DBSCAN.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='orange'>Clustering: DBSCAN\n",
    "--- \n",
    "</font>\n",
    "\n",
    "*@j*"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Es un algoritmo que se basa en la `densidad` de los datos, es decir funciona bajo la lógica de que elementos similares siempre están cerca unos de otros. La principal característica de `DBSCAN` es que solo asigna elementos a clusters cuando realmente pertenecen a él, de modo que \"separa\" aquellos elementos anómalos (ruido), contrario a otros algoritmos en los que siempre se asignan todos los datos.\n",
    "\n",
    "Otra característica es que no se selecciona la cantidad de grupos a crear, estos se crean automáticamente por el modelo tomando en cuenta el `radio` y la cantidad de puntos vecinos `M` que se definen en las condiciones iniciales.\n",
    "\n",
    "Problema: Clasificar a los clientes de una empresa según su \"cercanía\" unos con otros."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Librerías:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "from sklearn.cluster import DBSCAN "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>edad</th>\n",
       "      <th>genero</th>\n",
       "      <th>nivel_ingresos</th>\n",
       "      <th>estudios</th>\n",
       "      <th>total_empleos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   edad  genero  nivel_ingresos  estudios  total_empleos\n",
       "0    19       1               1         0              1\n",
       "1    18       0               0         0              0\n",
       "2    29       1               3         2              2\n",
       "3    25       1               2         1              1\n",
       "4    27       0               2         1              3"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data.csv')\n",
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Crear nArray:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.values"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Crear modelo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = DBSCAN(eps=3.8, min_samples=5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Entrenar modelo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "DBSCAN(eps=3.8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obtener clasificaciones (-1 = elementos sin grupo):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{-1, 0, 1, 2}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = model.labels_\n",
    "set(labels)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agregar columna de clasificaciones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>edad</th>\n",
       "      <th>genero</th>\n",
       "      <th>nivel_ingresos</th>\n",
       "      <th>estudios</th>\n",
       "      <th>total_empleos</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   edad  genero  nivel_ingresos  estudios  total_empleos  labels\n",
       "0    19       1               1         0              1       0\n",
       "1    18       0               0         0              0       0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['labels'] = labels\n",
    "df.head(2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Librerías para graficar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.set(font_scale=1.5)\n",
    "sns.set_style(\"darkgrid\")    "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gráfica 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "sns.scatterplot(data=df, x='edad', y='total_empleos', hue='labels', palette='bright', s=70)\n",
    "\n",
    "plt.title(\"Total de empleos\", fontdict={'fontsize': 20}, pad=15)\n",
    "plt.show();"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gráfica 2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "sns.scatterplot(data=df, x='edad', y='nivel_ingresos', hue='labels', palette='bright', s=70)\n",
    "\n",
    "plt.title(\"Nivel de Ingresos\", fontdict={'fontsize': 20}, pad=15)\n",
    "plt.show();"
   ]
  }
 ],
 "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": "3.9.4"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "b5f98589597bf928df7c3b4b7221661d9594090b96ffc8472b8fdbc1b88b3ff7"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}