03_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3장 – 분류**\n",
    "\n",
    "_이 노트북은 3장의 모든 샘플 코드와 연습 문제 정답을 담고 있습니다._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/handson-ml2/blob/master/03_classification.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩에서 실행하기</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 설정"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "먼저 몇 개의 모듈을 임포트합니다. 맷플롯립 그래프를 인라인으로 출력하도록 만들고 그림을 저장하는 함수를 준비합니다. 또한 파이썬 버전이 3.5 이상인지 확인합니다(파이썬 2.x에서도 동작하지만 곧 지원이 중단되므로 파이썬 3을 사용하는 것이 좋습니다). 사이킷런 버전이 0.20 이상인지도 확인합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 파이썬 ≥3.5 필수\n",
    "import sys\n",
    "assert sys.version_info >= (3, 5)\n",
    "\n",
    "# 사이킷런 ≥0.20 필수\n",
    "import sklearn\n",
    "assert sklearn.__version__ >= \"0.20\"\n",
    "\n",
    "# 공통 모듈 임포트\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# 노트북 실행 결과를 동일하게 유지하기 위해\n",
    "np.random.seed(42)\n",
    "\n",
    "# 깔끔한 그래프 출력을 위해\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "# 그림을 저장할 위치\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"classification\"\n",
    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
    "    print(\"그림 저장:\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'details', 'categories', 'url'])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "mnist = fetch_openml('mnist_784', version=1)\n",
    "mnist.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000, 784)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y = mnist[\"data\"], mnist[\"target\"]\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000,)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "784"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "28 * 28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "그림 저장: some_digit_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "some_digit = X[0]\n",
    "some_digit_image = some_digit.reshape(28, 28)\n",
    "plt.imshow(some_digit_image, cmap=mpl.cm.binary)\n",
    "plt.axis(\"off\")\n",
    "\n",
    "save_fig(\"some_digit_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y.astype(np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_digit(data):\n",
    "    image = data.reshape(28, 28)\n",
    "    plt.imshow(image, cmap = mpl.cm.binary,\n",
    "               interpolation=\"nearest\")\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 숫자 그림을 위한 추가 함수\n",
    "def plot_digits(instances, images_per_row=10, **options):\n",
    "    size = 28\n",
    "    images_per_row = min(len(instances), images_per_row)\n",
    "    images = [instance.reshape(size,size) for instance in instances]\n",
    "    n_rows = (len(instances) - 1) // images_per_row + 1\n",
    "    row_images = []\n",
    "    n_empty = n_rows * images_per_row - len(instances)\n",
    "    images.append(np.zeros((size, size * n_empty)))\n",
    "    for row in range(n_rows):\n",
    "        rimages = images[row * images_per_row : (row + 1) * images_per_row]\n",
    "        row_images.append(np.concatenate(rimages, axis=1))\n",
    "    image = np.concatenate(row_images, axis=0)\n",
    "    plt.imshow(image, cmap = mpl.cm.binary, **options)\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "그림 저장: more_digits_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,9))\n",
    "example_images = X[:100]\n",
    "plot_digits(example_images, images_per_row=10)\n",
    "save_fig(\"more_digits_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 이진 분류기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_5 = (y_train == 5)\n",
    "y_test_5 = (y_test == 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**노트**: `max_iter`와 `tol` 같은 일부 매개변수는 사이킷런 다음 버전에서 기본값이 바뀝니다. 버전이 업데이트되더라도 결과가 바뀌지 않도록 아예 나중에 바뀔 기본값을 사용해 명시적으로 지정하겠습니다. 번거로움을 피하기 위해 책에는 따로 표시하지 않았습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n",
       "              early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
       "              l1_ratio=0.15, learning_rate='optimal', loss='hinge',\n",
       "              max_iter=1000, n_iter_no_change=5, n_jobs=None, penalty='l2',\n",
       "              power_t=0.5, random_state=42, shuffle=True, tol=0.001,\n",
       "              validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_clf = SGDClassifier(max_iter=1000, tol=1e-3, random_state=42)\n",
    "sgd_clf.fit(X_train, y_train_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.95035, 0.96035, 0.9604 ])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "cross_val_score(sgd_clf, X_train, y_train_5, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.95035\n",
      "0.96035\n",
      "0.9604\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.base import clone\n",
    "\n",
    "skfolds = StratifiedKFold(n_splits=3, random_state=42)\n",
    "\n",
    "for train_index, test_index in skfolds.split(X_train, y_train_5):\n",
    "    clone_clf = clone(sgd_clf)\n",
    "    X_train_folds = X_train[train_index]\n",
    "    y_train_folds = y_train_5[train_index]\n",
    "    X_test_fold = X_train[test_index]\n",
    "    y_test_fold = y_train_5[test_index]\n",
    "\n",
    "    clone_clf.fit(X_train_folds, y_train_folds)\n",
    "    y_pred = clone_clf.predict(X_test_fold)\n",
    "    n_correct = sum(y_pred == y_test_fold)\n",
    "    print(n_correct / len(y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator\n",
    "class Never5Classifier(BaseEstimator):\n",
    "    def fit(self, X, y=None):\n",
    "        pass\n",
    "    def predict(self, X):\n",
    "        return np.zeros((len(X), 1), dtype=bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.91125, 0.90855, 0.90915])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "never_5_clf = Never5Classifier()\n",
    "cross_val_score(never_5_clf, X_train, y_train_5, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "\n",
    "y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[53892,   687],\n",
       "       [ 1891,  3530]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[54579,     0],\n",
       "       [    0,  5421]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_perfect_predictions = y_train_5  # pretend we reached perfection\n",
    "confusion_matrix(y_train_5, y_train_perfect_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8370879772350012"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score\n",
    "\n",
    "precision_score(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7290850836596654"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4096 / (4096 + 1522)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6511713705958311"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7555801512636044"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4096 / (4096 + 1325)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7325171197343846"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "\n",
    "f1_score(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7420962043663375"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4096 / (4096 + (1522 + 1325) / 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2164.22030239])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_scores = sgd_clf.decision_function([some_digit])\n",
    "y_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = 0\n",
    "y_some_digit_pred = (y_scores > threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_some_digit_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = 8000\n",
    "y_some_digit_pred = (y_scores > threshold)\n",
    "y_some_digit_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3,\n",
    "                             method=\"decision_function\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precisions, recalls, thresholds = precision_recall_curve(y_train_5, y_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "그림 저장: precision_recall_vs_threshold_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAEYCAYAAABRMYxdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzdeZyN1R/A8c+ZfcaMsU/WsUcpZMiSKFmyJr9SzWSJSCWkErJGqWQpqVTILkSoJGtZS1kKWbITg2EWs8+c3x/Hnc2M2e6dOzP3+369zus+57nP8r3zzPKd5zmL0lojhBBCCFGYONk7ACGEEEIIa5MERwghhBCFjiQ4QgghhCh0JMERQgghRKEjCY4QQgghCh1JcIQQQghR6EiCI4QQQohCJ0sJjlLqZaXUHqVUjFJqbibbDlFKXVRKhSqlZiul3K0SqRBCCCFEFmX1Ds4FYAIw+3YbKaXaAm8CrYDKQFVgXC7iE0IIIYTItiwlOFrrb7XWq4CrmWzaE/hKa31Qa30NeBvolbsQhRBCCCGyx8XKx7sb+C5FfT/gp5QqqbVOlRwppfoB/QDwpAHFrBxJfqNAoZKqzsoZhcJJOSWtT3o/zbaWZSfldMu69PZL79hpt0t1XJxQKjkGy74ZxZTesQuqxMREnJykKVp+JteoYJDrZDuJiYqQEDciIlyIjU3/a1y1agQuLmbqpfPnPblxI/0/715ecVSoEA1AfLzixAnvDM9bvnwURYrEA3D1qjtXr7qlu52zs6ZatYik+r//epOQkP7fh5IlYyhZMhaAiAgXLlzwzPD8GX0mH584ypaNTtru6NGjV7TWpdPub+0ExxsITVG3LPuQ5u6P1noWMAugaJWi+v7x96O1RqMt76PRWObKsiyn93529olLjCMmPibVe7fb73bnzOi9uMQ4ouKi0GgSdWLyZyZ53q944nPzdc43XJ1c8Xbzxs3ZDWcnZ5yUE87KGWcn56TXou5FcXN2w83ZDXdnd7xcvZLed1JOSfs5YZZdnFwo6l4Ud2d33Jzd8HH3wc3ZDVcnV1ycXHBxcsHbzRtXZ9dbzmV5z8vVC08XT8r5lEtK3tKzZcsWWrZsmXdfMJFtco0KBrlOtpWQAIsXQ2Rk+u8HBYGXl1letw7OnEl/u+vX9/PGG3UBuHEDFi7M+Jxt24K/v1neswf+/DP97by8zPkt5s+HqKj0t23QwBSAU6dg/fqMz5/RZ6pWDVq1St5OKXU6vf2tneBEAEVT1C3L4bfbqWbJmvz87M9WDsW+LD/sWpskJ1EnkqATSNSJxMTHEBYTxo24G4TFhCW9f7sSFRdFbEJsquOkLQmJCcQkxBAeE05oTCiRcZGZHjc+MZ4bcTeIS4gjLjGO+MR4IuMiiYqLSnWuhMSEpOW4hDgiYiOISYghLjGOa9HX7P3lzpBCUaZIGUp5lUpKsop5FMPH3YeKRSsSHRzNwd8O4uLkgquzK54unvh5+1HUvSilvUpTyqsURdyK2PtjCCEcnLNz6iTidtq1y/i9LVuSf18XKQL9+mXtmAEBpmTFs89mbbvKlbN+/tt9poxYO8E5CNQFvrlZrwtcSvt4ypEoZR4ZOeOMK64AeLl6UdyzuJ0jyz2tNbEJsYTGhJKQmJCUEKVMhmLiY4iMiyQ2IZa4RJMYRcdHp0rK0iaAUXFRRMWbhC4qLoqwmDDiE+NN0fHExMcQERuR6lwJiQnEJ8YTlxhHeEw4MQkxXI28SmhMKJduXOLSjUsZf5ATt/+cHi4e+BXxo0yRMvi4+1DCswTlfcpTyqsUxT2KU9yzOJ4unpTyKkW1EtUo51POul9oIYRD27sXrl2De+6B0rc8iBEZyVKCo5RyubmtM+CslPIA4rXWaZ+zzAPmKqUWAv8BbwFzrReuyE+UUri7uFPGpYy9Q8lQTHwMZ8POEh0fnZQwXY++ztWoq5wJPcPeo3vxK+uXlBxdvnGZsJgwQmNCCYkK4VLEJaLjozkdeprToeneBb1FSc+SlPMpR+Vilbmr9F34FfHDy9WLMkXKULV4VWqVqoW7i4yeIITImunT4euv4YsvoG9fe0dTcGT1Ds5bwJgU9SBgnFJqNnAIuEtrfUZrvU4p9T6wGfAEVqTZT4g85e7iTvUS1TN8f0vi7dsNaK2JiI3g0o1LBN8IJiwmjAvhFwiJCuFq5FWuRF4hPDacyLhI/ov4j78u/cXVqKtcjbrKX8F/sebomluOqVD4evhSuVhlHq/1OI/VeozqJarj6ZpxYzshhOM6e9a8Vqpk3zgKmiwlOFrrscDYDN5O1QRbaz0FmJKrqITIJ5RS+Lj74OPuc9tEySIuIY4rkVc4H36ef0P+5fCVw1yPvk5YTBiXIy9z+PJhTl4/yfXo6+y7uI99F/cxestonJQTNUvWpH319jzf4HlqlaqVB59OOKLwcNP2wtLh6eJF2LQJOncG75u/zU+fNg1Q77rLNARt0wYaNoROnaBjx+TtRN6wNK6VBCd7rN0GRwiH5ursSlmfspT1KUtAufRb5MUlxBESFcIPx35g/Yn1bDq5ieAbwfxz5R/+ufIPU3ZNwd/Xn0blG9Guejs639mZUl6l8viTiPxg9274+WfTg+bff03vFCcnePhhWLYMihUz65cuhcaNQWtQCnx9TRJTq5bpzfLEE9CkCbz1ljlu2bLmcUeHDrBqFQwYAC4uUK+e6S0D5ng7d5r6sWOmLFoEbm5Qpw40amR62Tz6KLi7Q1SUs/2+UIVYYmLyHZyKFe0bS0EjCY4QeczV2RU/bz961+9N7/q9AQiNDmXbmW0s/nsx3x35LqnNz7JDywDTrqeSbyXaVW9H11pdaVi+oT0/grCyGzfgpZdMO4vSpWHrVqhdG06ehFGjUm+bmAh//22SGDCJynvv3XrMDh1g7Vpz3O+/N8Xiv//MPlpDuXLQtCns2pWc3ICpA3TpYhq5bt4MK1bA9u2mu/Cff8Ls2XD1qklwRo6sw8yZ0L27udNjiU/kzuXLEBMDJUqYpFVknSQ4QuQDvh6+dKjZgQ41O5CQmMCeC3vYfnY7a4+uZfvZ7UntevZe3Mu7296lRokadKjRgb739eXuMnfbO3yRC59/Di+8kFy/fBn69DFJjlLQogVcugT//GMeGb39NlSoYN4Dkwi5u5s/gikdOmTu/EyZAn/9ZRKRuDgoVQo++QTOnzd3gry8zOOpsDCTtGzebMZaGT7cHMfV1dzZqVcPhgyB0FCz3bZtpmePtzdcvw6HDxdl716TVIFJjDp2NF2G3aVNfY7J46mcU5bB7ewpICBA70n5r0MhIINeFQwF4TolJCZwJfIKO87uYMXhFXx35DsiYpNHDX3y7icZ22IstUvXtmOUtlMQrlFOBQeDn1/qdWXLQu/eMGFCchKTHZZf6TnZNze+/XYH5841ZepU027HonRpOHhQujfn1LffQrdu5q7Y6tW5P15h/HlSSv2htb6lTYDcwREin3N2csbP24+utbvStXZXYhNi2Xl2J3P3z2XhgYV8c/Abvjn4DW2qteGJu56g+93d8XH3sXfY4iatzR+mxESoW9eMZRIZae6mpBztdft286got/I6sbEoUSKWxx+HV16Bc+fM46w5c+DoUfN4xWL7djNgnLu7eVRWtKg8ermdrl3NXb3o6My3FanJxCFCFDBuzm60qNyCOV3msO+FfXSq2Qk3ZzfW/7ue59c8T7WPqjHjtxlExmUwprvIU+XLw2OPweOPmyHmLUPtDxtmhsGfPds8LrJGcpNfVKgAgwaZtjt79phReME8amvVCqpWhUmTTPsfb2/TeFmkTynzWLFCBXtHUvBIgiNEAXZX6btY/fRqzg05x8z2M6lTpg6XIy8z8MeBVJ5WmTl75xCfWDjmPcuPtDZtW8LC4Phx+Phj8wdJKThyxGzz/PPp7ztihHnt3dv8oS+MlDLthiwuXIDq1c2rpY0PSO8gYRuS4AhRCJQuUpoBDQdw4IUDLOm2hLp+dbkceZnnVj9H8feK03NVT45dPWbvMAuFvXtNd20wCU6TJqbHUI0a5vGMxeOPm8dQgYGwb5/ZNjHRvGoNJUvaJ357ql8fDhyAadOS5zUqXhyaN0/e5tgx83USRlCQaax9IpMpZcStJMERohBRStG9Tnf29t/LnC5z8Pf1JyI2gnn751H7k9qM3TJW7ujkwKZNyXdm7rvP3H347TfzyKV9+1u39/Y2611doWZN0/YG7Nc+Jj9xcjKPr37/3dzJuXRzmjit4f33Ta+wBg3M7NECNm40XfxdXe0dScEjCY4QhZBSil71enFy0En+6PcHD1d5mASdwLit4yj3YTnm7ptLopZ/kzNz+rRJSlq1Sr3+/Hn44APT42nqVDM6cFxc8t2Z8HDzvri9smVT/+H28jK9yvbtMwMIfvSR/WLLD2JizEjTzs7mayWyRxIcIQoxpRT3lb2PjT02MrfLXCr5VuJy5GV6f9ebez69h4PBB+0dYr6htRm5t0wZk9SsWmXahtSrl7xN/fqwZo3ZdpkZgxGlzB0bF+mTmitKwcsvm0EM+/Uz61591Yzh46jOnzev5cvL91dOSIIjhIPoWa8nJwedZFbHWRRxLcKhy4doNrsZCw8stHdodqM1PPWUaUPj5GR6Ml2+bN7r2tWsGzfOrNPaDHDXsaN9Yy7sihc3gx926WIGKhw1yrw6Issgf9IIO2ckwRHCgTgpJ55v8DznXj1HqyqtCI0JJWhlEP/75n+cuOYYrRi1hh9/NMuRkWaen7Cw1NvccYdpYwNmlN9SMhVYnrPMm/XTT5LgyCjGOSMJjhAOqJhHMX5+9mfGtBiDi5MLKw6v4M4Zd/L1vq/tHZr1LVwIlSujnZyIKFWZQKeFBAWZt4oUgV69zB2cL780CY/WZgC6hjLdl10FBJjeaoGBZoJPRyQJTu5IgiOEg1JKMbblWI68fITHaj1GfGI8vb/rzaRtk+wdmvUsXGgadJw+jdIa76un+YJ+tA1ZyMaNZpPnnzdzKfXpA56e9g1XpBYUZBJPC8vjQ0dxzz3Qo4cZikBkX/5IcI4cgblzzXJcHLRsCQsWmHpkpKkvXWrqoaGm/u23pn7liqmvWWPqFy+auqWP4dmzpr5hg6mfOGHqW7cmn7tlS9ixw9T//tvUf//d1PftM/V9+0z9999N/e+/TX3HDlO3jOq1dSu0bInHhQumvmGDed8y3/26daZ+8aKpr1lj6leumPq335p6aKipL11q6pbhTxcsMPW4OFOfO9fULb74Ah55JLk+c6bpjmAxfbq5524xebKZ6MRi0iTTKMHi7bdJ+ncXYPRoMzKZxfDhyS0CAV57zUxfbDF4sCkWL71ktrHo1y/1iF+9e5tzWAQFmRgsnnrKxGjRrZv5DBadO5vPaPHoo+ZrYPHII+ZrdFO9wYML3fde0oAZWfzeq5pQlJXdV7JAP86mOZp3vx/OyI0jC8f33oAByfHfVIRI5pUfeUvPKJF/aW16pdWq5VhTFnTpYmaY79LF3pEUTPkjwRFC2F3gvYHcWaomAO9se4ctp7bYNyAr0OHh6a53uXAmjyMRuaGUyadDQpLbRgmRKa213UuDBg10YbN582Z7hyCyQK7Trd799V3NWDRj0dN3TdeJiYl2jSe71ygyUus33jAj0sRX8LcMTZO6+PvbIlSHZuufpddeM5euRg2to6Jseqp8ITFR6w0btD5yxCxbS2H8nQfs0enkFnIHRwiRypsPvMkbTd8AYNC6QbRd0JYbsTfsHFXWTJtmBot7/31T39FxolmRkpcXTJyY98GJXLE82T52DH7+2b6x5IXQUPPEt0EDe0dScEmCI4S4xaRHJjGlzRTcnd35+cTPtJrXiuvR1+0d1m09/TQMGZJ6XVjHQJg1y/T7VspM3z1rlumaIwoUPz944QWz3KMH/PGHfeOxtZRj4MgUHzkjCY4Q4hZKKYY0GcL257ZT1L0ou8/vpv3C9kTERtg7tHQNGwZLliTXL1wwz6I6dMAkM3feCQ8+CKdOSXJTgL3zjulZdP06PPusuca2cP267Y5tceKEaXNv6XOQlnQRzz1JcIQQGWpQrgHbn9tOaa/S7Dy3k+7Lu5OQmP9GXUt55yYxMZ15e2bMMEUUaMWLm4lP+/SBCRNsd2fjmWfM91CPHsmdEK1t5UrTkfGxx+D48Vvft5xXEpyckwRHCHFbdcrU4Zfev+Dt5s0Px35g1OZR9g4JgKNHzR+448fNE6j4ePNfd7p/9OrUMUUUeKVKmbFxHn/c1GNjzbJl9IbMxMTcui4yEipUMDPFaw3//mtmOZ8/Hz77zHqxpxQVZV6vXzfTgkSkuTkq0zTkniQ4QohM1SpVi6X/M+MBvbvtXcZsHmPXeM6eNU+dAGrUMK/OzrfZYceO5PGGRKHy44/mbki3bma8mO3bb91m7VoziGPdulCtGmzenPr9sDAzseX58yZB/uef5OG3Zs82zbYswz+lpbXZ5qefshd3ykSrShVz5zEleUSVe5LgCCGypH2N9nz86McAjP9lPIv/WmyXOJ58MvUv/ZRtbzI0YoQpotBp0cIMAujmBqtXwwMPwNChMGiQKVqbu3vR0XDggEliUo4lCmAZLsnHx7wqZR5P1a1rxsXs3x/uuit5TM2UVq82j8yCgswUH1llGbDw1VfNzPVFi6Z+XxKc3JMJ2IUQWfZyo5cJiwlj5KaR9F3Tl7tK30XdO+rmybkTEsAlzW+s5ctTD4acoc8/t0lMwv6KFTNdyAMD4aOPTLIzZUry+82bJ3/ftGwJO3fCtm3mLk2tWma9ZbJVS4IDJrH4809YtszMaF6x4q1tu7ZvTx4Y/coVM5B2Vh+VPfaYOWazZmbWejB3ic6dM3d0vvvOJDnVq2fryyFSkDs4QohsGf7AcNrXaE9kXCTPfPsMYTFhme9kBb/8krzcpYu5pZ+l5AbM8yzLMy1RKJUtC+++mzyzisWqVck9onx9oWdPszxoUPJjobR3cCycnKB7dzNbyg8/3HqX5cIF+P57uP9+s+/KlbeePyPNmsErrySPc3Pxorn71KqVGQOnRAmoVw+8vbN2PHErSXCEENmilGLp/5ZSo0QNDl0+RJcltp8oJzHR5Cd3322muFq1Kps9aLZuzbg/rihUHn7Y3B2xWLjQ3A0B8PCAceNMb6z16+HDD816S4KTNoGx8PICd3ezHB9v7uxAcuJUsWLyFHlPPWUeox46lL24ixc3d3BOnoQXX7R9N3VHIAmOECLbvN28Wf30arzdvNlyagtL/s5KQ5jsu3IFHnqoJatWmf/Q//7bzJ+ZbWPGmCIKveLFzZ2UxMTk0Y+//tq8enmZHndffWXqU6aYtjDpPaJKT2iomdm7QwdTtyQhSplBCN980yRCy5bBvHm3P9bGjSau06dN3d0dFi82MS5aZO4evfde9j67SE0SHCFEjtQqVYt3Hn4HgB4re7Dqn1VWPX5sLJQubZa7dbu1l0m2zJ5tinAYSpkE4cknk+/2WWbt6NjRJDo1apjed3XrwsiR0Lbt7Y9ZtKgZnuDiRdONPGWC4+RkHpH9+6959PT668n7bduWnERZfPIJ9OoFe/Ykr7vzTvj44+R6VtvziPRJgiOEyLGXG73MK41eIS4xjmdWPMOp66esduw330xeXrw4k27gmala1RThUJycTC+733+H6dOTx85xdYV160x7GVdXM0TShAkm4bgdpUy7GDAJSsoEx6J8eXOukiVNPSbGNIB+7rnUj50svagsj74sevc2bXrAzEUlck4SHCFEjimlmNZuGq2qtCIqPoouS7pYZaTjl1+GqVPNco8ep3jqqVwecMMGU4TDUco05H3lFdM+x6Ju3XRGvM6CsWPNMSdMSB5P53btwYKDzWB+K1ak7sxnGQcnbYKjlDnuvHnJj9hEzkiCI4TIFaUU87rOo0yRMhy4dIAZv+VuSoRPPjEFzHgmvXufyn2QEyaYIkQKBw+aR0mNG5s26Flp2PvQQ/DWW2bbL76ARo2gfv2Mt69Y0QwUCDB4sBmLB5ITHA+PW/fx9DRzbRUvnr3PI1KTBEcIkWvlfMox8eGJAIzbOo7/wrMx4lkaly8nL1tGk821+fNNESKFkydh8mTYvRv+97+s98wbPdp06QYoVw7eeOP223fvDn37mqTmqafgxo2M7+AI65EERwhhFX3q96FJhSZci75Gv7X90Nns5xoRYRpivvqqSWzi4sx/slZRsaJM6iNukfIOiWXQv6xwcTE9ne67z8xknxXTp0Pt2nD4sBmDRxIc28tSgqOUKqGUWqmUuqGUOq2UeiaD7ZRSaoJS6rxSKlQptUUpdbd1QxZC5EdKKT7v+DnOypm1R9ey9GAWRzwDfv7Z/LE5fNj0VHnrrVtHLc6VdetMESKFgIDk5fj47O1bsaLpAdW4cda29/IyjZo9PEw39b/+MuslwbGdrN7B+QSIBfyAQODTDBKXJ4DngOZACWAnIPeFhXAQ9/jdw/ut3wfgtfWvcS3qWqb7hIWZ2/fx8WaEYpuYNCl5JDYhbnJ3T93eK7uyNdgkcM898OmnZpychAQzi3nNmtk/r8iaTBMcpVQRoBswSmsdobXeBqwGnk1n8yrANq31Ca11ArAAuMuaAQsh8reXG73MvX73cj78PC/+8GKm27drZ+bcqVXLtImwiSVLsjgrp3A0L74IISHZmPYjl3r1Mu19nJzMI1gnaShiMyqz5+RKqfrADq21Z4p1rwEttNad0mzrD6wEngJOAhOBmlrrx0hDKdUP6Afg5+fXYEkh++UTERGBt0wiku/JdbKNs5Fn6fl7TzSaj+t9TB3fOuluFxTUiPPnzehrn376B7Vqhd+yjVyjgkGuU8FQGK/TQw899IfWOiDt+qw85fYGQtOsCwXSG9T6P+BX4AiQAJwFHk5nO7TWs4BZAAEBAbply5ZZCKXg2LJlC4XtMxVGcp1s50+nP5m2exozzs7gYKeDODulHqnvs8/g/HmzPHAgvPBCg3SPY5VrtGaNee3U6fbbiRyTn6WsO3cuuc17VFT6XcVtxZGuU1ZujkUAaacgKwrc+q8WjAEaAhUBD2AcsEkp5ZWbIIUQBc/EVhMp612WI1ePsP7f9be8n3JOqY8+snEwH36YPLOiEHaWclRuqzamF6lkJcE5CrgopWqkWFcXOJjOtnWBpVrrc1rreK31XKA40g5HCIfj5erFiw1NG5xxW8fdMsLxvHmmLcK1zNsh597y5aYIkQ+kbJwsCY7tZJrgaK1vAN8C45VSRZRSzYAupN876nfgCaWUn1LKSSn1LOAKHLdm0EKIgmHQ/YMoU6QMu8/vZv6B1L8ynn3W9CYpViwPAilVyhQh8gFpWJw3svplfhHwBIKBxcAArfVBpVQlpVSEUqrSze3eA/YD+4DrwBCgm9b6upXjFkIUAD7uPkxqZbpnj9o8ihuxN1i0yMyacPp0Hgby7bcyNbPIN8qUMb23xo61dySFW5ZujmmtQ4BbekJprc9gGiFb6tHASzeLEELQs15PPv7tY/Ze3MvozWNZM/YDjh2Du+4Cf/88CsLSyMcynbQQdmYZf0fYjtwoE0LYlJNyYlq7aQDM2D2TY+euUrkydO6ch0F8950pQgiHIQmOEMLmHvR/kDZV2xKrI6HxdJ58Mo8bV/r6miKEcBiS4Agh8kSRP0eYhYYzefWN6Lw9+dKlpgghHIYkOEIIm4uPh5XTH4CL94LXVRYd/TRvA/j0U1OEEA5DEhwhhM1t2gRoJ9gyDoDRW0YTfCM47wL44QdThBAOQxIcIYTNPfSQ6Ra7cFQXGpZrSERsBAO+H5D5jtbi5WWKEMJhSIIjhLA5V1fTLfaZZxSLui3C3dmdlYdX8nfw33kTwIIFpgghHIYkOEIIm/nzTzMs/bZtyeuql6hO3/v6otH0Xd2X2IRY2wfy5ZemCCEchiQ4QgibmWQGMaZ9+9Trx7YcSzmfcuw+v5sv/vjC9oH8/LMpQgiHIQmOEMImLlwwc00B/J3mSVQpr1JMbzcdgPG/jOd6tI1nc3F1NUUI4TAkwRFC2ESnTua1c2eoVOnW97vV7kaj8o0IvhHMhzs+tG0wc+eaIoRwGJLgCCGsLiLCtL8BeOKJ9LdRSjGupek2Pn33dM6GnrVdQJLgCOFwJMERQljd4sXJy4GBGW/Xrno7Ot/ZmfDYcJ5f87ztAtqyxRQhhMOQBEcIYVVaQ79+Znn2bNOL6na+7PQl3m7e/PTvT/xwTAbjE0JYhyQ4QgirUgp69AB3dwgKynz70kVKM7bFWAAG/jiQ8Jhw6wf1xRemCCEchiQ4Qgir+/priI7OeselV+5/hXvK3MOJayeYtG2S9QOSyTaFcDiS4AghrOb0aQgLy/5+rs6uzGg/A4A5++aQqBOtG9iGDaYIIRyGJDhCCKtp0wbKlIHVq7O/7wOVHsDf15//Iv5jzZE11g9OCOFQJMERQljFr7/C0aMQFwcNGmR/fyflxJDGQwCYtH0SWmvrBTdzpilCCIchCY4QwioGDzavtWtD+fI5O0bf+/pSwrMEu87t4tczv1ovuDVrTBFCOAxJcIQQuXblSvLAfp98kvPjFHErwsBGAwGs29j4xx9NEUI4DElwhBC5NnaseX34YWjRInfHernRy3i5evHj8R/Zf3F/rmMTQjgmSXCEELkSHp5812bkyNwfr5RXKZ6/z4xqPGrzqNwfEGD6dFOEEA5DEhwhRK44O0ORImb5oYesc8wRzUfg7uzO2qNrOX3jdO4PuHGjKUIIhyEJjhAiV7y8zF2chITMp2XIqjJFytC7Xm80mnmn5+X+gKtX56zvuhCiwJIERwiRYyEhMGmSeXWy8m+TN5q9gUKx6fImtp7aat2DCyEKPUlwhBA5VqoUDB8OUVHWP3aV4lV4qeFLAMzck8sxbCZPNkUI4TAkwRFC5MiRI2bmcIBEK8+sYPFa09dwwollB5dxPOR4zg+0c6cpQgiHIQmOECJHpk41r82bQ6VKtjmHfzF/Wvu1RqP5ePfHOT/QihWmCCEchiQ4Qohsu3gRPv/cLFtebeXx8o8DMP/AfMJjwm17MiFEoSEJjhAi28aNM6/+/mZqBluq4V2D+8rex7Xoa8zeOztnB5k0yRQhhMOQBEcIkS2xsfDZZ2Z51izbn08pxbUkVRwAACAASURBVOgHRwPwwY4PiE+Mz/5B9u0zRQjhMCTBEUJky6ZNycutW+fNOTvf2ZnqJapzPvw83/3zXfYPsGSJKUIIhyEJjhAiW9q1g7NnYf9+6w3slxmlFIPuHwTA1F1T8+akQogCzSUrGymlSgBfAW2AK8BwrfWiDLatCnwEtABigNla6zesE64QIj+oUMGUvNSrXi9GbR7F9rPb2Xl2J00qNsn6zm+/bV5HWWluK5FnwsLCCA4OJi4uzt6hFAq+vr4cPnzY3mFkysXFBQ8PD0qXLo2Hh0fOjpHF7T4BYgE/oB7wvVJqv9b6YMqNlFJuwM83t+8OJAA1cxSZECLf2bcP6tQBl6z+5rAibzdv+tbvy+Sdk5m+e3r2EpwjR2wXmLCZsLAwLl26RPny5fH09ETl1S3DQiw8PBwfHx97h3FbWmvi4+OJiIjgzJkz+Pn54evrm+3jZPqISilVBOgGjNJaR2ittwGrgWfT2bwXcEFrPUVrfUNrHa21PpDtqIQQ+U5oqBnzpmpVuHbNPjG82PBF3JzdWHpwKX/+92fWd1ywwBRRoAQHB1O+fHm8vLwkuXEgSilcXV0pXrw4FSpU4OrVqzk6Tlb+D6sJJGitj6ZYtx/zCCqtxsAppdSPQEPgb2Cg1vqvtBsqpfoB/QD8/PzYsmVLNkPP3yIiIgrdZyqM5Dpl3bJlFYiIqE6NGtfYv39/np037TXqUrYLy84tY9CKQbxd5+08i0Pcni1+lnx9fYmPjyc8XMY/spaEhIQC9fXUWhMaGpqj762sJDjeQGiadaFAeve4KgAPAZ2BjcAg4DulVC2tdWyaoGcBswACAgJ0y5Ytsxd5PrdlyxYK22cqjOQ6ZU1CAvTubZbHji2ep1+ztNeoSr0qrPhoBTtDduJ3tx+1S2dhIJ7Rpps548fbJkhhk5+lw4cPU7RoUase09EVhEdUaXl4eFC/fv1s75eVXlQRQNrvsKJAeilgFLBNa/3jzYRmMlASsPFQYEIIW/ruOzh1CqpVgw4d7BuLfzF/gu4NIkEnMOuPLA7Ec/asKUIIh5GVBOco4KKUqpFiXV3gYDrbHgC0NQITQuQf06aZ10GDwNnZvrEA9G/QH4AlB5dkbeC/OXNMEUI4jEwTHK31DeBbYLxSqohSqhnQBZifzuYLgMZKqUeUUs7AYEy38vzfJ00Ika5//oFff4WiRaFXL3tHYzSp0IQ7S97JxYiL/HjsR3uHI0SWzZ07F6VUUvHx8aFu3brMmDGD+PgcjNKdQ2PHjs12w+2WLVsWqEf6WR3o70XAEwgGFgMDtNYHlVKVlFIRSqlKAFrrI0AQ8BlwDZMIdU7b/kYIUXCEhED16tC1K+SXR/dKKZ6r/xwA8w7My3yH4cNNESKfWLZsGTt37mTFihU0atSIgQMHMj4P24j17duXnTt3ZmufmTNnMnPmTBtFZH1ZGs1Cax0CPJbO+jOYRsgp132LueMjhCgEmjY1w8iEpu1qYGfd7+7OsA3D+PHYj0TFReHp6pnxxjnsZiqErdSrV4/q1asD0KZNG44fP860adPSTXK01sTFxeHm5ma181eoUIEK2Ryt86677rLa+fOCTNUghMiUkxMUL27vKFLzL+ZPQLkAbsTdYNFf6Q6snmzWrLyZGVSIHGrYsCHh4eEEBwdTuXJlgoKCmD17NrVq1cLNzY3vv/8egMjISIYNG0aVKlVwc3OjSpUqTJw4kcTExFTHu3z5Mi+++CIVK1bE3d2dihUr8uyzzxITEwOk/4hq+vTp1K5dG09PT4oXL05AQAArV65Mej+9R1RHjhyha9euFCtWDE9PTxo3bsy6detSbWM517Fjx+jQoQPe3t74+/szfvz4W+K2JjuMRyqEKCi++gpatDCPqPKjwfcPJmhlEO9tf4/e9XvjpOR/NlEwnTx5EmdnZ7y9zUORzZs3s2/fPsaMGUOZMmWoXLky8fHxtG3blkOHDjFq1Cjuuecedu3axdtvv01ISAgffvghANeuXaNp06aEhITw1ltvce+99xIcHMx3331HbGz6LUYWLlzI0KFDGT16NM2bNycqKooDBw4QEhKSYcwXLlzggQcewMfHhxkzZuDr68snn3xChw4dWLt2LY8++miq7bt27Urv3r0ZMmQIa9asYcyYMVSsWJHeljEorE1rbffSoEEDXdhs3rzZ3iGILJDrlLGTJ7UGrb28tI6MtF8ct7tGcQlxutLUSpqx6GUHl2V8kKFDTRE2Y4ufpUOHDqW7HjIun3+evN3nn99+25Tuuy/j7Z5/Pnm7PXty95nmzJmjAf3PP//ouLg4HRISoj/77DPt5OSku3TporXW2t/fX3t6eur//vsv1b7z5s3TgN66dWuq9RMmTNCurq760qVLWmutR40apZ2cnPSff/55y/nDwsK01lqPGTNGk+KL8NJLL+n69evfNvYWLVroFi1aJNWHDh2qnZ2d9bFjx5LWxcfH65o1a6Y6luVcs2fPTnW8OnXq6NatW9/2nFpn/H1gAezR6eQW8u+OECJdS5aY1y5dwPM2zVvsycXJhWHNhgEw4ZcJmN916YiKMkWIfKJWrVq4urpSokQJXnzxRQIDA5k9e3bS+40bN+aOO+5Itc+6devw9/enadOmxMfHJ5U2bdoQFxfHrl27AFi/fj0NGzbM1uB4DRs2ZN++fQwcOJANGzYQGRmZ6T6//PILjRs3TmpLBODs7MzTTz/Nvn37CAsLS7V9hzSDaNWpU4czZ85kOcbskkdUQohbJCYmDxvz9NP2jSUzz9V/jom/TmT/pf2sObqGznd2vnWjTz7J+8CEzWSUx6bVr58pWfHHH1nbrkGDrG2XmZUrV1KhQgV8fHzw9/e/ZcbssmXL3rJPcHAwp0+fxtXVNd1jWuZsunr1KnXr1s1WPD169CA6OpqvvvqKmTNn4urqSvv27ZkyZQqVK1dOd5+QkJB0k6g77rgDrTXXrl1LNRJ1iRIlUm3n7u5OdHR0tuLMDklwhBC32LQJjh6F8uUhzWP0fMfDxYM3mr7B4J8GM3bLWDrW7ChtcUS+V6dOnVR3PtJKb4yakiVLUqVKFb755pt097EkIqVKleL8+fPZikcpRf/+/enfvz/Xrl1j/fr1DB06lO7du7N79+509ylRogQXL168Zf3FixdRSt2S0OQ1+S0ghLjFl1+a1379wKUA/BvUr0E/yvuUZ+/FvSw4kM6s4YMHmyJEAdauXTvOnj2Lt7c3AQEBt5RSpUoBptv5b7/9luNJcYsXL0737t158skn+fvvvzPcrkWLFuzatYtTp04lrUtISGDp0qXUr1/f7nNeSYIjhEjl6lVYtQqUgp497R1N1ni6ejKmxRgApuycknFbHCEKsMDAQJo2bUqrVq2YMmUKGzdu5Mcff2TGjBm0adMmqd3MkCFDqFq1Ko888gjTp09n06ZNfPPNNwQGBmY4k3i/fv0YOnQoy5cv55dffuHLL79k/vz5tGnTJsN4hgwZQrFixWjdujWLFi1i7dq1dOrUiaNHjzJx4kSbfA2yowD8byaEyEsREdCtm2mT6+9v72iyLujeIEZuGsn+S/vZenorLSu3TH7TMpmWEAWYq6srP/30E5MmTWLWrFmcPHmSIkWKUK1aNTp06JA0EGCxYsXYvn07b731FpMmTeLq1av4+fnx8MMPZzhYYLNmzZgzZw7z588nNDSUcuXKERQUxLhx4zKMp1y5cmzbto1hw4YxYMAAYmJiqFevHt9//z3t2rWzydcgO1R++E8nICBA79mzx95hWNWWLVsK1JwdjkquU8a0Nndx7C0712jslrGM2zqOdtXb8WOgzFGVl2zxs3T48GFq165t1WM6uvDwcLs/OsquzL4PlFJ/aK0D0q6XR1RCiCRhYXBzwNR8kdxk18uNXsbL1Yt1x9ex5O8lyW+89JIpQgiHIQmOECJJ2bLQsSP8+6+9I8mZUl6l+LCNGc112IZhxCbcHLXV0zP/DuYjhLAJSXCEEIBJaixjexXEuzcW/Rr0467Sd3Em9Ayz994cOG3yZFOEEA5DEhwhBACffmpee/aEqlXtG0tuOCknRj04CoARG0dw+cZlO0ckhLAHSXCEEERGmok1AV5+2b6xWMOTdz9J4wqNuRZ9jXn752VvSFshRKEgCY4QgsWL4fp1aNQIAm7pi1DwOCknhjYZCsDYrWOJ8fWBkiXtHJUQIi9JgiOEg9MaZswwy4Xh7o3F/+76H80rNSciNoKPu5aFd9+1d0hCiDwkCY4QDm7XLti3D0qVgieesHc01jX8geE8fQC6dxiGdnKCypVh4UJ7hyWEyAMykrEQDi4gAJYuhfBwSDOhcYHX7rcQWq51wjM20aw4fTq5LU5goP0CE0LYXP64g3PkCMyda5bj4qBlS1hwc8K8yEhTX7rU1ENDTf3bb039yhVTX7PG1C9eNPV160z97FlT37DB1E+cMPWtW5PP3bIl7Nhh6n//beq//27q+/aZ+r59pv7776ZumYBsxw5TP3LE1LduhZYt8bhwwdQ3bDDvnz1r6uvWmbplBtY1a0z9yhVT//ZbUw8NNfWlS03d0n93wQJTj4sz9blzTd3iiy/gkUeS6zNnpp4Oevp06Nw5uT55shmX32LSJHjqqeT6229DUFByffRo6N07uT58eOrGm6+9lnpAtbSTHL70ktnGol8/cwyL3r3NOSyCgkwMFk89ZWK06NYtdfffzp3NZ7R49FHzNbB45BHzNbqp3uDBhe57jxMnTD2L33uuoVd48knoU7zwfe+pAQOSkxuLyEgYORIhROGWPxIcIYRdJNp/phbbymBiQc6cyds4hLhp7ty5KKWSipubG9WqVWPEiBFER0fbNbbKlSvTq1evpLol1pSzhRcoWmu7lwYNGujCZvPmzfYOQWSBI1+nqCitK1bUum9frSMj7R1NxnJ1jfz9tTbtqFMXf38rRScsbPGzdOjQIasf097mzJmjAb1s2TK9c+dOvX79ej1gwAAN6Jdfftnm5w8LC8vwPX9/f92zZ89bYj158qTN47qdzL4PgD06ndxC7uAI4aAWLzZPr/bsKXxtb5JMnAheXqlWRbs5oSdOtFNAQhj16tWjcePGtG7dmpkzZ/LII4/w1VdfkZiYmPnOIkskwRHCASUmwnPPmeUhQwr21Ay3FRgIs2aBry8aOFNM8VzHRH66X8bEEfnLfffdR1RUFFcs7TGBkydPEhgYSOnSpXF3d6devXqsXLnyln33799P165dKVmyJJ6entx55528m2JYhPXr19O+fXvKli2Ln58fderU4cMPPyQhISFPPpu9SC8qIRzQDz+Y14oV4Zln7BuLzQUGQmAgCli87T0Wb3yTP9YN4q8Bf+Hm7Gbv6IQA4NSpU/j6+lLy5oCUZ8+e5f7776dMmTJMnTqV0qVLs3TpUrp168aqVavofLPB/m+//UbLli2pXr06U6dOpUKFChw7dowDBw4kHfvEiRO0atWKgQMHkpiYyKFDhxg7diyXL19mUspOG4WMJDhCOCBLR7IuXcDFgX4LDGkyhNn7ZnP06lEW/bWIXvV62TskkU1qXP643ajH5K6FfkJCAvHx8YSHh7Ny5UpWrFjBtGnTcHZ2BmDs2LFordm6dWtS0tO2bVvOnj3L6NGjkxKc1157jZIlS7Jr1y68bj6Offjhh1Od64UXXkhaDgsLo23btsTGxjJ58mTeeecdnJwK58OcwvmphBAZOnnS9BB3c4O33rJ3NHnkqafgqadwc3bjzWZvAvDBjg+IT4y3c2DCUdWqVQtXV1dKlChBnz596N+/Py+nGEp83bp1tG/fHl9fX+Lj45NK27Zt2b9/P2FhYURGRrJ9+3YCAwOTkpv0/Pfff/Tv3x9/f39KliyJq6srb731FtevXyc4ODgvPq5dOND/bkIIgE2bTFeip54CPz97R5NH6tVLWnz6nqcZ/8t4Dl0+xNSdU3m92et2DExkV27vnOQXK1eupEKFCly+fJkpU6Ywc+ZM7r//fnr06AFAcHAw8+bNY968eenuf/XqVdzc3EhMTKRChQoZnicxMZHOnTtz4cIFxo4dS6VKlShZsiSrVq1i4sSJdu+abkuS4AjhYPr0gaZNwdXV3pHkoTffTFr0cPFgxqMz6Li4I2O2jKFfg374evjaMTjhiOrUqUP16tUB80jp3nvv5fXXX6dbt24UKVKEkiVL0rx5c4YNG5bu/uXKlSMhIQEnJyfOnz+f4Xn+/fdf9uzZw/z58wkKCiI8PBwfHx/WWAYoLcTkEZUQDqh2bbj5u9UhdajZgeaVmhMVH8XyQ8vtHY5wcO7u7nzwwQcEBwcz8+bI6+3atePAgQPcfffdBAQE3FLc3d3x8vLigQceYMGCBURFRaV77MibI5G7pviPJi4ujoUOMCebJDhCOIi4OFi/3nQRdzjduqWeFgLoU78PAGO2jCEqLv0/DkLklc6dO9OwYUMmT55MVFQU48ePJzQ0lAcffJCvv/6arVu3smrVKiZMmMBzljEegMmTJ3P16lWaNGnC/Pnz2bx5M1999RUDBw4EoHbt2vj7+zNy5EiWL1/O999/T+vWre31MfOUJDhCOIipU6FtW2jTxt6R2EGTJqakEHhvIHX96nI+/DwL/yr8/82K/G/ChAkEBwfz2WefUalSJfbs2UPdunUZMWIErVu3ZsCAAWzdujVVL6mGDRuyfft2KlasyMCBA2nfvj0ffPBBUrscNzc3Vq1axR133EGPHj0YOnQoDz74IG+meGxbWCkzyrF9BQQE6D179tg7DKvasmULLVNORCjyJUe5TnFxUKECBAfDRx/BzX/uCgRbXqOv931Nr+96Uf+O+vzR7w9UoR3x0PZscZ0OHz5M7dq1rXpMR2dpg1OQZPZ9oJT6Q2sdkHa93MERwgEsX26Sm6pVYcAAe0eTf3Sv050yRcqw9+Jefjn9i73DEUJYkSQ4QjiAr74yr0OHOtbAfkk6dzYlDQ8XDwYEmIxvxKYR5Ic72kII68hSgqOUKqGUWqmUuqGUOq2UynRwd6XUJqWUVko54q9TIfKNEyfM2Ddubg4wLUNGWrUyJR2DGw/Gr4gfO87u4OcTP+dxYEIIW8nqHZxPgFjADwgEPlVK3Z3RxkqpQGSMHSHyhffeMwP7Pf00FCtm72jsZNAgU9JRzKMYr9z/CgCTthXeeXmEcDSZJjhKqSJAN2CU1jpCa70NWA08m8H2vsAY4A1rBiqEyJn69aFSJRg+3N6R5F8vNXwJX3dfNp/azE/Hf7J3OEIIK8i0F5VSqj6wQ2vtmWLda0ALrXWndLb/BDgOrAROAq5a61smfFFK9QP6Afj5+TVYsmRJbj5HvhMREYG3t7e9wxCZcJTrlJAAN+fwK3CscY3uuTka7F/vvZfhNgtOL+CrU19Rw7sGn9/3ufSoyiZb/Cz5+vpSrVo1uRZWlJCQkDShZ0Ggtebff/8lNDQ0w20eeuihdHtRZeUxkjeQ9sihwC39zJRSAUAzYBCQ8eQYJuhZwCww3cQLW1ddR+l+XNDJdcr/rHKNevYEuO1x7o+7n9XTVnMs4hge1T1oUrFJhtuKW9niZ+n48eO4uLjcdiJJkT0FrZt4ZGQkvr6+1K9fP9v7ZqUNTgRQNM26okB4yhVKKSdgJjAovTs2Qoi89c47MHo0hITYO5J84MUXTbkNT1fPpNGNh64fKj2q8oEyZcpw/vx5IiMj5Xo4EK01cXFxhISEcO7cOUqWLJmj42TlDs5RwEUpVUNrfezmurrAwTTbFQUCgKU3byda7oGdU0o9obX+NUcRCiGyLSQE3n0XIiKgY0do1MjeERUMI5qPYPa+2ew8t5PZe2fT574+9g7JoRUtav63vnDhAnFxcXaOpnCIjo7Gw8PD3mFkysXFBQ8PDypVqpTjeDNNcLTWN5RS3wLjlVJ9gXpAF6Bpmk1DgXIp6hWB34AGwOUcRSeEyJGPPjLJTZs2ktwA8Mgj5nXDhttu5uPuw+TWk+mxqgcjNo0g8N5APFzy/x+Dwqxo0aJJiY7IvS1btuTocU9BlNVu4i8CnkAwsBgYoLU+qJSqpJSKUEpV0sZFSyE5qbmktY61QexCiHRERJgEB2DkSPvGkm90725KFgTdG0Rdv7oE3whm2cFlNg5MCGErWUpwtNYhWuvHtNZFtNaVtNaLbq4/o7X21lqfSWefU1prJe1xhMhbX3wB165B06bQvLm9o8knnn/elCxQSiWNbvzhzg+JT5RfYUIURDJVgxCFSGgoTLo5Vt3w4SC9a3Mm6N4g/H392X9pP5/v+dze4QghckASHCEKkfnzzaSaTZtChw72jiYfadnSlCwq4laEyW0mAzBq8ygiYiNsE5cQwmZkOgUhCpGXXoJateCOO+TuTSq9emV7l261u9G4QmN2ndvF+K3jeb/1+9aPSwhhM3IHR4hCRCnTYahOHXtHks/06pXtJEcpxUftPkKhmLprKvsv7rdJaEII25AER4hCYN8+2LXL3lHkY3FxpmRTw/INGRAwgPjEePqv7S+DzQlRgEiCI0QBpzUMGABNmsCKFfaOJp9q3dqUHJj0yCT8ivix+/xulvxduObME6IwkwRHiAJu0SJz9+aOO8zAfiIdffuakgM+7j5MeHgCAEN+GkJIlMx9IURBIAmOEAXYjRtwc6Js3nkHCtAcenkrKMiUHHqu/nM0r9ScSzcu8epPr1oxMCGErUiCI0QB9v77cP48NGiQNGG2SE9kpCk55KSc+LLzl3i4ePD1/q/5ePfHVgxOCGELkuAIUUAdP24SHIBp08BJfpoz1r69KblQs2RNZrafCcCwDcO4EnnFGpEJIWxEfiUKUUANGwbR0RAYCA88YO9o8rkBA0zJpd71e/No9UeJio9i3JZxVghMCGErkuAIUUB99hn072/u3ohMZGOyzcyMazkOZ+XMjN9n8MOxH6xyTCGE9UmCI0QBVbq0SXJKlbJ3JAVAaKgpVtCwfENGtxgNQL81/bh847JVjiuEsC5JcIQoQOLjYfJk82hKZEOXLqZYyVsPvsX95e/nfPh5un3TTWYcFyIfkgRHiALk44/h9dfhiSfsHUkB88orpliJk3JixZMrKOdTjl/P/MqUnVOsdmwhhHVIgiNEAXHwILz5plnu18++sRQ4jz9uihWVL1qeWR1nAfDmhjfZcmqLVY8vhMgdSXCEKADi4sxMA7GxZkDeTp3sHVEBc+WKKVbWoWYH+tbvi0bz+NLHuRRxyernEELkjCQ4QhQAr78O//1nlqfI05Ds+9//TLGBmR1m8qD/g1yLvsbbv7xtk3MIIbJPEhwh8rmffoLp083y99/LdAw5MnSoKTbg6uzKtLamr/7svbM5ee2kTc4jhMgeSXCEyOc2bjSvo0blejBex9Wpk02f69UvW5/Od3YmKj6Kfmv7kagTbXYuIUTWSIIjRD73/vuwahWMGWPvSAqwixdNsaFPO3xKaa/SbDixgQ93fGjTcwkhMicJjhD5VExM8nKXLuDsbL9YCrynnjLFhsr5lGNOlzkAjNw0kj8u/GHT8wkhbk8SHCHyoUWL4N574Z9/7B1JIfHmm8l97G2oQ80OvNzwZeIS43hzo+3PJ4TImCQ4QuQzGzdC795w9Chs3mzvaAqJdu1MyQPjHxqPp4snG05s4Ot9X+fJOYUQt5IER4h8ZM8eeOwxM97NK6/ACy/YO6JC4uxZU/JAcc/ijG05FoA+q/uw9ujaPDmvECI1SXCEyCf++QcefRQiIiAwEKZOBaXsHVUh8eyzpuSRN5q9Qd/6fUnQCTy57EmCbwTn2bmFEIYkOELkA2fPQps2ZrDdRx+FOXPASX46reett0zJQ591/IwmFZoQFR/Fqz+9mqfnFkJIgiNEvvD99ybJadIEli0DV1d7R1TIPPKIKXnI2cmZBY8vwNXJlUV/LWLbmW15en4hHJ0kOELkAy+8AAsWwNq1UKSIvaMphE6cMCWPVS1elYGNBqLRdFrcieMhx/M8BiEclSQ4QthJRAScOpVcDwyEEiXsFk7h9txzptjBe63fo2PNjlyPvk7PVT1llGMh8ogkOELYwcWL0KIFNG8Ox+WfetsbN84UO3BxcmFul7mU9S7LjrM7pD2OEHlEEhwh8tjRo9C0Kfz5J7i72zsaB9GihSl2UtKrJJ92+BSA6bun88vpX+wWixCOQhIcIfLQzp0muTl5Eho2hB07oHp1e0flAI4cMcWOutTqwugHRwPQdWlXdp3bZdd4hCjsJMERIo8sXw6tWsHVq2ZW8M2boUwZe0flIPr3N8XOhjcfTgv/FoREhfDw1w/LnRwhbChLCY5SqoRSaqVS6oZS6rRS6pkMtuuplPpDKRWmlDqnlHpfKeVi3ZCFKHhOnIDu3SEqCp5/Hr77TnpL5al33jHFzjxcPPgp6Cc61OhAVHwUj8x7hJ///dneYQlRKGX1Ds4nQCzgBwQCnyql7k5nOy9gMFAKuB9oBbxmhTiFKNCqVoXx42HyZPj8c3CRtD9vNW1qSj7g7uLOt92/pfvd3YlLjCNoZRD/hvxr77CEKHQy/TWrlCoCdAPqaK0jgG1KqdXAs0Cq6XK11p+mqJ5XSi0EHrJivEIUGLt3Q1gYtG5t6iNH2jceh/b33+a1Th37xnGTm7Mb87rO43ToaXad20XT2U05+OJBSnmVsndoQhQaSmt9+w2Uqg/s0Fp7plj3GtBCa90pk31XAf9ord9M571+QD8APz+/BkuWLMlB+PlXREQE3t7e9g5DZMIW1ykmxokFC/xZtKgSPj5xzJ69hxIlYq16DkdijWtUb/BgAPZNm2aNkKwmNC6UQfsGcTryNAHFA5h0zySclbO9w8oR+Z1XMBTG6/TQQw/9obUOSLs+KzfKvYHQNOtCAZ/b7aSU6g0EAH3Te19rPQuYBRAQEKBbtmyZhVAKji1btlDYPlNhZO3rtGOHGZXY0mGnTx83OnRoKt3Bc8Eq1+iLLwBo2bBh7gOyuRPOxwAAGBBJREFUsvsa3Ue9z+ux59oeZl+bzdwuc3F1LnhzdcjvvILBka5TVtrgRABF06wrCoRntINS6jFgEvCo1vpKzsMTomC4cQP69YNmzUxyU7s2bNsGH34oY93kCw0bmpIPVfStyPfPfI+3mzeL/lrE82ueJ7M760KIzGUlwTkKuCilaqRYVxc4mN7GSql2wBdAJ631X7kPUYj878knzU0CJyd4/XUziF+zZvaOSiTZt8+UfKpxhcb8FPQTXq5efL3/a97d9q69QxKiwMs0wdFa3wC+BcYrpYoopZoBXYD5abdVSj0MLAS6aa1/s3awQuQnKf/JHjYMihUzs4K//z54eNgvLpGOwYNNyceaVmzKvMfmATBy00gGrB0gd3KEyIWsdhN/EfAEgoHFwACt9UGlVCWlVIRSqtLN7UYBvsAPN9dHKKV+tH7YQtjPhQswcCD07Jm87sEHzQB+7drZLy5xG9OmmZLPdburG1PaTAHgsz8+Y9TmUXaOSIiCK0ujcWitQ4DH0ll/BtMI2VKXLuGi0Lp4Ed5914xjExMDzs4wcSJUrGjed5JxwfOvevXsHUGWDWkyhOolqtN1aVcm/jqRmiVr0qNuD3uHJUSBI7+ShcjE/v3Qpw9UrgwffWSSm27dYO/e5ORG5HO//25KAdHpzk5MfHgiAL2/6830XdPtHJEQBY+MpyrEbVy7Bo0aQWwsKAWPPQbjxsG999o7MpEtr79uXrdssWsY2fFGszcIiwnjnW3vMPinwbi7uPNCwAv2DkuIAkMSHCFSOHoUliyBESPMdArFi0Pv3uDmZtrd1KiR+TFEPjRjhr0jyDalFBNbTaSSbyVe+P4FBnw/gF/P/MrM9jPx9fC1d3hC5HuS4AiHd/UqfPMNzJsHu3aZdXXrQpcuZvmzz+wXm7CSfDJFQ070D+jPtehrDN84nEV/LeKfK/+wscdGinkUs3doQuRr0gZHOCSt4csv4dVX61KqFLz4okluvL2hVy8zOaYoRHbsMKWAevOBN1kftJ4qxarw539/0mx2M86GnrV3WELka5LgCIegNRw6lFxXyvQa3ru3OE5O0LYtzJ9vekrNmQP33GO/WIUNjBhhSgHWulprtvTawp0l7+TQ5UM8uvBRouOj7R2WEPmWPKIShda5c7B1K2zcCOvXw/nzcPw4VKtm3n/jDdi37wjDh99J6dL2jVXY2Oef2zsCq6jkW4mdfXbS6MtGHLx8kO7Lu7P8ieUFcu4qIWxN7uCIQuXqVXj2WahSxXThDgoyd2TOn4c77oCTJ5O37dEDOnf+T5IbR3DnnaYUAsU9i7O422KKexRn9ZHVdF/enYjYCHuHJUS+I3dwRIGTmGjuxOzZA3/8AXFxZnwagKJFYflyiI42yw88AC1bQps2pmu3UnYNXdjL1q3mtUUL+8ZhJQHlAljz9BraLWzHyn9W0vSrpuzptwc3Zzd7hyZEviEJjigQtm2DBQvMoHt//WVm77bw8THtaZycwNXVtKWpXt20o3F2tl/MIh8ZM8a8FqBxcDLTrFIzdvfdzYNzHuSv4L/ovLgzy59cjrebd+Y7C+EAJMERdqU1XL4MJ06Yx0cnT5rlY8fMuDP/+397dx6dRXX/cfz9zUIIAYQQIEIUkEVZVCxFRWTrD4W6VAQrheqv1lZKXc5x6WnR42lrq9Wq6LHubaGibLYi4kKhAnEHKv5sVISIVQFZYgIiSwIk8f7+uPOQJ49PNiB5lnxe59wz88y9M3OfuUyeLzN35l7iy61fX70bRdeu8K1vwbe/DYMHVx/4MrSOyCEzZ8a6Bo2iX8d+LLh0ARfOu5Cl/11Kv4f7MW/CPIYer6HsRRTgSKNyzveL2bLFBy+7dvnHsEO6doVt26Kve9ZZVcHKqFFw333+/TSnnAI5OY1edUkmSfzc/4juI1j101WMmz+ODTs3MGrWKGZeNJPLTrks1lUTiSkFOHJYDhzwgUtJiU+9e1eNy/T88/6W0ebN/kmm/WFPsmZm+lG4Q31hOnXy+T16VE+9e1d/VLtnT7jhhqb7fpJkli3z09GjY1uPRtKvYz8KphZw49Ibeeydx7h84eVs3LWRXw79pZ6wkmZLAU4zt3cv7Nzpp7t2+bGXvvzSL8vMhKuu8uWcgyFD/O2k4mLYs6f6dh5+2L8sD/z6+flVecccA126+MEqe/TwwVHLlj5v5Uq/H5FGdfvtfpqkAQ5AZnomj5z/CFktspi+cjq35t/KvA/mcc859zC211hMPeylmVGAk0BKSnzwUFrqO9nu21c1f+yx/jZOqNwdd1Tl7d3rA5Ldu32aNQvOPNOX/c1v/K2faHr2rApwzKCw0AdB4Dvv5uRUTyHnnANLl/orOscd598OXBMFN9Iknnoq1jVoEmbGvefey+gTRnPdP69jbfFazpt7Huf1Po+54+dqDCtpVpI6wHEOKit9qqiomk9N9Y8Qg1++aZOfhsqE5isq/KszsrN92cJC/zbcigr/aHIoVVT4KxLhfUvmzDmexYt9/sGDVdODB2HCBJ8AXnvNv3Du4EF/q2b/figrq5rftIlD72n54Q/9C+uiGTeuKsApK/O3iGpSUlI136kT5OX5IKRtW/9d27f307y86uutWOGfWMrJ8VdlavoPYZcuPonEjdD902ZibK+x/Odn/+HBfz/InW/cyeINi+n/SH/uOeceJg6YSIrpFWiS/OIiwPn4YzjjDP9+k6+/9kFGaH7qVLj2Wl9uxQq48srq+eEBzPr1/koGwPe+By+8EH1/558PL77o54uLq95sG82iRX5bAPPmwW23RS+Xm1s9wHnuua7VAolwvXtXBTi7d8Pq1TXv/8CBqvm8PF/XVq0gK6v69PTTq8p16AD33luV17q1D0jatPFBTPjf+l/9yqf6OO20+pUTiTtLlvjp2LGxrUcTymqRxbSzpzG+73gu/celFBQVMPnZycz9YC5zxs+hbUbbWFdRpFHFRYBTWgr//nf0vKKiqvn9+2Hjxpq3U1FRNR+6upCS4q/YpKX5aWqq/9EPadHC9wsJlQlPqak+MAjp29ePMJ2W5t+3EkppadAuYmDfSZM2kZfXu1q5Fi0gI6N659mzzoI33/TLMzP9laDwFH4LZ8aMmr97uFat4Kab6ldWpFm46y4/bUYBTkifDn14Z8o7zHh3BtOWTePFj17k9L+czkuTX6Jndi3/uxNJcHER4PTq5V+nn5JSlVJT/bRTp6pyI0f6R40jy4WCkbZh/yFZuNAHOXX1q+vQwb93pT4mTvSpPsaP38LIkb3rLJed7YMcEWlE8+fHugYxlZqSypRBUxjZfSSjZo2icEchpz52KjeceQPTzp5GVousWFdR5KiLiwCnVSt/i6o+5bp3r982U3SLWURCcnNjXYO4ELqaM2nBJF757BVuf/12nih4gvvH3M8l/fSGTEkuCgNEJPm98ELNnfKamdzWueT/KJ+XL3+ZQccO4vPdn/P9f3yf6xZfx54De+regEiCUIAjIslv+nSf5JDRJ4xm9U9X89B3HyI9JZ2H3n6I3Om5XL7wcgpLCmNdPZEjpgBHRJLfM8/4JNWkpqRyzenXsPx/lzO823BKy0uZ/d5s+j/Sn+uXXE/B9gJc+EBvIglEAY6IJL/It1FKNcO6DePVK16l8NpCxvQcQ6Wr5IHVDzDw8YHkTs/lb+/+jYOVB2NdTZEGUYAjIsnv2Wd9klr16dCHJZct4bUrXuPHA38MwBf7vuDK56/k2OnH8sjbj1BeWR7jWorUjwIcEUl+f/qTT1Ivw7oNY+ZFMym9pZTHL3icEzucyM6ynVyz+BpOfexUbll+C2u2rtHtK4lrCnBEJPktWuSTNEhmeiZTBk1h3TXrmD9hPscfczzrStZx5xt3Mvgvgznt8dO458172Lpna6yrKvINcfEeHBGRRnWMBpk8EmbGxAETubjvxbz40Yvkf5rPnPfnUFBUQEFRAbesuIVzO51Lj4E96NauW6yrKwLoCo6INAdPP+2THJEWqS0Y33c8D573INtu2sbCiQsZ22ssFV9XsHj7Yro/0J2hM4dy87Kb+eTLer4iXqSR6AqOiCS/Rx/10/qOtSJ1ykjLYNxJ4xh30jg+2vERV//9apZ/sZy3Nr/FW5vf4q4376Jfx35cfNLFnNH1DAZ1GUSXNl1iXW1pRhTgiEjyW7w41jVIan069OHWvrey8MqFzP9gPss+XcaSj5fwYfGHfFj84aFyp3Q+hdE9RjP6hNEM6zaM1i1ax7DWkuwU4IhI8mvVKtY1aBbaZLThqkFXcdWgqyivLGfFpytY8vES3tn2Dm9vfZv3it7jvaL3uG/VfaRaKn069GFw18Fc2OdCxvQcQ5uMNrH+CpJEFOCISPKbPdtPL7sstvVoRtJT0xnTawxjeo0BYH/FflZuXsmyT5ax7NNlrNm6hnUl61hXso4nC54kLSWNLm26MKr7KE7tfCpDjhvCwNyBtExrGeNvIolKAY6IJL+//tVPFeDETMu0lozqMYpRPUZxB3dQVl7GB198wPJPl7Ng3QLWbF3Dpq82Matg1qF1MlIzmNBvAn1z+jKg0wBGdBtB+8z2MfwWkkgU4IhI8nv55VjXQCJkpmcyuOtgBncdzLSzp7GzbCfvF71PQVEB725/l6UfL2Xb3m3MfX9u1TppmZyZdya9s3vTKasT7Vq2qzFlZ2ZjZjH8hhJr9QpwzCwbmAGcC5QANzvn5tZQ9gbgV0AmsAD4uXPuwNGprojIYUhPj3UNpA7ZmdmM6D6CEd1HHFr2ftH7rPx8JRt2bGD1ltW8vul18j/LJ/+z/Dq3l56STm7rXDpmdaRjq47ktMqhY6uOdMyqms9plUNOqxzatWxHi9QWpKWkkZ6aTnpKOmkpaQqQElx9r+A8DBwEOgMDgZfMrMA5tza8kJmNAaYB3wG2AguB24JlIiKx8cQTfnrFFbGshTTQyZ1P5uTOJx/6/Pnuz1mzdQ1bdm9hR9kOdu3fFTVt37udsooyNu/ezObdmw97/6mWeijgCU0z0jJomdaSjFQ/DU8ZaRmkp6RjZqRYSlXCT7+xPCwZ0fNqWqeh5UPrFBYVsuW9LfXeR2SQZ1R9buq88PxOWZ04KeekWtuvzgDHzLKACcAA59xe4A0zex64nG8GLj8CZoQCHzP7PTAnSjkRkaajACcp5LXNI69tXp3lnHPsK99H8b5iSkpLKC4tpnhfMcWlweew+aJ9Rew9uJeKrysoryyn/OtyyivLqXSVPlVUsp/9TfDtmtD6WFfgyE0aMIm5E6LeSDrE6hoszcxOA95yzmWGLfsFMMI5d2FE2QLgD865p4PPOUAxkOOc2xFRdgowJfh4IlBYny+VQHLwt/Mkvqmd4p/aKDGonRJDMrZTN+dcx8iF9blF1Rr4KmLZV0C0FxZElg3NtwGqBTjOuT8Df67H/hOSma1xzn071vWQ2qmd4p/aKDGonRJDc2qn+oxFtRdoG7GsLbCnHmVD89HKioiIiDSK+gQ4HwFpZtY7bNmpwNooZdcGeeHliiJvT4mIiIg0pjoDHOfcPuBZ4HdmlmVmQ4GLgKeiFH8S+ImZ9TOz9sCtwBNHsb6JJGlvvyUZtVP8UxslBrVTYmg27VRnJ2M49B6cmcA5+L4005xzc83seOBDoJ9zblNQ9kaqvwdnqt6DIyIiIk2pXgGOiIiISCKpTx8cERERkYSiAEdERESSjgKcejKz3ma238xmRyz/HzNbb2alZpZvZt3C8szM/mhmO4J0t4W9h9rMugfrlAbbGB2x7clmttHM9pnZc0FfKIlgZhlmNiM4VnvM7F0z+25EGbVTgjGzbDNbGBzXjWY2OdZ1SkZ1nT86d+JLtN8itVENnHNK9UjAv4DXgdlhy3LwLzP8PtASuAdYFZb/M/wbmvOArvgO2VPD8lcC9+E7ZE8AdgEdg7z++PcHDce/QHEuMD/WxyEeE5AF/Bbojg/aLwiOXXe1U+ImYB7wdHBczw7asH+s65VsqbbzR+dO/KXI3yK1US3HKtYVSIQE/AD4e/BHIDzAmYIfxiL0OQsoA04KPr8FTAnL/0noHx7QBzgAtAnLfz30Dw/4AzA3LK8nfsDTNkfzuyVrAt4DJqidEjMFbXQQ6BO27CngrljXrTmk0Pmjcye+UrTfIrVRzUm3qOpgZm2B3wE3RcnuDxSEPjj/zqD/Bsu/kR/Mh+d94pzbU0t++Lb/S/AH/3C/S3NhZp3xxyn0Mkq1U+LpA1Q65z4KWxZ+3KWRRJw/OnfiRC2/RWqjGijAqdvv8SOkb46SV9c4XdHG5mod3P9s6LqR+RKFmaXjR7Cf5ZwLjZmrdko8Oq4xEOX80bkTP2r6LVIb1aBZBzhm9oqZuRrSG2Y2EBgN3F/DJuoapyva2Fx7nb/W19B1I/ObjbraKaxcCv42xkHg2rBNqJ0Sj45rE6vh/NG5Ewfq+C1SG9WgWQc4zrmRzjmrIZ0NjMR3tNtkZtuBXwATzOz/gk1UG3vLzLLw9yjXRsun+hhea4ETzKxNLfnh2z4ByMCPDdas1KOdCP43MgPojO97Ux62CbVT4mnIGHhyhGo5f3TuxIeR1PxbpDaqSaw7AcVzAloBuWHpXuAZqnqYd8RfrpuA773+R6r3Xp8KrMP3XO+C/8cS3nt9VbDNlsDFfLP3+m5gGL7T2GwSqPd6DNrqseB4to6Sp3ZKwATMxz9JlQUMRU9RNeaxjnr+6NyJj1Tbb5HaqJbjFusKJFIi4imqYNloYD2+1/orBI8mB3kG3A3sDNLdBMNjBPndg3XK8I/xjY7Y9mRgE7APWARkx/oYxGMCugEO2I+/pBpKP1Q7JW4CsoHnguO6CZgc6zolY6rr/NG5E38p8rdIbRQ9aSwqERERSTrNug+OiIiIJCcFOCIiIpJ0FOCIiIhI0lGAIyIiIklHAY6IiIgkHQU4IiIiknQU4IgItQyFEZ4+C8o+YWafx7jKAJjZb4O6pR3N7dWj3MhgvyOPxn5F5Og7Kn8URCThDYn4vBA/ivBvw5YdaLLaiIgcIQU4IoJzblX4ZzM7AJRELj9SZpbhnFOgJCKNTreoROSwmNlpZva6mZWa2QYzmxqRf0VwG2e4mf3DzHYBq8PyR5jZcjPbY2b7zGypmQ2I2MYYM3vLzL4ys71mVmhmv45SnR5m9lJQZqOZ/ToYHTt8Wyea2UIz22VmZWa2yszG1uN7djSzuWa2O1j3SaBdgw6WiDQ5BTgicjjaAnPxg+9dBLwNPGpmo6KUnQN8ClwCTAMws/OB5fgxjy7Dj3fTBnjdzI4LypwAPB+sOxH4HnAfftC/SAuBFcA4/PhVtwE/CmWaWRfgDfzIyNcCl+IHFXzJzL5bx3d9FrgAuCWoRwXwYB3riEiM6RaViByONsDVzrl8ADN7DTgXmATkR5R9xjn3y4hlDwCvOucuCi0ws3zgE+Am4HrgW0AL4OfOud1BsRU11Ge6c+5vwfwyM/tOUJfQshuB9sAQ59zHwf4WAx8CdwD/jLZRMzsHOBuY5JybHyxeamb/BPJqqIuIxAFdwRGRw1EaCm4Agn41G4Djo5RdGP7BzHoDPYE5ZpYWSkApsBIYHhT9D1AOzDezS8ysUy31eSni8wcRdRkOrAoFN0GdK4F5wEAza1vDdocAlcCCiOXzo5QVkTiiAEdEDseXUZYdAFpGWb4t4nMoUJmBD2DC0wVAB4AgGBmD/zv1FLDdzFab2Ygo+9hZR12yo9QDYDtg+Ks70RwLfOmcK49YXlRDeRGJE7pFJSKNLfK9MjuC6c3AsijlDx5a0V8lyjezDGAo8Dt8v5nuzrmSBtRhJ5AbZXluUL/IAClkG9DezNIjgpzODdi3iMSAAhwRaWqFwGdAf+fcXfVZIbgFtsLMWgOLgB5AQwKcV4Hrg8DoMwAzS8V3Gn7XObenhvVWAqnABKrflvpBA/YtIjGgAEdEmpRzzpnZNcAiM2sB/B0frHQGzgI2OefuCx47Hw4sBjYDOfirPlvxfWwa4n7gCuBlM/sNsBu4GugDnF9LXV82szeAx80sB9/PaCIwoKZ1RCQ+qA+OiDQ559xifPCSBfwVWArcjb9ltDIoVhDk3wn8C3gI/8j4d5xzZQ3c31b801BrgUeBZ/D9cs53zi2pY/Xx+CDrTuBp/H8Mr23I/kWk6ZlzdQ67IiIiIpJQdAVHREREko4CHBEREUk6CnBEREQk6SjAERERkaSjAEdERESSjgIcERERSToKcERERCTpKMARERGRpPP/PeEkw21e2VwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_precision_recall_vs_threshold(precisions, recalls, thresholds):\n",
    "    plt.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\", linewidth=2)\n",
    "    plt.plot(thresholds, recalls[:-1], \"g-\", label=\"Recall\", linewidth=2)\n",
    "    plt.legend(loc=\"center right\", fontsize=16) # Not shown in the book\n",
    "    plt.xlabel(\"Threshold\", fontsize=16)        # Not shown\n",
    "    plt.grid(True)                              # Not shown\n",
    "    plt.axis([-50000, 50000, 0, 1])             # Not shown\n",
    "\n",
    "\n",
    "\n",
    "recall_90_precision = recalls[np.argmax(precisions >= 0.90)]\n",
    "threshold_90_precision = thresholds[np.argmax(precisions >= 0.90)]\n",
    "\n",
    "\n",
    "plt.figure(figsize=(8, 4))                                                                  # Not shown\n",
    "plot_precision_recall_vs_threshold(precisions, recalls, thresholds)\n",
    "plt.plot([threshold_90_precision, threshold_90_precision], [0., 0.9], \"r:\")                 # Not shown\n",
    "plt.plot([-50000, threshold_90_precision], [0.9, 0.9], \"r:\")                                # Not shown\n",
    "plt.plot([-50000, threshold_90_precision], [recall_90_precision, recall_90_precision], \"r:\")# Not shown\n",
    "plt.plot([threshold_90_precision], [0.9], \"ro\")                                             # Not shown\n",
    "plt.plot([threshold_90_precision], [recall_90_precision], \"ro\")                             # Not shown\n",
    "save_fig(\"precision_recall_vs_threshold_plot\")                                              # Not shown\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y_train_pred == (y_scores > 0)).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "그림 저장: precision_vs_recall_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_precision_vs_recall(precisions, recalls):\n",
    "    plt.plot(recalls, precisions, \"b-\", linewidth=2)\n",
    "    plt.xlabel(\"Recall\", fontsize=16)\n",
    "    plt.ylabel(\"Precision\", fontsize=16)\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.grid(True)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plot_precision_vs_recall(precisions, recalls)\n",
    "plt.plot([0.4368, 0.4368], [0., 0.9], \"r:\")\n",
    "plt.plot([0.0, 0.4368], [0.9, 0.9], \"r:\")\n",
    "plt.plot([0.4368], [0.9], \"ro\")\n",
    "save_fig(\"precision_vs_recall_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold_90_precision = thresholds[np.argmax(precisions >= 0.90)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3370.0194991439557"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold_90_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred_90 = (y_scores >= threshold_90_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9000345901072293"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_train_5, y_train_pred_90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4799852425751706"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred_90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ROC 곡선"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_train_5, y_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "그림 저장: roc_curve_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'k--') # dashed diagonal\n",
    "    plt.axis([0, 1, 0, 1])                                    # Not shown in the book\n",
    "    plt.xlabel('False Positive Rate (Fall-Out)', fontsize=16) # Not shown\n",
    "    plt.ylabel('True Positive Rate (Recall)', fontsize=16)    # Not shown\n",
    "    plt.grid(True)                                            # Not shown\n",
    "\n",
    "plt.figure(figsize=(8, 6))                         # Not shown\n",
    "plot_roc_curve(fpr, tpr)\n",
    "plt.plot([4.837e-3, 4.837e-3], [0., 0.4368], \"r:\") # Not shown\n",
    "plt.plot([0.0, 4.837e-3], [0.4368, 0.4368], \"r:\")  # Not shown\n",
    "plt.plot([4.837e-3], [0.4368], \"ro\")               # Not shown\n",
    "save_fig(\"roc_curve_plot\")                         # Not shown\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9604938554008616"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "roc_auc_score(y_train_5, y_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**노트**: 사이킷런 0.22 버전에서 바뀔 기본 값을 사용해 `n_estimators=100`로 지정합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "forest_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "y_probas_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3,\n",
    "                                    method=\"predict_proba\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores_forest = y_probas_forest[:, 1] # 점수 = 양성 클래스의 확률\n",
    "fpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train_5,y_scores_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "그림 저장: roc_curve_comparison_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, \"b:\", linewidth=2, label=\"SGD\")\n",
    "plot_roc_curve(fpr_forest, tpr_forest, \"Random Forest\")\n",
    "plt.plot([4.837e-3, 4.837e-3], [0., 0.4368], \"r:\")\n",
    "plt.plot([0.0, 4.837e-3], [0.4368, 0.4368], \"r:\")\n",
    "plt.plot([4.837e-3], [0.4368], \"ro\")\n",
    "plt.plot([4.837e-3, 4.837e-3], [0., 0.9487], \"r:\")\n",
    "plt.plot([4.837e-3], [0.9487], \"ro\")\n",
    "plt.grid(True)\n",
    "plt.legend(loc=\"lower right\", fontsize=16)\n",
    "save_fig(\"roc_curve_comparison_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9983436731328145"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_train_5, y_scores_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9905083315756169"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3)\n",
    "precision_score(y_train_5, y_train_pred_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8662608374838591"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred_forest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 다중 분류"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5], dtype=uint8)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svm_clf = SVC(gamma=\"auto\", random_state=42)\n",
    "svm_clf.fit(X_train[:1000], y_train[:1000]) # y_train_5이 아니라 y_train입니다\n",
    "svm_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.81585438,  7.09167958,  3.82972099,  0.79365551,  5.8885703 ,\n",
       "         9.29718395,  1.79862509,  8.10392157, -0.228207  ,  4.83753243]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_digit_scores = svm_clf.decision_function([some_digit])\n",
    "some_digit_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(some_digit_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.classes_[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5], dtype=uint8)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "ovr_clf = OneVsRestClassifier(SVC(gamma=\"auto\", random_state=42))\n",
    "ovr_clf.fit(X_train[:1000], y_train[:1000])\n",
    "ovr_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ovr_clf.estimators_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3], dtype=uint8)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.fit(X_train, y_train)\n",
    "sgd_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-31893.03095419, -34419.69069632,  -9530.63950739,\n",
       "          1823.73154031, -22320.14822878,  -1385.80478895,\n",
       "        -26188.91070951, -16147.51323997,  -4604.35491274,\n",
       "        -12050.767298  ]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.decision_function([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.87082583, 0.87089354, 0.88628294])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(sgd_clf, X_train, y_train, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/haesun/anaconda3/envs/homl2/lib/python3.7/site-packages/sklearn/linear_model/stochastic_gradient.py:561: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.89957009, 0.89344467, 0.89963495])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train.astype(np.float64))\n",
    "cross_val_score(sgd_clf, X_train_scaled, y_train, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/haesun/anaconda3/envs/homl2/lib/python3.7/site-packages/sklearn/linear_model/stochastic_gradient.py:561: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[5576,    0,   21,    6,    9,   43,   37,    6,  224,    1],\n",
       "       [   0, 6398,   38,   23,    4,   44,    4,    8,  213,   10],\n",
       "       [  26,   27, 5242,   90,   71,   26,   62,   36,  371,    7],\n",
       "       [  24,   17,  117, 5220,    2,  208,   28,   40,  405,   70],\n",
       "       [  12,   14,   48,   10, 5192,   10,   36,   26,  330,  164],\n",
       "       [  28,   15,   33,  166,   55, 4437,   76,   14,  538,   59],\n",
       "       [  30,   14,   41,    2,   43,   95, 5560,    4,  128,    1],\n",
       "       [  21,    9,   52,   27,   51,   12,    3, 5693,  188,  209],\n",
       "       [  17,   63,   46,   90,    3,  125,   25,   10, 5429,   43],\n",
       "       [  23,   18,   31,   66,  116,   32,    1,  179,  377, 5106]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred = cross_val_predict(sgd_clf, X_train_scaled, y_train, cv=3)\n",
    "conf_mx = confusion_matrix(y_train, y_train_pred)\n",
    "conf_mx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(matrix):\n",
    "    \"\"\"If you prefer color and a colorbar\"\"\"\n",
    "    fig = plt.figure(figsize=(8,8))\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(matrix)\n",
    "    fig.colorbar(cax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "그림 저장: confusion_matrix_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(conf_mx, cmap=plt.cm.gray)\n",
    "save_fig(\"confusion_matrix_plot\", tight_layout=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "row_sums = conf_mx.sum(axis=1, keepdims=True)\n",
    "norm_conf_mx = conf_mx / row_sums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "그림 저장: confusion_matrix_errors_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.fill_diagonal(norm_conf_mx, 0)\n",
    "plt.matshow(norm_conf_mx, cmap=plt.cm.gray)\n",
    "save_fig(\"confusion_matrix_errors_plot\", tight_layout=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "그림 저장: error_analysis_digits_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cl_a, cl_b = 3, 5\n",
    "X_aa = X_train[(y_train == cl_a) & (y_train_pred == cl_a)]\n",
    "X_ab = X_train[(y_train == cl_a) & (y_train_pred == cl_b)]\n",
    "X_ba = X_train[(y_train == cl_b) & (y_train_pred == cl_a)]\n",
    "X_bb = X_train[(y_train == cl_b) & (y_train_pred == cl_b)]\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.subplot(221); plot_digits(X_aa[:25], images_per_row=5)\n",
    "plt.subplot(222); plot_digits(X_ab[:25], images_per_row=5)\n",
    "plt.subplot(223); plot_digits(X_ba[:25], images_per_row=5)\n",
    "plt.subplot(224); plot_digits(X_bb[:25], images_per_row=5)\n",
    "save_fig(\"error_analysis_digits_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 다중 레이블 분류"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "y_train_large = (y_train >= 7)\n",
    "y_train_odd = (y_train % 2 == 1)\n",
    "y_multilabel = np.c_[y_train_large, y_train_odd]\n",
    "\n",
    "knn_clf = KNeighborsClassifier()\n",
    "knn_clf.fit(X_train, y_multilabel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False,  True]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**경고**: 다음 셀은 실행하는데 매우 오래 걸립니다(하드웨어에 따라 몇 시간이 걸릴 수 있습니다)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.976410265560605"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_knn_pred = cross_val_predict(knn_clf, X_train, y_multilabel, cv=3)\n",
    "f1_score(y_multilabel, y_train_knn_pred, average=\"macro\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 다중 출력 분류"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise = np.random.randint(0, 100, (len(X_train), 784))\n",
    "X_train_mod = X_train + noise\n",
    "noise = np.random.randint(0, 100, (len(X_test), 784))\n",
    "X_test_mod = X_test + noise\n",
    "y_train_mod = X_train\n",
    "y_test_mod = X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "그림 저장: noisy_digit_example_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "some_index = 0\n",
    "plt.subplot(121); plot_digit(X_test_mod[some_index])\n",
    "plt.subplot(122); plot_digit(y_test_mod[some_index])\n",
    "save_fig(\"noisy_digit_example_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "그림 저장: cleaned_digit_example_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_clf.fit(X_train_mod, y_train_mod)\n",
    "clean_digit = knn_clf.predict([X_test_mod[some_index]])\n",
    "plot_digit(clean_digit)\n",
    "save_fig(\"cleaned_digit_example_plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 추가 내용"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 더미 (즉 랜덤) 분류기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.dummy import DummyClassifier\n",
    "dmy_clf = DummyClassifier()\n",
    "y_probas_dmy = cross_val_predict(dmy_clf, X_train, y_train_5, cv=3, method=\"predict_proba\")\n",
    "y_scores_dmy = y_probas_dmy[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fprr, tprr, thresholdsr = roc_curve(y_train_5, y_scores_dmy)\n",
    "plot_roc_curve(fprr, tprr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN 분류기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=4, p=2,\n",
       "                     weights='distance')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn_clf = KNeighborsClassifier(weights='distance', n_neighbors=4)\n",
    "knn_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_knn_pred = knn_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9714"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, y_knn_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.ndimage.interpolation import shift\n",
    "def shift_digit(digit_array, dx, dy, new=0):\n",
    "    return shift(digit_array.reshape(28, 28), [dy, dx], cval=new).reshape(784)\n",
    "\n",
    "plot_digit(shift_digit(some_digit, 5, 1, new=100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((300000, 784), (300000,))"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_expanded = [X_train]\n",
    "y_train_expanded = [y_train]\n",
    "for dx, dy in ((1, 0), (-1, 0), (0, 1), (0, -1)):\n",
    "    shifted_images = np.apply_along_axis(shift_digit, axis=1, arr=X_train, dx=dx, dy=dy)\n",
    "    X_train_expanded.append(shifted_images)\n",
    "    y_train_expanded.append(y_train)\n",
    "\n",
    "X_train_expanded = np.concatenate(X_train_expanded)\n",
    "y_train_expanded = np.concatenate(y_train_expanded)\n",
    "X_train_expanded.shape, y_train_expanded.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=4, p=2,\n",
       "                     weights='distance')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf.fit(X_train_expanded, y_train_expanded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_knn_expanded_pred = knn_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9763"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_knn_expanded_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.24579675, 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.75420325]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ambiguous_digit = X_test[2589]\n",
    "knn_clf.predict_proba([ambiguous_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_digit(ambiguous_digit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 연습문제 해답"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 97% 정확도의 MNIST 분류기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**경고**: 다음 셀은 하드웨어 따라 실행하는데 몇 시간이 걸리 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n",
      "[CV] n_neighbors=3, weights=uniform ..................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ...... n_neighbors=3, weights=uniform, score=0.972, total=11.4min\n",
      "[CV] n_neighbors=3, weights=uniform ..................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 11.4min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ...... n_neighbors=3, weights=uniform, score=0.971, total=11.4min\n",
      "[CV] n_neighbors=3, weights=uniform ..................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed: 22.9min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ...... n_neighbors=3, weights=uniform, score=0.969, total=11.4min\n",
      "[CV] n_neighbors=3, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=3, weights=uniform, score=0.969, total=11.4min\n",
      "[CV] n_neighbors=3, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=3, weights=uniform, score=0.970, total=11.4min\n",
      "[CV] n_neighbors=3, weights=distance .................................\n",
      "[CV] ..... n_neighbors=3, weights=distance, score=0.972, total=11.4min\n",
      "[CV] n_neighbors=3, weights=distance .................................\n",
      "[CV] ..... n_neighbors=3, weights=distance, score=0.972, total=11.4min\n",
      "[CV] n_neighbors=3, weights=distance .................................\n",
      "[CV] ..... n_neighbors=3, weights=distance, score=0.970, total=11.4min\n",
      "[CV] n_neighbors=3, weights=distance .................................\n",
      "[CV] ..... n_neighbors=3, weights=distance, score=0.970, total=11.4min\n",
      "[CV] n_neighbors=3, weights=distance .................................\n",
      "[CV] ..... n_neighbors=3, weights=distance, score=0.971, total=11.4min\n",
      "[CV] n_neighbors=4, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=4, weights=uniform, score=0.969, total=11.4min\n",
      "[CV] n_neighbors=4, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=4, weights=uniform, score=0.968, total=11.4min\n",
      "[CV] n_neighbors=4, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=4, weights=uniform, score=0.968, total=11.4min\n",
      "[CV] n_neighbors=4, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=4, weights=uniform, score=0.967, total=11.4min\n",
      "[CV] n_neighbors=4, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=4, weights=uniform, score=0.970, total=11.4min\n",
      "[CV] n_neighbors=4, weights=distance .................................\n",
      "[CV] ..... n_neighbors=4, weights=distance, score=0.973, total=11.4min\n",
      "[CV] n_neighbors=4, weights=distance .................................\n",
      "[CV] ..... n_neighbors=4, weights=distance, score=0.972, total=11.4min\n",
      "[CV] n_neighbors=4, weights=distance .................................\n",
      "[CV] ..... n_neighbors=4, weights=distance, score=0.970, total=11.4min\n",
      "[CV] n_neighbors=4, weights=distance .................................\n",
      "[CV] ..... n_neighbors=4, weights=distance, score=0.971, total=11.4min\n",
      "[CV] n_neighbors=4, weights=distance .................................\n",
      "[CV] ..... n_neighbors=4, weights=distance, score=0.972, total=11.4min\n",
      "[CV] n_neighbors=5, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=5, weights=uniform, score=0.970, total=11.4min\n",
      "[CV] n_neighbors=5, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=5, weights=uniform, score=0.970, total=11.4min\n",
      "[CV] n_neighbors=5, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=5, weights=uniform, score=0.969, total=11.4min\n",
      "[CV] n_neighbors=5, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=5, weights=uniform, score=0.968, total=11.4min\n",
      "[CV] n_neighbors=5, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=5, weights=uniform, score=0.969, total=11.4min\n",
      "[CV] n_neighbors=5, weights=distance .................................\n",
      "[CV] ..... n_neighbors=5, weights=distance, score=0.970, total=11.4min\n",
      "[CV] n_neighbors=5, weights=distance .................................\n",
      "[CV] ..... n_neighbors=5, weights=distance, score=0.971, total=11.4min\n",
      "[CV] n_neighbors=5, weights=distance .................................\n",
      "[CV] ..... n_neighbors=5, weights=distance, score=0.970, total=11.4min\n",
      "[CV] n_neighbors=5, weights=distance .................................\n",
      "[CV] ..... n_neighbors=5, weights=distance, score=0.969, total=11.4min\n",
      "[CV] n_neighbors=5, weights=distance .................................\n",
      "[CV] ..... n_neighbors=5, weights=distance, score=0.971, total=11.4min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed: 343.2min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30,\n",
       "                                            metric='minkowski',\n",
       "                                            metric_params=None, n_jobs=None,\n",
       "                                            n_neighbors=5, p=2,\n",
       "                                            weights='uniform'),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid=[{'n_neighbors': [3, 4, 5],\n",
       "                          'weights': ['uniform', 'distance']}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=3)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [{'weights': [\"uniform\", \"distance\"], 'n_neighbors': [3, 4, 5]}]\n",
    "\n",
    "knn_clf = KNeighborsClassifier()\n",
    "grid_search = GridSearchCV(knn_clf, param_grid, cv=5, verbose=3)\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 4, 'weights': 'distance'}"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9716166666666667"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9714"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "y_pred = grid_search.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 데이터 증식"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.ndimage.interpolation import shift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "def shift_image(image, dx, dy):\n",
    "    image = image.reshape((28, 28))\n",
    "    shifted_image = shift(image, [dy, dx], cval=0, mode=\"constant\")\n",
    "    return shifted_image.reshape([-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = X_train[1000]\n",
    "shifted_image_down = shift_image(image, 0, 5)\n",
    "shifted_image_left = shift_image(image, -5, 0)\n",
    "\n",
    "plt.figure(figsize=(12,3))\n",
    "plt.subplot(131)\n",
    "plt.title(\"Original\", fontsize=14)\n",
    "plt.imshow(image.reshape(28, 28), interpolation=\"nearest\", cmap=\"Greys\")\n",
    "plt.subplot(132)\n",
    "plt.title(\"Shifted down\", fontsize=14)\n",
    "plt.imshow(shifted_image_down.reshape(28, 28), interpolation=\"nearest\", cmap=\"Greys\")\n",
    "plt.subplot(133)\n",
    "plt.title(\"Shifted left\", fontsize=14)\n",
    "plt.imshow(shifted_image_left.reshape(28, 28), interpolation=\"nearest\", cmap=\"Greys\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_augmented = [image for image in X_train]\n",
    "y_train_augmented = [label for label in y_train]\n",
    "\n",
    "for dx, dy in ((1, 0), (-1, 0), (0, 1), (0, -1)):\n",
    "    for image, label in zip(X_train, y_train):\n",
    "        X_train_augmented.append(shift_image(image, dx, dy))\n",
    "        y_train_augmented.append(label)\n",
    "\n",
    "X_train_augmented = np.array(X_train_augmented)\n",
    "y_train_augmented = np.array(y_train_augmented)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "shuffle_idx = np.random.permutation(len(X_train_augmented))\n",
    "X_train_augmented = X_train_augmented[shuffle_idx]\n",
    "y_train_augmented = y_train_augmented[shuffle_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_clf = KNeighborsClassifier(**grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=4, p=2,\n",
       "                     weights='distance')"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf.fit(X_train_augmented, y_train_augmented)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9763"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = knn_clf.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "간단히 데이터를 증식해서 0.5% 정확도를 높였습니다. :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 타이타닉 데이터셋 도전"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "승객의 나이, 성별, 승객 등급, 승선 위치 같은 속성을 기반으로 하여 승객의 생존 여부를 예측하는 것이 목표입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "먼저 [캐글](https://www.kaggle.com)에 로그인 하고 [타이타닉 챌린지](https://www.kaggle.com/c/titanic)에서 `train.csv`와 `test.csv`를 다운로드합니다. 두 파일을 `datasets/titanic` 디렉토리에 저장하세요."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그다음 데이터를 적재합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "TITANIC_PATH = os.path.join(\"datasets\", \"titanic\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def load_titanic_data(filename, titanic_path=TITANIC_PATH):\n",
    "    csv_path = os.path.join(titanic_path, filename)\n",
    "    return pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = load_titanic_data(\"train.csv\")\n",
    "test_data = load_titanic_data(\"test.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "데이터는 이미 훈련 세트와 테스트 세트로 분리되어 있습니다. 그러나 테스트 데이터는 레이블을 가지고 있지 않습니다: 훈련 데이터를 이용하여 가능한 최고의 모델을 만들고 테스트 데이터에 대한 예측을 캐글(Kaggle)에 업로드하여 최종 점수를 확인하는 것이 목표입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "훈련 세트에서 맨 위 몇 개의 열을 살펴 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "속성은 다음과 같은 의미를 가집니다:\n",
    "* **Survived**: 타깃입니다. 0은 생존하지 못한 것이고 1은 생존을 의미합니다.\n",
    "* **Pclass**: 승객 등급. 1, 2, 3등석.\n",
    "* **Name**, **Sex**, **Age**: 이름 그대로 의미입니다.\n",
    "* **SibSp**: 함께 탑승한 형제, 배우자의 수.\n",
    "* **Parch**: 함께 탑승한 자녀, 부모의 수.\n",
    "* **Ticket**: 티켓 아이디\n",
    "* **Fare**: 티켓 요금 (파운드)\n",
    "* **Cabin**: 객실 번호\n",
    "* **Embarked**: 승객이 탑승한 곳. C(Cherbourg), Q(Queenstown), S(Southampton)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "누락된 데이터가 얼마나 되는지 알아보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "괜찮네요. **Age**, **Cabin**, **Embarked** 속성의 일부가 null입니다(891개의 non-null 보다 작습니다). 특히 **Cabin**은 77%가 null입니다. 일단 **Cabin**은 무시하고 나머지를 활용하겠습니다. **Age**는 19%가 null이므로 이를 어떻게 처리할지 결정해야 합니다. null을 중간 나이로 바꾸는 것이 괜찮아 보입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Name**과 **Ticket** 속성도 값을 가지고 있지만 머신러닝 모델이 사용할 수 있는 숫자로 변환하는 것이 조금 까다롭습니다. 그래서 지금은 이 두 속성을 무시하겠습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "통계치를 살펴 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 이크, 38%만 **Survived**입니다. :( 거의 40%에 가까우므로 정확도를 사용해 모델을 평가해도 괜찮을 것 같습니다.\n",
    "* 평균 **Fare**는 32.20 파운드라 그렇게 비싸보이지는 않습니다(아마 요금을 많이 반환해 주었기 때문일 것입니다)\n",
    "* 평균 **Age**는 30보다 작습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "타깃이 0과 1로 이루어졌는지 확인합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Survived\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "범주형 특성들을 확인해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    491\n",
       "1    216\n",
       "2    184\n",
       "Name: Pclass, dtype: int64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Pclass\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Sex\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Embarked\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Embarked** 특성은 승객이 탑승한 곳을 알려 줍니다: C=Cherbourg, Q=Queenstown, S=Southampton."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**노트**: 아래 코드는 `Pipeline`, `FeatureUnion`와 사용자 정의 `DataFrameSelector` 클래스를 사용해 각 열을 다르게 전처리합니다. 사이킷런 0.20부터는 이전 장에서처럼 `ColumnTransformer`를 사용하는 것이 좋습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "전처리 파이프라인을 만들어 보죠. 이전 장에서 만든 `DataframeSelector`를 재사용하여 `DataFrame`에서 특정 열을 선택하겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, attribute_names):\n",
    "        self.attribute_names = attribute_names\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[self.attribute_names]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "숫자 특성을 위한 파이프라인을 만듭니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "        (\"select_numeric\", DataFrameSelector([\"Age\", \"SibSp\", \"Parch\", \"Fare\"])),\n",
    "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22.    ,  1.    ,  0.    ,  7.25  ],\n",
       "       [38.    ,  1.    ,  0.    , 71.2833],\n",
       "       [26.    ,  0.    ,  0.    ,  7.925 ],\n",
       "       ...,\n",
       "       [28.    ,  1.    ,  2.    , 23.45  ],\n",
       "       [26.    ,  0.    ,  0.    , 30.    ],\n",
       "       [32.    ,  0.    ,  0.    ,  7.75  ]])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_pipeline.fit_transform(train_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "문자열로된 범주형 열을 위해 별도의 Imputer 클래스가 필요합니다(일반 `SimpleImputer` 클래스는 이를 처리하지 못합니다):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stackoverflow.com/questions/25239958 에서 착안했습니다\n",
    "class MostFrequentImputer(BaseEstimator, TransformerMixin):\n",
    "    def fit(self, X, y=None):\n",
    "        self.most_frequent_ = pd.Series([X[c].value_counts().index[0] for c in X],\n",
    "                                        index=X.columns)\n",
    "        return self\n",
    "    def transform(self, X, y=None):\n",
    "        return X.fillna(self.most_frequent_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 범주형 특성을 위한 파이프라인을 만듭니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_pipeline = Pipeline([\n",
    "        (\"select_cat\", DataFrameSelector([\"Pclass\", \"Sex\", \"Embarked\"])),\n",
    "        (\"imputer\", MostFrequentImputer()),\n",
    "        (\"cat_encoder\", OneHotEncoder(sparse=False)),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 1., ..., 0., 0., 1.],\n",
       "       [1., 0., 0., ..., 1., 0., 0.],\n",
       "       [0., 0., 1., ..., 0., 0., 1.],\n",
       "       ...,\n",
       "       [0., 0., 1., ..., 0., 0., 1.],\n",
       "       [1., 0., 0., ..., 1., 0., 0.],\n",
       "       [0., 0., 1., ..., 0., 1., 0.]])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_pipeline.fit_transform(train_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "마지막으로 숫자와 범주형 파이프라인을 연결합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import FeatureUnion\n",
    "preprocess_pipeline = FeatureUnion(transformer_list=[\n",
    "        (\"num_pipeline\", num_pipeline),\n",
    "        (\"cat_pipeline\", cat_pipeline),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "좋습니다! 이제 원본 데이터를 받아 머신러닝 모델에 주입할 숫자 입력 특성을 출력하는 전처리 파이프라인을 만들었습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22.,  1.,  0., ...,  0.,  0.,  1.],\n",
       "       [38.,  1.,  0., ...,  1.,  0.,  0.],\n",
       "       [26.,  0.,  0., ...,  0.,  0.,  1.],\n",
       "       ...,\n",
       "       [28.,  1.,  2., ...,  0.,  0.,  1.],\n",
       "       [26.,  0.,  0., ...,  1.,  0.,  0.],\n",
       "       [32.,  0.,  0., ...,  0.,  1.,  0.]])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = preprocess_pipeline.fit_transform(train_data)\n",
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "레이블을 가져옵니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = train_data[\"Survived\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 분류기를 훈련시킬 차례입니다. 먼저 `SVC`를 사용해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "    tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svm_clf = SVC(gamma=\"auto\")\n",
    "svm_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "모델이 잘 훈련된 것 같습니다. 이를 사용해서 테스트 세트에 대한 예측을 만듭니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = preprocess_pipeline.transform(test_data)\n",
    "y_pred = svm_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 예측 결과를 (캐글에서 기대하는 형태인) CSV 파일로 만들어 업로드하고 평가를 받아볼 수 있습니다. 하지만 그냥 좋을거라 기대하는 것보다 교차 검증으로 모델이 얼마나 좋은지 평가하는 것이 좋습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7365250822835092"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "svm_scores = cross_val_score(svm_clf, X_train, y_train, cv=10)\n",
    "svm_scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "정확도가 73% 이상입니다. 확실히 무작위로 선택한 것보다는 좋습니다. 하지만 아주 높은 점수는 아닙니다. 캐글에서 타이타닉 경연 대회의 [리더보드](https://www.kaggle.com/c/titanic/leaderboard)를 보면 상위 10%내에 들려면 80% 이상의 정확도를 내야합니다. 어떤 사람들은 100%를 달성했습니다. 하지만 타이타닉의 [희생자 목록](https://www.encyclopedia-titanica.org/titanic-victims/)을 쉽게 찾을 수 있으므로 머신러닝을 사용하지 않고도 이런 정확도를 달성할 수 있습니다! ;-) 우리는 80% 정도의 정확도를 내는 모델을 만들어 보겠습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`RandomForestClassifier`를 적용해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8149526160481217"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "forest_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "forest_scores = cross_val_score(forest_clf, X_train, y_train, cv=10)\n",
    "forest_scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "훨씬 좋네요!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10 폴드 교차 검증에 대한 평균 정확도를 보는 대신 모델에서 얻은 10개의 점수를 1사분위, 3사분위를 명료하게 표현해주는 상자 수염 그림(box-and-whisker) 그래프를 만들어 보겠습니다(이 방식을 제안해 준 Nevin Yilmaz에게 감사합니다). `boxplot()` 함수는 이상치(플라이어(flier)라고 부릅니다)를 감지하고 수염 부분에 이를 포함시키지 않습니다. 1사분위가 $Q_1$이고 3사분위가 $Q_3$이라면 사분위수 범위는 $IQR = Q_3 - Q_1$가 됩니다(이 값이 박스의 높이가 됩니다). $Q_1 - 1.5 \\times IQR$ 보다 낮거나 $Q3 + 1.5 \\times IQR$ 보다 높은 점수는 이상치로 간주됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))\n",
    "plt.plot([1]*10, svm_scores, \".\")\n",
    "plt.plot([2]*10, forest_scores, \".\")\n",
    "plt.boxplot([svm_scores, forest_scores], labels=(\"SVM\",\"Random Forest\"))\n",
    "plt.ylabel(\"Accuracy\", fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 결과를 더 향상시키려면:\n",
    "* 교차 검증과 그리드 탐색을 사용하여 더 많은 모델을 비교하고 하이퍼파라미터를 튜닝하세요.\n",
    "* 특성 공학을 더 시도해 보세요, 예를 들면:\n",
    "  * **SibSp**와 **Parch**을 이 두 특성의 합으로 바꿉니다.\n",
    "  * **Survived** 특성과 관련된 이름을 구별해 보세요(가령, 이름에 \"Countess\"가 있는 경우 생존할 가능성이 높습니다).\n",
    "* 수치 특성을 범주형 특성으로 바꾸어 보세요: 예를 들어, 나이대가 다른 경우 다른 생존 비율을 가질 수 있습니다(아래 참조). 그러므로 나이 구간을 범주로 만들어 나이 대신 사용하는 것이 도움이 될 수 있스니다. 비슷하게 생존자의 30%가 혼자 여행하는 사람이기 때문에 이들을 위한 특별한 범주를 만드는 것이 도움이 될 수 있습니다(아래 참조)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AgeBucket</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.576923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>0.362745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30.0</th>\n",
       "      <td>0.423256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45.0</th>\n",
       "      <td>0.404494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60.0</th>\n",
       "      <td>0.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75.0</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Survived\n",
       "AgeBucket          \n",
       "0.0        0.576923\n",
       "15.0       0.362745\n",
       "30.0       0.423256\n",
       "45.0       0.404494\n",
       "60.0       0.240000\n",
       "75.0       1.000000"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"AgeBucket\"] = train_data[\"Age\"] // 15 * 15\n",
    "train_data[[\"AgeBucket\", \"Survived\"]].groupby(['AgeBucket']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RelativesOnboard</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.303538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.552795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.578431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.724138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Survived\n",
       "RelativesOnboard          \n",
       "0                 0.303538\n",
       "1                 0.552795\n",
       "2                 0.578431\n",
       "3                 0.724138\n",
       "4                 0.200000\n",
       "5                 0.136364\n",
       "6                 0.333333\n",
       "7                 0.000000\n",
       "10                0.000000"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"RelativesOnboard\"] = train_data[\"SibSp\"] + train_data[\"Parch\"]\n",
    "train_data[[\"RelativesOnboard\", \"Survived\"]].groupby(['RelativesOnboard']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 스팸 필터"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "먼저 데이터를 다운받습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tarfile\n",
    "import urllib\n",
    "\n",
    "DOWNLOAD_ROOT = \"http://spamassassin.apache.org/old/publiccorpus/\"\n",
    "HAM_URL = DOWNLOAD_ROOT + \"20030228_easy_ham.tar.bz2\"\n",
    "SPAM_URL = DOWNLOAD_ROOT + \"20030228_spam.tar.bz2\"\n",
    "SPAM_PATH = os.path.join(\"datasets\", \"spam\")\n",
    "\n",
    "def fetch_spam_data(spam_url=SPAM_URL, spam_path=SPAM_PATH):\n",
    "    if not os.path.isdir(spam_path):\n",
    "        os.makedirs(spam_path)\n",
    "    for filename, url in ((\"ham.tar.bz2\", HAM_URL), (\"spam.tar.bz2\", SPAM_URL)):\n",
    "        path = os.path.join(spam_path, filename)\n",
    "        if not os.path.isfile(path):\n",
    "            urllib.request.urlretrieve(url, path)\n",
    "        tar_bz2_file = tarfile.open(path)\n",
    "        tar_bz2_file.extractall(path=SPAM_PATH)\n",
    "        tar_bz2_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "fetch_spam_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "다음, 모든 이메일을 읽어 들입니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "HAM_DIR = os.path.join(SPAM_PATH, \"easy_ham\")\n",
    "SPAM_DIR = os.path.join(SPAM_PATH, \"spam\")\n",
    "ham_filenames = [name for name in sorted(os.listdir(HAM_DIR)) if len(name) > 20]\n",
    "spam_filenames = [name for name in sorted(os.listdir(SPAM_DIR)) if len(name) > 20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2500"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ham_filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(spam_filenames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "파이썬의 `email` 모듈을 사용해 이메일을 파싱합니다(헤더, 인코딩 등을 처리합니다):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "import email\n",
    "import email.policy\n",
    "\n",
    "def load_email(is_spam, filename, spam_path=SPAM_PATH):\n",
    "    directory = \"spam\" if is_spam else \"easy_ham\"\n",
    "    with open(os.path.join(spam_path, directory, filename), \"rb\") as f:\n",
    "        return email.parser.BytesParser(policy=email.policy.default).parse(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "ham_emails = [load_email(is_spam=False, filename=name) for name in ham_filenames]\n",
    "spam_emails = [load_email(is_spam=True, filename=name) for name in spam_filenames]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "데이터가 어떻게 구성되어 있는지 감을 잡기 위해 햄 메일과 스팸 메일을 하나씩 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Martin A posted:\n",
      "Tassos Papadopoulos, the Greek sculptor behind the plan, judged that the\n",
      " limestone of Mount Kerdylio, 70 miles east of Salonika and not far from the\n",
      " Mount Athos monastic community, was ideal for the patriotic sculpture. \n",
      " \n",
      " As well as Alexander's granite features, 240 ft high and 170 ft wide, a\n",
      " museum, a restored amphitheatre and car park for admiring crowds are\n",
      "planned\n",
      "---------------------\n",
      "So is this mountain limestone or granite?\n",
      "If it's limestone, it'll weather pretty fast.\n",
      "\n",
      "------------------------ Yahoo! Groups Sponsor ---------------------~-->\n",
      "4 DVDs Free +s&p Join Now\n",
      "http://us.click.yahoo.com/pt6YBB/NXiEAA/mG3HAA/7gSolB/TM\n",
      "---------------------------------------------------------------------~->\n",
      "\n",
      "To unsubscribe from this group, send an email to:\n",
      "forteana-unsubscribe@egroups.com\n",
      "\n",
      " \n",
      "\n",
      "Your use of Yahoo! Groups is subject to http://docs.yahoo.com/info/terms/\n"
     ]
    }
   ],
   "source": [
    "print(ham_emails[1].get_content().strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help wanted.  We are a 14 year old fortune 500 company, that is\n",
      "growing at a tremendous rate.  We are looking for individuals who\n",
      "want to work from home.\n",
      "\n",
      "This is an opportunity to make an excellent income.  No experience\n",
      "is required.  We will train you.\n",
      "\n",
      "So if you are looking to be employed from home with a career that has\n",
      "vast opportunities, then go:\n",
      "\n",
      "http://www.basetel.com/wealthnow\n",
      "\n",
      "We are looking for energetic and self motivated people.  If that is you\n",
      "than click on the link and fill out the form, and one of our\n",
      "employement specialist will contact you.\n",
      "\n",
      "To be removed from our link simple go to:\n",
      "\n",
      "http://www.basetel.com/remove.html\n",
      "\n",
      "\n",
      "4139vOLW7-758DoDY1425FRhM1-764SMFc8513fCsLl40\n"
     ]
    }
   ],
   "source": [
    "print(spam_emails[6].get_content().strip())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "어떤 이메일은 이미지나 첨부 파일을 가진 멀티파트(multipart)입니다(메일에 포함되어 있을수 있습니다). 어떤 파일들이 있는지 살펴 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_email_structure(email):\n",
    "    if isinstance(email, str):\n",
    "        return email\n",
    "    payload = email.get_payload()\n",
    "    if isinstance(payload, list):\n",
    "        return \"multipart({})\".format(\", \".join([\n",
    "            get_email_structure(sub_email)\n",
    "            for sub_email in payload\n",
    "        ]))\n",
    "    else:\n",
    "        return email.get_content_type()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "def structures_counter(emails):\n",
    "    structures = Counter()\n",
    "    for email in emails:\n",
    "        structure = get_email_structure(email)\n",
    "        structures[structure] += 1\n",
    "    return structures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('text/plain', 2408),\n",
       " ('multipart(text/plain, application/pgp-signature)', 66),\n",
       " ('multipart(text/plain, text/html)', 8),\n",
       " ('multipart(text/plain, text/plain)', 4),\n",
       " ('multipart(text/plain)', 3),\n",
       " ('multipart(text/plain, application/octet-stream)', 2),\n",
       " ('multipart(text/plain, text/enriched)', 1),\n",
       " ('multipart(text/plain, application/ms-tnef, text/plain)', 1),\n",
       " ('multipart(multipart(text/plain, text/plain, text/plain), application/pgp-signature)',\n",
       "  1),\n",
       " ('multipart(text/plain, video/mng)', 1),\n",
       " ('multipart(text/plain, multipart(text/plain))', 1),\n",
       " ('multipart(text/plain, application/x-pkcs7-signature)', 1),\n",
       " ('multipart(text/plain, multipart(text/plain, text/plain), text/rfc822-headers)',\n",
       "  1),\n",
       " ('multipart(text/plain, multipart(text/plain, text/plain), multipart(multipart(text/plain, application/x-pkcs7-signature)))',\n",
       "  1),\n",
       " ('multipart(text/plain, application/x-java-applet)', 1)]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structures_counter(ham_emails).most_common()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('text/plain', 218),\n",
       " ('text/html', 183),\n",
       " ('multipart(text/plain, text/html)', 45),\n",
       " ('multipart(text/html)', 20),\n",
       " ('multipart(text/plain)', 19),\n",
       " ('multipart(multipart(text/html))', 5),\n",
       " ('multipart(text/plain, image/jpeg)', 3),\n",
       " ('multipart(text/html, application/octet-stream)', 2),\n",
       " ('multipart(text/plain, application/octet-stream)', 1),\n",
       " ('multipart(text/html, text/plain)', 1),\n",
       " ('multipart(multipart(text/html), application/octet-stream, image/jpeg)', 1),\n",
       " ('multipart(multipart(text/plain, text/html), image/gif)', 1),\n",
       " ('multipart/alternative', 1)]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structures_counter(spam_emails).most_common()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "햄 메일은 평범한 텍스트가 많고 스팸은 HTML일 경우가 많습니다. 적은 수의 햄 이메일이 PGP로 서명되어 있지만 스팸 메일에는 없습니다. 요약하면 이메일 구조는 유용한 정보입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 이메일 헤더를 살펴보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Return-Path : <12a1mailbot1@web.de>\n",
      "Delivered-To : zzzz@localhost.spamassassin.taint.org\n",
      "Received : from localhost (localhost [127.0.0.1])\tby phobos.labs.spamassassin.taint.org (Postfix) with ESMTP id 136B943C32\tfor <zzzz@localhost>; Thu, 22 Aug 2002 08:17:21 -0400 (EDT)\n",
      "Received : from mail.webnote.net [193.120.211.219]\tby localhost with POP3 (fetchmail-5.9.0)\tfor zzzz@localhost (single-drop); Thu, 22 Aug 2002 13:17:21 +0100 (IST)\n",
      "Received : from dd_it7 ([210.97.77.167])\tby webnote.net (8.9.3/8.9.3) with ESMTP id NAA04623\tfor <zzzz@spamassassin.taint.org>; Thu, 22 Aug 2002 13:09:41 +0100\n",
      "From : 12a1mailbot1@web.de\n",
      "Received : from r-smtp.korea.com - 203.122.2.197 by dd_it7  with Microsoft SMTPSVC(5.5.1775.675.6);\t Sat, 24 Aug 2002 09:42:10 +0900\n",
      "To : dcek1a1@netsgo.com\n",
      "Subject : Life Insurance - Why Pay More?\n",
      "Date : Wed, 21 Aug 2002 20:31:57 -1600\n",
      "MIME-Version : 1.0\n",
      "Message-ID : <0103c1042001882DD_IT7@dd_it7>\n",
      "Content-Type : text/html; charset=\"iso-8859-1\"\n",
      "Content-Transfer-Encoding : quoted-printable\n"
     ]
    }
   ],
   "source": [
    "for header, value in spam_emails[0].items():\n",
    "    print(header,\":\",value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "보낸사람의 이메일 주소와 같이 헤더에는 유용한 정보가 많이 있지만 여기서는 `Subject` 헤더만 다뤄 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Life Insurance - Why Pay More?'"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam_emails[0][\"Subject\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "좋습니다. 데이터에를 더 살펴보기 전에 훈련 세트와 테스트 세트로 나누도록 하겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = np.array(ham_emails + spam_emails)\n",
    "y = np.array([0] * len(ham_emails) + [1] * len(spam_emails))\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 전처리 함수를 작성하겠습니다. 먼저 HTML을 일반 텍스트로 변환하는 함수가 필요합니다. 이 작업에는 당연히 [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/) 라이브러리를 사용하는게 좋지만 의존성을 줄이기 위해서 정규식을 사용하여 대강 만들어 보겠습니다([un̨ho͞ly radiańcé destro҉ying all enli̍̈́̂̈́ghtenment](https://stackoverflow.com/a/1732454/38626)의 위험에도 불구하고). 다음 함수는 `<head>` 섹션을 삭제하고 모든 `<a>` 태그를 HYPERLINK 문자로 바꿉니다. 그런 다음 모든 HTML 태그를 제거하고 텍스트만 남깁니다. 보기 편하게 여러개의 개행 문자를 하나로 만들고 (`&gt;`나 `&nbsp;` 같은) html 엔티티를 복원합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "from html import unescape\n",
    "\n",
    "def html_to_plain_text(html):\n",
    "    text = re.sub('<head.*?>.*?</head>', '', html, flags=re.M | re.S | re.I)\n",
    "    text = re.sub('<a\\s.*?>', ' HYPERLINK ', text, flags=re.M | re.S | re.I)\n",
    "    text = re.sub('<.*?>', '', text, flags=re.M | re.S)\n",
    "    text = re.sub(r'(\\s*\\n)+', '\\n', text, flags=re.M | re.S)\n",
    "    return unescape(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "잘 작동하는지 확인해 보겠습니다. 다음은 HTML 스팸입니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<HTML><HEAD><TITLE></TITLE><META http-equiv=\"Content-Type\" content=\"text/html; charset=windows-1252\"><STYLE>A:link {TEX-DECORATION: none}A:active {TEXT-DECORATION: none}A:visited {TEXT-DECORATION: none}A:hover {COLOR: #0033ff; TEXT-DECORATION: underline}</STYLE><META content=\"MSHTML 6.00.2713.1100\" name=\"GENERATOR\"></HEAD>\n",
      "<BODY text=\"#000000\" vLink=\"#0033ff\" link=\"#0033ff\" bgColor=\"#CCCC99\"><TABLE borderColor=\"#660000\" cellSpacing=\"0\" cellPadding=\"0\" border=\"0\" width=\"100%\"><TR><TD bgColor=\"#CCCC99\" valign=\"top\" colspan=\"2\" height=\"27\">\n",
      "<font size=\"6\" face=\"Arial, Helvetica, sans-serif\" color=\"#660000\">\n",
      "<b>OTC</b></font></TD></TR><TR><TD height=\"2\" bgcolor=\"#6a694f\">\n",
      "<font size=\"5\" face=\"Times New Roman, Times, serif\" color=\"#FFFFFF\">\n",
      "<b>&nbsp;Newsletter</b></font></TD><TD height=\"2\" bgcolor=\"#6a694f\"><div align=\"right\"><font color=\"#FFFFFF\">\n",
      "<b>Discover Tomorrow's Winners&nbsp;</b></font></div></TD></TR><TR><TD height=\"25\" colspan=\"2\" bgcolor=\"#CCCC99\"><table width=\"100%\" border=\"0\"  ...\n"
     ]
    }
   ],
   "source": [
    "html_spam_emails = [email for email in X_train[y_train==1]\n",
    "                    if get_email_structure(email) == \"text/html\"]\n",
    "sample_html_spam = html_spam_emails[7]\n",
    "print(sample_html_spam.get_content().strip()[:1000], \"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "변환된 텍스트입니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "OTC\n",
      " Newsletter\n",
      "Discover Tomorrow's Winners \n",
      "For Immediate Release\n",
      "Cal-Bay (Stock Symbol: CBYI)\n",
      "Watch for analyst \"Strong Buy Recommendations\" and several advisory newsletters picking CBYI.  CBYI has filed to be traded on the OTCBB, share prices historically INCREASE when companies get listed on this larger trading exchange. CBYI is trading around 25 cents and should skyrocket to $2.66 - $3.25 a share in the near future.\n",
      "Put CBYI on your watch list, acquire a position TODAY.\n",
      "REASONS TO INVEST IN CBYI\n",
      "A profitable company and is on track to beat ALL earnings estimates!\n",
      "One of the FASTEST growing distributors in environmental & safety equipment instruments.\n",
      "Excellent management team, several EXCLUSIVE contracts.  IMPRESSIVE client list including the U.S. Air Force, Anheuser-Busch, Chevron Refining and Mitsubishi Heavy Industries, GE-Energy & Environmental Research.\n",
      "RAPIDLY GROWING INDUSTRY\n",
      "Industry revenues exceed $900 million, estimates indicate that there could be as much as $25 billi ...\n"
     ]
    }
   ],
   "source": [
    "print(html_to_plain_text(sample_html_spam.get_content())[:1000], \"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "아주 좋습니다! 이제 포맷에 상관없이 이메일을 입력으로 받아서 일반 텍스트를 출력하는 함수를 만들겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "def email_to_text(email):\n",
    "    html = None\n",
    "    for part in email.walk():\n",
    "        ctype = part.get_content_type()\n",
    "        if not ctype in (\"text/plain\", \"text/html\"):\n",
    "            continue\n",
    "        try:\n",
    "            content = part.get_content()\n",
    "        except: # in case of encoding issues\n",
    "            content = str(part.get_payload())\n",
    "        if ctype == \"text/plain\":\n",
    "            return content\n",
    "        else:\n",
    "            html = content\n",
    "    if html:\n",
    "        return html_to_plain_text(html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "OTC\n",
      " Newsletter\n",
      "Discover Tomorrow's Winners \n",
      "For Immediate Release\n",
      "Cal-Bay (Stock Symbol: CBYI)\n",
      "Wat ...\n"
     ]
    }
   ],
   "source": [
    "print(email_to_text(sample_html_spam)[:100], \"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "어간 추출을 해보죠! 이 작업을 하려면 자연어 처리 툴킷([NLTK](http://www.nltk.org/))을 설치해야 합니다. 다음 명령으로 간단히 설치할 수 있습니다(먼저 virtualenv 환경을 활성화시켜야 합니다. 별도의 환경이 없다면 어드민 권한이 필요할지 모릅니다. 아니면 `--user` 옵션을 사용하세요):\n",
    "\n",
    "`$ pip install nltk`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computations => comput\n",
      "Computation => comput\n",
      "Computing => comput\n",
      "Computed => comput\n",
      "Compute => comput\n",
      "Compulsive => compuls\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    import nltk\n",
    "\n",
    "    stemmer = nltk.PorterStemmer()\n",
    "    for word in (\"Computations\", \"Computation\", \"Computing\", \"Computed\", \"Compute\", \"Compulsive\"):\n",
    "        print(word, \"=>\", stemmer.stem(word))\n",
    "except ImportError:\n",
    "    print(\"Error: stemming requires the NLTK module.\")\n",
    "    stemmer = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "인터넷 주소는 \"URL\" 문자로 바꾸겠습니다. [정규식](https://mathiasbynens.be/demo/url-regex)을 하드 코딩할 수도 있지만 [urlextract](https://github.com/lipoja/URLExtract) 라이브러리를 사용하겠습니다. 다음 명령으로 설치합니다(먼저 virtualenv 환경을 활성화시켜야 합니다. 별도의 환경이 없다면 어드민 권한이 필요할지 모릅니다. 아니면 `--user` 옵션을 사용하세요):\n",
    "\n",
    "`$ pip install urlextract`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 코랩에서 이 노트북을 실행하려면 먼저 pip install urlextract을 실행합니다\n",
    "try:\n",
    "    import google.colab\n",
    "    !pip install -q -U urlextract\n",
    "except ImportError:\n",
    "    pass # not running on Colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['github.com', 'https://youtu.be/7Pq-S557XQU?t=3m32s']\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    import urlextract # 루트 도메인 이름을 다운로드하기 위해 인터넷 연결이 필요할지 모릅니다\n",
    "    \n",
    "    url_extractor = urlextract.URLExtract()\n",
    "    print(url_extractor.find_urls(\"Will it detect github.com and https://youtu.be/7Pq-S557XQU?t=3m32s\"))\n",
    "except ImportError:\n",
    "    print(\"Error: replacing URLs requires the urlextract module.\")\n",
    "    url_extractor = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이들을 모두 하나의 변환기로 연결하여 이메일을 단어 카운트로 바꿀 것입니다. 파이썬의 `split()` 메서드를 사용하면 구둣점과 단어 경계를 기준으로 문장을 단어로 바꿉니다. 이 방법이 많은 언어에 통하지만 전부는 아닙니다. 예를 들어 중국어와 일본어는 일반적으로 단어 사이에 공백을 두지 않습니다. 베트남어는 음절 사이에 공백을 두기도 합니다. 여기서는 데이터셋이 (거의) 영어로 되어 있기 때문에 문제없습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "class EmailToWordCounterTransformer(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, strip_headers=True, lower_case=True, remove_punctuation=True,\n",
    "                 replace_urls=True, replace_numbers=True, stemming=True):\n",
    "        self.strip_headers = strip_headers\n",
    "        self.lower_case = lower_case\n",
    "        self.remove_punctuation = remove_punctuation\n",
    "        self.replace_urls = replace_urls\n",
    "        self.replace_numbers = replace_numbers\n",
    "        self.stemming = stemming\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X, y=None):\n",
    "        X_transformed = []\n",
    "        for email in X:\n",
    "            text = email_to_text(email) or \"\"\n",
    "            if self.lower_case:\n",
    "                text = text.lower()\n",
    "            if self.replace_urls and url_extractor is not None:\n",
    "                urls = list(set(url_extractor.find_urls(text)))\n",
    "                urls.sort(key=lambda url: len(url), reverse=True)\n",
    "                for url in urls:\n",
    "                    text = text.replace(url, \" URL \")\n",
    "            if self.replace_numbers:\n",
    "                text = re.sub(r'\\d+(?:\\.\\d*(?:[eE]\\d+))?', 'NUMBER', text)\n",
    "            if self.remove_punctuation:\n",
    "                text = re.sub(r'\\W+', ' ', text, flags=re.M)\n",
    "            word_counts = Counter(text.split())\n",
    "            if self.stemming and stemmer is not None:\n",
    "                stemmed_word_counts = Counter()\n",
    "                for word, count in word_counts.items():\n",
    "                    stemmed_word = stemmer.stem(word)\n",
    "                    stemmed_word_counts[stemmed_word] += count\n",
    "                word_counts = stemmed_word_counts\n",
    "            X_transformed.append(word_counts)\n",
    "        return np.array(X_transformed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 변환기를 몇 개의 이메일에 적용해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([Counter({'chuck': 1, 'murcko': 1, 'wrote': 1, 'stuff': 1, 'yawn': 1, 'r': 1}),\n",
       "       Counter({'the': 11, 'of': 9, 'and': 8, 'all': 3, 'christian': 3, 'to': 3, 'by': 3, 'jefferson': 2, 'i': 2, 'have': 2, 'superstit': 2, 'one': 2, 'on': 2, 'been': 2, 'ha': 2, 'half': 2, 'rogueri': 2, 'teach': 2, 'jesu': 2, 'some': 1, 'interest': 1, 'quot': 1, 'url': 1, 'thoma': 1, 'examin': 1, 'known': 1, 'word': 1, 'do': 1, 'not': 1, 'find': 1, 'in': 1, 'our': 1, 'particular': 1, 'redeem': 1, 'featur': 1, 'they': 1, 'are': 1, 'alik': 1, 'found': 1, 'fabl': 1, 'mytholog': 1, 'million': 1, 'innoc': 1, 'men': 1, 'women': 1, 'children': 1, 'sinc': 1, 'introduct': 1, 'burnt': 1, 'tortur': 1, 'fine': 1, 'imprison': 1, 'what': 1, 'effect': 1, 'thi': 1, 'coercion': 1, 'make': 1, 'world': 1, 'fool': 1, 'other': 1, 'hypocrit': 1, 'support': 1, 'error': 1, 'over': 1, 'earth': 1, 'six': 1, 'histor': 1, 'american': 1, 'john': 1, 'e': 1, 'remsburg': 1, 'letter': 1, 'william': 1, 'short': 1, 'again': 1, 'becom': 1, 'most': 1, 'pervert': 1, 'system': 1, 'that': 1, 'ever': 1, 'shone': 1, 'man': 1, 'absurd': 1, 'untruth': 1, 'were': 1, 'perpetr': 1, 'upon': 1, 'a': 1, 'larg': 1, 'band': 1, 'dupe': 1, 'import': 1, 'led': 1, 'paul': 1, 'first': 1, 'great': 1, 'corrupt': 1}),\n",
       "       Counter({'url': 4, 's': 3, 'group': 3, 'to': 3, 'in': 2, 'forteana': 2, 'martin': 2, 'an': 2, 'and': 2, 'we': 2, 'is': 2, 'yahoo': 2, 'unsubscrib': 2, 'y': 1, 'adamson': 1, 'wrote': 1, 'for': 1, 'altern': 1, 'rather': 1, 'more': 1, 'factual': 1, 'base': 1, 'rundown': 1, 'on': 1, 'hamza': 1, 'career': 1, 'includ': 1, 'hi': 1, 'belief': 1, 'that': 1, 'all': 1, 'non': 1, 'muslim': 1, 'yemen': 1, 'should': 1, 'be': 1, 'murder': 1, 'outright': 1, 'know': 1, 'how': 1, 'unbias': 1, 'memri': 1, 'don': 1, 't': 1, 'html': 1, 'rob': 1, 'sponsor': 1, 'number': 1, 'dvd': 1, 'free': 1, 'p': 1, 'join': 1, 'now': 1, 'from': 1, 'thi': 1, 'send': 1, 'email': 1, 'egroup': 1, 'com': 1, 'your': 1, 'use': 1, 'of': 1, 'subject': 1})],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_few = X_train[:3]\n",
    "X_few_wordcounts = EmailToWordCounterTransformer().fit_transform(X_few)\n",
    "X_few_wordcounts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "제대로 작동하는 것 같네요!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 단어 카운트를 벡터로 변환해야 합니다. 이를 위해서 또 다른 변환기를 만들겠습니다. 이 변환기는 (자주 나타나는 단어 순으로 정렬된) 어휘 목록을 구축하는 `fit()` 메서드와 어휘 목록을 사용해 단어를 벡터로 바꾸는 `transform()` 메서드를 가집니다. 출력은 희소 행렬이 됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.sparse import csr_matrix\n",
    "\n",
    "class WordCounterToVectorTransformer(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, vocabulary_size=1000):\n",
    "        self.vocabulary_size = vocabulary_size\n",
    "    def fit(self, X, y=None):\n",
    "        total_count = Counter()\n",
    "        for word_count in X:\n",
    "            for word, count in word_count.items():\n",
    "                total_count[word] += min(count, 10)\n",
    "        most_common = total_count.most_common()[:self.vocabulary_size]\n",
    "        self.most_common_ = most_common\n",
    "        self.vocabulary_ = {word: index + 1 for index, (word, count) in enumerate(most_common)}\n",
    "        return self\n",
    "    def transform(self, X, y=None):\n",
    "        rows = []\n",
    "        cols = []\n",
    "        data = []\n",
    "        for row, word_count in enumerate(X):\n",
    "            for word, count in word_count.items():\n",
    "                rows.append(row)\n",
    "                cols.append(self.vocabulary_.get(word, 0))\n",
    "                data.append(count)\n",
    "        return csr_matrix((data, (rows, cols)), shape=(len(X), self.vocabulary_size + 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<3x11 sparse matrix of type '<class 'numpy.longlong'>'\n",
       "\twith 20 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab_transformer = WordCounterToVectorTransformer(vocabulary_size=10)\n",
    "X_few_vectors = vocab_transformer.fit_transform(X_few_wordcounts)\n",
    "X_few_vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [99, 11,  9,  8,  3,  1,  3,  1,  3,  2,  3],\n",
       "       [67,  0,  1,  2,  3,  4,  1,  2,  0,  1,  0]], dtype=int64)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_few_vectors.toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 행렬은 무엇을 의미하나요? 세 번째 행의 첫 번째 열의 65는 세 번째 이메일이 어휘 목록에 없는 단어를 65개 가지고 있다는 뜻입니다. 그 다음의 0은 어휘 목록에 있는 첫 번째 단어가 한 번도 등장하지 않는다는 뜻이고 그 다음의 1은 한 번 나타난다는 뜻입니다. 이 단어들이 무엇인지 확인하려면 어휘 목록을 보면 됩니다. 첫 번째 단어는 \"the\"이고 두 번째 단어는 \"of\"입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'the': 1,\n",
       " 'of': 2,\n",
       " 'and': 3,\n",
       " 'to': 4,\n",
       " 'url': 5,\n",
       " 'all': 6,\n",
       " 'in': 7,\n",
       " 'christian': 8,\n",
       " 'on': 9,\n",
       " 'by': 10}"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab_transformer.vocabulary_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 스팸 분류기를 훈련시킬 준비를 마쳤습니다! 전체 데이터셋을 변환시켜보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "preprocess_pipeline = Pipeline([\n",
    "    (\"email_to_wordcount\", EmailToWordCounterTransformer()),\n",
    "    (\"wordcount_to_vector\", WordCounterToVectorTransformer()),\n",
    "])\n",
    "\n",
    "X_train_transformed = preprocess_pipeline.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: to be future-proof, we set `solver=\"lbfgs\"` since this will be the default value in Scikit-Learn 0.22."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "/home/haesun/anaconda3/envs/homl2/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "/home/haesun/anaconda3/envs/homl2/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  ................................................................\n",
      "[CV] .................................... , score=0.981, total=   0.1s\n",
      "[CV]  ................................................................\n",
      "[CV] .................................... , score=0.985, total=   0.1s\n",
      "[CV]  ................................................................\n",
      "[CV] .................................... , score=0.990, total=   0.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/haesun/anaconda3/envs/homl2/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9854166666666666"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "log_clf = LogisticRegression(solver=\"lbfgs\", random_state=42)\n",
    "score = cross_val_score(log_clf, X_train_transformed, y_train, cv=3, verbose=3)\n",
    "score.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "98.7%가 넘네요. 첫 번째 시도치고 나쁘지 않습니다! :) 그러나 이 데이터셋은 비교적 쉬운 문제입니다. 더 어려운 데이터셋에 적용해 보면 결과가 그리 높지 않을 것입니다. 여러개의 모델을 시도해 보고 제일 좋은 것을 골라 교차 검증으로 세밀하게 튜닝해 보세요.\n",
    "\n",
    "하지만 전체 내용을 파악했으므로 여기서 멈추겠습니다. 테스트 세트에서 정밀도/재현율을 출력해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정밀도: 95.88%\n",
      "재현율: 97.89%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/haesun/anaconda3/envs/homl2/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:947: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score\n",
    "\n",
    "X_test_transformed = preprocess_pipeline.transform(X_test)\n",
    "\n",
    "log_clf = LogisticRegression(solver=\"lbfgs\", random_state=42)\n",
    "log_clf.fit(X_train_transformed, y_train)\n",
    "\n",
    "y_pred = log_clf.predict(X_test_transformed)\n",
    "\n",
    "print(\"정밀도: {:.2f}%\".format(100 * precision_score(y_test, y_pred)))\n",
    "print(\"재현율: {:.2f}%\".format(100 * recall_score(y_test, y_pred)))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  },
  "nav_menu": {},
  "toc": {
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 6,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}