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 href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/03_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/master/03_classification.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></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",
    "# Is this notebook running on Colab or Kaggle?\n",
    "IS_COLAB = \"google.colab\" in sys.modules\n",
    "IS_KAGGLE = \"kaggle_secrets\" in sys.modules\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": "markdown",
   "metadata": {},
   "source": [
    "**Warning:** since Scikit-Learn 0.24, `fetch_openml()` returns a Pandas `DataFrame` by default. To avoid this and keep the same code as in the book, we use `as_frame=False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'categories', 'feature_names', 'target_names', 'DESCR', 'details', 'url'])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "mnist = fetch_openml('mnist_784', version=1, as_frame=False)\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",
    "    # This is equivalent to n_rows = ceil(len(instances) / images_per_row):\n",
    "    n_rows = (len(instances) - 1) // images_per_row + 1\n",
    "\n",
    "    # Append empty images to fill the end of the grid, if needed:\n",
    "    n_empty = n_rows * images_per_row - len(instances)\n",
    "    padded_instances = np.concatenate([instances, np.zeros((n_empty, size * size))], axis=0)\n",
    "\n",
    "    # Reshape the array so it's organized as a grid containing 28×28 images:\n",
    "    image_grid = padded_instances.reshape((n_rows, images_per_row, size, size))\n",
    "\n",
    "    # Combine axes 0 and 2 (vertical image grid axis, and vertical image axis),\n",
    "    # and axes 1 and 3 (horizontal axes). We first need to move the axes that we\n",
    "    # want to combine next to each other, using transpose(), and only then we\n",
    "    # can reshape:\n",
    "    big_image = image_grid.transpose(0, 2, 1, 3).reshape(n_rows * size,\n",
    "                                                         images_per_row * size)\n",
    "    # Now that we have a big image, we just need to show it:\n",
    "    plt.imshow(big_image, cmap = mpl.cm.binary, **options)\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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": [
    "# Training a 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(random_state=42)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_clf = SGDClassifier(max_iter=1000, tol=1e-3, random_state=42)\n",
    "sgd_clf.fit(X_train, y_train_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": "markdown",
   "metadata": {},
   "source": [
    "# Performance Measures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Measuring Accuracy Using Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9669\n",
      "0.91625\n",
      "0.96785\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.base import clone\n",
    "\n",
    "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 dicts 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": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "## Precision and Recall"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "## Precision/Recall Trade-off"
   ]
  },
  {
   "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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5fegqbroJHnzQDEUg8s8xEpxDh2DOHPM8IQE6dYL5881ydLRZXrLELIeHm+VvvzXLly6Z5RUrzPL582bZ0sfw9GmzvHatWT52zCxv2pT22p06wZYtZvmff8zyjh1mefdus7x7t1nescMs//OPWd6yxSxbRvXatAk6dcLn3DmzvHat2W6Z7/7nn83y+fNmecUKs3zpkln+9luzHB5ulpcsMcuW4U/nzzfLCQlmec4cs2zx+efQtWva8vTppjuCxbRp5pq7xeTJZqITi0mTTKMEiwkTSP13F+C118zIZBZjx6a1CAR4/nkzfbHFyJGmWDzxhKljERKSccSvYcPMa1gMGmRisLjvPhOjRf/+5j1Y9Olj3qPFHXeYz8Cia1fzGaVoOnKk0/3spQ6YkcefvVpJJVg+YDnz9d2sn615+8exvLzuZef42Rs+PC3+FP5EM6/yy9f1jBKOS2vTK61+fdeasqBvXzPDfN++9o6keHKMBEcIYXcDGw/khuB6ALy1+S02ntho34CsQEdEZLne49ypIo5EFIZSJp8OC0trGyVErrTWdi/NmzfXzmbDhg32DkHkgZyn673929uacWjGoadtm6aTk5PtGk9+z1F0tNYvvmhGpEmsUt0yNE3GUr26LUJ1abb+Lj3/vDl1detqHRNj05dyCMnJWq9dq/WhQ+a5tTjj7zxgp84it5ArOEKIDMbcOoYX274IwDM/P0P3+d2Jio+yc1R5M3WqGSzu3XfN8pY7J5oV6fn5wcSJRR6bKBzLne0jR+CXX+wbS1EIDzd3fJs3t3ckxZckOEKI60zqOokp3abg7e7NL8d+ocu8LlyNvWrvsHJ0//0walTGddfuHAgzZ5p+30qZ6btnzjRdc0SxUr48PP64ef7gg/Dnn/aNx9bSj4EjU3wUjCQ4QojrKKUY1WYUvz/0OyW8S7D97HZ6LuhJZHykvUPL0ujRsHhx2vK5c+ZeVK9emGTmhhugQwc4cUKSm2LsrbdMz6KrV2HwYHOObeHqVdsd2+LYMdPm3tLnIDPpIl54kuAIIbLVvFJzfn/od8r6lWXrma0M+GYAScmON+pa+is3yclZzNvz8cemiGKtVCkz8enDD8Obb9ruysYDD5ifoQcfTOuEaG3Ll5uOjP36wdGj12+3vK4kOAUnCY4QIkeNyjXi12G/EuAVwKojq3h1w6v2DgmAw4fNH7ijR80dqMRE8193ln/0GjUyRRR7wcFmbJy77zbL8fHmuWX0htzExV2/LjoaqlQxM8VrDf/+a2Y5/+or+Owz68WeXkyMebx61UwLEpnp4qhM01B4kuAIIXJVP7g+S/5nxgN6e/PbvL7hdbvGc/q0uesEULeueXR3z2GHLVvSxhsSTuWnn8zVkP79zXgxv/9+fZ2VK80gjk2aQO3asGFDxu3XrpmJLc+eNQnywYNpw2/NmmWabVmGf8pMa1Nn9er8xZ0+0apZ01x5TE9uURWeJDhCiDzpWbcnH93xEQBv/PoGi/Yusksc996b8Zd++rY32XrpJVOE0+nY0QwC6OUFP/wAt94Kzz0Hzzxjitbm6l5sLPz9t0li0o8lCmAZLikw0DwqZW5PNWlixsV87DFo2DBtTM30fvjB3DIbNMhM8ZFXlgELn33WzFxfokTG7ZLgFJ5MwC6EyLMnWz3JtbhrvLz+ZR5Z8QgNyzakSYUmRfLaSUngkek31jffZBwMOVszZtgkJmF/JUuaLuQDB8KHH5pkZ8qUtO3t26f93HTqBFu3wubN5ipN/fpmvWWyVUuCAyax+OsvWLrUzGheter1bbt+/z1tYPRLl8xA2nm9Vdavnzlmu3Zm1nowV4nOnDFXdL7/3iQ5derk48MQGcgVHCFEvoy9dSw96/YkOiGaB759gGtx13LfyQp+/TXted++5pJ+npIbMPezLPe0hFOqWBHefjttZhWL775L6xEVFARDhpjnzzyTdlso8xUcCzc3GDDAzJayatX1V1nOnYMff4RbbjH7Ll9+/etnp107ePrptHFuzp83V5+6dDFj4JQuDU2bQkBA3o4nricJjhAiX5RSLPnfEuqWrsv+0P30XWz7iXKSk01+cuONZoqr777LZw+aTZuy748rnMptt5mrIxYLFpirIQA+PjB+vOmNtWYNvP++WW9JcDInMBZ+fuDtbZ4nJporO5CWOFWtmjZF3n33mduo+/fnL+5SpcwVnOPHYcQI23dTdwWS4Agh8i3AK4Af7v+BAK8ANp7YyOJ/8tIQJv8uXYLOnTvx3XfmP/R//jHzZ+bb66+bIpxeqVLmSkpyctrox3Pnmkc/P9Pj7ssvzfKUKaYtTFa3qLISHm5m9u7VyyxbkhClzCCEY8aYRGjpUpg3L+djrVtn4jp50ix7e8OiRSbGhQvN1aN33snfexcZSYIjhCiQ+sH1eeu2twB4cPmDfHfwO6sePz4eypY1z/v3v76XSb7MmmWKcBlKmQTh3nvTrvZZZu24806T6NSta3rfNWkCL78M3bvnfMwSJczwBOfPm27k6RMcNzdzi+zff82tpxdeSNtv8+a0JMrik09g6FDYuTNt3Q03wEcfpS3ntT2PyJokOEKIAnuy1ZM83eppEpITeGDZA5y4esJqxx4zJu35okW5dAPPTa1apgiX4uZmetnt2AHTpqWNnePpCT//bNrLeHqaIZLefNMkHDlRyrSLAZOgpE9wLCpXNq9VpoxZjoszDaAfeijjbSdLLyrLrS+LYcNMmx4wc1GJgpMERwhRYEoppvaYSpeaXYhJjKHv4r5WGen4ySfhgw/M8wcfPMF99xXygGvXmiJcjlKmIe/TT5v2ORZNmmQx4nUejBtnjvnmm2nj6eTUHuziRTOY37JlGTvzWcbByZzgKGWOO29e2i02UTCS4AghCkUpxby75lHOvxx/X/ibj/8o3JQIn3xiCpjxTIYNO1H4IN980xQh0tm3z9xKat3atEHPS8Pezp3hlVdM3c8/h1atoFmz7OtXrWoGCgQYOdKMxQNpCY6Pz/X7+PqaubZKlcrX2xGZSIIjhCi0SoGVmHjbRADGbxrPfxH5GPEsk9DQtOeW0WQL7auvTBEinePHYfJk2L4d/ve/vPfMe+0106UboFIlePHFnOsPGACPPGKSmvvug6io7K/gCOuRBEcIYRUPN3uYNlXacCX2CiErQ9D57OcaGWkaYj77rElsEhLMf7JWUbWqTOojrpP+Coll0L+88PAwPZ1uvtnMZJ8X06ZBgwZw4IAZg0cSHNvLU4KjlCqtlFqulIpSSp1USj2QTT2llHpTKXVWKRWulNqolLrRuiELIRyRUooZd87AXbmz8vBKluzL44hnwC+/mD82Bw6YniqvvHL9qMWF8vPPpgiRTosWac8TE/O3b9WqpgdU69Z5q+/nZxo1+/iYbup795r1kuDYTl6v4HwCxAPlgYHAp9kkLvcADwHtgdLAVkCuCwvhIm4qfxPv3v4uAM+veZ4rMVdy3efaNXP5PjHRjFBsE5MmpY3EJkQKb++M7b3yK1+DTQI33QSffmrGyUlKMrOY16uX/9cVeZNrgqOU8gf6A69qrSO11puBH4DBWVSvCWzWWh/TWicB84GG1gxYCOHYnmz1JI3LN+ZsxFlGrBqRa/0ePcycO/XrmzYRNrF4cR5n5RSuZsQICAvLx7QfhTR0qGnv4+ZmbsG6SUMRm1G53SdXSjUDtmitfdOtex7oqLXunaludWA5cB9wHJgI1NNa98viuCFACED58uWbL3ayXz6RkZEEyCQiDk/Ok22cjj7NkB1D0Gg+avoRjYIaZVlv0KBWnD1rRl/79NM/qV8/4ro6co6KBzlPxYMznqfOnTv/qbVukXl9Xu5yBwDhmdaFA1kNav0f8BtwCEgCTgO3ZVEPrfVMYCZAixYtdKdOnfIQSvGxceNGnO09OSM5T7bzl9tfTN0+lY9Pf8y+3vtwd8s4Ut9nn8HZs+b5U0/B4483z/I4VjlHK1aYx969c64nCky+S3l35kxam/eYmKy7ituKK52nvFwciwQyT0FWArj+Xy14HWgJVAV8gPHAeqWUX2GCFEIUPxO7TKRiQEUOXT7Emn/XXLc9/ZxSH35o42Defz9tZkUh7Cz9qNxWbUwvMshLgnMY8FBK1U23rgmwL4u6TYAlWuszWutErfUcoBTSDkcIl+Pn6ceIlqYNzvhN468b4XjePNMW4Uru7ZAL75tvTBHCAaRvnCwJju3kmuBoraOAb4E3lFL+Sql2QF+y7h21A7hHKVVeKeWmlBoMeAJHrRm0EKJ4eOaWZyjnX47tZ7fz1d8Zf2UMHmx6k5QsWQSBBAebIoQDkIbFRSOvH/MIwBe4CCwChmut9ymlqimlIpVS1VLqvQPsAXYDV4FRQH+t9VVrBi2EKB4CvQOZ1MV0z351w6tExUexcKGZNeHkySIM5NtvZWpm4TDKlTO9t8aNs3ckzi1PF8e01mFAvyzWn8I0QrYsxwJPpBQhhGBI0yF89MdH7Dq/i9c2jGPFuPc4cgQaNoTq1YsoCEsjH8t00kLYmWX8HWE7cqFMCGFTbsqNqT2mAvDx9ukcOXOZGjWgT58iDOL7700RQrgMSXCEEDbXoXoHutXqTryOhtbTuPfeIm5cGRRkihDCZUiCI4QoEv5/vWSetJzOsy/GFu2LL1liihDCZUiCI4SwucREWD7tVjjfGPwus/Dwp0UbwKefmiKEcBmS4AghbG79ekC7wcbxALy28TUuRl0sugBWrTJFCOEyJMERQthc586mW+yCV/vSslJLIuMjGf7j8Nx3tBY/P1OEEC5DEhwhhM15eppusQ88oFjYfyHe7t4sP7Ccfy7+UzQBzJ9vihDCZUiCI4Swmb/+MsPSb96ctq5O6To8cvMjaDSP/PAI8Unxtg/kiy9MEUK4DElwhBA2M8kMYkzPnhnXj+s0jkqBldh+djuf//m57QP55RdThBAuQxIcIYRNnDtn5poC+CfTnahgv2Cm9ZgGwBu/vsHV2Ku2DcbT0xQhhMuQBEcIYRO9e5vHPn2gWrXrt/dv0J9WlVtxMeoi729537bBzJljihDCZUiCI4SwushI0/4G4J57sq6jlGJ8J9NtfNr2aZwOP227gCTBEcLlSIIjhLC6RYvSng8cmH29HnV60OeGPkTER/DoikdtF9DGjaYIIVyGJDhCCKvSGkJCzPNZs0wvqpx80fsLArwCWP3valYdkcH4hBDWIQmOEMKqlIIHHwRvbxg0KPf6Zf3LMq7jOACe+ukpIuIirB/U55+bIoRwGZLgCCGsbu5ciI3Ne8elp295mpvK3cSxK8eYtHmS9QOSyTaFcDmS4AghrObkSbh2Lf/7ebp78nHPjwGYvXs2yTrZuoGtXWuKEMJlSIIjhLCabt2gXDn44Yf873trtVupHlSd/yL/Y8WhFdYPTgjhUiTBEUJYxW+/weHDkJAAzZvnf3835cao1qMAmPT7JLTW1gtu+nRThBAuQxIcIYRVjBxpHhs0gMqVC3aMR25+hNK+pdl2Zhu/nfrNarGxYoUpQgiXIQmOEKLQLl1KG9jvk08Kfhx/L3+eavUUgHUbG//0kylCCJchCY4QotDGjTOPt90GHTsW7lhPtnoSP08/fjr6E3vO7yl0bEII1yQJjhCiUCIi0q7avPxy4Y8X7BfMozebUY1f3fBq4Q8IMG2aKUIIlyEJjhCiUNzdwd/fPO/c2TrHfKn9S3i7e7Py8EpORp0s/AHXrTNFCOEyJMERQhSKn5+5ipOUlPu0DHlVzr8cw5oOQ6OZd3Je4Q/4ww8F67suhCi2JMERQhRYWBhMmmQe3az82+TFdi+iUKwPXc+mE5use3AhhNOTBEcIUWDBwTB2LMTEWP/YNUvV5ImWTwAwfWchx7CZPNkUIYTLkARHCFEghw6ZmcMBkq08s4LF822fxw03lu5bytGwowU/0NatpgghXIYkOEKIAvngA/PYvj1Uq2ab16hesjq3l78djeaj7R8V/EDLlpkihHAZkuAIIfLt/HmYMcM8tzzayt2V7wbgq7+/IiIuwrYvJoRwGpLgCCHybfx481i9upmawZbqBtTl5oo3cyX2CrN2zSrYQSZNMkUI4TIkwRFC5Et8PHz2mXk+c6btX08pxWsdXgPgvS3vkZicmP+D7N5tihDCZUiCI4TIl/Xr057ffnvRvGafG/pQp3Qdzkac5fuD3+f/AIsXmyKEcBmS4Agh8qVHDzh9Gvbssd7AfrlRSvHMLc8A8MG2D4rmRYUQxZpHXioppUoDXwLdgEvAWK31wmzq1gI+BDoCccAsrfWL1glXCOEIqlQxpSgNbTqUVze8yu+nf2fr6a20qdom7ztPmGAeX7XS3FaiyMTGxhIaGkpsbCyJiQW4PSkyCAoK4sCBA/YOI1ceHh74+PhQtmxZfHx8CnaMPNb7BIgHygNNgR+VUnu01vvSV1JKeQG/pNQfACQB9QoUmRDC4ezeDY0agUdef3NYUYBXAI80e4TJWyczbfu0/CU4hw7ZLjBhM+Hh4Vy4cIGyZctSoUIFPDw8UEV12dBJRUREEBgYaO8wcqS1JjExkcjISE6dOkX58uUJCgrK93FyvUWllPIH+gOvaq0jtdabgR+AwVlUHwqc01pP0VpHaa1jtdZ/5zsqIYTDCQ83Y97UqgVXrtgnhhEtR+Dl7sWSfUv467+/8r7j/PmmiGLl0qVLVKlShVKlSuHp6SnJjYtQSuHp6UmpUqWoUqUKly9fLtBx8vJ/WD0gSWt9ON26PZhbUJm1Bk4opX4CWgL/AE9prfdmrqiUCgFCAMqXL8/GjRvzGbpji4yMdLr35IzkPOXd0qVViIysQ926V9izZ0+RvW7mc9S3Yl+WnlnKM8ueYUKjCUUWh8iZLb5LQUFBlC1blogIGf/IWpKSkorV56m1Jjw8vEA/W3lJcAKA8EzrwoGsrnFVAToDfYB1wDPA90qp+lrr+ExBzwRmArRo0UJ36tQpf5E7uI0bN+Js78kZyXnKm6QkGDbMPB83rlSRfmaZz1HNpjVZ9uEytoZtpfyN5WlQNg8D8bxmupnzxhu2CVLY5Lt04MABSpQoYdVjurricIsqMx8fH5o1a5bv/fLSiyoSyPwTVgLIKgWMATZrrX9KSWgmA2UAGw8FJoSwpe+/hxMnoHZt6NXLvrFUL1mdQY0HkaSTmPlnHgfiOX3aFCGEy8hLgnMY8FBK1U23rgmwL4u6fwPaGoEJIRzH1Knm8ZlnwN3drqEA8FjzxwBYvG9x3gb+mz3bFCGEy8g1wdFaRwHfAm8opfyVUu2AvsBXWVSfD7RWSnVVSrkDIzHdyh2/T5oQIksHD8Jvv0GJEjB0qL2jMdpUacMNZW7gfOR5fjryk73DESLP5syZg1IqtQQGBtKkSRM+/vjjIusGf+LECZRSzJkzJ8/7WOI+ceKEzeKytrwO9DcC8AUuAouA4VrrfUqpakqpSKVUNQCt9SFgEPAZcAWTCPXJ3P5GCFF8hIVBnTpw113gKLfulVI81OwhAOb9PS/3HcaONUUIB7F06VK2bt3KsmXLaNWqFU899RRvFFEbsYoVK7J161Z65eN+c69evdi6dSsVK1a0YWTWlafRLLTWYUC/LNafwjRCTr/uW8wVHyGEE2jb1gwjE565q4GdDbhxAKPXjuanIz8RkxCDr6dv9pUL2M1UCFtp2rQpderUAaBbt24cPXqUqVOnZpnkJCQkWHUMIG9vb1q3bp2vfcqWLUvZsmWt8vpFRaZqEELkys0NSpWydxQZVS9ZnRaVWhCVEMXCvVkOrJ5m5syimRlUiAJq2bIlERER/PHHHyilmD59Oi+++CKVKlXC29ubq1evAvDtt9/SunVr/Pz8KFmyJPfccw+nTp267niff/45N998M76+vpQqVYqOHTuyZcsWIOtbVDt27OD222+nTJky+Pn5UatWLUaMGJG6PatbVAkJCbzyyivUqFEDLy8vatSowSuvvEJCQkJqHctrzZgxg9dee42KFStSsmRJevfuzZkzZ6z7IWYiCY4QIltffglHj9o7iuyNvGUkAO/8/g7JOtm+wQhRCMePH8fd3Z2AAHNTZOLEiRw+fJiZM2eyfPlyfHx8+Oyzz+jfvz8NGzbkm2++YcaMGfzzzz907Ngxw9g2zz//PCEhIdx88818/fXXzJ8/nw4dOmSZCIEZw6h79+64u7szZ84cVq1axWuvvZZrm6AhQ4YwadIkHnzwQVauXMmwYcN45513GDJkyHV13377bY4ePcqsWbOYNm0aW7duZeDAgYX4xPJAa2330rx5c+1sNmzYYO8QRB7Iecre8eNag9Z+flpHR9svjpzOUUJSgq72QTXNOPTSfUuzP8hzz5kibMYW36X9+/dnuw2yLzNmpNWbMSPnuundfHP29R59NK3ezp0Ff0+zZ8/WgD548KBOSEjQYWFh+rPPPtNubm66b9+++vjx4xrQzZo108nJyan7RURE6BIlSuhhw4ZlON7x48e1p6en/uCDD7TWWh85ckS7ubnpUaNGZfn6165dS32N2bNna6213rFjhwb0nj17co37+PHjWmut9+7dqwH9+uuvZ6g3YcKEDMeyvFaHDh0y1Hvvvfc0oM+ePZvbR5bjz4HWWgM7dRa5hVzBEUJkafFi89i3L/jm0LzFnjzcPBjdbjQAb/76JuZ3XRZiYkwRwkHUr18fT09PSpcuzYgRIxg4cCCzZs1K3d6vX78MbW62bt3KtWvXGDhwIImJiamlSpUq1K9fn19//RWAtWvXkpycTEhISJ5jqVu3LiVLluSxxx5j/vz5nM7DmFGW1xs0aFCG9ZblTZs2ZVifuUHzTTfdBJDtVSVrkARHCHGd5OS0YWPuv9++seTmoWYPUSmwEnsu7GHF4RVZV/rkE1OE08jpukz6v+0hITnXTe/PP7Ovl74JV/PmhY9/+fLl7Nixg4MHDxIVFcW8efMoXbp06vbMvZUuXrwIQNeuXfH09MxQ9u7dmzpfk+WxSpUqeY4lKCiIDRs2UKlSJUaMGEG1atVo1KgRy5Yty3afsLCwLOOsUKFChu0W6d8bmIbOYGaLtxU7zAkshHB069fD4cNQuTLccYe9o8mZj4cPL7Z9kZGrRzJu4zjurHcnbkr+dxOOrVGjRqm9qLKSucdUmTJlANPY98Ybb7yuvmX6heDgYADOnj3LDTfckOd4mjZtyrJly0hMTGTnzp28/fbb3HvvvezZs4dGjRpdV9+SsJw/f57atWunrj9//nyGeO1JfgsIIa7zxRfmMSQEPIrBv0EhzUOoHFiZXed3Mf/vLGYNHznSFCGKqbZt2xIYGMjRo0dp0aLFdcWSzHTt2hU3NzdmFrDXoIeHB61bt2bChAkkJydz4EDW4/R27Gjm215suZedYsGCBQB06NChQK9vTcXgV5cQoihdvgzffQdKQRadIRySr6cvr3d8nZCVIUzZOoXBjQdbbcwQIRxBiRIleO+993jiiScIDQ3ljjvuICgoiLNnz7Jp0yY6derEAw88QO3atRk1ahRTpkwhIiKCPn364O7uzh9//EH9+vXp2bPndcdeuXIlM2fOpF+/ftSsWZOoqCg+/PBDAgMDadOmTZbx3Hjjjdx///2MGzeOxMRE2rZty9atW5kwYQL3338/jRs3tvVHkitJcIQQGURGQv/+pk1u9er2jibvBjUexMvrX2bPhT1sOrmJTjU6pW20TKYlRDH22GOPUbVqVd577z0WLlxIQkIClStXpkOHDjRt2jS13uTJk6lTpw7Tp09n7ty5+Pv707hxY7p165blcevWrYuvry8TJkzgv//+IzAwkJYtW/LLL7/k2JZn7ty51KpVi1mzZvHmm29SqVIlRo8ezeuvv27tt14gKtteB0WoRYsWeufOnfYOw6o2btxIp06d7B2GyIWcp+xpba7i2Ft+ztG4jeMYv2k8Per04KeBMkdVUbLFd+nAgQM0aNDAqsd0dREREantdYqL3H4OlFJ/aq1bZF4vbXCEEKmuXYMffzTPHSG5ya8nWz2Jn6cfPx/9mcX/pGsb8MQTpgghXIYkOEKIVBUrwp13wr//2juSggn2C+b9bu8DMHrtaOKTUub59fV13MF8hBA2IQmOEAIwSU10tHleHK/eWIQ0D6Fh2YacCj/FrF0pA6dNnmyKEMJlSIIjhADg00/N45AhUKuWfWMpDDflxqsdXgXgpXUvERoVaueIhBD2IAmOEILoaDOxJsCTT9o3Fmu498Z7aV2lNVdirzBvzzwzoE8+hq4XQhR/kuAIIVi0CK5ehVatoMV1fRGKHzflxnNtngNg3KZxxAUFggOMrCqEKDqS4Ajh4rSGjz82z53h6o3F/xr+j/bV2hMZH8lHd1WEt9+2d0hCiCIkCY4QLm7bNti9G4KD4Z577B2NdY29dSz3/w0Deo1Gu7lBjRqQMpS8EMK5yUjGQri4Fi1gyRKIiAAfH3tHY109/gij00o3fOOTzYqTJ9Pa4gwcaL/AhBA25xhXcA4dgjlzzPOEBOjUCeanTJgXHW2Wlywxy+HhZvnbb83ypUtmecUKs3z+vFn++WezfPq0WV671iwfO2aWN21Ke+1OnWDLFrP8zz9meccOs7x7t1nevdss79hhlv/5xyxv2WKWDx0yy5s2QadO+Jw7Z5bXrjXbT582yz//bJZTZlxlxQqzfOmSWf72W7McHm6Wlywxy5b+u/Pnm+WEBLM8Z45Ztvj8c+jaNW15+vSM00FPmwZ9+qQtT55sxuW3mDQJ7rsvbXnCBBg0KG35tddg2LC05bFjMzbefP75jAOqZZ7k8IknTB2LkBBzDIthw8xrWAwaZGKwuO8+E6NF//4Zu//26WPeo8Udd5jPwKJrV/MZpWg6cqTT/exx7JhZzuPPnmf4Je69Fx4u5Xw/e2r48LTkxiI6Gl5+GSGEc3OMBEcIYRfJ9p+pxbYiIrJef+pU0cYhRIo5c+aglEotXl5e1K5dm5deeonY2Fi7xTV06FBq1KiRunzixAmUUsyx/ANYHGmt7V6aN2+unc2GDRvsHYLIA1c+TzExWletqvUjj2gdHW3vaLJXqHNUvbrWph11xlK9upWiExa2+C7t37/f6se0t9mzZ2tAL126VG/dulWvWbNGDx8+XAP6ySeftPnrX7t2Lcv1Q4YM0dXTfS+OHz+uAT179mybx5Sb3H4OgJ06i9xCruAI4aIWLTJ3r3budL62N6kmTgQ/vwyrYr3c0BMn2ikgIYymTZvSunVrbr/9dqZPn07Xrl358ssvSU5Ozn1nkSeS4AjhgpKT4aGHzPNRo4r31Aw5GjgQZs6EoCA0cKqk4qE7k1l9i4yJIxzLzTffTExMDJdS2mNGR0czevRoatasiZeXFzVr1mTixInXJUChoaGMGDGCqlWr4u3tTdWqVRk8eDBxcXEAHD16lMGDB1OzZk18fX1p3Lgxw4cP58qVK0X+Houa9KISwgWtWmUeq1aFBx6wbyw2N3AgDByIAhZtfodF68bw58/PsHf4XrzcvewdnRCAafMSFBREmTJlSExMpHv37uzfv59XX32Vm266iW3btjFhwgTCwsJ4/30zoeyVK1do27YtYWFhvPLKKzRu3JiLFy/y/fffEx8fj7e3N+fOnaNKlSpMnTqVUqVKsX//fqZMmULPnj3ZunWrnd+1bUmCI4QLsnQk69sXPFzot8CoNqOYtXsWhy8fZuHehQxtOtTeIYkCUOMd45Kjfr3grfSTkpJITEwkIiKC5cuXs2zZMqZOnYq7uztfffUVmzdvZtOmTXTo0AGALl26ADB+/HhGjx5NuXLl+OCDDzh27Bg7d+6kWbNmqce+//77U5936NAh9RgAN910E40aNaJ9+/bs2rUrw37ORm5RCeFijh83PcS9vOCVV+wdTRG57z647z683L0Y024MAO9teY/E5EQ7ByZcVf369fH09KR06dI8/PDDPPbYYzyZMpT4zz//TPXq1Wnbti2JiYmppVu3biQkJLBt2zYA1qxZQ8uWLXNMUuLj43nrrbeoX78+vr6+lC5dmvbt2wNwyDLEhJNyof/dhBAA69ebrkT33Qfly9s7miLStGnq0/tvup83fn2D/aH7+WDrB7zQ7gX7xSUKpDBXThzF8uXLqVKlCqGhoUyZMoXp06dzyy238OCDD3Lx4kVOnjyJp6dnlvtevnw59bFJkyY5vs7YsWP56KOPeO2112jbti1ubm5cuXKFu+++267d0ouCJDhCuJiHH4a2bSGb353OacyY1Kc+Hj58fMfH3LnoTl7f+DohzUMI8gmyY3DCFTVq1Ig6deoAcNttt9G4cWNeeOEF+vfvT5kyZahZsyZff/11lvtaxqsJDg7m7NmzOb7O4sWLefDBB3kl5XJtREQEO3futN4bcWByi0oIF9SgAaT8bnVJver1on219sQkxvDN/m/sHY5wcd7e3rz33ntcvHiR6dOn06NHD06fPk1AQAAtWrS4rgQHBwPQrVs3/vjjD/bs2ZPtsaOjo6+7EjR79mybvh9HIQmOEC4iIQHWrDFdxF1O//4Zp4UAHm72MACvb3ydmIQYe0QlRKo+ffrQsmVLJk+eTP/+/Wnbti1dunRhypQprFu3jp9++omPP/6Ybt26EZ0yfcqoUaOoVasWXbt2Zdq0aaxfv56vv/6agQMHEpEyinePHj2YO3cu06dPZ82aNYwcOZItlulhnJwkOEK4iA8+gO7doVs3e0diB23amJLOwMYDaVK+CWcjzrJgr8wwLuzvzTff5OLFi3zxxResXr2aRx99lJkzZ9KzZ08GDhzI3Llzadu2LV5eZniDkiVL8vvvv3PXXXcxadIkevTowXPPPYeHh0dqnY8++og+ffrw8ssvM2DAACIiIli0aJE932aRUWaUY/tq0aKFdrZ7ghs3bqRT+okIhUNylfOUkABVqsDFi/Dhh/DUU/aOKO9seY7m7p7L0O+H0qxCM/4M+RPltCMe2p4tztOBAwdo0KCBVY/p6iIiIggMDLR3GPmS28+BUupPrXWLzOvlCo4QLuCbb0xyU6sWDB9u72gcx4BGAyjnX45d53fx68lf7R2OEMKKJMERwgV8+aV5fO451xrYL1WfPqZk4uPhw/AWJuN7af1LOMIVbSGEdeQpwVFKlVZKLVdKRSmlTiqlch3cXSm1XimllVKu+OtUCIdx7JgZ+8bLywWmZchOly6mZGFk65GU9y/PltNb+OXYL0UcmBDCVvJ6BecTIB4oDwwEPlVK3ZhdZaXUQGSMHSEcwjvvmIH97r8fSpa0dzR28swzpmShpE9Jnr7laQAmbZ5UlFEJIWwo1wRHKeUP9Ade1VpHaq03Az8Ag7OpHwS8DrxozUCFEAXTrBlUqwZjx9o7Esf1RMsnCPIOYsOJDaw+utre4QghrCDXXlRKqWbAFq21b7p1zwMdtda9s6j/CXAUWA4cBzy11tdN+KKUCgFCAMqXL9988eLFhXkfDicyMpKAgAB7hyFy4SrnKSkJ3N3tHUXBWOMc3TR6NAB733kn2zrzT87nyxNfUjegLjNuniE9qvLJFt+loKAgateuLefCipKSknAvRr8MtNb8+++/hIeHZ1unc+fOWfaiysttpAAg85HDgev6mSmlWgDtgGeAKrkEPROYCaabuLN11XWV7sfFnZwnx2eVczRkCECOx7kl4RZ+mPoDRyKP4FPHhzZV22RbV1zPFt+lf//9Fw8PD/z8/Kx6XFdW3LqJR0dHExQUVKBZz/PSBicSKJFpXQkgIv0KpZQbMB14JqsrNkKIovXWW/DaaxAWZu9IHMCIEabkwNfTN3V04+fWPCc9qhxAcHAwZ86cISwsjISEBDknLkJrTUJCAmFhYZw5c4YyZcoU6Dh5uYJzGPBQStXVWh9JWdcE2JepXgmgBbAk5XKi5RrYGaXUPVrr3woUoRAi38LC4O23ITIS7rwTWrWyd0TFw0vtX2LW7llsPbOVWbtm8fDND9s7JJcWFBSEt7c3oaGhXL58mcRE+d+5sGJjY/Hx8bF3GLny8PDAx8eHatWqFTjeXBMcrXWUUupb4A2l1CNAU6Av0DZT1XCgUrrlqsAfQHMgtEDRCSEK5MMPTXLTrZskNwB07Woe167NsVqgdyCTb5/Mg989yEvrX2Jg44H4eDj+HwNn5uPjQ9WqVe0dhtPYuHFjgW73FEd57SY+AvAFLgKLgOFa631KqWpKqUilVDVtnLcU0pKaC1rreBvELoTIQmSkSXAAXn7ZvrE4jAEDTMmDQY0H0aR8Ey5GXWTpvqU2DkwIYSt5SnC01mFa635aa3+tdTWt9cKU9ae01gFa61NZ7HNCa62kPY4QRevzz+HKFWjbFtq3t3c0DuLRR03JA6VU6ujG7299n8Rk+RUmRHEkUzUI4UTCw2FSylh1Y8eC9K4tmEGNB1E9qDp7Luxhxs4Z9g5HCFEAkuAI4US++spMqtm2LfTqZe9oHEinTqbkkb+XP5O7TQbg1Q2vEhkfaZu4hBA2I9MpCOFEnngC6teHChXk6k0GQ4fme5f+DfrTukprtp3Zxhub3uDd29+1flxCCJuRKzhCOBGlTIehRo3sHYmDGTo030mOUooPe3yIQvHBtg/Yc36PTUITQtiGJDhCOIHdu2HbNntH4cASEkzJp5aVWzK8xXASkxN5bOVjMtCcEMWIJDhCFHNaw/Dh0KYNLFtm72gc1O23m1IAk7pOorx/ebaf3c7if5xrzjwhnJkkOEIUcwsXmqs3FSqYgf1EFh55xJQCCPQO5M3b3gRg1OpRhMXI3BdCFAeS4AhRjEVFQcpE2bz1FhSjOfSK1qBBphTQQ80eon219lyIusCzq5+1YmBCCFuRBEeIYuzdd+HsWWjePHXCbJGV6GhTCshNufFFny/w8fBh7p65fLT9IysGJ4SwBUlwhCimjh41CQ7A1KngJt/m7PXsaUoh1CtTj+k9pwMweu1oLkVfskZkQggbkV+JQhRTo0dDbCwMHAi33mrvaBzc8OGmFNKwZsO4o84dxCTGMH7jeCsEJoSwFUlwhCimPvsMHnvMXL0RucjHZJu5Gd9pPO7KnY93fMyqI6usckwhhPVJgiNEMVW2rElygoPtHUkxEB5uihW0rNyS1zq+BkDIihBCo0KtclwhhHVJgiNEMZKYCJMnm1tTIh/69jXFSl7p8Aq3VL6FsxFn6f91f5lxXAgHJAmOEMXIRx/BCy/APffYO5Ji5umnTbESN+XGsnuXUSmwEr+d+o0pW6dY7dhCCOuQBEeIYmLfPhgzxjwPCbFvLMXO3XebYkWVS1Rm5p0zARizdgwbT2y06vGFEIUjCY4QxUBCgplpID7eDMjbu7e9IypmLl0yxcp61evFI80eQaO5e8ndXIi8YPXXEEIUjCQ4QhQDL7wA//1nnk+RuyH597//mWID03tNp0P1DlyJvcKEXyfY5DWEEPknCY4QDm71apg2zTz/8UeZjqFAnnvOFBvwdPdkavepAMzaNYvjV47b5HWEEPkjCY4QDm7dOvP46quFHozXdfXubdP7es0qNqPPDX2ISYwhZGUIyTrZZq8lhMgbSXCEcHDvvgvffQevv27vSIqx8+dNsaFPe31KWb+yrD22lve3vG/T1xJC5E4SHCEcVFxc2vO+fcHd3X6xFHv33WeKDVUKrMTsvrMBeHn9y/x57k+bvp4QImeS4AjhgBYuhMaN4eBBe0fiJMaMSetjb0O96vXiyZZPkpCcwJh1tn89IUT2JMERwsGsWwfDhsHhw7Bhg72jcRI9ephSBN7o/Aa+Hr6sPbaWubvnFslrCiGuJwmOEA5k507o18+Md/P00/D44/aOyEmcPm1KESjlW4pxncYB8PAPD7Py8MoieV0hREaS4AjhIA4ehDvugMhIGDgQPvgAlLJ3VE5i8GBTisiL7V7kkWaPkKSTuHfpvVyMulhkry2EMCTBEcIBnD4N3bqZwXbvuANmzwY3+XZazyuvmFKEPrvzM9pUaUNMYgzPrn62SF9bCCEJjhAO4ccfTZLTpg0sXQqenvaOyMl07WpKEXJ3c2f+3fPxdPNk4d6FbD61uUhfXwhXJwmOEA7g8cdh/nxYuRL8/e0djRM6dsyUIlarVC2eavUUGk3vRb05Gna0yGMQwlVJgiOEnURGwokTacsDB0Lp0nYLx7k99JApdvDO7e9wZ707uRp7lSHfDZFRjoUoIpLgCGEH589Dx47Qvj0clX/qbW/8eFPswMPNgzl951AxoCJbTm+R9jhCFBFJcIQoYocPQ9u28Ndf4O1t72hcRMeOpthJGb8yfNrrUwCmbZ/Gryd/tVssQrgKSXCEKEJbt5rk5vhxaNkStmyBOnXsHZULOHTIFDvqW78vr3V4DYC7ltzFtjPb7BqPEM5OEhwhisg330CXLnD5spkVfMMGKFfO3lG5iMceM8XOxrYfS8fqHQmLCeO2ubfJlRwhbChPCY5SqrRSarlSKkopdVIp9UA29YYopf5USl1TSp1RSr2rlPKwbshCFD/HjsGAARATA48+Ct9/L72litRbb5liZz4ePqwetJpedXsRkxhD13ld+eXfX+wdlhBOKa9XcD4B4oHywEDgU6XUjVnU8wNGAsHALUAX4PnChylE8VarFrzxBkyeDDNmgIek/UWrbVtTHIC3hzffDviWATcOICE5gUHLB/Fv2L/2DksIp5Prr1mllD/QH2iktY4ENiulfgAGAxmmy9Vaf5pu8axSagHQ2YrxClFsbN8O167B7beb5Zdftm88Lu2ff8xjo0b2jSOFl7sX8+6ax8nwk2w7s422s9qyb8Q+gv2C7R2aEE5Daa1zrqBUM2CL1to33brngY5a69657PsdcFBrPSaLbSFACED58uWbL168OP/RO7DIyEgCAgLsHYbIhS3OU1ycG/PnV2fhwmoEBiYwa9ZOSpeOt+pruBJrnKOmI0cCsHvq1MIHZEXhCeE8s/sZTkafpEWpFky6aRLuyt3eYRWI/M4rHpzxPHXu3PlPrXWLzOvzcqE8AAjPtC4cCMxpJ6XUMKAF8EhW27XWM4GZAC1atNCdOnXKQyjFx8aNG3G29+SMrH2etmwxoxJbOuw8/LAXvXq1le7ghWCVc/T55wB0atmy8AFZ2c2tbqbpjKbsvLKTWVdmMafvHDzdi99cHfI7r3hwpfOUlzY4kUCJTOtKABHZ7aCU6gdMAu7QWl8qcHRCFBNRURASAu3ameSmQQPYvBnef1/GunEILVua4oCqBlXlxwd+JMArgIV7F/LoikfJ7cq6ECJ3eUlwDgMeSqm66dY1AfZlVVkp1QP4HOittd5b+BCFcHz33msuEri5wQsvmEH82rWzd1Qi1e7dpjio1lVas3rQavw8/Zi7Zy5vb37b3iEJUezlmuBoraOAb4E3lFL+Sql2QF/gq8x1lVK3AQuA/lrrP6wdrBCOJP0/2aNHQ8mSZlbwd98FHx+7hSWyMnKkKQ6sbdW2zOs3D4CX17/M8JXD5UqOEIWQ127iIwBf4CKwCBiutd6nlKqmlIpUSlVLqfcqEASsSlkfqZT6yfphC2E/587BU0/BkCFp6zp0MAP49ehhv7hEDqZONcXB9W/YnyndpgDw2Z+f8eqGV+0ckRDFV55G49BahwH9slh/CtMI2bIsXcKF0zp/Ht5+24xjExcH7u4wcSJUrWq2u8m44I6raVN7R5Bno9qMok7pOty15C4m/jaRemXq8WCTB+0dlhDFjvxKFiIXe/bAww9DjRrw4YcmuenfH3btSktuhIPbscOUYqL3Db2ZeNtEAIZ9P4xp26bZOSIhih8ZT1WIHFy5Aq1aQXw8KAX9+sH48dC4sb0jE/nywgvmceNGu4aRHy+2e5Frcdd4a/NbjFw9Em8Pbx5v8bi9wxKi2JAER4h0Dh+GxYvhpZfMdAqlSsGwYeDlZdrd1K2b+zGEA/r4Y3tHkG9KKSZ2mUi1oGo8/uPjDP9xOL+d+o3pPacT5BNk7/CEcHiS4AiXd/kyfP01zJsH27aZdU2aQN++5vlnn9kvNmElDjJFQ0E81uIxrsReYey6sSzcu5CDlw6y7sF1lPQpae/QhHBo0gZHuCSt4Ysv4NlnmxAcDCNGmOQmIACGDjWTYwonsmWLKcXUmFvHsGbQGmqWrMlf//1Fu1ntOB1+2t5hCeHQJMERLkFr2L8/bVkp02t4165SuLlB9+7w1Vemp9Ts2XDTTXYLVdjCSy+ZUozdXvt2Ng7dyA1lbmB/6H7uWHAHsYmx9g5LCIclt6iE0zpzBjZtgnXrYM0aOHsWjh6F2rXN9hdfhN27DzF27A2ULWvfWIWNzZhh7wisolpQNbY+vJVWX7RiX+g+BnwzgG/u+aZYzl0lhK3JFRzhVC5fhsGDoWZN04V70CBzRebsWahQAY4fT6v74IPQp89/kty4ghtuMMUJlPItxaL+iyjlU4ofDv3AgG8GEBkfae+whHA4cgVHFDvJyeZKzM6d8OefkJBgxqcBKFECvvkGYmPN81tvhU6doFs307VbKbuGLuxl0ybz2LGjfeOwkhaVWrDi/hX0WNCD5QeX0/bLtuwM2YmXu5e9QxPCYUiCI4qFzZth/nwz6N7evWb2bovAQNOexs0NPD1NW5o6dUw7Gnd3u4UsHMnrr5vHYjQOTm7aVWvH9ke202F2B/Ze3EufRX345t5vCPAKyH1nIVyAJDjCrrSG0FA4dszcPjp+3Dw/csSMO/O//5l6Bw9mbEZRuTLcfDO0aAEtW2ac+NKyjxCpZs2ydwQ20bBsQ5bdu4zei3qz+t/VNPykIYv6L6JdNZnKXghJcIRNaW3axZw9a5KXq1dNN2yLypXhv/+y3rdt27RkpXNnmDLFjE/TuDEEB9s6cuFUnLjff8caHdn2yDb6Le7HkbAjdJ7bmVl9ZzGo8SB7hyaEXUmCIwokLs4kLpcumVK3btq8TD/8YG4ZnT5tejLFpuvJ6utrZuG2tIUpV85sr1kzY6lbN2NX7dq1YdSoInt7wtmsXWseu3a1bxw20rBsQ/Y8vodnVz/LZ39+xuDlgzl59SQvtntRelgJlyUJjouLjISwMPN49aqZe+nKFbPO1xcefdTU0xratDG3k0JDISIi43E++cQMlgdm/w0b0rYFBUGlSmayypo1TXLk42O2bd1qXkcIm3rzTfPopAkOgK+nL9N7Tcffy5/3t77PKxteYdE/i3jv9vfoUacHSlrYCxcjCU4xcumSSR6io00j26iotOcVK5rbOJZ6EyembYuMNAnJtWumzJ0LrVubuq+/bm79ZKV27bQERyk4dMgkQWAa7wYHZywWt98Oq1ebKzpVq5rRgbMjyY0oEl99Ze8IioRSisndJtO1Vlee+ukp9oXuo+fCnvSs25OFdy+UOayES3HqBEdrSEoyJTEx7bm7u+lCDGb9qVPm0VLH8jwx0QydUbq0qXvokBkNNzHRdE22lMREc0UifduSBQuqsWqV2R4fn/YYHw/9+5sC8OuvZsC5+HhzqyY2FmJi0p6fOkXqOC0DB5oB67LSr19aghMTY24RZefSpbTn5cpBlSomCSlRwrzXUqXMY5UqGfdbv970WAoONldlsvuHsFIlU4RwGJb7py6iR50e7H5sNx/98RFvb36bVUdWceP0G3nv9vcY0GgAbkqGQBPOzyESnKNH4ZZbzPgmyckmybA8f/xxePJJU2/9enjooYzb0ycwBw+aKxkAffrAihVZv16vXrBypXkeGpo2sm1Wvv/eHAtg0SIYPz7rehUqZExwvvuucoZEIr26ddMSnGvXYPv27F8/Li7teZUqJlY/P/D3z/jYqlVavTJlYPLktG0BASYhCQw0SUz63/WjR5uSF82a5a2eEA7n55/NY48e9o2jCPl7+TPm1jHc3eBu7l16L3su7OGBbx9g4T8LWXD3Akp4l7B3iELYlEMkONHR8McfWW+7cCHteWwsnDyZ/XESE9OeW64uuLmZKzYeHubR3d380bfw8jLtQix10hd3d5MYWDRoYGaY9vAw461YiocHlCyZMZb77z9FlSp1M9Tz8gJv74yNZ9u2hd9/N+t9fc2VoPQl/S2cL7/M/r2n5+cHzz2Xt7pCuIRJk8yjCyU4FvXK1OPPkD/5cteXjFk7hpWHV9Lq81b8+MCP1C6dw393QhRzDpHg1KljhtN3c0sr7u7msVy5tHqdOpmuxpnrWZKREun+IVm+3CQ5ubWrK1PGjLuSFwMGmJIXd999lk6d6uZar3Rpk+QIIWxo8WJ7R2BX7m7uhDQPoVONTnSe25lDlw/R5LMmjGo9ijG3jsHfy9/eIQphdQ6R4Pj5mVtUealXo0bejukmt5iFEBYVKtg7AodguZpz/7L72XhiI2/+9iZz9szhg+4f8L+GMkKmcC6SBgghnN+KFdk3ynMxFQIqsGHIBn4Z/AvNKzbnzLUz3LP0Hp5a9RQRcRG5H0CIYkISHCGE83v/fVNEqq61urL9ke18fMfHeLp58vGOj6nwfgUGLx/MoUuH7B2eEIUmCY4Qwvl9840pIgN3N3eeaPUE6x5cR4fqHYhOiGb+3/O5cfqNjPx5JHvO70Gnn+hNiGJEEhwhhPPLPBqlyKB99fZsGrqJQ08eonvt7iTpJKZtn0bTGU2p8H4FZu+aTXxSvL3DFCJfJMERQji/b781ReSoXpl6/DzoZ34d+ivDmg4D4GLURR764SEqvl+R6Tumk5CUYOcohcgbSXCEEM7vww9NEXnSvnp7ZvWdRfRL0cy4cwY3lLmBsJgwnlj1BE0+a8JL615i57mdcvtKODRJcIQQzu/7700R+eLr6UtI8xAOPHGAxf0XUy2oGgcuHeDtzW/T8vOWNJvRjPd+f49zEefsHaoQ13GIcXCEEMKmgmSSycJQSjGg0QDuanAXKw+vZMPxDSzYu4A9F/aw58IeXlr/Et3KdaNm05pUL1nd3uEKAcgVHCGEK1iyxBRRKF7uXtzd4G4+6vkR/z33H8sHLKdHnR4kJiey6vwqakyrQbtZ7Ri7dizHruRxiHghbESu4AghnN+nn5rHvM61InLl7eFNv/r96Fe/H4cvH2bE1yNYd3EdW05vYcvpLUz6fRINyzbkrvp3cUvlW2heqTmVAivZO2zhQiTBEUI4v1Wr7B2BU6tXph6vNHiF5Q8tZ/E/i1l7fC0/H/2Z/aH72R+6P7Ve4/KN6VqzK11rdaV99fYEeAXYMWrh7CTBEUI4Pz8/e0fgEgK9A3m0+aM82vxREpISWH98PT8f/Zk///uTHed28PeFv/n7wt9M2TYFd+VOvTL1aFm5Jb3r9aZ77e4Eegfa+y0IJyIJjhDC+c2fbx4HDbJvHC7E092T7nW6071OdwBiE2PZenora4+tZe3xtew8t5MDlw5w4NIB5u2Zh4ebB5UCK9G5RmealG9Cm6ptaFqhKT4ePnZ+J6K4kgRHCOH8vvjCPEqCYzc+Hj50rtmZzjU7M5GJxCTE8M/Ff1h3fB3LDixj57mdnAo/xdw9c1P38Xb3pn/D/jQIbkCjco3oWL0jpXxL2fFdiOJEEhwhhPP75Rd7RyAy8fX0pWXllrSs3JIxt44hLCaMvRf2sufCHnad38Xqo6v5L/I/Fu5dmLaPhy+tq7Smbum6lPMvR0mfktmW0r6lUUrZ8R0Ke8tTgqOUKg18CXQDLgFjtdYLs6k7ChgN+ALLgOFa6zjrhCuEEAXg6WnvCEQuSvuWpmONjnSs0TF13d4Le9l6ZitHLh9h+9nt/HbqNzac2MCGExtyPZ6nmycVAipQ1r8sZf3KEuwXTFm/spT1T3se7BdMsF8wJX1K4uXuhYebB57unni6eeLh5iEJUjGX1ys4nwDxQHmgKfCjUmqP1npf+kpKqe7AGOA24BywHBifsk4IIexjzhzzOHSoPaMQ+XRT+Zu4qfxNqctnrp1h57mdnL12lssxl7kaezXLcj7yPDGJMZy+dprT104X+PXdlXtqwmN59PbwxsfDB29385i+eHt44+nmiVIKN+WWVjCP161PVxRZb8tun/zWt+xz6MIhzv59Ns+vkTnJU6QtF/W29NvL+ZejfnD9HM9frgmOUsof6A800lpHApuVUj8Ag7k+cRkCfGlJfJRSE4AFWdQTQoiiIwmOU6hSogpVSlTJtZ7WmqiEKEKjQrkUfYnQ6FBCo0IJjU5ZTvf8QtQFIuMjSUxOJCEpgYTkBBKSEkjSSaYkJhFLbBG8uyJ00N4BFN79je5nYf8sbySlUrlNlqaUagZs0Vr7plv3PNBRa907U909wFta6yUpy8FAKBCstb6cqW4IEJKyeANwKC9vqhgJxtzOE45NzpPjk3NUPMh5Kh6c8TxV11qXzbwyL7eoAoDwTOvCgawGLMhc1/I8EMiQ4GitZwIz8/D6xZJSaqfWuoW94xA5k/Pk+OQcFQ9ynooHVzpPeZmLKhIokWldCSAiD3Utz7OqK4QQQghhE3lJcA4DHkqpuunWNQH2ZVF3X8q29PUuZL49JYQQQghhS7kmOFrrKOBb4A2llL9Sqh3QF/gqi+rzgIeVUg2VUqWAV4A5Voy3OHHa229ORs6T45NzVDzIeSoeXOY85drIGFLHwZkF3I5pSzNGa71QKVUN2A801FqfSqn7LBnHwXlcxsERQgghRFHKU4IjhBBCCFGc5KUNjhBCCCFEsSIJjhBCCCGcjiQ4eaSUqquUilVKzc+0votS6qBSKloptUEpVT3dNqWUekcpdTmlvKvSjUOtlKqRsk90yjG6Zjr2A0qpk0qpKKXUdyltoUQmSilvpdSXKZ9VhFJql1Lqjkx15DwVM0qp0kqp5Smf60ml1AP2jskZ5fb9ke+OY8nqb5Gco2xoraXkoQBrgN+A+enWBWMGM7wH8AHeA7al2/4YZoTmKkBlTIPsx9Nt3wpMwTTI7g9cBcqmbLsRM35QB8wAiguBxfb+HByxAP7AOKAGJmm/M+WzqyHnqfgWYBGwJOVzvTXlHN5o77icreT0/ZHvjuOVzH+L5Bzl8FnZO4DiUID7gK9TfgmkT3BCMNNYWJb9gRigfsryFiAk3faHLT94QD0gDghMt/03yw8e8BawMN222pgJTwOt+d6ctQB/A/3lPBXPknKO4oF66dZ9BUyyd2yuUCzfH/nuOFbJ6m+RnKPsi9yiyoVSqgTwBvBcFptvBPZYFrQZM+jflPXXbU95nn7bMa11RA7b0x/7X1J+4Rf0vbgKpVR5zOdkGYxSzlPxUw9I0lofTrcu/ecubCTT90e+Ow4ih79Fco6yIQlO7iZgZkg/ncW23ObpympuroCU+5/53TfzdpEFpZQnZgb7uVpry5y5cp6KH/lc7SCL7498dxxHdn+L5Bxlw6UTHKXURqWUzqZsVko1BboCH2RziNzm6cpqbq5Iba715XffzNtdRm7nKV09N8xtjHjgyXSHkPNU/MjnWsSy+f7Id8cB5PK3SM5RNlw6wdFad9Jaq2zKrUAnTEO7U0qp88DzQH+l1F8ph8gw95ZSyh9zj3JfVtvJOIfXPqCWUiowh+3pj10L8MbMDeZS8nCeSPlv5EugPKbtTUK6Q8h5Kn7yMweeKKQcvj/y3XEMncj+b5Gco+zYuxGQIxfAD6iQrkwGviGthXlZzOW6/pjW6++QsfX648ABTMv1SpgflvSt17elHNMHuIvrW69fA9pjGo3Npxi1XrfDufos5fMMyGKbnKdiWIDFmJ5U/kA7pBeVLT/rLL8/8t1xjJLT3yI5Rzl8bvYOoDgVMvWiSlnXFTiIabW+kZSuySnbFPAuEJZS3iVleoyU7TVS9onBdOPrmunYDwCngCjge6C0vT8DRyxAdUADsZhLqpYyUM5T8S1AaeC7lM/1FPCAvWNyxpLb90e+O45XMv8tknOUdZG5qIQQQgjhdFy6DY4QQgghnJMkOEIIIYRwOpLgCCGEEMLpSIIjhBBCCKcjCY4QQgghnI4kOEIIIYRwOpLgCCHIYSqM9OVESt05Sqkzdg4ZAKXUCaXUfCsfb04e6s2xfB5CCMfkYe8AhBAOoU2m5eWYWYTHpVsXV2TRCCFEIUmCI4RAa70t/bJSKg64lHl9YSmlvLXWkigJIWxOblEJIQpEKdVMKfWbUipaKXVEKfV4pu1DU25tdVBKLVVKXQW2p2zzUEqNVUodVErFKaXOKaXeV0r5pNvfQyk1QSn1r1IqVil1SSm1WSl1axax3KeUOqCUilJK7cymziCl1J50x/pKKVUxD++zi1Lqr5T9/lVKPVaQz0sIUbTkCo4QoiBKAAuBqcAbwDDgU6XUIa31hkx1F2Amzfwfab9z5gO9MRMDbgEaABMw8+L0T6kzGhgFvAzsTnnNFpg5qtJrD9wAvIqZT2kCsFIpVUNrfRVAKRUCzACWAGMxkw6+BdyilLpZax2Z1ZtUSjUAVgE7gfswMymPAwKApJw/IiGEPUmCI4QoiEBghCWZUUr9CnQD7gcyJzjfaK1ftCwopdoDA4AhWut5KavXKqXCgPlKqaZa692YdkFrtNbT0h1rRRaxlACaaq2vpBz/PLAD6AksVEq5Y5KejVrr+9LFcRD4DXgI+DCb9/kKEAF001pHpey3BfgXOJfdhyOEsD+5RSWEKIjo9FdqUtrVHAGqZVF3eablHkA8sCzlNpSHUsoDWJOyvUPK4w6gp1JqolLqVqWUVzaxbLUkNyn2pjxaYrkBKIe5kpRKa70ZOAl0zO5NYpKsVZbkJmW/08DvOewjhHAAkuAIIQriShbr4gCfLNb/l2m5HOAFRAIJ6crFlO1lUh7fAl4H+mCutFxWSs1WSgVnOl5Y+oV0jZgtsVhuaWWOA+A819/ySq8icCGL9VmtE0I4ELlFJYSwNZ1p+TKmrUz7bOqfA9BaJ2Da6LyjlKoA3AlMAfwwt7jyypIAVchiWwVM+5rs/AeUz2J9VuuEEA5EruAIIYraz5irK0Fa651ZlOvatmitz2utvwDWAo3y+XqHMFdc7ku/UinVFqgObMph362Y22T+6farCrTLZwxCiCImV3CEEEVKa71RKbUI+EYpNQX4A0jG9KDqCYzWWh9WSn2PGWzwL8wtsWaY9jsz8vl6SUqp14AZKaMezwcqAxMx7YZm57D7m8A9wBql1HuYW2vjkVtUQjg8SXCEEPYwCHgK04PpZUz7nRPAatKSh18xycUTmNtSp4B3MYlJvmitZyqlooEXgO8x7X9WAS9m10U8Zb8DSqmewHuYLuZnMbfN2gCd8huHEKLoKK0z3x4XQgghhCjepA2OEEIIIZyOJDhCCCGEcDqS4AghhBDC6UiCI4QQQginIwmOEEIIIZyOJDhCCCGEcDqS4AghhBDC6UiCI4QQQgin83/VkFRzCv0C/wAAAABJRU5ErkJggg==\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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\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": [
    "## The ROC Curve"
   ]
  },
  {
   "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": "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": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: the following two cells may take close to 30 minutes to run, or more depending on your hardware."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.87365, 0.85835, 0.8689 ])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(sgd_clf, X_train, y_train, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.8983, 0.891 , 0.9018])"
      ]
     },
     "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": "markdown",
   "metadata": {},
   "source": [
    "# Error Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5577,    0,   22,    5,    8,   43,   36,    6,  225,    1],\n",
       "       [   0, 6400,   37,   24,    4,   44,    4,    7,  212,   10],\n",
       "       [  27,   27, 5220,   92,   73,   27,   67,   36,  378,   11],\n",
       "       [  22,   17,  117, 5227,    2,  203,   27,   40,  403,   73],\n",
       "       [  12,   14,   41,    9, 5182,   12,   34,   27,  347,  164],\n",
       "       [  27,   15,   30,  168,   53, 4444,   75,   14,  535,   60],\n",
       "       [  30,   15,   42,    3,   44,   97, 5552,    3,  131,    1],\n",
       "       [  21,   10,   51,   30,   49,   12,    3, 5684,  195,  210],\n",
       "       [  17,   63,   48,   86,    3,  126,   25,   10, 5429,   44],\n",
       "       [  25,   18,   30,   64,  118,   36,    1,  179,  371, 5107]])"
      ]
     },
     "execution_count": 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()"
      ]
     },
     "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": 73,
   "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": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure noisy_digit_example_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAADWCAYAAACE7RbFAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAATR0lEQVR4nO3dSWyWZdvG8bNioVDa0jK0UKBlKDMCZbAQIijgFN2YuNLEjdFEEhNdaFxAJCRq4rBxiEYWRlcmsjAxBuOLVQYFWplnSqFMBQottAwdgH6bb/G9keOo36Pghfx/23+ep3dL6+mTnPd1Z3V3dwcAAKm555++AAAAboYBBQBIEgMKAJAkBhQAIEkMKABAkhhQAIAk3evixYsX5Q760aNH5euysrJkKy4uthd06NAh2SorK2W7cOGCbM3NzbI1NTXJNnToUNkKCgpki4g4c+aMbOXl5bL9/vvvsg0aNEi2kpIS2fbs2SPbtGnTZIuIaGlpka2srEy21tZW2QoLC2Vztz3s2LFDttLSUtkiIu65R/+/2L59+2SbM2eObDk5OfoX/Y+4nwPQbvq3xCcoAECSGFAAgCQxoAAASWJAAQCSxIACACSJAQUASJJdM3er0m49ua6uTja3Kh0RMXDgQNk2btwoW69evWSrqqqSzX2Po0ePlq2nU+B37twpW0dHh2y5ubmyjRgxQraampqM3vPIkSOyRURUVFTIVl1dLdv8+fNlc7cSuN+P/Px82QYMGCBbRMSlS5dku3r1qmy1tbWyue8RwF/HJygAQJIYUACAJDGgAABJYkABAJLEgAIAJIkBBQBIkl0zdyeEu/Xs+vr6jFpExMKFC21X3Er45cuXZcvLy5PNrVH37t3bXo/rRUVFsrW1tcnW2dkpm1vBdt/j6dOnZYuIOHDggGzu32r37t2yuVsU3OsmTZok27Zt22SLiJg4caJs996r/wzc7xWAW4tPUACAJDGgAABJYkABAJLEgAIAJIkBBQBIEgMKAJAku2bu1rPdidRLliyRbcuWLfaC3MnS7qTzs2fPyubWiF3r27evbNevX5ctImLw4MGylZSUyHblyhXZ3Er4+PHjZduzZ49sOTk5skX4lXB3Erp7X3cqveNW3ltbW+1r+/fvL9uMGTNkO3jwoGzDhg2zXxPAX8MnKABAkhhQAIAkMaAAAEliQAEAksSAAgAkiQEFAEgSAwoAkCR7H5S716esrEy2rKws2dx9PhER586dk62wsFA2d09OV1eXbAsWLJDN3Xe0f/9+2SIihgwZIpu7L2nMmDGyNTc3y+buA6qoqJDN3VsW4R//kZ2dbV+rVFVVybZu3TrZ3O9jr1697Nd099+579HdJwfg1uITFAAgSQwoAECSGFAAgCQxoAAASWJAAQCSxIACACTJ7tDOnj1bNrcqXVpaKptbT4+IGDVqlGwtLS2yTZo0SbaamhrZjh8/Lpv7Ptz3HxFx6dIl2To6OmQ7fPiwbHPmzJHNPW7ENbe6HhExbtw42dya+YkTJ2Rz/46OW/ufPHmyfW13d7ds7jaEM2fO9HxhAG4JPkEBAJLEgAIAJIkBBQBIEgMKAJAkBhQAIEkMKABAkrLc+u3mzZtlzM3Nla+7cOGCbDk5OfaC3Knco0ePzuh96+vrZZsyZYps7mT1AQMGyBbhT9d2P58bN27Idu3aNdny8/NlKy4ulu3q1auy9fQ13cn0DQ0NsrmfzYwZM2TbunWrbHl5ebJFRJSUlMh2/vx52dy1lpWV6WP7/0j/oQG46d8Sn6AAAEliQAEAksSAAgAkiQEFAEgSAwoAkCQGFAAgSfY0c7eaO3LkSNncqdN9+vSxF+RWid1J6O+8845sp06dku3y5cuyuRXsF154QbaIiGPHjsn23HPPyeZO1nY/V3cquzvNvKioSLYIv2rf1tYm24gRI2RzPxv3/Y8ZM0Y2d3p8hL/VwJ2E39jYaN/33+ibb76R7fPPP5dt2LBhsrnbQJ555hl7Pe6/Q2PHjrWvxZ2NT1AAgCQxoAAASWJAAQCSxIACACSJAQUASBIDCgCQJHuaeWNjY0YnMF+/fl22/fv329fOmTNHtrq6OtneeOMN2Xbt2iWbW1N1K+g9rcs3NzfL5k5Cd19z4cKFstXU1Mg2depU2dzPNMKvBxcWFmb0uhdffFE2ty4+fPhw2dwJ8RH+d7J3796yVVRUyFZQUPCvPM181KhRsh09evT2Xcj/cif1u1sE/i3cLRuvvfaabLNmzboVl3OrcJo5AODOwYACACSJAQUASBIDCgCQJAYUACBJDCgAQJLsaeZZWXqLtri4WLYjR47ItnjxYntBFy9etF158803ZautrZXNrWK6E8JXrVplr8f9DBoaGmSbN2+ebG5dfseOHbL169dPtitXrsgWEdHR0SHb+fPnZdu3b19GX/Orr76Sbffu3bK5U9Aj/L/zhg0bZHPr6wUFBfZr3qnc77b7PXMr33v37pVt27Zt9np+/vln2TZt2iSbe+KCO1E/U9nZ2bYPGjRINndqvvse3Qr6HbZmflN8ggIAJIkBBQBIEgMKAJAkBhQAIEkMKABAkhhQAIAk2TVzt4rp1qg7Oztla2pqshdUWlqa0fu6Fc+lS5dmdD3u6y1btky2iIi8vDzZ3Lr0Qw89JNuWLVtka29vl23w4MGyuTXyCH/S96uvvirbtWvXZBs2bJhsvXr1ks2ddF5ZWSlbRMT69etlmzJlimzu5Pl/q0WLFmXUnEcffTTTy4mWlhbZ3Iq6W7N2p/9nqqcnHIwfP162CRMmyOaejDBmzJieL+wOxicoAECSGFAAgCQxoAAASWJAAQCSxIACACSJAQUASBIDCgCQpKzu7m4Zf/jhBxndvTXuCPi2tjZ7Qe6+E/eYBvdICfdYBHdPknt8QF1dnWwR/mfg7hFy93W4e8TcPULuESZz586VLSLirbfeku3tt9+Wzd2D8umnn8o2atQo2fLz82Xr6d/D3aPifufc4yXmz5+vn0fzR/oPDXeF1atXy/b000/LNnXqVNmqq6tlKyoq+nMXloab/i3xCQoAkCQGFAAgSQwoAECSGFAAgCQxoAAASWJAAQCSZB+3MWjQINnKyspkc2vUOTk59oLc0fLuUQwHDhyQzR1z71bQ3Xu664yIGDdunGxulfzBBx+Urb6+XrbJkyfLtnPnTtncCnpERG1trWzuMRXPP/+8bMXFxbK5x7i417nbDCIi9u7dK1t5eblsbl0e+L/Onj1r+0svvSSbu91n+fLlst1hq+T/b3yCAgAkiQEFAEgSAwoAkCQGFAAgSQwoAECSGFAAgCTZNfPKykrZjh07JltXV5dsubm59oLcKeluPdudyn3+/HnZ8vLyZHPXOnDgQNki/OniQ4cOlW39+vWyuZ/r6dOnZbt+/bps3377rWwREdu3b5ftvvvuk839XPv27SvbkCFDZOvo6Mjo60VElJSUyNba2ipbU1OTbO7Eetx9Pv74Y9vdGro7Ud/dJvNvxycoAECSGFAAgCQxoAAASWJAAQCSxIACACSJAQUASFKWO0W3q6tLxuPHj8vX3bhxQ7aeVnMPHz4sm1vtbmtrk+3o0aOyjR49WraGhgbZ3EneERGFhYWyrV27VrZJkybJ1tLSItvVq1dlc+vyVVVVskVE9O/fX7avv/5aNrf27U7JP3jwoGxuXb6nf4+6ujrZ3In2I0eOlK2oqCjLftH/pv/QcMfYsGGDbIsWLbKv7ezslO2XX36R7YEHHuj5wu58N/1b4hMUACBJDCgAQJIYUACAJDGgAABJYkABAJLEgAIAJMmeZu5OyD506JB+03v12547d85eUGlpqWwnT56UzZ34m52dLVtjY6NsZWVlGb1nRER7e7tsM2fOlM2dauxW192p2++//75sly9fli0i4qmnnpLNrai709zd7QLuZPHy8nLZevo+3Pq+O7HdvQ53n++//142t0YeEbF48WLZ3NMY7mZ8ggIAJIkBBQBIEgMKAJAkBhQAIEkMKABAkhhQAIAk2TXzK1euyOZOpHZr1OvXr/8Tl3VzbgX92LFjso0dO1Y2txK/f/9+2dwp6BERv/32W0avdT/zrVu3yjZ58mTZamtrZZswYYJsERHPPvusbO5k53HjxsnWp08f2UpKSmTbtGmTbPfff79sEREHDhyQbdq0abK5U6aXLFlivybuTO7JAGvWrJHN/V5HRKxYsUK2nm5buVvxCQoAkCQGFAAgSQwoAECSGFAAgCQxoAAASWJAAQCSZNfMjx8/ntGbdnV1yTZx4kT72n79+sm2Y8cO2dyKZ319vWx9+/aVza0uNzQ0yBYRUVlZKZtbY62rq5PNrcR/+eWXsrl1ebcOHuHXX93p4u53p1evXrJduHBBtp6u1XGr5O4080WLFmX8NXFnevfdd2Xbtm2bbI899ph933nz5mV8TXcrPkEBAJLEgAIAJIkBBQBIEgMKAJAkBhQAIEkMKABAkhhQAIAk2fugxo8fL5u7t8jd53Pt2jV7Qe4emc7OTtncPUvXr1+XbePGjbIVFhbK1tjYKFtERG5urmzu3iv3uIlVq1bJ9p///Cej9/zkk09ki4ioqKiQLT8/X7aDBw/KVlxcLFtra6ts/fv3l+3IkSOyRUQMHz5ctiFDhsi2bt062RYuXGi/JtL13XffybZy5UrZCgoKZFu2bNlfuib8EZ+gAABJYkABAJLEgAIAJIkBBQBIEgMKAJAkBhQAIEl2zfzAgQOyFRUVyeZWxc+dO2cvyK0g5+TkyJaVlSWbW0936+C7du2Sza1fR/hHXLjXumv94osvZHNr78uXL5fN/bwj/ONPMv257tu3Tza3un727FnZRowYIVtExKVLl2S7ceOGbN3d3fZ9ka7z58/L9vLLL8vmboV5/PHHZZs7d+6fuzD8aXyCAgAkiQEFAEgSAwoAkCQGFAAgSQwoAECSGFAAgCRluTXauro6GVtaWuTr3GnmU6ZMsRdUU1Mj2/Tp02U7fvy4bDNmzJDNrRjv3LlTNneydoRflz59+rRs7iTlH3/8UbaqqirZPvzwQ9l69+4tW0REaWmpbM3NzbJ1dXXJ5n53Lly4IJtbXR89erRsERFHjx6Vrb29XbbBgwfLVlFRoe9t+CP21W8B96QC9zdRW1sr29ixY2Vbs2aNbGPGjJENPbrp3xKfoAAASWJAAQCSxIACACSJAQUASBIDCgCQJAYUACBJ9jRzt4LsVozdOnB9fb29IHdi+bFjx2QbMGCAbJs3b5bt8uXLsrkV49bWVtkiIk6ePGm74n6uQ4YMkW3FihWyDR06VDa3nh8R8euvv8qWl5cnmzsFvby8XLbt27fL5k5e7+n7cKvk7iR8dzI//nmHDx+Wza2SOx988IFsrJLfXnyCAgAkiQEFAEgSAwoAkCQGFAAgSQwoAECSGFAAgCTZNXN30vncuXNla2xslM2dKh3hV9TdCvbkyZNlc6vrdXV1srlTtysqKmSL8Gvmr7zyimxuBfv111+XzZ0efubMGdl6WqN2p0VfvHhRNrf2704sd6v97uv19O+xYcMG2crKymQ7deqUbD2doI6/R0NDg2wPP/xwRu/53nvvyfbEE09k9J74+/EJCgCQJAYUACBJDCgAQJIYUACAJDGgAABJYkABAJJk18zdeqc7HfrEiROyjRgxwl7QhAkTZHOnE7trdavSpaWlsrnT3N0J2BERq1evzui17vTwlStXyuZ+rsOGDZPNrV9H+NsJ9u7dK5s7JT47O1u2tra2v/09IyLmz58vW1NTk2zuBHXcHp999pls7u/eWbBggWw9/W3j9uETFAAgSQwoAECSGFAAgCQxoAAASWJAAQCSxIACACTJrpl3dnbK5lawFy1aJNv27dvtBbmT0MePHy/brl27ZJs3b55s1dXVsk2cOFG2n376SbaIiB07dsjmThB3p7Lv379ftlmzZsl27ty5jF4XEdHR0SGbW193p7m709WnTp0qW58+fWSrqamRLcKfSu5WyQcOHGjfF3/d+vXrbf/oo49u05UgNXyCAgAkiQEFAEgSAwoAkCQGFAAgSQwoAECSGFAAgCQxoAAASbL3QXV3d8vmHn1w8OBB2XJzc+0FXb16VTZ3/0xeXp5sra2tsk2ZMkW2e+7R83vjxo2yRfh7hNw9OyNHjszoetz9Y+4RFkOHDpUtwl/rxYsXZSsvL5etoKBAtsOHD8vWr18/2R555BHZIiK6urpkc/fm8biNW6+nR764319n7NixsvXv3z+j98TtxScoAECSGFAAgCQxoAAASWJAAQCSxIACACSJAQUASJJdM79x44ZsbuU3OztbtqamJntBbnXZcY/icOvpbh3arVi7VekIv4b/5JNPyrZ06VLZ3KM4mpubZXOPjOjpUQeFhYWyuZV4txrsbiUoKiqSzX2PPf3euMfDTJgwQbaWlhbZeBTHP2/69OmyrV27Vjb3e4Z08AkKAJAkBhQAIEkMKABAkhhQAIAkMaAAAEliQAEAkpTlTiyvq6uT0a1Z79q1Sza3FhoRce3aNdncKekdHR2yufXsffv2yeZOD+/p+3Br5u3t7bKdPXtWNneytjuVvL6+Xrae1rPd70efPn1kmzlzpmzV1dWyLViwQLaffvpJttmzZ8sWEXHy5EnZ3Jr51q1bZZs1a1aW/aL/Tf8gAdz0b4lPUACAJDGgAABJYkABAJLEgAIAJIkBBQBIEgMKAJAku2YOAMA/hU9QAIAkMaAAAEliQAEAksSAAgAkiQEFAEgSAwoAkKT/ASomcaBOpAPwAAAAAElFTkSuQmCC\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": 75,
   "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": 76,
   "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": 77,
   "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": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=4, weights='distance')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn_clf = KNeighborsClassifier(weights='distance', n_neighbors=4)\n",
    "knn_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_knn_pred = knn_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9714"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, y_knn_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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": [
    "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": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((300000, 784), (300000,))"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_expanded = [X_train]\n",
    "y_train_expanded = [y_train]\n",
    "for dx, dy in ((1, 0), (-1, 0), (0, 1), (0, -1)):\n",
    "    shifted_images = np.apply_along_axis(shift_digit, axis=1, arr=X_train, dx=dx, dy=dy)\n",
    "    X_train_expanded.append(shifted_images)\n",
    "    y_train_expanded.append(y_train)\n",
    "\n",
    "X_train_expanded = np.concatenate(X_train_expanded)\n",
    "y_train_expanded = np.concatenate(y_train_expanded)\n",
    "X_train_expanded.shape, y_train_expanded.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=4, weights='distance')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf.fit(X_train_expanded, y_train_expanded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_knn_expanded_pred = knn_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9763"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_knn_expanded_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.24579675, 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.75420325]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ambiguous_digit = X_test[2589]\n",
    "knn_clf.predict_proba([ambiguous_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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": [
    "# 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 close to 16 hours to run, or more 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=168.0min\n",
      "[CV] n_neighbors=3, weights=uniform ..................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 168.0min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ...... n_neighbors=3, weights=uniform, score=0.971, total=12.3min\n",
      "[CV] n_neighbors=3, weights=uniform ..................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed: 180.3min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ...... n_neighbors=3, weights=uniform, score=0.969, total=11.9min\n",
      "[CV] n_neighbors=3, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=3, weights=uniform, score=0.969, total=12.5min\n",
      "[CV] n_neighbors=3, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=3, weights=uniform, score=0.970, total=12.7min\n",
      "[CV] n_neighbors=3, weights=distance .................................\n",
      "[CV] ..... n_neighbors=3, weights=distance, score=0.972, total=12.5min\n",
      "[CV] n_neighbors=3, weights=distance .................................\n",
      "[CV] ..... n_neighbors=3, weights=distance, score=0.972, total=12.8min\n",
      "[CV] n_neighbors=3, weights=distance .................................\n",
      "[CV] ..... n_neighbors=3, weights=distance, score=0.970, total=12.6min\n",
      "[CV] n_neighbors=3, weights=distance .................................\n",
      "[CV] ..... n_neighbors=3, weights=distance, score=0.970, total=12.9min\n",
      "[CV] n_neighbors=3, weights=distance .................................\n",
      "[CV] ..... n_neighbors=3, weights=distance, score=0.971, total=11.3min\n",
      "[CV] n_neighbors=4, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=4, weights=uniform, score=0.969, total=11.0min\n",
      "[CV] n_neighbors=4, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=4, weights=uniform, score=0.968, total=11.0min\n",
      "[CV] n_neighbors=4, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=4, weights=uniform, score=0.968, total=11.0min\n",
      "[CV] n_neighbors=4, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=4, weights=uniform, score=0.967, total=11.0min\n",
      "[CV] n_neighbors=4, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=4, weights=uniform, score=0.970, total=11.0min\n",
      "[CV] n_neighbors=4, weights=distance .................................\n",
      "[CV] ..... n_neighbors=4, weights=distance, score=0.973, total=11.0min\n",
      "[CV] n_neighbors=4, weights=distance .................................\n",
      "[CV] ..... n_neighbors=4, weights=distance, score=0.972, total=11.0min\n",
      "[CV] n_neighbors=4, weights=distance .................................\n",
      "[CV] ..... n_neighbors=4, weights=distance, score=0.970, total=11.0min\n",
      "[CV] n_neighbors=4, weights=distance .................................\n",
      "[CV] ..... n_neighbors=4, weights=distance, score=0.971, total=11.0min\n",
      "[CV] n_neighbors=4, weights=distance .................................\n",
      "[CV] ..... n_neighbors=4, weights=distance, score=0.972, total=11.3min\n",
      "[CV] n_neighbors=5, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=5, weights=uniform, score=0.970, total=10.9min\n",
      "[CV] n_neighbors=5, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=5, weights=uniform, score=0.970, total=11.0min\n",
      "[CV] n_neighbors=5, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=5, weights=uniform, score=0.969, total=11.0min\n",
      "[CV] n_neighbors=5, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=5, weights=uniform, score=0.968, total=11.1min\n",
      "[CV] n_neighbors=5, weights=uniform ..................................\n",
      "[CV] ...... n_neighbors=5, weights=uniform, score=0.969, total=11.0min\n",
      "[CV] n_neighbors=5, weights=distance .................................\n",
      "[CV] ..... n_neighbors=5, weights=distance, score=0.970, total=93.6min\n",
      "[CV] n_neighbors=5, weights=distance .................................\n",
      "[CV] ..... n_neighbors=5, weights=distance, score=0.971, total=11.0min\n",
      "[CV] n_neighbors=5, weights=distance .................................\n",
      "[CV] ..... n_neighbors=5, weights=distance, score=0.970, total=10.9min\n",
      "[CV] n_neighbors=5, weights=distance .................................\n",
      "[CV] ..... n_neighbors=5, weights=distance, score=0.969, total=11.2min\n",
      "[CV] n_neighbors=5, weights=distance .................................\n",
      "[CV] ..... n_neighbors=5, weights=distance, score=0.971, total=11.1min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed: 582.5min 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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97+CTiNgj6bk8DmB8IV+B9kG+oqONtQCdJGlXVWyXpCOqG9pelF/nMjA4ODjGzQIYhbrzVSJngRYjX9HRxlqADkmaXBWbLGl3dcOIuCEi+iOif9q0aWPcLIBRqDtfJXIWaDHyFR1trLfifErSxw4+sd0n6YQ83lV2707+n5A0adKkZPziiy8uqjtACvlasAMHDiTj9913XzJeK8d37NjR0HZnzJgxLLZ9+/Zk2wULFiTjO3fuTManTJnSUF/QNOMyXxsZ6yTGO9RW79cwTbDdK6lHUo/tXtsTJK2VdIrtc/Pln5O0iQukgdYhX4H2Qb6iW9V7Cv4qSXslXSnpovznqyJiUNK5klZK+qmkeZIuaEI/AdSPfAXaB/mKrlTv1zAtl7S8xrL1kk4qrksAxoJ8BdoH+Ypuxa04AQAAUCoKUAAAAJRqrLPgkVu7dm3dbS+//PJk/MgjjyyqOwAK9NBDDyXjjzzySDK+bNmyhtZ/ySWXJONLlixJxo855phhsfPPPz/Zdv369cn4okWLkvE77rgjGQekxsY6ifEOtXEEFAAAAKWiAAUAAECpKEABAABQKgpQAAAAlIoCFAAAAKViFnyD9u7dm4xfe+21da/jzDPPLKo7TbN///5k/K677iq5J0C51qxZMyx23nnnJdtGRDI+ffr0ZHzjxo3J+HHHHZeM207GU9atW5eM9/b2JuO1ZjNv3bo1GT/++OPr7gs6Q2q8a2SskxjvUBtHQAEAAFAqClAAAACUigIUAAAApaIABQAAQKkoQAEAAFAqZsE3aPv27cn4Cy+8UPc6jjrqqKK60zS1ZvfW2s+JEycm47Vm4AKttm/fvmT86quvHharlQ99fX3J+GOPPZaMz5w5s87eNa6npycZnzt3bjL+5JNPJuO19hXdJzXeNTLWSYx3qI0joAAAACgVBSgAAABKRQEKAACAUlGAAgAAoFR1FaC2F9sesP2K7Zsr4rNth+2hisdnm9ZbACMiX4H2Qb6iW9U7C/4lSSskvU9SavrX6yMifTNVDHPGGWe0uguFO/XUU5PxGTNmlNwTiHytS61Z8Js2bap7HStXrkzGZ8+ePZoujUmtWfDz5s1LxmvNgkfpOjpfGe9QS10FaESskSTb/ZKOa2qPAIwJ+Qq0D/IV3aqoa0C32X7R9ldsTy1onQCag3wF2gf5io401gJ0p6TTJc2S9HZJR0i6LdXQ9qL8OpeBwcHBMW4WwCjUna8SOQu0GPmKjjamAjQihiJiICL2R8QOSYsl/VvbkxNtb4iI/ojonzZt2lg2C2AUGsnXvD05C7QI+YpOV/TXMB28n5ULXi+A4pGvQPsgX9FR6pqEZHtC3rZHUo/tXkn7lZ0W+JmkLZKOlPRFSRsiYldTeovS3H333Q21v/baa5vUEzSKfK3P7t276247adKkZPziiy8uqjvoUuRr6zHetUa9R0CvkrRX0pWSLsp/vkrSGyR9S9JuSZslvSLpj4rvJoAGkK9A+yBf0ZXq/Rqm5ZKW11j8taI6A2DsyFegfZCv6FbcihMAAAClogAFAABAqShAAQAAUKp67wWP3KxZs5LxOXPmJONPP/10M7tTiKGhoWGxxYsXN7SOuXPnFtUdoBRr166tu+3ll1+ejB955JFFdQcYd1LjXaeNdRLjXatwBBQAAAClogAFAABAqShAAQAAUCoKUAAAAJSKSUgN6unpScYPP/zwkntSnM2bNw+Lvfjii8m2tfbf5vbEGJ/27t2bjDdyO70zzzyzqO40zf79+5Pxu+66q+SeoFOk/r/vtLFOYrxrFY6AAgAAoFQUoAAAACgVBSgAAABKRQEKAACAUlGAAgAAoFTMgm+B3bt3J+NTpkxp6nb37NmTjC9ZsmRYrNbsv3vvvTcZ7+vrG33HgCbavn17Mv7CCy/UvY6jjjqqqO40TUQk47X2c+LEicl4b29vYX0CWjHeNTLWSYx3rcIRUAAAAJSKAhQAAAClogAFAABAqShAAQAAUCoKUAAAAJRqxFnwtl8j6UuSFkiaIulZSZ+JiLvz5e+RdL2k35b0uKSFEbGtaT0epy699NJk/IorrhgWu/POO5NtFy5cWEhfDhw4kIyvWrUqGX/00UeHxWbOnJlse/bZZ4++Y2g68rU5zjjjjFZ3oXCnnnpqMj5jxoySe9Ld2i1nGxnrpNaMd42MdRLjXavUcwR0gqQfSZov6XWSPivpG7Zn254qaU0emyJpQNLXm9RXACMjX4H2Qs6iK414BDQi9khaXhFaZ3urpLdLOkrSUxFxuyTZXi5pp+2TIuKZ4rsL4FDIV6C9kLPoVg1fA2r7aEknSnpK0smSvndwWZ5Iz+Xx6tctsj1ge2BwcHD0PQZQt9Hma/5achYoGWMsukVDBajtwyXdJumW/K+vSZJ2VTXbJemI6tdGxA0R0R8R/dOmTRttfwHUaSz5KpGzQNkYY9FN6i5AbR8m6VZJ+yQtzsNDkiZXNZ0sKX3vLQClIF+B9kLOotvUdS9425Z0o6SjJZ0TEb/KFz0l6WMV7foknZDHu0p/f3/dba+55ppk/IILLkjGG70388MPP5yML1u2LBlP/bX84IMPNrRNjB/ka3e6++67G2p/7bXXNqknaFQ75WwjY53UmvGukbFOYrxrlXqPgK6WNEfSByNib0V8raRTbJ9ru1fS5yRt4uJooKXIV6C9kLPoOiMWoLZnSbpM0lsl/cT2UP64MCIGJZ0raaWkn0qaJyn9Zw2ApiNfgfZCzqJb1fM1TNsk+RDL10s6qchOARgd8hVoL+QsuhW34gQAAECpKEABAABQqrpmwWNkp512WjI+ffr0YbEtW7Yk265evToZv+yyy5Lx22+/PRlfunRpMl7LihUrhsVmzZrV0DqA8arWZ3nOnDnJ+NNPP93M7hRiaGhoWGzx4sWJlrXNnTu3qO6gizQy1knja7xLjXUS412rcAQUAAAApaIABQAAQKkoQAEAAFAqClAAAACUigIUAAAApWIWfEEmTpyYjD/++OPDYieeeGKy7ZIlS5LxWvdsHhwcTMYPHDiQjH/iE59Ixj/+8Y8n40An6OnpScYPP/zwkntSnM2bNw+Lvfjii8m2tfY/u/040JhGxjqpNeMdY1174AgoAAAASkUBCgAAgFJRgAIAAKBUFKAAAAAoFQUoAAAASsUs+CZL3WP2pptuSrb95Cc/mYzv2LGjoW1+4QtfSMYXLVqUjB92GH+HAIeye/fuZHzKlClN3e6ePXuS8dQM4lqz3e+9995kvK+vb/QdA6rUup96K8Y7xrr2wLsBAACAUlGAAgAAoFQUoAAAACgVBSgAAABKNeIkJNuvkfQlSQskTZH0rKTPRMTdtmdL2iqp8kr5z0fEXzehrx3jwgsvbCgO1It8rd+ll16ajF9xxRXDYnfeeWey7cKFCwvpS63b565atSoZf/TRR4fFZs6cmWx79tlnj75jaLpOz1nGO9RSzyz4CZJ+JGm+pBcknSPpG7ZPrWjz+ojY34T+AWgM+Qq0F3IWXWnEU/ARsScilkfE8xFxICLWKfuL7O3N7x6ARpCvQHshZ9GtGr4G1PbRkk6U9FRFeJvtF21/xfbUGq9bZHvA9sDg4OAouwugEaPN1/y15CxQMsZYdIuGClDbh0u6TdItEfGMpJ2STpc0S9lfa0fky4eJiBsioj8i+qdNmza2XgMY0VjyVSJngbIxxqKb1H0nJNuHSbpV0j5JiyUpIoYkDeRNdtheLGm77ckR8fOiOwugPuQr0F7IWXSbugpQ25Z0o6SjJZ0TEb+q0TQOvqSAvgEYBfK1Pv39/XW3veaaa5LxCy64IBnv7e1tqC8PP/xwMr5s2bJkPHWE68EHH2xomxg/yFl0o3qPgK6WNEfSgojYezBoe56kn0naIulISV+UtCEidhXcTwD1I1+B9kLOouuMeA2o7VmSLpP0Vkk/sT2UPy6U9AZJ35K0W9JmSa9I+qPmdRfAoZCvQHshZ9GtRjwCGhHbdOjD/V8rrjsAxoJ8BdoLOYtuxa04AQAAUCoKUAAAAJSq7q9hAoBOctpppyXj06dPHxbbsmVLsu3q1auT8csuuywZv/3225PxpUuXJuO1rFixYlhs1qxZDa0DAFqJI6AAAAAoFQUoAAAASkUBCgAAgFJRgAIAAKBUFKAAAAAolSNi5FZFb9QelLQtfzpV0s7SO9Ea3bKv7bafsyJi+M218aouzVn2c3wiX0dAvna8dtrXmvnakgL0NzpgD0REf0s7UZJu2ddu2c9u1S3vL/uJTtAt72+37KfUOfvKKXgAAACUigIUAAAApRoPBegNre5AibplX7tlP7tVt7y/7Cc6Qbe8v92yn1KH7GvLrwEFAABAdxkPR0ABAADQRShAAQAAUKqWFaC2p9hea3uP7W22P9qqvhTJ9mLbA7ZfsX1z1bL32H7G9i9s3297Vou6OWa2X2P7xvy92237n2y/v2J5x+wrMuRs+36OydfuQ7629+e4G3K2lUdAr5e0T9LRki6UtNr2yS3sT1FekrRC0k2VQdtTJa2R9FlJUyQNSPp66b0rzgRJP5I0X9LrlO3XN2zP7sB9RYacbd/PMfnafcjX9v4cd3zOtupOSH2SfirplIj4YR67VdKPI+LK0jvUBLZXSDouIhbmzxdJWhgRv5M/71N2J4O3RcQzLetogWxvkvSfJB2lDt/XbkPOdt7nmHztXORrZ36OOy1nW3UE9ERJvz6YGLnvSeqEv85qOVnZPkqSImKPpOfUIfts+2hl7+tT6vB97VLkbAd9jsnXjke+dtjnuBNztlUF6CRJu6piuyQd0YK+lKVj99n24ZJuk3RL/tdXx+5rF+vG97Qj95l87Qrd+J527D53as62qgAdkjS5KjZZ0u4W9KUsHbnPtg+TdKuya40W5+GO3Ncu143vacftM/naNbrxPe3Ife7knG1VAfpDSRNsv6ki9hZlh5Y71VPK9lHSq9dsnKA23mfblnSjsovcz42IX+WLOm5fQc62++eYfO0q5GsHfI47PWdbUoDm1yuskXS17T7bvyvpQ8qq/LZme4LtXkk9knps99qeIGmtpFNsn5sv/5ykTe10wXDCaklzJH0wIvZWxDtxX7saOdsRn2PytUuQrx3zOe7snI2IljyUfXXAP0jaI+kFSR9tVV8K3q/lkqLqsTxftkDSM5L2StogaXar+zuG/ZyV79svlZ0OOPi4sNP2lcer7zk526afY/K1+x7ka3t/jrshZ7kXPAAAAErFrTgBAABQKgpQAAAAlIoCFAAAAKWiAAUAAECpKEABAABQKgpQAAAAlIoCFAAAAKWiAAUAAECpKEABAABQqv8H+rnGG/eWpVIAAAAASUVORK5CYII=\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": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: the following cell may take close to an hour to run, depending on your hardware."
   ]
  },
  {
   "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": [
    "Let's fetch the data and load it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import urllib.request\n",
    "\n",
    "TITANIC_PATH = os.path.join(\"datasets\", \"titanic\")\n",
    "DOWNLOAD_URL = \"https://raw.githubusercontent.com/ageron/handson-ml2/master/datasets/titanic/\"\n",
    "\n",
    "def fetch_titanic_data(url=DOWNLOAD_URL, path=TITANIC_PATH):\n",
    "    if not os.path.isdir(path):\n",
    "        os.makedirs(path)\n",
    "    for filename in (\"train.csv\", \"test.csv\"):\n",
    "        filepath = os.path.join(path, filename)\n",
    "        if not os.path.isfile(filepath):\n",
    "            print(\"Downloading\", filename)\n",
    "            urllib.request.urlretrieve(url + filename, filepath)\n",
    "\n",
    "fetch_titanic_data()    "
   ]
  },
  {
   "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",
    "* **PassengerId**: a unique identifier for each passenger\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 explicitly set the `PassengerId` column as the index column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = train_data.set_index(\"PassengerId\")\n",
    "test_data = test_data.set_index(\"PassengerId\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get more info to see how much data is missing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 1 to 891\n",
      "Data columns (total 11 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Survived  891 non-null    int64  \n",
      " 1   Pclass    891 non-null    int64  \n",
      " 2   Name      891 non-null    object \n",
      " 3   Sex       891 non-null    object \n",
      " 4   Age       714 non-null    float64\n",
      " 5   SibSp     891 non-null    int64  \n",
      " 6   Parch     891 non-null    int64  \n",
      " 7   Ticket    891 non-null    object \n",
      " 8   Fare      891 non-null    float64\n",
      " 9   Cabin     204 non-null    object \n",
      " 10  Embarked  889 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 83.5+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.0"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[train_data[\"Sex\"]==\"female\"][\"Age\"].median()"
   ]
  },
  {
   "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. We could be a bit smarter by predicting the age based on the other columns (for example, the median age is 37 in 1st class, 29 in 2nd class and 24 in 3rd class), but we'll keep things simple and just use the overall median age."
   ]
  },
  {
   "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": 107,
   "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",
       "      <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>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>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699113</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>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526507</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>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.416700</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>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>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>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>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": [
       "         Survived      Pclass         Age       SibSp       Parch        Fare\n",
       "count  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\n",
       "mean     0.383838    2.308642   29.699113    0.523008    0.381594   32.204208\n",
       "std      0.486592    0.836071   14.526507    1.102743    0.806057   49.693429\n",
       "min      0.000000    1.000000    0.416700    0.000000    0.000000    0.000000\n",
       "25%      0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n",
       "50%      0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
       "75%      1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n",
       "max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
      ]
     },
     "execution_count": 107,
     "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": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 108,
     "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": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    491\n",
       "1    216\n",
       "2    184\n",
       "Name: Pclass, dtype: int64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Pclass\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Sex\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 111,
     "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": [
    "Now let's build our preprocessing pipelines, starting with the pipeline for numerical attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
    "        (\"scaler\", StandardScaler())\n",
    "    ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can build the pipeline for the categorical attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_pipeline = Pipeline([\n",
    "        (\"imputer\", SimpleImputer(strategy=\"most_frequent\")),\n",
    "        (\"cat_encoder\", OneHotEncoder(sparse=False)),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's join the numerical and categorical pipelines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "num_attribs = [\"Age\", \"SibSp\", \"Parch\", \"Fare\"]\n",
    "cat_attribs = [\"Pclass\", \"Sex\", \"Embarked\"]\n",
    "\n",
    "preprocess_pipeline = ColumnTransformer([\n",
    "        (\"num\", num_pipeline, num_attribs),\n",
    "        (\"cat\", cat_pipeline, cat_attribs),\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": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.56573582,  0.43279337, -0.47367361, ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       [ 0.6638609 ,  0.43279337, -0.47367361, ...,  1.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-0.25833664, -0.4745452 , -0.47367361, ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       ...,\n",
       "       [-0.10463705,  0.43279337,  2.00893337, ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       [-0.25833664, -0.4745452 , -0.47367361, ...,  1.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.20276213, -0.4745452 , -0.47367361, ...,  0.        ,\n",
       "         1.        ,  0.        ]])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = preprocess_pipeline.fit_transform(\n",
    "    train_data[num_attribs + cat_attribs])\n",
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's not forget to get the labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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 a `RandomForestClassifier`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "forest_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "forest_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": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = preprocess_pipeline.transform(test_data[num_attribs + cat_attribs])\n",
    "y_pred = forest_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": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8137578027465668"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "forest_scores = cross_val_score(forest_clf, X_train, y_train, cv=10)\n",
    "forest_scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, not too bad! Looking at the [leaderboard](https://www.kaggle.com/c/titanic/leaderboard) for the Titanic competition on Kaggle, you can see that our score is in the top 2%, woohoo! Some Kagglers reached 100% accuracy, 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! 😆"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try an `SVC`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8249313358302123"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svm_clf = SVC(gamma=\"auto\")\n",
    "svm_scores = cross_val_score(svm_clf, X_train, y_train, cv=10)\n",
    "svm_scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! This model looks better."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But 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": 122,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "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": [
    "The random forest classifier got a very high score on one of the 10 folds, but overall it had a lower mean score, as well as a bigger spread, so it looks like the SVM classifier is more likely to generalize well."
   ]
  },
  {
   "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",
    "  * 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).\n",
    "  * Replace **SibSp** and **Parch** with their sum.\n",
    "  * Try to identify parts of names that correlate well with the **Survived** attribute.\n",
    "  * Use the **Cabin** column, for example take its first letter and treat it as a categorical attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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": 123,
     "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": 124,
   "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": 124,
     "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": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tarfile\n",
    "import urllib.request\n",
    "\n",
    "DOWNLOAD_ROOT = \"http://spamassassin.apache.org/old/publiccorpus/\"\n",
    "HAM_URL = DOWNLOAD_ROOT + \"20030228_easy_ham.tar.bz2\"\n",
    "SPAM_URL = DOWNLOAD_ROOT + \"20030228_spam.tar.bz2\"\n",
    "SPAM_PATH = os.path.join(\"datasets\", \"spam\")\n",
    "\n",
    "def fetch_spam_data(ham_url=HAM_URL, spam_url=SPAM_URL, spam_path=SPAM_PATH):\n",
    "    if not os.path.isdir(spam_path):\n",
    "        os.makedirs(spam_path)\n",
    "    for filename, url in ((\"ham.tar.bz2\", ham_url), (\"spam.tar.bz2\", spam_url)):\n",
    "        path = os.path.join(spam_path, filename)\n",
    "        if not os.path.isfile(path):\n",
    "            urllib.request.urlretrieve(url, path)\n",
    "        tar_bz2_file = tarfile.open(path)\n",
    "        tar_bz2_file.extractall(path=spam_path)\n",
    "        tar_bz2_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "fetch_spam_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's load all the emails:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2500"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ham_filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500"
      ]
     },
     "execution_count": 129,
     "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": "code",
   "execution_count": 130,
   "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": 131,
   "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": 132,
   "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": 133,
   "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": 134,
   "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": 135,
   "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": 136,
   "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": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structures_counter(ham_emails).most_common()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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": 137,
     "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": 138,
   "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": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Life Insurance - Why Pay More?'"
      ]
     },
     "execution_count": 139,
     "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": 140,
   "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": 141,
   "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": 142,
   "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": 143,
   "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 $25 billi ...\n"
     ]
    }
   ],
   "source": [
    "print(html_to_plain_text(sample_html_spam.get_content())[:1000], \"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! Now let's write a function that takes an email as input and returns its content as plain text, whatever its format is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "def email_to_text(email):\n",
    "    html = None\n",
    "    for part in email.walk():\n",
    "        ctype = part.get_content_type()\n",
    "        if not ctype in (\"text/plain\", \"text/html\"):\n",
    "            continue\n",
    "        try:\n",
    "            content = part.get_content()\n",
    "        except: # in case of encoding issues\n",
    "            content = str(part.get_payload())\n",
    "        if ctype == \"text/plain\":\n",
    "            return content\n",
    "        else:\n",
    "            html = content\n",
    "    if html:\n",
    "        return html_to_plain_text(html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "OTC\n",
      " Newsletter\n",
      "Discover Tomorrow's Winners \n",
      "For Immediate Release\n",
      "Cal-Bay (Stock Symbol: CBYI)\n",
      "Wat ...\n"
     ]
    }
   ],
   "source": [
    "print(email_to_text(sample_html_spam)[:100], \"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's throw in some stemming! For this to work, you need to install the Natural Language Toolkit ([NLTK](http://www.nltk.org/)). It's as simple as running the following command (don't forget to activate your virtualenv first; if you don't have one, you will likely need administrator rights, or use the `--user` option):\n",
    "\n",
    "`$ pip3 install nltk`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "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": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if running this notebook on Colab or Kaggle, we just pip install urlextract\n",
    "if IS_COLAB or IS_KAGGLE:\n",
    "    %pip install -q -U urlextract"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** inside a Jupyter notebook, always use `%pip` instead of `!pip`, as `!pip` may install the library inside the wrong environment, while `%pip` makes sure it's installed inside the currently running environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "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": "code",
   "execution_count": 149,
   "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": 150,
   "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": 150,
     "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": 151,
   "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.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": 152,
   "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": 152,
     "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": 153,
   "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": 153,
     "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": 154,
   "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": 154,
     "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": 155,
   "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": 156,
   "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",
      "[CV] .................................... , score=0.991, total=   0.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.5s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9858333333333333"
      ]
     },
     "execution_count": 156,
     "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": 157,
   "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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