Outcome_regression_diabete.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5563f26c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d4d8a4d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_raw = pd.read_csv('./stroke_data_dummies.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bb60236f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_r = df_raw[df_raw.Gender_Other == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7528bcb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def glucose_check(x):\n",
    "    if x >= 140:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "df_r['diabetes'] = df_r.avg_glucose_level.apply(glucose_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d940fa27",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_r.drop(columns = ['Unnamed: 0','id','Obesity','Gender_Other','avg_glucose_level'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0e652b83",
   "metadata": {},
   "outputs": [
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       "      <th>hypertension</th>\n",
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      "text/plain": [
       "    age  hypertension  heart_disease   bmi  stroke  marriage  Gender_Female  \\\n",
       "0  67.0             0              1  36.6       1         1              0   \n",
       "1  80.0             0              1  32.5       1         1              0   \n",
       "2  49.0             0              0  34.4       1         1              1   \n",
       "3  79.0             1              0  24.0       1         1              1   \n",
       "4  81.0             0              0  29.0       1         1              0   \n",
       "\n",
       "   Gender_Male  Work_Govt_job  Work_Never_worked  Work_Private  \\\n",
       "0            1              0                  0             1   \n",
       "1            1              0                  0             1   \n",
       "2            0              0                  0             1   \n",
       "3            0              0                  0             0   \n",
       "4            1              0                  0             1   \n",
       "\n",
       "   Work_Self-employed  Work_children  smoking_Unknown  \\\n",
       "0                   0              0                0   \n",
       "1                   0              0                0   \n",
       "2                   0              0                0   \n",
       "3                   1              0                0   \n",
       "4                   0              0                0   \n",
       "\n",
       "   smoking_formerly smoked  smoking_never smoked  smoking_smokes  \\\n",
       "0                        1                     0               0   \n",
       "1                        0                     1               0   \n",
       "2                        0                     0               1   \n",
       "3                        0                     1               0   \n",
       "4                        1                     0               0   \n",
       "\n",
       "   Residence_Rural  Residence_Urban  diabetes  \n",
       "0                0                1         1  \n",
       "1                1                0         0  \n",
       "2                0                1         1  \n",
       "3                1                0         1  \n",
       "4                0                1         1  "
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "09a10860",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "       age  hypertension  heart_disease   bmi  marriage  Gender_Female  \\\n",
       "0     67.0             0              1  36.6         1              0   \n",
       "1     80.0             0              1  32.5         1              0   \n",
       "2     49.0             0              0  34.4         1              1   \n",
       "3     79.0             1              0  24.0         1              1   \n",
       "4     81.0             0              0  29.0         1              0   \n",
       "...    ...           ...            ...   ...       ...            ...   \n",
       "4904  13.0             0              0  18.6         0              1   \n",
       "4905  81.0             0              0  40.0         1              1   \n",
       "4906  35.0             0              0  30.6         1              1   \n",
       "4907  51.0             0              0  25.6         1              0   \n",
       "4908  44.0             0              0  26.2         1              1   \n",
       "\n",
       "      Gender_Male  Work_Govt_job  Work_Never_worked  Work_Private  \\\n",
       "0               1              0                  0             1   \n",
       "1               1              0                  0             1   \n",
       "2               0              0                  0             1   \n",
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       "...           ...            ...                ...           ...   \n",
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       "\n",
       "      Work_Self-employed  Work_children  smoking_Unknown  \\\n",
       "0                      0              0                0   \n",
       "1                      0              0                0   \n",
       "2                      0              0                0   \n",
       "3                      1              0                0   \n",
       "4                      0              0                0   \n",
       "...                  ...            ...              ...   \n",
       "4904                   0              1                1   \n",
       "4905                   1              0                0   \n",
       "4906                   1              0                0   \n",
       "4907                   0              0                0   \n",
       "4908                   0              0                1   \n",
       "\n",
       "      smoking_formerly smoked  smoking_never smoked  smoking_smokes  \\\n",
       "0                           1                     0               0   \n",
       "1                           0                     1               0   \n",
       "2                           0                     0               1   \n",
       "3                           0                     1               0   \n",
       "4                           1                     0               0   \n",
       "...                       ...                   ...             ...   \n",
       "4904                        0                     0               0   \n",
       "4905                        0                     1               0   \n",
       "4906                        0                     1               0   \n",
       "4907                        1                     0               0   \n",
       "4908                        0                     0               0   \n",
       "\n",
       "      Residence_Rural  Residence_Urban  \n",
       "0                   0                1  \n",
       "1                   1                0  \n",
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       "4                   0                1  \n",
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       "4905                0                1  \n",
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       "4907                1                0  \n",
       "4908                0                1  \n",
       "\n",
       "[4908 rows x 18 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop(columns = ['stroke', 'diabetes'])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7cdff4d9",
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       "       age  hypertension  heart_disease   bmi  stroke  marriage  \\\n",
       "1     80.0             0              1  32.5       1         1   \n",
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       "\n",
       "      Gender_Female  Gender_Male  Work_Govt_job  Work_Never_worked  \\\n",
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       "5                 0            1              0                  0   \n",
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       "      Work_Private  Work_Self-employed  Work_children  smoking_Unknown  \\\n",
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       "6                1                   0              0                0   \n",
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       "...            ...                 ...            ...              ...   \n",
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       "\n",
       "      smoking_formerly smoked  smoking_never smoked  smoking_smokes  \\\n",
       "1                           0                     1               0   \n",
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       "6                           0                     1               0   \n",
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       "4903                        0                     0               0   \n",
       "4904                        0                     0               0   \n",
       "4905                        0                     1               0   \n",
       "4906                        0                     1               0   \n",
       "4908                        0                     0               0   \n",
       "\n",
       "      Residence_Rural  Residence_Urban  \n",
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       "\n",
       "[4154 rows x 19 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_0 = df[df['diabetes'] == 0]\n",
    "df_0 = df_0.drop(columns = ['diabetes'])\n",
    "df_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f4d3b7a0",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4865</th>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4866</th>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4878</th>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4888</th>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21.8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4907</th>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>754 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  hypertension  heart_disease   bmi  stroke  marriage  \\\n",
       "0     67.0             0              1  36.6       1         1   \n",
       "2     49.0             0              0  34.4       1         1   \n",
       "3     79.0             1              0  24.0       1         1   \n",
       "4     81.0             0              0  29.0       1         1   \n",
       "11    79.0             0              1  28.2       1         1   \n",
       "...    ...           ...            ...   ...     ...       ...   \n",
       "4865  39.0             0              0  27.7       0         1   \n",
       "4866  70.0             0              0  24.3       0         1   \n",
       "4878  34.0             0              0  23.0       0         1   \n",
       "4888  51.0             0              0  21.8       0         1   \n",
       "4907  51.0             0              0  25.6       0         1   \n",
       "\n",
       "      Gender_Female  Gender_Male  Work_Govt_job  Work_Never_worked  \\\n",
       "0                 0            1              0                  0   \n",
       "2                 1            0              0                  0   \n",
       "3                 1            0              0                  0   \n",
       "4                 0            1              0                  0   \n",
       "11                1            0              0                  0   \n",
       "...             ...          ...            ...                ...   \n",
       "4865              0            1              0                  0   \n",
       "4866              0            1              0                  0   \n",
       "4878              1            0              0                  0   \n",
       "4888              1            0              0                  0   \n",
       "4907              0            1              0                  0   \n",
       "\n",
       "      Work_Private  Work_Self-employed  Work_children  smoking_Unknown  \\\n",
       "0                1                   0              0                0   \n",
       "2                1                   0              0                0   \n",
       "3                0                   1              0                0   \n",
       "4                1                   0              0                0   \n",
       "11               1                   0              0                0   \n",
       "...            ...                 ...            ...              ...   \n",
       "4865             1                   0              0                1   \n",
       "4866             0                   1              0                1   \n",
       "4878             1                   0              0                0   \n",
       "4888             1                   0              0                1   \n",
       "4907             1                   0              0                0   \n",
       "\n",
       "      smoking_formerly smoked  smoking_never smoked  smoking_smokes  \\\n",
       "0                           1                     0               0   \n",
       "2                           0                     0               1   \n",
       "3                           0                     1               0   \n",
       "4                           1                     0               0   \n",
       "11                          0                     1               0   \n",
       "...                       ...                   ...             ...   \n",
       "4865                        0                     0               0   \n",
       "4866                        0                     0               0   \n",
       "4878                        0                     1               0   \n",
       "4888                        0                     0               0   \n",
       "4907                        1                     0               0   \n",
       "\n",
       "      Residence_Rural  Residence_Urban  \n",
       "0                   0                1  \n",
       "2                   0                1  \n",
       "3                   1                0  \n",
       "4                   0                1  \n",
       "11                  0                1  \n",
       "...               ...              ...  \n",
       "4865                0                1  \n",
       "4866                1                0  \n",
       "4878                1                0  \n",
       "4888                0                1  \n",
       "4907                1                0  \n",
       "\n",
       "[754 rows x 19 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1 = df[df['diabetes'] == 1]\n",
    "df_1 = df_1.drop(columns = ['diabetes'])\n",
    "df_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a80fb9c6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "y_0 = df_0['stroke']\n",
    "x_0 = df_0.drop(columns = ['stroke'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c2612ba0",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_1 = df_1['stroke']\n",
    "x_1 = df_1.drop(columns = ['stroke'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8bfa4273",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "445966eb",
   "metadata": {},
   "source": [
    "# Model for controlled group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2250af41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9665382106041495"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(random_state=60)\n",
    "score_pre_0 = cross_val_score(rfc,x_0,y_0,cv=5).mean()\n",
    "score_pre_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "75951716",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9670198483464544 41\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scorel = []\n",
    "for i in range(0,200,10):\n",
    "    rfc = RandomForestClassifier(n_estimators=i+1,\n",
    "                                 n_jobs=-1,\n",
    "                                random_state=60)\n",
    "    score = cross_val_score(rfc,x_0,y_0,cv=5).mean()\n",
    "    scorel.append(score)\n",
    "\n",
    "print(max(scorel),(scorel.index(max(scorel))*10)+1)\n",
    "plt.figure(figsize=[20,5])\n",
    "plt.plot(range(1,201,10),scorel)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "50caa82a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9670198483464544 40\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 4320x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scorel = []\n",
    "for i in range(1,71):\n",
    "    rfc = RandomForestClassifier(n_estimators=i+1,\n",
    "                                 n_jobs=-1,\n",
    "                                random_state=60)\n",
    "    score = cross_val_score(rfc,x_0,y_0,cv=5).mean()\n",
    "    scorel.append(score)\n",
    "\n",
    "print(max(scorel),([*range(1,71)][scorel.index(max(scorel))]))\n",
    "plt.figure(figsize=[60,20])\n",
    "plt.plot(range(1,71),scorel)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b5fc99af",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=40, random_state=60),\n",
       "             param_grid={'max_depth': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])})"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'max_depth':np.arange(1, 30, 1)}\n",
    "rfc = RandomForestClassifier(n_estimators=40\n",
    "                             ,random_state=60\n",
    "                             )\n",
    "GS = GridSearchCV(rfc,param_grid,cv=5)\n",
    "GS.fit(x_0,y_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "35099b1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 1}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "56a53ca6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9689458193785974"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d8438727",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=40, random_state=60),\n",
       "             param_grid={'max_features': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19])})"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'max_features':np.arange(1,20,1)}\n",
    "rfc = RandomForestClassifier(n_estimators=40\n",
    "                             ,random_state=60\n",
    "                             )\n",
    "GS = GridSearchCV(rfc,param_grid,cv=5)\n",
    "GS.fit(x_0,y_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a2ce1964",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 4}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "61f4ca98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9665382106041495"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "041a4745",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=40, random_state=60),\n",
       "             param_grid={'min_samples_leaf': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19])})"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid={'min_samples_leaf':np.arange(1, 20, 1)}\n",
    "rfc = RandomForestClassifier(n_estimators=40\n",
    "                             ,random_state=60\n",
    "                             )\n",
    "GS = GridSearchCV(rfc,param_grid,cv=5)\n",
    "GS.fit(x_0,y_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "69b5605a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_samples_leaf': 4}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7e6e4d3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9689458193785974"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d577bcce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=40, random_state=60),\n",
       "             param_grid={'min_samples_split': array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
       "       19, 20, 21])})"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid={'min_samples_split':np.arange(2, 2+20, 1)}\n",
    "rfc = RandomForestClassifier(n_estimators=40\n",
    "                             ,random_state=60\n",
    "                             )\n",
    "GS = GridSearchCV(rfc,param_grid,cv=5)\n",
    "GS.fit(x_0,y_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fff77457",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_samples_split': 11}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5315b785",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9689458193785974"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "35d5a303",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9689458193785974"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_0 = RandomForestClassifier(n_estimators=40,max_depth = 1,random_state=60)\n",
    "score_0 = cross_val_score(rfc_0,x_0,y_0,cv=5).mean()\n",
    "score_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "625befab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0024076087744479002"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_0 - score_pre_0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46d8b047",
   "metadata": {},
   "source": [
    "# Model for treatment group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "118c2d19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8833024282560705"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=100,random_state=90)\n",
    "score_pre_1 = cross_val_score(rfc,x_1,y_1,cv=5).mean()\n",
    "score_pre_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "518fd43e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8833112582781457 171\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scorel = []\n",
    "for i in range(0,200,10):\n",
    "    rfc = RandomForestClassifier(n_estimators=i+1,\n",
    "                                 n_jobs=-1,\n",
    "                                random_state=80)\n",
    "    score = cross_val_score(rfc,x_1,y_1,cv=5).mean()\n",
    "    scorel.append(score)\n",
    "\n",
    "print(max(scorel),(scorel.index(max(scorel))*10)+1)\n",
    "plt.figure(figsize=[20,5])\n",
    "plt.plot(range(0,200,10),scorel)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a20f6b17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8846357615894039 151\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scorel = []\n",
    "for i in range(141,201):\n",
    "    rfc = RandomForestClassifier(n_estimators=i+1,\n",
    "                                 n_jobs=-1,\n",
    "                                random_state=80)\n",
    "    score = cross_val_score(rfc,x_1,y_1,cv=5).mean()\n",
    "    scorel.append(score)\n",
    "\n",
    "print(max(scorel),([*range(141,201)][scorel.index(max(scorel))]))\n",
    "plt.figure(figsize=[20,5])\n",
    "plt.plot(range(141,201),scorel)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2e791d5b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=151,\n",
       "                                              random_state=80),\n",
       "             param_grid={'max_depth': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])})"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'max_depth':np.arange(1, 30, 1)}\n",
    "rfc = RandomForestClassifier(n_estimators=151\n",
    "                             ,random_state=80\n",
    "                             )\n",
    "GS = GridSearchCV(rfc,param_grid,cv=5)\n",
    "GS.fit(x_1,y_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8e4fe8c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 7}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ed762c26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8952317880794702"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e4d62d19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=151,\n",
       "                                              random_state=80),\n",
       "             param_grid={'max_features': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19])})"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'max_features':np.arange(1,20,1)}\n",
    "rfc = RandomForestClassifier(n_estimators=151\n",
    "                             ,random_state=80\n",
    "                             )\n",
    "GS = GridSearchCV(rfc,param_grid,cv=5)\n",
    "GS.fit(x_1,y_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5b8d2a7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 8}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "044f4cfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8833112582781457"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5b9afecb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=151,\n",
       "                                              random_state=80),\n",
       "             param_grid={'min_samples_leaf': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19])})"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid={'min_samples_leaf':np.arange(1, 20, 1)}\n",
    "rfc = RandomForestClassifier(n_estimators=151\n",
    "                             ,random_state=80\n",
    "                             )\n",
    "GS = GridSearchCV(rfc,param_grid,cv=5)\n",
    "GS.fit(x_1,y_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d06c6f38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_samples_leaf': 4}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b877f4a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8938984547461368"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5d217c08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=151,\n",
       "                                              random_state=80),\n",
       "             param_grid={'min_samples_split': array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
       "       19, 20, 21])})"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid={'min_samples_split':np.arange(2, 2+20, 1)}\n",
    "rfc = RandomForestClassifier(n_estimators=151\n",
    "                             ,random_state=80\n",
    "                             )\n",
    "GS = GridSearchCV(rfc,param_grid,cv=5)\n",
    "GS.fit(x_1,y_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "41f2c63b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_samples_split': 12}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4d44cd8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8938984547461368"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0d8cb3a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8952317880794702"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_1 = RandomForestClassifier(n_estimators=151,max_depth = 7, random_state=80)\n",
    "score_1 = cross_val_score(rfc_1,x_1,y_1,cv=5).mean()\n",
    "score_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e6311425",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01192935982339971"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_1 - score_pre_1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dee8914",
   "metadata": {},
   "source": [
    "# Outcome modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7823ba7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "import scipy.stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "727458a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc0 = rfc_0.fit(x_0, y_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7a4faaa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_0 = rfc0.predict(X)\n",
    "mean_0 = predict_0.mean()\n",
    "mean_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a7806102",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc1 = rfc_1.fit(x_1, y_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "39676bf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.002037489812550937"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_1 = rfc1.predict(X)\n",
    "mean_1 = predict_1.mean()\n",
    "mean_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "20398cf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.002037489812550937"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ATE_outcome = mean_1 - mean_0\n",
    "ATE_outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "72985245",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-3.165181645162925 0.0007796715253973722\n"
     ]
    }
   ],
   "source": [
    "t,pval = scipy.stats.ttest_ind(predict_0,predict_1,equal_var=False,alternative='less')\n",
    "print(t,pval)"
   ]
  }
 ],
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