mnist.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a1748090",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cf8f721",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0a9ed649",
   "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",
   "id": "97d207a1",
   "metadata": {},
   "source": [
    "## MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e2045e64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'categories', 'feature_names', 'target_names', 'DESCR', 'details', 'url'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "mnist = fetch_openml('mnist_784', version=1, as_frame=False, parser = 'auto')\n",
    "mnist.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cca3ffaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000, 784)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y = mnist[\"data\"], mnist[\"target\"]\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d7babd63",
   "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 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "some_digit = X[0]\n",
    "some_digit_image = some_digit.reshape(28, 28)\n",
    "plt.imshow(some_digit_image, cmap=mpl.cm.binary)\n",
    "plt.axis(\"off\")\n",
    "\n",
    "save_fig(\"some_digit_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e20f6ffa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2b39b093",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y.astype(np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "22d64b36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aa3ce4a3",
   "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,
   "id": "609ddfa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_digits(instances, images_per_row=10, **options):\n",
    "    size = 28\n",
    "    images_per_row = min(len(instances), images_per_row)\n",
    "    # This is equivalent to n_rows = ceil(len(instances) / images_per_row):\n",
    "    n_rows = (len(instances) - 1) // images_per_row + 1\n",
    "\n",
    "    # Append empty images to fill the end of the grid, if needed:\n",
    "    n_empty = n_rows * images_per_row - len(instances)\n",
    "    padded_instances = np.concatenate([instances, np.zeros((n_empty, size * size))], axis=0)\n",
    "\n",
    "    # Reshape the array so it's organized as a grid containing 28×28 images:\n",
    "    image_grid = padded_instances.reshape((n_rows, images_per_row, size, size))\n",
    "\n",
    "    # Combine axes 0 and 2 (vertical image grid axis, and vertical image axis),\n",
    "    # and axes 1 and 3 (horizontal axes). We first need to move the axes that we\n",
    "    # want to combine next to each other, using transpose(), and only then we\n",
    "    # can reshape:\n",
    "    big_image = image_grid.transpose(0, 2, 1, 3).reshape(n_rows * size,\n",
    "                                                         images_per_row * size)\n",
    "    # Now that we have a big image, we just need to show it:\n",
    "    plt.imshow(big_image, cmap = mpl.cm.binary, **options)\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2add88ac",
   "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 900x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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,
   "id": "8db53503",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc9b7264",
   "metadata": {},
   "source": [
    "# Training a Binary Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ae7232b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_5 = (y_train == 5)\n",
    "y_test_5 = (y_test == 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dd460b1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SGDClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SGDClassifier</label><div class=\"sk-toggleable__content\"><pre>SGDClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SGDClassifier(random_state=42)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_clf = SGDClassifier(max_iter=1000, tol=1e-3, random_state=42)\n",
    "sgd_clf.fit(X_train, y_train_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c3448e05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63b5641c",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2cbb883c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "\n",
    "y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6aeed0d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[53892,   687],\n",
       "       [ 1891,  3530]], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf5e8173",
   "metadata": {},
   "source": [
    "## Precision and Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "83f76188",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8370879772350012"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "\n",
    "precision_score(y_train_5, y_train_pred) \n",
    "\n",
    "# the above line of code returns the same result as the next lines of code\n",
    "\n",
    "# cm = confusion_matrix(y_train_5, y_train_pred)\n",
    "# cm[1, 1] / (cm[0, 1] + cm[1, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1b416da3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6511713705958311"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred)\n",
    "\n",
    "# the above line of code returns the same result as the next lines of code\n",
    "# cm = confusion_matrix(y_train_5, y_train_pred)\n",
    "# cm[1, 1] / (cm[1, 0] + cm[1, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cc11771c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7325171197343846"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_train_5, y_train_pred)\n",
    "\n",
    "# the above line of code returns the same result as the next lines of code\n",
    "# cm = confusion_matrix(y_train_5, y_train_pred)\n",
    "# cm[1, 1] / (cm[1, 1] + (cm[1, 0] + cm[0, 1]) / 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63ef822f",
   "metadata": {},
   "source": [
    "## Precision / Recall Trade-off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7b6932ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores = cross_val_predict(sgd_clf, X_train, y_train_5, cv = 3, method = \"decision_function\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7da79e14",
   "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": 25,
   "id": "60fc8d8f",
   "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 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 26,
   "id": "8e908724",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y_train_pred == (y_scores > 0)).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f2367dbd",
   "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 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 28,
   "id": "45a57081",
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold_90_precision = thresholds[np.argmax(precisions >= 0.90)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "827bb6c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3370.0194991439557"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold_90_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ae352892",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred_90 = (y_scores >= threshold_90_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d105bf2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9000345901072293"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_train_5, y_train_pred_90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "85c67e17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4799852425751706"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred_90)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32f8c6d0",
   "metadata": {},
   "source": [
    "## The ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c0888a28",
   "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": 34,
   "id": "1e2444c7",
   "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 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 35,
   "id": "c5c7d074",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9604938554008616"
      ]
     },
     "execution_count": 35,
     "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",
   "id": "b532a79a",
   "metadata": {},
   "source": [
    "### ROC Curve RandomForestClassifier X SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "673cf21d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\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, method = \"predict_proba\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5ae5cbb1",
   "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": 38,
   "id": "c22a260d",
   "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 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 39,
   "id": "c2e4af7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9983436731328145"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_train_5, y_scores_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c3929d58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9905083315756169"
      ]
     },
     "execution_count": 40,
     "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": 41,
   "id": "f4521c02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8662608374838591"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred_forest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6452210c",
   "metadata": {},
   "source": [
    "# Multiclass Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "996bac27",
   "metadata": {},
   "source": [
    "## Model Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "903c344f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svm_clf = SVC(gamma = \"auto\", random_state = 42)\n",
    "svm_clf.fit(X_train, y_train) # y_train, not y_train_5\n",
    "svm_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6ddaf3ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(svm_clf.decision_function([some_digit]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fa25a91",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train.astype(np.float64))\n",
    "cross_val_score(svm_clf, X_train_scaled, y_train, cv = 3, scoring = \"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "37f4bc12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5], dtype=uint8)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "\n",
    "ovr_clf = OneVsRestClassifier(SVC(gamma = \"auto\", random_state = 42))\n",
    "ovr_clf.fit(X_train, y_train)\n",
    "ovr_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7288bf1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(ovr_clf.decision_function([some_digit]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b08fc644",
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_val_score(ovr_clf, X_train_scaled, y_train, cv = 3, scoring = \"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "44c37961",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5], dtype=uint8)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.multiclass import OneVsOneClassifier\n",
    "ovo_clf = OneVsOneClassifier(SVC(gamma = \"auto\", random_state = 42))\n",
    "ovo_clf.fit(X_train, y_train)\n",
    "ovo_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "aea3a1ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(ovo_clf.decision_function([some_digit]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d71fd218",
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_val_score(ovo_clf, X_train_scaled, y_train, cv = 3, scoring = \"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "535938b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SGDClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SGDClassifier</label><div class=\"sk-toggleable__content\"><pre>SGDClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SGDClassifier(random_state=42)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf = SGDClassifier(max_iter = 1000, tol = 1e-3, random_state = 42)\n",
    "sgd_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c7c46027",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(sgd_clf.decision_function([some_digit]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1257d14",
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_val_score(sgd_clf, X_train_scaled, y_train, cv = 3, scoring = \"accuracy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "197894fe",
   "metadata": {},
   "source": [
    "## Hyperparameter Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0323d61d",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" Hyperparameter optimization code of best model: sgd_clf \"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bd97772",
   "metadata": {},
   "source": [
    "## Error Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11e77fbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = cross_val_predict(sgd_clf, X_train_scaled, y_train, cv=3)\n",
    "conf_mx = confusion_matrix(y_train, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc1b14b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# since sklearn 0.22, you can use sklearn.metrics.plot_confusion_matrix()\n",
    "def plot_confusion_matrix(matrix):\n",
    "    \"\"\"If you prefer color and a colorbar\"\"\"\n",
    "    fig = plt.figure(figsize=(8,8))\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(matrix)\n",
    "    fig.colorbar(cax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ef43b38",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.matshow(conf_mx, cmap=plt.cm.gray)\n",
    "save_fig(\"confusion_matrix_plot\", tight_layout=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "791dae9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "row_sums = conf_mx.sum(axis = 1, keepdims = True)\n",
    "norm_conf_mx = conf_mx / row_sums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5d1c20d",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.fill_diagonal(norm_conf_mx, 0)\n",
    "plt.matshow(norm_conf_mx, cmap=plt.cm.gray)\n",
    "save_fig(\"confusion_matrix_errors_plot\", tight_layout=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33d01df9",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_aa = X_train[(y_train == 3) & (y_train_pred == 3)]\n",
    "X_ab = X_train[(y_train == 3) & (y_train_pred == 5)]\n",
    "X_ba = X_train[(y_train == 5) & (y_train_pred == 3)]\n",
    "X_bb = X_train[(y_train == 5) & (y_train_pred == 5)]\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.subplot(221); plot_digits(X_aa[:25], images_per_row=5)\n",
    "plt.subplot(222); plot_digits(X_ab[:25], images_per_row=5)\n",
    "plt.subplot(223); plot_digits(X_ba[:25], images_per_row=5)\n",
    "plt.subplot(224); plot_digits(X_bb[:25], images_per_row=5)\n",
    "\n",
    "save_fig(\"error_analysis_digits_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bf2bb79",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" PR Curve/Score \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbc77e1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" F1 Score \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc9e7abb",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" ROC Curve \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63ecb755",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" AUC Score \"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4053b2a8",
   "metadata": {},
   "source": [
    "## Multilabel Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f94f789d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "y_train_large = (y_train >= 7)\n",
    "y_train_odd = (y_train % 2 == 1)\n",
    "y_multilabel = np.c_[y_train_large, y_train_odd]\n",
    "\n",
    "knn_clf = KNeighborsClassifier()\n",
    "knn_clf.fit(X_train, y_multilabel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34583bb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f24a068",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_knn_pred = cross_val_predict(knn_clf, X_train, y_multilabel, cv=3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7257c827",
   "metadata": {},
   "outputs": [],
   "source": [
    "f1_score(y_multilabel, y_train_knn_pred, average = \"macro\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b23bfe64",
   "metadata": {},
   "outputs": [],
   "source": [
    "f1_score(y_multilabel, y_train_knn_pred, average = \"weighted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10b7a577",
   "metadata": {},
   "source": [
    "# Multioutput Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14bbbf79",
   "metadata": {},
   "outputs": [],
   "source": [
    "noise = np.random.randint(0, 100, (len(X_train), 784))\n",
    "X_train_mod = X_train + noise\n",
    "noise = np.random.randint(0, 100, (len(X_test), 784))\n",
    "X_test_mod = X_test + noise\n",
    "y_train_mod = X_train\n",
    "y_test_mod = X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02b4a0b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "some_index = 0\n",
    "plt.subplot(121); plot_digit(X_test_mod[some_index])\n",
    "plt.subplot(122); plot_digit(y_test_mod[some_index])\n",
    "save_fig(\"noisy_digit_example_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b83a9b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_clf.fit(X_train_mod, y_train_mod)\n",
    "clean_digit = knn_clf.predict([X_test_mod[some_index]])\n",
    "plot_digit(clean_digit)\n",
    "save_fig(\"cleaned_digit_example_plot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "257383b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" F1 Score \"\"\""
   ]
  }
 ],
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