handson_ml2_03.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C04ROP_yajIU"
      },
      "source": [
        "# 3장 분류"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C11isoHEajIZ"
      },
      "source": [
        "## 주요내용"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "czj2EqvSajIa"
      },
      "source": [
        "* 분류 모델 소개\n",
        "* 분류 모델 성능 평가 방법 소개\n",
        "* 실전 예제: MNIST 데이터셋을 이용한 숫자 이미지 분류"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qcZbG3nYajIa"
      },
      "source": [
        "__참고:__\n",
        "핵심 설명과 코드는 &#x1f511;로 표시되었으며 굳이 알아둘 필요가 없는 코드는 &#x270b;로 표시되었다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tiAeHv-VajIb"
      },
      "source": [
        "## 기본 설정"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DfiqACN3ajIb"
      },
      "source": [
        "- 필수 모듈 불러오기\n",
        "- 그래프 출력 관련 기본 설정 지정"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xvV9jbAlajIc"
      },
      "outputs": [],
      "source": [
        "# 파이썬 ≥3.5 필수\n",
        "import sys\n",
        "assert sys.version_info >= (3, 5)\n",
        "\n",
        "# 사이킷런 ≥0.20 필수\n",
        "import sklearn\n",
        "assert sklearn.__version__ >= \"0.20\"\n",
        "\n",
        "# 공통 모듈 임포트\n",
        "import numpy as np\n",
        "import os\n",
        "\n",
        "# 노트북 실행 결과를 동일하게 유지하기 위해\n",
        "np.random.seed(42)\n",
        "\n",
        "# 깔끔한 그래프 출력을 위해\n",
        "%matplotlib inline\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "mpl.rc('axes', labelsize=14)\n",
        "mpl.rc('xtick', labelsize=12)\n",
        "mpl.rc('ytick', labelsize=12)\n",
        "\n",
        "# 그림을 저장할 위치\n",
        "PROJECT_ROOT_DIR = \".\"\n",
        "CHAPTER_ID = \"classification\"\n",
        "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
        "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
        "\n",
        "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
        "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
        "    print(\"그림 저장:\", fig_id)\n",
        "    if tight_layout:\n",
        "        plt.tight_layout()\n",
        "    plt.savefig(path, format=fig_extension, dpi=resolution)\n",
        "\n",
        "\n",
        "# 어레이 데이터를 csv 파일로 저장하기\n",
        "def save_data(fileName, arrayName, header=''):\n",
        "    np.savetxt(fileName, arrayName, delimiter=',', header=header, comments='')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mu_pcLDMajId"
      },
      "source": [
        "# 1부"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mL-8YyQEajIe"
      },
      "source": [
        "## 3.1 MNIST (p. 127)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jK7ZAD0tajIe"
      },
      "source": [
        "#### 데이터 불러오기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CV_tvSvJajIf"
      },
      "source": [
        "MNIST 데이터셋을 가져온다.\n",
        "`fetch_openml()` 함수의 반환값은 사전 자료형을 살짝 확장한 `Bunch` 객체이며,\n",
        "기본적으로 사전 자료형처럼 사용하면 된다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9w46EckrajIf"
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import fetch_openml\n",
        "mnist = fetch_openml('mnist_784', version=1, as_frame=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0os6CnvVajIf"
      },
      "source": [
        "사용되는 키(key)들은 다음과 같다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T0KBdyVpajIf",
        "outputId": "9a6e0e36-e619-4148-bf85-1ecc895140c8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'frame', 'categories', 'feature_names', 'target_names', 'DESCR', 'details', 'url'])"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "mnist.keys()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i43Y00PLajIg"
      },
      "source": [
        "`\"data\"` 속성에 이미지 데이터가, `\"target\"` 속성에 레이블 데이터가 들어 있다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kfTSWTYaajIg"
      },
      "outputs": [],
      "source": [
        "X, y = mnist[\"data\"], mnist[\"target\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XWNqpbiQajIg"
      },
      "source": [
        "샘플 수는 70,000개의 이미지이며, 이미지는 길이가 784인 1차원 어레이로 되어 있다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s7lIeuzrajIh",
        "outputId": "604e0412-c202-45da-c494-17070a60d0ad"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(70000, 784)"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ldz5Vr3qajIh"
      },
      "source": [
        "레이블 또한 70,000개 이며, 0부터 9까지의 숫자이다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m8QsMzkKajIh",
        "outputId": "f60ad4c3-671e-4326-de1b-94e3398e4330"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(70000,)"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VLkjiHFrajIh"
      },
      "source": [
        "784는 28x28과 같다.\n",
        "즉, 각 데이터는 원래 28x28 크기의 2차원 어레이를 1차원 어레이로 변환한 값이다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xjz5FUU8ajIh",
        "outputId": "b841cc1b-02d8-4f7f-d2ab-d9e398cb4b7c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "784"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "28 * 28"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HaU-4QcQajIh"
      },
      "source": [
        "첫째 손글씨 이미지를 확인하면 5로 보이는 이미지가 보인다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m-846c7dajIi",
        "outputId": "f3b4ed60-5884-471d-e763-4702c0dd32c3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "그림 저장: some_digit_plot\n"
          ]
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "some_digit = X[0]\n",
        "\n",
        "# reshape() 함수를 이용하여 28x28 모양의 2차원 어레이로 변환\n",
        "some_digit_image = some_digit.reshape(28, 28)\n",
        "\n",
        "# 흑백 이미지로 보여주기\n",
        "plt.imshow(some_digit_image, cmap=mpl.cm.binary)\n",
        "plt.axis(\"off\")\n",
        "\n",
        "save_fig(\"some_digit_plot\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zLXX6eU7ajIi"
      },
      "source": [
        "첫째 이미지의 실제 레이블 또한 5이다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FedPfn-gajIi",
        "outputId": "83e540bd-55f8-453e-a762-a306e1ecaf56"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'5'"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H5y7EgIJajIi"
      },
      "source": [
        "그런데 레이블이 문자형으로 저장되어 있기 때문에 모델 훈련을 위해 정수형으로 변환해야 한다.\n",
        "아래 코드는 8바이트를 사용하여 양의 정수를 표현하는 `uint8` 자료형을 사용한다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OIeheemrajIi"
      },
      "outputs": [],
      "source": [
        "y = y.astype(np.uint8)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PlROW6n8ajIj"
      },
      "source": [
        "이제 모든 레이블이 정수로 변했다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RH93iICVajIj",
        "outputId": "df535399-de2c-491c-8a4b-971451ea0667"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jGE3nYZJajIj"
      },
      "source": [
        "#### 숫자 이미지 출력 함수"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PenyFXQeajIj"
      },
      "source": [
        "아래 함수는 28x28 모양의 2차원 어레이로 변환하여 숫자 이미지를 보여주는 함수이다.\n",
        "즉, 앞서 설명한 과정을 함수로 지정하였다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ATRoLAqwajIj"
      },
      "outputs": [],
      "source": [
        "def plot_digit(data):\n",
        "    image = data.reshape(28, 28)\n",
        "    plt.imshow(image, cmap = mpl.cm.binary,\n",
        "               interpolation=\"nearest\")\n",
        "    plt.axis(\"off\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9hBxvXMhajIj"
      },
      "source": [
        "&#x270b; 아래 함수는 여러 개의 숫자 이미지를 한 줄에 10개씩 출력하는 함수이다.\n",
        "10개의 이미지를 이어붙인 다음에 여러 줄을 다시 이어붙여서 커다란 이미지를 만드는 방식을 사용한다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RDKufpjcajIj"
      },
      "outputs": [],
      "source": [
        "# 숫자 그림을 위한 추가 함수\n",
        "def plot_digits(instances, images_per_row=10, **options):\n",
        "    size = 28\n",
        "    images_per_row = min(len(instances), images_per_row)\n",
        "    images = [instance.reshape(size,size) for instance in instances]\n",
        "    n_rows = (len(instances) - 1) // images_per_row + 1\n",
        "    row_images = []\n",
        "    n_empty = n_rows * images_per_row - len(instances)\n",
        "    images.append(np.zeros((size, size * n_empty)))\n",
        "    for row in range(n_rows):\n",
        "        rimages = images[row * images_per_row : (row + 1) * images_per_row]\n",
        "        row_images.append(np.concatenate(rimages, axis=1))\n",
        "    image = np.concatenate(row_images, axis=0)\n",
        "    plt.imshow(image, cmap = mpl.cm.binary, **options)\n",
        "    plt.axis(\"off\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OUQ8k_dsajIk"
      },
      "source": [
        "처음 100개의 이미지는 다음과 같다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6hi42l1ZajIk",
        "outputId": "d31f4aa4-7f21-40c8-f405-a464678b4723"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "그림 저장: more_digits_plot\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(9,9))\n",
        "example_images = X[:100]\n",
        "plot_digits(example_images, images_per_row=10)\n",
        "save_fig(\"more_digits_plot\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NN2TdlObajIk"
      },
      "source": [
        "#### 훈련 세트와 테스트 세트로 분류"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PMsQwYmGajIk"
      },
      "source": [
        "처음 60,000개의 샘플은 훈련 세트로, 이후 10,000개의 샘플은 테스트 세트로 이미\n",
        "계층별 샘플링 방식으로 구분되어 있다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jIxg2gqKajIp"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Nt3TWKcajIq"
      },
      "source": [
        "## 3.2 이진 분류기 훈련 (p. 130)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uHP6b0x1ajIq"
      },
      "source": [
        "이미지가 숫자 5를 표현하는지 여부만을 판단하는 이진 분류기의 훈련을 위해 레이블을 0 또는 1로 변경한다.\n",
        "* 0: 숫자 5 아님.\n",
        "* 1: 숫자 5 맞음."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_nj1TbjoajIq"
      },
      "outputs": [],
      "source": [
        "y_train_5 = (y_train == 5)\n",
        "y_test_5 = (y_test == 5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n8VFigbpajIq"
      },
      "source": [
        "확률적 경사 하강법(SGD) 분류기를 훈련시키기 위해 `fit()` 메서드를 호출한다.\n",
        "* `max_iter=1000`: 훈련 데이터를 최대 1000번 반복해서 학습에 사용함.\n",
        "* `tol=le-3`: 전체 훈련 세트를 한 번 학습할 때마다 성능 향상이 연속적으로 지정된 값 이상으로 이루어지지 않을 때 학습을 멈추도록 하는 데에 사용됨. 기본적으로 5번 이상 성능 향상이 10의 -3승 이상 이루어지지 않으면 학습을 강제로 멈춤."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xLFx4CDbajIq",
        "outputId": "eba3b94a-8583-443c-fb0f-f96edd6061d7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "SGDClassifier(random_state=42)"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.linear_model import SGDClassifier\n",
        "\n",
        "sgd_clf = SGDClassifier(max_iter=1000, tol=1e-3, random_state=42)\n",
        "sgd_clf.fit(X_train, y_train_5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "80_-dSbKajIr"
      },
      "source": [
        "첫째 샘플의 예측값은 `True`, 즉, 숫자 5라고 정확하게 맞춘다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sFu91UfoajIr",
        "outputId": "0ef54ad9-70a7-4a22-cfb8-9f6ac38ca81e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([ True])"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sgd_clf.predict([some_digit])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "55HpcUdIajIr"
      },
      "source": [
        "## 3.3 성능 측정 (p. 131)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "585lcgcEajIr"
      },
      "source": [
        "분류기 성능을 측정하는 세 가지 방법을 소개한다.\n",
        "\n",
        "* 교차 검증을 활용한 정확도 측정\n",
        "* 정밀도/재현율 조율\n",
        "* AUC 측정"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "90CWdTyPajIs"
      },
      "source": [
        "### 3.3.1 교차 검증을 사용한 정확도 측정 (p. 131)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EsayPETdajIs"
      },
      "source": [
        "분류기의 정확도 평가는 교차 검증을 이용한다.\n",
        "아래 코드는 3겹 교차 검증을 실행한다.\n",
        "결과는 정확도 95% 이상이다.\n",
        "\n",
        "* `cv=3`: 폴더 3개 사용\n",
        "* `scoring=\"accuracy\"`: 정확도 검증"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S16kMCJwajIs",
        "outputId": "cb272617-e6e5-44e2-e675-80bccf944442"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.95035, 0.96035, 0.9604 ])"
            ]
          },
          "execution_count": 19,
          "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": "markdown",
      "metadata": {
        "id": "oGOAJ75XajIs"
      },
      "source": [
        "#### &#x270b; 부록: 교차 검증 모델 직접 구현하기 (p. 131)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ThdHXikcajIs"
      },
      "source": [
        "`cross_val_score()` 함수가 제공하지 않는 기능을 이용하려면 직접 교차 검증을 구현해야 한다.\n",
        "아래 코드는 3겹 교차 검증을 위해 계층별 샘플링 부터 세 번의 훈련 및 검증을 직접 진행한다.\n",
        "앞서 사용한 `cross_val_score()` 함수와 기본적으로 동일한 기능을 수행한다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0-HgvWAfajIs",
        "outputId": "d11937f1-53f4-499a-aa3d-839a6f662de7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.9669\n",
            "0.91625\n",
            "0.96785\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.base import clone\n",
        "\n",
        "# 계층별 샘플링 진행: 3개의 폴드 지정\n",
        "# 주의사항: shuffle=False가 기본값이기 때문에 random_state를 삭제하던지\n",
        "# shuffle=True로 지정하라는 경고가 발생한다.\n",
        "# 0.24버전부터는 에러가 발생할 예정이므로 향후 버전을 위해 shuffle=True을 지정한다.\n",
        "skfolds = StratifiedKFold(n_splits=3, random_state=42, shuffle=True)\n",
        "\n",
        "# 3개의 폴드를 대상으로 두 개씩 돌아가면 훈련 및 검증 진행\n",
        "for train_index, test_index in skfolds.split(X_train, y_train_5):\n",
        "    # 기존 모델에 영향을 주지 않도록 모델을 복제해서 사용한다.\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": "markdown",
      "metadata": {
        "id": "0qliCjz1ajIt"
      },
      "source": [
        "#### 정확도 평가의 문제점"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iebTLehqajIt"
      },
      "source": [
        "교차 검증 결과 95% 이상의 정확도가 나왔지만 전혀 믿음직스럽지 않다.\n",
        "이유는 무조건 5가 아니라고 찍는 분류기도 90%의 정확도를 보여주기 때문이다.\n",
        "\n",
        "* 무조건 `False` 라고 예측하는 분류기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-ebzqlhpajIt"
      },
      "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": "markdown",
      "metadata": {
        "id": "I3LPDqMIajIt"
      },
      "source": [
        "훈련 세트의 90% 정도가 5가 아닌 숫자 이미지이기에 정확도는 기본적으로 90%로 나온다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M48BCm8pajIt",
        "outputId": "8a2a2c0e-d5be-4375-f6d4-222476352346"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.91125, 0.90855, 0.90915])"
            ]
          },
          "execution_count": 22,
          "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": "markdown",
      "metadata": {
        "id": "soDC69TDajIu"
      },
      "source": [
        "### 참고사항"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yvKN7yP7ajIu"
      },
      "source": [
        "성능 평가 점수 등이 책의 내용과 조금 다를 수 있지만, 어떤 문제가 있는 것은 아니다.\n",
        "결과가 조금씩 달라지는 이유는 아래와 같다.\n",
        "\n",
        "* 첫째, 사이킷런과 다른 라이브러리들이 발전하면서 알고리즘이 조금씩 변경되기 때문에 얻어지는 결괏값이 바뀔 수 있다.\n",
        "* 둘째, 많은 훈련 알고리즘이 확률적, 즉 무작위성에 의존한다.\n",
        "    이론적으로 `random_state=42`나 `np.random.seed(42)` 등\n",
        "    난수 생성기에 시드를 지정하여 일관된 결과를 얻을 수 있지만 항상 보장되지는 않는다.\n",
        "* 셋째, 훈련 알고리즘이 여러 스레드(C로 구현된 알고리즘)나 여러 프로세스(예를 들어 n_jobs 매개변수를 사용할 때)로\n",
        "    실행되면 연산이 실행되는 정확한 순서가 항상 보장되지 않으며, 따라서 결괏값이 조금 다를 수 있다.\n",
        "* 넷째, 파이썬 사전(dict)이나 셋(set) 객체는 항목을 사용하는 순서가 경우에 따라 달라질 수 있다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cqyaPhEfajIu"
      },
      "source": [
        "### 3.3.2 오차 행렬 (p. 133)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ys1MQ6AsajIu"
      },
      "source": [
        "교차 검증을 이용하여 평가 점수 대신에 예측을 수행할 수 있다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zb7mL9JrajIu"
      },
      "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": "markdown",
      "metadata": {
        "id": "81ifdX_najIu"
      },
      "source": [
        "`confusion_matrix()` 함수가 오차 행렬을 만들어준다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1WoVrB2KajIv",
        "outputId": "1e832e2c-2441-49c2-e9bd-42e566252f31"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[53892,   687],\n",
              "       [ 1891,  3530]])"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_train_5, y_train_pred)\n",
        "\n",
        "cm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kTxvpZK8ajIv"
      },
      "source": [
        "위 결과는 다음을 보여준다.\n",
        "\n",
        "```\n",
        "TN | FP\n",
        "---|---\n",
        "FN | TP\n",
        "```\n",
        "\n",
        "* TN: 참 음성 개수. 즉, 5가 아닌 이미지를 5가 아니라고 정확하게 예측한 경우의 수\n",
        "* FP: 거짓 양성 개수. 즉, 5가 아닌 이미지를 5라고 잘못 예측한 경우의 수\n",
        "* FN: 거짓 음성 개수. 즉, 5를 나타내는 이미지를 5가 아니라고 잘못 예측한 경우의 수\n",
        "* TP: 참 양성 개수. 즉, 5를 나타내는 이미지를 5라고 정확하게 예측한 경우의 수"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JEgF6_l_ajIv"
      },
      "source": [
        "__참고:__ 완벽한 이진 분류기의 오차 행렬은 대각선 이외의 값이 모두 0인 대각 행렬이다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vkdUY-oiajIv",
        "outputId": "902d736e-392d-4a01-86ab-91c5a42c1255"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[54579,     0],\n",
              "       [    0,  5421]])"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_train_perfect_predictions = y_train_5  # 예측값을 실제 레이블로 지정. 즉, 완벽한 분류 결과로 사용\n",
        "confusion_matrix(y_train_5, y_train_perfect_predictions)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mv82hbiCajIv"
      },
      "source": [
        "### 3.3.3 정밀도와 재현율 (p. 135)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_phRcMY9ajIv"
      },
      "source": [
        "#### 이진 분류기의 정밀도"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SuD6Kg6wajIw",
        "outputId": "618b763e-68e2-41bb-e079-013908da71b7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8370879772350012"
            ]
          },
          "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": "markdown",
      "metadata": {
        "id": "gByhibp5ajIw"
      },
      "source": [
        "수동으로 정밀도 계산:\n",
        "\n",
        "$$\n",
        "\\text{정밀도} = \\frac{\\text{TP}}{\\text{TP} + \\text{FP}}\n",
        "$$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pqjaQznPajIw",
        "outputId": "caae5ed0-1469-423e-fea9-037f15061e70"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8370879772350012"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cm[1, 1] / (cm[0, 1] + cm[1, 1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OSuXSoaZajIw"
      },
      "source": [
        "#### 이진 분류기의 재현율"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bV34Sv2HajIw",
        "outputId": "4b425d91-fdb5-440e-f674-97138ef02ccd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.6511713705958311"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "recall_score(y_train_5, y_train_pred)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1-rMZGwbajIw"
      },
      "source": [
        "수동으로 재현율 계산:\n",
        "\n",
        "$$\n",
        "\\text{재현율} = \\frac{\\text{TP}}{\\text{TP} + \\text{FN}}\n",
        "$$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IKT5jfFOajIx",
        "outputId": "42b7d0f0-0b97-444b-e0e2-1540bdeeba61"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.6511713705958311"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cm[1, 1] / (cm[1, 0] + cm[1, 1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qclamyg8ajIx"
      },
      "source": [
        "#### F<sub>1</sub> 점수 계산"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W9HoN6fpajIx"
      },
      "source": [
        "정밀도와 재현율의 조화평균을 F<sub>1</sub> 점수라 한다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mxeMP1GhajIx",
        "outputId": "1f7ebe1c-6ac7-4624-8148-b5de310edf7c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.7325171197343846"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import f1_score\n",
        "\n",
        "f1_score(y_train_5, y_train_pred)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Wwbuo1rajIx"
      },
      "source": [
        "수동으로 F<sub>1</sub> 점수 계산:\n",
        "\n",
        "$$\n",
        "F_1\n",
        "= \\frac{2}{\\frac{1}{\\text{정밀도}} + \\frac{1}{\\text{재현율}}}\n",
        "= \\frac{\\text{TP}}{\\text{TP} + \\frac{\\text{FN} + \\text{FP}}{2}}\n",
        "$$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CRiDy0UbajIx",
        "outputId": "449833ea-edaa-489a-ea15-8f986fe71c3b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.7325171197343847"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cm[1, 1] / (cm[1, 1] + (cm[1, 0] + cm[0, 1]) / 2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G2odyD3zajIy"
      },
      "source": [
        "### 3.3.4 정밀도/재현율 트레이드오프 (p. 135)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yXpU18JXajIy"
      },
      "source": [
        "#### 샘플 점수"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g1ogNxXaajIy"
      },
      "source": [
        "SGD 분류기는 `decision_function()`을 이용하여 예측에 필요한 점수를 각 샘플에 대해 계산한다.\n",
        "예를 들어, 첫째 샘플의 점수는 아래와 같다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Becb2OjYajIy",
        "outputId": "bf936c76-ce78-47aa-ec26-37d3b70cb889"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([2164.22030239])"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_scores = sgd_clf.decision_function([some_digit])\n",
        "y_scores"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "09gKAyblajIy"
      },
      "source": [
        "예측한 점수가 지정된 임계갓보다 크면 양성, 즉 `True`로 판정한다.\n",
        "아래 코드는 임곗값을 0으로 지정한 결과를 보여준다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MJl7wI1cajIz"
      },
      "outputs": [],
      "source": [
        "threshold = 0\n",
        "y_some_digit_pred = (y_scores > threshold)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ODldJnKXajIz",
        "outputId": "9ba733c9-5870-4d64-83e2-afbc489ab131"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([ True])"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_some_digit_pred"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2PCgvzBQajIz"
      },
      "source": [
        "임곗값을 변경하면 예측값이 달라질 수 있다.\n",
        "아래 코드는 임곗값을 8000으로 할 때, 첫째 샘플에 대한 예측값이 `False`로 변경되는 것을 보여준다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_8qnJbHdajIz",
        "outputId": "a1906899-4b67-4dc5-bef9-a9c4c90ca293"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([False])"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "threshold = 8000\n",
        "y_some_digit_pred = (y_scores > threshold)\n",
        "y_some_digit_pred"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gOKqOIAcajIz"
      },
      "source": [
        "#### 임곗값 활용"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L5tZ_fvqajIz"
      },
      "source": [
        "사이킷런에서 임곗값을 임의로 직접 지정할 수는 없다.\n",
        "하지만 임곗값을 수동으로 변경하면서 정밀도와 재현율의 관계를 확인한 다음에\n",
        "목적에 따라 가장 적절한 임곗값을 찾을 수 있다.\n",
        "\n",
        "먼저, 교참 검증을 이용하여 모든 훈련 샘플에 대한 예측 점수를 확인한다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JRma01xlajIz"
      },
      "outputs": [],
      "source": [
        "y_scores = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3,\n",
        "                             method=\"decision_function\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aSKJqBMMajI0"
      },
      "source": [
        "`precision_recall_curve()` 함수는 가능한 모든 임곗값에 대한 정밀도와 재현율을 계산한 값을 반환한다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_-rD_UtoajI0"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import precision_recall_curve\n",
        "\n",
        "precisions, recalls, thresholds = precision_recall_curve(y_train_5, y_scores)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JCdBRZDsajI0"
      },
      "source": [
        "#### 임곗값, 정밀도, 재현율 그래프"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n4M2QZNjajI0"
      },
      "source": [
        "임곗값을 기준으로 정밀도와 재현율의 변화를 그래프로 그리면 다음과 같다.\n",
        "빨강 점들과 연결된 빨강 점선은 정밀도가 90%가 되는 지점의 정밀도와 재현율을 보여준다.\n",
        "\n",
        "__주의사항:__ 정밀도와 재현율을 담은 어레이의 마지막 항목은 각각 1과 0이며, 이는 기술적인 이유때문에 추가되었다.\n",
        "따라서 임곗값 어레이의 길이와 맞추기 위해 정밀도와 재현율의 어레이에서 마지막 항목은 무시해야 한다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p1ru8p2sajI0",
        "outputId": "53817ad6-d62e-4004-8d79-16553fec1943"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "그림 저장: precision_recall_vs_threshold_plot\n"
          ]
        },
        {
          "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": [
        "# 정밀도와 재현율 그래프 그리기. x 축은 임곗값을 가리킴.\n",
        "def plot_precision_recall_vs_threshold(precisions, recalls, thresholds):\n",
        "    plt.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\", linewidth=2) # 정밀도 어레이 마지막 항목 무시\n",
        "    plt.plot(thresholds, recalls[:-1], \"g-\", label=\"Recall\", linewidth=2)        # 재현율 어레이 마지막 항목 무시\n",
        "    plt.legend(loc=\"center right\", fontsize=16)\n",
        "    plt.xlabel(\"Threshold\", fontsize=16)\n",
        "    plt.grid(True)\n",
        "    plt.axis([-50000, 50000, 0, 1])\n",
        "\n",
        "# 정밀도 90%가 달성되는 지점에서의 재현율과 임곗값\n",
        "recall_90_precision = recalls[np.argmax(precisions >= 0.90)]\n",
        "threshold_90_precision = thresholds[np.argmax(precisions >= 0.90)]\n",
        "\n",
        "# 빨강 점과 빨강 점선 그리기\n",
        "plt.figure(figsize=(8, 4))\n",
        "plot_precision_recall_vs_threshold(precisions, recalls, thresholds)\n",
        "plt.plot([threshold_90_precision, threshold_90_precision], [0., 0.9], \"r:\")  # 수직 빨강 점선\n",
        "plt.plot([-50000, threshold_90_precision], [0.9, 0.9], \"r:\")                 # 위 수평 빨강 점선\n",
        "plt.plot([-50000, threshold_90_precision], [recall_90_precision, recall_90_precision], \"r:\")  # 아래 수평 빨강 점선\n",
        "plt.plot([threshold_90_precision], [0.9], \"ro\")                              # 위 빨강 점\n",
        "plt.plot([threshold_90_precision], [recall_90_precision], \"ro\")              # 아래 빨강 점\n",
        "\n",
        "save_fig(\"precision_recall_vs_threshold_plot\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jM76BRGbajI0"
      },
      "source": [
        "재현율 그래프와는 달리 정밀도 그래프는 100% 근처에서 상승과 하강이 살짝 이루지도 한다.\n",
        "이유는 임곗값을 올리면서 양성으로 판명된 샘플의 수는 기본적으로 줄어들지만 거짓 양성 샘플 수는\n",
        "순간적으로는 변하지 않을 수도 있기 때문이다.\n",
        "반면에 재현율은 전체 양성 샘플 수를 대상으로 하기에 그래프의 진동이 발생하지 않는다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "65mfFXI5ajI1"
      },
      "source": [
        "#### 사이킷런 분류기의 임곗값"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mjjCOkraajI1"
      },
      "source": [
        "SGD 분류기는 0을 임곗값으로 사용하며,\n",
        "이를 아래와 같이 확인할 수 있다.\n",
        "\n",
        "__참고:__ `all()` 메서드는 모든 항목이 참일 때만 참을 반환한다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4gNBpzFaajI1",
        "outputId": "ee74dd3b-4d58-484f-bc14-b361e541bf41"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(y_train_pred == (y_scores > 0)).all()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9qp2qG0JajI1"
      },
      "source": [
        "#### 정밀도와 재현율 사이의 관계"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "65aCjR_yajI1"
      },
      "source": [
        "아래 코드는 재현율과 정밀도 사이의 관계를 직접적으로 보여준다.\n",
        "빨강 점과 빨강 선은 앞 도표에서처럼 정밀도 90%가 달성되는 지점의 재현율을 보여주는 용도이다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C4Ge6mHhajI1",
        "outputId": "e236561a-fb0e-47e9-a906-f96a32613266"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "그림 저장: precision_vs_recall_plot\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# 정밀도와 재현율 사이의 관계 그래프 그리기\n",
        "def plot_precision_vs_recall(precisions, recalls):\n",
        "    plt.plot(recalls, precisions, \"b-\", linewidth=2)\n",
        "    plt.xlabel(\"Recall\", fontsize=16)\n",
        "    plt.ylabel(\"Precision\", fontsize=16)\n",
        "    plt.axis([0, 1, 0, 1])\n",
        "    plt.grid(True)\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plot_precision_vs_recall(precisions, recalls)\n",
        "\n",
        "# 빨강 점과 빨강 점선 그리기\n",
        "plt.plot([recall_90_precision, recall_90_precision], [0., 0.9], \"r:\") # 수직 빨강 점선\n",
        "plt.plot([0.0, recall_90_precision], [0.9, 0.9], \"r:\")                # 수평 빨강 직선\n",
        "plt.plot([recall_90_precision], [0.9], \"ro\") # 빨강 점 좌표\n",
        "save_fig(\"precision_vs_recall_plot\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0QAnSvrWajI2"
      },
      "source": [
        "#### 정밀도/재현율 조율 모델 생성"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yWFdbEZjajI2"
      },
      "source": [
        "아래 코드는 정밀도 90%가 달성되는 지점의 임곗값을 확인한다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "88bxCZ6sajI2"
      },
      "outputs": [],
      "source": [
        "threshold_90_precision = thresholds[np.argmax(precisions >= 0.90)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qifyiuntajI3",
        "outputId": "62da9226-5de8-4b08-d42d-ea4b3ae75bdc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3370.0194991439557"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "threshold_90_precision"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "adHRGvYYajI3"
      },
      "source": [
        "실제로 바로 위에서 구해진 임곗값을 사용하면 정밀도가 90%인 모델을 (수동)얻게 된다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Pmv6Xlb0ajI3"
      },
      "outputs": [],
      "source": [
        "y_train_pred_90 = (y_scores >= threshold_90_precision)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LYHsrV6PajI3",
        "outputId": "10a5671d-9944-4b9b-9e3b-095c949ab63c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9000345901072293"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "precision_score(y_train_5, y_train_pred_90)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ys6sPv1ZajI3"
      },
      "source": [
        "하지만 그렇게 얻어진 모델의 재현율은 47.9% 정도로 떨어지기에 사용 목적에 적절한 모델인지 판단해야 한다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E1V5SCchajI3",
        "outputId": "659f946d-dd4b-4ee7-e1b6-e9b26f11b5d6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.4799852425751706"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "recall_score(y_train_5, y_train_pred_90)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5m2-yK1UajI4"
      },
      "source": [
        "### 3.3.5 ROC 곡선 (p. 141)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MjNaFoG8ajI4"
      },
      "source": [
        "__수신기 조작 특성__(ROC)을 그려서 분류기의 성능을 측정할 수 있다.\n",
        "여기서 사용되는 기준은 재현율과 정밀도의 관계가 아니라\n",
        "__참 양성 비율__(true positive rate, TPR)과\n",
        "__거짓 양성 비율__(false positive rate, FPR) 사이의 관계이다.\n",
        "\n",
        "TPR은 재현율의 다른 명칭이며, FPR은 실제로 음성에 속하는 샘플 중에서 양성으로 잘못 판정되는 경우의 비율이다.\n",
        "\n",
        "```\n",
        "TN | FP\n",
        "---|---\n",
        "FN | TP\n",
        "```\n",
        "\n",
        "$$\n",
        "\\text{TPR} = \\frac{\\text{TP}}{\\text{FN} + \\text{TP}} \\qquad\n",
        "\\text{FPR} = \\frac{\\text{FP}}{\\text{TN} + \\text{FP}}\n",
        "$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OLuHTGB3ajI4"
      },
      "source": [
        "앞서 정밀도와 재현율의 관계를 그래프로 묘사한 것과 비슷한 방식으로\n",
        "TPR와 FPR 사이의 관계를 그래프로 그리면 다음과 같다.\n",
        "\n",
        "* `roc_curve()` 함수: 임곗값의 변화에 따른 TPR, FPR 계산.\n",
        "    `precision_recall_curve()` 함수와 유사한 기능 제공."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6QiH4U2uajI4"
      },
      "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": null,
      "metadata": {
        "id": "5feKWchQajI4",
        "outputId": "7e84fa9b-09d3-439a-f37e-2ff6bd8b15fb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "그림 저장: roc_curve_plot\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# SGD 분류기 모델의 TPR과 FPR 사이의 관계 그래프 그리기\n",
        "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--') # 대각 점선\n",
        "    plt.axis([0, 1, 0, 1])\n",
        "    plt.xlabel('False Positive Rate (Fall-Out)', fontsize=16)\n",
        "    plt.ylabel('True Positive Rate (Recall)', fontsize=16)\n",
        "    plt.grid(True)\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plot_roc_curve(fpr, tpr)\n",
        "\n",
        "# 정밀도가 90%를 넘어설 때의 재현율(TPR, 참 양성 비율)에 해당하는 거짓 양성 비율(FPR)\n",
        "fpr_90 = fpr[np.argmax(tpr >= recall_90_precision)]   # 0.0053 정도\n",
        "# 빨강 점과 원점 사이의 실선 그리기\n",
        "plt.plot([fpr_90, fpr_90], [0., recall_90_precision], \"r:\")   # 수직 빨강 점선. 좌표는 (0.0053, 0.48) 정도\n",
        "plt.plot([0.0, fpr_90], [recall_90_precision, recall_90_precision], \"r:\")  # y축과 빨강 점 사이의 수평 빨강 점선(거의 안보임))\n",
        "plt.plot([fpr_90], [recall_90_precision], \"ro\")               # 빨강 점\n",
        "save_fig(\"roc_curve_plot\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ELyf8K3ajI4"
      },
      "source": [
        "TPR이 증가하면 FPR도 증가한다.\n",
        "따라서 거짓 양성 비율을 최대한 낮추면서 재현율을 최대한 높일 수 있는 모델이 좋은 모델이다.\n",
        "이를 계량화 화면, ROC 곡선의 아랫쪽의 면적인 __AUC__(area under curve)가 1에 가깝게\n",
        "나오도록 해주는 모델이 좋은 모델이다.\n",
        "\n",
        "SGD 분류기의 AUC는 0.96 정도이다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tKGOVhcrajI5",
        "outputId": "63c22178-ae69-42b2-b4b6-0375816746ae"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9604938554008616"
            ]
          },
          "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": {
        "id": "dcsjDal3ajI5"
      },
      "source": [
        "#### 랜덤 포레스트 분류기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v_9CT3fVajI5"
      },
      "source": [
        "랜덤 포레스트 분류기 모델을 이용해보자.\n",
        "\n",
        "__주의사항:__ `RandomForestClassifier` 모델은 점수를 예측하는 `predict_function()` 메서드가 없다.\n",
        "대신에 확률을 예측하는 `predict_proba()` 메서드를 교차 검증 예측에 사용할 수 있다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RM4W3uaDajI5"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "forest_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "y_probas_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3,\n",
        "                                    method=\"predict_proba\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LkjKnB2JajI5"
      },
      "source": [
        "`y_probas_forest` 변수는 각 훈련 샘플에 대한 음성 클래스, 양성 클래스에 속할 확률을 포함한 2차원 어레이를\n",
        "가리킨다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EVvpM8h9ajI5",
        "outputId": "cd44fc01-e52c-40a6-e89c-da56793cff44"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[0.11, 0.89],\n",
              "       [0.99, 0.01],\n",
              "       [0.96, 0.04],\n",
              "       ...,\n",
              "       [0.02, 0.98],\n",
              "       [0.92, 0.08],\n",
              "       [0.94, 0.06]])"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_probas_forest"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WemKa3kGajI6"
      },
      "source": [
        "모든 샘플에 대한 양성 클래스에 속할 확률만 가져와서 ROC 곡선을 그려보자.\n",
        "즉, 양성 클래스에 속할 확률을 임곗점으로 활용하여 TPR과 FPR의 관계를 그래프로 나타낸다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9d6yWwNhajI6"
      },
      "outputs": [],
      "source": [
        "y_scores_forest = y_probas_forest[:, 1] # 점수 = 양성 클래스의 확률\n",
        "\n",
        "fpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train_5,y_scores_forest)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X7xN1iX2ajI6"
      },
      "source": [
        "램덤 포레스트 분류기의 ROC 곡선이 보다 1x1 크기의 직사각형에 가까워졌다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0EYGVGMDajI6",
        "outputId": "d3a0e60c-41f3-47a2-cb2f-08c1fbe49b96"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "그림 저장: roc_curve_comparison_plot\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# 램덤 포레스트 분류기의 ROC 곡선 그리기\n",
        "recall_for_forest = tpr_forest[np.argmax(fpr_forest >= fpr_90)]  # 90% FPR에 대응하는 TPR. 약 0.95\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "\n",
        "# 파랑 점 곡선: SGD 분류기의 ROC 곡선\n",
        "plt.plot(fpr, tpr, \"b:\", linewidth=2, label=\"SGD\")\n",
        "# 파랑 실선: 랜덤 포레스트 분류기의 ROC 곡선\n",
        "plot_roc_curve(fpr_forest, tpr_forest, \"Random Forest\")\n",
        "\n",
        "plt.plot([fpr_90, fpr_90], [0., recall_90_precision], \"r:\")      # 짧은 빵강 수직 점선\n",
        "plt.plot([0.0, fpr_90], [recall_90_precision, recall_90_precision], \"r:\")  # 아랫쪽 빨강 수평 점선 (거의 안보임)\n",
        "plt.plot([fpr_90], [recall_90_precision], \"ro\")                  # 아래쪽 빨강 점. 좌표는 (0.0053, 0.48) 정도\n",
        "plt.plot([fpr_90, fpr_90], [0., recall_for_forest], \"r:\")        # 윗쪽 빨강 수평 점선 (거의 안보임)\n",
        "plt.plot([fpr_90], [recall_for_forest], \"ro\")                    # 위쪽 빨강 점. 좌표는 (0.0053, 0.95) 정도\n",
        "plt.grid(True)\n",
        "plt.legend(loc=\"lower right\", fontsize=16)\n",
        "save_fig(\"roc_curve_comparison_plot\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E_RxC6J2ajI7"
      },
      "source": [
        "AUC 값도 SGD 분류기보다 훨씬 좋아졌음을 확인할 수 있다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p6LRZ14RajI7",
        "outputId": "ae841477-5104-41d1-b76e-0172b596a6fb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9983436731328145"
            ]
          },
          "execution_count": 53,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "roc_auc_score(y_train_5, y_scores_forest)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZnMfYUzLajI7"
      },
      "source": [
        "#### 랜덤 포레스트 분류기의 정밀도와 재현율"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XZ6w01BKajI7"
      },
      "source": [
        "교차 검증을 이용한 모델의 정밀도는 99%, 재현율은 86.6% 정도로 나쁘지 않은 편이다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zMhN4VGwajI7",
        "outputId": "a0e65d84-782b-44b9-d14d-c0f763de355b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9905083315756169"
            ]
          },
          "execution_count": 54,
          "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": null,
      "metadata": {
        "id": "udryJU8WajI7",
        "outputId": "13004e5a-7981-4040-9623-7da01189ceee"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8662608374838591"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "recall_score(y_train_5, y_train_pred_forest)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-wn6Kbp4ajI7"
      },
      "source": [
        "# 2부"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rtoL9z1OajI8"
      },
      "source": [
        "## 3. 4 다중 클래스 분류 (p. 145)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b45BqWmiajI8"
      },
      "source": [
        "이제부터 두 개 보다 많은 클래스로 분류하는 다중 클래스 분류를 진행한다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wnsd5W6uajI8"
      },
      "source": [
        "#### 서포트 벡터 머신 활용"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vsvn0W3sajI8"
      },
      "source": [
        "서포트 벡터 머신(SVC, support vector machine)은 기본적으로 이진 분류기이지만\n",
        "__일대일__(one-versus-one) 전략을 활용하여 다중 클래스 분류에도 사용된다.\n",
        "\n",
        "* `gamma=\"auto\"`: 이 하이퍼파라미터의 역할은 5장에서 자세히 설명한다.\n",
        "    다만, 사이킷런 버전 0.22부터 `gamma`의 기본값이 `\"auto\"`에서 `\"scale\"` 로 변경되어\n",
        "    여기서는 명시적으로 키워드 인자를 지정한다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VSHQUDmxajI8"
      },
      "source": [
        "__주의사항:__ 서포트 벡터 머신 분류기는 훈련 세트의 크기에 민감하다. 따라서\n",
        "아래 코드는 훈련 세트의 크기를 1,000으로 줄여서 훈련하는 것을 보여준다.\n",
        "6만 개의 훈련 세트 전체를 대상으로 하면 시간이 좀 걸릴 것이다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fT4aAgUEajI8",
        "outputId": "90ae479f-f476-4ecd-b5cd-249e94c8bf06"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "SVC(gamma='auto', random_state=42)"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.svm import SVC\n",
        "\n",
        "svm_clf = SVC(gamma=\"auto\", random_state=42)\n",
        "svm_clf.fit(X_train[:1000], y_train[:1000]) # 이제부터는 y_train_5 대신에 y_train 사용"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pTrGdazdajI9"
      },
      "source": [
        "첫째 훈련 샘플에 대해 숫자 5로 정확하게 예측한다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Og86YtKyajI9",
        "outputId": "e9a7da97-ebef-4468-a165-a6c77a381dc4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([5], dtype=uint8)"
            ]
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "svm_clf.predict([some_digit])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8WR9O4GOajI9"
      },
      "source": [
        "`decision_function()` 메서드는 샘플이 각각의 클래스에 대한 점수를 어레이로 반환한다.\n",
        "예를 들어 첫째 훈련 샘플에 대한 각 클래스별 점수는 다음과 같다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BD7SSZo6ajI9",
        "outputId": "abc84938-469d-4450-9e18-780978066b7b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[ 2.81585438,  7.09167958,  3.82972099,  0.79365551,  5.8885703 ,\n",
              "         9.29718395,  1.79862509,  8.10392157, -0.228207  ,  4.83753243]])"
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "some_digit_scores = svm_clf.decision_function([some_digit])\n",
        "some_digit_scores"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q7LexFUbajI9"
      },
      "source": [
        "5번 인덱스의 값이 가장 크다.\n",
        "\n",
        "__참고:__ 넘파이의 `argmax()` 함수는 최댓값이 위치한 인덱스를 반환한다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OmIEqFfjajI9",
        "outputId": "5ecc9728-1f46-4c19-a19b-0cc38edce9ac"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.argmax(some_digit_scores)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zXusT9vhajI9"
      },
      "source": [
        "5번 인덱스에 해당하는 클래스를 알아보기 위해 사용된 클래스를 확인하면 다음과 같다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g5fg_rekajI-",
        "outputId": "bc8c9a1b-f6de-43f8-ff57-828aae44ce52"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8)"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "svm_clf.classes_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-jN0f0IKajI-"
      },
      "source": [
        "5번 인덱스에 해당하는 클래스는 (우연히도) 숫자 5이다.\n",
        "\n",
        "__주의사항:__ 분류 클래스가 여기서는 0, 1, 2, ..., 9 이기에 5번 인덱스, 즉 6번째 클래스는 5이다.\n",
        "하지만 이것은 우연에 불과하다. 예를 들어, 만약에 클래스가 1, 2, 3, ..., 9, 0 으로 순서로\n",
        "사용된다면 5번 인덱스에 해당하는 클래스는 6이 된다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AhyNZRLLajI-",
        "outputId": "775bd02d-a715-4251-ccb4-210fbadcea8e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "svm_clf.classes_[5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ebCjiGBMajI-"
      },
      "source": [
        "#### 일대다 방식 활용"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WKxcAOjQajI-"
      },
      "source": [
        "서포트 벡터 머신 분류기는 기본적으로 일대일(OvO) 방식으로 다항 클래스 분률를 진행한다.\n",
        "하지만 일대다(OvR) 방식을 사용하려면 `OneVsRestClassfier` 가 SVC 모델을 상속하도록 하면 된다.\n",
        "\n",
        "__주의사항:__ 서포트 벡터 머신 분류기는 훈련 세트의 크기에 민감하며, 이런 이유로 적은 수의 훈련 세트에\n",
        "대해 많은 많은 수의 이진 분류를 진행하는 일대일 방식을 기본으로 사용한다.\n",
        "아래 코드는 훈련 세트의 크기를 1,000으로 줄여서 일대다 방식을 SVC 모델에 적용한다.\n",
        "만약에 6만개의 훈련 세트에 대해 아래 코드를 실행하면 꽤 오래 걸릴 것이다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OKrTDepMajI-",
        "outputId": "bb1d9a23-8fb5-4ada-a5f8-f489812b39b9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "OneVsRestClassifier(estimator=SVC(gamma='auto', random_state=42))"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.multiclass import OneVsRestClassifier\n",
        "ovr_clf = OneVsRestClassifier(SVC(gamma=\"auto\", random_state=42))\n",
        "ovr_clf.fit(X_train[:1000], y_train[:1000])   # 첫 1000개의 훈련 샘플만을 이용한 일대다 방식 진행"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QkKOpC7LajI_"
      },
      "source": [
        "첫째 샘플을 5라고 예측한다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WVdRo5roajI_",
        "outputId": "b03dcc07-91c3-46e2-b4c8-7ebcfd5098c8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([5], dtype=uint8)"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ovr_clf.predict([some_digit])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tlSkrZP5ajI_"
      },
      "source": [
        "아래 코드는 일대다 방식에 사용된 SVC 모델의 수가 10개임을 확인해준다.\n",
        "\n",
        "* `estimators_` 메서드: 일대다 방식에 사용된 예측기, 여기서는 SVC 모델을 저장한다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bjjwbe8qajI_",
        "outputId": "200f914d-9cbf-41cd-87ef-46c0ced52915"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(ovr_clf.estimators_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "05uFHysbajI_"
      },
      "source": [
        "#### `SGDClassifier`와 `RandomForestClassifier` 분류기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MMJcv3yPajI_"
      },
      "source": [
        "두 모델은 다중 클래스 분류를 기본으로 지원하기에 일대일, 일대다 방식을 고민할 필요가 없다.\n",
        "아래 코드는 SGD 분류기를 이용하여 10개 클래스 전체를 대상으로 분류를 진행한다.\n",
        "(실행에 몇 분 걸린다.)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kU9faMaXajJA",
        "outputId": "9e4fb585-541f-43fb-f6a0-a8c48a705823"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "SGDClassifier(random_state=42)"
            ]
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sgd_clf.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ltFRFCvGajJA"
      },
      "source": [
        "__주의사항:__ 책에서와는 달리 첫째 샘플이 5가 아니라고 예측할 수도 있다. 여기서는 3으로 예측한다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K6Jmx0hnajJA",
        "outputId": "c3b590b8-23ec-43cf-90b3-88c11cf55287"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([3], dtype=uint8)"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sgd_clf.predict([some_digit])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AETMBXOHajJA"
      },
      "source": [
        "실제로 3과 혼동할만 하다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d3RaVjAkajJA",
        "outputId": "fcbfcb8c-968f-4d7e-d140-ab4305f91329"
      },
      "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(some_digit)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f1aI2K6najJA"
      },
      "source": [
        "첫째 샘플에 대한 10개 클래스별 예측 점수는 다음과 같이 3번 인덱스에 위치한 값이 가장 크다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IL0gnmT9ajJB",
        "outputId": "ae6313c3-e166-4165-e8cb-eebed21e49b1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[-31893.03095419, -34419.69069632,  -9530.63950739,\n",
              "          1823.73154031, -22320.14822878,  -1385.80478895,\n",
              "        -26188.91070951, -16147.51323997,  -4604.35491274,\n",
              "        -12050.767298  ]])"
            ]
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sgd_clf.decision_function([some_digit])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ykTYbXnJajJB"
      },
      "source": [
        "아래 코드는 3-겹 교차 검증을 이용하여 SGD 모델의 성능을 정확도를 기준으로 측정한다.\n",
        "이진 분류의 경우와는 달리 여기서는 정확도를 충분히 신뢰할 수 있다.\n",
        "이유는 각 숫자의 비율이 비슷하기 때문이다.\n",
        "\n",
        "**경고**: 사용하는 하드웨어에 따라 다음 두 셀을 실행하는데 30분 또는 그 이상 걸릴 수 있다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3QT61ZhTajJB",
        "outputId": "9b02d8f8-808f-41c0-b5a8-b2b49731e97c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.87365, 0.85835, 0.8689 ])"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cross_val_score(sgd_clf, X_train, y_train, cv=3, scoring=\"accuracy\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EIdCXhC1ajJB"
      },
      "source": [
        "성능 결과는 87% 정도의 정확도이다. 임의로 찍는 랜덤 분류기가 10% 정도의 정확도를 가질 것이기에\n",
        "이진 분류의 경우와 달리 상대적으로 매우 높은 성능의 다중 클래스 분류기라 할 수 있다.\n",
        "하지만 예를 들어 2장에서 전처리 단계로 사용한 축척 조정(스케일링)만 적용하더라도 성능을 90%정도로\n",
        "좀 더 끌어올릴 수 있다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VXo82ik7ajJB",
        "outputId": "909b89de-417e-4771-9f3a-eba2afe47226"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.8983, 0.891 , 0.9018])"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 표준화 축척 조정 전처리 진행 후 SGD 분류기 훈련\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "\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": "markdown",
      "metadata": {
        "id": "8a57WZvZajJC"
      },
      "source": [
        "## 3.5 에러 분석 (p. 148)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CyDUoVc6ajJC"
      },
      "source": [
        "이제 좋은 모델을 하나 찾았다고 가정한 후에 모델의 성능을 향상시키는 방법을 하나 소개한다.\n",
        "(좋은 모델을 구하기 위한 하이퍼파라미터 튜닝 등은 2장에서 소개한 그리드 탐색, 랜덤 탐색 등을 이용한다.)\n",
        "\n",
        "어떤 방법으로 모델의 성능을 향상시켜야 하는가는 경우에 따라 다르다.\n",
        "여기서는 오차 행렬을 이용하여 분류 오류의 발생 원인을 찾고, 어떻게 해결할 수 있는지를 살펴본다.\n",
        "아래 코드는 SGD 분류기를 3겹 교차 검증으로 훈련시킨 후의 오차 행렬을 계산한다.\n",
        "10개의 클래스를 대상으로 하였기에 `10x10` 행렬 모양의 2차원 어레이가 생성된다.\n",
        "앞서 이진 분류기의 오차 행렬보도 복잡해졌지만 이해하는 방식은 동일하다.\n",
        "\n",
        "* 행: 0부터 9까지의 실제 레이블.\n",
        "* 열: 0부터 9까지의 예측 레이블.\n",
        "\n",
        "예를 들어, 실제로 0번을 담은 사진에 대한 예측은 0번 행을 보면 된다.\n",
        "그러면 0으로 제대로 예측된 이미지는 5,577개, 1로 잘못 예측된 이미지는 0개, 2로 잘못 예측된 이미지는 22개\n",
        "등이 된다.\n",
        "\n",
        "__주의사항:__ 앞서 설명한 대로 언급된 구체적인 수가 아래 코드를 실행할 때마다\n",
        "조금씩 달라질 수 있지만 그리 큰 차이가 나지는 않을 것이다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sykaqaGOajJC",
        "outputId": "69288262-6036-4575-bf3a-e40e31dd29c7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[5577,    0,   22,    5,    8,   43,   36,    6,  225,    1],\n",
              "       [   0, 6400,   37,   24,    4,   44,    4,    7,  212,   10],\n",
              "       [  27,   27, 5220,   92,   73,   27,   67,   36,  378,   11],\n",
              "       [  22,   17,  117, 5227,    2,  203,   27,   40,  403,   73],\n",
              "       [  12,   14,   41,    9, 5182,   12,   34,   27,  347,  164],\n",
              "       [  27,   15,   30,  168,   53, 4444,   75,   14,  535,   60],\n",
              "       [  30,   15,   42,    3,   44,   97, 5552,    3,  131,    1],\n",
              "       [  21,   10,   51,   30,   49,   12,    3, 5684,  195,  210],\n",
              "       [  17,   63,   48,   86,    3,  126,   25,   10, 5429,   44],\n",
              "       [  25,   18,   30,   64,  118,   36,    1,  179,  371, 5107]])"
            ]
          },
          "execution_count": 82,
          "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",
        "\n",
        "conf_mx"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JONXerRLajJC"
      },
      "source": [
        "위 오차 행렬을 흑백 이미지로 표현하면 다음과 같다.\n",
        "오차 행렬에 사용된 숫자가 클 수록 해당 칸의 색은 상대적으로 밝아진다.\n",
        "\n",
        "* 대각선: 대부분의 숫자 이미지를 제대로 예측했다는 의미\n",
        "* 숫자 5의 경우 상대적으로 어두움: 숫자 5 이미지가 상대적으로 적거나 다룬 숫자에 비해 숫자 5에 대한 분류기의 정확도가 떨어진다는 의미"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7LWAyY-kajJC",
        "outputId": "f2175cd2-a64a-4172-b166-1274d5592064"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "그림 저장: confusion_matrix_plot\n"
          ]
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.matshow(conf_mx, cmap=plt.cm.gray)\n",
        "save_fig(\"confusion_matrix_plot\", tight_layout=False)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OW0DVS2eajJC"
      },
      "source": [
        "#### 부록: `plot_confusion_matrix()` 함수 활용"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GQ3a73JUajJD"
      },
      "source": [
        "사이킷런 0.22 버전부터는 `sklearn.metrics.plot_confusion_matrix()` 함수를 사용할 수 있다.\n",
        "예를 들어, 지금까지 학습된 SGD 분류기의 오차행렬은 다음과 같이 구한다.\n",
        "\n",
        "* `include_values=False`: 오차 행렬에 사용된 값 함께 표시 여부 지정. 여기서는 표시 안함.\n",
        "* `cmap='gray'`: 흑백 이미지로 표현. 지정하지 않으면 컬러 이미지 보여줌."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gqv1hnSjajJD",
        "outputId": "3b627ece-7c12-4861-b8b9-e2dae831f76c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f96fa9b6040>"
            ]
          },
          "execution_count": 88,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.metrics import plot_confusion_matrix\n",
        "\n",
        "plot_confusion_matrix(sgd_clf, X_train, y_train, include_values=False, cmap='gray')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NGijRaZTajJD"
      },
      "source": [
        "#### 오차율 행렬 활용"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2pCDis9uajJD"
      },
      "source": [
        "숫자별 오차율을 계산해서 이미지로 표현하면 보다 정확한 이유를 알 수 있다.\n",
        "이를 위해 각 숫자별로 이미지 개수를 확인한 다음에 숫자별 오차율로 이루어진 행렬을 생성한다.\n",
        "즉, 오차 행렬을 숫자별 이미지 총 개수로 나눈다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uUdih-KzajJD"
      },
      "outputs": [],
      "source": [
        "row_sums = conf_mx.sum(axis=1, keepdims=True)  # 숫자별 총 이미지 개수\n",
        "norm_conf_mx = conf_mx / row_sums              # 숫자별 오차율 행렬"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BruV43-SajJD"
      },
      "source": [
        "오차율 행렬을 이미지로 표현하면 다음과 같다.\n",
        "\n",
        "* 정확하게 예측된 부분을 무시하기 위해 대각선은 0으로 채워야 오류가 있는 칸이 보다 선명하게 드러남.\n",
        "* 5번 행, 8번 열이 가장 밝음: 숫자 5 이미지를 8로 잘못 예측한 비율이 가장 높다는 의미임.\n",
        "* 5를 3으로 또는 3을 5로 혼동한 경우도 높은 편임."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-mQH-SBiajJD",
        "outputId": "99fe79c7-9e7d-4b6c-c5f0-49fbe3f29c89"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "그림 저장: confusion_matrix_errors_plot\n"
          ]
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "np.fill_diagonal(norm_conf_mx, 0)  # 대각선을 0으로 채우기\n",
        "plt.matshow(norm_conf_mx, cmap=plt.cm.gray)\n",
        "\n",
        "save_fig(\"confusion_matrix_errors_plot\", tight_layout=False)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VqIHzvl9ajJE"
      },
      "source": [
        "3과 5로 한정해서 두 숫자를 확인해 보자.\n",
        "아래 이미지는 3과 5로 한정한 다음에 오차 행렬과 비슷한 형식으로 숫자들을 모아 보여준다.\n",
        "\n",
        "* 상단 왼편: 3을 3으로 정확하게 예측한 경우 25개\n",
        "* 상단 오른편: 3을 5로 잘못 예측한 경우 25개\n",
        "* 하단 왼편: 5를 3으로 잘못 예측한 경우 25개\n",
        "* 하단 오른편: 5를 5로 정확하게 예측한 경우 25개"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5a2c_bx4ajJE",
        "outputId": "9a890bc2-2d5d-4edc-fcfd-70c478be5b2f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "그림 저장: error_analysis_digits_plot\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "cl_a, cl_b = 3, 5\n",
        "X_aa = X_train[(y_train == cl_a) & (y_train_pred == cl_a)]  # 3을 3으로 예측한 샘플들\n",
        "X_ab = X_train[(y_train == cl_a) & (y_train_pred == cl_b)]  # 3을 5로 예측한 샘플들\n",
        "X_ba = X_train[(y_train == cl_b) & (y_train_pred == cl_a)]  # 5를 3으로 예측한 샘플들\n",
        "X_bb = X_train[(y_train == cl_b) & (y_train_pred == cl_b)]  # 5를 5로 예측한 샘플들\n",
        "\n",
        "plt.figure(figsize=(8,8))\n",
        "plt.subplot(221); plot_digits(X_aa[:25], images_per_row=5)  # 이미지 상단 왼편\n",
        "plt.subplot(222); plot_digits(X_ab[:25], images_per_row=5)  # 이미지 상단 오른편\n",
        "plt.subplot(223); plot_digits(X_ba[:25], images_per_row=5)  # 이미지 하단 왼편\n",
        "plt.subplot(224); plot_digits(X_bb[:25], images_per_row=5)  # 이미지 하단 오른편\n",
        "save_fig(\"error_analysis_digits_plot\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sZOJJTC9ajJE"
      },
      "source": [
        "#### 데이터 증식"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ihA_Op0KajJE"
      },
      "source": [
        "위 그림을 보면 3과 5개 매우 구분하기 어려운 경우도 있지만 대부분은 그렇지 않다.\n",
        "이유는 SGD 분류기가 사용하는 선형 분류 알고리즘이 제대로 작동하지 않았기 때문이다.\n",
        "4장에서 살펴 보겠지만 선형 분류기는 단순히 각각의 픽셀에 대한 가중치를 조절하는 형식으로 훈련한다.\n",
        "따라서 픽셀값이 조금만 달라도 예측값을 혼동하기 쉽다.\n",
        "\n",
        "이에 대한 한 가지 해결책은 이미지를 중앙에 위치시키고 회전되어 있지 않도록 전처리 하거나,\n",
        "아니면 조금씩 회전된 이미지를 추가로 생성하여 훈련 세트를 확장한 후에 훈련하면 모델의 성능을 높일 수 있다.\n",
        "이렇게 데이터를 인위적으로 추가하는 작업을 __데이터 증식__(data augmentation)이라 한다.\n",
        "\n",
        "아래 부록 B와 연습문제 2번에서 이미지를 상하좌우 1픽쎌씩 이동하여 총 24만개의 이미지를 추가하는\n",
        "데이터 증식 기법을 소개한다.\n",
        "이 방법만을 적용해도 정확도가 97% 이상으로 좋아진다.\n",
        "\n",
        "&#x270b; __참고:__ MNIST 데이터셋에 데이터 증식을 적용하는 보다 일반적인 방법은\n",
        "[MNIST 데이터 증식](https://medium.com/the-data-science-publication/how-to-augment-the-mnist-dataset-using-tensorflow-4fbf113e99a0)을\n",
        "참조할 수 있다.\n",
        "단, 아직 다루지 않은 텐서플로우(Tensorflow)를 이용하기에 관심 있는 경우에만 살펴보기 바란다.\n",
        "사용법은 사실 어렵지 않다.\n",
        "\n",
        "__주의사항:__ 이미지를 너무 많이 회전시키지 않아야 한다. 예를 들어, 숫자 6을 90도 회전 시키면 숫자 9가 된다.\n",
        "숫자를 얼마만큼 회전시켜야 하는가는 실험을 통해 확인해야 한다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JAPQhD0UajJE"
      },
      "source": [
        "#### 다중 클래스 분류 일반화"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rkmeEbUNajJE"
      },
      "source": [
        "지금까지 분류기와 관련된 기초 개념과 사용법, 성능 평가 방법 등에 대해 자세히 알아보았으며,\n",
        "그 정도면 충분하다.\n",
        "마지막으로 다중 클래스 분류 개념을 일반화한 두 개념을 소개한다.\n",
        "\n",
        "* 다중 레이블 분류(multilabel classification)\n",
        "* 다중 출력 분류(multioutput classification)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hdm95TnCajJE"
      },
      "source": [
        "## 3.6 다중 레이블 분류 (p. 152)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u6MotU-jajJF"
      },
      "source": [
        "다중 레이블 분류는 하나의 샘플에 대해 한 개의 클래스가 아닌 여러 개의 클래스를 예측하는 분류이다.\n",
        "예를 들어, 하나의 사진에 지정된 세 사람의 인물이 포함되어 있는지 여부를 예측하고 길이가 3인 어레이로 반환하라고 할 수 있다.\n",
        "지정된 세 사람이 앨리스, 밥, 찰리이면 다중 레이블 분류기는 아래와 같은 예측값을 반환해야 한다.\n",
        "\n",
        "```python\n",
        "[True, False, True]\n",
        "```\n",
        "\n",
        "위 결과에 따르면 앨리스와 찰리는 사진에 있지만 밥은 사진에 없다고 예측된다.\n",
        "\n",
        "아래 코드는 MNIST 이미지 샘플 각각에 대해 이미지가 가리키는 숫자를 예측하는 것 대신에\n",
        "이미지가 나타내는 수가 홀수인지 여부와, 7보다 같거나 큰지 여부를 함께 예측한다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "46uKQLLAajJF",
        "outputId": "9e6486ab-7c08-44b8-d4dd-7c96b07c98df"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[False,  True]])"
            ]
          },
          "execution_count": 90,
          "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)\n",
        "\n",
        "knn_clf.predict([some_digit])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hjbSkhA4ajJF"
      },
      "source": [
        "#### 다중 레이블 분류기 평가"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XLLcO71bajJF"
      },
      "source": [
        "* 다중 레이블 분류기를 평가하는 방법은 모델과 목적에 따라 달라짐.\n",
        "    "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "znduofYAajJG"
      },
      "source": [
        "* 예를들어, 각 레이블의 F<sub>1</sub> 점수를 구하고 레이블에 대한 가중치를 적용한\n",
        "    평균 점수 계산\n",
        "    * 가중치 예제: 타깃 레이블에 속한 샘플 수인 __지지도__(support).\n",
        "        즉, 샘플 수가 많은 클래스의 가중치를 보다 크게 줄 수 있음."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1RTibSEtajJG"
      },
      "source": [
        "아래 코드는 모든 레이블의 중요도가 동일하다고 가정하면서 위 KNN 모델의 F<sub>1</sub> 점수를 계산한다.\n",
        "\n",
        "**경고**: 다음 셀은 실행하는데 매우 오래 걸린다(하드웨어에 따라 몇 시간이 걸릴 수 있다)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F4GGVWJkajJG",
        "outputId": "74945a6e-6c6a-4a85-8c4e-91a36e910e1f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.976410265560605"
            ]
          },
          "execution_count": 72,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_train_knn_pred = cross_val_predict(knn_clf, X_train, y_multilabel, cv=3)\n",
        "f1_score(y_multilabel, y_train_knn_pred, average=\"macro\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RK79usu_ajJH"
      },
      "source": [
        "## 3.7 다중 출력 분류 (p. 153)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f4uzp5gyajJH"
      },
      "source": [
        "* 다중 출력 다중 클래스 분류라고도 불림\n",
        "* 다중 레이블 분류에서 한 레이블이 이진 클래스가 아닌 다중 클래스를 대상으로 예측하는 분류"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ec0E91bajJH"
      },
      "source": [
        "#### 다중 출력 분류 예제"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jQe23KLsajJH"
      },
      "source": [
        "* 예제: 이미지에서 잡음을 제거하는 시스템\n",
        "    * 다중 레이블: 각각의 픽셀에 대해 레이블 예측해야 함.\n",
        "    * 다중 클래스: 각각의 픽셀에서 예측하는 레이블이 0부터 255 중에 하나임."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "96PCVaeaajJH"
      },
      "source": [
        "아래 코드는 MNIST 데이터셋에 포함된 모든 샘플에 잡음을 추가한다.\n",
        "단, 훈련 세트와 테스트 세트에 서로 다른 잡음을 이용한다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8mjqCd2AajJH"
      },
      "outputs": [],
      "source": [
        "# MNIST 훈련 세트의 모든 샘플에 잡음 추가\n",
        "noise = np.random.randint(0, 100, (len(X_train), 784))\n",
        "X_train_mod = X_train + noise\n",
        "\n",
        "# MNIST 테스트 세트의 모든 샘플에 잡음 추가\n",
        "noise = np.random.randint(0, 100, (len(X_test), 784))\n",
        "X_test_mod = X_test + noise\n",
        "\n",
        "# 레이블은 사진 원본\n",
        "y_train_mod = X_train\n",
        "y_test_mod = X_test"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B6ozofb_ajJI"
      },
      "source": [
        "잡음(noise)이 추가된 이미지와 원본 이미지를 비교하면 다음과 같다.\n",
        "아래 코드는 테스트 세트의 0번 샘플을 이용한다.\n",
        "\n",
        "__주의사항:__ 책에서는 테스트 세트의 0번 인덱스가 4를 가리키는 이미지로 사용되었다.\n",
        "무슨 이유인지 모르지만 여기서는 7을 나타내는 이미지가 사용된다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "gj04SsBwajJI",
        "outputId": "99ccdcaa-e20d-4559-d44b-7862c47374e1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "그림 저장: noisy_digit_example_plot\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "some_index = 0  # 0번 인덱스\n",
        "\n",
        "plt.subplot(121); plot_digit(X_test_mod[some_index])  # 잡음 추가된 이미지\n",
        "plt.subplot(122); plot_digit(y_test_mod[some_index])  # 원본 이미지\n",
        "\n",
        "save_fig(\"noisy_digit_example_plot\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0jQeDc_IajJI"
      },
      "source": [
        "KNN 모델을 이용하여 다중 출력 모델을 훈련시킨다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xg6Fd3tAajJI",
        "outputId": "a1b0b91b-effb-4762-e902-44007c8e38bb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ]
          },
          "execution_count": 94,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "knn_clf.fit(X_train_mod, y_train_mod)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k0ipdJ0OajJI"
      },
      "source": [
        "훈련된 모델이 테스트 세트 첫째 샘플에 대해 예측한 이미지는 다음과 같이\n",
        "잡음을 많이 제거한 이미지다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TEzuBHDYajJJ",
        "outputId": "0fb43e89-fb6a-4963-c3a1-a3733ae9a018"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "그림 저장: cleaned_digit_example_plot\n"
          ]
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "clean_digit = knn_clf.predict([X_test_mod[some_index]])\n",
        "\n",
        "plot_digit(clean_digit)\n",
        "save_fig(\"cleaned_digit_example_plot\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eHHRXBggajJJ"
      },
      "source": [
        "## 부록"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4qilK9ItajJJ"
      },
      "source": [
        "### 부록 A. 더미 (즉 랜덤) 분류기 (p. 142)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0slDKcqqajJJ"
      },
      "source": [
        "랜덤 분류기의 성능을 ROC 곡선으로 측정하면 `y = x` 함수의 그래프로 그려진다.\n",
        "\n",
        "__참고:__ <그림 3-6>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "laUAdw-LajJK"
      },
      "outputs": [],
      "source": [
        "from sklearn.dummy import DummyClassifier\n",
        "# 0.24버전부터 strategy의 기본값이 'stratified'에서 'prior'로 바뀌므로 명시적으로 지정합니다.\n",
        "dmy_clf = DummyClassifier(strategy='prior')\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": null,
      "metadata": {
        "scrolled": false,
        "id": "V-nkqnCoajJK",
        "outputId": "234dd6c8-863f-4179-ab80-02a540d6d629"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fprr, tprr, thresholdsr = roc_curve(y_train_5, y_scores_dmy)\n",
        "plot_roc_curve(fprr, tprr)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nrmGGYkrajJK"
      },
      "source": [
        "### 부록 B. 데이터 증식을 이용한 KNN 모델 성능 향상시키기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vji2rGawajJK"
      },
      "source": [
        "먼저, 기본 훈련세트를 이용한 KNN 분류기의 성능 평가를 진행한다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wDR7mHuzajJK",
        "outputId": "9cf9edb5-9b0d-4cea-cd7c-3325ade781f9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=4, weights='distance')"
            ]
          },
          "execution_count": 78,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn_clf = KNeighborsClassifier(weights='distance', n_neighbors=4)\n",
        "knn_clf.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eQfw59S0ajJK"
      },
      "outputs": [],
      "source": [
        "y_knn_pred = knn_clf.predict(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s--J732fajJL"
      },
      "source": [
        "`accuracy_score()` 함수를 이용한 정확도는 97.14% 이다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tlE2vDLtajJL",
        "outputId": "6e60b220-2b20-459d-85eb-0583af07fd44"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9714"
            ]
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(y_test, y_knn_pred)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qVH6hUQZajJL"
      },
      "source": [
        "####  1 픽셀 이동 데이터 증식"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "95ZpMWzDajJL"
      },
      "source": [
        "상하좌우 각각의 방향으로 1 픽셀씩 이동하는 방식을 이동하여 훈련 데이터를 증식한다.\n",
        "아래 `shift_digit()` 함수는 지정된 픽셀 만큼 이미지를 이동시킨다.\n",
        "\n",
        "* `shift()` 함수: scipy에서 제공. 이미지 픽셀을 지정된 크기만큼 이동시켜 새로운 이미지 생성.\n",
        "* `dx`, `dy`: 각각 x축, y축으로 이동 크기 지정\n",
        "* `cval`: 이동 후 빈 픽셀에 채울 값. 모든 빈 픽셀에 동일한 값 사용.\n",
        "    - 0에서 255 사이의 값 사용. 여기서는 100으로 지정."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LzBOzbddajJL",
        "outputId": "3657406b-a8fe-4528-9b25-c7920b7f3538"
      },
      "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",
        "\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": "markdown",
      "metadata": {
        "id": "_Tbt4MZiajJL"
      },
      "source": [
        "6만 개의 훈련 샘플 각각에 대해 상하좌우 1 픽셀씩 이동시킨 훈련 데이터를 총 24만 개 생성한다.\n",
        "따라서 최종 훈련 세트의 크기는 30만이 된다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rTVeh9kiajJM",
        "outputId": "f214f8a4-5676-41f5-83cd-8f4270a0049e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((300000, 784), (300000,))"
            ]
          },
          "execution_count": 82,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train_expanded = [X_train]\n",
        "y_train_expanded = [y_train]\n",
        "for dx, dy in ((1, 0), (-1, 0), (0, 1), (0, -1)):\n",
        "    shifted_images = np.apply_along_axis(shift_digit, axis=1, arr=X_train, dx=dx, dy=dy)\n",
        "    X_train_expanded.append(shifted_images)\n",
        "    y_train_expanded.append(y_train)\n",
        "\n",
        "X_train_expanded = np.concatenate(X_train_expanded)\n",
        "y_train_expanded = np.concatenate(y_train_expanded)\n",
        "X_train_expanded.shape, y_train_expanded.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tjVYo2-uajJM"
      },
      "source": [
        "확장된 훈련 세트를 이용하여 다시 학습하면 정확도가 97.63%까지 상승한다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nydlRAUHajJM",
        "outputId": "5b12658e-1471-452d-e635-3fa6c95afe19"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=4, weights='distance')"
            ]
          },
          "execution_count": 83,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "knn_clf.fit(X_train_expanded, y_train_expanded)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LG2pMq9yajJM"
      },
      "outputs": [],
      "source": [
        "y_knn_expanded_pred = knn_clf.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h1HzVeFIajJM",
        "outputId": "724a95d3-9508-4b73-d152-6ab4c77b73f5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9763"
            ]
          },
          "execution_count": 85,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "accuracy_score(y_test, y_knn_expanded_pred)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pckMlYQFajJM"
      },
      "source": [
        "성능이 향상된 모델은 아래 처럼 매우 어려운 손글씨도 정확하게 예측한다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NRRXdnIuajJM",
        "outputId": "3a0fba66-e314-4e06-a616-6843a155b845"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[0.24579675, 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.75420325]])"
            ]
          },
          "execution_count": 86,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ambiguous_digit = X_test[2589]\n",
        "knn_clf.predict_proba([ambiguous_digit])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9xQzjdIQajJN",
        "outputId": "bcd5cd6c-c091-40a2-f8eb-56cbb615be17"
      },
      "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,
        "id": "37nLkbnHajJN"
      },
      "source": [
        "# 연습문제 해답"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5vNfJ1TiajJN"
      },
      "source": [
        "## 1. 97% 정확도의 MNIST 분류기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FSL2JXzBajJN"
      },
      "source": [
        "**경고**: 사용하는 하드웨어에 따라 다음 셀을 실행하는데 16시간 또는 그 이상 걸릴 수 있습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bT98blDgajJN",
        "outputId": "2269531c-04fd-40d6-90cb-395b4b65b281"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n",
            "[CV 1/5] END .................n_neighbors=3, weights=uniform; total time=   9.5s\n",
            "[CV 2/5] END .................n_neighbors=3, weights=uniform; total time=   9.7s\n",
            "[CV 3/5] END .................n_neighbors=3, weights=uniform; total time=   9.5s\n",
            "[CV 4/5] END .................n_neighbors=3, weights=uniform; total time=   9.6s\n",
            "[CV 5/5] END .................n_neighbors=3, weights=uniform; total time=   9.5s\n",
            "[CV 1/5] END ................n_neighbors=3, weights=distance; total time=   9.1s\n",
            "[CV 2/5] END ................n_neighbors=3, weights=distance; total time=   9.1s\n",
            "[CV 3/5] END ................n_neighbors=3, weights=distance; total time=   9.1s\n",
            "[CV 4/5] END ................n_neighbors=3, weights=distance; total time=   9.2s\n",
            "[CV 5/5] END ................n_neighbors=3, weights=distance; total time=   9.2s\n",
            "[CV 1/5] END .................n_neighbors=4, weights=uniform; total time=  12.5s\n",
            "[CV 2/5] END .................n_neighbors=4, weights=uniform; total time=  12.4s\n",
            "[CV 3/5] END .................n_neighbors=4, weights=uniform; total time=  12.4s\n",
            "[CV 4/5] END .................n_neighbors=4, weights=uniform; total time=  12.6s\n",
            "[CV 5/5] END .................n_neighbors=4, weights=uniform; total time=  12.7s\n",
            "[CV 1/5] END ................n_neighbors=4, weights=distance; total time=  11.9s\n",
            "[CV 2/5] END ................n_neighbors=4, weights=distance; total time=  12.1s\n",
            "[CV 3/5] END ................n_neighbors=4, weights=distance; total time=  11.9s\n",
            "[CV 4/5] END ................n_neighbors=4, weights=distance; total time=  12.5s\n",
            "[CV 5/5] END ................n_neighbors=4, weights=distance; total time=  12.2s\n",
            "[CV 1/5] END .................n_neighbors=5, weights=uniform; total time=  12.2s\n",
            "[CV 2/5] END .................n_neighbors=5, weights=uniform; total time=  12.7s\n",
            "[CV 3/5] END .................n_neighbors=5, weights=uniform; total time=  12.2s\n",
            "[CV 4/5] END .................n_neighbors=5, weights=uniform; total time=  12.5s\n",
            "[CV 5/5] END .................n_neighbors=5, weights=uniform; total time=  12.7s\n",
            "[CV 1/5] END ................n_neighbors=5, weights=distance; total time=  12.0s\n",
            "[CV 2/5] END ................n_neighbors=5, weights=distance; total time=  12.3s\n",
            "[CV 3/5] END ................n_neighbors=5, weights=distance; total time=  12.1s\n",
            "[CV 4/5] END ................n_neighbors=5, weights=distance; total time=  12.1s\n",
            "[CV 5/5] END ................n_neighbors=5, weights=distance; total time=  12.4s\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
              "             param_grid=[{'n_neighbors': [3, 4, 5],\n",
              "                          'weights': ['uniform', 'distance']}],\n",
              "             verbose=3)"
            ]
          },
          "execution_count": 88,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "param_grid = [{'weights': [\"uniform\", \"distance\"], 'n_neighbors': [3, 4, 5]}]\n",
        "\n",
        "knn_clf = KNeighborsClassifier()\n",
        "grid_search = GridSearchCV(knn_clf, param_grid, cv=5, verbose=3)\n",
        "grid_search.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bP9YH_y0ajJN",
        "outputId": "be1ae6ef-5045-4a93-cf6e-b47c53b74d6d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'n_neighbors': 4, 'weights': 'distance'}"
            ]
          },
          "execution_count": 89,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "grid_search.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NEu9CKEsajJO",
        "outputId": "5c7ce4b5-c902-45a8-b042-c96a634a9d0f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9716166666666666"
            ]
          },
          "execution_count": 90,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "grid_search.best_score_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jtRONGSmajJO",
        "outputId": "010b808e-f53c-4db6-dbbd-a3a928fbeacd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9714"
            ]
          },
          "execution_count": 91,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "y_pred = grid_search.predict(X_test)\n",
        "accuracy_score(y_test, y_pred)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ItTXbk7ajJO"
      },
      "source": [
        "## 2. 데이터 증식"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pVE7qELHajJO"
      },
      "outputs": [],
      "source": [
        "from scipy.ndimage.interpolation import shift"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mm9boi-iajJO"
      },
      "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": null,
      "metadata": {
        "id": "Xz4RdjwhajJO",
        "outputId": "3a8db9af-84fc-4d8f-84a7-6ce27c0a1737"
      },
      "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": null,
      "metadata": {
        "id": "C1t5tRVqajJO"
      },
      "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": null,
      "metadata": {
        "id": "Hvjbr0PbajJP"
      },
      "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": null,
      "metadata": {
        "id": "vCxdeY5uajJP"
      },
      "outputs": [],
      "source": [
        "knn_clf = KNeighborsClassifier(**grid_search.best_params_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QV8w2uMfajJP",
        "outputId": "24e538b5-5e80-4e78-c767-cef50ddeed85"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=4, weights='distance')"
            ]
          },
          "execution_count": 98,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "knn_clf.fit(X_train_augmented, y_train_augmented)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XABosmVFajJP"
      },
      "source": [
        "**경고**: 사용하는 하드웨어에 따라 다음 셀을 실행하는데 1시간 또는 그 이상 걸릴 수 있습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rY9J5ovCajJP",
        "outputId": "2961592c-761f-4719-d700-021a902c6455"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9763"
            ]
          },
          "execution_count": 99,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_pred = knn_clf.predict(X_test)\n",
        "accuracy_score(y_test, y_pred)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pzsf9ASqajJP"
      },
      "source": [
        "간단히 데이터를 증식해서 0.5% 정확도를 높였습니다. :)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MbMNX5UAajJQ"
      },
      "source": [
        "## 3. 타이타닉 데이터셋 도전"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AkX_VzvEajJQ"
      },
      "source": [
        "승객의 나이, 성별, 승객 등급, 승선 위치 같은 속성을 기반으로 하여 승객의 생존 여부를 예측하는 것이 목표입니다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DFPZmOBfajJQ"
      },
      "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(번역서 깃허브에는 두 파일이 이미 포함되어 있습니다)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-bcTBBKxajJQ"
      },
      "source": [
        "그다음 데이터를 적재합니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Jrw6RbGMajJQ"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "TITANIC_PATH = os.path.join(\"datasets\", \"titanic\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aCUWmtV1ajJQ"
      },
      "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": null,
      "metadata": {
        "id": "PpanpmJxajJQ"
      },
      "outputs": [],
      "source": [
        "train_data = load_titanic_data(\"train.csv\")\n",
        "test_data = load_titanic_data(\"test.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hM0p_OjPajJQ"
      },
      "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": {
        "id": "tQ2dZCgpajJR"
      },
      "source": [
        "훈련 세트에서 맨 위 몇 개의 열을 살펴 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YvQofG5bajJR",
        "outputId": "b6c45d89-bfef-4232-c490-d9fc46de9f1a"
      },
      "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": 103,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z0l6OtKNajJR"
      },
      "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": {
        "id": "Vj4sf7LIajJR"
      },
      "source": [
        "누락된 데이터가 얼마나 되는지 알아보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HnrFRw6UajJR",
        "outputId": "4b39c491-4cac-42fc-b653-c4506079beff"
      },
      "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",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ]
        }
      ],
      "source": [
        "train_data.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WzGh4o24ajJR"
      },
      "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": {
        "id": "xATKrUcSajJR"
      },
      "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": {
        "id": "iZRV7K9WajJR"
      },
      "source": [
        "통계치를 살펴 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M_3FL4hDajJS",
        "outputId": "084708f9-f5c3-458e-854f-57ff25c42fae"
      },
      "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": 105,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_data.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EDgJf20VajJS"
      },
      "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": {
        "id": "IXwgHd0WajJS"
      },
      "source": [
        "타깃이 0과 1로 이루어졌는지 확인합니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k5Z6Id1pajJS",
        "outputId": "61737cf0-78aa-4a55-ce53-9d2f86870ba8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    549\n",
              "1    342\n",
              "Name: Survived, dtype: int64"
            ]
          },
          "execution_count": 106,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_data[\"Survived\"].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GQ-IKko4ajJS"
      },
      "source": [
        "범주형 특성들을 확인해 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_5bf-pmoajJS",
        "outputId": "c96c6e1a-f5fe-4158-a2a9-b5961a809239"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3    491\n",
              "1    216\n",
              "2    184\n",
              "Name: Pclass, dtype: int64"
            ]
          },
          "execution_count": 107,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_data[\"Pclass\"].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KI1VOVwVajJS",
        "outputId": "d9555471-269f-4d00-f3eb-34cb1848823f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "male      577\n",
              "female    314\n",
              "Name: Sex, dtype: int64"
            ]
          },
          "execution_count": 108,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_data[\"Sex\"].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m4eA-YlQajJT",
        "outputId": "e83af22e-1443-4196-d024-46a5f3ff3b2b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "S    644\n",
              "C    168\n",
              "Q     77\n",
              "Name: Embarked, dtype: int64"
            ]
          },
          "execution_count": 109,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_data[\"Embarked\"].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DlC6AgYlajJT"
      },
      "source": [
        "**Embarked** 특성은 승객이 탑승한 곳을 알려 줍니다: C=Cherbourg, Q=Queenstown, S=Southampton."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MF92ta_BajJT"
      },
      "source": [
        "**노트**: 아래 코드는 `Pipeline`, `FeatureUnion`와 사용자 정의 `DataFrameSelector` 클래스를 사용해 각 열을 다르게 전처리합니다. 사이킷런 0.20부터는 이전 장에서처럼 `ColumnTransformer`를 사용하는 것이 좋습니다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GND4GrNRajJT"
      },
      "source": [
        "전처리 파이프라인을 만들어 보죠. 이전 장에서 만든 `DataframeSelector`를 재사용하여 `DataFrame`에서 특정 열을 선택하겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rYUwnrpbajJT"
      },
      "outputs": [],
      "source": [
        "from sklearn.base import BaseEstimator, TransformerMixin\n",
        "\n",
        "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, attribute_names):\n",
        "        self.attribute_names = attribute_names\n",
        "    def fit(self, X, y=None):\n",
        "        return self\n",
        "    def transform(self, X):\n",
        "        return X[self.attribute_names]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t9_OaysDajJT"
      },
      "source": [
        "숫자 특성을 위한 파이프라인을 만듭니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lSH1dZEpajJU"
      },
      "outputs": [],
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.impute import SimpleImputer\n",
        "\n",
        "num_pipeline = Pipeline([\n",
        "        (\"select_numeric\", DataFrameSelector([\"Age\", \"SibSp\", \"Parch\", \"Fare\"])),\n",
        "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
        "    ])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tlxcWwDkajJU",
        "outputId": "406e68bb-8556-41e5-a11f-58765607bf68"
      },
      "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": 112,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "num_pipeline.fit_transform(train_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VMRasmL1ajJU"
      },
      "source": [
        "문자열로된 범주형 열을 위해 별도의 Imputer 클래스가 필요합니다(일반 `SimpleImputer` 클래스는 이를 처리하지 못합니다):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hJMcZgdVajJU"
      },
      "outputs": [],
      "source": [
        "# stackoverflow.com/questions/25239958 에서 착안했습니다\n",
        "class MostFrequentImputer(BaseEstimator, TransformerMixin):\n",
        "    def fit(self, X, y=None):\n",
        "        self.most_frequent_ = pd.Series([X[c].value_counts().index[0] for c in X],\n",
        "                                        index=X.columns)\n",
        "        return self\n",
        "    def transform(self, X, y=None):\n",
        "        return X.fillna(self.most_frequent_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kfbm7YABajJU"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import OneHotEncoder"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8OqyMNvpajJU"
      },
      "source": [
        "이제 범주형 특성을 위한 파이프라인을 만듭니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CLv3p22LajJU"
      },
      "outputs": [],
      "source": [
        "cat_pipeline = Pipeline([\n",
        "        (\"select_cat\", DataFrameSelector([\"Pclass\", \"Sex\", \"Embarked\"])),\n",
        "        (\"imputer\", MostFrequentImputer()),\n",
        "        (\"cat_encoder\", OneHotEncoder(sparse=False)),\n",
        "    ])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fkFNXSLAajJV",
        "outputId": "35f08fcd-2a37-4888-987b-51c755e9f09f"
      },
      "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": 116,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cat_pipeline.fit_transform(train_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M1z1lUEBajJV"
      },
      "source": [
        "마지막으로 숫자와 범주형 파이프라인을 연결합니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E0wl5RbpajJY"
      },
      "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": {
        "id": "p3Uy3ZgAajJY"
      },
      "source": [
        "좋습니다! 이제 원본 데이터를 받아 머신러닝 모델에 주입할 숫자 입력 특성을 출력하는 전처리 파이프라인을 만들었습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8v5cCsCBajJZ",
        "outputId": "bc7cd34e-3d55-4550-b27b-c8a62ae36cb4"
      },
      "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": 118,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train = preprocess_pipeline.fit_transform(train_data)\n",
        "X_train"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1PqAmTznajJZ"
      },
      "source": [
        "레이블을 가져옵니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v5P4HSROajJa"
      },
      "outputs": [],
      "source": [
        "y_train = train_data[\"Survived\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NUMZgaPzajJa"
      },
      "source": [
        "이제 분류기를 훈련시킬 차례입니다. 먼저 `SVC`를 사용해 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-61O5zekajJa",
        "outputId": "413a0053-3b2a-4c9d-8222-4d4bfe38410c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "SVC(gamma='auto')"
            ]
          },
          "execution_count": 120,
          "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": {
        "id": "SUMBlhQpajJa"
      },
      "source": [
        "모델이 잘 훈련된 것 같습니다. 이를 사용해서 테스트 세트에 대한 예측을 만듭니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NwPqsIj-ajJa"
      },
      "outputs": [],
      "source": [
        "X_test = preprocess_pipeline.transform(test_data)\n",
        "y_pred = svm_clf.predict(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nxfv7UM0ajJa"
      },
      "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": null,
      "metadata": {
        "id": "FQ0C6U_7ajJa",
        "outputId": "e9c1130e-579a-479c-e8bd-9a831da62880"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.7329588014981274"
            ]
          },
          "execution_count": 122,
          "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": {
        "id": "a3a9MJ-PajJb"
      },
      "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": {
        "id": "YFX5GER2ajJb"
      },
      "source": [
        "`RandomForestClassifier`를 적용해 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NdqQf4GQajJb",
        "outputId": "48843dce-4e6d-412f-dac0-567ce6e3c314"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8126466916354558"
            ]
          },
          "execution_count": 123,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "forest_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "forest_scores = cross_val_score(forest_clf, X_train, y_train, cv=10)\n",
        "forest_scores.mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pXnfg3cJajJb"
      },
      "source": [
        "훨씬 좋네요!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JgdifycUajJb"
      },
      "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": null,
      "metadata": {
        "id": "Ch6ueHjfajJb",
        "outputId": "bdc20d10-2d52-4e6e-f779-b09679e10faa"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 4))\n",
        "plt.plot([1]*10, svm_scores, \".\")\n",
        "plt.plot([2]*10, forest_scores, \".\")\n",
        "plt.boxplot([svm_scores, forest_scores], labels=(\"SVM\",\"Random Forest\"))\n",
        "plt.ylabel(\"Accuracy\", fontsize=14)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xwjyZkPFajJb"
      },
      "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": null,
      "metadata": {
        "id": "FHiCxL6BajJc",
        "outputId": "7cc3c80f-f756-4cf9-810c-b2a5a596f3ac"
      },
      "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": 125,
          "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": null,
      "metadata": {
        "id": "oF_IRShiajJc",
        "outputId": "5b102e25-0192-46f8-d015-e10a44cf4765"
      },
      "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": 126,
          "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": {
        "id": "0zDXl5CmajJc"
      },
      "source": [
        "## 4. 스팸 필터"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JvpRUFdtajJc"
      },
      "source": [
        "먼저 데이터를 다운받습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gmA2Nm2bajJc"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import tarfile\n",
        "import urllib.request\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(ham_url=HAM_URL, 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": null,
      "metadata": {
        "id": "HoLdB5IsajJc"
      },
      "outputs": [],
      "source": [
        "fetch_spam_data()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2mCDsQJWajJc"
      },
      "source": [
        "다음, 모든 이메일을 읽어 들입니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ut8YS1lWajJc"
      },
      "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": null,
      "metadata": {
        "id": "YnP9iWl3ajJc",
        "outputId": "41fdfc58-9152-46ea-be28-5b47348fb86a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2500"
            ]
          },
          "execution_count": 130,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(ham_filenames)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MA33-4dyajJd",
        "outputId": "2852d173-a278-493c-8a6f-92a60e53474f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "500"
            ]
          },
          "execution_count": 131,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(spam_filenames)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RTPcQQ8sajJd"
      },
      "source": [
        "파이썬의 `email` 모듈을 사용해 이메일을 파싱합니다(헤더, 인코딩 등을 처리합니다):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FwQT88w9ajJd"
      },
      "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": null,
      "metadata": {
        "id": "S197FL7XajJd"
      },
      "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": {
        "id": "D50oeLEZajJd"
      },
      "source": [
        "데이터가 어떻게 구성되어 있는지 감을 잡기 위해 햄 메일과 스팸 메일을 하나씩 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ADwV_i-pajJd",
        "outputId": "5e94b9d0-e588-4e92-bd7c-618b93c4b369"
      },
      "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": null,
      "metadata": {
        "id": "0NRNDoV2ajJd",
        "outputId": "471c2b51-8bd3-4347-f031-0f4e86a7a751"
      },
      "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": {
        "id": "AM4T88vWajJe"
      },
      "source": [
        "어떤 이메일은 이미지나 첨부 파일을 가진 멀티파트(multipart)입니다(메일에 포함되어 있을수 있습니다). 어떤 파일들이 있는지 살펴 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "whrmCkv1ajJe"
      },
      "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": null,
      "metadata": {
        "id": "DqmU4uILajJe"
      },
      "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": null,
      "metadata": {
        "id": "NfP9iyd0ajJe",
        "outputId": "22259c6c-e956-4434-c0ed-70639e49f9f5"
      },
      "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": 138,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "structures_counter(ham_emails).most_common()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mwMMA9BAajJe",
        "outputId": "93ddd551-17ab-42f5-c7e8-fc7af88cd6f6"
      },
      "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": 139,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "structures_counter(spam_emails).most_common()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oIJl67kaajJe"
      },
      "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": {
        "id": "SE4-exmqajJe"
      },
      "source": [
        "이제 이메일 헤더를 살펴보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PH4eumbQajJe",
        "outputId": "a7408029-faa8-4000-99b5-bb915b295fc1"
      },
      "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": {
        "id": "x6pCxpHCajJf"
      },
      "source": [
        "보낸사람의 이메일 주소와 같이 헤더에는 유용한 정보가 많이 있지만 여기서는 `Subject` 헤더만 다뤄 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-0Av9VJ2ajJf",
        "outputId": "8af85df0-8e8b-49ef-b5a7-b48349d714cb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'Life Insurance - Why Pay More?'"
            ]
          },
          "execution_count": 141,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "spam_emails[0][\"Subject\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4uywW79iajJf"
      },
      "source": [
        "좋습니다. 데이터에를 더 살펴보기 전에 훈련 세트와 테스트 세트로 나누도록 하겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V2TPO13-ajJf",
        "outputId": "6a376ac4-2ef2-461c-a1b5-e119a74c6481"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-142-c6d32b215bf4>:4: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  X = np.array(ham_emails + spam_emails)\n"
          ]
        }
      ],
      "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": {
        "id": "WXMrMyKOajJf"
      },
      "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": null,
      "metadata": {
        "id": "i7vciulCajJf"
      },
      "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": {
        "id": "6qY4SHtTajJf"
      },
      "source": [
        "잘 작동하는지 확인해 보겠습니다. 다음은 HTML 스팸입니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_ol860QLajJg",
        "outputId": "22cb7aa7-43cc-4c90-9c41-0ca3b19ddc2c"
      },
      "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": {
        "id": "8p4yAt-pajJg"
      },
      "source": [
        "변환된 텍스트입니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JoXBqYBbajJg",
        "outputId": "1b5cadcd-8b24-40f7-fefb-2e84ee180635"
      },
      "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": {
        "id": "QLp31Qs1ajJg"
      },
      "source": [
        "아주 좋습니다! 이제 포맷에 상관없이 이메일을 입력으로 받아서 일반 텍스트를 출력하는 함수를 만들겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xmEj515zajJg"
      },
      "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": null,
      "metadata": {
        "id": "tKtP7K5sajJh",
        "outputId": "09ce641f-ae2d-4821-b5b1-a9b8c47cdbc6"
      },
      "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": {
        "id": "uYjTvte8ajJh"
      },
      "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": null,
      "metadata": {
        "id": "7bQfUmcYajJh",
        "outputId": "b874fa57-aa14-40da-bc6d-54e5ad9dfdf3"
      },
      "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": {
        "id": "kDS0kI8ZajJh"
      },
      "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": null,
      "metadata": {
        "id": "PKHNfhaiajJh"
      },
      "outputs": [],
      "source": [
        "# 코랩에서 이 노트북을 실행하려면 먼저 pip install urlextract을 실행합니다\n",
        "try:\n",
        "    import google.colab\n",
        "    !pip install -q -U urlextract\n",
        "except ImportError:\n",
        "    pass # not running on Colab"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vx_oya6qajJh",
        "outputId": "bc5bb657-0cdc-46c1-ba68-0d8bbfdfb424"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Error: replacing URLs requires the urlextract module.\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": {
        "id": "ILBgHl--ajJh"
      },
      "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": null,
      "metadata": {
        "id": "AnFDEOQMajJi"
      },
      "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": {
        "id": "XqD7BVUKajJi"
      },
      "source": [
        "이 변환기를 몇 개의 이메일에 적용해 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IR0Tu9ggajJi",
        "outputId": "69e62c52-19bd-4689-a568-e06121e0afe4"
      },
      "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, 'http': 1, 'www': 1, 'postfun': 1, 'com': 1, 'pfp': 1, 'worboi': 1, 'html': 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({'number': 5, 'http': 4, 'yahoo': 4, 's': 3, 'group': 3, 'com': 3, 'to': 3, 'in': 2, 'forteana': 2, 'martin': 2, 'an': 2, 'and': 2, 'memri': 2, 'we': 2, 'is': 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, 'org': 1, 'bin': 1, 'articl': 1, 'cgi': 1, 'page': 1, 'archiv': 1, 'area': 1, 'ia': 1, 'id': 1, 'ianumb': 1, 'know': 1, 'how': 1, 'unbias': 1, 'don': 1, 't': 1, 'www': 1, 'guardian': 1, 'co': 1, 'uk': 1, 'elsewher': 1, 'journalist': 1, 'stori': 1, 'html': 1, 'rob': 1, 'sponsor': 1, 'dvd': 1, 'free': 1, 'p': 1, 'join': 1, 'now': 1, 'us': 1, 'click': 1, 'ptnumberybb': 1, 'nxieaa': 1, 'mvfiaa': 1, 'numbergsolb': 1, 'tm': 1, 'from': 1, 'thi': 1, 'send': 1, 'email': 1, 'egroup': 1, 'your': 1, 'use': 1, 'of': 1, 'subject': 1, 'doc': 1, 'info': 1, 'term': 1})],\n",
              "      dtype=object)"
            ]
          },
          "execution_count": 152,
          "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": {
        "id": "V_CmaFQ_ajJi"
      },
      "source": [
        "제대로 작동하는 것 같네요!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0VBjAbbsajJi"
      },
      "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": null,
      "metadata": {
        "id": "0UX3KgVPajJi"
      },
      "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.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": null,
      "metadata": {
        "id": "yM_UPl3wajJi",
        "outputId": "14276ec3-d21f-4436-da67-4325c7e3eba9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<3x11 sparse matrix of type '<class 'numpy.int64'>'\n",
              "\twith 20 stored elements in Compressed Sparse Row format>"
            ]
          },
          "execution_count": 154,
          "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": null,
      "metadata": {
        "id": "6KdxRNmZajJi",
        "outputId": "392b4f2c-f86d-4597-d9bd-a96ffce71114"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[  6,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
              "       [112,  11,   9,   8,   3,   1,   0,   1,   3,   0,   1],\n",
              "       [ 92,   0,   1,   2,   3,   4,   5,   3,   1,   4,   2]])"
            ]
          },
          "execution_count": 155,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_few_vectors.toarray()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E02qOzHbajJj"
      },
      "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": null,
      "metadata": {
        "id": "uJ3u6HOWajJj",
        "outputId": "2550c7b8-30ec-488c-df54-d9bb74011e31"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'the': 1,\n",
              " 'of': 2,\n",
              " 'and': 3,\n",
              " 'to': 4,\n",
              " 'http': 5,\n",
              " 'number': 6,\n",
              " 'com': 7,\n",
              " 'all': 8,\n",
              " 'yahoo': 9,\n",
              " 'in': 10}"
            ]
          },
          "execution_count": 156,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "vocab_transformer.vocabulary_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zU2rzb1cajJj"
      },
      "source": [
        "이제 스팸 분류기를 훈련시킬 준비를 마쳤습니다! 전체 데이터셋을 변환시켜보죠:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "knZCxHPwajJj"
      },
      "outputs": [],
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "preprocess_pipeline = Pipeline([\n",
        "    (\"email_to_wordcount\", EmailToWordCounterTransformer()),\n",
        "    (\"wordcount_to_vector\", WordCounterToVectorTransformer()),\n",
        "])\n",
        "\n",
        "X_train_transformed = preprocess_pipeline.fit_transform(X_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OIyzABemajJj"
      },
      "source": [
        "**Note**: to be future-proof, we set `solver=\"lbfgs\"` since this will be the default value in Scikit-Learn 0.22."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RujKS4fPajJj",
        "outputId": "5853765d-a7a5-4a5d-8fd5-86cf455d00b3"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[CV] END ................................ score: (test=0.983) total time=   0.3s\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.3s remaining:    0.0s\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[CV] END ................................ score: (test=0.984) total time=   0.3s\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[CV] END ................................ score: (test=0.993) total time=   0.5s\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    1.1s finished\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "0.9862500000000001"
            ]
          },
          "execution_count": 158,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "log_clf = LogisticRegression(solver=\"lbfgs\", max_iter=1000, 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": {
        "id": "MYHpXqniajJj"
      },
      "source": [
        "98.5%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": null,
      "metadata": {
        "id": "Nnwyq_R2ajJj",
        "outputId": "32fa3af1-7b39-4e75-b174-55784f10130a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "정밀도: 96.88%\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=\"lbfgs\", max_iter=1000, random_state=42)\n",
        "log_clf.fit(X_train_transformed, y_train)\n",
        "\n",
        "y_pred = log_clf.predict(X_test_transformed)\n",
        "\n",
        "print(\"정밀도: {:.2f}%\".format(100 * precision_score(y_test, y_pred)))\n",
        "print(\"재현율: {:.2f}%\".format(100 * recall_score(y_test, y_pred)))"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    },
    "nav_menu": {},
    "toc": {
      "navigate_menu": true,
      "number_sections": true,
      "sideBar": true,
      "threshold": 6,
      "toc_cell": false,
      "toc_section_display": "block",
      "toc_window_display": false
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}