08장_지도학습_5절_로지스틱회귀분석.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 개별 코드 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 불러오기\n",
    "from sklearn import datasets\n",
    "raw_cancer = datasets.load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 피쳐, 타겟 데이터 지정\n",
    "X = raw_cancer.data\n",
    "y = raw_cancer.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 트레이닝/테스트 데이터 분할\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_tn, X_te, y_tn, y_te=train_test_split(X,y,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#데이터 표준화\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "std_scale = StandardScaler()\n",
    "std_scale.fit(X_tn)\n",
    "X_tn_std = std_scale.transform(X_tn)\n",
    "X_te_std  = std_scale.transform(X_te)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 로지스틱 회귀분석(L2 제약식 적용)\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "clf_logi_l2 =  LogisticRegression(penalty='l2')\n",
    "clf_logi_l2.fit(X_tn_std, y_tn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.29792942 -0.58056355 -0.3109406  -0.377129   -0.11984232  0.42855478\n",
      "  -0.71131106 -0.85371164 -0.46688191  0.11762548 -1.38262136  0.0899184\n",
      "  -0.94778563 -0.94686238  0.18575731  0.99305313  0.11090349 -0.3458275\n",
      "   0.20290919  0.80470317 -0.91626377 -0.91726667 -0.8159834  -0.86539197\n",
      "  -0.45539191  0.10347391 -0.83009341 -0.98445173 -0.5920036  -0.61086989]]\n",
      "[0.02713751]\n"
     ]
    }
   ],
   "source": [
    "# 로지스틱 회귀분석 모형(L2 제약식 적용) 추정 계수\n",
    "print(clf_logi_l2.coef_)\n",
    "print(clf_logi_l2.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0 0 0 0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0 1\n",
      " 0 1 0 0 1 0 1 1 0 1 1 1 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 0 1 0 0 0 1 1 0 1 0\n",
      " 0 1 1 1 1 1 0 0 0 1 0 1 1 1 0 0 1 0 0 0 1 1 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1\n",
      " 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 0 0 0 1 1 1 0]\n"
     ]
    }
   ],
   "source": [
    "# 예측\n",
    "pred_logistic = clf_logi_l2.predict(X_te_std)\n",
    "print(pred_logistic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      " [1.27248974e-04 9.99872751e-01]\n",
      " [2.66411581e-04 9.99733588e-01]\n",
      " [2.13163719e-01 7.86836281e-01]\n",
      " [2.92511631e-02 9.70748837e-01]\n",
      " [2.37309476e-05 9.99976269e-01]\n",
      " [5.09465728e-01 4.90534272e-01]\n",
      " [6.17881971e-01 3.82118029e-01]\n",
      " [1.00000000e+00 1.46648090e-12]\n",
      " [8.41453252e-05 9.99915855e-01]\n",
      " [1.58701592e-03 9.98412984e-01]\n",
      " [1.26424968e-03 9.98735750e-01]\n",
      " [9.99999994e-01 5.81805301e-09]]\n"
     ]
    }
   ],
   "source": [
    "# 확률값으로 예측\n",
    "pred_proba = clf_logi_l2.predict_proba(X_te_std)\n",
    "print(pred_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9666666666666667\n"
     ]
    }
   ],
   "source": [
    "# 정밀도\n",
    "from sklearn.metrics import precision_score\n",
    "precision = precision_score(y_te, pred_logistic)\n",
    "print(precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[50  3]\n",
      " [ 3 87]]\n"
     ]
    }
   ],
   "source": [
    "# confusion matrix 확인 \n",
    "from sklearn.metrics import confusion_matrix\n",
    "conf_matrix = confusion_matrix(y_te, pred_logistic)\n",
    "print(conf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.94      0.94        53\n",
      "           1       0.97      0.97      0.97        90\n",
      "\n",
      "    accuracy                           0.96       143\n",
      "   macro avg       0.96      0.96      0.96       143\n",
      "weighted avg       0.96      0.96      0.96       143\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 분류 레포트 확인\n",
    "from sklearn.metrics import classification_report\n",
    "class_report = classification_report(y_te, pred_logistic)\n",
    "print(class_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 전체 코드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.29792942 -0.58056355 -0.3109406  -0.377129   -0.11984232  0.42855478\n",
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      "   0.20290919  0.80470317 -0.91626377 -0.91726667 -0.8159834  -0.86539197\n",
      "  -0.45539191  0.10347391 -0.83009341 -0.98445173 -0.5920036  -0.61086989]]\n",
      "[0.02713751]\n",
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      " [9.99999999e-01 1.44073341e-09]\n",
      " [9.97609027e-01 2.39097260e-03]\n",
      " [9.99257870e-01 7.42129950e-04]\n",
      " [3.14309030e-05 9.99968569e-01]\n",
      " [4.40044150e-03 9.95599559e-01]\n",
      " [9.99897373e-01 1.02627439e-04]\n",
      " [1.52976144e-01 8.47023856e-01]\n",
      " [1.00000000e+00 2.39185116e-13]\n",
      " [9.99998777e-01 1.22317020e-06]\n",
      " [9.99999046e-01 9.53579837e-07]\n",
      " [7.96239235e-04 9.99203761e-01]\n",
      " [3.87033734e-01 6.12966266e-01]\n",
      " [9.99993469e-01 6.53125942e-06]\n",
      " [2.97085842e-03 9.97029142e-01]\n",
      " [8.09412134e-01 1.90587866e-01]\n",
      " [9.99996998e-01 3.00240009e-06]\n",
      " [1.75950117e-02 9.82404988e-01]\n",
      " [4.94325863e-05 9.99950567e-01]\n",
      " [3.51047770e-02 9.64895223e-01]\n",
      " [4.25841119e-04 9.99574159e-01]\n",
      " [2.09232609e-05 9.99979077e-01]\n",
      " [9.82374564e-01 1.76254356e-02]\n",
      " [1.00000000e+00 3.57855006e-10]\n",
      " [9.99988747e-01 1.12526453e-05]\n",
      " [5.94724730e-05 9.99940528e-01]\n",
      " [9.62731634e-01 3.72683662e-02]\n",
      " [1.69452548e-03 9.98305475e-01]\n",
      " [6.14966533e-05 9.99938503e-01]\n",
      " [6.36886875e-06 9.99993631e-01]\n",
      " [9.99902779e-01 9.72205364e-05]\n",
      " [1.00000000e+00 8.14423797e-11]\n",
      " [3.47458432e-05 9.99965254e-01]\n",
      " [5.53589378e-01 4.46410622e-01]\n",
      " [6.91462937e-01 3.08537063e-01]\n",
      " [9.99996851e-01 3.14924112e-06]\n",
      " [2.01951834e-03 9.97980482e-01]\n",
      " [2.39759190e-03 9.97602408e-01]\n",
      " [9.99999992e-01 7.92006333e-09]\n",
      " [1.03400237e-02 9.89659976e-01]\n",
      " [9.23218910e-03 9.90767811e-01]\n",
      " [9.80048490e-04 9.99019952e-01]\n",
      " [5.45753731e-09 9.99999995e-01]\n",
      " [3.09034901e-03 9.96909651e-01]\n",
      " [6.22819445e-03 9.93771806e-01]\n",
      " [1.49494565e-01 8.50505435e-01]\n",
      " [9.99994787e-01 5.21292981e-06]\n",
      " [6.02188244e-04 9.99397812e-01]\n",
      " [9.99995658e-01 4.34219020e-06]\n",
      " [9.49795077e-02 9.05020492e-01]\n",
      " [3.27428663e-01 6.72571337e-01]\n",
      " [1.72350019e-02 9.82764998e-01]\n",
      " [3.75686888e-02 9.62431311e-01]\n",
      " [9.99975711e-01 2.42887910e-05]\n",
      " [9.99911399e-01 8.86014791e-05]\n",
      " [8.65663331e-02 9.13433667e-01]\n",
      " [8.21398481e-04 9.99178602e-01]\n",
      " [2.45946373e-02 9.75405363e-01]\n",
      " [1.43898490e-01 8.56101510e-01]\n",
      " [1.58128486e-03 9.98418715e-01]\n",
      " [1.79682971e-02 9.82031703e-01]\n",
      " [1.18803803e-03 9.98811962e-01]\n",
      " [1.55728346e-02 9.84427165e-01]\n",
      " [1.43822197e-03 9.98561778e-01]\n",
      " [3.86829219e-01 6.13170781e-01]\n",
      " [2.65232841e-02 9.73476716e-01]\n",
      " [9.99999918e-01 8.17382381e-08]\n",
      " [1.28424726e-01 8.71575274e-01]\n",
      " [4.67709202e-01 5.32290798e-01]\n",
      " [2.58725940e-04 9.99741274e-01]\n",
      " [3.25269018e-05 9.99967473e-01]\n",
      " [4.00075207e-05 9.99959992e-01]\n",
      " [9.99901036e-01 9.89636008e-05]\n",
      " [1.27248974e-04 9.99872751e-01]\n",
      " [2.66411581e-04 9.99733588e-01]\n",
      " [2.13163719e-01 7.86836281e-01]\n",
      " [2.92511631e-02 9.70748837e-01]\n",
      " [2.37309476e-05 9.99976269e-01]\n",
      " [5.09465728e-01 4.90534272e-01]\n",
      " [6.17881971e-01 3.82118029e-01]\n",
      " [1.00000000e+00 1.46648090e-12]\n",
      " [8.41453252e-05 9.99915855e-01]\n",
      " [1.58701592e-03 9.98412984e-01]\n",
      " [1.26424968e-03 9.98735750e-01]\n",
      " [9.99999994e-01 5.81805301e-09]]\n",
      "0.9666666666666667\n",
      "[[50  3]\n",
      " [ 3 87]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.94      0.94        53\n",
      "           1       0.97      0.97      0.97        90\n",
      "\n",
      "    accuracy                           0.96       143\n",
      "   macro avg       0.96      0.96      0.96       143\n",
      "weighted avg       0.96      0.96      0.96       143\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "\n",
    "# 데이터 불러오기\n",
    "raw_cancer = datasets.load_breast_cancer()\n",
    "\n",
    "# 피쳐, 타겟 데이터 지정\n",
    "X = raw_cancer.data\n",
    "y = raw_cancer.target\n",
    "\n",
    "# 트레이닝/테스트 데이터 분할\n",
    "X_tn, X_te, y_tn, y_te=train_test_split(X,y,random_state=0)\n",
    "\n",
    "# 데이터 표준화\n",
    "std_scale = StandardScaler()\n",
    "std_scale.fit(X_tn)\n",
    "X_tn_std = std_scale.transform(X_tn)\n",
    "X_te_std  = std_scale.transform(X_te)\n",
    "\n",
    "# 로지스틱 회귀분석(L2 제약식 적용)\n",
    "clf_logi_l2 =  LogisticRegression(penalty='l2')\n",
    "clf_logi_l2.fit(X_tn_std, y_tn)\n",
    "\n",
    "# 로지스틱 회귀분석 모형(L2 제약식 적용) 추정 계수\n",
    "print(clf_logi_l2.coef_)\n",
    "print(clf_logi_l2.intercept_)\n",
    "\n",
    "# 예측\n",
    "pred_logistic = clf_logi_l2.predict(X_te_std)\n",
    "print(pred_logistic)\n",
    "\n",
    "# 확률값으로 예측\n",
    "pred_proba = clf_logi_l2.predict_proba(X_te_std)\n",
    "print(pred_proba)\n",
    "\n",
    "# 정밀도\n",
    "precision = precision_score(y_te, pred_logistic)\n",
    "print(precision)\n",
    "\n",
    "# confusion matrix 확인 \n",
    "conf_matrix = confusion_matrix(y_te, pred_logistic)\n",
    "print(conf_matrix)\n",
    "\n",
    "# 분류 레포트 확인\n",
    "class_report = classification_report(y_te, pred_logistic)\n",
    "print(class_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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