{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 모델 평가와 성능 향상"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set score: 0.88\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# create a synthetic dataset\n",
    "X, y = make_blobs(random_state=0)\n",
    "\n",
    "# split data and labels into a training and a test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "\n",
    "# instantiate a model and fit it to the training set\n",
    "logreg = LogisticRegression().fit(X_train, y_train)\n",
    "\n",
    "# evaluate the model on the test set\n",
    "print(\"Test set score: {:.2f}\".format(logreg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 교차 검증\n",
    "- 교차 검증은 일반화 성능을 재기 위해 훈련 세트와 테스트 세트로 한번 나누는 것 보다 더 안정적이고, 뛰어난 통계적 방법\n",
    "- 데이터를 여러번 반복해서 나누고 여러 모델을 학습"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import sys \n",
    "sys.path.append('..')\n",
    "from preamble import *\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "교차 검증 점수: [0.961 0.922 0.958]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "iris = load_iris()\n",
    "logreg = LogisticRegression()\n",
    "\n",
    "scores = cross_val_score(logreg, iris.data, iris.target)\n",
    "print(\"교차 검증 점수: {}\".format(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "교차 검증 점수: [1.    0.967 0.933 0.9   1.   ]\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(logreg, iris.data, iris.target, cv=5)\n",
    "print(\"교차 검증 점수: {}\".format(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "교차 검증 평균 점수: 0.96\n"
     ]
    }
   ],
   "source": [
    "print(\"교차 검증 평균 점수: {:.2f}\".format(scores.mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1.2 교차 검증의 장점\n",
    "- 테스트 세트에 각 샘플이 정확하게 한 번씩 들어감\n",
    "- 각 샘플은 폴드 중 하나에 속하며 각 폴드는 한 번씩 테스트 세트가 됨"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1.3 계층별 k-겹 교차 검증과 그외 전략들"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris labels:\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "print(\"Iris labels:\\n{}\".format(iris.target))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 교차 검증 상세 옵션"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "kfold = KFold(n_splits=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores:\n",
      "[1.    0.933 0.433 0.967 0.433]\n"
     ]
    }
   ],
   "source": [
    "print(\"Cross-validation scores:\\n{}\".format(\n",
    "      cross_val_score(logreg, iris.data, iris.target, cv=kfold)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores:\n",
      "[0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "kfold = KFold(n_splits=3)\n",
    "print(\"Cross-validation scores:\\n{}\".format(\n",
    "    cross_val_score(logreg, iris.data, iris.target, cv=kfold)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores:\n",
      "[0.9  0.96 0.96]\n"
     ]
    }
   ],
   "source": [
    "kfold = KFold(n_splits=3, shuffle=True, random_state=0)\n",
    "print(\"Cross-validation scores:\\n{}\".format(\n",
    "    cross_val_score(logreg, iris.data, iris.target, cv=kfold)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "교차 검증 분할 횟수:  150\n",
      "평균 정확도: 0.95\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import LeaveOneOut\n",
    "loo = LeaveOneOut()\n",
    "scores = cross_val_score(logreg, iris.data, iris.target, cv=loo)\n",
    "print(\"교차 검증 분할 횟수: \", len(scores))\n",
    "print(\"평균 정확도: {:.2f}\".format(scores.mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 임의 분할 교차 검증"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "교차 검증 점수:\n",
      "[0.907 0.973 0.893 0.973 0.933 0.893 0.893 0.907 0.893 0.96 ]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import ShuffleSplit\n",
    "shuffle_split = ShuffleSplit(test_size=.5, train_size=.5, n_splits=10)\n",
    "scores = cross_val_score(logreg, iris.data, iris.target, cv=shuffle_split)\n",
    "print(\"교차 검증 점수:\\n{}\".format(scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 그룹별 교차 검증"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "교차 검증 점수:\n",
      "[0.75  0.8   0.667]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GroupKFold\n",
    "\n",
    "X, y = make_blobs(n_samples=12, random_state=0)\n",
    "\n",
    "groups = [0, 0, 0, 1, 1, 1, 1, 2, 2, 3, 3, 3]\n",
    "scores = cross_val_score(logreg, X, y, groups, cv=GroupKFold(n_splits=3))\n",
    "print(\"교차 검증 점수:\\n{}\".format(scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 그리드 서치\n",
    "### 5.2.1 간단한 그리드 서치"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of training set: 112   size of test set: 38\n",
      "최고 점수: 0.97\n",
      "최고 매개변수: {'C': 100, 'gamma': 0.001}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=0)\n",
    "print(\"Size of training set: {}   size of test set: {}\".format(X_train.shape[0], X_test.shape[0]))\n",
    "\n",
    "best_score = 0\n",
    "\n",
    "for gamma in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "    for C in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "        svm = SVC(gamma=gamma, C=C)\n",
    "        svm.fit(X_train, y_train)\n",
    "        score = svm.score(X_test, y_test)\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            best_parameters = {'C': C, 'gamma': gamma}\n",
    "\n",
    "print(\"최고 점수: {:.2f}\".format(best_score))\n",
    "print(\"최고 매개변수: {}\".format(best_parameters))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2.2 매개변수 과대적합과 검증 세트"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of training set: 84, size of validation set: 28,size of test set: 38\n",
      "\n",
      "검증 세트에서 최고 점수: 0.96\n",
      "최고 파라미터:  {'C': 10, 'gamma': 0.001}\n",
      "최적 매개변수에서 테스트 세트 점수: 0.92\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "X_trainval, X_test, y_trainval, y_test = train_test_split(iris.data, iris.target, random_state=0)\n",
    "\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(X_trainval, y_trainval, random_state=1)\n",
    "\n",
    "print(\"Size of training set: {}, size of validation set: {},size of test set: {}\\n\".format(\n",
    "    X_train.shape[0], \n",
    "    X_valid.shape[0], \n",
    "    X_test.shape[0]))\n",
    "\n",
    "best_score = 0\n",
    "\n",
    "for gamma in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "    for C in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "        svm = SVC(gamma=gamma, C=C)\n",
    "        svm.fit(X_train, y_train)\n",
    "        score = svm.score(X_valid, y_valid)\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            best_parameters = {'C': C, 'gamma': gamma}\n",
    "\n",
    "svm = SVC(**best_parameters)\n",
    "svm.fit(X_trainval, y_trainval)\n",
    "\n",
    "test_score = svm.score(X_test, y_test)\n",
    "print(\"검증 세트에서 최고 점수: {:.2f}\".format(best_score))\n",
    "print(\"최고 파라미터: \", best_parameters)\n",
    "print(\"최적 매개변수에서 테스트 세트 점수: {:.2f}\".format(test_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2.3 교차 검증을 사용한 그리드 서치"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma=0.01, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for gamma in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "    for C in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "        svm = SVC(gamma=gamma, C=C)\n",
    "        scores = cross_val_score(svm, X_trainval, y_trainval, cv=5)\n",
    "        score = np.mean(scores)\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            best_parameters = {'C': C, 'gamma': gamma}\n",
    "\n",
    "svm = SVC(**best_parameters)\n",
    "svm.fit(X_trainval, y_trainval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter grid:\n",
      "{'C': [0.001, 0.01, 0.1, 1, 10, 100], 'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}\n"
     ]
    }
   ],
   "source": [
    "param_grid = {    'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "              'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}\n",
    "print(\"Parameter grid:\\n{}\".format(param_grid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "grid_search = GridSearchCV(SVC(), param_grid, cv=5, return_train_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100], 'gamma': [0.001, 0.01, 0.1, 1, 10, 100]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "테스트 세트 점수: 0.97\n"
     ]
    }
   ],
   "source": [
    "print(\"테스트 세트 점수: {:.2f}\".format(grid_search.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "최고 매개변수: {'C': 100, 'gamma': 0.01}\n",
      "최적 매개변수에서 테스트 세트 점수: 0.97\n"
     ]
    }
   ],
   "source": [
    "print(\"최고 매개변수: {}\".format(grid_search.best_params_))\n",
    "print(\"최적 매개변수에서 테스트 세트 점수: {:.2f}\".format(grid_search.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "최고 성능 모델:\n",
      "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma=0.01, kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n"
     ]
    }
   ],
   "source": [
    "print(\"최고 성능 모델:\\n{}\".format(grid_search.best_estimator_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 교차 검증 결과 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['mean_fit_time', 'mean_score_time', 'mean_test_score',\n",
      "       'mean_train_score', 'param_C', 'param_gamma', 'params',\n",
      "       'rank_test_score', 'split0_test_score', 'split0_train_score',\n",
      "       'split1_test_score', 'split1_train_score', 'split2_test_score',\n",
      "       'split2_train_score', 'split3_test_score', 'split3_train_score',\n",
      "       'split4_test_score', 'split4_train_score', 'std_fit_time',\n",
      "       'std_score_time', 'std_test_score', 'std_train_score'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>...</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.86e-04</td>\n",
       "      <td>2.68e-04</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>...</td>\n",
       "      <td>1.76e-04</td>\n",
       "      <td>4.39e-05</td>\n",
       "      <td>0.01</td>\n",
       "      <td>2.85e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.11e-04</td>\n",
       "      <td>2.53e-04</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>...</td>\n",
       "      <td>2.05e-05</td>\n",
       "      <td>6.22e-06</td>\n",
       "      <td>0.01</td>\n",
       "      <td>2.85e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.77e-04</td>\n",
       "      <td>2.93e-04</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>...</td>\n",
       "      <td>8.31e-05</td>\n",
       "      <td>5.52e-05</td>\n",
       "      <td>0.01</td>\n",
       "      <td>2.85e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.89e-04</td>\n",
       "      <td>2.60e-04</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>...</td>\n",
       "      <td>1.95e-05</td>\n",
       "      <td>3.59e-05</td>\n",
       "      <td>0.01</td>\n",
       "      <td>2.85e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.35e-04</td>\n",
       "      <td>2.46e-04</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>...</td>\n",
       "      <td>1.99e-05</td>\n",
       "      <td>6.66e-06</td>\n",
       "      <td>0.01</td>\n",
       "      <td>2.85e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  mean_score_time  mean_test_score  mean_train_score  \\\n",
       "0       6.86e-04         2.68e-04             0.37              0.37   \n",
       "1       6.11e-04         2.53e-04             0.37              0.37   \n",
       "2       6.77e-04         2.93e-04             0.37              0.37   \n",
       "3       5.89e-04         2.60e-04             0.37              0.37   \n",
       "4       6.35e-04         2.46e-04             0.37              0.37   \n",
       "\n",
       "        ...        std_fit_time std_score_time std_test_score  std_train_score  \n",
       "0       ...            1.76e-04       4.39e-05           0.01         2.85e-03  \n",
       "1       ...            2.05e-05       6.22e-06           0.01         2.85e-03  \n",
       "2       ...            8.31e-05       5.52e-05           0.01         2.85e-03  \n",
       "3       ...            1.95e-05       3.59e-05           0.01         2.85e-03  \n",
       "4       ...            1.99e-05       6.66e-06           0.01         2.85e-03  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "results = pd.DataFrame(grid_search.cv_results_)\n",
    "\n",
    "print(results.columns)\n",
    "display(results.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x116d45fd0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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MD7Yuv7EiC611JezKmiCeK++hmbKx0djiYHAaJl6AFjdg6GmNGjV04YKmLpVCgcUl8Jl8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\n",
      "image/png": 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HlrSnV3rDF1sNQ3P52FJi46w89Vx0UFkAtnxUw7IF5XgNzbgJkUyZHrh+qpwGi+4sJW+v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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11640dc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = np.array(results.mean_test_score).reshape(6, 6)\n",
    "\n",
    "mglearn.tools.heatmap(\n",
    "    scores, \n",
    "    xlabel='gamma', \n",
    "    xticklabels=param_grid['gamma'],\n",
    "    ylabel='C', \n",
    "    yticklabels=param_grid['C'], \n",
    "    cmap=\"viridis\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x117a10d30>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": 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oIDMzOCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw1Ujmu3UAM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\nCg==\n",
      "image/png": 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ryd6YU1tm4t2Xsu2nHTx42Rw6pLdj+tzJ3Dnyodrjt5/zAGV79gc6WlhnVV7l\nDdesgXbStHWWxb1jz2HyK4spKNvPoilXsmLjNrYV7a0ts+CbNSz4Zg0Aw9K7cM1Z/SitqGpQ3UDy\neAz33FvG22+1IjnZzpgLihk1KpLu3Z21ZZ599gCnnOLk5ZdbkZnp5u57SnlnUeva4+++25pWrZpm\nZN7jMUy/u4hli9qTkuxg0Ohsxo6KISM9srbMI8/spW+vSBa/0o5NW6uZfnchy99NAWDGrCLOGx7N\nu/OTqa42lFd4g5433M7vM/cV8tjr7UlIcnLzuCzOGBFDare68/vWc3tJy4jioRfbk72tmmfuK+Dx\nNztgd1hMuSeB7r2iKD/gZcrYLPoPifarG0g2m8Ut94zl7htfobigjGcWTWXVio1kbyvyK7d+zU7u\nv+X1evWn/PEC1nyzldm3vo3DaScyylmvTKgdrU2zLKsVsAhIBXYCVxhj9h2l7vnAXwE7MN8Y85dA\n52tIf/Pj5+v5dsntAHTu3ZF7F93GpIwZ5GzZzZTT7qh9nbdzXmTle8F7kxhOWcMyr2Xx8Jkjuerj\nd8g/uJ8l467ms+xtbC3ZU1vmxXXf8+I63wD4uR27MrlXf0qrKhtUN9A8HsMjs/Yx780EEpPsXHVR\nIWePaEbXw9rm+XPLSM+I4KmX4tmR6eKRWSW89HYCEZHwt7cTiI6x4XIZrr+8kCHDouhzWnDaukN5\nb717Lx8tTKR9soOhY3ZzwXnR9OweUVtmzjOl9DklgkUL2rJ5azW33rOXpe8kAXDHfXsZOawZb/2t\nbZP0feHGGPCEySMJwbwaeBU4/1eO3wJsMMacCgwDnrAsK+JXyp+Q9IFp7M7MJ39HIW6Xmy8XreTM\ncQP8ynTqmcLaL9YDsGvzbhJTE2jRNvijduGcFZQ32MIpbzhlDYJXOQnaut4pSWTvKSFnXykuj5eP\n123mnJ5dj1l+TJ90lv68+YTq/rt+XOsiNdVOp04OIiIsxo1rxrJPq/zKbNnq5qyzfBdCaWkOcnI8\nFBV5gpbp13z3YyVdU5106eQkIsJi/Lg4liw76Fdmw5Zqhp8VDUCPbhHs3OWmoMhNaZmHr1dVMOlK\n312kiAiLFs3tQc0bbud300+VtO/kpF3HCJwRFsPHxvO/y/3Pb1ZmNX3P8M3K6Ng1gvwcN3uL3LRu\n66B7L98dq+hYG53SIijOdwcta3rvFPJ27SU/Zx9ul4d/Lf2ZM4b3bFDd6NhIevdP5ZN/rgbA7fJw\ncH9l0LJkEXiTAAAgAElEQVT+G16lfpv2B+BzY0w34POar/1YlmUHngNGAxnARMuyMgIdriH9TeXB\nuvMaFRPluzI/Qr9ze5G3LZ/C7OJARwzLrOGYt29CMjvL9rFrfykur5cPt29iZKe0Y5Yf16UHH2zb\ndEJ1A2H92mo6pDpI6ejAGWFx3thmfLm8wq/M9q1uBp7pa5s7pznZneNmT5EHy7KIjvG9bXO7DW4X\nWEF+r7n6xyq6pjroXNP3XT4uho+WlfuV2bjVxbAhvjY4vVsEWbvcFBR5KC3z8s2qKq670je7rSn6\nvnDkNbZGfYRK0H6yMeYr4NduTxkgzrIsC4itKRvwXr5N+1YU5dSNFhbn7KVN+9Z+Zbb/vJMhlw4C\nIP30NBI7JZCQ4itjDDy2/D6e+/5Rxtw4ItDxwjar8ipvuGYNtJOlrUuMjyW/tG6GRn7ZAdrGxx61\nbJTTwdBuqSz/ZWuj6wZCfp6Hdsl1Fw7JSTby8/zfrGZkOFj6se/C9Mcfq8nJ8ZCX57s7YVkwfsIe\nzh9dxBtv+F+8BENuvpsO7esm5LVPdpB7xJvSUzMieW/pAcA3wJCV4yJnt5sd2W4SWtu5YUYB/Udm\nc+PMAg6WB/cuS7id3+J8NwnJdec3IclBcb7Lr0yXnpF8s8x3fjetraAg11VvYCA/x0Xmhip69g3e\nlNfWifEU5ZXWZS8oo3Vi/YHPjH4deWHxdB6edy2durYFICmlFaX7ypk5+zLm/uMWZjx4CZHNTr4Z\nBsdo08YBr9V8/hpw8VGqDgQyjTHbjTHVwMKaegHVkP4G4KyLB/Lyhqf500d/5PFJL9Q7PmzCWaxY\nuDLQ8cI2K4Rf3qToWPIO1vVdeQf3kxR9jH7P7uDslM58vHNLo+sGSmG+h6TD2ubEZDuF+f5tc/cM\nJ59/4htEWLe2mrxcDwU1ZTwewxWjCzjntDwGD42kd7/gzS4A2J3voX07/75v9xF9Se+MCD5Y6usn\nvv+xiuwcN7l5bnZmu2jT2sbvby1m8MjdTJ1ZHPS+L9z4tlVs3EeohHKlhblAT2A3sA74H2NMSP4l\nLfzL+8Q2j2HeD3O4eNpoMn/cgdfji3Lr0FlMOe0O7hkzm4tuPo/eQxt2J0FZfZQ3uMIpbzhlDbCT\npq07ZFh6F37I3k1pRdXxC4fItFtiKSvzMnJUEQteKadXLye2muus9xa3ZvmnCbzxeitefe0gq1aF\n/ve4a3pLSso8nDYii7kvl9CvVyR2u4XbbfhhXRVTrm3BmuUdiWlm49Fn683kbnLhdn4nTmnJgTIv\nN43J4r3XSuh2SmRtXoCKg14emLqbm2clEBMX2rtYmRt2c/WIOUy99FmWvPkt9z17FQB2u420nsl8\ntPD/mHb5c1RWVDN+8tkhzdoIicaYvJrP84HEo5RpDxy+IENOzfdCYuX73zEpYwYPXPIY1z003u+Y\nw+ngjLED+Ne734Yonb9wygrhlxdgRKeurC7MpbTqpJzVU+uGqXHsL/NyxegCFr56gPRTnNhq3q3Z\n7RbvfJzIslXJrF/rInOz69dfrAncPq05JaVeBo3IZd6CMk7tFYHdBm4PrF1XzeRr4lm1vB0x0RaP\nzy09/gv+h/FiNeojVEK56OF5wFrgHKArsNyyrK+NMWVHFrQs6ybgJoAoohv1Q4pz99bexQRok9KK\n4lz/55PK91fw+KTna79+fftz5G0vAGDPbt8ge0lRGSvf/470gWms+3pjozL8FrMqr/KGa9YQOKG2\nzhHfuIXbCsoOkNQ8rvbrpPhYCssOHLWs73GETSdUNxCSku1+dyny8r1+d10A4uJsPPWkb3ElYwyD\nzyiiU0dfmeSasm3a2Bl9fhRr17oYPDh4d1raJznYlVt3Nzs3z037JP/uMz7OzoKnk2rzdh24ky6d\nHJRXGN+6B6f57npfdmEsj84NztoQh4Tb+W2T5KAor+78FuW7aZPkf+c9Js7OnXPqzu9VQ3eQ3MFX\nxu0yPDB1N+eOi2fo+XEE056CMhKS62YUtEmMZ0+B/0Vw+cG6AZbvv97CtFkXEd8imuKCUooLyti8\nzvc8+Nefrg+nAYNaxhhjWVb9eeiNEOzrusOt+3ojyV0SiW8dV7tOzumj+5L5ww5KCoP7BiacsoZj\n3vzyAyTH1P2fT46JI7/86H3X2C49WbKtrt9rTN1AaZtk95vtVZDnoW2Sf9scG2fjocd9i74aYxgz\nJJ+Ujkf0N81tnH5mJCu/rCQtPXizlNol2cnd7d/3tTuiL4mPs/HS021q8/YclEPnTk7KK7y0T7Yz\nsGaNhUsujNGAwRHCaVvFUM4wuB5YbHwygR1Aj6MVNMa8ZIwZYIwZ4KRxFy2bv8+kfbdkklLb4nA6\nGDb+LL5dstqvTEzzaBxO33/G0ZPPZd1XGynfX0FUdCTNYn0XeVHRkfQfeSo71wdvNeNwyqq8yhuu\nWUPghNo6R3RMo37I+tx8OrVuSfuW8TjtNkb3TmfFpu31ysVGRnB6agpfbNzW6LqB0vdUJzt2eMjO\ndlNdbfjggwpGjfRv20tLvVRX+96TvPVWBYMGRRAXZ6O83MuBA74JGuXlXv71VRXp6cEd+z69bxSZ\nO6rZke2iutqw6IP9jD3P/++npNRTm3f+m2UMHdyM+Dg7SW0ddGjnYHNmNQBffFNORveAL2HhJ9zO\nb48+UeTudJG3y4Wr2rDiwzLOHOF/fg+UeXDV5F26sJQ+A5sRE2fHGMPjd+XTMS2C300O7u4IAJvX\n59KuY2sS27fE4bRz9pg+rFqxya9MyzZ105q7907BslmUlZSzr/gARfmlpKT6Lq77De5K9rbCoGcO\nkALLspIBav48WvBcoMNhX6fUfK+eYF/XteuaVPt5Wr/OOCOdfovqDp8whBULv2nUzz0R4ZQ1HPP+\nVJRH5/iWdIhtjtNmY2yXHizPyqxXLs4ZweCkFD497FhD6wbSKadGkL3DTW62G1e1YdmHFZw90n/H\nnLJSb21bt3jhQfoPjCQ2zsbePR7KSn1tc2WlYdXXlXROC27b3L9vJJk7fI8XVFcb/vHBQS4Y5T/A\nd3jf98pbBxgyOIr4OBtJbR2ktHOwJdM3C2LF1xX07HbyPYIVauGyhkEoZxhkA+cCX1uWlQikAwG/\nQvV6vMyd/jKPfHIPNruNZa+sIGtDDhf+fiQAH724nI49U7jz1VswBrJ+2cUTk33PY7VIbM4Di30r\nvtoddla8/Q2rl60NdMSwzKq8yhuuWUOgSdo6j9cw+6Mv+Nu1l2KzWby35hcyC/cw/vQ+ACz6/mcA\nRmSksTIziwqX+7h1g8XhsPjTw/FcedVevF4YP74Z6elO/v66b6G7a66OYWummxkzSrAsSO/u4PHH\nfXfDi4q8TJrsm9Lv8cDFF0cxfHhwt2lyOCye+XNbRk/MxeOB6yfEc0p6JPNeKwFgyrUt2Li1muv/\npwDLgozuEcx/sm7G9l9nt+XqW/Kpdhk6d3Sy4OmjzeYObN5wOr92h8X0BxO465ocvF4Y/bt4UrtH\n8uGbvvM79qoWZGVW8+jMfCwLUrtHcvujvnO4fnUly9/bT+f0CG4akwXApDtaM2h4cJ5F9nq8PD/7\nQ2a/dB02m8Wn7/1A1rZCxlwxEICl73zHkFG9uHD8QDweL1WVLh65fVFt/ef//BF3PnoFTqedvJy9\nPHnvP4OSMwiWANcCf6n584OjlPke6GZZVmd8AwUTgCsDHaQh/c3QywYx4uqz8bg8VFVU86cJT9XW\n9w1M9+HpKS8FOlpYZw3HvB5juO9/P+Pvoy/Hbtl4Z8s6tpbs4aoepwLw5qafADgvtRtf5e6kwu06\nbt1gcjgs/vBQC6ZeU4zXYxh3RQxp3Z28+4ZvZsPv/iuWHZkuZs3ch2VB124OHpjjm21QXOhh1m37\n8HrB6zWMujCa/3ducLfndTgsnpzdiouuLMDjgWsmxJKRHsHf/u6bIHnjNfFs3urixhnFWEDP9Ahe\neKJuhsoTf2rF9dOKcLkMqR0dvPhU8LfoDSshXpegMSxzlNVNA/LClvU2vhXB2wAFwP2AE8AYM8+y\nrHb4VuJNBizgL8aYN473uvFWKzPIOjcomUUkfH1m/rHGGDPg+CUDK1htXbPkDqbzpNuClDrwPps6\nJ9QRGizJHtyFrQIt3xPcabKBtskV3McCAu2xCy4PdYRGWbbhz0Ft647Rpr0PvINve9gsfNsq7q1p\n3+YbY8bU1B0DPI1vW8UFxpjZx/t5uq6TQ7a8NDDUERplyahnQh2hUbo7Q3mfuPGi2+0MyXVdU2nZ\no605Z0Hj+p/FZ71w3HNyvO1tT2S776D9yzHGTDzO8d3AqGD9fBGRpqC2TkR+S36lTav3rr6mfRtz\n2NdLgaVBiiYi8psS6BkGh21vOxLfwrPfW5a1xBiz4bBih7b7HmtZVgKw2bKsN2t2tzmq8BpqEhER\nEREREQljQVr0sHZ7WwDLsg5tb3v4gEGjt/sO5aKHIiIiIiIiIvLva8j2to3e7lsDBiIiIiIiIiJN\nyFuz8GFDP4A2lmWtPuzjphP4sYe2+24H9AXmWpYV/2sV9EiCiIiIiIiISBMxnNAuCcXHWfSwIdvb\nXo9vAW4DZFqWdWi77++O9aKaYSAiIiIiIiLShLxYjfpogNrtbS3LisC3ve2SI8oc2u6bhm73rRkG\nIiIiIiIiIk3FBH7RQ2OM27KsacAy6ra3/cWyrCk1x+cBDwOvWpa1Dt9233cZY4p/7XU1YCAiIiIi\nIiLSRIK0S8JRt7etGSg49Hmjt/vWgIGIiIiIiIhIEwrGgEEwaMBAREREREREpImc4KKHIaEBAxER\nEREREZEmZDRgICIiIiIiIiJHauDOByGnAQMRERERERGRJmKCsEtCsGjAQERERERERKQJ6ZEEERER\nERERETmCFj0UERERERERkaPQDAMRERERERER8WPQGgYiIiIiIiIiciTjW/gwHGjAQEREREREQqr7\nTd+FOkKjXPTSf4c6QqMsGfVMqCPIEcJlW0VbqAOIiIiIiIiIyMlHMwxEREREREREmohBix6KiIiI\niIiISD3aVlFEREREREREjkKLHoqIiIiIiIhIPXokQURERERERET8GKMBAxERERERERE5Cq1hICIi\nIiIiIiL1aA0DEREREREREalHjySIiIiIiIiIiB+DpQEDEREREREREakvTJ5I0ICBiIiIiIiISJPR\nLgkiIiIiIiIiclRhMsVAAwYiIiIiIiIiTUgzDERERERERESkHm2rKCIiIiIiIiJ+DJphcFIZcF5f\nbn76emx2Gx+//DmLHn3f73hsixhmvnwz7bomUl3p4olJz7Pzl10AvL79OSr2V+L1ePG4Pdwy8A/K\nqrzKG+ZZf6uGdOvEH8cMw26z8Y8165n/1fd+x28Y0p8LT+0BgN1mo0tCK4Y8Mo/Siqrj1g20FSsq\nue/+MrwemDgxmmnTYv2Ol5R4mTmzhKwsD5GRFk880ZwePZwADBpcSGyMhc0ODofFx0vbBDUrwCdf\nHOTW+4rweGDSlfHcNb2V3/F9JR4m3VrA9iwXUZEW859KpFePSN/vUurhxpmF/LKpCsuC+U8lcsaA\nZkHNG27n97t/HeS5BwvxemHM+OZMnOp/fveXephzZz67s1xERFrc8VgSndMjKdzt4i8z89lX7MGy\n4IKJzbns+pZBzdp/SDem/uECbHYbn/xzNe/M/8rveJ/TO3P/s/9Ffu4+AFZ+9gtvvbACgJi4KGY8\ndAmpaYkYY3hq1mI2/rQrqHl/i47X35xx0QCue2gCxmvwuD08f+ur/LJyEynd23HvwltryyV1actr\n9y/ivb8uVVblbZK8Z6ekcv/gc7FbFgs3/8wLP3/nd/z3vU9nXFoGAA7LIq1Fa/q9+RylVZXHrRsM\nK7+s5LEHS/B6DJdMiOGGm+P9jpeVern/jr3kZHmIiIQH57QiLd1JVaXhhisKcVWD220YMaYZN9/W\nPOh5w4oB/tMHDCzLWgBcCBQaY3odo8ww4GnACRQbY84OdA6bzcb0uZO4a9TDFOfsZe53j/DtktVk\nb8ypLTPx7kvZ9tMOHrxsDh3S2zF97mTuHPlQ7fHbz3mAsj37Ax0trLMqr/KGa9ZAO2naOsvi3rHn\nMPmVxRSU7WfRlCtZsXEb24r21pZZ8M0aFnyzBoBh6V245qx+lFZUNahuIHk8hnvuLePtt1qRnGxn\nzAXFjBoVSffuztoyzz57gFNOcfLyy63IzHRz9z2lvLOode3xd99tTatWtqDkO1re6XcXsWxRe1KS\nHQwanc3YUTFkpEfWlnnkmb307RXJ4lfasWlrNdPvLmT5uykAzJhVxHnDo3l3fjLV1YbyCm/Q84bb\n+X3mvkIee709CUlObh6XxRkjYkjtVnd+33puL2kZUTz0Ynuyt1XzzH0FPP5mB+wOiyn3JNC9VxTl\nB7xMGZtF/yHRfnUDyWazuOWesdx94ysUF5TxzKKprFqxkextRX7l1q/Zyf23vF6v/pQ/XsCab7Yy\n+9a3cTjtREY565U5mR2vvbMsywL+CowByoHrjDE/BDJDQ/qbHz9fz7dLbgegc++O3LvoNiZlzCBn\ny26mnHZH7eu8nfMiK98L3puucMqqvE2Q17J4+MyRXPXxO+Qf3M+ScVfzWfY2tpbsqS3z4rrveXGd\nb8D+3I5dmdyrP6VVlQ2qG2gej+GRWfuY92YCiUl2rrqokLNHNKPrYX3J/LllpGdE8NRL8ezIdPHI\nrBJeejuBiEj429sJRMfYcLkM119eyJBhUfQ5LThtswRXMK8GXgXOP9ZBy7JaAM8DFxljTgF+F4wQ\n6QPT2J2ZT/6OQtwuN18uWsmZ4wb4lenUM4W1X6wHYNfm3SSmJtCibdOPgoVTVlDeYAunvOGUNQhe\n5SRo63qnJJG9p4ScfaW4PF4+XreZc3p2PWb5MX3SWfrz5hOq++/6ca2L1FQ7nTo5iIiwGDeuGcs+\nrfIrs2Wrm7PO8l1YpKU5yMnxUFTkCVqmX/Pdj5V0TXXSpZOTiAiL8ePiWLLsoF+ZDVuqGX5WNAA9\nukWwc5ebgiI3pWUevl5VwaQrfXdlIiIsWjS3BzVvuJ3fTT9V0r6Tk3YdI3BGWAwfG8//Lvc/v1mZ\n1fQ9wzcro2PXCPJz3OwtctO6rYPuvaIAiI610SktguJ8d9CypvdOIW/XXvJz9uF2efjX0p85Y3jP\nBtWNjo2kd/9UPvnnagDcLg8H91cGLWuQvMqvtHfAaKBbzcdNwAuBDtCQ/qbyYN15jYqJOuqDwv3O\n7UXetnwKs4sDHTEssypv8PP2TUhmZ9k+du0vxeX18uH2TYzslHbM8uO69OCDbZtOqG4grF9bTYdU\nBykdHTgjLM4b24wvl1f4ldm+1c3AM319Sec0J7tz3Owp8mBZFtExvreZbrfB7QIrPG6mNyljGvcR\nKkEbMDDGfAX82u2pK4HFxpjsmvKFwcjRpn0rinLqRt+Kc/bSpn1rvzLbf97JkEsHAZB+ehqJnRJI\nSPGVMQYeW34fz33/KGNuHBGMiGGZVXmVN1yzBtrJ0tYlxseSX1o3QyO/7ABt42OPWjbK6WBot1SW\n/7K10XUDIT/PQ7vkujfNyUk28vP836xmZDhY+rHvQu/HH6vJyfGQl+e7M29ZMH7CHs4fXcQbb5QH\nLechufluOrSvm5DXPtlB7hFvSk/NiOS9pQcA3wBDVo6LnN1udmS7SWht54YZBfQfmc2NMws4WB7c\nGQbhdn6L890kJNed34QkB8X5Lr8yXXpG8s0y3/ndtLaCglxXvYGB/BwXmRuq6Nk3KmhZWyfGU5RX\nWpe9oIzWifUHPjP6deSFxdN5eN61dOraFoCklFaU7itn5uzLmPuPW5jx4CVENguvGQYNaO/GAX83\nPquAFpZlJQcyQ0P6G4CzLh7Iyxue5k8f/ZHHJ9Uftxg24SxWLFwZyGhhnRWUN9iSomPJO1jX1+Yd\n3E9S9DH6abuDs1M68/HOLY2uGyiF+R6SDutLEpPtFOb79yXdM5x8/olvEGHd2mrycj0U1JTxeAxX\njC7gnNPyGDw0kt79NLugHtPIjxBpmvmGR9cdaGlZ1peWZa2xLOuaUAVZ+Jf3iW0ew7wf5nDxtNFk\n/rgDr8d34XTr0FlMOe0O7hkzm4tuPo/eQxt2J0FZfZQ3uMIpbzhlDbCTpq07ZFh6F37I3k1pRdXx\nC4fItFtiKSvzMnJUEQteKadXLye2muuW9xa3ZvmnCbzxeitefe0gq1aF/ve4a3pLSso8nDYii7kv\nl9CvVyR2u4XbbfhhXRVTrm3BmuUdiWlm49Fn94U6btid34lTWnKgzMtNY7J477USup0SWZsXoOKg\nlwem7ubmWQnExAV3BsfxZG7YzdUj5jD10mdZ8ua33PfsVQDY7TbSeibz0cL/Y9rlz1FZUc34yQF/\nOinU2gOHL8qQU/O9Jrfy/e+YlDGDBy55jOseGu93zOF0cMbYAfzr3W9DEa2ecMoKytsURnTqyurC\nXEqrTu5ZSDdMjWN/mZcrRhew8NUDpJ/ixFbz7tJut3jn40SWrUpm/VoXmZtdv/5i/3EsjGncR6iE\nctFDB9AfOBdoBnxrWdYqY8yWIwtalnUTvqltRBHdqB9SnLu39i4mQJuUVhTn+j/vU76/gscnPV/7\n9evbnyNvewEAe3b7BtJLispY+f53pA9MY93XGxuV4beYVXmVN1yzhsAJtXWO+MYt3FZQdoCk5nG1\nXyfFx1JYduCoZX2PI2w6obqBkJRsZ/dhd7zz8r1+dzEA4uJsPPVkCwCMMQw+o4hOHX1lkmvKtmlj\nZ/T5Uaxd62Lw4ODduWif5GBXbt3d7Nw8N+2T/LvP+Dg7C55Oqs3bdeBOunRyUF5hfOsenOa7633Z\nhbE8Ojc4a0McEm7nt02Sg6K8uvNblO+mTZL/nfeYODt3zqk7v1cN3UFyB18Zt8vwwNTdnDsunqHn\nxxFMewrKSEium1HQJjGePQWlfmXKD9YNsHz/9RamzbqI+BbRFBeUUlxQxuZ1vuerv/50/W9xwKBB\ngn1dd7h1X28kuUsi8a3jatfJOX10XzJ/2EFJYekx6wVCOGVV3uDnzS8/QHJMXRuVHBNHfvnR+9qx\nXXqyZFtdP92YuoHSNsnuNzutIM9D2yT/viQ2zsZDj/sWqTXGMGZIPikdj+gfm9s4/cxIVn5ZSVp6\neM2qCrow2VYxlDMMcoBlxpiDxphi4Cvg1KMVNMa8ZIwZYIwZ4KRxFy2bv8+kfbdkklLb4nA6GDb+\nLL5dstqvTEzzaBxO3z/u0ZPPZd1XGynfX0FUdCTNYn0XeVHRkfQfeSo71wdvNeNwyqq8yhuuWUPg\nhNo6R3RMo37I+tx8OrVuSfuW8TjtNkb3TmfFpu31ysVGRnB6agpfbNzW6LqB0vdUJzt2eMjOdlNd\nbfjggwpGjfRv20tLvVRX+3rSt96qYNCgCOLibJSXezlwwDczpbzcy7++qiI9Pbhj36f3jSJzRzU7\nsl1UVxsWfbCfsef5//2UlHpq885/s4yhg5sRH2cnqa2DDu0cbM6sBuCLb8rJ6B4R1Lzhdn579Iki\nd6eLvF0uXNWGFR+WceYI//N7oMyDqybv0oWl9BnYjJg4O8YYHr8rn45pEfxucnB3RwDYvD6Xdh1b\nk9i+JQ6nnbPH9GHVik1+ZVq2qZsm3L13CpbNoqyknH3FByjKLyUl1bfrRL/BXcneFpQnlEIpF+hw\n2NcpNd/zE+zrunZdk2o/T+vXGWek029R3eEThrBi4TeN+rknIpyyKm/w/VSUR+f4lnSIbY7TZmNs\nlx4sz8qsVy7OGcHgpBQ+PexYQ+sG0imnRpC9w01uthtXtWHZhxWcPdJ/h5+yUm9t27x44UH6D4wk\nNs7G3j0eykp9fUllpWHV15V0TvuP2Jyv4QyaYdAAHwBzLctyABHAIOCpQP8Qr8fL3Okv88gn92Cz\n21j2ygqyNuRw4e9HAvDRi8vp2DOFO1+9BWMg65ddPDHZ93xTi8TmPLDYt4Kq3WFnxdvfsHrZ2kBH\nDMusyqu84Zo1BJqkrfN4DbM/+oK/XXspNpvFe2t+IbNwD+NP7wPAou9/BmBERhorM7OocLmPWzdY\nHA6LPz0cz5VX7cXrhfHjm5Ge7uTvr/sWurvm6hi2ZrqZMaMEy4L07g4ef9x3N7yoyMukyb4p/R4P\nXHxxFMOHB++Z9UN5n/lzW0ZPzMXjgesnxHNKeiTzXisBYMq1Ldi4tZrr/6cAy4KM7hHMfzKxtv5f\nZ7fl6lvyqXYZOnd0suDpxGP9qIDlDafza3dYTH8wgbuuycHrhdG/iye1eyQfvuk7v2OvakFWZjWP\nzszHsiC1eyS3P+o7h+tXV7L8vf10To/gpjFZAEy6ozWDhgfn2V6vx8vzsz9k9kvXYbNZfPreD2Rt\nK2TMFQMBWPrOdwwZ1YsLxw/E4/FSVenikdsX1dZ//s8fceejV+B02snL2cuT9/4zKDlDaAkwzbKs\nhfjaulJjTF4gf0BD+puhlw1ixNVn43F5qKqo5k8T6ppc38B0H56e8lIgY4V9VuUNPo8x3Pe/n/H3\n0Zdjt2y8s2UdW0v2cFUP3z2ENzf9BMB5qd34KncnFW7XcesGk8Nh8YeHWjD1mmK8HsO4K2JI6+7k\n3Td8Mxt+91+x7Mh0MWvmPiwLunZz8MAc32yD4kIPs27bh9cLXq9h1IXR/L9zg7udcFgKkxkGlgnS\nkouWZb0NDAPaAAXA/fi2FMMYM6+mzB3A9YAXmG+Mefp4rxtvtTKDrHODkllEwtdn5h9rjDEDjl8y\nsILV1jVL7mA6T7otSKkD77Opc0IdocGS7MFdKCrQ8j3BnXYaaJtcwX0sINAeu+DyUEdolGUb/hyS\ntg6O397VbKs4F99OCuXA9caY1Ud/NR9d10m42vLSwFBHaJQlo54JdYRG6dspJ2RtXVOI7Jxikh+c\n3qg6Wdf+ISTnJGgzDIwxExtQZg4QPleZIiJHUFsnIv8pjtfeGd9dqFuaKM7/Z+/O46OqDv//v87M\nZCbYdzYAACAASURBVIEs7CQhYV8CEREFwQV+SgUVFHFrAf24FbS48KkWt7ovpVbRuoFFP4hYN9BW\n3L4o0oob1YIoCghoWAIJWYEkQALJzJzfHxOTTEhIgrmZDL6fj8c8nJl7zp23J8OZO2fOPVdEJLw5\n8Lu9MeZs4EnATeBHqr/U2H4LcGnFQw8wAOhkra1zgaVQrmEgIiIiIiIi8svTxJdVNMa4gTnAWCAN\nmGyMSQt6SWtnWWsHW2sHA38EPjncYAFowEBERERERESk+VjAmsbd6jcMSLfWbrHWlgELgQmHKT8Z\neK2+nWrAQERERERERKQZWdu4WwMkA9UvNZZZ8dwhjDGtCaw3U+/qu7q+hYiIiIiIiEhzavwaBh2N\nMdUXkn3OWnukl/kYD6yo73QE0ICBiIiIiIiISPNq2GkG1RXUc5WELKBrtccpFc/VZhINOB0BdEqC\niIiIiIiISLMytnG3BlgF9DXG9DTGRBIYFHjnkNc1pg1wGvB2Q3aqGQYiIiIiIiIizaWBVz5o1C6t\n9RpjbgCWEris4nxr7XpjzLSK7XMril4AfGit3d+Q/WrAQERERERERKTZNPjKB41irV0CLKnx3Nwa\njxcACxq6T52SICIiIiIiIiKH0AwDERERERERkebUxKckOEUDBiIiIiIiIiLNSQMGIiIiIiIiInII\nDRiIiIiIiIiISBCLI4seOkEDBiIiIiIiIiLNyITJDIPDXiXBGNPHGHNqLc+faozp7VwsEZHmo75O\nRMKN+i0RkTBnG3kLkfouq/gEUFzL88UV20REjgbq60Qk3KjfEhERx9V3SkKCtXZtzSettWuNMT0c\nSSQi0vzU14lIuFG/JSISxsLllIT6BgzaHmZbq6YMIiISQurrfqa9/lAnaLhEd6gTiDQJ9VsiIdTv\nmpWhjtAo5z33v6GO0Ei3hjqA88Jk0cP6Tkn4yhhzdc0njTFTgdXORBIRaXbq60Qk3KjfEhEJV41d\nvyCEsxHqm2FwI7DYGHMpVR8+Q4FI4AIng4mINCP1dSISbtRviYiEs6PhlARrbS5wijFmFDCw4un/\nZ639yPFkIiLNRH2diIQb9VsiIuHtaFnDAABr7XJgucNZRERCSn2diIQb9VsiImHqaBowEBERERER\nEZEmogEDEREREREREanO2KPslAQRERERERERaSJhcllFDRiIiIiIiIiINKcwmWHgCnUAERERERER\nEWl5NMNAREREREREpBlpDQMREREREREROZQGDEREREREREQkiK6SICIiIiIiIiK10oCBiIiIiIiI\niBxCAwYiIiIiIiIiUpNOSWhBhp41mOueuAqX28X7z/+bRQ+/FbQ9tm0MM56/ji69Eyg7UM5jU55h\n2/odALy0ZQ6lew/g9/nxeX1cP+x2ZVVe5Q3zrEerEX2788dxp+N2ufjH6nXM+3RV0PbfjhjCucf1\nB8DtctGrU3tGPDSXotKD9dZtap99fICZ9xXj98HFk1pzzfWxQduLCv3ceUsh2zN8REUZZj7ahn6p\nEZXbfT7LxecW0DnBzbML2juaFeCDj/Zz0z35+Hww5ZJ4bpse/Jp7Cn1MuSmXLRnlREcZ5j2ewMD+\nUQAUFvm4ekYe6zcexBiY93gCJw9t5Wje5csPcM+9gfadPLk1N9wQ3L6FhX5mzCgko6J9H3usDf37\nB9p3+El5xMYYXG7weAzvL+noaFaAlZ/sZ879efj9MG5iGyZfG9y+e4t8zLo1h50Z5URGGW55JJGe\nqVHk7SznLzNy2FPgwxg4Z3IbLrqqnaNZh4zoy7W3n4PL7eKDf37F6/M+Ddo+6MSe3Pv0/5CTtQeA\nFf9az6t/Ww5ATFw0Nz5wAT36JGCt5fG732TDtzsczXs0qu/z5uTzhnLlA5OwfovP6+OZmxawfsVG\nUvp14a6FN1WWS+zVmRfvXcTiJ5coq/Iqby1OS+nBvSedgdsYFm76jr99tzJo+++OPZEJfdIA8BhD\nn7YdOP6VORQdPFBvXQkfjg0YGGPmA+cCedbagYcpdyLwBTDJWvuPps7hcrmYPnsKt535IAWZu5m9\n8iG+eOcrtm/IrCwz+Y4L2fztVu6/aBZdU7swffZUbh3zQOX2m391H8W79jZ1tLDOqrzKG65Zm1qL\n6euM4a7xv2LqC2+SW7yXRdMuYfmGzWzO311ZZv7nq5n/+WoATk/txeWnHk9R6cEG1W1KPp/lgbuK\nmf9KexKS3Px6fAG/GhNFn35VAwLPztlH/7QIZv9fe7ake3ngriIWLOxQuf3v8/fTq4+HfXudH573\n+SzT78hn6aJkUpI8DB+7nfFnxpCWGlVZ5qGndjN4YBRvvtCFjT+WMf2OPJa9kQLAjXfnc9ao1rwx\nL4myMktJqd/xvHfeVcxrr7YnKcnNuHMKOPPMKPpVa9+nn97HMcdE8Pzz7UlP93LHnUW8vqiqfd94\nowPt27sczVk971P35PHIS8l0SozgugkZnDw6hh59q9r31Tm76ZMWzQPPJrN9cxlP3ZPLo690xe0x\nTLuzE/0GRlOyz8+08RkMGdE6qG5TcrkM1985njuufoGC3GKeWnQtXy7fwPbN+UHl1q3exr3Xv3RI\n/Wl/PIfVn//IzJtewxPhJio64pAyLZkx5mzgScANzLPW/qXG9v7AC8AJwJ3W2kebOkNDPm+++fc6\nvnjnZgB6HtuNuxb9gSlpN5L5w06mnXBL5X5ey3yWFYud+xITTlmVV3kPyWsMD54yhkvff52c/Xt5\nZ8Jl/Gv7Zn4s3FVZ5tm1q3h2beAHhjO69WbqwCEUHTzQoLpC2JyS4OTRwALg7MMVMMa4gYeBD50K\nkTqsDzvTc8jZmoe33MvHi1ZwyoShQWW6D0hhzUfrANixaScJPTrRtnMbpyIdFVlBeZ0WTnnDKasD\nFtAC+rpjUxLZvquQzD1FlPv8vL92E78a0LvO8uMGpbLku01HVPfn+m5NOd16uOna3UNkpGHc+Fb8\n+8ODQWU2/+jlpFMCX/p69fGQlemjIN8HQE62j0/+fZBfT2rtWMbqVn5zgN49IujVPYLISMPECXG8\ns3R/UJnvfyhj1KmBPP37RrJth5fcfC9FxT4++7KUKZfEAxAZaWjbxu1o3m/WlNOjh5vuFe07YUIr\nltZo3x9+9HLqqYH27dPHQ2amj/yK9m1uG789QHL3CLp0iyQi0jBqfDz/WRbcvhnpZQw+OTAro1vv\nSHIyvezO99Khs4d+A6MBaB3ronufSApyvI5lTT02hewdu8nJ3IO33McnS77j5FEDGlS3dWwUxw7p\nwQf//AoAb7mP/XsPOJa1qVX0Y3OAsUAaMNkYk1aj2G7gf4EmHyj4SUM+bw7sr2rX6JhosIcelR9/\nxkCyN+eQt73AqahhlVV5lbemwZ2S2Fa8hx17iyj3+3l3y0bGdO9TZ/kJvfrz9uaNR1T3F6niKgmN\nuYWKYwMG1tpPCXxwHM504J9AnlM5Oia3Jz+zajSrIHM3HZM7BJXZ8t02Rlw4HIDUE/uQ0L0TnVIC\nZayFR5bdw5xVDzPu6tFOxQy7rMqrvOGatam1lL4uIT6WnKKqGRo5xfvoHB9ba9noCA8j+/Zg2fof\nG123KeTm+EjqUvWlOTHJRW5u8JfV1AEeln0QOHD6bk0ZO7N85GQHfpn/833F3HxHPKZ5fgAnK8dL\n1+SqCXnJSR6yanwpPS4tisVL9gGBAYaMzHIyd3rZut1Lpw5ufntjLkPGbOfqGbnsL3F2hkFOto8u\nSVXtm5ToIic7uH3T0jwseT/Qvt98U0Zmpo/sivY1BiZO2sXZY/N5+eUSR7MCFOR46ZRU1b6dEj0U\n5JQHlek1IIrPlwbad+OaUnKzyg8ZGMjJLCf9+4MMGBztWNYOCfHkZxdVZc8tpkPCoQOfacd3429v\nTufBuVfQvXdnABJT2lO0p4QZMy9i9j+u58b7LyCqVVjNMBgGpFtrt1hry4CFwITqBay1edbaVUB5\nbTtoCg35vAE49fxhPP/9E/zpvT/y6JS/HbL99EmnsnzhCqdihl1WUF6nhVvexNaxZO+vOjbI3r+X\nxNZ1HFe4PZyW0pP3t/3Q6Lq/aLaRtxBppsOtQxljkoELgEP/JTSzhX95i9g2Mcz9ehbn3zCW9G+2\n4vcFDpxuGnk30064hTvHzeS8687i2JEN+yVBWQOU11nhlDecsjalltTX/eT01F58vX0nRaUH6y8c\nItdcF0txsZ/zz87n5RdKGHBMBG43LP/XATp0dDFwUMv6onXb9HYUFvs4YXQGs58v5PiBUbjdBq/X\n8vXag0y7oi2rl3UjppWLh5/eE+q43HB9oH3HnJnP/BdKGDgwAlfFGMPiNzuw7MNOvPxSexa8uJ8v\nvwz9+2TytHbsK/ZzzbgMFr9YSN9joirzApTu93PftTu57u5OxMQ5O4OjPunf7+Sy0bO49sKneeeV\nL7jn6UsBcLtd9BmQxHsL/8sNF8/hQGkZE6eeFtKsjZQMVF9wIbPiuRZpxVsrmZJ2I/dd8AhXPjAx\naJsnwsPJ44fyyRtfhChdsHDKCsrrtHDLCzC6e2++ysui6GD4zJpqEcJkwCCUix4+AdxmrfUbYw5b\n0BhzDXANQDSNm4JakLW78ldMgI4p7SnICj5/pmRvKY9Oeaby8Utb5pC9JReAXTsDPxwW5hez4q2V\npA7rw9rPNjQqw9GYVXmVN1yzhsAR9XWe+MYt3JZbvI/ENnGVjxPjY8kr3ldr2cDpCBuPqG5TSEh0\nk72z6hfvnGw/CQnBX/Ji41w89FhbAKy1nHFqPl27uVnybikfLTvAJ8sPUnbQsm+vn1t+v4dZTzq3\n0F1yoocdWVW/Zmdle0lODP74jI9zM/+JxMq8vYdto1d3DyWlNrDuwQmBX70vOjeWh2c7szbETxKT\n3OysNqMgO8dPYlJw+8bFuXj8r1Xte9LJ+XTvFiiTVFG2Y0c3Y8+OZs2ack46yZk1AQA6JnrIz65q\n3/wcLx0TgweEYuLc3Dqrqn0vHbmVpK6BMt5yy33X7uSMCfGMPDsOJ+3KLaZTUtWMgo4J8ezKLQoq\nU7K/aoBl1Wc/cMPd5xHftjUFuUUU5BazaW3gfOXPPlwXbgMGTcbp47rq1n62gaReCcR3iKtcJ+fE\nsYNJ/3orhXlFddZrCuGUVXmVt6ackn0kxVT1qUkxceSU1H5sML7XAN7ZXHVc0Zi6v1SG8LlKQshm\nGABDgYXGmG3AxcAzxpjzaytorX3OWjvUWjs0gsYdtGxalU5y3yQSe3TGE+Hh9Imn8sU7XwWViWnT\nGk9E4OBv7NQzWPvpBkr2lhLdOopWsYGDvOjWUQwZcxzb1jm3mnE4ZVVe5Q3XrCFwRH2dp3VMo15k\nXVYO3Tu0I7ldPBFuF2OPTWX5xi2HlIuNiuTEHil8tGFzo+s2lWOPiyBjq4/M7V7KyixL3i3lV2OC\n+/biIj9lZYFP0jdeK+XEYZHExrmYcXs8n6xM4KP/dOax2W0ZfkqUo4MFACcOjiZ9axlbt5dTVmZZ\n9PZexp8V/PcpLPJV5p33SjEjT2pFfJybxM4eunbxsCm9DICPPi8hrV+ko3kHHxfB1q0+tle079tv\nl3JmjfYtqta+r75ayvDhkcTFuSgp8bNvX2DmT0mJn08+PUhqqrO/LfQfFE3WtnKyd5RTXmZZ/m4x\np4wObt99xT7KK/IuWVjEoGGtiIlzY63l0dty6NYnkl9PdfZ9ALBpXRZdunUgIbkdngg3p40bxJfL\nNwaVadexatptv2NTMC5DcWEJewr2kZ9TREqPwFUnjj+pN9s3O3aWkhOygK7VHqdUPNdoTh/Xdemd\nWHm/z/E9iYiKCFpUd9SkESxf+PmRRD9qsyqv8tb0bX42PePb0TW2DREuF+N79WdZRvoh5eIiIjkp\nMYUPq21raN1fPM0wODxrbc+f7htjFgDvWWvfqrvGkfH7/Mye/jwPfXAnLreLpS8sJ+P7TM793RgA\n3nt2Gd0GpHDrguuxFjLW7+CxqYGZw20T2nDfm4EVSd0eN8tf+5yvlq5p6ohhmVV5lTdcsza35urr\nfH7LzPc+4v+uuBCXy7B49XrS83Yx8cRBACxa9R0Ao9P6sCI9g9Jyb711neLxGO5+MJ4pl+3G74OL\nJraib2oEC18KLHQ36bIYNqd7uf0PhRgDfft5+NMjbR3L05C8T/25M2MnZ+HzwVWT4jkmNYq5LxYC\nMO2Ktmz4sYyrfp+LMZDWL5J5f02orP/kzM5cdn0OZeWWnt0imP9EQl0v1WR5//RgPJdcuhu/HyZO\nbEVqagR/r2jfyy+L4cd0LzfeGGjf1H4eHn000L75+X6mTA2cMuHzwfnnRzNqlHNrAgC4PYbp93fi\ntssz8fth7K/j6dEvindfCbTv+EvbkpFexsMzcjAGevSL4uaHA2247qsDLFu8l56pkVwzLgOAKbd0\nYPgoZ86V9fv8PDPzXWY+dyUul+HDxV+TsTmPcb8ZBsCS11cy4syBnDtxGD6fn4MHynno5kWV9Z/5\n83vc+vBviIhwk525m7/e9U9HcjpkFdDXGNOTwEDBJOCS5g7RkM+bkRcNZ/Rlp+Er93GwtIw/TXq8\nsn5gYHoQT0x7TlmVV3kPw2ct9/znX/x97MW4jYvXf1jLj4W7uLT/cQC8svFbAM7q0ZdPs7ZR6i2v\nt65UE+KFDBvD2FpW32ySHRvzGnA60BHIBe4FIgCstXNrlF1A4CC63kuNxZv2drg5o6njikiY+5f9\nx2pr7dD6SzYtp/q6Vkldbc8pf2jquI5Z/LtZoY7QYH0jwmvhpRxfeE3j3Fju7GkBTe2Rcy4OdYRG\nWfr9n0PS1wEYY8YROM3KDcy31s40xkyDQH9njEkEvgLiAT+wD0iz1hbXtU8d14k0jx+eGxbqCI2y\n/epbQ9bXNYdWSV1tz9827jhvw5//EJI2cWyGgbV2ciPKXulUDhERJ6mvE5FfCmvtEmBJjefmVruf\nQ+BUBRERqU+YzDAI5RoGIiIiIiIiItJCacBAREREREREpBkZ27hbg/ZpzNnGmE3GmHRjzO11lDnd\nGLPGGLPeGPNJffsM5WUVRURERERERH55mviUBGOMG5gDjAEygVXGmHestd9XK9MWeAY421q73RjT\nub79aoaBiIiIiIiISHNp7CUVGza4MAxIt9ZusdaWAQuBCTXKXAK8aa3dDmCtrff6vhowEBERERER\nEWlGDpySkAzsqPY4s+K56voB7YwxHxtjVhtjLq9vpzolQURERERERKQ5Nf6UhI7GmK+qPX7OWvtc\nI/fhAYYAZwCtgC+MMV9aa384XAURERERERERaSYNXciwmgJr7dDDbM8CulZ7nFLxXHWZwC5r7X5g\nvzHmU+A4oM4BA52SICIiIiIiItKcmn4Ng1VAX2NMT2NMJDAJeKdGmbeBEcYYjzGmNTAc2HC4nWqG\ngYiIiIiIiEhzafggQMN3aa3XGHMDsBRwA/OtteuNMdMqts+11m4wxnwAfAf4gXnW2nWH268GDERE\nRERERESaiam4NTVr7RJgSY3n5tZ4PAuY1dB9asBAREREREREpDk18QwDp2jAQERERERERKQZHcGi\nhyGhAQMRERERERGR5qQBAxERERERERE5hAYMRERERERERCSI1SkJIiIiIiIiIlIbDRiIiIiIiIiI\nSE3hMsPAFeoAIiIiIiIiItLyaIaBiIj8bMU2MtQRREREpA79rlkZ6giNsj3UAZpDmMww0ICBiIiI\niIiISDMKl1MSNGAgIiIiIiIi0lwsmmEgIiIiIiIiIrXQgIGIiIiIiIiIVGfQKQkiIiIiIiIiUhsN\nGIiIiIiIiIhITcaGx4iBBgxEREREREREmosWPRQRERERERGR2mgNAxERERERERE5lAYMRERERERE\nRKQmzTAQERERERERkUNpwEBEREREREREgljNMBARERERERGR2mjAQERERERERESqM2iGgYiIiIiI\niIjUxobHiMEvYsBg6FmDue6Jq3C5Xbz//L9Z9PBbQdtj28Yw4/nr6NI7gbID5Tw25Rm2rd8BwEtb\n5lC69wB+nx+f18f1w25XVuVV3jDPerQa0bc7fxx3Om6Xi3+sXse8T1cFbf/tiCGce1x/ANwuF706\ntWfEQ3MpKj1Yb92m9sXHpTx2/278PpgwKZYrrmsTtL24yMeDt+wiK8NLZJTh7lkd6J0aycEDlt/9\nJoeyMovPC2eMa801f2jraFaADz7az0335OPzwZRL4rltevug7XsKfUy5KZctGeVERxnmPZ7AwP5R\nABQW+bh6Rh7rNx7EGJj3eAInD23laN7lyw9wz73F+H0weXJrbrghNmh7YaGfGTMKycjwERVleOyx\nNvTvHwHA8JPyiI0xuNzg8RjeX9LR0awAKz/Zz5z78/D7YdzENky+Nrh99xb5mHVrDjszyomMMtzy\nSCI9U6PI21nOX2bksKfAhzFwzuQ2XHRVO0ezDhnRl2tvPweX28UH//yK1+d9GrR90Ik9uffp/yEn\naw8AK/61nlf/thyAmLhobnzgAnr0ScBay+N3v8mGb3c4mvdoVN/nzcnnDeXKByZh/Raf18czNy1g\n/YqNpPTrwl0Lb6osl9irMy/eu4jFTy5RVuVV3qMgrzjDsQEDY8x84Fwgz1o7sJbtlwK3EZiRsRe4\n1lr7bVPncLlcTJ89hdvOfJCCzN3MXvkQX7zzFds3ZFaWmXzHhWz+div3XzSLrqldmD57KreOeaBy\n+82/uo/iXXubOlpYZ1Ve5Q3XrE2txfR1xnDX+F8x9YU3yS3ey6Jpl7B8w2Y25++uLDP/89XM/3w1\nAKen9uLyU4+nqPRgg+o2JZ/P8sjdu5n9Smc6J3q44rxsRo5uRa9+kZVlFswupl9aJLOe68y29HIe\nuXs3z7yWQGQUPPNaAq1jXHjLLVdfnMPJp7fi2BOiHMn6U97pd+SzdFEyKUkeho/dzvgzY0hLrXrN\nh57azeCBUbz5Qhc2/ljG9DvyWPZGCgA33p3PWaNa88a8JMrKLCWlfsey/pT3zruKee3V9iQluRl3\nTgFnnhlFv34RlWWefnofxxwTwfPPtyc93csddxbx+qIOldvfeKMD7du7HM1ZPe9T9+TxyEvJdEqM\n4LoJGZw8OoYefava99U5u+mTFs0DzyazfXMZT92Ty6OvdMXtMUy7sxP9BkZTss/PtPEZDBnROqhu\nU3K5DNffOZ47rn6Bgtxinlp0LV8u38D2zflB5dat3sa91790SP1pfzyH1Z//yMybXsMT4SYqOuKQ\nMi2BMeZs4EnADcyz1v6lxnZTsX0cUAJcaa39umLbYfvEn6shnzff/HsdX7xzMwA9j+3GXYv+wJS0\nG8n8YSfTTrilcj+vZT7LisUrmzpiWGZVXuUN97zhKFxOSXDyaGABcPZhtm8FTrPWHgs8CDznRIjU\nYX3YmZ5DztY8vOVePl60glMmDA0q031ACms+WgfAjk07SejRibad29S2O0eFU1ZQXqeFU95wyuqA\nBbSAvu7YlES27yokc08R5T4/76/dxK8G9K6z/LhBqSz5btMR1f251q8pI6WHh+RuEUREGs4cH8On\ny0qDymz9sZyhp0QD0KNPBNmZXnbl+zDG0Dom8NHl9Vq85WCMY1EBWPnNAXr3iKBX9wgiIw0TJ8Tx\nztL9QWW+/6GMUae2BqB/30i27fCSm++lqNjHZ1+WMuWSeAAiIw1t27gdzfvNmnJ69HDTvbuHyEjD\nhAmtWPrhwaAyP/zo5dRTA1+q+/TxkJnpIz/f52iuumz89gDJ3SPo0i2SiEjDqPHx/GdZcPtmpJcx\n+OTArIxuvSPJyfSyO99Lh84e+g0MvE9ax7ro3ieSghyvY1lTj00he8ducjL34C338cmS7zh51IAG\n1W0dG8WxQ3rwwT+/AsBb7mP/3gOOZT1Sxhg3MAcYC6QBk40xaTWKjQX6VtyuAf5WbdsCDt8n/iwN\n+bw5sL+qXaNjomud9nv8GQPJ3pxD3vYCp6KGVVblVd5wzxt27BHcQsSxAQNr7adAnT9PWWv/Y63d\nU/HwSyDFiRwdk9uTn7mr8nFB5m46JncIKrPlu22MuHA4AKkn9iGheyc6pXSoyAmPLLuHOaseZtzV\no52IGJZZlVd5wzVrU2spfV1CfCw5RVUzNHKK99E5PrbWstERHkb27cGy9T82um5TyM/xkpBUNcGt\nc5Kb/JzgL6t90yJY/kEJAOvXHCQny0texRdBn89y6didnHVCJsNGRjPweOdmFwBk5XjpmlyVNznJ\nQ1aNL6XHpUWxeMk+IDDAkJFZTuZOL1u3e+nUwc1vb8xlyJjtXD0jl/0lzs4wyMn20SWpalAiKdFF\nTnZw+6aleVjyfuBA75tvysjM9JGdHchlDEyctIuzx+bz8ssljmYFKMjx0qna+6FTooeCnPKgMr0G\nRPH50kD7blxTSm5W+SEDAzmZ5aR/f5ABg6Mdy9ohIZ787KKq7LnFdEg4dOAz7fhu/O3N6Tw49wq6\n9+4MQGJKe4r2lDBj5kXM/sf13Hj/BUS1apEzDIYB6dbaLdbaMmAhMKFGmQnA323Al0BbY0wS1N8n\n/lwN+bwBOPX8YTz//RP86b0/8uiUvx2y/fRJp7J84QqnYoZdVlBepymv1GT8jbuFSvPMN6zfFOD9\nUL34wr+8RWybGOZ+PYvzbxhL+jdb8fsCf5WbRt7NtBNu4c5xMznvurM4dmTDfklQ1gDldVY45Q2n\nrA4KaV/3k9NTe/H19p0UlR6sv3CIXH5tG/YV+7l07E5eX7CXfsdE4nYFphK43YZX3u/Ce1+m8P2a\ng2zeVBbitHDb9HYUFvs4YXQGs58v5PiBUbjdBq/X8vXag0y7oi2rl3UjppWLh5/eU/8OHXbD9bEU\nF/sZc2Y+818oYeDACFwVYwyL3+zAsg878fJL7Vnw4n6+/DL075PJ09qxr9jPNeMyWPxiIX2PiarM\nC1C638991+7kurs7ERPn7AyO+qR/v5PLRs/i2guf5p1XvuCepy8FwO120WdAEu8t/C83XDyHuSn0\nMAAAIABJREFUA6VlTJx6Wkiz1iEZqL6wQmbFc40tE1Ir3lrJlLQbue+CR7jygYlB2zwRHk4eP5RP\n3vgiROmChVNWUF6nKe8vSJjMMAj5oofGmFEEDqJHHKbMNQSmvBFN60btvyBrd+WvmAAdU9pTkLUr\nqEzJ3lIenfJM5eOXtswhe0suALt2BgbJC/OLWfHWSlKH9WHtZxsaleFozKq8yhuuWUOlsX2dJ75x\nC7flFu8jsU1c5ePE+FjyivfVWjZwOsLGI6rbFDolesjNrvp1OC/bR6fE4C95sXEu7nk0sNietZbz\nR2TRpVvwR1ZcGxdDTonmi49L6Z0aiVOSEz3syKrKm5XtJTkxOEt8nJv5TyRW5u09bBu9unsoKbWB\ndQ9OCPzqfdG5sTw827EfXwFITHKzs9qMguwcP4lJwe0bF+fi8b+2rcx70sn5dO8WKJNUUbZjRzdj\nz45mzZpyTjrJuVkcHRM95Fd7P+TneOmYGPzLe0ycm1tnVbXvpSO3ktQ1UMZbbrnv2p2cMSGekWfH\n4aRducV0SqqaUdAxIZ5duUVBZUr2Vw2wrPrsB264+zzi27amILeIgtxiNq0NnP/72YfrWuqAgeOc\nPq6rbu1nG0jqlUB8h7jKdXJOHDuY9K+3UphXVGe9phBOWZVXecM9bzjSGgYNYIwZBMwDJlhr63wH\nWmufs9YOtdYOjaBxBy2bVqWT3DeJxB6d8UR4OH3iqXzxzldBZWLatMYTETj4Gzv1DNZ+uoGSvaVE\nt46iVWzgIC+6dRRDxhzHtnXOrWYcTlmVV3nDNWsoHElf52kd06jXWJeVQ/cO7UhuF0+E28XYY1NZ\nvnHLIeVioyI5sUcKH23Y3Oi6TSXtuEh2bPWStb2c8jLLh+/uZ+SY4KsG7C3yU14W+CR9e+E+Bg+L\nJjbOxZ5dPvYWBWamHDjg57+fHaB7H2endZ84OJr0rWVs3V5OWZll0dt7GX9W8N+nsMhHWUXeea8U\nM/KkVsTHuUns7KFrFw+b0gOzID76vIS0fs4NbgAMPi6CrVt9bN/upazM8vbbpZw5Jvizs6jIX5n3\n1VdLGT48krg4FyUlfvbtC7RvSYmfTz49SGqqs78t9B8UTda2crJ3BN4Py98t5pTRwe27r9hX+X5Y\nsrCIQcNaERPnxlrLo7fl0K1PJL+e6uzVEQA2rcuiS7cOJCS3wxPh5rRxg/hy+cagMu06Vp3O0+/Y\nFIzLUFxYwp6CfeTnFJHSIzAQdvxJvdm+Oc/xzEcgC+ha7XFKxXONLVMnp4/ruvROrLzf5/ieRERF\nBC2qO2rSCJYv/LxRr3skwimr8ipvuOcNO5bAObmNuYVIyGYYGGO6AW8Cl1lrf3Dqdfw+P7OnP89D\nH9yJy+1i6QvLyfg+k3N/NwaA955dRrcBKdy64HqshYz1O3hsauD8m7YJbbjvzcAKn26Pm+Wvfc5X\nS9c4FTWssiqv8oZr1ubWXH2dz2+Z+d5H/N8VF+JyGRavXk963i4mnjgIgEWrvgNgdFofVqRnUFru\nrbeuUzwewy0PtOd/L8/D74Pxv4mld79I/vly4CDjov+JY2t6OffPKMAY6NU3grtmBX7lKMjzcf8f\nCvD7we+H0ee2ZuQZjfuF8kjyPvXnzoydnIXPB1dNiueY1CjmvlgIwLQr2rLhxzKu+n0uxkBav0jm\n/TWhsv6TMztz2fU5lJVbenaLYP4TCXW9VJPl/dOD8Vxy6W78fpg4sRWpqRH8/aXAQoKXXxbDj+le\nbryxEGMgtZ+HRx8NzDbIz/czZWrglAmfD84/P5pRo5xbEwDA7TFMv78Tt12eid8PY38dT49+Ubz7\nSqB9x1/aloz0Mh6ekYMx0KNfFDc/HGjDdV8dYNnivfRMjeSacRkATLmlA8NHObMGh9/n55mZ7zLz\nuStxuQwfLv6ajM15jPvNMACWvL6SEWcO5NyJw/D5/Bw8UM5DNy+qrP/Mn9/j1od/Q0SEm+zM3fz1\nrn86kvNnWgX0Ncb0JDAIMAm4pEaZd4AbjDELgeFAkbU2uznCNeTzZuRFwxl92Wn4yn0cLC3jT5Me\nr6wfGJgexBPTHFl/NmyzKq/yhnvecBQuMwyMdWi0whjzGnA60BHIBe4FIgCstXONMfOAi4CMiipe\na+3QWnYVJN60t8PNGY5kFpHw9S/7j9UN6UOamlN9XaukrrbnlD84ktkJL17zZKgjNNiQSGd/4W9q\nOT7nTg9xwsZyZ08LaGqPnHNxqCM0ytLv/+x4X2eMGQc8QeCyivOttTONMdOgsl8zwGwCV0MoAa6y\n1n5VUfeQPtFa+3xdr6XjOhGpTaiO65pLbLuudvCo3zeqzorFt4SkTRybYWCtnVzP9qnAVKdeX0Sk\nOaivE5GjjbV2CbCkxnNzq923wPV11D1snygiImAInxkGIV/0UEREREREROQXI8TrEjSGBgxERERE\nREREmlG4zDAI6VUSRERERERERH5xbCNvDWCMOdsYs8kYk26Mub2W7acbY4qMMWsqbvfUt0/NMBAR\nERERERFpRk09w8AY4wbmAGOATGCVMeYda+33NYp+Zq09t6H71QwDERERERERkeZiAb9t3K1+w4B0\na+0Wa20ZsBCY8HOjasBAREREREREpDk1/pSEjsaYr6rdrqmxx2RgR7XHmRXP1XSKMeY7Y8z7xphj\n6oupUxJEREREREREmtERnJJQYK0d+jNf9mugm7V2nzFmHPAW0PdwFTTDQERERERERCS8ZQFdqz1O\nqXiukrW22Fq7r+L+EiDCGNPxcDvVgIGIiIiIiIhIc7K2cbf6rQL6GmN6GmMigUnAO9ULGGMSjTGm\n4v4wAuMBuw63U52SICIiIiIiItKMmvoqCdZarzHmBmAp4AbmW2vXG2OmVWyfC1wMXGuM8QKlwCRr\nDz8aoQEDERERERERkeZStZBh0+42cJrBkhrPza12fzYwuzH71ICBiIiIiIiISDMxgGnYaQYhpwED\nERERERERkebkD3WAhtGAgYiIiIiIiEgz0gwDEREREREREQnm0BoGTtCAgYiIiIiIiEizafClEkNO\nAwYiIiIiIiIizaipL6voFA0YiIiIiIiIiDQnzTAQERERadl8G34IdQQREfmlsWB0lQQRERERERER\nOYRmGIiIiIiIiIjIIcJjvEADBiIiIiIiIiLNyWiGgYiIiIiIiIgcQgMGIiIiIiIiIhLEAlr0UERE\nRERERESqM9iwOSXBFeoAIiIiIiIiItLyaIaBiIiIiIiISHMKkxkGGjAQERERERERaU4aMBARERER\nERGRIFr0UERERERERERqEy6LHmrAQERERERERKQ5acBARERERERERIJZDRiIiIiIiIiISA0WDRiI\niIiIiIiISC206GHLMfSswVz3xFW43C7ef/7fLHr4raDtsW1jmPH8dXTpnUDZgXIem/IM29bvAOCl\nLXMo3XsAv8+Pz+vj+mG3K6vyKm+YZz1ajejbnT+OOx23y8U/Vq9j3qergrb/dsQQzj2uPwBul4te\nndoz4qG5FJUerLduU/vi41Ieu383fh9MmBTLFde1CdpeXOTjwVt2kZXhJTLKcPesDvROjeTgAcvv\nfpNDWZnF54UzxrXmmj+0dTQrwAcf7eeme/Lx+WDKJfHcNr190PY9hT6m3JTLloxyoqMM8x5PYGD/\nKAAKi3xcPSOP9RsPYgzMezyBk4e2cjTv8uUHuOfeYvw+mDy5NTfcEBu0vbDQz4wZhWRk+IiKMjz2\nWBv6948AYPhJecTGGFxu8HgM7y/p6GhWgJWf7GfO/Xn4/TBuYhsmXxvcvnuLfMy6NYedGeVERhlu\neSSRnqlR5O0s5y8zcthT4MMYOGdyGy66qp2jWevr6wadlsYDb91GztY8AD5f/F9efvAfAMx4/lqG\nnzOEwrwirhk0w9GcR7P6/gYnnzeUKx+YhPVbfF4fz9y0gPUrNpLSrwt3Lbypslxir868eO8iFj+5\nRFmVV3mPgrzh5he/6KExZj5wLpBnrR1Yy3YDPAmMA0qAK621Xzd1DpfLxfTZU7jtzAcpyNzN7JUP\n8cU7X7F9Q2Zlmcl3XMjmb7dy/0Wz6Jrahemzp3LrmAcqt9/8q/so3rW3qaOFdVblVd5wzdrUWkxf\nZwx3jf8VU194k9zivSyadgnLN2xmc/7uyjLzP1/N/M9XA3B6ai8uP/V4ikoPNqhuU/L5LI/cvZvZ\nr3Smc6KHK87LZuToVvTqF1lZZsHsYvqlRTLruc5sSy/nkbt388xrCURGwTOvJdA6xoW33HL1xTmc\nfHorjj0hypGsP+Wdfkc+Sxclk5LkYfjY7Yw/M4a01KrXfOip3QweGMWbL3Rh449lTL8jj2VvpABw\n4935nDWqNW/MS6KszFJS6uxPCj6f5c67innt1fYkJbkZd04BZ54ZRb9+EZVlnn56H8ccE8Hzz7cn\nPd3LHXcW8fqiDpXb33ijA+3buxzNWT3vU/fk8chLyXRKjOC6CRmcPDqGHn2r2vfVObvpkxbNA88m\ns31zGU/dk8ujr3TF7TFMu7MT/QZGU7LPz7TxGQwZ0TqoblNqSF8HsPazDdx93l8Oqf/hgo95e/YH\n3PriDY7kawxjzNkE+iY3MM9a+5ca2+vsu+qqa4z5NXAfMAAYZq39qqlzN+Rv8M2/1/HFOzcD0PPY\nbty16A9MSbuRzB92Mu2EWyr381rms6xYvLKpI4ZlVuVV3nDPG5bCZMDAyaOBBcDZh9k+FuhbcbsG\n+JsTIVKH9WFneg45W/Pwlnv5eNEKTpkwNKhM9wEprPloHQA7Nu0koUcn2nZuU9vuHBVOWUF5nRZO\necMpqwMW0AL6umNTEtm+q5DMPUWU+/y8v3YTvxrQu87y4walsuS7TUdU9+dav6aMlB4ekrtFEBFp\nOHN8DJ8uKw0qs/XHcoaeEg1Ajz4RZGd62ZXvwxhD65jAR5fXa/GWgzGORQVg5TcH6N0jgl7dI4iM\nNEycEMc7S/cHlfn+hzJGndoagP59I9m2w0tuvpeiYh+ffVnKlEviAYiMNLRt43Y07zdryunRw033\n7h4iIw0TJrRi6YcHg8r88KOXU08NfKnu08dDZqaP/Hyfo7nqsvHbAyR3j6BLt0giIg2jxsfzn2XB\n7ZuRXsbgkwOzMrr1jiQn08vufC8dOnvoNzDwPmkd66J7n0gKcryOZW1IX3c4az/bwN7d+xzL11DG\nGDcwh0D/lAZMNsak1ShWa99VT911wIXAp05lb8jf4MD+A5X3o2Oiaz0oP/6MgWRvziFve4FTUcMq\nq/Iqb7jnDTsW8NvG3ULEsQEDa+2nwOF+npoA/N0GfAm0NcYkNXWOjsntyc/cVfm4IHM3HZM7BJXZ\n8t02Rlw4HIDUE/uQ0L0TnVICZayFR5bdw5xVDzPu6tFNHS9ssyqv8oZr1qbWUvq6hPhYcoqqZmjk\nFO+jc3xsrWWjIzyM7NuDZet/bHTdppCf4yUhqWqCW+ckN/k5wV9W+6ZFsPyDEgDWrzlITpaXvIov\ngj6f5dKxOznrhEyGjYxm4PHOzS4AyMrx0jW5Km9ykoesGl9Kj0uLYvGSwBfBld8cICOznMydXrZu\n99Kpg5vf3pjLkDHbuXpGLvtLnJ1hkJPto0tS1aBEUqKLnOzg9k1L87Dk/cCB3jfflJGZ6SM7O5DL\nGJg4aRdnj83n5ZdLHM0KUJDjpVO190OnRA8FOeVBZXoNiOLzpYH23bimlNys8kMGBnIyy0n//iAD\nBkc7lrUhfR1A2impPLvmUWb+vzvonpbiWJ6fYRiQbq3dYq0tAxYS6Kuqq6vvqrOutXaDtXaTk8Eb\n+jc49fxhPP/9E/zpvT/y6JRDx2lPn3QqyxeucDJqWGUF5XWa8kqwiqskNOYWIs0z37B2ycCOao8z\nK55rdgv/8haxbWKY+/Uszr9hLOnfbMXvCxw43TTybqadcAt3jpvJededxbEjB4QiYlhmBeV1Wjjl\nDaesTazF9HU/OT21F19v30lR6cH6C4fI5de2YV+xn0vH7uT1BXvpd0wkbldgKoHbbXjl/S6892UK\n3685yOZNZSFOC7dNb0dhsY8TRmcw+/lCjh8Yhdtt8HotX689yLQr2rJ6WTdiWrl4+Ok9oY7LDdfH\nUlzsZ8yZ+cx/oYSBAyNwVYwxLH6zA8s+7MTLL7VnwYv7+fLL0L9PJk9rx75iP9eMy2Dxi4X0PSaq\nMi9A6X4/9127k+vu7kRMnLMzOOqT/vVWLu1+Lb8bfDNvz36f+xffGtI8dWhIv1RXmRbXp9VmxVsr\nmZJ2I/dd8AhXPjAxaJsnwsPJ44fyyRtfhChdsHDKCsrrNOX9BQmTAYOwWPTQGHMNgelwRNO6UXUL\nsnZX/ooJ0DGlPQVZu4LKlOwt5dEpz1Q+fmnLHLK35AKwa2fgh8PC/GJWvLWS1GF9WPvZhiP6/zia\nsiqv8oZr1pasel/niW/cwm25xftIbBNX+TgxPpa84tqnPgdOR9h4RHWbQqdED7nZVb8O52X76JQY\n/CUvNs7FPY8GFtuz1nL+iCy6dAv+yIpr42LIKdF88XEpvVMjcUpyoocdWVV5s7K9JCcGZ4mPczP/\nicTKvL2HbaNXdw8lpTaw7sEJgV+9Lzo3lodnO7M2xE8Sk9zsrDajIDvHT2JScPvGxbl4/K9tK/Oe\ndHI+3bsFyiRVlO3Y0c3Ys6NZs6ack05ybhZHx0QP+dXeD/k5XjomRgSViYlzc+usqva9dORWkroG\nynjLLfddu5MzJsQz8uw4nNTQvu4nK9//hulzphLfIS4s12hxitPHddWt/WwDSb0Sgv4GJ44dTPrX\nWynMKzqC9EdnVuVV3nDPG5a0hkG9soCu1R6nVDx3CGvtc9baodbaoRE07qBl06p0kvsmkdijM54I\nD6dPPJUv3glegyemTWs8EYGDv7FTz2Dtpxso2VtKdOsoWsUGDvKiW0cxZMxxbFu345DXaCrhlFV5\nlTdcs4bAEfV1ntYxjXqRdVk5dO/QjuR28US4XYw9NpXlG7ccUi42KpITe6Tw0YbNja7bVNKOi2TH\nVi9Z28spL7N8+O5+Ro4JvmrA3iI/5WWBD9K3F+5j8LBoYuNc7NnlY29RYGbKgQN+/vvZAbr3iTjk\nNZrSiYOjSd9axtbt5ZSVWRa9vZfxZwX/fQqLfJRV5J33SjEjT2pFfJybxM4eunbxsCk9MAvio89L\nSOvn3OAGwODjIti61cf27V7Kyixvv13KmWOCPzuLivyVeV99tZThwyOJi3NRUuJn375A+5aU+Pnk\n04Okpjr720L/QdFkbSsne0fg/bD83WJOGR3cvvuKfZXvhyULixg0rBUxcW6stTx6Ww7d+kTy66nO\nXh0BGtbXtUuoumpH6ol9cLlcLXGwoCH9Ul1lGtyn1cXp47ouvRMr7/c5vicRURFBf4NRk0awfOHn\njXrdIxFOWZVXecM9b9gJozUMQjnD4B3gBmPMQmA4UGStzW7qF/H7/Mye/jwPfXAnLreLpS8sJ+P7\nTM793RgA3nt2Gd0GpHDrguuxFjLW7+CxqYHzb9omtOG+NwMrfLo9bpa/9jlfLV3T1BHDMqvyKm+4\nZg2BZunrfH7LzPc+4v+uuBCXy7B49XrS83Yx8cRBACxa9R0Ao9P6sCI9g9Jyb711neLxGG55oD3/\ne3kefh+M/00svftF8s+XAwcZF/1PHFvTy7l/RgHGQK++Edw1K/ArR0Gej/v/UIDfD34/jD63NSPP\naNwvlEeS96k/d2bs5Cx8PrhqUjzHpEYx98VCAKZd0ZYNP5Zx1e9zMQbS+kUy768JlfWfnNmZy67P\noazc0rNbBPOfSKjrpZos758ejOeSS3fj98PEia1ITY3g7y8FFhK8/LIYfkz3cuONhRgDqf08PPpo\n4Etufr6fKVMDp0z4fHD++dGMGuXcmgAAbo9h+v2duO3yTPx+GPvreHr0i+LdVwLtO/7StmSkl/Hw\njByMgR79orj54UAbrvvqAMsW76VnaiTXjMsAYMotHRg+ypk1OBrS1/1/F5/EudPOxOf1UVZaxszJ\nj1fWv+OV3zPo9GNo0zGOV7fP5e/3vc4H8z9yJGs9VgF9jTE9CXzZnwRcUqNMrX2XMSa/AXUd05C/\nwciLhjP6stPwlfs4WFrGnyZV/Q0CA9ODeGLac8qqvMp7FOUNPxass2saNRVjHZoKYYx5DTgd6Ajk\nAvcCEQDW2rkVl+uZTWB18RLgqoZcfifetLfDzRmOZBaR8PUv+4/V1tqGL1feRJzq61oldbU9p/zB\nqdhN7sVrngx1hAYbEunsL/xNLccX+lX1G2NjubOnBTS1h3oNCnWERmmqvs4YMw54gsClEedba2ca\nY6ZB/X1XbXUrnr8AeBroBBQCa6y1Zx0uh47rRKQ2oTquay5tohLsKV0ubVSdD7Y9HpI2cWyGgbV2\ncj3bLXC9U68vItIc1NeJSDiy1i4BltR4bm61+3X2XbXVrXh+MbC4aZOKiBylwmQNg7BY9FBERERE\nRETkqPDTGgZhQAMGIiIiIiIiIs0pTGYYhPIqCSIiIiIiIiK/PNY27tYAxpizjTGbjDHpxpjbD1Pu\nRGOM1xhzcX371ICBiIiIiIiISLNp5GBBAwYMjDFuYA4wFkgDJhtj0uoo9zDwYUOSasBARERERERE\npLlYqLxGdENv9RsGpFtrt1hry4CFwIRayk0H/gnkNWSnGjAQERERERERaU5Nf0pCMrCj2uPMiucq\nGWOSgQuAvzU0phY9FBEREREREWlOjV/0sKMx5qtqj5+z1j7XyH08AdxmrfUbYxpUQQMGIiIiIiIi\nIs3GHsllFQustUMPsz0L6FrtcUrFc9UNBRZWDBZ0BMYZY7zW2rfq2qkGDERERERERESaiwVrG7Qu\nQWOsAvoaY3oSGCiYBFwS9LLW9vzpvjFmAfDe4QYLQAMGIiIiIiIiIs2r8TMMDsta6zXG3AAsBdzA\nfGvtemPMtIrtc49kvxowEBEREREREWlOjV/DoAG7tEuAJTWeq3WgwFp7ZUP2qQEDERERERERkeZi\nbUMvlRhyGjAQERERERERaU4OzDBwggYMRERERERERJqR1QwDEREREREREQlmw2aGgSvUAURERERE\nRESk5dEMAxEREREREZHmYmnyyyo6xdgwmQrxE2NMPpDhwK47AgUO7NcpyuuccMoKyvuT7tbaTg7s\nNyTU11VSXueEU1ZQ3p+or2sYvV+cpbzOCaesoL7uiLRxdbAnRZ7dqDofHnx1tbV2qEOR6hR2Mwyc\neuMYY74KxR/gSCmvc8IpKyjv0Up9XYDyOiecsoLyHq3U1wUor7PCKW84ZYXwy9tSWMCGyQyDsBsw\nEBEREREREQlb1oLVVRJEREREREREpAbNMAg/z4U6QCMpr3PCKSsorzROuLW/8jonnLKC8krjhFv7\nK6+zwilvOGWF8MvbIuxlz9J/+V/v2MhqIVnbIuwWPRQRERERERER57lCHUBEREREREREWp5f1ICB\nMWa+MSbPGLOuju3GGPOUMSbdGPOdMeaE5s5YI099eS+tyLnWGPMfY8xxzZ2xRp7D5q1W7kRjjNcY\nc3FzZasjR715jTGnG2PWGGPWG2M+ac58NXLU915oY4x51xjzbUXWq5o7Y408XY0xy40x31fk+X0t\nZVrUv7dwZIw52xizqaINb69le51tXFddY8yvK/5mfmOMY6se/8zsDeprnNKA7P2NMV8YYw4aY24O\nRcYaeVr0Z19t+Ywx7Y0xy4wxP1b8t10ddQ/7twhVRmPMHysybTLGnFXHPhv0/yg/79+8MWZIxXFS\nesX73FQ8/3jF5/saY8wPxpjClpy3Yttvqn2uvtqS8xpjrjTG5Fdr46k/M6MTn3e1/hs0xnQwgWOY\nfcaY2T8nt4PZ9Vktzcda+4u5Af8fcAKwro7t44D3AQOcBPy3hec9BWhXcX9sS89bUcYNfAQsAS5u\nyXmBtsD3QLeKx51bcNY7gIcr7ncCdgORIcybBJxQcT8O+AFIq1GmRf17C7dbxb+lzUAvIBL4tqFt\nfLi6wAAgFfgYGNrSsldsq7evCXG7dwZOBGYCN7eA90qL/uyrLR/wCHB7xf3bf+rfGvu3CEVGIK0i\nSxTQsyKju5Z91vv/qFvD3sP11F1Z8b42Fe/zsbWUmQ7Mb8l5gb7AN1Qd9zXZMYlDea8EZjdRPqc+\n7+r6NxwDjACm/dz/Bwez67Nat2a7/aJmGFhrPyXwRaouE4C/24AvgbbGmKTmSXeo+vJaa/9jrd1T\n8fBLIKVZgtWdp772hcCH8j+BPOcTHV4D8l4CvGmt3V5RPmSZG5DVAnEVI/uxFWW9zZGt1jDWZltr\nv664vxfYACTXKNai/r2FoWFAurV2i7W2DFhIoE2rq6uN66xrrd1grd3UgrM3tK9xSr3ZrbV51tpV\nQHkoAtbU0j/76sg3AXix4v6LwPm1VG3I+ygUGScAC621B621W4H0iqw1NeT/Uai9/Y0xvY0xHxhj\nVhtjPjPG9K9Zr+J9HG+t/dJaa4G/U3s7TwZea+F5rwbm/HTc15THJM3Qvj+XI5931PFv0Fq731r7\nOXCgpWbXZ7U0p1/UgEEDJAM7qj3O5NAvOS3VFAIjfC2WMSYZuAD4W6izNFA/oJ0x5uOKD8zLQx3o\nMGYTGG3eCawFfm9ty7i4qzGmB3A88N8am8L531tL0JD2q6tMqNv+52QPtZaa6+doif9PCdba7Ir7\nOUBCLWVCnbuujA3N1ZD/R6nbc8B0a+0Q/v/27jdErquM4/j31ya21vinWlDrvwQxBGtN0FRrzYtY\nKWqpxVLEgLZRRC36xkLFF6VFpKAgVBGtIFHWF1LBGNoNaKu1SmppJFqSpjE2aiMSCRGKbUwgMSaP\nL+7dOsnsmiU7s3N35/uBS2bvnLvz3DP3njN59pwzcBtwzzRlXkNT/1P63oskb6AZCfLwkOKcMtd4\nVwIrkzyaZHuS9w812sHU743tdIXNSV43h1iG1d/Nxz1oX60Fz69VXASSvIcmYbBu1LHWLES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      "text/plain": [
       "<matplotlib.figure.Figure at 0x116bd0160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(20, 5))\n",
    "\n",
    "param_grid_linear  = {'C': np.linspace(1, 2, 6),  'gamma':  np.linspace(1, 2, 6)}\n",
    "param_grid_one_log = {'C': np.linspace(1, 2, 6),  'gamma':  np.logspace(-3, 2, 6)}\n",
    "param_grid_range   = {'C': np.logspace(-3, 2, 6), 'gamma':  np.logspace(-7, -2, 6)}\n",
    "\n",
    "for param_grid, ax in zip([param_grid_linear, param_grid_one_log, param_grid_range], axes):\n",
    "    grid_search = GridSearchCV(SVC(), param_grid, cv=5)\n",
    "    grid_search.fit(X_train, y_train)\n",
    "    scores = grid_search.cv_results_['mean_test_score'].reshape(6, 6)\n",
    "\n",
    "    scores_image = mglearn.tools.heatmap(\n",
    "        scores, xlabel='gamma', ylabel='C', xticklabels=param_grid['gamma'],\n",
    "        yticklabels=param_grid['C'], cmap=\"viridis\", ax=ax)\n",
    "\n",
    "plt.colorbar(scores_image, ax=axes.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 비대칭 매개변수 그리드 탐색"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "그리드 목록:\n",
      "[{'kernel': ['rbf'], 'C': [0.001, 0.01, 0.1, 1, 10, 100], 'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}, {'kernel': ['linear'], 'C': [0.001, 0.01, 0.1, 1, 10, 100]}]\n"
     ]
    }
   ],
   "source": [
    "param_grid = [{'kernel': ['rbf'],\n",
    "               'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "               'gamma': [0.001, 0.01, 0.1, 1, 10, 100]},\n",
    "              {'kernel': ['linear'],\n",
    "               'C': [0.001, 0.01, 0.1, 1, 10, 100]}]\n",
    "print(\"그리드 목록:\\n{}\".format(param_grid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "최고 매개벼수: {'C': 100, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "최고 교차 검증 점수: 0.97\n"
     ]
    }
   ],
   "source": [
    "grid_search = GridSearchCV(SVC(), param_grid, cv=5, return_train_score=True)\n",
    "\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print(\"최고 매개벼수: {}\".format(grid_search.best_params_))\n",
    "print(\"최고 교차 검증 점수: {:.2f}\".format(grid_search.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>...</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean_fit_time</th>\n",
       "      <td>0.00065</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.00059</td>\n",
       "      <td>0.00062</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00035</td>\n",
       "      <td>0.00034</td>\n",
       "      <td>0.00034</td>\n",
       "      <td>0.00035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_score_time</th>\n",
       "      <td>0.00029</td>\n",
       "      <td>0.00025</td>\n",
       "      <td>0.00025</td>\n",
       "      <td>0.00025</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00021</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_test_score</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>...</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_train_score</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>...</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_C</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_gamma</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_kernel</th>\n",
       "      <td>rbf</td>\n",
       "      <td>rbf</td>\n",
       "      <td>rbf</td>\n",
       "      <td>rbf</td>\n",
       "      <td>...</td>\n",
       "      <td>linear</td>\n",
       "      <td>linear</td>\n",
       "      <td>linear</td>\n",
       "      <td>linear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>params</th>\n",
       "      <td>{'C': 0.001, 'gamma': 0.001, 'kernel': 'rbf'}</td>\n",
       "      <td>{'C': 0.001, 'gamma': 0.01, 'kernel': 'rbf'}</td>\n",
       "      <td>{'C': 0.001, 'gamma': 0.1, 'kernel': 'rbf'}</td>\n",
       "      <td>{'C': 0.001, 'gamma': 1, 'kernel': 'rbf'}</td>\n",
       "      <td>...</td>\n",
       "      <td>{'C': 0.1, 'kernel': 'linear'}</td>\n",
       "      <td>{'C': 1, 'kernel': 'linear'}</td>\n",
       "      <td>{'C': 10, 'kernel': 'linear'}</td>\n",
       "      <td>{'C': 100, 'kernel': 'linear'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank_test_score</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split0_test_score</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>...</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split0_train_score</th>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split1_test_score</th>\n",
       "      <td>0.35</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.35</td>\n",
       "      <td>...</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split1_train_score</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>...</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split2_test_score</th>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split2_train_score</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>...</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split3_test_score</th>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split3_train_score</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>...</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split4_test_score</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>...</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split4_train_score</th>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_fit_time</th>\n",
       "      <td>8.8e-05</td>\n",
       "      <td>1.6e-05</td>\n",
       "      <td>1.8e-05</td>\n",
       "      <td>1.7e-05</td>\n",
       "      <td>...</td>\n",
       "      <td>1.1e-05</td>\n",
       "      <td>1e-05</td>\n",
       "      <td>1.3e-05</td>\n",
       "      <td>2.9e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_score_time</th>\n",
       "      <td>6e-05</td>\n",
       "      <td>3.2e-06</td>\n",
       "      <td>1.5e-05</td>\n",
       "      <td>6.7e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>3.6e-06</td>\n",
       "      <td>1.1e-06</td>\n",
       "      <td>1.9e-06</td>\n",
       "      <td>1.8e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_test_score</th>\n",
       "      <td>0.011</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.011</td>\n",
       "      <td>...</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_train_score</th>\n",
       "      <td>0.0029</td>\n",
       "      <td>0.0029</td>\n",
       "      <td>0.0029</td>\n",
       "      <td>0.0029</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.0055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                               0   \\\n",
       "mean_fit_time                                             0.00065   \n",
       "mean_score_time                                           0.00029   \n",
       "mean_test_score                                              0.37   \n",
       "mean_train_score                                             0.37   \n",
       "param_C                                                     0.001   \n",
       "param_gamma                                                 0.001   \n",
       "param_kernel                                                  rbf   \n",
       "params              {'C': 0.001, 'gamma': 0.001, 'kernel': 'rbf'}   \n",
       "rank_test_score                                                27   \n",
       "split0_test_score                                            0.38   \n",
       "split0_train_score                                           0.36   \n",
       "split1_test_score                                            0.35   \n",
       "split1_train_score                                           0.37   \n",
       "split2_test_score                                            0.36   \n",
       "split2_train_score                                           0.37   \n",
       "split3_test_score                                            0.36   \n",
       "split3_train_score                                           0.37   \n",
       "split4_test_score                                            0.38   \n",
       "split4_train_score                                           0.36   \n",
       "std_fit_time                                              8.8e-05   \n",
       "std_score_time                                              6e-05   \n",
       "std_test_score                                              0.011   \n",
       "std_train_score                                            0.0029   \n",
       "\n",
       "                                                              1   \\\n",
       "mean_fit_time                                             0.0006   \n",
       "mean_score_time                                          0.00025   \n",
       "mean_test_score                                             0.37   \n",
       "mean_train_score                                            0.37   \n",
       "param_C                                                    0.001   \n",
       "param_gamma                                                 0.01   \n",
       "param_kernel                                                 rbf   \n",
       "params              {'C': 0.001, 'gamma': 0.01, 'kernel': 'rbf'}   \n",
       "rank_test_score                                               27   \n",
       "split0_test_score                                           0.38   \n",
       "split0_train_score                                          0.36   \n",
       "split1_test_score                                           0.35   \n",
       "split1_train_score                                          0.37   \n",
       "split2_test_score                                           0.36   \n",
       "split2_train_score                                          0.37   \n",
       "split3_test_score                                           0.36   \n",
       "split3_train_score                                          0.37   \n",
       "split4_test_score                                           0.38   \n",
       "split4_train_score                                          0.36   \n",
       "std_fit_time                                             1.6e-05   \n",
       "std_score_time                                           3.2e-06   \n",
       "std_test_score                                             0.011   \n",
       "std_train_score                                           0.0029   \n",
       "\n",
       "                                                             2   \\\n",
       "mean_fit_time                                           0.00059   \n",
       "mean_score_time                                         0.00025   \n",
       "mean_test_score                                            0.37   \n",
       "mean_train_score                                           0.37   \n",
       "param_C                                                   0.001   \n",
       "param_gamma                                                 0.1   \n",
       "param_kernel                                                rbf   \n",
       "params              {'C': 0.001, 'gamma': 0.1, 'kernel': 'rbf'}   \n",
       "rank_test_score                                              27   \n",
       "split0_test_score                                          0.38   \n",
       "split0_train_score                                         0.36   \n",
       "split1_test_score                                          0.35   \n",
       "split1_train_score                                         0.37   \n",
       "split2_test_score                                          0.36   \n",
       "split2_train_score                                         0.37   \n",
       "split3_test_score                                          0.36   \n",
       "split3_train_score                                         0.37   \n",
       "split4_test_score                                          0.38   \n",
       "split4_train_score                                         0.36   \n",
       "std_fit_time                                            1.8e-05   \n",
       "std_score_time                                          1.5e-05   \n",
       "std_test_score                                            0.011   \n",
       "std_train_score                                          0.0029   \n",
       "\n",
       "                                                           3   \\\n",
       "mean_fit_time                                         0.00062   \n",
       "mean_score_time                                       0.00025   \n",
       "mean_test_score                                          0.37   \n",
       "mean_train_score                                         0.37   \n",
       "param_C                                                 0.001   \n",
       "param_gamma                                                 1   \n",
       "param_kernel                                              rbf   \n",
       "params              {'C': 0.001, 'gamma': 1, 'kernel': 'rbf'}   \n",
       "rank_test_score                                            27   \n",
       "split0_test_score                                        0.38   \n",
       "split0_train_score                                       0.36   \n",
       "split1_test_score                                        0.35   \n",
       "split1_train_score                                       0.37   \n",
       "split2_test_score                                        0.36   \n",
       "split2_train_score                                       0.37   \n",
       "split3_test_score                                        0.36   \n",
       "split3_train_score                                       0.37   \n",
       "split4_test_score                                        0.38   \n",
       "split4_train_score                                       0.36   \n",
       "std_fit_time                                          1.7e-05   \n",
       "std_score_time                                        6.7e-06   \n",
       "std_test_score                                          0.011   \n",
       "std_train_score                                        0.0029   \n",
       "\n",
       "                                 ...                \\\n",
       "mean_fit_time                    ...                 \n",
       "mean_score_time                  ...                 \n",
       "mean_test_score                  ...                 \n",
       "mean_train_score                 ...                 \n",
       "param_C                          ...                 \n",
       "param_gamma                      ...                 \n",
       "param_kernel                     ...                 \n",
       "params                           ...                 \n",
       "rank_test_score                  ...                 \n",
       "split0_test_score                ...                 \n",
       "split0_train_score               ...                 \n",
       "split1_test_score                ...                 \n",
       "split1_train_score               ...                 \n",
       "split2_test_score                ...                 \n",
       "split2_train_score               ...                 \n",
       "split3_test_score                ...                 \n",
       "split3_train_score               ...                 \n",
       "split4_test_score                ...                 \n",
       "split4_train_score               ...                 \n",
       "std_fit_time                     ...                 \n",
       "std_score_time                   ...                 \n",
       "std_test_score                   ...                 \n",
       "std_train_score                  ...                 \n",
       "\n",
       "                                                38  \\\n",
       "mean_fit_time                              0.00035   \n",
       "mean_score_time                            0.00021   \n",
       "mean_test_score                               0.95   \n",
       "mean_train_score                              0.97   \n",
       "param_C                                        0.1   \n",
       "param_gamma                                    NaN   \n",
       "param_kernel                                linear   \n",
       "params              {'C': 0.1, 'kernel': 'linear'}   \n",
       "rank_test_score                                 11   \n",
       "split0_test_score                             0.96   \n",
       "split0_train_score                            0.97   \n",
       "split1_test_score                             0.91   \n",
       "split1_train_score                            0.98   \n",
       "split2_test_score                                1   \n",
       "split2_train_score                            0.94   \n",
       "split3_test_score                             0.91   \n",
       "split3_train_score                            0.98   \n",
       "split4_test_score                             0.95   \n",
       "split4_train_score                            0.97   \n",
       "std_fit_time                               1.1e-05   \n",
       "std_score_time                             3.6e-06   \n",
       "std_test_score                               0.033   \n",
       "std_train_score                              0.012   \n",
       "\n",
       "                                              39  \\\n",
       "mean_fit_time                            0.00034   \n",
       "mean_score_time                           0.0002   \n",
       "mean_test_score                             0.97   \n",
       "mean_train_score                            0.98   \n",
       "param_C                                        1   \n",
       "param_gamma                                  NaN   \n",
       "param_kernel                              linear   \n",
       "params              {'C': 1, 'kernel': 'linear'}   \n",
       "rank_test_score                                1   \n",
       "split0_test_score                              1   \n",
       "split0_train_score                          0.99   \n",
       "split1_test_score                           0.96   \n",
       "split1_train_score                          0.98   \n",
       "split2_test_score                              1   \n",
       "split2_train_score                          0.98   \n",
       "split3_test_score                           0.95   \n",
       "split3_train_score                          0.99   \n",
       "split4_test_score                           0.95   \n",
       "split4_train_score                          0.99   \n",
       "std_fit_time                               1e-05   \n",
       "std_score_time                           1.1e-06   \n",
       "std_test_score                             0.022   \n",
       "std_train_score                           0.0055   \n",
       "\n",
       "                                               40  \\\n",
       "mean_fit_time                             0.00034   \n",
       "mean_score_time                            0.0002   \n",
       "mean_test_score                              0.96   \n",
       "mean_train_score                             0.99   \n",
       "param_C                                        10   \n",
       "param_gamma                                   NaN   \n",
       "param_kernel                               linear   \n",
       "params              {'C': 10, 'kernel': 'linear'}   \n",
       "rank_test_score                                 3   \n",
       "split0_test_score                            0.96   \n",
       "split0_train_score                           0.99   \n",
       "split1_test_score                               1   \n",
       "split1_train_score                           0.99   \n",
       "split2_test_score                               1   \n",
       "split2_train_score                           0.98   \n",
       "split3_test_score                            0.91   \n",
       "split3_train_score                           0.99   \n",
       "split4_test_score                            0.95   \n",
       "split4_train_score                              1   \n",
       "std_fit_time                              1.3e-05   \n",
       "std_score_time                            1.9e-06   \n",
       "std_test_score                              0.034   \n",
       "std_train_score                             0.007   \n",
       "\n",
       "                                                41  \n",
       "mean_fit_time                              0.00035  \n",
       "mean_score_time                             0.0002  \n",
       "mean_test_score                               0.96  \n",
       "mean_train_score                              0.99  \n",
       "param_C                                        100  \n",
       "param_gamma                                    NaN  \n",
       "param_kernel                                linear  \n",
       "params              {'C': 100, 'kernel': 'linear'}  \n",
       "rank_test_score                                  3  \n",
       "split0_test_score                             0.96  \n",
       "split0_train_score                            0.99  \n",
       "split1_test_score                                1  \n",
       "split1_train_score                            0.99  \n",
       "split2_test_score                                1  \n",
       "split2_train_score                            0.99  \n",
       "split3_test_score                             0.91  \n",
       "split3_train_score                               1  \n",
       "split4_test_score                             0.95  \n",
       "split4_train_score                               1  \n",
       "std_fit_time                               2.9e-05  \n",
       "std_score_time                             1.8e-06  \n",
       "std_test_score                               0.034  \n",
       "std_train_score                             0.0055  \n",
       "\n",
       "[23 rows x 42 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = pd.DataFrame(grid_search.cv_results_)\n",
    "display(results.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 그리드 서치에 다양한 교차 검증 적용\n",
    "### 중첩 교차 검증"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "교차 검증 점수:  [0.967 1.    0.967 0.967 1.   ]\n",
      "교차 검증 평균 점수:  0.9800000000000001\n"
     ]
    }
   ],
   "source": [
    "param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "              'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}\n",
    "scores = cross_val_score(GridSearchCV(SVC(), param_grid, cv=5), iris.data, iris.target, cv=5)\n",
    "print(\"교차 검증 점수: \", scores)\n",
    "print(\"교차 검증 평균 점수: \", scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def nested_cv(X, y, inner_cv, outer_cv, Classifier, parameter_grid):\n",
    "    outer_scores = []\n",
    "    for training_samples, test_samples in outer_cv.split(X, y):\n",
    "        best_parms = {}\n",
    "        best_score = -np.inf\n",
    "\n",
    "        for parameters in parameter_grid:\n",
    "            cv_scores = []\n",
    "            for inner_train, inner_test in inner_cv.split(X[training_samples], y[training_samples]):\n",
    "                clf = Classifier(**parameters)\n",
    "                clf.fit(X[inner_train], y[inner_train])\n",
    "                score = clf.score(X[inner_test], y[inner_test])\n",
    "                cv_scores.append(score)\n",
    "\n",
    "            mean_score = np.mean(cv_scores)\n",
    "            if mean_score > best_score:\n",
    "                best_score = mean_score\n",
    "                best_params = parameters\n",
    "\n",
    "        clf = Classifier(**best_params)\n",
    "        clf.fit(X[training_samples], y[training_samples])\n",
    "        outer_scores.append(clf.score(X[test_samples], y[test_samples]))\n",
    "    return np.array(outer_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "교차 검증 점수: [0.967 1.    0.967 0.967 1.   ]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import ParameterGrid, StratifiedKFold\n",
    "scores = nested_cv(iris.data, iris.target, StratifiedKFold(5),\n",
    "                   StratifiedKFold(5), SVC, ParameterGrid(param_grid))\n",
    "print(\"교차 검증 점수: {}\".format(scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 교차 검증과 그리드 서치 병렬화"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3 평가 지표와 측정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "\n",
    "digits = load_digits()\n",
    "y = digits.target == 9\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    digits.data, y, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "예측된 유니크 레이블: [False]\n",
      "테스트 점수: 0.90\n"
     ]
    }
   ],
   "source": [
    "from sklearn.dummy import DummyClassifier\n",
    "dummy_majority = DummyClassifier(strategy='most_frequent').fit(X_train, y_train)\n",
    "pred_most_frequent = dummy_majority.predict(X_test)\n",
    "print(\"예측된 유니크 레이블: {}\".format(np.unique(pred_most_frequent)))\n",
    "print(\"테스트 점수: {:.2f}\".format(dummy_majority.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "테스트 점수: 0.92\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "tree = DecisionTreeClassifier(max_depth=2).fit(X_train, y_train)\n",
    "pred_tree = tree.predict(X_test)\n",
    "print(\"테스트 점수: {:.2f}\".format(tree.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dummy score: 0.83\n",
      "logreg 점수: 0.98\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "dummy = DummyClassifier().fit(X_train, y_train)\n",
    "pred_dummy = dummy.predict(X_test)\n",
    "print(\"dummy score: {:.2f}\".format(dummy.score(X_test, y_test)))\n",
    "\n",
    "logreg = LogisticRegression(C=0.1).fit(X_train, y_train)\n",
    "pred_logreg = logreg.predict(X_test)\n",
    "print(\"logreg 점수: {:.2f}\".format(logreg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 오차 행렬 (Confusion Matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      "[[401   2]\n",
      " [  8  39]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion = confusion_matrix(y_test, pred_logreg)\n",
    "print(\"Confusion matrix:\\n{}\".format(confusion))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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xIDQ3MSA2MTIgOTY5IDk2OSA5NjkgNTMxIDY4NCA2ODQgNjg0IDY4NCA2ODQg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\nIFIgL0luZm8gMzUgMCBSID4+CnN0YXJ0eHJlZgo4MjQ3CiUlRU9GCg==\n",
      "image/png": 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AAIBn1CEDgDTHZH4g+XGEDAAAwDMSMgBIc9QhA5IfdciQMNQhA/ygDhngD3XI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\nO1atWrVSx44d9cADDxT7BuD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      "text/plain": [
       "<matplotlib.figure.Figure at 0x117c17320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_confusion_matrix_illustration()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/pdf": 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jNKUcQ//WuMfrA4eKUvQKZW5kc3RyZWFtCmVuZG9iagoyNyAwIG9iago8PCAvTGVuZ3Ro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\ncnR4cmVmCjc3NjkKJSVFT0YK\n",
      "image/png": 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PpSkC2gIHOecuc879YGZjgDFm9g3wOr6QJq5MnGjRvgRcb2Z3A88AxwFlte0/As40s+fx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\nZkVJMROB3sAI/OH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      "text/plain": [
       "<matplotlib.figure.Figure at 0x117e75cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_binary_confusion_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "빈도 기반 더미 모델:\n",
      "[[403   0]\n",
      " [ 47   0]]\n",
      "\n",
      "무작위 더미 모델:\n",
      "[[359  44]\n",
      " [ 45   2]]\n",
      "\n",
      "결정 트리:\n",
      "[[390  13]\n",
      " [ 24  23]]\n",
      "\n",
      "Logistic Regression\n",
      "[[401   2]\n",
      " [  8  39]]\n"
     ]
    }
   ],
   "source": [
    "print(\"빈도 기반 더미 모델:\")\n",
    "print(confusion_matrix(y_test, pred_most_frequent))\n",
    "print(\"\\n무작위 더미 모델:\")\n",
    "print(confusion_matrix(y_test, pred_dummy))\n",
    "print(\"\\n결정 트리:\")\n",
    "print(confusion_matrix(y_test, pred_tree))\n",
    "print(\"\\nLogistic Regression\")\n",
    "print(confusion_matrix(y_test, pred_logreg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1 score most frequent: 0.00\n",
      "f1 더미 점수: 0.04\n",
      "f1 트리 점수: 0.55\n",
      "f1 회귀 점수: 0.89\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Kyo/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "print(\"f1 score most frequent: {:.2f}\".format(\n",
    "    f1_score(y_test, pred_most_frequent)))\n",
    "print(\"f1 더미 점수: {:.2f}\".format(f1_score(y_test, pred_dummy)))\n",
    "print(\"f1 트리 점수: {:.2f}\".format(f1_score(y_test, pred_tree)))\n",
    "print(\"f1 회귀 점수: {:.2f}\".format(\n",
    "    f1_score(y_test, pred_logreg)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "       9 아님       0.90      1.00      0.94       403\n",
      "          9       0.00      0.00      0.00        47\n",
      "\n",
      "avg / total       0.80      0.90      0.85       450\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Kyo/anaconda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, pred_most_frequent,\n",
    "                            target_names=[\"9 아님\", \"9\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 불확실성 고려"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from mglearn.datasets import make_blobs\n",
    "X, y = make_blobs(n_samples=(400, 50), centers=2, cluster_std=[7.0, 2],\n",
    "                  random_state=22)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "svc = SVC(gamma=.05).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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841ysRcdnLWkeqoJmhaeFRmK7056Ku9CK9U2ApNhFFd5FztsN2+rrOUnNqj/m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\nUiA+PgpzdGFydHhyZWYKNDE1OTcKJSVFT0YK\n",
      "image/png": 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TRGsIBloSXggRyeboIaXgHI48V0TxBp9zKv6ollERm2MAZZeFEImR+DR0EpuG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DxUSD1JSVbefDOV7u4otFo9k7UG7L1Gt9IdrEE4Lc3EqlVtvPHG9fOU0ShDFmVDMesMQ4YkLEHm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117757b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_decision_threshold()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.97      0.89      0.93       104\n",
      "          1       0.35      0.67      0.46         9\n",
      "\n",
      "avg / total       0.92      0.88      0.89       113\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, svc.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred_lower_threshold = svc.decision_function(X_test) > -.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.82      0.90       104\n",
      "          1       0.32      1.00      0.49         9\n",
      "\n",
      "avg / total       0.95      0.83      0.87       113\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_lower_threshold))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 정밀도-재현율 곡선과 ROC 곡선"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "precision, recall, thresholds = precision_recall_curve(\n",
    "    y_test, svc.decision_function(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11b63c550>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": 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phlyWEFYuF0wsB8wC0ZZwCiKeBgCffQy1CmVuZHN0cmVhbQplbmRvYmoKMzMg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\nJUVPRgo=\n",
      "image/png": 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Nw4fTaN8+gLp1fSyOTpV3RT3ZsqlMolBKlZopU/aQlJQFwLx58cybFw/YH2bb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\nUko5aVJQSinl9P/Ct0hqWysoWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117b939b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = make_blobs(n_samples=(4000, 500), centers=2, cluster_std=[7.0, 2],\n",
    "                  random_state=22)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "svc = SVC(gamma=.05).fit(X_train, y_train)\n",
    "precision, recall, thresholds = precision_recall_curve(\n",
    "    y_test, svc.decision_function(X_test))\n",
    "close_zero = np.argmin(np.abs(thresholds))\n",
    "plt.plot(precision[close_zero], recall[close_zero], 'o', markersize=10,\n",
    "         label=\"threshold zero\", fillstyle=\"none\", c='k', mew=2)\n",
    "\n",
    "plt.plot(precision, recall, label=\"precision recall curve\")\n",
    "plt.xlabel(\"Precision\")\n",
    "plt.ylabel(\"Recall\")\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x115e62b00>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": 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Cl41XTBZjGfRE5S7F9EGqs4WehPKifA6y+aghEl2inIEnBgejQDuw57afiVeF\noHV1n7aNoRopHU//NjQ1SSLkEyWc2dK4W/j+nnv9/AOmVFOfCmVuZHN0cmVhbQplbmRvYmoKMjkg\nMCBvYmoKPDwgL0xlbmd0aCAyMjcgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicNU87\nsgMhDOs5hS6QGYxtYM+zmVQv92+fZLINEv5I8vRERyZe5sgIrNnxthYZiBn4FlPxrz3tw4TqPbiH\nCOXiQphhJJw167ibp+PFv13lM9bBuw2+YpYXBLYwk/WVxZnLdsFYGidxTrIbY9dEbGNd6+kU1hFM\nKAMhne0wJcgcFSl9sqOMOTpO5InnYqrFLr/vYX3BpjGiwhxXBU/QZFCWPe8moB0X9N/Vjd9JNIte\nAjKRYGGdJObOWU741WtHx1GLIjEnpBnkMhHSnK5iCqEJxTo7CioVBZfqc8rdPv9oXVtNCmVuZHN0\ncmVhbQplbmRvYmoKMzAgMCBvYmoKPDwgL0xlbmd0aCAyNDUgL0ZpbHRlciAvRmxhdGVEZWNvZGUg\nPj4Kc3RyZWFtCnicRVC7jUMxDOs9BRcIYP0se553SJXbvz1KRnCFIVo/kloSmIjASwyxlG/iR0ZB\nPQu/F4XiM8TPF4VBzoSkQJz1GRCZeIbaRm7odnDOvMMzjDkCF8VacKbTmfZc2OScBycQzm2U8YxC\nuklUFXFUn3FM8aqyz43XgaW1bLPTkewhjYRLSSUml35TKv+0KVsq6NpFE7BI5IGTTTThLD9DkmLM\noJRR9zC1jvRxspFHddDJ2Zw5LZnZ7qftTHwPWCaZUeUpnecyPiep81xOfe6zHdHkoqVV+5z93pGW\n8iK126HV6VclUZmN1aeQuDz/jJ/x/gOOoFk+CmVuZHN0cmVhbQplbmRvYmoKMzEgMCBvYmoKPDwg\nL0xlbmd0aCAxMzMgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicTY9BEsMwCAPvfoWe\ngLEB8550ekr+fy2QNu4F7YyAkYYwCDxiDOswJbx6++FVpEtwNo75JRlFPAhqC9wXVAVHY4qd+Njd\noeyl4ukUTYvrEXPTtKR0N1Eqbb2dyPjAfZ/eH1W2JJ2CHlvqhC7RJPJFAnPYVDDP6sZLS4+n7dne\nH2Y+M9cKZW5kc3RyZWFtCmVuZG9iagozMiAwIG9iago8PCAvTGVuZ3RoIDE2MyAvRmlsdGVyIC9G\nbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFkLl1BDEMQ3NVgRJ4gDrqGT9Hs/2nC2m83kD6eIR4iD0J\nw3JdxYXRDT/etsw0vI4y3I31Zcb4qLFATtAHGCITV6NJ9e2KM1Tp4dVirqOiXC86IhLMkuOrQCN8\nOrLHQ1vbmX46r3/sIe8T/yoq525hAS6q7kD5Uh/x1I/ZUeqaoY8qK2seatq/CLsilLZ9XE5lnLp7\nB7TCZytX+30DqOc6gAplbmRzdHJlYW0KZW5kb2JqCjMzIDAgb2JqCjw8IC9MZW5ndGggNjggL0Zp\nbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicMzK3UDBQsDQBEoYWJgrmZgYKKYZcQL6piblC\nLhdIDMTKAbMMgLQlnIKIW0I0QZSCWBClZiZmEEk4AyKXBgDJtBXlCmVuZHN0cmVhbQplbmRvYmoK\nMzQgMCBvYmoKPDwgL0xlbmd0aCA0NSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJwz\nMrdQMFCwNAEShhYmCuZmBgophlyWEFYuF0wsB8wC0ZZwCiKeBgCffQy1CmVuZHN0cmVhbQplbmRv\nYmoKMzUgMCBvYmoKPDwgL0xlbmd0aCAxNjEgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFt\nCnicRZBLEsMgDEP3nEJH8EcGfJ50ukrvv60hTbOAp7FABncnBKm1BRPRBS9tS7oLPlsJzsZ46DZu\nNRLkBHWAVqTjaJRSfbnFaZV08Wg2cysLrRMdZg56lKMZoBA6Fd7touRypu7O+Udw9V/1R7HunM3E\nwGTlDoRm9SnufJsdUV3dZH/SY27Wa38V9qqwtKyl5YTbzl0zoATuqRzt/QWpczqECmVuZHN0cmVh\nbQplbmRvYmoKMzYgMCBvYmoKPDwgL0xlbmd0aCAyMTQgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4K\nc3RyZWFtCnicPVC7EUMxCOs9BQvkznztN8/Lpcv+bSScpEI2QhKUmkzJlIc6ypKsKU8dPktih7yH\n5W5kNiUqRS+TsCX30ArxfYnmFPfd1ZazQzSXaDl+CzMqqhsd00s2mnAqE7qg3MMz+g1tdANWhx6x\nWyDQpGDXtiByxw8YDMGZE4siDEpNBv+tcvdS3O89HG+iiJR08K755fTLzy28Tj2ORLq9+YprcaY6\nCkRwRmryinRhxbLIQ6TVBDU9A2u1AK7eevk3aEd0GYDsE4njNKUcQ//WuMfrA4eKUvQKZW5kc3Ry\nZWFtCmVuZG9iagozNyAwIG9iago8PCAvTGVuZ3RoIDE1NyAvRmlsdGVyIC9GbGF0ZURlY29kZSA+\nPgpzdHJlYW0KeJxFkLkRQzEIRHNVQQkSsAjqscfRd/+pF/lKtG8ALYevJVOqHyciptzXaPQweQ6f\nTSVWLNgmtpMachsWQUoxmHhOMaujt6GZh9TruKiquHVmldNpy8rFf/NoVzOTPcI16ifwTej4nzy0\nqehboK8LlH1AtTidSVAxfa9igaOcdn8inBjgPhlHmSkjcWJuCuz3GQBmvle4xuMF3QE3eQplbmRz\ndHJlYW0KZW5kb2JqCjM4IDAgb2JqCjw8IC9MZW5ndGggMzMyIC9GaWx0ZXIgL0ZsYXRlRGVjb2Rl\nID4+CnN0cmVhbQp4nC1SOY4kMQzL/Qp+YADr8vGeHkzU+/90SVUFBapsyzzkcsNEJX4skNtRa+LX\nRmagwvCvq8yF70jbyDqIa8hFXMmWwmdELOQxxDzEgu/b+Bke+azMybMHxi/Z9xlW7KkJy0LGizO0\nwyqOwyrIsWDrIqp7eFOkw6kk2OOL/z7FcxeCFr4jaMAv+eerI3i+pEXaPWbbtFsPlmlHlRSWg+1p\nzsvkS+ssV8fj+SDZ3hU7QmpXgKIwd8Z5Lo4ybWVEa2Fng6TGxfbm2I+lBF3oxmWkOAL5mSrCA0qa\nzGyiIP7I6SGnMhCmrulKJ7dRFXfqyVyzubydSTJb90WKzRTO68KZ9XeYMqvNO3mWE6VORfgZe7YE\nDZ3j6tlrmYVGtznBKyV8NnZ6cvK9mlkPyalISBXTugpOo8gUS9iW+JqKmtLUy/Dfl/cZf/8BM+J8\nAQplbmRzdHJlYW0KZW5kb2JqCjM5IDAgb2JqCjw8IC9MZW5ndGggMTMxIC9GaWx0ZXIgL0ZsYXRl\nRGVjb2RlID4+CnN0cmVhbQp4nEWPyw0EIQxD71ThEvIZPqmH1Z7Y/q/rMJpBQvhBIjvxMAis8/I2\n0MXw0aLDN/421atjlSwfunpSVg/pkIe88hVQaTBRxIVZTB1DYc6YysiWMrcb4bZNg6xslVStg3Y8\nBg+2p2WrCH6pbWHqLPEMwlVeuMcNP5BLrXe9Vb5/QlMwlwplbmRzdHJlYW0KZW5kb2JqCjQwIDAg\nb2JqCjw8IC9MZW5ndGggNzIgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicNYyxEcAw\nCAN7ptAINlhg75NLRfZvQ3xOAy8dD5eiwVoNuoIjcHWp/NEjXbkpRZdjzoLhcapfSDFGPagj497H\nT7lfcBYSfQplbmRzdHJlYW0KZW5kb2JqCjQxIDAgb2JqCjw8IC9MZW5ndGggNzQgL0ZpbHRlciAv\nRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicPYzBDYAwDAP/nSIjNIlNMhDiBft/aQrtxz6dZNMoXeAV\naUKEnNrISU9b7p6Eg4MUkLBfbejVvipLe6ogajL+Nnx31wt3HBdOCmVuZHN0cmVhbQplbmRvYmoK\nMTQgMCBvYmoKPDwgL1R5cGUgL0ZvbnQgL0Jhc2VGb250IC9EZWphVnVTYW5zIC9GaXJzdENoYXIg\nMCAvTGFzdENoYXIgMjU1Ci9Gb250RGVzY3JpcHRvciAxMyAwIFIgL1N1YnR5cGUgL1R5cGUzIC9O\nYW1lIC9EZWphVnVTYW5zCi9Gb250QkJveCBbIC0xMDIxIC00NjMgMTc5NCAxMjMzIF0gL0ZvbnRN\nYXRyaXggWyAwLjAwMSAwIDAgMC4wMDEgMCAwIF0KL0NoYXJQcm9jcyAxNSAwIFIKL0VuY29kaW5n\nIDw8IC9UeXBlIC9FbmNvZGluZwovRGlmZmVyZW5jZXMgWyAzMiAvc3BhY2UgNDYgL3BlcmlvZCA0\nOCAvemVybyAvb25lIC90d28gNTIgL2ZvdXIgL2ZpdmUgL3NpeCA1NiAvZWlnaHQgODAKL1AgODIg\nL1IgOTcgL2EgOTkgL2MgL2QgL2UgL2YgMTA0IC9oIC9pIDEwOCAvbCAxMTAgL24gL28gMTE0IC9y\nIC9zIC90IDExOAovdiAxMjIgL3ogXQo+PgovV2lkdGhzIDEyIDAgUiA+PgplbmRvYmoKMTMgMCBv\nYmoKPDwgL1R5cGUgL0ZvbnREZXNjcmlwdG9yIC9Gb250TmFtZSAvRGVqYVZ1U2FucyAvRmxhZ3Mg\nMzIKL0ZvbnRCQm94IFsgLTEwMjEgLTQ2MyAxNzk0IDEyMzMgXSAvQXNjZW50IDkyOSAvRGVzY2Vu\ndCAtMjM2IC9DYXBIZWlnaHQgMAovWEhlaWdodCAwIC9JdGFsaWNBbmdsZSAwIC9TdGVtViAwIC9N\nYXhXaWR0aCAxMzQyID4+CmVuZG9iagoxMiAwIG9iagpbIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAw\nIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwCjYwMCA2MDAg\nNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgMzE4IDQwMSA0\nNjAgODM4IDYzNgo5NTAgNzgwIDI3NSAzOTAgMzkwIDUwMCA4MzggMzE4IDM2MSAzMTggMzM3IDYz\nNiA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYKNjM2IDYzNiAzMzcgMzM3IDgzOCA4MzggODM4\nIDUzMSAxMDAwIDY4NCA2ODYgNjk4IDc3MCA2MzIgNTc1IDc3NSA3NTIgMjk1CjI5NSA2NTYgNTU3\nIDg2MyA3NDggNzg3IDYwMyA3ODcgNjk1IDYzNSA2MTEgNzMyIDY4NCA5ODkgNjg1IDYxMSA2ODUg\nMzkwIDMzNwozOTAgODM4IDUwMCA1MDAgNjEzIDYzNSA1NTAgNjM1IDYxNSAzNTIgNjM1IDYzNCAy\nNzggMjc4IDU3OSAyNzggOTc0IDYzNCA2MTIKNjM1IDYzNSA0MTEgNTIxIDM5MiA2MzQgNTkyIDgx\nOCA1OTIgNTkyIDUyNSA2MzYgMzM3IDYzNiA4MzggNjAwIDYzNiA2MDAgMzE4CjM1MiA1MTggMTAw\nMCA1MDAgNTAwIDUwMCAxMzQyIDYzNSA0MDAgMTA3MCA2MDAgNjg1IDYwMCA2MDAgMzE4IDMxOCA1\nMTggNTE4CjU5MCA1MDAgMTAwMCA1MDAgMTAwMCA1MjEgNDAwIDEwMjMgNjAwIDUyNSA2MTEgMzE4\nIDQwMSA2MzYgNjM2IDYzNiA2MzYgMzM3CjUwMCA1MDAgMTAwMCA0NzEgNjEyIDgzOCAzNjEgMTAw\nMCA1MDAgNTAwIDgzOCA0MDEgNDAxIDUwMCA2MzYgNjM2IDMxOCA1MDAKNDAxIDQ3MSA2MTIgOTY5\nIDk2OSA5NjkgNTMxIDY4NCA2ODQgNjg0IDY4NCA2ODQgNjg0IDk3NCA2OTggNjMyIDYzMiA2MzIg\nNjMyCjI5NSAyOTUgMjk1IDI5NSA3NzUgNzQ4IDc4NyA3ODcgNzg3IDc4NyA3ODcgODM4IDc4NyA3\nMzIgNzMyIDczMiA3MzIgNjExIDYwNQo2MzAgNjEzIDYxMyA2MTMgNjEzIDYxMyA2MTMgOTgyIDU1\nMCA2MTUgNjE1IDYxNSA2MTUgMjc4IDI3OCAyNzggMjc4IDYxMiA2MzQKNjEyIDYxMiA2MTIgNjEy\nIDYxMiA4MzggNjEyIDYzNCA2MzQgNjM0IDYzNCA1OTIgNjM1IDU5MiBdCmVuZG9iagoxNSAwIG9i\nago8PCAvc3BhY2UgMTYgMCBSIC9wZXJpb2QgMTcgMCBSIC96ZXJvIDE4IDAgUiAvb25lIDE5IDAg\nUiAvdHdvIDIwIDAgUgovZm91ciAyMSAwIFIgL2ZpdmUgMjIgMCBSIC9zaXggMjMgMCBSIC9laWdo\ndCAyNCAwIFIgL1AgMjUgMCBSIC9SIDI2IDAgUgovYSAyNyAwIFIgL2MgMjggMCBSIC9kIDI5IDAg\nUiAvZSAzMCAwIFIgL2YgMzEgMCBSIC9oIDMyIDAgUiAvaSAzMyAwIFIKL2wgMzQgMCBSIC9uIDM1\nIDAgUiAvbyAzNiAwIFIgL3IgMzcgMCBSIC9zIDM4IDAgUiAvdCAzOSAwIFIgL3YgNDAgMCBSCi96\nIDQxIDAgUiA+PgplbmRvYmoKMyAwIG9iago8PCAvRjEgMTQgMCBSID4+CmVuZG9iago0IDAgb2Jq\nCjw8IC9BMSA8PCAvVHlwZSAvRXh0R1N0YXRlIC9DQSAwIC9jYSAxID4+Ci9BMiA8PCAvVHlwZSAv\nRXh0R1N0YXRlIC9DQSAxIC9jYSAxID4+Ci9BMyA8PCAvVHlwZSAvRXh0R1N0YXRlIC9DQSAwLjgg\nL2NhIDAuOCA+PiA+PgplbmRvYmoKNSAwIG9iago8PCA+PgplbmRvYmoKNiAwIG9iago8PCA+Pgpl\nbmRvYmoKNyAwIG9iago8PCA+PgplbmRvYmoKMiAwIG9iago8PCAvVHlwZSAvUGFnZXMgL0tpZHMg\nWyAxMCAwIFIgXSAvQ291bnQgMSA+PgplbmRvYmoKNDIgMCBvYmoKPDwgL0NyZWF0b3IgKG1hdHBs\nb3RsaWIgMi4wLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZykKL1Byb2R1Y2VyIChtYXRwbG90bGli\nIHBkZiBiYWNrZW5kKSAvQ3JlYXRpb25EYXRlIChEOjIwMTgwNjE0MjAwMTEyKzA5JzAwJykKPj4K\nZW5kb2JqCnhyZWYKMCA0MwowMDAwMDAwMDAwIDY1NTM1IGYgCjAwMDAwMDAwMTYgMDAwMDAgbiAK\nMDAwMDAxMjYyNCAwMDAwMCBuIAowMDAwMDEyMzg3IDAwMDAwIG4gCjAwMDAwMTI0MTkgMDAwMDAg\nbiAKMDAwMDAxMjU2MSAwMDAwMCBuIAowMDAwMDEyNTgyIDAwMDAwIG4gCjAwMDAwMTI2MDMgMDAw\nMDAgbiAKMDAwMDAwMDA2NSAwMDAwMCBuIAowMDAwMDAwMzk4IDAwMDAwIG4gCjAwMDAwMDAyMDgg\nMDAwMDAgbiAKMDAwMDAwMzg2NSAwMDAwMCBuIAowMDAwMDExMDI0IDAwMDAwIG4gCjAwMDAwMTA4\nMjQgMDAwMDAgbiAKMDAwMDAxMDM3NCAwMDAwMCBuIAowMDAwMDEyMDc3IDAwMDAwIG4gCjAwMDAw\nMDM4ODYgMDAwMDAgbiAKMDAwMDAwMzk3NSAwMDAwMCBuIAowMDAwMDA0MDk2IDAwMDAwIG4gCjAw\nMDAwMDQzNzkgMDAwMDAgbiAKMDAwMDAwNDUzMSAwMDAwMCBuIAowMDAwMDA0ODUyIDAwMDAwIG4g\nCjAwMDAwMDUwMTQgMDAwMDAgbiAKMDAwMDAwNTMzNCAwMDAwMCBuIAowMDAwMDA1NzI0IDAwMDAw\nIG4gCjAwMDAwMDYxODkgMDAwMDAgbiAKMDAwMDAwNjQyNyAwMDAwMCBuIAowMDAwMDA2NzI3IDAw\nMDAwIG4gCjAwMDAwMDcxMDQgMDAwMDAgbiAKMDAwMDAwNzQwNyAwMDAwMCBuIAowMDAwMDA3NzA3\nIDAwMDAwIG4gCjAwMDAwMDgwMjUgMDAwMDAgbiAKMDAwMDAwODIzMSAwMDAwMCBuIAowMDAwMDA4\nNDY3IDAwMDAwIG4gCjAwMDAwMDg2MDcgMDAwMDAgbiAKMDAwMDAwODcyNCAwMDAwMCBuIAowMDAw\nMDA4OTU4IDAwMDAwIG4gCjAwMDAwMDkyNDUgMDAwMDAgbiAKMDAwMDAwOTQ3NSAwMDAwMCBuIAow\nMDAwMDA5ODgwIDAwMDAwIG4gCjAwMDAwMTAwODQgMDAwMDAgbiAKMDAwMDAxMDIyOCAwMDAwMCBu\nIAowMDAwMDEyNjg0IDAwMDAwIG4gCnRyYWlsZXIKPDwgL1NpemUgNDMgL1Jvb3QgMSAwIFIgL0lu\nZm8gNDIgMCBSID4+CnN0YXJ0eHJlZgoxMjgzMgolJUVPRgo=\n",
      "image/png": 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zcXKCQYMacuLE5TK/dE4rwVq2vrYgKjZpKQjhaElJkJ5u3NcaXn4ZUlIAWJjZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115df0fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=100, random_state=0, max_features=2)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "precision_rf, recall_rf, thresholds_rf = precision_recall_curve(\n",
    "    y_test, rf.predict_proba(X_test)[:, 1])\n",
    "\n",
    "plt.plot(precision, recall, label=\"svc\")\n",
    "\n",
    "plt.plot(precision[close_zero], recall[close_zero], 'o', markersize=10,\n",
    "         label=\"threshold zero svc\", fillstyle=\"none\", c='k', mew=2)\n",
    "\n",
    "plt.plot(precision_rf, recall_rf, label=\"rf\")\n",
    "\n",
    "close_default_rf = np.argmin(np.abs(thresholds_rf - 0.5))\n",
    "plt.plot(precision_rf[close_default_rf], recall_rf[close_default_rf], '^', c='k',\n",
    "         markersize=10, label=\"threshold 0.5 rf\", fillstyle=\"none\", mew=2)\n",
    "plt.xlabel(\"Precision\")\n",
    "plt.ylabel(\"Recall\")\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest f1_score: 0.610\n",
      "SVC f1_score: 0.656\n"
     ]
    }
   ],
   "source": [
    "print(\"Random Forest f1_score: {:.3f}\".format(\n",
    "    f1_score(y_test, rf.predict(X_test))))\n",
    "print(\"SVC f1_score: {:.3f}\".format(f1_score(y_test, svc.predict(X_test))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision of random forest: 0.666\n",
      "Average precision of svc: 0.663\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import average_precision_score\n",
    "ap_rf = average_precision_score(y_test, rf.predict_proba(X_test)[:, 1])\n",
    "ap_svc = average_precision_score(y_test, svc.decision_function(X_test))\n",
    "print(\"Average precision of random forest: {:.3f}\".format(ap_rf))\n",
    "print(\"Average precision of svc: {:.3f}\".format(ap_svc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC와 AUC 곡선"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1177c9ef0>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": 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u6HZwzrzDM4w5AhfFWnCm05n2XNjknAcnEM5tlPGMQrpJVBVxVJ9xTPGqss+N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\n",
      "image/png": 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ERAIKBRERCSgUREQkEGoomNlMM9thZrvMbGEL++eY2Stm9qqZrTez/DDrAbjqqnVhv4WI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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1177c9c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, svc.decision_function(X_test))\n",
    "\n",
    "plt.plot(fpr, tpr, label=\"ROC Curve\")\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR (recall)\")\n",
    "\n",
    "close_zero = np.argmin(np.abs(thresholds))\n",
    "plt.plot(fpr[close_zero], tpr[close_zero], 'o', markersize=10,\n",
    "         label=\"threshold zero\", fillstyle=\"none\", c='k', mew=2)\n",
    "plt.legend(loc=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x117711cf8>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": 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CiiEXSADEzOWCCeaAWQZAGqI4B64mhysNAMboDSYKZW5kc3RyZWFtCmVuZG9iagoyMCAw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\n",
      "image/png": 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4srKyWLp0aahDM8YYVwSaFB7AqYN0P87VQj8EbncrqPrC4/Ewb6tz/94jDz/MI488AsC8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b666470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr_rf, tpr_rf, thresholds_rf = roc_curve(y_test, rf.predict_proba(X_test)[:, 1])\n",
    "\n",
    "plt.plot(fpr, tpr, label=\"ROC Curve SVC\")\n",
    "plt.plot(fpr_rf, tpr_rf, label=\"ROC Curve RF\")\n",
    "\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR (recall)\")\n",
    "plt.plot(fpr[close_zero], tpr[close_zero], 'o', markersize=10,\n",
    "         label=\"threshold zero SVC\", fillstyle=\"none\", c='k', mew=2)\n",
    "close_default_rf = np.argmin(np.abs(thresholds_rf - 0.5))\n",
    "plt.plot(fpr_rf[close_default_rf], tpr[close_default_rf], '^', markersize=10,\n",
    "         label=\"threshold 0.5 RF\", fillstyle=\"none\", c='k', mew=2)\n",
    "\n",
    "plt.legend(loc=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC for Random Forest: 0.937\n",
      "AUC for SVC: 0.916\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "rf_auc = roc_auc_score(y_test, rf.predict_proba(X_test)[:, 1])\n",
    "svc_auc = roc_auc_score(y_test, svc.decision_function(X_test))\n",
    "print(\"AUC for Random Forest: {:.3f}\".format(rf_auc))\n",
    "print(\"AUC for SVC: {:.3f}\".format(svc_auc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gamma = 1.00  accuracy = 0.90  AUC = 0.50\n",
      "gamma = 0.10  accuracy = 0.90  AUC = 0.96\n",
      "gamma = 0.01  accuracy = 0.90  AUC = 1.00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1175783c8>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": 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e0rmzxfT4XnOyvDAVWPHmtRuQTbX4Ny/i+D3j6/n8A6ilWxYKZW5kc3RyZWFtCmVuZG9i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\nNDAKJSVFT0YK\n",
      "image/png": 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cq1PHBeeuv17BOTldfHw8pUuXpnbt2jrC9AhrLXv37iU+Pp46derk2+f19LWO\nuh/F+fnuu500azaX11/fAMBjjzVg5cob1CTkrBITE6lQoYKahIcYY6hQoUK+HwV6+ohCOYqcSUhI\n4rHHTg/ORUdH0KaNgnOSPTUJ7/HF35mnjygkeyeCc40azeadd7ZQvHghXn65KTEx16tJiPjQuHHj\nqFevHsYY9uzZk+V206ZNo379+tSvX59p06adfHzz5s20bduWevXqceedd5KUlAS47+lBgwZRr149\nmjdvztKlS33+tYAaRcjavv0YXbv+wh13LGDnzuO0a1eR5ctvYMiQRkpXi/jYVVddxbfffkutWrWy\n3Gbfvn0MHz6cRYsWsXjxYoYPH05CQgIAgwcP5pFHHmHTpk2UK1eOqVOnAvDNN9+wceNGNm7cyOTJ\nk+nbt69fvh79xAgxJ+4417jxbL788g9Kly7CxImtmT+/vdLV4kkvvvgil156KVdffTXdu3dn1KhR\nTJkyhTZt2tCiRQtuu+02jh49CsD9999P3759ufzyy6lbty7z58/ngQceoFGjRtx///0nP2epUqV4\n4oknaNKkCddffz2LFy+mffv21K1bl5kzZwKwZcsW2rVrR+vWrWndujW//PJLjmtu1aoVtWvXznab\nOXPm0LFjR8qXL0+5cuXo2LEjs2fPxlrLf//7X26//XYAevTowYwZMwD48ssvue+++zDGcPnll7N/\n/3527NhxHnszd3x6jsIY0xkYAxQGoq21r2Zafw8wGDDAIaCvtXa5L2sKZRs2HKJ371h++GE3ALfc\nUpUJE1pTvXrJAFcmXmfMpz75vNbeke36JUuW8Pnnn7N8+XKSk5Np3bo14eHhdOvWjcjISACGDh3K\n1KlTGThwIAAJCQksWLCAmTNn0qVLF37++Weio6Np06YNcXFxtGzZkiNHjnDdddcxcuRIunbtytCh\nQ5k3bx5r1qyhR48edOnShcqVKzNv3jzCwsLYuHEj3bt3JyYmhkOHDtGuXbuz1vvhhx/SuHHjHH3t\n27dvp0aNGieXq1evzvbt29m7dy9ly5alSJEipz2e3XOqVq2ao9fMLZ81CmNMYWA80BGIB5YYY2Za\na9dk2Gwz8BdrbYIx5kZgMtA2p6+hHIWTnJzG669v4PnnV58Mzr31VivuuEPBOfG2n3/+mVtvvZWw\nsDDCwsK45ZZbAFi1ahVDhw5l//79HD58mE6dOp18zi233IIxhmbNmlGlShWaNWsGQJMmTdiyZQst\nW7akWLE947GzAAAPdklEQVRidO7cGYBmzZpRvHhxihYtSrNmzdiyZQvgciQDBgwgLi6OwoULs2GD\nu1qwdOnSxMXF+XEvBJ4vjyguAzZZa38DMMZ8DNwKnGwU1tqMx3ILgeo+rCckxcYm0KtXDHFx+wEX\nnBs1qjkVKhQPcGUSSs71m7+/3X///cyYMYMWLVrw7rvvMn/+/JPrihd3//YLFSp08uMTyykpKQAU\nLVr05C9RGbfLuM3o0aOpUqUKy5cvJy0tjbCwMIB8O6KoVq3aaXXHx8fTvn17KlSowP79+0lJSaFI\nkSLEx8dTrVq1k8/Ztm3bac85sc6XfHmOohqwLcNyfPpjWekJfHO2FcaY3saYGGNMzO7du08+XpBz\nFEePpvDkkyu47LJviYvbT+3aJZk79xreeaeNmoSEjKuuuoqvvvqKxMREDh8+zNdffw24H9ZVq1Yl\nOTmZDz74wCevfeDAAapWrUqhQoV4//33SU1NBU4dUZztT06bBECnTp2YO3cuCQkJJCQkMHfuXDp1\n6oQxhmuvvZbPPvsMcFdG3XrrrQB06dKF9957D2stCxcupEyZMj5/2wmC5GS2MeZaXKMYfLb11trJ\n1toIa21EpUqVTj4+7waYV3+zn6oMHv/97y6aN5/LyJHrAXj00QasWtWJjh0VnJPQ0qZNG7p06ULz\n5s258cYbadasGWXKlOHFF1+kbdu2XHXVVTRs2NAnr92vXz+mTZtGixYtWLduHRdccEGOnzt27Fiq\nV69OfHw8zZs3p1evXgDExMSc/Lh8+fI8++yztGnThjZt2jBs2DDKl3eXrY8YMYI33niDevXqsXfv\nXnr27AnATTfdRN26dalXrx6RkZFMmDAhn7/qLFhrffIHuAKYk2H5aeDps2zXHPgVaJCTzxseHm6t\ntdZ+8on9W/p/BcW+fcftAw8stvCJhU9ss2Zz7KJFewNdloSoNWvWBLoEa621hw4dstZae+TIERse\nHm5jY2MDXFHwO9vfHRBjc/nz3JfnKJYA9Y0xdYDtwF3A3Rk3MMbUBL4A7rXWbvBhLZ5mreXzz7cz\nYMBSdu48TrFihRg2rDFPPHEpxYoFxUGhiM/07t2bNWvWkJiYSI8ePWjdunWgSypwfNYorLUpxpgB\nwBzc5bFvW2tXG2P6pK+PAoYBFYAJ6SeWUqy1Eb6qyYv++MPdcW7GDHfHuauvrsiUKeE0bHhhgCsT\n8Y8PP/ww0CUUeD7NUVhrZwGzMj0WleHjXkAvX9bgVWlplujozTzxxHIOHkyhdOkijBjRnAcf1B3n\nRMS/PD0UMFRzFBs3HiIyUsE5EQkOnm4UoSZzcK5SJRec+/vfFZwTkcDxdKOY0BeoE0u/EDikWLo0\ngZ49TwXnevSoxeuvt1AmQkQCztOXzIRCjuJUcO67k8G5OXPa8e67l6lJiHhUVmPCM+vcuTNly5bl\n5ptvztHzrcaMFzwZg3PWWh55pD6rVnXihhsuCnRpIpIHWY0Jz+yJJ57g/fffz/HzNWa8AElISKJX\nrxg6dPiBX389QtOmF7JgQQfeeKMlF1zg6XcDRfKd18aM22zGhGfWoUMHSpc+ffx/ds8PyTHjcqbP\nP49nwIBl/PlnIsWKFeLZZxvx5JMNFZyT4Na1q28+7/Tp2a724pjxypUrZzkmPCcK1JhxOd0ffxxj\nwIBlTJ/u/sKvuqoCU6ZE0KiRgnMiWfHimPHsbn3qVZ5uFF7IUVh7Iji3ggMHkilVqggjRjSjT59L\nFJwT7zjHb/7+Fsxjxhs1apTlmPCcKGhjxgu8jRsPcd11P9C7dywHDiTz179WZc2aTvTrV09NQiQH\nvDhmPLsx4TmhMeP5LFjvR5GcnMaIEeto3nwu8+fvplKl4nz0UVu++uoqatRQulokp7w6ZjyrMeEZ\nx4wDtGvXjjvuuIPvvvuO6tWrM2fOnGyfH6gx48ZNn/WOiIgIGxMTA59+Stc73LCw6QTPYfHSpe6O\nc8uWueDcfffV4o03FJwT71m7di2NGjUKdBkcPnyYUqVKcfToUa655homT56sCbLncLa/O2NMbG6H\nrnr6HEUwOXo0heHD1/D66xtITbXUrl2SSZPClYkQySONGQ88NYp88P33u4iMjOHXX49gDDz8cH1e\nfLEppUpp94rklcaMB55+kuXB/v1JPPHECqKj3RiRpk0vJDo6grZtKwS4MhGR/KNGkUtffBFP//6n\ngnNDhzZi8GAF50Qk9Hi6UQQiR7FjhwvOffGFgnMiUjB4ulH4k7WWqVM38/jjp4Jzr77ajL59FZwT\nkdDm6fdJ/JWj2LTpMB06/EBk5OnBuf79FZwTkTPldMz4tGnTqF+/PvXr12fatGknHx83bhz16tXD\nGHPaSBCNGc8FX9+PIiUljddeW0ezZnP4/vvdVKxYjA8/VHBORLKXkzHj+/btY/jw4SxatIjFixcz\nfPhwEhISAJdI//bbb6lVq9Zpz9GY8SCzbFkCl132HYMHryQxMY17763F2rWd6d69pm5LKuJHoTpm\nfM6cOXTs2JHy5ctTrlw5OnbsyOzZswFo1aoVtWvXPuM5GjMeJI4dS2X48NWMGuWCc7VqlSQqKpzO\nnRWck4KtK2eOGu9IR/rRL1frczJRIZTHjGc1Mjw7GjMeBObP30VkZCybNh3GGHjoofq89JKCcyKB\nojHjwUE/ATkzONekiQvOXX65gnMiJ5zrCCCv689HKIwZr1at2ml1x8fH0759+2y/bo0Zz4XpXWH6\nB7fn7XNM307jxnOIjt5MsWKFeOGFJixd2lFNQiQIhPKY8U6dOjF37lwSEhJISEhg7ty5px0ZnY3G\njPvZjh3HuO22X+jW7Rd27EjkyisrsGxZR559trHS1SJBIpTHjJcvX55nn32WNm3a0KZNG4YNG0b5\n8uUBGDt2LNWrVyc+Pp7mzZuffI7GjOdQxjHjE8p8CHXq0K/+Gzl+vrWWt9/ewuOPL2f/fgXnRLKi\nMePepTHjGcy7AWBz+jUV57Zp02F6947h++93A3DTTRcxcWI4NWsqEyESrDRmPPA83ShyKiUljdGj\nNzJs2CoSE9OoWLEYY8e24q67aigTIRLkNGY88EK+UcTF7adnzyUsXeruOPePf9Rk9OiWVKyoO86J\niOREyDaKY8dSeeGFNYwcuZ7UVEvNmu6OcwrOieSctVZH3R7ji/POIdkofvhhN5GRMWzc6IJzgwbV\n4+WXmyk4J3IewsLC2Lt3LxUqVFCz8AhrLXv37j2Z+cgvnv7Jmfl+FPv3JzF48EomT/4NUHBOJC9O\nXJ65e/fuQJci5yEsLIzq1avn6+f0aaMwxnQGxgCFgWhr7auZ1pv09TcBR4H7rbU5m5t7zTXQoAFU\nrgzAjBnb6ddvKTt2JFK0qOGZZxrx9NONlIkQyaWiRYtSp06dQJchQcBnjcIYUxgYD3QE4oElxpiZ\n1to1GTa7Eaif/qctMDH9/+dWpQpUqcKffyYy8I4FfPZZPABXXFGB6OgIGjfWHedERPKDL3/dvgzY\nZK39zVqbBHwMZM6x3wq8Z52FQFljTI7y6C44t5lGjWbz2WfxXHBBYcaObclPP12rJiEiko98+dZT\nNWBbhuV4zjxaONs21YBzDljv23cpkya5cxE33ngRUVEKzomI+IInTmYbY3oDvdMXDxtj1mdYXfGb\nb9iT6UZQBVFFIPTmG58/7QdH++EU7Qvn0tw+0ZeNYjtQI8Ny9fTHzncbrLWTgclnexFjTExu55eE\nEu0HR/vB0X44RfvCMcbE5Pa5vjxHsQSob4ypY4wpBtwFzMy0zUzgPuNcDhyw1vr+vn4iIpJjPjui\nsNamGGMGAHNwl8e+ba1dbYzpk74+CpiFuzR2E+7y2H/6qh4REckdn56jsNbOwjWDjI9FZfjYAv3z\n+DJnfUuqANJ+cLQfHO2HU7QvnFzvB8/dj0JERPxLsWUREcmWJxqFMaazMWa9MWaTMeaps6w3xpix\n6etXGGNC8s4mOdgP96R//SuNMb8YY1oEok5/ONe+yLBdG2NMijEmbzdXD1I52Q/GmPbGmDhjzGpj\nzA/+rtEfcvC9UcYY85UxZnn6fgjJ86HGmLeNMbuMMauyWJ+7n5XW2qD+gzsR/itQFygGLAcaZ9rm\nJuAbwACXA4sCXXeA9sOVQLn0j28Mxf2Q032RYbv/4s6T3R7ougP0b6IssAaomb5cOdB1B2g/DAFG\npH9cCdgHFAt07T7YF9cArYFVWazP1c9KLxxR+HQUiIeccz9Ya3+x1iakLy7E5VJCUU7+TQAMBD4H\ndvmzOD/KyX64G/jCWvs7gLU2FPdFTvaDBUqnDyIthWsUKf4t0/estT/ivras5OpnpRcaRVZjPs53\nG68736+xJ+43h1B0zn1hjKkGdMUNmgxVOfk30QAoZ4yZb4yJNcbc57fq/Ccn+2Ec0Aj4A1gJPGSt\nTfNPeUElVz8rPTHCQ86PMeZaXKO4OtC1BNCbwGBrbVoBv+lOESAc6ACUABYYYxZaazcEtiy/6wTE\nAdcBlwDzjDE/WWsPBrYsb/BCo8i3USAel6Ov0RjTHIgGbrTW7vVTbf6Wk30RAXyc3iQqAjcZY1Ks\ntTP8U6Jf5GQ/xAN7rbVHgCPGmB+BFkAoNYqc7Id/Aq9a90b9JmPMZqAhsNg/JQaNXP2s9MJbTxoF\n4pxzPxhjagJfAPeG+G+M59wX1to61tra1trawGdAvxBrEpCz740vgauNMUWMMSVxE5zX+rlOX8vJ\nfvgdd1SFMaYKbkDeb36tMjjk6mdl0B9RWI0CAXK8H4YBFYAJ6b9Jp9gQHIaWw30R8nKyH6y1a40x\ns4EVQBruTpNnvXTSq3L47+FF4F1jzErcFT+DrbUhN1HWGPMR0B6oaIyJB54DikLeflYqmS0iItny\nwltPIiISQGoUIiKSLTUKERHJlhqFiIhkS41CRESypUYhkkPGmNT0Kawn/tROn8x6IH15rTHmufRt\nMz6+zhgzKtD1i+RW0OcoRILIMWtty4wPGGNqAz9Za282xlwAxBljvkpffeLxEsAyY8x0a+3P/i1Z\nJO90RCGST9LHZMQC9TI9fgw3ZyjUBlVKAaFGIZJzJTK87TQ980pjTAXcjP/VmR4vB9QHfvRPmSL5\nS289ieTcGW89pWtnjFmGG5Hxavr4iPbpjy/HNYk3rbV/+rFWkXyjRiGSdz9Za2/O6nFjTB1goTHm\nE2ttnL+LE8krvfUk4mPW2s3Aq8DgQNcikhtqFCL+EQVck36VlIinaHqsiIhkS0cUIiKSLTUKERHJ\nlhqFiIhkS41CRESypUYhIiLZUqMQEZFsqVGIiEi21ChERCRb/w9Geb+rm/4voAAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115ee2128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = digits.target == 9\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    digits.data, y, random_state=0)\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "for gamma in [1, 0.1, 0.01]:\n",
    "    svc = SVC(gamma=gamma).fit(X_train, y_train)\n",
    "    accuracy = svc.score(X_test, y_test)\n",
    "    auc = roc_auc_score(y_test, svc.decision_function(X_test))\n",
    "    fpr, tpr, _ = roc_curve(y_test , svc.decision_function(X_test))\n",
    "    print(\"gamma = {:.2f}  accuracy = {:.2f}  AUC = {:.2f}\".format(\n",
    "        gamma, accuracy, auc))\n",
    "    plt.plot(fpr, tpr, label=\"gamma={:.3f}\".format(gamma))\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR\")\n",
    "plt.xlim(-0.01, 1)\n",
    "plt.ylim(0, 1.02)\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3.3 다중 분류의 평가 지표"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.953\n",
      "Confusion matrix:\n",
      "[[37  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 39  0  0  0  0  2  0  2  0]\n",
      " [ 0  0 41  3  0  0  0  0  0  0]\n",
      " [ 0  0  1 43  0  0  0  0  0  1]\n",
      " [ 0  0  0  0 38  0  0  0  0  0]\n",
      " [ 0  1  0  0  0 47  0  0  0  0]\n",
      " [ 0  0  0  0  0  0 52  0  0  0]\n",
      " [ 0  1  0  1  1  0  0 45  0  0]\n",
      " [ 0  3  1  0  0  0  0  0 43  1]\n",
      " [ 0  0  0  1  0  1  0  0  1 44]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    digits.data, digits.target, random_state=0)\n",
    "lr = LogisticRegression().fit(X_train, y_train)\n",
    "pred = lr.predict(X_test)\n",
    "print(\"Accuracy: {:.3f}\".format(accuracy_score(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n{}\".format(confusion_matrix(y_test, pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/pdf": 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9O5qx6RqbOOx+70bWyoyuaCF+yFcn6yVg3FMmRRJkTrZYbovVnu6hKKZzhnMZ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\nIDQ2IDAgUiA+PgpzdGFydHhyZWYKMTM2NjIKJSVFT0YK\n",
      "image/png": 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WW2/psmXLtGXLlnG1b9WqVanTuHHjNCcnR9955x1t1aqVXnDBBUeW3XffffrSSy8Vu56r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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115d99f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores_image = mglearn.tools.heatmap(\n",
    "    confusion_matrix(y_test, pred), xlabel='Predicted label',\n",
    "    ylabel='True label', xticklabels=digits.target_names,\n",
    "    yticklabels=digits.target_names, cmap=plt.cm.gray_r, fmt=\"%d\")\n",
    "plt.title(\"Confusion matrix\")\n",
    "plt.gca().invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00        37\n",
      "          1       0.89      0.91      0.90        43\n",
      "          2       0.95      0.93      0.94        44\n",
      "          3       0.90      0.96      0.92        45\n",
      "          4       0.97      1.00      0.99        38\n",
      "          5       0.98      0.98      0.98        48\n",
      "          6       0.96      1.00      0.98        52\n",
      "          7       1.00      0.94      0.97        48\n",
      "          8       0.93      0.90      0.91        48\n",
      "          9       0.96      0.94      0.95        47\n",
      "\n",
      "avg / total       0.95      0.95      0.95       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Micro average f1 score: 0.953\n",
      "Macro average f1 score: 0.954\n"
     ]
    }
   ],
   "source": [
    "print(\"Micro average f1 score: {:.3f}\".format(\n",
    "    f1_score(y_test, pred, average=\"micro\")))\n",
    "print(\"Macro average f1 score: {:.3f}\".format(\n",
    "    f1_score(y_test, pred, average=\"macro\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3.4 회귀의 평가 지표\n",
    "### 5.3.5 모델 선택에서 평가 지표 사용하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Default scoring: [0.9 0.9 0.9]\n",
      "Explicit accuracy scoring: [0.9 0.9 0.9]\n",
      "AUC scoring: [0.994 0.99  0.996]\n"
     ]
    }
   ],
   "source": [
    "print(\"Default scoring: {}\".format(\n",
    "    cross_val_score(SVC(), digits.data, digits.target == 9)))\n",
    "explicit_accuracy =  cross_val_score(SVC(), digits.data, digits.target == 9,\n",
    "                                     scoring=\"accuracy\")\n",
    "print(\"Explicit accuracy scoring: {}\".format(explicit_accuracy))\n",
    "roc_auc =  cross_val_score(SVC(), digits.data, digits.target == 9,\n",
    "                           scoring=\"roc_auc\")\n",
    "print(\"AUC scoring: {}\".format(roc_auc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid-Search with accuracy\n",
      "Best parameters: {'gamma': 0.0001}\n",
      "Best cross-validation score (accuracy)): 0.970\n",
      "Test set AUC: 0.992\n",
      "Test set accuracy: 0.973\n",
      "\n",
      "Grid-Search with AUC\n",
      "Best parameters: {'gamma': 0.01}\n",
      "Best cross-validation score (AUC): 0.997\n",
      "Test set AUC: 1.000\n",
      "Test set accuracy: 1.000\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    digits.data, digits.target == 9, random_state=0)\n",
    "\n",
    "param_grid = {'gamma': [0.0001, 0.01, 0.1, 1, 10]}\n",
    "grid = GridSearchCV(SVC(), param_grid=param_grid)\n",
    "grid.fit(X_train, y_train)\n",
    "print(\"Grid-Search with accuracy\")\n",
    "print(\"Best parameters:\", grid.best_params_)\n",
    "print(\"Best cross-validation score (accuracy)): {:.3f}\".format(grid.best_score_))\n",
    "print(\"Test set AUC: {:.3f}\".format(\n",
    "    roc_auc_score(y_test, grid.decision_function(X_test))))\n",
    "print(\"Test set accuracy: {:.3f}\".format(grid.score(X_test, y_test)))\n",
    "\n",
    "grid = GridSearchCV(SVC(), param_grid=param_grid, scoring=\"roc_auc\")\n",
    "grid.fit(X_train, y_train)\n",
    "print(\"\\nGrid-Search with AUC\")\n",
    "print(\"Best parameters:\", grid.best_params_)\n",
    "print(\"Best cross-validation score (AUC): {:.3f}\".format(grid.best_score_))\n",
    "print(\"Test set AUC: {:.3f}\".format(\n",
    "    roc_auc_score(y_test, grid.decision_function(X_test))))\n",
    "print(\"Test set accuracy: {:.3f}\".format(grid.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "가능한 평가 방식:\n",
      "['accuracy', 'adjusted_rand_score', 'average_precision', 'f1', 'f1_macro', 'f1_micro', 'f1_samples', 'f1_weighted', 'log_loss', 'mean_absolute_error', 'mean_squared_error', 'median_absolute_error', 'neg_log_loss', 'neg_mean_absolute_error', 'neg_mean_squared_error', 'neg_median_absolute_error', 'precision', 'precision_macro', 'precision_micro', 'precision_samples', 'precision_weighted', 'r2', 'recall', 'recall_macro', 'recall_micro', 'recall_samples', 'recall_weighted', 'roc_auc']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics.scorer import SCORERS\n",
    "print(\"가능한 평가 방식:\\n{}\".format(sorted(SCORERS.keys())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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