01_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Chapter 3 – Classification**\n",
    "\n",
    "_This notebook contains all the sample code and solutions to the exercises in chapter 3._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/UAPH4582/PH482_582/blob/main/01_Classification.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python ≥3.5 is required\n",
    "import sys\n",
    "assert sys.version_info >= (3, 5)\n",
    "\n",
    "# Scikit-Learn ≥0.20 is required\n",
    "import sklearn\n",
    "assert sklearn.__version__ >= \"0.20\"\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\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",
    "# Where to save the figures\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(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'details', 'categories', 'url'])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "mnist = fetch_openml('mnist_784', version=1)\n",
    "mnist.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000, 784)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y = mnist[\"data\"], mnist[\"target\"]\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000,)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "784"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "28 * 28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure some_digit_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "some_digit = X[0]\n",
    "some_digit_image = some_digit.reshape(28, 28)\n",
    "plt.imshow(some_digit_image, cmap=mpl.cm.binary)\n",
    "plt.axis(\"off\")\n",
    "\n",
    "save_fig(\"some_digit_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y.astype(np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_digit(data):\n",
    "    image = data.reshape(28, 28)\n",
    "    plt.imshow(image, cmap = mpl.cm.binary,\n",
    "               interpolation=\"nearest\")\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# EXTRA\n",
    "def plot_digits(instances, images_per_row=10, **options):\n",
    "    size = 28\n",
    "    images_per_row = min(len(instances), images_per_row)\n",
    "    images = [instance.reshape(size,size) for instance in instances]\n",
    "    n_rows = (len(instances) - 1) // images_per_row + 1\n",
    "    row_images = []\n",
    "    n_empty = n_rows * images_per_row - len(instances)\n",
    "    images.append(np.zeros((size, size * n_empty)))\n",
    "    for row in range(n_rows):\n",
    "        rimages = images[row * images_per_row : (row + 1) * images_per_row]\n",
    "        row_images.append(np.concatenate(rimages, axis=1))\n",
    "    image = np.concatenate(row_images, axis=0)\n",
    "    plt.imshow(image, cmap = mpl.cm.binary, **options)\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure more_digits_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,9))\n",
    "example_images = X[:100]\n",
    "plot_digits(example_images, images_per_row=10)\n",
    "save_fig(\"more_digits_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Binary classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_5 = (y_train == 5)\n",
    "y_test_5 = (y_test == 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: some hyperparameters will have a different defaut value in future versions of Scikit-Learn, such as `max_iter` and `tol`. To be future-proof, we explicitly set these hyperparameters to their future default values. For simplicity, this is not shown in the book."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 1\n",
    "Fit an SGD Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import SGDClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2\n",
    "Calculate cross-validation scores "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9559\n",
      "0.9559\n",
      "0.96565\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.base import clone\n",
    "\n",
    "skfolds = StratifiedKFold(n_splits=3, shuffle=True, random_state=42)\n",
    "\n",
    "for train_index, test_index in skfolds.split(X_train, y_train_5):\n",
    "    clone_clf = clone(sgd_clf)\n",
    "    X_train_folds = X_train[train_index]\n",
    "    y_train_folds = y_train_5[train_index]\n",
    "    X_test_fold = X_train[test_index]\n",
    "    y_test_fold = y_train_5[test_index]\n",
    "\n",
    "    clone_clf.fit(X_train_folds, y_train_folds)\n",
    "    y_pred = clone_clf.predict(X_test_fold)\n",
    "    n_correct = sum(y_pred == y_test_fold)\n",
    "    print(n_correct / len(y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: `shuffle=True` was omitted by mistake in previous releases of the book."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator\n",
    "class Never5Classifier(BaseEstimator):\n",
    "    def fit(self, X, y=None):\n",
    "        pass\n",
    "    def predict(self, X):\n",
    "        return np.zeros((len(X), 1), dtype=bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "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": {},
   "source": [
    "**Warning**: this output (and many others in this notebook and other notebooks) may differ slightly from those in the book. Don't worry, that's okay! There are several reasons for this:\n",
    "* first, Scikit-Learn and other libraries evolve, and algorithms get tweaked a bit, which may change the exact result you get. If you use the latest Scikit-Learn version (and in general, you really should), you probably won't be using the exact same version I used when I wrote the book or this notebook, hence the difference. I try to keep this notebook reasonably up to date, but I can't change the numbers on the pages in your copy of the book.\n",
    "* second, many training algorithms are stochastic, meaning they rely on randomness. In principle, it's possible to get consistent outputs from a random number generator by setting the seed from which it generates the pseudo-random numbers (which is why you will see `random_state=42` or `np.random.seed(42)` pretty often). However, sometimes this does not suffice due to the other factors listed here.\n",
    "* third, if the training algorithm runs across multiple threads (as do some algorithms implemented in C) or across multiple processes (e.g., when using the `n_jobs` argument), then the precise order in which operations will run is not always guaranteed, and thus the exact result may vary slightly.\n",
    "* lastly, other things may prevent perfect reproducibility, such as Python maps and sets whose order is not guaranteed to be stable across sessions, or the order of files in a directory which is also not guaranteed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "\n",
    "y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3\n",
    "Calculate the confusion matrix for \n",
    "- the train and predicted values \n",
    "- the train and perfect predictions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4\n",
    "Calculate precision and recall scores for training and predicted values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5\n",
    "Using the values from the confusion matrix, calculate \n",
    "- the rate of false positives, true positives, false negatives, true negatives \n",
    "- accuracy \n",
    "- precision "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 6\n",
    "Calculate the f1 score. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 7\n",
    "Explain what the scores in task 5 and 6 (accuracy, precision, f1) describe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "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": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = 0\n",
    "y_some_digit_pred = (y_scores > threshold)\n",
    "y_some_digit_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = 8000\n",
    "y_some_digit_pred = (y_scores > threshold)\n",
    "y_some_digit_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3,\n",
    "                             method=\"decision_function\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precisions, recalls, thresholds = precision_recall_curve(y_train_5, y_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure 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": [
    "def plot_precision_recall_vs_threshold(precisions, recalls, thresholds):\n",
    "    plt.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\", linewidth=2)\n",
    "    plt.plot(thresholds, recalls[:-1], \"g-\", label=\"Recall\", linewidth=2)\n",
    "    plt.legend(loc=\"center right\", fontsize=16) # Not shown in the book\n",
    "    plt.xlabel(\"Threshold\", fontsize=16)        # Not shown\n",
    "    plt.grid(True)                              # Not shown\n",
    "    plt.axis([-50000, 50000, 0, 1])             # Not shown\n",
    "\n",
    "\n",
    "\n",
    "recall_90_precision = recalls[np.argmax(precisions >= 0.90)]\n",
    "threshold_90_precision = thresholds[np.argmax(precisions >= 0.90)]\n",
    "\n",
    "\n",
    "plt.figure(figsize=(8, 4))                                                                  # Not shown\n",
    "plot_precision_recall_vs_threshold(precisions, recalls, thresholds)\n",
    "plt.plot([threshold_90_precision, threshold_90_precision], [0., 0.9], \"r:\")                 # Not shown\n",
    "plt.plot([-50000, threshold_90_precision], [0.9, 0.9], \"r:\")                                # Not shown\n",
    "plt.plot([-50000, threshold_90_precision], [recall_90_precision, recall_90_precision], \"r:\")# Not shown\n",
    "plt.plot([threshold_90_precision], [0.9], \"ro\")                                             # Not shown\n",
    "plt.plot([threshold_90_precision], [recall_90_precision], \"ro\")                             # Not shown\n",
    "save_fig(\"precision_recall_vs_threshold_plot\")                                              # Not shown\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y_train_pred == (y_scores > 0)).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure precision_vs_recall_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_precision_vs_recall(precisions, recalls):\n",
    "    plt.plot(recalls, precisions, \"b-\", linewidth=2)\n",
    "    plt.xlabel(\"Recall\", fontsize=16)\n",
    "    plt.ylabel(\"Precision\", fontsize=16)\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.grid(True)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plot_precision_vs_recall(precisions, recalls)\n",
    "plt.plot([recall_90_precision, recall_90_precision], [0., 0.9], \"r:\")\n",
    "plt.plot([0.0, recall_90_precision], [0.9, 0.9], \"r:\")\n",
    "plt.plot([recall_90_precision], [0.9], \"ro\")\n",
    "save_fig(\"precision_vs_recall_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold_90_precision = thresholds[np.argmax(precisions >= 0.90)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3370.0194991439557"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold_90_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred_90 = (y_scores >= threshold_90_precision)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ROC curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_train_5, y_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure roc_curve_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'k--') # dashed diagonal\n",
    "    plt.axis([0, 1, 0, 1])                                    # Not shown in the book\n",
    "    plt.xlabel('False Positive Rate (Fall-Out)', fontsize=16) # Not shown\n",
    "    plt.ylabel('True Positive Rate (Recall)', fontsize=16)    # Not shown\n",
    "    plt.grid(True)                                            # Not shown\n",
    "\n",
    "plt.figure(figsize=(8, 6))                                    # Not shown\n",
    "plot_roc_curve(fpr, tpr)\n",
    "fpr_90 = fpr[np.argmax(tpr >= recall_90_precision)]           # Not shown\n",
    "plt.plot([fpr_90, fpr_90], [0., recall_90_precision], \"r:\")   # Not shown\n",
    "plt.plot([0.0, fpr_90], [recall_90_precision, recall_90_precision], \"r:\")  # Not shown\n",
    "plt.plot([fpr_90], [recall_90_precision], \"ro\")               # Not shown\n",
    "save_fig(\"roc_curve_plot\")                                    # Not shown\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "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": {},
   "source": [
    "**Note**: we set `n_estimators=100` to be future-proof since this will be the default value in Scikit-Learn 0.22."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "forest_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "y_probas_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3,\n",
    "                                    method=\"predict_proba\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores_forest = y_probas_forest[:, 1] # score = proba of positive class\n",
    "fpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train_5,y_scores_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure 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": [
    "recall_for_forest = tpr_forest[np.argmax(fpr_forest >= fpr_90)]\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, \"b:\", linewidth=2, label=\"SGD\")\n",
    "plot_roc_curve(fpr_forest, tpr_forest, \"Random Forest\")\n",
    "plt.plot([fpr_90, fpr_90], [0., recall_90_precision], \"r:\")\n",
    "plt.plot([0.0, fpr_90], [recall_90_precision, recall_90_precision], \"r:\")\n",
    "plt.plot([fpr_90], [recall_90_precision], \"ro\")\n",
    "plt.plot([fpr_90, fpr_90], [0., recall_for_forest], \"r:\")\n",
    "plt.plot([fpr_90], [recall_for_forest], \"ro\")\n",
    "plt.grid(True)\n",
    "plt.legend(loc=\"lower right\", fontsize=16)\n",
    "save_fig(\"roc_curve_comparison_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9983436731328145"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_train_5, y_scores_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9905083315756169"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3)\n",
    "precision_score(y_train_5, y_train_pred_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8662608374838591"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred_forest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Textbook exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 1**\n",
    "\n",
    "Try to build different classifier. \n",
    "\n",
    "__Hint__: the KNeighborsClassifier works quite well for this task. Good hyperparameters are `{'n_neighbors': 4, 'weights': 'distance'}`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 2**\n",
    "(Optional)\n",
    "\n",
    "Write a function that can shift an MNIST image in any direction (left, right, up, or down) by five pixels. Then, for each image in the training set, create four shifted copies (one per direction) and add them to the training set. Finally, train your best model on this expanded training set and measure its accuracy on the test set. You should observe that your model performs even better now! This technique of artificially growing the training set is called data augmentation or training set expansion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = knn_clf.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 2 Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.ndimage.interpolation import shift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def shift_image(image, dx, dy):\n",
    "    image = image.reshape((28, 28))\n",
    "    shifted_image = shift(image, [dy, dx], cval=0, mode=\"constant\")\n",
    "    return shifted_image.reshape([-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": {},
   "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": {},
   "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": {},
   "outputs": [],
   "source": [
    "knn_clf = KNeighborsClassifier(**grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_clf.fit(X_train_augmented, y_train_augmented)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = knn_clf.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
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   "file_extension": ".py",
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