Sentiment_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trading View Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.base import clone\n",
    "import scipy.sparse as sparse\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PreProcessing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('/Users/rohitsuvarna/Downloads/Trading view project/idea_data.csv',usecols=['label','text','ticker'])\n",
    "data_clean = df.loc[df['label'].isin(['Long','Short'])]\n",
    "data_clean = data_clean[-pd.isnull(data_clean[\"text\"])]\n",
    "corpus = data_clean[\"text\"].tolist()\n",
    "#len(corpus)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vectorizer = CountVectorizer(\n",
    "    stop_words='english',\n",
    "    analyzer='word',\n",
    "    min_df = 2,\n",
    "    token_pattern='(?u)\\\\b[a-zA-Z]\\\\w+\\\\b' \n",
    ")\n",
    "X_corpus_docterm = vectorizer.fit_transform(corpus)\n",
    "features = vectorizer.get_feature_names()\n",
    "#features\n",
    "CV = X_corpus_docterm.toarray()\n",
    "df_sparse_vec = pd.DataFrame(CV)\n",
    "data_clean1 = data_clean['label'].reset_index()\n",
    "df_sparse_vec['label'] = data_clean1['label']\n",
    "df_sparse_vec['label'] = df_sparse_vec['label'].map({'Long': 1, 'Short': 0})\n",
    "#df_sparse_vec\n",
    "ticker = set(data_clean['ticker'])\n",
    "def checkTickerInDoc(doc, ticker):\n",
    "    ticker_list = []\n",
    "    doc = doc.split()\n",
    "    count = 0\n",
    "    for t in ticker:\n",
    "        if t in doc and t not in ticker_list:\n",
    "            #print(t)\n",
    "            ticker_list.append(t)\n",
    "            count += 1\n",
    "    if count == 0:\n",
    "        haveTicker = 0\n",
    "        multiTicker = 0\n",
    "    elif count == 1:\n",
    "        haveTicker = 1\n",
    "        multiTicker = 0\n",
    "    else:\n",
    "        haveTicker = 1\n",
    "        multiTicker = 1\n",
    "    return haveTicker, multiTicker\n",
    "counts = np.zeros([len(corpus),2])\n",
    "for count,text in enumerate(data_clean['text']):\n",
    "    counts[count,:] = checkTickerInDoc(text,ticker)\n",
    "#counts.shape\n",
    "res = checkTickerInDoc(\"Trade active: Active in this trade. Considering it's performing weak despite a bullish morning, this further confirms my trend suspicions. I estimate this is about a 4 day trade give or take\",ticker)\n",
    "#res\n",
    "count_cols = pd.DataFrame(counts)\n",
    "count_cols.columns = ['HaveTicker','Multiticker']\n",
    "df_sparse_vec['HaveTicker'] = count_cols['HaveTicker']\n",
    "df_sparse_vec['Multiticker'] = count_cols['Multiticker']\n",
    "df_sparse_vec['Output'] = df_sparse_vec['label']\n",
    "df_sparse_vec.drop(df_sparse_vec['label'])\n",
    "df_sparse_vec = df_sparse_vec.drop('label',1)\n",
    "#df_sparse_vec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Two Class Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = df_sparse_vec.drop('Output',1).as_matrix()\n",
    "y = df_sparse_vec['Output'].as_matrix()\n",
    "#y\n",
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n",
       "       eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n",
       "       learning_rate='optimal', loss='log', n_iter=5, n_jobs=1,\n",
       "       penalty='l2', power_t=0.5, random_state=42, shuffle=True, verbose=0,\n",
       "       warm_start=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_score, recall_score\n",
    "logit_clf = SGDClassifier(random_state=42, loss = 'log')\n",
    "logit_clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred_l = logit_clf.predict(X_test)\n",
    "#y_pred.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.792954086053\n"
     ]
    }
   ],
   "source": [
    "print(sum(y_pred_l == y_test)/len(y_pred_l))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precision  and  Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.921560222889\n",
      "0.801043219076\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred_l)\n",
    "#cm\n",
    "recall = recall_score(y_test, y_pred_l)\n",
    "precision = precision_score(y_test, y_pred_l)\n",
    "y_score = logit_clf.decision_function(X_test)\n",
    "print(recall)\n",
    "print(precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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djlKlih4qV6oYHD8OjzwCl11mQ/r552HJEtv1aNeu9hD5GV26QNOmnhvaAE93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\nqgdXB+DXPb9yJOmIw9UoVTQ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+EZG/XL9jHs2nTTcROZHjO/uCE7VerPN917z882uU43NZ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\neBkWnQ8PAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f95e5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precisions, recalls, thresholds = precision_recall_curve(y_test, y_score)\n",
    "\n",
    "def plot_precision_recall_vs_threshold(precisions, recalls, thresholds):\n",
    "    plt.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\", linewidth=2)\n",
    "    plt.plot(thresholds, recalls[:-1], \"g-\", label=\"Recall\", linewidth=2)\n",
    "    plt.xlabel(\"Threshold\", fontsize=16)\n",
    "    plt.legend(loc=\"upper left\", fontsize=16)\n",
    "    plt.ylim([0, 1])\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "plot_precision_recall_vs_threshold(precisions, recalls, thresholds)\n",
    "plt.xlim([-10, 10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uF/vhBlv7v8NbUiIs0MA0Qni+lJQURo0axZw5c2jVqhULFiygUqVKRscSPs7e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\nRJoRJX4REZFm5P8BxqdjwOQmx1cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f969240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "fpr,tpr, thresholds = roc_curve(y_test,y_score)\n",
    "def plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.xlabel('False Positive Rate', fontsize=16)\n",
    "    plt.ylabel('True Positive Rate', fontsize=16)\n",
    "plt.figure(figsize=(8, 6))\n",
    "plot_roc_curve(fpr, tpr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hinge_clf = SGDClassifier(random_state=42, loss='hinge') \n",
    "hinge_clf.fit(X_train, y_train)\n",
    "y_pred_h= hinge_clf.predict(X_test)\n",
    "y_score2= hinge_clf.decision_function(X_test)\n",
    "#y_pred.shape_h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accurcy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.789777649437\n"
     ]
    }
   ],
   "source": [
    "print(sum(y_pred_h == y_test)/len(y_pred_h))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precision  and  Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.929275610802\n",
      "0.793848407177\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred_h)\n",
    "#cm\n",
    "recall = recall_score(y_test, y_pred_h)\n",
    "precision = precision_score(y_test, y_pred_h)\n",
    "print(recall)\n",
    "print(precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S6vJp4lZKFXp+4ketMrUAOB1/msqvVtbew5TP0sStlCoSWlRpQf9G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\nRUGQTp8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110e85b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precisions, recalls, thresholds = precision_recall_curve(y_test, y_score2)\n",
    "\n",
    "def plot_precision_recall_vs_threshold(precisions, recalls, thresholds):\n",
    "    plt.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\", linewidth=2)\n",
    "    plt.plot(thresholds, recalls[:-1], \"g-\", label=\"Recall\", linewidth=2)\n",
    "    plt.xlabel(\"Threshold\", fontsize=16)\n",
    "    plt.legend(loc=\"upper left\", fontsize=16)\n",
    "    plt.ylim([0, 1])\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "plot_precision_recall_vs_threshold(precisions, recalls, thresholds)\n",
    "plt.xlim([-10, 10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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17mzPYMJ5/d2CF2B8x2oE+fkYmEYI53Xz5k1GjhzJokWLaNq0KUuWLKFMmTJG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\ncMUVV5CRkcH06dMZPnx4vR5fREQk2SLZq7+4xHf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WKMTfymkd9s+1Fi3OOcy5ylYtERER\niTj16hcREUkjSvwiIiJpRIlfREQkjSjxi4iIpBElfhERkTSixC8iIpJGlPhFRETSiBK/iIhIGlHi\nFxERSSP/DxvcAYEFUhK8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f95e898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "fpr,tpr, thresholds = roc_curve(y_test,y_score2)\n",
    "def plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.xlabel('False Positive Rate', fontsize=16)\n",
    "    plt.ylabel('True Positive Rate', fontsize=16)\n",
    "plt.figure(figsize=(8, 6))\n",
    "plot_roc_curve(fpr, tpr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enriched Corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "negative_word = pd.read_csv('/Users/rohitsuvarna/Downloads/LoughranMcDonald_Negative.csv', header = None)\n",
    "positive_word = pd.read_csv('/Users/rohitsuvarna/Downloads/LoughranMcDonald_Positive.csv', header = None)\n",
    "uncertainty_word = pd.read_csv('/Users/rohitsuvarna/Downloads/LoughranMcDonald_Uncertainty.csv', header = None)\n",
    "negative_word_list=negative_word.iloc[:, 0].tolist()\n",
    "positive_word_list=positive_word.iloc[:, 0].tolist()\n",
    "uncertainty_word_list=uncertainty_word.iloc[:, 0].tolist()\n",
    "corpus1=corpus+negative_word_list\n",
    "corpus2=corpus1+positive_word_list#This is the extend corpus\n",
    "vectorizer2 = CountVectorizer(\n",
    "    stop_words='english',\n",
    "    analyzer='word',\n",
    "    min_df = 2,\n",
    "    token_pattern='(?u)\\\\b[a-zA-Z]\\\\w+\\\\b' \n",
    ")\n",
    "X_corpus_docterm2 = vectorizer2.fit_transform(corpus2)\n",
    "CV2 = X_corpus_docterm2.toarray()\n",
    "df_sparse_vec2 = pd.DataFrame(CV2)\n",
    "#Here we only need to consider the Long/short ideas and not pos/neg words as our feature vectors\n",
    "df_sparse_vec2 = df_sparse_vec2[:34628] \n",
    "#df_sparse_vec2\n",
    "p1 = data_clean['label'].map({'Long': 1, 'Short': 0})\n",
    "p1 = p1.reset_index()\n",
    "p1 = p1.drop('index',1)\n",
    "#p1\n",
    "t1 = df_sparse_vec['HaveTicker']\n",
    "t1 = t1.reset_index()\n",
    "t1 = t1.drop('index',1)\n",
    "#t1\n",
    "s1 = df_sparse_vec['Multiticker']\n",
    "s1 = s1.reset_index()\n",
    "s1 = s1.drop('index',1)\n",
    "#s1\n",
    "df_sparse_vec2['HaveTicker'] = t1\n",
    "df_sparse_vec2['Multiticker'] = s1\n",
    "df_sparse_vec2['output'] = p1\n",
    "#df_sparse_vec2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X1 = df_sparse_vec2.drop('output',1).as_matrix()\n",
    "y1= df_sparse_vec2['output'].as_matrix()\n",
    "X_train1, X_test1, y_train1, y_test1 = train_test_split( X1, y1, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "logit_clf1 = SGDClassifier(random_state=42, loss = 'log')\n",
    "logit_clf1.fit(X_train1,y_train1)\n",
    "y_pred1 = logit_clf1.predict(X_test1)\n",
    "cm1 = confusion_matrix(y_test1, y_pred1)\n",
    "y_score1 = logit_clf1.decision_function(X_test1)\n",
    "#cm1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.794686687843\n"
     ]
    }
   ],
   "source": [
    "print(sum(y_pred1 == y_test1)/len(y_pred1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precision and Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.922203171882\n",
      "0.802499067512\n"
     ]
    }
   ],
   "source": [
    "recall1 = recall_score(y_test1, y_pred1)\n",
    "precision1 = precision_score(y_test1, y_pred1)\n",
    "print(recall1)\n",
    "print(precision1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kHug/fTr4+lohXZYjwcvDq7+/yrNLnuWRHo/w9uC37S5HKbchImuNMVFlWYaz\nTgdTSpVCRgb88gvMmwcLF0JMTP68Zs1g79784L71VntqLI0wf2t8gYTUBJsrUarq0eBWqpxlZlqb\nvsE6Cvzaa8Fx3CBhYfDQQ3D99dCypXWkuDtoGGyN3vLLrl/IysnCy0O/SpSqKPq/TSknS0uzetQ/\n/wzr18O6ddZR4DVrQmgoPPecdQT4hRdC797WOdfu5pIml9AouBF7k/cye+tsrmt7nd0lKVVlaHAr\n5QQ5OfDdd9b+6q++KjxPxDrwbJDjvIvKcGygt6c343qNY/T80Ty66FGGNR9GoE+g3WUpVSW49FHl\ndh04p1yDO73/xsCdd+aHdq1a8OqrVq87KSk/tCuT+6Luo2PdjhxIOcDMzTPtLkepKsNlg9vb27vE\ngUVU1ZCWllZo8BhXkZgI338P991nHQkO1ljgV11lhfX27XDkCDz9tDU2eEiIvfWWFy8PL8Z2HwvA\npNWT3OoPLaXcmctuKq9duzZxcXE0aNAAf39/HVqxCjHGkJaWRlxcHHVyB9q2tR7rOtZTp1r7q9et\ny58XEQH/+pd1f/p0e+qz01WtruK+OfexLn4dO47uoGXNlnaXpFSl57LBXb16dQAOHjxIZmamzdWo\niubt7U2dOnXyPgd26t0b/vyz8LT+/a2Dy66r4sdkBfsF0zO8J0v3LmVr4lYNbqUqgMsGN1jh7Qpf\n3KryS0uzDiDbudO6XXSRtZkbrCFGY2Jg6FC45hprf7U7HgleXoY2G8rSvUt5f9X7XNnqSrvLUarS\nc9mR05Qqbx9/DEuWwJo1sHt34XmPPw5vvGHdP3gw/5rW6nTJp5Jp8HYDTmaeZNdDu4gMjbS7JKVc\nlo6cplQJjLECd/Nm67ZpE2zcCPPnW+dTg3X61uLF+c9p1gyioqBpUygwJDz161ds7e4m2C+Yy1pe\nxtebvua7rd/xeK/H7S5JqUpNg1u5jYwMSEiwjthOTIRjx6xbo0YwZIjVZtMma3P2gQP5o5MVtG9f\nfnDfey/cdFN+WAcEVNy6VDbXtL6Grzd9zYy/Z2hwK1XONLiVS0hLs0I1Pt4K5SNH4K67rItsgHXN\n6ZklnCp83XX5wR0UBDt2WPdr1LCuqtW2rXVr0wZatCj8POUcl7W4jFC/UNbHr2fj4Y20r9Pe7pKU\nqrQ0uJXTGQPHj1sBnJBQ+GeHDjBwoNVuxQrrUpW5veeiBg+GJk2s+6dOWfuYa9aE2rWtnzVqWEOI\nFrzEZXg3+wlXAAAgAElEQVS41esOD4fg4HJfVeXg6+XLje1u5MPoD/nq7680uJUqRxrcqpCsLGuk\nr6NHrdG/cjcrb9sGS5daF8w4dQpSUiA52fp58iTMmpW/jPbtrfAszqhR+cHt5ZV/dSxvb+uc6Pr1\n81+34MFgU6ZYIX228Vi8vKzetap4vSJ68WH0h+xN3mt3KUpVahrcLignxwpIyN9UnJFh7bfNyLDm\n5f5MS7NuPXpYPVCwerLr1kF6unVLS7N6vMePW5eMfPPN/Nfq3h1OnLDm5QZxrg8/hPvvt+4vX55/\nvzjp6fm1BgdDYKAVvrkhnHu/T5/851xwAWzdmt979jjDOH61a5fud6fsE+xrbeJITE20uRKlKrcq\nH9y7d1uBmJ1tBWZ2dv59X9/8c3kBpk2zQjAz0+qZFvx5ySXQq5fVbuVK+OgjK1xzwzP3lpEBv/6a\nfx7w9dfDH3/kh3Hu8gFuvhlmzLDu79oFrVuXvB5Ll1rnHgN8+y28807x7Vq0KBzcmzdbPeZcIlaI\nhoWBn1/+9NatrYO5vL2t30twMFSvbt1CQwtfjvLXX8/eMwbrd9Cq1dnbKffQvk57BOG3Pb+xM2kn\nTWs0tbskpSqlKh/cH36Yf75uUU2bQmxs/uMxYwr3SAvy88sP7r174YsvSn7NU6fygzspCQ4dOr2N\nt3fhMPT3h8hIa7qPj/XT29t63cDAwvtze/e2XsPHx5rv52cFcWhofq881x9/WJuXg4KsZQQHF9/z\n7dUrf/3OxgWHF1cVoFFII27tcCvTNkxj4uqJvDvkXbtLUqpSqvIDsHz0kdWr9fCw9qkW/NmgAXzy\nSX7bhx6yes1eXlY4eXnl3x80KH8z8O7d8NtvVnD6+ubfch9HReWH2+HDVq/dxyc/aH18Coe2Uu5i\nTdwaun3SjdqBtYl7NA4vjyrfN1CqEGcMwFLlg1sp5TzGGFpMakFsUizzR8xnSLMhdpeklEtxRnC7\n7GU9lVLuR0QYccEIAP678b82V6NU5aTBrZRyqqtbXw3AqrhVNleiVOWkwa2Ucqrw6uEAxJ+IJ8fk\n2FyNUpWPBrdSyqlC/UJpFNyIExknWBO3xu5ylKp0NLiVUk4lIvSP7A/A+vj1NlejVOWjwa2UcroQ\n3xAAUjOLuUSbUqpMNLiVUk4X4mcF94oDK2yuRKnKR4NbKeV0t3a4FR9PH77d8q3u51bKyTS4lVJO\n1yS0CQ92fRCAd1aWMHC+Uuq8aHArpcrFmG5jAPhh2w+kpJcwyL9S6pxpcCulykVkaCR9GvYhLSuN\n6Rum212OUpWGBrdSqtw8EPUAAB+v/Ri7rougVGVTquAWkSEisl1EYkXkqRLa9BOR9SKyWUSWOrdM\npZQ7urr11dQOrM3fR/7mtz2/2V2OUpXCWYNbRDyBD4ChQBvgJhFpU6RNCDAZuNwY0xa4rhxqVUq5\nGV8vX+7tfC8AT/3ylPa6lXKC0vS4uwGxxphdxpgM4GvgiiJtbga+N8bsAzDGHHFumUopd/VE7yeo\nE1iH1XGrWRC7wO5ylHJ7pQnuBsD+Ao8POKYV1AIIFZHfRGStiNxW3IJEZKSIRItIdEJCwvlVrJRy\nK9V9q/NYz8cAmLB8gs3VKOX+nHVwmhfQBRgODAaeE5EWRRsZY6YYY6KMMVG1atVy0ksrpVzd/VH3\nE+AdwLK9y1i+b7nd5Sjl1koT3HFARIHH4Y5pBR0AFhpjThpjEoFlQAfnlKiUcnfVfKvxcPeHAXhm\nyTO6r1upMihNcK8BmotIpIj4ADcCPxVp8yNwoYh4iUgA0B3Y6txSlVLu7IneTxDiF8Kyvcv4fuv3\ndpejlNs6a3AbY7KA0cBCrDCeZYzZLCL3i8j9jjZbgQXARmA18IkxZlP5la2UcjfBfsG8MuAVAB6Y\n+wDxJ+Jtrkgp9yR2bbKKiooy0dHRtry2UsoeOSaHS6Zdwq97fqVvo778evuviIjdZSlVYURkrTEm\nqizL0JHTlFIVxkM8+PKaLwn1C2Xp3qWs2K+X/VTqXGlwK6UqVN2gutzT+R4A/v3Hv22uRin3o8Gt\nlKpwD3V/CG8Pb+bGzGVD/Aa7y1HKrWhwK6UqXHj18Lxe98drP7a5GqXciwa3UsoWI7uMBODTdZ+y\nM2mnzdUo5T40uJVStuhYtyMjLhhBRnYG7616z+5ylHIbGtxKKds82vNRwNpcrr1upUpHg1spZZvO\n9TpzbZtrycjOYOyCsXaXo5Rb0OBWStnq7UFvE+QTxNyYufyx7w+7y1HK5WlwK6VsFREcwYNdHwTg\n2lnX8s+pf2yuSCnXpsGtlLLds32epU2tNhw+eZgpa6fYXY5SLk2DWyllu2q+1Xj9ktcBeGnZS9rr\nVuoMNLiVUi5heIvh9G3UlxMZJ5i2YZrd5SjlsjS4lVIuY1TXUQC8+vurHDl5xOZqlHJNGtxKKZdx\ndeur6RXRi8MnD/PML8/YXY5SLkmDWynlMrw8vPjsis8QhOkbp5N8KtnukpRyORrcSimX0iKsBe3r\ntCcjO4NtidvsLkcpl6PBrZRyOZGhkQDM+HuGzZUo5Xo0uJVSLmdcr3EATFw9kQWxC2yuRinXosGt\nlHI5vSJ68XjPxwEYu2AsJzNO2lyRUq5Dg1sp5ZKe6/scDYMbsuPoDp78+Um7y1HKZWhwK6VcUnXf\n6nxx5Rd4iAcfrPmAxTsX212SUi5Bg1sp5bL6Ne7HMxda53Pf9sNt7Plnj70FKeUCNLiVUi7tub7P\n0bdRX+JPxDPyfyPtLkcp22lwK6Vcmo+nD19d8xV+Xn4s3rWYLQlb7C5JKVtpcCulXF69avW4o8Md\nALyx4g17i1HKZhrcSim3MLbHWLw9vPl8/efM3DTT7nKUso0Gt1LKLbSq2Yp3Br8DWOd26zW7VVWl\nwa2UchsPdH2A3hG9OXzyMP/32//ZXY5SttDgVkq5DQ/xYOLQiQBMXT+VpLQkmytSquJpcCul3Eqn\nep2Iqh9FSnoKP2z7we5ylKpwGtxKKbfTK7wXACnpKTZXolTF0+BWSrmdQJ9AAN1UrqokDW6llNvp\nXK8zAP/56z8cTz9uczVKVSwNbqWU27m69dX0CO9B/Il4Xvn9FbvLUapClSq4RWSIiGwXkVgReeoM\n7bqKSJaIXOu8EpVSqjAP8eD9Ie8DMGn1JN1krqqUswa3iHgCHwBDgTbATSLSpoR2rwGLnF2kUkoV\n1bVBVwZEDuBk5kkeXvCw3eUoVWFK0+PuBsQaY3YZYzKAr4Erimk3BvgOOOLE+pRSqkTvDn4XX09f\npm+czmfrPrO7HKUqRGmCuwGwv8DjA45peUSkAXAV8OGZFiQiI0UkWkSiExISzrVWpZQq5II6F/Bi\n/xcBGDVvFPuS99lckVLlz1kHp70LPGmMyTlTI2PMFGNMlDEmqlatWk56aaVUVTau1zgujryYU1mn\nePvPt+0uR6lyV5rgjgMiCjwOd0wrKAr4WkT2ANcCk0XkSqdUqJRSZyAivDnoTQA++esT7XWrSq80\nwb0GaC4ikSLiA9wI/FSwgTEm0hjT2BjTGPgWGGWM0bEIlVIVomPdjlzZ6kpOZp5k4PSBHE09andJ\nSpWbswa3MSYLGA0sBLYCs4wxm0XkfhG5v7wLVEqp0vj08k9pX6c9O47u4PpvrycjO8PukpQqF2KM\nseWFo6KiTHR0tC2vrZSqnHYm7aTLlC4kpyczuutoJg6baHdJShUiImuNMVFlWYaOnKaUqjSa1mjK\n/BHz8fbwZtKaSSzZvcTukpRyOg1upVSl0jOiJ7d3uB2Avw//bXM1SjmfBrdSqtKpV60eAPEn4m2u\nRCnn0+BWSlU6tQNrA7Bg5wIyszNtrkYp59LgVkpVOiMuGEGwbzDr49fz5M9P2l2OUk6lwa2UqnRC\n/UOZdd0sACaunsjOpJ02V6SU82hwK6UqpUFNB3F166vJysli1LxR2HXqq1LOpsGtlKq0Phr+EcG+\nwSzauYjFuxbbXY5STqHBrZSqtGoF1uLpC58G4Llfn9Net6oUNLiVUpXa6G6jqR1Ym9Vxq5mzY47d\n5ShVZhrcSqlKLdAnkGcufAaAB+Y+oBcgUW5Pg1spVendF3UfrWq2Iu54HGPmj7G7HKXKRINbKVXp\n+Xn58c113+Dl4cVXm75iQewCu0tS6rxpcCulqoR2tdvlbTJ/ffnrNlej1PnT4FZKVRmP9HyEQO9A\nft3zK6sOrLK7HKXOiwa3UqrKCPELYXS30QCMmT+GjOwMmytS6txpcCulqpSnLnyKMP8w1hxcw+OL\nHre7HKXOmQa3UqpKCfEL4ccbf8TH04eJqycy4Y8Jdpek1DnR4FZKVTm9G/bm40s/RhCe/uVpZmyc\nYXdJSpWaBrdSqkq6o+MdTLjE6m3fMvsWPlzzoc0VKVU6GtxKqSprXK9xeWOZj5o3islrJttckVJn\np8GtlKqyRIRXL36Vdwa/A8DoeaNZe3CtzVUpdWYa3EqpKu/hHg8ztvtYDIY7f7yT4+nH7S5JqRJp\ncCulFDC+33hahLXg7yN/M+L7EWTnZNtdklLF0uBWSims08Tm3DSHUL9Q/rfjf4yZP0bDW7kkDW6l\nlHJoHtacb677Bk/x5MPoDxnx/QjSs9LtLkupQjS4lVKqgIubXMyUy6YAMHPzTPp/0Z9jacdsrkqp\nfBrcSilVxF2d7uKb674hvHo4fx74kzaT27AwdqHdZSkFaHArpVSxrm1zLX/c+Qcd63Yk/kQ8Q2YM\nYeq6qXaXpZQGt1JKlaRRSCOi741mZOeRANzz0z3a81a20+BWSqkz8PTw5OPLPubpC5/GYLjjxztI\nTE20uyxVhWlwK6VUKbzU/yX6NOxD/Il4Lp52Mcmnku0uSVVRGtxKKVUKnh6efHXNV9SvVp+NhzfS\ne2pv9v6z1+6yVBWkwa2UUqXUoHoDlt+1nCahTdicsJkWk1ow4Y8JGGPsLk1VIaUKbhEZIiLbRSRW\nRJ4qZv4IEdkoIn+LyAoR6eD8UpVSyn6NQxqz9I6lXNfmOjKyM3j6l6cZ/uVw3e+tKsxZg1tEPIEP\ngKFAG+AmEWlTpNluoK8x5gLgJWCKswtVSilXEV49nFnXzeLLq7/E38uf+bHz6T21t4a3qhCl6XF3\nA2KNMbuMMRnA18AVBRsYY1YYY3KHFloJhDu3TKWUcj03XXATm0dtpnFIY3Yc3UH/L/qzMHahbjpX\n5ao0wd0A2F/g8QHHtJLcDcwvboaIjBSRaBGJTkhIKH2VSinloiJDI1l6x1Ka1WjGpiObGDJjCJdM\nv0R736rcOPXgNBHpjxXcTxY33xgzxRgTZYyJqlWrljNfWimlbNMwuCEr717J+L7j8fbwZsnuJbT5\noA2zt87W3rdyutIEdxwQUeBxuGNaISLSHvgEuMIYc9Q55SmllHsICwjjhX4vsPOhnfRp2IeE1ASu\nnnU1LSe15KftP2mAK6cpTXCvAZqLSKSI+AA3Aj8VbCAiDYHvgVuNMTucX6ZSSrmHiOAIlty+hBf7\nvUidwDrEJMVwxddXcPnXl7P72G67y1OVwFmD2xiTBYwGFgJbgVnGmM0icr+I3O9o9jwQBkwWkfUi\nEl1uFSullIvz8vDiub7Pse+Rfbw58E1C/EKYs2MObSe35ZvN39hdnnJzYtfmm6ioKBMdrfmulKr8\n9ifvZ/T80fy03dpY+WTvJ3n14lfxEB0Dq6oRkbXGmKiyLEM/NUopVc4igiP47vrvGNdrHB7iwWvL\nX+OqmVeRlplmd2nKDWlwK6VUBfDy8OL1ga+zYMQCgn2D+Wn7TwycPpCjqXosrzo3GtxKKVWBBjYd\nyO93/k549XCW719O0/ebMnfHXLvLUm5Eg1sppSrYBXUuYNkdy2gY3JDk9GQu/epSJq2epKeMqVLR\n4FZKKRtEhkayZdQWxnYfC8CY+WMY9uUwDh4/aHNlytVpcCullE0CfQJ5d8i7TBo6iVC/UBbELqDl\npJa8teItMrMz7S5PuSgNbqWUstmD3R5k7ci1XN7yck5knODxxY/T5P0mfBz9MVk5WXaXp1yMBrdS\nSrmAyNBIfrzxR+bePJeWYS05kHKA++feT8N3GvLjth91/7fKo8GtlFIuZFjzYWx5cAszr51Jo+BG\nHDpxiCtnXknvqb358u8vOZFxwu4Slc105DSllHJROSaH1/54jTdWvMGxU8cA8PX05fq213N7h9vp\n17gfnh6eNlepzoUzRk7T4FZKKRd3IuMEX6z/gmkbp7E6bnXe9LpBdbmh7Q3c3uF2OtbtiIjYWKUq\nDQ1upZSqYnYc3cGMjTP4ctOXxCbF5k1vXbM117S+hpFdRhIRHHGGJSg7aXArpVQVZYwh+mA0H6/9\nmGkbppGZk3/6WP/G/ZlwyQS6NehmY4WqOBrcSimlOJ5+nN/2/MbnGz5nzo45ZGRnANAktAnXt7me\ne7vcS2RIpG5KdwEa3EoppQr559Q//GvJv/h03aecyjqVN726b3UGRA7g3s73Mqz5MBsrrNo0uJVS\nShUrKyeLP/b9wTsr32HJ7iWFTiNrVbMVfRv1ZVTXUbSt1VaPTK9AGtxKKaVKZd2hdbyx4g3m7JjD\n8YzjedN9PX3p17gfN7a7kStbXUmIX4iNVVZ+GtxKKaXOyamsU2yI38DUdVOZEzOn0EVN/Lz8GBA5\ngN4RvenWoBv9G/fX3riTaXArpZQqk4STCczeNpuvNn3Fb3t+KzSvTmAdhjUfxqCmgxjSbIj2xp1A\ng1sppZTTHDp+iF92/0L0wWh+2v4Tu//ZnTfPUzzpH9mfm9vdzJBmQ6hXrZ6NlbovDW6llFLlwhjD\n30f+ZkHsAubFzGPp3qWF5neq24lLW1zKoKaDaBTciHrV6uHl4WVTte5Dg1sppVSFiEuJ49st37J4\n12KW7F5CWlZaofnBvsH0adSHLvW60KZWG9rXaU+zGs00zIvQ4FZKKVXhTmWdYtHORczdMZe1h9ay\nNXErqZmpp7UL9A6kc73ONK/RnI51O9KyZkua12hO45DGVXYwGA1upZRStsvdrL4tcRsrD6xka+JW\ntiRsYV/yvmLbN6jWgIubXEzzGs2Jqh9Fv8b98PPyq+Cq7aHBrZRSymUdPnGYjYc3si1xG38f+ZuY\npBg2H9lMQmpCoXbeHt60rtWa5jWa0zS0KU1CmxBePZzGIY1pWqNppQp1DW6llFJuxRjD+vj1rDyw\nktikWH7Z/QsbDm8osb0gRARHEOgdSJf6XYioHkGrmq1oX6c9F9S+wO3OM3dGcOtRA0oppSqMiNCp\nXic61euUNy0pLYmYozHsOrYr7xZ3PI6dx3ay+9juvE3uWxO3Fl4WwvAWw2lYvSF1gurQNLQpzcOa\n07xGc0L9Qyt0vSqS9riVUkq5rMzsTDYd2cTe5L0cOXmE+BPx/HXoL37Z/Uuh8deLCvMPo1mNZnlB\nHhkSSd2gukQER1AnsA4hfiG2HCCnm8qVUkpVSVk5WWxP3M7WxK0cOn6Ig8cPEnsslpijMcQkxRR7\nlHtBXh5e1AyoSd2gunSs25Gu9btSN6gu1XyqEeQTRJBPENV8q1E7sDYB3gFOq1uDWymllCrCGMOh\nE4fyQjw2KZa9yXuJS4nj4PGDJKQmkJKeUurlVfOpRt2guoRXD6dr/a40q9GMiOAIQvxCaFCtAeHV\nw0vde9fgVkoppc5DelY6iamJ7Evex5qDa1gXv45jacc4kXEi75aSnsKRk0dIz04/47Jye+7VfKpR\nzbcaIX4h1A6oTe3A2tQJqmP9DLR+NgtrpgenKaWUUufK18uXBtUb0KB6A3pG9CyxnTGG5PRkDh0/\nxLr4dew4uoPd/+wmLiWOlPQUYpNiSUxNJDE1scJq1+BWSimlSiAihPiFEOIXQutarU+bb4wh7ngc\nSWlJHE8/Tkp6CsdOHePIySN5t8MnD1s/TxxmL3vLXJMGt1JKKXWeRITw6uGEVw8vXftHyn4ku0eZ\nl6CUUkqpClOq4BaRISKyXURiReSpYuaLiLzvmL9RRDo7v1SllFJKnTW4RcQT+AAYCrQBbhKRNkWa\nDQWaO24jgQ+dXKdSSimlKF2PuxsQa4zZZYzJAL4GrijS5gpgmrGsBEJEpJ6Ta1VKKaWqvNIcnNYA\n2F/g8QGgeynaNAAOFWwkIiOxeuQA6SKy6ZyqdS81gYo7P6Di6fq5r8q8bqDr5+4q+/q1LOsCKvSo\ncmPMFGAKgIhEl/UkdFem6+feKvP6VeZ1A10/d1cV1q+syyjNpvI4IKLA43DHtHNto5RSSqkyKk1w\nrwGai0ikiPgANwI/FWnzE3Cb4+jyHkCyMeZQ0QUppZRSqmzOuqncGJMlIqOBhYAnMNUYs1lE7nfM\n/wiYBwwDYoFU4M5SvPaU867aPej6ubfKvH6Ved1A18/d6fqdhW0XGVFKKaXUudOR05RSSik3osGt\nlFJKuZFyDW4RuU5ENotIjohEFZn3tGOI1O0iMriE59cQkcUiEuP4GVqe9ZaFiMwUkfWO2x4RWV9C\nuz0i8rejndtckFxExotIXIF1HFZCuzMOj+uqROQNEdnmGLJ3toiElNDObd6/yjxUsYhEiMivIrLF\n8R0ztpg2/UQkucBn9nk7aj1fZ/usufn717LA+7JeRFJE5OEibdzq/RORqSJypOD4JKXNsHP+3jTG\nlNsNaI11svlvQFSB6W2ADYAvEAnsBDyLef7rwFOO+08Br5VnvU5c77eA50uYtweoaXeN57FO44HH\nz9LG0/FeNgF8HO9xG7trL+X6DQK8HPdfK+mz5i7vX2neC6wDSucDAvQAVtld9zmsXz2gs+N+NWBH\nMevXD5hjd61lWMczftbc+f0rsh6eQDzQyJ3fP+AioDOwqcC0s2bY+XxvlmuP2xiz1RizvZhZVwBf\nG2PSjTG7sY5G71ZCuy8c978AriyfSp1HRAS4HvjK7lpsUJrhcV2SMWaRMSbL8XAl1lgE7qxSD1Vs\njDlkjPnLcf84sBVrtMaqxG3fvyIuBnYaY8p+oWobGWOWAUlFJpcmw875e9OufdwlDZFaVB2Tfz54\nPFCnvAtzgj7AYWNMTAnzDfCziKx1DAHrTsY4NslNLWGTT2nfV1d3F1ZPpjju8v6V5r2oFO+XiDQG\nOgGripndy/GZnS8ibSu0sLI722etUrx/WGODlNTRcef3D0qXYef8PpZ5yFMR+RmoW8ysZ40xP5Z1\n+bmMMUZEbD13rZTrehNn7m1faIyJE5HawGIR2eb4S812Z1o/rCu+vYT1ZfIS1u6AuyquurIrzfsn\nIs8CWcCMEhbjsu9fVSQiQcB3wMPGmJQis/8CGhpjTjiOyfgB6wqG7qLSf9bEGtTrcuDpYma7+/tX\niDMzrMzBbYy55DyeVtohUg+LSD1jzCHHJqAj51Ojs5xtXUXEC7ga6HKGZcQ5fh4RkdlYm0lc4j9j\nad9LEfkPMKeYWS499G0p3r87gEuBi41j51Mxy3DZ96+ISj9UsYh4Y4X2DGPM90XnFwxyY8w8EZks\nIjWNMW5xAYtSfNbc+v1zGAr8ZYw5XHSGu79/DqXJsHN+H+3aVP4TcKOI+IpIJNZfUatLaHe74/7t\ngNN68OXkEmCbMeZAcTNFJFBEquXexzogyi2ukFZk39lVFF93aYbHdUkiMgR4ArjcGJNaQht3ev8q\n9VDFjmNJPgW2GmPeLqFNXUc7RKQb1vfd0Yqr8vyV8rPmtu9fASVuoXTn96+A0mTYuX9vlvNRdldh\nba9PBw4DCwvMexbrSLrtwNAC0z/BcQQ6EAb8AsQAPwM1yrNeJ6zv58D9RabVB+Y57jfBOmJwA7AZ\naxOt7XWXct2mA38DGx0fqnpF18/xeBjWEb473Wz9YrH2M6133D5y9/evuPcCuD/3M4p1NPIHjvl/\nU+DMD1e/ARdi7bbZWOA9G1Zk/UY73qcNWAcc9rK77nNYv2I/a5Xl/XPUH4gVxMEFprnt+4f1B8gh\nINORe3eXlGFl/d7UIU+VUkopN6IjpymllFJuRINbKaWUciMa3EoppZQb0eBWSiml3IgGt1JKKeVG\nNLiVKgciYkpx2+No+7mIFHvuf0UT6ypwxjGYkNOWV4p2/Ryv288Zr6tUZeaU/5xKqdP0LPJ4Ntb5\nqOMLTEuvsGqUUpWGBrdS5cBYV2vKIyLpQGLR6WUlIr7GGP0DQKkqRDeVK+UiRKSTiPwuIqkiEiMi\n9xeZf4djc/JFIvKNiPxDgStiiUhfEflFRI6LyEkRWSgi7YosY7CIrBCRZBE5ISLbReT5YsqJFJG5\njjZ7ReR5EfEosqyWIjJbRP4RkTQRWekYOvZs61lLRL4UkRTHc6cBIef0y1KqCtPgVso1VAe+BP6L\ndS3eNcCHItK/mLYzgN3AtcBTACIyHGtoxRPALcDNQDXgdxGJcLRpgjVc7W7gBqyrMr2NNfRkUbOB\nJVjXD/4B+D/yx1xGROoDfwAdsIamvB74B5grIkPPsq7fY13M5RlHHVnAxLM8RynloJvKlXIN1YBR\nxphfAURkGTAY6yIMvxZp+60x5oki094DlhpjrsidICK/AruAx4CHgc6AD/CAyb/y0pIS6nnLGPOZ\n4/7PIjLAUUvutEeBUKCnMSbW8XrzgC3AK5RwPXMRGYg1zvhNxpivHZMXish8rKsiKaXOQnvcSrmG\n1NzQBnDst94BNCym7eyCD0SkOdAUmCEiXrk3IBX4E7jI0XQ91gUQvhaRa8W6znNJ5hZ5vKlILRcB\nK3ND21FzNtaFFjqKSPUSltsTyMa6HGdBXxfTVilVDA1upVzDsWKmpQN+xUwveunG3AD+FCuYC94u\nxbpCEY6QHYz1/346EO/YL923mNdIOkstNYqpAyAe66pVocXMA6gHHDPGZBaZftr1mJVSxdNN5Uq5\nn6LnRedeo/hprEsHFpWR90SrV/+riPgCvYEXsfZLNzbGJJ5DDUlA3WKm13XUV9wfImCFfaiIeBcJ\n7zrn8NpKVWka3Eq5v+3AHqCtMWZCaZ7g2BS/RESCgB+BSOBcgnsp8LAj8PcAiIgn1sFm6wrsQy/q\nTxC7QY8AAAD+SURBVMATuIbCm8dvPIfXVqpK0+BWys0ZY4yIPAj8KCI+wCysEK4D9AL2GWPedpxe\ndhEwD9gP1MTqpR/E2od9Lt4B7gAWi8gLQAowCmgBDD9DrYtF5A/gYxGpCcRghX27kp6jlCpM93Er\nVQkYY+ZhhXIg8AmwEHgda9P1n45mGxzz/w0sAiZhnRo2wBiTdo6vdxDr6PDNwIfAt1j7vYcbYxac\n5elXY/3x8G9gJlYHYvS5vL5SVZkYc9ZhhJVSSinlIrTHrZRSSrkRDW6llFLKjWhwK6WUUm5Eg1sp\npZRyIxrcSimllBvR4FZKKaXciAa3Ukop5UY0uJVSSik38v8SPh2uB1v5AQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110c140f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precisions, recalls, thresholds = precision_recall_curve(y_test1, y_score1)\n",
    "\n",
    "def plot_precision_recall_vs_threshold(precisions, recalls, thresholds):\n",
    "    plt.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\", linewidth=2)\n",
    "    plt.plot(thresholds, recalls[:-1], \"g-\", label=\"Recall\", linewidth=2)\n",
    "    plt.xlabel(\"Threshold\", fontsize=16)\n",
    "    plt.legend(loc=\"upper left\", fontsize=16)\n",
    "    plt.ylim([0, 1])\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "plot_precision_recall_vs_threshold(precisions, recalls, thresholds)\n",
    "plt.xlim([-10, 10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t9H4ZYC9QENsKfqeAY64KKFwvOd3Ms3O3cDElA4AG5QpSIn+YwamE8H6PP/44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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110c143c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr1,tpr1, thresholds1 = roc_curve(y_test1,y_score1)\n",
    "plt.figure(figsize=(8, 6))\n",
    "plot_roc_curve(fpr1, tpr1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hinge_clf1 = SGDClassifier(random_state=42, loss='hinge') \n",
    "hinge_clf1.fit(X_train1, y_train1)\n",
    "y_pred_h1= hinge_clf1.predict(X_test1)\n",
    "y_score3 = hinge_clf1.decision_function(X_test1)\n",
    "#y_pred_h1.shape\n",
    "cm1 = confusion_matrix(y_test1, y_pred_h1)\n",
    "#cm1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.790932717297\n"
     ]
    }
   ],
   "source": [
    "print(sum(y_pred_h1 == y_test1)/len(y_pred_h1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precision and Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.92413201886\n",
      "0.797632260451\n"
     ]
    }
   ],
   "source": [
    "recall = recall_score(y_test1, y_pred_h1)\n",
    "precision = precision_score(y_test1, y_pred_h1)\n",
    "print(recall)\n",
    "print(precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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61O3P7tjR9a5FRAqqxAZ3rVq12L17N/Xr1ycsLEyb3coQay1xcXHs3r2b2rVr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\ni4iI+BEFt4iIiB/5fy7K5vq6NN0WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110ecef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precisions, recalls, thresholds = precision_recall_curve(y_test1, y_score3)\n",
    "\n",
    "def plot_precision_recall_vs_threshold(precisions, recalls, thresholds):\n",
    "    plt.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\", linewidth=2)\n",
    "    plt.plot(thresholds, recalls[:-1], \"g-\", label=\"Recall\", linewidth=2)\n",
    "    plt.xlabel(\"Threshold\", fontsize=16)\n",
    "    plt.legend(loc=\"upper left\", fontsize=16)\n",
    "    plt.ylim([0, 1])\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "plot_precision_recall_vs_threshold(precisions, recalls, thresholds)\n",
    "plt.xlim([-10, 10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ss4WW7/1l217yUkvaVC9uXCAh3Mzs2bNp0KABM2fOZP/+/UbHEV7O7umglFLV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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110eb5940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "fpr,tpr, thresholds = roc_curve(y_test1,y_score3)\n",
    "def plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.xlabel('False Positive Rate', fontsize=16)\n",
    "    plt.ylabel('True Positive Rate', fontsize=16)\n",
    "plt.figure(figsize=(8, 6))\n",
    "plot_roc_curve(fpr, tpr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Three Class Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>ticker</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>A</td>\n",
       "      <td>Could be working on an IHS here</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>A</td>\n",
       "      <td>Notes on chart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Long</td>\n",
       "      <td>A</td>\n",
       "      <td>Trade closed manually: looks like market comes...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Long</td>\n",
       "      <td>A</td>\n",
       "      <td>Long on positive earnings news and various upg...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Long</td>\n",
       "      <td>A</td>\n",
       "      <td>breakout and retest. bull flag . looks for LT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Short</td>\n",
       "      <td>A</td>\n",
       "      <td>Its going to be beautiful NOT INVESTMENT ADVICE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>A</td>\n",
       "      <td>This consolidation, if combined with large vol...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Short</td>\n",
       "      <td>A</td>\n",
       "      <td>Trade active: Active in this trade. Considerin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>A</td>\n",
       "      <td>Update. Could be a nice handle here</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>A</td>\n",
       "      <td>Look at the chart. Speaks for itself.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Short</td>\n",
       "      <td>A</td>\n",
       "      <td>target : 50.00 NYSE:A C&amp;A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Long</td>\n",
       "      <td>A</td>\n",
       "      <td>Although the entry is in the daily, weekly tim...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>A</td>\n",
       "      <td>Wait for the break - looks promising</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Long</td>\n",
       "      <td>A</td>\n",
       "      <td>The stop is adjustable, pattern great and anot...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>A</td>\n",
       "      <td>Pretty tight Gartley here. If price holds this...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>A</td>\n",
       "      <td>looks like this will test $44. was a major res...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Long</td>\n",
       "      <td>A</td>\n",
       "      <td>Although it's highly volatile, it's giving sig...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>A</td>\n",
       "      <td>Updated notes on chart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>A</td>\n",
       "      <td>My best guess - notes on chart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>A</td>\n",
       "      <td>Will prefer to wait and see if price comes bac...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Short</td>\n",
       "      <td>A</td>\n",
       "      <td>The Agilent Tech stock entered a rising wedge ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>A</td>\n",
       "      <td>Same chart - showing the GAP area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Long</td>\n",
       "      <td>A</td>\n",
       "      <td>Good ratio confluence and structure support at...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Short</td>\n",
       "      <td>A</td>\n",
       "      <td>A reporting after market today, and it seems v...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Long</td>\n",
       "      <td>A</td>\n",
       "      <td>If this figure is confirmed, may be on its way...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>A</td>\n",
       "      <td>Notes on chart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Short</td>\n",
       "      <td>A</td>\n",
       "      <td>Friends, Very high probability short, with man...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>A</td>\n",
       "      <td>Updated noted on chart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>A</td>\n",
       "      <td>Butterfly , but further market weakness could ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Long</td>\n",
       "      <td>AA</td>\n",
       "      <td>Reverse Head and shoulders w lo volume handle....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49881</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>ZTO</td>\n",
       "      <td>Comment: this escalated quickly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49882</th>\n",
       "      <td>Long</td>\n",
       "      <td>ZTO</td>\n",
       "      <td>ZTO long term. Chart looks great, but needs pa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49883</th>\n",
       "      <td>Long</td>\n",
       "      <td>ZTO</td>\n",
       "      <td>It's time to invest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49884</th>\n",
       "      <td>Long</td>\n",
       "      <td>ZTS</td>\n",
       "      <td>This may be worth watching if you are looking ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49885</th>\n",
       "      <td>Long</td>\n",
       "      <td>ZTS</td>\n",
       "      <td>Long term buy setup at monthly demand level ar...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49886</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>ZTS</td>\n",
       "      <td>Comment: Let's review the performance of the t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49887</th>\n",
       "      <td>Long</td>\n",
       "      <td>ZTS</td>\n",
       "      <td>ZTS - I am looking for this to breakout to the...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49888</th>\n",
       "      <td>Short</td>\n",
       "      <td>ZTS</td>\n",
       "      <td>https://www.tradingview.com/chart/3AbgIaEW/ Id...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49889</th>\n",
       "      <td>Short</td>\n",
       "      <td>ZTS</td>\n",
       "      <td>Trendline support was broken. Now the price is...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49890</th>\n",
       "      <td>Long</td>\n",
       "      <td>ZTS</td>\n",
       "      <td>Looking for medium term upside potential with ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49891</th>\n",
       "      <td>Long</td>\n",
       "      <td>ZTS</td>\n",
       "      <td>Bullish Harami on trending support line. Long ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49892</th>\n",
       "      <td>Long</td>\n",
       "      <td>ZTS</td>\n",
       "      <td>Ideally like to buy on a PB . Could potentiall...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49893</th>\n",
       "      <td>Long</td>\n",
       "      <td>ZUMZ</td>\n",
       "      <td>Trade active: Buy point reached</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49894</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>ZUMZ</td>\n",
       "      <td>..but if I did might look for a reversal as it...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49895</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>ZUMZ</td>\n",
       "      <td>With Gann , Fib and Support lines. MACD shows ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49896</th>\n",
       "      <td>Long</td>\n",
       "      <td>ZUMZ</td>\n",
       "      <td>bullish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49897</th>\n",
       "      <td>Long</td>\n",
       "      <td>ZUMZ</td>\n",
       "      <td>look for trend reversal soon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49898</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>ZUMZ</td>\n",
       "      <td>and now a breakdown...fugly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49899</th>\n",
       "      <td>Short</td>\n",
       "      <td>ZX</td>\n",
       "      <td>TP 1: 0.89 TP 2: 0.80 Stop @ 1.17 (Most broker...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49900</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>ZYNE midterm Performance was strict bullish . ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49901</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>Comment: ZYNE vs GWPh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49902</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>Wow. Another pharma Ending Diagonal example.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49903</th>\n",
       "      <td>Short</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>Trade closed: target reached: TAG your IT!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49904</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>Comment: is this a flag?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49905</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>Comment: The first step is done, no further low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49906</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>ZYNE IDEA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49907</th>\n",
       "      <td>Long</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>very bullish ZYNE swing to $23 price target.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49908</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>Possible multiple bottom forming.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49909</th>\n",
       "      <td>Long</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>met our indicators waited for pullback to pull...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49910</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>ZYNE</td>\n",
       "      <td>levels</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49911 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         label ticker                                               text\n",
       "0      Neutral      A                    Could be working on an IHS here\n",
       "1      Neutral      A                                     Notes on chart\n",
       "2         Long      A  Trade closed manually: looks like market comes...\n",
       "3         Long      A  Long on positive earnings news and various upg...\n",
       "4         Long      A      breakout and retest. bull flag . looks for LT\n",
       "5        Short      A    Its going to be beautiful NOT INVESTMENT ADVICE\n",
       "6      Neutral      A  This consolidation, if combined with large vol...\n",
       "7        Short      A  Trade active: Active in this trade. Considerin...\n",
       "8      Neutral      A                Update. Could be a nice handle here\n",
       "9      Neutral      A              Look at the chart. Speaks for itself.\n",
       "10       Short      A                          target : 50.00 NYSE:A C&A\n",
       "11        Long      A  Although the entry is in the daily, weekly tim...\n",
       "12     Neutral      A               Wait for the break - looks promising\n",
       "13        Long      A  The stop is adjustable, pattern great and anot...\n",
       "14     Neutral      A  Pretty tight Gartley here. If price holds this...\n",
       "15     Neutral      A  looks like this will test $44. was a major res...\n",
       "16        Long      A  Although it's highly volatile, it's giving sig...\n",
       "17     Neutral      A                             Updated notes on chart\n",
       "18     Neutral      A                     My best guess - notes on chart\n",
       "19     Neutral      A  Will prefer to wait and see if price comes bac...\n",
       "20       Short      A  The Agilent Tech stock entered a rising wedge ...\n",
       "21     Neutral      A                  Same chart - showing the GAP area\n",
       "22        Long      A  Good ratio confluence and structure support at...\n",
       "23       Short      A  A reporting after market today, and it seems v...\n",
       "24        Long      A  If this figure is confirmed, may be on its way...\n",
       "25     Neutral      A                                     Notes on chart\n",
       "26       Short      A  Friends, Very high probability short, with man...\n",
       "27     Neutral      A                             Updated noted on chart\n",
       "28     Neutral      A  Butterfly , but further market weakness could ...\n",
       "29        Long     AA  Reverse Head and shoulders w lo volume handle....\n",
       "...        ...    ...                                                ...\n",
       "49881  Neutral    ZTO                    Comment: this escalated quickly\n",
       "49882     Long    ZTO  ZTO long term. Chart looks great, but needs pa...\n",
       "49883     Long    ZTO                                It's time to invest\n",
       "49884     Long    ZTS  This may be worth watching if you are looking ...\n",
       "49885     Long    ZTS  Long term buy setup at monthly demand level ar...\n",
       "49886  Neutral    ZTS  Comment: Let's review the performance of the t...\n",
       "49887     Long    ZTS  ZTS - I am looking for this to breakout to the...\n",
       "49888    Short    ZTS  https://www.tradingview.com/chart/3AbgIaEW/ Id...\n",
       "49889    Short    ZTS  Trendline support was broken. Now the price is...\n",
       "49890     Long    ZTS  Looking for medium term upside potential with ...\n",
       "49891     Long    ZTS  Bullish Harami on trending support line. Long ...\n",
       "49892     Long    ZTS  Ideally like to buy on a PB . Could potentiall...\n",
       "49893     Long   ZUMZ                    Trade active: Buy point reached\n",
       "49894  Neutral   ZUMZ  ..but if I did might look for a reversal as it...\n",
       "49895  Neutral   ZUMZ  With Gann , Fib and Support lines. MACD shows ...\n",
       "49896     Long   ZUMZ                                            bullish\n",
       "49897     Long   ZUMZ                       look for trend reversal soon\n",
       "49898  Neutral   ZUMZ                        and now a breakdown...fugly\n",
       "49899    Short     ZX  TP 1: 0.89 TP 2: 0.80 Stop @ 1.17 (Most broker...\n",
       "49900  Neutral   ZYNE  ZYNE midterm Performance was strict bullish . ...\n",
       "49901  Neutral   ZYNE                              Comment: ZYNE vs GWPh\n",
       "49902  Neutral   ZYNE       Wow. Another pharma Ending Diagonal example.\n",
       "49903    Short   ZYNE         Trade closed: target reached: TAG your IT!\n",
       "49904  Neutral   ZYNE                           Comment: is this a flag?\n",
       "49905  Neutral   ZYNE    Comment: The first step is done, no further low\n",
       "49906  Neutral   ZYNE                                          ZYNE IDEA\n",
       "49907     Long   ZYNE       very bullish ZYNE swing to $23 price target.\n",
       "49908  Neutral   ZYNE                  Possible multiple bottom forming.\n",
       "49909     Long   ZYNE  met our indicators waited for pullback to pull...\n",
       "49910  Neutral   ZYNE                                             levels\n",
       "\n",
       "[49911 rows x 3 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/Users/rohitsuvarna/Downloads/Trading view project/idea_data.csv',usecols=['label','text','ticker'])\n",
    "df = df[-pd.isnull(df[\"text\"])]\n",
    "df['label'] = df['label'].replace(np.nan, 'Neutral')\n",
    "#df = df[-pd.isnull(df[\"label\"])]\n",
    "df = df.reset_index()\n",
    "df = df.drop('index',1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "corpus3 = df[\"text\"].tolist()\n",
    "corpus3 = corpus3 + negative_word_list + positive_word_list + uncertainty_word_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49881</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49882</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49883</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49884</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49885</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49886</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49887</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49888</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49889</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49890</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49891</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49892</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49893</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49894</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49895</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49896</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49897</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49898</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49899</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49900</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49901</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49902</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49903</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49904</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49905</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49906</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49907</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49908</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49909</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49910</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49911 rows × 14605 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0  1  2  3  4  5  6  7  8  9   ...    14595  14596  14597  14598  \\\n",
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       "...   .. .. .. .. .. .. .. .. .. ..   ...      ...    ...    ...    ...   \n",
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       "\n",
       "       14599  14600  14601  HaveTicker  Multiticker  Output  \n",
       "0          0      0      0         0.0          0.0       1  \n",
       "1          0      0      0         0.0          0.0       1  \n",
       "2          0      0      0         0.0          0.0       2  \n",
       "3          0      0      0         0.0          0.0       2  \n",
       "4          0      0      0         0.0          0.0       2  \n",
       "5          0      0      0         0.0          0.0       0  \n",
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       "8          0      0      0         0.0          0.0       1  \n",
       "9          0      0      0         0.0          0.0       1  \n",
       "10         0      0      0         0.0          0.0       0  \n",
       "11         0      0      0         0.0          0.0       2  \n",
       "12         0      0      0         0.0          0.0       1  \n",
       "13         0      0      0         0.0          0.0       2  \n",
       "14         0      0      0         0.0          0.0       1  \n",
       "15         0      0      0         0.0          0.0       1  \n",
       "16         0      0      0         0.0          0.0       2  \n",
       "17         0      0      0         0.0          0.0       1  \n",
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       "21         0      0      0         0.0          0.0       1  \n",
       "22         0      0      0         1.0          0.0       2  \n",
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       "24         0      0      0         0.0          0.0       2  \n",
       "25         0      0      0         0.0          0.0       1  \n",
       "26         0      0      0         0.0          0.0       0  \n",
       "27         0      0      0         0.0          0.0       1  \n",
       "28         0      0      0         0.0          0.0       1  \n",
       "29         0      0      0         0.0          0.0       2  \n",
       "...      ...    ...    ...         ...          ...     ...  \n",
       "49881      0      0      0         0.0          0.0       1  \n",
       "49882      0      0      0         1.0          0.0       2  \n",
       "49883      0      0      0         0.0          0.0       2  \n",
       "49884      0      0      0         1.0          0.0       2  \n",
       "49885      0      0      0         0.0          0.0       2  \n",
       "49886      0      0      0         1.0          0.0       1  \n",
       "49887      0      0      0         1.0          1.0       2  \n",
       "49888      0      0      0         0.0          0.0       0  \n",
       "49889      0      0      0         0.0          0.0       0  \n",
       "49890      0      0      0         0.0          0.0       2  \n",
       "49891      0      0      0         0.0          0.0       2  \n",
       "49892      0      0      0         1.0          0.0       2  \n",
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       "49894      0      0      0         1.0          0.0       1  \n",
       "49895      0      0      0         0.0          0.0       1  \n",
       "49896      0      0      0         0.0          0.0       2  \n",
       "49897      0      0      0         0.0          0.0       2  \n",
       "49898      0      0      0         0.0          0.0       1  \n",
       "49899      0      0      0         0.0          0.0       0  \n",
       "49900      2      0      0         1.0          0.0       1  \n",
       "49901      1      0      0         1.0          0.0       1  \n",
       "49902      0      0      0         0.0          0.0       1  \n",
       "49903      0      0      0         0.0          0.0       0  \n",
       "49904      0      0      0         0.0          0.0       1  \n",
       "49905      0      0      0         0.0          0.0       1  \n",
       "49906      1      0      0         1.0          0.0       1  \n",
       "49907      1      0      0         1.0          0.0       2  \n",
       "49908      0      0      0         0.0          0.0       1  \n",
       "49909      0      0      0         0.0          0.0       2  \n",
       "49910      0      0      0         0.0          0.0       1  \n",
       "\n",
       "[49911 rows x 14605 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer3 = CountVectorizer(\n",
    "    stop_words='english',\n",
    "    analyzer='word',\n",
    "    min_df = 2,\n",
    "    token_pattern='(?u)\\\\b[a-zA-Z]\\\\w+\\\\b' \n",
    ")\n",
    "ticker = set(data_clean['ticker'])\n",
    "X_corpus_docterm3 = vectorizer3.fit_transform(corpus3)\n",
    "features3 = vectorizer3.get_feature_names()\n",
    "CV3 = X_corpus_docterm3.toarray()\n",
    "df_sparse_vec3 = pd.DataFrame(CV3)\n",
    "df_sparse_vec3 = df_sparse_vec3[:49911]\n",
    "def checkTickerInDoc(doc, ticker):\n",
    "    ticker_list = []\n",
    "    doc = doc.split()\n",
    "    count = 0\n",
    "    for t in ticker:\n",
    "        if t in doc and t not in ticker_list:\n",
    "            #print(t)\n",
    "            ticker_list.append(t)\n",
    "            count += 1\n",
    "    if count == 0:\n",
    "        haveTicker = 0\n",
    "        multiTicker = 0\n",
    "    elif count == 1:\n",
    "        haveTicker = 1\n",
    "        multiTicker = 0\n",
    "    else:\n",
    "        haveTicker = 1\n",
    "        multiTicker = 1\n",
    "    return haveTicker, multiTicker\n",
    "counts3 = np.zeros([len(corpus3),2])\n",
    "for count,text in enumerate(df['text']):\n",
    "    counts3[count,:] = checkTickerInDoc(text,ticker)\n",
    "count_cols3 = pd.DataFrame(counts3)\n",
    "count_cols3.columns = ['HaveTicker','Multiticker']\n",
    "df_sparse_vec3['HaveTicker'] = count_cols3['HaveTicker']\n",
    "df_sparse_vec3['Multiticker'] = count_cols3['Multiticker']\n",
    "df_sparse_vec3['Output'] = df['label'].map({'Long': 2, 'Short': 0, 'Neutral':1,'Education':1})\n",
    "df_sparse_vec3\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#df_sparse_vec3.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#df['label'][4145]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#df_sparse_vec3['Output'] = df['label'].map({'Long': 1, 'Short': -1, 'Neutral':0,'Education':0 })\n",
    "#a = df_sparse_vec3['Output']\n",
    "#a = a.as_matrix()\n",
    "#np.argwhere(np.isnan(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X3 = df_sparse_vec3.drop('Output',1).as_matrix()\n",
    "y3= df_sparse_vec3['Output'].as_matrix()\n",
    "X_train3, X_test3, y_train3, y_test3 = train_test_split( X3, y3, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1076,  349,  901],\n",
       "       [ 333, 1098, 1611],\n",
       "       [ 229,  497, 3889]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logit_clf3 = SGDClassifier(random_state=42, loss = 'log')\n",
    "logit_clf3.fit(X_train3,y_train3)\n",
    "y_pred3 = logit_clf3.predict(X_test3)\n",
    "cm3 = confusion_matrix(y_test3, y_pred3)\n",
    "y_score3 = logit_clf3.decision_function(X_test3)\n",
    "cm3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.607332465191\n"
     ]
    }
   ],
   "source": [
    "print(sum(y_pred3 == y_test3)/len(y_pred3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#a = df['label'].map({'Long': 1, 'Short': -1, 'Neutral':0})\n",
    "#a = a.as_matrix()\n",
    "#np.argwhere(np.isnan(a))\n",
    "#a[4146]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "recall3 = recall_score(y_test3, y_pred3, average = 'micro')\n",
    "precision3 = precision_score(y_test3, y_pred3, average = 'micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.55541012290815972"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.60975826341914974"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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