train_randomforest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preperation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "dir = 'data/features'\n",
    "train = pd.read_pickle(os.path.join(dir, 'train.pkl'))\n",
    "valid = pd.read_pickle(os.path.join(dir, 'valid.pkl'))\n",
    "test = pd.read_pickle(os.path.join(dir, 'test.pkl'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "col_in_use = [\n",
    "    'num_morphemes',\n",
    "    'negative',\n",
    "    'self_ref',\n",
    "    'cognitive',\n",
    "    'hedges',\n",
    "    'hesitations',\n",
    "    'exlusion_words',\n",
    "    'negations',\n",
    "    'motion_words',\n",
    "    'sense_words',\n",
    "    'noun',\n",
    "    'verb',\n",
    "    'particle',\n",
    "    'adjective',\n",
    "    'conjunction',\n",
    "    'connections',\n",
    "]\n",
    "\n",
    "def make_data(df, type='sum'):\n",
    "    X, y = [], []\n",
    "    for _, row in df.iterrows():\n",
    "        _X, _y = _make_data(row, type=type)\n",
    "        X.append(_X)\n",
    "        y.append(_y)\n",
    "    return X, y\n",
    "\n",
    "def _make_data(row, type='sum'):\n",
    "    if type=='sum':\n",
    "        X = [row[f'{col}_sum'] for col in col_in_use]\n",
    "        X.append(row['TTR'])\n",
    "    elif type=='mean':\n",
    "        X = [row[f'{col}_mean'] for col in col_in_use]\n",
    "        X.append(row['TTR'])\n",
    "    elif type=='std':\n",
    "        X = [row[f'{col}_std'] for col in col_in_use]\n",
    "        X.append(row['TTR'])\n",
    "    elif type=='all':\n",
    "        X = [row[f'{col}_sum'] for col in col_in_use]\n",
    "        X.extend([row[f'{col}_mean'] for col in col_in_use])\n",
    "        X.extend([row[f'{col}_std'] for col in col_in_use])\n",
    "        X.append(row['TTR'])\n",
    "    y = row.labels\n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "type='sum'\n",
    "train_X, train_y = make_data(train, type=type)\n",
    "valid_X, valid_y = make_data(valid, type=type)\n",
    "train_X.extend(valid_X), train_y.extend(valid_y)\n",
    "test_X, test_y = make_data(test, type=type)\n",
    "train_X.extend(valid_X)\n",
    "train_y.extend(valid_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_morphemes</th>\n",
       "      <th>negative</th>\n",
       "      <th>self_ref</th>\n",
       "      <th>cognitive</th>\n",
       "      <th>hedges</th>\n",
       "      <th>hesitations</th>\n",
       "      <th>exlusion_words</th>\n",
       "      <th>negations</th>\n",
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       "      <th>TTR</th>\n",
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       "      <th>0</th>\n",
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       "      <td>123</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>459</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
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       "      <td>7</td>\n",
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       "      <th>2</th>\n",
       "      <td>936</td>\n",
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       "      <td>16</td>\n",
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       "      <th>3</th>\n",
       "      <td>2970</td>\n",
       "      <td>112</td>\n",
       "      <td>21</td>\n",
       "      <td>39</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>926</td>\n",
       "      <td>384</td>\n",
       "      <td>783</td>\n",
       "      <td>64</td>\n",
       "      <td>20</td>\n",
       "      <td>803</td>\n",
       "      <td>0.236027</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3129</td>\n",
       "      <td>93</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>71</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>71</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>1006</td>\n",
       "      <td>386</td>\n",
       "      <td>880</td>\n",
       "      <td>62</td>\n",
       "      <td>22</td>\n",
       "      <td>902</td>\n",
       "      <td>0.228508</td>\n",
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      ],
      "text/plain": [
       "   num_morphemes  negative  self_ref  cognitive  hedges  hesitations  \\\n",
       "0            561        15         8          2      16            2   \n",
       "1            459         9         5          2       8            3   \n",
       "2            936        21         7         16      14            7   \n",
       "3           2970       112        21         39      42            5   \n",
       "4           3129        93         8         35      71           13   \n",
       "\n",
       "   exlusion_words  negations  motion_words  sense_words  noun  verb  particle  \\\n",
       "0               1          8             0            6   171    58       123   \n",
       "1               0          7             0            0   161    44       104   \n",
       "2               0         15             0            0   246   129       205   \n",
       "3               8         31             3            7   926   384       783   \n",
       "4               5         71             4           11  1006   386       880   \n",
       "\n",
       "   adjective  conjunction  connections       TTR  labels  \n",
       "0         21            0          123  0.326203       0  \n",
       "1         12            0          104  0.455338       0  \n",
       "2         15            3          208  0.307692       1  \n",
       "3         64           20          803  0.236027       0  \n",
       "4         62           22          902  0.228508       1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_morphemes</th>\n",
       "      <th>negative</th>\n",
       "      <th>self_ref</th>\n",
       "      <th>cognitive</th>\n",
       "      <th>hedges</th>\n",
       "      <th>hesitations</th>\n",
       "      <th>exlusion_words</th>\n",
       "      <th>negations</th>\n",
       "      <th>motion_words</th>\n",
       "      <th>sense_words</th>\n",
       "      <th>noun</th>\n",
       "      <th>verb</th>\n",
       "      <th>particle</th>\n",
       "      <th>adjective</th>\n",
       "      <th>conjunction</th>\n",
       "      <th>connections</th>\n",
       "      <th>TTR</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>2233</td>\n",
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       "      <td>554</td>\n",
       "      <td>51</td>\n",
       "      <td>12</td>\n",
       "      <td>566</td>\n",
       "      <td>0.234214</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10884</td>\n",
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       "      <td>99</td>\n",
       "      <td>71</td>\n",
       "      <td>115</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>3572</td>\n",
       "      <td>1303</td>\n",
       "      <td>2826</td>\n",
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       "      <td>69</td>\n",
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       "      <td>0.146821</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2572</td>\n",
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       "      <td>26</td>\n",
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       "      <td>8</td>\n",
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       "      <td>0.243390</td>\n",
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       "      <th>3</th>\n",
       "      <td>6285</td>\n",
       "      <td>159</td>\n",
       "      <td>38</td>\n",
       "      <td>47</td>\n",
       "      <td>116</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>89</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>2210</td>\n",
       "      <td>746</td>\n",
       "      <td>1631</td>\n",
       "      <td>136</td>\n",
       "      <td>36</td>\n",
       "      <td>1667</td>\n",
       "      <td>0.145426</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4595</td>\n",
       "      <td>112</td>\n",
       "      <td>29</td>\n",
       "      <td>39</td>\n",
       "      <td>59</td>\n",
       "      <td>34</td>\n",
       "      <td>9</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1351</td>\n",
       "      <td>585</td>\n",
       "      <td>1165</td>\n",
       "      <td>86</td>\n",
       "      <td>18</td>\n",
       "      <td>1183</td>\n",
       "      <td>0.172797</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   num_morphemes  negative  self_ref  cognitive  hedges  hesitations  \\\n",
       "0           2233        81         7         25      32            9   \n",
       "1          10884       325        99         71     115           29   \n",
       "2           2572        63        16         26      52           11   \n",
       "3           6285       159        38         47     116           23   \n",
       "4           4595       112        29         39      59           34   \n",
       "\n",
       "   exlusion_words  negations  motion_words  sense_words  noun  verb  particle  \\\n",
       "0               4         38             0            6   712   288       554   \n",
       "1               8         23             3           21  3572  1303      2826   \n",
       "2               4         40             1            3   935   305       599   \n",
       "3               9         89             5           13  2210   746      1631   \n",
       "4               9         59             1           12  1351   585      1165   \n",
       "\n",
       "   adjective  conjunction  connections       TTR  labels  \n",
       "0         51           12          566  0.234214       0  \n",
       "1        176           69         2895  0.146821       0  \n",
       "2         53            8          607  0.243390       0  \n",
       "3        136           36         1667  0.145426       0  \n",
       "4         86           18         1183  0.172797       0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = col_in_use + ['TTR']\n",
    "train_df = pd.concat((pd.DataFrame(train_X, columns=features), pd.DataFrame(train_y, columns=['labels'])), axis=1)\n",
    "display(train_df.head())\n",
    "test_df = pd.concat((pd.DataFrame(test_X, columns=features), pd.DataFrame(test_y, columns=['labels'])), axis=1)\n",
    "display(test_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 900x1200 with 17 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# difference of distribution between train and test datasets.\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "features = col_in_use + ['TTR']\n",
    "train_df = pd.DataFrame(train_X, columns=features)\n",
    "test_df = pd.DataFrame(test_X, columns=features)\n",
    "\n",
    "plt.rcParams['figure.dpi'] = 150 #解像度（dots-per-inch）\n",
    "fig = plt.figure(figsize=(6,8) ,facecolor='#f6f5f5')\n",
    "gs = fig.add_gridspec(6, 3) # specify the geometry of the grid that a subplot will be placed.\n",
    "gs.update(wspace=0.2, hspace=0.4)\n",
    "\n",
    "background_color = \"#f6f5f5\"\n",
    "\n",
    "run_no = 0\n",
    "for row in range(0, 6):\n",
    "    for col in range(0, 3):\n",
    "        locals()[\"ax\"+str(run_no)] = fig.add_subplot(gs[row, col])\n",
    "        locals()[\"ax\"+str(run_no)].set_facecolor(background_color)\n",
    "        locals()[\"ax\"+str(run_no)].set_yticklabels([]) # y軸を見えなくする。\n",
    "        for s in [\"top\",\"right\"]: # グラフの周りの枠を削除している。\n",
    "            locals()[\"ax\"+str(run_no)].spines[s].set_visible(False)\n",
    "        run_no += 1\n",
    "\n",
    "run_no = 0\n",
    "for col in features:\n",
    "    sns.kdeplot(train_df[col], ax=locals()[\"ax\"+str(run_no)], shade=True, color='#287094', alpha=0.95, linewidth=0, zorder=2)\n",
    "    sns.kdeplot(test_df[col], ax=locals()[\"ax\"+str(run_no)], shade=True, color='#fcd12a', alpha=0.95, linewidth=0, zorder=2)\n",
    "    locals()[\"ax\"+str(run_no)].set_ylabel('')\n",
    "    locals()[\"ax\"+str(run_no)].set_xlabel(col, fontsize=5, fontweight='bold')\n",
    "    locals()[\"ax\"+str(run_no)].tick_params(labelsize=5, width=0.5, length=1.5)\n",
    "    locals()[\"ax\"+str(run_no)].grid(axis='x', zorder=0, color='#EEEEEE', linewidth=0.7)\n",
    "    locals()[\"ax\"+str(run_no)].grid(axis='y', zorder=0, color='#EEEEEE', linewidth=0.7)\n",
    "    run_no += 1\n",
    "\n",
    "# 左上の図にタイトルと説明を付ける事で、画像の左上になる。。\n",
    "#ax0.text(-10, 0.086, 'Features Distribution', fontsize=8, fontweight='bold')\n",
    "#ax0.text(-10, 0.081, 'Features comparison between train and test dataset', fontsize=5)\n",
    "fig.legend(['train', 'test'], ncol=2, facecolor=background_color, edgecolor=background_color, fontsize=4, bbox_to_anchor=(0.24, 0.91))\n",
    "\n",
    "ax17.remove() # 今回はグラフが18個なので、18個目のaxを消す。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas_profiling\n",
    "import pandas as pd\n",
    "\n",
    "pandas_profiling.ProfileReport(pd.DataFrame(test_X, columns=col_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[CV] START .....................................................................\n",
      "[CV] END  accuracy: (test=0.926) f1: (test=0.931) precision: (test=0.870) recall: (test=1.000) total time=  27.4s\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   27.4s remaining:    0.0s\n",
      "[CV] START .....................................................................\n",
      "[CV] END  accuracy: (test=0.933) f1: (test=0.937) precision: (test=0.882) recall: (test=1.000) total time=  27.1s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:   54.5s remaining:    0.0s\n",
      "[CV] START .....................................................................\n",
      "[CV] END  accuracy: (test=0.929) f1: (test=0.934) precision: (test=0.876) recall: (test=1.000) total time=  27.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:  1.4min remaining:    0.0s\n",
      "[CV] START .....................................................................\n",
      "[CV] END  accuracy: (test=0.921) f1: (test=0.927) precision: (test=0.864) recall: (test=1.000) total time=  26.7s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:  1.8min remaining:    0.0s\n",
      "[CV] START .....................................................................\n",
      "[CV] END  accuracy: (test=0.525) f1: (test=0.223) precision: (test=0.611) recall: (test=0.136) total time=  27.1s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:  2.3min remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:  2.3min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'fit_time': array([26.65292454, 26.36240315, 26.26918459, 26.01123142, 26.47163796]),\n",
       " 'score_time': array([0.71619534, 0.70540929, 0.6894834 , 0.69440556, 0.67215943]),\n",
       " 'estimator': [RandomForestClassifier(random_state=1),\n",
       "  RandomForestClassifier(random_state=1),\n",
       "  RandomForestClassifier(random_state=1),\n",
       "  RandomForestClassifier(random_state=1),\n",
       "  RandomForestClassifier(random_state=1)],\n",
       " 'test_accuracy': array([0.92553836, 0.93329562, 0.9294574 , 0.92141732, 0.52480808]),\n",
       " 'test_precision': array([0.87037558, 0.88229003, 0.87636312, 0.86418546, 0.61107091]),\n",
       " 'test_recall': array([1.        , 1.        , 1.        , 1.        , 0.13648485]),\n",
       " 'test_f1': array([0.93069605, 0.93746449, 0.93410823, 0.92714537, 0.22313231])}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "from sklearn.model_selection import cross_validate\n",
    "\n",
    "forest = RandomForestClassifier(random_state=1)\n",
    "\n",
    "cv_results = cross_validate(forest, train_X, train_y, cv=5, scoring=['accuracy', 'precision','recall','f1'], return_estimator=True, verbose=100)\n",
    "cv_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9042847833170414\n",
      "[1.0, 1.0, 1.0, 1.0, 0.5214239165852069]\n",
      "[1.0, 1.0, 1.0, 1.0, 0.5990957045968349]\n",
      "[1.0, 1.0, 1.0, 1.0, 0.1295210166177908]\n",
      "[1.0, 1.0, 1.0, 1.0, 0.21299397186872068]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      6138\n",
      "           1       1.00      1.00      1.00      6138\n",
      "\n",
      "    accuracy                           1.00     12276\n",
      "   macro avg       1.00      1.00      1.00     12276\n",
      "weighted avg       1.00      1.00      1.00     12276\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      6138\n",
      "           1       1.00      1.00      1.00      6138\n",
      "\n",
      "    accuracy                           1.00     12276\n",
      "   macro avg       1.00      1.00      1.00     12276\n",
      "weighted avg       1.00      1.00      1.00     12276\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      6138\n",
      "           1       1.00      1.00      1.00      6138\n",
      "\n",
      "    accuracy                           1.00     12276\n",
      "   macro avg       1.00      1.00      1.00     12276\n",
      "weighted avg       1.00      1.00      1.00     12276\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      6138\n",
      "           1       1.00      1.00      1.00      6138\n",
      "\n",
      "    accuracy                           1.00     12276\n",
      "   macro avg       1.00      1.00      1.00     12276\n",
      "weighted avg       1.00      1.00      1.00     12276\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.51      0.91      0.66      6138\n",
      "           1       0.60      0.13      0.21      6138\n",
      "\n",
      "    accuracy                           0.52     12276\n",
      "   macro avg       0.56      0.52      0.43     12276\n",
      "weighted avg       0.56      0.52      0.43     12276\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from statistics import mean\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, precision_recall_fscore_support, recall_score, precision_score, f1_score\n",
    "\n",
    "acc = []\n",
    "precision = []\n",
    "recall = []\n",
    "f1 = []\n",
    "clf_report = []\n",
    "\n",
    "for estimator in cv_results['estimator']:\n",
    "    predict = estimator.predict(valid_X)\n",
    "    acc.append(accuracy_score(valid_y, predict))\n",
    "    precision.append(precision_score(valid_y, predict))\n",
    "    recall.append(recall_score(valid_y, predict))\n",
    "    f1.append(f1_score(valid_y, predict))\n",
    "    clf_report.append(classification_report(valid_y, predict))\n",
    "\n",
    "print(mean(acc))\n",
    "print(acc)\n",
    "print(precision)\n",
    "print(recall)\n",
    "print(f1)\n",
    "for r in clf_report:\n",
    "    print(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "col_name = col_in_use + ['TTR']\n",
    "\n",
    "for i, estimator in enumerate(cv_results['estimator']):\n",
    "    # Creating a bar plot\n",
    "    feature_imp = pd.Series(estimator.feature_importances_,index=col_name).sort_values(ascending=False)\n",
    "    sns.barplot(x=feature_imp, y=feature_imp.index)\n",
    "    # Add labels to your graph\n",
    "    plt.xlabel('Feature Importance Score')\n",
    "    plt.ylabel('Features')\n",
    "    plt.title(f\"Visualizing Important Features(estimator {i+1})\")\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.06496616, 0.06403004, 0.05792305, 0.05863101, 0.06372628,\n",
       "       0.05480639, 0.03681158, 0.06070002, 0.03831964, 0.04967173,\n",
       "       0.07715507, 0.06741997, 0.05895169, 0.06033207, 0.05599299,\n",
       "       0.05951569, 0.07104662])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_results['estimator'][0].feature_importances_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hold out 検証"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5192622950819672\n",
      "      0    1\n",
      "0  5617  483\n",
      "1  5382  718\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.51      0.92      0.66      6100\n",
      "           1       0.60      0.12      0.20      6100\n",
      "\n",
      "    accuracy                           0.52     12200\n",
      "   macro avg       0.55      0.52      0.43     12200\n",
      "weighted avg       0.55      0.52      0.43     12200\n",
      "\n",
      "   precision    recall        f1  support\n",
      "0   0.510683  0.920820  0.656997   6100.0\n",
      "1   0.597835  0.117705  0.196685   6100.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, precision_recall_fscore_support\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "save_dir = ('outputs/nested/randomforest')\n",
    "os.makedirs(save_dir, exist_ok=True)\n",
    "\n",
    "forest = RandomForestClassifier(random_state=1).fit(train_X, train_y)\n",
    "\n",
    "predict = forest.predict(test_X)\n",
    "acc = accuracy_score(test_y, predict)\n",
    "c_report = classification_report(test_y, predict)\n",
    "cm = pd.DataFrame(confusion_matrix(test_y, predict))\n",
    "scores_df = pd.DataFrame(np.array(precision_recall_fscore_support(test_y, predict)).T,\n",
    "            columns=[\"precision\", \"recall\", \"f1\", \"support\"],\n",
    "        )\n",
    "\n",
    "print(acc)\n",
    "print(cm)\n",
    "print(c_report)\n",
    "print(scores_df)\n",
    "\n",
    "with open(os.path.join(save_dir, 'classification_report.txt'), 'w') as f:\n",
    "        f.write(str(acc)+'\\n')\n",
    "        f.write(c_report)\n",
    "cm.to_csv(os.path.join('cm.csv'))\n",
    "scores_df.to_csv(os.path.join(save_dir, 'scores.csv'))"
   ]
  }
 ],
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