feature selection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "from collections import Counter\n",
    "\n",
    "#d = pd.read_csv(\"Feature.csv\")\n",
    "#d = pd.read_csv(\"FeatureNew.csv\")\n",
    "#d = pd.read_csv(\"Features(BAP).csv\").iloc[:,1:]\n",
    "#pattern = pd.read_csv(\"PatternFeature.csv\").iloc[:,1:]\n",
    "#d = pd.read_csv(\"Feature(with pattern and type).csv\")\n",
    "#d = pd.read_csv(\"Feature(with pattern and type and usage).csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"Features(BAPPr).csv\").iloc[:,2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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     "execution_count": 3,
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    "data"
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    "columns = data.columns.tolist()\n",
    "columns[:42]"
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   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "basic = data.iloc[:,:42]\n",
    "adv = data.iloc[:,42:(43+105-1)]\n",
    "pattern = data.iloc[:,(43+105-1):161]\n",
    "profile = data[\"Type\"]\n",
    "target = data[\"Target\"]"
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  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
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    {
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  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
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   "source": [
    "#stats = pd.read_csv(\"StatisticsFeatures.csv\").iloc[:,1:]\n",
    "#stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#target = stats[\"Target\"]\n",
    "#stats.drop(columns = [\"Target\"], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'stats' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-140c8ab414f8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mstats\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'stats' is not defined"
     ]
    }
   ],
   "source": [
    "stats.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   e1  e6  e8  e13  e14  e17  e1e13  e6e8  e6e14  e8e14  e13e17\n",
       "0   0   0   0    0    0    0      0     0      0      0       0\n",
       "1   0   0   0    0    0    0      0     0      0      0       0\n",
       "2   0   0   0    0    0    0      0     0      0      0       0\n",
       "3   0   0   0    0    0    0      0     0      0      0       0\n",
       "4   0   0   0    0    0    0      0     0      0      0       0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pattern = pd.read_csv(\"PatternFeature.csv\").iloc[:,1:]\n",
    "#pattern.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'stats' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-11-30a9a94eb169>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mstats\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m42\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'stats' is not defined"
     ]
    }
   ],
   "source": [
    "stats.columns[:42]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "e1.m1     int64\n",
       "e2.m1     int64\n",
       "e3.m1     int64\n",
       "e4.m1     int64\n",
       "e5.m1     int64\n",
       "e6.m1     int64\n",
       "e7.m1     int64\n",
       "e8.m1     int64\n",
       "e9.m1     int64\n",
       "e10.m1    int64\n",
       "e11.m1    int64\n",
       "e12.m1    int64\n",
       "e13.m1    int64\n",
       "e14.m1    int64\n",
       "e15.m1    int64\n",
       "e16.m1    int64\n",
       "e17.m1    int64\n",
       "e18.m1    int64\n",
       "e19.m1    int64\n",
       "e20.m1    int64\n",
       "e21.m1    int64\n",
       "e1.m2     int64\n",
       "e2.m2     int64\n",
       "e3.m2     int64\n",
       "e4.m2     int64\n",
       "e5.m2     int64\n",
       "e6.m2     int64\n",
       "e7.m2     int64\n",
       "e8.m2     int64\n",
       "e9.m2     int64\n",
       "e10.m2    int64\n",
       "e11.m2    int64\n",
       "e12.m2    int64\n",
       "e13.m2    int64\n",
       "e14.m2    int64\n",
       "e15.m2    int64\n",
       "e16.m2    int64\n",
       "e17.m2    int64\n",
       "e18.m2    int64\n",
       "e19.m2    int64\n",
       "e20.m2    int64\n",
       "e21.m2    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basic.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index         66\n",
       "e1.m1     949216\n",
       "e2.m1     949216\n",
       "e3.m1     949216\n",
       "e4.m1     949216\n",
       "e5.m1     949216\n",
       "e6.m1     949216\n",
       "e7.m1     949216\n",
       "e8.m1     949216\n",
       "e9.m1     949216\n",
       "e10.m1    949216\n",
       "e11.m1    949216\n",
       "e12.m1    949216\n",
       "e13.m1    949216\n",
       "e14.m1    949216\n",
       "e15.m1    949216\n",
       "e16.m1    949216\n",
       "e17.m1    949216\n",
       "e18.m1    949216\n",
       "e19.m1    949216\n",
       "e20.m1    949216\n",
       "e21.m1    949216\n",
       "e1.m2     949216\n",
       "e2.m2     949216\n",
       "e3.m2     949216\n",
       "e4.m2     949216\n",
       "e5.m2     949216\n",
       "e6.m2     949216\n",
       "e7.m2     949216\n",
       "e8.m2     949216\n",
       "e9.m2     949216\n",
       "e10.m2    949216\n",
       "e11.m2    949216\n",
       "e12.m2    949216\n",
       "e13.m2    949216\n",
       "e14.m2    949216\n",
       "e15.m2    949216\n",
       "e16.m2    949216\n",
       "e17.m2    949216\n",
       "e18.m2    949216\n",
       "e19.m2    949216\n",
       "e20.m2    949216\n",
       "e21.m2    949216\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basic.memory_usage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#d[[\"ATM ID\"]] = d[[\"ATM ID\"]].astype('category')\n",
    "#d.memory_usage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index         66\n",
       "e1.m1     237304\n",
       "e2.m1     237304\n",
       "e3.m1     237304\n",
       "e4.m1     237304\n",
       "e5.m1     237304\n",
       "e6.m1     237304\n",
       "e7.m1     237304\n",
       "e8.m1     237304\n",
       "e9.m1     237304\n",
       "e10.m1    237304\n",
       "e11.m1    237304\n",
       "e12.m1    237304\n",
       "e13.m1    237304\n",
       "e14.m1    237304\n",
       "e15.m1    237304\n",
       "e16.m1    237304\n",
       "e17.m1    237304\n",
       "e18.m1    237304\n",
       "e19.m1    237304\n",
       "e20.m1    237304\n",
       "e21.m1    237304\n",
       "e1.m2     237304\n",
       "e2.m2     237304\n",
       "e3.m2     237304\n",
       "e4.m2     237304\n",
       "e5.m2     237304\n",
       "e6.m2     237304\n",
       "e7.m2     237304\n",
       "e8.m2     237304\n",
       "e9.m2     237304\n",
       "e10.m2    237304\n",
       "e11.m2    237304\n",
       "e12.m2    237304\n",
       "e13.m2    237304\n",
       "e14.m2    237304\n",
       "e15.m2    237304\n",
       "e16.m2    237304\n",
       "e17.m2    237304\n",
       "e18.m2    237304\n",
       "e19.m2    237304\n",
       "e20.m2    237304\n",
       "e21.m2    237304\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basic = basic.astype(\"int16\")\n",
    "basic.memory_usage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "e1       int64\n",
       "e4       int64\n",
       "e6       int64\n",
       "e8       int64\n",
       "e9       int64\n",
       "e13      int64\n",
       "e14      int64\n",
       "e1e9     int64\n",
       "e1e13    int64\n",
       "e4e9     int64\n",
       "e4e13    int64\n",
       "e6e8     int64\n",
       "e6e14    int64\n",
       "e9e13    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pattern.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index        66\n",
       "e1       949216\n",
       "e4       949216\n",
       "e6       949216\n",
       "e8       949216\n",
       "e9       949216\n",
       "e13      949216\n",
       "e14      949216\n",
       "e1e9     949216\n",
       "e1e13    949216\n",
       "e4e9     949216\n",
       "e4e13    949216\n",
       "e6e8     949216\n",
       "e6e14    949216\n",
       "e9e13    949216\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pattern.memory_usage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index        66\n",
       "e1       237304\n",
       "e4       237304\n",
       "e6       237304\n",
       "e8       237304\n",
       "e9       237304\n",
       "e13      237304\n",
       "e14      237304\n",
       "e1e9     237304\n",
       "e1e13    237304\n",
       "e4e9     237304\n",
       "e4e13    237304\n",
       "e6e8     237304\n",
       "e6e14    237304\n",
       "e9e13    237304\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pattern = pattern.astype(\"int16\")\n",
    "pattern.memory_usage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "237370"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile = profile.astype(\"int16\")\n",
    "profile.memory_usage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({1: 66364, 0: 52288})"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x = pd.DataFrame(d.iloc[:,1:-1])\n",
    "#y = d[\"Target\"]\n",
    "#x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 1234)#, stratify = y)\n",
    "#x_train = x\n",
    "#y_train = y\n",
    "#pd.concat([x_train,y_train],axis = 1).to_csv(\"train.csv\", index = False)\n",
    "#pd.concat([x_test,y_test],axis = 1).to_csv(\"test.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train = pd.read_csv(\"train.csv\")\n",
    "#test = pd.read_csv(\"test.csv\")\n",
    "#x_train = train.iloc[:,:-1]\n",
    "#x_test = test.iloc[:,:-1]\n",
    "#y_train = train[\"Target\"]\n",
    "#y_test = test[\"Target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#x = pd.concat([basic,adv,pattern,profile], axis = 1)\n",
    "x = pd.concat([basic, adv, pattern, profile], axis = 1)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, target, test_size = 0.3, random_state = 1234)#, stratify = y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 15711, 1: 19885})"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#from imblearn.over_sampling import SMOTE\n",
    "\n",
    "#smote = SMOTE(sampling_strategy = 0.7, random_state = 123)\n",
    "#x_train, y_train = smote.fit_sample(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6078067809670584 0.603831891223733\n",
      "0.6144840191671195 0.8028787194216743 0.6961607342735616\n",
      "0.6106201461417805 0.8026653256223284 0.6935946462715106\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6355601404018623"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import recall_score, precision_score, accuracy_score, f1_score, precision_recall_curve, auc, roc_curve\n",
    "\n",
    "rf_model = RandomForestClassifier(max_depth = 5, min_samples_split = 5, min_samples_leaf = 2, n_estimators = 50, max_samples = 0.8).fit(x_train, y_train)\n",
    "acc_train = rf_model.score(x_train, y_train)\n",
    "acc_test = rf_model.score(x_test, y_test)\n",
    "print(acc_train, acc_test)\n",
    "\n",
    "print(precision_score(y_train, rf_model.predict(x_train)), recall_score(y_train, rf_model.predict(x_train)),f1_score(y_train, rf_model.predict(x_train)))\n",
    "print(precision_score(y_test, rf_model.predict(x_test)), recall_score(y_test, rf_model.predict(x_test)),f1_score(y_test, rf_model.predict(x_test)))\n",
    " \n",
    "tp, fp, t = roc_curve(y_test, rf_model.predict_proba(x_test)[:,1])\n",
    "#rf_model.classes_\n",
    "auc(tp,fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "ename": "MemoryError",
     "evalue": "Unable to allocate 103. MiB for an array with shape (83056, 162) and data type float64",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-49-56cdd389a475>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m## lasso\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpenalty\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"l1\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msolver\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"liblinear\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mC\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.01\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mcoef\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\asus\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m   1340\u001b[0m             \u001b[0m_dtype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1341\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1342\u001b[1;33m         X, y = self._validate_data(X, y, accept_sparse='csr', dtype=_dtype,\n\u001b[0m\u001b[0;32m   1343\u001b[0m                                    \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"C\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1344\u001b[0m                                    accept_large_sparse=solver != 'liblinear')\n",
      "\u001b[1;32mc:\\users\\asus\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    430\u001b[0m                 \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    431\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 432\u001b[1;33m                 \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    433\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    434\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\asus\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\asus\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m    793\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"y cannot be None\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    794\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 795\u001b[1;33m     X = check_array(X, accept_sparse=accept_sparse,\n\u001b[0m\u001b[0;32m    796\u001b[0m                     \u001b[0maccept_large_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maccept_large_sparse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    797\u001b[0m                     \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\asus\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\asus\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    596\u001b[0m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"unsafe\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    597\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 598\u001b[1;33m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    599\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    600\u001b[0m                 raise ValueError(\"Complex data not supported\\n\"\n",
      "\u001b[1;32mc:\\users\\asus\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages\\numpy\\core\\_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m     83\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     84\u001b[0m     \"\"\"\n\u001b[1;32m---> 85\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     86\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mMemoryError\u001b[0m: Unable to allocate 103. MiB for an array with shape (83056, 162) and data type float64"
     ]
    }
   ],
   "source": [
    "## lasso\n",
    "model = LogisticRegression(penalty = \"l1\", solver = \"liblinear\", C = 0.01).fit(x_train, y_train)\n",
    "\n",
    "coef = pd.DataFrame(model.coef_)\n",
    "columns = pd.DataFrame(x_train.columns).transpose()\n",
    "coef_df = pd.concat([columns, coef]).transpose()\n",
    "coef_df.columns = [\"Feature\", \"Coefficient\"]\n",
    "\n",
    "relevant_features = list(coef_df[coef_df[\"Coefficient\"] != 0][\"Feature\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'relevant_features' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-50-14fb677f7635>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrelevant_features\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'relevant_features' is not defined"
     ]
    }
   ],
   "source": [
    "len(relevant_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['e10.m1',\n",
       " 'e11.m1',\n",
       " 'e12.m1',\n",
       " 'e5.m2',\n",
       " 'e11.m2',\n",
       " 'e12.m2',\n",
       " 'e19.m2',\n",
       " 'e5.m3',\n",
       " 'e9.m3',\n",
       " 'e12.m3',\n",
       " 'e18.m3',\n",
       " 'e19.m3',\n",
       " 'min(D1)',\n",
       " 'max(D1)',\n",
       " 'mean(D1)',\n",
       " 'mean(V1)',\n",
       " 'std(V1)',\n",
       " 'min(D2)',\n",
       " 'max(D2)',\n",
       " 'mean(D2)',\n",
       " 'mean(V2)',\n",
       " 'std(V2)',\n",
       " 'min(D3)',\n",
       " 'max(D3)',\n",
       " 'mean(D3)',\n",
       " 'mean(V3)',\n",
       " 'min(D4)',\n",
       " 'mean(D4)',\n",
       " 'mean(V4)',\n",
       " 'std(V4)',\n",
       " 'min(D5)',\n",
       " 'max(D5)',\n",
       " 'mean(D5)',\n",
       " 'mean(V5)',\n",
       " 'std(V5)',\n",
       " 'min(D6)',\n",
       " 'max(D6)',\n",
       " 'mean(D6)',\n",
       " 'mean(V6)',\n",
       " 'min(D7)',\n",
       " 'max(D7)',\n",
       " 'mean(D7)',\n",
       " 'mean(V7)',\n",
       " 'std(V7)',\n",
       " 'min(D8)',\n",
       " 'max(D8)',\n",
       " 'mean(D8)',\n",
       " 'mean(V8)',\n",
       " 'std(V8)',\n",
       " 'min(D9)',\n",
       " 'max(D9)',\n",
       " 'mean(D9)',\n",
       " 'mean(V9)',\n",
       " 'std(V9)',\n",
       " 'min(D10)',\n",
       " 'max(D10)',\n",
       " 'mean(D10)',\n",
       " 'mean(V10)',\n",
       " 'std(V10)',\n",
       " 'min(D11)',\n",
       " 'max(D11)',\n",
       " 'mean(D11)',\n",
       " 'mean(V11)',\n",
       " 'std(V11)',\n",
       " 'min(D12)',\n",
       " 'max(D12)',\n",
       " 'mean(D12)',\n",
       " 'mean(V12)',\n",
       " 'std(V12)',\n",
       " 'min(D13)',\n",
       " 'mean(D13)',\n",
       " 'mean(V13)',\n",
       " 'min(D14)',\n",
       " 'max(D14)',\n",
       " 'mean(D14)',\n",
       " 'mean(V14)',\n",
       " 'std(V14)',\n",
       " 'min(D15)',\n",
       " 'max(D15)',\n",
       " 'mean(D15)',\n",
       " 'mean(V15)',\n",
       " 'std(V15)',\n",
       " 'min(D16)',\n",
       " 'max(D16)',\n",
       " 'mean(V16)',\n",
       " 'max(D17)',\n",
       " 'min(D18)',\n",
       " 'max(D18)',\n",
       " 'mean(D18)',\n",
       " 'mean(V18)',\n",
       " 'std(V18)',\n",
       " 'min(D19)',\n",
       " 'max(D19)',\n",
       " 'mean(D19)',\n",
       " 'mean(V19)',\n",
       " 'std(V19)',\n",
       " 'min(D20)',\n",
       " 'max(D20)',\n",
       " 'mean(D20)',\n",
       " 'mean(V20)',\n",
       " 'std(V20)',\n",
       " 'min(D21)',\n",
       " 'max(D21)',\n",
       " 'mean(D21)',\n",
       " 'mean(V21)',\n",
       " 'std(V21)']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "relevant_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#logit_model = sm.Logit(y_train, sm.add_constant(x_train[relevant_features]))\n",
    "#result = logit_model.fit()\n",
    "#result.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692461\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692990\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
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      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692819\n",
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      "Optimization terminated successfully.\n",
      "         Current function value: 0.693059\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
      "         Current function value: 0.688430\n",
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      "Optimization terminated successfully.\n",
      "         Current function value: 0.690661\n",
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      "Optimization terminated successfully.\n",
      "         Current function value: 0.691257\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.690674\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.693002\n",
      "         Iterations 3\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692302\n",
      "         Iterations 3\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.693020\n",
      "         Iterations 3\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.693057\n",
      "         Iterations 3\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.690183\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.689870\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.689301\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.691739\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.691871\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.691761\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692917\n",
      "         Iterations 3\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692626\n",
      "         Iterations 3\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692052\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.693099\n",
      "         Iterations 3\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692569\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692371\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692576\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692975\n",
      "         Iterations 3\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692668\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692809\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.693032\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692905\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.693083\n",
      "         Iterations 3\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.693033\n",
      "         Iterations 3\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.693061\n",
      "         Iterations 3\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.693065\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.693073\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.693138\n",
      "         Iterations 3\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.693100\n",
      "         Iterations 3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692999\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692902\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692696\n",
      "         Iterations 3\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692164\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692390\n",
      "         Iterations 3\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692910\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692712\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692337\n",
      "         Iterations 3\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.693027\n",
      "         Iterations 3\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692822\n",
      "         Iterations 3\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.693142\n",
      "         Iterations 3\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692606\n",
      "         Iterations 3\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692540\n",
      "         Iterations 3\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692397\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692454\n",
      "         Iterations 3\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692663\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.693082\n",
      "         Iterations 4\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.693117\n",
      "         Iterations 3\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.693088\n",
      "         Iterations 3\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.693140\n",
      "         Iterations 3\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692898\n",
      "         Iterations 3\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692845\n",
      "         Iterations 4\n"
     ]
    }
   ],
   "source": [
    "## logistic regression\n",
    "\n",
    "var = []\n",
    "p = []\n",
    "for col in relevant_features:#x_train.columns.tolist():\n",
    "    logit_model = sm.Logit(y_train, sm.add_constant(x_train[col]))\n",
    "    result = logit_model.fit()\n",
    "    if result.pvalues[0] < 0.05:\n",
    "        var.append(col)\n",
    "        p.append(result.pvalues[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49, 49)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(var), len(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#corr_matrix = x_train[var].corr().abs()\n",
    "#high_corr_var=np.where(corr_matrix>0.9)\n",
    "#high_corr_var=[(corr_matrix.columns[x],corr_matrix.columns[y]) for x,y in zip(*high_corr_var) if x!=y and x<y]\n",
    "#len(high_corr_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pred = []\n",
    "#from scipy import stats\n",
    "#for col in var:\n",
    "#    pvalue = stats.pointbiserialr(x_train[col],y_train)[1]\n",
    "#    if pvalue < 0.05:\n",
    "#        pred.append(col)\n",
    "\n",
    "#var = pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "163"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#len(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import recall_score, precision_score, accuracy_score, f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5930567177582301 0.5818848516596028\n",
      "0.6045372050816696 0.5427954536195869 0.5720050657908858\n",
      "0.5906382978723405 0.5295688668447157 0.5584389458861396\n"
     ]
    }
   ],
   "source": [
    "rf_model = RandomForestClassifier(max_depth = 5, min_samples_split = 5, min_samples_leaf = 2, n_estimators = 50, max_samples = 0.8).fit(x_train[var], y_train)\n",
    "acc_train = rf_model.score(x_train[var], y_train)\n",
    "acc_test = rf_model.score(x_test[var], y_test)\n",
    "print(acc_train, acc_test)\n",
    "\n",
    "print(precision_score(y_train, rf_model.predict(x_train[var])), recall_score(y_train, rf_model.predict(x_train[var])),f1_score(y_train, rf_model.predict(x_train[var])))\n",
    "print(precision_score(y_test, rf_model.predict(x_test[var])), recall_score(y_test, rf_model.predict(x_test[var])),f1_score(y_test, rf_model.predict(x_test[var])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6166120354944327"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve, auc, roc_curve\n",
    "tp, fp, t = roc_curve(y_test, rf_model.predict_proba(x_test[var])[:,1])\n",
    "#rf_model.classes_\n",
    "auc(tp,fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00365233, 0.01659841, 0.00028513, 0.04315984, 0.00600597,\n",
       "       0.04265988, 0.00077783, 0.00927036, 0.02018866, 0.01952696,\n",
       "       0.00832801, 0.01074957, 0.01950977, 0.02398577, 0.00312933,\n",
       "       0.01193914, 0.01048727, 0.04840837, 0.05685939, 0.04044256,\n",
       "       0.06448887, 0.01943535, 0.01895088, 0.0139482 , 0.01561364,\n",
       "       0.05210123, 0.05251658, 0.05424994, 0.03468337, 0.00647707,\n",
       "       0.00991324, 0.00729593, 0.0048956 , 0.0067935 , 0.03558711,\n",
       "       0.039531  , 0.02894498, 0.01494015, 0.00870709, 0.00640946,\n",
       "       0.00863555, 0.00468827, 0.00790788, 0.01588587, 0.03975894,\n",
       "       0.00517826, 0.00696696, 0.00666551, 0.01286499])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## random forest feature importance\n",
    "\n",
    "rf_model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    feature  importance\n",
      "0  mean(V5)    0.064489\n",
      "1   max(D5)    0.056859\n",
      "2  mean(D9)    0.054250\n",
      "3   max(D9)    0.052517\n",
      "4   min(D9)    0.052101\n",
      "['mean(V5)', 'max(D5)', 'mean(D9)', 'max(D9)', 'min(D9)', 'min(D5)', 'e5.m3', 'e18.m3', 'mean(D5)', 'mean(D18)', 'max(D11)', 'min(D11)', 'mean(V9)', 'mean(D11)', 'mean(D3)', 'max(D1)', 'mean(D1)', 'max(D3)', 'std(V5)', 'min(D8)', 'e5.m2', 'max(D18)', 'mean(D8)', 'mean(V11)', 'max(D8)', 'std(V19)', 'min(D4)', 'min(D3)', 'mean(D4)', 'min(D10)', 'min(D1)', 'std(V11)', 'max(D12)', 'mean(V1)', 'mean(V12)', 'max(D10)', 'max(D19)', 'mean(V10)', 'mean(D19)', 'std(V9)', 'min(D12)', 'e9.m3', 'min(D19)', 'mean(D10)', 'mean(D12)', 'e10.m1', 'mean(V3)', 'e19.m3', 'e19.m2']\n"
     ]
    }
   ],
   "source": [
    "imp_df = pd.DataFrame({'feature': var, 'importance': rf_model.feature_importances_},).sort_values(by='importance', ascending=False, ignore_index = True)\n",
    "print(imp_df.head())\n",
    "var = imp_df.feature.tolist()\n",
    "print(var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "feature_list = []\n",
    "old_f1 = 0\n",
    "f1_list = []\n",
    "for name in var:\n",
    "    x = x_train[feature_list + [name]]\n",
    "    model = RandomForestClassifier(max_depth = 5, min_samples_split = 5, min_samples_leaf = 2, n_estimators = 50, max_samples = 0.8).fit(x, y_train)\n",
    "    new_f1 = f1_score(y_train, model.predict(x))\n",
    "    if (new_f1 - old_f1) > 0.0001 : \n",
    "        feature_list += [name]\n",
    "       # print(\"F1 score : %.5f\"%new_f1)\n",
    "        f1_list.append(new_f1)\n",
    "    old_f1 = new_f1\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(feature_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "plt.figure(figsize = (8,5))\n",
    "sns.barplot(imp_df.importance[:10],var[:10], orient = \"h\", color = \"teal\")\n",
    "plt.xlabel(\"Importance\", size = 13)\n",
    "plt.ylabel(\"Feature\", size = 13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(feature_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5901585811376207 0.5785037382732511\n",
      "0.5956235011990407 0.5666272864301136 0.5807636902649325\n",
      "0.5812033814022874 0.5574208317436093 0.5690637324115099\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import recall_score, precision_score, accuracy_score, f1_score\n",
    "\n",
    "rf_model = RandomForestClassifier(max_depth = 5, min_samples_split = 5, min_samples_leaf = 2, n_estimators = 50, max_samples = 0.8).fit(x_train[feature_list], y_train)\n",
    "acc_train = rf_model.score(x_train[feature_list], y_train)\n",
    "acc_test = rf_model.score(x_test[feature_list], y_test)\n",
    "print(acc_train, acc_test)\n",
    "\n",
    "print(precision_score(y_train, rf_model.predict(x_train[feature_list])), recall_score(y_train, rf_model.predict(x_train[feature_list])),f1_score(y_train, rf_model.predict(x_train[feature_list])))\n",
    "print(precision_score(y_test, rf_model.predict(x_test[feature_list])), recall_score(y_test, rf_model.predict(x_test[feature_list])),f1_score(y_test, rf_model.predict(x_test[feature_list])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6019244050337035"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve, auc, roc_curve\n",
    "tp, fp, t = roc_curve(y_test, rf_model.predict_proba(x_test[feature_list])[:,1])\n",
    "#rf_model.classes_\n",
    "auc(tp,fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"mean(D59)\" in feature_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[False,\n",
       " True,\n",
       " False,\n",
       " True,\n",
       " False,\n",
       " True,\n",
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       " True,\n",
       " True,\n",
       " False,\n",
       " False]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[t in feature_list for t in imp_df[imp_df[\"importance\"] == 0][\"feature\"].tolist()]"
   ]
  }
 ],
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