Random Forest analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "from sklearn.decomposition import PCA\n",
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('상장비상장(2008_2013).csv',header=None,skiprows=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.iloc[:,2:37].values\n",
    "y = data.iloc[:,[37]].values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_indices = []\n",
    "test_indices = []\n",
    "\n",
    "for index,element in data.iterrows():\n",
    "    if element[1] == 2013.0:\n",
    "        test_indices.append(index)\n",
    "    else:\n",
    "        train_indices.append(index)\n",
    "        \n",
    "train_indices=np.array(train_indices)\n",
    "train_indices=np.int_(train_indices)\n",
    "test_indices=np.array(test_indices)\n",
    "test_indices=np.int_(test_indices)\n",
    "\n",
    "X_train = X[train_indices]\n",
    "X_test = X[test_indices]\n",
    "y_train = y[train_indices]\n",
    "y_test = y[test_indices]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm = SMOTE(ratio='auto', kind='regular')\n",
    "X_train, y_train = sm.fit_sample(X_train,list(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\youngjae.kim\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_distribs = {'n_estimators': randint(low=10, high=1000), \n",
    "                  'max_features': ['auto', 'sqrt', 'log2']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_search=RandomizedSearchCV(RandomForestClassifier(class_weight={0:100}), \n",
    "                                 param_distributions=param_distribs, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise',\n",
       "          estimator=RandomForestClassifier(bootstrap=True, class_weight={0: 100},\n",
       "            criterion='gini', max_depth=None, max_features='auto',\n",
       "            max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "            min_impurity_split=None, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n",
       "            verbose=0, warm_start=False),\n",
       "          fit_params=None, iid=True, n_iter=10, n_jobs=1,\n",
       "          param_distributions={'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000001BAD7E00F98>, 'max_features': ['auto', 'sqrt', 'log2']},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score='warn', scoring=None, verbose=0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameter: {'max_features': 'log2', 'n_estimators': 69}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Parameter: {}\".format(random_search.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Cross-validity Score: 0.916\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Cross-validity Score: {:.3f}\".format(random_search.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set Score: 0.938\n"
     ]
    }
   ],
   "source": [
    "print(\"Test set Score: {:.3f}\".format(random_search.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "Forest=RandomForestClassifier(max_features='log2', n_estimators=400, class_weight={0:10})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight={0: 10}, criterion='gini',\n",
       "            max_depth=None, max_features='log2', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=400, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Forest.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_train=Forest.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "confusion_train=confusion_matrix(y_train, pred_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련데이터 오차행렬:\n",
      " [[4983    0]\n",
      " [   0 4983]]\n"
     ]
    }
   ],
   "source": [
    "print(\"훈련데이터 오차행렬:\\n\", confusion_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "테스트 데이터 오차행렬:\n",
      " [[961  31]\n",
      " [ 27  22]]\n"
     ]
    }
   ],
   "source": [
    "pred_test=Forest.predict(X_test)\n",
    "confusion_test=confusion_matrix(y_test, pred_test)\n",
    "print(\"테스트 데이터 오차행렬:\\n\", confusion_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_test_prob=Forest.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "cfreport=classification_report(y_test, pred_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "분류예측 레포트:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.97      0.97      0.97       992\n",
      "        1.0       0.42      0.45      0.43        49\n",
      "\n",
      "avg / total       0.95      0.94      0.95      1041\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"분류예측 레포트:\\n\", cfreport)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr, tpr, thresholds = metrics.roc_curve(y_test, pred_test_prob[:,1])\n",
    "roc_auc = metrics.auc(fpr, tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1bade7edf60>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.3f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], 'k--')  # random predictions curve\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlabel('False Positive Rate or (1 - Specifity)')\n",
    "plt.ylabel('True Positive Rate or (Sensitivity)')\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.legend(loc=\"lower right\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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