{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://nbviewer.jupyter.org/github/twMr7/Python-Machine-Learning/blob/master/15-Machine_Learning_with_Scikit.ipynb\">\n",
    "        Open In Jupyter nbviewer\n",
    "        <img style=\"float: center;\" src=\"https://nbviewer.jupyter.org/static/img/nav_logo.svg\" width=\"120\" />\n",
    "    </a>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/twMr7/Python-Machine-Learning/blob/master/15-Machine_Learning_with_Scikit.ipynb)\n",
    "\n",
    "# 15. Machine Learning with Scikit\n",
    "\n",
    "**Scikit-learn** 提供了簡單有效率的資料分析工具，其中整合了相當多在執行機器學習任務過程所需要的套件：\n",
    "+ 資料前處理： 數據正規化、類別欄位轉數值。\n",
    "+ 特徵工程： 特徵選取、特徵萃取、特徵降維。\n",
    "+ 學習模型： Classification/Regression, Parametric/Non-Parametric, Supervised/Unsupervised, ... 等方法。\n",
    "+ 模型選取： 交叉驗證、超參數搜尋、效能度量指標。\n",
    "\n",
    "本章節主要內容，介紹如何利用 scikit-learn 的工具來完成機器學習的任務流程。 按照一般機器學習專案的開發順序，一開始介紹準備資料時需要做的資料集切割、交叉驗證，前處理的數據正規化，再使用 pipeline 簡化工作流程，最後加入必要的度量指標。\n",
    "\n",
    "| 工具函式庫套件      | 網址                                                                    |\n",
    "|---------------------|-------------------------------------------------------------------------|\n",
    "| **`pandas`**        | [pandas.pydata.org](https://pandas.pydata.org/docs/reference/index.html)|\n",
    "| **`matplotlib`**    | [matplotlib.org](https://matplotlib.org/stable/contents.html)           |\n",
    "| **`scikit-learn`**  | [scikit-learn.org](https://scikit-learn.org/stable/modules/classes.html)|\n",
    "\n",
    "+ [**15.1 資料集切割與交叉驗證**](#cross-validation)\n",
    "+ [**15.2 數據正規化與數據洩漏問題**](#data-normalization)\n",
    "+ [**15.3 將工作流程封裝成 Pipeline**](#task-pipeline)\n",
    "+ [**15.4 效能度量指標**](#performance-metrics)\n",
    "+ [**15.5 理解ROC曲線**](#roc-explain)\n",
    "+ [**參考資料**](#references)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 預載入必要模組與環境設定\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-notebook')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 輔助圖片顯示及嵌入 notebook\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"cross-validation\"></a>\n",
    "\n",
    "## 15.1 資料集切割與交叉驗證\n",
    "\n",
    "在之前的章節中提到過，我們希望機器學習訓練出來的模型具有泛化能力，在上線輸入真實世界的資料時仍然維持相同的預測效能。 為了在訓練過程就能模擬真實世界面臨沒見過的新資料的狀況，在開始訓練模型之前會先把資料集做切割，保留一小部分當測試集，剩下的是訓練模型用的訓練集。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### § 資料集切割\n",
    "\n",
    "切割成兩份的大小如何分配？ 目前沒有一個固定有效的評估標準。 機器學習裡有幾種常見的的比例分配，如 **train：test = 7:3**、**8:2**、或**9:1**等等，如何決定分配的比例取決於資料集的大小。 一般來說，訓練集越多模型學得越好，但測試集太小也不能反映真實的狀況。 例如一個 600 筆的資料集，按照 9:1 的比例分配是 540:60 筆，直覺判斷可能會覺得測試集有點少，而按照 7:3 的比例分配是 420:180 筆，可能又會覺得訓練集太小。 但如果是 1000,000 筆的資料集，按照 9:1 的比例應該會覺得測試集可以再少一點，例如 20,000 筆的測試集可能就夠多了。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Scikit-learn 在 [`sklearn.model_selection`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.model_selection) 模組裡，提供了一個可以指定比例來做 train/test 分割的工具函式。\n",
    "+ [**`train_test_split()`**](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html)。 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X =\n",
      " [[ 0  1]\n",
      " [ 2  3]\n",
      " [ 4  5]\n",
      " [ 6  7]\n",
      " [ 8  9]\n",
      " [10 11]\n",
      " [12 13]\n",
      " [14 15]\n",
      " [16 17]\n",
      " [18 19]]\n",
      "y =\n",
      " [0. 0. 0. 0. 0. 0. 1. 1. 1. 1.]\n"
     ]
    }
   ],
   "source": [
    "# 生成 10x2 大小的 X，以及對應的 10 個 y 數列\n",
    "X = np.arange(20).reshape((10, 2)) \n",
    "y = np.zeros(10)\n",
    "y[6:] = 1.0\n",
    "\n",
    "print('X =\\n', X)\n",
    "print('y =\\n', y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train =\n",
      " [[12 13]\n",
      " [16 17]\n",
      " [14 15]\n",
      " [ 8  9]\n",
      " [ 0  1]\n",
      " [18 19]]\n",
      "y_train =\n",
      "\n",
      " [1. 1. 1. 0. 0. 1.]\n",
      "X_test =\n",
      " [[ 2  3]\n",
      " [10 11]\n",
      " [ 4  5]\n",
      " [ 6  7]]\n",
      "y_test =\n",
      " [0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 刻意指定一個很不剛好的比例\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)\n",
    "\n",
    "# 指定的比例如果沒有剛好整數，切割時還是會調整成整數\n",
    "print('X_train =\\n', X_train)\n",
    "print('y_train =\\n\\n', y_train)\n",
    "print('X_test =\\n', X_test)\n",
    "print('y_test =\\n', y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([0., 0., 0., 0., 0., 0., 1.]), array([1., 1., 1.])]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 沒有 shuffle 的話，切割是照原本順序\n",
    "train_test_split(y, test_size=0.3, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ★ 測試集要有代表性 ★\n",
    "\n",
    "測試集一定要盡可能反映模型實際使用的狀況。 不僅僅是樣本數量大小夠不夠的問題而已，要注意所選出來的測試集裡：\n",
    "1. **各類別的分布狀況是否符合實際使用狀況？**\n",
    "2. **所輸入的特徵數據是否符合實際使用狀況？**\n",
    "\n",
    "例如： 生產線上的瑕疵檢測，實際狀況常常是瑕疵的資料比正常的資料少很多，所以訓練資料常常呈現極度不平衡的比例分布狀況。 如果使用大多數是瑕疵的資料來當測試集，實際上線時可能會發現 *false positive* 的誤判過高。 這非常可能就是訓練過程不符合實際狀況的測試誤導的，因為大量的瑕疵鼓勵了模型這樣的判斷可以得高分。\n",
    "\n",
    "例如： 深度學習裡常見的手法，在訓練資料太少時，採用 *Data Augmentation* 的手法來擴充資料時常是有效的。 據說有使用 webcam 偵測室內公共場所人群的模型，在訓練集裡加入了大量網路上抓來的圖片，結果證實是有助於模型學習出更好的辨識能力的。 像這樣擴充資料的方式，在測試集裡就應該避免放進網路抓來的圖片，也應該避免室外、路上、野外的拍攝場景，應該就只使用符合實際使用的目標情境下的影像。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train =\n",
      " [[16 17]\n",
      " [ 6  7]\n",
      " [ 2  3]\n",
      " [ 4  5]\n",
      " [ 0  1]\n",
      " [18 19]\n",
      " [14 15]]\n",
      "y_train =\n",
      "\n",
      " [1. 0. 0. 0. 0. 1. 1.]\n",
      "X_test =\n",
      " [[12 13]\n",
      " [10 11]\n",
      " [ 8  9]]\n",
      "y_test =\n",
      " [1. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "# 指定訓練集測試集的類別分布要一致\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y)\n",
    "\n",
    "print('X_train =\\n', X_train)\n",
    "print('y_train =\\n\\n', y_train)\n",
    "print('X_test =\\n', X_test)\n",
    "print('y_test =\\n', y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### § 交叉驗證 Cross-Validation\n",
    "\n",
    "資料切割成訓練集跟測試集，也確認測試集符合實際狀況，那就可以直接拿訓練集來訓練模型，拿測試集來測試訓練後的模型效能了。\n",
    "\n",
    "Wait a minute，ちょっと待ってください，**請等一下**。\n",
    "\n",
    "機器學習模型的演算法都需要給適合的**超參數（hyperparameters）**，而且不是任何資料都適用同一組超參數。 實務上，針對一項機器學習任務的開發，我們會使用不同的超參數組合來分別訓練模型，再從測試這些模型的結果來決定最適合的超參數。 這個挑選的過程通常會由程式迭代自動反覆進行，如果使用測試集進行超參數的評估，所選的超參數仍然有可能是針對測試集過度擬合的結果，因為測試集已經洩漏了評估的結果，使得這個過程傾向於挑選讓模型過度擬合測試集的指標分數，因此所評估的效能指標已經沒辦法公正地代表泛化的程度。\n",
    "\n",
    "什麼？ 不測試不準，測試也不準，是在哈囉！\n",
    "\n",
    "為了解決這樣的問題，訓練模型的超參數挑選過程不使用測試集，會從訓練集裡面再保留一部份當作評估效能的驗證集（validation set）。 驗證完挑選了適當超參數後，使用這組超參數整個訓練集重新訓練一次，再跟測試集測試取得最後模型的效能指標。 所以典型的模型訓練就變成如下圖的流程。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "image/png": {
       "width": 480
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 典型的模型訓練流程圖\n",
    "Image(url='https://scikit-learn.org/stable/_images/grid_search_workflow.png', embed=True, width=480)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ★ K-fold Cross-Validation ★\n",
    "\n",
    "時常訓練集都已經不是很夠了，還要再保留一部分作驗證集那不是訓練集又變得更小了。 簡單直覺的解決方案就是： **全部的訓練集資料都分階段輪流當訓練集和驗證集**。 這個概念就是所謂的**交叉驗證（Cross-Validation）**。 基本的做法就是把訓練集拆成 k 等份，每次都只保留其中某一份作驗證，其餘的作訓練，全部 k 個等份都同樣輪流作一次，然後把 k 次的結果作平均。 這樣就所有的資料都有訓練過也驗證過，這種交叉驗證的手法我們稱為 **k-Fold Cross-Validation**。 一般常見 5-fold 或 10-fold 交叉驗證，但跟資料集切割一樣，k 是多少取決於資料集的大小跟資料特性，並沒有一體適用的方式。\n",
    "\n",
    "由於 k-fold 交叉驗證會花比較久的時間運算，所以如果輸入資料是像語音、影像這種大型的數據，而且是有百萬筆等級的資料集，隨機抽樣 1% 都還有個一萬筆，那就不必一定要切割成 k 等份，隨機取適當數量的驗證集作驗證即可。\n",
    "\n",
    "有一種比較極端狀況的交叉驗證，當訓練資料很少時，可以每次只保留一筆作驗證，總共有 m 筆訓練資料的話，k-fold 就要做 m 次，這個方法叫做 **Leave One Out**。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "image/png": {
       "width": 480
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# k-fold 交叉驗證\n",
    "Image(url='https://scikit-learn.org/stable/_images/grid_search_cross_validation.png', embed=True, width=480)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "在 `sklearn.model_selection` 模組中，提供了好幾種用來交叉驗證的不同 k-fold 分割的迭代類別，這些類別都會提供一個 `split()` 的生成函數，被呼叫時會回傳 k 等份的資料序號。 常用的如下：\n",
    "\n",
    "+ [**`KFold()`**](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html) - 一般的 k 等份分割。\n",
    "+ [**`RepeatedKFold()`**](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RepeatedKFold.html) - 重覆 n 次的 k 等份分割。\n",
    "+ [**`StratifiedKFold()`**](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedKFold.html) - 分割方式會維持每種類別的數量比例。\n",
    "+ [**`RepeatedStratifiedKFold()`**](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RepeatedStratifiedKFold.html) - 重覆 n 次的維持類別比例分割。\n",
    "+ [**`LeaveOneOut()`**](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeaveOneOut.html) - 每次都只留一筆驗證。\n",
    "+ [**`ShuffleSplit()`**](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.ShuffleSplit.html) - 每次隨機排列的分割，是會重複取樣的方式。\n",
    "+ [**`StratifiedShuffleSplit()`**](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html) - 每次維持類別比例的隨機排列分割。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> Fold # 0\n",
      " - Train index: [4 5 6 7 8 9] \n",
      " - X: [[ 8  9]\n",
      " [10 11]\n",
      " [12 13]\n",
      " [14 15]\n",
      " [16 17]\n",
      " [18 19]] \n",
      " - y: [0. 0. 1. 1. 1. 1.] \n",
      "\n",
      " - Test index: [0 1 2 3] \n",
      " - X: [[0 1]\n",
      " [2 3]\n",
      " [4 5]\n",
      " [6 7]] \n",
      " - y: [0. 0. 0. 0.] \n",
      "\n",
      ">> Fold # 1\n",
      " - Train index: [0 1 2 3 7 8 9] \n",
      " - X: [[ 0  1]\n",
      " [ 2  3]\n",
      " [ 4  5]\n",
      " [ 6  7]\n",
      " [14 15]\n",
      " [16 17]\n",
      " [18 19]] \n",
      " - y: [0. 0. 0. 0. 1. 1. 1.] \n",
      "\n",
      " - Test index: [4 5 6] \n",
      " - X: [[ 8  9]\n",
      " [10 11]\n",
      " [12 13]] \n",
      " - y: [0. 0. 1.] \n",
      "\n",
      ">> Fold # 2\n",
      " - Train index: [0 1 2 3 4 5 6] \n",
      " - X: [[ 0  1]\n",
      " [ 2  3]\n",
      " [ 4  5]\n",
      " [ 6  7]\n",
      " [ 8  9]\n",
      " [10 11]\n",
      " [12 13]] \n",
      " - y: [0. 0. 0. 0. 0. 0. 1.] \n",
      "\n",
      " - Test index: [7 8 9] \n",
      " - X: [[14 15]\n",
      " [16 17]\n",
      " [18 19]] \n",
      " - y: [1. 1. 1.] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "\n",
    "kfold = KFold(n_splits=3)\n",
    "# 使用之前生成的 10x2 大小的 X，以及 10 個 y 的數列當訓練集\n",
    "for k, (train_index, test_index) in enumerate(kfold.split(X, y)):\n",
    "    print('>> Fold #', k)\n",
    "    X_train, X_test = X[train_index], X[test_index]\n",
    "    y_train, y_test = y[train_index], y[test_index]\n",
    "    print(' - Train index:', train_index, '\\n - X:', X_train, '\\n - y:', y_train, '\\n')\n",
    "    print(' - Test index:', test_index, '\\n - X:', X_test, '\\n - y:', y_test, '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 生成 100 個新的範例資料點\n",
    "n_points = 100\n",
    "X = np.random.randn(100, 10)\n",
    "\n",
    "# y 包含三個分布非常不平衡的類別\n",
    "percentiles_classes = [0.1, 0.3, 0.6]\n",
    "y = np.hstack([[c] * int(100 * perc) for c, perc in enumerate(percentiles_classes)])\n",
    "\n",
    "# 要顯示的不同類別\n",
    "from sklearn.model_selection import (\n",
    "    KFold, ShuffleSplit, StratifiedKFold, StratifiedShuffleSplit\n",
    ")\n",
    "cvs = [KFold, ShuffleSplit, StratifiedKFold, StratifiedShuffleSplit]\n",
    "n_splits = 4\n",
    "\n",
    "# 用圖顯示不同 k-fold 類別的分割分配\n",
    "fig, axs = plt.subplots(2, 2, figsize=(12, 8))\n",
    "\n",
    "cmap_data = plt.cm.Paired\n",
    "cmap_cv = plt.cm.coolwarm\n",
    "x_lim = [0, n_points]\n",
    "y_lim = [n_splits+1.2, -0.2]\n",
    "y_ticks = np.arange(n_splits+1) + 0.5\n",
    "y_ticklabels = list(range(n_splits)) + ['class']\n",
    "\n",
    "from matplotlib.patches import Patch\n",
    "\n",
    "for n, (cv, ax) in enumerate(zip(cvs, axs.reshape(-1))):\n",
    "    cv_n = cv(n_splits=n_splits)\n",
    "    for k, (idxtrain, idxtest) in enumerate(cv_n.split(X=X, y=y)):\n",
    "        indices = np.zeros(X.shape[0])\n",
    "        indices[idxtest] = 1.0\n",
    "        ax.scatter(range(X.shape[0]), [k+0.5] * X.shape[0],\n",
    "                   c=indices, marker='_', lw=20, cmap=cmap_cv,\n",
    "                   vmin=-0.2, vmax=1.2)\n",
    "\n",
    "    # Plot the data classes at the end\n",
    "    ax.scatter(range(X.shape[0]), [k + 1.5] * X.shape[0],\n",
    "               c=y, marker='_', lw=20, cmap=cmap_data)\n",
    "\n",
    "    ax.set(xlim=x_lim, ylim=y_lim, yticks=y_ticks, yticklabels=y_ticklabels)\n",
    "    if n in [0, 2]:\n",
    "        ax.set_ylabel('CV iteration', fontsize=14)\n",
    "    if n in [2, 3]:\n",
    "        ax.set_xlabel('Sample index', fontsize=14)\n",
    "\n",
    "    ax.set_title(type(cv_n).__name__, fontsize=16, fontweight='bold')\n",
    "\n",
    "    ax.legend([Patch(color=cmap_cv(.8)), Patch(color=cmap_cv(.02))],\n",
    "              ['Testing set', 'Training set'], loc='lower right')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit-learn 的學習模型中，有一些是直接內建 **\"cv\"** 交叉驗證選項的，只需要指定如上述的這些分割類別。 如果模型沒有提供，或是自己寫的演算法需要另外做交叉驗證的流程時，在 `sklearn.model_selection` 模組中提供了 [**`cross_validate()`**](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_validate.html) 的函式可以使用。 在下一段落中有範例示範如何使用。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"data-normalization\"></a>\n",
    "\n",
    "## 15.2 數據正規化與數據洩漏問題\n",
    "\n",
    "原始數據時常會發現尺度差異大、或包含離群值（outlier）的現象，在前處理階段事先做適當的轉換，大多有助於模型的學習。 在 `sklearn.preprocessing` 模組中，提供了好幾種用來做正規化的轉換類別和函示，，類別都會提供 `fit()` 和 `transform()` 的方法來做轉換。\n",
    "\n",
    "+ [**`StandardScaler()`**](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html) - 標準化，將欄位數據轉換至平均為0及單位標準差。\n",
    "+ [**`MinMaxScaler()`**](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html) - 根據欄位數據的最大最小值，將數據轉換至 [0, 1] 之間。\n",
    "+ [**`RobustScaler()`**](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.RobustScaler.html) - 根據欄位數據的四分位數分布做調整，比較穩健不受離群值影響。\n",
    "+ [**`Normalizer()`**](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Normalizer.html) - 針對樣本空間做向量正規化的轉換。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 以下範例使用之前章節用過的 WDBC 資料集\n",
    "class WdbcDataset:\n",
    "    def __init__(self):\n",
    "        # 載入 WDBC (Wisconsin Diagnostic Breast Cancer) 資料集\n",
    "        wdbc_url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data'\n",
    "        self.df = pd.read_csv(wdbc_url, header=None)\n",
    "\n",
    "        # 說明中描述的欄位名稱\n",
    "        column_mean = [\n",
    "            \"radius_mean\", \"texture_mean\", \"perimeter_mean\", \"area_mean\", \"smoothness_mean\",\n",
    "            \"compactness_mean\", \"concavity_mean\", \"concave points_mean\", \"symmetry_mean\", \"fractal_dimension_mean\"\n",
    "        ]\n",
    "        column_se = [\n",
    "            \"radius_se\", \"texture_se\", \"perimeter_se\", \"area_se\", \"smoothness_se\",\n",
    "            \"compactness_se\", \"concavity_se\", \"concave points_se\", \"symmetry_se\", \"fractal_dimension_se\"\n",
    "        ]\n",
    "        column_worst = [\n",
    "            \"radius_worst\", \"texture_worst\", \"perimeter_worst\", \"area_worst\", \"smoothness_worst\",\n",
    "            \"compactness_worst\", \"concavity_worst\", \"concave points_worst\", \"symmetry_worst\", \"fractal_dimension_worst\"\n",
    "        ]\n",
    "        column_names = [\"id\", \"diagnosis\"] + column_mean + column_se + column_worst\n",
    "\n",
    "        # 指定欄位名稱\n",
    "        self.df.columns = column_names\n",
    "        # 丟掉不需要的 \"id\" 欄位\n",
    "        self.df.drop(columns=['id'], inplace=True)\n",
    "        # 將 diagnosis 欄位良性與惡性的類別轉為 0 與 1\n",
    "        self.df.loc[:,'diagnosis'] = self.df.loc[:,'diagnosis'].map({'B':0, 'M':1})\n",
    "    \n",
    "    def get_xy(self):\n",
    "        # 取所有的 X\n",
    "        X = self.df.drop(columns=['diagnosis']).to_numpy()\n",
    "        # 取所有的 Y\n",
    "        Y = self.df.loc[:,'diagnosis'].to_numpy()\n",
    "        # 資料分割 train:test = 9:1\n",
    "        X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1, stratify=Y)\n",
    "        # 回傳資料副本\n",
    "        return X_train.copy(), X_test.copy(), Y_train.copy(), Y_test.copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X: train shape = (512, 30), test shape = (57, 30)\n",
      "Y: train shape = (512,), test shape = (57,)\n",
      "Train Classes 0:1 = 321:191\n",
      "Test Classes 0:1 = 36:21\n"
     ]
    }
   ],
   "source": [
    "# 取得訓練集與測試集\n",
    "dsWdbc = WdbcDataset()\n",
    "X_train, X_test, Y_train, Y_test = dsWdbc.get_xy()\n",
    "\n",
    "# 顯示切割後的大小\n",
    "print('X: train shape = {}, test shape = {}'.format(X_train.shape, X_test.shape))\n",
    "print('Y: train shape = {}, test shape = {}'.format(Y_train.shape, Y_test.shape))\n",
    "\n",
    "# 顯示切割後的類別分布\n",
    "train_labels, train_counts = np.unique(Y_train, return_counts=True)\n",
    "print('Train Classes {}:{} = {}:{}'.format(train_labels[0], train_labels[1], train_counts[0], train_counts[1]))\n",
    "test_labels, test_counts = np.unique(Y_test, return_counts=True)\n",
    "print('Test Classes {}:{} = {}:{}'.format(test_labels[0], test_labels[1], test_counts[0], test_counts[1]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.074999</td>\n",
       "      <td>0.001002</td>\n",
       "      <td>0.923077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.056998</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.942308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.063000</td>\n",
       "      <td>0.001008</td>\n",
       "      <td>0.941176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.063001</td>\n",
       "      <td>0.000999</td>\n",
       "      <td>0.980392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.064998</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.941176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.073001</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.901961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.087520</td>\n",
       "      <td>0.001004</td>\n",
       "      <td>0.941176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.058000</td>\n",
       "      <td>0.005001</td>\n",
       "      <td>0.980392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.051519</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.921569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.036999</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.941176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fit_time  score_time  test_score\n",
       "0  0.074999    0.001002    0.923077\n",
       "1  0.056998    0.001000    0.942308\n",
       "2  0.063000    0.001008    0.941176\n",
       "3  0.063001    0.000999    0.980392\n",
       "4  0.064998    0.000000    0.941176\n",
       "5  0.073001    0.000000    0.901961\n",
       "6  0.087520    0.001004    0.941176\n",
       "7  0.058000    0.005001    0.980392\n",
       "8  0.051519    0.001000    0.921569\n",
       "9  0.036999    0.000000    0.941176"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_validate\n",
    "\n",
    "sklr1 = LogisticRegression(max_iter=100)\n",
    "# 10-fold 交叉驗證，使用所有處理核心平行運算\n",
    "scores1 = cross_validate(sklr1, X_train, Y_train, cv=10, n_jobs=-1)\n",
    "\n",
    "# 顯示每個 fold 的 accuracy 分數\n",
    "# 轉成 DataFrame 方便觀察，也方便輸出成檔案做記錄\n",
    "dfscores1 = pd.DataFrame(scores1)\n",
    "dfscores1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Average scores]:\n",
      " fit_time      0.063003\n",
      "score_time    0.001101\n",
      "test_score    0.941440\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 不同超參數訓練的模型比較，通常觀察平均分數\n",
    "print('[Average scores]:\\n', dfscores1.mean(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### § 用 k-fold 工具自己做交叉驗證\n",
    "\n",
    "請注意，上面使用 `cross_validate()` 函式的範例中，數據沒有事先做正規化。 但是交叉驗證的流程整個被包在函式中，正規化的轉換要如何在每個 fold `fit()` 訓練集，然後套用 `transform()` 到訓練集和驗證集？ 記得我們需要避免**數據洩漏（Data Leakage）**，所有會偷學到測試集或驗證集資訊的方法都要避免，數據轉換的方法不能拿來 `fit()` 測試集或驗證集，所以上面例子在進行 `cross_validate()` 之前，想要先對 `X_train` 做正規化有困難。\n",
    "\n",
    "Scikit-learn 提供的解決方式是使用 **`Pipiline`** 封裝處理流程，這留在下一小節中介紹。 我們首先用 scikit-learn 提供的 k-fold 類別工具，自己試著來處理交叉驗證看看，應該會發現這些處理的步驟有點小囉嗦。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold #0 accuracy = 0.981\n",
      "Fold #1 accuracy = 1.000\n",
      "Fold #2 accuracy = 0.942\n",
      "Fold #3 accuracy = 0.981\n",
      "Fold #4 accuracy = 0.942\n",
      "Fold #5 accuracy = 0.962\n",
      "Fold #6 accuracy = 0.962\n",
      "Fold #7 accuracy = 1.000\n",
      "Fold #8 accuracy = 0.981\n",
      "Fold #9 accuracy = 0.962\n",
      "\n",
      "10-fold cross-validation result: average accuracy = 0.971\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sklr2 = LogisticRegression(max_iter=100)\n",
    "ss10fold = StratifiedShuffleSplit(n_splits=10)\n",
    "\n",
    "scores2 = []\n",
    "for k, (idx_train, idx_validate) in enumerate(ss10fold.split(X_train, Y_train)):\n",
    "    # fold k 分割\n",
    "    Xk_train, Xk_validate = X_train[idx_train], X_train[idx_validate]\n",
    "    Yk_train, Yk_validate = Y_train[idx_train], Y_train[idx_validate]\n",
    "    # 正規化\n",
    "    scaler_std = StandardScaler()\n",
    "    # 注意 fit() 只套用在這個 fold 的訓練分割\n",
    "    scaler_std.fit(Xk_train)\n",
    "    Xk_train = scaler_std.transform(Xk_train)\n",
    "    Xk_validate = scaler_std.transform(Xk_validate)\n",
    "    # 訓練模型\n",
    "    sklr2.fit(Xk_train, Yk_train)\n",
    "    # 驗證這個模型的正確率\n",
    "    accuracy = sklr2.score(Xk_validate, Yk_validate)\n",
    "    # 記錄這個模型的驗證正確率\n",
    "    scores2.append(accuracy)\n",
    "    # 結束這個 fold，顯示訊息\n",
    "    print('Fold #{} accuracy = {:.3f}'.format(k, accuracy))\n",
    "# 交叉驗證完成，顯示平均分數\n",
    "print('\\n10-fold cross-validation result: average accuracy = {:.3f}'.format(np.mean(scores2)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"task-pipeline\"></a>\n",
    "\n",
    "## 15.3 將工作流程封裝成 Pipeline\n",
    "\n",
    "一個完整的機器學習專案，經常會包含多種輸入資料的清理、數據轉換，複雜一點的還會結合幾種不同的分類器模型、回歸模型在一起。 常用來簡化複雜工作流程的概念就是定義 [**`Pipeline`**](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html)，scikit-learn 提供的 *pipeline* 工具與套件裡的其他處理工具搭配運用，可以輔助讓複雜的工作流程比較容易且清楚的定義出來。\n",
    "\n",
    "1. 定義在 pipeline 裡的數據轉換，本身知道如何適當`fit()`和`transform()`來避免數據洩漏。\n",
    "2. scikit-learn 裡的類別工具大多有相同的介面，如`fit()`、`transform()`、`predict()` ... 等。 封裝多個物件在 pipeline 之後，所有的操作都只對 pipeline 做一次就可以。\n",
    "\n",
    "+ [**`sklearn.pipeline.Pipeline(steps)`**](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html)\n",
    "    - **steps**： 是工作流程的清單，照工作順序放 (*name*, *transform*) 的 tuple 清單，最後一個一定要是 estimator 類的學習模型物件。\n",
    "\n",
    "註： Scikit-learn 文件裡 **`Transformer`** 指的是有實作 `fit()` 及 `transform()` 方法的類別； **`Estimator`** 指的是任何有實作 `transfrom()` 方法的類別。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.016999</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.980769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.014000</td>\n",
       "      <td>0.000999</td>\n",
       "      <td>0.961538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.015000</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.961538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.018007</td>\n",
       "      <td>0.000997</td>\n",
       "      <td>0.942308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.016001</td>\n",
       "      <td>0.001004</td>\n",
       "      <td>0.980769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.016998</td>\n",
       "      <td>0.001004</td>\n",
       "      <td>0.980769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.016008</td>\n",
       "      <td>0.001002</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.015005</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.923077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.013992</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.980769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.012997</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>0.980769</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fit_time  score_time  test_score\n",
       "0  0.016999    0.000000    0.980769\n",
       "1  0.014000    0.000999    0.961538\n",
       "2  0.015000    0.001000    0.961538\n",
       "3  0.018007    0.000997    0.942308\n",
       "4  0.016001    0.001004    0.980769\n",
       "5  0.016998    0.001004    0.980769\n",
       "6  0.016008    0.001002    1.000000\n",
       "7  0.015005    0.000000    0.923077\n",
       "8  0.013992    0.000000    0.980769\n",
       "9  0.012997    0.001001    0.980769"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 再把剛剛不好處理的 cross-validate 用 Pipeline 做一次\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import StratifiedShuffleSplit, cross_validate\n",
    "\n",
    "# 定義包含正規化前處理的 pipeline\n",
    "pipelr3 = Pipeline(\n",
    "    steps=[\n",
    "        ('zscaler', StandardScaler()),\n",
    "        ('lr', LogisticRegression(max_iter=100))\n",
    "    ]\n",
    ")\n",
    "\n",
    "# 10-fold 交叉驗證，使用所有處理核心平行運算\n",
    "scores3 = cross_validate(pipelr3, X_train, Y_train, cv=StratifiedShuffleSplit(n_splits=10), n_jobs=-1)\n",
    "\n",
    "# 顯示每個 fold 的 accuracy 分數\n",
    "dfscores3 = pd.DataFrame(scores3)\n",
    "dfscores3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Average scores]:\n",
      " fit_time      0.015501\n",
      "score_time    0.000701\n",
      "test_score    0.969231\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 觀察平均分數\n",
    "print('[Average scores]:\\n', dfscores3.mean(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"performance-metrics\"></a>\n",
    "\n",
    "## 15.4 效能度量指標\n",
    "\n",
    "模型訓練後的效能度量方式，在模型設計初期的目標訂定就應決定。 不同模型有不同的適用量尺或評估指標，以下先介紹常用來評估二元分類模型的指標。 Scikit-learn 將這些工具放在 `sklearn.metrics` 模組中，常用的二元分類的度量指標如下：\n",
    "\n",
    "+ [**`accuracy_score()`**](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html) - 正確率。\n",
    "+ [**`precision_score()`**](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html) - 精確度。\n",
    "+ [**`recall_score()`**](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html) - 即**敏感度（Sensitivity）**，也可用來計算**特異性（Specificity）**。\n",
    "+ [**`confusion_matrix()`**](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html) - 計算真陰性（True Negative）、假陽性（False Positive）、假陰性（False Negative）、真陽性（True Positive）。\n",
    "+ [**`roc_auc_score()`**](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html) - Area Under the Receiver Operating Characteristic Curve (ROC AUC) 。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### § Confusion Matrix\n",
    "\n",
    "| Notation  | 說明                               |\n",
    "|-----------|------------------------------------|\n",
    "| **+**     | *Positive*，陽性，通常是標籤 $y = 1$ |\n",
    "| **−**    | *Negative*，陰性，通常是標籤 $y = 0$ |\n",
    "| **TP**    | *True Positive*，真陽性個數。 預測陽性，實際也是陽性的個數。 |\n",
    "| **FP**    | *False Positive*，假陽性個數。 預測陽性，實際是陰性的個數。  |\n",
    "| **TN**    | *True Negative*，真陰性個數。 預測陰性，實際也是陰性的個數。 |\n",
    "| **FN**    | *False Negative*，假陰性個數。 預測陰性，實際是陽性的個數。  |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "image/png": {
       "width": 480
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='images/ConfusionMatrix.png', width=480)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### § Metrics\n",
    "\n",
    "由不同的方向觀察 confusion matrix 表格中的個數，可對應出不同的度量指標。 基本的個數關係是：\n",
    "+ 1st column，實際陽性個數 $= TP + FN$。\n",
    "+ 2nd column，實際陰性個數 $= FP + TN$。\n",
    "+ 1st row，預測陽性個數 $= TP + FP$。\n",
    "+ 2nd row，預測陰性個數 $= TN + FN$。\n",
    "+ 總群體數 $= TP + TN + FP + FN$\n",
    "\n",
    "#### Sensitivity 敏感度\n",
    "\n",
    "真陽性率（True Positive Rate, TPR），所有實際是陽性的，預測也是陽性的正確比例。（又稱為 **recall**）\n",
    "$$\n",
    "    \\mathrm{Sensitivity} = \\frac{TP}{TP + FN}\n",
    "$$\n",
    "\n",
    "#### Specificity 特異度\n",
    "\n",
    "真陰性率（True Negative Rate, TPR），所有實際是陰性的，預測也是陰性的正確比例。\n",
    "$$\n",
    "    \\mathrm{Specificity} = \\frac{TN}{TN + FP}\n",
    "$$\n",
    "\n",
    "#### Precision 精確度\n",
    "\n",
    "所有預測是陽性的，實際也是陽性的正確比例。\n",
    "$$\n",
    "    \\mathrm{Precision} = \\frac{TP}{TP + FP}\n",
    "$$\n",
    "\n",
    "#### Accuracy 正確率\n",
    "\n",
    "對於所有群體，預測正確的比例。\n",
    "$$\n",
    "    \\mathrm{Accuracy} = \\frac{TP + TN}{TP + TN + FP + FN}\n",
    "$$\n",
    "\n",
    "#### False Positive Rate 假陽性率\n",
    "\n",
    "假陽性率（False Positive Rate, FPR），所有實際是陰性的，錯誤預測為陽性的比例。\n",
    "$$\n",
    "    \\mathrm{FPR} = \\frac{FP}{FP + TN}\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### § ROC AUC\n",
    "\n",
    "**Receiver Operating Characteristic (ROC)** 曲線是以 FPR 為 X 軸，TPR 為 Y 軸，根據二元分類模型對所有樣本預測的陽性機率，取不同機率門檻值後的累計機率分布所畫出來的曲線。\n",
    "\n",
    "ROC 的 **Area Under Curve (AUC)** 計算曲線下的面積，使用 AUC 值做不同模型的比較會比觀察曲線的分布來得方便。\n",
    "\n",
    " `sklearn.metrics` 模組中也有工具可以畫 ROC 曲線：\n",
    "\n",
    "+ [**`roc_curve()`**](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html) - 使用模型的預測機率，計算出可用來畫出 ROC 曲線的 FPR, TPR。\n",
    "+ [**`plot_roc_curve()`**](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_roc_curve.html) - 直接透過 Matplotlib 將 ROC 曲線畫出來。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_roc_auc</th>\n",
       "      <th>test_accuracy</th>\n",
       "      <th>test_sensitivity</th>\n",
       "      <th>test_specificity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.980769</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.980769</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.980769</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.961538</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.958533</td>\n",
       "      <td>0.961538</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.969697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.980769</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.980769</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.980769</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   test_roc_auc  test_accuracy  test_sensitivity  test_specificity\n",
       "0      1.000000       1.000000          1.000000          1.000000\n",
       "1      0.973684       0.980769          0.947368          1.000000\n",
       "2      0.973684       0.980769          0.947368          1.000000\n",
       "3      0.973684       0.980769          0.947368          1.000000\n",
       "4      1.000000       1.000000          1.000000          1.000000\n",
       "5      0.947368       0.961538          0.894737          1.000000\n",
       "6      0.958533       0.961538          0.947368          0.969697\n",
       "7      0.973684       0.980769          0.947368          1.000000\n",
       "8      0.973684       0.980769          0.947368          1.000000\n",
       "9      0.973684       0.980769          0.947368          1.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import StratifiedShuffleSplit, cross_validate\n",
    "# 示範各 metrics 的使用\n",
    "from sklearn.metrics import (\n",
    "    make_scorer,\n",
    "    accuracy_score,\n",
    "    recall_score,\n",
    "    roc_auc_score,\n",
    "    roc_curve,\n",
    "    plot_roc_curve,\n",
    "    confusion_matrix\n",
    ")\n",
    "\n",
    "# 與前面範例相同的 pipeline 定義\n",
    "pipelr4 = Pipeline(\n",
    "    steps=[\n",
    "        ('zscaler', StandardScaler()),\n",
    "        ('lr', LogisticRegression(max_iter=100))\n",
    "    ]\n",
    ")\n",
    "\n",
    "# 交叉驗證過程可以指定多項評估指標\n",
    "combo_scores = {\n",
    "    'roc_auc': make_scorer(roc_auc_score),\n",
    "    'accuracy': make_scorer(accuracy_score),\n",
    "    'sensitivity': make_scorer(recall_score, pos_label=1),\n",
    "    'specificity': make_scorer(recall_score, pos_label=0)\n",
    "}\n",
    "\n",
    "# 10-fold 交叉驗證，使用所有處理核心平行運算\n",
    "scores4 = cross_validate(\n",
    "    pipelr4,\n",
    "    X_train,\n",
    "    Y_train,\n",
    "    scoring=combo_scores,\n",
    "    cv=StratifiedShuffleSplit(n_splits=10),\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "# 顯示每個 fold 的指標分數，明確指定需要的欄位\n",
    "col_names = ['test_roc_auc', 'test_accuracy', 'test_sensitivity', 'test_specificity']\n",
    "dfscores4 = pd.DataFrame({k:v for k,v in scores4.items() if k in col_names})\n",
    "dfscores4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Average scores]:\n",
      " test_roc_auc        0.974801\n",
      "test_accuracy       0.980769\n",
      "test_sensitivity    0.952632\n",
      "test_specificity    0.996970\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 觀察平均分數\n",
    "print('[Average scores]:\\n', dfscores4.mean(axis=0))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### § 最後的模型評估\n",
    "\n",
    "記得交叉驗證目的是在找出最佳超參數，最後還是要拿所有訓練集來訓練一個最終模型與測試集做測試。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Confusion Matrix]:\n",
      "  | TP: 19 | FP: 0 |\n",
      "  | FN: 2 | TN: 36 |\n",
      "\n",
      "[Testing Scores]:\n",
      "  * Sensitivity: 0.905\n",
      "  * Specificity: 1.000\n",
      "  * Accuracy: 0.965\n",
      "  * ROC AUC: 0.996\n"
     ]
    }
   ],
   "source": [
    "# 與前面範例相同的 pipeline 定義，假設超參數就是最佳\n",
    "pipelr5 = Pipeline(\n",
    "    steps=[\n",
    "        ('zscaler', StandardScaler()),\n",
    "        ('lr', LogisticRegression(max_iter=100))\n",
    "    ]\n",
    ")\n",
    "\n",
    "# 直接訓練整個訓練集\n",
    "pipelr5.fit(X_train, Y_train)\n",
    "# 已訓練模型用來預測測試集\n",
    "Y_predict = pipelr5.predict(X_test)\n",
    "\n",
    "# confusion matrix\n",
    "TN, FP, FN, TP = confusion_matrix(Y_test, Y_predict).ravel()\n",
    "print('[Confusion Matrix]:\\n  | TP: {} | FP: {} |\\n  | FN: {} | TN: {} |\\n'.format(TP, FP, FN, TN))\n",
    "\n",
    "# sensitivity and specificity\n",
    "sensitivity = recall_score(Y_test, Y_predict, pos_label=1)\n",
    "specificity = recall_score(Y_test, Y_predict, pos_label=0)\n",
    "print('[Testing Scores]:\\n  * Sensitivity: {:.3f}\\n  * Specificity: {:.3f}'.format(sensitivity, specificity))\n",
    "\n",
    "# accuracy and ROC AUC\n",
    "accuracy = accuracy_score(Y_test, Y_predict)\n",
    "rocauc = roc_auc_score(Y_test, pipelr5.predict_proba(X_test)[:,1])\n",
    "print('  * Accuracy: {:.3f}\\n  * ROC AUC: {:.3f}'.format(accuracy, rocauc))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 畫出 ROC 曲線\n",
    "_, ax5 = plt.subplots(figsize=(8, 7))\n",
    "plot_roc_curve(pipelr5, X_test, Y_test, name='5th Logistic Regression', ax=ax5)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"roc-explain\"></a>\n",
    "\n",
    "## 15.5 理解ROC曲線\n",
    "\n",
    "已知 ROC 的 X 軸是 FPR，Y 軸是 TPR，那是怎麼從模型預測樣本的機率中取門檻值（threshold）畫出曲線的？\n",
    "\n",
    "首先觀察下圖中的維基百科範例，這個範例使用 Logistic Regression 模型由書的時間預測考試會pass的機率。 單一輸入變數比容易畫在機率擬合的圖上，比較方便解釋取 threshold 的過程，但以下解釋同樣適用於多變數輸入的情境。\n",
    "\n",
    "圖上在 $y=0$ (*fail*) 與 $y=1$ (*pass*) 的線上分別有 10 個點，這些 $x$ 點是分別對應正確的 $y$ 標籤，觀察以下取不同 threshold 的變化。\n",
    "+ **threshold = 0.0**： 所有的 $x$ 點都預測為 *pass*，所以 $\\mathrm{FPR}=1.0$，$\\mathrm{TPR}=1.0$，對應到 ROC 圖上最右上角的點。\n",
    "+ **threshold = 1.0**： 所有的 $x$ 點都預測為 *fail*，所以 $\\mathrm{FPR}=0.0$，$\\mathrm{TPR}=0.0$，對應到 ROC 圖上最左下角的點。 從這兩個 threshold 可以觀察一個現象，在 ROC 圖上由座標 (0,0) 到 (1,1) 的對角線上的點，都是 $\\mathrm{FPR} = \\mathrm{TPR}$，代表是沒有鑑別力的。\n",
    "+ **threshold = 0.125**： $\\mathrm{TN}=4$，$\\mathrm{FP}=6$，$\\mathrm{FN}=0$，$\\mathrm{TP}=10$，所以 $\\mathrm{FPR}=0.6$，$\\mathrm{TPR}=1.0$。 與 threshold=0.0 比起來，在 TPR 維持不變下 FPR 下降了，顯然取 threshold=0.125 的鑑別力比較好。 在 ROC 圖上，點 (0.6, 1.0) 比 (1.0, 1.0) 往左移，我們希望能取到一個最佳的 threshold 使得這樣的點一直往 ROC 的左上角靠，因為最左上角的點 (0.0, 1.0) 代表最好的沒有預測錯誤的狀況。\n",
    "+ **threshold = 0.25**： $\\mathrm{TN}=6$，$\\mathrm{FP}=4$，$\\mathrm{FN}=1$，$\\mathrm{TP}=9$，所以 $\\mathrm{FPR}=0.4$，$\\mathrm{TPR}=0.9$。\n",
    "+ **threshold = 0.5**： $\\mathrm{TN}=8$，$\\mathrm{FP}=2$，$\\mathrm{FN}=2$，$\\mathrm{TP}=8$，所以 $\\mathrm{FPR}=0.2$，$\\mathrm{TPR}=0.8$。\n",
    "+ **threshold = 0.75**： $\\mathrm{TN}=9$，$\\mathrm{FP}=1$，$\\mathrm{FN}=4$，$\\mathrm{TP}=6$，所以 $\\mathrm{FPR}=0.1$，$\\mathrm{TPR}=0.6$。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "image/jpeg": {
       "width": 600
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 維基百科範例： Logistic Regression 模型由念書的時間預測考試會pass的機率\n",
    "Image(url='https://upload.wikimedia.org/wikipedia/commons/6/6d/Exam_pass_logistic_curve.jpeg', embed=True, width=600)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### § 實際做做看\n",
    "\n",
    "許多 sciki-learn 的模型都有一個 `predict_proba()` 的方法，在模型訓練後輸入測試樣本可以輸出擬合的預測機率。 這些測試樣本對應的機率，就如同剛剛範例中取 threshold 之後，我們手動將圖上 x 軸樣本點通過 sigmoid 函數對應到 y 軸的機率是一樣的。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>y label</th>\n",
       "      <th>p(y=0)</th>\n",
       "      <th>p(y=1)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>9.811598e-01</td>\n",
       "      <td>1.884020e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>9.887362e-01</td>\n",
       "      <td>1.126377e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2.077201e-10</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>9.744399e-01</td>\n",
       "      <td>2.556009e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>6.172583e-08</td>\n",
       "      <td>9.999999e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>2.236679e-01</td>\n",
       "      <td>7.763321e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>9.707585e-01</td>\n",
       "      <td>2.924150e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>9.964413e-01</td>\n",
       "      <td>3.558722e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>9.999860e-01</td>\n",
       "      <td>1.401826e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>9.991837e-01</td>\n",
       "      <td>8.163274e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>1.895340e-02</td>\n",
       "      <td>9.810466e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>9.988891e-01</td>\n",
       "      <td>1.110874e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>3.673237e-07</td>\n",
       "      <td>9.999996e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>9.999446e-01</td>\n",
       "      <td>5.543915e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>9.983377e-01</td>\n",
       "      <td>1.662277e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>9.961365e-04</td>\n",
       "      <td>9.990039e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>6.152423e-05</td>\n",
       "      <td>9.999385e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>2.055393e-06</td>\n",
       "      <td>9.999979e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>9.984787e-01</td>\n",
       "      <td>1.521341e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>9.999505e-01</td>\n",
       "      <td>4.951016e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>9.987231e-01</td>\n",
       "      <td>1.276873e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>7.029572e-01</td>\n",
       "      <td>2.970428e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>9.999005e-01</td>\n",
       "      <td>9.948022e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>9.939873e-01</td>\n",
       "      <td>6.012731e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>1.498176e-03</td>\n",
       "      <td>9.985018e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>9.995396e-01</td>\n",
       "      <td>4.604009e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>3.720497e-03</td>\n",
       "      <td>9.962795e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>9.860299e-01</td>\n",
       "      <td>1.397006e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>9.961436e-01</td>\n",
       "      <td>3.856419e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>9.999392e-01</td>\n",
       "      <td>6.083539e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0</td>\n",
       "      <td>7.738409e-01</td>\n",
       "      <td>2.261591e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0</td>\n",
       "      <td>9.999996e-01</td>\n",
       "      <td>4.355531e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>1.219025e-13</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0</td>\n",
       "      <td>9.996205e-01</td>\n",
       "      <td>3.794777e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0</td>\n",
       "      <td>9.999851e-01</td>\n",
       "      <td>1.487948e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0</td>\n",
       "      <td>9.994774e-01</td>\n",
       "      <td>5.226429e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0</td>\n",
       "      <td>9.710374e-01</td>\n",
       "      <td>2.896259e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1</td>\n",
       "      <td>8.947722e-01</td>\n",
       "      <td>1.052278e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1</td>\n",
       "      <td>1.118804e-01</td>\n",
       "      <td>8.881196e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0</td>\n",
       "      <td>9.814283e-01</td>\n",
       "      <td>1.857174e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1</td>\n",
       "      <td>1.156100e-09</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1</td>\n",
       "      <td>2.589068e-06</td>\n",
       "      <td>9.999974e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1</td>\n",
       "      <td>1.056756e-07</td>\n",
       "      <td>9.999999e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0</td>\n",
       "      <td>9.749439e-01</td>\n",
       "      <td>2.505609e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0</td>\n",
       "      <td>6.274347e-01</td>\n",
       "      <td>3.725653e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0</td>\n",
       "      <td>9.998620e-01</td>\n",
       "      <td>1.379736e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1</td>\n",
       "      <td>1.648762e-02</td>\n",
       "      <td>9.835124e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0</td>\n",
       "      <td>9.943470e-01</td>\n",
       "      <td>5.653024e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0</td>\n",
       "      <td>9.999982e-01</td>\n",
       "      <td>1.775461e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0</td>\n",
       "      <td>9.943191e-01</td>\n",
       "      <td>5.680922e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1</td>\n",
       "      <td>1.726175e-11</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0</td>\n",
       "      <td>9.999895e-01</td>\n",
       "      <td>1.049783e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0</td>\n",
       "      <td>9.999144e-01</td>\n",
       "      <td>8.557925e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0</td>\n",
       "      <td>9.773547e-01</td>\n",
       "      <td>2.264531e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>0</td>\n",
       "      <td>9.999951e-01</td>\n",
       "      <td>4.944444e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1</td>\n",
       "      <td>1.751521e-05</td>\n",
       "      <td>9.999825e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>1</td>\n",
       "      <td>3.705312e-01</td>\n",
       "      <td>6.294688e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    y label        p(y=0)        p(y=1)\n",
       "0         0  9.811598e-01  1.884020e-02\n",
       "1         0  9.887362e-01  1.126377e-02\n",
       "2         1  2.077201e-10  1.000000e+00\n",
       "3         0  9.744399e-01  2.556009e-02\n",
       "4         1  6.172583e-08  9.999999e-01\n",
       "5         1  2.236679e-01  7.763321e-01\n",
       "6         0  9.707585e-01  2.924150e-02\n",
       "7         0  9.964413e-01  3.558722e-03\n",
       "8         0  9.999860e-01  1.401826e-05\n",
       "9         0  9.991837e-01  8.163274e-04\n",
       "10        1  1.895340e-02  9.810466e-01\n",
       "11        0  9.988891e-01  1.110874e-03\n",
       "12        1  3.673237e-07  9.999996e-01\n",
       "13        0  9.999446e-01  5.543915e-05\n",
       "14        0  9.983377e-01  1.662277e-03\n",
       "15        1  9.961365e-04  9.990039e-01\n",
       "16        1  6.152423e-05  9.999385e-01\n",
       "17        1  2.055393e-06  9.999979e-01\n",
       "18        0  9.984787e-01  1.521341e-03\n",
       "19        0  9.999505e-01  4.951016e-05\n",
       "20        0  9.987231e-01  1.276873e-03\n",
       "21        1  7.029572e-01  2.970428e-01\n",
       "22        0  9.999005e-01  9.948022e-05\n",
       "23        0  9.939873e-01  6.012731e-03\n",
       "24        1  1.498176e-03  9.985018e-01\n",
       "25        0  9.995396e-01  4.604009e-04\n",
       "26        1  3.720497e-03  9.962795e-01\n",
       "27        0  9.860299e-01  1.397006e-02\n",
       "28        0  9.961436e-01  3.856419e-03\n",
       "29        0  9.999392e-01  6.083539e-05\n",
       "30        0  7.738409e-01  2.261591e-01\n",
       "31        0  9.999996e-01  4.355531e-07\n",
       "32        1  1.219025e-13  1.000000e+00\n",
       "33        0  9.996205e-01  3.794777e-04\n",
       "34        0  9.999851e-01  1.487948e-05\n",
       "35        0  9.994774e-01  5.226429e-04\n",
       "36        0  9.710374e-01  2.896259e-02\n",
       "37        1  8.947722e-01  1.052278e-01\n",
       "38        1  1.118804e-01  8.881196e-01\n",
       "39        0  9.814283e-01  1.857174e-02\n",
       "40        1  1.156100e-09  1.000000e+00\n",
       "41        1  2.589068e-06  9.999974e-01\n",
       "42        1  1.056756e-07  9.999999e-01\n",
       "43        0  9.749439e-01  2.505609e-02\n",
       "44        0  6.274347e-01  3.725653e-01\n",
       "45        0  9.998620e-01  1.379736e-04\n",
       "46        1  1.648762e-02  9.835124e-01\n",
       "47        0  9.943470e-01  5.653024e-03\n",
       "48        0  9.999982e-01  1.775461e-06\n",
       "49        0  9.943191e-01  5.680922e-03\n",
       "50        1  1.726175e-11  1.000000e+00\n",
       "51        0  9.999895e-01  1.049783e-05\n",
       "52        0  9.999144e-01  8.557925e-05\n",
       "53        0  9.773547e-01  2.264531e-02\n",
       "54        0  9.999951e-01  4.944444e-06\n",
       "55        1  1.751521e-05  9.999825e-01\n",
       "56        1  3.705312e-01  6.294688e-01"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取得模型預測測試集的機率 \n",
    "predict_proba = pipelr5.predict_proba(X_test)\n",
    "# 機率 p(y=0), p(y=1)\n",
    "py0, py1 = predict_proba[:,0], predict_proba[:,1]\n",
    "pd.DataFrame({'y label': Y_test, 'p(y=0)': py0, 'p(y=1)': py1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1.])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 二元分類模型中，擬合的機率固定就是 p(y=0) + p(y=1) = 1\n",
    "py0 + py1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p(y=1) decreasing sorted:\n",
      " [1.00000000e+00 1.00000000e+00 1.00000000e+00 9.99999999e-01\n",
      " 9.99999938e-01 9.99999894e-01 9.99999633e-01 9.99997945e-01\n",
      " 9.99997411e-01 9.99982485e-01 9.99938476e-01 9.99003864e-01\n",
      " 9.98501824e-01 9.96279503e-01 9.83512385e-01 9.81046597e-01\n",
      " 8.88119586e-01 7.76332096e-01 6.29468771e-01 3.72565297e-01\n",
      " 2.97042844e-01 2.26159053e-01 1.05227811e-01 2.92414979e-02\n",
      " 2.89625927e-02 2.55600930e-02 2.50560931e-02 2.26453131e-02\n",
      " 1.88401991e-02 1.85717447e-02 1.39700575e-02 1.12637697e-02\n",
      " 6.01273136e-03 5.68092234e-03 5.65302350e-03 3.85641912e-03\n",
      " 3.55872235e-03 1.66227680e-03 1.52134060e-03 1.27687261e-03\n",
      " 1.11087425e-03 8.16327421e-04 5.22642900e-04 4.60400905e-04\n",
      " 3.79477730e-04 1.37973639e-04 9.94802246e-05 8.55792451e-05\n",
      " 6.08353892e-05 5.54391511e-05 4.95101569e-05 1.48794834e-05\n",
      " 1.40182578e-05 1.04978262e-05 4.94444380e-06 1.77546135e-06\n",
      " 4.35553061e-07]\n"
     ]
    }
   ],
   "source": [
    "# 取 p(y=1) 由大到小遞減排序，回傳排序後的原序號\n",
    "py1_dec_index = np.argsort(py1, kind='stable')[::-1]\n",
    "\n",
    "# 取得遞減的 p(y=1) \n",
    "py1_dec = py1[py1_dec_index]\n",
    "print('p(y=1) decreasing sorted:\\n', py1_dec)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "corresponding label y=1:\n",
      " [ True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True False  True False  True False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False]\n"
     ]
    }
   ],
   "source": [
    "# y=1 的標籤轉成 True/False 的陣列\n",
    "y_true = (Y_test == 1)\n",
    "\n",
    "# 標籤對應 p(y=1) 的序號\n",
    "y_true = y_true[py1_dec_index]\n",
    "print('corresponding label y=1:\\n', y_true)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cumulative True Positives:\n",
      " [ 1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17. 18.\n",
      " 19. 19. 20. 20. 21. 21. 21. 21. 21. 21. 21. 21. 21. 21. 21. 21. 21. 21.\n",
      " 21. 21. 21. 21. 21. 21. 21. 21. 21. 21. 21. 21. 21. 21. 21. 21. 21. 21.\n",
      " 21. 21. 21.]\n",
      "Cumulative False Positives:\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  1.  1.  2.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15.\n",
      " 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33.\n",
      " 34. 35. 36.]\n"
     ]
    }
   ],
   "source": [
    "# 每個 p(y=1) 都是 threshold，取累計 True Positive\n",
    "tps = np.cumsum(y_true, dtype=np.float64)\n",
    "print('Cumulative True Positives:\\n', tps)\n",
    "\n",
    "# 取累計 False Positive\n",
    "fps = np.cumsum((1 - y_true), dtype=np.float64)\n",
    "print('Cumulative False Positives:\\n', fps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Positive Rates:\n",
      " [0.04761905 0.0952381  0.14285714 0.19047619 0.23809524 0.28571429\n",
      " 0.33333333 0.38095238 0.42857143 0.47619048 0.52380952 0.57142857\n",
      " 0.61904762 0.66666667 0.71428571 0.76190476 0.80952381 0.85714286\n",
      " 0.9047619  0.9047619  0.95238095 0.95238095 1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.        ]\n",
      "False Positive Rates:\n",
      " [0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.02777778 0.02777778 0.05555556 0.05555556 0.08333333\n",
      " 0.11111111 0.13888889 0.16666667 0.19444444 0.22222222 0.25\n",
      " 0.27777778 0.30555556 0.33333333 0.36111111 0.38888889 0.41666667\n",
      " 0.44444444 0.47222222 0.5        0.52777778 0.55555556 0.58333333\n",
      " 0.61111111 0.63888889 0.66666667 0.69444444 0.72222222 0.75\n",
      " 0.77777778 0.80555556 0.83333333 0.86111111 0.88888889 0.91666667\n",
      " 0.94444444 0.97222222 1.        ]\n"
     ]
    }
   ],
   "source": [
    "tpr = tps / tps[-1]\n",
    "print('True Positive Rates:\\n', tpr)\n",
    "\n",
    "fpr = fps / fps[-1]\n",
    "print('False Positive Rates:\\n', fpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Positive Rates:\n",
      " [0.         0.04761905 0.0952381  0.14285714 0.19047619 0.23809524\n",
      " 0.28571429 0.33333333 0.38095238 0.42857143 0.47619048 0.52380952\n",
      " 0.57142857 0.61904762 0.66666667 0.71428571 0.76190476 0.80952381\n",
      " 0.85714286 0.9047619  0.9047619  0.95238095 0.95238095 1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.         1.         1.\n",
      " 1.         1.         1.         1.        ]\n",
      "False Positive Rates:\n",
      " [0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.02777778 0.02777778 0.05555556 0.05555556\n",
      " 0.08333333 0.11111111 0.13888889 0.16666667 0.19444444 0.22222222\n",
      " 0.25       0.27777778 0.30555556 0.33333333 0.36111111 0.38888889\n",
      " 0.41666667 0.44444444 0.47222222 0.5        0.52777778 0.55555556\n",
      " 0.58333333 0.61111111 0.63888889 0.66666667 0.69444444 0.72222222\n",
      " 0.75       0.77777778 0.80555556 0.83333333 0.86111111 0.88888889\n",
      " 0.91666667 0.94444444 0.97222222 1.        ]\n"
     ]
    }
   ],
   "source": [
    "# 畫 ROC 曲線前補上 (0,0) 的點\n",
    "tpr = np.r_[0.0, tpr]\n",
    "print('True Positive Rates:\\n', tpr)\n",
    "\n",
    "fpr = np.r_[0.0, fpr]\n",
    "print('False Positive Rates:\\n', fpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax6 = plt.subplots(figsize=(8, 7))\n",
    "# plot ROC curve\n",
    "ax6.plot(fpr, tpr, label='5th Logistic Regression (AUC = {:.3f})'.format(rocauc), lw=2)\n",
    "\n",
    "# the diagonal line\n",
    "ax6.plot([0, 1], [0, 1], linestyle='--', lw=2, color='#808080', alpha=0.8)\n",
    "\n",
    "ax6.set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05])\n",
    "ax6.set_xlabel('False Positive Rate', fontsize=16)\n",
    "ax6.set_ylabel('True Positive Rate', fontsize=16)\n",
    "ax6.legend(loc=\"lower right\", fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"references\"></a>\n",
    "\n",
    "## References:\n",
    "\n",
    "1. Scikit-Learn, \"*User Guide*\", scikit-learn.org, [[link]](https://scikit-learn.org/stable/user_guide.html).\n",
    "2. Wikipedia contributors, \"*Sensitivity and specificity*\", Wikipedia, The Free Encyclopedia, [[link]](https://en.wikipedia.org/wiki/Sensitivity_and_specificity)\n",
    "3. Wikipedia contributors, \"*Precision and recall*\", Wikipedia, The Free Encyclopedia, [[link]](https://en.wikipedia.org/wiki/Precision_and_recall)\n",
    "4. Wikipedia contributors, \"*Receiver operating characteristic*\", Wikipedia, The Free Encyclopedia, [[link]](https://en.wikipedia.org/wiki/Receiver_operating_characteristic)\n"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}