Step 7.2_Classifier (Russia) - by combination.ipynb

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      "/Users/becca/anaconda3/lib/python3.7/site-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
      "  \"(https://pypi.org/project/six/).\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# General\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "\n",
    "# Classifer\n",
    "from sklearn.neighbors import KNeighborsClassifier #kNN\n",
    "from sklearn.naive_bayes import GaussianNB #Naive Bayes\n",
    "from sklearn.tree import DecisionTreeClassifier #Decision Tree\n",
    "from sklearn import svm #SVM\n",
    "from sklearn.neural_network import MLPClassifier #Neural Network\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis #Linear Discriminant\n",
    "from sklearn.linear_model import LogisticRegression #Logistic Regression\n",
    "from sklearn.linear_model import SGDClassifier # Stochastic Gradient Descent\n",
    "\n",
    "# PR curve\n",
    "# from sklearn.metrics import average_precision_score\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import plot_precision_recall_curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Decision Tree plot\n",
    "from sklearn.externals.six import StringIO \n",
    "from IPython.display import Image \n",
    "from sklearn.tree import export_graphviz \n",
    "import pydotplus\n",
    "\n",
    "#Cross Validation\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def file_reader(path):\n",
    "    df=pd.DataFrame()\n",
    "    chunksize = 10 ** 6\n",
    "    for chunk in pd.read_csv(path, chunksize=chunksize, index_col=[0]):\n",
    "        df = df.append(chunk)\n",
    "    return df\n",
    "\n",
    "country='Russia'\n",
    "data=file_reader('/Volumes/BECCA/Project/Data/Input/02_Preprocessing/05_Final/%s.csv' %country)"
   ]
  },
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_text</th>\n",
       "      <th>polarity_number</th>\n",
       "      <th>subjectivity_number</th>\n",
       "      <th>repeat_tag</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>suspend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>Barcelona is so sunny and nice at Vmworld, tre...</td>\n",
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       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Linear -up and –out scaling on disk and node l...</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>open for a surprise https://t.co/YrU8x3AhGC</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MY HEART 😭❤️ https://t.co/iKrYLK5apF</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cows are just big grass puppies https://t.co/6...</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>26</td>\n",
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       "    <tr>\n",
       "      <th>54165</th>\n",
       "      <td>RT @spacebongmilk: My girl can be butt ass nak...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
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       "      <td>Not a win again, but people that understands f...</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54167</th>\n",
       "      <td>RT @alizawhoo: @lanascouture A girl can walk o...</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54168</th>\n",
       "      <td>People don't understand what football means to...</td>\n",
       "      <td>0.396875</td>\n",
       "      <td>0.443750</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>54169</th>\n",
       "      <td>@Hb_0L 😂😂😂 nah we’re fair people.. Liverpool d...</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>54164 rows × 8 columns</p>\n",
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       "                                              tweet_text  polarity_number  \\\n",
       "0      Barcelona is so sunny and nice at Vmworld, tre...         0.700000   \n",
       "1      Linear -up and –out scaling on disk and node l...         0.600000   \n",
       "2            open for a surprise https://t.co/YrU8x3AhGC         0.500000   \n",
       "3                   MY HEART 😭❤️ https://t.co/iKrYLK5apF         0.500000   \n",
       "4      cows are just big grass puppies https://t.co/6...         0.466667   \n",
       "...                                                  ...              ...   \n",
       "54165  RT @spacebongmilk: My girl can be butt ass nak...         0.500000   \n",
       "54166  Not a win again, but people that understands f...         0.600000   \n",
       "54167  RT @alizawhoo: @lanascouture A girl can walk o...         0.600000   \n",
       "54168  People don't understand what football means to...         0.396875   \n",
       "54169  @Hb_0L 😂😂😂 nah we’re fair people.. Liverpool d...         0.875000   \n",
       "\n",
       "       subjectivity_number  repeat_tag  day  hour  minute  suspend  \n",
       "0                 0.600000           0    6    10      32        1  \n",
       "1                 0.200000           0    1    12      30        1  \n",
       "2                 0.500000           0    5    11      27        1  \n",
       "3                 0.000000           0    1    12      21        1  \n",
       "4                 0.133333           0    4    20      26        1  \n",
       "...                    ...         ...  ...   ...     ...      ...  \n",
       "54165             0.233333           0    2    18      19        0  \n",
       "54166             0.400000           0    3     2      29        0  \n",
       "54167             0.400000           0    2    16      56        0  \n",
       "54168             0.443750           0    3     7       4        0  \n",
       "54169             0.825000           0    2    22      45        0  \n",
       "\n",
       "[54164 rows x 8 columns]"
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    "data"
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scaling data\n",
    "from sklearn import preprocessing\n",
    "\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "for i in data.columns:\n",
    "    if i != 'tweet_text':\n",
    "        x = np.array(data[i]).reshape(-1,1)\n",
    "        data[i] = min_max_scaler.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "      <th>3</th>\n",
       "      <td>MY HEART 😭❤️ https://t.co/iKrYLK5apF</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.521739</td>\n",
       "      <td>0.355932</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cows are just big grass puppies https://t.co/6...</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.869565</td>\n",
       "      <td>0.440678</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>54165</th>\n",
       "      <td>RT @spacebongmilk: My girl can be butt ass nak...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.782609</td>\n",
       "      <td>0.322034</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54166</th>\n",
       "      <td>Not a win again, but people that understands f...</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.491525</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54167</th>\n",
       "      <td>RT @alizawhoo: @lanascouture A girl can walk o...</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.695652</td>\n",
       "      <td>0.949153</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54168</th>\n",
       "      <td>People don't understand what football means to...</td>\n",
       "      <td>0.396875</td>\n",
       "      <td>0.443750</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54169</th>\n",
       "      <td>@Hb_0L 😂😂😂 nah we’re fair people.. Liverpool d...</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
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       "      <td>0.762712</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
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       "                                              tweet_text  polarity_number  \\\n",
       "0      Barcelona is so sunny and nice at Vmworld, tre...         0.700000   \n",
       "1      Linear -up and –out scaling on disk and node l...         0.600000   \n",
       "2            open for a surprise https://t.co/YrU8x3AhGC         0.500000   \n",
       "3                   MY HEART 😭❤️ https://t.co/iKrYLK5apF         0.500000   \n",
       "4      cows are just big grass puppies https://t.co/6...         0.466667   \n",
       "...                                                  ...              ...   \n",
       "54165  RT @spacebongmilk: My girl can be butt ass nak...         0.500000   \n",
       "54166  Not a win again, but people that understands f...         0.600000   \n",
       "54167  RT @alizawhoo: @lanascouture A girl can walk o...         0.600000   \n",
       "54168  People don't understand what football means to...         0.396875   \n",
       "54169  @Hb_0L 😂😂😂 nah we’re fair people.. Liverpool d...         0.875000   \n",
       "\n",
       "       subjectivity_number  repeat_tag       day      hour    minute  suspend  \n",
       "0                 0.600000         0.0  1.000000  0.434783  0.542373      1.0  \n",
       "1                 0.200000         0.0  0.166667  0.521739  0.508475      1.0  \n",
       "2                 0.500000         0.0  0.833333  0.478261  0.457627      1.0  \n",
       "3                 0.000000         0.0  0.166667  0.521739  0.355932      1.0  \n",
       "4                 0.133333         0.0  0.666667  0.869565  0.440678      1.0  \n",
       "...                    ...         ...       ...       ...       ...      ...  \n",
       "54165             0.233333         0.0  0.333333  0.782609  0.322034      0.0  \n",
       "54166             0.400000         0.0  0.500000  0.086957  0.491525      0.0  \n",
       "54167             0.400000         0.0  0.333333  0.695652  0.949153      0.0  \n",
       "54168             0.443750         0.0  0.500000  0.304348  0.067797      0.0  \n",
       "54169             0.825000         0.0  0.333333  0.956522  0.762712      0.0  \n",
       "\n",
       "[54164 rows x 8 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "data"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# kNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Selection:\n",
      "Score of k6 = 0.65 (+/- 0.16)\n",
      "Score of k5 = 0.63 (+/- 0.16)\n",
      "Score of k4 = 0.63 (+/- 0.17)\n",
      "Score of L1 = 0.65 (+/- 0.16)\n",
      "Score of Tree = 0.62 (+/- 0.15)\n"
     ]
    }
   ],
   "source": [
    "clf_name='kNN'\n",
    "parent_dir = \"/Volumes/BECCA/Project/Data/Result/Russia/By_model\"\n",
    "path = os.path.join(parent_dir, clf_name)\n",
    "# os.mkdir(path) \n",
    "# os.mkdir(path+'/PR_plot') \n",
    "\n",
    "k6=np.array(data)[:,1:7].astype('float')\n",
    "k5=np.array(data)[:,2:7].astype('float')\n",
    "k4=np.array(data[['subjectivity_number','day','hour','minute']]).astype('float')\n",
    "k3=np.array(data)[:,4:7].astype('float')\n",
    "k2=np.array(data)[:,4:6].astype('float')\n",
    "L1=np.array(data[['polarity_number','subjectivity_number','day','hour','minute']]).astype('float')\n",
    "Tree=np.array(data[['subjectivity_number','day','hour']]).astype('float')\n",
    "y=np.array(data)[:,7].astype('int')\n",
    "\n",
    "clf = KNeighborsClassifier(n_neighbors=5)\n",
    "\n",
    "scores_1 = cross_val_score(clf, k6, y, cv=10)\n",
    "scores_2 = cross_val_score(clf, k5, y, cv=10)\n",
    "scores_3 = cross_val_score(clf, k4, y, cv=10)\n",
    "scores_4 = cross_val_score(clf, k3, y, cv=10)\n",
    "scores_5 = cross_val_score(clf, k2, y, cv=10)\n",
    "scores_6 = cross_val_score(clf, L1, y, cv=10)\n",
    "scores_7 = cross_val_score(clf, Tree, y, cv=10)\n",
    "\n",
    "scores=[scores_1,scores_2,scores_3,scores_4,scores_5,scores_6,scores_7]\n",
    "\n",
    "print('Model Selection:')\n",
    "mean_score = 0\n",
    "std_score = 0\n",
    "for i in scores:\n",
    "    mean_score += i.mean()\n",
    "    std_score += i.std()\n",
    "mean_score = mean_score/len(scores)\n",
    "\n",
    "for i,j in zip(['k6','k5','k4','k3','k2','L1','Tree'], scores):    \n",
    "    if j.mean() > mean_score:\n",
    "        print('Score of %s = %0.2f (+/- %0.2f)' %(i, j.mean(), j.std()*2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Result collection\n",
    "Model=[]\n",
    "Score=[]\n",
    "TP=[]\n",
    "FP=[] \n",
    "FN=[]\n",
    "TN=[]\n",
    "Recall=[]\n",
    "Precision=[]\n",
    "F1_Score=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "name = ['k6','k5','k4','L1','Tree']\n",
    "model = [k6, k5, k4, L1, Tree]\n",
    "\n",
    "for X, md_name in zip(model, name):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "    clf=clf.fit(X_train, y_train)\n",
    "    y_pred=clf.predict(X_test)\n",
    "    \n",
    "    #Confusion metric & relative indexs\n",
    "    tp=0\n",
    "    fp=0\n",
    "    fn=0\n",
    "    tn=0\n",
    "\n",
    "    for i in range(len(y_test)):\n",
    "        if y_test[i] == 1:\n",
    "            if y_pred[i] == 1:\n",
    "                tp+=1\n",
    "            elif y_pred[i] == 0:\n",
    "                fn+=1\n",
    "        elif y_test[i] == 0:\n",
    "            if y_pred[i] == 0:\n",
    "                tn+=1\n",
    "            elif y_pred[i] == 1:\n",
    "                fp+=1\n",
    "    \n",
    "    recall=tp/(tp+fn)\n",
    "    precision=tp/(tp+fp)\n",
    "    accuracy=(tp+tn)/len(y_test)\n",
    "    f1_score=2*(recall*precision)/(recall+precision)\n",
    "    \n",
    "    #Result collection\n",
    "    Model.append(md_name)\n",
    "    Score.append(accuracy)\n",
    "    TP.append(tp)\n",
    "    FP.append(fp)\n",
    "    FN.append(fn)\n",
    "    TN.append(tn)\n",
    "    Recall.append(recall)\n",
    "    Precision.append(precision)\n",
    "    F1_Score.append(f1_score)\n",
    "    \n",
    "    #PR curve\n",
    "    disp = plot_precision_recall_curve(clf, X_test, y_test)\n",
    "    disp.ax_.set_title('2-class Precision-Recall curve')\n",
    "    plt.savefig('/Volumes/BECCA/Project/Data/Result/Russia/By_model/%s/PR_plot/PR_%s.png' %(clf_name, md_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Model</th>\n",
       "      <th>Classifier</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4309</td>\n",
       "      <td>1038</td>\n",
       "      <td>4167</td>\n",
       "      <td>1319</td>\n",
       "      <td>0.782424</td>\n",
       "      <td>0.765636</td>\n",
       "      <td>0.805872</td>\n",
       "      <td>0.785239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4297</td>\n",
       "      <td>1151</td>\n",
       "      <td>4054</td>\n",
       "      <td>1331</td>\n",
       "      <td>0.770885</td>\n",
       "      <td>0.763504</td>\n",
       "      <td>0.788730</td>\n",
       "      <td>0.775912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4284</td>\n",
       "      <td>1155</td>\n",
       "      <td>4050</td>\n",
       "      <td>1344</td>\n",
       "      <td>0.769316</td>\n",
       "      <td>0.761194</td>\n",
       "      <td>0.787645</td>\n",
       "      <td>0.774194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L1</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4321</td>\n",
       "      <td>1051</td>\n",
       "      <td>4154</td>\n",
       "      <td>1307</td>\n",
       "      <td>0.782332</td>\n",
       "      <td>0.767768</td>\n",
       "      <td>0.804356</td>\n",
       "      <td>0.785636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tree</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4426</td>\n",
       "      <td>1278</td>\n",
       "      <td>3927</td>\n",
       "      <td>1202</td>\n",
       "      <td>0.771070</td>\n",
       "      <td>0.786425</td>\n",
       "      <td>0.775947</td>\n",
       "      <td>0.781151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model Classifier    TP    FP    TN    FN  Accuracy    Recall  Precision  \\\n",
       "0    k6        kNN  4309  1038  4167  1319  0.782424  0.765636   0.805872   \n",
       "1    k5        kNN  4297  1151  4054  1331  0.770885  0.763504   0.788730   \n",
       "2    k4        kNN  4284  1155  4050  1344  0.769316  0.761194   0.787645   \n",
       "3    L1        kNN  4321  1051  4154  1307  0.782332  0.767768   0.804356   \n",
       "4  Tree        kNN  4426  1278  3927  1202  0.771070  0.786425   0.775947   \n",
       "\n",
       "   F1_Score  \n",
       "0  0.785239  \n",
       "1  0.775912  \n",
       "2  0.774194  \n",
       "3  0.785636  \n",
       "4  0.781151  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Classifier=[]\n",
    "for i in range(len(Score)):\n",
    "    Classifier.append(clf_name)\n",
    "result={'Model':Model, \\\n",
    "        'Classifier':Classifier,\\\n",
    "        'TP':TP, \\\n",
    "        'FP':FP, \\\n",
    "        'TN':TN,\\\n",
    "        'FN':FN, \\\n",
    "        'Accuracy':Score, \\\n",
    "        'Recall':Recall,\\\n",
    "        'Precision':Precision,\\\n",
    "        'F1_Score':F1_Score\n",
    "       }\n",
    "\n",
    "clf_result=pd.DataFrame(result)\n",
    "clf_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Selection:\n",
      "Score of k4 = 0.61 (+/- 0.11)\n",
      "Score of L1 = 0.61 (+/- 0.13)\n",
      "Score of Tree = 0.62 (+/- 0.12)\n"
     ]
    }
   ],
   "source": [
    "clf_name='gnb'\n",
    "parent_dir = \"/Volumes/BECCA/Project/Data/Result/Russia/By_model\"\n",
    "path = os.path.join(parent_dir, clf_name)\n",
    "# os.mkdir(path) \n",
    "# os.mkdir(path+'/PR_plot') \n",
    "\n",
    "k6=np.array(data)[:,1:7].astype('float')\n",
    "k5=np.array(data)[:,2:7].astype('float')\n",
    "k4=np.array(data[['subjectivity_number','day','hour','minute']]).astype('float')\n",
    "k3=np.array(data)[:,4:7].astype('float')\n",
    "k2=np.array(data)[:,4:6].astype('float')\n",
    "L1=np.array(data[['polarity_number','subjectivity_number','day','hour','minute']]).astype('float')\n",
    "Tree=np.array(data[['subjectivity_number','day','hour']]).astype('float')\n",
    "y=np.array(data)[:,7].astype('int')\n",
    "\n",
    "clf = GaussianNB()\n",
    "\n",
    "scores_1 = cross_val_score(clf, k6, y, cv=10)\n",
    "scores_2 = cross_val_score(clf, k5, y, cv=10)\n",
    "scores_3 = cross_val_score(clf, k4, y, cv=10)\n",
    "scores_4 = cross_val_score(clf, k3, y, cv=10)\n",
    "scores_5 = cross_val_score(clf, k2, y, cv=10)\n",
    "scores_6 = cross_val_score(clf, L1, y, cv=10)\n",
    "scores_7 = cross_val_score(clf, Tree, y, cv=10)\n",
    "\n",
    "scores=[scores_1,scores_2,scores_3,scores_4,scores_5,scores_6,scores_7]\n",
    "\n",
    "print('Model Selection:')\n",
    "mean_score = 0\n",
    "std_score = 0\n",
    "for i in scores:\n",
    "    mean_score += i.mean()\n",
    "    std_score += i.std()\n",
    "mean_score = mean_score/len(scores)\n",
    "\n",
    "for i,j in zip(['k6','k5','k4','k3','k2','L1','Tree'], scores):    \n",
    "    if j.mean() > mean_score:\n",
    "        print('Score of %s = %0.2f (+/- %0.2f)' %(i, j.mean(), j.std()*2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Result collection\n",
    "Model=[]\n",
    "Score=[]\n",
    "TP=[]\n",
    "FP=[] \n",
    "FN=[]\n",
    "TN=[]\n",
    "Recall=[]\n",
    "Precision=[]\n",
    "F1_Score=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "name = ['k4','L1','Tree']\n",
    "model = [k4, L1, Tree]\n",
    "\n",
    "for X, md_name in zip(model, name):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "    clf=clf.fit(X_train, y_train)\n",
    "    y_pred=clf.predict(X_test)\n",
    "    \n",
    "    #Confusion metric & relative indexs\n",
    "    tp=0\n",
    "    fp=0\n",
    "    fn=0\n",
    "    tn=0\n",
    "\n",
    "    for i in range(len(y_test)):\n",
    "        if y_test[i] == 1:\n",
    "            if y_pred[i] == 1:\n",
    "                tp+=1\n",
    "            elif y_pred[i] == 0:\n",
    "                fn+=1\n",
    "        elif y_test[i] == 0:\n",
    "            if y_pred[i] == 0:\n",
    "                tn+=1\n",
    "            elif y_pred[i] == 1:\n",
    "                fp+=1\n",
    "    \n",
    "    recall=tp/(tp+fn)\n",
    "    precision=tp/(tp+fp)\n",
    "    accuracy=(tp+tn)/len(y_test)\n",
    "    f1_score=2*(recall*precision)/(recall+precision)\n",
    "    \n",
    "    #Result collection\n",
    "    Model.append(md_name)\n",
    "    Score.append(accuracy)\n",
    "    TP.append(tp)\n",
    "    FP.append(fp)\n",
    "    FN.append(fn)\n",
    "    TN.append(tn)\n",
    "    Recall.append(recall)\n",
    "    Precision.append(precision)\n",
    "    F1_Score.append(f1_score)\n",
    "    \n",
    "    #PR curve\n",
    "    disp = plot_precision_recall_curve(clf, X_test, y_test)\n",
    "    disp.ax_.set_title('2-class Precision-Recall curve')\n",
    "    plt.savefig('/Volumes/BECCA/Project/Data/Result/Russia/By_model/%s/PR_plot/PR_%s.png' %(clf_name, md_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Model</th>\n",
       "      <th>Classifier</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4309</td>\n",
       "      <td>1038</td>\n",
       "      <td>4167</td>\n",
       "      <td>1319</td>\n",
       "      <td>0.782424</td>\n",
       "      <td>0.765636</td>\n",
       "      <td>0.805872</td>\n",
       "      <td>0.785239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4297</td>\n",
       "      <td>1151</td>\n",
       "      <td>4054</td>\n",
       "      <td>1331</td>\n",
       "      <td>0.770885</td>\n",
       "      <td>0.763504</td>\n",
       "      <td>0.788730</td>\n",
       "      <td>0.775912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4284</td>\n",
       "      <td>1155</td>\n",
       "      <td>4050</td>\n",
       "      <td>1344</td>\n",
       "      <td>0.769316</td>\n",
       "      <td>0.761194</td>\n",
       "      <td>0.787645</td>\n",
       "      <td>0.774194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L1</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4321</td>\n",
       "      <td>1051</td>\n",
       "      <td>4154</td>\n",
       "      <td>1307</td>\n",
       "      <td>0.782332</td>\n",
       "      <td>0.767768</td>\n",
       "      <td>0.804356</td>\n",
       "      <td>0.785636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tree</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4426</td>\n",
       "      <td>1278</td>\n",
       "      <td>3927</td>\n",
       "      <td>1202</td>\n",
       "      <td>0.771070</td>\n",
       "      <td>0.786425</td>\n",
       "      <td>0.775947</td>\n",
       "      <td>0.781151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k4</td>\n",
       "      <td>gnb</td>\n",
       "      <td>3888</td>\n",
       "      <td>1974</td>\n",
       "      <td>3231</td>\n",
       "      <td>1740</td>\n",
       "      <td>0.657159</td>\n",
       "      <td>0.690832</td>\n",
       "      <td>0.663255</td>\n",
       "      <td>0.676762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L1</td>\n",
       "      <td>gnb</td>\n",
       "      <td>4133</td>\n",
       "      <td>2120</td>\n",
       "      <td>3085</td>\n",
       "      <td>1495</td>\n",
       "      <td>0.666297</td>\n",
       "      <td>0.734364</td>\n",
       "      <td>0.660963</td>\n",
       "      <td>0.695733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tree</td>\n",
       "      <td>gnb</td>\n",
       "      <td>3897</td>\n",
       "      <td>2001</td>\n",
       "      <td>3204</td>\n",
       "      <td>1731</td>\n",
       "      <td>0.655497</td>\n",
       "      <td>0.692431</td>\n",
       "      <td>0.660732</td>\n",
       "      <td>0.676210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model Classifier    TP    FP    TN    FN  Accuracy    Recall  Precision  \\\n",
       "0    k6        kNN  4309  1038  4167  1319  0.782424  0.765636   0.805872   \n",
       "1    k5        kNN  4297  1151  4054  1331  0.770885  0.763504   0.788730   \n",
       "2    k4        kNN  4284  1155  4050  1344  0.769316  0.761194   0.787645   \n",
       "3    L1        kNN  4321  1051  4154  1307  0.782332  0.767768   0.804356   \n",
       "4  Tree        kNN  4426  1278  3927  1202  0.771070  0.786425   0.775947   \n",
       "0    k4        gnb  3888  1974  3231  1740  0.657159  0.690832   0.663255   \n",
       "1    L1        gnb  4133  2120  3085  1495  0.666297  0.734364   0.660963   \n",
       "2  Tree        gnb  3897  2001  3204  1731  0.655497  0.692431   0.660732   \n",
       "\n",
       "   F1_Score  \n",
       "0  0.785239  \n",
       "1  0.775912  \n",
       "2  0.774194  \n",
       "3  0.785636  \n",
       "4  0.781151  \n",
       "0  0.676762  \n",
       "1  0.695733  \n",
       "2  0.676210  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Classifier=[]\n",
    "for i in range(len(Score)):\n",
    "    Classifier.append(clf_name)\n",
    "result={'Model':Model, \\\n",
    "        'Classifier':Classifier,\\\n",
    "        'TP':TP, \\\n",
    "        'FP':FP, \\\n",
    "        'TN':TN,\\\n",
    "        'FN':FN, \\\n",
    "        'Accuracy':Score, \\\n",
    "        'Recall':Recall,\\\n",
    "        'Precision':Precision,\\\n",
    "        'F1_Score':F1_Score\n",
    "       }\n",
    "\n",
    "clf_result = clf_result.append(pd.DataFrame(result))\n",
    "clf_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Selection:\n",
      "Score of k6 = 0.59 (+/- 0.17)\n",
      "Score of k5 = 0.57 (+/- 0.19)\n",
      "Score of k4 = 0.57 (+/- 0.19)\n",
      "Score of L1 = 0.59 (+/- 0.17)\n",
      "Score of Tree = 0.57 (+/- 0.19)\n"
     ]
    }
   ],
   "source": [
    "clf_name='dtree'\n",
    "parent_dir = \"/Volumes/BECCA/Project/Data/Result/Russia/By_model\"\n",
    "path = os.path.join(parent_dir, clf_name)\n",
    "# os.mkdir(path) \n",
    "# os.mkdir(path+'/PR_plot') \n",
    "\n",
    "k6=np.array(data)[:,1:7].astype('float')\n",
    "k5=np.array(data)[:,2:7].astype('float')\n",
    "k4=np.array(data[['subjectivity_number','day','hour','minute']]).astype('float')\n",
    "k3=np.array(data)[:,4:7].astype('float')\n",
    "k2=np.array(data)[:,4:6].astype('float')\n",
    "L1=np.array(data[['polarity_number','subjectivity_number','day','hour','minute']]).astype('float')\n",
    "Tree=np.array(data[['subjectivity_number','day','hour']]).astype('float')\n",
    "y=np.array(data)[:,7].astype('int')\n",
    "\n",
    "clf = DecisionTreeClassifier(criterion=\"entropy\", max_depth=4)\n",
    "\n",
    "scores_1 = cross_val_score(clf, k6, y, cv=10)\n",
    "scores_2 = cross_val_score(clf, k5, y, cv=10)\n",
    "scores_3 = cross_val_score(clf, k4, y, cv=10)\n",
    "scores_4 = cross_val_score(clf, k3, y, cv=10)\n",
    "scores_5 = cross_val_score(clf, k2, y, cv=10)\n",
    "scores_6 = cross_val_score(clf, L1, y, cv=10)\n",
    "scores_7 = cross_val_score(clf, Tree, y, cv=10)\n",
    "\n",
    "scores=[scores_1,scores_2,scores_3,scores_4,scores_5,scores_6,scores_7]\n",
    "\n",
    "print('Model Selection:')\n",
    "mean_score = 0\n",
    "std_score = 0\n",
    "for i in scores:\n",
    "    mean_score += i.mean()\n",
    "    std_score += i.std()\n",
    "mean_score = mean_score/len(scores)\n",
    "\n",
    "for i,j in zip(['k6','k5','k4','k3','k2','L1','Tree'], scores):    \n",
    "    if j.mean() > mean_score:\n",
    "        print('Score of %s = %0.2f (+/- %0.2f)' %(i, j.mean(), j.std()*2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Result collection\n",
    "Model=[]\n",
    "Score=[]\n",
    "TP=[]\n",
    "FP=[] \n",
    "FN=[]\n",
    "TN=[]\n",
    "Recall=[]\n",
    "Precision=[]\n",
    "F1_Score=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "name = ['k6','k5','k4','L1','Tree']\n",
    "model = [k6, k5, k4, L1, Tree]\n",
    "\n",
    "for X, md_name in zip(model, name):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "    clf=clf.fit(X_train, y_train)\n",
    "    y_pred=clf.predict(X_test)\n",
    "    \n",
    "    #Confusion metric & relative indexs\n",
    "    tp=0\n",
    "    fp=0\n",
    "    fn=0\n",
    "    tn=0\n",
    "\n",
    "    for i in range(len(y_test)):\n",
    "        if y_test[i] == 1:\n",
    "            if y_pred[i] == 1:\n",
    "                tp+=1\n",
    "            elif y_pred[i] == 0:\n",
    "                fn+=1\n",
    "        elif y_test[i] == 0:\n",
    "            if y_pred[i] == 0:\n",
    "                tn+=1\n",
    "            elif y_pred[i] == 1:\n",
    "                fp+=1\n",
    "    \n",
    "    recall=tp/(tp+fn)\n",
    "    precision=tp/(tp+fp)\n",
    "    accuracy=(tp+tn)/len(y_test)\n",
    "    f1_score=2*(recall*precision)/(recall+precision)\n",
    "    \n",
    "    #Result collection\n",
    "    Model.append(md_name)\n",
    "    Score.append(accuracy)\n",
    "    TP.append(tp)\n",
    "    FP.append(fp)\n",
    "    FN.append(fn)\n",
    "    TN.append(tn)\n",
    "    Recall.append(recall)\n",
    "    Precision.append(precision)\n",
    "    F1_Score.append(f1_score)\n",
    "    \n",
    "    #PR curve\n",
    "    disp = plot_precision_recall_curve(clf, X_test, y_test)\n",
    "    disp.ax_.set_title('2-class Precision-Recall curve')\n",
    "    plt.savefig('/Volumes/BECCA/Project/Data/Result/Russia/By_model/%s/PR_plot/PR_%s.png' %(clf_name, md_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Model</th>\n",
       "      <th>Classifier</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4309</td>\n",
       "      <td>1038</td>\n",
       "      <td>4167</td>\n",
       "      <td>1319</td>\n",
       "      <td>0.782424</td>\n",
       "      <td>0.765636</td>\n",
       "      <td>0.805872</td>\n",
       "      <td>0.785239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4297</td>\n",
       "      <td>1151</td>\n",
       "      <td>4054</td>\n",
       "      <td>1331</td>\n",
       "      <td>0.770885</td>\n",
       "      <td>0.763504</td>\n",
       "      <td>0.788730</td>\n",
       "      <td>0.775912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4284</td>\n",
       "      <td>1155</td>\n",
       "      <td>4050</td>\n",
       "      <td>1344</td>\n",
       "      <td>0.769316</td>\n",
       "      <td>0.761194</td>\n",
       "      <td>0.787645</td>\n",
       "      <td>0.774194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L1</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4321</td>\n",
       "      <td>1051</td>\n",
       "      <td>4154</td>\n",
       "      <td>1307</td>\n",
       "      <td>0.782332</td>\n",
       "      <td>0.767768</td>\n",
       "      <td>0.804356</td>\n",
       "      <td>0.785636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tree</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4426</td>\n",
       "      <td>1278</td>\n",
       "      <td>3927</td>\n",
       "      <td>1202</td>\n",
       "      <td>0.771070</td>\n",
       "      <td>0.786425</td>\n",
       "      <td>0.775947</td>\n",
       "      <td>0.781151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k4</td>\n",
       "      <td>gnb</td>\n",
       "      <td>3888</td>\n",
       "      <td>1974</td>\n",
       "      <td>3231</td>\n",
       "      <td>1740</td>\n",
       "      <td>0.657159</td>\n",
       "      <td>0.690832</td>\n",
       "      <td>0.663255</td>\n",
       "      <td>0.676762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L1</td>\n",
       "      <td>gnb</td>\n",
       "      <td>4133</td>\n",
       "      <td>2120</td>\n",
       "      <td>3085</td>\n",
       "      <td>1495</td>\n",
       "      <td>0.666297</td>\n",
       "      <td>0.734364</td>\n",
       "      <td>0.660963</td>\n",
       "      <td>0.695733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tree</td>\n",
       "      <td>gnb</td>\n",
       "      <td>3897</td>\n",
       "      <td>2001</td>\n",
       "      <td>3204</td>\n",
       "      <td>1731</td>\n",
       "      <td>0.655497</td>\n",
       "      <td>0.692431</td>\n",
       "      <td>0.660732</td>\n",
       "      <td>0.676210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3556</td>\n",
       "      <td>1211</td>\n",
       "      <td>3994</td>\n",
       "      <td>2072</td>\n",
       "      <td>0.696945</td>\n",
       "      <td>0.631841</td>\n",
       "      <td>0.745962</td>\n",
       "      <td>0.684175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3303</td>\n",
       "      <td>811</td>\n",
       "      <td>4394</td>\n",
       "      <td>2325</td>\n",
       "      <td>0.710514</td>\n",
       "      <td>0.586887</td>\n",
       "      <td>0.802868</td>\n",
       "      <td>0.678095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3303</td>\n",
       "      <td>811</td>\n",
       "      <td>4394</td>\n",
       "      <td>2325</td>\n",
       "      <td>0.710514</td>\n",
       "      <td>0.586887</td>\n",
       "      <td>0.802868</td>\n",
       "      <td>0.678095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L1</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3556</td>\n",
       "      <td>1211</td>\n",
       "      <td>3994</td>\n",
       "      <td>2072</td>\n",
       "      <td>0.696945</td>\n",
       "      <td>0.631841</td>\n",
       "      <td>0.745962</td>\n",
       "      <td>0.684175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tree</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3303</td>\n",
       "      <td>811</td>\n",
       "      <td>4394</td>\n",
       "      <td>2325</td>\n",
       "      <td>0.710514</td>\n",
       "      <td>0.586887</td>\n",
       "      <td>0.802868</td>\n",
       "      <td>0.678095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model Classifier    TP    FP    TN    FN  Accuracy    Recall  Precision  \\\n",
       "0    k6        kNN  4309  1038  4167  1319  0.782424  0.765636   0.805872   \n",
       "1    k5        kNN  4297  1151  4054  1331  0.770885  0.763504   0.788730   \n",
       "2    k4        kNN  4284  1155  4050  1344  0.769316  0.761194   0.787645   \n",
       "3    L1        kNN  4321  1051  4154  1307  0.782332  0.767768   0.804356   \n",
       "4  Tree        kNN  4426  1278  3927  1202  0.771070  0.786425   0.775947   \n",
       "0    k4        gnb  3888  1974  3231  1740  0.657159  0.690832   0.663255   \n",
       "1    L1        gnb  4133  2120  3085  1495  0.666297  0.734364   0.660963   \n",
       "2  Tree        gnb  3897  2001  3204  1731  0.655497  0.692431   0.660732   \n",
       "0    k6      dtree  3556  1211  3994  2072  0.696945  0.631841   0.745962   \n",
       "1    k5      dtree  3303   811  4394  2325  0.710514  0.586887   0.802868   \n",
       "2    k4      dtree  3303   811  4394  2325  0.710514  0.586887   0.802868   \n",
       "3    L1      dtree  3556  1211  3994  2072  0.696945  0.631841   0.745962   \n",
       "4  Tree      dtree  3303   811  4394  2325  0.710514  0.586887   0.802868   \n",
       "\n",
       "   F1_Score  \n",
       "0  0.785239  \n",
       "1  0.775912  \n",
       "2  0.774194  \n",
       "3  0.785636  \n",
       "4  0.781151  \n",
       "0  0.676762  \n",
       "1  0.695733  \n",
       "2  0.676210  \n",
       "0  0.684175  \n",
       "1  0.678095  \n",
       "2  0.678095  \n",
       "3  0.684175  \n",
       "4  0.678095  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Classifier=[]\n",
    "for i in range(len(Score)):\n",
    "    Classifier.append(clf_name)\n",
    "result={'Model':Model, \\\n",
    "        'Classifier':Classifier,\\\n",
    "        'TP':TP, \\\n",
    "        'FP':FP, \\\n",
    "        'TN':TN,\\\n",
    "        'FN':FN, \\\n",
    "        'Accuracy':Score, \\\n",
    "        'Recall':Recall,\\\n",
    "        'Precision':Precision,\\\n",
    "        'F1_Score':F1_Score\n",
    "       }\n",
    "\n",
    "clf_result = clf_result.append(pd.DataFrame(result))\n",
    "clf_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Selection:\n",
      "Score of k6 = 0.64 (+/- 0.08)\n",
      "Score of k5 = 0.60 (+/- 0.13)\n",
      "Score of k4 = 0.60 (+/- 0.13)\n",
      "Score of L1 = 0.64 (+/- 0.08)\n",
      "Score of Tree = 0.58 (+/- 0.16)\n"
     ]
    }
   ],
   "source": [
    "clf_name='SVM'\n",
    "parent_dir = \"/Volumes/BECCA/Project/Data/Result/Russia/By_model\"\n",
    "path = os.path.join(parent_dir, clf_name)\n",
    "# os.mkdir(path) \n",
    "# os.mkdir(path+'/PR_plot') \n",
    "\n",
    "k6=np.array(data)[:,1:7].astype('float')\n",
    "k5=np.array(data)[:,2:7].astype('float')\n",
    "k4=np.array(data[['subjectivity_number','day','hour','minute']]).astype('float')\n",
    "k3=np.array(data)[:,4:7].astype('float')\n",
    "k2=np.array(data)[:,4:6].astype('float')\n",
    "L1=np.array(data[['polarity_number','subjectivity_number','day','hour','minute']]).astype('float')\n",
    "Tree=np.array(data[['subjectivity_number','day','hour']]).astype('float')\n",
    "y=np.array(data)[:,7].astype('int')\n",
    "\n",
    "clf = svm.SVC(kernel='poly')\n",
    "\n",
    "scores_1 = cross_val_score(clf, k6, y, cv=10)\n",
    "scores_2 = cross_val_score(clf, k5, y, cv=10)\n",
    "scores_3 = cross_val_score(clf, k4, y, cv=10)\n",
    "scores_4 = cross_val_score(clf, k3, y, cv=10)\n",
    "scores_5 = cross_val_score(clf, k2, y, cv=10)\n",
    "scores_6 = cross_val_score(clf, L1, y, cv=10)\n",
    "scores_7 = cross_val_score(clf, Tree, y, cv=10)\n",
    "\n",
    "scores=[scores_1,scores_2,scores_3,scores_4,scores_5,scores_6,scores_7]\n",
    "\n",
    "print('Model Selection:')\n",
    "mean_score = 0\n",
    "std_score = 0\n",
    "for i in scores:\n",
    "    mean_score += i.mean()\n",
    "    std_score += i.std()\n",
    "mean_score = mean_score/len(scores)\n",
    "\n",
    "for i,j in zip(['k6','k5','k4','k3','k2','L1','Tree'], scores):    \n",
    "    if j.mean() > mean_score:\n",
    "        print('Score of %s = %0.2f (+/- %0.2f)' %(i, j.mean(), j.std()*2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Result collection\n",
    "Model=[]\n",
    "Score=[]\n",
    "TP=[]\n",
    "FP=[] \n",
    "FN=[]\n",
    "TN=[]\n",
    "Recall=[]\n",
    "Precision=[]\n",
    "F1_Score=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "name = ['k6','k5','k4','L1','Tree']\n",
    "model = [k6, k5, k4, L1, Tree]\n",
    "\n",
    "for X, md_name in zip(model, name):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "    clf=clf.fit(X_train, y_train)\n",
    "    y_pred=clf.predict(X_test)\n",
    "    \n",
    "    #Confusion metric & relative indexs\n",
    "    tp=0\n",
    "    fp=0\n",
    "    fn=0\n",
    "    tn=0\n",
    "\n",
    "    for i in range(len(y_test)):\n",
    "        if y_test[i] == 1:\n",
    "            if y_pred[i] == 1:\n",
    "                tp+=1\n",
    "            elif y_pred[i] == 0:\n",
    "                fn+=1\n",
    "        elif y_test[i] == 0:\n",
    "            if y_pred[i] == 0:\n",
    "                tn+=1\n",
    "            elif y_pred[i] == 1:\n",
    "                fp+=1\n",
    "    \n",
    "    recall=tp/(tp+fn)\n",
    "    precision=tp/(tp+fp)\n",
    "    accuracy=(tp+tn)/len(y_test)\n",
    "    f1_score=2*(recall*precision)/(recall+precision)\n",
    "    \n",
    "    #Result collection\n",
    "    Model.append(md_name)\n",
    "    Score.append(accuracy)\n",
    "    TP.append(tp)\n",
    "    FP.append(fp)\n",
    "    FN.append(fn)\n",
    "    TN.append(tn)\n",
    "    Recall.append(recall)\n",
    "    Precision.append(precision)\n",
    "    F1_Score.append(f1_score)\n",
    "    \n",
    "    #PR curve\n",
    "    disp = plot_precision_recall_curve(clf, X_test, y_test)\n",
    "    disp.ax_.set_title('2-class Precision-Recall curve')\n",
    "    plt.savefig('/Volumes/BECCA/Project/Data/Result/Russia/By_model/%s/PR_plot/PR_%s.png' %(clf_name, md_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Model</th>\n",
       "      <th>Classifier</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4309</td>\n",
       "      <td>1038</td>\n",
       "      <td>4167</td>\n",
       "      <td>1319</td>\n",
       "      <td>0.782424</td>\n",
       "      <td>0.765636</td>\n",
       "      <td>0.805872</td>\n",
       "      <td>0.785239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4297</td>\n",
       "      <td>1151</td>\n",
       "      <td>4054</td>\n",
       "      <td>1331</td>\n",
       "      <td>0.770885</td>\n",
       "      <td>0.763504</td>\n",
       "      <td>0.788730</td>\n",
       "      <td>0.775912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4284</td>\n",
       "      <td>1155</td>\n",
       "      <td>4050</td>\n",
       "      <td>1344</td>\n",
       "      <td>0.769316</td>\n",
       "      <td>0.761194</td>\n",
       "      <td>0.787645</td>\n",
       "      <td>0.774194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L1</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4321</td>\n",
       "      <td>1051</td>\n",
       "      <td>4154</td>\n",
       "      <td>1307</td>\n",
       "      <td>0.782332</td>\n",
       "      <td>0.767768</td>\n",
       "      <td>0.804356</td>\n",
       "      <td>0.785636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tree</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4426</td>\n",
       "      <td>1278</td>\n",
       "      <td>3927</td>\n",
       "      <td>1202</td>\n",
       "      <td>0.771070</td>\n",
       "      <td>0.786425</td>\n",
       "      <td>0.775947</td>\n",
       "      <td>0.781151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k4</td>\n",
       "      <td>gnb</td>\n",
       "      <td>3888</td>\n",
       "      <td>1974</td>\n",
       "      <td>3231</td>\n",
       "      <td>1740</td>\n",
       "      <td>0.657159</td>\n",
       "      <td>0.690832</td>\n",
       "      <td>0.663255</td>\n",
       "      <td>0.676762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L1</td>\n",
       "      <td>gnb</td>\n",
       "      <td>4133</td>\n",
       "      <td>2120</td>\n",
       "      <td>3085</td>\n",
       "      <td>1495</td>\n",
       "      <td>0.666297</td>\n",
       "      <td>0.734364</td>\n",
       "      <td>0.660963</td>\n",
       "      <td>0.695733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tree</td>\n",
       "      <td>gnb</td>\n",
       "      <td>3897</td>\n",
       "      <td>2001</td>\n",
       "      <td>3204</td>\n",
       "      <td>1731</td>\n",
       "      <td>0.655497</td>\n",
       "      <td>0.692431</td>\n",
       "      <td>0.660732</td>\n",
       "      <td>0.676210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3556</td>\n",
       "      <td>1211</td>\n",
       "      <td>3994</td>\n",
       "      <td>2072</td>\n",
       "      <td>0.696945</td>\n",
       "      <td>0.631841</td>\n",
       "      <td>0.745962</td>\n",
       "      <td>0.684175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3303</td>\n",
       "      <td>811</td>\n",
       "      <td>4394</td>\n",
       "      <td>2325</td>\n",
       "      <td>0.710514</td>\n",
       "      <td>0.586887</td>\n",
       "      <td>0.802868</td>\n",
       "      <td>0.678095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3303</td>\n",
       "      <td>811</td>\n",
       "      <td>4394</td>\n",
       "      <td>2325</td>\n",
       "      <td>0.710514</td>\n",
       "      <td>0.586887</td>\n",
       "      <td>0.802868</td>\n",
       "      <td>0.678095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L1</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3556</td>\n",
       "      <td>1211</td>\n",
       "      <td>3994</td>\n",
       "      <td>2072</td>\n",
       "      <td>0.696945</td>\n",
       "      <td>0.631841</td>\n",
       "      <td>0.745962</td>\n",
       "      <td>0.684175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tree</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3303</td>\n",
       "      <td>811</td>\n",
       "      <td>4394</td>\n",
       "      <td>2325</td>\n",
       "      <td>0.710514</td>\n",
       "      <td>0.586887</td>\n",
       "      <td>0.802868</td>\n",
       "      <td>0.678095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>SVM</td>\n",
       "      <td>3792</td>\n",
       "      <td>1549</td>\n",
       "      <td>3656</td>\n",
       "      <td>1836</td>\n",
       "      <td>0.687529</td>\n",
       "      <td>0.673774</td>\n",
       "      <td>0.709979</td>\n",
       "      <td>0.691403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4485</td>\n",
       "      <td>2676</td>\n",
       "      <td>2529</td>\n",
       "      <td>1143</td>\n",
       "      <td>0.647466</td>\n",
       "      <td>0.796908</td>\n",
       "      <td>0.626309</td>\n",
       "      <td>0.701384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4487</td>\n",
       "      <td>2676</td>\n",
       "      <td>2529</td>\n",
       "      <td>1141</td>\n",
       "      <td>0.647651</td>\n",
       "      <td>0.797264</td>\n",
       "      <td>0.626414</td>\n",
       "      <td>0.701587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L1</td>\n",
       "      <td>SVM</td>\n",
       "      <td>3787</td>\n",
       "      <td>1545</td>\n",
       "      <td>3660</td>\n",
       "      <td>1841</td>\n",
       "      <td>0.687437</td>\n",
       "      <td>0.672886</td>\n",
       "      <td>0.710240</td>\n",
       "      <td>0.691058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tree</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4581</td>\n",
       "      <td>2868</td>\n",
       "      <td>2337</td>\n",
       "      <td>1047</td>\n",
       "      <td>0.638604</td>\n",
       "      <td>0.813966</td>\n",
       "      <td>0.614982</td>\n",
       "      <td>0.700619</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model Classifier    TP    FP    TN    FN  Accuracy    Recall  Precision  \\\n",
       "0    k6        kNN  4309  1038  4167  1319  0.782424  0.765636   0.805872   \n",
       "1    k5        kNN  4297  1151  4054  1331  0.770885  0.763504   0.788730   \n",
       "2    k4        kNN  4284  1155  4050  1344  0.769316  0.761194   0.787645   \n",
       "3    L1        kNN  4321  1051  4154  1307  0.782332  0.767768   0.804356   \n",
       "4  Tree        kNN  4426  1278  3927  1202  0.771070  0.786425   0.775947   \n",
       "0    k4        gnb  3888  1974  3231  1740  0.657159  0.690832   0.663255   \n",
       "1    L1        gnb  4133  2120  3085  1495  0.666297  0.734364   0.660963   \n",
       "2  Tree        gnb  3897  2001  3204  1731  0.655497  0.692431   0.660732   \n",
       "0    k6      dtree  3556  1211  3994  2072  0.696945  0.631841   0.745962   \n",
       "1    k5      dtree  3303   811  4394  2325  0.710514  0.586887   0.802868   \n",
       "2    k4      dtree  3303   811  4394  2325  0.710514  0.586887   0.802868   \n",
       "3    L1      dtree  3556  1211  3994  2072  0.696945  0.631841   0.745962   \n",
       "4  Tree      dtree  3303   811  4394  2325  0.710514  0.586887   0.802868   \n",
       "0    k6        SVM  3792  1549  3656  1836  0.687529  0.673774   0.709979   \n",
       "1    k5        SVM  4485  2676  2529  1143  0.647466  0.796908   0.626309   \n",
       "2    k4        SVM  4487  2676  2529  1141  0.647651  0.797264   0.626414   \n",
       "3    L1        SVM  3787  1545  3660  1841  0.687437  0.672886   0.710240   \n",
       "4  Tree        SVM  4581  2868  2337  1047  0.638604  0.813966   0.614982   \n",
       "\n",
       "   F1_Score  \n",
       "0  0.785239  \n",
       "1  0.775912  \n",
       "2  0.774194  \n",
       "3  0.785636  \n",
       "4  0.781151  \n",
       "0  0.676762  \n",
       "1  0.695733  \n",
       "2  0.676210  \n",
       "0  0.684175  \n",
       "1  0.678095  \n",
       "2  0.678095  \n",
       "3  0.684175  \n",
       "4  0.678095  \n",
       "0  0.691403  \n",
       "1  0.701384  \n",
       "2  0.701587  \n",
       "3  0.691058  \n",
       "4  0.700619  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Classifier=[]\n",
    "for i in range(len(Score)):\n",
    "    Classifier.append(clf_name)\n",
    "result={'Model':Model, \\\n",
    "        'Classifier':Classifier,\\\n",
    "        'TP':TP, \\\n",
    "        'FP':FP, \\\n",
    "        'TN':TN,\\\n",
    "        'FN':FN, \\\n",
    "        'Accuracy':Score, \\\n",
    "        'Recall':Recall,\\\n",
    "        'Precision':Precision,\\\n",
    "        'F1_Score':F1_Score\n",
    "       }\n",
    "\n",
    "clf_result = clf_result.append(pd.DataFrame(result))\n",
    "clf_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Selection:\n",
      "Score of k6 = 0.60 (+/- 0.13)\n",
      "Score of k5 = 0.58 (+/- 0.13)\n",
      "Score of k4 = 0.58 (+/- 0.13)\n",
      "Score of L1 = 0.60 (+/- 0.14)\n",
      "Score of Tree = 0.58 (+/- 0.14)\n"
     ]
    }
   ],
   "source": [
    "clf_name='nn'\n",
    "parent_dir = \"/Volumes/BECCA/Project/Data/Result/Russia/By_model\"\n",
    "path = os.path.join(parent_dir, clf_name)\n",
    "os.mkdir(path) \n",
    "os.mkdir(path+'/PR_plot') \n",
    "\n",
    "k6=np.array(data)[:,1:7].astype('float')\n",
    "k5=np.array(data)[:,2:7].astype('float')\n",
    "k4=np.array(data[['subjectivity_number','day','hour','minute']]).astype('float')\n",
    "k3=np.array(data)[:,4:7].astype('float')\n",
    "k2=np.array(data)[:,4:6].astype('float')\n",
    "L1=np.array(data[['polarity_number','subjectivity_number','day','hour','minute']]).astype('float')\n",
    "Tree=np.array(data[['subjectivity_number','day','hour']]).astype('float')\n",
    "y=np.array(data)[:,7].astype('int')\n",
    "\n",
    "clf = MLPClassifier(activation='identity',solver='adam', max_iter=1000)\n",
    "\n",
    "scores_1 = cross_val_score(clf, k6, y, cv=10)\n",
    "scores_2 = cross_val_score(clf, k5, y, cv=10)\n",
    "scores_3 = cross_val_score(clf, k4, y, cv=10)\n",
    "scores_4 = cross_val_score(clf, k3, y, cv=10)\n",
    "scores_5 = cross_val_score(clf, k2, y, cv=10)\n",
    "scores_6 = cross_val_score(clf, L1, y, cv=10)\n",
    "scores_7 = cross_val_score(clf, Tree, y, cv=10)\n",
    "\n",
    "scores=[scores_1,scores_2,scores_3,scores_4,scores_5,scores_6,scores_7]\n",
    "\n",
    "print('Model Selection:')\n",
    "mean_score = 0\n",
    "std_score = 0\n",
    "for i in scores:\n",
    "    mean_score += i.mean()\n",
    "    std_score += i.std()\n",
    "mean_score = mean_score/len(scores)\n",
    "\n",
    "for i,j in zip(['k6','k5','k4','k3','k2','L1','Tree'], scores):    \n",
    "    if j.mean() > mean_score:\n",
    "        print('Score of %s = %0.2f (+/- %0.2f)' %(i, j.mean(), j.std()*2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Result collection\n",
    "Model=[]\n",
    "Score=[]\n",
    "TP=[]\n",
    "FP=[] \n",
    "FN=[]\n",
    "TN=[]\n",
    "Recall=[]\n",
    "Precision=[]\n",
    "F1_Score=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "name = ['k6','k5','k4','L1','Tree']\n",
    "model = [k6, k5, k4, L1, Tree]\n",
    "\n",
    "for X, md_name in zip(model, name):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "    clf=clf.fit(X_train, y_train)\n",
    "    y_pred=clf.predict(X_test)\n",
    "    \n",
    "    #Confusion metric & relative indexs\n",
    "    tp=0\n",
    "    fp=0\n",
    "    fn=0\n",
    "    tn=0\n",
    "\n",
    "    for i in range(len(y_test)):\n",
    "        if y_test[i] == 1:\n",
    "            if y_pred[i] == 1:\n",
    "                tp+=1\n",
    "            elif y_pred[i] == 0:\n",
    "                fn+=1\n",
    "        elif y_test[i] == 0:\n",
    "            if y_pred[i] == 0:\n",
    "                tn+=1\n",
    "            elif y_pred[i] == 1:\n",
    "                fp+=1\n",
    "    \n",
    "    recall=tp/(tp+fn)\n",
    "    precision=tp/(tp+fp)\n",
    "    accuracy=(tp+tn)/len(y_test)\n",
    "    f1_score=2*(recall*precision)/(recall+precision)\n",
    "    \n",
    "    #Result collection\n",
    "    Model.append(md_name)\n",
    "    Score.append(accuracy)\n",
    "    TP.append(tp)\n",
    "    FP.append(fp)\n",
    "    FN.append(fn)\n",
    "    TN.append(tn)\n",
    "    Recall.append(recall)\n",
    "    Precision.append(precision)\n",
    "    F1_Score.append(f1_score)\n",
    "    \n",
    "    #PR curve\n",
    "    disp = plot_precision_recall_curve(clf, X_test, y_test)\n",
    "    disp.ax_.set_title('2-class Precision-Recall curve')\n",
    "    plt.savefig('/Volumes/BECCA/Project/Data/Result/Russia/By_model/%s/PR_plot/PR_%s.png' %(clf_name, md_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Model</th>\n",
       "      <th>Classifier</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4309</td>\n",
       "      <td>1038</td>\n",
       "      <td>4167</td>\n",
       "      <td>1319</td>\n",
       "      <td>0.782424</td>\n",
       "      <td>0.765636</td>\n",
       "      <td>0.805872</td>\n",
       "      <td>0.785239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4297</td>\n",
       "      <td>1151</td>\n",
       "      <td>4054</td>\n",
       "      <td>1331</td>\n",
       "      <td>0.770885</td>\n",
       "      <td>0.763504</td>\n",
       "      <td>0.788730</td>\n",
       "      <td>0.775912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4284</td>\n",
       "      <td>1155</td>\n",
       "      <td>4050</td>\n",
       "      <td>1344</td>\n",
       "      <td>0.769316</td>\n",
       "      <td>0.761194</td>\n",
       "      <td>0.787645</td>\n",
       "      <td>0.774194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L1</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4321</td>\n",
       "      <td>1051</td>\n",
       "      <td>4154</td>\n",
       "      <td>1307</td>\n",
       "      <td>0.782332</td>\n",
       "      <td>0.767768</td>\n",
       "      <td>0.804356</td>\n",
       "      <td>0.785636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tree</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4426</td>\n",
       "      <td>1278</td>\n",
       "      <td>3927</td>\n",
       "      <td>1202</td>\n",
       "      <td>0.771070</td>\n",
       "      <td>0.786425</td>\n",
       "      <td>0.775947</td>\n",
       "      <td>0.781151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k4</td>\n",
       "      <td>gnb</td>\n",
       "      <td>3888</td>\n",
       "      <td>1974</td>\n",
       "      <td>3231</td>\n",
       "      <td>1740</td>\n",
       "      <td>0.657159</td>\n",
       "      <td>0.690832</td>\n",
       "      <td>0.663255</td>\n",
       "      <td>0.676762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L1</td>\n",
       "      <td>gnb</td>\n",
       "      <td>4133</td>\n",
       "      <td>2120</td>\n",
       "      <td>3085</td>\n",
       "      <td>1495</td>\n",
       "      <td>0.666297</td>\n",
       "      <td>0.734364</td>\n",
       "      <td>0.660963</td>\n",
       "      <td>0.695733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tree</td>\n",
       "      <td>gnb</td>\n",
       "      <td>3897</td>\n",
       "      <td>2001</td>\n",
       "      <td>3204</td>\n",
       "      <td>1731</td>\n",
       "      <td>0.655497</td>\n",
       "      <td>0.692431</td>\n",
       "      <td>0.660732</td>\n",
       "      <td>0.676210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3556</td>\n",
       "      <td>1211</td>\n",
       "      <td>3994</td>\n",
       "      <td>2072</td>\n",
       "      <td>0.696945</td>\n",
       "      <td>0.631841</td>\n",
       "      <td>0.745962</td>\n",
       "      <td>0.684175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3303</td>\n",
       "      <td>811</td>\n",
       "      <td>4394</td>\n",
       "      <td>2325</td>\n",
       "      <td>0.710514</td>\n",
       "      <td>0.586887</td>\n",
       "      <td>0.802868</td>\n",
       "      <td>0.678095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3303</td>\n",
       "      <td>811</td>\n",
       "      <td>4394</td>\n",
       "      <td>2325</td>\n",
       "      <td>0.710514</td>\n",
       "      <td>0.586887</td>\n",
       "      <td>0.802868</td>\n",
       "      <td>0.678095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L1</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3556</td>\n",
       "      <td>1211</td>\n",
       "      <td>3994</td>\n",
       "      <td>2072</td>\n",
       "      <td>0.696945</td>\n",
       "      <td>0.631841</td>\n",
       "      <td>0.745962</td>\n",
       "      <td>0.684175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tree</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3303</td>\n",
       "      <td>811</td>\n",
       "      <td>4394</td>\n",
       "      <td>2325</td>\n",
       "      <td>0.710514</td>\n",
       "      <td>0.586887</td>\n",
       "      <td>0.802868</td>\n",
       "      <td>0.678095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>SVM</td>\n",
       "      <td>3792</td>\n",
       "      <td>1549</td>\n",
       "      <td>3656</td>\n",
       "      <td>1836</td>\n",
       "      <td>0.687529</td>\n",
       "      <td>0.673774</td>\n",
       "      <td>0.709979</td>\n",
       "      <td>0.691403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4485</td>\n",
       "      <td>2676</td>\n",
       "      <td>2529</td>\n",
       "      <td>1143</td>\n",
       "      <td>0.647466</td>\n",
       "      <td>0.796908</td>\n",
       "      <td>0.626309</td>\n",
       "      <td>0.701384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4487</td>\n",
       "      <td>2676</td>\n",
       "      <td>2529</td>\n",
       "      <td>1141</td>\n",
       "      <td>0.647651</td>\n",
       "      <td>0.797264</td>\n",
       "      <td>0.626414</td>\n",
       "      <td>0.701587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L1</td>\n",
       "      <td>SVM</td>\n",
       "      <td>3787</td>\n",
       "      <td>1545</td>\n",
       "      <td>3660</td>\n",
       "      <td>1841</td>\n",
       "      <td>0.687437</td>\n",
       "      <td>0.672886</td>\n",
       "      <td>0.710240</td>\n",
       "      <td>0.691058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tree</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4581</td>\n",
       "      <td>2868</td>\n",
       "      <td>2337</td>\n",
       "      <td>1047</td>\n",
       "      <td>0.638604</td>\n",
       "      <td>0.813966</td>\n",
       "      <td>0.614982</td>\n",
       "      <td>0.700619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>nn</td>\n",
       "      <td>4233</td>\n",
       "      <td>2657</td>\n",
       "      <td>2548</td>\n",
       "      <td>1395</td>\n",
       "      <td>0.625958</td>\n",
       "      <td>0.752132</td>\n",
       "      <td>0.614369</td>\n",
       "      <td>0.676306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>nn</td>\n",
       "      <td>4070</td>\n",
       "      <td>2742</td>\n",
       "      <td>2463</td>\n",
       "      <td>1558</td>\n",
       "      <td>0.603065</td>\n",
       "      <td>0.723170</td>\n",
       "      <td>0.597475</td>\n",
       "      <td>0.654341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>nn</td>\n",
       "      <td>3849</td>\n",
       "      <td>2465</td>\n",
       "      <td>2740</td>\n",
       "      <td>1779</td>\n",
       "      <td>0.608234</td>\n",
       "      <td>0.683902</td>\n",
       "      <td>0.609598</td>\n",
       "      <td>0.644616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L1</td>\n",
       "      <td>nn</td>\n",
       "      <td>4227</td>\n",
       "      <td>2618</td>\n",
       "      <td>2587</td>\n",
       "      <td>1401</td>\n",
       "      <td>0.629004</td>\n",
       "      <td>0.751066</td>\n",
       "      <td>0.617531</td>\n",
       "      <td>0.677784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tree</td>\n",
       "      <td>nn</td>\n",
       "      <td>3734</td>\n",
       "      <td>2260</td>\n",
       "      <td>2945</td>\n",
       "      <td>1894</td>\n",
       "      <td>0.616542</td>\n",
       "      <td>0.663468</td>\n",
       "      <td>0.622956</td>\n",
       "      <td>0.642574</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model Classifier    TP    FP    TN    FN  Accuracy    Recall  Precision  \\\n",
       "0    k6        kNN  4309  1038  4167  1319  0.782424  0.765636   0.805872   \n",
       "1    k5        kNN  4297  1151  4054  1331  0.770885  0.763504   0.788730   \n",
       "2    k4        kNN  4284  1155  4050  1344  0.769316  0.761194   0.787645   \n",
       "3    L1        kNN  4321  1051  4154  1307  0.782332  0.767768   0.804356   \n",
       "4  Tree        kNN  4426  1278  3927  1202  0.771070  0.786425   0.775947   \n",
       "0    k4        gnb  3888  1974  3231  1740  0.657159  0.690832   0.663255   \n",
       "1    L1        gnb  4133  2120  3085  1495  0.666297  0.734364   0.660963   \n",
       "2  Tree        gnb  3897  2001  3204  1731  0.655497  0.692431   0.660732   \n",
       "0    k6      dtree  3556  1211  3994  2072  0.696945  0.631841   0.745962   \n",
       "1    k5      dtree  3303   811  4394  2325  0.710514  0.586887   0.802868   \n",
       "2    k4      dtree  3303   811  4394  2325  0.710514  0.586887   0.802868   \n",
       "3    L1      dtree  3556  1211  3994  2072  0.696945  0.631841   0.745962   \n",
       "4  Tree      dtree  3303   811  4394  2325  0.710514  0.586887   0.802868   \n",
       "0    k6        SVM  3792  1549  3656  1836  0.687529  0.673774   0.709979   \n",
       "1    k5        SVM  4485  2676  2529  1143  0.647466  0.796908   0.626309   \n",
       "2    k4        SVM  4487  2676  2529  1141  0.647651  0.797264   0.626414   \n",
       "3    L1        SVM  3787  1545  3660  1841  0.687437  0.672886   0.710240   \n",
       "4  Tree        SVM  4581  2868  2337  1047  0.638604  0.813966   0.614982   \n",
       "0    k6         nn  4233  2657  2548  1395  0.625958  0.752132   0.614369   \n",
       "1    k5         nn  4070  2742  2463  1558  0.603065  0.723170   0.597475   \n",
       "2    k4         nn  3849  2465  2740  1779  0.608234  0.683902   0.609598   \n",
       "3    L1         nn  4227  2618  2587  1401  0.629004  0.751066   0.617531   \n",
       "4  Tree         nn  3734  2260  2945  1894  0.616542  0.663468   0.622956   \n",
       "\n",
       "   F1_Score  \n",
       "0  0.785239  \n",
       "1  0.775912  \n",
       "2  0.774194  \n",
       "3  0.785636  \n",
       "4  0.781151  \n",
       "0  0.676762  \n",
       "1  0.695733  \n",
       "2  0.676210  \n",
       "0  0.684175  \n",
       "1  0.678095  \n",
       "2  0.678095  \n",
       "3  0.684175  \n",
       "4  0.678095  \n",
       "0  0.691403  \n",
       "1  0.701384  \n",
       "2  0.701587  \n",
       "3  0.691058  \n",
       "4  0.700619  \n",
       "0  0.676306  \n",
       "1  0.654341  \n",
       "2  0.644616  \n",
       "3  0.677784  \n",
       "4  0.642574  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Classifier=[]\n",
    "for i in range(len(Score)):\n",
    "    Classifier.append(clf_name)\n",
    "result={'Model':Model, \\\n",
    "        'Classifier':Classifier,\\\n",
    "        'TP':TP, \\\n",
    "        'FP':FP, \\\n",
    "        'TN':TN,\\\n",
    "        'FN':FN, \\\n",
    "        'Accuracy':Score, \\\n",
    "        'Recall':Recall,\\\n",
    "        'Precision':Precision,\\\n",
    "        'F1_Score':F1_Score\n",
    "       }\n",
    "\n",
    "clf_result = clf_result.append(pd.DataFrame(result))\n",
    "clf_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Selection:\n",
      "Score of k6 = 0.60 (+/- 0.14)\n",
      "Score of k5 = 0.58 (+/- 0.13)\n",
      "Score of k4 = 0.58 (+/- 0.13)\n",
      "Score of L1 = 0.60 (+/- 0.14)\n",
      "Score of Tree = 0.58 (+/- 0.14)\n"
     ]
    }
   ],
   "source": [
    "clf_name='lr'\n",
    "parent_dir = \"/Volumes/BECCA/Project/Data/Result/Russia/By_model\"\n",
    "path = os.path.join(parent_dir, clf_name)\n",
    "os.mkdir(path) \n",
    "os.mkdir(path+'/PR_plot') \n",
    "\n",
    "k6=np.array(data)[:,1:7].astype('float')\n",
    "k5=np.array(data)[:,2:7].astype('float')\n",
    "k4=np.array(data[['subjectivity_number','day','hour','minute']]).astype('float')\n",
    "k3=np.array(data)[:,4:7].astype('float')\n",
    "k2=np.array(data)[:,4:6].astype('float')\n",
    "L1=np.array(data[['polarity_number','subjectivity_number','day','hour','minute']]).astype('float')\n",
    "Tree=np.array(data[['subjectivity_number','day','hour']]).astype('float')\n",
    "y=np.array(data)[:,7].astype('int')\n",
    "\n",
    "clf = LogisticRegression(random_state=0)\n",
    "\n",
    "scores_1 = cross_val_score(clf, k6, y, cv=10)\n",
    "scores_2 = cross_val_score(clf, k5, y, cv=10)\n",
    "scores_3 = cross_val_score(clf, k4, y, cv=10)\n",
    "scores_4 = cross_val_score(clf, k3, y, cv=10)\n",
    "scores_5 = cross_val_score(clf, k2, y, cv=10)\n",
    "scores_6 = cross_val_score(clf, L1, y, cv=10)\n",
    "scores_7 = cross_val_score(clf, Tree, y, cv=10)\n",
    "\n",
    "scores=[scores_1,scores_2,scores_3,scores_4,scores_5,scores_6,scores_7]\n",
    "\n",
    "print('Model Selection:')\n",
    "mean_score = 0\n",
    "std_score = 0\n",
    "for i in scores:\n",
    "    mean_score += i.mean()\n",
    "    std_score += i.std()\n",
    "mean_score = mean_score/len(scores)\n",
    "\n",
    "for i,j in zip(['k6','k5','k4','k3','k2','L1','Tree'], scores):    \n",
    "    if j.mean() > mean_score:\n",
    "        print('Score of %s = %0.2f (+/- %0.2f)' %(i, j.mean(), j.std()*2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Result collection\n",
    "Model=[]\n",
    "Score=[]\n",
    "TP=[]\n",
    "FP=[] \n",
    "FN=[]\n",
    "TN=[]\n",
    "Recall=[]\n",
    "Precision=[]\n",
    "F1_Score=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rRWStiAz2ZzwAqri7gzhT2wcKRYT9+vHcM3sNiRM/tbEQjDGVzm+JQERCgSnAIKAjMFpEOhZb7UHgXVU9DxgFvOi/eH59vSR1HwAxkeH+Olyl2fZk0dy4fZ9vzyEYY4yv/Fki6AmkqmqaquYAM4FhxdZRoPBx3nrAbj/G43bj/5xnCLZlHquKw52W0BAh7cnBjO7ZHICB/14c4IiMMTWNPxNBPODZvWeGa56nScAfRSQDmAfc4cd4SqgfdeaXCABCQoRb+7YCICevgI/XVEm+NMYECX8mAm+34xSv4B4NvKGqCcBg4E0RKRGTiNwsIskikpyZ6b0PH1/tPnTC/XpE94TT2ldVatHo13ES7pixOoCRGGNqGn8mggygmcd0AiWrfm4E3gVQ1aVAJBBbfEeqOlVVk1Q1KS4u7rSCesdjOMoeLRqc1r6qmme/Q/2e/obcfOva2hhz+vyZCFYAbUWkpYhE4DQGzy22zk/ApQAi0gEnEZzeT/5yvL96lz9373c3X+xUEaXvz+K177YHOBpjTE3gt0SgqnnAeOALYBPO3UEbRORRERnqWu0e4CYRWQPMAMao3R9Zpr8N7sD1vVsA8NRnP3Iyz0ZGM8acHr8+R6Cq81S1naq2VtUnXPMeVtW5rtcbVfUiVe2qqt1U9Ut/xlNTPDqss/v1OQ9+zvpdhwMYjTGmugvaJ4v/PrRToEM4LV95jJt8xQvfBTASY0x1F7SJoGfLhoEO4bS0bRJjg9YYYypF0CWC1nHObZgdzqrYsJRnqjoRTjcZw6csCXAkxpjqKugSQXStMNo1iQ50GJXm9Rt6Ak7HdNb9hDHmVARdIsjJV8JDa85p92zZkMHnOkNt/vaZhdYpnTGmwmrOFdFHm/YcITTkzByD4FQ9Mfxc9+uW98+zB82MMRUSNIlAXD1e1AoLcXdDXVM0qBNR5C6otg98xomcmnWOxhj/CZpEUEgV2jetGQ3Fnv50YSJjLkx0T3d/7Ksal/CMMf4RdIkgJ7+A4yfzAh2GX0wa2omtTwwCnBHY2j/0Oec9+iVrdh4KcGTGmDNZ0CUCgAU//hLoEPwmPDSE56/u6p4+mJXLsClLSJz4KR9W836WjDH+EZSJoKb73XkJbHx0ALe4xjAo9OdZKaxxjX9896wUu8PIGANAWKADMP5ROyKM+wd1YOLA9uw6dIKB/1rMsZN5DPN48OyXoyd5a9wFAYzSGHMmsBJBDSciJDSozbpJ/bm4nTOWQ2x0BADfpe7j6S9+JCfPbjc1JphZiSBIiAjTx/Z0T09dtI0n5/3IlG+2MeWbbQDMvLkX9WuH18i7qowxpfM5EYhIPNDCcxtVXeSPoIz/3Xxxa6LCQ3noow3ueaOmLnO//uD/LuS85tVrBDdjzKnxKRGIyD+Aq4GNQOHN6QpYIqjGruudyHW9E9mWeYxLn/22yLLfvfg9k67syDUXtEBRTuTkU792RIAiNcb4k68lguHAOap60p/BVJXqNlaxv7WOiy7SpXXixE8BmPTxRiZ9vLHE+l2b1edvg9rTo0UDwmpQv03GBCtf/xenAeH+DKQq1XZ13Wy8W/ngZVzXq0Wpy9fsPMTVU5fR5oHP6PDQ52zYbSOkGVOd+VoiyAJSRGQB4C4VqOqdfonKzxIaRAU6hDNao+haPDa8M48N71xk/v5jJ0necZAXF25zP618IjefIf/5jrqRYaydNCAQ4RpjTpOviWCu669GmHBpu0CHUC01iq7FgE5NGdDJ6fb6l6PZ9HxiAQBHsvO49r/L+N8NPa26yJhqxqdEoKr/E5EIoPAKullVc/0Xlh949DwdFlqzuqEOlMYxkaRPHsJ3W/fxx2nLWZK6nzYPfMaFrRsxfawlBGOqC5/+p4pIP2ArMAV4EdgiIheXuZGz3UAR2SwiqSIy0cvy50UkxfW3RUSqpHe0ULFEUJn6tI1ly+ODaNPYGfnt+21OQpjyTWqAIzPG+MLXqqFngf6quhlARNoBM4AepW0gIqE4ieNyIANYISJzVdV9G4qq3uWx/h3AeRU+g1MQUsMGpjkTRISFMP/uvvz48xHunLGaLXuP8fQXm+maUJ8+bWMDHZ4xpgy+JoLwwiQAoKpbRKS8u4h6AqmqmgYgIjOBYTjPIngzGnjEx3hOS00boexM0r5pXb68qy8rdxzgqpeW8sdpy4ssX3b/pTStFxmg6Iwx3vhaiZssItNEpJ/r71VgZTnbxAM7PaYzXPNKEJEWQEvg61KW3ywiySKSnJmZ6WPIpbOqIf/r0aIh/xldsoDX66kFbP75aAAiMsaUxtdEcBuwAbgTmIDzq/7WcrbxdrUtrd/jUcAcVfU6pJaqTlXVJFVNiouL8zHk0oVYG2aVGNr1bNInD2Hbk4PZ8vgg9/wB/1rEzB9+CmBkxhhPPl0SVfWkqj6nqiNU9Xeq+rwPTxlnAM08phOA3aWsOwqnzaFKhFkmqFKhIUJEWEiRp5cnvr+O3YdOBDAqY0yhMq+IIvKu6991IrK2+F85+14BtBWRlq5bT0fh5VkEETkHaAAsPbVTqDhrIwic9MlDiKnlNE1dOPlrMo/WiF5LjKnWymssnuD694qK7lhV80RkPPAFEAq8pqobRORRIFlVC5PCaGCm2nBZQSPlkf60/ts8AM5/Yj4Amx8fSK0w6/rDmEAos0SgqntcL/cBO1V1B1AL6Erp1Tye289T1Xaq2lpVn3DNe9gjCaCqk1S1xDMGpuYKDRHSJw/hwSEd3PPOefBzXvl2mw2faUwA+FpZvgiIdI1JsAC4AXjDX0GZ4DDuN6346q5fn0t86rMfaXn/POaszCh1G1W1ZGFMJfP1OQJR1SwRuRF4QVX/KSKr/RmYCQ5tm8SQPnkI6fuO0++ZhQDcO3sN985e414nqUUDknccrNB+b7m4FTsPZjG8Wzz9XX0jGWO88zkRiEhv4Frgxgpua0y5EmPrkPbkYPr/axGpvxwrsqysJNA4pha/eGlwfmVRGgDz1v1c6rad4+tyafsm3NavNZHh1j5hgpevF/M/A/cDH7gafFsB3/gvLBOMQkKE+Xf3dU8fysqhQKFOrdAyG5JVlSPZeUSFh7L70AmmfJPK7JUZPD68Mw9+uL7U7dbvOsL6XUf494KtReaHhgjNGkTRJaE+c9fs5rIOTfh9UgLnNIkhMbbO6Z+oMWcYqW71rUlJSZqcnFzh7d5N3slf5jh3vHrez26C15HsXP711VbeX53BoSynM93RPZsx44edpW4TGR7COU3rusdj6Bxfl27N6nPHJW1pUte6zjBnLhFZqapJXpeVlQhE5F+q+mcR+RgvTwWr6tDKC9M3p5oIZifv5L45a2nWMIrFf7nED5GZmkZVycrJZ/ehE2zcc4TpS3ewcfcRTuR6fQAeEdj+lP3IMGemshJBeVVDb7r+faZyQwoc8drzhTEliQh1aoXRtkkMbZvEMKxbya6yNu05wl/mrGXdrsOo/jrec992cZzIzeeH7Qf4TdtYeiY2JL5BFCO6J1T1aRhTrjITgaoWdiyXDJxQ1QJwdzFdy8+x+YU9VGwqU4ez6vLxHX04lJXDn2elsHCz0ynit1t+7Rxx8dZ9LN66D4C7311DiECBwkVtGvHPkV05u14kYh0hmgDytbF4AXAZUHg7RxTwJXChP4LyJ/sPZ/yhfu0I3rihJ+CM7Zy+P4tWsXWIigjliw0/07BOBLe/vYoj2XkUuCpZl6Tu56LJToe7l3VoQmx0BH8f1smesDZVztdEEKmq7nv6VPWYiNT2U0x+ZXnA+Fuj6Fo0iv61wFxYpbR20gD3vKPZuTwydwPvr9pFQoMo5m/aC8DMFTsZc2EiAzs3pVerRlUbuAlaviaC4yLSXVVXAYhID6Badh1pecCcCWIiw3nuD9147g/dACcxTJq7kfdWZfDG9+m88X06AK1i69CiUW3qRoVzRZezubB1I+rUskd4TOWqyHMEs0WksH+hs4Cr/ROSf4VYkcCcgWIiw3n2D115cEgHZqz4iX/N30pOXgFp+46Ttu84AB+l/Nq9V79z4mgVG02BKhe1ieXyjk0CFbqpAXxKBKq6QkTaA+fg/Kj+UVVz/RqZn1geMGeyBnUi+L9+bfi/fm3c8z5du4cFm/ZSNyrcXVJYuDnT3TBdOO/uy9sxskcCdaPCibZSg6kAn74trvaAu4EWqnqTiLQVkXNU9RP/hlf5rERgqpshXc5iSJezAJg0tBM5eQXsO3aS/cdyyFdl3P9WsO9YDs99tYXnvtri3q5B7XBm33ohzRvWJiLMBmMypfP1Z8PrOGMU93ZNZwCzgWqXCA5m5QQ6BGNOS0RYCGfXj+Ls+lEAJD94OSt3HGDU1GWM7tmczKMn+Wz9zxzMyuWy574FICxE6HdOHPkFysDOTbn6/OaBPAVzhvE1EbRW1atFZDSAqp6QanofZmx0tXz8wZgy9WjRkK1PDC4y772VGfyw/QCzkneSV6Bs2nOUXYdO8M3mTP763jq+++tviY2uRYiIlRiCnK+JIEdEonB1MyEirYFqOcag3XFhgsVVPRK4qkcC/xjZxT0v9ZdjXP78t6hCn3/82m9kRFgI5yc24Nnfd6NpPeszKdj4elV8BPgcaCYibwMXAWP8FZQ/VctijDGVpE3jaLY/NYRXF6VxPCeP4yfz2H88h/dX7WJJ6n56PbWAvww8p0hjtan5yk0EriqgH4ERQC+ca+kEVd3n59j8onpWaBlTuW66uFWR6Wd/35W3lv/EQx+u55+fb+afn2/mgpYNeXvcBYSFWrVRTVduIlBVFZEPVbUH8GkVxOQXhU0a1umcMSWJCNf1asEl7Rtz3bTlpGUeZ/n2A7R54DPAGQDob4M7MPy8kh3vmerP11S/TETO92skVcRKBMaULr5+FF/f04/UJwZxcbs49/xfjp7kz7NSOPeRL0ic+ClvLttBQUH1GsvElM7XNoLfAreKSDpwHKd6SFW1S1kbichA4N9AKPBfVZ3sZZ0/AJNwGqLXqOo1Pkd/CiwRGFO+sNAQpo/t6Z7+YsPPvLhwm3tAnoc+XM9DH66necPaTB/b00Zuq+Z8TQSDKrpjV1fVU4DLcZ47WCEic1V1o8c6bXGGwLxIVQ+KSOOKHqfCcVnVkDEVNqBTUwZ0agrAz4ez+feCLcz4YSc/Hcii3zMLAXhh9Hlc2LpRkQ73TPVQZiIQkUjgVqANsA6Ypqp5Pu67J5Cqqmmufc0EhgEbPda5CZiiqgcBVPWXioVfcVYiMOb0NK0XyVMjuvDUiC68sGArz7qeZr5jxmr3Or1bNWJsn5Zc1qGxdf1eDZRXIvgfkAssxikVdAQm+LjveMBz8NcM4IJi67QDEJElONVHk1T18+I7EpGbgZsBmje3JyKNOVPccWlb7ri0LT8fzmZZ2n5Sdh7ije/TWZq2n6Vp+4mvH8WSiTY07JmuvETQUVXPBRCRacAPFdi3t58BxVuXwoC2QD8gAVgsIp1V9VCRjVSnAlPBGbO4AjGUDMp+nRhT6ZrWi2T4efEMPy+eSUM7sfNAFr/55zfsOnSCxImfclGbRrw+pqc9wXyGKu9TcfcwWoEqoUIZQDOP6QRgt5d1PlLVXFXdDmzGSQx+Y2nAGP9r1rA2SyZewnW9WgDOaGztHvyMAc8vYu6a4pcBE2jlJYKuInLE9XcU6FL4WkSOlLPtCqCtiLQUkQhgFDC32Dof4tyRhIjE4lQVpVX8NMqXl18AwL5j1bJnDGOqnfj6UTw2vDPpk4fQv2MTEhvVZvPeo9w5YzWJEz/l7eU7yM7ND3SYhvIHrz/lwVNVNU9ExgNf4NT/v6aqG0TkUSBZVee6lvUXkY1APnCfqu4/1WOWJV+dGqUuCfX8sXtjTBmmXp8EwPpdh/nDK0vJysnngQ/W88AH67mqewJPj+xCSIiV1wPFrz2wqeo8YF6xeQ97vFaccQ7u9mccRdmXzZhA6Rxfj42PDuTwiVyum7actRmHeW9VBu+tyuDb+/rRopE9jxAIQdNyo/YQpDFnjHpR4cwd34cfHxvontf36YXc//5acl3VuKbqBE8icP1rNw0Zc+aIDA8lffIQZt7cC4AZP+yk7QOf8fqS7aj9eqsyQZMIClkeMObM06tVI7Y/NZjB5zpPL//94420vH8ePZ+Yz7CGCFIAABkWSURBVAMfrGNJarXs7LjasFFajDFnBBHhxWt7kJ2bzx9eWcrajMP8cvQkby//ibeX/wTAhEvbMuHSttawXMmCJxFYMdOYaiEyPJS54/u4p7fsPcqtb60kLfM4/16wlX8v2MpFbRrxwujuNKwTEcBIa46gSQTWRmBM9dSuSQxf39OPvUeyGfP6CjbtOcKS1P10f+wrAH7TNpYR3eP53XkJAY60+gqaRHAy17kT4VBWbjlrGmPORE3qRvLZhN9wJDuX6d+n8+XGvazfdZjFW/exeOs+Hv14I3PH96FZw9qBDrXakerWMp+UlKTJyckV3u7KF75j3a7DAKRPHlLZYRljAmT9rsOMeOl7cvKcH3sxtcK48Tctub53olUdeRCRlaqa5G1Z0Nw1VFDNEp4xxjed4+vx46MDeW1MErf2bc2xnDz+NX8r3R/7iqtfWcreI9mBDvGMFzRVQzaqnjE1V0iIcEn7JlzSvgm39WvNVxv38pc5a1i+/QAXPLmAOhGh1IsKp8NZdRnYuSlXdDmbqIhT7kGnxgmaRFDdqsCMMaemXlQ4I3skcFX3eJak7ufrH39h/qa9/HQgi92Hs1nw4y/cN2ete/3Y6FpEhArNGtYmIiyExVv30atVQ6JrhREXU4u6keEM6xZPx7PrBvCs/CuIEkGgIzDGVCURoU/bWPq0jeXhKzuiqqzJOMx7KzPIzs2ndkQo6fuz2HXoBGfVi+RkXgGLtzoPri1LO1BkX68scjpFjokMo1ZYKGP7JHLFuWfTvFHNaJgOnkRQYkwcY0wwERG6NatPt2b1fd7mwPEcFm/N5P1VuyhQZfHWfRwlj39+vpl/fr4Z+LVEkVeg3DfgHIZ1i692A/AETSKwNgJjTEU1rBPBsG7xDOsW756XX6CsyTjE3+duYP/xHKJrhZFXoKT+coz75qzlvjlraVK3FhMubceI7vFEhp/5bRFBlAgsExhjTl9oiNC9eQM+8nj6GeBwVi7v/PATL36Tyv5jOfztg3X87YN1RISGcFGbRvxjZBcax0QGKOqyBU0isJohY4w/1asdzm39WnNbv9Zk5+bz9Y+/8Mna3cxb9zPfbM6k5xMLAGjTOJrHh3emV6tGAY74V0GTCCwPGGOqSmR4KIPPPYvB555FTl4Bs1fuZOm2/Wzcc4TUX44xauoywBkxsXN8PS5uG0fn+LqcVS+KAlWOn8yjVlgoR7JziYuuRb4qR7PzqBUWQp1alX/ZDp5EYFVDxpgAiAgL4doLWnDtBS0ApxO9qYvSOHA8h7UZh1ibcZh3XL2rlufJ353LNRc0r/QYgycRBDoAY4zB6UTvmd93dU/vO3aSFdsPsONAFjl5Bew5fIK6keHUjQonNETIOJhFXHQk9aLC6NGigV9iCppEYI3FxpgzUWx0LQade1ZAY/Drza4iMlBENotIqohM9LJ8jIhkikiK62+cv2KxPGCMMd75rUQgIqHAFOByIANYISJzVXVjsVVnqep4f8VRyBKBMcZ4588SQU8gVVXTVDUHmAkM8+PxymSNxcYY450/E0E8sNNjOsM1r7irRGStiMwRkWbediQiN4tIsogkZ2ZmnlIw9mSxMcZ4589E4G1QyOKX44+BRFXtAswH/udtR6o6VVWTVDUpLi7ulIKxvoaMMcY7fyaCDMDzF34CsNtzBVXdr6onXZOvAj38FYzVDBljjHf+TAQrgLYi0lJEIoBRwFzPFUTE856pocAmfwVjVUPGGOOd3+4aUtU8ERkPfAGEAq+p6gYReRRIVtW5wJ0iMhTIAw4AY/wYj792bYwx1ZpfHyhT1XnAvGLzHvZ4fT9wvz9jcB+rKg5ijDHVUPUaPeE0WInAGGO8C5pEYG0ExhjjXdAkAmOMMd4FTSKwqiFjjPEuiBJBoCMwxpgzU9AkgtBQ50HniNCgOWVjjPFJ0FwV+7SJBeDOS9sEOBJjjDmzBE0iKCwJNK0XFeBIjDHmzBI0iaCwicBbT3jGGBPMgiYRFBLLBMYYU0TQJAK7fdQYY7wLnkTg+tdKBMYYU1TwJAJXJhBrJTDGmCKCJhEUshKBMcYUFTSJwFoIjDHGu6BJBHsPZwOQl28pwRhjPAVNIoiOdMbgqRsVHuBIjDHmzBI0iaBQiLURGGNMEUGXCIwxxhRlicAYY4KcJQJjjAlyfk0EIjJQRDaLSKqITCxjvZEioiKS5M94jDHGlOS3RCAiocAUYBDQERgtIh29rBcD3Aks91csxhhjSufPEkFPIFVV01Q1B5gJDPOy3mPAP4FsP8ZijDGmFP5MBPHATo/pDNc8NxE5D2imqp+UtSMRuVlEkkUkOTMzs/IjNcaYIObPRODtjn33Y70iEgI8D9xT3o5UdaqqJqlqUlxcXCWGaIwxxp+JIANo5jGdAOz2mI4BOgMLRSQd6AXMtQZjY4ypWv5MBCuAtiLSUkQigFHA3MKFqnpYVWNVNVFVE4FlwFBVTfZjTMYYY4rxWyJQ1TxgPPAFsAl4V1U3iMijIjLUX8c1xhhTMWH+3LmqzgPmFZv3cCnr9vNnLL8epyqOYowx1UfQPFlsfc0ZY4x3fi0RVJXc3FwyMjLIzi79UYQbOoUzqt1ZNMrLZNOmA1UYnTGVLzIykoSEBMLDrVt1c/pqRCLIyMggJiaGxMREpJSxKNP3HedIdi6JjerYmASmWlNV9u/fT0ZGBi1btgx0OKYGqBFVQ9nZ2TRq1KjUJGBMTSIiNGrUqMwSsDEVUSMSAWBJwAQV+76bylRjEoExxphTY4mgkkRHR5/2Pnbv3s3IkSNLXX7o0CFefPFFn9cH6NevH+eccw5du3bl/PPPJyUl5bTjrEwPP/ww8+fPr5R9rV69mnHjxhWZN2zYMHr37l1k3qRJk4iPj6dbt2507tyZuXPncrpWrlzJueeeS5s2bbjzzjvRUu5TXrhwId26daNTp0707dvXPf/QoUOMHDmS9u3b06FDB5YuXQrAvffey9dff33a8RlTJlWtVn89evTQ4jZu3FhiXnHbM4/pmp0H9XBWTrnrnoo6der4Zb+etm/frp06darQNn379tUVK1aoquprr72ml112WaXEkpubWyn7qUwjR47UlJQU9/TBgwc1ISFB27dvr2lpae75jzzyiD799NOq6nx3GjVqpPn5+ad17PPPP1+///57LSgo0IEDB+q8efNKrHPw4EHt0KGD7tixQ1VV9+7d6152/fXX66uvvqqqqidPntSDBw+qqmp6erpefvnlXo/py/femEJAspZyXa0Rdw15+vvHG9i4+0iJ+dm5+eQXKJHhoYRWcAT7jmfX5ZErO1U4lh07djB27FgyMzOJi4vj9ddfp3nz5mzbto1rr72W/Px8Bg0axHPPPcexY8dIT0/niiuuYP369WzYsIEbbriBnJwcCgoKeO+993jooYfYtm0b3bp14/LLL+f22293r5+fn89f//pXvvjiC0SEm266iTvuuKNIPL179+bpp592T3/55Zc88sgjnDx5ktatW/P6668THR3NvHnzuPvuu4mNjaV79+6kpaXxySefMGnSJHbv3k16ejqxsbG8+eabTJw4kYULF3Ly5Eluv/12brnlFvbs2cPVV1/NkSNHyMvL46WXXuLCCy/kxhtvJDk5GRFh7Nix3HXXXYwZM4YrrriCkSNHsmDBAu69917y8vI4//zzeemll6hVqxaJiYn86U9/4uOPPyY3N5fZs2fTvn37Iud29OhR1q5dS9euXd3z3nvvPa688kqaNGnCzJkzuf/++0t8Rh06dCAsLIx9+/bRuHHjCn/GAHv27OHIkSPuksf111/Phx9+yKBBg4qs98477zBixAiaN28O4D7ekSNHWLRoEW+88QYAERERREREANCiRQv279/Pzz//TNOmTU8pPmPKY1VDfjR+/Hiuv/561q5dy7XXXsudd94JwIQJE5gwYQIrVqzg7LPP9rrtyy+/zIQJE0hJSSE5OZmEhAQmT55M69atSUlJKXJBB5g6dSrbt29n9erV7uMV9/nnnzN8+HAA9u3bx+OPP878+fNZtWoVSUlJPPfcc2RnZ3PLLbfw2Wef8d1331G82++VK1fy0Ucf8c477zBt2jTq1avHihUrWLFiBa+++irbt2/nnXfeYcCAAaSkpLBmzRq6detGSkoKu3btYv369axbt44bbrihyH6zs7MZM2YMs2bNYt26de4EUig2NpZVq1Zx22238cwzz5Q4t+TkZDp37lxk3owZMxg9ejSjR49mxowZXt/n5cuXExISQvFebb/55hu6detW4u/CCy8ssY9du3aRkJDgnk5ISGDXrl0l1tuyZQsHDx6kX79+9OjRg+nTpwOQlpZGXFwcN9xwA+eddx7jxo3j+PHj7u26d+/OkiVLvMZvTGWocSWC0n65B+I5gqVLl/L+++8DcN111/GXv/zFPf/DDz8E4JprruHee+8tsW3v3r154oknyMjIYMSIEbRt27bMY82fP59bb72VsDDnI23YsKF72bXXXsvx48fJz89n1apVACxbtoyNGzdy0UUXAZCTk0Pv3r358ccfadWqlfv+9NGjRzN16lT3voYOHUpUVBTglCjWrl3LnDlzADh8+DBbt27l/PPPZ+zYseTm5jJ8+HC6detGq1atSEtL44477mDIkCH079+/SPybN2+mZcuWtGvXDoA//elPTJkyhT//+c8AjBgxAoAePXq431NPe/bsKXIx37t3L6mpqfTp0wcRISwsjPXr17uTxfPPP89bb71FTEwMs2bNKnEXzm9/+1uf21PUS3uAt7t68vLyWLlyJQsWLODEiRP07t2bXr16kZeXx6pVq3jhhRe44IILmDBhApMnT+axxx4DnJLD7t27S+zPmMpiJYIqVJFb/q655hrmzp1LVFQUAwYMKLfBUFVL3f/bb7/N9u3bueaaa7j99tvd619++eWkpKSQkpLCxo0bmTZtWqmNnIXq1KlT5JgvvPCCex/bt2+nf//+XHzxxSxatIj4+Hiuu+46pk+fToMGDVizZg39+vVjypQpJRp1yzturVq1AAgNDSUvL6/E8qioqCL31c+aNYuDBw/SsmVLEhMTSU9PZ+bMme7ld911FykpKSxevJjf/OY3JfZXkRJBQkICGRkZ7umMjAyvJb2EhAQGDhxInTp1iI2N5eKLL2bNmjUkJCSQkJDABRdcAMDIkSPdCRuc0lJh8jXGHywR+NGFF17ovvi8/fbb9OnTB4BevXrx3nvvARS5OHlKS0ujVatW3HnnnQwdOpS1a9cSExPD0aNHva7fv39/Xn75ZfdF8sCBot1ohIeH8/jjj7Ns2TI2bdpEr169WLJkCampqQBkZWWxZcsW2rdvT1paGunp6YBzQS3NgAEDeOmll8jNzQWcqo/jx4+zY8cOGjduzE033cSNN97IqlWr2LdvHwUFBVx11VU89thjRS50AO3btyc9Pd0dz5tvvlnkrprydOjQwb0tONVCn3/+Oenp6aSnp7Ny5cpS32tvCksExf++//77EuueddZZxMTEsGzZMlSV6dOnM2xYyVFZhw0bxuLFi8nLyyMrK4vly5fToUMHmjZtSrNmzdi8eTMACxYsoGPHX4f33rJlS4lqL2MqU42rGiqNv5+/ycrKKlJPfPfdd/Of//yHsWPH8vTTT7sbiwH+9a9/8cc//pFnn32WIUOGUK9evRL7mzVrFm+99Rbh4eE0bdqUhx9+mIYNG3LRRRfRuXNnBg0a5P51DzBu3Di2bNlCly5dCA8P56abbmL8+PFF9hkVFcU999zDM888w7Rp03jjjTcYPXo0J0+eBODxxx+nXbt2vPjiiwwcOJDY2Fh69uxZ6jmPGzeO9PR0unfvjqoSFxfHhx9+yMKFC3n66acJDw8nOjqa6dOns2vXLm644QYKCgoAeOqpp4rsKzIyktdff53f//737sbiW2+91ef3v3379hw+fJijR4+yf/9+fvrpJ3r16uVe3rJlS+rWrcvy5ct93mdFvPTSS4wZM4YTJ04waNAgd0Pxyy+/DMCtt95Khw4dGDhwIF26dCEkJIRx48a5L/AvvPAC1157LTk5ObRq1cr9XcnNzSU1NZWkJBuvyfiPlFckP9MkJSVpcnLRsWs2bdpEhw4dytwuL7+AfcdO0qRuZMCfyszKyiIqKgoRYebMmcyYMYOPPvoooDF5OnbsGNHR0agqt99+O23btuWuu+4KdFjlev7554mJiSlR7VSdffDBB6xatcrdXuDJl++9MYVEZKWqev1FETRVQ2GhITStFxXwJADOnTfdunWjS5cuvPjiizz77LOBDqmIV1991f3Q0+HDh7nlllsCHZJPbrvtNndbQk2Rl5fHPfeUO6y3MaclaEoExtQ09r03FREUJYLqltCMOR32fTeVqUYkgsjISPbv32//OUxQUNd4BJGRkYEOxdQQNeKuocL7uIs/BWtMTVU4QpkxlaFGJILw8HAbqckYY06RX6uGRGSgiGwWkVQRmehl+a0isk5EUkTkOxHp6G0/xhhj/MdviUBEQoEpwCCgIzDay4X+HVU9V1W7Af8EnvNXPMYYY7zzZ4mgJ5CqqmmqmgPMBIo8d6+qnv1F1wGstdcYY6qYP9sI4oGdHtMZwAXFVxKR24G7gQjgEm87EpGbgZtdk8dEZPMpxhQL7DvFbasrO+fgYOccHE7nnFuUtsCficDbI7wlfvGr6hRgiohcAzwI/MnLOlOBqcXnVzggkeTSHqioqeycg4Odc3Dw1zn7s2ooA2jmMZ0AlNWp+kxguB/jMcYY44U/E8EKoK2ItBSRCGAUUGSUcBHxHG1lCLDVj/EYY4zxwm9VQ6qaJyLjgS+AUOA1Vd0gIo/iDKI8FxgvIpcBucBBvFQLVbLTrl6qhuycg4Odc3DwyzlXu07njDHGVK4a0deQMcaYU2eJwBhjglyNTAQ+dG1RS0RmuZYvF5HEqo+ycvlwzneLyEYRWSsiC0Sk1HuKq4vyztljvZEioiJS7W819OWcReQPrs96g4i8U9UxVjYfvtvNReQbEVnt+n4PDkSclUVEXhORX0RkfSnLRUT+43o/1opI99M+qKrWqD+chultQCuch9TWAB2LrfN/wMuu16OAWYGOuwrO+bdAbdfr24LhnF3rxQCLgGVAUqDjroLPuS2wGmjgmm4c6Lir4JynAre5XncE0gMd92me88VAd2B9KcsHA5/hPKvVC1h+usesiSWCcru2cE3/z/V6DnCpnAljWJ46X7rz+EZVs1yTy3Ce66jOfPmcAR7D6ccquyqD8xNfzvkmYIqqHgRQ1V+qOMbK5ss5K1DX9boeZT+vdMZT1UXAgTJWGQZMV8cyoL6InHU6x6yJicBb1xbxpa2jqnnAYaBRlUTnH76cs6cbcX5RVGflnrOInAc0U9VPqjIwP/Llc24HtBORJSKyTEQGVll0/uHLOU8C/igiGcA84I6qCS1gKvr/vVw1YjyCYnzp2sKn7i+qEZ/PR0T+CCQBff0akf+Vec4iEgI8D4ypqoCqgC+fcxhO9VA/nFLfYhHprKqH/Bybv/hyzqOBN1T1WRHpDbzpOucC/4cXEJV+/aqJJQJfurZwryMiYTjFybKKYmc6n7rzcD289wAwVFVPVlFs/lLeOccAnYGFIpKOU5c6t5o3GPv63f5IVXNVdTuwGScxVFe+nPONwLsAqroUiMTpnK2mqmj3PeWqiYmg3K4tXNOFTzGPBL5WVytMNeVLdx7nAa/gJIHqXm8M5Zyzqh5W1VhVTVTVRJx2kaGqmhyYcCuFL9/tD3FuDEBEYnGqitKqNMrK5cs5/wRcCiAiHXASQU0et3YucL3r7qFewGFV3XM6O6xxVUPqW9cW03CKj6k4JYFRgYv49Pl4zk8D0cBsV7v4T6o6NGBBnyYfz7lG8fGcvwD6i8hGIB+4T1X3By7q0+PjOd8DvCoid+FUkYypzj/sRGQGTtVerKvd4xEgHEBVX8ZpBxkMpAJZwA2nfcxq/H4ZY4ypBDWxasgYY0wFWCIwxpggZ4nAGGOCnCUCY4wJcpYIjDEmyFkiMKYYEckXkRQRWS8iH4tI/Ure/xgR+X+u15NE5N7K3L8xFWWJwJiSTqhqN1XtjPOcye2BDsgYf7JEYEzZluLRoZeI3CciK1z9wP/dY/71rnlrRORN17wrXeNdrBaR+SLSJADxG1OuGvdksTGVRURCcboumOaa7o/Tb09PnI6/5orIxcB+nD6cLlLVfSLS0LWL74BeqqoiMg74C85TsMacUSwRGFNSlIikAInASuAr1/z+rr/VrulonMTQFZijqvsAVLWwA8MEYJarr/gIYHuVRG9MBVnVkDElnVDVbkALnAt4YRuBAE+52g+6qWobVZ3mmu+tr5YXgP+nqucCt+B0hmbMGccSgTGlUNXDwJ3AvSISjtPx2VgRiQYQkXgRaQwsAP4gIo1c8wurhuoBu1yv/4QxZyirGjKmDKq6WkTWAKNU9U1XN8dLXT24HgP+6OoN8wngWxHJx6k6GoMzctZsEdmF0w12y0CcgzHlsd5HjTEmyFnVkDHGBDlLBMYYE+QsERhjTJCzRGCMMUHOEoExxgQ5SwTGGBPkLBEYY0yQ+/8P99xgueuuhAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "name = ['k6','k5','k4','L1','Tree']\n",
    "model = [k6, k5, k4, L1, Tree]\n",
    "\n",
    "for X, md_name in zip(model, name):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "    clf=clf.fit(X_train, y_train)\n",
    "    y_pred=clf.predict(X_test)\n",
    "    \n",
    "    #Confusion metric & relative indexs\n",
    "    tp=0\n",
    "    fp=0\n",
    "    fn=0\n",
    "    tn=0\n",
    "\n",
    "    for i in range(len(y_test)):\n",
    "        if y_test[i] == 1:\n",
    "            if y_pred[i] == 1:\n",
    "                tp+=1\n",
    "            elif y_pred[i] == 0:\n",
    "                fn+=1\n",
    "        elif y_test[i] == 0:\n",
    "            if y_pred[i] == 0:\n",
    "                tn+=1\n",
    "            elif y_pred[i] == 1:\n",
    "                fp+=1\n",
    "    \n",
    "    recall=tp/(tp+fn)\n",
    "    precision=tp/(tp+fp)\n",
    "    accuracy=(tp+tn)/len(y_test)\n",
    "    f1_score=2*(recall*precision)/(recall+precision)\n",
    "    \n",
    "    #Result collection\n",
    "    Model.append(md_name)\n",
    "    Score.append(accuracy)\n",
    "    TP.append(tp)\n",
    "    FP.append(fp)\n",
    "    FN.append(fn)\n",
    "    TN.append(tn)\n",
    "    Recall.append(recall)\n",
    "    Precision.append(precision)\n",
    "    F1_Score.append(f1_score)\n",
    "    \n",
    "    #PR curve\n",
    "    disp = plot_precision_recall_curve(clf, X_test, y_test)\n",
    "    disp.ax_.set_title('2-class Precision-Recall curve')\n",
    "    plt.savefig('/Volumes/BECCA/Project/Data/Result/Russia/By_model/%s/PR_plot/PR_%s.png' %(clf_name, md_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Model</th>\n",
       "      <th>Classifier</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4309</td>\n",
       "      <td>1038</td>\n",
       "      <td>4167</td>\n",
       "      <td>1319</td>\n",
       "      <td>0.782424</td>\n",
       "      <td>0.765636</td>\n",
       "      <td>0.805872</td>\n",
       "      <td>0.785239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4297</td>\n",
       "      <td>1151</td>\n",
       "      <td>4054</td>\n",
       "      <td>1331</td>\n",
       "      <td>0.770885</td>\n",
       "      <td>0.763504</td>\n",
       "      <td>0.788730</td>\n",
       "      <td>0.775912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4284</td>\n",
       "      <td>1155</td>\n",
       "      <td>4050</td>\n",
       "      <td>1344</td>\n",
       "      <td>0.769316</td>\n",
       "      <td>0.761194</td>\n",
       "      <td>0.787645</td>\n",
       "      <td>0.774194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L1</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4321</td>\n",
       "      <td>1051</td>\n",
       "      <td>4154</td>\n",
       "      <td>1307</td>\n",
       "      <td>0.782332</td>\n",
       "      <td>0.767768</td>\n",
       "      <td>0.804356</td>\n",
       "      <td>0.785636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tree</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4426</td>\n",
       "      <td>1278</td>\n",
       "      <td>3927</td>\n",
       "      <td>1202</td>\n",
       "      <td>0.771070</td>\n",
       "      <td>0.786425</td>\n",
       "      <td>0.775947</td>\n",
       "      <td>0.781151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k4</td>\n",
       "      <td>gnb</td>\n",
       "      <td>3888</td>\n",
       "      <td>1974</td>\n",
       "      <td>3231</td>\n",
       "      <td>1740</td>\n",
       "      <td>0.657159</td>\n",
       "      <td>0.690832</td>\n",
       "      <td>0.663255</td>\n",
       "      <td>0.676762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L1</td>\n",
       "      <td>gnb</td>\n",
       "      <td>4133</td>\n",
       "      <td>2120</td>\n",
       "      <td>3085</td>\n",
       "      <td>1495</td>\n",
       "      <td>0.666297</td>\n",
       "      <td>0.734364</td>\n",
       "      <td>0.660963</td>\n",
       "      <td>0.695733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tree</td>\n",
       "      <td>gnb</td>\n",
       "      <td>3897</td>\n",
       "      <td>2001</td>\n",
       "      <td>3204</td>\n",
       "      <td>1731</td>\n",
       "      <td>0.655497</td>\n",
       "      <td>0.692431</td>\n",
       "      <td>0.660732</td>\n",
       "      <td>0.676210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3556</td>\n",
       "      <td>1211</td>\n",
       "      <td>3994</td>\n",
       "      <td>2072</td>\n",
       "      <td>0.696945</td>\n",
       "      <td>0.631841</td>\n",
       "      <td>0.745962</td>\n",
       "      <td>0.684175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3303</td>\n",
       "      <td>811</td>\n",
       "      <td>4394</td>\n",
       "      <td>2325</td>\n",
       "      <td>0.710514</td>\n",
       "      <td>0.586887</td>\n",
       "      <td>0.802868</td>\n",
       "      <td>0.678095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3303</td>\n",
       "      <td>811</td>\n",
       "      <td>4394</td>\n",
       "      <td>2325</td>\n",
       "      <td>0.710514</td>\n",
       "      <td>0.586887</td>\n",
       "      <td>0.802868</td>\n",
       "      <td>0.678095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L1</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3556</td>\n",
       "      <td>1211</td>\n",
       "      <td>3994</td>\n",
       "      <td>2072</td>\n",
       "      <td>0.696945</td>\n",
       "      <td>0.631841</td>\n",
       "      <td>0.745962</td>\n",
       "      <td>0.684175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tree</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3303</td>\n",
       "      <td>811</td>\n",
       "      <td>4394</td>\n",
       "      <td>2325</td>\n",
       "      <td>0.710514</td>\n",
       "      <td>0.586887</td>\n",
       "      <td>0.802868</td>\n",
       "      <td>0.678095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>SVM</td>\n",
       "      <td>3792</td>\n",
       "      <td>1549</td>\n",
       "      <td>3656</td>\n",
       "      <td>1836</td>\n",
       "      <td>0.687529</td>\n",
       "      <td>0.673774</td>\n",
       "      <td>0.709979</td>\n",
       "      <td>0.691403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4485</td>\n",
       "      <td>2676</td>\n",
       "      <td>2529</td>\n",
       "      <td>1143</td>\n",
       "      <td>0.647466</td>\n",
       "      <td>0.796908</td>\n",
       "      <td>0.626309</td>\n",
       "      <td>0.701384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4487</td>\n",
       "      <td>2676</td>\n",
       "      <td>2529</td>\n",
       "      <td>1141</td>\n",
       "      <td>0.647651</td>\n",
       "      <td>0.797264</td>\n",
       "      <td>0.626414</td>\n",
       "      <td>0.701587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L1</td>\n",
       "      <td>SVM</td>\n",
       "      <td>3787</td>\n",
       "      <td>1545</td>\n",
       "      <td>3660</td>\n",
       "      <td>1841</td>\n",
       "      <td>0.687437</td>\n",
       "      <td>0.672886</td>\n",
       "      <td>0.710240</td>\n",
       "      <td>0.691058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tree</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4581</td>\n",
       "      <td>2868</td>\n",
       "      <td>2337</td>\n",
       "      <td>1047</td>\n",
       "      <td>0.638604</td>\n",
       "      <td>0.813966</td>\n",
       "      <td>0.614982</td>\n",
       "      <td>0.700619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>nn</td>\n",
       "      <td>4233</td>\n",
       "      <td>2657</td>\n",
       "      <td>2548</td>\n",
       "      <td>1395</td>\n",
       "      <td>0.625958</td>\n",
       "      <td>0.752132</td>\n",
       "      <td>0.614369</td>\n",
       "      <td>0.676306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>nn</td>\n",
       "      <td>4070</td>\n",
       "      <td>2742</td>\n",
       "      <td>2463</td>\n",
       "      <td>1558</td>\n",
       "      <td>0.603065</td>\n",
       "      <td>0.723170</td>\n",
       "      <td>0.597475</td>\n",
       "      <td>0.654341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>nn</td>\n",
       "      <td>3849</td>\n",
       "      <td>2465</td>\n",
       "      <td>2740</td>\n",
       "      <td>1779</td>\n",
       "      <td>0.608234</td>\n",
       "      <td>0.683902</td>\n",
       "      <td>0.609598</td>\n",
       "      <td>0.644616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L1</td>\n",
       "      <td>nn</td>\n",
       "      <td>4227</td>\n",
       "      <td>2618</td>\n",
       "      <td>2587</td>\n",
       "      <td>1401</td>\n",
       "      <td>0.629004</td>\n",
       "      <td>0.751066</td>\n",
       "      <td>0.617531</td>\n",
       "      <td>0.677784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tree</td>\n",
       "      <td>nn</td>\n",
       "      <td>3734</td>\n",
       "      <td>2260</td>\n",
       "      <td>2945</td>\n",
       "      <td>1894</td>\n",
       "      <td>0.616542</td>\n",
       "      <td>0.663468</td>\n",
       "      <td>0.622956</td>\n",
       "      <td>0.642574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>ld</td>\n",
       "      <td>4188</td>\n",
       "      <td>2592</td>\n",
       "      <td>2613</td>\n",
       "      <td>1440</td>\n",
       "      <td>0.627804</td>\n",
       "      <td>0.744136</td>\n",
       "      <td>0.617699</td>\n",
       "      <td>0.675048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>ld</td>\n",
       "      <td>3868</td>\n",
       "      <td>2469</td>\n",
       "      <td>2736</td>\n",
       "      <td>1760</td>\n",
       "      <td>0.609619</td>\n",
       "      <td>0.687278</td>\n",
       "      <td>0.610383</td>\n",
       "      <td>0.646552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>ld</td>\n",
       "      <td>3872</td>\n",
       "      <td>2473</td>\n",
       "      <td>2732</td>\n",
       "      <td>1756</td>\n",
       "      <td>0.609619</td>\n",
       "      <td>0.687989</td>\n",
       "      <td>0.610244</td>\n",
       "      <td>0.646789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L1</td>\n",
       "      <td>ld</td>\n",
       "      <td>4190</td>\n",
       "      <td>2595</td>\n",
       "      <td>2610</td>\n",
       "      <td>1438</td>\n",
       "      <td>0.627712</td>\n",
       "      <td>0.744492</td>\n",
       "      <td>0.617539</td>\n",
       "      <td>0.675099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tree</td>\n",
       "      <td>ld</td>\n",
       "      <td>3867</td>\n",
       "      <td>2453</td>\n",
       "      <td>2752</td>\n",
       "      <td>1761</td>\n",
       "      <td>0.611003</td>\n",
       "      <td>0.687100</td>\n",
       "      <td>0.611867</td>\n",
       "      <td>0.647305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>lr</td>\n",
       "      <td>4171</td>\n",
       "      <td>2571</td>\n",
       "      <td>2634</td>\n",
       "      <td>1457</td>\n",
       "      <td>0.628173</td>\n",
       "      <td>0.741116</td>\n",
       "      <td>0.618659</td>\n",
       "      <td>0.674373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>lr</td>\n",
       "      <td>3862</td>\n",
       "      <td>2471</td>\n",
       "      <td>2734</td>\n",
       "      <td>1766</td>\n",
       "      <td>0.608880</td>\n",
       "      <td>0.686212</td>\n",
       "      <td>0.609822</td>\n",
       "      <td>0.645765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>lr</td>\n",
       "      <td>3865</td>\n",
       "      <td>2474</td>\n",
       "      <td>2731</td>\n",
       "      <td>1763</td>\n",
       "      <td>0.608880</td>\n",
       "      <td>0.686745</td>\n",
       "      <td>0.609718</td>\n",
       "      <td>0.645943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L1</td>\n",
       "      <td>lr</td>\n",
       "      <td>4172</td>\n",
       "      <td>2574</td>\n",
       "      <td>2631</td>\n",
       "      <td>1456</td>\n",
       "      <td>0.627989</td>\n",
       "      <td>0.741294</td>\n",
       "      <td>0.618441</td>\n",
       "      <td>0.674317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tree</td>\n",
       "      <td>lr</td>\n",
       "      <td>3870</td>\n",
       "      <td>2458</td>\n",
       "      <td>2747</td>\n",
       "      <td>1758</td>\n",
       "      <td>0.610819</td>\n",
       "      <td>0.687633</td>\n",
       "      <td>0.611568</td>\n",
       "      <td>0.647374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model Classifier    TP    FP    TN    FN  Accuracy    Recall  Precision  \\\n",
       "0    k6        kNN  4309  1038  4167  1319  0.782424  0.765636   0.805872   \n",
       "1    k5        kNN  4297  1151  4054  1331  0.770885  0.763504   0.788730   \n",
       "2    k4        kNN  4284  1155  4050  1344  0.769316  0.761194   0.787645   \n",
       "3    L1        kNN  4321  1051  4154  1307  0.782332  0.767768   0.804356   \n",
       "4  Tree        kNN  4426  1278  3927  1202  0.771070  0.786425   0.775947   \n",
       "0    k4        gnb  3888  1974  3231  1740  0.657159  0.690832   0.663255   \n",
       "1    L1        gnb  4133  2120  3085  1495  0.666297  0.734364   0.660963   \n",
       "2  Tree        gnb  3897  2001  3204  1731  0.655497  0.692431   0.660732   \n",
       "0    k6      dtree  3556  1211  3994  2072  0.696945  0.631841   0.745962   \n",
       "1    k5      dtree  3303   811  4394  2325  0.710514  0.586887   0.802868   \n",
       "2    k4      dtree  3303   811  4394  2325  0.710514  0.586887   0.802868   \n",
       "3    L1      dtree  3556  1211  3994  2072  0.696945  0.631841   0.745962   \n",
       "4  Tree      dtree  3303   811  4394  2325  0.710514  0.586887   0.802868   \n",
       "0    k6        SVM  3792  1549  3656  1836  0.687529  0.673774   0.709979   \n",
       "1    k5        SVM  4485  2676  2529  1143  0.647466  0.796908   0.626309   \n",
       "2    k4        SVM  4487  2676  2529  1141  0.647651  0.797264   0.626414   \n",
       "3    L1        SVM  3787  1545  3660  1841  0.687437  0.672886   0.710240   \n",
       "4  Tree        SVM  4581  2868  2337  1047  0.638604  0.813966   0.614982   \n",
       "0    k6         nn  4233  2657  2548  1395  0.625958  0.752132   0.614369   \n",
       "1    k5         nn  4070  2742  2463  1558  0.603065  0.723170   0.597475   \n",
       "2    k4         nn  3849  2465  2740  1779  0.608234  0.683902   0.609598   \n",
       "3    L1         nn  4227  2618  2587  1401  0.629004  0.751066   0.617531   \n",
       "4  Tree         nn  3734  2260  2945  1894  0.616542  0.663468   0.622956   \n",
       "0    k6         ld  4188  2592  2613  1440  0.627804  0.744136   0.617699   \n",
       "1    k5         ld  3868  2469  2736  1760  0.609619  0.687278   0.610383   \n",
       "2    k4         ld  3872  2473  2732  1756  0.609619  0.687989   0.610244   \n",
       "3    L1         ld  4190  2595  2610  1438  0.627712  0.744492   0.617539   \n",
       "4  Tree         ld  3867  2453  2752  1761  0.611003  0.687100   0.611867   \n",
       "0    k6         lr  4171  2571  2634  1457  0.628173  0.741116   0.618659   \n",
       "1    k5         lr  3862  2471  2734  1766  0.608880  0.686212   0.609822   \n",
       "2    k4         lr  3865  2474  2731  1763  0.608880  0.686745   0.609718   \n",
       "3    L1         lr  4172  2574  2631  1456  0.627989  0.741294   0.618441   \n",
       "4  Tree         lr  3870  2458  2747  1758  0.610819  0.687633   0.611568   \n",
       "\n",
       "   F1_Score  \n",
       "0  0.785239  \n",
       "1  0.775912  \n",
       "2  0.774194  \n",
       "3  0.785636  \n",
       "4  0.781151  \n",
       "0  0.676762  \n",
       "1  0.695733  \n",
       "2  0.676210  \n",
       "0  0.684175  \n",
       "1  0.678095  \n",
       "2  0.678095  \n",
       "3  0.684175  \n",
       "4  0.678095  \n",
       "0  0.691403  \n",
       "1  0.701384  \n",
       "2  0.701587  \n",
       "3  0.691058  \n",
       "4  0.700619  \n",
       "0  0.676306  \n",
       "1  0.654341  \n",
       "2  0.644616  \n",
       "3  0.677784  \n",
       "4  0.642574  \n",
       "0  0.675048  \n",
       "1  0.646552  \n",
       "2  0.646789  \n",
       "3  0.675099  \n",
       "4  0.647305  \n",
       "0  0.674373  \n",
       "1  0.645765  \n",
       "2  0.645943  \n",
       "3  0.674317  \n",
       "4  0.647374  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Classifier=[]\n",
    "for i in range(len(Score)):\n",
    "    Classifier.append(clf_name)\n",
    "result={'Model':Model, \\\n",
    "        'Classifier':Classifier,\\\n",
    "        'TP':TP, \\\n",
    "        'FP':FP, \\\n",
    "        'TN':TN,\\\n",
    "        'FN':FN, \\\n",
    "        'Accuracy':Score, \\\n",
    "        'Recall':Recall,\\\n",
    "        'Precision':Precision,\\\n",
    "        'F1_Score':F1_Score\n",
    "       }\n",
    "\n",
    "clf_result = clf_result.append(pd.DataFrame(result))\n",
    "clf_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Selection:\n",
      "Score of k6 = 0.60 (+/- 0.14)\n",
      "Score of k5 = 0.58 (+/- 0.11)\n",
      "Score of k4 = 0.58 (+/- 0.10)\n",
      "Score of L1 = 0.60 (+/- 0.13)\n",
      "Score of Tree = 0.58 (+/- 0.09)\n"
     ]
    }
   ],
   "source": [
    "clf_name='sgd'\n",
    "parent_dir = \"/Volumes/BECCA/Project/Data/Result/Russia/By_model\"\n",
    "path = os.path.join(parent_dir, clf_name)\n",
    "os.mkdir(path) \n",
    "os.mkdir(path+'/PR_plot') \n",
    "\n",
    "k6=np.array(data)[:,1:7].astype('float')\n",
    "k5=np.array(data)[:,2:7].astype('float')\n",
    "k4=np.array(data[['subjectivity_number','day','hour','minute']]).astype('float')\n",
    "k3=np.array(data)[:,4:7].astype('float')\n",
    "k2=np.array(data)[:,4:6].astype('float')\n",
    "L1=np.array(data[['polarity_number','subjectivity_number','day','hour','minute']]).astype('float')\n",
    "Tree=np.array(data[['subjectivity_number','day','hour']]).astype('float')\n",
    "y=np.array(data)[:,7].astype('int')\n",
    "\n",
    "clf = SGDClassifier(loss=\"hinge\", penalty=\"l2\", max_iter=1000)\n",
    "\n",
    "scores_1 = cross_val_score(clf, k6, y, cv=10)\n",
    "scores_2 = cross_val_score(clf, k5, y, cv=10)\n",
    "scores_3 = cross_val_score(clf, k4, y, cv=10)\n",
    "scores_4 = cross_val_score(clf, k3, y, cv=10)\n",
    "scores_5 = cross_val_score(clf, k2, y, cv=10)\n",
    "scores_6 = cross_val_score(clf, L1, y, cv=10)\n",
    "scores_7 = cross_val_score(clf, Tree, y, cv=10)\n",
    "\n",
    "scores=[scores_1,scores_2,scores_3,scores_4,scores_5,scores_6,scores_7]\n",
    "\n",
    "print('Model Selection:')\n",
    "mean_score = 0\n",
    "std_score = 0\n",
    "for i in scores:\n",
    "    mean_score += i.mean()\n",
    "    std_score += i.std()\n",
    "mean_score = mean_score/len(scores)\n",
    "\n",
    "for i,j in zip(['k6','k5','k4','k3','k2','L1','Tree'], scores):    \n",
    "    if j.mean() > mean_score:\n",
    "        print('Score of %s = %0.2f (+/- %0.2f)' %(i, j.mean(), j.std()*2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Result collection\n",
    "Model=[]\n",
    "Score=[]\n",
    "TP=[]\n",
    "FP=[] \n",
    "FN=[]\n",
    "TN=[]\n",
    "Recall=[]\n",
    "Precision=[]\n",
    "F1_Score=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v1dDp7KFkt7/rvA8QWysi4G1yu7vItjYCY0yIBHpHcBnQXlVL021nKtDcb74ZBat+bgCGAqjq9yISCcQC+0pxnIAEuzE2O0dZv/soYdWEZfddFPB2W/afAGD7wTRa2/sExpgQCLSNIBkIL+W+lwJtRaSliETgNAbPyFdmOzAYQEQ6ApHAfoIsGCmh9d9mAvC7PqWrumpQ07l7+CRpZ5nHZIwxgQg0EaQBSSLyvIg8lftT3AaqmgWMA2YD63GeDlorIg+KyAi32B3ATSKyEpgGXKtB+uoezKeGjqRl+qZvv6hdqbYd2bMpAE3qRpVpTIfTMkiY8DkJEz63JGOMKVagVUMzKPhtvkSqOhOYmW/ZRL/pdcA5pd3vaQlizdAtby4HIDK8GvVqBt4+AFA70rnRenbuFu4e2uG0jn8yM5tTWTnUiXL2dfD4Kc5++Gvf+vHTk4ivH033ZnWpZl1wG2PyCSgRqOprbvVO7tfdjaqaWdw2XrIy9TAAqycV/RZxILJztNRjJew/dorek78udN3dQ9vz6KyNAIx8dhEAz17Vk+FdzzqjOI0xVUtAVUMiMgj4CecFsWeBTSIyMIhxlTn/8QjKsmZo64ETpGVkM7BdQ8LDzuy1jDveTSpV+czsnCKTwG97NePWQW3Y+PDQPMtvfetH+v6j8G2MMd4U6JXrMWCIqp6nqgOBi4EnghdW5fHsnM0A/K5P8xJKFu3WQa0B+DhpV0DtF7sOpzNz9W7a/v0LAHq1qMfkkV2IrVWDq/rG88q1vXn0iu4A1KgeRsqUS/jgj78i92Zj71Ebs9kY84tA2wjCVXVj7oyqbhKR0j5FFFL+11ctowaDtIws3lueCsDQLqdf3XL30A48O3cL4FQPVQ8runrorR+28feP1vjmq1cT3v1Df6pVE67q26LI7c5uUY/kRy5h8GNz2bL/BOkZ2URF2GA4xpjA7wiWichLIjLI/XkRWB7MwMpankRQRlVDye47AL9qHdhbxMXJ7X9udzF9Dk38ZE2eJACw8eFhpWoAvqhTYwBWbD9U+iCNMVVSoIngj8Ba4C/AeJw3jG8JVlDBVlZNBHM2OO+9jR/c9oz39eToHgCcysrOs3z97qO+x0Bf/34bAPf/phMpUy4hZcolpW5cvqCDMzbCP2dtOOOYjTFVQ6BPDZ0CHnd/KqWyqg7y99hXmwDoEV/vjPcV5t4SLPjpAG3iYgCn6mnYkwvylHv1ut4Mcge6OR3tGjlvL69MPXLa+zDGVC3F3hGIyLvuv6vd8QLy/JRPiGWjrKuGtuw/7puOqH7mnbg2ruN0Pvfd5oOA89Jbp4mz85R57MruZ5QEAOpGl+49B2NM1VfSHcF4999fBzuQ8lQWdwfvLHU6Vn352l5nvC+Anu5dxY6f0wDo8dBXAMTUqM7qB87s/YT8rj+nJS9/t5WTmdlEhluDsTFeV+xXWVXd7U4eAHao6jagBtCdII4dEAxlXTH0wvxk4JfxiM9U7mhlG/ceY3HyQQ673VYsva/QnrnPSG7X10dP2juBxpjAG4vnA5HumATfANcBrwYrqGAoy6qh3G/tkHe4yTNVq4Zzgzb6hcUAfHPHeUH5xl7Drcpau/Nome/bGFP5BJoIRFXTgMuB/6jqSJzhJz0ptxO3x67sXqb7bR33SzfUE4Z1CFq31J2bOIPC/fubn4Kyf2NM5RJwInAHjrkK+NxdFvDoZhXD6d8GbD1wIs8IYv/60nla6LIeTc84Kn/+bxX/YWCrMt23v54tnPaIlTsO0+6+L4J2HGNM5RDoxfyvwL3AR25X0q2AOcELq+wdPJHhmy5NN9TvLN3OPR+spmFMDV68pheXPfOdb11pn+EvyYxx57L7SDr1a0aUaZVTfuFh1WhSJ5JdR06SkZXDT3uP0bZRTNCOZ4yp2AK6I1DVeao6QlX/6c4nq+pfghta2fLvEK40bQT3fLAacHr59E8CHRoH58J5Vp0oalQP/pM8i+4dzN+GO91eX/REcSOOGmOqupLeI/i3+++nIjIj/0/5hBg6xd05zPprpep8tVA3D2ztm15tL5gZ41klVQ294f77r2AHUp4CvSGYvXZvocs3PTys7IIJsSdHJzJ+ehIvf7eVJ0YlhjocY0wIFJsIVDW3Y7llQLqq5gCISBjO+wSVUqBVQ7kjj/VJqM9V/eIZ0qlxleux89LEpoyfnsRHK3bSuE4kV/drUebDZhpjKrZAnxr6Boj2m48CKu3oJjsPp5VYxv8poddv6MOliU2rXBLI779zt/CrKd+SlpEV6lCMMeUo0EQQqaq+znXc6ehiyldo7y5LLbHMV+t+qRaq6t0wrJo0hIQGv/w68/dxZIyp2gJNBCdEpGfujIicDaQHJ6SKIbda6MKOjUIcSfDVjgxn7l3n5xnWMmHC52Rm54QwKmNMeQk0EfwVeE9EFojIAuAdYFzwwgqCUr5P1iehPgD//X3PEkpWHTWqh3FpYhPffNu/f0HChM+598PVIYzKGBNsgb5HsBTogDNAza1AR7+G5Eonvn7JtVp7j52kXnT4GQ9IX9k8OboHn/zpnDzLpi3ZzvQl20MUkTEm2AK6yolINHAPMF5VVwMJIlJpu6bOHaWrKFnZOWw7mMbRk95sNO3evK5vBLRcEz5cTXpGdjFbGWMqq0C/7r4CZAD93flU4OGgRFQBfJzk9LDt/+SQV827a5BvuuPEWWRZu4ExVU6giaC1qj4KZAKoajoQvM5wQuzO91YCMO78NiGOJPRaNKjJ6klDfPNt/v5FgXGVC1Oa/pyMMaEVaKdzGSIShdvkKiKtgVNBiyrISrpIhVUTsnOU689tWU4RVWwxkeGsf3AoHSfOAqD9fbP471U9mbNxH1f1bUH35nV9Zd9cvI37Pl5TYB9DOzfmuavPDuh4x05mMuHD1fRoXpc5G/fx3eaDtI2rxT8u70pvtxHfGFN2Ak0E9wOzgOYi8hZwDnBtsIIKtfj60RxOy6B+TRvfN1dURBhL/jaYPv/4BoA/vvUjENg7GQCz1u4hYcLntGpYk6nX9KJ+zQhemJ9MiwbRjOzRDID3l6fyt49+eULp81W7fdM/7TvOlc99z4aHhhIZHkZ2jnL8VBY/JB/ks1W7GdalMYPax1X5l/6MCYYSE4E4/SFvwBmUph9OldB4VT0Q5NjKlP84xcXdDxxJy2TrgRN0bVon+EFVMnG1I3n9+j5c8/KSIsu0jK3J1LG9aFAzghMZ2dSPjuCZOZt5es5mAJL3n+CCx+bl2Sa3h1d/jWtH8vOJDNo2qsVliU2ZPHM9AB3+d1ahx52x0mnXGdnDeQN88mVdSD5wgoc/W8ecjfsB6Nq0Dn1a1ueui9tX+ZcEjSkNCaQuV0SWq2pg9/VB1qtXL122bFmpt7vyuUUsTTkEwNX9WvDQZV0KLdfu71+QkZ3DhR3jmDq29xnF6gUnM7OZu3EfF3VqXOz4DPuPneLP035kcfLPxe7vnqEd+OOg1gWWqyot751ZYPnwro05t03DPHcSgYgKD2P9Q0NLLmhMFeFex3sVti7QqqHFItLbfZ+gSstwn4qxB4YCExkextAuZ5VYrmFMDabf3L/EckUREbb8YzhQ+IBAv+sbzzfr9/LWD9v5dsM+wBkz4qHLulA3KpzoGtWZ8sUGPnXvHNIzs0mY4Ay2l9i8Lg9f1oUudhdoPCrQRHA+cIuIpAAncKqHVFW7FbeRiAwFngTCgKmqOiXf+ifcfYPTd1GcqtYlyDSA14yr7CNRlVhJI8IN7tiIwcV0CfKfMT34z5gepGdk+xq+AZJ2HObX/1kIQM2IMFbeP4TqHnuR0HhboImg1B3wu11VPwNchPPewVIRmaGq63LLqOptfuX/DPQo7XGC5T+/qzChmDIWFRHme1lu39GTvgZwgBMZ2bT5+xfceG5LbhnUmthalba3dWMCVmwiEJFI4BagDbAaeElVA33dtg+wWVWT3X1NBy4F1hVRfgzO00lBV1SzSG57yWWJTYiOCDRHmsosrnakLymczMz2NUZPXbiVqQu35inbtG4UOw+n89zve3Jhx0Z212CqjJKudq/hvES2AOeuoBMwPsB9NwV2+M2nAn0LKygiLYCWwLdFrL8ZuBkgPj4+wMMXLT2z8BeidvzsdKjarF6l7WHbnIHIcOdOYcfPaQx4dE6B9TsPO38ft7z5Y4F1tw5qzd1DOwQ9RmOCoaRE0ElVuwKIyEtA0c8NFlRYhW5RlfOjgfdVtdArtKq+ALwAzlNDpYjBbx+/TH/4404e/23BYRm3HHCGXGgdV/N0DmGqiOb1o/P0swTOmM4Z2dmkZ+QwccYaTmXm+BIDwLNztzBrzR5eurY3LWPt78dULiUlgszcCVXNcl4pCFgq0Nxvvhmwq4iyo4E/lWbnwbBxzzEA2sbFhDgSU9F0bfbLE0Xf3jEIcKoSRYT3l6dy53srST5wgvP/NddXbt5dg2jRwJKCqfhKSgTdReSoOy1AlDuf+9RQ7WK2XQq0FZGWwE6ci/3v8hcSkfZAPeD70gZf1tbvdk7VvtGZQOR+Mbri7GZc1KkRt7+TxDfuo6sA5/3fXN/0Y1d25/KeTSnllyljykVJg9ef9uuX7h3EOGA2zuOjL6vqWhF5EFimqjPcomOA6VoBein7xO11NNq6KTClVCcqnJeudV5AzM5Rbn59WZ6kcMd7K7nD7czwvHYNuf83nWjVsFZIYjUmv6A+GqOqM4GZ+ZZNzDc/KZgxnA771mbORFg18SUFgFWph7n3w9Ws3eXccc7btD9PNxsXdWrE0fRMrji7GQPbNSQupob9DZpyZc9I5jOsS+NQh2CqmG7N6vL5Xwagqsxeu9c3Hnaur9btBeCHrQW73/j78I70iK9Ll6Z1rH8kEzSWCPKpVsLbq8acLhFhaJfGvieSVJV1u4/SuHYk+4+fInn/CV5blJInIeR2tgcQUb0at5zXmt/3iycuJrLc4zdVl2cSQUkNEEfSnQekGte2/2CmfIgInZs4TyM1qFWDDo1rM7zrL/02rdh+iMXJP/NJ0k427DlGRlYOT33zE0998xPP/K4nl3QruY8nYwLhmURQkv/O3QLApr3HQhyJMY4e8fXoEV/P1xvrkbRMuj/4JQB/evtH/vQ2xNaK4P1bfkWCPelmzoC9I+/6Yo0zCEqtGpYbTcVUJzqclCmXMO+uQTStGwXAgeMZDPrXXKYv2R7i6Exl5rlE0DO+LpHhBU/71+5t9pTLi+1Q1ZiQa9GgJt9NuICUKZfw71HOG/ITPlxNwoTPWb6t+PEejCmM5xJB6qF0TmbmkJNvwIEDxzIAqB1ldwSm8risR1M+HXeub/5//vs9Fz8xv8DftzHF8Vwi2HfsFAAb9uRtC9j28wlia0XY89um0unarA5bHxnOi9c4g09t3HuMVn+byY6f00IcmaksPJcIcu09djLPfI46b4QaUxmJCBd1asSCu88n97vMgEfnkOWOuGdMcTyTCPL3YPFyvr7ml2z9mSZuA5wxlVXz+tFsfeSXnlPb/P0LXluUErqATKXgmUSQ34KfDvimV6ceAfB1AWBMZZf8j+G+hyLun7GWR75YX8IWxss8mwj8vbtsR8mFjKlEqlUTNjw0jJevddoNnp+XzL0frg5xVKaiskQAViVkqqwLOjRi9l8HAjBtyXYOHj8V4ohMRWSJANjqjkyW+x/GmKqkfeMYftfXGeL1L9NXhDgaUxFZIgBqum8Tt29sI5OZqunhS7sA8N3mg7z63dYSShuv8XQiOOkOYr8s5RAx1rWEqcKqVRP+ckEbACZ9uo6pC5JDHJGpSDxz9SvsDYG/fbiax0clUjc6nIjqns6JxgNuH9KeTk3qcMuby3n48/Ws332Mc9s24LvNBzmSnsn4wW3p0rROyTsyVY5nEkFhlm8/BMDRk1lWLWQ8YWiXxtx2YTue+HoTH/yYygc/pvrWfbVuL5seHmZfijzI04kgyh3xadOeY/RsUTfE0RhTPsZf2JabBrbkzcXbOLdNQ86qE8n1ry1lxfbDtLvvC1+5G85tyZ8vaEPd6IgQRmvKg6dT/4Y9x8jJUWrWCCM6wtM50XhMdER1bh7Ymk5NalOvZgQf/vFXDGzXME+ZlxZuJfHBr7hq6uICb+abqsVzV79bB7XmWXcQGnyjsXgAABlUSURBVICVqYc5cDyD+PrRIYzKmNASEV6/vo9v/kh6Jm/9sI1HZ23ku80HaXnvTGJqVOfYqSyu6d+CB92nkEzV4Lk7gsEdG+WZzx2i0jqcM+YXdaLCuXVQG+bfdT7X/iqBXi3qcexUFgCvf7+NhAmfs8JtYzOVn+fuCPJLz3AeIW0TVyvEkRhT8cQ3iGbSiM6++bSMLDpNnA3AyGcXUTuyOm/f1M+eNqrkPJMIiqrinP/Tfne93REYU5LoiOqkTLmEb9bv5ZXvUli4+QC//s9CAPq2rM/tF7WjV0J9wqrZuB6ViWcSQa78485MW+J0OJc7YI0xpmSDOzZicMdGrE49wl/fWcGW/Sf4YevPjHphsa/MFWc34+6L2xNXOzKEkZpAeC4RFKV1Q6saMqa0ujarwzd3DCInR7nzvZWcyMhi9tq9ALy/PJX3l6dSO7I6qyZdHOJITXEsEbguTWwS6hCMqbSqVRMeH5UIOF23LN92iPW7j/Lw5+s5ejKLhAmf079VA96+qa8NB1sBee6poaLYH6cxZSMyPIxz2sRy44BWzLlzkG/598nOY6irUg+HLjhTKE8mgvsu6Zhnfmz/FiGKxJiqrWVsTVKmXMKiCRf4lo14+jt2Hk4PYVQmP08mghsHtGLTw8N88+FhnvwYjCk3TepGkTLlEq79VQIA50z5lmfnbibH3t+pEILaRiAiQ4EngTBgqqpOKaTMb4FJOB2ErlTV3wUjlvx/bv5Pt23efzwYhzTG5HP/bzrx9g/bycjO4dFZG3l01kbOqhNJRlYOx05l8avWDXhydA/qRIWHOlRPCVoiEJEw4BngIiAVWCoiM1R1nV+ZtsC9wDmqekhE4oIVj++Y7r/V/e4C5m7cH+zDGmNw2uI2TR7Giu2H+PDHnWw9cII1u45QPzqCgycymLtxP90f+JJ+reozqH0ct5zXOtQhe0Iw7wj6AJtVNRlARKYDlwLr/MrcBDyjqocAVHVfEOMpUsvYmqE4rDGe1SO+Hj3i6+VZdiQ9k/s+XsPhtAwW/HSAxck/M+WLDVzTvwWX92xG5ya1rRo3SIKZCJoCO/zmU4G++cq0AxCR73Cqjyap6qz8OxKRm4GbAeLj48s80Gb1bPB6Y0KtTlQ4/xnTA4DDaRk88Ok6Plu1i9e/38br328D4NErunHl2c3sKb8yFsz0WthvKn9VfXWgLTAIGANMFZECAwOo6guq2ktVezVs2DD/6jP2yOVdy3yfxpjTVzc6gidGJbLmgYt5+8a+vi9rd7+/ipb3zmTT3mMhjrBqCWYiSAWa+803A3YVUuYTVc1U1a3ARpzEUK6sYcqYiqlG9TB+1SaWhfdcwI//exEXur0HD3liPpv32UMeZSWYiWAp0FZEWopIBDAamJGvzMfA+QAiEotTVVRuo2p/Ou5cxvSJJybSEoExFV39mhFMHduLkT2aAnDh4/N4b9mOErYygQhaIlDVLGAcMBtYD7yrqmtF5EERGeEWmw0cFJF1wBzgLlU9GKSACizq2qyOVQsZU8k8MSqR/+nZDIC73l/Fuf/81noPPkNBbYJX1Zmq2k5VW6vqZHfZRFWd4U6rqt6uqp1UtauqTg9mPGBdSRhTFTz22+58eOuvAEg9lE7Le2cyb5M9Bn667FksY0yl1DO+HlsfGU7rhs7j32NfXsJt7ySxYvshjrujqZnAWO+jxphKS0T45o5BPDprA8/O3cJHK3by0YqdvvVf3jaQdo1iQhhh5WCJwBhT6d09tAOje8fz4/ZD7D92ilcXpbDzcDpDnphP07pRvHxtb9o3toRQFEsExpgqIb5BNPENogG4aWArVmw/xOgXFrPzcDoX/3s+LWNr8oeBrRje7Sxq25OCeVgbgTGmSuoRX4+NDw9j2k39aNeoFlsPnGDCh6vpNulLPl+1O9ThVSieSQT2cJkx3tS/dQO+vO08Fk24gB7xTscFf3r7R46kZYY4sorDc1VD9vCoMd7UpG4UH916Dv/78RreWLyN7g9+yQUd4mhYqwaX92xKg1oRNKod6ckXTD2XCIwx3vbQZV0Iqya8uiiFbzc4HR6/k+8N5dsvasfNA1sRGR4WihDLnSUCY4znTBrRmUkjOpOekc2K7Yc4cCKD9buP8vy8LeQoPP7VJh7/ahOD2jckR53hbavyY6iWCIwxnhUV4XRqBzCiexPuGdqBA8dPcclTC9h79JRv0Kohm/bz3O97MrTLWaEMN2gsERhjjJ/YWjX44W8X+ubnbNjHda8u5ZY3fwRg6yPDq1xXNZ55asgYY07H+R3i+Oq2gb75q6b+EMJogsMzicA6JzTGnK62jWLYPHkYIrBoy0FGPvtdqEMqU55JBLmq2B2dMaacVA+rxroHhhJbqwYrth9m7a4joQ6pzHguERhjzOmKighj6theAFzy1EJSDpwIcURlwxKBMcaUQmLzuowf7Iyo+/z8chtQMagsERhjTCnddlE7OjepzbQl2xnw6LeVfvwDSwTGGHMa/j0qEYAdP6fT5f7Z3PXeShZtPlAp+zCy9wiMMeY0tG0Uw9ZHhjNl1gaen5fMe8tTeW95qm99h8YxXNajKWP7JxAVUbG7qvDMHYFa/6PGmDImItw7rCPrHxzKtJv6ceug1r51G/YcY8oXG+h0/yze+mEbS7b+TFZ2TgijLZrn7gjE+h81xpSxqIgw+rduQP/WDbh7aAcATpzK4sMVO/nfj9fw94/W+MqGhwkXdmzE07/rSVi1inE9Eq1kb1r16tVLly1blmdZZmYmqampnDx5ssjt9h09SUa2EhdTg4jqnrkRMuUsMjKSZs2aER7uva6MTeH2HTvJ9oNpvLN0B7ExNfhm/V427T0OwO/6xvOPkV3LJQ4RWa6qvQpdVxUSwdatW4mJiaFBgwZF9gHy095jpGdm0yauFtERnrsRMuVAVTl48CDHjh2jZcuWoQ7HVFCqygOfruPVRSm+ZWP6xPOPkV2C2odRcYmgSnw1PnnyZLFJwJjyICI0aNCg2DtTY0SESSM6s3LiEEb2aArAtCXbaXnvTP5v9gayc8r/y3mV+WpsScBUBPZ3aAJVJzqcJ0Yl8o+RXek4cRYAz8zZwjNztnBWnUj2Hj1JjkJUeBj3Du/A1f1aBO3vq8okAmOMqYyiIsJImXIJOTnKSwu3knzgBKcys9m49xg7D6dzOC2TiZ+s5bm5W3hyTA96J9Qv8xiqRNVQRTB58mQ6d+5Mt27dSExM5IcfnK5qs7Ky+Nvf/kbbtm1JTEwkMTGRyZMn+7YLCwsjMTGRzp070717dx5//HFycn55xGzJkiUMHDiQ9u3b06FDB2688UbS0tJ49dVXGTduXJnFP3z4cA4fPgzAU089RceOHbnqqquYMWMGU6ZMOaN97969m1//+td5lo0fP56mTZvmOddXX32Vhg0bkpiYSKdOnXjxxRfP6LjgtB/17duXtm3bMmrUKDIyMgott2rVKvr370/nzp3p2rUrJ0+e5NixY77fWWJiIrGxsfz1r38F4Omnn+aVV1454/iMyVWtmnDTwFY8cnlXHh+VyOd/GUDSxCGse/BiOp1Vm1NZOaQeSgvOwVW1Uv2cffbZmt+6desKLMtv056junLHIU07lVli2dJatGiR9uvXT0+ePKmqqvv379edO3eqquo999yjY8eO1fT0dFVVPXr0qN5///2+bWvWrOmb3rt3rw4ePFgnTpyoqqp79uzR+Ph4XbRokaqq5uTk6Hvvvad79uzRV155Rf/0pz+V+bmoqrZv316Tk5NPa9vMzIKf75133qkff/yxbz47O1ubN2+uffv21Tlz5viW+5/T3r17NTY2Vvfs2XNaceS68sorddq0aaqq+oc//EGfffbZQmPu2rWrJiUlqarqgQMHNCsrq0C5nj176rx581RV9cSJE5qYmFjoMQP5ezSmtHJycjQrO+e0tweWaRHX1SpXNfTAp2tZt+togeXpmdnk5ChREWFUK2U9W6cmtbn/N52LXL97925iY2OpUaMGALGxztB3aWlpvPjii6SkpBAZGQlATEwMkyZNKnQ/cXFxvPDCC/Tu3ZtJkybxzDPPMHbsWPr37w849c9XXHFFge0+/fRTHn74YTIyMmjQoAFvvfUWjRo1Yt68eYwfP9637fz58zl+/DijRo3i6NGjZGVl8d///pcBAwaQkJDAsmXLuO+++0hOTmbEiBFcf/311KtXj2XLlvH000+zf/9+brnlFrZv3w7Av//9b8455xwmTZrErl27SElJITY2lrfffjtPfB988AEPP/ywb37OnDl06dKFUaNGMW3aNAYNGlToZ9G6dWu2bdtGo0aNivzsi6OqfPvtt754xo4dy6RJk/jjH/+Yp9yXX35Jt27d6N69OwANGjQosK+ffvqJffv2MWDAAACio6NJSEhgyZIl9OnT57TiM6Y0RISwIDVBWdVQGRgyZAg7duygXbt23HrrrcybNw+AzZs3Ex8fT0xM4INet2rVipycHPbt28eaNWs4++yzS9zm3HPPZfHixaxYsYLRo0fz6KOPAvCvf/2LZ555hqSkJBYsWEBUVBRvv/02F198MUlJSaxcuZLExMQ8+3ruuedo0qQJc+bM4bbbbsuzbvz48dx2220sXbqUDz74gBtvvNG3bvny5XzyyScFksDWrVupV6+eL0kCTJs2jTFjxjBy5Eg+++wzMjML9s2SnJxMcnIybdq0ybN848aNeapr/H9yq7ZyHTx4kLp161K9uvN9p1mzZuzcubPAsTZt2oSIcPHFF9OzZ0/f5+dv2rRpjBo1Kk9jXa9evViwYEGBssZUNlXujqCob+657xG0jatFVBm/R1CrVi2WL1/OggULmDNnDqNGjWLKlCn07NkzT7lXXnmFJ598koMHD7Jo0SKaN29e6P60lO92pKamMmrUKHbv3k1GRobvGfZzzjmH22+/nauuuorLL7+cZs2a0bt3b66//noyMzO57LLLCiSC4nz99desW7fON3/06FGOHTsGwIgRI4iKiiqwze7du2nYsKFvPiMjg5kzZ/LEE08QExND3759+fLLL7nkkksAeOedd1i4cCE1atTg+eefp379vA1j7du3JykpKaB4C/scC3vqIisri4ULF7J06VKio6MZPHgwZ599NoMHD/aVmT59Om+88Uae7eLi4tiwYUNAsRhTkQX1jkBEhorIRhHZLCITCll/rYjsF5Ek9+fGwvZTGYSFhTFo0CAeeOABnn76aT744APatGnD9u3bfRfL6667jqSkJOrUqUN2dnah+0lOTiYsLIy4uDg6d+7M8uXLSzz2n//8Z8aNG8fq1at5/vnnfc+xT5gwgalTp5Kenk6/fv3YsGEDAwcOZP78+TRt2pSrr76a119/PeBzzMnJ4fvvvycpKYmkpCR27tzpu9upWbNmodtERUXlea5+1qxZHDlyhK5du5KQkMDChQuZNm2ab/2oUaNISkrihx9+YOTIkQX2V5o7gtjYWA4fPkxWltNFcGpqKk2aNCmwz2bNmnHeeecRGxtLdHQ0w4cP58cff/StX7lyJVlZWQXuzk6ePFlo8jOmsglaIhCRMOAZYBjQCRgjIp0KKfqOqia6P1ODFU8wbdy4kZ9++sk3n5SURIsWLYiOjuaGG25g3LhxvothdnZ2kU+u5NbBjxs3DhFh3LhxvPbaa74nkADefPNN9uzZk2e7I0eO0LSp82LKa6+95lu+ZcsWunbtyj333EOvXr3YsGED27ZtIy4ujptuuokbbrghzwWvJEOGDOHpp5/Oc54ladeuHSkpKb75adOmMXXqVFJSUkhJSWHr1q18+eWXpKUF9jRE7h1BYT9169bNU1ZEOP/883n//fcB57O59NJLC+zz4osvZtWqVaSlpZGVlcW8efPo1OmXP9Xcqqz8Nm3aRJcuXQKK25iKLJh3BH2AzaqarKoZwHSg4P/CchLMF32OHz/O2LFj6dSpE926dWPdunW+BuHJkydz1lln0aVLF3r06MGAAQMYO3as75tpenq67/HRCy+8kCFDhnD//fcD0KhRI6ZPn86dd95J+/bt6dixIwsWLKB27dp5jj9p0iSuvPJKBgwY4GuoBqcxt0uXLnTv3p2oqCiGDRvG3LlzSUxMpEePHnzwwQe+xuRAPPXUUyxbtoxu3brRqVMnnnvuuRK3qVmzJq1bt2bz5s2kpaUxe/ZsXzVQ7vpzzz2XTz/9NOA4SuOf//wnjz/+OG3atOHgwYPccMMNAMyYMYOJEycCUK9ePW6//XZ69+5NYmIiPXv2zBPju+++W2gi+O6777jwwguDErcx5SlofQ2JyBXAUFW90Z2/GuirquP8ylwLPALsBzYBt6nqjkL2dTNwM0B8fPzZ27Zty7N+/fr1dOzYsdh4MrJyOJSWQVxMDXv7s5x99NFHLF++PM+TQ5XdihUrePzxxwu0G0Bgf4/GlLdQ9TVU2NU2f9b5FEhQ1W7A18BrBTcBVX1BVXupai//hsfSiKhejUa1Iy0JhMDIkSNJSEgIdRhl6sCBAzz00EOhDsOYMhHMp4ZSAf/HYpoBu/wLqOpBv9kXgX8GMR4TQv6PmlYFF110UahDMKbMBPOOYCnQVkRaikgEMBqY4V9ARM7ymx0BrD/dgwWrisuY0rC/Q1MZBe2OQFWzRGQcMBsIA15W1bUi8iDOq84zgL+IyAggC/gZuPZ0jhUZGcnBgwetK2oTUuqOR5D7FrkxlUWVGJgmkBHKjCkPNkKZqaiKayyuEm8Wh4eH24hQxhhzmqyvIWOM8ThLBMYY43GWCIwxxuMqXWOxiOwHtpVYsHCxwIEyDKcysHP2BjtnbziTc26hqoW+kVvpEsGZEJFlRbWaV1V2zt5g5+wNwTpnqxoyxhiPs0RgjDEe57VE8EKoAwgBO2dvsHP2hqCcs6faCIwxxhTktTsCY4wx+VgiMMYYj6uSiUBEhorIRhHZLCITCllfQ0Tecdf/ICIJ5R9l2QrgnG8XkXUiskpEvhGRFqGIsyyVdM5+5a4QERWRSv+oYSDnLCK/dX/Xa0Xk7fKOsawF8LcdLyJzRGSF+/c9PBRxlhUReVlE9onImiLWi4g85X4eq0Sk5xkfVFWr1A9Ol9dbgFZABLAS6JSvzK3Ac+70aOCdUMddDud8PhDtTv/RC+fslosB5gOLgV6hjrscfs9tgRVAPXc+LtRxl8M5vwD80Z3uBKSEOu4zPOeBQE9gTRHrhwNf4IwC2Q/44UyPWRXvCPoAm1U1WVUzgOnApfnKXMovw2K+DwyWyj2QQYnnrKpzVDXNnV2MM2JcZRbI7xngIeBRoCr0UR7IOd8EPKOqhwBUdV85x1jWAjlnBWq703XINxJiZaOq83HGZynKpcDr6lgM1M03yFepVcVE0BTY4Tef6i4rtIyqZgFHgAblEl1wBHLO/m7A+UZRmZV4ziLSA2iuqp+VZ2BBFMjvuR3QTkS+E5HFIjK03KILjkDOeRLwexFJBWYCfy6f0EKmtP/fS1QlxiPIp7Bv9vmfkQ2kTGUS8PmIyO+BXsB5QY0o+Io9ZxGpBjzBaY56V0EF8nuujlM9NAjnrm+BiHRR1cNBji1YAjnnMcCrqvqYiPQH3nDPOSf44YVEmV+/quIdQSrQ3G++GQVvFX1lRKQ6zu1kcbdiFV0g54yIXAj8HRihqqfKKbZgKemcY4AuwFwRScGpS51RyRuMA/3b/kRVM1V1K7ARJzFUVoGc8w3AuwCq+j0QidM5W1UV0P/30qiKiWAp0FZEWopIBE5j8Ix8ZWYAY93pK4Bv1W2FqaRKPGe3muR5nCRQ2euNoYRzVtUjqhqrqgmqmoDTLjJCVZcVvrtKIZC/7Y9xHgxARGJxqoqSyzXKshXIOW8HBgOISEecRLC/XKMsXzOAa9ynh/oBR1R195nssMpVDalqloiMA2bjPHHwsqquFZEHgWWqOgN4Cef2cTPOncDo0EV85gI85/8DagHvue3i21V1RMiCPkMBnnOVEuA5zwaGiMg6IBu4S1UPhi7qMxPgOd8BvCgit+FUkVxbmb/Yicg0nKq9WLfd434gHEBVn8NpBxkObAbSgOvO+JiV+PMyxhhTBqpi1ZAxxphSsERgjDEeZ4nAGGM8zhKBMcZ4nCUCY4zxOEsExuQjItkikiQia0TkUxGpW8b7v1ZEnnanJ4nInWW5f2NKyxKBMQWlq2qiqnbBec/kT6EOyJhgskRgTPG+x69DLxG5S0SWuv3AP+C3/Bp32UoRecNd9ht3vIsVIvK1iDQKQfzGlKjKvVlsTFkRkTCcrgtecueH4PTb0wen468ZIjIQOIjTh9M5qnpAROq7u1gI9FNVFZEbgbtx3oI1pkKxRGBMQVEikgQkAMuBr9zlQ9yfFe58LZzE0B14X1UPAKhqbgeGzYB33L7iI4Ct5RK9MaVkVUPGFJSuqolAC5wLeG4bgQCPuO0HiaraRlVfcpcX1lfLf4CnVbUr8AecztCMqXAsERhTBFU9AvwFuFNEwnE6PrteRGoBiEhTEYkDvgF+KyIN3OW5VUN1gJ3u9FiMqaCsasiYYqjqChFZCYxW1Tfcbo6/d3twPQ783u0NczIwT0SycaqOrsUZOes9EdmJ0w12y1CcgzElsd5HjTHG46xqyBhjPM4SgTHGeJwlAmOM8ThLBMYY43GWCIwxxuMsERhjjMdZIjDGGI/7f4kaSXC6gmLYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "name = ['k6','k5','k4','L1','Tree']\n",
    "model = [k6, k5, k4, L1, Tree]\n",
    "\n",
    "for X, md_name in zip(model, name):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "    clf=clf.fit(X_train, y_train)\n",
    "    y_pred=clf.predict(X_test)\n",
    "    \n",
    "    #Confusion metric & relative indexs\n",
    "    tp=0\n",
    "    fp=0\n",
    "    fn=0\n",
    "    tn=0\n",
    "\n",
    "    for i in range(len(y_test)):\n",
    "        if y_test[i] == 1:\n",
    "            if y_pred[i] == 1:\n",
    "                tp+=1\n",
    "            elif y_pred[i] == 0:\n",
    "                fn+=1\n",
    "        elif y_test[i] == 0:\n",
    "            if y_pred[i] == 0:\n",
    "                tn+=1\n",
    "            elif y_pred[i] == 1:\n",
    "                fp+=1\n",
    "    \n",
    "    recall=tp/(tp+fn)\n",
    "    precision=tp/(tp+fp)\n",
    "    accuracy=(tp+tn)/len(y_test)\n",
    "    f1_score=2*(recall*precision)/(recall+precision)\n",
    "    \n",
    "    #Result collection\n",
    "    Model.append(md_name)\n",
    "    Score.append(accuracy)\n",
    "    TP.append(tp)\n",
    "    FP.append(fp)\n",
    "    FN.append(fn)\n",
    "    TN.append(tn)\n",
    "    Recall.append(recall)\n",
    "    Precision.append(precision)\n",
    "    F1_Score.append(f1_score)\n",
    "    \n",
    "    #PR curve\n",
    "    disp = plot_precision_recall_curve(clf, X_test, y_test)\n",
    "    disp.ax_.set_title('2-class Precision-Recall curve')\n",
    "    plt.savefig('/Volumes/BECCA/Project/Data/Result/Russia/By_model/%s/PR_plot/PR_%s.png' %(clf_name, md_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Model</th>\n",
       "      <th>Classifier</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4309</td>\n",
       "      <td>1038</td>\n",
       "      <td>4167</td>\n",
       "      <td>1319</td>\n",
       "      <td>0.782424</td>\n",
       "      <td>0.765636</td>\n",
       "      <td>0.805872</td>\n",
       "      <td>0.785239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4297</td>\n",
       "      <td>1151</td>\n",
       "      <td>4054</td>\n",
       "      <td>1331</td>\n",
       "      <td>0.770885</td>\n",
       "      <td>0.763504</td>\n",
       "      <td>0.788730</td>\n",
       "      <td>0.775912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4284</td>\n",
       "      <td>1155</td>\n",
       "      <td>4050</td>\n",
       "      <td>1344</td>\n",
       "      <td>0.769316</td>\n",
       "      <td>0.761194</td>\n",
       "      <td>0.787645</td>\n",
       "      <td>0.774194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L1</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4321</td>\n",
       "      <td>1051</td>\n",
       "      <td>4154</td>\n",
       "      <td>1307</td>\n",
       "      <td>0.782332</td>\n",
       "      <td>0.767768</td>\n",
       "      <td>0.804356</td>\n",
       "      <td>0.785636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tree</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4426</td>\n",
       "      <td>1278</td>\n",
       "      <td>3927</td>\n",
       "      <td>1202</td>\n",
       "      <td>0.771070</td>\n",
       "      <td>0.786425</td>\n",
       "      <td>0.775947</td>\n",
       "      <td>0.781151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k4</td>\n",
       "      <td>gnb</td>\n",
       "      <td>3888</td>\n",
       "      <td>1974</td>\n",
       "      <td>3231</td>\n",
       "      <td>1740</td>\n",
       "      <td>0.657159</td>\n",
       "      <td>0.690832</td>\n",
       "      <td>0.663255</td>\n",
       "      <td>0.676762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L1</td>\n",
       "      <td>gnb</td>\n",
       "      <td>4133</td>\n",
       "      <td>2120</td>\n",
       "      <td>3085</td>\n",
       "      <td>1495</td>\n",
       "      <td>0.666297</td>\n",
       "      <td>0.734364</td>\n",
       "      <td>0.660963</td>\n",
       "      <td>0.695733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tree</td>\n",
       "      <td>gnb</td>\n",
       "      <td>3897</td>\n",
       "      <td>2001</td>\n",
       "      <td>3204</td>\n",
       "      <td>1731</td>\n",
       "      <td>0.655497</td>\n",
       "      <td>0.692431</td>\n",
       "      <td>0.660732</td>\n",
       "      <td>0.676210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3556</td>\n",
       "      <td>1211</td>\n",
       "      <td>3994</td>\n",
       "      <td>2072</td>\n",
       "      <td>0.696945</td>\n",
       "      <td>0.631841</td>\n",
       "      <td>0.745962</td>\n",
       "      <td>0.684175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3303</td>\n",
       "      <td>811</td>\n",
       "      <td>4394</td>\n",
       "      <td>2325</td>\n",
       "      <td>0.710514</td>\n",
       "      <td>0.586887</td>\n",
       "      <td>0.802868</td>\n",
       "      <td>0.678095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3303</td>\n",
       "      <td>811</td>\n",
       "      <td>4394</td>\n",
       "      <td>2325</td>\n",
       "      <td>0.710514</td>\n",
       "      <td>0.586887</td>\n",
       "      <td>0.802868</td>\n",
       "      <td>0.678095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L1</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3556</td>\n",
       "      <td>1211</td>\n",
       "      <td>3994</td>\n",
       "      <td>2072</td>\n",
       "      <td>0.696945</td>\n",
       "      <td>0.631841</td>\n",
       "      <td>0.745962</td>\n",
       "      <td>0.684175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tree</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3303</td>\n",
       "      <td>811</td>\n",
       "      <td>4394</td>\n",
       "      <td>2325</td>\n",
       "      <td>0.710514</td>\n",
       "      <td>0.586887</td>\n",
       "      <td>0.802868</td>\n",
       "      <td>0.678095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>SVM</td>\n",
       "      <td>3792</td>\n",
       "      <td>1549</td>\n",
       "      <td>3656</td>\n",
       "      <td>1836</td>\n",
       "      <td>0.687529</td>\n",
       "      <td>0.673774</td>\n",
       "      <td>0.709979</td>\n",
       "      <td>0.691403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4485</td>\n",
       "      <td>2676</td>\n",
       "      <td>2529</td>\n",
       "      <td>1143</td>\n",
       "      <td>0.647466</td>\n",
       "      <td>0.796908</td>\n",
       "      <td>0.626309</td>\n",
       "      <td>0.701384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4487</td>\n",
       "      <td>2676</td>\n",
       "      <td>2529</td>\n",
       "      <td>1141</td>\n",
       "      <td>0.647651</td>\n",
       "      <td>0.797264</td>\n",
       "      <td>0.626414</td>\n",
       "      <td>0.701587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L1</td>\n",
       "      <td>SVM</td>\n",
       "      <td>3787</td>\n",
       "      <td>1545</td>\n",
       "      <td>3660</td>\n",
       "      <td>1841</td>\n",
       "      <td>0.687437</td>\n",
       "      <td>0.672886</td>\n",
       "      <td>0.710240</td>\n",
       "      <td>0.691058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tree</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4581</td>\n",
       "      <td>2868</td>\n",
       "      <td>2337</td>\n",
       "      <td>1047</td>\n",
       "      <td>0.638604</td>\n",
       "      <td>0.813966</td>\n",
       "      <td>0.614982</td>\n",
       "      <td>0.700619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>nn</td>\n",
       "      <td>4233</td>\n",
       "      <td>2657</td>\n",
       "      <td>2548</td>\n",
       "      <td>1395</td>\n",
       "      <td>0.625958</td>\n",
       "      <td>0.752132</td>\n",
       "      <td>0.614369</td>\n",
       "      <td>0.676306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>nn</td>\n",
       "      <td>4070</td>\n",
       "      <td>2742</td>\n",
       "      <td>2463</td>\n",
       "      <td>1558</td>\n",
       "      <td>0.603065</td>\n",
       "      <td>0.723170</td>\n",
       "      <td>0.597475</td>\n",
       "      <td>0.654341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>nn</td>\n",
       "      <td>3849</td>\n",
       "      <td>2465</td>\n",
       "      <td>2740</td>\n",
       "      <td>1779</td>\n",
       "      <td>0.608234</td>\n",
       "      <td>0.683902</td>\n",
       "      <td>0.609598</td>\n",
       "      <td>0.644616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L1</td>\n",
       "      <td>nn</td>\n",
       "      <td>4227</td>\n",
       "      <td>2618</td>\n",
       "      <td>2587</td>\n",
       "      <td>1401</td>\n",
       "      <td>0.629004</td>\n",
       "      <td>0.751066</td>\n",
       "      <td>0.617531</td>\n",
       "      <td>0.677784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tree</td>\n",
       "      <td>nn</td>\n",
       "      <td>3734</td>\n",
       "      <td>2260</td>\n",
       "      <td>2945</td>\n",
       "      <td>1894</td>\n",
       "      <td>0.616542</td>\n",
       "      <td>0.663468</td>\n",
       "      <td>0.622956</td>\n",
       "      <td>0.642574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>ld</td>\n",
       "      <td>4188</td>\n",
       "      <td>2592</td>\n",
       "      <td>2613</td>\n",
       "      <td>1440</td>\n",
       "      <td>0.627804</td>\n",
       "      <td>0.744136</td>\n",
       "      <td>0.617699</td>\n",
       "      <td>0.675048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>ld</td>\n",
       "      <td>3868</td>\n",
       "      <td>2469</td>\n",
       "      <td>2736</td>\n",
       "      <td>1760</td>\n",
       "      <td>0.609619</td>\n",
       "      <td>0.687278</td>\n",
       "      <td>0.610383</td>\n",
       "      <td>0.646552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>ld</td>\n",
       "      <td>3872</td>\n",
       "      <td>2473</td>\n",
       "      <td>2732</td>\n",
       "      <td>1756</td>\n",
       "      <td>0.609619</td>\n",
       "      <td>0.687989</td>\n",
       "      <td>0.610244</td>\n",
       "      <td>0.646789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L1</td>\n",
       "      <td>ld</td>\n",
       "      <td>4190</td>\n",
       "      <td>2595</td>\n",
       "      <td>2610</td>\n",
       "      <td>1438</td>\n",
       "      <td>0.627712</td>\n",
       "      <td>0.744492</td>\n",
       "      <td>0.617539</td>\n",
       "      <td>0.675099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tree</td>\n",
       "      <td>ld</td>\n",
       "      <td>3867</td>\n",
       "      <td>2453</td>\n",
       "      <td>2752</td>\n",
       "      <td>1761</td>\n",
       "      <td>0.611003</td>\n",
       "      <td>0.687100</td>\n",
       "      <td>0.611867</td>\n",
       "      <td>0.647305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>lr</td>\n",
       "      <td>4171</td>\n",
       "      <td>2571</td>\n",
       "      <td>2634</td>\n",
       "      <td>1457</td>\n",
       "      <td>0.628173</td>\n",
       "      <td>0.741116</td>\n",
       "      <td>0.618659</td>\n",
       "      <td>0.674373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>lr</td>\n",
       "      <td>3862</td>\n",
       "      <td>2471</td>\n",
       "      <td>2734</td>\n",
       "      <td>1766</td>\n",
       "      <td>0.608880</td>\n",
       "      <td>0.686212</td>\n",
       "      <td>0.609822</td>\n",
       "      <td>0.645765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>lr</td>\n",
       "      <td>3865</td>\n",
       "      <td>2474</td>\n",
       "      <td>2731</td>\n",
       "      <td>1763</td>\n",
       "      <td>0.608880</td>\n",
       "      <td>0.686745</td>\n",
       "      <td>0.609718</td>\n",
       "      <td>0.645943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L1</td>\n",
       "      <td>lr</td>\n",
       "      <td>4172</td>\n",
       "      <td>2574</td>\n",
       "      <td>2631</td>\n",
       "      <td>1456</td>\n",
       "      <td>0.627989</td>\n",
       "      <td>0.741294</td>\n",
       "      <td>0.618441</td>\n",
       "      <td>0.674317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tree</td>\n",
       "      <td>lr</td>\n",
       "      <td>3870</td>\n",
       "      <td>2458</td>\n",
       "      <td>2747</td>\n",
       "      <td>1758</td>\n",
       "      <td>0.610819</td>\n",
       "      <td>0.687633</td>\n",
       "      <td>0.611568</td>\n",
       "      <td>0.647374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>sgd</td>\n",
       "      <td>4735</td>\n",
       "      <td>3499</td>\n",
       "      <td>1706</td>\n",
       "      <td>893</td>\n",
       "      <td>0.594572</td>\n",
       "      <td>0.841329</td>\n",
       "      <td>0.575055</td>\n",
       "      <td>0.683163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>sgd</td>\n",
       "      <td>3561</td>\n",
       "      <td>2248</td>\n",
       "      <td>2957</td>\n",
       "      <td>2067</td>\n",
       "      <td>0.601680</td>\n",
       "      <td>0.632729</td>\n",
       "      <td>0.613014</td>\n",
       "      <td>0.622716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>sgd</td>\n",
       "      <td>3679</td>\n",
       "      <td>2359</td>\n",
       "      <td>2846</td>\n",
       "      <td>1949</td>\n",
       "      <td>0.602326</td>\n",
       "      <td>0.653696</td>\n",
       "      <td>0.609308</td>\n",
       "      <td>0.630722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L1</td>\n",
       "      <td>sgd</td>\n",
       "      <td>4567</td>\n",
       "      <td>3123</td>\n",
       "      <td>2082</td>\n",
       "      <td>1061</td>\n",
       "      <td>0.613773</td>\n",
       "      <td>0.811478</td>\n",
       "      <td>0.593888</td>\n",
       "      <td>0.685839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tree</td>\n",
       "      <td>sgd</td>\n",
       "      <td>4048</td>\n",
       "      <td>2980</td>\n",
       "      <td>2225</td>\n",
       "      <td>1580</td>\n",
       "      <td>0.579064</td>\n",
       "      <td>0.719261</td>\n",
       "      <td>0.575982</td>\n",
       "      <td>0.639697</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model Classifier    TP    FP    TN    FN  Accuracy    Recall  Precision  \\\n",
       "0    k6        kNN  4309  1038  4167  1319  0.782424  0.765636   0.805872   \n",
       "1    k5        kNN  4297  1151  4054  1331  0.770885  0.763504   0.788730   \n",
       "2    k4        kNN  4284  1155  4050  1344  0.769316  0.761194   0.787645   \n",
       "3    L1        kNN  4321  1051  4154  1307  0.782332  0.767768   0.804356   \n",
       "4  Tree        kNN  4426  1278  3927  1202  0.771070  0.786425   0.775947   \n",
       "0    k4        gnb  3888  1974  3231  1740  0.657159  0.690832   0.663255   \n",
       "1    L1        gnb  4133  2120  3085  1495  0.666297  0.734364   0.660963   \n",
       "2  Tree        gnb  3897  2001  3204  1731  0.655497  0.692431   0.660732   \n",
       "0    k6      dtree  3556  1211  3994  2072  0.696945  0.631841   0.745962   \n",
       "1    k5      dtree  3303   811  4394  2325  0.710514  0.586887   0.802868   \n",
       "2    k4      dtree  3303   811  4394  2325  0.710514  0.586887   0.802868   \n",
       "3    L1      dtree  3556  1211  3994  2072  0.696945  0.631841   0.745962   \n",
       "4  Tree      dtree  3303   811  4394  2325  0.710514  0.586887   0.802868   \n",
       "0    k6        SVM  3792  1549  3656  1836  0.687529  0.673774   0.709979   \n",
       "1    k5        SVM  4485  2676  2529  1143  0.647466  0.796908   0.626309   \n",
       "2    k4        SVM  4487  2676  2529  1141  0.647651  0.797264   0.626414   \n",
       "3    L1        SVM  3787  1545  3660  1841  0.687437  0.672886   0.710240   \n",
       "4  Tree        SVM  4581  2868  2337  1047  0.638604  0.813966   0.614982   \n",
       "0    k6         nn  4233  2657  2548  1395  0.625958  0.752132   0.614369   \n",
       "1    k5         nn  4070  2742  2463  1558  0.603065  0.723170   0.597475   \n",
       "2    k4         nn  3849  2465  2740  1779  0.608234  0.683902   0.609598   \n",
       "3    L1         nn  4227  2618  2587  1401  0.629004  0.751066   0.617531   \n",
       "4  Tree         nn  3734  2260  2945  1894  0.616542  0.663468   0.622956   \n",
       "0    k6         ld  4188  2592  2613  1440  0.627804  0.744136   0.617699   \n",
       "1    k5         ld  3868  2469  2736  1760  0.609619  0.687278   0.610383   \n",
       "2    k4         ld  3872  2473  2732  1756  0.609619  0.687989   0.610244   \n",
       "3    L1         ld  4190  2595  2610  1438  0.627712  0.744492   0.617539   \n",
       "4  Tree         ld  3867  2453  2752  1761  0.611003  0.687100   0.611867   \n",
       "0    k6         lr  4171  2571  2634  1457  0.628173  0.741116   0.618659   \n",
       "1    k5         lr  3862  2471  2734  1766  0.608880  0.686212   0.609822   \n",
       "2    k4         lr  3865  2474  2731  1763  0.608880  0.686745   0.609718   \n",
       "3    L1         lr  4172  2574  2631  1456  0.627989  0.741294   0.618441   \n",
       "4  Tree         lr  3870  2458  2747  1758  0.610819  0.687633   0.611568   \n",
       "0    k6        sgd  4735  3499  1706   893  0.594572  0.841329   0.575055   \n",
       "1    k5        sgd  3561  2248  2957  2067  0.601680  0.632729   0.613014   \n",
       "2    k4        sgd  3679  2359  2846  1949  0.602326  0.653696   0.609308   \n",
       "3    L1        sgd  4567  3123  2082  1061  0.613773  0.811478   0.593888   \n",
       "4  Tree        sgd  4048  2980  2225  1580  0.579064  0.719261   0.575982   \n",
       "\n",
       "   F1_Score  \n",
       "0  0.785239  \n",
       "1  0.775912  \n",
       "2  0.774194  \n",
       "3  0.785636  \n",
       "4  0.781151  \n",
       "0  0.676762  \n",
       "1  0.695733  \n",
       "2  0.676210  \n",
       "0  0.684175  \n",
       "1  0.678095  \n",
       "2  0.678095  \n",
       "3  0.684175  \n",
       "4  0.678095  \n",
       "0  0.691403  \n",
       "1  0.701384  \n",
       "2  0.701587  \n",
       "3  0.691058  \n",
       "4  0.700619  \n",
       "0  0.676306  \n",
       "1  0.654341  \n",
       "2  0.644616  \n",
       "3  0.677784  \n",
       "4  0.642574  \n",
       "0  0.675048  \n",
       "1  0.646552  \n",
       "2  0.646789  \n",
       "3  0.675099  \n",
       "4  0.647305  \n",
       "0  0.674373  \n",
       "1  0.645765  \n",
       "2  0.645943  \n",
       "3  0.674317  \n",
       "4  0.647374  \n",
       "0  0.683163  \n",
       "1  0.622716  \n",
       "2  0.630722  \n",
       "3  0.685839  \n",
       "4  0.639697  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Classifier=[]\n",
    "for i in range(len(Score)):\n",
    "    Classifier.append(clf_name)\n",
    "result={'Model':Model, \\\n",
    "        'Classifier':Classifier,\\\n",
    "        'TP':TP, \\\n",
    "        'FP':FP, \\\n",
    "        'TN':TN,\\\n",
    "        'FN':FN, \\\n",
    "        'Accuracy':Score, \\\n",
    "        'Recall':Recall,\\\n",
    "        'Precision':Precision,\\\n",
    "        'F1_Score':F1_Score\n",
    "       }\n",
    "\n",
    "clf_result = clf_result.append(pd.DataFrame(result))\n",
    "clf_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_result = clf_result.reset_index()\n",
    "clf_result = clf_result.drop(['index'],axis=1)\n",
    "clf_result.to_csv('/Volumes/BECCA/Project/Data/Result/Russia/By_model/Total_Result.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Model</th>\n",
       "      <th>Classifier</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FN</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4309</td>\n",
       "      <td>1038</td>\n",
       "      <td>4167</td>\n",
       "      <td>1319</td>\n",
       "      <td>0.782424</td>\n",
       "      <td>0.765636</td>\n",
       "      <td>0.805872</td>\n",
       "      <td>0.785239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4297</td>\n",
       "      <td>1151</td>\n",
       "      <td>4054</td>\n",
       "      <td>1331</td>\n",
       "      <td>0.770885</td>\n",
       "      <td>0.763504</td>\n",
       "      <td>0.788730</td>\n",
       "      <td>0.775912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4284</td>\n",
       "      <td>1155</td>\n",
       "      <td>4050</td>\n",
       "      <td>1344</td>\n",
       "      <td>0.769316</td>\n",
       "      <td>0.761194</td>\n",
       "      <td>0.787645</td>\n",
       "      <td>0.774194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L1</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4321</td>\n",
       "      <td>1051</td>\n",
       "      <td>4154</td>\n",
       "      <td>1307</td>\n",
       "      <td>0.782332</td>\n",
       "      <td>0.767768</td>\n",
       "      <td>0.804356</td>\n",
       "      <td>0.785636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tree</td>\n",
       "      <td>kNN</td>\n",
       "      <td>4426</td>\n",
       "      <td>1278</td>\n",
       "      <td>3927</td>\n",
       "      <td>1202</td>\n",
       "      <td>0.771070</td>\n",
       "      <td>0.786425</td>\n",
       "      <td>0.775947</td>\n",
       "      <td>0.781151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>k4</td>\n",
       "      <td>gnb</td>\n",
       "      <td>3888</td>\n",
       "      <td>1974</td>\n",
       "      <td>3231</td>\n",
       "      <td>1740</td>\n",
       "      <td>0.657159</td>\n",
       "      <td>0.690832</td>\n",
       "      <td>0.663255</td>\n",
       "      <td>0.676762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>L1</td>\n",
       "      <td>gnb</td>\n",
       "      <td>4133</td>\n",
       "      <td>2120</td>\n",
       "      <td>3085</td>\n",
       "      <td>1495</td>\n",
       "      <td>0.666297</td>\n",
       "      <td>0.734364</td>\n",
       "      <td>0.660963</td>\n",
       "      <td>0.695733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tree</td>\n",
       "      <td>gnb</td>\n",
       "      <td>3897</td>\n",
       "      <td>2001</td>\n",
       "      <td>3204</td>\n",
       "      <td>1731</td>\n",
       "      <td>0.655497</td>\n",
       "      <td>0.692431</td>\n",
       "      <td>0.660732</td>\n",
       "      <td>0.676210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>k6</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3556</td>\n",
       "      <td>1211</td>\n",
       "      <td>3994</td>\n",
       "      <td>2072</td>\n",
       "      <td>0.696945</td>\n",
       "      <td>0.631841</td>\n",
       "      <td>0.745962</td>\n",
       "      <td>0.684175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>k5</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3303</td>\n",
       "      <td>811</td>\n",
       "      <td>4394</td>\n",
       "      <td>2325</td>\n",
       "      <td>0.710514</td>\n",
       "      <td>0.586887</td>\n",
       "      <td>0.802868</td>\n",
       "      <td>0.678095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>k4</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3303</td>\n",
       "      <td>811</td>\n",
       "      <td>4394</td>\n",
       "      <td>2325</td>\n",
       "      <td>0.710514</td>\n",
       "      <td>0.586887</td>\n",
       "      <td>0.802868</td>\n",
       "      <td>0.678095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>L1</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3556</td>\n",
       "      <td>1211</td>\n",
       "      <td>3994</td>\n",
       "      <td>2072</td>\n",
       "      <td>0.696945</td>\n",
       "      <td>0.631841</td>\n",
       "      <td>0.745962</td>\n",
       "      <td>0.684175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Tree</td>\n",
       "      <td>dtree</td>\n",
       "      <td>3303</td>\n",
       "      <td>811</td>\n",
       "      <td>4394</td>\n",
       "      <td>2325</td>\n",
       "      <td>0.710514</td>\n",
       "      <td>0.586887</td>\n",
       "      <td>0.802868</td>\n",
       "      <td>0.678095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>k6</td>\n",
       "      <td>SVM</td>\n",
       "      <td>3792</td>\n",
       "      <td>1549</td>\n",
       "      <td>3656</td>\n",
       "      <td>1836</td>\n",
       "      <td>0.687529</td>\n",
       "      <td>0.673774</td>\n",
       "      <td>0.709979</td>\n",
       "      <td>0.691403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>k5</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4485</td>\n",
       "      <td>2676</td>\n",
       "      <td>2529</td>\n",
       "      <td>1143</td>\n",
       "      <td>0.647466</td>\n",
       "      <td>0.796908</td>\n",
       "      <td>0.626309</td>\n",
       "      <td>0.701384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>k4</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4487</td>\n",
       "      <td>2676</td>\n",
       "      <td>2529</td>\n",
       "      <td>1141</td>\n",
       "      <td>0.647651</td>\n",
       "      <td>0.797264</td>\n",
       "      <td>0.626414</td>\n",
       "      <td>0.701587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>L1</td>\n",
       "      <td>SVM</td>\n",
       "      <td>3787</td>\n",
       "      <td>1545</td>\n",
       "      <td>3660</td>\n",
       "      <td>1841</td>\n",
       "      <td>0.687437</td>\n",
       "      <td>0.672886</td>\n",
       "      <td>0.710240</td>\n",
       "      <td>0.691058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Tree</td>\n",
       "      <td>SVM</td>\n",
       "      <td>4581</td>\n",
       "      <td>2868</td>\n",
       "      <td>2337</td>\n",
       "      <td>1047</td>\n",
       "      <td>0.638604</td>\n",
       "      <td>0.813966</td>\n",
       "      <td>0.614982</td>\n",
       "      <td>0.700619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>k6</td>\n",
       "      <td>nn</td>\n",
       "      <td>4233</td>\n",
       "      <td>2657</td>\n",
       "      <td>2548</td>\n",
       "      <td>1395</td>\n",
       "      <td>0.625958</td>\n",
       "      <td>0.752132</td>\n",
       "      <td>0.614369</td>\n",
       "      <td>0.676306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>k5</td>\n",
       "      <td>nn</td>\n",
       "      <td>4070</td>\n",
       "      <td>2742</td>\n",
       "      <td>2463</td>\n",
       "      <td>1558</td>\n",
       "      <td>0.603065</td>\n",
       "      <td>0.723170</td>\n",
       "      <td>0.597475</td>\n",
       "      <td>0.654341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>k4</td>\n",
       "      <td>nn</td>\n",
       "      <td>3849</td>\n",
       "      <td>2465</td>\n",
       "      <td>2740</td>\n",
       "      <td>1779</td>\n",
       "      <td>0.608234</td>\n",
       "      <td>0.683902</td>\n",
       "      <td>0.609598</td>\n",
       "      <td>0.644616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>L1</td>\n",
       "      <td>nn</td>\n",
       "      <td>4227</td>\n",
       "      <td>2618</td>\n",
       "      <td>2587</td>\n",
       "      <td>1401</td>\n",
       "      <td>0.629004</td>\n",
       "      <td>0.751066</td>\n",
       "      <td>0.617531</td>\n",
       "      <td>0.677784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Tree</td>\n",
       "      <td>nn</td>\n",
       "      <td>3734</td>\n",
       "      <td>2260</td>\n",
       "      <td>2945</td>\n",
       "      <td>1894</td>\n",
       "      <td>0.616542</td>\n",
       "      <td>0.663468</td>\n",
       "      <td>0.622956</td>\n",
       "      <td>0.642574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>k6</td>\n",
       "      <td>lr</td>\n",
       "      <td>4171</td>\n",
       "      <td>2571</td>\n",
       "      <td>2634</td>\n",
       "      <td>1457</td>\n",
       "      <td>0.628173</td>\n",
       "      <td>0.741116</td>\n",
       "      <td>0.618659</td>\n",
       "      <td>0.674373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>k5</td>\n",
       "      <td>lr</td>\n",
       "      <td>3862</td>\n",
       "      <td>2471</td>\n",
       "      <td>2734</td>\n",
       "      <td>1766</td>\n",
       "      <td>0.608880</td>\n",
       "      <td>0.686212</td>\n",
       "      <td>0.609822</td>\n",
       "      <td>0.645765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>k4</td>\n",
       "      <td>lr</td>\n",
       "      <td>3865</td>\n",
       "      <td>2474</td>\n",
       "      <td>2731</td>\n",
       "      <td>1763</td>\n",
       "      <td>0.608880</td>\n",
       "      <td>0.686745</td>\n",
       "      <td>0.609718</td>\n",
       "      <td>0.645943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>L1</td>\n",
       "      <td>lr</td>\n",
       "      <td>4172</td>\n",
       "      <td>2574</td>\n",
       "      <td>2631</td>\n",
       "      <td>1456</td>\n",
       "      <td>0.627989</td>\n",
       "      <td>0.741294</td>\n",
       "      <td>0.618441</td>\n",
       "      <td>0.674317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Tree</td>\n",
       "      <td>lr</td>\n",
       "      <td>3870</td>\n",
       "      <td>2458</td>\n",
       "      <td>2747</td>\n",
       "      <td>1758</td>\n",
       "      <td>0.610819</td>\n",
       "      <td>0.687633</td>\n",
       "      <td>0.611568</td>\n",
       "      <td>0.647374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>k6</td>\n",
       "      <td>sgd</td>\n",
       "      <td>4735</td>\n",
       "      <td>3499</td>\n",
       "      <td>1706</td>\n",
       "      <td>893</td>\n",
       "      <td>0.594572</td>\n",
       "      <td>0.841329</td>\n",
       "      <td>0.575055</td>\n",
       "      <td>0.683163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>k5</td>\n",
       "      <td>sgd</td>\n",
       "      <td>3561</td>\n",
       "      <td>2248</td>\n",
       "      <td>2957</td>\n",
       "      <td>2067</td>\n",
       "      <td>0.601680</td>\n",
       "      <td>0.632729</td>\n",
       "      <td>0.613014</td>\n",
       "      <td>0.622716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>k4</td>\n",
       "      <td>sgd</td>\n",
       "      <td>3679</td>\n",
       "      <td>2359</td>\n",
       "      <td>2846</td>\n",
       "      <td>1949</td>\n",
       "      <td>0.602326</td>\n",
       "      <td>0.653696</td>\n",
       "      <td>0.609308</td>\n",
       "      <td>0.630722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>L1</td>\n",
       "      <td>sgd</td>\n",
       "      <td>4567</td>\n",
       "      <td>3123</td>\n",
       "      <td>2082</td>\n",
       "      <td>1061</td>\n",
       "      <td>0.613773</td>\n",
       "      <td>0.811478</td>\n",
       "      <td>0.593888</td>\n",
       "      <td>0.685839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Tree</td>\n",
       "      <td>sgd</td>\n",
       "      <td>4048</td>\n",
       "      <td>2980</td>\n",
       "      <td>2225</td>\n",
       "      <td>1580</td>\n",
       "      <td>0.579064</td>\n",
       "      <td>0.719261</td>\n",
       "      <td>0.575982</td>\n",
       "      <td>0.639697</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Model Classifier    TP    FP    TN    FN  Accuracy    Recall  Precision  \\\n",
       "0     k6        kNN  4309  1038  4167  1319  0.782424  0.765636   0.805872   \n",
       "1     k5        kNN  4297  1151  4054  1331  0.770885  0.763504   0.788730   \n",
       "2     k4        kNN  4284  1155  4050  1344  0.769316  0.761194   0.787645   \n",
       "3     L1        kNN  4321  1051  4154  1307  0.782332  0.767768   0.804356   \n",
       "4   Tree        kNN  4426  1278  3927  1202  0.771070  0.786425   0.775947   \n",
       "5     k4        gnb  3888  1974  3231  1740  0.657159  0.690832   0.663255   \n",
       "6     L1        gnb  4133  2120  3085  1495  0.666297  0.734364   0.660963   \n",
       "7   Tree        gnb  3897  2001  3204  1731  0.655497  0.692431   0.660732   \n",
       "8     k6      dtree  3556  1211  3994  2072  0.696945  0.631841   0.745962   \n",
       "9     k5      dtree  3303   811  4394  2325  0.710514  0.586887   0.802868   \n",
       "10    k4      dtree  3303   811  4394  2325  0.710514  0.586887   0.802868   \n",
       "11    L1      dtree  3556  1211  3994  2072  0.696945  0.631841   0.745962   \n",
       "12  Tree      dtree  3303   811  4394  2325  0.710514  0.586887   0.802868   \n",
       "13    k6        SVM  3792  1549  3656  1836  0.687529  0.673774   0.709979   \n",
       "14    k5        SVM  4485  2676  2529  1143  0.647466  0.796908   0.626309   \n",
       "15    k4        SVM  4487  2676  2529  1141  0.647651  0.797264   0.626414   \n",
       "16    L1        SVM  3787  1545  3660  1841  0.687437  0.672886   0.710240   \n",
       "17  Tree        SVM  4581  2868  2337  1047  0.638604  0.813966   0.614982   \n",
       "18    k6         nn  4233  2657  2548  1395  0.625958  0.752132   0.614369   \n",
       "19    k5         nn  4070  2742  2463  1558  0.603065  0.723170   0.597475   \n",
       "20    k4         nn  3849  2465  2740  1779  0.608234  0.683902   0.609598   \n",
       "21    L1         nn  4227  2618  2587  1401  0.629004  0.751066   0.617531   \n",
       "22  Tree         nn  3734  2260  2945  1894  0.616542  0.663468   0.622956   \n",
       "23    k6         lr  4171  2571  2634  1457  0.628173  0.741116   0.618659   \n",
       "24    k5         lr  3862  2471  2734  1766  0.608880  0.686212   0.609822   \n",
       "25    k4         lr  3865  2474  2731  1763  0.608880  0.686745   0.609718   \n",
       "26    L1         lr  4172  2574  2631  1456  0.627989  0.741294   0.618441   \n",
       "27  Tree         lr  3870  2458  2747  1758  0.610819  0.687633   0.611568   \n",
       "28    k6        sgd  4735  3499  1706   893  0.594572  0.841329   0.575055   \n",
       "29    k5        sgd  3561  2248  2957  2067  0.601680  0.632729   0.613014   \n",
       "30    k4        sgd  3679  2359  2846  1949  0.602326  0.653696   0.609308   \n",
       "31    L1        sgd  4567  3123  2082  1061  0.613773  0.811478   0.593888   \n",
       "32  Tree        sgd  4048  2980  2225  1580  0.579064  0.719261   0.575982   \n",
       "\n",
       "    F1_Score  \n",
       "0   0.785239  \n",
       "1   0.775912  \n",
       "2   0.774194  \n",
       "3   0.785636  \n",
       "4   0.781151  \n",
       "5   0.676762  \n",
       "6   0.695733  \n",
       "7   0.676210  \n",
       "8   0.684175  \n",
       "9   0.678095  \n",
       "10  0.678095  \n",
       "11  0.684175  \n",
       "12  0.678095  \n",
       "13  0.691403  \n",
       "14  0.701384  \n",
       "15  0.701587  \n",
       "16  0.691058  \n",
       "17  0.700619  \n",
       "18  0.676306  \n",
       "19  0.654341  \n",
       "20  0.644616  \n",
       "21  0.677784  \n",
       "22  0.642574  \n",
       "23  0.674373  \n",
       "24  0.645765  \n",
       "25  0.645943  \n",
       "26  0.674317  \n",
       "27  0.647374  \n",
       "28  0.683163  \n",
       "29  0.622716  \n",
       "30  0.630722  \n",
       "31  0.685839  \n",
       "32  0.639697  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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