Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optimal Cutoff for Biased Articles - Youden Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cutoff</th>\n",
       "      <th>TP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FP</th>\n",
       "      <th>FN</th>\n",
       "      <th>TPR</th>\n",
       "      <th>FPR</th>\n",
       "      <th>TNR</th>\n",
       "      <th>accuracy</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
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       "      <td>18</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
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       "      <td>63</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "   cutoff  TP  TN  FP  FN       TPR       FPR       TNR  accuracy\n",
       "0      15   0  66   0   2  0.000000  0.000000  1.000000  0.970588\n",
       "1      25  18  33   7  10  0.642857  0.175000  0.825000  0.750000\n",
       "2      27  32  23   7   6  0.842105  0.233333  0.766667  0.808824\n",
       "3      30  55   4   9   0  1.000000  0.692308  0.307692  0.867647\n",
       "4      33  63   1   2   2  0.969231  0.666667  0.333333  0.941176\n",
       "5      36  67   0   1   0  1.000000  1.000000  0.000000  0.985294"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "roc = {\n",
    "    'cutoff':[],\n",
    "    'TP':[],\n",
    "    'TN':[],\n",
    "    'FP':[],\n",
    "    'FN':[],\n",
    "    'TPR':[],\n",
    "    'FPR':[],\n",
    "    'TNR':[],\n",
    "    'accuracy':[]\n",
    "}\n",
    "\n",
    "df = pd.read_csv('calculated-data/result.csv')\n",
    "cutoff=[15,25,27,30,33,36]                                    #cutoff=[15,22,26,29,33]  these are perfect\n",
    "#df.loc[(df['BiasLexicon'] < 24 ) == True]\n",
    "\n",
    "df['Biased']=0\n",
    "\n",
    "for j in range(len(cutoff)):\n",
    "    df['Biased']=0\n",
    "    for i in range(df.shape[0]):\n",
    "        if df['BiasLexicon'][i] > cutoff[j]:\n",
    "            df['Biased'][i]=1                                                 # means  biased\n",
    "    #print(df)\n",
    "\n",
    "    X = df[['Hedge',\n",
    "            'HedgeContext',\n",
    "            'FativeVerb',\n",
    "            'FactiveVerbContext',\n",
    "            'AssertiveVerb',\n",
    "            'AssertiveVerbContext',\n",
    "            'ImplicativeVerb',\n",
    "            'ImplicativeVerbContext',\n",
    "            'ReportVerb',\n",
    "            'ReportVerbContext',\n",
    "            'StrongSub',\n",
    "            'StrongSubContext',\n",
    "            'WeakSub',\n",
    "            'WeakSubContext',\n",
    "            'PositiveWord',\n",
    "            'PositiveWordContext',\n",
    "            'NegativeWord',\n",
    "            'NegativeWordContext',\n",
    "           'BiasLexicon']]\n",
    "    y = df['Biased']\n",
    "\n",
    "    X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.30,random_state=0)\n",
    "\n",
    "    logistic_regression= LogisticRegression()\n",
    "    logistic_regression.fit(X_train,y_train)\n",
    "    y_pred=logistic_regression.predict(X_test)\n",
    "\n",
    "    confusion_matrix(y_test, y_pred)\n",
    "#     TP=confusion_matrix[0][0]\n",
    "#     FN=confusion_matrix[0][1]\n",
    "#     FP=confusion_matrix[1][0]\n",
    "#     TN=confusion_matrix[1][1]\n",
    "    roc['cutoff'].append(cutoff[j])\n",
    "    roc['TP'].append(confusion_matrix(y_test, y_pred)[0][0])\n",
    "    roc['FN'].append(confusion_matrix(y_test, y_pred)[0][1])\n",
    "    roc['FP'].append(confusion_matrix(y_test, y_pred)[1][0])\n",
    "    roc['TN'].append(confusion_matrix(y_test, y_pred)[1][1])\n",
    "    roc['TPR'].append(confusion_matrix(y_test, y_pred)[0][0]/(confusion_matrix(y_test, y_pred)[0][0]+confusion_matrix(y_test, y_pred)[0][1]))\n",
    "    roc['FPR'].append(confusion_matrix(y_test, y_pred)[1][0]/(confusion_matrix(y_test, y_pred)[1][0]+confusion_matrix(y_test, y_pred)[1][1]))\n",
    "    roc['TNR'].append(confusion_matrix(y_test, y_pred)[1][1]/(confusion_matrix(y_test, y_pred)[1][1]+confusion_matrix(y_test, y_pred)[1][0]))\n",
    "    roc['accuracy'].append(metrics.accuracy_score(y_test, y_pred))\n",
    "    \n",
    "df_roc = pd.DataFrame(roc)\n",
    "df_roc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>cutoff</th>\n",
       "      <th>TP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FP</th>\n",
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       "      <th>TPR</th>\n",
       "      <th>FPR</th>\n",
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       "      <td>0.750000</td>\n",
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       "      <td>3</td>\n",
       "      <td>30</td>\n",
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       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.692308</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.867647</td>\n",
       "      <td>0.307692</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.969231</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.302564</td>\n",
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       "      <td>5</td>\n",
       "      <td>36</td>\n",
       "      <td>67</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.985294</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   cutoff  TP  TN  FP  FN       TPR       FPR       TNR  accuracy  TPR+TNR-1\n",
       "0      15   0  66   0   2  0.000000  0.000000  1.000000  0.970588   0.000000\n",
       "1      25  18  33   7  10  0.642857  0.175000  0.825000  0.750000   0.467857\n",
       "2      27  32  23   7   6  0.842105  0.233333  0.766667  0.808824   0.608772\n",
       "3      30  55   4   9   0  1.000000  0.692308  0.307692  0.867647   0.307692\n",
       "4      33  63   1   2   2  0.969231  0.666667  0.333333  0.941176   0.302564\n",
       "5      36  67   0   1   0  1.000000  1.000000  0.000000  0.985294   0.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculating The Youden Index\n",
    "\n",
    "sum_column = df_roc[\"TPR\"] + df_roc[\"TNR\"]\n",
    "df_roc[\"TPR+TNR-1\"] = sum_column - 1\n",
    "df_roc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting The ROC Curve\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = df_roc['FPR'] \n",
    "y = df_roc['TPR']\n",
    "    \n",
    "plt.plot(x, y) \n",
    "  \n",
    "plt.xlabel('FPR')  \n",
    "plt.ylabel('TPR') \n",
    "  \n",
    "plt.title('ROC curve') \n",
    "  \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# checking for labels\n",
    "\n",
    "# df=pd.read_csv('label.csv')\n",
    "\n",
    "# for i in range(df.shape[0]):\n",
    "#     if df['bjp_count'][i] > 3:\n",
    "#         df['label'][i] = 'bjp'\n",
    "# X = df[['Hedge',\n",
    "#     'HedgeContext',\n",
    "#     'FativeVerb',\n",
    "#     'FactiveVerbContext',\n",
    "#     'AssertiveVerb',\n",
    "#     'AssertiveVerbContext',\n",
    "#     'ImplicativeVerb',\n",
    "#     'ImplicativeVerbContext',\n",
    "#     'ReportVerb',\n",
    "#     'ReportVerbContext',\n",
    "#     'StrongSub',\n",
    "#     'StrongSubContext',\n",
    "#     'WeakSub',\n",
    "#     'WeakSubContext',\n",
    "#     'PositiveWord',\n",
    "#     'PositiveWordContext',\n",
    "#     'NegativeWord',\n",
    "#     'NegativeWordContext',\n",
    "#     'bjp_count',\n",
    "#    'BiasLexicon']]\n",
    "# y = df['label']\n",
    "\n",
    "# X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.20,random_state=0)\n",
    "\n",
    "# logistic_regression= LogisticRegression()\n",
    "# logistic_regression.fit(X_train,y_train)\n",
    "# y_pred=logistic_regression.predict(X_test)\n",
    "\n",
    "# print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optimal Cutoff for Ruling Goverment - Youden Index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:26: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:26: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:26: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:26: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cutoff</th>\n",
       "      <th>TP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FP</th>\n",
       "      <th>FN</th>\n",
       "      <th>TPR</th>\n",
       "      <th>FPR</th>\n",
       "      <th>TNR</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.894737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.842105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.947368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.947368</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cutoff  TP  TN  FP  FN     TPR       FPR       TNR  accuracy\n",
       "0       1  15   2   1   1  0.9375  0.333333  0.666667  0.894737\n",
       "1       2   9   7   2   1  0.9000  0.222222  0.777778  0.842105\n",
       "2       4   5  13   1   0  1.0000  0.071429  0.928571  0.947368\n",
       "3       7   0  18   0   1  0.0000  0.000000  1.000000  0.947368"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "\n",
    "roc = {\n",
    "    'cutoff':[],\n",
    "    'TP':[],\n",
    "    'TN':[],\n",
    "    'FP':[],\n",
    "    'FN':[],\n",
    "    'TPR':[],\n",
    "    'FPR':[],\n",
    "    'TNR':[],\n",
    "    'accuracy':[]\n",
    "}\n",
    "\n",
    "df = pd.read_csv('calculated-data/label1.csv')\n",
    "cutoff=[1,2,4,7]\n",
    "for j in range(len(cutoff)):\n",
    "    df['label']='others'\n",
    "    for i in range(df.shape[0]):\n",
    "        if df['bjp_count'][i] > cutoff[j]:\n",
    "            df['label'][i]='bjp'\n",
    "    \n",
    "    X = df[['Hedge',\n",
    "        'HedgeContext',\n",
    "        'FativeVerb',\n",
    "        'FactiveVerbContext',\n",
    "        'AssertiveVerb',\n",
    "        'AssertiveVerbContext',\n",
    "        'ImplicativeVerb',\n",
    "        'ImplicativeVerbContext',\n",
    "        'ReportVerb',\n",
    "        'ReportVerbContext',\n",
    "        'StrongSub',\n",
    "        'StrongSubContext',\n",
    "        'WeakSub',\n",
    "        'WeakSubContext',\n",
    "        'PositiveWord',\n",
    "        'PositiveWordContext',\n",
    "        'NegativeWord',\n",
    "        'NegativeWordContext',\n",
    "        'bjp_count',\n",
    "       'BiasLexicon']]\n",
    "    y = df['label']\n",
    "    \n",
    "    X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.20,random_state=0)\n",
    "\n",
    "    logistic_regression= LogisticRegression()\n",
    "    logistic_regression.fit(X_train,y_train)\n",
    "    y_pred=logistic_regression.predict(X_test)\n",
    "    \n",
    "    confusion_matrix(y_test, y_pred)\n",
    "#     TP=confusion_matrix[0][0]\n",
    "#     FN=confusion_matrix[0][1]\n",
    "#     FP=confusion_matrix[1][0]\n",
    "#     TN=confusion_matrix[1][1]\n",
    "    roc['cutoff'].append(cutoff[j])\n",
    "    roc['TP'].append(confusion_matrix(y_test, y_pred)[0][0])\n",
    "    roc['FN'].append(confusion_matrix(y_test, y_pred)[0][1])\n",
    "    roc['FP'].append(confusion_matrix(y_test, y_pred)[1][0])\n",
    "    roc['TN'].append(confusion_matrix(y_test, y_pred)[1][1])\n",
    "    roc['TPR'].append(confusion_matrix(y_test, y_pred)[0][0]/(confusion_matrix(y_test, y_pred)[0][0]+confusion_matrix(y_test, y_pred)[0][1]))\n",
    "    roc['FPR'].append(confusion_matrix(y_test, y_pred)[1][0]/(confusion_matrix(y_test, y_pred)[1][0]+confusion_matrix(y_test, y_pred)[1][1]))\n",
    "    roc['TNR'].append(confusion_matrix(y_test, y_pred)[1][1]/(confusion_matrix(y_test, y_pred)[1][1]+confusion_matrix(y_test, y_pred)[1][0]))\n",
    "    roc['accuracy'].append(metrics.accuracy_score(y_test, y_pred))\n",
    "    \n",
    "df_roc = pd.DataFrame(roc)\n",
    "df_roc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cutoff</th>\n",
       "      <th>TP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FP</th>\n",
       "      <th>FN</th>\n",
       "      <th>TPR</th>\n",
       "      <th>FPR</th>\n",
       "      <th>TNR</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>TPR+TNR-1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.604167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.677778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.928571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cutoff  TP  TN  FP  FN     TPR       FPR       TNR  accuracy  TPR+TNR-1\n",
       "0       1  15   2   1   1  0.9375  0.333333  0.666667  0.894737   0.604167\n",
       "1       2   9   7   2   1  0.9000  0.222222  0.777778  0.842105   0.677778\n",
       "2       4   5  13   1   0  1.0000  0.071429  0.928571  0.947368   0.928571\n",
       "3       7   0  18   0   1  0.0000  0.000000  1.000000  0.947368   0.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_column = df_roc[\"TPR\"] + df_roc[\"TNR\"]\n",
    "df_roc[\"TPR+TNR-1\"] = sum_column - 1\n",
    "df_roc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df_roc['FPR'] \n",
    "y = df_roc['TPR']\n",
    "    \n",
    "plt.plot(x, y) \n",
    "  \n",
    "plt.xlabel('FPR')  \n",
    "plt.ylabel('TPR') \n",
    "  \n",
    "plt.title('ROC curve') \n",
    "  \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:28: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:28: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:28: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cutoff</th>\n",
       "      <th>TP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FP</th>\n",
       "      <th>FN</th>\n",
       "      <th>TPR</th>\n",
       "      <th>FPR</th>\n",
       "      <th>TNR</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cutoff  TP  TN  FP  FN       TPR       FPR       TNR  accuracy\n",
       "0       1   9   2   1   0  1.000000  0.333333  0.666667  0.916667\n",
       "1       2   6   5   1   0  1.000000  0.166667  0.833333  0.916667\n",
       "2       3   2   8   1   1  0.666667  0.111111  0.888889  0.833333"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# testing for congress\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "\n",
    "roc = {\n",
    "    'cutoff':[],\n",
    "    'TP':[],\n",
    "    'TN':[],\n",
    "    'FP':[],\n",
    "    'FN':[],\n",
    "    'TPR':[],\n",
    "    'FPR':[],\n",
    "    'TNR':[],\n",
    "    'accuracy':[]\n",
    "}\n",
    "\n",
    "df = pd.read_csv('congress.csv')\n",
    "cutoff=[1,2,3]\n",
    "for j in range(len(cutoff)):\n",
    "    df['label']='others'\n",
    "    for i in range(df.shape[0]):\n",
    "        if df['congress_count'][i] > cutoff[j]:\n",
    "            df['label'][i]='congress'\n",
    "    \n",
    "    X = df[['Hedge',\n",
    "        'HedgeContext',\n",
    "        'FativeVerb',\n",
    "        'FactiveVerbContext',\n",
    "        'AssertiveVerb',\n",
    "        'AssertiveVerbContext',\n",
    "        'ImplicativeVerb',\n",
    "        'ImplicativeVerbContext',\n",
    "        'ReportVerb',\n",
    "        'ReportVerbContext',\n",
    "        'StrongSub',\n",
    "        'StrongSubContext',\n",
    "        'WeakSub',\n",
    "        'WeakSubContext',\n",
    "        'PositiveWord',\n",
    "        'PositiveWordContext',\n",
    "        'NegativeWord',\n",
    "        'NegativeWordContext',\n",
    "        'bjp_count',\n",
    "        'congress_count',\n",
    "       'BiasLexicon']]\n",
    "    y = df['label']\n",
    "    \n",
    "    X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.20,random_state=0)\n",
    "\n",
    "    logistic_regression= LogisticRegression()\n",
    "    logistic_regression.fit(X_train,y_train)\n",
    "    y_pred=logistic_regression.predict(X_test)\n",
    "    \n",
    "    confusion_matrix(y_test, y_pred)\n",
    "#     TP=confusion_matrix[0][0]\n",
    "#     FN=confusion_matrix[0][1]\n",
    "#     FP=confusion_matrix[1][0]\n",
    "#     TN=confusion_matrix[1][1]\n",
    "    roc['cutoff'].append(cutoff[j])\n",
    "    roc['TP'].append(confusion_matrix(y_test, y_pred)[0][0])\n",
    "    roc['FN'].append(confusion_matrix(y_test, y_pred)[0][1])\n",
    "    roc['FP'].append(confusion_matrix(y_test, y_pred)[1][0])\n",
    "    roc['TN'].append(confusion_matrix(y_test, y_pred)[1][1])\n",
    "    roc['TPR'].append(confusion_matrix(y_test, y_pred)[0][0]/(confusion_matrix(y_test, y_pred)[0][0]+confusion_matrix(y_test, y_pred)[0][1]))\n",
    "    roc['FPR'].append(confusion_matrix(y_test, y_pred)[1][0]/(confusion_matrix(y_test, y_pred)[1][0]+confusion_matrix(y_test, y_pred)[1][1]))\n",
    "    roc['TNR'].append(confusion_matrix(y_test, y_pred)[1][1]/(confusion_matrix(y_test, y_pred)[1][1]+confusion_matrix(y_test, y_pred)[1][0]))\n",
    "    roc['accuracy'].append(metrics.accuracy_score(y_test, y_pred))\n",
    "    \n",
    "df_roc = pd.DataFrame(roc)\n",
    "df_roc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cutoff</th>\n",
       "      <th>TP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FP</th>\n",
       "      <th>FN</th>\n",
       "      <th>TPR</th>\n",
       "      <th>FPR</th>\n",
       "      <th>TNR</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>TPR+TNR-1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.555556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cutoff  TP  TN  FP  FN       TPR       FPR       TNR  accuracy  TPR+TNR-1\n",
       "0       1   9   2   1   0  1.000000  0.333333  0.666667  0.916667   0.666667\n",
       "1       2   6   5   1   0  1.000000  0.166667  0.833333  0.916667   0.833333\n",
       "2       3   2   8   1   1  0.666667  0.111111  0.888889  0.833333   0.555556"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_column = df_roc[\"TPR\"] + df_roc[\"TNR\"]\n",
    "df_roc[\"TPR+TNR-1\"] = sum_column - 1\n",
    "df_roc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df_roc['FPR'] \n",
    "y = df_roc['TPR']\n",
    "    \n",
    "plt.plot(x, y) \n",
    "  \n",
    "plt.xlabel('FPR')  \n",
    "plt.ylabel('TPR') \n",
    "  \n",
    "plt.title('ROC curve') \n",
    "  \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6 0]\n",
      " [1 5]]\n",
      "Accuracy:  0.9166666666666666\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    congress       0.86      1.00      0.92         6\n",
      "      others       1.00      0.83      0.91         6\n",
      "\n",
      "    accuracy                           0.92        12\n",
      "   macro avg       0.93      0.92      0.92        12\n",
      "weighted avg       0.93      0.92      0.92        12\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_csv('congress.csv')\n",
    "for i in range(df.shape[0]):\n",
    "    if df['congress_count'][i] > 2:\n",
    "        df['label'][i]='congress'\n",
    "\n",
    "\n",
    "\n",
    "X = df[['Hedge',\n",
    "        'HedgeContext',\n",
    "        'FativeVerb',\n",
    "        'FactiveVerbContext',\n",
    "        'AssertiveVerb',\n",
    "        'AssertiveVerbContext',\n",
    "        'ImplicativeVerb',\n",
    "        'ImplicativeVerbContext',\n",
    "        'ReportVerb',\n",
    "        'ReportVerbContext',\n",
    "        'StrongSub',\n",
    "        'StrongSubContext',\n",
    "        'WeakSub',\n",
    "        'WeakSubContext',\n",
    "        'PositiveWord',\n",
    "        'PositiveWordContext',\n",
    "        'NegativeWord',\n",
    "        'NegativeWordContext',\n",
    "        'bjp_count',\n",
    "        'congress_count',\n",
    "       'BiasLexicon']]\n",
    "y = df['label']\n",
    "\n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.20,random_state=0)\n",
    "logistic_regression= LogisticRegression()\n",
    "logistic_regression.fit(X_train,y_train)\n",
    "y_pred=logistic_regression.predict(X_test)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print('Accuracy: ',metrics.accuracy_score(y_test, y_pred))\n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "print(classification_report(y_test,y_pred))\n",
    "#print(metrics.roc_auc_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.loc[(df['label'] == 'congress' ) == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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