Step 7.2_Classifier (China) - 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": 3,
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   "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='China'\n",
    "data=file_reader('/Volumes/BECCA/Project/Data/Input/02_Preprocessing/05_Final/%s.csv' %country)"
   ]
  },
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       "      <th>4</th>\n",
       "      <td>Maybe should call that Kekong? #HongKong #Keki...</td>\n",
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       "      <td>RT @YeOldHongKonger: .\\nBioterorist Xi Jinping...</td>\n",
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       "3      Want to see the walking dead, but feel a littl...         0.403125   \n",
       "4      Maybe should call that Kekong? #HongKong #Keki...         0.500000   \n",
       "...                                                  ...              ...   \n",
       "44293  @ace_critic @realDonaldTrump @narendramodi The...         0.500000   \n",
       "44294  RT @F1Bathurst2015: @catherine___c @tyrelle123...         0.538889   \n",
       "44295  RT @YeOldHongKonger: .\\nBioterorist Xi Jinping...         0.500000   \n",
       "44296  @GauravS101418 @narendramodi Hahaha... So u ex...         0.600000   \n",
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       "44297             0.369444           0         0    2    16      58        0  \n",
       "\n",
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  {
   "cell_type": "code",
   "execution_count": 48,
   "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": 49,
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       "                                              tweet_text  polarity_number  \\\n",
       "0                   Necessity is the mother of invention         0.500000   \n",
       "1      #病毒  Constantly defying the will of the citize...         0.425000   \n",
       "2      #肺炎  #病毒  The priceless life should not be the...         0.654167   \n",
       "3      Want to see the walking dead, but feel a littl...         0.403125   \n",
       "4      Maybe should call that Kekong? #HongKong #Keki...         0.500000   \n",
       "...                                                  ...              ...   \n",
       "44293  @ace_critic @realDonaldTrump @narendramodi The...         0.500000   \n",
       "44294  RT @F1Bathurst2015: @catherine___c @tyrelle123...         0.538889   \n",
       "44295  RT @YeOldHongKonger: .\\nBioterorist Xi Jinping...         0.500000   \n",
       "44296  @GauravS101418 @narendramodi Hahaha... So u ex...         0.600000   \n",
       "44297  RT @LawrenceGostin: The public hardly knows al...         0.534722   \n",
       "\n",
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       "...                    ...         ...       ...       ...       ...   \n",
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       "\n",
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       "...         ...      ...  \n",
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       "\n",
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# kNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Selection:\n",
      "Score of k7 = 0.79 (+/- 0.07)\n",
      "Score of k6 = 0.78 (+/- 0.07)\n",
      "Score of k5 = 0.76 (+/- 0.07)\n",
      "Score of Tree = 0.78 (+/- 0.05)\n"
     ]
    }
   ],
   "source": [
    "clf_name='kNN'\n",
    "parent_dir = \"/Volumes/BECCA/Project/Data/Result/China/By_model\"\n",
    "path = os.path.join(parent_dir, clf_name)\n",
    "# os.mkdir(path) \n",
    "# os.mkdir(path+'/PR_plot') \n",
    "\n",
    "k7=np.array(data)[:,1:8].astype('float')\n",
    "k6=np.array(data)[:,2:8].astype('float')\n",
    "k5=np.array(data)[:,3:8].astype('float')\n",
    "k4=np.array(data)[:,3:7].astype('float')\n",
    "k3=np.array(data[['repeat_tag','freq_tag','hour']]).astype('float')\n",
    "k2=np.array(data[['freq_tag','hour']]).astype('float')\n",
    "Tree=np.array(data[['polarity_number','subjectivity_number','hour','minute']]).astype('float')\n",
    "y=np.array(data)[:,8].astype('int')\n",
    "\n",
    "clf = KNeighborsClassifier(n_neighbors=5)\n",
    "\n",
    "scores_1 = cross_val_score(clf, k7, y, cv=10)\n",
    "scores_2 = cross_val_score(clf, k6, y, cv=10)\n",
    "scores_3 = cross_val_score(clf, k5, y, cv=10)\n",
    "scores_4 = cross_val_score(clf, k4, y, cv=10)\n",
    "scores_5 = cross_val_score(clf, k3, y, cv=10)\n",
    "scores_6 = cross_val_score(clf, k2, 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(['k7','k6','k5','k4','k3','k2','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"
    },
    {
     "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 = ['k7','k6','k5','Tree']\n",
    "model = [k7, k6, k5, 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/China/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>k7</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5942</td>\n",
       "      <td>971</td>\n",
       "      <td>1327</td>\n",
       "      <td>619</td>\n",
       "      <td>0.820522</td>\n",
       "      <td>0.905655</td>\n",
       "      <td>0.859540</td>\n",
       "      <td>0.881995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k6</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5932</td>\n",
       "      <td>1031</td>\n",
       "      <td>1267</td>\n",
       "      <td>629</td>\n",
       "      <td>0.812620</td>\n",
       "      <td>0.904130</td>\n",
       "      <td>0.851932</td>\n",
       "      <td>0.877255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k5</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5894</td>\n",
       "      <td>1220</td>\n",
       "      <td>1078</td>\n",
       "      <td>667</td>\n",
       "      <td>0.786996</td>\n",
       "      <td>0.898339</td>\n",
       "      <td>0.828507</td>\n",
       "      <td>0.862011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tree</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5904</td>\n",
       "      <td>1188</td>\n",
       "      <td>1110</td>\n",
       "      <td>657</td>\n",
       "      <td>0.791737</td>\n",
       "      <td>0.899863</td>\n",
       "      <td>0.832487</td>\n",
       "      <td>0.864865</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model Classifier    TP    FP    TN   FN  Accuracy    Recall  Precision  \\\n",
       "0    k7        kNN  5942   971  1327  619  0.820522  0.905655   0.859540   \n",
       "1    k6        kNN  5932  1031  1267  629  0.812620  0.904130   0.851932   \n",
       "2    k5        kNN  5894  1220  1078  667  0.786996  0.898339   0.828507   \n",
       "3  Tree        kNN  5904  1188  1110  657  0.791737  0.899863   0.832487   \n",
       "\n",
       "   F1_Score  \n",
       "0  0.881995  \n",
       "1  0.877255  \n",
       "2  0.862011  \n",
       "3  0.864865  "
      ]
     },
     "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=pd.DataFrame(result)\n",
    "clf_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Selection:\n",
      "Score of Tree = 0.76 (+/- 0.07)\n"
     ]
    }
   ],
   "source": [
    "clf_name='gnb'\n",
    "parent_dir = \"/Volumes/BECCA/Project/Data/Result/China/By_model\"\n",
    "path = os.path.join(parent_dir, clf_name)\n",
    "os.mkdir(path) \n",
    "os.mkdir(path+'/PR_plot') \n",
    "\n",
    "k7=np.array(data)[:,1:8].astype('float')\n",
    "k6=np.array(data)[:,2:8].astype('float')\n",
    "k5=np.array(data)[:,3:8].astype('float')\n",
    "k4=np.array(data)[:,3:7].astype('float')\n",
    "k3=np.array(data[['repeat_tag','freq_tag','hour']]).astype('float')\n",
    "k2=np.array(data[['freq_tag','hour']]).astype('float')\n",
    "Tree=np.array(data[['polarity_number','subjectivity_number','hour','minute']]).astype('float')\n",
    "y=np.array(data)[:,8].astype('int')\n",
    "\n",
    "clf = GaussianNB()\n",
    "\n",
    "scores_1 = cross_val_score(clf, k7, y, cv=10)\n",
    "scores_2 = cross_val_score(clf, k6, y, cv=10)\n",
    "scores_3 = cross_val_score(clf, k5, y, cv=10)\n",
    "scores_4 = cross_val_score(clf, k4, y, cv=10)\n",
    "scores_5 = cross_val_score(clf, k3, y, cv=10)\n",
    "scores_6 = cross_val_score(clf, k2, 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(['k7','k6','k5','k4','k3','k2','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": 16,
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = ['Tree']\n",
    "model = [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/China/By_model/%s/PR_plot/PR_%s.png' %(clf_name, md_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>k7</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5942</td>\n",
       "      <td>971</td>\n",
       "      <td>1327</td>\n",
       "      <td>619</td>\n",
       "      <td>0.820522</td>\n",
       "      <td>0.905655</td>\n",
       "      <td>0.859540</td>\n",
       "      <td>0.881995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k6</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5932</td>\n",
       "      <td>1031</td>\n",
       "      <td>1267</td>\n",
       "      <td>629</td>\n",
       "      <td>0.812620</td>\n",
       "      <td>0.904130</td>\n",
       "      <td>0.851932</td>\n",
       "      <td>0.877255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k5</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5894</td>\n",
       "      <td>1220</td>\n",
       "      <td>1078</td>\n",
       "      <td>667</td>\n",
       "      <td>0.786996</td>\n",
       "      <td>0.898339</td>\n",
       "      <td>0.828507</td>\n",
       "      <td>0.862011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tree</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5904</td>\n",
       "      <td>1188</td>\n",
       "      <td>1110</td>\n",
       "      <td>657</td>\n",
       "      <td>0.791737</td>\n",
       "      <td>0.899863</td>\n",
       "      <td>0.832487</td>\n",
       "      <td>0.864865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tree</td>\n",
       "      <td>gnb</td>\n",
       "      <td>5979</td>\n",
       "      <td>1514</td>\n",
       "      <td>784</td>\n",
       "      <td>582</td>\n",
       "      <td>0.763404</td>\n",
       "      <td>0.911294</td>\n",
       "      <td>0.797945</td>\n",
       "      <td>0.850861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model Classifier    TP    FP    TN   FN  Accuracy    Recall  Precision  \\\n",
       "0    k7        kNN  5942   971  1327  619  0.820522  0.905655   0.859540   \n",
       "1    k6        kNN  5932  1031  1267  629  0.812620  0.904130   0.851932   \n",
       "2    k5        kNN  5894  1220  1078  667  0.786996  0.898339   0.828507   \n",
       "3  Tree        kNN  5904  1188  1110  657  0.791737  0.899863   0.832487   \n",
       "0  Tree        gnb  5979  1514   784  582  0.763404  0.911294   0.797945   \n",
       "\n",
       "   F1_Score  \n",
       "0  0.881995  \n",
       "1  0.877255  \n",
       "2  0.862011  \n",
       "3  0.864865  \n",
       "0  0.850861  "
      ]
     },
     "execution_count": 18,
     "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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Selection:\n",
      "Score of k3 = 0.77 (+/- 0.06)\n",
      "Score of k2 = 0.76 (+/- 0.05)\n",
      "Score of Tree = 0.75 (+/- 0.04)\n"
     ]
    }
   ],
   "source": [
    "clf_name='dtree'\n",
    "parent_dir = \"/Volumes/BECCA/Project/Data/Result/China/By_model\"\n",
    "path = os.path.join(parent_dir, clf_name)\n",
    "os.mkdir(path) \n",
    "os.mkdir(path+'/PR_plot') \n",
    "\n",
    "k7=np.array(data)[:,1:8].astype('float')\n",
    "k6=np.array(data)[:,2:8].astype('float')\n",
    "k5=np.array(data)[:,3:8].astype('float')\n",
    "k4=np.array(data)[:,3:7].astype('float')\n",
    "k3=np.array(data[['repeat_tag','freq_tag','hour']]).astype('float')\n",
    "k2=np.array(data[['freq_tag','hour']]).astype('float')\n",
    "Tree=np.array(data[['polarity_number','subjectivity_number','hour','minute']]).astype('float')\n",
    "y=np.array(data)[:,8].astype('int')\n",
    "\n",
    "clf = DecisionTreeClassifier(criterion=\"entropy\", max_depth=4)\n",
    "\n",
    "scores_1 = cross_val_score(clf, k7, y, cv=10)\n",
    "scores_2 = cross_val_score(clf, k6, y, cv=10)\n",
    "scores_3 = cross_val_score(clf, k5, y, cv=10)\n",
    "scores_4 = cross_val_score(clf, k4, y, cv=10)\n",
    "scores_5 = cross_val_score(clf, k3, y, cv=10)\n",
    "scores_6 = cross_val_score(clf, k2, 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(['k7','k6','k5','k4','k3','k2','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": 20,
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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F9erfkhsoOEEUR75nMW0Efpy+bC91Ud9u/kO1Xcq91eKqM0FImhwR50v6M9m32z6mYJGZmVlR1deD+Fb67+BCB2JmZruXOs9iiogl6eRHwMKI+ADYBziWGs92MDOzpiXf01xfAFqlz4R4FrgcuL9QQZmZWfHlexaTImKdpCuAn0bE7eljQs3MCiqfU6t9Kmxh5J0gJH2W5PqHKxq4rpnZDsnn1Oq5S1YBPhW2EPLdyX+b5JGfj6VPhesBzCxcWGZm+Z1a7VNhCyff6yCeB57PmX+P5KI5MzNrouq7DuKuiPi2pN+SfR3E2QWLzMzMiqq+HkTVvZfuKHQgZma2e6kzQURE1Q35yoD1EVEJIKk5yfUQZmbWROV7HcSzQJuc+dbAjMYPx8zMdhf5JohWEbGmaiadblNHfTMz28PlmyDWSjq+akbSZ4D1hQnJzMx2Bw25DuIRSVX3X/oUcEFhQjIzs91BvtdBzJJ0BHA4yQOD3oqIzQWNzMzMiiqvISZJbYDvA9+KiD8D3SX5FuBmZk1Yvscg7gM2AZ9N5yuAHxQkIjMz2y3kmyB6RsTtwGaAiFhPMtRkZmZNVL4JYpOk1qS325DUE9hY30qSzpA0T9J8SSMylneTNFPSbElvShqUlneXtF5Sefoa04A2mZlZI8j3LKYbgWlAV0kPAf2Ay+paIb3a+h7gCyRDUrMkTYmIuTnVbgAmR8S9knoBU4Hu6bJ3I6JPvg0xM7PGVW+CkCTgLeBc4CSSoaVvRcRH9ax6IjA/vfMrkiYBQ4HcBBHAfun0/vgxpmZmu416E0REhKTHI+IzwJMN2HZnYGHOfAXQt0adUcAzkr4J7AuclrPs4PSpdauAGyLixQa8t5mZ7aR8j0H8UdIJDdx21kHsmrcMvxC4PyK6AIOAiZKaAUuAbhFxHHAd8EtJ+9VYF0lXSyqTVLZ06dIGhmdmZnXJN0F8niRJvJseTP6zpDfrWacC6Joz34Xth5CuACYDRMQrQCugQ0RsjIhlafnrwLvAYTXfICLGRURpRJR27Ngxz6aYmVk+8j1IfeYObHsWcKikg4FFwHDgohp1FgCnAvdLOpIkQSyV1BFYHhFb08ebHgq8twMxmJnZDqrviXKtgGuAQ4A/A+MjYks+G46ILZKuBZ4GmgMT0udZjwbKImIK8F3gZ5K+QzL8dFl6zONfgNGStgBbgWsiYvkOttHMzHZAfT2IB0gujnuRpBfRC/hWvhuPiKkkp67mlo3MmZ5LcspszfV+Dfw63/cxM7PGV1+C6BURvQEkjQdeK3xIZma2O6jvIHX1HVvzHVoyM7Omob4exLGSVqXTAlqn8yK5RGK7U0/NzKxpqDNBRETzXRWImZntXvK9DsLMzPYyThBmZpbJCcLMzDI5QZiZWSYnCDMzy+QEYWZmmZwgzMwskxOEmZllcoIwM7NMThBmZpbJCcLMzDI5QZiZWSYnCDMzy+QEYWZmmZwgzMwskxOEmZllcoIwM7NMThBmZpbJCcLMzDI5QZiZWSYnCDMzy+QEYWZmmZwgzMwskxOEmZllcoIwM7NMBU0Qks6QNE/SfEkjMpZ3kzRT0mxJb0oalLPs39P15kk6vZBxmpnZ9koKtWFJzYF7gC8AFcAsSVMiYm5OtRuAyRFxr6RewFSgezo9HDgK+DQwQ9JhEbG1UPGamdm2CtmDOBGYHxHvRcQmYBIwtEadAPZLp/cHFqfTQ4FJEbExIv4GzE+3Z2Zmu0ghE0RnYGHOfEValmsU8BVJFSS9h282YF0zMyugQiYIZZRFjfkLgfsjogswCJgoqVme6yLpakllksqWLl260wGbmdnHCnYMguRXf9ec+S58PIRU5QrgDICIeEVSK6BDnusSEeOAcQClpaXbJRAz2zvMXbKKC8a+Uuww6jW0T2cu6tut2GHkrZA9iFnAoZIOltSS5KDzlBp1FgCnAkg6EmgFLE3rDZe0j6SDgUOB1woYq5ntoYb26UyvT+1Xf8Uim7tkFU+ULyp2GA1SsB5ERGyRdC3wNNAcmBARcySNBsoiYgrwXeBnkr5DMoR0WUQEMEfSZGAusAX4hs9gMrMsF/Xttkf8Kt8Tejg1FXKIiYiYSnLwObdsZM70XKBfLeveAtxSyPjMzKx2vpLazMwyOUGYmVkmJwgzM8vkBGFmZpmcIMzMLJMThJmZZXKCMDOzTE4QZmaWyQnCzMwyOUGYmVkmJwgzM8vkBGFmZpmcIMzMLJMThJmZZXKCMDOzTE4QZmaWyQnCzMwyOUGYmVkmJwgzM8vkBGFmZpmcIMzMLJMThJmZZXKCMDOzTE4QZmaWyQnCzMwyOUGYmVkmJwgzM8tUUuwACmnz5s1UVFSwYcOGYoditkdp1aoVXbp0oUWLFsUOxYqoSSeIiooK2rVrR/fu3ZFU7HDM9ggRwbJly6ioqODggw8udjhWRE16iGnDhg0ceOCBTg5mDSCJAw880D1vK2yCkHSGpHmS5ksakbH8Tknl6ettSStylm3NWTZlJ2LY0VXN9lr+uzEo4BCTpObAPcAXgApglqQpETG3qk5EfCen/jeB43I2sT4i+hQqPjMzq1shexAnAvMj4r2I2ARMAobWUf9C4OECxlMUzZs3p0+fPhx11FEce+yx/PjHP6aysnKHtjVy5EhmzJhR6/IxY8bw4IMPNni7Tz/9NH369KFPnz60bduWww8/nD59+nDJJZfsUJw1rVq1iquuuoqePXty1FFHMWDAAGbNmsWWLVs44IADGuU9AO655x4eeughAObOncuxxx7Lcccdx7vvvsspp5yy09v/4he/yAcffFA9P2vWLCTx7LPPVpdt2bKl+js/+uijueCCC1i/fv1OvW9E8PWvf51DDjmEY489lvLy8sx6v/jFL+jduzfHHHMMgwYNYvny5QDMnj2bk046id69ezN06FDWrFkDQHl5OVdeeeVOxWZNXEQU5AUMA36eM38xcHctdQ8ClgDNc8q2AGXAH4Fzalnv6rROWbdu3aKmuXPnble2q+27777V0//4xz/i1FNPjZEjRxYxorr1798/Zs2albls8+bNO7TN8847L2644YaorKyMiIh33nknpk6dGps3b479999/h2Oty8033xyjR4/eoXUrKytj69at25SVl5fHsGHDtin7zne+EyeffHJcccUV1WW5baqsrIzzzz8/fvKTn+xQHFWeeOKJGDx4cEREvPjii/G5z31uuzobN26Mjh07xrJly6pju/nmmyMiok+fPvHSSy9FRMTYsWNj1KhR1esNGDAgKioqMt93d/j7aUrOH/NynD/m5WKHsR2gLGrZjxfyLKasQcyope5w4NGI2JpT1i0iFkvqAfxe0p8j4t1tNhYxDhgHUFpaWtu2Abjpt3OYu3hV/tHnoden9+PGIUflXb9Tp06MGzeOE044gVGjRlFZWcmIESN47rnn2LhxI9/4xjf42te+BsDtt9/OxIkTadasGWeeeSa33XYbl112GYMHD2bYsGGMGDGCKVOmUFJSwsCBA7njjjsYNWoUbdu25Xvf+x7l5eVcc801rFu3jp49ezJhwgTat2/PgAED6Nu3LzNnzmTFihWMHz++zl/XP//5z5kxYwZr1qxh48aNTJ8+ndtuu43f/OY3bNiwgWHDhjFy5EgAHnjgAe655x42bdrE5z73Oe6++27eeecdysvLmTx5cvW49iGHHMIhhxzCli1bqt9n1apVnHPOOaxYsYItW7Zw6623MnjwYFavXs3555/P4sWL2bp1K6NGjWLYsGFcf/31PPnkk5SUlHDmmWfyox/9iBtuuIEOHTrQo0cP7r77bpo3b87zzz/PtGnT6NChAytWJIe4suKfP38+55xzDieffDKvvvoqv/vd7+jcuXN1fA899BBDh37cAa6srOTXv/41M2fO5JRTTmHTpk20bNlym89OEqeccgpvv/123v9HsjzxxBPVvbmTTz6Zv//97yxdupSOHTtW16n6g167di3t27dn9erVHH300QC8++679OvXD4AvfOELDB06lBtvvBGAwYMH86tf/Yrrrrtup2K0pqmQCaIC6Joz3wVYXEvd4cA3cgsiYnH673uSniM5PvHu9qvuWXr06EFlZSUffvghTzzxBPvvvz+zZs1i48aN9OvXj4EDB/LWW2/x+OOP8+qrr9KmTZvqoYIqy5cv57HHHuOtt95CUvWOL9cll1zCT3/6U/r378/IkSO56aabuOuuu4BkGOS1115j6tSp3HTTTXUOWwG88sorlJeX0759e6ZOncqCBQt49dVXiQgGDRrEyy+/zH777cdjjz3Gyy+/TElJCVdffTWTJk2iVatWHHfccTRrVvdoZuvWrXniiSdo164dH374If369WPw4MFMnTqV7t2789RTTwGwcuVK/vGPfzB16lTmzJmT2f6zzz6b1157jQ4dOvDtb397m0RUW/ydOnVi7ty53HfffYwZM2a7+P7whz9w+eWXV8+/8MILHHHEEfTo0YN+/foxbdo0zj777G3W2bx5M9OmTdsmsVQZNmwY8+fP3678+uuv58tf/vI2ZYsWLaJr14//lLp06cKiRYu2SRD77LMPd999N7169aJt27YcccQRjB07FoAjjjiCJ598krPOOotHHnmEhQsXVq9XWlrKXXfd5QRhmQqZIGYBh0o6GFhEkgQuqllJ0uFAe+CVnLL2wLqI2CipA9APuH1ngmnIL/1CS3p18Mwzz/Dmm2/y6KOPAsnO75133mHGjBlcfvnltGnTBoBPfOIT26y/33770apVK6688krOOussBg8evM3ylStXsmLFCvr37w/ApZdeype+9KXq5eeeey4An/nMZ3j//ffrjXfgwIG0b9++OuannnqK445LzidYs2YNb7/9NitWrGDWrFmUlpYCsH79erp27cpRR+X3uUcE3//+93nppZdo1qwZCxcu5KOPPuKYY45hxIgRjBgxgiFDhtCvXz/atGlDs2bNuOqqqzLbX5fa4u/UqRM9e/bkhBNOyFxvyZIl2+yQH374YYYPHw7A8OHDefjhh6sTxOrVq+nTJzm/on///lx22WXbba/qO89H1f+XXDXPMtq0aRNjx47lzTff5KCDDuLrX/86t99+OyNGjOD+++/nW9/6FiNHjmTo0KHbXPzWqVMnFi+u7Xeb7e0KliAiYouka4GngebAhIiYI2k0yZhX1amrFwKTYtu/giOBsZIqSQ6k3xY5Zz/tyd577z2aN29Op06diAh++tOfcvrpp29TZ9q0aXWeZlhSUsJrr73Gs88+y6RJk7j77rv5/e9/n3cM++yzD5AcQM/9dV2bfffdt3o6Irjhhhu44oortqlz55138tWvfpWbb755m/J58+ZRXl5OZWVlnb2IBx98kJUrV/KnP/2JkpISunTpwoYNGzjyyCMpKytj6tSpXH/99QwePJj/+I//oKysjOnTpzNp0iTuvfdennnmmbzaXlv88+fP36adNbVu3br6uoDNmzfz2GOPVffAKisrWbFiBWvXrmWfffahXbt2tR5IrtKQHkSXLl1YuHAhJ510EpBcAPrpT396mzqvv/46LVq0qL6w7fzzz6/uMfbq1Yvp06cDycH7adOmVa+3YcMGWrduXWestvcq6HUQETE1Ig6LiJ4RcUtaNjInORARoyJiRI31Xo6I3hFxbPrv+ELGuassXbqUa665hmuvvRZJnH766dx7771s3rwZgLfffpu1a9cycOBAJkyYwLp16wC2G2Jas2YNK1euZNCgQdx1113b7Yz2339/2rdvz4svvgjAxIkTq3sTO+v0009n/PjxrF27Fkh2Vh999BGnnXYakydP5qOPPgJg2bJlLFiwgMMPP5zevXszevTo6l/C8+bN47e//e022125ciWdOnWipKSE6dOns2jRIiAZXmnbti0XX3wx1113HX/6059YvXo1q1atYvDgwdx5553Mnj17p+Ovz5FHHlm9Q3/mmWc44YQTWLhwIe+//z4LFixgyJAhTJmS/+U6jz76KOXl5du9aiYHSIbMqs5Oe+mll/jkJz+5TW8GkiTy5z//mWXLlgEwY8YMjjzySAA+/PBDIDlu8oMf/IBrrrmmer233367+liFWU1N+lYbu4P169fTp08fNm/eTElJSfWODo0oi4YAAAfRSURBVODKK6/k/fff5/jjjyci6NixI48//jhnnHEG5eXllJaW0rJlSwYNGsStt95avc3Vq1czdOhQNmzYQERw5513bve+DzzwQPVB6h49enDfffc1SnsGDRrEW2+9Vf1rtl27dvzyl7+kd+/e3HjjjZx22mlUVlbSokULxowZQ7du3bjvvvu47rrrOOSQQ2jdujUdO3bkjjvu2Ga7F198MUOGDKG0tJTjjz+eQw89FIA33niDESNG0KxZM1q2bMmYMWNYuXIl5557Lhs3bqSyspIf//jHOx1/fc466yyee+45BgwYwMMPP8wXv/jFbZafd9553HfffdsM5TWWIUOG8NRTT9GzZ0/23XdfHnjgAQC2bt1K3759KSsro2vXrtxwww2cfPLJtGjRgu7du1fXmzhxIuPGjSMi+NKXvsTFF19cve2ZM2dmHiOxwpi7ZBUXjH2l/ooN1NATZvKlrPHNPVFpaWmUlZVtU/bXv/61+leU2c5Yt24dp556Ki+99BLNmzcvdjiNYv369Xz+85/nD3/4Q2ab/PfTuH756gKeKF9UkG3vTIKQ9HpElGYtcw/CLA9t2rRh5MiRLFmyhC5duhQ7nEaxYMECbr/99iaT8HZ3F/XtxkV9uxU7jAZxgjDL05lnnlnsEBrV4YcfzuGHH17sMGw31qTv5grZpwiaWd38d2PQxBNEq1atWLZsmf+zmzVApM+DaNWqVbFDsSJr0kNMXbp0oaKigqVLlxY7FLM9StUT5Wzv1qQTRO6FQ2Zm1jBNeojJzMx2nBOEmZllcoIwM7NMTeZKaklLgQ/qrVi7DkD9N+VpWva2Nu9t7QW3eW+xM20+KCI6Zi1oMgliZ0kqq+1y86Zqb2vz3tZecJv3FoVqs4eYzMwskxOEmZllcoL42LhiB1AEe1ub97b2gtu8tyhIm30MwszMMrkHYWZmmZwgzMws016VICSdIWmepPmSRmQs30fSr9Llr0rqvuujbFx5tPk6SXMlvSnpWUkHFSPOxlRfm3PqDZMUkvb4UyLzabOk89Pveo6k+p+zupvL4/92N0kzJc1O/38PKkacjUXSBEkfSvpLLcsl6b/Tz+NNScfv9JtGxF7xApoD7wI9gJbAG0CvGnW+DoxJp4cDvyp23LugzZ8H2qTT/7o3tDmt1w54AfgjUFrsuHfB93woMBton853Knbcu6DN44B/Tad7Ae8XO+6dbPO/AMcDf6ll+SDgKUDAScCrO/uee1MP4kRgfkS8FxGbgElAzae1DwUeSKcfBU6VpF0YY2Ort80RMTMi1qWzfwT29Hs85/M9A9wM3A5s2JXBFUg+bb4KuCci/gkQER/u4hgbWz5tDmC/dHp/YPEujK/RRcQLwPI6qgwFHozEH4EDJH1qZ95zb0oQnYGFOfMVaVlmnYjYAqwEDtwl0RVGPm3OdQXJL5A9Wb1tlnQc0DUifrcrAyugfL7nw4DDJP1B0h8lnbHLoiuMfNo8CviKpApgKvDNXRNa0TT0771eTfp5EDVk9QRqnuObT509Sd7tkfQVoBToX9CICq/ONktqBtwJXLarAtoF8vmeS0iGmQaQ9BJflHR0RKwocGyFkk+bLwTuj4j/kvRZYGLa5srCh1cUjb7/2pt6EBVA15z5Lmzf5ayuI6mEpFtaV5dud5dPm5F0GvB/gbMjYuMuiq1Q6mtzO+Bo4DlJ75OM1U7Zww9U5/t/+4mI2BwRfwPmkSSMPVU+bb4CmAwQEa8ArUhuatdU5fX33hB7U4KYBRwq6WBJLUkOQk+pUWcKcGk6PQz4faRHf/ZQ9bY5HW4ZS5Ic9vRxaainzRGxMiI6RET3iOhOctzl7IgoK064jSKf/9uPk5yQgKQOJENO7+3SKBtXPm1eAJwKIOlIkgTRlJ8/PAW4JD2b6SRgZUQs2ZkN7jVDTBGxRdK1wNMkZ0BMiIg5kkYDZRExBRhP0g2dT9JzGF68iHdenm3+T6At8Eh6PH5BRJxdtKB3Up5tblLybPPTwEBJc4GtwPURsax4Ue+cPNv8XeBnkr5DMtRy2Z78g0/SwyRDhB3S4yo3Ai0AImIMyXGWQcB8YB1w+U6/5x78eZmZWQHtTUNMZmbWAE4QZmaWyQnCzMwyOUGYmVkmJwgzM8vkBGHWAJK2SiqX9BdJv5V0QCNv/zJJd6fToyR9rzG3b9YQThBmDbM+IvpExNEk18p8o9gBmRWKE4TZjnuFnJuhSbpe0qz0Xvw35ZRfkpa9IWliWjYkfebIbEkzJH2yCPGb1WmvuZLarDFJak5yG4fx6fxAknsbnUhy07Qpkv4FWEZyn6t+EfGRpE+km3gJOCkiQtKVwP8hufLXbLfhBGHWMK0llQPdgdeB6Wn5wPQ1O51vS5IwjgUejYiPACKi6uaPXYBfpffrbwn8bZdEb9YAHmIya5j1EdEHOIhkx151DELAD9PjE30i4pCIGJ+WZ93P5qfA3RHRG/gayY3kzHYrThBmOyAiVgL/BnxPUguSm8Z9VVJbAEmdJXUCngXOl3RgWl41xLQ/sCidvhSz3ZCHmMx2UETMlvQGMDwiJqa3lH4lvSvuGuAr6R1GbwGel7SVZAjqMpKnnT0iaRHJLccPLkYbzOriu7mamVkmDzGZmVkmJwgzM8vkBGFmZpmcIMzMLJMThJmZZXKCMDOzTE4QZmaW6f8Dl7ngQCwSptUAAAAASUVORK5CYII=\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 = ['k3','k2','Tree']\n",
    "model = [k3, k2, 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/China/By_model/%s/PR_plot/PR_%s.png' %(clf_name, md_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>k7</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5942</td>\n",
       "      <td>971</td>\n",
       "      <td>1327</td>\n",
       "      <td>619</td>\n",
       "      <td>0.820522</td>\n",
       "      <td>0.905655</td>\n",
       "      <td>0.859540</td>\n",
       "      <td>0.881995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k6</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5932</td>\n",
       "      <td>1031</td>\n",
       "      <td>1267</td>\n",
       "      <td>629</td>\n",
       "      <td>0.812620</td>\n",
       "      <td>0.904130</td>\n",
       "      <td>0.851932</td>\n",
       "      <td>0.877255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k5</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5894</td>\n",
       "      <td>1220</td>\n",
       "      <td>1078</td>\n",
       "      <td>667</td>\n",
       "      <td>0.786996</td>\n",
       "      <td>0.898339</td>\n",
       "      <td>0.828507</td>\n",
       "      <td>0.862011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tree</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5904</td>\n",
       "      <td>1188</td>\n",
       "      <td>1110</td>\n",
       "      <td>657</td>\n",
       "      <td>0.791737</td>\n",
       "      <td>0.899863</td>\n",
       "      <td>0.832487</td>\n",
       "      <td>0.864865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tree</td>\n",
       "      <td>gnb</td>\n",
       "      <td>5979</td>\n",
       "      <td>1514</td>\n",
       "      <td>784</td>\n",
       "      <td>582</td>\n",
       "      <td>0.763404</td>\n",
       "      <td>0.911294</td>\n",
       "      <td>0.797945</td>\n",
       "      <td>0.850861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k3</td>\n",
       "      <td>dtree</td>\n",
       "      <td>6096</td>\n",
       "      <td>1559</td>\n",
       "      <td>739</td>\n",
       "      <td>465</td>\n",
       "      <td>0.771532</td>\n",
       "      <td>0.929127</td>\n",
       "      <td>0.796342</td>\n",
       "      <td>0.857625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k2</td>\n",
       "      <td>dtree</td>\n",
       "      <td>6311</td>\n",
       "      <td>1789</td>\n",
       "      <td>509</td>\n",
       "      <td>250</td>\n",
       "      <td>0.769839</td>\n",
       "      <td>0.961896</td>\n",
       "      <td>0.779136</td>\n",
       "      <td>0.860924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tree</td>\n",
       "      <td>dtree</td>\n",
       "      <td>6257</td>\n",
       "      <td>1686</td>\n",
       "      <td>612</td>\n",
       "      <td>304</td>\n",
       "      <td>0.775370</td>\n",
       "      <td>0.953666</td>\n",
       "      <td>0.787738</td>\n",
       "      <td>0.862796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model Classifier    TP    FP    TN   FN  Accuracy    Recall  Precision  \\\n",
       "0    k7        kNN  5942   971  1327  619  0.820522  0.905655   0.859540   \n",
       "1    k6        kNN  5932  1031  1267  629  0.812620  0.904130   0.851932   \n",
       "2    k5        kNN  5894  1220  1078  667  0.786996  0.898339   0.828507   \n",
       "3  Tree        kNN  5904  1188  1110  657  0.791737  0.899863   0.832487   \n",
       "0  Tree        gnb  5979  1514   784  582  0.763404  0.911294   0.797945   \n",
       "0    k3      dtree  6096  1559   739  465  0.771532  0.929127   0.796342   \n",
       "1    k2      dtree  6311  1789   509  250  0.769839  0.961896   0.779136   \n",
       "2  Tree      dtree  6257  1686   612  304  0.775370  0.953666   0.787738   \n",
       "\n",
       "   F1_Score  \n",
       "0  0.881995  \n",
       "1  0.877255  \n",
       "2  0.862011  \n",
       "3  0.864865  \n",
       "0  0.850861  \n",
       "0  0.857625  \n",
       "1  0.860924  \n",
       "2  0.862796  "
      ]
     },
     "execution_count": 22,
     "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 k3 = 0.77 (+/- 0.07)\n",
      "Score of k2 = 0.77 (+/- 0.07)\n"
     ]
    }
   ],
   "source": [
    "clf_name='SVM'\n",
    "parent_dir = \"/Volumes/BECCA/Project/Data/Result/China/By_model\"\n",
    "path = os.path.join(parent_dir, clf_name)\n",
    "os.mkdir(path) \n",
    "os.mkdir(path+'/PR_plot') \n",
    "\n",
    "k7=np.array(data)[:,1:8].astype('float')\n",
    "k6=np.array(data)[:,2:8].astype('float')\n",
    "k5=np.array(data)[:,3:8].astype('float')\n",
    "k4=np.array(data)[:,3:7].astype('float')\n",
    "k3=np.array(data[['repeat_tag','freq_tag','hour']]).astype('float')\n",
    "k2=np.array(data[['freq_tag','hour']]).astype('float')\n",
    "Tree=np.array(data[['polarity_number','subjectivity_number','hour','minute']]).astype('float')\n",
    "y=np.array(data)[:,8].astype('int')\n",
    "\n",
    "clf = svm.SVC(kernel='poly')\n",
    "\n",
    "scores_1 = cross_val_score(clf, k7, y, cv=10)\n",
    "scores_2 = cross_val_score(clf, k6, y, cv=10)\n",
    "scores_3 = cross_val_score(clf, k5, y, cv=10)\n",
    "scores_4 = cross_val_score(clf, k4, y, cv=10)\n",
    "scores_5 = cross_val_score(clf, k3, y, cv=10)\n",
    "scores_6 = cross_val_score(clf, k2, 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(['k7','k6','k5','k4','k3','k2','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"
    }
   ],
   "source": [
    "name = ['k3','k2']\n",
    "model = [k3, k2]\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/China/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>k7</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5942</td>\n",
       "      <td>971</td>\n",
       "      <td>1327</td>\n",
       "      <td>619</td>\n",
       "      <td>0.820522</td>\n",
       "      <td>0.905655</td>\n",
       "      <td>0.859540</td>\n",
       "      <td>0.881995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k6</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5932</td>\n",
       "      <td>1031</td>\n",
       "      <td>1267</td>\n",
       "      <td>629</td>\n",
       "      <td>0.812620</td>\n",
       "      <td>0.904130</td>\n",
       "      <td>0.851932</td>\n",
       "      <td>0.877255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k5</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5894</td>\n",
       "      <td>1220</td>\n",
       "      <td>1078</td>\n",
       "      <td>667</td>\n",
       "      <td>0.786996</td>\n",
       "      <td>0.898339</td>\n",
       "      <td>0.828507</td>\n",
       "      <td>0.862011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tree</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5904</td>\n",
       "      <td>1188</td>\n",
       "      <td>1110</td>\n",
       "      <td>657</td>\n",
       "      <td>0.791737</td>\n",
       "      <td>0.899863</td>\n",
       "      <td>0.832487</td>\n",
       "      <td>0.864865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tree</td>\n",
       "      <td>gnb</td>\n",
       "      <td>5979</td>\n",
       "      <td>1514</td>\n",
       "      <td>784</td>\n",
       "      <td>582</td>\n",
       "      <td>0.763404</td>\n",
       "      <td>0.911294</td>\n",
       "      <td>0.797945</td>\n",
       "      <td>0.850861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k3</td>\n",
       "      <td>dtree</td>\n",
       "      <td>6096</td>\n",
       "      <td>1559</td>\n",
       "      <td>739</td>\n",
       "      <td>465</td>\n",
       "      <td>0.771532</td>\n",
       "      <td>0.929127</td>\n",
       "      <td>0.796342</td>\n",
       "      <td>0.857625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k2</td>\n",
       "      <td>dtree</td>\n",
       "      <td>6311</td>\n",
       "      <td>1789</td>\n",
       "      <td>509</td>\n",
       "      <td>250</td>\n",
       "      <td>0.769839</td>\n",
       "      <td>0.961896</td>\n",
       "      <td>0.779136</td>\n",
       "      <td>0.860924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tree</td>\n",
       "      <td>dtree</td>\n",
       "      <td>6257</td>\n",
       "      <td>1686</td>\n",
       "      <td>612</td>\n",
       "      <td>304</td>\n",
       "      <td>0.775370</td>\n",
       "      <td>0.953666</td>\n",
       "      <td>0.787738</td>\n",
       "      <td>0.862796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k3</td>\n",
       "      <td>SVM</td>\n",
       "      <td>6096</td>\n",
       "      <td>1559</td>\n",
       "      <td>739</td>\n",
       "      <td>465</td>\n",
       "      <td>0.771532</td>\n",
       "      <td>0.929127</td>\n",
       "      <td>0.796342</td>\n",
       "      <td>0.857625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k2</td>\n",
       "      <td>SVM</td>\n",
       "      <td>6081</td>\n",
       "      <td>1557</td>\n",
       "      <td>741</td>\n",
       "      <td>480</td>\n",
       "      <td>0.770064</td>\n",
       "      <td>0.926840</td>\n",
       "      <td>0.796151</td>\n",
       "      <td>0.856539</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model Classifier    TP    FP    TN   FN  Accuracy    Recall  Precision  \\\n",
       "0    k7        kNN  5942   971  1327  619  0.820522  0.905655   0.859540   \n",
       "1    k6        kNN  5932  1031  1267  629  0.812620  0.904130   0.851932   \n",
       "2    k5        kNN  5894  1220  1078  667  0.786996  0.898339   0.828507   \n",
       "3  Tree        kNN  5904  1188  1110  657  0.791737  0.899863   0.832487   \n",
       "0  Tree        gnb  5979  1514   784  582  0.763404  0.911294   0.797945   \n",
       "0    k3      dtree  6096  1559   739  465  0.771532  0.929127   0.796342   \n",
       "1    k2      dtree  6311  1789   509  250  0.769839  0.961896   0.779136   \n",
       "2  Tree      dtree  6257  1686   612  304  0.775370  0.953666   0.787738   \n",
       "0    k3        SVM  6096  1559   739  465  0.771532  0.929127   0.796342   \n",
       "1    k2        SVM  6081  1557   741  480  0.770064  0.926840   0.796151   \n",
       "\n",
       "   F1_Score  \n",
       "0  0.881995  \n",
       "1  0.877255  \n",
       "2  0.862011  \n",
       "3  0.864865  \n",
       "0  0.850861  \n",
       "0  0.857625  \n",
       "1  0.860924  \n",
       "2  0.862796  \n",
       "0  0.857625  \n",
       "1  0.856539  "
      ]
     },
     "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 = 0.77 (+/- 0.07)\n",
      "Score of k5 = 0.77 (+/- 0.07)\n",
      "Score of k4 = 0.77 (+/- 0.07)\n",
      "Score of k3 = 0.77 (+/- 0.07)\n",
      "Score of k2 = 0.77 (+/- 0.07)\n"
     ]
    }
   ],
   "source": [
    "clf_name='nn'\n",
    "parent_dir = \"/Volumes/BECCA/Project/Data/Result/China/By_model\"\n",
    "path = os.path.join(parent_dir, clf_name)\n",
    "os.mkdir(path) \n",
    "os.mkdir(path+'/PR_plot') \n",
    "\n",
    "k7=np.array(data)[:,1:8].astype('float')\n",
    "k6=np.array(data)[:,2:8].astype('float')\n",
    "k5=np.array(data)[:,3:8].astype('float')\n",
    "k4=np.array(data)[:,3:7].astype('float')\n",
    "k3=np.array(data[['repeat_tag','freq_tag','hour']]).astype('float')\n",
    "k2=np.array(data[['freq_tag','hour']]).astype('float')\n",
    "Tree=np.array(data[['polarity_number','subjectivity_number','hour','minute']]).astype('float')\n",
    "y=np.array(data)[:,8].astype('int')\n",
    "\n",
    "clf = MLPClassifier(activation='identity',solver='adam', max_iter=1000)\n",
    "\n",
    "scores_1 = cross_val_score(clf, k7, y, cv=10)\n",
    "scores_2 = cross_val_score(clf, k6, y, cv=10)\n",
    "scores_3 = cross_val_score(clf, k5, y, cv=10)\n",
    "scores_4 = cross_val_score(clf, k4, y, cv=10)\n",
    "scores_5 = cross_val_score(clf, k3, y, cv=10)\n",
    "scores_6 = cross_val_score(clf, k2, 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(['k7','k6','k5','k4','k3','k2','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','k3','k2']\n",
    "model = [k6, k5, k4, k3, k2]\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/China/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>k7</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5942</td>\n",
       "      <td>971</td>\n",
       "      <td>1327</td>\n",
       "      <td>619</td>\n",
       "      <td>0.820522</td>\n",
       "      <td>0.905655</td>\n",
       "      <td>0.859540</td>\n",
       "      <td>0.881995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k6</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5932</td>\n",
       "      <td>1031</td>\n",
       "      <td>1267</td>\n",
       "      <td>629</td>\n",
       "      <td>0.812620</td>\n",
       "      <td>0.904130</td>\n",
       "      <td>0.851932</td>\n",
       "      <td>0.877255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k5</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5894</td>\n",
       "      <td>1220</td>\n",
       "      <td>1078</td>\n",
       "      <td>667</td>\n",
       "      <td>0.786996</td>\n",
       "      <td>0.898339</td>\n",
       "      <td>0.828507</td>\n",
       "      <td>0.862011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tree</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5904</td>\n",
       "      <td>1188</td>\n",
       "      <td>1110</td>\n",
       "      <td>657</td>\n",
       "      <td>0.791737</td>\n",
       "      <td>0.899863</td>\n",
       "      <td>0.832487</td>\n",
       "      <td>0.864865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tree</td>\n",
       "      <td>gnb</td>\n",
       "      <td>5979</td>\n",
       "      <td>1514</td>\n",
       "      <td>784</td>\n",
       "      <td>582</td>\n",
       "      <td>0.763404</td>\n",
       "      <td>0.911294</td>\n",
       "      <td>0.797945</td>\n",
       "      <td>0.850861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k3</td>\n",
       "      <td>dtree</td>\n",
       "      <td>6096</td>\n",
       "      <td>1559</td>\n",
       "      <td>739</td>\n",
       "      <td>465</td>\n",
       "      <td>0.771532</td>\n",
       "      <td>0.929127</td>\n",
       "      <td>0.796342</td>\n",
       "      <td>0.857625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k2</td>\n",
       "      <td>dtree</td>\n",
       "      <td>6311</td>\n",
       "      <td>1789</td>\n",
       "      <td>509</td>\n",
       "      <td>250</td>\n",
       "      <td>0.769839</td>\n",
       "      <td>0.961896</td>\n",
       "      <td>0.779136</td>\n",
       "      <td>0.860924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tree</td>\n",
       "      <td>dtree</td>\n",
       "      <td>6257</td>\n",
       "      <td>1686</td>\n",
       "      <td>612</td>\n",
       "      <td>304</td>\n",
       "      <td>0.775370</td>\n",
       "      <td>0.953666</td>\n",
       "      <td>0.787738</td>\n",
       "      <td>0.862796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k3</td>\n",
       "      <td>SVM</td>\n",
       "      <td>6096</td>\n",
       "      <td>1559</td>\n",
       "      <td>739</td>\n",
       "      <td>465</td>\n",
       "      <td>0.771532</td>\n",
       "      <td>0.929127</td>\n",
       "      <td>0.796342</td>\n",
       "      <td>0.857625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k2</td>\n",
       "      <td>SVM</td>\n",
       "      <td>6081</td>\n",
       "      <td>1557</td>\n",
       "      <td>741</td>\n",
       "      <td>480</td>\n",
       "      <td>0.770064</td>\n",
       "      <td>0.926840</td>\n",
       "      <td>0.796151</td>\n",
       "      <td>0.856539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>nn</td>\n",
       "      <td>6069</td>\n",
       "      <td>1506</td>\n",
       "      <td>792</td>\n",
       "      <td>492</td>\n",
       "      <td>0.774467</td>\n",
       "      <td>0.925011</td>\n",
       "      <td>0.801188</td>\n",
       "      <td>0.858659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>nn</td>\n",
       "      <td>6050</td>\n",
       "      <td>1485</td>\n",
       "      <td>813</td>\n",
       "      <td>511</td>\n",
       "      <td>0.774692</td>\n",
       "      <td>0.922116</td>\n",
       "      <td>0.802920</td>\n",
       "      <td>0.858400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>nn</td>\n",
       "      <td>5980</td>\n",
       "      <td>1427</td>\n",
       "      <td>871</td>\n",
       "      <td>581</td>\n",
       "      <td>0.773338</td>\n",
       "      <td>0.911446</td>\n",
       "      <td>0.807344</td>\n",
       "      <td>0.856243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>k3</td>\n",
       "      <td>nn</td>\n",
       "      <td>6006</td>\n",
       "      <td>1459</td>\n",
       "      <td>839</td>\n",
       "      <td>555</td>\n",
       "      <td>0.772661</td>\n",
       "      <td>0.915409</td>\n",
       "      <td>0.804555</td>\n",
       "      <td>0.856410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>k2</td>\n",
       "      <td>nn</td>\n",
       "      <td>6081</td>\n",
       "      <td>1557</td>\n",
       "      <td>741</td>\n",
       "      <td>480</td>\n",
       "      <td>0.770064</td>\n",
       "      <td>0.926840</td>\n",
       "      <td>0.796151</td>\n",
       "      <td>0.856539</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model Classifier    TP    FP    TN   FN  Accuracy    Recall  Precision  \\\n",
       "0    k7        kNN  5942   971  1327  619  0.820522  0.905655   0.859540   \n",
       "1    k6        kNN  5932  1031  1267  629  0.812620  0.904130   0.851932   \n",
       "2    k5        kNN  5894  1220  1078  667  0.786996  0.898339   0.828507   \n",
       "3  Tree        kNN  5904  1188  1110  657  0.791737  0.899863   0.832487   \n",
       "0  Tree        gnb  5979  1514   784  582  0.763404  0.911294   0.797945   \n",
       "0    k3      dtree  6096  1559   739  465  0.771532  0.929127   0.796342   \n",
       "1    k2      dtree  6311  1789   509  250  0.769839  0.961896   0.779136   \n",
       "2  Tree      dtree  6257  1686   612  304  0.775370  0.953666   0.787738   \n",
       "0    k3        SVM  6096  1559   739  465  0.771532  0.929127   0.796342   \n",
       "1    k2        SVM  6081  1557   741  480  0.770064  0.926840   0.796151   \n",
       "0    k6         nn  6069  1506   792  492  0.774467  0.925011   0.801188   \n",
       "1    k5         nn  6050  1485   813  511  0.774692  0.922116   0.802920   \n",
       "2    k4         nn  5980  1427   871  581  0.773338  0.911446   0.807344   \n",
       "3    k3         nn  6006  1459   839  555  0.772661  0.915409   0.804555   \n",
       "4    k2         nn  6081  1557   741  480  0.770064  0.926840   0.796151   \n",
       "\n",
       "   F1_Score  \n",
       "0  0.881995  \n",
       "1  0.877255  \n",
       "2  0.862011  \n",
       "3  0.864865  \n",
       "0  0.850861  \n",
       "0  0.857625  \n",
       "1  0.860924  \n",
       "2  0.862796  \n",
       "0  0.857625  \n",
       "1  0.856539  \n",
       "0  0.858659  \n",
       "1  0.858400  \n",
       "2  0.856243  \n",
       "3  0.856410  \n",
       "4  0.856539  "
      ]
     },
     "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 k5 = 0.77 (+/- 0.07)\n",
      "Score of k3 = 0.77 (+/- 0.07)\n",
      "Score of k2 = 0.77 (+/- 0.07)\n"
     ]
    }
   ],
   "source": [
    "clf_name='lr'\n",
    "parent_dir = \"/Volumes/BECCA/Project/Data/Result/China/By_model\"\n",
    "path = os.path.join(parent_dir, clf_name)\n",
    "os.mkdir(path) \n",
    "os.mkdir(path+'/PR_plot') \n",
    "\n",
    "k7=np.array(data)[:,1:8].astype('float')\n",
    "k6=np.array(data)[:,2:8].astype('float')\n",
    "k5=np.array(data)[:,3:8].astype('float')\n",
    "k4=np.array(data)[:,3:7].astype('float')\n",
    "k3=np.array(data[['repeat_tag','freq_tag','hour']]).astype('float')\n",
    "k2=np.array(data[['freq_tag','hour']]).astype('float')\n",
    "Tree=np.array(data[['polarity_number','subjectivity_number','hour','minute']]).astype('float')\n",
    "y=np.array(data)[:,8].astype('int')\n",
    "\n",
    "clf = LogisticRegression(random_state=0)\n",
    "\n",
    "scores_1 = cross_val_score(clf, k7, y, cv=10)\n",
    "scores_2 = cross_val_score(clf, k6, y, cv=10)\n",
    "scores_3 = cross_val_score(clf, k5, y, cv=10)\n",
    "scores_4 = cross_val_score(clf, k4, y, cv=10)\n",
    "scores_5 = cross_val_score(clf, k3, y, cv=10)\n",
    "scores_6 = cross_val_score(clf, k2, 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(['k7','k6','k5','k4','k3','k2','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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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = ['k5','k3','k2']\n",
    "model = [k5, k3, k2]\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/China/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>k7</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5942</td>\n",
       "      <td>971</td>\n",
       "      <td>1327</td>\n",
       "      <td>619</td>\n",
       "      <td>0.820522</td>\n",
       "      <td>0.905655</td>\n",
       "      <td>0.859540</td>\n",
       "      <td>0.881995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k6</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5932</td>\n",
       "      <td>1031</td>\n",
       "      <td>1267</td>\n",
       "      <td>629</td>\n",
       "      <td>0.812620</td>\n",
       "      <td>0.904130</td>\n",
       "      <td>0.851932</td>\n",
       "      <td>0.877255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k5</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5894</td>\n",
       "      <td>1220</td>\n",
       "      <td>1078</td>\n",
       "      <td>667</td>\n",
       "      <td>0.786996</td>\n",
       "      <td>0.898339</td>\n",
       "      <td>0.828507</td>\n",
       "      <td>0.862011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tree</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5904</td>\n",
       "      <td>1188</td>\n",
       "      <td>1110</td>\n",
       "      <td>657</td>\n",
       "      <td>0.791737</td>\n",
       "      <td>0.899863</td>\n",
       "      <td>0.832487</td>\n",
       "      <td>0.864865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tree</td>\n",
       "      <td>gnb</td>\n",
       "      <td>5979</td>\n",
       "      <td>1514</td>\n",
       "      <td>784</td>\n",
       "      <td>582</td>\n",
       "      <td>0.763404</td>\n",
       "      <td>0.911294</td>\n",
       "      <td>0.797945</td>\n",
       "      <td>0.850861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k3</td>\n",
       "      <td>dtree</td>\n",
       "      <td>6096</td>\n",
       "      <td>1559</td>\n",
       "      <td>739</td>\n",
       "      <td>465</td>\n",
       "      <td>0.771532</td>\n",
       "      <td>0.929127</td>\n",
       "      <td>0.796342</td>\n",
       "      <td>0.857625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k2</td>\n",
       "      <td>dtree</td>\n",
       "      <td>6311</td>\n",
       "      <td>1789</td>\n",
       "      <td>509</td>\n",
       "      <td>250</td>\n",
       "      <td>0.769839</td>\n",
       "      <td>0.961896</td>\n",
       "      <td>0.779136</td>\n",
       "      <td>0.860924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tree</td>\n",
       "      <td>dtree</td>\n",
       "      <td>6257</td>\n",
       "      <td>1686</td>\n",
       "      <td>612</td>\n",
       "      <td>304</td>\n",
       "      <td>0.775370</td>\n",
       "      <td>0.953666</td>\n",
       "      <td>0.787738</td>\n",
       "      <td>0.862796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k3</td>\n",
       "      <td>SVM</td>\n",
       "      <td>6096</td>\n",
       "      <td>1559</td>\n",
       "      <td>739</td>\n",
       "      <td>465</td>\n",
       "      <td>0.771532</td>\n",
       "      <td>0.929127</td>\n",
       "      <td>0.796342</td>\n",
       "      <td>0.857625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k2</td>\n",
       "      <td>SVM</td>\n",
       "      <td>6081</td>\n",
       "      <td>1557</td>\n",
       "      <td>741</td>\n",
       "      <td>480</td>\n",
       "      <td>0.770064</td>\n",
       "      <td>0.926840</td>\n",
       "      <td>0.796151</td>\n",
       "      <td>0.856539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>nn</td>\n",
       "      <td>6069</td>\n",
       "      <td>1506</td>\n",
       "      <td>792</td>\n",
       "      <td>492</td>\n",
       "      <td>0.774467</td>\n",
       "      <td>0.925011</td>\n",
       "      <td>0.801188</td>\n",
       "      <td>0.858659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>nn</td>\n",
       "      <td>6050</td>\n",
       "      <td>1485</td>\n",
       "      <td>813</td>\n",
       "      <td>511</td>\n",
       "      <td>0.774692</td>\n",
       "      <td>0.922116</td>\n",
       "      <td>0.802920</td>\n",
       "      <td>0.858400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>nn</td>\n",
       "      <td>5980</td>\n",
       "      <td>1427</td>\n",
       "      <td>871</td>\n",
       "      <td>581</td>\n",
       "      <td>0.773338</td>\n",
       "      <td>0.911446</td>\n",
       "      <td>0.807344</td>\n",
       "      <td>0.856243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>k3</td>\n",
       "      <td>nn</td>\n",
       "      <td>6006</td>\n",
       "      <td>1459</td>\n",
       "      <td>839</td>\n",
       "      <td>555</td>\n",
       "      <td>0.772661</td>\n",
       "      <td>0.915409</td>\n",
       "      <td>0.804555</td>\n",
       "      <td>0.856410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>k2</td>\n",
       "      <td>nn</td>\n",
       "      <td>6081</td>\n",
       "      <td>1557</td>\n",
       "      <td>741</td>\n",
       "      <td>480</td>\n",
       "      <td>0.770064</td>\n",
       "      <td>0.926840</td>\n",
       "      <td>0.796151</td>\n",
       "      <td>0.856539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k5</td>\n",
       "      <td>ld</td>\n",
       "      <td>5955</td>\n",
       "      <td>1405</td>\n",
       "      <td>893</td>\n",
       "      <td>606</td>\n",
       "      <td>0.772999</td>\n",
       "      <td>0.907636</td>\n",
       "      <td>0.809103</td>\n",
       "      <td>0.855542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k3</td>\n",
       "      <td>ld</td>\n",
       "      <td>5894</td>\n",
       "      <td>1345</td>\n",
       "      <td>953</td>\n",
       "      <td>667</td>\n",
       "      <td>0.772886</td>\n",
       "      <td>0.898339</td>\n",
       "      <td>0.814201</td>\n",
       "      <td>0.854203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k2</td>\n",
       "      <td>ld</td>\n",
       "      <td>5987</td>\n",
       "      <td>1457</td>\n",
       "      <td>841</td>\n",
       "      <td>574</td>\n",
       "      <td>0.770742</td>\n",
       "      <td>0.912513</td>\n",
       "      <td>0.804272</td>\n",
       "      <td>0.854980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k5</td>\n",
       "      <td>lr</td>\n",
       "      <td>6019</td>\n",
       "      <td>1448</td>\n",
       "      <td>850</td>\n",
       "      <td>542</td>\n",
       "      <td>0.775370</td>\n",
       "      <td>0.917391</td>\n",
       "      <td>0.806080</td>\n",
       "      <td>0.858141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k3</td>\n",
       "      <td>lr</td>\n",
       "      <td>6006</td>\n",
       "      <td>1459</td>\n",
       "      <td>839</td>\n",
       "      <td>555</td>\n",
       "      <td>0.772661</td>\n",
       "      <td>0.915409</td>\n",
       "      <td>0.804555</td>\n",
       "      <td>0.856410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k2</td>\n",
       "      <td>lr</td>\n",
       "      <td>6081</td>\n",
       "      <td>1557</td>\n",
       "      <td>741</td>\n",
       "      <td>480</td>\n",
       "      <td>0.770064</td>\n",
       "      <td>0.926840</td>\n",
       "      <td>0.796151</td>\n",
       "      <td>0.856539</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model Classifier    TP    FP    TN   FN  Accuracy    Recall  Precision  \\\n",
       "0    k7        kNN  5942   971  1327  619  0.820522  0.905655   0.859540   \n",
       "1    k6        kNN  5932  1031  1267  629  0.812620  0.904130   0.851932   \n",
       "2    k5        kNN  5894  1220  1078  667  0.786996  0.898339   0.828507   \n",
       "3  Tree        kNN  5904  1188  1110  657  0.791737  0.899863   0.832487   \n",
       "0  Tree        gnb  5979  1514   784  582  0.763404  0.911294   0.797945   \n",
       "0    k3      dtree  6096  1559   739  465  0.771532  0.929127   0.796342   \n",
       "1    k2      dtree  6311  1789   509  250  0.769839  0.961896   0.779136   \n",
       "2  Tree      dtree  6257  1686   612  304  0.775370  0.953666   0.787738   \n",
       "0    k3        SVM  6096  1559   739  465  0.771532  0.929127   0.796342   \n",
       "1    k2        SVM  6081  1557   741  480  0.770064  0.926840   0.796151   \n",
       "0    k6         nn  6069  1506   792  492  0.774467  0.925011   0.801188   \n",
       "1    k5         nn  6050  1485   813  511  0.774692  0.922116   0.802920   \n",
       "2    k4         nn  5980  1427   871  581  0.773338  0.911446   0.807344   \n",
       "3    k3         nn  6006  1459   839  555  0.772661  0.915409   0.804555   \n",
       "4    k2         nn  6081  1557   741  480  0.770064  0.926840   0.796151   \n",
       "0    k5         ld  5955  1405   893  606  0.772999  0.907636   0.809103   \n",
       "1    k3         ld  5894  1345   953  667  0.772886  0.898339   0.814201   \n",
       "2    k2         ld  5987  1457   841  574  0.770742  0.912513   0.804272   \n",
       "0    k5         lr  6019  1448   850  542  0.775370  0.917391   0.806080   \n",
       "1    k3         lr  6006  1459   839  555  0.772661  0.915409   0.804555   \n",
       "2    k2         lr  6081  1557   741  480  0.770064  0.926840   0.796151   \n",
       "\n",
       "   F1_Score  \n",
       "0  0.881995  \n",
       "1  0.877255  \n",
       "2  0.862011  \n",
       "3  0.864865  \n",
       "0  0.850861  \n",
       "0  0.857625  \n",
       "1  0.860924  \n",
       "2  0.862796  \n",
       "0  0.857625  \n",
       "1  0.856539  \n",
       "0  0.858659  \n",
       "1  0.858400  \n",
       "2  0.856243  \n",
       "3  0.856410  \n",
       "4  0.856539  \n",
       "0  0.855542  \n",
       "1  0.854203  \n",
       "2  0.854980  \n",
       "0  0.858141  \n",
       "1  0.856410  \n",
       "2  0.856539  "
      ]
     },
     "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 k5 = 0.77 (+/- 0.07)\n",
      "Score of k3 = 0.77 (+/- 0.07)\n",
      "Score of k2 = 0.77 (+/- 0.07)\n"
     ]
    }
   ],
   "source": [
    "clf_name='sgd'\n",
    "parent_dir = \"/Volumes/BECCA/Project/Data/Result/China/By_model\"\n",
    "path = os.path.join(parent_dir, clf_name)\n",
    "os.mkdir(path) \n",
    "os.mkdir(path+'/PR_plot') \n",
    "\n",
    "k7=np.array(data)[:,1:8].astype('float')\n",
    "k6=np.array(data)[:,2:8].astype('float')\n",
    "k5=np.array(data)[:,3:8].astype('float')\n",
    "k4=np.array(data)[:,3:7].astype('float')\n",
    "k3=np.array(data[['repeat_tag','freq_tag','hour']]).astype('float')\n",
    "k2=np.array(data[['freq_tag','hour']]).astype('float')\n",
    "Tree=np.array(data[['polarity_number','subjectivity_number','hour','minute']]).astype('float')\n",
    "y=np.array(data)[:,8].astype('int')\n",
    "\n",
    "sgd = SGDClassifier(loss=\"hinge\", penalty=\"elasticnet\", max_iter=1000)\n",
    "\n",
    "scores_1 = cross_val_score(clf, k7, y, cv=10)\n",
    "scores_2 = cross_val_score(clf, k6, y, cv=10)\n",
    "scores_3 = cross_val_score(clf, k5, y, cv=10)\n",
    "scores_4 = cross_val_score(clf, k4, y, cv=10)\n",
    "scores_5 = cross_val_score(clf, k3, y, cv=10)\n",
    "scores_6 = cross_val_score(clf, k2, 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(['k7','k6','k5','k4','k3','k2','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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\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 = ['k5', 'k3', 'k2']\n",
    "model = [k5, k3, k2]\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/China/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>k7</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5942</td>\n",
       "      <td>971</td>\n",
       "      <td>1327</td>\n",
       "      <td>619</td>\n",
       "      <td>0.820522</td>\n",
       "      <td>0.905655</td>\n",
       "      <td>0.859540</td>\n",
       "      <td>0.881995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k6</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5932</td>\n",
       "      <td>1031</td>\n",
       "      <td>1267</td>\n",
       "      <td>629</td>\n",
       "      <td>0.812620</td>\n",
       "      <td>0.904130</td>\n",
       "      <td>0.851932</td>\n",
       "      <td>0.877255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k5</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5894</td>\n",
       "      <td>1220</td>\n",
       "      <td>1078</td>\n",
       "      <td>667</td>\n",
       "      <td>0.786996</td>\n",
       "      <td>0.898339</td>\n",
       "      <td>0.828507</td>\n",
       "      <td>0.862011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tree</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5904</td>\n",
       "      <td>1188</td>\n",
       "      <td>1110</td>\n",
       "      <td>657</td>\n",
       "      <td>0.791737</td>\n",
       "      <td>0.899863</td>\n",
       "      <td>0.832487</td>\n",
       "      <td>0.864865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tree</td>\n",
       "      <td>gnb</td>\n",
       "      <td>5979</td>\n",
       "      <td>1514</td>\n",
       "      <td>784</td>\n",
       "      <td>582</td>\n",
       "      <td>0.763404</td>\n",
       "      <td>0.911294</td>\n",
       "      <td>0.797945</td>\n",
       "      <td>0.850861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k3</td>\n",
       "      <td>dtree</td>\n",
       "      <td>6096</td>\n",
       "      <td>1559</td>\n",
       "      <td>739</td>\n",
       "      <td>465</td>\n",
       "      <td>0.771532</td>\n",
       "      <td>0.929127</td>\n",
       "      <td>0.796342</td>\n",
       "      <td>0.857625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k2</td>\n",
       "      <td>dtree</td>\n",
       "      <td>6311</td>\n",
       "      <td>1789</td>\n",
       "      <td>509</td>\n",
       "      <td>250</td>\n",
       "      <td>0.769839</td>\n",
       "      <td>0.961896</td>\n",
       "      <td>0.779136</td>\n",
       "      <td>0.860924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tree</td>\n",
       "      <td>dtree</td>\n",
       "      <td>6257</td>\n",
       "      <td>1686</td>\n",
       "      <td>612</td>\n",
       "      <td>304</td>\n",
       "      <td>0.775370</td>\n",
       "      <td>0.953666</td>\n",
       "      <td>0.787738</td>\n",
       "      <td>0.862796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k3</td>\n",
       "      <td>SVM</td>\n",
       "      <td>6096</td>\n",
       "      <td>1559</td>\n",
       "      <td>739</td>\n",
       "      <td>465</td>\n",
       "      <td>0.771532</td>\n",
       "      <td>0.929127</td>\n",
       "      <td>0.796342</td>\n",
       "      <td>0.857625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k2</td>\n",
       "      <td>SVM</td>\n",
       "      <td>6081</td>\n",
       "      <td>1557</td>\n",
       "      <td>741</td>\n",
       "      <td>480</td>\n",
       "      <td>0.770064</td>\n",
       "      <td>0.926840</td>\n",
       "      <td>0.796151</td>\n",
       "      <td>0.856539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k6</td>\n",
       "      <td>nn</td>\n",
       "      <td>6069</td>\n",
       "      <td>1506</td>\n",
       "      <td>792</td>\n",
       "      <td>492</td>\n",
       "      <td>0.774467</td>\n",
       "      <td>0.925011</td>\n",
       "      <td>0.801188</td>\n",
       "      <td>0.858659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k5</td>\n",
       "      <td>nn</td>\n",
       "      <td>6050</td>\n",
       "      <td>1485</td>\n",
       "      <td>813</td>\n",
       "      <td>511</td>\n",
       "      <td>0.774692</td>\n",
       "      <td>0.922116</td>\n",
       "      <td>0.802920</td>\n",
       "      <td>0.858400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k4</td>\n",
       "      <td>nn</td>\n",
       "      <td>5980</td>\n",
       "      <td>1427</td>\n",
       "      <td>871</td>\n",
       "      <td>581</td>\n",
       "      <td>0.773338</td>\n",
       "      <td>0.911446</td>\n",
       "      <td>0.807344</td>\n",
       "      <td>0.856243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>k3</td>\n",
       "      <td>nn</td>\n",
       "      <td>6006</td>\n",
       "      <td>1459</td>\n",
       "      <td>839</td>\n",
       "      <td>555</td>\n",
       "      <td>0.772661</td>\n",
       "      <td>0.915409</td>\n",
       "      <td>0.804555</td>\n",
       "      <td>0.856410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>k2</td>\n",
       "      <td>nn</td>\n",
       "      <td>6081</td>\n",
       "      <td>1557</td>\n",
       "      <td>741</td>\n",
       "      <td>480</td>\n",
       "      <td>0.770064</td>\n",
       "      <td>0.926840</td>\n",
       "      <td>0.796151</td>\n",
       "      <td>0.856539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k5</td>\n",
       "      <td>ld</td>\n",
       "      <td>5955</td>\n",
       "      <td>1405</td>\n",
       "      <td>893</td>\n",
       "      <td>606</td>\n",
       "      <td>0.772999</td>\n",
       "      <td>0.907636</td>\n",
       "      <td>0.809103</td>\n",
       "      <td>0.855542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k3</td>\n",
       "      <td>ld</td>\n",
       "      <td>5894</td>\n",
       "      <td>1345</td>\n",
       "      <td>953</td>\n",
       "      <td>667</td>\n",
       "      <td>0.772886</td>\n",
       "      <td>0.898339</td>\n",
       "      <td>0.814201</td>\n",
       "      <td>0.854203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k2</td>\n",
       "      <td>ld</td>\n",
       "      <td>5987</td>\n",
       "      <td>1457</td>\n",
       "      <td>841</td>\n",
       "      <td>574</td>\n",
       "      <td>0.770742</td>\n",
       "      <td>0.912513</td>\n",
       "      <td>0.804272</td>\n",
       "      <td>0.854980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k5</td>\n",
       "      <td>lr</td>\n",
       "      <td>6019</td>\n",
       "      <td>1448</td>\n",
       "      <td>850</td>\n",
       "      <td>542</td>\n",
       "      <td>0.775370</td>\n",
       "      <td>0.917391</td>\n",
       "      <td>0.806080</td>\n",
       "      <td>0.858141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k3</td>\n",
       "      <td>lr</td>\n",
       "      <td>6006</td>\n",
       "      <td>1459</td>\n",
       "      <td>839</td>\n",
       "      <td>555</td>\n",
       "      <td>0.772661</td>\n",
       "      <td>0.915409</td>\n",
       "      <td>0.804555</td>\n",
       "      <td>0.856410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k2</td>\n",
       "      <td>lr</td>\n",
       "      <td>6081</td>\n",
       "      <td>1557</td>\n",
       "      <td>741</td>\n",
       "      <td>480</td>\n",
       "      <td>0.770064</td>\n",
       "      <td>0.926840</td>\n",
       "      <td>0.796151</td>\n",
       "      <td>0.856539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k5</td>\n",
       "      <td>sgd</td>\n",
       "      <td>6019</td>\n",
       "      <td>1448</td>\n",
       "      <td>850</td>\n",
       "      <td>542</td>\n",
       "      <td>0.775370</td>\n",
       "      <td>0.917391</td>\n",
       "      <td>0.806080</td>\n",
       "      <td>0.858141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k3</td>\n",
       "      <td>sgd</td>\n",
       "      <td>6006</td>\n",
       "      <td>1459</td>\n",
       "      <td>839</td>\n",
       "      <td>555</td>\n",
       "      <td>0.772661</td>\n",
       "      <td>0.915409</td>\n",
       "      <td>0.804555</td>\n",
       "      <td>0.856410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k2</td>\n",
       "      <td>sgd</td>\n",
       "      <td>6081</td>\n",
       "      <td>1557</td>\n",
       "      <td>741</td>\n",
       "      <td>480</td>\n",
       "      <td>0.770064</td>\n",
       "      <td>0.926840</td>\n",
       "      <td>0.796151</td>\n",
       "      <td>0.856539</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model Classifier    TP    FP    TN   FN  Accuracy    Recall  Precision  \\\n",
       "0    k7        kNN  5942   971  1327  619  0.820522  0.905655   0.859540   \n",
       "1    k6        kNN  5932  1031  1267  629  0.812620  0.904130   0.851932   \n",
       "2    k5        kNN  5894  1220  1078  667  0.786996  0.898339   0.828507   \n",
       "3  Tree        kNN  5904  1188  1110  657  0.791737  0.899863   0.832487   \n",
       "0  Tree        gnb  5979  1514   784  582  0.763404  0.911294   0.797945   \n",
       "0    k3      dtree  6096  1559   739  465  0.771532  0.929127   0.796342   \n",
       "1    k2      dtree  6311  1789   509  250  0.769839  0.961896   0.779136   \n",
       "2  Tree      dtree  6257  1686   612  304  0.775370  0.953666   0.787738   \n",
       "0    k3        SVM  6096  1559   739  465  0.771532  0.929127   0.796342   \n",
       "1    k2        SVM  6081  1557   741  480  0.770064  0.926840   0.796151   \n",
       "0    k6         nn  6069  1506   792  492  0.774467  0.925011   0.801188   \n",
       "1    k5         nn  6050  1485   813  511  0.774692  0.922116   0.802920   \n",
       "2    k4         nn  5980  1427   871  581  0.773338  0.911446   0.807344   \n",
       "3    k3         nn  6006  1459   839  555  0.772661  0.915409   0.804555   \n",
       "4    k2         nn  6081  1557   741  480  0.770064  0.926840   0.796151   \n",
       "0    k5         ld  5955  1405   893  606  0.772999  0.907636   0.809103   \n",
       "1    k3         ld  5894  1345   953  667  0.772886  0.898339   0.814201   \n",
       "2    k2         ld  5987  1457   841  574  0.770742  0.912513   0.804272   \n",
       "0    k5         lr  6019  1448   850  542  0.775370  0.917391   0.806080   \n",
       "1    k3         lr  6006  1459   839  555  0.772661  0.915409   0.804555   \n",
       "2    k2         lr  6081  1557   741  480  0.770064  0.926840   0.796151   \n",
       "0    k5        sgd  6019  1448   850  542  0.775370  0.917391   0.806080   \n",
       "1    k3        sgd  6006  1459   839  555  0.772661  0.915409   0.804555   \n",
       "2    k2        sgd  6081  1557   741  480  0.770064  0.926840   0.796151   \n",
       "\n",
       "   F1_Score  \n",
       "0  0.881995  \n",
       "1  0.877255  \n",
       "2  0.862011  \n",
       "3  0.864865  \n",
       "0  0.850861  \n",
       "0  0.857625  \n",
       "1  0.860924  \n",
       "2  0.862796  \n",
       "0  0.857625  \n",
       "1  0.856539  \n",
       "0  0.858659  \n",
       "1  0.858400  \n",
       "2  0.856243  \n",
       "3  0.856410  \n",
       "4  0.856539  \n",
       "0  0.855542  \n",
       "1  0.854203  \n",
       "2  0.854980  \n",
       "0  0.858141  \n",
       "1  0.856410  \n",
       "2  0.856539  \n",
       "0  0.858141  \n",
       "1  0.856410  \n",
       "2  0.856539  "
      ]
     },
     "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": 22,
   "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/China/By_model/Total_Result.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>k7</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5942</td>\n",
       "      <td>971</td>\n",
       "      <td>1327</td>\n",
       "      <td>619</td>\n",
       "      <td>0.820522</td>\n",
       "      <td>0.905655</td>\n",
       "      <td>0.859540</td>\n",
       "      <td>0.881995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k6</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5932</td>\n",
       "      <td>1031</td>\n",
       "      <td>1267</td>\n",
       "      <td>629</td>\n",
       "      <td>0.812620</td>\n",
       "      <td>0.904130</td>\n",
       "      <td>0.851932</td>\n",
       "      <td>0.877255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k5</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5894</td>\n",
       "      <td>1220</td>\n",
       "      <td>1078</td>\n",
       "      <td>667</td>\n",
       "      <td>0.786996</td>\n",
       "      <td>0.898339</td>\n",
       "      <td>0.828507</td>\n",
       "      <td>0.862011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tree</td>\n",
       "      <td>kNN</td>\n",
       "      <td>5904</td>\n",
       "      <td>1188</td>\n",
       "      <td>1110</td>\n",
       "      <td>657</td>\n",
       "      <td>0.791737</td>\n",
       "      <td>0.899863</td>\n",
       "      <td>0.832487</td>\n",
       "      <td>0.864865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tree</td>\n",
       "      <td>gnb</td>\n",
       "      <td>5979</td>\n",
       "      <td>1514</td>\n",
       "      <td>784</td>\n",
       "      <td>582</td>\n",
       "      <td>0.763404</td>\n",
       "      <td>0.911294</td>\n",
       "      <td>0.797945</td>\n",
       "      <td>0.850861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>k3</td>\n",
       "      <td>dtree</td>\n",
       "      <td>6096</td>\n",
       "      <td>1559</td>\n",
       "      <td>739</td>\n",
       "      <td>465</td>\n",
       "      <td>0.771532</td>\n",
       "      <td>0.929127</td>\n",
       "      <td>0.796342</td>\n",
       "      <td>0.857625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>k2</td>\n",
       "      <td>dtree</td>\n",
       "      <td>6311</td>\n",
       "      <td>1789</td>\n",
       "      <td>509</td>\n",
       "      <td>250</td>\n",
       "      <td>0.769839</td>\n",
       "      <td>0.961896</td>\n",
       "      <td>0.779136</td>\n",
       "      <td>0.860924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tree</td>\n",
       "      <td>dtree</td>\n",
       "      <td>6257</td>\n",
       "      <td>1686</td>\n",
       "      <td>612</td>\n",
       "      <td>304</td>\n",
       "      <td>0.775370</td>\n",
       "      <td>0.953666</td>\n",
       "      <td>0.787738</td>\n",
       "      <td>0.862796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>k3</td>\n",
       "      <td>SVM</td>\n",
       "      <td>6096</td>\n",
       "      <td>1559</td>\n",
       "      <td>739</td>\n",
       "      <td>465</td>\n",
       "      <td>0.771532</td>\n",
       "      <td>0.929127</td>\n",
       "      <td>0.796342</td>\n",
       "      <td>0.857625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>k2</td>\n",
       "      <td>SVM</td>\n",
       "      <td>6081</td>\n",
       "      <td>1557</td>\n",
       "      <td>741</td>\n",
       "      <td>480</td>\n",
       "      <td>0.770064</td>\n",
       "      <td>0.926840</td>\n",
       "      <td>0.796151</td>\n",
       "      <td>0.856539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>k6</td>\n",
       "      <td>nn</td>\n",
       "      <td>6069</td>\n",
       "      <td>1506</td>\n",
       "      <td>792</td>\n",
       "      <td>492</td>\n",
       "      <td>0.774467</td>\n",
       "      <td>0.925011</td>\n",
       "      <td>0.801188</td>\n",
       "      <td>0.858659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>k5</td>\n",
       "      <td>nn</td>\n",
       "      <td>6050</td>\n",
       "      <td>1485</td>\n",
       "      <td>813</td>\n",
       "      <td>511</td>\n",
       "      <td>0.774692</td>\n",
       "      <td>0.922116</td>\n",
       "      <td>0.802920</td>\n",
       "      <td>0.858400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>k4</td>\n",
       "      <td>nn</td>\n",
       "      <td>5980</td>\n",
       "      <td>1427</td>\n",
       "      <td>871</td>\n",
       "      <td>581</td>\n",
       "      <td>0.773338</td>\n",
       "      <td>0.911446</td>\n",
       "      <td>0.807344</td>\n",
       "      <td>0.856243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>k3</td>\n",
       "      <td>nn</td>\n",
       "      <td>6006</td>\n",
       "      <td>1459</td>\n",
       "      <td>839</td>\n",
       "      <td>555</td>\n",
       "      <td>0.772661</td>\n",
       "      <td>0.915409</td>\n",
       "      <td>0.804555</td>\n",
       "      <td>0.856410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>k2</td>\n",
       "      <td>nn</td>\n",
       "      <td>6081</td>\n",
       "      <td>1557</td>\n",
       "      <td>741</td>\n",
       "      <td>480</td>\n",
       "      <td>0.770064</td>\n",
       "      <td>0.926840</td>\n",
       "      <td>0.796151</td>\n",
       "      <td>0.856539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>k5</td>\n",
       "      <td>lr</td>\n",
       "      <td>6019</td>\n",
       "      <td>1448</td>\n",
       "      <td>850</td>\n",
       "      <td>542</td>\n",
       "      <td>0.775370</td>\n",
       "      <td>0.917391</td>\n",
       "      <td>0.806080</td>\n",
       "      <td>0.858141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>k3</td>\n",
       "      <td>lr</td>\n",
       "      <td>6006</td>\n",
       "      <td>1459</td>\n",
       "      <td>839</td>\n",
       "      <td>555</td>\n",
       "      <td>0.772661</td>\n",
       "      <td>0.915409</td>\n",
       "      <td>0.804555</td>\n",
       "      <td>0.856410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>k2</td>\n",
       "      <td>lr</td>\n",
       "      <td>6081</td>\n",
       "      <td>1557</td>\n",
       "      <td>741</td>\n",
       "      <td>480</td>\n",
       "      <td>0.770064</td>\n",
       "      <td>0.926840</td>\n",
       "      <td>0.796151</td>\n",
       "      <td>0.856539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>k5</td>\n",
       "      <td>sgd</td>\n",
       "      <td>6019</td>\n",
       "      <td>1448</td>\n",
       "      <td>850</td>\n",
       "      <td>542</td>\n",
       "      <td>0.775370</td>\n",
       "      <td>0.917391</td>\n",
       "      <td>0.806080</td>\n",
       "      <td>0.858141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>k3</td>\n",
       "      <td>sgd</td>\n",
       "      <td>6006</td>\n",
       "      <td>1459</td>\n",
       "      <td>839</td>\n",
       "      <td>555</td>\n",
       "      <td>0.772661</td>\n",
       "      <td>0.915409</td>\n",
       "      <td>0.804555</td>\n",
       "      <td>0.856410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>k2</td>\n",
       "      <td>sgd</td>\n",
       "      <td>6081</td>\n",
       "      <td>1557</td>\n",
       "      <td>741</td>\n",
       "      <td>480</td>\n",
       "      <td>0.770064</td>\n",
       "      <td>0.926840</td>\n",
       "      <td>0.796151</td>\n",
       "      <td>0.856539</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Model Classifier    TP    FP    TN   FN  Accuracy    Recall  Precision  \\\n",
       "0     k7        kNN  5942   971  1327  619  0.820522  0.905655   0.859540   \n",
       "1     k6        kNN  5932  1031  1267  629  0.812620  0.904130   0.851932   \n",
       "2     k5        kNN  5894  1220  1078  667  0.786996  0.898339   0.828507   \n",
       "3   Tree        kNN  5904  1188  1110  657  0.791737  0.899863   0.832487   \n",
       "4   Tree        gnb  5979  1514   784  582  0.763404  0.911294   0.797945   \n",
       "5     k3      dtree  6096  1559   739  465  0.771532  0.929127   0.796342   \n",
       "6     k2      dtree  6311  1789   509  250  0.769839  0.961896   0.779136   \n",
       "7   Tree      dtree  6257  1686   612  304  0.775370  0.953666   0.787738   \n",
       "8     k3        SVM  6096  1559   739  465  0.771532  0.929127   0.796342   \n",
       "9     k2        SVM  6081  1557   741  480  0.770064  0.926840   0.796151   \n",
       "10    k6         nn  6069  1506   792  492  0.774467  0.925011   0.801188   \n",
       "11    k5         nn  6050  1485   813  511  0.774692  0.922116   0.802920   \n",
       "12    k4         nn  5980  1427   871  581  0.773338  0.911446   0.807344   \n",
       "13    k3         nn  6006  1459   839  555  0.772661  0.915409   0.804555   \n",
       "14    k2         nn  6081  1557   741  480  0.770064  0.926840   0.796151   \n",
       "15    k5         lr  6019  1448   850  542  0.775370  0.917391   0.806080   \n",
       "16    k3         lr  6006  1459   839  555  0.772661  0.915409   0.804555   \n",
       "17    k2         lr  6081  1557   741  480  0.770064  0.926840   0.796151   \n",
       "18    k5        sgd  6019  1448   850  542  0.775370  0.917391   0.806080   \n",
       "19    k3        sgd  6006  1459   839  555  0.772661  0.915409   0.804555   \n",
       "20    k2        sgd  6081  1557   741  480  0.770064  0.926840   0.796151   \n",
       "\n",
       "    F1_Score  \n",
       "0   0.881995  \n",
       "1   0.877255  \n",
       "2   0.862011  \n",
       "3   0.864865  \n",
       "4   0.850861  \n",
       "5   0.857625  \n",
       "6   0.860924  \n",
       "7   0.862796  \n",
       "8   0.857625  \n",
       "9   0.856539  \n",
       "10  0.858659  \n",
       "11  0.858400  \n",
       "12  0.856243  \n",
       "13  0.856410  \n",
       "14  0.856539  \n",
       "15  0.858141  \n",
       "16  0.856410  \n",
       "17  0.856539  \n",
       "18  0.858141  \n",
       "19  0.856410  \n",
       "20  0.856539  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_result"
   ]
  }
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