03_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/ageron/handson-ml2/blob/master/03_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": 6,
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y.astype(np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "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": 11,
   "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Binary classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n",
       "              early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
       "              l1_ratio=0.15, learning_rate='optimal', loss='hinge',\n",
       "              max_iter=1000, n_iter_no_change=5, n_jobs=None, penalty='l2',\n",
       "              power_t=0.5, random_state=42, shuffle=True, tol=0.001,\n",
       "              validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "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,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.95035, 0.96035, 0.9604 ])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "cross_val_score(sgd_clf, X_train, y_train_5, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 19,
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.91125, 0.90855, 0.90915])"
      ]
     },
     "execution_count": 20,
     "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": 21,
   "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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[53892,   687],\n",
       "       [ 1891,  3530]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[54579,     0],\n",
       "       [    0,  5421]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_perfect_predictions = y_train_5  # pretend we reached perfection\n",
    "confusion_matrix(y_train_5, y_train_perfect_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8370879772350012"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score\n",
    "\n",
    "precision_score(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8370879772350012"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_train_5, y_train_pred)\n",
    "cm[1, 1] / (cm[0, 1] + cm[1, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6511713705958311"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6511713705958311"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm[1, 1] / (cm[1, 0] + cm[1, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7325171197343846"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "\n",
    "f1_score(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7325171197343847"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm[1, 1] / (cm[1, 1] + (cm[1, 0] + cm[0, 1]) / 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2164.22030239])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_scores = sgd_clf.decision_function([some_digit])\n",
    "y_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = 0\n",
    "y_some_digit_pred = (y_scores > threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_some_digit_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False])"
      ]
     },
     "execution_count": 33,
     "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": 34,
   "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": 35,
   "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": 36,
   "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": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y_train_pred == (y_scores > 0)).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure precision_vs_recall_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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aaNfO19bq0sWfvOrVywuXdet80sHJk+G008q+57PPfNByrVqwzTY+pc+bbyamkxKR3JSXBc4TT/gcbyHARx/5MRU4IjE3bVpieuM0q1UL9tvPFwMdM8b/bZg/H266qex5X3zh8zt27w4tWybG8hx5JPz0U0ZSE5EMycsCB/wfsYMPhg8+8P0OHaLMRkQ2qW7dDUcOZ1D79j4Rdgjwww8+aXVli4FOmQINGsCECbqVJZIr8rbAAR9E+Mwz3m7XLtpcRGQTxoxJLHWRZY0bw4UX+oDl0oVEly71MXxHHJE479hjvTfouONg1qxIUhWRKsrrAieZChyRmIuwwKlIs2Z+a3vKFJ+LMNmTT8Luu/vtqxYt4JRTfP/WWxNLu4lItAqiwKldG7baKuosRGSjios9YuiKK7xXZ9o07+1JtmSJP/w1axZccgnUq+eFz5lnwt//Hk2+IlIgBU6bNlCnTtRZiEiu693bx+usX+9PWd12my9Iv+OOG87F8+CDcPzxXuycdho8/TR8/XU0eYsUooL4tV/ZwEERiZEHHvDtmWdGm0cVmPnEoYMH+/7QoYnX7rrLHzv/9tvEsb/+1aPU0UfDIYfAm2+2Ze1aL5xEJL0KogenWbOoMxCRTRo/3iPHXXABfPONz6J8+unQqtWG50yaBGefDaNHb//zhIOTJ2c/V5F8VhAFToMGUWcgIps0fXperZ9Qvz489BAsWpR4Mus///GJCMGnsUjWt2/ZdbTOO0+PpIvUREEUOG3aRJ2BiAh06+azJIcAzz0HL75YzH33VXzuvff6I+ktWsAf/6iV0kVSVRAFTkVdxCISM/fe61FgzjrLC55vv/V5d/74x7KvL1kC119fdqX0b76JJleRXKICR0TiYcoUjwLVooXPu3PddV7wLFkCl11W8bmtWnlh9Mknuo0lUhkVOCISD888k5h6XGjeHG64ITF+Z8ECGDgw8fro0dCxo9/G+sUvfGxP8pNbIoVOBY6ISA7YZhv4y1/gyy83fG3uXJgzB7bcMnEba+ZMWLs2+3mKxEVBFDhdu0adgYhs0h13eMhGbbVVoldn0SKfRbki++7ra5eawT77+OSEIoUk7wucTp00i7FITnj+eQ+pslatoH//RMGzZg2cc86G5732ms+0bObLfWncjhSCvC9wioqizkBEqmTyZM12V0N16viDaKUFz+LFsMceZc85/XQft3PjjerVkfyW9wXOlltGnYGISDSKiuBf/4IZM+CXvyz72tCh3qujcTqSr/K+wLn99qgzEJEquflmD0m7ffeF99/3Xp2PPir7Wt26cPHFfntLJJ/kdYFz6aXQoUPUWYhIlbz2modkVMeOXuj07Zs4dtttUK9e2aUili6NLkeRdMjrAqdhw6gzEJEqmzDBQ7Li//4PvvrKe3AqUlQE220HV12lQcmSm/K6wGnUKOoMRETiq3VrWL3ax+EsXeoTBXbvnnh93jwYMcIHJb/+enR5ilSHChwRiYeRIz0k62rXhmbNfLmIWbNg6lTYddey5+y9d2JOnSuvhFdfheXLo8lXpCryusDRLSqRHDJnjodE7rDDYPZsvzU1fXrZ1157Da69FvbbDzbfHHbeGb7+Opo8RTYmrwsc9eCI5JDHH/eQWDnwwMTTV2eeueHr777rt7rM9BCcxEvWCxwza25mk8xshZl9ZmYnVXKemdl1ZvaFmS0zs2Iz2zGV79pii/TkLCJS6Dp29AU+SycR/OEHOO64sudceqkXOnrkXOIgih6ce4DVQCugPzCqksLlOGAg0ANoDrwGjK3qlxx9NBxwQM2TFZEsufZaD8kJjRvDE0/4QOT27cu+Vq8ejB3rA5hFopLVAsfMGgH9gOEhhOUhhBnAZOCUCk7fFpgRQvg0hLAOGAd0qep3TZzoA+dEJEfMneshOaVDB5g/f8NHyU89FTbbTAORJTrZ7sHpBKwLIXyYdOwtoKIenMeBjmbWyczqAqcBz2YhRxGJwrhxlS+NLTkhBHjqqbLHNt8chg3TuleSfRayOIOTmfUA/h5CaJ107EygfwihZ7lz6wE3ARcC64AFwAEhhHkVfO4gYJDvde8Os3jxxeLM/BCSkuXLl9O4ceOo05ASuh7xks/X44kntmHUqI5ljjVsuJbbb5/DDjvEs1snn69HLurVq9fsEMJu1X1/tgucXwGvhhAaJh27BOgZQuhT7tw/Ab2A44FFwMnAVcCOIYQfK/+O3QLM0sybMVFcXEzPnj2jTkNKxPp6XHmlb0eMiDaPLIr19UiDpUt9RuTyiop8pfO4yffrkWvMrEYFTrZvUX0I1DGzHZKOdQXeq+DcrsD4EMLCEMLaEMIYoBkpjMMRkRyyYIGH5I3mzf221VdfweGHJ44vWeKTCiavhyWSblktcEIIK4CJwAgza2Rm+wJ9qfjpqDeA48yslZnVMrNTgLrAx9nLWESy5uGHPSTvtG4N//gHrFqVOLZkCUye7I+Vb7ttdLlJ/oriMfFzgQbAN8BjwDkhhPfMrJ2ZLTezdiXn/RkfgDwH+A64COgXQvgugpxFRKSG6tXz3pz77it7fP582HdfLeop6VUn218YQlgKHFXB8c+Bxkn7PwHnlYSI5LvLL/ftDTdEm4dkVOvWcNZZHqtX+6PkADNn+qKea9dqig9Jj7xeqkFEcsiSJR5SMOrV84Jm880Tx+rUgSFD4KefostL8oMKHBGJh9GjPaSg1K4Ny5ZB06aJY7fcAg0aRJeT5Ie8K3A233wN99wTdRYiIlJVZvDdd/Cvf0GnTonjEyZEl5PkvrwrcLba6ifOPTfqLEQkZUOGeEjB2mOPsqt1HHusBh5L9eVdgSMiOWrlSg8peFOmJNq1asGMGdHlIrkr609RiYhUSPeWpcQRR/jYnHXrfL9HD1+5/Ljjos1Lcot6cEREJHbWroVXXknsH3+838ISqSoVOCISD4MHe4iU2G+/srer3njDBySX9uyIbIwKHBERia0jjtiwoKlTx29brV8fTU6SGzQGR0Ti4fbbo85AYqpWLR9/fvDBidtWM2b4OJ2VK6F+/Wjzk3hSD46IiMRe/frw8svw/vvQrFnieIMGUFTk8+iIJFOBIyLxcN55HiIb8ctfwtKlZRfsXLrUix7NgSbJVOCISDw0aKD5+aXKzjoLfvwRBg1KHBs1Cu6+O7qcJF5U4IhIPNx8s4dIFTVoAPff76uSl7rgAn/SSkQFjoiI5LS6deGtt8oeM4MPPogmH4kHFTgiEg+DBpW93yCSgl128cfGu3VLHOvSxQudFSuiy0uiowJHROKhqMhDpJrM4D//gWeeKXu8ceNo8pFoaR4cEYmHG26IOgPJE4ce6r05PXrAq6/6MTN4/XXYc89oc5PsUQ+OiIjkHTOfDPC3v00c22sv2Hbb6HKS7FKBIyLxcPrpHiJp9OijcMwxif358zUAuVCowBGReGjb1kMkjcxgwgRfnTxZly4wcmQ0OUl2qMARkXgYMcJDJANq14YQ4M47E8cuvxxmzYouJ8ksFTgiIlIwLrgA1qxJ7O++O3z8cXT5SOaowBGReDj5ZA+RDKtTB4qLE/s77ADffBNZOpIhKnBEJB46d/YQyYL994dJkxL7rVrBq69qHqZ8ogJHROJh+HAPkSw56ii44orE/rBhOzN1anT5SHqpwBERkYL1pz+VHWj8m9/4o+SS+1TgiEg8nHiih0iWde9etsjZdlv47rvo8pH0UIEjIvHQrVvZlRJFsqh7d7juund+3m/WDN57L8KEpMZU4IhIPFx2mYdIRPbddwlXXpnY32knuPrqyNKRGlKBIyIiUuKaa2Do0LL7//pXdPlI9anAEZF46NfPQyRiI0fCt98m9vv2jS4XqT4VOCISD3vv7SESAy1awFVXefvrr31Nq7lzo81JUqMCR0TiYcgQD5GYGDas7P4vfhFNHlI9KnBEREQqUKcOrF8Pt9+eOKYaPHeowBGReDjySA+RGDGD3/8+sX/LLXDssb4yucRbnagTEBEB4MADo85ApFLffQdbbOHtCROgVi1Yt863Ek8qcEQkHpL/N1kkZpo2hdWroUEDL2wAateGVaugXr1oc5OKqfYUERGpgrp1Ye1auOiixLHNNoPFi6PLSSqnAkdE4uGwwzxEYu7WW6FTp8R+nz7R5SKVU4EjIvHQp49+U0jO+OAD6NjR26+/DrNnR5uPbEgFjojEw7nneojkgFq14L//TezvtlvZx8kleipwREREqqF2bXjrrcT+RRf57SuJBxU4IhIPvXt7iOSQXXaBH35I7F9yCUydGl0+kqDHxEUkHk44IeoMRKqlcWNYtAhat/b93/wGVq6E+vWjzavQqQdHROLhzDM9RHJQq1ZlF+M8++zochGnAkdERCQNOnWCgw7y9iOPRJuLqMARkbjo2dNDJIc9/nii/cAD0eUhGoMjInExYEDUGYjUWPPmifagQb6Mw2mnRZdPIVMPjojEw4ABKnIkLxQXJ9oDBkCPHlp9PAoqcEQkHtas8RDJcfvvD6+8ktifMQO23jq6fAqVChwRiYeDDkqM0BTJcfvtBwsXJvYXLYK3344un0KkMTgiEg+/+13UGYikVZs28NNPiflwunbVrapsUg+OiMTDySd7iOSRzTaDO+5I7F91VXS5FBoVOCISDz/+6CGSZy68EMy8PWIEXHNNtPkUChU4IhIPhx/uIZKHvvsu0b76arjttshSKRgqcEQkHs45x0MkDzVpUrbIufji6HIpFBpkLCLxoMU2Jc81bQrTpiUeFjTToONMUg+OiMTDsmUeInmsd2+oWzexf+ut0eWS71TgiEg89O3rIZLnVq9OtC+5xB8ll/TTLSoRiYcLL4w6A5Gs+fZbaNnS2w0a6FZVJqgHR0Ti4ZhjPEQKQIsWZSfu3nvv6HLJVypwRCQeFi/2ECkQU6Yk2q+/Dv/7X3S55CMVOCISD8ce6yFSIDbbDFauTOxff310ueSjrBc4ZtbczCaZ2Qoz+8zMTtrIuduZ2dNm9oOZLTazG7OZq4hk0SWXeIgUkPr1Ya+9vH3zzWULHqmZKHpw7gFWA62A/sAoM9ux/ElmVg+YBrwAtAa2AcZlMU8RyaY+fTxECszjjyfaf/hDdHnkm6wWOGbWCOgHDA8hLA8hzAAmA6dUcPoA4MsQwq0hhBUhhJ9CCFpsXiRfLVrkIVJg2reHffbx9t13w4IF0eaTLyxk8dk0M/sVMDOE0CDp2BBg/xBCn3LnPgTUBVoAuwPvAheEEN6p4HMHAYMAWrZs2f2JJ57I3A8hKVm+fDmNGzeOOg0pEefr0W3wYADm3H57xJlkT5yvRyGK8nosWNCAU0/d8+f9hx76N9tuW9iLz/bq1Wt2CGG36r4/2/PgNAbKT1W6DNi8gnO3AXoBRwLPA78HnjKzX4QQViefGEIYDYwG6Ny5c+jZs2ea05bqKi4uRtcjPmJ9PUaOBIhvfhkQ6+tRgKK+HvPmwVVXeXvgwD1Yvz6xCrmkLttjcJYDTcodawL8UMG5K4EZIYRnSgqam4Ei4JeZTVFEInHooR4iBerKK2HixMR+LT3nXCPZ/s/3IVDHzHZIOtYVeK+Cc98GNLejSKFYsECDD6TgHX003HBDYn/OnOhyyXVZLXBCCCuAicAIM2tkZvsCfYGxFZw+DtjLzHqbWW1gMLAY+CBrCYtI9pxyiodIgbvsskT7V7+KLo9cF0UH2LlAA+Ab4DHgnBDCe2bWzsyWm1k7gBDCXOBk4D7gf3ghdGT58TcikieGDfMQEZLH2icv6SBVl/IgYzM7Dfgt0A6oX+7lEELYfmPvDyEsBY6q4Pjn+CDk5GMT8R4fEcl3vXtHnYFIbFxwgd+q+vprmD7dVyCvVy/qrHJLSgWOmQ0HrsEf2Z4DrMpEUiJSgD791LfbbRdtHiIxUKsWfPkl1K7t+2edBQ8/HG1OuSbVHpwzgDtCCBdlIhkRKWADB/q2uDjSNETiolYtf7Dw2WdhzBi45x5o2DDqrHJHqgVOETBlk2eJiKTqmmuizkAkdsaPh6ZNvd2oEWRxbt6cl+og45fwx7pFRNJr//09RORnTZr8PAcmoMU4U5FqgTMYON3MTjWzFmZWq3xkIkkRKQBz53qISBkXX5xo6xZV1aVakHwI7AQ8DHwNrCkXer822TQAAB//SURBVIRbRKrnrLM8RKSMunXh1FMT+5deGl0uuSTVMTgj0OzCIpIJ118fdQYisfXII/DOO/Cf/8DNN8NNN0WdUfylVOCEEK7OUB4iUuj22SfqDERibdo0aNHC29Ona+qoTan2mBkza2xmbc2sUToTEpEC9e67HiJSoaKiRFuzG29aygWOmR1iZrOA74D5wDIz+7eZ6T+3iFTf+ed7iEilkpdw+Prr6PLIBSkVOGZ2CPAPfEmFa/F1pa4DNgemqsgRkWq76SYNLBDZhAsvTLRffjm6PHJBqoOMrwb+CRwRQlhfetDMRgBP48s4TEtbdiJSOHbfPeoMRGLPDLbfHj75xAceH3dc1BnFV6q3qLoC9yQXNwAl+/cC3dKVmIgUmDlzPERkow4+2Lf/+Ee0ecRdqgXOKqBJJa9tjhbfFJHqGjzYQ0Q2Knniv169ossj7lK9RVUMXGtmr4cQ5pUeNLN2+O2rF9OXmogUlOTRkyJSqY4doUEDX7ahuNjXpzKLOqv4SbXAGQq8Csw1s9eBr4DWwF74U1VD05ueiBSMbrrDLVJVy5ZBvXrePvBAeOGFaPOJo5RuUYUQPgR2Ae4ENgN2BeoDdwDdQggfpT1DESkMb7zhISKbVLcu7LSTt1980Wc5lrJS7cEhhPAVMCQDuYhIIStdYKe4ONI0RHLFm28menF22cVvVUmCVv8WkXi4+24PEamSunXhoYcS+0uXRpdLHG2yB8fMXgDODSH8t6S9MSGEcGB6UhORglLa3y4iVXb66TBwoLeLiuDLL2GrraLNKS6q0oOTPDa7Vsl+ZaEeIRGpnpkzPUQkJZddlmhvvXV0ecTNJntwQgi9kto9M5qNiBSuK67wrcbgiKTkhhugWTMYWvIc8+uvw157RZtTHKjHRUTi4f77PUQkZX/4Q6K9994acAypL7bZ18xOT9pvb2avmdkPZvakmTVOf4oiUhA6d/YQkWp57rlE+8gjo8sjLlLtwRkGtEzavxXYBhgN/BqfzVhEJHUvveQhItVy8MFwyCHefvpp+P77aPOJWqoFzvbA2wBm1gA4HLg4hHAJcAVwdHrTE5GCcdVVHiJSbVOmJNrnnBNdHnGQaoFTH1hZ0t4HH6T8z5L9uYDGb4tI9Tz0UNlJPUQkZXXrQvfu3v7b36LNJWqpFjjzgf1K2n2B2SGEZSX7WwLLKnqTiMgmbbedh4jUyJNPJtrbbhtdHlFLtcC5H7jazGYB5wJ/SXptb+D9dCUmIgVm+nQPEamRDh3goIO8PX9+4U4vldJaVCGEO8xsMb56+J0hhL8mvbw58HA6kxORAnLddb7t3TvaPETywD//CVYyTe+++xbmY+PVWWzzUeDRCo6flZaMRKQwjR0bdQYieWXSJDi65NGf1asTC3MWCk30JyLx0Lath4ikxVFHJdqPPRZdHlHZZIFjZuvMbI+S9vqS/cpibeZTFpG89OyzHiKSNh06+Lb0DnAhqcotqhHAwqR2Ad7JE5GMGznSt4ceGm0eInnkggvgkkvg44+jziT7qrLY5jVJ7aszmo2IFK7HH486A5G8c9ppXuAAfPEFtGkTbT7ZlOpaVHXNrFElrzUys7rpSUtECk7r1h4ikjZFRYl2//7R5RGFVAcZ/wV4oJLX7i8JEZHUTZlSdp55EUmLXXf1baEt9ZZqgdMTeKqS1yYDB9YoGxEpXLfc4iEiaZU8s/EXX0SXR7alOg/OlsA3lbz2LdCqZumISMFK/ldYRNImebmGHj3g00+jyyWbUu3B+QbYuZLXdgaW1CwdESlYLVp4iEjanXSSb+fNg08+iTaXbEm1wHkaGG5muyQfNLOdgT8CuoEuItUzcaKHiKTd/UkjZHv2jCyNrEq1wLkS+A6YbWYzzewJM3sVeBNfSXxYuhMUkQJx550eIpJ2jRvD8OHeXrhw4+fmi5QKnBDCYmB34AbAgG4l2z8Bu5e8LiKSuqee8hCRjCidDwfg3HOjyyNbqrPY5nd4T86V6U9HRApW06ZRZyCS15L/io0aBXffDbXyeEXKav1oZtbCzI4ws9PMrHnJsfpmlsf/qUQko8aP9xCRjJk/P9Hu3j2yNLIipR4cMzPgRuACoB6+LtXuwFJ8fpwZwLVpzlFECsGoUb494YRo8xDJY+3bJ9pz5kSXRzak2uNyOXA+vujmnvj4m1JTgCPSlJeIFJqpUz1EJKO++irRvuuu6PLItFQLnN8BI0II1+NPTiX7GNg+LVmJSOFp2NBDRDIqecm3V1+NLo9MS7XAaQO8Xslrq4EKF+IUEdmkceM8RCTjrrrKt/k87C3VAucLYKdKXusKzKtZOiJSsB580ENEMu6AAxLt5FtW+STVx8T/DlxpZm+S6MkJZtYJuAQYnc7kRKSATJsWdQYiBePXv060t94aQogul0xJtQfnauC/wMvARyXH/g68U7I/Mm2ZiUhhqVvXQ0SyYmTSb+yVK6PLI1NSncl4JdATGADMBKYDbwCDgINCCKvTnJ+IFIoxYzxEJCuGDk20b7opujwypcoFjpnVNbO+QLsQwtgQwskhhINDCL8NITwSQlibwTxFJN+pwBHJui5dfJuPd4irXOCEENYATwAdMpaNiBSu4mIPEcmaU0/17YwZ0eaRCamOwfkU2DITiYiIiEh2/eY3ifb330eXRyakWuDcCPzRzFpmIhkRKWAPPOAhIlmz005gJWsSXHddtLmkW6oFzgFAc2CemU03s7Fm9tekeCQDOYpIIdBimyKR2H9/3+bbQONU58HpAawBvsWXZSi/NEMePkkvIlkxfXrUGYgUpJNOSgx/69EDXnkl0nTSJtUCZzdgeQjhp0wkIyIiItl1xhkwaJC382mw8SZvUZlZbTO72sy+A74GvjezCWa2RebTE5GCce+9HiKSVbVqwf/+l9hfvjy6XNKpKmNwzgauxFcPvxl4CugL3JbBvESk0EyZ4iEiWbdFUpfF/fdHl0c6VaXAORN4IIRwQAhhaAjhOOA84GQzq5fZ9ESkYDzzjIeIRKJzZ98OGRJtHulSlQJnO3y9qWTjgdpA+1S/0Myam9kkM1thZp+Z2UlVeM8LZhbMLNUxQyIiIlIFd92VaH/wQXR5pEtVCpzGQPnpf34o2W5eje+8B1gNtAL6A6PMbMfKTjaz/qQ+GFpEcs0dd3iISCR69060R42KLo90qeo8OG3MbLvSwHt1Njhe8lqlzKwR0A8YHkJYHkKYAUwGTqnk/KbAVcAfqpiniOSq55/3EJFImMGFF3r7nnuizSUdLISNT11jZuupeH4bq+h4CKH2Rj7rV8DMEEKDpGNDgP1DCH0qOP8e4GNgEjAPqFvRop5mNghf0ZyWLVt2f+KJJzb6M0n2LF++nMaNG0edhpTQ9YgXXY940fWAGTNaMHz4TgC8+GJxpLn06tVrdghht+q+vyoFzmmpfGAIodLZjM2sB/D3EELrpGNnAv1DCD3Lnbsb8CA+9842bKTASda5c+cwd+7cVFKWDCouLqZnz55RpyEldD3iRdcjXnQ9YN06qFMyKOTHH6FBg42fn0lmVqMCZ5NjWzZWsFTDcqBJuWNNSIzpAcDMagH3Ar8PIay10oUyRCR/3Xyzb/PlEQ6RHFQ76R7MBx/ArrtGl0tNpboWVU19CNQxsx2SjnUF3it3XhO852a8mS0C3ig5vrCkF0hE8s1rr3mISKS23NK3t94abR41ldWnk0IIK8xsIjDCzH4HdMMnDdyn3KnLgK2T9tsC/wa64+tgiUi+mTAh6gxEhMQtqqeeijaPmsp2Dw7AuUAD4BvgMeCcEMJ7ZtbOzJabWbvgFpUGiaLm6xDC6ghyFhERKQh//rNvly8vu4RDrsl6gRNCWBpCOCqE0CiE0C6E8LeS45+HEBqHED6v4D3zQwi2qQHGIpLDRo70EJFIHXdcon3IIdHlUVNR9OCIiGxozhwPEYnUZpvBCSd4+403YHWO3jdRgSMi8fD44x4iErnRoxPtYcOiy6MmVOCIiIhIGU2awG4lM9DcdFO0uVSXChwRiYdrr/UQkVhInpIqF29TqcARkXiYO9dDRGLh2GMT7cmTo8ujulTgiEg8jBvnISKxULs2dO7s7SuuiDaX6lCBIyIiIhU68UTffvQRrF8fbS6pUoEjIvFw5ZUeIhIbyeNwBg6MLo/qUIEjIvGwYIGHiMRG48ZwxBHefiSdS29ngQocEYmHhx/2EJFYue++RPvpp6PLI1UqcERERKRSbdok2oMGRZdHqlTgiEg8XH65h4jEzh//6Nuvvoo2j1SowBGReFiyxENEYufccxPtXFlhvE7UCYiIAGUXvxGRWNl660T7zTfhwAOjy6Wq1IMjIiIim9SokW+HD482j6pSgSMi8TBkSNlJN0QkVvr18+1rr0WbR1WpwBGReFi50kNEYumGGxLtl16KLo+q0hgcEYmHe+6JOgMR2YjkcThHHw1Ll0aXS1WoB0dERESqpHTRzR12iDaPqlCBIyLxMHiwh4jE1rHH+vbf/4a33442l01RgSMiIiJVsvPOiXbXrtHlURUqcEQkHm6/3UNEYqtOHRg5MrE/cWJ0uWyKChwRERGpsqFDE+3SW1ZxpAJHROLhvPM8RCT2Jk/2bQjxXbpBBY6IxEODBh4iEnt9+iTayfPjxIkKHBGJh5tv9hCRnHD88b696aZo86iMChwRERFJ2cUXJ9pxnPRPBY6IxMOgQR4ikhP23DPR/uc/o8ujMipwRCQeioo8RCRndOni20mTos2jIipwRCQebrghvqMVRaRCpeNwnnjCn6iKExU4IiIiUi1nnZVo33prdHlURAWOiMTD6ad7iEjOaN060X700ejyqIgKHBGJh7ZtPUQkp4we7dsPP4w2j/LqRJ2AiAgAI0ZEnYGIVEOvXr5dsQIWLoRttok2n1LqwREREZFq69gx0X7uuejyKE8FjojEw8kne4hIzildumHatGjzSKZbVCISD507R52BiFRTy5a+bdEi2jySqQdHROJh+HAPEck5++3n2/Hjo80jmQocERERqZGmTX27eHG0eSRTgSMi8XDiiR4iknNKe3DAn6SKAxU4IhIP3bp5iEjO2XLLRPu3v40uj2QaZCwi8XDZZVFnICI1MGiQT/o3Y4avS2UWbT7qwREREZEau/76RPvjj6PLo5QKHBGJh379PEQkJxUVwS9+4e04LLypAkdE4mHvvT1EJGeVLr55333R5gEagyMicTFkSNQZiEgNXXIJFBd7e/16qBVhN4p6cERERCQtfvObRHvu3OjyABU4IhIXRx7pISI5ywy23trbXbpEm4sKHBGJhwMP9BCRnPaHPyTar78eXR4qcEQkHn7/ew8RyWnJf42jfG5ABY6IiIik1YQJiXYI0eSgAkdE4uGwwzxEJOcdc0yi/dxz0eSgx8RFJB769Ik6AxHJgH/+Ew49NPvfqwJHROLh3HOjzkBE0uiEE2D8eJgzJ5rv1y0qERERSbvSXpsXX4S1a7P//SpwRCQeevf2EJG8kDwO56mnsv/9ukUlIvFwwglRZyAiadSkCey8M7zzDhx7LPz4IzRokL3vVw+OiMTDmWd6iEjeuPrqRPsf/8jud6vAERERkYw45hjYZx9vz5yZ3e9WgSMi8dCzp4eI5JXOnX27YEF2v1djcEQkHgYMiDoDEcmATp18++ST2f1e9eCISDwMGKAiRyQPHXFEor1mTfa+VwWOiMTDmjXZ/ddPRLJip50S7WxO+pf1AsfMmpvZJDNbYWafmdlJlZx3mpnNNrPvzWyhmd1oZrqlJpKvDjrIQ0TyTr16vn355ex9ZxQ9OPcAq4FWQH9glJntWMF5DYHBQAtgT+BAYEi2khSRLPvd7zxEJO/st59vszmjcVYLHDNrBPQDhocQlocQZgCTgVPKnxtCGBVCeCWEsDqE8AXwKLBvNvMVkSw6+WQPEck7u+7q2+nTs/ed2e7B6QSsCyF8mHTsLaCiHpzyfg28l5GsRCR6P/7oISJ5p6jIt9kscLI9pqUxsKzcsWXA5ht7k5mdDuwGVNh/bWaDgEEALVu2pLi4uMaJSnosX75c1yNG4nw9ug0eDMCc22+POJPsifP1KES6HpnTvn19YC8AJk6cSfPmqzP+nRZCyPiX/PxlZr8CXg0hNEw6dgnQM4TQp5L3HAXcD/QOIbyzqe/o3LlzmDt3brpSlhoqLi6mpyZvi41YX4/x431bQGtSxfp6FCBdj8wy8+3558Ndd1XlfJsdQtitut+X7VtUHwJ1zGyHpGNdqeTWk5kdCjwA9KlKcSMiOeyEEwqquBEpNIcf7tts9UFktcAJIawAJgIjzKyRme0L9AXGlj/XzA7ABxb3CyH8O5t5ikgEli3zEJG8dMEFvp02LTtTXkXxmPi5QAPgG+Ax4JwQwntm1s7MlptZu5LzhgNNgaklx5eb2TMR5Csi2dC3r4eI5KU990y0p07N/PdlfeK8EMJS4KgKjn+OD0Iu3e+VzbxEJGIXXhh1BiKSQc2aQfv28Nln/jRVpv9/RjMDi0g8HHNM1BmISIbttpsXOA0bbvrcmtJaVCISD4sXe4hI3ip9SO3BBzP/XerBEZF4OPZY32oeEpG81aiRb5cuzfx3qcARkXi45JKoMxCRDNtrr0R7/XqolcH7SCpwRCQe+lQ416eI5JHOnRPtqVPhiCMy910agyMi8bBokYeI5K1ataBjR28/+2yGvyuzHy8iUkUnnughInlt7719m+kCR7eoRCQeLrss6gxEJAv69YOxY+GTTyCExBpV6aYeHBGJh0MP9RCRvHbkkYn2o49m7ntU4IhIPCxY4CEieS25x+aFFzL3PSpwRCQeTjnFQ0Ty3tln+/bppzP3HRqDIyLxMGxY1BmISJb06QP33QdFRZn7DhU4IhIPvXtHnYGIZMmOO/r2v//N3HfoFpWIxMOnn3qISN7baqtEe/z4zHyHChwRiYeBAz1EJO/Vqwf163s7U9Nf6RaViMTDNddEnYGIZNFf/gL9+5ftzUknFTgiEg/77x91BiKSRT17+varr+CDD+CXv0zv5+sWlYjEw9y5HiJSELbaCpo08XYmFt1UgSMi8XDWWR4iUhDM4IwzEu100y0qEYmH66+POgMRybITT4TbbvN1qdJNBY6IxMM++0SdgYhkWadOiXa6F97ULSoRiYd33/UQkYKxxRaJ9kcfpfezVeCISDycf76HiBSU0iJn1Kj0fq5uUYlIPNx0U9QZiEgEtt8eZs9O/0TmKnBEJB523z3qDEQkAied5AXO5Mnp/VzdohKReJgzx0NECsqhhybaq1en73NV4IhIPAwe7CEiBaVLl0T7scfS97kqcEQkHm6/3UNECs4ee/h2wID0faYKHBGJh27dPESk4AwblmgvWZKez1SBIyLx8MYbHiJScJLXourcOT2fqQJHROLh0ks9RKTgmMFDD3l7yZL0PFGlx8RFJB7uvjvqDEQkQqefDgMHertv35p/ngocEYmHnXaKOgMRidhHH8EOO6Tns3SLSkTiYeZMDxEpWB07wv77p+ezVOCISDxccYWHiBS0F16Afv1q/jm6RSUi8XD//VFnICIxUKsWPPmkDzyuCRU4IhIP6Xo2VEQE3aISkbh46SUPEZE0UA+OiMTDVVf5trg40jREJD+owBGReCid5UtEJA1U4IhIPGy3XdQZiEge0RgcEYmH6dM9RETSQD04IhIP113n2969o81DRPKCChwRiYexY6POQETyiAocEYmHtm2jzkBE8ojG4IhIPDz7rIeISBqoB0dE4mHkSN8eemi0eYhIXlCBIyLx8PjjUWcgInlEBY6IxEPr1lFnICJ5RGNwRCQepkzxEBFJA/XgiEg83HKLb/v0iTYPEckLKnBEJB6efDLqDEQkj6jAEZF4aNEi6gxEJI9oDI6IxMPEiR4iImmgHhwRiYc77/TtMcdEm4eI5AUVOCISD089FXUGIpJHVOCISDw0bRp1BiKSRzQGR0TiYfx4DxGRNFAPjojEw6hRvj3hhGjzEJG8oAJHROJh6tSoMxCRPKICR0TioWHDqDMQkTyiMTgiEg/jxnmIiKSBenBEJB4efNC3J58cbR4ikhdU4IhIPEybFnUGIpJHsn6Lysyam9kkM1thZp+Z2UkbOfciM1tkZsvM7CEz2yybuYpIFtWt6yEikgZRjMG5B1gNtAL6A6PMbMfyJ5nZIcBlwIFAB2A74JrspSkiWTVmjIeISBpktcAxs0ZAP2B4CGF5CGEGMBk4pYLTTwP+EkJ4L4TwP+BaYEDWkhWR7FKBIyJplO0xOJ2AdSGED5OOvQXsX8G5OwJPlTuvlZkVhRCWJJ9oZoOAQSW7q8zs3TTmLDXTAlgcdRLys/hfD7OoM8im+F+PwqLrES+da/LmbBc4jYFl5Y4tAzavwrml7c2BMgVOCGE0MBrAzGaFEHZLS7ZSY7oe8aLrES+6HvGi6xEvZjarJu/P9hic5UCTcseaAD9U4dzSdkXnioiIiPws2wXOh0AdM9sh6VhX4L0Kzn2v5LXk874uf3tKREREpLysFjghhBXARGCEmTUys32BvsDYCk7/K3CGmXUxs2bAMGBMFb5mdLrylbTQ9YgXXY940fWIF12PeKnR9bAQQroSqdoXmjUHHgIOwsfSXBZC+JuZtQPeB7qEED4vOfdiYCjQAJgAnB1CWJXVhEVERCTnZL3AEREREck0LbYpIiIieUcFjoiIiOSdnCtwtJZVvFT1epjZaWY228y+N7OFZnajmWmx1zRL5e9H0nteMLOg65F+Kf57tZ2ZPW1mP5jZYjO7MZu5FoIU/r0yM7vOzL4o+f1RXNGSQlIzZna+mc0ys1VmNmYT56b8+zznChy0llXcVOl6AA2BwfhMoXvi12VItpIsIFW9HgCYWX+yP+FnIanqv1f1gGnAC0BrYBtgXBbzLBRV/ftxHDAQ6AE0B16j4qd9pWa+BK7DHzyqVHV/n+fUIOOStaz+B+xUutyDmY0FvgghXFbu3L8B80MIV5TsHwg8GkJoneW081Yq16OC914M9Aoh9Ml8poUh1ethZk2BN4BT8X/A64YQ1mYx5byW4r9Xg4BTQgg9sp9pYUjxegwFuocQji/Z3xGYHUKon+W0C4KZXQdsE0IYUMnr1fp9nms9OJWtZVVRBb5jyWvJ57Uys6IM5ldoUrke5f2aiid4lOpL9XpcD4wCFmU6sQKVyvXYC5hvZs+U3J4qNrOds5Jl4UjlejwOdDSzTmZWF1/8+dks5CgVq9bv81wrcNK1lpWkRyrX42dmdjqwG3BzhvIqVFW+Hma2G7AvcFcW8ipUqfz92AY4EbgT2Br4B/BUya0rSY9UrsdXwCvAXGAlfsvqooxmJxtTrd/nuVbgaC2reEnlegBgZkcBI4HDQghatTe9qnQ9zKwWcC/we92SyqhU/n6sBGaEEJ4JIazGi/8i4JeZTbGgpHI9rgJ2B9oC9fHxHi+YWcOMZiiVqdbv81wrcLSWVbykcj0ws0OBB4A+IYR3spBfoanq9WiC96CNN7NF+DgcgIVmpjEg6ZPK34+3gdwZEJmbUrkeXYHxIYSFIYS1IYQxQDOgS+bTlApU6/d5ThU4WVrLSqoolethZgcAjwL9Qgj/zm6mhSGF67EMvw3SrSQOLzneHfhXltLNeyn+ezUO2MvMeptZbfyJw8XAB1lLOM+leD3eAI4zs1ZmVsvMTgHqAh9nL+P8Z2Z1zKw+UBuobWb1K5muonq/z0MIORX4I3v/B6wAPgdOKjneDu/Gapd07sXA18D3wMPAZlHnn29R1esBvAisLTlWGs9EnX++RSp/P5Le0wHvPagTdf75Fin+e3UM/gv0e6AY2DHq/PMtUvj3qj7+SPlXJdfjTeDQqPPPtwCuLvm3JzmuTtfv85x6TFxERESkKnLqFpWIiIhIVajAERERkbyjAkdERETyjgocERERyTsqcERERCTvqMARERGRvKMCR0QywswGmFlIitVm9omZXV8yuVeUuc03szFJ+6W5dogsKRFJq4pmDBQRSafjgIX4wnhHA5eXtC+IMikRyW8qcEQk0+aEEEqnuJ9WshbQGWb2+xDC+igTE5H8pVtUIpJtbwINgBalB8xsWzN71My+NbNVZjbHzI4u/0Yz62pmk8xsiZmtNLO5ZnZ50usHm9lUM/vKzH40s3fN7JKS9Z1EpICoB0dEsq0DvuDnEgAza4sv8vkNcBHwLXACMMHMjgohTC45bw98jaaPS85bCOwA7JL02dsBzwN3AT/hq6ZfDbQELsvoTyUisaICR0QyrXbJCsGlY3D6AYNDCOtKXr8aMGD/EMKSkmPPlRQ+I4DJJcduxouivUIIP5YceyH5i0II95W2zcyAV4B6wBAzu0K3xEQKhwocEcm0/5bbvzeEcHfS/qHAVGBZSSFU6jngJjNrgq9Evy9wU1JxswEz2wovmA4Ftqbsv3FbAouq+0OISG5RgSMimXY0fjupJXAxcK6Z/SuE8NeS17cETi2JihQBq/Exgwsr+xIzq4X39myNFzn/BVYCRwF/BCJ9NF1EsksFjohk2rulT1GZ2QvA23jPzIQQwgr8ttMrwJ8ref+XQG1gPdBmI9+zPT7m5pQQwrjSg2bWp+Y/gojkGj1FJSJZE0JYBVyK99qcW3L4WXyg8HshhFkVxKqS21IzgJPNrEElH9+wZLum9ICZ1QX6Z+SHEZFYUw+OiGRVCGGymb2BD/y9G7gS+Dfwcsn+fKAZsBOwXQhhYMlbhwAvAa+Z2S347artgG4hhAuAD4DPgD+Z2Tq80Lkoez+ZiMSJenBEJArD8F6cs0MIn+O3lt4CrgemAaOA/Ul6SiqE8AY+0HgB/hj4VLw3aGHJ66vx8TaLgL8C9wAvAyOz8hOJSKxYCCHqHERERETSSj04IiIikndU4IiIiEjeUYEjIiIieUcFjoiIiOQdFTgiIiKSd1TgiIiISN5RgSMiIiJ5RwWOiIiI5J3/B6pyTO5oXp0WAAAAAElFTkSuQmCC\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": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold_90_precision = thresholds[np.argmax(precisions >= 0.90)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3370.0194991439557"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold_90_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred_90 = (y_scores >= threshold_90_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9000345901072293"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_train_5, y_train_pred_90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4799852425751706"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred_90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ROC curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": 45,
   "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": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9604938554008616"
      ]
     },
     "execution_count": 46,
     "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": 47,
   "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": 48,
   "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": "markdown",
   "metadata": {},
   "source": [
    "### Note: Could pay more attention to how the below graph is constructed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9983436731328145"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_train_5, y_scores_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9905083315756169"
      ]
     },
     "execution_count": 51,
     "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": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8662608374838591"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred_forest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiclass classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5], dtype=uint8)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svm_clf = SVC(gamma=\"auto\", random_state=42)\n",
    "svm_clf.fit(X_train[:1000], y_train[:1000]) # y_train, not y_train_5\n",
    "svm_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.81585438,  7.09167958,  3.82972099,  0.79365551,  5.8885703 ,\n",
       "         9.29718395,  1.79862509,  8.10392157, -0.228207  ,  4.83753243]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_digit_scores = svm_clf.decision_function([some_digit])\n",
    "some_digit_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(some_digit_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.classes_[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5], dtype=uint8)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "ovr_clf = OneVsRestClassifier(SVC(gamma=\"auto\", random_state=42))\n",
    "ovr_clf.fit(X_train[:1000], y_train[:1000])\n",
    "ovr_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ovr_clf.estimators_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3], dtype=uint8)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.fit(X_train, y_train)\n",
    "sgd_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-31893.03095419, -34419.69069632,  -9530.63950739,\n",
       "          1823.73154031, -22320.14822878,  -1385.80478895,\n",
       "        -26188.91070951, -16147.51323997,  -4604.35491274,\n",
       "        -12050.767298  ]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.decision_function([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.87082583, 0.87089354, 0.88628294])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(sgd_clf, X_train, y_train, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mislav/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/stochastic_gradient.py:561: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.89957009, 0.89344467, 0.89963495])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train.astype(np.float64))\n",
    "cross_val_score(sgd_clf, X_train_scaled, y_train, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mislav/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/linear_model/stochastic_gradient.py:561: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[5576,    0,   21,    6,    9,   43,   37,    6,  224,    1],\n",
       "       [   0, 6398,   38,   23,    4,   44,    4,    8,  213,   10],\n",
       "       [  26,   27, 5242,   90,   71,   26,   62,   36,  371,    7],\n",
       "       [  24,   17,  117, 5220,    2,  208,   28,   40,  405,   70],\n",
       "       [  12,   14,   48,   10, 5192,   10,   36,   26,  330,  164],\n",
       "       [  28,   15,   33,  166,   55, 4437,   76,   14,  538,   59],\n",
       "       [  30,   14,   41,    2,   43,   95, 5560,    4,  128,    1],\n",
       "       [  21,    9,   52,   27,   51,   12,    3, 5693,  188,  209],\n",
       "       [  17,   63,   46,   90,    3,  125,   25,   10, 5429,   43],\n",
       "       [  23,   18,   31,   66,  116,   32,    1,  179,  377, 5106]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred = cross_val_predict(sgd_clf, X_train_scaled, y_train, cv=3)\n",
    "conf_mx = confusion_matrix(y_train, y_train_pred)\n",
    "conf_mx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure confusion_matrix_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(conf_mx, cmap=plt.cm.gray)\n",
    "save_fig(\"confusion_matrix_plot\", tight_layout=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure confusion_matrix_errors_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.fill_diagonal(norm_conf_mx, 0)\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": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure error_analysis_digits_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cl_a, cl_b = 3, 5\n",
    "X_aa = X_train[(y_train == cl_a) & (y_train_pred == cl_a)]\n",
    "X_ab = X_train[(y_train == cl_a) & (y_train_pred == cl_b)]\n",
    "X_ba = X_train[(y_train == cl_b) & (y_train_pred == cl_a)]\n",
    "X_bb = X_train[(y_train == cl_b) & (y_train_pred == cl_b)]\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.subplot(221); plot_digits(X_aa[:25], images_per_row=5)\n",
    "plt.subplot(222); plot_digits(X_ab[:25], images_per_row=5)\n",
    "plt.subplot(223); plot_digits(X_ba[:25], images_per_row=5)\n",
    "plt.subplot(224); plot_digits(X_bb[:25], images_per_row=5)\n",
    "save_fig(\"error_analysis_digits_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multilabel classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "y_train_large = (y_train >= 7)\n",
    "y_train_odd = (y_train % 2 == 1)\n",
    "y_multilabel = np.c_[y_train_large, y_train_odd]\n",
    "\n",
    "knn_clf = KNeighborsClassifier()\n",
    "knn_clf.fit(X_train, y_multilabel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False,  True]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: the following cell may take a very long time (possibly hours depending on your hardware)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.976410265560605"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_knn_pred = cross_val_predict(knn_clf, X_train, y_multilabel, cv=3)\n",
    "f1_score(y_multilabel, y_train_knn_pred, average=\"macro\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multioutput classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure noisy_digit_example_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "some_index = 0\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": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure cleaned_digit_example_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_clf.fit(X_train_mod, y_train_mod)\n",
    "clean_digit = knn_clf.predict([X_test_mod[some_index]])\n",
    "plot_digit(clean_digit)\n",
    "save_fig(\"cleaned_digit_example_plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extra material"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dummy (ie. random) classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.dummy import DummyClassifier\n",
    "dmy_clf = DummyClassifier(strategy=\"prior\")\n",
    "y_probas_dmy = cross_val_predict(dmy_clf, X_train, y_train_5, cv=3, method=\"predict_proba\")\n",
    "y_scores_dmy = y_probas_dmy[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fprr, tprr, thresholdsr = roc_curve(y_train_5, y_scores_dmy)\n",
    "plot_roc_curve(fprr, tprr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=4, p=2,\n",
       "                     weights='distance')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn_clf = KNeighborsClassifier(weights='distance', n_neighbors=4)\n",
    "knn_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_knn_pred = knn_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9714"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, y_knn_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOcAAADnCAYAAADl9EEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAGnElEQVR4nO3dS4jO/R/GcWOc5ZgNkgWriRyiWShhwcppp0TUyMJhM5PSbCys2DFKzEIWlGQWsxLFQkqTaKJIClmwEQsUaf7b/1Puz3jGjLnm8Xot5+rHL3n3Ld/uW9PAwMA4IM/40X4B4OfECaHECaHECaHECaEmVGN3d7d/yoUR1tbW1vSznzs5IZQ4IZQ4IZQ4IZQ4IZQ4IZQ4IVR5z5msra1ttF8BRpSTE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0KJE0JNGO0XYOz48eNHuX/69GnEfu+urq5y//LlS7k/f/683M+dO1fuHR0dDberV6+Wz06ZMqXcv379+tOfOzkhlDghlDghlDghlDghlDghlKuUMebNmzfl/u3bt3K/f/9+ud+7d6/h9vHjx/LZ69evl/toWrRoUbkfOXKk3Ht6ehpuM2bMKJ9dsWJFuTfi5IRQ4oRQ4oRQ4oRQ4oRQ4oRQ4oRQ7jnDPHr0qNw3bdpU7iP5sa1kzc3N5X7y5Mlynz59ernv3r274bZgwYLy2Tlz5pR7I05OCCVOCCVOCCVOCCVOCCVOCCVOCOWeM8zixYvLfd68eeWefM/Z2tpa7tV94J07d8pnJ02aVO579uwp90ROTgglTgglTgglTgglTgglTgglTgjlnjPM3Llzy/306dPl3tvbW+6rVq0q96NHj5Z7ZeXKleV++/btcq8+U/nkyZPy2TNnzpT7WOTkhFDihFDihFDihFDihFDihFDihFDuOceYHTt2lPtg32s72P8l2d/f33Dr7u4un+3o6Cj3wb4btrJs2bJyv3DhwpB/7VROTgglTgglTgglTgglTgglTgjlKuU/ZubMmb/1/KxZs4b87GBXLbt27Sr38eOdFf/PnwaEEieEEieEEieEEieEEieEEieEcs/JP5w4caLh9vDhw/LZu3fvlvtgX425efPmcv/bODkhlDghlDghlDghlDghlDghlDghlHtO/qH6+sqLFy+Wz65evbrcDxw4UO4bN25suK1Zs6Z89tChQ+Xe1NRU7omcnBBKnBBKnBBKnBBKnBBKnBBKnBDKPSe/bMmSJeV+6dKlct+/f3+5X758eUjbuHHjxn3+/Lnc9+7dW+7z588v99Hg5IRQ4oRQ4oRQ4oRQ4oRQ4oRQ4oRQ7jkZNjt37iz3pUuXlnt7e3vDbbDvvD1+/Hi5v379utw7OzvLfeHCheU+EpycEEqcEEqcEEqcEEqcEEqcEMpVCn/M8uXLy/3atWsNt97e3vLZffv2lfv58+fL/cWLF+V+69atch8JTk4IJU4IJU4IJU4IJU4IJU4IJU4I1TQwMNBw7O7ubjyOsra2ttF+BYJMnjy53L9//17uEydOLPebN2823DZs2FA++wt++v8TOjkhlDghlDghlDghlDghlDghlDghlM9z8sf09/eX+/Xr1xtufX195bOD3WMOpqWlpdzXr1//W7/+UDg5IZQ4IZQ4IZQ4IZQ4IZQ4IZQ4IZR7Tn7Z8+fPy/3s2bPlfuPGjXJ/9+7dv36nXzVhQv1Xff78+eU+fvyfP8ecnBBKnBBKnBBKnBBKnBBKnBDKVcpfZrDriitXrjTcurq6ymdfvXo1lFcaFmvXri33zs7Oct+2bdtwvs6wcHJCKHFCKHFCKHFCKHFCKHFCKHFCKPecY8z79+/L/enTp+V++PDhcn/27Nm/fqfh0tra2nA7duxY+ez27dvLfTQ+8vW7xt4bw19CnBBKnBBKnBBKnBBKnBBKnBDKPeco+PDhQ8Pt4MGD5bOPHz8u95cvXw7pnYbDunXryr29vb3ct2zZ0nCbOnXqkN5pLHNyQihxQihxQihxQihxQihxQihxQij3nEPw4MGDcj916lS59/X1Ndzevn07pHcaLtOmTWu4HT16tHx2sO+GnT59+pDe6W/l5IRQ4oRQ4oRQ4oRQ4oRQ4oRQ4oRQ7jmHoKen57f239HS0lLuW7duLffm5uZy7+joaLjNnj27fJbh5eSEUOKEUOKEUOKEUOKEUOKEUE0DAwMNx+7u7sbjKGtraxvtV4Dh0vSzHzo5IZQ4IZQ4IZQ4IZQ4IZQ4IZQ4IZQ4IZQ4IZQ4IZQ4IZQ4IZQ4IZQ4IZQ4IVT5eU5g9Dg5IZQ4IZQ4IZQ4IZQ4IZQ4IdT/AHt48gfUZQlsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.ndimage.interpolation import shift\n",
    "def shift_digit(digit_array, dx, dy, new=0):\n",
    "    return shift(digit_array.reshape(28, 28), [dy, dx], cval=new).reshape(784)\n",
    "\n",
    "plot_digit(shift_digit(some_digit, 5, 1, new=100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((300000, 784), (300000,))"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_expanded = [X_train]\n",
    "y_train_expanded = [y_train]\n",
    "for dx, dy in ((1, 0), (-1, 0), (0, 1), (0, -1)):\n",
    "    shifted_images = np.apply_along_axis(shift_digit, axis=1, arr=X_train, dx=dx, dy=dy)\n",
    "    X_train_expanded.append(shifted_images)\n",
    "    y_train_expanded.append(y_train)\n",
    "\n",
    "X_train_expanded = np.concatenate(X_train_expanded)\n",
    "y_train_expanded = np.concatenate(y_train_expanded)\n",
    "X_train_expanded.shape, y_train_expanded.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=4, p=2,\n",
       "                     weights='distance')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf.fit(X_train_expanded, y_train_expanded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_knn_expanded_pred = knn_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9763"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_knn_expanded_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.24579675, 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.75420325]])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ambiguous_digit = X_test[2589]\n",
    "knn_clf.predict_proba([ambiguous_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_digit(ambiguous_digit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# My exercise solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. An MNIST Classifier With Over 97% Accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let me prepare the dataset first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "mnist = fetch_openml('mnist_784', version=1)\n",
    "mnist.keys()\n",
    "\n",
    "X, y = mnist[\"data\"], mnist[\"target\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mislav/anaconda3/envs/tf2/lib/python3.7/site-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "    {'weights': ['uniform', 'distance']},\n",
    "    {'n_neighbors': [2, 5, 10]}\n",
    "]\n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "knn_clf = GridSearchCV(knn, param_grid)\n",
    "knn_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_knn_pred = knn_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, y_knn_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import copy\n",
    "from scipy.ndimage.interpolation import shift\n",
    "import numpy as np\n",
    "\n",
    "def data_augmentation(X_train, y_train):\n",
    "    X_train_augmented = copy.deepcopy(X_train) # just to be safe\n",
    "    y_train_augmented = copy.deepcopy(y_train)\n",
    "    for dx, dy in [(1, 0), (-1, 0), (0, 1), (0, -1)]:\n",
    "        X_augmented_images = []\n",
    "        y_augmented_images = []\n",
    "        for (i, image) in zip(range(0, len(y_train)), X_train):\n",
    "            augmented_image = copy.deepcopy(image)\n",
    "            augmented_image = shift(augmented_image.reshape(28, 28), (dx, dy), cval=0).reshape(784)\n",
    "            X_augmented_images = np.append(X_augmented_images, augmented_image)\n",
    "            y_augmented_images.append(y_train[i])\n",
    "        X_train_augmented = np.append(X_train_augmented, X_augmented_images)\n",
    "        y_train_augmented = np.append(y_train_augmented, y_augmented_images)\n",
    "    return X_train_augmented, y_train_augmented"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_augmented, y_train_augmented = data_augmentation(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train_augmented\n",
      "[ 0.00000000e+00  0.00000000e+00  0.00000000e+00 ...  9.06779478e-26\n",
      " -7.18008749e-27  0.00000000e+00]\n",
      "X_train_augmented dims\n",
      "(235200000,)\n",
      "y_train_augmented\n",
      "['5' '0' '4' ... '5' '6' '8']\n",
      "y_train_augmented dims\n",
      "(300000,)\n",
      "X_train_augmented reshaped dims\n",
      "[[ 0.00000000e+00  0.00000000e+00  0.00000000e+00 ...  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00 ...  0.00000000e+00\n",
      "   1.74000000e+02  2.53000000e+02]\n",
      " [ 2.53000000e+02  2.53000000e+02  1.06000000e+02 ...  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00]\n",
      " ...\n",
      " [-5.16956540e-22 -3.48518458e-21 -1.88971750e-21 ...  2.94844685e-14\n",
      "   2.58993854e-14  2.82928883e-14]\n",
      " [ 1.10000000e+01  5.10000000e+01  5.10000000e+01 ...  2.11000000e+02\n",
      "   2.28000000e+02  2.53000000e+02]\n",
      " [ 2.47000000e+02  5.00000000e+01 -4.41345923e-16 ...  9.06779478e-26\n",
      "  -7.18008749e-27  0.00000000e+00]]\n"
     ]
    }
   ],
   "source": [
    "# test the dimensions\n",
    "\n",
    "print(\"X_train_augmented\")\n",
    "print(X_train_augmented)\n",
    "print(\"X_train_augmented dims\")\n",
    "print(X_train_augmented.shape)\n",
    "print(\"y_train_augmented\")\n",
    "print(y_train_augmented)\n",
    "print(\"y_train_augmented dims\")\n",
    "print(y_train_augmented.shape)\n",
    "print(\"X_train_augmented reshaped dims\")\n",
    "print(X_train_augmented.reshape(300000, 784))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=4, p=2,\n",
       "                     weights='distance')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=4, weights='distance')\n",
    "\n",
    "knn.fit(X_train_augmented.reshape(300000, 784), y_train_augmented)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_knn_pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9763"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, y_knn_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Tackle the Titanic dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the folder \"titanic\" in this repo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Build a spam classifier (a more challenging exercise)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Download examples of spam and ham from Apache SpamAssassin’s public\n",
    "datasets._\n",
    "\n",
    " - _Unzip the datasets and familiarize yourself with the data format (https://spamassassin.apache.org/old/publiccorpus/)._\n",
    " - _Split the datasets into a training set and a test set._\n",
    " - _Write a data preparation pipeline to convert each email into a feature vector._\n",
    "\n",
    "_Your preparation pipeline should transform an email into a (sparse) vector that\n",
    "indicates the presence or absence of each possible word. For example, if all\n",
    "emails only ever contain four words, “Hello,” “how,” “are,” “you,” then the email\n",
    "“Hello you Hello Hello you” would be converted into a vector [1, 0, 0, 1]\n",
    "(meaning [“Hello” is present, “how” is absent, “are” is absent, “you” is\n",
    "present]), or [3, 0, 0, 2] if you prefer to count the number of occurrences of\n",
    "each word._\n",
    "\n",
    "_You may want to add hyperparameters to your preparation pipeline to control\n",
    "whether or not to strip off email headers, convert each email to lowercase,\n",
    "remove punctuation, replace all URLs with “URL,” replace all numbers with\n",
    "“NUMBER,” or even perform stemming (i.e., trim off word endings; there are\n",
    "Python libraries available to do this)._\n",
    "\n",
    "_Finally, try out several classifiers and see if you can build a great spam classifier, with both high recall and high precision._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note: For this exercise, I'm going to look at the official solution to see what they did. I'm leaving a note here so that I know I looked at the official solution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. An MNIST Classifier With Over 97% Accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: the next cell may take hours to run, depending on your hardware."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n",
      "[CV] n_neighbors=3, weights=uniform ..................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ...... n_neighbors=3, weights=uniform, score=0.972, total=11.5min\n",
      "[CV] n_neighbors=3, weights=uniform ..................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 11.5min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ..... n_neighbors=3, weights=uniform, score=0.971, total=720.8min\n",
      "[CV] n_neighbors=3, weights=uniform ..................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed: 732.3min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ...... n_neighbors=3, weights=uniform, score=0.969, total=11.5min\n",
      "[CV] n_neighbors=3, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=3, weights=uniform, score=0.969, total=11.3min\n",
      "[CV] n_neighbors=3, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=3, weights=uniform, score=0.970, total=11.3min\n",
      "[CV] n_neighbors=3, weights=distance .................................\n",
      "[CV] ..... n_neighbors=3, weights=distance, score=0.972, total=11.4min\n",
      "[CV] n_neighbors=3, weights=distance .................................\n",
      "[CV] ..... n_neighbors=3, weights=distance, score=0.972, total=11.4min\n",
      "[CV] n_neighbors=3, weights=distance .................................\n",
      "[CV] ..... n_neighbors=3, weights=distance, score=0.970, total=11.3min\n",
      "[CV] n_neighbors=3, weights=distance .................................\n",
      "[CV] ..... n_neighbors=3, weights=distance, score=0.970, total=11.3min\n",
      "[CV] n_neighbors=3, weights=distance .................................\n",
      "[CV] ..... n_neighbors=3, weights=distance, score=0.971, total=11.4min\n",
      "[CV] n_neighbors=4, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=4, weights=uniform, score=0.969, total=11.4min\n",
      "[CV] n_neighbors=4, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=4, weights=uniform, score=0.968, total=11.4min\n",
      "[CV] n_neighbors=4, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=4, weights=uniform, score=0.968, total=11.4min\n",
      "[CV] n_neighbors=4, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=4, weights=uniform, score=0.967, total=11.4min\n",
      "[CV] n_neighbors=4, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=4, weights=uniform, score=0.970, total=11.4min\n",
      "[CV] n_neighbors=4, weights=distance .................................\n",
      "[CV] ..... n_neighbors=4, weights=distance, score=0.973, total=11.5min\n",
      "[CV] n_neighbors=4, weights=distance .................................\n",
      "[CV] ..... n_neighbors=4, weights=distance, score=0.972, total=11.4min\n",
      "[CV] n_neighbors=4, weights=distance .................................\n",
      "[CV] ..... n_neighbors=4, weights=distance, score=0.970, total=11.4min\n",
      "[CV] n_neighbors=4, weights=distance .................................\n",
      "[CV] ..... n_neighbors=4, weights=distance, score=0.971, total=11.4min\n",
      "[CV] n_neighbors=4, weights=distance .................................\n",
      "[CV] ..... n_neighbors=4, weights=distance, score=0.972, total=11.4min\n",
      "[CV] n_neighbors=5, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=5, weights=uniform, score=0.970, total=11.4min\n",
      "[CV] n_neighbors=5, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=5, weights=uniform, score=0.970, total=11.6min\n",
      "[CV] n_neighbors=5, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=5, weights=uniform, score=0.969, total=11.1min\n",
      "[CV] n_neighbors=5, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=5, weights=uniform, score=0.968, total=11.3min\n",
      "[CV] n_neighbors=5, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=5, weights=uniform, score=0.969, total=11.3min\n",
      "[CV] n_neighbors=5, weights=distance .................................\n",
      "[CV] ..... n_neighbors=5, weights=distance, score=0.970, total=11.3min\n",
      "[CV] n_neighbors=5, weights=distance .................................\n",
      "[CV] ..... n_neighbors=5, weights=distance, score=0.971, total=11.3min\n",
      "[CV] n_neighbors=5, weights=distance .................................\n",
      "[CV] ..... n_neighbors=5, weights=distance, score=0.970, total=11.3min\n",
      "[CV] n_neighbors=5, weights=distance .................................\n",
      "[CV] ..... n_neighbors=5, weights=distance, score=0.969, total=11.3min\n",
      "[CV] n_neighbors=5, weights=distance .................................\n",
      "[CV] ..... n_neighbors=5, weights=distance, score=0.971, total=11.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed: 1050.5min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid=[{'n_neighbors': [3, 4, 5],\n",
       "                          'weights': ['uniform', 'distance']}],\n",
       "             verbose=3)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [{'weights': [\"uniform\", \"distance\"], 'n_neighbors': [3, 4, 5]}]\n",
    "\n",
    "knn_clf = KNeighborsClassifier()\n",
    "grid_search = GridSearchCV(knn_clf, param_grid, cv=5, verbose=3)\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 4, 'weights': 'distance'}"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9716166666666666"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9714"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "y_pred = grid_search.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.ndimage.interpolation import shift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = X_train[1000]\n",
    "shifted_image_down = shift_image(image, 0, 5)\n",
    "shifted_image_left = shift_image(image, -5, 0)\n",
    "\n",
    "plt.figure(figsize=(12,3))\n",
    "plt.subplot(131)\n",
    "plt.title(\"Original\", fontsize=14)\n",
    "plt.imshow(image.reshape(28, 28), interpolation=\"nearest\", cmap=\"Greys\")\n",
    "plt.subplot(132)\n",
    "plt.title(\"Shifted down\", fontsize=14)\n",
    "plt.imshow(shifted_image_down.reshape(28, 28), interpolation=\"nearest\", cmap=\"Greys\")\n",
    "plt.subplot(133)\n",
    "plt.title(\"Shifted left\", fontsize=14)\n",
    "plt.imshow(shifted_image_left.reshape(28, 28), interpolation=\"nearest\", cmap=\"Greys\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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": 96,
   "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": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_clf = KNeighborsClassifier(**grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=4, weights='distance')"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf.fit(X_train_augmented, y_train_augmented)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9763"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = knn_clf.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By simply augmenting the data, we got a 0.5% accuracy boost. :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Tackle the Titanic dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal is to predict whether or not a passenger survived based on attributes such as their age, sex, passenger class, where they embarked and so on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, login to [Kaggle](https://www.kaggle.com/) and go to the [Titanic challenge](https://www.kaggle.com/c/titanic) to download `train.csv` and `test.csv`. Save them to the `datasets/titanic` directory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's load the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "TITANIC_PATH = os.path.join(\"datasets\", \"titanic\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def load_titanic_data(filename, titanic_path=TITANIC_PATH):\n",
    "    csv_path = os.path.join(titanic_path, filename)\n",
    "    return pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = load_titanic_data(\"train.csv\")\n",
    "test_data = load_titanic_data(\"test.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is already split into a training set and a test set. However, the test data does *not* contain the labels: your goal is to train the best model you can using the training data, then make your predictions on the test data and upload them to Kaggle to see your final score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a peek at the top few rows of the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The attributes have the following meaning:\n",
    "* **Survived**: that's the target, 0 means the passenger did not survive, while 1 means he/she survived.\n",
    "* **Pclass**: passenger class.\n",
    "* **Name**, **Sex**, **Age**: self-explanatory\n",
    "* **SibSp**: how many siblings & spouses of the passenger aboard the Titanic.\n",
    "* **Parch**: how many children & parents of the passenger aboard the Titanic.\n",
    "* **Ticket**: ticket id\n",
    "* **Fare**: price paid (in pounds)\n",
    "* **Cabin**: passenger's cabin number\n",
    "* **Embarked**: where the passenger embarked the Titanic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get more info to see how much data is missing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, the **Age**, **Cabin** and **Embarked** attributes are sometimes null (less than 891 non-null), especially the **Cabin** (77% are null). We will ignore the **Cabin** for now and focus on the rest. The **Age** attribute has about 19% null values, so we will need to decide what to do with them. Replacing null values with the median age seems reasonable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **Name** and **Ticket** attributes may have some value, but they will be a bit tricky to convert into useful numbers that a model can consume. So for now, we will ignore them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the numerical attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Yikes, only 38% **Survived**. :(  That's close enough to 40%, so accuracy will be a reasonable metric to evaluate our model.\n",
    "* The mean **Fare** was £32.20, which does not seem so expensive (but it was probably a lot of money back then).\n",
    "* The mean **Age** was less than 30 years old."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check that the target is indeed 0 or 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Survived\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's take a quick look at all the categorical attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    491\n",
       "1    216\n",
       "2    184\n",
       "Name: Pclass, dtype: int64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Pclass\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Sex\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Embarked\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Embarked attribute tells us where the passenger embarked: C=Cherbourg, Q=Queenstown, S=Southampton."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: the code below uses a mix of `Pipeline`, `FeatureUnion` and a custom `DataFrameSelector` to preprocess some columns differently.  Since Scikit-Learn 0.20, it is preferable to use a `ColumnTransformer`, like in the previous chapter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's build our preprocessing pipelines. We will reuse the `DataframeSelector` we built in the previous chapter to select specific attributes from the `DataFrame`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, attribute_names):\n",
    "        self.attribute_names = attribute_names\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[self.attribute_names]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's build the pipeline for the numerical attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "        (\"select_numeric\", DataFrameSelector([\"Age\", \"SibSp\", \"Parch\", \"Fare\"])),\n",
    "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22.    ,  1.    ,  0.    ,  7.25  ],\n",
       "       [38.    ,  1.    ,  0.    , 71.2833],\n",
       "       [26.    ,  0.    ,  0.    ,  7.925 ],\n",
       "       ...,\n",
       "       [28.    ,  1.    ,  2.    , 23.45  ],\n",
       "       [26.    ,  0.    ,  0.    , 30.    ],\n",
       "       [32.    ,  0.    ,  0.    ,  7.75  ]])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_pipeline.fit_transform(train_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need an imputer for the string categorical columns (the regular `SimpleImputer` does not work on those):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inspired from stackoverflow.com/questions/25239958\n",
    "class MostFrequentImputer(BaseEstimator, TransformerMixin):\n",
    "    def fit(self, X, y=None):\n",
    "        self.most_frequent_ = pd.Series([X[c].value_counts().index[0] for c in X],\n",
    "                                        index=X.columns)\n",
    "        return self\n",
    "    def transform(self, X, y=None):\n",
    "        return X.fillna(self.most_frequent_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can build the pipeline for the categorical attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_pipeline = Pipeline([\n",
    "        (\"select_cat\", DataFrameSelector([\"Pclass\", \"Sex\", \"Embarked\"])),\n",
    "        (\"imputer\", MostFrequentImputer()),\n",
    "        (\"cat_encoder\", OneHotEncoder(sparse=False)),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 1., ..., 0., 0., 1.],\n",
       "       [1., 0., 0., ..., 1., 0., 0.],\n",
       "       [0., 0., 1., ..., 0., 0., 1.],\n",
       "       ...,\n",
       "       [0., 0., 1., ..., 0., 0., 1.],\n",
       "       [1., 0., 0., ..., 1., 0., 0.],\n",
       "       [0., 0., 1., ..., 0., 1., 0.]])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_pipeline.fit_transform(train_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's join the numerical and categorical pipelines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import FeatureUnion\n",
    "preprocess_pipeline = FeatureUnion(transformer_list=[\n",
    "        (\"num_pipeline\", num_pipeline),\n",
    "        (\"cat_pipeline\", cat_pipeline),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cool! Now we have a nice preprocessing pipeline that takes the raw data and outputs numerical input features that we can feed to any Machine Learning model we want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22.,  1.,  0., ...,  0.,  0.,  1.],\n",
       "       [38.,  1.,  0., ...,  1.,  0.,  0.],\n",
       "       [26.,  0.,  0., ...,  0.,  0.,  1.],\n",
       "       ...,\n",
       "       [28.,  1.,  2., ...,  0.,  0.,  1.],\n",
       "       [26.,  0.,  0., ...,  1.,  0.,  0.],\n",
       "       [32.,  0.,  0., ...,  0.,  1.,  0.]])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = preprocess_pipeline.fit_transform(train_data)\n",
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's not forget to get the labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = train_data[\"Survived\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now ready to train a classifier. Let's start with an `SVC`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(gamma='auto')"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svm_clf = SVC(gamma=\"auto\")\n",
    "svm_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, our model is trained, let's use it to make predictions on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = preprocess_pipeline.transform(test_data)\n",
    "y_pred = svm_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now we could just build a CSV file with these predictions (respecting the format excepted by Kaggle), then upload it and hope for the best. But wait! We can do better than hope. Why don't we use cross-validation to have an idea of how good our model is?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7329588014981274"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "svm_scores = cross_val_score(svm_clf, X_train, y_train, cv=10)\n",
    "svm_scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, over 73% accuracy, clearly better than random chance, but it's not a great score. Looking at the [leaderboard](https://www.kaggle.com/c/titanic/leaderboard) for the Titanic competition on Kaggle, you can see that you need to reach above 80% accuracy to be within the top 10% Kagglers. Some reached 100%, but since you can easily find the [list of victims](https://www.encyclopedia-titanica.org/titanic-victims/) of the Titanic, it seems likely that there was little Machine Learning involved in their performance! ;-) So let's try to build a model that reaches 80% accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try a `RandomForestClassifier`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8126466916354558"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "forest_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "forest_scores = cross_val_score(forest_clf, X_train, y_train, cv=10)\n",
    "forest_scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's much better!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of just looking at the mean accuracy across the 10 cross-validation folds, let's plot all 10 scores for each model, along with a box plot highlighting the lower and upper quartiles, and \"whiskers\" showing the extent of the scores (thanks to Nevin Yilmaz for suggesting this visualization). Note that the `boxplot()` function detects outliers (called \"fliers\") and does not include them within the whiskers. Specifically, if the lower quartile is $Q_1$ and the upper quartile is $Q_3$, then the interquartile range $IQR = Q_3 - Q_1$ (this is the box's height), and any score lower than $Q_1 - 1.5 \\times IQR$ is a flier, and so is any score greater than $Q3 + 1.5 \\times IQR$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))\n",
    "plt.plot([1]*10, svm_scores, \".\")\n",
    "plt.plot([2]*10, forest_scores, \".\")\n",
    "plt.boxplot([svm_scores, forest_scores], labels=(\"SVM\",\"Random Forest\"))\n",
    "plt.ylabel(\"Accuracy\", fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To improve this result further, you could:\n",
    "* Compare many more models and tune hyperparameters using cross validation and grid search,\n",
    "* Do more feature engineering, for example:\n",
    "  * replace **SibSp** and **Parch** with their sum,\n",
    "  * try to identify parts of names that correlate well with the **Survived** attribute (e.g. if the name contains \"Countess\", then survival seems more likely),\n",
    "* try to convert numerical attributes to categorical attributes: for example, different age groups had very different survival rates (see below), so it may help to create an age bucket category and use it instead of the age. Similarly, it may be useful to have a special category for people traveling alone since only 30% of them survived (see below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AgeBucket</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.576923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>0.362745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30.0</th>\n",
       "      <td>0.423256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45.0</th>\n",
       "      <td>0.404494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60.0</th>\n",
       "      <td>0.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75.0</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Survived\n",
       "AgeBucket          \n",
       "0.0        0.576923\n",
       "15.0       0.362745\n",
       "30.0       0.423256\n",
       "45.0       0.404494\n",
       "60.0       0.240000\n",
       "75.0       1.000000"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"AgeBucket\"] = train_data[\"Age\"] // 15 * 15\n",
    "train_data[[\"AgeBucket\", \"Survived\"]].groupby(['AgeBucket']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RelativesOnboard</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.303538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.552795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.578431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.724138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Survived\n",
       "RelativesOnboard          \n",
       "0                 0.303538\n",
       "1                 0.552795\n",
       "2                 0.578431\n",
       "3                 0.724138\n",
       "4                 0.200000\n",
       "5                 0.136364\n",
       "6                 0.333333\n",
       "7                 0.000000\n",
       "10                0.000000"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"RelativesOnboard\"] = train_data[\"SibSp\"] + train_data[\"Parch\"]\n",
    "train_data[[\"RelativesOnboard\", \"Survived\"]].groupby(['RelativesOnboard']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Spam classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's fetch the data:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note: Pay attention to fetching the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tarfile\n",
    "import urllib\n",
    "\n",
    "DOWNLOAD_ROOT = \"http://spamassassin.apache.org/old/publiccorpus/\"\n",
    "HAM_URL = DOWNLOAD_ROOT + \"20030228_easy_ham.tar.bz2\"\n",
    "SPAM_URL = DOWNLOAD_ROOT + \"20030228_spam.tar.bz2\"\n",
    "SPAM_PATH = os.path.join(\"datasets\", \"spam\")\n",
    "\n",
    "def fetch_spam_data(spam_url=SPAM_URL, spam_path=SPAM_PATH):\n",
    "    if not os.path.isdir(spam_path):\n",
    "        os.makedirs(spam_path)\n",
    "    for filename, url in ((\"ham.tar.bz2\", HAM_URL), (\"spam.tar.bz2\", SPAM_URL)):\n",
    "        path = os.path.join(spam_path, filename)\n",
    "        if not os.path.isfile(path):\n",
    "            urllib.request.urlretrieve(url, path)\n",
    "        tar_bz2_file = tarfile.open(path)\n",
    "        tar_bz2_file.extractall(path=SPAM_PATH)\n",
    "        tar_bz2_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fetch_spam_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's load all the emails:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "HAM_DIR = os.path.join(SPAM_PATH, \"easy_ham\")\n",
    "SPAM_DIR = os.path.join(SPAM_PATH, \"spam\")\n",
    "ham_filenames = [name for name in sorted(os.listdir(HAM_DIR)) if len(name) > 20]\n",
    "spam_filenames = [name for name in sorted(os.listdir(SPAM_DIR)) if len(name) > 20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2500"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ham_filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(spam_filenames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use Python's `email` module to parse these emails (this handles headers, encoding, and so on):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note: Pay attention to data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import email\n",
    "import email.policy\n",
    "\n",
    "def load_email(is_spam, filename, spam_path=SPAM_PATH):\n",
    "    directory = \"spam\" if is_spam else \"easy_ham\"\n",
    "    with open(os.path.join(spam_path, directory, filename), \"rb\") as f:\n",
    "        return email.parser.BytesParser(policy=email.policy.default).parse(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ham_emails = [load_email(is_spam=False, filename=name) for name in ham_filenames]\n",
    "spam_emails = [load_email(is_spam=True, filename=name) for name in spam_filenames]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at one example of ham and one example of spam, to get a feel of what the data looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Martin A posted:\n",
      "Tassos Papadopoulos, the Greek sculptor behind the plan, judged that the\n",
      " limestone of Mount Kerdylio, 70 miles east of Salonika and not far from the\n",
      " Mount Athos monastic community, was ideal for the patriotic sculpture. \n",
      " \n",
      " As well as Alexander's granite features, 240 ft high and 170 ft wide, a\n",
      " museum, a restored amphitheatre and car park for admiring crowds are\n",
      "planned\n",
      "---------------------\n",
      "So is this mountain limestone or granite?\n",
      "If it's limestone, it'll weather pretty fast.\n",
      "\n",
      "------------------------ Yahoo! Groups Sponsor ---------------------~-->\n",
      "4 DVDs Free +s&p Join Now\n",
      "http://us.click.yahoo.com/pt6YBB/NXiEAA/mG3HAA/7gSolB/TM\n",
      "---------------------------------------------------------------------~->\n",
      "\n",
      "To unsubscribe from this group, send an email to:\n",
      "forteana-unsubscribe@egroups.com\n",
      "\n",
      " \n",
      "\n",
      "Your use of Yahoo! Groups is subject to http://docs.yahoo.com/info/terms/\n"
     ]
    }
   ],
   "source": [
    "print(ham_emails[1].get_content().strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help wanted.  We are a 14 year old fortune 500 company, that is\n",
      "growing at a tremendous rate.  We are looking for individuals who\n",
      "want to work from home.\n",
      "\n",
      "This is an opportunity to make an excellent income.  No experience\n",
      "is required.  We will train you.\n",
      "\n",
      "So if you are looking to be employed from home with a career that has\n",
      "vast opportunities, then go:\n",
      "\n",
      "http://www.basetel.com/wealthnow\n",
      "\n",
      "We are looking for energetic and self motivated people.  If that is you\n",
      "than click on the link and fill out the form, and one of our\n",
      "employement specialist will contact you.\n",
      "\n",
      "To be removed from our link simple go to:\n",
      "\n",
      "http://www.basetel.com/remove.html\n",
      "\n",
      "\n",
      "4139vOLW7-758DoDY1425FRhM1-764SMFc8513fCsLl40\n"
     ]
    }
   ],
   "source": [
    "print(spam_emails[6].get_content().strip())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some emails are actually multipart, with images and attachments (which can have their own attachments). Let's look at the various types of structures we have:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_email_structure(email):\n",
    "    if isinstance(email, str):\n",
    "        return email\n",
    "    payload = email.get_payload()\n",
    "    if isinstance(payload, list):\n",
    "        return \"multipart({})\".format(\", \".join([\n",
    "            get_email_structure(sub_email)\n",
    "            for sub_email in payload\n",
    "        ]))\n",
    "    else:\n",
    "        return email.get_content_type()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "def structures_counter(emails):\n",
    "    structures = Counter()\n",
    "    for email in emails:\n",
    "        structure = get_email_structure(email)\n",
    "        structures[structure] += 1\n",
    "    return structures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('text/plain', 2408),\n",
       " ('multipart(text/plain, application/pgp-signature)', 66),\n",
       " ('multipart(text/plain, text/html)', 8),\n",
       " ('multipart(text/plain, text/plain)', 4),\n",
       " ('multipart(text/plain)', 3),\n",
       " ('multipart(text/plain, application/octet-stream)', 2),\n",
       " ('multipart(text/plain, text/enriched)', 1),\n",
       " ('multipart(text/plain, application/ms-tnef, text/plain)', 1),\n",
       " ('multipart(multipart(text/plain, text/plain, text/plain), application/pgp-signature)',\n",
       "  1),\n",
       " ('multipart(text/plain, video/mng)', 1),\n",
       " ('multipart(text/plain, multipart(text/plain))', 1),\n",
       " ('multipart(text/plain, application/x-pkcs7-signature)', 1),\n",
       " ('multipart(text/plain, multipart(text/plain, text/plain), text/rfc822-headers)',\n",
       "  1),\n",
       " ('multipart(text/plain, multipart(text/plain, text/plain), multipart(multipart(text/plain, application/x-pkcs7-signature)))',\n",
       "  1),\n",
       " ('multipart(text/plain, application/x-java-applet)', 1)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structures_counter(ham_emails).most_common()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('text/plain', 218),\n",
       " ('text/html', 183),\n",
       " ('multipart(text/plain, text/html)', 45),\n",
       " ('multipart(text/html)', 20),\n",
       " ('multipart(text/plain)', 19),\n",
       " ('multipart(multipart(text/html))', 5),\n",
       " ('multipart(text/plain, image/jpeg)', 3),\n",
       " ('multipart(text/html, application/octet-stream)', 2),\n",
       " ('multipart(text/plain, application/octet-stream)', 1),\n",
       " ('multipart(text/html, text/plain)', 1),\n",
       " ('multipart(multipart(text/html), application/octet-stream, image/jpeg)', 1),\n",
       " ('multipart(multipart(text/plain, text/html), image/gif)', 1),\n",
       " ('multipart/alternative', 1)]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structures_counter(spam_emails).most_common()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that the ham emails are more often plain text, while spam has quite a lot of HTML. Moreover, quite a few ham emails are signed using PGP, while no spam is. In short, it seems that the email structure is useful information to have."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's take a look at the email headers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Return-Path : <12a1mailbot1@web.de>\n",
      "Delivered-To : zzzz@localhost.spamassassin.taint.org\n",
      "Received : from localhost (localhost [127.0.0.1])\tby phobos.labs.spamassassin.taint.org (Postfix) with ESMTP id 136B943C32\tfor <zzzz@localhost>; Thu, 22 Aug 2002 08:17:21 -0400 (EDT)\n",
      "Received : from mail.webnote.net [193.120.211.219]\tby localhost with POP3 (fetchmail-5.9.0)\tfor zzzz@localhost (single-drop); Thu, 22 Aug 2002 13:17:21 +0100 (IST)\n",
      "Received : from dd_it7 ([210.97.77.167])\tby webnote.net (8.9.3/8.9.3) with ESMTP id NAA04623\tfor <zzzz@spamassassin.taint.org>; Thu, 22 Aug 2002 13:09:41 +0100\n",
      "From : 12a1mailbot1@web.de\n",
      "Received : from r-smtp.korea.com - 203.122.2.197 by dd_it7  with Microsoft SMTPSVC(5.5.1775.675.6);\t Sat, 24 Aug 2002 09:42:10 +0900\n",
      "To : dcek1a1@netsgo.com\n",
      "Subject : Life Insurance - Why Pay More?\n",
      "Date : Wed, 21 Aug 2002 20:31:57 -1600\n",
      "MIME-Version : 1.0\n",
      "Message-ID : <0103c1042001882DD_IT7@dd_it7>\n",
      "Content-Type : text/html; charset=\"iso-8859-1\"\n",
      "Content-Transfer-Encoding : quoted-printable\n"
     ]
    }
   ],
   "source": [
    "for header, value in spam_emails[0].items():\n",
    "    print(header,\":\",value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's probably a lot of useful information in there, such as the sender's email address (12a1mailbot1@web.de looks fishy), but we will just focus on the `Subject` header:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Life Insurance - Why Pay More?'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam_emails[0][\"Subject\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, before we learn too much about the data, let's not forget to split it into a training set and a test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = np.array(ham_emails + spam_emails, dtype=object)\n",
    "y = np.array([0] * len(ham_emails) + [1] * len(spam_emails))\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, let's start writing the preprocessing functions. First, we will need a function to convert HTML to plain text. Arguably the best way to do this would be to use the great [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/) library, but I would like to avoid adding another dependency to this project, so let's hack a quick & dirty solution using regular expressions (at the risk of [un̨ho͞ly radiańcé destro҉ying all enli̍̈́̂̈́ghtenment](https://stackoverflow.com/a/1732454/38626)). The following function first drops the `<head>` section, then converts all `<a>` tags to the word HYPERLINK, then it gets rid of all HTML tags, leaving only the plain text. For readability, it also replaces multiple newlines with single newlines, and finally it unescapes html entities (such as `&gt;` or `&nbsp;`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "from html import unescape\n",
    "\n",
    "def html_to_plain_text(html):\n",
    "    text = re.sub('<head.*?>.*?</head>', '', html, flags=re.M | re.S | re.I)\n",
    "    text = re.sub('<a\\s.*?>', ' HYPERLINK ', text, flags=re.M | re.S | re.I)\n",
    "    text = re.sub('<.*?>', '', text, flags=re.M | re.S)\n",
    "    text = re.sub(r'(\\s*\\n)+', '\\n', text, flags=re.M | re.S)\n",
    "    return unescape(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see if it works. This is HTML spam:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<HTML><HEAD><TITLE></TITLE><META http-equiv=\"Content-Type\" content=\"text/html; charset=windows-1252\"><STYLE>A:link {TEX-DECORATION: none}A:active {TEXT-DECORATION: none}A:visited {TEXT-DECORATION: none}A:hover {COLOR: #0033ff; TEXT-DECORATION: underline}</STYLE><META content=\"MSHTML 6.00.2713.1100\" name=\"GENERATOR\"></HEAD>\n",
      "<BODY text=\"#000000\" vLink=\"#0033ff\" link=\"#0033ff\" bgColor=\"#CCCC99\"><TABLE borderColor=\"#660000\" cellSpacing=\"0\" cellPadding=\"0\" border=\"0\" width=\"100%\"><TR><TD bgColor=\"#CCCC99\" valign=\"top\" colspan=\"2\" height=\"27\">\n",
      "<font size=\"6\" face=\"Arial, Helvetica, sans-serif\" color=\"#660000\">\n",
      "<b>OTC</b></font></TD></TR><TR><TD height=\"2\" bgcolor=\"#6a694f\">\n",
      "<font size=\"5\" face=\"Times New Roman, Times, serif\" color=\"#FFFFFF\">\n",
      "<b>&nbsp;Newsletter</b></font></TD><TD height=\"2\" bgcolor=\"#6a694f\"><div align=\"right\"><font color=\"#FFFFFF\">\n",
      "<b>Discover Tomorrow's Winners&nbsp;</b></font></div></TD></TR><TR><TD height=\"25\" colspan=\"2\" bgcolor=\"#CCCC99\"><table width=\"100%\" border=\"0\"  ...\n"
     ]
    }
   ],
   "source": [
    "html_spam_emails = [email for email in X_train[y_train==1]\n",
    "                    if get_email_structure(email) == \"text/html\"]\n",
    "sample_html_spam = html_spam_emails[7]\n",
    "print(sample_html_spam.get_content().strip()[:1000], \"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And this is the resulting plain text:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "OTC\n",
      " Newsletter\n",
      "Discover Tomorrow's Winners \n",
      "For Immediate Release\n",
      "Cal-Bay (Stock Symbol: CBYI)\n",
      "Watch for analyst \"Strong Buy Recommendations\" and several advisory newsletters picking CBYI.  CBYI has filed to be traded on the OTCBB, share prices historically INCREASE when companies get listed on this larger trading exchange. CBYI is trading around 25 cents and should skyrocket to $2.66 - $3.25 a share in the near future.\n",
      "Put CBYI on your watch list, acquire a position TODAY.\n",
      "REASONS TO INVEST IN CBYI\n",
      "A profitable company and is on track to beat ALL earnings estimates!\n",
      "One of the FASTEST growing distributors in environmental & safety equipment instruments.\n",
      "Excellent management team, several EXCLUSIVE contracts.  IMPRESSIVE client list including the U.S. Air Force, Anheuser-Busch, Chevron Refining and Mitsubishi Heavy Industries, GE-Energy & Environmental Research.\n",
      "RAPIDLY GROWING INDUSTRY\n",
      "Industry revenues exceed $900 million, estimates indicate that there could be as much as $25 billi ...\n"
     ]
    }
   ],
   "source": [
    "print(html_to_plain_text(sample_html_spam.get_content())[:1000], \"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! Now let's write a function that takes an email as input and returns its content as plain text, whatever its format is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def email_to_text(email):\n",
    "    html = None\n",
    "    for part in email.walk():\n",
    "        ctype = part.get_content_type()\n",
    "        if not ctype in (\"text/plain\", \"text/html\"):\n",
    "            continue\n",
    "        try:\n",
    "            content = part.get_content()\n",
    "        except: # in case of encoding issues\n",
    "            content = str(part.get_payload())\n",
    "        if ctype == \"text/plain\":\n",
    "            return content\n",
    "        else:\n",
    "            html = content\n",
    "    if html:\n",
    "        return html_to_plain_text(html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "OTC\n",
      " Newsletter\n",
      "Discover Tomorrow's Winners \n",
      "For Immediate Release\n",
      "Cal-Bay (Stock Symbol: CBYI)\n",
      "Wat ...\n"
     ]
    }
   ],
   "source": [
    "print(email_to_text(sample_html_spam)[:100], \"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's throw in some stemming! For this to work, you need to install the Natural Language Toolkit ([NLTK](http://www.nltk.org/)). It's as simple as running the following command (don't forget to activate your virtualenv first; if you don't have one, you will likely need administrator rights, or use the `--user` option):\n",
    "\n",
    "`$ pip3 install nltk`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computations => comput\n",
      "Computation => comput\n",
      "Computing => comput\n",
      "Computed => comput\n",
      "Compute => comput\n",
      "Compulsive => compuls\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    import nltk\n",
    "\n",
    "    stemmer = nltk.PorterStemmer()\n",
    "    for word in (\"Computations\", \"Computation\", \"Computing\", \"Computed\", \"Compute\", \"Compulsive\"):\n",
    "        print(word, \"=>\", stemmer.stem(word))\n",
    "except ImportError:\n",
    "    print(\"Error: stemming requires the NLTK module.\")\n",
    "    stemmer = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need a way to replace URLs with the word \"URL\". For this, we could use hard core [regular expressions](https://mathiasbynens.be/demo/url-regex) but we will just use the [urlextract](https://github.com/lipoja/URLExtract) library. You can install it with the following command (don't forget to activate your virtualenv first; if you don't have one, you will likely need administrator rights, or use the `--user` option):\n",
    "\n",
    "`$ pip3 install urlextract`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if running this notebook on Colab, we just pip install urlextract\n",
    "try:\n",
    "    import google.colab\n",
    "    !pip install -q -U urlextract\n",
    "except ImportError:\n",
    "    pass # not running on Colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['github.com', 'https://youtu.be/7Pq-S557XQU?t=3m32s']\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    import urlextract # may require an Internet connection to download root domain names\n",
    "    \n",
    "    url_extractor = urlextract.URLExtract()\n",
    "    print(url_extractor.find_urls(\"Will it detect github.com and https://youtu.be/7Pq-S557XQU?t=3m32s\"))\n",
    "except ImportError:\n",
    "    print(\"Error: replacing URLs requires the urlextract module.\")\n",
    "    url_extractor = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are ready to put all this together into a transformer that we will use to convert emails to word counters. Note that we split sentences into words using Python's `split()` method, which uses whitespaces for word boundaries. This works for many written languages, but not all. For example, Chinese and Japanese scripts generally don't use spaces between words, and Vietnamese often uses spaces even between syllables. It's okay in this exercise, because the dataset is (mostly) in English."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note: I think it wouldn't hurt me to read the code for the transformers below (both of them) the second time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "class EmailToWordCounterTransformer(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, strip_headers=True, lower_case=True, remove_punctuation=True,\n",
    "                 replace_urls=True, replace_numbers=True, stemming=True):\n",
    "        self.strip_headers = strip_headers\n",
    "        self.lower_case = lower_case\n",
    "        self.remove_punctuation = remove_punctuation\n",
    "        self.replace_urls = replace_urls\n",
    "        self.replace_numbers = replace_numbers\n",
    "        self.stemming = stemming\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X, y=None):\n",
    "        X_transformed = []\n",
    "        for email in X:\n",
    "            text = email_to_text(email) or \"\"\n",
    "            if self.lower_case:\n",
    "                text = text.lower()\n",
    "            if self.replace_urls and url_extractor is not None:\n",
    "                urls = list(set(url_extractor.find_urls(text)))\n",
    "                urls.sort(key=lambda url: len(url), reverse=True)\n",
    "                for url in urls:\n",
    "                    text = text.replace(url, \" URL \")\n",
    "            if self.replace_numbers:\n",
    "                text = re.sub(r'\\d+(?:\\.\\d*(?:[eE]\\d+))?', 'NUMBER', text)\n",
    "            if self.remove_punctuation:\n",
    "                text = re.sub(r'\\W+', ' ', text, flags=re.M)\n",
    "            word_counts = Counter(text.split())\n",
    "            if self.stemming and stemmer is not None:\n",
    "                stemmed_word_counts = Counter()\n",
    "                for word, count in word_counts.items():\n",
    "                    stemmed_word = stemmer.stem(word)\n",
    "                    stemmed_word_counts[stemmed_word] += count\n",
    "                word_counts = stemmed_word_counts\n",
    "            X_transformed.append(word_counts)\n",
    "        return np.array(X_transformed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try this transformer on a few emails:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([Counter({'chuck': 1, 'murcko': 1, 'wrote': 1, 'stuff': 1, 'yawn': 1, 'r': 1}),\n",
       "       Counter({'the': 11, 'of': 9, 'and': 8, 'all': 3, 'christian': 3, 'to': 3, 'by': 3, 'jefferson': 2, 'i': 2, 'have': 2, 'superstit': 2, 'one': 2, 'on': 2, 'been': 2, 'ha': 2, 'half': 2, 'rogueri': 2, 'teach': 2, 'jesu': 2, 'some': 1, 'interest': 1, 'quot': 1, 'url': 1, 'thoma': 1, 'examin': 1, 'known': 1, 'word': 1, 'do': 1, 'not': 1, 'find': 1, 'in': 1, 'our': 1, 'particular': 1, 'redeem': 1, 'featur': 1, 'they': 1, 'are': 1, 'alik': 1, 'found': 1, 'fabl': 1, 'mytholog': 1, 'million': 1, 'innoc': 1, 'men': 1, 'women': 1, 'children': 1, 'sinc': 1, 'introduct': 1, 'burnt': 1, 'tortur': 1, 'fine': 1, 'imprison': 1, 'what': 1, 'effect': 1, 'thi': 1, 'coercion': 1, 'make': 1, 'world': 1, 'fool': 1, 'other': 1, 'hypocrit': 1, 'support': 1, 'error': 1, 'over': 1, 'earth': 1, 'six': 1, 'histor': 1, 'american': 1, 'john': 1, 'e': 1, 'remsburg': 1, 'letter': 1, 'william': 1, 'short': 1, 'again': 1, 'becom': 1, 'most': 1, 'pervert': 1, 'system': 1, 'that': 1, 'ever': 1, 'shone': 1, 'man': 1, 'absurd': 1, 'untruth': 1, 'were': 1, 'perpetr': 1, 'upon': 1, 'a': 1, 'larg': 1, 'band': 1, 'dupe': 1, 'import': 1, 'led': 1, 'paul': 1, 'first': 1, 'great': 1, 'corrupt': 1}),\n",
       "       Counter({'url': 4, 's': 3, 'group': 3, 'to': 3, 'in': 2, 'forteana': 2, 'martin': 2, 'an': 2, 'and': 2, 'we': 2, 'is': 2, 'yahoo': 2, 'unsubscrib': 2, 'y': 1, 'adamson': 1, 'wrote': 1, 'for': 1, 'altern': 1, 'rather': 1, 'more': 1, 'factual': 1, 'base': 1, 'rundown': 1, 'on': 1, 'hamza': 1, 'career': 1, 'includ': 1, 'hi': 1, 'belief': 1, 'that': 1, 'all': 1, 'non': 1, 'muslim': 1, 'yemen': 1, 'should': 1, 'be': 1, 'murder': 1, 'outright': 1, 'know': 1, 'how': 1, 'unbias': 1, 'memri': 1, 'don': 1, 't': 1, 'html': 1, 'rob': 1, 'sponsor': 1, 'number': 1, 'dvd': 1, 'free': 1, 'p': 1, 'join': 1, 'now': 1, 'from': 1, 'thi': 1, 'send': 1, 'email': 1, 'egroup': 1, 'com': 1, 'your': 1, 'use': 1, 'of': 1, 'subject': 1})],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_few = X_train[:3]\n",
    "X_few_wordcounts = EmailToWordCounterTransformer().fit_transform(X_few)\n",
    "X_few_wordcounts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks about right!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have the word counts, and we need to convert them to vectors. For this, we will build another transformer whose `fit()` method will build the vocabulary (an ordered list of the most common words) and whose `transform()` method will use the vocabulary to convert word counts to vectors. The output is a sparse matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.sparse import csr_matrix\n",
    "\n",
    "class WordCounterToVectorTransformer(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, vocabulary_size=1000):\n",
    "        self.vocabulary_size = vocabulary_size\n",
    "    def fit(self, X, y=None):\n",
    "        total_count = Counter()\n",
    "        for word_count in X:\n",
    "            for word, count in word_count.items():\n",
    "                total_count[word] += min(count, 10)\n",
    "        most_common = total_count.most_common()[:self.vocabulary_size]\n",
    "        self.most_common_ = most_common\n",
    "        self.vocabulary_ = {word: index + 1 for index, (word, count) in enumerate(most_common)}\n",
    "        return self\n",
    "    def transform(self, X, y=None):\n",
    "        rows = []\n",
    "        cols = []\n",
    "        data = []\n",
    "        for row, word_count in enumerate(X):\n",
    "            for word, count in word_count.items():\n",
    "                rows.append(row)\n",
    "                cols.append(self.vocabulary_.get(word, 0))\n",
    "                data.append(count)\n",
    "        return csr_matrix((data, (rows, cols)), shape=(len(X), self.vocabulary_size + 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<3x11 sparse matrix of type '<class 'numpy.longlong'>'\n",
       "\twith 20 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab_transformer = WordCounterToVectorTransformer(vocabulary_size=10)\n",
    "X_few_vectors = vocab_transformer.fit_transform(X_few_wordcounts)\n",
    "X_few_vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [99, 11,  9,  8,  3,  1,  3,  1,  3,  2,  3],\n",
       "       [67,  0,  1,  2,  3,  4,  1,  2,  0,  1,  0]], dtype=int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_few_vectors.toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What does this matrix mean? Well, the 99 in the second row, first column, means that the second email contains 99 words that are not part of the vocabulary. The 11 next to it means that the first word in the vocabulary is present 11 times in this email. The 9 next to it means that the second word is present 9 times, and so on. You can look at the vocabulary to know which words we are talking about. The first word is \"the\", the second word is \"of\", etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'the': 1,\n",
       " 'of': 2,\n",
       " 'and': 3,\n",
       " 'to': 4,\n",
       " 'url': 5,\n",
       " 'all': 6,\n",
       " 'in': 7,\n",
       " 'christian': 8,\n",
       " 'on': 9,\n",
       " 'by': 10}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab_transformer.vocabulary_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now ready to train our first spam classifier! Let's transform the whole dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "preprocess_pipeline = Pipeline([\n",
    "    (\"email_to_wordcount\", EmailToWordCounterTransformer()),\n",
    "    (\"wordcount_to_vector\", WordCounterToVectorTransformer()),\n",
    "])\n",
    "\n",
    "X_train_transformed = preprocess_pipeline.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: to be future-proof, we set `solver=\"lbfgs\"` since this will be the default value in Scikit-Learn 0.22."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  ................................................................\n",
      "[CV] .................................... , score=0.981, total=   0.1s\n",
      "[CV]  ................................................................\n",
      "[CV] .................................... , score=0.985, total=   0.2s\n",
      "[CV]  ................................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.3s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .................................... , score=0.991, total=   0.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.5s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9858333333333333"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "log_clf = LogisticRegression(solver=\"lbfgs\", max_iter=1000, random_state=42)\n",
    "score = cross_val_score(log_clf, X_train_transformed, y_train, cv=3, verbose=3)\n",
    "score.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Over 98.5%, not bad for a first try! :) However, remember that we are using the \"easy\" dataset. You can try with the harder datasets, the results won't be so amazing. You would have to try multiple models, select the best ones and fine-tune them using cross-validation, and so on.\n",
    "\n",
    "But you get the picture, so let's stop now, and just print out the precision/recall we get on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 95.88%\n",
      "Recall: 97.89%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score\n",
    "\n",
    "X_test_transformed = preprocess_pipeline.transform(X_test)\n",
    "\n",
    "log_clf = LogisticRegression(solver=\"lbfgs\", max_iter=1000, random_state=42)\n",
    "log_clf.fit(X_train_transformed, y_train)\n",
    "\n",
    "y_pred = log_clf.predict(X_test_transformed)\n",
    "\n",
    "print(\"Precision: {:.2f}%\".format(100 * precision_score(y_test, y_pred)))\n",
    "print(\"Recall: {:.2f}%\".format(100 * recall_score(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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