03_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPython 3.6.7\n",
      "IPython 6.5.0\n",
      "\n",
      "numpy 1.15.2\n",
      "scipy 1.1.0\n",
      "sklearn 0.20.0\n",
      "pandas 0.23.4\n",
      "matplotlib 3.0.2\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -v -p numpy,scipy,sklearn,pandas,matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3장 – 분류**\n",
    "\n",
    "_이 노트북은 3장에 있는 모든 샘플 코드와 연습문제 해답을 가지고 있습니다._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 설정"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "파이썬 2와 3을 모두 지원합니다. 공통 모듈을 임포트하고 맷플롯립 그림이 노트북 안에 포함되도록 설정하고 생성한 그림을 저장하기 위한 함수를 준비합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 파이썬 2와 파이썬 3 지원\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# 공통\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# 일관된 출력을 위해 유사난수 초기화\n",
    "np.random.seed(42)\n",
    "\n",
    "# 맷플롯립 설정\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "# 한글출력\n",
    "matplotlib.rc('font', family='NanumBarunGothic')\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 그림을 저장할 폴드\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"classification\"\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True):\n",
    "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/haesun/anaconda3/envs/handson-ml2/lib/python3.6/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function fetch_mldata is deprecated; fetch_mldata was deprecated in version 0.20 and will be removed in version 0.22\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "/home/haesun/anaconda3/envs/handson-ml2/lib/python3.6/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function mldata_filename is deprecated; mldata_filename was deprecated in version 0.20 and will be removed in version 0.22\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'DESCR': 'mldata.org dataset: mnist-original',\n",
       " 'COL_NAMES': ['label', 'data'],\n",
       " 'target': array([0., 0., 0., ..., 9., 9., 9.]),\n",
       " 'data': array([[0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_mldata\n",
    "mnist = fetch_mldata('MNIST original')\n",
    "mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000, 784)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y = mnist[\"data\"], mnist[\"target\"]\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "784"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "28*28"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`fetch_mldata`는 0.22 버전에서 삭제될 예정입니다. 0.20 버전에서 관련된 경고를 피하기 위해 대신 `fetch_openml` 함수를 사용하는 것이 좋습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "mnist = fetch_openml('mnist_784', version=1)\n",
    "X, y = mnist[\"data\"], mnist[\"target\"]\n",
    "y = y.astype(np.int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "some_digit = X[36000]\n",
    "some_digit_image = some_digit.reshape(28, 28)\n",
    "plt.imshow(some_digit_image, cmap = matplotlib.cm.binary,\n",
    "           interpolation=\"nearest\")\n",
    "plt.axis(\"off\")\n",
    "\n",
    "save_fig(\"some_digit_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_digit(data):\n",
    "    image = data.reshape(28, 28)\n",
    "    plt.imshow(image, cmap = matplotlib.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 = matplotlib.cm.binary, **options)\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "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 = np.r_[X[:12000:600], X[13000:30600:600], X[30600:60000:590]]\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": [
       "9"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[36000]"
   ]
  },
  {
   "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": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "shuffle_index = np.random.permutation(60000)\n",
    "X_train, y_train = X_train[shuffle_index], y_train[shuffle_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 이진 분류기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_5 = (y_train == 5)\n",
    "y_test_5 = (y_test == 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "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', max_iter=5,\n",
       "       n_iter=None, n_iter_no_change=5, n_jobs=None, penalty='l2',\n",
       "       power_t=0.5, random_state=42, shuffle=True, tol=None,\n",
       "       validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_clf = SGDClassifier(max_iter=5, random_state=42)\n",
    "sgd_clf.fit(X_train, y_train_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.9605 , 0.95595, 0.95375])"
      ]
     },
     "execution_count": 18,
     "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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9605\n",
      "0.95595\n",
      "0.95375\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": 20,
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.909  , 0.90745, 0.9125 ])"
      ]
     },
     "execution_count": 21,
     "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": 22,
   "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[52972,  1607],\n",
       "       [  989,  4432]])"
      ]
     },
     "execution_count": 23,
     "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": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_perfect_predictions = y_train_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[54579,     0],\n",
       "       [    0,  5421]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train_5, y_train_perfect_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7338963404537175"
      ]
     },
     "execution_count": 26,
     "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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7687135020350381"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4344 / (4344 + 1307)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8175613355469471"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.801328168234643"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4344 / (4344 + 1077)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7734729493891798"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "f1_score(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7846820809248555"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4344 / (4344 + (1077 + 1307)/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-176288.21703149])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_scores = sgd_clf.decision_function([some_digit])\n",
    "y_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = 0\n",
    "y_some_digit_pred = (y_scores > threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_some_digit_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = 200000\n",
    "y_some_digit_pred = (y_scores > threshold)\n",
    "y_some_digit_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3,\n",
    "                             method=\"decision_function\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "노트: 만약 사이킷런 0.19.0 버전을 사용하고 있다면 `method=\"decision_function\"` 옵션으로 `cross_val_predict()` 함수를 사용할 때 이진 분류에서 1차원 배열이 아니고 2차원 배열을 반환하는 [버그](https://github.com/scikit-learn/scikit-learn/issues/9589)가 있습니다. 사이킷런 0.19.1로 업그레이드하거나 다음 셀에서 y_scores\\[:, 1\\] 처럼 두 번째 열만 사용해야 합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_scores.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hack to work around issue #9589 introduced in Scikit-Learn 0.19.0\n",
    "# if y_scores.ndim == 2:\n",
    "#     y_scores = y_scores[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": 40,
   "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": [
    "def plot_precision_recall_vs_threshold(precisions, recalls, thresholds):\n",
    "    plt.plot(thresholds, precisions[:-1], \"b--\", label=\"정밀도\", linewidth=2)\n",
    "    plt.plot(thresholds, recalls[:-1], \"g-\", label=\"재현율\", linewidth=2)\n",
    "    plt.xlabel(\"임계값\", fontsize=16)\n",
    "    plt.legend(loc=\"upper left\", fontsize=16)\n",
    "    plt.ylim([0, 1])\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "plot_precision_recall_vs_threshold(precisions, recalls, thresholds)\n",
    "plt.xlim([-700000, 700000])\n",
    "save_fig(\"precision_recall_vs_threshold_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y_train_pred == (y_scores > 0)).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred_90 = (y_scores > 70000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.855198572066042"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_train_5, y_train_pred_90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7070651171370596"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred_90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "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(\"재현율\", fontsize=16)\n",
    "    plt.ylabel(\"정밀도\", fontsize=16)\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plot_precision_vs_recall(precisions, recalls)\n",
    "save_fig(\"precision_vs_recall_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ROC 곡선"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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": 47,
   "metadata": {},
   "outputs": [
    {
     "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--')\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.xlabel('거짓 양성 비율', fontsize=16)\n",
    "    plt.ylabel('진짜 양성 비율', fontsize=16)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plot_roc_curve(fpr, tpr)\n",
    "save_fig(\"roc_curve_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9614189997126434"
      ]
     },
     "execution_count": 48,
     "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": [
    "랜덤 포레스트 `n_estimator` 매개변수의 기본값이 0.22 버전에서 10에서 100으로 변경될 예정입니다. 경고를 피하기 위해서 `n_estimator=10`으로 지정합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "forest_clf = RandomForestClassifier(n_estimators=10, 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": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores_forest = y_probas_forest[:, 1] # 점수는 양상 클래스의 확률입니다\n",
    "fpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train_5,y_scores_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "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, \"랜덤 포레스트\")\n",
    "plt.legend(loc=\"lower right\", fontsize=16)\n",
    "save_fig(\"roc_curve_comparison_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9928250745111685"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_train_5, y_scores_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9870386643233744"
      ]
     },
     "execution_count": 53,
     "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": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8288138719793396"
      ]
     },
     "execution_count": 54,
     "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": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.fit(X_train, y_train)\n",
    "sgd_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-736165.13534356, -391182.59305387, -752094.90919363,\n",
       "        -199593.6564959 ,  -33428.88555026, -176288.21703149,\n",
       "        -856742.40238951, -145757.71304016, -253587.97052021,\n",
       "        -249064.39096412]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_digit_scores = sgd_clf.decision_function([some_digit])\n",
    "some_digit_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(some_digit_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.classes_[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.multiclass import OneVsOneClassifier\n",
    "ovo_clf = OneVsOneClassifier(SGDClassifier(max_iter=5, random_state=42))\n",
    "ovo_clf.fit(X_train, y_train)\n",
    "ovo_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ovo_clf.estimators_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_clf.fit(X_train, y_train)\n",
    "forest_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0. , 0. , 0. , 0. , 0.1, 0. , 0. , 0. , 0. , 0.9]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_clf.predict_proba([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81908618, 0.86379319, 0.84492674])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(sgd_clf, X_train, y_train, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.91131774, 0.90884544, 0.90883633])"
      ]
     },
     "execution_count": 65,
     "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": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5730,    2,   22,    9,   11,   52,   47,    8,   39,    3],\n",
       "       [   1, 6459,   54,   29,    6,   43,    6,   11,  123,   10],\n",
       "       [  53,   34, 5371,   90,   80,   23,   79,   57,  157,   14],\n",
       "       [  48,   37,  147, 5315,    3,  248,   36,   58,  143,   96],\n",
       "       [  20,   25,   45,   10, 5338,   10,   49,   36,   87,  222],\n",
       "       [  72,   40,   38,  170,   72, 4616,  108,   28,  186,   91],\n",
       "       [  35,   24,   56,    1,   39,   86, 5622,    6,   49,    0],\n",
       "       [  22,   20,   73,   28,   48,   12,    4, 5832,   18,  208],\n",
       "       [  53,  146,   84,  152,   14,  147,   55,   27, 5037,  136],\n",
       "       [  44,   33,   29,   89,  164,   39,    3,  211,   77, 5260]])"
      ]
     },
     "execution_count": 66,
     "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": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(matrix):\n",
    "    \"\"\"컬러 오차 행렬을 원할 경우\"\"\"\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": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": 69,
   "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": 70,
   "metadata": {},
   "outputs": [
    {
     "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": 71,
   "metadata": {},
   "outputs": [
    {
     "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": 72,
   "metadata": {
    "scrolled": true
   },
   "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": 72,
     "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": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True,  True]])"
      ]
     },
     "execution_count": 73,
     "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": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9768224011622285"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_knn_pred = cross_val_predict(knn_clf, X_train, y_multilabel, cv=3, n_jobs=-1)\n",
    "f1_score(y_multilabel, y_train_knn_pred, average=\"macro\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 다중 출력 분류"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAADVCAYAAAACeWRrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAE6xJREFUeJzt3d1PFvT/x/G34g3IjQiCghqkhnekBUI08a6Zc22tqWurXKvZPKm1dVRTaWuZszqog9ZJdlTH5ZnZgbXUbrxBUWcgciNqqNypQCAi9PsH3q/P73v5xcuPfZ+Pwxd7f67r4gLfXtub92fcP//8YwAAxGb8g34CAAB4aFAAgCjRoAAAUaJBAQCiRIMCAESJBgUAiBINCgAQpQnJfsDR0VH3D68OHjwoa/Ly8ty8qKhI1qi/72pqanLzvr4+edajjz7q5n///besKS0tdfMTJ064eU5OjjwrNzfXzY8dO5bQY5uZTZw40c0bGhpkjTJnzhw3b2xslDUrVqxw85aWFllz8+ZNNy8rK3Pz06dPy7MqKircfHBwUNb89ddfbr5o0aJxsig5+CNG/Fu4v0t8ggIARIkGBQCIEg0KABAlGhQAIEo0KABAlMYle5t5XV2d+4A9PT2yprq62s1HR0dlzcDAgJunpKS4uZqIMzNLT09388WLF8ua4eFhNz916pSbT5s2TZ6VlZWVUE1bW5s8KzU11c3V98XMrKCgwM1v3bqV8FlqWlI9LzOz4uJiN+/u7nbzoaEheVZ7e7ubq0lBM7Oqqio3z8rKYooPGBtM8QEAHh40KABAlGhQAIAo0aAAAFGiQQEAokSDAgBEKelj5n19fe4DpqWlyZrbt2+7+fHjx2WNGvNet26dm9+5c0eedeXKFTdX499megS8ubk54bPU2HRHR4ebl5SUJPy8QotvOzs73Xz69OluPn68/n+Pei9D7/+ZM2fcPDMz083V+xV6HLVE1kw/5+zsbMbMgbHBmDkA4OFBgwIARIkGBQCIEg0KABAlGhQAIEpJn+Lr7Ox0H7C2tlbWrF+/3s0vXrwoawoLC9386tWrbh76PqilqKEFo2qKUL2W0LLcCRMmuLmaCFy4cKE8Sy3YHTdOD6SNjIy4uZr8mzRpkjxLXa1+7do1WTNz5syEHif0+PX19W6u3mMzs+XLl7t5bm4uU3zA2GCKDwDw8KBBAQCiRIMCAESJBgUAiBINCgAQJRoUACBK/vzyfXT27NmEaxoaGtw8Oztb1pw8edLN1eLPsrIyeZYaAV+9erWsuXTpkpv39va6eVNTkzwrJyfHzUtLS9386NGj8qzJkye7eWhkPj8/380LCgrcvLu7W55148YNNw+Nhqvls+np6W7+22+/ybMyMjLcvLKyUtaEFvkCuH/4BAUAiBINCgAQJRoUACBKNCgAQJRoUACAKCV9iq+qqsrN1aSemZ6IC10t/sQTT7i5WjAbWlaqFqm2trbKmr6+PjdXi19Dy0rVNe1KaPGtmtYrLy+XNWrBq1oi+9Zbb8mz1ETiunXrZM2SJUvcXL0valGwmb4Ovr+/X9YMDQ25uVpiC4yViooK+bUpU6a4+WeffSZrQr/nMeITFAAgSjQoAECUaFAAgCjRoAAAUaJBAQCilPQpPjV5VVxcLGsef/xxN1fTfWZ6wiU1NdXNZ8+eLc9qaWlxc7ULzsysra3NzTs7O908LS1NnqUmD9UuQrU7z0xf097e3i5r5s+f7+Zq55267t7M7I8//nBzNXVpZvbUU0+5udrrF5quXLBggZurHX1mesKUKT6MFfXvQldXl6xR/8Z8/fXXsoYpPgAAxgANCgAQJRoUACBKNCgAQJRoUACAKNGgAABRSvqYuVqWqpaImpk1Nja6+ejoqKxRo8FqZFpdEW9m9sgjj7h5aDT8l19+cfNvvvnGzTds2CDPeuONN9xcXUUeei1qzHrq1KmyRo26Xrhwwc3Ve2ymx9nr6upkjRqNP3XqlJsvW7ZMnqWWAtfX18ua0J8g4P776KOP3Pz9999385qaGnnWrl27xuQ5jbXa2lo3V6PkZuGl0P8WfIICAESJBgUAiBINCgAQJRoUACBKNCgAQJTGJXsSpK+vz33A0JXnvb29bn737l1ZM2nSJDefPn26m6uFrGZ6+amaCDQz27p1q5ufO3fOzUMLTg8ePOjmjz32mJuHltiqiUi1XNfM7M8//3Tzjz/+2M3VElsz/T7v3r1b1qjJQ7XEt6qqSp6lrrxXU59mZtXV1W6elpbmbz5Onn/NGJdalmpmVllZ6eZqwk0tpDYzGxkZSeyJjTE1LbpmzRo3Dy2LXblypZuHrnwvKyvTT+7Bct80PkEBAKJEgwIARIkGBQCIEg0KABAlGhQAIEo0KABAlJK+LDYlJcXN+/v7Zc3ixYvdPLQU9fbt226en5+f8OOXl5e7+WuvvSZr1CJVNeZeUVEhzyopKXFzNTIbWryrRvNPnz4ta9599103z8zMdPPz58/Ls3bu3OnmTz/9tKxpbm52c/WnBKHRXLX4N/T+h8aWMTbU4lczPU6u/kRm1apVY/Kc7lVowasaJ+/o6HDz0M/eK6+84uYRj5InjE9QAIAo0aAAAFGiQQEAokSDAgBEiQYFAIhS0qf4Dh065OazZs2SNYcPH3bzFStWyBo14XP8+PGEz+rr63NztfjVTE/yqKWs6ip4M70sVz2vO3fuyLPUdej79u2TNeqadHUVemFhoTxr27Ztbj44OChr1BXyaolvaPJJTesNDQ3JmtBSYoyN0M+fej/z8vLcPLQsNRn27t0rv6YmTNVrVBPMZmabNm1K7Ik9hPgEBQCIEg0KABAlGhQAIEo0KABAlGhQAIAoJX2KT13Hra4VNzMbHR1184GBAVlz+fJlN1e72H766Sd51vfff+/moWvS1dfefPNNN1d75cz0FF9BQYGbZ2VlybO+++47N9+/f7+smTNnjpuPH+///2bLli3yLDUtp16LmdmlS5fcXE04hXYBqivfQ7v4VE1GRoasge/VV191c7WLzkxPuKmfs2TtolMTybt375Y16rWoqePQdK/a6/lvwicoAECUaFAAgCjRoAAAUaJBAQCiRIMCAESJBgUAiFLSx8ynTp3q5qGR8eLiYjcPXe29aNEiN29vb3fzpUuXyrM++OADN09LS5M1M2fOdHM1ApuamirPUmPWqiY0Zv7FF1+4eWhZq1o+W1lZ6eYLFy6UZ6kFn0ePHpU1apw7JSXFzdV7b6bHfNW18mbh7w0S09DQ4OahBb/qzwl27NgxJs/pXu3Zs8fNQ69FfW3jxo1uHvpd+l/AJygAQJRoUACAKNGgAABRokEBAKJEgwIARCnpU3xqIio9PV3WFBUVuXlra6usaWpqcnO1+FNNF5rpK89Dz3nDhg1unp+f7+YjIyPyrAULFrj5xYsX3fzTTz+VZ129etXN1ffFzGz9+vVuvnnzZjdX031mZhcuXHDz0OLV3NxcNz937pybhxbP9vX1uXl5ebmsQWJqa2sT/ppalmqmJ9ySsSy1pqZGfu3HH39089BrUVOsaonz/zo+QQEAokSDAgBEiQYFAIgSDQoAECUaFAAgSkmf4uvs7HTz0ESOmvxSV6Gbmc2dO9fN1RTNzz//LM+aMWOGm4d2bu3bt8/N1dXiaurQTE+YDQ8Pu3notajvc09Pj6y5ceOGm2/fvt3N1aSimZ68XLdunaxRU4HqmvDGxkZ51gsvvODmEyboXwW1ixC++vp6+bXQ74yifpcOHDiQ8FkrV650c/Wc1aSe2b29FlVTUVGR8FlKaBdldXV1QmeF/l3etGlTQmfdCz5BAQCiRIMCAESJBgUAiBINCgAQJRoUACBKNCgAQJTGhRYb3g8nT550HzB0ffuqVavcXC0LNTMrLS11c3VNeF1dnTzrxRdfdPPQNe1q+enEiRPd/O7du/Ks48ePy695QtdEq6W0apTczGx0dNTN1fi9WmJrphfshha8qkW+6jmrP2UwM3vppZfcfNu2bbJGvc68vLzE54zHVnJ/ef9DoWWxapw69O+QGs1WNaHx70RrxvJ53UvNWL6We6kJvZbPP//czd955x1ZE+A+AT5BAQCiRIMCAESJBgUAiBINCgAQJRoUACBKSV8We/v2bTdfs2aNrDl27Jibq+vjzfTV5kNDQ24+adIkedaOHTvc/Msvv5Q1ly5dcnO1xFZdK29mtnbtWjc/cuSIm4eub79165abL126VNYUFRW5+f79+908NEV4L5N/asJRfY+nTJkiz1qwYIGb5+TkyBo1eQhfaFnpzp073Tx05fn58+cTevyxXOI61jWbN292c/X6Q4+hfs8S/X79f48zljWJ4hMUACBKNCgAQJRoUACAKNGgAABRokEBAKJEgwIARCnpy2JbW1vdBwyNRqoR8GeeeUbWnD592s1LSkrcXI2lm+kFrz09PbJGjUYXFha6+cDAgDxry5Ytbn706FE3X7ZsmTzr9ddfd/Pnn39e1gwPD7u5Gs1ubW2VZxUXF7t5aPFvZmamm8+cOdPN8/Pz5VlqWW7o+69+/rKyslgWG6HQsmD1s3Evy1JramrcfNeuXYFnB4FlsQCAhwcNCgAQJRoUACBKNCgAQJRoUACAKCV9Waxa8FpeXi5rJkzwn+bly5dlzZ07d9y8ra3NzadNmybPUstH1USemVl/f7+bNzY2uvnevXvlWb29vW6upuhCi09XrFghv6aoySe14DX0vVQTcfPmzZM16vU3NTW5eXNzszxLPU5owayaMFXXl+PB2rNnj/yamtZT+caNG+VZ27dvT+yJIWF8ggIARIkGBQCIEg0KABAlGhQAIEo0KABAlJK+i6+np8d9wK6uLlmTmprq5rNmzZI1J0+edHN1fbfa92amJ8/U9fVm+jrylpYWN3/vvffkWYlqaGiQXxs/3v8/yYkTJ2TN/Pnz3Xx0dNTNQ1ek5+bmunlo8m727NlufvbsWTdXU59m+vWHdvGpCdPJkyezi+8BOnDggJs/99xzskb9e6cmVa9fv574E8O9YBcfAODhQYMCAESJBgUAiBINCgAQJRoUACBKNCgAQJSSvixWXZ+ulrua6dHQ48ePy5qqqio3V1exDw0NybNu3brl5qGlrFlZWW7+9ttvu/nChQvlWWrU9ZNPPnFz9XzNzDo6Otw8NJqtRuPVaG5aWpo8S43fd3d3yxo15n7jxg03nzFjhjxLLYsN/Syp79mcOXNkDe4/tRRWLX4N+fbbb//bp4P7gE9QAIAo0aAAAFGiQQEAokSDAgBEiQYFAIhS0qf4Lly44OZqIayZXvAZmvwbHh52c3UVu1oIa2aWl5fn5keOHJE1hw8fTuhx6urq5Flbt25185KSEjfv6+uTZ6kJv9D3Xy2FVZOHx44dk2elpKS4eWiKUn2fs7OzE3oMM71Id82aNbJGvTdM8SXHV1995eaHDh1y89AU34cffujm69evT/yJ4b7jExQAIEo0KABAlGhQAIAo0aAAAFGiQQEAokSDAgBEKelj5vcyMn7t2jU3X7VqlaxRCz6bm5vdfHBwUJ61ZMkSNw8teP3hhx/cPCMjw81DC15ffvllN58+fbqbh0bG1WtR3xczPbKvVFZWJnxWV1eXrFELZjMzM928t7dXnlVWVubmoTH3RYsWya/h/lN/GqDGyUNj5jU1NWPynJAcfIICAESJBgUAiBINCgAQJRoUACBKNCgAQJTGqevU75euri73AdX13Wb6mvjQUlA1laVeb2FhoTxLTbiFrhbfsGGDm0+bNk3WKOpqd/X6Z8+eLc9Si2TV99hMTwv++uuvbh5aoqqulg9N8c2cOdPN1XRnaIpQCU1+qQXHJSUlid8tPraS+8v7gFRUVLj5iRMn3Dz0XqrFx3jg3DeNT1AAgCjRoAAAUaJBAQCiRIMCAESJBgUAiFLSd/Glp6e7+dmzZ2WN2t+3cuVKWXP58mU3HxkZcfPQNKOaYgvtD1TTepMnT3bz0L43dU28qgntzlPTcqH9fd3d3W4+b948N1fvl5nZlClT3LypqUnWqP2FKj9z5ow8S+1cDL3/6j1Dcqif89raWjffvHnz/Xw6SCI+QQEAokSDAgBEiQYFAIgSDQoAECUaFAAgSjQoAECUkr4sdmBgwH1AtXjUzKyoqEidJWvUOLMa2VaLR83Mbt686ebV1dWyJi0tzc3VIsvQmL0ajc/Ly3Pz9vZ2eZa6Wn7t2rWyRo3zqu9laPx+6dKlbt7R0SFr1Ai8es86OzvlWXPnznVzda28mdndu3fd/Nlnn2VZLDA2WBYLAHh40KAAAFGiQQEAokSDAgBEiQYFAIhS0qf4fv/9d/cBCwoKZI2asCotLZU16pru/v5+N1+yZIk8S02LqSk6Mz3hpha51tfXy7OefPJJN1dXXq9YsUKedf36dTcPXUWvJg/r6urcfPXq1fKsK1euuHlbW5usUVfbl5WVubmaujPTU4y9vb2ypqSkxM3Hjx/PFB8wNpjiAwA8PGhQAIAo0aAAAFGiQQEAokSDAgBEiQYFAIjShGQ/oBoZvnjxYsJnpaeny6+pEXQ1Zp2fny/PUiPYJ0+elDVqbHn58uVunp2dLc9S5s2b5+atra0JP6+MjAxZ09TU5OZq8apaLmumF8mqs8zMBgcH3fyXX35x86lTp8qzysvL3bylpUXWqJ+NnJwcWQPgv8cnKABAlGhQAIAo0aAAAFGiQQEAokSDAgBEKenLYgEA+E/wCQoAECUaFAAgSjQoAECUaFAAgCjRoAAAUaJBAQCiRIMCAESJBgUAiBINCgAQJRoUACBKNCgAQJRoUACAKNGgAABRokEBAKJEgwIARIkGBQCIEg0KABAlGhQAIEo0KABAlGhQAIAo0aAAAFGiQQEAokSDAgBEiQYFAIgSDQoAEKX/A+64t/Y5o0dSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "some_index = 5500\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": 77,
   "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": [
    "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": 78,
   "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": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wI1WTpdq2/3Dfi0jek5CYxKVX3sBzD94JBQpQpesY7h51ARc1q6xRiQCZuw24G/BbVgciIrnXtz/+wZlt27Nz05cUq9mSiyfcxJ2DO1JWVYEllQwnFHd/PTsCEZHc52BCIgtWbeHulf8hpUoLarToyvwZ4zmvwcmRDk1yoXDFId/JzEHd/azMhSMiucUzr79LXNwIirUdQrFqTYkbO5FpXepRqphKzMvhhRuhbMmRKEQk19i5ey89hk3gn0uDYo4xJQrx4LDWtKl9UqRDk1wu3F1eQ3IqEBGJvEeee40rxoziwG/bKdH0fMZePYPresSqxLyki35LRIQ/E5KYtWI99y5YTmJSCk1HzeXRay6jRVVVBZb0CzeHUg6ol47j/J+7x4fv9rfjtwfuCsWRAIx1908O0+8k4EGgLsFDlG+6+9SMvp+I/N0dDwfFHA9WaU2p5hdw5ejhTOrcmKKFIv58skSZcCOU84DFqV47f19Ay4HawKaMvHFosa6lQFd3/9jMOgAvm1n10FLDh/oVIVjk6yp3/yDUVjYj7yUif7dx6490GzCcb1Ytp0jlBnScdA6z+zRRiXnJtHDFIZcCVUJfMQTJpA1BKZaKofbMPtHUCVjv7h8DuPt7BA9Qnp2m36XAJ8B4M1tjZov538W9RCQD3J2ps+ZTt149vvn4LU5sO4g7FjzHK+PaKJnIMQk3KX+AoLIwwKGnYX9z959Dr49lTFwD2JimbWOoPbV2QFOCByq3AjcBT/L3xIOZxQFxADExMccQmkje9Pu+eEbd9SwvXD+awhXrcPbEa3lw/EXUKq/KSXLsIjkpb0BymrYk/j5qKg887u6bAczsDmC3mZVw932pO7r7w8DDEBSHzI6gRaJRcnIydz/3Fks2FGTn/rJU7ncjM0YNYEjbmhQsoLIpkjUys4RaVn1QbyO4jJZaTKg9tV+APalep6T6EpEwPl73NZUbtmbSoG78vG0zZ9Yqy6r7JjO8fS0lE8lSR00oZnaRmW099BVqfsfMNpnZJuA/ZD7BvAw0NrNGofdqRXAX1ztmtipU5RiCeZy4VNWMJwDvpJ64F5G/S0xMZOD4azmzVXN+3vQNlTqP4c6h57FkWGutVyLZItwlr03AonQcZ2dG39jdd5tZH2ChmTnB5a7OQHGgKqGlh919mZnVAj41swMET+8Pzuj7ieQnm37eQ+szzuS3TV9RrFZruo+5nruGnMXJpYpGOjTJw466wFY00wJbkh8lJCax+P9+4M431vPTh89Sulwl5k0bzYVNKqnEvKRLti2wJSLR4/l/vM2w4SMo1nYoxao345K48czoVl8l5iXHKKGIRLlde/bSc+h43n1xEQVLlqVSicLcd2ks59SvEOnQJJ9RQhGJYgufe5Wxo0dx4PcfKdGsM8MnXssNvVuqxLxEhBKKSBQ6mJjMPW//h9mPvUliCjQaeRfzJw/izFoqMS+Ro4QiEmXmPPIkC/+5nv2ntOb4Jucxcthgpl3YhOKF9ecskRWu2vD3HP05k08IbuFdDHQB3gAGhUq2iEgW+v6HH+k2YBhff7iCIlUacsb4jszu00Ql5iXXCPdPmsdD/z0JGE1QRyu1zcB04BzgHmA4cD2g0vIiWcTduX7OfG6fcQ1J8Qco0+4Srp4yhSvOq6cS85KrhCsOeSOAmdUBRh96nZqZbQamuftDoRGNEopIFtl9IJEx857lyWljKFypDm0vu5b5Yy+kQSVVBZbcJyMXXf/7VJSZjQXeJig3HwMsD216A3jQzMq4+x9ZFqVIPpOSksIDz7/JExuO45e9ZajU7yauGdaXUR1rU6hgZkrwiWS/9P5m/gJcBWBmLYA5BOuinEgwx/JTqN9PBImnTNaGKZJ//N+6r6hcP5ZxA7qyfcsmWlQtw/t3T2TcOXWUTCRXS9cIJTTamGNmpwKvAC+5+yNmVjXUpTDBEr6FQ6/TlqUXkTASExMZftUNLL7/TqxgIU7uPJ6bLz2Hy86orqrAEhXC3eXVBNhFsLDVIIKJ938AQ0JdfiIYodQAvgCqE5SV/+lvBxORI/rh9720aHUGv276imKnns4FI65l3tCOVDlRVYEleoQboawjSBiJBMvu3uXukw9tdPd4M1tLkGAmEizXu87d47MpXpE8JTExkRfW7WDm69+QGNOKKi26M2vyCC5uGaNijhJ1wiWU1kAJoBHQC5hgZscDE1IljdnAc2Z2LsF6JgOyK1iRvGTpincYMnQYRdsOpVj15vQafDm39mhIhRNUYl6iU7jbhj8NffsucI+ZnU/wbEoDM+vk7gfc/QUzGwScD9zm7s9la8QiUW73nr30Gjaet19YRMETTqJC8SLM6d+Mro0ralQiUS1DtRrcfYWZtQM+BBYC/UPtTwFPZX14InnLohdeZfSokfz5+w5KNu/KwLFXc2vfVpx4fOHwO4vkchku/uPu35nZAOB2Myui+RKR8BKTU3jwvY3csuBNErwg9Ufcxd0T+qvEvOQpYRNKaESy2t0PmlkxoKW7rzSz0909MftDFIludz+6hMc+2MCuii0p2vg8Jlx2KdMvasoJRVViXvKW9IxQ3gXqAd8RPBX/LlBQyUTk6Db/sJ0LBwzjyw/foEhMI2JHt2VWryacoRLzkkel57Fb4+gVhzGzqmZWP2tCEolu7s6MOfdTu049vvz4HUq3v5Sr5y3mzYntlUwkTwv3YGNK6NuvU919YmZ26En4MUAr4LLQhn8CXdz9z2yIVSTX2x+fxPi7n2Xh1WMpckp9YgdezX2ju9GiqqoRSd4X7pLXUI4+OqlHcKfXGOAPgifpbwImH2UfkTwnJSWFR5a+xRMbCrF9V2kqXnwzEwf3Yvw5p1LkOJWYl/wh3HMojx9tu5m9C8xz9wdDr4sBd6CEIvnImn99xUX9LmH7f76k0vD5NGtYlzvGj1OJecl30l261MzKhxJGao2Bt1K9fhM4yczKZ0VwIrlZYmIiQyZMo1XL5uzYsoEKXa5gWr8OvDT6TCUTyZfSc9twD2AeUBlINrOlwDh3/xU4Afg1VfdfCSbxSxKUvBfJk37atZ8msa35ZePXFK9zJh0HX828oR2pVb5EpEMTiZhwk/LnEzwBPxd4B6gKXAusMLPWwD6gbKpdyhLMuezPlmhFIiwxMZHXv/qFG1/9msSY1pzSrAe3TBjKpadXo4BKzEs+F26Ecjsww91nHWoIzZt8C/QFvgY6AO+FNncAdru7ytdLnvPSircZPHQ4RULFHDsPiOO2no1UYl4kJFxCqUuw1O9/ufv3ZrYJOBV4GrjVzP5FcJfX7cCz2RGoSKTs3r2HPsPH8dYLT1CwVAXKFCvCHb0a0ye2soo5iqQSLqFsBZoBaw81mFkZgifmtxFUHu4CLCW41PUlwSUxkTxhyYuvMjJuBH/u/IWSLbrRM24yd/RrpRLzIoeRnktec0IPOL4HnEJwW/DPwNPungR0Dq0zXxT4xN21/K9EveQU57FV33PdwpUkUIhTh8/lzrF9VWJe5CjCPYey0MxOAu4lSBgGrAG6uvv+VP3WHuEQIlHF3bl3wRKe+HADv53ciiINz2VkvwHc1LOZSsyLhBH2tmF3n2Vm84EGwC53/zb7wxLJeVu3badb/yF88eFbFIlpTJO4ttzWqxFn1VWJeZH0SNd6KO6+F/gkm2MRiQh355a5D3DT9GtITkygdIfBxI0ez7XdGqrEvEgGZHiBLZG85GBiMlfe9zzzJ4+lSOUGNOk3hXtGdeaMmqoKLJJRSiiSLyUnJ7PolZUs2ViETb+VpEK/W7i8f3euOr8uxQvrz0IkM9JdywuCuvVmNjN06/AxM7P2ZvaZmX1hZmvM7LQw/WeYWaKZVcuK95f8ac3nX1KtQQuG9e7C+v/8h9rlS/CPO0Yz48KGSiYixyBDCSXU/2rgmBOKmZUmeH5ljLs3JqhQ/LKZHfaxYzPrBlQEth/re0v+lJiYyPAJU2nVogU/btlE+S4TuLJnW14b34bmMVqvRORYhavl1TdN06EE1NnMjlj80d2fS8d7dwLWu/vHoX3eM7MdwNnAq2niqANMADoD69NxbJH/8fueAzRoHsvPG/9N8bptOeOSydw9uAP1K50Q6dBE8oxw4/tnjtB+z1H2cSA9CaUGsDFN28ZQ+3+Z2QnAAmCgu8cf7aEyM4sD4gBiYmLSEYLkdYmJiby9/nemv/wVCVXPoGKznkwfM5gRbatzXMGMDtBF5GiO+hfl7gWO9gX0A05O057e5ekMSPtUfVLqmCzIHouAm919S7gDuvvD7h7r7rHlypVLZxiSV72yYiXlYmpx2U0P8+veeM7qPYQPHriayzvUVDIRyQaZ/qsys7HAE0DHTB5iG0FNsNQO1Qg7pCTQFLjRzD4xs08I5lGWmdngTL6v5HG7d+/m/L6X0v2Cc9l/IJ5ixYpx44UNeG7k6dQsp/VKRLJLuDmUGOB+gsWyvgE+AkoAU4FGBJehXszke78MzDWzRu7+pZm1Iqhu/I6ZrQIGu/t/gOppYtoM9HD3zZl8X8nDnnrxFeLi4tj/x6+UbHkRXQZfwax+rVRiXiQHhJtDKURQTXghcD5wHcEqjXuB9u7+r8y+sbvvNrM+wEIzc4LLXZ2B4gQLeWkNVUm3lBTn6U+3ctXj75BgRag5dC4zR/WkTwuVmBfJKebuR95oVhP4DjjO3d3MjgO6A0MJLnXNB25y9905EWxGxMbG+po1ayIdhmQzd+f+BYtZ8vEmfirXEnfnnDplua13M8qrxLxIhpnZWnePzcy+4UYoCcCmQy9C5epfNLOXgBHAGOBCM+umopGS037Ytp1u/Qfz+YcrKVq1CfWHncFN3RvRudHJGpWIREC4hJIELAemmtk3wIcEE+f3EMxtnAbcBnxkZjHuvi87gxWBYFQy8677ueG6qaFijkO5dMTl3NC9MWVUYl4kYsIllOOBscA/gSuAQxXzPgYauvtOYKCZdVIykZyQkJTC1Q+8yLxJ4yhSpSEN+17FnOGdVGJeJBcIl1AccHfvAGBmTYDRwGDgXjMb7e673f2NbI1S8r3k5GSWvPIWT28pzrc/Fad8v1sZ2rsL07rUp6RKzIvkCul5DsXMrACAu3/u7iOB1kBD4J9mVjE7AxRZu+5zqtVvxuDeXfnym/VULVucZbfEcVuvJkomIrlIuISyGaju7impG0O3C7cFdgPNsic0ye8SEhKIm3ANLVvG8uMPWyjX9UpGdT2DFVe003olIrlQuDXlk4HDljxx9z1m1jHURyRL7dp/kPpNWrBj478pXr89sRdPZN7gdjRTVWCRXCvck/LXhztA6PbMPe4+L7ROySuhcvQiGZaYmMhH3+9i2tIvOVjtTE5u1purRgxgzFm1KHJcesvEiUgkhJuUH5LO4/wIzAOKAA2OKSLJt15b8RaDBg+jSNthFKsZy5ndL2VW78bUq6gS8yLRINwlr+pH2y6SFXbv3k3/4WNZ/sISjitdkZLFj+eaC+oyvI1KzItEk0ytd2pm5xKUbXkzi+ORfOaZF19h+IgR7N/1Gye06slZA8cwp38raqgqsEjUyXBCMbNSwCPA/wFKKJIp7s6yddu56on3SChYjGpD5nLj8O4Mal2VAgVUNkUkGoWblN9JULL+CXd/LlQc8lmCxbFG5kB8kse4Ow88uohnVm/mh7ItKVD3LHp37smsvs2pXEYl5kWiWbgRSimCNUqeMbPpwG8ENbzOcvdd2R2c5C1bt/5A9wFD+NeqtylarRm1Lj2N67s1oLdKzIvkCeESSgrQCShNcBdXO2Cyu6ddC17kiFJSUrj9rvuYcd1UkpOSKXPWcHpdOpxbezRRiXmRPCQ9cyiF3X2tmbUD7gJuM7Mv3X0lQKgsyxaCul+ZmuSXvCs5xbn2oaXcMfkKilZtTN2ek7h98LkqMS+SB6UnARgEFSKBCWZWDFhqZs3dfQNBIlkQ+q8IAElJSTz3+js8+0NxPt9WjAr9b6P/hZ24vlsDlZgXyaMyM6IYC7QC7gUuCCWaG7IyKIlun637nB79L2Hrd19TacSDxFSrwczrh9KxbvlIhyYi2SjcU2OLgD9SN7h7IsEdXp3MrHN2BSbRJz4+npETriY2tgXbftjKSd0mc1mn1rw5sZ2SiUg+EO5J+eFHaF90Em07AAAUP0lEQVRtZiOBldkSlUSdfQfiqdOoGT9u/IbjG3Skce/xzLmkLafXLBvp0EQkh2R6Et3dH8nKQCQ6JSQksG7bXq5+8QsOVGtLheZ9GXdZX648tw7FCquYo0h+oruyJNNeW/4mlwwZSqG2wylesyWxXQYwq3cTmlYpHenQRCQCVHlPMmzXrl106TOQbp07sS/BKVSsBOPPrs2r49oomYjkYxqhSIY8u/Qlhg2PY/+u3zmhdW/O7DOKOQNaqsS8iCihSPot/3IHVy/+kIRCJYgZch3TLu3CMJWYF5EQJRQ5Kndn/qOP88LarWwqHYuf2oELz+nOrL7NVGJeRP6HEooc0ZYtW+gxYAjrPnqXotWbU31gK6Z2rsdAlZgXkcNQQpG/SUlJ4Y677uX6a6eRnJJMmbPj6NxvMLf3aqIS8yJyREoo8j9SUpwbFrzEzZMnULRqU2pdNJGbLzmLXs1PUTFHETkqJRQBgmKOL7y+kqU/lWL190WoMOB2unc6m5t7NKR8SZWYF5HwdHuOsPazddRs0JT+Pbqw6rOvOalEYRZecykPXRqrZCIi6aaEko8dPHiQUROuomXLlmzbvp2Tul/NxWfH8tbE9nRuVDHS4YlIlNElr3zqz4MJ1G7QjB83fcvxDc+m3kVjmDXoTDrWUVVgEckcJZR8Jj4+nm9/OcCUF77gYM0OlG/RnxEDenD1+XUpWbRQpMMTkSgW0YRiZu0JlhU+DkgAxrr7J2n6VABuIljPfi8QD4x29y9zONyo99o/VnDJkGEUaTeCojVb0ujcvtzeqzGn1VCJeRE5dhGbQzGz0sBSYIy7NwYmAy+bWdoHHZoDb7h7PXdvBbwEzMnZaKPbH3/8Qdc+A+nW5QL2JRkFipUgrl0Nll/RTslERLJMJEconYD17v4xgLu/Z2Y7gLOBVw91cvflafbbgS7VpdtzS19i6PAR7N+1kxNO60PLHiOY078lTVQVWESyWCQ/mGsAG9O0bQy1H1aqy1/DjrA9DogDiImJyZooo9i7639h2lOrSCh0AlWGXM/Efp0Y07EWhY/TzX0ikvUimVAMSE7TlsQRLsOZWVngH8AN7v7+4fq4+8PAwwCxsbGedaFGD3dn/iMLeXnddtaXaoHXbE+ntl2YfXFz6p6sEvMikn0imVC2AeekaYsBXkjb0cwqAsuBWe7+VA7EFpU2b95Mr4GD+eyj9ylaowVV+sUyuVNdhp6pEvMikv0i+SnzMtDYzBoBmFkroC7wjpmtMrPaofaqwNvAzUomh5eSksLM2XOpXbc+69as5sRzR9F18t28MbE9ce1qKpmISI6I2AjF3XebWR9goZk5weWuzkBxoCpQKtR1DlABuMrMrgq1xbt7+5yOOTdyd25/4jWunTKJotWbU63reK4f0IGBrWJUYl5EclRE75Zy93eBlofZVDlVn945F1H0SExM5KU33uG1X8rw/ncFqTBwFud1bMdtvRpzSulikQ5PRPIh3X4bhdau/Yye/S9h64ZvqTTiIU6qFMOcif3pqRLzIhJBurgeRQ4ePMjlV0yiZatWbP9xB+W6X8OFbZvx1pXt6NWispKJiESURihR4sDBeGo3aMr2Tes5vtE51Oo2mtv6ncYFqgosIrmEEkoud/DgQb7/I4GrX/yCAzXPonzsIAb16sb0rvUoXbxwpMMTEfkvJZRc7NXX/8FlQ0dQpN1witRsRZ2OvZjZsxEdVGJeRHIhzaHkQr///jvdevfnwq5d2JdcEIqV4pLTqvLmle2VTEQk19IIJZd57oVlDB0xgv17dlHq9Itp3G0Isy+OpbWqAotILqeEkot8sul3rn/+ExKLlqFSjxmM7X0OE889laKFCkY6NBGRsJRQIszdefCRBbz+xQ6+KtEcr9aW9q06Mbtvc5WYF5GoooQSQd9//z19Bg5m7cf/pFjNllTq25yxZ53K6A4qMS8i0UcJJQKSk5OZPfdupk+/lmQ3TjxvNGd2uZjZfZtR5+SSkQ5PRCRTlFBymLsz7+nlTJ0yKSgx32U8U3u3YWib6hRUMUcRiWJKKDkkISGBV1a8zVu7y/HG18bJg2bT9swzmNW7CdVOOj7S4YmIHDMllBzw6aef0mfgZWzZsJ5KcQ9TpkJlbhnThwEqMS8ieYhmfrPRgQMHuHz8RFq3Po1tO36mXI9rObd1I96c2I5Bp1VVMhGRPEUjlGwSH58QFHP8/jtKNOlEtQviuLFPK3o0U4l5EcmblFCy2IEDB9ixLzko5lj7HMq3voxeXTtx44UNKVeySKTDExHJNkooWejVV1/jsmEjKNphJIVrtKR62x7cclEDzm+oEvMikvcpoWSB3377jcviRvOPZc9TqGwMRYqeQO8Wlbmui0rMi0j+oYRyjJ569nlGjBzFn3t3U+rM/tQ7/1Ju79uC9qeWi3RoIiI5Sgklk9ydN77+ieue/5SkYmWp3PtmLu/ZkQnn1KZ4YZ1WEcl/9MmXQe7OnffM55XPf+SH8qfjNdtxfvuu3NarKfUrnRDp8EREIkYJJQPWf/cfuve7lPXrPqFYrVbU6N+Gqy+oy4DWVVU2RUTyPSWUdEhOTmbKjbcz7/abcSvIiZ3GcvElg5nRrQEVTiga6fBERHIFJZQw9hxMZMI9z/HYzddRrFYrGvS5klmXdaSjluIVEfkfSihHEB8fz51PvMTLv5Tll72lqXTZXMb2PZ8J55xKscJaQVFEJC3V8jqM11Z+wMk1G3DdyAFs/2ELzWJKs/K2YUztXE/JRETkCDRCSWX33n30Hj6Blc8/RsHjyxDTbwYzh5xD/5aqCiwiEo4SSsj/bfiFs9u0Zv/PmynR9Hz6j53KLX1bUb6kJt1FRNIj3yeUHb/v5v4PtvLk/22lSNMuVK9ag/smXaIn3UVEMijfzqG4O9PveZyq1Wvy8OLnKGjG1RPGsvreK5RMREQyIV+OUNat30LPS0ewefVbFCpXjYanVmf++LbUOblkpEMTEYla+WqEkpicwogb7yO2WWM2r3mX8h0uYcGyt1h56xAlExGRY5RvRihrt+xk2tKvWPvpJo4rXZG+E29h7qhuWvRKRCSL5PmE8se+ePpPvpVPt+yiZJNO1G/XlZvunEKHuidHOjQRkTwlope8zKy9mX1mZl+Y2RozO+0wfczMbjaz9Wb2bzNbYmbHp+f481/+gJhGrXjjoZuJ3/QpYzrU5M2J7ZVMRESygbl7ZN7YrDSwEejq7h+bWQfgWaC6u/+Zqt9gYBzQxt0PmNljwH53H3u04x9/YgU/sHcXFCxE8z7jeGr2VE49WeXlRUSOxszWuntsZvaN5AilE7De3T8GcPf3gB3A2Wn6XQw85O4HQq/vBvqHO/iff/xCyZotuH/pu6xeNFPJREQkm0VyDqUGwQgltY2h9qP12wicaGal3H136o5mFgfEhV7G71n/8Veju7RidBYGHaVOAn6LdBC5hM7FX3Qu/qJz8Zc6md0xkgnFgOQ0bUn8fdSUtl9S6L9/G125+8PAwwBmtiazw7a8RufiLzoXf9G5+IvOxV/MbE1m943kJa9tQEyatphQ+9H6xQD7gF3ZF5qIiGRUJBPKy0BjM2sEYGatgLrAO2a2ysxqh/otBoabWeHQ63HAUo/U3QQiInJYEbvk5e67zawPsNDMnOBSVmegOFAVKBXq+gRQC1htZknAv4Gj3uEV8nDWRx21dC7+onPxF52Lv+hc/CXT5yJitw2LiEjekq9qeYmISPZRQhERkSwR1Qklu0u3RJN0nosKZvaQmX1jZqvN7INDN0XkJek5F2n6zzCzRDOrljMR5pz0ngszO8nMXjCzr8xsrZndltOxZqcM/H08b2brQn8fH5pZm0jEm53MrJCZTQ79zvc7Qp/MfW66e1R+AaWB34HTQ687AD8DxdP0GwysBYqFXj8G3Bfp+CN0Li4AeqZ6PQl4M9LxR+JcpOrfDXgQ2AxUi3T8Efq9KAJ8ArRN1VY20vFH4DwsAObz19xyT2B7pOPPhvMxOvS3/wHQ7wh9MvW5Gc0jlGwt3RJl0nUu3H25uy9N1bSDvFdxOr2/F5hZHWACcEVOBpiD0nsuLiVIKOND/3pfDBTKyUCzWXrPw3aC5HNoTYtyobY8xd0fcPc5/P3B8tQy9bkZzQnlmEu3ZGNsOS295+K/zKwCcBNwYzbGFQnpOhdmdgLBv0iHunt8DsWW09L7e9GO4MP1KqAVsAV4MtujyznpPQ8zCB6a/sXMthKUcbow+8PLlTL1uRnNCSXLS7dEsfSei6CzWVngH8AN7v5+NseW08KeCzMzYBFws7tvycHYclp6fy/KA4+7+2Z3TwHuADqaWYkciDEnpPc8TAVOAaq4ewzBpdDXzKxg9oeY62TqczOaP1RVuuUv6T0XmFlF4G1gjrsvyYHYclp6zkVJoClwo5l9YmafABWBZaHlEvKK9P5e/ALsSfU6JdVXXpDe8zAAuNtDRWfd/RGCBNMk2yPMfTL3uRnpCaJjmFgqRVAdtFHodSvgD6AssAqoHWofAvwTKBx6fR+wKNLxR+hcVCWoNNAr0jFH+lwcZr/N5L1J+fT+XvQAPgVKhl5fSx66WSMD52Ep8BBQIPS6HbAfOCnSP0M2nZf3CE3KZ9XnZtROyHr2l26JGhk4F3OACsBVZnZVqC3e3dvndMzZJQPnIs9L77lw92VmVgv41MwOEMyhDI5M1FkvA78To4G7gM/M7NC8Wi93zw9l7bPkc1OlV0REJEtE8xyKiIjkIkooIiKSJZRQREQkSyihiIhIllBCERGRLKGEIiIiWUIJRUREsoQSikgaoVpfIpJBSigS1cysqpnVSvNV28zqmVmhVP02m9kNqV5PMbOkw3w5cHmqfo+b2XthYhhqZv8xs/jQAk4XpNk+OHTcI+3fyszczJodZtsNZrb5KPs2MbMeadqKm9mgUEXpo/4coUWlbguVrd8VOgcHzGyDmT1tZnmmioJkv6gtvSIS8j5ByYhDnKCoYUHgBCDxCPstJKi4fEgS0I+ghPlH6X1zM5tCUPtqCvAZQRn4ZWY20N1fTNP3UFLp6MGaHIccquqbmYKlfQjKpCxL1XYisBjoSLCQ1JFiLwasIagqe1vo+90EZTiqASOAd82ss7uvyERsks8ooUi0q0lQahvA3T3ZzK4GrnD3vUfaKVSf6b81mkIlynsDH7n7v9LzxqE1VW4IvdcjoeZPQ+3zzGyp/1XbKBloGPp+a5pDNSRIfFtDxz2RoKQ8wEnpiSWTyhBU073W3R9Ks+0LM/sX0DUUnxKKhKWEIlHN3Q+36lwHgpFLRtwJNACOuv58Gg2AYsC7adrfIVhbowLw06FGd//2CMfpRbBC4iCCdVqGArNTbc+WNVvc/UczGwPcbGbnA18Ce4GiQBWCEc4zBOuCiISlORTJU8zsFILLTs9lYJ+xBEsBOxCbgbc7dInqlDTtMQQjkv8ZIZlZ3dBX8VRt5wNtCarc3m1mdd39Tnc3dzfSt6LmKaE5GA9dVvshvT+Au88HKhGsMf4uQVXZ1cADBKXM+7v7vvQeT/I3jVAkr7meYHGgDUebCAcws8LAPIJJ+HnA/wFLzKw0MNPDlOJ292/MbA1wR2ieYaeZVQeuA5a5+/5U3QsC34S+7wi8Z2anEoxIHnX3K83sZOAdM7vQ3ddk4Gf+kf9dH/0k4IOj/NxdgVfTc+A0N7wtcvfBGYhL8hklFMkzzOxcgonki4H1QL1Um99O1c8ILjPdCNQGJrj73aFtRYCHgbPMrHc63nYw8DqwLbQOeQ2Cf+WPS9sxNOI4FMM5wNPAd8AVoeYhBJPpH5nZZHe/Jx3vD5CY+nJaKDEdzUqCS1qH9AHmApX5a9nXxaHvh6fqlzpBivyNEorkCWbWguAy1yJ3fz7UnPpDNvXdXmcSzA2sIlix7stDG9x9kZl9DVzs7n+EeyTF3b82s0ZAf4IP5O+AZ939SHeXHRoZ3UFwiWmIux8IHSvezC4G4oAP0/eTZ5y7HyTV8rdm9kfo25/dPSnUFg8kufvflpEWORIlFIl6ZtYXeJRgSdO4cP3d/UMzq+Huae+2OrR9DcEttOni7nvN7DFgJMHls/vMrATBOu1fEFxKG5Oqf4KZtTmUSFL9HEWBkwlGEDtDzZuBT8LFYGap/5YLpifuUIyp53kS0ybQ0GXDEe7+aHqOKfmbJuUlaplZFTN7kWC08RDQ42gjg9SOlEzMbJaZ/ZKq6WtgbToO+TIwk+DSWk+gBdCXIJlcQbCOeer3/28yMbPeZraa4JLS98AGYKeZbSG4JDc4zHtXJbjt+NBXekcV+wkufR3tK/6Ie4ukoRGKRLM9wEGgrbuvOtaDhZ6s7wnsM7NC7p7o7rPTsV8scAHQO+3DjMDK0JPuD5jZje7+fZp9BwBPAvcSjK42AQkEI5ULgFsJkkrfI7z9XODxI2zbfrS43d3N7ODR+ohkhBKKRC133w0MzIpjhSbq5xHMg+wEFprZMHdPSMfuRUP/3XmE7Yfajz/MtguAb9x9fJr2zcB8M4vhr0n7v3H3nUd536MKXfL6NTP7ihyOEorke2bWmOBBwrYEd4h9R3Dp6iMzu8rd0z64mNYnBHMli0JP6b8H/A6UA84FZgEfuPtXh9n3DWCgmd0JPEFwyevQCOU8grus3jymHzC8ywnmbY7kwFG2ifyXEorkW6EijrMJnnh/H2jp7l+HtjUhuAz1tpmtB+5w98cPdxx3TzKzswkuT80HSqXa/BuwBJh+hH2XmFkScBVwJX+VkYHg+ZLHSN/Djcdifpjtq4A22RyD5AEW5tktkTzLzEoSPCH+urt/eoQ+dQhqfC1x97AlUEKXzioSXN7a6+4/hdkl9b7FCEYmBYE/3P339O4rkhsooYiISJbQbcMiIpIllFBERCRLKKGIiEiWUEIREZEsoYQiIiJZQglFRESyhBKKiIhkif8Hp3mlYA79510AAAAASUVORK5CYII=\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": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=-1, n_neighbors=4, p=2,\n",
       "           weights='distance')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn_clf = KNeighborsClassifier(n_jobs=-1, weights='distance', n_neighbors=4)\n",
    "knn_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_knn_pred = knn_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9714"
      ]
     },
     "execution_count": 82,
     "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": 83,
   "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": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((300000, 784), (300000,))"
      ]
     },
     "execution_count": 84,
     "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": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=-1, n_neighbors=4, p=2,\n",
       "           weights='distance')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf.fit(X_train_expanded, y_train_expanded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_knn_expanded_pred = knn_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9763"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_knn_expanded_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.24579675, 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.75420325]])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ambiguous_digit = X_test[2589]\n",
    "knn_clf.predict_proba([ambiguous_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed: 469.2min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "           weights='uniform'),\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid=[{'weights': ['uniform', 'distance'], 'n_neighbors': [3, 4, 5]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=3)"
      ]
     },
     "execution_count": 90,
     "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_jobs=-1)\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 4, 'weights': 'distance'}"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9731333333333333"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9714"
      ]
     },
     "execution_count": 93,
     "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": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.ndimage.interpolation import shift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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": 96,
   "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": 97,
   "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": 98,
   "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": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_clf = KNeighborsClassifier(**grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf.fit(X_train_augmented, y_train_augmented)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9763"
      ]
     },
     "execution_count": 101,
     "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": [
    "간단한 데이터 증식으로 0.5%의 정확도가 향상되었습니다. :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 타이타닉 데이터셋에 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": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "TITANIC_PATH = os.path.join(\"datasets\", \"titanic\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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: 83.6+ 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": [
    "`CategoricalEncoder` 클래스는 범주형 특성을 원-핫 벡터로 변환시켜 줍니다. 이 클래스는 사이킷런 0.20에 포함될 예정이지만 지금은 아래 코드를 사용합니다([#9151](https://github.com/scikit-learn/scikit-learn/pull/9151) 풀 리퀘스트에서 복사했습니다)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PR #9151에서 가져온 CategoricalEncoder 클래스의 정의.\n",
    "# 이 클래스는 사이킷런 0.20에 포함될 예정입니다.\n",
    "# 이 셀을 실행하거나 복사해서 사용하세요. 이 코드를 모두 이해할 필요는 없습니다.\n",
    "\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from sklearn.utils import check_array\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from scipy import sparse\n",
    "\n",
    "class CategoricalEncoder(BaseEstimator, TransformerMixin):\n",
    "    \"\"\"Encode categorical features as a numeric array.\n",
    "    The input to this transformer should be a matrix of integers or strings,\n",
    "    denoting the values taken on by categorical (discrete) features.\n",
    "    The features can be encoded using a one-hot aka one-of-K scheme\n",
    "    (``encoding='onehot'``, the default) or converted to ordinal integers\n",
    "    (``encoding='ordinal'``).\n",
    "    This encoding is needed for feeding categorical data to many scikit-learn\n",
    "    estimators, notably linear models and SVMs with the standard kernels.\n",
    "    Read more in the :ref:`User Guide <preprocessing_categorical_features>`.\n",
    "    Parameters\n",
    "    ----------\n",
    "    encoding : str, 'onehot', 'onehot-dense' or 'ordinal'\n",
    "        The type of encoding to use (default is 'onehot'):\n",
    "        - 'onehot': encode the features using a one-hot aka one-of-K scheme\n",
    "          (or also called 'dummy' encoding). This creates a binary column for\n",
    "          each category and returns a sparse matrix.\n",
    "        - 'onehot-dense': the same as 'onehot' but returns a dense array\n",
    "          instead of a sparse matrix.\n",
    "        - 'ordinal': encode the features as ordinal integers. This results in\n",
    "          a single column of integers (0 to n_categories - 1) per feature.\n",
    "    categories : 'auto' or a list of lists/arrays of values.\n",
    "        Categories (unique values) per feature:\n",
    "        - 'auto' : Determine categories automatically from the training data.\n",
    "        - list : ``categories[i]`` holds the categories expected in the ith\n",
    "          column. The passed categories are sorted before encoding the data\n",
    "          (used categories can be found in the ``categories_`` attribute).\n",
    "    dtype : number type, default np.float64\n",
    "        Desired dtype of output.\n",
    "    handle_unknown : 'error' (default) or 'ignore'\n",
    "        Whether to raise an error or ignore if a unknown categorical feature is\n",
    "        present during transform (default is to raise). When this is parameter\n",
    "        is set to 'ignore' and an unknown category is encountered during\n",
    "        transform, the resulting one-hot encoded columns for this feature\n",
    "        will be all zeros.\n",
    "        Ignoring unknown categories is not supported for\n",
    "        ``encoding='ordinal'``.\n",
    "    Attributes\n",
    "    ----------\n",
    "    categories_ : list of arrays\n",
    "        The categories of each feature determined during fitting. When\n",
    "        categories were specified manually, this holds the sorted categories\n",
    "        (in order corresponding with output of `transform`).\n",
    "    Examples\n",
    "    --------\n",
    "    Given a dataset with three features and two samples, we let the encoder\n",
    "    find the maximum value per feature and transform the data to a binary\n",
    "    one-hot encoding.\n",
    "    >>> from sklearn.preprocessing import CategoricalEncoder\n",
    "    >>> enc = CategoricalEncoder(handle_unknown='ignore')\n",
    "    >>> enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]])\n",
    "    ... # doctest: +ELLIPSIS\n",
    "    CategoricalEncoder(categories='auto', dtype=<... 'numpy.float64'>,\n",
    "              encoding='onehot', handle_unknown='ignore')\n",
    "    >>> enc.transform([[0, 1, 1], [1, 0, 4]]).toarray()\n",
    "    array([[ 1.,  0.,  0.,  1.,  0.,  0.,  1.,  0.,  0.],\n",
    "           [ 0.,  1.,  1.,  0.,  0.,  0.,  0.,  0.,  0.]])\n",
    "    See also\n",
    "    --------\n",
    "    sklearn.preprocessing.OneHotEncoder : performs a one-hot encoding of\n",
    "      integer ordinal features. The ``OneHotEncoder assumes`` that input\n",
    "      features take on values in the range ``[0, max(feature)]`` instead of\n",
    "      using the unique values.\n",
    "    sklearn.feature_extraction.DictVectorizer : performs a one-hot encoding of\n",
    "      dictionary items (also handles string-valued features).\n",
    "    sklearn.feature_extraction.FeatureHasher : performs an approximate one-hot\n",
    "      encoding of dictionary items or strings.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, encoding='onehot', categories='auto', dtype=np.float64,\n",
    "                 handle_unknown='error'):\n",
    "        self.encoding = encoding\n",
    "        self.categories = categories\n",
    "        self.dtype = dtype\n",
    "        self.handle_unknown = handle_unknown\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        \"\"\"Fit the CategoricalEncoder to X.\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : array-like, shape [n_samples, n_feature]\n",
    "            The data to determine the categories of each feature.\n",
    "        Returns\n",
    "        -------\n",
    "        self\n",
    "        \"\"\"\n",
    "\n",
    "        if self.encoding not in ['onehot', 'onehot-dense', 'ordinal']:\n",
    "            template = (\"encoding should be either 'onehot', 'onehot-dense' \"\n",
    "                        \"or 'ordinal', got %s\")\n",
    "            raise ValueError(template % self.handle_unknown)\n",
    "\n",
    "        if self.handle_unknown not in ['error', 'ignore']:\n",
    "            template = (\"handle_unknown should be either 'error' or \"\n",
    "                        \"'ignore', got %s\")\n",
    "            raise ValueError(template % self.handle_unknown)\n",
    "\n",
    "        if self.encoding == 'ordinal' and self.handle_unknown == 'ignore':\n",
    "            raise ValueError(\"handle_unknown='ignore' is not supported for\"\n",
    "                             \" encoding='ordinal'\")\n",
    "\n",
    "        X = check_array(X, dtype=np.object, accept_sparse='csc', copy=True)\n",
    "        n_samples, n_features = X.shape\n",
    "\n",
    "        self._label_encoders_ = [LabelEncoder() for _ in range(n_features)]\n",
    "\n",
    "        for i in range(n_features):\n",
    "            le = self._label_encoders_[i]\n",
    "            Xi = X[:, i]\n",
    "            if self.categories == 'auto':\n",
    "                le.fit(Xi)\n",
    "            else:\n",
    "                valid_mask = np.in1d(Xi, self.categories[i])\n",
    "                if not np.all(valid_mask):\n",
    "                    if self.handle_unknown == 'error':\n",
    "                        diff = np.unique(Xi[~valid_mask])\n",
    "                        msg = (\"Found unknown categories {0} in column {1}\"\n",
    "                               \" during fit\".format(diff, i))\n",
    "                        raise ValueError(msg)\n",
    "                le.classes_ = np.array(np.sort(self.categories[i]))\n",
    "\n",
    "        self.categories_ = [le.classes_ for le in self._label_encoders_]\n",
    "\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        \"\"\"Transform X using one-hot encoding.\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : array-like, shape [n_samples, n_features]\n",
    "            The data to encode.\n",
    "        Returns\n",
    "        -------\n",
    "        X_out : sparse matrix or a 2-d array\n",
    "            Transformed input.\n",
    "        \"\"\"\n",
    "        X = check_array(X, accept_sparse='csc', dtype=np.object, copy=True)\n",
    "        n_samples, n_features = X.shape\n",
    "        X_int = np.zeros_like(X, dtype=np.int)\n",
    "        X_mask = np.ones_like(X, dtype=np.bool)\n",
    "\n",
    "        for i in range(n_features):\n",
    "            valid_mask = np.in1d(X[:, i], self.categories_[i])\n",
    "\n",
    "            if not np.all(valid_mask):\n",
    "                if self.handle_unknown == 'error':\n",
    "                    diff = np.unique(X[~valid_mask, i])\n",
    "                    msg = (\"Found unknown categories {0} in column {1}\"\n",
    "                           \" during transform\".format(diff, i))\n",
    "                    raise ValueError(msg)\n",
    "                else:\n",
    "                    # Set the problematic rows to an acceptable value and\n",
    "                    # continue `The rows are marked `X_mask` and will be\n",
    "                    # removed later.\n",
    "                    X_mask[:, i] = valid_mask\n",
    "                    X[:, i][~valid_mask] = self.categories_[i][0]\n",
    "            X_int[:, i] = self._label_encoders_[i].transform(X[:, i])\n",
    "\n",
    "        if self.encoding == 'ordinal':\n",
    "            return X_int.astype(self.dtype, copy=False)\n",
    "\n",
    "        mask = X_mask.ravel()\n",
    "        n_values = [cats.shape[0] for cats in self.categories_]\n",
    "        n_values = np.array([0] + n_values)\n",
    "        indices = np.cumsum(n_values)\n",
    "\n",
    "        column_indices = (X_int + indices[:-1]).ravel()[mask]\n",
    "        row_indices = np.repeat(np.arange(n_samples, dtype=np.int32),\n",
    "                                n_features)[mask]\n",
    "        data = np.ones(n_samples * n_features)[mask]\n",
    "\n",
    "        out = sparse.csc_matrix((data, (row_indices, column_indices)),\n",
    "                                shape=(n_samples, indices[-1]),\n",
    "                                dtype=self.dtype).tocsr()\n",
    "        if self.encoding == 'onehot-dense':\n",
    "            return out.toarray()\n",
    "        else:\n",
    "            return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 전처리 파이프라인을 만듭니다. `DataFrame`으로부터 특정 속성만 선택하기 위해 이전 장에서 만든 `DataframeSelector`를 재사용하겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "# 사이킷런이 DataFrame을 바로 사용하지 못하므로\n",
    "# 수치형이나 범주형 컬럼을 선택하는 클래스를 만듭니다.\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(`Imputer` 클래스는 0.22 버전에서 삭제될 예정입니다. 0.20 버전에서 추가된 `SimpleImputer`를 사용합니다):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "#from sklearn.preprocessing import Imputer\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "imputer = SimpleImputer(strategy=\"median\")\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "        (\"select_numeric\", DataFrameSelector([\"Age\", \"SibSp\", \"Parch\", \"Fare\"])),\n",
    "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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": 115,
     "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 `Imputer` 클래스는 이를 처리하지 못합니다):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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": "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\", CategoricalEncoder(encoding='onehot-dense')),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### future_encoders.py를 사용한 방법 ==========================\n",
    "\n",
    "주의: 번역서는 `CategoricalEncoder`를 사용하여 각 범주형 값을 원-핫 벡터로 변경합니다. `OneHotEncoder`를 사용하는 것이 더 낫습니다. 지금은 정수형 범주 입력만 다룰 수 있지만 사이킷런 0.20에서는 문자열 범주 입력도 다룰 수 있을 것입니다(PR #10521). 지금은 `future_encoders.py` 파일에서 임포트하지만 사이킷런 0.20 버전이 릴리스되면 `sklearn.preprocessing`에서 바로 임포팅할 수 있습니다.\n",
    "\n",
    "0.20에 추가된 `OneHotEncoder`를 사용하도록 코드를 변경합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from future_encoders import OneHotEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "cat_pipeline = Pipeline([\n",
    "        (\"select_cat\", DataFrameSelector([\"Pclass\", \"Sex\", \"Embarked\"])),\n",
    "        (\"imputer\", MostFrequentImputer()),\n",
    "        (\"cat_encoder\", OneHotEncoder(sparse=False)),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0.20 버전에 추가된 `SimpleImputer` 클래스에는 `most_frequent` 옵션을 사용할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_pipeline = Pipeline([\n",
    "        (\"select_cat\", DataFrameSelector([\"Pclass\", \"Sex\", \"Embarked\"])),\n",
    "        (\"imputer\", SimpleImputer(strategy='most_frequent')),\n",
    "        (\"cat_encoder\", OneHotEncoder(sparse=False)),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ===================================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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": 120,
     "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": 121,
   "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": [
    "사이킷런 0.20 버전에 추가된 `ColumnTransformer`를 사용하면 더 간단히 전처리 파이프라인을 구성할 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "        (\"imputer\", SimpleImputer(strategy=\"median\"))\n",
    "    ])\n",
    "\n",
    "cat_pipeline = Pipeline([\n",
    "        (\"imputer\", SimpleImputer(strategy='most_frequent')),\n",
    "        (\"cat_encoder\", OneHotEncoder(sparse=False)),\n",
    "    ])\n",
    "\n",
    "preprocess_pipeline = ColumnTransformer([\n",
    "        (\"num_pipeline\", num_pipeline, [\"Age\", \"SibSp\", \"Parch\", \"Fare\"]),\n",
    "        (\"cat_pipeline\", cat_pipeline, [\"Pclass\", \"Sex\", \"Embarked\"]),\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": 123,
   "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": 123,
     "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": 124,
   "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": 125,
   "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": 125,
     "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": 126,
   "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": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7365250822835092"
      ]
     },
     "execution_count": 127,
     "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": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8115690614005221"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "forest_clf = RandomForestClassifier(n_estimators=10, 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": 129,
   "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(\"정확도\", 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": 130,
   "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": 130,
     "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": 131,
   "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": 131,
     "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": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tarfile\n",
    "from six.moves 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": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "fetch_spam_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "다음, 모든 이메일을 읽어 들입니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "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": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2500"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ham_filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500"
      ]
     },
     "execution_count": 136,
     "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": 137,
   "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": 138,
   "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": 139,
   "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": 140,
   "metadata": {
    "scrolled": true
   },
   "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": 141,
   "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": 142,
   "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": 143,
   "metadata": {
    "scrolled": true
   },
   "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": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structures_counter(ham_emails).most_common()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "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": 144,
     "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": 145,
   "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": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Life Insurance - Why Pay More?'"
      ]
     },
     "execution_count": 146,
     "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": 147,
   "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": 148,
   "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": 149,
   "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": 150,
   "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 $25 billi ...\n"
     ]
    }
   ],
   "source": [
    "print(html_to_plain_text(sample_html_spam.get_content())[:1000], \"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "아주 좋습니다! 이제 포맷에 상관없이 이메일을 입력으로 받아서 일반 텍스트를 출력하는 함수를 만들겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "def email_to_text(email):\n",
    "    html = None\n",
    "    for part in email.walk():\n",
    "        ctype = part.get_content_type()\n",
    "        if not ctype in (\"text/plain\", \"text/html\"):\n",
    "            continue\n",
    "        try:\n",
    "            content = part.get_content()\n",
    "        except: # in case of encoding issues\n",
    "            content = str(part.get_payload())\n",
    "        if ctype == \"text/plain\":\n",
    "            return content\n",
    "        else:\n",
    "            html = content\n",
    "    if html:\n",
    "        return html_to_plain_text(html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "OTC\n",
      " Newsletter\n",
      "Discover Tomorrow's Winners \n",
      "For Immediate Release\n",
      "Cal-Bay (Stock Symbol: CBYI)\n",
      "Wat ...\n"
     ]
    }
   ],
   "source": [
    "print(email_to_text(sample_html_spam)[:100], \"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "어간 추출을 해보죠! 이 작업을 하려면 자연어 처리 툴킷([NLTK](http://www.nltk.org/))을 설치해야 합니다. 다음 명령으로 간단히 설치할 수 있습니다(먼저 virtualenv 환경을 활성화시켜야 합니다. 별도의 환경이 없다면 어드민 권한이 필요할지 모릅니다. 아니면 `--user` 옵션을 사용하세요):\n",
    "\n",
    "`$ pip install nltk`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "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": 154,
   "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": 155,
   "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": 156,
   "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': 5, '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, 'your': 1, 'use': 1, 'of': 1, 'subject': 1})],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 156,
     "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": 157,
   "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": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<3x11 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 20 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 158,
     "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": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [99, 11,  9,  8,  1,  3,  3,  1,  3,  2,  3],\n",
       "       [65,  0,  1,  2,  5,  3,  1,  2,  0,  1,  0]], dtype=int64)"
      ]
     },
     "execution_count": 159,
     "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": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'the': 1,\n",
       " 'of': 2,\n",
       " 'and': 3,\n",
       " 'url': 4,\n",
       " 'to': 5,\n",
       " 'all': 6,\n",
       " 'in': 7,\n",
       " 'christian': 8,\n",
       " 'on': 9,\n",
       " 'by': 10}"
      ]
     },
     "execution_count": 160,
     "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": 161,
   "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": "code",
   "execution_count": 162,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  ................................................................\n",
      "[CV] .................................. , score=0.98375, total=   0.0s\n",
      "[CV]  ................................................................\n",
      "[CV] .................................... , score=0.985, total=   0.0s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................... , score=0.9925, total=   0.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9870833333333334"
      ]
     },
     "execution_count": 162,
     "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='liblinear', 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": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정밀도: 94.90%\n",
      "재현율: 97.89%\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='liblinear', 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)))"
   ]
  }
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