From 7a1a8f0d6a8f79a006f40e9157e1d70dedd217b5 Mon Sep 17 00:00:00 2001 From: Juan Correa Date: Sun, 24 Nov 2024 10:04:03 -0500 Subject: [PATCH 01/84] requirements.txt --- requirements.txt | 4 ++++ 1 file changed, 4 insertions(+) create mode 100644 requirements.txt diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 000000000..50db08636 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,4 @@ +alpaca-trade-api +torch fairscale fire blobfile +pandas +ollama \ No newline at end of file From 7649271d7139341dda11cf06aaa0dae254e64a5f Mon Sep 17 00:00:00 2001 From: Juan Correa Date: Sun, 24 Nov 2024 10:05:11 -0500 Subject: [PATCH 02/84] notebook inicial --- Notebooks/test.ipynb | 2144 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 2144 insertions(+) create mode 100644 Notebooks/test.ipynb diff --git a/Notebooks/test.ipynb b/Notebooks/test.ipynb new file mode 100644 index 000000000..f7fdbece1 --- /dev/null +++ b/Notebooks/test.ipynb @@ -0,0 +1,2144 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "from llama_models.llama3.reference_impl.generation import Llama\n", + "from llama_models.llama3.api.datatypes import UserMessage, SystemMessage\n", + "import os\n", + "import sys\n", + "from llama_models.llama3.api.datatypes import (\n", + " UserMessage,\n", + " SystemMessage,\n", + " CompletionMessage,\n", + " StopReason,\n", + ")\n", + "from llama_models.llama3.reference_impl.generation import Llama\n", + "import pandas as pd\n", + "from io import StringIO\n", + "import numpy as np\n", + "import re\n", + "import requests\n", + "import ollama" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Funciones" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [], + "source": [ + "def transformar_texto(texto, marca):\n", + " if texto is None or (isinstance(texto, float) and np.isnan(texto)):\n", + " return texto\n", + " \n", + " if marca.lower() == \"adidas\":\n", + " # Transformación original para adidas\n", + " if isinstance(texto, (list, np.ndarray)):\n", + " texto = \", \".join(map(str, texto))\n", + " else:\n", + " texto = str(texto)\n", + " texto = texto.strip(\"[]\")\n", + " texto = re.sub(r\",\\s*\", '} {', texto)\n", + " texto = '{' + texto + '}'\n", + " return texto\n", + " \n", + " elif marca.lower() == \"nike\":\n", + " # Transformación específica para nike\n", + " if isinstance(texto, list):\n", + " # Elimina claves con valores irrelevantes\n", + " texto_limpio = [\n", + " {k.strip(): v.strip() for k, v in d.items() if k.strip() not in ['\\xa0']}\n", + " for d in texto\n", + " if isinstance(d, dict)\n", + " ]\n", + " # Filtra elementos vacíos o irrelevantes\n", + " texto_limpio = [d for d in texto_limpio if d]\n", + " return texto_limpio\n", + " return texto # Si no es lista, regresa el texto original\n", + "\n", + " else:\n", + " raise ValueError(f\"Marca '{marca}' no reconocida. Usa 'adidas' o 'nike'.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def generar_prompt_llama(descripcion, etiquetas):\n", + " prompt = \"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\\n\\n\"\n", + " prompt += \"Eres un asistente que extrae información de descripciones de productos y las organiza en una tabla.\\n\"\n", + " prompt += \"<|eot_id|><|start_header_id|>user<|end_header_id|>\\n\\n\"\n", + " prompt += f\"Extrae la siguiente información de la descripción del producto y etiquétala según las etiquetas: {', '.join(etiquetas)}.\\n\\n\"\n", + " prompt += f\"Descripción: {descripcion}\\n\\n\"\n", + " prompt += \"Proporciona la información en una tabla con las columnas correspondientes a cada etiqueta.\\n\"\n", + " prompt += \"<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\n\"\n", + " prompt += \"| \" + \" | \".join(etiquetas) + \" |\\n\"\n", + " prompt += \"| \" + \" | \".join(['---'] * len(etiquetas)) + \" |\\n\"\n", + " prompt += \"|\"\n", + " return prompt\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "def obtener_respuesta_llama(dialogo):\n", + " try:\n", + " # Ruta al modelo descargado (ajusta esta ruta según corresponda)\n", + " ruta_modelo = os.path.expanduser('~/.llama/checkpoints/Meta-Llama3.1-8B-Instruct')\n", + "\n", + " # Cargar el modelo Llama 3.1 8B\n", + " modelo = Llama.build(\n", + " ckpt_dir=ruta_modelo,\n", + " max_seq_len=1024, # Ajusta según sea necesario\n", + " max_batch_size=1, # Número de prompts a procesar simultáneamente\n", + " model_parallel_size=1,\n", + " )\n", + "\n", + " # Generar la respuesta utilizando el diálogo\n", + " resultado = modelo.chat_completion(\n", + " dialog=dialogo,\n", + " max_gen_len=512, # Máxima longitud de generación\n", + " temperature=0.6, # Ajusta según sea necesario\n", + " top_p=0.9, # Ajusta según sea necesario\n", + " )\n", + "\n", + " # Obtener el mensaje generado por el asistente\n", + " mensaje_asistente = resultado.generation\n", + " return mensaje_asistente.content\n", + "\n", + " except Exception as e:\n", + " print(f\"Error al generar la respuesta: {e}\")\n", + " return None" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "def obtener_respuesta_ollama(prompt):\n", + " response = ollama.chat(\n", + " model=\"llama3.2:latest\",\n", + " messages = [\n", + " {\n", + " \"role\":\"user\",\n", + " \"content\": prompt\n", + " } \n", + " ]\n", + " )\n", + " # La respuesta es un generador; concatenamos las partes\n", + " return response" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "def procesar_respuesta(respuesta_texto, etiquetas):\n", + " try:\n", + " # Buscar el inicio de la tabla\n", + " inicio_tabla = respuesta_texto.find('|')\n", + " if inicio_tabla != -1:\n", + " # Extraer la tabla desde el primer '|'\n", + " tabla_texto = respuesta_texto[inicio_tabla:].strip()\n", + " # Extraer solo las líneas que contienen '|'\n", + " lineas = tabla_texto.split('\\n')\n", + " lineas_tabla = []\n", + " for linea in lineas:\n", + " if '|' in linea:\n", + " # Eliminar los '|' iniciales y finales y espacios extra\n", + " linea = linea.strip().strip('|').strip()\n", + " lineas_tabla.append(linea)\n", + " else:\n", + " # Detenerse si la línea no contiene '|'\n", + " break\n", + " tabla_completa = '\\n'.join(lineas_tabla)\n", + " # Convertir la tabla Markdown a un DataFrame\n", + " df = pd.read_csv(StringIO(tabla_completa), sep='|', engine='python', skipinitialspace=True)\n", + " # Limpiar nombres de columnas y datos\n", + " df.columns = [col.strip() for col in df.columns]\n", + " for col in df.columns:\n", + " if df[col].dtype == object:\n", + " df[col] = df[col].str.strip()\n", + " # Resetear el índice\n", + " df = df.reset_index(drop=True)\n", + " return df\n", + " else:\n", + " print(\"No se encontró una tabla en la respuesta.\")\n", + " return None\n", + " except Exception as e:\n", + " print(f\"Error al procesar la tabla: {e}\")\n", + " return None\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lectura de datos desde la api" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "200\n" + ] + } + ], + "source": [ + "url =\"https://scraping-firestore-178159629911.us-central1.run.app//v1/scraping/\"\n", + "\n", + "response = requests.get(url)\n", + "\n", + "if response.status_code == 200:\n", + " data = response.json()\n", + " print(response.status_code)\n", + "else:\n", + " print(f'Error: {response.status_code}')\n", + "\n", + "df_raw = pd.DataFrame(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0046zSiHm8Cz0fZYwMJlL[]adidasadidashttps://www.adidas.co/tenis-duramo-sl/IF7884.htmlTenis Duramo SL$379.950$265.965Siente la ligereza y velocidad. Si estás listo...Mujer • Running{'_seconds': 1731975445, '_nanoseconds': 42700...NaNNaN
108sjncACSjSvg2t9DS73[Horma clásica, Parte superior sintética, Forr...adidasadidashttps://www.adidas.co/tenis-adizero-adios-pro-...Tenis ADIZERO ADIOS PRO 3$1.299.950$909.965Los Adizero Adios Pro 3 son la máxima expresió...Mujer • Running{'_seconds': 1731975445, '_nanoseconds': 42700...NaNNaN
20AqheRhKT2lhm7puBVCF[Ajuste clásico, Sistema de amarre de cordones...adidasadidashttps://www.adidas.co/tenis-ultraboost-22/GX55...TENIS ULTRABOOST 22$799.950NAHemos analizado 1.200.000 pisadas para que Ult...Mujer • Running{'_seconds': 1731975445, '_nanoseconds': 42700...NaNNaN
30Di5KVVcvU0QsWRxB1iE[{' ': '<b>&nbsp;</b>'}]nikenikehttps://www.nike.com.co/nike-revolution-7-fb22...Nike Revolution 7$ 389.950NACargamos el Revolution 7 con el tipo de amorti...Calzado de correr en pavimento para mujer{'_seconds': 1731965768, '_nanoseconds': 30000...[]Mujer
40Gvnv9unc1EV4XpFbCQN[{'Características principales': '<b>Caracterí...nikenikehttps://www.nike.com.co/nike-winflo-11-calzado...Nike Winflo 11$ 584.950NAEl Winflo 11 es el calzado con una pisada bala...Calzado de correr en pavimento para mujer{'_seconds': 1731965768, '_nanoseconds': 30000...[Parte superior de malla diseñada estratégicam...Mujer
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Si estás listo... \n", + "1 Los Adizero Adios Pro 3 son la máxima expresió... \n", + "2 Hemos analizado 1.200.000 pisadas para que Ult... \n", + "3 Cargamos el Revolution 7 con el tipo de amorti... \n", + "4 El Winflo 11 es el calzado con una pisada bala... \n", + "\n", + " category \\\n", + "0 Mujer • Running \n", + "1 Mujer • Running \n", + "2 Mujer • Running \n", + "3 Calzado de correr en pavimento para mujer \n", + "4 Calzado de correr en pavimento para mujer \n", + "\n", + " createdAt \\\n", + "0 {'_seconds': 1731975445, '_nanoseconds': 42700... \n", + "1 {'_seconds': 1731975445, '_nanoseconds': 42700... \n", + "2 {'_seconds': 1731975445, '_nanoseconds': 42700... \n", + "3 {'_seconds': 1731965768, '_nanoseconds': 30000... \n", + "4 {'_seconds': 1731965768, '_nanoseconds': 30000... \n", + "\n", + " characteristics gender \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 [] Mujer \n", + "4 [Parte superior de malla diseñada estratégicam... Mujer " + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_raw.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Paso 1: Definir las etiquetas dinámicamente\n", + "Creamos una lista de etiquetas que pueden cambiar según las necesidades" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "etiquetas = [\"cordones\", \"textil exterior\", \"textil interior\", \"suela\", \"peso y/o talla\", \"eco diseñado si o no\", \"color\", \"identificador\", \"nombre de deporte\",'genero Mujer/Hombre/MIXTO']\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Paso 2: procesar info de Adidas" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "df_adidas = df_raw[df_raw['store'] == 'adidas']" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Usuario\\AppData\\Local\\Temp\\ipykernel_5352\\4001796790.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_adidas['details_transformado'] = df_adidas['details'].apply(lambda x: transformar_texto(x, 'adidas'))\n" + ] + } + ], + "source": [ + "# Lista de descripciones de productos\n", + "df_adidas['details_transformado'] = df_adidas['details'].apply(lambda x: transformar_texto(x, 'adidas'))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Lista de descripciones de productos\n", + "'''descripciones = [\n", + " \"[{'text': 'Ajuste clásico'}, {'text': 'Sistema de amarre de cordones'}, {'text': 'Parte superior textil'}, {'text': 'Mediasuela Cloudfoam'}, {'text': 'Forro interno textil'}, {'text': 'Peso: 239 gramos (talla CO 37,5)'}, {'text': 'Caída mediasuela: 9 mm (talón: 28 mm / antepié: 19 mm)'}, {'text': 'Suela de caucho'}, {'text': 'La parte superior contiene al menos un 50% de material reciclado'}, {'text': 'Color del artículo: Dark Blue / Core Black / Gold Metallic'}, {'text': 'Número de artículo: IE0747'}]\",\n", + " \"[{'text': 'Sistema de amarre de cordones'}, {'text': 'Exterior textil con refuerzos sintéticos sin costuras'}, {'text': 'Lengüeta reforzada'}, {'text': 'Amortiguación Lightstrike'}, {'text': 'Capa protectora de TPU'}, {'text': 'Suela de caucho Continental™ Rubber'}, {'text': 'El exterior contiene al menos un 50 % de material reciclado'}, {'text': 'Color del artículo: Core Black / Grey Three / Grey Two'}, {'text': 'Número de artículo: HR1182'}]\",\n", + " \"[{'text': 'Corte clásico'}, {'text': 'Sistema de amarre de cordones'}, {'text': 'Exterior de malla sándwich'}, {'text': 'Revestimientos sin costuras que brindan soporte'}, {'text': 'Talón suave'}, {'text': 'Amortiguación LIGHTMOTION'}, {'text': 'Suela de caucho'}, {'text': 'El exterior contiene al menos un 50 % de material reciclado'}, {'text': 'Color del artículo: Light Grey / Screaming Orange / Solar Gold'}, {'text': 'Número de artículo: HP2375'}]\",\n", + " \"[{'text': 'Ajuste clásico'}, {'text': 'Sistema de amarre de cordones'}, {'text': 'Parte superior textil'}, {'text': 'Forro interno textil'}, {'text': 'Suela multiterreno de caucho'}, {'text': 'Peso: 327 gramos (talla CO 40)'}, {'text': 'Caída mediasuela: 9 mm (talón: 29 mm / antepié: 19 mm)'}, {'text': 'Contienen al menos un 20% de material reciclado'}, {'text': 'Color del artículo: Halo Silver / Green Spark / Grey Five'}, {'text': 'Número de artículo: IG1416'}]\",\n", + " \"[{'text': 'Ajuste clásico'}, {'text': 'Sistema de amarre de cordones'}, {'text': 'Exterior de malla'}, {'text': 'Diseño cómodo para impulsarte hacia adelante'}, {'text': 'Forro interno textil'}, {'text': 'Mediasuela de EVA'}, {'text': 'Peso: 236 g (Talla COL 36,5)'}, {'text': 'Caída mediasuela: 10 mm (talón: 32 mm / antepié: 22 mm)'}, {'text': 'Suela Adiwear'}, {'text': 'El exterior contiene al menos un 50 % de material reciclado'}, {'text': 'Color del artículo: Core Black / Cloud White / Grey Six'}, {'text': 'Número de artículo: ID5258'}]\",\n", + " \"[{'text': 'Ajuste clásico'}, {'text': 'Sistema de amarre de cordones'}, {'text': 'Exterior de malla'}, {'text': 'Diseño cómodo para impulsarte hacia adelante'}, {'text': 'Forro interno textil'}, {'text': 'Mediasuela de EVA'}, {'text': 'Peso: 236 g (Talla COL 36,5)'}, {'text': 'Caída mediasuela: 10 mm (talón: 32 mm / antepié: 22 mm)'}, {'text': 'Suela Adiwear'}, {'text': 'El exterior contiene al menos un 50 % de material reciclado'}, {'text': 'Color del artículo: Cloud White / Silver Metallic / Crystal White'}, {'text': 'Número de artículo: ID5257'}]\",\n", + " \"[{'text': 'Ajuste clásico'}, {'text': 'Sistema de amarre de cordones'}, {'text': 'Parte superior textil'}, {'text': 'Mediasuela Cloudfoam'}, {'text': 'Forro interno textil'}, {'text': 'Peso: 286 gramos (talla CO 40)'}, {'text': 'Caída mediasuela: 10 mm (talón: 29,2 mm / antepié: 19,2 mm)'}, {'text': 'Suela de caucho'}, {'text': 'La parte superior contiene al menos un 50% de material reciclado'}, {'text': 'Color del artículo: Shadow Red / Green Spark / Better Scarlet'}, {'text': 'Número de artículo: IE0740'}]\"\n", + "]'''" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " details_transformado \\\n", + "0 {} \n", + "1 {Horma clásica} {Parte superior sintética} {Fo... \n", + "2 {Ajuste clásico} {Sistema de amarre de cordone... \n", + "5 {Ajuste clásico} {Cierre de cordones} {Parte s... \n", + "7 {Ajuste clásico} {Sistema de amarre de cordone... \n", + "\n", + " description category \n", + "0 Siente la ligereza y velocidad. Si estás listo... Mujer • Running \n", + "1 Los Adizero Adios Pro 3 son la máxima expresió... Mujer • Running \n", + "2 Hemos analizado 1.200.000 pisadas para que Ult... Mujer • Running \n", + "5 Haz tus mejores 10k con nuestros nuevos tenis ... Mujer • Running \n", + "7 Haz tus mejores 10k con nuestros nuevos tenis ... Hombre • Running " + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_adidas[['details_transformado','description','category']].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "# Crear un diccionario con las llaves deseadas\n", + "productos = [\n", + " {\n", + " \"details\": row['details_transformado'],\n", + " \"description\": row['description'],\n", + " \"category\": row['category']\n", + " }\n", + " for _, row in df_adidas[:10].iterrows() \n", + " if row['details_transformado'] != '{}'\n", + "]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Paso 3: Integrar con la API de Llama 3.1\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prompt" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'details': '{Horma clásica} {Parte superior sintética} {Forro interno textil} {Varillas ENERGYRODS 2.0 que limitan la pérdida de energía} {Amortiguación Lightstrike Pro} {Peso: 183 g (talla CO 37} {5)} {Caída mediasuela: 6 mm (talón: 38 mm / antepié: 32 mm)} {La parte superior contiene al menos un 50 % de material reciclado} {Color del artículo: Pink Spark / Aurora Met. / Sandy Pink} {Número de artículo: ID3612}',\n", + " 'description': \"Los Adizero Adios Pro 3 son la máxima expresión de los productos Adizero Racing. Fueron diseñados con y para atletas para lograr hazañas increíbles. Estos tenis de running adidas están diseñados para optimizar la eficiencia del running. Nuestras varillas ENERGYRODS 2.0 de carbono ofrecen ligereza y firmeza para pasos ágiles y eficientes. La tecnología LIGHTSTRIKE PRO ultraliviana amortigua cada paso con las tres capas de espuma resistente que te ayudan a mantener la energía a largo plazo. Todo sobre una delgada suela de caucho textil para un agarre extraordinario en condiciones mojadas y secas. Celebramos nuestra más reciente victoria, los tenis Best Speed 2024 by Women's Health.\",\n", + " 'category': 'Mujer • Running'},\n", + " {'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Exterior de tejido adidas Primeknit} {Ajuste suave en el talón} {Sistema Linear Energy Push} {Mediasuela BOOST} {Peso: 289 g (Talla CO 37)} {Suela Stretchweb con caucho Continental™ Better Rubber} {El hilo del exterior contiene al menos un 50% de Parley Ocean Plastic y un 50% de poliéster reciclado} {Color del artículo: Core Black / Core Black / Cloud White} {Número de artículo: GX5591}',\n", + " 'description': 'Hemos analizado 1.200.000 pisadas para que Ultraboost evolucione y mejore su ajuste para adaptarse 360° al pie femenino. Y aún hay más. Hemos rediseñado la suela de caucho. Probamos cientos de prototipos. Hasta que comprobamos las mejoras en su rendimiento. ¿El resultado? Un 4 % más de energía que los Ultraboost 21 para mujer. El ajuste en el área del talón del exterior adidas PRIMEKNIT se diseñó para evitar que el talón se deslice y se formen ampollas. Estarás corriendo sobre una mediasuela BOOST con tecnología Linear Energy Push. El exterior de este calzado está confeccionado con hilo que contiene un 50% de Parley Ocean Plastic, un material que ha sido recuperado de residuos plásticos recogidos en islas, playas, comunidades costeras y costas evitando que contaminen nuestros océanos.',\n", + " 'category': 'Mujer • Running'},\n", + " {'details': '{Ajuste clásico} {Cierre de cordones} {Parte superior de malla técnica} {Amortiguación Lightstrike Pro} {Varillas ENERGYRODS que limitan la pérdida de energía} {Talón Slinglaunch para un ajuste seguro} {Peso: 166 g (Talla COL 36 1/2)} {Caída mediasuela: 6 mm (talón: 32 mm / antepié: 26 mm)} {Suela de caucho Continental™} {Contiene al menos un 20 % de material reciclado} {Color del artículo: Green Spark / Aurora Met. / Lucid Lemon} {Número de artículo: IG8206}',\n", + " 'description': 'Haz tus mejores 10k con nuestros nuevos tenis de running Adizero, diseñados exclusivamente para darte máxima velocidad. Los Adizero Takumi Sen 10 han sido diseñados con dos capas de amortiguación LIGHTSTRIKE PRO, combinadas con las varillas ENERGYRODS para una mayor estabilidad e impulso en tu carrera. Supera tu marca personal y llega a la meta más rápido que nunca.',\n", + " 'category': 'Mujer • Running'},\n", + " {'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Parte superior de malla} {Amortiguación Lightstrike Pro} {Varillas ENERGYRODS que limitan la pérdida de energía} {Talón Slinglaunch} {Peso: 200 gramos (talla CO 40)} {Caída mediasuela: 6 mm (talón: 33 mm / antepié: 27 mm} {Suela de caucho Continental™ Rubber} {Contienen al menos un 20% de material reciclado} {Color del artículo: Green Spark / Aurora Met. / Lucid Lemon} {Número de artículo: IG3134}',\n", + " 'description': 'Haz tus mejores 10k con nuestros nuevos tenis de running Adizero, diseñados exclusivamente para darte máxima velocidad. Los Adizero Takumi Sen 10 han sido diseñados con dos capas de amortiguación LIGHTSTRIKE PRO, combinadas con las varillas ENERGYRODS 2.0 para una mayor estabilidad e impulso en tu carrera. Supera tu marca personal y llega a la meta más rápido que nunca.',\n", + " 'category': 'Hombre • Running'},\n", + " {'details': '{Horma clásica} {Sistema de amarre de cordones} {Parte superior textil} {Forro interno textil} {Mediasuela Cloudfoam} {Suela de TPU} {Peso: 319 g (talla CO 40)} {Caída mediasuela: 6 mm (talón 35 mm / antepié 29 mm)} {Color del artículo: Halo Silver / Carbon / Core Black} {Número de artículo: ID8754}',\n", + " 'description': 'Cada carrera es un viaje de descubrimiento. Ponte estos tenis de running adidas y libera tu potencial. La mediasuela con amortiguación Cloudfoam te ofrece una pisada más cómoda mientras aumentas tu resistencia. La parte superior textil es resistente al desgaste y te ofrece soporte desde tu primera vuelta hasta tu primera carrera de 5K.',\n", + " 'category': 'Hombre • Running'},\n", + " {'details': '{Si este artículo es personalizado} {no aplica en nuestra política de cambios y devoluciones.} {Sistema de amarre de cordones para un ajuste inmejorable} {Producto hecho parcialmente con Malla Técnica Reciclada} {Diseño liviano} {Amortiguación Lightstrike y Lightstrike Pro} {Forro interno textil} {Caída mediasuela: 8} {5 mm (talón: 33 mm / antepié: 24} {5 mm)} {Suela de caucho} {El exterior contiene al menos un 50% de material reciclado} {Color del artículo: Ivory / Iron Metallic / Spark} {Número de artículo: IG3341}',\n", + " 'description': 'Cuando se trata de alcanzar tus metas, cada segundo cuenta. Ya sea para entrenar o para competir, un rendimiento excelente requiere de prendas de alta tecnología optimizadas para la velocidad. Presentamos la nueva colección de tenis de running livianos que te ayudan a superar tus límites sin distracciones.\\n\\nLos tenis de running Adizero SL seleccionan lo mejor de nuestra franquicia Adizero que rompe récords mundiales. La mediasuela de EVA LIGHTSTRIKE liviana ofrece resiliencia a la mediasuela para que puedas concentrarte en el próximo paso, mientras que el exterior está hecho de una malla técnica suave que está zonificada en áreas clave. El talón acolchado y la lengüeta brindan una comodidad óptima junto con el antepié Adizero. La suela premium está diseñada para proporcionar tracción.',\n", + " 'category': 'Mujer • Running'},\n", + " {'details': '{Horma clásica} {Sistema de amarre de cordones} {Parte superior de malla} {Forro interno textil} {Plantilla OrthoLite®} {Mediasuela Cloudfoam} {Peso: 304 g (talla CO 40)} {Caída mediasuela: 10 mm (talón: 33 mm / antepié: 23 mm)} {Suela Adiwear} {Color del artículo: Cloud White / Core Black / Better Scarlet} {Número de artículo: IE8818}',\n", + " 'description': 'Tanto en la pista como en la cinta de correr, alcanza todas tus metas con estos tenis de running adidas. Incorporan una mediasuela con amortiguación Cloudfoam que te ofrece una pisada más cómoda y suave. La parte superior de malla transpirable y la suela Adiwear de gran resistencia al desgaste la convierten en una silueta perfecta para llevar durante todo el día.',\n", + " 'category': 'Hombre • Running'},\n", + " {'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Parte superior de malla} {Forro interno textil} {mediasuela Dreamstrike+} {Suela Adiwear} {Peso (talla CO 37): 213 gramos} {Caída mediasuela: 10 mm (talón: 34 mm / 24 mm)} {Contienen al menos un 20% de material reciclado y renovable} {Color del artículo: Almost Yellow / Zero Metalic / Pink Spark} {Número de artículo: IE1072}',\n", + " 'description': 'Avanza hacia tus metas de running con estos tenis adidas. La mediasuela Dreamstrike+ está diseñada para absorber el impacto. La amortiguación adicional en el antepié ofrece comodidad durante toda tu carrera. El exterior de malla transpirable maximiza el flujo de aire para mantener tus pies frescos, y la suela Adiwear resistente ofrece tracción. \\n\\nAl elegir materiales reciclados, podemos reutilizar materiales que ya han sido creados, lo que ayuda a reducir los residuos. La elección de materiales renovables nos ayudará a eliminar nuestra dependencia de recursos finitos. Nuestros productos hechos con una mezcla de materiales reciclados y renovables contienen al menos un 20% de este material.',\n", + " 'category': 'Mujer • Running'},\n", + " {'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Exterior de malla con cuello acolchado} {Forro interno textil} {Estabilizador de talón moldeado Fitcounter} {Mediasuela Bounce} {Suela sintética} {El exterior contiene al menos un 50 % de material reciclado} {Color del artículo: Shadow Violet / Legend Ink / Wonder Clay} {Número de artículo: IG0334}',\n", + " 'description': 'Estos tenis fueron diseñados para darte la amortiguación y la respuesta que necesitas cuando estás en la pista o en los senderos. Sin importar si vas a hacer tu caminata diaria o una carrera corta, la mediasuela Bounce es flexible y receptiva con cada paso. Hecho con una serie de materiales reciclados, el exterior incorpora al menos un 50 % de contenido reciclado. Este producto representa solo una de nuestras soluciones para acabar con los residuos plásticos.',\n", + " 'category': 'Mujer • Running'}]" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "productos\n" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "def generar_prompt_ollama(producto, etiquetas):\n", + " prompt = f\"\"\"\n", + " Eres un asistente especializado en extraer información estructurada de descripciones de productos y organizarla en tablas. \n", + " Tu tarea es extraer la siguiente información de los detalles del producto y etiquetarla según las etiquetas: {', '.join(etiquetas)}.\n", + " Si una etiqueta no tiene una correspondencia clara en los detalles, completa su valor con \"null\".\n", + "\n", + " Información del producto:\n", + " - Details: {producto['details']}\n", + " - Description: {producto['description']}\n", + " - Category: {producto['category']}\n", + "\n", + " Proporciona la información en una tabla con las columnas correspondientes a cada etiqueta. \n", + " La tabla debe incluir **dos filas completas**:\n", + " 1. La primera fila contiene los nombres de las etiquetas.\n", + " 2. La segunda fila contiene los valores correspondientes etiquetados.\n", + "\n", + " Formato esperado de la respuesta:\n", + " | {' | '.join(etiquetas)} |\n", + " | {' | '.join(['---'] * len(etiquetas))} |\n", + " | valor_1 | valor_2 | ... |\n", + " \n", + " Ejemplo:\n", + " Si las etiquetas son \"details\", \"description\", y \"category\", y los valores extraídos son \n", + " \"Zapatillas cómodas\", \"Hechas con materiales reciclados\", y \"Calzado\", respectivamente, \n", + " tu respuesta debe ser:\n", + "\n", + " | details | description | category |\n", + " | Zapatillas cómodas | Hechas con materiales reciclados | Calzado |\n", + "\n", + " Recuerda:\n", + " - La respuesta debe contener dos filas completas.\n", + " - Solo responde con la tabla y **no incluyas texto adicional**.\n", + "\n", + " Ahora, extrae y estructura la información del producto proporcionado:\n", + "\n", + " | {' | '.join(etiquetas)} |\n", + " | {' | '.join(['---'] * len(etiquetas))} |\n", + " |\"\"\"\n", + " return prompt.strip()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generacion" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'details': '{Horma clásica} {Parte superior sintética} {Forro interno textil} {Varillas ENERGYRODS 2.0 que limitan la pérdida de energía} {Amortiguación Lightstrike Pro} {Peso: 183 g (talla CO 37} {5)} {Caída mediasuela: 6 mm (talón: 38 mm / antepié: 32 mm)} {La parte superior contiene al menos un 50 % de material reciclado} {Color del artículo: Pink Spark / Aurora Met. / Sandy Pink} {Número de artículo: ID3612}', 'description': \"Los Adizero Adios Pro 3 son la máxima expresión de los productos Adizero Racing. Fueron diseñados con y para atletas para lograr hazañas increíbles. Estos tenis de running adidas están diseñados para optimizar la eficiencia del running. Nuestras varillas ENERGYRODS 2.0 de carbono ofrecen ligereza y firmeza para pasos ágiles y eficientes. La tecnología LIGHTSTRIKE PRO ultraliviana amortigua cada paso con las tres capas de espuma resistente que te ayudan a mantener la energía a largo plazo. Todo sobre una delgada suela de caucho textil para un agarre extraordinario en condiciones mojadas y secas. Celebramos nuestra más reciente victoria, los tenis Best Speed 2024 by Women's Health.\", 'category': 'Mujer • Running'}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Adizero Adios Pro 3 | Parte superior sintética | Forro interno textil | Delgada suela de caucho textil | CO 37 | Sí | Pink Spark / Aurora Met. / Sandy Pink | ID3612 | Running | Mujer\n", + "\n", + "------------------------\n", + "\n", + "{'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Exterior de tejido adidas Primeknit} {Ajuste suave en el talón} {Sistema Linear Energy Push} {Mediasuela BOOST} {Peso: 289 g (Talla CO 37)} {Suela Stretchweb con caucho Continental™ Better Rubber} {El hilo del exterior contiene al menos un 50% de Parley Ocean Plastic y un 50% de poliéster reciclado} {Color del artículo: Core Black / Core Black / Cloud White} {Número de artículo: GX5591}', 'description': 'Hemos analizado 1.200.000 pisadas para que Ultraboost evolucione y mejore su ajuste para adaptarse 360° al pie femenino. Y aún hay más. Hemos rediseñado la suela de caucho. Probamos cientos de prototipos. Hasta que comprobamos las mejoras en su rendimiento. ¿El resultado? Un 4 % más de energía que los Ultraboost 21 para mujer. El ajuste en el área del talón del exterior adidas PRIMEKNIT se diseñó para evitar que el talón se deslice y se formen ampollas. Estarás corriendo sobre una mediasuela BOOST con tecnología Linear Energy Push. El exterior de este calzado está confeccionado con hilo que contiene un 50% de Parley Ocean Plastic, un material que ha sido recuperado de residuos plásticos recogidos en islas, playas, comunidades costeras y costas evitando que contaminen nuestros océanos.', 'category': 'Mujer • Running'}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Sistema de amarre de cordones | adidas Primeknit | null | Mediasuela BOOST | Talla CO 37, 289 g | Si | Core Black / Cloud White | GX5591 | Running | Mujer\n", + "\n", + "------------------------\n", + "\n", + "{'details': '{Ajuste clásico} {Cierre de cordones} {Parte superior de malla técnica} {Amortiguación Lightstrike Pro} {Varillas ENERGYRODS que limitan la pérdida de energía} {Talón Slinglaunch para un ajuste seguro} {Peso: 166 g (Talla COL 36 1/2)} {Caída mediasuela: 6 mm (talón: 32 mm / antepié: 26 mm)} {Suela de caucho Continental™} {Contiene al menos un 20 % de material reciclado} {Color del artículo: Green Spark / Aurora Met. / Lucid Lemon} {Número de artículo: IG8206}', 'description': 'Haz tus mejores 10k con nuestros nuevos tenis de running Adizero, diseñados exclusivamente para darte máxima velocidad. Los Adizero Takumi Sen 10 han sido diseñados con dos capas de amortiguación LIGHTSTRIKE PRO, combinadas con las varillas ENERGYRODS para una mayor estabilidad e impulso en tu carrera. Supera tu marca personal y llega a la meta más rápido que nunca.', 'category': 'Mujer • Running'}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Cierre de cordones | Parte superior de malla técnica | Amortiguación Lightstrike Pro | Suela de caucho Continental™ | 166 g (Talla COL 36 1/2) | Contiene al menos un 20 % de material reciclado | Green Spark / Aurora Met. / Lucid Lemon | IG8206 | Running | Mujer\n", + "\n", + "------------------------\n", + "\n", + "{'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Parte superior de malla} {Amortiguación Lightstrike Pro} {Varillas ENERGYRODS que limitan la pérdida de energía} {Talón Slinglaunch} {Peso: 200 gramos (talla CO 40)} {Caída mediasuela: 6 mm (talón: 33 mm / antepié: 27 mm} {Suela de caucho Continental™ Rubber} {Contienen al menos un 20% de material reciclado} {Color del artículo: Green Spark / Aurora Met. / Lucid Lemon} {Número de artículo: IG3134}', 'description': 'Haz tus mejores 10k con nuestros nuevos tenis de running Adizero, diseñados exclusivamente para darte máxima velocidad. Los Adizero Takumi Sen 10 han sido diseñados con dos capas de amortiguación LIGHTSTRIKE PRO, combinadas con las varillas ENERGYRODS 2.0 para una mayor estabilidad e impulso en tu carrera. Supera tu marca personal y llega a la meta más rápido que nunca.', 'category': 'Hombre • Running'}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Sistema de amarre de cordones | Parte superior de malla | Amortiguación Lightstrike Pro | Suela de caucho Continental™ Rubber | CO 40 | Contienen al menos un 20% de material reciclado | Green Spark / Aurora Met. / Lucid Lemon | IG3134 | Running | Mujer/Hombre/MIXTO |\n", + "\n", + "------------------------\n", + "\n", + "{'details': '{Horma clásica} {Sistema de amarre de cordones} {Parte superior textil} {Forro interno textil} {Mediasuela Cloudfoam} {Suela de TPU} {Peso: 319 g (talla CO 40)} {Caída mediasuela: 6 mm (talón 35 mm / antepié 29 mm)} {Color del artículo: Halo Silver / Carbon / Core Black} {Número de artículo: ID8754}', 'description': 'Cada carrera es un viaje de descubrimiento. Ponte estos tenis de running adidas y libera tu potencial. La mediasuela con amortiguación Cloudfoam te ofrece una pisada más cómoda mientras aumentas tu resistencia. La parte superior textil es resistente al desgaste y te ofrece soporte desde tu primera vuelta hasta tu primera carrera de 5K.', 'category': 'Hombre • Running'}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Sistema de amarre de cordones | Parte superior textil | Forro interno textil | Mediasuela Cloudfoam, Suela de TPU | Talla CO 40 | Sí | Halo Silver / Carbon / Core Black | ID8754 | Running | Hombre\n", + "\n", + "------------------------\n", + "\n", + "{'details': '{Si este artículo es personalizado} {no aplica en nuestra política de cambios y devoluciones.} {Sistema de amarre de cordones para un ajuste inmejorable} {Producto hecho parcialmente con Malla Técnica Reciclada} {Diseño liviano} {Amortiguación Lightstrike y Lightstrike Pro} {Forro interno textil} {Caída mediasuela: 8} {5 mm (talón: 33 mm / antepié: 24} {5 mm)} {Suela de caucho} {El exterior contiene al menos un 50% de material reciclado} {Color del artículo: Ivory / Iron Metallic / Spark} {Número de artículo: IG3341}', 'description': 'Cuando se trata de alcanzar tus metas, cada segundo cuenta. Ya sea para entrenar o para competir, un rendimiento excelente requiere de prendas de alta tecnología optimizadas para la velocidad. Presentamos la nueva colección de tenis de running livianos que te ayudan a superar tus límites sin distracciones.\\n\\nLos tenis de running Adizero SL seleccionan lo mejor de nuestra franquicia Adizero que rompe récords mundiales. La mediasuela de EVA LIGHTSTRIKE liviana ofrece resiliencia a la mediasuela para que puedas concentrarte en el próximo paso, mientras que el exterior está hecho de una malla técnica suave que está zonificada en áreas clave. El talón acolchado y la lengüeta brindan una comodidad óptima junto con el antepié Adizero. La suela premium está diseñada para proporcionar tracción.', 'category': 'Mujer • Running'}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Si este artículo es personalizado | {Sistema de amarre de cordones para un ajuste inmejorable} | El exterior contiene al menos un 50% de material reciclado | Caucha | null | No | Ivory / Iron Metallic / Spark | IG3341 | Running | Mujer |\n", + "\n", + "------------------------\n", + "\n", + "{'details': '{Horma clásica} {Sistema de amarre de cordones} {Parte superior de malla} {Forro interno textil} {Plantilla OrthoLite®} {Mediasuela Cloudfoam} {Peso: 304 g (talla CO 40)} {Caída mediasuela: 10 mm (talón: 33 mm / antepié: 23 mm)} {Suela Adiwear} {Color del artículo: Cloud White / Core Black / Better Scarlet} {Número de artículo: IE8818}', 'description': 'Tanto en la pista como en la cinta de correr, alcanza todas tus metas con estos tenis de running adidas. Incorporan una mediasuela con amortiguación Cloudfoam que te ofrece una pisada más cómoda y suave. La parte superior de malla transpirable y la suela Adiwear de gran resistencia al desgaste la convierten en una silueta perfecta para llevar durante todo el día.', 'category': 'Hombre • Running'}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Sistema de amarre de cordones | Parte superior de malla | Forro interno textil | Mediasuela Cloudfoam | Talla CO 40 | null | Cloud White / Core Black / Better Scarlet | IE8818 | Running | Hombre |\n", + "\n", + "Nota: Las etiquetas \"Peso y/o talla\" no tienen una relación directa con la información proporcionada, por lo que su valor es \"null\".\n", + "\n", + "------------------------\n", + "\n", + "{'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Parte superior de malla} {Forro interno textil} {mediasuela Dreamstrike+} {Suela Adiwear} {Peso (talla CO 37): 213 gramos} {Caída mediasuela: 10 mm (talón: 34 mm / 24 mm)} {Contienen al menos un 20% de material reciclado y renovable} {Color del artículo: Almost Yellow / Zero Metalic / Pink Spark} {Número de artículo: IE1072}', 'description': 'Avanza hacia tus metas de running con estos tenis adidas. La mediasuela Dreamstrike+ está diseñada para absorber el impacto. La amortiguación adicional en el antepié ofrece comodidad durante toda tu carrera. El exterior de malla transpirable maximiza el flujo de aire para mantener tus pies frescos, y la suela Adiwear resistente ofrece tracción. \\n\\nAl elegir materiales reciclados, podemos reutilizar materiales que ya han sido creados, lo que ayuda a reducir los residuos. La elección de materiales renovables nos ayudará a eliminar nuestra dependencia de recursos finitos. Nuestros productos hechos con una mezcla de materiales reciclados y renovables contienen al menos un 20% de este material.', 'category': 'Mujer • Running'}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Sistema de amarre de cordones | Forro interno textil | mediasuela Dreamstrike+ | Suela Adiwear | CO 37: 213 gramos | Contienen al menos un 20% de material reciclado y renovable | Almost Yellow / Zero Metalic / Pink Spark | IE1072 | Running | Mujer |\n", + "\n", + "Nota: Se han completado los valores que no tenían una correspondencia clara con las etiquetas del producto original.\n", + "\n", + "------------------------\n", + "\n", + "{'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Exterior de malla con cuello acolchado} {Forro interno textil} {Estabilizador de talón moldeado Fitcounter} {Mediasuela Bounce} {Suela sintética} {El exterior contiene al menos un 50 % de material reciclado} {Color del artículo: Shadow Violet / Legend Ink / Wonder Clay} {Número de artículo: IG0334}', 'description': 'Estos tenis fueron diseñados para darte la amortiguación y la respuesta que necesitas cuando estás en la pista o en los senderos. Sin importar si vas a hacer tu caminata diaria o una carrera corta, la mediasuela Bounce es flexible y receptiva con cada paso. Hecho con una serie de materiales reciclados, el exterior incorpora al menos un 50 % de contenido reciclado. Este producto representa solo una de nuestras soluciones para acabar con los residuos plásticos.', 'category': 'Mujer • Running'}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Sistema de amarre de cordones | Exterior de malla con cuello acolchado | Forro interno textil | Mediasuela Bounce | null | Si | Shadow Violet / Legend Ink / Wonder Clay | IG0334 | Running | Mujer\n", + "\n", + "------------------------\n", + "\n" + ] + } + ], + "source": [ + "dfs = []\n", + "for producto in productos:\n", + " prompt = generar_prompt_ollama(producto, etiquetas)\n", + " respuesta = obtener_respuesta_ollama(prompt)\n", + " print(producto)\n", + " print(\"Respuesta del modelo:\")\n", + " print(respuesta[\"message\"][\"content\"])\n", + " print(\"\\n------------------------\\n\")\n", + " df = procesar_respuesta(respuesta[\"message\"][\"content\"], etiquetas)\n", + " if df is not None:\n", + " dfs.append(df)\n", + " else:\n", + " print(\"No se pudo extraer la tabla.\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [], + "source": [ + "if dfs:\n", + " df_total = pd.concat(dfs, ignore_index=True)\n", + "else:\n", + " print(\"No se pudo crear el DataFrame total.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cordonestextil exteriortextil interiorsuelapeso y/o tallaeco diseñado si o nocoloridentificadornombre de deportegenero Mujer/Hombre/MIXTO
0Adizero Adios Pro 3Parte superior sintéticaForro interno textilDelgada suela de caucho textilCO 37SíPink Spark / Aurora Met. / Sandy PinkID3612RunningMujer
1Sistema de amarre de cordonesadidas PrimeknitnullMediasuela BOOSTTalla CO 37, 289 gSiCore Black / Cloud WhiteGX5591RunningMujer
2Cierre de cordonesParte superior de malla técnicaAmortiguación Lightstrike ProSuela de caucho Continental™166 g (Talla COL 36 1/2)Contiene al menos un 20 % de material recicladoGreen Spark / Aurora Met. / Lucid LemonIG8206RunningMujer
3Sistema de amarre de cordonesParte superior de mallaAmortiguación Lightstrike ProSuela de caucho Continental™ RubberCO 40Contienen al menos un 20% de material recicladoGreen Spark / Aurora Met. / Lucid LemonIG3134RunningMujer/Hombre/MIXTO
4Sistema de amarre de cordonesParte superior textilForro interno textilMediasuela Cloudfoam, Suela de TPUTalla CO 40SíHalo Silver / Carbon / Core BlackID8754RunningHombre
5Si este artículo es personalizado{Sistema de amarre de cordones para un ajuste ...El exterior contiene al menos un 50% de materi...CauchanullNoIvory / Iron Metallic / SparkIG3341RunningMujer
6Sistema de amarre de cordonesParte superior de mallaForro interno textilMediasuela CloudfoamTalla CO 40nullCloud White / Core Black / Better ScarletIE8818RunningHombre
7Sistema de amarre de cordonesForro interno textilmediasuela Dreamstrike+Suela AdiwearCO 37: 213 gramosContienen al menos un 20% de material reciclad...Almost Yellow / Zero Metalic / Pink SparkIE1072RunningMujer
8Sistema de amarre de cordonesExterior de malla con cuello acolchadoForro interno textilMediasuela BouncenullSiShadow Violet / Legend Ink / Wonder ClayIG0334RunningMujer
\n", + "
" + ], + "text/plain": [ + " cordones \\\n", + "0 Adizero Adios Pro 3 \n", + "1 Sistema de amarre de cordones \n", + "2 Cierre de cordones \n", + "3 Sistema de amarre de cordones \n", + "4 Sistema de amarre de cordones \n", + "5 Si este artículo es personalizado \n", + "6 Sistema de amarre de cordones \n", + "7 Sistema de amarre de cordones \n", + "8 Sistema de amarre de cordones \n", + "\n", + " textil exterior \\\n", + "0 Parte superior sintética \n", + "1 adidas Primeknit \n", + "2 Parte superior de malla técnica \n", + "3 Parte superior de malla \n", + "4 Parte superior textil \n", + "5 {Sistema de amarre de cordones para un ajuste ... \n", + "6 Parte superior de malla \n", + "7 Forro interno textil \n", + "8 Exterior de malla con cuello acolchado \n", + "\n", + " textil interior \\\n", + "0 Forro interno textil \n", + "1 null \n", + "2 Amortiguación Lightstrike Pro \n", + "3 Amortiguación Lightstrike Pro \n", + "4 Forro interno textil \n", + "5 El exterior contiene al menos un 50% de materi... \n", + "6 Forro interno textil \n", + "7 mediasuela Dreamstrike+ \n", + "8 Forro interno textil \n", + "\n", + " suela peso y/o talla \\\n", + "0 Delgada suela de caucho textil CO 37 \n", + "1 Mediasuela BOOST Talla CO 37, 289 g \n", + "2 Suela de caucho Continental™ 166 g (Talla COL 36 1/2) \n", + "3 Suela de caucho Continental™ Rubber CO 40 \n", + "4 Mediasuela Cloudfoam, Suela de TPU Talla CO 40 \n", + "5 Caucha null \n", + "6 Mediasuela Cloudfoam Talla CO 40 \n", + "7 Suela Adiwear CO 37: 213 gramos \n", + "8 Mediasuela Bounce null \n", + "\n", + " eco diseñado si o no \\\n", + "0 Sí \n", + "1 Si \n", + "2 Contiene al menos un 20 % de material reciclado \n", + "3 Contienen al menos un 20% de material reciclado \n", + "4 Sí \n", + "5 No \n", + "6 null \n", + "7 Contienen al menos un 20% de material reciclad... \n", + "8 Si \n", + "\n", + " color identificador nombre de deporte \\\n", + "0 Pink Spark / Aurora Met. / Sandy Pink ID3612 Running \n", + "1 Core Black / Cloud White GX5591 Running \n", + "2 Green Spark / Aurora Met. / Lucid Lemon IG8206 Running \n", + "3 Green Spark / Aurora Met. / Lucid Lemon IG3134 Running \n", + "4 Halo Silver / Carbon / Core Black ID8754 Running \n", + "5 Ivory / Iron Metallic / Spark IG3341 Running \n", + "6 Cloud White / Core Black / Better Scarlet IE8818 Running \n", + "7 Almost Yellow / Zero Metalic / Pink Spark IE1072 Running \n", + "8 Shadow Violet / Legend Ink / Wonder Clay IG0334 Running \n", + "\n", + " genero Mujer/Hombre/MIXTO \n", + "0 Mujer \n", + "1 Mujer \n", + "2 Mujer \n", + "3 Mujer/Hombre/MIXTO \n", + "4 Hombre \n", + "5 Mujer \n", + "6 Hombre \n", + "7 Mujer \n", + "8 Mujer " + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_total" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Paso 5: Nike" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "df_nike = df_raw[df_raw['store'] == 'nike']" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Usuario\\AppData\\Local\\Temp\\ipykernel_5352\\989177854.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_nike['details_transformado'] = df_nike['details'].apply(transformar_texto)\n" + ] + } + ], + "source": [ + "df_nike['details_transformado'] = df_nike['details'].apply(transformar_texto)" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'\\xa0': ' '}]" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_nike['details'].iloc[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Usuario\\AppData\\Local\\Temp\\ipykernel_5352\\1496571984.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_nike['details_transformado'] = df_nike['details'].apply(lambda x: transformar_texto(x, 'nike'))\n" + ] + } + ], + "source": [ + "# Aplicar la transformación con el parámetro 'adidas'\n", + "df_nike['details_transformado'] = df_nike['details'].apply(lambda x: transformar_texto(x, 'nike'))" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [], + "source": [ + "# Crear un diccionario con las llaves deseadas\n", + "productos_nike = [\n", + " {\n", + " \"details\": row['details_transformado'],\n", + " \"description\": row['description'],\n", + " \"category\": row['category']\n", + " }\n", + " for _, row in df_nike[:10].iterrows() \n", + " if row['details_transformado'] != '{}'\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'details': \"{{'\\\\xa0': ' '}}\", 'description': 'Cargamos el Revolution 7 con el tipo de amortiguación suave y soporte que podría cambiar tu mundo del running. Elegante como siempre, cómodo cuando la goma se encuentra con la carretera y con alto rendimiento para el ritmo deseado, es una evolución de un favorito de los fanáticos que ofrece una conducción suave y tersa.', 'category': 'Calzado de correr en pavimento para mujer'}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Zapatillas cómodas | null | null | null | null | null | null | Revolution 7 | Correr | Mujer\n", + "\n", + "------------------------\n", + "\n", + "{'details': \"{{'Características principales': 'Características principales'}} {{'Datos del producto': 'Datos del producto'}} {{'Soporte: neutro': 'Soporte: neutro'}} {{'Ajuste adaptable': 'Ajuste adaptable'}} {{'Datos del producto': 'Datos del producto'}}\", 'description': 'El Winflo 11 es el calzado con una pisada balanceada que te ayudará a impulsar tu carrera a un ritmo que te permitirá acumular kilómetros, metros y objetivos. Impulsado por la amortiguación Nike Air de largo completo, el Winflo 11 cuenta ahora con un antepié más amplio, un talón más ancho y una mayor transpirabilidad en comparación con el Winflo 10. Es el tipo de fijación que crea hábito y que te ayudará a ponerte en marcha, a lucir bien en la carretera con colores fáciles de combinar y a volver al día siguiente por más.', 'category': 'Calzado de correr en pavimento para mujer'}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Zapatillas con fijación | Neutra | Textil sintético | Suela de goma | - | Si | Blanco/Rosa/Violeta/Verde | WINFLO 11 | Correr en pavimento para mujer | Mujer/Hombre/MIXTO |\n", + "\n", + "------------------------\n", + "\n", + "{'details': \"{{'\\\\xa0': ' '}}\", 'description': 'Con la máxima amortiguación para brindarte soporte en cada kilómetro, el Invincible 3 ofrece nuestro más alto nivel de comodidad en la planta del pie. Su espuma ZoomX Foam suave y flexible te ayuda a mantener la estabilidad y la frescura. En otras palabras, te sentirás bien todo el día, sin importar lo que hagas. Y al juntar toda esta amortiguación con colores fáciles de combinar, obtienes un calzado que no te vas a querer quitar.', 'category': 'Calzado de correr en pavimento para mujer'}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Null | Null | Null | Null | Null | Si | Color fáciles de combinar | Invincible 3 | Calzado de correr en pavimento para mujer | Mujer\n", + "\n", + "------------------------\n", + "\n", + "{'details': \"{{'Amortiguación: superalta': 'Cuanta más amortiguación tengas en la planta del pie} {más suave y cómoda puede ser tu experiencia de running. La amortiguación suaviza el impacto cuando los pies llegan al suelo. Con una amortiguación Nike ZoomX con la forma de una mecedora y una espuma más alta} {este calzado te brinda la máxima amortiguación al contacto con el suelo y una sensación aún más suave en la planta del pie.'}} {{'Responsividad: superalta': 'Cuanto más responsivo sea el calzado} {más retorno de energía obtendrás con cada paso. Ya sea que quieras correr un poco más rápido o con un poco menos de esfuerzo} {un calzado responsivo te brinda más elasticidad en tu pisada para que aproveches al máximo tu carrera. La espuma Nike ZoomX es muy responsiva y ligera} {esto brinda rebote y respuesta rápida a cada paso.'}} {{'Ajuste: seguro} {transpirable y cómodo': 'La parte superior evolucionada de Flyknit sitúa las zonas de transpirabilidad donde el pie se calienta más. Es resistente y duradera para mantener el pie seguro a cada kilómetro.'}} {{'¿Qué novedades tiene el Invincible 3?': 'Creamos el clip del talón más pequeño que el de nuestra versión anterior y lo colocamos en una ubicación más precisa. La entresuela más ancha agrega estabilidad. La espuma apilada más alta que la de nuestra versión anterior eleva la vara en términos de amortiguación y comodidad en un diseño más elegante.'}}\", 'description': 'Con la máxima amortiguación para soportar cada kilómetro, el Invincible 3 tiene nuestro más alto nivel de comodidad a tus pies. Su espuma ZoomX Foam suave y flexible te ayuda a mantener la estabilidad y la frescura. En otras palabras, te sentirás bien todo el día, sin importar lo que hagas. Tiene todo lo que necesitas para que puedas impulsarte por tu camino preferido y volver a tu próxima carrera sintiéndote listo y revitalizado.', 'category': 'Calzado de running en carretera para hombre'}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Amortiguación: superalta | Nike ZoomX con forma de mecedora y espuma más alta | transpirable | Cuanta más amortiguación tengas en la planta del pie | null | Si | Blanco/Sierra Verde | Invincible 3 | Calzado de running en carretera para hombre | Hombre\n", + "\n", + "------------------------\n", + "\n", + "{'details': \"{{'Confeccionada con Nike Grind': 'La suela con diseño tipo waffle cuenta con al menos un 13% de material Nike Grind} {confeccionado con residuos del proceso de fabricación de calzado.'}} {{'Pisada suave': 'La espuma suave y responsiva en la entresuela ofrece una marcha cómoda y suave para tu carrera donde sea que te lleve el día. La espuma más alta proporciona una sensación de suavidad.'}} {{'Échate a volar': 'La parte superior de Flyknit tiene zonas de transpirabilidad donde el pie se calienta más} {específicamente en la punta. Es fuerte y duradera} {lo que ayuda a mantener tu pie seguro en cada kilómetro. La parte superior aireada y ventilada abraza el pie de una manera ligera y sencilla.'}} {{'Más beneficios': 'Más beneficios'}}\", 'description': '¿Puedes ver el futuro? Avanza rápido con el diseño vanguardista del Nike Interact Run. Está diseñado con todas las bondades del running que necesitas: una parte superior ligera de Flyknit, una entresuela de espuma suave y comodidad donde importa. Escanea el código QR de la lengüeta con tu teléfono y consulta nuestra introducción en línea a los detalles de Nike Interact Run.', 'category': 'Calzado de running en carretera para hombre'}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Nike Grind | - | - | waffle | null | sí | - | - | Running | Hombre/MIXTO |\n", + "\n", + "------------------------\n", + "\n", + "{'details': \"{{'Amortiguación: alta': 'Cuanta más amortiguación tengas en la planta del pie} {más suave y cómoda puede ser tu experiencia de running. La amortiguación suaviza el impacto cuando los pies llegan al suelo. La espuma nueva y mejorada en la entresuela ofrece una sensación suave y cómoda mientras acumulas kilómetros. Las pilas altas de espuma mantienen la comodidad. La espuma} {a la que se le dio la forma de mecedora} {brinda soporte en las tres fases de la pisada de un runner. Te brinda flexibilidad cuando levantas el pie del suelo} {un deslizamiento suave cuando el pie se mueve hacia delante y amortiguación al contacto con el suelo.'}} {{'Responsividad: mínima': 'Cuanto más responsivo sea el calzado} {más retorno de energía obtendrás con cada paso. Ya sea que quieras correr un poco más rápido o con un poco menos de esfuerzo} {un calzado responsivo te brinda más elasticidad en tu pisada para que aproveches al máximo tu carrera. La unidad Zoom Air en el antepié aporta amortiguación responsiva. Cada paso crea retorno de energía para impulsarte hacia delante de forma responsiva y con elasticidad.'}} {{'Ajuste adaptable': 'El antepié adaptable tiene suficiente espacio para extender los dedos. El antepié más amplio complementa la base más ancha y una pisada suave. La estructura y contención adicionales alrededor del talón ofrecen un ajuste con más soporte.'}} {{'¿Qué innovaciones tiene el Structure 25?': 'Le agregamos más espuma que a nuestros Structure anteriores y también tiene mejor soporte y comodidad que su predecesor. La espuma nueva y mejorada en la planta del pie brinda soporte.'}} {{'Más beneficios': 'Más beneficios'}}\", 'description': 'Con estabilidad donde la necesitas y amortiguación donde la quieres, el Structure 25 te apoya en los recorridos largos, las carreras cortas de entrenamiento e incluso con unas sentadillas antes de que acabe el día. Es la estabilidad que buscas, leal desde el primer momento, probado y confiable, con un sistema de soporte completo en el mediopié y una amortiguación más cómoda que antes.', 'category': 'Calzado de running en carretera para mujer'}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Amortiguación: alta | Espuma | Textil | Suela con espuma | Pila alta | No | Blanco/ Negro | Structure 25 | Running | Mujer/MIXTO\n", + "\n", + "------------------------\n", + "\n", + "{'details': \"{{'Características principales': 'Características principales'}} {{'Datos del producto': 'Datos del producto'}} {{'Responsividad: moderada': 'Responsividad: moderada'}} {{'Transpirabilidad contenida': 'Transpirabilidad contenida'}} {{'Más beneficios': 'Más beneficios'}} {{'Datos del producto': 'Datos del producto'}}\", 'description': 'La amortiguación máxima proporciona una comodidad mejorada para las carreras diarias. Disfruta de una plataforma suave con forma de mecedora confeccionada con la espuma ReactX Foam nueva en la planta del pie, así como de un cuello y una lengüeta ultracómodos para ofrecer un ajuste ceñido. Además, se añadió una membrana resistente al agua a esta versión para ayudar a evitar la humedad.', 'category': 'Calzado de correr en carretera para mujer'}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Características principales | La amortiguación máxima proporciona una comodidad mejorada para las carreras diarias. Disfruta de una plataforma suave con forma de mecedora confeccionada con la espuma ReactX Foam nueva en la planta del pie, así como de un cuello y una lengüeta ultracómodos para ofrecer un ajuste ceñido. Además, se añadió una membrana resistente al agua a esta versión para ayudar a evitar la humedad. | Hechas con materiales reciclados | Transpirabilidad contenida | Moderada | Negro | X | | Calzado de correr en carretera para mujer | Mujer\n", + "\n", + "------------------------\n", + "\n", + "{'details': \"{{'Amortiguación: superalta': 'Cuanta más amortiguación tengas en la planta del pie} {más suave y cómoda puede ser tu experiencia de running. La amortiguación suaviza el impacto cuando los pies llegan al suelo. La espuma ReactX Foam te brinda una sensación increíblemente suave que te ayuda a ir más allá de tus límites.'}} {{'Soporte: alto': 'Cuanto más soporte ofrece el calzado} {más estabilidad puede darle a tu pisada natural. La combinación de soporte optimizado y amortiguación colocada intencionalmente te ayuda a sentir seguridad en cada paso. La suela curva ayuda a que tu pie se balancee suavemente desde el talón hasta la punta y en cada paso hasta la pisada. Hace que cada paso se sienta más natural y agrega eficiencia a tu carrera} {lo que te ayuda a desperdiciar menos energía en cada pisada. La nueva banda de ajuste Flyknit interna (como una banda de goma alrededor de la parte media del pie) ofrece un soporte elástico y seguro.'}} {{'Responsividad: moderada': 'Cuanto más responsivo sea el calzado} {más retorno de energía obtendrás con cada paso. Ya sea que quieras correr un poco más rápido o con un poco menos de esfuerzo} {un calzado responsivo te brinda más elasticidad en tu pisada para que aproveches al máximo tu carrera. La espuma ReactX Foam te brinda un 13% adicional de retorno de energía en comparación con la espuma React Foam} {lo que te ayuda a mantener la frescura y la elasticidad durante la carrera.'}} {{'Transpirabilidad contenida': 'El forro repelente al agua en la punta te ayuda a mantenerte libre de humedad cuando cambia el tiempo.'}} {{'Más beneficios': 'Más beneficios'}}\", 'description': 'El Nike InfinityRN 4, con una amortiguación con soporte diseñada para ofrecer una carrera uniforme, es una nueva versión de un favorito conocido. Está hecho con nuestra nueva espuma Nike ReactX Foam que te brinda un 13% adicional de retorno de energía en comparación con la espuma Nike React Foam, lo que te ayuda a mantener la frescura y la elasticidad. (¿Y qué más? Nike ReactX reduce su huella de carbono en un par de entresuelas en al menos un 43% en comparación con la espuma Nike React Foam*). Combinamos la espuma ReactX Foam con el Flyknit más adaptable de Nike Running hasta la fecha, para que puedas despegar en cualquier momento y en cualquier lugar con un soporte seguro y transpirabilidad en la parte superior. Es el tipo de calzado que puede concederte esa tranquilidad que no tiene precio para ir más lejos y más rápido gracias a un diseño intuitivo que brinda soporte a cada paso. *La huella de carbono de ReactX se basa en un análisis de todo el proceso de producción, verificado por Intertek China y PRé Sustainability B.V. No se consideraron otros componentes de la entresuela, como cámaras de aire, placas u otras formulaciones de espuma.', 'category': 'Calzado de running en carretera para hombre'}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Amortiguación: superalta | Nike ReactX Foam | Flyknit más adaptable | Suela curva | - | No | Negro/Blanco | InfinityRN 4 | Calzado de running en carretera para hombre | Hombre/MIXTO |\n", + "\n", + "------------------------\n", + "\n", + "{'details': \"{{'Responsividad: superalta': 'Cuanto más responsivo sea el calzado} {más retorno de energía obtendrás con cada paso. Ya sea que quieras correr un poco más rápido o con un poco menos de esfuerzo} {un calzado responsivo te brinda más elasticidad en tu pisada para que aproveches al máximo tu carrera. En el Vomero 17} {nuestro calzado con el mejor resorte} {el pie descansa sobre una pila de ZoomX Foam} {la espuma más ligera y con mayor retorno de energía de Nike Running} {en la parte superior} {y espuma suave en la parte inferior para tener una pisada energizada} {pero suave.'}} {{'Soporte: neutro': 'Cuanto más soporte ofrece el calzado} {más estabilidad puede darle a tu pisada natural. La combinación de soporte optimizado y amortiguación colocada intencionalmente te ayuda a sentir seguridad en cada paso. El Vomero 17 tiene un soporte neutro. Te brinda equilibrio} {ya sea que pises con el talón o con el antepié. También es bueno para carreras largas y cortas; ofrece una transición suave del talón a la punta que complementa la pisada natural.'}} {{'Ajuste y sensación': 'Este calzado se enorgullece en tener comodidad premium que te pone en modo crucero para distancias largas o cortas. La malla diseñada estratégicamente le da más ligereza y transpirabilidad a la parte superior en comparación con los Vomero anteriores. La plantilla suave ofrece comodidad al ponértelo y el cuello es cómodo en el talón al momento de correr. La lengüeta acolchada y las agujetas suaves y elásticas completan una experiencia general ultracómoda.'}} {{'¿Qué novedades tiene el Vomero 17?': 'Simplificamos la estructura del 17 al quitar la unidad Zoom Air del antepié y apilar espuma ZoomX Foam premium arriba de la entresuela para amplificar la comodidad acolchada. El resultado es una pisada más suave y responsiva.'}} {{'Más beneficios': 'Más beneficios'}}\", 'description': 'El Vomero 17 proporciona una pisada elástica y suave para energizar cada kilómetro, la cual te lleva a tu lugar feliz de running. Su espuma apilada proporciona una responsividad superior para ayudarte a mejorar cuando estés listo para esa velocidad adicional. Y con mejoras generales que ofrecen un nuevo nivel de comodidad ligera y transpirabilidad, este calzado es para aquellos runners de carretera que buscan la emoción del vroom y la sensación de suavidad que te pone en modo crucero para distancias cortas o largas.', 'category': 'Calzado de running en carretera para hombre'}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Responsividad: superalta | ZoomX Foam, espuma más ligera y con mayor retorno de energía de Nike Running | - | suela sin especificar | null | No | Color negro/negro escuro | Vomero 17 | Calzado de running en carretera para hombre | Mujer/Hombre/MIXTO |\n", + "\n", + "------------------------\n", + "\n", + "{'details': \"{{'La innovación es nuestra inspiración': 'La innovación es nuestra inspiración'}} {{'Soporte: alto': 'Soporte: alto'}} {{'Amortiguación: superalta': 'Amortiguación: superalta'}} {{'Responsividad: superalta': 'Responsividad: superalta'}} {{'Ajuste: seguro} {transpirable y cómodo': 'Ajuste: seguro} {transpirable y cómodo'}} {{'¿Qué novedades tienen los Invincible 3?': '¿Qué novedades tienen los Invincible 3?'}} {{'Datos del producto': 'Datos del producto'}}\", 'description': 'Con la máxima amortiguación para brindarte soporte en cada kilómetro, el Invincible 3 ofrece nuestro más alto nivel de comodidad en la planta del pie. Sigue corriendo hoy, mañana y siempre. La espuma ZoomX Foam ultraresponsiva y ligera, que cuenta con un diseño avanzado según las especificaciones exactas de atletas campeones, te ayuda a seguir corriendo. Puede ayudarte a impulsarte por tu camino preferido y volver a tu próxima carrera sintiéndote con energía y vitalidad.', 'category': 'Calzado de correr en carretera para hombre'}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| La innovación es nuestra inspiración | transpirable y cómodo | superalta | alta | | no | negro, gris | Invincible 3 | Calzado de correr en carretera para hombre | Hombre\n", + "\n", + "------------------------\n", + "\n" + ] + } + ], + "source": [ + "dfs_nike = []\n", + "for producto in productos_nike:\n", + " prompt = generar_prompt_ollama(producto, etiquetas)\n", + " respuesta = obtener_respuesta_ollama(prompt)\n", + " print(producto)\n", + " print(\"Respuesta del modelo:\")\n", + " print(respuesta[\"message\"][\"content\"])\n", + " print(\"\\n------------------------\\n\")\n", + " df_nike = procesar_respuesta(respuesta[\"message\"][\"content\"], etiquetas)\n", + " if df_nike is not None:\n", + " dfs_nike.append(df_nike)\n", + " else:\n", + " print(\"No se pudo extraer la tabla.\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [], + "source": [ + "if dfs_nike:\n", + " df_total_nike = pd.concat(dfs_nike, ignore_index=True)\n", + "else:\n", + " print(\"No se pudo crear el DataFrame total.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cordonestextil exteriortextil interiorsuelapeso y/o tallaeco diseñado si o nocoloridentificadornombre de deportegenero Mujer/Hombre/MIXTO
0Zapatillas cómodasnullnullnullnullnullnullRevolution 7CorrerMujer
1Zapatillas con fijaciónNeutraTextil sintéticoSuela de goma-SiBlanco/Rosa/Violeta/VerdeWINFLO 11Correr en pavimento para mujerMujer/Hombre/MIXTO
2NullNullNullNullNullSiColor fáciles de combinarInvincible 3Calzado de correr en pavimento para mujerMujer
3Amortiguación: superaltaNike ZoomX con forma de mecedora y espuma más ...transpirableCuanta más amortiguación tengas en la planta d...nullSiBlanco/Sierra VerdeInvincible 3Calzado de running en carretera para hombreHombre
4Nike Grind--wafflenullsí--RunningHombre/MIXTO
5Amortiguación: altaEspumaTextilSuela con espumaPila altaNoBlanco/ NegroStructure 25RunningMujer/MIXTO
6Características principalesLa amortiguación máxima proporciona una comodi...Hechas con materiales recicladosTranspirabilidad contenidaModeradaNegroXNaNCalzado de correr en carretera para mujerMujer
7Amortiguación: superaltaNike ReactX FoamFlyknit más adaptableSuela curva-NoNegro/BlancoInfinityRN 4Calzado de running en carretera para hombreHombre/MIXTO
8Responsividad: superaltaZoomX Foam, espuma más ligera y con mayor reto...-suela sin especificarnullNoColor negro/negro escuroVomero 17Calzado de running en carretera para hombreMujer/Hombre/MIXTO
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\n", + "2 Null Null \n", + "3 Cuanta más amortiguación tengas en la planta d... null \n", + "4 waffle null \n", + "5 Suela con espuma Pila alta \n", + "6 Transpirabilidad contenida Moderada \n", + "7 Suela curva - \n", + "8 suela sin especificar null \n", + "9 alta NaN \n", + "\n", + " eco diseñado si o no color identificador \\\n", + "0 null null Revolution 7 \n", + "1 Si Blanco/Rosa/Violeta/Verde WINFLO 11 \n", + "2 Si Color fáciles de combinar Invincible 3 \n", + "3 Si Blanco/Sierra Verde Invincible 3 \n", + "4 sí - - \n", + "5 No Blanco/ Negro Structure 25 \n", + "6 Negro X NaN \n", + "7 No Negro/Blanco InfinityRN 4 \n", + "8 No Color negro/negro escuro Vomero 17 \n", + "9 no negro, gris Invincible 3 \n", + "\n", + " nombre de deporte genero Mujer/Hombre/MIXTO \n", + "0 Correr Mujer \n", + "1 Correr en pavimento para mujer Mujer/Hombre/MIXTO \n", + "2 Calzado de correr en pavimento para mujer Mujer \n", + "3 Calzado de running en carretera para hombre Hombre \n", + "4 Running Hombre/MIXTO \n", + "5 Running Mujer/MIXTO \n", + "6 Calzado de correr en carretera para mujer Mujer \n", + "7 Calzado de running en carretera para hombre Hombre/MIXTO \n", + "8 Calzado de running en carretera para hombre Mujer/Hombre/MIXTO \n", + "9 Calzado de correr en carretera para hombre Hombre " + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_total_nike" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Nacion Runer" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [], + "source": [ + "df_nr = df_raw[df_raw['store'] == 'nacionRunner']" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(263, 13)" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_nr.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['id', 'details', 'store', 'manufacturer', 'url', 'title',\n", + " 'regularPrice', 'undiscounted_price', 'description', 'category',\n", + " 'createdAt', 'characteristics', 'gender'],\n", + " dtype='object')" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_nr.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Usuario\\AppData\\Local\\Temp\\ipykernel_5352\\2945244906.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_nr['details_transformado'] = df_nr['details'].apply(lambda x: transformar_texto(x, 'adidas'))\n" + ] + } + ], + "source": [ + "# Aplicar la transformación con el parámetro 'adidas'\n", + "df_nr['details_transformado'] = df_nr['details'].apply(lambda x: transformar_texto(x, 'adidas'))" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [], + "source": [ + "# Crear un diccionario con las llaves deseadas\n", + "productos_nr = [\n", + " {\n", + " \"details\": row['details_transformado'],\n", + " \"description\": row['description'],\n", + " \"category\": row['category']\n", + " }\n", + " for _, row in df_nr[:10].iterrows() \n", + " if row['details_transformado'] != '{}'\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'details': \"{{'Pisada': 'Neutro • Supinador'}} {{'Peso del Corredor': 'Ideal hasta 85 Kg'}} {{'Amortiguación': 'Alta'}} {{'Ritmo De Carrera': 'Entre 4:30 y 6:00 min/Km'}} {{'Distancia Recomendada': 'Desde 5 hasta 42 Km'}} {{'Superficie': 'Asfalto'}}\", 'description': [{'Comodidad y Amortiguación Superior': 'Las Brooks Ghost 15 se destacan por su amortiguación DNA Loft v2, que proporciona una sensación de suavidad y confort bajo los pies, perfecta para quienes buscan un calzado que se adapte a ritmos de carrera que van desde 4:30 min/km hasta 6:00 min/km. Con un drop de 11 mm, estas zapatillas están diseñadas para corredoras con pisada neutra o supinadora, ofreciendo un soporte equilibrado que facilita la transición en cada zancada.'}, {'Diseñadas para Ritmos y Distancias Variadas': 'Ideales para distancias que van desde los 5 km hasta el maratón, las Brooks Ghost 15 están construidas para soportar volúmenes de entrenamiento semanal que oscilan entre 20 km y más de 50 km. Su capacidad de adaptarse tanto a ritmos rápidos como a entrenamientos más tranquilos las convierte en una opción versátil para el entrenamiento diario en asfalto, asegurando estabilidad y respuesta en cada paso.'}, {'Upper y Suela Mejorados para un Rendimiento Óptimo': 'El upper de malla de ingeniería de las Brooks Ghost 15 ha sido diseñado para mejorar la transpirabilidad y el ajuste, utilizando aproximadamente un 25% de materiales reciclados. Esta estructura permite una excelente ventilación y un ajuste seguro sin comprometer la ligereza. La suela de goma proporciona una tracción duradera y fiable, ideal para mantener la seguridad en todo tipo de superficies durante tus entrenamientos.'}], 'category': nan}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Neutro • Supinador | Malla de ingeniería | Upper con materiales reciclados | Goma | Ideal hasta 85 Kg | Sí | Neutro | Brooks Ghost 15 | Corredora | Mujer |\n", + "\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Entre 4:30 y 6:00 min/Km | Ventilación | Ajuste seguro sin comprometer la ligereza | Duradera y fiable | | Sí | Asfalto | | Corredora | Mujer |\n", + "\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Desde 5 hasta 42 Km | Ventilación | Ajuste seguro sin comprometer la ligerezza | Duradera y fiable | | Sí | Asfalto | | Corredora | Mujer\n", + "\n", + "------------------------\n", + "\n", + "{'details': \"{{'Pisada': 'Neutro'}} {{'Peso del Corredor': 'Ideal hasta 85 Kg'}} {{'Amortiguación': 'Alta'}} {{'Ritmo De Carrera': 'Entre 4:00 y 5:45 min/Km'}} {{'Distancia Recomendada': 'Entre 5 y 21 Km | Entreno en pista'}} {{'Superficie': 'Asfalto'}}\", 'description': [{'El equilibrio perfecto entre velocidad y comodidad': '¡Hey Runner! Las Hoka Mach 6 son ideales para quienes buscan correr rápido sin perder comodidad. Estas zapatillas han sido diseñadas para corredores avanzados que necesitan una opción confiable tanto para entrenamientos de calidad como para competiciones de hasta 21 km. Con una amortiguación reactiva y un diseño ligero, este modelo es perfecto para mantener ritmos rápidos y consistentes, sin la necesidad de una placa de carbono. Si pesas hasta 85 kg y cuentas con una buena base de entrenamiento, las Mach 6 serán tus aliadas en el asfalto. ¡Estas zapatillas son una competencia directa de las New Balance Rebel v4!'}, {'Versatilidad y rendimiento en cada paso': 'Las Hoka Mach 6 destacan por su mediasuela de espuma Supercrítica, que proporciona una pisada dinámica y un excelente retorno de energía. El MetaRocker en la suela te impulsa hacia adelante con cada zancada, haciéndolas ideales para ritmos vivos entre 4:00 min/km y 5:45 min/km. Su capellada de malla Jacquard Creel garantiza transpirabilidad y un ajuste cómodo, mientras que los refuerzos estratégicos de goma en la suela aseguran tracción en superficies mojadas.'}], 'category': nan}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Pisada | Neutro | null | MetaRocker | Ideal hasta 85 Kg | Si | Asfalto | Mach 6 | Corre | Mujer |\n", + "\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Entre 4:00 y 5:45 min/Km | null | null | Altimetral con espuma Supercrítica | Entre 5 y 21 Km | Si | Color neutro | New Balance Rebel v4 | Corre | Hombre\n", + "\n", + "------------------------\n", + "\n", + "{'details': \"{{'Pisada': 'Neutro • Supinador'}} {{'Peso del Corredor': 'Ideal hasta 80 Kg'}} {{'Amortiguación': 'Alta'}} {{'Ritmo De Carrera': '5:00 min/Km o más despacio'}} {{'Distancia Recomendada': 'Desde 5 hasta 21 Km'}} {{'Superficie': 'Asfalto'}}\", 'description': [{'Ligereza y durabilidad para tus entrenamientos diarios': '¡Hey Runner! Las Hoka Rincon 4 están diseñadas para ofrecer una combinación perfecta de ligereza y durabilidad, ideales para quienes buscan un zapato de entrenamiento diario en asfalto. Con su espuma de doble capa en la mediasuela, esta versión mejora notablemente la durabilidad, haciendo que cada zancada se sienta más consistente, incluso en distancias largas. Si eres un corredor que pesa menos de 80 kg y buscas un zapato ligero para entrenar distancias de hasta 21 km, el Rincon 4 es una opción a considerar seriamente. Para quienes pesen más de 80 kg, recomendamos modelos como el Hoka Clifton o el Bondi, que ofrecen mayor soporte.'}, {'Ideal para entrenamientos y distancias medias': 'Con un drop de 8 mm y una amortiguación alta, las Hoka Rincon 4 son perfectas para corredores de pisada neutral o supinador que manejan ritmos cómodos entre 5:00 min/km y más de 6:15 min/km. Su capellada de malla Jacquard es ligera y transpirable, asegurando un ajuste cómodo en cada carrera. Además, su suela de EVA con goma en zonas estratégicas proporciona un buen agarre, mientras que el MetaRocker ayuda a una transición suave de cada paso, maximizando la eficiencia en cada kilómetro.'}], 'category': nan}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Neutro • Supinador | Null | Null | Null | Ideal hasta 80 Kg | No | Asfalto | Hoka Rincon 4 | Corredor | Mujer |\n", + "\n", + "Nota: Se extrae la información según las etiquetas proporcionadas y se completa con valores \"null\" donde corresponda.\n", + "\n", + "------------------------\n", + "\n", + "{'details': \"{{'Pisada': 'Neutro'}} {{'Peso del Corredor': 'Ideal hasta 80 Kg'}} {{'Amortiguación': 'Alta'}} {{'Ritmo De Carrera': 'Entre 3:45 y 5:30 min/Km'}} {{'Distancia Recomendada': 'Entre 5 y 21 Km | Entreno en pista'}} {{'Superficie': 'Asfalto'}}\", 'description': [{'Ligereza y Respuesta para Distancias Medias': 'Las Hoka Mach 5 están diseñadas para ofrecer una excelente respuesta en ritmos que van desde 3:45 min/km hasta 5:15 min/km. Con un drop de 5 mm y un peso de 242 gramos en la versión para hombre, estas zapatillas son ideales para distancias que van desde 5 km hasta 21 km. La mediasuela incorpora la espuma ProFly+, que combina una amortiguación suave con una sensación de rebote, permitiendo una transición fluida en cada zancada.'}, {'Comodidad y Ajuste Superior': 'Estas zapatillas cuentan con una capellada de malla ligera que asegura una transpirabilidad óptima, manteniendo tus pies frescos incluso en carreras intensas. Además, el diseño del calzado, con una lengüeta plana y una sujeción firme, se adapta perfectamente a tu pie, evitando movimientos indeseados y garantizando un ajuste seguro durante todo el recorrido.'}, {'Ideal para Entrenamientos Diarios y Diversos Tipos de Corredores': 'Las Hoka Mach 5 no solo son una opción excelente para velocidad, sino que también se destacan como calzado de entrenamiento diario. Son ideales para corredores con un peso de entre menos de 60 kg hasta 80 kg, y que tengan un volumen de entrenamiento semanal de entre 30 km y 50 km o más. Ofrecen un equilibrio ideal entre amortiguación y ligereza, lo que las convierte en una excelente opción para aquellos corredores que buscan un calzado que les permita rendir al máximo día tras día.'}, {'Competencia Directa en Colombia': 'En el mercado colombiano, las Hoka Mach 5 compiten directamente con modelos destacados como la ASICS Noosa Tri 15, el Brooks Hyperion Max, y el New Balance Rebel v3. Sin embargo, las Mach 5 se destacan por su combinación única de ligereza y capacidad de respuesta, haciéndolas una opción preferida para corredores que buscan superar sus límites en cada entrenamiento.'}], 'category': nan}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Neutro | Asfalto | Espuma ProFly+ | Mediasuela | Ideal hasta 80 Kg | Sí | Neutro | Hoka Mach 5 | Corredor | Hombre |\n", + "\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", + "| null | Asfalto | Espuma ProFly+ | Mediasuela | Ideal hasta 80 Kg | Sí | Neutro | Hoka Mach 5 | Corredor | Hombre |\n", + "\n", + "Nota: En la primera fila, se indican los nombres de las etiquetas. En la segunda fila, se muestran los valores correspondientes a cada etiqueta.\n", + "\n", + "------------------------\n", + "\n", + "{'details': \"{{'Pisada': 'Neutro • Supinador'}} {{'Peso del Corredor': 'Ideal hasta 85 Kg'}} {{'Amortiguación': 'Superior'}} {{'Ritmo De Carrera': 'Entre 4:00 y 6:00 min/Km'}} {{'Distancia Recomendada': 'Entre 5 y 42 Km | Entreno en pista'}} {{'Superficie': 'Asfalto'}}\", 'description': [{'Rendimiento en Asfalto': 'La Asics Novablast 4 LE está optimizada para asfalto, ofreciendo una experiencia de carrera segura y controlada. Con un drop de 8 mm y una pisada neutra o supinadora, esta zapatilla es ideal para corredores que buscan entrenamientos de velocidad. Su diseño más \"contundente\" y sólido proporciona una estabilidad superior, permitiendo girar y voltear con confianza, incluso en circuitos cerrados. Además, la base ensanchada y el talón prominente mejoran aún más la estabilidad, eliminando cualquier rastro de inestabilidad presente en versiones anteriores.'}, {'Amortiguación Superior y Comodidad Avanzada': 'Equipada con la innovadora mediasuela FlyteFoam Blast+ ECO, la Novablast 4 LE ofrece una amortiguación superior, adaptándose tanto a ritmos moderados como intensos. Este modelo se destaca por su capacidad de proporcionar una carrera suave y cómoda, aunque a ritmos muy altos puede sentirse un poco menos reactiva. Sin embargo, su enfoque está en ofrecer una experiencia de entrenamiento segura y eficiente, ideal para distancias que van desde los 5 km hasta los 42 km.'}, {'Tecnología y Diseño de Vanguardia': 'La Novablast 4 LE no solo se distingue por su ligereza, sino también por la inclusión de un nuevo caucho AHAR en la suela, mejorando significativamente la tracción y la durabilidad. Su capellada de material Woven, elástica y sin costuras, brinda un ajuste cómodo y lujoso, aunque sacrifica un poco de ventilación. Sin embargo, su capacidad de secado rápido compensa este aspecto, haciendo que la zapatilla esté lista para usar nuevamente en poco tiempo, incluso después de entrenamientos bajo la lluvia.'}], 'category': nan}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Supinador | Neutro • Supinador | null | AhAR | Ideal hasta 85 Kg | null | Neutro • Supinador | Novablast 4 LE | Entre 5 y 42 Km | Mujer/Hombre/MIXTO |\n", + "\n", + "------------------------\n", + "\n", + "{'details': \"{{'Pisada': 'Pronador'}} {{'Peso del Corredor': 'Ideal hasta 85 Kg'}} {{'Amortiguación': 'Alta'}} {{'Ritmo De Carrera': '5:15 min/Km o más despacio'}} {{'Distancia Recomendada': 'Desde 5 hasta 21 Km'}} {{'Superficie': 'Asfalto'}}\", 'description': [{'Estabilidad y confort a un precio que convence': '¡Hey Runner! Las ASICS GT-2000 13 llegan al mercado con la promesa de ser una de las mejores opciones para corredores que buscan estabilidad y amortiguación alta sin romper el presupuesto. Este modelo no es un Kayano, pero está muy cerca en términos de calidad y soporte. Por su precio, es una elección que harías a ojos cerrados, y con lo que ahorras, ¡podrías inscribirte en tu próxima carrera!'}, {'Tecnología avanzada para un rendimiento superior': 'Las GT-2000 13 están diseñadas para corredores con pisada pronadora que buscan entrenar de forma regular en asfalto y distancias de hasta 21 km. Con un drop de 8 mm y un peso de 275 g, estas zapatillas brindan una pisada suave y protegida. La espuma FlyteFoam Blast Plus y la tecnología PureGEL en el talón aseguran una amortiguación que se siente como correr sobre una nube, mientras que el sistema 3D Guidance mejora la estabilidad y guía tu pisada de manera natural.'}], 'category': nan}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Pisada pronadora | Pronador | Algunos puestos pueden tener materiales sintéticos como la poliéster, el nylon y la paño, con una mezcla de cuero y poliéster en los bordes | Alta densidad | Ideal hasta 85 Kg | Sí | Negro/Rosa | GT-2000 13 | Corredor | Mujer/Hombre/MIXTO |\n", + "\n", + "------------------------\n", + "\n", + "{'details': \"{{'Pisada': 'Neutro • Supinador'}} {{'Peso del Corredor': 'Ideal hasta 85 Kg'}} {{'Amortiguación': 'Alta'}} {{'Ritmo De Carrera': 'Entre 4:00 y 5:45 min/Km'}} {{'Distancia Recomendada': 'Desde 5 hasta 42 Km'}} {{'Superficie': 'Asfalto'}}\", 'description': [{'Tecnología y Rendimiento en Asfalto': 'La Asics Glideride Max está optimizada para entrenamientos diarios y largas distancias en asfalto. Diseñada para corredores avanzados con pisada neutra o supinadora, esta zapatilla cuenta con un drop de 6 mm y es ideal para mantener ritmos entre 4:00 min/km y 5:30 min/km en distancias que van desde los 5 km hasta la maratón. Lo que hace destacar a este modelo es la incorporación de la innovadora espuma FF Blast Max, la cual promete un rebote superior y una sensación de ligereza, sin precedentes en la línea Glideride.'}, {'Placa Interna y Amortiguación Superior': 'Para aquellos que buscan mejorar sus tiempos sin recurrir a una placa de carbono, la Glideride Max ofrece una solución perfecta. Este modelo incorpora una placa interna de nylon o plástico que añade rigidez y mejora la eficiencia en cada zancada, impulsándote a correr más rápido. Además, la combinación de FlyteFoam Blast+ ECO en la capa inferior de la mediasuela y el nuevo FF Blast Max en la parte superior, asegura una sensación de amortiguación alta que te mantendrá protegido en cada paso, incluso en tus entrenamientos más intensos.'}, {'Diseño y Durabilidad': 'El upper de la Glideride Max está construido con un mesh técnico similar al de la Nimbus 26, ofreciendo transpirabilidad y un ajuste seguro. La suela, diseñada con una combinación de ASICS Grip y AHARPLUS, garantiza durabilidad y tracción en diversas superficies, lo que la convierte en una opción confiable para corredores que necesitan un calzado resistente para entrenamientos de volumen.'}, {'Competencia en el Mercado Colombiano': 'En el mercado colombiano, la Asics Glideride Max competirá directamente con modelos como el Brooks Hyperion Max. Ambos zapatos están dirigidos a corredores que buscan velocidad sin renunciar al confort de un calzado maximalista. La Glideride Max se presenta como una opción sólida para aquellos que desean una zapatilla con tecnología avanzada que les permita afrontar largas distancias con mayor eficiencia y menor riesgo de lesión.'}], 'category': nan}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Neutro • Supinador | Neutro | Nylon o plástico | ASICS Grip + AHARPLUS | Ideal hasta 85 Kg | Si | Neutro | Glideride Max | Corredora | Hombre\n", + "\n", + "------------------------\n", + "\n", + "{'details': \"{{'Pisada': 'Pronador'}} {{'Peso del Corredor': 'Ideal desde 65 Kg en adelante'}} {{'Amortiguación': 'Superior'}} {{'Ritmo De Carrera': '5:30 min/Km o más despacio'}} {{'Distancia Recomendada': 'Desde 5 hasta 42 Km'}} {{'Superficie': 'Asfalto'}}\", 'description': [{'Estabilidad y confort para corredores avanzados': 'Las Gel Kayano 31 están equipadas con el innovador 4D Guidance System, una tecnología que proporciona el soporte exacto que cada corredor necesita, adaptándose de manera dinámica a la forma de correr, sin restringir el movimiento natural del pie. Este sistema es esencial para corredores con pisada pronadora, ayudando a controlar la sobrepronación y ofreciendo una carrera más equilibrada y segura. Además, con una sensación de amortiguación superior proporcionada por la tecnología FF BLAST PLUS ECO en la mediasuela, estas zapatillas garantizan una absorción de impactos excepcional, devolviendo la energía en cada zancada.'}, {'Amortiguación dinámica para largas distancias': 'Ideales para corredores que entrenan distancias que van desde 10 km hasta los 42 km, las Gel Kayano 31 están diseñadas para mantener la comodidad y estabilidad durante los entrenamientos más largos. Su capacidad para manejar ritmos de carrera entre 5:30 min/km y más de 6:15 min/km las hace perfectas para corredores con un volumen de entrenamiento semanal que oscila entre 20 km y más de 50 km. Con un drop de 10 mm y un peso de 305 gramos para la versión masculina, estas zapatillas están preparadas para corredores con un peso de 65 kg a más de 95 kg.'}, {'Diseño renovado y adaptabilidad': 'La capellada de malla técnica mejorada no solo asegura una mayor transpirabilidad, sino que también proporciona un ajuste más cómodo y personalizado, adaptándose a la forma del pie en cada paso. Además, la suela exterior, actualizada con el material de goma HYBRID ASICSGRIP, mejora la tracción y la estabilidad en cada kilómetro que recorres. Estas características hacen que las Gel Kayano 31 sean ideales para entrenamientos en asfalto y corredores que buscan una zapatilla de entrenamiento diario que pueda manejar altos volúmenes de entrenamiento semanal.'}, {'Un competidor destacado en el mercado de estabilidad': 'Las Gel Kayano 31 se enfrentan cara a cara con otras zapatillas de estabilidad premium, como el New Balance Vongo y el Brooks Glycerine GTS, ofreciendo una experiencia de carrera más dinámica gracias a su amortiguación mejorada que se adapta a tu ritmo y técnica de carrera.'}], 'category': nan}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Pisada pronadora | Asfalto | Mediasuela tech | Suela de goma HYBRID ASICSGRIP | Ideal desde 65 Kg en adelante | Si | Blanco | Kayano 31 | Corredor | Hombre\n", + "\n", + "------------------------\n", + "\n", + "{'details': \"{{'Pisada': 'Pronador'}} {{'Peso del Corredor': 'Ideal hasta 85 Kg'}} {{'Amortiguación': 'Alta'}} {{'Ritmo De Carrera': '5:15 min/Km o más despacio'}} {{'Distancia Recomendada': 'Desde 5 hasta 21 Km'}} {{'Superficie': 'Asfalto'}}\", 'description': [{'Rendimiento Óptimo para Pronadores': 'La Asics GT-1000 13 es ideal para corredores con pisada pronadora, ofreciendo un equilibrio perfecto entre estabilidad y amortiguación. Con un drop de 8 mm y un peso de 270 gramos, esta zapatilla es adecuada para corredores que pesan entre menos de 60 kg y 85 kg. Gracias a la incorporación de la tecnología PureGel en la zona del talón, esta zapatilla proporciona una absorción de impactos superior, permitiendo una sensación de comodidad en cada zancada.'}, {'Amortiguación y Estabilidad en Ritmos Moderados': 'Con una sensación de amortiguación alta, la GT-1000 13 está diseñada para corredores que mantienen ritmos entre 5:15 min/km y 6:15 min/km o más lentos. Es ideal para distancias que van desde 5 km hasta 21 km, proporcionando el soporte necesario para mantener un ritmo constante y seguro. La tecnología FlyteFoam en la mediasuela asegura una pisada suave y ligera, mientras que la suela, con una alta concentración de caucho, garantiza durabilidad y tracción en superficies de asfalto.'}, {'Confort y Durabilidad para el Entrenamiento Diario': 'El upper de la GT-1000 13 está confeccionado con una malla técnica que asegura una excelente transpirabilidad y un ajuste preciso, envolviendo tu pie con suavidad en cada carrera. Además, la plantilla OrthoLite Hybrid Max añade una capa adicional de confort, lo que hace que cada entrenamiento sea más placentero. Esta zapatilla está diseñada para un volumen de entrenamiento semanal de 20 km a 40 km, siendo una opción confiable para corredores que buscan estabilidad en sus entrenamientos diarios.'}, {'Accesibilidad y Valor Inigualable': 'Una de las ventajas más destacadas de la Asics GT-1000 13 es su relación calidad-precio. A pesar de incorporar tecnologías de gama alta como el PureGel, esta zapatilla se mantiene en un rango de precio accesible, lo que la coloca en una posición competitiva en el mercado colombiano. Aunque no tiene rivales directos en su rango de precio, podría compararse con el Brooks Adrenaline, pero a un costo significativamente menor, ofreciendo así un valor insuperable para corredores pronadores.'}], 'category': nan}\n", + "Respuesta del modelo:\n", + "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", + "| Pronador | Algodón/Polipropileno | Malla técnica con poliéster y spandex | Suela de caucho | Ideal hasta 85 Kg | Si | Negro/Cream | GT-1000 13 | Pronador | Hombre\n", + "\n", + "------------------------\n", + "\n" + ] + } + ], + "source": [ + "dfs_nr = []\n", + "for producto in productos_nr:\n", + " prompt = generar_prompt_ollama(producto, etiquetas)\n", + " respuesta = obtener_respuesta_ollama(prompt)\n", + " print(producto)\n", + " print(\"Respuesta del modelo:\")\n", + " print(respuesta[\"message\"][\"content\"])\n", + " print(\"\\n------------------------\\n\")\n", + " df_nr = procesar_respuesta(respuesta[\"message\"][\"content\"], etiquetas)\n", + " if df_nr is not None:\n", + " dfs_nr.append(df_nr)\n", + " else:\n", + " print(\"No se pudo extraer la tabla.\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cordonestextil exteriortextil interiorsuelapeso y/o tallaeco diseñado si o nocoloridentificadornombre de deportegenero Mujer/Hombre/MIXTO
0Neutro • SupinadorMalla de ingenieríaUpper con materiales recicladosGomaIdeal hasta 85 KgSíNeutroBrooks Ghost 15CorredoraMujer
1PisadaNeutronullMetaRockerIdeal hasta 85 KgSiAsfaltoMach 6CorreMujer
2Neutro • SupinadorNullNullNullIdeal hasta 80 KgNoAsfaltoHoka Rincon 4CorredorMujer
3NeutroAsfaltoEspuma ProFly+MediasuelaIdeal hasta 80 KgSíNeutroHoka Mach 5CorredorHombre
4SupinadorNeutro • SupinadornullAhARIdeal hasta 85 KgnullNeutro • SupinadorNovablast 4 LEEntre 5 y 42 KmMujer/Hombre/MIXTO
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cordonestextil exteriortextil interiorsuelapeso y/o tallaeco diseñado si o nocoloridentificadornombre de deportegenero Mujer/Hombre/MIXTO
0Neutro • SupinadorMalla de ingenieríaUpper con materiales recicladosGomaIdeal hasta 85 KgSíNeutroBrooks Ghost 15CorredoraMujer
1PisadaNeutronullMetaRockerIdeal hasta 85 KgSiAsfaltoMach 6CorreMujer
2Neutro • SupinadorNullNullNullIdeal hasta 80 KgNoAsfaltoHoka Rincon 4CorredorMujer
3NeutroAsfaltoEspuma ProFly+MediasuelaIdeal hasta 80 KgSíNeutroHoka Mach 5CorredorHombre
4SupinadorNeutro • SupinadornullAhARIdeal hasta 85 KgnullNeutro • SupinadorNovablast 4 LEEntre 5 y 42 KmMujer/Hombre/MIXTO
5Pisada pronadoraPronadorAlgunos puestos pueden tener materiales sintét...Alta densidadIdeal hasta 85 KgSíNegro/RosaGT-2000 13CorredorMujer/Hombre/MIXTO
6Neutro • SupinadorNeutroNylon o plásticoASICS Grip + AHARPLUSIdeal hasta 85 KgSiNeutroGlideride MaxCorredoraHombre
7Pisada pronadoraAsfaltoMediasuela techSuela de goma HYBRID ASICSGRIPIdeal desde 65 Kg en adelanteSiBlancoKayano 31CorredorHombre
8PronadorAlgodón/PolipropilenoMalla técnica con poliéster y spandexSuela de cauchoIdeal hasta 85 KgSiNegro/CreamGT-1000 13PronadorHombre
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" + ], + "text/plain": [ + " cordones textil exterior \\\n", + "0 Neutro • Supinador Malla de ingeniería \n", + "1 Pisada Neutro \n", + "2 Neutro • Supinador Null \n", + "3 Neutro Asfalto \n", + "4 Supinador Neutro • Supinador \n", + "5 Pisada pronadora Pronador \n", + "6 Neutro • Supinador Neutro \n", + "7 Pisada pronadora Asfalto \n", + "8 Pronador Algodón/Polipropileno \n", + "\n", + " textil interior \\\n", + "0 Upper con materiales reciclados \n", + "1 null \n", + "2 Null \n", + "3 Espuma ProFly+ \n", + "4 null \n", + "5 Algunos puestos pueden tener materiales sintét... \n", + "6 Nylon o plástico \n", + "7 Mediasuela tech \n", + "8 Malla técnica con poliéster y spandex \n", + "\n", + " suela peso y/o talla \\\n", + "0 Goma Ideal hasta 85 Kg \n", + "1 MetaRocker Ideal hasta 85 Kg \n", + "2 Null Ideal hasta 80 Kg \n", + "3 Mediasuela Ideal hasta 80 Kg \n", + "4 AhAR Ideal hasta 85 Kg \n", + "5 Alta densidad Ideal hasta 85 Kg \n", + "6 ASICS Grip + AHARPLUS Ideal hasta 85 Kg \n", + "7 Suela de goma HYBRID ASICSGRIP Ideal desde 65 Kg en adelante \n", + "8 Suela de caucho Ideal hasta 85 Kg \n", + "\n", + " eco diseñado si o no color identificador nombre de deporte \\\n", + "0 Sí Neutro Brooks Ghost 15 Corredora \n", + "1 Si Asfalto Mach 6 Corre \n", + "2 No Asfalto Hoka Rincon 4 Corredor \n", + "3 Sí Neutro Hoka Mach 5 Corredor \n", + "4 null Neutro • Supinador Novablast 4 LE Entre 5 y 42 Km \n", + "5 Sí Negro/Rosa GT-2000 13 Corredor \n", + "6 Si Neutro Glideride Max Corredora \n", + "7 Si Blanco Kayano 31 Corredor \n", + "8 Si Negro/Cream GT-1000 13 Pronador \n", + "\n", + " genero Mujer/Hombre/MIXTO \n", + "0 Mujer \n", + "1 Mujer \n", + "2 Mujer \n", + "3 Hombre \n", + "4 Mujer/Hombre/MIXTO \n", + "5 Mujer/Hombre/MIXTO \n", + "6 Hombre \n", + "7 Hombre \n", + "8 Hombre " + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_total_nr" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From f214cdf63ae488ebc6f6b20f5d57d0b92f12688d Mon Sep 17 00:00:00 2001 From: jumcorrealom Date: Sun, 24 Nov 2024 10:14:00 -0500 Subject: [PATCH 03/84] Update project_charter.md --- docs/business_understanding/project_charter.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/business_understanding/project_charter.md b/docs/business_understanding/project_charter.md index 38810d19c..5c4852e75 100644 --- a/docs/business_understanding/project_charter.md +++ b/docs/business_understanding/project_charter.md @@ -2,11 +2,11 @@ ## Nombre del Proyecto -[Nombre del proyecto aquí] +Análisis comparativo de productos de Running entre Nike, Adidas y Nation Runner ## Objetivo del Proyecto -[Descripción breve del objetivo del proyecto y por qué es importante] +Desarrollar una herramienta computacional que permita realizar un análisis de comparación de zapatos para running, con datos seleccionados de varias tiendas retail mediante técnicas de procesamiento de lenguaje natural y técnicas de pre-procesamiento de datos que incluyan Grandes Modelos de Lenguaje. ## Alcance del Proyecto @@ -22,7 +22,7 @@ ## Metodología -[Descripción breve de la metodología que se utilizará para llevar a cabo el proyecto] +CRISP-DM y SCRUM ## Cronograma From e29717feb8cb61efc78716d759d4cdc9fcf847eb Mon Sep 17 00:00:00 2001 From: azacipa <52643112+azacipa@users.noreply.github.com> Date: Sun, 24 Nov 2024 10:18:45 -0500 Subject: [PATCH 04/84] prueba de cargue del archivo --- docs/business_understanding/prueba_azacipa.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 docs/business_understanding/prueba_azacipa.txt diff --git a/docs/business_understanding/prueba_azacipa.txt b/docs/business_understanding/prueba_azacipa.txt new file mode 100644 index 000000000..d931ceffb --- /dev/null +++ b/docs/business_understanding/prueba_azacipa.txt @@ -0,0 +1 @@ +prueba azacipa \ No newline at end of file From a0eb48b4e3587537aeb6bc37d54ae42a02011781 Mon Sep 17 00:00:00 2001 From: azacipa <52643112+azacipa@users.noreply.github.com> Date: Sun, 24 Nov 2024 10:24:42 -0500 Subject: [PATCH 05/84] Update prueba_azacipa.txt --- docs/business_understanding/prueba_azacipa.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/business_understanding/prueba_azacipa.txt b/docs/business_understanding/prueba_azacipa.txt index d931ceffb..f9b3dfbfd 100644 --- a/docs/business_understanding/prueba_azacipa.txt +++ b/docs/business_understanding/prueba_azacipa.txt @@ -1 +1,2 @@ -prueba azacipa \ No newline at end of file +prueba azacipa +para el modulo 6 del diplomado \ No newline at end of file From 06f0f5a53baa8e065533a3283d7134f9fbcef9a7 Mon Sep 17 00:00:00 2001 From: Juan Correa Date: Mon, 25 Nov 2024 20:22:35 -0500 Subject: [PATCH 06/84] generated api_reader, and re ubicated main.py for better execution, requirements.txt and .gitignore actualizated too --- .gitignore | 2 + docs/data/data_dictionary.md | 40 ++++++-------- main.py | 14 +++++ requirements.txt | 10 ++-- .../data_acquisition}/__init__.py | 0 scripts/data_acquisition/api_reader.py | 54 +++++++++++++++++++ .../__init__.py | 0 .../database}/__init__.py | 0 .../evaluation}/__init__.py | 0 .../models}/__init__.py | 0 .../preprocessing}/__init__.py | 0 .../training}/__init__.py | 0 .../visualization/__init__.py | 0 13 files changed, 93 insertions(+), 27 deletions(-) create mode 100644 main.py rename {src/nombre_paquete => scripts/data_acquisition}/__init__.py (100%) create mode 100644 scripts/data_acquisition/api_reader.py rename src/{nombre_paquete/database => comparative_analysis}/__init__.py (100%) rename src/{nombre_paquete/evaluation => comparative_analysis/database}/__init__.py (100%) rename src/{nombre_paquete/models => comparative_analysis/evaluation}/__init__.py (100%) rename src/{nombre_paquete/preprocessing => comparative_analysis/models}/__init__.py (100%) rename src/{nombre_paquete/training => comparative_analysis/preprocessing}/__init__.py (100%) rename src/{nombre_paquete/visualization => comparative_analysis/training}/__init__.py (100%) rename scripts/data_acquisition/main.py => src/comparative_analysis/visualization/__init__.py (100%) diff --git a/.gitignore b/.gitignore index 411470fe7..e1989f769 100644 --- a/.gitignore +++ b/.gitignore @@ -3,3 +3,5 @@ env_vars* credentials* **/build/* +venv +*.xlsx \ No newline at end of file diff --git a/docs/data/data_dictionary.md b/docs/data/data_dictionary.md index 72552d48c..add175a86 100644 --- a/docs/data/data_dictionary.md +++ b/docs/data/data_dictionary.md @@ -4,35 +4,27 @@ **Agregar una descripción de la tabla o fuente de datos. -| Variable | Descripción | Tipo de dato | Rango/Valores posibles | Fuente de datos | +| Variable | Descripción | Tipo de dato | Rango/Valores posibles | Example Value | | --- | --- | --- | --- | --- | -| variable_1 | Descripción de la variable 1 | Tipo de dato | Rango/Valores posibles | Fuente de datos | -| variable_2 | Descripción de la variable 2 | Tipo de dato | Rango/Valores posibles | Fuente de datos | -| variable_3 | Descripción de la variable 3 | Tipo de dato | Rango/Valores posibles | Fuente de datos | -| variable_4 | Descripción de la variable 4 | Tipo de dato | Rango/Valores posibles | Fuente de datos | -| variable_5 | Descripción de la variable 5 | Tipo de dato | Rango/Valores posibles | Fuente de datos | +| id | Descripción de la variable 1 | string | | 046zSiHm8Cz0fZYwMJlL | +| details | detalles del producto scrapeado, data semi estructurada que contiene detalles técnicos del mismo | string | - | '{Horma clásica} {Parte superior sintética}...' | +| store | nombre de la tienda que vende el producto | string | | adidas | +| manufacturer | nombre de la empesa que crea el producto | | adidas | +| url | url desde la que se extrajo el producto | string | | https://www.adidas.co/tenis-duramo-sl/IF7884.html | +| title | nombre del producto | string | | Tenis Duramo SL | +| regularPrice | precio del producto sin descuento | string | | $379.950 | +| undiscounted_price | precio con descuento aplicado | string | | $265.965 | +| description | descripción general del producto | string | | 'description': "Los Adizero Adios Pro 3 son la ..." | +| category | categoria a la que la página scrapeada asigna el producto | string | | Mujer • Running | +| createdAt | fecha de creación del registro | string | | '_seconds': 1731975445, '_nanoseconds': 42700.. | +| characteristics | características adicionales del producto | string | | Parte superior de malla diseñada estratégicam.. | +| gender | genero del producto | string | | Mujer | -- **Variable**: nombre de la variable. -- **Descripción**: breve descripción de la variable. -- **Tipo de dato**: tipo de dato que contiene la variable. -- **Rango/Valores posibles**: rango o valores que puede tomar la variable. -- **Fuente de datos**: fuente de los datos de la variable. - -## Base de datos 2 - -**Agregar una descripción de la tabla o fuente de datos. - -| Variable | Descripción | Tipo de dato | Rango/Valores posibles | Fuente de datos | -| --- | --- | --- | --- | --- | -| variable_1 | Descripción de la variable 1 | Tipo de dato | Rango/Valores posibles | Fuente de datos | -| variable_2 | Descripción de la variable 2 | Tipo de dato | Rango/Valores posibles | Fuente de datos | -| variable_3 | Descripción de la variable 3 | Tipo de dato | Rango/Valores posibles | Fuente de datos | -| variable_4 | Descripción de la variable 4 | Tipo de dato | Rango/Valores posibles | Fuente de datos | -| variable_5 | Descripción de la variable 5 | Tipo de dato | Rango/Valores posibles | Fuente de datos | - **Variable**: nombre de la variable. - **Descripción**: breve descripción de la variable. - **Tipo de dato**: tipo de dato que contiene la variable. - **Rango/Valores posibles**: rango o valores que puede tomar la variable. -- **Fuente de datos**: fuente de los datos de la variable. +- **Example Value**: Ejemplo de un valor que se puede encontrar en el campo. +- **Fuente de datos**: API en la que se disponibilizan los datos scrapeados. diff --git a/main.py b/main.py new file mode 100644 index 000000000..be5883d03 --- /dev/null +++ b/main.py @@ -0,0 +1,14 @@ +from scripts.data_acquisition.api_reader import ApiReader + +def main(): + # Crear instancia de ApiReader + api_url = "https://scraping-firestore-178159629911.us-central1.run.app//v1/scraping/" + output_path = "src/comparative_analysis/database/raw_data.xlsx" + + api_reader = ApiReader(api_url, output_path) + + # Leer datos de la API y guardar en Excel + api_reader.fetch_and_save_data() + +if __name__ == "__main__": + main() diff --git a/requirements.txt b/requirements.txt index 50db08636..c0b612c10 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,8 @@ -alpaca-trade-api -torch fairscale fire blobfile +torch +fairscale +fire +blobfile pandas -ollama \ No newline at end of file +ollama +requests +openpyxl \ No newline at end of file diff --git a/src/nombre_paquete/__init__.py b/scripts/data_acquisition/__init__.py similarity index 100% rename from src/nombre_paquete/__init__.py rename to scripts/data_acquisition/__init__.py diff --git a/scripts/data_acquisition/api_reader.py b/scripts/data_acquisition/api_reader.py new file mode 100644 index 000000000..8f6bfd85c --- /dev/null +++ b/scripts/data_acquisition/api_reader.py @@ -0,0 +1,54 @@ +import os +import requests +import pandas as pd + +class ApiReader: + def __init__(self, api_url, output_path): + """ + Inicializa el objeto ApiReader con la URL de la API y la ruta de salida. + + :param api_url: URL de la API + :param output_path: Ruta donde se guardará el archivo Excel + """ + self.api_url = api_url + self.output_path = output_path + + def fetch_data(self): + """ + Realiza la solicitud GET a la API y devuelve los datos en formato JSON. + + :return: Datos JSON de la API + :raises Exception: Si la solicitud falla + """ + response = requests.get(self.api_url) + if response.status_code == 200: + return response.json() + else: + raise Exception(f"Error al obtener datos de la API: {response.status_code}") + + def save_to_excel(self, data): + """ + Guarda los datos en un archivo Excel. + + :param data: Datos en formato JSON + """ + # Crear un DataFrame con los datos + df_raw = pd.DataFrame(data) + + # Asegurarse de que la carpeta de salida exista + output_dir = os.path.dirname(self.output_path) + os.makedirs(output_dir, exist_ok=True) + + # Guardar el DataFrame en un archivo Excel + df_raw.to_excel(self.output_path, index=False) + print(f"Datos guardados exitosamente en {self.output_path}") + + def fetch_and_save_data(self): + """ + Realiza la lectura de datos desde la API y los guarda en un archivo Excel. + """ + try: + data = self.fetch_data() + self.save_to_excel(data) + except Exception as e: + print(f"Error: {e}") diff --git a/src/nombre_paquete/database/__init__.py b/src/comparative_analysis/__init__.py similarity index 100% rename from src/nombre_paquete/database/__init__.py rename to src/comparative_analysis/__init__.py diff --git a/src/nombre_paquete/evaluation/__init__.py b/src/comparative_analysis/database/__init__.py similarity index 100% rename from src/nombre_paquete/evaluation/__init__.py rename to src/comparative_analysis/database/__init__.py diff --git a/src/nombre_paquete/models/__init__.py b/src/comparative_analysis/evaluation/__init__.py similarity index 100% rename from src/nombre_paquete/models/__init__.py rename to src/comparative_analysis/evaluation/__init__.py diff --git a/src/nombre_paquete/preprocessing/__init__.py b/src/comparative_analysis/models/__init__.py similarity index 100% rename from src/nombre_paquete/preprocessing/__init__.py rename to src/comparative_analysis/models/__init__.py diff --git a/src/nombre_paquete/training/__init__.py b/src/comparative_analysis/preprocessing/__init__.py similarity index 100% rename from src/nombre_paquete/training/__init__.py rename to src/comparative_analysis/preprocessing/__init__.py diff --git a/src/nombre_paquete/visualization/__init__.py b/src/comparative_analysis/training/__init__.py similarity index 100% rename from src/nombre_paquete/visualization/__init__.py rename to src/comparative_analysis/training/__init__.py diff --git a/scripts/data_acquisition/main.py b/src/comparative_analysis/visualization/__init__.py similarity index 100% rename from scripts/data_acquisition/main.py rename to src/comparative_analysis/visualization/__init__.py From e6e5c71e63028f940493d81a8ee46d5867de590b Mon Sep 17 00:00:00 2001 From: jumcorrealom Date: Mon, 25 Nov 2024 20:24:39 -0500 Subject: [PATCH 07/84] Update data_dictionary.md --- docs/data/data_dictionary.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/data/data_dictionary.md b/docs/data/data_dictionary.md index add175a86..ff4e420c3 100644 --- a/docs/data/data_dictionary.md +++ b/docs/data/data_dictionary.md @@ -9,7 +9,7 @@ | id | Descripción de la variable 1 | string | | 046zSiHm8Cz0fZYwMJlL | | details | detalles del producto scrapeado, data semi estructurada que contiene detalles técnicos del mismo | string | - | '{Horma clásica} {Parte superior sintética}...' | | store | nombre de la tienda que vende el producto | string | | adidas | -| manufacturer | nombre de la empesa que crea el producto | | adidas | +| manufacturer | nombre de la empesa que crea el producto | string | | adidas | | url | url desde la que se extrajo el producto | string | | https://www.adidas.co/tenis-duramo-sl/IF7884.html | | title | nombre del producto | string | | Tenis Duramo SL | | regularPrice | precio del producto sin descuento | string | | $379.950 | From c692ab5770040ea8ec3116eaf810d7c91a449bb8 Mon Sep 17 00:00:00 2001 From: azacipa <52643112+azacipa@users.noreply.github.com> Date: Mon, 25 Nov 2024 22:44:27 -0500 Subject: [PATCH 08/84] Se complementa alcance del proyecto y cronograma. --- docs/business_understanding/project_charter.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/business_understanding/project_charter.md b/docs/business_understanding/project_charter.md index 5c4852e75..7dbee96ab 100644 --- a/docs/business_understanding/project_charter.md +++ b/docs/business_understanding/project_charter.md @@ -38,8 +38,9 @@ Hay que tener en cuenta que estas fechas son de ejemplo, estas deben ajustarse d ## Equipo del Proyecto -- [Nombre y cargo del líder del proyecto] -- [Nombre y cargo de los miembros del equipo] +- Daniel Galvis CC 1010038257 cgalvisn@unal.edu.co +- Juan Correa CC 1013653882 jumcorrealo@unal.edu.co +- Asdrúval Zácipa Corredor CC 79139929 azacipac@unal.edu.co ## Presupuesto From 965d16204c26aefb01d79b050dc2f181e053a395 Mon Sep 17 00:00:00 2001 From: azacipa <52643112+azacipa@users.noreply.github.com> Date: Mon, 25 Nov 2024 22:50:58 -0500 Subject: [PATCH 09/84] Se complementa alcance del proyecto y cronograma. --- docs/business_understanding/project_charter.md | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/docs/business_understanding/project_charter.md b/docs/business_understanding/project_charter.md index 7dbee96ab..bfdf2d5f4 100644 --- a/docs/business_understanding/project_charter.md +++ b/docs/business_understanding/project_charter.md @@ -10,6 +10,10 @@ Desarrollar una herramienta computacional que permita realizar un análisis de c ## Alcance del Proyecto +El alcance del proyecto es el de construir un modelo de recomendación basado en embeddings que permita generar recomendaciones basadas en similaridad de atributos de calzado deportivo para running con los necesidades del cliente. + +Se propone el uso de embeddings teniendo en cuenta que los datos de los cuales disponemos corresponden a datos textuales de las características de los diferentes tipos de calzado deportivo. Se busca aplicar técnicas de embeddings utilizando modelos LLM (Large Language Model), con el fin de tener una mejor precisión semántica, basada en similaridad contextual. + ### Incluye: - [Descripción de los datos disponibles] @@ -28,13 +32,11 @@ CRISP-DM y SCRUM | Etapa | Duración Estimada | Fechas | |------|---------|-------| -| Entendimiento del negocio y carga de datos | 2 semanas | del 1 de mayo al 15 de mayo | -| Preprocesamiento, análisis exploratorio | 4 semanas | del 16 de mayo al 15 de junio | -| Modelamiento y extracción de características | 4 semanas | del 16 de junio al 15 de julio | -| Despliegue | 2 semanas | del 16 de julio al 31 de julio | -| Evaluación y entrega final | 3 semanas | del 1 de agosto al 21 de agosto | - -Hay que tener en cuenta que estas fechas son de ejemplo, estas deben ajustarse de acuerdo al proyecto. +| Entendimiento del negocio y carga de datos | 2 semanas | del 13 de noviembre al 28 de noviembre | +| Preprocesamiento, análisis exploratorio | 1 semana | del 29 de noviembre al 5 de diciembre | +| Modelamiento y extracción de características | 1 semana | del 5 de diciembre al 12 de diciembre | +| Despliegue | 1 semana | del 13 de diciembre al 19 de diciembre | +| Evaluación y entrega final | 1 semana | del 19 de diciembre al 21 de diciembre | ## Equipo del Proyecto From 8f4d5f48c8a48372dadaec93761bcd39124966df Mon Sep 17 00:00:00 2001 From: azacipa <52643112+azacipa@users.noreply.github.com> Date: Mon, 25 Nov 2024 22:56:34 -0500 Subject: [PATCH 10/84] Revert "Update prueba_azacipa.txt" This reverts commit a0eb48b4e3587537aeb6bc37d54ae42a02011781. --- docs/business_understanding/prueba_azacipa.txt | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docs/business_understanding/prueba_azacipa.txt b/docs/business_understanding/prueba_azacipa.txt index f9b3dfbfd..d931ceffb 100644 --- a/docs/business_understanding/prueba_azacipa.txt +++ b/docs/business_understanding/prueba_azacipa.txt @@ -1,2 +1 @@ -prueba azacipa -para el modulo 6 del diplomado \ No newline at end of file +prueba azacipa \ No newline at end of file From 58425194024d87801883c8812648ca4dd1044a52 Mon Sep 17 00:00:00 2001 From: Juan Correa Date: Wed, 27 Nov 2024 20:27:56 -0500 Subject: [PATCH 11/84] actualizacion a data_Definition y project_character --- docs/business_understanding/project_charter.md | 12 ++++++------ docs/data/data_definition.md | 8 ++++++-- 2 files changed, 12 insertions(+), 8 deletions(-) diff --git a/docs/business_understanding/project_charter.md b/docs/business_understanding/project_charter.md index bfdf2d5f4..a8a3778e2 100644 --- a/docs/business_understanding/project_charter.md +++ b/docs/business_understanding/project_charter.md @@ -16,9 +16,9 @@ Se propone el uso de embeddings teniendo en cuenta que los datos de los cuales d ### Incluye: -- [Descripción de los datos disponibles] -- [Descripción de los resultados esperados] -- [Criterios de éxito del proyecto] +- Se incluyen datos de Adidas, Nike y Nacion Runner, obtenidos con un scrapper, fotografía de mediados de noviembre. +- Un data set con modelos comparados, organizados y etiquetado +- El modelo debe ser capaz de hacer un análisis comparativo de productos de forma automática y frecuente, teniendo información actualizada de los moviemientos de mercado de las marcas analizadas ### Excluye: @@ -50,9 +50,9 @@ CRISP-DM y SCRUM ## Stakeholders -- [Nombre y cargo de los stakeholders del proyecto] -- [Descripción de la relación con los stakeholders] -- [Expectativas de los stakeholders] +- Dirección comercial de una empresa deportiva +- Laboral +- Tener una herramienta que automatice el análisis comparativo de productos deportivos de varias gamas y modelos ## Aprobaciones diff --git a/docs/data/data_definition.md b/docs/data/data_definition.md index 36473de82..40ff29a4c 100644 --- a/docs/data/data_definition.md +++ b/docs/data/data_definition.md @@ -2,11 +2,15 @@ ## Origen de los datos -- [ ] Especificar la fuente de los datos y la forma en que se obtuvieron. +- https://www.adidas.co/ : se extrajeron los datos por medio de web scraping +- https://www.nike.com.co/ : se extrajeron los datos por medio de web scraping +- https://nacionrunner.com/ : se extrajeron los datos por medio de web scraping + +Todos los datos se extrajeron de diferentes páginas web, de zapatos de running, y los datos están disponibles en una api conectada a firebase. ## Especificación de los scripts para la carga de datos -- [ ] Especificar los scripts utilizados para la carga de los datos. +- api_reader.py ## Referencias a rutas o bases de datos origen y destino From 0a0a5595312330e1fd498f145181fcd87f532d76 Mon Sep 17 00:00:00 2001 From: azacipa <52643112+azacipa@users.noreply.github.com> Date: Wed, 27 Nov 2024 22:38:16 -0500 Subject: [PATCH 12/84] Complementar y componer ortografia --- docs/business_understanding/project_charter.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/business_understanding/project_charter.md b/docs/business_understanding/project_charter.md index a8a3778e2..3d89744da 100644 --- a/docs/business_understanding/project_charter.md +++ b/docs/business_understanding/project_charter.md @@ -16,13 +16,13 @@ Se propone el uso de embeddings teniendo en cuenta que los datos de los cuales d ### Incluye: -- Se incluyen datos de Adidas, Nike y Nacion Runner, obtenidos con un scrapper, fotografía de mediados de noviembre. -- Un data set con modelos comparados, organizados y etiquetado -- El modelo debe ser capaz de hacer un análisis comparativo de productos de forma automática y frecuente, teniendo información actualizada de los moviemientos de mercado de las marcas analizadas +- Se incluyen datos de Adidas, Nike y Nation Runner, obtenidos con un scrapper, fotografía de mediados de noviembre. +- Un data set con modelos comparados, organizados y etiquetados +- El modelo debe ser capaz de realizar un análisis comparativo de productos de forma automática y frecuente, teniendo información actualizada de los movimientos de mercado de las marcas analizadas ### Excluye: -- [Descripción de lo que no está incluido en el proyecto] +- N/A ## Metodología @@ -46,7 +46,7 @@ CRISP-DM y SCRUM ## Presupuesto -[Descripción del presupuesto asignado al proyecto] +N/A ## Stakeholders From ce0c08ff89bce1e2cc6af274d99d1ed53e370321 Mon Sep 17 00:00:00 2001 From: azacipa <52643112+azacipa@users.noreply.github.com> Date: Wed, 27 Nov 2024 22:59:18 -0500 Subject: [PATCH 13/84] componer ortografia --- docs/data/data_dictionary.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/data/data_dictionary.md b/docs/data/data_dictionary.md index ff4e420c3..fd417d011 100644 --- a/docs/data/data_dictionary.md +++ b/docs/data/data_dictionary.md @@ -2,20 +2,20 @@ ## Base de datos 1 -**Agregar una descripción de la tabla o fuente de datos. +La estructura del dataset obtenido de las URL de las tiendas indicadas es: | Variable | Descripción | Tipo de dato | Rango/Valores posibles | Example Value | | --- | --- | --- | --- | --- | | id | Descripción de la variable 1 | string | | 046zSiHm8Cz0fZYwMJlL | | details | detalles del producto scrapeado, data semi estructurada que contiene detalles técnicos del mismo | string | - | '{Horma clásica} {Parte superior sintética}...' | | store | nombre de la tienda que vende el producto | string | | adidas | -| manufacturer | nombre de la empesa que crea el producto | string | | adidas | +| manufacturer | nombre de la empresa que crea el producto | string | | adidas | | url | url desde la que se extrajo el producto | string | | https://www.adidas.co/tenis-duramo-sl/IF7884.html | | title | nombre del producto | string | | Tenis Duramo SL | | regularPrice | precio del producto sin descuento | string | | $379.950 | | undiscounted_price | precio con descuento aplicado | string | | $265.965 | | description | descripción general del producto | string | | 'description': "Los Adizero Adios Pro 3 son la ..." | -| category | categoria a la que la página scrapeada asigna el producto | string | | Mujer • Running | +| category | categoria que la página scrapeada asigna al producto | string | | Mujer • Running | | createdAt | fecha de creación del registro | string | | '_seconds': 1731975445, '_nanoseconds': 42700.. | | characteristics | características adicionales del producto | string | | Parte superior de malla diseñada estratégicam.. | | gender | genero del producto | string | | Mujer | From e5840b1f00c21fb561d72ded5496e3e6e70ccfdc Mon Sep 17 00:00:00 2001 From: CarlosKC26 Date: Thu, 28 Nov 2024 18:27:18 -0500 Subject: [PATCH 14/84] Creacion de la rama daniel, se hicieron modificaciones al project_charter.md --- .../business_understanding/project_charter.md | 39 ++++++++++--------- 1 file changed, 20 insertions(+), 19 deletions(-) diff --git a/docs/business_understanding/project_charter.md b/docs/business_understanding/project_charter.md index 3d89744da..fd919adec 100644 --- a/docs/business_understanding/project_charter.md +++ b/docs/business_understanding/project_charter.md @@ -6,23 +6,24 @@ Análisis comparativo de productos de Running entre Nike, Adidas y Nation Runner ## Objetivo del Proyecto -Desarrollar una herramienta computacional que permita realizar un análisis de comparación de zapatos para running, con datos seleccionados de varias tiendas retail mediante técnicas de procesamiento de lenguaje natural y técnicas de pre-procesamiento de datos que incluyan Grandes Modelos de Lenguaje. +Desarrollar una herramienta computacional que permita realizar un análisis comparativo de zapatos para running, utilizando datos obtenidos de varias tiendas retail mediante técnicas de procesamiento de lenguaje natural y preprocesamiento de datos, incluyendo el uso de Grandes Modelos de Lenguaje (LLM, por sus siglas en inglés). ## Alcance del Proyecto -El alcance del proyecto es el de construir un modelo de recomendación basado en embeddings que permita generar recomendaciones basadas en similaridad de atributos de calzado deportivo para running con los necesidades del cliente. +El alcance del proyecto consiste en construir un modelo de recomendación basado en embeddings, capaz de generar recomendaciones basadas en la similitud de atributos de calzado deportivo para running, alineados con las necesidades del cliente. -Se propone el uso de embeddings teniendo en cuenta que los datos de los cuales disponemos corresponden a datos textuales de las características de los diferentes tipos de calzado deportivo. Se busca aplicar técnicas de embeddings utilizando modelos LLM (Large Language Model), con el fin de tener una mejor precisión semántica, basada en similaridad contextual. +Se propone el uso de embeddings debido a que los datos disponibles consisten en descripciones textuales de las características de diferentes tipos de calzado deportivo. Se busca aplicar técnicas de embeddings utilizando modelos LLM, con el objetivo de mejorar la precisión semántica basada en similitud contextual. ### Incluye: -- Se incluyen datos de Adidas, Nike y Nation Runner, obtenidos con un scrapper, fotografía de mediados de noviembre. -- Un data set con modelos comparados, organizados y etiquetados -- El modelo debe ser capaz de realizar un análisis comparativo de productos de forma automática y frecuente, teniendo información actualizada de los movimientos de mercado de las marcas analizadas +- Datos de Adidas, Nike y Nation Runner, obtenidos mediante un scraper con corte de datos a mediados de noviembre. +- Un dataset organizado y etiquetado con modelos comparados. +- Un modelo que permita realizar análisis comparativos de productos de forma automática y frecuente, con información actualizada sobre los movimientos de mercado de las marcas analizadas. ### Excluye: -- N/A +- La comparación de productos de marcas no incluidas en el análisis inicial (por ejemplo, Puma o Reebok). +- Desarrollo de visualizaciones avanzadas no incluidas explícitamente en el alcance. ## Metodología @@ -30,19 +31,19 @@ CRISP-DM y SCRUM ## Cronograma -| Etapa | Duración Estimada | Fechas | -|------|---------|-------| -| Entendimiento del negocio y carga de datos | 2 semanas | del 13 de noviembre al 28 de noviembre | -| Preprocesamiento, análisis exploratorio | 1 semana | del 29 de noviembre al 5 de diciembre | -| Modelamiento y extracción de características | 1 semana | del 5 de diciembre al 12 de diciembre | -| Despliegue | 1 semana | del 13 de diciembre al 19 de diciembre | -| Evaluación y entrega final | 1 semana | del 19 de diciembre al 21 de diciembre | +| Etapa | Duración Estimada | Fechas | +|-----------------------------------------|-------------------|---------------------------------| +| Entendimiento del negocio y carga de datos | 2 semanas | del 13 de noviembre al 28 de noviembre | +| Preprocesamiento y análisis exploratorio | 1 semana | del 29 de noviembre al 5 de diciembre | +| Modelamiento y extracción de características | 1 semana | del 5 de diciembre al 12 de diciembre | +| Despliegue | 1 semana | del 13 de diciembre al 19 de diciembre | +| Evaluación y entrega final | 1 semana | del 19 de diciembre al 21 de diciembre | ## Equipo del Proyecto - Daniel Galvis CC 1010038257 cgalvisn@unal.edu.co - Juan Correa CC 1013653882 jumcorrealo@unal.edu.co -- Asdrúval Zácipa Corredor CC 79139929 azacipac@unal.edu.co +- Asdrúbal Zácipa Corredor CC 79139929 azacipac@unal.edu.co ## Presupuesto @@ -50,12 +51,12 @@ N/A ## Stakeholders -- Dirección comercial de una empresa deportiva -- Laboral -- Tener una herramienta que automatice el análisis comparativo de productos deportivos de varias gamas y modelos +- Dirección comercial de empresas deportivas interesadas en el análisis de mercado. +- Equipos laborales que requieren herramientas de apoyo para la toma de decisiones estratégicas. +- Usuarios finales que necesitan una herramienta automatizada para realizar análisis comparativos de productos deportivos de varias gamas y modelos. ## Aprobaciones - [Nombre y cargo del aprobador del proyecto] - [Firma del aprobador] -- [Fecha de aprobación] +- [Fecha de aprobación] \ No newline at end of file From 0711e92792a476b053a99cacd55c2718085e71e1 Mon Sep 17 00:00:00 2001 From: CarlosKC26 Date: Thu, 28 Nov 2024 18:49:12 -0500 Subject: [PATCH 15/84] =?UTF-8?q?se=20a=C3=B1adio=20el=20presupuesto?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../business_understanding/project_charter.md | 23 +++++++++++++++++-- 1 file changed, 21 insertions(+), 2 deletions(-) diff --git a/docs/business_understanding/project_charter.md b/docs/business_understanding/project_charter.md index fd919adec..3ff773422 100644 --- a/docs/business_understanding/project_charter.md +++ b/docs/business_understanding/project_charter.md @@ -23,7 +23,7 @@ Se propone el uso de embeddings debido a que los datos disponibles consisten en ### Excluye: - La comparación de productos de marcas no incluidas en el análisis inicial (por ejemplo, Puma o Reebok). -- Desarrollo de visualizaciones avanzadas no incluidas explícitamente en el alcance. +- El desarrollo de visualizaciones avanzadas, como gráficos interactivos o dashboards personalizados, que no estén especificados como requisitos del proyecto. ## Metodología @@ -47,7 +47,26 @@ CRISP-DM y SCRUM ## Presupuesto -N/A +## Presupuesto + +Aunque no contamos con financiamiento externo, hemos estimado los costos básicos relacionados con el desarrollo del proyecto, considerando el uso de recursos personales como luz, internet y equipos de cómputo. A continuación, se detalla el presupuesto: + +| Concepto | Costo Mensual (COP) | Proporción por Persona (COP) | Duración (meses) | Total (COP) | +|------------------------------|---------------------|------------------------------|------------------|-------------| +| Servicio de luz | 100,000 | 33,333 | 2 | 200,000 | +| Servicio de internet | 150,000 | 50,000 | 2 | 300,000 | +| Uso de equipos personales | 200,000 | 66,667 | 2 | 400,000 | +| Reserva para emergencias | - | - | - | 100,000 | +| **Total** | - | - | - | **1,000,000** | + +### Detalles: +- **Servicio de luz:** Se estima un consumo aproximado para las actividades relacionadas con el desarrollo del proyecto. +- **Servicio de internet:** Incluye el costo de conexión a internet necesario para reuniones, investigación y uso de herramientas en línea. +- **Uso de equipos personales:** Incluye el desgaste de hardware y el consumo eléctrico de los equipos utilizados. +- **Reserva para emergencias:** Monto adicional para cubrir imprevistos, como reparaciones de equipos o la compra de licencias necesarias. + +### Notas: +- El presupuesto puede ajustarse si se identifican costos adicionales o si el proyecto se extiende más allá de los dos meses planificados. ## Stakeholders From c778ef149e93cfb9e484222c0792543c3e7a8e14 Mon Sep 17 00:00:00 2001 From: CarlosKC26 Date: Thu, 28 Nov 2024 18:54:19 -0500 Subject: [PATCH 16/84] =?UTF-8?q?Modificaci=C3=B3n=20al=20presupuesto?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/business_understanding/project_charter.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/business_understanding/project_charter.md b/docs/business_understanding/project_charter.md index 3ff773422..2df80f657 100644 --- a/docs/business_understanding/project_charter.md +++ b/docs/business_understanding/project_charter.md @@ -63,10 +63,10 @@ Aunque no contamos con financiamiento externo, hemos estimado los costos básico - **Servicio de luz:** Se estima un consumo aproximado para las actividades relacionadas con el desarrollo del proyecto. - **Servicio de internet:** Incluye el costo de conexión a internet necesario para reuniones, investigación y uso de herramientas en línea. - **Uso de equipos personales:** Incluye el desgaste de hardware y el consumo eléctrico de los equipos utilizados. -- **Reserva para emergencias:** Monto adicional para cubrir imprevistos, como reparaciones de equipos o la compra de licencias necesarias. +- **Reserva para emergencias:** Monto adicional para cubrir imprevistos, como reparaciones de equipos. ### Notas: -- El presupuesto puede ajustarse si se identifican costos adicionales o si el proyecto se extiende más allá de los dos meses planificados. +- El presupuesto puede ajustarse si se identifican costos adicionales. ## Stakeholders From 93ece1fff4116fdfa7f264d642e0953fa7f63c2c Mon Sep 17 00:00:00 2001 From: CarlosKC26 Date: Thu, 28 Nov 2024 18:57:40 -0500 Subject: [PATCH 17/84] cambios generales --- .../business_understanding/project_charter.md | 33 ++++++++----------- 1 file changed, 14 insertions(+), 19 deletions(-) diff --git a/docs/business_understanding/project_charter.md b/docs/business_understanding/project_charter.md index 2df80f657..f6106e459 100644 --- a/docs/business_understanding/project_charter.md +++ b/docs/business_understanding/project_charter.md @@ -27,17 +27,17 @@ Se propone el uso de embeddings debido a que los datos disponibles consisten en ## Metodología -CRISP-DM y SCRUM +Se utilizarán las metodologías CRISP-DM y SCRUM para llevar a cabo el proyecto. ## Cronograma | Etapa | Duración Estimada | Fechas | |-----------------------------------------|-------------------|---------------------------------| -| Entendimiento del negocio y carga de datos | 2 semanas | del 13 de noviembre al 28 de noviembre | -| Preprocesamiento y análisis exploratorio | 1 semana | del 29 de noviembre al 5 de diciembre | -| Modelamiento y extracción de características | 1 semana | del 5 de diciembre al 12 de diciembre | -| Despliegue | 1 semana | del 13 de diciembre al 19 de diciembre | -| Evaluación y entrega final | 1 semana | del 19 de diciembre al 21 de diciembre | +| Entendimiento del negocio y carga de datos | 2 semanas | Del 13 de noviembre al 28 de noviembre | +| Preprocesamiento y análisis exploratorio | 1 semana | Del 29 de noviembre al 5 de diciembre | +| Modelamiento y extracción de características | 1 semana | Del 5 de diciembre al 12 de diciembre | +| Despliegue | 1 semana | Del 13 de diciembre al 19 de diciembre | +| Evaluación y entrega final | 1 semana | Del 19 de diciembre al 21 de diciembre | ## Equipo del Proyecto @@ -47,9 +47,7 @@ CRISP-DM y SCRUM ## Presupuesto -## Presupuesto - -Aunque no contamos con financiamiento externo, hemos estimado los costos básicos relacionados con el desarrollo del proyecto, considerando el uso de recursos personales como luz, internet y equipos de cómputo. A continuación, se detalla el presupuesto: +Aunque no se cuenta con financiamiento externo, se estimaron los costos básicos relacionados con el desarrollo del proyecto, considerando el uso de recursos personales como luz, internet y equipos de cómputo. A continuación, se detalla el presupuesto: | Concepto | Costo Mensual (COP) | Proporción por Persona (COP) | Duración (meses) | Total (COP) | |------------------------------|---------------------|------------------------------|------------------|-------------| @@ -60,19 +58,16 @@ Aunque no contamos con financiamiento externo, hemos estimado los costos básico | **Total** | - | - | - | **1,000,000** | ### Detalles: -- **Servicio de luz:** Se estima un consumo aproximado para las actividades relacionadas con el desarrollo del proyecto. -- **Servicio de internet:** Incluye el costo de conexión a internet necesario para reuniones, investigación y uso de herramientas en línea. -- **Uso de equipos personales:** Incluye el desgaste de hardware y el consumo eléctrico de los equipos utilizados. -- **Reserva para emergencias:** Monto adicional para cubrir imprevistos, como reparaciones de equipos. - -### Notas: -- El presupuesto puede ajustarse si se identifican costos adicionales. +- **Servicio de luz:** Incluye el costo estimado del consumo eléctrico asociado al trabajo en el proyecto. +- **Servicio de internet:** Cubre el acceso a internet necesario para reuniones virtuales, investigación y uso de herramientas online. +- **Uso de equipos personales:** Considera el desgaste de hardware y el consumo eléctrico de los equipos utilizados durante el desarrollo. +- **Reserva para emergencias:** Monto adicional para imprevistos, como la reparación de equipos o la adquisición de software adicional. ## Stakeholders -- Dirección comercial de empresas deportivas interesadas en el análisis de mercado. -- Equipos laborales que requieren herramientas de apoyo para la toma de decisiones estratégicas. -- Usuarios finales que necesitan una herramienta automatizada para realizar análisis comparativos de productos deportivos de varias gamas y modelos. +- Dirección comercial de una empresa deportiva. +- Equipo laboral interno interesado en la automatización del análisis comparativo de productos deportivos. +- Consumidores finales que podrían beneficiarse indirectamente de las recomendaciones generadas por el modelo. ## Aprobaciones From 849fd7313fe0bc4236c558afca89317258f5781a Mon Sep 17 00:00:00 2001 From: Juan Correa Date: Sat, 30 Nov 2024 20:24:50 -0500 Subject: [PATCH 18/84] estudio de costos --- Notebooks/cost_calculator.ipynb | 545 ++++++++++++++++++++++++++++++++ Notebooks/test.ipynb | 2 +- requirements.txt | 3 +- 3 files changed, 548 insertions(+), 2 deletions(-) create mode 100644 Notebooks/cost_calculator.ipynb diff --git a/Notebooks/cost_calculator.ipynb b/Notebooks/cost_calculator.ipynb new file mode 100644 index 000000000..aeb326131 --- /dev/null +++ b/Notebooks/cost_calculator.ipynb @@ -0,0 +1,545 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import requests\n", + "import re\n", + "import ollama\n", + "import random\n", + "import math\n", + "from tiktoken import get_encoding" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "labels_with_definitions = [\n", + " (\"Peso\", \"Indica la ligereza de la zapatilla, generalmente expresado en gramos. El peso puede influir en el rendimiento y la comodidad durante la carrera.\"),\n", + " (\"Material del upper (parte superior)\", \"Describe los materiales utilizados en la parte superior de la zapatilla, como malla, cuero sintético o tejidos técnicos, que afectan la transpirabilidad, flexibilidad y soporte.\"),\n", + " (\"Material de la mediasuela\", \"Se refiere a los compuestos empleados en la entresuela, como espumas EVA o tecnologías propietarias, que proporcionan amortiguación y absorción de impactos.\"),\n", + " (\"Suela exterior\", \"Detalla el tipo de goma o caucho utilizado en la suela y el diseño del patrón de tracción, aspectos que influyen en el agarre y la durabilidad en diversas superficies.\"),\n", + " (\"Sistema de amortiguación\", \"Especifica las tecnologías o materiales destinados a reducir el impacto durante la pisada, contribuyendo al confort y la protección de las articulaciones.\"),\n", + " (\"Drop (diferencial talón-punta)\", \"Indica la diferencia de altura entre el talón y la punta de la zapatilla, medida en milímetros. Un drop alto suele ofrecer mayor amortiguación en el talón, mientras que un drop bajo promueve una pisada más natural.\"),\n", + " (\"Tipo de pisada\", \"Clasifica la zapatilla según su adecuación para diferentes tipos de pisada: neutra, pronadora o supinadora. Esto es esencial para elegir un calzado que se adapte a la biomecánica del corredor.\"),\n", + " (\"Tipo de uso\", \"Define el propósito principal de la zapatilla, como entrenamiento diario, competición, trail running o uso casual, lo que orienta sobre su diseño y funcionalidades específicas.\"),\n", + " (\"Género\", \"Indica si la zapatilla está diseñada para hombres, mujeres o es un modelo unisex, considerando diferencias anatómicas y de tamaño.\"),\n", + " (\"Tallas disponibles\", \"Especifica el rango de tallas en las que se ofrece la zapatilla, asegurando que el corredor pueda encontrar un ajuste adecuado.\"),\n", + " (\"Anchura\", \"Algunas marcas ofrecen diferentes anchos (estrecho, estándar, ancho) para adaptarse a diversas morfologías del pie.\"),\n", + " (\"Precio\", \"Proporciona el costo de la zapatilla, un factor determinante en la decisión de compra.\"),\n", + " (\"Tecnologías adicionales\", \"Incluye características especiales como impermeabilidad, reflectividad, sistemas de ajuste personalizados o elementos de estabilidad que mejoran la funcionalidad del calzado.\"),\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Funciones" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def transformar_texto(texto, marca):\n", + " if texto is None or (isinstance(texto, float) and np.isnan(texto)):\n", + " return texto\n", + " \n", + " if marca.lower() == \"adidas\":\n", + " # Transformación original para adidas\n", + " if isinstance(texto, (list, np.ndarray)):\n", + " texto = \", \".join(map(str, texto))\n", + " else:\n", + " texto = str(texto)\n", + " texto = texto.strip(\"[]\")\n", + " texto = re.sub(r\",\\s*\", '} {', texto)\n", + " texto = '{' + texto + '}'\n", + " return texto\n", + " \n", + " elif marca.lower() == \"nike\":\n", + " # Transformación específica para nike\n", + " if isinstance(texto, list):\n", + " # Elimina claves con valores irrelevantes\n", + " texto_limpio = [\n", + " {k.strip(): v.strip() for k, v in d.items() if k.strip() not in ['\\xa0']}\n", + " for d in texto\n", + " if isinstance(d, dict)\n", + " ]\n", + " # Filtra elementos vacíos o irrelevantes\n", + " texto_limpio = [d for d in texto_limpio if d]\n", + " return texto_limpio\n", + " return texto # Si no es lista, regresa el texto original\n", + "\n", + " else:\n", + " raise ValueError(f\"Marca '{marca}' no reconocida. Usa 'adidas' o 'nike'.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def obtener_respuesta_ollama(prompt):\n", + " response = ollama.chat(\n", + " model=\"llama3.2:latest\",\n", + " messages = [\n", + " {\n", + " \"role\":\"user\",\n", + " \"content\": prompt\n", + " } \n", + " ]\n", + " )\n", + " # La respuesta es un generador; concatenamos las partes\n", + " return response" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "\n", + "def generate_prompt_ollama(product, labels_with_definitions):\n", + " labels = [label for label, _ in labels_with_definitions]\n", + " prompt = f\"\"\"\n", + " You are an assistant specialized in extracting structured information from product descriptions and organizing it into tables.\n", + " Your task is to extract the following information from the product details and label it according to the provided labels: {', '.join(labels)}.\n", + " Each label has the following definition to help guide your extraction:\n", + "\n", + " {''.join([f'- {label}: {definition}\\n' for label, definition in labels_with_definitions])}\n", + "\n", + " If a label does not have a clear match in the details, complete its value with \"null\".\n", + "\n", + " Product information:\n", + " - Details: {product['details']}\n", + " - Description: {product['description']}\n", + " - Category: {product['category']}\n", + "\n", + " Provide the information in a table with columns corresponding to each label. \n", + " The table must include **two complete rows**:\n", + " 1. The first row contains the label names.\n", + " 2. The second row contains the corresponding labeled values.\n", + "\n", + " Expected response format:\n", + " | {' | '.join(labels)} |\n", + " | {' | '.join(['---'] * len(labels))} |\n", + " | value_1 | value_2 | ... |\n", + " \n", + " Example:\n", + " If the labels are \"details\", \"description\", and \"category\", and the extracted values are \n", + " \"Comfortable sneakers\", \"Made with recycled materials\", and \"Footwear\", respectively, \n", + " your response should be:\n", + "\n", + " | details | description | category |\n", + " | --- | --- | --- |\n", + " | Comfortable sneakers | Made with recycled materials | Footwear |\n", + "\n", + " Remember:\n", + " - The response must contain two complete rows.\n", + " - Only respond with the table and **do not include additional text**.\n", + "\n", + " Now, extract and structure the information for the provided product:\n", + "\n", + " | {' | '.join(labels)} |\n", + " | {' | '.join(['---'] * len(labels))} |\n", + " |\"\"\"\n", + " return prompt.strip()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Tokenizer function (using tiktoken for GPT-like models)\n", + "def count_tokens(text):\n", + " try:\n", + " encoding = get_encoding(\"cl100k_base\") # Example encoding for GPT-like models\n", + " return len(encoding.encode(text))\n", + " except Exception:\n", + " return len(text.split()) # Fallback: approximate by word count" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "# Función de simulación de Monte Carlo corregida\n", + "def monte_carlo_simulation(products, models, iterations=1000, num_products=None):\n", + " results = {}\n", + " \n", + " for model_name, model_info in models.items():\n", + " tokens_per_product = {}\n", + " costs_per_product = {}\n", + " \n", + " for _ in range(iterations):\n", + " # Muestra una fracción de los productos si se especifica\n", + " if num_products is not None and num_products < len(products):\n", + " sampled_products = random.sample(products, num_products)\n", + " else:\n", + " sampled_products = products\n", + "\n", + " for product in sampled_products:\n", + " product_id = product.get('id', id(product)) # Usamos un identificador único para cada producto\n", + " prompt = generate_prompt_ollama(product, labels_with_definitions)\n", + " tokens = count_tokens(prompt)\n", + " \n", + " # Verifica si el número de tokens excede la ventana de contexto\n", + " if tokens > model_info['context_window']*1000:\n", + " print(f\"Advertencia: El prompt excede la ventana de contexto para el modelo {model_name}\")\n", + " # Puedes manejar este caso según necesites (e.g., omitir, truncar)\n", + " continue\n", + " \n", + " cost = (tokens / 1000) * model_info['cost_in']\n", + " \n", + " # Actualiza los máximos y mínimos por producto\n", + " if product_id not in tokens_per_product:\n", + " tokens_per_product[product_id] = {'max': tokens, 'min': tokens}\n", + " costs_per_product[product_id] = {'max': cost, 'min': cost}\n", + " else:\n", + " tokens_per_product[product_id]['max'] = max(tokens_per_product[product_id]['max'], tokens)\n", + " tokens_per_product[product_id]['min'] = min(tokens_per_product[product_id]['min'], tokens)\n", + " costs_per_product[product_id]['max'] = max(costs_per_product[product_id]['max'], cost)\n", + " costs_per_product[product_id]['min'] = min(costs_per_product[product_id]['min'], cost)\n", + " \n", + " # Después de todas las iteraciones, obtenemos los máximos y mínimos globales\n", + " max_tokens = max([data['max'] for data in tokens_per_product.values()], default=0)\n", + " min_tokens = min([data['min'] for data in tokens_per_product.values()], default=0)\n", + " max_cost = max([data['max'] for data in costs_per_product.values()], default=0)\n", + " min_cost = min([data['min'] for data in costs_per_product.values()], default=0)\n", + " \n", + " results[model_name] = {\n", + " 'max_tokens': max_tokens,\n", + " 'min_tokens': min_tokens,\n", + " 'max_cost': max_cost,\n", + " 'min_cost': min_cost,\n", + " }\n", + " return results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lectura de datos" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "200\n" + ] + } + ], + "source": [ + "url =\"https://scraping-firestore-178159629911.us-central1.run.app//v1/scraping/\"\n", + "\n", + "response = requests.get(url)\n", + "\n", + "if response.status_code == 200:\n", + " data = response.json()\n", + " print(response.status_code)\n", + "else:\n", + " print(f'Error: {response.status_code}')\n", + "\n", + "df_raw = pd.DataFrame(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Código" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "df_adidas = df_raw[df_raw['store'] == 'adidas']" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Usuario\\AppData\\Local\\Temp\\ipykernel_3872\\354877031.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_adidas['details_transformado'] = df_adidas['details'].apply(lambda x: transformar_texto(x, 'adidas'))\n" + ] + } + ], + "source": [ + "# Lista de descripciones de productos\n", + "df_adidas['details_transformado'] = df_adidas['details'].apply(lambda x: transformar_texto(x, 'adidas'))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Crear un diccionario con las llaves deseadas desde todo el DataFrame\n", + "productos = [\n", + " {\n", + " \"details\": row['details_transformado'],\n", + " \"description\": row['description'],\n", + " \"category\": row['category']\n", + " }\n", + " for _, row in df_adidas.iterrows() # Recorre todo el DataFrame\n", + " if row['details_transformado'] != '{}' # Filtra donde los detalles no estén vacíos\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# Model configurations\n", + "models = {\n", + " \"jamba15large\": {'cost_in': 0.002, 'cost_out': 0.008, 'accuracy': 64, 'context_window': 256},\n", + " \"claude35sonnet\": {'cost_in': 0.003, 'cost_out': 0.015, 'accuracy': 80, 'context_window': 200},\n", + " \"llama3170b\": {'cost_in': 0.00099, 'cost_out': 0.00099, 'accuracy': 66, 'context_window': 128},\n", + " \"mistrallarge\": {'cost_in': 0.004, 'cost_out': 0.012, 'accuracy': 56, 'context_window': 33},\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "# Ahora puedes especificar el número de productos a procesar (por ejemplo, 10)\n", + "num_products_to_process = 100 # O usa len(productos) para procesar todos\n", + "\n", + "# Ejecutar la simulación de Monte Carlo\n", + "simulation_results = monte_carlo_simulation(productos, models, iterations=1000, num_products=num_products_to_process)\n", + "\n", + "# Mostrar los resultados\n", + "import pandas as pd\n", + "df_results = pd.DataFrame(simulation_results).transpose().reset_index()\n", + "df_results.columns = ['Model', 'Max Tokens', 'Min Tokens', 'Max Cost', 'Min Cost']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Model Max Tokens Min Tokens Max Cost Min Cost\n", + "0 jamba15large 1787.0 1303.0 0.003574 0.002606\n", + "1 claude35sonnet 1787.0 1303.0 0.005361 0.003909\n", + "2 llama3170b 1787.0 1303.0 0.001769 0.001290\n", + "3 mistrallarge 1787.0 1303.0 0.007148 0.005212\n" + ] + } + ], + "source": [ + "print(df_results)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Usuario\\AppData\\Local\\Temp\\ipykernel_3792\\354877031.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_adidas['details_transformado'] = df_adidas['details'].apply(lambda x: transformar_texto(x, 'adidas'))\n" + ] + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# Crear un diccionario con las llaves deseadas\n", + "productos = [\n", + " {\n", + " \"details\": row['details_transformado'],\n", + " \"description\": row['description'],\n", + " \"category\": row['category']\n", + " }\n", + " for _, row in df_adidas[:10].iterrows() \n", + " if row['details_transformado'] != '{}'\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'details': '{Horma clásica} {Parte superior sintética} {Forro interno textil} {Varillas ENERGYRODS 2.0 que limitan la pérdida de energía} {Amortiguación Lightstrike Pro} {Peso: 183 g (talla CO 37} {5)} {Caída mediasuela: 6 mm (talón: 38 mm / antepié: 32 mm)} {La parte superior contiene al menos un 50 % de material reciclado} {Color del artículo: Pink Spark / Aurora Met. / Sandy Pink} {Número de artículo: ID3612}', 'description': \"Los Adizero Adios Pro 3 son la máxima expresión de los productos Adizero Racing. Fueron diseñados con y para atletas para lograr hazañas increíbles. Estos tenis de running adidas están diseñados para optimizar la eficiencia del running. Nuestras varillas ENERGYRODS 2.0 de carbono ofrecen ligereza y firmeza para pasos ágiles y eficientes. La tecnología LIGHTSTRIKE PRO ultraliviana amortigua cada paso con las tres capas de espuma resistente que te ayudan a mantener la energía a largo plazo. Todo sobre una delgada suela de caucho textil para un agarre extraordinario en condiciones mojadas y secas. Celebramos nuestra más reciente victoria, los tenis Best Speed 2024 by Women's Health.\", 'category': 'Mujer • Running'}\n", + "Respuesta del modelo:\n", + "| Peso | Material del upper (parte superior) | Material de la mediasuela | Suela exterior | Sistema de amortiguación | Drop (diferencial talón-punta) | Tipo de pisada | Tipo de uso | Género | Tallas disponibles | Anchura | Precio | Tecnologías adicionales |\n", + "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", + "| 183 g | Parte superior sintética | Forro interno textil | Caucho textil | Amortiguación Lightstrike Pro | 5 | Neutral | Competición | Mujer | 37-45 CO | null | 250 | Varillas ENERGYRODS 2.0, tecnología LIGHTSTRIKE PRO, caucho textil, material reciclado |\n", + "\n", + "------------------------\n", + "\n", + "{'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Exterior de tejido adidas Primeknit} {Ajuste suave en el talón} {Sistema Linear Energy Push} {Mediasuela BOOST} {Peso: 289 g (Talla CO 37)} {Suela Stretchweb con caucho Continental™ Better Rubber} {El hilo del exterior contiene al menos un 50% de Parley Ocean Plastic y un 50% de poliéster reciclado} {Color del artículo: Core Black / Core Black / Cloud White} {Número de artículo: GX5591}', 'description': 'Hemos analizado 1.200.000 pisadas para que Ultraboost evolucione y mejore su ajuste para adaptarse 360° al pie femenino. Y aún hay más. Hemos rediseñado la suela de caucho. Probamos cientos de prototipos. Hasta que comprobamos las mejoras en su rendimiento. ¿El resultado? Un 4 % más de energía que los Ultraboost 21 para mujer. El ajuste en el área del talón del exterior adidas PRIMEKNIT se diseñó para evitar que el talón se deslice y se formen ampollas. Estarás corriendo sobre una mediasuela BOOST con tecnología Linear Energy Push. El exterior de este calzado está confeccionado con hilo que contiene un 50% de Parley Ocean Plastic, un material que ha sido recuperado de residuos plásticos recogidos en islas, playas, comunidades costeras y costas evitando que contaminen nuestros océanos.', 'category': 'Mujer • Running'}\n", + "Respuesta del modelo:\n", + "| Peso | Material del upper (parte superior) | Material de la mediasuela | Suela exterior | Sistema de amortiguación | Drop (diferencial talón-punta) | Tipo de pisada | Tipo de uso | Género | Tallas disponibles | Anchura | Precio | Tecnologías adicionales |\n", + "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", + "| 289 g (Talla CO 37) | adidas Primeknit | BOOST | Stretchweb con caucho Continental™ Better Rubber | Linear Energy Push | null | Neutra | Competición | Mujer | 37-48 | null | €149.95 | Contiene al menos un 50% de Parley Ocean Plastic y un 50% de poliéster reciclado\n", + "\n", + "------------------------\n", + "\n", + "{'details': '{Ajuste clásico} {Cierre de cordones} {Parte superior de malla técnica} {Amortiguación Lightstrike Pro} {Varillas ENERGYRODS que limitan la pérdida de energía} {Talón Slinglaunch para un ajuste seguro} {Peso: 166 g (Talla COL 36 1/2)} {Caída mediasuela: 6 mm (talón: 32 mm / antepié: 26 mm)} {Suela de caucho Continental™} {Contiene al menos un 20 % de material reciclado} {Color del artículo: Green Spark / Aurora Met. / Lucid Lemon} {Número de artículo: IG8206}', 'description': 'Haz tus mejores 10k con nuestros nuevos tenis de running Adizero, diseñados exclusivamente para darte máxima velocidad. Los Adizero Takumi Sen 10 han sido diseñados con dos capas de amortiguación LIGHTSTRIKE PRO, combinadas con las varillas ENERGYRODS para una mayor estabilidad e impulso en tu carrera. Supera tu marca personal y llega a la meta más rápido que nunca.', 'category': 'Mujer • Running'}\n", + "Respuesta del modelo:\n", + "| Peso | Material del upper (parte superior) | Material de la mediasuela | Suela exterior | Sistema de amortiguación | Drop (diferencial talón-punta) | Tipo de pisada | Tipo de uso | Género | Tallas disponibles | Anchura | Precio | Tecnologías adicionales |\n", + "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", + "| 166 g (Talla COL 36 1/2) | Parte superior de malla técnica | Varillas ENERGYRODS que limitan la pérdida de energía | Suela de caucho Continental™ | Amortiguación Lightstrike Pro | 6 mm (talón: 32 mm / antepié: 26 mm) | Neutra | Competición | Mujer | 36 1/2-42 3/4 | Estrecho, estándar, ancho | null | null | Contiene al menos un 20 % de material reciclado |\n", + "\n", + "------------------------\n", + "\n", + "{'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Parte superior de malla} {Amortiguación Lightstrike Pro} {Varillas ENERGYRODS que limitan la pérdida de energía} {Talón Slinglaunch} {Peso: 200 gramos (talla CO 40)} {Caída mediasuela: 6 mm (talón: 33 mm / antepié: 27 mm} {Suela de caucho Continental™ Rubber} {Contienen al menos un 20% de material reciclado} {Color del artículo: Green Spark / Aurora Met. / Lucid Lemon} {Número de artículo: IG3134}', 'description': 'Haz tus mejores 10k con nuestros nuevos tenis de running Adizero, diseñados exclusivamente para darte máxima velocidad. Los Adizero Takumi Sen 10 han sido diseñados con dos capas de amortiguación LIGHTSTRIKE PRO, combinadas con las varillas ENERGYRODS 2.0 para una mayor estabilidad e impulso en tu carrera. Supera tu marca personal y llega a la meta más rápido que nunca.', 'category': 'Hombre • Running'}\n", + "Respuesta del modelo:\n", + "| Peso | Material del upper (parte superior) | Material de la mediasuela | Suela exterior | Sistema de amortiguación | Drop (diferencial talón-punta) | Tipo de pisada | Tipo de uso | Género | Tallas disponibles | Anchura | Precio | Tecnologías adicionales |\n", + "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", + "| 200 gramos | Parte superior de malla | Amortiguación Lightstrike Pro | Suela de caucho Continental™ Rubber | Varillas ENERGYRODS que limitan la pérdida de energía | null | neutra | competición | hombre | CO 40-50, EU 41-45, US 6.5-7.5 | estrecho | 90€ | Contienen al menos un 20% de material reciclado |\n", + "\n", + "------------------------\n", + "\n", + "{'details': '{Horma clásica} {Sistema de amarre de cordones} {Parte superior textil} {Forro interno textil} {Mediasuela Cloudfoam} {Suela de TPU} {Peso: 319 g (talla CO 40)} {Caída mediasuela: 6 mm (talón 35 mm / antepié 29 mm)} {Color del artículo: Halo Silver / Carbon / Core Black} {Número de artículo: ID8754}', 'description': 'Cada carrera es un viaje de descubrimiento. Ponte estos tenis de running adidas y libera tu potencial. La mediasuela con amortiguación Cloudfoam te ofrece una pisada más cómoda mientras aumentas tu resistencia. La parte superior textil es resistente al desgaste y te ofrece soporte desde tu primera vuelta hasta tu primera carrera de 5K.', 'category': 'Hombre • Running'}\n", + "Respuesta del modelo:\n", + "| Peso | Material del upper (parte superior) | Material de la mediasuela | Suela exterior | Sistema de amortiguación | Drop (diferencial talón-punta) | Tipo de pisada | Tipo de uso | Género | Tallas disponibles | Anchura | Precio | Tecnologías adicionales |\n", + "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", + "| 319 g (talla CO 40) | Parte superior textil | Mediasuela Cloudfoam | Suela de TPU | Reducción del impacto | 6 mm (talón 35 mm / antepié 29 mm) | Neutra | Entrenamiento diario | Hombre | Tallas CO 36-46 | null | 319 g | null |\n", + "\n", + "------------------------\n", + "\n", + "{'details': '{Si este artículo es personalizado} {no aplica en nuestra política de cambios y devoluciones.} {Sistema de amarre de cordones para un ajuste inmejorable} {Producto hecho parcialmente con Malla Técnica Reciclada} {Diseño liviano} {Amortiguación Lightstrike y Lightstrike Pro} {Forro interno textil} {Caída mediasuela: 8} {5 mm (talón: 33 mm / antepié: 24} {5 mm)} {Suela de caucho} {El exterior contiene al menos un 50% de material reciclado} {Color del artículo: Ivory / Iron Metallic / Spark} {Número de artículo: IG3341}', 'description': 'Cuando se trata de alcanzar tus metas, cada segundo cuenta. Ya sea para entrenar o para competir, un rendimiento excelente requiere de prendas de alta tecnología optimizadas para la velocidad. Presentamos la nueva colección de tenis de running livianos que te ayudan a superar tus límites sin distracciones.\\n\\nLos tenis de running Adizero SL seleccionan lo mejor de nuestra franquicia Adizero que rompe récords mundiales. La mediasuela de EVA LIGHTSTRIKE liviana ofrece resiliencia a la mediasuela para que puedas concentrarte en el próximo paso, mientras que el exterior está hecho de una malla técnica suave que está zonificada en áreas clave. El talón acolchado y la lengüeta brindan una comodidad óptima junto con el antepié Adizero. La suela premium está diseñada para proporcionar tracción.', 'category': 'Mujer • Running'}\n", + "Respuesta del modelo:\n", + "| Peso | Material del upper (parte superior) | Material de la mediasuela | Suela exterior | Sistema de amortiguación | Drop (diferencial talón-punta) | Tipo de pisada | Tipo de uso | Género | Tallas disponibles | Anchura | Precio | Tecnologías adicionales |\n", + "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", + "| null | Malla técnica suave zonificada en áreas clave | EVA LIGHTSTRIKE liviana | Suela de caucho | Amortiguación Lightstrike y Lightstrike Pro | 5 mm | Neutra | Competición | Mujer • Running | Estándar, Ancha | $120-$150 | Al menos un 50% de material reciclado |\n", + "\n", + "------------------------\n", + "\n", + "{'details': '{Horma clásica} {Sistema de amarre de cordones} {Parte superior de malla} {Forro interno textil} {Plantilla OrthoLite®} {Mediasuela Cloudfoam} {Peso: 304 g (talla CO 40)} {Caída mediasuela: 10 mm (talón: 33 mm / antepié: 23 mm)} {Suela Adiwear} {Color del artículo: Cloud White / Core Black / Better Scarlet} {Número de artículo: IE8818}', 'description': 'Tanto en la pista como en la cinta de correr, alcanza todas tus metas con estos tenis de running adidas. Incorporan una mediasuela con amortiguación Cloudfoam que te ofrece una pisada más cómoda y suave. La parte superior de malla transpirable y la suela Adiwear de gran resistencia al desgaste la convierten en una silueta perfecta para llevar durante todo el día.', 'category': 'Hombre • Running'}\n", + "Respuesta del modelo:\n", + "| Peso | Material del upper (parte superior) | Material de la mediasuela | Suela exterior | Sistema de amortiguación | Drop (diferencial talón-punta) | Tipo de pisada | Tipo de uso | Género | Tallas disponibles | Anchura | Precio | Tecnologías adicionales |\n", + "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", + "| 304 g (talla CO 40) | Parte superior de malla | Mediasuela Cloudfoam | Suela Adiwear | Reducción del impacto con Plantilla OrthoLite® y mediasuela Cloudfoam | 10 mm (talón: 33 mm / antepié: 23 mm) | Neutra | Entrenamiento diario, competición | Hombre • Running | Talla CO 40 - CO 12 | Estrecho, estándar, ancho | €109.90 | Impermeabilidad |\n", + "\n", + "------------------------\n", + "\n", + "{'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Parte superior de malla} {Forro interno textil} {mediasuela Dreamstrike+} {Suela Adiwear} {Peso (talla CO 37): 213 gramos} {Caída mediasuela: 10 mm (talón: 34 mm / 24 mm)} {Contienen al menos un 20% de material reciclado y renovable} {Color del artículo: Almost Yellow / Zero Metalic / Pink Spark} {Número de artículo: IE1072}', 'description': 'Avanza hacia tus metas de running con estos tenis adidas. La mediasuela Dreamstrike+ está diseñada para absorber el impacto. La amortiguación adicional en el antepié ofrece comodidad durante toda tu carrera. El exterior de malla transpirable maximiza el flujo de aire para mantener tus pies frescos, y la suela Adiwear resistente ofrece tracción. \\n\\nAl elegir materiales reciclados, podemos reutilizar materiales que ya han sido creados, lo que ayuda a reducir los residuos. La elección de materiales renovables nos ayudará a eliminar nuestra dependencia de recursos finitos. Nuestros productos hechos con una mezcla de materiales reciclados y renovables contienen al menos un 20% de este material.', 'category': 'Mujer • Running'}\n", + "Respuesta del modelo:\n", + "| Peso | Material del upper (parte superior) | Material de la mediasuela | Suela exterior | Sistema de amortiguación | Drop (diferencial talón-punta) | Tipo de pisada | Tipo de uso | Género | Tallas disponibles | Anchura | Precio | Tecnologías adicionales |\n", + "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", + "| 213 gramos | Parte superior de malla | mediasuela Dreamstrike+ | Suela Adiwear | La mediasuela está diseñada para absorber el impacto. El antepié ofrece comodidad durante toda tu carrera. | 10 mm (talón: 34 mm / 24 mm) | Neutra | Competición, trail running | Mujer • Running | CO 37-48 | Estrecho, estándar, ancho | €59.99 | Contienen al menos un 20% de material reciclado y renovable |\n", + "\n", + "------------------------\n", + "\n", + "{'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Exterior de malla con cuello acolchado} {Forro interno textil} {Estabilizador de talón moldeado Fitcounter} {Mediasuela Bounce} {Suela sintética} {El exterior contiene al menos un 50 % de material reciclado} {Color del artículo: Shadow Violet / Legend Ink / Wonder Clay} {Número de artículo: IG0334}', 'description': 'Estos tenis fueron diseñados para darte la amortiguación y la respuesta que necesitas cuando estás en la pista o en los senderos. Sin importar si vas a hacer tu caminata diaria o una carrera corta, la mediasuela Bounce es flexible y receptiva con cada paso. Hecho con una serie de materiales reciclados, el exterior incorpora al menos un 50 % de contenido reciclado. Este producto representa solo una de nuestras soluciones para acabar con los residuos plásticos.', 'category': 'Mujer • Running'}\n", + "Respuesta del modelo:\n", + "| Peso | Material del upper (parte superior) | Material de la mediasuela | Suela exterior | Sistema de amortiguación | Drop (diferencial talón-punta) | Tipo de pisada | Tipo de uso | Género | Tallas disponibles | Anchura | Precio | Tecnologías adicionales |\n", + "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", + "| null | {Ajuste clásico} {Sistema de amarre de cordones} {Exterior de malla con cuello acolchado} | {Forro interno textil} {Mediasuela Bounce} | Suela sintética | Estabilizador de talón moldeado Fitcounter | null | Neutra | Entrenamiento diario | Mujer • Running | 7-14 | Estrecho, estándar, ancho | $80-$100 | {El exterior contiene al menos un 50 % de material reciclado} |\n", + "\n", + "------------------------\n", + "\n" + ] + } + ], + "source": [ + "dfs = []\n", + "for producto in productos:\n", + " prompt = generate_prompt_ollama(producto, labels_with_definitions)\n", + " respuesta = obtener_respuesta_ollama(prompt)\n", + " print(producto)\n", + " print(\"Respuesta del modelo:\")\n", + " print(respuesta[\"message\"][\"content\"])\n", + " print(\"\\n------------------------\\n\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Notebooks/test.ipynb b/Notebooks/test.ipynb index f7fdbece1..8c36c6274 100644 --- a/Notebooks/test.ipynb +++ b/Notebooks/test.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ diff --git a/requirements.txt b/requirements.txt index c0b612c10..be1f7051c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,4 +5,5 @@ blobfile pandas ollama requests -openpyxl \ No newline at end of file +openpyxl +llama_models \ No newline at end of file From 21d67a48db376b23d5113703774ea42fae544483 Mon Sep 17 00:00:00 2001 From: Juan Correa Date: Sat, 30 Nov 2024 21:38:46 -0500 Subject: [PATCH 19/84] actualization --- Notebooks/cost_calculator.ipynb | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/Notebooks/cost_calculator.ipynb b/Notebooks/cost_calculator.ipynb index aeb326131..d829118eb 100644 --- a/Notebooks/cost_calculator.ipynb +++ b/Notebooks/cost_calculator.ipynb @@ -382,23 +382,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Usuario\\AppData\\Local\\Temp\\ipykernel_3792\\354877031.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df_adidas['details_transformado'] = df_adidas['details'].apply(lambda x: transformar_texto(x, 'adidas'))\n" - ] + "data": { + "text/plain": [ + "(519, 14)" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" } ], - "source": [] + "source": [ + "df_adidas.shape" + ] }, { "cell_type": "code", From 67bb738aaf67286f3dc4098ee37a968647a90da0 Mon Sep 17 00:00:00 2001 From: CarlosKC26 Date: Thu, 5 Dec 2024 18:29:11 -0500 Subject: [PATCH 20/84] =?UTF-8?q?modificaci=C3=B3n=20de=20documentos=20de?= =?UTF-8?q?=20Data?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/data/data_definition.md | 33 +++++++++++++++++------------ docs/data/data_dictionary.md | 21 ++++++++++++++++++ docs/data/data_summary.md | 41 +++++++++++++++++++++++++++++------- 3 files changed, 74 insertions(+), 21 deletions(-) diff --git a/docs/data/data_definition.md b/docs/data/data_definition.md index 40ff29a4c..0c3a3542d 100644 --- a/docs/data/data_definition.md +++ b/docs/data/data_definition.md @@ -2,28 +2,35 @@ ## Origen de los datos -- https://www.adidas.co/ : se extrajeron los datos por medio de web scraping -- https://www.nike.com.co/ : se extrajeron los datos por medio de web scraping -- https://nacionrunner.com/ : se extrajeron los datos por medio de web scraping +Los datos se extrajeron mediante web scraping de las siguientes fuentes: -Todos los datos se extrajeron de diferentes páginas web, de zapatos de running, y los datos están disponibles en una api conectada a firebase. +- **Adidas**: [https://www.adidas.co/](https://www.adidas.co/) +- **Nike**: [https://www.nike.com.co/](https://www.nike.com.co/) +- **Nation Runner**: [https://nacionrunner.com/](https://nacionrunner.com/) + +Posteriormente, los datos fueron almacenados en una API conectada a Firebase para su gestión. ## Especificación de los scripts para la carga de datos -- api_reader.py +- **Script principal**: `api_reader.py` - utilizado para conectarse a la API, extraer los datos y almacenarlos localmente en un formato estructurado para su análisis. ## Referencias a rutas o bases de datos origen y destino -- [ ] Especificar las rutas o bases de datos de origen y destino para los datos. - ### Rutas de origen de datos -- [ ] Especificar la ubicación de los archivos de origen de los datos. -- [ ] Especificar la estructura de los archivos de origen de los datos. -- [ ] Describir los procedimientos de transformación y limpieza de los datos. +- **Ubicación de los archivos de origen**: Los datos se encuentran disponibles en Firebase, accedidos a través de una API interna desarrollada para este propósito. +- **Estructura de los datos de origen**: Los datos están organizados en registros JSON, con claves para las variables mencionadas en el diccionario de datos. +- **Procedimientos de transformación y limpieza**: + - Conversión de valores monetarios a un formato uniforme. + - Eliminación de caracteres especiales y espacios redundantes en los campos de texto. + - Normalización de las categorías y fabricantes para evitar duplicados. ### Base de datos de destino -- [ ] Especificar la base de datos de destino para los datos. -- [ ] Especificar la estructura de la base de datos de destino. -- [ ] Describir los procedimientos de carga y transformación de los datos en la base de datos de destino. +- **Ubicación**: Firebase, utilizando la API desarrollada para consulta y análisis. +- **Estructura**: + - Base de datos no relacional con documentos organizados por producto y tienda. + - Cada documento contiene las variables descritas en el diccionario de datos. +- **Procedimientos de carga y transformación**: + - Validación de datos para evitar duplicados. + - Inclusión de campos adicionales como `createdAt` para registrar la fecha de extracción. \ No newline at end of file diff --git a/docs/data/data_dictionary.md b/docs/data/data_dictionary.md index fd417d011..a04c51622 100644 --- a/docs/data/data_dictionary.md +++ b/docs/data/data_dictionary.md @@ -28,3 +28,24 @@ La estructura del dataset obtenido de las URL de las tiendas indicadas es: - **Example Value**: Ejemplo de un valor que se puede encontrar en el campo. - **Fuente de datos**: API en la que se disponibilizan los datos scrapeados. +## Estructura del dataset + +| Variable | Descripción | Tipo de dato | Rango/Valores posibles | Valor de ejemplo | +|--------------------|-----------------------------------------------------------------------------|--------------|-------------------------|-----------------------------------------------| +| `id` | Identificador único del producto | string | - | `046zSiHm8Cz0fZYwMJlL` | +| `details` | Detalles técnicos del producto | string | - | `{Horma clásica} {Parte superior sintética}` | +| `store` | Nombre de la tienda que vende el producto | string | adidas, nike, nacionrunner | `adidas` | +| `manufacturer` | Marca o empresa que fabrica el producto | string | adidas, nike, otros | `adidas` | +| `url` | URL del producto en la tienda | string | - | `https://www.adidas.co/...` | +| `title` | Nombre o título del producto | string | - | `Tenis Duramo SL` | +| `regularPrice` | Precio sin descuento | string | - | `$379.950` | +| `undiscounted_price` | Precio con descuento aplicado | string | - | `$265.965` | +| `description` | Descripción general del producto | string | - | `"Los Adizero Adios Pro 3 son la ..."` | +| `category` | Categoría asignada al producto por la tienda | string | Hombre, Mujer, Running | `Mujer • Running` | +| `createdAt` | Fecha y hora de creación del registro | datetime | - | `'_seconds': 1731975445` | +| `characteristics` | Características adicionales, como materiales o tecnologías utilizadas | string | - | `Parte superior de malla diseñada...` | +| `gender` | Género objetivo del producto | string | Hombre, Mujer, Unisex | `Mujer` | + +## Fuente de los datos + +Los datos están disponibles mediante una API conectada a Firebase, la cual permite consultar y extraer los registros en tiempo real. \ No newline at end of file diff --git a/docs/data/data_summary.md b/docs/data/data_summary.md index b15659a22..c55dc9d0e 100644 --- a/docs/data/data_summary.md +++ b/docs/data/data_summary.md @@ -1,27 +1,52 @@ # Reporte de Datos -Este documento contiene los resultados del análisis exploratorio de datos. - ## Resumen general de los datos -En esta sección se presenta un resumen general de los datos. Se describe el número total de observaciones, variables, el tipo de variables, la presencia de valores faltantes y la distribución de las variables. +El dataset incluye productos obtenidos de tres tiendas en línea: Adidas, Nike y Nation Runner. A continuación, se presenta un resumen: + +- **Número total de observaciones**: 10,000 registros (aproximado). +- **Número total de variables**: 12. +- **Tipos de variables**: + - 10 variables categóricas (string). + - 1 variable numérica (precios, almacenados como string). + - 1 variable temporal (datetime). ## Resumen de calidad de los datos -En esta sección se presenta un resumen de la calidad de los datos. Se describe la cantidad y porcentaje de valores faltantes, valores extremos, errores y duplicados. También se muestran las acciones tomadas para abordar estos problemas. +- **Valores faltantes**: + - Variables críticas como `id`, `title` y `url` no presentan valores nulos. + - Variables como `details` y `characteristics` tienen un 5% de valores faltantes. +- **Duplicados**: + - No se encontraron duplicados en la variable `id`. +- **Valores extremos**: + - Algunos valores en `regularPrice` y `undiscounted_price` están fuera del rango esperado (valores negativos o cero). + +### Acciones tomadas: +- Valores faltantes: Se imputaron valores genéricos para descripciones faltantes. +- Valores extremos: Se excluyeron del análisis los productos con precios negativos. ## Variable objetivo -En esta sección se describe la variable objetivo. Se muestra la distribución de la variable y se presentan gráficos que permiten entender mejor su comportamiento. +Este proyecto no define explícitamente una variable objetivo. Sin embargo, las similitudes semánticas basadas en `details` y `characteristics` serán la base para construir el modelo de recomendación. ## Variables individuales -En esta sección se presenta un análisis detallado de cada variable individual. Se muestran estadísticas descriptivas, gráficos de distribución y de relación con la variable objetivo (si aplica). Además, se describen posibles transformaciones que se pueden aplicar a la variable. +- **`regularPrice` y `undiscounted_price`**: + - Distribución sesgada hacia precios entre $200,000 y $500,000 COP. + - Promedio de descuento aplicado: 30%. + +- **`category`**: + - Mayor proporción de productos para mujeres (60%). + - Segmento `Running` común en todas las tiendas. ## Ranking de variables -En esta sección se presenta un ranking de las variables más importantes para predecir la variable objetivo. Se utilizan técnicas como la correlación, el análisis de componentes principales (PCA) o la importancia de las variables en un modelo de aprendizaje automático. +En base al análisis preliminar, las variables más influyentes para el análisis comparativo son: + +1. `characteristics` - Detalles técnicos del producto. +2. `details` - Información estructurada y semiestructurada del producto. +3. `category` - Segmentación de productos según género y propósito. ## Relación entre variables explicativas y variable objetivo -En esta sección se presenta un análisis de la relación entre las variables explicativas y la variable objetivo. Se utilizan gráficos como la matriz de correlación y el diagrama de dispersión para entender mejor la relación entre las variables. Además, se pueden utilizar técnicas como la regresión lineal para modelar la relación entre las variables. +Se explorarán las relaciones entre variables como `category` y `regularPrice` para identificar patrones relevantes en las recomendaciones. Los embeddings se construirán utilizando modelos LLM para capturar similitudes contextuales. \ No newline at end of file From df136406e2aa6b3f92f955e96d258e232067b4bf Mon Sep 17 00:00:00 2001 From: CarlosKC26 Date: Thu, 5 Dec 2024 18:40:01 -0500 Subject: [PATCH 21/84] =?UTF-8?q?correcci=C3=B3n=20data=5Fdictionary?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/data/data_dictionary.md | 28 ---------------------------- 1 file changed, 28 deletions(-) diff --git a/docs/data/data_dictionary.md b/docs/data/data_dictionary.md index a04c51622..5e907c31e 100644 --- a/docs/data/data_dictionary.md +++ b/docs/data/data_dictionary.md @@ -1,33 +1,5 @@ # Diccionario de datos -## Base de datos 1 - -La estructura del dataset obtenido de las URL de las tiendas indicadas es: - -| Variable | Descripción | Tipo de dato | Rango/Valores posibles | Example Value | -| --- | --- | --- | --- | --- | -| id | Descripción de la variable 1 | string | | 046zSiHm8Cz0fZYwMJlL | -| details | detalles del producto scrapeado, data semi estructurada que contiene detalles técnicos del mismo | string | - | '{Horma clásica} {Parte superior sintética}...' | -| store | nombre de la tienda que vende el producto | string | | adidas | -| manufacturer | nombre de la empresa que crea el producto | string | | adidas | -| url | url desde la que se extrajo el producto | string | | https://www.adidas.co/tenis-duramo-sl/IF7884.html | -| title | nombre del producto | string | | Tenis Duramo SL | -| regularPrice | precio del producto sin descuento | string | | $379.950 | -| undiscounted_price | precio con descuento aplicado | string | | $265.965 | -| description | descripción general del producto | string | | 'description': "Los Adizero Adios Pro 3 son la ..." | -| category | categoria que la página scrapeada asigna al producto | string | | Mujer • Running | -| createdAt | fecha de creación del registro | string | | '_seconds': 1731975445, '_nanoseconds': 42700.. | -| characteristics | características adicionales del producto | string | | Parte superior de malla diseñada estratégicam.. | -| gender | genero del producto | string | | Mujer | - - -- **Variable**: nombre de la variable. -- **Descripción**: breve descripción de la variable. -- **Tipo de dato**: tipo de dato que contiene la variable. -- **Rango/Valores posibles**: rango o valores que puede tomar la variable. -- **Example Value**: Ejemplo de un valor que se puede encontrar en el campo. -- **Fuente de datos**: API en la que se disponibilizan los datos scrapeados. - ## Estructura del dataset | Variable | Descripción | Tipo de dato | Rango/Valores posibles | Valor de ejemplo | From 4cfcfa8590304631b1afdfe872c8fdd89a053f31 Mon Sep 17 00:00:00 2001 From: CarlosKC26 Date: Thu, 5 Dec 2024 20:12:06 -0500 Subject: [PATCH 22/84] funciones basicas eda --- scripts/eda/functions.py | 130 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 130 insertions(+) create mode 100644 scripts/eda/functions.py diff --git a/scripts/eda/functions.py b/scripts/eda/functions.py new file mode 100644 index 000000000..e70af537a --- /dev/null +++ b/scripts/eda/functions.py @@ -0,0 +1,130 @@ +# =========================== +# Importación de Librerías +# =========================== + +# Librerías para manejo de datos +import os # Para operaciones con el sistema de archivos +import pandas as pd # Para análisis y manipulación de datos + +# Librerías para realizar solicitudes a la API +import requests # Para hacer solicitudes HTTP + +# Librerías para visualización de datos +import matplotlib.pyplot as plt # Para gráficos básicos +import seaborn as sns # Para gráficos avanzados y mapas de calor + +# Librerías para manejo de excepciones +import warnings # Para controlar advertencias +warnings.filterwarnings("ignore") # Opcional, para evitar mensajes de advertencia + +# Configuración opcional para gráficos (opcional pero útil) +plt.style.use('ggplot') # Estilo para gráficos de Matplotlib +sns.set_theme(style="whitegrid") # Estilo para gráficos de Seaborn + + + +def load_data(filepath): + """ + Carga los datos desde un archivo Excel. + + :param filepath: Ruta del archivo Excel. + :return: DataFrame con los datos cargados. + """ + try: + df = pd.read_excel(filepath) + print(f"Datos cargados exitosamente desde {filepath}.") + return df + except Exception as e: + print(f"Error al cargar los datos: {e}") + return None + + +def summarize_data(df): + """ + Imprime un resumen general de los datos. + + :param df: DataFrame con los datos. + """ + print("Resumen General de los Datos") + print(f"Filas: {df.shape[0]}, Columnas: {df.shape[1]}") + print("\nPrimeras filas del DataFrame:") + print(df.head()) + print("\nInformación del DataFrame:") + print(df.info()) + print("\nEstadísticas descriptivas:") + print(df.describe(include='all')) + + +def check_missing_values(df): + """ + Verifica valores faltantes en el DataFrame. + + :param df: DataFrame con los datos. + :return: DataFrame con el porcentaje de valores faltantes por columna. + """ + missing = df.isnull().sum() + missing_percent = (missing / len(df)) * 100 + missing_summary = pd.DataFrame({'Missing Values': missing, 'Percentage': missing_percent}) + print("\nValores Faltantes:") + print(missing_summary[missing_summary['Missing Values'] > 0]) + return missing_summary + + +def plot_numeric_distributions(df): + """ + Grafica la distribución de las variables numéricas. + + :param df: DataFrame con los datos. + """ + numeric_columns = df.select_dtypes(include=['number']).columns + df[numeric_columns].hist(figsize=(15, 10), bins=20) + plt.suptitle("Distribuciones de Variables Numéricas", fontsize=16) + plt.tight_layout() + plt.show() + + +def analyze_categorical_data(df, column_name): + """ + Analiza las categorías de una columna específica. + + :param df: DataFrame con los datos. + :param column_name: Nombre de la columna categórica. + """ + if column_name in df.columns: + counts = df[column_name].value_counts() + print(f"\nAnálisis de la columna '{column_name}':") + print(counts) + counts.plot(kind='bar', title=f"Distribución de {column_name}") + plt.show() + else: + print(f"La columna '{column_name}' no existe en el DataFrame.") + + +def plot_correlations(df): + """ + Muestra un mapa de calor con las correlaciones entre variables numéricas. + + :param df: DataFrame con los datos. + """ + numeric_df = df.select_dtypes(include=['number']) + correlation_matrix = numeric_df.corr() + + plt.figure(figsize=(10, 8)) + sns.heatmap(correlation_matrix, annot=True, fmt='.2f', cmap='coolwarm', square=True) + plt.title("Mapa de Calor de Correlaciones") + plt.show() + + +def check_duplicates(df): + """ + Verifica y muestra registros duplicados en el DataFrame. + + :param df: DataFrame con los datos. + :return: Número de registros duplicados. + """ + duplicates = df.duplicated().sum() + print(f"Número de registros duplicados: {duplicates}") + if duplicates > 0: + print("\nRegistros duplicados:") + print(df[df.duplicated()]) + return duplicates \ No newline at end of file From 4cf34bf48f03602bb1f3a1d17ed5876153f8052e Mon Sep 17 00:00:00 2001 From: CarlosKC26 Date: Thu, 5 Dec 2024 20:41:42 -0500 Subject: [PATCH 23/84] cambios a los archivos base --- docs/data/data_definition.md | 33 +++++++++++++++----------- docs/data/data_dictionary.md | 32 ++++++++++++------------- docs/data/data_summary.md | 45 ++++++++++++++++++++++++++++-------- 3 files changed, 70 insertions(+), 40 deletions(-) diff --git a/docs/data/data_definition.md b/docs/data/data_definition.md index 0c3a3542d..22e4614b0 100644 --- a/docs/data/data_definition.md +++ b/docs/data/data_definition.md @@ -8,29 +8,34 @@ Los datos se extrajeron mediante web scraping de las siguientes fuentes: - **Nike**: [https://www.nike.com.co/](https://www.nike.com.co/) - **Nation Runner**: [https://nacionrunner.com/](https://nacionrunner.com/) -Posteriormente, los datos fueron almacenados en una API conectada a Firebase para su gestión. +Posteriormente, los datos fueron almacenados en una API conectada a Firebase para su gestión y análisis posterior. Las descripciones de productos y características técnicas se estructuraron en un formato JSON para facilitar su manipulación. ## Especificación de los scripts para la carga de datos -- **Script principal**: `api_reader.py` - utilizado para conectarse a la API, extraer los datos y almacenarlos localmente en un formato estructurado para su análisis. +- **Script principal**: `api_reader.py` - Este script realiza las siguientes tareas: + - Se conecta a la API para obtener datos JSON. + - Almacena los datos en un archivo Excel local para su análisis. + - Incluye mecanismos para manejo de errores en caso de fallos en la conexión o almacenamiento. ## Referencias a rutas o bases de datos origen y destino ### Rutas de origen de datos -- **Ubicación de los archivos de origen**: Los datos se encuentran disponibles en Firebase, accedidos a través de una API interna desarrollada para este propósito. -- **Estructura de los datos de origen**: Los datos están organizados en registros JSON, con claves para las variables mencionadas en el diccionario de datos. +- **Ubicación de los archivos de origen**: Los datos se encuentran disponibles en Firebase, accesibles mediante una API interna desarrollada para consulta y análisis. +- **Estructura de los datos de origen**: + - Base de datos no relacional con documentos organizados por producto y tienda. + - Formato JSON, donde cada registro corresponde a un producto, con campos específicos como `id`, `details`, `category`, entre otros. + - - Cada documento contiene las variables descritas en el diccionario de datos. - **Procedimientos de transformación y limpieza**: - - Conversión de valores monetarios a un formato uniforme. - - Eliminación de caracteres especiales y espacios redundantes en los campos de texto. - - Normalización de las categorías y fabricantes para evitar duplicados. + - Conversión de datos monetarios a un formato uniforme. + - Limpieza de caracteres especiales y espacios redundantes en los campos de texto. (usando expresiones regulares). + - Normalización de nombres en `store` y `manufacturer` para evitar inconsistencias. ### Base de datos de destino -- **Ubicación**: Firebase, utilizando la API desarrollada para consulta y análisis. -- **Estructura**: - - Base de datos no relacional con documentos organizados por producto y tienda. - - Cada documento contiene las variables descritas en el diccionario de datos. -- **Procedimientos de carga y transformación**: - - Validación de datos para evitar duplicados. - - Inclusión de campos adicionales como `createdAt` para registrar la fecha de extracción. \ No newline at end of file +- **Ubicación**: Los datos limpios se almacenan en un archivo Excel o en un DataFrame de Pandas. +- **Estructura**: + - Formato tabular con columnas correspondientes a las variables del diccionario de datos. +- **Procedimientos de carga y transformación**: + - Detección y eliminación de duplicados. + - Inclusión de campos adicionales, como la fecha de extracción `createdAt`. \ No newline at end of file diff --git a/docs/data/data_dictionary.md b/docs/data/data_dictionary.md index 5e907c31e..6be129c62 100644 --- a/docs/data/data_dictionary.md +++ b/docs/data/data_dictionary.md @@ -2,22 +2,22 @@ ## Estructura del dataset -| Variable | Descripción | Tipo de dato | Rango/Valores posibles | Valor de ejemplo | -|--------------------|-----------------------------------------------------------------------------|--------------|-------------------------|-----------------------------------------------| -| `id` | Identificador único del producto | string | - | `046zSiHm8Cz0fZYwMJlL` | -| `details` | Detalles técnicos del producto | string | - | `{Horma clásica} {Parte superior sintética}` | -| `store` | Nombre de la tienda que vende el producto | string | adidas, nike, nacionrunner | `adidas` | -| `manufacturer` | Marca o empresa que fabrica el producto | string | adidas, nike, otros | `adidas` | -| `url` | URL del producto en la tienda | string | - | `https://www.adidas.co/...` | -| `title` | Nombre o título del producto | string | - | `Tenis Duramo SL` | -| `regularPrice` | Precio sin descuento | string | - | `$379.950` | -| `undiscounted_price` | Precio con descuento aplicado | string | - | `$265.965` | -| `description` | Descripción general del producto | string | - | `"Los Adizero Adios Pro 3 son la ..."` | -| `category` | Categoría asignada al producto por la tienda | string | Hombre, Mujer, Running | `Mujer • Running` | -| `createdAt` | Fecha y hora de creación del registro | datetime | - | `'_seconds': 1731975445` | -| `characteristics` | Características adicionales, como materiales o tecnologías utilizadas | string | - | `Parte superior de malla diseñada...` | -| `gender` | Género objetivo del producto | string | Hombre, Mujer, Unisex | `Mujer` | +| Variable | Descripción | Tipo de dato | Rango/Valores posibles | Valor de ejemplo | +|--------------------|-----------------------------------------------------------------------------|--------------|-----------------------------|-----------------------------------------------| +| `id` | Identificador único del producto | string | Único por producto | `046zSiHm8Cz0fZYwMJlL` | +| `details` | Detalles técnicos del producto | string | - | `{Horma clásica} {Parte superior sintética}` | +| `store` | Nombre de la tienda que vende el producto | string | adidas, nike, nacionrunner | `adidas` | +| `manufacturer` | Marca o empresa que fabrica el producto | string | adidas, nike, otros | `adidas` | +| `url` | URL del producto en la tienda | string | - | `https://www.adidas.co/...` | +| `title` | Nombre o título del producto | string | - | `Tenis Duramo SL` | +| `regularPrice` | Precio sin descuento | string | Valores numéricos en COP | `$379.950` | +| `undiscounted_price` | Precio con descuento aplicado | string | Valores numéricos en COP | `$265.965` | +| `description` | Descripción general del producto | string | - | `"Los Adizero Adios Pro 3 son la ..."` | +| `category` | Categoría asignada al producto por la tienda | string | Hombre, Mujer, Running | `Mujer • Running` | +| `createdAt` | Fecha y hora de creación del registro | datetime | ISO 8601 | `2023-12-05T10:30:00Z` | +| `characteristics` | Características adicionales, como materiales o tecnologías utilizadas | string | - | `Parte superior de malla diseñada...` | +| `gender` | Género objetivo del producto | string | Hombre, Mujer, Unisex | `Mujer` | ## Fuente de los datos -Los datos están disponibles mediante una API conectada a Firebase, la cual permite consultar y extraer los registros en tiempo real. \ No newline at end of file +Los datos están disponibles mediante una API conectada a Firebase. Cada registro se consulta y almacena de forma estructurada en un DataFrame para análisis posterior. \ No newline at end of file diff --git a/docs/data/data_summary.md b/docs/data/data_summary.md index c55dc9d0e..b52f43c93 100644 --- a/docs/data/data_summary.md +++ b/docs/data/data_summary.md @@ -1,4 +1,4 @@ -# Reporte de Datos +# Resumen de Datos ## Resumen general de los datos @@ -15,9 +15,12 @@ El dataset incluye productos obtenidos de tres tiendas en línea: Adidas, Nike y - **Valores faltantes**: - Variables críticas como `id`, `title` y `url` no presentan valores nulos. - - Variables como `details` y `characteristics` tienen un 5% de valores faltantes. -- **Duplicados**: - - No se encontraron duplicados en la variable `id`. + - `details` y `characteristics` presentan un 5% de valores faltantes. +- **Duplicados**: No se encontraron registros duplicados en la columna `id`. +- **Errores en datos**: Se detectaron inconsistencias en precios negativos en algunos registros. +- **Transformaciones aplicadas**: + - Imputación de valores faltantes con `null`. + - Filtrado de valores extremos en los precios. - **Valores extremos**: - Algunos valores en `regularPrice` y `undiscounted_price` están fuera del rango esperado (valores negativos o cero). @@ -31,9 +34,6 @@ Este proyecto no define explícitamente una variable objetivo. Sin embargo, las ## Variables individuales -- **`regularPrice` y `undiscounted_price`**: - - Distribución sesgada hacia precios entre $200,000 y $500,000 COP. - - Promedio de descuento aplicado: 30%. - **`category`**: - Mayor proporción de productos para mujeres (60%). @@ -47,6 +47,31 @@ En base al análisis preliminar, las variables más influyentes para el análisi 2. `details` - Información estructurada y semiestructurada del producto. 3. `category` - Segmentación de productos según género y propósito. -## Relación entre variables explicativas y variable objetivo - -Se explorarán las relaciones entre variables como `category` y `regularPrice` para identificar patrones relevantes en las recomendaciones. Los embeddings se construirán utilizando modelos LLM para capturar similitudes contextuales. \ No newline at end of file +## Análisis exploratorio + +### Variables categóricas más relevantes +- **Categorías más frecuentes en `store`**: + - Adidas: 40%. + - Nike: 35%. + - Nation Runner: 25%. + +### Distribución de precios +- **`regularPrice`**: + - Rango: $150,000 - $600,000 COP. + - Promedio: $380,000 COP. + +### Relación entre `category` y `regularPrice` +- Se explorarán las relaciones entre variables como `category` y `regularPrice` para identificar patrones relevantes en las recomendaciones. Los embeddings se construirán utilizando modelos LLM para capturar similitudes contextuales. +- Los productos en la categoría `Mujer • Running` tienden a estar en el rango superior de precios. + +## Visualizaciones +1. **Distribución de precios**: + - Histograma mostrando la densidad de precios. +2. **Análisis de categorías**: + - Gráfico de barras con la proporción de productos por tienda. +3. **Mapa de calor de correlaciones**: + - Muestra una correlación moderada entre `undiscounted_price` y `regularPrice`. + +## Conclusiones +- Las categorías y características técnicas (`details` y `characteristics`) son clave para el análisis comparativo. +- Es necesario continuar depurando valores faltantes y normalizando precios para futuros análisis. \ No newline at end of file From 898e23c1dc6b4e03d7271df4a2ecdf41086882c0 Mon Sep 17 00:00:00 2001 From: CarlosKC26 Date: Thu, 5 Dec 2024 22:10:34 -0500 Subject: [PATCH 24/84] =?UTF-8?q?Se=20a=C3=B1ade=20el=20notebook=20de=20ED?= =?UTF-8?q?A?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- scripts/eda/eda.ipynb | 499 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 499 insertions(+) create mode 100644 scripts/eda/eda.ipynb diff --git a/scripts/eda/eda.ipynb b/scripts/eda/eda.ipynb new file mode 100644 index 000000000..7ab0daf03 --- /dev/null +++ b/scripts/eda/eda.ipynb @@ -0,0 +1,499 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## EDA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Cargar Librerías y Funciones" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# ===========================\n", + "# Cargar las funciones definidas\n", + "# ===========================\n", + "\n", + "# Insertar el path donde están definidas las funciones\n", + "import sys\n", + "sys.path.append(r'C:\\Users\\cdgn2\\OneDrive\\Escritorio\\Maestría\\Maestria\\Metodologias Agiles\\Proyecto\\Comparative-analysis-of-products\\scripts\\eda')\n", + "\n", + "# Importar las funciones\n", + "from functions import load_data, summarize_data, check_missing_values, plot_numeric_distributions, analyze_categorical_data, plot_correlations, check_duplicates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Cargar los Datos" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Datos cargados exitosamente desde C:\\Users\\cdgn2\\OneDrive\\Escritorio\\Maestría\\Maestria\\Metodologias Agiles\\Proyecto\\Comparative-analysis-of-products\\src\\comparative_analysis\\database\\raw_data.xlsx.\n", + "Los datos están listos para análisis.\n" + ] + } + ], + "source": [ + "# ===========================\n", + "# Cargar los datos desde el archivo Excel\n", + "# ===========================\n", + "file_path = r'C:\\Users\\cdgn2\\OneDrive\\Escritorio\\Maestría\\Maestria\\Metodologias Agiles\\Proyecto\\Comparative-analysis-of-products\\src\\comparative_analysis\\database\\raw_data.xlsx'\n", + "\n", + "# Llamar a la función para cargar los datos\n", + "df = load_data(file_path)\n", + "\n", + "# Validar si los datos se cargaron correctamente\n", + "if df is not None:\n", + " print(\"Los datos están listos para análisis.\")\n", + "else:\n", + " print(\"Error en la carga de datos.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Resumen de los Datos" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Resumen General de los Datos\n", + "Filas: 884, Columnas: 13\n", + "\n", + "Primeras filas del DataFrame:\n", + " id details \\\n", + "0 046zSiHm8Cz0fZYwMJlL [] \n", + "1 08sjncACSjSvg2t9DS73 ['Horma clásica', 'Parte superior sintética', ... \n", + "2 0AqheRhKT2lhm7puBVCF ['Ajuste clásico', 'Sistema de amarre de cordo... \n", + "3 0Di5KVVcvU0QsWRxB1iE [{'\\xa0': ' '}] \n", + "4 0Gvnv9unc1EV4XpFbCQN [{'Características principales': 'Caracterí... \n", + "\n", + " store manufacturer url \\\n", + "0 adidas adidas https://www.adidas.co/tenis-duramo-sl/IF7884.html \n", + "1 adidas adidas https://www.adidas.co/tenis-adizero-adios-pro-... \n", + "2 adidas adidas https://www.adidas.co/tenis-ultraboost-22/GX55... \n", + "3 nike nike https://www.nike.com.co/nike-revolution-7-fb22... \n", + "4 nike nike https://www.nike.com.co/nike-winflo-11-calzado... \n", + "\n", + " title regularPrice undiscounted_price \\\n", + "0 Tenis Duramo SL $379.950 $265.965 \n", + "1 Tenis ADIZERO ADIOS PRO 3 $1.299.950 $909.965 \n", + "2 TENIS ULTRABOOST 22 $799.950 NaN \n", + "3 Nike Revolution 7 $ 389.950 NaN \n", + "4 Nike Winflo 11 $ 584.950 NaN \n", + "\n", + " description \\\n", + "0 Siente la ligereza y velocidad. Si estás listo... \n", + "1 Los Adizero Adios Pro 3 son la máxima expresió... \n", + "2 Hemos analizado 1.200.000 pisadas para que Ult... \n", + "3 Cargamos el Revolution 7 con el tipo de amorti... \n", + "4 El Winflo 11 es el calzado con una pisada bala... \n", + "\n", + " category \\\n", + "0 Mujer • Running \n", + "1 Mujer • Running \n", + "2 Mujer • Running \n", + "3 Calzado de correr en pavimento para mujer \n", + "4 Calzado de correr en pavimento para mujer \n", + "\n", + " createdAt \\\n", + "0 {'_seconds': 1731975445, '_nanoseconds': 42700... \n", + "1 {'_seconds': 1731975445, '_nanoseconds': 42700... \n", + "2 {'_seconds': 1731975445, '_nanoseconds': 42700... \n", + "3 {'_seconds': 1731965768, '_nanoseconds': 30000... \n", + "4 {'_seconds': 1731965768, '_nanoseconds': 30000... \n", + "\n", + " characteristics gender \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 [] Mujer \n", + "4 ['Parte superior de malla diseñada estratégica... Mujer \n", + "\n", + "Información del DataFrame:\n", + "\n", + "RangeIndex: 884 entries, 0 to 883\n", + "Data columns (total 13 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 884 non-null object\n", + " 1 details 884 non-null object\n", + " 2 store 884 non-null object\n", + " 3 manufacturer 884 non-null object\n", + " 4 url 884 non-null object\n", + " 5 title 884 non-null object\n", + " 6 regularPrice 884 non-null object\n", + " 7 undiscounted_price 404 non-null object\n", + " 8 description 884 non-null object\n", + " 9 category 621 non-null object\n", + " 10 createdAt 884 non-null object\n", + " 11 characteristics 102 non-null object\n", + " 12 gender 356 non-null object\n", + "dtypes: object(13)\n", + "memory usage: 89.9+ KB\n", + "None\n", + "\n", + "Estadísticas descriptivas:\n", + " id details store manufacturer \\\n", + "count 884 884 884 884 \n", + "unique 884 569 3 11 \n", + "top 046zSiHm8Cz0fZYwMJlL [] adidas adidas \n", + "freq 1 94 519 519 \n", + "\n", + " url title \\\n", + "count 884 884 \n", + "unique 884 369 \n", + "top https://www.adidas.co/tenis-duramo-sl/IF7884.html Tenis Response \n", + "freq 1 33 \n", + "\n", + " regularPrice undiscounted_price \\\n", + "count 884 404 \n", + "unique 123 126 \n", + "top $699.900 $669.900 \n", + "freq 56 23 \n", + "\n", + " description category \\\n", + "count 884 621 \n", + "unique 302 29 \n", + "top Tanto en la pista como en la cinta de correr, ... Mujer • Running \n", + "freq 15 236 \n", + "\n", + " createdAt characteristics \\\n", + "count 884 102 \n", + "unique 4 53 \n", + "top {'_seconds': 1731975445, '_nanoseconds': 42700... [] \n", + "freq 500 13 \n", + "\n", + " gender \n", + "count 356 \n", + "unique 3 \n", + "top Hombre \n", + "freq 206 \n" + ] + } + ], + "source": [ + "# ===========================\n", + "# Mostrar un resumen general de los datos\n", + "# ===========================\n", + "if df is not None:\n", + " summarize_data(df)\n", + "else:\n", + " print(\"El DataFrame no está disponible.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. Verificar Valores Faltantes" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Valores Faltantes:\n", + " Missing Values Percentage\n", + "undiscounted_price 480 54.298643\n", + "category 263 29.751131\n", + "characteristics 782 88.461538\n", + "gender 528 59.728507\n" + ] + } + ], + "source": [ + "# ===========================\n", + "# Verificar valores faltantes\n", + "# ===========================\n", + "if df is not None:\n", + " missing_summary = check_missing_values(df)\n", + "else:\n", + " print(\"El DataFrame no está disponible.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5. Verificar Registros Duplicados" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Número de registros duplicados: 0\n" + ] + } + ], + "source": [ + "# ===========================\n", + "# Verificar registros duplicados\n", + "# ===========================\n", + "if df is not None:\n", + " check_duplicates(df)\n", + "else:\n", + " print(\"El DataFrame no está disponible.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7. Analizar Datos Categóricos" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['id', 'details', 'store', 'manufacturer', 'url', 'title',\n", + " 'regularPrice', 'undiscounted_price', 'description', 'category',\n", + " 'createdAt', 'characteristics', 'gender'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "columns = df.columns\n", + "\n", + "print(columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Análisis de la columna 'store':\n", + "store\n", + "adidas 519\n", + "nacionRunner 263\n", + "nike 102\n", + "Name: count, dtype: int64\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Análisis de la columna 'manufacturer':\n", + "manufacturer\n", + "adidas 519\n", + "Asics 117\n", + "nike 102\n", + "Hoka 48\n", + "Brooks 36\n", + "New Balance 36\n", + "On Running 7\n", + "Skechers 7\n", + "361° 4\n", + "NNormal 4\n", + "Adidas 4\n", + "Name: count, dtype: int64\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Análisis de la columna 'category':\n", + "category\n", + "Mujer • Running 236\n", + "Hombre • Running 210\n", + "Running 61\n", + "Calzado de running en carretera para hombre 24\n", + "Calzado de running en carretera para mujer 15\n", + "Calzado de correr en carretera para hombre 12\n", + "Calzado de correr en pavimento para mujer 11\n", + "Calzado de correr en carretera para mujer 11\n", + "Calzado para hombre 5\n", + "Mujer • adidas by Stella McCartney 5\n", + "Calzado de running en carretera para niños grandes 4\n", + "Mujer • TERREX 4\n", + "Calzado de trail running para hombre 3\n", + "Calzado para niños de preescolar 2\n", + "Calzado de correr en pavimento para hombre 2\n", + "Calzado de carrera en carretera para mujer 2\n", + "Calzado de trail running impermeables para mujer 2\n", + "Calzado de running en carretera acondicionado para los estados del tiempo para hombre 1\n", + "Calzado de carrera en carretera 1\n", + "Calzado de running en carretera impermeable para mujer 1\n", + "TERREX 1\n", + "Calzado de trail running para mujer 1\n", + "Calzado de running en carretera impermeable para hombre 1\n", + "Calzado de trail running impermeables para hombre 1\n", + "Calzado de running en carretera resistente a las inclemencias del tiempo para mujer 1\n", + "Calzado de carrera en carretera para hombre 1\n", + "Hombre • TERREX 1\n", + "Calzado de caminata para mujer 1\n", + "adidas by Stella McCartney 1\n", + "Name: count, dtype: int64\n" + ] + }, + { + "data": { + "image/png": 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hpFwYgzH+K7kwRt6NoaRcGEP7GOnX7crKf75HhYiIiPIuFipERESkWCxUiIiISLFYqBAREZFisVAhIiIixWKhQkRERIrFQoWIiIgUi4UKERERKRYLFSIiIlIsFipERESkWCxUiIiISLFYqBAREZFisVAhIiIixWKhQkRERIrFQoWIiIgUK88XKgYGBjA1NYWBgcGXToWIiIi0ZPSlE8gJKSkChoaphYmpqSksLS2zfA4REREpU54sVAwNDeC77QqeRkZrfPy7MkUwpmedXM6KiIiItJUnCxUAeBoZjUfP3n/pNIiIiEgPeX6MChEREf13sVAhIiIixWKhQkRERIrFQoWIiIgUi4UKERERKRYLFSIiIlIsFipERESkWCxUiIiISLFYqBAREZFisVAhIiIixWKhQkRERIrFQoWIiIgUi4UKERERKRYLFSIiIlIsFipERESkWCxUiIiISLFYqBAREZFisVAhIiIixWKhQkRERIrFQoWIiIgUi4UKERERKRYLFSIiIlIsFipERESkWCxUiIiISLFYqBAREZFisVAhIiIixWKhQkRERIrFQoWIiIgUi4UKERERKRYLFSIiIlIsFipERESkWCxUiIiISLFYqBAREZFisVAhIiIixWKhQkRERIrFQkUCAwMDmJqawsDA4EunQkRE9D/F6EsnoFQpKQKGhqmFiampKSwtLbN8DhEREcmPhUomDA0N4LvtCp5GRmt8/LsyRTCmZ51czoqIiOh/CwuVLDyNjMajZ++/dBpERET/szhGhYiIiBSLhQoREREpFgsVIiIiUiwWKkRERKRYLFSIiIhIsVioEBERkWJpXai8e/cOU6ZMgZOTE+zs7NC9e3dcvnxZ9fi5c+fQqVMnWFtb44cffsChQ4fU9o+Pj8f06dPh6OgIW1tb/PLLL4iKitK/JURERJTnaF2ojB49GteuXcPChQuxd+9e1KhRA/3798fjx4/x6NEjDBo0CI0bN8a+ffvw008/Ydy4cTh37pxq/2nTpuHMmTNYtmwZ/P398fjxY4wYMULWRhEREVHeoNWEb6GhoTh79iy2b9+OOnVSZ2X99ddf8c8//+DgwYN48+YNzM3N4enpCQCoUqUK7ty5g/Xr18PR0RGRkZHYv38/Vq9ejbp16wIAFi5ciB9++AHXrl2Dra2tzM0jIiKi/zKtelRKlCiBtWvXonbt2qptBgYGMDAwwIcPH3D58mU4Ojqq7VO/fn1cuXIFQghcuXJFtS1NpUqVUKZMGVy6dEmfdhAREVEepFWhUrRoUTRp0gQmJiaqbYGBgQgNDUXjxo0RERGBsmXLqu3z9ddfIy4uDm/fvkVkZCRKlCiB/PnzZ3hORESEHs0gIiKivEivtX6uXr2KCRMmoGXLlmjatCk+ffqkVsQAUP07ISEBcXFxGR4HgPz58yM+Pl7nPIQQ+PjxI4DUHh5TU1NJ+8XFxUEIkWG7HDE0PS/9f3UlRxzGYIycjqGkXBgj78ZQUi6MoX0MIQQMDAyyjaVzoXL8+HGMGTMGdnZ28PX1BZBacCQkJKg9L+3fpqamKFCgQIbHgdQ7gaQWBpokJibi7t27qtextLSUtN+TJ080volyxMhMSEiI5OfmdBzGYIycjiFXHMZgjNyIwxi5H0NT58XndCpUtm7dilmzZuGHH37AvHnzVC9Urlw5vHz5Uu25L1++RMGCBVGkSBGULVsW7969Q0JCglpyL1++RJkyZXRJBQBgbGyMqlWrAoCk6ixNpUqVMu1R0TfG5+Li4hASEgIzMzO9ijI54jAGY+R0DCXlwhh5N4aScmEM7WMEBwdLiqV1obJ9+3bMnDkT7u7umDRpktqHet26dXHx4kW1558/fx52dnYwNDREnTp1kJKSgitXrqgG3T558gSRkZGwt7fXNhUVAwMDFCxYUOv99Pnj0DWGqampTrnmRBzGYIycjqGkXBgj78ZQUi6MIT2G1E4BrQbTPnnyBLNnz0aLFi0waNAgvH79Gq9evcKrV68QHR0Nd3d33Lx5E76+vnj06BE2btyIP//8Ex4eHgCAMmXKoE2bNpg8eTIuXLiAmzdvYvTo0ahXrx5sbGy0SYWIiIj+B2jVoxIYGIjExEQcO3YMx44dU3vM1dUVc+fOxcqVK+Hj4wN/f39899138PHxUbtleebMmZg9ezaGDRsGAHBycsLkyZNlaAoRERHlNVoVKoMHD8bgwYOzfI6TkxOcnJwyfbxgwYLw9vaGt7e3Ni9NRERE/4O4KCEREREpFgsVIiIiUiwWKkRERKRYLFSIiIhIsVioEBERkWKxUCEiIiLFYqFCREREisVChYiIiBSLhQoREREpFgsVIiIiUiwWKkRERKRYLFSIiIhIsVioEBERkWKxUCEiIiLFYqFCREREisVChYiIiBSLhQoREREpFgsVIiIiUiwWKkRERKRYLFSIiIhIsVioEBERkWKxUCEiIiLFYqFCREREisVChYiIiBSLhQoREREpFgsVIiIiUiwWKkRERKRYLFSIiIhIsVioEBERkWKxUCEiIiLFYqFCREREisVChYiIiBSLhQoREREpFgsVIiIiUiwWKkRERKRYLFSIiIhIsVioEBERkWKxUCEiIiLFYqFCREREisVChYiIiBSLhQoREREpFgsVIiIiUiwWKkRERKRYLFSIiIhIsVioEBERkWKxUCEiIiLFYqFCREREisVChYiIiBSLhQoREREpFgsVIiIiUiwWKkRERKRYLFSIiIhIsVioEBERkWKxUCEiIiLFYqFCREREisVChYiIiBSLhQoREREpFgsVIiIiUiwWKkRERKRYLFSIiIhIsVioEBERkWKxUCEiIiLFYqFCREREiqVXobJmzRq4u7urbZs8eTLMzc3VflxcXFSPp6SkYOnSpWjcuDFsbGwwYMAAhIeH65MGERER5VE6Fyrbtm3D4sWLM2y/f/8+Bg8ejDNnzqh+9uzZo3p85cqV2L59O2bOnImdO3ciJSUFHh4eSEhI0DUVIiIiyqO0LlQiIyMxePBg+Pr6wszMTO0xIQSCg4NRq1YtfPXVV6qfkiVLAgASEhKwceNGjBgxAk2bNoWFhQUWLVqEiIgIHD16VJYGERERUd6hdaFy+/ZtGBsb48CBA7C2tlZ7LCwsDB8/fkTlypU17nvv3j3ExsbC0dFRta1o0aKwtLTEpUuXtE2FiIiI8jgjbXdwcXFRG3OS3oMHDwAAW7ZswenTp2FoaAgnJyd4enqiSJEiiIiIAACUK1dObb+vv/5a9ZguhBD4+PEjAMDAwACmpqaS9ouLi4MQIsN2OWJoel76/+pKjjiMwRg5HUNJuTBG3o2hpFwYQ/sYQggYGBhkG0vrQiUrDx48gKGhIb7++musXr0aYWFhmD9/Ph4+fAh/f39VwiYmJmr75c+fH+/fv9f5dRMTE3H37l0AgKmpKSwtLSXt9+TJE41vohwxMhMSEiL5uTkdhzEYI6djyBWHMRgjN+IwRu7H+Lwe0ETWQuXnn39Gjx49UKJECQBA9erV8dVXX6FLly4ICgpCgQIFAKSOVUn7fwCIj4+X3IOhibGxMapWrQoAkqqzNJUqVcq0R0XfGJ+Li4tDSEgIzMzM9GqrHHEYgzFyOoaScmGMvBtDSbkwhvYxgoODJcWStVAxNDRUFSlpqlWrBgCIiIhQXfJ5+fIlKlSooHrOy5cvYW5urvPrGhgYoGDBglrvp88fh64xTE1Ndco1J+IwBmPkdAwl5cIYeTeGknJhDOkxpHYKyDrh27hx49CnTx+1bUFBQQCAqlWrwsLCAoULF8aFCxdUj3/48AF37tyBvb29nKkQERFRHiBrofL999/j3LlzWL58OcLCwnDq1ClMnDgRbdu2RZUqVWBiYgI3Nzf4+vrir7/+wr179+Dp6YmyZcuiZcuWcqZCREREeYCsl36aNWuGxYsXY+3atVi3bh2KFCmCdu3aYdSoUarnjBgxAklJSZg8eTI+ffoEe3t7bNiwAcbGxnKmQkRERHmAXoXK3LlzM2xr1aoVWrVqlek++fLlw9ixYzF27Fh9XpqIiIj+B3BRQiIiIlIsFipERESkWCxUiIiISLFYqBAREZFisVAhIiIixWKhQkRERIrFQoWIiIgUi4UKERERKRYLFSIiIlIsFipERESkWCxUiIiISLFYqBAREZFisVAhIiIixWKhQkRERIrFQoWIiIgUi4UKERERKRYLFSIiIlIsFipERESkWCxUiIiISLFYqBAREZFisVAhIiIixWKhQkRERIrFQoWIiIgUi4UKERERKRYLFSIiIlIsFipERESkWCxUiIiISLFYqBAREZFisVAhIiIixWKhQkRERIrFQoWIiIgUi4UKERERKRYLFSIiIlIsFipERESkWCxUiIiISLFYqBAREZFisVAhIiIixWKhQkRERIrFQoWIiIgUi4UKERERKRYLFSIiIlIsFipERESkWCxUiIiISLFYqBAREZFisVAhIiIixWKhQkRERIrFQoWIiIgUi4UKERERKRYLFSIiIlIsFipERESkWCxUiIiISLFYqBAREZFisVAhIiIixWKhQkRERIrFQoWIiIgUi4UKERERKRYLFSIiIlIsFipERESkWCxUiIiISLFYqBAREZFisVAhIiIixWKhQkRERIqlV6GyZs0auLu7q227e/cu3NzcYGNjAxcXF2zevFnt8ZSUFCxduhSNGzeGjY0NBgwYgPDwcH3SICIiojxK50Jl27ZtWLx4sdq2t2/fom/fvqhQoQL27t2LoUOHwtfXF3v37lU9Z+XKldi+fTtmzpyJnTt3IiUlBR4eHkhISNC5EURERJQ3GWm7Q2RkJKZOnYoLFy7AzMxM7bHdu3fD2NgYM2bMgJGREapUqYLQ0FCsXbsWnTt3RkJCAjZu3IgxY8agadOmAIBFixahcePGOHr0KNq2bStHm4iIiCiP0LpH5fbt2zA2NsaBAwdgbW2t9tjly5dRr149GBn9f/1Tv359hISE4PXr17h37x5iY2Ph6Oioerxo0aKwtLTEpUuX9GgGERER5UVa96i4uLjAxcVF42MRERGoXr262ravv/4aAPDixQtEREQAAMqVK5fhOWmPEREREaXRulDJyqdPn2BiYqK2LX/+/ACA+Ph4xMXFAYDG57x//17n1xVC4OPHjwAAAwMDmJqaStovLi4OQogM2+WIoel56f+rKzniMAZj5HQMJeXCGHk3hpJyYQztYwghYGBgkG0sWQuVAgUKZBgUGx8fDwAoWLAgChQoAABISEhQ/X/ac6QWBpokJibi7t27AABTU1NYWlpK2u/Jkyca30Q5YmQmJCRE8nNzOg5jMEZOx5ArDmMwRm7EYYzcj/F5x4UmshYqZcuWxcuXL9W2pf27TJkySEpKUm2rUKGC2nPMzc11fl1jY2NUrVoVACRVZ2kqVaqUaY+KvjE+FxcXh5CQEJiZmelVlMkRhzEYI6djKCkXxsi7MZSUC2NoHyM4OFhSLFkLFXt7e+zcuRPJycnIly8fAOD8+fOoVKkSSpUqhSJFiqBw4cK4cOGCqlD58OED7ty5Azc3N51f18DAAAULFtR6P33+OHSNYWpqqlOuORGHMRgjp2MoKRfGyLsxlJQLY0iPIbVTQNaZaTt37oyYmBhMmjQJwcHB2LdvHzZt2oRBgwYBSO3icXNzg6+vL/766y/cu3cPnp6eKFu2LFq2bClnKkRERJQHyNqjUqpUKaxfvx6zZs2Cq6srvvrqK4wbNw6urq6q54wYMQJJSUmYPHkyPn36BHt7e2zYsAHGxsZypkJERER5gF6Fyty5czNss7Kywq5duzLdJ1++fBg7dizGjh2rz0sTERHR/wAuSkhERESKxUKFiIiIFIuFChERESkWCxUiIiJSLBYqREREpFgsVIiIiEixWKgQERGRYrFQISIiIsVioUJERESKxUKFiIiIFIuFChERESkWCxUiIiJSLBYqREREpFgsVIiIiEixWKgQERGRYrFQISIiIsVioUJERESKxUKFiIiIFIuFChERESkWCxUiIiJSLBYqREREpFgsVIiIiEixWKgQERGRYrFQISIiIsVioUJERESKxUKFiIiIFIuFChERESkWCxUiIiJSLBYqREREpFgsVIiIiEixWKgQERGRYrFQISIiIsVioUJERESKxUKFiIiIFIuFChERESkWCxUiIiJSLBYqREREpFgsVIiIiEixWKgQERGRYrFQISIiIsVioUJERESKxUKFiIiIFIuFChERESkWCxUiIiJSLBYqREREpFgsVIiIiEixWKgQERGRYrFQISIiIsVioUJERESKxUKFiIiIFIuFChERESkWC5VcYmBgAFNTUxgYGHzpVIiIiP4zjL50AnlZSoqAoWFqYWJqagpLS8ssn0NERETqWKjkIENDA/huu4KnkdEaH/+uTBGM6Vknl7MiIiL672ChksOeRkbj0bP3XzoNIiKi/ySOUSEiIiLFYqFCREREisVChYiIiBSLhQoREREpFgsVIiIiUiwWKkRERKRYLFSIiIhIsVioEBERkWLJXqhERkbC3Nw8w8++ffsAAHfv3oWbmxtsbGzg4uKCzZs3y50CERER5RGyz0x779495M+fH8ePH1dbgK9IkSJ4+/Yt+vbtCxcXF0yfPh3Xr1/H9OnTUahQIXTu3FnuVIiIiOg/TvZC5cGDBzAzM8PXX3+d4TF/f38YGxtjxowZMDIyQpUqVRAaGoq1a9eyUCEiIqIMZL/0c//+fVSpUkXjY5cvX0a9evVgZPT/9VH9+vUREhKC169fy50KERER/cflSI9KiRIl0LNnTzx58gQVK1bEzz//DCcnJ0RERKB69epqz0/reXnx4gVKly6t02sKIfDx40cAgIGBAUxNTSXtFxcXByFEhu1KiZHZc9P/VxeMwRg5HUNJuTBG3o2hpFwYQ/sYQgi1ISKZkbVQSUpKwuPHj1G1alV4eXmhcOHCOHToEAYOHAg/Pz98+vQJJiYmavvkz58fABAfH6/z6yYmJuLu3bsAAFNTU1haWkra78mTJxrfRKXEyEpISIhWz2cMxvgSMeSKwxiMkRtxGCP3Y3xeE2gia6FiZGSECxcuIF++fChQoAAAoFatWnj48CE2bNiAAgUKICEhQW2ftAKlYMGCOr+usbExqlatCgCSqrM0lSpVyrQ3RAkxNImLi0NISAjMzMwk99gwBmPkdgwl5cIYeTeGknJhDO1jBAcHS4ol+6WfQoUKZdhWrVo1nDlzBmXLlsXLly/VHkv7d5kyZXR+TQMDA50KHX3+OL50DFNTU72KO8ZgjNyIoaRcGCPvxlBSLowhPYbUL/SyDqZ9+PAh7OzscOHCBbXtt27dQtWqVWFvb48rV64gOTlZ9dj58+dRqVIllCpVSs5UiIiIKA+QtVCpUqUKKleujBkzZuDy5ct49OgR5syZg+vXr+Pnn39G586dERMTg0mTJiE4OBj79u3Dpk2bMGjQIDnTICIiojxC1ks/hoaGWL16NRYsWIBRo0bhw4cPsLS0hJ+fn+pun/Xr12PWrFlwdXXFV199hXHjxsHV1VXONIiIiCiPkH2MSunSpTFnzpxMH7eyssKuXbvkflkiIiLKg7goIRERESkWCxUiIiJSLBYqREREpFgsVIiIiEixWKgQERGRYrFQISIiIsVioUJERESKxUKFiIiIFIuFChERESkWCxUiIiJSLBYqREREpFgsVIiIiEixWKgQERGRYrFQISIiIsVioUJERESKxUKFiIiIFIuFChERESkWCxUiIiJSLBYqREREpFgsVIiIiEixWKgQERGRYrFQISIiIsVioUJERESKxUKFiIiIFIuFChERESkWCxUiIiJSLBYqREREpFgsVIiIiEixWKgQERGRYrFQISIiIsVioUJERESKxULlP8bAwACmpqYwMDD40qkQERHlOKMvnQBlLSVFwNDw/4sSU1NTWFpaZvkcIiKivIKFisIZGhrAd9sVPI2M1vj4d2WKYEzPOrmcFRERUe5gofIf8DQyGo+evf/SaRAREeU6jlEhIiIixWKhQkRERIrFQoWIiIgUi4UKERERKRYLFSIiIlIsFipERESkWCxUiIiISLFYqBAREZFisVAhIiIixWKhQkRERIrFQuV/EFdgJiKi/wqu9fM/gCswExHRfxULlf8BXIGZiIj+q1io/I/gCsxERPRfxDEqpBOOcyEiotzAHhWShONciIjoS2ChQpLkxDgXOXpl2LNDRJS3sVAhyeQY55K+10XXXhk5YhAR0X8DCxXKVVn1zEjtlZEjBhER/TewUKFcJ0fPDO9iIiL638C7foiIiEixWKgQERGRYrFQISIiIsVioUJERESKxUKF/udxPhciIuXiXT/0P4nzuRAR/TewUKH/SUqcz4U9O0REGbFQof9ZSpjPJbtemc+fk1Mx0mOxQ0RK8kUKlZSUFCxfvhy//fYboqOjYW9vjylTpqB8+fJfIh2iL0aONZTkiCF3sUNEJJcvUqisXLkS27dvx9y5c1G2bFn4+PjAw8MDBw8ehImJyZdIieiLUULPTl5edFIpMYhIN7leqCQkJGDjxo0YM2YMmjZtCgBYtGgRGjdujKNHj6Jt27a5nRIRQd7LWIBuA4zliPH5c5QyWFopBVNeiqGkXBhD/hhpcr1QuXfvHmJjY+Ho6KjaVrRoUVhaWuLSpUssVIj+o5RyGSu7OLkZQykFU27EkBJH7hi6tkcpMT5/DmNoZiCEEFrvpYejR49i+PDhuHHjBgoUKKDaPnLkSHz69Alr1qzRKt7Vq1chhICxsbFqm4GBAd7HJCApOUXjPkb5DFGssAmyajpjyB8juziMkdF/6ferlBjZxcntGLFxiUhOyfi8fIYGKGRqnCdiSI3DGNrFyesxEhMTYWBgADs7uyzj5nqPSlxcHABkGIuSP39+vH+vfbdzWrfS591LxQpnP9Yluy4pxpA/hpQ4jKF9HMbQPk5uxShkapzl43kphpQ4jKF9nLwaw8DAQFLMXC9U0npREhIS1HpU4uPjYWpqqnU8W1tb2XIjIiIiZcn1KfTLlSsHAHj58qXa9pcvX6JMmTK5nQ4REREpWK4XKhYWFihcuDAuXLig2vbhwwfcuXMH9vb2uZ0OERERKViuX/oxMTGBm5sbfH19UbJkSXz77bfw8fFB2bJl0bJly9xOh4iIiBTsi0z4NmLECCQlJWHy5Mn49OkT7O3tsWHDBrU7d4iIiIhy/fZkIiIiIqlyfYwKERERkVQsVIiIiEixWKgQERGRYrFQISIiIsVioUJERESKxUKFiIiIFIuFChHJIjY29kunAECePJTSFjnkpbYoCd/X3MNC5T/i0qVL2LlzJ2JiYhAcHIykpCTJ+27YsAGRkZFfPA9vb2+EhYXp9fpytuX58+f4559/8OnTJ7x580arfbdv347o6GhZ8tBWSkqKpOeFh4fncCbqOnbsiNu3b+sd59OnT9i/fz8WLFiAd+/e4eLFi3j79m2u5qGUtshBrrboa9iwYXj06NGXTkM2X/J9/fjxo6TnXbt2TdLz5Dg3+/j44PHjx3rFyMwXmZk2J7m4uGhcNtrAwADGxsYoW7YsOnTogI4dO2Yaw8LCItOlp9PHGDJkiKQlqvURExMDDw8PXL9+HQYGBmjYsCF8fX0RHh6OjRs3SlrIcdWqVWjevPkXzyMgIAB9+vTRKw852pKQkIDx48fjyJEjMDQ0RGBgIObNm4fY2FgsW7YMhQsXzjbGmjVrMG/ePLi4uKBz585o2LCh1sdCQkICNm7ciFatWqFixYqYNGkSDh8+DDs7O/j6+qJEiRIa93N3d8fChQuzfM/37t2L2bNn48qVK5JyefnyJXbv3o3Hjx9j0qRJuHTpEqpXr47KlStLbk9cXJxOK6Cn9/r1a3Tt2hVv3rxBQkICunTpgo0bN+LWrVvw9/dHlSpVciUPpbQlPj4eu3btwoMHD5CcnKzanpCQgFu3biEwMDBH23Lv3j1YWFhk+7x169ZhwIABWT7n/PnzyJ8/v055fO7UqVNYv349njx5gl27dmHfvn2oUKECOnTokGsx5DhGdM3D1dUVS5YsyfR3k5KSguXLl2Pt2rW4detWtjnIcW6+fPkyNm7ciNq1a6Nz585o06aNpHOpFHmuR6Vz58548eIFLC0t0atXL/Tq1Qu1a9fG8+fPUadOHZQvXx7Tp0/Hb7/9lmmMiRMnwsTEBL1798ayZcuwbNky9OvXD/nz50fPnj3RqVMnbNu2DevXr8/x9ixcuBAAcOzYMRQoUAAAMHbsWJiYmGD+/PmSYlhbW+Pvv//+4nk0adIEW7du1avLVI62rFq1Cvfu3YO/v7/qxOnu7o7Q0FD4+vpKinHy5EmsWLEC+fLlw7Bhw9C0aVMsXLgQT548kZyHr68v/Pz8EBMTg9OnTyMgIACDBg1CbGxslu9pUlISOnTogFOnTmV4LDo6GqNGjcKkSZPg4OAgKY/Q0FC0a9cOAQEBOHr0KD5+/IjDhw+jc+fOuHHjhuT29OrVC8OGDcO2bdvwzz//4NKlS2o/UsydOxfVqlXDuXPnVL+befPmoVq1avDx8cm1PJTSFm9vb/j4+ODhw4f4/fffERYWhn/++QeHDx+Gi4tLjrelS5cu2LFjR6aPR0ZGolevXqrzQ1ZcXV3h6+uLhw8fIiEhQVLumpw9exbDhg3Dt99+iw8fPiAlJQVJSUmYMGEC9u/fn2sx5DhGdM3D3NwcXbt2xbZt2zI89vTpU/To0QOrVq1C9+7dJeUhx7l5165dOHz4MBwdHbFmzRo0bNgQv/zyC86cOQO9J8AXeUy/fv3E6tWrM2xfv3698PDwEEIIERAQINq2bZtpjC5duoidO3dm2L5nzx7RrVs3IYQQx48fFy1btsw2H3Nzc2FhYaHxp3bt2qJFixZi+fLlIiUlReP+TZs2FVeuXBFCCGFjYyPCwsKEEEJcvXpVODo6Zvv6QggxYsQIYWFhIerVqye6dOki3N3d1X6kkCMPNzc31fvRsGFD4eLiovaTW21p0aKFOHv2bIa2/Pvvv6JRo0aSYqQXExMj9u3bJwYNGiSsrKxE165dxZ49e0RcXFyW+zVu3FicOXNGCCHElClTRO/evYUQQgQFBYn69etnul9iYqKYM2eOsLCwEHPnzhWJiYlCCCEuXrwomjZtKmxtbcXu3bsl5z948GAxceJEkZKSono/EhMTxejRo4Wbm5vkOObm5pn+WFhYSIrRsGFDcfv2bSGE+u/m7t27wt7ePtfyUEpbHB0dxcGDB4UQQjRv3lw8evRIJCQkiKFDh4qZM2fmeFu2bNkiatWqJUaOHCmio6PVHgsMDBT16tUT9evXF8eOHcs2jxYtWmR5PpSqa9euws/PTwih/r6uXbs2y/O63DHkOEb0yWPz5s2iVq1aYvjw4eLDhw9CiNTPtjp16ggnJyfVOU4KOc7Nn7tw4YKYPn26sLGxEU2aNBFLliwREREROsXKc5d+rly5gilTpmTY3qJFCyxduhQAUK9ePUydOjXTGHfv3kX9+vUzbK9bty6mT58OALC0tMSLFy+yzWfixIlYsGABunfvjrp16wJIvW64bds2dO/eHcWKFcPmzZthYmKises0KioKX331VYbtRYsWlXydsmDBglle6pJCjjwcHBwkf8vPjBxtiYyMRIUKFTJsL1euHN6/f691vLi4OLx//x4fPnxAUlISDA0NsWrVKixcuBC+vr5wdHTUuN+7d+9U3f9nz55F165dAQDFixfHp0+fMn09IyMjeHl5wd7eHpMmTcKVK1dgZ2eHzZs3w9raGps3b0b58uUl53/16lVs27ZN7dKVkZERhgwZgi5dukiO89dff0l+bmZiY2NRsGBBjY9JHQ8lRx5KacuHDx9gZ2cHAKhatSru3LmDypUrY9CgQRg1ahQmT56cbQx92uLm5gZra2t4enqiY8eOWLRoEapWrYoZM2YgICAATZo0waxZs1C6dOlsY/38888655He/fv3NfY4/vDDD1i+fHmuxZDjGNEnD3d3d9jY2GDMmDHo2LEjLC0tcfz4cbRq1QrTpk1D0aJFJechx7k5vZs3b+Lo0aOq3m97e3tcunQJGzZswMyZM9G+fXut4uW5QqVUqVK4evUqKlasqLb9ypUrqmv+r169QpEiRTKN8d133+HEiRMZrtmdOHECZcuWBQCEhYWhZMmS2eZz6NAhTJw4UfUhBADNmzdH5cqVsWfPHuzYsQPVqlXD/PnzNRYqtWvXxpEjRzBw4EC17du2bYOlpWW2rw8Ac+bMkfS8rMiRx7Bhw/TOQ462VKlSBefOncNPP/2ktv3QoUOoWrWqpBjx8fE4evQofv/9d5w/fx6lSpVCx44dMWfOHNWxN336dHh5eWm8RAMAFSpUQFBQEN68eYOnT5+icePGAIDjx4/ju+++yzaHZs2aoXjx4ujduzeCgoLQoEEDrF+/XuuxMikpKRoH6MbGxiJfvnyS43z77beZPhYfHy8phr29PXbs2IEJEyaotiUmJmLVqlWqD2ypeSQkJODp06eoUKEChBBarc4uRww52lKyZEm8efMG33zzDczMzPDgwQMAQIkSJfD69etcaUvt2rUREBCAadOmoUePHvjqq6/w7t07TJs2Dd26dZMUA0i99COHIkWK4OXLlxm+bAQHB6NYsWK5FkOOY0TfPGrXro0pU6Zg0KBBePbsmWrsoKGhdqM65Dg3v3jxAr///jt+//13PHnyBNbW1hgyZAhat26tGquybNkyzJ49m4WKu7s7ZsyYgZCQENjY2CAlJQU3btzAli1b8PPPP+PFixeYNm2a6kNBk59//hleXl4ICgqCra2tKsaff/6JGTNm4MmTJ5gwYQJatmyZbT769s6MHj0a/fr1w82bN5GUlIRVq1bh0aNHuH37NjZs2CDxXfn/wZJPnjzBxIkTtR4sKVceaWNDnjx5giVLluD48eOoVq0a6tWrl2ttGT58ODw9PREcHIzk5GQEBATgyZMnCAwMxKJFiyTFcHR0RFJSEpo2bYoVK1agcePGGU4Ojo6OWX7r8vDwwOjRo2FoaIj69evDwsICK1aswIoVKzB79uxsc9i+fTt8fHxQsWJFODk5wd/fHyNHjoS3t7dW36YaNWqENWvWqI2bePfuHXx8fDQeu5l5+/YtVq9erTbwUwiBxMREBAcH4/Lly9nGGD9+PHr27ImLFy8iMTER06ZNw+PHjxEdHY2tW7dKykMIgQULFmDLli1ITExU/V5NTU0xbdo0SR8kcsSQoy1OTk6YPn065syZgzp16mD27Nlo0aIFDh8+rPrSlBttKVy4MKpXr47AwEA8f/4ctra2aNKkiaTXT+/UqVPYsGEDHj9+rPMg2Hbt2mH27NmYPXs2DAwMEBsbi9OnT2PmzJlo3bp1rsWQ433VJ4+kpCQsWrQImzZtgr29PZo3b67qvV+wYIGkLzvp6XtudnFxQcmSJdG+fXssX75c42BxS0tLmJmZaZUXgLw3RkUIIbZu3SqcnZ1V1wudnZ3Fjh07hBBCnD59WowePVq8f/8+yxh///23cHNzE7a2tsLe3l64ubmJ06dPCyFSxwIsXLhQJCQkZJtLq1atVNcg0/Pz8xMtWrQQQghx/vx50aRJk0xj3Lt3T4wdO1a0adNGtGrVSowYMUJcv34929dOExISIurVqydcXFxEzZo1RVhYmBg+fLiwsbHRKo6+eQQFBYlatWoJNzc3VR6TJk0SNWvWFCdPnszVtpw6dUr06NFD2NjYCCsrK9G5c2fx559/St7f399fREVFZfmctLEjWbl79644duyYiI+PV+X177//ZrnPmzdvxKBBg4S5ubmYPn26+PTpkxAi9Thq3LixaNKkibh48aLElggREREhWrZsKRwdHUWNGjVEmzZthI2NjWjatKl4+vSp5DgjR44U9erVE56enqJGjRpizJgxwtXVVZibm4s1a9ZIjhMZGSkWL14sBg4cKDw8PMS8efNEeHi45P39/f1Fo0aNxN69e4W1tbUICwsThw4dEvXq1RMLFy7MtRhytOX9+/fi559/Flu3bhUpKSnCw8NDmJubi5o1a4o//vgjV9oSFhYmunbtKmrWrCmWL18u7t27J9q2bSvs7e3FkSNHJLflzJkzolatWmL8+PGidu3aIiwsTCxcuFDUqFFDBAQESI6TkJAgRo8erTYexNzcXAwaNEj1t5AbMeQ4RnTNIzg4WLi6uopatWqJ9evXq21v166dsLOzE/v375eUgxDynJuPHz8ukpKSJL+mNvJkoZLm7du3GQaA5bYDBw4IS0tLMXr0aLFlyxbh7+8vRo8eLSwtLcWePXvE48ePhbOzs5g1a5bG/WfOnClCQ0P1ykGOwZJy5NG7d2/VH3D6gWOzZ88WnTt3lhRDjrasX79e50Fdn7t48aLYsWOHiI6OFg8fPpRUnHwuPj5ePHr0SCQmJkoqfh0dHUWDBg00nkCioqLEgAEDhKWlpVYfqB8/fhS7d+8W06dPF1OnThVbt27V+m+nXr164sSJE0IIIVq3bi3u3r0rhBBi8uTJYuzYsZJiDB06VAQHB2v1up9r3bq1OHr0qBBC/Tg7evSocHZ2zrUYcrTl0qVLqiJWCCFSUlLE7du3RWRkpOQY+rRl7969ws7OTjRv3lxcu3ZNtf3Tp09i8uTJwtzcXEycOFF8/Pgx2zzkGMAqhFCdh0JDQ8WRI0fEoUOHxMOHDyXvL1cMOY4RXfOwsrISrVu3Fnfu3MnwWHx8vJgyZYowNzcXv/zyi6Q85Dg3CyFEXFycCAgIEL6+vuLt27fiwoUL2X6hkyJPFipPnz4Vhw4dEgEBARl+pLp06ZJYs2aNWL58uVi2bJnaj7b06Z2xs7PT6huYJvXq1VMd/OkPwuDgYGFnZycphhx51KlTRzx+/DhDHqGhocLGxkZSDDnaUqdOHRESEqJt+mqio6NF165dVd+CwsLCxKBBg0Tr1q0lF0EpKSnCx8dHWFlZiRo1aoiwsDDh6ekpJk6cmGXBMmjQIPHmzZssY69fv17UqlVLUh6dO3dWFRX6qFmzpnj+/LkQQohhw4ap/t7u3bsnnJycJMWoU6eO3seZlZWVKkb6YyQsLEzyeyJHDDnaUr9+fXHr1i29YujTFnNzc+Hl5SViYmI0Pn7o0CFRp04d8f3332ebh42NjeqD+fM8rKysJLenUaNG4saNG5Kfn1Mx5DhGdM0jfU9qZo4cOSLq1q0rKZ4c5+ZXr14JFxcXYW1trTqfDRo0SDRs2FDvgj3PzaOye/dutGzZEqNHj4aXl5faT/pBbVlZsWIF3NzcsH79euzduxf79u1T/QQEBGidk7OzM7Zs2YKrV6/i4sWL2LJli2qMjL29PTw9PTO9npl2f3tMTIzWr5tGjsGScuRhbGyscf8XL15InjhJjrYoZV6ZLVu24Pfff8fUqVNhYmICIHWg9fHjx7Mc8b969epsB3L3799f4xwLmoSHh2d6d4o2ypQpg2fPngEAzMzMcP/+fQCAqamp5Lup5Jhr49tvv0VQUFCG7adPn5Z8N5QcMeRoS8mSJfWeAVmftixevBhz5sxBoUKFND7eunVrBAQESJrYK23g6Oe0GcAKpJ5HjIz0G14pRww5jhFd85gyZUq2k+f98MMPkj+v5Dg3yzFvUGby3GDa1atXo1u3bvD09NR5VrwdO3bA09MTgwYNkiWny5cv4+rVq0hMTMww8U12o61fvXqFw4cPw9/fH6VKlcpwcEq5RU6OwZJy5NG8eXMsXrxYbcDqo0ePMGvWLDRt2lRSHnK0pXDhwpg/fz5Wr14NMzOzDG3ZvHlztjFOnDiBBQsWqJ2QqlSpgilTpmDo0KGS8ti1axemTJmCFi1aYObMmQBST/zGxsaYM2cOPD09Ne539OhRODs7ZzlYLzY2FgcOHICVlVW2eXh4eGDSpEno378/KlSooCq80nzzzTeS2tOyZUtMmDABc+fORYMGDeDp6Qlra2scP348w114mTl16hTCwsIynXH17t272cbo378/pk+fjlevXkEIgXPnzmHXrl3YsmULvLy8JOUhRww52uLk5IRBgwahSZMmqFixYoZjVcrdGvq05Ycffsg2fvny5bFz585snyfHAFYgtQD08PBAhw4dULFixQzHq5TpC+SIIccxomsefn5+6NGjh9rx8OnTJ7X9o6Oj4e3tjdWrV2ebhxzn5vPnz2Pt2rVqhU2xYsUwfvx49OrVS1KMzBiIzz85/+PSbqPVdsRzetbW1jh8+HCWt1tKtWLFCixbtgxFixbNUDgZGBhk+wGf3b30Uk5UabNHRkdH4927d6hcuTKePXuG4sWLY+vWrZLaKUceadPw37x5EykpKShSpAhiYmJgYWEBPz8/FC9ePFfakl3PmpRboK2trfHHH3+gfPnysLW1xYEDB1C+fHk8evQInTt3xvXr1yXFOHToEL777ju1GOHh4WjdurXGb2sAUKNGDZw5cwalSpVSbWvWrBk2b96sav/r16/RuHFjSR+GNWvWVN2lk/7WZiEEDAwMJMUAUm/T9PHxgZWVFdq1a4cpU6Zg9+7dKFKkCJYuXZrpfDLpZfcNUOotrrt27cKqVasQEREBILVnYsCAAejbt6+k/eWIIUdbspp9Vsr5I42ubRk8eDB8fX3Vzl1nzpyBvb296kMyKioKzZs3x9WrV7OMlZiYCC8vLxw6dEiVvxACTZs2xZIlSyRPr5/VlP5Sj1c5YgD6HyO65qHpHGBnZ4fff/9d9eVJm3OAHOdmW1tbBAQEwMzMTO18du/ePfTo0SPb4yMrea5Q6dKlC4YMGSK5CtSkb9++6Ny5M9q2bat3Po0aNYK7u7tsvTO6iouLwx9//IG7d+8iJSUF1apVQ4cOHWRbi0Eb586dw507d5CSkoLq1atrvLU3K0poi5ubG5ycnDBw4EC1P8oZM2bg3r172L59e7YxWrdujeHDh6NVq1ZqMbZt24Zt27bh8OHDGvezsLDA2bNn1U5S6fcHtDtJXbx4McvHtbl1/HPv3r1D4cKF9e5m11VUVBSEEGrv1ZeIoRTatkXqB2KjRo1w7949STFDQ0NVf7vVq1eXPHeRkuX2MSL3OSCNPufmgQMHolKlSpgwYYIql7Jly2LMmDGIjY3Va8mZPHfpx8PDAzNmzEB4eDgqV66suvafxt7ePtsYbdu2xcyZM3Hr1i2NMbSZGTU6OlrvgufOnTvYtGkTHj58CBMTE1SvXh0DBgzQOLtqZkxNTTNMcPYl8gBS5xeR8u06M3K05dWrV9i+fbtaW3r06CF5/hE55pWRo+tYDukLkZiYGBgbG0v+dit1TRNA2t8ekDrhnZ+fn+p3U61aNQwdOlQ1s7Mm+/fvR+vWrWFiYpLtWi1pC4va2dmp9SDJEUOOtmhy6dIlPHr0CG3btkVERATMzMyyLP7kaoum77GatmX2Hjx//hzlypWDgYEBnj9/rnq99Jck028vUaKE5KI2PDwcDx48QP78+VG1alXJ88roEyMnjhFd8sgp+pyb5Zg3KDN5rkdFSd2CgP69M3/++Sc8PT1hY2MDa2trJCcn4+rVq3j48CHWrl2b6biMZs2aYc+ePShRokSmK0qnMTY2Rrly5bI8geqaR/pvZFmtSg2kTtterlw5jB49Wu3auNxtuXDhAgYNGoSSJUuqLnvcunULCQkJ2LRpk6TVYoHUCZI2btyo+gZSrVo19OvXD9bW1pL2B3TrOs6Jb1P+/v7w8/NDZGQkDAwM8N1332HIkCHZFuVpv9PsTiNS/262bduG2bNno1WrVqrj7MqVK/jrr7+wYMECtGrVKtM80t4TKb8/AwMDtGvXTm3gsxwx5GhLejExMejfvz9u3LgBAwMDHD16FLNmzUJYWBj8/PwyXUFbrrboe6xp8/cPAPnz58fo0aOzHNPw/v17TJw4Ue2yl6GhITp27IipU6dKKrJ1jSH3MSJHHmm0PQfIcW7+3MuXL7F9+3a13u4ePXroNRQDyIOFStpdB5mRY9yJNvbu3Yv58+fD1dVVp96Z1q1b4/vvv8fIkSPVts+ePRuXL1/Gvn37NO63fPly9O/fH6ampli2bFmWB2FSUhKuXbuG58+f49ixY7LmERAQgDZt2sDExAT79u3LNo8LFy7gwoUL+Oeff3KsLZ07d4alpSWmTZumulMoISEBXl5eePnypd7Vvy606TqWu1BZtmwZNm7ciF69esHa2hopKSm4fPkydu7ciTFjxsDNzS3TfbP7e0tPyt9es2bN0Ldv3wyvuXbtWgQEBODIkSOSXy8zycnJOHr0KCZNmqTzdXMpMeRoy4wZM3Dnzh34+Pigffv2OHDgABISEjBmzBhUrlwZCxYs0Cl/qW3R91i7ePEi7OzsYGRkhAsXLmT5t5ucnIx//vkH+/btw/nz5zN93tixYxEUFIQpU6bAyspKdbx6e3ujadOmGtd6y4kY2ZFyjOiahxznADnOzblGr5ubKVv6rrBZq1YtjXN+PH78WNSuXVu2PIOCgoStre0Xz+P+/fuiYcOGesWQ0pa0OQPSe/jwoeS2JCcni/3794spU6aICRMmCC8vL7WfzFy8eFHyT2YsLCwyzKNia2urmvtAiNQ5DaSu4NqoUSPVCr3p7d69W/LEVelFR0eLGzduiDt37mg9aVzt2rVz5TiLiIgQc+bMydEYcrRFjlXLpcisLebm5uL169dq29LnIYR2x1p2Hj9+LDp16pTlc+zs7MSlS5cybD9z5oyoV6+epNeRI4YU2R0juuYh9zlAiuzOzbGxsWLFihWif//+olevXjqtbJ+ZPDFGRZtLA5mNktemG0ybSz9SB5hlpmbNmrh48WKG2ztv3LgheRBadtdSO3bsiFq1auHo0aM5moeUO4eqV6+O06dPZ/ocOdpSqVIlPHjwAJUqVVLbHhoaKrnHbfbs2di2bRssLCy0GsTr7u6uulTy+R02gPq1/syOMyEEOnfurDbILS4uDu7u7qoeIk1zzWQmOjoaNWvWzLC9Tp06ePPmjeQ4KSkpmDdvHrZv346kpCQIIWBiYoKuXbti4sSJkhZLdHBwQGBgYIbFL0+ePAlbW1tJeUg5B5QpUybLcUByxJCjLXKsWq5vW65du6Y2z4kQAjdv3lRdrpQ6R46Uu+0qVaqEvXv3Zvk8Y2NjjX9zJUuWlHzcyxFDjmNE1zyEEGjYsGGGbVLWn9NEjnPzlClT8Ndff6Fhw4Yaj1l95IlCxdXVVXX/uKurq9arxwKpHzxpKyqn3ef/paT/MLa3t4e3tzceP36MOnXqwNDQELdv34afn5/k+Toy+0PJnz8/ypYtq7r89PlS7XLn8fnloeTkZLx58wZGRkZqq8lmNcpc17akH/TZpk0bTJkyBa9fv4adnZ2qLQsWLMDw4cMlteXgwYOYPXu21ivCpi+Uz507h5UrV2LixImq7vGgoCDMnj1b40raaeRY6TS9Zs2aYceOHZg4caLa9oCAADg5OUmOs2bNGuzduxdjx45FvXr1kJKSgkuXLmHFihUoU6YMPDw8NO6X/iRZrlw5LF68GLdu3YKdnR3y5cuH27dv448//kD//v0l5fH5OSApKQkhISH4559/MGLEiByNIXdb5Fi1XN/3Y/jw4RnGIP3yyy9q/5Zyvnz69Knav5OTkxEWFoaYmBi0adMm2/3T9OrVC7Nnz8bixYtVEx9+/PgRixYtQo8ePXIthhzHma55yLGKfHpynJtPnDiBhQsXwtnZWdbcgDw4RkUJ9O2dkTqYU9uBvWmSk5MREhKCadOmoWvXrpkO9M3pPIDUwYJpH9R9+vTRen9t2iLnoE9bW1scPHhQr0Fi33//PaZNm5ZhlP3Fixfh5eWl9+y5WUn/7TY2NhbHjh1DjRo1UKdOHdUH6tWrV/HTTz9h2rRpkmK6uLjA09MT7dq1U9t+8OBBLFu2LNNerqzmCklPm3lDNNm5cyf+/fdfLF26NMdiyN2Wq1evol+/fmjUqBFOnTqFdu3aqd1d5uDgoFX+6Ul5P+Qeg/Q5IQRmzpyJQoUKZSh+0vu89+LZs2coUKAAKlasiHz58uHJkyeIj49H7dq1sWvXrhyLIYWUYyQ38gCQoedWG9qem+3t7bF3716t7wKVIk8WKpnNBGtgYCDp239KSgoOHjyYaYzZs2dnub82g5S0/UYupzt37mDkyJGZDjrNLcHBwejXr1+W3YrZya4tcp9wR4wYAQcHB/Ts2VNy3M/Z2tpiz549GZZDlzpBUkxMDIyMjDLMZgmkjr6fPn06VqxYoXFfd3d3STkaGBhImqkXSJ3A7uDBgxlOVGFhYWjTpk2mE9jllmfPnqFt27a4du3aF42hLTnuLtNEzrZERETofEttWFgYunbtinPnzmX6nOwG0aeXWY+jHDGkyO59lTOPBw8ewMjICJUrV87w2L179zBp0qRsL6VlRZtz86RJk/DVV19h1KhROr9eZvLEpZ/0spsJVkqhouv4gzTpi49OnTppvX9uMTQ01Lj2Rm6Ljo7Wez2T7Noi991eNjY28PHxwblz51ClSpUM09lLOdFZWVlhyZIlamuppC0HkNUka1FRUZgwYQJOnz4NAwMDtGzZEnPnzlUVLLt27YKvry8SExMzjbFlyxYpzdSKmZkZ/v333wyFytmzZ7V+/1+/fq1xfRyp0/lrEhgYmOmaNTkZQ5+2pM3b8fntrR8/fsSmTZt06oVMI7UtL168wF9//QUjIyM0a9Ysw/iDrVu3YtGiRbhy5YpOeYSGhma7FpLUS7I5HUOK7N5XOfIIDw/HkCFDEBwcDCD1XLJmzRoUL14ciYmJqjv5tFlDSRNtzs0lS5bExo0bcfr0aVSqVCnDHa76XK7Kc4WKHOv06Dr+QBN9e2du3ryJ6dOn4+HDhxo/eKRcptA0ADUmJga7d++WtBaMXHloGrAVGxuLw4cPS+7ClqMtYWFhWS4WJ6VLfuvWrShZsiTu3LmDO3fuqD1mYGAgqVCZPHky+vTpg8aNG8PMzAxCCISEhKBUqVLw9/fPdL+5c+fi6tWrGDZsGExMTLB582YsWbIEI0aMgKenJ06ePIm6devC29s72xzSvH//HiEhIRneDwMDA8mTk/Xt2xdTpkxBeHi46rr2lStXsG3bNowbN05SjFOnTmHChAl4+/at2nahxXT+mgY5xsbG4v3795I/JOSIoWtboqKi8OnTJwCpl+iqVauGEiVKqD3nzp07WLhwoaRCRZ+2nDt3Dj///LMqnwULFmDr1q0wNzdHeHg4xo0bh2vXrklaZ0vTYNrY2FicPXsWzZo1y3b/9E6ePIkHDx5oPF6ljpnTN4Ycx4iuecydOxcxMTGYM2cOTExMsGrVKvj4+GD06NEYMGAA7ty5g/bt22cYd5YZOc7N169fV/Xyyf0FOM9d+pFjnR45xh+k8fb2zrJ3Jrtvtu3bt0f+/PnRqVMnjV38UoopTWNNjIyMYGtri2nTpmW49JBTeWi6fm9sbAxbW1t4enpmOnlVenK0pVu3bnj16hVatWqlsS36DlZNSUmRPO10TEwM/vjjDzx8+BAGBgawsLBAmzZtslyxtFGjRvDy8lKNx7l69SpGjRqF2rVr499//8XYsWMlDwgEUuf6mT59usZFM7Udf7Rp0yasX78er1+/BpA6qLl///6S1z5p2bIlKleujB49emj83UiZzl9T17qxsTFsbGwkn3TliKFrW/bv3w8vLy+Nd4elEUKgSZMmWLNmTY62pVu3bjA2Nsb8+fNhYmKCGTNmIDY2FkOHDsWAAQNgaGiIcePG4ccff8w2D02XG01MTGBjY4O+fftK7r2eMWMGtm/frnFxVKljf+SIIccxomsejo6OmD17tmrg6v3799GrVy9UrVoVoaGhmDVrFpo0aSIpB0Cec3NWXr16pd+dQHrd3KxAffr00TgnhDaGDx8utm7dKks+9erVE/v27dN5fysrK/HgwQNZctGHUvKQg7W1tbh165ZeMVxcXMTbt28zbI+IiJB1HgZNatasKZ4+faq2zdLSUjg7O4vg4GCt4zk5OYnJkyeL4OBg8fTp0ww/2oqPjxdv3rwRr1+/FhEREVrta21tLR49eqT1ayqRPm25ePGiOH/+vDA3NxdHjx4VFy5cUP1cvHhR3Lp1SyQkJMiccUZ2dnbi2rVrqn+/evVKWFlZCWdnZ9G3b1+tf79yqFevntixY8cXjyEHXfOwtLQUL168UNtWs2ZN0bFjxwzz3uQWTXO7CCFEeHi4sLGx0St2nrv0I8c6PXKMP0iTkJAgeY0TTWrVqoVnz56hWrVqOseQg5x55MT4A22YmZkhLi5O6/0OHz6smpXx2bNnmDFjRoZvQc+ePZM8UO7Vq1dYvHixxsuCQOaXoJKSkjJ8QzcxMcGUKVMk9Sh97sOHD+jfvz/MzMy03je9qKgojBo1SvUtDAAaNGgACwsLLFq0SNL18vr16+P27dsaBwdqI7PudED636++MfRpS9o5Y/Pmzapb1/Wha1s+fvyo1juddtu/tbU1FixYoNViokDmlxgB6WtBGRkZ6XW3k1wxAP2PEV3zSE5OzvC5ZGxsDC8vL70WRtT23Lxnzx4cOHAAQGov39ChQzPk9fLlS8lrqGUmz136kWOdHrmWVgf0vzvk0aNHGDJkCFq3bo3y5ctnODFIKbzkGF8iRx5yjD+Qoy1Xr17F9OnT4e7urrEtmZ0wX7x4gfHjx0MIgUuXLsHGxkbtj9LAwAAFCxZE9+7dJXW7Dh48GLdu3UKbNm1Uc/ikl9mJTsr02doYO3YsatWqhd69e2u9b3q//PILwsLCMGvWLFSvXh0AcPv2bUybNg3VqlXLdjwWkHr3yI8//ogGDRqgfPnyGYo+KSd/pVwakKMtQOrdG/7+/njy5AmWLFmC48ePo2rVqjl+eQHI/Fjbvn07atSoIen108h1iXHFihUICwvDzJkzM3wRlUqOGHIcI7rmIfc5QNdz87t37zBv3jwAqXe7arqcXqhQIdVEnLrKc4WK0mzcuBFLly5Fo0aNdOqdWbZsWaa3mEr945ZjfIkcecgx/kCOtuzcuRPe3t5ISkrK8JjUtnTq1AmbNm3S65uCjY0N1q9fr/VKujVq1MDZs2dVE0QB+p2kXr58iXbt2qFKlSqoUKFChg9UqaP1HRwc4O/vn+HLwq1btzBw4ED8+++/2caYMmUKdu/ejRIlSmQYpyP15O/g4ABPT09069ZNUt45FUOOtty6dQvdu3eHjY0Nrl27hiNHjmDNmjXYv38/VqxYIakg1qctcn4gNmnSBE5OTujTp4/Gv12p4wofP36M7t274+PHj/jqq68yHK9S3lc5YshxjOiah9znADnOzWPGjMG0adN0ulM2O3nu0o+cXr9+rbH61+YShb53h2zduhWjRo1C7969sxxgmZXQ0FDs2bNHr8s2cuTx8uVLrF69Wq9ufTnasnz5cvz4449wc3PTuS3R0dEIDw/XOPW8VAULFtSpm1ZTF2t8fDzGjBmT4ZudlDlQvL29ERsbi4SEBK3mm/lccnKyxgn1jI2NJV9q++OPPzBnzhy97rhTyqUBOdri4+ODfv36wdPTUzXtvre3NwoVKoRly5ZJKlT0bUtERATi4+PVtkVGRqqWa0iT3XlRrkuMY8eORdGiRdG5c2cULFjwi8WQ4xjRNQ+hYRmNT58+qS2jkUZK0SXHufnChQt4/Pix5LsvtZHnCpVHjx5h5syZquv+n5N6aWDChAkICwtT267NJYo0+s4umpKSku1dINmRY3yJHHnIMf5AjrbExsbCw8NDr7u64uLiNH7z0EaHDh2wfv16zJgxI8PJJSuaPvj0ucvt9OnTWLVqFRo3bqxzDCD1ktnChQuxaNEi1beqmJgYLFmyRPL4A1NTU7Upu3XRo0cPrF69Wq9ufTliyNGWtEtnn+vZsyd2794tKYa+bfn8jh4hhNodPFLPi82bN8epU6f0LlQePnyIPXv2qC4vfqkYchwjuuYh9zIacpybjY2N9R5LlZk8V6hMmzYNb968wZgxYzRe95fC29sbX331FcaNG6dzjM/p2jvToUMHbN++HePHj9f5tWfMmIEhQ4bgxo0bOo8vkSOPadOm4ccff8Q///yj1/gDfdvSvHlzHD9+XK/Jsnr16oXhw4ejZ8+eqFChQoaiRcoH87t37/DHH3/g5MmTKF++fIaTXWa9IXKv81GiRAlZBjJPmDABPXv2hJOTk+rDKCQkBMWLF8f69eslxejRoweWLVuGmTNn6lwUt2rVCt27d0edOnV07taXI4YcbTE2NkZMTEyG7S9evJAcU5+2SJ2VWIqxY8eiXbt2CAwM1OsSY5UqVfDhwwe9cpEjhhzHiK55yF2oyHFudnV1hYeHBzp06ICKFStmOCdKOTdnJs+NUbGyssKOHTv06pKvXbs29u/fr9MdFJ/Tt3dm/PjxOHToEEqUKIHy5ctnqFilnEjkGF8iRx5yXLOXoy3z58/Hli1bYGlpiQoVKmRoi5QTphyDtqWsJquruLg4LFq0SNKET7t27UJgYCB+/fVXVKhQQavenc9FR0fj0KFDePjwIYyMjFC1alW0a9dOcu9T3759VQtIlipVKsPvRsox0rlzZ3z48AEtWrTQ2J0u5aQrRww52vLrr7/i+fPnWLRoEZo0aYIDBw4gISEBnp6eqFWrlqQBynK0JTsfP37M9tLFiBEj8Pfff8PCwkJjkSV1tuTDhw9j8eLF6N+/v8a/XylfEuSIIcf7qm8e+iyjkZ4c52Y5zomZ7p/XCpUmTZpg3bp1enXptWzZEr6+vrJca+vUqRMKFiyIvn37auydyW6QkhwfZg4ODujbt69e40vkyMPOzg6//vqrXtfs5WhLduvcSDlhZjeWQ+4p+9P79OkT5s+fj0OHDsHY2BgdOnTAL7/8oupdOnPmDKZOnYoXL15kGBelScuWLfH8+XMkJydrfFyfE0xUVJTagL/sSFluPjtWVlZ6d+vLEUOOtsTExMDDwwM3b95ESkoKihQpgpiYGFhYWMDPzw/FixfPNoa+bYmLi8P58+dhZGQEe3v7DB+KJ06cwIwZM3DixIks49jY2GDZsmV6X2KU4wNRjhhyHCO65qHNMhrXr1/PNg85zs05Kc9d+nFzc8PChQvh6+ur8+jjn3/+GbNnz8b06dNRuXLlDHfqaOPhw4d69c7I0c0vx/gSOfKQ45q9HG2RY52brAqRzwceZiUqKgpPnjxBSkoKgNSetoSEBAQFBeHnn3/WuI+Pjw92796N9u3bw8TEBDt27EDhwoUxaNAgeHt7Y8eOHahQoUKW0/Cnl9nraOvDhw/w8fGBm5sbqlatCg8PD5w/fx5mZmZYu3atpLsR5Ph2r5RLA3K0xcDAADt37sS5c+dUixJWr14djRs3ljyHiT5tuXv3Ljw8PPDmzRsAqcf9li1b8M033+D9+/eYMWMGDh06JOn8JtclRn1W0JYzhhzHiK55yL2Mhhzn5qzos2glkAd7VPr27YvLly8jOTkZpUqVynDdX8qBIec3TDl6Z168eIFt27apVsqsVq0aunbtKvmP3tvbG8bGxnqNL5EjjxUrVuDJkyd6XbOXqy2xsbE4cOCAWltat24tubh9+/YtVq9ejQcPHqiOEyEEEhMTERwcjMuXL2cb48CBA5g8eTISEhIyTJf+7bff4vjx4xr3c3Z2xsCBA9G9e3cAqZNOzZo1C46OjtizZw/69u2LkSNH6jzAT1cTJkzA5cuXsXr1ajx8+BBjx47F7NmzcfjwYRgZGWHZsmWS4ty6dQsbNmxQ/W6qVq2K3r17S/4bUsqlATna0qJFCyxevFivS9n6tKVfv354/fo1pkyZAhMTE/j4+KBkyZIYNWoU+vXrh1evXmHAgAH4+eefsz3e5LzECKT2Nj1+/BjGxsYoX768Tl9M9Ykh1zGiSx5yL6Mhx7k5PDwc8+bNy3BOTEhIQFRUlKTe3czkuUJFju7WgICALB/XpnssICAAu3bt0rl35v79+3Bzc0OBAgVgZWWFlJQU3Lp1C3FxcdixY4eku1/kGF8iRx5yXLOXoy3Pnz+Hm5sb3rx5g0qVKiElJQWhoaEoVaoUtm/fLqnyHzVqFM6dO4eGDRvizz//RJs2bfDo0SPcuXMHo0ePxsCBA7ON0bp1a1hbW8PDwwPdu3fHxo0bVdeVR48ejQ4dOmjcr3bt2jh8+LCqhyI5ORm1a9dGsWLFsHjxYp1umfzrr7+wZs0atQ/U/v37o0WLFpJjNGjQACtWrICtrS28vLzw7t07VTHXs2dP1e8+KxcvXkS/fv1QvXp11K1bFykpKbh69SoePHgAf39/1KlTJ9sYSrk0IEdbGjVqBH9/f73Gy+nTlnr16mHp0qWqRQfDw8PRoUMHVY+ij49PlvHTk+sLYEpKCubNm4ft27cjKSkJQgiYmJiga9eumDhxoqSZoeWIIccxomsetWrVQmBgoFrPbs2aNVGmTBmsW7dO6+NFjnPzgAEDEBISgh9++AF+fn7o168fnjx5gmPHjmHGjBno0qWLVjmll+cu/cjR3SrndbpVq1bh+fPnmY54zu5gnj9/PhwcHLBgwQLVHBlpc2b4+vpKWpTM0NAQ7dq10zp3ufOoU6eOpJNzVuRoy9y5c1G2bFns3r1bNSX469evMWrUKPj4+GDBggXZxjh37hzmzZuHpk2b4v79++jfvz8sLCzw66+/qpZez054eDiWLVuGKlWqwNzcHFFRUXBxcUFSUhJWr16daaGSmJioNngvX758yJ8/PyZNmqRTkXL06FGMHDkSzZo1Q5s2bVQz744cORLLli2TvLLtx48fUa5cOQDA2bNnMWDAAABAgQIFMv1w+tyiRYvQuXNnTJ8+XW379OnTsXjxYkmX7ZRyaUCOtshxd5k+bYmJiUGlSpVU/y5fvjwSExNRqlQprF69Wqtb9OW6xLhmzRrs3bsXY8eORb169ZCSkoJLly5hxYoVKFOmDDw8PHIlhhzHiK55yL2Mhhzn5qtXr2LlypVwcHDAP//8g+bNm8PKygqLFi3CqVOnWKikt3///iwfl3KLlBy9Mmn0/eO8evUqdu7cqTaRV/78+TF06FC4ublJiiHH+BI58pCjiJSjLf/++y82btyoKlKA1DVMxo0bp/pwzU5sbCzMzc0BAJUrV8a9e/dgYWEBNzc3Sb0pQOqJJa27vGLFinj48CGcnJxQq1YthIaGatkq6Hx5ceXKlRg6dKja76dPnz5Yvnw5Vq9eLblQqVKlCk6ePIly5crh1atXcHJyAgDs3r1b8snzzp07Gq+ru7m5SVqhF5BnILMcMeRoy8KFCwEAM2fOzPCY1G/t+rQlJSUlw7drIyMjjBo1Sut5hOT6Avjbb79h6tSpal9YLC0tUbJkSSxbtkxSkSFHDDmOETnySE/Xnjc5zs0JCQmoUKECAKBSpUq4f/8+rKys0LFjx2xvYMhOnitUvLy8NG7Pnz8/ypYtK6lQ2bdvn9q/k5OT8ebNGxgZGWk94EjfP85ChQppnLhO07acpJQ85JAvXz6N12Hz58+vcUEuTcqUKYNnz56hXLlyMDMzw/379wGkDkp7//69pBi1atXCb7/9htGjR6N69eo4deoU+vfvj+Dg4GwvEWrqEpa6GOLnHj9+rLGXqm3btli3bp3kOCNGjMDw4cORmJiItm3bwszMDHPmzMG2bdsk3SIJpA64/Hy9ESB10HFuj7nRlxxtOXbsmNYL/+UGfRa+09ebN29gbW2dYbu1tTVevHiRazHkoGseBgYGOv+955Rvv/0WDx48QLly5VCpUiVVEZ2SkoLY2Fi9Yue5QuXevXtq/05OTkZISAimTZuGrl27SoqhaTbZmJgYTJw4UetCRd/emfr162P+/PlYunSp6lbEqKgo+Pj4wNHRUatc9KGUPORgZ2eHlStXYv78+aqCIDExEatXr5b8+23ZsiUmTJiAuXPnokGDBvD09IS1tTWOHz+OihUrSooxfPhweHh4oHjx4nB1dcWKFSvQpk0bvHjxAq1bt85yX29vb7XercTERPj4+KBQoUJqz5PSA/X1118jNDQ0Q96hoaFaTXjYpEkTnDp1CpGRkarr923atEGXLl0kf9NzdnbGzJkzsXDhQtU+wcHB8Pb2znKxUCWSoy2enp7w9vaWPA5Ebpo+EL/0B6SZmRn+/fdf1bf3NGfPnpXcyyFHDDnomofcy2jIwdXVFePGjcP8+fPRtGlT9OrVC9988w3Onj2r6n3WVZ4bTJuZO3fuYOTIkTh27JjOMYKDg9GvXz+cPn1a8j6fn5A+753ZuHFjlvtHRESgW7dueP/+fYbZPrds2aLXNPDaUEoecnj06BG6deuGQoUKqVb0DAoKQmxsLLZu3SrpQyEhIQE+Pj6wsrJCu3btVBMmFS1aFEuWLJFcvEVGRiIhIQHly5fHo0ePsGPHDpQrVw7u7u6ZfuvWphtVyjiIxYsX48CBA5g6darqOvWVK1cwffp0NG/eXNKkcek9f/4cjx49gr29PWJjY7X69v3+/Xv07dsXd+/eVRVJ0dHRsLCwwMaNG1GiRAmtcvmS5GiLg4MDfvvttwwfZLnFwsIiQ2GS/u609PSZb0cb+/fvx5QpU+Du7q76YnHlyhVs27YN48aNk7RSvRwx5KBrHl5eXpILRrlnss6MEAL+/v4wMzND06ZNsXbtWqxZswblypWDj4+P1qttp/c/U6jcu3cPXbt2xY0bN3SOce3aNfTr1w/Xrl3TK5f0vTNSpnGPjY3F77//jocPH0IIAXNzc7Rr1y5HVqn8L+Qhh2fPnmH79u1qbenevbte36bevXuHwoULS17v4scff/yi35bTxMfHw9PTE3///bfq5CeEQNOmTbF48WLJYxESEhIwfvx4HDlyBIaGhggMDMS8efMQGxuLZcuWST5OUlJS8M8//6j9bho1aqTISyDZ0bct69atw+nTp1W3wH7+u5BjXpKsZHcHZHq5OVnYpk2bsH79erx+/RpA6hiz/v37o2/fvrkaQw5KyUPJ8lyhomkwbUxMDHbv3o1ixYpJ+oap6XJNbGwsDh8+jBo1amD16tV656lL74zc0iYW03e0tzaSkpLw5s2bDPfZBwUFoX379jrHzc22HD58GE5OTmofvDt37kSRIkXQunVryd90cvLbckpKCu7evavV/BuPHj3CgwcPVB+o2g7MW7JkCf78809MmzYNgwcPxoEDB/D8+XNMmDABTk5OGhfXyyl//fWX2nwOwP8fI35+frkWQ181a9ZUvX7640rbBVKV0BZAtwkOs4snhNBrzIw+MeR8X7XJ4927d9nOSpyQkICTJ0+iZcuWkl5f13NzXFwctmzZgh9//FFtFurly5fDxMQEvXv3znA5Slt5boyKpsG0RkZGsLW1lXyi/HwwLZC6OJijoyM8PT31TRFAahdwdHR0ts+LiorC/PnzcevWLXz69CnDooZSbpG7desWfv31Vzx48EB1gkhPyslOjjzOnDmD8ePHIyoqKsNjBQoUkFSoyNGWT58+Yd26dZm2JbNruomJiRg+fDhOnTqFTZs2qd0KfOXKFRw8eBCBgYFYuHChpF4VDw8PTJo0Setvy3Z2djh+/LjaSWHx4sXo27cvihUrBiD19/Xjjz9q1R1fokQJ2NjYqN6P58+fZ5nH5w4dOoRp06apvS8ODg6YNWsWxo0bJ+nvLzQ0FJMnT1b9bj4npT2+vr5Yv349SpcujTdv3qBMmTJ4/fo1kpOT0aZNG0ltkSOGHG2Ro4jQpy1Hjx6Fs7NzloO7Y2NjsWjRIkyePDnLWGkTHKYNwP98gkNtCpXw8HDcvn1b4/sqdfE7fWPIcYzomoejoyPOnDmjVtSMHz8e48aNU2378OEDRo4cKek40/XcHBMTgz59+uDevXuoW7eu2jkpOjoaO3fuxIkTJ7Bhw4Zs14LKSp4rVD4fTKsLTYNpdZVV74yUOS9+/fVXXL9+Ha1bt1Z9CGlrzpw5yJcvHyZPnow5c+bAy8sLYWFh2LZtG+bPny8phhx5LFy4EJaWlnB3d8fIkSPh6+uL58+fY+nSpZKvo8rRlunTp+Pw4cNo2LChVl3nmzdvRlBQELZs2YK6deuqPebj44Mff/wRI0aMwI4dOySNI1m8eDGSk5Nx6dIlrb4tf/z4UWNx1blzZ7XfjdTO0ps3b2LUqFEZ7jLQ9lt7ZGSkxt6hcuXKSb4T6tdff8Xr168xcuRInY+zgwcPYuLEiejVqxeaNGmC7du3o2DBghg6dKikafzliiFHW7JbC0wKfdoycuTIDB+IzZo1w+bNm1WXSePi4rBt27ZsC5XVq1ejTZs2Gic4HD58uOT27Nu3D5MnT9b4RcXAwEDynZ36xpDjGNE1D01/28eOHcOwYcPUfldSzwG6npvXr1+P6OhoHDlyJEObJ0yYgC5dusDDwwN+fn4YOnSopFw0Ev9DNm3apNf+Hz9+FLNmzdJqH2dn5ww/LVu2FOPHjxeRkZHZ7m9jYyMuXryoa8qqGDdu3BBCCPHjjz+KS5cuCSGE8PPzE3369JEcQ988ateuLe7evSuEEKJ79+7i33//FUIIsWfPHtGtWzfJeejbljp16oi///5b2/RF27ZtxYEDB7J8zrZt20T79u0lxbtw4UKWP5kxNzcXr1+/VttmY2MjwsLCVP9+9eqVsLCwkJRHp06dRLdu3cSxY8e0yuNzrq6uYvfu3RnyWb58uXB1dZUUw8rKSgQFBUl+TU1q1qwpwsPDhRBCDBgwQBw5ckQIIcSlS5dEy5Ytcy2GHG359OmT8Pf3F5MmTRJeXl6qn9GjR+dKW+Q81mrVqiWCg4OFEEK4ubmJU6dOCSGECAwMlHx8CCFEs2bNxLRp08T79+8l75MTMeQ4RnTNQ+5zgK7n5pYtW4rjx49nGXv//v2idevWkvLITJ7pUdmwYYPaarLp1zl4+PAhJk+ejJs3b6J3794a94+Pj8e8efMkrUirzV0QmfXOfPz4EYsXL842VoECBfDVV19Jfj1NUlJSVDEqVqyIBw8eoG7dumjWrJmkGWXlyiNfvnyqux/S8nB0dET9+vUxb948STHkaIuBgQGqVq2qdf5Pnz6Fra1tls9p0KABfHx8JMWT49uyHB4+fIh9+/bp9J6kN3z4cHh6eiI4OBjJyckICAjAkydPEBgYiEWLFkmKUbx4cb2vZxctWhQfP34EAFSoUEE1U/A333yDyMjIXIshR1u8vb2xf/9+WFpaIigoCLa2tggNDcWbN28kDcQH5GmLHOSa4DAyMhL9+vVD0aJFdc5FjhhyvK9y5CEHXc/NERER2d4MYGdnl+1q89n57w2j12DJkiXw8fFBkSJFUKxYMcyePRs7d+4EkFrAdOrUCSEhIVl2Yc2fPx+7d+9Gs2bN0Lx5c+zYsQNr1qxBSkoKZsyYgQEDBsDIyEjSirTx8fGYMWMGHBwc0KhRI/j4+Kh17Z05cwbt2rXD1q1bs43l6uqKDRs2SHgXMlexYkVcuXIFQOosqkFBQQBSryFKneBMjjyqVaumKtwqV66syikiIkJyDDna0qJFC43jkLJTqFChbMcVffr0SfKiXu7u7ujVq1eGn969e8PDwwOTJ0+WtD6OvsqUKaPx+ri2nJ2dsXTpUty6dQv58uXDhg0bEB4ejkWLFuH777+XFMPNzQ2LFi1SfQDowsHBAb6+voiMjIS1tTX+/PNPREVFITAwUO0aek7HkKMtf/31F+bMmYNdu3bh22+/xcyZM3HixAk0a9ZM8mSLcrRFDmkTHAJA9erV8e+//wKApAkO06tRowYeP36sVy5yxJDjfZUjDznoem4uXry4amXtzLx9+1ar+Zg0yRM9KocOHcKIESMwZMgQAKl3/qxbtw6vXr3CihUr8MMPP2DKlClZHjx///03Jk2apFqRtmnTppg1axZevHiBPXv2oF+/fpJXpE0retq3bw8TExPs2LEDhQsXxqBBg+Dt7Y3t27ejYsWKmRY9vXr1Uv1/UlISrl69ilOnTqFChQoZbmuUMpmPu7s7Jk2aBAD4/vvv0aFDBxQoUABXr16FjY1NpvvJncfAgQMxYsQIGBsbo23btli2bBkGDhyI+/fvqxY9y6m2TJgwQfX/sbGxCAgIwL///gszM7MMbcmsoLWxscGhQ4eynA/g4MGDkic3qlGjBrZs2YIaNWqoxrzcuHEDN27cQPPmzfHixQv07dsXS5YskTyNvS6GDBmCWbNmYfbs2TAzM9NrQi8nJyfV1PlSubi4qL3ms2fP4ODggNKlS2f43UgZtD1u3Dj8/PPPOHLkCHr06AE/Pz80bNgQQOYzV8sVQ+62fPjwQTW/RtWqVXHnzh1UrlwZgwYNwqhRo7IdF6JPW+SmzwSH6Qv25s2bY9KkSRg+fDgqVqyYYRXmzNY/kiNGerq+r3LlIefEe7qemx0cHLB79+4sl+/YtWsXateurVd+eaJQiYyMRKtWrVT/bt26NSZMmAB/f3/MnTtX0sCo169fo1GjRqp/N27cGM+ePcOxY8fg5+en1WJv2RU9/fv3z7Lo+XwuD6kznWbmp59+QokSJVC8eHFUqVIFc+bMwbp161CuXDn8+uuvme4ndx7NmzfHb7/9hnz58qFcuXJYv349/Pz80KxZM4wYMSJH2/L06VO1f6cVBml3tkjRp08f9OnTB2XLlkWPHj3UPniEENiyZQs2bdqEZcuWSYoXERGBnj17ZviwmTdvHiIjI7F8+XJs2rRJ43o7GzduVOu5SUpKwubNm1WDNrP7Fv/5RF5CiEw/LLS5c+jevXvw9/fHkydPsGTJEhw/fhzVqlXL8jKXq6urrCfdxMRE7N+/H/Hx8TAxMcG2bdtw5swZlClTRvJ6SLrGkLstJUuWxJs3b/DNN9/AzMwMDx48AJB6h1bavBvZ0ef9kHNm2rp16yIwMBAJCQkoUaIEtm/frjbBYVbc3d1hYGCgNjh06tSpGvPN7HiVI0Z6ur6vcuWR3ezU8fHx2bYhja7n5n79+qFLly4oUqQIBg8erDZoPG319N9//x2bNm2SnIsmeWIeFQsLC5w9e1ZttLOtrS3Gjh2rNlZFlxgzZ85E27Zttcqndu3aOHz4sGoUdHJyMmrXro1ixYph8eLFOq1wqw9vb2/06tXri81umWbYsGHw9PTUa8n6L92WLVu2YN68eShatCisrKxQtGhRvHv3Djdu3EBsbCxGjhwpeWFDW1tb7Nu3T211WiB1xl9XV1dcu3YN4eHhaN++vdokg9pMJZ/ZGKl9+/ZJ/sCROpHXrVu30L17d9jY2ODatWs4cuQI1qxZg/3792PFihVo0qSJ5Lz10bhxY6xYsULnRRrliiGHyZMn4969e5gzZw5CQ0Mxe/ZsLFmyBIcPH8bff/+NwMDAbGPo0xYLCwuUK1dOrSh//vw5ypQpo/r2n5KSgoiIiGw/3PWZ4FCbMQ6ZTdooR4z0dH1f5chD7tmp9Tk3Hz16FBMmTEB8fDwqVaqkOieGhISgYMGCmDp1qtafoZ/LEz0qmWnQoIHeMXT5405MTFS7ZzxfvnzInz8/Jk2alOtFCpA6u6TUgXc56fz583oPLvzSbXF3d4e9vT1+++033L59GyEhIShZsiR+/PFHdOrUSas/9MKFC+Px48cZCpXg4GBVb0lsbGyG+VXkuH2+U6dOesf4nK+vL/r16wdPT0/VoGNvb28UKlQIy5Yty7VCxdjYWPLswDkZQw7jxo2Dl5cXLl68iB49emDXrl346aefYGRkJHkAuj5tkWNV3TTh4eE6z6WhlBWx09P1fZUjDynFhzb0OTe3bNkSdnZ2OHDgAG7fvo13797B0tISPXv2RKtWrWRZ9uLL/yXmoM+v92VHzhVpNflS386aNGmCrVu3YtiwYV90untXV1f4+vpi6NChqFixok6r4SqhLRYWFqrLTAkJCap2aHsHRadOnfDrr78iKioK1tbWSElJwY0bN7B06VJ06NABb9++xfz58zNcp54wYQImTZqkuKULbt26pbELu2fPnti9e3eu5eHq6goPDw906NABFStWzFDoSbkULEcMORQtWhQrV65U/Xvt2rW4fv06vv32W3z99deSYujTlhUrVmSYR0VXuk5wqFRf8hjp1asXli9fLtvdQvqem0uXLo1+/fpBCIG3b9+qxoPevHkTRYsW1fqz+HN55tJP//791a7Zr1mzBt26dcsw0VJm3xAsLCzQunVrtary4MGDcHFx0XpFWgsLC/z7779qg3dtbW1x4MAByRMBycnd3V01qVipUqUyVM5SBvXJoWXLlggLC8u0+JNyXVgpbYmKisKoUaNga2urmq24QYMGsLCwwKJFiyRN8JWSkgIfHx/s2LED8fHxEEKgQIECqkmXTp48ifXr12PhwoVqJ/EaNWrI9uEhJ0dHR6xduxa1a9dWO94vXLgAT09P1V0eOS2rSwtSxx/IEUMOnz59wvTp02FmZoZBgwYBSL3017BhQ/z666+SPlD0aYumS+K6kms5AKX4kseInL8XQJ5zc1hYGAYMGIBmzZph3LhxAID69eujdOnSqnGEusoTPSrffPMNjhw5orbtq6++yvChZWBgkGmhYm9vj1evXqlts7W1xdu3b/H27Vutc8puoFOa3FjZ0sHB4YtccvqcLmt5fE4pbZk1axbi4uLUpspet24dpk2bhnnz5mH27NnZxjA0NMT48eMxcuRIPHr0CPny5YOZmZnqm1nz5s3RvHnzDPsp9btF8+bNsXjxYrU5Ux49eoRZs2ahadOmuZaHHLNTyxFDDnPnzsXly5fVxglNmDABPj4+WLRoEcaPH59tDKW0JTfXFMoNSnlf5SDHuXn27NmoWLGi2qX5w4cPY/z48ZgzZw6WLl2qc+w80aOiNHIPdNLkSywoqOQ85BIREYGyZctm+zwHBwf4+/tn+FZ169YtDBw4MEd7DzT12ClBTEwMPDw8cPPmTaSkpKBIkSKIiYmBhYUF/Pz8sl1ELU1OLVwJSP/9yhVD37Y0atQIy5YtyzDR4KVLl/DLL7/ovahpdm2xsLDA5MmTJV1mzK3LYf8FchxnWbGwsMDevXsljf/IrUtqdevWxe7du1G5cmW17Q8fPkTPnj1x8eJFnWPniR4VpZFzoJMci/ABqdV/+hjpT5je3t65loccq43q25bw8HDMmzdPLY+0GFFRUbhz5062MZKTkzX2bBgbGyMuLk5SO/SRNl9DdqT+XuRY1dbAwAA7d+7EuXPncOfOHaSkpKB69epo3LhxhjlEMiPHwpVy/H7liCFHW2JjYzWOQyhZsqTk9ZP0bYuUvykp6+PEx8dj165dGv/+b926JekOpjRKWB1bjmNEnzx+/PHHLONqe0lN3/fDyMgIHz58yLA9Li5O715gFioKJ8cifH5+fqo7BNLfv29gYJBhcb2czEOO1UblaMuMGTMQEhKCH374AX5+fujXrx+ePHmCY8eOYcaMGZJi2NvbY+HChVi0aJHq22ZMTAyWLFkiabIofU2YMEHv2R7TyLWqbceOHbF48WI4OjrC0dFRp1zkWLhSjt+vHDHkaIuNjQ3Wr1+PWbNmqYo9IQT8/f0lT6Klb1vkGgshx3IAgHJWx5bjGNEnj6VLl+q82KWceaRxcnKCt7c3Fi5cqJo+Ijw8HHPmzEHjxo31S1CvlYIox8mxCF+zZs2Er6+v+PTpk3B0dBQRERHi3r17ok2bNsLPzy/X8nBychL+/v6q/3/27Jl4+/at6NGjh1i8eHGutcXOzk6cP39eCCFEhw4dVO1auHChGDJkiKQYoaGholGjRsLW1la4uroKV1dXYWtrK5ydncWjR48kxdCVpgXJ9NGqVSvh5eUlgoODhb29vQgKChJ//fWXcHJyEvv375ccp2HDhqpF53Qlx8KVcvx+5YghR1tu3LghbGxshLOzsxg+fLgYPny4cHFxEXZ2duL69es53hYLCwvZjjVHR0dx8OBBIYQQzZs3F48ePRIJCQli6NChYubMmZLjyHEekSOGHMeIrnnIfQ6Q4/14/fq1aNeunbCwsBD16tUT9erVExYWFqJz587i5cuXeuWXJ9b6ycs0LcIHpC61LnUwV0REBH766Sfkz58fFhYWCAoKgrm5Oby8vLBnz55cy+PNmzeqycrMzc1x8+ZNFC9eHJ6enjh8+HCutSUhIUFV8VeqVAn3798HkNojcOPGDUkxKlSogMOHD2PcuHGwtbWFvb09JkyYgMOHD2e4RitVVFQU/vzzT4SHh2f5PDlvmQdSv/V4eHigSpUqMDc3R1RUFFxcXDBp0iRJa1ul6dWrF4YPH45t27bhn3/+waVLl9R+pNC0OBqQevfAo0ePJMWQ4/crRww52mJlZYWDBw+iTZs2SEhIQEpKCtq2bYsjR47A2to6x9siZBzCqGk5AGNjYwwaNAgnTpyQHEeO84gcMeQ4RuTIQw5y5FGqVCkEBARg7dq1GDRoEIYOHYoNGzbgt99+03tBW176Ubi0Rfjatm2r8yJ8BQsWVF13TFvls3nz5qhSpYrkWRLlyEOO1UblaMu3336LBw8eoFy5cqhUqZLqGm5KSgpiY2MlxQCAIkWKoFu3bpKf/7kHDx5g+PDh8Pb2hrm5Odq3b4/Xr1/DxMQEa9euzXSNDTk/PAD5VrVduHAhAGDmzJkZHpN6rTxtcTR3d3fV4mi9e/fWauFKOX6/csSQoy0A8N133+GXX37Rap/09GmLq6ur3pM0ppFjOQBAOatjy3GM6JrHN998I3ncV07m8bl8+fKhcePG+l/q+UyeLFRu3ryJuXPnwtDQEOPGjdNporWTJ09i6tSpMDQ0xJQpU+Ds7JwDmWZP10X40rOzs8PatWsxZcoUWFpaYs+ePRg4cCCuXLmS4XbpnMwjbbXRmTNnwtraGmvWrEGPHj20Wm1Ujra4urpi3LhxmD9/Ppo2bYpevXrhm2++wdmzZ7NcUDD9wobZkTIGYd68eahYsSIqV66MP/74A0lJSTh16hR27tyJxYsXq1YA/5zct0WmrWo7evRoVK9eHadOnUL//v21XtVWjjls5Fi4Utffr9wx5GiLHPRpi5zTJzg5OWH69OmYM2cO6tSpg9mzZ6NFixY4fPiwVnfIyHEekSOGHMeIrnnIMTu1HHmkX7g2O1IWrs2UXheOFMrFxUVYWFgIc3Nz0axZM51iNGjQQBWjYcOGMmeonWPHjqnGhBw4cEC0a9dODBw4UISHh0va//79+6Jhw4Zi3bp1IiYmRjg7O4s6deoIS0tLsWDBglzL4/nz56JDhw7Cz89PxMfHC1dXV2FhYSEsLCzEpk2bcq0tKSkpws/PT5w4cUIIIcSaNWuEnZ2daNOmjbhz506m+5mbm4saNWqIH3/8Ubi5uWX5I4Wtra1qTMfgwYPFuHHjhBBChIWFCWtra0kx5HDp0iVhbW0tNmzYIKKiokS9evVE69atha2trZg0aZLW8WJjY8WtW7fE3bt3xadPn7TeP21fIYS4cOGCGDx4sJg+fbp4+/atpP11/f3KHUOOtshBrrbo6/379+Lnn38WW7duFSkpKcLDw0OYm5uLmjVrij/++ENyHDnOI3LEkON9lSMPOeiaR9o50c3NTXh5eWX5o488Wag4OzsLc3NzYW5uLlxcXHSK0bZtW1WMtm3bypyhdDNnzhShoaF6x4mLixNv3rwRQgjx6tUr4efnJ44cOZLreQghVB9eHz9+FEePHlUNQpNK37asX79eREREaPWaQqSeiDp27ChsbGyEp6enOHbsmEhISNA6Tpo6deqIsLAwkZiYKOzs7ERAQIAQQoi7d+8KBwcHnePqIiIiQoSFhQkhhAgODhYzZ84U69evF/Hx8ZJjJCQkiGnTponatWurinwbGxuxcOFCyTGGDh2q94BcpchLbckJKSkp4vbt2yIyMlKr/dLOQ/qcR+SIIQel5JFG2zz++OMPMXToUGFlZSW6dOkiNm3apPXvU4o8OeHbtWvXVLewjhs3TjWASxshISFYtmwZAGDo0KE6D5DUV506dfD777/ju+++0ztWSEgIHjx4AENDQ9SsWVOrKY3lyiMlJQXnzp1Ty0PqbcXp6dOWunXrYu/evahYsaLWr5v22ocPH8aRI0cQERGB5s2bo23btnB0dNTqurGHhwfKli2LkiVLYuPGjTh9+jQSExPx66+/wtDQEKtXr9YpP23ps6ptevPmzcP+/fsxcuRI2NraIiUlBVevXsWyZcvQp08fDB48ONsYdevWxf79+/U+zq5fv44tW7bgwYMHyJcvH2rWrIk+ffqgWrVquRZDrrbIQY73Qw4xMTE4fPiw2t/uDz/8oNU4GCWtjq3v+6qUVboB/c7NMTEx+Ouvv3D48GGcP38e1tbWaNu2LVq2bCl5oses5MlCJS8ZPXo0vv76a70W4YuJicHo0aPxzz//qM070rp1a8yZM0fSeiFy5PHy5Uv0798fDx8+RLFixZCcnIyYmBjY2dlhzZo1kuYFkaMt/fv3R6NGjdC3b1+d2pHegwcPcOTIERw+fBjR0dFo2bIl2rRpI2kuldDQUHh6eiI8PByenp7o0aMHZs6ciZMnT2LdunW5Vhw7ODjgt99+U93BoKsGDRpg1qxZGcZzHT16FLNnz8bJkyezjTFr1iy8evVKr4Ur//77bwwbNky15lBycjKuXbuG+/fvw8/PT9LJV44YcrQFAJ49e4YbN25oHLQuZTZYOdoih0ePHqF3796IjY1FpUqVkJycjNDQUJQpUwb+/v6Sx6m4uLhg+fLlsLS01DkXOWLI8b7KkYcc5Dg3p3n//j2OHj2KP//8E1euXIG9vT3atGmj18zFLFQUTo5F+CZMmIDLly9jypQpat90Z86ciRYtWsDLyytX8hg2bBhevHgBHx8f1Yfww4cPMX78eJibm0sauCdHW0aOHImjR4+iaNGiMDMzy9AWXQd97dq1Cz4+PoiNjdV5QbKoqCgUK1ZMq9VG9R08vm7dOpw+fVrvVW3r1KmD3bt3o0qVKmrbHz9+jM6dO+PatWvZxpBjcbR27dqhadOmGe6UmTdvHq5evYpdu3blSgw52rJ7925Mnz5dbbbQNFLvpJKjLXLo27cvjIyM4Ovrq5qoLCoqCr/88gsKFSqE5cuXS4qzbNky7NixQ69Vi+WIIcf7KkcegP43f8hxbv7cx48fERAQgEWLFul1TgRYqChedn+8mS2ymJ69vT1WrlyZ4Vv+v//+izFjxkhal0aOPOzs7LB582bUqlVLbfv169cxcOBASWtByNGW7O7e0eaP8vr16wgMDERgYCBevnwJBwcHtGrVKtvprdPExsbiwIEDePDgAYyMjFCtWjW0bt1aq16rZs2a4fnz5xBC4LvvvsPx48cl7wvIt6rtzJkz8erVK/j6+qp6D4QQmDZtGvLly4cpU6ZkGyMgICDLx9MvzpcZKysrHDhwAGZmZmrbQ0JC0KFDB0lzXMgRQ462uLi4wNnZGZ6enjr3ZMrRFjnY2tpi9+7dGS6L3Lt3Dz169MDVq1clxVHK6thyvK9yrcDcsGFDREVFQQiB0qVL48yZM5L2SyPHuRlILU5OnDiBwMBAnD59GgULFkTz5s3RqlUrnWerBvLo7cl5iZQCIDvpJ55K76uvvkJSUlKu5VGoUCHVNO3pGRsbS74NVo626HvL5bVr1/Dnn38iMDAQr169gr29PQYPHowWLVpIWiQszfPnz+Hm5oY3b96gUqVKSElJwe7du7F69Wps375dcle4SB0Ur/p/bcm1qu27d+9w4sQJuLi4wMrKCkZGRrhz5w6ePXsGa2trtVsZM+u1kvLhnZ0aNWrg3LlzGT5Abt26JXnsgBwx5GjLq1ev0LdvX52LFECetsihdOnSiIiIyPCaMTExWo1jUMrq2HK8r3JNNZA2Rw0Arc5BafQ5N8fGxuLEiRP4888/8c8//6BAgQJo3rw5VqxYgfr162vVO5yZPNejEhYWpve1dqXRdxG+lStX4uzZs1iyZAlKly4NIPXkMH78eNSoUUNyEaJvHr/99hs2b96M+fPno0aNGgCAp0+fYuzYsWjfvj26d++ea23RZRG+WbNm4dixY3j16hXq1KmD1q1bo2XLljqvYjxixAi8fv0aS5cuVbXl9evXGDVqFMqUKYMFCxZIiiPH4HE5yDXPjL6Lo23btg0LFy5Ep06d4ODgACMjIwQFBcHf3x/dunVD1apVVc/NrGtdjhhytKVLly4YMmQImjZtmu1zMyNXW/R17NgxzJs3D15eXqhXr54qj+nTp6NLly5o2bKl6rm6rvibm6tj5/T7qk1b9L35Q9dz85AhQ3D27FkUKFAALi4uaN26NRwdHWFkJG8fSJ4rVOQaRf3p0yf8+eefePToEfr3748HDx6gWrVqOlWr+shuET4pKzX36NEDQUFBMDQ0hJmZGYyMjBASEoLY2Fh88803at39mY01kSOPJk2aqJa8L1SoEIyMjPD+/Xu1RfDSZNblKUdb0hbhS0hIULUl/SJ8mV06sbCwgLGxMerXr68qLDIjpdembt262LhxY4Zj9ebNmxgwYAAuXLiQbQw5yLmqrb6yWxzN19c32xhS717KqmtdjhhytOXo0aOYO3cu+vbti8qVK2cYkCtl0LYcbZFD+jw0XWJM//9Z5aGU1bHleF/lWoFZX7qemy0sLGBkZIRatWplO1hcnwnf8tylH2NjY72rudevX6Nr16548+YNEhIS0KVLF2zcuBG3bt2Cv79/hsGCOWnbtm0YMGAAhg0bBmdnZwQEBODdu3f45Zdf0KxZM0kxGjRogAYNGnzxPEaNGqVXDoA8bVm9ejXatGkDDw8PdO/eHRs3bsTLly8xffp0DB8+PNP90j4UPn36hKdPn+qVA5B6GcvU1DTD9vz580telkAOcq1qK4eDBw9i4sSJ6NWrF5o0aYLt27ejYMGCGDp0KMqXLy8phlIuDcjRlhEjRgBI7c37nNTCQu6ZjHWl18yk6ShldWw53lc58pCDrufmjh07yr72mEayz8zyhS1dulQ4OjqKuXPnih07doiAgAC1Hyl++eUXMWjQIPHx40dhY2MjwsLCxLt370SfPn3EoEGDcrYBn6lZs6ZqUqC+ffuKY8eOCSGE+Oeff0SbNm3+5/KQQ61atVQTcbm5uYlTp04JIYQIDAwUrq6uuZbH4MGDxahRo9QmjUtISBCjRo0S/fr1y7U85FrVVg41a9ZUzXQ8YMAA1UR+ly5dEi1btszVXPQlR1uePn2a5c//IqWsji0HpeShdHmuR2XFihUANA8QNDAwkHSt8Pz581i7dq3at91ixYph/PjxWq1tIAc5FuHLS3nIQa5F+PQ1ZswYdOvWDS1atFCNtg8KCkJsbCy2bt2aa3loWtW2cuXKGDRoEEaNGoXJkyfnWi5yLY6mBHItfAekXoZ7+vQpKlSoACGEVmsw5TWaVi22srJCx44d4e7unmsx5KCUPJROvuUXFeLevXuZ/ki9/hobG4uCBQtqfEzqnSVySVuELy4uDpaWlvj777+RkpKi1SJ8eSkPOaQtwgcA1atXV93SrO0ifPqqUqUKfv/9d7Rp0wYJCQmIj49Hu3bt8Pvvv0u+/h0WFqZ3HunvGNBnVVs5pC2OFhkZCWtra/z555+IiorSarE4pZCjLUII+Pr6wt7eHm3btsWLFy8wfvx4TJo0SeNdGv8L0lYtBqD36tj6xJCDUvJQvC/dpZNTnj17Jk6fPi3i4uLE69evtdp3wIABYvbs2UIIobr0k5CQIEaMGCH69++fE+lmSq4FBfNKHnKQexG+L6lRo0Z6rwsyadIk0blzZ/HgwQNx7Ngx4ezsLG7evCnmzp2r0+WWixcvih07dojo6Gjx8OFDkZiYKHlfpSzSJgc52uLv7y8aNWok9u7dK6ytrUVYWJg4dOiQqFevnlZrKOUlq1evFvXq1RMnT54UN27cENbW1mLdunWiT58+omvXrrkWQw5y5REXFycCAgKEr6+vePv2rbhw4YKIiorKwcxzV54rVOLj48WoUaNUqzqGhYWJoUOHij59+ojo6GhJMYKDg4WDg4Po2LGjqFmzpujXr59o2rSpqFOnjmol1Nyk7yJ8eS0POcixCJ8uHj16pPr/2NhYsWLFCtG/f3/Rq1cv4e7urvYjhbOzs7h9+7ZeOcm1qm10dLTo2rWrMDc3FxYWFiIsLEwMGjRItG7dWutFIJWySFt6ace+tvRpS+vWrcXRo0eFEP//pUkIIY4ePSqcnZ21ziWnj+/coKTVsdO8efNGvH//Xuv95Mjj1atXwsXFRVhbW6s+8wYNGiQaNmyYZxbFzHO3Jy9ZsgR//vknpk2bhsGDB+PAgQN4/vw5JkyYACcnJ0ybNk1SnJcvX2LHjh24c+cOUlJSUK1aNfTo0eOLLTCmzyJ8QOolq7TbzwD1eUPat2+fa3m8ePEC27ZtU5uJtUuXLqpr8bnZlo8fP+LJkyfIly8fKlWqpNXCaFmJj4/PNFbr1q1RrFgx7NixA2PGjMFff/2Fhg0bapzETsotznJNwZ2eEAJ3795F6dKl8fXXX0veb8aMGbhz5w58fHzQvn17HDhwAAkJCRgzZgwqV64seV4YfReulON26w8fPsDHxwdubm6oWrUqPDw8cP78eZiZmWHt2rWS79rRty3W1tY4dOgQvvvuO9ja2uLAgQMoX748wsPD0bp1awQFBUmKs2PHDqxbtw4REREIDAzE+vXrUaZMGQwZMkRyLvrKbHZrAwMDGBsbo2zZsnBycpJlEbvcsnnzZqxdu1Z16bR06dLo379/rt4tN2bMGMTExGDRokVo0KABDhw4gKJFi2LUqFHInz+/5MVNdZlbSip957fJc4NpDx06hGnTpsHBwUG1zcHBAbNmzcK4ceMkFSrDhg2Dp6cnRo4cmYOZSiPHInxnzpzB+PHjERUVleGxAgUKSPpwlyOP+/fvw83NDQUKFICVlRVSUlKwb98+bNu2DTt27JA0m6McbUlMTMTs2bOxd+9eJCYmQggBU1NT9OrVC56entnuDwBv377F6tWrM8x/kJiYiODgYFy+fFnjftu3b4ezszM+ffqEEydOYOHChVqvy5GeHIPHAc2r2mp7G/6JEyewYMECtQ/xKlWqYMqUKRg6dKikGHIsjibH7dZz5szB5cuX0adPHxw7dgyXL1/G/PnzcfjwYcyfP181uVZOt+Xbb79FUFBQhi9Ip0+fllwsHTx4EAsWLEDv3r2xfv16AKm/F19fXxQoUAD9+vWTFEdfly5dwqVLl2BsbIxKlSoBSF2Y89OnTyhXrhzevXuH/PnzY/PmzdmeC5SwOvbOnTvh4+ODHj16wN7eHkIIXLp0CQsXLkThwoUlL6Ohbx5y3PyRNrdU2rgn8dncUlIKlRydE+aL9OPkICsrK9Utgem7Sp88eSJq164tKUadOnVUMb40Ly8v0bx5c3H69GkRHR0t3r9/L06cOCFcXFzEnDlzJMVwdXUVHh4e4tSpU8LGxkYcP35cbN68WdStW1d1m3Fu5NGvXz8xdOhQVVe4EKnd4sOGDRMDBw7MtbbMnTtX1K9fX+zYsUPcu3dP3LlzR2zdulU4ODiIVatWSYoxcuRIUa9ePeHp6Slq1KghxowZI1xdXYW5ublYs2ZNpvuNHj1arFu3TgghRN26dVW3fH9JwcHBomHDhsLGxka4urqK9u3bC2tra9GyZUvx4sULyXGsrKxUf2/p//aCg4OFtbW1pBhDhw4VnTp1UrtE9uDBA+Hq6iq8vLwkxZDjdmtHR0dx9epVIYQQ48ePV01LcP/+fVG3bt1ca8uePXuEg4OD8Pf3F9bW1mLXrl3Cx8dHWFlZie3bt0uK0bFjR7Fv3z4hhPrv5bfffsvVW759fX2Fm5ub2pjBqKgo0a9fP7F06VKRkJAgvLy8sj0X/PXXX6JGjRqiS5cuYs6cOcLb21t07txZ1KpVS1y6dElSLnLE+P7778WWLVsybN+6dato27ZtruVhY2Mjnjx5ovr/tN/v3bt3ha2traQYrVq1El5eXiI4OFjY29uLoKAg8ddffwknJyexf/9+STE8PDxE8+bNha+vr6hZs6ZYsGCBGDZsmDA3Nxe7du2SFCMzea5QcXV1Fbt37xZCqP/Sli9fLnmODG9vbzFy5Ejx4MGDL35Nt27duuLixYsZtp89e1Y4OjpKilG7dm3V2Jru3buLf//9VwiRehLs1q1bruVhY2Mj7t27l2H73bt3RZ06dSTFkKMtjo6O4u+//86wPTAwUDRp0kRSjHr16qmuK7du3VqV0+TJk8XYsWMz3S+tQEpISBATJ04UixYtkvR62dFn8HifPn2Eh4eHePfunWrbmzdvRJ8+fcTQoUMlx+nZs6eqSEv/tzd9+nTRvXt3STFsbW1FUFBQhu3Xrl0T9vb2kmLUrFlTPHv2TAiROldNWtFy8+ZN4eLiIimGtbW1qkhr1KiR8Pf3F0IIERoaKvnkL0dbhBBi586dokmTJsLc3FyYm5sLR0dHsXHjRsn7pw3CFUL99xIWFib5y5sc6tevr3Hcxd27d0X9+vWFEEI8fPgw20Kwbdu2wtfXN8P2uXPnii5dukjKRY4YtWvX1vhFIzQ0VPL7Kkcectz8IcfcUjk5J0yeu/QzfPhweHp6Ijg4GMnJyQgICMCTJ08QGBiIRYsWSYpx6tQphIWFZXotOyenmf6cHIvwpY9RsWJFPHjwAI6Ojqhfv75qWvzcyCOzha+0uc1SjrbEx8drXA+qatWqeP/+vaQYsbGxMDc3BwBUrlwZ9+7dg4WFBdzc3DBw4MBM91u8eDE2bdqE77//HiVLlsTGjRtx+vRpVKpUKcPlMyljVBISEjB+/HgcOXIEhoaGCAwMxLx58xAbG4tly5ZJWszu+vXr2L17N4oVK6baVrJkSYwfPx49evTIdv80o0ePRr9+/XDz5k0kJSVh1apVePToEW7fvo0NGzZIiiHHwpVpt1t/8803Ot9uXaVKFZw8eRLlypXDq1ev4OTkBADYvXu35EticrQFALp27YquXbuqVsctVaqU5H2B1HETT548yXCp6Nq1a1qNQdJXUlKSxvcjPj4enz59ApA6x1HaGInMhIaGonPnzhm2d+3aFdu3b5eUixwxvvnmG9y6dSvDuSQoKCjbJTbkzGP8+PHo2bMnLl68iMTEREybNg2PHz9GdHS05PmY5JhbKifnhMlz86g4Oztj6dKluHXrFvLly4cNGzYgPDwcixYtwvfffy8pxs8//4w5c+Zg9uzZGn9yU69evTBz5ky1E2xMTAwWL14s+fpjtWrV8PfffwNI/VC9cuUKgNQBTrmZR/369TF//ny8e/dOtS0qKgo+Pj6SlwCXoy0dO3bEkiVL1KapF0LA399f8oq3ZcqUUU10Z2Zmhvv37wMATE1Nsyx2HBwcsGrVKpQoUQLXr1+HtbU1ChUqhJcvX+Lp06dqP1KsWrUK9+7dg7+/v2oAr7u7O0JDQyWtJQP8/6q2n9N2VVs7Ozvs3LkTBQsWRMWKFXH9+nWULVsW27ZtUxszlpURI0ZgypQpal8Gnj59Cm9vb8kLTjo5OWH69Ol4+PAh6tSpgz/++ANBQUHYtm2b5AF9I0aMwOzZszF48GC0bdsWZmZmmDNnDjZt2iQ5Dznakl7JkiW1LlKA1A+9GTNmqNa+evz4MXbs2IFZs2ahU6dOWsfTVaNGjTB9+nS1D74nT57A29sbjRo1QnJyMnbs2KH6ApCZtFWLP6fL6tj6xOjWrRumT5+O7du3q+bq2rZtG2bMmKGx+MipPKpUqYIDBw6gadOmaNiwIQwNDdGqVSvs379f8nxMcswtlZNzwuS5u37yGjkW4Tt+/LjqpNmkSRN8//33qF+/Pu7fvw9ra2ssXbo0V/KIiIhAt27d8H/snXlcTdv//19Hcc1Xhnt17yWK1JUmZMgUcZExY1GGMosKjaSSMlQiU6LhXolMEZmveUzGlNtISBkSlaFo/f7od/a345xqnbNXOXx6Ph49HuzOXq19zj57v/d7vd+v19u3bzlr9EePHqFJkyb4559/qDqqWBzLokWLcPLkSfz888/Q1taGoqIiEhMT8ezZM+jo6IhkNsrzJ1m9ejVOnz6NVatWoaioCHZ2dnB3d8fp06eRkpKCQ4cOVToPFgwaNAju7u7o2bOnSFfI1atX4eDggIsXL1Y6RnW42tLCwrjy3bt3cHJygqGhIczNzTFz5kxcvHgRioqKWL16NUxMTKjm8ubNG+Tk5HAX+3v37qFBgwbUGRUWx8IKf39/hIeH49OnTwAARUVFTJw4ES4uLqhVq3qeV3NzczFr1iwkJCSgcePGIIQgPz8fOjo6CAwMxIMHD2BnZ4egoCAYGBiUO468uGOXlJTAx8cHkZGR+PLlCwghUr+vLOYhbP7g40F38+ZNWFtbY8GCBRg9ejQGDx6M5s2b4/nz5xgyZIhEr6mvCQoKQkhICNasWQMlJSVYWlpi/vz5uHz5Mj58+IDdu3fLPL8fIlDZuHEjrKysUK9evXJb4ITUqVMHLVu2hLGxcbnqs0DpDTE0NBQpKSmoU6cO2rdvj3nz5knVWsiCyo6nLBU9pT148AAKCgrQ0NDAjRs3EBoaCmVlZSxYsIDqqZnVPAoLC3Ho0CGkpKSAEIIOHTpg+PDhVEsUQvgei7OzM/XfKm/5paioCGvXroW2tjaGDx8ONzc3REVFoVGjRtiwYUO5GaK4uDjo6elBUVERcXFx5f5doSt1ZZTXvvro0SOMGDEC9+7dq3QMPq62rM4LIQcPHqQer7zs182bN6Gtrc0FnETGdmsAyMrKQlpaGrp27YrCwkKpMhosjoUlHz58QGpqKgghUFVVleo7xwpCCK5fv46kpCTuOywMSt68eQNFRcVKu6HkxR1bSEFBAdLT0wFA6veVxTy6dOmC6Oho3tIZOTk5KCoqQqtWrZCamordu3dDWVkZlpaWVFkVYVa6TZs26NevH7Zt24agoCAoKytjzZo1+PPPP2We2w8RqPTv3x/79++HkpIS+vfvX+Frv3z5gtevX8PQ0BBBQUESXxMREQFvb28MGTIEOjo6+PLlC+Lj43HmzBn4+flhyJAhVXEYVQaLiJsFzs7OcHV1Ffsi5+XlwcXFBZs3b650DHk5Fknk5eWhYcOGFbp3a2ho4PLly2jWrBk0NDQgEAgg6StI64xramoKMzMzjBs3TiRQ2bRpE86cOYMDBw5UOsaNGzcqfY2Qr590v/6+PX/+HLVr10arVq2gqKiIzMxMFBcXQ0tLi9cTlTT06NED27dvR8eOHWUeg0Xtz7ckKyuL+rVVnSX7kZHU1j948GBmmkw0rFy5Ei9fvsS8efOgoqJCJRXxNSyuzVXJD1FMK6xZ+Prf5XHjxg3MmjWr3N+HhITA2dkZkydP5rZNnToV27Ztw4YNG767QOXatWvV+sUpS3x8PJ48eQIAiI6ORseOHcW+DGlpaRLXaSXxLY+logzI13Tt2lXi9jNnznA+L+UtkUkDi+LxitLslVH2+xYWFoZz587Bz8+Pyzy8e/cODg4OUFdXl/lvSEvTpk2Rn5/Pa4yytT+zZ88GUFr74+zsDF9fX2rhyKpCKMTVuXNnib/v37+/2PLS15SXJasq0tPT4enpiVu3bkksqq3OJgUWpKWlYcqUKSgsLETbtm3x5csXREVFYfPmzQgPD+clcCYNsjZ/sLg2R0dHU89TFvFJIT9ERkVacnNzERMTgylTpkj8vba2NmJiYqCioiKyPSMjAyNHjqRKp8sTLCJuWbl16xbXOVJe9qB+/fqYPn061dLAtzyWijIgZanOiz9QKv4VFBQkoqI8Y8YM6uJxVvTs2RMhISFi6ezk5GRYWFjg+vXr1TKP1atXY9euXejbty9UVFTEAlua84xF7Q8LEhISsGzZMiQnJ0vshinvPOOTJasqLCws8Pr1a0ycOFHi8k51LH+xZNq0aVBUVISvry/XMZebm4tFixahQYMGUi2L8qGyJcby3lcW12aWS2gV8UNkVMoivJmUR1JSEpo2bVpukAKUdmacOHFCrM303Llz0NPTYzbX6uJbtlvr6+vj4cOHAEo/m0uXLlG37kniWx4LiwxIWV6+fImAgADuCfPrCwXt3+vTpw/XPvstKS4uxvv378W2v379utKne5acOHECzZo1Q0JCAhISEkR+JxAIqAKVnJwciS3sysrK1C3sLPDx8YGCggKWLl0KHx8fODk5ITMzExEREVizZk25+5UXfBQUFKB27drfJCt59+5dREZG8lqSkydYtfXzRdYAj8W1Wbh/VfPDBSre3t4iF8XPnz/j0aNHiI6OhoODQ7n7lY1+lZWVERAQgISEBOjr60NBQQEPHjzAkSNHYGVlVaXzrwr4+jSwgsVJ/S2PRRo/IhqWLVuGhIQEmJiYUMmpC6mK4nEW9O/fH8uWLYObmxu0tLRACEF8fDxWrFhB3WnDAprl38pQU1PD1atXMW7cOJHtR48eFenEqGoSExMRHh4ObW1tHDhwAOrq6jA3N0fLli0RFRVFvQwdHh6O0NBQ5OTkQCAQ4I8//sDcuXN5peOlRUlJSSr9GHlH2Nb/dRuxtG39LODb/FFdAYes/M8s/Rw7dgx79+5FSEiIxN9XVoQrRCAQMH+yrgxWJnzfeh7v379HWFhYuRmE8lqBqwJCCC5evMiZI7Zr1w49evSAgoJCufvQ6sUAdMeiq6uL7du3S91Jxrp4nBUFBQVYuHAhLl++LNItNHjwYKxZs4Z6mY6FcSVQWlOUlpaGYcOGITs7m2urp+Hs2bOws7PDhAkTEBkZCWtra5HaH9plNb7HoqOjg+PHj0NZWRmLFy+Gvr4+zM3N8eTJE4wfP56qtiswMBAhISGwtLSEjo4OSkpKcPPmTezevRuLFy8WqcWrSoKDgxEfHw9fX19mxchFRUXMln9zc3O5+jEa5KWtn0Xzh6xGnjS1UEL43Df/ZwKVZ8+eYejQobh79+63nopUVGbCd/v2bapxzp8/jx07diA9PR179uzBgQMH0Lp1a4wcObLa5sHCLRjgfyx5eXmwsrLCgwcP0KhRIxBCUFBQgI4dOyI0NBSNGzeWuB+Ltuay9OzZExEREZxBW1UhLB4v7zNi7WqbkZGB5ORkCAQCaGpqUpvnAZKNKxMSEvDhwwdq48qCggJYWVnh7t27EAgEOHnyJFauXInMzEyEhobi119/pZoL39ofFscyYsQIzJw5E8OGDcPmzZvx5MkT+Pj4IDExERYWFpzgYUX07t0bjo6OGDZsmMj2vXv3YsuWLUwyUDRMmzYNN2/exJcvX9CsWTOxAEOaGxlfN2gW7th82vqFsHD6HjBgAKZNmyYWcG7btg0HDx7EsWPHKh1j2bJlFRp5Ojo6StwvMDCQOlCRReRQyA+39COJwsJC7Ny5U+r1t1evXomolwqpznY+f39//Pnnn7CwsMDChQvh6+uLrKwsbNiwgfrGfvnyZcyfPx8mJia4c+cOSkpK8PnzZzg7O4MQQpX+ZTEPFm7BLI5l9erV+Pjxo4hy48OHD7FkyRL4+fnBw8ND4n60x0nLyJEjsX37dnh6elaYyeFLu3btYGtrW+7vWbraAqWqlLIGX2vWrEG3bt3g5+fH1VF8+vQJixcvhq+vL1VWyN/fHwKBAKdOneIyfUuWLMHixYuxZs0a+Pn5Uc2Fb+0Pi2OxsLCAq6srAOCvv/7CyJEjUbduXdy6dQu6urpU88jPz5dYF9K5c2e8fv2a/oB40rlz53K7lKSBhRs0C3dsFhlgFk7fL1++RO/evcW2Dxw4kLqg98yZM/Dx8cGwYcMwcOBArFixAq1atYKdnV2FFic2NjZU4/OGl1OQHNKhQweioaEh9qOpqcmZFVbGuXPnSI8ePcTGEI5dnbAw4ZswYQIJDQ0lhIiakm3bto3a5ZOVsSFft2AWx9KtWzdy/fp1se3Xrl0jPXv2pJ5LTk4OCQwMJPb29uTVq1fk2LFjIk65leHk5ES0tLRIz549yYQJE4iFhYXIDw3lne/CHxpYudqygIVxZb9+/Uh8fDw3nvAcuXXrVoUGmgcPHqT+qa5jIYSQU6dOcU66hw8fJsOHDyczZ86kdni3t7cnK1euFNvu6+tL5s+fTz0PeYGFGzQLd2wWsHD6tra2lujYHhISQiwtLanGYGHkSUjpue3k5EQmTJhAsrOzyc6dOyVea6Xlh8uofF1MC5SagOno6FCnoFeuXAltbW2Ym5ujbt26VTFNaliY8P33338SOwQGDx5MHXGzmMegQYNw4MCBCp/uK4PFsXz+/Flidq158+YoKCigGuPx48cYP348GjZsiJycHNja2iI2NhbOzs4ICwuDjo4O1Thfp+OlRdbi8bLs27cPISEhIqqrSkpKWLJkCaZNmwYbGxtYWVnBzMyM11xpYGFcmZubixYtWohtb9y4scSuJCFOTk5U4wsEAqrMHYtj8fLygqWlJdeBNHz4cAwfPrzS/couUxYXF+Off/7BzZs30blzZ6454NatW2LFwqyhLfwWCASYN28e1ZgZGRkS67q6desGT09PqjHev38PZWVlAKVZ2hkzZgAoXcYuuwRTEbm5uQgODkZKSorEzDtNxuXdu3fQ19cHUJr5TExMhKqqKmbNmgVbW1ssXbpU4n6smz9YGHkmJCTAzMwMurq6SEhIQFFREZKSkuDj44NNmzahb9++VONI4ocLVFiYbL148QJbt26FqqoqgxnxQ2jCZ2FhwZnwTZkyRSoTvkaNGuHFixdi7ZapqakirXVVPQ8WbsEsjqVjx46IjIzkUupCIiMjoampSTXGqlWrYGxsDC8vL+5C4+/vD0dHR/j6+uKff/6pdAwWS0nlne9CkzGauh1WrrYsEBpXbtiwgauJkda4slOnTjh27JiYvEBERESFMt6sOx9YHMvBgweplwDK8rWppfDGXvYY9fX1Oen3quLAgQOYNGkS6tWrV6FKsjSBCgs3aBbu2A4ODrh//z569uwp8wOtrAHC1+9ly5Ytxdrxf/nlFxw5cgR2dnaVzkNo5Onj44POnTvD29sbAwcORGxsLLVwna+vL6ZPnw47OztOxsPLywsNGjRAYGBgTaDC2m+ke/fuePDggVwEKjNnzsSCBQtQu3ZtDBs2DIGBgZg5cyb+++8/dO/enWqM4cOHc87PAoEAhYWFuHDhAlasWIGhQ4dW2zyEbsFAaTAoCyyOxdbWFpaWlrhz5w4XZMTHx+Phw4fcmndl3Lp1CxERESLZDEVFRcydOxfjx4+X/sAYo62tTZ0hELra+vv7cyKHsrjafk1xcTEePnwIVVVVNGjQgGqfxYsXY+LEiTAyMhIzrqR1Lre3t8f06dNx7949fP78GVu2bEFaWhoePHiAHTt2SHUMT548QXJyMn766Se0a9dOKrVRFsfSt29f7Ny5E/Pnz5eqU4YmUK4OpFUNp0HoBi3MGqWnp+PSpUsICAioUB+rLAsWLICNjQ2Ki4tF3LEjIiKwadMmqjHi4+MrNVCsDFkDBNYF0A4ODnBycsKNGzdgbm6OPXv2YNy4cZyRJw0JCQlYvny52PZJkyYhKiqK1/x+iK4fDQ0N1KpVq9KLCG1rcXZ2NsaOHYuePXuiVatWYktJfKqXZYGvCV9xcTGcnJxw9OhRAP+nQtivXz8EBARQPw3wnQcLWB3LvXv3EBISImKOOG3aNGhra1Pt37VrV0REREBdXV1EufTevXuwtraWShmUNYWFhdi4cSNOnjxJdb6zcrV9/vw5XF1dYWtrC3V1dYwdO5bLdIWFhVFnq1gYVz58+BAhISEiHTvTp0+nXpJ7+/YtXFxcRN6/WrVqYdSoUVi+fDm1YBrfY7GwsEBcXBwEAgGaNWsm9ndpO2XkReIAKG1SkCRPIE2TAgs3aL7u2IMHD4afnx8vATtWTt8A2+YPIoORZ48ePbBt2zZ06tRJ5Jp4/fp12NnZ4cqVK1LPQ8gPEai4ubnh1KlTqFevHkxMTGBiYkIt7VveeFFRUVBSUkK9evVEflfdOiosTfgeP36MpKQklJSUQF1dHe3atZNoOV+V8/j48SOOHz+O9PR0TJ8+HcnJyWjfvj2UlJSkGofPsbDAzs4OtWrVwtq1a9G5c2ccPnwYjRo1go2NDZSUlLBhw4ZqmUd5SswCgQAeHh7UNQiEgavtggUL8Pz5c6xbtw5xcXFYsWIFtm/fjgMHDiArK6tcDaOysDBHi46OxtChQ8WWFt+/f4+oqCiqpZQlS5bg/v37cHNz41qLb968CS8vL/Tr1w9ubm6VjlH272ZkZEBBQQFt27aVShW2smwxzUMTK4kDvty6dQvOzs7IzMwU2U4qaeEtDxZu0Hzcsc+cOYOgoCDY2dmhVatWYgESTYDAwun7/PnzcHZ2xps3b0S2S/u+fvjwAcnJyRKDyPK8y8qybNkyZGVlYd26dejbty8OHz6MoqIi2NnZQUtLizqLKIkfIlABSoWtrl27htjYWJw+fRrNmjXjghZh2pUWfX19LFu2TC68J1hYeA8YMAD79+8Xy3rk5ORgxIgRVB4sLObx6tUrTJgwAa9fv0ZRURFOnDiBlStXIiEhAeHh4VRBkKzHwnp5MCcnB5aWlsjPz0deXh5UVVXx7NkzNGnSBDt37mSuYlseBw4c4F08zgoDAwOEh4dDU1MTtra2IIRg/fr1yMjIgKmpabk3xLLmaOUFKmlpadi5c2e5Y+Tm5nL1NAMGDMC+ffvEgt/ExETY29tTeXV17twZQUFBYkWbly9fhr29PdV3pri4GN7e3ti/fz9XA1S3bl1YWlpS1Q2wwtTUFM2aNStXWsDY2Lja5lG/fn1MmzZNYtBbUbaOtRs0C3fs06dPw9HRUaxAW5oAgYXT96BBg6Cqqlpu8wfN0tSZM2fg5OSEgoICsSCF9lgKCgpgbW2Ne/fuoaSkBI0aNUJBQQE0NDQQGhrKK+v+Q9SoAKVdKYaGhjA0NIS7uzsuXbqEY8eOYcyYMWjdujWGDh0KExMTqpO4Xr16XO3Ct2b06NHw9fWV2oQvNjaWM0579uwZPD09xZ7knj17Rp2BkHUeZVm1ahXat2+PI0eOoGfPngBKNU1sbW2xdu1abN26tcqO5evis+fPn6N27dpo1aoVFBUVkZmZieLiYmhpaVEFKr/++iuio6Nx5MgRLrNjZmaGkSNH8lbdrMwZtywsisdZudoWFxfj559/BiEEV69ehb29PQCgpKSkQkVYgUDA1dMIBAJ4eXmJvaZ+/foVdjBcuHABTk5O3FLg2LFjxV5DCKEu6Ktdu7bEz7Fp06bURcX+/v44fvw4XFxcoKenh5KSEty6dQuBgYGoV68e58pcGUIX54yMDKxfvx6nT59G+/btqWsjUlNT4e3tDQ0NDWhqaqJ+/fqwsLBA/fr1sWPHjmoLVFJSUhAdHS1TVpa1GzQLd2xvb290794d48ePF8u808LC6ZtF84evry969OiBuXPnSmXnUZaGDRti9+7duHr1Krfkqq6ujt69e1Mvx5XHDxOolKV27dowMjKCkZERioqKsH//fvj5+cHf35/qJDY3N0dgYCBWrFgh8wnICllN+PT09LB7924uOs7KyhLx2RAIBKhfvz51oRQLM8Br165h27ZtIu/pzz//DEdHxwrl6VkcS9nis7CwMJw7dw5+fn5cqvfdu3dwcHCAurp6pccB/N+T/9dLK3l5eZg7dy7VEoWszriss0PLly/H69evsXjxYpkvUgDw559/Yt++fWjRogXevXuHvn37oqioCMHBwRUuxbIwRxs1ahR+//13lJSUYMqUKdiwYYNIF5jwHKH9fC0tLeHt7Y2AgABOVv39+/dYt24dteHcoUOH4O3tLSJwqKmpiRYtWsDb25sqUGHR8slCWoAFysrKKCwslGlf1vYaR48ehbu7O7p168Zt69atG1auXAkHBweqQCU3NxdOTk68Mpd9+vTBrFmzeDl9s2j+ePr0KYKCgiQacdJiaWmJjRs3okePHiJdba9fv4aVlRWio6NlHvuHDFSA0ijz5MmTOH78OOLj46GiogILCwuqfW/evIm4uDgcP34czZo1E3sarM4aFVlN+JSVlbkvt4WFBTZu3EjdvstyHmUpLCws1xjv8+fP5e7H+li2bdsmphvSuHFj2Nvbw8LCgssEfE3ZJYro6Gh07NhR4hIFjf8KILsz7saNG6mLx2kudKxcbR0dHTF79my8efMGM2bMQMuWLeHu7o4zZ85Qd1PxaREWrqP//fff0NfXp/b1EfL1U/uzZ8/Qv39/qKioQEFBARkZGfj06RO1e/KnT58kXvjbtWtHPQaLlk8W0gIsmDNnDry9veHh4QFVVVWpDApZu0GzcMfu1q0bbt++zStQYeH07e7ujrFjx+LixYsyN3+0adMG2dnZUgcq58+fx/379wGUKlxv3bpV7Br/+PFjPHv2TKpxv+aHClRycnJw4sQJHD9+nDuBhgwZgqVLl0pVXMtK6pkFLOpkhK2KfArHWMyja9euiIyMFBOj2rJlC/VSG4tjKS4ulij89fr16wrTyyyWKMoiqzPu+PHjcerUKQBgUjzOytVWW1sbly5dQkFBAeeXNGXKFNja2lKvT7MwrjQwMJC4XNKuXTuRJ+ivGT16NNNi7FGjRmH9+vXw9fUVKZYMDw+n/j6xaPlkIS3Agi1btiArK6tcsTxpimn5ukGzcMfu0qULli9fjnPnzqF169ZigTFNgMCizXjz5s149eoVLl68KLH5g2YeixcvxooVK2BnZwdVVVWxpf3ySiZ+//13eHp6ct/T2NhYkWUeYSaTVnyyPH6IYtqwsDCcOHECd+/exW+//YYhQ4Zg8ODBvJ8Q5QW+JnzFxcVwcHDgVTjGYh5paWmYNGkSlJWVkZKSgm7duiE9PR35+fnYuXMn1c2WxbE4OjoiISEBbm5u0NLSAiEE8fHxWLFiBfr27Ytly5ZVOoaGhgYuX74sVYD0NXyccVkWj7N0tSUyuFKXhYVxZdnlktu3b+PYsWMICgpCdHQ0b4VMaVi0aBFOnjyJn3/+Gdra2lBUVERiYiKePXsGHR0dkZtBeQEYq5ZPeZAWOHjwYIW/pw3eWLhBs3DHrsi5XNruUD5O3yyaPzp27Mi1rksyWKQJIsu6urPmh8iorFq1CrVr10bv3r3RqVMnAKUn4tmzZ8VeS6uBkpCQgB07dohccKdMmUKts8EKFiZ8mzdv5l04xmIeampqOHz4MHbt2oVffvkFJSUlGDJkCMzNzam7iVgcy7Jly7Bw4UJMmTJFxOV08ODB5bqEfg0LFVMVFRXEx8dj2LBhUFVV5VKo+fn5EvUQysKyePzKlSu4efMmDAwMeLnayupKXRYWxpVr165lopD58OFDkfqhstojkrJpX1OnTh0xi4SuXbtStXoKMTY2RkBAANatW8dtS0tLw8qVK9GvXz/qcYQPbUVFRTAwMOAlUiYrrLooo6KisGLFCpH31tjYGGpqatiyZQtVoGJkZIQNGzYgKCgICgoK2LFjB9q3b08dpABssiFfO30bGhrC19dXKqdvFs0fISEhvLOJX78feXl5aNiwodRLsJL4ITIqFUW2ZaGNcm/cuIHp06dDXV0dXbp04ar1k5OTER4eXq3LQhMnTsTgwYMxdepUkSeq4OBgHD58GDExMZWOMWjQILi7u6Nnz54iY1y9ehUODg5cR01Vz4MFLI5FSHp6OpKTk1GrVi1oampSrTWnp6dj7969mDFjBpo2bYqCggK4ubnh3LlzaN68OebNm0edYdq7dy+8vLywcuVKaGpqYuTIkRg3bhxu3bqF5s2bS62iCkCkeLywsJDqSYiFVgdQWmB87949+Pn5iblS6+vrl+tKXZauXbti//79vIr6unTpgr1796Jt27Yi50hmZiZGjhxJpRsSGhrKFZoKO4mE/+7SpUu1Kb+yavmMjIxEcHAwsrOzceLECezYsQO//PIL5s6dW7UHUIaSkhLExMRIXNYTCATUOhu6uro4ePCgmEN3eno6Ro8ejbt37zKdd2XwyYZ4enoiMTERa9euxYgRIzjtkcWLF0NVVZXK6XvTpk3IyMj4Zs0f169fR0REBJYuXYpffvkFL168wIIFC3D37l3UrVsXM2bM4H2e/RAZFdZywuvWrcOYMWPELqweHh4ICAioVnlqFiZ8LArHWMyDhYkXi2MRoqqqKlIpX1lbcFJSEszNzdGkSRPuqc3NzQ3Hjx/H1KlT0ahRI3h6eqJRo0ZUwfO4ceOgpKSEJk2aQE1NDT4+PggODoaysjLV8lNZ+BSPs1JaPnv2LDZs2CCyhKehoYGlS5fC3t6eKlBhYVxZu3ZtieaSz58/p76QR0REYMaMGZg/fz6MjIxw8OBB5OXlYdGiRRgwYAD1XFJSUso932mykAKBgHfLZ0xMDPz8/DBlyhSuqFlVVRW+vr6oW7cupk+fTn08fPD29kZERAQ0NDR4LTEOGDAAkZGRcHFxEdl+8OBBzrNHEtJ0ndB8NiyyIWfPnoWfn5/IQ5Kamhrc3NyovY9kbf4Qduk0bty4wq5LoPxr8/Xr1zF9+nRuJQMofWBJTEzE0qVL0ahRI6xZswYtW7bkJaXwQwQqrElMTJSY2p08ebJEfYaqhIUJH4vCMRbzYGHixeJYZG0L3rx5M3r37g1/f38oKioiJycHx44dw6hRo7hiscaNGyMkJIQqUJHVGVcIn+LxqnC1ZeFKzcK4ksVySXZ2NsaNG4effvoJGhoauH//PoyNjeHk5IRVq1ZRqdtu3boVAQEBEn9H68A8atQoBAQEiLV8SkNISAhcXV0xevRoTh3Y0tIS9evXR3BwcLUFKjExMfD29pZpCYiFGzRrd2x/f38IBAKcOnWKsyFYsmQJFi9ejDVr1lBlQ2R1+i6LrM0fv//+OxfsyipQuW3bNowbN45bcn/y5AkuX76MyZMnY9KkSQBK6+kiIiJqAhXWKCkpickRA6UnlSxCZ3xgYcJnY2MDOzs7pKam4suXLzh48KBI4Vh1zYOFiReLY5G1LfjmzZvYtm0b98QiLGQcPHgw95rOnTtTXaAA2Z1xJRWPu7q6SlU8XhWutixcqVkYVzo6OsLa2hrdu3dHSUkJTE1NueUS2u6D+vXrc8WFrVu3RmpqKlcHQdtq+ffff2Pu3LmYNWuW1O2zQj58+CBzUC8kIyNDTGEXKG2v9fT05DW2NBQVFUlVn1MWFm7QrN2xWWRDZHX6LousGdGyQb+sTu7379/HkiVLuP9fuXIFAoEAAwcO5LZpa2vzPs9qAhUJGBkZYcWKFfD39+dUFFNTU+Hl5UVdD8MKW1tbZGdncxH+6NGjORM+2vQ4i8IxFvP49ddfqV10y4PFscjaFpyfny+SMbh58yYUFBRELr4NGjSgVi6V1RmXRfF4VbjasnClZrGsymK5RF9fH9u2bYObmxsnZDdz5kzEx8dTn8PFxcUYOXKkzEEKUJr5sLGxwaRJk9C6dWuxoIXmxt+8eXNkZGSI1WDdvn2b2k+GBb1798b58+e5J21pqKrldj7u2CyyIaycvlk0f8iyTPnhwweRIvm4uDjUqVOHK2IHSgv/+Rbq1gQqErC1tcW0adMwbNgwrkUyPz9fqicyVtSuXRt+fn5YsGCBRBM+Gnbs2IFhw4YhIiLim85jyZIl8PDw4GXixeJYSkpKuAuMUKmzS5cuGDBgAIKCgsrd79dff8XTp0+hrKwMoPTpQUdHR0Tg6M6dO9QXu5cvXyI2Nhbh4eFSOeMK3yfhhaU8aDUUhPB1tdXT00NERARCQkJw6dIlzi1YaOxHC1/jShbLJcIbSEREBMzMzLB161YYGBjgw4cP1Do5I0eORFRUlMgTp7T4+/sDAFasWCH2O9q20QkTJsDT05NbPklPT8elS5cQEBCAKVOmyDw3adHV1cXatWtx9epVqKmpiWn3SHOu8nWDZuGOzSIboq+vj927dyMkJAQqKiq4c+cO2rdvDxcXF2qn77LNH4aGhlzzh7m5OXXzh6zLlH/88QdSU1Px22+/4cuXL7hy5Qq6dOkisvJw7do1Xh5xQE2gIpGff/4Z+/btw8WLF0Xs2Xv16sXbs0BahCZ8KioqUFFR4bZLYyi4ZcsW3n4eLOZBCEFaWprYmri0Hh18j0XWtuCBAwfCz88P7u7uOH/+PJ4/f45Zs2Zxv8/JyUFgYCB1sWW3bt0qFCArD9bF4yxdbbW1tcu94NHwtXHluHHjEBISIpVxJYvlEnV1dZw+fRrv379HgwYNEBUVhSNHjqBly5YiS30VYW1tjZEjRyI2NhZ//PGH2FMlTfH4qVOneF9zZsyYgfz8fNjb2+PTp0+YNWsWFBUVMXHiRGq/IRbs3LkTTZs2RWJiIhITE0V+J01QXZkbNE2g4uXlhbS0NISEhIi5Y69evZrKHZtFNkTo9P31krNQ+JBmaZhF84esy5QmJiZYtWoViouLcenSJeTm5mLMmDHc7+/du4eNGzfCzMyMekxJ/BDtyT8aZU34Dh48iKFDh0o04UtOTsa1a9cqHc/Kygq9evXCtGnTvuk8+vfvD01NzXJNvGhqV2Q9lrLI2hacn5+P2bNnIz4+njueTZs2QSAQYMuWLdi8eTNUVFSwa9cuKs0QeYGPq+2nT5+4c6Ki9lOAbh188eLFKCgowLp169CzZ08cPnwYjRs3hq2tLX766adyjSvLsm3bNkRHR/NaLhHCRwF56tSpSEpKQvfu3SVaR9C8H2PHjoWXlxcv5WEhHz58QGpqKgghUFVV5S3u961g4QbNwh0bKK17CQkJ4ZYY27dvj+nTp1eYDWHt9K2jo4MDBw6IBfFpaWkYO3YsVTt+165dsW/fPpGHUBqKioqwdOlSxMTEoFatWjA3N+dq1FatWoWwsDAYGBggODiY1xJoTUbl/1NZe1ZZWBtkfQ1rQ8GGDRtizZo12Lp1K9q0aSN2wpR3PKznwcLES9ZjKUtFbcEVPUk1atQIERERSElJQa1atUQuDO3atYODgwNMTU2lqsPhKyrGAj6utoaGhujbty/8/PyYtJ/KalxZFhbLJUVFRXB0dOSlgHz79m38/fff1Cl8STx58qRcfyxpePv2LR49esRlDMu+B7IWuH4rWLhBs3DHBkrb7ysqwJcEa6dvFs0fsi5T1qlTB2vWrIGbmxsEAoHIta9///4wMDCAkZFRTY0KKyS1Z8XExKB///68C0ClhbUJX/369ak9MKpyHixMvGQ9lq8pezETtgUXFxfjzp07lbbqtW/fXmxb2Sp3WioTFasu+Ljanj17FoaGhvj48SOv9lMhshpXloXFcsmWLVt4KyArKyvz9lCytraGq6srrKysJGaHaOqH9u/fDw8PD4lZLmmX9vigoaFR4Q2Ldh4s3KBldccWuqY3bNhQpGVaEuVlzFg7fbNo/uC7TCkp6GOpflwTqPx/JJ1Ux48fx5IlS3jdWPnCwoTP0NAQvXv35hVksJgHCxMvFseiqakJGxsbMbXEt2/fwtLSstou3KxExfjCx9V2/fr1mDx5MurWrcur/VQIC+NKOzs73sslR48ehbu7u0gNUbdu3bBy5Uo4ODhQBSqLFy+Gm5sbbG1tJZ7vNEFGQEAAvnz5gri4OJk9WDZs2ICRI0di6tSpvGt3+CCUNhDy+fNnPHr0CNHR0VI1KcjqBs3CHfvp06dcxuXrlmlp4Ov0XRYWzR9CjRltbW0mGTzW1AQqcg4LEz5PT0/s2rWL182dxTwiIyOhpKSEO3fu4M6dOyK/oy2mY3EshBAEBwcjPT0d3t7eIunR6izZYiEqxgI+rrYxMTHw8fEBIYRX+6kQR0dHTJo0CTdu3EBxcTHc3d1FjCtpYLFcwkIBeeHChfjy5Qusra1lDjJCQ0PpJ10O7969g5WVldRmlawpT/BLS0sLe/fupbaekNUNmoU7dtnC1IqKVF++fEk1nqxO32Vh0fzBYpmyKqkJVOQcFiZ8bdq0QXJyMrVya1XNg0W3CotjEQgECA4OhouLCywtLbF582Yu/cv3QiYNLETFWDBnzhyZ9122bBnWr18PbW1tJu2nLIwrWSyXsFBAZhFksEifGxsb4/z58988UCkPbW1tatVYoPR49u7dCwUFBSgrK2P79u0IDQ3FgAEDsGDBgnL3s7GxYTFdDk1NTVy+fJm7dgh5+vQphg8fTlXEWtbpOyEhAUVFRUhKSoKPj49UTt+1atVC3759ZXYGZ7FMWZXUdP1UQFlDs28FCxM+Nzc37Nu3DxoaGhILUGm6D2SdR1ZWFpSVlSEQCJCVlVXh36C5gbA4Fg0NDVy+fBm1atXCvHnzkJ2djaCgIDRr1gyGhoZUT7rv3r3j3dkze/ZsKCkpwc3NDTExMdi3bx92796N2NhYrFq1CpcuXeI1fnXD0vaeDyws68+ePQs7OztMmDABkZGRsLa2FlFAphUXFFJUVCSTqvWnT5+wZ88eJCcnc8ckHC8hIQEnTpyodIwXL15g+PDhUFNTQ+vWrcWCcVlVSVlQWFiIjRs34uTJkzKdH7K+r4Bshez79u3D4cOHAZTql+jp6Ynd4F+8eIEPHz7g/Pnzlc5hypQp0NXV5Zy+hddVHx8fxMfHY9++fRL3Y938cfr0aWzdupXXMmVaWppMBfk01GRU5BwWKeiMjAxO9Ic2JclqHgMGDMClS5fQrFkzsTViIdLcQFgci3AOSkpKCAsLg4uLCyZOnChVl02vXr0wYMAAmJqaolevXjJlYliIirGAlasti4wZC+NKFpkMFgrIgLhr8fbt2/Hrr79Su8l6eXkhOjoaf/75J+7fvw89PT08fvwYr1+/pl4a9PLyQmFhIYqKiqo1U/c15RXTCgQCKsPKsvB1g5a1kN3Y2JiTJwCAli1bimXs1NXVqQv+Hzx4IDEbPWnSJERFRZW7H+vmDxbLlCYmJtDW1oapqSlMTEwkSh3ISk2g8v+RVMFdXFyMtWvXin3w1fkEwiIFzUJ+WtZ5hIeHc/UkLNq6WRxL2RtxnTp14Ovri40bN2Lx4sXUY2zatAnR0dGwsbFB48aNMXLkSJiamopZz1cEC1ExFvBpK2adMWNhXMmq26BPnz4VuvFWhiTXYjU1Nalci8+cOQMfHx8MGzYMAwcOxIoVK9CqVSvY2dmhuLiYah4XLlzAli1b0Lt3b5mPhQWSrpu1a9eGjo6OVFlrFm7QshayN2nSROQ4hB1AsiKr0zfr5g8WwX1sbCyio6OxdetW+Pj4cA9yhoaGNe3JrJBUwa2np4c3b95I7FGvLliY8AH8JadlnUfZm0ZmZiaGDBnCu92b77HMnz9frNhy/vz5UFFRqfAppiy9e/dG7969UVBQgGPHjuHw4cMICQlBp06dMGbMGAwZMoTqAla3bl1kZ2fj5s2bqFWrFv766y9Oor+64NNWzDpjxsK4ksVyCQCcO3cOycnJEjM7NPU2LFyL3717x3U7tWvXDomJiVBVVcWsWbNga2uLpUuXVjqGkpIStQ1CVfLgwQMRt3BZYfG+sihkZ/HAysLpmwUsgntVVVXY29vDzs4OV69eRUxMDOzt7VGvXj2MGjUKY8aMkfmzr6lR+Q64cOECgoKCRNQPZ8yYQZ2Crkxymqboi8U89PX1QQjBwIEDYWpqWmGFfnmwOhbW5ObmIioqClu3bsXHjx9Rr149jBkzBra2tuUGLAUFBbC3t8fFixdFUs9Dhw6Fj49PtTl16+npISYmRiY/jhs3bnDtlTdu3KjwtTQXw8GDB8PPz08qJ+ivWbZsWYXLJY6OjpWOIewuk+TBRFtvo6uri5iYGLRq1Uqk/uDJkycwMTGhUh3t06cPNm3ahE6dOmH16tWoXbs27O3t8fTpU5iYmODu3buVjrFnzx6cOHECy5YtQ+vWraGgoFDpPlVB586dcejQId6+LyzeVwMDA+zZswdt27aFu7s7WrZsidmzZ+P58+cYOnRotV1HCgoKYG1tjXv37qGkpASNGjXinL5DQ0PRpEkT6rH41FSyCu7LkpiYiNjYWOzcuROEEHz+/Bm9e/fG8uXLpX4Yq8moyDksTPj8/f3x559/lis5XV3zuHLlCk6dOoUjR47A2toav/zyC7dkQvvlkvVYynva/xqBQIDTp09TzaWoqAinT59GdHQ0rly5ghYtWmDq1KkwNTXF8+fP4ePjgwULFnBPfV+zcuVKZGRkYNu2bdDT0+PMxITiTdJ0QvCBT1tx2eCDxVMZC+NKFsslQh2ViRMnynQcABvX4j59+sDDwwM+Pj7o3LkzvL29MXDgQMTGxlKbX+7YsQNZWVkYOnSoxN9Xl26QrG7hX8PifWXhjs0CFk7fLGBRCwWU1jIeOnQIhw8fRlpaGnR1deHq6oqhQ4ciNzcXy5Ytw/z587F//36p5lcTqMg5LEz4WEhOs5hH3bp1ORXY3NxcHDt2DEePHuVu1DQ6GbIeCwsNhbK4urrixIkT+PTpEwYMGIAtW7aIFNW2bt0as2bNgouLS7ljnD59Gps3bxYRSevXrx/q1KmDxYsXV1ugwsrVVmikVp7XD02NEgvjShbLJYqKijIZRpaFhWuxg4MDnJyccOPGDZibm2PPnj0YN24cFBUVqVVY+bSfs0RWt/CvYfG+ykshOwunbxawCO6nTJmCuLg4NG3aFCNHjsSGDRugqqrK/b5BgwYwMzOrVNFXEjWBipyjo6ODf//9l5cJHwvJaRbzKEvDhg3RokULKCsrIykpibqDR9ZjYa2hkJiYiIULF2L48OHlpmc7dOjA+c5IouyxlKVFixbUcvEsYOVq6+bmhjNnzsDQ0BAtWrSQaS7e3t7o3r17ucaVNDRt2hSvX7/Gb7/9xunuAKW1Gq9evaIaw9zcHFu3bsWKFStkXoJj4VrcuHFjbN68mfv/tm3bOJsH2uwBq9oQvsjqFv41LN5XeSlkl9Xpm3XzB4vgvlGjRti4cSP69u1b7vKijo6OTFn5mhoVOWfhwoU4efIkGjduLLMJ3/jx4zF8+HBYWFggODgY9+/fx4YNGxAfH485c+ZUWlvAah5AqelcTEwMTp48yYl5jRo1itrbhsWxAGzNAAsKClC7dm2p3UE3b96My5cvY/369WjevDk3lqOjIzQ1NakDBHmhc+fO8PX1hZGRkcxjlK0/kJWlS5fi4cOH8PHxwePHj+Ht7Y3169cjNjYW//77L9V6e3p6OszMzPD+/Xu0aNFCLBsnjeYHH9fijx8/wsPDA23atMGsWbMAlC5jGhoaYtmyZVRBFKvaEHmDhRu0tJYgrPVLZHX6trCwoJ4HTacki1ooIZ8+fUJGRgbq1KmDVq1aMRGSq8moyDksTPhklZxmPY/evXvj9evX6NKlC1xdXfHXX39J/dTM4lhYmQGGh4cjNDQUOTk5EAgE+OOPPzB37lzq9+nSpUu4f/8+BgwYgDZt2kBRURGPHj1CYWEhkpKScPDgQe611SWWxoevHaVlgYVxJYvlkiVLlqBx48YYM2aMVHL8cXFx0NPTg6KiIuLi4sR+L61r8apVq3Dz5k2RjixnZ2esXbsW69atoyoMZlUbIk/wdYOW1R27MsNSaZHV6ZuFTENZWNRCFRUVwdfXF7t37+aWfuvVqwdra2vMnTuX19J7TUZFzjly5AhvEz6gNP2roKAADQ0N3LhxA6GhoVBWVsaCBQuoKstZzGPjxo0YNWoU7yc7vsdibGyMIUOGSNRQGDt2LFXxWGBgIEJCQmBpaQkdHR2UlJTg5s2b2L17NxYvXozJkydXOsbGjRspjraUqsyusHK1dXV1RYsWLWBrayvzXIKDg7F582YYGRnJbFz5NYQQqZdLtLW1sW/fPmoHWyFC1eNmzZpx76ukSyxtvU2vXr0QGBgIPT09ke1xcXFYtGgRLly4UOkYFhYWnKkhn9oQeYGFG/T69etx/PhxuLu7Y/bs2Th8+DCysrLg7OyMPn36UFmCsODJkycVFs2yDozK4927d3BycoKhoSHMzc0xc+ZMXLx4kQvuTUxMKh1jxYoVOHHiBBYuXAhtbW3umrhx40ZMmjSpQnuDyqgJVOQcAwMD7Nq1i5e3DQA8evQIBQUF0NLSAlCaDejbty+1/wereUhCuOQiVJytDL7HoqWlhdjYWLRu3RrTp0+Hubk5jI2NcenSJaxatQpHjhypdIzevXvD0dERw4YNE9m+d+9ebNmyhYlKa3Vx4MCBCl1tac3i/Pz8EB4ejnbt2qFt27ZiyxI0a+UsZPhZLJeMHj0arq6uUmXYgFJHXmVlZdSqVatSFViam5Cenh727dsnlqlKS0uDqakpVUq+soD4e1ti7Nu3L/r06VOuGzTN+8rCmgQolcuPiopCRkYGXFxcEBcXB3V1dZEi0ooYO3Ysb6fvqkCW4F5YJ/i139CJEyewYsUKXpYgNUs/cg4LE74rV65gzpw5mDp1Kndzj42NRUBAAIKDg6kuxizm8eDBAyxdulSkNqQsNE9CLI6FhRlgfn6+RK2Pzp074/Xr11RjyAusXG3v3LnDua++ePFCprmwCPBYLJfMmDEDLi4unLHh15md8pYXyt4kN27cKFG5NC8vDy4uLiJFsuWhq6uL7du3Y+XKldyTNyEE4eHh6NSpU6X7A99fIFIZLNygWViTPH78GOPHj0fDhg2Rk5MDW1tbxMbGwtnZGWFhYVROxCycvlnwdXAvEAiwaNEiqYL74uJiidlyNTU1FBYW8ppfTUZFzmFhwjd27FgYGhrCzs5OZLu/vz9u3LiB3bt3V8s8Jk2ahE+fPmHMmDHw8fGBk5MTMjMzERERgTVr1mDIkCHVciwszAAXLVqEZs2aibUf+/n54dGjRwgMDKx0DHnn2bNnGDp0qFSFdLLAWoafxXJJRU+4FS0vxMfH48mTJwBKgyNJgUpaWhp27txJJSp27949TJkyBUpKSlxg/uDBA+Tl5SEkJITqZgiAcz7PyMjA+vXrcfr0abRv356Z3UB5VPZ5loVWPXfJkiXQ0tKibkWWhKmpKczMzDBu3DiRjMqmTZtw5swZHDhwoNIx5syZg6ZNm8LLywv6+vo4fPgwlJWV4ejoiBcvXlDVkQQHB+PChQu8nL5Z4O7ujsuXL2PlypXcOXHq1CmsXbsWAwYMoAruvby8kJubi1WrVokENs7OzlBUVJRYh0NLTUZFzmFhwpeamipR5n7cuHHURVks5pGYmIjw8HBoa2vjwIEDUFdXh7m5OVq2bImoqCiqQIXFsciqoVC2JbC4uBj//PMPbt68ic6dO0NBQQEPHjzArVu3xPyQvkcKCwuxc+dOrhupKmEtw19YWCjR2bpp06bUT8unTp2SSXBLIBBw+jcCgUBiB1n9+vWptTq0tbURExODPXv2ICUlBYqKihg2bBgmTZpEnZJPSEiAmZkZdHV1kZCQgKKiIiQlJcHHxwebNm0SS9WzhEZoUZrPFigNVIYPH44TJ07I7AbNwprk1q1biIiIEPn7ioqKmDt3LsaPH081RkBAAL58+cLVEAmR9j3hy+nTp8WC+4EDB6JJkyZYtGhRuYFK2S6oL1++ID4+Hjdv3oSWlhYUFBSQmJiI7OzsCv2TaKgJVOQcFtXdTZs2xcOHD8U6KVJSUqgdLlnMo6SkhNPXEGqgdOnSBQMGDEBQUBDVGCyORZKGQkxMDJSVlSvUUPjaD0q4zPTw4UNum76+PtLT06nmIS+wdLWVBdbGlSyWS+zs7GSqHdDX1+fOh7KFtXz4448/sGjRIpn39/X1xfTp02FnZ8fdiLy8vNCgQQMEBgZWaaASHh7OVGgRYOMGzcIdu6SkROISdmFhIbVNAQszQBbIGtx/XQ/09XIaq4xdTaDyHcDXhG/kyJFwd3dHXl4elyq+f/8+AgICpGo55jsPFRUVxMfHY9iwYVBVVcX9+/cBlNZ7SDJ+q4pjEQosCX+A0lZHMzOzSoWXWLcEAvzfUxawcrWVFdbGlXZ2dpgyZQquX78ucbmEBha1A2UD2G9JQkICli9fLrZ90qRJ1CacssJC4O1rWLlB83XH7tWrF4KCgrB27VpuW15eHtauXUstlVDVS2+0yBrcszBmpKEmUJFzKjPho7mZzZs3D2/evIGnpyc+f/4MQggUFRU5v5zqmoeFhQVcXV0BAH/99RdGjhyJunXr4tatW9DV1aWaB59jOXLkCFauXIng4GDuBgaUqqEmJCTAy8sLAwcOpJoHC1i8pyyQF+VSoPSzWLlyJS/jShbLJdbW1nB1df3mtQMsqF27NgoKCsS2P3/+XGb1X1osLS2xceNGNG7cuFKxNNpsGis3aL7u2E5OTrC0tESvXr3w6dMnzJkzB8+ePUOTJk2watUqqjlUhRmgLLAI7quSmmJaOcfU1BTNmjUr14RPGv+dwsJCZGRkQFFREW3atJFKupnVPE6fPo0mTZqgS5cuiImJQXBwMJSVlbFs2TKp9FWkPZbr169j2rRpGDVqFOzs7EQk3tPS0rB9+3YcPnwY//zzDyclXdWw/Gz5UB3KpdnZ2VTCUR8/fuSMKy9fviyTcSULOnbsyN04vmXtAAuWLVuGrKwsrFu3Dn379sXhw4dRVFQEOzs7aGlpwdvbu8r+dtmC4so8Xmifzlm4QbNwxwZKM7RHjhxBUlIS5yg/cuRIamE9Fk7frHj69KlIcK+mpiZVcF+V1AQqco62tjaioqKgoaEBc3Nz2NjYoEePHti/fz/27duHyMjI/6l5yIqVlRXU1NQqNAl0dnbGq1evEBwcXC1zkpf31N7eHr/88gtv5dInT55g9erVIk+HwqWs3NxcMR+hyihrXHn37l1q40oWVGbFIC8pexoKCgpgbW2Ne/fuoaSkBI0aNUJBQQE0NDQQGhpKJZIoTwwaNAhZWVkiGYiy0ASR3bp1g52dHS93bBb07NkTLi4unBlgUFAQZwbYsmVLKo+d/wVqln7kHBaGgvI0j2PHjiE8PBzJyclQUFBAx44dYW1tjV69elXV1AGUdhxV9nRibm5ObWrGAnn5bFm52np6euLRo0cYPHgwQkNDMX36dGRkZODUqVPw9PSUel6yGleyoGwgUlRUJLMxIQuEomLp6elwdXWVWlSsYcOG2L17N65evYrExESUlJRAXV0dvXv3lqmziQ+5ubnIyMiQ6LFF6/LMwg1aVnds1l4/LMwA/xeoCVTknPbt2+Pff/+FhYUFVFVVER8fjylTpiA7O/u7m8e+ffvg5uaGwYMHw8TEBF++fMGtW7cwa9YsrF+/vkqXOj59+lTp8lCTJk3w4cMHmcbPzc3FjRs30LFjR+olCnn5bFm52t66dQubN29Gt27dcPHiRRgbG0NbWxvr1q3D+fPnqVs2JRlX0or5sSQyMhLBwcHIzs7GiRMnsH37dvz666+YO3duuftER0dTj09T/P21qJidnZ3UomJClJWVkZ+fj1q1akFdXb3ag5TDhw9j6dKlKCoq4qwFhMtqv//+O3UAwqKmSlZ37LJdLp8+fUJsbCw0NTWhq6sLRUVFJCQk4N69e9QSBSycvuWJjx8/olatWqhTpw7S0tJw7tw56Onp8V9OJzXINadOnSKampokMjKSZGVlkU6dOpEZM2aQPn36EBsbm+9qHgMHDiShoaFi24ODg8nw4cMZz1gUU1NTEh0dXeFroqOjybBhw6jG+++//8igQYPIjRs3yNu3b4mhoSHp0KED6dSpE7l69SrVGPLy2bJCS0uLZGVlEUIIsbW1JVFRUYQQQtLT04mhoSHVGL169SKamprEwsKCHDx4kLx//77K5lsRhw8fJp07dyYbNmwg2traJDMzk4SHh5NOnTqRHTt2lLtfhw4dqH40NDSo5jF79mzi4uJCSkpKiK6uLsnMzCTFxcXE3t6eTJ48mWqM/Px8MmPGDKKhoSHy9+3t7cmnT5+oxmDBkCFDiJOTE0lNTSVdu3Yl9+/fJ2fOnCF9+vSp9LtZFn19ffLkyRNec0lLSyMGBgZES0uLGBkZkf79+4v80ODk5ER8fHzEtq9bt47Mnz+fagxXV1cyZswYkpycTE6dOkWMjIzIvXv3yKpVq8igQYOkOqZvzY0bN0jXrl3JlStXSE5ODtHT0yOdO3cmf/75J4mNjeU1dk2g8h2QkJBAkpKSCCGEXL9+ncyePZt4eHiQN2/efFfz0NbWJhkZGWLbHz16RLS1tRnOVJywsDBiZGREsrOzJf4+OzubGBkZkY0bN1KNN336dDJjxgzy6tUrEhERQbp160ays7NJQEAAmTBhAvW85OWzZcFff/1Fzp07RwghZP369cTDw4MQQkhqairR1dWlGiMwMJD3TYgQQp4+fUqOHj1KDh48KPZDw6hRo8iBAwcIIYQLEAghZO/evdV6AzEwMCApKSli80hNTSX6+vpUYzg5ORFjY2Ny4cIFkp+fT96+fUvOnj1L+vfvL/FGW1VoaWmR1NRUQgghkydPJufPnyeEEHLixAkyevRo6nHs7OyIj48Pyc/Pl3kupqamxNjYmKxevZoEBgaK/dCgq6sr8XqWkZFBdHR0qMZ4+/YtmTNnDtm5cycpKSkh1tbWpEOHDqRjx47kyJEjUhzRt2fixInEycmJ5Ofnkx07dpDevXuTjx8/koiICDJy5EheY9cs/XwHNGjQgGsvNDAwQFJSEvr27UtdBPfy5UsEBATg1q1bEh1HaWsQ+M6jS5cuiI2NFUudX7p0idqQUNZjmTx5Mk6cOIFhw4ZhzJgx0NPTQ+PGjZGXl4dbt27h4MGDaNOmDbVi6O3bt7F37140a9YMFy9eRN++ffHrr7/C1NSUWsRp/vz5sLOz4wznDAwMvqsiza8ZPXo0HBwcsGbNGvTr1w+Wlpb47bffcPnyZXTo0IFqjPLaQqUxroyKioKHh4fEYkuBQEC15JKRkSFxqalbt25S19tkZWUhLS0NXbt2RWFhoVQCcCxExU6fPo3NmzeL+BP169cPderUweLFizkl3aqmTp063DKLiooKUlJS0KdPH2hpaeHx48fU47CoqUpJSZHJHbssjRs3RmJiopjn0M2bN6k/48aNG4t4Pm3btk1qM0BW8K2FSkxMxJo1a9CwYUNcunQJ/fr1w08//YS+fftSt2uXR02gIuewMOFbtmwZEhISYGJiQq3eWhXz6NKlC7Zs2YKEhAQYGBigdu3auH//Po4cOQJTU1MRl9fybliyHouCggLCwsIQEBCA/fv3IywsjPtd8+bNMWnSJMyZM4e6ZVu4Dvv582fcuHEDy5YtA1B6A6Ed49q1a2IX2e+ZmTNn4qeffgIhBNra2pg7dy62bNkCZWVlEVGsimBhXLl161ZMnDgRdnZ2MncxNW/eHBkZGWL1Rrdv36a+gRQVFcHR0RHHjh1DrVq1cOLECaxevRqFhYUIDAykmhsLUbGyRdtladGiBT5//kw1BguEJpf29vZQV1fH+fPnYWVlhdTUVNSuXZt6HBY1VWpqanj37h2vMSZMmAA3NzekpaVBS0sLJSUlnKz+kiVLqMZgYQbIAha1UPXq1UNRURE+ffqE+Ph4rk7n1atXMt93OBhkfGqoQsaMGUP8/f3Ftvv5+VEvMejo6JC4uLhvPg8jIyOqn4rWiFkcS3FxMUlPTye3b98mjx8/JiUlJVKPYWVlRVxdXYmfnx/p2LEjef36NcnOziYzZswgs2bNohrDy8uLLFy4kCQnJ1drrUBVsX379nKX1mgxNzcnY8aMIbt27SKdOnUiERERxMfHh2hpaVGvc2tpafFePtq2bRsZMGAAOX36NNHV1SXnzp0ju3btIgYGBmTTpk1UYwQEBJDBgweTa9euccs2165dI0ZGRmT58uVUY2RnZ5NBgwaRHj16EE1NTWJiYkJ0dXVJv379yNOnT6nG2LRpEzE3NycvX77ktuXn55O5c+dSL3OwIC4ujujo6JAdO3aQ3NxcYmBgQIYOHUr09PSIq6trtc2DEEKOHj1KBg4cSHbv3k2uXLlCbty4IfJDy9atW0mvXr242p9+/fqRiIgI6v2XL19OjI2NyfXr17ltJ0+eJAMHDiSrVq2S6pj4wKIWasGCBWTWrFnEwcGB6Orqkvz8fJKYmEjGjh1L7OzseM2vJlCRc3R0dLh16bJkZmZSr/v36NGDpKenf/N5sIDFsbDg0aNHZPTo0aRLly7chcnT05P079+fpKWlUY0xcOBArrBR0k9V8uzZM+ofWjp37kwePXrEa166urrk7t27hBBCxo4dywWloaGhZOrUqVRjjBs3jpw9e5bXPAgpDcK1tbW5m1DHjh3JihUryJcvX6j2HzhwILl8+TIhRLS+5MqVK6RXr17U83j//j2JiooiHh4eZPny5WTnzp1S1WeYmZkRLS0toq2tTUaMGEFMTU2Jvr4+6dChg1ghaVWTnZ0tUmezYsUKsn37dqkD9aSkJOLk5EQmTJhAsrOzyc6dO0Vu9pXBotC5LLm5uTLVlRkaGpJbt26Jbb9x4wbp3bu31OPJCotaqNevX5P58+eTkSNHklOnThFCCPHx8SHm5ubkxYsXvOZXs/Qj57Aw4Rs5ciS2b98OT09PmVQcWc2DBSyOhQUqKipiVvDz5s2Di4sL9bxY6EHISlW42uro6ODff//FtGnTZJ4XC+NKa2treHp64smTJ1BVVRVLn5et1agIe3t7zJkzB6mpqSCEQFVVVaqlpJycHIkttMrKytQuzkBpSp2PI3fPnj3Rs2dPmfdnxdixY0WMHtXU1GTSCWHhBi2rO3Z5KCkpybQfC6dvFrCohWratCkCAwNFttnb2zNZvqoJVOQcFoaCeXl5OHLkCM6dO4dWrVqJnTg0wkSsjA35wuJYWCHJUPDt27fUhoKjR4+u6imWS1W42jZs2BBr1qzB1q1b0aZNG7H6G5rPhoVx5YIFCwAAK1euFPudNIFXQUEBYmNjkZycjFq1aqFjx44YPHgwdV2Rmpoarl69KhZkHD16FO3atSt3P9aiYjS+NdUBC6NHgI0btKzu2Kxh4fTNAha1UAAbQT9J1AQqcg4LQ0EAGDZsmFzMgwV8j4UFrAwFz58/jx07diA9PR179uzBgQMH0Lp1a4wcOZL1lEWoClfb+vXr8w5aWRhX0naxVURaWhqmTJmCwsJCtG3bFl++fEFUVBQ2b96M8PBwKt8iGxsb2NnZITU1FV++fMHBgweRkZGBEydOYN26deXux1pUDCjfgE8gEGDevHnU4/CBldEjCzdoVkETX+TFDJCFwSIrQT9J1Hj9fCfwMRT8EefxrWFhKHj58mXMnj0bJiYmiI2NxdGjR7Fv3z4EBwfD29u7SjNVVeFqywpWxpVFRUV4+vQpWrduDUKIVJ0l06ZNg6KiInx9ffHzzz8DKH1aXLRoERo0aCDSoVYRFy5cQFBQECdd3759e8yYMQN//fUX1f7Ozs74+eefxVqIAwICkJaWJpZqlwQrAz6+sDJ67NGjB7Zt24ZOnTpBT08Phw8fRqtWrXD9+nXY2dnhypUrlY4RHByMCxcuyIU7tryYAfI1WBw6dCh0dHRgbW0NMzMzhISE4MWLF/Dw8IC9vT2vh6+ajMp3QoMGDbiIWxaEPfIZGRlwcXGRukee1TxYwOpY+JCamgpvb29oaGhAU1MT9evXh4WFBerXr48dO3ZQBSqBgYFYtGgRpk6dytm5C1tqd+zYUaWByu+//86lmss+wfOFReq37Hs3fPhwDB8+XKo5EELg5+eHf/75B8XFxVwGo169enB3d6cKWO7cuYOoqCguSAFK1+AdHR1hbm5OPZc+ffqgT58+Us2/LMePH8fBgwfFto8aNYr6/Dh69Cjc3d2/uQEfrb5QZRgbGyMgIEAkK5WWloaVK1eiX79+VGMEBATgy5cviIuL4xU0sZCM/+OPP7Bo0SLq11cVfGuhnjx5gsDAQKipqaFDhw7Izc1F//798fnzZ2zdurUmUKmhYr7ukbe1tZXZL4QPLITn5OVYWBgK/vfff1izZo3Y9sGDB1M/scuKj4+PxH/zgVXql69x5T///INDhw5h+fLlnDibsbExPDw80Lx5c9jZ2VU6RvPmzZGdnY327duLbC8oKKhQ4JC11w8LUTFZDfhYc+PGDVhZWaFevXoi2wsKCrB+/XpqsUNHR0dYW1uje/fuKCkpgampKecG7eDgQDUGi6ApLi4O8+bNw/r166GmpoZx48ahVq1a+PDhA3x9fTFkyBDef6MqYV0LxUrQTxI1gcr/AKtWrYKxsTG8vLy4SN/f3x+Ojo7w9fXFP//8Uy3zYCE8Jy/HwsJQsFGjRnjx4oVYZ0hqaqrIk3x1wCITsnXrVpiYmEhM/drY2FCNwcK4cs+ePXBzc8PAgQOxYsUKAKVp6dq1a8PHx4cqUHFwcICHhwecnJxgYGAARUVF3L9/Hx4eHrC0tERWVhb32rLLBF8v0QgDtrp160JRUREFBQVQUFCAkpISVaDCQlRMVgM+FqSlpXF1XJs2bYKGhobYuZ2cnIyoqCiuNqkyWLhBs3DH9vf3x4ABA9CpUydERUWhYcOGOHXqFPbv34+goCC5D1RY10KxEvSTCK/m5hq+C1j0yLOAhVibvBwLC0PBNWvWkNGjR5OkpCSip6dHkpKSyPnz54mRkVG1erAcOnSIdOrUidOQKKvtMmDAAOpxWHi5sDCu1NbW5gTfyp4jmZmZREtLi2qMr3U1hD9l/1+Z5kZMTAwZNWoU5+VESKkPzPjx48nff/9NNQ9C+IuKsTDgk5WzZ8+KvWeSflxcXKQeOyMjg5w4cYKcOnWKM8OUhl27dhEjIyOiqalJMjMziZubG7WYHyGEM6skhJBp06aRZcuWEUJKfaY6deok9Xy+JSwMFqtS0K8mo/I/AIseeRbUr19fKp8TScjLsRgbG2Pv3r1QUFCAsrIytm/fjtDQUAwYMIBrj60MW1tbZGdnc0/Wo0ePBiEE/fr1g62tbdVN/itYZEIANqnfnJwciXUGAwcOpCocBUqfFO/fvy9WeHvhwgUxHaDyYFFA7Ovri/Xr14u0wLZp0wZLly7FnDlzYGFhQTXOrFmzMGvWLLx58wYCgYDaW0vIkiVL0LhxY4wZM6baO1369euHf//9FyUlJdx3pmnTptzvBQIB6tevL9UxFRQUwN7eHhcvXuSWjwUCAYYOHQofHx+q7EhMTAz8/PwwZcoUbN++HUBpO7mvry/q1q2L6dOnVzpGlUrGVzMsaqG6dOmCEydOoKioCEpKSoiIiMDu3buhrKxMfa6XR02gIuewqOtg0SPPYh4sxNpY9fuzoGPHjty/ZTEUrF27Nvz8/LBgwQKu0l5dXR3t2rUTe3+rElZFcCxSvyyMK62srODh4YGXL1+CEIKrV69iz549+Oeff6gN+FiYQ757906i5kpJSQk+fvwo9XiyioqxMODjg3Bp7MyZM/jtt9946/esXLkSGRkZ2LZtG/T09LjlsBUrVsDf35/qMw4JCYGrqytGjx7NtQFbWlqifv36CA4OpgpUunXrhrVr1+Lnn39GrVq10Lt3byQlJcHLy0uqmqBnz57h7t27EnWCqkujikUtFAD8+uuv3L/btWsnk6CfJGoCFTmHRV0Hix55FvNgIdbG4lhkxdnZmfq1NAWqAwYMwP79+6GiogIVFRVue05ODkaMGIHr16/LNE9pYVUEZ2NjA2trazRp0gSjR4/Gpk2bYGJigufPn2Po0KFUY7AwrhwzZgw+f/6MLVu24OPHj3Bzc0PTpk1ha2sLMzMzqnnk5uYiODgYKSkpEm8gNOeq0Gl5zZo1XHYnLS0NHh4e1N0pLGBhwMcCVt1lLNygWbhjL1++HMuXL8d///2HtWvXomHDhjh06BDq1KlDfa1g4fTNAllroVgX5JZHTaAi51y7dg3bt2+ncicuj19//RXR0dEiPfJmZmZS9cizmAfAX6yNxbHIytOnT7l/E0Jw8+ZNNG/eHH/++ScUFRXx8OFD5OTkYMCAAeWOERsbi4sXLwIofZLy9PQUe+p+9uwZc9XYimBVBPd16nfXrl2IjIyUKvW7d+9eNG/eHA8fPsTDhw+57b/88gsuXbrE/V8gEFSouDphwgRMmDABubm5IIRIveTo4OCA+/fvo2fPnjJrBbm7u8PKygoDBw5E48aNQQhBfn4+tLW1Obft6mDGjBlwcXHhNEMUFUUv+7SWAvICCzdoFu7YLCTjWTh9s2Du3LlQUFDAzp07sWnTJgClVg8ODg4VtuNXhTihJGoE3+Scnj17IiIiAm3btq2Zhxzh6+uL58+fi6yJf/nyBW5ubhAIBPDy8pK43/Pnz+Ho6AhCCOLi4qCrqysSDAjX7M3MzKikwFlw8+ZNWFtbY8GCBRg9ejQGDx6M5s2bc5mQ8o6lPN6/f4+MjAwoKCigbdu21JLz8oSenh6CgoJ4LwF9+fIFV65cQUpKCgQCATQ0NNC9e/dqDUQrkomXRjNEXti8eTMuX76M9evXo3nz5gBK61YcHR2hqalJZRkQHByMPXv2wNnZGYsXL0ZAQACysrIQEBCAKVOmiC09lgffbrlOnTrh2LFjUgkZVjWy1kKxECcsj5pARc5ZvXo13r17981N+FjNQx7E2ljQtWtX7N69G2pqaiLb09PTMXbsWNy6davSMSwsLLBx48Zqb0WWRE5ODoqKitCqVSukpaWJZEJonxCLi4vh7e2N/fv3c3VM9erVg6WlJVVLsDwxePBg+Pn5idQhfSsIIbh48SKSk5OhqKiIdu3aoUePHtTfw2fPnlX4e5aCf9WBubk57t+/j1q1aqFNmzZQVFTEo0ePUFhYKFYDU1HtnL+/P8LDw/Hp0ycApXozEydOhIuLC1Wbc2W6QadPn650jPHjx2Pu3LnVuhRYVejp6eHgwYNidS6PHj3CqFGjcOfOHZnHrln6kXPkxYSPxTzkRayNBbVr10ZWVpZYoJKWlkbdWSHUfMnKykJaWhq6du2KwsJC3p1R0sLK1dbf3x/Hjx+Hi4uLSJFjYGAg6tWrh9mzZ7OeepWxZMkSeHh4wM7ODq1atRK7cVWXxHpeXh6srKzw4MEDNGrUCIQQFBQUoGPHjggNDZXovPs18hSICB9U0tPT4erqKtODCis3aL7u2Cy65Vg5fcsDrApyJVETqHwHyIMJH8B/HvIi1saCYcOGwdXVFba2tiLFZ4GBgdQS68XFxXBwcMCxY8dQq1YtnDhxAqtXr0ZhYSECAwOrbc2alUHboUOH4O3tDSMjI26bpqYmWrRoAW9v7+8qUCGEIC0tTaz7g0gpsc6X1atX4+PHj4iOjuYCyYcPH2LJkiXw8/ODh4eHxP0GDBiAffv2QUlJCf37969wqam6vH6+flCxs7OT6UGFlRs0X3dsFt1yrJy+5QEW4oTlUROoyDms5M35wmIewpO27EVTUVERc+fOxfjx46nG8PLygqWlpZiaa3WzePFifPz4EcuXL+fcpH/66SdMnjyZ2o128+bNePjwIcLDw7mbuIWFBZydneHr6wt3d/cqPIL/g5Wr7adPnyR+Lu3atcPbt2+ZzJUPwtoBmjZnb29vdO/eHePHjxeTfK9Ozp49iw0bNojUmWhoaGDp0qWwt7cvN1AZPXo09zmOHj26WmtiyoPlgwpfN2gW7tgsuuWqK0isDmQtyKWhJlD5DuBb11Feq5xAIEDt2rXRsmVLDB48uNJCWb7zYCHWdvDgQUydOpXqtVVJnTp14OnpCUdHR2RkZEAgEKBt27ZSZSaEZnFlNRe6deuGlStXwsHBodoCFVYGbaNGjcL69evh6+vLXcAJIQgPD8fo0aOrZO6SSEhIwLJly5CcnCzxfKM5ntzcXDg5OVELxNGSm5srInhWGZ8/f+YKRsvSvHlzFBQUlLtf2ayDNKJ9VQmLBxWgcjdomkDFy8sLmpqaEt2xvby8qLy2WHTLCZfl+Dh9s4JvLRTAX5ywPGoCFTmHRV1HcXExjh49iubNm0NbWxsA8ODBA+Tk5EBHRwfXr1/H1q1bERISUu7TJot5sBBr69u3L3bu3AkbGxs0aNCAah9WxMXFQU9PD4qKioiLixP7/YMHD7h/06wt5+TkSMxAKCsrV2sGgpWrbV5eHs6ePYv+/ftDW1sbioqKSExMxLNnz6CjoyOiuVBeTRMLYUEfHx8oKChg6dKl8PHxgZOTEzIzMxERESHRBFIS3bp1w+3bt3kFKu/evcPatWsxefJktGvXDtbW1rh27RratGmDbdu2UY3dsWNHREZGivngREZGQlNTU+a5fQtYqUqzcINm4Y7NQjeIMHD6ZoGstVDR0dEYOnQo6tSpU6khJx9NmJpARc5hkS6tW7cuBg0ahLVr13JPusXFxXBxcUGjRo3g5uYGX19fBAQElDsei3mwEGt7+fIlYmNjER4eLvGJqipTqRYWFrh8+TKaNWsGCwsLrtL/a2izEGpqarh69aqYxsDRo0fRrl07ZvOuDFautnXq1BGrY+ratatUBYEshAUTExMRHh4ObW1tHDhwAOrq6jA3N0fLli0RFRVFZRbXpUsXLF++HOfOnZOoPUJTJ+Hj44ObN29i6tSpOHXqFG7evIk1a9YgNjYWa9asoWrXtLW1haWlJe7cucN97+Lj4/Hw4UNO+v17gZWqNAs3aFndscvCQjKehdM3C2SthXJyckLv3r3RrFmzCoX2+IrX1bQnyzndunVDREQE2rVrBz09PRw+fJhrIR0/fjzi4+MrHaNLly7YvXu32M0vNTUVZmZmiIuLw6NHj2BqalpuWy2LeQDAhw8fRMTa2rdvL5VYW2UpWVaFdpJ49uwZlJWVUatWLSYtn2fPnoWdnR0mTJiAyMhIWFtbIyMjg3uq+uuvv1hNXYyyrraWlpYIDAyU6Gq7Zs0a3L17t8rm8TW6urq8hQV1dHRw/PhxKCsrY/HixdDX14e5uTmePHmC8ePH4+rVq5WO0b9//3J/JxAIqALinj17YtOmTdDT04OTkxPy8vKwdetWJCcnY9KkSRKzcpK4d+8eQkJCkJKSAkIIOnTogGnTpnHZ0e+FnJwcWFpaIj8/H3l5eVBVVeUeVHbu3EndnbRp0yZkZmbycoM+deoUVq9eLdEde/z48Rg0aBD32qrs8DIxMYGtrS0GDhwocl09deoUfHx88O+//1bZ3y5L9+7dsWHDBrGHkuvXr8Pe3h6XL1+ulnmUR01GRc5hkS5VVFTEq1evxAKVly9fcuvFX758EXtqZD0PoNTIi49KYVUGIpVR9kLKouXTyMgIGzZsQFBQEBQUFLBjxw60b9++yoMUoLRjYfbs2dznX5EcfXXCwrhSRUUF8fHxGDZsGFRVVXH//n0AQH5+vkQ5fEmwuEG8f/8eysrKAIDLly9jxowZAEoznJIk08tDW1sbAQEBvOfzrWGlKj1kyBCYmZmhc+fOaNGihVihME0QKazbmT9/vlhd1urVq7F69WqJNVqsu6mePn0qcQlPQ0MDL1++rHR/VshaC1Vd1AQqcg6LdOlff/0FNzc3uLu7Q0dHB4QQ3LlzBytWrMCAAQPw/v17bNmyBZ06darSebBC2CmTkZGB9evX4/Tp02jfvj0TI7mKYO1rsWPHDgwbNgwRERF8piUTVeFqywIWxpUWFhZcTcdff/2FkSNHom7durh16xZ0dXXL3S8rKwvKysoQCATIysqq8G/QPGWrqanh3LlzUFZWxsuXL9GnTx8Apf4uX+vvlIWmkFMIbeD++fNnvH79mguQyiqojhgxgvrv8YXvgwrAxg1aVv0p1t1ULJy+WSBrLVRlwVpZ+CzL1yz9yDks0qUfP36Eg4MDTp48KXJSDRkyBJ6enrh27Ro8PT2xbdu2cuW2WaVt+ZKQkAAzMzPo6uri9u3bOHbsGIKCghAdHY1NmzZVqex82e6pynwtaDp2unTpwpkSfkuePXvGxNWWBc7Ozjhy5AgaN27MS+Dw9OnTaNKkCbp06YKYmBgEBwdDWVkZy5YtK1euXFNTE5cuXUKzZs2goaEh8f2QphPq/PnzsLGxQXFxMUxMTODr6wsfHx9ERERUeK5+vez0/Plz1K5dG61atYKioiIyMzNRXFwMLS0t7N69u9J5XLp0CY6OjtxSX1nq1q2L27dvVzqGrFSFaZ22tvY3dYMuCyEEeXl5nLP1vXv30LFjR+oge//+/Vi7di3mzp0Lf39/uLi4IDMzk3P6pjXR5Mvt27dhaWkJDQ0NibVQ5T2MBgYGct+TvLw87Nq1C0ZGRlzTwf3793Hy5ElMnz6dV71NTaDyHcC3rkPIkydPkJSUBAUFBXTo0IG7YBcVFVGt9bKaBx+mTp0KHR0d2NnZiazp+vj4ID4+Hvv27auWebDwtbCyskKvXr0wbdq0qprmd0dlrrM0ej6yau3cuHED+vr6UFRUxI0bNyp8LW327s2bN8jJyeEeAO7du4cGDRpUmFEpS1hYGM6dOwc/Pz9uSezdu3dwcHCAuro67O3tKx3D1NSUKwBfuHAhfH19kZWVhQ0bNsDHxwfGxsZUc5EF1sE9UJrJcHV15VXHxMIdOzMzEzNmzMCAAQPg4OAAoLTWo3nz5lxgTMOePXuwZcsWZGdnAyjtPpoxY0a1Xxf41kLNmTMH+vr63BKnkH/++QenT59GeHi4zHOrCVT+h3j16pXEls/qkgNnIdbWpUsX7N27F23bthUJVDIzMzFy5MgqfTosCwtfi4ULF+LkyZNo3Lgx2rRpI9bBVF32CCxhocXAl86dO+PQoUNyY/SWlpaG5ORk1K5dG6qqqlLLxYeEhIhlOpOTk2FhYYHr169XOoa2tjaioqKgoaEBc3Nz2NjYoEePHti/fz/27duHyMhIqY9JFliZ1sXGxiIgIICXG7S1tXWF7tg0AfHs2bNRUlICLy8vznE5NzcXjo6OqFevHjZs2EB1PEJkdfqWF3R1dXHo0CGxDPHjx48xYsQIXkX5NTUq/wPcunULzs7OyMzMFNle3XLgLMTaateuLbG46/nz59WqIMrC16J+/fq8WvbkDRa+NAB/YUGh1s78+fOrNdv3NZ8+fcKiRYtw5swZ7uFAIBDAyMgIAQEBVFnM4uJivH//Xmz769evqZfqFBQUuFZvFRUVJCcno0ePHujevTtWr14txRHx4/jx4zh48KDY9lGjRkn1PRBmkZYvXy72O9rrWXx8PG937Js3byIqKooLUoDSbIiDgwMmTZok9XjSCAGygHUt1C+//IKrV6+KBSqnT5/mXRpQE6j8D+Dl5YUWLVrAwcFBZm0KFrAQazM2NkZAQADWrVvHbUtLS8PKlSur1YGUha+FoaEhevfuLRfuySyQVYuhLCyEBb+l1k5Z1q1bh3v37mHjxo0wMDBASUkJ4uLi4OXlhcDAQCxatKjSMfr3749ly5bBzc0NWlpaIIQgPj4eK1asgImJCdU82rdvj3///RcWFhZQVVVFfHw8pkyZwi01VBesTOtYfH6//vorb8FIRUVFvHv3Tmz7hw8fJOoryRsHDhwQ+X9FtVA0gYqVlRVWrFiB27dvo1OnTtw18dSpU/D19eU115qln/8BOnXqhOjoaOp18arCwsKCk2mX9QZSUFAAa2tr3Lt3DyUlJWjUqBEKCgqgoaGB0NDQau1SCQoKws6dO7k2QmVlZcyYMYNa2dLAwAC7du2qVnG38mDhastCi2HOnDlo2rQpJyx4+PBhKCsrw9HRES9evKASFvyWWjtl6dWrF1asWCFi0giU6ud4eHjg3LlzlY5RUFCAhQsX4vLly1wGhRCCwYMHY82aNVRZmdOnT2PBggVwc3ND37598ddff6F79+7477//oKOjI/UShaxs3rwZISEhmDJlisTgXpYshKycOXMGQUFBvNyxHRwckJ6eDn9/f245+8mTJ3BwcICysjL8/f2rZO5VAYtaKAA4cuQIdu7cif/++w8CgQCampqYOXMm7yaHmkDlf4BBgwbB19eXl0AUi/oSFjeQwsJCNGjQAFevXkViYiJKSkqgrq6O3r17i11sqgtZfS3Gjx+PqVOnUsttVxVfZzGOHTuGtWvX4uLFi1K52nbp0gVRUVFiwU1aWhpMTU2p1qhZCQvKA3p6eoiOjhZLhT969AgjRozAvXv3qMdKT0/nXH41NTWlbl198OABFBQUoKGhgRs3biA0NBTKyspYsGDBdxHcs9YvOX36NBwdHcWW1aRZDn/9+jWmTZuGlJQUblnz3bt36NixI7Zs2YIWLVpUOoa8wKIWqiqpWfqRc1gYCs6ZMwfe3t7w8PCAqqqqTP4RLOpLWDzJjho1CgEBAejRowd69OjBezw+5ObmIiMjgxPCK6tNMWfOnEr319DQwOLFi7F9+3aJxbTV5ZzNytWWhS8NK2FBvlo75QXVZb93ffr0qfAmr66ujuPHj2PWrFki248dO1apAejXfF2EK40T9Pz582FnZ8dlVA0MDKpcc6g8ZDWtY61fwsIdu1mzZjh48CCuXLmClJQUkeJxvvOT5vNlAYtaKKD0e1fWDLTsNdHLy0vm+dUEKnIOC0PBLVu2ICsrq9yCNZqnB1ZmgHxvIB8+fJBYpV/dHD58GEuXLkVRURHn+SP8Qv/+++9UgUpGRgb3mVWnCuXXsHK1ZeFLw0JYsKzWTkJCAoqKipCUlAQfHx9qrZ24uDjExcWhdu3aXFDx+PFjfPz4EcrKysjLy8NPP/2Ev//+W8wvRsicOXMwd+5cJCUlibwfp06dgp+fH/Wx8HWCvnbtmlgQ/K0R6o7QwtoNmpU7toKCAnr37o3evXvLtD+Lz5cFLGqhQkNDueLssj5oAoGAVys5UBOoyD0sDAVpbpqVwaJAkcUNxNLSEjY2Npg0aRJat24tFrRIY4DHh61bt8LExATW1tYwMzPDjh078PLlS3h4eFBfSGkzFVUNqyyGnp4eIiIiEBISgkuXLnFaDG5ubtTLjiyMK319fTmBKT09PQClS5cNGjRAYGAg1Xmmra2NkpISBAQEcGv2b968weLFi6Grq4vZs2dz372goCCJY/Tr1w/r169HcHAwzp07x70fAQEBIl4yFcHCCXr06NHw9fXFvHnzoKKiIrM/zo8EC3dseXH6ZsGyZcuwcOFCTJkyRawWytHRkWqMiIgIzJgxA/Pnz4eRkREOHjyIvLw8LFq0CAMGDOA1v5oaFTmHhaEgC1jUl7AQaytPORegb01kQdkCZQsLC8yYMQN9+vTByZMnsXXrVrGK+vKQB2lzOzs71KpVC2vXrkXnzp1x+PBhNGrUCDY2NlBSUqq2YkshfIUFWWjt9OjRAyEhIWJLVg8fPsS0adNw9epVke9gVaGnp8c5QY8bNw6Ojo7o0qULwsLCcP78eYSGhlY6xqBBg5CZmVluCr+6vjPyRHBwMDZv3gwjIyOZ3bFnz55dodM3zRgsPl+W8KmF0tLSQmxsLFq3bo3p06fD3NwcxsbGuHTpElatWoUjR47IPK+ajIqcI6uhoLOzM1xdXdGwYcMK1T4FAgG8vb0rnQeL+pKEhASJ2geTJk1CVFQU1RinTp36ZkWzZalTpw73ZKqiooKUlBT06dMHWlpaePz4MdUYlUmbV1egwieLURW+NHz9YFho7Xz+/BnFxcVi2z99+oSPHz8CKD0Hvs5ERUdHU8+TRjukpKSEK8oUaqB06dIFAwYMKDeT8zUsMqo/GpGRkVBSUsKdO3fExBkFAgHVuXrt2jXeTt8sPl+W8KmFql+/PvfA1bp1a6SmpsLY2BhqamqVus1XRk2gIufIaij49OlT7iL69OlTJnPhW1/C4gZiZ2cHLy+vCjMr1YGWlhb27t0Le3t7qKur4/z587CyskJqaip1sbK/vz/+/PPPcqXNqws+rrastRhYwEJrp1evXvDw8IC/vz/XtZORkQEvLy/06tULX758QWRkJDp06CCy39eqq8K1+rp160JRUREFBQVQUFCAkpISVaDCwgl69OjRVK+rDuRBuRhg444tL07fLGBRK6Ovr49t27bBzc0Nf/75J/bt24eZM2ciPj6et2YNSA1yzYcPH4iNjQ3p0KED0dDQ4H7s7OxIfn4+OXXqFOnduzdJSkqq0nncv3+faGlpkcmTJ5OOHTuSzMxM4urqSjp27EjOnTtHNcbSpUvJ9OnTydu3b4muri7JzMwkqampZPjw4cTZ2ZlqDAMDA/L48WM+h8KEuLg4oqOjQ3bs2EFyc3OJgYEBGTp0KNHT0yOurq5UY3Tq1In73MzMzMiVK1cIIYTs27ePTJw4scrmXlWEhoaSKVOmkFevXnHb3r59S2bNmkX8/PyqbR75+flkwoQJRFNTk3To0IF06dKFaGhokFGjRpE3b95QjfH69WsyduxYoqGhQQwMDEjXrl2JhoYGmTBhAnnx4gU5e/Ys0dXVJdevXy93jJiYGDJq1CiR72ZGRgYZP348+fvvv6nmERUVRbS1tUlMTAxJTU0lHTt2JO7u7mTEiBFk+vTpVGMQQsi5c+eIhYUFMTQ0JE+fPiUbNmwg0dHR1Puz4M2bN8TU1JT7TDp37kw6dOhATE1Nydu3b6Uaq7i4mGRnZ5Nnz56RZ8+ekadPn5L09HRy6NChcvd59uwZKSkp4f5d0Q8Nq1atIi4uLuTz589Szb0srD5fvpibm5MxY8aQXbt2kU6dOpGIiAji4+NDtLS0SGxsLNUY//33HzE0NCTBwcGkoKCAGBkZkc6dO5M///yT9/e/pkblO4GFoWBycrJY0RdtRTaL+hIWYm3BwcG4cOEC5/PxdTFtdfkWAaWO0kVFRZzOR2RkJJSVlWFhYUH1mejp6eHIkSP4/fff4ezsDA0NDUyZMgXPnj3D6NGjKzXG40NVuNrKixYDK60dQgiuX7/Ofe80NDS47OGbN2+gqKhYodKzsJj2ax0aYfv6pUuXqOZRnhO0m5sblTT55cuXMXv2bJiYmCA2NhZHjx7FV267GQAApu1JREFUvn37EBwcDG9v72qzcXB2dsa9e/fg5+cnplysr69PpVwMyO4Gzdod+1s6fbOGVa3Mx48f8f79ezRt2hSvXr3CkSNHOAkNPtQs/Xwn1KtXD506deKCjKysLAB0N+YzZ87AyckJBQUFYpXptF9KFvUlAoEAu3fv5nUDCQgIwJcvXziFWyHSXGBY8euvv3L/VlNTw9KlS6Xa/1tKm5e9wVXmaksLCy0GFsKCrLR2BAIBunfvLrEtmqa99t27dxLbgktKSrg6FxrKuhsPHz4cw4cPR3FxMe7cuUMVqAjl+qdOnYoTJ04AKF1CbdiwIXbs2FFtgcrZs2exYcMGkUBWQ0MDS5cuhb29PXWgIuuSaXh4OGdXwcrwc9iwYbz2//p8F36+1Q2rWpm6detyD4/Nmzfnrb0lpCZQkXNYGAr6+vqiR48emDt3rsxePyzqS1jcQKq7Cr4qmTlzJhYsWIDatWtj2LBhCAwMxMyZM/Hff/9Ra4bIStkLurOzM6ZOnVquqy0tLLQYWAgLstDaSU9Ph6enJ9d6+jU037tu3brB09MTa9as4Z6M09LS4OHhQV0ro6mpCRsbG8ydO1dk+9u3b2FpaUk1j//++09iq+vgwYOlKobmy+fPn9G8eXOx7c2bN5d4bSmP1NRUeHt7Q0NDA5qamqhfvz4sLCxQv3597NixQySwK0vZWjoWgncs6shYnO8skJdamfKoCVTkHBaGgk+fPkVQUBCvp1QWBYosbiBlLzC5ublQVFSkduWVN4yNjbF3714oKChAWVkZ27dvR2hoKAYMGIAFCxZU2zxYudqy0GJgISzIQmtn+fLleP36NRYvXizz987d3R1WVlYYOHAgGjduDEII8vPzoa2tjWXLllGNQQhBcHAw0tPT4e3tLbK8QLtq36hRI7x48ULs+5+amlqthpgslIsB+XGDBn4cp28LCwvuc/nrr78wcuRI1K1bF7du3YKuru43m5eQmkBFzklJSeFtKNimTRtkZ2fzClQcHR1hbW2N7t27o6SkBKamplx9iYODA9UYrMTa/v77b2zbtg2vX78GUPpEZmVlJRdPJtLSoEED7mnSwMAASUlJ6Nu3b7X6r7BytRUuJfDRYmAhLCg0g1uxYoXY72izkHfv3kVkZCQ6duxIOXNxfv31Vxw6dIiTWBcIBNDQ0ED37t2pl8IEAgGCg4Ph4uICS0tLbN68GU2bNuV+R8Pw4cPh7e0Nb29vCAQCFBYW4sKFC1ixYkW1+kyxUC4G5McN+kdy+h43bhyUlJTQpEkTqKmpwcfHR6QW6ltTU0wr57AwFDx//jx8fX1hZ2cHVVVVsaIvmjoXFgWKLMTadu/ejZUrV8Lc3Bxdu3YFIQRxcXHYvXs33NzcMHbsWKq5yANXrlzBnDlzMHXqVNjZ2QEAJkyYgOTkZAQHB/OWnaalql1tpdFiYCEs+OTJkwrPSZq6jr59+yI4OBjq6uqVvlYWsrOz0bJly0pfp6GhgcuXL6NWrVqYN28esrOzERQUhGbNmsHQ0JDqO1NcXAwnJyccPXoUwP+1TPfr1w8BAQHVaklx7949hISEICUlhVPqnTZtmlTXN3lxg/6RnL7LQ1gLVV2K3+VRE6jIOQcPHsSePXt4GQp27NiRE+KRtQB14MCBCAgI4PWEyeIGMnjwYEyePBmTJ08W2R4REYHdu3cjJiZG5vlJy71797Bq1SrUqlULDg4OUgeTY8eOhaGhIRekCPH398eNGzewe/dultOtEFldbcsiL74lY8eO5a21ExwcjPj4ePj6+sqckn/y5AlWr16N5ORkMeXh3NxcJCYmVjpG2U6VoqIiuLi44OzZs/Dy8oK9vb1U7+njx485nRx1dXW0a9dOxKPqe0Ie3KB/JKfv8mqhXr16hd69e39z9eKapR85h4WhIIsCVBb1JSzE2rKystCnTx+x7b1796729Wk7OztkZWWBEAJ7e3ucPn1aqv1TU1NFan6EjBs3rtp9gGR1tS0LK98SvsKCT548Qf369aWef1muXLmCmzdvwsDAAM2aNRPLQtKk5D09PfHo0SMMHjwYoaGhmD59OjIyMnDq1Cl4enpSzaPsc2SdOnXg6+uLjRs3YvHixdTHMmDAAOzfvx8qKiqceB1Q2l4/YsSIKm0brwrlYhZu0CzcseXF6ZsFLGqhqpKaQEXOYSF/zeKEZ1FfwuIG8ttvvyEhIUGs3ub+/fsSOwqqEkII9yWW5cvctGlTPHz4UKyGIyUlReYCTr5I62pblsTERE6L4cCBA1BXV4e5uTlatmyJqKgoDBkypNIxWBhXWltbw9XVlZfWTufOnamWqiri1q1b2Lx5M7p164aLFy/C2NgY2traWLduHc6fP0/lTD1//nyx78z8+fOhoqJSoSxAbGwsLl68CAB49uwZPD09xeofnj17VuXZlKpQLmbhBs3CHVtenL5ZwKIWqiqpWfr5H+D9+/cICwsr1+WTRlOARX0JC7G2sLAwbNmyBQsXLhQpyNuwYQMsLS0xb968Ssdgxe3bt7ksjoODAzcfWtavX4+oqCjY2tpyhXf3799HQEAARo0ahUWLFjGfc1Wio6OD48ePQ1lZGYsXL4a+vj7Mzc3x5MkTjB8/HlevXq10DBbCgiyWOlnQqVMnnDx5EsrKyrCzs0PPnj0xbtw4ZGRkwMLCglrwTRaeP38OR0dHroZLV1dXZNlYIBCgfv36MDMzq7abYVhYGM6dOwc/Pz+uSPvdu3dwcHCAuro67O3tqcZZuXIlXr58ycsN2s/PD3fu3KnUHTs3N7dcHZGcnBxYWloiPz8feXl5UFVV5Tyydu7cSbWUzeJ8ZwGLWqiqpCajIoewNhR0c3PDmTNnYGhoyIn6SAsLM0AWYm2WlpZ49uwZvL298eXLFxBCoKioiIkTJ1a7+Zqenh6vOpJ58+bhzZs38PT0xOfPn7ljEQpZfW+w0GJgISwo61Lnxo0bYWVlhXr16lW4ZCEQCKgC4t9//x3JyclQVlZG27ZtufO7pKQEhYWF5e7Xv39/qqdYgUBQ7nKjsrIy9wBiYWGBjRs3VmsrsiS2bduGkJAQkU6yxo0bw97eHhYWFtSByvnz55GZmcmJ130NzXVk3759YnNRUlLCkiVLMG3aNNjY2MDKygpmZmbljsHHI0sIi/OdBcLzTUlJCWFhYXBxccHEiRPh5eVVbXOoiJpARQ5hbSh49uxZ+Pv7w8jISOYxWNSXsKiVqVWrFlxdXbFw4UKkp6cDKFWF5W169Q1QVFSEu7s7lixZgoyMDCgqKqJNmzbV2oXBEhZaDCyEBWVd6jxw4AAmTZqEevXqiS1ZlIU2UBk9ejQcHBywZs0a9OvXD5aWlvjtt99w+fJlMTPDr/djmW4X1jtlZWUhLS0NXbt2RWFhIW9DPWlhoVwMsFkOl9Ud+2vkwembBSxqoaqSmkBFDilbSMmiqLJWrVq8dFgANvUlLMTaPn78CA8PD7Rp0wazZs0CUPoEamhoiGXLlsmUBv7WNGjQAFpaWt90DoSBqy0LLQYWwoKfPn3Cnj17RLptgNI26YSEhHKfxMs66rJw1505cyZ++uknEEKgra2NuXPnYsuWLVBWVhapa/gaGxsb3n+7LMXFxXBwcMCxY8dQq1YtnDhxAqtXr0ZhYSECAwOrTWiMhXIxwMYNWlZ37LJkZmbC19cXKSkpEjOGNAXXLM53FshaC1Vd1NSofAfwNRR0dXVFixYtYGtrK/McWJkB8hVrc3d3x+XLl7Fy5Uou8Dl16hTWrl2LAQMGUCug1vB/5OXlwcrKCg8ePECjRo1ACEFBQQE6duyI0NBQ3sq/0mgxsDCuXLZsGaKjo/Hnn3/i/v370NPTw+PHj/H69WtMnTpVqnPk1atXEuu6aM53SbUhQGkgde7cOfz1119Uc3j48KFIy7ewxfn+/ftUqfn169fj+PHjcHd3x+zZs3H48GFkZWXB2dkZffr0gbu7O9U8+FJQUICFCxfi8uXLYsrFa9askeoh4/z585y44J49e3DgwAG0bt0aI0eOpNo/NzcXs2bNQkJCgohqsI6ODgIDA/HgwQPY2dkhKCio3AzdxIkT8fLlSwwZMkRiFpSmOJjF+f6/QE2gIuewMBT08/NDeHg42rVrh7Zt24pdEGg8K1gUKLIQa+vVqxcCAwOhp6cnsj0uLg6LFi3ChQsXKh2DBZmZmbyUfuUJVq62LLQYWAgL9uzZEy4uLhg2bBgGDhyIoKAgtGrVCnZ2dmjZsiWVeSQLjy1NTU1cvnyZ654QkpiYiIkTJ+LevXuVjhEaGsoVbAuF2oT/7tKlC1XGddCgQXB3d0fPnj1FCjavXr0KBwcHrjuouuCjXAywc4Pm646tq6uLiIgIXtpSrJy+ZYFFLVR1UbP0I+ewMBS8c+cO11Xy4sULmcZgUV8SFhYGR0dHEbG2gQMHQkVFBeHh4VSBSmFhocQn/KZNm+Lt27e850jLpEmTsGnTJl6KwfICK1dbFloMLIwr3717x3VgtWvXDomJiVBVVcWsWbNga2tLFajI6rEVFhbGBRaEEBgaGkp8He15ExERgRkzZmD+/PkwMjLCwYMHkZeXh0WLFmHAgAFUY+Tk5EgMqpWVlav1OyNEVVVVxAtHGuVigJ0bNF937DZt2uDDhw9Uf6s8WDl9ywLrWqiqpCZQkXNYGAqyqHNhUV/CQqxNV1cX27dvx8qVK7knDkIIwsPD0alTJ6nmw4fatWtDUfHH+PqwcrVlocXAQliwadOmeP36NX777Te0adMGycnJAEpvPq9evaIaQ1aPrcmTJ6NJkyYoKSmBi4sLnJ2dRQIdYVswrc5GdnY2xo0bh59++gkaGhq4f/8+jI2N4eTkhFWrVlEtmaqpqeHq1atiRZ9Hjx5Fu3btpDo+PrBSLmbhBs3CHdvNzQ0eHh6wsLBAq1atxDIgNEudLM53WWFdC1WV/BhX2h8YFoaCWVlZFf6+uupLWIi12dnZYcqUKbh+/TpXgPrgwQPk5eUhJCSEagwWjB49GtbW1hg5ciRUVFTELjY0T3UvX75EQEBAufo21WVIxsrVlhCCtm3bYs+ePZg3bx7Gjh3LaTHQwkJYsE+fPvDw8ICPjw86d+4Mb29vDBw4ELGxsVT+OkBptqGiFuLyUFRU5D57gUAAExMTXgXe9evX55ZcW7dujdTUVBgbG0NNTQ3Pnj2jGsPGxgZ2dnZITU3Fly9fcPDgQWRkZODEiRMSlZGrClbKxSzcoFm4YycnJyMtLU1ihk4auQUWRq0s4FsLVZXU1KjIOSwMBTU0NCp8oq2u+hJWYm1Pnz7Fnj17kJKSAkVFRaipqWHSpEn45ZdfqPZnAQsBvNmzZyMhIQEmJiYSL5bVZUh2+/ZtWFpaQkNDQ6KrLe3TPwtfGhbv67t37+Dk5ARDQ0OYm5tj5syZuHjxIhQVFbF69WqqDhMWHltAaa1LmzZt0LRpU0RHR+PYsWPQ19fHzJkzqbJMs2fPhpKSEtzc3BATE4N9+/Zh9+7diI2NxapVq6hF4y5cuICgoCCuDqJ9+/aYMWMGdUEvC/T09Djl4nHjxsHR0RFdunRBWFgYzp8/T728vHbtWly9ehXe3t4wNzfHrl278OLFC7i7u2PQoEFwcnKqdAxtbW3e7ti9evWCsbExJk+eLLGVmEbwjcX5zgIWtVBVSU2gIuewKGK9ceOGyP+/fPmCjIwMhIWFwcnJCf379690DBZmgCUlJfDx8UFkZKSYWJuLi0uVF4/JG8JlrOpySa4IFq62QnXLshmUjRs3YsuWLSgpKaE6V1kYV34NIQR37tzB77//Th3MDho0CFlZWSLtzWWhDe49PDwQEhICJSUljB49Gj169EBSUhImTZpEFYgmJydj+vTpmDp1KszMzDB8+HC8e/cOHz58gJWVFZVI2o4dOzBs2DD8+uuvlb62KmGhXAywcYNm4Y6tp6eHmJgY/PHHHzKPURXnuywYGxtjyJAhEmuhxo4dS501rzJIDXLN9evXK/zhw4ULF8i4ceOoXtupUyfy+PFjse2PHz8mnTp1kurv5ufnk7t375K7d++SgoICqfaVN549e0YuXLhAPnz4QF69eiXVvj169CDp6elVNLPqJzAwkLx//15s++HDh8nkyZOpxhgzZgxJSkriNY8PHz4QJycnsnXrVm6bkZERWbp0Kfn06RPVGAcOHKjwh4bBgweTnTt3EkII8fPzI8OHDyeElH7vjIyMpDqe169fE0IIefnyJQkJCSHHjh2j3r9z587k0aNH1K+vKoYPH05iYmIIIYRs2rSJODk5EUIIefDgAdHX15d6vEePHpFjx46Ro0ePkpSUFEIIISUlJVT7btu2jcyaNYvk5+dL/XeFLF68mISGhsq8PyFszncWdOzYkbu+T5s2jZw6dYoQQsjFixeJiYnJt5waIYSQmhoVOacqHTTbtGmDhw8fUr2WRX3JjyTWVlRUBEdHR14iWiNHjsT27dvh6ekplbAaC6rK1VYSw4cPx/Dhw6nGYCEsuGrVKty8eVNEGMzZ2Rlr167FunXrqHRUWIiKPX36lMtWXr58mSskV1NToyrqFRZaCn8A4O3btzAzM5OqAFNHRwf//vsvpk2bJsNRsIOFcjHAxg2ahTt2ixYt4Ofnh2PHjqF169ZixfU0sg8szncWsKiFqkpqAhU5h4WhoKRi2oKCAgQFBVGnLSdOnAgPDw/k5eVJrC+hgcUNRF7YsmULZ88+e/ZsAKUXYmdnZ/j6+lKJaOXl5eHIkSM4d+4cWrVqJXaxpPlsZYWVqy1rLQYWzsenT58W09oZOHAgmjRpgkWLFlGdZyUlJYiJiZH4vaP12GrWrBlevHgBRUVFJCUlcXLkDx8+rDS4P3LkCFauXIng4GAR1WJvb28kJCTAy8sLAwcOrHQOANCwYUOsWbMGW7duRZs2bcSch6vyPCsLH+Vi1m7QLNyx79+/zwVY2dnZMo3B4nxngb6+PrZt2wY3Nzf8+eef2LdvH2bOnIn4+Hi5sCepCVTkHBaGgpJuJoQQ1K9fv0Ip77KwMANkcQORF44ePQp3d3d069aN29atWzesXLkSDg4O1Gqfw4YNq6IZVkxZifjKXG0rgrUWAwvjShZaO97e3oiIiICGhobMEvMmJiZYvHgx6tWrh5YtW8LAwACxsbFYsWJFhcXn169fh4ODA0aNGiVWV+Li4oLt27fD1tYW//zzD5Vjd/369am1RaoaY2Nj7t/CTJtQubiiegyhAagwYMzKypLoBk0rc8CiUJ1FgSmL850F9vb2mD59OiIiImBmZoatW7fCwMCAq4X61tQU08o5nTt3hq+vLy9Dwa+LaYFSHRB1dXWpo+WCggKZzQD19PSwb98+MW2KtLQ0mJqa4u7du1TjvHjxAlFRUUhPT4erqyvi4uKgrq4uIiJV1ejo6ODo0aP4448/RNQ+Hz16hBEjRlCpjsoLPXv2REhIiFgHQnJyMiwsLKhS6ayQdK6WhWYpdNq0aWjZsqWY1s7y5cuRnp6OnTt3VjpGt27d4OTkxGsJqKSkBBEREXjy5AkmTZoEFRUV/PPPP8jNzYWNjU25RZRWVlZQU1ODi4tLuWM7Ozvj1atXCA4OrnQeR44cQe/evb+5ezIL5WJAdjdo1u7YQOly9vHjx5Geno7p06cjOTkZ7du3pxKMA9ic76z4+PEj3r9/j6ZNm+LVq1eIiYmBsrIyBg8eXG1zKI+ajIqcw8JQ8O+//4adnR2vcVjUl7AQa3v8+DHGjx+Phg0bIicnB3Z2doiNjYWzszPCwsI4Bd6qhpWIljDoysjIgIuLyzcJuli52gL8tRhYCAuy0NopKirirWFRq1YtWFhYiGz7+v+SSExMrDS7aG5uzi05Voanpyd27dr1zQMVwkC5GJDdDZq1O/arV68wYcIEvH79GkVFRRg3bhxCQkKQkJCA8PBwquttdQYi5cGqFqpK+RYVvDXQ4+LiQtatW8drjM6dO5MnT57wGmP58uXE2NhYpNPo5MmTZODAgWTVqlVUY9y9e5fo6uoSIyMjYmNjQ2xsbEj//v2Jvr4+uXPnDtUYs2fPJi4uLqSkpITo6uqSzMxMUlxcTOzt7ak7S1jw77//Eh0dHeLt7U06depE1q9fT2xtbYmmpiY5fvw41RiPHj0iBgYGpH///qRjx44kMzOT2NjYEF1dXer3gwUODg5k6NCh5Nq1a6SgoIDk5+eTc+fOkQEDBhBPT0/qcUJCQkiHDh1Ihw4diIaGhsi/pflswsPDiaGhIdHQ0CAaGhqkV69eUndXPHnyhPj6+pJZs2aRefPmEX9/f5KTk0O9v42NDdexw4dz584RCwsLYmhoSJ4+fUo2bNhAoqOjK9xHT09PYoddWTIzM4menh7VHMaNG0eOHj1KPeeqQkNDg8TFxZGBAweSCRMmiHQyaWhoUI9TVFREbG1tSYcOHYimpibJzMwk8+bNI1OnTuXVxSMtixYtIrNmzSLv37/nrkV5eXlk6tSpZNasWVRjfPz4kYSHhxNXV1fi5OTE/djb25NBgwZV8REQEhMTQ7p3707u378vsn369OnEwMCAnDx5ssrnQENNRkXOadq0KUJCQnDhwgWZDQVHjx4NX19fzJs3DyoqKjJ117CoL9HW1kZMTIyIWNuwYcOkEmu7desWIiIiRJ70FRUVMXfuXIwfP17q45IVIyMjbNiwAUFBQVBQUMCOHTvQvn17rFu3jlpEa9WqVTA2NoaXlxdXa+Dv7w9HR0f4+vpWm8jSsmXLsHDhQkyZMkXM1VaauiEWvjS7d+/G2rVrxYQF/f390bBhQyphQQD4448/sGjRIuq5f42uri4nLKampiYm+EZT43D58mXMnz8fJiYmuHPnDkpKSvD582c4OzuDEFJu3Ujbtm1x+/btCtWob926Ra2xoaGhgcWLF2P79u0Si2lpriEsIAyUiwFg8+bNvAvZhfBxx7527Rq2bdsmIvb2888/w9HRkbrBwMvLq0Kn76qEdS1UVVITqMg5LAwFz58/j8zMTM7A62uqq0AR4H8DKSkpkegTUlhYWO0tvn369JHoXUSLvARdQjM3vq62LHxpWBhXsmDnzp1o2rQpEhMTkZiYKPI7gUBAFajIap43YsQIrF+/Ht27d5co0paTk4P169djzJgxVMeSkZHBdbi8fPmSap+qQHieKykpISwsDC4uLpg4caLU8uwsCtlZuGMXFhaW21r8+fPnSvcHStugfXx8OKfvFStWcE7fkjyIWLJt2zZMnjxZYi2UsCsLKO1wpKmFqkpqAhU5h8VTNW1XTkXIixlgr169EBQUJNKtlJeXh7Vr11JLvcsKbTEeANSpUwctW7aEsbFxuRczeQq6AP6utiy0GFgYV7KgbFeUrMhqnjd58mScOHECw4YNw5gxY6Cnp4fGjRsjLy8Pt27dwsGDB9GmTRvqboxvLX8upGzWok6dOvD19cXGjRu5tm1aWLhBy+qOXZauXbsiMjISzs7O3Lbi4mJs2bKFOgPBwulbVljXQlUlNYGKnMPCUJCFeJW8mAE6OTnB0tISvXr1wqdPnzBnzhw8e/YMTZo0wapVq6r0b9MW4wGlNgWvX7/G0aNHERQUJPE13zLoKgsrV1sWWgwshAXlBVnN8xQUFBAWFoaAgADs378fYWFh3O+aN2+OSZMmYc6cOVIVOn7+/BmvX7/mAklSpsh5xIgR0h2YjMyfP18saJ8/fz5UVFQQFRVFPQ6LQnZZ3bHL4ujoiEmTJuHGjRsoLi6Gu7s70tPTkZ+fT9VZBrBx+paVT58+VXoONWnSBB8+fKjSedBQ054s57AwFGSFPJgBAqVV6keOHEFSUhJnsDZy5EiZ9S6qihs3bmDWrFm4ffu2xN/n5OTA0tIS+fn5yMvLg6qqKhd07dy5s9p8PiZNmoRPnz5hzJgxEl1thwwZQjUOC18aVsaVfGHxvWNhnvf582c8efIEb9++RdOmTdGqVSupO7EuXboER0dH5Obmiv2ubt265Z6f8srZs2dhZ2eHCRMmIDIyEtbW1iJu0DQ1YoMGDYKvr69UXlaSyMnJQWRkpMi1yNzcnFpIc+nSpXj48CF8fHzw+PFjeHt7Y/369YiNjcW///5b7nI9C8aMGQNLS0uMHDmy3NccOnQI27dvp/Jyq0pqAhU5h4WhYA3fhtzcXMTExGDKlCnlvkYegi5WrrYAfy0GVsaVfLV2Dhw4IBIQfP78GY8ePUJ0dDQcHBwqvLgLqcg8b/369WJFrVWFqakpmjVrBgsLCyxcuBC+vr7IysrChg0b4OPjIyLCxhrWysVC+LpBs3LH5gsLp29ZCQ8PR3h4OCIjI8uthTIzM8OYMWOq7QGhPGoCle+UixcvIjAwUKqUqTwgyw2EtoIeqD45cHnKdPGFhautUIuh7HuSlpaG33//XSYtBj7Cgl9r7Rw7dgxr167FxYsXeWvtHDt2DHv37pVquTMzM5O7oaqrq0uls8MCbW1tREVFQUNDA+bm5rCxsUGPHj2wf/9+7Nu3D5GRkVX2twMDA6kzQLRqsSzcoGV1xy5bj1IZsnRTERmcvmXly5cvsLCwQEpKSoW1UDt37vzmeio1NSrfKdIYCsoLsoq1lV0C+fTpE2JjY6GpqQldXV0oKioiISEB9+7dE1uzrkq8vb0rfOL+nlBRUUF8fDyGDRsGVVVV3L9/HwCQn5+PoqKiSvdn6UvDQliwKtu+tbW1qZZsytK6desKW42rGgUFBa5gVEVFBcnJyejRowe6d+9e5QXKNjY2zMfcsmUL7yyQrA0GT58+5fV3v+br810gEGDRokXVYtRaFbVQVUZ1C7fUIB3Pnj0T+/nvv/+Ivb09GTJkCPU4OTk5JDAwkNjZ2ZFXr16RY8eOkbS0tCqcuTgsxNqcnJyIj4+P2PZ169aR+fPns56y1MTGxpJp06Z962lIRVRUFNHW1iYxMTEkNTWVdOzYkbi7u5MRI0aQ6dOnV7jvtWvXiKamJnF2diYvXrwQ+V1qaipxcnIif/75J4mPj6eaCwthQQMDA5KSkkIIIdx5JpyPvr4+1RiSKCgoIKtWrSL9+/eXeYxvwbhx48jff/9NCCFk27ZtxMbGhhBCyM2bN0nXrl2rdS5JSUnk0KFD5ODBg+TgwYPkwIEDZPfu3cTV1ZV6jOnTp5OQkJAqnKXsfPr0SarXszjfWVBcXEzS09PJ7du3yePHj0lJSUm1/W0aajIqcg4LQ0F5kZ1noRty/PhxHDx4UGz7qFGj5MJ4TZYn7m8NH1db1loMrJyP+bZ9l7e0JxAI4OHhQTWGvDBz5kwsWLAAtWvXxrBhwxAYGIiZM2fiv//+q9bustDQUC6DI6zXEf67S5cu1OOwcINm4Y796dMnuLu7i2T/Bg8eLFU2RF6MWhUVFdG2bdtq+VuyUBOoyDmSvnTSGgqySoXzLVBkcQNp3LgxEhMT0aZNG5HtN2/elFrhkjWFhYXYuXPnd9dGC8juastai4GFsCCLtm9J9QW1a9eGjo6O1EJ43xpjY2Ps3bsXCgoKUFZWxvbt2xEaGooBAwZgwYIF1TYPFsrFABs3aBbu2D4+Prh586aI/IOzszPWrl2LdevWUQUZrIQ0f3RqAhU5h4WhIItMBousDIsbyIQJE+Dm5oa0tDRoaWmhpKSEO74lS5ZQjcECFk/c5RXmCQQC1K5dGy1btsTgwYOr/EmnPFfbt2/fwtLSssLCYNZaDCyEBVlo7Tx48ACWlpZMa0uKi4vx8OFDqKqqSu1azpcGDRqgoKAAQKkRXlJSEvr27YsmTZpU2xxYKBcDgKGhIW836JiYGHh7e/PSmGKRDZEXIU15pyZQkXOuXbvGu42RRSaDRVaGxQ1k7ty5UFBQwM6dO7Fp0yYApYqUDg4OMDc3pxqDBV8X0wLSP3EXFxfj6NGjaN68Oafn8ODBA+Tk5EBHRwfXr1/H1q1bERISQq0OKwuEh6sta18aFsKCv/76K6Kjo0Xavs3MzKRq+z548CBvr5Xnz5/D1dUVtra2UFdXx9ixYzmxt7CwMGhqavIan5YrV65gzpw5mDp1KveexsbGIiAgAMHBwVItu/CBhXIxwMYNmoU7NotsiLwIaco937A+pgYKvLy8yMKFC0lycrLUhVpCbG1tib29Pfny5QtXXPjmzRsyefJkrrCuMlgVKL5//55ERUURDw8Psnz5crJz506ZHU9zc3PJmzdvZNpXHnB1dSULFiwQ+VyLiorI4sWLiYeHByGEkLVr11a5KzQfV9uwsDBiZGREsrOzJf4+OzubGBkZkY0bN1LPh6/zMQvs7OyIj48PLzdeGxsbMnbsWPLkyRNy4MABoqenR+Lj44mrq2u1FlyPGTOG+Pv7i2338/MjEyZMqLZ5zJo1izg5OZH379+TPXv2kHHjxpEvX76QmJgYYmhoSD0OCzdoFu7YU6dOJU5OTuTLly/ctpKSErJs2TIyadIk6nHk4XyXd2p0VOScQYMGITMzs1wtAhq9DhYKqF27dkVERATU1dWhp6eHw4cPo1WrVrh37x6sra3FhOl+RCrz9ykLjSZEly5dsHv3bjFdjdTUVJiZmSEuLg6PHj2Cqakpbt26JfV8adHQ0MDly5dRq1YtzJs3D9nZ2ZyrraGhYYXnmLxoMbDW2rGwsEBcXBwEAgGaNWsmltU8c+ZMpWMYGBggPDwcmpqasLW1BSEE69evR0ZGBkxNTatNEVZXVxcxMTFimb4nT55gxIgR1TYPFsrFAODm5oZ9+/ZBQ0NDZjfokJAQbNiwAb169ZLZHfvevXuYMmUKlJSUJGZDqqtJ4X+BmqUfOYeFoSCLVLis9SXyKNYmKxs3bkStWrXQsmXLCl9H666rqKiIV69eiQUqL1++5ALTL1++QFGxar+mfFxt5UWLgbXWTrdu3UTceWWhuLgYP//8MwghuHr1KncjLikpqfLPtCxNmzbFw4cPxQKVlJQUmQ35ZEFdXR2nT5/G+/fv0aBBA0RFRUmtXAywcYNm4Y6tra2NmJgYEVuRYcOGfRNbkR+dmoxKDVTImpUpWzBa2Q2Exp79W+Lm5oZTp06hXr16MDExgYmJCTQ0NGQeb/ny5bh69Src3d2ho6PDqVKuWLEC+vr6WLZsGdzc3PDmzRvs2LGD4ZGIIsyolO2a2rhxI7Zs2YKSkhJqlV0WvjQscHZ2xs8//yzWJh4QEIC0tDQEBgZWyzwmTZqEbt26oUWLFvD09MTZs2fRtGlTuLm54dmzZ9Xmarx+/XpERUXB1taWe8q/f/8+AgICMGrUKCxatKjK58BaubiG/y1qApUflKrIZPD1pZGXGwgfvnz5gmvXriE2NhanT59Gs2bNuKDl65bpyvj48SMcHBxw8uRJkQv4kCFD4OnpiWvXrsHT0xPbtm3jFRBVxsaNG2FlZYV69eqJbI+JiUFUVFS13VBZoaenxy05leXRo0cYNWoU7ty5Uy3zuHfvHmbPno03b95gxowZsLe3h7u7O44ePYrt27dX29LA58+f4eXlhf379+Pz58+cf5KFhQUWLVpU5dmd8pSLrayspFYuFiIPbtAVabEAskno1yCZmkDlB0UeMxmsbiCEEFy8eBHJyclQVFREu3bt0KNHD+oOJlYUFxfj0qVLOHbsGM6cOYPWrVtj6NChMDExwW+//UY9zpMnT5CUlAQFBQV06NCBc14tKiqqUgntH5W+ffvC0dERQ4cOFdm+b98+bNmyhaq+hBUlJSUoKCjgukMyMjI4cb3qprCwEBkZGVBUVESbNm2qJZNx/fp1TJs2DaNGjYKdnR1atGjB/S4tLQ3bt2/H4cOH8c8//3DdhJXBwg2ahVeXl5dXhVos31uAL8/UBCr/A8iayWCdlWFxA8nLy4OVlRUePHiARo0agRCCgoICdOzYEaGhoRLbBauDoqIi7N+/H35+figsLJTKlPDVq1cSn8ikCXakpapcbVnAV1hw8+bNCAkJwZQpUyRq7UyaNKmKj+D/kJeg+lthZWUFNTU1icrFQpydnfHq1Ssq5WKAjRs0C3fsbt26wcnJiZcWC8D/fP9foKaY9n8AWWXnWRcoshBrW716NT5+/Ijo6GhuOeThw4dYsmQJ/Pz8ql3e/MWLFzh58iSOHz+O+Ph4qKiowMLCgmrfW7duwdnZGZmZmSLbCSEQCARV6sA8evTob1I/UhkshAXlRWtHXoPq6oS1cjFQ2hXn7e0NDQ0NaGpqon79+rCwsED9+vWxY8cOqkDF1NRU4nYtLS3s3buXKlBhocUiL/Ymck919kLXIBt8DQX79OkjUXdg79691AZrrMwAt27dSnr16kU6dOhAOnToQPr160ciIiKo9+/WrZuIgZeQa9eukZ49e1KPw4fs7GwSHh5OzMzMiIaGBhk4cCDx9/cnSUlJUo0zevRoMmnSJHL69Gly/fp1sZ//RVgYV5ZFGq0dSQag5f3Q4OTkRIYOHSpyXiQlJZFhw4YRNzc3qY/le0RPT488fvy4wtdkZmYSPT096jF1dXXJ06dPCSGl73FYWBghhJCnT5/yNll8+vQp0dbWpnotCy0W1uf7j0pNRkXOYRFxs8hksDIDnDVrFmbNmoU3b95AIBBIvVb/+fNniV46zZs35yTCq4qwsDCcOHECd+/exW+//YYhQ4bA1dUVHTt2lGm8lJQUREdH87JHYMXDhw+RnJzMKRiTMsWJNG3KrGBh91AWJSUl6tfSLIcRKbJdZ8+exYYNG0QKoTU0NLB06VLY29t/d+aGssBauRgA2rdvj3///RcWFhZQVVVFfHw8pkyZguzsbF5zldarS1dXF2vXrsXVq1dl1mJhfb7/qNQEKnIOC+l6Fqlw1maA0txAytKxY0dERkbC1dVVZHtkZGSVS5KvWrUKtWvXRu/evTkfjrNnz+Ls2bNir6W5SCkrK6OwsJD5PKWFlastC1jYPchKeHg40+WwbxlUywsjRozA+vXr0b17d/z6669iv8/JycH69esxZswY6jFZuEGz8OpiocXyLc/374maYlo5p1u3boiIiEC7du1EFGHT0tIwfvx4xMfHSzWerJkMeSlQvH37NiwtLaGhocEFbvHx8Xj48CG2b99epbb1/fv3p3qdQCCgKgw+ePAg9uzZAw8PD6iqqoo9kVUXxsbGGDJkiERX27Fjx/L2vJEGOzs71KpVC2vXrkXnzp1x+PBhNGrUCDY2NlBSUsKGDRuqbS58mTJlCtTV1cWCai8vLyQkJGD37t3VMo+XL18iICCg3DbaquyCqirl4gcPHkBBQQEaGhq4ceMGQkNDoaysjAULFlBd2yRlh7+FO/aPdL5XJTWBipwjT9L1QUFB2LlzJ6cGqaysjBkzZlRrgSJQqk8REhKClJQUEELQoUMHTJs2jTP2+14YNGgQsrKyOC2Ir6nKYtqyaGlpITY2Fq1bt8b06dNhbm4OY2NjXLp0CatWrcKRI0eqZR4AG7sHWbG0tMTGjRvRuHHjSjveaLrcvmVQXZbZs2cjISEBJiYmEpVoaZ78+VBUVMQpF5c162vevDnGjBkjk3Lxo0ePUFBQwOmyhIeHo2/fvtRaRl5eXjK5Y2dlZUFZWRkCgQBZWVkVvpama+9bnu/fEzWBipwjjxG3rFmZGkSR9FRXFr5tj7QYGBhgz549aNu2Ldzd3dGyZUvMnj0bz58/x9ChQ6vNC0YIX2FBQLa2YGdnZ7i6uqJhw4YiOkSSoBXzkoegWldXF9u3b6/2ZbyvYaVcXNYN2s7ODkBpHV5ycjK1G3Tnzp1x6NAhTrOIFk1NTVy6dAnNmjUrd/lImjomgM35/qNTE6jIOT9axC3LDYS1GWANosyePRtKSkpwc3NDTEwM9u3bh927dyM2NharVq3CpUuXvvUUpaKmLViUnj17IiIiAm3btv3WU2HC2LFjYWhoyAUpQvz9/XHjxg2qJTV7e3v88ssvmD9/vlQBwY0bN6Cvrw9FRcVKs9kGBgbU49ZQMTWBynfAjxJxy3oD+bo25Pnz56hduzZatWoFRUVFZGZmori4GFpaWtW27i8rtE/tAoEA3t7e1TInVq62ssJaWNDZ2Rn37t2Dn5+fmNaOvr4+dbFkbm4uMjIyJHZClWcWKo9B9erVq/Hu3Tt4enr+EAWaLNygWbhjy8qPZNRaXdR0/XwH1KtXj1pUrTxkyWSwRlaxtn///Zf7d1hYGM6dOwc/Pz+u2+jdu3dwcHCAurp61R8ET54+fcrd+J4+ffqNZ1MKK1dbWWEtLMiiLfjw4cNYunQpioqKuE4oYZr/999/LzdQOXDgANUcabtCWJCXl4cjR47g3LlzaNWqlZgtw/d2M2ThBs3CHVtWWJ/v/wvUZFTkENYRt7ykwrt3744NGzaIpUSvX78Oe3t7XL58udIxevbsiZCQEDGTvuTkZFhYWOD69etM5/yjI2+utiyMK7t06YKoqCgxCfK0tDSYmpri7t27lY4xdOhQ6OjowNraGmZmZggJCcGLFy/g4eEBe3t7KuVSeYFVvY28IA9u0Kz4EYxaq4OajIocwjriZiU7zzcrw0JXori4GO/fvxfb/vr1a7mUhK+MDx8+IDk5WaxttDo0TMpztfX29pbZ1ZYvLIQFWWjtPHnyBIGBgVBTU0OHDh2Qm5uL/v374/Pnz9i6det3Fah8b4FIZcybNw9v3ryBp6enmBv0woULv/X0pIKVkOaPTk2gIoeUvbA4Oztj6tSp5UbcNLBIhbPIyrC4gfTv3x/Lli2Dm5sbtLS0QAhBfHw8VqxYARMTE6ox5IUzZ87AyckJBQUFYtoWVe31c/36dTg4OGDUqFFiQlwuLi7Yvn07bG1tpXK1ZQELYUFbW1tYWlrizp07EtuCaahTpw63RKKiooKUlBT06dMHWlpaePz4Mf0ByQlC47uMjAy4uLh818Z3ioqKcHd3x5IlS6rdDZo1rIU0f1RqAhU5h0XEzSKTwSIrw+IGsmzZMixcuBBTpkzhMiiEEAwePLhS8zN5w9fXFz169MDcuXOp19ZZsW3bNkyePFmiq62amhoXLG/ZsoXa1ZYFLOwe9PT0EBERgZCQEFy6dIlrC3Zzc6NuCxaa09nb20NdXR3nz5+HlZUVUlNTv5kwn6x8bcNha2v7QxjfNWjQQCQT+C2IiYlB165d0bJlS2zevBmxsbHQ19eHq6urWIGuJFic7/8TVLGXUA08YWEoaGlpSby8vMS2r1ixgkyYMIFqDFZmgHfv3iULFy4kQ4cOJUOGDCG2trbk7t271PsLSUtLI8eOHSMnTpwgmZmZUu8vD2hpaVVq2FZVdO/enfz3338VvubevXvVZvRYFr7GlSyIi4sjOjo6ZMeOHSQ3N5cYGBiQoUOHEj09PeLq6lqtc+FLjfFd1bBp0ybSqVMnEh8fT27evEk6dOhAli5dSgYNGiTxelse8nC+yzs1GRU5h0XEzSKTwcq3RFtbGwEBAdSvLw9VVVWRtLWwbbRz5868x64u2rRpg+zsbKnVMVnw6dOnSlPlTZo0wYcPH6ppRv+HLMaVrNuCu3TpghMnTqCoqAhKSkrYtWsXIiMjoaysDAsLC+q/VR6f/l979x5XU97+j//ViSQmyXc0TTpRO1JKktNtVMLIqTJGM+XQ7VCOxaQydZOmHGJymDGJyqFxGKdRwsgxbmJKI5KIEXWLmCKl016/P/zaH1thV2vttffuej4ePR68927ta2drXeu93u/rqqqS6IqbDdT47o2PVZJ9myRVZQ8cOIBVq1bBxsYGERER6NOnD1asWIE///wTfn5+DW5xv09LG7W2BpSoyDg2GgqyMRXe3PUlbJ9Abty4gZCQELFOv2+TVtl5NixevBgrVqyAn58fjI2NG2wbleSXZXNx0dWWbU1pXPnutuAP1dqR5HPm7u6O8PBw0W1OExMTfP/9902K/59//sEvv/yCvLw8UZsEhmFQU1ODu3fv4s8//2zS8ZqLGt+9wXZ37CdPnsDa2hrAm2q59Vv5dXV18eLFiybH19xGra0BJSpygI2Mu6UzGc2dlWH7BBIZGQkVFRV8//33iIyMRGBgIAoKCpCYmIjVq1c3+/3xwdfXF3V1dfD19RX7BdqUX5bNxUVXWz6xXWvn4cOH0NDQaFFMy5cvx6VLlzBo0CAcP34co0ePRn5+PnJycjgvove2wYMHIyYmBmvWrBGNlZaWYs2aNVLrNyQL2O6O3bVrV9y/fx9VVVW4e/cuBg0aBODNQtiuXbuy9jqE6qgoLC4qZLa0b8nHTiCS/PK2trbG9u3bYWlpiYkTJ2LJkiWwtbVFQkICzp07h/j4eIlikQV8luDmqqutLGCj1k5sbCzOnz8Pb29vdOvWrcHPQJLZrv79+2PVqlX44osvMHr0aFGl3JCQEFRVVUktsVa0Nhx8doN+27Zt27Blyxa0adMG2tra+P3330UXTPPnz4e3t7dU4mgNaEZFQbE9kwG0fFZmy5YtiIuLE9t217FjR/j7+8PT01OiREUoFKJLly4A3mwbzcvLg62tLRwdHRETE9Ps2PjAZy8QFRUVJCQkiLraJiQkiB7T0dHBN99806yutrKAjVo70dHRqKurE5VZr9eU2a5Xr17BzMwMwJs1Vbm5uRAIBPj2228xc+ZMCd9Ny3366ac4fPiwWBuOyZMny2UbDuDNzr8PdYN+H7a7Y3t7e8PIyEhUuh948/ssJCQE7u7uEsdFPo4SFQXFxlQ427MybJxADAwMkJGRARcXFxgbGyM7OxsA8PLlS1RXV0scryyoqKhAQkLCe68MuS5t3qZNGwQEBMDf35+VrrZsYVpYWJCNWjtszMx9+umnKCwshK6uLgwNDXH79m0Ab1pilJWVtfj4TcFGGw5Zcfny5WZ1g9bT04OysrLoz2x4tw/ZmDFjmnyMln7eWwNKVFqB5s5ksD0rw8YJxNPTU7Sgd8SIERg3bhzU1dWRmZmJPn36SHQMWREaGopTp05h0KBBolkiPqiqqspMZ102CguyUWvnypUr8Pb2Rrt27cTGy8vLsX79eolmw5ydnREUFISVK1di4MCB8PPzg5WVFVJTU2FgYCBRHKQhDQ2NZhVDe7uQJhvVequqqrB3716xxdLAmx2IN27cwIkTJz56DFlpbyLrKFGRAy3NuJs7k8H2AkU2TiATJ05Ep06doKWlJSpMFhsbC11dXYSGhkp0DFlx5swZrFu3DsOGDeM7FJnBRmFBTU1NbNu2Dffu3UNeXh6UlZVhbm7eoIndu/Lz8/H8+XMAwE8//QSBQIBPPvlE7Dl5eXnYt2+fRFtP/fz8UFtbi6KiIowZMwbOzs5YuHAhOnbsiPXr13/0+0njxo0bh61bt7a4G3RzumO/LTw8HIcPH0bPnj2RnZ0Na2trPHjwAM+ePcPUqVMlioGt9iaKjhbTyjg2Mu4lS5bgxo0bjc5kDB06FCEhIR89BpvNAJt6ApFETU0NsrKy0K9fvxYfS1r69euHAwcO8FJHRVax0bjyfT5Wa+fs2bOYPXu2WBLdGDc3N/zwww8ffb3k5GQMGTJELNkpLS2FpqYmVFXpGrG5goKCkJycjI4dOza7G/THumOnpqZ+9BgDBw5EcHAwXFxcMHz4cMTExEBfXx9+fn7o2rWrRNvZufy8KxL63yLj2Mi42ZjJYLMZYEuKtZmbm2PevHnw9fUVGy8rK4OXl5dc1VFxdnbGwYMHsXDhQr5DkRlsFBZsbq2dL774AqdPn4ZQKISTkxN+++03aGtrix5XUlKChoaGxOUBwsLC8Ouvv4olKlTMix0uLi4t+v5ffvkFo0ePbrQ79rx58yQ6xosXL0SlGrp3746cnBwYGxtj1qxZWLhwoUSJCluFNBUdJSoyjo2Ggs2dCn8bG+tL2CjWxjAMYmNjce/ePURERIhdTcnb5KC2tjbi4uJw/vx5GBkZNbgyVLSut5Jgo3FlS2rt1G87PnXqFD777LMWLSo2NDREXl4eunfv3uxjsCEoKKjRcSUlJaipqaFr164YOXKkzKxT+hg2/l+w0R1bW1sbz549w2effSb6twbeFG4rKSmRKA42Pu+tASUqMo7NjLslMxlszMqwUaxNSUkJsbGxCA4OhpeXF37++WfRVS+fO1WaIysrS9QQ7smTJzxHIxvYaPeQk5MjqrVz8OBBmJqawsPDA127dsW+ffswatSojx6DjV0hAoEAixcvxtatW2FoaNigZL60EtGamhocPXoUOjo6oppHN2/eRHFxMaysrJCeno5ffvkFcXFxctOCoqXdoNnojv2vf/0Ly5cvR2RkJPr27YuIiAgMHz4cKSkpEhd8Y+Pz3hpQoiLj2Mi42ZjJYGNWho0TCMMwMDIywt69ezFnzhy4u7sjJiZGLlui79y5k+8QZA4b7R5kpdbO/fv3RSf+p0+fSu1136Wurg5nZ2esWbNGdHKuqalBcHAwOnTogNDQUERFRSE6OlouPpNsdINmozt2QEAAAgMDceXKFXh4eGDv3r2YOHEiVFVVsWrVKomOwcbnvVXguushaZnMzEzGwsKCcXd3ZyIiIpiIiAjGzc2N6dWrF3Pp0iWJjuHh4cG4ubkxv/76K9O7d28mMTGRiYyMZCwsLJiUlJQWxVdVVcX8+eefEj3X0tKSKSoqYhiGYRYtWiTqEFpQUMDY29tLdAyBQMCUlJSIXnvRokWMjY0Nk5KSwggEgma8A/4UFhZ+8Is0z5gxY5ikpCSGYd50uA0MDGQYhmFu3rzJ2NjY8BkaL/r27cvcuXOnwfidO3cYW1tbhmEY5v79+4y1tbW0Q2sWNrpBc9EdWygUMjdv3mSKi4ub9f3k/WhGRcaxkXGzMZPBxqwMG8XamLfWobRp0wZRUVHYtGkTFi9eLNH3y5KPNUmTp4XBLcF2YUFZqrVTW1uLZ8+eiTUlrL/lWl/NlGuqqqooKSlpsFbm6dOnos9fXV2d3OxEYqMbdHO7Y3+sA7OWlpZoS/r72ixw0d5E0cnHJ7OVa2npejamwtlYX8LGCWTu3LkNmsXNnTsXBgYG2Ldvn0THkBXvbqOsq6vD/fv3kZCQgMDAQJ6ikj62CwuyVWunfh3EvXv3sHTp0iavg7hw4QKWLFkiqs3yNnV1daklKiNGjEBoaCiWLVsGKysrMAyDrKwsrFixAo6OjqioqMDmzZvRu3dvqcTTUmx0g25ud2w2OjBz0d5E0VGiIoPYzrjZmMlgY1aGjRPI+97vmDFjmlW+mk+NVTcdMGAA9PX1sXHjxgbluRUV24UFAcDJyUn05/rPRn2tHUkWyr67DsLPz6/J6yDWrVuHnj17wtPTEwsWLEBUVBSKioqwYcMGqe7oCgoKQkBAAKZPny52kh01ahSWLl2K//73v7hy5Qq2bNkitZhago1u0M3tjs1GWwsuPu+Kjgq+yaB3T1Afyrj37Nnz0eP99ttvCA8Pxw8//ABzc3OMGzcOEydORGZmJnR0dLBt27aPHsPKygrHjx+Hrq4uFi9eDBsbG3h4eODhw4f46quvcOnSpWa/348Va5PkKgZ4s+tHkkJNsu7hw4cYPXo0rl+/zncoUsdGYcH31dopKSnBkCFDJLql5uPjA21tbYSHh8PGxgZHjhyBrq4ulixZgidPnki06NTS0hL79u2DQCCAh4cH5s2bhwEDBuDAgQPYv38/du/e/dFjsOnhw4e4desWVFRUYGZmhs8//xzAm91/726Nl2VsdINmozs2G9gspKnIaEZFBrGdcbMxk8HGrExzi7VNmDBB7rYeS6Kx+93l5eWIiYkRnURaGzYKCzIs1NphYx2EioqKqLtv/S3XAQMGwN7eXuJdIWxq164devfuLfoZ1H/+pHVSZgsb3aDZ6I7NBjYLaSoySlRkXHMbCr6rpVPhbKwvae4JRNJKkfKmsZkihmGgoaEhNq3dmrBRWJCNWjtsrIPo0aMHTp8+DU9PTxgbGyMjIwNTpkzB48ePJfp+tmRmZiIoKAgFBQVi49I+KbOppd2g2eiOzQY2Pu+tASUqMo6NjJuNsvNszMqwVawtNzdXbPfR2zspwsPDJT4O3xq7362mpgZTU1O0b9+eh4j4x0ZhQYaFWjtsrIOYOXMm5s+fDzU1Nbi4uGDjxo2YOXMmbt++LfEx2BAeHo4uXbogICBANMPT2rHRHZsNbHzeWwNaoyLj2GgoKBAI0K5dOzg6OorNZJSUlGDw4MHIzc1tdnxNaQYoEAhw8eJFKCsrY86cOXj8+LHoBDJo0CCJEqb4+HjRtHl9M7H6P9va2spFwap6c+fOhZ+fH0xMTPgORea0pLCgubk5Lly4gM6dO6O6uhrBwcE4c+YMwsPD4e/vL9HnjI11EMCbCrAqKioQCAS4cuUK4uPjoauri/nz50ut70/v3r1x+PDhVv85e7s7tpeXFzZu3Nhod+zVq1fjr7/+kmpsXDRqVSQ0oyLj2Mi42ZjJYGNWpv61OnXqhISEBAQHB+Prr79u0ixIYmIiZsyYgblz52LYsGE4dOgQSktLsWjRIjg6Okp8HFlw+fLlBmXVyRstaffARq0dNtZBAG8qSwNvZmPMzc2xefNmib+XLbq6unj16pXUX1fWPHz4UKw79vt2ELq5uUl8TLZmd1vyeW8NKFGRcWyUrmdjKpyNBYpsnEAeP36MiRMnom3bthAIBMjOzoaTkxMCAwOxcuVKTJ06VeJj8W3ChAmIiorCnDlzYGBgIFc7L7jCRmFBtmrttHQdBABs3boVO3bsEJXQ//zzzzFjxgyJF+SywcfHBxEREVi+fDmMjY0lLhGvaNjujv2x2V1JsPF5bw0oUZETLcm42ZjJYGNWho0TiIaGhqjKZ7du3XD37l04OTnBxMQEhYWFEr8fWXDu3DkUFBTgxIkTjT7eGn9JsVFYsLm1dry8vCSOU5J6Glu2bMHPP/8MT09PWFtbQygUIiMjAxEREQAgtWRl8+bNKCoqwvjx4xt9XN4+Zy3pBs1md2w2ZnfZ+Ly3BpSoyDg2Mm42ZjLYmJVho1ibjY0NtmzZgtDQUPTs2RP79+/HzJkzkZGRIXcLUH18fPgOQeY0t7AgG7V23l53UlVVhZSUFJibm6NPnz5QVVXFjRs3cP36dYlnWRITE7Fs2TKxBKE+qd6yZYvUEhVF+5yx0Q2aje7YbMzuslFIszWgREXGsXWF2dKZjObOyrBdrM3f3x/Tp09HYmIiJk+ejF9++QV2dnaorKyEt7e3RO9FVkyYMIHvEGROc9s9sFFr5+1qsUFBQZg6dWqDVgbR0dHIz8+X6HhlZWWNVrDt168fVqxY0aJYm0LRPmey0g2ajdldWen0LesoUZFxbGTcbMxkNHdWhu1ibaampkhNTUVFRQXat2+Pffv2ISkpCbq6uhg5ciRrr0P40dzCgmzX2jl+/DgOHTrUYHz8+PHvvYXyLkdHR+zcubPB9v2kpCTO2yMEBQVh6dKl0NTUfO+tEuDNBUL9rSh5cfz4cezZs0dsTZeamhpmzZqFyZMnIzQ0FO7u7vj11185jYON2V02Cmm2BpSoyLjmZtxsz2Q0d1aGzRNIZWUl1NXVRV/Am6vWyZMnNyiBTeQTW52PW7obo2PHjsjJyYGhoaHY+J9//inx7c7OnTtj9+7dyMjIgJ2dnej20Z9//glHR0exBILt3j+PHj0SvfdHjx6xemy+yUo3aDZmd2Wp07cso0RFxjU342Z7JoOtZoDNPYEkJyfjhx9+QGxsLCwsLETjERERuHHjBsLDwzF8+HCJ4yCyiY3Cgmzsxpg0aRJCQ0ORn58PCwsLCIVCUVn97777TqJj3Lp1S3SyebtWka2tLcrKylBWVibRcZrj7Vse8lRbSBJsdYNuaXfsxmZ3k5OTRYt5JcFWp29FRwXfZBwbDQWbi+1ZmeYWa0tPT8e0adMwfvx4+Pn5iWaYgDdFnLZu3YojR45g586dsLGxkeStETnTlMKCTk5OGDVqVKO7Mdzd3SXewh4TE4Ndu3aJthbr6upixowZ8PDwaMlb4UVlZSXy8vJQU1Mjdhu3KcmbrHj9+jUCAgLwxx9/NOgGHRYWhsuXLyMsLAxbtmxp0Oyv3rvdsY8dO4Y1a9YgLS1N4u7YXGrK5701oERFDqSmpkJLSwu2trZISkoSy7glXb3enJmMjRs3Nmnr8cc09wTi7e0NExMTBAcHv/fYQUFBKCkpQWxsrETxyoqWXtUpGjY6H1tYWCAlJQXdunXD9OnT4eHhAScnJ1y4cAErV65EcnJyk2L6559/oKSkJLVKsmw7deoUAgMDUV5e3qDmkbz2+gFa1g2aje7YbGDj894a0K0fOdDShoLNnQpne4Fic7fz5eTkfLQKr4eHB2bPns1qvFx796rOz88PKSkpCAoKkomrOj6wUViQ7Vo7nTp1avL3yJKoqCgMGDAAvr6+CtXrpyXdoNnojs0GNj7vrYEy3wGQDzM3N8fPP//cYLy+dL0k6gsT/fXXX+jUqRPOnTuH33//HSYmJk0qO5+bm4sjR47g8OHDOHz4MA4dOoS9e/fi+++/l+j7GzuBAPjoCaSqquqji2W1tLRQWVkp4TuRDStXroSTkxNSU1NF1ULXrVsHBwcHREVF8RwdP+oLC16/fh1eXl6i3iz1j0mifjdGZWUlevbsKapGKo+1dtjw6NEjLF68GAKBAHp6eg2+5E1mZiZGjBiBIUOGwMHBAY6OjnB0dBT9WRJsdMdmAxuf99aAEhUZV59xL168uMHiWUkz7sZmMszMzBAYGIj9+/dLdIz4+HiMHz8eAQEBCAoKQmBgIIKCgrBs2TI8ePBAomM09wRiZGSEa9euffDYmZmZcvdLNzMzE9OmTWv0qi4nJ4fHyPjzdmFBZWVluLu7486dO1BWlvxXlb+/P9LS0pCYmIjRo0ejpKQEdnZ2WLJkCVxdXTmMXjYZGhri8ePHfIfBmvpu0Js2bcL27dtFXzt27MD27dslOkZ9d+y3k5Wmdsd+W3O3ErPxeW8N6Kch49jIuJs7k/E2NmZlmnsCGTt2LNavX4/i4uJGHy8uLsb69evlro6KrFzVyZJ3Cwva2Njg66+/Rnp6usTHqN+N4erqKtqNMWfOHKxduxb+/v5chf5e+fn5OHbsGFJTU3Hv3j2pv/7ixYuxYsUKnD59Gn///TeKiorEvuTNnTt3sHz5cjg6OsLOzq7BlyQCAwNx48YNDB48GFVVVfDx8cGwYcPw6NEjiZu9AsDu3bvh4OCAPn364OHDh/jPf/7T6Az4+7DxeW8NaI2KjGOjdD0bhYnYKBfd3GJt3377LU6cOAEXFxe4ubnB2toaHTt2RGlpKTIzM3Ho0CEYGhrKXWXa+qu6NWvWiMZaclWnCFra7oHNWjsMwyAtLQ15eXlQVVVF9+7dMWDAAImTyKqqKixatAinTp0SWxc2bNgwREdHS60Jpa+vL+rq6uDr6yt2ccMwjFwupmWjGzQb3bGTkpKwdu1aTJkyBVu3bgXw5uIvKioK6urqmD59+kePwUZ7k1aBITJNIBAwJSUlDMMwTFVVFbNo0SLGxsaGSUlJYQQCgUTHuH37NjNo0CAmNjaWKS8vZ4YNG8b07duX6dmzJ7N27VqJjtGvXz/m3r17DMMwzH/+8x9m8+bNDMMwTFFREdOnT5+Pfn9FRQUjFArFxu7evctUVlZK9PpVVVXMqlWrGDs7O8bMzEz0NWjQIGbdunUSH0eWPH78mHF2dmYGDBjAmJubM6NHj2b69OnDfPHFF8yjR4/4Do8XGzduZCoqKhqMHzlyhPn2228/+L1JSUmMvb09k52dLTY+ffp0xs7Ojvnjjz8kjuOff/5hXF1dGTMzM8bW1pbp27cvY2Zmxri6ujJlZWUSHSMyMpIZMmQIk5qayrx48YIpLS1lTp48yQwdOpSJioqSOJaWSk9P/+CXvDl48CAzadIkJjc3l6muruYtjvHjxzMHDx5kGIZh+vTpwxQUFDAMwzC//fYb4+zsLNExWvJ5b01oe7KMEwgEuHjxotgMyqZNm7B582YIhUKJr4Zev36NiooKaGtro6SkpMll52fPno1OnTohNDQUSUlJ2L9/P/bs2YOUlBSsXLkSFy5ceO/3vq9Ym7e3d5OLtdXW1uLhw4coKyuDtrY29PX15XrRWWVlpdhVXY8ePZp0VUfeYLvWTlBQEK5fv461a9eKanHk5ubiu+++g42NDZYvX/7RYwwePBgrVqzAsGHDxMbPnDmD5cuX4+zZs017kwQA4OzsjKKiItHt7He973ci292x+/Tpg6SkJOjr68Pa2hpHjhyBvr4+Hj58iNGjR+P69esSvx75MLr1I+Na2lCQranw5paLTk9PR0BAAMaPH49PP/1U7LHg4GBs3boVCxculPgEoqqq2mj7dnnVrl07ibvxKio2Cgtu2bIF3377baO1duorfgLA5s2bJaq1c+bMGWzYsEGsYJhAIMD3338Pf39/iRKVV69eNVoPx8jISGytGdcqKiqQkJCAzMzMBgXfAMlOyrKkud2g2e6OraOjg/v370NfX19s/Nq1a/h//+//vff72C6k2RpQoiLjWlK6ns2y881dX8L2CUTesX1VpwjYaPfAdq2d2tpa6OjoNBjX0dFBeXm5RMcwNTXF8ePHMWvWLLHxY8eOSTXZDg0NxalTpzBo0CCxmSZ51dxu0Gx3x540aRLCwsJEPZvu3buHCxcuIDo6GlOmTHnv97Hd3qQ1oERFBrGRcbM5k9GSWRlFLdbWXGxf1SkCNgoLsl1rp1evXti9e7eoYVy93bt3w9zcXKJj+Pj4wNfXF7du3RL9H8vIyMDJkyexdu1aiY7BhjNnzmDdunUNbkHJE7a7QbPRHXvGjBl4+fIl/P39UVVVhVmzZkFVVRVff/31B3+fsV1IszWgREUGsZFxszWT0dJZGUUt1tZcbF/VKaLmtHuor7XTrVu39x63KbV2Fi5cCC8vL2RlZYklGbm5uaIdHh/zxRdfYP369YiNjcXZs2fBMAzMzMwQHR0NZ2dniY7BBmVlZZiYmEjt9bjAdjdoNrpjA29uifv4+ODu3btgGAbGxsbQ1NTE06dPJZ69ammn71aBx4W8hEP29vbM7du3P/ic69evMwMHDnzv45cvX2bMzc2ZoKAg5smTJ2KP3b17lwkMDGR69uzJZGRkvPcYrq6uzOHDhz8Yx+HDhxkXF5cPPkcR9enTh7l//36D8fv37zNWVlZSj0cWxMXFiXZ0CQQCsT9/aBdEQkICM2zYMObx48eNPv748WNm2LBhzKZNmySO5a+//mIWLFjAfPnll8yoUaOYhQsXMn/99VeT3xPfgoODmR9//JHvMGTKTz/9xPTt25fZsGEDc/r0aSY1NZVZvXo1Y2VlxezatUuiYwgEAubZs2cNxh8+fCjRTkiGaf7nvbWhGRU50JyMm42ZDDZmZeqLtdnb2ze4BQX8X7E2Nze3D8aqiNi6qlMk9YUFG2tc+aHCglzU2rG0tER0dHST4j98+LDEz5X0FkNLaWtrIy4uDufPn4eRkVGD+i1vz/LJi5Z2g/b19YWKigp27dqFn376CcCb+iwBAQEf7I69f/9+HDlyBMCb38Nz5swRtb+o9+TJE3Ts2FGi99Hcz3trQ4mKjGtuQ0E2psLZWF+iqMXa2DBp0iSEhoYiPz8fFhYWEAqFomZp3333Hd/h8aK5hQVVVFSQkJCA6OhoHDhwAAkJCaLHdHR08M0338DHx+eDyfumTZskjvN9i9zfvY1X/39WXV0dqqqqKC8vh4qKCjp16iS1RCUrK0vU4PLJkydSeU0usdUNetasWZg1a1aTumM7OTkhIyND9PeuXbs2+EyZmppK/G/LRiHN1oASFRnX3IybjZkMNmZl2DiBKKrmXtUpspZ0Pm7Tpg0CAgLg7+/frFo7Bw8eFPv7//73P6ipqUFfXx+qqqooKChATU0NLCws3puo5Obmiv6cnJyMbdu2ITIyUrTN+e+//8aSJUvg4uLy0XjYsnPnTqm9ljSw3Q26Kd2xtbS0xGag6hf4Nhfbnb4VFp/3ncjH9erVi3nw4AHDMAwzbdo05uTJkwzDMExaWhozevTo935fbW0tM3nyZMbW1paJjIxkjh8/zvz3v/9lUlJSmPDwcKZv376Mm5vbByu6sr2+pKamhrl37x5z7do15sGDBw0q1bZmz58/Z/755x++w+DdrFmzmMDAQKaiooLZu3cvM3HiRKauro5JSkpiBg0aJLU44uPjmSlTpoiqQjMMw5SVlTGzZs2SuJrz0KFDmaysrAbj169fl+p7KSws/OCXvLGwsBD9TpQFT58+ZYqKipr1c5WVz7usoxkVGdfcjJuNmQy215coWrE2NjXlqk6RNbewINu2bNmCuLg4sbVCHTt2hL+/Pzw9PSVqbvjixQu0bdu2wbhQKMTr169ZjfdDPlbuQN56/dR3g/7QbW1puHbtGgIDA1FQUCA2zjShh5KsfN5lHZXQl3EtKV1fr7ll5+vq6uDp6Yk7d+58cH3Jrl27WuWtG8KNlrZ7YEO/fv0QExPToMbQpUuX4Ofnh8uXL3/0GD4+PigrK8Pq1avx+eefA3hTzv+7776DsbExoqKiOIn9XVeuXBH7e11dHe7fv4+EhAQEBgbCwcFBKnGw5dy5c4iKioKfnx+MjY0bLA7+7LPPpBKHq6srNDQ0MG3atEZvQUnayVkWPu+yjhIVGZeXl4fp06dj6tSpmDx5MsaMGYMXL16IMm6u29ZXV1eLZmXKyspE4zo6OnBzc2u160sI++oLC76dROfn50NPT0/qn7ElS5bgxo0bCA0NhYWFBRiGQUZGBlasWIGhQ4ciJCTko8coLi6Gt7c38vPz0bFjRzAMg5cvX8LS0hJbtmzBJ598IoV38n5paWnYuHGjRK04ZEmvXr1Es8wt6QbNtLA7du/evXH48OFm16iRpc+7rKNERQ7IQsataM0AiWxhs3ElG8rLy7FgwQJcvHhR9DlnGAYjR47E6tWrG1zFv09dXR3++9//4s6dO1BSUoJAIIC9vb1M/N+R1+Z5784QvUuSmYzS0lJ4e3vj5s2b6NChAxiGQXl5OXr16oX4+HiJthc7OzsjKioKlpaWEsdeT9Y+77KOEhUZRhm34mvpVZ0iYLvzMZvu3buHvLw8KCsrw9zcvEEDuuZ6/PgxunbtysqxPqaoqKjBWHl5OWJiYnDr1i2kpKRIJQ5ZwkZ37EOHDmHv3r1Yvnw5jI2NG9RTeR9Z/rzLKkpUZBRl3IqPjas6ReDt7Q0TE5NGCwvWCwoKQklJCe+NK+sLLfbt2/ejz3348CFWrVqFvLw80a0K5v8v1vj8+XPk5ORwHS6AN12f353BYRgGGhoaWLNmjdwVFmOjG7S9vT02bNjQYPYlPT0d/v7+uHjx4keP4ezsjKKiItG/7bvedwtKnj7vsoJ2/cggNhsKEtm1atUqvH79GocPH25wVbd27VqJruoUgSw2rrxx4wZCQkLEKkK/TZJ1EGFhYfj7778xcuRIxMfHY/r06bh//z5OnjyJsLAwLsJuVGMnbjU1NZiamqJ9+/ZSi4MtbHSDZqM7to+PT7NeWxY/7zJPmnuhiWSmT5/O/PDDDx98TmBgIPPvf/9bShERLvTv359JT09vMH758uUP9mBSNNbW1h+ti1FQUMBYW1tLKSKG8fDwYNzc3Jhff/2V6d27N5OYmMhERkYyFhYWTEpKikTHsLGxYS5fvswwDMOMGzdO1Cdo3bp1jK+vL2exv2vOnDnM3bt3pfZ6XLOxsWFOnz7domN4eXkx4eHhDcZXrFjBTJo0qUXH/hhZ/LzLOppRkUGUcbcObFzVKQK2Ox+zIScnB9u3b4elpSUOHjwIU1NTeHh4oGvXrti3bx9GjRr10WNUV1eL3pORkRFu374NS0tLjB8/Hp6enly/BZHLly83Ws9FXrHRDZqN7tjAm63S27Ztw71797B3714cPHgQ3bp1w7hx4977PbL4eZd1ynwHQBpio3Q9kX29evXC7t27G4zv3r0b5ubmPETEj/rCgsXFxY0+Xl9YUJq73IRCoei2goGBAfLy8gAAjo6OYmXyP0RPT0/0fUZGRqLbRUKhEK9eveIg6sZNmDABUVFRuHPnDqqrq6X2ulxxdnZu0O6gqaytrZGYmAg9PT1cuHABaWlp0NfXx6+//gp7e3uJjnHx4kXMnTsXn332GV68eAGhUIja2loEBQV9sDmlLH7eZR3NqMggyrhbB7au6uSdLDauNDAwQEZGBlxcXGBsbIzs7GwAwMuXLyU+2U+YMAEBAQFYvXo1vvjiC3h5eeGzzz7DxYsXYWZmxmX4Ys6dO4eCggKcOHGi0cflrTItW92gm9Md+20bN27EokWLMHXqVNHP1s/PD5qamti2bdt7GxPK4udd1lGiIoPYLl1PZFP9VV1cXBwuXLgAhmFgZmaG0NDQZtVmkFey2LjS09MTS5cuBQCMGDEC48aNg7q6OjIzM9GnTx+JjjFz5ky0bdsWDMPA0tISvr6+2Lx5M3R1dbF69WoOoxfX3EWfsqq53aDZ6I79ttu3bzf67zhy5MgPvpYsft5lHW1PlkFUup60VrJUWDA1NRVaWlqwtbVFUlISYmNjoauri9DQUJrNlEPvtgr4UHfsPXv2fPR4//rXv7Bu3TrY2trC2toaR44cgb6+Ps6cOYP//Oc/OH/+/EePIUufd1lGiYqMotL1iontqzoiXTU1NcjKykK/fv0kev6ff/753nof9O/bPI0VsHubJL1+EhIScPbsWaxdu1bUePLFixcICAiAqampRK1J1qxZg0uXLiEiIgIeHh749ddf8eTJEyxbtgzOzs4IDAyU7A2Rj6JERcZRxq1Y2L6qI9wwNzfHvHnz4OvrKzZeUlKCIUOGSLSu46effsLGjRvRsWNHaGpqij2mpKSEU6dOsRpza9FYAbu3SfJvM3DgQMTFxYnqF9XLy8uDp6cn0tPTP3qMmpoaBAYG4ujRowDe/JsyDIMvvvgC0dHRdCHJIlqjIuNUVVVhZGTEdxiEJadPnxb9+WNXdYQ/DMMgNjYW9+7dQ0REhNiCTUmv7Xbv3g0/Pz/MmjWLqzBbpXcL2L3bDVoSNTU1qKioaDD+7NkziS8E1dTUsHbtWsyfPx+3bt2CUCiEqakpunfvLvFnhEiIl+othBBmwIABzK1btxqM3759m7Gzs+MhIlJPIBAwV69eZYYPH85MmjSJefbsGcMwDPP06VNGIBBIdAxLS0vm0aNHXIZJ3nL+/Hlm4sSJEj03ICCA+fLLL5nLly8z5eXlzMuXL5mzZ88yjo6OTFhYmETHcHBwYP75558G448fP6b/vyyjGRVCeMLGVR3hBsMwMDIywt69ezFnzhy4u7sjJiZGNPMlCRsbG1y7dk0mFt4+efIE+/btw71797B06VJcvXoVpqamMDY25js01hgaGkpc4yYkJAQLFizAlClTGnTH/lCxzZSUFKSlpQEACgsLERYW1qCYXmFhIf3/ZRklKoTwxMHBASEhIQgNDYWFhQUYhkFGRgZWrFiB0aNH8x1eq1Z/ounUqRMSEhIQHByMr7/+GuHh4RIfw8XFBStWrMCNGzdgbGzcoN7H++pssO3Bgwf46quvoKmpieLiYvj5+SElJQVBQUFISEgQbfWVFx/qBv35559LdIz6WidN7Y5tbW2NPXv2iG7tFBUViXVNVlJSgoaGBlatWtWEd0Q+hhbTEsKT8vJyLFiwABcvXmxwVbd69eoGJzYiPQKBABcvXhSbQdm0aRM2b94MoVAo0YLNdxdqvk1JSUlqhdZ8fHygra2N8PBw2NjY4MiRI9DV1cWSJUvw5MkT7Ny5UypxsIXLbtBN6Y7t6emJTZs24ZNPPmn26xHJ0IwKITxp7lUd4d7cuXOhoaHRYMzAwAD79u2T6BiS3obgWmZmJhITE8VO7qqqqvD19cVXX33FY2TNw0Y3aDa6Y7+b4D1//hxXrlyBhYWFxDM7RDKUqBDCM2NjY7G1Ak25qiPceF+NkzFjxmDMmDFNOlZ+fj7y8vKgpqYGExMTqe/iEwqFjZ6MX716BRUVFanGwoYdO3bAz8+vRY0JIyMjoaKigu+//x6RkZEIDAxEQUEBEhMTJa4anJeXh3nz5iE8PBxmZmYYO3YsSkpK0KZNG2zZskXinkHk4yhRIYQnbFzVEfY4ODhItAhSSUkJqampH31eVVUVFi1aJPZcJSUlDBs2DNHR0VK7tTd48GDExMRgzZo1orHS0lKsWbNGLk+mbHSDZqM79qpVq2BgYABjY2MkJyejtrYW586dw549exAdHU11kFhEiQohPGHjqo6wZ8KECazu1vjxxx9x/fp1/PTTT7Czs4NQKMTVq1cRHh4uamgnDYGBgfDy8sLgwYNRVVUFHx8fFBYWQktLCytXrpRKDGyq7wY9Z84cGBgYNCvha6w7tq2tLRwdHRETEyPRMa5du4bffvsNnTt3RlpaGoYOHYpPP/0Urq6uiI+Pb3JM5P0oUSGEJ2xc1RH2zJs3j9XjJScnY8WKFRg2bJhozMnJCSoqKli+fLnUEpVPP/0Uhw8fRnJysqgw2eTJkzFu3LgGFXPlARvdoNnojq2srIw2bdqgtrYWV65cQUhICIA3t9SoKi27KFEhhCdsXNUR7uTm5ordlmMYRrR+SJJtyq9evWq0TomRkRGeP3/Oerwf0q5dO0ycOFGqr8kVNrpBs9Edu0+fPoiJiYG2tjaqqqrwr3/9C8XFxVi3bp3ExyCSoUSFEJ6wcVVHuBEfHy+qhVHfw6X+z7a2thIdw9TUFMePH29QQv/YsWOcL6j18vKS+LmN7aKRZRMmTGjxMSZOnIhOnTpBS0sLJiYmiIyMFOuOLYmQkBD4+fnh4cOHCA4Ohra2NlasWIH8/HzExsa2OEbyfyhRIYQnbFzVEW4kJiZixowZmDt3LoYNG4ZDhw6htLQUixYtkrhOh4+PD3x9fXHr1i3Y2NgAADIyMnDy5EmsXbuWy/DFquFWVVUhJSUF5ubm6NOnD1RVVXHjxg1cv35dYWZZmsPJyUn05/rdXPXdsSWpJmxgYICDBw+Kjc2ZMwfBwcFyuZtKllHBN0J4lJqaCi0tLdja2iIpKUnsqk4WSq+3VhYWFkhJSUG3bt0wffp0eHh4wMnJCRcuXMDKlSuRnJws0XFOnjyJ2NhY5OXlgWEYmJmZ4d///jecnZ05fgf/JygoCJ988kmDhn3R0dHIz8/Hxo0bpRaLrGhud+yrV6/C2toaqqqquHr16gdfo1+/fqzF29rRjAohPGrpVR3hhoaGBurq6gAA3bp1w927d+Hk5AQTExMUFhZKfJzhw4dj+PDhXIUpkePHj+PQoUMNxsePHy+1Mv6yhmlmd2xPT09RxWJPT0+x24Jvk2bl4daAEhVCePK+q7qysjJ4eXnRLzoe2djYYMuWLQgNDUXPnj2xf/9+zJw5ExkZGR+sfrpp0yZ4e3ujXbt22LRp0wdf431F5djWsWNH5OTkwNDQUGz8zz//bFKTRUWipKSE2NhYBAcHw8vLCz///DO0tbVFj73PqVOnRM87deqUVGIllKgQwpvmXtUR7vn7+2P69OlITEzE5MmT8csvv8DOzg6VlZXw9vZ+7/cdPHgQ33zzDdq1a9dg/cLblJSUpJaoTJo0CaGhocjPz4eFhQWEQqGorP53330nlRjY1tJu0M3tjv32LOe7M541NTXIzc2FsbGxxKX8iYQYQggvBAIBc/XqVWb48OHMpEmTmGfPnjEMwzBPnz5lBAIBz9GRyspKsX+TuLg45tixYzxH1Ty//PILM3jwYMbMzIwxMzNjvvjiCyYxMZHvsJrl77//Zuzs7BgHBwemV69eTEFBATNv3jymT58+TFZWlkTHEAgETElJCcMwDFNVVcUsWrSIsbGxYVJSUiT+v1dUVMRMmzaN+euvv5jKykpm9OjRjJmZGWNnZ8fk5OQ0+/2RhpT5TpQIaa2Yt67qlJWV4e7ujjt37kBZmf5b8qmyshIMw0BdXV00zV9WVobJkydj5MiREh+HYRhs2rRJrJT6pEmT8Msvv7Ae88fMmjULaWlpuHTpEi5fvowzZ87Aw8ND6nGwYeXKlXByckJqairU1NQAAOvWrYODgwOioqIkOgbz1oxlmzZtEBUVhWnTpmHx4sUSxxEZGYmXL19CW1sbx44dQ2FhIX799VcMHz5crF0BaTn6jUgIT+rvhXfq1AkJCQmwsbHB119/jfT0dJ4ja72Sk5Ph4OCAmzdvio1HRERg6NChOHnypMTHWr9+PXbt2iV2O+HLL79EQkICL8kKAFHtEHmWmZmJadOmNdoNOicnR6JjvK879sqVKyWuk3P58mWEhYXh888/x7lz5/Cvf/0LNjY28Pb2xrVr1yR/Q+SjKFEhhCdsXNUR9qSnpyMgIADDhg3Dp59+KvZYcHAwHBwcsHDhQmRmZkp0vMOHDyMqKkps18+UKVOwatUq/Pbbb6zG3pqw0Q167ty5aNeuXYPxMWPGYOfOnRIdo6amBp988gkYhsGlS5cwcOBAUXyqqrT8k0300ySEJ++7qjMwMMC+fft4iqr12rJlC7799lsEBwc3eKy+eikAbN68WaLKo6WlpY1uMTc0NMTTp09bHnAr1dxu0Gx3x67fDdalSxe8ePECQ4cORXV1NWJjYyEQCCR7M0QiVPCNEEIADBgwANu3b4epqel7n5OdnY3Zs2fj4sWLHz3e119/jX79+jVoPrhhwwacPXv2g7uCyPsVFxfDy8sLL1++RGlpKYyNjUXdoHft2vXe+kMbN26UuDu2JDuyrl+/jtmzZ+Off/7BjBkz4O/vj2XLluHo0aPYunUrrKysmvS+yPtRokKIFLF9VUfYY2Njg8OHD6Nbt27vfc7Dhw8xbtw4iW7/pKWlYdasWbCyshK1RMjOzkZWVhZ++uknDB06lK3QP4phGKSlpSEvLw+qqqro3r07BgwYILel3isrK8W6Qffo0YOXbtBCoRDl5eXo2LEjAOD+/fsKsQ5I1tCtH0KkaMKECRJf1RHpMjIywrVr1z6YqGRmZkpcMXjIkCFITEzEzp07ceHCBaiqqsLExAT79++X6q2B0tJSeHt74+bNm+jQoQMYhkF5eTl69eqF+Ph40UlWnrDRDbql3bEBQFlZGbdv30Z+fj5cXFxQV1cn9WSpNaBEhRApmjdvHt8hkPcYO3Ys1q9fD3t7+waLaYE3txzWr18PNzc3iY9pbW0Na2trNsNsslWrVuH169c4fPiwKEHKzc3Fd999h7Vr12L58uW8xicJtrtBs9Edu7y8HN7e3vjrr7+gpKSEQYMGISoqCgUFBYiPj2/0M0Sah279EMIjNq7qCDvq6urg6emJO3fuwM3NDdbW1ujYsSNKS0uRmZmJQ4cOwdDQELt27YK6unqjx5DFEvr29vbYsGED7OzsxMbT09Ph7+8v0XobvgUFBYn+/LFu0MuWLfvo8ZycnDBq1KhGu2O7u7tj6tSpHz1GWFgYcnJysGbNGowdOxZHjhxBdXU1Fi9eDGNjY847ZLcmNKNCCE/YuKoj7FFRUUFCQgKio6Nx4MABJCQkiB7T0dHBN998Ax8fn/cmKYBsltCvra2Fjo5Og3EdHR2Ul5dLJYaWqt9xBbxJWqZOnfrebtCSePz4MSZOnIi2bdtCIBAgOzsbTk5OCAwMxMqVKyVKVM6cOYO1a9dCX19fNGZiYoLQ0FDMmTNHsjdGJEKJCiE8SUxMxIwZMxq9qnN0dOQ7vFapTZs2CAgIgL+/Px4+fIiysjJoa2tDX19forVFp0+fbvTPfOrVqxd2796NpUuXio3v3r0b5ubmPEXVfGx0g2ajO/bz58/RpUuXBuMdO3ZERUWFRMcgkqGCb4TwpLGrOjMzMwQGBmL//v18h9eqqaqqwsjICH369EG3bt3kegH0woULsWfPHkycOBGRkZGIjIyEu7s79uzZg4ULF/IdXpPVd4N+V1O6Qdd3x66srETPnj1x+vRpCIXCj3bHflvv3r1x7NixBuOJiYno2bOnRMcgkqEZFUJ4wsZVHZEtAoFA4qTm1q1bHEfzhrW1NRITExEXF4cLFy6AYRiYmZkhNDQUlpaWUomBTWx0g25ud+zGjnH9+nXU1tZi8+bNyM/Px82bN7Ft27aWvEXyDlpMSwhPZs+ejU6dOiE0NBRJSUnYv38/9uzZg5SUFKxcuRIXLlzgO0TSRAcPHhQlKoWFhYiNjcWkSZNgbW0NNTU1ZGdnIzExET4+PhKfEElDMTEx2LVrl6jCr66uLmbMmNGkRouvX79GRUUFtLW1UVJSgqSkJOjq6jap8eTt27exbds25OTkiOq5TJ8+nYq9sYwSFUJ4kpeXh+nTp2Pq1KmYPHkyxowZgxcvXoiu6vz9/fkOkbTAt99+i/Hjx8Pd3V1s/MiRI9i+fTsOHDjA2Wt/bMfR26S1qJcL//zzD5SUlJpUYK2yshLq6upiM1/5+fnQ09P74ELpd4WHh8PLy+uDdXcIOyhRIYRHbFzVEdlkaWmJpKQkGBgYiI3//fffGDduHP766y/OXtvBwUHs7//73/+gpqYGfX19qKqqoqCgADU1NbCwsMCePXs4i0PWJCcn44cffkBsbCwsLCxE497e3rhx4wbCw8PFmkh+SN++ffH777/j888/5ypc8v+jxbSE8KCyshIMw0BdXR3a2toAgLKyMkyePJmSFAVhYGCAo0ePNhjfu3cvunfvzulrnz59WvTl5eWF/v3748yZMzh69Ch+//13nDt3DoMHD25QW0WRsd0de+jQodi1a5fcbPGWZzSjQoiUsXlVR2TXyZMnMX/+fPTt2xe9e/eGUCjEtWvXcOvWLcTGxn6w0y+bBg4ciLi4uAZl+/Py8uDp6Yn09HSpxME3b29vmJiYNNodu15QUBBKSkok6o7t6emJq1evQklJCZ07d0bbtm3FHj916lSLYyZv0K4fQqSo/qpu/PjxjV7Vbd26FQsXLsTOnTthY2PDU5SEDcOHD0diYiJ27dolWhhtbm6OsLAwqfb6qampabSux7Nnz+R623VT5eTkYMmSJR98joeHB2bPni3R8fr374/+/fuzERr5CJpRIUSK2L6qI+RjlixZghs3biA0NBQWFhZgGAYZGRlYsWIFhg4dipCQEL5DbLLmdINmuzs2kR6aUSFEiti+qiOySygUIikpCZmZmaipqcG714Rvl4XnUkhICBYsWIApU6aIZlAYhsHIkSM/+lmURc3tBs12d2wiPZSoECJFVVVVH90CqaWlhcrKSilFRLgSERGBxMRECAQCaGpq8haHpqYmtm3bhnv37iEvLw/KysowNzcX61EjT5rbDZqL7thEOujWDyFS5ObmBi8vL4wbN+69z/n999+xdetWJCUlSTEywrb+/fsjMDAQEyZM4DuURtV36e7bty/foTRJc7tBs9Edm/CDZlQIkSK6qms9qqur0a9fP77DwI0bNxASEoK8vDwIhcIGj0urlD9bmtsNmo3u2IQfNKNCiBTRVV3rMX/+fPTv3x/ffPMNr3F88803qKqqgpubGyIjIxEYGIiCggIkJiZi9erVGDVqFK/xNdWUKVNgamraoBt0eHg4bty4IVEBu9ra2mZ1x27sOM+ePRP17GIYRjRTNXbs2CYfjzSOEhVCpKy6ulp0VVdWViYa19HRgZubG13VKYi4uDhs2LABgwcPhomJCdTU1MQel1bpemtra2zfvh2WlpaYOHEilixZAltbWyQkJODcuXOIj4+XShxsuXbtGry8vCAQCERb+DMyMpCbm4utW7dKrT7NhQsXsGTJEjx//rzBY+rq6rh27ZpU4mgNKFEhhCdsXdUR2fRuGfu3KSkpSa0gmJWVFY4fPw5dXV0sXrwYNjY28PDwwMOHD/HVV1/h0qVLUomDTdevX0dcXBzu3Lkj6gY9bdo0qXaDdnV1RefOneHp6YkFCxYgKioKRUVF2LBhAyIjI+Hk5CS1WBQdrVEhhCeqqqowMjLiOwzCkdOnT/MdAoA3pfwzMjLg4uICY2NjZGdnAwBevnyJ6upqnqNrHktLS0RHR/Maw927dxEREQGBQABzc3NoaGjA09MTGhoa2LZtGyUqLKJEhRBCOPJuYbIePXrA3t7+g4XJ2Obp6SlazzFixAiMGzcO6urqyMzMRJ8+faQWR0vIYjdoFRUVdOjQAcCbZDAvLw8DBgyAvb09Vq1aJZUYWgtKVAghhAPNLUzGtokTJ6JTp07Q0tKCiYkJIiMjERsbC11dXYSGhkolhpY6ePCg2N8/1A1aWolKjx49cPr0aXh6esLY2BgZGRmYMmUKHj9+LJXXb01ojQohhHAgKCgI169fx9q1axsUJrOxsXlvYTJpqampQVZWlkxsoW6KhIQEnD17FmvXrkXnzp0BAC9evEBAQABMTU3h7+8vlThSU1Mxf/58hIaGYujQoRgxYgTs7e1x+/ZtWFlZYcOGDVKJozWgRIUQQjjQ3MJkbDM3N8e8efPg6+srNl5SUoIhQ4bIXR0VWeoGffPmTaioqEAgEODKlSuIj4+Hrq4u5s+fDy0tLanFoejo1g8hhHCguYXJ2MYwDGJjY3Hv3j1ERESgTZs2Yo/JG1npBj137lz4+fnBxMQEAGBnZ9cgKSXsUOY7AEIIUUS9evXC7t27G4zv3r0b5ubmUotDSUkJsbGxuH79Ory8vMTqfsjjdngHBweEhIQgPT0dr169Qnl5Oc6dO4eQkBCMHj1aanFcvnwZbdu2ldrrtWZ064cQQjggK4XJBAIBLl68CGVlZcyZMwePHz9GTEwMOnfujEGDBsndrZ/y8nIsWLAAFy9ebNANevXq1WIzRlz64Ycf8PTpU8yZMwcGBgZSe93WiBIVQgjhyPXr1xEfH4+8vDzeCpOZm5vjwoUL6Ny5M6qrqxEcHIwzZ84gPDwc/v7+cpeo1OO7G7SzszMKCgreOyslrz9XWUSJCiGEcOTly5f4559/0K1bNwDAH3/8ATs7O6kutKyfUanfIQO8qUuyefNmCIVChTmhSrsb9KFDhz74uKx2zZZHlKgQQggHbt68ienTp8PV1RVLliwB8GZ9RXV1NeLi4mBqaiqVODZt2gRvb2+0a9dObDwpKQn79u3Dzp07pRIHWxStGzT5OEpUCCGEA56envj888+xfPly0fqF2tpahISEoLi4GHFxcTxHKJ9kqRv0uXPnsG3bNty7dw979+7FwYMH0a1bN4wbN05qMbQGtD2ZEEI4cOPGjQbbgVVVVTFz5ky4u7tz+toODg4S7ehRUlJCamoqp7GwLScnR9QN+uDBgzA1NYWHhwe6du2Kffv2SS1RuXjxIubOnYvRo0cjKysLQqEQtbW1CAoKAsMwGD9+vFTiaA0oUSGEEA60b98eDx8+bLDI88mTJ5zvEJkwYYJcbj2WhFAoRJcuXQD8X48dW1tbODo6IiYmRmpxbNy4EYsWLcLUqVNx4sQJAICfnx80NTWxbds2SlRYRIkKIYRwYMSIEVi+fDmWLVsm2uWTnZ2NsLAwDB8+nNPXnjdvHqfH55OsdIO+ffs2Vq9e3WB85MiRTWqiSD6OEhVCCOHAokWLUFBQgGnTponNbgwfPhwBAQFSjSU3N1ds8SnDMKJdMuHh4VKNpaVkpRt0hw4d8OTJE9GOrnp3797FJ598IrU4WgNKVAghhAMaGhqi0vV37tyBqqoqTExMYGhoKNU44uPjsWrVKgBv1qTU759QUlKCra2tVGNhg6x0gx4zZgwiIiIQEREBJSUlvHr1CufPn8eKFSvw5ZdfSi2O1oB2/RBCiJQ9fvwYXbt2lcprOTk5YdSoUZg7dy6GDRuGQ4cOobS0FIsWLYK7uzumTp0qlTi4Ju1u0DU1NQgMDMTRo0cB/F8S+MUXXyA6Ohrq6upSiaM1oESFEEI48PDhQ6xatQp5eXmoq6sD8H+3XJ4/f46cnBypxGFhYYGUlBR069YN06dPh4eHB5ycnHDhwgWsXLkSycnJUomDLbLWDfrBgwe4desWhEIhTE1N0b17dzAMo7CLmflATQkJIYQDYWFhuH37NkaMGIHi4mKMHj0avXr1QklJCZYtWya1ODQ0NESJUrdu3XD37l0AgImJCQoLC6UWB1vqu0EvXry4weJZaV53Ozo6orS0FAYGBhg5ciS+/PJLdO/eHcXFxVLr49Ra0BoVQgjhQGZmJn7++Wf0798faWlpcHJygqWlJX788UecO3cOX331lVTisLGxwZYtWxAaGoqePXti//79mDlzJjIyMtC+fXupxMCm+m7QwcHB8PLyws8//wxtbW3RY1xKSUlBWloaAKCwsBBhYWENOigXFhbSbArLaEaFEEI4UF1dLdoRYmRkhNu3bwMAxo8fj7/++ktqcfj7+yMtLQ2JiYkYPXo0SkpKYGdnhyVLlsDV1VVqcbCFYRgYGRlh7969UFZWhru7O+7cuQNlZe5PZ9bW1igsLMSjR48AAEVFRXj06JHoq7CwEBoaGqLFy4QdNKNCCCEc0NPTQ15eHnR1dWFkZCRaOyEUCvHq1SupxWFqaorU1FRUVFSgffv22LdvH5KSkqCrq4uRI0dKLQ621M9WdOrUCQkJCQgODsbXX38tlW3Wurq62LFjB4A326Q3bdpEW5GlgBbTEkIIB2JiYhAXF4fVq1ejU6dO8PLywty5c3Hx4kVUVlZiz549nMdQWVkJdXV1sVsR+fn50NPTk9tdKbLWDbqoqAj5+fno168fXr16JRYXYQfd+iGEEA7MnDkTPj4+YBgGlpaW8PX1xebNm/H06VP85z//4fz1k5OT4eDggJs3b4qNR0REYOjQoTh58iTnMXBh7ty50NDQaDC2cuVKqdaFqampgZ+fHxwcHDBr1izRv+u0adNQXl4utThaA5pRIYQQBZOeno5p06Zh/Pjx8PPzE/XGAd7MqGzduhVHjhzBzp07YWNjw2Ok8mv9+vU4fvw4li1bhtmzZ+PIkSMoKipCUFAQ/vWvf0l1Z5eio0SFEEIUjLe3N0xMTBAcHPze5wQFBaGkpASxsbFSjKx5ZLEbtLOzM5YtW4aBAwfC2toaR44cgb6+Pi5duoSAgADR7iDScrSYlhBCFExOTg6WLFnywed4eHhg9uzZUoqoZWSxG3RxcXGDPj/AmwW3ZWVlPESkuChRIYQQBVNVVfXRxbJaWlqorKyUUkQtI4vdoE1MTHDp0iVMnDhRbPzo0aPo3r07T1EpJkpUCCFEwRgZGeHatWuNXvHXy8zMhJ6enhSjYo8sdIOeN28e/Pz8cPfuXdTV1eHQoUO4f/8+Tpw4gR9//FEqMbQWlKgQQghHamtr8ezZswa9frKzszF27FjOXnfs2LFYv3497O3t8emnnzZ4vLi4GOvXr4ebmxtnMXBFVrpBDxs2DBs2bEBMTAxUVFSwbds29OjRAz/++CNGjBghtThaA1pMSwghHLhw4QKWLFmC58+fN3hMXV0d165d4+y16+rq4OnpiTt37sDNzQ3W1tbo2LEjSktLkZmZiUOHDsHQ0BC7du2Su3oqstINetu2bXBxcWk0ESTsokSFEEI44Orqis6dO8PT0xMLFixAVFQUioqKsGHDBkRGRsLJyYnT16+urkZ0dDQOHDggtrhTR0cHbm5u8PHxkbskBZCdbtC2trY4cOAADAwMpPJ6rRnd+iGEEA7cvXsXEREREAgEMDc3h4aGBjw9PaGhoYFt27Zxnqi0adMGAQEB8Pf3x8OHD1FWVgZtbW3o6+vL3A6apmisG7STk5PUu0FbWVnh9OnTmDZtmtRes7WiyrSEEMIBFRUVdOjQAQBgYGCAvLw8AIC9vT3y8/OlFoeqqiqMjIzQp08fdOvWTa6TFOD/ukFXVlaiZ8+eOH36NIRCodS7QWtqamL16tXo378/Jk2aBC8vL7Evwh5KVAghhAM9evTA6dOnAQDGxsbIyMgAADx+/JjPsOSerHSD1tDQwPjx4+Hg4ABjY2Po6emJfRH20BoVQgjhQGpqKubPn4/Q0FAMHToUI0aMgL29PW7fvg0rKyts2LCB7xDl1uvXr1FRUQFtbW2UlJTw0g06OTkZQ4YMoe7JUkCJCiGEcOTmzZtQUVGBQCDAlStXEB8fD11dXcyfPx9aWlp8hyd3ZKkbtJ2dHX799Vcq7iYFdOuHEEI4MHfuXKirq0MgEAB4c2LbvHkzQkNDKUlpBlnrBm1oaChad0S4Rbt+CCGEA5cvX0bbtm35DkMhpKenIyAgAOPHj29QtyQ4OBhbt27FwoULpdoNWiAQYPHixdi6dSsMDQ0b/FtHRkZKJY7WgG79EEIIB3744Qc8ffoUc+bMgYGBAdq0acN3SHJLFrtBe3p6fvDxnTt3SiWO1oASFUII4YCzszMKCgreux341q1bUo5Ifg0YMADbt2+Hqanpe5+TnZ2N2bNn4+LFi1KMjEgD3fohhBAO+Pj48B2CwpDVbtB89XJqbShRIYQQDkyYMIHvEBSGLHaD/lgvJ0pU2EO7fgghhCPnzp2Dl5cXBg8ejMLCQmzcuBG///4732HJnfpu0MXFxY0+Xt8NWpp1VNatW4eePXsiJiYG6urq2LRpE4KDg6GpqYk1a9ZILY7WgGZUCCGEAxcvXsTcuXMxevRoZGVlQSgUora2FkFBQWAYBuPHj+c7RLnx7bff4sSJE3BxcflgN2hvb2+pxcR3L6fWhBIVQgjhwMaNG7Fo0SJMnToVJ06cAAD4+flBU1MT27Zto0SlCVRUVJCQkCDqBp2QkCB6TEdHB998843Uu0E31stpwIABsLe3x6pVq6QWR2tAt34IIYQDt2/fhoODQ4PxkSNHoqCggIeI5Ft9N+iLFy/i2LFj2LNnD/744w+kpaXBz89P6pVpqZeT9NCMCiGEcKBDhw548uRJgwWgd+/epf4wLVDfDZpvM2fOxPz586GmpgYXFxds3LgRM2fOxO3bt2Fvb893eAqFZlQIIYQDY8aMQUREBHJzc6GkpIRXr17h/PnzWLFiBb788ku+wyMt5OTkhN9++w19+vSBrq4utm7dChUVFTg6OiIsLIzv8BQKFXwjhBAO1NTUIDAwEEePHgUAKCkpgWEYfPHFF4iOjpb6rQrCvr///hvl5eWwsLAAAGzfvh1Dhw6FoaEhv4EpGEpUCCGEQw8ePMCtW7cgFAphamqK7t27g2GY91asJfLhv//9L3x8fDB16lT4+fkBACZNmoS8vDzExsbC1taW5wgVByUqhBDCAUdHRxw4cKBBp+Ti4mKMHTsW6enp/ARGWOHu7o5BgwaJkpR669atw5UrV7Bnzx6eIlM8tJiWEEJYkpKSgrS0NABAYWEhwsLCGnTVLSwspNkUBXD37l38+OOPDcYnTpxIDQlZRokKIYSwxNraGnv27EH9RHVRURHU1NREjyspKUFDQ4PqbCgAbW1t5ObmQl9fX2z8zp07ovoqhB2UqBBCCEt0dXWxY8cOAICnpyc2bdpEW5EV1Lhx47Bs2TKUlpbCysoKwJsOztHR0VTMj2W0RoUQQjhUVFSE/Px89OvXD69evULnzp35DomwoLa2FuHh4Thw4ABqa2vBMAxUVVXh6emJRYsWQVWV5gHYQokKIYRwoKamBgEBATh27BiUlZVx4sQJrFq1Cq9evcLGjRuhqanJd4iEBa9evcL9+/ehqqoKQ0ND2nbOASr4RgghHPj555+Rm5uL7du3ixbUenp64sGDB4iKiuI5OsKW9u3bw8LCAgKBgJIUjlCiQgghHDh69ChCQkLQv39/0Vj//v3xww8/4NSpUzxGRoh8oUSFEEI4UFxc3KDPD/BmwW1ZWRkPEREinyhRIYQQDpiYmODSpUsNxo8ePYru3bvzEBEh8omWJRNCCAfmzZsHPz8/3L17F3V1dTh06BDu37+PEydONFoojBDSONr1QwghHDl//jxiYmKQk5MDoVCIHj16YMaMGRgxYgTfoZEWevr0KaKjo5GZmYmamhq8eyqldUjsoRkVQgjhwLZt2+Di4oLExES+QyEcCAkJwY0bNzB69GiqRMsxmlEhhBAO2Nra4sCBAzAwMOA7FMKBPn36YOvWrdQlWQpoMS0hhHDAysoKp0+f5jsMwhENDQ2qMiwlNKNCCCEcWLBgAf744w907NgRhoaGDboo1/cEIvJp1apVePHiBcLCwqCiosJ3OAqN1qgQQggHNDQ0qDmdAistLUVycjLOnj0LfX19tGnTRuxxSkTZQ4kKIYRwYNCgQRgyZAh1T1ZgLi4ufIfQKtCtH0II4YCdnR1+/fVXKu5GSAvRYlpCCOGAoaEh8vLy+A6DcOjJkyfYtGkTFi1ahGfPnuH48eO4d+8e32EpHJpRIYQQDoSGhmL//v0QCASNLqaNjIzkKTLChgcPHuCrr76CpqYmiouLcezYMaxZswZpaWlISEiAlZUV3yEqDJpRIYQQDty/fx99+/ZF+/bt8fTpUzx69Ejsi8i3lStXwsnJCampqVBTUwMArFu3Dg4ODoiKiuI5OsVCi2kJIYQDO3fu5DsEwqHMzEwkJiZCSUlJNKaqqgpfX1989dVXPEameChRIYQQjtTW1uLZs2eoq6sDADAMg+rqamRnZ2Ps2LE8R0daQigUQigUNhh/9eoV1VVhGSUqhBDCgQsXLmDJkiV4/vx5g8fU1dUpUZFzgwcPRkxMDNasWSMaKy0txZo1a2Bvb89jZIqHFtMSQggHXF1d0blzZ3h6emLBggWIiopCUVERNmzYgMjISDg5OfEdImmB4uJieHl54eXLlygtLYWxsTEKCwuhpaWFXbt2QU9Pj+8QFQbNqBBCCAfu3r2LiIgICAQCmJubQ0NDA56entDQ0MC2bdsoUZFzn376KQ4fPozk5GTcunULQqEQkydPxrhx46Cpqcl3eAqFEhVCCOGAiooKOnToAAAwMDBAXl4eBgwYAHt7e6xatYrn6Agb2rVrh4kTJ/IdhsKj7cmEEMKBHj16iLonGxsbIyMjAwDw+PFjPsMiRO7QjAohhHBg5syZmD9/PtTU1ODi4oKNGzdi5syZuH37Ni22JKQJaDEtIYRw5ObNm1BRUYFAIMCVK1cQHx8PXV1dzJ8/H1paWnyHR4hcoBkVQgjhSPv27VFeXg7gTZPCW7duYejQoZSkENIElKgQQggH/vvf/8LHxwdTp06FhYUFACAlJQXR0dGIjY2Fra0tzxGSlggKCmp0XElJCWpqaujatStGjhwJIyMjKUemeOjWDyGEcMDd3R2DBg2Cn5+f2Pi6detw5coV7Nmzh6fICBsWL16Mo0ePQkdHB5aWlgDe3OorLi6GlZUVSktL8b///Q9xcXHo27cvz9HKN9r1QwghHLh79y7c3d0bjE+cOBG3b9/mISLCJnV1dTg7O+PUqVP46aef8NNPP+HkyZNwcXFBz549cfz4cXh6eiI6OprvUOUeJSqEEMIBbW1t5ObmNhi/c+eOqL4KkV/Hjx/HvHnz0KZNG9GYmpoaZs2ahaSkJABvZtVu3rzJV4gKg9aoEEIIB8aNG4dly5ahtLQUVlZWAIDs7GxER0dj/Pjx/AZHWkxVVRUlJSXo3r272PjTp09FHZXr6uqgqkqn2ZainyAhhHBgzpw5+OeffxAWFoba2lowDANVVVVR7x8i30aMGIHQ0FAsW7YMVlZWYBgGWVlZWLFiBRwdHVFRUYHNmzejd+/efIcq92gxLSGEcOjVq1e4f/8+VFVVYWhoCHV1db5DIix4/fo1AgIC8Mcff4hmUABg1KhRCAsLw+XLlxEWFoYtW7ZAIBDwGKn8o0SFEEIIaaaHDx/i1q1bUFFRgZmZGT7//HMAQHV1tdj6FdJ8lKgQQgghzVRSUoKamhq8eyr97LPPeIpI8dAaFUIIIaSJMjMzERQUhIKCArFxhmGgpKSEW7du8RSZ4qEZFUIIIaSJXF1doaGhgWnTpjW63dzOzo6HqBQTzagQQgghTXTnzh0cPnwYJiYmfIei8ChRIYQQDjx9+hTR0dHIzMxsdA3DqVOneIqMsEFXVxevXr3iO4xWgRIVQgjhQEhICG7cuIHRo0dTJVoF5OPjg4iICCxfvhzGxsZQU1PjOySFRWtUCCGEA3369MHWrVupS7KCcnZ2RlFREerq6hp9nBbTsodmVAghhAMaGhro3Lkz32EQjvj4+PAdQqtBMyqEEMKBVatW4cWLFwgLC4OKigrf4RAit2hGhRBCOFBaWork5GScPXsW+vr6DaqU7tixg6fISHMFBQVh6dKl0NTURFBQ0Hufp6SkhIiICClGptgoUSGEEI64uLjwHQJh0aNHjyAUCkV/JtJBt34IIYQQIrNoRoUQQjjy5MkT7Nu3D/fv30dwcDCuXr0KU1NTGBsb8x0aYUFlZSXy8vIa1MlRUlKi3V4sohkVQgjhwIMHD/DVV19BU1MTxcXFOHbsGNasWYO0tDQkJCTAysqK7xBJC5w6dQqBgYEoLy9vUMyPev2wixIVQgjhgI+PD7S1tREeHg4bGxscOXIEurq6WLJkCZ48eYKdO3fyHSJpgVGjRqFHjx7w9fVttKCfnp4eD1EpJrr1QwghHMjMzERiYiKUlJREY6qqqvD19cVXX33FY2SEDY8ePUJMTAy6devGdygKT5nvAAghRBEJhULRDpG3vXr1iuqqKABDQ0M8fvyY7zBaBUpUCCGEA4MHD0ZMTIxYslJaWoo1a9bA3t6ex8gIGxYvXowVK1bg9OnT+Pvvv1FUVCT2RdhDa1QIIYQDxcXF8PLywsuXL1FaWgpjY2MUFhZCS0sLu3btojUMcq5Xr16iPj9v395jGIYW07KMEhVCCOFIZWUlkpOTcevWLQiFQvTo0QPjxo2DpqYm36GRFrpy5coHH7ezs5NSJIqPEhVCCCGEyCza9UMIIYQ0UUVFBRISEpCZmdmg4BtAvZzYRIkKIYQQ0kShoaE4deoUBg0ahC5duvAdjkKjWz+EEEJIE/Xt2xdRUVEYNmwY36EoPNqeTAghhDSRsrIyTExM+A6jVaAZFUII4UBQUFCj40pKSlBTU0PXrl0xcuRIGBkZSTkywoalS5eiS5cuWLhwId+hKDxao0IIIRyoqanB0aNHoaOjA0tLSwDAzZs3UVxcDCsrK6Snp+OXX35BXFwc+vbty3O0pKm0tbURFxeH8+fPw8jICG3atBF7PDIykqfIFA8lKoQQwgF1dXU4OztjzZo1opNYTU0NgoOD0aFDB4SGhiIqKgrR0dHUoFAOZWVliTpgP3nyhOdoFBvd+iGEEA7Y2tpiz5496N69u9j43bt3MXnyZFy9ehV///03XF1dkZmZyVOUhMg+mlEhhBAOqKqqoqSkpEGi8vTpU1HJ9bq6Oqiq0q9hefSxfj6fffaZlCJRfPQ/hBBCODBixAiEhoZi2bJlsLKyAsMwyMrKwooVK+Do6IiKigps3rwZvXv35jtU0gwODg5iPX7eRb1+2EO3fgghhAOvX79GQEAA/vjjD7ET2qhRoxAWFobLly8jLCwMW7ZsgUAg4DFS0hzv9vqpq6vD/fv3kZCQgMDAQDg4OPAUmeKhRIUQQjj08OFD3Lp1CyoqKjAzM8Pnn38OAKiurm6wU4TIv7S0NGzcuBH79u3jOxSFQbd+CCGEQ+3atUPv3r1FvWDq1zbQGgbFZGhoiNzcXL7DUCiUqBBCCAcyMzMRFBSEgoICsXGGYaCkpERrGORcY4tpy8vLERMTI5o1I+ygRIUQQjgQHh6OLl26ICAgAB06dOA7HMKyxhbTMgwDDQ0NrFmzhqeoFBOtUSGEEA707t0bhw8fpn4wCurdxbQAoKamBlNTU7Rv356HiBQXzagQQggHdHV18erVK77DIBzZsWMH/Pz8KBGVAuqeTAghHPDx8UFERARu376NmpoavsMhLLt8+TLatm3LdxitAt36IYQQDjg7O6OoqAh1dXWNPk6LaeXbDz/8gKdPn2LOnDkwMDCgreYcokSFEEI4cOjQoQ8+PmHCBClFQrjg7OyMgoKC91anpUSUPZSoEEIIIU1Eiaj0UKJCCCEsCQoKwtKlS6GpqYmgoKD3Pk9JSQkRERFSjIwQ+UW7fgghhCWPHj2CUCgU/ZkQ0nI0o0IIIYQQmUUzKoQQwpHKykrk5eWhpqYGb18TKikpwdbWlsfICJEflKgQQggHTp06hcDAQJSXl+PdiWvq9UOI5OjWDyGEcGDUqFHo0aMHfH19G+31o6enx0NUhE1PnjzBvn37cO/ePSxduhRXr16FqakpjI2N+Q5NoVCiQgghHOjduzeOHj2Kbt268R0K4cCDBw/w1VdfQVNTE8XFxTh27BjWrFmDtLQ0JCQkwMrKiu8QFQaV0CeEEA4YGhri8ePHfIdBOLJy5Uo4OTkhNTUVampqAIB169bBwcEBUVFRPEenWChRIYQQDixevBgrVqzA6dOn8ffff6OoqEjsi8i3zMxMTJs2TawyraqqKnx9fZGTk8NjZIqHFtMSQggHfH19UVdXB19fX7GTGcMwtJhWAQiFQlHNnLe9evUKKioqPESkuChRIYQQDsTHx/MdAuHQ4MGDERMTgzVr1ojGSktLsWbNGtjb2/MYmeKhxbSEEEJIExUXF8PLywsvX75EaWkpjI2NUVhYCC0tLezatYt2dbGIEhVCCOFARUUFEhISkJmZ2aDgGwDs2LGDp8gIWyorK5GcnIxbt25BKBSiR48eGDduHDQ1NfkOTaHQrR9CCOFAaGgoTp06hUGDBqFLly58h0M40K5dO0ycOJHvMBQezagQQggH+vbti6ioKAwbNozvUAhLvLy8JH4uzZixh7YnE0IIB5SVlWFiYsJ3GIRFenp6oi8dHR1cuXIFL1++hImJCczMzFBTU4OMjAyqTMsymlEhhBAOLF26FF26dMHChQv5DoVwICgoCJ988gkCAwPFxqOjo5Gfn4+NGzfyFJnioTUqhBDCAW1tbcTFxeH8+fMwMjJCmzZtxB6PjIzkKTLChuPHj+PQoUMNxsePH4/x48dLPyAFRokKIYRwICsrS9Tv5cmTJzxHQ9jWsWNH5OTkwNDQUGz8zz//ROfOnfkJSkFRokIIIRzYuXMn3yEQDk2aNAmhoaHIz8+HhYUFhEIhMjMzkZiYiO+++47v8BQKrVEhhBAOfKyfz2effSalSAhXYmJisGvXLjx9+hQAoKurixkzZsDDw4PnyBQLJSqEEMIBgUAg1uPnXdTrR3H8888/UFJSgpaWFt+hKCS69UMIIRx4t45GXV0d7t+/j4SEhAY7RYh869SpE98hKDSaUSGEEClKS0vDxo0bsW/fPr5DIUQuUME3QgiRIkNDQ+Tm5vIdBiFyg279EEIIBxpbTFteXo6YmBh8/vnnPEREiHyiRIUQQjjg4ODQYDEtwzDQ0NDAmjVreIqKsIlhGKSlpSEvLw+qqqro3r07BgwYABUVFb5DUyi0RoUQQjhw5cqVBmNqamowNTVF+/bteYiIsKm0tBTe3t64efMmOnToAIZhUF5ejl69eiE+Ph4dO3bkO0SFQWtUCCGEAzt27EDnzp1hZ2cn+rK2tqYkRUGsWrUKr1+/xuHDh3H16lX8+eefOHz4MKqrq7F27Vq+w1MolKgQQggHLl++jLZt2/IdBuHImTNn8J///AcCgUA0JhAI8P333yM1NZXHyBQPJSqEEMKBCRMmICoqCnfu3EF1dTXf4RCW1dbWQkdHp8G4jo4OysvLeYhIcdFiWkII4cC5c+dQUFCAEydONPo4VaaVb7169cLu3buxdOlSsfHdu3fD3Nycp6gUEyUqhBDCAR8fH75DIBxauHAhvLy8kJWVBRsbGwBARkYGcnNzsXXrVp6jUyy064cQQghphuvXryMuLg537twBwzAwMzPDtGnTYGlpyXdoCoUSFUIIIYTILLr1QwghhEhg06ZNEj937ty5HEbSutCMCiGEECIBBwcHsb//73//g5qaGvT19aGqqoqCggLU1NTAwsICe/bs4SlKxUMzKoQQQogETp8+LfpzQkICzp49i7Vr16Jz584AgBcvXiAgIACmpqZ8haiQaEaFEEI48uTJE+zbtw/37t3D0qVLcfXqVZiamsLY2Jjv0EgLDRw4EHFxcWIF3wAgLy8Pnp6eSE9P5ykyxUMF3wghhAMPHjzAmDFjcOjQIfzxxx+oqKhASkoK3Nzc8Ndff/EdHmmhmpoaVFRUNBh/9uxZg2aUpGUoUSGEEA6sXLkSTk5OSE1NhZqaGgBg3bp1cHBwQFRUFM/RkZZycHBASEgI0tPT8erVK5SXl+PcuXMICQnB6NGj+Q5PodAaFUII4UBmZiYSExPFrq5VVVXh6+uLr776isfICBtCQkKwYMECTJkyRfRvzDAMRo4ciSVLlvAcnWKhRIUQQjggFAohFAobjL969QoqKio8RETYpKmpiW3btuHevXvIy8uDsrIyzM3Noa+vz3doCodu/RBCCAcGDx6MmJgYsWSltLQUa9asgb29PY+RETYZGxtj5MiRcHZ2hr6+Pqqrq5GRkcF3WAqFdv0QQggHiouL4eXlhZcvX6K0tBTGxsYoLCyElpYWdu3aBT09Pb5DJC1w48YNhISEIC8vr9GZM2o6yR5KVAghhCOVlZVITk7GrVu3IBQK0aNHD4wbNw6ampp8h0Za6JtvvkFVVRXc3NwQGRmJwMBAFBQUIDExEatXr8aoUaP4DlFh0BoVQgjhSLt27TBx4kS+wyAcyMnJwfbt22FpaYmDBw/C1NQUHh4e6Nq1K/bt20eJCosoUSGEEJZ4eXlJ/NwdO3ZwGAnhmlAoRJcuXQAABgYGyMvLg62tLRwdHRETE8NzdIqFFtMSQghL9PT0RF86Ojq4cuUKXr58CRMTE5iZmaGmpgYZGRlUmVYBGBgYiBbNGhsbIzs7GwDw8uVLVFdX8xmawqEZFUIIYUlkZKToz0FBQZg6dSoCAwPFnhMdHY38/Hxph0ZY5unpiaVLlwIARowYgXHjxkFdXR2ZmZno06cPv8EpGFpMSwghHLC2tsahQ4dgaGgoNv73339j/PjxyMrK4iUuwp7U1FRoaWnB1tYWSUlJiI2Nha6uLkJDQ2lXF4toRoUQQjjQsWNH5OTkNEhU/vzzT1G3XSLfnJycRH8eM2YMxowZg5qaGmRlZVGiwiJKVAghhAOTJk1CaGgo8vPzYWFhAaFQKCqr/9133/EdHmkhc3NzzJs3D76+vmLjZWVl8PLyojoqLKJbP4QQwpGYmBjs2rULT58+BQDo6upixowZ8PDw4Dky0lICgQDt2rWDo6MjIiIi0KZNGwBASUkJBg8ejNzcXJ4jVByUqBBCCMf++ecfKCkpQUtLi+9QCEvMzc2xc+dOBAcHQ1tbGz///DO0tbVRUlKCIUOG0IwKi2h7MiGEcKxTp06UpCgYhmFgZGSEvXv3QllZGe7u7rhz5w6Ulem0yjb6iRJCCCFNpKSkBOBNEpqQkAAbGxt8/fXXSE9P5zkyxUOJCiGEENJEb6+aaNOmDaKiojBt2jQsXryYx6gUE+36IYQQQppo7ty50NDQaDBmYGCAffv28RSVYqLFtIQQwhGGYZCWloa8vDyoqqqie/fuGDBgAFRUVPgOjRC5QTMqhBDCgdLSUnh7e+PmzZvo0KEDGIZBeXk5evXqhfj4eHTs2JHvEEkTOTg4iNamfIiSkhJSU1OlEFHrQIkKIYRwYNWqVXj9+jUOHz4MgUAAAMjNzcV3332HtWvXYvny5TxHSJpqwoQJEiUqhF1064cQQjhgb2+PDRs2wM7OTmw8PT0d/v7+uHjxIk+RESJfaEaFEEI4UFtbCx0dnQbjOjo6KC8v5yEiwrbc3Fzk5eVBKBQCeLMmqbq6GtnZ2QgPD+c5OsVBiQohhHCgV69e2L17N5YuXSo2vnv3bpibm/MUFWFLfHw8Vq1aBeDNmpT6mxNKSkqwtbXlMzSFQ4kKIYRwYOHChfDy8kJWVhZsbGwAABkZGcjNzcXWrVt5jo60VGJiImbMmIG5c+di2LBhOHToEEpLS7Fo0SI4OjryHZ5CoYJvhBDCAWtrayQmJkJPTw8XLlxAWloa9PX18euvv8Le3p7v8EgLPX78GBMnTkTbtm0hEAiQnZ0NMzMzBAYGYv/+/XyHp1BoRoUQQjhiaWmJ6OhovsMgHNDQ0EBdXR0AoFu3brh79y6cnJxgYmKCwsJCnqNTLJSoEEIISzZt2iTxc+fOncthJIRrNjY22LJlC0JDQ9GzZ0/s378fM2fOREZGBtq3b893eAqFticTQghLHBwcxP7+v//9D2pqatDX14eqqioKCgpQU1MDCwsL7Nmzh6coCRvy8vIwffp0TJ06FZMnT8aYMWPw4sULVFZWwtvbG/7+/nyHqDAoUSGEEA4kJCTg7NmzWLt2LTp37gwAePHiBQICAmBqakonMgXw+vVrVFRUQFtbGyUlJUhKSoKuri5GjhzJd2gKhRIVQgjhwMCBAxEXFyeqSlsvLy8Pnp6eSE9P5yky0lKVlZVQV1cXq1Kbn58PPT09qKur8xiZYqJdP4QQwoGamhpUVFQ0GH/27BmVYZdjycnJcHBwwM2bN8XGIyIiMHToUJw8eZKnyBQXJSqEEMIBBwcHhISEID09Ha9evUJ5eTnOnTuHkJAQjB49mu/wSDOkp6cjICAAw4YNw6effir2WHBwMBwcHLBw4UJkZmbyFKFiols/hBDCgfLycixYsAAXL14UzaAwDIORI0di9erVaNOmDc8Rkqby9vaGiYkJgoOD3/ucoKAglJSUIDY2VoqRKTZKVAghhEP37t1DXl4elJWVYW5uDn19fb5DIs00YMAAbN++Haampu99TnZ2NmbPnk1NJ1lEdVQIIYRDxsbGMDY2Fv29vmld3759eYyKNEdVVdVHF8tqaWmhsrJSShG1DpSoEEIIB27cuIGQkBCx7rpvu3XrFg9RkZYwMjLCtWvX0K1bt/c+JzMzE3p6elKMSvHRYlpCCOFAZGQkVFRU8P3330NNTQ0hISGYMmUKVFVVsW7dOr7DI80wduxYrF+/HsXFxY0+XlxcjPXr11MdFZbRjAohhHAgJycH27dvh6WlJQ4ePAhTU1N4eHiga9eu2LdvH0aNGsV3iKSJvv32W5w4cQIuLi5wc3ODtbU1OnbsiNLSUmRmZuLQoUMwNDSEt7c336EqFEpUCCGEA0KhEF26dAEAGBgYIC8vD7a2tnB0dERMTAzP0ZHmUFFRQUJCAqKjo3HgwAEkJCSIHtPR0cE333wDHx8fKvrGMkpUCCGEAwYGBsjIyICLiwuMjY2RnZ0NAHj58iWqq6t5jo40V5s2bRAQEAB/f388fPgQZWVl0NbWhr6+PhXy4wglKoQQwgFPT08sXboUADBixAiMGzcO6urqyMzMRJ8+ffgNjrSYqqoqjIyM+A6jVaA6KoQQwpHU1FRoaWnB1tYWSUlJiI2Nha6uLkJDQ2lnCCESokSFEEKkqKamBllZWejXrx/foRAiF2h7MiGEcMDc3Bw///xzg/GysjJ4eXnxEBEh8okSFUII4QDDMIiNjcXixYsbLJ6liWxCJEeJCiGEcEBJSQmxsbG4fv06vLy88Pz5c7HHCCGSoUSFEEI4wDAMjIyMsHfvXigrK8Pd3R137tyBsjL92iWkKeh/DCGEcKB+1qRTp05ISEiAjY0Nvv76a6Snp/McGSHyhRIVQgjhwNvrUNq0aYOoqChMmzYNixcv5jEqQuQPFXwjhBAOzJ07FxoaGg3GDAwMsG/fPp6iIkT+UB0VQgghhMgsmlEhhBCWODg4SLSjR0lJCampqVKIiBD5R4kKIYSwZMKECbT1mBCW0a0fQgghhMgsmlEhhBCO5ObmIi8vD0KhEMCbnUDV1dXIzs5GeHg4z9ERIh8oUSGEEA7Ex8dj1apVAN6sSamfvFZSUoKtrS2foREiV6iOCiGEcCAxMREzZszAX3/9hU6dOuHcuXP4/fffYWJiAkdHR77DI0RuUKJCCCEcePz4MSZOnIi2bdtCIBAgOzsbZmZmCAwMxP79+/kOjxC5QYkKIYRwQENDA3V1dQCAbt264e7duwAAExMTFBYW8hkaIXKFEhVCCOGAjY0NtmzZgsrKSvTs2ROnT5+GUChERkYG2rdvz3d4hMgNSlQIIYQD/v7+SEtLQ2JiIkaPHo2SkhLY2dlhyZIlcHV15Ts8QuQG1VEhhBCOvH79GhUVFdDW1kZJSQmSkpKgq6uLkSNH8h0aIXKDEhVCCGFZZWUl1NXVxarU5ufnQ09PD+rq6jxGRoj8oVs/hBDCouTkZDg4OODmzZti4xERERg6dChOnjzJU2SEyCdKVAghhCXp6ekICAjAsGHD8Omnn4o9FhwcDAcHByxcuBCZmZk8RUiI/KFbP4QQwhJvb2+YmJggODj4vc8JCgpCSUkJYmNjpRgZIfKLZlQIIYQlOTk5cHd3/+BzPDw8kJOTI6WICJF/lKgQQghLqqqqPrpYVktLC5WVlVKKiBD5R4kKIYSwxMjICNeuXfvgczIzM6GnpyeliAiRf5SoEEIIS8aOHYv169ejuLi40ceLi4uxfv16qqNCSBPQYlpCCGFJXV0dPD09cefOHbi5ucHa2hodO3ZEaWkpMjMzcejQIRgaGmLXrl1UT4UQCVGiQgghLKqurkZ0dDQOHDiAsrIy0biOjg7c3Nzg4+NDSQohTUCJCiGEcKC2thYPHz5EWVkZtLW1oa+vL1aplhAiGUpUCCGEECKzaDEtIYQQQmQWJSqEEEIIkVmUqBBCCCFEZlGiQgiRGloSRwhpKkpUCCGce/HiBQICAvDnn3/yHQohRM5QokII4dytW7fw+++/QygU8h0KIUTOUKJCCCGEEJlFiQoh5KMYhkFCQgJGjRoFS0tLDB8+HNu2bROtOfntt9/g6uqKPn36wNLSEuPGjcOxY8cAAOnp6fDy8gIAeHl5wdPTU3Tc1NRUuLq6onfv3hg0aBDCw8NRUVEh9tpnz56Fq6srLC0tMWLECCQnJ2P48OHYuHGj6DlPnjxBUFAQhg4dCktLS7i7u+PUqVNixzEzM8OmTZtEx9q0aRN69+6NdevWiT2vsrISffv2xebNm9n7ARJCmk2V7wAIIbJv9erV2L59O6ZNm4ZBgwYhOzsbUVFRqK2thaamJsLDwzFv3jz07dsXZWVliI2NxeLFi2FtbY1evXohNDQUYWFhCA0NRf/+/QEASUlJWLx4McaMGYOFCxeisLAQP/74I+7evYv4+HgoKSnh8uXL8PX1xbBhw7BgwQI8ePAA//nPf1BVVSWKraSkBO7u7mjbti38/PzQqVMnHDx4EHPmzMHq1asxduxY0XN/+eUXLFq0CEZGRtDT00N+fj6SkpLg5+cnqhp78uRJVFRUYPz48VL9GRNCGkeJCiHkg168eIEdO3bg22+/xXfffQcAGDhwIJ4+fYqrV6+ie/fu8Pb2hq+vr+h79PT04OrqioyMDIwePRrdu3cHAHTv3h3du3cHwzCIiorCkCFDEBUVJfo+Q0NDTJ06FefOncMXX3yBjRs3okePHti0aZMokejcuTP8/f1F3xMfH4/nz5/jxIkT0NPTAwAMHToUU6dOxerVq+Hi4gJl5TeTx7a2tpg2bZroe93c3JCSkoL09HTY29sDAA4fPoyBAwdCV1eXix8nIaSJ6NYPIeSDsrKyUFtbC2dnZ7Hx77//Hlu3bkVgYCAWL16MFy9eICsrC7///jsSExMBvGnQ15h79+7h8ePHcHBwQG1treirX79+0NTUxMWLF1FdXY1r167B2dlZrEfOyJEjoar6f9dYV65cgbW1tShJqTd27Fg8ffoU9+7dE42Zm5uLPWfgwIH47LPP8PvvvwMAHj9+jEuXLmHChAnN+EkRQrhAiQoh5INKS0sBANra2o0+XlBQgKlTp6Jfv3749ttvsW3bNtTW1gJ4f92U+mMuX74cvXr1EvsqLy/HkydPUFpairq6OnTu3Fnse1VUVKClpSX6e1lZGbp06dLgNXR0dAC8mRGqp6GhIfYcZWVluLq64sSJE3j9+jV+//13aGpqYvjw4e//gRBCpIpu/RBCPqhjx44AgOfPn8PY2Fg0XlRUhAcPHiAkJATt2rXD/v37YW5uDlVVVdy9e1c0S/GhYwYEBMDOzq7B45988gk6d+4MNTU1lJSUiD0mFApFiU79c58+fdrgGPVjnTp1+uD7c3V1xU8//YTz58/j2LFj+PLLL9G2bdsPfg8hRHpoRoUQ8kGWlpZQU1PDmTNnxMbj4uIwdepUPHz4EO7u7ujdu7folsz58+cBQFQ3RUVFRex7jY2N0blzZzx69Ai9e/cWfX366adYu3YtcnJyoKKiAhsbmwa7d06fPi2asQGAfv364dq1aygsLBR73pEjR9ClSxcYGBh88P3p6elhwIAB2LFjB27dugVXV9cm/HQIIVyjGRVCyAdpa2vDy8sLCQkJaNOmDezs7PDXX39h9+7dCA4Oxvbt25GYmIiuXbuiY8eOSEtLw44dOwC82eoLAB06dADwZqvxJ598AoFAAD8/P4SGhkJFRQXDhg3Dixcv8PPPP6O4uBi9evUCAMyfPx+enp6YP38+3N3dUVRUhPXr1wOAaN3KtGnTcOTIEUydOhVz586FlpYWDh8+jMuXLyMiIkK0kPZD3N3d4e/vDxMTE1hZWbH+MySENJ8SQ803CCEfwTAM4uLisGfPHjx+/Biff/45pkyZgq+//hq5ubn44YcfcOPGDbRp0wbdu3fH7NmzERERAVNTU6xfvx5CoRDfffcdTp48iW7duiE5ORkAkJKSgq1bt+LOnTvQ0NCAjY0NFi5cCDMzM9Frp6amYv369bh//z709PSwYMEC+Pn5ITAwULSD5+HDh1i7di0uXryImpoaCAQCzJgxA46OjqLjmJmZYe7cuZg3b16D91deXg5bW1ssXrwY//73vzn+aRJCmoISFUKIzDp16hS6du0qmmEBgDt37sDFxQU///yzWCLSEikpKQgICMC5c+caLN4lhPCLbv0QQmTWhQsXkJKSgsWLF8PIyAjFxcXYvHkzjI2NMXjw4BYfPzU1FdnZ2dizZw9cXV0pSSFEBlGiQgiRWUuWLIG6ujo2b96MJ0+eQEtLC0OGDMGiRYtY2Znz6NEjbN++HX379hUVsyOEyBa69UMIIYQQmUXbkwkhhBAisyhRIYQQQojMokSFEEIIITKLEhVCCCGEyCxKVAghhBAisyhRIYQQQojMokSFEEIIITKLEhVCCCGEyCxKVAghhBAis/4/+rtx5zq6I3AAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Análisis de la columna 'createdAt':\n", + "createdAt\n", + "{'_seconds': 1731975445, '_nanoseconds': 427000000} 500\n", + "{'_seconds': 1732123040, '_nanoseconds': 262000000} 263\n", + "{'_seconds': 1731965768, '_nanoseconds': 30000000} 102\n", + "{'_seconds': 1731975447, '_nanoseconds': 767000000} 19\n", + "Name: count, dtype: int64\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Análisis de la columna 'gender':\n", + "gender\n", + "Hombre 206\n", + "Mujer 144\n", + "Niño 6\n", + "Name: count, dtype: int64\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ===========================\n", + "# Analizar una columna categórica\n", + "# ===========================\n", + "for column in [\"store\", \"manufacturer\", \"category\", \"createdAt\", \"gender\"]:\n", + " categorical_column = column # Reemplazar por una columna categórica del DataFrame\n", + "\n", + " if df is not None:\n", + " analyze_categorical_data(df, categorical_column)\n", + " else:\n", + " print(\"El DataFrame no está disponible.\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From b756eefc4552c7a1d63f02587ad24d326a63c02e Mon Sep 17 00:00:00 2001 From: CarlosKC26 Date: Thu, 5 Dec 2024 22:11:44 -0500 Subject: [PATCH 25/84] =?UTF-8?q?Correcci=C3=B3n=20de=20numeraci=C3=B3n=20?= =?UTF-8?q?en=20el=20notebook=20de=20EDA?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- scripts/eda/eda.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/eda/eda.ipynb b/scripts/eda/eda.ipynb index 7ab0daf03..b24e5811e 100644 --- a/scripts/eda/eda.ipynb +++ b/scripts/eda/eda.ipynb @@ -280,7 +280,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 7. Analizar Datos Categóricos" + "### 6. Analizar Datos Categóricos" ] }, { From b610501640663fb3315fe21e81e214178ea1fe82 Mon Sep 17 00:00:00 2001 From: CarlosKC26 Date: Thu, 5 Dec 2024 22:47:49 -0500 Subject: [PATCH 26/84] arreglos finales --- docs/data/data_dictionary.md | 12 ++-- docs/data/data_summary.md | 79 +++++++++++-------------- scripts/eda/eda.ipynb | 110 +++++++++++++++++++++++++++++++++++ 3 files changed, 149 insertions(+), 52 deletions(-) diff --git a/docs/data/data_dictionary.md b/docs/data/data_dictionary.md index 6be129c62..561b6bd8c 100644 --- a/docs/data/data_dictionary.md +++ b/docs/data/data_dictionary.md @@ -7,16 +7,16 @@ | `id` | Identificador único del producto | string | Único por producto | `046zSiHm8Cz0fZYwMJlL` | | `details` | Detalles técnicos del producto | string | - | `{Horma clásica} {Parte superior sintética}` | | `store` | Nombre de la tienda que vende el producto | string | adidas, nike, nacionrunner | `adidas` | -| `manufacturer` | Marca o empresa que fabrica el producto | string | adidas, nike, otros | `adidas` | +| `manufacturer` | Marca o empresa que fabrica el producto | string | adidas, Asics, nike, Hoka, otros | `adidas` | | `url` | URL del producto en la tienda | string | - | `https://www.adidas.co/...` | | `title` | Nombre o título del producto | string | - | `Tenis Duramo SL` | -| `regularPrice` | Precio sin descuento | string | Valores numéricos en COP | `$379.950` | -| `undiscounted_price` | Precio con descuento aplicado | string | Valores numéricos en COP | `$265.965` | +| `regularPrice` | Precio sin descuento | string | Valores en COP | `$379.950` | +| `undiscounted_price` | Precio con descuento aplicado | string | Valores en COP | `$265.965` | | `description` | Descripción general del producto | string | - | `"Los Adizero Adios Pro 3 son la ..."` | -| `category` | Categoría asignada al producto por la tienda | string | Hombre, Mujer, Running | `Mujer • Running` | -| `createdAt` | Fecha y hora de creación del registro | datetime | ISO 8601 | `2023-12-05T10:30:00Z` | +| `category` | Categoría asignada al producto por la tienda | string | - | `Mujer • Running` | +| `createdAt` | Fecha y hora de creación del registro | datetime | - | `2023-12-05T10:30:00Z` | | `characteristics` | Características adicionales, como materiales o tecnologías utilizadas | string | - | `Parte superior de malla diseñada...` | -| `gender` | Género objetivo del producto | string | Hombre, Mujer, Unisex | `Mujer` | +| `gender` | Género objetivo del producto | string | Hombre, Mujer, Niño | `Mujer` | ## Fuente de los datos diff --git a/docs/data/data_summary.md b/docs/data/data_summary.md index b52f43c93..dab3c42c6 100644 --- a/docs/data/data_summary.md +++ b/docs/data/data_summary.md @@ -4,74 +4,61 @@ El dataset incluye productos obtenidos de tres tiendas en línea: Adidas, Nike y Nation Runner. A continuación, se presenta un resumen: -- **Número total de observaciones**: 10,000 registros (aproximado). -- **Número total de variables**: 12. -- **Tipos de variables**: - - 10 variables categóricas (string). - - 1 variable numérica (precios, almacenados como string). - - 1 variable temporal (datetime). +- **Número total de observaciones**: 10,000 registros (aproximado). +- **Número total de variables**: 12. +- **Tipos de variables**: + - 10 variables categóricas (string). + - 1 variable numérica (precios, almacenados como string). + - 1 variable temporal (datetime). ## Resumen de calidad de los datos -- **Valores faltantes**: - - Variables críticas como `id`, `title` y `url` no presentan valores nulos. - - `details` y `characteristics` presentan un 5% de valores faltantes. -- **Duplicados**: No se encontraron registros duplicados en la columna `id`. -- **Errores en datos**: Se detectaron inconsistencias en precios negativos en algunos registros. -- **Transformaciones aplicadas**: - - Imputación de valores faltantes con `null`. - - Filtrado de valores extremos en los precios. -- **Valores extremos**: - - Algunos valores en `regularPrice` y `undiscounted_price` están fuera del rango esperado (valores negativos o cero). +- **Valores faltantes**: + - Variables críticas como `id`, `title` y `url` no presentan valores nulos. + - `gender`, `category`, `undiscounted_price` y `characteristics` presentan un porcentaje alto de valores faltantes y no hay mas columnas con valores faltantes. +- **Duplicados**: No se encontraron registros duplicados. ### Acciones tomadas: -- Valores faltantes: Se imputaron valores genéricos para descripciones faltantes. -- Valores extremos: Se excluyeron del análisis los productos con precios negativos. +- **Valores faltantes**: Se planea imputar valores genéricos como "unisex" para descripciones faltantes en columnas como "genero" pero se necesita hacer una revision exahustiva para decidir esto. ## Variable objetivo -Este proyecto no define explícitamente una variable objetivo. Sin embargo, las similitudes semánticas basadas en `details` y `characteristics` serán la base para construir el modelo de recomendación. +Este proyecto no define explícitamente una variable objetivo. Sin embargo, las similitudes semánticas basadas en `details` y `characteristics` serán la base para construir el modelo de recomendación. ## Variables individuales - -- **`category`**: - - Mayor proporción de productos para mujeres (60%). - - Segmento `Running` común en todas las tiendas. +- **`category`**: + - Mayor proporción de productos para mujeres (60%). + - El segmento `Running` es común en todas las tiendas. ## Ranking de variables -En base al análisis preliminar, las variables más influyentes para el análisis comparativo son: +En base al análisis preliminar, las variables más influyentes para el análisis comparativo son: -1. `characteristics` - Detalles técnicos del producto. -2. `details` - Información estructurada y semiestructurada del producto. -3. `category` - Segmentación de productos según género y propósito. +1. `characteristics` - Detalles técnicos del producto. +2. `details` - Información estructurada y semiestructurada del producto. +3. `category` - Segmentación de productos según género y propósito. +4. `regularPrice` - Precio del producto sin descuento. +5. `undiscounted_price` - Precio del producto después de aplicar descuento (si aplica). +6. `store` - Tienda que vende el producto. +7. `manufacturer` - Empresa/Marca del producto. +8. `gender` - Género del público objetivo del producto. ## Análisis exploratorio ### Variables categóricas más relevantes -- **Categorías más frecuentes en `store`**: - - Adidas: 40%. - - Nike: 35%. - - Nation Runner: 25%. - -### Distribución de precios -- **`regularPrice`**: - - Rango: $150,000 - $600,000 COP. - - Promedio: $380,000 COP. +- **Categorías más frecuentes en `store`**: + - Adidas: 59%. + - Nike: 30%. + - Nation Runner: 11%. ### Relación entre `category` y `regularPrice` -- Se explorarán las relaciones entre variables como `category` y `regularPrice` para identificar patrones relevantes en las recomendaciones. Los embeddings se construirán utilizando modelos LLM para capturar similitudes contextuales. -- Los productos en la categoría `Mujer • Running` tienden a estar en el rango superior de precios. +- Se explorarán las relaciones entre variables como `category` y `regularPrice` para identificar patrones relevantes en las recomendaciones. Los embeddings se construirán utilizando modelos LLM para capturar similitudes contextuales. ## Visualizaciones -1. **Distribución de precios**: - - Histograma mostrando la densidad de precios. -2. **Análisis de categorías**: - - Gráfico de barras con la proporción de productos por tienda. -3. **Mapa de calor de correlaciones**: - - Muestra una correlación moderada entre `undiscounted_price` y `regularPrice`. +1. **Análisis de categorías**: + - Gráficos de barras con la proporción de distintas características de los datos. ## Conclusiones -- Las categorías y características técnicas (`details` y `characteristics`) son clave para el análisis comparativo. -- Es necesario continuar depurando valores faltantes y normalizando precios para futuros análisis. \ No newline at end of file +- Las categorías y características técnicas (`details` y `characteristics`) son clave para el análisis comparativo. +- Es necesario normalizar distintos valores, como los precios, para futuros análisis. \ No newline at end of file diff --git a/scripts/eda/eda.ipynb b/scripts/eda/eda.ipynb index b24e5811e..d78e85f32 100644 --- a/scripts/eda/eda.ipynb +++ b/scripts/eda/eda.ipynb @@ -305,6 +305,116 @@ "print(columns)" ] }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Porcentajes de los elementos en la columna 'store':\n", + " Count Percentage\n", + "store \n", + "adidas 519 58.710407\n", + "nacionRunner 263 29.751131\n", + "nike 102 11.538462\n", + "\n", + "Porcentajes de los elementos en la columna 'manufacturer':\n", + " Count Percentage\n", + "manufacturer \n", + "adidas 519 58.710407\n", + "Asics 117 13.235294\n", + "nike 102 11.538462\n", + "Hoka 48 5.429864\n", + "Brooks 36 4.072398\n", + "New Balance 36 4.072398\n", + "On Running 7 0.791855\n", + "Skechers 7 0.791855\n", + "361° 4 0.452489\n", + "NNormal 4 0.452489\n", + "Adidas 4 0.452489\n", + "\n", + "Porcentajes de los elementos en la columna 'category':\n", + " Count Percentage\n", + "category \n", + "Mujer • Running 236 38.003221\n", + "Hombre • Running 210 33.816425\n", + "Running 61 9.822866\n", + "Calzado de running en carretera para hombre 24 3.864734\n", + "Calzado de running en carretera para mujer 15 2.415459\n", + "Calzado de correr en carretera para hombre 12 1.932367\n", + "Calzado de correr en pavimento para mujer 11 1.771337\n", + "Calzado de correr en carretera para mujer 11 1.771337\n", + "Calzado para hombre 5 0.805153\n", + "Mujer • adidas by Stella McCartney 5 0.805153\n", + "Calzado de running en carretera para niños grandes 4 0.644122\n", + "Mujer • TERREX 4 0.644122\n", + "Calzado de trail running para hombre 3 0.483092\n", + "Calzado para niños de preescolar 2 0.322061\n", + "Calzado de correr en pavimento para hombre 2 0.322061\n", + "Calzado de carrera en carretera para mujer 2 0.322061\n", + "Calzado de trail running impermeables para mujer 2 0.322061\n", + "Calzado de running en carretera acondicionado p... 1 0.161031\n", + "Calzado de carrera en carretera 1 0.161031\n", + "Calzado de running en carretera impermeable par... 1 0.161031\n", + "TERREX 1 0.161031\n", + "Calzado de trail running para mujer 1 0.161031\n", + "Calzado de running en carretera impermeable par... 1 0.161031\n", + "Calzado de trail running impermeables para hombre 1 0.161031\n", + "Calzado de running en carretera resistente a la... 1 0.161031\n", + "Calzado de carrera en carretera para hombre 1 0.161031\n", + "Hombre • TERREX 1 0.161031\n", + "Calzado de caminata para mujer 1 0.161031\n", + "adidas by Stella McCartney 1 0.161031\n", + "\n", + "Porcentajes de los elementos en la columna 'createdAt':\n", + " Count Percentage\n", + "createdAt \n", + "{'_seconds': 1731975445, '_nanoseconds': 427000... 500 56.561086\n", + "{'_seconds': 1732123040, '_nanoseconds': 262000... 263 29.751131\n", + "{'_seconds': 1731965768, '_nanoseconds': 30000000} 102 11.538462\n", + "{'_seconds': 1731975447, '_nanoseconds': 767000... 19 2.149321\n", + "\n", + "Porcentajes de los elementos en la columna 'gender':\n", + " Count Percentage\n", + "gender \n", + "Hombre 206 57.865169\n", + "Mujer 144 40.449438\n", + "Niño 6 1.685393\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "def calculate_percentage(df, column_name):\n", + " \"\"\"\n", + " Calcula y muestra los porcentajes de cada elemento en una columna específica.\n", + " \n", + " :param df: DataFrame con los datos.\n", + " :param column_name: Nombre de la columna.\n", + " \"\"\"\n", + " if column_name in df.columns:\n", + " counts = df[column_name].value_counts()\n", + " percentages = (counts / counts.sum()) * 100\n", + " percentage_df = pd.DataFrame({\n", + " 'Count': counts,\n", + " 'Percentage': percentages\n", + " })\n", + " print(f\"\\nPorcentajes de los elementos en la columna '{column_name}':\")\n", + " print(percentage_df)\n", + " return percentage_df\n", + " else:\n", + " print(f\"La columna '{column_name}' no existe en el DataFrame.\")\n", + " return None\n", + "\n", + "for column_name in [\"store\", \"manufacturer\", \"category\", \"createdAt\", \"gender\"]:\n", + " calculate_percentage(df, column_name)" + ] + }, { "cell_type": "code", "execution_count": 16, From f32148ead7cca389d5efeafaff8505be350dd073 Mon Sep 17 00:00:00 2001 From: Juan Correa Date: Thu, 5 Dec 2024 23:07:30 -0500 Subject: [PATCH 27/84] =?UTF-8?q?notebook=20de=20exploraci=C3=B3n=20de=20e?= =?UTF-8?q?tiquetado?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Notebooks/preprocessing.ipynb | 540 ++++++++++++++++++++++++++++++++++ 1 file changed, 540 insertions(+) create mode 100644 Notebooks/preprocessing.ipynb diff --git a/Notebooks/preprocessing.ipynb b/Notebooks/preprocessing.ipynb new file mode 100644 index 000000000..297620ba6 --- /dev/null +++ b/Notebooks/preprocessing.ipynb @@ -0,0 +1,540 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "from llama_models.llama3.reference_impl.generation import Llama\n", + "from llama_models.llama3.api.datatypes import UserMessage, SystemMessage\n", + "import os\n", + "import sys\n", + "from llama_models.llama3.api.datatypes import (\n", + " UserMessage,\n", + " SystemMessage,\n", + " CompletionMessage,\n", + " StopReason,\n", + ")\n", + "from llama_models.llama3.reference_impl.generation import Llama\n", + "import pandas as pd\n", + "from io import StringIO\n", + "import numpy as np\n", + "import re\n", + "import requests\n", + "import ollama\n", + "import random\n", + "from tiktoken import get_encoding" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'labels_with_definitions = [\\n (\"Peso\", \"Indica la ligereza de la zapatilla, generalmente expresado en gramos. El peso puede influir en el rendimiento y la comodidad durante la carrera.\"),\\n (\"Material del upper (parte superior)\", \"Describe los materiales utilizados en la parte superior de la zapatilla, como malla, cuero sintético o tejidos técnicos, que afectan la transpirabilidad, flexibilidad y soporte.\"),\\n (\"Material de la mediasuela\", \"Se refiere a los compuestos empleados en la entresuela, como espumas EVA o tecnologías propietarias, que proporcionan amortiguación y absorción de impactos.\"),\\n (\"Suela exterior\", \"Detalla el tipo de goma o caucho utilizado en la suela y el diseño del patrón de tracción, aspectos que influyen en el agarre y la durabilidad en diversas superficies.\"),\\n (\"Sistema de amortiguación\", \"Especifica las tecnologías o materiales destinados a reducir el impacto durante la pisada, contribuyendo al confort y la protección de las articulaciones.\"),\\n (\"Drop (diferencial talón-punta)\", \"Indica la diferencia de altura entre el talón y la punta de la zapatilla, medida en milímetros. Un drop alto suele ofrecer mayor amortiguación en el talón, mientras que un drop bajo promueve una pisada más natural.\"),\\n (\"Tipo de pisada\", \"Clasifica la zapatilla según su adecuación para diferentes tipos de pisada: neutra, pronadora o supinadora. Esto es esencial para elegir un calzado que se adapte a la biomecánica del corredor.\"),\\n (\"Tipo de uso\", \"Define el propósito principal de la zapatilla, como entrenamiento diario, competición, trail running o uso casual, lo que orienta sobre su diseño y funcionalidades específicas.\"),\\n (\"Género\", \"Indica si la zapatilla está diseñada para hombres, mujeres o es un modelo unisex, considerando diferencias anatómicas y de tamaño.\"),\\n (\"Tallas disponibles\", \"Especifica el rango de tallas en las que se ofrece la zapatilla, asegurando que el corredor pueda encontrar un ajuste adecuado.\"),\\n (\"Anchura\", \"Algunas marcas ofrecen diferentes anchos (estrecho, estándar, ancho) para adaptarse a diversas morfologías del pie.\"),\\n (\"Precio\", \"Proporciona el costo de la zapatilla, un factor determinante en la decisión de compra.\"),\\n (\"Tecnologías adicionales\", \"Incluye características especiales como impermeabilidad, reflectividad, sistemas de ajuste personalizados o elementos de estabilidad que mejoran la funcionalidad del calzado.\"),\\n]'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'''labels_with_definitions = [\n", + " (\"Peso\", \"Indica la ligereza de la zapatilla, generalmente expresado en gramos. El peso puede influir en el rendimiento y la comodidad durante la carrera.\"),\n", + " (\"Material del upper (parte superior)\", \"Describe los materiales utilizados en la parte superior de la zapatilla, como malla, cuero sintético o tejidos técnicos, que afectan la transpirabilidad, flexibilidad y soporte.\"),\n", + " (\"Material de la mediasuela\", \"Se refiere a los compuestos empleados en la entresuela, como espumas EVA o tecnologías propietarias, que proporcionan amortiguación y absorción de impactos.\"),\n", + " (\"Suela exterior\", \"Detalla el tipo de goma o caucho utilizado en la suela y el diseño del patrón de tracción, aspectos que influyen en el agarre y la durabilidad en diversas superficies.\"),\n", + " (\"Sistema de amortiguación\", \"Especifica las tecnologías o materiales destinados a reducir el impacto durante la pisada, contribuyendo al confort y la protección de las articulaciones.\"),\n", + " (\"Drop (diferencial talón-punta)\", \"Indica la diferencia de altura entre el talón y la punta de la zapatilla, medida en milímetros. Un drop alto suele ofrecer mayor amortiguación en el talón, mientras que un drop bajo promueve una pisada más natural.\"),\n", + " (\"Tipo de pisada\", \"Clasifica la zapatilla según su adecuación para diferentes tipos de pisada: neutra, pronadora o supinadora. Esto es esencial para elegir un calzado que se adapte a la biomecánica del corredor.\"),\n", + " (\"Tipo de uso\", \"Define el propósito principal de la zapatilla, como entrenamiento diario, competición, trail running o uso casual, lo que orienta sobre su diseño y funcionalidades específicas.\"),\n", + " (\"Género\", \"Indica si la zapatilla está diseñada para hombres, mujeres o es un modelo unisex, considerando diferencias anatómicas y de tamaño.\"),\n", + " (\"Tallas disponibles\", \"Especifica el rango de tallas en las que se ofrece la zapatilla, asegurando que el corredor pueda encontrar un ajuste adecuado.\"),\n", + " (\"Anchura\", \"Algunas marcas ofrecen diferentes anchos (estrecho, estándar, ancho) para adaptarse a diversas morfologías del pie.\"),\n", + " (\"Precio\", \"Proporciona el costo de la zapatilla, un factor determinante en la decisión de compra.\"),\n", + " (\"Tecnologías adicionales\", \"Incluye características especiales como impermeabilidad, reflectividad, sistemas de ajuste personalizados o elementos de estabilidad que mejoran la funcionalidad del calzado.\"),\n", + "]'''" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "labels_with_definitions = [\n", + " (\"Weight\", \"Indicates the lightness of the shoe, usually expressed in grams. Weight can influence performance and comfort during running.\"),\n", + " (\"Upper Material\", \"Describes the materials used in the shoe's upper part, such as mesh, synthetic leather, or technical fabrics, which affect breathability, flexibility, and support.\"),\n", + " (\"Midsole Material\", \"Refers to the compounds used in the midsole, such as EVA foams or proprietary technologies, which provide cushioning and shock absorption.\"),\n", + " (\"Outsole\", \"Details the type of rubber or material used in the sole and the traction pattern design, which influence grip and durability on various surfaces.\"),\n", + " (\"Cushioning System\", \"Specifies the technologies or materials aimed at reducing impact during strides, contributing to comfort and joint protection.\"),\n", + " (\"Drop (heel-to-toe differential)\", \"Indicates the height difference between the heel and the toe of the shoe, measured in millimeters. A higher drop typically offers more heel cushioning, while a lower drop promotes a more natural stride.\"),\n", + " (\"Pronation Type\", \"Classifies the shoe according to its suitability for different pronation types: neutral, overpronation, or supination. This is essential for choosing footwear that matches the runner's biomechanics.\"),\n", + " (\"Usage Type\", \"Defines the primary purpose of the shoe, such as daily training, racing, trail running, or casual use, guiding its specific design and features.\"),\n", + " (\"Gender\", \"Indicates whether the shoe is designed for men, women, or is a unisex model, considering anatomical and sizing differences.\"),\n", + " (\"Available Sizes\", \"Specifies the range of sizes in which the shoe is offered, ensuring the runner can find a suitable fit.\"),\n", + " (\"Width\", \"Some brands offer different widths (narrow, standard, wide) to accommodate various foot shapes.\"),\n", + " (\"Price\", \"Provides the cost of the shoe, a determining factor in purchasing decisions.\"),\n", + " (\"Additional Technologies\", \"Includes special features such as waterproofing, reflectivity, customized fit systems, or stability elements that enhance the shoe's functionality.\"),\n", + "]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Funciones" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def transformar_texto(texto, marca):\n", + " if texto is None or (isinstance(texto, float) and np.isnan(texto)):\n", + " return texto\n", + " \n", + " if marca.lower() == \"adidas\":\n", + " # Transformación original para adidas\n", + " if isinstance(texto, (list, np.ndarray)):\n", + " texto = \", \".join(map(str, texto))\n", + " else:\n", + " texto = str(texto)\n", + " texto = texto.strip(\"[]\")\n", + " texto = re.sub(r\",\\s*\", '} {', texto)\n", + " texto = '{' + texto + '}'\n", + " return texto\n", + " \n", + " elif marca.lower() == \"nike\":\n", + " # Transformación específica para nike\n", + " if isinstance(texto, list):\n", + " # Elimina claves con valores irrelevantes\n", + " texto_limpio = [\n", + " {k.strip(): v.strip() for k, v in d.items() if k.strip() not in ['\\xa0']}\n", + " for d in texto\n", + " if isinstance(d, dict)\n", + " ]\n", + " # Filtra elementos vacíos o irrelevantes\n", + " texto_limpio = [d for d in texto_limpio if d]\n", + " return texto_limpio\n", + " return texto # Si no es lista, regresa el texto original\n", + "\n", + " else:\n", + " raise ValueError(f\"Marca '{marca}' no reconocida. Usa 'adidas' o 'nike'.\")\n", + "\n", + "def obtener_respuesta_ollama(prompt):\n", + " response = ollama.chat(\n", + " model=\"llama3.2:latest\",\n", + " messages = [\n", + " {\n", + " \"role\":\"user\",\n", + " \"content\": prompt\n", + " } \n", + " ]\n", + " )\n", + " # La respuesta es un generador; concatenamos las partes\n", + " return response\n", + "\n", + "\n", + "\n", + "def generate_prompt_ollama(product, labels_with_definitions):\n", + " labels = [label for label, _ in labels_with_definitions]\n", + " prompt = f\"\"\"\n", + " You are an assistant specialized in extracting structured information from product descriptions and organizing it into tables.\n", + " Your task is to extract the following information from the product details and label it according to the provided labels: {', '.join(labels)}.\n", + " Each label has the following definition to help guide your extraction:\n", + "\n", + " {''.join([f'- {label}: {definition}\\n' for label, definition in labels_with_definitions])}\n", + "\n", + " If a label does not have a clear match in the details, complete its value with \"null\".\n", + "\n", + " Product information:\n", + " - Details: {product['details']}\n", + " - Description: {product['description']}\n", + " - Category: {product['category']}\n", + "\n", + " Provide the information in a table with columns corresponding to each label. \n", + " The table must include **two complete rows**:\n", + " 1. The first row contains the label names.\n", + " 2. The second row contains the corresponding labeled values.\n", + "\n", + " Expected response format:\n", + " | {' | '.join(labels)} |\n", + " | {' | '.join(['---'] * len(labels))} |\n", + " | value_1 | value_2 | ... |\n", + " \n", + " Example:\n", + " If the labels are \"details\", \"description\", and \"category\", and the extracted values are \n", + " \"Comfortable sneakers\", \"Made with recycled materials\", and \"Footwear\", respectively, \n", + " your response should be:\n", + "\n", + " | details | description | category |\n", + " | --- | --- | --- |\n", + " | Comfortable sneakers | Made with recycled materials | Footwear |\n", + "\n", + " Remember:\n", + " - The response must contain two complete rows.\n", + " - Only respond with the table and **do not include additional text**.\n", + "\n", + " Now, extract and structure the information for the provided product:\n", + "\n", + " | {' | '.join(labels)} |\n", + " | {' | '.join(['---'] * len(labels))} |\n", + " |\"\"\"\n", + " return prompt.strip()\n", + "\n", + "# Tokenizer function (using tiktoken for GPT-like models)\n", + "def count_tokens(text):\n", + " try:\n", + " encoding = get_encoding(\"cl100k_base\") # Example encoding for GPT-like models\n", + " return len(encoding.encode(text))\n", + " except Exception:\n", + " return len(text.split()) # Fallback: approximate by word count\n", + "# Función de simulación de Monte Carlo corregida\n", + "def monte_carlo_simulation(products, models, iterations=1000, num_products=None):\n", + " results = {}\n", + " \n", + " for model_name, model_info in models.items():\n", + " tokens_per_product = {}\n", + " costs_per_product = {}\n", + " \n", + " for _ in range(iterations):\n", + " # Muestra una fracción de los productos si se especifica\n", + " if num_products is not None and num_products < len(products):\n", + " sampled_products = random.sample(products, num_products)\n", + " else:\n", + " sampled_products = products\n", + "\n", + " for product in sampled_products:\n", + " product_id = product.get('id', id(product)) # Usamos un identificador único para cada producto\n", + " prompt = generate_prompt_ollama(product, labels_with_definitions)\n", + " tokens = count_tokens(prompt)\n", + " \n", + " # Verifica si el número de tokens excede la ventana de contexto\n", + " if tokens > model_info['context_window']*1000:\n", + " print(f\"Advertencia: El prompt excede la ventana de contexto para el modelo {model_name}\")\n", + " # Puedes manejar este caso según necesites (e.g., omitir, truncar)\n", + " continue\n", + " \n", + " cost = (tokens / 1000) * model_info['cost_in']\n", + " \n", + " # Actualiza los máximos y mínimos por producto\n", + " if product_id not in tokens_per_product:\n", + " tokens_per_product[product_id] = {'max': tokens, 'min': tokens}\n", + " costs_per_product[product_id] = {'max': cost, 'min': cost}\n", + " else:\n", + " tokens_per_product[product_id]['max'] = max(tokens_per_product[product_id]['max'], tokens)\n", + " tokens_per_product[product_id]['min'] = min(tokens_per_product[product_id]['min'], tokens)\n", + " costs_per_product[product_id]['max'] = max(costs_per_product[product_id]['max'], cost)\n", + " costs_per_product[product_id]['min'] = min(costs_per_product[product_id]['min'], cost)\n", + " \n", + " # Después de todas las iteraciones, obtenemos los máximos y mínimos globales\n", + " max_tokens = max([data['max'] for data in tokens_per_product.values()], default=0)\n", + " min_tokens = min([data['min'] for data in tokens_per_product.values()], default=0)\n", + " max_cost = max([data['max'] for data in costs_per_product.values()], default=0)\n", + " min_cost = min([data['min'] for data in costs_per_product.values()], default=0)\n", + " \n", + " results[model_name] = {\n", + " 'max_tokens': max_tokens,\n", + " 'min_tokens': min_tokens,\n", + " 'max_cost': max_cost,\n", + " 'min_cost': min_cost,\n", + " }\n", + " return results" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "def procesar_respuesta(respuesta_texto, etiquetas):\n", + " try:\n", + " # Buscar el inicio de la tabla\n", + " inicio_tabla = respuesta_texto.find('|')\n", + " if inicio_tabla != -1:\n", + " # Extraer la tabla desde el primer '|'\n", + " tabla_texto = respuesta_texto[inicio_tabla:].strip()\n", + " # Extraer solo las líneas que contienen '|'\n", + " lineas = tabla_texto.split('\\n')\n", + " lineas_tabla = []\n", + " for linea in lineas:\n", + " if '|' in linea:\n", + " # Eliminar los '|' iniciales y finales y espacios extra\n", + " linea = linea.strip().strip('|').strip()\n", + " lineas_tabla.append(linea)\n", + " else:\n", + " # Detenerse si la línea no contiene '|'\n", + " break\n", + " tabla_completa = '\\n'.join(lineas_tabla)\n", + " # Convertir la tabla Markdown a un DataFrame\n", + " df = pd.read_csv(StringIO(tabla_completa), sep='|', engine='python', skipinitialspace=True)\n", + " # Limpiar nombres de columnas y datos\n", + " df.columns = [col.strip() for col in df.columns]\n", + " for col in df.columns:\n", + " if df[col].dtype == object:\n", + " df[col] = df[col].str.strip()\n", + " # Resetear el índice\n", + " df = df.reset_index(drop=True)\n", + " return df\n", + " else:\n", + " print(\"No se encontró una tabla en la respuesta.\")\n", + " return None\n", + " except Exception as e:\n", + " print(f\"Error al procesar la tabla: {e}\")\n", + " return None\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lectura de datos" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "200\n" + ] + } + ], + "source": [ + "url =\"https://scraping-firestore-178159629911.us-central1.run.app//v1/scraping/\"\n", + "\n", + "response = requests.get(url)\n", + "\n", + "if response.status_code == 200:\n", + " data = response.json()\n", + " print(response.status_code)\n", + "else:\n", + " print(f'Error: {response.status_code}')\n", + "\n", + "df_raw = pd.DataFrame(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Clasificacion" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "df_adidas = df_raw[df_raw['store'] == 'adidas']" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Usuario\\AppData\\Local\\Temp\\ipykernel_15108\\354877031.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_adidas['details_transformado'] = df_adidas['details'].apply(lambda x: transformar_texto(x, 'adidas'))\n" + ] + } + ], + "source": [ + "# Lista de descripciones de productos\n", + "df_adidas['details_transformado'] = df_adidas['details'].apply(lambda x: transformar_texto(x, 'adidas'))" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['id', 'details', 'store', 'manufacturer', 'url', 'title',\n", + " 'regularPrice', 'undiscounted_price', 'description', 'category',\n", + " 'createdAt', 'characteristics', 'gender', 'details_transformado'],\n", + " dtype='object')" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_adidas.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# Crear un diccionario con las llaves deseadas desde todo el DataFrame\n", + "productos = [\n", + " {\n", + " \"id\": row['id'],\n", + " \"regularPrice\" : row[\"regularPrice\"],\n", + " \"undiscounted_price\": row[\"undiscounted_price\"],\n", + " \"details\": row['details_transformado'],\n", + " \"description\": row['description'],\n", + " \"category\": row['category']\n", + " }\n", + " for _, row in df_adidas.iterrows() # Recorre todo el DataFrame\n", + " if row['details_transformado'] != '{}' # Filtra donde los detalles no estén vacíos\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'id': '0AqheRhKT2lhm7puBVCF',\n", + " 'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Exterior de tejido adidas Primeknit} {Ajuste suave en el talón} {Sistema Linear Energy Push} {Mediasuela BOOST} {Peso: 289 g (Talla CO 37)} {Suela Stretchweb con caucho Continental™ Better Rubber} {El hilo del exterior contiene al menos un 50% de Parley Ocean Plastic y un 50% de poliéster reciclado} {Color del artículo: Core Black / Core Black / Cloud White} {Número de artículo: GX5591}',\n", + " 'description': 'Hemos analizado 1.200.000 pisadas para que Ultraboost evolucione y mejore su ajuste para adaptarse 360° al pie femenino. Y aún hay más. Hemos rediseñado la suela de caucho. Probamos cientos de prototipos. Hasta que comprobamos las mejoras en su rendimiento. ¿El resultado? Un 4 % más de energía que los Ultraboost 21 para mujer. El ajuste en el área del talón del exterior adidas PRIMEKNIT se diseñó para evitar que el talón se deslice y se formen ampollas. Estarás corriendo sobre una mediasuela BOOST con tecnología Linear Energy Push. El exterior de este calzado está confeccionado con hilo que contiene un 50% de Parley Ocean Plastic, un material que ha sido recuperado de residuos plásticos recogidos en islas, playas, comunidades costeras y costas evitando que contaminen nuestros océanos.',\n", + " 'category': 'Mujer • Running'}]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "productos[1:2]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'id': '0AqheRhKT2lhm7puBVCF', 'regularPrice': '$799.950', 'undiscounted_price': 'NA', 'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Exterior de tejido adidas Primeknit} {Ajuste suave en el talón} {Sistema Linear Energy Push} {Mediasuela BOOST} {Peso: 289 g (Talla CO 37)} {Suela Stretchweb con caucho Continental™ Better Rubber} {El hilo del exterior contiene al menos un 50% de Parley Ocean Plastic y un 50% de poliéster reciclado} {Color del artículo: Core Black / Core Black / Cloud White} {Número de artículo: GX5591}', 'description': 'Hemos analizado 1.200.000 pisadas para que Ultraboost evolucione y mejore su ajuste para adaptarse 360° al pie femenino. Y aún hay más. Hemos rediseñado la suela de caucho. Probamos cientos de prototipos. Hasta que comprobamos las mejoras en su rendimiento. ¿El resultado? Un 4 % más de energía que los Ultraboost 21 para mujer. El ajuste en el área del talón del exterior adidas PRIMEKNIT se diseñó para evitar que el talón se deslice y se formen ampollas. Estarás corriendo sobre una mediasuela BOOST con tecnología Linear Energy Push. El exterior de este calzado está confeccionado con hilo que contiene un 50% de Parley Ocean Plastic, un material que ha sido recuperado de residuos plásticos recogidos en islas, playas, comunidades costeras y costas evitando que contaminen nuestros océanos.', 'category': 'Mujer • Running'}\n", + "Respuesta del modelo:\n", + "| Weight | Upper Material | Midsole Material | Outsole | Cushioning System | Drop (heel-to-toe differential) | Pronation Type | Usage Type | Gender | Available Sizes | Width | Price | Additional Technologies |\n", + "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", + "| 289 g | adidas Primeknit | Mediasuela BOOST | Suela Stretchweb con caucho Continental™ Better Rubber | Sistema Linear Energy Push | null | neutral | Daily training | mujer | CO 37 - ?? | null | 4% más de energía que Ultraboost 21 para mujer | El hilo del exterior contiene al menos un 50% de Parley Ocean Plastic y un 50% de poliéster reciclado |\n", + "\n", + "------------------------\n", + "\n", + "{'id': '0IgYTzUHkE7zIdcVyFCK', 'regularPrice': '$1.049.950', 'undiscounted_price': '$629.970', 'details': '{Ajuste clásico} {Cierre de cordones} {Parte superior de malla técnica} {Amortiguación Lightstrike Pro} {Varillas ENERGYRODS que limitan la pérdida de energía} {Talón Slinglaunch para un ajuste seguro} {Peso: 166 g (Talla COL 36 1/2)} {Caída mediasuela: 6 mm (talón: 32 mm / antepié: 26 mm)} {Suela de caucho Continental™} {Contiene al menos un 20 % de material reciclado} {Color del artículo: Green Spark / Aurora Met. / Lucid Lemon} {Número de artículo: IG8206}', 'description': 'Haz tus mejores 10k con nuestros nuevos tenis de running Adizero, diseñados exclusivamente para darte máxima velocidad. Los Adizero Takumi Sen 10 han sido diseñados con dos capas de amortiguación LIGHTSTRIKE PRO, combinadas con las varillas ENERGYRODS para una mayor estabilidad e impulso en tu carrera. Supera tu marca personal y llega a la meta más rápido que nunca.', 'category': 'Mujer • Running'}\n", + "Respuesta del modelo:\n", + "| Weight | Upper Material | Midsole Material | Outsole | Cushioning System | Drop (heel-to-toe differential) | Pronation Type | Usage Type | Gender | Available Sizes | Width | Price | Additional Technologies |\n", + "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", + "| 166 g | Parte superior de malla técnica | Amortiguación Lightstrike Pro | Suela de caucho Continental™ | Contains at least a 20% recycled material | 6 mm (talón: 32 mm / antepié: 26 mm) | Neutral | Daily training | Women | Talla COL 36 1/2, null | null | 166 g | Contains al menos un 20 % de material reciclado |\n", + "\n", + "------------------------\n", + "\n" + ] + } + ], + "source": [ + "dfs = []\n", + "for producto in productos:\n", + " prompt = generate_prompt_ollama(producto, labels_with_definitions)\n", + " respuesta = obtener_respuesta_ollama(prompt)\n", + " print(producto)\n", + " print(\"Respuesta del modelo:\")\n", + " print(respuesta[\"message\"][\"content\"])\n", + " print(\"\\n------------------------\\n\")\n", + " df = procesar_respuesta(respuesta[\"message\"][\"content\"], labels_with_definitions)\n", + " if df is not None:\n", + " attribute_columns = df.columns[:-3]\n", + " dfs = []\n", + " df['id'] = producto['id']\n", + " df['regularPrice'] = producto['regularPrice']\n", + " df['undiscounted_price'] = producto['undiscounted_price']\n", + " df = df[~df[attribute_columns].eq('---').all(axis=1)]\n", + " df = df.dropna(how='all')\n", + " dfs.append(df)\n", + " \n", + " else:\n", + " print(\"No se pudo extraer la tabla.\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "if dfs:\n", + " df_total = pd.concat(dfs, ignore_index=True)\n", + "else:\n", + " print(\"No se pudo crear el DataFrame total.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "output_path = \"../src/comparative_analysis/database/adidas_etiquetado.xlsx\"\n", + "df_total.to_excel(output_path, index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From b19670d06cb175bca86bf860bbf04d79e25f94ba Mon Sep 17 00:00:00 2001 From: Juan Correa Date: Sun, 8 Dec 2024 19:53:23 -0500 Subject: [PATCH 28/84] preprocessiong nike and nr --- Notebooks/preprocessing.ipynb | 902 +++++++++++++++++++++++++++++----- 1 file changed, 786 insertions(+), 116 deletions(-) diff --git a/Notebooks/preprocessing.ipynb b/Notebooks/preprocessing.ipynb index 297620ba6..a1edf0a85 100644 --- a/Notebooks/preprocessing.ipynb +++ b/Notebooks/preprocessing.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -25,7 +25,9 @@ "import requests\n", "import ollama\n", "import random\n", - "from tiktoken import get_encoding" + "from tiktoken import get_encoding\n", + "import time\n", + "import httpx\n" ] }, { @@ -64,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -80,7 +82,6 @@ " (\"Gender\", \"Indicates whether the shoe is designed for men, women, or is a unisex model, considering anatomical and sizing differences.\"),\n", " (\"Available Sizes\", \"Specifies the range of sizes in which the shoe is offered, ensuring the runner can find a suitable fit.\"),\n", " (\"Width\", \"Some brands offer different widths (narrow, standard, wide) to accommodate various foot shapes.\"),\n", - " (\"Price\", \"Provides the cost of the shoe, a determining factor in purchasing decisions.\"),\n", " (\"Additional Technologies\", \"Includes special features such as waterproofing, reflectivity, customized fit systems, or stability elements that enhance the shoe's functionality.\"),\n", "]\n" ] @@ -94,14 +95,23 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "\n", + "\n", + "def remove_html_tags(text):\n", + " if text:\n", + " text = re.sub(r\"<.*?>\", \"\", text) # Remueve etiquetas HTML\n", + " text = text.replace(' ', ' ')\n", + " text = text.strip()\n", + " return text\n", + "\n", "def transformar_texto(texto, marca):\n", " if texto is None or (isinstance(texto, float) and np.isnan(texto)):\n", " return texto\n", - " \n", + "\n", " if marca.lower() == \"adidas\":\n", " # Transformación original para adidas\n", " if isinstance(texto, (list, np.ndarray)):\n", @@ -112,36 +122,82 @@ " texto = re.sub(r\",\\s*\", '} {', texto)\n", " texto = '{' + texto + '}'\n", " return texto\n", - " \n", + "\n", " elif marca.lower() == \"nike\":\n", - " # Transformación específica para nike\n", " if isinstance(texto, list):\n", - " # Elimina claves con valores irrelevantes\n", - " texto_limpio = [\n", - " {k.strip(): v.strip() for k, v in d.items() if k.strip() not in ['\\xa0']}\n", - " for d in texto\n", - " if isinstance(d, dict)\n", - " ]\n", - " # Filtra elementos vacíos o irrelevantes\n", - " texto_limpio = [d for d in texto_limpio if d]\n", - " return texto_limpio\n", - " return texto # Si no es lista, regresa el texto original\n", + " # Si es lista de diccionarios (details)\n", + " if all(isinstance(item, dict) for item in texto):\n", + " texto_limpio = []\n", + " for d in texto:\n", + " if isinstance(d, dict):\n", + " d_limpio = {}\n", + " for k, v in d.items():\n", + " k_clean = k.strip() if k else \"\"\n", + " v_clean = remove_html_tags(v) if isinstance(v, str) else v\n", + " # Si clave y valor no están vacíos\n", + " if k_clean and v_clean:\n", + " # Aquí verificamos si la info es irrelevante: \n", + " # Caso en el que el valor es igual a la clave (sin html), \n", + " # Ejemplo: { \"Datos del producto\": \"Datos del producto\" }\n", + " # No aporta información adicional, así que no la incluimos.\n", + " if v_clean != k_clean:\n", + " d_limpio[k_clean] = v_clean\n", + "\n", + " if d_limpio:\n", + " texto_limpio.append(d_limpio)\n", + "\n", + " # Si después de limpiar no quedó nada (porque todo era texto irrelevante),\n", + " # devolvemos lista vacía\n", + " return texto_limpio\n", + "\n", + " else:\n", + " # Si es lista de strings (characteristics)\n", + " seen = set()\n", + " texto_limpio = []\n", + " for s in texto:\n", + " if s and isinstance(s, str):\n", + " s_clean = remove_html_tags(s)\n", + " if s_clean and s_clean not in seen:\n", + " seen.add(s_clean)\n", + " texto_limpio.append(s_clean)\n", + " return texto_limpio\n", + "\n", + " else:\n", + " # Si no es lista (solo string)\n", + " if isinstance(texto, str):\n", + " return remove_html_tags(texto)\n", + " return texto\n", "\n", " else:\n", " raise ValueError(f\"Marca '{marca}' no reconocida. Usa 'adidas' o 'nike'.\")\n", "\n", - "def obtener_respuesta_ollama(prompt):\n", - " response = ollama.chat(\n", - " model=\"llama3.2:latest\",\n", - " messages = [\n", - " {\n", - " \"role\":\"user\",\n", - " \"content\": prompt\n", - " } \n", - " ]\n", - " )\n", - " # La respuesta es un generador; concatenamos las partes\n", - " return response\n", + "\n", + "def obtener_respuesta_ollama(prompt, max_retries=3, delay=5):\n", + " attempt = 0\n", + " while attempt < max_retries:\n", + " try:\n", + " response = ollama.chat(\n", + " model=\"llama3.2:latest\",\n", + " messages = [\n", + " {\n", + " \"role\":\"user\",\n", + " \"content\": prompt\n", + " } \n", + " ]\n", + " )\n", + " # Si llega aquí, la respuesta se obtuvo exitosamente\n", + " return response\n", + " except httpx.HTTPError as e:\n", + " attempt += 1\n", + " print(f\"Error en la solicitud: {e}. Reintento {attempt} de {max_retries} en {delay} segundos...\")\n", + " time.sleep(delay)\n", + " \n", + " # Si sale del while sin retornar, significa que falló en todos los intentos\n", + " raise RuntimeError(f\"No se pudo obtener respuesta de Ollama después de {max_retries} intentos.\")\n", + "\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -160,6 +216,7 @@ " - Details: {product['details']}\n", " - Description: {product['description']}\n", " - Category: {product['category']}\n", + " - Characteristics: {product['characteristics']}\n", "\n", " Provide the information in a table with columns corresponding to each label. \n", " The table must include **two complete rows**:\n", @@ -253,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 94, "metadata": {}, "outputs": [], "source": [ @@ -261,34 +318,85 @@ " try:\n", " # Buscar el inicio de la tabla\n", " inicio_tabla = respuesta_texto.find('|')\n", - " if inicio_tabla != -1:\n", - " # Extraer la tabla desde el primer '|'\n", - " tabla_texto = respuesta_texto[inicio_tabla:].strip()\n", - " # Extraer solo las líneas que contienen '|'\n", - " lineas = tabla_texto.split('\\n')\n", - " lineas_tabla = []\n", - " for linea in lineas:\n", - " if '|' in linea:\n", - " # Eliminar los '|' iniciales y finales y espacios extra\n", - " linea = linea.strip().strip('|').strip()\n", - " lineas_tabla.append(linea)\n", - " else:\n", - " # Detenerse si la línea no contiene '|'\n", - " break\n", + " if inicio_tabla == -1:\n", + " print(\"No se encontró una tabla en la respuesta.\")\n", + " return None\n", + "\n", + " # Extraer la tabla desde el primer '|'\n", + " tabla_texto = respuesta_texto[inicio_tabla:].strip()\n", + " # Extraer solo las líneas que contengan '|'\n", + " lineas = tabla_texto.split('\\n')\n", + " lineas_tabla = []\n", + " for linea in lineas:\n", + " if '|' in linea:\n", + " # Eliminar los '|' iniciales y finales y espacios extra\n", + " linea = linea.strip().strip('|').strip()\n", + " lineas_tabla.append(linea)\n", + " else:\n", + " # Detenerse si la línea no contiene '|'\n", + " break\n", + "\n", + " if not lineas_tabla:\n", + " print(\"No se encontraron líneas válidas de tabla.\")\n", + " return None\n", + "\n", + " # Verificar si hay separadores '---'\n", + " tiene_separador = any(\n", + " all(c.strip('-') == '' for c in fila.split('|')) \n", + " for fila in lineas_tabla\n", + " )\n", + "\n", + " # Caso 1: Hay separadores (modo normal de Markdown)\n", + " if tiene_separador:\n", " tabla_completa = '\\n'.join(lineas_tabla)\n", - " # Convertir la tabla Markdown a un DataFrame\n", " df = pd.read_csv(StringIO(tabla_completa), sep='|', engine='python', skipinitialspace=True)\n", - " # Limpiar nombres de columnas y datos\n", + " # Limpiar columnas y datos\n", " df.columns = [col.strip() for col in df.columns]\n", " for col in df.columns:\n", " if df[col].dtype == object:\n", " df[col] = df[col].str.strip()\n", - " # Resetear el índice\n", + " df = df.reset_index(drop=True)\n", + " return df\n", + "\n", + " # Caso 2: No hay separadores\n", + " # Verificar la cantidad de líneas\n", + " if len(lineas_tabla) == 2:\n", + " # La primera línea son los nombres de columnas, la segunda línea datos\n", + " columnas = [c.strip() for c in lineas_tabla[0].split('|')]\n", + " datos = [c.strip() for c in lineas_tabla[1].split('|')]\n", + " df = pd.DataFrame([datos], columns=columnas)\n", + " for col in df.columns:\n", + " if df[col].dtype == object:\n", + " df[col] = df[col].str.strip()\n", + " df = df.reset_index(drop=True)\n", + " return df\n", + " elif len(lineas_tabla) == 1:\n", + " # Solo una línea de datos, usar etiquetas como nombres de columnas\n", + " datos = [c.strip() for c in lineas_tabla[0].split('|')]\n", + " # Ajustar si el número de columnas difiere del número de etiquetas\n", + " if len(etiquetas) != len(datos):\n", + " print(\"Advertencia: El número de etiquetas no coincide con las columnas detectadas. Se ajustará al mínimo.\")\n", + " col_names = etiquetas[:len(datos)]\n", + " else:\n", + " col_names = etiquetas\n", + "\n", + " df = pd.DataFrame([datos], columns=col_names)\n", + " for col in df.columns:\n", + " if df[col].dtype == object:\n", + " df[col] = df[col].str.strip()\n", " df = df.reset_index(drop=True)\n", " return df\n", " else:\n", - " print(\"No se encontró una tabla en la respuesta.\")\n", - " return None\n", + " # Hay más de 2 líneas pero sin separador. Asumimos que la primera es columnas y el resto datos.\n", + " columnas = [c.strip() for c in lineas_tabla[0].split('|')]\n", + " filas_datos = [ [x.strip() for x in fila.split('|')] for fila in lineas_tabla[1:] ]\n", + " df = pd.DataFrame(filas_datos, columns=columnas)\n", + " for col in df.columns:\n", + " if df[col].dtype == object:\n", + " df[col] = df[col].str.strip()\n", + " df = df.reset_index(drop=True)\n", + " return df\n", + "\n", " except Exception as e:\n", " print(f\"Error al procesar la tabla: {e}\")\n", " return None\n" @@ -303,7 +411,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -337,7 +445,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -346,14 +454,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\Usuario\\AppData\\Local\\Temp\\ipykernel_15108\\354877031.py:2: SettingWithCopyWarning: \n", + "C:\\Users\\Usuario\\AppData\\Local\\Temp\\ipykernel_2256\\354877031.py:2: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -369,30 +477,7 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['id', 'details', 'store', 'manufacturer', 'url', 'title',\n", - " 'regularPrice', 'undiscounted_price', 'description', 'category',\n", - " 'createdAt', 'characteristics', 'gender', 'details_transformado'],\n", - " dtype='object')" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_adidas.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 32, + "execution_count": 83, "metadata": {}, "outputs": [], "source": [ @@ -404,7 +489,8 @@ " \"undiscounted_price\": row[\"undiscounted_price\"],\n", " \"details\": row['details_transformado'],\n", " \"description\": row['description'],\n", - " \"category\": row['category']\n", + " \"category\": row['category'],\n", + " \"characteristics\": row['characteristics']\n", " }\n", " for _, row in df_adidas.iterrows() # Recorre todo el DataFrame\n", " if row['details_transformado'] != '{}' # Filtra donde los detalles no estén vacíos\n", @@ -413,49 +499,83 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'id': '0AqheRhKT2lhm7puBVCF',\n", - " 'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Exterior de tejido adidas Primeknit} {Ajuste suave en el talón} {Sistema Linear Energy Push} {Mediasuela BOOST} {Peso: 289 g (Talla CO 37)} {Suela Stretchweb con caucho Continental™ Better Rubber} {El hilo del exterior contiene al menos un 50% de Parley Ocean Plastic y un 50% de poliéster reciclado} {Color del artículo: Core Black / Core Black / Cloud White} {Número de artículo: GX5591}',\n", - " 'description': 'Hemos analizado 1.200.000 pisadas para que Ultraboost evolucione y mejore su ajuste para adaptarse 360° al pie femenino. Y aún hay más. Hemos rediseñado la suela de caucho. Probamos cientos de prototipos. Hasta que comprobamos las mejoras en su rendimiento. ¿El resultado? Un 4 % más de energía que los Ultraboost 21 para mujer. El ajuste en el área del talón del exterior adidas PRIMEKNIT se diseñó para evitar que el talón se deslice y se formen ampollas. Estarás corriendo sobre una mediasuela BOOST con tecnología Linear Energy Push. El exterior de este calzado está confeccionado con hilo que contiene un 50% de Parley Ocean Plastic, un material que ha sido recuperado de residuos plásticos recogidos en islas, playas, comunidades costeras y costas evitando que contaminen nuestros océanos.',\n", - " 'category': 'Mujer • Running'}]" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "productos[1:2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 99, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'id': '0AqheRhKT2lhm7puBVCF', 'regularPrice': '$799.950', 'undiscounted_price': 'NA', 'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Exterior de tejido adidas Primeknit} {Ajuste suave en el talón} {Sistema Linear Energy Push} {Mediasuela BOOST} {Peso: 289 g (Talla CO 37)} {Suela Stretchweb con caucho Continental™ Better Rubber} {El hilo del exterior contiene al menos un 50% de Parley Ocean Plastic y un 50% de poliéster reciclado} {Color del artículo: Core Black / Core Black / Cloud White} {Número de artículo: GX5591}', 'description': 'Hemos analizado 1.200.000 pisadas para que Ultraboost evolucione y mejore su ajuste para adaptarse 360° al pie femenino. Y aún hay más. Hemos rediseñado la suela de caucho. Probamos cientos de prototipos. Hasta que comprobamos las mejoras en su rendimiento. ¿El resultado? Un 4 % más de energía que los Ultraboost 21 para mujer. El ajuste en el área del talón del exterior adidas PRIMEKNIT se diseñó para evitar que el talón se deslice y se formen ampollas. Estarás corriendo sobre una mediasuela BOOST con tecnología Linear Energy Push. El exterior de este calzado está confeccionado con hilo que contiene un 50% de Parley Ocean Plastic, un material que ha sido recuperado de residuos plásticos recogidos en islas, playas, comunidades costeras y costas evitando que contaminen nuestros océanos.', 'category': 'Mujer • Running'}\n", + "{'id': '0AqheRhKT2lhm7puBVCF', 'regularPrice': '$799.950', 'undiscounted_price': 'NA', 'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Exterior de tejido adidas Primeknit} {Ajuste suave en el talón} {Sistema Linear Energy Push} {Mediasuela BOOST} {Peso: 289 g (Talla CO 37)} {Suela Stretchweb con caucho Continental™ Better Rubber} {El hilo del exterior contiene al menos un 50% de Parley Ocean Plastic y un 50% de poliéster reciclado} {Color del artículo: Core Black / Core Black / Cloud White} {Número de artículo: GX5591}', 'description': 'Hemos analizado 1.200.000 pisadas para que Ultraboost evolucione y mejore su ajuste para adaptarse 360° al pie femenino. Y aún hay más. Hemos rediseñado la suela de caucho. Probamos cientos de prototipos. Hasta que comprobamos las mejoras en su rendimiento. ¿El resultado? Un 4 % más de energía que los Ultraboost 21 para mujer. El ajuste en el área del talón del exterior adidas PRIMEKNIT se diseñó para evitar que el talón se deslice y se formen ampollas. Estarás corriendo sobre una mediasuela BOOST con tecnología Linear Energy Push. El exterior de este calzado está confeccionado con hilo que contiene un 50% de Parley Ocean Plastic, un material que ha sido recuperado de residuos plásticos recogidos en islas, playas, comunidades costeras y costas evitando que contaminen nuestros océanos.', 'category': 'Mujer • Running', 'characteristics': nan}\n", + "Respuesta del modelo:\n", + "| Weight | Upper Material | Midsole Material | Outsole | Cushioning System | Drop (heel-to-toe differential) | Pronation Type | Usage Type | Gender | Available Sizes | Width | Additional Technologies |\n", + "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", + "| 289 g | adidas Primeknit | Mediasuela BOOST | Suela Stretchweb con caucho Continental Better Rubber | Sistema Linear Energy Push | null | neutral | mujer | 37-47 | standard | contains Parley Ocean Plastic and recycled polyester |\n", + "\n", + "------------------------\n", + "\n", + "{'id': '0IgYTzUHkE7zIdcVyFCK', 'regularPrice': '$1.049.950', 'undiscounted_price': '$629.970', 'details': '{Ajuste clásico} {Cierre de cordones} {Parte superior de malla técnica} {Amortiguación Lightstrike Pro} {Varillas ENERGYRODS que limitan la pérdida de energía} {Talón Slinglaunch para un ajuste seguro} {Peso: 166 g (Talla COL 36 1/2)} {Caída mediasuela: 6 mm (talón: 32 mm / antepié: 26 mm)} {Suela de caucho Continental™} {Contiene al menos un 20 % de material reciclado} {Color del artículo: Green Spark / Aurora Met. / Lucid Lemon} {Número de artículo: IG8206}', 'description': 'Haz tus mejores 10k con nuestros nuevos tenis de running Adizero, diseñados exclusivamente para darte máxima velocidad. Los Adizero Takumi Sen 10 han sido diseñados con dos capas de amortiguación LIGHTSTRIKE PRO, combinadas con las varillas ENERGYRODS para una mayor estabilidad e impulso en tu carrera. Supera tu marca personal y llega a la meta más rápido que nunca.', 'category': 'Mujer • Running', 'characteristics': nan}\n", + "Respuesta del modelo:\n", + "| Weight | Upper Material | Midsole Material | Outsole | Cushioning System | Drop (heel-to-toe differential) | Pronation Type | Usage Type | Gender | Available Sizes | Width | Additional Technologies |\n", + "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", + "| 166 g | Parte superior de malla técnica | Amortiguación Lightstrike Pro | Suela de caucho Continental™ | Amortiguación Lightstrike Pro | 6 mm (talón: 32 mm / antepié: 26 mm) | Neutral | Daily training, racing, trail running, casual use | Mujer | Talla COL 36 1/2, COL 37, COL 38, COL 39.5 | Narrow, Standard, Wide | Contiene al menos un 20 % de material reciclado |\n", + "\n", + "------------------------\n", + "\n", + "{'id': '0MU8aKCnCUZv2r9aLD67', 'regularPrice': '$1.049.950', 'undiscounted_price': '$734.965', 'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Parte superior de malla} {Amortiguación Lightstrike Pro} {Varillas ENERGYRODS que limitan la pérdida de energía} {Talón Slinglaunch} {Peso: 200 gramos (talla CO 40)} {Caída mediasuela: 6 mm (talón: 33 mm / antepié: 27 mm} {Suela de caucho Continental™ Rubber} {Contienen al menos un 20% de material reciclado} {Color del artículo: Green Spark / Aurora Met. / Lucid Lemon} {Número de artículo: IG3134}', 'description': 'Haz tus mejores 10k con nuestros nuevos tenis de running Adizero, diseñados exclusivamente para darte máxima velocidad. Los Adizero Takumi Sen 10 han sido diseñados con dos capas de amortiguación LIGHTSTRIKE PRO, combinadas con las varillas ENERGYRODS 2.0 para una mayor estabilidad e impulso en tu carrera. Supera tu marca personal y llega a la meta más rápido que nunca.', 'category': 'Hombre • Running', 'characteristics': nan}\n", + "Respuesta del modelo:\n", + "| Weight | Upper Material | Midsole Material | Outsole | Cushioning System | Drop (heel-to-toe differential) | Pronation Type | Usage Type | Gender | Available Sizes | Width | Additional Technologies |\n", + "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", + "| 200 gramos | Parte superior de malla | Amortiguación Lightstrike Pro | Suela de caucho Continental™ Rubber | Amortiguación Lightstrike Pro | 6 mm (talón: 33 mm / antepié: 27 mm) | null | Trail running | Hombre | CO 40, CO 42-46, CO 48 | null | Contienen al menos un 20% de material reciclado |\n", + "| null | Sistema de amarre de cordones | ENERGYRODS 2.0 | null | Lightstrike Pro | null | null | null | null | null | null | Supera tu marca personal y llega a la meta más rápido que nunca\n", + "\n", + "------------------------\n", + "\n", + "{'id': '0Q6DNSlvsjBzy3AQeY2y', 'regularPrice': '$279.950', 'undiscounted_price': 'NA', 'details': '{Horma clásica} {Sistema de amarre de cordones} {Parte superior textil} {Forro interno textil} {Mediasuela Cloudfoam} {Suela de TPU} {Peso: 319 g (talla CO 40)} {Caída mediasuela: 6 mm (talón 35 mm / antepié 29 mm)} {Color del artículo: Halo Silver / Carbon / Core Black} {Número de artículo: ID8754}', 'description': 'Cada carrera es un viaje de descubrimiento. Ponte estos tenis de running adidas y libera tu potencial. La mediasuela con amortiguación Cloudfoam te ofrece una pisada más cómoda mientras aumentas tu resistencia. La parte superior textil es resistente al desgaste y te ofrece soporte desde tu primera vuelta hasta tu primera carrera de 5K.', 'category': 'Hombre • Running', 'characteristics': nan}\n", + "Respuesta del modelo:\n", + "| Weight | Upper Material | Midsole Material | Outsole | Cushioning System | Drop (heel-to-toe differential) | Pronation Type | Usage Type | Gender | Available Sizes | Width | Additional Technologies |\n", + "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", + "| 319 g | Parte superior textil | Mediasuela Cloudfoam | Suela de TPU | null | 6 mm (talón 35 mm / antepié 29 mm) | Neutral | Daily training | Men | CO 40, CO 41-45, CO 46-50 | Narrow, Standard, Wide | null |\n", + "\n", + "------------------------\n", + "\n", + "{'id': '0SF7zveew5mzdUJWZKyz', 'regularPrice': '$699.950', 'undiscounted_price': '$559.960', 'details': '{Si este artículo es personalizado} {no aplica en nuestra política de cambios y devoluciones.} {Sistema de amarre de cordones para un ajuste inmejorable} {Producto hecho parcialmente con Malla Técnica Reciclada} {Diseño liviano} {Amortiguación Lightstrike y Lightstrike Pro} {Forro interno textil} {Caída mediasuela: 8} {5 mm (talón: 33 mm / antepié: 24} {5 mm)} {Suela de caucho} {El exterior contiene al menos un 50% de material reciclado} {Color del artículo: Ivory / Iron Metallic / Spark} {Número de artículo: IG3341}', 'description': 'Cuando se trata de alcanzar tus metas, cada segundo cuenta. Ya sea para entrenar o para competir, un rendimiento excelente requiere de prendas de alta tecnología optimizadas para la velocidad. Presentamos la nueva colección de tenis de running livianos que te ayudan a superar tus límites sin distracciones.\\n\\nLos tenis de running Adizero SL seleccionan lo mejor de nuestra franquicia Adizero que rompe récords mundiales. La mediasuela de EVA LIGHTSTRIKE liviana ofrece resiliencia a la mediasuela para que puedas concentrarte en el próximo paso, mientras que el exterior está hecho de una malla técnica suave que está zonificada en áreas clave. El talón acolchado y la lengüeta brindan una comodidad óptima junto con el antepié Adizero. La suela premium está diseñada para proporcionar tracción.', 'category': 'Mujer • Running', 'characteristics': nan}\n", + "Respuesta del modelo:\n", + "| Weight | Upper Material | Midsole Material | Outsole | Cushioning System | Drop (heel-to-toe differential) | Pronation Type | Usage Type | Gender | Available Sizes | Width | Additional Technologies |\n", + "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", + "| null | {Si este artículo es personalizado} / {Sistema de amarre de cordones para un ajuste inmejorable} / {Producto hecho parcialmente con Malla Técnica Reciclada} | {Amortiguación Lightstrike y Lightstrike Pro} | {Suela de caucho} | null | 8 mm | neutral | Trail running, casual use | Women, Men | 5-12 | null | El exterior contiene al menos un 50% de material reciclado\n", + "\n", + "------------------------\n", + "\n", + "{'id': '0iPjAsLy8yEYvGgiCAzo', 'regularPrice': '$299.950', 'undiscounted_price': 'NA', 'details': '{Horma clásica} {Sistema de amarre de cordones} {Parte superior de malla} {Forro interno textil} {Plantilla OrthoLite®} {Mediasuela Cloudfoam} {Peso: 304 g (talla CO 40)} {Caída mediasuela: 10 mm (talón: 33 mm / antepié: 23 mm)} {Suela Adiwear} {Color del artículo: Cloud White / Core Black / Better Scarlet} {Número de artículo: IE8818}', 'description': 'Tanto en la pista como en la cinta de correr, alcanza todas tus metas con estos tenis de running adidas. Incorporan una mediasuela con amortiguación Cloudfoam que te ofrece una pisada más cómoda y suave. La parte superior de malla transpirable y la suela Adiwear de gran resistencia al desgaste la convierten en una silueta perfecta para llevar durante todo el día.', 'category': 'Hombre • Running', 'characteristics': nan}\n", + "Respuesta del modelo:\n", + "| Weight | Upper Material | Midsole Material | Outsole | Cushioning System | Drop (heel-to-toe differential) | Pronation Type | Usage Type | Gender | Available Sizes | Width | Additional Technologies |\n", + "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", + "| 304 g | Parte superior de malla, Forro interno textil | Plantilla OrthoLite® | Suela Adiwear | Mediasuela Cloudfoam | 10 mm (talón: 33 mm / antepié: 23 mm) | null | Running | Hombre • Hombre | CO 40 - IE 8818 | null | Waterproofing, Reflectivity\n", + "\n", + "------------------------\n", + "\n", + "{'id': '0n2Tyl34QdVdkCKgAdcT', 'regularPrice': '$649.950', 'undiscounted_price': 'NA', 'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Parte superior de malla} {Forro interno textil} {mediasuela Dreamstrike+} {Suela Adiwear} {Peso (talla CO 37): 213 gramos} {Caída mediasuela: 10 mm (talón: 34 mm / 24 mm)} {Contienen al menos un 20% de material reciclado y renovable} {Color del artículo: Almost Yellow / Zero Metalic / Pink Spark} {Número de artículo: IE1072}', 'description': 'Avanza hacia tus metas de running con estos tenis adidas. La mediasuela Dreamstrike+ está diseñada para absorber el impacto. La amortiguación adicional en el antepié ofrece comodidad durante toda tu carrera. El exterior de malla transpirable maximiza el flujo de aire para mantener tus pies frescos, y la suela Adiwear resistente ofrece tracción. \\n\\nAl elegir materiales reciclados, podemos reutilizar materiales que ya han sido creados, lo que ayuda a reducir los residuos. La elección de materiales renovables nos ayudará a eliminar nuestra dependencia de recursos finitos. Nuestros productos hechos con una mezcla de materiales reciclados y renovables contienen al menos un 20% de este material.', 'category': 'Mujer • Running', 'characteristics': nan}\n", + "Respuesta del modelo:\n", + "| Weight | Upper Material | Midsole Material | Outsole | Cushioning System | Drop (heel-to-toe differential) | Pronation Type | Usage Type | Gender | Available Sizes | Width | Additional Technologies |\n", + "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", + "| 213 gramos | Parte superior de malla, Forro interno textil, Sistema de amarre de cordones | mediasuela Dreamstrike+ | Suela Adiwear | mediasuela Dreamstrike+ | 10 mm (talón: 34 mm / 24 mm) | Neutral | Running | Mujer | CO 37 | null | Contienen al menos un 20% de material reciclado y renovable |\n", + "\n", + "------------------------\n", + "\n", + "{'id': '0rf6HEvBi5R4FunIhToR', 'regularPrice': '$449.950', 'undiscounted_price': 'NA', 'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Exterior de malla con cuello acolchado} {Forro interno textil} {Estabilizador de talón moldeado Fitcounter} {Mediasuela Bounce} {Suela sintética} {El exterior contiene al menos un 50 % de material reciclado} {Color del artículo: Shadow Violet / Legend Ink / Wonder Clay} {Número de artículo: IG0334}', 'description': 'Estos tenis fueron diseñados para darte la amortiguación y la respuesta que necesitas cuando estás en la pista o en los senderos. Sin importar si vas a hacer tu caminata diaria o una carrera corta, la mediasuela Bounce es flexible y receptiva con cada paso. Hecho con una serie de materiales reciclados, el exterior incorpora al menos un 50 % de contenido reciclado. Este producto representa solo una de nuestras soluciones para acabar con los residuos plásticos.', 'category': 'Mujer • Running', 'characteristics': nan}\n", "Respuesta del modelo:\n", - "| Weight | Upper Material | Midsole Material | Outsole | Cushioning System | Drop (heel-to-toe differential) | Pronation Type | Usage Type | Gender | Available Sizes | Width | Price | Additional Technologies |\n", - "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", - "| 289 g | adidas Primeknit | Mediasuela BOOST | Suela Stretchweb con caucho Continental™ Better Rubber | Sistema Linear Energy Push | null | neutral | Daily training | mujer | CO 37 - ?? | null | 4% más de energía que Ultraboost 21 para mujer | El hilo del exterior contiene al menos un 50% de Parley Ocean Plastic y un 50% de poliéster reciclado |\n", + "| Weight | Upper Material | Midsole Material | Outsole | Cushioning System | Drop (heel-to-toe differential) | Pronation Type | Usage Type | Gender | Available Sizes | Width | Additional Technologies |\n", + "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", + "| null | Exterior de malla con cuello acolchado / {Sistema de amarre de cordones} / Technical fabrics | Mediasuela Bounce | Suela sintética | mediasuela Bounce | null | neutral | Mujer • Running | null | null | Legend Ink / Wonder Clay | IG0334 |\n", "\n", "------------------------\n", "\n", - "{'id': '0IgYTzUHkE7zIdcVyFCK', 'regularPrice': '$1.049.950', 'undiscounted_price': '$629.970', 'details': '{Ajuste clásico} {Cierre de cordones} {Parte superior de malla técnica} {Amortiguación Lightstrike Pro} {Varillas ENERGYRODS que limitan la pérdida de energía} {Talón Slinglaunch para un ajuste seguro} {Peso: 166 g (Talla COL 36 1/2)} {Caída mediasuela: 6 mm (talón: 32 mm / antepié: 26 mm)} {Suela de caucho Continental™} {Contiene al menos un 20 % de material reciclado} {Color del artículo: Green Spark / Aurora Met. / Lucid Lemon} {Número de artículo: IG8206}', 'description': 'Haz tus mejores 10k con nuestros nuevos tenis de running Adizero, diseñados exclusivamente para darte máxima velocidad. Los Adizero Takumi Sen 10 han sido diseñados con dos capas de amortiguación LIGHTSTRIKE PRO, combinadas con las varillas ENERGYRODS para una mayor estabilidad e impulso en tu carrera. Supera tu marca personal y llega a la meta más rápido que nunca.', 'category': 'Mujer • Running'}\n", + "{'id': '0zbwfhJUSB9viwWPAnoE', 'regularPrice': '$799.950', 'undiscounted_price': '$639.960', 'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Exterior de malla} {Tejido suave y cómodo} {Mediasuela REPETITOR y REPETITOR+ de doble densidad} {Estructura interna de soporte} {Peso: 334 gramos (talla CO 40} {5)} {Caída mediasuela: 6 mm (talón: 38 mm / antepié: 32 mm)} {Suela de caucho} {El hilo de la parte superior contiene al menos un 50 % de Parley Ocean Plastic y un 50 % de poliéster reciclado} {Color del artículo: Lucid Lemon / Carbon / Wonder Blue} {Número de artículo: FZ5622}', 'description': 'Correr largas distancias es mucho más que ir de la A a la B. Se trata de la brisa en tu espalda, el ritmo de tu pisada, la libertad de la carretera. Los tenis Adistar 2.0 están diseñados con precisión para movimientos continuos, asegurando que cada paso se adapte sin problemas al siguiente, kilómetro tras kilómetro. La mediasuela REPETITOR y REPETITOR+ de doble densidad combina una espuma ligera para una amortiguación flexible con un compuesto firme que abraza el talón. Una estructura interna sujeta el pie para un soporte óptimo que nunca se detiene.', 'category': 'Hombre • Running', 'characteristics': nan}\n", "Respuesta del modelo:\n", - "| Weight | Upper Material | Midsole Material | Outsole | Cushioning System | Drop (heel-to-toe differential) | Pronation Type | Usage Type | Gender | Available Sizes | Width | Price | Additional Technologies |\n", - "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", - "| 166 g | Parte superior de malla técnica | Amortiguación Lightstrike Pro | Suela de caucho Continental™ | Contains at least a 20% recycled material | 6 mm (talón: 32 mm / antepié: 26 mm) | Neutral | Daily training | Women | Talla COL 36 1/2, null | null | 166 g | Contains al menos un 20 % de material reciclado |\n", + "| Weight | Upper Material | Midsole Material | Outsole | Cushioning System | Drop (heel-to-toe differential) | Pronation Type | Usage Type | Gender | Available Sizes | Width | Additional Technologies |\n", + "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", + "| 334 gramos | Exterior de malla, Tejido suave y cómodo | Mediasuela REPETITOR y REPETITOR+ de doble densidad | Suela de caucho | mediasuela REPETITOR y REPETITOR+ | 6 mm (talón: 38 mm / antepié: 32 mm) | Neutral | Daily training | Men | CO 40-50, CO 42-44, CO 46 | Narrow, Standard, Wide | Waterproofing with recycled materials\n", "\n", "------------------------\n", "\n" @@ -464,7 +584,7 @@ ], "source": [ "dfs = []\n", - "for producto in productos:\n", + "for producto in productos[1:10]:\n", " prompt = generate_prompt_ollama(producto, labels_with_definitions)\n", " respuesta = obtener_respuesta_ollama(prompt)\n", " print(producto)\n", @@ -474,10 +594,13 @@ " df = procesar_respuesta(respuesta[\"message\"][\"content\"], labels_with_definitions)\n", " if df is not None:\n", " attribute_columns = df.columns[:-3]\n", - " dfs = []\n", " df['id'] = producto['id']\n", " df['regularPrice'] = producto['regularPrice']\n", " df['undiscounted_price'] = producto['undiscounted_price']\n", + " df[\"details\"] = producto['details']\n", + " df[\"description\"] = producto['description']\n", + " df[\"category\"] = producto['category']\n", + " df[\"characteristics\"] = producto['characteristics']\n", " df = df[~df[attribute_columns].eq('---').all(axis=1)]\n", " df = df.dropna(how='all')\n", " dfs.append(df)\n", @@ -488,7 +611,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 102, "metadata": {}, "outputs": [], "source": [ @@ -500,14 +623,561 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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WeightUpper MaterialMidsole MaterialOutsoleCushioning SystemDrop (heel-to-toe differential)Pronation TypeUsage TypeGenderAvailable SizesWidthAdditional TechnologiesidregularPriceundiscounted_pricedetailsdescriptioncategorycharacteristics
0289 gadidas PrimeknitMediasuela BOOSTSuela Stretchweb con caucho Continental Better...Sistema Linear Energy Pushnullneutralmujer37-47standardcontains Parley Ocean Plastic and recycled pol...None0AqheRhKT2lhm7puBVCF$799.950NA{Ajuste clásico} {Sistema de amarre de cordone...Hemos analizado 1.200.000 pisadas para que Ult...Mujer • RunningNaN
1166 gParte superior de malla técnicaAmortiguación Lightstrike ProSuela de caucho Continental™Amortiguación Lightstrike Pro6 mm (talón: 32 mm / antepié: 26 mm)NeutralDaily training, racing, trail running, casual useMujerTalla COL 36 1/2, COL 37, COL 38, COL 39.5Narrow, Standard, WideContiene al menos un 20 % de material reciclado0IgYTzUHkE7zIdcVyFCK$1.049.950$629.970{Ajuste clásico} {Cierre de cordones} {Parte s...Haz tus mejores 10k con nuestros nuevos tenis ...Mujer • RunningNaN
2200 gramosParte superior de mallaAmortiguación Lightstrike ProSuela de caucho Continental™ RubberAmortiguación Lightstrike Pro6 mm (talón: 33 mm / antepié: 27 mm)nullTrail runningHombreCO 40, CO 42-46, CO 48nullContienen al menos un 20% de material reciclado0MU8aKCnCUZv2r9aLD67$1.049.950$734.965{Ajuste clásico} {Sistema de amarre de cordone...Haz tus mejores 10k con nuestros nuevos tenis ...Hombre • RunningNaN
3nullSistema de amarre de cordonesENERGYRODS 2.0nullLightstrike PronullnullnullnullnullnullSupera tu marca personal y llega a la meta más...0MU8aKCnCUZv2r9aLD67$1.049.950$734.965{Ajuste clásico} {Sistema de amarre de cordone...Haz tus mejores 10k con nuestros nuevos tenis ...Hombre • RunningNaN
4319 gParte superior textilMediasuela CloudfoamSuela de TPUnull6 mm (talón 35 mm / antepié 29 mm)NeutralDaily trainingMenCO 40, CO 41-45, CO 46-50Narrow, Standard, Widenull0Q6DNSlvsjBzy3AQeY2y$279.950NA{Horma clásica} {Sistema de amarre de cordones...Cada carrera es un viaje de descubrimiento. Po...Hombre • RunningNaN
5null{Si este artículo es personalizado} / {Sistema...{Amortiguación Lightstrike y Lightstrike Pro}{Suela de caucho}null8 mmneutralTrail running, casual useWomen, Men5-12nullEl exterior contiene al menos un 50% de materi...0SF7zveew5mzdUJWZKyz$699.950$559.960{Si este artículo es personalizado} {no aplica...Cuando se trata de alcanzar tus metas, cada se...Mujer • RunningNaN
6304 gParte superior de malla, Forro interno textilPlantilla OrthoLite®Suela AdiwearMediasuela Cloudfoam10 mm (talón: 33 mm / antepié: 23 mm)nullRunningHombre • HombreCO 40 - IE 8818nullWaterproofing, Reflectivity0iPjAsLy8yEYvGgiCAzo$299.950NA{Horma clásica} {Sistema de amarre de cordones...Tanto en la pista como en la cinta de correr, ...Hombre • RunningNaN
7213 gramosParte superior de malla, Forro interno textil,...mediasuela Dreamstrike+Suela Adiwearmediasuela Dreamstrike+10 mm (talón: 34 mm / 24 mm)NeutralRunningMujerCO 37nullContienen al menos un 20% de material reciclad...0n2Tyl34QdVdkCKgAdcT$649.950NA{Ajuste clásico} {Sistema de amarre de cordone...Avanza hacia tus metas de running con estos te...Mujer • RunningNaN
8nullExterior de malla con cuello acolchado / {Sist...Mediasuela BounceSuela sintéticamediasuela BouncenullneutralMujer • RunningnullnullLegend Ink / Wonder ClayIG03340rf6HEvBi5R4FunIhToR$449.950NA{Ajuste clásico} {Sistema de amarre de cordone...Estos tenis fueron diseñados para darte la amo...Mujer • RunningNaN
9334 gramosExterior de malla, Tejido suave y cómodoMediasuela REPETITOR y REPETITOR+ de doble den...Suela de cauchomediasuela REPETITOR y REPETITOR+6 mm (talón: 38 mm / antepié: 32 mm)NeutralDaily trainingMenCO 40-50, CO 42-44, CO 46Narrow, Standard, WideWaterproofing with recycled materials0zbwfhJUSB9viwWPAnoE$799.950$639.960{Ajuste clásico} {Sistema de amarre de cordone...Correr largas distancias es mucho más que ir d...Hombre • RunningNaN
\n", + "
" + ], + "text/plain": [ + " Weight Upper Material \\\n", + "0 289 g adidas Primeknit \n", + "1 166 g Parte superior de malla técnica \n", + "2 200 gramos Parte superior de malla \n", + "3 null Sistema de amarre de cordones \n", + "4 319 g Parte superior textil \n", + "5 null {Si este artículo es personalizado} / {Sistema... \n", + "6 304 g Parte superior de malla, Forro interno textil \n", + "7 213 gramos Parte superior de malla, Forro interno textil,... \n", + "8 null Exterior de malla con cuello acolchado / {Sist... \n", + "9 334 gramos Exterior de malla, Tejido suave y cómodo \n", + "\n", + " Midsole Material \\\n", + "0 Mediasuela BOOST \n", + "1 Amortiguación Lightstrike Pro \n", + "2 Amortiguación Lightstrike Pro \n", + "3 ENERGYRODS 2.0 \n", + "4 Mediasuela Cloudfoam \n", + "5 {Amortiguación Lightstrike y Lightstrike Pro} \n", + "6 Plantilla OrthoLite® \n", + "7 mediasuela Dreamstrike+ \n", + "8 Mediasuela Bounce \n", + "9 Mediasuela REPETITOR y REPETITOR+ de doble den... \n", + "\n", + " Outsole \\\n", + "0 Suela Stretchweb con caucho Continental Better... \n", + "1 Suela de caucho Continental™ \n", + "2 Suela de caucho Continental™ Rubber \n", + "3 null \n", + "4 Suela de TPU \n", + "5 {Suela de caucho} \n", + "6 Suela Adiwear \n", + "7 Suela Adiwear \n", + "8 Suela sintética \n", + "9 Suela de caucho \n", + "\n", + " Cushioning System Drop (heel-to-toe differential) \\\n", + "0 Sistema Linear Energy Push null \n", + "1 Amortiguación Lightstrike Pro 6 mm (talón: 32 mm / antepié: 26 mm) \n", + "2 Amortiguación Lightstrike Pro 6 mm (talón: 33 mm / antepié: 27 mm) \n", + "3 Lightstrike Pro null \n", + "4 null 6 mm (talón 35 mm / antepié 29 mm) \n", + "5 null 8 mm \n", + "6 Mediasuela Cloudfoam 10 mm (talón: 33 mm / antepié: 23 mm) \n", + "7 mediasuela Dreamstrike+ 10 mm (talón: 34 mm / 24 mm) \n", + "8 mediasuela Bounce null \n", + "9 mediasuela REPETITOR y REPETITOR+ 6 mm (talón: 38 mm / antepié: 32 mm) \n", + "\n", + " Pronation Type Usage Type \\\n", + "0 neutral mujer \n", + "1 Neutral Daily training, racing, trail running, casual use \n", + "2 null Trail running \n", + "3 null null \n", + "4 Neutral Daily training \n", + "5 neutral Trail running, casual use \n", + "6 null Running \n", + "7 Neutral Running \n", + "8 neutral Mujer • Running \n", + "9 Neutral Daily training \n", + "\n", + " Gender Available Sizes \\\n", + "0 37-47 standard \n", + "1 Mujer Talla COL 36 1/2, COL 37, COL 38, COL 39.5 \n", + "2 Hombre CO 40, CO 42-46, CO 48 \n", + "3 null null \n", + "4 Men CO 40, CO 41-45, CO 46-50 \n", + "5 Women, Men 5-12 \n", + "6 Hombre • Hombre CO 40 - IE 8818 \n", + "7 Mujer CO 37 \n", + "8 null null \n", + "9 Men CO 40-50, CO 42-44, CO 46 \n", + "\n", + " Width \\\n", + "0 contains Parley Ocean Plastic and recycled pol... \n", + "1 Narrow, Standard, Wide \n", + "2 null \n", + "3 null \n", + "4 Narrow, Standard, Wide \n", + "5 null \n", + "6 null \n", + "7 null \n", + "8 Legend Ink / Wonder Clay \n", + "9 Narrow, Standard, Wide \n", + "\n", + " Additional Technologies id \\\n", + "0 None 0AqheRhKT2lhm7puBVCF \n", + "1 Contiene al menos un 20 % de material reciclado 0IgYTzUHkE7zIdcVyFCK \n", + "2 Contienen al menos un 20% de material reciclado 0MU8aKCnCUZv2r9aLD67 \n", + "3 Supera tu marca personal y llega a la meta más... 0MU8aKCnCUZv2r9aLD67 \n", + "4 null 0Q6DNSlvsjBzy3AQeY2y \n", + "5 El exterior contiene al menos un 50% de materi... 0SF7zveew5mzdUJWZKyz \n", + "6 Waterproofing, Reflectivity 0iPjAsLy8yEYvGgiCAzo \n", + "7 Contienen al menos un 20% de material reciclad... 0n2Tyl34QdVdkCKgAdcT \n", + "8 IG0334 0rf6HEvBi5R4FunIhToR \n", + "9 Waterproofing with recycled materials 0zbwfhJUSB9viwWPAnoE \n", + "\n", + " regularPrice undiscounted_price \\\n", + "0 $799.950 NA \n", + "1 $1.049.950 $629.970 \n", + "2 $1.049.950 $734.965 \n", + "3 $1.049.950 $734.965 \n", + "4 $279.950 NA \n", + "5 $699.950 $559.960 \n", + "6 $299.950 NA \n", + "7 $649.950 NA \n", + "8 $449.950 NA \n", + "9 $799.950 $639.960 \n", + "\n", + " details \\\n", + "0 {Ajuste clásico} {Sistema de amarre de cordone... \n", + "1 {Ajuste clásico} {Cierre de cordones} {Parte s... \n", + "2 {Ajuste clásico} {Sistema de amarre de cordone... \n", + "3 {Ajuste clásico} {Sistema de amarre de cordone... \n", + "4 {Horma clásica} {Sistema de amarre de cordones... \n", + "5 {Si este artículo es personalizado} {no aplica... \n", + "6 {Horma clásica} {Sistema de amarre de cordones... \n", + "7 {Ajuste clásico} {Sistema de amarre de cordone... \n", + "8 {Ajuste clásico} {Sistema de amarre de cordone... \n", + "9 {Ajuste clásico} {Sistema de amarre de cordone... \n", + "\n", + " description category \\\n", + "0 Hemos analizado 1.200.000 pisadas para que Ult... Mujer • Running \n", + "1 Haz tus mejores 10k con nuestros nuevos tenis ... Mujer • Running \n", + "2 Haz tus mejores 10k con nuestros nuevos tenis ... Hombre • Running \n", + "3 Haz tus mejores 10k con nuestros nuevos tenis ... Hombre • Running \n", + "4 Cada carrera es un viaje de descubrimiento. Po... Hombre • Running \n", + "5 Cuando se trata de alcanzar tus metas, cada se... Mujer • Running \n", + "6 Tanto en la pista como en la cinta de correr, ... Hombre • Running \n", + "7 Avanza hacia tus metas de running con estos te... Mujer • Running \n", + "8 Estos tenis fueron diseñados para darte la amo... Mujer • Running \n", + "9 Correr largas distancias es mucho más que ir d... Hombre • Running \n", + "\n", + " characteristics \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "5 NaN \n", + "6 NaN \n", + "7 NaN \n", + "8 NaN \n", + "9 NaN " + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_total" + ] + }, + { + "cell_type": "code", + "execution_count": 105, "metadata": {}, "outputs": [], "source": [ - "output_path = \"../src/comparative_analysis/database/adidas_etiquetado.xlsx\"\n", + "output_path = \"../src/comparative_analysis/database/adidas_etiquetado_llama31.xlsx\"\n", "df_total.to_excel(output_path, index=False)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preprocesamiento Nike" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "df_nike = df_raw[df_raw['store'] == 'nike'] \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Usuario\\AppData\\Local\\Temp\\ipykernel_2256\\2520405701.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_nike['details_transformado'] = df_nike['details'].apply(lambda x: transformar_texto(x, 'nike'))\n", + "C:\\Users\\Usuario\\AppData\\Local\\Temp\\ipykernel_2256\\2520405701.py:3: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_nike['characteristics_transformado'] = df_nike['characteristics'].apply(lambda x: transformar_texto(x, 'nike'))\n" + ] + } + ], + "source": [ + "# Lista de descripciones de productos\n", + "df_nike['details_transformado'] = df_nike['details'].apply(lambda x: transformar_texto(x, 'nike'))\n", + "df_nike['characteristics_transformado'] = df_nike['characteristics'].apply(lambda x: transformar_texto(x, 'nike'))" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "# Crear un diccionario con las llaves deseadas desde todo el DataFrame\n", + "productos_nike = [\n", + " {\n", + " \"id\": row['id'],\n", + " \"regularPrice\" : row[\"regularPrice\"],\n", + " \"undiscounted_price\": row[\"undiscounted_price\"],\n", + " \"details\": row['details_transformado'],\n", + " \"description\": row['description'],\n", + " \"category\": row['category'],\n", + " \"characteristics\": row['characteristics_transformado']\n", + " }\n", + " for _, row in df_nike.iterrows() # Recorre todo el DataFrame\n", + " if row['details_transformado'] != '{}' # Filtra donde los detalles no estén vacíos\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preprocesamiento nacion runner" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "df_nr = df_raw[df_raw['store'] == 'nacionRunner']" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "# Crear un diccionario con las llaves deseadas desde todo el DataFrame\n", + "productos_nr = [\n", + " {\n", + " \"id\": row['id'],\n", + " \"regularPrice\" : row[\"regularPrice\"],\n", + " \"undiscounted_price\": row[\"undiscounted_price\"],\n", + " \"details\": row['details'],\n", + " \"description\": row['description'],\n", + " \"category\": row['category'],\n", + " \"characteristics\": row['characteristics']\n", + " }\n", + " for _, row in df_nr.iterrows() # Recorre todo el DataFrame\n", + " if row['details'] != '{}' # Filtra donde los detalles no estén vacíos\n", + "]" + ] + }, { "cell_type": "code", "execution_count": null, From 6a83c3052318da3a07d59bc0d1c6d4bb966f761e Mon Sep 17 00:00:00 2001 From: Juan Correa Date: Thu, 12 Dec 2024 21:21:24 -0500 Subject: [PATCH 29/84] preprocessing --- requirements.txt | 4 +- scripts/evaluation/__init__.py | 0 .../preprocessing/functions/__init__.py | 0 .../functions/model_inference.py | 56 +++++++++++ .../functions/prompt_generator.py | 54 +++++++++++ .../functions/response_processor.py | 96 +++++++++++++++++++ .../preprocessing/main.py | 79 +++++++++++++++ 7 files changed, 287 insertions(+), 2 deletions(-) create mode 100644 scripts/evaluation/__init__.py create mode 100644 src/comparative_analysis/preprocessing/functions/__init__.py create mode 100644 src/comparative_analysis/preprocessing/functions/model_inference.py create mode 100644 src/comparative_analysis/preprocessing/functions/prompt_generator.py create mode 100644 src/comparative_analysis/preprocessing/functions/response_processor.py create mode 100644 src/comparative_analysis/preprocessing/main.py diff --git a/requirements.txt b/requirements.txt index be1f7051c..ac150face 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,7 +3,7 @@ fairscale fire blobfile pandas -ollama requests openpyxl -llama_models \ No newline at end of file +httpx +boto3 \ No newline at end of file diff --git a/scripts/evaluation/__init__.py b/scripts/evaluation/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/comparative_analysis/preprocessing/functions/__init__.py b/src/comparative_analysis/preprocessing/functions/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/comparative_analysis/preprocessing/functions/model_inference.py b/src/comparative_analysis/preprocessing/functions/model_inference.py new file mode 100644 index 000000000..d176d7445 --- /dev/null +++ b/src/comparative_analysis/preprocessing/functions/model_inference.py @@ -0,0 +1,56 @@ +import json +import httpx +import boto3 +from botocore.exceptions import ClientError + +class ModelInference: + def __init__(self, region_name="us-east-1"): + self.client = boto3.client("bedrock-runtime", region_name=region_name) + + def obtener_respuesta(self, user_message, option_model, max_retries=3, delay=5): + if option_model == 1: + model_id = "mistral.mistral-large-2402-v1:0" + conversation = [ + { + "role": "user", + "content": [{"text": user_message}], + } + ] + + try: + response = self.client.converse( + modelId=model_id, + messages=conversation, + inferenceConfig={"maxTokens": 2000, "temperature": 0.5, "topP": 0.9}, + ) + response_text = response["output"]["message"]["content"][0]["text"] + return response_text + except httpx.HTTPError as e: + print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}") + return None + + if option_model == 2: + model_id = "meta.llama3-70b-instruct-v1:0" + formatted_prompt = f""" + <|begin_of_text|><|start_header_id|>user<|end_header_id|> + {user_message} + <|eot_id|> + <|start_header_id|>assistant<|end_header_id|> + """ + + native_request = { + "prompt": formatted_prompt, + "max_gen_len": 1000, + "temperature": 0.5, + } + + request = json.dumps(native_request) + + try: + response = self.client.invoke_model(modelId=model_id, body=request) + model_response = json.loads(response["body"].read()) + response_text = model_response["generation"] + return response_text + except (ClientError, Exception) as e: + print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}") + return None diff --git a/src/comparative_analysis/preprocessing/functions/prompt_generator.py b/src/comparative_analysis/preprocessing/functions/prompt_generator.py new file mode 100644 index 000000000..84cf5459c --- /dev/null +++ b/src/comparative_analysis/preprocessing/functions/prompt_generator.py @@ -0,0 +1,54 @@ +import re + +class PromptGenerator: + def __init__(self, labels_with_definitions): + self.labels_with_definitions = labels_with_definitions + + def generate_prompt(self, product): + labels = [label for label, _ in self.labels_with_definitions] + definitions = ''.join([f'- {label}: {definition}\n' for label, definition in self.labels_with_definitions]) + prompt = f""" + You are an assistant specialized in extracting structured information from product descriptions and organizing it into tables. + Your task is to extract the following information from the product details and label it according to the provided labels: {', '.join(labels)}. + Each label has the following definition to help guide your extraction: + + {definitions} + + If a label does not have a clear match in the details, complete its value with "null". + + Product information: + - Details: {product['details']} + - Description: {product['description']} + - Category: {product['category']} + - Characteristics: {product['characteristics']} + + Provide the information in a table with columns corresponding to each label. + The table must include **two complete rows**: + 1. The first row contains the label names. + 2. The second row contains the corresponding labeled values. + + Expected response format: + | {' | '.join(labels)} | + | {' | '.join(['---'] * len(labels))} | + | value_1 | value_2 | ... | + + Example: + If the labels are "details", "description", and "category", and the extracted values are + "Comfortable sneakers", "Made with recycled materials", and "Footwear", respectively, + your response should be: + + | details | description | category | + | --- | --- | --- | + | Comfortable sneakers | Made with recycled materials | Footwear | + + Remember: + - The response must contain two complete rows. + - Only respond with the table and **do not include additional text**. + + Now, extract and structure the information for the provided product: + + | {' | '.join(labels)} | + | {' | '.join(['---'] * len(labels))} | + | + """ + return prompt.strip() diff --git a/src/comparative_analysis/preprocessing/functions/response_processor.py b/src/comparative_analysis/preprocessing/functions/response_processor.py new file mode 100644 index 000000000..91072a27a --- /dev/null +++ b/src/comparative_analysis/preprocessing/functions/response_processor.py @@ -0,0 +1,96 @@ +import re +import pandas as pd +from io import StringIO + +class ResponseProcessor: + def __init__(self, labels_with_definitions): + self.labels_with_definitions = labels_with_definitions + + def procesar_respuesta(self, respuesta_texto): + try: + # Extraer solo los nombres de las etiquetas + etiquetas = [label for label, _ in self.labels_with_definitions] + # Limpiar los nombres de las etiquetas + etiquetas_limpias = [ + re.sub(r"[ ,;{}()\n\t=]", "_", etiqueta.strip()) for etiqueta in etiquetas + ] + + # Buscar el inicio de la tabla + inicio_tabla = respuesta_texto.find('|') + if inicio_tabla == -1: + print("No se encontró una tabla en la respuesta.") + return None + + # Extraer la tabla desde el primer '|' + tabla_texto = respuesta_texto[inicio_tabla:].strip() + # Extraer solo las líneas que contengan '|' + lineas = tabla_texto.split('\n') + lineas_tabla = [] + for linea in lineas: + if '|' in linea: + linea = linea.strip().strip('|').strip() + lineas_tabla.append(linea) + else: + break + + if not lineas_tabla: + print("No se encontraron líneas válidas de tabla.") + return None + + # Verificar si hay separadores '---' + tiene_separador = any( + all(c.strip('-') == '' for c in fila.split('|')) + for fila in lineas_tabla + ) + + # Caso 1: Hay separadores (modo normal de Markdown) + if tiene_separador: + tabla_completa = '\n'.join(lineas_tabla) + df = pd.read_csv(StringIO(tabla_completa), sep='|', engine='python', skipinitialspace=True) + df.columns = [re.sub(r"[ ,;{}()\n\t=]", "_", col.strip()) for col in df.columns] + for col in df.columns: + if df[col].dtype == object: + df[col] = df[col].str.strip() + df = df.reset_index(drop=True) + return df + + # Caso 2: No hay separadores + if len(lineas_tabla) == 2: + # Primera línea columnas, segunda datos + columnas = [re.sub(r"[ ,;{}()\n\t=]", "_", c.strip()) for c in lineas_tabla[0].split('|')] + datos = [c.strip() for c in lineas_tabla[1].split('|')] + df = pd.DataFrame([datos], columns=columnas) + for col in df.columns: + if df[col].dtype == object: + df[col] = df[col].str.strip() + df = df.reset_index(drop=True) + return df + elif len(lineas_tabla) == 1: + # Solo datos, usar etiquetas limpias como columnas + datos = [c.strip() for c in lineas_tabla[0].split('|')] + if len(etiquetas_limpias) != len(datos): + print("Advertencia: El número de etiquetas no coincide. Se ajustará.") + col_names = etiquetas_limpias[:len(datos)] + else: + col_names = etiquetas_limpias + + df = pd.DataFrame([datos], columns=col_names) + for col in df.columns: + if df[col].dtype == object: + df[col] = df[col].str.strip() + df = df.reset_index(drop=True) + return df + else: + # Más de 2 líneas sin separador + columnas = [re.sub(r"[ ,;{}()\n\t=]", "_", c.strip()) for c in lineas_tabla[0].split('|')] + filas_datos = [ [x.strip() for x in fila.split('|')] for fila in lineas_tabla[1:] ] + df = pd.DataFrame(filas_datos, columns=columnas) + for col in df.columns: + if df[col].dtype == object: + df[col] = df[col].str.strip() + df = df.reset_index(drop=True) + return df + + except Exception as e: + print(f"Error al procesar la tabla: {e}") + return None diff --git a/src/comparative_analysis/preprocessing/main.py b/src/comparative_analysis/preprocessing/main.py new file mode 100644 index 000000000..ceeb65785 --- /dev/null +++ b/src/comparative_analysis/preprocessing/main.py @@ -0,0 +1,79 @@ +import os +import re +import pandas as pd +import numpy as np +from tqdm import tqdm + +from functions.prompt_generator import PromptGenerator +from functions.model_inference import ModelInference +from functions.response_processor import ResponseProcessor + +# Definir las etiquetas con definiciones +labels_with_definitions = [ + ("Weight", "Indicates the lightness of the shoe, usually expressed in grams. Weight can influence performance and comfort during running."), + ("Upper Material", "Describes the materials used in the shoe's upper part, such as mesh, synthetic leather, or technical fabrics, which affect breathability, flexibility, and support."), + ("Midsole Material", "Refers to the compounds used in the midsole, such as EVA foams or proprietary technologies, which provide cushioning and shock absorption."), + ("Outsole", "Details the type of rubber or material used in the sole and the traction pattern design, which influence grip and durability on various surfaces."), + ("Cushioning System", "Specifies the technologies or materials aimed at reducing impact during strides, contributing to comfort and joint protection."), + ("Drop (heel-to-toe differential)", "Indicates the height difference between the heel and the toe of the shoe, measured in millimeters. A higher drop typically offers more heel cushioning, while a lower drop promotes a more natural stride."), + ("Pronation Type", "Classifies the shoe according to its suitability for different pronation types: neutral, overpronation, or supination. This is essential for choosing footwear that matches the runner's biomechanics."), + ("Usage Type", "Defines the primary purpose of the shoe, such as daily training, racing, trail running, or casual use, guiding its specific design and features."), + ("Gender", "Indicates whether the shoe is designed for men, women, or is a unisex model, considering anatomical and sizing differences."), + ("Available Sizes", "Specifies the range of sizes in which the shoe is offered, ensuring the runner can find a suitable fit."), + ("Width", "Some brands offer different widths (narrow, standard, wide) to accommodate various foot shapes."), + ("Additional Technologies", "Includes special features such as waterproofing, reflectivity, customized fit systems, or stability elements that enhance the shoe's functionality."), +] + +# Asumimos que spark está inicializado en el entorno +spark_df = spark.table("preprod_colombia.scraping_pp_adidas") +df_adidas = spark_df.toPandas() + +# Crear lista de productos +productos = [ + { + "id": row['id'], + "regularPrice" : row["regularPrice"], + "undiscounted_price": row["undiscounted_price"], + "details": row['details_transformado'], + "description": row['description'], + "category": row['category'], + "characteristics": row['characteristics'] + } + for _, row in df_adidas.iterrows() + if row['details_transformado'] != '{}' +] + +# Instanciar las clases +prompt_generator = PromptGenerator(labels_with_definitions) +model_inference = ModelInference() +response_processor = ResponseProcessor(labels_with_definitions) + +option_model = 2 +dfs = [] +for producto in tqdm(productos, desc="Procesando productos"): + user_message = prompt_generator.generate_prompt(producto) + respuesta = model_inference.obtener_respuesta(user_message, option_model) + df = response_processor.procesar_respuesta(respuesta) + + if df is not None: + attribute_columns = df.columns[:-3] # Ajustar según cuántas columnas sean las de atributos + df['id'] = producto['id'] + df['regularPrice'] = producto['regularPrice'] + df['undiscounted_price'] = producto['undiscounted_price'] + df["details"] = producto['details'] + df["description"] = producto['description'] + df["category"] = producto['category'] + df["characteristics"] = producto['characteristics'] + df = df[~df[attribute_columns].eq('---').all(axis=1)] + df = df.dropna(how='all') + dfs.append(df) + else: + print("No se pudo extraer la tabla.\n") + +if dfs: + df_total = pd.concat(dfs, ignore_index=True) + # Guardar df_total como Excel + os.makedirs("src/database", exist_ok=True) + df_total.to_excel("src/database/df_total.xlsx", index=False) +else: + print("Fail") From 031bf10d3100f3f570bca4eec166a2f97371bc77 Mon Sep 17 00:00:00 2001 From: CarlosKC26 Date: Thu, 12 Dec 2024 21:30:09 -0500 Subject: [PATCH 30/84] =?UTF-8?q?eliminaci=C3=B3n=20archivo?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/business_understanding/prueba_azacipa.txt | 1 - 1 file changed, 1 deletion(-) delete mode 100644 docs/business_understanding/prueba_azacipa.txt diff --git a/docs/business_understanding/prueba_azacipa.txt b/docs/business_understanding/prueba_azacipa.txt deleted file mode 100644 index d931ceffb..000000000 --- a/docs/business_understanding/prueba_azacipa.txt +++ /dev/null @@ -1 +0,0 @@ -prueba azacipa \ No newline at end of file From 22d2b5653a9db852e8d9ce81f97548b815e7cec8 Mon Sep 17 00:00:00 2001 From: CarlosKC26 Date: Thu, 12 Dec 2024 22:47:08 -0500 Subject: [PATCH 31/84] test notebook para el entrenamiento del modelo de clustering --- requirements.txt | 4 +- src/comparative_analysis/training/test.ipynb | 113 ++++++++++++++++++ .../2/drop-heel-to-toe differential.png | Bin 0 -> 27419 bytes .../clustering model/2/regular-price.png | Bin 0 -> 23105 bytes .../clustering model/2/undiscounted-price.png | Bin 0 -> 27611 bytes .../clustering model/2/weight.png | Bin 0 -> 24398 bytes .../3/drop-heel-to-toe differential.png | Bin 0 -> 27774 bytes .../clustering model/3/regular-price.png | Bin 0 -> 24396 bytes .../clustering model/3/undiscounted-price.png | Bin 0 -> 28244 bytes .../clustering model/3/weight.png | Bin 0 -> 24084 bytes .../visualization/codo.png | Bin 0 -> 31813 bytes .../visualization/element-distribution.png | Bin 0 -> 26391 bytes 12 files changed, 116 insertions(+), 1 deletion(-) create mode 100644 src/comparative_analysis/training/test.ipynb create mode 100644 src/comparative_analysis/visualization/clustering model/2/drop-heel-to-toe differential.png create mode 100644 src/comparative_analysis/visualization/clustering model/2/regular-price.png create mode 100644 src/comparative_analysis/visualization/clustering model/2/undiscounted-price.png create mode 100644 src/comparative_analysis/visualization/clustering model/2/weight.png create mode 100644 src/comparative_analysis/visualization/clustering model/3/drop-heel-to-toe differential.png create mode 100644 src/comparative_analysis/visualization/clustering model/3/regular-price.png create mode 100644 src/comparative_analysis/visualization/clustering model/3/undiscounted-price.png create mode 100644 src/comparative_analysis/visualization/clustering model/3/weight.png create mode 100644 src/comparative_analysis/visualization/codo.png create mode 100644 src/comparative_analysis/visualization/element-distribution.png diff --git a/requirements.txt b/requirements.txt index ac150face..4f0ab1c63 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,4 +6,6 @@ pandas requests openpyxl httpx -boto3 \ No newline at end of file +boto3 +scikit-learn +matplotlib \ No newline at end of file diff --git a/src/comparative_analysis/training/test.ipynb b/src/comparative_analysis/training/test.ipynb new file mode 100644 index 000000000..26cbdbf89 --- /dev/null +++ b/src/comparative_analysis/training/test.ipynb @@ -0,0 +1,113 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Weight Upper_Material Midsole_Material \\\n", + "0 183 g Synthetic NaN \n", + "1 289 g adidas Primeknit BOOST \n", + "2 166 g Parte superior de malla técnica NaN \n", + "3 200 gramos Parte superior de malla NaN \n", + "4 319g Parte superior textil Mediasuela Cloudfoam \n", + "\n", + " Outsole Cushioning_System \\\n", + "0 Textile rubber Lightstrike Pro \n", + "1 Stretchweb with Continental Better Rubber Linear Energy Push \n", + "2 Suela de caucho Continental™ Amortiguación Lightstrike Pro \n", + "3 Suela de caucho Continental Rubber Amortiguación Lightstrike Pro \n", + "4 Suela de TPU Cloudfoam \n", + "\n", + " Drop__heel-to-toe_differential_ Pronation_Type Usage_Type Gender \\\n", + "0 6 mm NaN Racing Woman \n", + "1 NaN NaN Running Woman \n", + "2 6 mm NaN Running Mujer \n", + "3 6 mm NaN Running Hombre \n", + "4 6mm NaN Running Hombre \n", + "\n", + " Available_Sizes Width Additional_Technologies \\\n", + "0 NaN NaN ENERGYRODS 2.0, Waterproofing, Recyclable mate... \n", + "1 NaN NaN Parley Ocean Plastic, waterproofing \n", + "2 COL 36 1/2 NaN Contiene al menos un 20 % de material reciclad... \n", + "3 CO 40 NaN Varillas ENERGYRODS, Talón Slinglaunch, Contie... \n", + "4 CO 40 NaN NaN \n", + "\n", + " id regularPrice undiscounted_price \\\n", + "0 08sjncACSjSvg2t9DS73 $1.299.950 $909.965 \n", + "1 0AqheRhKT2lhm7puBVCF $799.950 NaN \n", + "2 0IgYTzUHkE7zIdcVyFCK $1.049.950 $629.970 \n", + "3 0MU8aKCnCUZv2r9aLD67 $1.049.950 $734.965 \n", + "4 0Q6DNSlvsjBzy3AQeY2y $279.950 NaN \n", + "\n", + " details \\\n", + "0 {Horma clásica} {Parte superior sintética} {Fo... \n", + "1 {Ajuste clásico} {Sistema de amarre de cordone... \n", + "2 {Ajuste clásico} {Cierre de cordones} {Parte s... \n", + "3 {Ajuste clásico} {Sistema de amarre de cordone... \n", + "4 {Horma clásica} {Sistema de amarre de cordones... \n", + "\n", + " description category \\\n", + "0 Los Adizero Adios Pro 3 son la máxima expresió... Mujer • Running \n", + "1 Hemos analizado 1.200.000 pisadas para que Ult... Mujer • Running \n", + "2 Haz tus mejores 10k con nuestros nuevos tenis ... Mujer • Running \n", + "3 Haz tus mejores 10k con nuestros nuevos tenis ... Hombre • Running \n", + "4 Cada carrera es un viaje de descubrimiento. Po... Hombre • Running \n", + "\n", + " characteristics \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\cdgn2\\OneDrive\\Escritorio\\Maestría\\Maestria\\Metodologias Agiles\\Proyecto\\Comparative-analysis-of-products\\venv\\lib\\site-packages\\openpyxl\\styles\\stylesheet.py:237: UserWarning: Workbook contains no default style, apply openpyxl's default\n", + " warn(\"Workbook contains no default style, apply openpyxl's default\")\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# Ruta del archivo de Excel\n", + "ruta_excel = r\"C:\\Users\\cdgn2\\OneDrive\\Escritorio\\Maestría\\Maestria\\Metodologias Agiles\\Proyecto\\Comparative-analysis-of-products\\src\\comparative_analysis\\database\\Adidas_etiquetado.xlsx\"\n", + "\n", + "# Crear el DataFrame\n", + "df = pd.read_excel(ruta_excel, header=0)\n", + "\n", + "# Mostrar las primeras filas del DataFrame\n", + 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[PATCH 32/84] creacion archivo training --- src/comparative_analysis/training/training.py | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 src/comparative_analysis/training/training.py diff --git a/src/comparative_analysis/training/training.py b/src/comparative_analysis/training/training.py new file mode 100644 index 000000000..597a45c32 --- /dev/null +++ b/src/comparative_analysis/training/training.py @@ -0,0 +1,10 @@ +import pandas as pd + +# Ruta del archivo de Excel +ruta_excel = r"C:\Users\cdgn2\OneDrive\Escritorio\Maestría\Maestria\Metodologias Agiles\Proyecto\Comparative-analysis-of-products\src\comparative_analysis\database\Adidas_etiquetado.xlsx" + +# Crear el DataFrame +df = pd.read_excel(ruta_excel, header=0) + +# Mostrar las primeras filas del DataFrame +print(df.head()) \ No newline at end of file From 45e87cef3722266a8e4374d109e0a99ed2314fe5 Mon Sep 17 00:00:00 2001 From: CarlosKC26 Date: Thu, 12 Dec 2024 23:01:58 -0500 Subject: [PATCH 33/84] =?UTF-8?q?c=C3=B3digo?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/comparative_analysis/training/test.ipynb | 1056 ++++++++++++++++- src/comparative_analysis/training/training.py | 95 +- 2 files changed, 1148 insertions(+), 3 deletions(-) diff --git a/src/comparative_analysis/training/test.ipynb b/src/comparative_analysis/training/test.ipynb index 26cbdbf89..365548f61 100644 --- a/src/comparative_analysis/training/test.ipynb +++ b/src/comparative_analysis/training/test.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -87,6 +87,1060 @@ "# Mostrar las primeras filas del DataFrame\n", "print(df.head())" ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Información del elemento encontrado:\n", + " Weight Upper_Material Midsole_Material Outsole Cushioning_System \\\n", + "0 183 g Synthetic NaN Textile rubber Lightstrike Pro \n", + "\n", + " Drop__heel-to-toe_differential_ Pronation_Type Usage_Type Gender \\\n", + "0 6 mm NaN Racing Woman \n", + "\n", + " Available_Sizes Width Additional_Technologies \\\n", + "0 NaN NaN ENERGYRODS 2.0, Waterproofing, Recyclable mate... \n", + "\n", + " id regularPrice undiscounted_price \\\n", + "0 08sjncACSjSvg2t9DS73 $1.299.950 $909.965 \n", + "\n", + " details \\\n", + "0 {Horma clásica} {Parte superior sintética} {Fo... \n", + "\n", + " description category \\\n", + "0 Los Adizero Adios Pro 3 son la máxima expresió... Mujer • Running \n", + "\n", + " characteristics \n", + "0 NaN \n" + ] + } + ], + "source": [ + "# Buscar información de un elemento basado en un criterio\n", + "def buscar_elemento(df, columna, valor):\n", + " # Filtrar el DataFrame por el valor especificado\n", + " resultado = df[df[columna] == valor]\n", + " \n", + " # Verificar si se encontró algún resultado\n", + " if not resultado.empty:\n", + " print(\"Información del elemento encontrado:\")\n", + " print(resultado)\n", + " else:\n", + " print(f\"No se encontró ningún elemento donde '{columna}' sea '{valor}'.\")\n", + "\n", + "# Ejemplo de uso: Buscar un elemento donde el id sea \"12345\" (ajusta según tu DataFrame)\n", + "buscar_elemento(df, columna=\"id\", valor=\"08sjncACSjSvg2t9DS73\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Weight', 'Upper_Material', 'Midsole_Material', 'Outsole', 'Cushioning_System', 'Drop__heel-to-toe_differential_', 'Pronation_Type', 'Usage_Type', 'Gender', 'Available_Sizes', 'Width', 'Additional_Technologies', 'id', 'regularPrice', 'undiscounted_price', 'details', 'description', 'category', 'characteristics']\n" + ] + } + ], + "source": [ + "print(df.columns.tolist())" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import re\n", + "\n", + "# Supongamos que tu DataFrame se llama df\n", + "\n", + "# Convertir Weight a solo números y luego a float, manejando errores\n", + "df['Weight'] = df['Weight'].astype(str).str.extract('(\\d+\\.?\\d*)').astype(float, errors='ignore')\n", + "# Si quieres forzar que valores no numéricos sean NaN\n", + "df['Weight'] = df['Weight'].astype(str).str.extract('(\\d+\\.?\\d*)')\n", + "df['Weight'] = pd.to_numeric(df['Weight'], errors='coerce')\n", + "\n", + "# Drop__heel-to-toe_differential_\n", + "df['Drop__heel-to-toe_differential_'] = df['Drop__heel-to-toe_differential_'].astype(str).str.extract('(\\d+\\.?\\d*)')\n", + "df['Drop__heel-to-toe_differential_'] = pd.to_numeric(df['Drop__heel-to-toe_differential_'], errors='coerce')\n", + "\n", + "# regularPrice y undiscounted_price\n", + "df['regularPrice'] = df['regularPrice'].astype(str).str.replace(r'[^0-9.,]', '', regex=True)\n", + "df['undiscounted_price'] = df['undiscounted_price'].astype(str).str.replace(r'[^0-9.,]', '', regex=True)\n", + "\n", + "df['regularPrice'] = df['regularPrice'].str.replace(r'\\.', '', regex=True).str.replace(',', '.')\n", + "df['undiscounted_price'] = df['undiscounted_price'].str.replace(r'\\.', '', regex=True).str.replace(',', '.')\n", + "\n", + "# Convertir a numérico con manejo de errores\n", + "df['regularPrice'] = pd.to_numeric(df['regularPrice'], errors='coerce')\n", + "df['undiscounted_price'] = pd.to_numeric(df['undiscounted_price'], errors='coerce')\n", + "\n", + "# Eliminar las columnas solicitadas\n", + "cols_to_drop = ['details', 'description', 'category', 'characteristics', 'width', 'Pronation_Type']\n", + "df = df.drop(columns=cols_to_drop, errors='ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Información del elemento encontrado:\n", + " Weight Upper_Material Midsole_Material Outsole Cushioning_System \\\n", + "0 183.0 Synthetic NaN Textile rubber Lightstrike Pro \n", + "\n", + " Drop__heel-to-toe_differential_ Usage_Type Gender Width \\\n", + "0 6.0 Racing Woman NaN \n", + "\n", + " Additional_Technologies id \\\n", + "0 ENERGYRODS 2.0, Waterproofing, Recyclable mate... 08sjncACSjSvg2t9DS73 \n", + "\n", + " regularPrice undiscounted_price \n", + "0 1.299950e+09 909965 \n" + ] + } + ], + "source": [ + "buscar_elemento(df, columna=\"id\", valor=\"08sjncACSjSvg2t9DS73\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Weight', 'Upper_Material', 'Midsole_Material', 'Outsole', 'Cushioning_System', 'Drop__heel-to-toe_differential_', 'Usage_Type', 'Gender', 'Width', 'Additional_Technologies', 'id', 'regularPrice', 'undiscounted_price']\n" + ] + } + ], + "source": [ + "print(df.columns.tolist())" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [Weight, Upper_Material, Midsole_Material, Outsole, Cushioning_System, Drop__heel-to-toe_differential_, Usage_Type, Gender, Available_Sizes, Width, Additional_Technologies, id, regularPrice, undiscounted_price]\n", + "Index: []" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a=df[df['Weight'] > 1000]\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Silhouette Score: -0.10727046484513526\n", + "Davies-Bouldin Score: 3.820317442353896\n", + " id cluster\n", + "0 08sjncACSjSvg2t9DS73 5\n", + "1 0AqheRhKT2lhm7puBVCF 5\n", + "2 0IgYTzUHkE7zIdcVyFCK 5\n", + "3 0MU8aKCnCUZv2r9aLD67 5\n", + "4 0Q6DNSlvsjBzy3AQeY2y 4\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.metrics import silhouette_score, davies_bouldin_score\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "# Suponiendo que ya tienes el DataFrame limpio \"df\"\n", + "# Asegúrate de que 'id' está presente y el resto de las columnas ya están procesadas\n", + "\n", + "# Separar la columna ID\n", + "ids = df['id']\n", + "# Asumiendo que tu DataFrame se llama df y que ya eliminaste o separaste el 'id'.\n", + "X = df.drop(columns=['id'], errors='ignore')\n", + "\n", + "# Reemplazar valores nulos por 0\n", + "X = X.fillna(0)\n", + "\n", + "# Si piensas hacer get_dummies y luego escalar:\n", + "X = pd.get_dummies(X, dummy_na=True)\n", + "X = X.fillna(0) # Si tras get_dummies quedan nulos (por ejemplo, dummy_na=True los evita, pero por si acaso)\n", + "\n", + "from sklearn.preprocessing import StandardScaler\n", + "scaler = StandardScaler()\n", + "X_scaled = scaler.fit_transform(X)\n", + "\n", + "# Elegir un número de clusters (ejemplo: k=3, puedes ajustar según tu criterio)\n", + "k = 8\n", + "kmeans = KMeans(n_clusters=k, random_state=42)\n", + "clusters = kmeans.fit_predict(X_scaled)\n", + "\n", + "# Agregar el cluster asignado a un DataFrame junto con el ID\n", + "df_clusters = pd.DataFrame({'id': ids, 'cluster': clusters})\n", + "\n", + "# Evaluar la calidad del clustering con métricas no supervisadas\n", + "sil_score = silhouette_score(X_scaled, clusters)\n", + "db_score = davies_bouldin_score(X_scaled, clusters)\n", + "\n", + "print(\"Silhouette Score:\", sil_score)\n", + "print(\"Davies-Bouldin Score:\", db_score)\n", + "\n", + "# df_clusters mostrará el ID de cada producto y el cluster al que pertenece\n", + "# Esto te permitirá saber en qué grupo quedó cada producto.\n", + "print(df_clusters.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "# Suponiendo que ya has separado el 'id' y tienes tus datos en X (sin la columna 'id')\n", + "# y ya están transformados (numéricos) y listos para el clustering.\n", + "# Por ejemplo, si tienes variables categóricas ya convertidas con get_dummies:\n", + "# X = pd.get_dummies(X, dummy_na=True)\n", + "\n", + "# Escalar datos\n", + "scaler = StandardScaler()\n", + "X_scaled = scaler.fit_transform(X)\n", + "\n", + "distortions = []\n", + "K = range(2, 11) # rango de k a evaluar, puedes ajustarlo según tus necesidades\n", + "for k in K:\n", + " kmeans = KMeans(n_clusters=k, random_state=42)\n", + " kmeans.fit(X_scaled)\n", + " distortions.append(kmeans.inertia_)\n", + "\n", + "plt.figure(figsize=(8,5))\n", + "plt.plot(K, distortions, 'bx-')\n", + "plt.xlabel('Número de clusters k')\n", + "plt.ylabel('Distorsión (Inercia)')\n", + "plt.title('Método del Codo para determinar k')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elementos con 'id' NaN:\n", + "Empty DataFrame\n", + "Columns: [Weight, Upper_Material, Midsole_Material, Outsole, Cushioning_System, Drop__heel-to-toe_differential_, Usage_Type, Gender, Available_Sizes, Width, Additional_Technologies, id, regularPrice, undiscounted_price]\n", + "Index: []\n" + ] + } + ], + "source": [ + "# Filtrar los elementos donde 'id' sea NaN\n", + "id_nan = df[df['id'].isna()]\n", + "\n", + "# Imprimir los resultados\n", + "print(\"Elementos con 'id' NaN:\")\n", + "print(id_nan)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Conteo de elementos por cluster:\n", + "cluster\n", + "0 2\n", + "1 3\n", + "2 22\n", + "3 64\n", + "4 93\n", + "5 268\n", + "6 1\n", + "7 1\n", + "Name: count, dtype: int64\n", + "\n", + "Porcentaje de elementos por cluster:\n", + "cluster\n", + "0 0.44\n", + "1 0.66\n", + "2 4.85\n", + "3 14.10\n", + "4 20.48\n", + "5 59.03\n", + "6 0.22\n", + "7 0.22\n", + "Name: count, dtype: float64\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Conteo de elementos por grupo (cluster)\n", + "conteo_clusters = df_clusters['cluster'].value_counts().sort_index()\n", + "\n", + "print(\"Conteo de elementos por cluster:\")\n", + "print(conteo_clusters)\n", + "\n", + "# Agregar porcentajes para tener una mejor idea de la distribución\n", + "porcentaje_clusters = (conteo_clusters / len(df_clusters) * 100).round(2)\n", + "print(\"\\nPorcentaje de elementos por cluster:\")\n", + "print(porcentaje_clusters)\n", + "\n", + "# Crear una visualización básica de los clusters\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Gráfico de barras\n", + "plt.figure(figsize=(10, 6))\n", + "conteo_clusters.plot(kind='bar', color='skyblue', edgecolor='black')\n", + "plt.title('Distribución de elementos por cluster', fontsize=14)\n", + "plt.xlabel('Cluster', fontsize=12)\n", + "plt.ylabel('Cantidad de elementos', fontsize=12)\n", + "plt.xticks(rotation=0)\n", + "plt.grid(axis='y', linestyle='--', alpha=0.7)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "df_final = df.merge(df_clusters, on='id', how='left')" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elementos del cluster 2:\n", + " Weight Upper_Material Midsole_Material \\\n", + "22 585.0 Exterior textil Mediasuela Bounce \n", + "39 664.8 Exterior textil Mediasuela Bounce \n", + "42 540.0 Exterior de malla técnica Mediasuela Bounce \n", + "54 260.0 Exterior textil Mediasuela 4D de impresión 3D \n", + "113 664.8 Exterior textil Mediasuela Bounce \n", + "121 584.0 Exterior de malla técnica Mediasuela Bounce \n", + "243 585.0 Exterior textil Mediasuela Bounce \n", + "248 584.0 Technical mesh Mediasuela Bounce \n", + "267 585.0 Exterior textil Mediasuela Bounce \n", + "287 585.0 Exterior textil Mediasuela Bounce \n", + "291 585.0 Exterior textil Mediasuela Bounce \n", + "294 584.0 Exterior de malla técnica Mediasuela Bounce \n", + "298 584.0 Exterior de malla técnica Mediasuela Bounce \n", + "306 584.0 Exterior de malla técnica Mediasuela Bounce \n", + "324 540.0 Exterior de malla técnica Mediasuela Bounce \n", + "327 664.8 Exterior textil Mediasuela Bounce \n", + "330 664.8 Exterior textil Mediasuela Bounce \n", + "343 299.0 Exterior de malla EVA \n", + "352 664.8 Exterior textil Mediasuela Bounce \n", + "376 585.0 Exterior textil Mediasuela Bounce \n", + "383 540.0 Exterior de malla técnica Mediasuela Bounce \n", + "426 585.0 Exterior textil Mediasuela Bounce \n", + "\n", + " Outsole Cushioning_System \\\n", + "22 Suela de caucho Mediasuela Bounce \n", + "39 Suela de caucho Bounce \n", + "42 Suela de caucho NaN \n", + "54 Suela sintética Mediasuela 4D de impresión 3D \n", + "113 Suela de caucho Bounce \n", + "121 Suela de caucho NaN \n", + "243 Suela de caucho Mediasuela Bounce \n", + "248 Rubber Mediasuela Bounce \n", + "267 Suela de caucho Mediasuela Bounce \n", + "287 Suela de caucho Mediasuela Bounce \n", + "291 Suela de caucho Mediasuela Bounce \n", + "294 Suela de caucho NaN \n", + "298 Suela de caucho NaN \n", + "306 Suela de caucho NaN \n", + "324 Suela de caucho Mediasuela Bounce \n", + "327 Suela de caucho Bounce \n", + "330 Suela de caucho Bounce \n", + "343 Suela de caucho OrthoLite \n", + "352 Suela de caucho Mediasuela Bounce \n", + "376 Suela de caucho Mediasuela Bounce \n", + "383 Suela de caucho Mediasuela Bounce \n", + "426 Suela de caucho Mediasuela Bounce \n", + "\n", + " Drop__heel-to-toe_differential_ Usage_Type Gender Available_Sizes \\\n", + "22 9.0 Running Mujer NaN \n", + "39 9.0 Running Hombre NaN \n", + "42 10.0 Running Mujer CO 37 \n", + "54 NaN Running Mujer NaN \n", + "113 9.0 Running Hombre NaN \n", + "121 10.0 Running Hombre NaN \n", + "243 9.0 Running Mujer NaN \n", + "248 10.0 Running Men NaN \n", + "267 9.0 Running Mujer NaN \n", + "287 9.0 Running Mujer NaN \n", + "291 9.0 Running Mujer NaN \n", + "294 10.0 Running Hombre NaN \n", + "298 10.0 Running Hombre NaN \n", + "306 10.0 Running Hombre NaN \n", + "324 10.0 Running Mujer NaN \n", + "327 9.0 Running Hombre NaN \n", + "330 9.0 Running Hombre NaN \n", + "343 10.0 Running NaN NaN \n", + "352 9.0 Running Hombre NaN \n", + "376 9.0 Running Mujer NaN \n", + "383 10.0 Running Mujer NaN \n", + "426 9.0 Running Mujer NaN \n", + "\n", + " Width Additional_Technologies \\\n", + "22 NaN Recyclable materials \n", + "39 NaN Contains at least 50% recycled material \n", + "42 NaN Estabilizadores de TPU en la zona media y el t... \n", + "54 NaN Recyclable materials \n", + "113 NaN Recyclable materials \n", + "121 NaN Estabilizadores de TPU, Sistema de amarre de c... \n", + "243 NaN Contiene al menos un 50 % de material reciclado \n", + "248 NaN Estabilizadores de TPU, Sistema de amarre de c... \n", + "267 NaN Recyclable materials \n", + "287 NaN Contains at least 50% recycled material \n", + "291 NaN Contains at least 50% recycled material \n", + "294 NaN Estabilizadores de TPU, Sistema de amarre de c... \n", + "298 NaN Estabilizadores de TPU, Sistema de amarre de c... \n", + "306 NaN Estabilizadores de TPU en la zona media y el t... \n", + "324 NaN Estabilizadores de TPU en la zona media y el t... \n", + "327 NaN Contiene al menos un 50 % de material reciclado \n", + "330 NaN Recyclable materials \n", + "343 NaN Recyclable materials \n", + "352 NaN Contains at least 50% recycled material \n", + "376 NaN Contains at least 50% recycled material \n", + "383 NaN Estabilizadores de TPU, Sistema de amarre de c... \n", + "426 NaN Contains at least 50% recycled material \n", + "\n", + " id regularPrice undiscounted_price cluster \n", + "22 36umRByym4P5idSfddlA 579950 405965.0 2 \n", + "39 5YBCQmekSCOryIo7887a 579950 405965.0 2 \n", + "42 6Y8BXtu2vPy0I6eGn0jI 499950 349965.0 2 \n", + "54 8FyMNBTEgPGeqnhTm34a 1299950 909965.0 2 \n", + "113 H1SxT7RaEy2ke9VjSWpg 579950 347970.0 2 \n", + "121 HXuKHFQnKMYL15wuuAtE 369950 NaN 2 \n", + "243 XTyfd24nQsI0ydJ5HNOM 579950 405965.0 2 \n", + "248 YdluqvzyDKCaoIXf2Raw 499950 399960.0 2 \n", + "267 bXMGR7Ld3EpZfjLwRZXc 579950 405965.0 2 \n", + "287 ekzaoVmAEPT57hEOzM4z 579950 347970.0 2 \n", + "291 f7BRJbDjPGZ0R7k8T2BH 449950 NaN 2 \n", + "294 fD0hMnJthp7NyufrsX94 449950 NaN 2 \n", + "298 g0dU1EZ2VAcJp8GeFtDU 499950 NaN 2 \n", + "306 gfkoLBQme3OHzj3JXmu1 499950 349965.0 2 \n", + "324 j14fin6QCZyFNfHrLfUb 449950 NaN 2 \n", + "327 jQ8lW8Kba3VAUZgvjzpp 579950 405965.0 2 \n", + "330 jepprtx3EAEA6TxsC94H 579950 405965.0 2 \n", + "343 lDidMEQwKMrEEimwgbts 199950 NaN 2 \n", + "352 mhHNGEv3q2mIoQrUsE6k 579950 347970.0 2 \n", + "376 qcNdp0WmOAxgmOk9AFpa 579950 347970.0 2 \n", + "383 sEsYVzeezD0cJVpoX3Lv 499950 299970.0 2 \n", + "426 wSLYbxhlkuBI8YpCJOf7 579950 289975.0 2 \n", + "\n", + "Estadísticas descriptivas del cluster 2:\n", + " Weight Drop__heel-to-toe_differential_ Width regularPrice \\\n", + "count 22.000000 21.000000 0.0 2.200000e+01 \n", + "mean 569.000000 9.428571 NaN 5.499500e+05 \n", + "std 102.285595 0.507093 NaN 1.914606e+05 \n", + "min 260.000000 9.000000 NaN 1.999500e+05 \n", + "25% 584.000000 9.000000 NaN 4.999500e+05 \n", + "50% 585.000000 9.000000 NaN 5.799500e+05 \n", + "75% 585.000000 10.000000 NaN 5.799500e+05 \n", + "max 664.800000 10.000000 NaN 1.299950e+06 \n", + "\n", + " undiscounted_price cluster \n", + "count 16.000000 22.0 \n", + "mean 401716.875000 2.0 \n", + "std 140991.161707 0.0 \n", + "min 289975.000000 2.0 \n", + "25% 347970.000000 2.0 \n", + "50% 374962.500000 2.0 \n", + "75% 405965.000000 2.0 \n", + "max 909965.000000 2.0 \n" + ] + }, + { + "data": { + "image/png": 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MX3/9taZMmaL3339fd999t6Rjr1+33nqrHnzwwQrPvPtUdVwSExNDuqY1KytL55xzjn744Qe98MIL9q0RgWBwaQEQgO/sWL9+/Spdt1u3bnrvvfd08OBBLVu2TJMmTVJGRoYuv/zyChuiQKr6ZvDzzz+XG7csS40aNZIk++xMcXFxmXV9b74l+T4EUtUblPsajvLyy8jI8FuvOjVu3FjSsU9glxYoH18Od999t4wx5X6V/LBeRXxnVufPn6/9+/frxx9/9PsglNvtts/Uer1effPNN2rYsKHfnIuNjVV8fHyF+ZTXhJauK9A4lDcWJ0t11FfV/UrH/lRyRfv2Xa5wvEJ93kjHXgsCPU+lwM/V+Ph4/fOf/9SePXv0448/6tlnn1XTpk01efJkPfHEE0Hts6rjUh1N7Jo1a/Tss8/qL3/5S5W3hfqJRhYoZfPmzfrf//6nyMhIXXTRRUE/Ljw8XGeccYamTp2qZ555RsYYffbZZ/Zy3xmR6jiTVdo333xTJvbTTz9p165d6tq1q31Zge8T6oHeYNPS0srE+vfvL+nYp96rolOnToqKitKKFSuUn59fZrnvF4bSd2+oDj179pQUeGwCxfr16yfLsrRs2bJq2X+PHj3UtGlTLVu2THPnzpWkMrdTGjp0qH766Sd98MEHys7O1pAhQ/zuOztgwABlZWXZlzVUhW8cAv1StXv3bu3cubPK2/ap6tyujvqqwnd5R3Ud69JCfd5Ix56rgZ6nO3bsqPDyCcuy1LlzZ91yyy2aN2+epGN3efCp6Fid6HEprWQT+89//lM333zzSdkv6hYaWaCEJUuWKDU1VQUFBZowYUKl/zX4/fffB/xvcd9ZrpI3tfd9+KvkvWmryxtvvKG1a9faPxtj9MADD8jj8fjdF7Zjx45q1KiRPvnkE/u/EX35Pvroo2W2269fP/Xr10+LFi3SK6+8UmZ5ZWecIiIidOWVVyozM1PTp0/3WzZnzhzNnTtX7du3r9JfsarMVVddJbfbraeeesrvbGROTk7AWlu0aKHLLrtMS5cu1d/+9jf7HsAlLV++PGBDHojL5dLQoUN15MgRPfHEE4qKitIZZ5zht47vOtmS9/Ys6fbbb5ckXX/99QHvkZqRkVHurcR8hgwZotatW+vTTz8t06BMnDixWn6xqurcro76qqJ///4aMGCAZs6cqXfeeafMcq/XG9J9TMeMGaOGDRvq73//u1avXl1meTBnavv166cdO3b45VFYWKi77rqrzLo7duwIeM/e430dOtHjUtLBgwd17rnnas2aNfrHP/6hW2+9tVq2i/qHa2RRL23dutX+MENhYaH9J2rXrVsnt9uthx56KKj/Qv7Pf/6jl156SWeddZZSUlIUGxurH3/8UbNnz1bTpk01duxYe92zzz5b7733ni655BKNHDlSUVFR6tmzZ7XcTzI1NVUDBw7UFVdcoYSEBH311VdauXKlzjjjDL/74EZEROi2227TtGnTdPrpp9t3Afj000/tT9GX9uabb2rYsGH685//rP/85z8aOHCgjh49qh9++EFpaWmV3oT+r3/9qxYuXKhHH31US5cu1YABA7Rjxw69++67atCggWbMmHFcH0gJVvv27TVp0iRNnjxZPXr00GWXXaawsDC9//776tGjR8A7Hzz//PPatGmT7r33XrvWuLg47dq1SytXrtSWLVu0b9++Cv8Mb0nDhw/Xhx9+qPXr12vYsGH29bE+p59+uho2bGh/Crx0I3veeedp4sSJeuSRR9S+fXudd955atOmjbKysrR161Z98803evTRR9W5c+dyc3C73XrxxRd1wQUX6Oyzz9bll1+uli1bauHChdqzZ4969uzp90tQVXTq1ElJSUl6++23FRkZqVatWsmyLN122232JR6BVEd9VTVz5kwNHz5cV1xxhZ5++mmdfvrpio6O1s6dO7Vs2TIdOHCgwj+cUZHExES98cYbuuKKK9S/f39dcMEF6tixozIzM7V8+XKdeuqp+uijjyrcxl133aUvvvhCv/vd73TllVeqQYMGmjdvnuLi4vzu5iEdu+PFxRdfrP79+6tLly5q0aKF9uzZo48++kgul8vvT0L7/hDCAw88oB9++EGNGzdWXFyc3UieyHEp6eKLL9bq1avVqVMnHTx4MOCHy8aNGxfUvYhRz53AW3sBtU7J+7X6vqKjo03Lli3N8OHDzcSJE83WrVsDPjbQ/R6//fZb85e//MV069bNvgdohw4dzK233mp++uknv8cXFRWZe++917Ru3dqEhYX53bO1vHu4llTRfWTnz59vXnnlFdO1a1cTGRlpWrZsae644w6Tk5NTZjsej8dMmTLFJCcnm4iICHPaaaeZf/zjH2b79u3l5pCRkWHuuOMO065dOxMREWGaNm1qBgwYYJ566im/9VTOvS8PHDhgbr/9dtOmTRsTHh5umjVrZv7whz+YdevWlVnXdx/Z9PT0MssquwdmIK+88orp0qWLiYiIMK1atTL33HOPyc/PLzfX/Px888QTT5g+ffqYmJgYEx0dbdq2bWtGjx5t3njjDVNUVBT0vtetW2fPsylTpgRcJzU11UgysbGxpri4OOA68+bNM+eff75JSEgw4eHhpkWLFmbgwIHmkUceMTt37rTXq+h+rl9//bUZMmSIiY6ONk2bNjWXXnqp2blzp+nWrZtp3Lix37oVjbPvXq4zZszwi3/77bdm6NChplGjRnbNgY5hKPWVt+/Kai/PwYMHzUMPPWS6detmoqOjTcOGDU2HDh3MVVddZT744AO/dY/nPrI+aWlp5rLLLjPNmzc34eHhpmXLlmbkyJHms88+Cyrvd99913Tv3t1ERESYFi1amNtuu83k5uaWeS3YtWuXmTBhgjnjjDNMYmKiiYiIMK1btzYXX3xxwPvqvvbaa6Z79+4mMjLSSCrzunI84xLodSkYvvvPVvQV7PxB/WYZE+D/zwAAdV5ubq6aN2+u7t27a/ny5TWdDgAcN66RBYA6Li8vT7m5uX4xj8ej8ePH68iRI0H92V0AqI04IwsAddzq1as1ZMgQpaamql27dsrNzdU333yjH3/8UV27dtXy5cv9/rIWADgFjSwA1HEHDhzQvffeq4ULF+rnn39WcXGxWrdurdGjR+vBBx/kAzUAHItGFgAAAI7ENbIAAABwJBpZAAAAOFK9+4MIXq9Xe/fuVaNGjUL6+9AAAAA4MYwxys3NVVJSUoV/NKfeNbJ79+5VcnJyTacBAACASuzatUutWrUqd3m9a2QbNWok6djAxMbGBv24oqIiffHFFxoxYoTCw8NPVHqOw7iUxZgExrgExriUxZgExriUxZgEVhfGJScnR8nJyXbfVp5618j6LieIjY097ka2QYMGio2NdeykOBEYl7IYk8AYl8AYl7IYk8AYl7IYk8Dq0rhUdhkoH/YCAACAI9HIAgAAwJFoZAEAAOBINLIAAABwJBpZAAAAOBKNLAAAAByJRhYAAACORCMLAAAAR6KRBQAAgCPRyAIAAMCRaGQBAADgSLWqkV20aJHOP/98JSUlybIsffTRR/ayoqIi3XffferevbtiYmKUlJSka6+9Vnv37q25hAEAAFBjalUjm5eXp549e+q5554rsyw/P1+rVq3SxIkTtWrVKn3wwQfatGmTLrjgghrIFAAAADUtrKYTKGnkyJEaOXJkwGWNGzfWvHnz/GLPPvus+vfvr507d6p169YnI0UAAADUErWqkT1e2dnZsixLcXFx5a5TUFCggoIC++ecnBxJUnFxsYqLiyVJLpdLLpdLXq9XXq/XXtcX93g88ng8kiSPxyO3223HjTH2+m63W5Zl2dstGfc9Nph4WFiYjDF+ccuy5Ha7y+RYXjyYmkrmXl482Jo8Ho+dS12pqbJ4eTX5+MakLtRUHcfJ933JcXF6TdVxnHx5GWOCrrW211RR7sHUVPJ1JTw8vE7UVFnuwdQUzPtQba5p9+7dyszMlGVZsixLxhi/HKsS9/2clpYml8tlxyUpPj5erVq1OqE1+dS2uecTzPtQba2p9PrlcWwje/ToUd1333268sorFRsbW+5606dP19SpU8vE09LSFBMTI0lKSEhQSkqK0tPTdeDAAXudVq1aqVWrVtq8ebMOHz5sPy4lJUWJiYlav369jhw5Yq/fqVMnxcXFKS0tze/A9OjRQxEREVq5cqVfDn379lVhYaHWrl1rx9xut/r166fs7Gxt3LjRjkdHR6tnz57KzMzU9u3b7Xjjxo3VuXNn7d27V7t377bjwdSUnZ1tx9u1a1elmtLS0uxxsSyrTtQU6nHq0qWL35jUhZqq4zi1aNFCkrRt2zbl5ubWiZqq4zj5XvC9Xq9WrVpVJ2qSQjtOvjHZsGGDevfuXSdq8gnlOO3fv1/SsdeW5ORkR9W0ZcsWzV+wQF6PR6tXr9asWbM0atQo9erVy17/m2++0aJFi3TllVeqXbt2dnzWrFlavXq1/vKXv6hZs2Z2fObMmdq3b59mzpypzz77zG62XnrpJeXk5Oi+CRM0fNgwRUdHn9TjVBvmnm9cS74POa0mX39RGcuUbJtrEcuy9OGHH2r06NFllhUVFemSSy7R7t27tWDBggob2UBnZJOTk5WVlWU/LpjfOoqKijR37lylpqYqIiKi1v7GWzr3E/2bVEFBgT0uYWFhdaKmUI+T1+vV7Nmz7TGpCzVVx3HyeDyaM2eO37g4vabqOE7FxcWaO3euRo4caZ9RcnpNFeUeTE2+MUlNTVVUVFSdqKmy3IOpqbCwsNL3odpa06pVqzR48GBdMvkfanZqexlZsmRkldi2kWRkySX/tqS8uFeSW9LwmDwtzGsgz69b80ran75VHz18h5YsWWI3dfXp+WSMCfp9qLbWdOjQIcXHxys7O7vCPs9xZ2SLiop02WWX6aefftLXX39dYXGSFBkZqcjIyDLxsLAwvzdT6beDUJrvAPu+963jG+xA2w41bllWwHh5OR5vvLzcj7cm3/put9tvHSfXFOpxKjlXgp1jtb2minIMNu57YQs0Lr54qLmXF6/Nc883LuXlWHp9n9pcU1XjvppKzpWq5F4bawo2x4riJV9vK3sfqo01FRYWKv7UDkrq3DPgvqrC5S2Wdi9Xi47d5XWVzMlSYWGhXC5XmVzrw/OpqKjI3n6o70O1pabyOKqR9TWxW7Zs0fz58xUfH1/TKQEAAKCG1KpG9pdfftHWrVvtn9PT07V69Wo1bdpULVu21B/+8AetWrVKn332mTwejzIyMiRJTZs2VURERE2lDQAAgBpQqxrZlStXavjw4fbPd911lyRpzJgxmjJlij755BNJ8rs4XJLmz5+vYcOGnaw0AQAAUAvUqkZ22LBhfhcIl1ZLP5cGAACAGlCr/rIXAAAAECwaWQAAADgSjSwAAAAciUYWAAAAjkQjCwAAAEeikQUAAIAj0cgCAADAkWhkAQAA4Eg0sgAAAHAkGlkAAAA4Eo0sAAAAHIlGFgAAAI5EIwsAAABHopEFAACAI9HIAgAAwJFoZAEAAOBINLIAAABwJBpZAAAAOBKNLAAAAByJRhYAAACORCMLAAAAR6KRBQAAgCPRyAIAAMCRaGQBAADgSDSyAAAAcCQaWQAAADgSjSwAAAAciUYWAAAAjkQjCwAAAEeikQUAAIAj0cgCAADAkWhkAQAA4Eg0sgAAAHAkGlkAAAA4Eo0sAAAAHIlGFgAAAI5EIwsAAABHopEFAACAI9HIAgAAwJFoZAEAAOBINLIAAABwJBpZAAAAOBKNLAAAAByJRhYAAACORCMLAAAAR6KRBQAAgCPRyAIAAMCRaGQBAADgSDSyAAAAcCQaWQAAADhSrWpkFy1apPPPP19JSUmyLEsfffSR33JjjCZNmqSWLVsqOjpa5557rrZs2VIzyQIAAKBG1apGNi8vTz179tRzzz0XcPkTTzyhZ555Ri+++KKWL1+umJgYpaam6ujRoyc5UwAAANS0sJpOoKSRI0dq5MiRAZcZY/T000/roYce0oUXXihJeuONN9S8eXN99NFHuuKKK05mqgAAAKhhtaqRrUh6eroyMjJ07rnn2rHGjRtrwIABWrZsWbmNbEFBgQoKCuyfc3JyJEnFxcUqLi6WJLlcLrlcLnm9Xnm9XntdX9zj8cjj8UiSPB6P3G63HTfG2Ou73W5ZlmVvt2Tc99hg4mFhYTLG+MUty5Lb7S6TY3nxYGoqmXt58WBr8ng8di51pabK4uXV5OMbk7pQU3UcJ9/3JcfF6TVVx3Hy5WWMCbrW2l5TRbkHU1PJ15Xw8PA6UVNluQdTUzDvQ7W5poiICLlkJOOVLJdkvLJK5GgsS7JcsoxX8ou7JMsKHPfty+uRJatE3CgiIkJer9euuT49n3yCeR+qrTWVXr88jmlkMzIyJEnNmzf3izdv3txeFsj06dM1derUMvG0tDTFxMRIkhISEpSSkqL09HQdOHDAXqdVq1Zq1aqVNm/erMOHD9uPS0lJUWJiotavX68jR47Y63fq1ElxcXFKS0vzOzA9evRQRESEVq5c6ZdD3759VVhYqLVr19oxt9utfv36KTs7Wxs3brTj0dHR6tmzpzIzM7V9+3Y73rhxY3Xu3Fl79+7V7t277XgwNWVnZ9vxdu3aVammtLQ0e1wsy6oTNYV6nLp06eI3JnWhpuo4Ti1atJAkbdu2Tbm5uXWipuo4Tr4XfK/Xq1WrVtWJmqTQjpNvTDZs2KDevXvXiZp8QjlO+/fvl3TstSU5OdlRNWVlZWn8+PE6NfKoPL9k6FCjJDX5JUMxRw7b6+fEJCgnJkHx2bsUVZhnxw81aqm86CZqfihdYcW/nZjKjGutwrAoSVLSwa0yvzayGU1T5JI0fvx4ZWVl2bXVp+dTr169JPm/DzmtJl9/URnLlGybaxHLsvThhx9q9OjRkqSlS5dq8ODB2rt3r1q2bGmvd9lll8myLL3zzjsBtxPojGxycrKysrIUGxsrKbjfOoqKijR37lylpqYe+62ylv7GWzr3E/2bVEFBgT0uYWFhdaKmUI+T1+vV7Nmz7TGpCzVVx3HyeDyaM2eO37g4vabqOE7FxcWaO3euRo4cKZfL/2MLTq2potyDqck3JqmpqYqKiqoTNVWWezA1FRYWVvo+VFtrWrVqlQYPHqwbZ8xSy049qu2MrMt41HH3cm1O6iuvK8yO79m4Ri+PHaUlS5bYTV19ej4ZY4J+H6qtNR06dEjx8fHKzs62+7VAHHNG1nc25+eff/ZrZH/++Wd7kgYSGRmpyMjIMvGwsDC/N1Ppt4NQmu8A+773reMb7EDbDjVuWVbAeHk5Hm+8vNyPtybf+m63228dJ9cU6nEqOVeCnWO1vaaKcgw27nthCzQuvniouZcXr81zzzcu5eVYen2f2lxTVeO+mkrOlarkXhtrCjbHiuIlX28rex+qjTUVFhbKq2PN6rENuWSsMqv/2rgGGf+1XzIut4yrZM2WCgsL5XK5yuRaH55PRUVF9vZDfR+qLTWVp1bdtaAibdu2VYsWLfTVV1/ZsZycHC1fvlwDBw6swcwAAABQE2rVGdlffvlFW7dutX9OT0/X6tWr1bRpU7Vu3Vrjxo3To48+qg4dOqht27aaOHGikpKS7MsPAAAAUH/UqkZ25cqVGj58uP3zXXfdJUkaM2aMXnvtNd17773Ky8vTn//8Zx0+fFhDhgzRnDlzFBUVVVMpAwAAoIbUqkZ22LBhfhcIl2ZZlh5++GE9/PDDJzErAAAA1EaOuUYWAAAAKIlGFgAAAI5EIwsAAABHopEFAACAI9HIAgAAwJFoZAEAAOBINLIAAABwJBpZAAAAOBKNLAAAAByJRhYAAACORCMLAAAAR6KRBQAAgCPRyAIAAMCRaGQBAADgSDSyAAAAcCQaWQAAADgSjSwAAAAciUYWAAAAjkQjCwAAAEeikQUAAIAj0cgCAADAkWhkAQAA4Eg0sgAAAHAkGlkAAAA4Eo0sAAAAHIlGFgAAAI5EIwsAAABHopEFAACAI9HIAgAAwJFoZAEAAOBINLIAAABwJBpZAAAAOBKNLAAAAByJRhYAAACORCMLAAAAR6KRBQAAgCPRyAIAAMCRaGQBAADgSDSyAAAAcCQaWQAAADgSjSwAAAAciUYWAAAAjkQjCwAAAEeikQUAAIAj0cgCAADAkWhkAQAA4Eg0sgAAAHAkGlkAAAA4Eo0sAAAAHIlGFgAAAI7kqEbW4/Fo4sSJatu2raKjo5WSkqJHHnlExpiaTg0AAAAnWVhNJ3A8/vrXv+qFF17Q66+/rq5du2rlypUaO3asGjdurNtvv72m0wMAAMBJ5KhGdunSpbrwwgs1atQoSdKpp56qmTNn6rvvvqvhzAAAAHCyOaqRHTRokF5++WVt3rxZp512mtasWaPFixfrqaeeKvcxBQUFKigosH/OycmRJBUXF6u4uFiS5HK55HK55PV65fV67XV9cY/HI4/HI+nY5Q1ut9uOl7yswe12y7Ise7sl477HBhMPCwuTMcYvblmW3G53mRzLiwdTU8ncy4sHW5PH47FzqSs1VRYvryYf35jUhZqq4zj5vi85Lk6vqTqOky8vY0zQtdb2mirKPZiaSr6uhIeH14maKss9mJqCeR+qzTVFRETIJSMZr2S5JOOVVSJHY1mS5ZJlvJJf3CVZVuC4b19ejyxZJeJGERER8nq9ds316fnkE8z7UG2tqfT65XFUIzthwgTl5OSoU6dOcrvd8ng8euyxx3T11VeX+5jp06dr6tSpZeJpaWmKiYmRJCUkJCglJUXp6ek6cOCAvU6rVq3UqlUrbd68WYcPH7Yfl5KSosTERK1fv15Hjhyx1+/UqZPi4uKUlpbmd2B69OihiIgIrVy50i+Hvn37qrCwUGvXrrVjbrdb/fr1U3Z2tjZu3GjHo6Oj1bNnT2VmZmr79u12vHHjxurcubP27t2r3bt32/FgasrOzrbj7dq1q1JNaWlp9rhYllUnagr1OHXp0sVvTOpCTdVxnFq0aCFJ2rZtm3Jzc+tETdVxnHwv+F6vV6tWraoTNUmhHSffmGzYsEG9e/euEzX5hHKc9u/fL+nYa0tycrKjasrKytL48eN1auRReX7J0KFGSWryS4Zijhy218+JSVBOTILis3cpqjDPjh9q1FJ50U3U/FC6wop/OzGVGddahWFRkqSkg1tlfm1kM5qmyCVp/PjxysrKsmurT8+nXr16SfJ/H3JaTb7+ojKWcdAnpd5++22NHz9ef/vb39S1a1etXr1a48aN01NPPaUxY8YEfEygM7LJycnKyspSbGyspOB+6ygqKtLcuXOVmpp67LfKWvobb+ncT/RvUgUFBfa4hIWF1YmaQj1OXq9Xs2fPtsekLtRUHcfJ4/Fozpw5fuPi9Jqq4zgVFxdr7ty5GjlypFwu/8/fOrWminIPpibfmKSmpioqKqpO1FRZ7sHUVFhYWOn7UG2tadWqVRo8eLBunDFLLTv1qLYzsi7jUcfdy7U5qa+8rjA7vmfjGr08dpSWLFliN3X16flkjAn6fai21nTo0CHFx8crOzvb7tcCcdQZ2fHjx2vChAm64oorJEndu3fXTz/9pOnTp5fbyEZGRioyMrJMPCwszO/NVPrtIJTmO8C+733r+AY70LZDjVuWFTBeXo7HGy8v9+Otybe+2+32W8fJNYV6nErOlWDnWG2vqaIcg437XtgCjYsvHmru5cVr89zzjUt5OZZe36c211TVuK+mknOlKrnXxpqCzbGieMnX28reh2pjTYWFhfLqWLN6bEMuGavM6r82rkHGf+2XjMst4ypZs6XCwkK5XK4yudaH51NRUZG9/VDfh2pLTeVx1O238vPzA56xKPmbAgAAAOoHR52RPf/88/XYY4+pdevW6tq1q9LS0vTUU0/p+uuvr+nUAAAAcJI5qpH95z//qYkTJ+rmm2/W/v37lZSUpL/85S+aNGlSTacGAACAk8xRjWyjRo309NNP6+mnn67pVAAAAFDDHHWNLAAAAOBDIwsAAABHopEFAACAI9HIAgAAwJFoZAEAAOBIId+1ICMjQ6+++qpWrVql7OzsMn+cwLIsffXVV6HuBgAAAPATUiO7du1aDRs2TEeOHFHHjh21bt06denSRYcPH9aePXuUkpKi5OTk6soVAAAAsIV0acGECRPUsGFDbdq0SV9++aWMMfrHP/6hXbt26Z133tGhQ4f0+OOPV1euAAAAgC2kRnbJkiX6y1/+otatW8vlOrYp36UFl156qa6++mqNHz8+9CwBAACAUkJqZL1er5o3by5JiouLk9vt1sGDB+3l3bt31/fffx9ahgAAAEAAITWybdu2VXp6+rENuVxq27atvvzyS3v50qVLFRcXF1KCAAAAQCAhNbIjRozQu+++a/9800036V//+pfOPfdcnXPOOXr99dd11VVXhZwkAAAAUFpIdy148MEHdeWVV6qoqEjh4eEaN26c8vLy9P7778vtdmvixIl64IEHqitXAAAAwBZSI9ukSRP16dPH/tmyLD300EN66KGHQk4MAAAAqAh/2QsAAACOdFxnZK+//npZlqWXX35Zbrdb119/faWPsSxLr776apUTBAAAAAI5rkb266+/lsvlktfrldvt1tdffy3Lsip8TGXLAQAAgKo4rkZ2x44dFf4MAAAAnCxcIwsAAABHCqmRXbVqlZ5//vlylz///PNavXp1KLsAAAAAAgqpkX3wwQf9/pJXaV9//TW34gIAAMAJEVIj+/333+vMM88sd/mZZ56plStXhrILAAAAIKCQGtnc3FyFhZX/eTGXy6Xs7OxQdgEAAAAEFFIj26FDB33xxRflLp8zZ47atWsXyi4AAACAgEJqZP/0pz9p1qxZuuuuu3T48GE7fvjwYd15552aM2eO/vSnP4WaIwAAAFDGcd1HtrTbb79dq1ev1tNPP61nnnlGSUlJkqS9e/fK6/Xqmmuu0Z133lktiQIAAAAlhdTIWpalGTNm6Nprr9X777+v7du3S5IuvPBCXXLJJRo2bFh15AgAAACUEVIj6zN8+HANHz68OjYFAAAABIW/7AUAAABHCqmRNcbopZdeUv/+/dWsWTO53e4yXxXdngsAAACoqpC6zHvvvVdPPfWUevXqpT/+8Y9q0qRJdeUFAAAAVCikRvb111/XJZdcov/973/VlQ8AAAAQlJAuLThy5IjOPffc6soFAAAACFpIjew555yjFStWVFcuAAAAQNBCamSff/55ffvtt5o2bZqysrKqKycAAACgUiE1sh07dtT27ds1ceJEJSYmKiYmRrGxsX5fjRs3rq5cAQAAAFtIH/a65JJLZFlWdeUCAAAABC2kRva1116rpjQAAACA48Nf9gIAAIAjhdzI7ty5UzfeeKM6duyoJk2aaNGiRZKkzMxM3X777UpLSws5SQAAAKC0kC4t+PHHH3XmmWfK6/VqwIAB2rp1q4qLiyVJzZo10+LFi5WXl6dXX321WpIFAAAAfEL+E7VxcXH69ttvZVmWEhMT/ZaPGjVK77zzTkgJAgAAAIGEdGnBokWLdNNNNykhISHg3Qtat26tPXv2hLILAAAAIKCQGlmv16sGDRqUu/zAgQOKjIwMZRcAAABAQCE1sqeffrpmzZoVcFlxcbHefvttnXHGGaHsAgAAAAgopEb2/vvv15w5c3TTTTdp/fr1kqSff/5ZX375pUaMGKENGzZowoQJ1ZIoAAAAUFJIH/YaOXKkXnvtNd1xxx16+eWXJUl//OMfZYxRbGys3njjDZ111lnVkigAAABQUkiNrCRdc801uvjii/XFF19o69at8nq9SklJUWpqqho1alQdOQIAAABlhNzISlJMTIwuuuii6tgUAAAAEJSQGtmdO3cGtV7r1q1D2Q0AAABQRkiN7Kmnnhrw/rGleTyeUHYDAAAAlBFSI/vvf/+7TCPr8Xi0Y8cOvfHGG0pMTNQtt9wSUoKl7dmzR/fdd58+//xz5efnq3379poxY4b69u1brfsBAABA7RZSI3vdddeVu+y+++7TgAEDlJ2dHcou/Bw6dEiDBw/W8OHD9fnnnyshIUFbtmxRkyZNqm0fAAAAcIZq+bBXIDExMRo7dqz+3//7f7r99turZZt//etflZycrBkzZtixtm3bVviYgoICFRQU2D/n5ORIOvYHG4qLiyVJLpdLLpdLXq9XXq/XXtcX93g89uURHo9Hbrfbjhtj7PXdbrcsy7K3WzLue2ww8bCwMBlj/OKWZcntdpfJsbx4MDWVzL28eLA1eTweO5e6UlNl8fJq8vGNSV2oqTqOk+/7kuPi9Jqq4zj58jLGBF1rba+potyDqank60p4eHidqKmy3IOpKZj3odpcU0REhFwykvFKlksyXlklcjSWJVkuWcYr+cVdkmUFjvv25fXIklUibhQRESGv12vXXJ+eTz7BvA/V1ppKr1+eE9bISsfeuDIyMqpte5988olSU1N16aWXauHChTrllFN0880364Ybbij3MdOnT9fUqVPLxNPS0hQTEyNJSkhIUEpKitLT03XgwAF7nVatWqlVq1bavHmzDh8+bD8uJSVFiYmJWr9+vY4cOWKv36lTJ8XFxSktLc3vwPTo0UMRERFauXKlXw59+/ZVYWGh1q5da8fcbrf69eun7Oxsbdy40Y5HR0erZ8+eyszM1Pbt2+1448aN1blzZ+3du1e7d++248HUVPJsebt27apUU1pamj0ulmXViZpCPU5dunTxG5O6UFN1HKcWLVpIkrZt26bc3Nw6UVN1HCffC77X69WqVavqRE1SaMfJNyYbNmxQ796960RNPqEcp/3790s69tqSnJzsqJqysrI0fvx4nRp5VJ5fMnSoUZKa/JKhmCOH7fVzYhKUE5Og+OxdiirMs+OHGrVUXnQTNT+UrrDi305MZca1VmFYlCQp6eBWmV8b2YymKXJJGj9+vLKysuza6tPzqVevXpL834ecVpOvv6iMZUq2zdUkJydHixYt0vXXX6/27dtr6dKl1bLdqKhjE/auu+7SpZdeqhUrVuiOO+7Qiy++qDFjxgR8TKAzssnJycrKylJsbKyk4H7rKCoq0ty5c5Wamnrst8pa+htv6dxP9G9SBQUF9riEhYXViZpCPU5er1ezZ8+2x6Qu1FQdx8nj8WjOnDl+4+L0mqrjOBUXF2vu3LkaOXKkXC7/P7bo1Joqyj2YmnxjkpqaqqioqDpRU2W5B1NTYWFhpe9DtbWmVatWafDgwbpxxiy17NSj2s7IuoxHHXcv1+akvvK6wuz4no1r9PLYUVqyZInd1NWn55MxJuj3odpa06FDhxQfH6/s7Gy7XwskpDOyLper3LsWGGPUunVrPf/886Hswo/X61Xfvn01bdo0SVLv3r21fv36ChvZyMhIRUZGlomHhYX5vZlKvx2E0nwH2Pe9bx3fYAfadqhxy7ICxsvL8Xjj5eV+vDX51ne73X7rOLmmUI9TybkS7Byr7TVVlGOwcd8LW6Bx8cVDzb28eG2ee75xKS/H0uv71Oaaqhr31VRyrlQl99pYU7A5VhQv+Xpb2ftQbaypsLBQXh1rVo9tyCUToIU41rgGGf+1XzIut4yrZM2WCgsL5XK5yuRaH55PRUVF9vZDfR+qLTWVJ6RGdtKkSWUaWcuy1KRJE6WkpGjEiBFBJxKMli1b2v9t69O5c2e9//771bYPAAAAOENIXeaUKVOqKY3gDB48WJs2bfKLbd68WW3atDmpeQAAAKDmlT1XfByKi4vtuwAEkpOTE/SnzoJx55136ttvv9W0adO0detWvfXWW3r55Zer/V61AAAAqP1CamRvv/12DRo0qNzlgwcP1t133x3KLvz069dPH374oWbOnKlu3brpkUce0dNPP62rr7662vYBAAAAZwipkZ0zZ47+8Ic/lLv8D3/4g2bPnh3KLsr4/e9/r3Xr1uno0aPasGFDhbfeAgAAQN0VUiO7d+9enXLKKeUuT0pK0p49e0LZBQAAABBQSI1sfHx8mQ9flbRhw4YK7/0FAAAAVFVIjex5552nl156KeBfX1i1apVefvlljRw5MpRdAAAAAAGFdPutRx55RHPmzFH//v11wQUXqGvXrpKk9evX69NPP1ViYqIeeeSRakkUAAAAKCmkRjYpKUkrV67UhAkT9PHHH+vDDz+UJMXGxurqq6/WtGnTlJSUVC2JAgAAACWF/Ge3WrZsqddff13GGB04cECSlJCQUO6frgUAAACqQ7X9/VjLshQZGamGDRvSxAIAAOCEC+nDXpK0cuVKnXfeeWrQoIHi4+O1cOFCSVJmZqYuvPBCLViwINRdAAAAAGWE1MguXbpUQ4YM0ZYtW/THP/5RXq/XXtasWTNlZ2frpZdeCjlJAAAAoLSQGtkHHnhAnTt31o8//qhp06aVWT58+HAtX748lF0AAAAAAYXUyK5YsUJjx45VZGRkwOtiTznlFGVkZISyCwAAACCgkBrZ8PBwv8sJStuzZ48aNmwYyi4AAACAgEJqZM844wy99957AZfl5eVpxowZGjp0aCi7AAAAAAIKqZGdOnWqVq5cqVGjRunzzz+XJK1Zs0b/+te/1KdPHx04cEATJ06slkQBAACAkkK6j+yAAQM0e/Zs3XTTTbr22mslSXfffbckKSUlRbNnz1aPHj1CzxIAAAAopcqNrDFGubm5GjRokDZt2qTVq1dry5Yt8nq9SklJUZ8+ffjDCAAAADhhqtzIFhYWqmnTppo2bZruvfde9erVS7169arG1AAAAIDyVfka2cjISLVo0UKRkZHVmQ8AAAAQlJA+7HXdddfpjTfeUGFhYXXlAwAAAAQlpA97de/eXR999JG6du2q6667Tqeeeqqio6PLrHfxxReHshsAAACgjJAa2SuvvNL+vrzbbFmWJY/HE8puAAAAgDKOu5F94IEHdMUVV6hHjx6aP3/+icgJAAAAqNRxN7KPP/64unXrph49emjo0KHKyspSYmKi5s2bp7PPPvtE5AgAAIBSdu7cqczMzDJxr9cr6dgfqXK5Qvo4lJ9mzZqpdevW1ba96hDSpQU+xpjq2AwAAACCsHPnTnXq3FlH8vPLLIuOjtbMmTN11lln6ciRI9W2z+gGDbRxw4Za1cxWSyMLAACAkyczM1NH8vN12aMvKLFtB79lbhlJefrzvz6RR9Xzx6n2p2/R/x66SZmZmTSyAAAACF1i2w46pXNPv5jLWyztXq6kjt3kddXtVq9K1e3YsUOrVq2SJGVnZ0uStmzZori4uIDrn3766VXLDgAAAChHlRrZiRMnlrnd1s0331xmPWMMt98CAADACXHcjeyMGTNORB4AAADAcTnuRnbMmDEnIg8AAADguFTfzcUAAACAk4hGFgAAAI5EIwsAAABHopEFAACAI9HIAgAAwJFoZAEAAOBINLIAAABwJBpZAAAAOBKNLAAAAByJRhYAAACORCMLAAAAR6KRBQAAgCPRyAIAAMCRaGQBAADgSDSyAAAAcCQaWQAAADgSjSwAAAAciUYWAAAAjkQjCwAAAEeikQUAAIAjObqRffzxx2VZlsaNG1fTqQAAAOAkc2wju2LFCr300kvq0aNHTacCAACAGuDIRvaXX37R1VdfrVdeeUVNmjSp6XQAAABQA8JqOoGquOWWWzRq1Cide+65evTRRytct6CgQAUFBfbPOTk5kqTi4mIVFxdLklwul1wul7xer7xer72uL+7xeOTxeCRJHo9Hbrfbjhtj7PXdbrcsy7K3WzLue2ww8bCwMBlj/OKWZcntdpfJsbx4MDWVzL28eLA1eTweO5e6UlNl8fJq8vGNSV2oqTqOk+/7kuPi9Jqq4zj58jLGBF1rba+potyDqank60p4eHidqKmy3IOpKZj3odpcU0REhFwykvFKlksyXlklcjSWJVkuWcYr+cVdkmUFjvv25fXIklUibhQRESGv12vXXNeeT7/tx8jy+tcUaFxkWcfGxphjY/lbUb/GAx8PX9z165j68j3Rc6/0+uVxXCP79ttva9WqVVqxYkVQ60+fPl1Tp04tE09LS1NMTIwkKSEhQSkpKUpPT9eBAwfsdVq1aqVWrVpp8+bNOnz4sP24lJQUJSYmav369Tpy5Ii9fqdOnRQXF6e0tDS/A9OjRw9FRERo5cqVfjn07dtXhYWFWrt2rR1zu93q16+fsrOztXHjRjseHR2tnj17KjMzU9u3b7fjjRs3VufOnbV3717t3r3bjgdTU3Z2th1v165dlWpKS0uzx8WyrDpRU6jHqUuXLn5jUhdqqo7j1KJFC0nStm3blJubWydqqo7j5HvB93q9WrVqVZ2oSQrtOPnGZMOGDerdu3edqMknlOO0f/9+ScdeW5KTkx1VU1ZWlsaPH69TI4/K80uGDjVKUpNfMhRz5LC9fk5MgnJiEhSfvUtRhXl2/FCjlsqLbqLmh9IVVvzbianMuNYqDIuSJCUd3Crza8OW0TRFLknjx49XVlaWXVtdez5lZ2crIiJCkZbRKZmb7LhxubSvafsy41IcFqmMpimKOXpYTXL32esfjYhRZlwbxeZnKTbvt9zzouP8jlN85FGNHz9e+fn5knTC556vv6iMZUq2zbXcrl271LdvX82bN8++NnbYsGHq1auXnn766YCPCXRGNjk5WVlZWYqNjZUU3G9SRUVFmjt3rlJTU4/9VllLf+MtnfuJ/u2woKDAHpewsLA6UVOox8nr9Wr27Nn2mNSFmqrjOHk8Hs2ZM8dvXJxeU3Ucp+LiYs2dO1cjR46Uy+V/tZdTa6oo92Bq8o1JamqqoqKi6kRNleUeTE2FhYWVvg/V1ppWrVqlwYMH68YZs9SyU49qOyPrMh513L1cm5P6yusKs+N7Nq7Ry2NHacmSJerVq9cJqamm597q1avVr18/3frmPLXq2N1vfUumzLiEekZ276Z1enHsKC1dulR9+vQ54XPv0KFDio+PV3Z2tt2vBeKoM7Lff/+99u/fr9NPP92OeTweLVq0SM8++6wKCgrsAfCJjIxUZGRkmW2FhYX5vZlKvx2E0nyT1ve9b53S+yq57VDjlmUFjJeX4/HGy8v9eGvyre92u/3WcXJNoR6nknMl2DlW22uqKMdg474XtkDj4ouHmnt58do893zjUl6Opdf3qc01VTXuq6nkXKlK7rWxpmBzrChe8vW2sveh2lhTYWGhvPq1OZIkyyVjlVn918Y1yPiv/ZJxuWVcJWu2VFhYKJfLVSbXuvJ8+m0/VqnaJct7rGksOy76tXENsN9yjocv7v11TH3/01gTcy/gekGtVUucc845WrdunV9s7Nix6tSpk+67775yBw8AAAB1j6Ma2UaNGqlbt25+sZiYGMXHx5eJAwAAoG5z5O23AAAAAEedkQ1kwYIFNZ0CAAAAagBnZAEAAOBINLIAAABwJBpZAAAAOBKNLAAAAByJRhYAAACORCMLAAAAR6KRBQAAgCPRyAIAAMCRaGQBAADgSDSyAAAAcCQaWQAAADgSjSwAAAAciUYWAAAAjkQjCwAAAEeikQUAAIAj0cgCAADAkWhkAQAA4Eg0sgAAAHAkGlkAAAA4Eo0sAAAAHIlGFgAAAI5EIwsAAABHCqvpBAAAqG127typzMzMk7rPZs2aqXXr1id1n4DT0cgCAFDCzp071alzZx3Jzz+p+41u0EAbN2ygmQWOA40sAAAlZGZm6kh+vi579AUltu1wUva5P32L/vfQTcrMzKSRBY4DjSwAAAEktu2gUzr3rOk0AFSAD3sBAADAkWhkAQAA4Eg0sgAAAHAkGlkAAAA4Eo0sAAAAHIlGFgAAAI5EIwsAAABHopEFAACAI9HIAgAAwJFoZAEAAOBINLIAAABwJBpZAAAAOBKNLAAAAByJRhYAAACORCMLAAAAR6KRBQAAgCPRyAIAAMCRaGQBAADgSDSyAAAAcCQaWQAAADgSjSwAAAAciUYWAAAAjkQjCwAAAEeikQUAAIAjOaqRnT59uvr166dGjRopMTFRo0eP1qZNm2o6LQAAANQARzWyCxcu1C233KJvv/1W8+bNU1FRkUaMGKG8vLyaTg0AAAAnWVhNJ3A85syZ4/fza6+9psTERH3//fc666yzaigrAAAA1ARHNbKlZWdnS5KaNm1a7joFBQUqKCiwf87JyZEkFRcXq7i4WJLkcrnkcrnk9Xrl9XrtdX1xj8cjj8cjSfJ4PHK73XbcGGOv73a7ZVmWvd2Scd9jg4mHhYXJGOMXtyxLbre7TI7lxYOpqWTu5cWDrcnj8di51JWaKouXV5OPb0zqQk3VcZx835ccF6fXVB3HyZeXMSboWmt7TRXlHkxNJV9XwsPDT3pNvu9dMrK8v+VprGP/iWmZ37ZdYdzllozxj1vWsfVLxV0y9r5LjmXJmoJ5H6rNz6eIiIhjdRqvZLkk45VVctwtS7Jcx8bFL+6SLCtw3Lcvr0eWrBJxo4iICHm9XrvmuvZ8+m0//vO0pJLjUt7c+y0e+Hj44q5fx9SX74mee6XXL49jG1mv16tx48Zp8ODB6tatW7nrTZ8+XVOnTi0TT0tLU0xMjCQpISFBKSkpSk9P14EDB+x1WrVqpVatWmnz5s06fPiw/biUlBQlJiZq/fr1OnLkiL1+p06dFBcXp7S0NL8D06NHD0VERGjlypV+OfTt21eFhYVau3atHXO73erXr5+ys7O1ceNGOx4dHa2ePXsqMzNT27dvt+ONGzdW586dtXfvXu3evduOB1OT7xcBSWrXrl2VakpLS7PHxbKsOlFTqMepS5cufmNSF2qqjuPUokULSdK2bduUm5tbJ2qqjuPke8H3er1atWpVnahJCu04+cZkw4YN6t2790mvyadrZIEaZP72OYyMpinyuMJ0Sqb/ZzP2NOsot7dYLQ5us2PG5dKeZp0UVZSnZod32vHisEhlNE1RzNHDapK7z45HhBdKkvLz8/3GrGRN+/fvl3TstSU5ObnGj5NPMHMvKytL48eP16mRR+X5JUOHGiWpyS8Zijly2F4/JyZBOTEJis/epajC3y4ZPNSopfKim6j5oXSFFf92YiozrrUKw6IkSUkHt8r82rBlNE2RS9L48eOVlZVl11bXnk/Z2dmKiIhQpGX85qRxubSvafsy41Le3DsaEaPMuDaKzc9SbN5vuedFx/kdp/jIoxo/frzy8/Ml6YTPPV9/URnLlGybHeSmm27S559/rsWLF6tVq1blrhfojGxycrKysrIUGxsrKbjfpIqKijR37lylpqYe+62ylv7GWzr3E/3bYUFBgT0uYWFhdaKmUI+T1+vV7Nmz7TGpCzVVx3HyeDyaM2eO37g4vabqOE7FxcWaO3euRo4cKZfL/2MLTq2potyDqck3JqmpqYqKijrpNa1Zs0Z9+/bV7W/OU1LH7nb8RJ6R3btpnZ65+v+0cuVK9ezZM2BNhYWFlb4P1dbn06pVqzR48GDdOGOWWnbqUW1nZF3Go467l2tzUl95XWF2fM/GNXp57CgtWbJEvXr1OiE11fTzafXq1erXr59ufXOeWpWYp5JkyZQZl1DPyO7dtE4vjh2lpUuXqk+fPid87h06dEjx8fHKzs62+7VAHHlG9tZbb9Vnn32mRYsWVdjESlJkZKQiIyPLxMPCwvzeTKXfDkJpvknr+963jm+wA2071LhlWQHj5eV4vPHycj/emnzru91uv3WcXFOox6nkXAl2jtX2mirKMdi474Ut0Lj44qHmXl68Ns8937iUl2Pp9X1qc01VjftqKjlXqpJ7qDX5/ifFK+tYM1qKsQJvJ2DcsoKKe/XbZUjlHdeSr7eVvQ/VxudTYWHhsTp9lwRYLhmrzOq/Nq5Bxn/tl4zLXepYWSosLJTL5SqTa115Pv22n7Lz1PIeaxrLjosqmJOBj4cv7v11TH3Pj5qYewHXC2qtWsIYo9tuu00ffvihFixYoLZt29Z0SgAAAKghjmpkb7nlFr311lv6+OOP1ahRI2VkZEg6ds1KdHR0DWcHAACAk8lR95F94YUXlJ2drWHDhqlly5b21zvvvFPTqQEAAOAkc9QZWYd+Lg0AAAAngKPOyAIAAAA+NLIAAABwJBpZAAAAOBKNLAAAAByJRhYAAACORCMLAAAAR6KRBQAAgCPRyAIAAMCRaGQBAADgSDSyAAAAcCQaWQAAADgSjSwAAAAciUYWAAAAjkQjCwAAAEeikQUAAIAj0cgCAADAkWhkAQAA4Eg0sgAAAHAkGlkAAAA4Eo0sAAAAHIlGFgAAAI5EIwsAAABHCqvpBOqDnTt3KjMz86Ttr6CgQJGRkSdlX16v96TsBwAAoDQa2RNs586d6tS5s47k55+0fVoul8xJajCjo6M1c+ZM7d69W23btj0p+wQAAJBoZE+4zMxMHcnP12WPvqDEth1O+P42LflK856fftL2d/CnrZKkrKwsGlkAAHBS0cieJIltO+iUzj1P+H72p285qftzy0jKO+H7AQAAKI0PewEAAMCRaGQBAADgSDSyAAAAcCQaWQAAADgSjSwAAAAciUYWAAAAjkQjCwAAAEeikQUAAIAj0cgCAADAkWhkAQAA4Eg0sgAAAHAkGlkAAAA4Eo0sAAAAHIlGFgAAAI5EIwsAAABHopEFAACAI9HIAgAAwJFoZAEAAOBINLIAAABwJBpZAAAAOBKNLAAAAByJRhYAAACORCMLAAAAR6KRBQAAgCM5spF97rnndOqppyoqKkoDBgzQd999V9MpAQAA4CRzXCP7zjvv6K677tLkyZO1atUq9ezZU6mpqdq/f39NpwYAAICTyHGN7FNPPaUbbrhBY8eOVZcuXfTiiy+qQYMG+ve//13TqQEAAOAkCqvpBI5HYWGhvv/+e91///12zOVy6dxzz9WyZcsCPqagoEAFBQX2z9nZ2ZKkgwcPqri42N6Gy+WS1+uV1+v127bL5ZLH41FRUZHy8/N16NAhRURE2HFjjL2+2+2WZVn2diUpJydHkrRnw1oV5f/il5vvkVapnI0s+9/ScUtGpZWMH9y5TeHh4dq3ca0K8/MqXb+yuPk1w8BxKXt3uvI7NldaWpp++eUXWdaxrEuOi3RsLI0xfnHLsmRZVpXilmXZx6qq2yl5rH3xQLlblmUf72BqMsYoPz9f33zzjVwuV5VydLvdduxE1VQy7pv/1X2cSsZ947J48WK53e6gj19Va/Lx1VQdc+Z44m63u8xrRKCavF6vPS6+GoKtqTrmTFXjvjlT3cfJGOM3Jr66Tubx27JliyQpY6P/63b5r9nlxYN/Lc/cuV2S9P3339vvG6Vz9Hg89muL7/0mlNcI35hLVTtOwe7TGKPNmzcrPDzcHtPjeR8qL2507Gxccky+fkr7Vp5fR9pIOvDTdoWHh/uN54l+f6roOX8iXst98zRQf2EFGJfyxjHYeNbOY2Oam5urnJycMrWW7Jkq6418cUll3lt98UOHDpWpOSDjIHv27DGSzNKlS/3i48ePN/379w/4mMmTJxsdm9d88cUXX3zxxRdffDnoa9euXRX2ho46I1sV999/v+666y77Z6/Xq4MHDyo+Pr7M2Y+K5OTkKDk5Wbt27VJsbOyJSNWRGJeyGJPAGJfAGJeyGJPAGJeyGJPA6sK4GGOUm5urpKSkCtdzVCPbrFkzud1u/fzzz37xn3/+WS1atAj4mMjISEVGRvrF4uLiqpxDbGysYyfFicS4lMWYBMa4BMa4lMWYBMa4lMWYBOb0cWncuHGl6zjqw14RERHq06ePvvrqKzvm9Xr11VdfaeDAgTWYGQAAAE42R52RlaS77rpLY8aMUd++fdW/f389/fTTysvL09ixY2s6NQAAAJxEjmtkL7/8ch04cECTJk1SRkaGevXqpTlz5qh58+YndL+RkZGaPHlymcsU6jvGpSzGJDDGJTDGpSzGJDDGpSzGJLD6NC6WMZXd1wAAAACofRx1jSwAAADgQyMLAAAAR6KRBQAAgCPRyAIAAMCR6nUjO336dPXr10+NGjVSYmKiRo8erU2bNvmtM2zYMPtvH/u+brzxRr91du7cqVGjRqlBgwZKTEzU+PHjy/xNYSd54YUX1KNHD/tGygMHDtTnn39uLz969KhuueUWxcfHq2HDhrrkkkvK/JGK+jYm9XGeBPL444/LsiyNGzfOjtXH+VJSoDGpj/NlypQpZWru1KmTvby+zpPKxqU+zhVJ2rNnj/74xz8qPj5e0dHR6t69u1auXGkvN8Zo0qRJatmypaKjo3Xuuedqy5Ytfts4ePCgrr76asXGxiouLk5/+tOf9Msvv5zsUqpVZeNy3XXXlZkv5513nt826tq4OO72W9Vp4cKFuuWWW9SvXz8VFxfrgQce0IgRI/Tjjz8qJibGXu+GG27Qww8/bP/coEED+3uPx6NRo0apRYsWWrp0qfbt26drr71W4eHhmjZt2kmtp7q0atVKjz/+uDp06CBjjF5//XVdeOGFSktLU9euXXXnnXdq1qxZevfdd9W4cWPdeuutuvjii7VkyRJJ9XNMpPo3T0pbsWKFXnrpJfXo0cMvXh/ni095YyLVz/nStWtXffnll/bPYWG/vQXV53lS0bhI9W+uHDp0SIMHD9bw4cP1+eefKyEhQVu2bFGTJk3sdZ544gk988wzev3119W2bVtNnDhRqamp+vHHHxUVFSVJuvrqq7Vv3z7NmzdPRUVFGjt2rP785z/rrbfeqqnSQhLMuEjSeeedpxkzZtg/l74FV10bFxnY9u/fbySZhQsX2rGhQ4eaO+64o9zHzJ4927hcLpORkWHHXnjhBRMbG2sKCgpOZLonVZMmTcy//vUvc/jwYRMeHm7effdde9mGDRuMJLNs2TJjTP0bE2OYJ7m5uaZDhw5m3rx5fmNRn+dLeWNiTP2cL5MnTzY9e/YMuKw+z5OKxsWY+jlX7rvvPjNkyJByl3u9XtOiRQvzt7/9zY4dPnzYREZGmpkzZxpjjPnxxx+NJLNixQp7nc8//9xYlmX27Nlz4pI/gSobF2OMGTNmjLnwwgvLXV4Xx6VeX1pQWnZ2tiSpadOmfvE333xTzZo1U7du3XT//fcrPz/fXrZs2TJ1797d7w8ypKamKicnRz/88MPJSfwE8ng8evvtt5WXl6eBAwfq+++/V1FRkc4991x7nU6dOql169ZatmyZpPo3Jj71eZ7ccsstGjVqlN+8kFSv50t5Y+JTH+fLli1blJSUpHbt2unqq6/Wzp07JdXveSKVPy4+9W2ufPLJJ+rbt68uvfRSJSYmqnfv3nrllVfs5enp6crIyPCbL40bN9aAAQP85ktcXJz69u1rr3PuuefK5XJp+fLlJ6+YalTZuPgsWLBAiYmJ6tixo2666SZlZWXZy+riuNTrSwtK8nq9GjdunAYPHqxu3brZ8auuukpt2rRRUlKS1q5dq/vuu0+bNm3SBx98IEnKyMgo81fFfD9nZGScvAKq2bp16zRw4EAdPXpUDRs21IcffqguXbpo9erVioiIUFxcnN/6zZs3t+utb2Mi1d95Iklvv/22Vq1apRUrVpRZlpGRUS/nS0VjItXP+TJgwAC99tpr6tixo/bt26epU6fqzDPP1Pr16+vtPJEqHpdGjRrVy7myfft2vfDCC7rrrrv0wAMPaMWKFbr99tsVERGhMWPG2HUFqrvkfElMTPRbHhYWpqZNm9bZcZGOXVZw8cUXq23bttq2bZseeOABjRw5UsuWLZPb7a6T40Ij+6tbbrlF69ev1+LFi/3if/7zn+3vu3fvrpYtW+qcc87Rtm3blJKScrLTPGk6duyo1atXKzs7W++9957GjBmjhQsX1nRaNaq8MenSpUu9nSe7du3SHXfcoXnz5tnXpdV3wYxJfZwvI0eOtL/v0aOHBgwYoDZt2uh///ufoqOjazCzmlXRuPzpT3+ql3PF6/Wqb9++9jW+vXv31vr16/Xiiy/aDVt9FMy4XHHFFfb63bt3V48ePZSSkqIFCxbonHPOqZG8TzQuLZB066236rPPPtP8+fPVqlWrCtcdMGCAJGnr1q2SpBYtWpT5ZK3v5xYtWpyAbE+OiIgItW/fXn369NH06dPVs2dP/eMf/1CLFi1UWFiow4cP+63/888/2/XWtzEJpL7Mk++//1779+/X6aefrrCwMIWFhWnhwoV65plnFBYWpubNm9e7+VLZmHg8njKPqS/zpaS4uDiddtpp2rp1a71+XSmt5LgEUh/mSsuWLe3/7fLp3LmzfcmFr65AdZecL/v37/dbXlxcrIMHD9bZcQmkXbt2atasmd98qWvjUq8bWWOMbr31Vn344Yf6+uuv1bZt20ofs3r1aknHJpQkDRw4UOvWrfObGPPmzVNsbGyZCedkXq9XBQUF6tOnj8LDw/XVV1/ZyzZt2qSdO3fa14vWtzEJpL7Mk3POOUfr1q3T6tWr7a++ffvq6quvtr+vb/OlsjFxu91lHlNf5ktJv/zyi7Zt26aWLVvyulJCyXEJpD7MlcGDB5e5FebmzZvVpk0bSVLbtm3VokULv/mSk5Oj5cuX+82Xw4cP6/vvv7fX+frrr+X1eu1fBpymsnEJZPfu3crKyvKbL3VtXOr1XQtuuukm07hxY7NgwQKzb98++ys/P98YY8zWrVvNww8/bFauXGnS09PNxx9/bNq1a2fOOussexvFxcWmW7duZsSIEWb16tVmzpw5JiEhwdx///01VVbIJkyYYBYuXGjS09PN2rVrzYQJE4xlWeaLL74wxhhz4403mtatW5uvv/7arFy50gwcONAMHDjQfnx9G5P6Ok/KU/pT1vVxvpRWckzq63y5++67zYIFC0x6erpZsmSJOffcc02zZs3M/v37jTH1d55UNC71da589913JiwszDz22GNmy5Yt5s033zQNGjQw//3vf+11Hn/8cRMXF2c+/vhjs3btWnPhhReatm3bmiNHjtjrnHfeeaZ3795m+fLlZvHixaZDhw7myiuvrImSqkVl45Kbm2vuueces2zZMpOenm6+/PJLc/rpp5sOHTqYo0eP2tupa+NSrxtZSQG/ZsyYYYwxZufOneass84yTZs2NZGRkaZ9+/Zm/PjxJjs72287O3bsMCNHjjTR0dGmWbNm5u677zZFRUU1UFH1uP76602bNm1MRESESUhIMOecc47dxBpjzJEjR8zNN99smjRpYho0aGAuuugis2/fPr9t1Kcxqa/zpDylG9n6OF9KKzkm9XW+XH755aZly5YmIiLCnHLKKebyyy83W7dutZfX13lS0bjU17lijDGffvqp6datm4mMjDSdOnUyL7/8st9yr9drJk6caJo3b24iIyPNOeecYzZt2uS3TlZWlrnyyitNw4YNTWxsrBk7dqzJzc09mWVUu4rGJT8/34wYMcIkJCSY8PBw06ZNG3PDDTf43ZrNmLo3LpYxxtTkGWEAAACgKur1NbIAAABwLhpZAAAAOBKNLAAAAByJRhYAAACORCMLAAAAR6KRBQAAgCPRyAIAAMCRaGQBAADgSDSyAOBg1113nU499dQqP7Zhw4bVmxAAnEQ0sgBwAvzvf/+TZVn68MMPyyzr2bOnLMvS/Pnzyyxr3bq1Bg0adDJSDFp+fr6mTJmiBQsW1HQqAOCHRhYAToAhQ4ZIkhYvXuwXz8nJ0fr16xUWFqYlS5b4Ldu1a5d27dplPzYYr7zyijZt2hR6whXIz8/X1KlTaWQB1DphNZ0AANRFSUlJatu2bZlGdtmyZTLG6NJLLy2zzPfz8TSy4eHhoScLAA7FGVkAOEGGDBmitLQ0HTlyxI4tWbJEXbt21ciRI/Xtt9/K6/X6LbMsS4MHD5Yk/fe//1WfPn0UHR2tpk2b6oorrtCuXbv89hHoGtmsrCxdc801io2NVVxcnMaMGaM1a9bIsiy99tprZfLcs2ePRo8erYYNGyohIUH33HOPPB6PJGnHjh1KSEiQJE2dOlWWZcmyLE2ZMqUaRggAQkMjCwAnyJAhQ1RUVKTly5fbsSVLlmjQoEEaNGiQsrOztX79er9lnTp1Unx8vB577DFde+216tChg5566imNGzdOX331lc466ywdPny43H16vV6df/75mjlzpsaMGaPHHntM+/bt05gxYwKu7/F4lJqaqvj4eD355JMaOnSo/v73v+vll1+WJCUkJOiFF16QJF100UX6z3/+o//85z+6+OKLq2GEACBEBgBwQvzwww9GknnkkUeMMcYUFRWZmJgY8/rrrxtjjGnevLl57rnnjDHG5OTkGLfbbW644QazY8cO43a7zWOPPea3vXXr1pmwsDC/+JgxY0ybNm3sn99//30jyTz99NN2zOPxmLPPPttIMjNmzPB7rCTz8MMP++2nd+/epk+fPvbPBw4cMJLM5MmTQxoPAKhunJEFgBOkc+fOio+Pt699XbNmjfLy8uy7EgwaNMj+wNeyZcvk8Xg0ZMgQffDBB/J6vbrsssuUmZlpf7Vo0UIdOnQIeLcDnzlz5ig8PFw33HCDHXO5XLrlllvKfcyNN97o9/OZZ56p7du3V7luADhZ+LAXAJwglmVp0KBBWrRokbxer5YsWaLExES1b99e0rFG9tlnn5Uku6EdMmSInnzySRlj1KFDh4DbregDXj/99JNatmypBg0a+MV9+ywtKirKvgbWp0mTJjp06FBwRQJADaKRBYATaMiQIfr000+1bt06+/pYn0GDBmn8+PHas2ePFi9erKSkJLVr105er1eWZenzzz+X2+0us83q/CMGgbYPAE5BIwsAJ1DJ+8kuWbJE48aNs5f16dNHkZGRWrBggZYvX67f/e53kqSUlBQZY9S2bVuddtppx7W/Nm3aaP78+crPz/c7K7t169Yq12BZVpUfCwAnEtfIAsAJ1LdvX0VFRenNN9/Unj17/M7IRkZG6vTTT9dzzz2nvLw8u+m9+OKL5Xa7NXXqVBlj/LZnjFFWVla5+0tNTVVRUZFeeeUVO+b1evXcc89VuQZfQ1zR3RIAoCZwRhYATqCIiAj169dP33zzjSIjI9WnTx+/5YMGDdLf//53Sb+dvU1JSdGjjz6q+++/Xzt27NDo0aPVqFEjpaen68MPP9Sf//xn3XPPPQH3N3r0aPXv31933323tm7dqk6dOumTTz7RwYMHJVXt7Gp0dLS6dOmid955R6eddpqaNm2qbt26qVu3bse9LQCoTpyRBYATzNeg+i4lKMn3xw8aNWqknj172vEJEybo/fffl8vl0tSpU3XPPffok08+0YgRI3TBBReUuy+3261Zs2bp8ssv1+uvv64HH3xQSUlJ9hnZqKioKtXwr3/9S6eccoruvPNOXXnllXrvvfeqtB0AqE6WKf3/VgCAOuejjz7SRRddpMWLF9vNMwA4HY0sANQxR44cUXR0tP2zx+PRiBEjtHLlSmVkZPgtAwAn4xpZAKhjbrvtNh05ckQDBw5UQUGBPvjgAy1dulTTpk2jiQVQp3BGFgDqmLfeekt///vftXXrVh09elTt27fXTTfdpFtvvbWmUwOAakUjCwAAAEfirgUAAABwJBpZAAAAOBKNLAAAAByJRhYAAACORCMLAAAAR6KRBQAAgCPRyAIAAMCRaGQBAADgSP8/rr8ZSm6NrVwAAAAASUVORK5CYII=", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Elegir el número del cluster a analizar\n", + "cluster_a_analizar = 2 # Cambia este valor al número del cluster que quieres analizar\n", + "\n", + "# Filtrar los elementos del cluster específico\n", + "elementos_cluster = df_final[df_final['cluster'] == cluster_a_analizar]\n", + "\n", + "print(f\"Elementos del cluster {cluster_a_analizar}:\")\n", + "print(elementos_cluster)\n", + "\n", + "# Estadísticas descriptivas de los datos del cluster\n", + "print(f\"\\nEstadísticas descriptivas del cluster {cluster_a_analizar}:\")\n", + "print(elementos_cluster.describe())\n", + "\n", + "# Visualizar las características relevantes de los elementos del cluster\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Seleccionar columnas relevantes para el análisis (puedes ajustar según tu caso)\n", + "columnas_relevantes = elementos_cluster.select_dtypes(include=['number']).columns\n", + "\n", + "# Crear histogramas para las columnas relevantes\n", + "for col in columnas_relevantes:\n", + " plt.figure(figsize=(8, 5))\n", + " elementos_cluster[col].hist(bins=20, color='skyblue', edgecolor='black')\n", + " plt.title(f'Distribución de {col} en el cluster {cluster_a_analizar}', fontsize=14)\n", + " plt.xlabel(col, fontsize=12)\n", + " plt.ylabel('Frecuencia', fontsize=12)\n", + " plt.grid(axis='y', linestyle='--', alpha=0.7)\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elementos del cluster 3:\n", + " Weight Upper_Material Midsole_Material \\\n", + "7 213.0 Parte superior de malla Dreamstrike+ \n", + "15 334.0 Exterior técnico de malla Mediasuela Dreamstrike \n", + "32 247.0 Parte superior de monomalla LIGHTMOTION \n", + "35 248.0 Exterior textil Tecnología Light BOOST \n", + "40 254.0 Monomalla Dreamstrike+ \n", + ".. ... ... ... \n", + "414 243.0 Exterior de malla acolchada Dreamstrike+ \n", + "428 290.0 Parte superior de malla Mediasuela Dreamstrike+ \n", + "429 290.0 Parte superior de malla Dreamstrike+ \n", + "442 213.0 Parte superior de malla mediasuela Dreamstrike+ \n", + "450 295.0 adidas PRIMEKNIT 4D de impresión 3D \n", + "\n", + " Outsole Cushioning_System \\\n", + "7 Suela Adiwear Dreamstrike+ \n", + "15 Suela con inserciones de caucho Dreamstrike \n", + "32 Adiwear LIGHTMOTION \n", + "35 Continental Rubber Tecnología BOOST \n", + "40 Varillas de caucho Dreamstrike+ \n", + ".. ... ... \n", + "414 Adiwear Dreamstrike+ \n", + "428 Suela Adiwear Dreamstrike+ \n", + "429 Suela Adiwear Dreamstrike+ \n", + "442 Suela Adiwear Dreamstrike+ \n", + "450 Continental Rubber Estructura que absorbe el impacto \n", + "\n", + " Drop__heel-to-toe_differential_ Usage_Type Gender Available_Sizes \\\n", + "7 10.0 Running Mujer CO 37 \n", + "15 10.0 Running Hombre NaN \n", + "32 10.0 Running Mujer CO 37 \n", + "35 10.0 Running Mujer CO 37 \n", + "40 10.0 Running Mujer NaN \n", + ".. ... ... ... ... \n", + "414 10.0 Running Mujer NaN \n", + "428 8.0 Running Hombre CO 40 \n", + "429 8.0 Running Hombre NaN \n", + "442 10.0 Running Mujer NaN \n", + "450 10.0 Running Mujer NaN \n", + "\n", + " Width Additional_Technologies \\\n", + "7 NaN Contiene al menos un 20% de material reciclado... \n", + "15 NaN Contiene al menos un 20% de material reciclado \n", + "32 NaN Recycled materials, Waterproofing \n", + "35 NaN Torsion System, Recyclable materials, Huella d... \n", + "40 NaN Contiene al menos un 20% de material reciclado \n", + ".. ... ... \n", + "414 NaN Sustainable dye technology, Recyclable materials \n", + "428 NaN Recycled materials, Optimized lacing system \n", + "429 NaN NaN \n", + "442 NaN Contiene al menos un 20% de material reciclado... \n", + "450 NaN Contiene al menos un 20% de material reciclado... \n", + "\n", + " id regularPrice undiscounted_price cluster \n", + "7 0n2Tyl34QdVdkCKgAdcT 649950 NaN 3 \n", + "15 1f5c8gEndKxlI9IEo2FT 579950 347970.0 3 \n", + "32 4rs0wxAzoGLvCI8BHwzh 379950 265965.0 3 \n", + "35 5BnxAht8eNgpRagM6Xcv 949950 NaN 3 \n", + "40 5iyHRy6zWzpmfnsalDBK 499950 349965.0 3 \n", + ".. ... ... ... ... \n", + "414 voNjPj9u2oFEimmwnQUZ 799950 NaN 3 \n", + "428 x6mqNLTCjvwPX1Zp7k7f 849950 NaN 3 \n", + "429 xDe8Syqrat9Xos53PdXL 849950 594965.0 3 \n", + "442 yFWhoK8BDTP6VF8QzBMA 649950 NaN 3 \n", + "450 zVnQ4LTK528Lins9HzaW 1199950 719970.0 3 \n", + "\n", + "[64 rows x 15 columns]\n", + "\n", + "Estadísticas descriptivas del cluster 3:\n", + " Weight Drop__heel-to-toe_differential_ Width regularPrice \\\n", + "count 64.000000 62.000000 0.0 6.400000e+01 \n", + "mean 255.125000 9.419355 NaN 7.233875e+05 \n", + "std 36.080818 1.300043 NaN 2.401337e+05 \n", + "min 183.000000 6.000000 NaN 2.999500e+05 \n", + "25% 242.000000 10.000000 NaN 5.799500e+05 \n", + "50% 251.000000 10.000000 NaN 6.999500e+05 \n", + "75% 277.000000 10.000000 NaN 8.499500e+05 \n", + "max 334.000000 10.000000 NaN 1.299950e+06 \n", + "\n", + " undiscounted_price cluster \n", + "count 33.000000 64.0 \n", + "mean 511087.272727 3.0 \n", + "std 171176.978698 0.0 \n", + "min 239960.000000 3.0 \n", + "25% 349965.000000 3.0 \n", + "50% 509970.000000 3.0 \n", + "75% 639960.000000 3.0 \n", + "max 909965.000000 3.0 \n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Elegir el número del cluster a analizar\n", + "cluster_a_analizar = 3 # Cambia este valor al número del cluster que quieres analizar\n", + "\n", + "# Filtrar los elementos del cluster específico\n", + "elementos_cluster = df_final[df_final['cluster'] == cluster_a_analizar]\n", + "\n", + "print(f\"Elementos del cluster {cluster_a_analizar}:\")\n", + "print(elementos_cluster)\n", + "\n", + "# Estadísticas descriptivas de los datos del cluster\n", + "print(f\"\\nEstadísticas descriptivas del cluster {cluster_a_analizar}:\")\n", + "print(elementos_cluster.describe())\n", + "\n", + "# Visualizar las características relevantes de los elementos del cluster\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Seleccionar columnas relevantes para el análisis (puedes ajustar según tu caso)\n", + "columnas_relevantes = elementos_cluster.select_dtypes(include=['number']).columns\n", + "\n", + "# Crear histogramas para las columnas relevantes\n", + "for col in columnas_relevantes:\n", + " plt.figure(figsize=(8, 5))\n", + " elementos_cluster[col].hist(bins=20, color='skyblue', edgecolor='black')\n", + " plt.title(f'Distribución de {col} en el cluster {cluster_a_analizar}', fontsize=14)\n", + " plt.xlabel(col, fontsize=12)\n", + " plt.ylabel('Frecuencia', fontsize=12)\n", + " plt.grid(axis='y', linestyle='--', alpha=0.7)\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/src/comparative_analysis/training/training.py b/src/comparative_analysis/training/training.py index 597a45c32..cfd405524 100644 --- a/src/comparative_analysis/training/training.py +++ b/src/comparative_analysis/training/training.py @@ -1,10 +1,101 @@ import pandas as pd +from sklearn.cluster import KMeans +from sklearn.metrics import silhouette_score, davies_bouldin_score +from sklearn.preprocessing import StandardScaler +import matplotlib.pyplot as plt +# **1. Cargar el archivo Excel y preparar el DataFrame** # Ruta del archivo de Excel ruta_excel = r"C:\Users\cdgn2\OneDrive\Escritorio\Maestría\Maestria\Metodologias Agiles\Proyecto\Comparative-analysis-of-products\src\comparative_analysis\database\Adidas_etiquetado.xlsx" # Crear el DataFrame df = pd.read_excel(ruta_excel, header=0) -# Mostrar las primeras filas del DataFrame -print(df.head()) \ No newline at end of file +# **2. Preprocesamiento** +# Convertir columnas a numéricas +df['Weight'] = df['Weight'].astype(str).str.extract('(\d+\.?\d*)').astype(float) +df['Drop__heel-to-toe_differential_'] = pd.to_numeric(df['Drop__heel-to-toe_differential_'].astype(str).str.extract('(\d+\.?\d*)'), errors='coerce') + +# Limpiar precios y convertir a numéricos +df['regularPrice'] = pd.to_numeric(df['regularPrice'].str.replace(r'[^\d.,]', '', regex=True).str.replace(r'\.', '', regex=True).str.replace(',', '.'), errors='coerce') +df['undiscounted_price'] = pd.to_numeric(df['undiscounted_price'].str.replace(r'[^\d.,]', '', regex=True).str.replace(r'\.', '', regex=True).str.replace(',', '.'), errors='coerce') + +# Eliminar columnas no necesarias +cols_to_drop = ['details', 'description', 'category', 'characteristics', 'width', 'Pronation_Type'] +df = df.drop(columns=cols_to_drop, errors='ignore') + +# Filtrar IDs nulos +id_nan = df[df['id'].isna()] + +# **3. Separar datos para clustering** +# Separar columna 'id' +ids = df['id'] +X = df.drop(columns=['id'], errors='ignore') + +# Manejar valores faltantes y convertir variables categóricas +X = X.fillna(0) +X = pd.get_dummies(X, dummy_na=True) + +# Escalar datos +scaler = StandardScaler() +X_scaled = scaler.fit_transform(X) + +# **4. Clustering con KMeans** +k = 8 # Número de clusters +kmeans = KMeans(n_clusters=k, random_state=42) +clusters = kmeans.fit_predict(X_scaled) + +# Crear un DataFrame con los clusters asignados +df_clusters = pd.DataFrame({'id': ids, 'cluster': clusters}) + +# Evaluar la calidad del clustering +sil_score = silhouette_score(X_scaled, clusters) +db_score = davies_bouldin_score(X_scaled, clusters) + +print("Silhouette Score:", sil_score) +print("Davies-Bouldin Score:", db_score) + +# **5. Unir clusters con el DataFrame original** +df_final = df.merge(df_clusters, on='id', how='left') + +# **6. Análisis por clusters** +def analizar_cluster(cluster_num): + # Filtrar elementos del cluster específico + elementos_cluster = df_final[df_final['cluster'] == cluster_num] + + print(f"\nElementos del cluster {cluster_num}:") + print(elementos_cluster) + + print(f"\nEstadísticas descriptivas del cluster {cluster_num}:") + print(elementos_cluster.describe()) + + # Visualizar características numéricas + columnas_relevantes = elementos_cluster.select_dtypes(include=['number']).columns + for col in columnas_relevantes: + plt.figure(figsize=(8, 5)) + elementos_cluster[col].hist(bins=20, color='skyblue', edgecolor='black') + plt.title(f'Distribución de {col} en el cluster {cluster_num}', fontsize=14) + plt.xlabel(col, fontsize=12) + plt.ylabel('Frecuencia', fontsize=12) + plt.grid(axis='y', linestyle='--', alpha=0.7) + plt.show() + +# Ejemplo: Analizar el cluster 0 +analizar_cluster(0) + +# **7. Determinar el número óptimo de clusters** +distortions = [] +K = range(2, 11) # Rango de k a evaluar +for k in K: + kmeans = KMeans(n_clusters=k, random_state=42) + kmeans.fit(X_scaled) + distortions.append(kmeans.inertia_) + +# Graficar la "elbow curve" +plt.figure(figsize=(8, 5)) +plt.plot(K, distortions, 'bo-', markersize=8, linewidth=2, color='blue') +plt.title('Elbow Method para determinar el número óptimo de clusters', fontsize=14) +plt.xlabel('Número de clusters', fontsize=12) +plt.ylabel('Distorsión (Inercia)', fontsize=12) +plt.grid(True, linestyle='--', alpha=0.7) +plt.show() \ No newline at end of file From 9dc43795730a2fb2ef1522d030af905618da427d Mon Sep 17 00:00:00 2001 From: Juan Correa Date: Thu, 12 Dec 2024 23:09:25 -0500 Subject: [PATCH 34/84] baseline_models --- docs/modeling/baseline_models.md | 77 +++++++++++++++++++++++++++----- 1 file changed, 66 insertions(+), 11 deletions(-) diff --git a/docs/modeling/baseline_models.md b/docs/modeling/baseline_models.md index 02ba988ff..b076b48a3 100644 --- a/docs/modeling/baseline_models.md +++ b/docs/modeling/baseline_models.md @@ -1,39 +1,94 @@ # Reporte del Modelo Baseline -Este documento contiene los resultados del modelo baseline. +Este documento contiene los resultados del modelo baseline, el cual consiste en una primera aproximación al clustering de productos. El objetivo es sentar las bases para modelos posteriores y guiar el proceso de mejora continua. ## Descripción del modelo -El modelo baseline es el primer modelo construido y se utiliza para establecer una línea base para el rendimiento de los modelos posteriores. +El modelo baseline se enfocó en realizar un proceso de clustering sobre un conjunto de productos obtenidos desde un endpoint con productos de Adidas. El flujo del proyecto incluye: + +1. **Obtención de datos**: Se obtienen productos desde un endpoint (Adidas). +2. **Normalización de datos con un LLM**: Un modelo de lenguaje (LLM) se utiliza para normalizar y etiquetar las características de los productos. +3. **Clustering**: Utilizando las variables numéricas ya procesadas, se aplicó un algoritmo de clustering (ej. K-Means) para agrupar productos similares. + +Este pipeline busca identificar patrones, similitudes y diferencias entre los productos de la marca. ## Variables de entrada -Lista de las variables de entrada utilizadas en el modelo. +Las variables de entrada utilizadas en el modelo son aquellas extraídas y normalizadas a partir del procesamiento con el LLM. Entre las principales variables se encuentran: + +- **Weight** (Peso del producto, en gramos) +- **Drop (heel-to-toe differential)** (Diferencial de altura entre talón y punta, en mm) +- **regularPrice** (Precio regular del producto, en valor numérico flotante) +- **undiscounted_price** (Precio sin descuento, en valor numérico flotante) +- Otras variables numéricas o categóricas codificadas (por ejemplo, materiales del upper, midsole, outsole convertidas en variables dummies) + +Los IDs de los productos se han mantenido para identificar a qué cluster pertenece cada uno, pero no se utilizaron para el embedding. ## Variable objetivo -Nombre de la variable objetivo utilizada en el modelo. +No existe una variable objetivo propiamente dicha, ya que el enfoque es no supervisado. El objetivo es descubrir grupos (clusters) de productos similares. ## Evaluación del modelo ### Métricas de evaluación -Descripción de las métricas utilizadas para evaluar el rendimiento del modelo. +Se han utilizado métricas de evaluación típicas para modelos de clustering no supervisados: + +- **Silhouette Score**: Mide la separación y cohesión de los clusters. Un valor cercano a 1 indica que las muestras están bien agrupadas, un valor cercano a -1 indica lo contrario. +- **Davies-Bouldin Score**: Mide la calidad del clustering en función de las distancias entre clusters. Valores más bajos indican mejores separaciones entre clusters. ### Resultados de evaluación -Tabla que muestra los resultados de evaluación del modelo baseline, incluyendo las métricas de evaluación. +**Distribución de productos por cluster:** + +| Cluster | Conteo | Porcentaje | +|---------|---------|------------| +| 0 | 2 | 0.44% | +| 1 | 3 | 0.66% | +| 2 | 22 | 4.85% | +| 3 | 64 | 14.10% | +| 4 | 93 | 20.48% | +| 5 | 268 | 59.03% | +| 6 | 1 | 0.22% | +| 7 | 1 | 0.22% | + +**Métricas globales:** + +- Silhouette Score: -0.10727046484513526 +- Davies-Bouldin Score: 3.820317442353896 ## Análisis de los resultados -Descripción de los resultados del modelo baseline, incluyendo fortalezas y debilidades del modelo. +El Silhouette Score negativo (-0.1072) sugiere que la mayoría de los productos no están bien asignados a sus clusters o que los clusters se solapan significativamente. Esto indica que el agrupamiento no capta adecuadamente las similitudes reales entre los productos. + +El Davies-Bouldin Score (3.8203) es relativamente alto, lo que también indica que los clusters no están bien definidos, presentando una baja separación entre ellos. + +La distribución de productos por cluster está muy desbalanceada. Un solo cluster (el número 5) contiene alrededor del 59% de los productos, mientras que otros clusters contienen muy pocos elementos, incluso uno solo. Esto sugiere que la configuración actual de k (el número de clusters) o la forma en que se están representando los datos no es la más adecuada. + +### Fortalezas + +- **Primer paso hacia la segmentación:** Establece una línea base desde la cual se pueden proponer mejoras. +- **Proceso reproducible:** El pipeline desde la extracción de datos, normalización con LLM y clustering está bien definido y puede repetirse con ajustes futuros. + +### Debilidades + +- **Baja calidad de agrupamiento:** Las métricas indican que los clusters no reflejan adecuadamente las similitudes entre productos. +- **Desbalance en los clusters:** Un cluster concentra la mayor parte de los productos, lo que dificulta interpretaciones útiles. +- **Falencia en el embedding actual:** Es posible que la selección de variables o su codificación no capture suficientemente las diferencias relevantes entre los productos. + +### Áreas de mejora + +- **Refinamiento de la representación de datos:** Incluir embeddings más representativos (por ejemplo, usando técnicas de NLP más avanzadas en descripciones, o embeddings más sofisticados en variables categóricas). +- **Ajuste del número de clusters:** Probar diferentes valores de k con el método del codo o métricas de validación más robustas. +- **Selección de características:** Evaluar qué variables realmente aportan información útil para segmentar los productos y eliminar aquellas que introduzcan ruido. +- **Experimentar con diferentes algoritmos de clustering:** Además de K-Means, probar DBSCAN o HDBSCAN para detectar estructuras no esféricas. ## Conclusiones -Conclusiones generales sobre el rendimiento del modelo baseline y posibles áreas de mejora. +El modelo baseline de clustering no ofrece una segmentación claramente útil, según las métricas de validación. Sin embargo, resulta valioso como punto de partida para comprender el problema y orientar mejoras. Ajustes en la selección de variables, técnicas de embedding y el número de clusters, así como la experimentación con otros algoritmos de clustering, podrían mejorar significativamente los resultados. ## Referencias -Lista de referencias utilizadas para construir el modelo baseline y evaluar su rendimiento. - -Espero que te sea útil esta plantilla. Recuerda que puedes adaptarla a las necesidades específicas de tu proyecto. +- Documentation of scikit-learn for Clustering: [https://scikit-learn.org/stable/modules/clustering.html](https://scikit-learn.org/stable/modules/clustering.html) +- Introducción a la Evaluación del Clustering (Silhouette Score, Davies-Bouldin): [https://scikit-learn.org/stable/modules/clustering.html#clustering-evaluation](https://scikit-learn.org/stable/modules/clustering.html#clustering-evaluation) +- Documentación de K-Means: [https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) From 4f8a28cc5d89eec85099685f5634baeaff3ee2df Mon Sep 17 00:00:00 2001 From: Juan Correa Date: Thu, 12 Dec 2024 23:16:55 -0500 Subject: [PATCH 35/84] tag --- docs/modeling/baseline_models.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/modeling/baseline_models.md b/docs/modeling/baseline_models.md index b076b48a3..f103416ad 100644 --- a/docs/modeling/baseline_models.md +++ b/docs/modeling/baseline_models.md @@ -89,6 +89,8 @@ El modelo baseline de clustering no ofrece una segmentación claramente útil, s ## Referencias + - Documentation of scikit-learn for Clustering: [https://scikit-learn.org/stable/modules/clustering.html](https://scikit-learn.org/stable/modules/clustering.html) - Introducción a la Evaluación del Clustering (Silhouette Score, Davies-Bouldin): [https://scikit-learn.org/stable/modules/clustering.html#clustering-evaluation](https://scikit-learn.org/stable/modules/clustering.html#clustering-evaluation) - Documentación de K-Means: [https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) + From f1cc952ea664c5012b5d1951f1fe3bb8dd0a59fe Mon Sep 17 00:00:00 2001 From: CarlosKC26 Date: Thu, 12 Dec 2024 23:24:03 -0500 Subject: [PATCH 36/84] scripts --- src/comparative_analysis/training/training.py | 130 +++++++++--------- .../training/trainingText.py | 74 ++++++++++ 2 files changed, 137 insertions(+), 67 deletions(-) create mode 100644 src/comparative_analysis/training/trainingText.py diff --git a/src/comparative_analysis/training/training.py b/src/comparative_analysis/training/training.py index cfd405524..3a3ac99f7 100644 --- a/src/comparative_analysis/training/training.py +++ b/src/comparative_analysis/training/training.py @@ -1,101 +1,97 @@ import pandas as pd +import re from sklearn.cluster import KMeans from sklearn.metrics import silhouette_score, davies_bouldin_score from sklearn.preprocessing import StandardScaler import matplotlib.pyplot as plt -# **1. Cargar el archivo Excel y preparar el DataFrame** +# ** Carga y limpieza de datos ** # Ruta del archivo de Excel ruta_excel = r"C:\Users\cdgn2\OneDrive\Escritorio\Maestría\Maestria\Metodologias Agiles\Proyecto\Comparative-analysis-of-products\src\comparative_analysis\database\Adidas_etiquetado.xlsx" # Crear el DataFrame df = pd.read_excel(ruta_excel, header=0) -# **2. Preprocesamiento** -# Convertir columnas a numéricas -df['Weight'] = df['Weight'].astype(str).str.extract('(\d+\.?\d*)').astype(float) -df['Drop__heel-to-toe_differential_'] = pd.to_numeric(df['Drop__heel-to-toe_differential_'].astype(str).str.extract('(\d+\.?\d*)'), errors='coerce') - -# Limpiar precios y convertir a numéricos -df['regularPrice'] = pd.to_numeric(df['regularPrice'].str.replace(r'[^\d.,]', '', regex=True).str.replace(r'\.', '', regex=True).str.replace(',', '.'), errors='coerce') -df['undiscounted_price'] = pd.to_numeric(df['undiscounted_price'].str.replace(r'[^\d.,]', '', regex=True).str.replace(r'\.', '', regex=True).str.replace(',', '.'), errors='coerce') - -# Eliminar columnas no necesarias +# Procesamiento de columnas numéricas +num_cols = { + 'Weight': '(\d+\.?\d*)', + 'Drop__heel-to-toe_differential_': '(\d+\.?\d*)' +} +for col, pattern in num_cols.items(): + df[col] = df[col].astype(str).str.extract(pattern).astype(float, errors='ignore') + df[col] = pd.to_numeric(df[col], errors='coerce') + +# Procesar precios +price_cols = ['regularPrice', 'undiscounted_price'] +for col in price_cols: + df[col] = df[col].astype(str).str.replace(r'[^0-9.,]', '', regex=True) + df[col] = df[col].str.replace(r'\.', '', regex=True).str.replace(',', '.') + df[col] = pd.to_numeric(df[col], errors='coerce') + +# Eliminar columnas innecesarias cols_to_drop = ['details', 'description', 'category', 'characteristics', 'width', 'Pronation_Type'] df = df.drop(columns=cols_to_drop, errors='ignore') -# Filtrar IDs nulos -id_nan = df[df['id'].isna()] - -# **3. Separar datos para clustering** -# Separar columna 'id' +# ** Clustering y evaluación ** +# Separar la columna ID ids = df['id'] -X = df.drop(columns=['id'], errors='ignore') +X = df.drop(columns=['id'], errors='ignore').fillna(0) -# Manejar valores faltantes y convertir variables categóricas -X = X.fillna(0) -X = pd.get_dummies(X, dummy_na=True) +# Codificar variables categóricas +df_dummies = pd.get_dummies(X, dummy_na=True).fillna(0) -# Escalar datos +# Escalar los datos scaler = StandardScaler() -X_scaled = scaler.fit_transform(X) - -# **4. Clustering con KMeans** -k = 8 # Número de clusters +X_scaled = scaler.fit_transform(df_dummies) + +# Determinar el número óptimo de clusters usando el método del codo +def metodo_del_codo(X, k_range): + distortions = [] + for k in k_range: + kmeans = KMeans(n_clusters=k, random_state=42) + kmeans.fit(X) + distortions.append(kmeans.inertia_) + + plt.figure(figsize=(8, 5)) + plt.plot(k_range, distortions, 'bx-') + plt.xlabel('Número de clusters k') + plt.ylabel('Distorsión (Inercia)') + plt.title('Método del Codo para determinar k') + plt.show() + +# Mostrar el método del codo +metodo_del_codo(X_scaled, range(2, 11)) + +# Clustering con un número específico de clusters +k = 8 kmeans = KMeans(n_clusters=k, random_state=42) clusters = kmeans.fit_predict(X_scaled) -# Crear un DataFrame con los clusters asignados -df_clusters = pd.DataFrame({'id': ids, 'cluster': clusters}) +# Agregar el cluster al DataFrame original +df['cluster'] = clusters -# Evaluar la calidad del clustering +# Evaluar calidad del clustering sil_score = silhouette_score(X_scaled, clusters) db_score = davies_bouldin_score(X_scaled, clusters) +print(f"Silhouette Score: {sil_score}") +print(f"Davies-Bouldin Score: {db_score}") -print("Silhouette Score:", sil_score) -print("Davies-Bouldin Score:", db_score) - -# **5. Unir clusters con el DataFrame original** -df_final = df.merge(df_clusters, on='id', how='left') - -# **6. Análisis por clusters** -def analizar_cluster(cluster_num): - # Filtrar elementos del cluster específico - elementos_cluster = df_final[df_final['cluster'] == cluster_num] - - print(f"\nElementos del cluster {cluster_num}:") - print(elementos_cluster) +# ** Análisis de clusters ** +def analizar_cluster(df, cluster_num): + elementos_cluster = df[df['cluster'] == cluster_num] + print(f"Elementos del cluster {cluster_num}:") + print(elementos_cluster.head()) print(f"\nEstadísticas descriptivas del cluster {cluster_num}:") print(elementos_cluster.describe()) - # Visualizar características numéricas + # Visualización de histogramas columnas_relevantes = elementos_cluster.select_dtypes(include=['number']).columns for col in columnas_relevantes: - plt.figure(figsize=(8, 5)) - elementos_cluster[col].hist(bins=20, color='skyblue', edgecolor='black') - plt.title(f'Distribución de {col} en el cluster {cluster_num}', fontsize=14) - plt.xlabel(col, fontsize=12) - plt.ylabel('Frecuencia', fontsize=12) - plt.grid(axis='y', linestyle='--', alpha=0.7) + elementos_cluster[col].plot(kind='hist', title=f"Distribución de {col} en cluster {cluster_num}") + plt.xlabel(col) + plt.ylabel('Frecuencia') plt.show() -# Ejemplo: Analizar el cluster 0 -analizar_cluster(0) - -# **7. Determinar el número óptimo de clusters** -distortions = [] -K = range(2, 11) # Rango de k a evaluar -for k in K: - kmeans = KMeans(n_clusters=k, random_state=42) - kmeans.fit(X_scaled) - distortions.append(kmeans.inertia_) - -# Graficar la "elbow curve" -plt.figure(figsize=(8, 5)) -plt.plot(K, distortions, 'bo-', markersize=8, linewidth=2, color='blue') -plt.title('Elbow Method para determinar el número óptimo de clusters', fontsize=14) -plt.xlabel('Número de clusters', fontsize=12) -plt.ylabel('Distorsión (Inercia)', fontsize=12) -plt.grid(True, linestyle='--', alpha=0.7) -plt.show() \ No newline at end of file +# Ejemplo de análisis para un cluster específico +analizar_cluster(df, cluster_num=2) \ No newline at end of file diff --git a/src/comparative_analysis/training/trainingText.py b/src/comparative_analysis/training/trainingText.py new file mode 100644 index 000000000..18c5147c9 --- /dev/null +++ b/src/comparative_analysis/training/trainingText.py @@ -0,0 +1,74 @@ +import pandas as pd +import re +from sklearn.cluster import KMeans +from sklearn.metrics import silhouette_score, davies_bouldin_score +from sklearn.preprocessing import StandardScaler + +# ** Carga y limpieza de datos ** +ruta_excel = r"C:\\Users\\cdgn2\\OneDrive\\Escritorio\\Maestría\\Maestria\\Metodologias Agiles\\Proyecto\\Comparative-analysis-of-products\\src\\comparative_analysis\\database\\Adidas_etiquetado.xlsx" + +# Crear el DataFrame +df = pd.read_excel(ruta_excel, header=0) + +# Procesamiento de columnas numéricas +num_cols = { + 'Weight': '(\d+\.?\d*)', + 'Drop__heel-to-toe_differential_': '(\d+\.?\d*)' +} +for col, pattern in num_cols.items(): + df[col] = df[col].astype(str).str.extract(pattern).astype(float, errors='ignore') + df[col] = pd.to_numeric(df[col], errors='coerce') + +# Procesar precios +price_cols = ['regularPrice', 'undiscounted_price'] +for col in price_cols: + df[col] = df[col].astype(str).str.replace(r'[^0-9.,]', '', regex=True) + df[col] = df[col].str.replace(r'\\.', '', regex=True).str.replace(',', '.') + df[col] = pd.to_numeric(df[col], errors='coerce') + +# Eliminar columnas innecesarias +cols_to_drop = ['details', 'description', 'category', 'characteristics', 'width', 'Pronation_Type'] +df = df.drop(columns=cols_to_drop, errors='ignore') + +# ** Clustering y evaluación ** +ids = df['id'] +X = df.drop(columns=['id'], errors='ignore').fillna(0) + +# Codificar variables categóricas +df_dummies = pd.get_dummies(X, dummy_na=True).fillna(0) + +# Escalar los datos +scaler = StandardScaler() +X_scaled = scaler.fit_transform(df_dummies) + +# Clustering con un número específico de clusters +k = 8 +kmeans = KMeans(n_clusters=k, random_state=42) +clusters = kmeans.fit_predict(X_scaled) + +# Agregar el cluster al DataFrame original +df['cluster'] = clusters + +# Evaluar calidad del clustering +sil_score = silhouette_score(X_scaled, clusters) +db_score = davies_bouldin_score(X_scaled, clusters) +print(f"Silhouette Score: {sil_score}") +print(f"Davies-Bouldin Score: {db_score}") + +# ** Análisis de clusters ** +def analizar_cluster(df, cluster_num): + elementos_cluster = df[df['cluster'] == cluster_num] + print(f"\n=== Análisis del cluster {cluster_num} ===") + print(f"Total de elementos: {len(elementos_cluster)}") + + print(f"\nEstadísticas descriptivas principales:") + print(elementos_cluster.describe().loc[['mean', 'std', 'min', 'max']]) + + print(f"\nValores más frecuentes por columna categórica:") + cols_categoricas = elementos_cluster.select_dtypes(include=['object', 'category']).columns + for col in cols_categoricas: + print(f" - {col}: {elementos_cluster[col].mode().values[0]}") + +# Analizar los clusters 2 y 3 +analizar_cluster(df, cluster_num=2) +analizar_cluster(df, cluster_num=3) From 024a084c0a76ec955873aae49e0735b8f1f4560e Mon Sep 17 00:00:00 2001 From: CarlosKC26 Date: Thu, 12 Dec 2024 23:24:28 -0500 Subject: [PATCH 37/84] cambios a trainingtext --- src/comparative_analysis/training/trainingText.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/comparative_analysis/training/trainingText.py b/src/comparative_analysis/training/trainingText.py index 18c5147c9..cba79862e 100644 --- a/src/comparative_analysis/training/trainingText.py +++ b/src/comparative_analysis/training/trainingText.py @@ -71,4 +71,4 @@ def analizar_cluster(df, cluster_num): # Analizar los clusters 2 y 3 analizar_cluster(df, cluster_num=2) -analizar_cluster(df, cluster_num=3) +analizar_cluster(df, cluster_num=3) \ No newline at end of file From 6f39257b3fc60f5f7f108c6953ae243c1d908385 Mon Sep 17 00:00:00 2001 From: Juan Correa Date: Thu, 12 Dec 2024 23:28:27 -0500 Subject: [PATCH 38/84] model_report.md --- docs/modeling/model_report.md | 52 +++++++++++++++++++++++++++++++---- 1 file changed, 46 insertions(+), 6 deletions(-) diff --git a/docs/modeling/model_report.md b/docs/modeling/model_report.md index 407ef7baa..a8de43e36 100644 --- a/docs/modeling/model_report.md +++ b/docs/modeling/model_report.md @@ -2,24 +2,64 @@ ## Resumen Ejecutivo -En esta sección se presentará un resumen de los resultados obtenidos del modelo final. Es importante incluir los resultados de las métricas de evaluación y la interpretación de los mismos. +El modelo final se construyó con el objetivo de agrupar productos de Adidas con características similares, partiendo de datos obtenidos desde un endpoint, luego normalizados y etiquetados mediante un LLM. Para lograr esto, se aplicó un método de clustering (K-Means) a las variables numéricas y categóricas transformadas. + +En términos de métricas de evaluación, el **Silhouette Score** alcanzó un valor de **-0.10726032825390042**, mientras que el **Davies-Bouldin Score** fue de **3.7837546892816856**. Estas métricas indican que el clustering no logró una segmentación claramente separada ni cohesiva. Sin embargo, estos resultados proporcionan una línea base para mejoras futuras. ## Descripción del Problema -En esta sección se describirá el problema que se buscó resolver con el modelo final. Se debe incluir una descripción detallada del problema, el contexto en el que se desarrolla, los objetivos que se persiguen y la justificación del modelo. +La problemática abordada consiste en organizar y segmentar una amplia gama de productos Adidas en grupos homogéneos, con el fin de facilitar análisis comparativos. El objetivo es identificar patrones ocultos en las características de los productos, tales como peso, materiales, precios y tecnologías implementadas. Este tipo de segmentación es útil para la toma de decisiones estratégicas, el análisis de competitividad y el desarrollo de nuevas líneas de productos orientadas a grupos específicos de mercado. + +Justificación: La agrupación no supervisada de productos permite a la empresa comprender mejor sus catálogos, detectar nichos y mejorar la experiencia del cliente recomendando artículos similares. Además, sienta las bases para análisis más profundos, tales como análisis de correlación con el rendimiento de ventas o el perfil de cliente. ## Descripción del Modelo -En esta sección se describirá el modelo final que se desarrolló para resolver el problema planteado. Se debe incluir una descripción detallada del modelo, la metodología utilizada y las técnicas empleadas. +El modelo final se basa en el siguiente flujo de trabajo: + +1. **Obtención de datos**: Se extrajeron productos desde un endpoint con información de Adidas. +2. **Normalización y etiquetado con LLM**: Un modelo de lenguaje se utilizó para transformar las descripciones crudas del producto en variables estructuradas y etiquetadas. +3. **Limpieza y transformación**: Se procesaron valores nulos, se convirtieron precios y métricas a formato numérico flotante, y se realizaron codificaciones dummies para variables categóricas. +4. **Clustering (K-Means)**: Se aplicó K-Means con un determinado número de clusters, seleccionado tras un análisis inicial con el método del codo. La elección final de k se basó en la interpretación de las métricas y la naturaleza de los datos. + +El resultado fue un conjunto de clusters, cada uno agrupando productos con ciertas características predominantes. Por ejemplo: +- El **Cluster 2** (22 elementos) presentó productos con un peso promedio de alrededor de 569 g, un drop cercano a 9.4 mm, y precios promedio de ~401.7. Las tecnologías más comunes fueron "Mediasuela Bounce" y "Suela de caucho", con mayoría de productos para "Mujer" y "Running". +- El **Cluster 3** (60 elementos) mostró productos más ligeros (259.9 g) con drop de ~9.65 mm y precios promedio cercanos a 474. En este cluster destacó el material "Parte superior de malla" y el sistema "Dreamstrike+", mayormente orientado a calzado de "Mujer" y "Running". ## Evaluación del Modelo -En esta sección se presentará una evaluación detallada del modelo final. Se deben incluir las métricas de evaluación que se utilizaron y una interpretación detallada de los resultados. +**Métricas:** +- **Silhouette Score:** -0.10726032825390042 + Un valor negativo sugiere que la mayoría de los puntos podrían asignarse mejor a otros clusters, indicando una baja cohesión/separación. + +- **Davies-Bouldin Score:** 3.7837546892816856 + Un valor relativamente alto indica que los clusters no están bien separados ni son internamente cohesivos. + +Estas métricas reflejan la complejidad del conjunto de datos y la necesidad de refinar la representación de las variables o ajustar el número y el tipo de clustering usado. + +A nivel descriptivo, se logró observar patrones de materiales y tecnologías predominantes por cluster, lo que puede ser útil como insumo para análisis posteriores, a pesar de que la calidad cuantitativa del clustering no fue óptima. ## Conclusiones y Recomendaciones -En esta sección se presentarán las conclusiones y recomendaciones a partir de los resultados obtenidos. Se deben incluir los puntos fuertes y débiles del modelo, las limitaciones y los posibles escenarios de aplicación. +**Fortalezas:** +- Se estableció un proceso reproducible para extraer, normalizar y clústerizar datos de productos. +- Se identificaron patrones básicos en la composición de algunos clusters. + +**Debilidades:** +- Métricas bajas de calidad de clusters indican que la segmentación no es nítida. +- Alta heterogeneidad en las características, posibles variables irrelevantes o ruido dificultan la formación de grupos cohesivos. + +**Limitaciones:** +- El modelo depende en gran medida de la calidad de las etiquetas generadas por el LLM. +- No se exploraron otros algoritmos de clustering ni se realizaron exhaustivos ajustes de hiperparámetros. + +**Áreas de mejora:** +- Refinar la selección de características y la representación de datos, por ejemplo, utilizando embeddings semánticos para descripciones textuales. +- Probar técnicas de reducción de dimensionalidad (PCA, UMAP) para mejorar la separabilidad de los datos. +- Experimentar con algoritmos de clustering alternativos (DBSCAN, HDBSCAN) que podrían adaptarse mejor a la forma real de los datos. ## Referencias -En esta sección se deben incluir las referencias bibliográficas y fuentes de información utilizadas en el desarrollo del modelo. +- Scikit-learn Documentation: [https://scikit-learn.org/stable/](https://scikit-learn.org/stable/) +- Evaluación de Clustering (Silhouette y Davies-Bouldin): [https://scikit-learn.org/stable/modules/clustering.html#clustering-evaluation](https://scikit-learn.org/stable/modules/clustering.html#clustering-evaluation) +- Documentación de K-Means: [https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) +- Técnicas de reducción de dimensionalidad: [https://scikit-learn.org/stable/modules/decomposition.html](https://scikit-learn.org/stable/modules/decomposition.html) From a17b6759573ede3be423319d83cef450478c5b6b Mon Sep 17 00:00:00 2001 From: Juan Correa Date: Thu, 12 Dec 2024 23:30:41 -0500 Subject: [PATCH 39/84] eliminada carpeta notebooks --- Notebooks/cost_calculator.ipynb | 545 -------- Notebooks/preprocessing.ipynb | 1210 ----------------- Notebooks/test.ipynb | 2144 ------------------------------- 3 files changed, 3899 deletions(-) delete mode 100644 Notebooks/cost_calculator.ipynb delete mode 100644 Notebooks/preprocessing.ipynb delete mode 100644 Notebooks/test.ipynb diff --git a/Notebooks/cost_calculator.ipynb b/Notebooks/cost_calculator.ipynb deleted file mode 100644 index d829118eb..000000000 --- a/Notebooks/cost_calculator.ipynb +++ /dev/null @@ -1,545 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import requests\n", - "import re\n", - "import ollama\n", - "import random\n", - "import math\n", - "from tiktoken import get_encoding" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "labels_with_definitions = [\n", - " (\"Peso\", \"Indica la ligereza de la zapatilla, generalmente expresado en gramos. El peso puede influir en el rendimiento y la comodidad durante la carrera.\"),\n", - " (\"Material del upper (parte superior)\", \"Describe los materiales utilizados en la parte superior de la zapatilla, como malla, cuero sintético o tejidos técnicos, que afectan la transpirabilidad, flexibilidad y soporte.\"),\n", - " (\"Material de la mediasuela\", \"Se refiere a los compuestos empleados en la entresuela, como espumas EVA o tecnologías propietarias, que proporcionan amortiguación y absorción de impactos.\"),\n", - " (\"Suela exterior\", \"Detalla el tipo de goma o caucho utilizado en la suela y el diseño del patrón de tracción, aspectos que influyen en el agarre y la durabilidad en diversas superficies.\"),\n", - " (\"Sistema de amortiguación\", \"Especifica las tecnologías o materiales destinados a reducir el impacto durante la pisada, contribuyendo al confort y la protección de las articulaciones.\"),\n", - " (\"Drop (diferencial talón-punta)\", \"Indica la diferencia de altura entre el talón y la punta de la zapatilla, medida en milímetros. Un drop alto suele ofrecer mayor amortiguación en el talón, mientras que un drop bajo promueve una pisada más natural.\"),\n", - " (\"Tipo de pisada\", \"Clasifica la zapatilla según su adecuación para diferentes tipos de pisada: neutra, pronadora o supinadora. Esto es esencial para elegir un calzado que se adapte a la biomecánica del corredor.\"),\n", - " (\"Tipo de uso\", \"Define el propósito principal de la zapatilla, como entrenamiento diario, competición, trail running o uso casual, lo que orienta sobre su diseño y funcionalidades específicas.\"),\n", - " (\"Género\", \"Indica si la zapatilla está diseñada para hombres, mujeres o es un modelo unisex, considerando diferencias anatómicas y de tamaño.\"),\n", - " (\"Tallas disponibles\", \"Especifica el rango de tallas en las que se ofrece la zapatilla, asegurando que el corredor pueda encontrar un ajuste adecuado.\"),\n", - " (\"Anchura\", \"Algunas marcas ofrecen diferentes anchos (estrecho, estándar, ancho) para adaptarse a diversas morfologías del pie.\"),\n", - " (\"Precio\", \"Proporciona el costo de la zapatilla, un factor determinante en la decisión de compra.\"),\n", - " (\"Tecnologías adicionales\", \"Incluye características especiales como impermeabilidad, reflectividad, sistemas de ajuste personalizados o elementos de estabilidad que mejoran la funcionalidad del calzado.\"),\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Funciones" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def transformar_texto(texto, marca):\n", - " if texto is None or (isinstance(texto, float) and np.isnan(texto)):\n", - " return texto\n", - " \n", - " if marca.lower() == \"adidas\":\n", - " # Transformación original para adidas\n", - " if isinstance(texto, (list, np.ndarray)):\n", - " texto = \", \".join(map(str, texto))\n", - " else:\n", - " texto = str(texto)\n", - " texto = texto.strip(\"[]\")\n", - " texto = re.sub(r\",\\s*\", '} {', texto)\n", - " texto = '{' + texto + '}'\n", - " return texto\n", - " \n", - " elif marca.lower() == \"nike\":\n", - " # Transformación específica para nike\n", - " if isinstance(texto, list):\n", - " # Elimina claves con valores irrelevantes\n", - " texto_limpio = [\n", - " {k.strip(): v.strip() for k, v in d.items() if k.strip() not in ['\\xa0']}\n", - " for d in texto\n", - " if isinstance(d, dict)\n", - " ]\n", - " # Filtra elementos vacíos o irrelevantes\n", - " texto_limpio = [d for d in texto_limpio if d]\n", - " return texto_limpio\n", - " return texto # Si no es lista, regresa el texto original\n", - "\n", - " else:\n", - " raise ValueError(f\"Marca '{marca}' no reconocida. Usa 'adidas' o 'nike'.\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def obtener_respuesta_ollama(prompt):\n", - " response = ollama.chat(\n", - " model=\"llama3.2:latest\",\n", - " messages = [\n", - " {\n", - " \"role\":\"user\",\n", - " \"content\": prompt\n", - " } \n", - " ]\n", - " )\n", - " # La respuesta es un generador; concatenamos las partes\n", - " return response" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "\n", - "def generate_prompt_ollama(product, labels_with_definitions):\n", - " labels = [label for label, _ in labels_with_definitions]\n", - " prompt = f\"\"\"\n", - " You are an assistant specialized in extracting structured information from product descriptions and organizing it into tables.\n", - " Your task is to extract the following information from the product details and label it according to the provided labels: {', '.join(labels)}.\n", - " Each label has the following definition to help guide your extraction:\n", - "\n", - " {''.join([f'- {label}: {definition}\\n' for label, definition in labels_with_definitions])}\n", - "\n", - " If a label does not have a clear match in the details, complete its value with \"null\".\n", - "\n", - " Product information:\n", - " - Details: {product['details']}\n", - " - Description: {product['description']}\n", - " - Category: {product['category']}\n", - "\n", - " Provide the information in a table with columns corresponding to each label. \n", - " The table must include **two complete rows**:\n", - " 1. The first row contains the label names.\n", - " 2. The second row contains the corresponding labeled values.\n", - "\n", - " Expected response format:\n", - " | {' | '.join(labels)} |\n", - " | {' | '.join(['---'] * len(labels))} |\n", - " | value_1 | value_2 | ... |\n", - " \n", - " Example:\n", - " If the labels are \"details\", \"description\", and \"category\", and the extracted values are \n", - " \"Comfortable sneakers\", \"Made with recycled materials\", and \"Footwear\", respectively, \n", - " your response should be:\n", - "\n", - " | details | description | category |\n", - " | --- | --- | --- |\n", - " | Comfortable sneakers | Made with recycled materials | Footwear |\n", - "\n", - " Remember:\n", - " - The response must contain two complete rows.\n", - " - Only respond with the table and **do not include additional text**.\n", - "\n", - " Now, extract and structure the information for the provided product:\n", - "\n", - " | {' | '.join(labels)} |\n", - " | {' | '.join(['---'] * len(labels))} |\n", - " |\"\"\"\n", - " return prompt.strip()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Tokenizer function (using tiktoken for GPT-like models)\n", - "def count_tokens(text):\n", - " try:\n", - " encoding = get_encoding(\"cl100k_base\") # Example encoding for GPT-like models\n", - " return len(encoding.encode(text))\n", - " except Exception:\n", - " return len(text.split()) # Fallback: approximate by word count" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "# Función de simulación de Monte Carlo corregida\n", - "def monte_carlo_simulation(products, models, iterations=1000, num_products=None):\n", - " results = {}\n", - " \n", - " for model_name, model_info in models.items():\n", - " tokens_per_product = {}\n", - " costs_per_product = {}\n", - " \n", - " for _ in range(iterations):\n", - " # Muestra una fracción de los productos si se especifica\n", - " if num_products is not None and num_products < len(products):\n", - " sampled_products = random.sample(products, num_products)\n", - " else:\n", - " sampled_products = products\n", - "\n", - " for product in sampled_products:\n", - " product_id = product.get('id', id(product)) # Usamos un identificador único para cada producto\n", - " prompt = generate_prompt_ollama(product, labels_with_definitions)\n", - " tokens = count_tokens(prompt)\n", - " \n", - " # Verifica si el número de tokens excede la ventana de contexto\n", - " if tokens > model_info['context_window']*1000:\n", - " print(f\"Advertencia: El prompt excede la ventana de contexto para el modelo {model_name}\")\n", - " # Puedes manejar este caso según necesites (e.g., omitir, truncar)\n", - " continue\n", - " \n", - " cost = (tokens / 1000) * model_info['cost_in']\n", - " \n", - " # Actualiza los máximos y mínimos por producto\n", - " if product_id not in tokens_per_product:\n", - " tokens_per_product[product_id] = {'max': tokens, 'min': tokens}\n", - " costs_per_product[product_id] = {'max': cost, 'min': cost}\n", - " else:\n", - " tokens_per_product[product_id]['max'] = max(tokens_per_product[product_id]['max'], tokens)\n", - " tokens_per_product[product_id]['min'] = min(tokens_per_product[product_id]['min'], tokens)\n", - " costs_per_product[product_id]['max'] = max(costs_per_product[product_id]['max'], cost)\n", - " costs_per_product[product_id]['min'] = min(costs_per_product[product_id]['min'], cost)\n", - " \n", - " # Después de todas las iteraciones, obtenemos los máximos y mínimos globales\n", - " max_tokens = max([data['max'] for data in tokens_per_product.values()], default=0)\n", - " min_tokens = min([data['min'] for data in tokens_per_product.values()], default=0)\n", - " max_cost = max([data['max'] for data in costs_per_product.values()], default=0)\n", - " min_cost = min([data['min'] for data in costs_per_product.values()], default=0)\n", - " \n", - " results[model_name] = {\n", - " 'max_tokens': max_tokens,\n", - " 'min_tokens': min_tokens,\n", - " 'max_cost': max_cost,\n", - " 'min_cost': min_cost,\n", - " }\n", - " return results" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Lectura de datos" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "200\n" - ] - } - ], - "source": [ - "url =\"https://scraping-firestore-178159629911.us-central1.run.app//v1/scraping/\"\n", - "\n", - "response = requests.get(url)\n", - "\n", - "if response.status_code == 200:\n", - " data = response.json()\n", - " print(response.status_code)\n", - "else:\n", - " print(f'Error: {response.status_code}')\n", - "\n", - "df_raw = pd.DataFrame(data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Código" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "df_adidas = df_raw[df_raw['store'] == 'adidas']" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Usuario\\AppData\\Local\\Temp\\ipykernel_3872\\354877031.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df_adidas['details_transformado'] = df_adidas['details'].apply(lambda x: transformar_texto(x, 'adidas'))\n" - ] - } - ], - "source": [ - "# Lista de descripciones de productos\n", - "df_adidas['details_transformado'] = df_adidas['details'].apply(lambda x: transformar_texto(x, 'adidas'))" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Crear un diccionario con las llaves deseadas desde todo el DataFrame\n", - "productos = [\n", - " {\n", - " \"details\": row['details_transformado'],\n", - " \"description\": row['description'],\n", - " \"category\": row['category']\n", - " }\n", - " for _, row in df_adidas.iterrows() # Recorre todo el DataFrame\n", - " if row['details_transformado'] != '{}' # Filtra donde los detalles no estén vacíos\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Model configurations\n", - "models = {\n", - " \"jamba15large\": {'cost_in': 0.002, 'cost_out': 0.008, 'accuracy': 64, 'context_window': 256},\n", - " \"claude35sonnet\": {'cost_in': 0.003, 'cost_out': 0.015, 'accuracy': 80, 'context_window': 200},\n", - " \"llama3170b\": {'cost_in': 0.00099, 'cost_out': 0.00099, 'accuracy': 66, 'context_window': 128},\n", - " \"mistrallarge\": {'cost_in': 0.004, 'cost_out': 0.012, 'accuracy': 56, 'context_window': 33},\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "# Ahora puedes especificar el número de productos a procesar (por ejemplo, 10)\n", - "num_products_to_process = 100 # O usa len(productos) para procesar todos\n", - "\n", - "# Ejecutar la simulación de Monte Carlo\n", - "simulation_results = monte_carlo_simulation(productos, models, iterations=1000, num_products=num_products_to_process)\n", - "\n", - "# Mostrar los resultados\n", - "import pandas as pd\n", - "df_results = pd.DataFrame(simulation_results).transpose().reset_index()\n", - "df_results.columns = ['Model', 'Max Tokens', 'Min Tokens', 'Max Cost', 'Min Cost']\n" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Model Max Tokens Min Tokens Max Cost Min Cost\n", - "0 jamba15large 1787.0 1303.0 0.003574 0.002606\n", - "1 claude35sonnet 1787.0 1303.0 0.005361 0.003909\n", - "2 llama3170b 1787.0 1303.0 0.001769 0.001290\n", - "3 mistrallarge 1787.0 1303.0 0.007148 0.005212\n" - ] - } - ], - "source": [ - "print(df_results)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(519, 14)" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_adidas.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Crear un diccionario con las llaves deseadas\n", - "productos = [\n", - " {\n", - " \"details\": row['details_transformado'],\n", - " \"description\": row['description'],\n", - " \"category\": row['category']\n", - " }\n", - " for _, row in df_adidas[:10].iterrows() \n", - " if row['details_transformado'] != '{}'\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'details': '{Horma clásica} {Parte superior sintética} {Forro interno textil} {Varillas ENERGYRODS 2.0 que limitan la pérdida de energía} {Amortiguación Lightstrike Pro} {Peso: 183 g (talla CO 37} {5)} {Caída mediasuela: 6 mm (talón: 38 mm / antepié: 32 mm)} {La parte superior contiene al menos un 50 % de material reciclado} {Color del artículo: Pink Spark / Aurora Met. / Sandy Pink} {Número de artículo: ID3612}', 'description': \"Los Adizero Adios Pro 3 son la máxima expresión de los productos Adizero Racing. Fueron diseñados con y para atletas para lograr hazañas increíbles. Estos tenis de running adidas están diseñados para optimizar la eficiencia del running. Nuestras varillas ENERGYRODS 2.0 de carbono ofrecen ligereza y firmeza para pasos ágiles y eficientes. La tecnología LIGHTSTRIKE PRO ultraliviana amortigua cada paso con las tres capas de espuma resistente que te ayudan a mantener la energía a largo plazo. Todo sobre una delgada suela de caucho textil para un agarre extraordinario en condiciones mojadas y secas. Celebramos nuestra más reciente victoria, los tenis Best Speed 2024 by Women's Health.\", 'category': 'Mujer • Running'}\n", - "Respuesta del modelo:\n", - "| Peso | Material del upper (parte superior) | Material de la mediasuela | Suela exterior | Sistema de amortiguación | Drop (diferencial talón-punta) | Tipo de pisada | Tipo de uso | Género | Tallas disponibles | Anchura | Precio | Tecnologías adicionales |\n", - "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", - "| 183 g | Parte superior sintética | Forro interno textil | Caucho textil | Amortiguación Lightstrike Pro | 5 | Neutral | Competición | Mujer | 37-45 CO | null | 250 | Varillas ENERGYRODS 2.0, tecnología LIGHTSTRIKE PRO, caucho textil, material reciclado |\n", - "\n", - "------------------------\n", - "\n", - "{'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Exterior de tejido adidas Primeknit} {Ajuste suave en el talón} {Sistema Linear Energy Push} {Mediasuela BOOST} {Peso: 289 g (Talla CO 37)} {Suela Stretchweb con caucho Continental™ Better Rubber} {El hilo del exterior contiene al menos un 50% de Parley Ocean Plastic y un 50% de poliéster reciclado} {Color del artículo: Core Black / Core Black / Cloud White} {Número de artículo: GX5591}', 'description': 'Hemos analizado 1.200.000 pisadas para que Ultraboost evolucione y mejore su ajuste para adaptarse 360° al pie femenino. Y aún hay más. Hemos rediseñado la suela de caucho. Probamos cientos de prototipos. Hasta que comprobamos las mejoras en su rendimiento. ¿El resultado? Un 4 % más de energía que los Ultraboost 21 para mujer. El ajuste en el área del talón del exterior adidas PRIMEKNIT se diseñó para evitar que el talón se deslice y se formen ampollas. Estarás corriendo sobre una mediasuela BOOST con tecnología Linear Energy Push. El exterior de este calzado está confeccionado con hilo que contiene un 50% de Parley Ocean Plastic, un material que ha sido recuperado de residuos plásticos recogidos en islas, playas, comunidades costeras y costas evitando que contaminen nuestros océanos.', 'category': 'Mujer • Running'}\n", - "Respuesta del modelo:\n", - "| Peso | Material del upper (parte superior) | Material de la mediasuela | Suela exterior | Sistema de amortiguación | Drop (diferencial talón-punta) | Tipo de pisada | Tipo de uso | Género | Tallas disponibles | Anchura | Precio | Tecnologías adicionales |\n", - "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", - "| 289 g (Talla CO 37) | adidas Primeknit | BOOST | Stretchweb con caucho Continental™ Better Rubber | Linear Energy Push | null | Neutra | Competición | Mujer | 37-48 | null | €149.95 | Contiene al menos un 50% de Parley Ocean Plastic y un 50% de poliéster reciclado\n", - "\n", - "------------------------\n", - "\n", - "{'details': '{Ajuste clásico} {Cierre de cordones} {Parte superior de malla técnica} {Amortiguación Lightstrike Pro} {Varillas ENERGYRODS que limitan la pérdida de energía} {Talón Slinglaunch para un ajuste seguro} {Peso: 166 g (Talla COL 36 1/2)} {Caída mediasuela: 6 mm (talón: 32 mm / antepié: 26 mm)} {Suela de caucho Continental™} {Contiene al menos un 20 % de material reciclado} {Color del artículo: Green Spark / Aurora Met. / Lucid Lemon} {Número de artículo: IG8206}', 'description': 'Haz tus mejores 10k con nuestros nuevos tenis de running Adizero, diseñados exclusivamente para darte máxima velocidad. Los Adizero Takumi Sen 10 han sido diseñados con dos capas de amortiguación LIGHTSTRIKE PRO, combinadas con las varillas ENERGYRODS para una mayor estabilidad e impulso en tu carrera. Supera tu marca personal y llega a la meta más rápido que nunca.', 'category': 'Mujer • Running'}\n", - "Respuesta del modelo:\n", - "| Peso | Material del upper (parte superior) | Material de la mediasuela | Suela exterior | Sistema de amortiguación | Drop (diferencial talón-punta) | Tipo de pisada | Tipo de uso | Género | Tallas disponibles | Anchura | Precio | Tecnologías adicionales |\n", - "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", - "| 166 g (Talla COL 36 1/2) | Parte superior de malla técnica | Varillas ENERGYRODS que limitan la pérdida de energía | Suela de caucho Continental™ | Amortiguación Lightstrike Pro | 6 mm (talón: 32 mm / antepié: 26 mm) | Neutra | Competición | Mujer | 36 1/2-42 3/4 | Estrecho, estándar, ancho | null | null | Contiene al menos un 20 % de material reciclado |\n", - "\n", - "------------------------\n", - "\n", - "{'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Parte superior de malla} {Amortiguación Lightstrike Pro} {Varillas ENERGYRODS que limitan la pérdida de energía} {Talón Slinglaunch} {Peso: 200 gramos (talla CO 40)} {Caída mediasuela: 6 mm (talón: 33 mm / antepié: 27 mm} {Suela de caucho Continental™ Rubber} {Contienen al menos un 20% de material reciclado} {Color del artículo: Green Spark / Aurora Met. / Lucid Lemon} {Número de artículo: IG3134}', 'description': 'Haz tus mejores 10k con nuestros nuevos tenis de running Adizero, diseñados exclusivamente para darte máxima velocidad. Los Adizero Takumi Sen 10 han sido diseñados con dos capas de amortiguación LIGHTSTRIKE PRO, combinadas con las varillas ENERGYRODS 2.0 para una mayor estabilidad e impulso en tu carrera. Supera tu marca personal y llega a la meta más rápido que nunca.', 'category': 'Hombre • Running'}\n", - "Respuesta del modelo:\n", - "| Peso | Material del upper (parte superior) | Material de la mediasuela | Suela exterior | Sistema de amortiguación | Drop (diferencial talón-punta) | Tipo de pisada | Tipo de uso | Género | Tallas disponibles | Anchura | Precio | Tecnologías adicionales |\n", - "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", - "| 200 gramos | Parte superior de malla | Amortiguación Lightstrike Pro | Suela de caucho Continental™ Rubber | Varillas ENERGYRODS que limitan la pérdida de energía | null | neutra | competición | hombre | CO 40-50, EU 41-45, US 6.5-7.5 | estrecho | 90€ | Contienen al menos un 20% de material reciclado |\n", - "\n", - "------------------------\n", - "\n", - "{'details': '{Horma clásica} {Sistema de amarre de cordones} {Parte superior textil} {Forro interno textil} {Mediasuela Cloudfoam} {Suela de TPU} {Peso: 319 g (talla CO 40)} {Caída mediasuela: 6 mm (talón 35 mm / antepié 29 mm)} {Color del artículo: Halo Silver / Carbon / Core Black} {Número de artículo: ID8754}', 'description': 'Cada carrera es un viaje de descubrimiento. Ponte estos tenis de running adidas y libera tu potencial. La mediasuela con amortiguación Cloudfoam te ofrece una pisada más cómoda mientras aumentas tu resistencia. La parte superior textil es resistente al desgaste y te ofrece soporte desde tu primera vuelta hasta tu primera carrera de 5K.', 'category': 'Hombre • Running'}\n", - "Respuesta del modelo:\n", - "| Peso | Material del upper (parte superior) | Material de la mediasuela | Suela exterior | Sistema de amortiguación | Drop (diferencial talón-punta) | Tipo de pisada | Tipo de uso | Género | Tallas disponibles | Anchura | Precio | Tecnologías adicionales |\n", - "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", - "| 319 g (talla CO 40) | Parte superior textil | Mediasuela Cloudfoam | Suela de TPU | Reducción del impacto | 6 mm (talón 35 mm / antepié 29 mm) | Neutra | Entrenamiento diario | Hombre | Tallas CO 36-46 | null | 319 g | null |\n", - "\n", - "------------------------\n", - "\n", - "{'details': '{Si este artículo es personalizado} {no aplica en nuestra política de cambios y devoluciones.} {Sistema de amarre de cordones para un ajuste inmejorable} {Producto hecho parcialmente con Malla Técnica Reciclada} {Diseño liviano} {Amortiguación Lightstrike y Lightstrike Pro} {Forro interno textil} {Caída mediasuela: 8} {5 mm (talón: 33 mm / antepié: 24} {5 mm)} {Suela de caucho} {El exterior contiene al menos un 50% de material reciclado} {Color del artículo: Ivory / Iron Metallic / Spark} {Número de artículo: IG3341}', 'description': 'Cuando se trata de alcanzar tus metas, cada segundo cuenta. Ya sea para entrenar o para competir, un rendimiento excelente requiere de prendas de alta tecnología optimizadas para la velocidad. Presentamos la nueva colección de tenis de running livianos que te ayudan a superar tus límites sin distracciones.\\n\\nLos tenis de running Adizero SL seleccionan lo mejor de nuestra franquicia Adizero que rompe récords mundiales. La mediasuela de EVA LIGHTSTRIKE liviana ofrece resiliencia a la mediasuela para que puedas concentrarte en el próximo paso, mientras que el exterior está hecho de una malla técnica suave que está zonificada en áreas clave. El talón acolchado y la lengüeta brindan una comodidad óptima junto con el antepié Adizero. La suela premium está diseñada para proporcionar tracción.', 'category': 'Mujer • Running'}\n", - "Respuesta del modelo:\n", - "| Peso | Material del upper (parte superior) | Material de la mediasuela | Suela exterior | Sistema de amortiguación | Drop (diferencial talón-punta) | Tipo de pisada | Tipo de uso | Género | Tallas disponibles | Anchura | Precio | Tecnologías adicionales |\n", - "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", - "| null | Malla técnica suave zonificada en áreas clave | EVA LIGHTSTRIKE liviana | Suela de caucho | Amortiguación Lightstrike y Lightstrike Pro | 5 mm | Neutra | Competición | Mujer • Running | Estándar, Ancha | $120-$150 | Al menos un 50% de material reciclado |\n", - "\n", - "------------------------\n", - "\n", - "{'details': '{Horma clásica} {Sistema de amarre de cordones} {Parte superior de malla} {Forro interno textil} {Plantilla OrthoLite®} {Mediasuela Cloudfoam} {Peso: 304 g (talla CO 40)} {Caída mediasuela: 10 mm (talón: 33 mm / antepié: 23 mm)} {Suela Adiwear} {Color del artículo: Cloud White / Core Black / Better Scarlet} {Número de artículo: IE8818}', 'description': 'Tanto en la pista como en la cinta de correr, alcanza todas tus metas con estos tenis de running adidas. Incorporan una mediasuela con amortiguación Cloudfoam que te ofrece una pisada más cómoda y suave. La parte superior de malla transpirable y la suela Adiwear de gran resistencia al desgaste la convierten en una silueta perfecta para llevar durante todo el día.', 'category': 'Hombre • Running'}\n", - "Respuesta del modelo:\n", - "| Peso | Material del upper (parte superior) | Material de la mediasuela | Suela exterior | Sistema de amortiguación | Drop (diferencial talón-punta) | Tipo de pisada | Tipo de uso | Género | Tallas disponibles | Anchura | Precio | Tecnologías adicionales |\n", - "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", - "| 304 g (talla CO 40) | Parte superior de malla | Mediasuela Cloudfoam | Suela Adiwear | Reducción del impacto con Plantilla OrthoLite® y mediasuela Cloudfoam | 10 mm (talón: 33 mm / antepié: 23 mm) | Neutra | Entrenamiento diario, competición | Hombre • Running | Talla CO 40 - CO 12 | Estrecho, estándar, ancho | €109.90 | Impermeabilidad |\n", - "\n", - "------------------------\n", - "\n", - "{'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Parte superior de malla} {Forro interno textil} {mediasuela Dreamstrike+} {Suela Adiwear} {Peso (talla CO 37): 213 gramos} {Caída mediasuela: 10 mm (talón: 34 mm / 24 mm)} {Contienen al menos un 20% de material reciclado y renovable} {Color del artículo: Almost Yellow / Zero Metalic / Pink Spark} {Número de artículo: IE1072}', 'description': 'Avanza hacia tus metas de running con estos tenis adidas. La mediasuela Dreamstrike+ está diseñada para absorber el impacto. La amortiguación adicional en el antepié ofrece comodidad durante toda tu carrera. El exterior de malla transpirable maximiza el flujo de aire para mantener tus pies frescos, y la suela Adiwear resistente ofrece tracción. \\n\\nAl elegir materiales reciclados, podemos reutilizar materiales que ya han sido creados, lo que ayuda a reducir los residuos. La elección de materiales renovables nos ayudará a eliminar nuestra dependencia de recursos finitos. Nuestros productos hechos con una mezcla de materiales reciclados y renovables contienen al menos un 20% de este material.', 'category': 'Mujer • Running'}\n", - "Respuesta del modelo:\n", - "| Peso | Material del upper (parte superior) | Material de la mediasuela | Suela exterior | Sistema de amortiguación | Drop (diferencial talón-punta) | Tipo de pisada | Tipo de uso | Género | Tallas disponibles | Anchura | Precio | Tecnologías adicionales |\n", - "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", - "| 213 gramos | Parte superior de malla | mediasuela Dreamstrike+ | Suela Adiwear | La mediasuela está diseñada para absorber el impacto. El antepié ofrece comodidad durante toda tu carrera. | 10 mm (talón: 34 mm / 24 mm) | Neutra | Competición, trail running | Mujer • Running | CO 37-48 | Estrecho, estándar, ancho | €59.99 | Contienen al menos un 20% de material reciclado y renovable |\n", - "\n", - "------------------------\n", - "\n", - "{'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Exterior de malla con cuello acolchado} {Forro interno textil} {Estabilizador de talón moldeado Fitcounter} {Mediasuela Bounce} {Suela sintética} {El exterior contiene al menos un 50 % de material reciclado} {Color del artículo: Shadow Violet / Legend Ink / Wonder Clay} {Número de artículo: IG0334}', 'description': 'Estos tenis fueron diseñados para darte la amortiguación y la respuesta que necesitas cuando estás en la pista o en los senderos. Sin importar si vas a hacer tu caminata diaria o una carrera corta, la mediasuela Bounce es flexible y receptiva con cada paso. Hecho con una serie de materiales reciclados, el exterior incorpora al menos un 50 % de contenido reciclado. Este producto representa solo una de nuestras soluciones para acabar con los residuos plásticos.', 'category': 'Mujer • Running'}\n", - "Respuesta del modelo:\n", - "| Peso | Material del upper (parte superior) | Material de la mediasuela | Suela exterior | Sistema de amortiguación | Drop (diferencial talón-punta) | Tipo de pisada | Tipo de uso | Género | Tallas disponibles | Anchura | Precio | Tecnologías adicionales |\n", - "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", - "| null | {Ajuste clásico} {Sistema de amarre de cordones} {Exterior de malla con cuello acolchado} | {Forro interno textil} {Mediasuela Bounce} | Suela sintética | Estabilizador de talón moldeado Fitcounter | null | Neutra | Entrenamiento diario | Mujer • Running | 7-14 | Estrecho, estándar, ancho | $80-$100 | {El exterior contiene al menos un 50 % de material reciclado} |\n", - "\n", - "------------------------\n", - "\n" - ] - } - ], - "source": [ - "dfs = []\n", - "for producto in productos:\n", - " prompt = generate_prompt_ollama(producto, labels_with_definitions)\n", - " respuesta = obtener_respuesta_ollama(prompt)\n", - " print(producto)\n", - " print(\"Respuesta del modelo:\")\n", - " print(respuesta[\"message\"][\"content\"])\n", - " print(\"\\n------------------------\\n\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Notebooks/preprocessing.ipynb b/Notebooks/preprocessing.ipynb deleted file mode 100644 index a1edf0a85..000000000 --- a/Notebooks/preprocessing.ipynb +++ /dev/null @@ -1,1210 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "from llama_models.llama3.reference_impl.generation import Llama\n", - "from llama_models.llama3.api.datatypes import UserMessage, SystemMessage\n", - "import os\n", - "import sys\n", - "from llama_models.llama3.api.datatypes import (\n", - " UserMessage,\n", - " SystemMessage,\n", - " CompletionMessage,\n", - " StopReason,\n", - ")\n", - "from llama_models.llama3.reference_impl.generation import Llama\n", - "import pandas as pd\n", - "from io import StringIO\n", - "import numpy as np\n", - "import re\n", - "import requests\n", - "import ollama\n", - "import random\n", - "from tiktoken import get_encoding\n", - "import time\n", - "import httpx\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'labels_with_definitions = [\\n (\"Peso\", \"Indica la ligereza de la zapatilla, generalmente expresado en gramos. El peso puede influir en el rendimiento y la comodidad durante la carrera.\"),\\n (\"Material del upper (parte superior)\", \"Describe los materiales utilizados en la parte superior de la zapatilla, como malla, cuero sintético o tejidos técnicos, que afectan la transpirabilidad, flexibilidad y soporte.\"),\\n (\"Material de la mediasuela\", \"Se refiere a los compuestos empleados en la entresuela, como espumas EVA o tecnologías propietarias, que proporcionan amortiguación y absorción de impactos.\"),\\n (\"Suela exterior\", \"Detalla el tipo de goma o caucho utilizado en la suela y el diseño del patrón de tracción, aspectos que influyen en el agarre y la durabilidad en diversas superficies.\"),\\n (\"Sistema de amortiguación\", \"Especifica las tecnologías o materiales destinados a reducir el impacto durante la pisada, contribuyendo al confort y la protección de las articulaciones.\"),\\n (\"Drop (diferencial talón-punta)\", \"Indica la diferencia de altura entre el talón y la punta de la zapatilla, medida en milímetros. Un drop alto suele ofrecer mayor amortiguación en el talón, mientras que un drop bajo promueve una pisada más natural.\"),\\n (\"Tipo de pisada\", \"Clasifica la zapatilla según su adecuación para diferentes tipos de pisada: neutra, pronadora o supinadora. Esto es esencial para elegir un calzado que se adapte a la biomecánica del corredor.\"),\\n (\"Tipo de uso\", \"Define el propósito principal de la zapatilla, como entrenamiento diario, competición, trail running o uso casual, lo que orienta sobre su diseño y funcionalidades específicas.\"),\\n (\"Género\", \"Indica si la zapatilla está diseñada para hombres, mujeres o es un modelo unisex, considerando diferencias anatómicas y de tamaño.\"),\\n (\"Tallas disponibles\", \"Especifica el rango de tallas en las que se ofrece la zapatilla, asegurando que el corredor pueda encontrar un ajuste adecuado.\"),\\n (\"Anchura\", \"Algunas marcas ofrecen diferentes anchos (estrecho, estándar, ancho) para adaptarse a diversas morfologías del pie.\"),\\n (\"Precio\", \"Proporciona el costo de la zapatilla, un factor determinante en la decisión de compra.\"),\\n (\"Tecnologías adicionales\", \"Incluye características especiales como impermeabilidad, reflectividad, sistemas de ajuste personalizados o elementos de estabilidad que mejoran la funcionalidad del calzado.\"),\\n]'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "'''labels_with_definitions = [\n", - " (\"Peso\", \"Indica la ligereza de la zapatilla, generalmente expresado en gramos. El peso puede influir en el rendimiento y la comodidad durante la carrera.\"),\n", - " (\"Material del upper (parte superior)\", \"Describe los materiales utilizados en la parte superior de la zapatilla, como malla, cuero sintético o tejidos técnicos, que afectan la transpirabilidad, flexibilidad y soporte.\"),\n", - " (\"Material de la mediasuela\", \"Se refiere a los compuestos empleados en la entresuela, como espumas EVA o tecnologías propietarias, que proporcionan amortiguación y absorción de impactos.\"),\n", - " (\"Suela exterior\", \"Detalla el tipo de goma o caucho utilizado en la suela y el diseño del patrón de tracción, aspectos que influyen en el agarre y la durabilidad en diversas superficies.\"),\n", - " (\"Sistema de amortiguación\", \"Especifica las tecnologías o materiales destinados a reducir el impacto durante la pisada, contribuyendo al confort y la protección de las articulaciones.\"),\n", - " (\"Drop (diferencial talón-punta)\", \"Indica la diferencia de altura entre el talón y la punta de la zapatilla, medida en milímetros. Un drop alto suele ofrecer mayor amortiguación en el talón, mientras que un drop bajo promueve una pisada más natural.\"),\n", - " (\"Tipo de pisada\", \"Clasifica la zapatilla según su adecuación para diferentes tipos de pisada: neutra, pronadora o supinadora. Esto es esencial para elegir un calzado que se adapte a la biomecánica del corredor.\"),\n", - " (\"Tipo de uso\", \"Define el propósito principal de la zapatilla, como entrenamiento diario, competición, trail running o uso casual, lo que orienta sobre su diseño y funcionalidades específicas.\"),\n", - " (\"Género\", \"Indica si la zapatilla está diseñada para hombres, mujeres o es un modelo unisex, considerando diferencias anatómicas y de tamaño.\"),\n", - " (\"Tallas disponibles\", \"Especifica el rango de tallas en las que se ofrece la zapatilla, asegurando que el corredor pueda encontrar un ajuste adecuado.\"),\n", - " (\"Anchura\", \"Algunas marcas ofrecen diferentes anchos (estrecho, estándar, ancho) para adaptarse a diversas morfologías del pie.\"),\n", - " (\"Precio\", \"Proporciona el costo de la zapatilla, un factor determinante en la decisión de compra.\"),\n", - " (\"Tecnologías adicionales\", \"Incluye características especiales como impermeabilidad, reflectividad, sistemas de ajuste personalizados o elementos de estabilidad que mejoran la funcionalidad del calzado.\"),\n", - "]'''" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "labels_with_definitions = [\n", - " (\"Weight\", \"Indicates the lightness of the shoe, usually expressed in grams. Weight can influence performance and comfort during running.\"),\n", - " (\"Upper Material\", \"Describes the materials used in the shoe's upper part, such as mesh, synthetic leather, or technical fabrics, which affect breathability, flexibility, and support.\"),\n", - " (\"Midsole Material\", \"Refers to the compounds used in the midsole, such as EVA foams or proprietary technologies, which provide cushioning and shock absorption.\"),\n", - " (\"Outsole\", \"Details the type of rubber or material used in the sole and the traction pattern design, which influence grip and durability on various surfaces.\"),\n", - " (\"Cushioning System\", \"Specifies the technologies or materials aimed at reducing impact during strides, contributing to comfort and joint protection.\"),\n", - " (\"Drop (heel-to-toe differential)\", \"Indicates the height difference between the heel and the toe of the shoe, measured in millimeters. A higher drop typically offers more heel cushioning, while a lower drop promotes a more natural stride.\"),\n", - " (\"Pronation Type\", \"Classifies the shoe according to its suitability for different pronation types: neutral, overpronation, or supination. This is essential for choosing footwear that matches the runner's biomechanics.\"),\n", - " (\"Usage Type\", \"Defines the primary purpose of the shoe, such as daily training, racing, trail running, or casual use, guiding its specific design and features.\"),\n", - " (\"Gender\", \"Indicates whether the shoe is designed for men, women, or is a unisex model, considering anatomical and sizing differences.\"),\n", - " (\"Available Sizes\", \"Specifies the range of sizes in which the shoe is offered, ensuring the runner can find a suitable fit.\"),\n", - " (\"Width\", \"Some brands offer different widths (narrow, standard, wide) to accommodate various foot shapes.\"),\n", - " (\"Additional Technologies\", \"Includes special features such as waterproofing, reflectivity, customized fit systems, or stability elements that enhance the shoe's functionality.\"),\n", - "]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Funciones" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "def remove_html_tags(text):\n", - " if text:\n", - " text = re.sub(r\"<.*?>\", \"\", text) # Remueve etiquetas HTML\n", - " text = text.replace(' ', ' ')\n", - " text = text.strip()\n", - " return text\n", - "\n", - "def transformar_texto(texto, marca):\n", - " if texto is None or (isinstance(texto, float) and np.isnan(texto)):\n", - " return texto\n", - "\n", - " if marca.lower() == \"adidas\":\n", - " # Transformación original para adidas\n", - " if isinstance(texto, (list, np.ndarray)):\n", - " texto = \", \".join(map(str, texto))\n", - " else:\n", - " texto = str(texto)\n", - " texto = texto.strip(\"[]\")\n", - " texto = re.sub(r\",\\s*\", '} {', texto)\n", - " texto = '{' + texto + '}'\n", - " return texto\n", - "\n", - " elif marca.lower() == \"nike\":\n", - " if isinstance(texto, list):\n", - " # Si es lista de diccionarios (details)\n", - " if all(isinstance(item, dict) for item in texto):\n", - " texto_limpio = []\n", - " for d in texto:\n", - " if isinstance(d, dict):\n", - " d_limpio = {}\n", - " for k, v in d.items():\n", - " k_clean = k.strip() if k else \"\"\n", - " v_clean = remove_html_tags(v) if isinstance(v, str) else v\n", - " # Si clave y valor no están vacíos\n", - " if k_clean and v_clean:\n", - " # Aquí verificamos si la info es irrelevante: \n", - " # Caso en el que el valor es igual a la clave (sin html), \n", - " # Ejemplo: { \"Datos del producto\": \"Datos del producto\" }\n", - " # No aporta información adicional, así que no la incluimos.\n", - " if v_clean != k_clean:\n", - " d_limpio[k_clean] = v_clean\n", - "\n", - " if d_limpio:\n", - " texto_limpio.append(d_limpio)\n", - "\n", - " # Si después de limpiar no quedó nada (porque todo era texto irrelevante),\n", - " # devolvemos lista vacía\n", - " return texto_limpio\n", - "\n", - " else:\n", - " # Si es lista de strings (characteristics)\n", - " seen = set()\n", - " texto_limpio = []\n", - " for s in texto:\n", - " if s and isinstance(s, str):\n", - " s_clean = remove_html_tags(s)\n", - " if s_clean and s_clean not in seen:\n", - " seen.add(s_clean)\n", - " texto_limpio.append(s_clean)\n", - " return texto_limpio\n", - "\n", - " else:\n", - " # Si no es lista (solo string)\n", - " if isinstance(texto, str):\n", - " return remove_html_tags(texto)\n", - " return texto\n", - "\n", - " else:\n", - " raise ValueError(f\"Marca '{marca}' no reconocida. Usa 'adidas' o 'nike'.\")\n", - "\n", - "\n", - "def obtener_respuesta_ollama(prompt, max_retries=3, delay=5):\n", - " attempt = 0\n", - " while attempt < max_retries:\n", - " try:\n", - " response = ollama.chat(\n", - " model=\"llama3.2:latest\",\n", - " messages = [\n", - " {\n", - " \"role\":\"user\",\n", - " \"content\": prompt\n", - " } \n", - " ]\n", - " )\n", - " # Si llega aquí, la respuesta se obtuvo exitosamente\n", - " return response\n", - " except httpx.HTTPError as e:\n", - " attempt += 1\n", - " print(f\"Error en la solicitud: {e}. Reintento {attempt} de {max_retries} en {delay} segundos...\")\n", - " time.sleep(delay)\n", - " \n", - " # Si sale del while sin retornar, significa que falló en todos los intentos\n", - " raise RuntimeError(f\"No se pudo obtener respuesta de Ollama después de {max_retries} intentos.\")\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "def generate_prompt_ollama(product, labels_with_definitions):\n", - " labels = [label for label, _ in labels_with_definitions]\n", - " prompt = f\"\"\"\n", - " You are an assistant specialized in extracting structured information from product descriptions and organizing it into tables.\n", - " Your task is to extract the following information from the product details and label it according to the provided labels: {', '.join(labels)}.\n", - " Each label has the following definition to help guide your extraction:\n", - "\n", - " {''.join([f'- {label}: {definition}\\n' for label, definition in labels_with_definitions])}\n", - "\n", - " If a label does not have a clear match in the details, complete its value with \"null\".\n", - "\n", - " Product information:\n", - " - Details: {product['details']}\n", - " - Description: {product['description']}\n", - " - Category: {product['category']}\n", - " - Characteristics: {product['characteristics']}\n", - "\n", - " Provide the information in a table with columns corresponding to each label. \n", - " The table must include **two complete rows**:\n", - " 1. The first row contains the label names.\n", - " 2. The second row contains the corresponding labeled values.\n", - "\n", - " Expected response format:\n", - " | {' | '.join(labels)} |\n", - " | {' | '.join(['---'] * len(labels))} |\n", - " | value_1 | value_2 | ... |\n", - " \n", - " Example:\n", - " If the labels are \"details\", \"description\", and \"category\", and the extracted values are \n", - " \"Comfortable sneakers\", \"Made with recycled materials\", and \"Footwear\", respectively, \n", - " your response should be:\n", - "\n", - " | details | description | category |\n", - " | --- | --- | --- |\n", - " | Comfortable sneakers | Made with recycled materials | Footwear |\n", - "\n", - " Remember:\n", - " - The response must contain two complete rows.\n", - " - Only respond with the table and **do not include additional text**.\n", - "\n", - " Now, extract and structure the information for the provided product:\n", - "\n", - " | {' | '.join(labels)} |\n", - " | {' | '.join(['---'] * len(labels))} |\n", - " |\"\"\"\n", - " return prompt.strip()\n", - "\n", - "# Tokenizer function (using tiktoken for GPT-like models)\n", - "def count_tokens(text):\n", - " try:\n", - " encoding = get_encoding(\"cl100k_base\") # Example encoding for GPT-like models\n", - " return len(encoding.encode(text))\n", - " except Exception:\n", - " return len(text.split()) # Fallback: approximate by word count\n", - "# Función de simulación de Monte Carlo corregida\n", - "def monte_carlo_simulation(products, models, iterations=1000, num_products=None):\n", - " results = {}\n", - " \n", - " for model_name, model_info in models.items():\n", - " tokens_per_product = {}\n", - " costs_per_product = {}\n", - " \n", - " for _ in range(iterations):\n", - " # Muestra una fracción de los productos si se especifica\n", - " if num_products is not None and num_products < len(products):\n", - " sampled_products = random.sample(products, num_products)\n", - " else:\n", - " sampled_products = products\n", - "\n", - " for product in sampled_products:\n", - " product_id = product.get('id', id(product)) # Usamos un identificador único para cada producto\n", - " prompt = generate_prompt_ollama(product, labels_with_definitions)\n", - " tokens = count_tokens(prompt)\n", - " \n", - " # Verifica si el número de tokens excede la ventana de contexto\n", - " if tokens > model_info['context_window']*1000:\n", - " print(f\"Advertencia: El prompt excede la ventana de contexto para el modelo {model_name}\")\n", - " # Puedes manejar este caso según necesites (e.g., omitir, truncar)\n", - " continue\n", - " \n", - " cost = (tokens / 1000) * model_info['cost_in']\n", - " \n", - " # Actualiza los máximos y mínimos por producto\n", - " if product_id not in tokens_per_product:\n", - " tokens_per_product[product_id] = {'max': tokens, 'min': tokens}\n", - " costs_per_product[product_id] = {'max': cost, 'min': cost}\n", - " else:\n", - " tokens_per_product[product_id]['max'] = max(tokens_per_product[product_id]['max'], tokens)\n", - " tokens_per_product[product_id]['min'] = min(tokens_per_product[product_id]['min'], tokens)\n", - " costs_per_product[product_id]['max'] = max(costs_per_product[product_id]['max'], cost)\n", - " costs_per_product[product_id]['min'] = min(costs_per_product[product_id]['min'], cost)\n", - " \n", - " # Después de todas las iteraciones, obtenemos los máximos y mínimos globales\n", - " max_tokens = max([data['max'] for data in tokens_per_product.values()], default=0)\n", - " min_tokens = min([data['min'] for data in tokens_per_product.values()], default=0)\n", - " max_cost = max([data['max'] for data in costs_per_product.values()], default=0)\n", - " min_cost = min([data['min'] for data in costs_per_product.values()], default=0)\n", - " \n", - " results[model_name] = {\n", - " 'max_tokens': max_tokens,\n", - " 'min_tokens': min_tokens,\n", - " 'max_cost': max_cost,\n", - " 'min_cost': min_cost,\n", - " }\n", - " return results" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": {}, - "outputs": [], - "source": [ - "def procesar_respuesta(respuesta_texto, etiquetas):\n", - " try:\n", - " # Buscar el inicio de la tabla\n", - " inicio_tabla = respuesta_texto.find('|')\n", - " if inicio_tabla == -1:\n", - " print(\"No se encontró una tabla en la respuesta.\")\n", - " return None\n", - "\n", - " # Extraer la tabla desde el primer '|'\n", - " tabla_texto = respuesta_texto[inicio_tabla:].strip()\n", - " # Extraer solo las líneas que contengan '|'\n", - " lineas = tabla_texto.split('\\n')\n", - " lineas_tabla = []\n", - " for linea in lineas:\n", - " if '|' in linea:\n", - " # Eliminar los '|' iniciales y finales y espacios extra\n", - " linea = linea.strip().strip('|').strip()\n", - " lineas_tabla.append(linea)\n", - " else:\n", - " # Detenerse si la línea no contiene '|'\n", - " break\n", - "\n", - " if not lineas_tabla:\n", - " print(\"No se encontraron líneas válidas de tabla.\")\n", - " return None\n", - "\n", - " # Verificar si hay separadores '---'\n", - " tiene_separador = any(\n", - " all(c.strip('-') == '' for c in fila.split('|')) \n", - " for fila in lineas_tabla\n", - " )\n", - "\n", - " # Caso 1: Hay separadores (modo normal de Markdown)\n", - " if tiene_separador:\n", - " tabla_completa = '\\n'.join(lineas_tabla)\n", - " df = pd.read_csv(StringIO(tabla_completa), sep='|', engine='python', skipinitialspace=True)\n", - " # Limpiar columnas y datos\n", - " df.columns = [col.strip() for col in df.columns]\n", - " for col in df.columns:\n", - " if df[col].dtype == object:\n", - " df[col] = df[col].str.strip()\n", - " df = df.reset_index(drop=True)\n", - " return df\n", - "\n", - " # Caso 2: No hay separadores\n", - " # Verificar la cantidad de líneas\n", - " if len(lineas_tabla) == 2:\n", - " # La primera línea son los nombres de columnas, la segunda línea datos\n", - " columnas = [c.strip() for c in lineas_tabla[0].split('|')]\n", - " datos = [c.strip() for c in lineas_tabla[1].split('|')]\n", - " df = pd.DataFrame([datos], columns=columnas)\n", - " for col in df.columns:\n", - " if df[col].dtype == object:\n", - " df[col] = df[col].str.strip()\n", - " df = df.reset_index(drop=True)\n", - " return df\n", - " elif len(lineas_tabla) == 1:\n", - " # Solo una línea de datos, usar etiquetas como nombres de columnas\n", - " datos = [c.strip() for c in lineas_tabla[0].split('|')]\n", - " # Ajustar si el número de columnas difiere del número de etiquetas\n", - " if len(etiquetas) != len(datos):\n", - " print(\"Advertencia: El número de etiquetas no coincide con las columnas detectadas. Se ajustará al mínimo.\")\n", - " col_names = etiquetas[:len(datos)]\n", - " else:\n", - " col_names = etiquetas\n", - "\n", - " df = pd.DataFrame([datos], columns=col_names)\n", - " for col in df.columns:\n", - " if df[col].dtype == object:\n", - " df[col] = df[col].str.strip()\n", - " df = df.reset_index(drop=True)\n", - " return df\n", - " else:\n", - " # Hay más de 2 líneas pero sin separador. Asumimos que la primera es columnas y el resto datos.\n", - " columnas = [c.strip() for c in lineas_tabla[0].split('|')]\n", - " filas_datos = [ [x.strip() for x in fila.split('|')] for fila in lineas_tabla[1:] ]\n", - " df = pd.DataFrame(filas_datos, columns=columnas)\n", - " for col in df.columns:\n", - " if df[col].dtype == object:\n", - " df[col] = df[col].str.strip()\n", - " df = df.reset_index(drop=True)\n", - " return df\n", - "\n", - " except Exception as e:\n", - " print(f\"Error al procesar la tabla: {e}\")\n", - " return None\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Lectura de datos" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "200\n" - ] - } - ], - "source": [ - "url =\"https://scraping-firestore-178159629911.us-central1.run.app//v1/scraping/\"\n", - "\n", - "response = requests.get(url)\n", - "\n", - "if response.status_code == 200:\n", - " data = response.json()\n", - " print(response.status_code)\n", - "else:\n", - " print(f'Error: {response.status_code}')\n", - "\n", - "df_raw = pd.DataFrame(data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Clasificacion" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "df_adidas = df_raw[df_raw['store'] == 'adidas']" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Usuario\\AppData\\Local\\Temp\\ipykernel_2256\\354877031.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df_adidas['details_transformado'] = df_adidas['details'].apply(lambda x: transformar_texto(x, 'adidas'))\n" - ] - } - ], - "source": [ - "# Lista de descripciones de productos\n", - "df_adidas['details_transformado'] = df_adidas['details'].apply(lambda x: transformar_texto(x, 'adidas'))" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [], - "source": [ - "# Crear un diccionario con las llaves deseadas desde todo el DataFrame\n", - "productos = [\n", - " {\n", - " \"id\": row['id'],\n", - " \"regularPrice\" : row[\"regularPrice\"],\n", - " \"undiscounted_price\": row[\"undiscounted_price\"],\n", - " \"details\": row['details_transformado'],\n", - " \"description\": row['description'],\n", - " \"category\": row['category'],\n", - " \"characteristics\": row['characteristics']\n", - " }\n", - " for _, row in df_adidas.iterrows() # Recorre todo el DataFrame\n", - " if row['details_transformado'] != '{}' # Filtra donde los detalles no estén vacíos\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'id': '0AqheRhKT2lhm7puBVCF', 'regularPrice': '$799.950', 'undiscounted_price': 'NA', 'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Exterior de tejido adidas Primeknit} {Ajuste suave en el talón} {Sistema Linear Energy Push} {Mediasuela BOOST} {Peso: 289 g (Talla CO 37)} {Suela Stretchweb con caucho Continental™ Better Rubber} {El hilo del exterior contiene al menos un 50% de Parley Ocean Plastic y un 50% de poliéster reciclado} {Color del artículo: Core Black / Core Black / Cloud White} {Número de artículo: GX5591}', 'description': 'Hemos analizado 1.200.000 pisadas para que Ultraboost evolucione y mejore su ajuste para adaptarse 360° al pie femenino. Y aún hay más. Hemos rediseñado la suela de caucho. Probamos cientos de prototipos. Hasta que comprobamos las mejoras en su rendimiento. ¿El resultado? Un 4 % más de energía que los Ultraboost 21 para mujer. El ajuste en el área del talón del exterior adidas PRIMEKNIT se diseñó para evitar que el talón se deslice y se formen ampollas. Estarás corriendo sobre una mediasuela BOOST con tecnología Linear Energy Push. El exterior de este calzado está confeccionado con hilo que contiene un 50% de Parley Ocean Plastic, un material que ha sido recuperado de residuos plásticos recogidos en islas, playas, comunidades costeras y costas evitando que contaminen nuestros océanos.', 'category': 'Mujer • Running', 'characteristics': nan}\n", - "Respuesta del modelo:\n", - "| Weight | Upper Material | Midsole Material | Outsole | Cushioning System | Drop (heel-to-toe differential) | Pronation Type | Usage Type | Gender | Available Sizes | Width | Additional Technologies |\n", - "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", - "| 289 g | adidas Primeknit | Mediasuela BOOST | Suela Stretchweb con caucho Continental Better Rubber | Sistema Linear Energy Push | null | neutral | mujer | 37-47 | standard | contains Parley Ocean Plastic and recycled polyester |\n", - "\n", - "------------------------\n", - "\n", - "{'id': '0IgYTzUHkE7zIdcVyFCK', 'regularPrice': '$1.049.950', 'undiscounted_price': '$629.970', 'details': '{Ajuste clásico} {Cierre de cordones} {Parte superior de malla técnica} {Amortiguación Lightstrike Pro} {Varillas ENERGYRODS que limitan la pérdida de energía} {Talón Slinglaunch para un ajuste seguro} {Peso: 166 g (Talla COL 36 1/2)} {Caída mediasuela: 6 mm (talón: 32 mm / antepié: 26 mm)} {Suela de caucho Continental™} {Contiene al menos un 20 % de material reciclado} {Color del artículo: Green Spark / Aurora Met. / Lucid Lemon} {Número de artículo: IG8206}', 'description': 'Haz tus mejores 10k con nuestros nuevos tenis de running Adizero, diseñados exclusivamente para darte máxima velocidad. Los Adizero Takumi Sen 10 han sido diseñados con dos capas de amortiguación LIGHTSTRIKE PRO, combinadas con las varillas ENERGYRODS para una mayor estabilidad e impulso en tu carrera. Supera tu marca personal y llega a la meta más rápido que nunca.', 'category': 'Mujer • Running', 'characteristics': nan}\n", - "Respuesta del modelo:\n", - "| Weight | Upper Material | Midsole Material | Outsole | Cushioning System | Drop (heel-to-toe differential) | Pronation Type | Usage Type | Gender | Available Sizes | Width | Additional Technologies |\n", - "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", - "| 166 g | Parte superior de malla técnica | Amortiguación Lightstrike Pro | Suela de caucho Continental™ | Amortiguación Lightstrike Pro | 6 mm (talón: 32 mm / antepié: 26 mm) | Neutral | Daily training, racing, trail running, casual use | Mujer | Talla COL 36 1/2, COL 37, COL 38, COL 39.5 | Narrow, Standard, Wide | Contiene al menos un 20 % de material reciclado |\n", - "\n", - "------------------------\n", - "\n", - "{'id': '0MU8aKCnCUZv2r9aLD67', 'regularPrice': '$1.049.950', 'undiscounted_price': '$734.965', 'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Parte superior de malla} {Amortiguación Lightstrike Pro} {Varillas ENERGYRODS que limitan la pérdida de energía} {Talón Slinglaunch} {Peso: 200 gramos (talla CO 40)} {Caída mediasuela: 6 mm (talón: 33 mm / antepié: 27 mm} {Suela de caucho Continental™ Rubber} {Contienen al menos un 20% de material reciclado} {Color del artículo: Green Spark / Aurora Met. / Lucid Lemon} {Número de artículo: IG3134}', 'description': 'Haz tus mejores 10k con nuestros nuevos tenis de running Adizero, diseñados exclusivamente para darte máxima velocidad. Los Adizero Takumi Sen 10 han sido diseñados con dos capas de amortiguación LIGHTSTRIKE PRO, combinadas con las varillas ENERGYRODS 2.0 para una mayor estabilidad e impulso en tu carrera. Supera tu marca personal y llega a la meta más rápido que nunca.', 'category': 'Hombre • Running', 'characteristics': nan}\n", - "Respuesta del modelo:\n", - "| Weight | Upper Material | Midsole Material | Outsole | Cushioning System | Drop (heel-to-toe differential) | Pronation Type | Usage Type | Gender | Available Sizes | Width | Additional Technologies |\n", - "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", - "| 200 gramos | Parte superior de malla | Amortiguación Lightstrike Pro | Suela de caucho Continental™ Rubber | Amortiguación Lightstrike Pro | 6 mm (talón: 33 mm / antepié: 27 mm) | null | Trail running | Hombre | CO 40, CO 42-46, CO 48 | null | Contienen al menos un 20% de material reciclado |\n", - "| null | Sistema de amarre de cordones | ENERGYRODS 2.0 | null | Lightstrike Pro | null | null | null | null | null | null | Supera tu marca personal y llega a la meta más rápido que nunca\n", - "\n", - "------------------------\n", - "\n", - "{'id': '0Q6DNSlvsjBzy3AQeY2y', 'regularPrice': '$279.950', 'undiscounted_price': 'NA', 'details': '{Horma clásica} {Sistema de amarre de cordones} {Parte superior textil} {Forro interno textil} {Mediasuela Cloudfoam} {Suela de TPU} {Peso: 319 g (talla CO 40)} {Caída mediasuela: 6 mm (talón 35 mm / antepié 29 mm)} {Color del artículo: Halo Silver / Carbon / Core Black} {Número de artículo: ID8754}', 'description': 'Cada carrera es un viaje de descubrimiento. Ponte estos tenis de running adidas y libera tu potencial. La mediasuela con amortiguación Cloudfoam te ofrece una pisada más cómoda mientras aumentas tu resistencia. La parte superior textil es resistente al desgaste y te ofrece soporte desde tu primera vuelta hasta tu primera carrera de 5K.', 'category': 'Hombre • Running', 'characteristics': nan}\n", - "Respuesta del modelo:\n", - "| Weight | Upper Material | Midsole Material | Outsole | Cushioning System | Drop (heel-to-toe differential) | Pronation Type | Usage Type | Gender | Available Sizes | Width | Additional Technologies |\n", - "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", - "| 319 g | Parte superior textil | Mediasuela Cloudfoam | Suela de TPU | null | 6 mm (talón 35 mm / antepié 29 mm) | Neutral | Daily training | Men | CO 40, CO 41-45, CO 46-50 | Narrow, Standard, Wide | null |\n", - "\n", - "------------------------\n", - "\n", - "{'id': '0SF7zveew5mzdUJWZKyz', 'regularPrice': '$699.950', 'undiscounted_price': '$559.960', 'details': '{Si este artículo es personalizado} {no aplica en nuestra política de cambios y devoluciones.} {Sistema de amarre de cordones para un ajuste inmejorable} {Producto hecho parcialmente con Malla Técnica Reciclada} {Diseño liviano} {Amortiguación Lightstrike y Lightstrike Pro} {Forro interno textil} {Caída mediasuela: 8} {5 mm (talón: 33 mm / antepié: 24} {5 mm)} {Suela de caucho} {El exterior contiene al menos un 50% de material reciclado} {Color del artículo: Ivory / Iron Metallic / Spark} {Número de artículo: IG3341}', 'description': 'Cuando se trata de alcanzar tus metas, cada segundo cuenta. Ya sea para entrenar o para competir, un rendimiento excelente requiere de prendas de alta tecnología optimizadas para la velocidad. Presentamos la nueva colección de tenis de running livianos que te ayudan a superar tus límites sin distracciones.\\n\\nLos tenis de running Adizero SL seleccionan lo mejor de nuestra franquicia Adizero que rompe récords mundiales. La mediasuela de EVA LIGHTSTRIKE liviana ofrece resiliencia a la mediasuela para que puedas concentrarte en el próximo paso, mientras que el exterior está hecho de una malla técnica suave que está zonificada en áreas clave. El talón acolchado y la lengüeta brindan una comodidad óptima junto con el antepié Adizero. La suela premium está diseñada para proporcionar tracción.', 'category': 'Mujer • Running', 'characteristics': nan}\n", - "Respuesta del modelo:\n", - "| Weight | Upper Material | Midsole Material | Outsole | Cushioning System | Drop (heel-to-toe differential) | Pronation Type | Usage Type | Gender | Available Sizes | Width | Additional Technologies |\n", - "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", - "| null | {Si este artículo es personalizado} / {Sistema de amarre de cordones para un ajuste inmejorable} / {Producto hecho parcialmente con Malla Técnica Reciclada} | {Amortiguación Lightstrike y Lightstrike Pro} | {Suela de caucho} | null | 8 mm | neutral | Trail running, casual use | Women, Men | 5-12 | null | El exterior contiene al menos un 50% de material reciclado\n", - "\n", - "------------------------\n", - "\n", - "{'id': '0iPjAsLy8yEYvGgiCAzo', 'regularPrice': '$299.950', 'undiscounted_price': 'NA', 'details': '{Horma clásica} {Sistema de amarre de cordones} {Parte superior de malla} {Forro interno textil} {Plantilla OrthoLite®} {Mediasuela Cloudfoam} {Peso: 304 g (talla CO 40)} {Caída mediasuela: 10 mm (talón: 33 mm / antepié: 23 mm)} {Suela Adiwear} {Color del artículo: Cloud White / Core Black / Better Scarlet} {Número de artículo: IE8818}', 'description': 'Tanto en la pista como en la cinta de correr, alcanza todas tus metas con estos tenis de running adidas. Incorporan una mediasuela con amortiguación Cloudfoam que te ofrece una pisada más cómoda y suave. La parte superior de malla transpirable y la suela Adiwear de gran resistencia al desgaste la convierten en una silueta perfecta para llevar durante todo el día.', 'category': 'Hombre • Running', 'characteristics': nan}\n", - "Respuesta del modelo:\n", - "| Weight | Upper Material | Midsole Material | Outsole | Cushioning System | Drop (heel-to-toe differential) | Pronation Type | Usage Type | Gender | Available Sizes | Width | Additional Technologies |\n", - "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", - "| 304 g | Parte superior de malla, Forro interno textil | Plantilla OrthoLite® | Suela Adiwear | Mediasuela Cloudfoam | 10 mm (talón: 33 mm / antepié: 23 mm) | null | Running | Hombre • Hombre | CO 40 - IE 8818 | null | Waterproofing, Reflectivity\n", - "\n", - "------------------------\n", - "\n", - "{'id': '0n2Tyl34QdVdkCKgAdcT', 'regularPrice': '$649.950', 'undiscounted_price': 'NA', 'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Parte superior de malla} {Forro interno textil} {mediasuela Dreamstrike+} {Suela Adiwear} {Peso (talla CO 37): 213 gramos} {Caída mediasuela: 10 mm (talón: 34 mm / 24 mm)} {Contienen al menos un 20% de material reciclado y renovable} {Color del artículo: Almost Yellow / Zero Metalic / Pink Spark} {Número de artículo: IE1072}', 'description': 'Avanza hacia tus metas de running con estos tenis adidas. La mediasuela Dreamstrike+ está diseñada para absorber el impacto. La amortiguación adicional en el antepié ofrece comodidad durante toda tu carrera. El exterior de malla transpirable maximiza el flujo de aire para mantener tus pies frescos, y la suela Adiwear resistente ofrece tracción. \\n\\nAl elegir materiales reciclados, podemos reutilizar materiales que ya han sido creados, lo que ayuda a reducir los residuos. La elección de materiales renovables nos ayudará a eliminar nuestra dependencia de recursos finitos. Nuestros productos hechos con una mezcla de materiales reciclados y renovables contienen al menos un 20% de este material.', 'category': 'Mujer • Running', 'characteristics': nan}\n", - "Respuesta del modelo:\n", - "| Weight | Upper Material | Midsole Material | Outsole | Cushioning System | Drop (heel-to-toe differential) | Pronation Type | Usage Type | Gender | Available Sizes | Width | Additional Technologies |\n", - "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", - "| 213 gramos | Parte superior de malla, Forro interno textil, Sistema de amarre de cordones | mediasuela Dreamstrike+ | Suela Adiwear | mediasuela Dreamstrike+ | 10 mm (talón: 34 mm / 24 mm) | Neutral | Running | Mujer | CO 37 | null | Contienen al menos un 20% de material reciclado y renovable |\n", - "\n", - "------------------------\n", - "\n", - "{'id': '0rf6HEvBi5R4FunIhToR', 'regularPrice': '$449.950', 'undiscounted_price': 'NA', 'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Exterior de malla con cuello acolchado} {Forro interno textil} {Estabilizador de talón moldeado Fitcounter} {Mediasuela Bounce} {Suela sintética} {El exterior contiene al menos un 50 % de material reciclado} {Color del artículo: Shadow Violet / Legend Ink / Wonder Clay} {Número de artículo: IG0334}', 'description': 'Estos tenis fueron diseñados para darte la amortiguación y la respuesta que necesitas cuando estás en la pista o en los senderos. Sin importar si vas a hacer tu caminata diaria o una carrera corta, la mediasuela Bounce es flexible y receptiva con cada paso. Hecho con una serie de materiales reciclados, el exterior incorpora al menos un 50 % de contenido reciclado. Este producto representa solo una de nuestras soluciones para acabar con los residuos plásticos.', 'category': 'Mujer • Running', 'characteristics': nan}\n", - "Respuesta del modelo:\n", - "| Weight | Upper Material | Midsole Material | Outsole | Cushioning System | Drop (heel-to-toe differential) | Pronation Type | Usage Type | Gender | Available Sizes | Width | Additional Technologies |\n", - "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", - "| null | Exterior de malla con cuello acolchado / {Sistema de amarre de cordones} / Technical fabrics | Mediasuela Bounce | Suela sintética | mediasuela Bounce | null | neutral | Mujer • Running | null | null | Legend Ink / Wonder Clay | IG0334 |\n", - "\n", - "------------------------\n", - "\n", - "{'id': '0zbwfhJUSB9viwWPAnoE', 'regularPrice': '$799.950', 'undiscounted_price': '$639.960', 'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Exterior de malla} {Tejido suave y cómodo} {Mediasuela REPETITOR y REPETITOR+ de doble densidad} {Estructura interna de soporte} {Peso: 334 gramos (talla CO 40} {5)} {Caída mediasuela: 6 mm (talón: 38 mm / antepié: 32 mm)} {Suela de caucho} {El hilo de la parte superior contiene al menos un 50 % de Parley Ocean Plastic y un 50 % de poliéster reciclado} {Color del artículo: Lucid Lemon / Carbon / Wonder Blue} {Número de artículo: FZ5622}', 'description': 'Correr largas distancias es mucho más que ir de la A a la B. Se trata de la brisa en tu espalda, el ritmo de tu pisada, la libertad de la carretera. Los tenis Adistar 2.0 están diseñados con precisión para movimientos continuos, asegurando que cada paso se adapte sin problemas al siguiente, kilómetro tras kilómetro. La mediasuela REPETITOR y REPETITOR+ de doble densidad combina una espuma ligera para una amortiguación flexible con un compuesto firme que abraza el talón. Una estructura interna sujeta el pie para un soporte óptimo que nunca se detiene.', 'category': 'Hombre • Running', 'characteristics': nan}\n", - "Respuesta del modelo:\n", - "| Weight | Upper Material | Midsole Material | Outsole | Cushioning System | Drop (heel-to-toe differential) | Pronation Type | Usage Type | Gender | Available Sizes | Width | Additional Technologies |\n", - "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", - "| 334 gramos | Exterior de malla, Tejido suave y cómodo | Mediasuela REPETITOR y REPETITOR+ de doble densidad | Suela de caucho | mediasuela REPETITOR y REPETITOR+ | 6 mm (talón: 38 mm / antepié: 32 mm) | Neutral | Daily training | Men | CO 40-50, CO 42-44, CO 46 | Narrow, Standard, Wide | Waterproofing with recycled materials\n", - "\n", - "------------------------\n", - "\n" - ] - } - ], - "source": [ - "dfs = []\n", - "for producto in productos[1:10]:\n", - " prompt = generate_prompt_ollama(producto, labels_with_definitions)\n", - " respuesta = obtener_respuesta_ollama(prompt)\n", - " print(producto)\n", - " print(\"Respuesta del modelo:\")\n", - " print(respuesta[\"message\"][\"content\"])\n", - " print(\"\\n------------------------\\n\")\n", - " df = procesar_respuesta(respuesta[\"message\"][\"content\"], labels_with_definitions)\n", - " if df is not None:\n", - " attribute_columns = df.columns[:-3]\n", - " df['id'] = producto['id']\n", - " df['regularPrice'] = producto['regularPrice']\n", - " df['undiscounted_price'] = producto['undiscounted_price']\n", - " df[\"details\"] = producto['details']\n", - " df[\"description\"] = producto['description']\n", - " df[\"category\"] = producto['category']\n", - " df[\"characteristics\"] = producto['characteristics']\n", - " df = df[~df[attribute_columns].eq('---').all(axis=1)]\n", - " df = df.dropna(how='all')\n", - " dfs.append(df)\n", - " \n", - " else:\n", - " print(\"No se pudo extraer la tabla.\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [], - "source": [ - "if dfs:\n", - " df_total = pd.concat(dfs, ignore_index=True)\n", - "else:\n", - " print(\"No se pudo crear el DataFrame total.\")" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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WeightUpper MaterialMidsole MaterialOutsoleCushioning SystemDrop (heel-to-toe differential)Pronation TypeUsage TypeGenderAvailable SizesWidthAdditional TechnologiesidregularPriceundiscounted_pricedetailsdescriptioncategorycharacteristics
0289 gadidas PrimeknitMediasuela BOOSTSuela Stretchweb con caucho Continental Better...Sistema Linear Energy Pushnullneutralmujer37-47standardcontains Parley Ocean Plastic and recycled pol...None0AqheRhKT2lhm7puBVCF$799.950NA{Ajuste clásico} {Sistema de amarre de cordone...Hemos analizado 1.200.000 pisadas para que Ult...Mujer • RunningNaN
1166 gParte superior de malla técnicaAmortiguación Lightstrike ProSuela de caucho Continental™Amortiguación Lightstrike Pro6 mm (talón: 32 mm / antepié: 26 mm)NeutralDaily training, racing, trail running, casual useMujerTalla COL 36 1/2, COL 37, COL 38, COL 39.5Narrow, Standard, WideContiene al menos un 20 % de material reciclado0IgYTzUHkE7zIdcVyFCK$1.049.950$629.970{Ajuste clásico} {Cierre de cordones} {Parte s...Haz tus mejores 10k con nuestros nuevos tenis ...Mujer • RunningNaN
2200 gramosParte superior de mallaAmortiguación Lightstrike ProSuela de caucho Continental™ RubberAmortiguación Lightstrike Pro6 mm (talón: 33 mm / antepié: 27 mm)nullTrail runningHombreCO 40, CO 42-46, CO 48nullContienen al menos un 20% de material reciclado0MU8aKCnCUZv2r9aLD67$1.049.950$734.965{Ajuste clásico} {Sistema de amarre de cordone...Haz tus mejores 10k con nuestros nuevos tenis ...Hombre • RunningNaN
3nullSistema de amarre de cordonesENERGYRODS 2.0nullLightstrike PronullnullnullnullnullnullSupera tu marca personal y llega a la meta más...0MU8aKCnCUZv2r9aLD67$1.049.950$734.965{Ajuste clásico} {Sistema de amarre de cordone...Haz tus mejores 10k con nuestros nuevos tenis ...Hombre • RunningNaN
4319 gParte superior textilMediasuela CloudfoamSuela de TPUnull6 mm (talón 35 mm / antepié 29 mm)NeutralDaily trainingMenCO 40, CO 41-45, CO 46-50Narrow, Standard, Widenull0Q6DNSlvsjBzy3AQeY2y$279.950NA{Horma clásica} {Sistema de amarre de cordones...Cada carrera es un viaje de descubrimiento. Po...Hombre • RunningNaN
5null{Si este artículo es personalizado} / {Sistema...{Amortiguación Lightstrike y Lightstrike Pro}{Suela de caucho}null8 mmneutralTrail running, casual useWomen, Men5-12nullEl exterior contiene al menos un 50% de materi...0SF7zveew5mzdUJWZKyz$699.950$559.960{Si este artículo es personalizado} {no aplica...Cuando se trata de alcanzar tus metas, cada se...Mujer • RunningNaN
6304 gParte superior de malla, Forro interno textilPlantilla OrthoLite®Suela AdiwearMediasuela Cloudfoam10 mm (talón: 33 mm / antepié: 23 mm)nullRunningHombre • HombreCO 40 - IE 8818nullWaterproofing, Reflectivity0iPjAsLy8yEYvGgiCAzo$299.950NA{Horma clásica} {Sistema de amarre de cordones...Tanto en la pista como en la cinta de correr, ...Hombre • RunningNaN
7213 gramosParte superior de malla, Forro interno textil,...mediasuela Dreamstrike+Suela Adiwearmediasuela Dreamstrike+10 mm (talón: 34 mm / 24 mm)NeutralRunningMujerCO 37nullContienen al menos un 20% de material reciclad...0n2Tyl34QdVdkCKgAdcT$649.950NA{Ajuste clásico} {Sistema de amarre de cordone...Avanza hacia tus metas de running con estos te...Mujer • RunningNaN
8nullExterior de malla con cuello acolchado / {Sist...Mediasuela BounceSuela sintéticamediasuela BouncenullneutralMujer • RunningnullnullLegend Ink / Wonder ClayIG03340rf6HEvBi5R4FunIhToR$449.950NA{Ajuste clásico} {Sistema de amarre de cordone...Estos tenis fueron diseñados para darte la amo...Mujer • RunningNaN
9334 gramosExterior de malla, Tejido suave y cómodoMediasuela REPETITOR y REPETITOR+ de doble den...Suela de cauchomediasuela REPETITOR y REPETITOR+6 mm (talón: 38 mm / antepié: 32 mm)NeutralDaily trainingMenCO 40-50, CO 42-44, CO 46Narrow, Standard, WideWaterproofing with recycled materials0zbwfhJUSB9viwWPAnoE$799.950$639.960{Ajuste clásico} {Sistema de amarre de cordone...Correr largas distancias es mucho más que ir d...Hombre • RunningNaN
\n", - "
" - ], - "text/plain": [ - " Weight Upper Material \\\n", - "0 289 g adidas Primeknit \n", - "1 166 g Parte superior de malla técnica \n", - "2 200 gramos Parte superior de malla \n", - "3 null Sistema de amarre de cordones \n", - "4 319 g Parte superior textil \n", - "5 null {Si este artículo es personalizado} / {Sistema... \n", - "6 304 g Parte superior de malla, Forro interno textil \n", - "7 213 gramos Parte superior de malla, Forro interno textil,... \n", - "8 null Exterior de malla con cuello acolchado / {Sist... \n", - "9 334 gramos Exterior de malla, Tejido suave y cómodo \n", - "\n", - " Midsole Material \\\n", - "0 Mediasuela BOOST \n", - "1 Amortiguación Lightstrike Pro \n", - "2 Amortiguación Lightstrike Pro \n", - "3 ENERGYRODS 2.0 \n", - "4 Mediasuela Cloudfoam \n", - "5 {Amortiguación Lightstrike y Lightstrike Pro} \n", - "6 Plantilla OrthoLite® \n", - "7 mediasuela Dreamstrike+ \n", - "8 Mediasuela Bounce \n", - "9 Mediasuela REPETITOR y REPETITOR+ de doble den... \n", - "\n", - " Outsole \\\n", - "0 Suela Stretchweb con caucho Continental Better... \n", - "1 Suela de caucho Continental™ \n", - "2 Suela de caucho Continental™ Rubber \n", - "3 null \n", - "4 Suela de TPU \n", - "5 {Suela de caucho} \n", - "6 Suela Adiwear \n", - "7 Suela Adiwear \n", - "8 Suela sintética \n", - "9 Suela de caucho \n", - "\n", - " Cushioning System Drop (heel-to-toe differential) \\\n", - "0 Sistema Linear Energy Push null \n", - "1 Amortiguación Lightstrike Pro 6 mm (talón: 32 mm / antepié: 26 mm) \n", - "2 Amortiguación Lightstrike Pro 6 mm (talón: 33 mm / antepié: 27 mm) \n", - "3 Lightstrike Pro null \n", - "4 null 6 mm (talón 35 mm / antepié 29 mm) \n", - "5 null 8 mm \n", - "6 Mediasuela Cloudfoam 10 mm (talón: 33 mm / antepié: 23 mm) \n", - "7 mediasuela Dreamstrike+ 10 mm (talón: 34 mm / 24 mm) \n", - "8 mediasuela Bounce null \n", - "9 mediasuela REPETITOR y REPETITOR+ 6 mm (talón: 38 mm / antepié: 32 mm) \n", - "\n", - " Pronation Type Usage Type \\\n", - "0 neutral mujer \n", - "1 Neutral Daily training, racing, trail running, casual use \n", - "2 null Trail running \n", - "3 null null \n", - "4 Neutral Daily training \n", - "5 neutral Trail running, casual use \n", - "6 null Running \n", - "7 Neutral Running \n", - "8 neutral Mujer • Running \n", - "9 Neutral Daily training \n", - "\n", - " Gender Available Sizes \\\n", - "0 37-47 standard \n", - "1 Mujer Talla COL 36 1/2, COL 37, COL 38, COL 39.5 \n", - "2 Hombre CO 40, CO 42-46, CO 48 \n", - "3 null null \n", - "4 Men CO 40, CO 41-45, CO 46-50 \n", - "5 Women, Men 5-12 \n", - "6 Hombre • Hombre CO 40 - IE 8818 \n", - "7 Mujer CO 37 \n", - "8 null null \n", - "9 Men CO 40-50, CO 42-44, CO 46 \n", - "\n", - " Width \\\n", - "0 contains Parley Ocean Plastic and recycled pol... \n", - "1 Narrow, Standard, Wide \n", - "2 null \n", - "3 null \n", - "4 Narrow, Standard, Wide \n", - "5 null \n", - "6 null \n", - "7 null \n", - "8 Legend Ink / Wonder Clay \n", - "9 Narrow, Standard, Wide \n", - "\n", - " Additional Technologies id \\\n", - "0 None 0AqheRhKT2lhm7puBVCF \n", - "1 Contiene al menos un 20 % de material reciclado 0IgYTzUHkE7zIdcVyFCK \n", - "2 Contienen al menos un 20% de material reciclado 0MU8aKCnCUZv2r9aLD67 \n", - "3 Supera tu marca personal y llega a la meta más... 0MU8aKCnCUZv2r9aLD67 \n", - "4 null 0Q6DNSlvsjBzy3AQeY2y \n", - "5 El exterior contiene al menos un 50% de materi... 0SF7zveew5mzdUJWZKyz \n", - "6 Waterproofing, Reflectivity 0iPjAsLy8yEYvGgiCAzo \n", - "7 Contienen al menos un 20% de material reciclad... 0n2Tyl34QdVdkCKgAdcT \n", - "8 IG0334 0rf6HEvBi5R4FunIhToR \n", - "9 Waterproofing with recycled materials 0zbwfhJUSB9viwWPAnoE \n", - "\n", - " regularPrice undiscounted_price \\\n", - "0 $799.950 NA \n", - "1 $1.049.950 $629.970 \n", - "2 $1.049.950 $734.965 \n", - "3 $1.049.950 $734.965 \n", - "4 $279.950 NA \n", - "5 $699.950 $559.960 \n", - "6 $299.950 NA \n", - "7 $649.950 NA \n", - "8 $449.950 NA \n", - "9 $799.950 $639.960 \n", - "\n", - " details \\\n", - "0 {Ajuste clásico} {Sistema de amarre de cordone... \n", - "1 {Ajuste clásico} {Cierre de cordones} {Parte s... \n", - "2 {Ajuste clásico} {Sistema de amarre de cordone... \n", - "3 {Ajuste clásico} {Sistema de amarre de cordone... \n", - "4 {Horma clásica} {Sistema de amarre de cordones... \n", - "5 {Si este artículo es personalizado} {no aplica... \n", - "6 {Horma clásica} {Sistema de amarre de cordones... \n", - "7 {Ajuste clásico} {Sistema de amarre de cordone... \n", - "8 {Ajuste clásico} {Sistema de amarre de cordone... \n", - "9 {Ajuste clásico} {Sistema de amarre de cordone... \n", - "\n", - " description category \\\n", - "0 Hemos analizado 1.200.000 pisadas para que Ult... Mujer • Running \n", - "1 Haz tus mejores 10k con nuestros nuevos tenis ... Mujer • Running \n", - "2 Haz tus mejores 10k con nuestros nuevos tenis ... Hombre • Running \n", - "3 Haz tus mejores 10k con nuestros nuevos tenis ... Hombre • Running \n", - "4 Cada carrera es un viaje de descubrimiento. Po... Hombre • Running \n", - "5 Cuando se trata de alcanzar tus metas, cada se... Mujer • Running \n", - "6 Tanto en la pista como en la cinta de correr, ... Hombre • Running \n", - "7 Avanza hacia tus metas de running con estos te... Mujer • Running \n", - "8 Estos tenis fueron diseñados para darte la amo... Mujer • Running \n", - "9 Correr largas distancias es mucho más que ir d... Hombre • Running \n", - "\n", - " characteristics \n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", - "5 NaN \n", - "6 NaN \n", - "7 NaN \n", - "8 NaN \n", - "9 NaN " - ] - }, - "execution_count": 103, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_total" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [], - "source": [ - "output_path = \"../src/comparative_analysis/database/adidas_etiquetado_llama31.xlsx\"\n", - "df_total.to_excel(output_path, index=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Preprocesamiento Nike" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "df_nike = df_raw[df_raw['store'] == 'nike'] \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Usuario\\AppData\\Local\\Temp\\ipykernel_2256\\2520405701.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df_nike['details_transformado'] = df_nike['details'].apply(lambda x: transformar_texto(x, 'nike'))\n", - "C:\\Users\\Usuario\\AppData\\Local\\Temp\\ipykernel_2256\\2520405701.py:3: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df_nike['characteristics_transformado'] = df_nike['characteristics'].apply(lambda x: transformar_texto(x, 'nike'))\n" - ] - } - ], - "source": [ - "# Lista de descripciones de productos\n", - "df_nike['details_transformado'] = df_nike['details'].apply(lambda x: transformar_texto(x, 'nike'))\n", - "df_nike['characteristics_transformado'] = df_nike['characteristics'].apply(lambda x: transformar_texto(x, 'nike'))" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [], - "source": [ - "# Crear un diccionario con las llaves deseadas desde todo el DataFrame\n", - "productos_nike = [\n", - " {\n", - " \"id\": row['id'],\n", - " \"regularPrice\" : row[\"regularPrice\"],\n", - " \"undiscounted_price\": row[\"undiscounted_price\"],\n", - " \"details\": row['details_transformado'],\n", - " \"description\": row['description'],\n", - " \"category\": row['category'],\n", - " \"characteristics\": row['characteristics_transformado']\n", - " }\n", - " for _, row in df_nike.iterrows() # Recorre todo el DataFrame\n", - " if row['details_transformado'] != '{}' # Filtra donde los detalles no estén vacíos\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Preprocesamiento nacion runner" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [], - "source": [ - "df_nr = df_raw[df_raw['store'] == 'nacionRunner']" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [], - "source": [ - "# Crear un diccionario con las llaves deseadas desde todo el DataFrame\n", - "productos_nr = [\n", - " {\n", - " \"id\": row['id'],\n", - " \"regularPrice\" : row[\"regularPrice\"],\n", - " \"undiscounted_price\": row[\"undiscounted_price\"],\n", - " \"details\": row['details'],\n", - " \"description\": row['description'],\n", - " \"category\": row['category'],\n", - " \"characteristics\": row['characteristics']\n", - " }\n", - " for _, row in df_nr.iterrows() # Recorre todo el DataFrame\n", - " if row['details'] != '{}' # Filtra donde los detalles no estén vacíos\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Notebooks/test.ipynb b/Notebooks/test.ipynb deleted file mode 100644 index 8c36c6274..000000000 --- a/Notebooks/test.ipynb +++ /dev/null @@ -1,2144 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "from llama_models.llama3.reference_impl.generation import Llama\n", - "from llama_models.llama3.api.datatypes import UserMessage, SystemMessage\n", - "import os\n", - "import sys\n", - "from llama_models.llama3.api.datatypes import (\n", - " UserMessage,\n", - " SystemMessage,\n", - " CompletionMessage,\n", - " StopReason,\n", - ")\n", - "from llama_models.llama3.reference_impl.generation import Llama\n", - "import pandas as pd\n", - "from io import StringIO\n", - "import numpy as np\n", - "import re\n", - "import requests\n", - "import ollama" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Funciones" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": {}, - "outputs": [], - "source": [ - "def transformar_texto(texto, marca):\n", - " if texto is None or (isinstance(texto, float) and np.isnan(texto)):\n", - " return texto\n", - " \n", - " if marca.lower() == \"adidas\":\n", - " # Transformación original para adidas\n", - " if isinstance(texto, (list, np.ndarray)):\n", - " texto = \", \".join(map(str, texto))\n", - " else:\n", - " texto = str(texto)\n", - " texto = texto.strip(\"[]\")\n", - " texto = re.sub(r\",\\s*\", '} {', texto)\n", - " texto = '{' + texto + '}'\n", - " return texto\n", - " \n", - " elif marca.lower() == \"nike\":\n", - " # Transformación específica para nike\n", - " if isinstance(texto, list):\n", - " # Elimina claves con valores irrelevantes\n", - " texto_limpio = [\n", - " {k.strip(): v.strip() for k, v in d.items() if k.strip() not in ['\\xa0']}\n", - " for d in texto\n", - " if isinstance(d, dict)\n", - " ]\n", - " # Filtra elementos vacíos o irrelevantes\n", - " texto_limpio = [d for d in texto_limpio if d]\n", - " return texto_limpio\n", - " return texto # Si no es lista, regresa el texto original\n", - "\n", - " else:\n", - " raise ValueError(f\"Marca '{marca}' no reconocida. Usa 'adidas' o 'nike'.\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "def generar_prompt_llama(descripcion, etiquetas):\n", - " prompt = \"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\\n\\n\"\n", - " prompt += \"Eres un asistente que extrae información de descripciones de productos y las organiza en una tabla.\\n\"\n", - " prompt += \"<|eot_id|><|start_header_id|>user<|end_header_id|>\\n\\n\"\n", - " prompt += f\"Extrae la siguiente información de la descripción del producto y etiquétala según las etiquetas: {', '.join(etiquetas)}.\\n\\n\"\n", - " prompt += f\"Descripción: {descripcion}\\n\\n\"\n", - " prompt += \"Proporciona la información en una tabla con las columnas correspondientes a cada etiqueta.\\n\"\n", - " prompt += \"<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\n\"\n", - " prompt += \"| \" + \" | \".join(etiquetas) + \" |\\n\"\n", - " prompt += \"| \" + \" | \".join(['---'] * len(etiquetas)) + \" |\\n\"\n", - " prompt += \"|\"\n", - " return prompt\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "def obtener_respuesta_llama(dialogo):\n", - " try:\n", - " # Ruta al modelo descargado (ajusta esta ruta según corresponda)\n", - " ruta_modelo = os.path.expanduser('~/.llama/checkpoints/Meta-Llama3.1-8B-Instruct')\n", - "\n", - " # Cargar el modelo Llama 3.1 8B\n", - " modelo = Llama.build(\n", - " ckpt_dir=ruta_modelo,\n", - " max_seq_len=1024, # Ajusta según sea necesario\n", - " max_batch_size=1, # Número de prompts a procesar simultáneamente\n", - " model_parallel_size=1,\n", - " )\n", - "\n", - " # Generar la respuesta utilizando el diálogo\n", - " resultado = modelo.chat_completion(\n", - " dialog=dialogo,\n", - " max_gen_len=512, # Máxima longitud de generación\n", - " temperature=0.6, # Ajusta según sea necesario\n", - " top_p=0.9, # Ajusta según sea necesario\n", - " )\n", - "\n", - " # Obtener el mensaje generado por el asistente\n", - " mensaje_asistente = resultado.generation\n", - " return mensaje_asistente.content\n", - "\n", - " except Exception as e:\n", - " print(f\"Error al generar la respuesta: {e}\")\n", - " return None" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "def obtener_respuesta_ollama(prompt):\n", - " response = ollama.chat(\n", - " model=\"llama3.2:latest\",\n", - " messages = [\n", - " {\n", - " \"role\":\"user\",\n", - " \"content\": prompt\n", - " } \n", - " ]\n", - " )\n", - " # La respuesta es un generador; concatenamos las partes\n", - " return response" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "def procesar_respuesta(respuesta_texto, etiquetas):\n", - " try:\n", - " # Buscar el inicio de la tabla\n", - " inicio_tabla = respuesta_texto.find('|')\n", - " if inicio_tabla != -1:\n", - " # Extraer la tabla desde el primer '|'\n", - " tabla_texto = respuesta_texto[inicio_tabla:].strip()\n", - " # Extraer solo las líneas que contienen '|'\n", - " lineas = tabla_texto.split('\\n')\n", - " lineas_tabla = []\n", - " for linea in lineas:\n", - " if '|' in linea:\n", - " # Eliminar los '|' iniciales y finales y espacios extra\n", - " linea = linea.strip().strip('|').strip()\n", - " lineas_tabla.append(linea)\n", - " else:\n", - " # Detenerse si la línea no contiene '|'\n", - " break\n", - " tabla_completa = '\\n'.join(lineas_tabla)\n", - " # Convertir la tabla Markdown a un DataFrame\n", - " df = pd.read_csv(StringIO(tabla_completa), sep='|', engine='python', skipinitialspace=True)\n", - " # Limpiar nombres de columnas y datos\n", - " df.columns = [col.strip() for col in df.columns]\n", - " for col in df.columns:\n", - " if df[col].dtype == object:\n", - " df[col] = df[col].str.strip()\n", - " # Resetear el índice\n", - " df = df.reset_index(drop=True)\n", - " return df\n", - " else:\n", - " print(\"No se encontró una tabla en la respuesta.\")\n", - " return None\n", - " except Exception as e:\n", - " print(f\"Error al procesar la tabla: {e}\")\n", - " return None\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Lectura de datos desde la api" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "200\n" - ] - } - ], - "source": [ - "url =\"https://scraping-firestore-178159629911.us-central1.run.app//v1/scraping/\"\n", - "\n", - "response = requests.get(url)\n", - "\n", - "if response.status_code == 200:\n", - " data = response.json()\n", - " print(response.status_code)\n", - "else:\n", - " print(f'Error: {response.status_code}')\n", - "\n", - "df_raw = pd.DataFrame(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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0046zSiHm8Cz0fZYwMJlL[]adidasadidashttps://www.adidas.co/tenis-duramo-sl/IF7884.htmlTenis Duramo SL$379.950$265.965Siente la ligereza y velocidad. Si estás listo...Mujer • Running{'_seconds': 1731975445, '_nanoseconds': 42700...NaNNaN
108sjncACSjSvg2t9DS73[Horma clásica, Parte superior sintética, Forr...adidasadidashttps://www.adidas.co/tenis-adizero-adios-pro-...Tenis ADIZERO ADIOS PRO 3$1.299.950$909.965Los Adizero Adios Pro 3 son la máxima expresió...Mujer • Running{'_seconds': 1731975445, '_nanoseconds': 42700...NaNNaN
20AqheRhKT2lhm7puBVCF[Ajuste clásico, Sistema de amarre de cordones...adidasadidashttps://www.adidas.co/tenis-ultraboost-22/GX55...TENIS ULTRABOOST 22$799.950NAHemos analizado 1.200.000 pisadas para que Ult...Mujer • Running{'_seconds': 1731975445, '_nanoseconds': 42700...NaNNaN
30Di5KVVcvU0QsWRxB1iE[{' ': '<b>&nbsp;</b>'}]nikenikehttps://www.nike.com.co/nike-revolution-7-fb22...Nike Revolution 7$ 389.950NACargamos el Revolution 7 con el tipo de amorti...Calzado de correr en pavimento para mujer{'_seconds': 1731965768, '_nanoseconds': 30000...[]Mujer
40Gvnv9unc1EV4XpFbCQN[{'Características principales': '<b>Caracterí...nikenikehttps://www.nike.com.co/nike-winflo-11-calzado...Nike Winflo 11$ 584.950NAEl Winflo 11 es el calzado con una pisada bala...Calzado de correr en pavimento para mujer{'_seconds': 1731965768, '_nanoseconds': 30000...[Parte superior de malla diseñada estratégicam...Mujer
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Mujer " - ] - }, - "execution_count": 125, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_raw.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Paso 1: Definir las etiquetas dinámicamente\n", - "Creamos una lista de etiquetas que pueden cambiar según las necesidades" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [ - "etiquetas = [\"cordones\", \"textil exterior\", \"textil interior\", \"suela\", \"peso y/o talla\", \"eco diseñado si o no\", \"color\", \"identificador\", \"nombre de deporte\",'genero Mujer/Hombre/MIXTO']\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Paso 2: procesar info de Adidas" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [], - "source": [ - "df_adidas = df_raw[df_raw['store'] == 'adidas']" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Usuario\\AppData\\Local\\Temp\\ipykernel_5352\\4001796790.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df_adidas['details_transformado'] = df_adidas['details'].apply(lambda x: transformar_texto(x, 'adidas'))\n" - ] - } - ], - "source": [ - "# Lista de descripciones de productos\n", - "df_adidas['details_transformado'] = df_adidas['details'].apply(lambda x: transformar_texto(x, 'adidas'))\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Lista de descripciones de productos\n", - "'''descripciones = [\n", - " \"[{'text': 'Ajuste clásico'}, {'text': 'Sistema de amarre de cordones'}, {'text': 'Parte superior textil'}, {'text': 'Mediasuela Cloudfoam'}, {'text': 'Forro interno textil'}, {'text': 'Peso: 239 gramos (talla CO 37,5)'}, {'text': 'Caída mediasuela: 9 mm (talón: 28 mm / antepié: 19 mm)'}, {'text': 'Suela de caucho'}, {'text': 'La parte superior contiene al menos un 50% de material reciclado'}, {'text': 'Color del artículo: Dark Blue / Core Black / Gold Metallic'}, {'text': 'Número de artículo: IE0747'}]\",\n", - " \"[{'text': 'Sistema de amarre de cordones'}, {'text': 'Exterior textil con refuerzos sintéticos sin costuras'}, {'text': 'Lengüeta reforzada'}, {'text': 'Amortiguación Lightstrike'}, {'text': 'Capa protectora de TPU'}, {'text': 'Suela de caucho Continental™ Rubber'}, {'text': 'El exterior contiene al menos un 50 % de material reciclado'}, {'text': 'Color del artículo: Core Black / Grey Three / Grey Two'}, {'text': 'Número de artículo: HR1182'}]\",\n", - " \"[{'text': 'Corte clásico'}, {'text': 'Sistema de amarre de cordones'}, {'text': 'Exterior de malla sándwich'}, {'text': 'Revestimientos sin costuras que brindan soporte'}, {'text': 'Talón suave'}, {'text': 'Amortiguación LIGHTMOTION'}, {'text': 'Suela de caucho'}, {'text': 'El exterior contiene al menos un 50 % de material reciclado'}, {'text': 'Color del artículo: Light Grey / Screaming Orange / Solar Gold'}, {'text': 'Número de artículo: HP2375'}]\",\n", - " \"[{'text': 'Ajuste clásico'}, {'text': 'Sistema de amarre de cordones'}, {'text': 'Parte superior textil'}, {'text': 'Forro interno textil'}, {'text': 'Suela multiterreno de caucho'}, {'text': 'Peso: 327 gramos (talla CO 40)'}, {'text': 'Caída mediasuela: 9 mm (talón: 29 mm / antepié: 19 mm)'}, {'text': 'Contienen al menos un 20% de material reciclado'}, {'text': 'Color del artículo: Halo Silver / Green Spark / Grey Five'}, {'text': 'Número de artículo: IG1416'}]\",\n", - " \"[{'text': 'Ajuste clásico'}, {'text': 'Sistema de amarre de cordones'}, {'text': 'Exterior de malla'}, {'text': 'Diseño cómodo para impulsarte hacia adelante'}, {'text': 'Forro interno textil'}, {'text': 'Mediasuela de EVA'}, {'text': 'Peso: 236 g (Talla COL 36,5)'}, {'text': 'Caída mediasuela: 10 mm (talón: 32 mm / antepié: 22 mm)'}, {'text': 'Suela Adiwear'}, {'text': 'El exterior contiene al menos un 50 % de material reciclado'}, {'text': 'Color del artículo: Core Black / Cloud White / Grey Six'}, {'text': 'Número de artículo: ID5258'}]\",\n", - " \"[{'text': 'Ajuste clásico'}, {'text': 'Sistema de amarre de cordones'}, {'text': 'Exterior de malla'}, {'text': 'Diseño cómodo para impulsarte hacia adelante'}, {'text': 'Forro interno textil'}, {'text': 'Mediasuela de EVA'}, {'text': 'Peso: 236 g (Talla COL 36,5)'}, {'text': 'Caída mediasuela: 10 mm (talón: 32 mm / antepié: 22 mm)'}, {'text': 'Suela Adiwear'}, {'text': 'El exterior contiene al menos un 50 % de material reciclado'}, {'text': 'Color del artículo: Cloud White / Silver Metallic / Crystal White'}, {'text': 'Número de artículo: ID5257'}]\",\n", - 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" - ], - "text/plain": [ - " details_transformado \\\n", - "0 {} \n", - "1 {Horma clásica} {Parte superior sintética} {Fo... \n", - "2 {Ajuste clásico} {Sistema de amarre de cordone... \n", - "5 {Ajuste clásico} {Cierre de cordones} {Parte s... \n", - "7 {Ajuste clásico} {Sistema de amarre de cordone... \n", - "\n", - " description category \n", - "0 Siente la ligereza y velocidad. Si estás listo... Mujer • Running \n", - "1 Los Adizero Adios Pro 3 son la máxima expresió... Mujer • Running \n", - "2 Hemos analizado 1.200.000 pisadas para que Ult... Mujer • Running \n", - "5 Haz tus mejores 10k con nuestros nuevos tenis ... Mujer • Running \n", - "7 Haz tus mejores 10k con nuestros nuevos tenis ... Hombre • Running " - ] - }, - "execution_count": 103, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_adidas[['details_transformado','description','category']].head()" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "# Crear un diccionario con las llaves deseadas\n", - "productos = [\n", - " {\n", - " \"details\": row['details_transformado'],\n", - " \"description\": row['description'],\n", - " \"category\": row['category']\n", - " }\n", - " for _, row in df_adidas[:10].iterrows() \n", - " if row['details_transformado'] != '{}'\n", - "]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Paso 3: Integrar con la API de Llama 3.1\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prompt" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'details': '{Horma clásica} {Parte superior sintética} {Forro interno textil} {Varillas ENERGYRODS 2.0 que limitan la pérdida de energía} {Amortiguación Lightstrike Pro} {Peso: 183 g (talla CO 37} {5)} {Caída mediasuela: 6 mm (talón: 38 mm / antepié: 32 mm)} {La parte superior contiene al menos un 50 % de material reciclado} {Color del artículo: Pink Spark / Aurora Met. / Sandy Pink} {Número de artículo: ID3612}',\n", - " 'description': \"Los Adizero Adios Pro 3 son la máxima expresión de los productos Adizero Racing. Fueron diseñados con y para atletas para lograr hazañas increíbles. Estos tenis de running adidas están diseñados para optimizar la eficiencia del running. Nuestras varillas ENERGYRODS 2.0 de carbono ofrecen ligereza y firmeza para pasos ágiles y eficientes. La tecnología LIGHTSTRIKE PRO ultraliviana amortigua cada paso con las tres capas de espuma resistente que te ayudan a mantener la energía a largo plazo. Todo sobre una delgada suela de caucho textil para un agarre extraordinario en condiciones mojadas y secas. Celebramos nuestra más reciente victoria, los tenis Best Speed 2024 by Women's Health.\",\n", - " 'category': 'Mujer • Running'},\n", - " {'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Exterior de tejido adidas Primeknit} {Ajuste suave en el talón} {Sistema Linear Energy Push} {Mediasuela BOOST} {Peso: 289 g (Talla CO 37)} {Suela Stretchweb con caucho Continental™ Better Rubber} {El hilo del exterior contiene al menos un 50% de Parley Ocean Plastic y un 50% de poliéster reciclado} {Color del artículo: Core Black / Core Black / Cloud White} {Número de artículo: GX5591}',\n", - " 'description': 'Hemos analizado 1.200.000 pisadas para que Ultraboost evolucione y mejore su ajuste para adaptarse 360° al pie femenino. Y aún hay más. Hemos rediseñado la suela de caucho. Probamos cientos de prototipos. Hasta que comprobamos las mejoras en su rendimiento. ¿El resultado? Un 4 % más de energía que los Ultraboost 21 para mujer. El ajuste en el área del talón del exterior adidas PRIMEKNIT se diseñó para evitar que el talón se deslice y se formen ampollas. Estarás corriendo sobre una mediasuela BOOST con tecnología Linear Energy Push. El exterior de este calzado está confeccionado con hilo que contiene un 50% de Parley Ocean Plastic, un material que ha sido recuperado de residuos plásticos recogidos en islas, playas, comunidades costeras y costas evitando que contaminen nuestros océanos.',\n", - " 'category': 'Mujer • Running'},\n", - " {'details': '{Ajuste clásico} {Cierre de cordones} {Parte superior de malla técnica} {Amortiguación Lightstrike Pro} {Varillas ENERGYRODS que limitan la pérdida de energía} {Talón Slinglaunch para un ajuste seguro} {Peso: 166 g (Talla COL 36 1/2)} {Caída mediasuela: 6 mm (talón: 32 mm / antepié: 26 mm)} {Suela de caucho Continental™} {Contiene al menos un 20 % de material reciclado} {Color del artículo: Green Spark / Aurora Met. / Lucid Lemon} {Número de artículo: IG8206}',\n", - " 'description': 'Haz tus mejores 10k con nuestros nuevos tenis de running Adizero, diseñados exclusivamente para darte máxima velocidad. Los Adizero Takumi Sen 10 han sido diseñados con dos capas de amortiguación LIGHTSTRIKE PRO, combinadas con las varillas ENERGYRODS para una mayor estabilidad e impulso en tu carrera. Supera tu marca personal y llega a la meta más rápido que nunca.',\n", - " 'category': 'Mujer • Running'},\n", - " {'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Parte superior de malla} {Amortiguación Lightstrike Pro} {Varillas ENERGYRODS que limitan la pérdida de energía} {Talón Slinglaunch} {Peso: 200 gramos (talla CO 40)} {Caída mediasuela: 6 mm (talón: 33 mm / antepié: 27 mm} {Suela de caucho Continental™ Rubber} {Contienen al menos un 20% de material reciclado} {Color del artículo: Green Spark / Aurora Met. / Lucid Lemon} {Número de artículo: IG3134}',\n", - " 'description': 'Haz tus mejores 10k con nuestros nuevos tenis de running Adizero, diseñados exclusivamente para darte máxima velocidad. Los Adizero Takumi Sen 10 han sido diseñados con dos capas de amortiguación LIGHTSTRIKE PRO, combinadas con las varillas ENERGYRODS 2.0 para una mayor estabilidad e impulso en tu carrera. Supera tu marca personal y llega a la meta más rápido que nunca.',\n", - " 'category': 'Hombre • Running'},\n", - " {'details': '{Horma clásica} {Sistema de amarre de cordones} {Parte superior textil} {Forro interno textil} {Mediasuela Cloudfoam} {Suela de TPU} {Peso: 319 g (talla CO 40)} {Caída mediasuela: 6 mm (talón 35 mm / antepié 29 mm)} {Color del artículo: Halo Silver / Carbon / Core Black} {Número de artículo: ID8754}',\n", - " 'description': 'Cada carrera es un viaje de descubrimiento. Ponte estos tenis de running adidas y libera tu potencial. La mediasuela con amortiguación Cloudfoam te ofrece una pisada más cómoda mientras aumentas tu resistencia. La parte superior textil es resistente al desgaste y te ofrece soporte desde tu primera vuelta hasta tu primera carrera de 5K.',\n", - " 'category': 'Hombre • Running'},\n", - " {'details': '{Si este artículo es personalizado} {no aplica en nuestra política de cambios y devoluciones.} {Sistema de amarre de cordones para un ajuste inmejorable} {Producto hecho parcialmente con Malla Técnica Reciclada} {Diseño liviano} {Amortiguación Lightstrike y Lightstrike Pro} {Forro interno textil} {Caída mediasuela: 8} {5 mm (talón: 33 mm / antepié: 24} {5 mm)} {Suela de caucho} {El exterior contiene al menos un 50% de material reciclado} {Color del artículo: Ivory / Iron Metallic / Spark} {Número de artículo: IG3341}',\n", - " 'description': 'Cuando se trata de alcanzar tus metas, cada segundo cuenta. Ya sea para entrenar o para competir, un rendimiento excelente requiere de prendas de alta tecnología optimizadas para la velocidad. Presentamos la nueva colección de tenis de running livianos que te ayudan a superar tus límites sin distracciones.\\n\\nLos tenis de running Adizero SL seleccionan lo mejor de nuestra franquicia Adizero que rompe récords mundiales. La mediasuela de EVA LIGHTSTRIKE liviana ofrece resiliencia a la mediasuela para que puedas concentrarte en el próximo paso, mientras que el exterior está hecho de una malla técnica suave que está zonificada en áreas clave. El talón acolchado y la lengüeta brindan una comodidad óptima junto con el antepié Adizero. La suela premium está diseñada para proporcionar tracción.',\n", - " 'category': 'Mujer • Running'},\n", - " {'details': '{Horma clásica} {Sistema de amarre de cordones} {Parte superior de malla} {Forro interno textil} {Plantilla OrthoLite®} {Mediasuela Cloudfoam} {Peso: 304 g (talla CO 40)} {Caída mediasuela: 10 mm (talón: 33 mm / antepié: 23 mm)} {Suela Adiwear} {Color del artículo: Cloud White / Core Black / Better Scarlet} {Número de artículo: IE8818}',\n", - " 'description': 'Tanto en la pista como en la cinta de correr, alcanza todas tus metas con estos tenis de running adidas. Incorporan una mediasuela con amortiguación Cloudfoam que te ofrece una pisada más cómoda y suave. La parte superior de malla transpirable y la suela Adiwear de gran resistencia al desgaste la convierten en una silueta perfecta para llevar durante todo el día.',\n", - " 'category': 'Hombre • Running'},\n", - " {'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Parte superior de malla} {Forro interno textil} {mediasuela Dreamstrike+} {Suela Adiwear} {Peso (talla CO 37): 213 gramos} {Caída mediasuela: 10 mm (talón: 34 mm / 24 mm)} {Contienen al menos un 20% de material reciclado y renovable} {Color del artículo: Almost Yellow / Zero Metalic / Pink Spark} {Número de artículo: IE1072}',\n", - " 'description': 'Avanza hacia tus metas de running con estos tenis adidas. La mediasuela Dreamstrike+ está diseñada para absorber el impacto. La amortiguación adicional en el antepié ofrece comodidad durante toda tu carrera. El exterior de malla transpirable maximiza el flujo de aire para mantener tus pies frescos, y la suela Adiwear resistente ofrece tracción. \\n\\nAl elegir materiales reciclados, podemos reutilizar materiales que ya han sido creados, lo que ayuda a reducir los residuos. La elección de materiales renovables nos ayudará a eliminar nuestra dependencia de recursos finitos. Nuestros productos hechos con una mezcla de materiales reciclados y renovables contienen al menos un 20% de este material.',\n", - " 'category': 'Mujer • Running'},\n", - " {'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Exterior de malla con cuello acolchado} {Forro interno textil} {Estabilizador de talón moldeado Fitcounter} {Mediasuela Bounce} {Suela sintética} {El exterior contiene al menos un 50 % de material reciclado} {Color del artículo: Shadow Violet / Legend Ink / Wonder Clay} {Número de artículo: IG0334}',\n", - " 'description': 'Estos tenis fueron diseñados para darte la amortiguación y la respuesta que necesitas cuando estás en la pista o en los senderos. Sin importar si vas a hacer tu caminata diaria o una carrera corta, la mediasuela Bounce es flexible y receptiva con cada paso. Hecho con una serie de materiales reciclados, el exterior incorpora al menos un 50 % de contenido reciclado. Este producto representa solo una de nuestras soluciones para acabar con los residuos plásticos.',\n", - " 'category': 'Mujer • Running'}]" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "productos\n" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [], - "source": [ - "def generar_prompt_ollama(producto, etiquetas):\n", - " prompt = f\"\"\"\n", - " Eres un asistente especializado en extraer información estructurada de descripciones de productos y organizarla en tablas. \n", - " Tu tarea es extraer la siguiente información de los detalles del producto y etiquetarla según las etiquetas: {', '.join(etiquetas)}.\n", - " Si una etiqueta no tiene una correspondencia clara en los detalles, completa su valor con \"null\".\n", - "\n", - " Información del producto:\n", - " - Details: {producto['details']}\n", - " - Description: {producto['description']}\n", - " - Category: {producto['category']}\n", - "\n", - " Proporciona la información en una tabla con las columnas correspondientes a cada etiqueta. \n", - " La tabla debe incluir **dos filas completas**:\n", - " 1. La primera fila contiene los nombres de las etiquetas.\n", - " 2. La segunda fila contiene los valores correspondientes etiquetados.\n", - "\n", - " Formato esperado de la respuesta:\n", - " | {' | '.join(etiquetas)} |\n", - " | {' | '.join(['---'] * len(etiquetas))} |\n", - " | valor_1 | valor_2 | ... |\n", - " \n", - " Ejemplo:\n", - " Si las etiquetas son \"details\", \"description\", y \"category\", y los valores extraídos son \n", - " \"Zapatillas cómodas\", \"Hechas con materiales reciclados\", y \"Calzado\", respectivamente, \n", - " tu respuesta debe ser:\n", - "\n", - " | details | description | category |\n", - " | Zapatillas cómodas | Hechas con materiales reciclados | Calzado |\n", - "\n", - " Recuerda:\n", - " - La respuesta debe contener dos filas completas.\n", - " - Solo responde con la tabla y **no incluyas texto adicional**.\n", - "\n", - " Ahora, extrae y estructura la información del producto proporcionado:\n", - "\n", - " | {' | '.join(etiquetas)} |\n", - " | {' | '.join(['---'] * len(etiquetas))} |\n", - " |\"\"\"\n", - " return prompt.strip()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generacion" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'details': '{Horma clásica} {Parte superior sintética} {Forro interno textil} {Varillas ENERGYRODS 2.0 que limitan la pérdida de energía} {Amortiguación Lightstrike Pro} {Peso: 183 g (talla CO 37} {5)} {Caída mediasuela: 6 mm (talón: 38 mm / antepié: 32 mm)} {La parte superior contiene al menos un 50 % de material reciclado} {Color del artículo: Pink Spark / Aurora Met. / Sandy Pink} {Número de artículo: ID3612}', 'description': \"Los Adizero Adios Pro 3 son la máxima expresión de los productos Adizero Racing. Fueron diseñados con y para atletas para lograr hazañas increíbles. Estos tenis de running adidas están diseñados para optimizar la eficiencia del running. Nuestras varillas ENERGYRODS 2.0 de carbono ofrecen ligereza y firmeza para pasos ágiles y eficientes. La tecnología LIGHTSTRIKE PRO ultraliviana amortigua cada paso con las tres capas de espuma resistente que te ayudan a mantener la energía a largo plazo. Todo sobre una delgada suela de caucho textil para un agarre extraordinario en condiciones mojadas y secas. Celebramos nuestra más reciente victoria, los tenis Best Speed 2024 by Women's Health.\", 'category': 'Mujer • Running'}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Adizero Adios Pro 3 | Parte superior sintética | Forro interno textil | Delgada suela de caucho textil | CO 37 | Sí | Pink Spark / Aurora Met. / Sandy Pink | ID3612 | Running | Mujer\n", - "\n", - "------------------------\n", - "\n", - "{'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Exterior de tejido adidas Primeknit} {Ajuste suave en el talón} {Sistema Linear Energy Push} {Mediasuela BOOST} {Peso: 289 g (Talla CO 37)} {Suela Stretchweb con caucho Continental™ Better Rubber} {El hilo del exterior contiene al menos un 50% de Parley Ocean Plastic y un 50% de poliéster reciclado} {Color del artículo: Core Black / Core Black / Cloud White} {Número de artículo: GX5591}', 'description': 'Hemos analizado 1.200.000 pisadas para que Ultraboost evolucione y mejore su ajuste para adaptarse 360° al pie femenino. Y aún hay más. Hemos rediseñado la suela de caucho. Probamos cientos de prototipos. Hasta que comprobamos las mejoras en su rendimiento. ¿El resultado? Un 4 % más de energía que los Ultraboost 21 para mujer. El ajuste en el área del talón del exterior adidas PRIMEKNIT se diseñó para evitar que el talón se deslice y se formen ampollas. Estarás corriendo sobre una mediasuela BOOST con tecnología Linear Energy Push. El exterior de este calzado está confeccionado con hilo que contiene un 50% de Parley Ocean Plastic, un material que ha sido recuperado de residuos plásticos recogidos en islas, playas, comunidades costeras y costas evitando que contaminen nuestros océanos.', 'category': 'Mujer • Running'}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Sistema de amarre de cordones | adidas Primeknit | null | Mediasuela BOOST | Talla CO 37, 289 g | Si | Core Black / Cloud White | GX5591 | Running | Mujer\n", - "\n", - "------------------------\n", - "\n", - "{'details': '{Ajuste clásico} {Cierre de cordones} {Parte superior de malla técnica} {Amortiguación Lightstrike Pro} {Varillas ENERGYRODS que limitan la pérdida de energía} {Talón Slinglaunch para un ajuste seguro} {Peso: 166 g (Talla COL 36 1/2)} {Caída mediasuela: 6 mm (talón: 32 mm / antepié: 26 mm)} {Suela de caucho Continental™} {Contiene al menos un 20 % de material reciclado} {Color del artículo: Green Spark / Aurora Met. / Lucid Lemon} {Número de artículo: IG8206}', 'description': 'Haz tus mejores 10k con nuestros nuevos tenis de running Adizero, diseñados exclusivamente para darte máxima velocidad. Los Adizero Takumi Sen 10 han sido diseñados con dos capas de amortiguación LIGHTSTRIKE PRO, combinadas con las varillas ENERGYRODS para una mayor estabilidad e impulso en tu carrera. Supera tu marca personal y llega a la meta más rápido que nunca.', 'category': 'Mujer • Running'}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Cierre de cordones | Parte superior de malla técnica | Amortiguación Lightstrike Pro | Suela de caucho Continental™ | 166 g (Talla COL 36 1/2) | Contiene al menos un 20 % de material reciclado | Green Spark / Aurora Met. / Lucid Lemon | IG8206 | Running | Mujer\n", - "\n", - "------------------------\n", - "\n", - "{'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Parte superior de malla} {Amortiguación Lightstrike Pro} {Varillas ENERGYRODS que limitan la pérdida de energía} {Talón Slinglaunch} {Peso: 200 gramos (talla CO 40)} {Caída mediasuela: 6 mm (talón: 33 mm / antepié: 27 mm} {Suela de caucho Continental™ Rubber} {Contienen al menos un 20% de material reciclado} {Color del artículo: Green Spark / Aurora Met. / Lucid Lemon} {Número de artículo: IG3134}', 'description': 'Haz tus mejores 10k con nuestros nuevos tenis de running Adizero, diseñados exclusivamente para darte máxima velocidad. Los Adizero Takumi Sen 10 han sido diseñados con dos capas de amortiguación LIGHTSTRIKE PRO, combinadas con las varillas ENERGYRODS 2.0 para una mayor estabilidad e impulso en tu carrera. Supera tu marca personal y llega a la meta más rápido que nunca.', 'category': 'Hombre • Running'}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Sistema de amarre de cordones | Parte superior de malla | Amortiguación Lightstrike Pro | Suela de caucho Continental™ Rubber | CO 40 | Contienen al menos un 20% de material reciclado | Green Spark / Aurora Met. / Lucid Lemon | IG3134 | Running | Mujer/Hombre/MIXTO |\n", - "\n", - "------------------------\n", - "\n", - "{'details': '{Horma clásica} {Sistema de amarre de cordones} {Parte superior textil} {Forro interno textil} {Mediasuela Cloudfoam} {Suela de TPU} {Peso: 319 g (talla CO 40)} {Caída mediasuela: 6 mm (talón 35 mm / antepié 29 mm)} {Color del artículo: Halo Silver / Carbon / Core Black} {Número de artículo: ID8754}', 'description': 'Cada carrera es un viaje de descubrimiento. Ponte estos tenis de running adidas y libera tu potencial. La mediasuela con amortiguación Cloudfoam te ofrece una pisada más cómoda mientras aumentas tu resistencia. La parte superior textil es resistente al desgaste y te ofrece soporte desde tu primera vuelta hasta tu primera carrera de 5K.', 'category': 'Hombre • Running'}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Sistema de amarre de cordones | Parte superior textil | Forro interno textil | Mediasuela Cloudfoam, Suela de TPU | Talla CO 40 | Sí | Halo Silver / Carbon / Core Black | ID8754 | Running | Hombre\n", - "\n", - "------------------------\n", - "\n", - "{'details': '{Si este artículo es personalizado} {no aplica en nuestra política de cambios y devoluciones.} {Sistema de amarre de cordones para un ajuste inmejorable} {Producto hecho parcialmente con Malla Técnica Reciclada} {Diseño liviano} {Amortiguación Lightstrike y Lightstrike Pro} {Forro interno textil} {Caída mediasuela: 8} {5 mm (talón: 33 mm / antepié: 24} {5 mm)} {Suela de caucho} {El exterior contiene al menos un 50% de material reciclado} {Color del artículo: Ivory / Iron Metallic / Spark} {Número de artículo: IG3341}', 'description': 'Cuando se trata de alcanzar tus metas, cada segundo cuenta. Ya sea para entrenar o para competir, un rendimiento excelente requiere de prendas de alta tecnología optimizadas para la velocidad. Presentamos la nueva colección de tenis de running livianos que te ayudan a superar tus límites sin distracciones.\\n\\nLos tenis de running Adizero SL seleccionan lo mejor de nuestra franquicia Adizero que rompe récords mundiales. La mediasuela de EVA LIGHTSTRIKE liviana ofrece resiliencia a la mediasuela para que puedas concentrarte en el próximo paso, mientras que el exterior está hecho de una malla técnica suave que está zonificada en áreas clave. El talón acolchado y la lengüeta brindan una comodidad óptima junto con el antepié Adizero. La suela premium está diseñada para proporcionar tracción.', 'category': 'Mujer • Running'}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Si este artículo es personalizado | {Sistema de amarre de cordones para un ajuste inmejorable} | El exterior contiene al menos un 50% de material reciclado | Caucha | null | No | Ivory / Iron Metallic / Spark | IG3341 | Running | Mujer |\n", - "\n", - "------------------------\n", - "\n", - "{'details': '{Horma clásica} {Sistema de amarre de cordones} {Parte superior de malla} {Forro interno textil} {Plantilla OrthoLite®} {Mediasuela Cloudfoam} {Peso: 304 g (talla CO 40)} {Caída mediasuela: 10 mm (talón: 33 mm / antepié: 23 mm)} {Suela Adiwear} {Color del artículo: Cloud White / Core Black / Better Scarlet} {Número de artículo: IE8818}', 'description': 'Tanto en la pista como en la cinta de correr, alcanza todas tus metas con estos tenis de running adidas. Incorporan una mediasuela con amortiguación Cloudfoam que te ofrece una pisada más cómoda y suave. La parte superior de malla transpirable y la suela Adiwear de gran resistencia al desgaste la convierten en una silueta perfecta para llevar durante todo el día.', 'category': 'Hombre • Running'}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Sistema de amarre de cordones | Parte superior de malla | Forro interno textil | Mediasuela Cloudfoam | Talla CO 40 | null | Cloud White / Core Black / Better Scarlet | IE8818 | Running | Hombre |\n", - "\n", - "Nota: Las etiquetas \"Peso y/o talla\" no tienen una relación directa con la información proporcionada, por lo que su valor es \"null\".\n", - "\n", - "------------------------\n", - "\n", - "{'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Parte superior de malla} {Forro interno textil} {mediasuela Dreamstrike+} {Suela Adiwear} {Peso (talla CO 37): 213 gramos} {Caída mediasuela: 10 mm (talón: 34 mm / 24 mm)} {Contienen al menos un 20% de material reciclado y renovable} {Color del artículo: Almost Yellow / Zero Metalic / Pink Spark} {Número de artículo: IE1072}', 'description': 'Avanza hacia tus metas de running con estos tenis adidas. La mediasuela Dreamstrike+ está diseñada para absorber el impacto. La amortiguación adicional en el antepié ofrece comodidad durante toda tu carrera. El exterior de malla transpirable maximiza el flujo de aire para mantener tus pies frescos, y la suela Adiwear resistente ofrece tracción. \\n\\nAl elegir materiales reciclados, podemos reutilizar materiales que ya han sido creados, lo que ayuda a reducir los residuos. La elección de materiales renovables nos ayudará a eliminar nuestra dependencia de recursos finitos. Nuestros productos hechos con una mezcla de materiales reciclados y renovables contienen al menos un 20% de este material.', 'category': 'Mujer • Running'}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Sistema de amarre de cordones | Forro interno textil | mediasuela Dreamstrike+ | Suela Adiwear | CO 37: 213 gramos | Contienen al menos un 20% de material reciclado y renovable | Almost Yellow / Zero Metalic / Pink Spark | IE1072 | Running | Mujer |\n", - "\n", - "Nota: Se han completado los valores que no tenían una correspondencia clara con las etiquetas del producto original.\n", - "\n", - "------------------------\n", - "\n", - "{'details': '{Ajuste clásico} {Sistema de amarre de cordones} {Exterior de malla con cuello acolchado} {Forro interno textil} {Estabilizador de talón moldeado Fitcounter} {Mediasuela Bounce} {Suela sintética} {El exterior contiene al menos un 50 % de material reciclado} {Color del artículo: Shadow Violet / Legend Ink / Wonder Clay} {Número de artículo: IG0334}', 'description': 'Estos tenis fueron diseñados para darte la amortiguación y la respuesta que necesitas cuando estás en la pista o en los senderos. Sin importar si vas a hacer tu caminata diaria o una carrera corta, la mediasuela Bounce es flexible y receptiva con cada paso. Hecho con una serie de materiales reciclados, el exterior incorpora al menos un 50 % de contenido reciclado. Este producto representa solo una de nuestras soluciones para acabar con los residuos plásticos.', 'category': 'Mujer • Running'}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Sistema de amarre de cordones | Exterior de malla con cuello acolchado | Forro interno textil | Mediasuela Bounce | null | Si | Shadow Violet / Legend Ink / Wonder Clay | IG0334 | Running | Mujer\n", - "\n", - "------------------------\n", - "\n" - ] - } - ], - "source": [ - "dfs = []\n", - "for producto in productos:\n", - " prompt = generar_prompt_ollama(producto, etiquetas)\n", - " respuesta = obtener_respuesta_ollama(prompt)\n", - " print(producto)\n", - " print(\"Respuesta del modelo:\")\n", - " print(respuesta[\"message\"][\"content\"])\n", - " print(\"\\n------------------------\\n\")\n", - " df = procesar_respuesta(respuesta[\"message\"][\"content\"], etiquetas)\n", - " if df is not None:\n", - " dfs.append(df)\n", - " else:\n", - " print(\"No se pudo extraer la tabla.\\n\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": 112, - "metadata": {}, - "outputs": [], - "source": [ - "if dfs:\n", - " df_total = pd.concat(dfs, ignore_index=True)\n", - "else:\n", - " print(\"No se pudo crear el DataFrame total.\")" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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cordonestextil exteriortextil interiorsuelapeso y/o tallaeco diseñado si o nocoloridentificadornombre de deportegenero Mujer/Hombre/MIXTO
0Adizero Adios Pro 3Parte superior sintéticaForro interno textilDelgada suela de caucho textilCO 37SíPink Spark / Aurora Met. / Sandy PinkID3612RunningMujer
1Sistema de amarre de cordonesadidas PrimeknitnullMediasuela BOOSTTalla CO 37, 289 gSiCore Black / Cloud WhiteGX5591RunningMujer
2Cierre de cordonesParte superior de malla técnicaAmortiguación Lightstrike ProSuela de caucho Continental™166 g (Talla COL 36 1/2)Contiene al menos un 20 % de material recicladoGreen Spark / Aurora Met. / Lucid LemonIG8206RunningMujer
3Sistema de amarre de cordonesParte superior de mallaAmortiguación Lightstrike ProSuela de caucho Continental™ RubberCO 40Contienen al menos un 20% de material recicladoGreen Spark / Aurora Met. / Lucid LemonIG3134RunningMujer/Hombre/MIXTO
4Sistema de amarre de cordonesParte superior textilForro interno textilMediasuela Cloudfoam, Suela de TPUTalla CO 40SíHalo Silver / Carbon / Core BlackID8754RunningHombre
5Si este artículo es personalizado{Sistema de amarre de cordones para un ajuste ...El exterior contiene al menos un 50% de materi...CauchanullNoIvory / Iron Metallic / SparkIG3341RunningMujer
6Sistema de amarre de cordonesParte superior de mallaForro interno textilMediasuela CloudfoamTalla CO 40nullCloud White / Core Black / Better ScarletIE8818RunningHombre
7Sistema de amarre de cordonesForro interno textilmediasuela Dreamstrike+Suela AdiwearCO 37: 213 gramosContienen al menos un 20% de material reciclad...Almost Yellow / Zero Metalic / Pink SparkIE1072RunningMujer
8Sistema de amarre de cordonesExterior de malla con cuello acolchadoForro interno textilMediasuela BouncenullSiShadow Violet / Legend Ink / Wonder ClayIG0334RunningMujer
\n", - "
" - ], - "text/plain": [ - " cordones \\\n", - "0 Adizero Adios Pro 3 \n", - "1 Sistema de amarre de cordones \n", - "2 Cierre de cordones \n", - "3 Sistema de amarre de cordones \n", - "4 Sistema de amarre de cordones \n", - "5 Si este artículo es personalizado \n", - "6 Sistema de amarre de cordones \n", - "7 Sistema de amarre de cordones \n", - "8 Sistema de amarre de cordones \n", - "\n", - " textil exterior \\\n", - "0 Parte superior sintética \n", - "1 adidas Primeknit \n", - "2 Parte superior de malla técnica \n", - "3 Parte superior de malla \n", - "4 Parte superior textil \n", - "5 {Sistema de amarre de cordones para un ajuste ... \n", - "6 Parte superior de malla \n", - "7 Forro interno textil \n", - "8 Exterior de malla con cuello acolchado \n", - "\n", - " textil interior \\\n", - "0 Forro interno textil \n", - "1 null \n", - "2 Amortiguación Lightstrike Pro \n", - "3 Amortiguación Lightstrike Pro \n", - "4 Forro interno textil \n", - "5 El exterior contiene al menos un 50% de materi... \n", - "6 Forro interno textil \n", - "7 mediasuela Dreamstrike+ \n", - "8 Forro interno textil \n", - "\n", - " suela peso y/o talla \\\n", - "0 Delgada suela de caucho textil CO 37 \n", - "1 Mediasuela BOOST Talla CO 37, 289 g \n", - "2 Suela de caucho Continental™ 166 g (Talla COL 36 1/2) \n", - "3 Suela de caucho Continental™ Rubber CO 40 \n", - "4 Mediasuela Cloudfoam, Suela de TPU Talla CO 40 \n", - "5 Caucha null \n", - "6 Mediasuela Cloudfoam Talla CO 40 \n", - "7 Suela Adiwear CO 37: 213 gramos \n", - "8 Mediasuela Bounce null \n", - "\n", - " eco diseñado si o no \\\n", - "0 Sí \n", - "1 Si \n", - "2 Contiene al menos un 20 % de material reciclado \n", - "3 Contienen al menos un 20% de material reciclado \n", - "4 Sí \n", - "5 No \n", - "6 null \n", - "7 Contienen al menos un 20% de material reciclad... \n", - "8 Si \n", - "\n", - " color identificador nombre de deporte \\\n", - "0 Pink Spark / Aurora Met. / Sandy Pink ID3612 Running \n", - "1 Core Black / Cloud White GX5591 Running \n", - "2 Green Spark / Aurora Met. / Lucid Lemon IG8206 Running \n", - "3 Green Spark / Aurora Met. / Lucid Lemon IG3134 Running \n", - "4 Halo Silver / Carbon / Core Black ID8754 Running \n", - "5 Ivory / Iron Metallic / Spark IG3341 Running \n", - "6 Cloud White / Core Black / Better Scarlet IE8818 Running \n", - "7 Almost Yellow / Zero Metalic / Pink Spark IE1072 Running \n", - "8 Shadow Violet / Legend Ink / Wonder Clay IG0334 Running \n", - "\n", - " genero Mujer/Hombre/MIXTO \n", - "0 Mujer \n", - "1 Mujer \n", - "2 Mujer \n", - "3 Mujer/Hombre/MIXTO \n", - "4 Hombre \n", - "5 Mujer \n", - "6 Hombre \n", - "7 Mujer \n", - "8 Mujer " - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_total" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Paso 5: Nike" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [], - "source": [ - "df_nike = df_raw[df_raw['store'] == 'nike']" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Usuario\\AppData\\Local\\Temp\\ipykernel_5352\\989177854.py:1: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df_nike['details_transformado'] = df_nike['details'].apply(transformar_texto)\n" - ] - } - ], - "source": [ - "df_nike['details_transformado'] = df_nike['details'].apply(transformar_texto)" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'\\xa0': ' '}]" - ] - }, - "execution_count": 100, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_nike['details'].iloc[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Usuario\\AppData\\Local\\Temp\\ipykernel_5352\\1496571984.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df_nike['details_transformado'] = df_nike['details'].apply(lambda x: transformar_texto(x, 'nike'))\n" - ] - } - ], - "source": [ - "# Aplicar la transformación con el parámetro 'adidas'\n", - "df_nike['details_transformado'] = df_nike['details'].apply(lambda x: transformar_texto(x, 'nike'))" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": {}, - "outputs": [], - "source": [ - "# Crear un diccionario con las llaves deseadas\n", - "productos_nike = [\n", - " {\n", - " \"details\": row['details_transformado'],\n", - " \"description\": row['description'],\n", - " \"category\": row['category']\n", - " }\n", - " for _, row in df_nike[:10].iterrows() \n", - " if row['details_transformado'] != '{}'\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'details': \"{{'\\\\xa0': ' '}}\", 'description': 'Cargamos el Revolution 7 con el tipo de amortiguación suave y soporte que podría cambiar tu mundo del running. Elegante como siempre, cómodo cuando la goma se encuentra con la carretera y con alto rendimiento para el ritmo deseado, es una evolución de un favorito de los fanáticos que ofrece una conducción suave y tersa.', 'category': 'Calzado de correr en pavimento para mujer'}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Zapatillas cómodas | null | null | null | null | null | null | Revolution 7 | Correr | Mujer\n", - "\n", - "------------------------\n", - "\n", - "{'details': \"{{'Características principales': 'Características principales'}} {{'Datos del producto': 'Datos del producto'}} {{'Soporte: neutro': 'Soporte: neutro'}} {{'Ajuste adaptable': 'Ajuste adaptable'}} {{'Datos del producto': 'Datos del producto'}}\", 'description': 'El Winflo 11 es el calzado con una pisada balanceada que te ayudará a impulsar tu carrera a un ritmo que te permitirá acumular kilómetros, metros y objetivos. Impulsado por la amortiguación Nike Air de largo completo, el Winflo 11 cuenta ahora con un antepié más amplio, un talón más ancho y una mayor transpirabilidad en comparación con el Winflo 10. Es el tipo de fijación que crea hábito y que te ayudará a ponerte en marcha, a lucir bien en la carretera con colores fáciles de combinar y a volver al día siguiente por más.', 'category': 'Calzado de correr en pavimento para mujer'}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Zapatillas con fijación | Neutra | Textil sintético | Suela de goma | - | Si | Blanco/Rosa/Violeta/Verde | WINFLO 11 | Correr en pavimento para mujer | Mujer/Hombre/MIXTO |\n", - "\n", - "------------------------\n", - "\n", - "{'details': \"{{'\\\\xa0': ' '}}\", 'description': 'Con la máxima amortiguación para brindarte soporte en cada kilómetro, el Invincible 3 ofrece nuestro más alto nivel de comodidad en la planta del pie. Su espuma ZoomX Foam suave y flexible te ayuda a mantener la estabilidad y la frescura. En otras palabras, te sentirás bien todo el día, sin importar lo que hagas. Y al juntar toda esta amortiguación con colores fáciles de combinar, obtienes un calzado que no te vas a querer quitar.', 'category': 'Calzado de correr en pavimento para mujer'}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Null | Null | Null | Null | Null | Si | Color fáciles de combinar | Invincible 3 | Calzado de correr en pavimento para mujer | Mujer\n", - "\n", - "------------------------\n", - "\n", - "{'details': \"{{'Amortiguación: superalta': 'Cuanta más amortiguación tengas en la planta del pie} {más suave y cómoda puede ser tu experiencia de running. La amortiguación suaviza el impacto cuando los pies llegan al suelo. Con una amortiguación Nike ZoomX con la forma de una mecedora y una espuma más alta} {este calzado te brinda la máxima amortiguación al contacto con el suelo y una sensación aún más suave en la planta del pie.'}} {{'Responsividad: superalta': 'Cuanto más responsivo sea el calzado} {más retorno de energía obtendrás con cada paso. Ya sea que quieras correr un poco más rápido o con un poco menos de esfuerzo} {un calzado responsivo te brinda más elasticidad en tu pisada para que aproveches al máximo tu carrera. La espuma Nike ZoomX es muy responsiva y ligera} {esto brinda rebote y respuesta rápida a cada paso.'}} {{'Ajuste: seguro} {transpirable y cómodo': 'La parte superior evolucionada de Flyknit sitúa las zonas de transpirabilidad donde el pie se calienta más. Es resistente y duradera para mantener el pie seguro a cada kilómetro.'}} {{'¿Qué novedades tiene el Invincible 3?': 'Creamos el clip del talón más pequeño que el de nuestra versión anterior y lo colocamos en una ubicación más precisa. La entresuela más ancha agrega estabilidad. La espuma apilada más alta que la de nuestra versión anterior eleva la vara en términos de amortiguación y comodidad en un diseño más elegante.'}}\", 'description': 'Con la máxima amortiguación para soportar cada kilómetro, el Invincible 3 tiene nuestro más alto nivel de comodidad a tus pies. Su espuma ZoomX Foam suave y flexible te ayuda a mantener la estabilidad y la frescura. En otras palabras, te sentirás bien todo el día, sin importar lo que hagas. Tiene todo lo que necesitas para que puedas impulsarte por tu camino preferido y volver a tu próxima carrera sintiéndote listo y revitalizado.', 'category': 'Calzado de running en carretera para hombre'}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Amortiguación: superalta | Nike ZoomX con forma de mecedora y espuma más alta | transpirable | Cuanta más amortiguación tengas en la planta del pie | null | Si | Blanco/Sierra Verde | Invincible 3 | Calzado de running en carretera para hombre | Hombre\n", - "\n", - "------------------------\n", - "\n", - "{'details': \"{{'Confeccionada con Nike Grind': 'La suela con diseño tipo waffle cuenta con al menos un 13% de material Nike Grind} {confeccionado con residuos del proceso de fabricación de calzado.'}} {{'Pisada suave': 'La espuma suave y responsiva en la entresuela ofrece una marcha cómoda y suave para tu carrera donde sea que te lleve el día. La espuma más alta proporciona una sensación de suavidad.'}} {{'Échate a volar': 'La parte superior de Flyknit tiene zonas de transpirabilidad donde el pie se calienta más} {específicamente en la punta. Es fuerte y duradera} {lo que ayuda a mantener tu pie seguro en cada kilómetro. La parte superior aireada y ventilada abraza el pie de una manera ligera y sencilla.'}} {{'Más beneficios': 'Más beneficios'}}\", 'description': '¿Puedes ver el futuro? Avanza rápido con el diseño vanguardista del Nike Interact Run. Está diseñado con todas las bondades del running que necesitas: una parte superior ligera de Flyknit, una entresuela de espuma suave y comodidad donde importa. Escanea el código QR de la lengüeta con tu teléfono y consulta nuestra introducción en línea a los detalles de Nike Interact Run.', 'category': 'Calzado de running en carretera para hombre'}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Nike Grind | - | - | waffle | null | sí | - | - | Running | Hombre/MIXTO |\n", - "\n", - "------------------------\n", - "\n", - "{'details': \"{{'Amortiguación: alta': 'Cuanta más amortiguación tengas en la planta del pie} {más suave y cómoda puede ser tu experiencia de running. La amortiguación suaviza el impacto cuando los pies llegan al suelo. La espuma nueva y mejorada en la entresuela ofrece una sensación suave y cómoda mientras acumulas kilómetros. Las pilas altas de espuma mantienen la comodidad. La espuma} {a la que se le dio la forma de mecedora} {brinda soporte en las tres fases de la pisada de un runner. Te brinda flexibilidad cuando levantas el pie del suelo} {un deslizamiento suave cuando el pie se mueve hacia delante y amortiguación al contacto con el suelo.'}} {{'Responsividad: mínima': 'Cuanto más responsivo sea el calzado} {más retorno de energía obtendrás con cada paso. Ya sea que quieras correr un poco más rápido o con un poco menos de esfuerzo} {un calzado responsivo te brinda más elasticidad en tu pisada para que aproveches al máximo tu carrera. La unidad Zoom Air en el antepié aporta amortiguación responsiva. Cada paso crea retorno de energía para impulsarte hacia delante de forma responsiva y con elasticidad.'}} {{'Ajuste adaptable': 'El antepié adaptable tiene suficiente espacio para extender los dedos. El antepié más amplio complementa la base más ancha y una pisada suave. La estructura y contención adicionales alrededor del talón ofrecen un ajuste con más soporte.'}} {{'¿Qué innovaciones tiene el Structure 25?': 'Le agregamos más espuma que a nuestros Structure anteriores y también tiene mejor soporte y comodidad que su predecesor. La espuma nueva y mejorada en la planta del pie brinda soporte.'}} {{'Más beneficios': 'Más beneficios'}}\", 'description': 'Con estabilidad donde la necesitas y amortiguación donde la quieres, el Structure 25 te apoya en los recorridos largos, las carreras cortas de entrenamiento e incluso con unas sentadillas antes de que acabe el día. Es la estabilidad que buscas, leal desde el primer momento, probado y confiable, con un sistema de soporte completo en el mediopié y una amortiguación más cómoda que antes.', 'category': 'Calzado de running en carretera para mujer'}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Amortiguación: alta | Espuma | Textil | Suela con espuma | Pila alta | No | Blanco/ Negro | Structure 25 | Running | Mujer/MIXTO\n", - "\n", - "------------------------\n", - "\n", - "{'details': \"{{'Características principales': 'Características principales'}} {{'Datos del producto': 'Datos del producto'}} {{'Responsividad: moderada': 'Responsividad: moderada'}} {{'Transpirabilidad contenida': 'Transpirabilidad contenida'}} {{'Más beneficios': 'Más beneficios'}} {{'Datos del producto': 'Datos del producto'}}\", 'description': 'La amortiguación máxima proporciona una comodidad mejorada para las carreras diarias. Disfruta de una plataforma suave con forma de mecedora confeccionada con la espuma ReactX Foam nueva en la planta del pie, así como de un cuello y una lengüeta ultracómodos para ofrecer un ajuste ceñido. Además, se añadió una membrana resistente al agua a esta versión para ayudar a evitar la humedad.', 'category': 'Calzado de correr en carretera para mujer'}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Características principales | La amortiguación máxima proporciona una comodidad mejorada para las carreras diarias. Disfruta de una plataforma suave con forma de mecedora confeccionada con la espuma ReactX Foam nueva en la planta del pie, así como de un cuello y una lengüeta ultracómodos para ofrecer un ajuste ceñido. Además, se añadió una membrana resistente al agua a esta versión para ayudar a evitar la humedad. | Hechas con materiales reciclados | Transpirabilidad contenida | Moderada | Negro | X | | Calzado de correr en carretera para mujer | Mujer\n", - "\n", - "------------------------\n", - "\n", - "{'details': \"{{'Amortiguación: superalta': 'Cuanta más amortiguación tengas en la planta del pie} {más suave y cómoda puede ser tu experiencia de running. La amortiguación suaviza el impacto cuando los pies llegan al suelo. La espuma ReactX Foam te brinda una sensación increíblemente suave que te ayuda a ir más allá de tus límites.'}} {{'Soporte: alto': 'Cuanto más soporte ofrece el calzado} {más estabilidad puede darle a tu pisada natural. La combinación de soporte optimizado y amortiguación colocada intencionalmente te ayuda a sentir seguridad en cada paso. La suela curva ayuda a que tu pie se balancee suavemente desde el talón hasta la punta y en cada paso hasta la pisada. Hace que cada paso se sienta más natural y agrega eficiencia a tu carrera} {lo que te ayuda a desperdiciar menos energía en cada pisada. La nueva banda de ajuste Flyknit interna (como una banda de goma alrededor de la parte media del pie) ofrece un soporte elástico y seguro.'}} {{'Responsividad: moderada': 'Cuanto más responsivo sea el calzado} {más retorno de energía obtendrás con cada paso. Ya sea que quieras correr un poco más rápido o con un poco menos de esfuerzo} {un calzado responsivo te brinda más elasticidad en tu pisada para que aproveches al máximo tu carrera. La espuma ReactX Foam te brinda un 13% adicional de retorno de energía en comparación con la espuma React Foam} {lo que te ayuda a mantener la frescura y la elasticidad durante la carrera.'}} {{'Transpirabilidad contenida': 'El forro repelente al agua en la punta te ayuda a mantenerte libre de humedad cuando cambia el tiempo.'}} {{'Más beneficios': 'Más beneficios'}}\", 'description': 'El Nike InfinityRN 4, con una amortiguación con soporte diseñada para ofrecer una carrera uniforme, es una nueva versión de un favorito conocido. Está hecho con nuestra nueva espuma Nike ReactX Foam que te brinda un 13% adicional de retorno de energía en comparación con la espuma Nike React Foam, lo que te ayuda a mantener la frescura y la elasticidad. (¿Y qué más? Nike ReactX reduce su huella de carbono en un par de entresuelas en al menos un 43% en comparación con la espuma Nike React Foam*). Combinamos la espuma ReactX Foam con el Flyknit más adaptable de Nike Running hasta la fecha, para que puedas despegar en cualquier momento y en cualquier lugar con un soporte seguro y transpirabilidad en la parte superior. Es el tipo de calzado que puede concederte esa tranquilidad que no tiene precio para ir más lejos y más rápido gracias a un diseño intuitivo que brinda soporte a cada paso. *La huella de carbono de ReactX se basa en un análisis de todo el proceso de producción, verificado por Intertek China y PRé Sustainability B.V. No se consideraron otros componentes de la entresuela, como cámaras de aire, placas u otras formulaciones de espuma.', 'category': 'Calzado de running en carretera para hombre'}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Amortiguación: superalta | Nike ReactX Foam | Flyknit más adaptable | Suela curva | - | No | Negro/Blanco | InfinityRN 4 | Calzado de running en carretera para hombre | Hombre/MIXTO |\n", - "\n", - "------------------------\n", - "\n", - "{'details': \"{{'Responsividad: superalta': 'Cuanto más responsivo sea el calzado} {más retorno de energía obtendrás con cada paso. Ya sea que quieras correr un poco más rápido o con un poco menos de esfuerzo} {un calzado responsivo te brinda más elasticidad en tu pisada para que aproveches al máximo tu carrera. En el Vomero 17} {nuestro calzado con el mejor resorte} {el pie descansa sobre una pila de ZoomX Foam} {la espuma más ligera y con mayor retorno de energía de Nike Running} {en la parte superior} {y espuma suave en la parte inferior para tener una pisada energizada} {pero suave.'}} {{'Soporte: neutro': 'Cuanto más soporte ofrece el calzado} {más estabilidad puede darle a tu pisada natural. La combinación de soporte optimizado y amortiguación colocada intencionalmente te ayuda a sentir seguridad en cada paso. El Vomero 17 tiene un soporte neutro. Te brinda equilibrio} {ya sea que pises con el talón o con el antepié. También es bueno para carreras largas y cortas; ofrece una transición suave del talón a la punta que complementa la pisada natural.'}} {{'Ajuste y sensación': 'Este calzado se enorgullece en tener comodidad premium que te pone en modo crucero para distancias largas o cortas. La malla diseñada estratégicamente le da más ligereza y transpirabilidad a la parte superior en comparación con los Vomero anteriores. La plantilla suave ofrece comodidad al ponértelo y el cuello es cómodo en el talón al momento de correr. La lengüeta acolchada y las agujetas suaves y elásticas completan una experiencia general ultracómoda.'}} {{'¿Qué novedades tiene el Vomero 17?': 'Simplificamos la estructura del 17 al quitar la unidad Zoom Air del antepié y apilar espuma ZoomX Foam premium arriba de la entresuela para amplificar la comodidad acolchada. El resultado es una pisada más suave y responsiva.'}} {{'Más beneficios': 'Más beneficios'}}\", 'description': 'El Vomero 17 proporciona una pisada elástica y suave para energizar cada kilómetro, la cual te lleva a tu lugar feliz de running. Su espuma apilada proporciona una responsividad superior para ayudarte a mejorar cuando estés listo para esa velocidad adicional. Y con mejoras generales que ofrecen un nuevo nivel de comodidad ligera y transpirabilidad, este calzado es para aquellos runners de carretera que buscan la emoción del vroom y la sensación de suavidad que te pone en modo crucero para distancias cortas o largas.', 'category': 'Calzado de running en carretera para hombre'}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Responsividad: superalta | ZoomX Foam, espuma más ligera y con mayor retorno de energía de Nike Running | - | suela sin especificar | null | No | Color negro/negro escuro | Vomero 17 | Calzado de running en carretera para hombre | Mujer/Hombre/MIXTO |\n", - "\n", - "------------------------\n", - "\n", - "{'details': \"{{'La innovación es nuestra inspiración': 'La innovación es nuestra inspiración'}} {{'Soporte: alto': 'Soporte: alto'}} {{'Amortiguación: superalta': 'Amortiguación: superalta'}} {{'Responsividad: superalta': 'Responsividad: superalta'}} {{'Ajuste: seguro} {transpirable y cómodo': 'Ajuste: seguro} {transpirable y cómodo'}} {{'¿Qué novedades tienen los Invincible 3?': '¿Qué novedades tienen los Invincible 3?'}} {{'Datos del producto': 'Datos del producto'}}\", 'description': 'Con la máxima amortiguación para brindarte soporte en cada kilómetro, el Invincible 3 ofrece nuestro más alto nivel de comodidad en la planta del pie. Sigue corriendo hoy, mañana y siempre. La espuma ZoomX Foam ultraresponsiva y ligera, que cuenta con un diseño avanzado según las especificaciones exactas de atletas campeones, te ayuda a seguir corriendo. Puede ayudarte a impulsarte por tu camino preferido y volver a tu próxima carrera sintiéndote con energía y vitalidad.', 'category': 'Calzado de correr en carretera para hombre'}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| La innovación es nuestra inspiración | transpirable y cómodo | superalta | alta | | no | negro, gris | Invincible 3 | Calzado de correr en carretera para hombre | Hombre\n", - "\n", - "------------------------\n", - "\n" - ] - } - ], - "source": [ - "dfs_nike = []\n", - "for producto in productos_nike:\n", - " prompt = generar_prompt_ollama(producto, etiquetas)\n", - " respuesta = obtener_respuesta_ollama(prompt)\n", - " print(producto)\n", - " print(\"Respuesta del modelo:\")\n", - " print(respuesta[\"message\"][\"content\"])\n", - " print(\"\\n------------------------\\n\")\n", - " df_nike = procesar_respuesta(respuesta[\"message\"][\"content\"], etiquetas)\n", - " if df_nike is not None:\n", - " dfs_nike.append(df_nike)\n", - " else:\n", - " print(\"No se pudo extraer la tabla.\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "metadata": {}, - "outputs": [], - "source": [ - "if dfs_nike:\n", - " df_total_nike = pd.concat(dfs_nike, ignore_index=True)\n", - "else:\n", - " print(\"No se pudo crear el DataFrame total.\")" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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2NullNullNullNullNullSiColor fáciles de combinarInvincible 3Calzado de correr en pavimento para mujerMujer
3Amortiguación: superaltaNike ZoomX con forma de mecedora y espuma más ...transpirableCuanta más amortiguación tengas en la planta d...nullSiBlanco/Sierra VerdeInvincible 3Calzado de running en carretera para hombreHombre
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"4 Running Hombre/MIXTO \n", - "5 Running Mujer/MIXTO \n", - "6 Calzado de correr en carretera para mujer Mujer \n", - "7 Calzado de running en carretera para hombre Hombre/MIXTO \n", - "8 Calzado de running en carretera para hombre Mujer/Hombre/MIXTO \n", - "9 Calzado de correr en carretera para hombre Hombre " - ] - }, - "execution_count": 110, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_total_nike" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Nacion Runer" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "metadata": {}, - "outputs": [], - "source": [ - "df_nr = df_raw[df_raw['store'] == 'nacionRunner']" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(263, 13)" - ] - }, - "execution_count": 116, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_nr.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['id', 'details', 'store', 'manufacturer', 'url', 'title',\n", - " 'regularPrice', 'undiscounted_price', 'description', 'category',\n", - " 'createdAt', 'characteristics', 'gender'],\n", - " dtype='object')" - ] - }, - "execution_count": 118, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_nr.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Usuario\\AppData\\Local\\Temp\\ipykernel_5352\\2945244906.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df_nr['details_transformado'] = df_nr['details'].apply(lambda x: transformar_texto(x, 'adidas'))\n" - ] - } - ], - "source": [ - "# Aplicar la transformación con el parámetro 'adidas'\n", - "df_nr['details_transformado'] = df_nr['details'].apply(lambda x: transformar_texto(x, 'adidas'))" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "metadata": {}, - "outputs": [], - "source": [ - "# Crear un diccionario con las llaves deseadas\n", - "productos_nr = [\n", - " {\n", - " \"details\": row['details_transformado'],\n", - " \"description\": row['description'],\n", - " \"category\": row['category']\n", - " }\n", - " for _, row in df_nr[:10].iterrows() \n", - " if row['details_transformado'] != '{}'\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'details': \"{{'Pisada': 'Neutro • Supinador'}} {{'Peso del Corredor': 'Ideal hasta 85 Kg'}} {{'Amortiguación': 'Alta'}} {{'Ritmo De Carrera': 'Entre 4:30 y 6:00 min/Km'}} {{'Distancia Recomendada': 'Desde 5 hasta 42 Km'}} {{'Superficie': 'Asfalto'}}\", 'description': [{'Comodidad y Amortiguación Superior': 'Las Brooks Ghost 15 se destacan por su amortiguación DNA Loft v2, que proporciona una sensación de suavidad y confort bajo los pies, perfecta para quienes buscan un calzado que se adapte a ritmos de carrera que van desde 4:30 min/km hasta 6:00 min/km. Con un drop de 11 mm, estas zapatillas están diseñadas para corredoras con pisada neutra o supinadora, ofreciendo un soporte equilibrado que facilita la transición en cada zancada.'}, {'Diseñadas para Ritmos y Distancias Variadas': 'Ideales para distancias que van desde los 5 km hasta el maratón, las Brooks Ghost 15 están construidas para soportar volúmenes de entrenamiento semanal que oscilan entre 20 km y más de 50 km. Su capacidad de adaptarse tanto a ritmos rápidos como a entrenamientos más tranquilos las convierte en una opción versátil para el entrenamiento diario en asfalto, asegurando estabilidad y respuesta en cada paso.'}, {'Upper y Suela Mejorados para un Rendimiento Óptimo': 'El upper de malla de ingeniería de las Brooks Ghost 15 ha sido diseñado para mejorar la transpirabilidad y el ajuste, utilizando aproximadamente un 25% de materiales reciclados. Esta estructura permite una excelente ventilación y un ajuste seguro sin comprometer la ligereza. La suela de goma proporciona una tracción duradera y fiable, ideal para mantener la seguridad en todo tipo de superficies durante tus entrenamientos.'}], 'category': nan}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Neutro • Supinador | Malla de ingeniería | Upper con materiales reciclados | Goma | Ideal hasta 85 Kg | Sí | Neutro | Brooks Ghost 15 | Corredora | Mujer |\n", - "\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Entre 4:30 y 6:00 min/Km | Ventilación | Ajuste seguro sin comprometer la ligereza | Duradera y fiable | | Sí | Asfalto | | Corredora | Mujer |\n", - "\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Desde 5 hasta 42 Km | Ventilación | Ajuste seguro sin comprometer la ligerezza | Duradera y fiable | | Sí | Asfalto | | Corredora | Mujer\n", - "\n", - "------------------------\n", - "\n", - "{'details': \"{{'Pisada': 'Neutro'}} {{'Peso del Corredor': 'Ideal hasta 85 Kg'}} {{'Amortiguación': 'Alta'}} {{'Ritmo De Carrera': 'Entre 4:00 y 5:45 min/Km'}} {{'Distancia Recomendada': 'Entre 5 y 21 Km | Entreno en pista'}} {{'Superficie': 'Asfalto'}}\", 'description': [{'El equilibrio perfecto entre velocidad y comodidad': '¡Hey Runner! Las Hoka Mach 6 son ideales para quienes buscan correr rápido sin perder comodidad. Estas zapatillas han sido diseñadas para corredores avanzados que necesitan una opción confiable tanto para entrenamientos de calidad como para competiciones de hasta 21 km. Con una amortiguación reactiva y un diseño ligero, este modelo es perfecto para mantener ritmos rápidos y consistentes, sin la necesidad de una placa de carbono. Si pesas hasta 85 kg y cuentas con una buena base de entrenamiento, las Mach 6 serán tus aliadas en el asfalto. ¡Estas zapatillas son una competencia directa de las New Balance Rebel v4!'}, {'Versatilidad y rendimiento en cada paso': 'Las Hoka Mach 6 destacan por su mediasuela de espuma Supercrítica, que proporciona una pisada dinámica y un excelente retorno de energía. El MetaRocker en la suela te impulsa hacia adelante con cada zancada, haciéndolas ideales para ritmos vivos entre 4:00 min/km y 5:45 min/km. Su capellada de malla Jacquard Creel garantiza transpirabilidad y un ajuste cómodo, mientras que los refuerzos estratégicos de goma en la suela aseguran tracción en superficies mojadas.'}], 'category': nan}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Pisada | Neutro | null | MetaRocker | Ideal hasta 85 Kg | Si | Asfalto | Mach 6 | Corre | Mujer |\n", - "\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Entre 4:00 y 5:45 min/Km | null | null | Altimetral con espuma Supercrítica | Entre 5 y 21 Km | Si | Color neutro | New Balance Rebel v4 | Corre | Hombre\n", - "\n", - "------------------------\n", - "\n", - "{'details': \"{{'Pisada': 'Neutro • Supinador'}} {{'Peso del Corredor': 'Ideal hasta 80 Kg'}} {{'Amortiguación': 'Alta'}} {{'Ritmo De Carrera': '5:00 min/Km o más despacio'}} {{'Distancia Recomendada': 'Desde 5 hasta 21 Km'}} {{'Superficie': 'Asfalto'}}\", 'description': [{'Ligereza y durabilidad para tus entrenamientos diarios': '¡Hey Runner! Las Hoka Rincon 4 están diseñadas para ofrecer una combinación perfecta de ligereza y durabilidad, ideales para quienes buscan un zapato de entrenamiento diario en asfalto. Con su espuma de doble capa en la mediasuela, esta versión mejora notablemente la durabilidad, haciendo que cada zancada se sienta más consistente, incluso en distancias largas. Si eres un corredor que pesa menos de 80 kg y buscas un zapato ligero para entrenar distancias de hasta 21 km, el Rincon 4 es una opción a considerar seriamente. Para quienes pesen más de 80 kg, recomendamos modelos como el Hoka Clifton o el Bondi, que ofrecen mayor soporte.'}, {'Ideal para entrenamientos y distancias medias': 'Con un drop de 8 mm y una amortiguación alta, las Hoka Rincon 4 son perfectas para corredores de pisada neutral o supinador que manejan ritmos cómodos entre 5:00 min/km y más de 6:15 min/km. Su capellada de malla Jacquard es ligera y transpirable, asegurando un ajuste cómodo en cada carrera. Además, su suela de EVA con goma en zonas estratégicas proporciona un buen agarre, mientras que el MetaRocker ayuda a una transición suave de cada paso, maximizando la eficiencia en cada kilómetro.'}], 'category': nan}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Neutro • Supinador | Null | Null | Null | Ideal hasta 80 Kg | No | Asfalto | Hoka Rincon 4 | Corredor | Mujer |\n", - "\n", - "Nota: Se extrae la información según las etiquetas proporcionadas y se completa con valores \"null\" donde corresponda.\n", - "\n", - "------------------------\n", - "\n", - "{'details': \"{{'Pisada': 'Neutro'}} {{'Peso del Corredor': 'Ideal hasta 80 Kg'}} {{'Amortiguación': 'Alta'}} {{'Ritmo De Carrera': 'Entre 3:45 y 5:30 min/Km'}} {{'Distancia Recomendada': 'Entre 5 y 21 Km | Entreno en pista'}} {{'Superficie': 'Asfalto'}}\", 'description': [{'Ligereza y Respuesta para Distancias Medias': 'Las Hoka Mach 5 están diseñadas para ofrecer una excelente respuesta en ritmos que van desde 3:45 min/km hasta 5:15 min/km. Con un drop de 5 mm y un peso de 242 gramos en la versión para hombre, estas zapatillas son ideales para distancias que van desde 5 km hasta 21 km. La mediasuela incorpora la espuma ProFly+, que combina una amortiguación suave con una sensación de rebote, permitiendo una transición fluida en cada zancada.'}, {'Comodidad y Ajuste Superior': 'Estas zapatillas cuentan con una capellada de malla ligera que asegura una transpirabilidad óptima, manteniendo tus pies frescos incluso en carreras intensas. Además, el diseño del calzado, con una lengüeta plana y una sujeción firme, se adapta perfectamente a tu pie, evitando movimientos indeseados y garantizando un ajuste seguro durante todo el recorrido.'}, {'Ideal para Entrenamientos Diarios y Diversos Tipos de Corredores': 'Las Hoka Mach 5 no solo son una opción excelente para velocidad, sino que también se destacan como calzado de entrenamiento diario. Son ideales para corredores con un peso de entre menos de 60 kg hasta 80 kg, y que tengan un volumen de entrenamiento semanal de entre 30 km y 50 km o más. Ofrecen un equilibrio ideal entre amortiguación y ligereza, lo que las convierte en una excelente opción para aquellos corredores que buscan un calzado que les permita rendir al máximo día tras día.'}, {'Competencia Directa en Colombia': 'En el mercado colombiano, las Hoka Mach 5 compiten directamente con modelos destacados como la ASICS Noosa Tri 15, el Brooks Hyperion Max, y el New Balance Rebel v3. Sin embargo, las Mach 5 se destacan por su combinación única de ligereza y capacidad de respuesta, haciéndolas una opción preferida para corredores que buscan superar sus límites en cada entrenamiento.'}], 'category': nan}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Neutro | Asfalto | Espuma ProFly+ | Mediasuela | Ideal hasta 80 Kg | Sí | Neutro | Hoka Mach 5 | Corredor | Hombre |\n", - "\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n", - "| null | Asfalto | Espuma ProFly+ | Mediasuela | Ideal hasta 80 Kg | Sí | Neutro | Hoka Mach 5 | Corredor | Hombre |\n", - "\n", - "Nota: En la primera fila, se indican los nombres de las etiquetas. En la segunda fila, se muestran los valores correspondientes a cada etiqueta.\n", - "\n", - "------------------------\n", - "\n", - "{'details': \"{{'Pisada': 'Neutro • Supinador'}} {{'Peso del Corredor': 'Ideal hasta 85 Kg'}} {{'Amortiguación': 'Superior'}} {{'Ritmo De Carrera': 'Entre 4:00 y 6:00 min/Km'}} {{'Distancia Recomendada': 'Entre 5 y 42 Km | Entreno en pista'}} {{'Superficie': 'Asfalto'}}\", 'description': [{'Rendimiento en Asfalto': 'La Asics Novablast 4 LE está optimizada para asfalto, ofreciendo una experiencia de carrera segura y controlada. Con un drop de 8 mm y una pisada neutra o supinadora, esta zapatilla es ideal para corredores que buscan entrenamientos de velocidad. Su diseño más \"contundente\" y sólido proporciona una estabilidad superior, permitiendo girar y voltear con confianza, incluso en circuitos cerrados. Además, la base ensanchada y el talón prominente mejoran aún más la estabilidad, eliminando cualquier rastro de inestabilidad presente en versiones anteriores.'}, {'Amortiguación Superior y Comodidad Avanzada': 'Equipada con la innovadora mediasuela FlyteFoam Blast+ ECO, la Novablast 4 LE ofrece una amortiguación superior, adaptándose tanto a ritmos moderados como intensos. Este modelo se destaca por su capacidad de proporcionar una carrera suave y cómoda, aunque a ritmos muy altos puede sentirse un poco menos reactiva. Sin embargo, su enfoque está en ofrecer una experiencia de entrenamiento segura y eficiente, ideal para distancias que van desde los 5 km hasta los 42 km.'}, {'Tecnología y Diseño de Vanguardia': 'La Novablast 4 LE no solo se distingue por su ligereza, sino también por la inclusión de un nuevo caucho AHAR en la suela, mejorando significativamente la tracción y la durabilidad. Su capellada de material Woven, elástica y sin costuras, brinda un ajuste cómodo y lujoso, aunque sacrifica un poco de ventilación. Sin embargo, su capacidad de secado rápido compensa este aspecto, haciendo que la zapatilla esté lista para usar nuevamente en poco tiempo, incluso después de entrenamientos bajo la lluvia.'}], 'category': nan}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Supinador | Neutro • Supinador | null | AhAR | Ideal hasta 85 Kg | null | Neutro • Supinador | Novablast 4 LE | Entre 5 y 42 Km | Mujer/Hombre/MIXTO |\n", - "\n", - "------------------------\n", - "\n", - "{'details': \"{{'Pisada': 'Pronador'}} {{'Peso del Corredor': 'Ideal hasta 85 Kg'}} {{'Amortiguación': 'Alta'}} {{'Ritmo De Carrera': '5:15 min/Km o más despacio'}} {{'Distancia Recomendada': 'Desde 5 hasta 21 Km'}} {{'Superficie': 'Asfalto'}}\", 'description': [{'Estabilidad y confort a un precio que convence': '¡Hey Runner! Las ASICS GT-2000 13 llegan al mercado con la promesa de ser una de las mejores opciones para corredores que buscan estabilidad y amortiguación alta sin romper el presupuesto. Este modelo no es un Kayano, pero está muy cerca en términos de calidad y soporte. Por su precio, es una elección que harías a ojos cerrados, y con lo que ahorras, ¡podrías inscribirte en tu próxima carrera!'}, {'Tecnología avanzada para un rendimiento superior': 'Las GT-2000 13 están diseñadas para corredores con pisada pronadora que buscan entrenar de forma regular en asfalto y distancias de hasta 21 km. Con un drop de 8 mm y un peso de 275 g, estas zapatillas brindan una pisada suave y protegida. La espuma FlyteFoam Blast Plus y la tecnología PureGEL en el talón aseguran una amortiguación que se siente como correr sobre una nube, mientras que el sistema 3D Guidance mejora la estabilidad y guía tu pisada de manera natural.'}], 'category': nan}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Pisada pronadora | Pronador | Algunos puestos pueden tener materiales sintéticos como la poliéster, el nylon y la paño, con una mezcla de cuero y poliéster en los bordes | Alta densidad | Ideal hasta 85 Kg | Sí | Negro/Rosa | GT-2000 13 | Corredor | Mujer/Hombre/MIXTO |\n", - "\n", - "------------------------\n", - "\n", - "{'details': \"{{'Pisada': 'Neutro • Supinador'}} {{'Peso del Corredor': 'Ideal hasta 85 Kg'}} {{'Amortiguación': 'Alta'}} {{'Ritmo De Carrera': 'Entre 4:00 y 5:45 min/Km'}} {{'Distancia Recomendada': 'Desde 5 hasta 42 Km'}} {{'Superficie': 'Asfalto'}}\", 'description': [{'Tecnología y Rendimiento en Asfalto': 'La Asics Glideride Max está optimizada para entrenamientos diarios y largas distancias en asfalto. Diseñada para corredores avanzados con pisada neutra o supinadora, esta zapatilla cuenta con un drop de 6 mm y es ideal para mantener ritmos entre 4:00 min/km y 5:30 min/km en distancias que van desde los 5 km hasta la maratón. Lo que hace destacar a este modelo es la incorporación de la innovadora espuma FF Blast Max, la cual promete un rebote superior y una sensación de ligereza, sin precedentes en la línea Glideride.'}, {'Placa Interna y Amortiguación Superior': 'Para aquellos que buscan mejorar sus tiempos sin recurrir a una placa de carbono, la Glideride Max ofrece una solución perfecta. Este modelo incorpora una placa interna de nylon o plástico que añade rigidez y mejora la eficiencia en cada zancada, impulsándote a correr más rápido. Además, la combinación de FlyteFoam Blast+ ECO en la capa inferior de la mediasuela y el nuevo FF Blast Max en la parte superior, asegura una sensación de amortiguación alta que te mantendrá protegido en cada paso, incluso en tus entrenamientos más intensos.'}, {'Diseño y Durabilidad': 'El upper de la Glideride Max está construido con un mesh técnico similar al de la Nimbus 26, ofreciendo transpirabilidad y un ajuste seguro. La suela, diseñada con una combinación de ASICS Grip y AHARPLUS, garantiza durabilidad y tracción en diversas superficies, lo que la convierte en una opción confiable para corredores que necesitan un calzado resistente para entrenamientos de volumen.'}, {'Competencia en el Mercado Colombiano': 'En el mercado colombiano, la Asics Glideride Max competirá directamente con modelos como el Brooks Hyperion Max. Ambos zapatos están dirigidos a corredores que buscan velocidad sin renunciar al confort de un calzado maximalista. La Glideride Max se presenta como una opción sólida para aquellos que desean una zapatilla con tecnología avanzada que les permita afrontar largas distancias con mayor eficiencia y menor riesgo de lesión.'}], 'category': nan}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Neutro • Supinador | Neutro | Nylon o plástico | ASICS Grip + AHARPLUS | Ideal hasta 85 Kg | Si | Neutro | Glideride Max | Corredora | Hombre\n", - "\n", - "------------------------\n", - "\n", - "{'details': \"{{'Pisada': 'Pronador'}} {{'Peso del Corredor': 'Ideal desde 65 Kg en adelante'}} {{'Amortiguación': 'Superior'}} {{'Ritmo De Carrera': '5:30 min/Km o más despacio'}} {{'Distancia Recomendada': 'Desde 5 hasta 42 Km'}} {{'Superficie': 'Asfalto'}}\", 'description': [{'Estabilidad y confort para corredores avanzados': 'Las Gel Kayano 31 están equipadas con el innovador 4D Guidance System, una tecnología que proporciona el soporte exacto que cada corredor necesita, adaptándose de manera dinámica a la forma de correr, sin restringir el movimiento natural del pie. Este sistema es esencial para corredores con pisada pronadora, ayudando a controlar la sobrepronación y ofreciendo una carrera más equilibrada y segura. Además, con una sensación de amortiguación superior proporcionada por la tecnología FF BLAST PLUS ECO en la mediasuela, estas zapatillas garantizan una absorción de impactos excepcional, devolviendo la energía en cada zancada.'}, {'Amortiguación dinámica para largas distancias': 'Ideales para corredores que entrenan distancias que van desde 10 km hasta los 42 km, las Gel Kayano 31 están diseñadas para mantener la comodidad y estabilidad durante los entrenamientos más largos. Su capacidad para manejar ritmos de carrera entre 5:30 min/km y más de 6:15 min/km las hace perfectas para corredores con un volumen de entrenamiento semanal que oscila entre 20 km y más de 50 km. Con un drop de 10 mm y un peso de 305 gramos para la versión masculina, estas zapatillas están preparadas para corredores con un peso de 65 kg a más de 95 kg.'}, {'Diseño renovado y adaptabilidad': 'La capellada de malla técnica mejorada no solo asegura una mayor transpirabilidad, sino que también proporciona un ajuste más cómodo y personalizado, adaptándose a la forma del pie en cada paso. Además, la suela exterior, actualizada con el material de goma HYBRID ASICSGRIP, mejora la tracción y la estabilidad en cada kilómetro que recorres. Estas características hacen que las Gel Kayano 31 sean ideales para entrenamientos en asfalto y corredores que buscan una zapatilla de entrenamiento diario que pueda manejar altos volúmenes de entrenamiento semanal.'}, {'Un competidor destacado en el mercado de estabilidad': 'Las Gel Kayano 31 se enfrentan cara a cara con otras zapatillas de estabilidad premium, como el New Balance Vongo y el Brooks Glycerine GTS, ofreciendo una experiencia de carrera más dinámica gracias a su amortiguación mejorada que se adapta a tu ritmo y técnica de carrera.'}], 'category': nan}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Pisada pronadora | Asfalto | Mediasuela tech | Suela de goma HYBRID ASICSGRIP | Ideal desde 65 Kg en adelante | Si | Blanco | Kayano 31 | Corredor | Hombre\n", - "\n", - "------------------------\n", - "\n", - "{'details': \"{{'Pisada': 'Pronador'}} {{'Peso del Corredor': 'Ideal hasta 85 Kg'}} {{'Amortiguación': 'Alta'}} {{'Ritmo De Carrera': '5:15 min/Km o más despacio'}} {{'Distancia Recomendada': 'Desde 5 hasta 21 Km'}} {{'Superficie': 'Asfalto'}}\", 'description': [{'Rendimiento Óptimo para Pronadores': 'La Asics GT-1000 13 es ideal para corredores con pisada pronadora, ofreciendo un equilibrio perfecto entre estabilidad y amortiguación. Con un drop de 8 mm y un peso de 270 gramos, esta zapatilla es adecuada para corredores que pesan entre menos de 60 kg y 85 kg. Gracias a la incorporación de la tecnología PureGel en la zona del talón, esta zapatilla proporciona una absorción de impactos superior, permitiendo una sensación de comodidad en cada zancada.'}, {'Amortiguación y Estabilidad en Ritmos Moderados': 'Con una sensación de amortiguación alta, la GT-1000 13 está diseñada para corredores que mantienen ritmos entre 5:15 min/km y 6:15 min/km o más lentos. Es ideal para distancias que van desde 5 km hasta 21 km, proporcionando el soporte necesario para mantener un ritmo constante y seguro. La tecnología FlyteFoam en la mediasuela asegura una pisada suave y ligera, mientras que la suela, con una alta concentración de caucho, garantiza durabilidad y tracción en superficies de asfalto.'}, {'Confort y Durabilidad para el Entrenamiento Diario': 'El upper de la GT-1000 13 está confeccionado con una malla técnica que asegura una excelente transpirabilidad y un ajuste preciso, envolviendo tu pie con suavidad en cada carrera. Además, la plantilla OrthoLite Hybrid Max añade una capa adicional de confort, lo que hace que cada entrenamiento sea más placentero. Esta zapatilla está diseñada para un volumen de entrenamiento semanal de 20 km a 40 km, siendo una opción confiable para corredores que buscan estabilidad en sus entrenamientos diarios.'}, {'Accesibilidad y Valor Inigualable': 'Una de las ventajas más destacadas de la Asics GT-1000 13 es su relación calidad-precio. A pesar de incorporar tecnologías de gama alta como el PureGel, esta zapatilla se mantiene en un rango de precio accesible, lo que la coloca en una posición competitiva en el mercado colombiano. Aunque no tiene rivales directos en su rango de precio, podría compararse con el Brooks Adrenaline, pero a un costo significativamente menor, ofreciendo así un valor insuperable para corredores pronadores.'}], 'category': nan}\n", - "Respuesta del modelo:\n", - "| cordones | textil exterior | textil interior | suela | peso y/o talla | eco diseñado si o no | color | identificador | nombre de deporte | genero Mujer/Hombre/MIXTO |\n", - "| Pronador | Algodón/Polipropileno | Malla técnica con poliéster y spandex | Suela de caucho | Ideal hasta 85 Kg | Si | Negro/Cream | GT-1000 13 | Pronador | Hombre\n", - "\n", - "------------------------\n", - "\n" - ] - } - ], - "source": [ - "dfs_nr = []\n", - "for producto in productos_nr:\n", - " prompt = generar_prompt_ollama(producto, etiquetas)\n", - " respuesta = obtener_respuesta_ollama(prompt)\n", - " print(producto)\n", - " print(\"Respuesta del modelo:\")\n", - " print(respuesta[\"message\"][\"content\"])\n", - " print(\"\\n------------------------\\n\")\n", - " df_nr = procesar_respuesta(respuesta[\"message\"][\"content\"], etiquetas)\n", - " if df_nr is not None:\n", - " dfs_nr.append(df_nr)\n", - " else:\n", - " print(\"No se pudo extraer la tabla.\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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cordonestextil exteriortextil interiorsuelapeso y/o tallaeco diseñado si o nocoloridentificadornombre de deportegenero Mujer/Hombre/MIXTO
0Neutro • SupinadorMalla de ingenieríaUpper con materiales recicladosGomaIdeal hasta 85 KgSíNeutroBrooks Ghost 15CorredoraMujer
1PisadaNeutronullMetaRockerIdeal hasta 85 KgSiAsfaltoMach 6CorreMujer
2Neutro • SupinadorNullNullNullIdeal hasta 80 KgNoAsfaltoHoka Rincon 4CorredorMujer
3NeutroAsfaltoEspuma ProFly+MediasuelaIdeal hasta 80 KgSíNeutroHoka Mach 5CorredorHombre
4SupinadorNeutro • SupinadornullAhARIdeal hasta 85 KgnullNeutro • SupinadorNovablast 4 LEEntre 5 y 42 KmMujer/Hombre/MIXTO
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" - ], - "text/plain": [ - " cordones textil exterior textil interior \\\n", - "0 Neutro • Supinador Malla de ingeniería Upper con materiales reciclados \n", - "1 Pisada Neutro null \n", - "2 Neutro • Supinador Null Null \n", - "3 Neutro Asfalto Espuma ProFly+ \n", - "4 Supinador Neutro • Supinador null \n", - "\n", - " suela peso y/o talla eco diseñado si o no color \\\n", - "0 Goma Ideal hasta 85 Kg Sí Neutro \n", - "1 MetaRocker Ideal hasta 85 Kg Si Asfalto \n", - "2 Null Ideal hasta 80 Kg No Asfalto \n", - "3 Mediasuela Ideal hasta 80 Kg Sí Neutro \n", - "4 AhAR Ideal hasta 85 Kg null Neutro • Supinador \n", - "\n", - " identificador nombre de deporte genero Mujer/Hombre/MIXTO \n", - "0 Brooks Ghost 15 Corredora Mujer \n", - "1 Mach 6 Corre Mujer \n", - "2 Hoka Rincon 4 Corredor Mujer \n", - "3 Hoka Mach 5 Corredor Hombre \n", - "4 Novablast 4 LE Entre 5 y 42 Km Mujer/Hombre/MIXTO " - ] - }, - "execution_count": 123, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "if dfs_nr:\n", - " df_total_nr = pd.concat(dfs_nr, ignore_index=True)\n", - "else:\n", - " print(\"No se pudo crear el DataFrame total.\")\n", - " \n", - "df_total_nr.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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cordonestextil exteriortextil interiorsuelapeso y/o tallaeco diseñado si o nocoloridentificadornombre de deportegenero Mujer/Hombre/MIXTO
0Neutro • SupinadorMalla de ingenieríaUpper con materiales recicladosGomaIdeal hasta 85 KgSíNeutroBrooks Ghost 15CorredoraMujer
1PisadaNeutronullMetaRockerIdeal hasta 85 KgSiAsfaltoMach 6CorreMujer
2Neutro • SupinadorNullNullNullIdeal hasta 80 KgNoAsfaltoHoka Rincon 4CorredorMujer
3NeutroAsfaltoEspuma ProFly+MediasuelaIdeal hasta 80 KgSíNeutroHoka Mach 5CorredorHombre
4SupinadorNeutro • SupinadornullAhARIdeal hasta 85 KgnullNeutro • SupinadorNovablast 4 LEEntre 5 y 42 KmMujer/Hombre/MIXTO
5Pisada pronadoraPronadorAlgunos puestos pueden tener materiales sintét...Alta densidadIdeal hasta 85 KgSíNegro/RosaGT-2000 13CorredorMujer/Hombre/MIXTO
6Neutro • SupinadorNeutroNylon o plásticoASICS Grip + AHARPLUSIdeal hasta 85 KgSiNeutroGlideride MaxCorredoraHombre
7Pisada pronadoraAsfaltoMediasuela techSuela de goma HYBRID ASICSGRIPIdeal desde 65 Kg en adelanteSiBlancoKayano 31CorredorHombre
8PronadorAlgodón/PolipropilenoMalla técnica con poliéster y spandexSuela de cauchoIdeal hasta 85 KgSiNegro/CreamGT-1000 13PronadorHombre
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" - ], - "text/plain": [ - " cordones textil exterior \\\n", - "0 Neutro • Supinador Malla de ingeniería \n", - "1 Pisada Neutro \n", - "2 Neutro • Supinador Null \n", - "3 Neutro Asfalto \n", - "4 Supinador Neutro • Supinador \n", - "5 Pisada pronadora Pronador \n", - "6 Neutro • Supinador Neutro \n", - "7 Pisada pronadora Asfalto \n", - "8 Pronador Algodón/Polipropileno \n", - "\n", - " textil interior \\\n", - "0 Upper con materiales reciclados \n", - "1 null \n", - "2 Null \n", - "3 Espuma ProFly+ \n", - "4 null \n", - "5 Algunos puestos pueden tener materiales sintét... \n", - "6 Nylon o plástico \n", - "7 Mediasuela tech \n", - "8 Malla técnica con poliéster y spandex \n", - "\n", - " suela peso y/o talla \\\n", - "0 Goma Ideal hasta 85 Kg \n", - "1 MetaRocker Ideal hasta 85 Kg \n", - "2 Null Ideal hasta 80 Kg \n", - "3 Mediasuela Ideal hasta 80 Kg \n", - "4 AhAR Ideal hasta 85 Kg \n", - "5 Alta densidad Ideal hasta 85 Kg \n", - "6 ASICS Grip + AHARPLUS Ideal hasta 85 Kg \n", - "7 Suela de goma HYBRID ASICSGRIP Ideal desde 65 Kg en adelante \n", - "8 Suela de caucho Ideal hasta 85 Kg \n", - "\n", - " eco diseñado si o no color identificador nombre de deporte \\\n", - "0 Sí Neutro Brooks Ghost 15 Corredora \n", - "1 Si Asfalto Mach 6 Corre \n", - "2 No Asfalto Hoka Rincon 4 Corredor \n", - "3 Sí Neutro Hoka Mach 5 Corredor \n", - "4 null Neutro • Supinador Novablast 4 LE Entre 5 y 42 Km \n", - "5 Sí Negro/Rosa GT-2000 13 Corredor \n", - "6 Si Neutro Glideride Max Corredora \n", - "7 Si Blanco Kayano 31 Corredor \n", - "8 Si Negro/Cream GT-1000 13 Pronador \n", - "\n", - " genero Mujer/Hombre/MIXTO \n", - "0 Mujer \n", - "1 Mujer \n", - "2 Mujer \n", - "3 Hombre \n", - "4 Mujer/Hombre/MIXTO \n", - "5 Mujer/Hombre/MIXTO \n", - "6 Hombre \n", - "7 Hombre \n", - "8 Hombre " - ] - }, - "execution_count": 124, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_total_nr" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From ca6eccb3ca97805a6b45e81bfe4513bd18e0093a Mon Sep 17 00:00:00 2001 From: CarlosKC26 Date: Thu, 12 Dec 2024 23:33:02 -0500 Subject: [PATCH 40/84] =?UTF-8?q?eliminaci=C3=B3n=20test.ipynb?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/comparative_analysis/training/test.ipynb | 1167 ------------------ 1 file changed, 1167 deletions(-) delete mode 100644 src/comparative_analysis/training/test.ipynb diff --git a/src/comparative_analysis/training/test.ipynb b/src/comparative_analysis/training/test.ipynb deleted file mode 100644 index 365548f61..000000000 --- a/src/comparative_analysis/training/test.ipynb +++ /dev/null @@ -1,1167 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Weight Upper_Material Midsole_Material \\\n", - "0 183 g Synthetic NaN \n", - "1 289 g adidas Primeknit BOOST \n", - "2 166 g Parte superior de malla técnica NaN \n", - "3 200 gramos Parte superior de malla NaN \n", - "4 319g Parte superior textil Mediasuela Cloudfoam \n", - "\n", - " Outsole Cushioning_System \\\n", - "0 Textile rubber Lightstrike Pro \n", - "1 Stretchweb with Continental Better Rubber Linear Energy Push \n", - "2 Suela de caucho Continental™ Amortiguación Lightstrike Pro \n", - "3 Suela de caucho Continental Rubber Amortiguación Lightstrike Pro \n", - "4 Suela de TPU Cloudfoam \n", - "\n", - " Drop__heel-to-toe_differential_ Pronation_Type Usage_Type Gender \\\n", - "0 6 mm NaN Racing Woman \n", - "1 NaN NaN Running Woman \n", - "2 6 mm NaN Running Mujer \n", - "3 6 mm NaN Running Hombre \n", - "4 6mm NaN Running Hombre \n", - "\n", - " Available_Sizes Width Additional_Technologies \\\n", - "0 NaN NaN ENERGYRODS 2.0, Waterproofing, Recyclable mate... \n", - "1 NaN NaN Parley Ocean Plastic, waterproofing \n", - "2 COL 36 1/2 NaN Contiene al menos un 20 % de material reciclad... \n", - "3 CO 40 NaN Varillas ENERGYRODS, Talón Slinglaunch, Contie... \n", - "4 CO 40 NaN NaN \n", - "\n", - " id regularPrice undiscounted_price \\\n", - "0 08sjncACSjSvg2t9DS73 $1.299.950 $909.965 \n", - "1 0AqheRhKT2lhm7puBVCF $799.950 NaN \n", - "2 0IgYTzUHkE7zIdcVyFCK $1.049.950 $629.970 \n", - "3 0MU8aKCnCUZv2r9aLD67 $1.049.950 $734.965 \n", - "4 0Q6DNSlvsjBzy3AQeY2y $279.950 NaN \n", - "\n", - " details \\\n", - "0 {Horma clásica} {Parte superior sintética} {Fo... \n", - "1 {Ajuste clásico} {Sistema de amarre de cordone... \n", - "2 {Ajuste clásico} {Cierre de cordones} {Parte s... \n", - "3 {Ajuste clásico} {Sistema de amarre de cordone... \n", - "4 {Horma clásica} {Sistema de amarre de cordones... \n", - "\n", - " description category \\\n", - "0 Los Adizero Adios Pro 3 son la máxima expresió... Mujer • Running \n", - "1 Hemos analizado 1.200.000 pisadas para que Ult... Mujer • Running \n", - "2 Haz tus mejores 10k con nuestros nuevos tenis ... Mujer • Running \n", - "3 Haz tus mejores 10k con nuestros nuevos tenis ... Hombre • Running \n", - "4 Cada carrera es un viaje de descubrimiento. Po... Hombre • Running \n", - "\n", - " characteristics \n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\cdgn2\\OneDrive\\Escritorio\\Maestría\\Maestria\\Metodologias Agiles\\Proyecto\\Comparative-analysis-of-products\\venv\\lib\\site-packages\\openpyxl\\styles\\stylesheet.py:237: UserWarning: Workbook contains no default style, apply openpyxl's default\n", - " warn(\"Workbook contains no default style, apply openpyxl's default\")\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "# Ruta del archivo de Excel\n", - "ruta_excel = r\"C:\\Users\\cdgn2\\OneDrive\\Escritorio\\Maestría\\Maestria\\Metodologias Agiles\\Proyecto\\Comparative-analysis-of-products\\src\\comparative_analysis\\database\\Adidas_etiquetado.xlsx\"\n", - "\n", - "# Crear el DataFrame\n", - "df = pd.read_excel(ruta_excel, header=0)\n", - "\n", - "# Mostrar las primeras filas del DataFrame\n", - "print(df.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Información del elemento encontrado:\n", - " Weight Upper_Material Midsole_Material Outsole Cushioning_System \\\n", - "0 183 g Synthetic NaN Textile rubber Lightstrike Pro \n", - "\n", - " Drop__heel-to-toe_differential_ Pronation_Type Usage_Type Gender \\\n", - "0 6 mm NaN Racing Woman \n", - "\n", - " Available_Sizes Width Additional_Technologies \\\n", - "0 NaN NaN ENERGYRODS 2.0, Waterproofing, Recyclable mate... \n", - "\n", - " id regularPrice undiscounted_price \\\n", - "0 08sjncACSjSvg2t9DS73 $1.299.950 $909.965 \n", - "\n", - " details \\\n", - "0 {Horma clásica} {Parte superior sintética} {Fo... \n", - "\n", - " description category \\\n", - "0 Los Adizero Adios Pro 3 son la máxima expresió... Mujer • Running \n", - "\n", - " characteristics \n", - "0 NaN \n" - ] - } - ], - "source": [ - "# Buscar información de un elemento basado en un criterio\n", - "def buscar_elemento(df, columna, valor):\n", - " # Filtrar el DataFrame por el valor especificado\n", - " resultado = df[df[columna] == valor]\n", - " \n", - " # Verificar si se encontró algún resultado\n", - " if not resultado.empty:\n", - " print(\"Información del elemento encontrado:\")\n", - " print(resultado)\n", - " else:\n", - " print(f\"No se encontró ningún elemento donde '{columna}' sea '{valor}'.\")\n", - "\n", - "# Ejemplo de uso: Buscar un elemento donde el id sea \"12345\" (ajusta según tu DataFrame)\n", - "buscar_elemento(df, columna=\"id\", valor=\"08sjncACSjSvg2t9DS73\")" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Weight', 'Upper_Material', 'Midsole_Material', 'Outsole', 'Cushioning_System', 'Drop__heel-to-toe_differential_', 'Pronation_Type', 'Usage_Type', 'Gender', 'Available_Sizes', 'Width', 'Additional_Technologies', 'id', 'regularPrice', 'undiscounted_price', 'details', 'description', 'category', 'characteristics']\n" - ] - } - ], - "source": [ - "print(df.columns.tolist())" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import re\n", - "\n", - "# Supongamos que tu DataFrame se llama df\n", - "\n", - "# Convertir Weight a solo números y luego a float, manejando errores\n", - "df['Weight'] = df['Weight'].astype(str).str.extract('(\\d+\\.?\\d*)').astype(float, errors='ignore')\n", - "# Si quieres forzar que valores no numéricos sean NaN\n", - "df['Weight'] = df['Weight'].astype(str).str.extract('(\\d+\\.?\\d*)')\n", - "df['Weight'] = pd.to_numeric(df['Weight'], errors='coerce')\n", - "\n", - "# Drop__heel-to-toe_differential_\n", - "df['Drop__heel-to-toe_differential_'] = df['Drop__heel-to-toe_differential_'].astype(str).str.extract('(\\d+\\.?\\d*)')\n", - "df['Drop__heel-to-toe_differential_'] = pd.to_numeric(df['Drop__heel-to-toe_differential_'], errors='coerce')\n", - "\n", - "# regularPrice y undiscounted_price\n", - "df['regularPrice'] = df['regularPrice'].astype(str).str.replace(r'[^0-9.,]', '', regex=True)\n", - "df['undiscounted_price'] = df['undiscounted_price'].astype(str).str.replace(r'[^0-9.,]', '', regex=True)\n", - "\n", - "df['regularPrice'] = df['regularPrice'].str.replace(r'\\.', '', regex=True).str.replace(',', '.')\n", - "df['undiscounted_price'] = df['undiscounted_price'].str.replace(r'\\.', '', regex=True).str.replace(',', '.')\n", - "\n", - "# Convertir a numérico con manejo de errores\n", - "df['regularPrice'] = pd.to_numeric(df['regularPrice'], errors='coerce')\n", - "df['undiscounted_price'] = pd.to_numeric(df['undiscounted_price'], errors='coerce')\n", - "\n", - "# Eliminar las columnas solicitadas\n", - "cols_to_drop = ['details', 'description', 'category', 'characteristics', 'width', 'Pronation_Type']\n", - "df = df.drop(columns=cols_to_drop, errors='ignore')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Información del elemento encontrado:\n", - " Weight Upper_Material Midsole_Material Outsole Cushioning_System \\\n", - "0 183.0 Synthetic NaN Textile rubber Lightstrike Pro \n", - "\n", - " Drop__heel-to-toe_differential_ Usage_Type Gender Width \\\n", - "0 6.0 Racing Woman NaN \n", - "\n", - " Additional_Technologies id \\\n", - "0 ENERGYRODS 2.0, Waterproofing, Recyclable mate... 08sjncACSjSvg2t9DS73 \n", - "\n", - " regularPrice undiscounted_price \n", - "0 1.299950e+09 909965 \n" - ] - } - ], - "source": [ - "buscar_elemento(df, columna=\"id\", valor=\"08sjncACSjSvg2t9DS73\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Weight', 'Upper_Material', 'Midsole_Material', 'Outsole', 'Cushioning_System', 'Drop__heel-to-toe_differential_', 'Usage_Type', 'Gender', 'Width', 'Additional_Technologies', 'id', 'regularPrice', 'undiscounted_price']\n" - ] - } - ], - "source": [ - "print(df.columns.tolist())" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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0183.0SyntheticNaNTextile rubberLightstrike Pro6.0RacingWomanNaNNaNENERGYRODS 2.0, Waterproofing, Recyclable mate...08sjncACSjSvg2t9DS731299950909965.0
1289.0adidas PrimeknitBOOSTStretchweb with Continental Better RubberLinear Energy PushNaNRunningWomanNaNNaNParley Ocean Plastic, waterproofing0AqheRhKT2lhm7puBVCF799950NaN
2166.0Parte superior de malla técnicaNaNSuela de caucho Continental™Amortiguación Lightstrike Pro6.0RunningMujerCOL 36 1/2NaNContiene al menos un 20 % de material reciclad...0IgYTzUHkE7zIdcVyFCK1049950629970.0
3200.0Parte superior de mallaNaNSuela de caucho Continental RubberAmortiguación Lightstrike Pro6.0RunningHombreCO 40NaNVarillas ENERGYRODS, Talón Slinglaunch, Contie...0MU8aKCnCUZv2r9aLD671049950734965.0
4319.0Parte superior textilMediasuela CloudfoamSuela de TPUCloudfoam6.0RunningHombreCO 40NaNNaN0Q6DNSlvsjBzy3AQeY2y279950NaN
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" - ], - "text/plain": [ - "Empty DataFrame\n", - "Columns: [Weight, Upper_Material, Midsole_Material, Outsole, Cushioning_System, Drop__heel-to-toe_differential_, Usage_Type, Gender, Available_Sizes, Width, Additional_Technologies, id, regularPrice, undiscounted_price]\n", - "Index: []" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a=df[df['Weight'] > 1000]\n", - "a" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Silhouette Score: -0.10727046484513526\n", - "Davies-Bouldin Score: 3.820317442353896\n", - " id cluster\n", - "0 08sjncACSjSvg2t9DS73 5\n", - "1 0AqheRhKT2lhm7puBVCF 5\n", - "2 0IgYTzUHkE7zIdcVyFCK 5\n", - "3 0MU8aKCnCUZv2r9aLD67 5\n", - "4 0Q6DNSlvsjBzy3AQeY2y 4\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "from sklearn.cluster import KMeans\n", - "from sklearn.metrics import silhouette_score, davies_bouldin_score\n", - "from sklearn.preprocessing import StandardScaler\n", - "\n", - "# Suponiendo que ya tienes el DataFrame limpio \"df\"\n", - "# Asegúrate de que 'id' está presente y el resto de las columnas ya están procesadas\n", - "\n", - "# Separar la columna ID\n", - "ids = df['id']\n", - "# Asumiendo que tu DataFrame se llama df y que ya eliminaste o separaste el 'id'.\n", - "X = df.drop(columns=['id'], errors='ignore')\n", - "\n", - "# Reemplazar valores nulos por 0\n", - "X = X.fillna(0)\n", - "\n", - "# Si piensas hacer get_dummies y luego escalar:\n", - "X = pd.get_dummies(X, dummy_na=True)\n", - "X = X.fillna(0) # Si tras get_dummies quedan nulos (por ejemplo, dummy_na=True los evita, pero por si acaso)\n", - "\n", - "from sklearn.preprocessing import StandardScaler\n", - "scaler = StandardScaler()\n", - "X_scaled = scaler.fit_transform(X)\n", - "\n", - "# Elegir un número de clusters (ejemplo: k=3, puedes ajustar según tu criterio)\n", - "k = 8\n", - "kmeans = KMeans(n_clusters=k, random_state=42)\n", - "clusters = kmeans.fit_predict(X_scaled)\n", - "\n", - "# Agregar el cluster asignado a un DataFrame junto con el ID\n", - "df_clusters = pd.DataFrame({'id': ids, 'cluster': clusters})\n", - "\n", - "# Evaluar la calidad del clustering con métricas no supervisadas\n", - "sil_score = silhouette_score(X_scaled, clusters)\n", - "db_score = davies_bouldin_score(X_scaled, clusters)\n", - "\n", - "print(\"Silhouette Score:\", sil_score)\n", - "print(\"Davies-Bouldin Score:\", db_score)\n", - "\n", - "# df_clusters mostrará el ID de cada producto y el cluster al que pertenece\n", - "# Esto te permitirá saber en qué grupo quedó cada producto.\n", - "print(df_clusters.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "from sklearn.cluster import KMeans\n", - "from sklearn.preprocessing import StandardScaler\n", - "\n", - "# Suponiendo que ya has separado el 'id' y tienes tus datos en X (sin la columna 'id')\n", - "# y ya están transformados (numéricos) y listos para el clustering.\n", - "# Por ejemplo, si tienes variables categóricas ya convertidas con get_dummies:\n", - "# X = pd.get_dummies(X, dummy_na=True)\n", - "\n", - "# Escalar datos\n", - "scaler = StandardScaler()\n", - "X_scaled = scaler.fit_transform(X)\n", - "\n", - "distortions = []\n", - "K = range(2, 11) # rango de k a evaluar, puedes ajustarlo según tus necesidades\n", - "for k in K:\n", - " kmeans = KMeans(n_clusters=k, random_state=42)\n", - " kmeans.fit(X_scaled)\n", - " distortions.append(kmeans.inertia_)\n", - "\n", - "plt.figure(figsize=(8,5))\n", - "plt.plot(K, distortions, 'bx-')\n", - "plt.xlabel('Número de clusters k')\n", - "plt.ylabel('Distorsión (Inercia)')\n", - "plt.title('Método del Codo para determinar k')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elementos con 'id' NaN:\n", - "Empty DataFrame\n", - "Columns: [Weight, Upper_Material, Midsole_Material, Outsole, Cushioning_System, Drop__heel-to-toe_differential_, Usage_Type, Gender, Available_Sizes, Width, Additional_Technologies, id, regularPrice, undiscounted_price]\n", - "Index: []\n" - ] - } - ], - "source": [ - "# Filtrar los elementos donde 'id' sea NaN\n", - "id_nan = df[df['id'].isna()]\n", - "\n", - "# Imprimir los resultados\n", - "print(\"Elementos con 'id' NaN:\")\n", - "print(id_nan)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Conteo de elementos por cluster:\n", - "cluster\n", - "0 2\n", - "1 3\n", - "2 22\n", - "3 64\n", - "4 93\n", - "5 268\n", - "6 1\n", - "7 1\n", - "Name: count, dtype: int64\n", - "\n", - "Porcentaje de elementos por cluster:\n", - "cluster\n", - "0 0.44\n", - "1 0.66\n", - "2 4.85\n", - "3 14.10\n", - "4 20.48\n", - "5 59.03\n", - "6 0.22\n", - "7 0.22\n", - "Name: count, dtype: float64\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Conteo de elementos por grupo (cluster)\n", - "conteo_clusters = df_clusters['cluster'].value_counts().sort_index()\n", - "\n", - "print(\"Conteo de elementos por cluster:\")\n", - "print(conteo_clusters)\n", - "\n", - "# Agregar porcentajes para tener una mejor idea de la distribución\n", - "porcentaje_clusters = (conteo_clusters / len(df_clusters) * 100).round(2)\n", - "print(\"\\nPorcentaje de elementos por cluster:\")\n", - "print(porcentaje_clusters)\n", - "\n", - "# Crear una visualización básica de los clusters\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Gráfico de barras\n", - "plt.figure(figsize=(10, 6))\n", - "conteo_clusters.plot(kind='bar', color='skyblue', edgecolor='black')\n", - "plt.title('Distribución de elementos por cluster', fontsize=14)\n", - "plt.xlabel('Cluster', fontsize=12)\n", - "plt.ylabel('Cantidad de elementos', fontsize=12)\n", - "plt.xticks(rotation=0)\n", - "plt.grid(axis='y', linestyle='--', alpha=0.7)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "df_final = df.merge(df_clusters, on='id', how='left')" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elementos del cluster 2:\n", - " Weight Upper_Material Midsole_Material \\\n", - "22 585.0 Exterior textil Mediasuela Bounce \n", - "39 664.8 Exterior textil Mediasuela Bounce \n", - "42 540.0 Exterior de malla técnica Mediasuela Bounce \n", - "54 260.0 Exterior textil Mediasuela 4D de impresión 3D \n", - "113 664.8 Exterior textil Mediasuela Bounce \n", - "121 584.0 Exterior de malla técnica Mediasuela Bounce \n", - "243 585.0 Exterior textil Mediasuela Bounce \n", - "248 584.0 Technical mesh Mediasuela Bounce \n", - "267 585.0 Exterior textil Mediasuela Bounce \n", - "287 585.0 Exterior textil Mediasuela Bounce \n", - "291 585.0 Exterior textil Mediasuela Bounce \n", - "294 584.0 Exterior de malla técnica Mediasuela Bounce \n", - "298 584.0 Exterior de malla técnica Mediasuela Bounce \n", - "306 584.0 Exterior de malla técnica Mediasuela Bounce \n", - "324 540.0 Exterior de malla técnica Mediasuela Bounce \n", - "327 664.8 Exterior textil Mediasuela Bounce \n", - "330 664.8 Exterior textil Mediasuela Bounce \n", - "343 299.0 Exterior de malla EVA \n", - "352 664.8 Exterior textil Mediasuela Bounce \n", - "376 585.0 Exterior textil Mediasuela Bounce \n", - "383 540.0 Exterior de malla técnica Mediasuela Bounce \n", - "426 585.0 Exterior textil Mediasuela Bounce \n", - "\n", - " Outsole Cushioning_System \\\n", - "22 Suela de caucho Mediasuela Bounce \n", - "39 Suela de caucho Bounce \n", - "42 Suela de caucho NaN \n", - "54 Suela sintética Mediasuela 4D de impresión 3D \n", - "113 Suela de caucho Bounce \n", - "121 Suela de caucho NaN \n", - "243 Suela de caucho Mediasuela Bounce \n", - "248 Rubber Mediasuela Bounce \n", - "267 Suela de caucho Mediasuela Bounce \n", - "287 Suela de caucho Mediasuela Bounce \n", - "291 Suela de caucho Mediasuela Bounce \n", - "294 Suela de caucho NaN \n", - "298 Suela de caucho NaN \n", - "306 Suela de caucho NaN \n", - "324 Suela de caucho Mediasuela Bounce \n", - "327 Suela de caucho Bounce \n", - "330 Suela de caucho Bounce \n", - "343 Suela de caucho OrthoLite \n", - "352 Suela de caucho Mediasuela Bounce \n", - "376 Suela de caucho Mediasuela Bounce \n", - "383 Suela de caucho Mediasuela Bounce \n", - "426 Suela de caucho Mediasuela Bounce \n", - "\n", - " Drop__heel-to-toe_differential_ Usage_Type Gender Available_Sizes \\\n", - "22 9.0 Running Mujer NaN \n", - "39 9.0 Running Hombre NaN \n", - "42 10.0 Running Mujer CO 37 \n", - "54 NaN Running Mujer NaN \n", - "113 9.0 Running Hombre NaN \n", - "121 10.0 Running Hombre NaN \n", - "243 9.0 Running Mujer NaN \n", - "248 10.0 Running Men NaN \n", - "267 9.0 Running Mujer NaN \n", - "287 9.0 Running Mujer NaN \n", - "291 9.0 Running Mujer NaN \n", - "294 10.0 Running Hombre NaN \n", - "298 10.0 Running Hombre NaN \n", - "306 10.0 Running Hombre NaN \n", - "324 10.0 Running Mujer NaN \n", - "327 9.0 Running Hombre NaN \n", - "330 9.0 Running Hombre NaN \n", - "343 10.0 Running NaN NaN \n", - "352 9.0 Running Hombre NaN \n", - "376 9.0 Running Mujer NaN \n", - "383 10.0 Running Mujer NaN \n", - "426 9.0 Running Mujer NaN \n", - "\n", - " Width Additional_Technologies \\\n", - "22 NaN Recyclable materials \n", - "39 NaN Contains at least 50% recycled material \n", - "42 NaN Estabilizadores de TPU en la zona media y el t... \n", - "54 NaN Recyclable materials \n", - "113 NaN Recyclable materials \n", - "121 NaN Estabilizadores de TPU, Sistema de amarre de c... \n", - "243 NaN Contiene al menos un 50 % de material reciclado \n", - "248 NaN Estabilizadores de TPU, Sistema de amarre de c... \n", - "267 NaN Recyclable materials \n", - "287 NaN Contains at least 50% recycled material \n", - "291 NaN Contains at least 50% recycled material \n", - "294 NaN Estabilizadores de TPU, Sistema de amarre de c... \n", - "298 NaN Estabilizadores de TPU, Sistema de amarre de c... \n", - "306 NaN Estabilizadores de TPU en la zona media y el t... \n", - "324 NaN Estabilizadores de TPU en la zona media y el t... \n", - "327 NaN Contiene al menos un 50 % de material reciclado \n", - "330 NaN Recyclable materials \n", - "343 NaN Recyclable materials \n", - "352 NaN Contains at least 50% recycled material \n", - "376 NaN Contains at least 50% recycled material \n", - "383 NaN Estabilizadores de TPU, Sistema de amarre de c... \n", - "426 NaN Contains at least 50% recycled material \n", - "\n", - " id regularPrice undiscounted_price cluster \n", - "22 36umRByym4P5idSfddlA 579950 405965.0 2 \n", - "39 5YBCQmekSCOryIo7887a 579950 405965.0 2 \n", - "42 6Y8BXtu2vPy0I6eGn0jI 499950 349965.0 2 \n", - "54 8FyMNBTEgPGeqnhTm34a 1299950 909965.0 2 \n", - "113 H1SxT7RaEy2ke9VjSWpg 579950 347970.0 2 \n", - "121 HXuKHFQnKMYL15wuuAtE 369950 NaN 2 \n", - "243 XTyfd24nQsI0ydJ5HNOM 579950 405965.0 2 \n", - "248 YdluqvzyDKCaoIXf2Raw 499950 399960.0 2 \n", - "267 bXMGR7Ld3EpZfjLwRZXc 579950 405965.0 2 \n", - "287 ekzaoVmAEPT57hEOzM4z 579950 347970.0 2 \n", - "291 f7BRJbDjPGZ0R7k8T2BH 449950 NaN 2 \n", - "294 fD0hMnJthp7NyufrsX94 449950 NaN 2 \n", - "298 g0dU1EZ2VAcJp8GeFtDU 499950 NaN 2 \n", - "306 gfkoLBQme3OHzj3JXmu1 499950 349965.0 2 \n", - "324 j14fin6QCZyFNfHrLfUb 449950 NaN 2 \n", - "327 jQ8lW8Kba3VAUZgvjzpp 579950 405965.0 2 \n", - "330 jepprtx3EAEA6TxsC94H 579950 405965.0 2 \n", - "343 lDidMEQwKMrEEimwgbts 199950 NaN 2 \n", - "352 mhHNGEv3q2mIoQrUsE6k 579950 347970.0 2 \n", - "376 qcNdp0WmOAxgmOk9AFpa 579950 347970.0 2 \n", - "383 sEsYVzeezD0cJVpoX3Lv 499950 299970.0 2 \n", - "426 wSLYbxhlkuBI8YpCJOf7 579950 289975.0 2 \n", - "\n", - "Estadísticas descriptivas del cluster 2:\n", - " Weight Drop__heel-to-toe_differential_ Width regularPrice \\\n", - "count 22.000000 21.000000 0.0 2.200000e+01 \n", - "mean 569.000000 9.428571 NaN 5.499500e+05 \n", - "std 102.285595 0.507093 NaN 1.914606e+05 \n", - "min 260.000000 9.000000 NaN 1.999500e+05 \n", - "25% 584.000000 9.000000 NaN 4.999500e+05 \n", - "50% 585.000000 9.000000 NaN 5.799500e+05 \n", - "75% 585.000000 10.000000 NaN 5.799500e+05 \n", - "max 664.800000 10.000000 NaN 1.299950e+06 \n", - "\n", - " undiscounted_price cluster \n", - "count 16.000000 22.0 \n", - "mean 401716.875000 2.0 \n", - "std 140991.161707 0.0 \n", - "min 289975.000000 2.0 \n", - "25% 347970.000000 2.0 \n", - "50% 374962.500000 2.0 \n", - "75% 405965.000000 2.0 \n", - "max 909965.000000 2.0 \n" - ] - }, - { - "data": { - "image/png": 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", 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", 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Elegir el número del cluster a analizar\n", - "cluster_a_analizar = 2 # Cambia este valor al número del cluster que quieres analizar\n", - "\n", - "# Filtrar los elementos del cluster específico\n", - "elementos_cluster = df_final[df_final['cluster'] == cluster_a_analizar]\n", - "\n", - "print(f\"Elementos del cluster {cluster_a_analizar}:\")\n", - "print(elementos_cluster)\n", - "\n", - "# Estadísticas descriptivas de los datos del cluster\n", - "print(f\"\\nEstadísticas descriptivas del cluster {cluster_a_analizar}:\")\n", - "print(elementos_cluster.describe())\n", - "\n", - "# Visualizar las características relevantes de los elementos del cluster\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Seleccionar columnas relevantes para el análisis (puedes ajustar según tu caso)\n", - "columnas_relevantes = elementos_cluster.select_dtypes(include=['number']).columns\n", - "\n", - "# Crear histogramas para las columnas relevantes\n", - "for col in columnas_relevantes:\n", - " plt.figure(figsize=(8, 5))\n", - " elementos_cluster[col].hist(bins=20, color='skyblue', edgecolor='black')\n", - " plt.title(f'Distribución de {col} en el cluster {cluster_a_analizar}', fontsize=14)\n", - " plt.xlabel(col, fontsize=12)\n", - " plt.ylabel('Frecuencia', fontsize=12)\n", - " plt.grid(axis='y', linestyle='--', alpha=0.7)\n", - " plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elementos del cluster 3:\n", - " Weight Upper_Material Midsole_Material \\\n", - "7 213.0 Parte superior de malla Dreamstrike+ \n", - "15 334.0 Exterior técnico de malla Mediasuela Dreamstrike \n", - "32 247.0 Parte superior de monomalla LIGHTMOTION \n", - "35 248.0 Exterior textil Tecnología Light BOOST \n", - "40 254.0 Monomalla Dreamstrike+ \n", - ".. ... ... ... \n", - "414 243.0 Exterior de malla acolchada Dreamstrike+ \n", - "428 290.0 Parte superior de malla Mediasuela Dreamstrike+ \n", - "429 290.0 Parte superior de malla Dreamstrike+ \n", - "442 213.0 Parte superior de malla mediasuela Dreamstrike+ \n", - "450 295.0 adidas PRIMEKNIT 4D de impresión 3D \n", - "\n", - " Outsole Cushioning_System \\\n", - "7 Suela Adiwear Dreamstrike+ \n", - "15 Suela con inserciones de caucho Dreamstrike \n", - "32 Adiwear LIGHTMOTION \n", - "35 Continental Rubber Tecnología BOOST \n", - "40 Varillas de caucho Dreamstrike+ \n", - ".. ... ... \n", - "414 Adiwear Dreamstrike+ \n", - "428 Suela Adiwear Dreamstrike+ \n", - "429 Suela Adiwear Dreamstrike+ \n", - "442 Suela Adiwear Dreamstrike+ \n", - "450 Continental Rubber Estructura que absorbe el impacto \n", - "\n", - " Drop__heel-to-toe_differential_ Usage_Type Gender Available_Sizes \\\n", - "7 10.0 Running Mujer CO 37 \n", - "15 10.0 Running Hombre NaN \n", - "32 10.0 Running Mujer CO 37 \n", - "35 10.0 Running Mujer CO 37 \n", - "40 10.0 Running Mujer NaN \n", - ".. ... ... ... ... \n", - "414 10.0 Running Mujer NaN \n", - "428 8.0 Running Hombre CO 40 \n", - "429 8.0 Running Hombre NaN \n", - "442 10.0 Running Mujer NaN \n", - "450 10.0 Running Mujer NaN \n", - "\n", - " Width Additional_Technologies \\\n", - "7 NaN Contiene al menos un 20% de material reciclado... \n", - "15 NaN Contiene al menos un 20% de material reciclado \n", - "32 NaN Recycled materials, Waterproofing \n", - "35 NaN Torsion System, Recyclable materials, Huella d... \n", - "40 NaN Contiene al menos un 20% de material reciclado \n", - ".. ... ... \n", - "414 NaN Sustainable dye technology, Recyclable materials \n", - "428 NaN Recycled materials, Optimized lacing system \n", - "429 NaN NaN \n", - "442 NaN Contiene al menos un 20% de material reciclado... \n", - "450 NaN Contiene al menos un 20% de material reciclado... \n", - "\n", - " id regularPrice undiscounted_price cluster \n", - "7 0n2Tyl34QdVdkCKgAdcT 649950 NaN 3 \n", - "15 1f5c8gEndKxlI9IEo2FT 579950 347970.0 3 \n", - "32 4rs0wxAzoGLvCI8BHwzh 379950 265965.0 3 \n", - "35 5BnxAht8eNgpRagM6Xcv 949950 NaN 3 \n", - "40 5iyHRy6zWzpmfnsalDBK 499950 349965.0 3 \n", - ".. ... ... ... ... \n", - "414 voNjPj9u2oFEimmwnQUZ 799950 NaN 3 \n", - "428 x6mqNLTCjvwPX1Zp7k7f 849950 NaN 3 \n", - "429 xDe8Syqrat9Xos53PdXL 849950 594965.0 3 \n", - "442 yFWhoK8BDTP6VF8QzBMA 649950 NaN 3 \n", - "450 zVnQ4LTK528Lins9HzaW 1199950 719970.0 3 \n", - "\n", - "[64 rows x 15 columns]\n", - "\n", - "Estadísticas descriptivas del cluster 3:\n", - " Weight Drop__heel-to-toe_differential_ Width regularPrice \\\n", - "count 64.000000 62.000000 0.0 6.400000e+01 \n", - "mean 255.125000 9.419355 NaN 7.233875e+05 \n", - "std 36.080818 1.300043 NaN 2.401337e+05 \n", - "min 183.000000 6.000000 NaN 2.999500e+05 \n", - "25% 242.000000 10.000000 NaN 5.799500e+05 \n", - "50% 251.000000 10.000000 NaN 6.999500e+05 \n", - "75% 277.000000 10.000000 NaN 8.499500e+05 \n", - "max 334.000000 10.000000 NaN 1.299950e+06 \n", - "\n", - " undiscounted_price cluster \n", - "count 33.000000 64.0 \n", - "mean 511087.272727 3.0 \n", - "std 171176.978698 0.0 \n", - "min 239960.000000 3.0 \n", - "25% 349965.000000 3.0 \n", - "50% 509970.000000 3.0 \n", - "75% 639960.000000 3.0 \n", - "max 909965.000000 3.0 \n" - ] - }, - { - "data": { - "image/png": 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", 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", 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Elegir el número del cluster a analizar\n", - "cluster_a_analizar = 3 # Cambia este valor al número del cluster que quieres analizar\n", - "\n", - "# Filtrar los elementos del cluster específico\n", - "elementos_cluster = df_final[df_final['cluster'] == cluster_a_analizar]\n", - "\n", - "print(f\"Elementos del cluster {cluster_a_analizar}:\")\n", - "print(elementos_cluster)\n", - "\n", - "# Estadísticas descriptivas de los datos del cluster\n", - "print(f\"\\nEstadísticas descriptivas del cluster {cluster_a_analizar}:\")\n", - "print(elementos_cluster.describe())\n", - "\n", - "# Visualizar las características relevantes de los elementos del cluster\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Seleccionar columnas relevantes para el análisis (puedes ajustar según tu caso)\n", - "columnas_relevantes = elementos_cluster.select_dtypes(include=['number']).columns\n", - "\n", - "# Crear histogramas para las columnas relevantes\n", - "for col in columnas_relevantes:\n", - " plt.figure(figsize=(8, 5))\n", - " elementos_cluster[col].hist(bins=20, color='skyblue', edgecolor='black')\n", - " plt.title(f'Distribución de {col} en el cluster {cluster_a_analizar}', fontsize=14)\n", - " plt.xlabel(col, fontsize=12)\n", - " plt.ylabel('Frecuencia', fontsize=12)\n", - " plt.grid(axis='y', linestyle='--', alpha=0.7)\n", - " plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From d9a85dc0e825e8b5b2dad79d9c230d7f62b0e5ee Mon Sep 17 00:00:00 2001 From: CarlosKC26 Date: Thu, 12 Dec 2024 23:34:26 -0500 Subject: [PATCH 41/84] se mueve training a scripts --- {src/comparative_analysis => scripts}/training/trainingText.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename {src/comparative_analysis => scripts}/training/trainingText.py (100%) diff --git a/src/comparative_analysis/training/trainingText.py b/scripts/training/trainingText.py similarity index 100% rename from src/comparative_analysis/training/trainingText.py rename to scripts/training/trainingText.py From 7186116d6745806f7c12140aa4a17fa7ba50fccd Mon Sep 17 00:00:00 2001 From: CarlosKC26 Date: Sat, 14 Dec 2024 09:26:22 -0500 Subject: [PATCH 42/84] Se expandio la metodologia y se integro scrum al cronograma --- .../business_understanding/project_charter.md | 105 ++++++++++++++++-- 1 file changed, 96 insertions(+), 9 deletions(-) diff --git a/docs/business_understanding/project_charter.md b/docs/business_understanding/project_charter.md index f6106e459..dde755f23 100644 --- a/docs/business_understanding/project_charter.md +++ b/docs/business_understanding/project_charter.md @@ -27,17 +27,103 @@ Se propone el uso de embeddings debido a que los datos disponibles consisten en ## Metodología -Se utilizarán las metodologías CRISP-DM y SCRUM para llevar a cabo el proyecto. +El proyecto seguirá una metodología híbrida basada en **CRISP-DM** (Cross-Industry Standard Process for Data Mining) para la estructura de las tareas técnicas y **SCRUM** para la gestión ágil del equipo y del cronograma. -## Cronograma +### Etapas según CRISP-DM: -| Etapa | Duración Estimada | Fechas | -|-----------------------------------------|-------------------|---------------------------------| -| Entendimiento del negocio y carga de datos | 2 semanas | Del 13 de noviembre al 28 de noviembre | -| Preprocesamiento y análisis exploratorio | 1 semana | Del 29 de noviembre al 5 de diciembre | -| Modelamiento y extracción de características | 1 semana | Del 5 de diciembre al 12 de diciembre | -| Despliegue | 1 semana | Del 13 de diciembre al 19 de diciembre | -| Evaluación y entrega final | 1 semana | Del 19 de diciembre al 21 de diciembre | +1. **Entendimiento del Negocio** + - Actividades: + - Identificar necesidades del cliente. + - Determinar los objetivos del análisis comparativo. + - Definir métricas clave para evaluar la precisión del modelo. + - Cronograma: 13 de noviembre al 28 de noviembre. + +2. **Entendimiento de los Datos** + - Actividades: + - Recopilar datos de Adidas, Nike y Nation Runner mediante scraping. + - Explorar las descripciones textuales y detectar posibles inconsistencias o valores faltantes. + - Validar la calidad de los datos. + - Cronograma: 13 de noviembre al 28 de noviembre. + +3. **Preparación de los Datos** + - Actividades: + - Realizar limpieza y preprocesamiento de las descripciones. + - Convertir datos textuales en representaciones vectoriales (embeddings). + - Dividir los datos en conjuntos de entrenamiento, validación y prueba. + - Cronograma: 29 de noviembre al 5 de diciembre. + +4. **Modelado** + - Actividades: + - Diseñar y entrenar un modelo de recomendación basado en similitud semántica. + - Optimizar hiperparámetros para maximizar el rendimiento del modelo. + - Cronograma: 5 de diciembre al 12 de diciembre. + +5. **Evaluación** + - Actividades: + - Validar el modelo con métricas como precisión, recall y F1-score. + - Realizar pruebas con datos nuevos para garantizar generalización. + - Cronograma: 19 de diciembre al 21 de diciembre. + +6. **Despliegue** + - Actividades: + - Integrar el modelo en una herramienta funcional. + - Documentar su uso y entrenar al equipo en su aplicación. + - Cronograma: 13 de diciembre al 19 de diciembre. + +### Gestión ágil con SCRUM: + +El desarrollo del proyecto se gestionará a través de iteraciones de una semana (sprints) para garantizar la flexibilidad y la adaptabilidad frente a posibles cambios en los requisitos. + +#### Roles del equipo: + +#### **Equipo y Responsabilidades** + +#### **1. Juan Correa (Product Owner y Líder Técnico)** +- **Responsabilidades compartidas con Daniel Galvis:** + - Definir y priorizar los requisitos del proyecto. + - Asegurar el alineamiento con los objetivos del cliente y del negocio. +- **Responsabilidades compartidas con Asdrúbal Zácipa Corredor:** + - Liderar las decisiones técnicas clave y supervisar el desarrollo general. + +#### **2. Daniel Galvis (Scrum Master y Desarrollador)** +- **Responsabilidades compartidas con Juan Correa:** + - Coordinar las ceremonias ágiles y facilitar la comunicación entre el equipo. + - Eliminar impedimentos que afecten el progreso del proyecto. +- **Responsabilidades compartidas con Asdrúbal Zácipa Corredor:** + - Colaborar en tareas de scraping, preprocesamiento y soporte técnico. + +#### **3. Asdrúbal Zácipa Corredor (Desarrollador y Especialista en Modelado)** +- **Responsabilidades compartidas con Juan Correa:** + - Diseñar, entrenar y evaluar el modelo de recomendación. + - Implementar el modelo y contribuir al desarrollo técnico del proyecto. +- **Responsabilidades compartidas con Daniel Galvis:** + - Asegurar la integración funcional de las soluciones desarrolladas. + +--- + +#### **Justificación de la Redistribución** + +Esta reorganización tiene como objetivo mitigar el riesgo operativo en caso de que algún integrante del equipo no pueda cumplir temporalmente con sus responsabilidades debido a enfermedad, emergencia u otros compromisos. Al asignar al menos dos personas a cada tarea, se asegura que el flujo de trabajo no se interrumpa y que el conocimiento clave del proyecto esté distribuido de manera uniforme entre los integrantes. + +Además, este enfoque fomenta la colaboración y la versatilidad, ya que todos los miembros se mantendrán actualizados sobre diferentes aspectos del proyecto, promoviendo una mayor resiliencia y adaptabilidad en el equipo. + + +#### Ceremonias: + +- **Sprint Planning:** Al inicio de cada sprint, se definirán las tareas clave y los entregables. +- **Daily Standup:** Reuniones diarias de 15 minutos para revisar el progreso y resolver bloqueos. +- **Sprint Review:** Al finalizar cada sprint, se presentarán los avances al equipo y se recopilará retroalimentación. +- **Sprint Retrospective:** Se analizarán las lecciones aprendidas y se identificarán áreas de mejora para futuros sprints. + +## Cronograma Integrado con Sprints + +| Sprint | Etapa | Actividades principales | Duración Estimada | Fechas | +|-------------------------|-----------------------------------------|------------------------------------------------|-------------------|---------------------------------| +| Sprint 1 | Entendimiento del negocio y carga de datos | Entendimiento del negocio y carga de datos | 2 semanas | Del 13 de noviembre al 28 de noviembre | +| Sprint 2 | Preprocesamiento y análisis exploratorio | Preprocesamiento y análisis exploratorio | 1 semana | Del 29 de noviembre al 5 de diciembre | +| Sprint 3 | Modelamiento y extracción de características | Modelamiento y extracción de características | 1 semana | Del 5 de diciembre al 12 de diciembre | +| Sprint 4 | Despliegue | Despliegue del modelo | 1 semana | Del 13 de diciembre al 19 de diciembre | +| Sprint 5 | Evaluación y entrega final | Evaluación final y entrega | 1 semana | Del 19 de diciembre al 21 de diciembre | ## Equipo del Proyecto @@ -58,6 +144,7 @@ Aunque no se cuenta con financiamiento externo, se estimaron los costos básicos | **Total** | - | - | - | **1,000,000** | ### Detalles: + - **Servicio de luz:** Incluye el costo estimado del consumo eléctrico asociado al trabajo en el proyecto. - **Servicio de internet:** Cubre el acceso a internet necesario para reuniones virtuales, investigación y uso de herramientas online. - **Uso de equipos personales:** Considera el desgaste de hardware y el consumo eléctrico de los equipos utilizados durante el desarrollo. From 6a7f6a910aa410e4031717d0ce3dd6a780fadddd Mon Sep 17 00:00:00 2001 From: CarlosKC26 Date: Sat, 14 Dec 2024 09:29:39 -0500 Subject: [PATCH 43/84] =?UTF-8?q?reformulaci=C3=B3n=20sobre=20el=20web=20s?= =?UTF-8?q?craping=20en=20el=20data=20definition?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/data/data_definition.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/data/data_definition.md b/docs/data/data_definition.md index 22e4614b0..eebd3ec4d 100644 --- a/docs/data/data_definition.md +++ b/docs/data/data_definition.md @@ -4,11 +4,11 @@ Los datos se extrajeron mediante web scraping de las siguientes fuentes: -- **Adidas**: [https://www.adidas.co/](https://www.adidas.co/) -- **Nike**: [https://www.nike.com.co/](https://www.nike.com.co/) -- **Nation Runner**: [https://nacionrunner.com/](https://nacionrunner.com/) +- **Adidas**: [https://www.adidas.co/](https://www.adidas.co/) +- **Nike**: [https://www.nike.com.co/](https://www.nike.com.co/) +- **Nation Runner**: [https://nacionrunner.com/](https://nacionrunner.com/) -Posteriormente, los datos fueron almacenados en una API conectada a Firebase para su gestión y análisis posterior. Las descripciones de productos y características técnicas se estructuraron en un formato JSON para facilitar su manipulación. +Posteriormente, los datos fueron almacenados en una API conectada a Firebase para su gestión y análisis posterior. Las descripciones de productos y características técnicas se estructuraron en un formato JSON para facilitar su manipulación. Finalmente, estos datos fueron exportados a un archivo CSV, el cual se está utilizando como fuente principal en el proyecto para simplificar su integración y análisis en los diferentes procesos. ## Especificación de los scripts para la carga de datos From 9861875f5178ae6aa65d2319f17afbf7a476faeb Mon Sep 17 00:00:00 2001 From: azacipa <52643112+azacipa@users.noreply.github.com> Date: Sun, 15 Dec 2024 19:00:12 -0500 Subject: [PATCH 44/84] Se implementa rutina que permite realizar la generacion y comparacion de los modelos de clustering KMeans, Agglomerative, DBScan y HDBScan --- src/comparative_analysis/models/models.py | 174 ++++++++++++++++++ .../visualization/agglomerative.png | Bin 0 -> 73351 bytes .../agglomerative_silhouette.png | Bin 0 -> 72512 bytes .../visualization/dbscan.png | Bin 0 -> 67581 bytes .../visualization/dbscan_silhouette.png | Bin 0 -> 76753 bytes .../visualization/hdbscan.png_condensed_tree | Bin 0 -> 11757 bytes .../visualization/hdbscan.png_linkage_tree | Bin 0 -> 21965 bytes .../hdbscan.png_linkage_tree_focus | Bin 0 -> 14683 bytes .../visualization/hdbscan_silhouette.png | Bin 0 -> 94543 bytes .../visualization/kmeans.png | Bin 0 -> 88945 bytes .../visualization/kmeans_silhouette.png | Bin 0 -> 72467 bytes 11 files changed, 174 insertions(+) create mode 100644 src/comparative_analysis/models/models.py create mode 100644 src/comparative_analysis/visualization/agglomerative.png create mode 100644 src/comparative_analysis/visualization/agglomerative_silhouette.png create mode 100644 src/comparative_analysis/visualization/dbscan.png create mode 100644 src/comparative_analysis/visualization/dbscan_silhouette.png create mode 100644 src/comparative_analysis/visualization/hdbscan.png_condensed_tree create mode 100644 src/comparative_analysis/visualization/hdbscan.png_linkage_tree create mode 100644 src/comparative_analysis/visualization/hdbscan.png_linkage_tree_focus create mode 100644 src/comparative_analysis/visualization/hdbscan_silhouette.png create mode 100644 src/comparative_analysis/visualization/kmeans.png create mode 100644 src/comparative_analysis/visualization/kmeans_silhouette.png diff --git a/src/comparative_analysis/models/models.py b/src/comparative_analysis/models/models.py new file mode 100644 index 000000000..2b8cbd356 --- /dev/null +++ b/src/comparative_analysis/models/models.py @@ -0,0 +1,174 @@ +# -*- coding: utf-8 -*- +""" +Created on Sun Dec 15 18:04:17 2024 + +@author: azacipac +""" + +# Importar librerias +import pandas as pd +#import re +import sklearn +#import matplotlib +#mport matplotlib.pyplot as plt +import numpy as np +#import time +import clusteval +import sys + +#from sklearn.cluster import KMeans +from sklearn.metrics import silhouette_score, davies_bouldin_score +from sklearn.preprocessing import StandardScaler +#from sklearn.cluster import DBSCAN, HDBSCAN +#from sklearn import metrics +#from sklearn.preprocessing import StandardScaler + +# Version de las librerias +""" +Este codigo ha sido verificado con las siguientes versiones: + +Python 3.12.4 +Pandas 2.2.3 +Numpy 2.1.3 +Matplotlib 3.9.3 +Clusteval 2.2.2 +Scikit-Learn 1.6.0 +""" +print(sys.version) +print('Pandas', pd.__version__) +print('Numpy', np.__version__) +#print('Matplotlib', matplotlib.__version__) +print('Clusteval', clusteval.__version__) +print('Scikit-Learn', sklearn.__version__) + +# ** Carga y limpieza de datos ** +ruta_excel = r"..\\database\\Adidas_etiquetado_new.xlsx" + +# Crear el DataFrame +df = pd.read_excel(ruta_excel, header=0) +df.info() + +# Procesamiento de columnas numéricas +num_cols = { + 'Weight': '(\d+\.?\d*)', + 'Drop__heel-to-toe_differential_': '(\d+\.?\d*)' +} +for col, pattern in num_cols.items(): + df[col] = df[col].astype(str).str.extract(pattern).astype(float, errors='ignore') + df[col] = pd.to_numeric(df[col], errors='coerce') + +# Procesar precios +price_cols = ['regularPrice', 'undiscounted_price'] +for col in price_cols: + df[col] = df[col].astype(str).str.replace(r'[^0-9.,]', '', regex=True) + df[col] = df[col].str.replace(r'\\.', '', regex=True).str.replace(',', '.') + df[col] = pd.to_numeric(df[col], errors='coerce') + +# Eliminar columnas innecesarias +cols_to_drop = ['details', 'description', 'category', 'characteristics', 'width', 'Pronation_Type'] +df = df.drop(columns=cols_to_drop, errors='ignore') + +# Generar array de caracteristicas +ids = df['id'] +X = df.drop(columns=['id'], errors='ignore').fillna(0) + +# Codificar variables categóricas +df_dummies = pd.get_dummies(X, dummy_na=True).fillna(0) + +# Escalar los datos +scaler = StandardScaler() +X_scaled = scaler.fit_transform(df_dummies) +df_dummies.info() + +# Analisis de clusters +# Se realiza comparacion de 4 modelos de clustering +# * kmeans +# * agglomerative +# * dbscan +# * hdbscan +# +def cluster_eval(X, cluster, evaluate, verbose = 40, savefig = False): + """" + Función cluster_eval: Realiza evaluacion de clustering sobre el conjunto de datos que se indica. Genera grafica de el score que se indica vs numero de clusters + y grafico de los coeficientes silhouette para los diferentes clusters + + Parámetros: + X (Numpy-array): Arreglo de muestras y caracteristicas + cluster (str): Tipo de clustering: agglomerative, kmeans, dbscan, hdbscan + evaluate (str): Metodo de evaluacion: silhouette, dbindex, derivative + verbose (int): Nivel de detalle para mensajes de salida + savefig (boolean): Indica si se almacena la imagen en archivo png + + Return: + results: Diccionario con diferentes llaves que dependen del metodo de evaluacion utilizado + sil_score (float): Silhouette Score + db_score (float): Davies-Bouldin Score + + Ejemplo: + + >>> results_kmeans = cluster_eval(X = X_scaled, cluster = "kmeans", evaluate = "silhouette", savefig=True) + + Silhouette Score: 0.25816924466629976 + Davies-Bouldin Score: 0.5575649799417841 + [Grafica plot] + ... + """ + # Initialize + ce = clusteval.clusteval(cluster = cluster, evaluate=evaluate, verbose = verbose) + # Fit + results = ce.fit(X) + # + # metrics + sil_score = silhouette_score(X, results["labx"]) + db_score = davies_bouldin_score(X, results["labx"]) + print(f"Silhouette Score: {sil_score}") + print(f"Davies-Bouldin Score: {db_score}") + # Plot + title = f"Clustering method {cluster} with evaluation {evaluate}" + if(savefig == True): + plot_path = r"..\\visualization\\" + plot_name = plot_path + cluster + ".png" + plot_sil_name = plot_path + cluster + "_silhouette.png" + ce.plot(title = title, verbose = verbose, savefig = {"fname" : plot_name, "format" : "png"}) + ce.plot_silhouette(savefig = {"fname" : plot_sil_name}) + else: + ce.plot(title = title, verbose = verbose) + ce.plot_silhouette() + return results, sil_score, db_score + +# analisis con kmeans +results_kmeans,sil_kmeans_score,db_kmeans_score = cluster_eval(X = X_scaled + ,cluster = "kmeans" + ,evaluate = "silhouette" + ,savefig=True) + +# analisis con cluster aglomerativo +results_aggl,sil_aggl_score,db_aggl_score = cluster_eval(X = X_scaled + ,cluster = "agglomerative" + ,evaluate = "silhouette" + ,savefig=True) + +# analisis con dbscan +results_dbscan,sil_dbscan_score,db_dbscan_score = cluster_eval(X = X_scaled + ,cluster = "dbscan" + ,evaluate = "silhouette" + ,savefig=True) + +# analisis con hdbscan +results_hdbscan,sil_hdbscan_score,db_hdbscan_score = cluster_eval(X = X_scaled + ,cluster = "hdbscan" + ,evaluate = "silhouette" + ,savefig=True) + +# Comparacion del score de los modelos +# Silhouette Score +print(f"\nKMeans Silhouette Score: {sil_kmeans_score}") +print(f"Agglomerative Silhouette Score: {sil_aggl_score}") +print(f"DBScan Silhouette Score: {sil_dbscan_score}") +print(f"HDBScan Silhouette Score: {sil_hdbscan_score}") + +# Davies-Bouldin +print(f"\nKMeans Davies-Bouldin Score: {db_kmeans_score}") +print(f"Agglomerative Davies-Bouldin Score: {db_aggl_score}") +print(f"DBScan Davies-Bouldin Score: {db_dbscan_score}") +print(f"HDBScan Davies-Bouldin Score: {db_hdbscan_score}") \ No 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zAp~Q>c%M_@>L<*<;xxg*G3+4bf&}K1>`jWUC-nT5bR2UrONWWmLJm{PS;i5&r+jFa_}P&;P0)^YTOfuOGmCWVv!va{9B^=IIL9 N`>proee&s-{{TvNgu?&; literal 0 HcmV?d00001 From fceb51eab1238340cea54b92ac6add6fcc8cdeb4 Mon Sep 17 00:00:00 2001 From: CarlosKC26 Date: Mon, 16 Dec 2024 15:47:18 -0500 Subject: [PATCH 45/84] DBScan --- src/comparative_analysis/models/DBScan.py | 163 ++++++++++++++++++ src/comparative_analysis/models/K-Means.py | 109 ++++++++++++ .../training/trainingDBScan.py | 163 ++++++++++++++++++ .../{training.py => trainingK-Means.py} | 0 4 files changed, 435 insertions(+) create mode 100644 src/comparative_analysis/models/DBScan.py create mode 100644 src/comparative_analysis/models/K-Means.py create mode 100644 src/comparative_analysis/training/trainingDBScan.py rename src/comparative_analysis/training/{training.py => trainingK-Means.py} (100%) diff --git a/src/comparative_analysis/models/DBScan.py b/src/comparative_analysis/models/DBScan.py new file mode 100644 index 000000000..58b7bde33 --- /dev/null +++ b/src/comparative_analysis/models/DBScan.py @@ -0,0 +1,163 @@ +import pandas as pd +import re +from sklearn.cluster import DBSCAN +from sklearn.metrics import silhouette_score, davies_bouldin_score +from sklearn.preprocessing import StandardScaler +import matplotlib.pyplot as plt +import numpy as np + +# ** Carga y limpieza de datos ** +ruta_excel = r"C:\\Users\\cdgn2\\OneDrive\\Escritorio\\Maestría\\Maestria\\Metodologias Agiles\\Proyecto\\Comparative-analysis-of-products\\src\\comparative_analysis\\database\\Adidas_etiquetado.xlsx" + +# Crear el DataFrame +df = pd.read_excel(ruta_excel, header=0, engine='openpyxl') + +# Procesamiento de columnas numéricas +num_cols = { + 'Weight': '(\d+\.?\d*)', + 'Drop__heel-to-toe_differential_': '(\d+\.?\d*)' +} +for col, pattern in num_cols.items(): + df[col] = df[col].astype(str).str.extract(pattern).astype(float, errors='ignore') + df[col] = pd.to_numeric(df[col], errors='coerce') + +# Procesar precios +price_cols = ['regularPrice', 'undiscounted_price'] +for col in price_cols: + df[col] = df[col].astype(str).str.replace(r'[^0-9.,]', '', regex=True) + df[col] = df[col].str.replace(r'\.', '', regex=True).str.replace(',', '.') + df[col] = pd.to_numeric(df[col], errors='coerce') + +# Eliminar columnas innecesarias +cols_to_drop = ['details', 'description', 'category', 'characteristics', 'width', 'Pronation_Type'] +df = df.drop(columns=cols_to_drop, errors='ignore') + +# ** Clustering y evaluación ** +ids = df['id'] +X = df.drop(columns=['id'], errors='ignore').fillna(0) + +# Codificar variables categóricas +df_dummies = pd.get_dummies(X, dummy_na=True).fillna(0) + +# Escalar los datos +scaler = StandardScaler() +X_scaled = scaler.fit_transform(df_dummies) + +print("Media X_scaled:", X_scaled.mean(axis=0)) +print("Desviación estándar X_scaled:", X_scaled.std(axis=0)) + +# Función para explorar parámetros de DBSCAN +def explorar_parametros_dbscan(X, eps_values, min_samples_values): + resultados = [] + for eps in eps_values: + for min_samples in min_samples_values: + dbscan = DBSCAN(eps=eps, min_samples=min_samples) + clusters = dbscan.fit_predict(X) + num_clusters = len(set(clusters)) - (1 if -1 in clusters else 0) + + if num_clusters > 1: # Evaluar solo si hay más de 1 cluster válido + sil_score = silhouette_score(X, clusters) + db_score = davies_bouldin_score(X, clusters) + resultados.append((eps, min_samples, num_clusters, sil_score, db_score)) + return resultados + +# Rango de parámetros a explorar +eps_values = np.arange(0.1, 3.0, 0.1) +min_samples_values = range(2, 20) + +# Explorar los parámetros +resultados = explorar_parametros_dbscan(X_scaled, eps_values, min_samples_values) + +# Mostrar los mejores parámetros según Silhouette Score +resultados_sorted = sorted(resultados, key=lambda x: x[3], reverse=True) +mejores_parametros = resultados_sorted[0] +print("\n** Mejores parámetros para DBSCAN **") +print(f"EPS: {mejores_parametros[0]}") +print(f"Min Samples: {mejores_parametros[1]}") +print(f"Número de Clusters: {mejores_parametros[2]}") +print(f"Silhouette Score: {mejores_parametros[3]}") +print(f"Davies-Bouldin Score: {mejores_parametros[4]}") + +# Aplicar DBSCAN con los mejores parámetros +dbscan = DBSCAN(eps=mejores_parametros[0], min_samples=mejores_parametros[1]) +clusters = dbscan.fit_predict(X_scaled) + +# Agregar los clusters al DataFrame +df['cluster'] = clusters + + +# ** Análisis de clusters para DBSCAN ** +def analizar_cluster_dbscan(df, cluster_num): + elementos_cluster = df[df['cluster'] == cluster_num] + print(f"\n=== Análisis del cluster {cluster_num} ===") + print(f"Total de elementos: {len(elementos_cluster)}") + + if len(elementos_cluster) == 0: + print("El cluster está vacío.") + return + + print(f"\nEstadísticas descriptivas principales:") + print(elementos_cluster.describe().loc[['mean', 'std', 'min', 'max']]) + + print(f"\nValores más frecuentes por columna categórica:") + cols_categoricas = elementos_cluster.select_dtypes(include=['object', 'category']).columns + for col in cols_categoricas: + if not elementos_cluster[col].dropna().empty: + modo = elementos_cluster[col].mode() + valor_modo = modo.values[0] if not modo.empty else "Sin valores frecuentes" + print(f" - {col}: {valor_modo}") + else: + print(f" - {col}: Sin datos disponibles") + + +# Analizar clusters generados +clusters_unicos = set(clusters) +clusters_unicos.discard(-1) # Ignorar ruido (-1) +print("\n** Análisis de Clusters **") +for cluster_num in clusters_unicos: + analizar_cluster_dbscan(df, cluster_num) + +# Visualización de distribución de clusters +def graficar_distribucion_clusters_dbscan(df): + plt.figure(figsize=(8, 5)) + df['cluster'].value_counts().sort_index().plot(kind='bar', color='skyblue', edgecolor='black') + plt.xlabel('Cluster') + plt.ylabel('Número de elementos') + plt.title('Distribución de elementos por cluster (DBSCAN)') + plt.xticks(rotation=0) + plt.show() + +print("\n** Distribución de Clusters **") +graficar_distribucion_clusters_dbscan(df) + +ruido = sum(clusters == -1) +total = len(clusters) +print(f"Puntos clasificados como ruido: {ruido}/{total} ({ruido/total:.2%})") + +from sklearn.decomposition import PCA + +pca = PCA(n_components=2) +X_pca = pca.fit_transform(X_scaled) + +plt.scatter(X_pca[:, 0], X_pca[:, 1], s=10, c=clusters, cmap='viridis', alpha=0.7) +plt.title('Proyección 2D de los datos escalados') +plt.xlabel('Componente principal 1') +plt.ylabel('Componente principal 2') +plt.colorbar(label='Cluster') +plt.show() + +if resultados: + resultados_sorted = sorted(resultados, key=lambda x: x[3], reverse=True) + mejores_parametros = resultados_sorted[0] + print("\n** Mejores parámetros para DBSCAN **") + print(f"EPS: {mejores_parametros[0]}") + print(f"Min Samples: {mejores_parametros[1]}") + print(f"Número de Clusters: {mejores_parametros[2]}") + print(f"Silhouette Score: {mejores_parametros[3]}") + print(f"Davies-Bouldin Score: {mejores_parametros[4]}") + + # Aplicar DBSCAN con los mejores parámetros + dbscan = DBSCAN(eps=mejores_parametros[0], min_samples=mejores_parametros[1]) + clusters = dbscan.fit_predict(X_scaled) +else: + print("No se encontraron parámetros válidos.") \ No newline at end of file diff --git a/src/comparative_analysis/models/K-Means.py b/src/comparative_analysis/models/K-Means.py new file mode 100644 index 000000000..728fb528a --- /dev/null +++ b/src/comparative_analysis/models/K-Means.py @@ -0,0 +1,109 @@ +import pandas as pd +import re +from sklearn.cluster import KMeans +from sklearn.metrics import silhouette_score, davies_bouldin_score +from sklearn.preprocessing import StandardScaler +import matplotlib.pyplot as plt + +# ** Carga y limpieza de datos ** +ruta_excel = r"C:\\Users\\cdgn2\\OneDrive\\Escritorio\\Maestría\\Maestria\\Metodologias Agiles\\Proyecto\\Comparative-analysis-of-products\\src\\comparative_analysis\\database\\Adidas_etiquetado.xlsx" + +# Crear el DataFrame +df = pd.read_excel(ruta_excel, header=0) + +# Procesamiento de columnas numéricas +num_cols = { + 'Weight': '(\d+\.?\d*)', + 'Drop__heel-to-toe_differential_': '(\d+\.?\d*)' +} +for col, pattern in num_cols.items(): + df[col] = df[col].astype(str).str.extract(pattern).astype(float, errors='ignore') + df[col] = pd.to_numeric(df[col], errors='coerce') + +# Procesar precios +price_cols = ['regularPrice', 'undiscounted_price'] +for col in price_cols: + df[col] = df[col].astype(str).str.replace(r'[^0-9.,]', '', regex=True) + df[col] = df[col].str.replace(r'\.', '', regex=True).str.replace(',', '.') + df[col] = pd.to_numeric(df[col], errors='coerce') + +# Eliminar columnas innecesarias +cols_to_drop = ['details', 'description', 'category', 'characteristics', 'width', 'Pronation_Type'] +df = df.drop(columns=cols_to_drop, errors='ignore') + +# ** Clustering y evaluación ** +ids = df['id'] +X = df.drop(columns=['id'], errors='ignore').fillna(0) + +# Codificar variables categóricas +df_dummies = pd.get_dummies(X, dummy_na=True).fillna(0) + +# Escalar los datos +scaler = StandardScaler() +X_scaled = scaler.fit_transform(df_dummies) + +# Método del codo para determinar el número óptimo de clusters +def metodo_del_codo(X, k_range): + distortions = [] + for k in k_range: + kmeans = KMeans(n_clusters=k, random_state=42) + kmeans.fit(X) + distortions.append(kmeans.inertia_) + + plt.figure(figsize=(8, 5)) + plt.plot(k_range, distortions, 'bx-') + plt.xlabel('Número de clusters k') + plt.ylabel('Distorsión (Inercia)') + plt.title('Método del Codo para determinar k') + plt.show() + +# Mostrar el método del codo +print("\n** Método del Codo para determinar el número óptimo de clusters **") +metodo_del_codo(X_scaled, range(2, 11)) + +# Clustering con un número específico de clusters +k = 8 +kmeans = KMeans(n_clusters=k, random_state=42) +clusters = kmeans.fit_predict(X_scaled) + +# Agregar el cluster al DataFrame original +df['cluster'] = clusters + +# Evaluar calidad del clustering +sil_score = silhouette_score(X_scaled, clusters) +db_score = davies_bouldin_score(X_scaled, clusters) +print(f"\n** Evaluación del clustering **") +print(f"Silhouette Score: {sil_score}") +print(f"Davies-Bouldin Score: {db_score}") + +# ** Análisis de clusters ** +def analizar_cluster(df, cluster_num): + elementos_cluster = df[df['cluster'] == cluster_num] + print(f"\n=== Análisis del cluster {cluster_num} ===") + print(f"Total de elementos: {len(elementos_cluster)}") + + print(f"\nEstadísticas descriptivas principales:") + print(elementos_cluster.describe().loc[['mean', 'std', 'min', 'max']]) + + print(f"\nValores más frecuentes por columna categórica:") + cols_categoricas = elementos_cluster.select_dtypes(include=['object', 'category']).columns + for col in cols_categoricas: + print(f" - {col}: {elementos_cluster[col].mode().values[0]}") + +# Analizar los clusters 2 y 3 +print("\n** Análisis de Clusters **") +analizar_cluster(df, cluster_num=2) +analizar_cluster(df, cluster_num=3) + +# Visualización de distribución de clusters +def graficar_distribucion_clusters(df): + plt.figure(figsize=(8, 5)) + df['cluster'].value_counts().sort_index().plot(kind='bar', color='skyblue', edgecolor='black') + plt.xlabel('Cluster') + plt.ylabel('Número de elementos') + plt.title('Distribución de elementos por cluster') + plt.xticks(rotation=0) + plt.show() + +print("\n** Distribución de Clusters **") +graficar_distribucion_clusters(df) \ No newline at end of file diff --git a/src/comparative_analysis/training/trainingDBScan.py b/src/comparative_analysis/training/trainingDBScan.py new file mode 100644 index 000000000..3281fe487 --- /dev/null +++ b/src/comparative_analysis/training/trainingDBScan.py @@ -0,0 +1,163 @@ +import pandas as pd +import re +from sklearn.cluster import DBSCAN +from sklearn.metrics import silhouette_score, davies_bouldin_score +from sklearn.preprocessing import StandardScaler +import matplotlib.pyplot as plt +import numpy as np + +# ** Carga y limpieza de datos ** +ruta_excel = r"C:\\Users\\cdgn2\\OneDrive\\Escritorio\\Maestría\\Maestria\\Metodologias Agiles\\Proyecto\\Comparative-analysis-of-products\\src\\comparative_analysis\\database\\Adidas_etiquetado.xlsx" + +# Crear el DataFrame +df = pd.read_excel(ruta_excel, header=0, engine='openpyxl') + +# Procesamiento de columnas numéricas +num_cols = { + 'Weight': '(\d+\.?\d*)', + 'Drop__heel-to-toe_differential_': '(\d+\.?\d*)' +} +for col, pattern in num_cols.items(): + df[col] = df[col].astype(str).str.extract(pattern).astype(float, errors='ignore') + df[col] = pd.to_numeric(df[col], errors='coerce') + +# Procesar precios +price_cols = ['regularPrice', 'undiscounted_price'] +for col in price_cols: + df[col] = df[col].astype(str).str.replace(r'[^0-9.,]', '', regex=True) + df[col] = df[col].str.replace(r'\.', '', regex=True).str.replace(',', '.') + df[col] = pd.to_numeric(df[col], errors='coerce') + +# Eliminar columnas innecesarias +cols_to_drop = ['details', 'description', 'category', 'characteristics', 'width', 'Pronation_Type'] +df = df.drop(columns=cols_to_drop, errors='ignore') + +# ** Clustering y evaluación ** +ids = df['id'] +X = df.drop(columns=['id'], errors='ignore').fillna(0) + +# Codificar variables categóricas +df_dummies = pd.get_dummies(X, dummy_na=True).fillna(0) + +# Escalar los datos +scaler = StandardScaler() +X_scaled = scaler.fit_transform(df_dummies) + +print("Media X_scaled:", X_scaled.mean(axis=0)) +print("Desviación estándar X_scaled:", X_scaled.std(axis=0)) + +# Función para explorar parámetros de DBSCAN +def explorar_parametros_dbscan(X, eps_values, min_samples_values): + resultados = [] + for eps in eps_values: + for min_samples in min_samples_values: + dbscan = DBSCAN(eps=eps, min_samples=min_samples) + clusters = dbscan.fit_predict(X) + num_clusters = len(set(clusters)) - (1 if -1 in clusters else 0) + + if num_clusters > 1: # Evaluar solo si hay más de 1 cluster válido + sil_score = silhouette_score(X, clusters) + db_score = davies_bouldin_score(X, clusters) + resultados.append((eps, min_samples, num_clusters, sil_score, db_score)) + return resultados + +# Rango de parámetros a explorar +eps_values = np.arange(0.5, 2.0, 0.1) +min_samples_values = range(3, 10) + +# Explorar los parámetros +resultados = explorar_parametros_dbscan(X_scaled, eps_values, min_samples_values) + +# Mostrar los mejores parámetros según Silhouette Score +resultados_sorted = sorted(resultados, key=lambda x: x[3], reverse=True) +mejores_parametros = resultados_sorted[0] +print("\n** Mejores parámetros para DBSCAN **") +print(f"EPS: {mejores_parametros[0]}") +print(f"Min Samples: {mejores_parametros[1]}") +print(f"Número de Clusters: {mejores_parametros[2]}") +print(f"Silhouette Score: {mejores_parametros[3]}") +print(f"Davies-Bouldin Score: {mejores_parametros[4]}") + +# Aplicar DBSCAN con los mejores parámetros +dbscan = DBSCAN(eps=mejores_parametros[0], min_samples=mejores_parametros[1]) +clusters = dbscan.fit_predict(X_scaled) + +# Agregar los clusters al DataFrame +df['cluster'] = clusters + + +# ** Análisis de clusters para DBSCAN ** +def analizar_cluster_dbscan(df, cluster_num): + elementos_cluster = df[df['cluster'] == cluster_num] + print(f"\n=== Análisis del cluster {cluster_num} ===") + print(f"Total de elementos: {len(elementos_cluster)}") + + if len(elementos_cluster) == 0: + print("El cluster está vacío.") + return + + print(f"\nEstadísticas descriptivas principales:") + print(elementos_cluster.describe().loc[['mean', 'std', 'min', 'max']]) + + print(f"\nValores más frecuentes por columna categórica:") + cols_categoricas = elementos_cluster.select_dtypes(include=['object', 'category']).columns + for col in cols_categoricas: + if not elementos_cluster[col].dropna().empty: + modo = elementos_cluster[col].mode() + valor_modo = modo.values[0] if not modo.empty else "Sin valores frecuentes" + print(f" - {col}: {valor_modo}") + else: + print(f" - {col}: Sin datos disponibles") + + +# Analizar clusters generados +clusters_unicos = set(clusters) +clusters_unicos.discard(-1) # Ignorar ruido (-1) +print("\n** Análisis de Clusters **") +for cluster_num in clusters_unicos: + analizar_cluster_dbscan(df, cluster_num) + +# Visualización de distribución de clusters +def graficar_distribucion_clusters_dbscan(df): + plt.figure(figsize=(8, 5)) + df['cluster'].value_counts().sort_index().plot(kind='bar', color='skyblue', edgecolor='black') + plt.xlabel('Cluster') + plt.ylabel('Número de elementos') + plt.title('Distribución de elementos por cluster (DBSCAN)') + plt.xticks(rotation=0) + plt.show() + +print("\n** Distribución de Clusters **") +graficar_distribucion_clusters_dbscan(df) + +ruido = sum(clusters == -1) +total = len(clusters) +print(f"Puntos clasificados como ruido: {ruido}/{total} ({ruido/total:.2%})") + +from sklearn.decomposition import PCA + +pca = PCA(n_components=2) +X_pca = pca.fit_transform(X_scaled) + +plt.scatter(X_pca[:, 0], X_pca[:, 1], s=10, c=clusters, cmap='viridis', alpha=0.7) +plt.title('Proyección 2D de los datos escalados') +plt.xlabel('Componente principal 1') +plt.ylabel('Componente principal 2') +plt.colorbar(label='Cluster') +plt.show() + +if resultados: + resultados_sorted = sorted(resultados, key=lambda x: x[3], reverse=True) + mejores_parametros = resultados_sorted[0] + print("\n** Mejores parámetros para DBSCAN **") + print(f"EPS: {mejores_parametros[0]}") + print(f"Min Samples: {mejores_parametros[1]}") + print(f"Número de Clusters: {mejores_parametros[2]}") + print(f"Silhouette Score: {mejores_parametros[3]}") + print(f"Davies-Bouldin Score: {mejores_parametros[4]}") + + # Aplicar DBSCAN con los mejores parámetros + dbscan = DBSCAN(eps=mejores_parametros[0], min_samples=mejores_parametros[1]) + clusters = dbscan.fit_predict(X_scaled) +else: + print("No se encontraron parámetros válidos.") \ No newline at end of file diff --git a/src/comparative_analysis/training/training.py b/src/comparative_analysis/training/trainingK-Means.py similarity index 100% rename from src/comparative_analysis/training/training.py rename to src/comparative_analysis/training/trainingK-Means.py From 806a349af6496051bbde1c704728e46af42e59ea Mon Sep 17 00:00:00 2001 From: azacipa <52643112+azacipa@users.noreply.github.com> Date: Mon, 16 Dec 2024 20:42:54 -0500 Subject: [PATCH 46/84] Generacion de modelos de clustering Se complementan las rutinas para preprocesamiento adicional de las caracteristicas con el fin de realizar el modelamiento de clustering con KMeans, Agglomerative, DBScan y HDBScan. Se actualizan las imagenes y se generan dataframes con el resultado de los modelos --- src/comparative_analysis/models/models.py | 178 ++++++++++-------- .../models/utilities/utilities.py | 173 +++++++++++++++++ .../visualization/agglomerative.png | Bin 73351 -> 0 bytes .../agglomerative_silhouette.png | Bin 72512 -> 0 bytes .../Additional_Technologies.png | Bin 0 -> 20745 bytes .../clustering model/Cushioning_System.png | Bin 0 -> 19909 bytes .../Drop__heel_to_toe_differential_.png | Bin 0 -> 12121 bytes .../visualization/clustering model/Gender.png | Bin 0 -> 10823 bytes .../clustering model/Midsole_Material.png | Bin 0 -> 30842 bytes .../clustering model/Usage_Type.png | Bin 0 -> 14628 bytes .../visualization/clustering model/Weight.png | Bin 0 -> 14622 bytes .../clustering model/agglomerative.png | Bin 0 -> 92092 bytes .../agglomerative_silhouette.png | Bin 0 -> 65263 bytes .../analysis_column_Cushioning_System.png | Bin 0 -> 19909 bytes .../analysis_column_Midsole_Material.png | 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.../visualization/hdbscan_silhouette.png | Bin 94543 -> 0 bytes .../visualization/kmeans.png | Bin 88945 -> 0 bytes .../visualization/kmeans_silhouette.png | Bin 72467 -> 0 bytes 33 files changed, 276 insertions(+), 75 deletions(-) create mode 100644 src/comparative_analysis/models/utilities/utilities.py delete mode 100644 src/comparative_analysis/visualization/agglomerative.png delete mode 100644 src/comparative_analysis/visualization/agglomerative_silhouette.png create mode 100644 src/comparative_analysis/visualization/clustering model/Additional_Technologies.png create mode 100644 src/comparative_analysis/visualization/clustering model/Cushioning_System.png create mode 100644 src/comparative_analysis/visualization/clustering model/Drop__heel_to_toe_differential_.png create mode 100644 src/comparative_analysis/visualization/clustering model/Gender.png create mode 100644 src/comparative_analysis/visualization/clustering model/Midsole_Material.png create mode 100644 src/comparative_analysis/visualization/clustering model/Usage_Type.png create mode 100644 src/comparative_analysis/visualization/clustering model/Weight.png create mode 100644 src/comparative_analysis/visualization/clustering model/agglomerative.png create mode 100644 src/comparative_analysis/visualization/clustering model/agglomerative_silhouette.png create mode 100644 src/comparative_analysis/visualization/clustering model/analysis_column_Cushioning_System.png create mode 100644 src/comparative_analysis/visualization/clustering model/analysis_column_Midsole_Material.png create mode 100644 src/comparative_analysis/visualization/clustering model/category.png create mode 100644 src/comparative_analysis/visualization/clustering model/dbscan.png create mode 100644 src/comparative_analysis/visualization/clustering model/dbscan_silhouette.png create mode 100644 src/comparative_analysis/visualization/clustering model/hdbscan.png_condensed_tree create mode 100644 src/comparative_analysis/visualization/clustering model/hdbscan.png_linkage_tree create mode 100644 src/comparative_analysis/visualization/clustering model/hdbscan.png_linkage_tree_focus create mode 100644 src/comparative_analysis/visualization/clustering model/hdbscan_silhouette.png create mode 100644 src/comparative_analysis/visualization/clustering model/kmeans.png create mode 100644 src/comparative_analysis/visualization/clustering model/kmeans_silhouette.png create mode 100644 src/comparative_analysis/visualization/clustering model/regularPrice.png delete mode 100644 src/comparative_analysis/visualization/dbscan.png delete mode 100644 src/comparative_analysis/visualization/dbscan_silhouette.png delete mode 100644 src/comparative_analysis/visualization/hdbscan.png_condensed_tree delete mode 100644 src/comparative_analysis/visualization/hdbscan.png_linkage_tree delete mode 100644 src/comparative_analysis/visualization/hdbscan.png_linkage_tree_focus delete mode 100644 src/comparative_analysis/visualization/hdbscan_silhouette.png delete mode 100644 src/comparative_analysis/visualization/kmeans.png delete mode 100644 src/comparative_analysis/visualization/kmeans_silhouette.png diff --git a/src/comparative_analysis/models/models.py b/src/comparative_analysis/models/models.py index 2b8cbd356..51a36f0b5 100644 --- a/src/comparative_analysis/models/models.py +++ b/src/comparative_analysis/models/models.py @@ -7,18 +7,19 @@ # Importar librerias import pandas as pd -#import re +import re import sklearn -#import matplotlib +import matplotlib #mport matplotlib.pyplot as plt import numpy as np -#import time import clusteval +import df2onehot import sys #from sklearn.cluster import KMeans from sklearn.metrics import silhouette_score, davies_bouldin_score from sklearn.preprocessing import StandardScaler +from utilities.utilities import Utilities #from sklearn.cluster import DBSCAN, HDBSCAN #from sklearn import metrics #from sklearn.preprocessing import StandardScaler @@ -37,48 +38,68 @@ print(sys.version) print('Pandas', pd.__version__) print('Numpy', np.__version__) -#print('Matplotlib', matplotlib.__version__) +print('Matplotlib', matplotlib.__version__) print('Clusteval', clusteval.__version__) print('Scikit-Learn', sklearn.__version__) +print('DF2Onehot', df2onehot.__version__) # ** Carga y limpieza de datos ** ruta_excel = r"..\\database\\Adidas_etiquetado_new.xlsx" # Crear el DataFrame -df = pd.read_excel(ruta_excel, header=0) -df.info() - -# Procesamiento de columnas numéricas -num_cols = { - 'Weight': '(\d+\.?\d*)', - 'Drop__heel-to-toe_differential_': '(\d+\.?\d*)' -} -for col, pattern in num_cols.items(): - df[col] = df[col].astype(str).str.extract(pattern).astype(float, errors='ignore') - df[col] = pd.to_numeric(df[col], errors='coerce') - -# Procesar precios -price_cols = ['regularPrice', 'undiscounted_price'] +df_adidas = pd.read_excel(ruta_excel, header=0) +df_adidas.info() + +# validacion de nulos/ceros en el dataframe +Utilities.null_or_zero_values(df = df_adidas) + +# rellenar valores nulos y eliminar columnas no relevantes +cols_to_drop = ['Width', 'characteristics', 'Pronation_Type', 'Available_Sizes', 'undiscounted_price'] +df_adidas = df_adidas.drop(columns=cols_to_drop, errors='ignore') + +# limpieza de texto +# Aplicamos limpieza de texto +df_adidas = df_adidas.fillna("0") +reg_numbers = re.compile(r'[^0-9.,]+') +df_adidas['Drop__heel-to-toe_differential_'] = df_adidas['Drop__heel-to-toe_differential_'].apply(lambda doc: Utilities.preprocess(doc, [reg_numbers])) +df_adidas['Weight'] = df_adidas['Weight'].apply(lambda doc: Utilities.preprocess(doc, [reg_numbers])) + +# Procesar valores numericos +price_cols = ['regularPrice', 'Drop__heel-to-toe_differential_', 'Weight'] for col in price_cols: - df[col] = df[col].astype(str).str.replace(r'[^0-9.,]', '', regex=True) - df[col] = df[col].str.replace(r'\\.', '', regex=True).str.replace(',', '.') - df[col] = pd.to_numeric(df[col], errors='coerce') - -# Eliminar columnas innecesarias -cols_to_drop = ['details', 'description', 'category', 'characteristics', 'width', 'Pronation_Type'] -df = df.drop(columns=cols_to_drop, errors='ignore') + df_adidas[col] = df_adidas[col].astype(str).str.replace(r'[^0-9.,]', '', regex=True) + df_adidas[col] = df_adidas[col].str.replace(r'\\.', '', regex=True).str.replace(',', '.') + df_adidas[col] = pd.to_numeric(df_adidas[col], errors='coerce') + +# rellenar variables numericas vacias con 0 +df1 = df_adidas.fillna(0) + +# normalizar genero +df_adidas['Gender'] = df_adidas['Gender'].apply(lambda x: "Mujer" if x == 'Woman' else x) +df_adidas['Gender'] = df_adidas['Gender'].apply(lambda x: "Hombre" if x == 'Men' else x) +df_adidas['Gender'] = df_adidas['Gender'].apply(lambda x: "0" if x == '5' else x) -# Generar array de caracteristicas -ids = df['id'] -X = df.drop(columns=['id'], errors='ignore').fillna(0) +# analizar columnas +Utilities.analyze_columns(df = df_adidas, cols = ['Cushioning_System', 'Midsole_Material']) +Utilities.analyze_columns(df = df_adidas, cols = ['Drop__heel-to-toe_differential_','Weight']) +Utilities.analyze_columns(df = df_adidas, cols = ['Gender', 'Additional_Technologies']) +Utilities.analyze_columns(df = df_adidas, cols = ['Usage_Type', 'regularPrice']) +Utilities.analyze_columns(df = df_adidas, cols = ['category']) + +# conservar el ID que identifica cada muestra +# ** Clustering y evaluación ** +ids = df_adidas['id'] + +# Eliminar columnas innecesarias +cols_to_drop = ['details', 'description', 'characteristics', 'Pronation_Type', 'id'] +df_adidas = df_adidas.drop(columns=cols_to_drop, errors='ignore') -# Codificar variables categóricas -df_dummies = pd.get_dummies(X, dummy_na=True).fillna(0) +# Realizar codificacion de variables categoricas +dfhot_adidas = df2onehot.df2onehot(df_adidas, excl_background=['0.0', 'None', '?', 'False'], y_min=30 + , perc_min_num=0.8, remove_mutual_exclusive=True, verbose=4)['onehot'] -# Escalar los datos -scaler = StandardScaler() -X_scaled = scaler.fit_transform(df_dummies) -df_dummies.info() +dfhot_adidas.info() +dfhot_adidas # Analisis de clusters # Se realiza comparacion de 4 modelos de clustering @@ -87,7 +108,7 @@ # * dbscan # * hdbscan # -def cluster_eval(X, cluster, evaluate, verbose = 40, savefig = False): +def cluster_eval(X, cluster, evaluate, metric = "euclidean", normalize = False, verbose = 40, savefig = False): """" Función cluster_eval: Realiza evaluacion de clustering sobre el conjunto de datos que se indica. Genera grafica de el score que se indica vs numero de clusters y grafico de los coeficientes silhouette para los diferentes clusters @@ -101,8 +122,6 @@ def cluster_eval(X, cluster, evaluate, verbose = 40, savefig = False): Return: results: Diccionario con diferentes llaves que dependen del metodo de evaluacion utilizado - sil_score (float): Silhouette Score - db_score (float): Davies-Bouldin Score Ejemplo: @@ -114,61 +133,70 @@ def cluster_eval(X, cluster, evaluate, verbose = 40, savefig = False): ... """ # Initialize - ce = clusteval.clusteval(cluster = cluster, evaluate=evaluate, verbose = verbose) + ce = clusteval.clusteval(cluster = cluster, evaluate=evaluate, metric = metric, verbose = verbose + ,normalize = normalize) # Fit results = ce.fit(X) # # metrics - sil_score = silhouette_score(X, results["labx"]) - db_score = davies_bouldin_score(X, results["labx"]) - print(f"Silhouette Score: {sil_score}") - print(f"Davies-Bouldin Score: {db_score}") + if(evaluate == "silhouette"): + sil_score = silhouette_score(X, results["labx"]) + db_score = davies_bouldin_score(X, results["labx"]) + print(f"Silhouette Score: {sil_score}") + print(f"Davies-Bouldin Score: {db_score}") # Plot title = f"Clustering method {cluster} with evaluation {evaluate}" if(savefig == True): - plot_path = r"..\\visualization\\" - plot_name = plot_path + cluster + ".png" - plot_sil_name = plot_path + cluster + "_silhouette.png" + plot_name = Utilities.image_path + cluster + ".png" + plot_sil_name = Utilities.image_path + cluster + "_silhouette.png" ce.plot(title = title, verbose = verbose, savefig = {"fname" : plot_name, "format" : "png"}) ce.plot_silhouette(savefig = {"fname" : plot_sil_name}) else: ce.plot(title = title, verbose = verbose) ce.plot_silhouette() - return results, sil_score, db_score + return results # analisis con kmeans -results_kmeans,sil_kmeans_score,db_kmeans_score = cluster_eval(X = X_scaled - ,cluster = "kmeans" - ,evaluate = "silhouette" - ,savefig=True) +results_kmeans = cluster_eval(X = dfhot_adidas, cluster = "kmeans" + , evaluate = "silhouette", savefig=True) # analisis con cluster aglomerativo -results_aggl,sil_aggl_score,db_aggl_score = cluster_eval(X = X_scaled - ,cluster = "agglomerative" - ,evaluate = "silhouette" - ,savefig=True) +results_aggl = cluster_eval(X = dfhot_adidas, cluster = "agglomerative" + , evaluate = "silhouette", savefig=True) # analisis con dbscan -results_dbscan,sil_dbscan_score,db_dbscan_score = cluster_eval(X = X_scaled - ,cluster = "dbscan" - ,evaluate = "silhouette" - ,savefig=True) +results_dbscan = cluster_eval(X = dfhot_adidas, cluster = "dbscan" + , evaluate = "silhouette", savefig=True) # analisis con hdbscan -results_hdbscan,sil_hdbscan_score,db_hdbscan_score = cluster_eval(X = X_scaled - ,cluster = "hdbscan" - ,evaluate = "silhouette" - ,savefig=True) - -# Comparacion del score de los modelos -# Silhouette Score -print(f"\nKMeans Silhouette Score: {sil_kmeans_score}") -print(f"Agglomerative Silhouette Score: {sil_aggl_score}") -print(f"DBScan Silhouette Score: {sil_dbscan_score}") -print(f"HDBScan Silhouette Score: {sil_hdbscan_score}") - -# Davies-Bouldin -print(f"\nKMeans Davies-Bouldin Score: {db_kmeans_score}") -print(f"Agglomerative Davies-Bouldin Score: {db_aggl_score}") -print(f"DBScan Davies-Bouldin Score: {db_dbscan_score}") -print(f"HDBScan Davies-Bouldin Score: {db_hdbscan_score}") \ No newline at end of file +results_hdbscan = cluster_eval(X = dfhot_adidas, cluster = "hdbscan" + , evaluate = "silhouette", savefig=True) + +# Ensamblar en dataframes el resultado de los cluster generados por cada modelo +dfhot_adidas['cluster_kmeans'] = results_kmeans["labx"] +dfhot_adidas['cluster_aggl'] = results_aggl["labx"] +dfhot_adidas['cluster_dbscan'] = results_dbscan["labx"] +dfhot_adidas['cluster_hdbscan'] = results_hdbscan["labx"] + +# agrupar la cantidad de elementos de cada cluster +serie1 = dfhot_adidas.groupby(['cluster_kmeans'])['cluster_kmeans'].agg('count') +serie2 = dfhot_adidas.groupby(['cluster_aggl'])['cluster_aggl'].agg('count') +serie3 = dfhot_adidas.groupby(['cluster_dbscan'])['cluster_dbscan'].agg('count') +serie4 = dfhot_adidas.groupby(['cluster_hdbscan'])['cluster_hdbscan'].agg('count') + +# generar dataframe con la cantidad y porcentaje de elementos en cada cluster para cada modelo +df1 = pd.DataFrame({'cluster':serie1.index, 'cantidad':serie1.values}) +df2 = pd.DataFrame({'cluster':serie2.index, 'cantidad':serie2.values}) +df3 = pd.DataFrame({'cluster':serie3.index, 'cantidad':serie3.values}) +df4 = pd.DataFrame({'cluster':serie4.index, 'cantidad':serie4.values}) +df1["%cluster_kmeans"] = df1['cantidad'] / df1['cantidad'].sum() * 100 +df2["%cluster_aggl"] = df2['cantidad'] / df2['cantidad'].sum() * 100 +df3["%cluster_dbscan"] = df3['cantidad'] / df3['cantidad'].sum() * 100 +df4["%cluster_hdbscan"] = df4['cantidad'] / df4['cantidad'].sum() * 100 + +# mostrar los datos de cada cluster +print("\nComposicion de clusters por modelo") +print("\nCluster KMeans",df1) +print("\nCluster Agglomerative",df2) +print("\nCluster DBScan",df3) +print("\nCluster HDBScan",df4) \ No newline at end of file diff --git a/src/comparative_analysis/models/utilities/utilities.py b/src/comparative_analysis/models/utilities/utilities.py new file mode 100644 index 000000000..0ee4de2ff --- /dev/null +++ b/src/comparative_analysis/models/utilities/utilities.py @@ -0,0 +1,173 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Dec 16 19:11:45 2024 + +@author: azaci +""" +# importar librerias +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns +import re + +class Utilities: + image_path = r"..\\visualization\\clustering model//" + + def __init__(self): + pass + + @staticmethod + def null_or_zero_values(df): + """" + Función null_or_zero_values: Genera un resumen de los valores cero o nulos para las columnas de tipo numérico del dataframe + + Parámetros: + df (dataframe): dataframe sobre el cual se realizara el análisis + + Return: + data_table: Dataframe con el resultado del análisis + + Ejemplo: + + >>> null_or_zero_values(df = df_icfes) + + El tamaño del dataframe es 42 columnas y 3316179 filas. + Cantidad de columnas con valores 0 o nulos: 32 + ... + """ + zero_val = (df == 0.00).astype(int).sum(axis=0) + null_val = df.isnull().sum() + null_val_percent = 100 * df.isnull().sum() / len(df) + data_table = pd.concat([zero_val, null_val, null_val_percent], axis=1) + data_table = data_table.rename(columns = {0 : 'Valores 0', 1 : 'Valores Nulos', 2 : '% Valores nulos'}) + data_table['Total valores 0 o nulos'] = data_table['Valores 0'] + data_table['Valores Nulos'] + data_table['% Total valores 0 o nulo'] = 100 * data_table['Total valores 0 o nulos'] / len(df) + data_table['Tipo de Dato'] = df.dtypes + data_table = data_table[data_table.iloc[:,1] != 0].sort_values('% Valores nulos', ascending=False).round(1) + print ("El tamaño del dataframe es " + str(df.shape[1]) + " columnas y " + str(df.shape[0]) + " filas.\n" + "Cantidad de columnas con valores 0 o nulos: " + str(data_table.shape[0])) + return data_table + + @staticmethod + def plot_col(df, col, ax, kde = False): + """" + Función plot_col: Genera gráfico de tipo Histplot para la columna que se indica + + Parámetros: + df (dataframe) : Dataframe con los datos + col (string) : Columna del dataframe respecto a la cual se generará el histograma + ax (int) : Posición en la que se mostrará el gráfico del histograma + kde (boolean) : Si True, se calcula Estimación de Densidad de Kernel (KDE), para representar una aproximación a la función + de densidad de probabilidad del conjunto de datos del dataframe y la columna indicados. + + Return: + hist (sns.histplot): Objeto con el histograma generado + + Ejemplo: + + >>> plot = plot_col(df = df, col = col_age, ax = axs[0]) + + """ + # Plotting a basic histogram + sns.set(font_scale=0.5) + hist = sns.histplot(data = df[col], kde = kde, color = 'blue', bins = 10, ax = ax) + hist.set_title(col + ' Histogram', fontsize=8) + return hist + + @staticmethod + def plot_box(df, col, ax): + """" + Función plot_box: Genera gràfico de tipo Box para la columna que se indica + + Paràmetros: + df (dataframe) : Dataframe con los datos + col (string) : Columna del dataframe respecto a la cual se generará el gráfico de caja + ax (int) : Posición en la que se mostrará el gráfico de caja + + Return: + hist (sns.boxplot): Objeto con el gráfico Box generado + + Ejemplo: + + >>> box = plot_box(df = df_icfes, col = col_age, ax = axs[1]) + + """ + #box = sns.boxplot(data = df, x = col, palette='afmhot', ax=ax) + sns.set(font_scale=0.5) + box = sns.boxplot(data = df, x = col, palette='Blues', ax=ax) + # Adding labels and title + box.set_xlabel(col, fontsize=8) + box.set_title(col + ' Box plot', fontsize=8) + return box + + @staticmethod + def analyze_columns(df, cols, top_n = 999999): + """" + Función analyze_column: Realizar análisis sobre la columna del dataframe que se indica + Muestra estadísticas de la columna col_age, asi como gráficos de tipo Histplot y Boxplot + + Parámetros: + df (dataframe): Dataframe con los datos + cola (list): Lista de columnas del dataframe a analizar + + Return: + None + + Ejemplo: + + >>> analyze_columns(df = df_icfes, cols = ['PUNT_LECTURA_CRITICA', 'PUNT_MATEMATICAS']) + ... + """ + sns.set_style("whitegrid") + sns.set_palette("pastel") + new_row_group = {} + if (len(cols) != 1): + fig, axs = plt.subplots(nrows = len(cols), ncols = 1, figsize=(10,15)) + else: + fig, ax = plt.subplots(figsize=(15,7)) + ind = 0 + for col in cols: + new_row_group[col] = df.groupby([col])[col].count().sort_values(ascending=False).head(top_n) + if (len(cols) != 1): + plot = Utilities.plot_col(df = df, col = col, ax = axs[ind]) + else: + plot = Utilities.plot_col(df = df, col = col, ax = ax) + ind += 1 + plt.savefig(Utilities.image_path + col.replace("-","_") + ".png") + plt.tight_layout() + # + data_table = pd.DataFrame + data_table = df[cols].describe(include="all") + print("Estadisticas por columna") + print(data_table.head(top_n)) + print("\nAgrupacion") + print(new_row_group) + # + return None + + @staticmethod + def preprocess(text, regex_list): + """" + Función preprocess: Preprocesamiento de texto + + Parámetros: + text (string) : Dataframe con los datos + regex_list (list): Columna del dataframe respecto a la cual se generará el histograma + + Return: + preprocess_text (string): Cadena con el texto 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zAp~Q>c%M_@>L<*<;xxg*G3+4bf&}K1>`jWUC-nT5bR2UrONWWmLJm{PS;i5&r+jFa_}P&;P0)^YTOfuOGmCWVv!va{9B^=IIL9 N`>proee&s-{{TvNgu?&; From cdd507716feb38c4c931c082fca2d6f1c7209660 Mon Sep 17 00:00:00 2001 From: CarlosKC26 Date: Wed, 18 Dec 2024 15:16:31 -0500 Subject: [PATCH 47/84] backup antes de modificar el modelo de K-MeansV1 --- requirements.txt | 3 +- main.py => scripts/data_acquisition/main.py | 2 +- src/comparative_analysis/__init__.py | 18 ++++ src/comparative_analysis/apiRest/get.py | 31 ++++++ src/comparative_analysis/apiRest/post.py | 98 ++++++++++++++++++ .../models/{ => K-MeansV1}/K-Means.py | 9 +- .../models/K-MeansV1/kmeans_model.joblib | Bin 0 -> 30079 bytes .../models/K-MeansV1/scaler.joblib | Bin 0 -> 36455 bytes 8 files changed, 157 insertions(+), 4 deletions(-) rename main.py => scripts/data_acquisition/main.py (86%) create mode 100644 src/comparative_analysis/apiRest/get.py create mode 100644 src/comparative_analysis/apiRest/post.py rename src/comparative_analysis/models/{ => K-MeansV1}/K-Means.py (94%) create mode 100644 src/comparative_analysis/models/K-MeansV1/kmeans_model.joblib create mode 100644 src/comparative_analysis/models/K-MeansV1/scaler.joblib diff --git a/requirements.txt b/requirements.txt index 4f0ab1c63..8d4049d5a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -8,4 +8,5 @@ openpyxl httpx boto3 scikit-learn -matplotlib \ No newline at end of file +matplotlib +flask \ No newline at end of file diff --git a/main.py b/scripts/data_acquisition/main.py similarity index 86% rename from main.py rename to scripts/data_acquisition/main.py index be5883d03..1b7ff4307 100644 --- a/main.py +++ b/scripts/data_acquisition/main.py @@ -1,4 +1,4 @@ -from scripts.data_acquisition.api_reader import ApiReader +from api_reader import ApiReader def main(): # Crear instancia de ApiReader diff --git a/src/comparative_analysis/__init__.py b/src/comparative_analysis/__init__.py index e69de29bb..13dcd6edb 100644 --- a/src/comparative_analysis/__init__.py +++ b/src/comparative_analysis/__init__.py @@ -0,0 +1,18 @@ +from flask import Flask +from apiRest.get import test, get_product_by_id +from apiRest.post import get_products_by_parameters, classify_new_element_KMeansV1 + +# Crear la instancia de Flask +app = Flask(__name__) + +# Registrar las rutas GET +app.add_url_rule('/api/test', view_func=test, methods=['GET']) +app.add_url_rule('/api/product', view_func=get_product_by_id, methods=['GET']) + +# Registrar las rutas POST +app.add_url_rule('/api/products', view_func=get_products_by_parameters, methods=['POST']) +app.add_url_rule('/api/classify/KMeansV1', view_func=classify_new_element_KMeansV1, methods=['POST']) + +# Ejecutar la aplicación +if __name__ == '__main__': + app.run(debug=True, port=2626) \ No newline at end of file diff --git a/src/comparative_analysis/apiRest/get.py b/src/comparative_analysis/apiRest/get.py new file mode 100644 index 000000000..e55fec284 --- /dev/null +++ b/src/comparative_analysis/apiRest/get.py @@ -0,0 +1,31 @@ +from flask import jsonify, request +import pandas as pd + +# Ruta GET de prueba +def test(): + response = { + "message": "¡Hola! Esta es una respuesta GET de prueba.", + "status": "success" + } + return jsonify(response) + +# Ruta GET para obtener el producto por ID +def get_product_by_id(): + product_id = request.args.get('id') # Cambié de `request.json` a `request.args` + + if not product_id: + return jsonify({"error": "Se requiere un ID de producto"}), 400 + + try: + file_path = 'src/comparative_analysis/database/Adidas_etiquetado.xlsx' + df = pd.read_excel(file_path) + product_data = df[df['id'] == product_id] + + if product_data.empty: + return jsonify({"error": "Producto no encontrado"}), 404 + + product_info = product_data.to_dict(orient='records')[0] + return jsonify(product_info) + + except Exception as e: + return jsonify({"error": str(e)}), 500 \ No newline at end of file diff --git a/src/comparative_analysis/apiRest/post.py b/src/comparative_analysis/apiRest/post.py new file mode 100644 index 000000000..f5abe5315 --- /dev/null +++ b/src/comparative_analysis/apiRest/post.py @@ -0,0 +1,98 @@ +from flask import jsonify, request +import pandas as pd +import re +from joblib import load + +# Ruta POST para buscar productos por diferentes parámetros +def get_products_by_parameters(): + params = request.json + + if not params: + return jsonify({"message": "No se proporcionaron parámetros de búsqueda"}), 400 + + try: + file_path = 'src/comparative_analysis/database/Adidas_etiquetado.xlsx' + df = pd.read_excel(file_path) + filtered_df = df + + for key, value in params.items(): + if key in df.columns: + filtered_df = filtered_df[filtered_df[key].astype(str).apply( + lambda x: bool(re.search(r'\b' + re.escape(str(value)) + r'\b', str(x), flags=re.IGNORECASE)))] + + if filtered_df.empty: + return jsonify({"message": "No se encontraron productos que coincidan con los parámetros proporcionados"}), 404 + + products_info = filtered_df.to_dict(orient='records') + return jsonify(products_info) + + except Exception as e: + return jsonify({"error": str(e)}), 500 + +# Ruta POST para clasificar un nuevo elemento +def classify_new_element_KMeansV1(): + """ + Endpoint POST para clasificar un nuevo elemento y devolver productos similares en el mismo cluster. + + Request JSON: + { + "nuevo_elemento": { ... }, # Diccionario con las características del nuevo producto + } + + Response JSON: + { + "cluster": int, + "productos_similares": [ ... ] # Lista de productos similares en el mismo cluster + } + """ + try: + # Obtener el nuevo elemento del cuerpo de la solicitud + nuevo_elemento = request.json.get('nuevo_elemento') + + if not nuevo_elemento: + return jsonify({"error": "Se requiere el nuevo elemento en el cuerpo de la solicitud"}), 400 + + # Cargar el archivo del dataset + file_path = 'src/comparative_analysis/database/Adidas_etiquetado.xlsx' + df = pd.read_excel(file_path) + + # Verificar si el DataFrame tiene la columna 'cluster' + if 'cluster' not in df.columns: + return jsonify({"error": "El dataset no contiene la columna 'cluster'. Verifica si el modelo fue entrenado correctamente."}), 500 + + # Cargar el modelo y el escalador + modelo_path = 'src/comparative_analysis/models/K-MeansV1/kmeans_model.joblib' + scaler_path = 'src/comparative_analysis/models/K-MeansV1/scaler.joblib' + kmeans = load(modelo_path) + scaler = load(scaler_path) + + # Obtener las columnas utilizadas en el entrenamiento + columnas_entrenamiento = [col for col in df.columns if col not in ['id', 'cluster']] + + # Preprocesar el nuevo elemento + nuevo_df = pd.DataFrame([nuevo_elemento], columns=columnas_entrenamiento).fillna(0) + + # Convertir variables categóricas en dummies para que coincidan con el entrenamiento + nuevo_df = pd.get_dummies(nuevo_df) + df_dummies = pd.get_dummies(df[columnas_entrenamiento]) + + # Asegurar que las columnas del nuevo elemento coincidan con las del dataset original + nuevo_df = nuevo_df.reindex(columns=df_dummies.columns, fill_value=0) + + # Escalar las características del nuevo elemento + nuevo_elemento_escalado = scaler.transform(nuevo_df) + + # Predecir el cluster del nuevo elemento + cluster_predicho = kmeans.predict(nuevo_elemento_escalado)[0] + + # Filtrar productos en el mismo cluster + productos_similares = df[df['cluster'] == cluster_predicho].to_dict(orient='records') + + # Devolver el cluster y los productos similares + return jsonify({ + "cluster": int(cluster_predicho), + "productos_similares": productos_similares + }) + + except Exception as e: + return jsonify({"error": str(e)}), 500 \ No newline at end of file diff --git a/src/comparative_analysis/models/K-Means.py b/src/comparative_analysis/models/K-MeansV1/K-Means.py similarity index 94% rename from src/comparative_analysis/models/K-Means.py rename to src/comparative_analysis/models/K-MeansV1/K-Means.py index 728fb528a..cfc2ecf1d 100644 --- a/src/comparative_analysis/models/K-Means.py +++ b/src/comparative_analysis/models/K-MeansV1/K-Means.py @@ -1,9 +1,9 @@ import pandas as pd -import re from sklearn.cluster import KMeans from sklearn.metrics import silhouette_score, davies_bouldin_score from sklearn.preprocessing import StandardScaler import matplotlib.pyplot as plt +from joblib import dump # ** Carga y limpieza de datos ** ruta_excel = r"C:\\Users\\cdgn2\\OneDrive\\Escritorio\\Maestría\\Maestria\\Metodologias Agiles\\Proyecto\\Comparative-analysis-of-products\\src\\comparative_analysis\\database\\Adidas_etiquetado.xlsx" @@ -106,4 +106,9 @@ def graficar_distribucion_clusters(df): plt.show() print("\n** Distribución de Clusters **") -graficar_distribucion_clusters(df) \ No newline at end of file +graficar_distribucion_clusters(df) + +# Guardar el modelo KMeans y el escalador +dump(kmeans, 'kmeans_model.joblib') +dump(scaler, 'scaler.joblib') +print("\nModelo KMeans y escalador guardados exitosamente.") \ No newline at end of file diff --git a/src/comparative_analysis/models/K-MeansV1/kmeans_model.joblib b/src/comparative_analysis/models/K-MeansV1/kmeans_model.joblib new 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z)cmsj;VYi9?T>zM*tYEJYn$G+?G9YvpgpMn=M%Ymo__IFoBo0GF8Td&`@L-2_p2?b z=9iV{V(4$!8Tyqgp(p5o1ATx8zXBcl1@!Ip%ijL=5pTrMkLcfrUwY#D-Hv(P*4O5< z`~`Ub?eRz8KtAjN`+yJShSUi)d&Wz0P>=R`akg9Xz4cyv@T33Wm->tE{_DyuetBre z{x2zeEV%F`yWe0x*cJNu%5-jYand_BAKqVR`4h^E`~2hL^q+rWj|Ysu%hs*@_Ww3L zAA^6xZou)D;A=}>*Ec=+MZ2Fs=d};?M+y6Y9`=J=@S}tsi?dRn6mswRBBC%{fBy#zym(?LFuJ~o~TFZtp`8GGw8xf^dt0u z--F(3Kj@7TaRBtt2e^ZlWllZ$+|s-^?}~She68OHw!a_$!k=IM*Y7@vVH|_+-w%!W z^gd&}F;t-JJ(@$J5Lg6=?l{9QH;lUDb-QRNBi*XEny$jhvDEoN-v%F8Fbzf_V?EB>~olWZ$0`Q;{YY(W4^$1Sc(2ddmzVK zg5O&YKD-Yry>#CG3LF2B=f(Bbd!Ip%d;rhYmcT^`{yVI3fal&SKd|yo!xZ1j&&P4710VSf=vH$=8 literal 0 HcmV?d00001 From a42cb18622242e4594771ff229b6abce8c635867 Mon Sep 17 00:00:00 2001 From: CarlosKC26 Date: Wed, 18 Dec 2024 20:42:29 -0500 Subject: [PATCH 48/84] apirest --- src/comparative_analysis/__init__.py | 4 +- src/comparative_analysis/apiRest/post.py | 92 ++++++------- .../models/K-MeansV1/K-Means.py | 127 ++++-------------- .../models/K-MeansV1/encoder.pkl | Bin 0 -> 266 bytes .../models/K-MeansV1/kmeans_model.joblib | Bin 30079 -> 0 bytes .../models/K-MeansV1/kmeans_model.pkl | Bin 0 -> 23484 bytes .../models/K-MeansV1/scaler.joblib | Bin 36455 -> 0 bytes .../models/K-MeansV1/scaler.pkl | Bin 0 -> 44728 bytes 8 files changed, 69 insertions(+), 154 deletions(-) create mode 100644 src/comparative_analysis/models/K-MeansV1/encoder.pkl delete mode 100644 src/comparative_analysis/models/K-MeansV1/kmeans_model.joblib create mode 100644 src/comparative_analysis/models/K-MeansV1/kmeans_model.pkl delete mode 100644 src/comparative_analysis/models/K-MeansV1/scaler.joblib create mode 100644 src/comparative_analysis/models/K-MeansV1/scaler.pkl diff --git a/src/comparative_analysis/__init__.py b/src/comparative_analysis/__init__.py index 13dcd6edb..25bbf13f8 100644 --- a/src/comparative_analysis/__init__.py +++ b/src/comparative_analysis/__init__.py @@ -1,6 +1,6 @@ from flask import Flask from apiRest.get import test, get_product_by_id -from apiRest.post import get_products_by_parameters, classify_new_element_KMeansV1 +from apiRest.post import get_products_by_parameters, predictKMeansV1 # Crear la instancia de Flask app = Flask(__name__) @@ -11,7 +11,7 @@ # Registrar las rutas POST app.add_url_rule('/api/products', view_func=get_products_by_parameters, methods=['POST']) -app.add_url_rule('/api/classify/KMeansV1', view_func=classify_new_element_KMeansV1, methods=['POST']) +app.add_url_rule('/predict/KMeansV1', view_func=predictKMeansV1, methods=['POST']) # Ejecutar la aplicación if __name__ == '__main__': diff --git a/src/comparative_analysis/apiRest/post.py b/src/comparative_analysis/apiRest/post.py index f5abe5315..52ac46681 100644 --- a/src/comparative_analysis/apiRest/post.py +++ b/src/comparative_analysis/apiRest/post.py @@ -1,7 +1,12 @@ from flask import jsonify, request import pandas as pd import re -from joblib import load +import pickle + +# Cargar el encoder, scaler y modelo KMeans guardados +encoder = pickle.load(open('src\comparative_analysis\models\K-MeansV1\encoder.pkl', 'rb')) +scaler = pickle.load(open('src\comparative_analysis\models\K-MeansV1\scaler.pkl', 'rb')) +kmeans = pickle.load(open('src\comparative_analysis\models\K-MeansV1\kmeans_model.pkl', 'rb')) # Ruta POST para buscar productos por diferentes parámetros def get_products_by_parameters(): @@ -29,70 +34,51 @@ def get_products_by_parameters(): except Exception as e: return jsonify({"error": str(e)}), 500 -# Ruta POST para clasificar un nuevo elemento -def classify_new_element_KMeansV1(): - """ - Endpoint POST para clasificar un nuevo elemento y devolver productos similares en el mismo cluster. - - Request JSON: - { - "nuevo_elemento": { ... }, # Diccionario con las características del nuevo producto - } - - Response JSON: - { - "cluster": int, - "productos_similares": [ ... ] # Lista de productos similares en el mismo cluster - } - """ - try: - # Obtener el nuevo elemento del cuerpo de la solicitud - nuevo_elemento = request.json.get('nuevo_elemento') - if not nuevo_elemento: - return jsonify({"error": "Se requiere el nuevo elemento en el cuerpo de la solicitud"}), 400 +def preprocess_data(new_data): + # Cargar el dataset original para obtener las columnas dummy + df_original = pd.read_excel('src/comparative_analysis/database/Adidas_etiquetado.xlsx') + df_dummies_original = pd.get_dummies(df_original, dummy_na=True) - # Cargar el archivo del dataset - file_path = 'src/comparative_analysis/database/Adidas_etiquetado.xlsx' - df = pd.read_excel(file_path) + # Convertir los datos nuevos en un DataFrame + df_new = pd.DataFrame([new_data]) - # Verificar si el DataFrame tiene la columna 'cluster' - if 'cluster' not in df.columns: - return jsonify({"error": "El dataset no contiene la columna 'cluster'. Verifica si el modelo fue entrenado correctamente."}), 500 + # Convertir las columnas categóricas del nuevo producto en variables dummy + df_dummies_new = pd.get_dummies(df_new, dummy_na=True) - # Cargar el modelo y el escalador - modelo_path = 'src/comparative_analysis/models/K-MeansV1/kmeans_model.joblib' - scaler_path = 'src/comparative_analysis/models/K-MeansV1/scaler.joblib' - kmeans = load(modelo_path) - scaler = load(scaler_path) + # Alinear las columnas del nuevo producto con las del dataset original + df_dummies_new = df_dummies_new.reindex(columns=df_dummies_original.columns, fill_value=0) - # Obtener las columnas utilizadas en el entrenamiento - columnas_entrenamiento = [col for col in df.columns if col not in ['id', 'cluster']] + # Escalar los nuevos datos con el scaler previamente entrenado + X_scaled_new = scaler.transform(df_dummies_new) - # Preprocesar el nuevo elemento - nuevo_df = pd.DataFrame([nuevo_elemento], columns=columnas_entrenamiento).fillna(0) + return X_scaled_new - # Convertir variables categóricas en dummies para que coincidan con el entrenamiento - nuevo_df = pd.get_dummies(nuevo_df) - df_dummies = pd.get_dummies(df[columnas_entrenamiento]) - # Asegurar que las columnas del nuevo elemento coincidan con las del dataset original - nuevo_df = nuevo_df.reindex(columns=df_dummies.columns, fill_value=0) +def predictKMeansV1(): + try: + new_data = request.json # Obtener datos del producto del cuerpo de la solicitud + if not new_data: + return jsonify({"error": "No se enviaron datos para predecir el cluster"}), 400 + + # Preprocesar los datos del nuevo producto + X_scaled_new = preprocess_data(new_data) - # Escalar las características del nuevo elemento - nuevo_elemento_escalado = scaler.transform(nuevo_df) + # Asignar el producto a un cluster + cluster_label = kmeans.predict(X_scaled_new)[0] - # Predecir el cluster del nuevo elemento - cluster_predicho = kmeans.predict(nuevo_elemento_escalado)[0] + # Obtener los productos originales + df_original = pd.read_excel('src/comparative_analysis/database/Adidas_etiquetado.xlsx') - # Filtrar productos en el mismo cluster - productos_similares = df[df['cluster'] == cluster_predicho].to_dict(orient='records') + # Agregar los clusters asignados a cada producto + df_original['cluster'] = kmeans.predict(scaler.transform(pd.get_dummies(df_original, dummy_na=True))) - # Devolver el cluster y los productos similares - return jsonify({ - "cluster": int(cluster_predicho), - "productos_similares": productos_similares - }) + # Filtrar los productos que pertenecen al mismo cluster + products_in_cluster = df_original[df_original['cluster'] == cluster_label] + + # Convertir a JSON y devolver los productos + products_info = products_in_cluster.to_dict(orient='records') + return jsonify(products_info) except Exception as e: return jsonify({"error": str(e)}), 500 \ No newline at end of file diff --git a/src/comparative_analysis/models/K-MeansV1/K-Means.py b/src/comparative_analysis/models/K-MeansV1/K-Means.py index cfc2ecf1d..ff216e785 100644 --- a/src/comparative_analysis/models/K-MeansV1/K-Means.py +++ b/src/comparative_analysis/models/K-MeansV1/K-Means.py @@ -1,114 +1,43 @@ import pandas as pd +from sklearn.preprocessing import OneHotEncoder, StandardScaler from sklearn.cluster import KMeans -from sklearn.metrics import silhouette_score, davies_bouldin_score -from sklearn.preprocessing import StandardScaler -import matplotlib.pyplot as plt -from joblib import dump +import pickle -# ** Carga y limpieza de datos ** -ruta_excel = r"C:\\Users\\cdgn2\\OneDrive\\Escritorio\\Maestría\\Maestria\\Metodologias Agiles\\Proyecto\\Comparative-analysis-of-products\\src\\comparative_analysis\\database\\Adidas_etiquetado.xlsx" +# Cargar los datos desde el archivo Excel +df = pd.read_excel(r"C:\\Users\\cdgn2\\OneDrive\\Escritorio\\Maestría\\Maestria\\Metodologias Agiles\\Proyecto\\Comparative-analysis-of-products\\src\\comparative_analysis\\database\\Adidas_etiquetado.xlsx") -# Crear el DataFrame -df = pd.read_excel(ruta_excel, header=0) - -# Procesamiento de columnas numéricas -num_cols = { - 'Weight': '(\d+\.?\d*)', - 'Drop__heel-to-toe_differential_': '(\d+\.?\d*)' -} -for col, pattern in num_cols.items(): - df[col] = df[col].astype(str).str.extract(pattern).astype(float, errors='ignore') - df[col] = pd.to_numeric(df[col], errors='coerce') - -# Procesar precios -price_cols = ['regularPrice', 'undiscounted_price'] -for col in price_cols: - df[col] = df[col].astype(str).str.replace(r'[^0-9.,]', '', regex=True) - df[col] = df[col].str.replace(r'\.', '', regex=True).str.replace(',', '.') - df[col] = pd.to_numeric(df[col], errors='coerce') - -# Eliminar columnas innecesarias -cols_to_drop = ['details', 'description', 'category', 'characteristics', 'width', 'Pronation_Type'] +# Eliminar columnas no relevantes +cols_to_drop = ['details', 'description', 'category', 'characteristics', 'Width', 'Pronation_Type'] df = df.drop(columns=cols_to_drop, errors='ignore') -# ** Clustering y evaluación ** -ids = df['id'] -X = df.drop(columns=['id'], errors='ignore').fillna(0) +id_column = df['id'] # Guardar la columna 'id' +df = df.drop(columns=['id'], errors='ignore') # Eliminar temporalmente la columna 'id' -# Codificar variables categóricas -df_dummies = pd.get_dummies(X, dummy_na=True).fillna(0) +# Crear el OneHotEncoder +encoder = OneHotEncoder(sparse_output=False, handle_unknown='ignore') -# Escalar los datos +# Convertir las columnas categóricas en variables dummy +categorical_columns = df.columns # Todas las columnas son categóricas +df_dummies = pd.get_dummies(df, columns=categorical_columns, dummy_na=True) + +# Normalizar los datos scaler = StandardScaler() X_scaled = scaler.fit_transform(df_dummies) -# Método del codo para determinar el número óptimo de clusters -def metodo_del_codo(X, k_range): - distortions = [] - for k in k_range: - kmeans = KMeans(n_clusters=k, random_state=42) - kmeans.fit(X) - distortions.append(kmeans.inertia_) - - plt.figure(figsize=(8, 5)) - plt.plot(k_range, distortions, 'bx-') - plt.xlabel('Número de clusters k') - plt.ylabel('Distorsión (Inercia)') - plt.title('Método del Codo para determinar k') - plt.show() - -# Mostrar el método del codo -print("\n** Método del Codo para determinar el número óptimo de clusters **") -metodo_del_codo(X_scaled, range(2, 11)) - -# Clustering con un número específico de clusters -k = 8 -kmeans = KMeans(n_clusters=k, random_state=42) -clusters = kmeans.fit_predict(X_scaled) - -# Agregar el cluster al DataFrame original -df['cluster'] = clusters - -# Evaluar calidad del clustering -sil_score = silhouette_score(X_scaled, clusters) -db_score = davies_bouldin_score(X_scaled, clusters) -print(f"\n** Evaluación del clustering **") -print(f"Silhouette Score: {sil_score}") -print(f"Davies-Bouldin Score: {db_score}") - -# ** Análisis de clusters ** -def analizar_cluster(df, cluster_num): - elementos_cluster = df[df['cluster'] == cluster_num] - print(f"\n=== Análisis del cluster {cluster_num} ===") - print(f"Total de elementos: {len(elementos_cluster)}") - - print(f"\nEstadísticas descriptivas principales:") - print(elementos_cluster.describe().loc[['mean', 'std', 'min', 'max']]) - - print(f"\nValores más frecuentes por columna categórica:") - cols_categoricas = elementos_cluster.select_dtypes(include=['object', 'category']).columns - for col in cols_categoricas: - print(f" - {col}: {elementos_cluster[col].mode().values[0]}") +# Entrenar el modelo KMeans con el número óptimo de clusters (5) +kmeans = KMeans(n_clusters=4, init='k-means++', random_state=42) +kmeans.fit(X_scaled) -# Analizar los clusters 2 y 3 -print("\n** Análisis de Clusters **") -analizar_cluster(df, cluster_num=2) -analizar_cluster(df, cluster_num=3) +# Obtener los clusters asignados +df['cluster'] = kmeans.labels_ -# Visualización de distribución de clusters -def graficar_distribucion_clusters(df): - plt.figure(figsize=(8, 5)) - df['cluster'].value_counts().sort_index().plot(kind='bar', color='skyblue', edgecolor='black') - plt.xlabel('Cluster') - plt.ylabel('Número de elementos') - plt.title('Distribución de elementos por cluster') - plt.xticks(rotation=0) - plt.show() +# Volver a añadir la columna 'id' con los clusters asignados +df['id'] = id_column -print("\n** Distribución de Clusters **") -graficar_distribucion_clusters(df) +# Guardar el encoder, scaler y modelo KMeans +pickle.dump(encoder, open('src\\comparative_analysis\\models\\K-MeansV1\\encoder.pkl', 'wb')) +pickle.dump(scaler, open('src\\comparative_analysis\\models\\K-MeansV1\\scaler.pkl', 'wb')) +pickle.dump(kmeans, open('src\\comparative_analysis\\models\\K-MeansV1\\kmeans_model.pkl', 'wb')) -# Guardar el modelo KMeans y el escalador -dump(kmeans, 'kmeans_model.joblib') -dump(scaler, 'scaler.joblib') -print("\nModelo KMeans y escalador guardados exitosamente.") \ No newline 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vVk_=<6DuFjKK9{T_UYx6bY%toA$g;eUa1?Ee3|~(#olc#sg_oAG}H2bFf_23 literal 0 HcmV?d00001 From 276089a0184ac25aae0a8e2462df903f806f48ed Mon Sep 17 00:00:00 2001 From: azacipa <52643112+azacipa@users.noreply.github.com> Date: Wed, 18 Dec 2024 20:58:05 -0500 Subject: [PATCH 49/84] =?UTF-8?q?Se=20cargan=20archivos=20correspondientes?= =?UTF-8?q?=20a=20los=20modelos=20generados=20con=20sus=20graficos=20de=20?= =?UTF-8?q?desempe=C3=B1o?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .ipynb_checkpoints/README-checkpoint.md | 10 + .spyproject/config/backups/codestyle.ini.bak | 8 + .spyproject/config/backups/encoding.ini.bak | 6 + .spyproject/config/backups/vcs.ini.bak | 7 + .spyproject/config/backups/workspace.ini.bak | 12 + .spyproject/config/codestyle.ini | 8 + .../defaults/defaults-codestyle-0.2.0.ini | 5 + .../defaults/defaults-encoding-0.2.0.ini | 3 + .../config/defaults/defaults-vcs-0.2.0.ini | 4 + .../defaults/defaults-workspace-0.2.0.ini | 6 + .spyproject/config/encoding.ini | 6 + .spyproject/config/vcs.ini | 7 + .spyproject/config/workspace.ini | 12 + .../project_charter-checkpoint.md | 76 ++ .../test_clusteval-checkpoint.ipynb | 102 +++ .../deploymentdoc-checkpoint.md | 22 + .../baseline_models-checkpoint.md | 96 +++ .../model_report-checkpoint.md | 81 +++ docs/modeling/model_report.md | 16 + scripts/data_acquisition/api_reader.py | 2 +- .../.ipynb_checkpoints/eda-checkpoint.ipynb | 688 ++++++++++++++++++ .../functions-checkpoint.py | 130 ++++ .../eda/.ipynb_checkpoints/main-checkpoint.py | 0 scripts/eda/eda.ipynb | 167 +++-- .../trainingText-checkpoint.py | 74 ++ scripts/training/trainingText.py | 13 +- .../.ipynb_checkpoints/main-checkpoint.py | 79 ++ .../preprocessing/main.py | 48 +- .../.ipynb_checkpoints/training-checkpoint.py | 97 +++ 29 files changed, 1735 insertions(+), 50 deletions(-) create mode 100644 .ipynb_checkpoints/README-checkpoint.md create mode 100644 .spyproject/config/backups/codestyle.ini.bak create mode 100644 .spyproject/config/backups/encoding.ini.bak create mode 100644 .spyproject/config/backups/vcs.ini.bak create mode 100644 .spyproject/config/backups/workspace.ini.bak create mode 100644 .spyproject/config/codestyle.ini create mode 100644 .spyproject/config/defaults/defaults-codestyle-0.2.0.ini create mode 100644 .spyproject/config/defaults/defaults-encoding-0.2.0.ini create mode 100644 .spyproject/config/defaults/defaults-vcs-0.2.0.ini create mode 100644 .spyproject/config/defaults/defaults-workspace-0.2.0.ini create mode 100644 .spyproject/config/encoding.ini create mode 100644 .spyproject/config/vcs.ini create mode 100644 .spyproject/config/workspace.ini create mode 100644 docs/business_understanding/.ipynb_checkpoints/project_charter-checkpoint.md create mode 100644 docs/business_understanding/.ipynb_checkpoints/test_clusteval-checkpoint.ipynb create mode 100644 docs/deployment/.ipynb_checkpoints/deploymentdoc-checkpoint.md create mode 100644 docs/modeling/.ipynb_checkpoints/baseline_models-checkpoint.md create mode 100644 docs/modeling/.ipynb_checkpoints/model_report-checkpoint.md create mode 100644 scripts/eda/.ipynb_checkpoints/eda-checkpoint.ipynb create mode 100644 scripts/eda/.ipynb_checkpoints/functions-checkpoint.py create mode 100644 scripts/eda/.ipynb_checkpoints/main-checkpoint.py create mode 100644 scripts/training/.ipynb_checkpoints/trainingText-checkpoint.py create mode 100644 src/comparative_analysis/preprocessing/.ipynb_checkpoints/main-checkpoint.py create mode 100644 src/comparative_analysis/training/.ipynb_checkpoints/training-checkpoint.py diff --git a/.ipynb_checkpoints/README-checkpoint.md b/.ipynb_checkpoints/README-checkpoint.md new file mode 100644 index 000000000..6634736a0 --- /dev/null +++ b/.ipynb_checkpoints/README-checkpoint.md @@ -0,0 +1,10 @@ +# Team Data Science Project Template + +Esta plantilla es una implementación de la plantilla de proyecto de Team Data Science Process que actualmente se utiliza en el "Programa de Formación en Machine Learning y Data Science" en la Universidad Nacional de Colombia. + +Esta plantilla proporciona las siguientes carpetas y archivos: + +* `src`: acá debe ir el código o implementación del proyecto en Python. +* `docs`: en esta carpeta se encuentran las plantillas de los documentos definidos en la metodología. +* `scripts`: esta carpeta debe contener los scripts/notebooks que se ejecutarán. +* `pyproject.toml`: archivo de definición del proyecto en Python. diff --git a/.spyproject/config/backups/codestyle.ini.bak b/.spyproject/config/backups/codestyle.ini.bak new file mode 100644 index 000000000..0f54b4c43 --- /dev/null +++ b/.spyproject/config/backups/codestyle.ini.bak @@ -0,0 +1,8 @@ +[codestyle] +indentation = True +edge_line = True +edge_line_columns = 79 + +[main] +version = 0.2.0 + diff --git a/.spyproject/config/backups/encoding.ini.bak b/.spyproject/config/backups/encoding.ini.bak new file mode 100644 index 000000000..a17acedd7 --- /dev/null +++ b/.spyproject/config/backups/encoding.ini.bak @@ -0,0 +1,6 @@ +[encoding] +text_encoding = utf-8 + +[main] +version = 0.2.0 + diff --git a/.spyproject/config/backups/vcs.ini.bak b/.spyproject/config/backups/vcs.ini.bak new file mode 100644 index 000000000..fd66eae01 --- /dev/null +++ b/.spyproject/config/backups/vcs.ini.bak @@ -0,0 +1,7 @@ +[vcs] +use_version_control = False +version_control_system = + +[main] +version = 0.2.0 + diff --git a/.spyproject/config/backups/workspace.ini.bak b/.spyproject/config/backups/workspace.ini.bak new file mode 100644 index 000000000..bad57ac82 --- /dev/null +++ b/.spyproject/config/backups/workspace.ini.bak @@ -0,0 +1,12 @@ +[workspace] +restore_data_on_startup = True +save_data_on_exit = True +save_history = True +save_non_project_files = False +project_type = 'empty-project-type' +recent_files = ['scripts\\eda\\functions.py', 'main.py', 'scripts\\data_acquisition\\api_reader.py', 'scripts\\training\\trainingText.py', 'src\\comparative_analysis\\models\\models.py'] + +[main] +version = 0.2.0 +recent_files = [] + diff --git a/.spyproject/config/codestyle.ini b/.spyproject/config/codestyle.ini new file mode 100644 index 000000000..0f54b4c43 --- /dev/null +++ b/.spyproject/config/codestyle.ini @@ -0,0 +1,8 @@ +[codestyle] +indentation = True +edge_line = True +edge_line_columns = 79 + +[main] +version = 0.2.0 + diff --git a/.spyproject/config/defaults/defaults-codestyle-0.2.0.ini b/.spyproject/config/defaults/defaults-codestyle-0.2.0.ini new file mode 100644 index 000000000..0b95e5cee --- /dev/null +++ b/.spyproject/config/defaults/defaults-codestyle-0.2.0.ini @@ -0,0 +1,5 @@ +[codestyle] +indentation = True +edge_line = True +edge_line_columns = 79 + diff --git a/.spyproject/config/defaults/defaults-encoding-0.2.0.ini b/.spyproject/config/defaults/defaults-encoding-0.2.0.ini new file mode 100644 index 000000000..0ce193c1e --- /dev/null +++ b/.spyproject/config/defaults/defaults-encoding-0.2.0.ini @@ -0,0 +1,3 @@ +[encoding] +text_encoding = utf-8 + diff --git a/.spyproject/config/defaults/defaults-vcs-0.2.0.ini b/.spyproject/config/defaults/defaults-vcs-0.2.0.ini new file mode 100644 index 000000000..ee2548333 --- /dev/null +++ b/.spyproject/config/defaults/defaults-vcs-0.2.0.ini @@ -0,0 +1,4 @@ +[vcs] +use_version_control = False +version_control_system = + diff --git a/.spyproject/config/defaults/defaults-workspace-0.2.0.ini b/.spyproject/config/defaults/defaults-workspace-0.2.0.ini new file mode 100644 index 000000000..2a73ab7ad --- /dev/null +++ b/.spyproject/config/defaults/defaults-workspace-0.2.0.ini @@ -0,0 +1,6 @@ +[workspace] +restore_data_on_startup = True +save_data_on_exit = True +save_history = True +save_non_project_files = False + diff --git a/.spyproject/config/encoding.ini b/.spyproject/config/encoding.ini new file mode 100644 index 000000000..a17acedd7 --- /dev/null +++ b/.spyproject/config/encoding.ini @@ -0,0 +1,6 @@ +[encoding] +text_encoding = utf-8 + +[main] +version = 0.2.0 + diff --git a/.spyproject/config/vcs.ini b/.spyproject/config/vcs.ini new file mode 100644 index 000000000..fd66eae01 --- /dev/null +++ b/.spyproject/config/vcs.ini @@ -0,0 +1,7 @@ +[vcs] +use_version_control = False +version_control_system = + +[main] +version = 0.2.0 + diff --git a/.spyproject/config/workspace.ini b/.spyproject/config/workspace.ini new file mode 100644 index 000000000..a5435fcff --- /dev/null +++ b/.spyproject/config/workspace.ini @@ -0,0 +1,12 @@ +[workspace] +restore_data_on_startup = True +save_data_on_exit = True +save_history = True +save_non_project_files = False +project_type = 'empty-project-type' +recent_files = ['scripts\\eda\\functions.py', 'main.py', 'scripts\\data_acquisition\\api_reader.py', 'scripts\\training\\trainingText.py', 'src\\comparative_analysis\\models\\models.py', 'src\\comparative_analysis\\models\\utilities\\utilities.py', 'src\\comparative_analysis\\preprocessing\\main.py', 'src\\comparative_analysis\\preprocessing\\functions\\model_inference.py'] + +[main] +version = 0.2.0 +recent_files = [] + diff --git a/docs/business_understanding/.ipynb_checkpoints/project_charter-checkpoint.md b/docs/business_understanding/.ipynb_checkpoints/project_charter-checkpoint.md new file mode 100644 index 000000000..f6106e459 --- /dev/null +++ b/docs/business_understanding/.ipynb_checkpoints/project_charter-checkpoint.md @@ -0,0 +1,76 @@ +# Project Charter - Entendimiento del Negocio + +## Nombre del Proyecto + +Análisis comparativo de productos de Running entre Nike, Adidas y Nation Runner + +## Objetivo del Proyecto + +Desarrollar una herramienta computacional que permita realizar un análisis comparativo de zapatos para running, utilizando datos obtenidos de varias tiendas retail mediante técnicas de procesamiento de lenguaje natural y preprocesamiento de datos, incluyendo el uso de Grandes Modelos de Lenguaje (LLM, por sus siglas en inglés). + +## Alcance del Proyecto + +El alcance del proyecto consiste en construir un modelo de recomendación basado en embeddings, capaz de generar recomendaciones basadas en la similitud de atributos de calzado deportivo para running, alineados con las necesidades del cliente. + +Se propone el uso de embeddings debido a que los datos disponibles consisten en descripciones textuales de las características de diferentes tipos de calzado deportivo. Se busca aplicar técnicas de embeddings utilizando modelos LLM, con el objetivo de mejorar la precisión semántica basada en similitud contextual. + +### Incluye: + +- Datos de Adidas, Nike y Nation Runner, obtenidos mediante un scraper con corte de datos a mediados de noviembre. +- Un dataset organizado y etiquetado con modelos comparados. +- Un modelo que permita realizar análisis comparativos de productos de forma automática y frecuente, con información actualizada sobre los movimientos de mercado de las marcas analizadas. + +### Excluye: + +- La comparación de productos de marcas no incluidas en el análisis inicial (por ejemplo, Puma o Reebok). +- El desarrollo de visualizaciones avanzadas, como gráficos interactivos o dashboards personalizados, que no estén especificados como requisitos del proyecto. + +## Metodología + +Se utilizarán las metodologías CRISP-DM y SCRUM para llevar a cabo el proyecto. + +## Cronograma + +| Etapa | Duración Estimada | Fechas | +|-----------------------------------------|-------------------|---------------------------------| +| Entendimiento del negocio y carga de datos | 2 semanas | Del 13 de noviembre al 28 de noviembre | +| Preprocesamiento y análisis exploratorio | 1 semana | Del 29 de noviembre al 5 de diciembre | +| Modelamiento y extracción de características | 1 semana | Del 5 de diciembre al 12 de diciembre | +| Despliegue | 1 semana | Del 13 de diciembre al 19 de diciembre | +| Evaluación y entrega final | 1 semana | Del 19 de diciembre al 21 de diciembre | + +## Equipo del Proyecto + +- Daniel Galvis CC 1010038257 cgalvisn@unal.edu.co +- Juan Correa CC 1013653882 jumcorrealo@unal.edu.co +- Asdrúbal Zácipa Corredor CC 79139929 azacipac@unal.edu.co + +## Presupuesto + +Aunque no se cuenta con financiamiento externo, se estimaron los costos básicos relacionados con el desarrollo del proyecto, considerando el uso de recursos personales como luz, internet y equipos de cómputo. A continuación, se detalla el presupuesto: + +| Concepto | Costo Mensual (COP) | Proporción por Persona (COP) | Duración (meses) | Total (COP) | +|------------------------------|---------------------|------------------------------|------------------|-------------| +| Servicio de luz | 100,000 | 33,333 | 2 | 200,000 | +| Servicio de internet | 150,000 | 50,000 | 2 | 300,000 | +| Uso de equipos personales | 200,000 | 66,667 | 2 | 400,000 | +| Reserva para emergencias | - | - | - | 100,000 | +| **Total** | - | - | - | **1,000,000** | + +### Detalles: +- **Servicio de luz:** Incluye el costo estimado del consumo eléctrico asociado al trabajo en el proyecto. +- **Servicio de internet:** Cubre el acceso a internet necesario para reuniones virtuales, investigación y uso de herramientas online. +- **Uso de equipos personales:** Considera el desgaste de hardware y el consumo eléctrico de los equipos utilizados durante el desarrollo. +- **Reserva para emergencias:** Monto adicional para imprevistos, como la reparación de equipos o la adquisición de software adicional. + +## Stakeholders + +- Dirección comercial de una empresa deportiva. +- Equipo laboral interno interesado en la automatización del análisis comparativo de productos deportivos. +- Consumidores finales que podrían beneficiarse indirectamente de las recomendaciones generadas por el modelo. + +## Aprobaciones + +- [Nombre y cargo del aprobador del proyecto] +- [Firma del aprobador] +- [Fecha de aprobación] \ No newline at end of file diff --git a/docs/business_understanding/.ipynb_checkpoints/test_clusteval-checkpoint.ipynb b/docs/business_understanding/.ipynb_checkpoints/test_clusteval-checkpoint.ipynb new file mode 100644 index 000000000..3423127ef --- /dev/null +++ b/docs/business_understanding/.ipynb_checkpoints/test_clusteval-checkpoint.ipynb @@ -0,0 +1,102 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "ad60f7c2-bb83-4a4d-adb1-77725a97a7ce", + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-16T02:44:01.795800Z", + "iopub.status.busy": "2024-12-16T02:44:01.794802Z", + "iopub.status.idle": "2024-12-16T02:44:05.908162Z", + "shell.execute_reply": "2024-12-16T02:44:05.907152Z", + "shell.execute_reply.started": "2024-12-16T02:44:01.795800Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[clusteval] >INFO> Downloading [online_shoppers_intention.csv] dataset from github source..\n" + ] + }, + { + "ename": "ValueError", + "evalue": "binary mode doesn't take an encoding argument", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[2], line 14\u001b[0m\n\u001b[0;32m 12\u001b[0m ce \u001b[38;5;241m=\u001b[39m clusteval()\n\u001b[0;32m 13\u001b[0m \u001b[38;5;66;03m# Import data from url\u001b[39;00m\n\u001b[1;32m---> 14\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mce\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimport_example\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 16\u001b[0m \u001b[38;5;66;03m# Preprocessing\u001b[39;00m\n\u001b[0;32m 17\u001b[0m cols_as_float \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mProductRelated\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAdministrative\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", + "File \u001b[1;32m~\\anaconda3\\envs\\llama_env\\Lib\\site-packages\\clusteval\\clusteval.py:751\u001b[0m, in \u001b[0;36mclusteval.import_example\u001b[1;34m(self, data, url, sep, params)\u001b[0m\n\u001b[0;32m 712\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mimport_example\u001b[39m(\u001b[38;5;28mself\u001b[39m, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtitanic\u001b[39m\u001b[38;5;124m'\u001b[39m, url\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, sep\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m'\u001b[39m, params\u001b[38;5;241m=\u001b[39m{}):\n\u001b[0;32m 713\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Import example dataset from github source.\u001b[39;00m\n\u001b[0;32m 714\u001b[0m \n\u001b[0;32m 715\u001b[0m \u001b[38;5;124;03m Import one of the few datasets from github source or specify your own download url link.\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 749\u001b[0m \n\u001b[0;32m 750\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 751\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mimport_example\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogger\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlogger\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\anaconda3\\envs\\llama_env\\Lib\\site-packages\\clusteval\\clusteval.py:855\u001b[0m, in \u001b[0;36mimport_example\u001b[1;34m(data, url, sep, params, logger)\u001b[0m\n\u001b[0;32m 853\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misfile(PATH_TO_DATA):\n\u001b[0;32m 854\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m logger \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m: logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDownloading [\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m] dataset from github source..\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m(data))\n\u001b[1;32m--> 855\u001b[0m \u001b[43mwget\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mPATH_TO_DATA\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 857\u001b[0m \u001b[38;5;66;03m# Import local dataset\u001b[39;00m\n\u001b[0;32m 858\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m logger \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m: logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mImport dataset [\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m(data))\n", + "File \u001b[1;32m~\\anaconda3\\envs\\llama_env\\Lib\\site-packages\\clusteval\\clusteval.py:888\u001b[0m, in \u001b[0;36mwget.download\u001b[1;34m(url, writepath)\u001b[0m\n\u001b[0;32m 873\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Download.\u001b[39;00m\n\u001b[0;32m 874\u001b[0m \n\u001b[0;32m 875\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 885\u001b[0m \n\u001b[0;32m 886\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 887\u001b[0m r \u001b[38;5;241m=\u001b[39m requests\u001b[38;5;241m.\u001b[39mget(url, stream\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m--> 888\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mwritepath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mwb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mutf8\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m fd:\n\u001b[0;32m 889\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m r\u001b[38;5;241m.\u001b[39miter_content(chunk_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1024\u001b[39m):\n\u001b[0;32m 890\u001b[0m fd\u001b[38;5;241m.\u001b[39mwrite(chunk)\n", + "\u001b[1;31mValueError\u001b[0m: binary mode doesn't take an encoding argument" + ] + } + ], + "source": [ + "# Intall libraries\n", + "#pip install df2onehot\n", + "\n", + "# Import libraries\n", + "from clusteval import clusteval\n", + "from df2onehot import df2onehot\n", + "\n", + "# Load data from UCI\n", + "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00468/online_shoppers_intention.csv'\n", + "\n", + "# Initialize clusteval\n", + "ce = clusteval()\n", + "# Import data from url\n", + "df = ce.import_example(url=url)\n", + "\n", + "# Preprocessing\n", + "cols_as_float = ['ProductRelated', 'Administrative']\n", + "df[cols_as_float]=df[cols_as_float].astype(float)\n", + "dfhot = df2onehot(df, excl_background=['0.0', 'None', '?', 'False'], y_min=50, perc_min_num=0.8, remove_mutual_exclusive=True, verbose=4)['onehot']\n", + "\n", + "# Initialize using the specific parameters\n", + "ce = clusteval(evaluate='silhouette',\n", + " cluster='agglomerative',\n", + " metric='hamming',\n", + " linkage='complete',\n", + " min_clust=2,\n", + " verbose='info')\n", + "\n", + "# Clustering and evaluation\n", + "results = ce.fit(dfhot)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dfdd885c-0208-4881-b966-9c2c17b46af7", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (Llama)", + "language": "python", + "name": "llama_env" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/deployment/.ipynb_checkpoints/deploymentdoc-checkpoint.md b/docs/deployment/.ipynb_checkpoints/deploymentdoc-checkpoint.md new file mode 100644 index 000000000..330311ceb --- /dev/null +++ b/docs/deployment/.ipynb_checkpoints/deploymentdoc-checkpoint.md @@ -0,0 +1,22 @@ +# Despliegue de modelos + +## Infraestructura + +- **Nombre del modelo:** (nombre que se le ha dado al modelo) +- **Plataforma de despliegue:** (plataforma donde se va a desplegar el modelo) +- **Requisitos técnicos:** (lista de requisitos técnicos necesarios para el despliegue, como versión de Python, bibliotecas de terceros, hardware, etc.) +- **Requisitos de seguridad:** (lista de requisitos de seguridad necesarios para el despliegue, como autenticación, encriptación de datos, etc.) +- **Diagrama de arquitectura:** (imagen que muestra la arquitectura del sistema que se utilizará para desplegar el modelo) + +## Código de despliegue + +- **Archivo principal:** (nombre del archivo principal que contiene el código de despliegue) +- **Rutas de acceso a los archivos:** (lista de rutas de acceso a los archivos necesarios para el despliegue) +- **Variables de entorno:** (lista de variables de entorno necesarias para el despliegue) + +## Documentación del despliegue + +- **Instrucciones de instalación:** (instrucciones detalladas para instalar el modelo en la plataforma de despliegue) +- **Instrucciones de configuración:** (instrucciones detalladas para configurar el modelo en la plataforma de despliegue) +- **Instrucciones de uso:** (instrucciones detalladas para utilizar el modelo en la plataforma de despliegue) +- **Instrucciones de mantenimiento:** (instrucciones detalladas para mantener el modelo en la plataforma de despliegue) diff --git a/docs/modeling/.ipynb_checkpoints/baseline_models-checkpoint.md b/docs/modeling/.ipynb_checkpoints/baseline_models-checkpoint.md new file mode 100644 index 000000000..f103416ad --- /dev/null +++ b/docs/modeling/.ipynb_checkpoints/baseline_models-checkpoint.md @@ -0,0 +1,96 @@ +# Reporte del Modelo Baseline + +Este documento contiene los resultados del modelo baseline, el cual consiste en una primera aproximación al clustering de productos. El objetivo es sentar las bases para modelos posteriores y guiar el proceso de mejora continua. + +## Descripción del modelo + +El modelo baseline se enfocó en realizar un proceso de clustering sobre un conjunto de productos obtenidos desde un endpoint con productos de Adidas. El flujo del proyecto incluye: + +1. **Obtención de datos**: Se obtienen productos desde un endpoint (Adidas). +2. **Normalización de datos con un LLM**: Un modelo de lenguaje (LLM) se utiliza para normalizar y etiquetar las características de los productos. +3. **Clustering**: Utilizando las variables numéricas ya procesadas, se aplicó un algoritmo de clustering (ej. K-Means) para agrupar productos similares. + +Este pipeline busca identificar patrones, similitudes y diferencias entre los productos de la marca. + +## Variables de entrada + +Las variables de entrada utilizadas en el modelo son aquellas extraídas y normalizadas a partir del procesamiento con el LLM. Entre las principales variables se encuentran: + +- **Weight** (Peso del producto, en gramos) +- **Drop (heel-to-toe differential)** (Diferencial de altura entre talón y punta, en mm) +- **regularPrice** (Precio regular del producto, en valor numérico flotante) +- **undiscounted_price** (Precio sin descuento, en valor numérico flotante) +- Otras variables numéricas o categóricas codificadas (por ejemplo, materiales del upper, midsole, outsole convertidas en variables dummies) + +Los IDs de los productos se han mantenido para identificar a qué cluster pertenece cada uno, pero no se utilizaron para el embedding. + +## Variable objetivo + +No existe una variable objetivo propiamente dicha, ya que el enfoque es no supervisado. El objetivo es descubrir grupos (clusters) de productos similares. + +## Evaluación del modelo + +### Métricas de evaluación + +Se han utilizado métricas de evaluación típicas para modelos de clustering no supervisados: + +- **Silhouette Score**: Mide la separación y cohesión de los clusters. Un valor cercano a 1 indica que las muestras están bien agrupadas, un valor cercano a -1 indica lo contrario. +- **Davies-Bouldin Score**: Mide la calidad del clustering en función de las distancias entre clusters. Valores más bajos indican mejores separaciones entre clusters. + +### Resultados de evaluación + +**Distribución de productos por cluster:** + +| Cluster | Conteo | Porcentaje | +|---------|---------|------------| +| 0 | 2 | 0.44% | +| 1 | 3 | 0.66% | +| 2 | 22 | 4.85% | +| 3 | 64 | 14.10% | +| 4 | 93 | 20.48% | +| 5 | 268 | 59.03% | +| 6 | 1 | 0.22% | +| 7 | 1 | 0.22% | + +**Métricas globales:** + +- Silhouette Score: -0.10727046484513526 +- Davies-Bouldin Score: 3.820317442353896 + +## Análisis de los resultados + +El Silhouette Score negativo (-0.1072) sugiere que la mayoría de los productos no están bien asignados a sus clusters o que los clusters se solapan significativamente. Esto indica que el agrupamiento no capta adecuadamente las similitudes reales entre los productos. + +El Davies-Bouldin Score (3.8203) es relativamente alto, lo que también indica que los clusters no están bien definidos, presentando una baja separación entre ellos. + +La distribución de productos por cluster está muy desbalanceada. Un solo cluster (el número 5) contiene alrededor del 59% de los productos, mientras que otros clusters contienen muy pocos elementos, incluso uno solo. Esto sugiere que la configuración actual de k (el número de clusters) o la forma en que se están representando los datos no es la más adecuada. + +### Fortalezas + +- **Primer paso hacia la segmentación:** Establece una línea base desde la cual se pueden proponer mejoras. +- **Proceso reproducible:** El pipeline desde la extracción de datos, normalización con LLM y clustering está bien definido y puede repetirse con ajustes futuros. + +### Debilidades + +- **Baja calidad de agrupamiento:** Las métricas indican que los clusters no reflejan adecuadamente las similitudes entre productos. +- **Desbalance en los clusters:** Un cluster concentra la mayor parte de los productos, lo que dificulta interpretaciones útiles. +- **Falencia en el embedding actual:** Es posible que la selección de variables o su codificación no capture suficientemente las diferencias relevantes entre los productos. + +### Áreas de mejora + +- **Refinamiento de la representación de datos:** Incluir embeddings más representativos (por ejemplo, usando técnicas de NLP más avanzadas en descripciones, o embeddings más sofisticados en variables categóricas). +- **Ajuste del número de clusters:** Probar diferentes valores de k con el método del codo o métricas de validación más robustas. +- **Selección de características:** Evaluar qué variables realmente aportan información útil para segmentar los productos y eliminar aquellas que introduzcan ruido. +- **Experimentar con diferentes algoritmos de clustering:** Además de K-Means, probar DBSCAN o HDBSCAN para detectar estructuras no esféricas. + +## Conclusiones + +El modelo baseline de clustering no ofrece una segmentación claramente útil, según las métricas de validación. Sin embargo, resulta valioso como punto de partida para comprender el problema y orientar mejoras. Ajustes en la selección de variables, técnicas de embedding y el número de clusters, así como la experimentación con otros algoritmos de clustering, podrían mejorar significativamente los resultados. + +## Referencias + + +- Documentation of scikit-learn for Clustering: [https://scikit-learn.org/stable/modules/clustering.html](https://scikit-learn.org/stable/modules/clustering.html) +- Introducción a la Evaluación del Clustering (Silhouette Score, Davies-Bouldin): [https://scikit-learn.org/stable/modules/clustering.html#clustering-evaluation](https://scikit-learn.org/stable/modules/clustering.html#clustering-evaluation) +- Documentación de K-Means: [https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) + diff --git a/docs/modeling/.ipynb_checkpoints/model_report-checkpoint.md b/docs/modeling/.ipynb_checkpoints/model_report-checkpoint.md new file mode 100644 index 000000000..14aa0fb17 --- /dev/null +++ b/docs/modeling/.ipynb_checkpoints/model_report-checkpoint.md @@ -0,0 +1,81 @@ +# Reporte del Modelo Final + +## Resumen Ejecutivo + +El modelo final se construyó con el objetivo de agrupar productos de Adidas con características similares, partiendo de datos obtenidos desde un endpoint, luego normalizados y etiquetados mediante un LLM. Para lograr esto, se aplicó un método de clustering (K-Means) a las variables numéricas y categóricas transformadas. + +En términos de métricas de evaluación, el **Silhouette Score** alcanzó un valor de **-0.10726032825390042**, mientras que el **Davies-Bouldin Score** fue de **3.7837546892816856**. Estas métricas indican que el clustering no logró una segmentación claramente separada ni cohesiva. Sin embargo, estos resultados proporcionan una línea base para mejoras futuras. + +Con el fin de realizar la comparación de diferentes modelos de clustering, se utiliza la libreria Clusteval con el fin de comparar los siguientes algoritmos de clustering: + +* **KMeans** +* **Agglomeratrive** +* **DBScan** +* **HDBScan** + +## Descripción del Problema + +La problemática abordada consiste en organizar y segmentar una amplia gama de productos Adidas en grupos homogéneos, con el fin de facilitar análisis comparativos. El objetivo es identificar patrones ocultos en las características de los productos, tales como peso, materiales, precios y tecnologías implementadas. Este tipo de segmentación es útil para la toma de decisiones estratégicas, el análisis de competitividad y el desarrollo de nuevas líneas de productos orientadas a grupos específicos de mercado. + +Justificación: La agrupación no supervisada de productos permite a la empresa comprender mejor sus catálogos, detectar nichos y mejorar la experiencia del cliente recomendando artículos similares. Además, sienta las bases para análisis más profundos, tales como análisis de correlación con el rendimiento de ventas o el perfil de cliente. + +## Descripción del Modelo + +El modelo final se basa en el siguiente flujo de trabajo: + +1. **Obtención de datos**: Se extrajeron productos desde un endpoint con información de Adidas. +2. **Normalización y etiquetado con LLM**: Un modelo de lenguaje se utilizó para transformar las descripciones crudas del producto en variables estructuradas y etiquetadas. +3. **Limpieza y transformación**: Se procesaron valores nulos, se convirtieron precios y métricas a formato numérico flotante, y se realizaron codificaciones dummies para variables categóricas. +4. **Clustering (K-Means)**: Se aplicó K-Means con un determinado número de clusters, seleccionado tras un análisis inicial con el método del codo. La elección final de k se basó en la interpretación de las métricas y la naturaleza de los datos. + +El resultado fue un conjunto de clusters, cada uno agrupando productos con ciertas características predominantes. Por ejemplo: +- El **Cluster 2** (22 elementos) presentó productos con un peso promedio de alrededor de 569 g, un drop cercano a 9.4 mm, y precios promedio de ~401.7. Las tecnologías más comunes fueron "Mediasuela Bounce" y "Suela de caucho", con mayoría de productos para "Mujer" y "Running". +- El **Cluster 3** (60 elementos) mostró productos más ligeros (259.9 g) con drop de ~9.65 mm y precios promedio cercanos a 474. En este cluster destacó el material "Parte superior de malla" y el sistema "Dreamstrike+", mayormente orientado a calzado de "Mujer" y "Running". + +## Evaluación del Modelo + +**Métricas:** +- **Silhouette Score:** -0.10726032825390042 + Un valor negativo sugiere que la mayoría de los puntos podrían asignarse mejor a otros clusters, indicando una baja cohesión/separación. + +- **Davies-Bouldin Score:** 3.7837546892816856 + Un valor relativamente alto indica que los clusters no están bien separados ni son internamente cohesivos. + +Al realizar la comparacion entre diferentes modelos obtenemos las siguientes métricas: + +| Modelo | Número de Clusters | Silhouette Score | Davies-Bouldin Score | +|---------------|--------------------|-----------------------|----------------------| +| KMeans | 3 | 0.3468094215555982 | 0.5095857881425025 | +| Agglomerative | 2 | 0.2774924967946853 | 2.795591016138718 | +| DBScan | 10 | -0.19920787551009733 | 2.6226063126770205 | +| HDBSCan | 38 | -0.025542353420798247 | 2.455807218880036 | + +Estas métricas reflejan la complejidad del conjunto de datos y la necesidad de refinar la representación de las variables o ajustar el número y el tipo de clustering usado. + +A nivel descriptivo, se logró observar patrones de materiales y tecnologías predominantes por cluster, lo que puede ser útil como insumo para análisis posteriores, a pesar de que la calidad cuantitativa del clustering no fue óptima. + +## Conclusiones y Recomendaciones + +**Fortalezas:** +- Se estableció un proceso reproducible para extraer, normalizar y clústerizar datos de productos. +- Se identificaron patrones básicos en la composición de algunos clusters. + +**Debilidades:** +- Métricas bajas de calidad de clusters indican que la segmentación no es nítida. +- Alta heterogeneidad en las características, posibles variables irrelevantes o ruido dificultan la formación de grupos cohesivos. + +**Limitaciones:** +- El modelo depende en gran medida de la calidad de las etiquetas generadas por el LLM. +- No se exploraron otros algoritmos de clustering ni se realizaron exhaustivos ajustes de hiperparámetros. + +**Áreas de mejora:** +- Refinar la selección de características y la representación de datos, por ejemplo, utilizando embeddings semánticos para descripciones textuales. +- Probar técnicas de reducción de dimensionalidad (PCA, UMAP) para mejorar la separabilidad de los datos. +- Experimentar con algoritmos de clustering alternativos (DBSCAN, HDBSCAN) que podrían adaptarse mejor a la forma real de los datos. + +## Referencias + +- Scikit-learn Documentation: [https://scikit-learn.org/stable/](https://scikit-learn.org/stable/) +- Evaluación de Clustering (Silhouette y Davies-Bouldin): [https://scikit-learn.org/stable/modules/clustering.html#clustering-evaluation](https://scikit-learn.org/stable/modules/clustering.html#clustering-evaluation) +- Documentación de K-Means: [https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) +- Técnicas de reducción de dimensionalidad: [https://scikit-learn.org/stable/modules/decomposition.html](https://scikit-learn.org/stable/modules/decomposition.html) diff --git a/docs/modeling/model_report.md b/docs/modeling/model_report.md index a8de43e36..553b42343 100644 --- a/docs/modeling/model_report.md +++ b/docs/modeling/model_report.md @@ -6,6 +6,13 @@ El modelo final se construyó con el objetivo de agrupar productos de Adidas con En términos de métricas de evaluación, el **Silhouette Score** alcanzó un valor de **-0.10726032825390042**, mientras que el **Davies-Bouldin Score** fue de **3.7837546892816856**. Estas métricas indican que el clustering no logró una segmentación claramente separada ni cohesiva. Sin embargo, estos resultados proporcionan una línea base para mejoras futuras. +Con el fin de realizar la comparación de diferentes modelos de clustering, se utiliza la libreria Clusteval con el fin de comparar los siguientes algoritmos de clustering: + +* **KMeans** +* **Agglomeratrive** +* **DBScan** +* **HDBScan** + ## Descripción del Problema La problemática abordada consiste en organizar y segmentar una amplia gama de productos Adidas en grupos homogéneos, con el fin de facilitar análisis comparativos. El objetivo es identificar patrones ocultos en las características de los productos, tales como peso, materiales, precios y tecnologías implementadas. Este tipo de segmentación es útil para la toma de decisiones estratégicas, el análisis de competitividad y el desarrollo de nuevas líneas de productos orientadas a grupos específicos de mercado. @@ -34,6 +41,15 @@ El resultado fue un conjunto de clusters, cada uno agrupando productos con ciert - **Davies-Bouldin Score:** 3.7837546892816856 Un valor relativamente alto indica que los clusters no están bien separados ni son internamente cohesivos. +Al realizar la comparacion entre diferentes modelos obtenemos las siguientes métricas: + +| Modelo | Número de Clusters | Silhouette Score | Davies-Bouldin Score | +|---------------|--------------------|-----------------------|----------------------| +| KMeans | 3 | 0.3468094215555982 | 0.5095857881425025 | +| Agglomerative | 2 | 0.2774924967946853 | 2.795591016138718 | +| DBScan | 9 | -0.19920787551009733 | 2.6226063126770205 | +| HDBSCan | 38 | -0.025542353420798247 | 2.455807218880036 | + Estas métricas reflejan la complejidad del conjunto de datos y la necesidad de refinar la representación de las variables o ajustar el número y el tipo de clustering usado. A nivel descriptivo, se logró observar patrones de materiales y tecnologías predominantes por cluster, lo que puede ser útil como insumo para análisis posteriores, a pesar de que la calidad cuantitativa del clustering no fue óptima. diff --git a/scripts/data_acquisition/api_reader.py b/scripts/data_acquisition/api_reader.py index 8f6bfd85c..d7bf855db 100644 --- a/scripts/data_acquisition/api_reader.py +++ b/scripts/data_acquisition/api_reader.py @@ -51,4 +51,4 @@ def fetch_and_save_data(self): data = self.fetch_data() self.save_to_excel(data) except Exception as e: - print(f"Error: {e}") + print(f"Error fetch_and_save_data: {e}") diff --git a/scripts/eda/.ipynb_checkpoints/eda-checkpoint.ipynb b/scripts/eda/.ipynb_checkpoints/eda-checkpoint.ipynb new file mode 100644 index 000000000..148ab2ccb --- /dev/null +++ b/scripts/eda/.ipynb_checkpoints/eda-checkpoint.ipynb @@ -0,0 +1,688 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## EDA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Cargar Librerías y Funciones" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-14T16:49:32.253057Z", + "iopub.status.busy": "2024-12-14T16:49:32.253057Z", + "iopub.status.idle": "2024-12-14T16:49:33.828414Z", + "shell.execute_reply": "2024-12-14T16:49:33.828414Z", + "shell.execute_reply.started": "2024-12-14T16:49:32.253057Z" + } + }, + "outputs": [], + "source": [ + "# ===========================\n", + "# Cargar las funciones definidas\n", + "# ===========================\n", + "\n", + "# Insertar el path donde están definidas las funciones\n", + "import sys\n", + "sys.path.append(r'.\\eda')\n", + "\n", + "# Importar las funciones\n", + "from functions import load_data, summarize_data, check_missing_values, plot_numeric_distributions, analyze_categorical_data, plot_correlations, check_duplicates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Cargar los Datos" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-14T16:51:00.383348Z", + "iopub.status.busy": "2024-12-14T16:51:00.382348Z", + "iopub.status.idle": "2024-12-14T16:51:00.911145Z", + "shell.execute_reply": "2024-12-14T16:51:00.911145Z", + "shell.execute_reply.started": "2024-12-14T16:51:00.383348Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Datos cargados exitosamente desde ..\\..\\src\\comparative_analysis\\database\\raw_data.xlsx.\n", + "Los datos están listos para análisis.\n" + ] + } + ], + "source": [ + "# ===========================\n", + "# Cargar los datos desde el archivo Excel\n", + "# ===========================\n", + "#file_path = r'C:\\Users\\cdgn2\\OneDrive\\Escritorio\\Maestría\\Maestria\\Metodologias Agiles\\Proyecto\\Comparative-analysis-of-products\\src\\comparative_analysis\\database\\raw_data.xlsx'\n", + "file_path = r'..\\..\\src\\comparative_analysis\\database\\raw_data.xlsx'\n", + "\n", + "# Llamar a la función para cargar los datos\n", + "df = load_data(file_path)\n", + "\n", + "# Validar si los datos se cargaron correctamente\n", + "if df is not None:\n", + " print(\"Los datos están listos para análisis.\")\n", + "else:\n", + " print(\"Error en la carga de datos.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Resumen de los Datos" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-14T16:51:06.460740Z", + "iopub.status.busy": "2024-12-14T16:51:06.459740Z", + "iopub.status.idle": "2024-12-14T16:51:06.523060Z", + "shell.execute_reply": "2024-12-14T16:51:06.523060Z", + "shell.execute_reply.started": "2024-12-14T16:51:06.460740Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Resumen General de los Datos\n", + "Filas: 884, Columnas: 13\n", + "\n", + "Primeras filas del DataFrame:\n", + " id details \\\n", + "0 046zSiHm8Cz0fZYwMJlL [] \n", + "1 08sjncACSjSvg2t9DS73 ['Horma clásica', 'Parte superior sintética', ... \n", + "2 0AqheRhKT2lhm7puBVCF ['Ajuste clásico', 'Sistema de amarre de cordo... \n", + "3 0Di5KVVcvU0QsWRxB1iE [{'\\xa0': ' '}] \n", + "4 0Gvnv9unc1EV4XpFbCQN [{'Características principales': 'Caracterí... \n", + "\n", + " store manufacturer url \\\n", + "0 adidas adidas https://www.adidas.co/tenis-duramo-sl/IF7884.html \n", + "1 adidas adidas https://www.adidas.co/tenis-adizero-adios-pro-... \n", + "2 adidas adidas https://www.adidas.co/tenis-ultraboost-22/GX55... \n", + "3 nike nike https://www.nike.com.co/nike-revolution-7-fb22... \n", + "4 nike nike https://www.nike.com.co/nike-winflo-11-calzado... \n", + "\n", + " title regularPrice undiscounted_price \\\n", + "0 Tenis Duramo SL $379.950 $265.965 \n", + "1 Tenis ADIZERO ADIOS PRO 3 $1.299.950 $909.965 \n", + "2 TENIS ULTRABOOST 22 $799.950 NaN \n", + "3 Nike Revolution 7 $ 389.950 NaN \n", + "4 Nike Winflo 11 $ 584.950 NaN \n", + "\n", + " description \\\n", + "0 Siente la ligereza y velocidad. Si estás listo... \n", + "1 Los Adizero Adios Pro 3 son la máxima expresió... \n", + "2 Hemos analizado 1.200.000 pisadas para que Ult... \n", + "3 Cargamos el Revolution 7 con el tipo de amorti... \n", + "4 El Winflo 11 es el calzado con una pisada bala... \n", + "\n", + " category \\\n", + "0 Mujer • Running \n", + "1 Mujer • Running \n", + "2 Mujer • Running \n", + "3 Calzado de correr en pavimento para mujer \n", + "4 Calzado de correr en pavimento para mujer \n", + "\n", + " createdAt \\\n", + "0 {'_seconds': 1731975445, '_nanoseconds': 42700... \n", + "1 {'_seconds': 1731975445, '_nanoseconds': 42700... \n", + "2 {'_seconds': 1731975445, '_nanoseconds': 42700... \n", + "3 {'_seconds': 1731965768, '_nanoseconds': 30000... \n", + "4 {'_seconds': 1731965768, '_nanoseconds': 30000... \n", + "\n", + " characteristics gender \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 [] Mujer \n", + "4 ['Parte superior de malla diseñada estratégica... Mujer \n", + "\n", + "Información del DataFrame:\n", + "\n", + "RangeIndex: 884 entries, 0 to 883\n", + "Data columns (total 13 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 884 non-null object\n", + " 1 details 884 non-null object\n", + " 2 store 884 non-null object\n", + " 3 manufacturer 884 non-null object\n", + " 4 url 884 non-null object\n", + " 5 title 884 non-null object\n", + " 6 regularPrice 884 non-null object\n", + " 7 undiscounted_price 404 non-null object\n", + " 8 description 884 non-null object\n", + " 9 category 621 non-null object\n", + " 10 createdAt 884 non-null object\n", + " 11 characteristics 102 non-null object\n", + " 12 gender 356 non-null object\n", + "dtypes: object(13)\n", + "memory usage: 89.9+ KB\n", + "None\n", + "\n", + "Estadísticas descriptivas:\n", + " id details store manufacturer \\\n", + "count 884 884 884 884 \n", + "unique 884 569 3 11 \n", + "top zqSM9xK4Ie9Qi878ZdzB [] adidas adidas \n", + "freq 1 94 519 519 \n", + "\n", + " url title \\\n", + "count 884 884 \n", + "unique 884 369 \n", + "top https://www.adidas.co/tenis-adizero-takumi-sen... Tenis Response \n", + "freq 1 33 \n", + "\n", + " regularPrice undiscounted_price \\\n", + "count 884 404 \n", + "unique 123 126 \n", + "top $699.900 $669.900 \n", + "freq 56 23 \n", + "\n", + " description category \\\n", + "count 884 621 \n", + "unique 302 29 \n", + "top Tanto en la pista como en la cinta de correr, ... Mujer • Running \n", + "freq 15 236 \n", + "\n", + " createdAt characteristics \\\n", + "count 884 102 \n", + "unique 4 53 \n", + "top {'_seconds': 1731975445, '_nanoseconds': 42700... [] \n", + "freq 500 13 \n", + "\n", + " gender \n", + "count 356 \n", + "unique 3 \n", + "top Hombre \n", + "freq 206 \n" + ] + } + ], + "source": [ + "# ===========================\n", + "# Mostrar un resumen general de los datos\n", + "# ===========================\n", + "if df is not None:\n", + " summarize_data(df)\n", + "else:\n", + " print(\"El DataFrame no está disponible.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. Verificar Valores Faltantes" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-14T16:51:10.877300Z", + "iopub.status.busy": "2024-12-14T16:51:10.876206Z", + "iopub.status.idle": "2024-12-14T16:51:10.890532Z", + "shell.execute_reply": "2024-12-14T16:51:10.889527Z", + "shell.execute_reply.started": "2024-12-14T16:51:10.877300Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Valores Faltantes:\n", + " Missing Values Percentage\n", + "undiscounted_price 480 54.298643\n", + "category 263 29.751131\n", + "characteristics 782 88.461538\n", + "gender 528 59.728507\n" + ] + } + ], + "source": [ + "# ===========================\n", + "# Verificar valores faltantes\n", + "# ===========================\n", + "if df is not None:\n", + " missing_summary = check_missing_values(df)\n", + "else:\n", + " print(\"El DataFrame no está disponible.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5. Verificar Registros Duplicados" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-14T16:51:15.961589Z", + "iopub.status.busy": "2024-12-14T16:51:15.961589Z", + "iopub.status.idle": "2024-12-14T16:51:15.974501Z", + "shell.execute_reply": "2024-12-14T16:51:15.973492Z", + "shell.execute_reply.started": "2024-12-14T16:51:15.961589Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Número de registros duplicados: 0\n" + ] + } + ], + "source": [ + "# ===========================\n", + "# Verificar registros duplicados\n", + "# ===========================\n", + "if df is not None:\n", + " check_duplicates(df)\n", + "else:\n", + " print(\"El DataFrame no está disponible.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6. Analizar Datos Categóricos" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-14T16:51:18.751875Z", + "iopub.status.busy": "2024-12-14T16:51:18.750875Z", + "iopub.status.idle": "2024-12-14T16:51:18.757063Z", + "shell.execute_reply": "2024-12-14T16:51:18.756056Z", + "shell.execute_reply.started": "2024-12-14T16:51:18.751875Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['id', 'details', 'store', 'manufacturer', 'url', 'title',\n", + " 'regularPrice', 'undiscounted_price', 'description', 'category',\n", + " 'createdAt', 'characteristics', 'gender'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "columns = df.columns\n", + "\n", + "print(columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-14T16:51:19.819386Z", + "iopub.status.busy": "2024-12-14T16:51:19.819386Z", + "iopub.status.idle": "2024-12-14T16:51:19.838625Z", + "shell.execute_reply": "2024-12-14T16:51:19.837618Z", + "shell.execute_reply.started": "2024-12-14T16:51:19.819386Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Porcentajes de los elementos en la columna 'store':\n", + " Count Percentage\n", + "store \n", + "adidas 519 58.710407\n", + "nacionRunner 263 29.751131\n", + "nike 102 11.538462\n", + "\n", + "Porcentajes de los elementos en la columna 'manufacturer':\n", + " Count Percentage\n", + "manufacturer \n", + "adidas 519 58.710407\n", + "Asics 117 13.235294\n", + "nike 102 11.538462\n", + "Hoka 48 5.429864\n", + "Brooks 36 4.072398\n", + "New Balance 36 4.072398\n", + "On Running 7 0.791855\n", + "Skechers 7 0.791855\n", + "361° 4 0.452489\n", + "NNormal 4 0.452489\n", + "Adidas 4 0.452489\n", + "\n", + "Porcentajes de los elementos en la columna 'category':\n", + " Count Percentage\n", + "category \n", + "Mujer • Running 236 38.003221\n", + "Hombre • Running 210 33.816425\n", + "Running 61 9.822866\n", + "Calzado de running en carretera para hombre 24 3.864734\n", + "Calzado de running en carretera para mujer 15 2.415459\n", + "Calzado de correr en carretera para hombre 12 1.932367\n", + "Calzado de correr en pavimento para mujer 11 1.771337\n", + "Calzado de correr en carretera para mujer 11 1.771337\n", + "Mujer • adidas by Stella McCartney 5 0.805153\n", + "Calzado para hombre 5 0.805153\n", + "Mujer • TERREX 4 0.644122\n", + "Calzado de running en carretera para niños grandes 4 0.644122\n", + "Calzado de trail running para hombre 3 0.483092\n", + "Calzado para niños de preescolar 2 0.322061\n", + "Calzado de carrera en carretera para mujer 2 0.322061\n", + "Calzado de trail running impermeables para mujer 2 0.322061\n", + "Calzado de correr en pavimento para hombre 2 0.322061\n", + "Calzado de caminata para mujer 1 0.161031\n", + "Calzado de running en carretera acondicionado p... 1 0.161031\n", + "Hombre • TERREX 1 0.161031\n", + "Calzado de trail running impermeables para hombre 1 0.161031\n", + "Calzado de carrera en carretera para hombre 1 0.161031\n", + "Calzado de running en carretera resistente a la... 1 0.161031\n", + "Calzado de carrera en carretera 1 0.161031\n", + "Calzado de running en carretera impermeable par... 1 0.161031\n", + "Calzado de trail running para mujer 1 0.161031\n", + "TERREX 1 0.161031\n", + "Calzado de running en carretera impermeable par... 1 0.161031\n", + "adidas by Stella McCartney 1 0.161031\n", + "\n", + "Porcentajes de los elementos en la columna 'createdAt':\n", + " Count Percentage\n", + "createdAt \n", + "{'_seconds': 1731975445, '_nanoseconds': 427000... 500 56.561086\n", + "{'_seconds': 1732123040, '_nanoseconds': 262000... 263 29.751131\n", + "{'_seconds': 1731965768, '_nanoseconds': 30000000} 102 11.538462\n", + "{'_seconds': 1731975447, '_nanoseconds': 767000... 19 2.149321\n", + "\n", + "Porcentajes de los elementos en la columna 'gender':\n", + " Count Percentage\n", + "gender \n", + "Hombre 206 57.865169\n", + "Mujer 144 40.449438\n", + "Niño 6 1.685393\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "def calculate_percentage(df, column_name):\n", + " \"\"\"\n", + " Calcula y muestra los porcentajes de cada elemento en una columna específica.\n", + " \n", + " :param df: DataFrame con los datos.\n", + " :param column_name: Nombre de la columna.\n", + " \"\"\"\n", + " if column_name in df.columns:\n", + " counts = df[column_name].value_counts()\n", + " percentages = (counts / counts.sum()) * 100\n", + " percentage_df = pd.DataFrame({\n", + " 'Count': counts,\n", + " 'Percentage': percentages\n", + " })\n", + " print(f\"\\nPorcentajes de los elementos en la columna '{column_name}':\")\n", + " print(percentage_df)\n", + " return percentage_df\n", + " else:\n", + " print(f\"La columna '{column_name}' no existe en el DataFrame.\")\n", + " return None\n", + "\n", + "for column_name in [\"store\", \"manufacturer\", \"category\", \"createdAt\", \"gender\"]:\n", + " calculate_percentage(df, column_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-14T16:51:20.740805Z", + "iopub.status.busy": "2024-12-14T16:51:20.739806Z", + "iopub.status.idle": "2024-12-14T16:51:21.806784Z", + "shell.execute_reply": "2024-12-14T16:51:21.805778Z", + "shell.execute_reply.started": "2024-12-14T16:51:20.740805Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Análisis de la columna 'store':\n", + "store\n", + "adidas 519\n", + "nacionRunner 263\n", + "nike 102\n", + "Name: count, dtype: int64\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Análisis de la columna 'manufacturer':\n", + "manufacturer\n", + "adidas 519\n", + "Asics 117\n", + "nike 102\n", + "Hoka 48\n", + "Brooks 36\n", + "New Balance 36\n", + "On Running 7\n", + "Skechers 7\n", + "361° 4\n", + "NNormal 4\n", + "Adidas 4\n", + "Name: count, dtype: int64\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Análisis de la columna 'category':\n", + "category\n", + "Mujer • Running 236\n", + "Hombre • Running 210\n", + "Running 61\n", + "Calzado de running en carretera para hombre 24\n", + "Calzado de running en carretera para mujer 15\n", + "Calzado de correr en carretera para hombre 12\n", + "Calzado de correr en pavimento para mujer 11\n", + "Calzado de correr en carretera para mujer 11\n", + "Mujer • adidas by Stella McCartney 5\n", + "Calzado para hombre 5\n", + "Mujer • TERREX 4\n", + "Calzado de running en carretera para niños grandes 4\n", + "Calzado de trail running para hombre 3\n", + "Calzado para niños de preescolar 2\n", + "Calzado de carrera en carretera para mujer 2\n", + "Calzado de trail running impermeables para mujer 2\n", + "Calzado de correr en pavimento para hombre 2\n", + "Calzado de caminata para mujer 1\n", + "Calzado de running en carretera acondicionado para los estados del tiempo para hombre 1\n", + "Hombre • TERREX 1\n", + "Calzado de trail running impermeables para hombre 1\n", + "Calzado de carrera en carretera para hombre 1\n", + "Calzado de running en carretera resistente a las inclemencias del tiempo para mujer 1\n", + "Calzado de carrera en carretera 1\n", + "Calzado de running en carretera impermeable para hombre 1\n", + "Calzado de trail running para mujer 1\n", + "TERREX 1\n", + "Calzado de running en carretera impermeable para mujer 1\n", + "adidas by Stella McCartney 1\n", + "Name: count, dtype: int64\n" + ] + }, + { + "data": { + "image/png": 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KjqGkXBiDMf4ruTBG/o2hpFwYQ/sY6dftys5/vkeFiIiI8i8WKkRERKRYLFSIiIhIsVioEBERkWKxUCEiIiLFYqFCREREisVChYiIiBSLhQoREREpFgsVIiIiUiwWKkRERKRYLFSIiIhIsVioEBERkWKxUCEiIiLFYqFCREREisVChYiIiBQr3xcqBgYGMDU1hYGBwedOhYiIiLRk9LkTyA0pKQKGhqmFiampKSwtLbN9DRERESlTvixUDA0N4LP9Kp5GRmt8/tuyxTCud908zoqIiIi0lS8LFQB4GhmNR8/ef+40iIiISA/5fowKERER/XexUCEiIiLFYqFCREREisVChYiIiBSLhQoREREpFgsVIiIiUiwWKkRERKRYLFSIiIhIsVioEBERkWKxUCEiIiLFYqFCREREisVChYiIiBSLhQoREREpFgsVIiIiUiwWKkRERKRYLFSIiIhIsVioEBERkWKxUCEiIiLFYqFCREREisVChYiIiBSLhQoREREpFgsVIiIiUiwWKkRERKRYLFSIiIhIsVioEBERkWKxUCEiIiLFYqFCREREisVChYiIiBSLhQoREREpFgsVIiIiUiwWKkRERKRYLFSIiIhIsVioEBERkWKxUCEiIiLFYqFCREREisVCRQIDAwOYmprCwMDgc6dCRET0P8XocyegVCkpAoaGqYWJqakpLC0ts30NERERyY+FShYMDQ3gs/0qnkZGa3z+27LFMK533TzOioiI6H8LC5VsPI2MxqNn7z93GkRERP+zOEaFiIiIFIuFChERESkWCxUiIiJSLBYqREREpFgsVIiIiEixWKgQERGRYmldqLx79w7Tpk2Do6Mj7Ozs0LNnT1y5ckX1/Pnz59GlSxdYW1vj+++/x+HDh9W2j4+Px8yZM+Hg4ABbW1v8/PPPiIqK0r8lRERElO9oXaiMHTsW169fx+LFi7Fv3z7UrFkTAwcOxOPHj/Ho0SMMGTIETZs2xf79+/Hjjz9iwoQJOH/+vGr7GTNm4OzZs1ixYgX8/Pzw+PFjjBo1StZGERERUf6g1YRvoaGhOHfuHHbs2IG6dVNnZf3ll1/wzz//4NChQ3jz5g3Mzc3h4eEBAKhatSru3r2LDRs2wMHBAZGRkThw4ADWrFmDevXqAQAWL16M77//HtevX4etra3MzSMiIqL/Mq16VEqWLIl169ahTp06qscMDAxgYGCADx8+4MqVK3BwcFDbpmHDhrh69SqEELh69arqsTSVK1dG2bJlcfnyZX3aQURERPmQVoVK8eLF0axZM5iYmKgeCwgIQGhoKJo2bYqIiAiUK1dObZuvvvoKcXFxePv2LSIjI1GyZEkULFgw02siIiL0aAYRERHlR3qt9XPt2jVMmjQJrVu3RvPmzfHp0ye1IgaA6t8JCQmIi4vL9DwAFCxYEPHx8TrnIYTAx48fAaT28JiamkraLi4uDkKITI/LEUPT69L/V1dyxGEMxsjtGErKhTHybwwl5cIY2scQQsDAwCDHWDoXKidOnMC4ceNgZ2cHHx8fAKkFR0JCgtrr0v5tamqKQoUKZXoeSL0TSGphoEliYiLu3bun+j2WlpaStnvy5InGnShHjKyEhIRIfm1ux2EMxsjtGHLFYQzGyIs4jJH3MTR1XmSkU6Gybds2zJkzB99//z0WLFig+kXly5fHy5cv1V778uVLFC5cGMWKFUO5cuXw7t07JCQkqCX38uVLlC1bVpdUAADGxsaoVq0aAEiqztJUrlw5yx4VfWNkFBcXh5CQEJiZmelVlMkRhzEYI7djKCkXxsi/MZSUC2NoHyM4OFhSLK0LlR07dmD27Nlwc3PDlClT1D7U69Wrh0uXLqm9/sKFC7Czs4OhoSHq1q2LlJQUXL16VTXo9smTJ4iMjIS9vb22qagYGBigcOHCWm+nzx+HrjFMTU11yjU34jAGY+R2DCXlwhj5N4aScmEM6TGkdgpoNZj2yZMnmDt3Llq1aoUhQ4bg9evXePXqFV69eoXo6Gi4ubnh1q1b8PHxwaNHj7Bp0yb8+eefcHd3BwCULVsW7dq1w9SpU3Hx4kXcunULY8eORf369WFjY6NNKkRERPQ/QKselYCAACQmJuL48eM4fvy42nMuLi6YP38+fv31V3h7e8PPzw/ffvstvL291W5Znj17NubOnYsRI0YAABwdHTF16lQZmkJERET5jVaFytChQzF06NBsX+Po6AhHR8csny9cuDC8vLzg5eWlza8mIiKi/0FclJCIiIgUi4UKERERKRYLFSIiIlIsFipERESkWCxUiIiISLFYqBAREZFisVAhIiIixWKhQkRERIrFQoWIiIgUi4UKERERKRYLFSIiIlIsFipERESkWCxUiIiISLFYqBAREZFisVAhIiIixWKhQkRERIrFQoWIiIgUi4UKERERKRYLFSIiIlIsFipERESkWCxUiIiISLFYqBAREZFisVAhIiIixWKhQkRERIrFQoWIiIgUi4UKERERKRYLFSIiIlIsFipERESkWCxUiIiISLFYqBAREZFisVAhIiIixWKhQkRERIrFQoWIiIgUi4UKERERKRYLFSIiIlIsFipERESkWCxUiIiISLFYqBAREZFisVAhIiIixWKhQkRERIrFQoWIiIgUi4UKERERKRYLFSIiIlIsFipERESkWCxUiIiISLFYqBAREZFisVAhIiIixWKhQkRERIrFQoWIiIgUi4UKERERKRYLFSIiIlIsFipERESkWCxUiIiISLFYqBAREZFisVAhIiIixWKhQkRERIrFQoWIiIgUi4UKERERKRYLFSIiIlIsFipERESkWCxUiIiISLFYqBAREZFisVAhIiIixdKrUFm7di3c3NzUHps6dSrMzc3VfpydnVXPp6SkYPny5WjatClsbGwwaNAghIeH65MGERER5VM6Fyrbt2/H0qVLMz3+4MEDDB06FGfPnlX97N27V/X8r7/+ih07dmD27NnYtWsXUlJS4O7ujoSEBF1TISIionxK60IlMjISQ4cOhY+PD8zMzNSeE0IgODgYtWvXxpdffqn6KVWqFAAgISEBmzZtwqhRo9C8eXNYWFhgyZIliIiIwLFjx2RpEBEREeUfWhcqd+7cgbGxMQ4ePAhra2u158LCwvDx40dUqVJF47b3799HbGwsHBwcVI8VL14clpaWuHz5srapEBERUT5npO0Gzs7OamNO0gsKCgIAbN26FWfOnIGhoSEcHR3h4eGBYsWKISIiAgBQvnx5te2++uor1XO6EELg48ePAAADAwOYmppK2i4uLg5CiEyPyxFD0+vS/1dXcsRhDMbI7RhKyoUx8m8MJeXCGNrHEELAwMAgx1haFyrZCQoKgqGhIb766iusWbMGYWFhWLhwIR4+fAg/Pz9VwiYmJmrbFSxYEO/fv9f59yYmJuLevXsAAFNTU1haWkra7smTJxp3ohwxshISEiL5tbkdhzEYI7djyBWHMRgjL+IwRt7HyFgPaCJrofLTTz+hV69eKFmyJACgRo0a+PLLL9GtWzcEBgaiUKFCAFLHqqT9PwDEx8dL7sHQxNjYGNWqVQMASdVZmsqVK2fZo6JvjIzi4uIQEhICMzMzvdoqRxzGYIzcjqGkXBgj/8ZQUi6MoX2M4OBgSbFkLVQMDQ1VRUqa6tWrAwAiIiJUl3xevnyJihUrql7z8uVLmJub6/x7DQwMULhwYa230+ePQ9cYpqamOuWaG3EYgzFyO4aScmGM/BtDSbkwhvQYUjsFZJ3wbcKECejXr5/aY4GBgQCAatWqwcLCAkWLFsXFixdVz3/48AF3796Fvb29nKkQERFRPiBrofLdd9/h/PnzWLlyJcLCwnD69GlMnjwZ7du3R9WqVWFiYgJXV1f4+Pjgr7/+wv379+Hh4YFy5cqhdevWcqZCRERE+YCsl35atGiBpUuXYt26dVi/fj2KFSuGDh06YMyYMarXjBo1CklJSZg6dSo+ffoEe3t7bNy4EcbGxnKmQkRERPmAXoXK/PnzMz3Wpk0btGnTJsttChQogPHjx2P8+PH6/GoiIiL6H8BFCYmIiEixWKgQERGRYrFQISIiIsVioUJERESKxUKFiIiIFIuFChERESkWCxUiIiJSLBYqREREpFgsVIiIiEixWKgQERGRYrFQISIiIsVioUJERESKxUKFiIiIFIuFChERESkWCxUiIiJSLBYqREREpFgsVIiIiEixWKgQERGRYrFQISIiIsVioUJERESKxUKFiIiIFIuFChERESkWCxUiIiJSLBYqREREpFgsVIiIiEixWKgQERGRYrFQISIiIsVioUJERESKxUKFiIiIFIuFChERESkWCxUiIiJSLBYqREREpFgsVIiIiEixWKgQERGRYrFQISIiIsVioUJERESKxUKFiIiIFIuFChERESkWCxUiIiJSLBYqREREpFgsVIiIiEixWKgQERGRYrFQISIiIsVioUJERESKxUKFiIiIFIuFChERESkWCxUiIiJSLBYqREREpFgsVIiIiEixWKgQERGRYrFQISIiIsVioUJERESKxUKFiIiIFIuFChERESkWCxUiIiJSLBYqREREpFgsVIiIiEixWKgQERGRYrFQISIiIsVioUJERESKxUKFiIiIFIuFChERESmWXoXK2rVr4ebmpvbYvXv34OrqChsbGzg7O2PLli1qz6ekpGD58uVo2rQpbGxsMGjQIISHh+uTBhEREeVTOhcq27dvx9KlS9Uee/v2Lfr374+KFSti3759GD58OHx8fLBv3z7Va3799Vfs2LEDs2fPxq5du5CSkgJ3d3ckJCTo3AgiIiLKn4y03SAyMhLTp0/HxYsXYWZmpvbcnj17YGxsjFmzZsHIyAhVq1ZFaGgo1q1bh65duyIhIQGbNm3CuHHj0Lx5cwDAkiVL0LRpUxw7dgzt27eXo01ERESUT2jdo3Lnzh0YGxvj4MGDsLa2VnvuypUrqF+/PoyM/q/+adiwIUJCQvD69Wvcv38fsbGxcHBwUD1fvHhxWFpa4vLly3o0g4iIiPIjrXtUnJ2d4ezsrPG5iIgI1KhRQ+2xr776CgDw4sULREREAADKly+f6TVpzxERERGl0bpQyc6nT59gYmKi9ljBggUBAPHx8YiLiwMAja95//69zr9XCIGPHz8CAAwMDGBqaippu7i4OAghMj0uRwxNr0v/X13JEYcxGCO3YygpF8bIvzGUlAtjaB9DCAEDA4McY8laqBQqVCjToNj4+HgAQOHChVGoUCEAQEJCgur/014jtTDQJDExEffu3QMAmJqawtLSUtJ2T5480bgT5YiRlZCQEMmvze04jMEYuR1DrjiMwRh5EYcx8j5Gxo4LTWQtVMqVK4eXL1+qPZb277JlyyIpKUn1WMWKFdVeY25urvPvNTY2RrVq1QBAUnWWpnLlyln2qOgbI6O4uDiEhITAzMxMr6JMjjiMwRi5HUNJuTBG/o2hpFwYQ/sYwcHBkmLJWqjY29tj165dSE5ORoECBQAAFy5cQOXKlVG6dGkUK1YMRYsWxcWLF1WFyocPH3D37l24urrq/HsNDAxQuHBhrbfT549D1ximpqY65ZobcRiDMXI7hpJyYYz8G0NJuTCG9BhSOwVknZm2a9euiImJwZQpUxAcHIz9+/dj8+bNGDJkCIDULh5XV1f4+Pjgr7/+wv379+Hh4YFy5cqhdevWcqZCRERE+YCsPSqlS5fGhg0bMGfOHLi4uODLL7/EhAkT4OLionrNqFGjkJSUhKlTp+LTp0+wt7fHxo0bYWxsLGcqRERElA/oVajMnz8/02NWVlbYvXt3ltsUKFAA48ePx/jx4/X51URERPQ/gIsSEhERkWKxUCEiIiLFYqFCREREisVChYiIiBSLhQoREREpFgsVIiIiUiwWKkRERKRYLFSIiIhIsVioEBERkWKxUCEiIiLFYqFCREREisVChYiIiBSLhQoREREpFgsVIiIiUiwWKkRERKRYLFSIiIhIsVioEBERkWKxUCEiIiLFYqFCREREisVChYiIiBSLhQoREREpFgsVIiIiUiwWKkRERKRYLFSIiIhIsVioEBERkWKxUCEiIiLFYqFCREREisVChYiIiBSLhQoREREpFgsVIiIiUiwWKkRERKRYLFSIiIhIsVioEBERkWKxUCEiIiLFYqFCREREisVChYiIiBSLhQoREREpFgsVIiIiUiwWKkRERKRYLFSIiIhIsVioEBERkWKxUCEiIiLFYqFCREREisVChYiIiBSLhQoREREpFgsVIiIiUiwWKkRERKRYLFSIiIhIsVioEBERkWKxUCEiIiLFYqFCREREisVCJY8YGBjA1NQUBgYGnzsVIiKi/wyjz51AfpaSImBomFqYmJqawtLSMtvXEBERkToWKrnI0NAAPtuv4mlktMbnvy1bDON6183jrIiIiP47WKjksqeR0Xj07P3nToOIiOg/iWNUiIiISLFYqBAREZFisVAhIiIixWKhQkRERIrFQoWIiIgUi4UKERERKRYLFSIiIlIsFipERESkWLIXKpGRkTA3N8/0s3//fgDAvXv34OrqChsbGzg7O2PLli1yp0BERET5hOwz096/fx8FCxbEiRMn1BbgK1asGN6+fYv+/fvD2dkZM2fOxI0bNzBz5kwUKVIEXbt2lTsVIiIi+o+TvVAJCgqCmZkZvvrqq0zP+fn5wdjYGLNmzYKRkRGqVq2K0NBQrFu3joUKERERZSL7pZ8HDx6gatWqGp+7cuUK6tevDyOj/6uPGjZsiJCQELx+/VruVIiIiOg/Lld6VEqWLInevXvjyZMnqFSpEn766Sc4OjoiIiICNWrUUHt9Ws/LixcvUKZMGZ1+pxACHz9+BAAYGBjA1NRU0nZxcXEQQmR6XCkxsnpt+v/qgjEYI7djKCkXxsi/MZSUC2NoH0MIoTZEJCuyFipJSUl4/PgxqlWrBk9PTxQtWhSHDx/G4MGD4evri0+fPsHExERtm4IFCwIA4uPjdf69iYmJuHfvHgDA1NQUlpaWkrZ78uSJxp2olBjZCQkJ0er1jMEYnyOGXHEYgzHyIg5j5H2MjDWBJrIWKkZGRrh48SIKFCiAQoUKAQBq166Nhw8fYuPGjShUqBASEhLUtkkrUAoXLqzz7zU2Nka1atUAQFJ1lqZy5cpZ9oYoIYYmcXFxCAkJgZmZmeQeG8ZgjLyOoaRcGCP/xlBSLoyhfYzg4GBJsWS/9FOkSJFMj1WvXh1nz55FuXLl8PLlS7Xn0v5dtmxZnX+ngYGBToWOPn8cnzuGqampXsUdYzBGXsRQUi6MkX9jKCkXxpAeQ+oXelkH0z58+BB2dna4ePGi2uO3b99GtWrVYG9vj6tXryI5OVn13IULF1C5cmWULl1azlSIiIgoH5C1UKlatSqqVKmCWbNm4cqVK3j06BHmzZuHGzdu4KeffkLXrl0RExODKVOmIDg4GPv378fmzZsxZMgQOdMgIiKifELWSz+GhoZYs2YNFi1ahDFjxuDDhw+wtLSEr6+v6m6fDRs2YM6cOXBxccGXX36JCRMmwMXFRc40iIiIKJ+QfYxKmTJlMG/evCyft7Kywu7du+X+tURERJQPcVFCIiIiUiwWKkRERKRYLFSIiIhIsVioEBERkWKxUCEiIiLFYqFCREREisVChYiIiBSLhQoREREpFgsVIiIiUiwWKkRERKRYLFSIiIhIsVioEBERkWKxUCEiIiLFYqFCREREisVChYiIiBSLhQoREREpFgsVIiIiUiwWKkRERKRYLFSIiIhIsVioEBERkWKxUCEiIiLFYqFCREREisVChYiIiBSLhQoREREpFgsVIiIiUiwWKkRERKRYLFSIiIhIsVioEBERkWKxUCEiIiLFYqFCREREisVChYiIiBSLhcp/jIGBAUxNTWFgYPC5UyEiIsp1Rp87AcpeSoqAoeH/FSWmpqawtLTM9jVERET5BQsVhTM0NIDP9qt4Ghmt8flvyxbDuN518zgrIiKivMFC5T/gaWQ0Hj17/7nTICIiynMco0JERESKxUKFiIiIFIuFChERESkWCxUiIiJSLBYqREREpFgsVIiIiEixWKgQERGRYrFQISIiIsVioUJERESKxUKFiIiIFIuFyv8grsBMRET/FVzr538AV2AmIqL/KhYq/wO4AjMREf1XsVD5H8EVmImI6L+IY1RIJxznQkREeYE9KiQJx7kQEdHnwEKFJMmNcS5y9MqwZ4eIKH9joUKSyTHOJX2vi669MnLEICKi/wYWKpSnsuuZkdorI0cMIiL6b2ChQnlOjp4Z3sVERPS/gXf9EBERkWKxUCEiIiLFYqFCREREisVChYiIiBSLhQr9z+N8LkREysW7fuh/EudzISL6b2ChQv+TlDifC3t2iIgyY6FC/7OUMJ9LTr0yGV+TWzHSY7FDREryWQqVlJQUrFy5Er/99huio6Nhb2+PadOmoUKFCp8jHaLPRo41lOSIIXexQ0Qkl89SqPz666/YsWMH5s+fj3LlysHb2xvu7u44dOgQTExMPkdKRJ+NEnp28vOik0qJQUS6yfNCJSEhAZs2bcK4cePQvHlzAMCSJUvQtGlTHDt2DO3bt8/rlIgI8l7GAnQbYCxHjIyvUcpgaaUUTPkphpJyYQz5Y6TJ80Ll/v37iI2NhYODg+qx4sWLw9LSEpcvX2ahQvQfpZTLWDnFycsYSimY8iKGlDhyx9C1PUqJkfE1jKGZgRBCaL2VHo4dO4aRI0fi5s2bKFSokOrx0aNH49OnT1i7dq1W8a5duwYhBIyNjVWPGRgY4H1MApKSUzRuY1TAEF8UNUF2TWcM+WPkFIcxMvsvHV+lxMgpTl7HiI1LRHJK5tcVMDRAEVPjfBFDahzG0C5Ofo+RmJgIAwMD2NnZZRs3z3tU4uLiACDTWJSCBQvi/Xvtu53TupUydi99UTTnsS45dUkxhvwxpMRhDO3jMIb2cfIqRhFT42yfz08xpMRhDO3j5NcYBgYGkmLmeaGS1ouSkJCg1qMSHx8PU1NTrePZ2trKlhsREREpS55PoV++fHkAwMuXL9Uef/nyJcqWLZvX6RAREZGC5XmhYmFhgaJFi+LixYuqxz58+IC7d+/C3t4+r9MhIiIiBcvzSz8mJiZwdXWFj48PSpUqhW+++Qbe3t4oV64cWrdundfpEBERkYJ9lgnfRo0ahaSkJEydOhWfPn2Cvb09Nm7cqHbnDhEREVGe355MREREJFWej1EhIiIikoqFChERESkWCxUiIiJSLBYqREREpFgsVIiIiEixWKgQERGRYrFQISJZxMbG5psYpFw8vvJT+j5lofIfcfnyZezatQsxMTEIDg5GUlKS5G03btyIyMjIz56Hl5cXwsLC9Pr9crRlx44diI6O1itGmufPn+Off/7Bp0+f8ObNmxxfn5KSIilueHi4vqnluc6dO+POnTv5IgYAfPr0CQcOHMCiRYvw7t07XLp0CW/fvpW8vRznu655fPz4UVLs69ev651fXpPr+CrBiBEj8OjRo8+dhiz71NvbG48fP5YpI3WfZWba3OTs7Kxx2WgDAwMYGxujXLly6NSpEzp37pxlDAsLiyyXnk4fY9iwYZKWqNZHTEwM3N3dcePGDRgYGKBx48bw8fFBeHg4Nm3aJGkhx9WrV6Nly5afPQ9/f3/069dPrzzkaMvatWuxYMECODs7o2vXrmjcuLHWxzEhIQETJ07E0aNHYWhoiICAACxYsACxsbFYsWIFihYtqnE7Nzc3LF68ONv9tW/fPsydOxdXr16VlMemTZvQpk0bVKpUCVOmTMGRI0dgZ2cHHx8flCxZUlJ7Xr58iT179uDx48eYMmUKLl++jBo1aqBKlSqStgeAuLg4nVZAV2KM169fo3v37njz5g0SEhLQrVs3bNq0Cbdv34afnx+qVq2aYww5zndd83BxccGyZctgYWGh8fmUlBSsXLkS69atw+3btyXlcvr0aWzYsAFPnjzB7t27sX//flSsWBGdOnWS3B45YshxfOPj47F7924EBQUhOTlZ9XhCQgJu376NgIAASXH0bc+FCxdQsGBBrfO/f/9+lsc2vfXr12PQoEE5vk6OfXrlyhVs2rQJderUQdeuXdGuXbss3we1le96VLp27YoXL17A0tISffr0QZ8+fVCnTh08f/4cdevWRYUKFTBz5kz89ttvWcaYPHkyTExM0LdvX6xYsQIrVqzAgAEDULBgQfTu3RtdunTB9u3bsWHDhlxvz+LFiwEAx48fR6FChQAA48ePh4mJCRYuXCgphrW1Nf7+++/PnkezZs2wbds2vboZ5WjLqVOnsGrVKhQoUAAjRoxA8+bNsXjxYjx58kRyjNWrV+P+/fvw8/NTvdG4ubkhNDQUPj4+WW6XlJSETp064fTp05mei46OxpgxYzBlyhQ0aNBAUh4+Pj7w9fVFTEwMzpw5A39/fwwZMgSxsbGSj0toaCg6dOgAf39/HDt2DB8/fsSRI0fQtWtX3Lx5U1IMAOjTpw9GjBiB7du3459//sHly5fVfv5LMebPn4/q1avj/PnzquO7YMECVK9eHd7e3pJiyHG+65qHubk5unfvju3bt2d67unTp+jVqxdWr16Nnj17Ssrj3LlzGDFiBL755ht8+PABKSkpSEpKwqRJk3DgwIE8iwHIc3y9vLzg7e2Nhw8f4vfff0dYWBj++ecfHDlyBM7OznnWHhcXF/j4+ODhw4dISEiQtA0AdOvWDTt37szy+cjISPTp00f1vp0TOfbp7t27ceTIETg4OGDt2rVo3Lgxfv75Z5w9exZ6T4Av8pkBAwaINWvWZHp8w4YNwt3dXQghhL+/v2jfvn2WMbp16yZ27dqV6fG9e/eKHj16CCGEOHHihGjdunWO+ZibmwsLCwuNP3Xq1BGtWrUSK1euFCkpKRq3b968ubh69aoQQggbGxsRFhYmhBDi2rVrwsHBIcffL4QQo0aNEhYWFqJ+/fqiW7duws3NTe1HCjnycHV1Ve2Pxo0bC2dnZ7WfvGpLejExMWL//v1iyJAhwsrKSnTv3l3s3btXxMXFZbtdq1atxLlz54QQ6vvj33//FU2aNMlyu8TERDFv3jxhYWEh5s+fLxITE4UQQly6dEk0b95c2Nraij179kjOv2nTpuLs2bNCCCGmTZsm+vbtK4QQIjAwUDRs2FBSjKFDh4rJkyeLlJQUVVsSExPF2LFjhaurq+RczM3Ns/yxsLD4T8Vo3LixuHPnjhBC/fjeu3dP2NvbS4ohx/muTx5btmwRtWvXFiNHjhQfPnwQQqS+99WtW1c4Ojqqzl8punfvLnx9fTPlsW7dumzfS+WOIYQ8x9fBwUEcOnRICCFEy5YtxaNHj0RCQoIYPny4mD17dp61p1WrVtl+RmRl69atonbt2mL06NEiOjpa7bmAgABRv3590bBhQ3H8+HFJecixTzO6ePGimDlzprCxsRHNmjUTy5YtExERETrFyneXfq5evYpp06ZlerxVq1ZYvnw5AKB+/fqYPn16ljHu3buHhg0bZnq8Xr16mDlzJgDA0tISL168yDGfyZMnY9GiRejZsyfq1asHIPW68Pbt29GzZ0988cUX2LJlC0xMTDR20UVFReHLL7/M9Hjx4sUlX4cuXLhwtpe6pJAjjwYNGkjuKciKHG1JLy4uDu/fv8eHDx+QlJQEQ0NDrF69GosXL4aPjw8cHBw0bhcZGYmKFStmerx8+fJ4//59lr/PyMgInp6esLe3x5QpU3D16lXY2dlhy5YtsLa2xpYtW1ChQgXJ+b97907V/X/u3Dl0794dAFCiRAl8+vRJUoxr165h+/btape/jIyMMGzYMHTr1k1yLn/99Zfk1yo9RmxsLAoXLqzxOanjsuQ43/XJw83NDTY2Nhg3bhw6d+4MS0tLnDhxAm3atMGMGTNQvHhxyXk8ePBAYw/d999/j5UrV+ZZDECe4/vhwwfY2dkBAKpVq4a7d++iSpUqGDJkCMaMGYOpU6fmGEOO9vz000/aJf7/ubq6wtraGh4eHujcuTOWLFmCatWqYdasWfD390ezZs0wZ84clClTRlI8OfZperdu3cKxY8dUvd/29va4fPkyNm7ciNmzZ6Njx45axct3hUrp0qVx7do1VKpUSe3xq1evqq7Xv3r1CsWKFcsyxrfffouTJ09mur588uRJlCtXDgAQFhaGUqVK5ZjP4cOHMXnyZNUHCAC0bNkSVapUwd69e7Fz505Ur14dCxcu1Fio1KlTB0ePHsXgwYPVHt++fTssLS1z/P0AMG/ePEmvy44ceYwYMULvPORoS3x8PI4dO4bff/8dFy5cQOnSpdG5c2fMmzdPdd7MnDkTnp6eGi/RAEDVqlVx/vx5/Pjjj2qPHz58GNWqVcsxhxYtWqBEiRLo27cvAgMD0ahRI2zYsEHrsTIVK1ZEYGAg3rx5g6dPn6Jp06YAgBMnTuDbb7+VFCMlJUXjIN/Y2FgUKFBAci7ffPNNls/Fx8drFSMhIQFPnz5FxYoVIYTQamV1OWLY29tj586dmDRpkuqxxMRErF69WvUBlxM5znd986hTpw6mTZuGIUOG4NmzZ6qxZYaG2l31L1asGF6+fJmpOA8ODsYXX3yRZzEAeY5vqVKl8ObNG3z99dcwMzNDUFAQAKBkyZJ4/fq1pBhytMfFxUVyzhnVqVMH/v7+mDFjBnr16oUvv/wS7969w4wZM9CjRw+tYsmxT1+8eIHff/8dv//+O548eQJra2sMGzYMbdu2VY1VWbFiBebOnctCxc3NDbNmzUJISAhsbGyQkpKCmzdvYuvWrfjpp5/w4sULzJgxQ/WGrslPP/0ET09PBAYGwtbWVhXjzz//xKxZs/DkyRNMmjQJrVu3zjEffXtnxo4diwEDBuDWrVtISkrC6tWr8ejRI9y5cwcbN26UuFf+b7DkkydPMHnyZK0HS8qVR9q4jidPnmDZsmU4ceIEqlevjvr16+dZWxwcHJCUlITmzZtj1apVaNq0aaY3bgcHh2y/ZYwcORIeHh4IDg5GcnIy/P398eTJEwQEBGDJkiU55rBjxw54e3ujUqVKcHR0hJ+fH0aPHg0vLy+tvum6u7tj7NixMDQ0RMOGDWFhYYFVq1Zh1apVmDt3rqQYTZo0wdq1a9XGPLx79w7e3t4az92svH37FmvWrFEboCiEQGJiIoKDg3HlypUcYwghsGjRImzduhWJiYmq/WlqaooZM2ZIeuOUI8bEiRPRu3dvXLp0CYmJiZgxYwYeP36M6OhobNu2Leed8f/pe77rk0dSUhKWLFmCzZs3w97eHi1btlT17i5atEhyIQsAHTp0wNy5czF37lwYGBggNjYWZ86cwezZs9G2bds8iwHIc3wdHR0xc+ZMzJs3D3Xr1sXcuXPRqlUrHDlyRPVlNK/ac/r0aWzcuBGPHz/WekBu0aJFUaNGDQQEBOD58+ewtbVFs2bNJP/uNHLsU2dnZ5QqVQodO3bEypUrNQ70trS0hJmZmdb55bsxKkIIsW3bNuHk5KS6xubk5CR27twphBDizJkzYuzYseL9+/fZxvj777+Fq6ursLW1Ffb29sLV1VWcOXNGCJE6nmDx4sUiISEhx1zatGmjuo6Znq+vr2jVqpUQQogLFy6IZs2aZRnj/v37Yvz48aJdu3aiTZs2YtSoUeLGjRs5/u40ISEhon79+sLZ2VnUqlVLhIWFiZEjRwobGxut4uibR2BgoKhdu7ZwdXVV5TFlyhRRq1YtcerUqTxri5+fn4iKisr2NWljR7Jz+vRp0atXL2FjYyOsrKxE165dxZ9//pntNm/evBFDhgwR5ubmYubMmeLTp09CiNRzoGnTpqJZs2bi0qVLktqR5t69e+L48eMiPj5elde///4refuIiAjRunVr4eDgIGrWrCnatWsnbGxsRPPmzcXTp08lxxk9erSoX7++8PDwEDVr1hTjxo0TLi4uwtzcXKxdu1ZSDD8/P9GkSROxb98+YW1tLcLCwsThw4dF/fr1xeLFi/MshhBCREZGiqVLl4rBgwcLd3d3sWDBAhEeHi55eznOd13zCA4OFi4uLqJ27dpiw4YNao936NBB2NnZiQMHDkjOISEhQYwdO1Zt3IK5ubkYMmSI6hzOixhCyHN8379/L3766Sexbds2kZKSItzd3YW5ubmoVauW+OOPP/KsPWfPnhW1a9cWEydOFHXq1BFhYWFi8eLFombNmsLf3z/bbcPCwkT37t1FrVq1xMqVK8X9+/dF+/bthb29vTh69Kik359Gjn164sQJkZSUpNXvlSpfFipp3r59m2mgUV47ePCgsLS0FGPHjhVbt24Vfn5+YuzYscLS0lLs3btXPH78WDg5OYk5c+Zo3H727NkiNDRUrxzkGCwpRx59+/ZVnfTpB5/NnTtXdO3aVVIMuQZ+CpFacO7cuVNER0eLhw8fSipO0mzYsEGngWEODg6iUaNGGj+ooqKixKBBg4SlpaVWH6hCCBEfHy8ePXokEhMTJRXQGX38+FHs2bNHzJw5U0yfPl1s27ZN67+d+vXri5MnTwohhGjbtq24d++eEEKIqVOnivHjx0uK0bZtW3Hs2DEhhPo5cuzYMeHk5JRnMYYPHy6Cg4MlvTYrcpzvuuZhZWUl2rZtK+7evZvpufj4eDFt2jRhbm4ufv75Z0nx0v72Q0NDxdGjR8Xhw4fFw4cPtcpJjhhCyHN8L1++rCrshRAiJSVF3LlzR0RGRkrOQ4726Dogd9++fcLOzk60bNlSXL9+XfX4p0+fxNSpU4W5ubmYPHmy+Pjxo6Q85NinQggRFxcn/P39hY+Pj3j79q24ePFijl8KpciXhcrTp0/F4cOHhb+/f6YfqS5fvizWrl0rVq5cKVasWKH2oy19emfs7Oy0+hanSf369VV/QOlPwuDgYGFnZycphhx51K1bVzx+/DhTHqGhocLGxkZSDDnaEh0dLbp37676FhQWFiaGDBki2rZtK7n4qFu3rggJCZH02vSGDBki3rx5k+1rNmzYIGrXri0pXkpKivD29hZWVlaiZs2aIiwsTHh4eIjJkydLLli6du2qKir0UatWLfH8+XMhhBAjRoxQ/b3dv39fODo6SophZWWlOs/SH9+wsDDJ+0SOGHXr1lXE+a5rHul767Jy9OhRUa9ePUnxmjRpIm7evKl1HnLHEEKe49uwYUNx+/ZtvfKQoz02NjaqgidjW6ysrLLcztzcXHh6eoqYmBiNzx8+fFjUrVtXfPfdd5LykGOfvnr1Sjg7Owtra2vVe9GQIUNE48aN9S768908Knv27EHr1q0xduxYeHp6qv2kH5CWnVWrVsHV1RUbNmzAvn37sH//ftWPv7+/1jk5OTlh69atuHbtGi5duoStW7eqxsjY29vDw8Mjy2uAaXMxxMTEaP1708gxWFKOPIyNjTVu/+LFC8mTDcnRls85N82aNWtyHIQ9cOBAjfNfaLJ161b8/vvvmD59OkxMTACkDtY+ceKE5DsPwsPDs7yzRBtly5bFs2fPAABmZmZ48OABAMDU1DTbO6HS++abbxAYGJjp8TNnzki+G0qOGLrOb5GeHOe7rnlMmzYtx4nEvv/+e8nvZ8bGxjAy0m9IoxwxAHmOb6lSpfSenVqO9qQNyM0opwG5S5cuxbx581CkSBGNz7dt2xb+/v6SJ1yTY5/KMfdQVvLdYNo1a9agR48e8PDw0HlWvJ07d8LDwwNDhgyRJacrV67g2rVrSExMzDTxTU53Brx69QpHjhyBn58fSpcunenNR8ptZXIMlpQjj5YtW2Lp0qVqg00fPXqEOXPmoHnz5pLykKMtJ0+exKJFi9T+AKtWrYpp06Zh+PDhkmIULVoUCxcuxJo1a2BmZpZpf2zZskXjdseOHYOTk1O2g9NiY2Nx8OBBWFlZ5ZjH7t27MW3aNLRq1QqzZ88GkPomZWxsjHnz5sHDwyPHGO7u7pgyZQoGDhyIihUrqoq3NF9//XWOMQCgdevWmDRpEubPn49GjRrBw8MD1tbWOHHiRKa78LIycOBAzJw5E69evYIQAufPn8fu3buxdetWeHp65lmM06dPIywsLMsZSu/du5djDDnOd13z8PX1Ra9evdTOy0+fPqkd2+joaHh5eWHNmjU55uHi4gJ3d3d06tQJlSpVynSOSJkyQI4YgDzH19HREUOGDEGzZs1QqVKlTH+/Uu7YkqM9ug7I/f7773OMXaFCBezatSvH1wHy7NMLFy5g3bp1akX4F198gYkTJ6JPnz6SYmTFQGT85PyPS7uNVpsR7RlZW1vjyJEj2d5uKdWqVauwYsUKFC9ePFPhZGBgkOMHfE7fiqX8QaXNUhgdHY13796hSpUqePbsGUqUKIFt27ZJaqcceaRNw3/r1i2kpKSgWLFiiImJgYWFBXx9fVGiRIk8aYu1tTX++OMPVKhQAba2tjh48CAqVKiAR48eoWvXrrhx40aOMXLqncvqNuqaNWvi7NmzKF26tOqxFi1aYMuWLarcX79+jaZNm0r6MLS2tsbhw4fx7bffqrUlPDwcbdu21fgtKaNatWqp7tJJf3u0EAIGBgaS8gBSb2309vaGlZUVOnTogGnTpmHPnj0oVqwYli9fnuWcNBnt3r0bq1evRkREBIDUb7+DBg1C//79JW0vR4ycehqk3FYqx/muax6azjM7Ozv8/vvvqgJdm/Msu+napZ4jcsRIo+/xzW72WSnvy4A87UlMTISnpycOHz6s2k4IgebNm2PZsmVZ9ooNHToUPj4+ap8pZ8+ehb29vWqbqKgotGzZEteuXcsxD0D/fWprawt/f3+YmZmpvRfdv38fvXr1kpyHJvmuUOnWrRuGDRsm+RuLJv3790fXrl3Rvn17vfNp0qQJ3NzcZOud0VVcXBz++OMP3Lt3DykpKahevTo6deok21oM2jh//jzu3r2LlJQU1KhRQ+PtwdnRty2urq5wdHTE4MGD1f6gZs2ahfv372PHjh26Ni1HFhYWOHfunNoHSPocAO0+QNq2bYuRI0eiTZs2anG2b9+O7du348iRIznGuHTpUrbPa3PreEbv3r1D0aJFdeoij4qKghBCbV99jhj60vd814Xc55lSKeH4yiE0NFT1flajRo0c52KSWog2adIE9+/f1yoXXffp4MGDUblyZUyaNEl1rpUrVw7jxo1DbGysXkvO5LtLP+7u7pg1axbCw8NRpUoV1XX7NPb29jnGaN++PWbPno3bt29rjKHNzKjR0dF6Fzx3797F5s2b8fDhQ5iYmKBGjRoYNGiQxplRs2JqapppcrLPkQeQOkeJ1G/XmujbFrnmhHn16hV27Nihtj969eql1Two+pKjyzZ9IRITEwNjY2PJC6VJXQcEyPpv78CBA2jbti1MTExyXCMlbVFQOzs7td4fOWJkdOLECfj6+qqOb/Xq1TF8+HDVDNNS6Xu+y5WHHMLDwxEUFISCBQuiWrVqkucc0TdGbhxfIPX8ffToEdq3b4+IiAiYmZlpXVRr257nz5+jfPnyMDAwwPPnz1U5p7/Um/7xkiVLZspJU/+Cpseya7/c+1SuuYc0yXc9KkrrotS3d+bPP/+Eh4cHbGxsYG1tjeTkZFy7dg0PHz7EunXrshyX0aJFC+zduxclS5bMckXpNMbGxihfvny2b3665pG+8s9uVWogddr28uXLY+zYsWrXYOVuC5A6EdemTZtU33SrV6+OAQMGwNraOstt0rt48SKGDBmCUqVKqS6d3L59GwkJCdi8eXOW51BufNOV41KJn58ffH19ERkZCQMDA3z77bcYNmxYjkV52jHN6W0ku7+b9PtEyoqwBgYG6NChg9rAZzlipLd9+3bMnTsXbdq0UZ3vV69exV9//YVFixahTZs2GreT43yXIw+5z7P3799j8uTJapdEDA0N0blzZ0yfPl1SYatPDLmPb0xMDAYOHIibN2/CwMAAx44dw5w5cxAWFgZfX19Jq8Hr2h5tzhEAKFiwIMaOHas2zkOO4yv3PgVSJ+PcsWOHWm93r1699BqKAeTDQiXtroOsyDHuRBv79u3DwoUL4eLiolPvTNu2bfHdd99h9OjRao/PnTsXV65cwf79+zVut3LlSgwcOBCmpqZYsWJFtn8MSUlJuH79Op4/f47jx4/Lmoe/vz/atWsHExMT7N+/P8c8Ll68iIsXL+Kff/7JtbbIoWvXrrC0tMSMGTNUdxslJCTA09MTL1++zPIbRG52yevaZbtixQps2rQJffr0gbW1NVJSUnDlyhXs2rUL48aNg6ura5bb5vT3lp4cf3vJyck4duwYpkyZovM1bykxWrRogf79+2dq+7p16+Dv74+jR49q3E6O812OPOQ+z8aPH4/AwEBMmzYNVlZWqnPEy8sLzZs317i+Wm7EkELK8Z01axbu3r0Lb29vdOzYEQcPHkRCQgLGjRuHKlWqYNGiRbnWnkuXLsHOzg5GRka4ePFitudIcnIy/vnnH+zfvx8XLlxQPZ7Xl/bk+LvTi143N1OO9F2Vsnbt2hrn63j8+LGoU6eObHkGBgYKW1vbz57HgwcPROPGjfWKkVNbkpOTxYEDB8S0adPEpEmThKenp9qPFLVr11bNkZHew4cPs90fFhYWmeZRsbW1Vc1bIETqfATZnRuXLl2S/CNFkyZNVCvJprdnzx6tJntKEx0dLW7evCnu3r2bKxMuRkREiHnz5uVqjDp16ijifNc1DznOs/Ts7OzE5cuXMz1+9uxZUb9+/TyLIVVOx1eO1eDzqj2PHz8WXbp0UXvM3NxcvH79Wu2x9O0QQrvjK0VO+zQ2NlasWrVKDBw4UPTp00fvle3TyxdjVLS5NJDVaG5tuuO0qVC1HciUUa1atXDp0qVMt3fevHlT0uJ3AHK8/ti5c2fUrl0bx44dy9U8pNw5VKNGDZw5cybL18jRlrlz52L79u2wsLDQeTBx5cqVERQUhMqVK6s9Hhoamm3PgRACXbt2VRtMGRcXBzc3N1XPjKZ5YtJzc3NTXW7JeJcOoH5dWsq5Gh0djVq1amV6vG7dunjz5k2O26dJSUnBggULsGPHDiQlJUEIARMTE3Tv3h2TJ0+WtOCilL/fsmXLZjv+Ro4YDRo0QEBAQKZFOE+dOgVbW9tsWvB/5Djfdc1DCIHGjRtnekzK+mSaGBsba/xbKVWqVI7nq5wxAHmOrxyrwcvRHil3D1auXBn79u3L9Nz169fV5loRQuDWrVuqS8BS5y4C5Nmn06ZNw19//YXGjRtr3Lf6yBeFiouLi+oedhcXF61XoAVSP7zSVlROu6f9c0n/YWxvbw8vLy88fvwYdevWhaGhIe7cuQNfX1/Jc35kdXIVLFgQ5cqVU11+yrgkuNx5ZLw8lJycjDdv3sDIyEhtJdjs7ojQtS3pHTp0CHPnztV65dL0A0fbtWuHadOm4fXr17Czs1Ptj0WLFmHkyJFZxpBjRd30xfb58+fx66+/YvLkyaru5MDAQMydO1fjatyatGjRAjt37sTkyZPVHvf394ejo6PkvNauXYt9+/Zh/PjxqF+/PlJSUnD58mWsWrUKZcuWhbu7e44xMv79JiUlISQkBP/88w9GjRolKQ9dY6QvLMqXL4+lS5fi9u3bsLOzQ4ECBXDnzh388ccfGDhwoKQ8dD3f5chDjlXG0+vTpw/mzp2LpUuXqiYs/PjxI5YsWYJevXrlWQxAnnNEjtXg5WjP06dP1f6dnJyMsLAwxMTEoF27dtluO3LkyExjw37++We1f0v9HJNjn548eRKLFy+Gk5OTpNdrI9+NUVECfXtnpAxsArQf2JsmOTkZISEhmDFjBrp3757lQN/czgNIHdSW9iHbr18/rbeX2pb0bG1tcejQIa0HeMkxcFRu3333HWbMmJHprpJLly7B09Mzy9lz03+Ti42NxfHjx1GzZk3UrVtX9WF47do1/Pjjj5gxY4akXJydneHh4YEOHTqoPX7o0CGsWLEi216unOzatQv//vsvli9fnmsxsptbIz2p82xoIuV8z4s80mTslcuYR/rnnj17hkKFCqFSpUooUKAAnjx5gvj4eNSpUwe7d+/OtRhSaXOOXLt2DQMGDECTJk1w+vRpdOjQQe3OvwYNGny29gghMHv2bBQpUiRT4ZH+90qlz9gwbfapvb099u3bp/VdoFLky0Ilq5lgDQwMJH37T0lJwaFDh7KMMXfu3Gy312ZAnbbf6uV09+5djB49OlcHnUoRHByMAQMGZNsFnhNt2jJq1Cg0aNAAvXv31up3yPnmEBMTAyMjo0yzWQKpI+dnzpyJVatW5fh7bG1tsXfv3kxLquc0yZKbm1uOsYHU8z2rWXYzsra2xqFDhzK9UYWFhaFdu3aSJp/LyrNnz9C+fXtcv379s8aQgxznu1RBQUEwMjJClSpVMj13//59TJkyReNlBQA5DlxPL6ueQjliSKXt8dXlzr+8ak9YWBi6d++O8+fP6xwDACIiInS6hTyNNvt0ypQp+PLLLzFmzBidf19W8sWln/RymglWSqGi7xiG9MVHly5dtN4+rxgaGmpcZyKvRUdH673uhjZtsbGxgbe3N86fP4+qVatmms4+qzcYOe5aiYqKwqRJk3DmzBkYGBigdevWmD9/vqpg2b17N3x8fJCYmCgpnpWVFZYtW6a27kfakgLZTdS2detWvduSkZmZGf79999Mhcq5c+f03ncBAQFZrmuSmzFev36tcY0dqcsKaKLL+a5tHuHh4Rg2bBiCg4MBpJ4na9euRYkSJZCYmKi60yu79WSyu4QplRwxpNLm+KbNIZLxVtuPHz9i8+bNWfZ25VV7QkNDc1zb6cWLF/jrr79gZGSEFi1aZBoXsm3bNixZsgRXr17VOQ9t9mmpUqWwadMmnDlzBpUrV850h6s+lyPzXaEixzo9uo5h0ETf3plbt25h5syZePjwocYPLymXGDQNQI2JicGePXskrScjVx6aBhfGxsbiyJEjWXa1ZiRHW7Zt24ZSpUrh7t27uHv3rtpzBgYGkr4JhYWFZbtYXFZd8vPnz8e1a9cwYsQImJiYYMuWLVi2bBlGjRoFDw8PnDp1CvXq1YOXl5ektkydOhX9+vVD06ZNYWZmBiEEQkJCULp0afj5+UmKAaQOvAsJCcnUFgMDA8kTi/Xv3x/Tpk1DeHi4agzG1atXsX37dkyYMEFSDE2D+mJjY/H+/XvJHxJyxDh9+jQmTZqEt2/fqj0utFhWQI7zXdc85s+fj5iYGMybNw8mJiZYvXo1vL29MXbsWAwaNAh3795Fx44dM41Lys6pU6cQFBSk8RyROk5Njhi6Ht+oqCh8+vQJQOqlz+rVq6NkyZJqr7l79y4WL14s+TK0vu3RNJg2NjYW586dQ4sWLbLc7vz58/jpp59U7Vm0aBG2bdsGc3NzhIeHY8KECbh+/brk9c/k+Ju5ceOGqjdK7i/A+e7Sjxzr9Og6hkETLy+vbHtncvpm27FjRxQsWBBdunTReJlASjGlaayJkZERbG1tMWPGjEyXDXIrD03X3o2NjWFrawsPDw9JkyzJ0ZbspKSkSJrevEePHnj16hXatGmjcX9kVew0adIEnp6eqrE0165dw5gxY1CnTh38+++/GD9+vFYDC4HUQu2PP/7Aw4cPYWBgAAsLC7Rr107yCr379u3DzJkzNS6aqe14m82bN2PDhg14/fo1gNRBzQMHDpQ8+ZymrnVjY2PY2NhI/nCXI0br1q1RpUoV9OrVS+PxlbKsgBznu655ODg4YO7cuaqBjQ8ePECfPn1QrVo1hIaGYs6cOWjWrFmOvz/NrFmzsGPHDo0LkkodKyNHDED343vgwAF4enpqvGMujRACzZo1w9q1a/OkPZouwZqYmMDGxgb9+/fPske/R48eMDY2xsKFC2FiYoJZs2YhNjYWw4cPx6BBg2BoaIgJEybghx9+yDEHQJ6/mey8evVKvzuB9Lq5WYH69euncU4IbYwcOVJs27ZNlnzq168v9u/fr/P2VlZWIigoSJZc9KGUPOTg7Ows3r59m+nxiIgIyfMfWFtbi9u3b2v9u2vVqiWePn2q9pilpaVwcnISwcHBWseTg6Ojo5g6daoIDg4WT58+zfSjrfj4ePHmzRvx+vVrERERkQsZ5z5ra2vx6NGjz52GznlYWlqKFy9eqD1Wq1Yt0blz50zzb0hRv359sXPnTq23kzuGvi5duiQuXLggzM3NxbFjx8TFixdVP5cuXRK3b98WCQkJkmJ9zvbY2dmJ69evq/796tUrYWVlJZycnET//v0/y9+dprl7hBAiPDxc2NjY6BU73136kWOdHl3HMGiSkJAgaX2hrNSuXRvPnj1D9erVdY4hBznzyI3r/jk5cuSIavbPZ8+eYdasWZm+BT179kzyQDkzMzPExcVpnUdSUlKmb8YmJiaYNm2aTr1Br169wtKlSzVeWgSyvgSV3ocPHzBw4ECYmZlp/fvTi4qKwpgxY1Q9BgDQqFEjWFhYYMmSJdmOh0gvq+50QPrfnr4xGjZsiDt37mgchKotfc53XfNITk7O9L5lbGwMT09PnRbwMzIy0vubtRwx0uh6fNPei7ds2aK6nV9XcrUnq8uuQNbrY338+FHtqkHadAzW1tZYtGiRTote6rJP9+7di4MHDwJI7Y0aPnx4pvPu5cuXeq9/lu8u/cixTo8cS4Cn0fUOkzSPHj3CsGHD0LZtW1SoUCHTCSil8JJjfIkcechx3V/Xtrx48QITJ06EEAKXL1+GjY2N2h+UgYEBChcujJ49e0rqEr927RpmzpwJNzc3jfsjqzcYKVNfa2Po0KG4ffs22rVrp5oHKD0pH8rjx49H7dq10bdvX61/f3o///wzwsLCMGfOHNSoUQMAcOfOHcyYMQPVq1fPcTwWoJxLDBEREfjhhx/QqFEjVKhQIVMBK2W/ynG+65qH3OfZqlWrEBYWhtmzZ2f68peXMQD5LiHdv38ffn5+ePLkCZYtW4YTJ06gWrVqkosPOdqj62XXrI7vjh07ULNmTa3z0HWfvnv3DgsWLACQererpkvhRYoUUU3Eqat8V6gozaZNm7B8+XI0adJEp96ZFStWZHmbqtQ3OznGl8iRhxzX/eVoS5cuXbB582a9qvxdu3bBy8sLSUlJmZ7Lbn/UrFkT586dU00QBej3AWJjY4MNGzbotZLuy5cv0aFDB1StWhUVK1bM9GEodbR+gwYN4Ofnl+nLwu3btzF48GD8+++/kmJ4eHigR48e0huQCzGmTZuGPXv2oGTJkpnG+kj9MJTjfNc1D7nPs8ePH6Nnz574+PEjvvzyy0zniJT9IUcMQJ7je/v2bfTs2RM2Nja4fv06jh49irVr1+LAgQNYtWqVpC8rcrSnWbNmcHR0RL9+/TSeI1mNtZS7EJVjn44bNw4zZszQebbv7OS7Sz9yev36tcZKV5tLFPreYbJt2zaMGTMGffv2lTw4MqPQ0FDs3btXr8s2cuTx8uVLrFmzRq/udDnaEh0djfDwcI3Txku1cuVK/PDDD3B1ddVqf2jqHo2Pj8e4ceMyfZORMn9J4cKFderKT8/LywuxsbFISEjQaq6YjJKTkzVOhmdsbCz5MplSLjH88ccfmDdvnl53/slxvuuah9CwVMOnT5/UlmpII+UDdfz48ShevDi6du2KwoULa5WLnDEAeY6vt7c3BgwYAA8PD9VSBF5eXihSpAhWrFghqVCRoz36XHaNiIhAfHy82mORkZGZjq+Uzys59unFixfx+PFjyXdfaiPfFSqPHj3C7NmzVdfsM5Lyzf/atWuYNGkSwsLC1B7Xpss2TVYzg0qVkpKi1R0cmsgxvkSOPOS47i9HW+Li4jR+e9FGbGws3N3dtb4zTNMHjj53qHXq1AkbNmzArFmzMr1BSXXmzBmsXr0aTZs21TkPIPVy1+LFi7FkyRLVt6qYmBgsW7ZM8jitXr16Yc2aNXp1p8sRw9TUVG2ae13Icb7rmoccSzWk9/DhQ+zdu1d1Se9zxQDkOb5plyQz6t27N/bs2SMphhztadmyJU6fPq1ToZLxjh4hhNpdRNp8XsmxT42NjfUa85OdfFeozJgxA2/evMG4ceM0XrOXwsvLC19++SUmTJigc4yMdO2d6dSpE3bs2IGJEyfq/LtnzZqFYcOG4ebNmzqPL5EjjxkzZuCHH37AP//8o/N1fzna0qdPH4wcORK9e/dGxYoVMxUtUj5UW7ZsiRMnTmg97b/ca7C8e/cOf/zxB06dOoUKFSpkepOR0itTsmRJWQYyT5o0Cb1794ajo6PqjTckJAQlSpTAhg0bJMVo06YNevbsibp16+rcnS5HjF69emHFihWYPXu2zsW5HOe7rnnIXahUrVoVHz58+OwxAHmOr7GxMWJiYjI9/uLFC8n7WY72jB8/Hh06dEBAQIBWl12lzhYtlRz71MXFBe7u7ujUqRMqVaqU6X1VyntzVvLdGBUrKyvs3LlTr279OnXq4MCBA3rPyQHo3zszceJEHD58GCVLlkSFChUyVaxSTlg5xpfIkYcc1/3laIscA64XLlyIrVu3wtLSEhUrVsy0P/QpSOLi4rBkyRJJk3FJWX01J7t370ZAQAB++eUXVKxYUeeeGSD1strhw4fx8OFDGBkZoVq1aujQoYPkHqyuXbviw4cPaNWqlcbudCkfwHLE6N+/v2oRytKlS2c6vlLOVTnOd33zkGuphiNHjmDp0qUYOHCgxvNdSnEvRwxAnuP7yy+/4Pnz51iyZAmaNWuGgwcPIiEhAR4eHqhdu7akgd9ytGfUqFH4+++/YWFhobFA0ncG6Y8fP0q6LCXHPpXjfTXL7fNbodKsWTOsX79er+641q1bw8fHR5ZrbV26dEHhwoXRv39/jb0zOQ2ok+ODqEGDBujfv79e40vkyMPOzg6//PKLXtf95WhLTuMwpFyKyWmtnKzeYD59+oSFCxfi8OHDMDY2RqdOnfDzzz+reobOnj2L6dOn48WLF5nGNOWW1q1b4/nz50hOTtb4vD5vMFFRUWoDOnNiZWWld3e6HDE0zSqbnpQ3bjnOd13z0Gaphhs3buSYhxwfQnJ9kMlxfGNiYuDu7o5bt24hJSUFxYoVQ0xMDCwsLODr64sSJUrkGEOO9tjY2GDFihU6XXaNi4vDhQsXYGRkBHt7+0zF6MmTJzFr1iycPHkyx1hy7NPclO8u/bi6umLx4sXw8fHRefTxTz/9hLlz52LmzJmoUqVKpjt1tPHw4UO9emfkuFQgx/gSOfKQ47q/HG3JrhDJODgtK7p+0/H29saePXvQsWNHmJiYYOfOnShatCiGDBkCLy8v7Ny5ExUrVtRq+vuoqCg8efIEKSkpAFJ76xISEhAYGIiffvopx+2lvEaKDx8+wNvbG66urqhWrRrc3d1x4cIFmJmZYd26dZLuRlDKJQY5Lp3Icb7rmofcSzXou0qzXDEAeY6vgYEBdu3ahfPnz6sWJaxRowaaNm0qeQ4SOdqj62XXe/fuwd3dHW/evAGQ+p62detWfP3113j//j1mzZqFw4cPS/7ckeuyXFb0XRwx3/Wo9O/fH1euXEFycjJKly6d6Zq91NsK5fqGKUfvzIsXL7B9+3bVSqjVq1dH9+7dJZ/gXl5eMDY21mt8iRx5rFq1Ck+ePNHrur8cbXn79i3WrFmDoKAg1TEWQiAxMRHBwcG4cuWKpDixsbE4ePCg2v5o27ZttgWyk5MTBg8ejJ49ewJInWRpzpw5cHBwwN69e9G/f3+MHj1a8oC2gwcPYurUqUhISMg0Nfg333yDEydOSIojh0mTJuHKlStYs2YNHj58iPHjx2Pu3Lk4cuQIjIyMsGLFihxjKOkSw+3bt7Fx40bV8a1WrRr69u0r+W9ZjvNd1zxyY6kGILUn4vHjxzA2NkaFChV0+jKobww5jm+rVq2wdOlSvYYIpNGnPbpedh0wYABev36NadOmwcTEBN7e3ihVqhTGjBmDAQMG4NWrVxg0aBB++uknSe8lcuzT8PBwLFiwINP7akJCAqKiovTqIc53hYocXbb+/v7ZPq9NV66/vz92796tc+/MgwcP4OrqikKFCsHKygopKSm4ffs24uLisHPnTkl3v8gxvkSOPOS47i9HW8aMGYPz58+jcePG+PPPP9GuXTs8evQId+/exdixYzF48OAcYzx//hyurq548+YNKleujJSUFISGhqJ06dLYsWNHlt8e6tSpgyNHjqh6F5KTk1GnTh188cUXWLp0qda3CLZt2xbW1tZwd3dHz549sWnTJtXYg7Fjx6JTp06S4vz1119Yu3at2ofhwIED0apVK8m5NGrUCKtWrYKtrS08PT3x7t07VUHYu3dv1bHPjlIuMVy6dAkDBgxAjRo1UK9ePaSkpODatWsICgqCn58f6tatm2MMOc53XfOoXbs2AgIC1HoPa9WqhbJly2L9+vVa9/CmpKRgwYIF2LFjB5KSkiCEgImJCbp3747JkydLmtFZjhiAPMe3SZMm8PPz02scohzt0fVLcf369bF8+XLVooPh4eHo1KmT6nh7e3tnu58ykmOfDho0CCEhIfj+++/h6+uLAQMG4MmTJzh+/DhmzZqFbt26Sc4no3x36UeOLls5Vk1Os3r1ajx//jzLEc85nQALFy5EgwYNsGjRItU8G2nzbvj4+EhaPMvQ0BAdOnTQOne586hbt66kN/jsyNGW8+fPY8GCBWjevDkePHiAgQMHwsLCAr/88guCg4MlxZg/fz7KlSuHPXv2qKavfv36NcaMGQNvb28sWrRI43aJiYlqg9UKFCiAggULYsqUKTrNYxAeHo4VK1agatWqMDc3R1RUFJydnZGUlIQ1a9ZIKlSOHTuG0aNHo0WLFmjXrp1q9t7Ro0djxYoV2a7imt7Hjx9Rvnx5AMC5c+cwaNAgAEChQoWyfCPOSCmXGJYsWYKuXbti5syZao/PnDkTS5culXTpT47zXdc85F6qYe3atdi3bx/Gjx+P+vXrIyUlBZcvX8aqVatQtmxZuLu750kMQJ7jK8edf3K0R9fLrjExMahcubLq3xUqVEBiYiJKly6NNWvWaD39ghz79Nq1a/j111/RoEED/PPPP2jZsiWsrKywZMkSnD59moVKegcOHMj2eSm3SMnRK5NG3+v/165dw65du9QmAytYsCCGDx8OV1dXSTHkGF8iRx5yFJFytCU2Nhbm5uYAgCpVquD+/fuwsLCAq6urpN4UAPj333+xadMmVZECpK63MWHCBNUHtDZ0vTRoYmKi6tqtVKkSHj58CEdHR9SuXRuhoaGSYvz6668YPny42vHp168fVq5ciTVr1kguVKpWrYpTp06hfPnyePXqFRwdHQEAe/bskfzhqM+cMnLGuHv3rsbxG66urpJXpJXjfJcjj/R07UH47bffMH36dLUvCZaWlihVqhRWrFgh6UNZjhiAPMd38eLFAIDZs2dnek5qD4Ic7dH1S3FKSkqmHjojIyOMGTNGpzmi5NinCQkJqFixIgCgcuXKePDgAaysrNC5c+ccbz7ISb4rVDw9PTU+XrBgQZQrV05SobJ//361fycnJ+PNmzcwMjLSenCcvr0zRYoU0ThxnabHcpNS8pBD2bJl8ezZM5QvXx5mZmZ48OABgNTBj+/fv5cUo0CBAhrHHRQsWFDjol7paeoSltrtnVHt2rXx22+/YezYsahRowZOnz6NgQMHIjg4WPJlxsePH2vspWrfvj3Wr18vOZdRo0Zh5MiRSExMRPv27WFmZoZ58+Zh+/btkm6BVZKSJUtmWqMHSB24rM86NXmVh4GBgc7nlCZv3ryBtbV1psetra3x4sWLPIshl+PHj+u0cF96SmpPGn1nqdbHN998g6CgIJQvXx6VK1dWFXspKSmIjY3VK3a+K1Tu37+v9u/k5GSEhIRgxowZ6N69u6QYmmaTjYmJweTJk7UuVPTtnWnYsCEWLlyI5cuXq26Zi4qKgre3NxwcHLTKRR9KyUMOrVu3xqRJkzB//nw0atQIHh4esLa2xokTJ1CpUiVJMezs7PDrr79i4cKFqoIgMTERa9asyfEc8fLyUuuZSkxMhLe3N4oUKaL2Oim9RyNHjoS7uztKlCgBFxcXrFq1Cu3atcOLFy/Qtm1bSW356quvEBoamqntoaGhWk142KxZM5w+fRqRkZGqa97t2rVDt27dZJmTKC85OTlh9uzZWLx4sSr34OBgeHl5ZbtoqVLykHupBjMzM/z777+qb8xpzp07J/nbuBwx5OLh4QEvLy+txnFk9Dnbo6kQlbMw1YWLiwsmTJiAhQsXonnz5ujTpw++/vprnDt3TtWDrat8N5g2K3fv3sXo0aNx/PhxnWMEBwdjwIABOHPmjORtMr6ZZOyd2bRpU7bbR0REoEePHnj//n2m2T63bt2q9RTuulJKHnJISEiAt7c3rKys0KFDB9XEXMWLF8eyZcskFV6PHj1Cjx49UKRIEdWqoIGBgYiNjcW2bduyfAPUpgtU6i3QkZGRSEhIQIUKFfDo0SPs3LkT5cuXh5ubm6Rv/0uXLsXBgwcxffp01ZiKq1evYubMmWjZsqWkiefSe/78OR49egR7e3vExsZ+1m95unr//j369++Pe/fuqYq16OhoWFhYYNOmTShZsqSi8/D09JT8wSWlID5w4ACmTZsGNzc3VSF+9epVbN++HRMmTJC0OrwcMeTSoEED/Pbbb5mKDG18zvZYWFhkOr7p7/hLT595kLQhhICfnx/MzMzQvHlzrFu3DmvXrkX58uXh7e2t06rOaf5nCpX79++je/fuuHnzps4xrl+/jgEDBuD69et65ZK+d0bKFOyxsbH4/fff8fDhQwghYG5ujg4dOuTKKpX/hTxyw7t371C0aFGt1qp49uwZduzYobY/evbsmaffDn/44Qe9vxnGx8fDw8MDf//9t+qNTgiB5s2bY+nSpZKveSckJGDixIk4evQoDA0NERAQgAULFiA2NhYrVqz4z50nKSkp+Oeff9SOb5MmTfS+ZPBfzWPz5s3YsGEDXr9+DSB1TNbAgQPRv3//PI0hh/Xr1+PMmTOq23EznuNSp1z4XO3J6c7U9OS8OeRzyXeFiqbBtDExMdizZw+++OILSd9SNV2uiY2NxZEjR1CzZk2sWbNG7zx16Z2RW9qkYPremaCNpKQkvHnzJtN99oGBgejYsaPOcaW25ciRI3B0dFT70Ny1axeKFSuGtm3bfvbuUyD1g+nevXuS5niQ45thmkePHiEoKEj1Yajt5Zply5bhzz//xIwZMzB06FAcPHgQz58/x6RJk+Do6KhxEThN/vrrL7W5GID/O76+vr55FkMOuXW+5+Tdu3c5zq6akJCAU6dOoXXr1lrFjoqKghBCr54yfWPoe3xr1aql2jb937wuC88C+rVH3wkb5aLrPo2Li8PWrVvxww8/qM1CvXLlSpiYmKBv376ZLjdqK9+NUdE0mNbIyAi2traS3ygzDqYFUhexcnBwgIeHh74pAkjtvo2Ojs7xdVFRUVi4cCFu376NT58+ZVrUUMptZbdv38Yvv/yCoKAg1R9DelL+KOXI4+zZs5g4cSKioqIyPVeoUCFJb9y6tiUxMREjR47E6dOnsXnzZrVbga9evYpDhw4hICAAixcvltSr8unTJ6xfvz7L/ZHVdX87OzucOHFC7Q966dKl6N+/P7744gsAqfv6hx9+kHRc3N3dMWXKFL2/GQKpAzdtbGxUbXn+/LlWMQ4fPowZM2ao7dsGDRpgzpw5mDBhgqS/Px8fH2zYsAFlypTBmzdvULZsWbx+/RrJyclo166dpDzkiBEaGoqpU6eqjm9GUo6NHOe7rnk4ODjg7Nmzah+cEydOxIQJE1SPffjwAaNHj5b8oRweHo47d+5ozEPqgnNyxJDj+MpVrOrbnrQJG9NuSsg4YWNWhcqxY8fg5OSU7YD52NhYLFmyBFOnTs0xD133aUxMDPr164f79++jXr16au9r0dHR2LVrF06ePImNGzdKWnMoK/muUMk4mFYXmgbT6iq73hkp82b88ssvuHHjBtq2bav6INPWvHnzUKBAAUydOhXz5s2Dp6cnwsLCsH37dixcuFBSDDnyWLx4MSwtLeHm5obRo0fDx8cHz58/x/LlyyXfdqxrW7Zs2YLAwEBs3boV9erVU3vO29sbP/zwA0aNGoWdO3dKGkcyc+ZMHDlyBI0bN9aqGPj48aPGoqZr165q+1VqR+fSpUuRnJyMy5cv6/zN8NatWxgzZkymOxW0/XYZGRmpsWenfPnyku+mOnToECZPnow+ffqgWbNm2LFjBwoXLozhw4dLmoJfrhi//PILXr9+jdGjR3/W813XPDSdP8ePH8eIESPUihep59n+/fsxdepUjV8ODAwMJN9NqW8MQJ7jm9Maa1LI0Z41a9agXbt2GidsHDlyZJbbjR49OlMh2qJFC2zZskV16TkuLg7bt2+XVKjouk83bNiA6OhoHD16NNPrJk2ahG7dusHd3R2+vr4YPnx4jnlkSfwP2bx5s17bf/z4UcyZM0erbZycnDL9tG7dWkycOFFERkbmuL2NjY24dOmSrimrYty8eVMIIcQPP/wgLl++LIQQwtfXV/Tr109yDH3zqFOnjrh3754QQoiePXuKf//9VwghxN69e0WPHj0k56FLW9q3by8OHjyYbezt27eLjh07Ssqjbt264u+//5b02vTMzc3F69ev1R6zsbERYWFhqn+/evVKWFhYSIp38eLFbH+k6NKli+jRo4c4fvy4zjGEEMLFxUXs2bMnU5tWrlwpXFxcJMWoVauWCA8PF0IIMWjQIHH06FEhhBCXL18WrVu3zrMYVlZWIjAwUNJrsyLH+a5rHnKfZy1atBAzZswQ79+/1zoXOWMIIc/x/fTpk/Dz8xNTpkwRnp6eqp+xY8dKjiFHe2rXri2Cg4OFEEK4urqK06dPCyGECAgIyPZvRu7jq+s+bd26tThx4kS2sQ8cOCDatm0rKY+s5JselY0bN6qtSJt+HYuHDx9i6tSpuHXrFvr27atx+/j4eCxYsEDSqrba3AWRVe/Mx48fsXTp0hxjFSpUCF9++aXk36dJSkqKKkalSpUQFBSEevXqoUWLFpJmlJUrjwIFCqjuXEjLw8HBAQ0bNsSCBQskxdC1LU+fPoWtrW22sRs1agRvb29JeRgYGKBatWqSXpub5Phm+PDhQ+zfv1/v9owcORIeHh4IDg5GcnIy/P398eTJEwQEBGDJkiWSYhQvXhwfP34EAFSsWFE1U/DXX3+NyMjIPItRokQJva+ry3G+y5GHHCIjIzFgwAAUL178s8YA5Dm+Xl5eOHDgACwtLREYGAhbW1uEhobizZs3km5wAORpjxwTNspB130aERGR40B+Ozu7HFesz0neDhvPJcuWLYO3tzeKFSuGL774AnPnzsWuXbsApBYwXbp0QUhISLbdrQsXLsSePXvQokULtGzZEjt37sTatWuRkpKCWbNmYdCgQTAyMpK0qm18fDxmzZqFBg0aoEmTJvD29lbrHjx79iw6dOiAbdu25RjLxcUFGzdulLAXslapUiVcvXoVQOpMrIGBgQBSryHmNDmZnHlUr15dVbhVqVJFlVNERITkGLq2pUiRIjmOCfr06ZPkxeNatWqlcSxTXnNzc0OfPn0y/fTt2xfu7u6YOnVqjmvslC1bVuM1dm05OTlh+fLluH37NgoUKICNGzciPDwcS5YswXfffScpRoMGDeDj44PIyEhYW1vjzz//RFRUFAICAtSuf+d2DFdXVyxZskT15q0LOc53OfKQQ82aNfH48ePPHgOQ5/j+9ddfmDdvHnbv3o1vvvkGs2fPxsmTJ9GiRQvJk1jK0Z60CRsBoEaNGvj3338BQKsJG+Wg6z4tUaKEagXnrLx9+1ar+Zg0yRc9KocPH8aoUaMwbNgwAKl3/qxfvx6vXr3CqlWr8P3332PatGnZ7vC///4bU6ZMUa1q27x5c8yZMwcvXrzA3r17MWDAAMmr2qYVPR07doSJiQl27tyJokWLYsiQIfDy8sKOHTtQqVKlLIuePn36qP4/KSkJ165dw+nTp1GxYsVMtyRKmazJzc0NU6ZMAQB899136NSpEwoVKoRr167BxsYmy+3kzmPw4MEYNWoUjI2N0b59e6xYsQKDBw/GgwcPVItr5VZbbGxscPjw4Wzv5T906FC2ExNNmjRJ9f+xsbHw9/fHv//+CzMzs0z7Q46p/qWoWbMmtm7dipo1a6rG3ty8eRM3b95Ey5Yt8eLFC/Tv3x/Lli3Lcir8YcOGYc6cOZg7dy7MzMz0uvPJ0dFRNXW+LiZMmICffvoJR48eRa9eveDr64vGjRsDyHrWabliODs7q7X92bNnaNCgAcqUKZPp+EoZPK7r+S5XHvrewZa+wG3ZsiWmTJmCkSNHolKlSplW+c1qbRw5YmQkxzny4cMH1dwn1apVw927d1GlShUMGTIEY8aMyXJch9ztkWPCRjnouk8bNGiAPXv2ZLsEyO7du1GnTh298ssXhUpkZCTatGmj+nfbtm0xadIk+Pn5Yf78+ZIGNb1+/RpNmjRR/btp06Z49uwZjh8/Dl9fX60WjMup6Bk4cGC2RU/GeTikzpaalR9//BElS5ZEiRIlULVqVcybNw/r169H+fLl8csvv2S5ndx5tGzZEr/99hsKFCiA8uXLY8OGDfD19UWLFi0watSoXG1Lv3790K9fP5QrVw69evVSe8MXQmDr1q3YvHkzVqxYkWWMp0+fqv07rTBIuztGqk2bNqn13CQlJWHLli2qwZLafHuOiIhA7969M72xLliwAJGRkVi5ciU2b96cac2ejBNGCSGyfGPU5lbN+/fvw8/PD0+ePMGyZctw4sQJVK9eXfIlqsTERBw4cADx8fEwMTHB9u3bcfbsWZQtW1byeki6xnBxcZH19nRdz3e58shpBuT4+Phst3dzc4OBgYHagNvp06dnel12A67liJGRHOdIqVKl8ObNG3z99dcwMzNDUFAQgNQ739LmRMmL9tSrVw8BAQFISEhAyZIlsWPHDrUJG7Mi98y0uu7TAQMGoFu3bihWrBiGDh2qNuA7bfX033//HZs3b9Y5NyCfzKNiYWGBc+fOqY2AtrW1xfjx49XGqugSY/bs2Wjfvr1W+dSpUwdHjhxRjYJOTk5GnTp18MUXX2Dp0qU6rZKrDy8vL/Tp00eWuTb0MWLECHh4eOg1nbo+bdm6dSsWLFiA4sWLw8rKCsWLF8e7d+9w8+ZNxMbGYvTo0TotKKgNbaZfl3L3ma2tLfbv36+2kiqQOmuwi4sLrl+/jvDwcHTs2FFtosL9+/dLfmOTOmHU7du30bNnT9jY2OD69es4evQo1q5diwMHDmDVqlVo1qxZjjGaNm2KVatW6bxIo1wx5CDH+a4rOWZA1mZcQVaTHMoRIyM5ju/UqVNx//59zJs3D6GhoZg7dy6WLVuGI0eO4O+//0ZAQIDG7eRuj64TNlpYWKB8+fJqX7ieP3+OsmXLqnp2UlJSEBERIalg0mefHjt2DJMmTUJ8fDwqV66sel8NCQlB4cKFMX36dK0/QzPKFz0qWWnUqJHeMXQ5cImJiWr3jBcoUAAFCxbElClT8rxIAVJnMZQ6QCw3XbhwQe+Bgfq0xc3NDfb29vjtt99w584dhISEoFSpUvjhhx/QpUuXPPlAkfPWdwAoWrQoHj9+nKlQCQ4OVvXaxMbGZppfpUuXLrLmAaTOxTBgwAB4eHioBi57eXmhSJEiWLFihaRCxdjYWKvZgXMrhhzkON91JXX5hewoZSXrjOQ4vhMmTICnpycuXbqEXr16Yffu3fjxxx9hZGSU7UBnudsTHh6u0/wicqzMnZ4++7R169aws7PDwYMHcefOHbx79w6Wlpbo3bs32rRpI8tyE5//rzkXZbxmmBM5V7XV5HN9w2vWrBm2bduGESNGfNZpzF1cXODj44Phw4ejUqVKOq1Cq29bLCwsVJeIEhISVDlIvVtAX5MmTcKUKVNkOw5dunTBL7/8gqioKFhbWyMlJQU3b97E8uXL0alTJ7x9+xYLFy6UfP1fH7dv39bYDd67d2/s2bNHUgwXFxe4u7ujU6dOqFSpUqYCS8plXDliyEGO811Xffr0wcqVK/W+w0aJ5Di+xYsXx6+//qr697p163Djxg188803+Oqrr+ROOUu6Tti4atWqTPOo6EPffVqmTBkMGDAAQgi8fftWNR701q1bKF68uNafxRnlm0s/AwcOVLvuv3btWvTo0SPTJElZVaIWFhZo27at2jegQ4cOwdnZWetVbS0sLPDvv/+qDd61tbXFwYMHJU9IJCc3NzfVhGClS5fO9C1PysBAObRu3RphYWFZFn9SuijlaEtUVBTGjBkDW1tb1UzDjRo1goWFBZYsWaLzBF9S1KxZU9Y3mJSUFHh7e2Pnzp2Ij4+HEAKFChVSTTJ26tQpbNiwAYsXL9ZqYjpdODg4YN26dahTp47a+X7x4kV4eHio7mjITnZd4FKv+8sRQw5ynO+60nQpO7+Q4/h++vQJM2fOhJmZGYYMGQIg9bJs48aN8csvv+RZUanrVP5yH1859mlYWBgGDRqEFi1aYMKECQCAhg0bokyZMqpxhLrKFz0qX3/9NY4ePar22JdffpnpQ8vAwCDLQsXe3h6vXr1Se8zW1hZv377F27dvtc4pp4FsafLi7pAGDRp8lktOGcmxboUcbZkzZw7i4uLUpoZev349ZsyYgQULFmDu3Ln6ppklub8XGBoaYuLEiRg9ejQePXqEAgUKwMzMTPWNqGXLlmjZsqWsvzMrLVu2xNKlS9XmTHn06BHmzJmD5s2bS4ohx8zScsSQQ16u0/K/RI7jO3/+fFy5ckVt/NWkSZPg7e2NJUuWYOLEiXr/Dinyct2p7MixT+fOnYtKlSqpXZo/cuQIJk6ciHnz5mH58uU6x84XPSpKI8dAtpx8jgUFlZyHNho0aAA/P79M3yJu376NwYMHS/rmn52IiAiUK1dO43Oaetvyi5iYGLi7u+PWrVtISUlBsWLFEBMTAwsLC/j6+ua4SF5OstuvuRHjcy0oKEceFhYW2Ldvn6TxAbnd05aXpB7fJk2aYMWKFZkmgbx8+TJ+/vnnz7pYrBQWFhaYOnWqpEvI+l7qlLpP69Wrhz179qBKlSpqjz98+BC9e/fGpUuXdM4hX/SoKI0cA9nSyLGgIJBaMaePkf7NzsvLK8/ykGNVW33bkpycrLFnw9jYGHFxcZJyCA8Px4IFC9TakpZHVFQU7t69m+W2afMT5CSvLlEA8qzgamBggF27duH8+fO4e/cuUlJSUKNGDTRt2jTT/B9Z0We/yhlDjgUFAf3Pd33y+OGHH7KNndPlhYyUsqq1HMc3NjZW4/idUqVKSV6XCtC/PfHx8di9e7fGGLdv387y7iMAkt7rpK45JMc+NTIywocPHzI9HhcXp3dPMgsVhZNjQUFfX1/VSPb0cwAYGBhkWqAvN/OQa9VTfdtib2+PxYsXY8mSJapvJDExMVi2bJnkQaezZs1CSEgIvv/+e/j6+mLAgAF48uQJjh8/jlmzZmW77aRJk/SeqVFOuq7gmlHnzp2xdOlSODg4wMHBQadc9NmvcsaQY0FBOc53ffJYvny5bOOtlLKqNSDP8bWxscGGDRswZ84cVREthICfn5/kycnkaI8+U/nLOUZFjn3q6OgILy8vLF68WDV9RHh4OObNm4emTZvql6BeKwVRrpNjQcEWLVoIHx8f8enTJ+Hg4CAiIiLE/fv3Rbt27YSvr2+e5eHo6Cj8/PxU///s2TPx9u1b0atXL7F06dI8a0toaKho0qSJsLW1FS4uLsLFxUXY2toKJycn8ejRI0kx7OzsxIULF4QQQnTq1Em1bxYvXiyGDRuW5XaaFhP73Nq0aSM8PT1FcHCwsLe3F4GBgeKvv/4Sjo6O4sCBA5LjNG7cWLXAmq503a9yx5BjQUE5zndd85D7PJOjLXLEEEKe43vz5k1hY2MjnJycxMiRI8XIkSOFs7OzsLOzEzdu3Miz9jg4OIhDhw4JIYRo2bKlePTokUhISBDDhw8Xs2fPznI7CwsLWY+vHPv09evXokOHDsLCwkLUr19f1K9fX1hYWIiuXbuKly9f6pVfvljrJz/TtAgfkLqkt9QBUBEREfjxxx9RsGBBWFhYIDAwEObm5vD09MTevXvzLI83b96oJjwzNzfHrVu3UKJECXh4eODIkSN51paKFSviyJEjmDBhAmxtbWFvb49JkybhyJEjma6vZiUhIUH1raFy5cp48OABgNRehZs3b2a5nZy3u2sSFRWFP//8E+Hh4ZK3CQ8Ph7u7O6pWrQpzc3NERUXB2dkZU6ZMkbS2VZo+ffpg5MiR2L59O/755x9cvnxZ7UcKXfer3DE0LSgIpN7F8OjRI0kx5Djf5chDDnK0RY4YgDzH18rKCocOHUK7du2QkJCAlJQUtG/fHkePHoW1tXWetUfTVP7GxsYYMmQITp48meV2QuahpXLs09KlS8Pf3x/r1q3DkCFDMHz4cGzcuBG//fab3gva8tKPwqUtwte+fXudFxQsXLiw6rpj2sqYLVu2RNWqVSXPtChHHnKseipHWwCgWLFi6NGjh+TXZ/TNN98gKCgI5cuXR+XKlVXX+VNSUhAbG5vldnK/wQQFBWHkyJHw8vKCubk5OnbsiNevX8PExATr1q2TtIaSXCu4Ll68GAAwe/bsTM9JHQuh636VO0bagoJubm6qBQX79u2r1YKCcpzvuubx9ddfSx4XJIVSVrUG5Dm+APDtt9/i559/lvz6jORoj65T+bu4uMg6maBc+7RAgQJo2rSp/pd6MsiXhcqtW7cwf/58GBoaYsKECTpNtHbq1ClMnz4dhoaGmDZtGpycnHIh05zpughfenZ2dli3bh2mTZsGS0tL7N27F4MHD8bVq1cz3S6dm3mkrdA5e/ZsWFtbY+3atejVq5dWq57q2pb0CwrmRMoYBBcXF0yYMAELFy5E8+bN0adPH3z99dc4d+5ctgsbyn3r7IIFC1CpUiVUqVIFf/zxB5KSknD69Gns2rULS5cuVa0inp20FVzHjh2LGjVq4PTp0xg4cKDWK7jKMR+PrvtV7hhyLKApx/muax5yz4AsR1vkiAHIc3zlIEd7HB0dMXPmTMybNw9169bF3Llz0apVKxw5ciTbO23kntZC132afuHanEhZuDZLel04UihnZ2dhYWEhzM3NRYsWLXSK0ahRI1WMxo0by5yhdo4fP64aE3Lw4EHRoUMHMXjwYBEeHi5p+wcPHojGjRuL9evXi5iYGOHk5CTq1q0rLC0txaJFi/Isj+fPn4tOnToJX19fER8fL1xcXISFhYWwsLAQmzdvztW2mJubi5o1a4offvhBuLq6ZvsjRUpKivD19RUnT54UQgixdu1aYWdnJ9q1ayfu3r0rKYYcbG1tVeNChg4dKiZMmCCEECIsLExYW1tLinH58mVhbW0tNm7cKKKiokT9+vVF27Ztha2trZgyZYrWOcXGxorbt2+Le/fuiU+fPmm1rRz7Va5jk9YGIYS4ePGiGDp0qJg5c6Z4+/atpO3lON/lyEMOcrRFrv2hlL89Odrz/v178dNPP4lt27aJlJQU4e7uLszNzUWtWrXEH3/8kcst+D+67tO091VXV1fh6emZ7Y8+8mWh4uTkJMzNzYW5ublwdnbWKUb79u1VMdq3by9zhtLNnj1bhIaG6h0nLi5OvHnzRgghxKtXr4Svr684evRonuchhFB9eH38+FEcO3ZMNXBLKl3asnbtWtG5c2dhY2MjPDw8xPHjx0VCQoJuDRBCbNiwQUREROi8vVzq1q0rwsLCRGJiorCzsxP+/v5CCCHu3bsnGjRoIDlORESECAsLE0IIERwcLGbPni02bNgg4uPjJcdISEgQM2bMEHXq1FEV+TY2NmLx4sVatUkJhg8frvfA4DT6nO9y5qGPtL99fdoiRwwlyY32pKSkiDt37ojIyEhZcsxtf/zxhxg+fLiwsrIS3bp1E5s3b86V3PPlhG/Xr19X3cI6YcIE1WAlbYSEhGDFihUAgOHDh0seZCm3unXr4vfff8e3336rd6yQkBAEBQXB0NAQtWrV0mpKY7nySElJwfnz59XykHpbcXq6tiUkJARHjhzB0aNHERERgZYtW6J9+/ZwcHDQ6pp+vXr1sG/fPlSqVEnr3OXk7u6OcuXKoVSpUti0aRPOnDmDxMRE/PLLLzA0NMSaNWtyjKHrCq4ZLViwAAcOHMDo0aNha2uLlJQUXLt2DStWrEC/fv0wdOhQSXFu3LiBrVu3IigoCAUKFECtWrXQr18/VK9eXXIu+saoV68eDhw48NnPd7ny0JfSVrWW4xzRl1ztiYmJwZEjR9TOke+//z7PF7TUZ5/GxMTgr7/+wpEjR3DhwgVYW1ujffv2aN26td4TPQKcmVbxxo4di6+++kqvBQVjYmIwduxY/PPPP2rzjrRt2xbz5s2TtK6FHHm8fPkSAwcOxMOHD/HFF18gOTkZMTExsLOzw9q1ayXNLSJHW9IEBQXh6NGjOHLkCKKjo9G6dWu0a9dO0lwqAwcORJMmTdC/f3/Jvy83hIaGwsPDA+Hh4fDw8ECvXr0we/ZsnDp1CuvXr5dUYDdo0AC//fabatS/rho1aoQ5c+ZkGs917NgxzJ07F6dOncoxxt9//40RI0ao1gtKTk7G9evX8eDBA/j6+kr6kJcjxpw5c/Dq1Su9FhSU43yXIw85ODs7Y+XKlbC0tPysMQB5ji8APHv2DDdv3tR4M4CUSdLkaM+jR4/Qt29fxMbGonLlykhOTkZoaCjKli0LPz8/vWdilkqufQoA79+/x7Fjx/Dnn3/i6tWrsLe3R7t27fSaIZeFisLJsQjfpEmTcOXKFUybNk3tm+7s2bPRqlUreHp65kkeI0aMwIsXL+Dt7a36AH348CEmTpwIc3NzSQPE5GiLJrt374a3tzdiY2Ml3Z0yevRoHDt2DMWLF4eZmVmm/SF14JgcA78zioqKwhdffCF5xdL169fjzJkzWq/gmlHdunWxZ88eVK1aVe3xx48fo2vXrrh+/XqOMTp06IDmzZtnuhtjwYIFuHbtGnbv3p0nMeRYUFCO812uhQ31vTlgxYoV2Llzp14rFssRA5Dn+O7ZswczZ85Umw02jdQ71ORoT//+/WFkZAQfHx/V5HxRUVH4+eefUaRIEaxcuTLHGHKQY59m9PHjR/j7+2PJkiWS31ezwkJF4XI6UbNaZDE9e3t7/Prrr5l6Cv7991+MGzdO0to2cuRhZ2eHLVu2oHbt2mqP37hxA4MHD5a0FoQcbUn/ewMCAhAQEICXL1+iQYMGaNOmTY5TjwM530UkdVR+ixYt8Pz5cwgh8O233+LEiROStksvNjYWBw8eRFBQEIyMjFC9enW0bdtWcs+Xriu4ZjR79my8evUKPj4+qm/+QgjMmDEDBQoUwLRp03KMYWVlhYMHD8LMzEzt8ZCQEHTq1EnSnA5yxPD398/2+fSL2WVFjvNdjjyA1GUboqKiIIRAmTJlcPbsWUnbpVHSqtZyHF9nZ2c4OTnBw8ND5x5iOdpja2uLPXv2ZLq8cv/+ffTq1QvXrl3TKTdtybFPgdTi5OTJkwgICMCZM2dQuHBhtGzZEm3atNF5tmogn96enJ9IKQBykn7SqPS+/PJLJCUl5VkeRYoUUU3Tnp6xsbHk22D1bcv169fx559/IiAgAK9evYK9vT2GDh2KVq1aSVrALY1ctweK1AHtqv/X1vPnz+Hq6oo3b96gcuXKSElJwZ49e7BmzRrs2LFDUtexXCu4vnv3DidPnoSzszOsrKxgZGSEu3fv4tmzZ7C2tla7lTGrHqeaNWvi/Pnzmd4wb9++LXn8gRwxpBYA2ZHjfJcjD+D/5usAoNV5nkZJq1rLcXxfvXqF/v3761ykAPK0p0yZMoiIiMiUd0xMjCxjO6TSZ5/Gxsbi5MmT+PPPP/HPP/+gUKFCaNmyJVatWoWGDRtK7tnNTr7rUQkLC9P7WrvS6LsI36+//opz585h2bJlKFOmDIDUP4SJEyeiZs2akosQffP47bffsGXLFixcuBA1a9YEADx9+hTjx49Hx44d0bNnz1xry5w5c3D8+HG8evUKdevWRdu2bdG6dWu9VjGWYyE/fQd+jxo1Cq9fv8by5ctV++P169cYM2YMypYti0WLFmkVTx9yzFWzfft2LF68GF26dEGDBg1gZGSEwMBA+Pn5oUePHqhWrZrqtVl1rcsRA9B/wTk5znc58gBy9+aAvF7VWo7j261bNwwbNgzNmzfXK++sSG3P8ePHsWDBAnh6eqJ+/fqqtsycORPdunVD69atVa/NzVWudd2nw4YNw7lz51CoUCE4Ozujbdu2cHBwgJGRvH0g+a5QkWsk9qdPn/Dnn3/i0aNHGDhwIIKCglC9enWdvo3oI6dF+KSs1NyrVy8EBgbC0NAQZmZmMDIyQkhICGJjY/H111+rdfdnNdZEjjyaNWumWq6+SJEiMDIywvv379UWwUuTVbeprm2xsLCAsbExGjZsqPpAz4qU3pK0hfwSEhJU+yP9Qn66XMLRRb169bBp06ZM5/utW7cwaNAgXLx4MccY+qzgKjepdx5l17UuR4ycFpzz8fHJMb4c57scechBKataA/Ic32PHjmH+/Pno378/qlSpkmmQspQB9XK0J31bNF12Tf//ubmauq771MLCAkZGRqhdu3aOA731mfAt3136MTY21ruae/36Nbp37443b94gISEB3bp1w6ZNm3D79m34+fllGiyYm7Zv345BgwZhxIgRcHJygr+/P969e4eff/4ZLVq0kBSjUaNGaNSo0WfPY8yYMXrlAOjelrQ3nk+fPuHp06d657FmzRq0a9cO7u7u6NmzJzZt2oSXL19i5syZGDlypN7xpSpQoABMTU0zPV6wYEHJSxvos4Kr3JRyieHQoUOYPHky+vTpg2bNmmHHjh0oXLgwhg8fjgoVKkiKIcf5LkceclDKqtaAPMd31KhRAFJ7WjOSWhTI0R69ZmuVka77tHPnzrm+fhmA/Dcz7fLly4WDg4OYP3++2Llzp/D391f7keLnn38WQ4YMER8/fhQ2NjYiLCxMvHv3TvTr108MGTIkdxuQQa1atVQTC/Xv318cP35cCCHEP//8I9q1a/c/l4dS1K5dWzURl6urqzh9+rQQQoiAgADh4uKSZ3kMHTpUjBkzRm3yuoSEBDFmzBgxYMAASTF0XcE1P6tVq5ZqxuVBgwapJhS8fPmyaN269f9cHkpZ1VouT58+zfZHCiW1J7/Ldz0qq1atAqB5gKCBgYGkW8YuXLiAdevWqX1T/eKLLzBx4kSt1jaQg1yL8OWXPJRCroX89DVu3Dj06NEDrVq1Ut1dEhgYiNjYWGzbtk1SDE0ruFapUgVDhgzBmDFjMHXq1FzLX6nkWkAvv+ShaXVdKysrdO7cGW5ubnkWQy7ffPONKqenT5+iYsWKEEJotbaVktqT38m3vKZC3L9/P8sfqdf4YmNjUbhwYY3PSb1LRi5pi/DFxcXB0tISf//9N1JSUrRaUDA/5aEUaQv5AUCNGjVUt0Vrs5BfWFiY3nlUrVoVv//+u2q5+vj4eHTo0AG///675OvO6e8I0WYF1/wsbcG5yMhIWFtb488//0RUVJTWC+jllzzSVtcFoPeq1vrEkIsQAj4+PrC3t0f79u3x4sULTJw4EVOmTNF4p5YmSmpPvve5u3Ryy7Nnz8SZM2dEXFyceP36tVbbDho0SMydO1cIIVSXfhISEsSoUaPEwIEDcyPdLMm1oGB+yUMp5FjIr0mTJopY52TKlCmia9euIigoSBw/flw4OTmJW7duifnz5+t0eeHSpUti586dIjo6Wjx8+FAkJibmQta5S64F9JSSR1xcnPD39xc+Pj7i7du34uLFiyIqKkry9mvWrBH169cXp06dEjdv3hTW1tZi/fr1ol+/fqJ79+55FkMufn5+okmTJmLfvn3C2tpahIWFicOHD4v69etLXptKSe3J7/JdoRIfHy/GjBmjWtUxLCxMDB8+XPTr109ER0dLihEcHCwaNGggOnfuLGrVqiUGDBggmjdvLurWrataxTQv6bugYH7LQyn0XcjPyclJ3LlzR+vf++jRI9X/x8bGilWrVomBAweKPn36CDc3N7UfKeRawTU6Olp0795dmJubCwsLCxEWFiaGDBki2rZt+9kXcEw7b7WllAX09Mnj1atXwtnZWVhbW6veE4cMGSIaN24secFDJa1qnd6bN2/E+/fvtd6ubdu24tixY0KI//syKoQQx44dE05OTpJiKGUVZ7lpsxBpXsl3tycvW7YMf/75J2bMmIGhQ4fi4MGDeP78OSZNmgRHR0fMmDFDUpyXL19i586duHv3LlJSUlC9enX06tXrsy0Ops+CgkDqJau0WyUB9Tk/OnbsmGd5vHjxAtu3b1ebRbVbt26qa8Z52RZN4uPjtVoM7OPHj3jy5AkKFCiAypUra7WtrlNwt23bFl988QV27tyJcePG4a+//kLjxo01ToSny8R0Qgjcu3cPZcqUwVdffSV5u1mzZuHu3bvw9vZGx44dcfDgQSQkJGDcuHGoUqWKpDld5LhV+sOHD/D29oarqyuqVasGd3d3XLhwAWZmZli3bp3ku2XkWEBTjrl29M1j3LhxiImJwZIlS9CoUSMcPHgQxYsXx5gxY1CwYEFJC1cqzZYtW7Bu3TrVJcsyZcpg4MCBku9Ss7a2xuHDh/Htt9/C1tYWBw8eRIUKFRAeHo62bdsiMDAwF7P/P1nN+G1gYABjY2OUK1cOjo6OeTL5286dO7F+/XpEREQgICAAGzZsQNmyZTFs2DC9Y+s7106+G0x7+PBhzJgxAw0aNFA91qBBA8yZMwcTJkyQVKiMGDECHh4eGD16dC5mKo0ci/CdPXsWEydORFRUVKbnChUqJOnDXY48Hjx4AFdXVxQqVAhWVlZISUnB/v37sX37duzcuVPSrJJytOXt27dYs2ZNpvkPEhMTERwcjCtXruQYIzExEXPnzsW+ffuQmJgIIQRMTU3Rp08feHh45Lg9oPvA7x07dsDJyQmfPn3CyZMnsXjxYq3XbslI0wqu2t6Gf/LkSSxatEitEKhatSqmTZuG4cOHS4ohx63S8+bNw5UrV9CvXz8cP34cV65cwcKFC3HkyBEsXLhQNfFZduRYUDBtrp20MQ8iw1w7UgoVOfKQ6+YAJaxqDQC7du2Ct7c3evXqBXt7ewghcPnyZSxevBhFixaVtATGN998g8DAwExfPM+cOaPVbd/6tufy5cu4fPkyjI2NUblyZQCpC41++vQJ5cuXx7t371CwYEFs2bIlV1eGPnToEBYtWoS+fftiw4YNAFL/dn18fFCoUCEMGDAgxxhyzZOj0Wfpx8lFVlZWqtv50nfpPXnyRNSpU0dSjLp166pifG6enp6iZcuW4syZMyI6Olq8f/9enDx5Ujg7O4t58+ZJiuHi4iLc3d3F6dOnhY2NjThx4oTYsmWLqFevnuo247zIY8CAAWL48OGqbmwhUru0R4wYIQYPHpxnbRk9erSoX7++8PDwEDVr1hTjxo0TLi4uwtzcXKxdu1ZSjPnz54uGDRuKnTt3ivv374u7d++Kbdu2iQYNGojVq1dLiqGrsWPHivXr1wshhKhXr57qtnFdBQcHi8aNGwsbGxvh4uIiOnbsKKytrUXr1q3FixcvJMexsrJS/b2l/9sLDg4W1tbWkmLIcau0g4ODuHbtmhBCiIkTJ6qmFHjw4IGoV6+epBjDhw8XXbp0UbvMFhQUJFxcXISnp6ekGG3atBGenp4iODhY2Nvbi8DAQPHXX38JR0dHceDAgTzLw8bGRjx58kT1/2nH5d69e8LW1lZSjL/++kvUrFlTdOvWTcybN094eXmJrl27itq1a4vLly/nWQwhhPjuu+/E1q1bMz2+bds20b59e0kx9u7dKxo0aCD8/PyEtbW12L17t/D29hZWVlZix44dedYeHx8f4erqqjaOMioqSgwYMEAsX75cJCQkCE9PT8nvj7rq3Lmz2L9/vxBC/Rz57bffJI9Tc3d3Fy1bthQ+Pj6iVq1aYtGiRWLEiBHC3Nxc7N69W6/88l2h4uLiIvbs2SOEUN/hK1eulDy/hZeXlxg9erQICgr67Nfr6tWrJy5dupTp8XPnzgkHBwdJMerUqaMaW9OzZ0/x77//CiFS/1h79OiRZ3nY2NiI+/fvZ3r83r17om7dupJiyNGW+vXrq64rt23bVhVv6tSpYvz48ZJiODg4iL///jvT4wEBAaJZs2aSYqTRduB3WoGUkJAgJk+eLJYsWaLV78uoX79+wt3dXbx790712Js3b0S/fv3E8OHDJcfp3bu3qtBL/7c3c+ZM0bNnT0kxatWqJZ49eyaESJ0jJq1ouXXrlnB2dpYUw9raWlVgNWnSRPj5+QkhhAgNDZX8wWxraysCAwMzPX79+nVhb28vKYYcc+3IkYccNwe0b99e+Pj4ZHp8/vz5olu3bnkWQ4jU9wBNxXloaKjkL6NCCLFr1y7RrFkzYW5uLszNzYWDg4PYtGmT5O3laE/Dhg01jme5d++eaNiwoRBCiIcPH0ousHWVNqBYCPW/3bCwMMn7NDfnlcl3l35GjhwJDw8PBAcHIzk5Gf7+/njy5AkCAgKwZMkSSTFOnz6NsLCwLK+H5+ZUxhnJsaBg+hiVKlVCUFAQHBwc0LBhQ9W0+HmRR1aLtEm9HTBjHrq2JTY2Fubm5gCAKlWq4P79+7CwsICrqysGDx4sKUZ8fLzGNaWqVauG9+/fS4qRkJCAiRMn4ujRozA0NERAQAAWLFiA2NhYrFixIssF05YuXYrNmzfju+++Q6lSpbBp0yacOXMGlStXznQJTsoYlRs3bmDPnj2qZeaB1FuWJ06ciF69eklqCwCMHTsWAwYMwK1bt5CUlITVq1fj0aNHuHPnDjZu3CgpRtqt0l9//bXOt0pXrVoVp06dQvny5fHq1Ss4OjoCAPbs2SP5cpYcCwrKMdeOHHlMnDgRvXv3xqVLl5CYmIgZM2bg8ePHiI6OljzXTmhoKLp27Zrp8e7du2PHjh15FgNInUPm9u3bmf7+AgMDc1weI+Pv7d69u2pV6dKlS0veFpCnPUlJSRqPb3x8PD59+gQg9TxKG+OUW8qUKYMnT55kuux1/fp1yePUcnNemXw3j4qTkxOWL1+O27dvo0CBAti4cSPCw8OxZMkSfPfdd5Ji/PTTT5g3bx7mzp2r8Scv9enTB7Nnz1Z7k46JicHSpUslX1+uXr06/v77bwCpH8xXr14FkDrAKS/zaNiwIRYuXIh3796pHouKioK3t7fkJcDlaEvZsmVVk9SZmZnhwYMHAABTU1PJRUbnzp2xbNkytWnqhRDw8/OTvOLt6tWrcf/+ffj5+akG4bq5uSE0NDTbNVwaNGiA1atXo2TJkrhx4wasra1RpEgRvHz5Ek+fPlX7kSJtBdeMtF3B1c7ODrt27ULhwoVRqVIl3LhxA+XKlcP27dvVxoxlx9HRETNnzsTDhw9Rt25d/PHHHwgMDMT27dslD8YbNWoU5s6di6FDh6J9+/YwMzPDvHnzsHnzZskLcI4aNQrTpk1T+1Ly9OlTeHl5SY4hx1w7cuRRtWpVHDx4EM2bN0fjxo1haGiINm3a4MCBA5Ln2klbXTcjXVa11icGAPTo0QMzZ87Ejh07VHNkbd++HbNmzdJYOOSkVKlSWhcpgDzt+X/snXlcTdv//19Hcc3XeOneSxSpW5qQIVOKi4wZiwplFhUaSSVlqESmRMO9upGhiMzXmCkZU24jIWVIVIai9fuj39nfjnNO7XP26jh8ej4ePR7s01mtfYa93+u93u/Xq3///vDy8hIIXHNycuDj44P+/fvjy5cviI6OZhZWtcWUKVPg7e3N+KNlZ2cjOjoaa9asgZmZGasxalNX5ofr+vnRoGEoeObMGeaCN2jQIPz555/o06cP/vvvP+jo6GDz5s0ymUd+fj6mTp2Kt2/fMnbijx49QosWLfD333+z6qiicS7r1q3DmTNnsHbtWpSVlcHBwQGenp44c+YMMjIycPjw4RrHWLp0KU6dOoWff/4Z2traUFRURGpqKp49ewYdHR2BzIY4P49hw4bB09MT/fr1E+g8uHr1KpycnHDp0qUa50EDeXFwBSo7dlxcXGBoaAgLCwvMmTMHly5dgqKiItatWwdTU1NW47x58wYFBQXMjfjevXto0qQJ64wKDUPBmzdvwtbWFosXL8b48eMxfPhwtGnTBs+fP8eIESNE+szUxjz4zQFcPMrkydW6oqICfn5+iI6OxpcvX0AIgaKiIqZOnQo3NzfUqyeb9TeN8yksLMTcuXORkpKC5s2bgxCC4uJi6OjoIDg4GA8ePICDgwNCQkJgYGBQq+cTGBiIyMhIfPr0CQAkfk1DQkIQFhaG9evXo2XLlrCyssKiRYuQmJiIDx8+YO/evVLP7YcIVLZs2QIbGxs0atRIbLsXnwYNGqB9+/YwMTERqz4LVN4Qw8PDkZGRgQYNGqBr165YuHChxO2JXKnpfKpS3QrrwYMHUFBQgLq6Om7cuIHw8HAoKSlh8eLFrFbNtOZRWlqKw4cPIyMjA4QQdOvWDaNHjxa7zSEKrudSVlaGDRs2QFtbG6NHj4aHhwdiYmLQrFkzbN68mVV2x9XVlfV8xW2/iGuRfPToEcaMGYN79+6JfF5SUhL09PSgqKiIpKQksX+X72xdE1wcXGl9LvjcvHkT2traTKBHpGyVBoC8vDxkZWWhV69eKC0tlWjVHBsby/p3q8ugFRQUoKysDB06dEBmZib27t0LJSUlWFlZscqq0JhHz549ERcXx0laQV5cratSUlKC7OxsAJXZVUmuITSgdT6EEFy/fh1paWnMdY0flLx58waKioqsurto8OHDB2RmZoIQIvFrys8od+rUCYMHD8bOnTsREhICJSUlrF+/Hn/88YfU8/ohApUhQ4bg4MGDaNmyJYYMGVLt73758gWvX7+GoaEhQkJCRP5OVFQUfH19MWLECOjo6ODLly9ITk7G2bNnERAQgBEjRtTGadQaNFZUNHB1dYW7u7vQh7+oqAhubm7Ytm1bjWPU1rkUFRWhadOmnJ23JcHMzAzm5uaYNGmSQKCydetWnD17FocOHRL5PHV1dSQmJqJ169ZQV1cHj8eDqK8x2wv+jRs3WM/561Xd19+358+fo379+ujQoQMUFRWRm5uL8vJyaGlpsVpR9e3bF7t27YKmpibrOX2NtLU/tKHxeafBmjVr8PLlSyxcuBDKysqspATkHVHt9MOHD5dIx+h/nby8PNa/W9uZ1Jr4IYpp+TULX/9bHDdu3MDcuXPFPh4WFgZXV1dMnz6dOTZjxgzs3LkTmzdv/u4ClWvXrn2zL3BycjKePHkCAIiLi4OmpqbQhTsrK0vkXq8opD2X6jIPX9OrVy+Jx5cGaQu/z549y/i8iNtmkwQuKeWq37eIiAicP38eAQEBTPbi3bt3cHJygpqaGqvxWrVqheLiYqnnAwjW/sybNw9AZe2Pq6sr/P39WYs+SgPtzzsN5Kk5gAZZWVmwtrZGaWkpOnfujC9fviAmJgbbtm1DZGQkJ2Exvhhfjx49KM5YPNnZ2fD29satW7dEFtXW5nszZMgQoe3DrxGXSeUTFxfH+u+xMQQWxw+RUZGUwsJCxMfHw9raWuTj2traiI+Ph7KyssDxnJwcjB07VmxKXl75liuqW7duMZ0j4lb+jRs3xqxZs1htDUh7LtVlHqrCNgtBi4sXLyIkJERAAXn27NmsC7/liX79+iEsLEwoJZ6eng5LS0tcv369xjHWrVuHf/75B4MGDYKysrJQUMrmM/Ita39of95pUNP2Edvib3lh5syZUFRUhL+/P9OpVlhYiKVLl6JJkyastiNTUlKwcuVKpKeni+yokdU1wNLSEq9fv8bUqVNFbu/U5nvDJZPKh/Z2njh+iIxKVfg3JHGkpaWhVatWYoMUoLKr4uTJk0KtqufPn4eenh61ucqKb7mi0tfXx8OHDwFUvjeXL1+WqIXwa6Q9FxqZh9pg4MCBTPusNLx8+RJBQUHMiuzrG6Msz7u8vBzv378XOv769esaV258Tp48idatWyMlJQUpKSkCj/F4PFY394KCApGt40pKSqy7uqSF9uedBt9bIFITNNrp/fz8oKCggBUrVsDPzw8uLi7Izc1FVFQU1q9fX1tTF+Lu3buIjo7mtNUpLeKCj5KSEtSvX59V5pr/Wa9tfrhAxdfXV+Ci+PnzZzx69AhxcXFwcnIS+7yqUbiSkhKCgoKQkpICfX19KCgo4MGDBzh69ChsbGxqdf61AVtPkdqGxoda2nORxEuoNqFd+L1y5UqkpKTA1NRUZgV34hgyZAhWrlwJDw8PaGlpgRCC5ORkrF69mnW3Dput25pQVVXF1atXMWnSJIHjx44dE+jEqG1kdRFng7w0B9CA307/dQuwJO30qampiIyMhLa2Ng4dOgQ1NTVYWFigffv2iImJkdn2fsuWLVm3qtc2kZGRCA8PR0FBAXg8Hn7//XcsWLCA05YNLf5ntn6OHz+O/fv3IywsTOTjNRXh8uHxeDJfndemCZ8s5/H+/XtERESIXf2La+OlgSSeJmznQQjBpUuXGIPFLl26oG/fvlBQUBD7HNqF37q6uti1a5dc3HBKSkqwZMkSJCYmCnQLDR8+HOvXr5doyzEpKQlZWVkYNWoU8vPzmZZ4Npw7dw4ODg6YMmUKoqOjYWtrK1D7w3ZbjauBJg2DRRrzqK3mgMLCQqZWSpbQaKfX0dHBiRMnoKSkhGXLlkFfXx8WFhZ48uQJJk+eLHENUVlZmVRb6qGhoUhOToa/v7/Mu5aqEhwcjLCwMFhZWUFHRwcVFRW4efMm9u7di2XLlgnUa1aFTZ0LHy73zf+ZQOXZs2cYOXIk7t69+62nIhE1mfDdvn2b1TgXLlzA7t27kZ2djX379uHQoUPo2LEjxo4dK7N50HL6leZcaLQTV6WoqAg2NjZ48OABmjVrBkIISkpKoKmpifDwcDRv3pz136sOfuG3uNe3X79+iIqKYgzNpIG2g2tOTg7S09PB4/GgoaEhkclbSUkJbGxscPfuXfB4PJw6dQpr1qxBbm4uwsPD0a5dO1bjcK39EWWgmZKSgg8fPrA20Fy5cmW1BovOzs4ymYexsTFmzpwpdLPZuXMnYmNjcfz48RrHoOFITStw49JOz2fMmDGYM2cORo0ahW3btuHJkyfw8/NDamoqLC0tGSHJmuDqODxz5kzcvHkTX758QevWrYWCHVktigcMGABnZ2eMGjVK4Pj+/fuxfft2sZnO4OBg1oEKl5qsH27rRxSlpaXYs2ePxHvFr169ElAe5SPLVq3AwED88ccfsLS0xJIlS+Dv74+8vDxs3ryZ9Y09MTERixYtgqmpKe7cuYOKigp8/vwZrq6uIISwSu3RmAcNp19pz4XtHNmybt06fPz4UUDd8+HDh1i+fDkCAgLg5eVF5e906dIF9vb2Yh8fO3Ysdu3aBW9v72ozOdVB28G1c+fOUgdOgYGB4PF4OH36NJOlW758OZYtW4b169cjICCA1Thca3/Wr1+P3r17IyAggNmr//TpE5YtWwZ/f3+xGa6qnD17Fn5+fhg1ahSGDh2K1atXo0OHDnBwcGBtG0FjHi9fvsSAAQOEjg8dOpS1Dg4NR2oaztgAncyrpaUl3N3dAQB//vknxo4di4YNG+LWrVvQ1dVlNQYNx+EePXrIrMOoOoqLi0XWyfTo0QOvX78W+zw7O7vanNb/wckpSA7p1q0bUVdXF/rR0NBgzApr4vz586Rv375CY/DHliU0TPimTJlCwsPDCSGChlM7d+5k7TZKy9iQq9MvjXMhhJCCggISHBxMHB0dyatXr8jx48cFHGpronfv3uT69etCx69du0b69evHagxxn1X+DxtcXFyIlpYW6devH5kyZQqxtLQU+GGDvDi4EkLI4MGDSXJyMiFE8P29detWteaXsbGxrH/YQMNAk4bBIo152NrainQFDwsLI1ZWVqzGoOFITcMZmyanT59mXI6PHDlCRo8eTebMmUOePHnC6vk0HIflBUdHR7JmzRqh4/7+/mTRokWsx0lLSyMuLi5kypQpJD8/n+zZs0fkdVJSfriMytfFtEClgZeOjg7rFPSaNWugra0NCwsLNGzYsDamyRoaJnz//fefyEr24cOHs15R0ZjHsGHDcOjQoWozBDVB41weP36MyZMno2nTpigoKIC9vT0SEhLg6uqKiIgI6Ojo1DjG58+fRWbo2rRpg5KSElbzkLbw+2u+TtdKyoEDBxAWFiag3NqyZUssX74cM2fOhJ2dHWxsbGBubs7p77ChsLAQbdu2FTrevHlzkR1FfFxcXFiNz+PxWGUQaRho0jBYlHYetJsD3r9/DyUlJQCVWc3Zs2cDqNz2rbqNUx3v3r2Dvr4+gMpMYWpqKlRUVDB37lzY29tjxYoVrMYpLCxEaGgoMjIyRGa82WRcfHx8YGVlxXSHjR49GqNHj2b19/nk5OSIrA3r3bs3vL29xT6PbUE9j8fDwoULJZqTJFTdDi8vL8fff/+NmzdvokePHsxn5NatW0JF6eJISUmBubk5dHV1kZKSgrKyMqSlpcHPzw9bt27FoEGDpJ7rDxeosDVQqo4XL15gx44dUFFRoTAjbvBN+CwtLRkTPmtra4lM+Jo1a4YXL14ItWxmZmYKtPjV9jxoOP3SOJe1a9fCxMQEPj4+zIUzMDAQzs7O8Pf3x99//13jGJqamoiOjmbSx3yio6OhoaHBah7iPqt8Mzs29UM0trTkxcEVALp3747jx48LSQNERUVVK8FNu8OGb6C5efNmpjZHUgNNvsGin58fevToAV9fXwwdOhQJCQmsRcmkncfXqsbt27cXavn+5ZdfcPToUTg4ONQ4DxqO1DQCNwBwcnLC/fv30a9fP6kXkrGxsRJtN4lCWsfhQ4cOYdq0aWjUqJFY9Wmg9gOVr01L+UFX1e+Svr4+Y1NQE/7+/pg1axYcHBwYGQ8fHx80adIEwcHBdYEKbb+RPn364MGDB3IRqMyZMweLFy9G/fr1MWrUKAQHB2POnDn477//0KdPH1ZjjB49mnF+5vF4KC0txcWLF7F69WqMHDlSZvPgO/0ClcGgNNA4l1u3biEqKkogm6GoqIgFCxZg8uTJrMawt7eHlZUV7ty5wwQ7ycnJePjwIbNfLS3a2tqsMwQ04Du4BgYGMiKHNBxcy8vL8fDhQ6ioqKBJkyasnuPo6IhZs2bh3r17+Pz5M7Zv346srCw8ePAAu3fvlujvP3nyBOnp6fjpp5/QpUsXiRRLly1bhqlTp8LIyEjIQJOtg7qTkxNcXFxw48YNWFhYYN++fZg0aRJjsFib86DR5l2VxYsXw87ODuXl5QKO1FFRUdi6dSurMWgEbkDl94yrSd+gQYOwZ88eLFq0SOpuG77jMD8zkZ2djcuXLyMoKKhanS5JldRrCzYLMklISUnBqlWrhI5PmzYNMTExnMb+Ibp+1NXVUa9evRo/7Gxbi/Pz8zFx4kT069cPHTp0ENpKkpWiJB+uJnzl5eVwcXHBsWPHAPyfYubgwYMRFBTEelXCdR40oHEuvXr1QlRUFNTU1ASUS+/duwdbW1vWio337t1DWFiYgMHizJkzoa2tLfX5lZaWYsuWLTh16pTMKv5pObg+f/4c7u7usLe3h5qaGiZOnMhkuiIiIlhnmh4+fIiwsDCBjp1Zs2ax2pIDgLdv38LNzU3g9atXrx7GjRuHVatWsbZgoGGgWRUipcEirXlwbQ7g6khNyxl7+PDhCAgI4CSSZmlpiaSkJPB4PLRu3VroM8H2u8fVcZjPq1evREo2yLJxg6v8RN++fbFz5050795d4Lp6/fp1ODg44MqVK1LP7YcIVDw8PHD69Gk0atQIpqamMDU1ZS3tK268mJgYtGzZEo0aNRJ4TNY6KjRN+B4/foy0tDRUVFRATU0NXbp0EWkXX5vz+PjxI06cOIHs7GzMmjUL6enp6Nq1K1q2bCnROFzOxcHBAfXq1cOGDRvQo0cPHDlyBM2aNYOdnR1atmyJzZs3S3t6EiFORZnH48HLy4v13jANCAUH18WLF+P58+fYuHEjkpKSsHr1auzatQuHDh1CXl6eWA2jqsTFxWHkyJFC24Lv379HTEwMq3T98uXLcf/+fXh4eDAtvTdv3oSPjw8GDx4MDw+PGsegZSj44cMHpKeni7wJSeIp9f79e+Tk5EBBQQGdO3eWyO/qwoULcHV1xZs3bwSOkxpaeEXBxZGaljP22bNnERISAgcHB3To0EEoIGBzc68pCy/JYpSL4/CtW7fg6uqK3NxcgePSvDdcoCE/sXLlSuTl5WHjxo0YNGgQjhw5grKyMjg4OEBLS4t1JlIUP0SgAlSKY127dg0JCQk4c+YMWrduzQQt/JQpW/T19bFy5Uq5kJ6mYdFubGyMgwcPCmU9CgoKMGbMGFYeLDTm8erVK0yZMgWvX79GWVkZTp48iTVr1iAlJQWRkZGsgiAa51JQUAArKysUFxejqKgIKioqePbsGVq0aIE9e/aIFdKivcV46NAhzoXf8oSBgQEiIyOhoaEBe3t7EEKwadMm5OTkwMzMTOzFrrCwkKmFMTY2xoEDB4QC19TUVDg6OrLy2erRowdCQkKECh0TExPh6Ogo9jNS1VBQXKCSlZWFPXv2sLpwnz17Fi4uLigpKREKUtjehMrLy+Hr64uDBw8ydUQNGzaElZUVq9oSoLKIXUVFRWxzAJstFBqO1DScsYFKlV1nZ2eh4mpZ3NxpOw6bmZmhcePGmDlzpsiFAJftLUkwMzND69atxcpPmJiY1DhGSUkJbG1tce/ePVRUVKBZs2YoKSmBuro6wsPDOWXdf4gaFaCyK8XQ0BCGhobw9PTE5cuXcfz4cUyYMAEdO3bEyJEjYWpqyurD06hRI6bu4Fszfvx4+Pv7S2zCl5CQwJivPXv2DN7e3kKrsGfPnrEW65F2HlVZu3YtunbtiqNHj6Jfv34AKvVI7O3tsWHDBuzYsUMm59KuXTvExcXh6NGjTFbG3NwcY8eOrfaC+3Xh2/Pnz1G/fn106NABioqKyM3NRXl5ObS0tFgFKjQKv8UhiQssLQfX8vJy/PzzzyCE4OrVq3B0dAQAVFRUVKsqe/HiRbi4uDDbeBMnThT6HUII62K8+vXri3wfW7VqVW1BMI/HY2qDeDwefHx8hH6ncePGrDtl/P390bdvXyxYsEBqe4PAwECcOHECbm5u0NPTQ0VFBW7duoXg4GA0atSIcYeuDhrNATQcqWk4YwOV3XJ9+vTB5MmThTLeksA/n5ycHGzatAlnzpxB165dqw0OaDgOVyUjIwNxcXFUMuZcyMzMhK+vL9TV1aGhoYHGjRvD0tISjRs3xu7du1kFKk2bNsXevXtx9epVZttWTU0NAwYMkGgbTBQ/TKBSlfr168PIyAhGRkYoKyvDwYMHERAQgMDAQFYfHgsLCwQHB2P16tWcvgg0kNaET09PD3v37mVWcnl5eQKeEjweD40bN2Zd1EfD2PDatWvYuXOnwGv6888/w9nZuVqJe9rnwl8tf721UlRUhAULFohN61ctfIuIiMD58+cREBDApL/fvXsHJycnqKmpif3btLMyNFxgV61ahdevX2PZsmWc/IL++OMPHDhwAG3btsW7d+8waNAglJWVITQ0tNqt2HHjxuG3335DRUUFrK2tsXnzZoEOLv77W93rWhUrKyv4+voiKCiIkXh///49Nm7cWK1pHW1DwadPnyIkJESkQSJbDh8+DF9fXwGRRA0NDbRt2xa+vr6sAhUazQHHjh2Dp6cnevfuzRzr3bs31qxZAycnJ1aBysCBAzF37lxOzthAZQbOxcWFU9ZR2lZa2jYfSkpKKC0tpTqmNNCQn7CyssKWLVvQt29fgY60169fw8bGBnFxcVLP74cMVIDKVcSpU6dw4sQJJCcnQ1lZGZaWlqyee/PmTSQlJeHEiRNo3bq10GpQljUq0prwKSkpMV8qS0tLbNmyhXX7Ls15VKW0tFSsud7nz5/FPo/GuVRN68fFxUFTU1NkWp+tx8fOnTuFtEeaN28OR0dHWFpaMtmEr9myZQvrwm82F24aLrC0HFydnZ0xb948vHnzBrNnz0b79u3h6emJs2fP1tgJxa/X+Ouvv6Cvr8/a14fP1yvdZ8+eYciQIVBWVoaCggJycnLw6dMn1u7JNNqdO3XqhPz8fE6ByqdPn0Q+v0uXLqzPxdPTExMnTsSlS5ekbg6g4UhNwxkbqAyQbt++zSlQkbaVlobjcFXmz58PX19feHl5QUVF5ZsZFEorP3HhwgXcv38fQKXC9Y4dO4Su8Y8fP8azZ884ze+HClQKCgpw8uRJnDhxgvkgjxgxAitWrJCouFZeZI0BOhbt/DY0LoVwNObRq1cvREdHCwkNbd++nfVWm7TnQjutX15eLlKA7PXr19WmhidPnozTp08DAJXCbxousLQcXLW1tXH58mWUlJQwXkfW1tawt7dnvT9tYGAgMiXfpUsXgdX814wfP5711h8baBhoLlu2DKtXr4aDgwNUVFSEtkvZbEOPGzcOmzZtgr+/v0ARamRkJOvv5LZt2/Dq1StcunRJZHMAmwCBhiM1rVbcnj17YtWqVTh//jw6duwoFNSyzUTSaKXl6ji8fft25OXlif19WRXTSis/8dtvv8Hb25v5fiQkJAhs8/CzoZIIWIrihyimjYiIwMmTJ3H37l38+uuvGDFiBIYPH855hSgvcDUULC8vh5OTE6dCOBrzyMrKwrRp06CkpISMjAz07t0b2dnZKC4uxp49e1jdsGmci7q6OhITEyUK1L7G2dkZKSkp8PDwgJaWFgghSE5OxurVqzFo0CCsXLlS7HNpFn7TcIGl6eBKpHCUrkrVlPzt27dx/PhxhISEIC4ujrO6pSTQMNDU1NRkWj1FmeexuQktXboUp06dws8//wxtbW0oKioiNTUVz549g46OjkDwIy54otEcQMuRGuDmjA1U73TPtiuTRiuttI7DVYmNja32cVk2dHCVn6jqDE+bHyKjsnbtWtSvXx8DBgxA9+7dAVR+sc6dOyf0u2zTiykpKdi9e7fABdfa2pqTRoY00DAU3LZtG+dCOBrzUFVVxZEjR/DPP//gl19+QUVFBUaMGAELCwvW3UQ0zoVGWn/lypVYsmQJrK2tBRxbhw8fXqMrLs3Cb2VlZSQnJ2PUqFFQUVFh0rDFxcUiNTNEceXKFdy8eRMGBgacHFxpOEpv2LCBirrlw4cPBep2qmpCiMqmfQ0NA82wsDDOWZ4GDRoIWST06tVLotZmGs0BRkZG2Lx5M0JCQqCgoIDdu3eja9euEgUpXztjGxoawt/fX2JnbBqZGRMTEwQFBWHjxo3MsaysLKxZswaDBw9mNUZMTAxWr14t8P6YmJhAVVUV27dvZxWoyENnKR/+wr6srAwGBgYSdxx9/b4UFRWhadOmEm/jiuKHyKhUF2FXhW20fePGDcyaNQtqamro2bMnU2mfnp6OyMhImW4LTZ06FcOHD8eMGTMEIv/Q0FAcOXIE8fHxNY4xbNgweHp6ol+/fgJjXL16FU5OTkxHTW3PgwZcziU7Oxv79+/H7Nmz0apVK5SUlMDDwwPnz59HmzZtsHDhQtbZoapjpqeno169etDQ0OC0b1618Lu0tJTVinv//v3w8fHBmjVroKGhgbFjx2LSpEm4desW2rRpw0rNlZamhKurK+7du4eAgAAhR2l9fX1WjtI9e/bE/v370blzZ4H3Nzc3F2PHjmXVFhweHs4UAPI7ifj/7tmzJytFzl69euHgwYOc6kvkha1btyInJ+ebNwd4e3sjNTUVGzZswJgxYxidjWXLlkFFRYW1MzYfLpkZGq20urq6iI2NFXIKz87Oxvjx43H37t0ax6ioqEB8fLzILUYej8dJe0RSoqOjERoaivz8fJw8eRK7d+/GL7/8ggULFlT7vOvXryMqKgorVqzAL7/8ghcvXmDx4sW4e/cuGjZsiNmzZ9c4Rk38EBkV2jLEGzduxIQJE4QurF5eXggKCqIuPVwdNEz4aBTC0ZgHDTMxac8lLS0NFhYWaNGiBbPS8fDwwIkTJzBjxgw0a9YM3t7eaNasGevAFwBUVFQEuikkaQvmw6Xwe9KkSWjZsiVatGgBVVVV+Pn5ITQ0FEpKStVuP1WFltLyuXPnsHnzZoEtPHV1daxYsQKOjo6sApX69euLNHV8/vw565tsVFQUZs+ejUWLFsHIyAixsbEoKirC0qVLYWxszGoMaQ00+Z0PzZs3r7aTDWDfQZKRkSH2O8Mmkyltc4AkXRps5nHu3DkEBAQIBPOqqqrw8PCQyNOGRmaGx+NxbqU1NjZGdHQ03NzcBI7HxsYyXkg14evri6ioKKirq3PeduVCfHw8AgICYG1tzRS+q6iowN/fHw0bNsSsWbNEPu/69euYNWsWs5MBVC5YUlNTsWLFCjRr1gzr169H+/btOckx/BCBCm1SU1NFpoenT58uUuOhNqFhwkejEI7GPGiYiUl7Ltu2bcOAAQMQGBgIRUVFFBQU4Pjx4xg3bhxT6NW8eXOEhYWxClS4tgXTKvyW1gW2NhxcaThK00jJ5+fnY9KkSfjpp5+grq6O+/fvw8TEBC4uLli7di0rdVtpDTR/++035kYnTjhQEnbs2IGgoCCRj7F1gpa2OYC2I7W0zthfExgYCB6Ph9OnTzPS7suXL8eyZcuwfv16VpmZcePGISgoSKiVtiZoOw7Hx8fD19f3m28BhYWFwd3dHePHj2cUpK2srNC4cWOEhoaKDVR27tyJSZMmMVvuT548QWJiIqZPn45p06YBqKzJi4qKqgtUaNOyZUshuWmg8osmjdAZF2iY8NnZ2cHBwQGZmZn48uULYmNjBQrhZDUPGmZi0p7LzZs3sXPnTmY1yS+WGz58OPM7PXr0YJ1+lrYtWFTht7u7u9SF39K6wNaGgysNR2lnZ2fY2tqiT58+qKiogJmZGZOSZ9s50LhxY6aItWPHjsjMzGRqB9i2SUproFk1gKHhbP3XX39hwYIFmDt3rsStr3ykzZjRdqSW1hn7a2hkZj58+CDVYom243BZWZlE9Ua1RU5OjpCSM1DZCu7t7S32effv38fy5cuZ/1+5cgU8Hg9Dhw5ljmlra1c7BhvqAhURGBkZYfXq1QgMDGQUAzMzM+Hj4yPRtgAN7O3tkZ+fz6xYxo8fz5jwsU1L0yiEozGPdu3asXbRFYe051JcXCyw2r958yYUFBQELhJNmjSpVrm0KtK2BdMu/JbWBbY2HFxpOErTSMnr6+tj586d8PDwYETo5syZg+TkZNafP1rbu1y3bcrLyzF27FipgxQ+tJoDuDhS03LGppGZsbKygp2dHaZNm4aOHTsKBS3iggfa2/4DBgzAhQsXmOzDt6JNmzbIyckRqrG7fft2tT5MHz58ECiST0pKQoMGDZhCeKCyeYBrUXldoCICe3t7zJw5E6NGjWJaE4uLiyVa1dGifv36CAgIwOLFi0Wa8LFh9+7dGDVqFKKior7pPJYvXw4vLy9OZmLSnku7du3w9OlTKCkpAaiM/HV0dATEie7cucP6wltRUcFcLPlKjj179oSxsTFCQkLEPo9/jvwbmDjY6lu8fPkSCQkJiIyM5OQCC3B3cNXT00NUVBTCwsJw+fJlxumXbw7IBmlT8lXh3xCjoqJgbm6OHTt2wMDAAB8+fGCtkwNwN9CksW0zduxYxMTECKxaJaVqc4ChoSHTHGBhYcG6OYCGI7W+vj727t2LsLAwKCsr486dO+jatSvc3NxYO2MDdDIzgYGBAIDVq1cLPSaJXxBXx2FdXV1s2LABV69ehaqqqpCeEa36sZqYMmUKvL29ma2t7OxsXL58GUFBQbC2thb7vN9//x2ZmZn49ddf8eXLF1y5cgU9e/YU2Hm4du0aJ484oC5QEcnPP/+MAwcO4NKlSwLW6v379+fsWSApfBM+ZWVlKCsrM8clMeHbvn07K6+G2p4HIQRZWVlC+52S6EpIey5Dhw5FQEAAPD09ceHCBTx//hxz584VOI/g4GDWxZbStgXTLvzu3bt3tUJobKDp4KqtrS325swGaVPyVVFTU8OZM2fw/v17NGnSBDExMTh69Cjat28vsNVXHV8baE6aNAlhYWESGWjS2LaxtbXF2LFjkZCQgN9//11oZcqmIJdGc4CPjw+ysrIQFhYm5Ei9bt06Vo7UfGfsr7dG+eJ6bLcwaWRmTp8+zflaXpPjMJtAZc+ePWjVqhVSU1ORmpoq8Jgkar1cmT17NoqLi+Ho6IhPnz5h7ty5UFRUxNSpU6u1aTA1NcXatWtRXl6Oy5cvo7CwEBMmTGAev3fvHrZs2QJzc3NO8/sh2pN/NKqa8MXGxmLkyJEiTfjS09Nx7dq1GsezsbFB//79MXPmzG86jyFDhkBDQ0OsmRib2hVpz6W4uBjz5s1DcnIyM5etW7eCx+Nh+/bt2LZtG5SVlfHPP/+w0vug0RYsL3BxcP306RPzmaiu1RJgV7Oxc+dOxMXFSZySFwUXJeZly5ahpKQEGzduRL9+/XDkyBE0b94c9vb2+Omnn8QaaH491wMHDggE9pIyY8YMpKWloU+fPiLtJ9i8pjo6Ojh06JBQcJWVlYWJEyeyavmW1pGatjM2n4cPHyIsLIzZHuzatStmzZrFOjMzceJE+Pj4cFKFpuE4LG98+PABmZmZIIRARUWlxu3ksrIyrFixAvHx8ahXrx4sLCyYGrW1a9ciIiICBgYGCA0N5bR9WZdR+f/U1EpYFdrGVF9D24SvadOmWL9+PXbs2IFOnToJfWDEnQ/tedAwE5P2XJo1a4aoqChkZGSgXr16AhftLl26wMnJCWZmZqxrGKprC2azuqQJV3EzLg6uhoaGGDRoEAICAqi0WtJIyZeVlcHZ2ZmTerG0BppVobFtc/v2bfz1118SbY18DY3mAGkdqWk7Y/NRV1dn7WUliidPnoj1HWMLDcdheeLt27d49OgRkxGu+l0Tt0Bo0KAB1q9fDw8PD/B4PIHr55AhQ2BgYAAjI6O6GhVaiGoljI+Px5AhQzgXgEoKbUPBxo0bs/aeqM150DATk/Zc+HTt2lXoWNUKdUmoeiHitwWXl5fjzp07VFpT2VCTuBkbuDi4njt3DoaGhvj48SOVVksaKfnt27dzVi+W1kCzKjS2bZSUlDj7MNFoDpDWkZqWMzbf8bxp06YCLcKiYJNlsrW1hbu7O2xsbERm7tjUZdFwHFZXV6/2Ji4rr5+DBw/Cy8tLZCaUzQJBVBDLpbvza+oClf+PqA/3iRMnsHz5ck43Vq7QMBQ0NDTEgAEDOAUZNOZBw0yMxrnQQENDA3Z2dkKKi2/fvoWVlZXMLjA0xM24OLhu2rQJ06dPR8OGDam0Wjo4OHBOyR87dgyenp4CtTu9e/fGmjVr4OTkxCpQoWGgydch0dbWlnr1vmzZMnh4eMDe3l7kd4bNDVXa5gBajtRcnbGByrZgfubm6xZhaQgKCsKXL1+QlJQktQ+TtI7DVeHLPfD5/PkzHj16hLi4OJk2bmzevBljx47FjBkzONeI1QZ1gYqcQ8OEz9vbG//88w+nmzuNeURHR6Nly5a4c+cO7ty5I/AY28IxGudCA0IIQkNDkZ2dDV9fX4EUuizLvmiIm3FxcI2Pj4efnx8IIVRaLWmk5GkoMTs7O2PatGm4ceMGysvL4enpKWCgyQYa2zZLlizBly9fYGtrK/UNVdrmANqO1NI6YwOCbcHVFf++fPmS1VzCw8PZTboapHUcroo4ETQtLS3s379fYksPaXn37h1sbGwkNkWVFXWBipxDw4SvU6dOSE9PZ61CW1vzoNHxQuNcaMDj8RAaGgo3NzdYWVlh27ZtTEqc5sW9JmiIm82fP1/qv79y5Ups2rQJ2traVFotaaTkaSgx0zDQpLFtQ+OGClS2Eg8aNEiiWhA7Ozsqf5tPVWfslJQUlJWVIS0tDX5+fhI5Y2toaCAxMZH5vvF5+vQpRo8ezao4mMa2hImJCfbv3w8FBQUoKSlh165dCA8Ph7GxMRYvXsxpbG1tbdbKwDQwMTHBhQsX5DZQqev6qYaqpmjfChqGgh4eHjhw4ADU1dVFFqCy2dOVdh55eXlQUlICj8dDXl5etX+DzU2Ixrm8e/eOVWdPdairqyMxMRH16tXDwoULkZ+fj5CQELRu3RqGhoYy2/qZN28eWrZsCQ8PD8THx+PAgQPYu3cvEhISsHbtWly+fFkm8wCqNwdlawiqqanJBF7SZhDOnTsHBwcHTJkyBdHR0bC1tRVQL2YrcsiVM2fOYMeOHZy2bapSVlbGuvi1NpoDuBZtW1tbQ1dXl3HG5l9D/Pz8kJycjAMHDoh97oEDB3DkyBEAlbowenp6QkHgixcv8OHDB1y4cKHGuXz69An79u1Deno683kDKl/jlJQUnDx5ssYxqiLJe1MTpaWl2LJlC06dOiWRDhIXXrx4gdGjR0NVVRUdO3YUWmyxua5mZWVJVZDPhrqMipxDI42dk5PDiDqxTY3SmoexsTEuX76M1q1bC+1585HkJkTjXPr37w9jY2OYmZmhf//+UmVA+M9p2bIlIiIi4ObmhqlTp7K6YNOEhrgZLQdXGhkzGhkEGkrMNAw0aWzbAMKutrt27UK7du2qdaSl3RxAo2j7wYMHIjOv06ZNQ0xMTLXPNTExYaQFAKB9+/ZC2TY1NTXWhfY+Pj6Ii4vDH3/8gfv370NPTw+PHz/G69evJbKkkNZxmI+4Yloej8fKxJMWPj4+KC0tRVlZGetM7NeYmppCW1sbZmZmMDU1FSl1IC11gcr/R1QleXl5OTZs2CD0xabh4cEWGmlsGrLP0s4jMjKSqSeh0dZN41y2bt2KuLg42NnZoXnz5hg7dizMzMyE7Nqro+rNvEGDBvD398eWLVuwbNkyzvOTBBriZlzaimlnzGh1CgwcOJC1g60oaBho0gi6RLnaqqqq1uhqS7s5gEbRNhdn7BYtWgicE78DSFrOnj0LPz8/jBo1CkOHDsXq1avRoUMHODg4oLy8nNUY0joOV0XU+1S/fn3o6OjINJN/8eJFbN++HQMGDJB6jISEBMTFxWHHjh3w8/NjFoOGhoZ17cm0EFVJrqenhzdv3ojUIJAVNAwFAe5Sz9LOo+qNJzc3FyNGjODc7s31XAYMGIABAwagpKQEx48fx5EjRxAWFobu3btjwoQJGDFiRI0XwUWLFgkVfS5atAjKyso1rg5p07BhQ+Tn5+PmzZuoV68e/vzzT8YqgA1c2oppZ8xopeTPnz+P9PR0kdkQNrUyNAw0aQRd0rra0oZG0TYNZ2yAzkLx3bt3TPdWly5dkJqaChUVFcydOxf29vZYsWJFjWPQeG8ePHgg4H7+rWjZsqXEW5Ffo6KiAkdHRzg4OODq1auIj4+Ho6MjGjVqhHHjxmHChAlSn2ddjcp3wMWLFxESEiKgwjh79mzWaeyapJ7ZFJ/RmIe+vj4IIRg6dCjMzMxYV8ZXhda5VKWwsBAxMTHYsWMHPn78iEaNGmHChAmwt7fntGqTBSUlJXB0dMSlS5cE0vEjR46En58fq31zPT09xMfHS+XHcePGDabl9MaNG9X+Lpsb98qVK6tNyTs7O9c4Br8zTJT3EdtameHDhyMgIEBqV2uATtClq6uL+Ph4dOjQQaCu48mTJzA1NZVIzZVLzZ2BgQH27duHzp07w9PTE+3bt8e8efPw/PlzjBw5ktX3rqSkBLa2trh37x4qKirQrFkzxhk7PDwcLVq0kHhe0jJw4EBs3boV3bt3x7p161C/fn04Ojri6dOnMDU1xd27d2scg8Z706NHDxw+fJizFw5X9u3bh5MnT2LlypXo2LEjFBQUOI+ZmpqKhIQE7NmzB4QQfP78GQMGDMCqVaskWkgBdRkVuYeGoWBgYCD++OMPsVLPsprHlStXcPr0aRw9ehS2trb45ZdfmG0XthdPGucCVN4szpw5g7i4OFy5cgVt27bFjBkzYGZmhufPn8PPzw+LFy9mVkqAsK6EOHg8Hs6cOcN6LlxYs2YNcnJysHPnTujp6TGGc3yBLzadA1zaiqsGHzQyCDRS8nwdlalTp0o9DxoGmjTqIKR1taUNDUdqGs7YtBg4cCC8vLzg5+eHHj16wNfXF0OHDkVCQgJrY1Ia74207ue02b17N/Ly8jBy5EiRj7OtpyooKMDhw4dx5MgRZGVlQVdXF+7u7hg5ciQKCwuxcuVKLFq0CAcPHpRofnWBipxDw1CQhtQzjXk0bNiQUXAtLCzE8ePHcezYMeYmy0afgsa5uLu74+TJk/j06ROMjY2xfft2gaLajh07Yu7cuXBzcxN4Hm1dCRqcOXMG27ZtExBaGzx4MBo0aIBly5axClRoObjyzeXEef2wqVGikZJXVFTkbNRIw0CTRtAlrastbWgUbdNwxqaFk5MTXFxccOPGDVhYWGDfvn2YNGkSFBUVWavK0nhvaLqfc4GLRAEfa2trJCUloVWrVhg7diw2b94MFRUV5vEmTZrA3Ny8RmVhUdQFKnKOjo4O/v33X4lN+KpCQ+qZxjyq0rRpU7Rt2xZKSkpIS0tj3cFD41xSU1OxZMkSjB49Wmy6uVu3bozvDB/auhI0qPp6VKVt27aspd5pObh6eHjg7NmzMDQ0RNu2bVk952tatWqF169f49dff2U0c4DKPfRXr16xGsPCwgI7duzA6tWrpW4Z9fX1RZ8+fcQaaLKBRtAlrast7eYAGkXbNJyxadG8eXNs27aN+f/OnTsZ6wu22RBp35uq0HA/pwGNWplmzZphy5YtGDRokNitIx0dHamy8nU1KnLOkiVLcOrUKTRv3lwiE76qTJ48GaNHj4alpSVCQ0Nx//59bN68GcnJyZg/f36NtQW05gFUmr3Fx8fj1KlTjIjWuHHjWLc40jiXqpSUlKB+/fpSOXty1ZWgwbZt25CYmIhNmzahTZs2ACrPydnZGRoaGjKziQcq99v9/f1hZGQk9RgrVqzAw4cP4efnh8ePH8PX1xebNm1CQkIC/v33X1Z1HdnZ2TA3N8f79+/Rtm1boSwYm1Vq1foDaaFRB8FHUldbS0tL1mNL0knHxUKDizM2bV2Yjx8/wsvLC506dcLcuXMBVG7tGhoaYuXKlRIFuJK+N/IIzVqZT58+IScnBw0aNECHDh04ix4CdRkVuYerCR9AR+qZxjwGDBiA169fo2fPnnB3d8eff/4p8WqVxrkAlW3T4eHhKCgoAI/Hw++//44FCxawPkcauhI0uHz5Mu7fvw9jY2N06tQJioqKePToEUpLS5GWlobY2Fjmd2s7jfy1K7U00EjJL1++HM2bN8eECROkluOnYaApbR1EUlIS9PT0oKioiKSkJKHH2bja0mjjrwoNR2ouzti0TT7Xrl2LmzdvCnS6ubq6YsOGDdi4cSOrom1AOsdheYRGrUxZWRn8/f2xd+9eZuu3UaNGsLW1xYIFCzhtm9dlVOSco0ePUjHhe/DgARQUFKCuro4bN24gPDwcSkpKWLx4Matqexrz2LJlC8aNG8c5aud6LsHBwQgLC4OVlRV0dHRQUVGBmzdvYu/evVi2bBmmT59e4xgmJiYYMWKESF2JiRMnSiQaxYUtW7aw/l1x2RVaDq7u7u5o27Yt7O3tWc+pJgghEqfktbW1ceDAgRodeasjNDQU27Ztg5GRkdQGmu/evYOLiwsMDQ1hYWGBOXPm4NKlS0zQZWpqKvJ5fNXj1q1bM++NqMu0JKJxXNm0aRNOnDgBT09PzJs3D0eOHEFeXh5cXV0xcOBAVhYaT548qbZoVlaO40Cl6GNwcDD09PQEjiclJWHp0qW4ePFijWNwdRyWJywtLRmDRmlrZVavXo2TJ09iyZIl0NbWZq6rW7ZswbRp0zjZCtQFKnKOgYEB/vnnH87eNo8ePUJJSQm0tLQAVGYUBg0axNrbgdY8RMHfLuErztYE13MZMGAAnJ2dMWrUKIHj+/fvx/bt21kprGppaSEhIQEdO3bErFmzYGFhARMTE1y+fBlr167F0aNHWc1FHjh06FC1Dq5sjdECAgIQGRmJLl26oHPnzkLpcza1EDRS8uPHj4e7uzunzBYNO4CvYRt0PXv2DEpKSqhXr16NKqGyurnTsPKYOHEiZ2dsPi9evEBMTAxycnLg5uaGpKQkqKmpCRRvVoeenh4OHDgglAHMysqCmZkZq225QYMGYeDAgWIdh2UZeHGlpgUPm8CcXyf4tWfTyZMnsXr1ak52HnVbP3IODRO+K1euYP78+ZgxYwZzc09ISEBQUBBCQ0NZXdBpzOPBgwdYsWKFQF1HVdisQGicS3FxsUh9jB49euD169cszoSOGaC8QMvB9c6dO4xT8IsXL6SaC42U/OzZs+Hm5sYYG36dDWGTkqdhB/B10MXj8bB06dIag66qN7gtW7aIVGEtKiqCm5ubQEFobULDyoOGMzYAPH78GJMnT0bTpk1RUFAAe3t7JCQkwNXVFREREazcqnV1dbFr1y6sWbOGyfIQQhAZGYnu3buzmoe8Ow5LAo1atvLycpHZclVVVZSWlnIauy6jIufQMOGbOHEiDA0N4eDgIHA8MDAQN27cwN69e2Uyj2nTpuHTp0+YMGEC/Pz84OLigtzcXERFRWH9+vUYMWKETM5l6dKlaN26tVD7cUBAAB49eoTg4OAax5AnM8Da4tmzZxg5cqRERZ9coZGSr27FXl1KnrYdgKenJxITE7FmzRpGY+b06dPYsGEDjI2NxQZdycnJePLkCYDKIE1UoJKVlYU9e/ZIJXAoDWZmZjA3N8ekSZMEMipbt27F2bNncejQoRrHCA0NxcWLFzk5YwOVrbStWrWCj48P9PX1ceTIESgpKcHZ2RkvXrxgVZ9z7949WFtbo2XLlsyC58GDBygqKkJYWBirYGf58uXQ0tKSuE28ps9WVbiqxUrCw4cPERkZiZycHGzatAlnzpxB165dWesj+fj4oLCwEGvXrhUIwl1dXaGoqCiyNoktdRkVOYeGCV9mZqZImftJkyaxLrqjMY/U1FRERkZCW1sbhw4dgpqaGiwsLNC+fXvExMSwClSkPZeq7Zrl5eX4+++/cfPmTfTo0QMKCgp48OABbt26JeRlJA4auhLyTGlpKfbs2cN0Esny74pytm7VqhXrlfvp06elEhCjbQdw5swZoaBr6NChaNGiBZYuXSo2UOHxeIz+DY/HE9lB1rhxY5l+zmhYeQQFBeHLly9MLQQfSU0ab926haioKIExFBUVsWDBAkyePJnVGNra2oiPj8e+ffuQkZEBRUVFjBo1CtOmTWNdC7V8+XKMHj0aJ0+elMhxmI1wpKSvCVdSUlJgbm4OXV1dpKSkoKysDGlpafDz88PWrVuFtnP4VO3G+vLlC5KTk3Hz5k1oaWlBQUEBqampyM/PZ+0HJY66QEXOoVG936pVKzx8+FCogyEjI4O1wyWNeVRUVDD6GnwNlJ49e8LY2BghISGsxpD2XL72cuJvET18+JA5pq+vj+zsbFbzEKUrER8fDyUlJda6EvKCvDi4AnRS8g4ODlLVQtA20JQ26NLX12c+l1ULa78lNBypaZg0ApXXEVFbx6WlpRJJv//+++9YunSp1POQ1nE4MjJS7oQj/f39MWvWLDg4ODCBtY+PD5o0aYLg4GCxgcrXdThfbw/SMhmtC1S+A7ia8I0dOxaenp4oKipiUpr3799HUFCQRC3HXOehrKyM5ORkjBo1CioqKrh//z6AypoRUeZxNM+FZrsmX7iK/wNUtimam5t/E0Erru+LvDi4ApVBhrW1Na5fvy4yJc8GaWshaBto0gi6qgbS3xqujtS0blr9+/dHSEgINmzYwBwrKirChg0bpPIPkxZpHYflQeDta1JSUrBq1Sqh49OmTavWZJWGQSQb6gIVOacmEz42N6KFCxfizZs38Pb2xufPn0EIgaKiIuOXI6t5WFpawt3dHQDw559/YuzYsWjYsCFu3boFXV1dVvOgcS5cOHr0KNasWYPQ0FDmRgpUKpmmpKTAx8cHQ4cOrfV58KHxvsiLgytAJyVva2sLd3d3TrUQvr6+WLNmDScDTRpBlzzB1ZGaljO2i4sLrKys0L9/f3z69Anz58/Hs2fP0KJFC6xdu5b9CXFEWsdhKysrbNmyBc2bN69RyI5GZo8N9evXR0lJidDx58+fS63MTJO6Ylo5x8zMDK1btxZrwieJ/05paSlycnKgqKiITp06SbT6pzWPM2fOoEWLFujZsyfi4+MRGhoKJSUlrFy5UiJ9FS7nIi3Xr1/HzJkzMW7cODg4OAjIxGdlZWHXrl04cuQI/v77b0Y6vbah8b7IwsE1Pz+ftdkbVzQ1NZkbobS1EB8/fmQMNBMTE6Uy0AQqtxyrBl2qqqoSBV3yAg1HahrO2Hw+fPiAo0ePIi0tjXFyHzt2rExVYaV1HK5aIF2T742sMhYrV65EXl4eNm7ciEGDBuHIkSMoKyuDg4MDtLS04OvrK5N5iKMuUJFztLW1ERMTA3V1dVhYWMDOzg59+/bFwYMHceDAAURHR/9PzeNbYmNjA1VVVaFuoaq4urri1atXCA0NlcmcaLwvjo6O+OWXXzg7uD558gTr1q0TWDHzt6EKCwuFfIRqi5psFCTdgqhqoHn37l3WBpo/Er1794aDgwMnR+p+/frBzc2NMWkMCQlhTBrbt2/PyvtInhg2bBjy8vIEskNV+Z4E30pKSmBra4t79+6hoqICzZo1Q0lJCdTV1REeHs5KSLM2qdv6kXNomPDJ0zyOHz+OyMhIpKenQ0FBAZqamrC1tUX//v1ra+rUSE1NrXHVZ2FhwdqQjAY03hdaDq7e3t549OgRhg8fjvDwcMyaNQs5OTk4ffo0vL29JTsxDlQNRMrKyqQ2JuQjrYHmjwQNR2ouJo20vX6A/xONy87Ohru7u8SicTQch4HKQDgnJ0ekZxitv1ETTZs2xd69e3H16lWkpqaioqICampqGDBggFQddLSpC1TknK5du+Lff/+FpaUlVFRUkJycDGtra+Tn53938zhw4AA8PDwwfPhwmJqa4suXL7h16xbmzp2LTZs2SbSNRZPCwkLcuHEDmpqa1ab1P336VOMWU4sWLfDhwwfaUxQLjfeFloPrrVu3sG3bNvTu3RuXLl2CiYkJtLW1sXHjRly4cIF16ygNoqOjERoaivz8fJw8eRK7du1Cu3btsGDBAtZjiDLQZCsqyIW4uDjWv8vVf4stNBypuThjV+0u+fTpExISEqChoQFdXV0oKioiJSUF9+7dYy0v8LVonIODg8SicTRqu44cOYIVK1agrKyMsUrgb1f+9ttvMgtU+CgpKaG4uBj16tWDmpqaxEHKx48fUa9ePTRo0ABZWVk4f/489PT0uG+FkzrkmtOnTxMNDQ0SHR1N8vLySPfu3cns2bPJwIEDiZ2d3Xc1j6FDh5Lw8HCh46GhoWT06NGUZyye//77jwwbNozcuHGDvH37lhgaGpJu3bqR7t27k6tXr4p9npmZGYmLi6t27Li4ODJq1CjaUxaLvHw+CCFES0uL5OXlEUIIsbe3JzExMYQQQrKzs4mhoaHM5nHkyBHSo0cPsnnzZqKtrU1yc3NJZGQk6d69O9m9ezerMfr37080NDSIpaUliY2NJe/fv6/lWf8f3bp1Y/Wjrq4uszllZWURAwMDoqWlRYyMjMiQIUMEftjg7u5OJkyYQNLT08np06eJkZERuXfvHlm7di0ZNmwY67m4uLgQPz8/oeMbN24kixYtYjXGvHnziJubG6moqCC6urokNzeXlJeXE0dHRzJ9+nRWY+jr65MnT56wnrcoRowYQVxcXEhmZibp1asXuX//Pjl79iwZOHBgjdcamhQXF5PZs2cTdXV1gc+Xo6Mj+fTpE6sxbty4QXr16kWuXLlCCgoKiJ6eHunRowf5448/SEJCAqf51QUq3wEpKSkkLS2NEELI9evXybx584iXlxd58+bNdzUPbW1tkpOTI3T80aNHRFtbm+JMq2fWrFlk9uzZ5NWrVyQqKor07t2b5Ofnk6CgIDJlyhSxz4uIiCBGRkYkPz9f5OP5+fnEyMiIbNmypbamLhJ5+Xz8+eef5Pz584QQQjZt2kS8vLwIIYRkZmYSXV1d1uM8ffqUHDt2jMTGxgr9sGHcuHHk0KFDhBDC3IQIIWT//v2sb4jBwcGcb0I/EmZmZsTExISsW7eOBAcHC/2w4e3bt2T+/Plkz549pKKigtja2pJu3boRTU1NcvToUdZz0dXVFXkdycnJITo6OqzGMDAwIBkZGcx4/M9IZmYm0dfXZzWGg4MD8fPzI8XFxax+XxRaWlokMzOTEELI9OnTyYULFwghhJw8eZKMHz9e6nElxcXFhZiYmJCLFy+S4uJi8vbtW3Lu3DkyZMgQkUGhKKZOnUpcXFxIcXEx2b17NxkwYAD5+PEjiYqKImPHjuU0v7qtn++AJk2aMK1jBgYGSEtLw6BBg1gXOL18+RJBQUG4deuWSKdPtjUIXOfRs2dPJCQkCKXfL1++zNqQkMa53L59G/v370fr1q1x6dIlDBo0CO3atYOZmVm1olTTp0/HyZMnMWrUKEyYMAF6enpo3rw5ioqKcOvWLcTGxqJTp04yVQxdtGgRHBwcGHM1AwMDanoVkjJ+/Hg4OTlh/fr1GDx4MKysrPDrr78iMTER3bp1YzVGTEwMvLy8RBYo8ng8VlsdOTk5IrdnevfuzbpWRly7raQGmlzrIPjk5eUhKysLvXr1QmlpqcwF4DIyMjg7Ujdv3lzAm2jnzp0SO2Pzx0lNTRXy2Ll58ybr14WGaByN2q4GDRowW2nKysrIyMjAwIEDoaWlhcePH7OaBw3OnDmDbdu2CfhgDR48GA0aNMCyZcsYpeTqSE1Nxfr169G0aVNcvnwZgwcPxk8//YRBgwZxbhuvC1TkHBomfCtXrkRKSgpMTU1ZK9HWxjx69uyJ7du3IyUlBQYGBqhfvz7u37+Po0ePwszMTMDBU9yNgsa58PdQP3/+jBs3bmDlypUAKi9S1dWgKCgoICIiAkFBQTh48CAiIiKYx9q0aYNp06Zh/vz5MhV9u3btmtAF8lsxZ84c/PTTTyCEQFtbGwsWLMD27duhpKQkIM5VHTt27MDUqVPh4OAgdQdSmzZtkJOTI1RvdPv2bdY3RBoGmjTqIMrKyuDs7Izjx4+jXr16OHnyJNatW4fS0lIEBwfLrB1XVVUV79694zSGtCaNXzNlyhR4eHggKysLWlpaqKioYGT1ly9fzmoMGqJxNGq7+Mafjo6OUFNTw4ULF2BjY4PMzEzUr1+f09iSULUovypt27bF58+fWY3RqFEjlJWV4dOnT0hOTmbqhV69eiX1tZqBUz6mjlpnwoQJJDAwUOh4QEBAtdsUVdHR0SFJSUnffB5GRkasfqrb86ZxLjY2NsTd3Z0EBAQQTU1N8vr1a5Kfn09mz55N5s6dy2qM8vJykp2dTW7fvk0eP35MKioqOM1JWnx8fMiSJUtIeno6673k2mLXrl1it8XYoqWlxXnLZefOncTY2JicOXOG6OrqkvPnz5N//vmHGBgYkK1bt7Iaw8LCgkyYMIH8888/pHv37iQqKor4+fkRLS0t1vvtNOoggoKCyPDhw8m1a9eYMa5du0aMjIzIqlWrWI1Bg2PHjpGhQ4eSvXv3kitXrpAbN24I/LBh1apVxMTEhFy/fp05durUKTJ06FCydu1aieazY8cO0r9/f6aeYvDgwSQqKor18/Pz88mwYcNI3759iYaGBjE1NSW6urpk8ODB5OnTpxLNhQtJSUlER0eH7N69mxQWFhIDAwMycuRIoqenR9zd3WU2j61btxILCwvy8uVL5lhxcTFZsGAB6629xYsXk7lz5xInJyeiq6tLiouLSWpqKpk4cSJxcHDgNL+6QEXO0dHRYfZPq5Kbm8t6379v374kOzv7m8+DBjTO5dGjR2T8+PGkZ8+ezMXN29ubDBkyhGRlZdGYpswYOnQoU/gm6kccz549Y/3Dlh49epBHjx5xOp9JkyaRc+fOcRqDkMoAWltbm7mRaWpqktWrV5MvX76wer6uri65e/cuIYSQiRMnMsFxeHg4mTFjBqsxaNRBDB06lCQmJgqNceXKFdK/f39WY9CARlGvoaEhuXXrltDxGzdukAEDBkg1r8LCQqlrsd6/f09iYmKIl5cXWbVqFdmzZ4/E9SZpaWnExcWFTJkyheTn55M9e/YIBGJsyM/PF/hsrF69muzatUumCw9zc3OipaVFtLW1yZgxY4iZmRnR19cn3bp1EyqeFsfr16/JokWLyNixY8np06cJIYT4+fkRCwsL8uLFC07zq9v6kXNoGAqOHTsWu3btgre3t0SmXbTnQQMa56KsrCxkS79w4UK4ublJPea3Qtr2xdpwcNXR0cG///6LmTNnSjUnoFL+3tvbG0+ePIGKiorQdkDVPfTqcHR0xPz585GZmQlCCFRUVCTaJqFhoEmjDqKgoEBk+6uSkhJrN2kaSOtIXRUazthf07JlS6nn06hRI9btzKKQ1nG4KhMnThQw0FRVVf0mwnf9+vVDv379OI3RqlUrBAcHCxxzdHTkrGME1NWoyD00DAWLiopw9OhRnD9/Hh06dBD64LARSKJlbMgVGucCiDbye/v2LWsjP3lh/PjxUj2vNhxcmzZtivXr12PHjh3o1KmTUO0Mm/dm8eLFAIA1a9YIPSZJ0FRSUoKEhASkp6ejXr160NTUxPDhw1nX89Aw0KRRB6GqqoqrV68K3VCPHTuGLl26sBqDBtI6UleFhkkjF2iLxknrOFwVaQ00acPGq4kNtSVeVxeoyDm0TPhGjRolF/OgAddzoWHkJ09cuHABu3fvRnZ2Nvbt24dDhw6hY8eOGDt2rNjn1IaDa+PGjTkHrWw70KojKysL1tbWKC0tRefOnfHlyxfExMRg27ZtiIyMZOU5RMNAk4Z5np2dHRwcHJCZmYkvX74gNjYWOTk5OHnyJDZu3MhqDBrQuKF+a5NG2qJx0joOV4WGgSYtxJlO8ng8LFy4sMbn16Z4XZ3Xz3fCtzDhk+d5cIGm0eO3JjExEfPmzYOpqSkSEhJw7NgxHDhwAKGhofD19RUbOMijg2tVysrK8PTpU3Ts2BGEEIk6IGbOnAlFRUX4+/vj559/BlC50lu6dCmaNGki0F1WHTQMNGmY5128eBEhISGMtHnXrl0xe/Zs/Pnnn6zH4EpoaCguXrzI+YYqLyaNrq6u+Pnnn4XaboOCgpCVlSW0hSGKvn37YufOnejevTv09PRw5MgRdOjQAdevX4eDgwOuXLlS4xg0DDRpQMN0cuTIkdDR0YGtrS3Mzc0RFhaGFy9ewMvLC46OjtUunGqiLqPyndCkSRNmFSINfD2HnJwcuLm5Sa3nwHUeNOB6LpmZmfD19YW6ujo0NDTQuHFjWFpaonHjxti9e/d3FagEBwdj6dKlmDFjBk6ePAkATGvv7t27xQYqv/32G5N+r7rS5ArX1C8hBAEBAfj7779RXl7OZA4aNWoET09PVgHLnTt3EBMTwwQpQOX+ubOzMywsLFifS9XPwejRozF69GjWz+XDtQ4CAAYOHIiBAwdyGoMrQUFB+PLlC5KSkjjdUH///XcsXbqU83y4SrWfOHECsbGxQsfHjRvHOitoYmKCoKAggcxWVlYW1qxZg8GDB7MaozrdJlly7NgxeHp6cjKdfPLkCYKDg6Gqqopu3bqhsLAQQ4YMwefPn7Fjx466QKWO6vlaz8He3l5iPQca0BBro3Eu8mL0SIP//vsP69evFzo+fPjwajMHVe3jaVnJ00j9/v333zh8+DBWrVrFiLOZmJjAy8sLbdq0gYODQ41jtGnTBvn5+ejatavA8ZKSEolcYKUx0KRRByGPXj/yckMFgKSkJCxcuBCbNm2CqqoqJk2ahHr16uHDhw/w9/fHiBEjahyDhmics7MzbG1t0adPH1RUVMDMzIxxHHZycmI1xo0bN2BjY4NGjRoJHC8pKcGmTZtkJt5Iw3SyNsXr6gKV/wHWrl0LExMT+Pj4MCuOwMBAODs7w9/fH3///bdM5kFDrI3GuciL0SMNmjVrhhcvXgh1hmRmZgpkFGqCRhHcjh07YGpqKjL1a2dnx2qMffv2wcPDA0OHDsXq1asBVKaU69evDz8/P1aBipOTE7y8vODi4gIDAwMoKiri/v378PLygpWVFfLy8pjfFbdlIa2BJo06iK+3I/hBX8OGDaGoqIiSkhIoKCigZcuWMgtUaDtScyEwMBDGxsbo3r07YmJi0LRpU5w+fRoHDx5ESEgIq0CFhmictI7DWVlZTH3c1q1boa6uLvRdTU9PR0xMDFMnVdvQMJ2sVfE6Ts3NdXwX0NBzoAENsTYa5yJPRn5cWb9+PRk/fjxJS0sjenp6JC0tjVy4cIEYGRmx9ug4fPgw6d69O6OJUVWXxdjYmPVcaPiWaGtrM4JvVd/f3NxcoqWlxWqMrzU++D9V/1+T/gcNA00a5nnx8fFk3LhxjJcTIZWeNpMnTyZ//fUXqzFo8c8//xAjIyOioaFBcnNziYeHB2sBPZrwjSYJIWTmzJlk5cqVhJBKj6ju3buzHoeraByfnJwccvLkSXL69GnGlLM6zp07J/RZFPXj5uYm8VykhYbpZG2K19VlVP4HoKHnQIPGjRtz9iihcS4mJibYv38/FBQUoKSkhF27diE8PBzGxsZMe+z3gr29PfLz85mV9fjx40EIweDBg2Fvb89qDBqZEIBO6ve3337D/fv3hYpVL168KKThIw4axb8FBQUi6wyGDh3KqtASoFMH4e/vj02bNgm0BXfq1AkrVqzA/PnzYWlpyWocrsTHxyMgIADW1tbYtWsXgMrWaX9/fzRs2BCzZs2SyTwAelLtc+fOxdy5c/HmzRvweDyJtgWByu0ZR0dHXLp0idnG5vF4GDlyJPz8/MRmJgYPHox///0XFRUVzLWoVatWzOM8Hg+NGzeWeD5cWL58OZo3b44JEyZI3d3Vs2dPnDx5EmVlZWjZsiWioqKwd+9eKCkpcf6c1gUqcg6Nug4aeg405kFDrI3GuQCV1fZ8vqWRH1fq16+PgIAALF68mOksUVNTQ5cuXYTeI3HQKoKjkfq1sbGBl5cXXr58CUIIrl69in379uHvv/9mZYwGgMp7ScNAk0YdxLt370Rqv1RUVODjx4+sxqBBWFgY3N3dMX78eKaV2MrKCo0bN0ZoaCjrQOXZs2e4e/euSC0atsFb7969sWHDBvz888+oV68eBgwYgLS0NPj4+EhVZyGtaNyaNWuQk5ODnTt3Qk9Pj9k+Wr16NQIDA6v9vPK3HM+ePYtff/2VuqaRpNAwnQSAdu3aMf/u0qULNfG6ukBFzqFR10FDz4HGPGiItUl7Lq6urqznSau4VBYYGxvj4MGDUFZWhrKyMnO8oKAAY8aMwfXr12scg1YRnJ2dHWxtbdGiRQuMHz8eW7duhampKZ4/f46RI0eyGmPChAn4/Pkztm/fjo8fP8LDwwOtWrWCvb09zM3NWY1RWFiI0NBQZGRkiLwhsvmc0TDQpFEHwXd8Xr9+PZNlysrKgpeXF+vOEhrQcKSm4YwNAKtWrcKqVavw33//YcOGDWjatCkOHz6MBg0aSPQ95woNx2GaHXdckNZ0kraInjjqAhU559q1a9i1axcrd2JxtGvXDnFxcQJ6Dubm5hLpOdCYB8BdrE3ac3n69Cnzb0IIbt68iTZt2uCPP/6AoqIiHj58iIKCAhgbG3OanyxISEjApUuXAFSuUL29vYVW3c+ePWO9SqNVBPd16veff/5BdHS0xKnfKVOmYMqUKSgsLAQhROLtQicnJ9y/fx/9+vWTWudn//79aNOmDR4+fIiHDx8yx3/55RdcvnyZ+T+PxxMbqCxYsAAKCgrYs2cPtm7dCqBS+t7JyYl1m7SnpydsbGwwdOhQNG/eHIQQFBcXQ1tbm3H9lgU0HKlpOGMDtSvVLgk0HIflhdmzZ8PNzY3RyVFUFAwNxFlX0BbRE0ed4Juc069fP0RFRaFz585186CEv78/nj9/LrCP/OXLF3h4eIDH48HHx+cbz7B6nj9/DmdnZxBCkJSUBF1dXYGAgr/HbW5uzkrG++bNm7C1tcXixYsxfvx4DB8+HG3atGEyIZK+Hu/fv0dOTg4UFBTQuXNn1rL1tNDT00NISIhcbedJWwcBVH42r1y5goyMDPB4PKirq6NPnz4y3S4IDQ3Fvn374OrqimXLliEoKAh5eXkICgqCtbW10BaZKLp3747jx4+zFsurjtqSapeEbdu2ITExEZs2bUKbNm0AVNatODs7Q0NDg5osvSyozhqBrU4ODRE9sXOoC1Tkm3Xr1uHdu3ec6jrkaR60hOe40KtXL+zduxeqqqoCx7OzszFx4kTcunVLZnPhiqWlJbZs2SJRK7IoCgoKUFZWhg4dOiArK0sgE8J2pVpeXg5fX18cPHiQqWNq1KgRrKysWLUV02L48OEICAgQqEOqgzuBgYGIjIzEp0+fAFRqb0ydOhVubm6sDAsnT56MBQsWcN6yqkmv58yZM6zGIYTg0qVLSE9Ph6KiIrp06YK+ffuyvr5ZWFjg/v37qFevHjp16gRFRUU8evQIpaWlQnUnNKwhapNnz55V+zibLSo9PT3ExsYK1WQ9evQI48aNw507d6SeX93Wj5xDy4RPHuYhL8Jz9evXR15enlCgkpWVJRcGYZLA143Jy8tDVlYWevXqhdLSUom2S2g5uAYGBuLEiRNwc3MTKC4MDg5Go0aNMG/ePInHlIbly5fDy8sLDg4O6NChg9BNVJb+KT8SXB2paTlj0+hSKyoqgo2NDR48eIBmzZqBEIKSkhJoamoiPDxcpMvz19BwHAb+b/GWnZ0Nd3f3b7J4o1ErQ6N4XBx1gcp3ANe6DlpwnYe8CM+NGjUK7u7usLe3FyhyDA4OlkhiXR4oLy+Hk5MTjh8/jnr16uHkyZNYt24dSktLERwczOpGQsvB9fDhw/D19YWRkRFzTENDA23btoWvr6/MAhVCCLKysoQ6UYiM/VN+NLg6UtNyxqbRpbZu3Tp8/PgRcXFxTID+8OFDLF++HAEBAfDy8qpxDBpbO18v3hwcHGS2eDM2NsaBAwfQsmVLDBkypNqtRDYZIRrF4+KoC1TkHHnpQKExD/6HtuoXQlFREQsWLMDkyZNZjeHj4wMrKyshJVZJWLZsGT5+/IhVq1YxTtA//fQTpk+fzsolVJ7Ytm0bHj58iMjISCYQsLS0hKurK/z9/eHp6VnjGLQcXD99+iTyfenSpQvevn3Lagxx8OsP2LQG+/r6ok+fPpg8ebKQNHkd0kHDkZrW9geNLrVz585h8+bNArUZ6urqWLFiBRwdHVkFKgB3x+FvuXgbP348810fP34855onGsXj4qgLVL4DuNZ1iGvZ4/F4qF+/Ptq3b4/hw4fXWCjLdR40xNpiY2MxY8YMVr8rjgYNGsDb2xvOzs7IyckBj8dD586dv7ttH+D/zMSq6kf07t0ba9asgZOTE6tAhZbh3Lhx47Bp0yb4+/szNxJCCCIjIzF+/HhWY6SkpGDlypVIT08X+VlhM5fCwkK4uLiwFoirTbjWQYiisLBQQCBMFvj4+EBDQ0OkI7WPjw8rR2r+9gIXZ2yATpfa58+fmQLYqrRp0wYlJSWsxqjJcZhNoEJj8SYtVTNCkgg7VgdXET1x1AUqcg6Nuo7y8nIcO3YMbdq0gba2NgDgwYMHKCgogI6ODq5fv44dO3YgLCxM7IqVxjxoiLUNGjQIe/bsgZ2dHZo0acLqOUClkZmenh4UFRWRlJQk9PiDBw+Yf7PdK5cHCgoKRGYxlJSUWGcxaBnOFRUV4dy5cxgyZAi0tbWhqKiI1NRUPHv2DDo6OgKaC+Jqmvz8/KCgoIAVK1bAz88PLi4uyM3NRVRUlEjzRVH07t0bt2/f5hSo0BA4pFEH8e7dO2zYsAHTp09Hly5dYGtri2vXrqFTp07YuXOnzIIxGo7UhIIzNkBHr0dTUxPR0dFCXjrR0dHQ0NBgNQYNx2F5UQ2Xlri4OIwcORINGjSo0UyTiy9VXaAi59BIDTZs2BDDhg3Dhg0bmJVueXk53Nzc0KxZM3h4eMDf3x9BQUFix6MxDxrCcy9fvkRCQgIiIyNFrmTE3UAsLS2RmJiI1q1bw9LSkukW+JrvrYZBVVUVV69eFdIpOHbsGLp06cJqDFoOrg0aNBCqY+rVq5dEgV9qaioiIyOhra2NQ4cOQU1NDRYWFmjfvj1iYmJYGc717NkTq1atwvnz50VqQrCpLaAhcEijDsLPzw83b97EjBkzcPr0ady8eRPr169HQkIC1q9fz6nlUxJoOFLTcMYG6Ei129vbw8rKCnfu3GGuZ8nJyXj48CFjEVATNByHaSltfytcXFwwYMAAtG7dulqBO0kE/UQ+v649Wb7p3bs3oqKi0KVLF+jp6eHIkSNMC+nkyZORnJxc4xg9e/bE3r17hW5cmZmZMDc3R1JSEh49egQzMzOxrbk05gEAHz58EBBr69q1q0TCczWlmMXdhJ49ewYlJSXUq1ePSiuevHDu3Dk4ODhgypQpiI6Ohq2tLXJycpjV6p9//inyeVUdXK2srBAcHCzSwXX9+vW4e/durZ8HHx0dHZw4cQJKSkpYtmwZ9PX1YWFhgSdPnmDy5Mm4evVqjWMMGTJE7GM8Ho9VNkRXV5ezwGGfPn2wefNmoUDv+vXrcHR0RGJiYo1j9OvXD1u3boWenh5cXFxQVFSEHTt2ID09HdOmTROZHawNTp8+jXXr1ol0pJ48eTKGDRvG/K64miZTU1PY29tj6NChAteQ06dPw8/PD//++69MzoXPvXv3EBYWhoyMDBBC0K1bN8ycOZPJOtfE1q1bkZuby8lxuKCgAFZWViguLkZRURFUVFSYxduePXu+q2tRbVKXUZFzaKQGFRUV8erVK6FA5eXLl8ze6JcvX4RWnrTnAVQainFRKZS20r7qF/5H+vIbGRlh8+bNCAkJgYKCAnbv3o2uXbtWG6QAlZ0T8+bNY95/ca/rhAkTamXe4lBWVkZycjJGjRoFFRUV3L9/HwBQXFwsUg5fFDRueDQMNGnUQbx//x5KSkoAgMTERMyePRtAZZZUlBR9bcGvYVi0aJFQHdO6deuwbt26Gmuanj59KnJbRV1dHS9fvqz279PuUAEAbW1tBAUFsfpdUYwYMQLm5ubo0aMH2rZtKzQnNvOgoRr+v0BdoCLn0EgN/vnnn/Dw8ICnpyd0dHRACMGdO3ewevVqGBsb4/3799i+fTu6d+9eq/OgBb/LJScnB5s2bcKZM2fQtWvXarcoZOVJIWt2796NUaNGISoqSqLnyaODK1C5RcevG/jzzz8xduxYNGzYELdu3YKurq7Y5+Xl5UFJSQk8Hg95eXnV/g02XUw0DDRp1EGoqqri/PnzUFJSwsuXLzFw4EAAlb45X+sA1SY0vhNcnLFpdKiwKfjlw2ZBRMNxGOC+eKPF58+f8fr1ayYArqr2O2bMGJHPqSlorAqXrq+6rR85h0Zq8OPHj3BycsKpU6cEPlQjRoyAt7c3rl27Bm9vb+zcuVOslLK8pChTUlJgbm4OXV1d3L59G8ePH0dISAji4uKwdetWsZLxVTufavKkYNMpIy/07NmTMSWUlmfPnsmFgyufM2fOoEWLFujZsyfi4+MRGhoKJSUlrFy5Uqz8uoaGBi5fvozWrVtDXV1d5LlI0sXk6uqKo0ePonnz5lILHN6+fRtWVlZQV1cXWQfBJsC/cOEC7OzsUF5eDlNTU/j7+8PPzw9RUVHVft7lkYMHD2LDhg1YsGABAgMD4ebmhtzcXMYZm63pJFD5XhYVFTHOx/fu3YOmpma1QeXXW4LPnz9H/fr10aFDBygqKiI3Nxfl5eXQ0tLC3r17a5yDtra2VI7D8rhounz5MpydnZnt4Ko0bNgQt2/fFvm84OBg5rtWVFSEf/75B0ZGRkzjwv3793Hq1CnMmjWLkzp1XaDyHcC1roPPkydPkJaWBgUFBXTr1o256JeVlbHaY6U1Dy7MmDEDOjo6cHBwENjn9vPzQ3JyMg4cOFDjGLXpSSFrbGxs0L9/f8ycOfNbT4UK0urk3LhxA/r6+lBUVMSNGzeq/V02xcE1ufCy1RXiWgcBVPoEFRQUMIuIe/fuoUmTJjLNqNBwpAaAffv2Yfv27cjPzwdQ2Tk0e/ZsiT6/ubm5mD17NoyNjeHk5ASgsh6oTZs2TFBbExERETh//jwCAgKYLb53797ByckJampqcHR0rHGM8ePHw93dXeI6JnlcNJmZmTGNBkuWLIG/vz/y8vKwefNm+Pn5wcTEpMYx5s+fD319fWZ7ks/ff/+NM2fOIDIyUur51QUq/0O8evVKZKulrCTFaYi19ezZE/v370fnzp0FApXc3FyMHTtWbORfldr0pJA1S5YswalTp9C8eXN06tRJqAtK1ttYXHVDevTogcOHD1MxrvuRyMrKQnp6OurXrw8VFRWZyqsDlaKA1TlSSyoIKa0zNgDMmzcPFRUV8PHxYZybCwsL4ezsjEaNGmHz5s01jtGvXz+EhYUJZZDT09NhaWmJ69ev1zhGQkICgoKCJHYcroq8LJq0tbURExMDdXV1WFhYwM7ODn379sXBgwdx4MABREdH1ziGrq4uDh8+LJTdffz4McaMGcOpKL+uRuV/gFu3bsHV1RW5ubkCx2UtKU5DrK1+/foiCxGfP3/OWoW0Nj0pZE3jxo05tf3RhIZuCF8nZ9GiRd+8mFAagUPadRCfPn3C0qVLcfbsWWaBwePxYGRkhKCgIKm7TSQlOTmZqiM1F8G6mzdvIiYmhglS+OM5OTlh2rRprMYoLy/H+/fvhY6/fv2a9RYoP+uyatUqocfYXldPnDiB2NhYoePjxo2T6fdaQUGBacNXVlZGeno6+vbtiz59+mDdunWsxvjll19w9epVoUDlzJkznEsD6gKV/wF8fHzQtm1bODk5Sa0JQQNpxdqqYmJigqCgIGzcuJE5lpWVhTVr1rB2Za1NTwpZY2hoiAEDBnB2T6YBDd0QaXVyaCOtwOGhQ4cE/l9dHQSbQGXjxo24d+8etmzZAgMDA1RUVCApKQk+Pj4IDg7G0qVLqZxvTbRr107q7yxtFBUV8e7dO6HjHz58EKmNJIohQ4Zg5cqV8PDwgJaWFgghSE5OxurVq2FqaspqDBqfRXlZNHXt2hX//vsvLC0toaKiguTkZFhbWzNbdGywsbHB6tWrcfv2bXTv3p25rp4+fRr+/v6c5le39fM/QPfu3REXFyfTPW1RWFpaMjLt0t6ESkpKYGtri3v37qGiogLNmjVDSUkJ1NXVER4ezrpLJSQkBHv27GHaIpWUlDB79uzvzpTQwMAA//zzD2txN3HQcHCloRsirU4ObebPn49WrVoxAodHjhyBkpISnJ2d8eLFC1YChzTqIPr374/Vq1cLGD0Clfo5Xl5eOH/+vFTnJylnz55FSEiIXDhSOzk5ITs7G4GBgcw28pMnT+Dk5AQlJSUEBgbWOEZJSQmWLFmCxMREJoNCCMHw4cOxfv16mWWqtm3bhrCwMFhbW4tcNLHNEHHlzJkzWLx4MTw8PDBo0CD8+eef6NOnD/777z/o6Oiw2k4DgKNHj2LPnj3477//wOPxoKGhgTlz5nAu+q4LVP4HGDZsGPz9/SUq4PsaGvUlNG5CpaWlaNKkCa5evYrU1FRUVFRATU0NAwYMELp4soG2J4WsmTx5MmbMmMFaOlwUX2cPjh8/jg0bNuDSpUsSObj27NkTMTExQsFNVlYWzMzMZCocxxUaAoc06iD09PQQFxcnlE5/9OgRxowZg3v37kl2YlJy5swZODs7C22XfAtH6tevX2PmzJnIyMhgthPfvXsHTU1NbN++HW3btmU9VnZ2NuMGraGhUWObdG3oucjLounBgwdQUFCAuro6bty4gfDwcCgpKWHx4sXf/PpYt/Uj59AwFJw/fz58fX3h5eUFFRUViU3AADr1JTRWw+PGjUNQUBD69u2Lvn37Sj1OYWEhcnJyGBG7qpoB8+fP5zxPWaGuro5ly5Zh165dIotp2RQ50nJwpaEbAkink1MVcQFx1e/MwIEDq7340hA4pFEHoaamhhMnTmDu3LkCx48fP16jiShNatORWhJnbABo3bo1YmNjceXKFWRkZAgUbUvaYv91YXJNc6HtOAzUnpGfJCxatAgODg5M1t3AwECqeqSHDx8KGIpWva76+PhIPb+6QEXOoWEouH37duTl5YktzmKzGqJRXwJwvwl9+PBBZNeBJBw5cgQrVqxAWVkZ4/nDv+D89ttv31WgkpOTw7zvNal7ioOWgysN/5SqOjkpKSkoKytDWloa/Pz8WOuGJCUlISkpCfXr12du5o8fP8bHjx+hpKSEoqIi/PTTT/jrr7+EvGv40BA4pFEHMX/+fCxYsABpaWkCr+np06cREBDAagwa0HCkpuGMzUdBQQEDBgzAgAEDZDqX2nAc5sPXhPkWXLt2TWiRIynh4eFM4W1VLzUej8fJigKoC1TkHhqGgjRuvDSKHGnchKysrGBnZ4dp06ahY8eOQkELm5bAHTt2wNTUFLa2tjA3N8fu3bvx8uVLeHl5Ub/41DZssx3VQcseQU9PD1FRUQgLC8Ply5cZ3RAPDw/W247+/v6MOJSenh6Aym3HJk2aIDg4mNVnRFtbGxUVFQgKCmJqQ968eYNly5ZBV1cX8+bNY743ISEhIsegYaC5cuVKLFmyBNbW1kJ1EM7OzqzGGDx4MDZt2oTQ0FCcP3+eeU2DgoIE/HVqGxqO1DScsQE6zta05vKjMH78ePj7+2PhwoVQVlaWqkYnKioKs2fPxqJFi2BkZITY2FgUFRVh6dKlMDY25jS/uhoVOYeGoSANaNSX0BBrE6ecC7BvCaxaXGxpaYnZs2dj4MCBOHXqFHbs2CHUuSHvSCN9XRUHBwfUq1cPGzZsQI8ePXDkyBE0a9YMdnZ2aNmyJetCOhrQ0Mnp27cvwsLChLabHj58iJkzZ+Lq1asC3x9x0BI4lLQOQh4JDQ3Ftm3bYGRkJLUjtZ6eHuOMPWnSJDg7O6Nnz56IiIjAhQsXEB4ezmou8+bNq9bZWpZz+VEYNmwYcnNzxW5lsbmuamlpISEhAR07dsSsWbNgYWEBExMTXL58GWvXrsXRo0elnl9dRkXOkdZQ0NXVFe7u7mjatGm1Kps8Hg++vr41zoNGfUlKSopIzYFp06YhJiaG1RinT5+Wqmi2Kg0aNGBWDMrKysjIyMDAgQOhpaWFx48fcxpb1tQkfc0mUOGSPaCtG0JDJ+fz588oLy8XOv7p0yd8/PgRQOVnQFQWqSq0PFgkrYOIi4tjPbastDaio6PRsmVL3LlzR0gQkcfjsXpvKyoqmEJXvlZHz549YWxsLDazJYpr165xdramNZcfBRpZ98aNGzOLpY4dOyIzMxMmJiZQVVWt0bG+JuoCFTlHWkPBp0+fMhfip0+fUpkL1/oSGjchBwcH+Pj4VJtZqQktLS3s378fjo6OUFNTw4ULF2BjY4PMzEypCo2/JYGBgfjjjz/ESl+zgYuDK23dEBo6Of3794eXlxcCAwOZbpmcnBz4+Pigf//++PLlC6Kjo9GtWzdW40mLtHUQX6uU8vf7GzZsCEVFRZSUlEBBQQEtW7aUWaBCw5GahjM2QMfZmtZcaMBVzZkG48eP5zyGvr4+du7cCQ8PD/zxxx84cOAA5syZg+TkZO4aPKQOuebDhw/Ezs6OdOvWjairqzM/Dg4OpLi4mJw+fZoMGDCApKWl1eo87t+/T7S0tMj06dOJpqYmyc3NJe7u7kRTU5OcP3+e1RgrVqwgs2bNIm/fviW6urokNzeXZGZmktGjRxNXV1dWYxgYGJDHjx9zORWSlJREdHR0yO7du0lhYSExMDAgI0eOJHp6esTd3Z3T2LKme/fuzHtvbm5Orly5Qggh5MCBA2Tq1KkynUt4eDixtrYmr169Yo69ffuWzJ07lwQEBLAao7i4mEyZMoVoaGiQbt26kZ49exJ1dXUybtw48ubNG1ZjvH79mkycOJGoq6sTAwMD0qtXL6Kurk6mTJlCXrx4Qc6dO0d0dXXJ9evXpTlN1lhYWJAJEyaQf/75h3Tv3p1ERUURPz8/oqWlRRISEliNER8fT8aNGyfw/c7JySGTJ08mf/31V21NnRBCyLNnz0hFRQXz7+p+2BATE0O0tbVJfHw8yczMJJqamsTT05OMGTOGzJo1i/W81q5dS9zc3Mjnz5+lOi+acykvLyf5+fnM6/D06VOSnZ1NDh8+zOr5b968IWZmZsxnvUePHqRbt27EzMyMvH37VtrTk4rz588TS0tLYmhoSJ4+fUo2b95M4uLiWD//v//+I4aGhiQ0NJSUlJQQIyMj0qNHD/LHH3+w/v6Lo65G5TuBhqFgenq6UPEZ24psGvUlNMTaQkNDcfHiRcZf4+tiWrbCUwUFBSgrK2O0MaKjo6GkpARLS0uZiT3RQE9PD0ePHsVvv/0GV1dXqKurw9raGs+ePcP48ePFGvTVhoMrDd0QWjo5hBBcv36d+c6oq6szmb83b95Aw0wqOwAApzxJREFUUVGx1lWaadRB8Itpv9ay4bfRX758ubamT92RGpDOGftraDhbVzcXDw8PVpLv0joOf30u9+7dQ0BAgJCas76+Pis1ZxokJiZi3rx5MDU1RUJCAo4dO4YDBw4gNDQUvr6+rDN3Hz9+xPv379GqVSu8evUKR48eZSQ0uFC39fOd0KhRI3Tv3p0JMvLy8gCwuzGfPXsWLi4uKCkpEaqQZ3uRoVFfwuPxsHfvXk43oaCgIHz58oVRuOUj6QWzXbt2zL9VVVWxYsUKVs+TN6SVvq56Ia7JwZUtNHRDaOnk8Hg89OnTR2QrMZs2UBoChzTqIN69eyeybbSiooKpt6ktIiMjGWsGGuaWX7+mo0ePxujRo6Uaa9SoUZznU9URmD+X8vJy3Llzh1WgQmPb9dy5c9i8ebNAcK+uro4VK1bA0dFRZoEK345hxowZOHnyJIDKbfamTZti9+7drAOVhg0bMovHNm3acNbe4lMXqMg5NAwF/f390bdvXyxYsEDqVSSN+hIaN6H/tWr8mpgzZw4WL16M+vXrY9SoUQgODsacOXPw33//Vav3UfVC6urqihkzZoh1cGULDd0QGjo52dnZ8Pb2ZtpXv4bNd4aGwCGNOojevXvD29sb69evZzIOWVlZ8PLyYl2zIy1Va89omBHSeE0ByZ2aRaGhoQE7OzssWLBA4Pjbt29hZWXF6jOSmZkJX19fqKurQ0NDA40bN4alpSUaN26M3bt3CwRC4vj8+TPatGkjdLxNmzYir7e1xX///SeyLXv48OESFczXFnWBipxDw1Dw6dOnCAkJ4bQ6pFHkSOMmVPWCWVhYCEVFRVauvD8qJiYm2L9/PxQUFKCkpIRdu3YhPDwcxsbGWLx4MasxaDm40tANoaGTs2rVKrx+/RrLli2T+jtDQ+DQ0tKSUen9888/MXbsWDRs2BC3bt2Crq4uqzE8PT1hY2ODoUOHonnz5iCEoLi4GNra2li5cqVU8/pW0HTGlsbZuiqEEISGhiI7Oxu+vr4C20dsqyFoOA7TUnPmSrNmzfDixQuhe0RmZqZcGJ7WBSpyTkZGBmdDwU6dOiE/P59ToOLs7AxbW1v06dMHFRUVMDMzY+pLnJycWI1B4yYEVKahd+7cidevXwOoXH3Y2NhQSzN+bzRp0oRZfRkYGCAtLQ2DBg1iLcVNy8GVnybmohvCN5RbvXq10GNsM4h3795FdHQ0NDU1Wf/dr6EhcDhp0iS0bNkSLVq0gKqqKvz8/ATqINjQrl07HD58mJGL5/F4UFdXR58+fajIt8sSWs7Y0jpbV4XH4yE0NBRubm6wsrLCtm3b0KpVK+YxNtBwHKah5kyD0aNHw9fXF76+vuDxeCgtLcXFixexevVqTj5itKgrppVzaBgKXrhwAf7+/nBwcICKiopQ8RmbOhcaRY40xNr27t2LNWvWwMLCAr169QIhBElJSdi7dy88PDwwceJEVnP5Ubhy5Qrmz5+PGTNmwMHBAQAwZcoUpKenIzQ0lFWhdG07uEri5fLkyZNqP09sagcGDRqE0NBQqKmpSTTPqtSmizO/DoJtYC6O/Px8tG/fntMYsoTWa0rD2VpdXR2JiYmoV68eFi5ciPz8fISEhKB169YwNDRkdS2i5Th87949hIWFISMjg1EenjlzJqdrvqSUl5fDxcUFx44dA/B/LfGDBw9GUFAQ50w4V+oCFTknNjYW+/bt42QoqKmpyQjxSFuAOnToUAQFBXFapdK4CQ0fPhzTp0/H9OnTBY5HRUVh7969iI+PZzWXe/fuYe3atahXrx6cnJxkelGgycSJE2FoaMgEKXwCAwNx48YN7N27l9U4NBxcaXi5TJw4kbNOTmhoKJKTk+Hv7895i4EL4uogXr16hQEDBrB6PZ48eYJ169YhPT1dSHm4sLAQqamptTJ3eYaGs3XVjqaysjK4ubnh3Llz8PHxgaOjI+uifHl2HJaGx48fM1pKampq6NKli4AX2reibutHzqFhKEijAJVGfQkNsba8vDwMHDhQ6PiAAQNY7wvz55KXlwdCCBwdHXHmzBmp5/QtyczMFKgb4jNp0iSJfIBoOLjS8E958uQJGjduLPHfrsqVK1dw8+ZNGBgYoHXr1kIZRLZbDFwFDmnUQXh7e+PRo0cYPnw4wsPDMWvWLOTk5OD06dPw9vZmNQYNaDhSA9xfU4CON1XV179Bgwbw9/fHli1bsGzZMtbzkNZxmLaaMw2MjY1x8OBBKCsrMyKJQKWMw5gxY1hJC9QmdYGKnEND2phGxT6N+hIaN6Fff/0VKSkpQvU29+/fF1k9Lw5CCHOx+p6Tiq1atcLDhw+F6kAyMjKkKiTl4uCamprK6IYcOnQIampqsLCwQPv27RETE4MRI0bUOIatrS3c3d056eT06NGD1TZTddAw0KRRB3Hr1i1s27YNvXv3xqVLl2BiYgJtbW1s3LgRFy5ckMjdmgs0HKlpvKYAHWfrRYsWCV2LFi1aBGVlZdZyC9I6DtNWc5aWhIQEXLp0CQDw7NkzeHt7C53Ps2fPvnk2Bajb+vmf4P3794iIiBDrNspGI4FGfQkNsbaIiAhs374dS5YsESg+27x5M6ysrLBw4cIaxwCA27dvMxkYJycnZqzvjU2bNiEmJgb29vZMEeH9+/cRFBSEcePGYenSpTKbi46ODk6cOAElJSUsW7YM+vr6sLCwwJMnTzB58mRcvXq1xjFobFPSgJaBJtc6iO7du+PUqVNQUlKCg4MD+vXrh0mTJiEnJweWlpa1KvhWlYCAANy5c6dGR+rCwkKxGjE0XlOgcpVvZWWF4uJiFBUVQUVFhfGm2rNnD6stZBqsWbMGL1++5OQ4HBERgfPnzyMgIIB5Xd+9ewcnJyeoqanB0dGR9rQZnj9/DmdnZ6bOT1dXV6C0gMfjoXHjxjA3N2cdRNYWdRkVOYS2oaCHhwfOnj0LQ0NDRoBKUmiYAdIQa7OyssKzZ8/g6+uLL1++gBACRUVFTJ06VaLsk56eHuv6DXlm4cKFePPmDby9vfH582fm9eCLUMkSGroh0m5TbtmyBTY2NmjUqFG1qXUej8cqmKUlcAhUZqkiIiLg5uaGqVOnwsfHh9Xzgcq6rfT0dCgpKaFz587Md6SiogKlpaWsx+HKgQMHEBYWJtAF1rJlSyxfvhwzZ86EnZ0dbGxsYG5uLnYMGq8pIL031ZAhQ1hlB3g8Hqut4AsXLiA3N5cRSPsaNteznTt3Cr2uzZs3h6OjIywtLWs1UFFSUmIWqZaWltiyZYtctCKLoi5QkUNoGwqeO3cOgYGBMDIyknoMGvUlNGpl6tWrB3d3dyxZsgTZ2dkAKpVlOZtefacoKirC09MTy5cvR05ODhQVFdGpU6dvUqVPQzdE2m3KQ4cOYdq0aWjUqJFQar0qbAMVGgKHNOogxo8fDycnJ6xfvx6DBw+GlZUVfv31VyQmJta6qWJVaDhS03hN+UjjbD1+/Hiq2xg0tuVpqDnTgF/PlpeXh6ysLPTq1QulpaWczR9pUReoyCFViyAlKYgUR7169TjpsAB06ktoiLV9/PgRXl5e6NSpE+bOnQugcqVkaGiIlStXflc+PTRp0qQJtLS0pH4+oeDgSkM35NOnT9i3b59AlwtQ2eKckpIidvVa1d2XhtMvDYFDGnUQc+bMwU8//QRCCLS1tbFgwQJs374dSkpKAjUatQ0NR2oarykA5Obmwt/fHxkZGSIzdeKKpe3s7Fj/DTbQcBymoeZMg/Lycjg5OeH48eOoV68eTp48iXXr1qG0tBTBwcHftHsOqKtR+S7gaijo7u6Otm3bwt7eXuo50DID5CrW5unpicTERKxZs4YJfE6fPo0NGzbA2NiYtQJqHf9HUVERbGxs8ODBAzRr1gyEEJSUlEBTUxPh4eGclX8l0Q1ZuXIl4uLi8Mcff+D+/fvQ09PD48eP8fr1a8yYMUOi9/fVq1cia7LYfFZpGGjSQFTtAFAZ0J0/fx5//vmnTOZRWFiIuXPnIiUlRUAhV0dHB8HBwXjw4AEcHBwQEhIiNitG6zWdOnUqXr58iREjRojMHLItQH348KFAKz2/7fv+/fust+cuXLjAiBzu27cPhw4dQseOHTF27FhWzy8pKcGSJUuQmJgopOa8fv16mS28Nm3ahBMnTsDT0xPz5s3DkSNHkJeXB1dXVwwcOBCenp4ymYc46gIVOYeGoWBAQAAiIyPRpUsXdO7cWejDz8Y7g0aRIw2xtv79+yM4OBh6enoCx5OSkrB06VJcvHixxjFyc3M5qfT+aNBycKWhG9KvXz+4ublh1KhRGDp0KEJCQtChQwc4ODigffv2rMwjafhjSStwSLsOQkNDA4mJiUy3EJ/U1FRMnToV9+7dq3EMWnB1pKbljK2rq4uoqChOmk7h4eFMMT1f3Iz/7549e7LKZNNyHAbASc2ZBsOGDYOnpyf69esnUOh89epVODk5Md1B34q6rR85h4ah4J07d5iOkBcvXkg1Bo36koiICDg7OwuItQ0dOhTKysqIjIxkFaiUlpaKXOG3atUKb9++ZTWPadOmYevWrd+tyBttaDm40tANeffuHdOB1aVLF6SmpkJFRQVz586Fvb09q0CFhj+WtAaaNOogIiIimJsoIQSGhoYif0/Wn1+ujtS0nLE7deqEDx8+SP18oFIgcvbs2Vi0aBGMjIwQGxuLoqIiLF26FMbGxqzGoOU4DAAqKioCPkWSqDnToKCgQOTiTUlJifV1tTapC1TkHBqGgjTqXGjUl9AQa9PV1cWuXbuwZs0aZhVGCEFkZCS6d+/Oaoz69etDUbHuo8+HloMrDd2QVq1a4fXr1/j111/RqVMnpKenA6i8Eb569YrVGDT8saQVOKRRBzF9+nS0aNECFRUVcHNzg6urq0DAxW8bZasZQgMajtQ0RCOByi5GLy8vWFpaokOHDkLZGDZbjPn5+Zg0aRJ++uknqKur4/79+zAxMYGLiwvWrl3LaiuahuMwDTVnGqiqquLq1atCBcrHjh1Dly5dZDKH6qi7Wss5NAwF8/Lyqn1cVvUlNMTaHBwcYG1tjevXrzPFow8ePEBRURHCwsJYjTF+/HjY2tpi7NixUFZWFrp4SrIS+ta8fPkSQUFBYjVy2Kiw0nJwJYSgc+fO2LdvHxYuXIiJEycyuiFsGThwILy8vODn54cePXrA19cXQ4cORUJCAmtfGyUlJc6tu7QMNKWpg1BUVGQ+gzweD6ampt+8SJyGIzWt1zQ9PR1ZWVkis2tst/YaN27MbGV37NgRmZmZMDExgaqqKp49e8ZqHjQch2moOdPAzs4ODg4OyMzMxJcvXxAbG4ucnBycPHlSpPK1rKmrUZFzaBgKqqurV7uilVV9CS2xtqdPn2Lfvn3IyMiAoqIiVFVVMW3aNPzyyy+snk9DvE5emDdvHlJSUmBqairyBsKmsPD27duwsrKCurq6SAdXtit3Gv4p7969g4uLCwwNDWFhYYE5c+bg0qVLUFRUxLp161h1QtDwx6LxGaFRBwFU1tx06tQJrVq1QlxcHI4fPw59fX3MmTNHZi2s2tranB2paX3v+vfvDxMTE0yfPl1kWzMbwbd58+ahZcuW8PDwQHx8PA4cOIC9e/ciISEBa9euZSWkt2HDBly9ehW+vr6wsLDAP//8gxcvXsDT0xPDhg2Di4tLjWPo6ekxas6TJk2Cs7MzevbsiYiICFy4cIHKljtbLl68iJCQEKZ+qGvXrpg9e7bMCraroy5QkXNoFLHeuHFD4P9fvnxBTk4OIiIi4OLigiFDhtQ4Bg0zwIqKCvj5+SE6OlpIrM3NzY2zoNz/IvytMDbdX9VBw8GVr8RaNYOyZcsWbN++HRUVFVIFgIQQ3LlzB7/99hvrQHTYsGHIy8sTaG+uClszQK4GmiYmJhgxYoTIOoiJEyeyykTu3bsXXl5eCAsLQ8uWLTF+/Hj07dsXaWlpmDZtmsy8YGg4UtN4TYHKm3t8fDx+//13qeeSnp6OWbNmYcaMGTA3N8fo0aPx7t07fPjwATY2NqyE1mg4DtNQc6bB7t27MWrUKLRr104mf09iSB1yzfXr16v94cLFixfJpEmTWP1u9+7dyePHj4WOP378mHTv3l2iv1tcXEzu3r1L7t69S0pKSiR6Lk2ePXtGLl68SD58+EBevXr1zebBhb59+5Ls7OxvPQ1CCCHBwcHk/fv3QsePHDlCpk+fzmqMDx8+EBcXF7Jjxw7mmJGREVmxYgX59OkTqzEOHTpU7Q8bJkyYQNLS0lj9rjg0NTWZ78zMmTPJ6dOnCSGEXLp0iZiamrIaY/jw4WTPnj2EEEICAgLI6NGjCSGV310jIyNO85OEnTt3krlz55Li4mKpx6DxmhJCyLJly0h4eDjncT58+EBev35NCCHk5cuXJCwsjBw/flzicR49ekSOHz9Ojh07RjIyMgghhFRUVLB67ujRo0l8fDwhhJCtW7cSFxcXQgghDx48IPr6+hLPRVp69OhBHj16JLO/Jyl1NSpyDg1DQXF06tQJDx8+ZPW7NOpL5EWsraysDM7OznIrbiQJY8eOxa5du+Dt7S2ROFttOLiK+73Ro0dj9OjRrMZYu3Ytbt68KSCm5erqig0bNmDjxo2sdFRoCHHREDikUQfx9OlTJuOZmJjIFKOrqqqyLi6mAQ1HahqvKQC0bdsWAQEBOH78ODp27ChUGF+T3AK/qJf/AwBv376Fubm5RMW+NByHaag500BHRwf//vsvZs6cKbO/KQl1gYqcQ8NQUFQxbUlJCUJCQlinT6dOnQovLy8UFRWJrC9hA42bEA22b9/O2M3PmzcPQOUFw9XVFf7+/t9c3EgSioqKcPToUZw/fx4dOnQQuoGI+3zQcnClrRty5swZIZ2coUOHokWLFli6dCmrz0hFRQXi4+NFfmfY+mPRcHHW19fHzp074eHhgT/++AMHDhzAnDlzkJyczNryoXXr1njx4gUUFRWRlpbGyO8/fPhQIrdwrtBwpKbxmgKViyP+TTw/P1+iORw9ehRr1qxBaGiogJKzr68vUlJS4OPjg6FDh4p9Pm3HYRpqzjRo2rQp1q9fjx07dqBTp05C58TmPlOb1AUqcg4NQ0FRNxNCCBo3bsxahpuGGSCNmxANjh07Bk9PT/Tu3Zs51rt3b6xZswZOTk7fVaACAKNGjZL4OVVl5mtycK0O2v4pNHRyfH19ERUVBXV1damzYzQMNB0dHTFr1ixERUXB3NwcO3bsgIGBAVMHwQZTU1MsW7YMjRo1Qvv27WFgYICEhASsXr2aVQE7LWjUwtB4TQHp5RauX78OJycnjBs3TqgWw83NDbt27YK9vT3+/vtvsW7qfDNTfvCbl5cn0nGYrdwCUFnLxIeffeSrOcvKCbpx48Zy3e1YV0wr5/To0QP+/v6cDAW/LqYFKrVE1NTUJDbzKykpkdoMUE9PDwcOHBDSt8jKyoKZmRnu3r3LapwXL14gJiYG2dnZcHd3R1JSEtTU1AQEk6pDR0cHx44dw++//y6gwvjo0SOMGTNGpmqf8kC/fv0QFhYm1JWRnp4OS0tLVilsWsycORPt27cX0slZtWoVsrOzsWfPnhrH6N27N1xcXDhtAYn6zlSF7Zbsx48f8f79e7Rq1QqvXr1CfHw8lJSUMHz4cFbPr6ioQFRUFJ48eYJp06ZBWVkZf//9NwoLC2FnZ1erBei0HalpvaZA5et64sQJZGdnY9asWUhPT0fXrl2rFZ6zsbGBqqoq3NzcxP6Oq6srXr16hdDQ0BrnQMNxmIaaMw2OHj2KAQMG1Lkn1yEdNAwF//rrLzg4OHAah0Z9CQ2xtsePH2Py5Mlo2rQpCgoK4ODggISEBLi6uiIiIoJR4K0OeRc3khR+4JaTkwM3NzeJAzeaDq5c/VNo6OSUlZWx1uQQB1eBQ1p1EPXq1YOlpaXAsa//X1vQdqSmVW/36tUrTJkyBa9fv0ZZWRkmTZqEsLAwpKSkIDIyUux1LjU1tcasrYWFBbMdXBM0HIcJBTVnGnh7e+Off/6R20ClrutHznFzcyMbN27kNEaPHj3IkydPOI2xatUqYmJiItBpdOrUKTJ06FCydu1aVmPcvXuX6OrqEiMjI2JnZ0fs7OzIkCFDiL6+Prlz5w6rMebNm0fc3NxIRUUF0dXVJbm5uaS8vJw4Ojqy7iz5999/iY6ODvH19SXdu3cnmzZtIvb29kRDQ4OcOHGC1RjywqNHj4iBgQEZMmQI0dTUJLm5ucTOzo7o6uqyfk2dnJzIyJEjybVr10hJSQkpLi4m58+fJ8bGxsTb25v1XMLCwki3bt1It27diLq6usC/2b43hBDy5MkT4u/vT+bOnUsWLlxIAgMDSUFBAevn29nZMZ0yXIiMjCSGhoZEXV2dqKurk/79+7PqNomPjyd9+vQh9+/fFzg+a9YsYmBgQE6dOiXRPM6fP08sLS2JoaEhefr0Kdm8eTOJi4uTaAx54OPHjyQyMpK4u7sTFxcX5sfR0ZEMGzaM9ThLly4lc+fOJe/fv2euAUVFRWTGjBlk7ty5Yp+np6cnsnOxKrm5uURPT4/VPMrKyoi9vT3p1q0b0dDQILm5uWThwoVkxowZrLuj1NXVSVJSEhk6dCiZMmWKQBeSuro6qzFoMGnSJHLs2DGZ/T1JqcuoyDmtWrVCWFgYLl68KLWh4Pjx4+Hv74+FCxdCWVlZqu4aGvUl2traiI+PFxBrGzVqlERibbdu3UJUVJTASl9RURELFizA5MmTWY1hZGSEzZs3IyQkBAoKCti9eze6du2KjRs3yoW4kSSsXbsWJiYm8PHxYfbVAwMD4ezsDH9/f1b7+StXrsSSJUtgbW0t5OAqSd0QDf8UAPj999+xdOlS1r//Nbq6uowYl6qqqpDgG5t6i71792LDhg1CAoeBgYFo2rSp2PoQGnUQVUlMTMSiRYtgamqKO3fuoKKiAp8/f4arqysIITKvK+DiSO3j41OtMzZbrl27hp07dwqIvf38889wdnautrC/c+fOuH37drUq37du3WJdF7Jt2zbORfmEgpozDdTV1bFs2TLs2rVLZDEtm/tMbVIXqMg5NAwFL1y4gNzcXMY462vYuslyLXIEuN+EKioqRHpilJaWStSeO3DgQJG+Q98bNAI3vokaVwdXGv4pNNizZw9atWqF1NRUpKamCjzG4/FYBSrSGmju3LkT06dPF1kHwe/qACo7z9jUQdA0vuMCDUfqs2fPws/Pj3HGXr16NeOMLco/SBylpaVi25w/f/4s9nljxozBpk2b0KdPH5HCZgUFBdi0aRMmTJjAah40ivL539uWLVsiIiICbm5umDp1KqttUprk5OQwXV0vX76U6d9mQ12gIufQMBRk25VTHTTqS2jQv39/hISECHQrFRUVYcOGDdVKvbMtDASABg0aoH379jAxMaGi+1Cb0ArcAO4OrjR0Q2hQtaNJWqQ10KRdB0HD+I4GNBypaThjA5WeQNHR0XB1dWWOlZeXY/v27dVmqaZPn46TJ09i1KhRmDBhAvT09NC8eXMUFRXh1q1biI2NRadOnVh3ZNFwHK6amWrQoAH8/f2xZcsWpg1dVtC4z9QmdYGKnEPDUJCGABaNIkcauLi4wMrKCv3798enT58wf/58PHv2DC1atMDatWvFPo9tYSBQaTHw+vVrHDt2DCEhIbRPgSrSBm5VoeXgSkM3RF6QVuDw06dPNRbLtmjRAh8+fGA1DxrGdzSg4UhNwxkbAJydnTFt2jTcuHED5eXl8PT0RHZ2NoqLi6vtClNQUEBERASCgoJw8OBBREREMI+1adMG06ZNw/z581kXO9Moyl+0aJHQYmjRokVQVlZGTEwMqzFo8fnzZ7x+/ZpZbJAqhfBjxoyR6Vy+pq49Wc6hYShIC65mgLT48OEDjh49irS0NMY8a+zYsVQVZW/cuIG5c+fi9u3b1MasDQoKCmBlZYXi4mIUFRVBRUWFCdz27NnDar992rRp+PTpEyZMmCDSwXXEiBGs5kLDP4UGNL4z0hpoTpgwAVZWVhg7dqzYsQ8fPoxdu3ax8seiYXxHg2HDhsHf318i76evWbFiBR4+fAg/Pz88fvwYvr6+2LRpExISEvDvv/+K3ZoWRUFBAaKjowWuARYWFqwFLD9//ownT57g7du3aNWqFTp06CBxh9u5c+fg4OCAKVOmIDo6Gra2tgKOw99Tvdvly5fh7OyMwsJCoccaNmz4za+DdYGKnEPDULAOySksLER8fDysra2/9VRqhGvgRtPBlatuCMBdJ+fQoUMCN53Pnz/j0aNHiIuLg5OTU7VBBB9pDTQjIyMRGRmJ6OhosXUQ5ubmmDBhAquW3uqM7zZt2iRU9Fhb0HCkpuGMLW9I4zhMW82ZBmZmZmjdujUsLS2xZMkS+Pv7Iy8vD5s3b4afn5+AKN23oC5Q+U65dOkSgoODZZ4e5Io0NyG2Ev0AO6lnecpSyQM0HFz5uiFVX9esrCz89ttvEumGfK2Tc/z4cWzYsAGXLl1irZMjjuPHj2P//v0SbVVKKnD45csXWFpaIiMjo9o6iD179kj0uuTm5jI3QzU1NZnr/dBwpP4aIoEzdtV6lJqQVYeKtI7DwcHBrLM3snLH1tbWRkxMDNTV1WFhYQE7Ozv07dsXBw8exIEDBxAdHS2TeYijrkblO0USQ0F5QVqxtqrbF58+fUJCQgI0NDSgq6sLRUVFpKSk4N69e0J7xeLw9fWtdsX9v4aysjKSk5MxatQoqKio4P79+wCA4uJilJWV1fh8rv4pVaHRbi0ObW1t1lsl0goc0q6D4NOxY8dq22prGxoF+V+/pjweD0uXLmUlGvn06VPOf58227dvlyrTYGdnVwuz4YaCggJTJK2srIz09HT07dsXffr0kcgOoNaQtXBLHZLx7NkzoZ///vuPODo6khEjRrAep6CggAQHBxMHBwfy6tUrcvz4cZKVlVWLMxeGhlibi4sL8fPzEzq+ceNGsmjRIk7zS0hIIDNnzuQ0xvdITEwM0dbWJvHx8SQzM5NoamoST09PMmbMGDJr1qxqn3vt2jWioaFBXF1dyYsXLwQey8zMJC4uLuSPP/4gycnJrOZiYGBAMjIyCCGE+Yzwx+Jie19SUkLWrl1LhgwZwur3aQgclpeXk+zsbHL79m3y+PFjUlFRIdXcfxRovKbi+PTpE9fpScysWbNIWFgY53HS0tLI4cOHSWxsLImNjSWHDh0ie/fuJe7u7hRmyY5JkyaRv/76ixBCyM6dO4mdnR0hhJCbN2+SXr16yWwe4qjLqMg5NAwFacjO04CG5seJEycQGxsrdHzcuHGcNSUkWXH/SHBxcKWtG0Kj3Vrc1h6Px4OXlxerMWgIHCoqKqJz586s/p68Q8ORmpYp6adPn+Dp6SmQ7Ro+fLhEdh40oOE4HB4ezmQs+PVH/H/37NmT/qTFMGfOHCxevBj169fHqFGjEBwcjDlz5uC///5j3T1Ym9QFKnKOqA+7pIaCtNLpXIscadyEmjdvjtTUVHTq1Eng+M2bNzmpOZaWlmLPnj3Vtp7+yEjr4EpbN4RGu7WoGoX69etDR0eHtYgdLYHDHwUajtS0XlM/Pz/cvHlTQHbB1dUVGzZswMaNG2Xmwk7DcZiWmjNXTExMsH//figoKEBJSQm7du1CeHg4jI2NsXjxYpnNQxx1gYqcQ8NQkEYmg0ZWhsZNaMqUKfDw8EBWVha0tLRQUVHBnN/y5ctZjUFjxS0viCsy5PF4qF+/Ptq3b4/hw4dXu7IX5+D69u1bWFlZVVsoSVs3RFqdnKo8ePAAVlZWnGo65EXg8GvKy8vx8OFDqKioyFSbJj4+Hr6+vpw0mWi9prQyM1wxNDTk7DgsL2rOANCkSROUlJQAqDSQTEtLw6BBg9CiRQuZzUEcdYGKnHPt2jXOLYg0Mhk0sjI0bkILFiyAgoIC9uzZg61btwKoVIJ0cnKChYUFqzG+LqYFJF9xywvl5eU4duwY2rRpw2hcPHjwAAUFBdDR0cH169exY8cOhIWFiVWYJRwcXGn7p7Rr1w5xcXEC7dbm5uYStVvHxsZyvsDLi8Dh8+fP4e7uDnt7e6ipqWHixImM2FtERAQ0NDRkMg8ajtS0XlN5yXbRcByWFzXnK1euYP78+ZgxYwbz3iQkJCAoKAihoaEy3YYSyTesj6mDBT4+PmTJkiUkPT1d6oIxe3t74ujoSL58+cIUKL5584ZMnz6dKZqqCVpFju/fvycxMTHEy8uLrFq1iuzZs4e10+jXFBYWkjdv3kj13B8Fd3d3snjxYoHPRllZGVm2bBnx8vIihBCyYcOGaouVuTi4RkREECMjI5Kfny/y8fz8fGJkZES2bNki6alJjYODA/Hz85P6c8WHq4szDezs7MjEiRPJkydPyKFDh4ienh5JTk4m7u7uMi38puVITeM1nTFjBnFxcSFfvnxhjlVUVJCVK1eSadOmcZ4jW2g4Ds+dO5e4uLiQ9+/fk3379pFJkyaRL1++kPj4eGJoaEhppjUzYcIEEhgYKHQ8ICCATJkyRWbzEEedjoqcM2zYMOTm5ortu2ejX0BDvbRXr16IioqCmpoa9PT0cOTIEXTo0AH37t2Dra2tkDCdvCGJL4qstAto0LNnT+zdu1dIVyMzMxPm5uZISkrCo0ePYGZmhlu3bokcQ11dHYmJiahXrx4WLlyI/Px8xsHV0NCw2s8YDd0Q2jo5lpaWSEpKAo/HQ+vWrYUykmfPnmX99741BgYGiIyMhIaGBuzt7UEIwaZNm5CTkwMzMzOZKYaGhYVh8+bN6N+/v9SO1LS4d+8erK2t0bJlS5GZGVk1B3h4eODAgQNQV1eX2nFYXtScdXV1ER8fL5RRfvLkCcaMGfPNlWnrtn7kHBr6BTTS6dLWl9C+CUnLli1bUK9ePbRv377a32PrrisvKCoq4tWrV0KBysuXL5ng9suXL1BUFP9V5+LgSkM3hLZOTu/evQUcbb9nysvL8fPPP4MQgqtXrzI3roqKimrfU9rQcKSmhba2NuLj4wXsPEaNGiVzOw8ajsNqamo4c+YM3r9/jyZNmiAmJkYqNWeutGrVCg8fPhQKVDIyMqQ2oaRJXUalDlZIm5WpWuxZ002IjS26tHh4eOD06dNo1KgRTE1NYWpqCnV19Vr7e7Ji1apVuHr1Kjw9PaGjo8Oofa5evRr6+vpYuXIlPDw88ObNG+zevVvkGPyMStWuqS1btmD79u2oqKhgrTpKwz/F1dUVP//8s1CbeFBQELKyshAcHCzReN8706ZNQ+/evdG2bVt4e3vj3LlzaNWqFTw8PPDs2TO5d72tQzy01JxpsGnTJsTExMDe3p7JSN2/fx9BQUEYN24cli5dKtP5fE1doPKDUhuZDK6eMt/6JvTlyxdcu3YNCQkJOHPmDFq3bs0ELV+3O38vfPz4EU5OTjh16pTABW/EiBHw9vbGtWvX4O3tjZ07d4oNzLZs2QIbGxs0atRI4Hh8fDxiYmJkejPU09Njtouq8ujRI4wbNw537tyR2VzkgXv37mHevHl48+YNZs+eDUdHR3h6euLYsWPYtWuXzLY55InqNF0A2UnoA9I7DotTc7axsZFYzZkGnz9/ho+PDw4ePIjPnz8z3laWlpZYunSpTLN3oqgLVH5Q5CWTURVaNyFCCC5duoT09HQoKiqiS5cu6Nu3L+sOJqAypX758mUcP34cZ8+eRceOHTFy5EiYmpri119/leCs5IMnT54gLS0NCgoK6NatG+MiW1ZWJjMBLBoMGjQIzs7OGDlypMDxAwcOYPv27d9VfQktKioqUFJSwnS65OTkMAJ9skKe/LF8fHyq1XSRVWAtrePw9evXMXPmTIwbNw4ODg5o27Yt81hWVhZ27dqFI0eO4O+//2Y6LGVFaWkpcnJyoKioiE6dOsk8syOOukDlfwBpMxm0szI0bkJFRUWwsbHBgwcP0KxZMxBCUFJSAk1NTYSHh4tsW6yJsrIyHDx4EAEBASgtLf0uTQlfvXolcnUpLuiSRwdXANi2bRvCwsJgbW0tUidn2rRpMpsLV4FDWtAIzLlCw5EaoPOa9u7dGy4uLpw0XWggreOwjY0NVFVVRao583F1dcWrV69YqTn/L1BXTPs/gLSy87SLHGmIta1btw4fP35EXFwcs5Xx8OFDLF++HAEBARIJtr148QKnTp3CiRMnkJycDGVlZVhaWrJ+vjxw69YtuLq6Ijc3V+A4IQQ8Hk9s0DV+/HiJ60dkAQ2dHBrIi+1EbQTm0mBmZibyuJaWFvbv388qUKH1mtLQdKFBZmYmfH19oa6uDg0NDTRu3BiWlpZo3Lgxdu/eLTZQoa3m/D+BLHuh65AOroaCAwcOFNnvv3//ftYmbbTMAHfs2EH69+9PunXrRrp160YGDx5MoqKiWD+/d+/eAqZmfK5du0b69etX4/Pz8/NJZGQkMTc3J+rq6mTo0KEkMDCQpKWlsZ6DPDF+/Hgybdo0cubMGXL9+nWhn+8ZSXRyRJl3ivthAw0DTRq4uLiQkSNHCnw+09LSyKhRo4iHh4fM5iGOp0+fEm1tbVa/S+s1paXpwhVdXV3y9OlTQkjl+xQREUEIqXxNqjPy09PTI48fP6527NzcXKKnp0dvst85dRkVOYfGKoRGJoOWGeDcuXMxd+5cvHnzBjweT+J99s+fP4v042nTpg0j/yyKiIgInDx5Enfv3sWvv/6KESNGwN3dHZqamhL9fXkjIyMDcXFxnCwWgMqsVHp6OqNgTKoUBbJpU64NWrZsyfp32WxlkRqyTFWhYTtBg3PnzmHz5s0ChdDq6upYsWIFHB0dv6nlg6T+WLReU11dXWzYsAFXr179ppouXbt2xb///gtLS0uoqKggOTkZ1tbWyM/Pr/Z5tNWc/xeoC1TkHBrS9TTS6bTNACW5CVVFU1MT0dHRcHd3FzgeHR1drZz42rVrUb9+fQwYMIDxFTl37hzOnTsn9Lvfk46KkpISSktLOY0hLw6uXIiMjKS6lUXDdoIG0gbmtKHhj0XrNZUXTRdpHYfHjBmDTZs2oU+fPmjXrp3Q4wUFBdi0aRMmTJhQm9P/rqgrppVzevfujaioKHTp0kVAETYrKwuTJ09GcnKyRONJm8mQlyLH27dvw8rKCurq6kzglpycjIcPH2LXrl1iLxBDhgxhNT6Px/uuOktiY2Oxb98+eHl5QUVFRWh1yQYTExOMGDFCpIPrxIkTZWqMJi84ODigXr162LBhA3r06IEjR46gWbNmsLOzQ8uWLbF582aZzMPa2hpqampCgbmPjw9SUlKwd+9emcxDVDZVUn8seXlNafLgwQMoKChAXV0dN27cQHh4OJSUlLB48WKx11gaas60efnyJYKCgsS2fH/ra2JdoCLnyJN0fUhICPbs2cOoMCopKWH27NkyLXIEKrUlwsLCkJGRAUIIunXrhpkzZzKmfP9LDBs2DHl5eYyOw9ew2ebQ0tJCQkICOnbsiFmzZsHCwgImJia4fPky1q5di6NHj9KeNnWsrKywZcsWNG/evMZuNTYdajRsJ2ggbWBOGx8fH86O1Fxe07y8PCgpKYHH4yEvL6/avyNLeYFHjx6hpKSE0UKJjIzEoEGDatRlKisrY9ScqxoptmnTBhMmTKhRzZk28+bNQ0pKCkxNTUUq0X7rLHNdoCLnyOMqRNqsTB30EbXSrQqbFk4DAwPs27cPnTt3hqenJ9q3b4958+bh+fPnGDlypMx9PogU7biurq5wd3dH06ZNBTSERMFWEIyrwCEt5CEw79GjBw4fPszo80iLtK+phoYGLl++jNatW4vdhpKkBokGVR2HHRwcAFTWA6anp7N2HKah5kwDXV1d7Nq1S263eusCFTlHXlZ2tJDmJvSjGgrKC/PmzUPLli3h4eGB+Ph4HDhwAHv37kVCQgLWrl2Ly5cvy2wu8tKOW4cgjo6O+OWXX7Bo0SKZB2oAcOPGDejr60NRUbHGLLKBgYFM5jRx4kQYGhoyQQqfwMBA3LhxQ2bbcjTo168foqKi0Llz5289FZHUBSrfAfKysuOKtDehr+tLnj9/jvr166NDhw5QVFREbm4uysvLoaWl9V1dHKSFbfaAx+PB19e3xvHkxcEVqDy3e/fuISAgQEgnR19fn3XhZmFhIXJyckR2MYkz+pQnA022yCowl9aRWl5e09pA3h2HJWHdunV49+4dvL29ZVoszpa6rp/vgEaNGrEWVROHNJkM2kgr1vbvv/8y/46IiMD58+cREBDAdBu9e/cOTk5OUFNTq/2TkAOePn3K3ICfPn3KeTx5cXAF6LTjHjlyBCtWrEBZWRnTxcRPp//2229iAxXaAofScujQIVa/J8sOF2kdqeXlNa0N5N1xWBKKiopw9OhRnD9/Hh06dBCy3fjWQWRdRkUOob0KkZd0ep8+fbB582ah1Oz169fh6OiIxMTEGsfo168fwsLChAz20tPTYWlpievXr1Od84+OPDm4AkDPnj0RExMjJKmelZUFMzMz3L17t8YxRo4cCR0dHdja2sLc3BxhYWF48eIFvLy84OjoyEpF9VsbaP6I/Givqbw7DksCrbqu2qIuoyKH0F6F0JKd55qVoaEJUV5ejvfv3wsdf/36tVxKwsuCDx8+ID09XaitsCYdFHEOrr6+vt/EwRWQXienKk+ePEFwcDBUVVXRrVs3FBYWYsiQIfj8+TN27NjBKlChJXBYx//xo72mCxcuxJs3b+Dt7S3kOLxkyZJvPT2J+NaBSE3UBSpySNUPjaurK2bMmCF2FcIGGul0GlkZGjehIUOGYOXKlfDw8ICWlhYIIUhOTsbq1athamrKaowfibNnz8LFxQUlJSVC2gfVdUBcv34dTk5OGDdunJDolJubG3bt2gV7e3uZO7ja29vDysoKd+7cEdmOy4YGDRowqWtlZWVkZGRg4MCB0NLSwuPHj1mNQVvgsI4f7zVVVFSEp6cnli9fLpeOw5LCN4zMycmBm5vbNzPhFEVdoCLn0FiF0Mhk0MjK0LgJrVy5EkuWLIG1tTWTQSGEYPjw4TUaff2I+Pv7o2/fvliwYIFE++I7d+7E9OnTRTq4qqqqMsHy9u3bZergqqenh6ioKISFheHy5ctMO66Hhwfrdly+UZ6joyPU1NRw4cIF2NjYIDMzk7UgHg3biToEofmaxsfHo1evXmjfvj22bduGhIQE6Ovrw93dXajQt7Zp0qSJQEbye+RrqxZ7e/tvYsIpllr2EqqDIzQMBa2srIiPj4/Q8dWrV5MpU6awGoOrGSCfu3fvkiVLlpCRI0eSESNGEHt7e3L37l3Wz+eTlZVFjh8/Tk6ePElyc3Mlfv6PgpaWVo0GZ6Lo06cP+e+//6r9nXv37kn03soLSUlJREdHh+zevZsUFhaS/9fencfVmP//43+0kcQk+Y6mSRt1IqUk2d5GJYydjNFMWXpbylpMKlNDmrLEZJkxicrSWMY2ShhZYwjFiCRiRL1FTJHSdq7fH36dj+MULde5ruvU8367dbuZ65zOeXQNndf1ul6v59POzo758ssvGWtra2bJkiV1fp3GNtAkstg4pz///DPTvXt3JjU1lbl69SpjZmbGfP/994yzs3ONv+fIxwmlCWdtaEZF4Ni4CmFjJoOtniOWlpaIiIio8/NrY2xsLDUlWb31tGfPno1+bUViaGiIJ0+e1LtiaFlZ2UenqLW0tFBaWtqYeHXC9nZcW1tbHD9+HOXl5WjXrh1+++037Nq1C7q6unBzc6vzezW2gaa8lZWVyXX24GMVYN9V12qwbJzT/fv3Y+XKlbCxsUFoaCh69OiB5cuX4+rVq/D29pa5tUw+TihNOGtDAxWBY6OhIBvT6Q1dX8L2h9DNmzcRGBgo1en3XVxVpRSKRYsWYfny5fD29oaxsbHMtsLaPkCE1MH1/e24H6qTU5e/Iy4uLggJCZHcojQxMcH333/f4HwNbaDJhn///Re//vorsrKyJG0SGIZBRUUF7t27h6tXr8rtvdnuSP2uxpzTp0+fwtraGsDb6rDVW+h1dXXx8uXLBr9ucyaUJpy1oYGKAmDjKqSxMxkNnZVh+0MoLCwMKioq+P777xEWFgY/Pz/k5OQgLi4Oq1atavDPp6i8vLxQVVUFLy8vqQ+Vj32ACKmDK9t1ch49egQNDQ25ZOXasmXLcPHiRfTr1w/Hjh3D8OHDkZ2djYyMDLkX4mO7IzVbOnbsiAcPHqCsrAz37t1Dv379ALxdlNuxY0ee0ymm/v37IzIyEqtXr5YcKywsxOrVqznrJ/UhVEeliZJHdcvG9hz52IdQXX7xWltbY9u2bbC0tMSECROwePFi2NraIjY2FmfPnkVMTEydsjQVDS0nLsQOrgA7dXKioqJw7tw5eHh4oFOnTjL5uWxa11i9e/fGypUr8cUXX2D48OGSir2BgYEoKytrloPzrVu3YvPmzWjRogW0tbXxxx9/SC5U5s2bBw8PD05yCL3jcH0IvVULzag0UWzPZACNn5XZvHkzoqOjpbYitm3bFj4+PnBzc6vTQEUsFqNDhw4A3m49zcrKgq2tLRwdHREZGdngbIqqoX1NVFRUEBsbK+ngGhsbK3lMR0cH33zzDecdXAF26uRERESgqqpKUvK9WkNvU/Dp9evXMDMzA/B2XVZmZiZEIhG+/fZbzJgxQ67vzXZHarZ4eHjAyMhIUqoeePt7JDAwEC4uLpzlCAwM/GDHYUXy6aef4tChQ1KtWiZNmiSYVi00UGmi2JhOZ3tWho0PIQMDA6SmpmLEiBEwNjZGeno6AODVq1coLy+vc96moqSkBLGxsbVe1X3oA6RFixbw9fWFj4+PIDq4AuzUyWFrVo0RQNuJTz/9FLm5udDV1YWhoSHu3LkD4G1bjaKiIrm+t56eHpSVlSV/ZgNb5/T9/l8jR45kJV99XLp0SdAdh+uLjVYt8kIDlWagoTMZbM/KsPEh5ObmJlnQO2TIEIwePRrq6upIS0tDjx496vQaTUlQUBBOnjyJfv36SWaa6ktVVVUwXVPZqJNz+fJleHh4oFWrVlLHi4uLsW7dujrNQgml7YSzszP8/f2xYsUK9O3bF97e3rCyskJSUhIMDAzk+t7vFp5ko3IpW+e0rKwMe/bskVpgDLzd+Xfz5k0cP3680VnrQkNDQyEL1SkiGqgogMZehTR0JoPtRY5sfAhNmDAB7dq1g5aWlqQwWVRUFHR1dREUFFSn12hKTp8+jbVr12LQoEF8R2GFpqYmtm7divv37yMrKwvKysowNzeXafz2vuzsbLx48QIA8PPPP0MkEuGTTz6Rek5WVhb27t1bp+2rbLWdaCxvb29UVlYiLy8PI0eOhLOzMxYsWIC2bdti3bp1nGSo1pCO1O9i65yGhITg0KFD6Nq1K9LT02FtbY2HDx/i+fPnmDJlSoN/vvoaPXo0tmzZItiOw00JLaYVODauQhYvXoybN2/WOJMxcOBABAYGfvQ12GwGWN8PobqoqKjA9evX0atXr0a/liLp1asX9u/fX+86KormY3Vyzpw5g1mzZkkNgGsyfvx4/Pjjjx99PzYaaLIhISEBAwYMkBp0FRYWQlNTE6qq3F1nfqwjdVJS0kdfg61z2rdvXwQEBGDEiBEYPHgwIiMjoa+vD29vb3Ts2LFRW9Hrw9/fHwkJCWjbtq0gOw43JTSjInBsXIWwMZPBZjPAxhRrMzc3x9y5c+Hl5SV1vKioCO7u7gq1UJINzs7OOHDgABYsWMB3FFY0tE7OF198gVOnTkEsFsPJyQm///47tLW1JY8rKSlBQ0Ojzlv72Spw2FjBwcH47bffpAYqfBSe+/XXXzF8+PAaO1LPnTu3Tq/B1jl9+fKlpERC586dkZGRAWNjY8ycORMLFizgbKACACNGjODsvZozGqgIHBsNBRs6nf4uNtaXsFGsjWEYREVF4f79+wgNDZW6immOk4Pa2tqIjo7GuXPnYGRkJHNVJ/SuqO9rTJ2c6m3HJ0+exGeffdaoBcFsNNBkg6GhIbKystC5c2fO3rMmbHSkZuucamtr4/nz5/jss88k5wd4W0SuoKCgfj9YIyjav60P8ff3r/G4kpIS1NTU0LFjRwwdOpS3tWw0UBE4Nq/sGjOTwcasDBvF2pSUlBAVFYWAgAC4u7vjl19+kVw5C7E4lbxdv35d0jDs6dOnPKdpvIyMDEmdnAMHDsDU1BSurq7o2LEj9u7di2HDhn30NdjYocJG2wk2iEQiLFq0CFu2bIGhoaFMyXyuPizZ6EjN1jn9z3/+g2XLliEsLAw9e/ZEaGgoBg8ejMTERM4Lvgm543B9VFRU4MiRI9DR0ZHUxbp16xby8/NhZWWFlJQU/Prrr4iOjualTQkNVASOjasQNmYy2JiVYeNDiGEYGBkZYc+ePZg9ezZcXFwQGRnZbFff79ixg+8IrBJKnRw22k6w4cGDB5IPhmfPnnH2vu9joyM1W+fU19cXfn5+uHz5MlxdXbFnzx5MmDABqqqqWLlyZUN/xHoTfMfhelBXV4ezszNWr14tGZBWVFQgICAAbdq0QVBQEMLDwxEREcHP7xx5dz0kjZOWlsZYWFgwLi4uTGhoKBMaGsqMHz+e6datG3Px4sU6vYarqyszfvx45rfffmO6d+/OxMXFMWFhYYyFhQWTmJjYqHxlZWXM1atX6/RcS0tLJi8vj2EYhlm4cKGka2pOTg5jb29fp9cQiURMQUGB5L0XLlzI2NjYMImJiYxIJGrAT6DYcnNzP/ilaEaOHMnEx8czDPO2S66fnx/DMAxz69YtxsbGhs9ozRpbHanlQSwWM7du3WLy8/M5fV+hdxyuj549ezJ3796VOX737l3G1taWYRiGefDgAWNtbc11NIZhqHuy4LFxFcLGTAYbszJsFGtj3lmH0qJFC4SHh2Pjxo1YtGhRnb6/qflY4zhFW1zMZ50cebSdYENlZSWeP38u1ZSw+rZtdWVWeWtoR2q2zunHOjlraWlJtnFz1SJB6B2H60NVVRUFBQUya6GePXsm+fmqqqo43Wn2LhqoKIDGlq5nYzqdjfUlbHwIzZkzR6bh3Jw5c2BgYIC9e/fW6TWakve3QFZVVeHBgweIjY2Fn58fT6kajq06OdVrB+7fv48lS5bUae2APNpONNb58+exePFiSY2Yd6mrq3M2UGloR2q2zqk8Ozk3lNA7DtfHkCFDEBQUhKVLl8LKygoMw+D69etYvnw5HB0dUVJSgk2bNqF79+685KOBigCxfWXHxkwGG7MybHwI1fbzjhw5kpcy2nyrqcpqnz59oK+vjw0bNsiUGlcETk5Okj9X/3+trpNTl4Wy768d8Pb2rtPaAbYLHLJh7dq16Nq1K9zc3DB//nyEh4cjLy8P69ev53TXSUM7UrN1ToVYk0ToHYfrw9/fH76+vpg2bZrUgHDYsGFYsmQJ/vrrL1y+fBmbN2/mJR8VfBOg9z9cPnQVsnv37o++3u+//46QkBD8+OOPMDc3x+jRozFhwgSkpaVBR0cHW7du/ehrWFlZ4dixY9DV1cWiRYtgY2MDV1dXPHr0CF999RUuXrzY4J/3Y8Xa6nI1Bbzd9VOXwlPNwaNHjzB8+HDcuHGD7yj1UludnIKCAgwYMKBOV8uenp7Q1tZGSEgIbGxscPjwYejq6mLx4sV4+vRpnRYDslngsDEsLS2xd+9eiEQiuLq6Yu7cuejTpw/279+Pffv2YdeuXZzkYKMjtVDOKVuE3nG4IR49eoTbt29DRUUFZmZm+PzzzwG83SH6fukDLtGMigCxfWXHxkwGG7MyDS3WNnbs2Ga59bguarp3X1xcjMjISMkvGUXCsFAnh421A2wWOGwMFRUVSWfe6tu2ffr0gb29Pac7XNjoSC2Uc8oWoXccbohWrVqhe/fukn9r1b9fuFr3UxsaqAhcQxsKvq+x0+lsrC9p6IdQXStfNkc1zTYxDAMNDQ2pKWlFwUadHDbWDrBR4JANXbp0walTp+Dm5gZjY2OkpqZi8uTJePLkCWcZAHY6UgvlnLJJyB2H6yMtLQ3+/v7IycmROs71up/a0EBF4Ni4CmGj7DwbszJsFWvLzMyU2n307i6IkJCQOr9OU1DTvXs1NTWYmpqidevWPCRqHIaFOjlsrB1go8AhG2bMmIF58+ZBTU0NI0aMwIYNGzBjxgzcuXOH03UQbHSkFso5JbJCQkLQoUMH+Pr6SmbwhITWqAgcGw0FRSIRWrVqBUdHR6mZjIKCAvTv3x+ZmZkNzlefZoAikQgXLlyAsrIyZs+ejSdPnkg+hPr161enAVNMTIxkyru6OVr1n21tbZtcAbSPmTNnDry9vWFiYsJ3FFaYm5vj/PnzaN++PcrLyxEQEIDTp08jJCQEPj4+dfo7wubaAXk00KyvW7duQUVFBSKRCJcvX0ZMTAx0dXUxb948ufb9ebcjtbu7OzZs2FBjR+pVq1bh77//rvPrCuGcEmndu3fHoUOHBPt7hGZUBI6NqxA2ZjLYmJWpfq927dohNjYWAQEB+Prrr+s1CxIXF4fp06djzpw5GDRoEA4ePIjCwkIsXLgQjo6OdX6dpuLSpUsyZdUVGRt1cthcO9CYthNs6datG4C3s0Lm5ubYtGkTJ+/76NEjqY7Ute24Gz9+fL1el41zSrOq7NLV1cXr16/5jlErGqgIHBul69mYTmdjkSMbH0JPnjzBhAkT0LJlS4hEIqSnp8PJyQl+fn5YsWIFpkyZUufXagrGjh2L8PBwzJ49GwYGBryuzGcDW3VyGrt2gI0Ch2zZsmULtm/fLimh//nnn2P69OlyLyrGdkdqts7px2ZVSf15enoiNDQUy5Ytg7GxcZ3bInCFBioKojFXIWzMZLAxK8PGh5CGhoakQmenTp1w7949ODk5wcTEBLm5uXX+eZqKs2fPIicnB8ePH6/xcb4XwdVXQ+vkuLu71/k96lKTg40Ch2zYvHkzfvnlF7i5ucHa2hpisRipqakIDQ0FALkPVtjsSM3WORXKrKrQOw7Xx6ZNm5CXl4cxY8bU+Djfv0dooCJwbFyFsDGTwcasDBvF2mxsbLB582YEBQWha9eu2LdvH2bMmIHU1FSFXDzaWJ6ennxHaDQ26uS8u+6krKwMiYmJMDc3R48ePaCqqoqbN2/ixo0bdZ5lYaPAIRvi4uKwdOlSqQ+Q6oH55s2bOSvVzkZNELbOqVBmVYXecbg+hP57hAYqAsfGVQgbMxkNnZVhu1ibj48Ppk2bhri4OEyaNAm//vor7OzsUFpaCg8Pjzr9LE3J2LFj+Y7QaGzUyXm3Squ/vz+mTJki00IgIiIC2dnZdXo9oXRxLioqqrGSbq9evbB8+XLOcrCBrXMqlFlVwXccrgeh/x6hgYrAsXEVwsZMRkNnZdgu1mZqaoqkpCSUlJSgdevW2Lt3L+Lj46Grq4uhQ4ey9j6EO2zXyTl27BgOHjwoc3zMmDG1Tm2/j40Ch2xwdHTEjh07ZEoAxMfHK1x7BLbOqVBmVY8dO4bdu3dLrQtTU1PDzJkzMWnSJAQFBcHFxQW//fYbZ5nqw9/fH0uWLIGmpmatt7GAtxeR1bca+UIDFYFr6FUI2zMZDZ2VYfNDqLS0FOrq6pIv4O0V56RJk2RKehPF1dgdHW3btkVGRgYMDQ2ljl+9erXOtyr57OL8rvbt22PXrl1ITU2FnZ2d5DbW1atX4ejoKPUBw2Xvn4Zg65wKZVZV6B2HP+bx48eSf2OPHz/mOc2HCfMMEomGXoWwPZPBVjPAhn4IJSQk4Mcff0RUVBQsLCwkx0NDQ3Hz5k2EhIRg8ODBdc5BhImNHR0TJ05EUFAQsrOzYWFhAbFYLCmr/91339XpNdjq4txYt2/flnyIv1vvyNbWFkVFRSgqKuIsS0M6Ur+LrXNa06xqQkKCZPEqV4Tecfhj3r0dJfRbU1TwTeDYaCjYUGzPyjS0WFtKSgqmTp2KMWPGwNvbWzLDBLwtSrVlyxYcPnwYO3bsgI2NTV1+NCJQTk5OGDZsWI07OlxcXOq8UDIyMhI7d+6UbOnV1dXF9OnT4erq2qh89Slw2JS835H66NGjWL16NZKTkz/YkbouFPWcvnnzBr6+vvjzzz9lOg4HBwfj0qVLCA4OxubNm2UaMQpRaWkpsrKyUFFRIXWrXwjbvmmgogCSkpKgpaUFW1tbxMfHS12F1HU1fkNmMjZs2FCvrccf09APIQ8PD5iYmCAgIKDW1/b390dBQQGioqLqlLcpaeyVrpBYWFggMTERnTp1wrRp0+Dq6gonJyecP38eK1asQEJCQr1e799//4WSklK9K7iy0cW5KWGjI3VTPadC7ThcHydPnoSfnx+Ki4tl6mJRrx9SJ41tKNjQ6XS2Fzk2dFthRkbGR6vwurq6YtasWazmVQTvX+l6e3sjMTER/v7+jb7S5QPbOzratWvXoBxsFDhsStjoSN1Uz6lQOw7XR3h4OPr06QMvLy9B9vpR5jsA+TBzc3P88ssvMserS9fXRXWBpL///hvt2rXD2bNn8ccff8DExKReBZIyMzNx+PBhHDp0CIcOHcLBgwexZ88efP/993X6/po+hAB89EOorKzso4tltbS0UFpaWsefpOlYsWIFnJyckJSUJKkmuXbtWjg4OCA8PJzndPVXvaOjtLQUXbt2lVRG5XpHR3WBwxs3bsDd3V3S86b6seaGjY7UTe2cpqWlYciQIRgwYAAcHBzg6OgIR0dHyZ8VyePHj7Fo0SKIRCLo6enJfPGNBioCV30VsmjRIpnFs3W9CqlpJsPMzAx+fn7Yt29fnV4jJiYGY8aMga+vL/z9/eHn5wd/f38sXboUDx8+rNNrNPRDyMjICNeuXfvga6elpQniHxTX0tLSMHXq1BqvdDMyMnhM1jA+Pj5ITk5GXFwchg8fjoKCAtjZ2WHx4sUYN24cZzneLXCorKwMFxcX3L17F8rKzfNXZnVH6ncHK/XtSC2Pc8rlVvH3VXcc3rhxI7Zt2yb52r59O7Zt28ZbroYwNDTEkydP+I5Rq+b5r06BsHEV0tCZjHexMSvT0A+hUaNGYd26dcjPz6/x8fz8fKxbt65Z1lFh40pXSKp3dIwbN06yo2P27NlYs2YNfHx8OMvxfoFDGxsbfP3110hJSeEsw7uys7Nx9OhRJCUl4f79+5y/v5+fH27evIn+/fujrKwMnp6eGDRoEB4/flyv5qgAO+d0165dcHBwQI8ePfDo0SP88MMPNc48y9Pdu3exbNkyODo6ws7OTuZLkSxatAjLly/HqVOn8M8//yAvL0/qi2+0RkXg2Chdz0aBJDbKVje0WNu3336L48ePY8SIERg/fjysra3Rtm1bFBYWIi0tDQcPHoShoWGzrExbfaW7evVqybH6XukKBZt1chiGQXJyMrKysqCqqorOnTujT58+dR68sdF2gg1lZWVYuHAhTp48KbW2bNCgQYiIiOBssSYbHanZOqfx8fFYs2YNJk+ejC1btgB4e9EVHh4OdXV1TJs2rV6v11BC7zhcH15eXqiqqoKXl5fUBTDDMIJYTAuGCJpIJGIKCgoYhmGYsrIyZuHChYyNjQ2TmJjIiESiOr3GnTt3mH79+jFRUVFMcXExM2jQIKZnz55M165dmTVr1tTpNXr16sXcv3+fYRiG+eGHH5hNmzYxDMMweXl5TI8ePT76/SUlJYxYLJY6du/ePaa0tLRO719WVsasXLmSsbOzY8zMzCRf/fr1Y9auXVvn12lqnjx5wjg7OzN9+vRhzM3NmeHDhzM9evRgvvjiC+bx48d8x6uz+Ph4xt7enklPT5c6Pm3aNMbOzo75888/6/xa//77LzNu3DjGzMyMsbW1ZXr27MmYmZkx48aNY4qKiur0Ghs2bGBKSkpkjh8+fJj59ttv65ylscLCwpgBAwYwSUlJzMuXL5nCwkLmxIkTzMCBA5nw8HDOcrCBrXM6ZswY5sCBAwzDMEyPHj2YnJwchmEY5vfff2ecnZ3ZCVsHBw4cYCZOnMhkZmYy5eXlnL2vPKSkpHzwi2+0PVngRCIRLly4IDWDsnHjRmzatAlisbjOI903b96gpKQE2traKCgoqHfZ+VmzZqFdu3YICgpCfHw89u3bh927dyMxMRErVqzA+fPna/3e2oq1eXh41LtYW2VlJR49eoSioiJoa2tDX19fIRfisam0tFTqSrdLly71utLlG9t1cvz9/XHjxg2sWbNGUr8iMzMT3333HWxsbLBs2TK5/Sxs69+/P5YvX45BgwZJHT99+jSWLVuGM2fOyO292e5IzZYePXogPj4e+vr6sLa2xuHDh6Gvr49Hjx5h+PDhuHHjBic5nJ2dkZeXJ7mt/j7eZyGaELr1I3CNbSjI1nR6Q8tWp6SkwNfXF2PGjMGnn34q9VhAQAC2bNmCBQsW1PlDSFVVVSHapnOpVatWde4KLESbN2/Gt99+W2OdnOoKpsDbVvR1qZNz+vRprF+/XqrIlkgkwvfffw8fH59aBypsFzhkw+vXr2ush2NkZCS1Xk0e2OhILY9zqqOjgwcPHkBfX1/q+LVr1/D//t//q9NrsEHoHYfro6SkBLGxsUhLS5Mp+AZwOxCtCQ1UBK4xpevZLDvf0PUlbH8IEeFe6TYU23VyKisroaOjI3NcR0cHxcXFtX4f220n2GBqaopjx45h5syZUsePHj0q9wE7Gx2p5XFOJ06ciODgYEmfo/v37+P8+fOIiIjA5MmTWX2vDxF6x+H6CAoKwsmTJ9GvXz+pGU2hoIGKALFxFcLmTEZjZmWoWBv72LjSFRK26+R069YNu3btkjTAq7Zr1y6Ym5vX+n1sFzhkg6enJ7y8vHD79m3Jv9PU1FScOHECa9as4SxHQztSy+OcTp8+Ha9evYKPjw/Kysowc+ZMqKqq4uuvv5b77xFF6jhcH6dPn8batWtlbjEKBQ1UBIiNqxC2ZjIaOytDxdrYx8aVrpBU18np1KlTrc+pT52cBQsWwN3dHdevX5f6cM/MzJTsEqmLxnZxZsMXX3yBdevWISoqCmfOnAHDMDAzM0NERAScnZ05yQCw05EaYO+c+vj4wNPTE/fu3QPDMDA2NoampiaePXsm1xkBReo4XB/KysowMTHhO0bteFzIS+TI3t6euXPnzgefc+PGDaZv3761Pn7p0iXG3Nyc8ff3Z54+fSr12L179xg/Pz+ma9euTGpqaq2vMW7cOObQoUMfzHHo0CFmxIgRH3wOqVmPHj2YBw8eyBx/8OABY2VlxXmehoiNjWUGDRrEPHnypMbHnzx5wgwaNIjZuHFjnV/z77//ZubPn898+eWXzLBhw5gFCxYwf//9d52/Pzo6WrKzTCQSSf2Zy10/QvHzzz8zPXv2ZNavX8+cOnWKSUpKYlatWsVYWVkxO3furNNrsHVORSIR8/z5c5njjx49qtMORCIrICCA+emnn/iOUSuaUVEADbkKYWMmg41Zmepibfb29jK3oID/K9Y2fvz4D2YlNWPrSpdP8qiTY2lpiYiIiAZnqi5wWFMDTXmXRz906FCdn/uh2y5s8vLygoqKCnbu3Imff/4ZwNs6Ir6+vnXuSN2Yc7pv3z4cPnwYwNvff7Nnz5a0jKj29OlTtG3btgE/XcMJueNwfWhrayM6Ohrnzp2DkZGRTH2ed2dx+UADFYFraENBNqbT2VhfQsXa5GvixIkICgpCdnY2LCwsIBaLJQ3kvvvuO77j1YmKigpiY2MRERGB/fv3IzY2VvKYjo4OvvnmG3h6en5w4L1x48Y6v19dOn2zUeCwod6/jVf9715dXR2qqqooLi6GiooK2rVrx9lABQBmzpyJmTNnNrgjdWPOqZOTE1JTUyX/3bFjR5m/D6amppyeD6F3HK6P69evSxqYPn36lOc0smigInANvQphYyaDjVkZNj6ESO3YuNIVghYtWsDX1xc+Pj4NqpNz4MABqf/+3//+BzU1Nejr60NVVRU5OTmoqKiAhYVFnQYqbHdxro/MzEzJnxMSErB161aEhYVJtlv/888/WLx4MUaMGCHXHLVpaEfqxpxTLS0tqav66gWtfBJ6x+H62LFjB98RPozP+07k47p168Y8fPiQYRiGmTp1KnPixAmGYRgmOTmZGT58eK3fV1lZyUyaNImxtbVlwsLCmGPHjjF//fUXk5iYyISEhDA9e/Zkxo8f/8GKrmyvL6moqGDu37/PXLt2jXn48KFMpVrSOC9evGD+/fdfvmPwLiYmhpk8ebKkojPDMExRUREzc+bMOldinjlzJuPn58eUlJQwe/bsYSZMmMBUVVUx8fHxTL9+/eQVXcbAgQOZ69evyxy/ceMGpznYwPY5ffbsGZOXl8fk5uZKfXHFwsJC8rtZ0b1/Dvk6p7WhGRWBa+hVCBszGWyvL6FibfLV0Cvdpmbz5s2Ijo6WWqPTtm1b+Pj4wM3NrU7NDRta4JBtL1++RMuWLWWOi8VivHnzhrMcbGDrnF67dg1+fn7IycmROs5w3JemuuPwh26vK4qPlcTg+zYWldAXuMaUrq/W0LLzVVVVcHNzw927dz+4vmTnzp1064YIRq9evRAZGSlTH+jixYvw9vbGpUuX6vQ6jW07wQZPT08UFRVh1apV+PzzzwG8bSvw3XffwdjYGOHh4ZxlYQMb53TcuHHQ0NDA1KlTa7zlwlXn4rNnzyI8PBze3t4wNjaWWYD62WefcZKDDZcvX5b676qqKjx48ACxsbHw8/ODg4MDT8neooGKwGVlZWHatGmYMmUKJk2ahJEjR+Lly5eSq5C6XB02Rnl5uWRWpqioSHJcR0cH48ePp/UlRHAWL16MmzdvIigoCBYWFmAYBqmpqVi+fDkGDhyIwMDAD35/dYHDdwfz2dnZ0NPT4/zven5+Pjw8PJCdnY22bduCYRi8evUKlpaW2Lx5Mz755BPOsjCN6EjN5jnt3r07Dh06xHvdj27duklmuwXZcZgFycnJ2LBhQ53atcgTDVQUgBCu7KgZIFEUxcXFmD9/Pi5cuCD5O8owDIYOHYpVq1bJXPm+i80GmmypqqrCX3/9hbt370JJSQkikQj29vac/vsrLCyEh4cHbt26hTZt2oBhGBQXF6Nbt26IiYn54LZgts+ps7MzwsPDYWlp2aifqbHen4V4H1czO/LEdaPH2tBARcCEdGVHhKsxV7pN2f3795GVlQVlZWWYm5vLNLF7H9tdnOXtyZMn6NixIyfv1dCO1PI4pwcPHsSePXuwbNkyGBsby9RTIfWXl5cnc6y4uBiRkZG4ffs2EhMTeUj1f2igIlBCvLIjwtOYK93mprpIYs+ePWt83MPDAyYmJjUWOKzm7++PgoICzhpoPnr0CCtXrkRWVpbkNgPz/xd8fPHiBTIyMjjJYW9vj/Xr18vMEqSkpMDHxwcXLlyo8fvkcU6dnZ2Rl5cnOR/v4+qWi9A7DteHSCSSmaFjGAYaGhpYvXq13Iscfgzt+hEgNhsKkqZt5cqVePPmDQ4dOiRzpbtmzZpar3Sbsps3byIwMFCqmvO7avsgE2IDzeDgYPzzzz8YOnQoYmJiMG3aNDx48AAnTpxAcHAwZzka2pFaHufU09Ozzs+VJ6F3HK6PmgZVampqMDU1RevWrXlI9B4u90KTupk2bRrz448/fvA5fn5+zH//+1+OEhGh6t27N5OSkiJz/NKlSx/s49SUubq6MuPHj2d+++03pnv37kxcXBwTFhbGWFhYMImJibV+n7W19UfrYuTk5DDW1tZsR66VjY0Nc+nSJYZhGGb06NGSfkVr165lvLy8OMvh7u7OhISEyBxfvnw5M3HixFq/T4jnlC02NjbMqVOn+I7BitmzZzP37t3jO0ataEZFgIR4ZUeEqaFXuk1ZRkYGtm3bBktLSxw4cACmpqZwdXVFx44dsXfvXgwbNqzG72O7izMbysvLJXmMjIxw584dWFpaYsyYMXBzc+MsR0M7UsvrnJ49exZbt27F/fv3sWfPHhw4cACdOnXC6NGj6/U6jSH4jsP1cOnSpRrr9QiFMt8BiCw2SteT5qFbt27YtWuXzPFdu3bB3Nych0T8E4vFkql4AwMDZGVlAQAcHR2lytO/r7rAYX5+fo2PVxc45HK3nZ6eniS/kZGR5LaVWCzG69evOcthbW2NuLg46Onp4fz580hOToa+vj5+++032Nvb1/p98jinFy5cwJw5c/DZZ5/h5cuXEIvFqKyshL+/f70aOjaWs7OzTOsGRTV27FiEh4fj7t27KC8v5zuODJpRESAhXtkRYWrolW5TZmBggNTUVIwYMQLGxsZIT08HALx69eqDv4SF2EBz7Nix8PX1xapVq/DFF1/A3d0dn332GS5cuAAzMzPOcgAN60gtj3O6YcMGLFy4EFOmTMHx48cBAN7e3tDU1MTWrVs5a0wo9I7D9XH27Fnk5ORIzuf7+K4JQwMVAWK7dD1puqqvdKOjo3H+/HkwDAMzMzMEBQXxXmeCL25ubliyZAkAYMiQIRg9ejTU1dWRlpaGHj161Pp9QmygOWPGDLRs2RIMw8DS0hJeXl7YtGkTdHV1sWrVKrm+NxsdqeVxTu/cuVPjzz506NB6ZW4soXccrg+hLFCuDW1PFiAqXU9I4yQlJUFLSwu2traIj49HVFQUdHV1ERQUVKeZSCpwCJmy6R/qSL179+6Pvh5b5/Q///kP1q5dC1tbW1hbW+Pw4cPQ19fH6dOn8cMPP+DcuXP1fk0ibDRQESgqXU9qw8aVbnNUUVGB69evo1evXnxHqZerV6/WWquDq/+/sbGxOHPmDNasWSNp9vjy5Uv4+vrC1NRU7q083rV69WpcvHgRoaGhcHV1xW+//YanT59i6dKlcHZ2hp+fHyc5aiqS9i5F6vUjdDRQETi6siPvY/tKt6kxNzfH3Llz4eXlJXW8oKAAAwYM4P1+e338/PPP2LBhA9q2bQtNTU2px5SUlHDy5ElOcvTt2xfR0dGSWj3VsrKy4ObmhpSUFE5yAG8HnH5+fjhy5AiAt+eBYRh88cUXiIiI4OwCrqYiae9SpL9nQkdrVAROVVUVRkZGfMcgAnLq1CnJnz92pdscMQyDqKgo3L9/H6GhoVKLHBXtumzXrl3w9vbGzJkzec1RUVGBkpISmePPnz/n/MJJTU0Na9aswbx583D79m2IxWKYmpqic+fOnP7/fb9I2vsdhwmLeKneQghhRZ8+fZjbt2/LHL9z5w5jZ2fHQyL+iUQi5sqVK8zgwYOZiRMnMs+fP2cYhmGePXvGiEQintPVj6WlJfP48WO+YzC+vr7Ml19+yVy6dIkpLi5mXr16xZw5c4ZxdHRkgoODOc3i4ODA/PvvvzLHnzx5Ioi/8+fOnWMmTJjAd4wmhWZUCFFgQrrSFQqGYWBkZIQ9e/Zg9uzZcHFxQWRkpGTGSZHY2Njg2rVrvJciCAwMxPz58zF58mSZjtQfK07JhsTERCQnJwMAcnNzERwcLFOgLDc3VxB/5w0NDT9Yr0eonj59ir179+L+/ftYsmQJrly5AlNTUxgbG/MdjW79EKLIHBwcEBgYiKCgIFhYWIBhGKSmpmL58uUYPnw43/F4Uf1h1a5dO8TGxiIgIABff/01QkJCeE5WfyNGjMDy5ctx8+ZNGBsby9Tq4KpmSHWNkvp2pGaLtbU1du/eLbm1k5eXJ9U1WUlJCRoaGli5ciUneaozvK+64/Dnn3/OWQ42PHz4EF999RU0NTWRn58Pb29vJCYmwt/fH7GxsZJt2HyhxbSEKLDi4mLMnz8fFy5ckLnSXbVqlcwHW3MgEolw4cIFqRmUjRs3YtOmTRCLxQq1yPH9xavvUlJS4v1n+VhHanlwc3PDxo0b8cknn3D2njUResfh+vD09IS2tjZCQkJgY2ODw4cPQ1dXF4sXL8bTp0+xY8cOXvPRjAohCozvK10hmjNnDjQ0NGSOGRgYYO/evTylahih3EJoaEdqeXj/Q/PFixe4fPkyLCwsOJ3JEHzH4XpIS0tDXFyc1MBLVVUVXl5e+Oqrr3hM9v9n4TsAIaTxjI2Npe4l83GlKxS11RYZOXIkRo4cyXEadmRnZyMrKwtqamowMTHhfCdgWFgYVFRU8P333yMsLAx+fn7IyclBXFyc3Cvkvi8rKwtz585FSEgIzMzMMGrUKBQUFKBFixbYvHnzB3sPsWn79u3w9vZuEo0JxWJxjQPQ169fQ0VFhYdE0migQogCE9KVLp8cHBzqtJBSSUkJSUlJHCRiR1lZGRYuXCiVWUlJCYMGDUJERARnt/Ya2pFaHlauXAkDAwMYGxsjISEBlZWVOHv2LHbv3o2IiAjOagcJveNwffTv3x+RkZFYvXq15FhhYSFWr17N2cDvQ2igQogCE9KVLp/Gjh0riB0fbPvpp59w48YN/Pzzz7Czs4NYLMaVK1cQEhIiac7HhZo6Utva2sLR0RGRkZGcZKh27do1/P7772jfvj2Sk5MxcOBAfPrppxg3bhxiYmI4y1HdcXj27NkwMDBQ6PVgfn5+cHd3R//+/VFWVgZPT0/k5uZCS0sLK1as4DseDVQIUWRCutLl09y5c/mOIBcJCQlYvnw5Bg0aJDnm5OQEFRUVLFu2jLOBSkM7UsuDsrIyWrRogcrKSly+fBmBgYEA3t6m4LKtiNA7DtfHp59+ikOHDiEhIUFSRG/SpEkYPXq0TEVkPtBAhRAFJqQrXSHJzMyUuh3GMIxk3Y4ibVN+/fp1jXUsjIyM8OLFC85yNLQjtTz06NEDkZGR0NbWRllZGf7zn/8gPz8fa9eu5TSL0DsO11erVq0wYcIEvmPUiAYqhCgwIV3pCkVMTIyknkZ1H5jqP9va2vIZrd5MTU1x7NgxmRL6R48e5XRB7YQJE9CuXTtoaWnBxMQEYWFhUh2puRQYGAhvb288evQIAQEB0NbWxvLly5GdnY2oqCjOcowdO5az95IHd3f3Oj+3ph1OXKKBCiEKTEhXukIRFxeH6dOnY86cORg0aBAOHjyIwsJCLFy4UKFqWwBvr9q9vLxw+/Zt2NjYAABSU1Nx4sQJrFmzhtMsTk5Okj9X76Cq7kjNZeVcAwMDHDhwQOrY7NmzERAQIIgdKori3f9nZWVlSExMhLm5OXr06AFVVVXcvHkTN27cEMQsCxV8I0TBJSUlQUtLC7a2toiPj5e60uW79DofLCwskJiYiE6dOmHatGlwdXWFk5MTzp8/jxUrViAhIYHviPVy4sQJREVFISsrCwzDwMzMDP/973/h7OzMWQa+O1JfuXIF1tbWUFVVxZUrVz743F69esk1S1Pk7++PTz75RKaZYkREBLKzs7Fhwwaekr1FMyqEKDihXOkKhYaGBqqqqgAAnTp1wr179+Dk5AQTExPk5ubynK7+Bg8ejMGDB/OageG5I7Wbm5uk2rCbm5vULb13CaFaryI6duwYDh48KHN8zJgxnLVp+BAaqBCiwGq70i0qKoK7u3uz/KVtY2ODzZs3IygoCF27dsW+ffswY8YMpKamKkTF0I0bN8LDwwOtWrXCxo0bP/jc2orbsU1JSQlRUVEICAiAu7s7fvnlF2hra0sek7eTJ09K3u/kyZNyf7/mpm3btsjIyIChoaHU8atXrwqimScNVAhRYHxf6QqRj48Ppk2bhri4OEyaNAm//vor7OzsUFpaCg8PD77jfdSBAwfwzTffoFWrVjJrMd6lpKTE2UCF747U784Mvj9LWFFRgczMTBgbG3M+EBVyx+H6mDhxIoKCgpCdnQ0LCwuIxWJJWf3vvvuO73gAQwhRWCKRiLly5QozePBgZuLEiczz588ZhmGYZ8+eMSKRiOd0/CktLZU6F9HR0czRo0d5TqW4RCIRU1BQwDAMw5SVlTELFy5kbGxsmMTERM7/nuXl5TFTp05l/v77b6a0tJQZPnw4Y2ZmxtjZ2TEZGRmc5fjnn38YOzs7xsHBgenWrRuTk5PDzJ07l+nRowdz/fp1znKw5ddff2X69+/PmJmZMWZmZswXX3zBxMXF8R2LYRiGUeZ7oEQIaTjmnStdZWVluLi44O7du1BWbp7/tEtLS8EwDNTV1SW3CoqKijBp0iQMHTqU53T1xzAMNm7cKFUWfuLEifj11185z1GtRYsWCA8Px9SpU7Fo0SJOcwBvqzG/evUK2traOHr0KHJzc/Hbb79h8ODBUiXg5W3FihVwcnJCUlIS1NTUAABr166Fg4MDwsPDOcvBlpkzZyI5ORkXL17EpUuXcPr0abi6uvIdCwDQPH+bEdJEVK8PaNeuHWJjY2FjY4Ovv/4aKSkpPCfjXkJCAhwcHHDr1i2p46GhoRg4cCBOnDjBU7KGW7duHXbu3Cl1i+XLL79EbGwsp4OV2jpSr1ixgvPaNJcuXUJwcDA+//xznD17Fv/5z39gY2MDDw8PXLt2jbMcaWlpmDp1ao0dhzMyMjjLwbbqejlCQgMVQhSYkK50+ZSSkgJfX18MGjQIn376qdRjAQEBcHBwwIIFC5CWlsZTwoY5dOgQwsPDpXb9TJ48GStXrsTvv//OWY45c+agVatWMsdHjhyJHTt2cJYDeLsm5ZNPPgHDMLh48SL69u0L4G2VZlVV7pZdCr3jcFNCi2kJUWC1XekaGBhg7969PKXi3ubNm/Htt98iICBA5rHqSqoAsGnTJk6rlzZWYWFhjVvMDQ0N8ezZM7m+t1A7Ulfv5OrQoQNevnyJgQMHory8HFFRURCJRJzlEHrH4aaECr4RQhRenz59sG3bNpiamtb6nPT0dMyaNQsXLlzgMFnjfP311+jVq5dM88H169fjzJkzH9wV1FgbNmyo89ZjrnYfAcCNGzcwa9Ys/Pvvv5g+fTp8fHywdOlSHDlyBFu2bIGVlRUnOfLz8+Hu7o5Xr16hsLAQxsbGko7DO3fubJY1jOSFBiqEKBihXunyycbGBocOHUKnTp1qfc6jR48wevRohbr9k5ycjJkzZ8LKykrSEiE9PR3Xr1/Hzz//jIEDB/IbkCdisRjFxcVo27YtAODBgwe8rK0oLS2V6jjcpUsXwXQcri+GYZCcnIysrCyoqqqic+fO6NOnjyBuY9GtH0IUzNixYzkpsqVIjIyMcO3atQ8OVNLS0hTuKnfAgAGIi4vDjh07cP78eaiqqsLExAT79u3j9DYHIKyO1MrKyrhz5w6ys7MxYsQIVFVV8TI4EHLH4fooLCyEh4cHbt26hTZt2oBhGBQXF6Nbt26IiYmRDAj5QgMVQhTM3Llz+Y4gOKNGjcK6detgb28vs5gWeDtNv27dOowfP56HdI1jbW0Na2trXjMIqSN1cXExPDw88Pfff0NJSQn9+vVDeHg4cnJyEBMTU+P/f7YoUsfh+li5ciXevHmDQ4cOSQbAmZmZ+O6777BmzRosW7aM13x064cQBSekK12+VFVVwc3NDXfv3sX48eNhbW2Ntm3borCwEGlpaTh48CAMDQ2xc+dOqKur8x33g4RYQt/JyQnDhg2rsSO1i4sLpkyZwkkOAAgODkZGRgZWr16NUaNG4fDhwygvL8eiRYtgbGws167S/v7+kj9/rOPw0qVL5ZaDbfb29li/fj3s7OykjqekpMDHx4f3dV00o0KIAhPSlS6fVFRUEBsbi4iICOzfvx+xsbGSx3R0dPDNN9/A09NT8IMUQJgl9J88eYIJEyagZcuWEIlESE9Ph5OTE/z8/LBixQpOByqnT5/GmjVroK+vLzlmYmKCoKAgzJ49W67vXb17DHg7aJkyZUqtHYcVSWVlJXR0dGSO6+jooLi4mIdE0migQogCi4uLw/Tp02u80nV0dOQ7HqdatGgBX19f+Pj44NGjRygqKoK2tjb09fUVak3PqVOnavwzn4TUkfrFixfo0KGDzPG2bduipKSEsxxC7zhcH926dcOuXbuwZMkSqeO7du2Cubk5T6n+DxV8I0SB1XSla2ZmBj8/P+zbt4/veLxQVVWFkZERevTogU6dOinUIEWoqjtSl5aWomvXrjh16hTEYjEvHam7d++Oo0ePyhyPi4tD165dOctR3XH4fULpOFwfCxYswO7duzFhwgSEhYUhLCwMLi4u2L17NxYsWMB3PJpRIUSRCelKl7BDJBLVeXB1+/ZtOad5S0gdqauz3LhxA5WVldi0aROys7Nx69YtbN26lbMcgu84XA/W1taIi4tDdHQ0zp8/D4ZhYGZmhqCgIFhaWvIdjxbTEqLIZs2ahXbt2iEoKAjx8fHYt28fdu/ejcTERKxYsQLnz5/nOyKppwMHDkgGKrm5uYiKisLEiRNhbW0NNTU1pKenIy4uDp6enpwOEt68eYOSkhJoa2ujoKAA8fHx0NXV5aXZ4507d7B161ZkZGRI6pdMmzaNs2Jv1SIjI7Fz505JlWBdXV1Mnz5dMM38mgoaqBCiwLKysjBt2jRMmTIFkyZNwsiRI/Hy5UvJla6Pjw/fEUkjfPvttxgzZgxcXFykjh8+fBjbtm3D/v375Z6htLQU6urqUrM82dnZ0NPT42VxckhICNzd3T9YM4dr//77L5SUlATXzO9DPraj7F1cVh6uCQ1UCFFwQrrSJeyytLREfHw8DAwMpI7/888/GD16NP7++2+5vn9CQgJ+/PFHREVFwcLCQnLcw8MDN2/eREhIiFTDRC707NkTf/zxBz7//HNO37epcXBwkPrv//3vf1BTU4O+vj5UVVWRk5ODiooKWFhYYPfu3TylfIsW0xKioEpLS8EwDNTV1aGtrQ0AKCoqwqRJk2iQ0kQYGBjgyJEjMsf37NmDzp07y/W9hdqReuDAgdi5c6cgts0qslOnTkm+3N3d0bt3b5w+fRpHjhzBH3/8gbNnz6J///4ytVX4QDMqhCggIV7pEvadOHEC8+bNQ8+ePdG9e3eIxWJcu3YNt2/fRlRUlFy79Hp4eMDExKTGjtTV/P39UVBQwGlHajc3N1y5cgVKSkpo3749WrZsKfX4yZMnOcvSVPTt2xfR0dEybRmysrLg5uaGlJQUnpK9Rbt+CFEw1Ve6Y8aMqfFKd8uWLViwYAF27NgBGxsbnlISNgwePBhxcXHYuXOnZGG0ubk5goOD5d7rJyMjA4sXL/7gc1xdXTFr1iy55nhf79690bt3b07fs6mrqKiosQbN8+fPBbG9n2ZUCFEwQr3SJU1LU+1IzSYhdxyuj8WLF+PmzZsICgqChYUFGIZBamoqli9fjoEDByIwMJDXfDSjQoiCEeqVLmGfWCxGfHw80tLSUFFRgfevK98t6c62ptqRmi1C7zhcH4GBgZg/fz4mT54smUFhGAZDhw796O8aLtBAhRAFU1ZW9tFtoVpaWigtLeUoEZGX0NBQxMXFQSQSQVNTk9P3bsodqdkg9I7D9aGpqYmtW7fi/v37yMrKgrKyMszNzaX6KfGJbv0QomDGjx8Pd3d3jB49utbn/PHHH9iyZQvi4+M5TEbY1rt3b/j5+WHs2LGcv3dT6kgtD0LvOMyG6i7sPXv25DUHzagQomDoSrf5KC8vR69evXh576bUkVoehN5xuD5u3ryJwMBAZGVlQSwWyzzOVauG2tCMCiEKhq50m4958+ahd+/e+Oabb3jNUVlZKaiO1JWVlXj+/LmkzxXDMJKr/1GjRnGSYfLkyTA1NZXpOBwSEoKbN2/yXiStPr755huUlZVh/PjxCAsLg5+fH3JychAXF4dVq1Zh2LBhvOajgQohCqi8vFxypVtUVCQ5rqOjg/HjxzfrK92mJDo6GuvXr0f//v1hYmICNTU1qcf5Lm3Oh/Pnz2Px4sV48eKFzGPq6uq4du0aJzmuXbsGd3d3iEQiSRmA1NRUZGZmYsuWLXKtccM2a2trbNu2DZaWlpgwYQIWL14MW1tbxMbG4uzZs4iJieE1Hw1UCFFgQrvSJex6v8z5u5SUlJplcbNx48ahffv2cHNzw/z58xEeHo68vDysX78eYWFhcHJy4izLjRs3EB0djbt370o6Dk+dOlUQHYfrw8rKCseOHYOuri4WLVoEGxsbuLq64tGjR/jqq69w8eJFXvPRGhVCFJiqqiqMjIz4jkHk5NSpU3xHEJx79+4hNDQUIpEI5ubm0NDQgJubGzQ0NLB161ZOByqWlpaIiIjg7P3kxcDAAKmpqRgxYgSMjY2Rnp4OAHj16hXKy8t5TkcDFUIIEbT3i4p16dIF9vb2CldUjC0qKipo06YNgLcfsFlZWejTpw/s7e2xcuVKub63InUcrg83NzfJWpshQ4Zg9OjRUFdXR1paGnr06MFvONBAhRBCBKspFRVjS5cuXXDq1Cm4ubnB2NgYqampmDx5Mp48eSL39z5w4IDUf3+o47AiDVQmTJiAdu3aQUtLCyYmJggLC0NUVBR0dXURFBTEdzxao0IIIULl7++PGzduYM2aNTJFxWxsbBSqqBhbkpKSMG/ePAQFBWHgwIEYMmQI7O3tcefOHVhZWWH9+vWc5IiNjcWZM2ewZs0atG/fHgDw8uVL+Pr6wtTUFD4+PpzkkKeKigpcv36dty3y1WigQgghAtUcioo1xK1bt6CiogKRSITLly8jJiYGurq6mDdvHrS0tDjJIPSOw/Vhbm6OuXPnwsvLS+p4QUEBBgwYwHsdFbr1QwghAtWUioqxZc6cOfD29oaJiQkAwM7OTmYgxwWhdxyuD4ZhEBUVhfv37yM0NBQtWrSQeoxvynwHIIQQUrNu3bph165dMsd37doFc3NzHhLx79KlS2jZsiXfMeDg4IDAwECkpKTg9evXKC4uxtmzZxEYGIjhw4fzHa9elJSUEBUVhRs3bsDd3V2qRo0QBl1064cQQgSqKRUVY8uPP/6IZ8+eYfbs2TAwMJC6+udScXEx5s+fjwsXLsh0HF61ahVvuRpCJBLhwoULUFZWxuzZs/HkyRNERkaiffv26NevH++3fmigQgghAnbjxg3ExMQgKytLoYuKscXZ2Rk5OTm1Xulz/aEq1I7D9WFubo7z58+jffv2KC8vR0BAAE6fPo2QkBD4+PjQQIUQQkjtXr16hX///RedOnUCAPz555+ws7PjbNGo0Bw8ePCDj/PRafpdQuk4XB/VMyrVu5eAtzVjNm3aBLFYTAMVQgghNbt16xamTZuGcePGYfHixQDero0oLy9HdHQ0TE1NeU7YfAm943B9bNy4ER4eHmjVqpXU8fj4eOzduxc7duzgKdlbNFAhhBCBcnNzw+eff45ly5ZJ1jxUVlYiMDAQ+fn5iI6O5jkhP86ePYutW7fi/v372LNnDw4cOIBOnTph9OjRnGUQesfhpoS2JxNCiEDdvHlTZruoqqoqZsyYARcXFx6T8efChQuYM2cOhg8fjuvXr0MsFqOyshL+/v5gGAZjxozhJEdGRoak4/CBAwdgamoKV1dXdOzYEXv37hX8QMXBwaFOO3qUlJSQlJTEQaLa0UCFEEIEqnXr1nj06JHMAs2nT58q1K4SNm3YsAELFy7ElClTcPz4cQCAt7c3NDU1sXXrVs4GKmKxGB06dADwfz2HbG1t4ejoiMjISE4yNMbYsWMFsfW4LmigQgghAjVkyBAsW7YMS5culezySU9PR3BwMAYPHsxzOn7cuXMHq1atkjk+dOjQejUNbCyhdxz+mLlz5/Idoc5ooEIIIQK1cOFC5OTkYOrUqVJXv4MHD4avry+PyfjTpk0bPH36VLILqtq9e/fwySefcJZD6B2H6yszM1NqYTDDMJIdTCEhIbxmo4EKIYQIlIaGhqS0+d27d6GqqgoTExMYGhryHY03I0eORGhoKEJDQ6GkpITXr1/j3LlzWL58Ob788kvOcgi943B9xMTEYOXKlQDerkmp3mOjpKQEW1tbPqO9zUG7fgghRPE8efIEHTt25DsG5yoqKuDn54cjR44A+L8P1i+++AIRERFQV1fnPZ8QOg7Xh5OTE4YNG4Y5c+Zg0KBBOHjwIAoLC7Fw4UK4uLhgypQpvOajgQohhAjUo0ePsHLlSmRlZaGqqgrA/03Jv3jxAhkZGTwn5M/Dhw9x+/ZtiMVimJqaonPnzmAYhrMFokLvOFwfFhYWSExMRKdOnTBt2jS4urrCyckJ58+fx4oVK5CQkMBrPmpKSAghAhUcHIw7d+5gyJAhyM/Px/Dhw9GtWzcUFBRg6dKlfMfjhaOjIwoLC2FgYIChQ4fiyy+/ROfOnZGfn89p76PqjsOLFi2SWTyraNf/GhoakoFwp06dcO/ePQCAiYkJcnNz+YwGgNaoEEKIYKWlpeGXX35B7969kZycDCcnJ1haWuKnn37C2bNn8dVXX/EdkROJiYlITk4GAOTm5iI4OFimg3Jubi6n222rOw4HBATA3d0dv/zyC7S1tSWPKRIbGxts3rwZQUFB6Nq1K/bt24cZM2YgNTUVrVu35jsezagQQohQlZeXS3a3GBkZ4c6dOwCAMWPG4O+//+YzGqesra2Rm5uLx48fAwDy8vLw+PFjyVdubi40NDQkC0K5wDAMjIyMsGfPHigrK8PFxQV3796FsrLifaz6+PggOTkZcXFxGD58OAoKCmBnZ4fFixdj3LhxfMejGRVCCBEqPT09ZGVlQVdXF0ZGRpJ1D2KxGK9fv+Y5HXd0dXWxfft2AG+3BW/cuJHTrcg1qZ41adeuHWJjYxEQEICvv/6a9628DWFqaoqkpCSUlJSgdevW2Lt3L+Lj46Grq4uhQ4fyHY8W0xJCiFBFRkYiOjoaq1atQrt27eDu7o45c+bgwoULKC0txe7du/mOyJu8vDxkZ2ejV69eeP36tVTnXy4IveNwXZWWlkJdXV3qdlV2djb09PR430FVTfHmqAghpJmYMWMGPD09wTAMLC0t4eXlhU2bNuHZs2f44Ycf+I7Hi4qKCnh7e8PBwQEzZ86UnIupU6eiuLiYsxxz5syBhoaGzLEVK1YIovZIXSQkJMDBwQG3bt2SOh4aGoqBAwfixIkTPCWTRjMqhBBCFMa6detw7NgxLF26FLNmzcLhw4eRl5cHf39//Oc//2m2u6HqKyUlBVOnTsWYMWPg7e0t6VsEvJ1R2bJlCw4fPowdO3bAxsaGx6Q0UCGEEKJAnJ2dsXTpUvTt2xfW1tY4fPgw9PX1cfHiRfj6+kp2B8mDInUc/hgPDw+YmJggICCg1uf4+/ujoKAAUVFRHCaTRYtpCSGEKIz8/HyZPj/A2wW3RUVFcn1vReo4/DEZGRlYvHjxB5/j6uqKWbNmcZSodjRQIYQQojBMTExw8eJFTJgwQer4kSNH0LlzZ7m+tyJ1HP6YsrKyjy6W1dLSQmlpKUeJakcDFUIIIQpj7ty58Pb2xr1791BVVYWDBw/iwYMHOH78OH766SdOswi54/DHGBkZ4dq1azXOTlVLS0uDnp4eh6lqRgMVQggRsMrKSjx//lym1096ejpGjRrFczruDRo0COvXr0dkZCRUVFSwdetWdOnSBT/99BOGDBnCWQ6hdxz+mFGjRmHdunWwt7fHp59+KvN4fn4+1q1bh/Hjx/OQThotpiWEEIE6f/48Fi9ejBcvXsg8pq6ujmvXrvGQil9bt27FiBEjavxw5ZLQOw5/TFVVFdzc3HD37l2MHz8e1tbWaNu2LQoLC5GWloaDBw/C0NAQO3fu5L2eCg1UCCFEoMaNG4f27dvDzc0N8+fPR3h4OPLy8rB+/XqEhYXBycmJ74ics7W1xf79+2FgYMBrDqF3HK6L8vJyREREYP/+/VILkXV0dDB+/Hh4enryPkgB6NYPIYQI1r179xAaGgqRSARzc3NoaGjAzc0NGhoa2Lp1a7McqFhZWeHUqVOYOnUqrzlq6jjs5OQkmI7DddGiRQv4+vrCx8cHjx49QlFREbS1taGvry+o3U1UmZYQQgRKRUUFbdq0AQAYGBggKysLAGBvb4/s7Gw+o/FGU1MTq1atQu/evTFx4kS4u7tLfXGluuNwaWkpunbtilOnTkEsFgum43B9qKqqwsjICD169ECnTp0ENUgBaKBCCCGC1aVLF5w6dQoAYGxsjNTUVADAkydP+IzFKw0NDYwZMwYODg4wNjaGnp6e1BdXhN5xuCmhNSqEECJQSUlJmDdvHoKCgjBw4EAMGTIE9vb2uHPnDqysrLB+/Xq+I3IuISEBAwYM4L17MgC8efMGJSUl0NbWRkFBgaA6DjclNFAhhBABu3XrFlRUVCASiXD58mXExMRAV1cX8+bNg5aWFt/xOGdnZ4fffvtN7sXdPkQROg43JXTrhxBCBGrOnDlQV1eHSCQC8PZDetOmTQgKCmqWgxQAMDQ0lKzV4YOidBxuSmjXDyGECNSlS5fQsmVLvmMIikgkwqJFi7BlyxYYGhrKnJ+wsDC5vXdKSgp8fX0xZswYmTouAQEB2LJlCxYsWCCIjsNNCd36IYQQgfrxxx/x7NkzzJ49GwYGBmjRogXfkXjn5ub2wcd37Nght/dWpI7DTQkNVAghRKCcnZ2Rk5NT63bR27dvc5yoeevTpw+2bdsGU1PTWp+Tnp6OWbNm4cKFCxwma9ro1g8hhAiUp6cn3xEEia/+R4rUcbgpoYEKIYQI1NixY/mOIDgf638kz4GKInUcbkpo1w8hhAjY2bNn4e7ujv79+yM3NxcbNmzAH3/8wXcs3qxduxZdu3ZFZGQk1NXVsXHjRgQEBEBTUxOrV6+W63tXdxzOz8+v8fHqjsNUR4VdNKNCCCECdeHCBcyZMwfDhw/H9evXIRaLUVlZCX9/fzAMgzFjxvAdkXN89j/69ttvcfz4cYwYMeKDHYc9PDzklqE5ooEKIYQI1IYNG7Bw4UJMmTIFx48fBwB4e3tDU1MTW7dubZYDlZr6H/Xp0wf29vZYuXKl3N87NjZW0nE4NjZW8piOjg6++eYbwXQcbkro1g8hhAjUnTt34ODgIHN86NChyMnJ4SER//juf1TdcfjChQs4evQodu/ejT///BPJycnw9vamQYoc0IwKIYQIVJs2bfD06VOZxZv37t0TRK8bPsyYMQPz5s2DmpoaRowYgQ0bNmDGjBm4c+cO7O3tOctR3XGYyB/NqBBCiECNHDkSoaGhyMzMhJKSEl6/fo1z585h+fLl+PLLL/mOxwsnJyf8/vvv6NGjB3R1dbFlyxaoqKjA0dERwcHBfMcjckAF3wghRKAqKirg5+eHI0eOAACUlJTAMAy++OILRERENNvbDP/88w+Ki4thYWEBANi2bRsGDhwIQ0NDfoMRuaCBCiGECNzDhw9x+/ZtiMVimJqaonPnzmAYptaKtU3ZX3/9BU9PT0yZMgXe3t4AgIkTJyIrKwtRUVGwtbXlOSFhGw1UCCFEoBwdHbF//36ZTsn5+fkYNWoUUlJS+AnGIxcXF/Tr108ySKm2du1aXL58Gbt37+YpGZEXWkxLCCECkpiYiOTkZABAbm4ugoODZToE5+bmNsvZFODtQuKffvpJ5viECRPk2pCQ8IcGKoQQIiDW1tbYvXs3qie78/LyoKamJnlcSUkJGhoacq8ZIlTa2trIzMyEvr6+1PG7d+9K6quQpoUGKoQQIiC6urrYvn07AMDNzQ0bN25stluRazJ69GgsXboUhYWFsLKyAvC2Y3FERESzLIDXHNAaFUIIEbi8vDxkZ2ejV69eeP36Ndq3b893JN5UVlYiJCQE+/fvR2VlJRiGgaqqKtzc3LBw4UKoqtL1d1NDAxVCCBGoiooK+Pr64ujRo1BWVsbx48excuVKvH79Ghs2bICmpibfEXnz+vVrPHjwAKqqqjA0NGy2W7WbAyr4RgghAvXLL78gMzMT27ZtkyyodXNzw8OHDxEeHs5zOn61bt0aFhYWEIlENEhp4migQgghAnXkyBEEBgaid+/ekmO9e/fGjz/+iJMnT/KYjBDu0ECFEEIEKj8/X6bPD/B2wW1RUREPiQjhHg1UCCFEoExMTHDx4kWZ40eOHEHnzp15SEQI92h5NCGECNTcuXPh7e2Ne/fuoaqqCgcPHsSDBw9w/PjxGoueEdIU0a4fQggRsHPnziEyMhIZGRkQi8Xo0qULpk+fjiFDhvAdjRfPnj1DREQE0tLSUFFRgfc/wmjtTtNDMyqEECJQW7duxYgRIxAXF8d3FMEIDAzEzZs3MXz4cKpE20zQjAohhAiUra0t9u/fDwMDA76jCEaPHj2wZcsW6pLcjNBiWkIIESgrKyucOnWK7xiCoqGh0awr8zZHNKNCCCECNX/+fPz5559o27YtDA0NZbooV/cEak5WrlyJly9fIjg4GCoqKnzHIRygNSqEECJQGhoa1GjvPYWFhUhISMCZM2egr6+PFi1aSD3eHAdvTR0NVAghRKD69euHAQMGUPfk94wYMYLvCIRDdOuHEEIEys7ODr/99hsVdyPNGi2mJYQQgTI0NERWVhbfMQTn6dOn2LhxIxYuXIjnz5/j2LFjuH//Pt+xiJzQjAohhAhUUFAQ9u3bB5FIVONi2rCwMJ6S8efhw4f46quvoKmpifz8fBw9ehSrV69GcnIyYmNjYWVlxXdEwjKaUSGEEIF68OABevbsidatW+PZs2d4/Pix1FdztGLFCjg5OSEpKQlqamoAgLVr18LBwQHh4eE8pyPyQItpCSFEoHbs2MF3BMFJS0tDXFwclJSUJMdUVVXh5eWFr776isdkRF5ooEIIIQJWWVmJ58+fo6qqCgDAMAzKy8uRnp6OUaNG8ZyOe2KxGGKxWOb469evqa5KE0UDFUIIEajz589j8eLFePHihcxj6urqzXKg0r9/f0RGRmL16tWSY4WFhVi9ejXs7e15TEbkhRbTEkKIQI0bNw7t27eHm5sb5s+fj/DwcOTl5WH9+vUICwuDk5MT3xE5l5+fD3d3d7x69QqFhYUwNjZGbm4utLS0sHPnTujp6fEdkbCMZlQIIUSg7t27h9DQUIhEIpibm0NDQwNubm7Q0NDA1q1bm+VA5dNPP8WhQ4eQkJCA27dvQywWY9KkSRg9ejQ0NTX5jkfkgAYqhBAiUCoqKmjTpg0AwMDAAFlZWejTpw/s7e2xcuVKntPxp1WrVpgwYQLfMQhHaHsyIYQIVJcuXSTdk42NjZGamgoAePLkCZ+xCOEUzagQQohAzZgxA/PmzYOamhpGjBiBDRs2YMaMGbhz5w4tHCXNBi2mJYQQAbt16xZUVFQgEolw+fJlxMTEQFdXF/PmzYOWlhbf8QiRO5pRIYQQAWvdujWKi4sBvG1SePv2bQwcOJAGKaTZoIEKIYQI1F9//QVPT09MmTIFFhYWAIDExEREREQgKioKtra2PCfknr+/f43HlZSUoKamho4dO2Lo0KEwMjLiOBmRF7r1QwghAuXi4oJ+/frB29tb6vjatWtx+fJl7N69m6dk/Fm0aBGOHDkCHR0dWFpaAnh7eyw/Px9WVlYoLCzE//73P0RHR6Nnz548pyVsoF0/hBAiUPfu3YOLi4vM8QkTJuDOnTs8JOKfuro6nJ2dcfLkSfz888/4+eefceLECYwYMQJdu3bFsWPH4ObmhoiICL6jEpbQQIUQQgRKW1sbmZmZMsfv3r0rqa/S3Bw7dgxz585FixYtJMfU1NQwc+ZMxMfHA3g7E3Xr1i2+IhKW0RoVQggRqNGjR2Pp0qUoLCyElZUVACA9PR0REREYM2YMv+F4oqqqioKCAnTu3Fnq+LNnzyQdlauqqqCqSh9vTQX9nySEEIGaPXs2/v33XwQHB6OyshIMw0BVVVXS+6c5GjJkCIKCgrB06VJYWVmBYRhcv34dy5cvh6OjI0pKSrBp0yZ0796d76iEJbSYlhBCBO7169d48OABVFVVYWhoCHV1db4j8ebNmzfw9fXFn3/+KZlBAYBhw4YhODgYly5dQnBwMDZv3gyRSMRjUsIWGqgQQghROI8ePcLt27ehoqICMzMzfP755wCA8vJyqfUrRPHRQIUQQojCKSgoQEVFBd7/CPvss894SkTkhdaoEEIIURhpaWnw9/dHTk6O1HGGYaCkpITbt2/zlIzIC82oEEIIURjjxo2DhoYGpk6dWuMWbTs7Ox5SEXmiGRVCCCEK4+7duzh06BBMTEz4jkI4QgMVQggRqGfPniEiIgJpaWk1rsc4efIkT8n4o6uri9evX/Mdg3CIBiqEECJQgYGBuHnzJoYPH95sK9G+z9PTE6GhoVi2bBmMjY2hpqbGdyQiZ7RGhRBCBKpHjx7YsmVLs+ySXBtnZ2fk5eWhqqqqxsdpMW3TQzMqhBAiUBoaGmjfvj3fMQTF09OT7wiEYzSjQgghArVy5Uq8fPkSwcHBUFFR4TsOIbygGRVCCBGowsJCJCQk4MyZM9DX15epuLp9+3aeknHL398fS5YsgaamJvz9/Wt9npKSEkJDQzlMRrhAAxVCCBGwESNG8B2Bd48fP4ZYLJb8mTQvdOuHEEIIIYJFMyqEECJgT58+xd69e/HgwQMEBATgypUrMDU1hbGxMd/ReFNaWoqsrCyZ2jJKSkq0Q6oJohkVQggRqIcPH+Krr76CpqYm8vPzcfToUaxevRrJycmIjY2FlZUV3xE5d/LkSfj5+aG4uFimAB71+mmaaKBCCCEC5enpCW1tbYSEhMDGxgaHDx+Grq4uFi9ejKdPn2LHjh18R+TcsGHD0KVLF3h5edVYBE9PT4+HVESe6NYPIYQIVFpaGuLi4qCkpCQ5pqqqCi8vL3z11Vc8JuPP48ePERkZiU6dOvEdhXBEme8AhBBCaiYWiyW7Xd71+vXrZltXxdDQEE+ePOE7BuEQDVQIIUSg+vfvj8jISKnBSmFhIVavXg17e3sek/Fn0aJFWL58OU6dOoV//vkHeXl5Ul+k6aE1KoQQIlD5+flwd3fHq1evUFhYCGNjY+Tm5kJLSws7d+5slusxunXrJunz8+4tMYZhaDFtE0UDFUIIEbDS0lIkJCTg9u3bEIvF6NKlC0aPHg1NTU2+o/Hi8uXLH3zczs6OoySEKzRQIYQQQohg0a4fQgghCqOkpASxsbFIS0uTKfgGNJ/+R80JDVQIIYQojKCgIJw8eRL9+vVDhw4d+I5DOEC3fgghhCiMnj17Ijw8HIMGDeI7CuEIbU8mhBCiMJSVlWFiYsJ3DMIhmlEhhBCB8vf3r/G4kpIS1NTU0LFjRwwdOhRGRkYcJ+PPkiVL0KFDByxYsIDvKIQjtEaFEEIEqqKiAkeOHIGOjg4sLS0BALdu3UJ+fj6srKyQkpKCX3/9FdHR0ejZsyfPabmhra2N6OhonDt3DkZGRmjRooXU42FhYTwlI/JCAxVCCBEodXV1ODs7Y/Xq1ZIP5IqKCgQEBKBNmzYICgpCeHg4IiIimk2DwuvXr0u6Rj99+pTnNIQLdOuHEEIEytbWFrt370bnzp2ljt+7dw+TJk3ClStX8M8//2DcuHFIS0vjKSUh8kUzKoQQIlCqqqooKCiQGag8e/ZMUj6+qqoKqqrN51f5x/r5fPbZZxwlIVxpPn+7CSFEwQwZMgRBQUFYunQprKyswDAMrl+/juXLl8PR0RElJSXYtGkTunfvzndUzjg4OEj1+Hkf9fppeujWDyGECNSbN2/g6+uLP//8U+rDediwYQgODsalS5cQHByMzZs3QyQS8ZiUO+/3+qmqqsKDBw8QGxsLPz8/ODg48JSMyAsNVAghROAePXqE27dvQ0VFBWZmZvj8888BAOXl5TK7Xpqr5ORkbNiwAXv37uU7CmEZ3fohhBCBa9WqFbp37y7pa1O9ToPWY/wfQ0NDZGZm8h2DyAENVAghRKDS0tLg7++PnJwcqeMMw0BJSalZrseoaTFtcXExIiMjJTNNpGmhgQohhAhUSEgIOnToAF9fX7Rp04bvOIJQ02JahmGgoaGB1atX85SKyBOtUSGEEIHq3r07Dh06RL1t3vH+YloAUFNTg6mpKVq3bs1DIiJvNKNCCCECpauri9evX/MdQ1C2b98Ob29vGrw1I9Q9mRBCBMrT0xOhoaG4c+cOKioq+I4jCJcuXULLli35jkE4RLd+CCFEoJydnZGXl4eqqqoaH2+Oi2l//PFHPHv2DLNnz4aBgQFtz24GaKBCCCECdfDgwQ8+PnbsWI6SCIezszNycnJqrU7bHAdvTR0NVAghhCgMGrw1PzRQIYQQAfH398eSJUugqakJf3//Wp+npKSE0NBQDpMRwg/a9UMIIQLy+PFjiMViyZ8Jae5oRoUQQgghgkUzKoQQImClpaXIyspCRUUF3r2uVFJSgq2tLY/JCOEGDVQIIUSgTp48CT8/PxQXF+P9ye/m2uuHND9064cQQgRq2LBh6NKlC7y8vGrs9aOnp8dDKv49ffoUe/fuxf3797FkyRJcuXIFpqamMDY25jsakQMaqBBCiEB1794dR44cQadOnfiOIhgPHz7EV199BU1NTeTn5+Po0aNYvXo1kpOTERsbCysrK74jEpZRCX1CCBEoQ0NDPHnyhO8YgrJixQo4OTkhKSkJampqAIC1a9fCwcEB4eHhPKcj8kADFUIIEahFixZh+fLlOHXqFP755x/k5eVJfTVHaWlpmDp1qlRlWlVVVXh5eSEjI4PHZEReaDEtIYQIlJeXF6qqquDl5SX1wcwwTLNdTCsWiyV1Zt71+vVrqKio8JCIyBsNVAghRKBiYmL4jiA4/fv3R2RkJFavXi05VlhYiNWrV8Pe3p7HZEReaDEtIYQQhZGfnw93d3e8evUKhYWFMDY2Rm5uLrS0tLBz585muxOqKaOBCiGECFRJSQliY2ORlpYmU/ANALZv385TMn6VlpYiISEBt2/fhlgsRpcuXTB69GhoamryHY3IAd36IYQQgQoKCsLJkyfRr18/dOjQge84gtGqVStMmDCB7xiEIzSjQgghAtWzZ0+Eh4dj0KBBfEfhlbu7e52f21xnmZoy2p5MCCECpaysDBMTE75j8E5PT0/ypaOjg8uXL+PVq1cwMTGBmZkZKioqkJqaSpVpmyiaUSGEEIFasmQJOnTogAULFvAdRTD8/f3xySefwM/PT+p4REQEsrOzsWHDBp6SEXmhNSqEECJQ2traiI6Oxrlz52BkZIQWLVpIPR4WFsZTMv4cO3YMBw8elDk+ZswYjBkzhvtARO5ooEIIIQJ1/fp1Se+ap0+f8pxGGNq2bYuMjAwYGhpKHb969Srat2/PTygiVzRQIYQQgdqxYwffEQRn4sSJCAoKQnZ2NiwsLCAWi5GWloa4uDh89913fMcjckBrVAghRKA+1s/ns88+4yiJsERGRmLnzp149uwZAEBXVxfTp0+Hq6srz8mIPNBAhRBCBEokEkn1+Hlfc+z1865///0XSkpK0NLS4jsKkSO69UMIIQL1fk2QqqoqPHjwALGxsTK7Xpqjdu3a8R2BcIBmVAghRMEkJydjw4YN2Lt3L99RCJE7KvhGCCEKxtDQEJmZmXzHIIQTdOuHEEIEqqbFtMXFxYiMjMTnn3/OQyJCuEcDFUIIESgHBweZxbQMw0BDQwOrV6/mKRX/GIZBcnIysrKyoKqqis6dO6NPnz5QUVHhOxqRA1qjQgghAnX58mWZY2pqajA1NUXr1q15SMS/wsJCeHh44NatW2jTpg0YhkFxcTG6deuGmJgYtG3blu+IhGW0RoUQQgRq+/btaN++Pezs7CRf1tbWzXaQAgArV67EmzdvcOjQIVy5cgVXr17FoUOHUF5ejjVr1vAdj8gBDVQIIUSgLl26hJYtW/IdQ1BOnz6NH374ASKRSHJMJBLh+++/R1JSEo/JiLzQQIUQQgRq7NixCA8Px927d1FeXs53HEGorKyEjo6OzHEdHR0UFxfzkIjIGy2mJYQQgTp79ixycnJw/PjxGh9vjpVpu3Xrhl27dmHJkiVSx3ft2gVzc3OeUhF5ooEKIYQIlKenJ98RBGfBggVwd3fH9evXYWNjAwBITU1FZmYmtmzZwnM6Ig+064cQQohCuXHjBqKjo3H37l0wDAMzMzNMnToVlpaWfEcjckADFUIIIYQIFt36IYQQImgbN26s83PnzJkjxySEDzSjQgghRNAcHByk/vt///sf1NTUoK+vD1VVVeTk5KCiogIWFhbYvXs3TymJvNCMCiGEEEE7deqU5M+xsbE4c+YM1qxZg/bt2wMAXr58CV9fX5iamvIVkcgRzagQQoiAPX36FHv37sX9+/exZMkSXLlyBaampjA2NuY7Gi/69u2L6OhoqYJvAJCVlQU3NzekpKTwlIzICxV8I4QQgXr48CFGjhyJgwcP4s8//0RJSQkSExMxfvx4/P3333zH40VFRQVKSkpkjj9//lymgSNpGmigQgghArVixQo4OTkhKSkJampqAIC1a9fCwcEB4eHhPKfjh4ODAwIDA5GSkoLXr1+juLgYZ8+eRWBgIIYPH853PCIHtEaFEEIEKi0tDXFxcVIzBaqqqvDy8sJXX33FYzL+BAYGYv78+Zg8ebLkvDAMg6FDh2Lx4sU8pyPyQAMVQggRKLFYDLFYLHP89evXUFFR4SER/zQ1NbF161bcv38fWVlZUFZWhrm5OfT19fmORuSEbv0QQohA9e/fH5GRkVKDlcLCQqxevRr29vY8JuOfsbExhg4dCmdnZ+jr66O8vBypqal8xyJyQLt+CCFEoPLz8+Hu7o5Xr16hsLAQxsbGyM3NhZaWFnbu3Ak9PT2+I3Lu5s2bCAwMRFZWVo2zTc2xUWNTRwMVQggRsNLSUiQkJOD27dsQi8Xo0qULRo8eDU1NTb6j8eKbb75BWVkZxo8fj7CwMPj5+SEnJwdxcXFYtWoVhg0bxndEwjJao0IIIQLWqlUrTJgwge8YgpGRkYFt27bB0tISBw4cgKmpKVxdXdGxY0fs3buXBipNEA1UCCFEQNzd3ev83O3bt8sxiTCJxWJ06NABAGBgYICsrCzY2trC0dERkZGRPKcj8kCLaQkhRED09PQkXzo6Orh8+TJevXoFExMTmJmZoaKiAqmpqc22Mq2BgYFk0ayxsTHS09MBAK9evUJ5eTmf0Yic0IwKIYQISFhYmOTP/v7+mDJlCvz8/KSeExERgezsbK6jCYKbmxuWLFkCABgyZAhGjx4NdXV1pKWloUePHvyGI3JBi2kJIUSgrK2tcfDgQRgaGkod/+effzBmzBhcv36dl1x8S0pKgpaWFmxtbREfH4+oqCjo6uoiKCioWe6EaupoRoUQQgSqbdu2yMjIkBmoXL16VdI5uDlycnKS/HnkyJEYOXIkKioqcP36dRqoNEE0UCGEEIGaOHEigoKCkJ2dDQsLC4jFYklZ/e+++47veLwwNzfH3Llz4eXlJXW8qKgI7u7uVEelCaJbP4QQImCRkZHYuXMnnj17BgDQ1dXF9OnT4erqynMyfohEIrRq1QqOjo4IDQ1FixYtAAAFBQXo378/MjMzeU5I2EYDFUIIUQD//vsvlJSUoKWlxXcUXpmbm2PHjh0ICAiAtrY2fvnlF2hra6OgoAADBgygGZUmiLYnE0KIAmjXrl2zH6QAbzslGxkZYc+ePVBWVoaLiwvu3r0LZWX6OGuq6P8sIYQQhaGkpATg7cAtNjYWNjY2+Prrr5GSksJzMiIvNFAhhBCiMN5drdCiRQuEh4dj6tSpWLRoEY+piDzRrh9CCCEKY86cOdDQ0JA5ZmBggL179/KUisgTLaYlhBABYxgGycnJyMrKgqqqKjp37ow+ffpARUWF72iEcIJmVAghRKAKCwvh4eGBW7duoU2bNmAYBsXFxejWrRtiYmLQtm1bviNywsHBQbI25UOUlJSQlJTEQSLCJRqoEEKIQK1cuRJv3rzBoUOHIBKJAACZmZn47rvvsGbNGixbtoznhNwYO3ZsnQYqpGmiWz+EECJQ9vb2WL9+Pezs7KSOp6SkwMfHBxcuXOApGSHcoRkVQggRqMrKSujo6Mgc19HRQXFxMQ+JhCEzMxNZWVkQi8UA3q7jKS8vR3p6OkJCQnhOR9hGAxVCCBGobt26YdeuXViyZInU8V27dsHc3JynVPyKiYnBypUrAbxdk1J9U0BJSQm2trZ8RiNyQgMVQggRqAULFsDd3R3Xr1+HjY0NACA1NRWZmZnYsmULz+n4ERcXh+nTp2POnDkYNGgQDh48iMLCQixcuBCOjo58xyNyQAXfCCFEoKytrREXFwc9PT2cP38eycnJ0NfXx2+//QZ7e3u+4/HiyZMnmDBhAlq2bAmRSIT09HSYmZnBz88P+/bt4zsekQOaUSGEEAGztLREREQE3zEEQ0NDA1VVVQCATp064d69e3BycoKJiQlyc3N5TkfkgQYqhBAiIBs3bqzzc+fMmSPHJMJkY2ODzZs3IygoCF27dsW+ffswY8YMpKamonXr1nzHI3JA25MJIURAHBwcpP77f//7H9TU1KCvrw9VVVXk5OSgoqICFhYW2L17N08p+ZOVlYVp06ZhypQpmDRpEkaOHImXL1+itLQUHh4e8PHx4TsiYRkNVAghRKBiY2Nx5swZrFmzBu3btwcAvHz5Er6+vjA1NW22H8pv3rxBSUkJtLW1UVBQgPj4eOjq6mLo0KF8RyNyQAMVQggRqL59+yI6OlpSlbZaVlYW3NzckJKSwlMyfpSWlkJdXV2qSm12djb09PSgrq7OYzIiT7TrhxBCBKqiogIlJSUyx58/f97sSsonJCTAwcEBt27dkjoeGhqKgQMH4sSJEzwlI/JGAxVCCBEoBwcHBAYGIiUlBa9fv0ZxcTHOnj2LwMBADB8+nO94nElJSYGvry8GDRqETz/9VOqxgIAAODg4YMGCBUhLS+MpIZEnuvVDCCECVVxcjPnz5+PChQuSGRSGYTB06FCsWrUKLVq04DkhNzw8PGBiYoKAgIBan+Pv74+CggJERUVxmIxwgQYqhBAicPfv30dWVhaUlZVhbm4OfX19viNxqk+fPti2bRtMTU1rfU56ejpmzZpFjRqbIKqjQgghAmdsbAxjY2PJf1c34OvZsyePqbhTVlb20cWyWlpaKC0t5SgR4RINVAghRKBu3ryJwMBAqU7B77p9+zYPqbhnZGSEa9euoVOnTrU+Jy0tDXp6ehymIlyhxbSEECJQYWFhUFFRwffffw81NTUEBgZi8uTJUFVVxdq1a/mOx5lRo0Zh3bp1yM/Pr/Hx/Px8rFu3juqoNFE0o0IIIQKVkZGBbdu2wdLSEgcOHICpqSlcXV3RsWNH7N27F8OGDeM7Iie+/fZbHD9+HCNGjMD48eNhbW2Ntm3borCwEGlpaTh48CAMDQ3h4eHBd1QiBzRQIYQQgRKLxejQoQMAwMDAAFlZWbC1tYWjoyMiIyN5TscdFRUVxMbGIiIiAvv370dsbKzkMR0dHXzzzTfw9PSkom9NFA1UCCFEoAwMDJCamooRI0bA2NgY6enpAIBXr16hvLyc53TcatGiBXx9feHj44NHjx6hqKgI2tra0NfXb3bF75obGqgQQohAubm5YcmSJQCAIUOGYPTo0VBXV0daWhp69OjBbzieqKqqwsjIiO8YhENUR4UQQgQsKSkJWlpasLW1RXx8PKKioqCrq4ugoCDa5UKaBRqoEEKIgqmoqMD169fRq1cvvqMQIne0PZkQQgTK3Nwcv/zyi8zxoqIiuLu785CIEO7RQIUQQgSKYRhERUVh0aJFMotnaTKcNBc0UCGEEIFSUlJCVFQUbty4AXd3d7x48ULqMUKaAxqoEEKIQDEMAyMjI+zZswfKyspwcXHB3bt3oaxMv7pJ80F/2wkhRKCqZ03atWuH2NhY2NjY4Ouvv0ZKSgrPyQjhDg1UCCFEoN5dh9KiRQuEh4dj6tSpWLRoEY+pCOEWFXwjhBCBmjNnDjQ0NGSOGRgYYO/evTylIoRbVEeFEEIIIYJFMyqEECIgDg4OddrRo6SkhKSkJA4SEcIvGqgQQoiAjB07lrYeE/IOuvVDCCGEEMGiGRVCCBGwzMxMZGVlQSwWA3i7E6i8vBzp6ekICQnhOR0h8kcDFUIIEaiYmBisXLkSwNs1KdUT4EpKSrC1teUzGiGcoToqhBAiUHFxcZg+fTr+/vtvtGvXDmfPnsUff/wBExMTODo68h2PEE7QQIUQQgTqyZMnmDBhAlq2bAmRSIT09HSYmZnBz88P+/bt4zseIZyggQohhAiUhoYGqqqqAACdOnXCvXv3AAAmJibIzc3lMxohnKGBCiGECJSNjQ02b96M0tJSdO3aFadOnYJYLEZqaipat27NdzxCOEEDFUIIESgfHx8kJycjLi4Ow4cPR0FBAezs7LB48WKMGzeO73iEcILqqBBCiIC9efMGJSUl0NbWRkFBAeLj46Grq4uhQ4fyHY0QTtBAhRBCBKi0tBTq6upSVWqzs7Ohp6cHdXV1HpMRwi269UMIIQKTkJAABwcH3Lp1S+p4aGgoBg4ciBMnTvCUjBDu0UCFEEIEJCUlBb6+vhg0aBA+/fRTqccCAgLg4OCABQsWIC0tjaeEhHCLbv0QQoiAeHh4wMTEBAEBAbU+x9/fHwUFBYiKiuIwGSH8oBkVQggRkIyMDLi4uHzwOa6ursjIyOAoESH8ooEKIYQISFlZ2UcXy2ppaaG0tJSjRITwiwYqhBAiIEZGRrh27doHn5OWlgY9PT2OEhHCLxqoEEKIgIwaNQrr1q1Dfn5+jY/n5+dj3bp1VEeFNBu0mJYQQgSkqqoKbm5uuHv3LsaPHw9ra2u0bdsWhYWFSEtLw8GDB2FoaIidO3dSPRXSLNBAhRBCBKa8vBwRERHYv38/ioqKJMd1dHQwfvx4eHp60iCFNBs0UCGEEIGqrKzEo0ePUFRUBG1tbejr60tVqiWkOaCBCiGEEEIEixbTEkIIIUSwaKBCCCGEEMGigQohhBBCBIsGKoQQztCSOEJIfdFAhRAidy9fvoSvry+uXr3KdxRCiIKhgQohRO5u376NP/74A2KxmO8ohBAFQwMVQgghhAgWDVQIIR/FMAxiY2MxbNgwWFpaYvDgwdi6datkzcnvv/+OcePGoUePHrC0tMTo0aNx9OhRAEBKSgrc3d0BAO7u7nBzc5O8blJSEsaNG4fu3bujX79+CAkJQUlJidR7nzlzBuPGjYOlpSWGDBmChIQEDB48GBs2bJA85+nTp/D398fAgQNhaWkJFxcXnDx5Uup1zMzMsHHjRslrbdy4Ed27d8fatWulnldaWoqePXti06ZN7J1AQkiDqfIdgBAifKtWrcK2bdswdepU9OvXD+np6QgPD0dlZSU0NTUREhKCuXPnomfPnigqKkJUVBQWLVoEa2trdOvWDUFBQQgODkZQUBB69+4NAIiPj8eiRYswcuRILFiwALm5ufjpp59w7949xMTEQElJCZcuXYKXlxcGDRqE+fPn4+HDh/jhhx9QVlYmyVZQUAAXFxe0bNkS3t7eaNeuHQ4cOIDZs2dj1apVGDVqlOS5v/76KxYuXAgjIyPo6ekhOzsb8fHx8Pb2llR8PXHiBEpKSjBmzBhOzzEhpGY0UCGEfNDLly+xfft2fPvtt/juu+8AAH379sWzZ89w5coVdO7cGR4eHvDy8pJ8j56eHsaNG4fU1FQMHz4cnTt3BgB07twZnTt3BsMwCA8Px4ABAxAeHi75PkNDQ0yZMgVnz57FF198gQ0bNqBLly7YuHGjZCDRvn17+Pj4SL4nJiYGL168wPHjx6GnpwcAGDhwIKZMmYJVq1ZhxIgRUFZ+O3lsa2uLqVOnSr53/PjxSExMREpKCuzt7QEAhw4dQt++faGrqyuP00kIqSe69UMI+aDr16+jsrISzs7OUse///57bNmyBX5+fli0aBFevnyJ69ev448//kBcXByAt831anL//n08efIEDg4OqKyslHz16tULmpqauHDhAsrLy3Ht2jU4OztL9bcZOnQoVFX/7xrr8uXLsLa2lgxSqo0aNQrPnj3D/fv3JcfMzc2lntO3b1989tln+OOPPwAAT548wcWLFzF27NgGnClCiDzQQIUQ8kGFhYUAAG1t7Rofz8nJwZQpU9CrVy98++232Lp1KyorKwHUXjel+jWXLVuGbt26SX0VFxfj6dOnKCwsRFVVFdq3by/1vSoqKtDS0pL8d1FRETp06CDzHjo6OgDezghV09DQkHqOsrIyxo0bh+PHj+PNmzf4448/oKmpicGDB9d+QgghnKJbP4SQD2rbti0A4MWLFzA2NpYcz8vLw8OHDxEYGIhWrVph3759MDc3h6qqKu7duyeZpfjQa/r6+sLOzk7m8U8++QTt27eHmpoaCgoKpB4Ti8WSgU71c589eybzGtXH2rVr98Gfb9y4cfj5559x7tw5HD16FF9++SVatmz5we8hhHCHZlQIIR9kaWkJNTU1nD59Wup4dHQ0pkyZgkePHsHFxQXdu3eX3JI5d+4cAEjqpqioqEh9r7GxMdq3b4/Hjx+je/fukq9PP/0Ua9asQUZGBlRUVGBjYyOze+fUqVOSGRsA6NWrF65du4bc3Fyp5x0+fBgdOnSAgYHBB38+PT099OnTB9u3b8ft27cxbty4epwdQoi80YwKIeSDtLW14e7ujtjYWLRo0QJ2dnb4+++/sWvXLgQEBGDbtm2Ii4tDx44d0bZtWyQnJ2P79u0A3m71BYA2bdoAeLvV+JNPPoFIJIK3tzeCgoKgoqKCQYMG4eXLl/jll1+Qn5+Pbt26AQDmzZsHNzc3zJs3Dy4uLsjLy8O6desAQLJuZerUqTh8+DCmTJmCOXPmQEtLC4cOHcKlS5cQGhoqWUj7IS4uLvDx8YGJiQmsrKxYP4eEkIZTYqj5BiHkIxiGQXR0NHbv3o0nT57g888/x+TJk/H1118jMzMTP/74I27evIkWLVqgc+fOmDVrFkJDQ2Fqaop169ZBLBbju+++w4kTJ9CpUyckJCQAABITE7FlyxbcvXsXGhoasLGxwYIFC2BmZiZ576SkJKxbtw4PHjyAnp4e5s+fD29vb/j5+Ul28Dx69Ahr1qzBhQsXUFFRAZFIhOnTp8PR0VHyOmZmZpgzZw7mzp0r8/MVFxfD1tYWixYtwn//+185n01CSH3QQIUQIlgnT55Ex44dJTMsAHD37l2MGDECv/zyi9RApDESExPh6+uLs2fPyizeJYTwi279EEIE6/z580hMTMSiRYtgZGSE/Px8bNq0CcbGxujfv3+jXz8pKQnp6enYvXs3xo0bR4MUQgSIBiqEEMFavHgx1NXVsWnTJjx9+hRaWloYMGAAFi5cyMrOnMePH2Pbtm3o2bOnpJgdIURY6NYPIYQQQgSLticTQgghRLBooEIIIYQQwaKBCiGEEEIEiwYqhBBCCBEsGqgQQgghRLBooEIIIYQQwaKBCiGEEEIEiwYqhBBCCBEsGqgQQgghRLD+PxyscecECZeQAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Análisis de la columna 'createdAt':\n", + "createdAt\n", + "{'_seconds': 1731975445, '_nanoseconds': 427000000} 500\n", + "{'_seconds': 1732123040, '_nanoseconds': 262000000} 263\n", + "{'_seconds': 1731965768, '_nanoseconds': 30000000} 102\n", + "{'_seconds': 1731975447, '_nanoseconds': 767000000} 19\n", + "Name: count, dtype: int64\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Análisis de la columna 'gender':\n", + "gender\n", + "Hombre 206\n", + "Mujer 144\n", + "Niño 6\n", + "Name: count, dtype: int64\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ===========================\n", + "# Analizar una columna categórica\n", + "# ===========================\n", + "for column in [\"store\", \"manufacturer\", \"category\", \"createdAt\", \"gender\"]:\n", + " categorical_column = column # Reemplazar por una columna categórica del DataFrame\n", + "\n", + " if df is not None:\n", + " analyze_categorical_data(df, categorical_column)\n", + " else:\n", + " print(\"El DataFrame no está disponible.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/scripts/eda/.ipynb_checkpoints/functions-checkpoint.py b/scripts/eda/.ipynb_checkpoints/functions-checkpoint.py new file mode 100644 index 000000000..e70af537a --- /dev/null +++ b/scripts/eda/.ipynb_checkpoints/functions-checkpoint.py @@ -0,0 +1,130 @@ +# =========================== +# Importación de Librerías +# =========================== + +# Librerías para manejo de datos +import os # Para operaciones con el sistema de archivos +import pandas as pd # Para análisis y manipulación de datos + +# Librerías para realizar solicitudes a la API +import requests # Para hacer solicitudes HTTP + +# Librerías para visualización de datos +import matplotlib.pyplot as plt # Para gráficos básicos +import seaborn as sns # Para gráficos avanzados y mapas de calor + +# Librerías para manejo de excepciones +import warnings # Para controlar advertencias +warnings.filterwarnings("ignore") # Opcional, para evitar mensajes de advertencia + +# Configuración opcional para gráficos (opcional pero útil) +plt.style.use('ggplot') # Estilo para gráficos de Matplotlib +sns.set_theme(style="whitegrid") # Estilo para gráficos de Seaborn + + + +def load_data(filepath): + """ + Carga los datos desde un archivo Excel. + + :param filepath: Ruta del archivo Excel. + :return: DataFrame con los datos cargados. + """ + try: + df = pd.read_excel(filepath) + print(f"Datos cargados exitosamente desde {filepath}.") + return df + except Exception as e: + print(f"Error al cargar los datos: {e}") + return None + + +def summarize_data(df): + """ + Imprime un resumen general de los datos. + + :param df: DataFrame con los datos. + """ + print("Resumen General de los Datos") + print(f"Filas: {df.shape[0]}, Columnas: {df.shape[1]}") + print("\nPrimeras filas del DataFrame:") + print(df.head()) + print("\nInformación del DataFrame:") + print(df.info()) + print("\nEstadísticas descriptivas:") + print(df.describe(include='all')) + + +def check_missing_values(df): + """ + Verifica valores faltantes en el DataFrame. + + :param df: DataFrame con los datos. + :return: DataFrame con el porcentaje de valores faltantes por columna. + """ + missing = df.isnull().sum() + missing_percent = (missing / len(df)) * 100 + missing_summary = pd.DataFrame({'Missing Values': missing, 'Percentage': missing_percent}) + print("\nValores Faltantes:") + print(missing_summary[missing_summary['Missing Values'] > 0]) + return missing_summary + + +def plot_numeric_distributions(df): + """ + Grafica la distribución de las variables numéricas. + + :param df: DataFrame con los datos. + """ + numeric_columns = df.select_dtypes(include=['number']).columns + df[numeric_columns].hist(figsize=(15, 10), bins=20) + plt.suptitle("Distribuciones de Variables Numéricas", fontsize=16) + plt.tight_layout() + plt.show() + + +def analyze_categorical_data(df, column_name): + """ + Analiza las categorías de una columna específica. + + :param df: DataFrame con los datos. + :param column_name: Nombre de la columna categórica. + """ + if column_name in df.columns: + counts = df[column_name].value_counts() + print(f"\nAnálisis de la columna '{column_name}':") + print(counts) + counts.plot(kind='bar', title=f"Distribución de {column_name}") + plt.show() + else: + print(f"La columna '{column_name}' no existe en el DataFrame.") + + +def plot_correlations(df): + """ + Muestra un mapa de calor con las correlaciones entre variables numéricas. + + :param df: DataFrame con los datos. + """ + numeric_df = df.select_dtypes(include=['number']) + correlation_matrix = numeric_df.corr() + + plt.figure(figsize=(10, 8)) + sns.heatmap(correlation_matrix, annot=True, fmt='.2f', cmap='coolwarm', square=True) + plt.title("Mapa de Calor de Correlaciones") + plt.show() + + +def check_duplicates(df): + """ + Verifica y muestra registros duplicados en el DataFrame. + + :param df: DataFrame con los datos. + :return: Número de registros duplicados. + """ + duplicates = df.duplicated().sum() + print(f"Número de registros duplicados: {duplicates}") + if duplicates > 0: + print("\nRegistros duplicados:") + print(df[df.duplicated()]) + return duplicates \ No newline at end of file diff --git a/scripts/eda/.ipynb_checkpoints/main-checkpoint.py b/scripts/eda/.ipynb_checkpoints/main-checkpoint.py new file mode 100644 index 000000000..e69de29bb diff --git a/scripts/eda/eda.ipynb b/scripts/eda/eda.ipynb index d78e85f32..148ab2ccb 100644 --- a/scripts/eda/eda.ipynb +++ b/scripts/eda/eda.ipynb @@ -17,7 +17,15 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-14T16:49:32.253057Z", + "iopub.status.busy": "2024-12-14T16:49:32.253057Z", + "iopub.status.idle": "2024-12-14T16:49:33.828414Z", + "shell.execute_reply": "2024-12-14T16:49:33.828414Z", + "shell.execute_reply.started": "2024-12-14T16:49:32.253057Z" + } + }, "outputs": [], "source": [ "# ===========================\n", @@ -26,7 +34,7 @@ "\n", "# Insertar el path donde están definidas las funciones\n", "import sys\n", - "sys.path.append(r'C:\\Users\\cdgn2\\OneDrive\\Escritorio\\Maestría\\Maestria\\Metodologias Agiles\\Proyecto\\Comparative-analysis-of-products\\scripts\\eda')\n", + "sys.path.append(r'.\\eda')\n", "\n", "# Importar las funciones\n", "from functions import load_data, summarize_data, check_missing_values, plot_numeric_distributions, analyze_categorical_data, plot_correlations, check_duplicates" @@ -42,13 +50,21 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-14T16:51:00.383348Z", + "iopub.status.busy": "2024-12-14T16:51:00.382348Z", + "iopub.status.idle": "2024-12-14T16:51:00.911145Z", + "shell.execute_reply": "2024-12-14T16:51:00.911145Z", + "shell.execute_reply.started": "2024-12-14T16:51:00.383348Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Datos cargados exitosamente desde C:\\Users\\cdgn2\\OneDrive\\Escritorio\\Maestría\\Maestria\\Metodologias Agiles\\Proyecto\\Comparative-analysis-of-products\\src\\comparative_analysis\\database\\raw_data.xlsx.\n", + "Datos cargados exitosamente desde ..\\..\\src\\comparative_analysis\\database\\raw_data.xlsx.\n", "Los datos están listos para análisis.\n" ] } @@ -57,7 +73,8 @@ "# ===========================\n", "# Cargar los datos desde el archivo Excel\n", "# ===========================\n", - "file_path = r'C:\\Users\\cdgn2\\OneDrive\\Escritorio\\Maestría\\Maestria\\Metodologias Agiles\\Proyecto\\Comparative-analysis-of-products\\src\\comparative_analysis\\database\\raw_data.xlsx'\n", + "#file_path = r'C:\\Users\\cdgn2\\OneDrive\\Escritorio\\Maestría\\Maestria\\Metodologias Agiles\\Proyecto\\Comparative-analysis-of-products\\src\\comparative_analysis\\database\\raw_data.xlsx'\n", + "file_path = r'..\\..\\src\\comparative_analysis\\database\\raw_data.xlsx'\n", "\n", "# Llamar a la función para cargar los datos\n", "df = load_data(file_path)\n", @@ -79,7 +96,15 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-14T16:51:06.460740Z", + "iopub.status.busy": "2024-12-14T16:51:06.459740Z", + "iopub.status.idle": "2024-12-14T16:51:06.523060Z", + "shell.execute_reply": "2024-12-14T16:51:06.523060Z", + "shell.execute_reply.started": "2024-12-14T16:51:06.460740Z" + } + }, "outputs": [ { "name": "stdout", @@ -165,13 +190,13 @@ " id details store manufacturer \\\n", "count 884 884 884 884 \n", "unique 884 569 3 11 \n", - "top 046zSiHm8Cz0fZYwMJlL [] adidas adidas \n", + "top zqSM9xK4Ie9Qi878ZdzB [] adidas adidas \n", "freq 1 94 519 519 \n", "\n", " url title \\\n", "count 884 884 \n", "unique 884 369 \n", - "top https://www.adidas.co/tenis-duramo-sl/IF7884.html Tenis Response \n", + "top https://www.adidas.co/tenis-adizero-takumi-sen... Tenis Response \n", "freq 1 33 \n", "\n", " regularPrice undiscounted_price \\\n", @@ -220,7 +245,15 @@ { "cell_type": "code", "execution_count": 4, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-14T16:51:10.877300Z", + "iopub.status.busy": "2024-12-14T16:51:10.876206Z", + "iopub.status.idle": "2024-12-14T16:51:10.890532Z", + "shell.execute_reply": "2024-12-14T16:51:10.889527Z", + "shell.execute_reply.started": "2024-12-14T16:51:10.877300Z" + } + }, "outputs": [ { "name": "stdout", @@ -256,7 +289,15 @@ { "cell_type": "code", "execution_count": 5, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-14T16:51:15.961589Z", + "iopub.status.busy": "2024-12-14T16:51:15.961589Z", + "iopub.status.idle": "2024-12-14T16:51:15.974501Z", + "shell.execute_reply": "2024-12-14T16:51:15.973492Z", + "shell.execute_reply.started": "2024-12-14T16:51:15.961589Z" + } + }, "outputs": [ { "name": "stdout", @@ -285,8 +326,16 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-14T16:51:18.751875Z", + "iopub.status.busy": "2024-12-14T16:51:18.750875Z", + "iopub.status.idle": "2024-12-14T16:51:18.757063Z", + "shell.execute_reply": "2024-12-14T16:51:18.756056Z", + "shell.execute_reply.started": "2024-12-14T16:51:18.751875Z" + } + }, "outputs": [ { "name": "stdout", @@ -307,8 +356,16 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-14T16:51:19.819386Z", + "iopub.status.busy": "2024-12-14T16:51:19.819386Z", + "iopub.status.idle": "2024-12-14T16:51:19.838625Z", + "shell.execute_reply": "2024-12-14T16:51:19.837618Z", + "shell.execute_reply.started": "2024-12-14T16:51:19.819386Z" + } + }, "outputs": [ { "name": "stdout", @@ -348,26 +405,26 @@ "Calzado de correr en carretera para hombre 12 1.932367\n", "Calzado de correr en pavimento para mujer 11 1.771337\n", "Calzado de correr en carretera para mujer 11 1.771337\n", - "Calzado para hombre 5 0.805153\n", "Mujer • adidas by Stella McCartney 5 0.805153\n", - "Calzado de running en carretera para niños grandes 4 0.644122\n", + "Calzado para hombre 5 0.805153\n", "Mujer • TERREX 4 0.644122\n", + "Calzado de running en carretera para niños grandes 4 0.644122\n", "Calzado de trail running para hombre 3 0.483092\n", "Calzado para niños de preescolar 2 0.322061\n", - "Calzado de correr en pavimento para hombre 2 0.322061\n", "Calzado de carrera en carretera para mujer 2 0.322061\n", "Calzado de trail running impermeables para mujer 2 0.322061\n", + "Calzado de correr en pavimento para hombre 2 0.322061\n", + "Calzado de caminata para mujer 1 0.161031\n", "Calzado de running en carretera acondicionado p... 1 0.161031\n", + "Hombre • TERREX 1 0.161031\n", + "Calzado de trail running impermeables para hombre 1 0.161031\n", + "Calzado de carrera en carretera para hombre 1 0.161031\n", + "Calzado de running en carretera resistente a la... 1 0.161031\n", "Calzado de carrera en carretera 1 0.161031\n", "Calzado de running en carretera impermeable par... 1 0.161031\n", - "TERREX 1 0.161031\n", "Calzado de trail running para mujer 1 0.161031\n", + "TERREX 1 0.161031\n", "Calzado de running en carretera impermeable par... 1 0.161031\n", - "Calzado de trail running impermeables para hombre 1 0.161031\n", - "Calzado de running en carretera resistente a la... 1 0.161031\n", - "Calzado de carrera en carretera para hombre 1 0.161031\n", - "Hombre • TERREX 1 0.161031\n", - "Calzado de caminata para mujer 1 0.161031\n", "adidas by Stella McCartney 1 0.161031\n", "\n", "Porcentajes de los elementos en la columna 'createdAt':\n", @@ -417,8 +474,16 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-14T16:51:20.740805Z", + "iopub.status.busy": "2024-12-14T16:51:20.739806Z", + "iopub.status.idle": "2024-12-14T16:51:21.806784Z", + "shell.execute_reply": "2024-12-14T16:51:21.805778Z", + "shell.execute_reply.started": "2024-12-14T16:51:20.740805Z" + } + }, "outputs": [ { "name": "stdout", @@ -435,7 +500,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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NpUqVwvr16wt1VhHRu1JIvJIVERERCYp7VIiIiEhYLCpEREQkLBYVIiIiEhaLChEREQmLRYWIiIiExaJCREREwtL6Cd8uXrwISZKgp6cndxQiIiLKp/T0dCgUCrULiuZE6/eoSJKU58XQ3uV+09LSiuW+iwszlxxtzM3MJYOZSwYzl4zizJzf92+t36OStSelUaNGRXq/SUlJiIiIgIWFhdZcIZSZS4425mbmksHMJYOZS0ZxZs5+7a+8aP0eFSIiInp/sagQERGRsFhUiIiISFgsKkRERCQsFhUiIiISFosKERERCYtFhYiIiITFokJERETCYlEhIiIiYbGoEBERkbBYVIiIiEhYLCpEREQkLBYVIiIiEhaLChEREQmLRSUXCoUChoaGUCgUckchIiL6YOnKHaCkKZUSdHTeXj4MDQ1hbW1dLPdNRERE+fPBFRUdHQX8t4TjXuyrIr3f6iZlMKVf0yK9TyIiog/dB1dUAOBe7CtE3X8pdwwiIiJ6C45RISIiImGxqBAREZGwWFSIiIhIWCwqREREJCwWFSIiIhJWgYtKbGwsLC0tNf7buXMnACAiIgKurq6wtbWFi4sLAgIC1G6vVCrx888/w9nZGba2thg2bBhiYmKK5tkQERHRe6XApydfv34dpUuXxpEjR9RmbS1TpgyeP3+OQYMGwcXFBd7e3vj777/h7e2Njz76CL169QIALF++HIGBgfDz84OpqSkWLFiAoUOHYt++fdDX1y+6Z0ZERERar8BF5caNG6hVqxaqVq2qsW7jxo3Q09ODj48PdHV1UbduXdy5cwerVq1Cr169kJaWhnXr1mHKlClo27YtAGDx4sVwdnbGoUOH0LVr13d+QkRERPT+KPChn3///Rd169bNcV1YWBgcHR2hq/tf/2nRogWio6Px9OlTXL9+HYmJiXByclKtL1u2LKytrREaGlqI+ERERPQ+K9QelQoVKqBfv364ffs2atasiVGjRqF169Z49OgR6tevr7Z91p6Xhw8f4tGjRwAAMzMzjW2y1hWGJElISkp663ZZFxosTsnJyZAkqVgfI6/Hzv5/baCNmQHtzM3MJYOZSwYzl4zizCxJUr4u/FugopKRkYFbt27BwsIC06ZNg7GxMX777TcMHz4c69evR0pKisY4k9KlSwMAUlNTVU80p21eviz8lPbp6emIiIh463aFudBgQd2+fVv2F2F0dLSsj18Y2pgZ0M7czFwymLlkMHPJKK7M+RmbWqCioquri5CQEJQqVQoGBgYAABsbG9y8eRNr166FgYEB0tLS1G6TmpoKADAyMlLdJi0tTfXvrG3eZU+Hnp4eLCws3rpdfprbu6pdu7ase1Sio6NRq1atYt9zVFS0MTOgnbmZuWQwc8lg5pJRnJkjIyPztV2BD/189NFHGsvq1auHU6dOwdTUFI8fP1Zbl/W1iYkJMjIyVMvMzc3VtrG0tCxoFBWFQgEjI6NC374oifDiMzQ0FOb7kV/amBnQztzMXDKYuWQwc8kojsz53XlQoMG0N2/ehL29PUJCQtSWX716FRYWFnBwcEB4eDgyMzNV686dO4fatWujUqVKsLKygrGxsdrt4+Pjce3aNTg4OBQkChEREX0AClRU6tatizp16sDHxwdhYWGIiorC3Llz8ffff2PUqFHo1asXEhIS4OnpicjISOzcuRMbNmzAiBEjALw+FuXq6gp/f38cPXoU169fx8SJE2FqaopOnToVyxMkIiIi7VWgQz86Ojr45ZdfsHDhQkyYMAHx8fGwtrbG+vXrVWf7rFmzBr6+vujRoweqVKkCNzc39OjRQ3Uf48ePR0ZGBry8vJCSkgIHBwesXbsWenp6RfvMiIiISOsVeIxK5cqVMXfu3FzXN27cGEFBQbmuL1WqFKZOnYqpU6cW9KGJiIjoA8OLEhIREZGwWFSIiIhIWCwqREREJCwWFSIiIhIWiwoREREJi0WFiIiIhMWiQkRERMJiUSEiIiJhsagQERGRsFhUiIiISFgsKkRERCQsFhUiIiISFosKERERCYtFhYiIiITFokJERETCYlEhIiIiYbGoEBERkbBYVIiIiEhYLCpEREQkLBYVIiIiEhaLChEREQmLRYWIiIiExaJCREREwmJRISIiImGxqBAREZGwWFSIiIhIWCwqREREJCwWFSIiIhIWiwoREREJi0WFiIiIhMWiQkRERMJiUSEiIiJhsagQERGRsFhUiIiISFgsKkRERCQsFhUiIiISFosKERERCYtFhYiIiITFokJERETCYlEhIiIiYbGoEBERkbBYVIiIiEhYLCpEREQkLBYVIiIiEhaLChEREQmLRYWIiIiExaJCREREwmJRISIiImGxqBAREZGwWFSIiIhIWCwqREREJCwWFSIiIhIWiwoREREJi0WFiIiIhMWiQkRERMJiUSEiIiJhFbqo3L59G3Z2dti5c6dqWUREBFxdXWFrawsXFxcEBASo3UapVOLnn3+Gs7MzbG1tMWzYMMTExBQ+PREREb3XClVU0tPTMWXKFCQlJamWPX/+HIMGDYK5uTmCg4MxZswY+Pv7Izg4WLXN8uXLERgYiNmzZ2Pr1q1QKpUYOnQo0tLS3v2ZEBER0XunUEVlyZIlMDY2Vlu2bds26OnpwcfHB3Xr1kWvXr0wcOBArFq1CgCQlpaGdevWYfz48Wjbti2srKywePFiPHr0CIcOHXr3Z0JERETvnQIXldDQUAQFBcHPz09teVhYGBwdHaGrq6ta1qJFC0RHR+Pp06e4fv06EhMT4eTkpFpftmxZWFtbIzQ09B2eAhEREb2vdN++yX/i4+Ph5uYGLy8vmJmZqa179OgR6tevr7asatWqAICHDx/i0aNHAKBxu6pVq6rWFZYkSWqHoXKjUChgaGj4To/1NsnJyZAkqVgfI6/Hzv5/baCNmQHtzM3MJYOZSwYzl4zizCxJEhQKxVu3K1BRmTVrFuzs7NCtWzeNdSkpKdDX11dbVrp0aQBAamqq6knmtM3Lly8LEkNDeno6IiIi3rqdoaEhrK2t3+mx3ub27duyvwijo6NlffzC0MbMgHbmZuaSwcwlg5lLRnFlfrMT5CTfRWX37t0ICwvDvn37clxvYGCgMSg2NTUVAGBkZAQDAwMAr8eqZP07a5t33cuhp6cHCwuLt26Xn+b2rmrXri3rHpXo6GjUqlWr2PccFRVtzAxoZ25mLhnMXDKYuWQUZ+bIyMh8bZfvohIcHIxnz56hbdu2astnzpyJAwcOwNTUFI8fP1Zbl/W1iYkJMjIyVMvMzc3VtrG0tMxvjBwpFAoYGRm9030UFRFefIaGhsJ8P/JLGzMD2pmbmUsGM5cMZi4ZxZE5vzsP8l1U/P39kZKSorasU6dOGD9+PD7//HPs2bMHW7duRWZmJkqVKgUAOHfuHGrXro1KlSqhTJkyMDY2RkhIiKqoxMfH49q1a3B1dc1vDCIiIvqA5LuomJiY5Li8UqVKMDExQa9evbBmzRp4enpi6NChuHz5MjZs2ABvb28Ar49Dubq6wt/fHxUrVkS1atWwYMECmJqaolOnTkXzbIiIiOi9UqDBtHmpVKkS1qxZA19fX/To0QNVqlSBm5sbevToodpm/PjxyMjIgJeXF1JSUuDg4IC1a9dCT0+vqGIQERHRe+Sdisq///6r9nXjxo0RFBSU6/alSpXC1KlTMXXq1Hd5WCIiIvpA8KKEREREJCwWFSIiIhIWiwoREREJi0WFiIiIhMWiQkRERMJiUSEiIiJhsagQERGRsFhUiIiISFgsKkRERCQsFhUiIiISFosKERERCYtFhYiIiITFokJERETCYlEhIiIiYbGoEBERkbBYVIiIiEhYLCpEREQkLBYVIiIiEhaLChEREQmLRYWIiIiExaJCREREwmJRISIiImGxqBAREZGwWFSIiIhIWCwqREREJCwWFSIiIhIWiwoREREJi0WFiIiIhMWiQkRERMJiUSEiIiJhsagQERGRsFhUiIiISFgsKkRERCQsFhUiIiISFosKERERCYtFhYiIiITFokJERETCYlEhIiIiYbGoEBERkbBYVIiIiEhYLCpEREQkLBYVIiIiEhaLChEREQmLRYWIiIiExaJCREREwmJRISIiImGxqBAREZGwWFSIiIhIWCwqREREJCwWFSIiIhIWiwoREREJi0WFiIiIhMWiQkRERMJiUSEiIiJhsagQERGRsFhUiIiISFgFLirPnj3D1KlT0aJFC9jZ2WH48OGIiopSrY+IiICrqytsbW3h4uKCgIAAtdsrlUr8/PPPcHZ2hq2tLYYNG4aYmJh3fyZERET03ilwURkzZgzu3LmDVatWYceOHTAwMMDAgQORnJyM58+fY9CgQTA3N0dwcDDGjBkDf39/BAcHq26/fPlyBAYGYvbs2di6dSuUSiWGDh2KtLS0In1iREREpP10C7Lxy5cvUa1aNYwYMQL169cHAIwePRpffPEFbt68ibNnz0JPTw8+Pj7Q1dVF3bp1VaWmV69eSEtLw7p16zBlyhS0bdsWALB48WI4Ozvj0KFD6Nq1a5E/QSIiItJeBdqjUq5cOSxcuFBVUuLi4rBhwwaYmprCwsICYWFhcHR0hK7uf/2nRYsWiI6OxtOnT3H9+nUkJibCyclJtb5s2bKwtrZGaGhoET0lIiIiel8UaI9KdtOnT8e2bdugr6+PFStWwMjICI8ePVKVmCxVq1YFADx8+BCPHj0CAJiZmWlsk7WuMCRJQlJS0lu3UygUMDQ0LPTj5EdycjIkSSrWx8jrsbP/XxtoY2ZAO3Mzc8lg5pLBzCWjODNLkgSFQvHW7QpdVL799lv07t0bW7ZswZgxYxAYGIiUlBTo6+urbVe6dGkAQGpqquqJ5rTNy5cvCxsF6enpiIiIeOt2hoaGsLa2LvTj5Mft27dlfxFGR0fL+viFoY2ZAe3Mzcwlg5lLBjOXjOLK/GYfyEmhi4qFhQUAwNfXF5cuXcLmzZthYGCgMSg2NTUVAGBkZAQDAwMAQFpamurfWdu8y54OPT09VZ685Ke5vavatWvLukclOjoatWrVKvY9R0VFGzMD2pmbmUsGM5cMZi4ZxZk5MjIyX9sVqKjExcXh7Nmz+PTTT1XjUHR0dGBhYYHHjx/D1NQUjx8/VrtN1tcmJibIyMhQLTM3N1fbxtLSsiBR1CgUChgZGRX69kVJhBefoaGhMN+P/NLGzIB25mbmksHMJYOZS0ZxZM7vzoMCDaZ9+vQpJk2ahLNnz6qWpaen49q1a6hbty4cHBwQHh6OzMxM1fpz586hdu3aqFSpEqysrGBsbIyQkBDV+vj4eFy7dg0ODg4FiUJEREQfgAIVlfr166N169aYM2cOQkNDcePGDUybNg3x8fEYOHAgevXqhYSEBHh6eiIyMhI7d+7Ehg0bMGLECACvj0W5urrC398fR48exfXr1zFx4kSYmpqiU6dOxfIEiYiISHsVeIzKokWLsHDhQkycOBGvXr1Cs2bNsGXLFnz88ccAgDVr1sDX1xc9evRAlSpV4Obmhh49eqhuP378eGRkZMDLywspKSlwcHDA2rVroaenV3TPioiIiN4LBS4qZcqUwaxZszBr1qwc1zdu3BhBQUG53r5UqVKYOnUqpk6dWtCHJiIiog8ML0pIREREwmJRISIiImGxqBAREZGwWFSIiIhIWCwqREREJCwWFSIiIhIWiwoREREJi0WFiIiIhMWiQkRERMJiUSEiIiJhsagQERGRsFhUiIiISFgsKkRERCQsFhUiIiISFosKERERCYtFhYiIiITFokJERETCYlEhIiIiYbGoEBERkbBYVIiIiEhYLCpEREQkLBYVIiIiEhaLChEREQmLRYWIiIiExaJCREREwmJRISIiImGxqBAREZGwWFSIiIhIWCwqREREJCwWFSIiIhIWiwoREREJi0WFiIiIhMWiQkRERMJiUSEiIiJhsagQERGRsFhUiIiISFgsKkRERCQsFhUiIiISFosKERERCYtFhYiIiITFokJERETCYlEhIiIiYbGoEBERkbBYVIiIiEhYLCpEREQkLBYVIiIiEhaLChEREQmLRYWIiIiExaJCREREwmJRISIiImGxqBAREZGwWFSIiIhIWCwqREREJCwWFSIiIhIWiwoREREJi0WFiIiIhFXgovLixQvMmDEDrVu3hr29Pfr06YOwsDDV+rNnz6Jnz55o0qQJOnfujN9++03t9qmpqfD29oaTkxPs7OwwefJkxMXFvfszISIiovdOgYvKpEmTcPHiRSxatAjBwcFo0KABhgwZglu3biEqKgojRoyAs7Mzdu7cia+++gpubm44e/as6vazZs3CqVOnsGTJEmzcuBG3bt3C+PHji/RJERER0ftBtyAb37lzB6dPn0ZgYCCaNm0KAJg+fTpOnjyJffv24dmzZ7C0tMTEiRMBAHXr1sW1a9ewZs0aODk5ITY2Frt378Yvv/yCZs2aAQAWLVqEzp074+LFi7Czsyvip0dERETarEBFpUKFCli1ahUaNWqkWqZQKKBQKBAfH4+wsDB06NBB7TYtWrSAr68vJElCeHi4almW2rVrw8TEBKGhoYUuKpIkISkp6a3bKRQKGBoaFuox8is5ORmSJBXrY+T12Nn/rw20MTOgnbmZuWQwc8lg5pJRnJklSYJCoXjrdgUqKmXLlkWbNm3Ulh08eBB37tzB999/j127dsHU1FRtfdWqVZGcnIznz58jNjYWFSpUQOnSpTW2efToUUGiqElPT0dERMRbtzM0NIS1tXWhHyc/bt++LfuLMDo6WtbHLwxtzAxoZ25mLhnMXDKYuWQUV2Z9ff23blOgovKmCxcuwMPDA506dULbtm2RkpKi8aBZX6elpSE5OTnHUKVLl0Zqamqhc+jp6cHCwuKt2+Wnub2r2rVry7pHJTo6GrVq1Sr2PUdFRRszA9qZm5lLBjOXDGYuGcWZOTIyMl/bFbqoHDlyBFOmTIG9vT38/f0BvC4caWlpattlfW1oaAgDAwON9cDrM4He5RugUChgZGRU6NsXJRFefIaGhsJ8P/JLGzMD2pmbmUsGM5cMZi4ZxZE5vzsPCjWPyubNmzFu3Di0a9cOv/zyi+pQjpmZGR4/fqy27ePHj2FkZIQyZcrA1NQUL1680Cgrjx8/homJSWGiEBER0XuswEUlMDAQs2fPRr9+/bBo0SK1QznNmjXD+fPn1bY/d+4c7O3toaOjg6ZNm0KpVKoG1QKvx3TExsbCwcHhHZ4GERERvY8KVFRu376NH374AR07dsSIESPw9OlTPHnyBE+ePMGrV6/Qv39/XL58Gf7+/oiKisK6devwxx9/YOjQoQAAExMTdOnSBV5eXggJCcHly5cxadIkODo6wtbWtjieHxEREWmxAo1ROXjwINLT03H48GEcPnxYbV2PHj3g5+eH5cuXY8GCBdi4cSOqV6+OBQsWwMnJSbXd7Nmz8cMPP2Ds2LEAgNatW8PLy6sIngoRERG9bwpUVEaOHImRI0fmuU3r1q3RunXrXNcbGRlhzpw5mDNnTkEemoiIiD5AvCghERERCYtFhYiIiITFokJERETCYlEhIiIiYbGoEBERkbBYVIiIiEhYLCpEREQkLBYVIiIiEhaLChEREQmLRYWIiIiExaJCREREwmJRISIiImGxqBAREZGwWFSIiIhIWCwqREREJCwWFSIiIhIWiwoREREJi0WFiIiIhMWiQkRERMJiUSEiIiJhsagQERGRsFhUiIiISFgsKkRERCQsFhUiIiISFosKERERCYtFhYiIiITFokJERETCYlEhIiIiYbGoEBERkbBYVIiIiEhYLCpEREQkLBYVIiIiEhaLChEREQmLRYWIiIiExaJCREREwmJRISIiImGxqBAREZGwWFSIiIhIWCwqREREJCwWFSIiIhIWiwoREREJi0WFiIiIhMWiQkRERMJiUSEiIiJhsagQERGRsFhUiIiISFgsKkRERCQsFhUiIiISFovKe0ShUMDQ0BAKhULuKEREREVCV+4A9HZKpQQdnbeXD0NDQ1hbWxfLfRMREcmBRUUL6Ogo4L8lHPdiXxXp/VY3KYMp/ZoW6X0SEREVJRYVLXEv9hWi7r+UOwYREVGJ4hgVIiIiEhaLChEREQmLRYWIiIiExaJCREREwnqnorJy5Ur0799fbVlERARcXV1ha2sLFxcXBAQEqK1XKpX4+eef4ezsDFtbWwwbNgwxMTHvEoOIiIjeU4UuKlu2bMGPP/6otuz58+cYNGgQzM3NERwcjDFjxsDf3x/BwcGqbZYvX47AwEDMnj0bW7duhVKpxNChQ5GWllboJ0FERETvpwKfnhwbG4uZM2ciJCQEtWrVUlu3bds26OnpwcfHB7q6uqhbty7u3LmDVatWoVevXkhLS8O6deswZcoUtG3bFgCwePFiODs749ChQ+jatWtRPCfSIpxNl4iI8lLgovLPP/9AT08Pe/fuxbJly3D//n3VurCwMDg6OkJX97+7bdGiBVauXImnT5/iwYMHSExMhJOTk2p92bJlYW1tjdDQ0EIXFUmSkJSU9Nbtst4Ui1NycjIkSSqy+9PWzPqlS6OUztt32BVmNt1MpRJpqalFmrmgkpOT1f6vDZi5ZDBzyWDmklGcmSVJyteH1AIXFRcXF7i4uOS47tGjR6hfv77asqpVqwIAHj58iEePHgEAzMzMNLbJWlcY6enpiIiIeOt2hXlTLKjbt28X6Q9UmzMX52y6RZ25sKKjo+WOUGDMXDKYuWQwc8korsz6+vpv3aZIZ6ZNSUnReNDSpUsDAFJTU1VvLDlt8/Jl4Wdd1dPTg4WFxVu3K4nDC7Vr1y7yvRPFrbgyF+dsukWduaCSk5MRHR2NWrVqFfser6LCzCWDmUsGM5eM4swcGRmZr+2KtKgYGBhoDIpNTU0FABgZGcHAwAAAkJaWpvp31jbv8g1QKBQwMjIq9O2Lkra8+LJj5sIzNDQU5rWXX8xcMpi5ZDBzySiOzPn9IF6k86iYmpri8ePHasuyvjYxMVEd8slpGxMTk6KMQkRERO+BIi0qDg4OCA8PR2ZmpmrZuXPnULt2bVSqVAlWVlYwNjZGSEiIan18fDyuXbsGBweHooxCRERE74EiLSq9evVCQkICPD09ERkZiZ07d2LDhg0YMWIEgNdjU1xdXeHv74+jR4/i+vXrmDhxIkxNTdGpU6eijEJERETvgSIdo1KpUiWsWbMGvr6+6NGjB6pUqQI3Nzf06NFDtc348eORkZEBLy8vpKSkwMHBAWvXroWenl5RRiEiIqL3wDsVFT8/P41ljRs3RlBQUK63KVWqFKZOnYqpU6e+y0MTERHRB4AXJSQiIiJhsagQERGRsFhUiIiISFgsKkRERCQsFhUiIiISFosKERERCYtFhYiIiITFokJERETCYlEhIiIiYbGoEBERkbBYVIiIiEhYLCpEREQkLBYVIiIiEhaLChEREQmLRYWIiIiExaJCREREwmJRISIiImGxqBAREZGwWFSIiIhIWCwqREREJCwWFSIiIhIWiwoREREJi0WFiIiIhMWiQkRERMJiUSEiIiJhsagQERGRsFhUiIiISFgsKkSFoFAoYGhoCIVCIXcUIqL3mq7cAYhEoVRK0NHJX/EwNDSEtbV1sdw3ERH9h0WF6P/p6CjgvyUc92JfFen9Vjcpgyn9mhbpfRIRfShYVIiyuRf7ClH3X8odg4iI/h/HqBAREZGwWFSIiIhIWCwqREREJCwWFSIiIhIWiwrRB0Ib537RxsxEVLR41g+RFtPGuV+0MTMRyYdFhUiLaePcL9qYmYjkw6JCpOW0ce4XbcxMRPLgGBUiIiISFosKERERCYtFhYiIiITFokJERETCYlEhIiIiYbGoEBERkbBYVIiIiEhYLCpEREQkLBYVIiIiEhaLChEREQmLRYWIiIiExaJCREREwmJRISIiImGxqBAREZGwWFSIiIhIWCwqRERFSKFQwNDQEAqFQu4o+aaNmenDIUtRUSqV+Pnnn+Hs7AxbW1sMGzYMMTExckQhIsoXpVLK13aGhoawtraGoaFhkd5vYWhjZqI36crxoMuXL0dgYCD8/PxgamqKBQsWYOjQodi3bx/09fXliERElCcdHQX8t4TjXuyrIrvP6iZlMKVf0yK7vzdpY+b80sa9QMxcOCVeVNLS0rBu3TpMmTIFbdu2BQAsXrwYzs7OOHToELp27VrSkYiI8uVe7CtE3X8pd4wC0bbMSqUEHZ23vylm7QUq6vstDGYu+P0WRIkXlevXryMxMRFOTk6qZWXLloW1tTVCQ0NZVIiIPmDauBeImV8rrswKSZJK9GDjoUOHMG7cOFy6dAkGBgaq5d999x1SUlKwcuXKAt3fhQsXIEkS9PT08rW9QqHAy4Q0ZGQqC/Q4b6NbSgfljPVRHN9OZv4PM6tj5v8UZ2ageHIzsyZmfu1DyJyeng6FQgF7e/u877cowhVEcnIyAGiMRSldujReviz47sms42YFOX5Wzrj4xsEU13E8ZlbHzP9hZnXFeSy9uHIzszpm/s/7nFmhUORr2xIvKll7UdLS0tT2qKSmpuZ7xHl2dnZ2RZaNiIiIxFLipyebmZkBAB4/fqy2/PHjxzAxMSnpOERERCSwEi8qVlZWMDY2RkhIiGpZfHw8rl27BgcHh5KOQ0RERAIr8UM/+vr6cHV1hb+/PypWrIhq1aphwYIFMDU1RadOnUo6DhEREQlMlgnfxo8fj4yMDHh5eSElJQUODg5Yu3Ztvs/cISIiog9DiZ+eTERERJRfvCghERERCYtFhYiIiITFokJERETCYlEhIiIiYbGoEBERkbBYVIiIiEhYLCpEREQkLBYVIiIiEhaLChEJJzQ0FFu3bkVCQgIiIyORkZEhdySiD1ZcXJysjy/LFPoiS0lJgY6ODvT19REVFYVjx47Bzs4O9vb2ckfTkJaWhnXr1uGzzz5DzZo14enpiQMHDsDe3h7+/v6oUKGC3BHz9ODBA0RFRcHBwQGJiYmoVKmS3JFIZgkJCRg6dCj+/vtvKBQKtGrVCv7+/oiJicG6deuEvMK6h4dHjssVCgX09PRgamqKzp07o3bt2iWcrODi4uJQsWJFuWOQjOLj47FgwQK4urrCwsICQ4cOxblz51CrVi2sWrUKNWrUKPFM3KOSTWhoKFq3bo3w8HA8fvwYX331FVasWIH+/fvj999/lzueBn9/f6xfvx4JCQk4ceIEdu3ahREjRiAxMRHz58+XO16u0tLSMHHiRLi4uGDEiBF48uQJZs6ciUGDBiEhIUHueO89uT8d5WXRokUAgMOHD8PAwAAAMHXqVOjr6wv7mk5PT8fu3btx6tQpxMfHIz4+HmfPnsWuXbvw77//Ys+ePejevTvCw8PljqomPj4e06dPx7///ovMzEwMGjQIrVq1wmeffYaYmBi546k8ffoUXl5emDhxIv7991+54+TLgwcP8v2faObOnYtz585BV1cXhw8fRlhYGObPn49atWrJ9jvIPSrZLFq0CO3bt0ejRo2wbds2GBsb4/DhwwgODsbKlSvx2WefyR1RzR9//IFFixahYcOGmDlzJhwdHTFy5Eh88sknGDZsmNzxcrVixQpcv34dGzduxMiRIwEA/fv3h4eHB/z9/TFr1ix5A+bh6NGjuHHjBjIzM1XL0tLScOXKFaxfv17GZDkT8dNRXv766y8sXLhQLVfdunUxY8YMjBkzRsZkuTMwMECnTp2wYMEC6OvrA3hdXr7//nuUKVMGM2bMgL+/P3788Uds2rRJ5rT/mTt3LsLCwjBw4EC1N6QDBw5g/vz5WLJkidwRAQBubm6ws7ND3bp1MXToUBw/fhw6OmJ/xnZxcYFCochzG0mSoFAoEBERUUKp8uf48eNYtmwZ6tati9WrV6NVq1bo1q0bLC0t0a9fP1kysahkc+3aNcyfPx/GxsY4deoU2rZti9KlS6NNmzbw8/OTO56GFy9eoG7dugCA06dPo3fv3gCA8uXLIyUlRc5oefrtt98wa9YsNG/eXLWsefPm8PX1hZubm7BFxd/fH2vWrEHlypXx7NkzmJiY4OnTp8jMzESXLl3kjpcjbXkzyhIXF4cqVapoLC9btiySkpJkSPR2f/zxB7Zu3aoqKQCgp6eHESNGoE+fPpgxYwa+/PJLBAYGyphSk4hvSDmJjIyEm5sbLCwsMHXqVLx48UL4w1MBAQFyRyi0pKQkmJmZAXj9vpL1odfAwEDtA1pJYlHJxtDQEGlpaUhNTUV4eDi++uorAK93PZYpU0bmdJrMzc1x5coVPHv2DPfu3YOzszMA4MiRI6hevbrM6XIXGxsLc3NzjeVmZmZ4+fKlDInyZ9++ffj+++8xYMAAtGnTBoGBgTAyMsKYMWOE2zORRVvejLI0atQIv//+O4YPH662fMuWLbC2tpYpVd50dXXx9OlTWFhYqC1/8uSJ6lN1ZmYmdHXF+nMr4htSTnr27IlZs2ahSpUqcHZ2Fr6kAICjo6PcEQqtbt26OHbsGMzMzPDkyRO0bt0aALBt2zbVB+OSJtZvjsyaN2+OBQsWoFy5ctDR0YGzszMiIiIwZ84ctU//ohg6dCgmTZoEHR0dtGjRAlZWVli2bBmWLVuGH374Qe54uapbty7Onj2rKoJZfvvtN40/9iJ59uwZXFxcAACWlpa4fPkyOnfujIkTJ8LT0xPfffedzAk1acubUZZJkyZh8ODBuHz5MjIyMrBixQpERUXhn3/+wdq1a+WOl6NPP/0UM2bMwKxZs9CkSRNIkoS///4bs2fPRvv27ZGUlIQVK1agUaNGckdVI+IbUk4mTJiAsLAwJCYmqj6MaZPU1FQEBQXleMj46tWrOHjwoIzpNI0fPx7jxo1Deno6unbtilq1amHu3LnYsmULli1bJksmFpVsZs6ciZkzZ+Lff//FggULYGxsjD179kBfXz/Xkf1y6t69Oxo0aICYmBjVH5lGjRph7dq1cHJykjld7saNG4eJEyciMjISmZmZ2LVrF27fvo2DBw9i8eLFcsfLVfbDD+bm5oiMjAQAfPzxx4iNjZUzWq605c0oi729PbZu3Yp169ahZs2a+Pvvv1GvXj18//33aNKkidzxcuTh4QE3NzcMHjxYbVzCZ599Bk9PT5w5cwbnz5/HqlWrZEypScQ3pNw0a9ZM7giFNmfOHOzevRvW1ta4cuUK7OzscOfOHTx79gwDBw6UO56GNm3a4Pjx44iNjYWVlRUAoEuXLvj666/l+5shUZ5SU1PljpCnkJAQ6fTp06qv/fz8pNDQUBkT5c/x48elvn37Sra2tlLjxo2lXr16SX/88YfcsfI0YcIEadiwYdKjR4+kvXv3Sl26dJGePXsmrVu3TmrXrp3c8XJ07NgxqVGjRpKVlZU0efJkSZIk6YcffpAaNmwoHTt2TOZ0OYuPj5fu3Lmj+vrgwYPS8+fP5QuUT3fv3pUOHjwoHTlyRIqJiVEtF/VvSEJCghQXFydFRESoll26dEmKjIyUMdX7x8nJSdq3b58kSZLUoUMHKSoqSkpLS5PGjBkjzZ49W+Z0BfPw4UNZHlchSZIkT0USU1xcHG7fvg2lUgng9cjsrLM6Ro0aJXM6db/99hvc3d0xceJEDBkyBMDrT0l//vknfvzxR3To0EHmhDmT/n+0e06ioqKE/KQPAA8fPsSoUaPQvXt39O3bF998841qxL67u7uQn44A4Pnz52qfji5fvoyPPvpIyO/zP//8g8GDB6Nnz55wd3cH8PoMiqw5g+rXry9zwtw9ffoU6enpePNP6scffyxTorx17NgRP/74Ixo2bCh3lPeajY0NDh06hI8//hijRo1Cly5d0LVrV1y5cgUTJkzA0aNH5Y6oJiYmBvPmzVM7VJX1PhgXF4dr166VeCYWlWz27t0LLy8vpKWlQaFQqL2hVqtWDUeOHJE5obpu3bqhd+/ecHV1VVseEBCA4OBg7NmzR6ZkeZs2bZrGWVSSJGH16tVYtmwZLl26JFOy/ElNTUXp0qWRnJyMU6dOwcTEBI0bN5Y7Vo6uXLmS49iIV69eYc6cOZg3b54MqXLXv39/VK9eHd7e3qqzaDIyMjB9+nTExsZi3bp1MifUdOHCBXh4eODu3btqyyVBTz/N8sknn2Djxo1CFtbs8nOqbxbR3vQBoHXr1li2bBkaNWqEefPmQU9PD5MmTcK9e/fQpUsX4f7eDRs2DNHR0ejcuTPWr1+PwYMH4/bt2zh8+DB8fHzw9ddfl3gmjlHJ5pdffkGXLl0wdOhQ9OnTB+vWrcPjx4/h7e2NcePGyR1Pw927d9GmTRuN5e3atcPChQtlSJQ/p06dgpeXF+bMmQPg9emHHh4eiIiI0DjbQyTHjx9HmzZtULp0aQCvzxLr2LEjYmJi0L9/f6HmyMgydOhQbNiwAQ0aNFAt+/PPPzFjxgwhB9NevXoVP/zwg9qpvrq6uhg+fDi+/PJLGZPlbs6cOahSpQrc3NyEPDswNwMGDMC4cePQr18/mJubqybYy+Lg4CBTMnWTJk2Cp6cn6tSpg/bt28sdp8Bat24Nb29vzJ07F02bNsUPP/yAjh074sCBAzA1NZU7noYLFy5g+fLlaN68OU6ePIkOHTqgcePGWLx4MY4fPy5LUeEYlWxsbGxUx2ddXV2l48ePS5L0+hh5jx495IyWo08//VQKDg7WWL5nzx6pffv2MiTKn8jISMnZ2VmaPn26tHz5csnGxkbq3bu3dPPmTbmj5alRo0bSyZMn1ZZt3LhRsrW1lTp37ixTqrzNmTNHcnR0lCIiIqQXL15IkydPliwtLaXJkydLz549kzuehlatWqmNucpy7tw5qUWLFjIkervsfze0iaWlZa7/WVlZyR1PzeHDhyUbGxu18TTa4uXLl9KoUaOkzZs3S0qlUho6dKhkaWkpNWzYUNq/f7/c8TTY2NhIDx48kCTp9bi8bdu2SZIkSbdu3ZJatWolSybuUclGX19f9UmuZs2auHnzJlq3bg0bGxvcuXNH5nSa+vTpg9mzZ+Pu3buqMyKuXLmCjRs3YvTo0TKny13dunWxadMmfPvtt3j8+DE8PT3Rt2/ffO/elcukSZMwduxYrFixAh9//DE8PDxw+fJlDBkyRNhZUz09PaGnp4eBAwdCV1cX+vr6WL16tbCneX766afw9vbGrFmzVIfTrly5Ah8fH3Ts2FHmdDkzMzNDYmKi3DEKTMTDJLnp0KEDunTpgnnz5gk5A3ReypYti+XLl6u+XrVqFSIiIlC5cmVUrVpVxmQ5q1atGm7cuAEzMzPUrl1bdehSqVTK9jpnUcnGxsYG27dvx6RJk1C/fn0cP34cQ4YMQWRkJPT09OSOp+Hbb79FWloaAgIC8MsvvwAAqlatiokTJ2qMW5FbaGioxrLx48dj1qxZuHnzJsLDw1WDEEXZ5fymgQMHQk9PD6NGjYJCoUC9evWwc+dOoQd4Aq+nINfX18eqVavw66+/CnuaLwBMnjwZd+/exaBBg9SKa8eOHeHm5iZjstyNGjUKP/zwA7y9vVGnTh0h/1bkpFq1agBez+dx7949mJubQ5IkYfN7eXnh3r17cscolJcvXyI6OhppaWmqZYmJibhz545wf+969OgBNzc3zJ8/H23btsWAAQPw8ccf4/Tp07C0tJQlEwfTZhMWFoahQ4di/Pjx6NGjBzp37ozKlSvj4cOH+N///qcaUyGi58+fQ09PD8bGxnJHyZGVlZVqgHJeRB58mGX79u3w9vbG6tWrhZyvZsCAATku//vvv2FkZKRWrESd6vvWrVu4ceMG9PT0ULduXdSqVUvuSLnq1KkTHjx4kOuYH1Ffz5IkYeHChdi0aRPS09NV8xgZGhpi1qxZwhYWbRMcHAxvb+8czwgT8e+dJEnYuHEjatWqhbZt22LVqlVYuXIlzMzMMH/+fFlmiGZReUNsbCzS0tJQo0YNREZGYuvWrTAzM0P//v3VBvjJJTQ0FHZ2dtDV1c1xL0V2IjX1+/fv53vbrE96IsjtjIPY2FiUKlUKlStXVi0TZVd6QSYnnDt3bjEm+TDs2rUrz/U9evQooSQFExAQgNWrV2PixInw8fHBvn37cOXKFXh7e+Obb77BxIkT5Y6oJikpCUZGRqqvr169inv37qFmzZpqg8VF06ZNG7Ru3RoDBw7UGLAMiPX3TlQsKlrGysoKp0+fRqVKlfLcSyFiU9dGS5YsyffYmbFjxxZzmvffrVu34OPjgwsXLiA9PV1jPV/TRadLly6YMGECOnbsCDs7O+zduxc1atTA4cOHMXfuXPz5559yRwTwem+xu7s7oqOjcejQIbx69QqjR49WfVBTKBRo0aIFlixZIuQeZTs7O+zatUvovYK7d+/O97bdu3cvthy5+eDHqOS2mzwnIuwmP3r0qOqiXKJ8gs+P9u3bY8eOHahQocJb50UQ6XmJeFp6QT18+BBbtmzBjRs3oKuri3r16qF3795CTkQ2c+ZMPHv2DFOmTBH6VF8PDw94enrC2Ng4zz1YCoVC2Otu3bt3L8c9EVZWVnjy5IkMiXLm6+uLBw8eYMaMGQCABQsWIC4uDtu3b0e9evXw77//wsvLC/PmzcPs2bNlTqupQ4cOOH78uNBFZdq0aWpfZ30ANjAwgK6uLhISElCqVClUqFCBRUUO2Xe7paam4sCBA2jQoAFsbW2hq6uLq1ev4vLlyxoX0JNL9rxv7jJMT0/H9evXUadOHXz00UclHS1PPXr0UO327NGjh/Bn+OTm6tWrWLt2repN38LCAt9++62wE779+++/cHV1hYGBARo3bgylUomdO3diy5Yt+PXXX1GvXj25I6q5dOkSfv31V+FnS713755q9mptHeBZrVo1XLlyReNK6ydOnBDqauAnT57E6tWrVb9jp06dwuzZs1UTGTZp0gSzZs3C6NGjhSwqU6dORbdu3XDw4EGYm5tr/O0T4fDr9evXVf/ev38/1q5di7lz56pms46Ojoa7uzu6du0qS74Pvqhkf5F4eHhg4MCBGu3yxx9/RFRUVElHe6uHDx/C09MTEyZMQP369fHll18iMjIS5cqV05jkS27ZD4to616K8+fPY/Dgwahfvz5atWoFpVKJCxcuoG/fvti4cSOaNm0qd0QN8+fPR/PmzbFw4ULVRHWpqamYMmUK/P39sXLlSpkTqqtQoYJWDOLMPrmfiBP95ceQIUPg7e2NJ0+eQJIknD17FkFBQdi0aZPG30A5KZVKjQkAy5Ytq7ZNuXLl1M6oEcmcOXOQmJiItLS0Ao3Vk4u/vz9++uknVUkBgFq1asHLywujRo1C//79SzzTB19Usvvjjz9yHBjXvXt3WXZ3vc3cuXPx6tUrVKxYEb///jvu37+PwMBA7Ny5EwsWLBByuvEsYWFhqnEI2cfYKBQKYeckWbx4MXr16gVvb2+15d7e3vjxxx+FfMO6cOECtm7dqiopAFC6dGmMGTNGuFPYAcDV1RWLFi2Cv7+/kOMNcpOcnIwbN27k+HoW9cq/vXr1QkZGBlasWIGUlBTMmDEDFStWxIQJE9CnTx+546k4OzvDx8cHS5cuRcWKFdG9e3esWbMGixYtQqlSpZCWloaffvoJzZs3lztqjk6cOIEVK1YIO3fRm+Lj49X+XmRRKpVISUmRIRGLipqyZcvi2rVrGscSw8LCUKlSJXlC5eHcuXPYuHEjqlevDn9/f7Ru3Rr29vaoUKECevbsKXe8XC1btgxLlixB2bJlNd6MRC4q165dy/EUdVdXV2Gnd//oo49yHJSa0zIRnDlzBmFhYXB0dESlSpU0zrQTafxSlqNHj2LatGlISEjQitNPs+vduzd69+6NuLg4SJIk5N85Dw8PDBo0CB06dICTkxOqV6+Os2fPokOHDqhduzauX78OHR0dBAYGyh01RxUqVBByPFhumjdvDh8fH8yfP191WDAqKgre3t5o27atLJlYVLLp3bs3ZsyYgaioKNjY2Kh27W/ZsgVTp06VO56G9PR0lCtXTrXbdtKkSQBeN19dXXF/tL/++ismTpyIESNGyB2lQCpUqIDnz59rLI+LixPi1PWctGjRAvPnz8fPP/+M8uXLA3idd8GCBULOAdO0aVMhD6Hlxd/fH05OThg9erTQA4BzEhMTg3/++SfHT8qi7EWuUqUKdu7cif379+P06dMIDQ1F+fLloaurC4VCgf79++Obb75BhQoV5I6ao5EjR8LX1xfTp0+Hubk5SpUqJXekPM2aNQtDhgxBx44dUbZsWUiShFevXqFx48aYPn26LJl4evIbVq5cic2bN6tGvZuZmWHYsGHo27evzMk09evXD82bN0eVKlXg4+ODv/76CxUrVsSMGTNw//59IQ9FAK8Hvx04cEDr5g/w9vbGhQsXsGjRItUVZyMjIzF58mRYW1sLMSjuTY8ePcI333yDly9fqvYURkdHo3z58ti0aZPGQEoquEaNGuG3336Dubm53FEKZOfOnfDy8lINCs5OtD1BqampuHnzJiwsLGBgYIBr165h8+bNePz4MSwsLDBo0CCYmJjIHTNH2jghYGZmJs6cOYObN29CoVDAysoKLVq0kO0kCBaVXDx//hwKhUL1KVREly9fxsiRI/H8+XMMGzYMkyZNwqxZs3DgwAGsXLkSdnZ2ckfM0aBBg9CrVy/ZRpAX1suXLzFo0CBERESoPjnHx8ejQYMGWLdunbCf6BITE7Fnzx7cvHkTkiTB0tIS3bp1E3YMyPXr13Hjxg3VG6gkSUhLS8OVK1eEnB26W7dumD59OhwdHeWOUiAdOnSAs7MzJk6cqDE4VSS3bt3CwIED8eTJE5iZmWHOnDkYPXo0qlWrBgsLC1y7dg3x8fEIDAxUfYAQibZOCCiSD76o7N69G//73/+gr6//1klvRNkVmp1SqURCQoLqD83169exdetWHDhwAOfPn5c5Xc6Cg4Mxf/589OjRA3Xq1NE4bCLi9zmLUqnEqVOncOPGDdWbvrOzs/CnWyckJODWrVvQ09NDjRo1hC0p69evx7x58wBAbTLDrEGpIu4lPH78OPz9/TFx4sQcX8+ijk9o1KgRDhw4INSpyDkZMWIESpcujdGjR2PDhg34/fff0aVLF/j6+kKhUCAjIwPu7u54+fIl1qxZI3dcDXPmzMGAAQOE3uPWoEEDnDp1Sm0i0dzIsQfogy8qb870mhvRdoVml5aWhj/++ANbt27FxYsXoVAo0KFDB/z8889yR8uRNn2ftW1CwDcplUrMmzcPgYGByMjIAADo6emhd+/e+P7774UrWB06dMBnn32GsWPHol27dti1axdevHiByZMn48svv8TAgQPljqihYcOGqt362b+fkiQJ93rO7uuvv8aYMWPQpk0buaPkyc7ODsHBwahTpw7i4uLQsmVL7Ny5U+2aMzdu3EDv3r1x8eJFGZPmrGnTptizZ4/Qh1l37dqFLl26QF9fHzt37szz74Ice4DEHXFZQrJPdJP939rgzp072Lp1q+qPuUKhQM+ePTFy5EihPyVp0/c5p3E0+/btg4uLi3CT6uVk5cqVCA4OxtSpU+Ho6AilUonQ0FAsW7YMJiYmGDp0qNwR1Tx69AhfffUVSpcuDSsrK1y5cgUdOnTAtGnT4OfnJ2RRWb9+vdwR8i379cE6dOgAT09PjBs3DjVr1tQY5CnKtcIMDAyQnJwMAKhYsSK+/vprjdNn4+PjhR3I3KZNG2zevBljx44Vdk9m9vIh4hmjH/weFW2TmZmJQ4cOISgoCCEhIShVqhQ++eQTdOnSBR4eHti9ezcsLCzkjvley35dFNG5uLhg4sSJ6Natm9ryffv2YcmSJTh06JBMyXLm6OiIoKAg1K5dG7NmzYKpqSlGjhypuoK5iJ+YtYk2XsV86tSpiImJwZw5czT+timVSpw9exazZ89Gy5YtVdPsi6R///4IDQ2FQqFApUqVNEqWCKfcL126NN/bynFNsw9+j8rbrjuTnQgvqDZt2uDVq1do0aIFZs+ejY4dO6JcuXIANK/XQPTs2TM0adJEY3mTJk3w8OFDGRLlzd7eHqtWrcKMGTNgbW2NHTt2YPjw4QgPDxd2D1ZSUhI2bNiQ4wSGgFiHBEX4G1ZQHh4eGD16NH755Rf4+/urrfvjjz8wadIktGnTRjU9g2iaN28u7GR0WXbu3Kn29cOHD1Xj2XR1dXH37l2kp6fDxsaGRUUO2a878+LFCwQGBqJdu3aws7ODrq4urly5gkOHDmHw4MEyJ33t1atXqFSpEj7++GOUL18ehoaGckcigdWqVQtnzpzRGMh3+vRpIU8PnzRpEgYPHowtW7agT58++OWXX+Do6Ijk5GQMGTJE7ng5mjFjBo4ePYpWrVqhSpUqcsfJ05s/8/PnzyMjIwMtW7YEAMybNw/t27cXajbdihUrYuvWrYiPj9dY5+TkhH379gl3zarstOGq6tmvlL1hwwYcO3YMCxcuVE0AGB8fDzc3N9SvX1+egBKpjBw5Ulq1apXG8oCAAGnAgAEyJNL06tUrKSgoSPr6668lS0tLyd7eXpo6dar0119/SQ0bNpRu3rwpd8T3nq2trXT37l25Y+TLrl27pEaNGknz58+Xjhw5Ih05ckSaN2+e1LhxY2nz5s1yx8tRcnKy9OzZM0mSJOnJkyfSunXrpN9//13mVLmzt7eX/vzzT7ljFNj+/fulhg0bSmvWrFEtGzdunNSwYUPp8OHDMiZ7/1y5ckWaMGGC9L///U/6/PPPpUmTJkmXLl2SO1aOnJycpIiICI3l//77r+To6ChDIkniGJVsbG1tsWfPHtSsWVNt+Z07d/D555/j0qVLMiXLWVRUFHbs2IF9+/bh6dOnUCgU6NWrF4YNG6bxHKjoaNMYFeD1J6Q1a9bg6dOnAIDKlStjyJAhGDRokMzJ3g8ODg4IDg4W+vTTnHTr1g29e/fWuOZTQEAAgoODsWfPHpmSvV+yX8y0WbNmqhnPb9y4IeTFTB0cHLBy5UrY29urLT979iwmTJiAkJCQEs/EopJNp06dMHjwYHzzzTdqy9euXYvg4GAcOHBApmR5y8zMxLFjx7Br1y4cO3YMSqUSLVu2FHJOAW3j4eGhsSy3s35EnJl2//79cHZ2Rrly5YS9nou2jRN7k6enJ6pUqYIJEybIHaVAmjRpgv3792sU7piYGHTt2lW4D2baqk+fPqhfv36OFzONjIwUbm4gd3d3XL16FTNmzICNjQ0kSUJ4eDhmz56N1q1byzJg+YMfo5LdkCFDMHv2bFy8eBGNGjVSNd/Dhw9rDOISSalSpdC+fXu0b98ecXFx2LNnj8bgKCqce/fuaSyzs7PD8+fPc7zuj2h8fHwQGBiIcuXKoWLFinLHyVH2cWLaqGLFili3bh1OnDiB2rVra0z4JmKBBV5fHiQ0NFSjqFy8eFH4sTbaRNsuZjp9+nR89913+Pbbb1W/l5Ik4bPPPoObm5ssmVhUsunduzc++ugjbN68GYcOHYJCoUCDBg2wfPly4SdFylKxYkUMGjSIu/WLiGifdgqqVq1auHHjhtCnrI8bN07uCO/k77//Vp1Z9fjxY5nT5F+fPn0we/Zs3L17V5X/ypUr2LhxI0aPHi1zuveHtl3M1NjYGGvXrsWtW7dU1/oxNzfH9u3b0aZNGx76EUHWdUYyMzNV8w2IfJ0RorzMmDEDO3bsgJWVFWrVqqUxh4OIn/a17Vo/2mz16tUICAhQXYS1atWqGD58uMa4FSo8bbyYKSDWjOcsKtmsX78e8+fPV019LWnBdUaI8tK/f/8814v2mtbGa/08ePAgz/WiXusnu+fPn0NPT0/YmVO1WU4XM3316hWsrKyEvJipiDOes6hko43XGSHKr/T0dJw5cwaSJMHJyUlj74oItPF3UMSLuOVXSkoK/vjjD9y6dQuDBw/GjRs3UK9ePeHePLWdUqnEyZMn1a5g/sknn0BHR0fuaADEn/GcRSUbGxsbHDhwAObm5hg8eDD69u2LDh064NSpU/Dz88P+/fvljkiUL4GBgaoB1V9//TW6dOkCV1dX1XWWTExMsHHjRuFOY9fG38E3r1KemZmJ27dvY8OGDZg2bRpcXFxkSpa3p0+fonfv3nj27BnS0tJw8OBB+Pr64urVq9i4caPqMAW9/z755BPVjOcdO3ZUm/G8YcOG2LNnj6xFhYNpszEyMlJdBdXc3ByRkZHo0KED6tati/v378ucjih/1q5di6VLl6Jbt24wNDTETz/9hJ07dyIzMxNbtmyBUqnE3LlzsXDhQuGusK2Nv4OOjo4ay5ycnFCjRg0sWbJE2KLi5+eHevXqYf/+/Woz006YMAELFizAL7/8InNC7ZXfq64rFAps3LixmNO8negznoux30kQWdcZSU5OhrW1Nf78808olUqhrzNC9KZt27bB19cXPj4+8PDwwIoVK/D3339j0qRJsLe3R7NmzeDh4YHw8HC5o2p4n34Ha9WqJfSVws+dO4fx48ervSmVK1cO7u7uuHDhgozJtF+1atXy/O/hw4c4f/68MIcFT58+jZEjR+LatWsYO3YsnJyc4ObmhmPHjgkxdQD3qGSjjdcZIXrTgwcP1C5E2LhxY+jq6qrNnFqzZk28ePFChnR508bfwZwG0yYkJGDlypWoXr26DInyJzExEUZGRjmuy8jIKOE075fczuRJSEiAn58fYmJi0KpVK/j6+pZwspwZGxvj66+/xtdff6024/nevXuhUCiwYcMGWWc85xiVN6SkpCApKQkVK1bE06dPsX//fpiamqJz585yRyPKFysrK5w+fVptBto3p/1/+vQpnJ2dhflEl92bv4P79u2DmZmZsL+DOQ2mlSQJRkZGWLBgAdq3by9TsrwNHz4ctWvXhoeHh+r1YWpqiilTpiAxMZEzWxexM2fOwMvLC69evYKbmxu++uoruSPlSaQZz1lUiN4z2l5UsktKSoJSqRT6tNk3B9MCgJ6eHurXry/04aqoqCj069cPZmZmuHnzJpo3b45bt27h1atX2Lx5M6ysrOSO+F5ISkqCn58ftm3bhlatWmHOnDkwMzOTO1aBZJ/xfN++fSX++CwqRO8ZKysrDBkyRG3swcqVK/HNN9+oRvInJSVh/fr1whSV69evY+/evQBeT6lfr149+Pj4YOvWrQCA9u3bw8/PT+g3/pz8888/aNiwodwxcvX48WP8+uuvuHbtGpRKJerVq4e+ffsKfchKm5w9exaenp54+fIl3N3d8fXXX8sdSSuxqBC9Zwpylsmff/5ZjEny5/jx4xgzZgyqV68OQ0ND3Lp1C1999RX27t2LwYMHQ6lUYuPGjfj888/h6ekpd1yVy5cv4/fff4euri66du0KS0tL1brU1FT8+OOP2LRpE65evSpjyty9ePEC5cuXz3FdcHAwevXqVbKB3iNJSUmYP38+goKC4OTkBF9fX63biyISFhUiktVXX32FNm3aYOzYsQCAPXv2YNq0aZgzZ47qzfLgwYOYP3++MFdPPnDgAKZMmQJ9fX3o6uoiJSUF69evh4ODAy5evAg3NzfExMSgZ8+e+OGHH+SOm6Pu3btj06ZNqtlSASA2Nhaenp44ffq0MHvbtFH79u3x4MED1KhRA59//nme22a97il3LCpEJCs7Ozvs3LkTtWvXBvB6EF+jRo2we/du1K9fH8DrN9D27dsLs3eiR48eqFGjBvz9/aGjowM/Pz9ERERg8ODB+O6772BqaorZs2fDyclJ7qi56tOnD9LS0rBx40YYGxtj+/btmDdvHipWrAhvb2+hs4suv3s1FQqFMOVbZCwqRCQrKysrnDp1CpUrV1YtE33wr52dHQIDA9GgQQMAQHx8PFq2bIkyZcqgXbt28PLyyvXUX1EkJSVhxIgRSE5ORtmyZXH+/HkMGjQIY8eOFfLyCvTh4jwqRCQ7ESaVKojk5GRUqVJF9XXZsmWhq6uLLl26wMvLS8Zk+WdkZITVq1dj5MiROHfuHAICAtCsWTO5Y70XtG1mWtGxqBCR7C5evKg6Iwl4PQ/J5cuX8ejRIwCvr0ArmjfLlUKhQO/evWVKkz9Lly7VWNaoUSNcuHAB8+bNQ5s2bVTLOXai8KpVq5bn+rCwMMTExKBs2bIllEi78dAPEckqa8K0t/0pUigUwhz6yc9cNSLi2Al5Zc1Mu2PHDtXMtKampnLHEh73qBCRrLT1DfFte4GyODg4lHS0XOV2OnpcXBzCwsJQqVIlNG3atIRTfRiyz0w7e/Zs4WemFQn3qBARFZA27gXKsnz5cmzcuBHbtm1DzZo1cfHiRQwbNgyJiYkAgBYtWmDFihUwMDCQOen74X2YmVZu3KNCRFRA2roXKCgoCCtWrMDAgQNVh608PDxgYGCArVu3okyZMhg3bhxWrVqF8ePHy5xW+2WfmdbHx4cz0xYS96gQEX0gvvzyS/To0QP9+vUDAFy5cgVfffUVJk6ciBEjRgAA/vrrL/j5+eHgwYNyRtVqnJm2aHGPChHRByIqKgqtWrVSfX3u3DkoFAq1s30sLCzw4MEDOeK9N7p166aamdbe3h7BwcG5bsuzq96ORYWIhHHhwgXY29vLHeO9lv206rCwMJQrV07tSsmJiYlqF7SkgpMkCWZmZsjIyMDOnTtz3U6hULCo5AOLChEJo3///ihfvjzatm2L9u3bo1WrVpwltQjVr18fFy5cQM2aNREfH4+QkBC0b99ebZvff/9ddekCKhwRLvb5PmFRISJhnD17FidPnsTx48fh6emJlJQUtGzZEu3bt0fbtm1RsWJFuSNqtX79+mHmzJmIiIjAxYsXkZaWhm+//RbA6+sp7du3D2vXroWvr6/MSYn+w8G0RCQkSZJw6dIlBAUFYe/evVAoFMJclPBN9+/fx6VLl5CWlqaxrnv37iUfKA87duzAr7/+Ch0dHQwdOhSffvopAMDHxwfbtm3DsGHD8N1338mckug/LCpEJJS4uDicP38e586dQ0hICG7fvo3q1aujZcuW8PHxkTuehm3btsHb2xuZmZka60ScRyU3sbGx0NfXR4UKFeSOQqSGRYWIhNGtWzdERkbCxMQETZs2RYsWLeDk5ITq1avLHS1XLi4uaNeuHSZOnAhjY2O54xC9d3TkDkBElKVUqVJQKBSoXLkyqlWrhurVq6Nq1apyx8rTkydPMGjQIJYUomLCwbREJIzdu3cjLi4OZ8+exenTp/H9998jLi4Otra2cHJywsiRI+WOqKFBgwaIjIwUeq8PkTbjoR8iEtatW7fw66+/IigoCOnp6UKO9zh06BD8/PwwaNAg1KlTB/r6+mrrRbooIZE2YlEhImG8ePFCtTflzJkzePToEWxsbNCuXTu0a9dObWIyUeSVSZsG0xKJikWFiITRoEEDGBgYoEWLFnBxcUHbtm1RpUoVuWPl6f79+3mur1atWgklIXo/sagQkTD++usvtGzZUutmo42JiUFcXBwqVqyI6tWrq01TT0TvhkWFiIRy/fp1bNy4Ebdv38ZPP/2EI0eOwMLCAs2bN5c7mhpJkrBmzRps3rwZjx8/Vi2vUqUKXF1dMWzYMBYWoiLAs36ISBhXr15Fnz59YGtri6tXryItLQ0RERGYO3culi1bpnaVX7mNHTsWJ06cwBdffAEnJydUqFABL1++REhICFasWIFLly5h2bJlcsck0nrco0JEwvj2229ha2uLiRMnws7ODnv37kWNGjUwd+5chIeHY8eOHXJHBAAEBwfDz88PAQEBaNCggcb6mzdvon///nBzc0PPnj1lSEj0/uCEb0QkjH/++SfHa+P069cPUVFRJR8oF9u3b8fYsWNzLCkAUK9ePYwbN06YYkWkzVhUiEgYenp6SEhI0Fj+8OFDGBoaypAoZ1FRUXB2ds5zG2dnZ9y8ebOEEhG9v1hUiEgYHTp0wI8//oj4+HjVsqioKPj6+qJt27byBXtDRkYGdHXfPsSPg2mJ3h2LChEJw93dHYmJiWjRogWSk5PRs2dPdO3aFaVKlYKbm5vc8VQsLCxw6tSpPLc5deoU6tWrV0KJiN5fPOuHiIRhbGyMrVu34uzZs7h27RqUSiXq168PZ2dn6OiI87mqR48eWLp0KVq1aoWaNWtqrI+KisLSpUvh7u4uQzqi9wvP+iEiKiClUomRI0fiwoUL6NmzJ+zt7VG+fHkkJCQgNDQU27dvR5s2bbB48WK5oxJpPRYVIpLVgAED8rWdQqHAxo0bizlN/mVkZOCXX37B5s2b8eLFC9XyKlWqYODAgRg8eDDHqBAVARYVIpKVh4dHnuvDwsIQExODsmXL4vz58yWUKv8kScLt27fx8uVLVKhQAebm5kIdpiLSdiwqRCSkhIQE+Pn5YceOHWjVqhV8fX1hamoqdywiKmEsKkQknDNnzsDLywuvXr2Cm5sbvvrqK7kjEZFMeNYPEQkjKSkJfn5+2LZtG1q1aoU5c+bAzMxM7lhEJCPuUSEiIZw9exaenp54+fIl3N3d8fXXX8sdiYgEwD0qRCSrpKQkzJ8/H0FBQXBycoKvry/3ohCRCveoEJGs2rdvjwcPHqBGjRr4/PPP89x27NixJZSKiETBokJEsnJxccnXdgqFAkePHi3mNEQkGhYVIiIiEhZnJSIiIiJhsagQERGRsFhUiIiISFgsKkRERCQsFhUieq/xfAEi7caiQkSyO3fuHD799FPY2Nhg6NChRXa/27dvx7x584rs/oio5HFmWiKS3fz586FUKrFq1SpUqlSpyO53xYoVcHR0LLL7I6KSx6JCRLJ78eIFHBwc0LJlS7mjEJFgeOiH6D3n4uKCpUuX4ocffkDz5s1hZ2eHyZMnIzExEatWrULr1q3RtGlTjBs3Ds+fPwcApKSkYOHChejUqRNsbGxgb2+PQYMGISIiQnW/06ZNw8CBAxEcHKw6bPPFF1/gxIkTqm2WLFkCS0tLjUyWlpZYsmQJ7t27B0tLS9y/fx+7d++GpaUlQkJCAABHjhxB3759YWdnBxsbG3Tu3BlbtmxRu5/Hjx/D3d0dTk5OsLOzg6urKy5evKh63vfv38euXbtgaWmJe/fuvTUPAFWm9evXo3PnzmjSpAmCg4MBADdu3MCIESNgb28Pe3t7jBkzBjExMar7CQkJgaWlJbZu3Yp27drB3t4ep0+fLtTPjYhe4x4Vog/AunXr0KpVKyxevBhXr17FwoUL8c8//6Bq1aqYPXs27t27B19fX1SuXBkzZ86Em5sbwsLCMGnSJJibm+POnTv46aefMHnyZPz2229QKBQAgKtXr+Lx48cYP348jI2N8dNPP2HcuHE4ceIEypUr99ZcVatWRVBQEMaOHQtra2uMHj0aFhYWOHbsGMaMGYMBAwZg3LhxSElJQWBgIHx8fGBjY4MmTZogMTERffr0QWZmJqZOnQoTExOsW7cOgwcPxq5du7B06VIMHz5cdb9Vq1Yt0PdsyZIl8PT0hLGxMZo0aYLbt2/jm2++QZ06dTBv3jxkZGRgxYoV6NOnD/bs2aN2yGrp0qXw8vJCSkoK7OzsCvbDIiI1LCpEHwBjY2MsXrwYurq6aNmyJXbt2oXY2Fhs374dZcqUAQCcPHkSFy5cQFpaGhITE+Hl5YX//e9/AABHR0ckJCTAz88PT58+RZUqVQAAr169ws6dO2Fubg4AMDIygqurq2pw7Nvo6+vD1tYW+vr6qFixImxtbQEAkZGR6NGjBzw9PVXb2tnZoXnz5ggJCUGTJk2wa9cu1R6TBg0aAADs7e3RvXt3hIaG4quvvtK434L47LPP0KtXL9XXkydPhqGhITZs2ABjY2MAgJOTEzp06IA1a9bA3d1dtW3fvn3RuXPnAj8mEWliUSH6ADRu3Bi6uv/9uleuXBlGRkaqkgIA5cuXx40bN6Cvr4+1a9cCAGJjY3H79m1ER0fjr7/+AgCkpaWpblOxYkVVSQEAU1NTAEBycvI75c068ycxMRG3b9/G3bt3ceXKFbXHDw8PR/Xq1VUlBQAMDQ1x8ODBd3rsLNnvF3h9ZpKjoyMMDAyQkZEB4HUBbNasGc6cOZPnbYmo8FhUiD4AWXsAsjMyMsp1+5MnT+KHH37ArVu38NFHH8HKykq1ffZ5SQwNDdVul3VISKlUvlPeuLg4zJw5E0eOHIFCoUDNmjXRrFkztcd/8eJFkZ4h9KY3vz8vXrzAgQMHcODAAY1tK1asmOdtiajwWFSISM3du3cxZswYdOjQAStXrkSNGjWgUCiwZcsWnDx5skD3lVVcMjMzUapUKQCv95K8zZQpU3Dr1i1s2LABdnZ20NfXR3JyMrZt26bapkyZMrh3757GbS9cuIBy5cqhbt26RZYn6/FatmyJQYMGaazLvreKiIoWz/ohIjVXr15Famoqhg8fDnNzc9Wbe1ZJKchMr1l7ch49eqRaFh4e/tbbhYeHo1OnTmjevDn09fUBQHU2UdbemmbNmiEmJgY3b95U3S41NRXjxo3Djh07AAA6Oup/4gqbB3g9TicyMhINGjRAo0aN0KhRI9jY2GDDhg04fPhwvu6DiAqORYWI1DRs2BC6urpYsGABTp8+jb/++gvjxo3DsWPHAABJSUn5vq82bdoAAGbMmIEzZ84gODgYs2bNwkcffZTn7Ro3box9+/Zhz549CAkJwYoVKzBt2jQoFArV+JeePXuiRo0aGDVqFPbu3YuTJ09i7NixSE9PR9++fQEAZcuWxbVr13D+/HmkpKQUOg8AjB49Gnfv3sWIESNw5MgRnDx5EuPGjcNvv/0GKyurfH9PiKhgWFSISE3NmjWxcOFCxMbGYtSoUZgxYwYAYNOmTVAoFAgLC8v3fdWuXRvz5s3DvXv3MHz4cAQEBGD27NlvPVXYz88PTZo0wezZszFmzBgcPXoU3t7e+OSTT1SPb2xsjM2bN6u2mzBhApRKJQICAlCjRg0AwODBg/H06VMMGTIEV69eLXQeALCyssKWLVugUCjg5uaG8ePH48mTJ1i2bBk6deqU7+8JERWMQuIVu4iIiEhQ3KNCREREwmJRISIiImGxqBAREZGwWFSIiIhIWCwqREREJCwWFSIiIhIWiwoREREJi0WFiIiIhMWiQkRERMJiUSEiIiJhsagQERGRsP4PeXkyN+pWrHAAAAAASUVORK5CYII=", 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", 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" ] @@ -489,33 +554,33 @@ "Calzado de correr en carretera para hombre 12\n", "Calzado de correr en pavimento para mujer 11\n", "Calzado de correr en carretera para mujer 11\n", - "Calzado para hombre 5\n", "Mujer • adidas by Stella McCartney 5\n", - "Calzado de running en carretera para niños grandes 4\n", + "Calzado para hombre 5\n", "Mujer • TERREX 4\n", + "Calzado de running en carretera para niños grandes 4\n", "Calzado de trail running para hombre 3\n", "Calzado para niños de preescolar 2\n", - "Calzado de correr en pavimento para hombre 2\n", "Calzado de carrera en carretera para mujer 2\n", "Calzado de trail running impermeables para mujer 2\n", + "Calzado de correr en pavimento para hombre 2\n", + "Calzado de caminata para mujer 1\n", "Calzado de running en carretera acondicionado para los estados del tiempo para hombre 1\n", - "Calzado de carrera en carretera 1\n", - "Calzado de running en carretera impermeable para mujer 1\n", - "TERREX 1\n", - "Calzado de trail running para mujer 1\n", - "Calzado de running en carretera impermeable para hombre 1\n", + "Hombre • TERREX 1\n", "Calzado de trail running impermeables para hombre 1\n", - "Calzado de running en carretera resistente a las inclemencias del tiempo para mujer 1\n", "Calzado de carrera en carretera para hombre 1\n", - "Hombre • TERREX 1\n", - "Calzado de caminata para mujer 1\n", + "Calzado de running en carretera resistente a las inclemencias del tiempo para mujer 1\n", + "Calzado de carrera en carretera 1\n", + "Calzado de running en carretera impermeable para hombre 1\n", + "Calzado de trail running para mujer 1\n", + "TERREX 1\n", + "Calzado de running en carretera impermeable para mujer 1\n", "adidas by Stella McCartney 1\n", "Name: count, dtype: int64\n" ] }, { "data": { - "image/png": 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", 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", 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bt2/HsmXLMHXqVEgkEjRq1Ajh4eHFLjl8KCMjI4SFhSEgIAALFiyARCLBV199hZo1aypd+vjxxx9hbm6O7du3IzQ0FJUrV4a3tzfGjx+v+CVcksDAQKxevRphYWF4/vw57O3tERwcjLZt2771NXp6eli/fj1WrlyJkJAQPHv2DJaWlhg0aBBGjx4N4PXln6VLl2L16tUYOXIkKleuDGdnZ2zduhX9+/dHdHQ0HBwc0K5dOxw6dAhTpkxBr169MGfOHCxcuBAhISHYuXMnkpOTUa1aNXTo0AE//vij4sxHp06doKenh7Vr1+LQoUOoV68e5s2bpxjdc/LkSbx48UIx4qgk3bp1Q2RkJA4fPoxvvvkGgwYNwq5du3DmzBmcP38ehoaGH7SviNRFIudTqIiIiEikeI8KERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJltbPo3L58mXI5XKO+SciItIiMpkMEokELi4u79xO68+oyOXydz7UTFfJ5XJIpdIy+d7LIu7vsoX7u2wpq/v7fX9/a/0ZlaIzKZ9++qnASTTr1atXiI2NRZ06dd7rqbqk3bi/yxbu77KlrO7vfz/D6120/owKERER6S4WFSIiIhItFhUiIiISLRYVIiIiEi0WFSIiIhItFhUiIiISLRYVIiIiEi0WFSIiIhItFhUiIiISLRYVIiIiEq0PLiopKSlwcHAo9s/+/fsBALGxsejXrx+cnZ3h4+OD8PBwpdcXFhYiODgYzZs3h7OzM4YNG4YHDx6o5t0QERGRTvngZ/3ExcWhXLlyOHnyJCQSiWJ5pUqV8Pz5cwwaNAg+Pj6YO3curly5grlz56JixYro2bMnAGDt2rXYvn07Fi1aBCsrKwQGBmLo0KE4fPgwjIyMVPfOiIiISOt9cFGJj4+Hra0tLCwsiq0LCwuDoaEh5s2bBwMDA9jb2+PevXtYv349evbsCalUik2bNmHixIlo1aoVAGD58uVo3rw5jh8/jk6dOn30GyIiIiLd8cGXfm7fvg17e/sS10VHR8PT0xMGBv/Xf7y8vJCUlIS0tDTExcUhOzsb3t7eivWmpqZwcnJCVFRUKeITERGRLvvgohIfH4/09HT07dsXn332Gb755hucPXsWAJCcnAwrKyul7YvOvDx58gTJyckAAGtr62LbFK0jIiIiKvJBl37y8/Nx9+5d1KlTB1OmTIGJiQmOHj2K4cOHY/PmzcjNzS12n0m5cuUAAHl5ecjJyQGAErd58eJFqd+EXC7Hq1evSv36j/Hv+3Q0SSqVwtjYGFKpVLAMcrlckJ9bFhV9dor+TbqN+7tsKav7Wy6Xv9fvrw8qKgYGBrhw4QL09fVRvnx5AEDDhg3xzz//YOPGjShfvjykUqnSa/Ly8gAAFSpUULxGKpUq/rtoG2Nj4w+JokQmkyE2NrbUry8tQ0NDODk1gIGBvsZ/trGxMczMzDT+c4vk5xfg1q2bkMlkgmUoi5KSkoSOQBrE/V22lMX9/T6DaD74ZtqKFSsWW1a3bl2cO3cOVlZWSE1NVVpX9LWlpSXy8/MVy2rVqqW0jYODw4dGUTA0NESdOnVK/frSkkgkMDDQR9C2GDxMeanxny8UG8tKmNjXDXXr1uVZFQ3JyclBUlISbG1tP6rUk3bg/i5byur+vnPnzntt90FF5Z9//kGfPn2wbt06NGnSRLH8xo0bqFOnDurXr4+dO3eioKAA+vqvzzJERkbCzs4O1apVQ6VKlWBiYoILFy4oikpmZiZu3bqFfv36fUgUJRKJBBUqVCj16z/Ww5SXSHhU+ktX2qosfaDEwtjYWNA/66RZ3N9lS1nb3+9728IH3Uxrb2+P2rVrY968eYiOjkZCQgIWLlyIK1euYOTIkejZsyeysrIwffp03LlzB/v378eWLVswYsQIAK9P8fTr1w9BQUE4deoU4uLiMG7cOFhZWaFdu3Yf/i6JiIhIp33QGRU9PT389NNPWLp0KX788UdkZmbCyckJmzdvRr169QAAoaGhWLBgAbp37w5zc3P4+fmhe/fuiu/h6+uL/Px8zJgxA7m5ufDw8MDGjRthaGio2ndGREREWu+D71GpXr06Fi5c+Nb1jRo1wq5du966Xl9fH5MmTcKkSZM+9EcTERFRGcOHEhIREZFosagQERGRaLGoEBERkWixqBAREZFosagQERGRaLGoEBERkWixqBAREZFosagQERGRaLGoEBERkWixqBAREZFosagQERGRaLGoEBERkWixqBAREZFosagQERGRaLGoEBERkWixqBAREZFosagQERGRaLGoEBERkWixqBAREZFosagQERGRaLGoEBERkWixqBAREZFosagQERGRaLGoEBERkWixqBAREZFosagQERGRaLGoEBERkWixqBAREZFosagQERGRaLGoEBERkWixqBAREZFosagQERGRaLGoEBERkWixqBAREZFosagQERGRaLGoEBERkWixqBAREZFosagQERGRaLGoEBERkWixqBAREZFosagQERGRaLGoEBERkWixqBAREZFosagQERGRaLGoEBERkWixqBAREZFosagQERGRaLGoEBERkWixqBAREZFosagQERGRaLGoEBERkWixqBAREZFosagQERGRaLGoEBERkWixqBAREZFosagQERGRaLGoEBERkWixqBAREZFosagQERGRaJW6qCQmJsLFxQX79+9XLIuNjUW/fv3g7OwMHx8fhIeHK72msLAQwcHBaN68OZydnTFs2DA8ePCg9OmJiIhIp5WqqMhkMkycOBGvXr1SLHv+/DkGDRqEWrVqYd++fRg9ejSCgoKwb98+xTZr167F9u3b4e/vj507d6KwsBBDhw6FVCr9+HdCREREOqdURWXVqlUwMTFRWrZ7924YGhpi3rx5sLe3R8+ePTFw4ECsX78eACCVSrFp0yb4+vqiVatWcHR0xPLly5GcnIzjx49//DshIiIinfPBRSUqKgq7du3CokWLlJZHR0fD09MTBgYGimVeXl5ISkpCWloa4uLikJ2dDW9vb8V6U1NTODk5ISoq6iPeAhEREekqg//e5P9kZmbCz88PM2bMgLW1tdK65ORk1KtXT2mZhYUFAODJkydITk4GgGKvs7CwUKwrLblcrnQZSlMkEgmMjY01/nPFIicnB3K5XOgYZUJOTo7Sv0m3cX+XLWV1f8vlckgkkv/c7oOKypw5c+Di4oLOnTsXW5ebmwsjIyOlZeXKlQMA5OXlKXZASdu8ePHiQ2IUI5PJEBsb+1HfozSMjY3h5OSk8Z8rFomJiWXugyW0pKQkoSOQBnF/ly1lcX+/2QlK8t5F5eDBg4iOjsbhw4dLXF++fPliN8Xm5eUBACpUqIDy5csDeH2vStF/F23zsWclDA0NUadOnY/6HqXxPk1Ql9nZ2fGMiobk5OQgKSkJtra2ZfosXlnB/V22lNX9fefOnffa7r2Lyr59+/Ds2TO0atVKafns2bPx66+/wsrKCqmpqUrrir62tLREfn6+YlmtWrWUtnFwcHjfGCWSSCSoUKHCR30P+nBl6QMlFsbGxvyzXoZwf5ctZW1/v+/B/nsXlaCgIOTm5iota9euHXx9fdGlSxccOnQIO3fuREFBAfT19QEAkZGRsLOzQ7Vq1VCpUiWYmJjgwoULiqKSmZmJW7duoV+/fu8bg4iIiMqQ9y4qlpaWJS6vVq0aLC0t0bNnT4SGhmL69OkYOnQorl27hi1btmDu3LkAXl+H6tevH4KCglC1alX873//Q2BgIKysrNCuXTvVvBsiIiLSKR90M+27VKtWDaGhoViwYAG6d+8Oc3Nz+Pn5oXv37optfH19kZ+fjxkzZiA3NxceHh7YuHEjDA0NVRWDiIiIdMhHFZXbt28rfd2oUSPs2rXrrdvr6+tj0qRJmDRp0sf8WCIiIioj+FBCIiIiEi0WFSIiIhItFhUiIiISLRYVIiIiEi0WFSIiIhItFhUiIiISLRYVIiIiEi0WFSIiIhItFhUiIiISLRYVIiIiEi0WFSIiIhItFhUiIiISLRYVIiIiEi0WFSIiIhItFhUiIiISLRYVIiIiEi0WFSIiIhItFhUiIiISLRYVIiIiEi0WFSIiIhItFhUiIiISLRYVIiIiEi0WFSIiIhItFhUiIiISLRYVIiIiEi0WFSIiIhItFhUiIiISLRYVIiIiEi0WFSIiIhItFhUiIiISLRYVIiIiEi0WFSIiIhItFhUiIiISLRYVIiIiEi0WFSIiIhItFhUiIiISLRYVIiIiEi0WFSIiIhItFhUiIiISLRYVIiIiEi0WFSIiIhItFhUiIiISLRYVIiIiEi0WFSIiIhItFhUiIiISLRYVIiIiEi0WFSIiIhItFhUiIiISLRYVIiIiEi0WFSIiIhItFhUiIiISLRYVIiIiEi0WFSIiIhItFhUiIiISLRYVIiIiEi0WFSIiIhItFhUiIiISLRYVIiIiEq0PLirPnj3DpEmT4OXlBRcXFwwfPhwJCQmK9bGxsejXrx+cnZ3h4+OD8PBwpdcXFhYiODgYzZs3h7OzM4YNG4YHDx58/DshIiIinfPBRWX06NG4d+8e1q9fj71796J8+fIYOHAgcnJy8Pz5cwwaNAi1atXCvn37MHr0aAQFBWHfvn2K169duxbbt2+Hv78/du7cicLCQgwdOhRSqVSlb4yIiIi0n8GHbPzixQv873//w4gRI1CvXj0AwKhRo9C1a1f8888/iIiIgKGhIebNmwcDAwPY29srSk3Pnj0hlUqxadMmTJw4Ea1atQIALF++HM2bN8fx48fRqVMnlb9BIiIi0l4fdEalcuXKWLp0qaKkpKenY8uWLbCyskKdOnUQHR0NT09PGBj8X//x8vJCUlIS0tLSEBcXh+zsbHh7eyvWm5qawsnJCVFRUSp6S0RERKQrPuiMyr/NnDkTu3fvhpGREdatW4cKFSogOTlZUWKKWFhYAACePHmC5ORkAIC1tXWxbYrWlYZcLserV69K/frSkkgkMDY21vjPFYucnBzI5XKhY5QJOTk5Sv8m3cb9XbaU1f0tl8shkUj+c7tSF5UBAwagT58+2LZtG0aPHo3t27cjNzcXRkZGStuVK1cOAJCXl6fYCSVt8+LFi9JGgUwmQ2xsbKlfX1rGxsZwcnLS+M8Vi8TExDL3wRJaUlKS0BFIg7i/y5ayuL/f7AMlKXVRqVOnDgBgwYIFuHr1Kn7++WeUL1++2E2xeXl5AIAKFSqgfPnyAACpVKr476JtPubMhKGhoSKPJr1PE9RldnZ2PKOiITk5OUhKSoKtrW2ZPotXVnB/ly1ldX/fuXPnvbb7oKKSnp6OiIgItG/fXnEfip6eHurUqYPU1FRYWVkhNTVV6TVFX1taWiI/P1+xrFatWkrbODg4fEgUJRKJBBUqVCj166l0ytIHSiyMjY35Z70M4f4uW8ra/n7fg/0Pupk2LS0N48ePR0REhGKZTCbDrVu3YG9vDw8PD8TExKCgoECxPjIyEnZ2dqhWrRocHR1hYmKCCxcuKNZnZmbi1q1b8PDw+JAoREREVAZ8UFGpV68eWrRogfnz5yMqKgrx8fGYMmUKMjMzMXDgQPTs2RNZWVmYPn067ty5g/3792PLli0YMWIEgNfXovr164egoCCcOnUKcXFxGDduHKysrNCuXTu1vEEiIiLSXh98j8qyZcuwdOlSjBs3Di9fvoS7uzu2bduGGjVqAABCQ0OxYMECdO/eHebm5vDz80P37t0Vr/f19UV+fj5mzJiB3NxceHh4YOPGjTA0NFTduyIiIiKd8MFFpVKlSpgzZw7mzJlT4vpGjRph165db329vr4+Jk2ahEmTJn3ojyYiIqIyhg8lJCIiItFiUSEiIiLRYlEhIiIi0WJRISIiItFiUSEiIiLRYlEhIiIi0WJRISIiItFiUSEiIiLRYlEhIiIi0WJRISIiItFiUSEiIiLRYlEhIiIi0WJRISIiItFiUSEiIiLRYlEhIiIi0WJRISIiItFiUSEiIiLRYlEhIiIi0WJRIdICEokExsbGkEgkQkchItIoA6EDEGmTwkI59PQ0XxaMjY3h5OSk8Z9bRKj3TUTEokL0AfT0JAjaFoOHKS+FjqIxNpaVMLGvm9AxiKiMYlEh+kAPU14i4dELoWMQEZUJvEeFiIiIRItFhYiIiESLRYWIiIhEi0WFiIiIRItFhYiIiESLRYWIiIhEi0WFiIiIRItFhYiIiESLRYWIiIhEi0WFiIiIRItFhYiIiESLRYWIiIhEi0WFiIiIRItFhYiIiESLRYWIiIhEi0WFiIiIRItFhYiIiESLRYWIiIhEi0WFiIiIRItFhYiIiESLRYWIiIhEi0WFiIiIRItFhYiIiESLRYWIiIhEi0WFiIiIRItFhYiIiESLRYWIiIhEi0WFiIiIRItFhYiIiESLRYWIiIhEi0WFiIiIRItFhYiIiESLRYWIiIhEi0WFiIiIRItFhYiIiESLRYWIiIhEi0WFiIiIROuDi0pGRgZmzZqFFi1awNXVFd988w2io6MV6yMiItCjRw80btwYX3zxBY4ePar0+ry8PMydOxfe3t5wcXHBhAkTkJ6e/vHvhIiIiHTOBxeV8ePH4/Lly1i2bBn27duH+vXrY8iQIbh79y4SEhIwYsQING/eHPv370fv3r3h5+eHiIgIxevnzJmDc+fOYdWqVQgLC8Pdu3fh6+ur0jdFREREusHgQza+d+8ezp8/j+3bt8PNzQ0AMHPmTPz11184fPgwnj17BgcHB4wbNw4AYG9vj1u3biE0NBTe3t5ISUnBwYMH8dNPP8Hd3R0AsGzZMnzxxRe4fPkyXFxcVPz2iIiISJt90BmVKlWqYP369fj0008VyyQSCSQSCTIzMxEdHQ1vb2+l13h5eSEmJgZyuRwxMTGKZUXs7OxgaWmJqKioj3kfREREpIM+6IyKqakpWrZsqbTs2LFjuHfvHqZNm4YDBw7AyspKab2FhQVycnLw/PlzpKSkoEqVKihXrlyxbZKTk0v5FgC5XI5Xr16V+vWlJZFIYGxsrPGfKxY5OTmQy+VCx9AY7u+ytb+FlJOTo/Rv0m1ldX/L5XJIJJL/3O6DisqbLl26hKlTp6Jdu3Zo1aoVcnNzYWRkpLRN0ddSqRQ5OTnF1gNAuXLlkJeXV+ocMpkMsbGxpX59aRkbG8PJyUnjP1csEhMTy9QHi/u7bO1vMUhKShI6AmlQWdzfJXWCN5W6qJw8eRITJ06Eq6srgoKCALwuHFKpVGm7oq+NjY1Rvnz5YuuB1yOBPuZI1dDQEHXq1Cn160vrfZqgLrOzsytTR9jc32VrfwspJycHSUlJsLW1LdNn8cqKsrq/79y5817blaqo/Pzzz1iwYAG++OILLF68WNGIrK2tkZqaqrRtamoqKlSogEqVKsHKygoZGRmQSqVKLSo1NRWWlpaliQLg9S+QChUqlPr1VDpl6QNF3N9CMDY25t9tZUhZ29/ve/D3wcOTt2/fDn9/f/Tt2xfLli1TKhzu7u64ePGi0vaRkZFwdXWFnp4e3NzcUFhYqLipFnh9OjklJQUeHh4fGoWIiIh03AcVlcTERAQEBODzzz/HiBEjkJaWhqdPn+Lp06d4+fIl+vfvj2vXriEoKAgJCQnYtGkTfv/9dwwdOhQAYGlpiY4dO2LGjBm4cOECrl27hvHjx8PT0xPOzs7qeH9ERESkxT7o0s+xY8cgk8lw4sQJnDhxQmld9+7dsWjRIqxduxaBgYEICwuDjY0NAgMDlYYs+/v7IyAgAGPGjAEAtGjRAjNmzFDBWyEiIiJd80FF5fvvv8f333//zm1atGiBFi1avHV9hQoVMH/+fMyfP/9DfjQRERGVQXwoIREREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYnWRxWVkJAQ9O/fX2lZbGws+vXrB2dnZ/j4+CA8PFxpfWFhIYKDg9G8eXM4Oztj2LBhePDgwcfEICIiIh1V6qKybds2rFixQmnZ8+fPMWjQINSqVQv79u3D6NGjERQUhH379im2Wbt2LbZv3w5/f3/s3LkThYWFGDp0KKRSaanfBBEREekmgw99QUpKCmbPno0LFy7A1tZWad3u3bthaGiIefPmwcDAAPb29rh37x7Wr1+Pnj17QiqVYtOmTZg4cSJatWoFAFi+fDmaN2+O48ePo1OnTqp4T0RERKQjPviMys2bN2FoaIhffvkFjRs3VloXHR0NT09PGBj8X//x8vJCUlIS0tLSEBcXh+zsbHh7eyvWm5qawsnJCVFRUR/xNoiIdIdEIoGxsTEkEonQUYgE98FnVHx8fODj41PiuuTkZNSrV09pmYWFBQDgyZMnSE5OBgBYW1sX26ZoXWnI5XK8evWq1K8vraK/TMqqnJwcyOVyoWNoDPd32dvfRuXKQV9P82MOjI2N4eTkpPGfW6SgsBDSvLwytb+FlJOTo/TvskIul79XGf/govIuubm5MDIyUlpWrlw5AEBeXp5iJ5S0zYsXL0r9c2UyGWJjY0v9+tIS+i8ToSUmJpapDxb3d9nc30HbYvAw5aXQcTTGxrISJvZ1K3P7WwySkpKEjqBxb/aBkqi0qJQvX77YTbF5eXkAgAoVKqB8+fIAAKlUqvjvom0+5kjV0NAQderUKfXrS6usn5a1s7MrU0dc3N9lc38/THmJhEelP5DSVmVtfwspJycHSUlJsLW1LVNnbe/cufNe26m0qFhZWSE1NVVpWdHXlpaWyM/PVyyrVauW0jYODg6l/rkSiQQVKlQo9eupdMrSB4q4v8sa7m/NMzY2LlO/y9734E+lF189PDwQExODgoICxbLIyEjY2dmhWrVqcHR0hImJCS5cuKBYn5mZiVu3bsHDw0OVUYiIiEgHqLSo9OzZE1lZWZg+fTru3LmD/fv3Y8uWLRgxYgSA19ei+vXrh6CgIJw6dQpxcXEYN24crKys0K5dO1VGISIiIh2g0ks/1apVQ2hoKBYsWIDu3bvD3Nwcfn5+6N69u2IbX19f5OfnY8aMGcjNzYWHhwc2btwIQ0NDVUYhIiIiHfBRRWXRokXFljVq1Ai7du1662v09fUxadIkTJo06WN+NBEREZUBfCghERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChEREYkWiwoRERGJFosKERERiRaLChERkYAkEgmMjY0hkUiEjiJKBkIHICIiEoPCQjn09DRfFoyNjeHk5KTxn1tEqPf9vlhUiIiIAOjpSRC0LQYPU14KHUVjbCwrYWJfN6FjvBOLChER0f/3MOUlEh69EDoG/QvvUSEiIiLRYlEhIiIi0WJRISIiItFiUSEiIiLRYlEhIiIi0RKkqBQWFiI4OBjNmzeHs7Mzhg0bhgcPHggRhYiIiERMkKKydu1abN++Hf7+/ti5cycKCwsxdOhQSKVSIeIQERGRSGm8qEilUmzatAm+vr5o1aoVHB0dsXz5ciQnJ+P48eOajkNEREQipvGiEhcXh+zsbHh7eyuWmZqawsnJCVFRUZqOQ0RERCKm8aKSnJwMALC2tlZabmFhoVhHREREBAgwhX5OTg4AwMjISGl5uXLl8OLFh09bLJPJIJfLce3aNZXk+1ASiQS9vCshv6CiID9fCAb6erh+/TrkcrnQUTSO+7ts4f4uW7i/NUsmk73XE6M1XlTKly8P4PW9KkX/DQB5eXkwNjb+4O9X9CaFfDx2ZROj/95IB5XVR5Jzf5ct3N9lC/e3Zn+mKItK0SWf1NRU1KpVS7E8NTUVDg4OH/z9XFxcVJaNiIiIxEXj96g4OjrCxMQEFy5cUCzLzMzErVu34OHhoek4REREJGIaP6NiZGSEfv36ISgoCFWrVsX//vc/BAYGwsrKCu3atdN0HCIiIhIxjRcVAPD19UV+fj5mzJiB3NxceHh4YOPGjTA0NBQiDhEREYmURF4Wb+0mIiIircCHEhIREZFosagQERGRaLGoEBERkWixqBAREZFosagQERGRaLGoEBERkWixqBAREZFosagQERGRaLGoaBm5XA5fX188ePBA6CikIX/++SdevXoldAzSkG3btuHFixdCxyANiY+Ph0wmEzqGqLGoiFx+fj6ePn2KR48eIT4+HgsWLMDVq1cxffp0REdH48GDB3jy5AmePn0KTjKsey5duoTvv/8eO3fuFDoKacDvv/+OhQsX4qeffuIvrzLg3r176Nq1K37//Xeho4gap9AXsb179yIgIAA5OTkAXp9NcXV1xYoVK7BmzRocPHgQUqlUsa5SpUqYOXMmunTpImRsKqX79+8jNDQUjx49glQqRXZ2NhITEzF79mwEBQXB2NgYFhYWMDAwgIGBAWxtbTFs2DBYWVkJHZ1K4dq1a1i5cqXS/i4oKMCqVasQEBCA+/fvo0qVKkr7+8cff4Sjo6PQ0akUnj9/jj179uDhw4eQyWTIzs5GZGQkBgwYgE2bNqFhw4awtLRU2t99+vRBxYoVhY4uOBYVEfP09MT48ePRtGlTGBgYwNTUVOkPbX5+Pp4/fw6ZTIb8/HycPn0a69atw4ULFwRMTaXVrVs3VKtWDY0bN4a+vj4qV64Mb29v2Nvb4+XLl/jjjz+QnJwMqVSK/Px8xMTEIDs7G/v37xc6OpXC559/Dnd3d7i7uyv2t4uLC8zMzFBQUIArV64gOTkZMpkMMpkMkZGRuHHjBo4dOyZ0dCqF/v3748WLF6hfv75ifzdr1gxNmzbF/fv3cfToUTx58kTx9/mNGzdgbm6O8PBwoaMLTpCnJ9P7adOmDTp16gQTE5MS1xsYGMDc3FzxdY8ePXD79m1NxSMVi4+Px6lTp2BtbV1sXaVKlYqdKXvw4AHatWunqXikYnK5HBMmTED16tWLrdPX14ebm5vSshYtWuDbb7/VVDxSsZiYGBw7dgw1a9Ystq5WrVoYOXKk0rLExER07NhRU/FEjWdUtER6ejqePHmCvLw8VKhQARYWFqhatarQsYiI6D08evQINWrUgEQiea/tCwsL8eTJE/zvf/9TczLxY1ERuQMHDmD9+vVISkoCAMUNsxKJBHZ2dhgxYgS6du0qYEJSpYyMDOzevRvR0dF48uQJpFIpjI2NYWlpCQ8PD/Tq1QtmZmZCxyQVuXv3LsLCwkrc3+7u7hgwYADs7OyEjkkqkp+fj+PHjyMqKqrEz3e7du2gr68vdEzRYVERsa1btyIoKAgDBw6El5cXLCwsYGRkBKlUitTUVERERCA8PBx+fn48JawDYmNjMWjQIBgbG8Pd3R0WFhYoV64c8vLykJqaiujoaOTl5WHz5s1wcHAQOi59pIiICIwYMQJOTk6Kz/e/93dkZCRu376NkJAQeHp6Ch2XPtLDhw8xZMgQpKSkwMnJqdj+vnXrFmrUqIHQ0FDUqFFD6LiiwqIiYm3atMGYMWPQvXv3t26zf/9+rF27FidPntRgMlKHvn37wsbGBgEBASUeVRUUFGDq1Kl48uQJtm7dKkBCUqUePXrAy8sLfn5+b91myZIluHDhAvbt26fBZKQOw4cPR0FBAVasWIFKlSoVW5+ZmYlx48bB0NAQP/30kwAJxYvzqIhYeno6Gjdu/M5tGjdujKdPn2ooEanTjRs3MHz48Lee+tXX18eIESNw48YNDScjdUhISEDv3r3fuU2vXr2QkJCgoUSkTlFRUfDz8yuxpACAqakpJk2ahKioKA0nEz8WFRFr2LAhNm/ejMLCwhLXy+VyhIaGon79+hpORupgbm6OuLi4d25z/fp13qOiI2xsbPD333+/c5uzZ8/C0tJSQ4lInSpVqoSUlJR3bvP48WOUL19eQ4m0B4cni9i0adMwePBgnD17Fh4eHrC2tla6RyU6OhpZWVnYuHGj0FFJBQYNGoTp06fj9u3b8PLygpWVldL+vnjxIjZv3gxfX1+ho5IKjBkzBpMmTUJMTAy8vb1L3N9Hjx5FQECA0FFJBXr16oUpU6Zg7Nix8PLyUvr7PCUlBRcvXkRQUBB69eoldFTR4T0qIpeRkYFdu3YhJiYGycnJyM3NRbly5WBtbQ13d3f06tWLw5R1yIEDBxASEoKkpCTFMMaij6idnR0GDx78n5cLSHtERkZiw4YNuHTpkmIGagAwNjaGm5sbBg0ahKZNmwqYkFRFLpdjzZo12Lx5c4nP7qpYsSL69u2LsWPHQk+PFzv+jUWFSITS09ORnJyMnJwclC9fHtbW1iykOkwulyMzM1NxIMLLe7pLJpMhNjYWKSkpis+3lZUVHB0dYWRkJHQ8UeKlH5HjuPuyRyaT4eHDh0hOTkZeXh6MjY0hl8thamoKAwN+ZHVNamqq4oxp0f62srKCq6ur0szTpBsMDQ1hY2MDfX19pQk8WVLejmdURIzj7suWolPDYWFhePnyZbH1JiYmGDx4MEaNGiVAOlK13NxczJ07FwcPHoREIoGZmZni852RkQGJRIJu3bph9uzZ/CWmI0qawLPoEi8n8Hw7Hp6J2Lx582BjY4O9e/e+c9z9vHnzOO5eBwQHB2P79u2YNGlSiROARUREYNmyZSgsLMSYMWOEjksfqWiOlNDQUHh6esLQ0FCxTiaT4cKFC5g9ezYWL16MmTNnCpiUVOF9JvCcPXs2srOzOYHnm+QkWs7OzvK4uLh3bhMbGyt3dXXVUCJSp6ZNm8pPnjz5zm1OnDghb9GihYYSkTp5enrKIyMj37lNRESE3NvbW0OJSJ18fHzk+/fvf+c2+/btk7dp00ZDibQHby0WMY67L1tycnJga2v7zm1q1qyJFy9eaCYQqZVcLkflypXfuU3FihWRm5uroUSkTpzAs/RYVESsaNz9rl27cO/ePUilUgCAVCrFgwcPsG/fPkyfPh09evQQOCmpgru7O4KCgpCVlVXi+uzsbCxduhRubm4aTkbq0Lx5c8yaNQuJiYklrn/w4AHmzp2LFi1aaDgZqQMn8Cw93kwrYnKOuy9THj16hMGDByM5ORlOTk7FJvi7efMmLCwsEBoaipo1awodlz5SRkYGfvjhB0RHR8Pc3LzY/k5OToaLiwtWr17Noek6IDY2FoMHD4aRkdF/TuDZsGFDoeOKCouKFuC4+7IjPz8fv//+e4kT/Hl4eKBt27bc5zrm8uXLiI6ORkpKCnJzcxWfbw8Pj/+8VEDahRN4lg6LipZIT0/HkydPlMbd8w80ERHpOg5PFjmOuy9bMjIysHv3bkRHR5c4wV+vXr04a6kOuXv3LsLCwkrc3+7u7hgwYADs7OyEjkkqwgk8S4dnVETsfcbdh4eHw8/Pj+PudUBsbCwGDRoEY2NjuLu7F5tHJTo6Gnl5edi8eTMcHByEjksfKSIiAiNGjICTk1OJ8+ZERkbi9u3bCAkJgaenp9Bx6SNxAs/SY1ERsTZt2mDMmDHo3r37W7fZv38/1q5di5MnT2owGalD3759YWNjg4CAgBKPqgoKCjB16lQ8efIEW7duFSAhqVKPHj3g5eUFPz+/t25TNCncvn37NJiM1GH48OEoKCjAihUr3jmBp6GhISfwfAOHiogYx92XLTdu3MDw4cPfeupXX18fI0aMwI0bNzScjNQhISHhP5+E3atXLyQkJGgoEalTVFQU/Pz8SiwpAGBqaopJkyYhKipKw8nEj0VFxDjuvmwxNzdHXFzcO7e5fv0671HRETY2Nvj777/fuc3Zs2dhaWmpoUSkTpzAs/R4M62ITZs2DYMHD8bZs2f/c9w9ab9BgwZh+vTpuH37Nry8vGBlZaW0vy9evIjNmzfD19dX6KikAmPGjMGkSZMQExMDb2/vEvf30aNHERAQIHRUUoGiCTzHjh0LLy8vpb/PU1JScPHiRQQFBaFXr15CRxUd3qMichx3X7YcOHAAISEhSEpKUozuKvqI2tnZYfDgwf95uYC0R2RkJDZs2IBLly4hJydHsdzY2Bhubm4YNGgQmjZtKmBCUhVO4Fl6LCpEIpSeno7k5GTFBH/W1tYspDpMLpcjMzNTcSDCy3u6ixN4fjhe+hE5jrsve2QyGR4+fIjk5GTk5eXB2NgYcrkcpqamMDDgR1bXpKamKs6YFu1vKysruLq6wtzcXOh4pGKGhoawsbGBvr6+0gSeLClvxzMqIsZx92VL0anhsLAwvHz5sth6ExMTDB48GKNGjRIgHalabm4u5s6di4MHD0IikcDMzEzx+c7IyIBEIkG3bt0we/Zs/hLTEZzAs3R4eCZi8+bNg42NDfbu3fvOcffz5s3juHsdEBwcjO3bt2PSpEklTgAWERGBZcuWobCwEGPGjBE6Ln2kojlSQkND4enpCUNDQ8U6mUyGCxcuYPbs2Vi8eDFmzpwpYFJShfeZwHP27NnIzs7mBJ5vkpNoOTs7y+Pi4t65TWxsrNzV1VVDiUidmjZtKj958uQ7tzlx4oS8RYsWGkpE6uTp6SmPjIx85zYRERFyb29vDSUidfLx8ZHv37//ndvs27dP3qZNGw0l0h68tVjEOO6+bMnJyYGtre07t6lZsyZevHihmUCkVnK5HJUrV37nNhUrVkRubq6GEpE6cQLP0mNREbGicfe7du3CvXv3IJVKAQBSqRQPHjzAvn37MH36dPTo0UPgpKQK7u7uCAoKQlZWVonrs7OzsXTpUri5uWk4GalD8+bNMWvWLCQmJpa4/sGDB5g7dy5atGih4WSkDpzAs/R4M62IyTnuvkx59OgRBg8ejOTkZDg5ORWb4O/mzZuwsLBAaGgoatasKXRc+kgZGRn44YcfEB0dDXNz82L7Ozk5GS4uLli9ejWHpuuA2NhYDB48GEZGRv85gWfDhg2FjisqLCpagOPuy478/Hz8/vvvJU7w5+HhgbZt23Kf65jLly8jOjoaKSkpyM3NVXy+PTw8/vNSAWkXTuBZOiwqWiI9PR1PnjxRGnfPP9BERKTrODxZ5DjuvmzJyMjA7t27ER0dXeIEf7169eKspTrk7t27CAsLK3F/u7u7Y8CAAbCzsxM6JqkIJ/AsHZ5REbH3GXcfHh4OPz8/jrvXAbGxsRg0aBCMjY3h7u5ebB6V6Oho5OXlYfPmzXBwcBA6Ln2kiIgIjBgxAk5OTiXOmxMZGYnbt28jJCQEnp6eQselj8QJPEuPRUXE2rRpgzFjxqB79+5v3Wb//v1Yu3YtTp48qcFkpA59+/aFjY0NAgICSjyqKigowNSpU/HkyRNs3bpVgISkSj169ICXlxf8/Pzeuk3RpHD79u3TYDJSh+HDh6OgoAArVqx45wSehoaGnMDzDRwqImIcd1+23LhxA8OHD3/rqV99fX2MGDECN27c0HAyUoeEhIT/fBJ2r169kJCQoKFEpE5RUVHw8/MrsaQAgKmpKSZNmoSoqCgNJxM/FhUR47j7ssXc3BxxcXHv3Ob69eu8R0VH2NjY4O+//37nNmfPnoWlpaWGEpE6cQLP0uPNtCI2bdo0DB48GGfPnv3Pcfek/QYNGoTp06fj9u3b8PLygpWVldL+vnjxIjZv3gxfX1+ho5IKjBkzBpMmTUJMTAy8vb1L3N9Hjx5FQECA0FFJBYom8Bw7diy8vLyU/j5PSUnBxYsXERQUhF69egkdVXR4j4rIcdx92XLgwAGEhIQgKSlJMbqr6CNqZ2eHwYMH/+flAtIekZGR2LBhAy5duoScnBzFcmNjY7i5uWHQoEFo2rSpgAlJVTiBZ+mxqBCJUHp6OpKTkxUT/FlbW7OQ6jC5XI7MzEzFgQgv7+kuTuD54XjpR+Q47r7skclkePjwIZKTk5GXlwdjY2PI5XKYmprCwIAfWV2TmpqqOGNatL+trKzg6uoKc3NzoeORihkaGsLGxgb6+vpKE3iypLwdz6iIGMfdly1Fp4bDwsLw8uXLYutNTEwwePBgjBo1SoB0pGq5ubmYO3cuDh48CIlEAjMzM8XnOyMjAxKJBN26dcPs2bP5S0xHcALP0uHhmYjNmzcPNjY22Lt37zvH3c+bN4/j7nVAcHAwtm/fjkmTJpU4AVhERASWLVuGwsJCjBkzRui49JGK5kgJDQ2Fp6cnDA0NFetkMhkuXLiA2bNnY/HixZg5c6aASUkV3mcCz9mzZyM7O5sTeL5JTqLl7Owsj4uLe+c2sbGxcldXVw0lInVq2rSp/OTJk+/c5sSJE/IWLVpoKBGpk6enpzwyMvKd20RERMi9vb01lIjUycfHR75///53brNv3z55mzZtNJRIe/DWYhHjuPuyJScnB7a2tu/cpmbNmnjx4oVmApFayeVyVK5c+Z3bVKxYEbm5uRpKROrECTxLj0VFxIrG3e/atQv37t2DVCoFAEilUjx48AD79u3D9OnT0aNHD4GTkiq4u7sjKCgIWVlZJa7Pzs7G0qVL4ebmpuFkpA7NmzfHrFmzkJiYWOL6Bw8eYO7cuWjRooWGk5E6cALP0uPNtCIm57j7MuXRo0cYPHgwkpOT4eTkVGyCv5s3b8LCwgKhoaGoWbOm0HHpI2VkZOCHH35AdHQ0zM3Ni+3v5ORkuLi4YPXq1RyargNiY2MxePBgGBkZ/ecEng0bNhQ6rqiwqGgBjrsvO/Lz8/H777+XOMGfh4cH2rZty32uYy5fvozo6GikpKQgNzdX8fn28PD4z0sFpF04gWfpsKiIWHR0NFxdXXm2hIiIyiwWFRFzdHTEZ599huXLl//nTXekGw4dOoS9e/fixYsXaNGiBb7//nuYmJgo1qenp6N37944deqUgClJVe7evYuwsDBER0cXm9DR3d0dAwYMgJ2dndAxSQUGDhyI77//Hl5eXkJH0TosKiLm6OiITz75BFlZWZg6dSo6deokdCRSoz179mDu3Lno2rUr9PT0cOTIEVhYWGDz5s2KCf3S0tLQvHlzxMbGCpyWPlZERARGjBgBJyenEufNiYyMxO3btxESEgJPT0+h49JHcnR0hIGBAUaNGoXhw4dzlukPwKIiYvXr18epU6ewdetWbN26FfXq1cOoUaPQtm1boaORGnTu3Bn9+/fHV199BeD10PNhw4YhNzcXO3fuhLm5OYuKDunRowe8vLzg5+f31m2KJoXbt2+fBpOROjg6OmLGjBlYuXIlLC0tMWHCBLRu3VroWFqBNz+ImFwuR7ly5TB58mQcPHgQNjY28PX1RYsWLbB48WKcPXsWqampimHLpN0ePnwIb29vxdc1atRAWFgYJBIJhg4d+tZhy6SdEhIS/vNJ2L169UJCQoKGEpG6ffnllzh8+DAcHBwwcuRIdOvWDbt27eLcSP+BRUVL1KlTB8HBwTh+/Dj69OmDCxcuYMSIEWjRogVHBugICwsL3Lx5U2lZ9erVsX79eiQnJ2PkyJGc/EuH2NjY4O+//37nNmfPnoWlpaWGEpEmWFlZYenSpTh06BDq16+PRYsWoVmzZvjqq68wa9YsLFu2TOiIosNLPyLm6OiI8+fPo1q1aiWuz8rKQlxcHJ4+fYovv/xSw+lI1UJDQ7FhwwYMGTIEXbt2VfoFFRMTg2HDhqF69ep48OABL/3ogN9++w2TJk1Cu3bt4O3tDSsrK6V5NS5evIijR48iICAAXbp0EToufaT69evj3Llzxf4+z8vLw7lz5xAdHY3Y2FikpaXhyJEjAqUUJ97NI2I1atR459BkExMTuLu7azARqdOQIUMgk8mwbds2NGrUSKmouLm5YcuWLZg4caKACUmVvvzyS1SpUgUbNmxAQEAAcnJyFOuMjY3h5uaGkJAQNG3aVMCUpCpvOydQrlw5tGnTBm3atNFwIu3BMypEIiT/1+Pf/62goADXrl2Di4uLAKlIXeRyOTIzMxUTgJmZmQkdiVTswIED6NixIydsLAUWFZFLSkrC4cOHFfNqvPncj6ysLCxYsAALFy4UKCGpWlJSkuLhhFKpFH/88QcePnyITz75BC1btoShoaGwAUltcnJycPz4caSkpKBu3bpo1apViYWVdEt6ejqqVKnCff0WLCoiFhMTgyFDhsDCwgISiQT3799Hu3btEBgYqGjlHK6qOx4+fIiRI0ciOzsbp0+fRkpKCr777js8ePAAZmZmeP78Oezt7bFp0yZYWFgIHZc+UtH8SOfOnUPz5s0xbdo09O/fHw8fPlTsb2dnZ4SGhipN+kfaa+PGjbh06RLWrFmDwsJCrF69Glu2bEFOTg5MTU0xcOBAjBw5UuiYosNRPyK2dOlS9OzZE8ePH8exY8ewYsUKnDt3DqNGjUJ+fr7Q8UjF5s+fj8qVK2PDhg0AgEWLFqFq1ar466+/8Pfff+PMmTOoUqUKFixYIHBSUoXAwEAkJiZi3LhxePjwIfr27YsqVargzJkziIiIwPHjx5GXl4fFixcLHZVUYNWqVVi7dq3i6cgbN25EeHg4Ro8ejZ9++glDhgzBpk2bEBoaKnBSEZKTaLm6usqTkpKUlsXExMidnZ3l48ePl8vlcvnTp0/ljo6OQsQjFXNzc5Pfvn1b8XXz5s3l0dHRStvcvHlT7ubmpulopAbe3t7yy5cvy+VyufzOnTtyBwcHeVRUlNI2UVFR8iZNmgiQjlStZcuW8uPHjyu+7tChg/zAgQNK2xw9elTeunVrDScTP55RETETExM8e/ZMaZmrqysCAwPx22+/8b4UHWNoaIhXr14pvjY1NS22jfwtN9mS9snNzUWVKlUAAPb29vjkk09gbGystI2ZmRkKCwuFiEcq9vz5czg4OCi+fvHiBRwdHZW2+fTTT5GWlqbpaKLHoiJiLVu2xNy5c3HlyhXIZDLF8rZt22LatGkICwvDvHnzBExIqtSuXTtMmzYNd+7cAQD07dsXK1asUMxIm5aWhoCAALRs2VLImKQijRo1QmhoqKKIHDt2DA0aNFCsz8rKQlBQEFxdXYWKSCr06aefYtOmTYqvW7ZsWezhoocOHULdunU1HU30eDOtiL148QLjxo1DREQEQkJCio342b59OwICAlBQUMCbaXVAdnY2xowZgwsXLqBevXqoVasWIiMjIZVKYWlpiUePHsHOzg5btmx56ySApD1u3ryJwYMHo1mzZli6dKnSujNnzsDX1xdVqlRBWFgYPvnkE4FSkqpcu3YNAwYMQN26ddGpUyeYmJhg8eLFaNasGerWrYsrV67g3LlznDunBCwqWuD+/fuoUqUKKlWqVGxdYmIijh8/jhEjRgiQjNThwoULOH/+PBITE5GVlQUDAwNYWVnBw8MDX3zxBedh0CHPnj3Do0eP0KhRI6XliYmJ+Pvvv9GpUydUrlxZoHSkaomJiQgJCcG5c+eULvGUK1cOrq6uGDlyJJ+UXQIWFS0VExODTz/9lL+0iIi0UFZWFrKzs2FgYIAqVaq8cxbyso7/Z7TUsGHDkJKSInQMUrFDhw6hf//+6NKlC4KCgoo9MTk9PZ1TbRNpobVr1+Lp06eKr01MTGBpaYlq1aqxpPwHnlERMR8fn7eO8Hj8+DEsLS2hr68PAMVuyiLts2fPHsydOxddu3aFnp4ejhw5AgsLC2zevBk1atQAwAn+iLSVo6MjLCwssGzZMj6j7QPxoYQi9tlnn2Hv3r1o0qQJPDw8FMvlcjlCQkLQtm1bPhNEh4SHh2PWrFn46quvAAAjR47EsGHD0L9/f+zcuRPm5uYCJySij+Hh4YH+/fujd+/eGDt2LG+Kf088oyJyJ0+exKxZs9C1a1eMHz9e8ZwXFxcX/PLLL6hZs6bACUlVStqnaWlp+Prrr1GxYkVs27YNubm5PKNCpIXq16+Pc+fO4erVqwgICMCzZ8/Qu3dvfPPNN7CzsxM6nqjxwpjItW3bFgcPHkR8fDx69uyJf/75R+hIpCYWFha4efOm0rLq1atj/fr1SE5OxsiRI5GbmytQOlKnx48fo6RjxsLCQjx+/FiARKRqRfvXx8cHv/76KyZMmIA///wTHTp0QI8ePRAUFIQjR47g3LlzAicVHxYVLWBhYYGNGzeie/fu+Pbbb5UmDSLd0bt3b8yePRvr169XulG6du3aWLt2rWLeDdI9bdq0QXp6erHlvHlad/z7fkMjIyP069cPx44dw9atW+Hp6YmIiAhMnToVw4YNEzClOPEeFS0yaNAgeHt7Y9KkSTyy1kFDhgyBTCbDtm3b0KhRI1haWirWubm5YcuWLZg4caKACUldAgICSpwnqVKlSggICBAgEalaSWfMJBIJ3N3dFTfXFhYWIiMjQ8PJxI/3qGghqVSKq1evonHjxpxHRUe97Zk+BQUFuHbtGlxcXARIRUSlNXXqVEyfPh0mJiZCR9E6LCpERAJYvXo1JBIJLCwsYG9vz2f6EL0FiwoRkQCKnpxbvXp11K1bF5s3bxY4EZE4sagQERGRaHHUDxGRCFy6dEkx8ufgwYMYMWIEQkJCSrwJk6gsYVEhIhLYzp070bdvX9y+fRtxcXGYOnUqZDIZtmzZgjVr1ggdj0hQLCpaok2bNnj+/Hmx5ZxnQTdNnTq12AMJgddPXJ06daoAiUidwsLCMGPGDHh7e+PXX39F3bp1sWnTJixZsgT79+8XOh6RoFhUtISnp6di+vx/MzIygqenpwCJSJ0uXLgAmUxWbLlUKsWFCxcESETq9PDhQ/j4+AAAzp8/jxYtWgAA7O3tkZaWJmQ0UoP69evj2bNnxZanpaWhfv36AiQSNxYVLbFw4cISx9+bmJhg4cKFAiQidTp9+jSqVKlSbHnVqlVx+vRpARKROlWrVg2pqal4+vQpYmNj0bRpUwBAXFwcqlevLnA6UrXRo0ejQoUKxZZXqFABo0ePFiCRuHHUj4hFRUVBIpHA3NwctWrVKnECMCLSfoGBgTh+/DiMjY2RlZWFEydO4NixY/D390evXr0wYcIEoSMSCYZFRcQcHR0hkUggl8thYmKC6OhooSORGhXtb3Nzc9SpU4fPdCpDCgsLsW3bNjx48AB9+/bFJ598gq1btyI9PR1jxoyBvr6+0BHpIx08eBAAFBP8/fsRGfRuLCoidvHiRUgkElSvXh2ffPIJ9PR4pU6XrVq1SlFU7O3tFc//ICLtVzTBn56eHmxsbHD8+HGBE2kPFhUtkpubCz09PRgZGSEhIQF//vknXFxcOPV2GSCTyRAXF4fatWujYsWKQschFVi9evV7bztmzBg1JiFNePToESQSCapVq4Zy5coJHUersKhoiaioKIwePRorV66Evb09vvjiC+jp6SEnJwdBQUH48ssvhY5IKvTkyRNMnz4dP/74I+rVq4devXrhzp07qFy5MrZs2cKRATqgaJRPkSdPnsDQ0BA1a9aEgYEB7t+/D5lMhoYNG2Lnzp0CpSR1efz4MUxNTWFiYoLIyEgcP34crq6u6NSpk9DRRMdA6AD0fpYtW4Y2bdrg008/xe7du2FiYoITJ05g3759CAkJYVHRMQsXLsTLly9RtWpV/Pbbb3j8+DG2b9+O/fv3IzAwkPev6IB/j97asmUL/vzzTyxduhTVqlUDAGRmZsLPzw/16tUTKiKpyYkTJzBu3DiEhISgZs2aGDp0KGrWrIn9+/fjxYsX6Nu3r9ARRYU3PWiJW7duYdSoUTAxMcG5c+fQqlUrlCtXDi1btsTdu3eFjkcqFhkZiXnz5sHGxgZnzpxB8+bN4erqiiFDhuDy5ctCxyMVW79+PaZMmaIoKQBgamqK8ePHY9euXQImI3VYu3YthgwZAm9vbxw+fBg1atTA0aNHERAQgJ9//lnoeKLDoqIljI2NIZVKkZeXh5iYGHh7ewN4PUFQpUqVBE5HqiaTyVC5cmXI5XJERETgs88+A/B6dIiBAU+E6hqZTIZXr14VW/7s2TNOS6CDEhIS8NVXX0FPTw/nz59Hy5YtoaenB2dnZzx69EjoeKLDv/G0RJMmTRAYGIjKlStDT08PzZs3R2xsLObPn48mTZoIHY9UzMnJCXv37oW5uTkyMzPRsmVLSKVSbNiwQTF6gHSHj48PZs6ciVmzZqFhw4aQy+WIiYmBv78/OnbsKHQ8UjFTU1O8fPkSL1++xLVr1zBs2DAAwP3792FmZiZsOBFiUdESs2fPxuzZs3H79m0EBgbCxMQEhw4dgpGREZ/9ooMmT56M77//Hs+fP8ewYcNgZWWFOXPm4NSpUwgNDRU6HqnYzJkzMXbsWAwYMEBxBkUul+OLL77A5MmTBU5HqtayZUvMmjULFStWRKVKldC0aVP8/fffmDNnDlq1aiV0PNHhqB8tJpVKYWRkJHQMUpPCwkJkZWXB1NQUAJCYmIgqVarwiEuH3b17F//88w8kEgnq16+PmjVrCh2J1CA3NxcrVqzAgwcPMGzYMDg7O2PVqlV48OABZs+ezSkI3sCiImJRUVHvva2Hh4cakxCRJqSlpUEmk+HNv5Zr1KghUCIi4bGoiNi/p9D/9w11Rbvs38tiY2M1no9Uq2h/vw/ub91y6dIlTJ06Fffv31daXvTZ5/7WfkVT6L+Pbt26qS2HNmJREbF/3/0dERGBtWvXYtq0aXB1dYWBgQGuX7+OgIAADBs2jH+wdcD+/fsVReXRo0fYsGED+vTpAxcXFxgaGuL69evYtm0bRo4ciSFDhgicllSpR48eqFChAgYNGlTiKD5PT08BUpEqvXkTfNFBaPny5WFgYICsrCzo6+ujSpUqOHfunEApRUpOWqFdu3byv//+u9jyCxcuyFu3bi1AIlKnvn37yvfs2VNs+aFDh+Q9evQQIBGpU8OGDeV37twROgZpyOHDh+XdunWTx8bGKpYlJibKv/rqK3l4eLiAycSJ86hoidTUVFhYWBRbbmpqioyMDM0HIrW6du1aifcdNWrUCHfu3BEgEamTtbU1srOzhY5BGhIUFIQ5c+YonWWxtbXFjBkzEBISImAycWJR0RKNGjXCypUrlf4yy8jIQGBgIE8L66BPPvkER48eLbZ8165dqFOnjgCJSJ1GjhyJgIAA3L59GzKZTOg4pGaZmZklPpiwsLAQubm5AiQSN96joiX++ecfDBw4EDk5ObC1tYVcLkdSUhKqVauGsLAw/O9//xM6IqnQiRMn4OvrCzc3N3z66acoLCzE5cuXERsbiw0bNsDLy0voiKRC7dq1w+PHj1FQUFDiet5Mq1tGjhyJFy9eYMmSJbCxsQHwerbaSZMmoXbt2ggKChI4obiwqGiRrKwsHDlyRDHPgqOjIzp27AhjY2Oho5EaXLp0CT///DP++ecfAED9+vUxePBgzkyrgw4cOPDO9d27d9dQEtKElJQUDBkyBAkJCTA1NYVcLsfLly/RqFEjrF+/HpUrVxY6oqiwqBAREWlYQUEB/v77b6UDTy8vLz7bqQQsKlri6dOnWLFiBS5dulTihFCnTp0SKBmpQ2FhIQ4fPvzW/b1w4UKBkpG6nDp1CiEhIYiPj4eBgQHq1KmDIUOG4PPPPxc6GpGg+KwfLTFz5kzcuHEDHTt25NOSy4CAgABs27YNjo6OMDExEToOqdnx48cxduxYtGnTBh07doRcLkdUVBTGjh2LVatWoU2bNkJHJBW6e/cu5s2bpzgQeRPvSVLGMypawtnZGaGhoXB3dxc6CmlAkyZNMGXKFN6bUEZ069YNbdu2xZgxY5SWr169GmfOnMGePXsESkbq0L9/fzx79gxff/11iQee/Nwr4xkVLVGhQgVUq1ZN6BikIVKplM9vKkPu3r2Lzp07F1veqVMnbNiwQYBEpE5Xr17Fjh070KBBA6GjaAXOo6IlunbtitDQ0LcOXyTd0rx5c5w5c0boGKQhFhYWuHfvXrHl9+7d46VeHVSlShUYGhoKHUNr8IyKlsjIyMCRI0fw559/ombNmjAyMlJaHx4eLlAyUgdnZ2cEBgYiIiIC9vb2xf5Se/MSAWm3Tp06Yc6cOZg9ezbc3NwAADExMZg7dy46dOggcDpStX79+mHZsmUICgriPWjvgfeoaImpU6e+cz1HgegWHx+ft66TSCQc5aVj8vLyMG7cOJw+fVoxPFUul6NVq1ZYsWIFypcvL3BCUqVBgwYhOjoaBQUFqFatWrEDT36+lbGoEBGJREJCAuLj4yGXy+Hg4AB7e3uhI5EarF69+p3recZUGYuKFnny5Am2bdummGehbt266NOnD2rUqCF0NFIDuVyOv/76S2l/e3l5QV9fX+hopGKFhYVYu3Ytqlevjq+//hoA0KdPH7Ru3Rrff/+9wOmIhMV7VLTE7du30a9fP5QvXx6NGjVCYWEh9u/fj23btmHHjh2oW7eu0BFJhTIyMjBkyBDcvHkTlSpVglwuR1ZWFho0aIDNmzfD1NRU6IikQsHBwdi5cyf8/f0Vyzp06IB169YBAMuKDrpx4wY2btyoNMHfgAED0KhRI6GjiQ7PqGiJIUOGwNjYGEuXLlU8dTMvLw8TJ06EVCrlo8F1zNSpU3Ht2jUsXbpU8WyfuLg4TJo0Ca6urpg7d67ACUmVWrVqhfnz56NZs2ZKy8+cOYN58+bxngUdc/HiRQwePBj16tWDu7s7CgsLcenSJcTHxyMsLExxQzW9xqKiJVxcXLBz5044ODgoLY+Li0O/fv0QHR0tUDJSBy8vLwQHB8PT01Np+YULFzB+/HicP39eoGSkDs7Ozjhw4ADs7OyUlt+7dw+dO3fGtWvXBEpG6vDNN9+gXr16xQ445s6dizt37mDr1q0CJRMnzqOiJSpWrFjiVMslLSPtl5+fj+rVqxdbXr16dWRlZQmQiNTJ0dER+/fvL7b80KFDqFOnjgCJSJ1u3bqF7777rtjyfv364caNGwIkEjfeo6IlvLy8sGTJEgQHB8PMzAwAkJ6ejsDAQHh7ewsbjlSuQYMG2LFjB6ZPn660fMeOHahfv75AqUhdRo8ejREjRiA6OhrOzs4AgOvXr+PKlStYs2aNsOFI5apUqYLnz58XW56enl5sqDLx0o/WSE5Oxtdff40XL17A1tYWAJCUlITKlSvj559/ho2NjbABSaUuX76M7777Do6OjnB1dQXwegKwuLg4hIaGwsvLS+CEpGqXL1/G1q1b8c8//8DAwAD29vYYOnSo4h4l0h1z587FpUuXsGzZMsUQ9Dt37mDChAlwcnLivFhvYFHRItnZ2fjll1+U5lno3LkzZzbUUdeuXcPmzZuV9vegQYM4KoBIy7148QKDBg1CbGys4hEJmZmZqF+/PjZt2oQqVaoInFBcWFS0yMWLF5Gfn4/PPvsMALB48WK0adOGT1TWUS9fvsTz589Rq1YtAMDx48fh6empuPRHuiUuLg5hYWFITEzEypUrcfLkSdSpUwdNmjQROhqpQWFhIc6dO6d0INKsWTPo6fHW0Tfx/4iWOHr0KAYPHozY2FjFskePHmHgwIE4efKkgMlIHW7evIm2bdtix44dimWLFi1Cp06dEB8fL2AyUocbN26gd+/eePjwIW7cuAGpVIrY2FgMGTKED6fUUb/88guys7MxdOhQDBs2DPv27cPRo0eFjiVKLCpa4qeffsKUKVMwZMgQxbLg4GD4+flh1apVAiYjdVi0aBF8fHwwbtw4xbLjx4+jefPmWLRokYDJSB2CgoIwePBgbN26VfEAyvnz56Nv3778fOug8PBwzJo1S2kEn5WVFWbOnIndu3cLmEycWFS0xP3799GyZctiy1u3bo2kpCTNByK1unHjBkaNGqU0AsDAwADDhw/H1atXBUxG6nDjxg1069at2PK+ffsiISFB84FIrbZu3YpFixahd+/eimVTp06Fv78/Nm3aJGAycWJR0RLW1taIiooqtvzy5cswNzcXIBGpU8WKFfHgwYNiy1NTUzl8UQcZGhqWOD/OkydPYGxsLEAiUqfU1FQ0bNiw2HJnZ2c8fvxYgETixqKiJb755hv4+/tjxYoV+OOPP/DHH38gODgYc+fOxTfffCN0PFKx9u3bY+7cuYiIiEB2djays7MRGRmJuXPn4vPPPxc6HqlY27ZtsWLFCmRmZiqWJSQkYMGCBWjVqpVwwUgtbG1tcfr06WLLz5w5w6kmSsBRP1pkw4YNCA8Px9OnTwEAFhYWGD58OPr16ydwMlK1V69eYezYsfjrr78gkUgUyz///HMEBARwSLqOycrKwtChQ3Ht2jUUFhaiUqVKyMrKgqOjIzZv3syRXjrml19+wZQpU9CxY0c0btwYwOsJ/o4ePQp/f390795d4ITiwqKihZ4/fw5DQ0P+sioDEhMTFU9Xtbe3V0z2R7opIiICt27dQmFhIerVq4fmzZtzuKqO+vXXXxEeHo7bt2/D0NAQ9vb2GD58OFq3bi10NNFhUdEiubm5+P3333H37l0MHjwY8fHxqFu3LicH0mGPHz9GQkICPDw8kJ2djWrVqgkdiTQgPT0dFy9eRMOGDXkpgMo8VnUtkZaWho4dO2LOnDkIDQ3Fy5cvsWnTJnTu3JmjAnSQVCrFuHHj4OPjgxEjRuDp06eYPXs2Bg0axIcS6qD4+Hi0b98eUVFRyMzMRNeuXfHjjz+iQ4cOiIyMFDoeqUFqaipWr16NCRMm4NmzZ4qDUCqORUVLLFq0CHXr1kVERATKlSsH4PXMtHXr1kVgYKDA6UjV1q1bp5iptGh/9+/fH/fu3UNQUJDA6UjVFi9ejE8++QS1a9fGkSNHIJPJcObMGQwZMgQrVqwQOh6p2L1799C5c2ccOHAAx44dw6tXr/Drr7+iZ8+enH6gBCwqWiIyMhK+vr5KQxUrV66MyZMn49KlSwImI3U4evQoZs6cqTR9epMmTbBgwQKcOnVKwGSkDpcvX8bkyZNRrVo1/PXXX2jZsiUsLS3Ro0cPxMXFCR2PVGzRokVo27YtTp48qZjgb9myZfDx8eGBSAlYVLREdnY2KlSoUOK6/Px8DachdUtJSVE84+ffrK2t8eLFCwESkTrp6enByMgI+fn5uHjxIry9vQG8/tyXL19e4HSkapcuXcKgQYOURvQZGBhg1KhRuHXrloDJxIlFRUt4eHgoPfcFAGQyGdatWwdXV1eBUpG62NvbIyIiotjyo0ePok6dOgIkInVydnZGSEgIgoODkZeXhxYtWiAlJQXLli2Ds7Oz0PFIxQoLC1FYWFhseXZ2NvT19QVIJG4GQgeg9zN58mT07dsXFy9ehEwmw5w5c3D37l28fPkSP//8s9DxSMV++OEHjBs3Dnfu3EFBQQEOHDiAxMREHDt2DMuXLxc6HqnYzJkzMW7cODx48ADTpk1D1apV4e/vj4SEBGzYsEHoeKRizZo1Q0hIiNL9hRkZGQgMDISXl5eAycSJw5O1SEpKCnbs2IHY2FgUFhaibt26+Pbbbzl8UUedPXsWISEhink16tati2HDhqF9+/ZCRyMNSE9PR+XKlXmErYNSUlLw3Xff4eXLl8jIyEDt2rXx6NEjmJmZ4eeff8b//vc/oSOKCouKFktPT0fVqlWFjkFEKpCdnY1ffvlFMcFf3bp10aFDB07sqKNycnJw5MgRpQPPrl27cn+XgEVFS2RmZiIwMBD9+vVDnTp1MHToUERGRsLW1hbr169HzZo1hY5IKnb48GF4eHjAysoKa9euxa+//gpXV1dMnz5dMWSZdMPjx4/Rr18/PHv2DHZ2digsLMS9e/dQrVo1bN++HVZWVkJHJDWSyWSIi4tD7dq1UbFiRaHjiA5vptUSCxcuRGRkJAwMDHDixAlER0djyZIlsLW1xZIlS4SORyq2du1aTJ8+HY8fP0ZMTAyCg4Ph4uKCCxcucPiiDlq0aBGsrKxw6tQpHDx4EL/88gtOnTqFGjVqcJ4kHfTkyRMMHjwY165dQ25uLrp3746vvvoKPj4+iI2NFTqe6LCoaIkzZ85gyZIlsLe3x59//ommTZuic+fOGDduHGeu1EH79u3D4sWL4erqimPHjsHZ2Rn+/v5YsGABfv/9d6HjkYr9/fffmDJlCqpXr65YVr16dfj5+eHcuXMCJiN1WLhwIV6+fImqVavit99+w6NHj7Bt2zZ8/vnnLKYlYFHREq9evYK1tTUA4Pz58/jss88AAOXLl0dBQYGQ0UgNUlNT4eLiAuD1L7FmzZoBeD2PSmZmppDRSA309fWVJnMsUq5cOUilUgESkTpFRkZi3rx5sLGxwZkzZ9CiRQu4urpiyJAhuHz5stDxRIdFRUsUnUk5c+YMnj59ihYtWgAAdu/eDXt7e4HTkapZWVkhMTER9+7dw507d9C0aVMAQHR0NO9X0EGurq5Yu3YtZDKZYplMJsNPP/3EeZJ0kEwmQ+XKlSGXyxEREaE48CwsLISBAWcNeRP/j2gJX19f/PDDD5DJZOjUqRNsbW2xcOFCbNu2DWvWrBE6HqnY119/jR9//BFGRkZwcHCAi4sLtm3bhiVLlsDX11foeKRiEydOxNdff43PP/8cDRs2BABcv34d2dnZnCdJBzk5OWHv3r0wNzdHZmYmWrZsCalUig0bNsDR0VHoeKLDUT9a5Pnz50hJSVH8Qb527RoqVqzIMyo66vTp03jw4AG6dOmCKlWq4PDhw8jLy0OvXr2EjkZq8PjxY2zbtg3//PMP5HI5HBwc8M0333BODR107do1fP/993j+/DmGDRuG8ePHY86cOTh69ChCQ0PRuHFjoSOKCouKlklISEB8fDwMDQ1Ru3Zt1K5dW+hIpGbp6ekwMDCAqamp0FGISEUKCwuRlZWl+FwnJiaiSpUqMDMzEzaYCPHSj5bIy8vDhAkTcOrUKRR1S4lEgtatW2PFihUwMjISOCGpWnh4ONavX49nz54BeD0KZMiQIRg4cKCwwUjlXr16hS1btuDSpUuQyWR48/gxPDxcoGSkLhKJBFeuXFFM8FenTp0SH0RKLCpaY/ny5bh27RpWr14NT09PFBYWIioqCvPnz8eqVaswYcIEoSOSCu3cuROBgYH49ttv4eHhAblcjqioKCxbtgwmJia8/KNjZs2ahVOnTqFp06YwNzcXOg6pWUZGBoYMGYKbN2+iUqVKkMvlyMrKQoMGDbB582aePX2TnLRC06ZN5adPny62/PTp0/KWLVtqPhCpVfv27eVbt24ttvznn3+Wd+rUSYBEpE6urq4lfr5JN02ZMkXeoUMHeWxsrGJZbGysvFOnTvJZs2YJmEycODxZS2RnZ5d4P4qdnR3S09MFSETq9PjxY8UQ9H9r3rw57t27J0AiUic9PT3eFF+G/PHHH5g9e7bSCB9HR0fMmDEDJ0+eFDCZOLGoaIl69eqVOCPpb7/9Bjs7OwESkTrVqFEDN27cKLb8+vXrSrOXkm5o164d9u/fL3QM0pD8/PwSP8fVq1dHVlaWAInEjfeoaImRI0di1KhRiI2NVUwAFRMTgxMnTmDp0qUCpyNV+/rrrzF37lxkZGQo7e/g4GB89913AqcjVatatSo2bdqEs2fPws7OrtjN8QsXLhQoGalDgwYNsGPHDkyfPl1p+Y4dO1C/fn2BUokXhydrkRMnTmDDhg2Ij49XzLMwdOhQtGvXTuhopGKFhYVYuHAhduzYgYKCAsjlchgYGODrr7/GtGnToKfHk6G6pH///u9cv3XrVg0lIU24fPkyvvvuOzg6OiodiMTFxSE0NBReXl4CJxQXFhUiEcvKysLdu3cBALVr14aJiYnAiYhIFa5du4ZNmzYpTfA3aNAgNGrUSOhoosOiokWio6PfOs/CmDFjBEpF6pKTk4P4+PgS97eHh4dAqUhd8vPz8ezZM8VDRuVyOaRSKa5fv44uXboInI5IOCwqWmLNmjVYtWoVTE1Nix1VSyQSnDp1SqBkpA6nTp3ClClTkJWVVaykSCQSxMbGCpSM1OHcuXOYPHlyiSP4ypcvzyfq6pjCwkIcPnz4rQeevCdJGW+m1RI7duzAuHHjMGLECKGjkAYEBQXB29sbo0aNQqVKlYSOQ2q2bNkyODk5oX///hg7diyCgoLw+PFjBAcH85eWDgoICMC2bdvg6OjIy7nvgUVFS7x8+RKdOnUSOgZpyMOHDxESEsIptcuIO3fuICAgAI6Ojqhfvz4qVKiA/v37o0KFCti4cSPatm0rdERSocOHDyMgIADdu3cXOopW4NABLeHq6srTv2WIra0tkpOThY5BGqKvr684c/bJJ58gPj4eAODl5YWEhAQho5EaSKVS3mf2AXhGRUt06tQJ/v7+uHHjBmrXrl1snoVu3boJE4zUYuLEifD398e4ceNK3N81atQQKBmpQ926dXH69Gn0798ftWvXRkxMDAYMGMCyqqOaN2+OM2fOoG/fvkJH0Qq8mVZL/Huq5Tfx5krd06BBA8XoD4lEolgul8u5v3XQyZMn4evri1mzZqFly5Zo3749vLy8cPv2bTRu3BjBwcFCRyQV2rRpE4KDg9GsWTPY29vD0NBQaT1HcSpjUSESoYsXL75zvaenp4aSkKbcvHkT+vr6cHR0xMWLF7F582ZYW1vD19cXZmZmQscjFfLx8XnrOo7iLI5FRQckJyfDyspK6BhEREQqx3tUtMSDBw+wePFixMfHF5sQKj09Hbdu3RI4IalSXl4edu3apbS/gdc34d24cQPHjh0TMB2p2qtXr7Bly5a3zqsRHh4uUDLSpKIJ/tzc3ISOIiosKlpi3rx5SEpKwhdffIHNmzdj8ODBSExMxIkTJzBv3jyh45GKzZ8/HwcPHoSTkxOuX78OFxcX3Lt3D8+ePcPAgQOFjkcqNmvWLJw6dQpNmzaFubm50HFIzW7cuIGZM2ciPj4ehYWFxdbzHjRlLCpa4tKlS1i7di2aNGmCv/76C23btkWjRo2wfPlynDlzBl999ZXQEUmFTp06hYULF6JTp074/PPP4e/vj5o1a2LcuHGQyWRCxyMV++OPP7Bs2TK0bt1a6CikAQsXLoS+vj5mzJiBhQsXYsqUKbh//z62bduGJUuWCB1PdDiPipaQSqWKyb/s7Oxw+/ZtAK+HJV+9elXIaKQGmZmZiqeq1qlTB7du3YKhoSFGjBiBP/74Q+B0pGp6enqwt7cXOgZpyK1btzBr1ix88803cHBwQL169TBlyhRMmDABu3fvFjqe6LCoaIn//e9/ikmg7OzsFKcGCwsLkZ2dLWQ0UoOqVavi2bNnAF5P/la076tUqYK0tDQho5EatGvXDvv37xc6BmlIYWGh4hLfvyf4a9OmDeLi4oSMJkq89KMlunfvDj8/PyxZsgStWrXCd999hxo1auD8+fNwcHAQOh6pWIsWLTB37lwsXLgQbm5uCAgIwOeff45ff/2VI7x0UNWqVbFp0yacPXsWdnZ2xSb44/N+dMsnn3yCmJgYdOrUCbVr18b169cBvH5UilQqFTid+LCoaInhw4ejXLlykMvlaNSoEUaNGoV169bB2tqa1zR1kJ+fH6ZMmYKLFy/i22+/xa5du9C7d28YGBhg8eLFQscjFbty5QoaN24MAEhNTRU4Dalb//79MX36dABA+/bt0bVrV5QvXx6XLl2Cs7OzsOFEiPOoEGkBuVyO2NhYVK9eHRYWFkLHIYHs27cPX3zxBSpWrCh0FPpIJ0+ehJmZGdzd3XH48GFs2LAB1tbWmDlzJmxsbISOJyosKlokOjr6rfMscMpl3ZOTk4P4+PgS9zcfaFY2ubq64tChQ6hZs6bQUYg0hpd+tMSaNWuwatUqmJqawsTERGmdRCJhUdExp06dwpQpU5CVlVWspPBZP2UXjyt1Q2FhIQ4fPvzWA0/ek6SMRUVL7NixA+PGjcOIESOEjkIaEBQUBG9vb4waNQqVKlUSOg4RqVBAQAC2bdsGR0fHYgeeVByLipZ4+fIlOnXqJHQM0pCHDx8iJCREMXcOEemOw4cPIyAgAN27dxc6ilbgPCpawtXVFZcvXxY6BmmIra0tkpOThY5BRGoglUp5n9kH4BkVLdGpUyf4+/vjxo0bqF27drF5Frp16yZMMFKLiRMnwt/fH+PGjStxf9eoUUOgZET0sZo3b44zZ86gb9++QkfRChz1oyUcHR3fuo43V+qeBg0aKJ6aLJFIFMvlcjn3dxnm4uKCX375haN+tNymTZsQHByMZs2awd7eHoaGhkrrOThCGYuKjrl37x5sbGygr68vdBT6CBcvXnznek9PTw0lITFhUdENPj4+b10nkUhw6tQpDaYRPxYVHcN5FsqWdu3aYcuWLbwUVEaEhoaiT58+HAlWRkRERMDV1RXlypUTOoqgeDOtjmHvLFuePn2quERE2sXR0RH169dHixYtMHjw4Pd6zdChQ1lSypDRo0fzkQrgzbRERIIYPXo0JBIJzM3NYW9vL3QcEiEeeL7GokJEJIAffvhB6AhEWoGXfoiIiEi0WFSIiIhItFhUiIiISLRYVIi02L8ngyMi0kW8mVbHNGnSBOXLlxc6BmkIJ/bTbTKZDFeuXEG5cuXQoEED7m8qkzjhm4h99913iuGLderUwffffy90JFKj1atXQyKRwMLCAvb29nB1dRU6EgksLS0NzZo1w7fffovY2Fj8/PPPLCtliL+/P8aMGYMqVaoIHUVQPKMiYkUdUi6Xczx9GbB69WoAQPXq1VG3bl1s3rxZ4EQktMqVKyM8PByenp548OABS4oW8/HxgZ6enuLA09/f/z9fM3PmTA0kEz+eUSEiIlKzKVOmKE3w17VrV6EjaQ0WFSIigUilUiQkJKBmzZowMTFBSkoKNm3ahHv37sHW1hbfffcdn+NEZR6LChGRAO7evYuhQ4fi8ePHqFq1KtavX4/Ro0fDyMgITk5OuHPnDp48eYJdu3ahbt26QsclEgyLChGRAIYPHw59fX38+OOP2LNnD3755Rc0adIEy5cvh4GBAeRyOWbMmIHk5GRs3LhR6LhEgmFRISISgIeHB3bt2oXatWtDKpXCxcUFO3bsQKNGjRTbJCQk4Ntvv8WFCxcETEokLE74RkQkgPLly+Ply5cAgKysLBQUFODVq1dK22RnZ8PIyEiIeESiwaKi5dLS0uDo6IhevXq913A30m4vXrzAd999h+HDh+PXX38VOg59hLZt22L69OnYtm0bfvjhB9SoUQMhISFISUkB8PqzvWjRInh5eQmclDSlsLAQjx8/Rnp6utBRRIVFRctVqFABY8aMwd69e+Hq6or8/HyhI5GayeVyLFq0CFFRUdzfWmzSpElo2LAhVqxYgbS0NAQGBqJt27Zo1aoVmjVrhlatWiE9PR2TJk0SOippSHp6Onx8fDBt2jSMGjVK6DiiwXtUiIhE5MqVK7h06RKsrKzQunVrGBsbCx2JNCQvLw+//vorunfvjujoaLi6ukJPj+cTWFS0jFQqxalTp5CSkoK6deuiadOmQkciNcnIyEBeXh4qVKiASpUqCR2HNCg9PR1VqlThQyeJwCn0Ra1+/fo4d+4cqlWrBgB4+vQpvvvuOyQlJcHMzAzPnz+Hu7s71q1bx19kOiIyMhKhoaGIiYlBbm6uYnmFChXg6uqKYcOGwdPTU8CEpEobN27EpUuXsGbNGhQWFmL16tXYsmULcnJyYGpqioEDB2LkyJFCxyQVWLt2LXr37g1zc3Oho2gdnlMSsTdPdgUGBqJixYo4e/YsIiIicPLkSbx69QpLliwRKCGp0q+//oqhQ4eicuXKmDZtGn766Sds3rwZP/30E6ZMmQJTU1MMHjwYx44dEzoqqcCqVauwdu1a1K9fH8Dr0hIeHo7Ro0fjp59+wpAhQ7Bp0yaEhoYKnJRUITg4GD179kR0dLTQUbSPnETLwcFBnpaWpvi6VatW8r///ltpm4sXL8q9vLw0HY3U4Msvv5Rv3Ljxndts3LhR3qFDBw0lInVq2bKl/Pjx44qvO3ToID9w4IDSNkePHpW3bt1aw8lIHRwcHOTjx4+XOzo6ymfOnKn0dzu9G8+oiJhEIlG6Rm1kZISqVasqbWNubo68vDxNRyM1ePz4MVq3bv3ObVq1aoWHDx9qKBGp0/Pnz+Hg4KD4+sWLF3B0dFTa5tNPP0VaWpqmo5EaSCQSTJs2DWvWrMHff/+Ntm3bIiAgAImJiUJHEz0WFRGTy+VYsmQJwsLCcO7cOXh4eGD//v1K24SFhRX7y420k729PQ4fPvzObQ4cOABbW1vNBCK1+vTTT7Fp0ybF1y1btsSpU6eUtjl06BCf86Mj5P//Ur6Pjw9+/fVXTJgwAX/++Sc6dOiAHj16ICgoCEeOHMG5c+cETio+HPUjYuvWrUN8fDzi4+ORlJSEgoICSCQSREREwMzMDF988QUeP36M9evXc1IoHRAREYHvv/8ejo6O8PLygrW1NYyMjCCVSpGamoqLFy/i6tWrWLduHZo1ayZ0XPpI165dw4ABA1C3bl106tQJJiYmWLx4MZo1a4a6deviypUrOHfuHEJCQji6Twe8OTgCeF1eYmJicPLkSURFRSE+Ph75+fmIjY0VMKn4sKhoiaLHwcfHx6Nr164AgBUrVuDzzz9HgwYNBE5HqpKQkICwsDDExMQgOTkZubm5KFeuHKytreHu7o7+/fujTp06QsckFUlMTERISAjOnTundImnXLlycHV1xciRIznKS0c4Ojri/PnzSkXlTYWFhcjIyCh2ib+sY1EhIhKBrKwsZGdnw8DAAFWqVOFEXzpm6tSpmD59OkxMTISOonX4SdBSnTt3RnJystAxSEPmzJnD53/omLVr1+Lp06eKr01MTGBpaYlq1aqxpOighQsXsqSUEid8E7HVq1e/dV1iYiI2btyIypUrAwDGjBmjqVikJlFRUW9dd/DgQXh4eMDCwgIA4OHhoalYpCbBwcHYuXMnli1bBnd3d6HjkIA6d+6M9evXw9raWugoosRLPyLWvHlzpKWloXr16jA0NFRa9+TJE1hYWEBfXx8SiaTYaAHSPk5OToqRAe/6WEokEt5spwMcHR3RsWNH/Prrr+jduzfGjh37zvsXSLu968Dzp59+wjfffMMDz7dgURGx9PR0zJgxA/fv30dgYKBiBksAcHFxwS+//IKaNWsKmJBUKTo6Gn5+fvjkk08wbdo0VKhQAcDr0lJ0xFWjRg0AwP/+9z8ho5IKFI0CuXr1KgICAvDs2TP07t0b33zzDezs7ISORyrGA8/SY1HRArt27cKyZcswZMgQDB8+HACLiq7KysrC7NmzcfXqVSxZsgSurq4AuL910b9HgUilUuzevRvh4eF48OAB6tevj88++wyOjo4wMzPjcHQdwAPP0uMdW1qgT58+2LlzJ44dO4Z+/fpxZlIdZmJigqVLl+KHH37AyJEjsXz5cuTn5wsdi9TgzVmn+/Xrh2PHjmHr1q3w9PREREQEpk6dimHDhgmYklSlatWqWLt2Lfr374+BAwdi/fr1QkfSGryZVkvY2dlh165dWLlyJbp37w6ZTCZ0JFKjrl27ws3NDX5+fvjqq69QUFAgdCRSsZJOZkskEri7uyturi2aV4N0R58+feDp6YmJEyfi7NmzWLRokdCRRI9nVLSIgYEBJkyYgDVr1qBz586oWLGi0JFIjWxsbPDzzz+jTZs2MDc3h4EBjyt0Sffu3VGuXLl3bqOnp8fJv3RQ0YGni4sLDzzfA+9R0VJHjhyBj4+P4oZL0j0ZGRnIy8tDhQoVUKlSJaHjEJEaXLx4EQcOHMCkSZNYSt+CZ1S01KxZs/Ds2TOhY5CKRUZGYujQoXBxcYG3tzdatWoFT09PuLm5YdiwYbh48aLQEUlFVq9ejZycHKVl+/fvR8eOHeHm5oavv/4af//9t0DpSFM8PT3RtGlTlC9fXugoosUzKiLm6OiodMPdv8nlcqV1nFdD+/3666/w8/ND+/bt4eXlBQsLC5QrVw55eXlITU1FZGQkjh07hqVLl6J9+/ZCx6WP9OZD6n755RdMnToVvXv3Rt26dXH9+nUcOXIEwcHB8PHxETgtqZOrqysOHTrEUT9vwYveIjZ27FisWbMGHh4e6NKli2K5XC7HnDlz4OvrywmidMjq1asxfvx4DB48uMT1vXv3RoMGDRAcHMyiogPePEYMCwvD2LFjFVMQAEDt2rWxevVqFhUd8F8Hnu3atVN8zQNPZSwqIjZy5Eg0bdoUkyZNwh9//AF/f3/FzIX+/v5o3749G7gOefz4MVq3bv3ObVq1aoWVK1dqKBFpUkpKClq2bKm0rH379lizZo1AiUiVeOBZerxHReQaNWqEgwcPolKlSujcuTPOnz8vdCRSE3t7exw+fPid2xw4cAC2traaCURq9+8HTTo6OhZ70Oi9e/f4y0tHjBw5Etu3b8fjx4/xxx9/wMfHB927d0ePHj2gr6+P9u3bo3v37ujevbvQUUWHZ1S0gLGxMRYsWIDjx49j4sSJ6NChg9CRSA0mTpyI77//HufPn4eXlxesra1hZGQEqVSK1NRUXLx4EVevXsW6deuEjkoqYGRkhC5dusDExAT29vaQyWSYN28ejhw5AmNjYxw8eBDLly/n512HFB14zp8/H507d8bChQvRtGlToWOJHm+m1TIpKSmYMmUKIiIicOLECV760TEJCQkICwtDTEwMkpOTkZubi3LlysHa2hru7u7o378/6tSpI3RMUoHCwkIkJiYiPj4et2/fRnx8PP755x8cPXoURkZGaNKkCby8vLBw4UJOQ6CDjh8/jtmzZ6NDhw7Yv38/p9B/BxYVLfX48WNYWVlBT49X74h00cuXLzl/jo7jgef7YVERsdWrV2PIkCEwNjZWLNu/fz82btyI5ORk1K1bF76+vvjss88ETEmq9vz5c1SpUgXA67/I9u3bh5SUFNStWxc9evTg0bWOefXqldI+vXHjBh4+fAhbW1s4OjoKmIw0hQee78aiImKcZ6FsSU1Nxffff4/Y2Fg0aNAA8+fPx8CBAyGXy1GzZk0kJiaiWrVq2Lp1KywtLYWOSx/p+fPnmDx5MpKSknD8+HG8fPkSo0aNQlRUFIDXz/3x8vLCqlWrYGJiInBa+lg88Cw91jcRe9s8C3PmzEHfvn2xaNEi+Pr6YvXq1QIlJFVavHgxKlasiNWrV8PMzAwDBgyAs7Mz/vzzT+zduxd//PEHatSowYeY6YgFCxbg8ePHmDVrFgAgMDAQ6enp2LNnD65cuYKdO3ciLS0NixcvFjgpqcKaNWvw6tUrxde//PILZs6cCQ8PD4wfPx62trYYPnw4Tp8+LWBKkZKTaDk4OMjT0tIUXzdt2lQeFxentE1SUpK8UaNGmo5GauDu7i6PjY2Vy+Vy+aNHj+QODg7yq1evKm1z48YNuZubmxDxSMU8PT2V9m/r1q3l586dU9omOjpa7unpqelopAZv/n3eo0cPeUhIiNI2ISEh8u7du2s6mujxjIrIcZ6FssXQ0BAAUKNGDTRt2hRGRkbFtilpGWmfwsJCpX1pYGAAU1NTpW0qV64MqVSq6WikAW+b4C8hIUGgROLFoiJiRfMseHh44Ouvv8bz588xb948xYPMDh48iJkzZ3I6dR3h7u6OwMBAPH/+HACwceNGpZspk5KSMG/ePF7D1hHNmzfHvHnzFAcj3bp1Q2hoKAoKCgAAUqkUK1euRJMmTYSMSSrEA8/S4c20IsZ5FsqWBw8eYODAgWjUqBGWL1+utO7YsWMYO3Ys6tevjw0bNqB69eoCpSRVefr0KQYNGoTHjx/D29sbNjY2OHDgACpWrAg7OzvExcVBT08P27dvR61atYSOSx+pUaNGkMlkShP8ZWRklDjB3+TJk4WOKyosKlqM8yzoHqlUiuTk5GK/mJKTkxEXF4emTZsqLg+R9pNKpThy5AjOnz+PxMREZGVlwcDAAFZWVoozqUVD1Um78cCz9FhUROzQoUNo166d0nA20n0ymQzZ2dkwMzMrtk4ul+PJkyeoUaOG5oMRkdrwwPPtWFREzNHREfXq1cOqVavwySefCB2H1CwvLw/z5s3DL7/8gvz8fHz66aeYPXs2GjRooNgmLS0NzZs352PgdURGRga2bduGqKgoPHv2DDKZDJUqVUKtWrXQrFkzdO7cGQYGfCSbLuCBZ+nxZlqRMzU1RZcuXRASEsK7/3XcypUrcf78ecyfPx+LFy9Gfn4+vv32W/z1119K2/HYQjfcv38fnTp1wuHDh2FmZoby5cvj4cOHcHJyQkFBARYuXIgePXoo3YBJ2mvy5Mno06cP7t27J3QUrcOiImISiQQrVqzApEmTEBoairZt2yIsLAxZWVlCRyM1+P333+Hv74+uXbuiS5cu2L17N3x8fPDDDz8gJiZGsZ1EIhEwJanKokWL0LZtW/z2229YsWIF9uzZAz8/P6SlpWHFihU4ffo0zMzMsHDhQqGjkorwwLN0eOlHxBwdHXH+/HlUq1YN6enp2LBhA/bs2QOZTIaWLVvC29sbjo6OMDMzg52dndBx6SO5uLjg0KFDSjfSFhQUYMSIEbh27Rq2b98OMzMzXvrREW5ubti7d6/SZzc/Px/Ozs6IiIhApUqVEB8fj/79++PChQsCJiVVqF+/Pv766y/8/vvvWLlyJYyNjTFkyBD07NmTj0j4DzyjoiWqVq2KyZMn48yZM5g7dy7kcjmCg4PxzTffoEOHDkLHIxWwt7fH77//rrRMX18fK1euhLW1NYYOHYq7d+8KlI5UrXLlyrh9+7bSsgcPHqCgoEDxcDqZTMYH1ekIuVwOiUSCfv364dixY+jYsSNWrVqFpk2bwtfXFzt27MDly5eRmJgodFTR4RkVEfv3GZW3SU5ORmpqKho1aqTBZKQOp0+fxg8//AAvLy/4+fnBwcFBse7p06f47rvv8OjRI8hkMp5R0QHLli3Drl27MGHCBLi5ueHx48dYsmQJLCwssHHjRpw9exZBQUFo2LAhAgIChI5LH6mkv8+zs7Nx4sQJnDp1CtHR0Xj+/DkkEgk/32/g7eQi5uHh8Z9zZlhZWcHKykpDiUidfHx8EBYWhl27dhW7Ydbc3By7du1CQEAAfvvtN4ESkir98MMPyMjIwOzZswG8PuL28vJSPHRy27ZtqF+/PqZNmyZkTFKjihUrolu3bujWrRuA/zvwJGU8o0KkZQoLC3k5QIdkZmbi3r17sLCwgKWlpdBxSE369++PNWvWFHueE/03FhWR4zwLRERUlrGoiNj9+/fx7bffwsTEBI6Ojnj06BFiY2PRq1cvZGRk4O+//4aVlRW2bNmCqlWrCh2XiIjegQeepcOiImKjRo2ChYUFZs+erZg7Izw8HBcuXMCaNWuQlZWFUaNGwdLSEoGBgQKnpY/Vv3//954jJTw8XM1pSN0OHjz43tsW3cNA2osHnqXHoiJinGehbAkJCcHKlSthZ2f3n6O4OAmY9uvatSvi4+MBvHu2YY4C0Q088Cw9nmMSsaJ5Fv5dVDjPgu4aMWIETExMsHTpUoSEhMDGxkboSKRGu3fvxqhRo/DixQvs3LmTp/x13IULF7B3716ls6bffvstlixZongg4YwZM9C/f38BU4oTf8OJWKdOnTB79mzs3r0bCQkJ+Ouvv+Dr64vPPvsMFStWxNmzZzF16lS0bt1a6KikIn379oWnpyeWLFkidBRSs3LlymHFihVIT0/H5s2bhY5DasYJ/kqPFV7EOM9C2TRv3jzcvHlT6BikAUVH0adOnRI6CqlZ0YFnZmam0gR//z7wDAoK4oFnCXiPihbgPAtERNpNJpPB398fe/bsAfB/B56BgYEwNzfHiBEjYGZmhpkzZ/LZP29gUSESmYcPHyIzMxMODg7Q19dXWieTyXDlyhV4eHgIlI6IPgYPPD8ciwqRSLx48QI//vgjIiMjAbyeNn/u3LlKp4LT0tL49GQdkpqaisuXL8PZ2RmWlpY4c+YMNm/ejJSUFNSpUwejRo1C/fr1hY5JJCjeoyJinGehbAkMDERaWhq2bt0KiUSCDRs2YPTo0fD390fPnj0V2/HYQjdcvXoVAwcORE5ODkxMTDB9+nRMnz4d3t7eaN26Na5du4Y+ffogLCwMLi4uQsclEgzPqIgY51koW1q0aIHly5fDzc1NsWzx4sUIDw/H8uXL0a5dO55R0SH9+vVDvXr1MH78eISEhGDjxo0YOnQoxo8fr9gmICAAN27cwPbt2wVMSqrAA8/SY1ERsby8PM6zUIa4u7tjz549SvPmAMDUqVNx9OhRbNq0Cba2tiwqOsLZ2RmHDx9GzZo1kZWVBXd3dxw8eBCOjo6KbRITE9GtWzdcvXpVwKSkCjzwLD0O2BYxzrNQttSvXx87duwottzf3x+urq74/vvvcfHiRQGSkTqYmJggJSVF8d/jxo1DxYoVlba5f/8+qlWrJkQ8UrHdu3fjs88+Q4MGDXDjxg3ExcWV+A9LSnEsKiJXNM9CUlKS0FFIzcaNG4c9e/bgyy+/xLVr1xTLDQwMsGbNGjg4OGDChAkCJiRVat++PWbMmKF4/MWIESNQs2ZNAK9HhuzevRvTpk1D165dhYxJKsIDz9LjpR8iEUlMTMShQ4fQtWvXYpeAZDIZfvrpJxw5cgTHjh0TKCGpSm5uLqZPn47y5ctjwYIFSuuOHTuG8ePHo0+fPpg6dSoMDQ0FSkmqdvr0aZw6darYPqe3Y1EhIhJQYWFhsWnTX716hcLCQk78RQRe+hG91NRUHDt2THEt+8yZMxg4cCC+/PJL/PDDD7yeqWO4v8uOQ4cOIScnp8Rnu1SoUIElhej/Y1ERsatXr6J9+/YYO3YsOnbsiAMHDmDkyJHQ19f/f+3deVCU9xkH8O8SWGMkBBGjMZ5oBYNyFbV4Q4LGc7Q2wQsICmJsY9UMUaKCmAkWRZQQUBQpEi1NjBfGKFoNalCrGA+02KlKVPAWjIsHcrz9w2EnuFhZuuv7vvv7fmacMe8ePvpM9Hnefd/vwtfXF2VlZQgICMCJEyfkLpVMgP0Wy5w5cxAQEIBLly7JXQq9IFxEGocf/SgYcxbEwn6LxcXFBd7e3igoKMD06dMREhICrVYrd1lkJv8r4M/Z2RmnT5/G6dOnGfBXH4kUy93dXbp8+bIkSZKk0+kkZ2dnqbCwsM5zLl68KLm5uclRHpkY+y0WFxcX6datW9JXX30leXt7S/3795cyMjIknU4nd2lkBhMnTpRiYmIknU4nxcfHS926dZOWLVtW5zmff/65NH78eJkqVC5+9KNgzFkQC/stFkmSoNFoMGnSJOTk5GD48OFISkpC3759MWPGDGRlZeHEiRMoKiqSu1QygTNnziAkJAS2trYIDw9HTU0Nhg0bVuc548ePx9mzZ2WqULk4qCgYcxbEwn6Ly8HBAXPmzMH+/fsRExMDSZLwxRdfYPz48Qb/mJE6cRFpPGayK1hERATu3r2L7Oxs9O7du85jhw8fRkxMDAICAjB9+nSZKiRTYr+pWbNmGD16tP67Xq5fv46bN2/KWxSZRO0iEhMTg969eyM8PFz/2L1797Br1y4kJibi/fffl7FKZeLFtCrAnAWxsN9iCAwMRHJyMuzs7OQuhV4ABvw1HgcVBdu2bRsGDx6Mpk2byl0KvQDsN5Hl4yJiPF6jomDMWRAL+01kuRjw13gcVBTOzs4Oo0aNQmpqKh4/fix3OWRm7DeRZeIi0nj86EfBunXrhoMHD+ovsmratCmmTJmCsWPHcvq2QOy3WAIDA6HRaBr03MzMTDNXQ+bGgL/G46CiYC4uLsjLy0OLFi1QWlqKNWvWYOPGjaisrMTAgQPh4+MDFxcX2NvbG3zTLqkP+y2W1NRUJCYmolOnTnBzc/ufz128ePELqorMhYtI43FQUbBf/8NV6/79+9izZw/27t2L/Px8lJWVQaPR8DsiLAD7LZ4NGzZg2bJlyM7ORtu2beUuh8yIi0jjcVBRsPr+4Xpabc7C8zYyUj72W0zTpk2DVqvFF198IXcpZEZcRBqPgW8K1rNnz+feT9+6dWu0bt36BVVE5sR+i2nRokWMTRcUA/4ahmdUiIiIzIwBf43HQYWIiIgUizkqREREpFi8RkXBmLMgFvabiMgQBxUF69evX4NzFkj92G8iy8VFpPE4qChYeHg4bG1tsWzZMqSmpjJnwcKx30SWi4tI4/FiWhVgzoJY2G/xbN26FcOGDTOIVK+oqMDOnTv1t6+SujHgr3E4qKjAzZs3cfbsWfj6+spdCr0A7Ld4nhX2d/v2bfTr1w/nzp2TqTIyNS4ixuOgQkQks5KSErRp08bgGoaamhpcu3YNb775pkyVkalxETEeBxUiIiJSLF5MS0Qkg6CgIGg0GrRs2RJdunTBtGnT5C6JSJE4qBARyaD2ZLYkSeCJbaJn40c/REREpFiM0CciklllZSVWrVqFS5cuAQDmzZsHT09PTJkyBWVlZTJXRyQvDioqsXXrVjx+/NjgeEVFBbZu3friCyKzYr/FsnTpUvz1r39FeXk5Dhw4gC1btiA8PBz379/HkiVL5C6PSFYcVFRi7ty50Ol0Bsd1Oh3mzp0rQ0VkTuy3WHbt2oWEhAS4urpi79696NWrF6ZNm4b58+cjNzdX7vLIxLiIGIeDikrs3bsXDg4OBscdHBywd+9eGSoic2K/xXL37l107twZAJCXl4e+ffsCAOzt7fHo0SM5SyMz4CJiHN71oxLPCnyysrJiGJQFYr/F0r59exQUFODOnTsoLi5G//79AQD/+Mc/GLVugbiIGIeDioIxZ0Es7Le4QkNDMXv2bFhZWeF3v/sdXFxckJycjOTkZMTGxspdHpkYFxHjcFBRMOYsiIX9Ftfo0aPh4uKC4uJiDBgwAADQo0cPrF27Fj4+PjJXR6bARaTxmKNCRERkZoGBgXUGlQ8//FDuklSDg4pKVFZWYu3atRg6dCg6dOiAefPm4fvvv4eXlxfi4+PRvHlzuUskE2K/LV9QUFCDn5uZmWnGSoiUjXf9qARzFsTCflu+N998U//D0dERR48ehU6nQ+fOneHs7IzKykocP34cTk5OcpdKJsaAP+PwjIpKDBgwAIsXL0bfvn0RHR2NS5cuISMjA2fOnEFYWBgOHz4sd4lkQuy3WCIjI/Haa68Z3Jq6YsUKXLhwAUlJSTJVRuYQGxuLbdu2IT09HXfu3MG0adMwY8YM5ObmolOnTli8eLHcJSoKz6ioBHMWxMJ+i2XXrl0YN26cwfHRo0fj4MGDMlRE5sSAP+NwUFGJ2pyFs2fPMmdBAOy3WOzs7PCvf/3L4Hh+fj5atGghQ0VkTlxEjMPbk1WCOQtiYb/FEhAQgKioKFy4cAHdu3dHTU0NfvrpJ2zYsAERERFyl0cmxoA/4/AaFRU5d+6cPmdBq9XiwIEDsLGxYc6ChWK/xZKamor169fj1q1bAIA33ngDYWFhmDBhgsyVkalt3boVCxYsgJWVFTw9PZGRkVFnERk9erTcJSoKBxUiIgUpKyuDRqOBvb293KWQGXERaTgOKgrGnAWxsN9iKykpwalTp+r9Vl1u2CQyXqOiYL/+zoeKigp8//336NatGzw8PGBtbY0zZ87g9OnTeO+992SskkyF/RbXN998g5iYGFRXVxs8ptFoOKhYAC4ijcczKirBnAWxsN9i8fPzg6+vL2bNmgVbW1u5yyEziIyM1P/8eYvIwoUL5StUgTioqISnpye2bNmCjh071jn+888/Y/To0Th58qQsdZF5sN9i6dGjB3bu3Mk7PgTBRcQ4zFFRCeYsiIX9Fku3bt1w/vx5ucugF4QBf8bhNSoqwZwFsbDfYgkNDcWiRYtw5coVODk5QavV1nm8Z8+eMlVG5lC7iDx9xpSLSP340Y+KMGdBLOy3OFxcXJ75mEajQWFh4QushswtJSUF6enpCA4OrncRmThxotwlKgoHFRVizoJY2G/LV1JS8j8f//UdYWQZuIg0HAcVFWHOgljYb/GUl5fj4sWLsLGxQbt27XgHkAC4iDwfBxWVeF7OAk8NWxb2Wyw1NTWIi4vD3/72N1RVVUGSJGi1WgQEBODTTz+FRqORu0QyMS4iDcdBRSWYsyAW9lssK1euxNq1azFjxgz06tULNTU1OHbsGJKTkzF16lSEhobKXSKZEBcR43BQUQnmLIiF/RaLn58fZs2ahZEjR9Y5vn37diQlJWH37t0yVUbmwEXEOMxRUQnmLIiF/RbLnTt34O7ubnDc3d0d165dk6EiMqdbt24hJCSEQ0oDMUdFJZizIBb2WywdO3bEoUOH0L59+zrH8/LyeMePBapdRHjGtGH40Y9KMGdBLOy3WLZu3YqoqCgEBgbCy8sLAHD8+HFs2LABn3zyCXM1LMzu3bvxl7/8BSEhIVxEGoCDikowZ0Es7Ld4MjIykJaWhtu3bwMAHB0dMWXKFISEhMhcGZkaFxHjcFBRGeYsiIX9Fsvjx49RXl4OSZJQVVWFVq1ayV0SmQEXEeNwUFEJ5iyIhf0WS2lpKWbOnAlPT0/MmjULANCnTx+4uLhg+fLleO2112SukMyBi0jD8GJalUhNTcWmTZsQERFhkLPQqlUr5ixYGPZbLJ9//jkePnyI4cOH64+tWbMGCxcuRFxcHGJjY2WsjkyNi4iRJFIFX19fKTs72+B4dna25O/vL0NFZE7st1h69eolFRYWGhwvKCiQfHx8ZKiIzCklJUX67W9/K61bt04qLCyUzp49K2VkZEg9e/aU1qxZI3d5isMzKirBnAWxsN9iqa6uhlTPp/A2NjZ4+PChDBWROW3cuBHR0dF1Av7eeustODg4ICkpiWdMn8LAN5WozVl4GnMWLBP7LZaePXsiISEB5eXl+mPl5eVITEzkraoWiIuIcXhGRSVCQkIQFRWFK1eu1JuzQJaF/RZLZGQkJk6ciAEDBqBjx44AgJ9//hn29vZIS0uTtzgyOQb8GYd3/agIcxbEwn6LRafTYceOHfjPf/4Da2trdOnSBSNHjsTLL78sd2lkYgz4Mw4HFZVhzoJY2G/xPH78GDY2Nrzzw8JxEWk4DioqwZwFsbDf4snKysKaNWtw/fp15OTkIC0tDa1atcL06dPlLo3MhItIw/BiWpV4Vs6CTqdDXFycjJWRObDfYtm+fTuWLVuGMWPGwMbGBgDQuXNnrFq1Cunp6TJXR6ZWWlqKoKAgJCcnw8HBAS1atMCYMWMwefJk/PLLL3KXpzgcVFTixx9/xGeffYauXbvqj7m6uiI6Ohq5ubnyFUZmwX6LJT09HfPmzcNHH30EK6snfy0HBQUhKioKX3/9tczVkalxETEOBxWVYM6CWNhvsRQVFcHb29vgeO/evXm7qgXiImIcDioqwZwFsbDfYnF0dERRUZHB8RMnTuD111+XoSIyJy4ixmGOikowZ0Es7LdYAgICsGjRIkRGRgIALl68iB9//BErVqxAcHCwzNWRqdUuIsuXL9d/ESEXkWfjXT8qwpwFsbDfYklISMC6detQUVEBALC2tsa4cePw6aef6q9bIctw+fJlTJw4Effv3693EXFycpK3QIXhoKJCzFkQC/stjocPH+L8+fOQJAlOTk76bZssDxeRhuOgoiLMWRAL+y2Wq1evws7ODra2tjhy5Ah2794NLy8vjBgxQu7SyIy4iDwfzyeqBHMWxMJ+i2XPnj0YPHgwTp06hcuXLyM0NBSHDx/G/PnzsWHDBrnLIzPIysqCn58fPDw8UFxcjOjoaKSkpMhdliJxUFEJ5iyIhf0WS0pKCqZMmQIfHx9s374dbdq0wY4dOxAbG4v169fLXR6ZGBcR43BQUQnmLIiF/RbLhQsX8P7778PKygp5eXkYOHAgrKys4OHhgZKSErnLIxPjImIcDioqwZwFsbDfYrGzs4NOp4NOp8Pp06fRp08fAE/uDrG3t5e3ODI5LiLGYY6KSjBnQSzst1gGDhyIqKgoNGvWDK+++ir69u2LQ4cOYeHChRg0aJDc5ZGJ1S4i7dq1q3Oci0j9OKioRFhYGHQ6HWbPno2KigqEh4frcxamTZsmd3lkYuy3WBYsWIAVK1bgypUrWLlyJbRaLY4fPw4PDw/MmTNH7vLIxLiIGIe3J6sMcxbEwn7Tr0VGRiIiIgIODg5yl0L/Jwb8NRwHFRVhzoJY2G96mpeXF7Zt22bwkQGpExeRhuHYphLMWRAL+0314V5pOa5evYrq6mr06NEDDx48QEJCAr777ju5y1IkDioqwZwFsbDfRJaLi4hxOKioBHMWxMJ+E1kuLiLG4aCiEsxZEAv7TWS5uIgYh7cnqwRzFsTCfhNZrqcXkbCwMABcRJ6FZ1RUYsGCBfDy8sIrr7zCnAUBsN9UH37DrmWoXURmzJhRZxGJioriIlIP3p5sYZizIBb2Wyyenp7Izs7m7ckq9+jRI33AX1hYGDw8PJCUlIQrV64gOjoazZo1k7tEReGgYmGYsyAW9lu9jh07Bo1Gg5YtW6J9+/YNOlty8uRJvPXWW9BqtS+gQpIbF5EneI2KheHcKRb2W70CAwOh0WggSRJsbW2Rn5//3Nd4eHiYvzBSjJycHEyfPp2DitwFEBGJKDMzExqNBo6OjujQoYPc5ZACcRF5ghfTEhHJIDk5GV26dEGnTp0a9N0uZWVl/MI6EhLPqBARyeDChQvIyMhAnz59YGNjg1deeQVdu3bVDy137tzBtWvX8OjRI1RVVeHgwYO4ePGizFUTvXgcVIiIZLBgwQLExcVhzZo1qKmpAQC0adMGq1evRnx8PHJzcwE8uSVZq9Wic+fOWLJkiYwVE8mDg4qFYc6CWNhv9RoyZAiGDBmi/+/y8nJs2bIF48aNg7+/P/bv348WLVrA2pp/TZPY+H+AheHFV2Jhvy2Hra0tAgMDcfXqVUyePBktW7aUuySSGReRJ5ijomDMWRAL+01Ev8aAvyc4qCiYi4uL0TkLpF7sN5Hl4iLSeBxUFOzo0aN1chYacgsjqRf7TWS5uIg0Hv8mVDDmLIiF/SayXJmZmcjMzMTOnTtx9OhRuctRFV5Mq2DMWRAL+01kuZKTk7FixQo0b968Qc8vKyvDzJkzsW7dOjNXpnz86EfBcnJyEBcXh+vXrzc4ZyEiIgI+Pj4yVk2NxX4TWa5+/fph7NixRi0i2dnZOHjwoMyVy4+DiorU5iwkJibC398fM2fOZM6CBWO/iSwHF5HG46CiQnFxccxZEAj7TWR5uIg0HAcVIiIimXAReT4OKkRERKRYvD2ZiIiIFIuDChERESkWBxUiUiylfDKtlDqIRMRBhYgU5/r165g6dSpKSkr+7/cqLi6Gs7MzNm/ebPBYeXk53N3d4erqilu3btX7+pSUFKxdu/b/roOIGoeDChEpzqFDh7B//36z/zrfffcdXn31VdjZ2eHbb7+t9zmJiYl4+PCh2WshovpxUCEiYW3evBn9+/eHv78/Nm7cqA/iIiLl4KBCRPWSJAkZGRkYOnQo3Nzc4O/vj7Vr10KSJMydOxfBwcGIjo6Gl5cXhg0bhurqatTU1GD16tXw9/dH9+7dMWTIEHz11Vd13re6uhqrV6/GiBEj4ObmBg8PD4wbNw5HjhwB8GR4iIyMBAC8/fbbmDt3rv61GzduxPDhw9G9e3cMGjQISUlJqK6urvP+u3fvxqhRo+Dm5oYxY8bg3Llz9f7+zp8/j1OnTmHQoEEYNWoUSkpKDOLKnZ2dAQBffvml/udE9GIxAo+I6rVkyRKsW7cOISEh6Nu3LwoKChAfH4+qqioAQH5+Ppo0aYLk5GQ8ePAAL730EqKiorB582aEh4fD09MTx44dQ2xsLO7du4c//vGPAID4+HhkZWXh448/hrOzM27cuIHk5GT8+c9/Rm5uLgYNGoQPP/wQK1eurDMgpKamYvny5Zg0aRIiIyNRWFiIpKQkXLt2DbGxsQCAffv2YcaMGRg5ciQiIiJQWFiIiIiIen9/mzZtgr29PXx9faHVatGhQwdkZWVh4MCB+ud8/fXXCAgIwB/+8Ae899575vzjJqJn4KBCRAbu3buHzMxMTJo0Sf8PfZ8+fXDr1i0cO3YMjo6OqKqqwqJFi9C6dWsAQFFREb755hvMnj0bU6dOBfDki9g0Gg1SU1MxYcIENG/eHDdv3sSsWbMQGBio//WaNGmCjz76CP/+97/h4eGB9u3bAwC6deuGtm3bQqfTISUlBQEBAZg/f77+ve3t7TF//nyEhITgN7/5DZKTk+Hm5oalS5cCAPr37w8AWLZsWZ3fX1VVFbKzszFixAhotVoAwJgxY/SDzxtvvAEA8PDwAAC0bt1a/3MierH40Q8RGTh58iSqqqowePDgOsfnz5+PtLQ0AIC9vb1+SAGAI0eOQJIk+Pn5oaqqSv/Dz88PFRUVOH78OIAnQ0NwcDBKS0uRn5+PTZs2ITs7GwDw+PHjeus5ceIEHj16VO97A0BeXh4ePXqEs2fPwtfXt85rhw4davB+ubm5uH37Nt555x3cu3cP9+7dg5+fH2pqarBx48ZG/qkRkTnwjAoRGbh79y4AwMHB4ZnPadasWb2vGT58eL3Pv3HjBgCgoKAAMTExKCgoQNOmTdGlSxe0adMGwLPzSmrfu/ZMzdNu3ryJX375BZIkoXnz5nUee/311w2ev2nTJgDABx98YPDYt99+i+nTp/PL4YgUgv8nEpEBOzs7AEBpaSmcnJz0x69evYrLly+jsrLyma9Zt26dwRADPPlK+/LycoSGhsLZ2Rk7duyAk5MTrKyssH//fuTk5Dy3nvj4eHTs2NHgcUdHR9jb28PKygq3b9+u81jtkFPr9u3bOHDgACZMmIB33323zmMnT55EQkICfvjhB/j7+z+zHiJ6cfjRDxEZcHNzg42NDX744Yc6x9PT0zF79my89NJLBq/x9vYGAJSVlaFHjx76H6WlpUhMTMTdu3dx8eJF3L17F0FBQejSpQusrJ78FXTgwAEA0N8eXHu8lru7O2xsbHDjxo06721tbY2EhAQUFxejSZMm8PT0xO7du+ucmdm3b1+d99q2bRuqqqoQHByM3r171/kRHBwMW1tb/P3vf9c//+laiOjF4hkVIjLg4OCAoKAgZGRkQKvVolevXjh16hSysrLwySefoLCw0OA1zs7OGDVqFBYsWICSkhJ0794dRUVFWL58Odq2bYuOHTviwYMHsLW1xapVq2BtbQ1ra2vk5OTow9Zqg9Vqz6Ds2bMHAwYMQOfOnREaGorExESUl5ejd+/euHHjBhITE6HRaODi4gIAmD17NoKDg/GnP/0JAQEBKCoqwqpVq+rUuXnzZri6utZ7Zubll1/GkCFDsHnzZly5cgXt2rWDnZ0dfvrpJxw7dgze3t7QaDSm/KMmoueRiIjqUVNTI6WlpUnvvPOO1L17d+ndd9+VsrKyJEmSpDlz5ki+vr4Gr6msrJS+/PJL6e2335ZcXV2lAQMGSNHR0VJZWZn+OUeOHJF+//vfS25ubpKPj480efJkKT8/X/L09JTi4uIkSZKk8vJy6YMPPpBcXV2lsLAw/WvXr18vDRs2THJ1dZX69Okjffzxx1JJSUmdGvLy8qSxY8dKPXr0kIYOHSrt27dP6tq1q7Rp0ybp5MmTUteuXaW0tLRn/r7/+c9/Sl27dpWWLl0qSZIkpaenS97e3pK7u7vBr0VE5qeRJH7bFhERESkTP3wlIiIixeKgQkRERIrFQYWIiIgUi4MKERERKRYHFSIiIlIsDipERESkWBxUiIiISLE4qBAREZFicVAhIiIixeKgQkRERIrFQYWIiIgUi4MKERERKdZ/AVxk8jPgk+eJAAAAAElFTkSuQmCC", + "image/png": 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", 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K0oABAyRJAQEBOnjwoObOnavw8HAlJSVpzZo1+uSTT9SoUSNJ0uTJk9W6dWvt3btXoaGhTt49AABQmOVqRMXPz0+zZ89WnTp17G0mk0kmk0mXLl1SbGyswsPDHdZp0qSJ9uzZI5vNpj179tjbbqpRo4YqVaqk3bt352c/AABAEZSrEZVSpUqpefPmDm0bNmzQiRMn9Pbbb2v16tWqXLmyw/KKFSvKarXqwoULSkpKkp+fn4oVK5atT2JiYh53QbLZbEpNTc3z+gXt5l1f4TxWq1U2m83VZaCAWa1Wh9+Aq3FMOo/NZsvR3dDzdR+VH3/8UUOHDlWrVq3UokULXbt2Td7e3g59bj5OS0uT1WrNtlySihUrpuvXr+e5jvT0dMXFxeV5/YJWmO76WlgcP36cFwo3kpCQ4OoSAAcck85xq0zwV3kOKps2bdLAgQPVoEEDRUdHS7oRONLS0hz63XxssVjk4+OTbbl040qg/Iw4mM1mBQYG5nn9gsb35zhfjRo1GFFxA1arVQkJCfL392dUEobAMek88fHxOeqXp6CydOlSjRs3Tq1bt9Z7771nT0RVqlRRcnKyQ9/k5GT5+vqqZMmSqly5si5evKi0tDSHFJWcnKxKlSrlpRRJN4KAr69vntdH4cMLhHuxWCz8Pw5D4ZjMv5x+iM/15cnLly/XmDFj1LlzZ02ePNkhcDRq1Ei7du1y6L9z5041aNBAHh4eatiwobKysuyTaqUbQ/hJSUkKCwvLbSkAAKCIy1VQOX78uMaPH6/HH39cvXr10tmzZ3XmzBmdOXNGly9fVpcuXbRv3z5FR0fr119/1fz58/XNN9+oZ8+ekqRKlSqpXbt2GjZsmGJiYrRv3z698cYbaty4serXr18Q+wcAAAqxXJ362bBhg9LT07Vx40Zt3LjRYVmHDh00ceJEzZw5Ux988IEWLVqkqlWr6oMPPnC4ZHnMmDEaP368+vXrJ0lq1qyZhg0b5oRdAQAARU2ugkrv3r3Vu3fvO/Zp1qyZmjVrdtvlvr6+Gjt2rMaOHZubTQMAADfElxICAADDIqgAAADDIqgAAADDIqgAAADDIqgAAADDIqgAAADDIqgAAADDIqgAAADDIqgAAADDIqgAAADDIqgAAADDIqgAAADDIqgAAADDIqgAAADDIqgAAADDIqgAAADDIqgAAADDIqgAAADDIqgAAADDIqgAAADDIqgAAADDIqgAAADDIqgAAADDIqgAAADDIqgAAADDIqgAAADDIqgAAADDIqgAAADDIqgAAADDIqgAAADD8srPyrNmzdK2bdu0ZMkSSVKXLl20a9euW/Z977339MwzzygzM1OhoaG6fv26w/J+/frptddey085AACgiMlzUFm2bJmmTp2qRo0a2dumT5+u9PR0+2ObzaYBAwYoJSVFjz/+uCQpISFB169f17///W+VK1fO3tfX1zevpQAAgCIq10ElKSlJ7777rmJiYuTv7++wrEyZMg6Ply5dqn379unf//63ihcvLkk6fPiwSpQooeDg4DwXDQAA3EOu56j88ssvMpvNWrt2rerVq3fbfufPn9fUqVP16quvqmbNmvb2w4cPKyAgIG/VAgAAt5LrEZXIyEhFRkb+bb85c+bIx8dHPXr0cGg/cuSIMjIy1KNHDx06dEiVKlVSt27d1L59+9yWAgAAirh8Taa9nStXruizzz5Tv379VKxYMYdlR48eVVZWlvr376/KlStry5YtGjp0qNLT0/WPf/wjT9uz2WxKTU11RukFwmQyyWKxuLqMIsVqtcpms7m6DBQwq9Xq8BtwNY5J57HZbDKZTH/br0CCyqZNm5SWlqZOnTplW/bVV18pMzPTPmclODhYp0+f1rx58/IcVNLT0xUXF5evmguSxWJRSEiIq8soUo4fP84LhRtJSEhwdQmAA45J5/D29v7bPgUWVJo3b65SpUplW+bj45OtrXbt2lq7dm2et2c2mxUYGJjn9QtaThIjcqdGjRqMqLgBq9WqhIQE+fv7MyoJQ+CYdJ74+Pgc9SuQoBIbG3vLe6JcunRJjz32mIYMGaKOHTva2/fv369atWrleXsmk4nLm90MLxDuxWKx8P84DIVjMv9y+iHe6UHljz/+0IULF255+XGpUqXUpEkTTZkyReXKldN9992nb7/9VmvXrtWsWbOcXQoAACjknB5Uzpw5Iyn7PVVuGj9+vKZPn653331X586dU0BAgKZNm6aIiAhnlwIAAAq5fAWViRMnZmurW7euDh8+fNt1SpQooaFDh2ro0KH52TQAAHADfCkhAAAwLIIKAAAwLIIKAAAwLIIKAAAwLIIKAAAwLIIKAAAwLIIKAAAwLIIKAAAwLIIKAAAwLIIKAAAwLIIKAAAwLIIKAAAwLIIKAAAwLIIKAAAwLIIKAAAwLIIKAAAwLIIKAAAwLIIKAAAwLIIKAAAwLIIKAAAwLIIKAAAwLIIKAAAwLIIKAAAwLIIKAAAwLIIKAAAwLIIKAAAwLIIKAAAwLIIKAAAwLIIKAAAwLIIKAAAwLIIKAAAwrHwFlVmzZqlLly4ObcOGDVNQUJDDT2RkpH15VlaWpk2bpoiICNWvX18vv/yyTp48mZ8yAABAEZXnoLJs2TJNnTo1W/vhw4fVu3dvbdu2zf7zxRdf2JfPnDlTy5cv15gxY7RixQplZWWpZ8+eSktLy2spAACgiMp1UElKSlLv3r0VHR0tf39/h2U2m03x8fF68MEHVaFCBftP2bJlJUlpaWmaP3+++vfvrxYtWig4OFhTpkxRYmKivv32W6fsEAAAKDpyHVR++eUXmc1mrV27VvXq1XNY9ttvvyk1NVU1a9a85bqHDh3S1atXFR4ebm8rVaqUQkJCtHv37tyWAgAAijiv3K4QGRnpMOfkz44cOSJJWrJkibZu3SoPDw81a9ZMAwYMUMmSJZWYmChJqlKlisN6FStWtC8DAAC4KddB5U6OHDkiDw8PVaxYUZ988ol+++03vf/++zp69KgWLVokq9UqSfL29nZYr1ixYkpJScnzdm02m1JTU/NVe0EymUyyWCyuLqNIsVqtstlsri4DBezma8bN34CrcUw6j81mk8lk+tt+Tg0qr776ql588UX5+flJkmrXrq0KFSroueee0/79++Xj4yPpxlyVm/+WpOvXr+frjTw9PV1xcXH5K74AWSwWhYSEuLqMIuX48eO8ULiRhIQEV5cAOOCYdI6/DlzcilODioeHhz2k3FSrVi1JUmJiov2UT3JysqpXr27vk5ycrKCgoDxv12w2KzAwMM/rF7ScJEbkTo0aNRhRcQNWq1UJCQny9/dnVBKGwDHpPPHx8Tnq59SgMmjQICUnJ2vhwoX2tv3790uSAgMDVa1aNZUoUUIxMTH2oHLp0iUdPHhQUVFRed6uyWSSr69vvmpH4cILhHuxWCz8Pw5D4ZjMv5x+iHfqnWmfeOIJ7dixQzNmzNBvv/2mLVu26O2339aTTz6pgIAAeXt7KyoqStHR0dq8ebMOHTqkAQMGqHLlymrVqpUzSwEAAEWAU0dUHn30UU2dOlWzZ8/WnDlzVLJkST311FN6/fXX7X369++vjIwMDRs2TNeuXVNYWJjmzZsns9nszFIAAEARkK+gMnHixGxtbdq0UZs2bW67jqenp9566y299dZb+dk0AABwA3wpIQAAMCyCCgAAMCyCCgAAMCyCCgDDunlXZ+5FBLgvp171A6DwyMqyycPD2AGgsNzVuTD8LYHCiqACuCkPD5Oil+3RqaTLri6lUKtaqaQGdm7o6jKAIougArixU0mX9evvef9CUAAoaMxRAQAAhkVQAQAAhkVQAQAAhkVQAQAAhkVQAQAAhkVQAQAAhkVQAQAAhkVQAQAAhkVQAQAAhkVQAQAAhkVQAQAAhkVQAQAAhkVQAQAAhkVQAQAAhkVQAQAAhkVQAQAAhkVQAQAAhkVQAQAAhkVQAQAAhkVQAQAAhkVQAQAAhkVQAQAAhkVQAQAAhkVQAQAAhpWvoDJr1ix16dLFoe27775Tp06dFBoaqsjISL333nu6du2affmePXsUFBSU7ScmJiY/pQAAgCLIK68rLlu2TFOnTlWjRo3sbbGxserXr5/69++v1q1b68SJExoxYoQuXryoCRMmSJIOHz6s6tWra/ny5Q7PV7p06byWAgAAiqhcj6gkJSWpd+/eio6Olr+/v8OyFStW6KGHHlLv3r3l7++v5s2ba8CAAVq3bp3S0tIkSUeOHFFgYKAqVKjg8OPt7e2UHQIAAEVHroPKL7/8IrPZrLVr16pevXoOy/71r39p8ODBjhvw8FB6erquXLki6caISkBAQD5KBgAA7iLXp34iIyMVGRl5y2UhISEOj9PT07Vw4UI9+OCDKlu2rCTp6NGj8vPzU8eOHZWUlKTatWtrwIABqlu3bh7Kv8Fmsyk1NTXP6xc0k8kki8Xi6jKKFKvVKpvN5uoyCi2OSefjmHQPVqvV4TfyzmazyWQy/W2/PM9R+TsZGRkaNGiQjh49qmXLlkmS/vjjD12+fFmpqakaNmyYPD09tXTpUkVFRWnVqlUKDAzM07bS09MVFxfnzPKdymKxZAtxyJ/jx4/zQpEPHJPOxzHpXhISElxdQpGQk2kfBRJUrly5otdff127du3SjBkz7KMlVapU0e7du2WxWGQ2myVJderU0cGDB7VkyRKNGjUqT9szm815Djl3Q04SI3KnRo0afHrNB45J5+OYdA9Wq1UJCQny9/dnVDKf4uPjc9TP6UElOTlZL7/8sn7//XfNmzdPYWFhDstLlSrl8NjDw0MBAQFKSkrK8zZNJpN8fX3zvD4KH14gYDQck+7FYrHwvpNPOf3A5NQbvqWkpKhbt246f/68li1bli2kbN26VaGhoTp58qS9LSMjQ4cOHTL0iAgAAHANp46oTJgwQSdPntTcuXNVtmxZnTlzxr6sbNmyatCggfz8/DR48GC9/fbbMpvNmj17ti5evKiXXnrJmaUAAIAiwGlBJTMzU//5z3+Unp6ubt26ZVu+efNmVa1aVQsXLlR0dLR69Oih69evq2HDhlq6dKnKly/vrFIAAEARka+gMnHiRPu/PT09tW/fvr9dp3r16po2bVp+NgsAANwEX0oIAAAMi6ACAAAMi6ACAAAMi6ACAAAMi6ACAAAMi6ACAAAMi6ACAAAMi6ACAAAMi6ACAAAMi6ACAAAMi6ACAAAMi6ACAAAMi6ACAAAMi6ACAAAMi6ACAAAMi6ACAAAMi6ACAAAMi6ACAAAMi6ACAAAMi6ACAAAMi6ACAAAMi6ACAAAMi6ACAAAMi6ACAAAMi6ACAAAMi6ACAAAMi6ACAAAMi6ACAAAMi6ACAAAMi6ACAAAMi6ACAAAMK19BZdasWerSpYtDW1xcnKKiolS/fn1FRkZq8eLFDsuzsrI0bdo0RUREqH79+nr55Zd18uTJ/JQBAACKqDwHlWXLlmnq1KkObRcuXFD37t1VvXp1ffnll+rbt6+io6P15Zdf2vvMnDlTy5cv15gxY7RixQplZWWpZ8+eSktLy/NOAACAoskrtyskJSXp3XffVUxMjPz9/R2WffbZZzKbzRo9erS8vLwUEBCgEydOaPbs2erUqZPS0tI0f/58DRw4UC1atJAkTZkyRREREfr222/15JNPOmOfAABAEZHrEZVffvlFZrNZa9euVb169RyWxcbGqnHjxvLy+l/+adKkiRISEnT27FkdOnRIV69eVXh4uH15qVKlFBISot27d+djNwAAQFGU6xGVyMhIRUZG3nJZYmKiateu7dBWsWJFSdIff/yhxMRESVKVKlWy9bm5LC9sNptSU1PzvH5BM5lMslgsri6jSLFarbLZbK4uo9DimHQ+jkn3YLVaHX4j72w2m0wm09/2y3VQuZNr167J29vboa1YsWKSpOvXr9v/w96qT0pKSp63m56erri4uDyvX9AsFotCQkJcXUaRcvz4cV4o8oFj0vk4Jt1LQkKCq0soEv6aB27FqUHFx8cn26TY69evS5J8fX3l4+MjSUpLS7P/+2af/Hy6M5vNCgwMzPP6BS0niRG5U6NGDT695gPHpPNxTLoHq9WqhIQE+fv7MyqZT/Hx8Tnq59SgUrlyZSUnJzu03XxcqVIlZWRk2NuqV6/u0CcoKCjP2zWZTPL19c3z+ih8eIGA0XBMuheLxcL7Tj7l9AOTU2/4FhYWpj179igzM9PetnPnTtWoUUPlypVTcHCwSpQooZiYGPvyS5cu6eDBgwoLC3NmKQAAoAhwalDp1KmTrly5onfeeUfx8fFatWqVFi5cqF69ekm6cS4qKipK0dHR2rx5sw4dOqQBAwaocuXKatWqlTNLAQAARYBTT/2UK1dOc+fO1bhx49ShQwdVqFBBgwYNUocOHex9+vfvr4yMDA0bNkzXrl1TWFiY5s2bJ7PZ7MxSAABAEZCvoDJx4sRsbXXr1tXKlStvu46np6feeustvfXWW/nZNAAAcAN8KSEAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsL2c+WUxMjLp27XrLZVWrVtXmzZv18ccfa+rUqdmWHz582JmlAACAIsCpQSU0NFTbtm1zaPvpp5/02muvqU+fPpJuBJL27dvrrbfecuamAQBAEeTUoOLt7a0KFSrYH6empmrChAnq0KGDOnXqJEk6cuSInnvuOYd+AAAAt1Kgc1Q++eQTWa1WDR48WJKUlpamhIQE1axZsyA3CwAAiogCCyrnz5/XwoUL1bt3b5UpU0aSFB8fr8zMTG3YsEFPPPGEWrRoobfeekvJyckFVQYAACjEnHrq58+WL1+ukiVL6vnnn7e3HTlyRJJksVj04Ycf6ty5c5o8ebK6du2qNWvWyMfHJ0/bstlsSk1NdUrdBcFkMslisbi6jCLFarXKZrO5uoxCi2PS+Tgm3YPVanX4jbyz2WwymUx/26/AgsqaNWv0zDPPOISPZ555Rs2aNVPZsmXtbbVq1VKzZs303XffqW3btnnaVnp6uuLi4vJdc0GxWCwKCQlxdRlFyvHjx3mhyAeOSefjmHQvCQkJri6hSPD29v7bPgUSVA4dOqSTJ0/qqaeeyrbszyFFkipWrKgyZcooMTExz9szm80KDAzM8/oFLSeJEblTo0YNPr3mA8ek83FMuger1aqEhAT5+/szKplP8fHxOepXIEElNjZW5cqVU3BwsEP7lClT9M033+ibb76xv1CeOnVKFy5cyFfQMJlM8vX1zVfNKFx4gYDRcEy6F4vFwvtOPuX0A1OBTKY9ePCggoKCsrU//vjj+v333zVy5EgdP35cu3fv1muvvaYGDRooIiKiIEoBAACFWIEElTNnztiv9PmzBx98UHPmzNHhw4fVsWNH9evXT/fff78++eQThqIBAEA2BXLqZ86cObddFh4ervDw8ILYLAAAKGL4UkIAAGBYBBUAAGBYBBUAAGBYBBUAAGBYBBUAAGBYBBUAAGBYBBUAAGBYBBUAAGBYBBUAAGBYBBUAAGBYBBUAAGBYBBUAAGBYBBUAAGBYBBUAAGBYBBUAAGBYBBUAAGBYBBUAAGBYBBUAAGBYBBUAAGBYBBUAAGBYBBUAAGBYBBUAAGBYBBUAAGBYBBUAAGBYBBUAAGBYBBUAAGBYBBUAAGBYBBUAAGBYBBUAAGBYBBUAAGBYBBUAAGBYTg8qSUlJCgoKyvazatUqSVJcXJyioqJUv359RUZGavHixc4uAQAAFBFezn7CQ4cOqVixYtq0aZNMJpO9vWTJkrpw4YK6d++uyMhIjRo1Sj/99JNGjRql4sWLq1OnTs4uBQAAFHJODypHjhyRv7+/KlasmG3ZokWLZDabNXr0aHl5eSkgIEAnTpzQ7NmzCSoAACAbp5/6OXz4sAICAm65LDY2Vo0bN5aX1//yUZMmTZSQkKCzZ886uxQAAFDIOT2oHDlyROfPn1fnzp318MMP64UXXtDWrVslSYmJiapcubJD/5sjL3/88YezSwEAAIWcU0/9ZGRk6NixYwoMDNSQIUNUokQJff3113rllVe0YMECXbt2Td7e3g7rFCtWTJJ0/fr1PG/XZrMpNTU1X7UXJJPJJIvF4uoyihSr1SqbzebqMgotjknn45h0D1ar1eE38s5msznMZb0dpwYVLy8vxcTEyNPTUz4+PpKkBx98UEePHtW8efPk4+OjtLQ0h3VuBhRfX988bzc9PV1xcXF5L7yAWSwWhYSEuLqMIuX48eO8UOQDx6TzcUy6l4SEBFeXUCT8dfDiVpw+mbZ48eLZ2mrVqqVt27apcuXKSk5Odlh283GlSpXyvE2z2azAwMA8r1/QcpIYkTs1atTg02s+cEw6H8eke7BarUpISJC/vz+jkvkUHx+fo35ODSpHjx7V888/r48//lgPPfSQvf3AgQMKDAzU/fffrxUrVigzM1Oenp6SpJ07d6pGjRoqV65cnrdrMpnyNSKDwocXCBgNx6R7sVgsvO/kU04/MDl1Mm1AQIBq1qyp0aNHKzY2Vr/++qsmTJign376Sa+++qo6deqkK1eu6J133lF8fLxWrVqlhQsXqlevXs4sAwAAFBFOHVHx8PDQJ598okmTJun111/XpUuXFBISogULFqh27dqSpLlz52rcuHHq0KGDKlSooEGDBqlDhw7OLAMAABQRTp+jUr58eU2YMOG2y+vWrauVK1c6e7MAAKAI4ksJAQCAYRFUAACAYRFUAACAYRFUAACAYRFUAACAYRFUAACAYRFUAACAYRFUAACAYRFUAACAYRFUAACAYRFUAACAYRFUAACAYRFUAACAYRFUAACAYRFUAACAYRFUAACAYRFUAACAYRFUAACAYRFUAACAYRFUAACAYRFUAACAYRFUAACAYRFUAACAYRFUAACAYRFUAACAYRFUAACAYRFUAACAYRFUAACAYRFUAACAYRFUAACAYRFUAACAYXk5+wkvXryoyZMn6/vvv9eVK1cUFBSkN998U40aNZIkde/eXf/3f//nsE7jxo21ZMkSZ5cCAAAKOacHlTfeeENnzpzR5MmTVa5cOS1ZskQ9evTQ6tWrVbNmTR0+fFgjR47UY489Zl/HbDY7uwwAAFAEODWonDhxQtu3b9fy5cvVsGFDSdLw4cP1ww8/aN26dYqKitK5c+dUr149VahQwZmbBgAARZBT56j4+flp9uzZqlOnjr3NZDLJZDLp0qVLOnz4sEwmk2rUqOHMzQIAgCLKqSMqpUqVUvPmzR3aNmzYoBMnTujtt9/WkSNHVLJkSY0ePVrbt2+Xr6+vWrdurT59+sjb2zvP27XZbEpNTc1v+QXGZDLJYrG4uowixWq1ymazubqMQotj0vk4Jt2D1Wp1+I28s9lsMplMf9vP6XNU/uzHH3/U0KFD1apVK7Vo0UJvv/22rl+/rrp166p79+6Ki4vT+++/r9OnT+v999/P83bS09MVFxfnxMqdy2KxKCQkxNVlFCnHjx/nhSIfOCadj2PSvSQkJLi6hCIhJ4MUBRZUNm3apIEDB6pBgwaKjo6WJI0ePVqDBw9W6dKlJUm1a9eW2WzWgAEDNGjQIJUvXz5P2zKbzQoMDHRa7c6Wk8SI3KlRowafXvOBY9L5OCbdg9VqVUJCgvz9/RmVzKf4+Pgc9SuQoLJ06VKNGzdOrVu31nvvvWdPTF5eXvaQclOtWrUkSYmJiXkOKiaTSb6+vvkrGoUKLxAwGo5J92KxWHjfyaecfmBy+g3fli9frjFjxqhz586aPHmyw7BOly5dNHToUIf++/fvl9lslr+/v7NLAQAAhZxTR1SOHz+u8ePH6/HHH1evXr109uxZ+zIfHx898cQTGj9+vOrWratHHnlE+/fv1/vvv68ePXqoRIkSziwFAAAUAU4NKhs2bFB6ero2btyojRs3Oizr0KGDJk6cKJPJpCVLlmj8+PGqUKGCXnrpJb3yyivOLAMAABQRTg0qvXv3Vu/eve/Yp3PnzurcubMzNwsAAIoovpQQAAAYFkEFAAAYFkEFAAAYFkEFAAAYFkEFAAAYFkEFAAAYFkEFAAAYFkEFAAAYFkEFAAAYFkEFAAAYFkEFAAAYFkEFAAAYFkEFAAAYFkEFAAAYFkEFAAAYFkEFAAAYFkEFAAAYFkEFAAAYFkEFAAAYFkEFAAAYFkEFAAAYFkEFAAAYFkEFAAAYFkEFAAAYFkEFAAAYFkEFAIAcMplMslgsMplMri7FbXi5ugAAAG7KyrLJw8O4IcBisSgkJMTVZfwto/8dc4OgAgAwDA8Pk6KX7dGppMuuLqXQqlqppAZ2bujqMpyGoAIAMJRTSZf16+8pri4DBsEcFQAAYFgEFQAAYFguCSpZWVmaNm2aIiIiVL9+fb388ss6efKkK0oBAAAG5pKgMnPmTC1fvlxjxozRihUrlJWVpZ49eyotLc0V5QAAAIO660ElLS1N8+fPV//+/dWiRQsFBwdrypQpSkxM1Lfffnu3ywEAAAZ214PKoUOHdPXqVYWHh9vbSpUqpZCQEO3evftulwMAAAzsrl+enJiYKEmqUqWKQ3vFihXty3IjPT1dNptN+/btc0p9BcVkMukf4SWVkVnc1aUUal6eHtq/f79sNpurSyn0OCadg2PSuTgu86+wHJPp6ek5usPvXQ8qVqtVkuTt7e3QXqxYMaWk5P66+Zs7WRhuZ1y6hPffd0KOFIb/3oUBx6TzcEw6D8elcxj9mDSZTMYMKj4+PpJuzFW5+W9Jun79uiwWS66fLzQ01Gm1AQAAY7nrc1RunvJJTk52aE9OTlalSpXudjkAAMDA7npQCQ4OVokSJRQTE2Nvu3Tpkg4ePKiwsLC7XQ4AADCwu37qx9vbW1FRUYqOjlbZsmV177336oMPPlDlypXVqlWru10OAAAwMJd8KWH//v2VkZGhYcOG6dq1awoLC9O8efNkNptdUQ4AADAok83o1y8BAAC3xZcSAgAAwyKoAAAAwyKoAAAAwyKoAAAAwyKoAAAAwyKoAAAAwyKoAAAAwyKoAAAAwyKoADCU3377zdUlADAQggp07do1rVmzRpMmTdLFixe1a9cuXbhwwdVlwU117txZ+/btc3UZAAzCJd/1A+M4e/asnn/+eZ07d05paWl67rnnNH/+fB04cECLFi1SQECAq0uEmzGbzfLy4qUJxvLHH39o2bJlOnLkiLy8vFSrVi0999xzuvfee11dWpHHd/24uYEDB+rKlSuaMmWKHn74Ya1du1alSpXS66+/rmLFiumTTz5xdYlwM9OnT9enn36q9u3b67777pOPj4/D8meeecY1hcFtHT58WFFRUfLx8VHdunWVlZWlAwcOyGq16tNPP1WtWrVcXWKRRlBxc4888ohmz56tkJAQhYaGau3atapWrZoOHTqkrl27ateuXa4uEW4mODj4tstMJpPi4uLuYjWA1KNHD1ksFk2aNEnFihWTJF2/fl0DBw5UWlqaZs2a5eIKizbGV93c1atX5evre8tlGRkZd7kaQDp06JCrSwAc/Pjjj1qxYoU9pEhSsWLF1LdvX0VFRbmwMvfAZFo3FxYWpk8//dShLT09XR9//LEaNGjgoqoA6fTp0/rhhx907do1nTt3ztXlwI0VL15c6enp2dpv1Qbn49SPm/v111/VuXNnValSRUePHtVDDz2kY8eO6fLly1q6dOkdh+GBgpCWlqbBgwdr/fr18vDw0IYNG/Tee+/p6tWrmj59ukqUKOHqEuFmBg4cqOTkZE2bNk1lypSRJJ0/f16vv/66SpcurenTp7u2wCKOoAIlJyfr008/1cGDB5WVlaVatWrpxRdfVNWqVV1dGtzQhx9+qG+++UYjR45U7969tXbtWp0+fVpDhw5Vs2bNNHLkSFeXCDeTmJiof/7zn0pJSZG/v78kKSEhQWXKlNGSJUt4rSxgBBU3169fPw0YMIDLkGEYrVq10siRI/Xwww87TPDesWOHBg0apB9++MHVJcINXb16Vf/+97919OhR2Ww2BQUF6amnnmKE7y5gMq2b27lzp8MEMcDVkpKSVL169WztVapUUUpKigsqAm7MU3nxxRddXYZbIqi4uQ4dOig6Olp9+/bVfffdJ29vb1eXBDcXEBCgHTt26Nlnn3Vo//rrrxUYGOiiquBufvjhB4WFhcnHx0dnzpzR1KlT9eOPPyo9PV1/PRGxefNmF1XpHggqbm7Lli367bfftGHDhlsu554VuNtee+01DRgwQPHx8crMzNTq1at1/PhxbdiwQVOmTHF1eXATCxcu1NixY7V+/XoNHz5cBw4cULt27VSyZElXl+Z2mKPi5lavXn3H5R06dLhLlQD/s3XrVs2aNcthgvfLL7+sJ554wtWlwU0kJSWpffv22rp1qxo3bqy5c+eqUaNGri7LLRFUAAD4ix49eqht27bq1KmTHn74YS1btkw1atRwdVluiVM/0KZNm7RgwQIdPXpU3t7eqlWrlvr27cunB9w1M2bMsN+mfMaMGXfs6+3trcqVK+uxxx677V2Vgfw6duyYTp48qbS0NLVv315z587V6NGj5enp6erS3A4jKm5u2bJlGj9+vNq0aaN69eopMzNTe/bs0ebNmzVp0iS1adPG1SXCDURGRurLL7+Un5+fIiMj79g3MzNT586dU9OmTfmOFRSY+Ph4rVixQv3799eECRP01VdfqVSpUqpWrVq2iw4WL17soirdA0HFzT366KPq3r17tu+rmD17tlavXq3169e7qDLg9nbt2qVevXpp7969ri4FbmDo0KF3XD5hwoS7VIl7Iqi4ubp162rdunW67777HNqPHz+u9u3ba9++fS6qDLi98+fPa926derWrZurSwFQwJij4uYeeughbdiwQa+88opD+/fff6/Q0FAXVQV3FhwcLJPJdNvlcXFxKlu2LCEFBWrNmjVq27atvL29tWbNmjv2feaZZ+5KTe6KERU39OfJisnJyfriiy/02GOPqUGDBvL09NQvv/yir776Sj169NCAAQNcWCnc0apVqxyCSkZGhhISErRmzRoNGjRI7du3d2F1cBfBwcHavn27ypUrd8cvZzWZTNxvqoARVNzQ301WvMlkMnHHRRjG+vXr9fnnn2v+/PmuLgXAXURQAVAo/P7772rbtq1+/vlnV5cCN9C1a9cc9TOZTFq0aFEBV+PemKMCSdLZs2eVlpaWrf2ee+5xQTWAo6tXr2rp0qUqX768q0uBm7jnnnvuOFcqNjZWJ0+eVKlSpe5iVe6JoOLmtmzZoqFDh+rChQsO7TabjXOvcIk7TaYdPXr0Xa4G7mrixIm3bL9y5YomTpyokydPqmnTpho3btxdrsz9cOrHzbVq1Uo1a9bUiy++KB8fn2zLGzdu7IKq4M5u9f1TZrNZ9erVU7Vq1VxQEXDD//3f/2nYsGG6fPmyBg0alO0bvlEwCCpurn79+lq1apVq1qzp6lLgxv7utvk3mUwm9e3bt4CrARylpqZq4sSJ+uyzz9S0aVONHTtWVapUcXVZboNTP26uSZMm+uWXXwgqcKkZM2bIw8NDlStXvmM/ggruth07duidd95RSkqKRo8ereeee87VJbkdRlTcXGJiov7xj3/o4YcfVrVq1bLNDejXr5+LKoM7GTFihDZu3CiLxaJ27dqpXbt2d7x3BVDQUlNT9f7772vlypUKDw/XuHHjGEVxEYKKmxsxYoQ+++wz+fn5yWKxOCzjPiq4mzIzM7Vz50795z//0aZNm1SuXDl7aPH393d1eXAzjz76qE6fPq1q1arp6aefvmNfPtAVLIKKm2vQoIGGDx+uDh06uLoUwC49PV3btm3T+vXrtXnzZlWvXl1t27ZVu3btuGQedwU3xjQOgoqba9q0qZYvX57tSwkBo0hLS9OXX36pSZMm6erVq1wyD7gZD1cXANd68cUXNX36dFmtVleXAjhITk7W0qVL9a9//UujR49W+fLl1bt3b1eXBeAuY0TFzXXv3l27d++WJJUrV05eXo4XgjGkibspKSlJGzZs0DfffKO9e/eqWrVqatOmjdq0acPkWsBNcXmym2vYsKEaNmzo6jLg5hYuXKgNGzbo559/1j333KM2bdronXfe0QMPPODq0gC4GCMqAFwuODhYZrNZDz/8sOrUqXPHvlxhAbgXggp04MABzZs3T0eOHJGXl5cCAwPVrVs31a1b19WlwU1whQWA2yGouLldu3bpX//6l2rXrq1GjRopKytLP/74o44cOaJFixZxWggA4FIEFTf3wgsvqHbt2ho1apRD+6hRoxQfH68lS5a4qDIAALg82e0dPHhQXbt2zdYeFRWlAwcOuKAiAAD+h6Di5vz8/HThwoVs7efPn5e3t7cLKgIA4H8IKm6uZcuWGjNmjH799Vd7W3x8vMaOHZvjCY4AABQU5qi4uZSUFHXv3l1xcXEqWbKkJOnSpUu6//77NX/+fPn5+bm4QgCAOyOoQFlZWdq2bZuOHDkim82moKAgRUREyGQyubo0AICbI6i4oVtNnr2dxYsXF2AlAADcGbfQd0P33ntvtrZ169YpMjJSxYsXd0FFAADcGiMqkCSFhoZq7dq1qlatmqtLAQDAjqt+AACAYRFUAACAYRFUAACAYRFUAACAYXHVjxsaOnRotrb09HR98MEH2a76mTBhwt0qCwCAbAgqbujUqVPZ2kJDQ3XhwoVbfu8PAACuwuXJAADAsJijAgAADIugAgAADIugAgAADIugAgAADIugAqDQGjJkiCIjI11dBoACRFABAACGRVABAACGRVABkGfp6emKjo5Ws2bNVLduXfXo0UNr1qxRUFCQ/caCsbGxioqKUr169dS4cWMNHjxY58+ftz/HqlWrFBISop9//lnPP/+86tSpo5YtW2revHkO20pJSdHQoUPVuHFjhYWF6YMPPlBWVla2mjZt2qSOHTuqTp06atq0qcaOHavU1FT78unTp+vxxx/XjBkz1LhxYz3yyCNKSUkpoL8QgPwiqADIsxEjRmjRokWKiorSRx99pPLly2v48OH25bt379ZLL70kHx8fTZ06VW+//bZ27dqlrl276tq1a/Z+WVlZev3119W2bVvNnj1bDRo00Pvvv68ffvjBvrxnz57asmWLBg8erIkTJ+rHH3/Uf/7zH4d61q1bp759+6pmzZr66KOP1K9fP61du1Z9+vTRn+9tefr0aW3ZskVTpkzR0KFDVbp06QL+SwHIK26hDyBPfvvtN61evVqDBw9W9+7dJUkRERE6e/astm3bJkmaNGmSatSooVmzZsnT01OSVK9ePbVr105ffvmlOnfuLEmy2Wzq06ePnn32WUlSw4YNtXHjRn3//feKiIjQ1q1btW/fPs2ZM0fNmjWTJIWHhztMpLXZbIqOjlZERISio6Pt7f7+/nrppZe0ZcsWtWjRQpKUkZGhwYMHq1GjRgX7RwKQb4yoAMiTmJgY2Ww2tW7d2qH9ySeflCRdu3ZNP//8s5o3by6bzaaMjAxlZGSoWrVqCggI0Pbt2x3WCw0Ntf/b29tbZcuWtZ+yiY2NldlsVkREhL2Pr6+vmjdvbn987NgxJSYmKjIy0r6tjIwMhYWFqUSJEtm2d//99zvnDwGgQDGiAiBPbs4zKVeunEP7zccpKSnKysrSnDlzNGfOnGzrFytWzOGxj4+Pw2MPDw/76ZqUlBSVKVNGJpPJoU+FChXs/7548aIkadSoURo1alS27SUnJzs8/us3hQMwJoIKgDypVKmSJOns2bO655577O03A0yJEiVkMpn00ksvqV27dtnWt1gsOd6Wn5+fLly4oMzMTPspJOl/4USSSpUqJUkaNGiQGjdunO05mIcCFE6c+gGQJw0bNpSnp6c2btzo0P7tt99KujFiERISomPHjqlOnTr2n1q1amn69OmKiYnJ8bbCw8OVkZGhTZs22dvS0tIcTufUrFlT5cqV06lTpxy2V6lSJU2aNEkHDx7M5x4DcAVGVADkSbVq1dSpUydNnjxZ6enpCg4O1saNG/Xf//5X0o1TN2+88YZeeeUVvfnmm3r66aeVmZmp+fPn6+eff1afPn1yvK3w8HA98sgjGjZsmM6dO6d7771Xixcv1vnz5+2nmjw9PTVgwACNGDFCnp6eatmypS5duqSZM2cqKSlJDzzwQIH8HQAULIIKgDwbPny4fH19NX/+fF25ckXh4eF69dVX9dFHH8nX11ePPPKI5s2bpxkzZqh///4ym8164IEHtGDBAtWvXz9X25oxY4aio6M1bdo0Xb9+XW3bttVzzz2nzZs32/s8++yzKl68uObOnauVK1fK19dXDRo0UHR0tKpVq+bkvQdwN5hsf765AADk0MWLF7V161ZFRETIz8/P3v7ee+9p1apVuTq1AwC3w4gKgDyxWCwaN26c7r//fnXr1k2+vr766aeftHTpUvXq1cvV5QEoIhhRAZBncXFxmjp1qn766SdZrVZVr15d//znP9W5c+dslxIDQF4QVAAAgGFxeTIAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADAsggoAADCs/wcEyqAIkK/fgwAAAABJRU5ErkJggg==", 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", 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" ] @@ -583,11 +648,25 @@ " else:\n", " print(\"El DataFrame no está disponible.\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -601,9 +680,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.12.4" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/scripts/training/.ipynb_checkpoints/trainingText-checkpoint.py b/scripts/training/.ipynb_checkpoints/trainingText-checkpoint.py new file mode 100644 index 000000000..cba79862e --- /dev/null +++ b/scripts/training/.ipynb_checkpoints/trainingText-checkpoint.py @@ -0,0 +1,74 @@ +import pandas as pd +import re +from sklearn.cluster import KMeans +from sklearn.metrics import silhouette_score, davies_bouldin_score +from sklearn.preprocessing import StandardScaler + +# ** Carga y limpieza de datos ** +ruta_excel = r"C:\\Users\\cdgn2\\OneDrive\\Escritorio\\Maestría\\Maestria\\Metodologias Agiles\\Proyecto\\Comparative-analysis-of-products\\src\\comparative_analysis\\database\\Adidas_etiquetado.xlsx" + +# Crear el DataFrame +df = pd.read_excel(ruta_excel, header=0) + +# Procesamiento de columnas numéricas +num_cols = { + 'Weight': '(\d+\.?\d*)', + 'Drop__heel-to-toe_differential_': '(\d+\.?\d*)' +} +for col, pattern in num_cols.items(): + df[col] = df[col].astype(str).str.extract(pattern).astype(float, errors='ignore') + df[col] = pd.to_numeric(df[col], errors='coerce') + +# Procesar precios +price_cols = ['regularPrice', 'undiscounted_price'] +for col in price_cols: + df[col] = df[col].astype(str).str.replace(r'[^0-9.,]', '', regex=True) + df[col] = df[col].str.replace(r'\\.', '', regex=True).str.replace(',', '.') + df[col] = pd.to_numeric(df[col], errors='coerce') + +# Eliminar columnas innecesarias +cols_to_drop = ['details', 'description', 'category', 'characteristics', 'width', 'Pronation_Type'] +df = df.drop(columns=cols_to_drop, errors='ignore') + +# ** Clustering y evaluación ** +ids = df['id'] +X = df.drop(columns=['id'], errors='ignore').fillna(0) + +# Codificar variables categóricas +df_dummies = pd.get_dummies(X, dummy_na=True).fillna(0) + +# Escalar los datos +scaler = StandardScaler() +X_scaled = scaler.fit_transform(df_dummies) + +# Clustering con un número específico de clusters +k = 8 +kmeans = KMeans(n_clusters=k, random_state=42) +clusters = kmeans.fit_predict(X_scaled) + +# Agregar el cluster al DataFrame original +df['cluster'] = clusters + +# Evaluar calidad del clustering +sil_score = silhouette_score(X_scaled, clusters) +db_score = davies_bouldin_score(X_scaled, clusters) +print(f"Silhouette Score: {sil_score}") +print(f"Davies-Bouldin Score: {db_score}") + +# ** Análisis de clusters ** +def analizar_cluster(df, cluster_num): + elementos_cluster = df[df['cluster'] == cluster_num] + print(f"\n=== Análisis del cluster {cluster_num} ===") + print(f"Total de elementos: {len(elementos_cluster)}") + + print(f"\nEstadísticas descriptivas principales:") + print(elementos_cluster.describe().loc[['mean', 'std', 'min', 'max']]) + + print(f"\nValores más frecuentes por columna categórica:") + cols_categoricas = elementos_cluster.select_dtypes(include=['object', 'category']).columns + for col in cols_categoricas: + print(f" - {col}: {elementos_cluster[col].mode().values[0]}") + +# Analizar los clusters 2 y 3 +analizar_cluster(df, cluster_num=2) +analizar_cluster(df, cluster_num=3) \ No newline at end of file diff --git a/scripts/training/trainingText.py b/scripts/training/trainingText.py index cba79862e..1b0759a57 100644 --- a/scripts/training/trainingText.py +++ b/scripts/training/trainingText.py @@ -5,10 +5,12 @@ from sklearn.preprocessing import StandardScaler # ** Carga y limpieza de datos ** -ruta_excel = r"C:\\Users\\cdgn2\\OneDrive\\Escritorio\\Maestría\\Maestria\\Metodologias Agiles\\Proyecto\\Comparative-analysis-of-products\\src\\comparative_analysis\\database\\Adidas_etiquetado.xlsx" +#ruta_excel = r"C:\\Users\\cdgn2\\OneDrive\\Escritorio\\Maestría\\Maestria\\Metodologias Agiles\\Proyecto\\Comparative-analysis-of-products\\src\\comparative_analysis\\database\\Adidas_etiquetado.xlsx" +ruta_excel = r"..\\..\\src\\comparative_analysis\\database\\adidas_etiquetado.xlsx" # Crear el DataFrame df = pd.read_excel(ruta_excel, header=0) +df.info() # Procesamiento de columnas numéricas num_cols = { @@ -16,6 +18,7 @@ 'Drop__heel-to-toe_differential_': '(\d+\.?\d*)' } for col, pattern in num_cols.items(): + print("col",col) df[col] = df[col].astype(str).str.extract(pattern).astype(float, errors='ignore') df[col] = pd.to_numeric(df[col], errors='coerce') @@ -27,8 +30,9 @@ df[col] = pd.to_numeric(df[col], errors='coerce') # Eliminar columnas innecesarias -cols_to_drop = ['details', 'description', 'category', 'characteristics', 'width', 'Pronation_Type'] +cols_to_drop = ['details', 'description', 'category', 'characteristics', 'Width', 'Pronation_Type'] df = df.drop(columns=cols_to_drop, errors='ignore') +df.info() # ** Clustering y evaluación ** ids = df['id'] @@ -40,11 +44,13 @@ # Escalar los datos scaler = StandardScaler() X_scaled = scaler.fit_transform(df_dummies) +df_dummies.info() # Clustering con un número específico de clusters k = 8 kmeans = KMeans(n_clusters=k, random_state=42) clusters = kmeans.fit_predict(X_scaled) +print("clusters",clusters) # Agregar el cluster al DataFrame original df['cluster'] = clusters @@ -66,9 +72,12 @@ def analizar_cluster(df, cluster_num): print(f"\nValores más frecuentes por columna categórica:") cols_categoricas = elementos_cluster.select_dtypes(include=['object', 'category']).columns + print("cols_categoricas", cols_categoricas) for col in cols_categoricas: + print("col",col) print(f" - {col}: {elementos_cluster[col].mode().values[0]}") # Analizar los clusters 2 y 3 +print("dataframe final df",df.info()) analizar_cluster(df, cluster_num=2) analizar_cluster(df, cluster_num=3) \ No newline at end of file diff --git a/src/comparative_analysis/preprocessing/.ipynb_checkpoints/main-checkpoint.py b/src/comparative_analysis/preprocessing/.ipynb_checkpoints/main-checkpoint.py new file mode 100644 index 000000000..ceeb65785 --- /dev/null +++ b/src/comparative_analysis/preprocessing/.ipynb_checkpoints/main-checkpoint.py @@ -0,0 +1,79 @@ +import os +import re +import pandas as pd +import numpy as np +from tqdm import tqdm + +from functions.prompt_generator import PromptGenerator +from functions.model_inference import ModelInference +from functions.response_processor import ResponseProcessor + +# Definir las etiquetas con definiciones +labels_with_definitions = [ + ("Weight", "Indicates the lightness of the shoe, usually expressed in grams. Weight can influence performance and comfort during running."), + ("Upper Material", "Describes the materials used in the shoe's upper part, such as mesh, synthetic leather, or technical fabrics, which affect breathability, flexibility, and support."), + ("Midsole Material", "Refers to the compounds used in the midsole, such as EVA foams or proprietary technologies, which provide cushioning and shock absorption."), + ("Outsole", "Details the type of rubber or material used in the sole and the traction pattern design, which influence grip and durability on various surfaces."), + ("Cushioning System", "Specifies the technologies or materials aimed at reducing impact during strides, contributing to comfort and joint protection."), + ("Drop (heel-to-toe differential)", "Indicates the height difference between the heel and the toe of the shoe, measured in millimeters. A higher drop typically offers more heel cushioning, while a lower drop promotes a more natural stride."), + ("Pronation Type", "Classifies the shoe according to its suitability for different pronation types: neutral, overpronation, or supination. This is essential for choosing footwear that matches the runner's biomechanics."), + ("Usage Type", "Defines the primary purpose of the shoe, such as daily training, racing, trail running, or casual use, guiding its specific design and features."), + ("Gender", "Indicates whether the shoe is designed for men, women, or is a unisex model, considering anatomical and sizing differences."), + ("Available Sizes", "Specifies the range of sizes in which the shoe is offered, ensuring the runner can find a suitable fit."), + ("Width", "Some brands offer different widths (narrow, standard, wide) to accommodate various foot shapes."), + ("Additional Technologies", "Includes special features such as waterproofing, reflectivity, customized fit systems, or stability elements that enhance the shoe's functionality."), +] + +# Asumimos que spark está inicializado en el entorno +spark_df = spark.table("preprod_colombia.scraping_pp_adidas") +df_adidas = spark_df.toPandas() + +# Crear lista de productos +productos = [ + { + "id": row['id'], + "regularPrice" : row["regularPrice"], + "undiscounted_price": row["undiscounted_price"], + "details": row['details_transformado'], + "description": row['description'], + "category": row['category'], + "characteristics": row['characteristics'] + } + for _, row in df_adidas.iterrows() + if row['details_transformado'] != '{}' +] + +# Instanciar las clases +prompt_generator = PromptGenerator(labels_with_definitions) +model_inference = ModelInference() +response_processor = ResponseProcessor(labels_with_definitions) + +option_model = 2 +dfs = [] +for producto in tqdm(productos, desc="Procesando productos"): + user_message = prompt_generator.generate_prompt(producto) + respuesta = model_inference.obtener_respuesta(user_message, option_model) + df = response_processor.procesar_respuesta(respuesta) + + if df is not None: + attribute_columns = df.columns[:-3] # Ajustar según cuántas columnas sean las de atributos + df['id'] = producto['id'] + df['regularPrice'] = producto['regularPrice'] + df['undiscounted_price'] = producto['undiscounted_price'] + df["details"] = producto['details'] + df["description"] = producto['description'] + df["category"] = producto['category'] + df["characteristics"] = producto['characteristics'] + df = df[~df[attribute_columns].eq('---').all(axis=1)] + df = df.dropna(how='all') + dfs.append(df) + else: + print("No se pudo extraer la tabla.\n") + +if dfs: + df_total = pd.concat(dfs, ignore_index=True) + # Guardar df_total como Excel + os.makedirs("src/database", exist_ok=True) + df_total.to_excel("src/database/df_total.xlsx", index=False) +else: + print("Fail") diff --git a/src/comparative_analysis/preprocessing/main.py b/src/comparative_analysis/preprocessing/main.py index ceeb65785..61a5c4aa9 100644 --- a/src/comparative_analysis/preprocessing/main.py +++ b/src/comparative_analysis/preprocessing/main.py @@ -2,6 +2,7 @@ import re import pandas as pd import numpy as np +import spark from tqdm import tqdm from functions.prompt_generator import PromptGenerator @@ -25,8 +26,49 @@ ] # Asumimos que spark está inicializado en el entorno -spark_df = spark.table("preprod_colombia.scraping_pp_adidas") -df_adidas = spark_df.toPandas() +#spark_df = spark.table("preprod_colombia.scraping_pp_adidas") +# cargar dataset +path_i = r"..\\database\\" +#path_i = "/content/drive/MyDrive/MachineLearning_UNAL/datos/" +file_i = path_i + "raw_data.xlsx" +df_adidas = pd.read_excel(file_i) +df_adidas.head() +#df_adidas = spark_df.toPandas() + +def transformar_texto(texto, marca): + if texto is None or (isinstance(texto, float) and np.isnan(texto)): + return texto + + if marca.lower() == "adidas": + # Transformación original para adidas + if isinstance(texto, (list, np.ndarray)): + texto = ", ".join(map(str, texto)) + else: + texto = str(texto) + texto = texto.strip("[]") + texto = re.sub(r",\s*", '} {', texto) + texto = '{' + texto + '}' + return texto + + elif marca.lower() == "nike": + # Transformación específica para nike + if isinstance(texto, list): + # Elimina claves con valores irrelevantes + texto_limpio = [ + {k.strip(): v.strip() for k, v in d.items() if k.strip() not in ['\xa0']} + for d in texto + if isinstance(d, dict) + ] + # Filtra elementos vacíos o irrelevantes + texto_limpio = [d for d in texto_limpio if d] + return texto_limpio + return texto # Si no es lista, regresa el texto original + + else: + raise ValueError(f"Marca '{marca}' no reconocida. Usa 'adidas' o 'nike'.") + +# Lista de descripciones de productos +df_adidas['details_transformado'] = df_adidas['details'].apply(lambda x: transformar_texto(x, 'adidas')) # Crear lista de productos productos = [ @@ -39,7 +81,7 @@ "category": row['category'], "characteristics": row['characteristics'] } - for _, row in df_adidas.iterrows() + for _, row in df_adidas[:2].iterrows() if row['details_transformado'] != '{}' ] diff --git a/src/comparative_analysis/training/.ipynb_checkpoints/training-checkpoint.py b/src/comparative_analysis/training/.ipynb_checkpoints/training-checkpoint.py new file mode 100644 index 000000000..3a3ac99f7 --- /dev/null +++ b/src/comparative_analysis/training/.ipynb_checkpoints/training-checkpoint.py @@ -0,0 +1,97 @@ +import pandas as pd +import re +from sklearn.cluster import KMeans +from sklearn.metrics import silhouette_score, davies_bouldin_score +from sklearn.preprocessing import StandardScaler +import matplotlib.pyplot as plt + +# ** Carga y limpieza de datos ** +# Ruta del archivo de Excel +ruta_excel = r"C:\Users\cdgn2\OneDrive\Escritorio\Maestría\Maestria\Metodologias Agiles\Proyecto\Comparative-analysis-of-products\src\comparative_analysis\database\Adidas_etiquetado.xlsx" + +# Crear el DataFrame +df = pd.read_excel(ruta_excel, header=0) + +# Procesamiento de columnas numéricas +num_cols = { + 'Weight': '(\d+\.?\d*)', + 'Drop__heel-to-toe_differential_': '(\d+\.?\d*)' +} +for col, pattern in num_cols.items(): + df[col] = df[col].astype(str).str.extract(pattern).astype(float, errors='ignore') + df[col] = pd.to_numeric(df[col], errors='coerce') + +# Procesar precios +price_cols = ['regularPrice', 'undiscounted_price'] +for col in price_cols: + df[col] = df[col].astype(str).str.replace(r'[^0-9.,]', '', regex=True) + df[col] = df[col].str.replace(r'\.', '', regex=True).str.replace(',', '.') + df[col] = pd.to_numeric(df[col], errors='coerce') + +# Eliminar columnas innecesarias +cols_to_drop = ['details', 'description', 'category', 'characteristics', 'width', 'Pronation_Type'] +df = df.drop(columns=cols_to_drop, errors='ignore') + +# ** Clustering y evaluación ** +# Separar la columna ID +ids = df['id'] +X = df.drop(columns=['id'], errors='ignore').fillna(0) + +# Codificar variables categóricas +df_dummies = pd.get_dummies(X, dummy_na=True).fillna(0) + +# Escalar los datos +scaler = StandardScaler() +X_scaled = scaler.fit_transform(df_dummies) + +# Determinar el número óptimo de clusters usando el método del codo +def metodo_del_codo(X, k_range): + distortions = [] + for k in k_range: + kmeans = KMeans(n_clusters=k, random_state=42) + kmeans.fit(X) + distortions.append(kmeans.inertia_) + + plt.figure(figsize=(8, 5)) + plt.plot(k_range, distortions, 'bx-') + plt.xlabel('Número de clusters k') + plt.ylabel('Distorsión (Inercia)') + plt.title('Método del Codo para determinar k') + plt.show() + +# Mostrar el método del codo +metodo_del_codo(X_scaled, range(2, 11)) + +# Clustering con un número específico de clusters +k = 8 +kmeans = KMeans(n_clusters=k, random_state=42) +clusters = kmeans.fit_predict(X_scaled) + +# Agregar el cluster al DataFrame original +df['cluster'] = clusters + +# Evaluar calidad del clustering +sil_score = silhouette_score(X_scaled, clusters) +db_score = davies_bouldin_score(X_scaled, clusters) +print(f"Silhouette Score: {sil_score}") +print(f"Davies-Bouldin Score: {db_score}") + +# ** Análisis de clusters ** +def analizar_cluster(df, cluster_num): + elementos_cluster = df[df['cluster'] == cluster_num] + print(f"Elementos del cluster {cluster_num}:") + print(elementos_cluster.head()) + + print(f"\nEstadísticas descriptivas del cluster {cluster_num}:") + print(elementos_cluster.describe()) + + # Visualización de histogramas + columnas_relevantes = elementos_cluster.select_dtypes(include=['number']).columns + for col in columnas_relevantes: + elementos_cluster[col].plot(kind='hist', title=f"Distribución de {col} en cluster {cluster_num}") + plt.xlabel(col) + plt.ylabel('Frecuencia') + plt.show() + +# Ejemplo de análisis para un cluster específico +analizar_cluster(df, cluster_num=2) \ No newline at end of file From 725873ca0cdf3787fffc53b7fbed0dd3d437a69a Mon Sep 17 00:00:00 2001 From: azacipa <52643112+azacipa@users.noreply.github.com> Date: Wed, 18 Dec 2024 21:02:46 -0500 Subject: [PATCH 50/84] =?UTF-8?q?Se=20cargan=20archivos=20correspondientes?= =?UTF-8?q?=20a=20los=20modelos=20generados=20con=20sus=20graficos=20de=20?= =?UTF-8?q?desempe=C3=B1o?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../business_understanding/project_charter.md | 105 ++++++++++- docs/data/data_definition.md | 8 +- src/comparative_analysis/models/DBScan.py | 163 ++++++++++++++++++ src/comparative_analysis/models/K-Means.py | 109 ++++++++++++ .../training/trainingDBScan.py | 163 ++++++++++++++++++ .../{training.py => trainingK-Means.py} | 0 6 files changed, 535 insertions(+), 13 deletions(-) create mode 100644 src/comparative_analysis/models/DBScan.py create mode 100644 src/comparative_analysis/models/K-Means.py create mode 100644 src/comparative_analysis/training/trainingDBScan.py rename src/comparative_analysis/training/{training.py => trainingK-Means.py} (100%) diff --git a/docs/business_understanding/project_charter.md b/docs/business_understanding/project_charter.md index f6106e459..dde755f23 100644 --- a/docs/business_understanding/project_charter.md +++ b/docs/business_understanding/project_charter.md @@ -27,17 +27,103 @@ Se propone el uso de embeddings debido a que los datos disponibles consisten en ## Metodología -Se utilizarán las metodologías CRISP-DM y SCRUM para llevar a cabo el proyecto. +El proyecto seguirá una metodología híbrida basada en **CRISP-DM** (Cross-Industry Standard Process for Data Mining) para la estructura de las tareas técnicas y **SCRUM** para la gestión ágil del equipo y del cronograma. -## Cronograma +### Etapas según CRISP-DM: -| Etapa | Duración Estimada | Fechas | -|-----------------------------------------|-------------------|---------------------------------| -| Entendimiento del negocio y carga de datos | 2 semanas | Del 13 de noviembre al 28 de noviembre | -| Preprocesamiento y análisis exploratorio | 1 semana | Del 29 de noviembre al 5 de diciembre | -| Modelamiento y extracción de características | 1 semana | Del 5 de diciembre al 12 de diciembre | -| Despliegue | 1 semana | Del 13 de diciembre al 19 de diciembre | -| Evaluación y entrega final | 1 semana | Del 19 de diciembre al 21 de diciembre | +1. **Entendimiento del Negocio** + - Actividades: + - Identificar necesidades del cliente. + - Determinar los objetivos del análisis comparativo. + - Definir métricas clave para evaluar la precisión del modelo. + - Cronograma: 13 de noviembre al 28 de noviembre. + +2. **Entendimiento de los Datos** + - Actividades: + - Recopilar datos de Adidas, Nike y Nation Runner mediante scraping. + - Explorar las descripciones textuales y detectar posibles inconsistencias o valores faltantes. + - Validar la calidad de los datos. + - Cronograma: 13 de noviembre al 28 de noviembre. + +3. **Preparación de los Datos** + - Actividades: + - Realizar limpieza y preprocesamiento de las descripciones. + - Convertir datos textuales en representaciones vectoriales (embeddings). + - Dividir los datos en conjuntos de entrenamiento, validación y prueba. + - Cronograma: 29 de noviembre al 5 de diciembre. + +4. **Modelado** + - Actividades: + - Diseñar y entrenar un modelo de recomendación basado en similitud semántica. + - Optimizar hiperparámetros para maximizar el rendimiento del modelo. + - Cronograma: 5 de diciembre al 12 de diciembre. + +5. **Evaluación** + - Actividades: + - Validar el modelo con métricas como precisión, recall y F1-score. + - Realizar pruebas con datos nuevos para garantizar generalización. + - Cronograma: 19 de diciembre al 21 de diciembre. + +6. **Despliegue** + - Actividades: + - Integrar el modelo en una herramienta funcional. + - Documentar su uso y entrenar al equipo en su aplicación. + - Cronograma: 13 de diciembre al 19 de diciembre. + +### Gestión ágil con SCRUM: + +El desarrollo del proyecto se gestionará a través de iteraciones de una semana (sprints) para garantizar la flexibilidad y la adaptabilidad frente a posibles cambios en los requisitos. + +#### Roles del equipo: + +#### **Equipo y Responsabilidades** + +#### **1. Juan Correa (Product Owner y Líder Técnico)** +- **Responsabilidades compartidas con Daniel Galvis:** + - Definir y priorizar los requisitos del proyecto. + - Asegurar el alineamiento con los objetivos del cliente y del negocio. +- **Responsabilidades compartidas con Asdrúbal Zácipa Corredor:** + - Liderar las decisiones técnicas clave y supervisar el desarrollo general. + +#### **2. Daniel Galvis (Scrum Master y Desarrollador)** +- **Responsabilidades compartidas con Juan Correa:** + - Coordinar las ceremonias ágiles y facilitar la comunicación entre el equipo. + - Eliminar impedimentos que afecten el progreso del proyecto. +- **Responsabilidades compartidas con Asdrúbal Zácipa Corredor:** + - Colaborar en tareas de scraping, preprocesamiento y soporte técnico. + +#### **3. Asdrúbal Zácipa Corredor (Desarrollador y Especialista en Modelado)** +- **Responsabilidades compartidas con Juan Correa:** + - Diseñar, entrenar y evaluar el modelo de recomendación. + - Implementar el modelo y contribuir al desarrollo técnico del proyecto. +- **Responsabilidades compartidas con Daniel Galvis:** + - Asegurar la integración funcional de las soluciones desarrolladas. + +--- + +#### **Justificación de la Redistribución** + +Esta reorganización tiene como objetivo mitigar el riesgo operativo en caso de que algún integrante del equipo no pueda cumplir temporalmente con sus responsabilidades debido a enfermedad, emergencia u otros compromisos. Al asignar al menos dos personas a cada tarea, se asegura que el flujo de trabajo no se interrumpa y que el conocimiento clave del proyecto esté distribuido de manera uniforme entre los integrantes. + +Además, este enfoque fomenta la colaboración y la versatilidad, ya que todos los miembros se mantendrán actualizados sobre diferentes aspectos del proyecto, promoviendo una mayor resiliencia y adaptabilidad en el equipo. + + +#### Ceremonias: + +- **Sprint Planning:** Al inicio de cada sprint, se definirán las tareas clave y los entregables. +- **Daily Standup:** Reuniones diarias de 15 minutos para revisar el progreso y resolver bloqueos. +- **Sprint Review:** Al finalizar cada sprint, se presentarán los avances al equipo y se recopilará retroalimentación. +- **Sprint Retrospective:** Se analizarán las lecciones aprendidas y se identificarán áreas de mejora para futuros sprints. + +## Cronograma Integrado con Sprints + +| Sprint | Etapa | Actividades principales | Duración Estimada | Fechas | +|-------------------------|-----------------------------------------|------------------------------------------------|-------------------|---------------------------------| +| Sprint 1 | Entendimiento del negocio y carga de datos | Entendimiento del negocio y carga de datos | 2 semanas | Del 13 de noviembre al 28 de noviembre | +| Sprint 2 | Preprocesamiento y análisis exploratorio | Preprocesamiento y análisis exploratorio | 1 semana | Del 29 de noviembre al 5 de diciembre | +| Sprint 3 | Modelamiento y extracción de características | Modelamiento y extracción de características | 1 semana | Del 5 de diciembre al 12 de diciembre | +| Sprint 4 | Despliegue | Despliegue del modelo | 1 semana | Del 13 de diciembre al 19 de diciembre | +| Sprint 5 | Evaluación y entrega final | Evaluación final y entrega | 1 semana | Del 19 de diciembre al 21 de diciembre | ## Equipo del Proyecto @@ -58,6 +144,7 @@ Aunque no se cuenta con financiamiento externo, se estimaron los costos básicos | **Total** | - | - | - | **1,000,000** | ### Detalles: + - **Servicio de luz:** Incluye el costo estimado del consumo eléctrico asociado al trabajo en el proyecto. - **Servicio de internet:** Cubre el acceso a internet necesario para reuniones virtuales, investigación y uso de herramientas online. - **Uso de equipos personales:** Considera el desgaste de hardware y el consumo eléctrico de los equipos utilizados durante el desarrollo. diff --git a/docs/data/data_definition.md b/docs/data/data_definition.md index 22e4614b0..eebd3ec4d 100644 --- a/docs/data/data_definition.md +++ b/docs/data/data_definition.md @@ -4,11 +4,11 @@ Los datos se extrajeron mediante web scraping de las siguientes fuentes: -- **Adidas**: [https://www.adidas.co/](https://www.adidas.co/) -- **Nike**: [https://www.nike.com.co/](https://www.nike.com.co/) -- **Nation Runner**: [https://nacionrunner.com/](https://nacionrunner.com/) +- **Adidas**: [https://www.adidas.co/](https://www.adidas.co/) +- **Nike**: [https://www.nike.com.co/](https://www.nike.com.co/) +- **Nation Runner**: [https://nacionrunner.com/](https://nacionrunner.com/) -Posteriormente, los datos fueron almacenados en una API conectada a Firebase para su gestión y análisis posterior. Las descripciones de productos y características técnicas se estructuraron en un formato JSON para facilitar su manipulación. +Posteriormente, los datos fueron almacenados en una API conectada a Firebase para su gestión y análisis posterior. Las descripciones de productos y características técnicas se estructuraron en un formato JSON para facilitar su manipulación. Finalmente, estos datos fueron exportados a un archivo CSV, el cual se está utilizando como fuente principal en el proyecto para simplificar su integración y análisis en los diferentes procesos. ## Especificación de los scripts para la carga de datos diff --git a/src/comparative_analysis/models/DBScan.py b/src/comparative_analysis/models/DBScan.py new file mode 100644 index 000000000..58b7bde33 --- /dev/null +++ b/src/comparative_analysis/models/DBScan.py @@ -0,0 +1,163 @@ +import pandas as pd +import re +from sklearn.cluster import DBSCAN +from sklearn.metrics import silhouette_score, davies_bouldin_score +from sklearn.preprocessing import StandardScaler +import matplotlib.pyplot as plt +import numpy as np + +# ** Carga y limpieza de datos ** +ruta_excel = r"C:\\Users\\cdgn2\\OneDrive\\Escritorio\\Maestría\\Maestria\\Metodologias Agiles\\Proyecto\\Comparative-analysis-of-products\\src\\comparative_analysis\\database\\Adidas_etiquetado.xlsx" + +# Crear el DataFrame +df = pd.read_excel(ruta_excel, header=0, engine='openpyxl') + +# Procesamiento de columnas numéricas +num_cols = { + 'Weight': '(\d+\.?\d*)', + 'Drop__heel-to-toe_differential_': '(\d+\.?\d*)' +} +for col, pattern in num_cols.items(): + df[col] = df[col].astype(str).str.extract(pattern).astype(float, errors='ignore') + df[col] = pd.to_numeric(df[col], errors='coerce') + +# Procesar precios +price_cols = ['regularPrice', 'undiscounted_price'] +for col in price_cols: + df[col] = df[col].astype(str).str.replace(r'[^0-9.,]', '', regex=True) + df[col] = df[col].str.replace(r'\.', '', regex=True).str.replace(',', '.') + df[col] = pd.to_numeric(df[col], errors='coerce') + +# Eliminar columnas innecesarias +cols_to_drop = ['details', 'description', 'category', 'characteristics', 'width', 'Pronation_Type'] +df = df.drop(columns=cols_to_drop, errors='ignore') + +# ** Clustering y evaluación ** +ids = df['id'] +X = df.drop(columns=['id'], errors='ignore').fillna(0) + +# Codificar variables categóricas +df_dummies = pd.get_dummies(X, dummy_na=True).fillna(0) + +# Escalar los datos +scaler = StandardScaler() +X_scaled = scaler.fit_transform(df_dummies) + +print("Media X_scaled:", X_scaled.mean(axis=0)) +print("Desviación estándar X_scaled:", X_scaled.std(axis=0)) + +# Función para explorar parámetros de DBSCAN +def explorar_parametros_dbscan(X, eps_values, min_samples_values): + resultados = [] + for eps in eps_values: + for min_samples in min_samples_values: + dbscan = DBSCAN(eps=eps, min_samples=min_samples) + clusters = dbscan.fit_predict(X) + num_clusters = len(set(clusters)) - (1 if -1 in clusters else 0) + + if num_clusters > 1: # Evaluar solo si hay más de 1 cluster válido + sil_score = silhouette_score(X, clusters) + db_score = davies_bouldin_score(X, clusters) + resultados.append((eps, min_samples, num_clusters, sil_score, db_score)) + return resultados + +# Rango de parámetros a explorar +eps_values = np.arange(0.1, 3.0, 0.1) +min_samples_values = range(2, 20) + +# Explorar los parámetros +resultados = explorar_parametros_dbscan(X_scaled, eps_values, min_samples_values) + +# Mostrar los mejores parámetros según Silhouette Score +resultados_sorted = sorted(resultados, key=lambda x: x[3], reverse=True) +mejores_parametros = resultados_sorted[0] +print("\n** Mejores parámetros para DBSCAN **") +print(f"EPS: {mejores_parametros[0]}") +print(f"Min Samples: {mejores_parametros[1]}") +print(f"Número de Clusters: {mejores_parametros[2]}") +print(f"Silhouette Score: {mejores_parametros[3]}") +print(f"Davies-Bouldin Score: {mejores_parametros[4]}") + +# Aplicar DBSCAN con los mejores parámetros +dbscan = DBSCAN(eps=mejores_parametros[0], min_samples=mejores_parametros[1]) +clusters = dbscan.fit_predict(X_scaled) + +# Agregar los clusters al DataFrame +df['cluster'] = clusters + + +# ** Análisis de clusters para DBSCAN ** +def analizar_cluster_dbscan(df, cluster_num): + elementos_cluster = df[df['cluster'] == cluster_num] + print(f"\n=== Análisis del cluster {cluster_num} ===") + print(f"Total de elementos: {len(elementos_cluster)}") + + if len(elementos_cluster) == 0: + print("El cluster está vacío.") + return + + print(f"\nEstadísticas descriptivas principales:") + print(elementos_cluster.describe().loc[['mean', 'std', 'min', 'max']]) + + print(f"\nValores más frecuentes por columna categórica:") + cols_categoricas = elementos_cluster.select_dtypes(include=['object', 'category']).columns + for col in cols_categoricas: + if not elementos_cluster[col].dropna().empty: + modo = elementos_cluster[col].mode() + valor_modo = modo.values[0] if not modo.empty else "Sin valores frecuentes" + print(f" - {col}: {valor_modo}") + else: + print(f" - {col}: Sin datos disponibles") + + +# Analizar clusters generados +clusters_unicos = set(clusters) +clusters_unicos.discard(-1) # Ignorar ruido (-1) +print("\n** Análisis de Clusters **") +for cluster_num in clusters_unicos: + analizar_cluster_dbscan(df, cluster_num) + +# Visualización de distribución de clusters +def graficar_distribucion_clusters_dbscan(df): + plt.figure(figsize=(8, 5)) + df['cluster'].value_counts().sort_index().plot(kind='bar', color='skyblue', edgecolor='black') + plt.xlabel('Cluster') + plt.ylabel('Número de elementos') + plt.title('Distribución de elementos por cluster (DBSCAN)') + plt.xticks(rotation=0) + plt.show() + +print("\n** Distribución de Clusters **") +graficar_distribucion_clusters_dbscan(df) + +ruido = sum(clusters == -1) +total = len(clusters) +print(f"Puntos clasificados como ruido: {ruido}/{total} ({ruido/total:.2%})") + +from sklearn.decomposition import PCA + +pca = PCA(n_components=2) +X_pca = pca.fit_transform(X_scaled) + +plt.scatter(X_pca[:, 0], X_pca[:, 1], s=10, c=clusters, cmap='viridis', alpha=0.7) +plt.title('Proyección 2D de los datos escalados') +plt.xlabel('Componente principal 1') +plt.ylabel('Componente principal 2') +plt.colorbar(label='Cluster') +plt.show() + +if resultados: + resultados_sorted = sorted(resultados, key=lambda x: x[3], reverse=True) + mejores_parametros = resultados_sorted[0] + print("\n** Mejores parámetros para DBSCAN **") + print(f"EPS: {mejores_parametros[0]}") + print(f"Min Samples: {mejores_parametros[1]}") + print(f"Número de Clusters: {mejores_parametros[2]}") + print(f"Silhouette Score: {mejores_parametros[3]}") + print(f"Davies-Bouldin Score: {mejores_parametros[4]}") + + # Aplicar DBSCAN con los mejores parámetros + dbscan = DBSCAN(eps=mejores_parametros[0], min_samples=mejores_parametros[1]) + clusters = dbscan.fit_predict(X_scaled) +else: + print("No se encontraron parámetros válidos.") \ No newline at end of file diff --git a/src/comparative_analysis/models/K-Means.py b/src/comparative_analysis/models/K-Means.py new file mode 100644 index 000000000..728fb528a --- /dev/null +++ b/src/comparative_analysis/models/K-Means.py @@ -0,0 +1,109 @@ +import pandas as pd +import re +from sklearn.cluster import KMeans +from sklearn.metrics import silhouette_score, davies_bouldin_score +from sklearn.preprocessing import StandardScaler +import matplotlib.pyplot as plt + +# ** Carga y limpieza de datos ** +ruta_excel = r"C:\\Users\\cdgn2\\OneDrive\\Escritorio\\Maestría\\Maestria\\Metodologias Agiles\\Proyecto\\Comparative-analysis-of-products\\src\\comparative_analysis\\database\\Adidas_etiquetado.xlsx" + +# Crear el DataFrame +df = pd.read_excel(ruta_excel, header=0) + +# Procesamiento de columnas numéricas +num_cols = { + 'Weight': '(\d+\.?\d*)', + 'Drop__heel-to-toe_differential_': '(\d+\.?\d*)' +} +for col, pattern in num_cols.items(): + df[col] = df[col].astype(str).str.extract(pattern).astype(float, errors='ignore') + df[col] = pd.to_numeric(df[col], errors='coerce') + +# Procesar precios +price_cols = ['regularPrice', 'undiscounted_price'] +for col in price_cols: + df[col] = df[col].astype(str).str.replace(r'[^0-9.,]', '', regex=True) + df[col] = df[col].str.replace(r'\.', '', regex=True).str.replace(',', '.') + df[col] = pd.to_numeric(df[col], errors='coerce') + +# Eliminar columnas innecesarias +cols_to_drop = ['details', 'description', 'category', 'characteristics', 'width', 'Pronation_Type'] +df = df.drop(columns=cols_to_drop, errors='ignore') + +# ** Clustering y evaluación ** +ids = df['id'] +X = df.drop(columns=['id'], errors='ignore').fillna(0) + +# Codificar variables categóricas +df_dummies = pd.get_dummies(X, dummy_na=True).fillna(0) + +# Escalar los datos +scaler = StandardScaler() +X_scaled = scaler.fit_transform(df_dummies) + +# Método del codo para determinar el número óptimo de clusters +def metodo_del_codo(X, k_range): + distortions = [] + for k in k_range: + kmeans = KMeans(n_clusters=k, random_state=42) + kmeans.fit(X) + distortions.append(kmeans.inertia_) + + plt.figure(figsize=(8, 5)) + plt.plot(k_range, distortions, 'bx-') + plt.xlabel('Número de clusters k') + plt.ylabel('Distorsión (Inercia)') + plt.title('Método del Codo para determinar k') + plt.show() + +# Mostrar el método del codo +print("\n** Método del Codo para determinar el número óptimo de clusters **") +metodo_del_codo(X_scaled, range(2, 11)) + +# Clustering con un número específico de clusters +k = 8 +kmeans = KMeans(n_clusters=k, random_state=42) +clusters = kmeans.fit_predict(X_scaled) + +# Agregar el cluster al DataFrame original +df['cluster'] = clusters + +# Evaluar calidad del clustering +sil_score = silhouette_score(X_scaled, clusters) +db_score = davies_bouldin_score(X_scaled, clusters) +print(f"\n** Evaluación del clustering **") +print(f"Silhouette Score: {sil_score}") +print(f"Davies-Bouldin Score: {db_score}") + +# ** Análisis de clusters ** +def analizar_cluster(df, cluster_num): + elementos_cluster = df[df['cluster'] == cluster_num] + print(f"\n=== Análisis del cluster {cluster_num} ===") + print(f"Total de elementos: {len(elementos_cluster)}") + + print(f"\nEstadísticas descriptivas principales:") + print(elementos_cluster.describe().loc[['mean', 'std', 'min', 'max']]) + + print(f"\nValores más frecuentes por columna categórica:") + cols_categoricas = elementos_cluster.select_dtypes(include=['object', 'category']).columns + for col in cols_categoricas: + print(f" - {col}: {elementos_cluster[col].mode().values[0]}") + +# Analizar los clusters 2 y 3 +print("\n** Análisis de Clusters **") +analizar_cluster(df, cluster_num=2) +analizar_cluster(df, cluster_num=3) + +# Visualización de distribución de clusters +def graficar_distribucion_clusters(df): + plt.figure(figsize=(8, 5)) + df['cluster'].value_counts().sort_index().plot(kind='bar', color='skyblue', edgecolor='black') + plt.xlabel('Cluster') + plt.ylabel('Número de elementos') + plt.title('Distribución de elementos por cluster') + plt.xticks(rotation=0) + plt.show() + +print("\n** Distribución de Clusters **") +graficar_distribucion_clusters(df) \ No newline at end of file diff --git a/src/comparative_analysis/training/trainingDBScan.py b/src/comparative_analysis/training/trainingDBScan.py new file mode 100644 index 000000000..3281fe487 --- /dev/null +++ b/src/comparative_analysis/training/trainingDBScan.py @@ -0,0 +1,163 @@ +import pandas as pd +import re +from sklearn.cluster import DBSCAN +from sklearn.metrics import silhouette_score, davies_bouldin_score +from sklearn.preprocessing import StandardScaler +import matplotlib.pyplot as plt +import numpy as np + +# ** Carga y limpieza de datos ** +ruta_excel = r"C:\\Users\\cdgn2\\OneDrive\\Escritorio\\Maestría\\Maestria\\Metodologias Agiles\\Proyecto\\Comparative-analysis-of-products\\src\\comparative_analysis\\database\\Adidas_etiquetado.xlsx" + +# Crear el DataFrame +df = pd.read_excel(ruta_excel, header=0, engine='openpyxl') + +# Procesamiento de columnas numéricas +num_cols = { + 'Weight': '(\d+\.?\d*)', + 'Drop__heel-to-toe_differential_': '(\d+\.?\d*)' +} +for col, pattern in num_cols.items(): + df[col] = df[col].astype(str).str.extract(pattern).astype(float, errors='ignore') + df[col] = pd.to_numeric(df[col], errors='coerce') + +# Procesar precios +price_cols = ['regularPrice', 'undiscounted_price'] +for col in price_cols: + df[col] = df[col].astype(str).str.replace(r'[^0-9.,]', '', regex=True) + df[col] = df[col].str.replace(r'\.', '', regex=True).str.replace(',', '.') + df[col] = pd.to_numeric(df[col], errors='coerce') + +# Eliminar columnas innecesarias +cols_to_drop = ['details', 'description', 'category', 'characteristics', 'width', 'Pronation_Type'] +df = df.drop(columns=cols_to_drop, errors='ignore') + +# ** Clustering y evaluación ** +ids = df['id'] +X = df.drop(columns=['id'], errors='ignore').fillna(0) + +# Codificar variables categóricas +df_dummies = pd.get_dummies(X, dummy_na=True).fillna(0) + +# Escalar los datos +scaler = StandardScaler() +X_scaled = scaler.fit_transform(df_dummies) + +print("Media X_scaled:", X_scaled.mean(axis=0)) +print("Desviación estándar X_scaled:", X_scaled.std(axis=0)) + +# Función para explorar parámetros de DBSCAN +def explorar_parametros_dbscan(X, eps_values, min_samples_values): + resultados = [] + for eps in eps_values: + for min_samples in min_samples_values: + dbscan = DBSCAN(eps=eps, min_samples=min_samples) + clusters = dbscan.fit_predict(X) + num_clusters = len(set(clusters)) - (1 if -1 in clusters else 0) + + if num_clusters > 1: # Evaluar solo si hay más de 1 cluster válido + sil_score = silhouette_score(X, clusters) + db_score = davies_bouldin_score(X, clusters) + resultados.append((eps, min_samples, num_clusters, sil_score, db_score)) + return resultados + +# Rango de parámetros a explorar +eps_values = np.arange(0.5, 2.0, 0.1) +min_samples_values = range(3, 10) + +# Explorar los parámetros +resultados = explorar_parametros_dbscan(X_scaled, eps_values, min_samples_values) + +# Mostrar los mejores parámetros según Silhouette Score +resultados_sorted = sorted(resultados, key=lambda x: x[3], reverse=True) +mejores_parametros = resultados_sorted[0] +print("\n** Mejores parámetros para DBSCAN **") +print(f"EPS: {mejores_parametros[0]}") +print(f"Min Samples: {mejores_parametros[1]}") +print(f"Número de Clusters: {mejores_parametros[2]}") +print(f"Silhouette Score: {mejores_parametros[3]}") +print(f"Davies-Bouldin Score: {mejores_parametros[4]}") + +# Aplicar DBSCAN con los mejores parámetros +dbscan = DBSCAN(eps=mejores_parametros[0], min_samples=mejores_parametros[1]) +clusters = dbscan.fit_predict(X_scaled) + +# Agregar los clusters al DataFrame +df['cluster'] = clusters + + +# ** Análisis de clusters para DBSCAN ** +def analizar_cluster_dbscan(df, cluster_num): + elementos_cluster = df[df['cluster'] == cluster_num] + print(f"\n=== Análisis del cluster {cluster_num} ===") + print(f"Total de elementos: {len(elementos_cluster)}") + + if len(elementos_cluster) == 0: + print("El cluster está vacío.") + return + + print(f"\nEstadísticas descriptivas principales:") + print(elementos_cluster.describe().loc[['mean', 'std', 'min', 'max']]) + + print(f"\nValores más frecuentes por columna categórica:") + cols_categoricas = elementos_cluster.select_dtypes(include=['object', 'category']).columns + for col in cols_categoricas: + if not elementos_cluster[col].dropna().empty: + modo = elementos_cluster[col].mode() + valor_modo = modo.values[0] if not modo.empty else "Sin valores frecuentes" + print(f" - {col}: {valor_modo}") + else: + print(f" - {col}: Sin datos disponibles") + + +# Analizar clusters generados +clusters_unicos = set(clusters) +clusters_unicos.discard(-1) # Ignorar ruido (-1) +print("\n** Análisis de Clusters **") +for cluster_num in clusters_unicos: + analizar_cluster_dbscan(df, cluster_num) + +# Visualización de distribución de clusters +def graficar_distribucion_clusters_dbscan(df): + plt.figure(figsize=(8, 5)) + df['cluster'].value_counts().sort_index().plot(kind='bar', color='skyblue', edgecolor='black') + plt.xlabel('Cluster') + plt.ylabel('Número de elementos') + plt.title('Distribución de elementos por cluster (DBSCAN)') + plt.xticks(rotation=0) + plt.show() + +print("\n** Distribución de Clusters **") +graficar_distribucion_clusters_dbscan(df) + +ruido = sum(clusters == -1) +total = len(clusters) +print(f"Puntos clasificados como ruido: {ruido}/{total} ({ruido/total:.2%})") + +from sklearn.decomposition import PCA + +pca = PCA(n_components=2) +X_pca = pca.fit_transform(X_scaled) + +plt.scatter(X_pca[:, 0], X_pca[:, 1], s=10, c=clusters, cmap='viridis', alpha=0.7) +plt.title('Proyección 2D de los datos escalados') +plt.xlabel('Componente principal 1') +plt.ylabel('Componente principal 2') +plt.colorbar(label='Cluster') +plt.show() + +if resultados: + resultados_sorted = sorted(resultados, key=lambda x: x[3], reverse=True) + mejores_parametros = resultados_sorted[0] + print("\n** Mejores parámetros para DBSCAN **") + print(f"EPS: {mejores_parametros[0]}") + print(f"Min Samples: {mejores_parametros[1]}") + print(f"Número de Clusters: {mejores_parametros[2]}") + print(f"Silhouette Score: {mejores_parametros[3]}") + print(f"Davies-Bouldin Score: {mejores_parametros[4]}") + + # Aplicar DBSCAN con los mejores parámetros + dbscan = DBSCAN(eps=mejores_parametros[0], min_samples=mejores_parametros[1]) + clusters = dbscan.fit_predict(X_scaled) +else: + print("No se encontraron parámetros válidos.") \ No newline at end of file diff --git a/src/comparative_analysis/training/training.py b/src/comparative_analysis/training/trainingK-Means.py similarity index 100% rename from src/comparative_analysis/training/training.py rename to src/comparative_analysis/training/trainingK-Means.py From 77870eed03b41bbe907a6d3d434d9b76623b0ace Mon Sep 17 00:00:00 2001 From: jumcorrealo Date: Thu, 19 Dec 2024 22:33:38 -0500 Subject: [PATCH 51/84] Update project_charter.md images --- docs/business_understanding/project_charter.md | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/docs/business_understanding/project_charter.md b/docs/business_understanding/project_charter.md index dde755f23..67116b96b 100644 --- a/docs/business_understanding/project_charter.md +++ b/docs/business_understanding/project_charter.md @@ -8,6 +8,11 @@ Análisis comparativo de productos de Running entre Nike, Adidas y Nation Runner Desarrollar una herramienta computacional que permita realizar un análisis comparativo de zapatos para running, utilizando datos obtenidos de varias tiendas retail mediante técnicas de procesamiento de lenguaje natural y preprocesamiento de datos, incluyendo el uso de Grandes Modelos de Lenguaje (LLM, por sus siglas en inglés). +image +Img 1: Proceso del negocio manual para un análisis comparativo de productos + + + ## Alcance del Proyecto El alcance del proyecto consiste en construir un modelo de recomendación basado en embeddings, capaz de generar recomendaciones basadas en la similitud de atributos de calzado deportivo para running, alineados con las necesidades del cliente. @@ -160,4 +165,4 @@ Aunque no se cuenta con financiamiento externo, se estimaron los costos básicos - [Nombre y cargo del aprobador del proyecto] - [Firma del aprobador] -- [Fecha de aprobación] \ No newline at end of file +- [Fecha de aprobación] From 80f0b0e46af77a462e08d89ca05a6f3f3a28d25f Mon Sep 17 00:00:00 2001 From: jumcorrealo Date: Thu, 19 Dec 2024 22:33:52 -0500 Subject: [PATCH 52/84] Update project_charter.md --- docs/business_understanding/project_charter.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/business_understanding/project_charter.md b/docs/business_understanding/project_charter.md index 67116b96b..a1f9dbf0b 100644 --- a/docs/business_understanding/project_charter.md +++ b/docs/business_understanding/project_charter.md @@ -9,6 +9,7 @@ Análisis comparativo de productos de Running entre Nike, Adidas y Nation Runner Desarrollar una herramienta computacional que permita realizar un análisis comparativo de zapatos para running, utilizando datos obtenidos de varias tiendas retail mediante técnicas de procesamiento de lenguaje natural y preprocesamiento de datos, incluyendo el uso de Grandes Modelos de Lenguaje (LLM, por sus siglas en inglés). image + Img 1: Proceso del negocio manual para un análisis comparativo de productos From 2ab0b25943d6aa40a6ef251babd982df91657d29 Mon Sep 17 00:00:00 2001 From: jumcorrealo Date: Thu, 19 Dec 2024 22:40:38 -0500 Subject: [PATCH 53/84] Update deploymentdoc.md --- docs/deployment/deploymentdoc.md | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/docs/deployment/deploymentdoc.md b/docs/deployment/deploymentdoc.md index 330311ceb..182c4782a 100644 --- a/docs/deployment/deploymentdoc.md +++ b/docs/deployment/deploymentdoc.md @@ -16,6 +16,15 @@ ## Documentación del despliegue +![dataflow](https://github.com/user-attachments/assets/2f14053b-4d55-4ca5-8afd-05d1321add3a) + +Flujo de datos + + +![arquitectura (1)](https://github.com/user-attachments/assets/4565491e-0dc2-4345-948c-c88167d42f16) + +Arquitectura + - **Instrucciones de instalación:** (instrucciones detalladas para instalar el modelo en la plataforma de despliegue) - **Instrucciones de configuración:** (instrucciones detalladas para configurar el modelo en la plataforma de despliegue) - **Instrucciones de uso:** (instrucciones detalladas para utilizar el modelo en la plataforma de despliegue) From 07e0ab11ee6220df6c025bd0778e252f92c2e288 Mon Sep 17 00:00:00 2001 From: jumcorrealo Date: Thu, 19 Dec 2024 22:42:13 -0500 Subject: [PATCH 54/84] tests clustering --- docs/deployment/Clustering.ipynb | 3060 ++++++++++++++++++++++++++++++ 1 file changed, 3060 insertions(+) create mode 100644 docs/deployment/Clustering.ipynb diff --git a/docs/deployment/Clustering.ipynb b/docs/deployment/Clustering.ipynb new file mode 100644 index 000000000..f362b4e16 --- /dev/null +++ b/docs/deployment/Clustering.ipynb @@ -0,0 +1,3060 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "19c9ae5b-6e84-4ec3-8777-b85b2f92839e", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "import re\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# Preprocesamiento\n", + "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n", + "from sklearn.impute import SimpleImputer\n", + "\n", + "# Modelado\n", + "from sklearn.cluster import DBSCAN\n", + "from sklearn.cluster import KMeans\n", + "\n", + "# Embeddings\n", + "from sentence_transformers import SentenceTransformer\n", + "\n", + "# Reducción de dimensionalidad (opcional, pero recomendado para mejorar el rendimiento de DBSCAN)\n", + "from sklearn.decomposition import PCA\n", + "\n", + "# Para combinar diferentes tipos de features\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.pipeline import Pipeline\n", + "\n", + "# Para manejar matrices dispersas\n", + "from scipy.sparse import hstack\n", + "from scipy.sparse import csr_matrix\n", + "\n", + "# Evaluación\n", + "from sklearn.metrics import silhouette_score\n", + "\n", + "# Visualización\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.manifold import TSNE\n", + "\n", + "import pyspark.sql.functions as F\n", + "\n", + "from sklearn.base import BaseEstimator, TransformerMixin" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "bcde3eb7-f9b7-4caf-9a27-99ae8d119276", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "spark_df = spark.table(\"preprod_colombia.scraping_adidas_etiquetado\")\n", + "spark_df_dkt = spark.table(\"preprod_colombia.scraping_dkt_etiquetado_running\")" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "5bd4c26d-7f89-47ba-ada3-03624dac06ab", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "columns = ['Weight','Upper_Material','Midsole_Material','Outsole','Cushioning_System','Drop__heel-to-toe_differential_','regularPrice','undiscounted_price', 'Gender','Additional_Technologies',\"id\"]\n", + "columns_dkt = ['Weight','Upper_Material','Midsole_Material','Outsole','Cushioning_System','Drop__heel_to_toe_differential_','regularPrice','undiscounted_price', 'Gender','Additional_Technologies',\"model_code\"]\n", + "spark_df_selected = spark_df.select(columns)\n", + "spark_df_dkt_selected = spark_df_dkt.select(columns_dkt)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "ecc7987b-a9ae-436a-88d1-a1b8de22a510", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "spark_df_dkt_selected = spark_df_dkt_selected.withColumnRenamed(\"model_code\", \"id\")\n", + "spark_df_dkt_selected = spark_df_dkt_selected.withColumnRenamed(\"drop__heel_to_toe_differential_\", \"Drop__heel-to-toe_differential_\")\n", + "spark_df_dkt_selected = spark_df_dkt_selected.withColumn(\"Drop__heel-to-toe_differential_\",F.col(\"Drop__heel-to-toe_differential_\").cast(\"string\"))\n", + "spark_df_dkt_selected = spark_df_dkt_selected.withColumn(\"regularPrice\",F.col(\"regularPrice\").cast(\"int\"))\n", + "spark_df_dkt_selected = spark_df_dkt_selected.withColumn(\"undiscounted_price\",F.col(\"undiscounted_price\").cast(\"int\"))\n", + "spark_df_dkt_selected = spark_df_dkt_selected.withColumn(\n", + " \"Upper_Material\", \n", + " F.regexp_replace(F.col(\"Upper_Material\"), \"Poliéster\", \"\")\n", + ")\n", + "\n", + "spark_df_dkt_selected = spark_df_dkt_selected.withColumn(\n", + " \"Upper_Material\", \n", + " F.regexp_replace(F.col(\"Upper_Material\"), \"Poliuretano\", \"\")\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "608993b9-826d-4006-ae30-5288e124a35f", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "df_adidas = spark_df_selected.toPandas()\n", + "df_dkt = spark_df_dkt_selected.toPandas()\n", + "df = pd.concat([df_adidas, df_dkt], ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "867ef166-13f4-4146-bb6d-dde41a168195", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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WeightUpper_MaterialMidsole_MaterialOutsoleCushioning_SystemDrop__heel-to-toe_differential_regularPriceundiscounted_priceGenderAdditional_Technologiesid
469NaNPoliamida, ;EVAAcetato de etileno y viniloNone199000NaNHombre8803408
470251.0, termoplásticoEVACaucho sintético, Acetato de etileno y viniloflexibles (61 N/mm)4.0249000NaNHombreTranspirabilidad: Nueva tela mesh desarrollada...8670191
471295.0,Entresuela de espuma EVA y concepto de amortig...Caucho sintético, Acetato de etileno y vinilo10.0250000229000.0HombreSuela estriada de 3 mm para más agarre en cami...8488639
472366.0EVAAcetato de etileno y viniloflexibilidadNone139000NaNMujer8803078
473243.0,IMEVACaucho sintético, Acetato de etileno y viniloFlexibilidad4.0185000NaNHombre8757334
474295.0,Entresuela de espuma EVA y concepto de amortig...Caucho sintético, Acetato de etileno y vinilo10.0250000229000.0HombreAgarre: Suela estriada de 3 mm para más agarre...8767790
475251.0, termoplásticoEVACaucho sintético, Acetato de etileno y vinilotenis flexibles (61 N/mm)4.0249000NaNHombreLibertad de movimientos: Las hendiduras de fle...8670196
476NaNtermoplástico,EVACaucho sintético, Acetato de etileno y viniloFlex-HNone249000NaNMujerAdaptabilidad: Disponible en dos anchos de pie...8750403
477200.0,IMEVACaucho sintético, Acetato de etileno y viniloFlexibilidad4.0185000NaNMujer8757345
478180.0,Acetato de etileno y vinilo;10.08700079000.0Hombre8351755
479210.0,Suela de espumaCaucho sintético, Acetato de etileno y viniloFlexibilidad para mejorar el desarrollo del pie.4.0135000NaNMujerAdherencia: Refuerzo de caucho en la suela, en...8733475
480NaNEVAAcetato de etileno y viniloLa flexibilidad de la suela es ideal para todo...None169000NaNHombreTranspirabilidad: Su mesh 3D aireado permite q...8803079
481203.0, termoplásticoEVACaucho sintético, 70.0% Acetato de etileno y v...tenis flexibles (61 N/mm)4.0249000NaNMujerLas hendiduras de flexión acompañan la extensi...8670202
482235.0termoplástico, ,KALENSOLECaucho sintético, Acetato de etileno y viniloKALENSOLENone399000NaNMujertela mesh, más elástica.8772824
483205.0, termoplástico, ,MFOAMCaucho sintético, Acetato de etileno y viniloMFOAM6.0499000NaNMujerBuen agarre en piso mojado gracias a la textur...8772779
484216.0termoplástico, ;VFOAMCaucho sintético, Acetato de etileno y vinilo,...Pebax8.0399000240000.0mujergeometría de la suela de las KIPRUN KD500 2, c...8756260
485280.0termoplástico, ;KalensoleCaucho sintético, Acetato de etileno y viniloKalensole6.0309000NaNHombre8772865
486252.0, termoplástico,espuma MFOAMCaucho sintético, Acetato de etileno y viniloespuma MFOAM6.0499000NaNHombreAdherencia: Buen agarre en piso mojado gracias...8830204
487225.0,Caucho sintético, Carbono, Amida de bloque de ...8.0849000NaNHombreImpulso: Espuma Pebax® de Arkema y placa de ca...8666803
488218.0termoplástico,espuma Pebax® de Arkema.Caucho sintético, Amida de bloque de poliéter8.0699000599000.0HombreImpulso: Excelente retorno de energía gracias ...8798231
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" + ], + "text/plain": [ + " Weight ... id\n", + "469 NaN ... 8803408\n", + "470 251.0 ... 8670191\n", + "471 295.0 ... 8488639\n", + "472 366.0 ... 8803078\n", + "473 243.0 ... 8757334\n", + "474 295.0 ... 8767790\n", + "475 251.0 ... 8670196\n", + "476 NaN ... 8750403\n", + "477 200.0 ... 8757345\n", + "478 180.0 ... 8351755\n", + "479 210.0 ... 8733475\n", + "480 NaN ... 8803079\n", + "481 203.0 ... 8670202\n", + "482 235.0 ... 8772824\n", + "483 205.0 ... 8772779\n", + "484 216.0 ... 8756260\n", + "485 280.0 ... 8772865\n", + "486 252.0 ... 8830204\n", + "487 225.0 ... 8666803\n", + "488 218.0 ... 8798231\n", + "\n", + "[20 rows x 11 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.tail(20)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "1a02981c-9475-4bcb-adea-9692e3ce3819", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "source": [ + "## Funciones" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "605cc91a-a695-4dce-a870-9c7ac634a193", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "# Definir una función para combinar textos relevantes\n", + "def combine_text(row):\n", + " text_fields = categorical_cols\n", + " combined_text = ' '.join([str(row[field]) if pd.notnull(row[field]) else '' for field in text_fields])\n", + " return combined_text\n", + "\n", + "def preprocess_outsole(text):\n", + " if pd.isna(text):\n", + " return \"Desconocido\"\n", + " # Reemplazar porcentajes o valores numericos por una etiqueta genérica\n", + " text = re.sub(r'\\d+(\\.\\d+)?\\%?', 'X%', text)\n", + " # Unificar materiales\n", + " text = text.replace(\"Acetato de etileno y vinilo\", \"EVA\")\n", + " text = text.replace(\"Caucho sintético\", \"CauchoSintetico\")\n", + " # Quitar espacios extra\n", + " text = re.sub(r'\\s+', ' ', text).strip()\n", + " return text\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "b8594999-2770-46ec-83ed-1766c12305d5", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "source": [ + "## Procesamiento del dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "26c8c077-ba52-4485-b8cd-2477d0f7e597", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "#Remplaza los caracteres no numéricos\n", + "df['Weight'] = df['Weight'].apply(lambda x: re.sub(r'[^\\d.]', '', str(x)))\n", + "df['Drop__heel-to-toe_differential_'] = df['Drop__heel-to-toe_differential_'].apply(lambda x: re.sub(r'[^\\d.]', '', str(x)))\n", + "df['regularPrice'] = df['regularPrice'].apply(lambda x: re.sub(r'\\D', '', str(x)))\n", + "df['undiscounted_price'] = df['undiscounted_price'].apply(lambda x: re.sub(r'\\D', '', str(x)))" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "9a7fecad-6de2-4472-8e09-d4961eb94ab3", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "df['Weight'] = df['Weight'].replace('', np.nan)\n", + "df['Drop__heel-to-toe_differential_'] = df['Drop__heel-to-toe_differential_'].replace('', np.nan)\n", + "df['regularPrice'] = df['regularPrice'].replace('', np.nan)\n", + "df['undiscounted_price'] = df['undiscounted_price'].replace('', np.nan)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "771e495f-7a5b-4351-98fe-53922c6e7cd7", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "df['Weight'] = df['Weight'].astype(float)\n", + "df['Drop__heel-to-toe_differential_'] = df['Drop__heel-to-toe_differential_'].astype(float)\n", + "df['regularPrice'] = df['regularPrice'].astype(float)\n", + "df['undiscounted_price'] = df['undiscounted_price'].astype(float)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "dcc99e26-1ce7-4639-962e-32819c122bf9", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "df['percentil_discounted'] = 1-(df['undiscounted_price']/df['regularPrice'])" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "01a546ca-a735-4019-b003-a4a2fc8da694", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "df_reduced = df[['Weight','Upper_Material','Midsole_Material','Outsole','Cushioning_System','Drop__heel-to-toe_differential_','regularPrice','undiscounted_price','percentil_discounted', 'Gender','Additional_Technologies']]" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "31fe59c0-c9c5-459b-bf1c-610e7ff96d60", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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WeightUpper_MaterialMidsole_MaterialOutsoleCushioning_SystemDrop__heel-to-toe_differential_regularPriceundiscounted_pricepercentil_discountedGenderAdditional_Technologies
0183.0SyntheticnullTextile rubberLightstrike Pro6.01299950.0909965.00.300000WomanENERGYRODS 2.0, Waterproofing, Recyclable mate...
1289.0adidas PrimeknitBOOSTStretchweb with Continental Better RubberLinear Energy PushNaN799950.0NaNNaNWomanParley Ocean Plastic, waterproofing
2166.0Parte superior de malla técnicanullSuela de caucho Continental™Amortiguación Lightstrike Pro6.01049950.0629970.00.400000MujerContiene al menos un 20 % de material reciclad...
3200.0Parte superior de mallanullSuela de caucho Continental RubberAmortiguación Lightstrike Pro6.01049950.0734965.00.300000HombreVarillas ENERGYRODS, Talón Slinglaunch, Contie...
4319.0Parte superior textilMediasuela CloudfoamSuela de TPUCloudfoam6.0279950.0NaNNaNHombrenull
....................................
484216.0termoplástico, ;VFOAMCaucho sintético, Acetato de etileno y vinilo,...Pebax8.0399000.02400000.0-5.015038mujergeometría de la suela de las KIPRUN KD500 2, c...
485280.0termoplástico, ;KalensoleCaucho sintético, Acetato de etileno y viniloKalensole6.0309000.0NaNNaNHombre
486252.0, termoplástico,espuma MFOAMCaucho sintético, Acetato de etileno y viniloespuma MFOAM6.0499000.0NaNNaNHombreAdherencia: Buen agarre en piso mojado gracias...
487225.0,Caucho sintético, Carbono, Amida de bloque de ...8.0849000.0NaNNaNHombreImpulso: Espuma Pebax® de Arkema y placa de ca...
488218.0termoplástico,espuma Pebax® de Arkema.Caucho sintético, Amida de bloque de poliéter8.0699000.05990000.0-7.569385HombreImpulso: Excelente retorno de energía gracias ...
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489 rows × 11 columns

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" + ], + "text/plain": [ + " Weight ... Additional_Technologies\n", + "0 183.0 ... ENERGYRODS 2.0, Waterproofing, Recyclable mate...\n", + "1 289.0 ... Parley Ocean Plastic, waterproofing\n", + "2 166.0 ... Contiene al menos un 20 % de material reciclad...\n", + "3 200.0 ... Varillas ENERGYRODS, Talón Slinglaunch, Contie...\n", + "4 319.0 ... null\n", + ".. ... ... ...\n", + "484 216.0 ... geometría de la suela de las KIPRUN KD500 2, c...\n", + "485 280.0 ... \n", + "486 252.0 ... Adherencia: Buen agarre en piso mojado gracias...\n", + "487 225.0 ... Impulso: Espuma Pebax® de Arkema y placa de ca...\n", + "488 218.0 ... Impulso: Excelente retorno de energía gracias ...\n", + "\n", + "[489 rows x 11 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_reduced" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "cd5bc1e4-0b47-40de-b629-c7e47e446b19", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['Weight', 'Upper_Material', 'Midsole_Material', 'Outsole',\n", + " 'Cushioning_System', 'Drop__heel-to-toe_differential_', 'regularPrice',\n", + " 'undiscounted_price', 'percentil_discounted', 'Gender',\n", + " 'Additional_Technologies'],\n", + " dtype='object')" + ] + }, + "execution_count": 243, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_reduced.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "caa09e55-f642-4d32-b06c-96a8c7af0da8", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "output_type": "stream", + "text": [ + "/root/.ipykernel/1017/command-2628091764913739-2217468474:1: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n df_reduced['Outsole'] = df_reduced['Outsole'].apply(preprocess_outsole)\n" + ] + } + ], + "source": [ + "df_reduced['Outsole'] = df_reduced['Outsole'].apply(preprocess_outsole)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "0357324b-81b4-4974-90e9-e6232b0f8d1a", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "source": [ + "## Clustering" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "c6a33e05-a578-4ea5-aadc-008198155e57", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + " \n", + " # Identificar columnas numéricas y categóricas\n", + "numerical_cols = ['Drop__heel-to-toe_differential_','Weight', 'regularPrice','undiscounted_price','percentil_discounted']\n", + "categorical_cols = ['Midsole_Material', 'Cushioning_System', 'Outsole', 'Upper_Material', \n", + " 'Additional_Technologies', 'Gender']\n", + " \n", + "# Definir qué columnas numéricas se imputarán con mediana y escalado\n", + "numeric_impute_cols = ['regularPrice']\n", + "\n", + "# Definir columnas numéricas \"especiales\" que no se deben imputar con mediana\n", + "special_numeric_cols = ['Drop__heel-to-toe_differential_', 'percentil_discounted','Weight', 'undiscounted_price']\n", + "\n", + "###################################\n", + "# Transformador personalizado\n", + "###################################\n", + "class SpecialNumericToCategory(BaseEstimator, TransformerMixin):\n", + " \n", + " \"\"\"\n", + " Este transformador convierte las columnas numéricas \"especiales\" en categorías.\n", + " Por ejemplo:\n", + " - Si el valor es NaN, lo marca como \"NoValue\".\n", + " - Si tiene valor, lo convierte a una categoría del tipo \"Value:X\".\n", + " \"\"\"\n", + " def __init__(self, cols):\n", + " self.cols = cols\n", + " \n", + " def fit(self, X, y=None):\n", + " return self\n", + "\n", + " def transform(self, X):\n", + " X = X.copy()\n", + " for col in self.cols:\n", + " X[col] = X[col].apply(lambda val: 'NoValue' if pd.isna(val) else f'Value:{val}')\n", + " return X[self.cols]\n", + "\n", + "###################################\n", + "# Pipelines\n", + "###################################\n", + "\n", + "# Pipeline para columnas numéricas \"normales\"\n", + "numeric_pipeline = Pipeline(steps=[\n", + " ('imputer', SimpleImputer(strategy='median')), # Imputar con mediana\n", + " ('scaler', StandardScaler()) # Escalar a media=0, std=1\n", + "])\n", + "\n", + "# Pipeline para columnas numéricas \"especiales\", convertidas a categóricas\n", + "special_numeric_pipeline = Pipeline(steps=[\n", + " ('to_category', SpecialNumericToCategory(special_numeric_cols)),\n", + " ('imputer', SimpleImputer(strategy='constant', fill_value='Unknown')), \n", + " ('onehot', OneHotEncoder(handle_unknown='ignore'))\n", + "])\n", + "\n", + "# Pipeline para columnas categóricas normales\n", + "categorical_pipeline = Pipeline(steps=[\n", + " ('imputer', SimpleImputer(strategy='constant', fill_value='Unknown')),\n", + " ('onehot', OneHotEncoder(handle_unknown='ignore'))\n", + "])\n", + "\n", + "# Combinar todos los pipelines con ColumnTransformer\n", + "preprocessor = ColumnTransformer(transformers=[\n", + " ('num', numeric_pipeline, numeric_impute_cols),\n", + " ('special_num', special_numeric_pipeline, special_numeric_cols),\n", + " ('cat', categorical_pipeline, categorical_cols)\n", + "])\n", + "X_transformed = preprocessor.fit_transform(df_reduced)\n", + "feature_names = (preprocessor.named_transformers_['num'][-1].get_feature_names_out(numeric_impute_cols).tolist() \n", + " + preprocessor.named_transformers_['special_num'][-1].get_feature_names_out(special_numeric_cols).tolist()\n", + " + preprocessor.named_transformers_['cat'][-1].get_feature_names_out(categorical_cols).tolist())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "70b99eca-b229-47bf-a0c6-8f4eb86c230d", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "output_type": "stream", + "text": [ + "Preprocesando columnas numéricas...\nGenerando embeddings para columnas categóricas...\nConcatenando características numéricas y categóricas...\nAplicando PCA para reducir dimensiones...\nCalculando el Método del Codo para determinar el número óptimo de clusters...\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "output_type": "stream", + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculando el Silhouette Score para diferentes valores de k...\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "output_type": "stream", + "name": "stdout", + "output_type": "stream", + "text": [ + "El número óptimo de clusters según el Silhouette Score es: 13\nAplicando K-Means con k=13...\n\nNúmero de clusters encontrados: 13\nReduciendo dimensiones para visualización con t-SNE...\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "output_type": "stream", + "text": [ + "/databricks/python/lib/python3.10/site-packages/sklearn/manifold/_t_sne.py:795: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n warnings.warn(\n/databricks/python/lib/python3.10/site-packages/sklearn/manifold/_t_sne.py:805: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "output_type": "stream", + "name": "stdout", + "output_type": "stream", + "text": [ + "\nResumen de Clusters:\n3 80\n4 70\n1 62\n5 48\n10 45\n0 42\n6 38\n12 31\n7 28\n11 18\n9 16\n8 10\n2 1\nName: Cluster, dtype: int64\n" + ] + } + ], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.metrics import silhouette_score\n", + "from sklearn.manifold import TSNE\n", + "from sentence_transformers import SentenceTransformer\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Suponemos que ya tienes df, df_reduced, numerical_cols, categorical_cols definidos\n", + "# y que df_reduced contiene las columnas requeridas.\n", + "\n", + "model = SentenceTransformer('all-MiniLM-L6-v2')\n", + "\n", + "# Preprocesar columnas numéricas\n", + "print(\"Preprocesando columnas numéricas...\")\n", + "scaler = StandardScaler()\n", + "numerical_scaled = scaler.fit_transform(df_reduced[numerical_cols].fillna(0))\n", + "\n", + "# Generar embeddings para columnas categóricas\n", + "print(\"Generando embeddings para columnas categóricas...\")\n", + "categorical_embeddings = []\n", + "for col in categorical_cols:\n", + " embeddings = model.encode(df_reduced[col].fillna('Unknown').astype(str).tolist())\n", + " categorical_embeddings.append(embeddings)\n", + "\n", + "# Asignar importancia a las columnas categóricas según el orden dado\n", + "# Asumiendo el orden de categorical_cols:\n", + "# ['Midsole_Material', 'Cushioning_System', 'Additional_Technologies', 'Outsole', ...]\n", + "Midsole_Material_embeddings = categorical_embeddings[0] * 5.0 # 1ra prioridad\n", + "Cushioning_System_embeddings = categorical_embeddings[1] * 5.0 # 1ra prioridad\n", + "Additional_Technologies_embeddings = categorical_embeddings[2] * 2.0 # info adicional\n", + "Outsole_embeddings = categorical_embeddings[3] * 3.0 # 3ra prioridad\n", + "\n", + "# Otras columnas categóricas (si las hay)\n", + "if len(categorical_embeddings) > 4:\n", + " other_cat_embeddings = np.hstack(categorical_embeddings[4:])\n", + "else:\n", + " other_cat_embeddings = np.empty((len(df_reduced), 0))\n", + "\n", + "# Ajustar importancia en las variables numéricas:\n", + "# numerical_cols = ['Drop__heel-to-toe_differential_','Weight','regularPrice','undiscounted_price','percentil_discounted']\n", + "drop_idx = numerical_cols.index('Drop__heel-to-toe_differential_')\n", + "weight_idx = numerical_cols.index('Weight')\n", + "regular_price_idx = numerical_cols.index('regularPrice')\n", + "\n", + "# Aplicar factores\n", + "numerical_scaled[:, drop_idx] *= 4.0 # 2da prioridad\n", + "numerical_scaled[:, weight_idx] *= 2.0 # 5ta prioridad\n", + "numerical_scaled[:, regular_price_idx] *= 1.5 # 6ta prioridad\n", + "\n", + "# Combinar embeddings categóricos con sus factores\n", + "combined_categorical_embeddings = np.hstack([\n", + " Midsole_Material_embeddings,\n", + " Cushioning_System_embeddings,\n", + " Additional_Technologies_embeddings,\n", + " Outsole_embeddings,\n", + " other_cat_embeddings\n", + "])\n", + "\n", + "# Combinar características numéricas y categóricas sin volver a escalar,\n", + "# para no perder la ponderación manual\n", + "print(\"Concatenando características numéricas y categóricas...\")\n", + "combined_features = np.hstack([numerical_scaled, combined_categorical_embeddings])\n", + "\n", + "# Ahora aplicamos PCA, k-means, etc., sobre combined_features\n", + "print(\"Aplicando PCA para reducir dimensiones...\")\n", + "pca = PCA(n_components=400, random_state=42) # Ajusta n_components según necesidad\n", + "reduced_features = pca.fit_transform(combined_features)\n", + "\n", + "print(\"Calculando el Método del Codo para determinar el número óptimo de clusters...\")\n", + "wcss = []\n", + "k_values = range(10, 21) # Rango de k para explorar\n", + "\n", + "for k in k_values:\n", + " kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)\n", + " kmeans.fit(reduced_features)\n", + " wcss.append(kmeans.inertia_)\n", + "\n", + "# Visualizar el Método del Codo\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(k_values, wcss, marker='o')\n", + "plt.title('Método del Codo para Determinar el Número Óptimo de Clusters')\n", + "plt.xlabel('Número de Clusters (k)')\n", + "plt.ylabel('WCSS (Inercia)')\n", + "plt.xticks(k_values)\n", + "plt.grid(True)\n", + "plt.show()\n", + "\n", + "print(\"Calculando el Silhouette Score para diferentes valores de k...\")\n", + "silhouette_scores = []\n", + "\n", + "for k in k_values:\n", + " kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)\n", + " clusters = kmeans.fit_predict(reduced_features)\n", + " score = silhouette_score(reduced_features, clusters)\n", + " silhouette_scores.append(score)\n", + "\n", + "# Visualizar el Silhouette Score\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(k_values, silhouette_scores, marker='o', color='orange')\n", + "plt.title('Silhouette Score para Diferentes Valores de k')\n", + "plt.xlabel('Número de Clusters (k)')\n", + "plt.ylabel('Silhouette Score')\n", + "plt.xticks(k_values)\n", + "plt.grid(True)\n", + "plt.show()\n", + "\n", + "best_k = k_values[np.argmax(silhouette_scores)]\n", + "print(f\"El número óptimo de clusters según el Silhouette Score es: {best_k}\")\n", + "\n", + "print(f\"Aplicando K-Means con k={best_k}...\")\n", + "kmeans_final = KMeans(n_clusters=best_k, random_state=42, n_init=10)\n", + "clusters_final = kmeans_final.fit_predict(reduced_features)\n", + "\n", + "# Añadir etiquetas de cluster al DataFrame original\n", + "df['Cluster'] = clusters_final\n", + "\n", + "n_clusters_final = len(set(clusters_final))\n", + "print(f'\\nNúmero de clusters encontrados: {n_clusters_final}')\n", + "\n", + "print(\"Reduciendo dimensiones para visualización con t-SNE...\")\n", + "tsne = TSNE(n_components=2, random_state=42, perplexity=30, n_iter=1000)\n", + "tsne_results = tsne.fit_transform(reduced_features)\n", + "\n", + "df['tsne-2d-one'] = tsne_results[:, 0]\n", + "df['tsne-2d-two'] = tsne_results[:, 1]\n", + "\n", + "plt.figure(figsize=(12, 8))\n", + "palette = sns.color_palette(\"hsv\", n_clusters_final)\n", + "sns.scatterplot(\n", + " x=\"tsne-2d-one\", y=\"tsne-2d-two\",\n", + " hue=\"Cluster\",\n", + " palette=palette,\n", + " data=df,\n", + " legend=\"full\",\n", + " alpha=0.7\n", + ")\n", + "plt.title('Visualización de Clusters con t-SNE')\n", + "plt.xlabel('t-SNE 1')\n", + "plt.ylabel('t-SNE 2')\n", + "plt.legend(title='Cluster', bbox_to_anchor=(1.05, 1), loc='upper left')\n", + "plt.show()\n", + "\n", + "print(\"\\nResumen de Clusters:\")\n", + "print(df['Cluster'].value_counts())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "daf2d204-2dbb-4bf2-b7b8-573e94ce663b", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "output_type": "stream", + "text": [ + "Aplicando PCA para reducir dimensiones...\nCalculando el Método del Codo para determinar el número óptimo de clusters...\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "output_type": "stream", + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculando el Silhouette Score para diferentes valores de k...\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "output_type": "stream", + "name": "stdout", + "output_type": "stream", + "text": [ + "El número óptimo de clusters según el Silhouette Score es: 20\nAplicando K-Means con k=20...\n\nNúmero de clusters encontrados: 20\nReduciendo dimensiones para visualización con t-SNE...\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "output_type": "stream", + "name": "stdout", + "output_type": "stream", + "text": [ + "\nResumen de Clusters:\n5 49\n14 44\n8 38\n1 35\n2 31\n6 31\n19 30\n18 27\n7 27\n12 27\n4 25\n9 22\n10 20\n16 19\n15 15\n13 14\n17 13\n11 9\n3 9\n0 4\nName: Cluster, dtype: int64\n\nCluster 0:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n68 278.0 Exterior de malla técnica ... 19.815088 -10.885159\n74 NaN Exterior de malla ... 21.941719 -10.785176\n165 330.0 Exterior de malla técnica ... 20.837984 -10.885697\n282 330.0 Exterior de malla técnica ... 20.412170 -10.892855\n\n[4 rows x 15 columns]\n\nCluster 1:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n19 320.0 Parte superior textil ... -21.108912 -6.493203\n46 323.0 Parte superior de malla ... -15.119090 -1.197047\n47 364.0 Exterior textil ... -18.662340 -0.758213\n48 286.0 Parte superior textil ... -19.311958 -3.636598\n96 NaN Exterior de malla ... -12.407335 -4.614008\n\n[5 rows x 15 columns]\n\nCluster 2:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n10 275.0 Exterior de malla ... -6.962754 -21.797403\n13 NaN Exterior en tejido de malla ... -7.990644 -24.520367\n20 267.0 Parte superior de malla transpirable ... -10.867888 -24.307665\n45 239.0 Textil ... -22.723793 -15.788798\n52 275.0 Exterior de malla ... -4.509296 -22.302498\n\n[5 rows x 15 columns]\n\nCluster 3:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n5 NaN Malla Técnica Reciclada ... 17.060745 10.778292\n83 NaN Malla Técnica Reciclada ... 16.683235 10.083214\n180 NaN Malla Técnica Reciclada ... 16.605957 10.964303\n187 NaN Malla Técnica Reciclada ... 17.209896 11.870272\n189 NaN Malla Técnica Reciclada ... 17.400578 11.692134\n\n[5 rows x 15 columns]\n\nCluster 4:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n23 279.0 Parte superior de malla ... 24.868908 25.097902\n37 299.0 Exterior de malla ... 27.103107 24.626539\n71 279.0 Parte superior de malla ... 24.243345 24.382366\n78 310.0 Parte superior de malla ... 24.756914 24.047520\n97 310.0 Parte superior de malla ... 22.776726 23.787300\n\n[5 rows x 15 columns]\n\nCluster 5:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n25 268.0 Exterior en tejido de malla ... 9.414218 -11.095246\n33 NaN Exterior de malla ... 14.699205 -9.345591\n42 540.0 Exterior de malla técnica ... -0.360923 -1.651374\n55 295.0 Exterior de tejido adidas PRIMEKNIT ... 19.124922 -5.523604\n61 257.0 Exterior de malla con refuerzos de TPU ... 11.038683 -10.796019\n\n[5 rows x 15 columns]\n\nCluster 6:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n12 223.0 Synthetic ... 12.747667 27.678469\n17 295.0 Exterior en malla ... 16.390188 24.164738\n21 230.0 Exterior liviano en malla ... 14.460028 23.500111\n26 230.0 Exterior liviano en malla ... 13.874146 22.178349\n27 265.0 Parte superior de malla ... 17.188601 22.407335\n\n[5 rows x 15 columns]\n\nCluster 7:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n3 200.0 Parte superior de malla ... 1.755538 11.330632\n28 230.0 Exterior liviano en malla ... 0.703063 14.455057\n65 275.0 Exterior textil ... -23.018738 4.829057\n117 265.0 Parte superior de malla ... -0.186041 10.122837\n125 275.0 Exterior textil ... -22.996166 4.856834\n\n[5 rows x 15 columns]\n\nCluster 8:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n2 166.0 Parte superior de malla técnica ... 8.224779 10.782299\n8 NaN Exterior de malla con cuello acolchado ... 6.570870 6.397749\n11 238.0 Parte superior de malla ... 13.326708 9.309869\n24 230.0 Exterior liviano en malla ... 6.502467 13.272505\n38 NaN Parte superior de malla acolchada ... 9.434493 8.326558\n\n[5 rows x 15 columns]\n\nCluster 9:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n22 585.0 Exterior textil ... 5.375830 -1.062628\n43 540.0 malla técnica ... 1.160343 -2.220453\n54 260.0 Exterior textil ... 5.994472 -2.767516\n92 272.0 Parte superior textil ... 8.725078 -0.769287\n93 NaN Exterior de malla con cuello acolchado ... 1.027744 0.455712\n\n[5 rows x 15 columns]\n\nCluster 10:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n15 334.0 Exterior técnico de malla ... -5.793875 -10.719331\n76 277.0 Parte superior de malla acolchada ... -8.826781 -13.096778\n87 320.0 monomalla ... -3.203534 -17.623938\n89 320.0 Parte superior de monomalla ... -3.568880 -17.335764\n112 277.0 mesh ... -6.326353 -16.414343\n\n[5 rows x 15 columns]\n\nCluster 11:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n32 247.0 Parte superior de monomalla ... 17.916094 5.371136\n77 247.0 Monomalla ... 18.768627 4.620423\n136 247.0 monomalla ... 18.994259 4.688051\n185 247.0 monomalla ... 19.126192 5.276633\n262 230.0 Exterior Monomesh ... 10.349771 -20.242315\n\n[5 rows x 15 columns]\n\nCluster 12:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n7 213.0 Parte superior de malla ... 2.448739 -16.902372\n40 254.0 Monomalla ... -0.392282 -18.852966\n49 254.0 Forro interno textil ... 10.334534 -3.532351\n79 213.0 Parte superior de malla ... 2.740963 -16.275845\n85 242.0 Parte superior de malla ... 5.266422 -18.370689\n\n[5 rows x 15 columns]\n\nCluster 13:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n0 183.0 Synthetic ... 10.893925 27.395477\n1 289.0 adidas Primeknit ... 4.881872 22.404074\n44 278.0 Technical mesh ... -28.522972 -8.019142\n63 243.0 Exterior de malla acolchada ... -0.501422 -13.866938\n101 267.0 mesh ... -28.068459 -7.972044\n\n[5 rows x 15 columns]\n\nCluster 14:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n14 NaN Parte superior de malla ... -12.902549 1.474306\n30 270.0 Exterior de malla ... -7.744373 1.824502\n31 306.0 Parte superior de malla ... -10.323046 5.550931\n36 NaN Exterior de malla con cuello acolchado ... -6.863936 7.874070\n39 664.8 Exterior textil ... -10.836259 10.468987\n\n[5 rows x 15 columns]\n\nCluster 15:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n455 NaN termoplástico, ; . ... -7.866673 -1.848189\n456 203.0 mesh, , termoplástico ... -4.990084 -4.200123\n460 248.0 , termoplástico ... -4.633328 -3.072667\n464 240.0 , termoplástico, termoplástico ... -3.273659 0.947516\n465 NaN termoplástico, ... -16.431103 10.599090\n\n[5 rows x 15 columns]\n\nCluster 16:\n Weight Upper_Material Midsole_Material ... Cluster tsne-2d-one tsne-2d-two\n454 NaN , Cuero bovino ... 16 -20.825001 11.226727\n458 NaN EVA ... 16 -18.891546 14.067634\n459 169.0 , ... 16 -20.181263 13.110445\n461 315.0 , ; EVA ... 16 -16.950970 12.168805\n462 255.0 , ... 16 -19.486427 12.905893\n\n[5 rows x 15 columns]\n\nCluster 17:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n72 306.0 Parte superior de malla ... -18.173550 -12.906224\n95 286.0 Textil ... -23.084425 -12.398204\n122 286.0 Textil ... -22.423227 -13.125884\n181 343.0 Parte superior en tejido de malla ... -20.020899 -11.947750\n201 286.0 Textil ... -22.604563 -13.830274\n\n[5 rows x 15 columns]\n\nCluster 18:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n18 303.0 null ... 1.648412 -4.487958\n35 248.0 Exterior textil ... -0.826562 -7.918261\n51 292.0 adidas PRIMEKNIT ... 16.231256 -1.089768\n82 237.0 Exterior textil ... -0.378965 -7.250349\n115 216.0 Parte superior de malla técnica ... 11.755957 3.241534\n\n[5 rows x 15 columns]\n\nCluster 19:\n Weight ... tsne-2d-two\n4 319.0 ... -16.600161\n6 304.0 ... -15.279252\n9 334.0 ... 4.823231\n16 304.0 ... 3.862275\n34 304.0 ... -14.713323\n\n[5 rows x 15 columns]\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "# Reducción de dimensionalidad con PCA\n", + "print(\"Aplicando PCA para reducir dimensiones...\")\n", + "pca = PCA(n_components=489, random_state=42)\n", + "reduced_features = pca.fit_transform(scaled_features)\n", + "\n", + "# -------------------- Determinación del Número Óptimo de Clusters --------------------\n", + "print(\"Calculando el Método del Codo para determinar el número óptimo de clusters...\")\n", + "wcss = []\n", + "k_values = range(2, 21) # Rango de k para explorar\n", + "\n", + "for k in k_values:\n", + " kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)\n", + " kmeans.fit(reduced_features)\n", + " wcss.append(kmeans.inertia_)\n", + "\n", + "# Visualizar el Método del Codo\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(k_values, wcss, marker='o')\n", + "plt.title('Método del Codo para Determinar el Número Óptimo de Clusters')\n", + "plt.xlabel('Número de Clusters (k)')\n", + "plt.ylabel('WCSS (Inercia)')\n", + "plt.xticks(k_values)\n", + "plt.grid(True)\n", + "plt.show()\n", + "\n", + "# Silhouette Score\n", + "print(\"Calculando el Silhouette Score para diferentes valores de k...\")\n", + "silhouette_scores = []\n", + "\n", + "for k in k_values:\n", + " kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)\n", + " clusters = kmeans.fit_predict(reduced_features)\n", + " score = silhouette_score(reduced_features, clusters)\n", + " silhouette_scores.append(score)\n", + "\n", + "# Visualizar el Silhouette Score\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(k_values, silhouette_scores, marker='o', color='orange')\n", + "plt.title('Silhouette Score para Diferentes Valores de k')\n", + "plt.xlabel('Número de Clusters (k)')\n", + "plt.ylabel('Silhouette Score')\n", + "plt.xticks(k_values)\n", + "plt.grid(True)\n", + "plt.show()\n", + "\n", + "# Determinar el mejor k basado en el Silhouette Score\n", + "best_k = k_values[np.argmax(silhouette_scores)]\n", + "print(f\"El número óptimo de clusters según el Silhouette Score es: {best_k}\")\n", + "\n", + "# -------------------- Aplicar K-Means con el Número Óptimo de Clusters --------------------\n", + "print(f\"Aplicando K-Means con k={best_k}...\")\n", + "kmeans_final = KMeans(n_clusters=best_k, random_state=42, n_init=10)\n", + "clusters_final = kmeans_final.fit_predict(reduced_features)\n", + "\n", + "# Añadir etiquetas de cluster al DataFrame original\n", + "df['Cluster'] = clusters_final\n", + "\n", + "# Resumen final de clusters\n", + "n_clusters_final = len(set(clusters_final))\n", + "print(f'\\nNúmero de clusters encontrados: {n_clusters_final}')\n", + "\n", + "# -------------------- Visualización de los Clusters --------------------\n", + "print(\"Reduciendo dimensiones para visualización con t-SNE...\")\n", + "tsne = TSNE(n_components=2, random_state=42, perplexity=30, n_iter=1000)\n", + "tsne_results = tsne.fit_transform(reduced_features)\n", + "\n", + "df['tsne-2d-one'] = tsne_results[:, 0]\n", + "df['tsne-2d-two'] = tsne_results[:, 1]\n", + "\n", + "plt.figure(figsize=(12, 8))\n", + "palette = sns.color_palette(\"hsv\", n_clusters_final)\n", + "sns.scatterplot(\n", + " x=\"tsne-2d-one\", y=\"tsne-2d-two\",\n", + " hue=\"Cluster\",\n", + " palette=palette,\n", + " data=df,\n", + " legend=\"full\",\n", + " alpha=0.7\n", + ")\n", + "plt.title('Visualización de Clusters con t-SNE')\n", + "plt.xlabel('t-SNE 1')\n", + "plt.ylabel('t-SNE 2')\n", + "plt.legend(title='Cluster', bbox_to_anchor=(1.05, 1), loc='upper left')\n", + "plt.show()\n", + "\n", + "# -------------------- Análisis de Clusters --------------------\n", + "print(\"\\nResumen de Clusters:\")\n", + "print(df['Cluster'].value_counts())\n", + "\n", + "# Opcional: Ver registros en cada cluster\n", + "for cluster in range(n_clusters_final):\n", + " print(f'\\nCluster {cluster}:')\n", + " print(df[df['Cluster'] == cluster].head())\"\"\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "7bc44aab-490e-47b7-a4ed-483d26032ab5", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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WeightUpper_MaterialMidsole_MaterialOutsoleCushioning_SystemDrop__heel-to-toe_differential_regularPriceundiscounted_priceGenderAdditional_Technologiesidpercentil_discountedClustertsne-2d-onetsne-2d-two
459169.0,100.0 Acetato de etileno y vinilo10.087000.0790000.08351800-8.08046088.2911001.462351
460248.0, termoplásticoEVA70.0 Acetato de etileno y vinilo 30.0 CauchoEVA4.0330000.0NaNmujerLas hendiduras de flexión acompañan la extensi...8670212NaN016.4084996.210423
461315.0, ;EVA100.0 Caucho sintéticoEVA8.0299000.0NaNHombre8642811NaN016.4233956.361099
462255.0,60.0 Caucho sintético, 40.0 Acetato de etileno...10.0250000.02290000.0mujer8572326-8.16000087.7962931.183642
463NaN, .EVACaucho sintético, Acetato de etileno y viniloKalensole , CS (Circular System)10.0275000.0NaNHombre8666719NaN012.3348085.093338
464240.0, termoplástico, termoplásticoCaucho sintético, Acetato de etileno y viniloespuma Kalensole) y absorción impactos (CS).NaN426000.03090000.0Mujer8767800-6.25352186.8190941.120482
465NaNtermoplástico,EVACaucho sintético, Acetato de etileno y viniloNaN249000.0NaNHombre8759629NaN012.5556983.515450
466243.0,IMEVACaucho sintético, Acetato de etileno y viniloFlexibilidad4.0185000.0NaNHombre8757332NaN012.7504817.847188
467255.0,Entresuela de espuma EVA y concepto de amortig...Caucho sintético, Acetato de etileno y viniloEspumas gruesas en el talón y en la lengüeta.10.0250000.02290000.0MujerAgarre: Suela estriada 3 mm para mayor agarre ...8544265-8.16000087.923182-0.259708
468NaNEVACaucho sintético, 80.0% Acetato de etileno y v...FlexibilidadNaN199000.0NaNHombreAgarre: Gracias a las estrías y geometría de l...8803397NaN012.9298875.782633
469NaNPoliamida, ;EVAAcetato de etileno y viniloNaN199000.0NaNHombre8803408NaN012.2893243.397114
470251.0, termoplásticoEVACaucho sintético, Acetato de etileno y viniloflexibles (61 N/mm)4.0249000.0NaNHombreTranspirabilidad: Nueva tela mesh desarrollada...8670191NaN015.0184684.532474
471295.0,Entresuela de espuma EVA y concepto de amortig...Caucho sintético, Acetato de etileno y vinilo10.0250000.02290000.0HombreSuela estriada de 3 mm para más agarre en cami...8488639-8.16000087.8291120.046291
472366.0EVAAcetato de etileno y viniloflexibilidadNaN139000.0NaNMujer8803078NaN013.4003496.981575
473243.0,IMEVACaucho sintético, Acetato de etileno y viniloFlexibilidad4.0185000.0NaNHombre8757334NaN012.7505697.847071
474295.0,Entresuela de espuma EVA y concepto de amortig...Caucho sintético, Acetato de etileno y vinilo10.0250000.02290000.0HombreAgarre: Suela estriada de 3 mm para más agarre...8767790-8.16000087.8277690.047635
475251.0, termoplásticoEVACaucho sintético, Acetato de etileno y vinilotenis flexibles (61 N/mm)4.0249000.0NaNHombreLibertad de movimientos: Las hendiduras de fle...8670196NaN015.1024104.388563
476NaNtermoplástico,EVACaucho sintético, Acetato de etileno y viniloFlex-HNaN249000.0NaNMujerAdaptabilidad: Disponible en dos anchos de pie...8750403NaN013.4843074.651424
477200.0,IMEVACaucho sintético, Acetato de etileno y viniloFlexibilidad4.0185000.0NaNMujer8757345NaN012.7497627.856787
478180.0,Acetato de etileno y vinilo;10.087000.0790000.0Hombre8351755-8.08046088.2941701.447252
479210.0,Suela de espumaCaucho sintético, Acetato de etileno y viniloFlexibilidad para mejorar el desarrollo del pie.4.0135000.0NaNMujerAdherencia: Refuerzo de caucho en la suela, en...8733475NaN012.9493228.663089
480NaNEVAAcetato de etileno y viniloLa flexibilidad de la suela es ideal para todo...NaN169000.0NaNHombreTranspirabilidad: Su mesh 3D aireado permite q...8803079NaN012.7549235.676413
481203.0, termoplásticoEVACaucho sintético, 70.0% Acetato de etileno y v...tenis flexibles (61 N/mm)4.0249000.0NaNMujerLas hendiduras de flexión acompañan la extensi...8670202NaN015.0611804.392717
482235.0termoplástico, ,KALENSOLECaucho sintético, Acetato de etileno y viniloKALENSOLENaN399000.0NaNMujertela mesh, más elástica.8772824NaN0-17.9420076.408968
483205.0, termoplástico, ,MFOAMCaucho sintético, Acetato de etileno y viniloMFOAM6.0499000.0NaNMujerBuen agarre en piso mojado gracias a la textur...8772779NaN1-12.33320033.055355
484216.0termoplástico, ;VFOAMCaucho sintético, Acetato de etileno y vinilo,...Pebax8.0399000.02400000.0mujergeometría de la suela de las KIPRUN KD500 2, c...8756260-5.01503886.2822821.016790
485280.0termoplástico, ;KalensoleCaucho sintético, Acetato de etileno y viniloKalensole6.0309000.0NaNHombre8772865NaN0-17.9403346.414684
486252.0, termoplástico,espuma MFOAMCaucho sintético, Acetato de etileno y viniloespuma MFOAM6.0499000.0NaNHombreAdherencia: Buen agarre en piso mojado gracias...8830204NaN0-12.31372433.078747
487225.0,Caucho sintético, Carbono, Amida de bloque de ...8.0849000.0NaNHombreImpulso: Espuma Pebax® de Arkema y placa de ca...8666803NaN010.4167862.686541
488218.0termoplástico,espuma Pebax® de Arkema.Caucho sintético, Amida de bloque de poliéter8.0699000.05990000.0HombreImpulso: Excelente retorno de energía gracias ...8798231-7.56938586.781542-0.310688
\n", + "
" + ], + "text/plain": [ + " Weight Upper_Material ... tsne-2d-one tsne-2d-two\n", + "459 169.0 , ... 8.291100 1.462351\n", + "460 248.0 , termoplástico ... 16.408499 6.210423\n", + "461 315.0 , ; ... 16.423395 6.361099\n", + "462 255.0 , ... 7.796293 1.183642\n", + "463 NaN , . ... 12.334808 5.093338\n", + "464 240.0 , termoplástico, termoplástico ... 6.819094 1.120482\n", + "465 NaN termoplástico, ... 12.555698 3.515450\n", + "466 243.0 , ... 12.750481 7.847188\n", + "467 255.0 , ... 7.923182 -0.259708\n", + "468 NaN ... 12.929887 5.782633\n", + "469 NaN Poliamida, ; ... 12.289324 3.397114\n", + "470 251.0 , termoplástico ... 15.018468 4.532474\n", + "471 295.0 , ... 7.829112 0.046291\n", + "472 366.0 ... 13.400349 6.981575\n", + "473 243.0 , ... 12.750569 7.847071\n", + "474 295.0 , ... 7.827769 0.047635\n", + "475 251.0 , termoplástico ... 15.102410 4.388563\n", + "476 NaN termoplástico, ... 13.484307 4.651424\n", + "477 200.0 , ... 12.749762 7.856787\n", + "478 180.0 , ... 8.294170 1.447252\n", + "479 210.0 , ... 12.949322 8.663089\n", + "480 NaN ... 12.754923 5.676413\n", + "481 203.0 , termoplástico ... 15.061180 4.392717\n", + "482 235.0 termoplástico, , ... -17.942007 6.408968\n", + "483 205.0 , termoplástico, , ... -12.333200 33.055355\n", + "484 216.0 termoplástico, ; ... 6.282282 1.016790\n", + "485 280.0 termoplástico, ; ... -17.940334 6.414684\n", + "486 252.0 , termoplástico, ... -12.313724 33.078747\n", + "487 225.0 , ... 10.416786 2.686541\n", + "488 218.0 termoplástico, ... 6.781542 -0.310688\n", + "\n", + "[30 rows x 15 columns]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.tail(30)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "79d3ce3e-b470-4297-b973-07f95888a71f", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "output_type": "stream", + "text": [ + "/databricks/spark/python/pyspark/sql/pandas/conversion.py:401: UserWarning: createDataFrame attempted Arrow optimization because 'spark.sql.execution.arrow.pyspark.enabled' is set to true; however, failed by the reason below:\n Expected bytes, got a 'int' object\nAttempting non-optimization as 'spark.sql.execution.arrow.pyspark.fallback.enabled' is set to true.\n warn(msg)\n" + ] + } + ], + "source": [ + "spark_df_adidas = spark.createDataFrame(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "c7b73d63-1802-436f-830d-244db6ddf731", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "spark_df_adidas.write.mode(\"overwrite\").saveAsTable(\"preprod_colombia.scraping_adidas_dkt_clusters\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "0cf2cbda-19d0-4774-9699-ce83ae969123", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "source": [ + "## Clustering NIKE" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "65b5ec70-9e23-41b6-a4a2-5cd3d34f3777", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "spark_df_nr = spark.table(\"preprod_colombia.scraping_nacionrunner_etiquetado\")" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "dcdc7277-6135-4a76-b371-357e846d008c", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "spark_df_selected = spark_df_nr.select(['Weight',\n", + " 'Upper_Material',\n", + " 'Midsole_Material',\n", + " 'Outsole',\n", + " 'Cushioning_System',\n", + " 'Drop__heel-to-toe_differential_',\n", + " 'Pronation_Type',\n", + " 'Usage_Type',\n", + " 'Gender',\n", + " 'Width',\n", + " 'Additional_Technologies',\n", + " 'regularPrice',\n", + " 'undiscounted_price']\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "ce4ec8d9-0a4c-4ee5-9a08-21fbbf18f574", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "df_nr = spark_df_selected.toPandas()" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "b599d705-4ee7-478e-9598-ebbc85a946ab", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "#Remplaza los caracteres no numéricos\n", + "df_nr['Weight'] = df_nr['Weight'].apply(lambda x: re.sub(r'[^\\d.]', '', str(x)))\n", + "df_nr['Drop__heel-to-toe_differential_'] = df_nr['Drop__heel-to-toe_differential_'].apply(lambda x: re.sub(r'[^\\d.]', '', str(x)))\n", + "df_nr['regularPrice'] = df_nr['regularPrice'].apply(lambda x: re.sub(r'\\D', '', str(x)))\n", + "df_nr['undiscounted_price'] = df_nr['undiscounted_price'].apply(lambda x: re.sub(r'\\D', '', str(x)))" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "7cd09b49-0be3-4933-aafc-82e2e9909f28", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "df_nr['Weight'] = df_nr['Weight'].replace('', np.nan)\n", + "df_nr['Drop__heel-to-toe_differential_'] = df_nr['Drop__heel-to-toe_differential_'].replace('', np.nan)\n", + "df_nr['regularPrice'] = df_nr['regularPrice'].replace('', np.nan)\n", + "df_nr['undiscounted_price'] = df_nr['undiscounted_price'].replace('', np.nan)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "4fed4f5b-274e-4342-a3e3-f1c33fce5c2c", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "df_nr['Weight'] = df_nr['Weight'].astype(float)\n", + "df_nr['Drop__heel-to-toe_differential_'] = df_nr['Drop__heel-to-toe_differential_'].astype(float)\n", + "df_nr['regularPrice'] = df_nr['regularPrice'].astype(float)\n", + "df_nr['undiscounted_price'] = df_nr['undiscounted_price'].astype(float)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "49fd365e-5e42-4281-87d5-3abdb7813cf5", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "df_nr['percentil_discounted'] = 1-(df_nr['undiscounted_price']/df_nr['regularPrice'])" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "1cd243cc-19bc-4acb-a994-5604e61e26ae", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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WeightUpper_MaterialMidsole_MaterialOutsoleCushioning_SystemDrop__heel-to-toe_differential_Pronation_TypeUsage_TypeGenderWidthAdditional_TechnologiesregularPriceundiscounted_price
0242NeutroProFly+Asfaltonull5nullnullnullnullnull59990007499000
1WovenFlyteFoam Blast+ ECOAHARFlyteFoam Blast+ ECO8Neutro • SupinadorEntreno en pistanullnullWaterproofing, Reflectivity6999000
285Mesh técnicoFF Blast MaxASICS Grip y AHARPLUSFF Blast Max6Neutral or SupinatorDaily training, racingUnisexStandardWaterproofing, reflectivity8499000
3305nullFF BLAST PLUS ECOAsfalto4D Guidance System10nullDaily Training, RacingMennullWaterproofing, Reflectivity, Customized Fit Sy...8799000
4270Technical FabricsPureGelAsfaltoPureGel8PronadoresDaily TrainingMenStandardFlyteFoam, OrthoLite Hybrid Max, Waterproofing4999000
..........................................
18080Malla JacquardEVAAsfaltoMetaRocker8NeutralDaily trainingUnisexnullWaterproofing, Reflectivity6999000
181malla knitFlyteFoam BLAST PLUS ECO, PureGELASICSGRIP, caucho AHAR PLUSFlyteFoam BLAST PLUS ECO, PureGEL8Neutro, SupinadorDaily training, Racingnullnullnull64990008499000
182282AsfaltoJ-FrameAsfaltoJ-Frame5PronadorDaily trainingMennullWaterproofing, reflectivity, customized fit sy...7499000
183275234nullAmplifoam+AsfaltoEquilibrada y Versatilidad8Neutral, SupinadorDaily training, RacingnullBreathability, TranspirabilityNone3299000
184nullnullAsfaltoSuperior10PronadorDaily TrainingMennullnull69990007699000
\n", + "

185 rows × 13 columns

\n", + "
" + ], + "text/plain": [ + " Weight Upper_Material ... regularPrice undiscounted_price\n", + "0 242 Neutro ... 5999000 7499000\n", + "1 Woven ... 6999000 \n", + "2 85 Mesh técnico ... 8499000 \n", + "3 305 null ... 8799000 \n", + "4 270 Technical Fabrics ... 4999000 \n", + ".. ... ... ... ... ...\n", + "180 80 Malla Jacquard ... 6999000 \n", + "181 malla knit ... 6499000 8499000\n", + "182 282 Asfalto ... 7499000 \n", + "183 275234 null ... 3299000 \n", + "184 null ... 6999000 7699000\n", + "\n", + "[185 rows x 13 columns]" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_nr" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "b5f996d7-2228-482b-9d46-6dc0a3eb1eef", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['Weight', 'Upper_Material', 'Midsole_Material', 'Outsole',\n", + " 'Cushioning_System', 'Drop__heel-to-toe_differential_',\n", + " 'Pronation_Type', 'Usage_Type', 'Gender', 'Width',\n", + " 'Additional_Technologies', 'regularPrice', 'undiscounted_price'],\n", + " dtype='object')" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_nr.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "52ba5d71-54fa-452c-82e5-3f377e7cc5de", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "# Identificar columnas numéricas y categóricas\n", + "numerical_cols = ['Weight', 'Drop__heel-to-toe_differential_','regularPrice','undiscounted_price','percentil_discounted']\n", + "categorical_cols = ['Upper_Material', 'Midsole_Material', 'Outsole', 'Pronation_Type', 'Usage_Type', 'Gender', 'Width',\n", + " 'Cushioning_System', 'Additional_Technologies']" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "f96cb877-7fc3-446e-aaa4-eefd7f08dbb2", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "# Definir los pipelines de preprocesamiento\n", + "numerical_pipeline = Pipeline(steps=[\n", + " ('imputer', SimpleImputer(strategy='median')),\n", + " ('scaler', StandardScaler())\n", + "])\n", + "\n", + "categorical_pipeline = Pipeline(steps=[\n", + " ('imputer', SimpleImputer(strategy='constant', fill_value='Desconocido')),\n", + " ('onehot', OneHotEncoder(handle_unknown='ignore'))\n", + "])\n", + "\n", + "# Combinar pipelines\n", + "preprocessor = ColumnTransformer(transformers=[\n", + " ('num', numerical_pipeline, numerical_cols),\n", + " ('cat', categorical_pipeline, categorical_cols)\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "23a561f7-9e74-4c84-82a5-b872a702e66d", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "output_type": "stream", + "text": [ + "Preprocesando columnas numéricas...\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m\n", + "\u001B[0;31mNameError\u001B[0m Traceback (most recent call last)\n", + "File \u001B[0;32m, line 20\u001B[0m\n", + "\u001B[1;32m 18\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mPreprocesando columnas numéricas...\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", + "\u001B[1;32m 19\u001B[0m scaler \u001B[38;5;241m=\u001B[39m StandardScaler()\n", + "\u001B[0;32m---> 20\u001B[0m numerical_scaled \u001B[38;5;241m=\u001B[39m scaler\u001B[38;5;241m.\u001B[39mfit_transform(df_nr[numerical_cols]\u001B[38;5;241m.\u001B[39mfillna(\u001B[38;5;241m0\u001B[39m))\n", + "\u001B[1;32m 22\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mGenerando embeddings para columnas categóricas...\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", + "\u001B[1;32m 23\u001B[0m categorical_embeddings \u001B[38;5;241m=\u001B[39m []\n", + "\n", + "\u001B[0;31mNameError\u001B[0m: name 'df_nr' is not defined" + ] + }, + "metadata": { + "application/vnd.databricks.v1+output": { + "arguments": {}, + "data": "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m\n\u001B[0;31mNameError\u001B[0m Traceback (most recent call last)\nFile \u001B[0;32m, line 20\u001B[0m\n\u001B[1;32m 18\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mPreprocesando columnas numéricas...\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 19\u001B[0m scaler \u001B[38;5;241m=\u001B[39m StandardScaler()\n\u001B[0;32m---> 20\u001B[0m numerical_scaled \u001B[38;5;241m=\u001B[39m scaler\u001B[38;5;241m.\u001B[39mfit_transform(df_nr[numerical_cols]\u001B[38;5;241m.\u001B[39mfillna(\u001B[38;5;241m0\u001B[39m))\n\u001B[1;32m 22\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mGenerando embeddings para columnas categóricas...\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 23\u001B[0m categorical_embeddings \u001B[38;5;241m=\u001B[39m []\n\n\u001B[0;31mNameError\u001B[0m: name 'df_nr' is not defined", + "errorSummary": "NameError: name 'df_nr' is not defined", + "errorTraceType": "ansi", + "metadata": {}, + "type": "ipynbError" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.metrics import silhouette_score\n", + "from sklearn.manifold import TSNE\n", + "from sentence_transformers import SentenceTransformer\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Asumiendo que ya tienes: \n", + "# df_nr (DataFrame) con columnas numéricas en numerical_cols y categorías en categorical_cols\n", + "# df (DataFrame) que es el DataFrame base donde se añadirán los clusters\n", + "# model = SentenceTransformer('all-MiniLM-L6-v2') # si aún lo necesitas para las columnas categóricas\n", + "\n", + "# Preprocesar columnas numéricas y categóricas\n", + "print(\"Preprocesando columnas numéricas...\")\n", + "scaler = StandardScaler()\n", + "numerical_scaled = scaler.fit_transform(df_nr[numerical_cols].fillna(0))\n", + "\n", + "print(\"Generando embeddings para columnas categóricas...\")\n", + "categorical_embeddings = []\n", + "for col in categorical_cols:\n", + " embeddings = model.encode(df_nr[col].fillna('Unknown').astype(str).tolist())\n", + " categorical_embeddings.append(embeddings)\n", + "\n", + "combined_categorical_embeddings = np.hstack(categorical_embeddings)\n", + "\n", + "# Combinar características numéricas y categóricas\n", + "print(\"Concatenando características numéricas y categóricas...\")\n", + "combined_features = np.hstack([numerical_scaled, combined_categorical_embeddings])\n", + "\n", + "# Escalar las características combinadas\n", + "print(\"Escalando características combinadas...\")\n", + "scaled_features = scaler.fit_transform(combined_features)\n", + "\n", + "# Reducción de dimensionalidad con PCA\n", + "print(\"Aplicando PCA para reducir dimensiones...\")\n", + "pca = PCA(n_components=400, random_state=42)\n", + "reduced_features = pca.fit_transform(scaled_features)\n", + "\n", + "# -------------------- Determinación del Número Óptimo de Clusters --------------------\n", + "print(\"Calculando el Método del Codo para determinar el número óptimo de clusters...\")\n", + "wcss = []\n", + "k_values = range(2, 21) # Rango de k para explorar\n", + "\n", + "for k in k_values:\n", + " kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)\n", + " kmeans.fit(reduced_features)\n", + " wcss.append(kmeans.inertia_)\n", + "\n", + "# Visualizar el Método del Codo\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(k_values, wcss, marker='o')\n", + "plt.title('Método del Codo para Determinar el Número Óptimo de Clusters')\n", + "plt.xlabel('Número de Clusters (k)')\n", + "plt.ylabel('WCSS (Inercia)')\n", + "plt.xticks(k_values)\n", + "plt.grid(True)\n", + "plt.show()\n", + "\n", + "# Silhouette Score\n", + "print(\"Calculando el Silhouette Score para diferentes valores de k...\")\n", + "silhouette_scores = []\n", + "\n", + "for k in k_values:\n", + " kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)\n", + " clusters = kmeans.fit_predict(reduced_features)\n", + " score = silhouette_score(reduced_features, clusters)\n", + " silhouette_scores.append(score)\n", + "\n", + "# Visualizar el Silhouette Score\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(k_values, silhouette_scores, marker='o', color='orange')\n", + "plt.title('Silhouette Score para Diferentes Valores de k')\n", + "plt.xlabel('Número de Clusters (k)')\n", + "plt.ylabel('Silhouette Score')\n", + "plt.xticks(k_values)\n", + "plt.grid(True)\n", + "plt.show()\n", + "\n", + "# Determinar el mejor k basado en el Silhouette Score\n", + "best_k = k_values[np.argmax(silhouette_scores)]\n", + "print(f\"El número óptimo de clusters según el Silhouette Score es: {best_k}\")\n", + "\n", + "# -------------------- Aplicar K-Means con el Número Óptimo de Clusters --------------------\n", + "print(f\"Aplicando K-Means con k={best_k}...\")\n", + "kmeans_final = KMeans(n_clusters=best_k, random_state=42, n_init=10)\n", + "clusters_final = kmeans_final.fit_predict(reduced_features)\n", + "\n", + "# Añadir etiquetas de cluster al DataFrame original\n", + "df_nr['Cluster'] = clusters_final\n", + "\n", + "# Resumen final de clusters\n", + "n_clusters_final = len(set(clusters_final))\n", + "print(f'\\nNúmero de clusters encontrados: {n_clusters_final}')\n", + "\n", + "# -------------------- Visualización de los Clusters --------------------\n", + "print(\"Reduciendo dimensiones para visualización con t-SNE...\")\n", + "tsne = TSNE(n_components=2, random_state=42, perplexity=30, n_iter=1000)\n", + "tsne_results = tsne.fit_transform(reduced_features)\n", + "\n", + "df_nr['tsne-2d-one'] = tsne_results[:, 0]\n", + "df_nr['tsne-2d-two'] = tsne_results[:, 1]\n", + "\n", + "plt.figure(figsize=(12, 8))\n", + "palette = sns.color_palette(\"hsv\", n_clusters_final)\n", + "sns.scatterplot(\n", + " x=\"tsne-2d-one\", y=\"tsne-2d-two\",\n", + " hue=\"Cluster\",\n", + " palette=palette,\n", + " data=df_nr,\n", + " legend=\"full\",\n", + " alpha=0.7\n", + ")\n", + "plt.title('Visualización de Clusters con t-SNE')\n", + "plt.xlabel('t-SNE 1')\n", + "plt.ylabel('t-SNE 2')\n", + "plt.legend(title='Cluster', bbox_to_anchor=(1.05, 1), loc='upper left')\n", + "plt.show()\n", + "\n", + "# -------------------- Análisis de Clusters --------------------\n", + "print(\"\\nResumen de Clusters:\")\n", + "print(df_nr['Cluster'].value_counts())\n", + "\n", + "# Opcional: Ver registros en cada cluster\n", + "for cluster in range(n_clusters_final):\n", + " print(f'\\nCluster {cluster}:')\n", + " print(df_nr[df_nr['Cluster'] == cluster].head())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "3eef2a78-db14-45a5-bb35-abdd9363197d", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "spark_df_nr = spark.createDataFrame(df_nr)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "a010ca5a-7a89-4aa1-b237-cee64fe2f8a8", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "spark_df_nr.write.mode(\"overwrite\").saveAsTable(\"preprod_colombia.scraping_nr_clusters\")" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "cc9a01b0-0ceb-4b50-b8d0-7bd5af7cd620", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "application/vnd.databricks.v1+notebook": { + "computePreferences": null, + "dashboards": [], + "environmentMetadata": { + "base_environment": "", + "client": "1" + }, + "language": "python", + "notebookMetadata": { + "pythonIndentUnit": 4 + }, + "notebookName": "Clustering", + "widgets": {} + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file From e4eb8f66d16158410b0e9c20680599c9d5e33118 Mon Sep 17 00:00:00 2001 From: jumcorrealo Date: Thu, 19 Dec 2024 23:10:52 -0500 Subject: [PATCH 55/84] Update deploymentdoc.md --- docs/deployment/deploymentdoc.md | 221 +++++++++++++++++++++++++++++-- 1 file changed, 207 insertions(+), 14 deletions(-) diff --git a/docs/deployment/deploymentdoc.md b/docs/deployment/deploymentdoc.md index 182c4782a..92b3a4c64 100644 --- a/docs/deployment/deploymentdoc.md +++ b/docs/deployment/deploymentdoc.md @@ -2,30 +2,223 @@ ## Infraestructura -- **Nombre del modelo:** (nombre que se le ha dado al modelo) -- **Plataforma de despliegue:** (plataforma donde se va a desplegar el modelo) -- **Requisitos técnicos:** (lista de requisitos técnicos necesarios para el despliegue, como versión de Python, bibliotecas de terceros, hardware, etc.) -- **Requisitos de seguridad:** (lista de requisitos de seguridad necesarios para el despliegue, como autenticación, encriptación de datos, etc.) -- **Diagrama de arquitectura:** (imagen que muestra la arquitectura del sistema que se utilizará para desplegar el modelo) +- **Nombre del modelo:** Clasificador de productos +- **Plataforma de despliegue:** AWS +- **Requisitos técnicos:** +Librerías: torch, fairscale, fire, blobfile, pandas, requests, openpyxl, httpx, boto3, scikit-learn, matplotlib, flask (requirements.txt) +LLMs: AWS Bedrock o Llama 3.1, modelo de 70B de parámetros +Databricks: Ambiente conectado a Glue Storage + +- **Requisitos de seguridad:** En este momento la base de datos en firebase no requiere autenticación, pero se implementará en un futuro cercano. +- **Diagrama de arquitectura:** + +![arquitectura (1)](https://github.com/user-attachments/assets/4565491e-0dc2-4345-948c-c88167d42f16) + +Arquitectura ## Código de despliegue -- **Archivo principal:** (nombre del archivo principal que contiene el código de despliegue) -- **Rutas de acceso a los archivos:** (lista de rutas de acceso a los archivos necesarios para el despliegue) -- **Variables de entorno:** (lista de variables de entorno necesarias para el despliegue) +- **Archivo principal:** src/main.py +- **Rutas de acceso a los archivos:** https://scraping-firestore-178159629911.us-central1.run.app//v1/scraping/ +- **Variables de entorno:** + +En el despliegue en databricks se solicita: +ACCESS_TOKEN: Token de acceso generado por databricks +USERNAME: Usuario corporativo + ## Documentación del despliegue +**Flujo de datos** ![dataflow](https://github.com/user-attachments/assets/2f14053b-4d55-4ca5-8afd-05d1321add3a) Flujo de datos +### **Instrucciones de instalación** +Para iniciar un ambiente virtual e instalar las dependencias necesarias para la aplicación, sigue los siguientes pasos: -![arquitectura (1)](https://github.com/user-attachments/assets/4565491e-0dc2-4345-948c-c88167d42f16) +1. Asegúrate de tener Python 3.7 o superior instalado. +2. Crea un ambiente virtual: + ```bash + python -m venv venv + ``` +3. Activa el ambiente virtual: + - En Windows: + ```bash + venv\Scripts\activate + ``` + - En macOS/Linux: + ```bash + source venv/bin/activate + ``` +4. Instala las dependencias listadas en el archivo `requirements.txt`: + ```bash + pip install -r requirements.txt + ``` -Arquitectura +### **Instrucciones de configuración** +Para configurar el modelo en la plataforma de despliegue: + +1. Asegúrate de que los modelos entrenados estén ubicados en: + - `src\comparative_analysis\models\K-MeansV1\` + +2. Verifica que el archivo Excel requerido para la base de datos se encuentre en: + - `src/comparative_analysis/database/Adidas_etiquetado.xlsx` + +3. Configura el puerto de ejecución predeterminado (2626) si es necesario modificarlo, ajustando el archivo `main.py`. + +### **Instrucciones de uso** + +#### **Manual Técnico: Endpoints de la API REST** + +**Aplicación desarrollada en Flask.** + +1. **Endpoints disponibles:** + + - **GET /api/test** + - Descripción: Este endpoint es una prueba básica para verificar el funcionamiento del servidor. + - Respuesta esperada (200): + ``` + { + "message": "¡Hola! Esta es una respuesta GET de prueba.", + "status": "success" + } + ``` + + - **GET /api/product** + - Descripción: Permite obtener la información de un producto a partir de su ID. + - Parámetros: + - `id` (obligatorio): ID del producto a buscar. + - Ejemplo de solicitud: + ``` + GET http://127.0.0.1:2626/api/product?id=08sjncACSjSvg2t9DS73 + ``` + - Respuesta esperada (200): + ``` + { + "Additional_Technologies": "ENERGYRODS 2.0, Waterproofing, Recyclable material", + "Available_Sizes": NaN, + "Cushioning_System": "Lightstrike Pro", + "Drop__heel-to-toe_differential_": "6 mm", + "Gender": "Woman", + "Midsole_Material": NaN, + "Outsole": "Textile rubber", + "Pronation_Type": NaN, + "Upper_Material": "Synthetic", + "Usage_Type": "Racing", + "Weight": "183 g", + "Width": NaN, + "category": "Mujer • Running", + "characteristics": NaN, + "description": "Los Adizero Adios Pro 3 son la máxima expresión de los productos Adidas...", + "id": "08sjncACSjSvg2t9DS73", + ... + } + ``` + - Errores posibles: + - 400: Si no se proporciona el ID del producto. + - 404: Si el producto no se encuentra. + + - **POST /api/products** + - Descripción: Busca productos que coincidan con los parámetros proporcionados. + - Cuerpo de la solicitud: + ``` + { + "key1": "value1", + "key2": "value2", + ... + } + ``` + - Ejemplo de solicitud: + ``` + POST http://127.0.0.1:2626/api/products + { + "category": "Mujer • Running", + "Cushioning_System": "Lightstrike Pro" + } + ``` + - Respuesta esperada (200): + ``` + [ + { + "id": "08sjncACSjSvg2t9DS73", + "category": "Mujer • Running", + ... + }, + ... + ] + ``` + - Errores posibles: + - 400: Si no se proporcionan parámetros. + - 404: Si no se encuentran productos que coincidan con los parámetros. + + - **POST /predict/KMeansV1** + - Descripción: Realiza una predicción utilizando un modelo K-Means para asignar un producto a un cluster y devuelve productos similares en el mismo cluster. + - Cuerpo de la solicitud: + ``` + { + "key1": "value1", + "key2": "value2", + ... + } + ``` + - Ejemplo de solicitud: + ``` + POST http://127.0.0.1:2626/predict/KMeansV1 + { + "category": "Mujer • Running", + "Cushioning_System": "Lightstrike Pro", + ... + } + ``` + - Respuesta esperada (200): + ``` + [ + { + "id": "08sjncACSjSvg2t9DS73", + "category": "Mujer • Running", + ... + }, + ... + ] + ``` + - Errores posibles: + - 400: Si no se envían datos para la predicción. + - 500: Si ocurre un error interno. + +2. **Notas técnicas:** + - La API utiliza pandas para manipular datos en formato Excel y pickle para cargar los modelos entrenados. + - Todos los modelos (encoder, scaler y KMeans) deben estar en la ruta `src\comparative_analysis\models\K-MeansV1\`. + - El archivo Excel utilizado debe estar en `src/comparative_analysis/database/Adidas_etiquetado.xlsx`. + - El puerto de ejecución por defecto es el 2626. + +3. **Requerimientos:** + - Python 3.7 o superior. + - Flask. + - Pandas. + - Re. + - Pickle. + +4. **Cómo iniciar la aplicación:** + Ejecuta el archivo `main.py`: + ``` + python main.py + ``` + +### **Instrucciones de mantenimiento** +1. **Actualización del modelo:** + - Sustituye los archivos del modelo en la ruta `src\comparative_analysis\models\K-MeansV1\` con las versiones actualizadas. + - Reinicia el servidor para cargar los nuevos modelos. + +2. **Actualización de la base de datos:** + - Sustituye el archivo `Adidas_etiquetado.xlsx` en la ruta `src/comparative_analysis/database/`. + - Asegúrate de que el formato del archivo sea compatible con las funciones existentes. + +3. **Monitoreo del servidor:** + - Verifica los logs para identificar errores o problemas de rendimiento. + - Asegúrate de que el puerto de ejecución (2626) esté disponible y no haya conflictos. + +4. **Resolución de errores:** + - Consulta los mensajes de error en el log para diagnosticar problemas. + - Realiza pruebas con los endpoints usando herramientas como Postman o cURL para verificar su correcto funcionamiento. -- **Instrucciones de instalación:** (instrucciones detalladas para instalar el modelo en la plataforma de despliegue) -- **Instrucciones de configuración:** (instrucciones detalladas para configurar el modelo en la plataforma de despliegue) -- **Instrucciones de uso:** (instrucciones detalladas para utilizar el modelo en la plataforma de despliegue) -- **Instrucciones de mantenimiento:** (instrucciones detalladas para mantener el modelo en la plataforma de despliegue) From 14c066051ca809c15c5f095ad5f9e2f5a2514872 Mon Sep 17 00:00:00 2001 From: CarlosKC26 Date: Thu, 19 Dec 2024 23:31:45 -0500 Subject: [PATCH 56/84] cambios kmeansv2 y red neuronal --- .../{__init__.py => main.py} | 0 .../models/K-MeansV2/Clustering.ipynb | 3060 +++++++++++++++++ .../models/K-MeansV2/K-Means.py | 284 ++ .../models/K-MeansV2/message.txt | 111 + .../models/RedNeuronal/test.py | 10 + .../models/RedNeuronal/train.py | 118 + 6 files changed, 3583 insertions(+) rename src/comparative_analysis/{__init__.py => main.py} (100%) create mode 100644 src/comparative_analysis/models/K-MeansV2/Clustering.ipynb create mode 100644 src/comparative_analysis/models/K-MeansV2/K-Means.py create mode 100644 src/comparative_analysis/models/K-MeansV2/message.txt create mode 100644 src/comparative_analysis/models/RedNeuronal/test.py create mode 100644 src/comparative_analysis/models/RedNeuronal/train.py diff --git a/src/comparative_analysis/__init__.py b/src/comparative_analysis/main.py similarity index 100% rename from src/comparative_analysis/__init__.py rename to src/comparative_analysis/main.py diff --git a/src/comparative_analysis/models/K-MeansV2/Clustering.ipynb b/src/comparative_analysis/models/K-MeansV2/Clustering.ipynb new file mode 100644 index 000000000..f362b4e16 --- /dev/null +++ b/src/comparative_analysis/models/K-MeansV2/Clustering.ipynb @@ -0,0 +1,3060 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "19c9ae5b-6e84-4ec3-8777-b85b2f92839e", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "import re\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# Preprocesamiento\n", + "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n", + "from sklearn.impute import SimpleImputer\n", + "\n", + "# Modelado\n", + "from sklearn.cluster import DBSCAN\n", + "from sklearn.cluster import KMeans\n", + "\n", + "# Embeddings\n", + "from sentence_transformers import SentenceTransformer\n", + "\n", + "# Reducción de dimensionalidad (opcional, pero recomendado para mejorar el rendimiento de DBSCAN)\n", + "from sklearn.decomposition import PCA\n", + "\n", + "# Para combinar diferentes tipos de features\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.pipeline import Pipeline\n", + "\n", + "# Para manejar matrices dispersas\n", + "from scipy.sparse import hstack\n", + "from scipy.sparse import csr_matrix\n", + "\n", + "# Evaluación\n", + "from sklearn.metrics import silhouette_score\n", + "\n", + "# Visualización\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.manifold import TSNE\n", + "\n", + "import pyspark.sql.functions as F\n", + "\n", + "from sklearn.base import BaseEstimator, TransformerMixin" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "bcde3eb7-f9b7-4caf-9a27-99ae8d119276", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "spark_df = spark.table(\"preprod_colombia.scraping_adidas_etiquetado\")\n", + "spark_df_dkt = spark.table(\"preprod_colombia.scraping_dkt_etiquetado_running\")" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "5bd4c26d-7f89-47ba-ada3-03624dac06ab", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "columns = ['Weight','Upper_Material','Midsole_Material','Outsole','Cushioning_System','Drop__heel-to-toe_differential_','regularPrice','undiscounted_price', 'Gender','Additional_Technologies',\"id\"]\n", + "columns_dkt = ['Weight','Upper_Material','Midsole_Material','Outsole','Cushioning_System','Drop__heel_to_toe_differential_','regularPrice','undiscounted_price', 'Gender','Additional_Technologies',\"model_code\"]\n", + "spark_df_selected = spark_df.select(columns)\n", + "spark_df_dkt_selected = spark_df_dkt.select(columns_dkt)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "ecc7987b-a9ae-436a-88d1-a1b8de22a510", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "spark_df_dkt_selected = spark_df_dkt_selected.withColumnRenamed(\"model_code\", \"id\")\n", + "spark_df_dkt_selected = spark_df_dkt_selected.withColumnRenamed(\"drop__heel_to_toe_differential_\", \"Drop__heel-to-toe_differential_\")\n", + "spark_df_dkt_selected = spark_df_dkt_selected.withColumn(\"Drop__heel-to-toe_differential_\",F.col(\"Drop__heel-to-toe_differential_\").cast(\"string\"))\n", + "spark_df_dkt_selected = spark_df_dkt_selected.withColumn(\"regularPrice\",F.col(\"regularPrice\").cast(\"int\"))\n", + "spark_df_dkt_selected = spark_df_dkt_selected.withColumn(\"undiscounted_price\",F.col(\"undiscounted_price\").cast(\"int\"))\n", + "spark_df_dkt_selected = spark_df_dkt_selected.withColumn(\n", + " \"Upper_Material\", \n", + " F.regexp_replace(F.col(\"Upper_Material\"), \"Poliéster\", \"\")\n", + ")\n", + "\n", + "spark_df_dkt_selected = spark_df_dkt_selected.withColumn(\n", + " \"Upper_Material\", \n", + " F.regexp_replace(F.col(\"Upper_Material\"), \"Poliuretano\", \"\")\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "608993b9-826d-4006-ae30-5288e124a35f", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "df_adidas = spark_df_selected.toPandas()\n", + "df_dkt = spark_df_dkt_selected.toPandas()\n", + "df = pd.concat([df_adidas, df_dkt], ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "867ef166-13f4-4146-bb6d-dde41a168195", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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WeightUpper_MaterialMidsole_MaterialOutsoleCushioning_SystemDrop__heel-to-toe_differential_regularPriceundiscounted_priceGenderAdditional_Technologiesid
469NaNPoliamida, ;EVAAcetato de etileno y viniloNone199000NaNHombre8803408
470251.0, termoplásticoEVACaucho sintético, Acetato de etileno y viniloflexibles (61 N/mm)4.0249000NaNHombreTranspirabilidad: Nueva tela mesh desarrollada...8670191
471295.0,Entresuela de espuma EVA y concepto de amortig...Caucho sintético, Acetato de etileno y vinilo10.0250000229000.0HombreSuela estriada de 3 mm para más agarre en cami...8488639
472366.0EVAAcetato de etileno y viniloflexibilidadNone139000NaNMujer8803078
473243.0,IMEVACaucho sintético, Acetato de etileno y viniloFlexibilidad4.0185000NaNHombre8757334
474295.0,Entresuela de espuma EVA y concepto de amortig...Caucho sintético, Acetato de etileno y vinilo10.0250000229000.0HombreAgarre: Suela estriada de 3 mm para más agarre...8767790
475251.0, termoplásticoEVACaucho sintético, Acetato de etileno y vinilotenis flexibles (61 N/mm)4.0249000NaNHombreLibertad de movimientos: Las hendiduras de fle...8670196
476NaNtermoplástico,EVACaucho sintético, Acetato de etileno y viniloFlex-HNone249000NaNMujerAdaptabilidad: Disponible en dos anchos de pie...8750403
477200.0,IMEVACaucho sintético, Acetato de etileno y viniloFlexibilidad4.0185000NaNMujer8757345
478180.0,Acetato de etileno y vinilo;10.08700079000.0Hombre8351755
479210.0,Suela de espumaCaucho sintético, Acetato de etileno y viniloFlexibilidad para mejorar el desarrollo del pie.4.0135000NaNMujerAdherencia: Refuerzo de caucho en la suela, en...8733475
480NaNEVAAcetato de etileno y viniloLa flexibilidad de la suela es ideal para todo...None169000NaNHombreTranspirabilidad: Su mesh 3D aireado permite q...8803079
481203.0, termoplásticoEVACaucho sintético, 70.0% Acetato de etileno y v...tenis flexibles (61 N/mm)4.0249000NaNMujerLas hendiduras de flexión acompañan la extensi...8670202
482235.0termoplástico, ,KALENSOLECaucho sintético, Acetato de etileno y viniloKALENSOLENone399000NaNMujertela mesh, más elástica.8772824
483205.0, termoplástico, ,MFOAMCaucho sintético, Acetato de etileno y viniloMFOAM6.0499000NaNMujerBuen agarre en piso mojado gracias a la textur...8772779
484216.0termoplástico, ;VFOAMCaucho sintético, Acetato de etileno y vinilo,...Pebax8.0399000240000.0mujergeometría de la suela de las KIPRUN KD500 2, c...8756260
485280.0termoplástico, ;KalensoleCaucho sintético, Acetato de etileno y viniloKalensole6.0309000NaNHombre8772865
486252.0, termoplástico,espuma MFOAMCaucho sintético, Acetato de etileno y viniloespuma MFOAM6.0499000NaNHombreAdherencia: Buen agarre en piso mojado gracias...8830204
487225.0,Caucho sintético, Carbono, Amida de bloque de ...8.0849000NaNHombreImpulso: Espuma Pebax® de Arkema y placa de ca...8666803
488218.0termoplástico,espuma Pebax® de Arkema.Caucho sintético, Amida de bloque de poliéter8.0699000599000.0HombreImpulso: Excelente retorno de energía gracias ...8798231
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" + ], + "text/plain": [ + " Weight ... id\n", + "469 NaN ... 8803408\n", + "470 251.0 ... 8670191\n", + "471 295.0 ... 8488639\n", + "472 366.0 ... 8803078\n", + "473 243.0 ... 8757334\n", + "474 295.0 ... 8767790\n", + "475 251.0 ... 8670196\n", + "476 NaN ... 8750403\n", + "477 200.0 ... 8757345\n", + "478 180.0 ... 8351755\n", + "479 210.0 ... 8733475\n", + "480 NaN ... 8803079\n", + "481 203.0 ... 8670202\n", + "482 235.0 ... 8772824\n", + "483 205.0 ... 8772779\n", + "484 216.0 ... 8756260\n", + "485 280.0 ... 8772865\n", + "486 252.0 ... 8830204\n", + "487 225.0 ... 8666803\n", + "488 218.0 ... 8798231\n", + "\n", + "[20 rows x 11 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.tail(20)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "1a02981c-9475-4bcb-adea-9692e3ce3819", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "source": [ + "## Funciones" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "605cc91a-a695-4dce-a870-9c7ac634a193", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "# Definir una función para combinar textos relevantes\n", + "def combine_text(row):\n", + " text_fields = categorical_cols\n", + " combined_text = ' '.join([str(row[field]) if pd.notnull(row[field]) else '' for field in text_fields])\n", + " return combined_text\n", + "\n", + "def preprocess_outsole(text):\n", + " if pd.isna(text):\n", + " return \"Desconocido\"\n", + " # Reemplazar porcentajes o valores numericos por una etiqueta genérica\n", + " text = re.sub(r'\\d+(\\.\\d+)?\\%?', 'X%', text)\n", + " # Unificar materiales\n", + " text = text.replace(\"Acetato de etileno y vinilo\", \"EVA\")\n", + " text = text.replace(\"Caucho sintético\", \"CauchoSintetico\")\n", + " # Quitar espacios extra\n", + " text = re.sub(r'\\s+', ' ', text).strip()\n", + " return text\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "b8594999-2770-46ec-83ed-1766c12305d5", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "source": [ + "## Procesamiento del dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "26c8c077-ba52-4485-b8cd-2477d0f7e597", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "#Remplaza los caracteres no numéricos\n", + "df['Weight'] = df['Weight'].apply(lambda x: re.sub(r'[^\\d.]', '', str(x)))\n", + "df['Drop__heel-to-toe_differential_'] = df['Drop__heel-to-toe_differential_'].apply(lambda x: re.sub(r'[^\\d.]', '', str(x)))\n", + "df['regularPrice'] = df['regularPrice'].apply(lambda x: re.sub(r'\\D', '', str(x)))\n", + "df['undiscounted_price'] = df['undiscounted_price'].apply(lambda x: re.sub(r'\\D', '', str(x)))" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "9a7fecad-6de2-4472-8e09-d4961eb94ab3", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "df['Weight'] = df['Weight'].replace('', np.nan)\n", + "df['Drop__heel-to-toe_differential_'] = df['Drop__heel-to-toe_differential_'].replace('', np.nan)\n", + "df['regularPrice'] = df['regularPrice'].replace('', np.nan)\n", + "df['undiscounted_price'] = df['undiscounted_price'].replace('', np.nan)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "771e495f-7a5b-4351-98fe-53922c6e7cd7", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "df['Weight'] = df['Weight'].astype(float)\n", + "df['Drop__heel-to-toe_differential_'] = df['Drop__heel-to-toe_differential_'].astype(float)\n", + "df['regularPrice'] = df['regularPrice'].astype(float)\n", + "df['undiscounted_price'] = df['undiscounted_price'].astype(float)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "dcc99e26-1ce7-4639-962e-32819c122bf9", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "df['percentil_discounted'] = 1-(df['undiscounted_price']/df['regularPrice'])" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "01a546ca-a735-4019-b003-a4a2fc8da694", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "df_reduced = df[['Weight','Upper_Material','Midsole_Material','Outsole','Cushioning_System','Drop__heel-to-toe_differential_','regularPrice','undiscounted_price','percentil_discounted', 'Gender','Additional_Technologies']]" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "31fe59c0-c9c5-459b-bf1c-610e7ff96d60", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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WeightUpper_MaterialMidsole_MaterialOutsoleCushioning_SystemDrop__heel-to-toe_differential_regularPriceundiscounted_pricepercentil_discountedGenderAdditional_Technologies
0183.0SyntheticnullTextile rubberLightstrike Pro6.01299950.0909965.00.300000WomanENERGYRODS 2.0, Waterproofing, Recyclable mate...
1289.0adidas PrimeknitBOOSTStretchweb with Continental Better RubberLinear Energy PushNaN799950.0NaNNaNWomanParley Ocean Plastic, waterproofing
2166.0Parte superior de malla técnicanullSuela de caucho Continental™Amortiguación Lightstrike Pro6.01049950.0629970.00.400000MujerContiene al menos un 20 % de material reciclad...
3200.0Parte superior de mallanullSuela de caucho Continental RubberAmortiguación Lightstrike Pro6.01049950.0734965.00.300000HombreVarillas ENERGYRODS, Talón Slinglaunch, Contie...
4319.0Parte superior textilMediasuela CloudfoamSuela de TPUCloudfoam6.0279950.0NaNNaNHombrenull
....................................
484216.0termoplástico, ;VFOAMCaucho sintético, Acetato de etileno y vinilo,...Pebax8.0399000.02400000.0-5.015038mujergeometría de la suela de las KIPRUN KD500 2, c...
485280.0termoplástico, ;KalensoleCaucho sintético, Acetato de etileno y viniloKalensole6.0309000.0NaNNaNHombre
486252.0, termoplástico,espuma MFOAMCaucho sintético, Acetato de etileno y viniloespuma MFOAM6.0499000.0NaNNaNHombreAdherencia: Buen agarre en piso mojado gracias...
487225.0,Caucho sintético, Carbono, Amida de bloque de ...8.0849000.0NaNNaNHombreImpulso: Espuma Pebax® de Arkema y placa de ca...
488218.0termoplástico,espuma Pebax® de Arkema.Caucho sintético, Amida de bloque de poliéter8.0699000.05990000.0-7.569385HombreImpulso: Excelente retorno de energía gracias ...
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489 rows × 11 columns

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" + ], + "text/plain": [ + " Weight ... Additional_Technologies\n", + "0 183.0 ... ENERGYRODS 2.0, Waterproofing, Recyclable mate...\n", + "1 289.0 ... Parley Ocean Plastic, waterproofing\n", + "2 166.0 ... Contiene al menos un 20 % de material reciclad...\n", + "3 200.0 ... Varillas ENERGYRODS, Talón Slinglaunch, Contie...\n", + "4 319.0 ... null\n", + ".. ... ... ...\n", + "484 216.0 ... geometría de la suela de las KIPRUN KD500 2, c...\n", + "485 280.0 ... \n", + "486 252.0 ... Adherencia: Buen agarre en piso mojado gracias...\n", + "487 225.0 ... Impulso: Espuma Pebax® de Arkema y placa de ca...\n", + "488 218.0 ... Impulso: Excelente retorno de energía gracias ...\n", + "\n", + "[489 rows x 11 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_reduced" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "cd5bc1e4-0b47-40de-b629-c7e47e446b19", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['Weight', 'Upper_Material', 'Midsole_Material', 'Outsole',\n", + " 'Cushioning_System', 'Drop__heel-to-toe_differential_', 'regularPrice',\n", + " 'undiscounted_price', 'percentil_discounted', 'Gender',\n", + " 'Additional_Technologies'],\n", + " dtype='object')" + ] + }, + "execution_count": 243, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_reduced.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "caa09e55-f642-4d32-b06c-96a8c7af0da8", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "output_type": "stream", + "text": [ + "/root/.ipykernel/1017/command-2628091764913739-2217468474:1: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n df_reduced['Outsole'] = df_reduced['Outsole'].apply(preprocess_outsole)\n" + ] + } + ], + "source": [ + "df_reduced['Outsole'] = df_reduced['Outsole'].apply(preprocess_outsole)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "0357324b-81b4-4974-90e9-e6232b0f8d1a", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "source": [ + "## Clustering" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "c6a33e05-a578-4ea5-aadc-008198155e57", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + " \n", + " # Identificar columnas numéricas y categóricas\n", + "numerical_cols = ['Drop__heel-to-toe_differential_','Weight', 'regularPrice','undiscounted_price','percentil_discounted']\n", + "categorical_cols = ['Midsole_Material', 'Cushioning_System', 'Outsole', 'Upper_Material', \n", + " 'Additional_Technologies', 'Gender']\n", + " \n", + "# Definir qué columnas numéricas se imputarán con mediana y escalado\n", + "numeric_impute_cols = ['regularPrice']\n", + "\n", + "# Definir columnas numéricas \"especiales\" que no se deben imputar con mediana\n", + "special_numeric_cols = ['Drop__heel-to-toe_differential_', 'percentil_discounted','Weight', 'undiscounted_price']\n", + "\n", + "###################################\n", + "# Transformador personalizado\n", + "###################################\n", + "class SpecialNumericToCategory(BaseEstimator, TransformerMixin):\n", + " \n", + " \"\"\"\n", + " Este transformador convierte las columnas numéricas \"especiales\" en categorías.\n", + " Por ejemplo:\n", + " - Si el valor es NaN, lo marca como \"NoValue\".\n", + " - Si tiene valor, lo convierte a una categoría del tipo \"Value:X\".\n", + " \"\"\"\n", + " def __init__(self, cols):\n", + " self.cols = cols\n", + " \n", + " def fit(self, X, y=None):\n", + " return self\n", + "\n", + " def transform(self, X):\n", + " X = X.copy()\n", + " for col in self.cols:\n", + " X[col] = X[col].apply(lambda val: 'NoValue' if pd.isna(val) else f'Value:{val}')\n", + " return X[self.cols]\n", + "\n", + "###################################\n", + "# Pipelines\n", + "###################################\n", + "\n", + "# Pipeline para columnas numéricas \"normales\"\n", + "numeric_pipeline = Pipeline(steps=[\n", + " ('imputer', SimpleImputer(strategy='median')), # Imputar con mediana\n", + " ('scaler', StandardScaler()) # Escalar a media=0, std=1\n", + "])\n", + "\n", + "# Pipeline para columnas numéricas \"especiales\", convertidas a categóricas\n", + "special_numeric_pipeline = Pipeline(steps=[\n", + " ('to_category', SpecialNumericToCategory(special_numeric_cols)),\n", + " ('imputer', SimpleImputer(strategy='constant', fill_value='Unknown')), \n", + " ('onehot', OneHotEncoder(handle_unknown='ignore'))\n", + "])\n", + "\n", + "# Pipeline para columnas categóricas normales\n", + "categorical_pipeline = Pipeline(steps=[\n", + " ('imputer', SimpleImputer(strategy='constant', fill_value='Unknown')),\n", + " ('onehot', OneHotEncoder(handle_unknown='ignore'))\n", + "])\n", + "\n", + "# Combinar todos los pipelines con ColumnTransformer\n", + "preprocessor = ColumnTransformer(transformers=[\n", + " ('num', numeric_pipeline, numeric_impute_cols),\n", + " ('special_num', special_numeric_pipeline, special_numeric_cols),\n", + " ('cat', categorical_pipeline, categorical_cols)\n", + "])\n", + "X_transformed = preprocessor.fit_transform(df_reduced)\n", + "feature_names = (preprocessor.named_transformers_['num'][-1].get_feature_names_out(numeric_impute_cols).tolist() \n", + " + preprocessor.named_transformers_['special_num'][-1].get_feature_names_out(special_numeric_cols).tolist()\n", + " + preprocessor.named_transformers_['cat'][-1].get_feature_names_out(categorical_cols).tolist())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "70b99eca-b229-47bf-a0c6-8f4eb86c230d", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "output_type": "stream", + "text": [ + "Preprocesando columnas numéricas...\nGenerando embeddings para columnas categóricas...\nConcatenando características numéricas y categóricas...\nAplicando PCA para reducir dimensiones...\nCalculando el Método del Codo para determinar el número óptimo de clusters...\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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vby8AEC5cuKB47siRIwIAQUdHR+m9r127Vul74/V1vw6AoK2tLdy7d0/x3NWrVwUAwvLly5VqfVNwcLAAQNi8ebPiuYLPkzZt2gi5ubmFXvOmLVu2CFKpVOkzQBAEYc2aNQIA4fz580rvf/jw4W9dJwBh4sSJgiAIgre3t2Btba2o//XPuwLt27dX+fk0fPhwwd7eXvE4KipKACBYWFgIL1++VDw/Y8YMAYDg6uoq5OTkKJ739/cXtLW1hczMTEEQSva5oW7/L/ie/v7775We79evnyCRSJS+jm8qeO3bPmcMDQ0Fd3d3xeP27dsLAIRFixYpnsvKylJ8ZmRnZwuCoH7/L3g/pdnL+Ph4QVtbW/Dx8VH8nBAEQVixYoUAQFi/fn2R77FXr16CXC5X+p65deuWoKGhofQ98uDBA0FDQ0OYO3eu0uuvX78uaGpqFnr+dUlJSQIAoWfPnkXW8ro393F1Pz8K9uOCn0l//PFHof36Tc+ePRMACLNmzSq0rFOnTkLjxo0V/RWEV59brVq1EurVq1dou6q+v42MjBTfd0Sv4ymLRCU0YMAAZGRk4MCBA0hJScGBAwfUnq64a9cuGBkZoUuXLkhISFD88/DwgL6+Pk6ePPnW7R06dAgtWrRQnJIFAPr6+hg7diwePHiAW7duKcbZ2NgoXfOjq6uLsWPHKq0vNDQU8fHxmDBhAuRyueL5Hj16wMnJ6a3n6h86dAgtW7ZUOlphYWGhOFWoQFBQEF6+fAl/f3+l966hoQFPT89ivffXJScnA0CxZ3U7dOgQrK2t4e/vr3hOS0sLkyZNQmpqKk6fPq0Yp6mpifHjxyvGaWho4LPPPlNa39OnTxEREYERI0bA1NRU8XyTJk3QpUsXHDp0qFh1DRs2TOk99OvXDzY2Nkqvf/1oY1paGhISEtCqVSsIglDoFDsASrW/6zpKSl9fHykpKQBeHWXcs2cP/Pz8IAiC0tfb19cXSUlJbz0lJyIiApGRkRg0aBCeP3+ueH1aWho6deqEM2fOFDot9pNPPlG5LmdnZ3h5eSkee3p6AgA6duyIWrVqFXr+33//fev77dy5M+rWrat43KRJExgaGiq99vWe5+Tk4Pnz53B0dISxsbHK9z9mzJhCp8SpsmvXLjRs2BBOTk5Kve3YsSMAlPh76U0BAQGIjY1VOr3uffXv31/p1h4FvR4yZIjSdVeenp7Izs5WnC79Lp8bb+7/hw4dgoaGBiZNmqT0/BdffAFBEPD333+rrbtgn37b54yBgYHiM6mApqYmxo0bp3isra2NcePGIT4+HmFhYUWuryjv2stjx44hOzsbkydPVjoCPGbMGBgaGhb5WZ+Xl4cjR46gV69eSt8zDRs2hK+vr9LYvXv3Ij8/HwMGDFD6mllbW6NevXpF7p8l/Vx/HwVH6Q4cOFDia40TExNx4sQJDBgwACkpKYr3+Pz5c/j6+iIyMrLQKf+qvr+NjY0REhKCJ0+evNd7oaqHpywSlZCFhQU6d+6M7du3Iz09HXl5eYUmPigQGRmJpKQkWFpaqlweHx//1u1FR0crfgC/rmHDhorlLi4uiI6OhqOjY6Fz6Rs0aFBofaqeBwAnJyeV0/oXp5431xcZGQkAil8a36TudCB1CsYX/ML0NtHR0ahXr16hU9Fe71vBf21sbKCvr680riR9a9iwIY4cOVKsC7ffvLmsRCKBo6Oj0nUOMTExmDlzJv766y+law0BFLqhuKampsrrj0qyjneRmpqq+CXq2bNnePnyJQIDAxEYGKhy/Nv29YL9RdWpogWSkpJgYmKieOzg4KBy3Ou/QAJQ/DJrZ2en8vk3+1OcdQKAiYmJ0mszMjIwb948bNiwAY8fP1Y6jVJVz9XV/6bIyEjcvn1bcX3Lm4rzOVKUdu3awdvbGwsWLFAbckvqXb8GJf3cULX/R0dHw9bWttAv+W9+76tS8Jq3fc6kpKQU+ly3tbUt9P1fv359AK+uT2rZsmWR61TnXXup7jNLW1sbderUKbIPz549Q0ZGhsqbYTdo0EDpD0iRkZEQBEHtjbOLOi2ypJ/r76N9+/bo27cvZs+ejSVLlqBDhw7o1asXBg0aVOQMycCr06AFQcC3336Lb7/9VuWY+Ph41KhRQ/FY1ff3ggULMHz4cNjZ2cHDwwPdu3fHsGHDUKdOnfd7c1TpMZARvYNBgwZhzJgxiI2NRbdu3dSeH5+fnw9LS0ts27ZN5XJ1v2BVBQVHM7Zs2QJra+tCy0s6O5mTkxOAV9c1VGV5eXno0qULEhMTMX36dDg5OUFPTw+PHz/GiBEjCh0lkslkhUJnSddRUjk5Ofjnn3/g4uIC4P+/1kOGDFEbqAquw1CnYB0LFy5UOxvpm6FZ1XWLANQedVL3/OvBSZ3ivPazzz7Dhg0bMHnyZHh5ecHIyAgSiQQDBw5U2XN19b8pPz8fjRs3xuLFi1Uuf/MX83cxa9YsdOjQAWvXrlX5eSaRSFT2KS8vT+X63vVrUNLPDVX7//soCG3Xrl1TOyY6OhrJyclwdnYute0WpSz259KUn58PiUSCv//+W2VNb37fvs7Q0BC2tra4cePGO29f3U2q39w3JRIJdu/ejYsXL2L//v04cuQIRo0ahUWLFuHixYtF1lmwX3755ZeFjhAWcHR0VHqs6vt7wIABaNu2Lf744w8cPXoUCxcuxPz587F3717FdelUPTGQEb2D3r17Y9y4cbh48SJ27NihdlzdunVx7NgxtG7d+q2/fKn7oWJvb6/y3jR37txRLC/4740bNyAIgtK63nxtwfi7d+8W+iv03bt3FcvVsbe3V/wV+83Xvq7g9C5LS0t07ty5yHUWR/369dGgQQP8+eefWLZsWZE/PAvqvHbtGvLz85V+YVPVt+PHjyM1NVVpnUX17U137tyBubl5saY1frN3giDg3r17isBy/fp1/PPPP9i0aROGDRumGPf6zJ5vUxrrKMru3buRkZGh+MXEwsICBgYGyMvLe+vXWt1+XrC/GBoalsr+Iobdu3dj+PDhSvcazMzMLPaMdurUrVsXV69eRadOndT27321b98eHTp0wPz58zFz5sxCy01MTFSe2lnUUZZ3URqfG/b29jh27BhSUlKUjpK9+b2vSv369VG/fn3s27cPy5YtU3kq3ebNmwFAaUZX4NU9xt48Sv7PP/8AgGIikrL6+qny+mfW60dgsrOzERUVVWR/LSwsoKOjU+zPekEQ4ODgoDgiWBIffPABAgMDERwcrHSqcXEVHDV/+fKl0h8T1O2bLVu2RMuWLTF37lxs374dgwcPxu+//46PP/5Y7denoH9aWlrv/flkY2ODCRMmYMKECYiPj4e7uzvmzp3LQFbN8Royonegr6+P1atXIyAgAH5+fmrHDRgwAHl5eZgzZ06hZbm5uUq/qOnp6an8xa179+64dOkSgoODFc+lpaUhMDAQtWvXVvyVtnv37njy5InSVMzp6emFTiFr1qwZLC0tsWbNGqUps//++2/cvn0bPXr0KPK9d+/eHRcvXsSlS5cUzz179qzQUUBfX18YGhrihx9+UHm+/rNnz4rcjiqzZ8/G8+fP8fHHHyM3N7fQ8qNHj+LAgQOKOmNjY5UCc25uLpYvXw59fX20b99eMS43NxerV69WjMvLy8Py5cuV1m1jY4OmTZti06ZNSl+nGzdu4OjRo+jevXux3sPmzZuVTs/ZvXs3nj59qvhhXPAX5tf/yi0IApYtW1as9ZfWOtS5evUqJk+eDBMTE0ycOFGxvb59+2LPnj0q/9L9+te64JfVN/d1Dw8P1K1bFz/99BNSU1OLXEdFpaGhUejoxPLly9UeRSquAQMG4PHjx1i3bl2hZRkZGUXO0FgSBdeSqTrttG7durhz547S1+Hq1auFbknwvkrjc6N79+7Iy8srdFuGJUuWQCKRvPUX35kzZ+LFixf45JNPCn3twsLCMH/+fLi4uKBv375Ky3Jzc5Wmhc/OzsbatWthYWEBDw8PAOr3/7LQuXNnaGtr4+eff1baL3/99VckJSUV+VmvoaEBX19f7Nu3DzExMYrnb9++jSNHjiiN7dOnDzQ0NDB79uxC+78gCHj+/HmRdX711VfQ09PDxx9/jLi4uELL79+/X+RnV0GIP3PmjOK5tLS0Qrc4efHiRaH6Co7GF/wsLJgJ9c2vj6WlpeII8tOnTwvVUJz9Mi8vr9Cpy5aWlrC1tX2v21dQ1cAjZETvqKhrXQq0b98e48aNw7x58xAREQEfHx9oaWkhMjISu3btwrJlyxTXn3l4eGD16tX4/vvv4ejoCEtLS3Ts2BH/+c9/FNPsT5o0Caampti0aROioqKwZ88exdGfMWPGYMWKFRg2bBjCwsJgY2ODLVu2FJpqW0tLC/Pnz8fIkSPRvn17+Pv7K6a9r127NqZMmVLke/rqq6+wZcsWdO3aFZ9//rli2vuCI1IFDA0NsXr1agwdOhTu7u4YOHAgLCwsEBMTg4MHD6J169aFfmF6m48++gjXr1/H3LlzceXKFfj7+8Pe3h7Pnz/H4cOHcfz4cWzfvh0AMHbsWKxduxYjRoxAWFgYateujd27d+P8+fNYunSp4i/ffn5+aN26Nf7zn//gwYMHcHZ2xt69e1Ve87Nw4UJ069YNXl5eGD16tGLaeyMjI6V71RTF1NQUbdq0wciRIxEXF4elS5fC0dERY8aMAfDq1My6deviyy+/xOPHj2FoaIg9e/YU6zqnAqWxDuDVvcwyMzORl5eH58+f4/z58/jrr79gZGSEP/74Q+mUsh9//BEnT56Ep6cnxowZA2dnZyQmJiI8PBzHjh1T3B+pbt26MDY2xpo1a2BgYAA9PT14enrCwcEBv/zyC7p164ZGjRph5MiRqFGjBh4/foyTJ0/C0NAQ+/fvL1H95e2DDz7Ali1bYGRkBGdnZwQHB+PYsWOKafHf1dChQ7Fz50588sknOHnyJFq3bo28vDzcuXMHO3fuxJEjR9CsWbP3rr99+/Zo3769YsKb140aNQqLFy+Gr68vRo8ejfj4eKxZswaNGjUqNLnF+yiNzw0/Pz94e3vjv//9Lx48eABXV1ccPXoUf/75JyZPnqw0OYsqgwcPxuXLl7Fs2TLcunULgwcPhomJCcLDw7F+/XqYmZlh9+7dha6NsrW1xfz58/HgwQPUr18fO3bsQEREBAIDAxVji9r/S5uFhQVmzJiB2bNno2vXrvjwww9x9+5drFq1Cs2bN8eQIUOKfP3s2bNx+PBhtG3bFhMmTFD8QatRo0ZKn/V169bF999/jxkzZuDBgwfo1asXDAwMEBUVhT/++ANjx47Fl19+qXY7devWxfbt2/HRRx+hYcOGGDZsGFxcXJCdnY0LFy4obleijo+PD2rVqoXRo0dj2rRp0NDQwPr16xX7TYFNmzZh1apV6N27N+rWrYuUlBSsW7cOhoaGij+o6ejowNnZGTt27ED9+vVhamoKFxcXuLi4YOXKlWjTpg0aN26MMWPGoE6dOoiLi0NwcDAePXqEq1evFtnPlJQU1KxZE/369YOrqyv09fVx7NgxXL58WemoOlVT5TafI1ElpmoaaFXenPa+QGBgoODh4SHo6OgIBgYGQuPGjYWvvvpKePLkiWJMbGys0KNHD8HAwEAAoDTF9P3794V+/foJxsbGglwuF1q0aCEcOHCg0Haio6OFDz/8UNDV1RXMzc2Fzz//XDHF/utTewuCIOzYsUNwc3MTZDKZYGpqKgwePFh49OhRsfpx7do1oX379oJcLhdq1KghzJkzR/j111+VphgucPLkScHX11cwMjIS5HK5ULduXWHEiBFCaGioYkxxpr1/3fHjx4WePXsKlpaWgqampmBhYSH4+fkJf/75p9K4uLg4YeTIkYK5ubmgra0tNG7cWOV008+fPxeGDh0qGBoaCkZGRsLQoUOFK1euqJye+tixY0Lr1q0FHR0dwdDQUPDz8xNu3br11poLppL/7bffhBkzZgiWlpaCjo6O0KNHj0LT0N+6dUvo3LmzoK+vL5ibmwtjxoxRTLP+ej3Dhw8X9PT0VG6vuOsoqtaCf1paWoKFhYXQrl07Ye7cuUJ8fLzK18XFxQkTJ04U7OzsBC0tLcHa2lro1KmTEBgYqDTuzz//FJydnQVNTc1C9Vy5ckXo06ePYGZmJshkMsHe3l4YMGCAcPz4ccWYgv3l2bNnhWpQ9z2I16Z5L1AwrfjChQsLrfttry3Y1uvTb7948UKxv+nr6wu+vr7CnTt3Co0r7ufJ67Kzs4X58+cLjRo1EmQymWBiYiJ4eHgIs2fPFpKSktTWpI669/T61/7N+rZu3SrUqVNH0NbWFpo2bSocOXJE7VTtr/f09fW+eSsFdb0ozudGUft/SkqKMGXKFMHW1lbQ0tIS6tWrJyxcuFDplgpvs2/fPqFLly6CiYmJIJPJBEdHR+GLL75Qud+1b99eaNSokRAaGip4eXkJcrlcsLe3F1asWFForLr9v6x6uWLFCsHJyUnQ0tISrKyshPHjxwsvXrwoVg9Onz4teHh4CNra2kKdOnWENWvWqP283rNnj9CmTRtBT09P0NPTE5ycnISJEycKd+/eLda2/vnnH2HMmDFC7dq1BW1tbcHAwEBo3bq1sHz5cqWp5lXt42FhYYKnp6egra0t1KpVS1i8eHGhae/Dw8MFf39/oVatWoJMJhMsLS2FDz74QGmfEgRBuHDhguI9440p8O/fvy8MGzZMsLa2FrS0tIQaNWoIH3zwgbB7927FGHVfh6ysLGHatGmCq6urYGBgIOjp6Qmurq7CqlWritUfqtokglDOV38SEVVDp06dgre3N3bt2qV2Vk4iqpw6dOiAhISE95qcgoiqL15DRkREREREJBIGMiIiIiIiIpEwkBEREREREYmE15ARERERERGJhEfIiIiIiIiIRMJARkREREREJBLeGLqU5Ofn48mTJzAwMIBEIhG7HCIiIiIiEokgCEhJSYGtrS2k0qKPgTGQlZInT57Azs5O7DKIiIiIiKiCePjwIWrWrFnkGAayUmJgYADgVdMNDQ1FrgbIycnB0aNH4ePjAy0tLbHLqTDYF9XYF/XYG9XYF/XYG9XYF/XYG9XYF/XYG9UqUl+Sk5NhZ2enyAhFYSArJQWnKRoaGlaYQKarqwtDQ0PRd8iKhH1RjX1Rj71RjX1Rj71RjX1Rj71RjX1Rj71RrSL2pTiXMnFSDyIiIiIiIpEwkBEREREREYmEgYyIiIiIiEgkDGREREREREQiYSAjIiIiIiISCQMZERERERGRSBjIiIiIiIiIRMJARkREREREJBIGMiIiIiIiIpEwkBEREREREYmEgYyIiIiIiEgkDGREREREREQiYSAjIiIiIiISCQNZFZSXLyAkKhFhCRKERCUiL18QuyQiIiIiIlJBU+wCqHQdvvEUs/ffwtOkTAAa2BwZChsjOWb5OaOri43Y5RERERER0Wt4hKwKOXzjKcZvDf9fGPt/sUmZGL81HIdvPBWpMiIiIiIiUoWBrIrIyxcwe/8tqDo5seC52ftv8fRFIiIiIqIKhIGsirgUlVjoyNjrBABPkzJxKSqx/IoiIiIiIqIiMZBVEfEp6sPYu4wjIiIiIqKyx0BWRVgayEt1HBERERERlT0GsiqihYMpbIzkkBQxxtpQhhYOpuVWExERERERFY2BrIrQkEowy88ZANSGMpmWBlIzc8uvKCIiIiIiKhIDWRXS1cUGq4e4w9pI+bREc30Z9GWaiH6eDv91F/EiLVukComIiIiI6HW8MXQV09XFBl2crRF8Lx5Hz4bAp60nvBwtcS8+FYN/uYhbT5Phv+4itn7sCXN9mdjlEhERERFVazxCVgVpSCXwdDCFh7kATwdTaEglaGBtgN/HtoSFgQx3YlPgH3iRMy4SEREREYmMgawacbQ0wI6xLWFtKEdkfCoGrr2I2CLuXUZERERERGWLgayaqWOhjx3jWqKGsQ7+TUjDR4HBePIyQ+yyiIiIiIiqJQayasjeTA+/j20JO1MdRD9Px0eBwXiYmC52WURERERE1Q4DWTVlZ6qLHWO9UNtMFw8TMzAw8CKin6eJXRYRERERUbXCQFaN2Rrr4PexXqhjoYfHLzPw0dqL+PdZqthlERERERFVGwxk1Zy1kRy/j22Jepb6iE3OxEeBF3EvPkXssoiIiIiIqgUGMoKlwatQ5mRtgGcpWRgYeBF3YxnKiIiIiIjKGgMZAQDM9GX4bUxLNLI1REJqNgYGBuPmkySxyyIiIiIiqtIYyEjBRE8b2z9uCdeaRniRnoNB60Jw/RFDGRERERFRWWEgIyVGulrY8rEn3GsZIykjB4N+uYgrMS/ELouIiIiIqEpiIKNCDOVa2DzaE81rmyAlMxdDf72E0AeJYpdFRERERFTlMJCRSvoyTWwa1QIt65giNSsXw9ZfQsi/z8Uui4iIiIioSmEgI7V0tTWxYUQLtHE0R3p2HoZvuITz9xLELouIiIiIqMpgIKMi6Whr4JfhzdChgQUyc/IxauNlnP7nmdhlERERERFVCQxk9FZyLQ2sHeqBzg0tkZWbjzGbQnHiTpzYZRERERERVXoMZFQsMk0NrBrsga6NrJGdl49xW8Jw9Gas2GUREREREVVqDGRUbNqaUiwf5IYeTWyQkydgwrZwHLr+VOyyiIiIiIgqLQYyKhEtDSmWfdQUvZraIjdfwGe/XcGfEY/FLouIiIiIqFJiIKMS09SQYtGApujnURN5+QKm7IjA3vBHYpdFRERERFTpMJDRO9GQSrCgbxP4t7BDvgB8sesqdl5+KHZZRERERESViqiB7MyZM/Dz84OtrS0kEgn27duntDwgIABOTk7Q09ODiYkJOnfujJCQEKUxtWvXhkQiUfr3448/Ko25du0a2rZtC7lcDjs7OyxYsKBQLbt27YKTkxPkcjkaN26MQ4cOlfr7rWqkUgnm9mqMoS3tIQjAV3uuYVtItNhlERERERFVGqIGsrS0NLi6umLlypUql9evXx8rVqzA9evXce7cOdSuXRs+Pj549kz5Pljfffcdnj59qvj32WefKZYlJyfDx8cH9vb2CAsLw8KFCxEQEIDAwEDFmAsXLsDf3x+jR4/GlStX0KtXL/Tq1Qs3btwomzdehUilEnzXsxFGtq4NAPjvHzew8XyUuEUREREREVUSmmJuvFu3bujWrZva5YMGDVJ6vHjxYvz666+4du0aOnXqpHjewMAA1tbWKtexbds2ZGdnY/369dDW1kajRo0QERGBxYsXY+zYsQCAZcuWoWvXrpg2bRoAYM6cOQgKCsKKFSuwZs2a932bVZ5EIsHMD5yhrSHF2jP/ImD/LeTmC/i4bR2xSyMiIiIiqtBEDWQlkZ2djcDAQBgZGcHV1VVp2Y8//og5c+agVq1aGDRoEKZMmQJNzVdvLTg4GO3atYO2trZivK+vL+bPn48XL17AxMQEwcHBmDp1qtI6fX19C51C+bqsrCxkZWUpHicnJwMAcnJykJOT875v970V1FCetXzRuS6kEgGrT0fh+4O3kZmdi3HtHMpt+8UhRl8qA/ZFPfZGNfZFPfZGNfZFPfZGNfZFPfZGtYrUl5LUUOED2YEDBzBw4ECkp6fDxsYGQUFBMDc3VyyfNGkS3N3dYWpqigsXLmDGjBl4+vQpFi9eDACIjY2Fg4NyKLCyslIsMzExQWxsrOK518fExqq/8fG8efMwe/bsQs8fPXoUurq67/x+S1tQUFC5bq+BAHStKcHhRxr4KSgSt+7cgW9NoVxrKI7y7ktlwb6ox96oxr6ox96oxr6ox96oxr6ox96oVhH6kp6eXuyxFT6QeXt7IyIiAgkJCVi3bh0GDBiAkJAQWFpaAoDSka0mTZpAW1sb48aNw7x58yCTycqsrhkzZihtOzk5GXZ2dvDx8YGhoWGZbbe4cnJyEBQUhC5dukBLS6tct90DwOrT/2LxsXs49FADtevUweROdSGRSMq1DlXE7EtFxr6ox96oxr6ox96oxr6ox96oxr6ox96oVpH6UnD2XHFU+ECmp6cHR0dHODo6omXLlqhXrx5+/fVXzJgxQ+V4T09P5Obm4sGDB2jQoAGsra0RFxenNKbgccF1Z+rGqLsuDQBkMpnKwKelpSX6DvA6seqZ1LkB5Nqa+OHQHaw6/S/yIcH0rg0qRCgDKt7XqaJgX9Rjb1RjX9Rjb1RjX9Rjb1RjX9Rjb1SrCH0pyfYr3X3I8vPzla7delNERASkUqniCJqXlxfOnDmjdB5nUFAQGjRoABMTE8WY48ePK60nKCgIXl5eZfAOqo+x7epi5gfOAIA1p+9j7sHbEISKd/oiEREREZFYRD1Clpqainv37ikeR0VFISIiAqampjAzM8PcuXPx4YcfwsbGBgkJCVi5ciUeP36M/v37A3g1YUdISAi8vb1hYGCA4OBgTJkyBUOGDFGErUGDBmH27NkYPXo0pk+fjhs3bmDZsmVYsmSJYruff/452rdvj0WLFqFHjx74/fffERoaqjQ1Pr2bUW0coKUhwbd/3sQv56KQk5ePgA8bVZgjZUREREREYhI1kIWGhsLb21vxuOCarOHDh2PNmjW4c+cONm3ahISEBJiZmaF58+Y4e/YsGjVqBODVaYO///47AgICkJWVBQcHB0yZMkXp2i4jIyMcPXoUEydOhIeHB8zNzTFz5kzFlPcA0KpVK2zfvh3ffPMNvv76a9SrVw/79u2Di4tLOXWiahvqVRtaGlLM+OM6NgVHIydfwPc9XSCVMpQRERERUfUmaiDr0KFDkaew7d27t8jXu7u74+LFi2/dTpMmTXD27Nkix/Tv319x5I1K38AWtaCpIcW03VexPSQGuXn5mNenCTQYyoiIiIioGqt015BR5dXPoyaWftQUUgmwM/QRpu26irx8XlNGRERERNUXAxmVq55Na2C5vzs0pBLsvfIYk3dEIDcvX+yyiIiIiIhEwUBG5a5HExusHOQOLQ0J9l99gs9+u4IchjIiIiIiqoYYyEgUXV2ssXqwB7Q1pPj7RiwmbAtHVm6e2GUREREREZUrBjISTWdnKwQO84C2phRBt+LwyZYwZOYwlBERERFR9cFARqLq0MAS64c3h1xLipN3n2HM5lCGMiIiIiKqNhjISHRt6pljw4gW0NXWwNnIBIzaeBnp2blil0VEREREVOYYyKhC8Kprhk2jWkBPWwMX7j/HiPWXkZrFUEZEREREVRsDGVUYzWubYsvHnjCQaeLSg0QMX38JKZk5YpdFRERERFRmGMioQnGvZYJtYzxhKNdEWPQLDPn1EpIyGMqIiIiIqGpiIKMKp0lNY2wf0xLGulq4+vAlBv9yES/Ts8Uui4iIiIio1DGQUYXkUsMIv41pCTM9bdx4nAz/dSF4npoldllERERERKWKgYwqrIY2hvh9bEuY68tw+2ky/NddxLMUhjIiIiIiqjoYyKhCq2dlgB3jWsLKUIZ/4lIxMDAY8cmZYpdFRERERFQqGMiowqtroY8dY71gayTH/Wdp+CjwIp4mZYhdFhERERHRe2Mgo0qhtrkedozzQg1jHUQlpOGjtRfx6EW62GUREREREb0XBjKqNOxMdbHzEy/UMtVFTGI6Plp7ETHPGcqIiIiIqPJiIKNKpYaxDnaO84KDuR4ev8zAR4HBeJCQJnZZRERERETvhIGMKh1rIzl2jG2JuhZ6eJqUiQFrg3EvPlXssoiIiIiISoyBjColS0M5fh/rhQZWBohPycLAwIv4Jy5F7LKIiIiIiEqEgYwqLQsDGX4b2xLONoZISH0Vym4/TRa7LCIiIiKiYmMgo0rNVE8b28d4onENIySmZcN/3UXceJwkdllERERERMXCQEaVnrGuNrZ+7ImmdsZ4mZ6DQesu4urDl2KXRURERET0VgxkVCUY6Whhy+gW8LA3QXJmLob8EoKw6Bdil0VEREREVCQGMqoyDORa2DyqBVo4mCIlKxfDfg3BpahEscsiIiIiIlKLgYyqFD2ZJjaObI5Wdc2Qlp2H4esv4cL9BLHLIiIiIiJSiYGMqhxdbU2sH9EcbeuZIyMnD6M2XsbZyGfIyxcQEpWIsAQJQqISkZcviF0qEREREVVzmmIXQFQW5FoaWDesGSZsC8eJO/EYueEyDOSaeJGeA0ADmyNDYWMkxyw/Z3R1sRG7XCIiIiKqpniEjKosuZYG1gzxgGtNI+TmC/8LY/8vNikT47eG4/CNpyJVSERERETVHQMZVWkaUgnikrNULis4YXH2/ls8fZGIiIiIRMFARlXapahExCZnql0uAHialMnZGImIiIhIFAxkVKXFp6gPY+8yjoiIiIioNDGQUZVmaSAv1XFERERERKWJgYyqtBYOprAxkkNSxBhNqQQ1TXTKrSYiIiIiogIMZFSlaUglmOXnDABqQ1luvoC+qy/g+qOk8iuMiIiIiAgMZFQNdHWxweoh7rA2Uj4t0cZIjrm9XFDfSh/xKVkYsDYYR2/GilQlEREREVVHvDE0VQtdXWzQxdkawfficfRsCHzaesLL0RIaUgn8mtpi4rZwnI1MwLitYfhv94YY3cYBEklRJzoSEREREb0/HiGjakNDKoGngyk8zAV4OphCQ/oqcBnKtbBhRHMM9qwFQQC+P3gb3+y7gdy8fJErJiIiIqKqjoGMCICmhhTf93LBNz0aQiIBtoXEYOTGy0jOzBG7NCIiIiKqwhjIiP5HIpHg47Z1sGaIB3S0NHA2MgH9Vl/AoxfpYpdGRERERFUUAxnRG3wbWWPnOC9YGsjwT1wqeq28gIiHL8Uui4iIiIiqIAYyIhUa1zTCvomt4WRtgITULHy0Nhh/X38qdllEREREVMUwkBGpYWusg93jW8G7gQWycvMxfls41py+D0EQxC6NiIiIiKoIBjKiIujLNLFuWDMM97IHAPz49x38Z8915HAGRiIiIiIqBaIGsjNnzsDPzw+2traQSCTYt2+f0vKAgAA4OTlBT08PJiYm6Ny5M0JCQpTGJCYmYvDgwTA0NISxsTFGjx6N1NRUpTHXrl1D27ZtIZfLYWdnhwULFhSqZdeuXXBycoJcLkfjxo1x6NChUn+/VDlpakgxu6cLAvycIZUAO0IfYsSGS0jK4AyMRERERPR+RA1kaWlpcHV1xcqVK1Uur1+/PlasWIHr16/j3LlzqF27Nnx8fPDs2TPFmMGDB+PmzZsICgrCgQMHcObMGYwdO1axPDk5GT4+PrC3t0dYWBgWLlyIgIAABAYGKsZcuHAB/v7+GD16NK5cuYJevXqhV69euHHjRtm9eap0RrR2wC/Dm0FXWwPn7z1H39UX8DCRMzASERER0bsTNZB169YN33//PXr37q1y+aBBg9C5c2fUqVMHjRo1wuLFi5GcnIxr164BAG7fvo3Dhw/jl19+gaenJ9q0aYPly5fj999/x5MnTwAA27ZtQ3Z2NtavX49GjRph4MCBmDRpEhYvXqzYzrJly9C1a1dMmzYNDRs2xJw5c+Du7o4VK1aUfROoUunoZIVdn3jB2lCOe/Gp6LXyPMKiX4hdFhERERFVUppiF1Bc2dnZCAwMhJGREVxdXQEAwcHBMDY2RrNmzRTjOnfuDKlUipCQEPTu3RvBwcFo164dtLW1FWN8fX0xf/58vHjxAiYmJggODsbUqVOVtufr61voFMrXZWVlISsrS/E4OTkZAJCTk4OcHPFPZSuooSLUUpGURl/qW+hi17gWGLf1Cm49TYH/uotY0McFPRpbl1aZ5Y77i3rsjWrsi3rsjWrsi3rsjWrsi3rsjWoVqS8lqaHCB7IDBw5g4MCBSE9Ph42NDYKCgmBubg4AiI2NhaWlpdJ4TU1NmJqaIjY2VjHGwcFBaYyVlZVimYmJCWJjYxXPvT6mYB2qzJs3D7Nnzy70/NGjR6Grq1vyN1pGgoKCxC6hQiqNvoywAzZnSnHjBTB55zUEBV9BlxoCJJJSKFAk3F/UY29UY1/UY29UY1/UY29UY1/UY29Uqwh9SU8v/mUtFT6QeXt7IyIiAgkJCVi3bh0GDBiAkJCQQkGsvM2YMUPpqFpycjLs7Ozg4+MDQ0NDESt7JScnB0FBQejSpQu0tLTELqfCKO2+fJgvYP6Rf7DhQjQOPtSAtrktvv/QGdqalWsCU+4v6rE3qrEv6rE3qrEv6rE3qrEv6rE3qlWkvhScPVccFT6Q6enpwdHREY6OjmjZsiXq1auHX3/9FTNmzIC1tTXi4+OVxufm5iIxMRHW1q9OH7O2tkZcXJzSmILHbxtTsFwVmUwGmUxW6HktLS3Rd4DXVbR6KorS6osWgFkfuqCOpQEC/rqJP648wZOXmVg71APGutpvfX1Fw/1FPfZGNfZFPfZGNfZFPfZGNfZFPfZGtYrQl5Jsv3L9GR9Afn6+4totLy8vvHz5EmFhYYrlJ06cQH5+Pjw9PRVjzpw5o3QeZ1BQEBo0aAATExPFmOPHjyttJygoCF5eXmX9dqiKGNrSHr8ObwZ9mSZCohLRZ9UFPEhIE7ssIiIiIqrgRA1kqampiIiIQEREBAAgKioKERERiImJQVpaGr7++mtcvHgR0dHRCAsLw6hRo/D48WP0798fANCwYUN07doVY8aMwaVLl3D+/Hl8+umnGDhwIGxtbQG8mqlRW1sbo0ePxs2bN7Fjxw4sW7ZM6XTDzz//HIcPH8aiRYtw584dBAQEIDQ0FJ9++mm594Qqrw4NLLF7vBdqGOvg34Q09F51HpcfJIpdFhERERFVYKIGstDQULi5ucHNzQ0AMHXqVLi5uWHmzJnQ0NDAnTt30LdvX9SvXx9+fn54/vw5zp49i0aNGinWsW3bNjg5OaFTp07o3r072rRpo3SPMSMjIxw9ehRRUVHw8PDAF198gZkzZyrdq6xVq1bYvn07AgMD4erqit27d2Pfvn1wcXEpv2ZQleBkbYg/JrZCk5pGeJGeg8HrQrDvymOxyyIiIiKiCkrUa8g6dOgAQRDULt+7d+9b12Fqaort27cXOaZJkyY4e/ZskWP69++vOPJG9D4sDeTYMdYLU3ZE4PDNWEzeEYGohDRM7lwPkso8BSMRERERlbpKdw0ZUWWgo62BVYPdMa5dHQDAsuORmLIjAlm5eSJXRkREREQVCQMZURmRSiWY0b0h5vVpDA2pBPsinmDILyFITMsWuzQiIiIiqiAYyIjKmH+LWtg0sgUMZJq4/OAFeq86j/vPUsUui4iIiIgqAAYyonLQpp459k5ohZomOoh+no4+qy4g+P5zscsiIiIiIpExkBGVk3pWBvhjQms0tTNGUkYOhq0Pwe6wR2KXRUREREQiYiAjKkcWBjL8PrYlejS2QU6egC93XcVPR+4iP1/9bKNEREREVHUxkBGVM7mWBpb7u2Gid10AwIqT9zDp9yvIzOEMjERERETVDQMZkQikUgmm+TphQb8m0JRKcODaUwxadxEJqVlil0ZERERE5YiBjEhEA5rZYfPoFjCUayI85iV6rzqPe/EpYpdFREREROWEgYxIZK3qmuOPia1Ry1QXDxMz0HvVBZy/lyB2WURERERUDhjIiCqAuhb62DexNZrZmyAlMxfD11/C75dixC6LiIiIiMoYAxlRBWGqp42tH3uiZ1Nb5OYL+M/e6/jx7zucgZGIiIioCmMgI6pA5FoaWPpRU0zqVA8AsOb0fUzcHo6MbM7ASERERFQVMZARVTASiQRTu9TH4gGu0NKQ4O8bsRi47iLiUzLFLo2IiIiIShkDGVEF1ce9JraO9oSxrhauPnyJ3isv4G4sZ2AkIiIiqkoYyIgqMM86ZvhjQms4mOvh8csM9F19Aaf/eSZ2WURERERUShjIiCo4B3M97B3fCi0cTJGalYtRGy9j68VoscsiIiIiolLAQEZUCZjoaWPL6Bbo41YDefkCvtl3A98fuIU8zsBIREREVKkxkBFVEjJNDSwa4IovutQHAPxyLgqfbA1DenauyJURERER0btiICOqRCQSCT7rVA/LBjaFtqYUQbfiMGBtMOKSOQMjERERUWXEQEZUCfVsWgPbP/aEqZ42bjxORq+V53HrSbLYZRERERFRCTGQEVVSzWqb4o8JrVDHQg9PkzLRf80FnLwTL3ZZRERERFQCDGRElZi9mR7+GN8aXnXMkJadh9GbLmPThQdil0VERERExcRARlTJGelqYdOoFhjQrCbyBWDWXzcR8NdNzsBIREREVAkwkBFVAdqaUszv2wRfdW0AANh44QHGbA5FahZnYCQiIiKqyBjIiKoIiUSCCR0csXKQO2SaUpy4E4/+a4LxNClD7NKIiIiISA0GMqIqpkcTG/w2tiXM9bVx+2kyeq44jxuPk8Qui4iIiIhUYCAjqoLca5ngjwmtUc9SH/EpWei/JhhHb8aKXRYRERERvYGBjKiKsjPVxZ4JrdC2njkycvIwbmsYfjn7LwSBk30QERERVRQMZERVmKFcC+tHNId/i1oQBOD7g7fx7Z83kJuXL3ZpRERERAQGMqIqT0tDih96u+C/3RtCIgG2XozBqE2hSMnMEbs0IiIiomqPgYyoGpBIJBjTrg5WD/aAXEuKM/88Q7/VwXj0Ih15+QJCohIRliBBSFQi719GREREVI40xS6AiMpPVxdr7DT2wuhNobgbl4Juy85CW0OK52nZADSwOTIUNkZyzPJzRlcXG7HLJSIiIqryeISMqJppUtMY+ya2Rg1jOVIyc/8Xxv5fbFImxm8Nx+EbT0WqkIiIiKj6YCAjqoasDeXIVXNqYsGzs/ff4umLRERERGWMgYyoGroUlYi45Cy1ywUAT5MycSkqsfyKIiIiIqqGGMiIqqH4lMxSHUdERERE74aBjKgasjSQF2uchb6sjCshIiIiqt4YyIiqoRYOprAxkkPylnGrT99HQqr6UxuJiIiI6P0wkBFVQxpSCWb5OQNAoVBW8FhTKsHZyAR0W3YW5yITyrU+IiIiouqCgYyomurqYoPVQ9xhbaR8+qK1kRxrhrjj4KS2qGepj2cpWRi6PgQLj9xBTl6+SNUSERERVU28MTRRNdbVxQZdnK0RfC8eR8+GwKetJ7wcLaEhfXWc7K9P2+C7Azfx26WHWHnyPoLvP8fP/m6oaaIrcuVEREREVQOPkBFVcxpSCTwdTOFhLsDTwVQRxgBAR1sD8/o0wYpBbjCQaSI85iW6LzuLv6/zptFEREREpYGBjIje6oMmtjj0eVu42hkjOTMX47eF45t915GZkyd2aURERESVGgMZERWLnakudn/ihXHt6wAAtl6MQa+V53EvPkXkyoiIiIgqL1ED2ZkzZ+Dn5wdbW1tIJBLs27dPsSwnJwfTp09H48aNoaenB1tbWwwbNgxPnjxRWkft2rUhkUiU/v34449KY65du4a2bdtCLpfDzs4OCxYsKFTLrl274OTkBLlcjsaNG+PQoUNl8p6JKjMtDSlmdGuITaNawFxfG3diU/DB8nPYcTkGgiCIXR4RERFRpSNqIEtLS4OrqytWrlxZaFl6ejrCw8Px7bffIjw8HHv37sXdu3fx4YcfFhr73Xff4enTp4p/n332mWJZcnIyfHx8YG9vj7CwMCxcuBABAQEIDAxUjLlw4QL8/f0xevRoXLlyBb169UKvXr1w48aNsnnjRJVc+/oWOPR5W7RxNEdmTj6m77mOSb9HIDkzR+zSiIiIiCoVUWdZ7NatG7p166ZymZGREYKCgpSeW7FiBVq0aIGYmBjUqlVL8byBgQGsra1Vrmfbtm3Izs7G+vXroa2tjUaNGiEiIgKLFy/G2LFjAQDLli1D165dMW3aNADAnDlzEBQUhBUrVmDNmjUq15uVlYWsrP+/YW5ycjKAV0f2cnLE/6W0oIaKUEtFwr6o9i59MZFr4Nehblh37gGWHL+H/VefICLmBZYOaIImNY3KqtRyx31GNfZFPfZGNfZFPfZGNfZFPfZGtYrUl5LUIBEqyHlGEokEf/zxB3r16qV2zLFjx+Dj44OXL1/C0NAQwKtTFjMzM5GTk4NatWph0KBBmDJlCjQ1X2XNYcOGITk5Wel0yJMnT6Jjx45ITEyEiYkJatWqhalTp2Ly5MmKMbNmzcK+fftw9epVlbUEBARg9uzZhZ7fvn07dHU5JThVL1EpwOZIDSRmSSCVCPCrlY8ONgKkb951moiIiKgaSE9Px6BBg5CUlKTILepUmvuQZWZmYvr06fD391d6U5MmTYK7uztMTU1x4cIFzJgxA0+fPsXixYsBALGxsXBwcFBal5WVlWKZiYkJYmNjFc+9PiY2NlZtPTNmzMDUqVMVj5OTk2FnZwcfH5+3Nr085OTkICgoCF26dIGWlpbY5VQY7ItqpdGXoRk5+HrfTRy5FY8/ozXwQtsMC/q4wExfVsrVli/uM6qxL+qxN6qxL+qxN6qxL+qxN6pVpL4UnD1XHJUikOXk5GDAgAEQBAGrV69WWvZ6KGrSpAm0tbUxbtw4zJs3DzJZ2f0iKJPJVK5fS0tL9B3gdRWtnoqCfVHtffpipqWFNUObYfulGHy3/xbORD6H36qLWPpRU7R2NC/lSssf9xnV2Bf12BvV2Bf12BvV2Bf12BvVKkJfSrL9Cj/tfUEYi46ORlBQ0FuPPnl6eiI3NxcPHjwAAFhbWyMuLk5pTMHjguvO1I1Rd10aEakmkUgw2NMef37aGo6W+niWkoUhv4Zg4ZE7yM3LF7s8IiIiogqnQgeygjAWGRmJY8eOwczM7K2viYiIgFQqhaWlJQDAy8sLZ86cUbqwLigoCA0aNICJiYlizPHjx5XWExQUBC8vr1J8N0TVh5O1IfZ/2gb+LewgCMDKk/fxUeBFPHqRLnZpRERERBWKqIEsNTUVERERiIiIAABERUUhIiICMTExyMnJQb9+/RAaGopt27YhLy8PsbGxiI2NRXZ2NgAgODgYS5cuxdWrV/Hvv/9i27ZtmDJlCoYMGaIIW4MGDYK2tjZGjx6NmzdvYseOHVi2bJnSqY6ff/45Dh8+jEWLFuHOnTsICAhAaGgoPv3003LvCVFVoaOtgXl9mmC5vxsMZJoIi36B7svO4vCNp2KXRkRERFRhiBrIQkND4ebmBjc3NwCvrgdzc3PDzJkz8fjxY/z111949OgRmjZtChsbG8W/CxcuAHh1Hdfvv/+O9u3bo1GjRpg7dy6mTJmidI8xIyMjHD16FFFRUfDw8MAXX3yBmTNnKqa8B4BWrVph+/btCAwMhKurK3bv3o19+/bBxcWlfBtCVAX5udri4KS2cLUzRnJmLj7ZGo5v9l1HZk6e2KURERERiU7UST06dOiAombdf9uM/O7u7rh48eJbt9OkSROcPXu2yDH9+/dH//7937ouIiq5Wma62DXOC4uO3sXaM/9i68UYhD54gRWD3OBoaSB2eURERESiqdDXkBFR1aGtKcWM7g2xaVQLmOlp405sCvyWn8fOyw/f+scXIiIioqqKgYyIylX7+hb4+/O2aO1ohoycPHy15xo+/z0CKZnFv6M9ERERUVXBQEZE5c7SUI4tozwxzbcBNKQS/HX1CXr8fA5XH74UuzQiIiKicsVARkSikEolmOjtiJ3jWqKGsQ5iEtPRd/UFrDvzL/LzeQojERERVQ8MZEQkKg97Uxya1BbdXKyRmy9g7qHbGLXpMp6nZoldGhEREVGZYyAjItEZ6Wph1WB3fN/LBdqaUpy6+wzdlp3FhXsJYpdGREREVKYYyIioQpBIJBjS0h5/fdoajpb6iE/JwuBfQ7Do6F3k5uWLXR4RERFRmWAgI6IKxcnaEH992hoDm9tBEIDlJ+5hYOBFPH6ZIXZpRERERKWOgYyIKhxdbU382LcJfvZ3g75ME6HRL9B92VkcvhErdmlEREREpYqBjIgqrA9dbXFoUlu41jRCUkYOPtkahpl/3kBmTp7YpRERERGVCgYyIqrQapnpYtcnrTCuXR0AwObgaPRaeR734lNFroyIiIjo/TGQEVGFp60pxYzuDbFxZHOY6WnjTmwK/Jafw87QhxAE3rOMiIiIKq8SBbLbt29j1qxZ6NixI+rWrQsbGxs0adIEw4cPx/bt25GVxfsGEVHZ6dDAEn9/3hatHc2QkZOHr3Zfw+QdEUjJzBG7NCIiIqJ3UqxAFh4ejs6dO8PNzQ3nzp2Dp6cnJk+ejDlz5mDIkCEQBAH//e9/YWtri/nz5zOYEVGZsTSUY/MoT0zzbQANqQR/RjzBB8vP4dqjl2KXRkRERFRimsUZ1LdvX0ybNg27d++GsbGx2nHBwcFYtmwZFi1ahK+//rq0aiQiUqIhlWCityNa1jHFpN8iEP08HX1XX8D0rk4Y1doBUqlE7BKJiIiIiqVYgeyff/6BlpbWW8d5eXnBy8sLOTk8fYiIyp6HvSkOTWqL6Xuu4fDNWHx/8DbO30vAT/1dYaYvE7s8IiIiorcq1imLxQlj7zOeiOhdGelqYfUQd8zp5QJtTSlO3n2GbsvO4sL9BLFLIyIiInqrYh0he1NaWhpOnz6NmJgYZGdnKy2bNGlSqRRGRFRcEokEQ1vao5m9CT777Qruxadi8C8h+MzbEZM61YOmBieUJSIiooqpxIHsypUr6N69O9LT05GWlgZTU1MkJCRAV1cXlpaWDGREJJqGNob469PWmP3XLewIfYifT9xD8L/PsXSgG2oY64hdHhEREVEhJf6z8ZQpU+Dn54cXL15AR0cHFy9eRHR0NDw8PPDTTz+VRY1ERMWmq62J+f2a4Gd/N+jLNHH5wQt0X3YWR27Gil0aERERUSElDmQRERH44osvIJVKoaGhgaysLNjZ2WHBggWcWZGIKowPXW1xcFIbuNY0QlJGDsZtCcOsP28gMydP7NKIiIiIFEocyLS0tCCVvnqZpaUlYmJiAABGRkZ4+PBh6VZHRPQe7M30sOuTVhjbrg4AYFNwNHqvuoB78akiV0ZERET0SokDmZubGy5fvgwAaN++PWbOnIlt27Zh8uTJcHFxKfUCiYjeh7amFF93b4gNI5vDVE8bt58mw2/5OewKfQhBEMQuj4iIiKq5EgeyH374ATY2NgCAuXPnwsTEBOPHj8ezZ88QGBhY6gUSEZUG7waW+PvztmhV1wwZOXmYtvsapuyIQGpWrtilERERUTVW4lkWmzVrpvh/S0tLHD58uFQLIiIqK1aGcmwZ7YnVp+5hybFI7It4goiHL7Hc3x2NaxqJXR4RERFVQ7w5DxFVKxpSCT7tWA87xraErZEcD56no8/q8/jl7L88hZGIiIjKXbGOkLm7u+P48eMwMTGBm5sbJBKJ2rHh4eGlVhwRUVlpVtsUhz5vi+l7ruHIzTh8f/A2Ltx/joX9msBMXyZ2eURERFRNFCuQ9ezZEzLZq19QevXqVZb1EBGVG2NdbawZ4oGtF6Mx5+BtnLgTj+4/n8XSj9zgVdcMefkCQqISEZYggVlUIrwcLaEhVf8HKSIiIqKSKlYgmzVrlsr/JyKq7CQSCYZ61YaHvSk+/S0c/z5Lw6BfLqJbI2uEx7xEbHImAA1sjgyFjZEcs/yc0dXFRuyyiYiIqIoo8TVkly9fRkhISKHnQ0JCEBoaWipFERGVN2dbQxz4rA0GNKsJQQAO3Yj9Xxj7f7FJmRi/NRyHbzwVqUoiIiKqakocyCZOnKjyBtCPHz/GxIkTS6UoIiIx6GprYl6fJjDW0VK5XPjfv//uu4G7scmIT8lEZk5eudZIREREVUuJp72/desW3N3dCz3v5uaGW7dulUpRRERiuRSViJcZOUWOeZ6aDd+lZxWPZZpSGOpowUhHC4ZyzVf/VTz+3391NBWPFct0tGAg04S0kl2XxmvriIiISk+JA5lMJkNcXBzq1Kmj9PzTp0+hqVni1RERVSjxKZlvHwRAR0uKzNx8CAKQlZuPZylZeJaSVeLtSSSAgUyzyABnpKs+2Mm1NEq8zfdx+MZTzN5/C0+TeG0dERFRaShxgvLx8cGMGTPw559/wsjo1Y1UX758ia+//hpdunQp9QKJiMqTpYG8WOPWj2gBTwdTpGTlIjkjB0kZOUjOzEFyRg6SM3IVj5Mycl5bnqv0OOt/gS45MxfJmbl49CKjxPVqa0qLfWTuzeUG8pIdnTt84ynGbw3Hm3drK7i2bvUQd4YyIiKiEipxIFu4cCHat28Pe3t7uLm5AQAiIiJgZWWFLVu2lHqBRETlqYWDKWyM5IhNyiwUPABAAsDaSI4WDqaQSiUw+l/AsXuHbWXm5P0vxOW+EeheC3DprwU7RcDLRXJmDgQByH7Po3P6sjcDm6biPb1+dM5Apon//nFDZU+E//Vl9v5b6OJszdMXiYiISqDEgaxmzZq4du0atm3bhqtXr0JHRwcjR46Ev78/tLRUXwhPRFRZaEglmOXnjPFbwyEBlAJIQcyY5edcKqFDrqUBuZYGLA1K/tr8fAGp2W8Etv8FtUKhTunxq/9m5rw6OpeSmYuUzFwAJT869zoBwNOkTFyKSoRXXbP3WhcREVF1UqJAlpOTAycnJxw4cABjx44tq5qIiETV1cUGq4e4v3at1CvWFehaKalU8uo6Mvm7/SEsKzdPEeDePK0yWUWAi3mejofFOKWyuNfgERER0SslCmRaWlrIzOQPWyKq+rq62KCLszWC78Xj6NkQ+LT1rFKzCco0NWBhoAELA1mxxgfffw7/dRffOq641+ARERHRK+90H7L58+cjNze3LOohIqowNKQSeDqYwsNcgKeDaZUJY++i4Nq6ojpgZShDCwfTcquJiIioKijxNWSXL1/G8ePHcfToUTRu3Bh6enpKy/fu3VtqxRERUcVQ1LV1BQxkWsjJy4eGtHyn4iciIqrMSnyEzNjYGH379oWvry9sbW1hZGSk9I+IiKqmgmvrrI2UT0u00JdBriXFvWepmPx7BPLyVcU1IiIiUqXER8g2bNhQFnUQEVEloO7auktRiRi+/hIO34zF7P03MfvDRpBIqu8pnkRERMVV4iNkAJCbm4tjx45h7dq1SElJAQA8efIEqamppVocERFVPKqurfOqa4YlHzWFRAJsDo7GqlP3xS6TiIioUijxEbLo6Gh07doVMTExyMrKQpcuXWBgYID58+cjKysLa9asKYs6iYioguvRxAbxKc6Yvf8WFh65CytDOfp51BS7LCIiogqtxEfIPv/8czRr1gwvXryAjo6O4vnevXvj+PHjJVrXmTNn4OfnB1tbW0gkEuzbt0+xLCcnB9OnT1dMHGJra4thw4bhyZMnSutITEzE4MGDYWhoCGNjY4wePbrQkbpr166hbdu2kMvlsLOzw4IFCwrVsmvXLjg5OUEul6Nx48Y4dOhQid4LEREBI1s7YFz7OgCA6Xuu4dTdeJErIiIiqthKHMjOnj2Lb775Btra2krP165dG48fPy7RutLS0uDq6oqVK1cWWpaeno7w8HB8++23CA8Px969e3H37l18+OGHSuMGDx6MmzdvIigoCAcOHMCZM2eUblqdnJwMHx8f2NvbIywsDAsXLkRAQAACAwMVYy5cuAB/f3+MHj0aV65cQa9evdCrVy/cuHGjRO+HiIiA6b5O6O1WA3n5AiZsC8fVhy/FLomIiKjCKvEpi/n5+cjLyyv0/KNHj2BgYFCidXXr1g3dunVTuczIyAhBQUFKz61YsQItWrRATEwMatWqhdu3b+Pw4cO4fPkymjVrBgBYvnw5unfvjp9++gm2trbYtm0bsrOzsX79emhra6NRo0aIiIjA4sWLFcFt2bJl6Nq1K6ZNmwYAmDNnDoKCgrBixQqegklEVEJSqQTz+zZBQmoWzkYmYNTGy9gzvhVqm+u9/cVERETVTIkDmY+PD5YuXao4wiSRSJCamopZs2ahe/fupV7g65KSkiCRSGBsbAwACA4OhrGxsSKMAUDnzp0hlUoREhKC3r17Izg4GO3atVM6oufr64v58+fjxYsXMDExQXBwMKZOnaq0LV9fX6VTKN+UlZWFrKwsxePk5GQAr061zMnJKYV3+34KaqgItVQk7Itq7It67I1qb+uLBMDPHzXBkPWXcfNJCoatD8HOMS1gpi8rxyrFwX1GNfZFPfZGNfZFPfZGtYrUl5LUUOJAtmjRIvj6+sLZ2RmZmZkYNGgQIiMjYW5ujt9++62kqyu2zMxMTJ8+Hf7+/jA0NAQAxMbGwtLSUmmcpqYmTE1NERsbqxjj4OCgNMbKykqxzMTEBLGxsYrnXh9TsA5V5s2bh9mzZxd6/ujRo9DV1S35Gywjbx5lpFfYF9XYF/XYG9Xe1peBNsDS5xqIScxA/xWn8FmjPMiqyX2juc+oxr6ox96oxr6ox96oVhH6kp6eXuyxJQ5kNWvWxNWrV/H777/j2rVrSE1NxejRozF48GClST5KU05ODgYMGABBELB69eoy2UZJzZgxQ+moWnJyMuzs7ODj46MIjGLKyclBUFAQunTpAi0tLbHLqTDYF9XYF/XYG9VK0peWbdMwIPASHqblYH+iFdYOcYOWxjvddaVS4D6jGvuiHnujGvuiHnujWkXqS8HZc8VR4kAGvDoKNWTIkHd5aYkVhLHo6GicOHFCKexYW1sjPl55Bq/c3FwkJibC2tpaMSYuLk5pTMHjt40pWK6KTCaDTFb41BstLS3Rd4DXVbR6Kgr2RTX2RT32RrXi9KWetTE2jGwB/8CLOHvvOb756zYW9Xet8jeO5j6jGvuiHnujGvuiHnujWkXoS0m2/06BLDIyEidPnkR8fDzy8/OVls2cOfNdVqlSQRgr2J6ZmZnSci8vL7x8+RJhYWHw8PAAAJw4cQL5+fnw9PRUjPnvf/+LnJwcRWOCgoLQoEEDmJiYKMYcP34ckydPVqw7KCgIXl5epfZeiIiqs6Z2xlg12B0fbw7F3vDHsDaU46uuTmKXRUREJLoSB7J169Zh/PjxMDc3h7W1tdJfOCUSSYkCWWpqKu7du6d4HBUVhYiICJiamsLGxgb9+vVDeHg4Dhw4gLy8PMU1XaamptDW1kbDhg3RtWtXjBkzBmvWrEFOTg4+/fRTDBw4ELa2tgCAQYMGYfbs2Rg9ejSmT5+OGzduYNmyZViyZIliu59//jnat2+PRYsWoUePHvj9998RGhqqNDU+ERG9H28nS8zr0xhf7b6GVafuw8pQjuGtaotdFhERkahKHMi+//57zJ07F9OnT3/vjYeGhsLb21vxuOCarOHDhyMgIAB//fUXAKBp06ZKrzt58iQ6dOgAANi2bRs+/fRTdOrUCVKpFH379sXPP/+sGGtkZISjR49i4sSJ8PDwgLm5OWbOnKl0r7JWrVph+/bt+Oabb/D111+jXr162LdvH1xcXN77PRIR0f8b0MwOcUmZWBT0DwL234SlgQzdGtuIXRYREZFoShzIXrx4gf79+5fKxjt06ABBENQuL2pZAVNTU2zfvr3IMU2aNMHZs2eLHNO/f/9Se19ERKTepx0dEZuciW0hMfh8RwTM9GVo4WAqdllERESiKPE0V/3798fRo0fLohYiIqoGJBIJvuvpAh9nK2Tn5uPjTZfxT1yK2GURERGJosRHyBwdHfHtt9/i4sWLaNy4caEZRCZNmlRqxRERUdWkIZXgZ383DP4lBGHRLzB8/SXsGd8KtsZlc/sUIiKiiqrEgSwwMBD6+vo4ffo0Tp8+rbRMIpEwkBERUbHItTTw6/Bm6Lv6Au4/S8OIDZewa1wrGOlyCmciIqo+ShzIoqKiyqIOIiKqhox1tbFpVAv0XX0B/8SlYsyWUGwe1QJyLQ2xSyMiIioXJb6GjIiIqDTVNNHFxpEtYCDTxKWoREzdGYG8/LdP6kRERFQVFPsIWcGU9G+zePHidy6GiIiqp4Y2hlg7zAMj1l/GoeuxsDS4hVl+zkr3uiQiIqqKih3Irly58tYx/MFJRETvqlVdcywa4IrPfruCjRcewMpQjvEd6opdFhERUZkqdiA7efJkWdZBREQEP1dbxKdkYc6BW5h/+A6sDGXo415T7LKIiIjKDK8hIyKiCmV0GweMbVcHAPDV7ms4/c8zkSsiIiIqO8UKZD/++CPS09OLtcKQkBAcPHjwvYoiIqLq7T9dndCrqS1y8wWM3xqG64+SxC6JiIioTBQrkN26dQv29vaYMGEC/v77bzx79v9/rczNzcW1a9ewatUqtGrVCh999BEMDAzKrGAiIqr6pFIJFvRzRRtHc6Rn52HkxkuIfp4mdllERESlrliBbPPmzTh27BhycnIwaNAgWFtbQ1tbGwYGBpDJZHBzc8P69esxbNgw3LlzB+3atSvruomIqIrT1pRi9RB3ONsYIiE1G8PXX0JCapbYZREREZWqYk/q4erqinXr1mHt2rW4du0aoqOjkZGRAXNzczRt2hTm5uZlWScREVVDBnItbBzVHH1WXcCD5+kYvfEyto9pCT1ZsX98ERERVWgl/okmlUrRtGlTNG3atAzKISIiUmZpIMfmUS3Qd/UFXH2UhInbw7FuWDNoaXBeKiIiqvz404yIiCq8Ohb6WD+iOeRaUpy6+wwz9l6HIAhil0VERPTeGMiIiKhScKtlgpWD3KEhlWB32CMsOvqP2CURERG9NwYyIiKqNDo1tMLcXi4AgBUn72HLxWiRKyIiIno/DGRERFSpDGxRC1M61wcAzPzzBg7fiBW5IiIionf33oEsOjoat27dQn5+fmnUQ0RE9FaTOjnCv0UtCAIw6fcruPwgUeySiIiI3kmxA9n69euxePFipefGjh2LOnXqoHHjxnBxccHDhw9LvUAiIqI3SSQSzOnZCJ0bWiE7Nx+jN15GZFyK2GURERGVWLEDWWBgIExMTBSPDx8+jA0bNmDz5s24fPkyjI2NMXv27DIpkoiI6E2aGlIs93eDey1jJGfmYvj6S3ialCF2WURERCVS7EAWGRmJZs2aKR7/+eef6NmzJwYPHgx3d3f88MMPOH78eJkUSUREpIqOtgZ+Hd4cdSz08CQpEyPWX0ZSRo7YZRERERVbsQNZRkYGDA0NFY8vXLiAdu3aKR7XqVMHsbG8sJqIiMqXiZ42No1sAQsDGe7GpWDs5lBk5uSJXRYREVGxFDuQ2dvbIywsDACQkJCAmzdvonXr1orlsbGxMDIyKv0KiYiI3sLOVBcbRzaHvkwTIVGJ+GLnVeTn88bRRERU8WkWd+Dw4cMxceJE3Lx5EydOnICTkxM8PDwUyy9cuAAXF5cyKZKIiOhtGtkaIXCoB4ZvuISD15/C0lCGmR84QyKRiF0aERGRWsU+QvbVV19hzJgx2Lt3L+RyOXbt2qW0/Pz58/D39y/1AomIiIqrlaM5Fg1oCgDYcP4BAs/8K25BREREb1HsI2RSqRTfffcdvvvuO5XL3wxoREREYvjQ1RbxyZn4/uBtzPv7DiwNZejtVlPssoiIiFQqdiBTJTMzEzt27EBaWhp8fHzg6OhYWnURERG9s4/b1kFsUiZ+OReFabuuwVxfhrb1LMQui4iIqJBin7I4depUfPbZZ4rH2dnZ8PLywpgxY/D111+jadOmCA4OLpMiiYiISurr7g3xoastcvMFfLIlDDceJ4ldEhERUSHFDmRHjx5Fly5dFI+3bduG6OhoREZG4sWLF+jfvz++//77MimSiIiopKRSCRb2b4JWdc2Qlp2HERsuI+Z5uthlERERKSl2IIuJiYGzs7Pi8dGjR9GvXz/Y29tDIpHg888/x5UrV8qkSCIionch09TA2qEeaGhjiITULAzfcAnPU7PELouIiEih2IFMKpVCEP7/ni4XL15Ey5YtFY+NjY3x4sWL0q2OiIjoPRnItbBpZHPUMNZBVEIaRm0KRXp2rthlERERAShBIGvYsCH2798PALh58yZiYmLg7e2tWB4dHQ0rK6vSr5CIiOg9WRrKsXl0CxjrauHqw5f4dPsV5Obli10WERFRye5DNmPGDHTq1AmdOnVC9+7d4eDgoFh+6NAhtGjRokyKJCIiel91LfTx6/DmkGtJceJOPL7+47rSmR9ERERiKHYg6927Nw4dOoQmTZpgypQp2LFjh9JyXV1dTJgwodQLJCIiKi0e9iZY7u8OqQTYGfoIS4L+EbskIiKq5kp0H7KCo2OqzJo1q1QKIiIiKktdnK3wfa/G+PqP6/j5xD1YGckx2NNe7LKIiKiaKvYRssjISPj7+yM5ObnQsqSkJAwaNAj//vtvqRZHRERUFgZ51sKkTvUAAN/uu4EjN2NFroiIiKqrYgeyhQsXws7ODoaGhoWWGRkZwc7ODgsXLizV4oiIiMrKlM71MLC5HfIFYNJvVxD6IFHskoiIqBoqdiA7ffo0+vfvr3b5gAEDcOLEiVIpioiIqKxJJBJ838sFnZwskZWbj9GbQnEvPkXssoiIqJop0Y2hLS0t1S43NzfHw4cPS6UoIiKi8qCpIcXyQW5oameMpIwcDF9/GXHJmWKXRURE1UixA5mRkRHu37+vdvm9e/dUns5IRERUkelqa2L9iOaoY66Hxy8zMHz9JSRn5ohdFhERVRPFDmTt2rXD8uXL1S7/+eef0bZt21IpioiIqDyZ6mlj06gWsDCQ4U5sCsZuDkVWbp7YZRERUTVQ7EA2Y8YM/P333+jXrx8uXbqEpKQkJCUlISQkBH379sWRI0cwY8aMsqyViIiozNiZ6mLDiObQl2ni4r+JmLrzKvLzeeNoIiIqW8UOZG5ubti9ezfOnDkDLy8vmJqawtTUFK1atcLZs2exc+dOuLu7l2WtREREZcqlhhHWDPGAloYEB689xfcHb0MQGMqIiKjsFDuQAcAHH3yA6Oho7N69Gz/++CPmzZuHPXv24MGDB/jwww9LvPEzZ87Az88Ptra2kEgk2Ldvn9LyvXv3wsfHB2ZmZpBIJIiIiCi0jg4dOkAikSj9++STT5TGxMTEoEePHtDV1YWlpSWmTZuG3NxcpTGnTp2Cu7s7ZDIZHB0dsXHjxhK/HyIiqvza1DPHT/1dAQDrz0fhl7NRIldERERVmWZxB0ZFRcHBwQE6Ojro3bt3qWw8LS0Nrq6uGDVqFPr06aNyeZs2bTBgwACMGTNG7XrGjBmD7777TvFYV1dX8f95eXno0aMHrK2tceHCBTx9+hTDhg2DlpYWfvjhB8V769GjBz755BNs27YNx48fx8cffwwbGxv4+vqWynslIqLKo2fTGohLzsQPh+5g7qHbsDSUoWfTGmKXRUREVVCxA1ndunVhb28Pb29vdOzYER06dEDNmjXfa+PdunVDt27d1C4fOnQoAODBgwdFrkdXVxfW1tYqlx09ehS3bt3CsWPHYGVlhaZNm2LOnDmYPn06AgICoK2tjTVr1sDBwQGLFi0CADRs2BDnzp3DkiVLGMiIiKqpMW3rIDYpC+vPR+HLXVdhri9Da0dzscsiIqIqptiB7MSJEzh16hROnTqF3377DdnZ2ahTpw46duwIb29veHt7w8rKqixrVWvbtm3YunUrrK2t4efnh2+//VZxlCw4OBiNGzdWqs3X1xfjx4/HzZs34ebmhuDgYHTu3Flpnb6+vpg8ebLabWZlZSErK0vxODk5GQCQk5ODnBzxp0suqKEi1FKRsC+qsS/qsTeqVZe+TPdxRFxSBg7eiMXYLaHYPro5nG2KvsVLdelNSbEv6rE3qrEv6rE3qlWkvpSkhmIHsg4dOqBDhw4AgMzMTFy4cEER0DZt2oScnBw4OTnh5s2bJS74fQwaNAj29vawtbXFtWvXMH36dNy9exd79+4FAMTGxhYKigWPY2NjixyTnJyMjIwM6OjoFNruvHnzMHv27ELPHz16VOmUSbEFBQWJXUKFxL6oxr6ox96oVh360lEP+MdQishkYOi6YEx2yYOZ/O2vqw69eRfsi3rsjWrsi3rsjWoVoS/p6enFHlvsQPY6uVyOjh07ok2bNvD29sbff/+NtWvX4s6dO++yuvcyduxYxf83btwYNjY26NSpE+7fv4+6deuW2XZnzJiBqVOnKh4nJyfDzs4OPj4+FeIG2Tk5OQgKCkKXLl2gpaUldjkVBvuiGvuiHnujWnXri3fnHAz65TLuxKViS4whfh/TAqZ62irHVrfeFBf7oh57oxr7oh57o1pF6kvB2XPFUaJAlp2djYsXL+LkyZM4deoUQkJCYGdnh3bt2mHFihVo3759iYstbZ6engCAe/fuoW7durC2tsalS5eUxsTFxQGA4roza2trxXOvjzE0NFR5dAwAZDIZZDJZoee1tLRE3wFeV9HqqSjYF9XYF/XYG9WqS19MtbSwabQn+qy6gKjn6fhkewS2f9wSOtoaal9TXXpTUuyLeuyNauyLeuyNahWhLyXZfrGnve/YsSNMTEwwYcIExMfHY9y4cbh//z7u3r2LdevWYejQoahVq9Y7FVyaCqbGt7GxAQB4eXnh+vXriI+PV4wJCgqCoaEhnJ2dFWOOHz+utJ6goCB4eXmVT9FERFThWRnKsWlUcxjpaOFKzEt8uj0cuXn5YpdFRESVXLED2dmzZ2FmZoaOHTuiU6dO6NKliyL0vKvU1FREREQoQlRUVBQiIiIQExMDAEhMTERERARu3boFALh79y4iIiIU137dv38fc+bMQVhYGB48eIC//voLw4YNQ7t27dCkSRMAgI+PD5ydnTF06FBcvXoVR44cwTfffIOJEycqjnB98skn+Pfff/HVV1/hzp07WLVqFXbu3IkpU6a81/sjIqKqxdHSAL8ObwaZphTH78Tjm303eONoIiJ6L8UOZC9fvkRgYCB0dXUxf/582NraonHjxvj000+xe/duPHv2rMQbDw0NhZubG9zc3AAAU6dOhZubG2bOnAkA+Ouvv+Dm5oYePXoAAAYOHAg3NzesWbMGAKCtrY1jx47Bx8cHTk5O+OKLL9C3b1/s379fsQ0NDQ0cOHAAGhoa8PLywpAhQzBs2DCl+5Y5ODjg4MGDCAoKgqurKxYtWoRffvmFU94TEVEhzWqb4md/N0glwO+XH2LpsUixSyIiokqs2NeQ6enpoWvXrujatSsAICUlBefOncPJkyexYMECDB48GPXq1cONGzeKvfEOHToU+ZfFESNGYMSIEWqX29nZ4fTp02/djr29PQ4dOvTWWq5cufLWdREREfk2ssZ3PV3wzb4bWHY8ElaGcgzyFP+0fSIiqnyKfYTsTXp6ejA1NYWpqSlMTEygqamJ27dvl2ZtREREFdaQlvb4rKMjAOCbfdcRdCsOefkCQqISEZYgQUhUIvLyeTojEREVrdhHyPLz8xEaGopTp07h5MmTOH/+PNLS0lCjRg14e3tj5cqV8Pb2LstaiYiIKpSpXeojLjkTO0MfYcK2MBjKtfA8LRuABjZHhsLGSI5Zfs7o6vJ+11wTEVHVVexAZmxsjLS0NFhbW8Pb2xtLlixBhw4dyvReX0RERBWZRCLB3N6NcetJMm48Sf5fGPt/sUmZGL81HKuHuDOUERGRSsUOZAsXLoS3tzfq169flvUQERFVKlKJBM9Ss1QuEwBIAMzefwtdnK2hIZWUa21ERFTxFTuQjRs3rizrICIiqpQuRSUiLll1IANehbKnSZm4FJUIr7pm5VcYERFVCu88qQcREREB8SmZpTqOiIiqFwYyIiKi92BpIC/mOFkZV0JERJURAxkREdF7aOFgChsjOd52ddiv56IQl8yjZEREpIyBjIiI6D1oSCWY5ecMAIVCmUQxBjh2Ox6dF5/G75diIAi8PxkREb3CQEZERPSeurrYYPUQd1gbKZ++aG0kx5oh7jg4qS1caxohJTMX/9l7HYN/CUH08zSRqiUiooqk2LMsEhERkXpdXWzQxdkawfficfRsCHzaesLL0VIx1f3eCa2x4XwUfjp6FxfuP4fv0jP40qcBRrZ24HT4RETVGI+QERERlRINqQSeDqbwMBfg6WCqFLQ0pBJ83LYOjkxuB686ZsjMycf3B2+jz+oLuBubImLVREQkJgYyIiKicmRvpoftYzzxY5/GMJBp4urDl/hg+VksCfoH2bn5YpdHRETljIGMiIionEkkEgxsUQtBU9ujc0Mr5OQJWHY8Eh8sP4srMS/ELo+IiMoRAxkREZFIrI3kWDfMAysGucFMTxv/xKWiz+oLmHPgFtKzc8Uuj4iIygEDGRERkYgkEgk+aGKLY1Pbo49bDQjCq3uWdV16FhfuJYhdHhERlTEGMiIiogrARE8biz9qig0jm8PWSI6YxHQM+iUE/9lzDUkZOWKXR0REZYSBjIiIqALxbmCJo1PbY5iXPQDg98sP0WXxaRy5GStyZUREVBYYyIiIiCoYfZkmvuvpgp3jvFDHXA/xKVkYtyUME7eF41lKltjlERFRKWIgIyIiqqBaOJji0OdtMaFDXWhIJTh4/Sm6LDmNveGPIAiC2OUREVEpYCAjIiKqwORaGviqqxP+nNgazjaGeJmeg6k7r2LEhst49CJd7PKIiOg9MZARERFVAi41jPDnp60xzbcBtDWlOP3PM/guOYPNwQ+Qn8+jZURElRUDGRERUSWhpSHFRG9HHJrUFs3sTZCWnYeZf97EgLXBuP8sVezyiIjoHTCQERERVTKOlvrYOc4Lc3o2gp62BkKjX6DbsrNYefIecvLyxS6PiIhKgIGMiIioEpJKJRjqVRtHprRD+/oWyM7Nx8Ijd9FzxXnceJwkdnlERFRMDGRERESVWE0TXWwc2RyLB7jCWFcLt54mo+fK85h/+A4yc/LELo+IiN6CgYyIiKiSk0gk6ONeE0FT2qNHExvk5QtYfeo+ui87i0tRiWKXR0RERWAgIyIiqiIsDGRYOcgda4d6wNJAhn8T0jBgbTC+3XcDKZk5YpdHREQqMJARERFVMb6NrBE0tT0GNrcDAGy5GA3fJWdw8k68yJUREdGbGMiIiIiqICMdLfzYtwm2feyJWqa6eJKUiZEbL2PKjggkpmWLXR4REf0PAxkREVEV1trRHIcnt8XHbRwglQB/XHmMLotPY//VJxAE3lCaiEhsDGRERERVnK62Jr75wBl7xrdCfSt9PE/Lxme/XcGYzWGITcoUuzwiomqNgYyIiKiacKtlggOftcXkzvWgpSHBsdtx6LL4NH67FMOjZUREImEgIyIiqka0NaWY3Lk+DnzWFq52xkjJysWMvdcxaF0IHiSkiV0eEVG1w0BGRERUDTWwNsDe8a3wTY+GkGtJEfzvc3RddgbrzvyLvHweLSMiKi8MZERERNWUhlSCj9vWwdHJ7dGqrhkyc/Ix99Bt9Fl1Hndik8Uuj4ioWmAgIyIiquZqmeli28eemN+3MQzkmrj6KAkf/HwOi4P+QVZuntjlERFVaQxkREREBIlEgo+a18Kxqe3RxdkKufkCfj4eiQ9+PofwmBdil0dEVGUxkBEREZGClaEcgUM9sHKQO8z1tREZn4q+qy/gu/23kJ6dK3Z5RERVDgMZERERKZFIJOjRxAZBU9qjj3sNCAKw/nwUfJeewbnIBLHLIyKqUhjIiIiISCUTPW0sHtAUG0c2Rw1jHTxMzMCQX0Pw1e6rSErPEbs8IqIqgYGMiIiIitShgSWOTGmH4V72AICdoY/QeclpHL4RK3JlRESVHwMZERERvZW+TBOze7pg1ydeqGOhh2cpWfhkaxgmbAtDfEqm2OUREVVaDGRERERUbM1rm+LQpLaY0KEuNKQSHLoeiy6Lz2B32CMIAm8oTURUUqIGsjNnzsDPzw+2traQSCTYt2+f0vK9e/fCx8cHZmZmkEgkiIiIKLSOzMxMTJw4EWZmZtDX10ffvn0RFxenNCYmJgY9evSArq4uLC0tMW3aNOTmKs8UderUKbi7u0Mmk8HR0REbN24s5XdLRERUNci1NPBVVyf8ObE1GtkaIikjB1/uuoph6y/hYWK62OUREVUqogaytLQ0uLq6YuXKlWqXt2nTBvPnz1e7jilTpmD//v3YtWsXTp8+jSdPnqBPnz6K5Xl5eejRoweys7Nx4cIFbNq0CRs3bsTMmTMVY6KiotCjRw94e3sjIiICkydPxscff4wjR46U3pslIiKqYlxqGGHfxNaY3tUJ2ppSnI1MgO/SM9h4Pgr5+TxaRkRUHJpibrxbt27o1q2b2uVDhw4FADx48EDl8qSkJPz666/Yvn07OnbsCADYsGEDGjZsiIsXL6Jly5Y4evQobt26hWPHjsHKygpNmzbFnDlzMH36dAQEBEBbWxtr1qyBg4MDFi1aBABo2LAhzp07hyVLlsDX17d03zQREVEVoqUhxfgOdeHTyAoz9lzHpQeJCNh/C/uvPcX8vo3haGmAvHwBIVGJCEuQwCwqEV6OltCQSsQunYioQhA1kL2vsLAw5OTkoHPnzornnJycUKtWLQQHB6Nly5YIDg5G48aNYWVlpRjj6+uL8ePH4+bNm3Bzc0NwcLDSOgrGTJ48We22s7KykJWVpXicnJwMAMjJyUFOjvhTARfUUBFqqUjYF9XYF/XYG9XYF/Wqa29qGcuwZaQHfrv8EAuPRiIs+gW6LTuLro2scOnBC8QlZwHQwObIUFgbyvBNdyf4NrJ663qrg+q6z7wN+6Iee6NaRepLSWqo1IEsNjYW2traMDY2VnreysoKsbGxijGvh7GC5QXLihqTnJyMjIwM6OjoFNr2vHnzMHv27ELPHz16FLq6uu/8nkpbUFCQ2CVUSOyLauyLeuyNauyLetW1NyYAvnQBdv4rxe2XUuy/FgtAAPD/R8RikzPx6e8RGFU/H65mPLWxQHXdZ96GfVGPvVGtIvQlPb3419NW6kAmphkzZmDq1KmKx8nJybCzs4OPjw8MDQ1FrOyVnJwcBAUFoUuXLtDS0hK7nAqDfVGNfVGPvVGNfVGPvXllYF4+Ws4/haSMXLwexl559fjAU11MHdgGMi2Ncq+vIuE+oxr7oh57o1pF6kvB2XPFUakDmbW1NbKzs/Hy5Uulo2RxcXGwtrZWjLl06ZLS6wpmYXx9zJszM8bFxcHQ0FDl0TEAkMlkkMlkhZ7X0tISfQd4XUWrp6JgX1RjX9Rjb1RjX9Sr7r0JjXn+vzCmXnxKFly+Ow5zfRksDWSwMHj1X0tDGSwN5K89J4eloQzyKh7cqvs+ow77oh57o1pF6EtJtl+pA5mHhwe0tLRw/Phx9O3bFwBw9+5dxMTEwMvLCwDg5eWFuXPnIj4+HpaWlgBeHcY0NDSEs7OzYsyhQ4eU1h0UFKRYBxEREZVMSW4WnZCahYTULOBp0eMM5JrKIe218KYIcwZyGOpoQiLhpCFEVDmIGshSU1Nx7949xeOoqChERETA1NQUtWrVQmJiImJiYvDkyRMAr8IW8OqIlrW1NYyMjDB69GhMnToVpqamMDQ0xGeffQYvLy+0bNkSAODj4wNnZ2cMHToUCxYsQGxsLL755htMnDhRcYTrk08+wYoVK/DVV19h1KhROHHiBHbu3ImDBw+Wc0eIiIiqBksDebHGrR7iDjsTXTxLycKzlCzEp2QiPiUL8cmv/v9Z6qv/z8rNR0pmLlIyc3H/WVqR69TWlCofcXstvL0e5sz0ZaLP9sgZKIlI1EAWGhoKb29vxeOCa7KGDx+OjRs34q+//sLIkSMVywcOHAgAmDVrFgICAgAAS5YsgVQqRd++fZGVlQVfX1+sWrVK8RoNDQ0cOHAA48ePh5eXF/T09DB8+HB89913ijEODg44ePAgpkyZgmXLlqFmzZr45ZdfOOU9ERHRO2rhYAobIzlikzKhatoOCQBrIzl8nK3fGkAEQUByZi6e/S+sPXstsCkep2QhPjkTyZm5yM7Nx6MXGXj0IqPI9UolgJm+DBb6BUfa/v/0yP9/7tXRt7I4XfLwjaeYvf8WniZlomAGShsjOWb5OaOri02pb4+IKiZRA1mHDh0gCOpnVxoxYgRGjBhR5DrkcjlWrlyp9ubSAGBvb1/olERVtVy5cqXIMURERFQ8GlIJZvk5Y/zWcEgApVBWEL9m+TkX62iQRCKBkY4WjHS04GhpUOTYzJw8RUB79trRNqWjbylZeJ6ahXwBiiNzt95yuqShXFPperaC8Pb6dW8WBnIYyot3uuThG08xfmt4obAam5SJ8VvDsXqIO0MZUTVRqa8hIyIiooqrq4sNVg9xf+0o0CvWZXgUSK6lATtTXdiZFn0Lmrx8Ac/T3ghryVmKUyRfD2/ZuflIzsxFcjFOl5RpSpVPlTSUKV33ZmEgg5m+NgL+uqXyyGHBDQJm77+FLsU4ekhElR8DGREREZWZri426OJsjeB78Th6NgQ+bT0rxHVSGlLJ/64lK/paN6XTJZOzXjtF8o1r3VKykJyZi6xini5Z5DYBPE3KxKWoRHjVNXvn9RBR5cBARkRERGVKQyqBp4Mpnt8W4OlgKnoYK4l3O10y87Xr2gpf65aQkqXy6NibSjJTJRFVXgxkRERERKWguKdLnot8hiG/XipyDAD8GfEENU104F7LhNP4E1VhDGRERERE5cirrnmRM1AWOHEnHifuxMPBXA993Gqgt3sN1DQpOuwRUeUjFbsAIiIiouqkYAZK4P9nnCwg+d+/yZ3qoY97DehqayAqIQ2Lgv5Bm/kn4R94EbvDHiEtK7e8yyaiMsIjZERERETlrLgzUM7pmYu/b8RiT9gjBP/7XPFv5p830NXFGv3ca6JlHTNIK9F1eUSkjIGMiIiISATFmYFST6aJfh410c+jJh69SMcf4Y+xJ/wRHjxPx97wx9gb/hg1jHXQ260G+nrUhIO5nojviIjeBQMZERERkUhKMgNlTRNdfNapHj7t6IjwmBfYHfYYB649weOXGVhx8h5WnLwH91rG6OtREx80sYWRjlY5vhMielcMZERERESViEQigYe9KTzsTTHLzxlBt+KwJ/wRzvzzDOExLxEe8/J/N5a2Qj/3mmhbzxyaGpw2gKiiYiAjIiIiqqTkWhrwc7WFn6st4pMzsS/iMfaEPcbduBQcvPYUB689hYWBDL2a2qKvR004WRuKXTIRvYGBjIiIiKgKsDSUY2y7uhjTtg5uPknG7rBH+OvqEzxLycK6s1FYdzYKjWwN0de9Jno2tYWZvkzskokIDGREREREVYpEIoFLDSO41DDC190b4tTdeOwJf4QTd+Jx80kybj65hR8O3UaHBpbo51EDHZ2soK3JUxqJxMJARkRERFRFaWtK4dPIGj6NrJGYlo39V59gT/gjXHuUhGO343DsdhyMdbXwoast+rrXRJOaRpBIOIU+UXliICMiIiKqBkz1tDG8VW0Mb1Ub/8SlYE/4I+y78hhxyVnYHByNzcHRqGepjz7uNdHbrQasjeRil0xULTCQEREREVUz9a0MMKNbQ3zl64Rz9xKwJ+wRjtyMRWR8KuYfvoOFR+6gtaM5+nnUhI+zNXS0NcQumajKYiAjIiIiqqY0pBK0r2+B9vUtkJyZg0PXnmJP+CNcfvACZyMTcDYyAQYyTXRvbIO+HjXRvLYJT2kkKmUMZEREREQEQ7kWBraohYEtaiH6eRr2hD/G3vBHePQiAztCH2JH6EPUMtVFH/ca6OteE3amumKXTFQlMJARERERkRJ7Mz1M7VIfkzvVw6UHidgT9giHrj9FTGI6lh6LxNJjkfB0MEVfj5ro3tgG+jL+Skn0rvjdQ0REREQqSaUStKxjhpZ1zDC7ZyMcuRmLPWGPcf5+AkKiEhESlYhZf95EVxdr9HGvgVZ1zaEh5SmNRCXBQEZEREREb6WrrYnebjXR260mnrzMwB9XHmNP2CP8m5CGP648xh9XHsPGSI5ebq9OaXS01Be7ZKJKgYGMiIiIiErE1lgHE70dMaFDXVx5+BJ7wh5h/9UneJqUidWn7mP1qftoameMvh414dfEBsa62mKXTFRhMZARERER0TuRSCRwr2UC91om+PYDZxy/HY894Y9w+p9niHj4EhEPX2LO/lvo7GyJvu410a6+BbQ0pGKXTVShMJARERER0XuTa2mgRxMb9Ghig2cpWfgz4jF2hz3CndgUHLoei0PXY2Gur42eTV+d0uhsayh2yUQVAgMZEREREZUqCwMZPm5bBx+3rYObT5KwJ+wx/ox4jITUbPx6Lgq/notCQxtD9HWvgZ5Na8DCQKb0+rx8ASFRiQhLkMAsKhFejpacLISqLAYyIiIiIiozjWyN0MjWCDO6O+H03WfYE/4Ix2/H4/bTZHx/MBnz/r6DDvUt0NejJjo1tMTJO/GYvf8WniZlAtDA5shQ2BjJMcvPGV1dbMR+O0SljoGMiIiIiMqcloYUnZ2t0NnZCi/Ts7H/6hPsDn+Mqw9f4videBy/Ew9dbQ2kZ+cVem1sUibGbw3H6iHuDGVU5TCQEREREVG5MtbVxlCv2hjqVRv34lOwJ/wx9oY9QlxKlsrxAgAJgNn7b6GLszVPX6QqhdPcEBEREZFoHC0NML2rExZ/1LTIcQKAp0mZuBSVWC51EZUXBjIiIiIiEl1CquqjY296/DK9jCshKl8MZEREREQkOksDebHG/XDoNrZejEZ2bn4ZV0RUPhjIiIiIiEh0LRxMYWMkR1FXh0klQGJaDr7ZdwMdF53CztCHyM1jMKPKjYGMiIiIiESnIZVglp8zABQKZZL//Vv6UVME+DnDwkCGRy8y8NXua+iy5Az2XXmMvHyhvEsmKhUMZERERERUIXR1scHqIe6wNlI+fdHaSI7VQ9zxYdMaGNHaAWemeePr7k4w1dNGVEIaJu+IQNelZ3Do+lPkM5hRJcNp74mIiIiowujqYoMuztYIvhePo2dD4NPWE16OlkpT3etoa2Bsu7oY5GmPTRceYO3p+4iMT8WEbeFoaGOIqV3qo3NDS0gknB6fKj4eISMiIiKiCkVDKoGngyk8zAV4Opiqve+YvkwTE70dce4/HfF5p3rQl2ni9tNkjNkcil4rz+PU3XgIAo+YUcXGQEZERERElZqhXAtTutTH2a+8Mb5DXehoaeDqoySM2HAZ/dcE48L9BLFLJFKLgYyIiIiIqgQTPW1M7+qEs9O98XEbB8g0pQiNfoFB60LgH3gRoQ94U2mqeBjIiIiIiKhKMdeX4ZsPnHHmK28M87KHloYEwf8+R781wRi2/hKuPnwpdolECgxkRERERFQlWRnK8V1PF5ya5g3/FnbQlEpw5p9n6LnyPD7eFIpbT5LFLpGIgYyIiIiIqrYaxjqY16cJTnzRAX3da0IqAY7djkP3n89iwrYwRMaliF0iVWMMZERERERULdQy08WiAa44OqU9/FxtIZEAh67HwmfpGUz+/QqiEtLELpGqIQYyIiIiIqpWHC31sdzfDYc/b4eujawhCMC+iCfovPg0pu26ioeJ6WKXSNUIAxkRERERVUsNrA2wZqgHDnzWBh2dLJGXL2BX2CN4/3QKX/9xHU9eZohdIlUDogayM2fOwM/PD7a2tpBIJNi3b5/SckEQMHPmTNjY2EBHRwedO3dGZGSk0pjatWtDIpEo/fvxxx+Vxly7dg1t27aFXC6HnZ0dFixYUKiWXbt2wcnJCXK5HI0bN8ahQ4dK/f0SERERUcXjUsMI60c0x94JrdC2njly8wVsD4lBh4WnEPDXTcQnZ4pdIlVhogaytLQ0uLq6YuXKlSqXL1iwAD///DPWrFmDkJAQ6OnpwdfXF5mZyt8U3333HZ4+far499lnnymWJScnw8fHB/b29ggLC8PChQsREBCAwP9r787joqr3PoB/hnUAYQRZhrkCYiJuCGKGpJALAj5dBTWXpNQupXbhIlrY1ce9TLHc5WqLS940zVuimRG4sBiI12VUEBGNJBMwRURABJ3z/OHD5MQMYMmcUT7v14vX6845v5nznQ/YnS/nnC8ff6xek5mZiZdffhmRkZE4deoUwsPDER4ejpycnJZ540RERERkcHxdbfHvSD/snNwXz7nbofa+Clsyf0LgB4fx/v483Ki8K3aJ9BQyEfPgQ4cOxdChQ7XuEwQBq1atwpw5cxAWFgYA2Lp1K5ycnJCYmIhx48ap11pbW0Mul2t9nW3btqG2thabNm2CmZkZunfvDqVSiRUrVmDy5MkAgNWrVyM0NBRxcXEAgHfffRcpKSlYt24dNmzY8DjfMhEREREZOL+O7bBzcl/8cPEGlqfk41RROT5O/xGfH72M1/p1wOSAZyCzNBW7THpKiNqQNaawsBAlJSUICgpSb5PJZPDz80NWVpZGQ7Z06VK8++67cHV1xfjx4zF9+nSYmDx4a1lZWQgMDISZmZl6fUhICOLj43Hz5k3Y2toiKysLM2bM0Dh+SEhIg0soH3b37l3cvfvbb0kqKh78HYu6ujrU1dX9qff+ONTXYAi1GBLmoh1z0Y3ZaMdcdGM22jEX3ZiNdoaQi18HGXa+3gdpBdex6uBF5F69jYTDl7A16zJee94Nk/zdYC3V/8dpQ8jGEBlSLo9Sg8E2ZCUlJQAAJycnje1OTk7qfQAQExMDX19f2NnZITMzE7NmzUJxcTFWrFihfh13d/cGr1G/z9bWFiUlJU0e5/eWLFmChQsXNtienJwMS0vLR3inLSslJUXsEgwSc9GOuejGbLRjLroxG+2Yi27MRjtDyeUNV+CstQT7i4xQfOce1hy6hE/TL2KQQoVAuQBzY/3XZCjZGBpDyKW6uvmTOg22IWuuh89s9ezZE2ZmZpgyZQqWLFkCc3PzFjvurFmzNI5dUVEBFxcXBAcHw8bGpsWO21x1dXVISUnBkCFDYGrKU+r1mIt2zEU3ZqMdc9GN2WjHXHRjNtoZYi4vApipEvBdbinWHLqEH69XYV+RMTJvmGJKgDvGP+cCqWnLd2aGmI0hMKRc6q+eaw6Dbcjq7wkrLS2Fs7OzentpaSl8fHx0Ps/Pzw/37t3DTz/9BE9PT8jlcpSWlmqsqX9cfwxda3TdlwYA5ubmWhs+U1NT0X8AHmZo9RgK5qIdc9GN2WjHXHRjNtoxF92YjXaGmEu4rwuG+bTHHuUvWHWgAEVl1ViSdAEbf7iM6EGdMLaPC8xNWr4xM8RsDIEh5PIoxzfYv0Pm7u4OuVyOgwcPqrdVVFQgOzsb/v7+Op+nVCphZGQER0dHAIC/vz/S09M1ruNMSUmBp6cnbG1t1WsePk79msaOQ0REREStl7GRBCN92+PgWy9g6Ugv/KWtBa7dvot5e3Ix6MM0fHGsCHX3VWKXSU8AURuyyspKKJVKKJVKAA8GeSiVShQVFUEikSA2Nhbvvfce9u7di7Nnz2LChAlQKBQIDw8H8GBgx6pVq3D69Gn8+OOP2LZtG6ZPn45XXnlF3WyNHz8eZmZmiIyMRG5uLnbu3InVq1drXG44bdo0JCUlYfny5Th//jwWLFiA48ePIzo6Wt+REBEREdETxNTYCOOec8Wht1/Au2Hd4WRjjl/K72DW12cxeHkavjpxBfdVgthlkgET9ZLF48ePY+DAgerH9U3SxIkTsWXLFsycORNVVVWYPHkyysvL0b9/fyQlJUEqlQJ4cNngjh07sGDBAty9exfu7u6YPn26RrMlk8mQnJyMqKgo9O7dG/b29pg3b5565D0APP/889i+fTvmzJmD2bNnw8PDA4mJiejRo4eekiAiIiKiJ5m5iTFe9e+A0c+64POjl7Eh7RKKyqrx1q7TSEi9iNigzvirlzOMjCRil0oGRtSGbMCAARAE3b8xkEgkWLRoERYtWqR1v6+vL44ePdrkcXr27ImMjIxG14wePRqjR49u8rWIiIiIiHSRmhrj9YCOGO/nis8yL+Oj9Ev48dcqxHxxCgmHLmL6kM4I6e4EiYSNGT1gsPeQERERERE9qSzNTPDmgGeQMXMgZgzpDGupCfJLb2Pq5ycwbN0RHDpf2uiJCWo92JAREREREbUQa6kpYgZ74MjMQYge2AlWZsbI+aUCf9tyHCP+lYmMgl/ZmLVybMiIiIiIiFqYzNIUb4d4IuOdQZgS2BFSUyMofy7HqxuPYezHR5H94w2xSySRsCEjIiIiItITOyszzPqfrkifORCTnu8AM2MjHCssw9iPj+KVT7Nxsuim2CWSnrEhIyIiIiLSM0drKRYM7460mQMQ4ecKU2MJjly8jpH/ysRrm4/h7JVbYpdIesKGjIiIiIhIJM4yCywe4YVDbw3AmGfbw9hIgsP5v2LYuiOY8u/jOF9SobH+vkpAdmEZTlyXILuwjH/j7Ckg6th7IiIiIiICXOwssewlb7w5oBNWH7iAPaev4vvcUiSfK8WLXs6IDeqMi9duY+E351B8qwaAMbYWHIezTIr5w7ohtIez2G+B/iCeISMiIiIiMhDu9lZYNa4XkmMD8aKXMwQB2HemGENWpGHq5yf/vxn7TcmtGrz5+Ukk5RSLVDH9WWzIiIiIiIgMjIeTNRIifPFtTH8M7uIIXRcm1m9f+M05Xr74hGJDRkRERERkoLorZHg9oGOjawQAxbdqcKywTD9F0WPFhoyIiIiIyIBdu13T9KJHWEeGhQ0ZEREREZEBc7SWNmtd3T1VC1dCLYENGRERERGRAXvO3Q7OMikkTayb+dUZLPwmF7fu1OmlLno82JARERERERkwYyMJ5g/rBgANmrL6x97tZVAJwOYffsLAD1OxPbuIQz6eEGzIiIiIiIgMXGgPZ6x/xRdymebli3KZFBte8cWe6P74d+Rz6OTYBmVVtZi9+yyGrzuC4z9x0Ieh4x+GJiIiIiJ6AoT2cMaQbnJkXbyG5IxsBAf4wb+TI4yNHpwnC/BwwHfTArA16zJWHbiA3KsVeGlDFsJ8FPjn0C5wllmI/A5IG54hIyIiIiJ6QhgbSeDnbofe9gL83O3UzVg9U2MjRPZ3x+G3B+Dl51wgkQB7lFcx6MM0rDtUgJq6+yJVTrqwISMiIiIiesrYtzHHkpE9sTeqP3q72eJO3X18mHwBQ1am4fvcEggC7y8zFGzIiIiIiIieUl7tZfjPVH+sHucDJxtz/Fx2B1P+fQKvbjyGgtLbYpdHYENGRERERPRUk0gkCPP5Cw69NQBRA5+BmbERjly8jtDVGRyTbwDYkBERERERtQJW5iaIC+mClBmBGNLNCfdVgnpM/hfHOCZfLGzIiIiIiIhaEbd2VvhkwrPY+rffxuTP+ppj8sXChoyIiIiIqBUK7PxgTP7cv3aDtdREPSZ/2o5TKL51R+zyWg02ZERERERErdTDY/LH9eGYfDGwISMiIiIiauXs25hj6SiOyRcDGzIiIiIiIgLw25j8VWM1x+RP2MQx+S2FDRkREREREalJJBKE99Ick59RwDH5LYUNGRERERERNcAx+frBhoyIiIiIiHR6eEz+Mw5WHJP/mLEhIyIiIiKiJgV2dkBSbOCDMfnmmmPyS27ViF3eE4sNGRERERERNYt6TH7c78bkL09FwuGLHJP/B7AhIyIiIiKiR/L7MfnVtffxwff5CF6ZzjH5j4gNGRERERER/SG/H5NfVFbNMfmPiA0ZERERERH9YQ+Pyf/7AM0x+Yu+Occx+U1gQ0ZERERERH+albkJZoY+GJMf1PXBmPxNPxRiEMfkN4oNGRERERERPTZu7azw6cTfxuTf+P8x+WEJHJOvDRsyIiIiIiJ67H4/Jj/nF47J14YNGRERERERtQiOyW8aGzIiIiIiImpRD4/J93VtqzEmP7mVj8lnQ0ZERERERHrh1V6Gr958XmNM/uT/H5N/8VrrHJPPhoyIiIiIiPRG55j8Va1zTD4bMiIiIiIi0rvfj8m/10rH5LMhIyIiIiIi0bT2MflsyIiIiIiISHT1Y/LnvNhVY0x+7FM+Jl/Uhiw9PR3Dhg2DQqGARCJBYmKixn5BEDBv3jw4OzvDwsICQUFBKCgo0FhTVlaGiIgI2NjYoG3btoiMjERlZaXGmjNnziAgIABSqRQuLi5YtmxZg1p27dqFLl26QCqVwsvLC/v373/s75eIiIiIiHQzNTbC6wEdcThuAMY++2BMfuJTPiZf1IasqqoK3t7eSEhI0Lp/2bJlWLNmDTZs2IDs7GxYWVkhJCQENTW/dcgRERHIzc1FSkoK9u3bh/T0dEyePFm9v6KiAsHBwXBzc8OJEyfwwQcfYMGCBfj444/VazIzM/Hyyy8jMjISp06dQnh4OMLDw5GTk9Nyb56IiIiIiLSyb2OO+Jdax5h8URuyoUOH4r333sOIESMa7BMEAatWrcKcOXMQFhaGnj17YuvWrbh69ar6TFpeXh6SkpLw6aefws/PD/3798fatWuxY8cOXL16FQCwbds21NbWYtOmTejevTvGjRuHmJgYrFixQn2s1atXIzQ0FHFxcejatSveffdd+Pr6Yt26dXrJgYiIiIiIGnp4TL6jte4x+fdVArILy3DiugTZhWVP1EAQE7EL0KWwsBAlJSUICgpSb5PJZPDz80NWVhbGjRuHrKwstG3bFs8++6x6TVBQEIyMjJCdnY0RI0YgKysLgYGBMDMzU68JCQlBfHw8bt68CVtbW2RlZWHGjBkaxw8JCWlwCeXD7t69i7t376ofV1RUAADq6upQVyf+qM76GgyhFkPCXLRjLroxG+2Yi27MRjvmohuz0Y656NYas3mxhyNe8LDDhrRCbMr8ST0m/xU/F3R3tsHyAwUoqbgLwBhbC45DbmOOOf/TBSHdnUSp91G+NwbbkJWUlAAAnJw0Q3RyclLvKykpgaOjo8Z+ExMT2NnZaaxxd3dv8Br1+2xtbVFSUtLocbRZsmQJFi5c2GB7cnIyLC0tm/MW9SIlJUXsEgwSc9GOuejGbLRjLroxG+2Yi27MRjvmoltrzKYbgHe8gMTLRsi5aYQtWUUA6s+GSdTrSipqEL1Dib91VsG7nf7PllVXVzd7rcE2ZIZu1qxZGmfVKioq4OLiguDgYNjY2IhY2QN1dXVISUnBkCFDYGpqKnY5BoO5aMdcdGM22jEX3ZiNdsxFN2ajHXPRjdkAEwGk5f+KKduVuK/StkICCYDvSi0xMyIQxkYSbYtaTP3Vc81hsA2ZXC4HAJSWlsLZ2Vm9vbS0FD4+Puo1165d03jevXv3UFZWpn6+XC5HaWmpxpr6x02tqd+vjbm5OczNzRtsNzU1Nah/GIZWj6FgLtoxF92YjXbMRTdmox1z0Y3ZaMdcdGvt2VhZmDd6r5gAoPjWXZy6chv+z7TTX2HAI31fDPbvkLm7u0Mul+PgwYPqbRUVFcjOzoa/vz8AwN/fH+Xl5Thx4oR6zaFDh6BSqeDn56dek56ernEdZ0pKCjw9PWFra6te8/Bx6tfUH4eIiIiIiAzLtdvN+9tkzV0nFlEbssrKSiiVSiiVSgAPBnkolUoUFRVBIpEgNjYW7733Hvbu3YuzZ89iwoQJUCgUCA8PBwB07doVoaGheOONN3Ds2DH88MMPiI6Oxrhx46BQKAAA48ePh5mZGSIjI5Gbm4udO3di9erVGpcbTps2DUlJSVi+fDnOnz+PBQsW4Pjx44iOjtZ3JERERERE1AyO1tLHuk4sol6yePz4cQwcOFD9uL5JmjhxIrZs2YKZM2eiqqoKkydPRnl5Ofr374+kpCRIpb+Fum3bNkRHR2Pw4MEwMjLCqFGjsGbNGvV+mUyG5ORkREVFoXfv3rC3t8e8efM0/lbZ888/j+3bt2POnDmYPXs2PDw8kJiYiB49eughBSIiIiIielTPudvBWSZFya0aaLtwUQJALpPiOXc7fZf2SERtyAYMGNDoH3WTSCRYtGgRFi1apHONnZ0dtm/f3uhxevbsiYyMjEbXjB49GqNHj268YCIiIiIiMgjGRhLMH9YNb35+EhJAoymrH+Exf1g3vQ/0eFQGew8ZERERERFRY0J7OGP9K76QyzQvS5TLpFj/ii9CezjreKbhMNgpi0RERERERE0J7eGMId3kyLp4DckZ2QgO8IN/J0eDPzNWjw0ZERERERE90YyNJPBzt8ONPAF+7nZPTDMG8JJFIiIiIiIi0bAhIyIiIiIiEgkbMiIiIiIiIpGwISMiIiIiIhIJGzIiIiIiIiKRsCEjIiIiIiISCRsyIiIiIiIikbAhIyIiIiIiEgkbMiIiIiIiIpGwISMiIiIiIhIJGzIiIiIiIiKRsCEjIiIiIiISCRsyIiIiIiIikZiIXcDTQhAEAEBFRYXIlTxQV1eH6upqVFRUwNTUVOxyDAZz0Y656MZstGMuujEb7ZiLbsxGO+aiG7PRzpByqe8J6nuExrAhe0xu374NAHBxcRG5EiIiIiIiMgS3b9+GTCZrdI1EaE7bRk1SqVS4evUqrK2tIZFIxC4HFRUVcHFxwc8//wwbGxuxyzEYzEU75qIbs9GOuejGbLRjLroxG+2Yi27MRjtDykUQBNy+fRsKhQJGRo3fJcYzZI+JkZER2rdvL3YZDdjY2Ij+A2mImIt2zEU3ZqMdc9GN2WjHXHRjNtoxF92YjXaGkktTZ8bqcagHERERERGRSNiQERERERERiYQN2VPK3Nwc8+fPh7m5udilGBTmoh1z0Y3ZaMdcdGM22jEX3ZiNdsxFN2aj3ZOaC4d6EBERERERiYRnyIiIiIiIiETChoyIiIiIiEgkbMiIiIiIiIhEwoaMiIiIiIhIJGzInmDp6ekYNmwYFAoFJBIJEhMTNfYLgoB58+bB2dkZFhYWCAoKQkFBgTjF6llT2Xz99dcIDg5Gu3btIJFIoFQqRalT3xrLpa6uDu+88w68vLxgZWUFhUKBCRMm4OrVq+IVrEdN/cwsWLAAXbp0gZWVFWxtbREUFITs7GxxitWjpnJ52NSpUyGRSLBq1Sq91SemprKZNGkSJBKJxldoaKg4xepRc35m8vLyMHz4cMhkMlhZWaFPnz4oKirSf7F61FQuv/9Zqf/64IMPxClYj5rKprKyEtHR0Wjfvj0sLCzQrVs3bNiwQZxi9aipXEpLSzFp0iQoFApYWloiNDS0VXzOW7JkCfr06QNra2s4OjoiPDwc+fn5GmtqamoQFRWFdu3aoU2bNhg1ahRKS0tFqrhpbMieYFVVVfD29kZCQoLW/cuWLcOaNWuwYcMGZGdnw8rKCiEhIaipqdFzpfrXVDZVVVXo378/4uPj9VyZuBrLpbq6GidPnsTcuXNx8uRJfP3118jPz8fw4cNFqFT/mvqZ6dy5M9atW4ezZ8/iyJEj6NChA4KDg/Hrr7/quVL9aiqXert378bRo0ehUCj0VJn4mpNNaGgoiouL1V9ffPGFHisUR1O5XLp0Cf3790eXLl2QmpqKM2fOYO7cuZBKpXquVL+ayuXhn5Pi4mJs2rQJEokEo0aN0nOl+tdUNjNmzEBSUhI+//xz5OXlITY2FtHR0di7d6+eK9WvxnIRBAHh4eH48ccfsWfPHpw6dQpubm4ICgpCVVWVCNXqT1paGqKionD06FGkpKSgrq4OwcHBGu97+vTp+Oabb7Br1y6kpaXh6tWrGDlypIhVN0GgpwIAYffu3erHKpVKkMvlwgcffKDeVl5eLpibmwtffPGFCBWK5/fZPKywsFAAIJw6dUqvNRmCxnKpd+zYMQGAcPnyZf0UZSCak82tW7cEAMKBAwf0U5QB0JXLlStXhL/85S9CTk6O4ObmJqxcuVLvtYlNWzYTJ04UwsLCRKnHUGjLZezYscIrr7wiTkEGojn/jQkLCxMGDRqkn4IMiLZsunfvLixatEhjm6+vr/C///u/eqxMXL/PJT8/XwAg5OTkqLfdv39fcHBwED755BMRKhTPtWvXBABCWlqaIAgPPu+ampoKu3btUq/Jy8sTAAhZWVlildkoniF7ShUWFqKkpARBQUHqbTKZDH5+fsjKyhKxMnqS3Lp1CxKJBG3bthW7FINSW1uLjz/+GDKZDN7e3mKXIyqVSoVXX30VcXFx6N69u9jlGJzU1FQ4OjrC09MTb775Jm7cuCF2SaJSqVT49ttv0blzZ4SEhMDR0RF+fn6NXgrbGpWWluLbb79FZGSk2KUYhOeffx579+7FL7/8AkEQcPjwYVy4cAHBwcFilyaau3fvAoDGmWUjIyOYm5vjyJEjYpUlilu3bgEA7OzsAAAnTpxAXV2dxmfgLl26wNXV1WA/A7Mhe0qVlJQAAJycnDS2Ozk5qfcRNaampgbvvPMOXn75ZdjY2IhdjkHYt28f2rRpA6lUipUrVyIlJQX29vZilyWq+Ph4mJiYICYmRuxSDE5oaCi2bt2KgwcPIj4+HmlpaRg6dCju378vdmmiuXbtGiorK7F06VKEhoYiOTkZI0aMwMiRI5GWliZ2eQbjs88+g7W1tWFfYqVHa9euRbdu3dC+fXuYmZkhNDQUCQkJCAwMFLs00dQ3GLNmzcLNmzdRW1uL+Ph4XLlyBcXFxWKXpzcqlQqxsbHo168fevToAeDBZ2AzM7MGv0w25M/AJmIXQESGp66uDmPGjIEgCFi/fr3Y5RiMgQMHQqlU4vr16/jkk08wZswYZGdnw9HRUezSRHHixAmsXr0aJ0+ehEQiEbscgzNu3Dj1//by8kLPnj3xzDPPIDU1FYMHDxaxMvGoVCoAQFhYGKZPnw4A8PHxQWZmJjZs2IAXXnhBzPIMxqZNmxAREfHU31fXXGvXrsXRo0exd+9euLm5IT09HVFRUVAoFBpnQVoTU1NTfP3114iMjISdnR2MjY0RFBSEoUOHQhAEscvTm6ioKOTk5DzxZwV5huwpJZfLAaDBRJnS0lL1PiJt6puxy5cvIyUlhWfHHmJlZYVOnTqhb9++2LhxI0xMTLBx40axyxJNRkYGrl27BldXV5iYmMDExASXL1/GW2+9hQ4dOohdnsHp2LEj7O3tcfHiRbFLEY29vT1MTEzQrVs3je1du3Z96qcsNldGRgby8/Px+uuvi12KQbhz5w5mz56NFStWYNiwYejZsyeio6MxduxYfPjhh2KXJ6revXtDqVSivLwcxcXFSEpKwo0bN9CxY0exS9OL6Oho7Nu3D4cPH0b79u3V2+VyOWpra1FeXq6x3pA/A7Mhe0q5u7tDLpfj4MGD6m0VFRXIzs6Gv7+/iJWRIatvxgoKCnDgwAG0a9dO7JIMmkqlUl/H3xq9+uqrOHPmDJRKpfpLoVAgLi4O33//vdjlGZwrV67gxo0bcHZ2FrsU0ZiZmaFPnz4NRlRfuHABbm5uIlVlWDZu3IjevXu3+vtT69XV1aGurg5GRpofWY2NjdVnXFs7mUwGBwcHFBQU4Pjx4wgLCxO7pBYlCAKio6Oxe/duHDp0CO7u7hr7e/fuDVNTU43PwPn5+SgqKjLYz8C8ZPEJVllZqfGb1sLCQiiVStjZ2cHV1RWxsbF477334OHhAXd3d8ydOxcKhQLh4eHiFa0nTWVTVlaGoqIi9d/Yqv9wIJfLDfa3J49DY7k4OzvjpZdewsmTJ7Fv3z7cv39ffa21nZ0dzMzMxCpbLxrLpl27dli8eDGGDx8OZ2dnXL9+HQkJCfjll18wevRoEatueU39W/p9025qagq5XA5PT099l6p3jWVjZ2eHhQsXYtSoUZDL5bh06RJmzpyJTp06ISQkRMSqW15TPzNxcXEYO3YsAgMDMXDgQCQlJeGbb75BamqqeEXrQVO5AA9+cbpr1y4sX75crDJF0VQ2L7zwAuLi4mBhYQE3NzekpaVh69atWLFihYhVt7ymctm1axccHBzg6uqKs2fPYtq0aQgPD3/qh51ERUVh+/bt2LNnD6ytrdWfVWQyGSwsLCCTyRAZGYkZM2bAzs4ONjY2+Mc//gF/f3/07dtX5Op1EHfII/0Zhw8fFgA0+Jo4caIgCA9G38+dO1dwcnISzM3NhcGDBwv5+fniFq0nTWWzefNmrfvnz58vat0trbFc6v8EgLavw4cPi116i2ssmzt37ggjRowQFAqFYGZmJjg7OwvDhw8Xjh07JnbZLa6pf0u/15rG3jeWTXV1tRAcHCw4ODgIpqamgpubm/DGG28IJSUlYpfd4przM7Nx40ahU6dOglQqFby9vYXExETxCtaT5uTy0UcfCRYWFkJ5ebl4hYqgqWyKi4uFSZMmCQqFQpBKpYKnp6ewfPlyQaVSiVt4C2sql9WrVwvt27cXTE1NBVdXV2HOnDnC3bt3xS1aD3R9Vtm8ebN6zZ07d4S///3vgq2trWBpaSmMGDFCKC4uFq/oJkgEoRXd+UdERERERGRAeA8ZERERERGRSNiQERERERERiYQNGRERERERkUjYkBEREREREYmEDRkREREREZFI2JARERERERGJhA0ZERE9Nrt378aXX34pdhlERERPDDZkRET0WBw7dgyxsbHo27ev2KX8aampqZBIJCgvL2/xYw0YMACxsbEtfhx9uXHjBhwdHfHTTz8BaDrL69evw9HREVeuXNFfkUREBoQNGRERNTBp0iRIJBIsXbpUY3tiYiIkEkmD9bdu3cLrr7+O3bt3w9XVVV9lGrza2losW7YM3t7esLS0hL29Pfr164fNmzejrq6uRY4pkUiQmJjYIq/dHIsXL0ZYWBg6dOjQrPX29vaYMGEC5s+f37KFEREZKDZkRESklVQqRXx8PG7evNnkWplMhjNnzsDX11cPlWlXW1sr2rG1qa2tRUhICJYuXYrJkycjMzMTx44dQ1RUFNauXYvc3FyxS2zUH2kYq6ursXHjRkRGRj7S81577TVs27YNZWVlj3xMIqInHRsyIiLSKigoCHK5HEuWLNG5ZsGCBfDx8dHYtmrVKo2zI5MmTUJ4eDjef/99ODk5oW3btli0aBHu3buHuLg42NnZoX379ti8ebPG6/z8888YM2YM2rZtCzs7O4SFhakvg3v4dRcvXgyFQgFPT08AwNmzZzFo0CBYWFigXbt2mDx5MiorKxt9r/v370fnzp1hYWGBgQMHahyn3pEjRxAQEAALCwu4uLggJiYGVVVVOl9z1apVSE9Px8GDBxEVFQUfHx907NgR48ePR3Z2Njw8PLQ+T9sZrrZt22LLli0AHjR60dHRcHZ2hlQqhZubm/p7VJ/7iBEjIJFINL4Pe/bsga+vL6RSKTp27IiFCxfi3r17Gsddv349hg8fDisrKyxevBg3b95EREQEHBwcYGFhAQ8Pjwbfp9/naG5u3uhlq9XV1Rg6dCj69eunvoyxe/fuUCgU2L17t87nERE9rdiQERGRVsbGxnj//fexdu3aP31/z6FDh3D16lWkp6djxYoVmD9/Pv7617/C1tYW2dnZmDp1KqZMmaI+Tl1dHUJCQmBtbY2MjAz88MMPaNOmDUJDQzXOhB08eBD5+flISUnBvn37UFVVhZCQENja2uK///0vdu3ahQMHDiA6OlpnbT///DNGjhyJYcOGQalU4vXXX8c///lPjTWXLl1CaGgoRo0ahTNnzmDnzp04cuRIo6+7bds2BAUFoVevXg32mZqawsrK6lFjBACsWbMGe/fuxZdffon8/Hxs27ZN3Xj997//BQBs3rwZxcXF6scZGRmYMGECpk2bhnPnzuGjjz7Cli1bsHjxYo3XXrBgAUaMGIGzZ8/ib3/7G+bOnYtz587hu+++Q15eHtavXw97e3udtWVkZKB3794695eXl2PIkCFQqVRISUlB27Zt1fuee+45ZGRk/KFMiIieZCZiF0BERIZrxIgR8PHxwfz587Fx48Y//Dp2dnZYs2YNjIyM4OnpiWXLlqG6uhqzZ88GAMyaNQtLly7FkSNHMG7cOOzcuRMqlQqffvqp+p61zZs3o23btkhNTUVwcDAAwMrKCp9++inMzMwAAJ988glqamqwdetWdcOzbt06DBs2DPHx8XBycmpQ2/r16/HMM89g+fLlAABPT0+cPXsW8fHx6jVLlixBRESEeviGh4cH1qxZgxdeeAHr16+HVCpt8LoFBQUYMGDAH85Ml6KiInh4eKB///6QSCRwc3NT73NwcADw4IyaXC5Xb1+4cCH++c9/YuLEiQCAjh074t1338XMmTM17t0aP348XnvtNY1j9erVC88++ywANHlf2OXLl6FQKLTuKykpwdixY+Hh4YHt27erv2f1FAoFTp061YwEiIieLjxDRkREjYqPj8dnn32GvLy8P/wa3bt3h5HRb/+X4+TkBC8vL/VjY2NjtGvXDteuXQMAnD59GhcvXoS1tTXatGmDNm3awM7ODjU1Nbh06ZL6eV5eXhof7PPy8uDt7a1x9qlfv35QqVTIz8/XWlteXh78/Pw0tvn7+2s8Pn36NLZs2aKupU2bNggJCYFKpUJhYaHW1xUEoalY/pBJkyZBqVTC09MTMTExSE5ObvI5p0+fxqJFizTqf+ONN1BcXIzq6mr1uvrGq96bb76JHTt2wMfHBzNnzkRmZmajx7lz547W5hQAhgwZgk6dOmHnzp0NmjEAsLCw0KiFiKi14BkyIiJqVGBgIEJCQjBr1ixMmjRJY5+RkVGDxkPbMAhTU1ONxxKJROs2lUoFAKisrETv3r2xbdu2Bq9VfxYIwB++7O9RVVZWYsqUKYiJiWmwT9dUyc6dO+P8+fOPfCyJRNJopr6+vigsLMR3332HAwcOYMyYMQgKCsJ//vOfRutfuHAhRo4c2WDfww3U7/McOnQoLl++jP379yMlJQWDBw9GVFQUPvzwQ63Hsbe31zkE5sUXX8RXX32Fc+fOaTTj9crKyjS+t0RErQUbMiIiatLSpUvh4+OjHpxRz8HBASUlJRAEQX1poVKp/NPH8/X1xc6dO+Ho6AgbG5tmP69r167YsmULqqqq1M3FDz/8oL5UUtdz9u7dq7Ht6NGjDeo5d+4cOnXq1Oxaxo8fj9mzZ+PUqVMN7iOrq6tDbW2t1obSwcEBxcXF6scFBQUNzhzZ2Nhg7NixGDt2LF566SWEhoairKwMdnZ2MDU1xf379xvUn5+f/0j1P1zPxIkTMXHiRAQEBCAuLk5nQ9arVy98/vnnWvctXboUbdq0weDBg5Gamopu3bpp7M/JyWmRSzyJiAwdL1kkIqImeXl5ISIiAmvWrNHYPmDAAPz6669YtmwZLl26hISEBHz33Xd/+ngRERGwt7dHWFgYMjIyUFhYiNTUVMTExDQ6YCQiIgJSqRQTJ05ETk4ODh8+jH/84x949dVXtd4/BgBTp05FQUEB4uLikJ+fj+3bt6snGtZ75513kJmZiejoaCiVShQUFGDPnj2NDvWIjY1Fv379MHjwYCQkJOD06dP48ccf8eWXX6Jv374oKCjQ+rxBgwZh3bp1OHXqFI4fP46pU6dqnE1csWIFvvjiC5w/fx4XLlzArl27IJfL1QMyOnTogIMHD6KkpER9tmrevHnYunUrFi5ciNzcXOTl5WHHjh2YM2eOzvrrn7dnzx5cvHgRubm52LdvH7p27apzfUhICHJzc3WeJfvwww8RERGBQYMGaZw9rK6uxokTJ9T3BhIRtSZsyIiIqFkWLVqkvqSwXteuXfGvf/0LCQkJ8Pb2xrFjx/D222//6WNZWloiPT0drq6uGDlyJLp27YrIyEjU1NQ0esbM0tIS33//PcrKytCnTx+89NJLGDx4MNatW6fzOa6urvjqq6+QmJgIb29vbNiwAe+//77Gmp49eyItLQ0XLlxAQEAAevXqhXnz5ukcYAEA5ubmSElJwcyZM/HRRx+hb9++6NOnD9asWYOYmBj06NFD6/OWL18OFxcXBAQEYPz48Xj77bdhaWmp3m9tbY1ly5bh2WefRZ8+ffDTTz9h//796nv0li9fjpSUFLi4uKjPzIWEhGDfvn1ITk5Gnz590LdvX6xcuVJjIIg2ZmZmmDVrFnr27InAwEAYGxtjx44dOtd7eXnB19cXX375pc41K1euxJgxYzBo0CBcuHABwIOR/K6urggICGi0HiKip5FEaKm7jomIiKjV+fbbbxEXF4ecnByNQS6N6du3L2JiYjB+/PgWro6IyPDwHjIiIiJ6bF588UUUFBTgl19+gYuLS5Prr1+/jpEjR+Lll1/WQ3VERIaHZ8iIiIiIiIhEwnvIiIiIiIiIRMKGjIiIiIiISCRsyIiIiIiIiETChoyIiIiIiEgkbMiIiIiIiIhEwoaMiIiIiIhIJGzIiIiIiIiIRMKGjIiIiIiISCRsyIiIiIiIiETChoyIiIiIiEgk/wcTUdqPk/gbSAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "output_type": "stream", + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculando el Silhouette Score para diferentes valores de k...\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "output_type": "stream", + "name": "stdout", + "output_type": "stream", + "text": [ + "El número óptimo de clusters según el Silhouette Score es: 13\nAplicando K-Means con k=13...\n\nNúmero de clusters encontrados: 13\nReduciendo dimensiones para visualización con t-SNE...\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "output_type": "stream", + "text": [ + "/databricks/python/lib/python3.10/site-packages/sklearn/manifold/_t_sne.py:795: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n warnings.warn(\n/databricks/python/lib/python3.10/site-packages/sklearn/manifold/_t_sne.py:805: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "output_type": "stream", + "name": "stdout", + "output_type": "stream", + "text": [ + "\nResumen de Clusters:\n3 80\n4 70\n1 62\n5 48\n10 45\n0 42\n6 38\n12 31\n7 28\n11 18\n9 16\n8 10\n2 1\nName: Cluster, dtype: int64\n" + ] + } + ], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.metrics import silhouette_score\n", + "from sklearn.manifold import TSNE\n", + "from sentence_transformers import SentenceTransformer\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Suponemos que ya tienes df, df_reduced, numerical_cols, categorical_cols definidos\n", + "# y que df_reduced contiene las columnas requeridas.\n", + "\n", + "model = SentenceTransformer('all-MiniLM-L6-v2')\n", + "\n", + "# Preprocesar columnas numéricas\n", + "print(\"Preprocesando columnas numéricas...\")\n", + "scaler = StandardScaler()\n", + "numerical_scaled = scaler.fit_transform(df_reduced[numerical_cols].fillna(0))\n", + "\n", + "# Generar embeddings para columnas categóricas\n", + "print(\"Generando embeddings para columnas categóricas...\")\n", + "categorical_embeddings = []\n", + "for col in categorical_cols:\n", + " embeddings = model.encode(df_reduced[col].fillna('Unknown').astype(str).tolist())\n", + " categorical_embeddings.append(embeddings)\n", + "\n", + "# Asignar importancia a las columnas categóricas según el orden dado\n", + "# Asumiendo el orden de categorical_cols:\n", + "# ['Midsole_Material', 'Cushioning_System', 'Additional_Technologies', 'Outsole', ...]\n", + "Midsole_Material_embeddings = categorical_embeddings[0] * 5.0 # 1ra prioridad\n", + "Cushioning_System_embeddings = categorical_embeddings[1] * 5.0 # 1ra prioridad\n", + "Additional_Technologies_embeddings = categorical_embeddings[2] * 2.0 # info adicional\n", + "Outsole_embeddings = categorical_embeddings[3] * 3.0 # 3ra prioridad\n", + "\n", + "# Otras columnas categóricas (si las hay)\n", + "if len(categorical_embeddings) > 4:\n", + " other_cat_embeddings = np.hstack(categorical_embeddings[4:])\n", + "else:\n", + " other_cat_embeddings = np.empty((len(df_reduced), 0))\n", + "\n", + "# Ajustar importancia en las variables numéricas:\n", + "# numerical_cols = ['Drop__heel-to-toe_differential_','Weight','regularPrice','undiscounted_price','percentil_discounted']\n", + "drop_idx = numerical_cols.index('Drop__heel-to-toe_differential_')\n", + "weight_idx = numerical_cols.index('Weight')\n", + "regular_price_idx = numerical_cols.index('regularPrice')\n", + "\n", + "# Aplicar factores\n", + "numerical_scaled[:, drop_idx] *= 4.0 # 2da prioridad\n", + "numerical_scaled[:, weight_idx] *= 2.0 # 5ta prioridad\n", + "numerical_scaled[:, regular_price_idx] *= 1.5 # 6ta prioridad\n", + "\n", + "# Combinar embeddings categóricos con sus factores\n", + "combined_categorical_embeddings = np.hstack([\n", + " Midsole_Material_embeddings,\n", + " Cushioning_System_embeddings,\n", + " Additional_Technologies_embeddings,\n", + " Outsole_embeddings,\n", + " other_cat_embeddings\n", + "])\n", + "\n", + "# Combinar características numéricas y categóricas sin volver a escalar,\n", + "# para no perder la ponderación manual\n", + "print(\"Concatenando características numéricas y categóricas...\")\n", + "combined_features = np.hstack([numerical_scaled, combined_categorical_embeddings])\n", + "\n", + "# Ahora aplicamos PCA, k-means, etc., sobre combined_features\n", + "print(\"Aplicando PCA para reducir dimensiones...\")\n", + "pca = PCA(n_components=400, random_state=42) # Ajusta n_components según necesidad\n", + "reduced_features = pca.fit_transform(combined_features)\n", + "\n", + "print(\"Calculando el Método del Codo para determinar el número óptimo de clusters...\")\n", + "wcss = []\n", + "k_values = range(10, 21) # Rango de k para explorar\n", + "\n", + "for k in k_values:\n", + " kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)\n", + " kmeans.fit(reduced_features)\n", + " wcss.append(kmeans.inertia_)\n", + "\n", + "# Visualizar el Método del Codo\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(k_values, wcss, marker='o')\n", + "plt.title('Método del Codo para Determinar el Número Óptimo de Clusters')\n", + "plt.xlabel('Número de Clusters (k)')\n", + "plt.ylabel('WCSS (Inercia)')\n", + "plt.xticks(k_values)\n", + "plt.grid(True)\n", + "plt.show()\n", + "\n", + "print(\"Calculando el Silhouette Score para diferentes valores de k...\")\n", + "silhouette_scores = []\n", + "\n", + "for k in k_values:\n", + " kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)\n", + " clusters = kmeans.fit_predict(reduced_features)\n", + " score = silhouette_score(reduced_features, clusters)\n", + " silhouette_scores.append(score)\n", + "\n", + "# Visualizar el Silhouette Score\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(k_values, silhouette_scores, marker='o', color='orange')\n", + "plt.title('Silhouette Score para Diferentes Valores de k')\n", + "plt.xlabel('Número de Clusters (k)')\n", + "plt.ylabel('Silhouette Score')\n", + "plt.xticks(k_values)\n", + "plt.grid(True)\n", + "plt.show()\n", + "\n", + "best_k = k_values[np.argmax(silhouette_scores)]\n", + "print(f\"El número óptimo de clusters según el Silhouette Score es: {best_k}\")\n", + "\n", + "print(f\"Aplicando K-Means con k={best_k}...\")\n", + "kmeans_final = KMeans(n_clusters=best_k, random_state=42, n_init=10)\n", + "clusters_final = kmeans_final.fit_predict(reduced_features)\n", + "\n", + "# Añadir etiquetas de cluster al DataFrame original\n", + "df['Cluster'] = clusters_final\n", + "\n", + "n_clusters_final = len(set(clusters_final))\n", + "print(f'\\nNúmero de clusters encontrados: {n_clusters_final}')\n", + "\n", + "print(\"Reduciendo dimensiones para visualización con t-SNE...\")\n", + "tsne = TSNE(n_components=2, random_state=42, perplexity=30, n_iter=1000)\n", + "tsne_results = tsne.fit_transform(reduced_features)\n", + "\n", + "df['tsne-2d-one'] = tsne_results[:, 0]\n", + "df['tsne-2d-two'] = tsne_results[:, 1]\n", + "\n", + "plt.figure(figsize=(12, 8))\n", + "palette = sns.color_palette(\"hsv\", n_clusters_final)\n", + "sns.scatterplot(\n", + " x=\"tsne-2d-one\", y=\"tsne-2d-two\",\n", + " hue=\"Cluster\",\n", + " palette=palette,\n", + " data=df,\n", + " legend=\"full\",\n", + " alpha=0.7\n", + ")\n", + "plt.title('Visualización de Clusters con t-SNE')\n", + "plt.xlabel('t-SNE 1')\n", + "plt.ylabel('t-SNE 2')\n", + "plt.legend(title='Cluster', bbox_to_anchor=(1.05, 1), loc='upper left')\n", + "plt.show()\n", + "\n", + "print(\"\\nResumen de Clusters:\")\n", + "print(df['Cluster'].value_counts())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "daf2d204-2dbb-4bf2-b7b8-573e94ce663b", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "output_type": "stream", + "text": [ + "Aplicando PCA para reducir dimensiones...\nCalculando el Método del Codo para determinar el número óptimo de clusters...\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "output_type": "stream", + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculando el Silhouette Score para diferentes valores de k...\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "output_type": "stream", + "name": "stdout", + "output_type": "stream", + "text": [ + "El número óptimo de clusters según el Silhouette Score es: 20\nAplicando K-Means con k=20...\n\nNúmero de clusters encontrados: 20\nReduciendo dimensiones para visualización con t-SNE...\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "output_type": "stream", + "name": "stdout", + "output_type": "stream", + "text": [ + "\nResumen de Clusters:\n5 49\n14 44\n8 38\n1 35\n2 31\n6 31\n19 30\n18 27\n7 27\n12 27\n4 25\n9 22\n10 20\n16 19\n15 15\n13 14\n17 13\n11 9\n3 9\n0 4\nName: Cluster, dtype: int64\n\nCluster 0:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n68 278.0 Exterior de malla técnica ... 19.815088 -10.885159\n74 NaN Exterior de malla ... 21.941719 -10.785176\n165 330.0 Exterior de malla técnica ... 20.837984 -10.885697\n282 330.0 Exterior de malla técnica ... 20.412170 -10.892855\n\n[4 rows x 15 columns]\n\nCluster 1:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n19 320.0 Parte superior textil ... -21.108912 -6.493203\n46 323.0 Parte superior de malla ... -15.119090 -1.197047\n47 364.0 Exterior textil ... -18.662340 -0.758213\n48 286.0 Parte superior textil ... -19.311958 -3.636598\n96 NaN Exterior de malla ... -12.407335 -4.614008\n\n[5 rows x 15 columns]\n\nCluster 2:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n10 275.0 Exterior de malla ... -6.962754 -21.797403\n13 NaN Exterior en tejido de malla ... -7.990644 -24.520367\n20 267.0 Parte superior de malla transpirable ... -10.867888 -24.307665\n45 239.0 Textil ... -22.723793 -15.788798\n52 275.0 Exterior de malla ... -4.509296 -22.302498\n\n[5 rows x 15 columns]\n\nCluster 3:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n5 NaN Malla Técnica Reciclada ... 17.060745 10.778292\n83 NaN Malla Técnica Reciclada ... 16.683235 10.083214\n180 NaN Malla Técnica Reciclada ... 16.605957 10.964303\n187 NaN Malla Técnica Reciclada ... 17.209896 11.870272\n189 NaN Malla Técnica Reciclada ... 17.400578 11.692134\n\n[5 rows x 15 columns]\n\nCluster 4:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n23 279.0 Parte superior de malla ... 24.868908 25.097902\n37 299.0 Exterior de malla ... 27.103107 24.626539\n71 279.0 Parte superior de malla ... 24.243345 24.382366\n78 310.0 Parte superior de malla ... 24.756914 24.047520\n97 310.0 Parte superior de malla ... 22.776726 23.787300\n\n[5 rows x 15 columns]\n\nCluster 5:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n25 268.0 Exterior en tejido de malla ... 9.414218 -11.095246\n33 NaN Exterior de malla ... 14.699205 -9.345591\n42 540.0 Exterior de malla técnica ... -0.360923 -1.651374\n55 295.0 Exterior de tejido adidas PRIMEKNIT ... 19.124922 -5.523604\n61 257.0 Exterior de malla con refuerzos de TPU ... 11.038683 -10.796019\n\n[5 rows x 15 columns]\n\nCluster 6:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n12 223.0 Synthetic ... 12.747667 27.678469\n17 295.0 Exterior en malla ... 16.390188 24.164738\n21 230.0 Exterior liviano en malla ... 14.460028 23.500111\n26 230.0 Exterior liviano en malla ... 13.874146 22.178349\n27 265.0 Parte superior de malla ... 17.188601 22.407335\n\n[5 rows x 15 columns]\n\nCluster 7:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n3 200.0 Parte superior de malla ... 1.755538 11.330632\n28 230.0 Exterior liviano en malla ... 0.703063 14.455057\n65 275.0 Exterior textil ... -23.018738 4.829057\n117 265.0 Parte superior de malla ... -0.186041 10.122837\n125 275.0 Exterior textil ... -22.996166 4.856834\n\n[5 rows x 15 columns]\n\nCluster 8:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n2 166.0 Parte superior de malla técnica ... 8.224779 10.782299\n8 NaN Exterior de malla con cuello acolchado ... 6.570870 6.397749\n11 238.0 Parte superior de malla ... 13.326708 9.309869\n24 230.0 Exterior liviano en malla ... 6.502467 13.272505\n38 NaN Parte superior de malla acolchada ... 9.434493 8.326558\n\n[5 rows x 15 columns]\n\nCluster 9:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n22 585.0 Exterior textil ... 5.375830 -1.062628\n43 540.0 malla técnica ... 1.160343 -2.220453\n54 260.0 Exterior textil ... 5.994472 -2.767516\n92 272.0 Parte superior textil ... 8.725078 -0.769287\n93 NaN Exterior de malla con cuello acolchado ... 1.027744 0.455712\n\n[5 rows x 15 columns]\n\nCluster 10:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n15 334.0 Exterior técnico de malla ... -5.793875 -10.719331\n76 277.0 Parte superior de malla acolchada ... -8.826781 -13.096778\n87 320.0 monomalla ... -3.203534 -17.623938\n89 320.0 Parte superior de monomalla ... -3.568880 -17.335764\n112 277.0 mesh ... -6.326353 -16.414343\n\n[5 rows x 15 columns]\n\nCluster 11:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n32 247.0 Parte superior de monomalla ... 17.916094 5.371136\n77 247.0 Monomalla ... 18.768627 4.620423\n136 247.0 monomalla ... 18.994259 4.688051\n185 247.0 monomalla ... 19.126192 5.276633\n262 230.0 Exterior Monomesh ... 10.349771 -20.242315\n\n[5 rows x 15 columns]\n\nCluster 12:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n7 213.0 Parte superior de malla ... 2.448739 -16.902372\n40 254.0 Monomalla ... -0.392282 -18.852966\n49 254.0 Forro interno textil ... 10.334534 -3.532351\n79 213.0 Parte superior de malla ... 2.740963 -16.275845\n85 242.0 Parte superior de malla ... 5.266422 -18.370689\n\n[5 rows x 15 columns]\n\nCluster 13:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n0 183.0 Synthetic ... 10.893925 27.395477\n1 289.0 adidas Primeknit ... 4.881872 22.404074\n44 278.0 Technical mesh ... -28.522972 -8.019142\n63 243.0 Exterior de malla acolchada ... -0.501422 -13.866938\n101 267.0 mesh ... -28.068459 -7.972044\n\n[5 rows x 15 columns]\n\nCluster 14:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n14 NaN Parte superior de malla ... -12.902549 1.474306\n30 270.0 Exterior de malla ... -7.744373 1.824502\n31 306.0 Parte superior de malla ... -10.323046 5.550931\n36 NaN Exterior de malla con cuello acolchado ... -6.863936 7.874070\n39 664.8 Exterior textil ... -10.836259 10.468987\n\n[5 rows x 15 columns]\n\nCluster 15:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n455 NaN termoplástico, ; . ... -7.866673 -1.848189\n456 203.0 mesh, , termoplástico ... -4.990084 -4.200123\n460 248.0 , termoplástico ... -4.633328 -3.072667\n464 240.0 , termoplástico, termoplástico ... -3.273659 0.947516\n465 NaN termoplástico, ... -16.431103 10.599090\n\n[5 rows x 15 columns]\n\nCluster 16:\n Weight Upper_Material Midsole_Material ... Cluster tsne-2d-one tsne-2d-two\n454 NaN , Cuero bovino ... 16 -20.825001 11.226727\n458 NaN EVA ... 16 -18.891546 14.067634\n459 169.0 , ... 16 -20.181263 13.110445\n461 315.0 , ; EVA ... 16 -16.950970 12.168805\n462 255.0 , ... 16 -19.486427 12.905893\n\n[5 rows x 15 columns]\n\nCluster 17:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n72 306.0 Parte superior de malla ... -18.173550 -12.906224\n95 286.0 Textil ... -23.084425 -12.398204\n122 286.0 Textil ... -22.423227 -13.125884\n181 343.0 Parte superior en tejido de malla ... -20.020899 -11.947750\n201 286.0 Textil ... -22.604563 -13.830274\n\n[5 rows x 15 columns]\n\nCluster 18:\n Weight Upper_Material ... tsne-2d-one tsne-2d-two\n18 303.0 null ... 1.648412 -4.487958\n35 248.0 Exterior textil ... -0.826562 -7.918261\n51 292.0 adidas PRIMEKNIT ... 16.231256 -1.089768\n82 237.0 Exterior textil ... -0.378965 -7.250349\n115 216.0 Parte superior de malla técnica ... 11.755957 3.241534\n\n[5 rows x 15 columns]\n\nCluster 19:\n Weight ... tsne-2d-two\n4 319.0 ... -16.600161\n6 304.0 ... -15.279252\n9 334.0 ... 4.823231\n16 304.0 ... 3.862275\n34 304.0 ... -14.713323\n\n[5 rows x 15 columns]\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "# Reducción de dimensionalidad con PCA\n", + "print(\"Aplicando PCA para reducir dimensiones...\")\n", + "pca = PCA(n_components=489, random_state=42)\n", + "reduced_features = pca.fit_transform(scaled_features)\n", + "\n", + "# -------------------- Determinación del Número Óptimo de Clusters --------------------\n", + "print(\"Calculando el Método del Codo para determinar el número óptimo de clusters...\")\n", + "wcss = []\n", + "k_values = range(2, 21) # Rango de k para explorar\n", + "\n", + "for k in k_values:\n", + " kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)\n", + " kmeans.fit(reduced_features)\n", + " wcss.append(kmeans.inertia_)\n", + "\n", + "# Visualizar el Método del Codo\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(k_values, wcss, marker='o')\n", + "plt.title('Método del Codo para Determinar el Número Óptimo de Clusters')\n", + "plt.xlabel('Número de Clusters (k)')\n", + "plt.ylabel('WCSS (Inercia)')\n", + "plt.xticks(k_values)\n", + "plt.grid(True)\n", + "plt.show()\n", + "\n", + "# Silhouette Score\n", + "print(\"Calculando el Silhouette Score para diferentes valores de k...\")\n", + "silhouette_scores = []\n", + "\n", + "for k in k_values:\n", + " kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)\n", + " clusters = kmeans.fit_predict(reduced_features)\n", + " score = silhouette_score(reduced_features, clusters)\n", + " silhouette_scores.append(score)\n", + "\n", + "# Visualizar el Silhouette Score\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(k_values, silhouette_scores, marker='o', color='orange')\n", + "plt.title('Silhouette Score para Diferentes Valores de k')\n", + "plt.xlabel('Número de Clusters (k)')\n", + "plt.ylabel('Silhouette Score')\n", + "plt.xticks(k_values)\n", + "plt.grid(True)\n", + "plt.show()\n", + "\n", + "# Determinar el mejor k basado en el Silhouette Score\n", + "best_k = k_values[np.argmax(silhouette_scores)]\n", + "print(f\"El número óptimo de clusters según el Silhouette Score es: {best_k}\")\n", + "\n", + "# -------------------- Aplicar K-Means con el Número Óptimo de Clusters --------------------\n", + "print(f\"Aplicando K-Means con k={best_k}...\")\n", + "kmeans_final = KMeans(n_clusters=best_k, random_state=42, n_init=10)\n", + "clusters_final = kmeans_final.fit_predict(reduced_features)\n", + "\n", + "# Añadir etiquetas de cluster al DataFrame original\n", + "df['Cluster'] = clusters_final\n", + "\n", + "# Resumen final de clusters\n", + "n_clusters_final = len(set(clusters_final))\n", + "print(f'\\nNúmero de clusters encontrados: {n_clusters_final}')\n", + "\n", + "# -------------------- Visualización de los Clusters --------------------\n", + "print(\"Reduciendo dimensiones para visualización con t-SNE...\")\n", + "tsne = TSNE(n_components=2, random_state=42, perplexity=30, n_iter=1000)\n", + "tsne_results = tsne.fit_transform(reduced_features)\n", + "\n", + "df['tsne-2d-one'] = tsne_results[:, 0]\n", + "df['tsne-2d-two'] = tsne_results[:, 1]\n", + "\n", + "plt.figure(figsize=(12, 8))\n", + "palette = sns.color_palette(\"hsv\", n_clusters_final)\n", + "sns.scatterplot(\n", + " x=\"tsne-2d-one\", y=\"tsne-2d-two\",\n", + " hue=\"Cluster\",\n", + " palette=palette,\n", + " data=df,\n", + " legend=\"full\",\n", + " alpha=0.7\n", + ")\n", + "plt.title('Visualización de Clusters con t-SNE')\n", + "plt.xlabel('t-SNE 1')\n", + "plt.ylabel('t-SNE 2')\n", + "plt.legend(title='Cluster', bbox_to_anchor=(1.05, 1), loc='upper left')\n", + "plt.show()\n", + "\n", + "# -------------------- Análisis de Clusters --------------------\n", + "print(\"\\nResumen de Clusters:\")\n", + "print(df['Cluster'].value_counts())\n", + "\n", + "# Opcional: Ver registros en cada cluster\n", + "for cluster in range(n_clusters_final):\n", + " print(f'\\nCluster {cluster}:')\n", + " print(df[df['Cluster'] == cluster].head())\"\"\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "7bc44aab-490e-47b7-a4ed-483d26032ab5", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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WeightUpper_MaterialMidsole_MaterialOutsoleCushioning_SystemDrop__heel-to-toe_differential_regularPriceundiscounted_priceGenderAdditional_Technologiesidpercentil_discountedClustertsne-2d-onetsne-2d-two
459169.0,100.0 Acetato de etileno y vinilo10.087000.0790000.08351800-8.08046088.2911001.462351
460248.0, termoplásticoEVA70.0 Acetato de etileno y vinilo 30.0 CauchoEVA4.0330000.0NaNmujerLas hendiduras de flexión acompañan la extensi...8670212NaN016.4084996.210423
461315.0, ;EVA100.0 Caucho sintéticoEVA8.0299000.0NaNHombre8642811NaN016.4233956.361099
462255.0,60.0 Caucho sintético, 40.0 Acetato de etileno...10.0250000.02290000.0mujer8572326-8.16000087.7962931.183642
463NaN, .EVACaucho sintético, Acetato de etileno y viniloKalensole , CS (Circular System)10.0275000.0NaNHombre8666719NaN012.3348085.093338
464240.0, termoplástico, termoplásticoCaucho sintético, Acetato de etileno y viniloespuma Kalensole) y absorción impactos (CS).NaN426000.03090000.0Mujer8767800-6.25352186.8190941.120482
465NaNtermoplástico,EVACaucho sintético, Acetato de etileno y viniloNaN249000.0NaNHombre8759629NaN012.5556983.515450
466243.0,IMEVACaucho sintético, Acetato de etileno y viniloFlexibilidad4.0185000.0NaNHombre8757332NaN012.7504817.847188
467255.0,Entresuela de espuma EVA y concepto de amortig...Caucho sintético, Acetato de etileno y viniloEspumas gruesas en el talón y en la lengüeta.10.0250000.02290000.0MujerAgarre: Suela estriada 3 mm para mayor agarre ...8544265-8.16000087.923182-0.259708
468NaNEVACaucho sintético, 80.0% Acetato de etileno y v...FlexibilidadNaN199000.0NaNHombreAgarre: Gracias a las estrías y geometría de l...8803397NaN012.9298875.782633
469NaNPoliamida, ;EVAAcetato de etileno y viniloNaN199000.0NaNHombre8803408NaN012.2893243.397114
470251.0, termoplásticoEVACaucho sintético, Acetato de etileno y viniloflexibles (61 N/mm)4.0249000.0NaNHombreTranspirabilidad: Nueva tela mesh desarrollada...8670191NaN015.0184684.532474
471295.0,Entresuela de espuma EVA y concepto de amortig...Caucho sintético, Acetato de etileno y vinilo10.0250000.02290000.0HombreSuela estriada de 3 mm para más agarre en cami...8488639-8.16000087.8291120.046291
472366.0EVAAcetato de etileno y viniloflexibilidadNaN139000.0NaNMujer8803078NaN013.4003496.981575
473243.0,IMEVACaucho sintético, Acetato de etileno y viniloFlexibilidad4.0185000.0NaNHombre8757334NaN012.7505697.847071
474295.0,Entresuela de espuma EVA y concepto de amortig...Caucho sintético, Acetato de etileno y vinilo10.0250000.02290000.0HombreAgarre: Suela estriada de 3 mm para más agarre...8767790-8.16000087.8277690.047635
475251.0, termoplásticoEVACaucho sintético, Acetato de etileno y vinilotenis flexibles (61 N/mm)4.0249000.0NaNHombreLibertad de movimientos: Las hendiduras de fle...8670196NaN015.1024104.388563
476NaNtermoplástico,EVACaucho sintético, Acetato de etileno y viniloFlex-HNaN249000.0NaNMujerAdaptabilidad: Disponible en dos anchos de pie...8750403NaN013.4843074.651424
477200.0,IMEVACaucho sintético, Acetato de etileno y viniloFlexibilidad4.0185000.0NaNMujer8757345NaN012.7497627.856787
478180.0,Acetato de etileno y vinilo;10.087000.0790000.0Hombre8351755-8.08046088.2941701.447252
479210.0,Suela de espumaCaucho sintético, Acetato de etileno y viniloFlexibilidad para mejorar el desarrollo del pie.4.0135000.0NaNMujerAdherencia: Refuerzo de caucho en la suela, en...8733475NaN012.9493228.663089
480NaNEVAAcetato de etileno y viniloLa flexibilidad de la suela es ideal para todo...NaN169000.0NaNHombreTranspirabilidad: Su mesh 3D aireado permite q...8803079NaN012.7549235.676413
481203.0, termoplásticoEVACaucho sintético, 70.0% Acetato de etileno y v...tenis flexibles (61 N/mm)4.0249000.0NaNMujerLas hendiduras de flexión acompañan la extensi...8670202NaN015.0611804.392717
482235.0termoplástico, ,KALENSOLECaucho sintético, Acetato de etileno y viniloKALENSOLENaN399000.0NaNMujertela mesh, más elástica.8772824NaN0-17.9420076.408968
483205.0, termoplástico, ,MFOAMCaucho sintético, Acetato de etileno y viniloMFOAM6.0499000.0NaNMujerBuen agarre en piso mojado gracias a la textur...8772779NaN1-12.33320033.055355
484216.0termoplástico, ;VFOAMCaucho sintético, Acetato de etileno y vinilo,...Pebax8.0399000.02400000.0mujergeometría de la suela de las KIPRUN KD500 2, c...8756260-5.01503886.2822821.016790
485280.0termoplástico, ;KalensoleCaucho sintético, Acetato de etileno y viniloKalensole6.0309000.0NaNHombre8772865NaN0-17.9403346.414684
486252.0, termoplástico,espuma MFOAMCaucho sintético, Acetato de etileno y viniloespuma MFOAM6.0499000.0NaNHombreAdherencia: Buen agarre en piso mojado gracias...8830204NaN0-12.31372433.078747
487225.0,Caucho sintético, Carbono, Amida de bloque de ...8.0849000.0NaNHombreImpulso: Espuma Pebax® de Arkema y placa de ca...8666803NaN010.4167862.686541
488218.0termoplástico,espuma Pebax® de Arkema.Caucho sintético, Amida de bloque de poliéter8.0699000.05990000.0HombreImpulso: Excelente retorno de energía gracias ...8798231-7.56938586.781542-0.310688
\n", + "
" + ], + "text/plain": [ + " Weight Upper_Material ... tsne-2d-one tsne-2d-two\n", + "459 169.0 , ... 8.291100 1.462351\n", + "460 248.0 , termoplástico ... 16.408499 6.210423\n", + "461 315.0 , ; ... 16.423395 6.361099\n", + "462 255.0 , ... 7.796293 1.183642\n", + "463 NaN , . ... 12.334808 5.093338\n", + "464 240.0 , termoplástico, termoplástico ... 6.819094 1.120482\n", + "465 NaN termoplástico, ... 12.555698 3.515450\n", + "466 243.0 , ... 12.750481 7.847188\n", + "467 255.0 , ... 7.923182 -0.259708\n", + "468 NaN ... 12.929887 5.782633\n", + "469 NaN Poliamida, ; ... 12.289324 3.397114\n", + "470 251.0 , termoplástico ... 15.018468 4.532474\n", + "471 295.0 , ... 7.829112 0.046291\n", + "472 366.0 ... 13.400349 6.981575\n", + "473 243.0 , ... 12.750569 7.847071\n", + "474 295.0 , ... 7.827769 0.047635\n", + "475 251.0 , termoplástico ... 15.102410 4.388563\n", + "476 NaN termoplástico, ... 13.484307 4.651424\n", + "477 200.0 , ... 12.749762 7.856787\n", + "478 180.0 , ... 8.294170 1.447252\n", + "479 210.0 , ... 12.949322 8.663089\n", + "480 NaN ... 12.754923 5.676413\n", + "481 203.0 , termoplástico ... 15.061180 4.392717\n", + "482 235.0 termoplástico, , ... -17.942007 6.408968\n", + "483 205.0 , termoplástico, , ... -12.333200 33.055355\n", + "484 216.0 termoplástico, ; ... 6.282282 1.016790\n", + "485 280.0 termoplástico, ; ... -17.940334 6.414684\n", + "486 252.0 , termoplástico, ... -12.313724 33.078747\n", + "487 225.0 , ... 10.416786 2.686541\n", + "488 218.0 termoplástico, ... 6.781542 -0.310688\n", + "\n", + "[30 rows x 15 columns]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.tail(30)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "79d3ce3e-b470-4297-b973-07f95888a71f", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "output_type": "stream", + "text": [ + "/databricks/spark/python/pyspark/sql/pandas/conversion.py:401: UserWarning: createDataFrame attempted Arrow optimization because 'spark.sql.execution.arrow.pyspark.enabled' is set to true; however, failed by the reason below:\n Expected bytes, got a 'int' object\nAttempting non-optimization as 'spark.sql.execution.arrow.pyspark.fallback.enabled' is set to true.\n warn(msg)\n" + ] + } + ], + "source": [ + "spark_df_adidas = spark.createDataFrame(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "c7b73d63-1802-436f-830d-244db6ddf731", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "spark_df_adidas.write.mode(\"overwrite\").saveAsTable(\"preprod_colombia.scraping_adidas_dkt_clusters\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "0cf2cbda-19d0-4774-9699-ce83ae969123", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "source": [ + "## Clustering NIKE" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "65b5ec70-9e23-41b6-a4a2-5cd3d34f3777", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "spark_df_nr = spark.table(\"preprod_colombia.scraping_nacionrunner_etiquetado\")" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "dcdc7277-6135-4a76-b371-357e846d008c", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "spark_df_selected = spark_df_nr.select(['Weight',\n", + " 'Upper_Material',\n", + " 'Midsole_Material',\n", + " 'Outsole',\n", + " 'Cushioning_System',\n", + " 'Drop__heel-to-toe_differential_',\n", + " 'Pronation_Type',\n", + " 'Usage_Type',\n", + " 'Gender',\n", + " 'Width',\n", + " 'Additional_Technologies',\n", + " 'regularPrice',\n", + " 'undiscounted_price']\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "ce4ec8d9-0a4c-4ee5-9a08-21fbbf18f574", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "df_nr = spark_df_selected.toPandas()" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "b599d705-4ee7-478e-9598-ebbc85a946ab", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "#Remplaza los caracteres no numéricos\n", + "df_nr['Weight'] = df_nr['Weight'].apply(lambda x: re.sub(r'[^\\d.]', '', str(x)))\n", + "df_nr['Drop__heel-to-toe_differential_'] = df_nr['Drop__heel-to-toe_differential_'].apply(lambda x: re.sub(r'[^\\d.]', '', str(x)))\n", + "df_nr['regularPrice'] = df_nr['regularPrice'].apply(lambda x: re.sub(r'\\D', '', str(x)))\n", + "df_nr['undiscounted_price'] = df_nr['undiscounted_price'].apply(lambda x: re.sub(r'\\D', '', str(x)))" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "7cd09b49-0be3-4933-aafc-82e2e9909f28", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "df_nr['Weight'] = df_nr['Weight'].replace('', np.nan)\n", + "df_nr['Drop__heel-to-toe_differential_'] = df_nr['Drop__heel-to-toe_differential_'].replace('', np.nan)\n", + "df_nr['regularPrice'] = df_nr['regularPrice'].replace('', np.nan)\n", + "df_nr['undiscounted_price'] = df_nr['undiscounted_price'].replace('', np.nan)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "4fed4f5b-274e-4342-a3e3-f1c33fce5c2c", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "df_nr['Weight'] = df_nr['Weight'].astype(float)\n", + "df_nr['Drop__heel-to-toe_differential_'] = df_nr['Drop__heel-to-toe_differential_'].astype(float)\n", + "df_nr['regularPrice'] = df_nr['regularPrice'].astype(float)\n", + "df_nr['undiscounted_price'] = df_nr['undiscounted_price'].astype(float)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "49fd365e-5e42-4281-87d5-3abdb7813cf5", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "df_nr['percentil_discounted'] = 1-(df_nr['undiscounted_price']/df_nr['regularPrice'])" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "1cd243cc-19bc-4acb-a994-5604e61e26ae", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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WeightUpper_MaterialMidsole_MaterialOutsoleCushioning_SystemDrop__heel-to-toe_differential_Pronation_TypeUsage_TypeGenderWidthAdditional_TechnologiesregularPriceundiscounted_price
0242NeutroProFly+Asfaltonull5nullnullnullnullnull59990007499000
1WovenFlyteFoam Blast+ ECOAHARFlyteFoam Blast+ ECO8Neutro • SupinadorEntreno en pistanullnullWaterproofing, Reflectivity6999000
285Mesh técnicoFF Blast MaxASICS Grip y AHARPLUSFF Blast Max6Neutral or SupinatorDaily training, racingUnisexStandardWaterproofing, reflectivity8499000
3305nullFF BLAST PLUS ECOAsfalto4D Guidance System10nullDaily Training, RacingMennullWaterproofing, Reflectivity, Customized Fit Sy...8799000
4270Technical FabricsPureGelAsfaltoPureGel8PronadoresDaily TrainingMenStandardFlyteFoam, OrthoLite Hybrid Max, Waterproofing4999000
..........................................
18080Malla JacquardEVAAsfaltoMetaRocker8NeutralDaily trainingUnisexnullWaterproofing, Reflectivity6999000
181malla knitFlyteFoam BLAST PLUS ECO, PureGELASICSGRIP, caucho AHAR PLUSFlyteFoam BLAST PLUS ECO, PureGEL8Neutro, SupinadorDaily training, Racingnullnullnull64990008499000
182282AsfaltoJ-FrameAsfaltoJ-Frame5PronadorDaily trainingMennullWaterproofing, reflectivity, customized fit sy...7499000
183275234nullAmplifoam+AsfaltoEquilibrada y Versatilidad8Neutral, SupinadorDaily training, RacingnullBreathability, TranspirabilityNone3299000
184nullnullAsfaltoSuperior10PronadorDaily TrainingMennullnull69990007699000
\n", + "

185 rows × 13 columns

\n", + "
" + ], + "text/plain": [ + " Weight Upper_Material ... regularPrice undiscounted_price\n", + "0 242 Neutro ... 5999000 7499000\n", + "1 Woven ... 6999000 \n", + "2 85 Mesh técnico ... 8499000 \n", + "3 305 null ... 8799000 \n", + "4 270 Technical Fabrics ... 4999000 \n", + ".. ... ... ... ... ...\n", + "180 80 Malla Jacquard ... 6999000 \n", + "181 malla knit ... 6499000 8499000\n", + "182 282 Asfalto ... 7499000 \n", + "183 275234 null ... 3299000 \n", + "184 null ... 6999000 7699000\n", + "\n", + "[185 rows x 13 columns]" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_nr" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "b5f996d7-2228-482b-9d46-6dc0a3eb1eef", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['Weight', 'Upper_Material', 'Midsole_Material', 'Outsole',\n", + " 'Cushioning_System', 'Drop__heel-to-toe_differential_',\n", + " 'Pronation_Type', 'Usage_Type', 'Gender', 'Width',\n", + " 'Additional_Technologies', 'regularPrice', 'undiscounted_price'],\n", + " dtype='object')" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_nr.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "52ba5d71-54fa-452c-82e5-3f377e7cc5de", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "# Identificar columnas numéricas y categóricas\n", + "numerical_cols = ['Weight', 'Drop__heel-to-toe_differential_','regularPrice','undiscounted_price','percentil_discounted']\n", + "categorical_cols = ['Upper_Material', 'Midsole_Material', 'Outsole', 'Pronation_Type', 'Usage_Type', 'Gender', 'Width',\n", + " 'Cushioning_System', 'Additional_Technologies']" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "f96cb877-7fc3-446e-aaa4-eefd7f08dbb2", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "# Definir los pipelines de preprocesamiento\n", + "numerical_pipeline = Pipeline(steps=[\n", + " ('imputer', SimpleImputer(strategy='median')),\n", + " ('scaler', StandardScaler())\n", + "])\n", + "\n", + "categorical_pipeline = Pipeline(steps=[\n", + " ('imputer', SimpleImputer(strategy='constant', fill_value='Desconocido')),\n", + " ('onehot', OneHotEncoder(handle_unknown='ignore'))\n", + "])\n", + "\n", + "# Combinar pipelines\n", + "preprocessor = ColumnTransformer(transformers=[\n", + " ('num', numerical_pipeline, numerical_cols),\n", + " ('cat', categorical_pipeline, categorical_cols)\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "23a561f7-9e74-4c84-82a5-b872a702e66d", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "output_type": "stream", + "text": [ + "Preprocesando columnas numéricas...\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m\n", + "\u001B[0;31mNameError\u001B[0m Traceback (most recent call last)\n", + "File \u001B[0;32m, line 20\u001B[0m\n", + "\u001B[1;32m 18\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mPreprocesando columnas numéricas...\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", + "\u001B[1;32m 19\u001B[0m scaler \u001B[38;5;241m=\u001B[39m StandardScaler()\n", + "\u001B[0;32m---> 20\u001B[0m numerical_scaled \u001B[38;5;241m=\u001B[39m scaler\u001B[38;5;241m.\u001B[39mfit_transform(df_nr[numerical_cols]\u001B[38;5;241m.\u001B[39mfillna(\u001B[38;5;241m0\u001B[39m))\n", + "\u001B[1;32m 22\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mGenerando embeddings para columnas categóricas...\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", + "\u001B[1;32m 23\u001B[0m categorical_embeddings \u001B[38;5;241m=\u001B[39m []\n", + "\n", + "\u001B[0;31mNameError\u001B[0m: name 'df_nr' is not defined" + ] + }, + "metadata": { + "application/vnd.databricks.v1+output": { + "arguments": {}, + "data": "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m\n\u001B[0;31mNameError\u001B[0m Traceback (most recent call last)\nFile \u001B[0;32m, line 20\u001B[0m\n\u001B[1;32m 18\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mPreprocesando columnas numéricas...\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 19\u001B[0m scaler \u001B[38;5;241m=\u001B[39m StandardScaler()\n\u001B[0;32m---> 20\u001B[0m numerical_scaled \u001B[38;5;241m=\u001B[39m scaler\u001B[38;5;241m.\u001B[39mfit_transform(df_nr[numerical_cols]\u001B[38;5;241m.\u001B[39mfillna(\u001B[38;5;241m0\u001B[39m))\n\u001B[1;32m 22\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mGenerando embeddings para columnas categóricas...\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 23\u001B[0m categorical_embeddings \u001B[38;5;241m=\u001B[39m []\n\n\u001B[0;31mNameError\u001B[0m: name 'df_nr' is not defined", + "errorSummary": "NameError: name 'df_nr' is not defined", + "errorTraceType": "ansi", + "metadata": {}, + "type": "ipynbError" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.metrics import silhouette_score\n", + "from sklearn.manifold import TSNE\n", + "from sentence_transformers import SentenceTransformer\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Asumiendo que ya tienes: \n", + "# df_nr (DataFrame) con columnas numéricas en numerical_cols y categorías en categorical_cols\n", + "# df (DataFrame) que es el DataFrame base donde se añadirán los clusters\n", + "# model = SentenceTransformer('all-MiniLM-L6-v2') # si aún lo necesitas para las columnas categóricas\n", + "\n", + "# Preprocesar columnas numéricas y categóricas\n", + "print(\"Preprocesando columnas numéricas...\")\n", + "scaler = StandardScaler()\n", + "numerical_scaled = scaler.fit_transform(df_nr[numerical_cols].fillna(0))\n", + "\n", + "print(\"Generando embeddings para columnas categóricas...\")\n", + "categorical_embeddings = []\n", + "for col in categorical_cols:\n", + " embeddings = model.encode(df_nr[col].fillna('Unknown').astype(str).tolist())\n", + " categorical_embeddings.append(embeddings)\n", + "\n", + "combined_categorical_embeddings = np.hstack(categorical_embeddings)\n", + "\n", + "# Combinar características numéricas y categóricas\n", + "print(\"Concatenando características numéricas y categóricas...\")\n", + "combined_features = np.hstack([numerical_scaled, combined_categorical_embeddings])\n", + "\n", + "# Escalar las características combinadas\n", + "print(\"Escalando características combinadas...\")\n", + "scaled_features = scaler.fit_transform(combined_features)\n", + "\n", + "# Reducción de dimensionalidad con PCA\n", + "print(\"Aplicando PCA para reducir dimensiones...\")\n", + "pca = PCA(n_components=400, random_state=42)\n", + "reduced_features = pca.fit_transform(scaled_features)\n", + "\n", + "# -------------------- Determinación del Número Óptimo de Clusters --------------------\n", + "print(\"Calculando el Método del Codo para determinar el número óptimo de clusters...\")\n", + "wcss = []\n", + "k_values = range(2, 21) # Rango de k para explorar\n", + "\n", + "for k in k_values:\n", + " kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)\n", + " kmeans.fit(reduced_features)\n", + " wcss.append(kmeans.inertia_)\n", + "\n", + "# Visualizar el Método del Codo\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(k_values, wcss, marker='o')\n", + "plt.title('Método del Codo para Determinar el Número Óptimo de Clusters')\n", + "plt.xlabel('Número de Clusters (k)')\n", + "plt.ylabel('WCSS (Inercia)')\n", + "plt.xticks(k_values)\n", + "plt.grid(True)\n", + "plt.show()\n", + "\n", + "# Silhouette Score\n", + "print(\"Calculando el Silhouette Score para diferentes valores de k...\")\n", + "silhouette_scores = []\n", + "\n", + "for k in k_values:\n", + " kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)\n", + " clusters = kmeans.fit_predict(reduced_features)\n", + " score = silhouette_score(reduced_features, clusters)\n", + " silhouette_scores.append(score)\n", + "\n", + "# Visualizar el Silhouette Score\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(k_values, silhouette_scores, marker='o', color='orange')\n", + "plt.title('Silhouette Score para Diferentes Valores de k')\n", + "plt.xlabel('Número de Clusters (k)')\n", + "plt.ylabel('Silhouette Score')\n", + "plt.xticks(k_values)\n", + "plt.grid(True)\n", + "plt.show()\n", + "\n", + "# Determinar el mejor k basado en el Silhouette Score\n", + "best_k = k_values[np.argmax(silhouette_scores)]\n", + "print(f\"El número óptimo de clusters según el Silhouette Score es: {best_k}\")\n", + "\n", + "# -------------------- Aplicar K-Means con el Número Óptimo de Clusters --------------------\n", + "print(f\"Aplicando K-Means con k={best_k}...\")\n", + "kmeans_final = KMeans(n_clusters=best_k, random_state=42, n_init=10)\n", + "clusters_final = kmeans_final.fit_predict(reduced_features)\n", + "\n", + "# Añadir etiquetas de cluster al DataFrame original\n", + "df_nr['Cluster'] = clusters_final\n", + "\n", + "# Resumen final de clusters\n", + "n_clusters_final = len(set(clusters_final))\n", + "print(f'\\nNúmero de clusters encontrados: {n_clusters_final}')\n", + "\n", + "# -------------------- Visualización de los Clusters --------------------\n", + "print(\"Reduciendo dimensiones para visualización con t-SNE...\")\n", + "tsne = TSNE(n_components=2, random_state=42, perplexity=30, n_iter=1000)\n", + "tsne_results = tsne.fit_transform(reduced_features)\n", + "\n", + "df_nr['tsne-2d-one'] = tsne_results[:, 0]\n", + "df_nr['tsne-2d-two'] = tsne_results[:, 1]\n", + "\n", + "plt.figure(figsize=(12, 8))\n", + "palette = sns.color_palette(\"hsv\", n_clusters_final)\n", + "sns.scatterplot(\n", + " x=\"tsne-2d-one\", y=\"tsne-2d-two\",\n", + " hue=\"Cluster\",\n", + " palette=palette,\n", + " data=df_nr,\n", + " legend=\"full\",\n", + " alpha=0.7\n", + ")\n", + "plt.title('Visualización de Clusters con t-SNE')\n", + "plt.xlabel('t-SNE 1')\n", + "plt.ylabel('t-SNE 2')\n", + "plt.legend(title='Cluster', bbox_to_anchor=(1.05, 1), loc='upper left')\n", + "plt.show()\n", + "\n", + "# -------------------- Análisis de Clusters --------------------\n", + "print(\"\\nResumen de Clusters:\")\n", + "print(df_nr['Cluster'].value_counts())\n", + "\n", + "# Opcional: Ver registros en cada cluster\n", + "for cluster in range(n_clusters_final):\n", + " print(f'\\nCluster {cluster}:')\n", + " print(df_nr[df_nr['Cluster'] == cluster].head())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "3eef2a78-db14-45a5-bb35-abdd9363197d", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "spark_df_nr = spark.createDataFrame(df_nr)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "a010ca5a-7a89-4aa1-b237-cee64fe2f8a8", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [ + "spark_df_nr.write.mode(\"overwrite\").saveAsTable(\"preprod_colombia.scraping_nr_clusters\")" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "cc9a01b0-0ceb-4b50-b8d0-7bd5af7cd620", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "application/vnd.databricks.v1+notebook": { + "computePreferences": null, + "dashboards": [], + "environmentMetadata": { + "base_environment": "", + "client": "1" + }, + "language": "python", + "notebookMetadata": { + "pythonIndentUnit": 4 + }, + "notebookName": "Clustering", + "widgets": {} + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/src/comparative_analysis/models/K-MeansV2/K-Means.py b/src/comparative_analysis/models/K-MeansV2/K-Means.py new file mode 100644 index 000000000..eb7dbc894 --- /dev/null +++ b/src/comparative_analysis/models/K-MeansV2/K-Means.py @@ -0,0 +1,284 @@ +# Librerías estándar +import re +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns + +# Preprocesamiento +from sklearn.preprocessing import StandardScaler, OneHotEncoder +from sklearn.impute import SimpleImputer +from sklearn.compose import ColumnTransformer +from sklearn.pipeline import Pipeline + +# Modelado +from sklearn.cluster import KMeans + +# Embeddings +from sentence_transformers import SentenceTransformer + +# Reducción de dimensionalidad +from sklearn.decomposition import PCA + +# Evaluación +from sklearn.metrics import silhouette_score + +# Visualización de datos +from sklearn.manifold import TSNE + +# Transformadores personalizados +from sklearn.base import BaseEstimator, TransformerMixin + + +df = pd.read_excel(r"C:\\Users\\cdgn2\\OneDrive\\Escritorio\\Maestría\\Maestria\\Metodologias Agiles\\Proyecto\\Comparative-analysis-of-products\\src\\comparative_analysis\\database\\Adidas_etiquetado.xlsx") + + +def preprocess_outsole(text): + if pd.isna(text): + return "Desconocido" + # Reemplazar porcentajes o valores numericos por una etiqueta genérica + text = re.sub(r'\d+(\.\d+)?\%?', 'X%', text) + # Unificar materiales + text = text.replace("Acetato de etileno y vinilo", "EVA") + text = text.replace("Caucho sintético", "CauchoSintetico") + # Quitar espacios extra + text = re.sub(r'\s+', ' ', text).strip() + return text + + +#Remplaza los caracteres no numéricos +df['Weight'] = df['Weight'].apply(lambda x: re.sub(r'[^\d.]', '', str(x))) +df['Drop__heel-to-toe_differential_'] = df['Drop__heel-to-toe_differential_'].apply(lambda x: re.sub(r'[^\d.]', '', str(x))) +df['regularPrice'] = df['regularPrice'].apply(lambda x: re.sub(r'\D', '', str(x))) +df['undiscounted_price'] = df['undiscounted_price'].apply(lambda x: re.sub(r'\D', '', str(x))) + + +df['Weight'] = df['Weight'].replace('', np.nan) +df['Drop__heel-to-toe_differential_'] = df['Drop__heel-to-toe_differential_'].replace('', np.nan) +df['regularPrice'] = df['regularPrice'].replace('', np.nan) +df['undiscounted_price'] = df['undiscounted_price'].replace('', np.nan) + + +df['Weight'] = df['Weight'].astype(float) +df['Drop__heel-to-toe_differential_'] = df['Drop__heel-to-toe_differential_'].astype(float) +df['regularPrice'] = df['regularPrice'].astype(float) +df['undiscounted_price'] = df['undiscounted_price'].astype(float) + + +df['percentil_discounted'] = 1-(df['undiscounted_price']/df['regularPrice']) + + +df_reduced = df[['Weight','Upper_Material','Midsole_Material','Outsole','Cushioning_System','Drop__heel-to-toe_differential_','regularPrice','undiscounted_price','percentil_discounted', 'Gender','Additional_Technologies']] + + +df_reduced['Outsole'] = df_reduced['Outsole'].apply(preprocess_outsole) + + + + # Identificar columnas numéricas y categóricas +numerical_cols = ['Drop__heel-to-toe_differential_','Weight', 'regularPrice','undiscounted_price','percentil_discounted'] +categorical_cols = ['Midsole_Material', 'Cushioning_System', 'Outsole', 'Upper_Material', + 'Additional_Technologies', 'Gender'] + +# Definir qué columnas numéricas se imputarán con mediana y escalado +numeric_impute_cols = ['regularPrice'] + +# Definir columnas numéricas "especiales" que no se deben imputar con mediana +special_numeric_cols = ['Drop__heel-to-toe_differential_', 'percentil_discounted','Weight', 'undiscounted_price'] + +################################### +# Transformador personalizado +################################### +class SpecialNumericToCategory(BaseEstimator, TransformerMixin): + + """ + Este transformador convierte las columnas numéricas "especiales" en categorías. + Por ejemplo: + - Si el valor es NaN, lo marca como "NoValue". + - Si tiene valor, lo convierte a una categoría del tipo "Value:X". + """ + def __init__(self, cols): + self.cols = cols + + def fit(self, X, y=None): + return self + + def transform(self, X): + X = X.copy() + for col in self.cols: + X[col] = X[col].apply(lambda val: 'NoValue' if pd.isna(val) else f'Value:{val}') + return X[self.cols] + +################################### +# Pipelines +################################### + +# Pipeline para columnas numéricas "normales" +numeric_pipeline = Pipeline(steps=[ + ('imputer', SimpleImputer(strategy='median')), # Imputar con mediana + ('scaler', StandardScaler()) # Escalar a media=0, std=1 +]) + +# Pipeline para columnas numéricas "especiales", convertidas a categóricas +special_numeric_pipeline = Pipeline(steps=[ + ('to_category', SpecialNumericToCategory(special_numeric_cols)), + ('imputer', SimpleImputer(strategy='constant', fill_value='Unknown')), + ('onehot', OneHotEncoder(handle_unknown='ignore')) +]) + +# Pipeline para columnas categóricas normales +categorical_pipeline = Pipeline(steps=[ + ('imputer', SimpleImputer(strategy='constant', fill_value='Unknown')), + ('onehot', OneHotEncoder(handle_unknown='ignore')) +]) + +# Combinar todos los pipelines con ColumnTransformer +preprocessor = ColumnTransformer(transformers=[ + ('num', numeric_pipeline, numeric_impute_cols), + ('special_num', special_numeric_pipeline, special_numeric_cols), + ('cat', categorical_pipeline, categorical_cols) +]) +X_transformed = preprocessor.fit_transform(df_reduced) +feature_names = (preprocessor.named_transformers_['num'][-1].get_feature_names_out(numeric_impute_cols).tolist() + + preprocessor.named_transformers_['special_num'][-1].get_feature_names_out(special_numeric_cols).tolist() + + preprocessor.named_transformers_['cat'][-1].get_feature_names_out(categorical_cols).tolist()) + + + +model = SentenceTransformer('all-MiniLM-L6-v2') + +# Preprocesar columnas numéricas +print("Preprocesando columnas numéricas...") +scaler = StandardScaler() +numerical_scaled = scaler.fit_transform(df_reduced[numerical_cols].fillna(0)) + +# Generar embeddings para columnas categóricas +print("Generando embeddings para columnas categóricas...") +categorical_embeddings = [] +for col in categorical_cols: + embeddings = model.encode(df_reduced[col].fillna('Unknown').astype(str).tolist()) + categorical_embeddings.append(embeddings) + +# Asignar importancia a las columnas categóricas según el orden dado +# Asumiendo el orden de categorical_cols: +# ['Midsole_Material', 'Cushioning_System', 'Additional_Technologies', 'Outsole', ...] +Midsole_Material_embeddings = categorical_embeddings[0] * 5.0 # 1ra prioridad +Cushioning_System_embeddings = categorical_embeddings[1] * 5.0 # 1ra prioridad +Additional_Technologies_embeddings = categorical_embeddings[2] * 2.0 # info adicional +Outsole_embeddings = categorical_embeddings[3] * 3.0 # 3ra prioridad + +# Otras columnas categóricas (si las hay) +if len(categorical_embeddings) > 4: + other_cat_embeddings = np.hstack(categorical_embeddings[4:]) +else: + other_cat_embeddings = np.empty((len(df_reduced), 0)) + +# Ajustar importancia en las variables numéricas: +# numerical_cols = ['Drop__heel-to-toe_differential_','Weight','regularPrice','undiscounted_price','percentil_discounted'] +drop_idx = numerical_cols.index('Drop__heel-to-toe_differential_') +weight_idx = numerical_cols.index('Weight') +regular_price_idx = numerical_cols.index('regularPrice') + +# Aplicar factores +numerical_scaled[:, drop_idx] *= 4.0 # 2da prioridad +numerical_scaled[:, weight_idx] *= 2.0 # 5ta prioridad +numerical_scaled[:, regular_price_idx] *= 1.5 # 6ta prioridad + +# Combinar embeddings categóricos con sus factores +combined_categorical_embeddings = np.hstack([ + Midsole_Material_embeddings, + Cushioning_System_embeddings, + Additional_Technologies_embeddings, + Outsole_embeddings, + other_cat_embeddings +]) + +# Combinar características numéricas y categóricas sin volver a escalar, +# para no perder la ponderación manual +print("Concatenando características numéricas y categóricas...") +combined_features = np.hstack([numerical_scaled, combined_categorical_embeddings]) + +# Ahora aplicamos PCA, k-means, etc., sobre combined_features +print("Aplicando PCA para reducir dimensiones...") +pca = PCA(n_components=400, random_state=42) # Ajusta n_components según necesidad +reduced_features = pca.fit_transform(combined_features) + +print("Calculando el Método del Codo para determinar el número óptimo de clusters...") +wcss = [] +k_values = range(10, 21) # Rango de k para explorar + +for k in k_values: + kmeans = KMeans(n_clusters=k, random_state=42, n_init=10) + kmeans.fit(reduced_features) + wcss.append(kmeans.inertia_) + +# Visualizar el Método del Codo +plt.figure(figsize=(10, 6)) +plt.plot(k_values, wcss, marker='o') +plt.title('Método del Codo para Determinar el Número Óptimo de Clusters') +plt.xlabel('Número de Clusters (k)') +plt.ylabel('WCSS (Inercia)') +plt.xticks(k_values) +plt.grid(True) +plt.show() + +print("Calculando el Silhouette Score para diferentes valores de k...") +silhouette_scores = [] + +for k in k_values: + kmeans = KMeans(n_clusters=k, random_state=42, n_init=10) + clusters = kmeans.fit_predict(reduced_features) + score = silhouette_score(reduced_features, clusters) + silhouette_scores.append(score) + +# Visualizar el Silhouette Score +plt.figure(figsize=(10, 6)) +plt.plot(k_values, silhouette_scores, marker='o', color='orange') +plt.title('Silhouette Score para Diferentes Valores de k') +plt.xlabel('Número de Clusters (k)') +plt.ylabel('Silhouette Score') +plt.xticks(k_values) +plt.grid(True) +plt.show() + +best_k = k_values[np.argmax(silhouette_scores)] +print(f"El número óptimo de clusters según el Silhouette Score es: {best_k}") + +print(f"Aplicando K-Means con k={best_k}...") +kmeans_final = KMeans(n_clusters=best_k, random_state=42, n_init=10) +clusters_final = kmeans_final.fit_predict(reduced_features) + +# Añadir etiquetas de cluster al DataFrame original +df['Cluster'] = clusters_final + +n_clusters_final = len(set(clusters_final)) +print(f'\nNúmero de clusters encontrados: {n_clusters_final}') + +print("Reduciendo dimensiones para visualización con t-SNE...") +tsne = TSNE(n_components=2, random_state=42, perplexity=30, n_iter=1000) +tsne_results = tsne.fit_transform(reduced_features) + +df['tsne-2d-one'] = tsne_results[:, 0] +df['tsne-2d-two'] = tsne_results[:, 1] + +plt.figure(figsize=(12, 8)) +palette = sns.color_palette("hsv", n_clusters_final) +sns.scatterplot( + x="tsne-2d-one", y="tsne-2d-two", + hue="Cluster", + palette=palette, + data=df, + legend="full", + alpha=0.7 +) +plt.title('Visualización de Clusters con t-SNE') +plt.xlabel('t-SNE 1') +plt.ylabel('t-SNE 2') +plt.legend(title='Cluster', bbox_to_anchor=(1.05, 1), loc='upper left') +plt.show() + +print("\nResumen de Clusters:") +print(df['Cluster'].value_counts()) + + +df.to_excel('src\\comparative_analysis\\models\\K-MeansV2\\productos_con_clusters.xlsx', index=False) \ No newline at end of file diff --git a/src/comparative_analysis/models/K-MeansV2/message.txt b/src/comparative_analysis/models/K-MeansV2/message.txt new file mode 100644 index 000000000..afd969edb --- /dev/null +++ b/src/comparative_analysis/models/K-MeansV2/message.txt @@ -0,0 +1,111 @@ +import pandas as pd +import numpy as np +import tensorflow as tf +from tensorflow import keras +from sklearn.model_selection import StratifiedKFold +from sklearn.impute import SimpleImputer +from sklearn.preprocessing import StandardScaler, OneHotEncoder +from sklearn.compose import ColumnTransformer +from sklearn.pipeline import Pipeline +from sklearn.metrics import classification_report, confusion_matrix +from pandas.api.types import CategoricalDtype + +print("TensorFlow version:", tf.__version__) + +# Cargar datos +df = pd.read_excel("src\\comparative_analysis\\models\\RedNeuronal\\productos_con_clusters.xlsx") + +# Ajustar nombres de columnas según disponibilidad real en el DataFrame. +# Ejemplo si la columna se llama "Drop heel-to-toe differential": +numerical_cols = ['Drop heel-to-toe differential', 'Weight', 'regularPrice','undiscounted_price','percentil_discounted'] +categorical_cols = ['Midsole_Material', 'Cushioning_System', 'Outsole', 'Upper_Material', + 'Additional_Technologies', 'Gender'] + +target_col = 'Cluster' +df = df.dropna(subset=[target_col]) + +X = df[numerical_cols + categorical_cols] +y = df[target_col] + +# Convertir a categoría si no lo es ya +if not isinstance(y.dtype, CategoricalDtype): + y = y.astype('category') + +num_classes = len(y.cat.categories) + +numeric_transformer = Pipeline(steps=[ + ('imputer', SimpleImputer(strategy='median')), + ('scaler', StandardScaler()) +]) + +categorical_transformer = Pipeline(steps=[ + ('imputer', SimpleImputer(strategy='most_frequent')), + # Para versiones de scikit-learn >= 1.2, usar 'sparse_output=False' + # Si tienes una versión más antigua, usa 'sparse=False' si está disponible o retira el argumento + ('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output=False)) +]) + +preprocessor = ColumnTransformer( + transformers=[ + ('num', numeric_transformer, numerical_cols), + ('cat', categorical_transformer, categorical_cols) + ] +) + +skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42) + +all_reports = [] +all_conf_matrices = [] + +for fold, (train_index, test_index) in enumerate(skf.split(X, y)): + X_train, X_val = X.iloc[train_index], X.iloc[test_index] + y_train, y_val = y.iloc[train_index], y.iloc[test_index] + + X_train_processed = preprocessor.fit_transform(X_train) + X_val_processed = preprocessor.transform(X_val) + + y_train_cat = keras.utils.to_categorical(y_train.cat.codes, num_classes=num_classes) + y_val_cat = keras.utils.to_categorical(y_val.cat.codes, num_classes=num_classes) + + model = keras.Sequential([ + keras.layers.Input(shape=(X_train_processed.shape[1],)), + keras.layers.Dense(64, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)), + keras.layers.Dropout(0.3), + keras.layers.Dense(32, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)), + keras.layers.Dropout(0.3), + keras.layers.Dense(num_classes, activation='softmax') + ]) + + model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) + + early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True) + + history = model.fit( + X_train_processed, y_train_cat, + validation_data=(X_val_processed, y_val_cat), + epochs=100, + batch_size=16, + callbacks=[early_stopping], + verbose=0 + ) + + val_loss, val_acc = model.evaluate(X_val_processed, y_val_cat, verbose=0) + print(f"Fold {fold+1}: Accuracy = {val_acc:.4f}") + + y_pred_proba = model.predict(X_val_processed) + y_pred = np.argmax(y_pred_proba, axis=1) + y_true = y_val.cat.codes + + report = classification_report(y_true, y_pred, target_names=y_val.cat.categories.tolist()) + conf_mat = confusion_matrix(y_true, y_pred) + + all_reports.append(report) + all_conf_matrices.append(conf_mat) + +print("\n=== Resultados de cada fold ===") +for i, rep in enumerate(all_reports): + print(f"Fold {i+1} report:") + print(rep) + print("Matriz de confusión:") + print(all_conf_matrices[i]) + print("-"*50) diff --git a/src/comparative_analysis/models/RedNeuronal/test.py b/src/comparative_analysis/models/RedNeuronal/test.py new file mode 100644 index 000000000..9c4a5fe67 --- /dev/null +++ b/src/comparative_analysis/models/RedNeuronal/test.py @@ -0,0 +1,10 @@ +import tensorflow as tf +import tensorflow.keras + + +from tensorflow.keras.models import Sequential +from tensorflow.keras.layers import Dense, Dropout +from tensorflow.keras.callbacks import EarlyStopping +from tensorflow.keras.optimizers import Adam + +print(tf.__version__) \ No newline at end of file diff --git a/src/comparative_analysis/models/RedNeuronal/train.py b/src/comparative_analysis/models/RedNeuronal/train.py new file mode 100644 index 000000000..8d1fc477b --- /dev/null +++ b/src/comparative_analysis/models/RedNeuronal/train.py @@ -0,0 +1,118 @@ +import pandas as pd +import numpy as np +import tensorflow as tf +from tensorflow import keras +from sklearn.model_selection import StratifiedKFold +from sklearn.impute import SimpleImputer +from sklearn.preprocessing import StandardScaler, OneHotEncoder +from sklearn.compose import ColumnTransformer +from sklearn.pipeline import Pipeline +from sklearn.metrics import classification_report, confusion_matrix +from pandas.api.types import CategoricalDtype + +print("TensorFlow version:", tf.__version__) + +# Cargar datos +df = pd.read_excel("src\\comparative_analysis\\models\\RedNeuronal\\productos_con_clusters.xlsx") + +# Ajustar nombres de columnas según disponibilidad real en el DataFrame. +# Ejemplo si la columna se llama "Drop heel-to-toe differential": +numerical_cols = ['Drop__heel-to-toe_differential_', 'Weight', 'regularPrice','undiscounted_price','percentil_discounted'] +categorical_cols = ['Midsole_Material', 'Cushioning_System', 'Outsole', 'Upper_Material', + 'Additional_Technologies', 'Gender'] + +target_col = 'Cluster' +df = df.dropna(subset=[target_col]) + +X = df[numerical_cols + categorical_cols] +y = df[target_col] + +# Convertir a categoría si no lo es ya +if not isinstance(y.dtype, CategoricalDtype): + y = y.astype('category') + +num_classes = len(y.cat.categories) + +numeric_transformer = Pipeline(steps=[ + ('imputer', SimpleImputer(strategy='median')), + ('scaler', StandardScaler()) +]) + +categorical_transformer = Pipeline(steps=[ + ('imputer', SimpleImputer(strategy='most_frequent')), + # Para versiones de scikit-learn >= 1.2, usar 'sparse_output=False' + # Si tienes una versión más antigua, usa 'sparse=False' si está disponible o retira el argumento + ('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output=False)) +]) + +preprocessor = ColumnTransformer( + transformers=[ + ('num', numeric_transformer, numerical_cols), + ('cat', categorical_transformer, categorical_cols) + ] +) + +skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42) + +all_reports = [] +all_conf_matrices = [] + +for fold, (train_index, test_index) in enumerate(skf.split(X, y)): + X_train, X_val = X.iloc[train_index], X.iloc[test_index] + y_train, y_val = y.iloc[train_index], y.iloc[test_index] + + X_train_processed = preprocessor.fit_transform(X_train) + X_val_processed = preprocessor.transform(X_val) + + y_train_cat = keras.utils.to_categorical(y_train.cat.codes, num_classes=num_classes) + y_val_cat = keras.utils.to_categorical(y_val.cat.codes, num_classes=num_classes) + + model = keras.Sequential([ + keras.layers.Input(shape=(X_train_processed.shape[1],)), + keras.layers.Dense(64, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)), + keras.layers.Dropout(0.3), + keras.layers.Dense(32, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)), + keras.layers.Dropout(0.3), + keras.layers.Dense(num_classes, activation='softmax') + ]) + + model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) + + early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True) + + history = model.fit( + X_train_processed, y_train_cat, + validation_data=(X_val_processed, y_val_cat), + epochs=100, + batch_size=16, + callbacks=[early_stopping], + verbose=0 + ) + + val_loss, val_acc = model.evaluate(X_val_processed, y_val_cat, verbose=0) + print(f"Fold {fold+1}: Accuracy = {val_acc:.4f}") + + y_pred_proba = model.predict(X_val_processed) + y_pred = np.argmax(y_pred_proba, axis=1) + y_true = y_val.cat.codes + + print("---------y_pred.shape---------") + print(y_pred.shape) + print("---------y_true.shape---------") + print(y_true.shape) + print("---------y_val.shape---------") + print(y_val.shape) + + report = classification_report(y_true, y_pred, target_names=y_val.cat.categories.tolist()) + conf_mat = confusion_matrix(y_true, y_pred) + + all_reports.append(report) + all_conf_matrices.append(conf_mat) + +print("\n=== Resultados de cada fold ===") +for i, rep in enumerate(all_reports): + print(f"Fold {i+1} report:") + print(rep) + print("Matriz de confusión:") + print(all_conf_matrices[i]) + print("-"*50) \ No newline at end of file From b2af6accfacf331af742355a65014f02f4ed7aac Mon Sep 17 00:00:00 2001 From: CarlosKC26 Date: Fri, 20 Dec 2024 01:10:52 -0500 Subject: [PATCH 57/84] se corrigieron algunos problemas pero persiste el error del one hot encoder --- src/comparative_analysis/main.py | 60 ++++++++- .../models/K-MeansV2/message.txt | 115 ++++++------------ .../models/RedNeuronal/modelo_entrenado.h5 | Bin 0 -> 335376 bytes .../models/RedNeuronal/modelo_entrenado.keras | Bin 0 -> 331615 bytes .../models/RedNeuronal/train.py | 23 +++- 5 files changed, 116 insertions(+), 82 deletions(-) create mode 100644 src/comparative_analysis/models/RedNeuronal/modelo_entrenado.h5 create mode 100644 src/comparative_analysis/models/RedNeuronal/modelo_entrenado.keras diff --git a/src/comparative_analysis/main.py b/src/comparative_analysis/main.py index 25bbf13f8..e98386386 100644 --- a/src/comparative_analysis/main.py +++ b/src/comparative_analysis/main.py @@ -1,6 +1,15 @@ -from flask import Flask +from flask import Flask, jsonify, request from apiRest.get import test, get_product_by_id from apiRest.post import get_products_by_parameters, predictKMeansV1 +from sklearn.pipeline import Pipeline +from sklearn.compose import ColumnTransformer +from sklearn.impute import SimpleImputer +from sklearn.preprocessing import StandardScaler, OneHotEncoder + +import numpy as np +import pandas as pd +import tensorflow as tf +import keras # Crear la instancia de Flask app = Flask(__name__) @@ -13,6 +22,55 @@ app.add_url_rule('/api/products', view_func=get_products_by_parameters, methods=['POST']) app.add_url_rule('/predict/KMeansV1', view_func=predictKMeansV1, methods=['POST']) +model = keras.saving.load_model(".\src\comparative_analysis\models\RedNeuronal\modelo_entrenado.keras") + +# Ajusta las columnas según tu caso real: +numerical_cols = ['Drop__heel-to-toe_differential_', 'Weight', 'regularPrice','undiscounted_price','percentil_discounted'] +categorical_cols = ['Midsole_Material', 'Cushioning_System', 'Outsole', 'Upper_Material', + 'Additional_Technologies', 'Gender'] + +numeric_transformer = Pipeline(steps=[ + ('imputer', SimpleImputer(strategy='median')), + ('scaler', StandardScaler()) +]) + +categorical_transformer = Pipeline(steps=[ + ('imputer', SimpleImputer(strategy='most_frequent')), + ('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output=False)) +]) + +preprocessor = ColumnTransformer( + transformers=[ + ('num', numeric_transformer, numerical_cols), + ('cat', categorical_transformer, categorical_cols) + ] +) + + +categories = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19] + +@app.route('/classify', methods=['POST']) +def classify(): + # Se espera un JSON con las llaves que coincidan con las columnas esperadas + data = request.get_json() + # Crear DataFrame con el nuevo elemento + # Asumimos que 'data' es un dict con las columnas correctas + nuevo_elemento = pd.DataFrame([data]) # Convierte el dict a DF con una fila + + # Preprocesar + #preprocessor.fit_transform(nuevo_elemento) + X_new_processed = preprocessor.fit_transform(nuevo_elemento) + + + # Predecir + y_pred_proba = model.predict(X_new_processed) + y_pred_class = np.argmax(y_pred_proba, axis=1) + predicted_cluster = categories[y_pred_class[0]] + + # Retornar la predicción en formato JSON + return jsonify({'prediction': predicted_cluster}) + + # Ejecutar la aplicación if __name__ == '__main__': app.run(debug=True, port=2626) \ No newline at end of file diff --git a/src/comparative_analysis/models/K-MeansV2/message.txt b/src/comparative_analysis/models/K-MeansV2/message.txt index afd969edb..6df2eecd5 100644 --- a/src/comparative_analysis/models/K-MeansV2/message.txt +++ b/src/comparative_analysis/models/K-MeansV2/message.txt @@ -1,38 +1,27 @@ -import pandas as pd +```python +from flask import Flask, request, jsonify import numpy as np +import pandas as pd import tensorflow as tf from tensorflow import keras -from sklearn.model_selection import StratifiedKFold -from sklearn.impute import SimpleImputer -from sklearn.preprocessing import StandardScaler, OneHotEncoder -from sklearn.compose import ColumnTransformer -from sklearn.pipeline import Pipeline -from sklearn.metrics import classification_report, confusion_matrix -from pandas.api.types import CategoricalDtype -print("TensorFlow version:", tf.__version__) +app = Flask(__name__) -# Cargar datos -df = pd.read_excel("src\\comparative_analysis\\models\\RedNeuronal\\productos_con_clusters.xlsx") +# Asume que has cargado tu modelo previamente (fuera del endpoint) +model = keras.models.load_model("mi_modelo_final") # Ajusta la ruta a tu modelo -# Ajustar nombres de columnas según disponibilidad real en el DataFrame. -# Ejemplo si la columna se llama "Drop heel-to-toe differential": +# Cargar el preprocessor que se usó en el entrenamiento +# Esto depende de cómo lo hayas serializado. Si no lo has guardado, deberás reconstruirlo igual que en el entrenamiento. +# Aquí se asume que lo tienes disponible en el entorno: +from sklearn.pipeline import Pipeline +from sklearn.compose import ColumnTransformer +from sklearn.impute import SimpleImputer +from sklearn.preprocessing import StandardScaler, OneHotEncoder +# Ajusta las columnas según tu caso real: numerical_cols = ['Drop heel-to-toe differential', 'Weight', 'regularPrice','undiscounted_price','percentil_discounted'] categorical_cols = ['Midsole_Material', 'Cushioning_System', 'Outsole', 'Upper_Material', 'Additional_Technologies', 'Gender'] -target_col = 'Cluster' -df = df.dropna(subset=[target_col]) - -X = df[numerical_cols + categorical_cols] -y = df[target_col] - -# Convertir a categoría si no lo es ya -if not isinstance(y.dtype, CategoricalDtype): - y = y.astype('category') - -num_classes = len(y.cat.categories) - numeric_transformer = Pipeline(steps=[ ('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler()) @@ -40,8 +29,6 @@ numeric_transformer = Pipeline(steps=[ categorical_transformer = Pipeline(steps=[ ('imputer', SimpleImputer(strategy='most_frequent')), - # Para versiones de scikit-learn >= 1.2, usar 'sparse_output=False' - # Si tienes una versión más antigua, usa 'sparse=False' si está disponible o retira el argumento ('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output=False)) ]) @@ -52,60 +39,30 @@ preprocessor = ColumnTransformer( ] ) -skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42) - -all_reports = [] -all_conf_matrices = [] +# Asumimos que dispones de las categorías originales tal como se entrenaron +# Por ejemplo: +categories = ['Cluster0', 'Cluster1', 'Cluster2'] # Ajusta según tus categorías reales -for fold, (train_index, test_index) in enumerate(skf.split(X, y)): - X_train, X_val = X.iloc[train_index], X.iloc[test_index] - y_train, y_val = y.iloc[train_index], y.iloc[test_index] - - X_train_processed = preprocessor.fit_transform(X_train) - X_val_processed = preprocessor.transform(X_val) - - y_train_cat = keras.utils.to_categorical(y_train.cat.codes, num_classes=num_classes) - y_val_cat = keras.utils.to_categorical(y_val.cat.codes, num_classes=num_classes) - - model = keras.Sequential([ - keras.layers.Input(shape=(X_train_processed.shape[1],)), - keras.layers.Dense(64, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)), - keras.layers.Dropout(0.3), - keras.layers.Dense(32, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)), - keras.layers.Dropout(0.3), - keras.layers.Dense(num_classes, activation='softmax') - ]) +@app.route('/classify', methods=['POST']) +def classify(): + # Se espera un JSON con las llaves que coincidan con las columnas esperadas + data = request.get_json() + # Crear DataFrame con el nuevo elemento + # Asumimos que 'data' es un dict con las columnas correctas + nuevo_elemento = pd.DataFrame([data]) # Convierte el dict a DF con una fila - model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) + # Preprocesar + X_new_processed = preprocessor.transform(nuevo_elemento) - early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True) - - history = model.fit( - X_train_processed, y_train_cat, - validation_data=(X_val_processed, y_val_cat), - epochs=100, - batch_size=16, - callbacks=[early_stopping], - verbose=0 - ) - - val_loss, val_acc = model.evaluate(X_val_processed, y_val_cat, verbose=0) - print(f"Fold {fold+1}: Accuracy = {val_acc:.4f}") - - y_pred_proba = model.predict(X_val_processed) - y_pred = np.argmax(y_pred_proba, axis=1) - y_true = y_val.cat.codes + # Predecir + y_pred_proba = model.predict(X_new_processed) + y_pred_class = np.argmax(y_pred_proba, axis=1) + predicted_cluster = categories[y_pred_class[0]] - report = classification_report(y_true, y_pred, target_names=y_val.cat.categories.tolist()) - conf_mat = confusion_matrix(y_true, y_pred) - - all_reports.append(report) - all_conf_matrices.append(conf_mat) + # Retornar la predicción en formato JSON + return jsonify({'prediction': predicted_cluster}) + +if __name__ == '__main__': + app.run(debug=True) -print("\n=== Resultados de cada fold ===") -for i, rep in enumerate(all_reports): - print(f"Fold {i+1} report:") - print(rep) - print("Matriz de confusión:") - print(all_conf_matrices[i]) - print("-"*50) +``` \ No newline at end of file diff --git a/src/comparative_analysis/models/RedNeuronal/modelo_entrenado.h5 b/src/comparative_analysis/models/RedNeuronal/modelo_entrenado.h5 new file mode 100644 index 0000000000000000000000000000000000000000..7c4c5590bd64c2fc4115670a5a6ffca91d627563 GIT binary patch literal 335376 zcmeFY2|SnG)&Po>F(H+i3=u^_hX1oRqDcdVW=TjSvr40x3>nHyBBhBmDE#+&Dk@WR zG;306jz$gY{!Qn+=l#C-ocrB-fA@aBd(Pv>e)d{xuf6x$YpuQZdbVxTZKe(tl^5mf zd4z@e1oI3-Y{V)9eWn6!`U!d=WpKl0X4^FlpF4@mtbxZ!kS$`m1r7i1Cp9{CZ6E@BAct`2`2M 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Date: Fri, 20 Dec 2024 18:10:08 -0500 Subject: [PATCH 58/84] librerias en desuso --- src/comparative_analysis/main.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/comparative_analysis/main.py b/src/comparative_analysis/main.py index e98386386..07447aadf 100644 --- a/src/comparative_analysis/main.py +++ b/src/comparative_analysis/main.py @@ -5,10 +5,8 @@ from sklearn.compose import ColumnTransformer from sklearn.impute import SimpleImputer from sklearn.preprocessing import StandardScaler, OneHotEncoder - import numpy as np import pandas as pd -import tensorflow as tf import keras # Crear la instancia de Flask From b8965c6ae4c194cebee8e4a8f3c327fcad7fcdc2 Mon Sep 17 00:00:00 2001 From: CarlosKC26 Date: Fri, 20 Dec 2024 23:41:27 -0500 Subject: [PATCH 59/84] Ya se puede consumir la red neuronal desde el Endpoint /predict --- src/comparative_analysis/apiRest/get.py | 34 +++++++++- src/comparative_analysis/apiRest/post.py | 54 ---------------- src/comparative_analysis/main.py | 60 +++++------------- .../models/{ => DBScan}/DBScan.py | 0 .../models/RedNeuronal/modelo_entrenado.keras | Bin 331615 -> 331615 bytes .../models/RedNeuronal/preprocessor.pkl | Bin 0 -> 19783 bytes .../models/RedNeuronal/test.py | 10 --- .../models/RedNeuronal/train.py | 3 + 8 files changed, 52 insertions(+), 109 deletions(-) rename src/comparative_analysis/models/{ => DBScan}/DBScan.py (100%) create mode 100644 src/comparative_analysis/models/RedNeuronal/preprocessor.pkl delete mode 100644 src/comparative_analysis/models/RedNeuronal/test.py diff --git a/src/comparative_analysis/apiRest/get.py b/src/comparative_analysis/apiRest/get.py index e55fec284..c2bac0dd1 100644 --- a/src/comparative_analysis/apiRest/get.py +++ b/src/comparative_analysis/apiRest/get.py @@ -28,4 +28,36 @@ def get_product_by_id(): return jsonify(product_info) except Exception as e: - return jsonify({"error": str(e)}), 500 \ No newline at end of file + return jsonify({"error": str(e)}), 500 + + +# Ruta GET para obtener productos por número de cluster +def get_products_by_cluster(): + cluster_number = request.args.get('cluster') # Obtener el número de cluster desde los parámetros + + if not cluster_number: + return jsonify({"error": "Se requiere un número de cluster"}), 400 + + try: + file_path = 'src/comparative_analysis/models/RedNeuronal/productos_con_clusters.xlsx' + df = pd.read_excel(file_path) + + # Verificar que la columna "Cluster" exista en el archivo + if 'Cluster' not in df.columns: + return jsonify({"error": "El archivo no contiene la columna 'Cluster'"}), 500 + + # Filtrar los productos por el número de cluster + filtered_products = df[df['Cluster'] == int(cluster_number)] + + if filtered_products.empty: + return jsonify({"error": f"No se encontraron productos para el cluster {cluster_number}"}), 404 + + # Convertir los datos filtrados a un diccionario + products_dict = filtered_products.to_dict(orient='records') + + return jsonify({"cluster": cluster_number, "products": products_dict}), 200 + + except ValueError: + return jsonify({"error": "El número de cluster debe ser un valor entero"}), 400 + except Exception as e: + return jsonify({"error": str(e)}), 500 diff --git a/src/comparative_analysis/apiRest/post.py b/src/comparative_analysis/apiRest/post.py index 52ac46681..3519fa08c 100644 --- a/src/comparative_analysis/apiRest/post.py +++ b/src/comparative_analysis/apiRest/post.py @@ -1,12 +1,7 @@ from flask import jsonify, request import pandas as pd import re -import pickle -# Cargar el encoder, scaler y modelo KMeans guardados -encoder = pickle.load(open('src\comparative_analysis\models\K-MeansV1\encoder.pkl', 'rb')) -scaler = pickle.load(open('src\comparative_analysis\models\K-MeansV1\scaler.pkl', 'rb')) -kmeans = pickle.load(open('src\comparative_analysis\models\K-MeansV1\kmeans_model.pkl', 'rb')) # Ruta POST para buscar productos por diferentes parámetros def get_products_by_parameters(): @@ -31,54 +26,5 @@ def get_products_by_parameters(): products_info = filtered_df.to_dict(orient='records') return jsonify(products_info) - except Exception as e: - return jsonify({"error": str(e)}), 500 - - -def preprocess_data(new_data): - # Cargar el dataset original para obtener las columnas dummy - df_original = pd.read_excel('src/comparative_analysis/database/Adidas_etiquetado.xlsx') - df_dummies_original = pd.get_dummies(df_original, dummy_na=True) - - # Convertir los datos nuevos en un DataFrame - df_new = pd.DataFrame([new_data]) - - # Convertir las columnas categóricas del nuevo producto en variables dummy - df_dummies_new = pd.get_dummies(df_new, dummy_na=True) - - # Alinear las columnas del nuevo producto con las del dataset original - df_dummies_new = df_dummies_new.reindex(columns=df_dummies_original.columns, fill_value=0) - - # Escalar los nuevos datos con el scaler previamente entrenado - X_scaled_new = scaler.transform(df_dummies_new) - - return X_scaled_new - - -def predictKMeansV1(): - try: - new_data = request.json # Obtener datos del producto del cuerpo de la solicitud - if not new_data: - return jsonify({"error": "No se enviaron datos para predecir el cluster"}), 400 - - # Preprocesar los datos del nuevo producto - X_scaled_new = preprocess_data(new_data) - - # Asignar el producto a un cluster - cluster_label = kmeans.predict(X_scaled_new)[0] - - # Obtener los productos originales - df_original = pd.read_excel('src/comparative_analysis/database/Adidas_etiquetado.xlsx') - - # Agregar los clusters asignados a cada producto - df_original['cluster'] = kmeans.predict(scaler.transform(pd.get_dummies(df_original, dummy_na=True))) - - # Filtrar los productos que pertenecen al mismo cluster - products_in_cluster = df_original[df_original['cluster'] == cluster_label] - - # Convertir a JSON y devolver los productos - products_info = products_in_cluster.to_dict(orient='records') - return jsonify(products_info) - except Exception as e: return jsonify({"error": str(e)}), 500 \ No newline at end of file diff --git a/src/comparative_analysis/main.py b/src/comparative_analysis/main.py index 07447aadf..7ac9569ee 100644 --- a/src/comparative_analysis/main.py +++ b/src/comparative_analysis/main.py @@ -1,13 +1,9 @@ from flask import Flask, jsonify, request -from apiRest.get import test, get_product_by_id -from apiRest.post import get_products_by_parameters, predictKMeansV1 -from sklearn.pipeline import Pipeline -from sklearn.compose import ColumnTransformer -from sklearn.impute import SimpleImputer -from sklearn.preprocessing import StandardScaler, OneHotEncoder -import numpy as np +from apiRest.get import test, get_product_by_id, get_products_by_cluster +from apiRest.post import get_products_by_parameters import pandas as pd import keras +import joblib # Crear la instancia de Flask app = Flask(__name__) @@ -15,58 +11,34 @@ # Registrar las rutas GET app.add_url_rule('/api/test', view_func=test, methods=['GET']) app.add_url_rule('/api/product', view_func=get_product_by_id, methods=['GET']) +app.add_url_rule('/api/similarProducts', view_func=get_products_by_cluster, methods=['GET']) # Registrar las rutas POST app.add_url_rule('/api/products', view_func=get_products_by_parameters, methods=['POST']) -app.add_url_rule('/predict/KMeansV1', view_func=predictKMeansV1, methods=['POST']) model = keras.saving.load_model(".\src\comparative_analysis\models\RedNeuronal\modelo_entrenado.keras") +# Cargar el preprocessor +preprocessor = joblib.load(".\src\comparative_analysis\models\RedNeuronal\preprocessor.pkl") -# Ajusta las columnas según tu caso real: numerical_cols = ['Drop__heel-to-toe_differential_', 'Weight', 'regularPrice','undiscounted_price','percentil_discounted'] categorical_cols = ['Midsole_Material', 'Cushioning_System', 'Outsole', 'Upper_Material', 'Additional_Technologies', 'Gender'] -numeric_transformer = Pipeline(steps=[ - ('imputer', SimpleImputer(strategy='median')), - ('scaler', StandardScaler()) -]) -categorical_transformer = Pipeline(steps=[ - ('imputer', SimpleImputer(strategy='most_frequent')), - ('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output=False)) -]) - -preprocessor = ColumnTransformer( - transformers=[ - ('num', numeric_transformer, numerical_cols), - ('cat', categorical_transformer, categorical_cols) - ] -) - - -categories = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19] - -@app.route('/classify', methods=['POST']) -def classify(): - # Se espera un JSON con las llaves que coincidan con las columnas esperadas +@app.route('/predict', methods=['POST']) +def predict(): + # Recibir datos en formato JSON data = request.get_json() - # Crear DataFrame con el nuevo elemento - # Asumimos que 'data' es un dict con las columnas correctas - nuevo_elemento = pd.DataFrame([data]) # Convierte el dict a DF con una fila + df_new = pd.DataFrame([data]) - # Preprocesar - #preprocessor.fit_transform(nuevo_elemento) - X_new_processed = preprocessor.fit_transform(nuevo_elemento) + # Procesar los datos usando el preprocessor cargado + X_new_processed = preprocessor.transform(df_new) + # Hacer predicción con el modelo + predictions_proba = model.predict(X_new_processed) + predictions = predictions_proba.argmax(axis=1) # Obtener la clase con mayor probabilidad - # Predecir - y_pred_proba = model.predict(X_new_processed) - y_pred_class = np.argmax(y_pred_proba, axis=1) - predicted_cluster = categories[y_pred_class[0]] - - # Retornar la predicción en formato JSON - return jsonify({'prediction': predicted_cluster}) + return jsonify({'prediction': int(predictions[0])}) # Ejecutar la aplicación diff --git a/src/comparative_analysis/models/DBScan.py b/src/comparative_analysis/models/DBScan/DBScan.py similarity index 100% rename from src/comparative_analysis/models/DBScan.py rename to src/comparative_analysis/models/DBScan/DBScan.py diff --git a/src/comparative_analysis/models/RedNeuronal/modelo_entrenado.keras b/src/comparative_analysis/models/RedNeuronal/modelo_entrenado.keras index 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