03_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Chapter 3 – Classification**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_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-ml3/blob/main/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-ml3/blob/main/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": [
    "This project requires Python 3.7 or above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "assert sys.version_info >= (3, 7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It also requires Scikit-Learn ≥ 1.0.1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from packaging import version\n",
    "import sklearn\n",
    "\n",
    "assert version.parse(sklearn.__version__) >= version.parse(\"1.0.1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just like in the previous chapter, let's define the default font sizes to make the figures prettier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rc('font', size=14)\n",
    "plt.rc('axes', labelsize=14, titlesize=14)\n",
    "plt.rc('legend', fontsize=14)\n",
    "plt.rc('xtick', labelsize=10)\n",
    "plt.rc('ytick', labelsize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let's create the `images/classification` folder (if it doesn't already exist), and define the `save_fig()` function which is used through this notebook to save the figures in high-res for the book:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "IMAGES_PATH = Path() / \"images\" / \"classification\"\n",
    "IMAGES_PATH.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = IMAGES_PATH / f\"{fig_id}.{fig_extension}\"\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "\n",
    "mnist = fetch_openml('mnist_784', as_frame=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**Author**: Yann LeCun, Corinna Cortes, Christopher J.C. Burges  \n",
      "**Source**: [MNIST Website](https://yann.lecun.com/exdb/mnist/) - Date unknown  \n",
      "**Please cite**:  \n",
      "\n",
      "The MNIST database of handwritten digits with 784 features, raw data available at: https://yann.lecun.com/exdb/mnist/. It can be split in a training set of the first 60,000 examples, and a test set of 10,000 examples  \n",
      "\n",
      "It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image. It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting. The original black and white (bilevel) images from NIST were size normalized to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field.  \n",
      "\n",
      "With some classification methods (particularly template-based methods, such as SVM and K-nearest neighbors), the error rate improves when the digits are centered by bounding box rather than center of mass. If you do this kind of pre-processing, you should report it in your publications. The MNIST database was constructed from NIST's NIST originally designated SD-3 as their training set and SD-1 as their test set. However, SD-3 is much cleaner and easier to recognize than SD-1. The reason for this can be found on the fact that SD-3 was collected among Census Bureau employees, while SD-1 was collected among high-school students. Drawing sensible conclusions from learning experiments requires that the result be independent of the choice of training set and test among the complete set of samples. Therefore it was necessary to build a new database by mixing NIST's datasets.  \n",
      "\n",
      "The MNIST training set is composed of 30,000 patterns from SD-3 and 30,000 patterns from SD-1. Our test set was composed of 5,000 patterns from SD-3 and 5,000 patterns from SD-1. The 60,000 pattern training set contained examples from approximately 250 writers. We made sure that the sets of writers of the training set and test set were disjoint. SD-1 contains 58,527 digit images written by 500 different writers. In contrast to SD-3, where blocks of data from each writer appeared in sequence, the data in SD-1 is scrambled. Writer identities for SD-1 is available and we used this information to unscramble the writers. We then split SD-1 in two: characters written by the first 250 writers went into our new training set. The remaining 250 writers were placed in our test set. Thus we had two sets with nearly 30,000 examples each. The new training set was completed with enough examples from SD-3, starting at pattern # 0, to make a full set of 60,000 training patterns. Similarly, the new test set was completed with SD-3 examples starting at pattern # 35,000 to make a full set with 60,000 test patterns. Only a subset of 10,000 test images (5,000 from SD-1 and 5,000 from SD-3) is available on this site. The full 60,000 sample training set is available.\n",
      "\n",
      "Downloaded from openml.org.\n"
     ]
    }
   ],
   "source": [
    "# extra code – it's a bit too long\n",
    "print(mnist.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'categories', 'feature_names', 'target_names', 'DESCR', 'details', 'url'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist.keys()  # extra code – we only use data and target in this notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y = mnist.data, mnist.target\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000, 784)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['5', '0', '4', ..., '4', '5', '6'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "784"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "28 * 28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_digit(image_data):\n",
    "    image = image_data.reshape(28, 28)\n",
    "    plt.imshow(image, cmap=\"binary\")\n",
    "    plt.axis(\"off\")\n",
    "\n",
    "some_digit = X[0]\n",
    "plot_digit(some_digit)\n",
    "save_fig(\"some_digit_plot\")  # extra code\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 100 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code – this cell generates and saves Figure 3–2\n",
    "plt.figure(figsize=(9, 9))\n",
    "for idx, image_data in enumerate(X[:100]):\n",
    "    plt.subplot(10, 10, idx + 1)\n",
    "    plot_digit(image_data)\n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "save_fig(\"more_digits_plot\", tight_layout=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_5 = (y_train == '5')  # True for all 5s, False for all other digits\n",
    "y_test_5 = (y_test == '5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(random_state=42)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_clf = SGDClassifier(random_state=42)\n",
    "sgd_clf.fit(X_train, y_train_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performance Measures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Measuring Accuracy Using Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.95035, 0.96035, 0.9604 ])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "cross_val_score(sgd_clf, X_train, y_train_5, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.95035\n",
      "0.96035\n",
      "0.9604\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.base import clone\n",
    "\n",
    "skfolds = StratifiedKFold(n_splits=3)  # add shuffle=True if the dataset is not\n",
    "                                       # already shuffled\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": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "from sklearn.dummy import DummyClassifier\n",
    "\n",
    "dummy_clf = DummyClassifier()\n",
    "dummy_clf.fit(X_train, y_train_5)\n",
    "print(any(dummy_clf.predict(X_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.90965, 0.90965, 0.90965])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(dummy_clf, X_train, y_train_5, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[53892,   687],\n",
       "       [ 1891,  3530]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm = confusion_matrix(y_train_5, y_train_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[54579,     0],\n",
       "       [    0,  5421]])"
      ]
     },
     "execution_count": 26,
     "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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8370879772350012"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score\n",
    "\n",
    "precision_score(y_train_5, y_train_pred)  # == 3530 / (687 + 3530)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8370879772350012"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extra code – this cell also computes the precision: TP / (FP + TP)\n",
    "cm[1, 1] / (cm[0, 1] + cm[1, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6511713705958311"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred)  # == 3530 / (1891 + 3530)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6511713705958311"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extra code – this cell also computes the recall: TP / (FN + TP)\n",
    "cm[1, 1] / (cm[1, 0] + cm[1, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7325171197343846"
      ]
     },
     "execution_count": 31,
     "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": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7325171197343847"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extra code – this cell also computes the f1 score\n",
    "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": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2164.22030239])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_scores = sgd_clf.decision_function([some_digit])\n",
    "y_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = 0\n",
    "y_some_digit_pred = (y_scores > threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_some_digit_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extra code – just shows that y_scores > 0 produces the same result as\n",
    "#              calling predict()\n",
    "y_scores > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = 3000\n",
    "y_some_digit_pred = (y_scores > threshold)\n",
    "y_some_digit_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": 39,
   "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": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))  # extra code – it's not needed, just formatting\n",
    "plt.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\", linewidth=2)\n",
    "plt.plot(thresholds, recalls[:-1], \"g-\", label=\"Recall\", linewidth=2)\n",
    "plt.vlines(threshold, 0, 1.0, \"k\", \"dotted\", label=\"threshold\")\n",
    "\n",
    "# extra code – this section just beautifies and saves Figure 3–5\n",
    "idx = (thresholds >= threshold).argmax()  # first index ≥ threshold\n",
    "plt.plot(thresholds[idx], precisions[idx], \"bo\")\n",
    "plt.plot(thresholds[idx], recalls[idx], \"go\")\n",
    "plt.axis([-50000, 50000, 0, 1])\n",
    "plt.grid()\n",
    "plt.xlabel(\"Threshold\")\n",
    "plt.legend(loc=\"center right\")\n",
    "save_fig(\"precision_recall_vs_threshold_plot\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.patches as patches  # extra code – for the curved arrow\n",
    "\n",
    "plt.figure(figsize=(6, 5))  # extra code – not needed, just formatting\n",
    "\n",
    "plt.plot(recalls, precisions, linewidth=2, label=\"Precision/Recall curve\")\n",
    "\n",
    "# extra code – just beautifies and saves Figure 3–6\n",
    "plt.plot([recalls[idx], recalls[idx]], [0., precisions[idx]], \"k:\")\n",
    "plt.plot([0.0, recalls[idx]], [precisions[idx], precisions[idx]], \"k:\")\n",
    "plt.plot([recalls[idx]], [precisions[idx]], \"ko\",\n",
    "         label=\"Point at threshold 3,000\")\n",
    "plt.gca().add_patch(patches.FancyArrowPatch(\n",
    "    (0.79, 0.60), (0.61, 0.78),\n",
    "    connectionstyle=\"arc3,rad=.2\",\n",
    "    arrowstyle=\"Simple, tail_width=1.5, head_width=8, head_length=10\",\n",
    "    color=\"#444444\"))\n",
    "plt.text(0.56, 0.62, \"Higher\\nthreshold\", color=\"#333333\")\n",
    "plt.xlabel(\"Recall\")\n",
    "plt.ylabel(\"Precision\")\n",
    "plt.axis([0, 1, 0, 1])\n",
    "plt.grid()\n",
    "plt.legend(loc=\"lower left\")\n",
    "save_fig(\"precision_vs_recall_plot\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3370.0194991439557"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx_for_90_precision = (precisions >= 0.90).argmax()\n",
    "threshold_for_90_precision = thresholds[idx_for_90_precision]\n",
    "threshold_for_90_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred_90 = (y_scores >= threshold_for_90_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9000345901072293"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_train_5, y_train_pred_90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4799852425751706"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_at_90_precision = recall_score(y_train_5, y_train_pred_90)\n",
    "recall_at_90_precision"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_train_5, y_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx_for_threshold_at_90 = (thresholds <= threshold_for_90_precision).argmax()\n",
    "tpr_90, fpr_90 = tpr[idx_for_threshold_at_90], fpr[idx_for_threshold_at_90]\n",
    "\n",
    "plt.figure(figsize=(6, 5))  # extra code – not needed, just formatting\n",
    "plt.plot(fpr, tpr, linewidth=2, label=\"ROC curve\")\n",
    "plt.plot([0, 1], [0, 1], 'k:', label=\"Random classifier's ROC curve\")\n",
    "plt.plot([fpr_90], [tpr_90], \"ko\", label=\"Threshold for 90% precision\")\n",
    "\n",
    "# extra code – just beautifies and saves Figure 3–7\n",
    "plt.gca().add_patch(patches.FancyArrowPatch(\n",
    "    (0.20, 0.89), (0.07, 0.70),\n",
    "    connectionstyle=\"arc3,rad=.4\",\n",
    "    arrowstyle=\"Simple, tail_width=1.5, head_width=8, head_length=10\",\n",
    "    color=\"#444444\"))\n",
    "plt.text(0.12, 0.71, \"Higher\\nthreshold\", color=\"#333333\")\n",
    "plt.xlabel('False Positive Rate (Fall-Out)')\n",
    "plt.ylabel('True Positive Rate (Recall)')\n",
    "plt.grid()\n",
    "plt.axis([0, 1, 0, 1])\n",
    "plt.legend(loc=\"lower right\", fontsize=13)\n",
    "save_fig(\"roc_curve_plot\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9604938554008616"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "roc_auc_score(y_train_5, y_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning:** the following cell may take a few minutes to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "forest_clf = RandomForestClassifier(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_probas_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3,\n",
    "                                    method=\"predict_proba\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.11, 0.89],\n",
       "       [0.99, 0.01]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_probas_forest[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are _estimated probabilities_. Among the images that the model classified as positive with a probability between 50% and 60%, there are actually about 94% positive images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "94.0%\n"
     ]
    }
   ],
   "source": [
    "# Not in the code\n",
    "idx_50_to_60 = (y_probas_forest[:, 1] > 0.50) & (y_probas_forest[:, 1] < 0.60)\n",
    "print(f\"{(y_train_5[idx_50_to_60]).sum() / idx_50_to_60.sum():.1%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores_forest = y_probas_forest[:, 1]\n",
    "precisions_forest, recalls_forest, thresholds_forest = precision_recall_curve(\n",
    "    y_train_5, y_scores_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 5))  # extra code – not needed, just formatting\n",
    "\n",
    "plt.plot(recalls_forest, precisions_forest, \"b-\", linewidth=2,\n",
    "         label=\"Random Forest\")\n",
    "plt.plot(recalls, precisions, \"--\", linewidth=2, label=\"SGD\")\n",
    "\n",
    "# extra code – just beautifies and saves Figure 3–8\n",
    "plt.xlabel(\"Recall\")\n",
    "plt.ylabel(\"Precision\")\n",
    "plt.axis([0, 1, 0, 1])\n",
    "plt.grid()\n",
    "plt.legend(loc=\"lower left\")\n",
    "save_fig(\"pr_curve_comparison_plot\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could use `cross_val_predict(forest_clf, X_train, y_train_5, cv=3)` to compute `y_train_pred_forest`, but since we already have the estimated probabilities, we can just use the default threshold of 50% probability to get the same predictions much faster:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9274509803921569"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred_forest = y_probas_forest[:, 1] >= 0.5  # positive proba ≥ 50%\n",
    "f1_score(y_train_5, y_train_pred_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9983436731328145"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_train_5, y_scores_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9897468089558485"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_train_5, y_train_pred_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8725327430363402"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred_forest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiclass Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVMs do not scale well to large datasets, so let's only train on the first 2,000 instances, or else this section will take a very long time to run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(random_state=42)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svm_clf = SVC(random_state=42)\n",
    "svm_clf.fit(X_train[:2000], y_train[:2000])  # y_train, not y_train_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['5'], dtype=object)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.79,  0.73,  6.06,  8.3 , -0.29,  9.3 ,  1.75,  2.77,  7.21,\n",
       "         4.82]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_digit_scores = svm_clf.decision_function([some_digit])\n",
    "some_digit_scores.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_id = some_digit_scores.argmax()\n",
    "class_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'], dtype=object)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5'"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.classes_[class_id]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want `decision_function()` to return all 45 scores, you can set the `decision_function_shape` hyperparameter to `\"ovo\"`. The default value is `\"ovr\"`, but don't let this confuse you: `SVC` always uses OvO for training. This hyperparameter only affects whether or not the 45 scores get aggregated or not:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.11, -0.21, -0.97,  0.51, -1.01,  0.19,  0.09, -0.31, -0.04,\n",
       "        -0.45, -1.28,  0.25, -1.01, -0.13, -0.32, -0.9 , -0.36, -0.93,\n",
       "         0.79, -1.  ,  0.45,  0.24, -0.24,  0.25,  1.54, -0.77,  1.11,\n",
       "         1.13,  1.04,  1.2 , -1.42, -0.53, -0.45, -0.99, -0.95,  1.21,\n",
       "         1.  ,  1.  ,  1.08, -0.02, -0.67, -0.14, -0.3 , -0.13,  0.25]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extra code – shows how to get all 45 OvO scores if needed\n",
    "svm_clf.decision_function_shape = \"ovo\"\n",
    "some_digit_scores_ovo = svm_clf.decision_function([some_digit])\n",
    "some_digit_scores_ovo.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneVsRestClassifier(estimator=SVC(random_state=42))"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "\n",
    "ovr_clf = OneVsRestClassifier(SVC(random_state=42))\n",
    "ovr_clf.fit(X_train[:2000], y_train[:2000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['5'], dtype='<U1')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ovr_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ovr_clf.estimators_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['3'], dtype='<U1')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf = SGDClassifier(random_state=42)\n",
    "sgd_clf.fit(X_train, y_train)\n",
    "sgd_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-31893., -34420.,  -9531.,   1824., -22320.,  -1386., -26189.,\n",
       "        -16148.,  -4604., -12051.]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.decision_function([some_digit]).round()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning:** the following two cells may take a few minutes each to run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.87365, 0.85835, 0.8689 ])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(sgd_clf, X_train, y_train, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.8983, 0.891 , 0.9018])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train.astype(\"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": "markdown",
   "metadata": {},
   "source": [
    "**Warning:** the following cell will take a few minutes to run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "\n",
    "y_train_pred = cross_val_predict(sgd_clf, X_train_scaled, y_train, cv=3)\n",
    "plt.rc('font', size=9)  # extra code – make the text smaller\n",
    "ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HNWi4qtFoqieGJaFnkmaWZFvqaNwu/BwHXFceTd3IaTQat1BKsOtUSxqNpjaj88lpNJpai5FPTqda0mg0tRadGdg9TFiSYJar1uSozR7XnNT+ao9rAsbKUDNkTXKqEps5X1Uz4rUEBXpcE8xZQuIJFFxY6Fstqf6NnEajqdZ4cO+qKehGTqPRuI32eNBoNLUWI9WSHq5qNJpajH4mp9Foai1GFhI9XNVoNLUUY1uXbuQ8Qu8BaTzx75NYLYo1i0P4YmYYj/wjjt4D04ja78eUcS0Aw50oKDifbxY0qFTdnxc0YuvnDRCBRh0yuX1KFKeP+rHsxVbkZQsWm+KWfx+jWY8Mjv0eyNcvtcLmY+fu6UcIbZlNVpqVT8e25ZGPD5a6cia0cTYT/xtNvQa5KDus/qwByz9sxMOTYvjLgLMc3e/P288a5jGDbkkiMDiP5R+WnW33mSnH6DPoLKnJNp64tjMAD/89lr8MSOPofj/efqaVoTki2dD8wLUMs2bcM7NifXbqCfoMTiM1ycbjgzoCeOT7FRCYy9Ov7KNFm3OAMO3VzvQbkMhlVyYRdTCId17pCsDAG+IIqpPLisUtXNLtPSCNJ16Lc9Rtfb6YEcYjL8bRe2A6Ufv8mDKueYXiLT/VuydXmW5dzUTkBxE54DCgGFee8y0WxZjXY3npvtY8NrAjA28+43An8oADlid0lWLTR2E8vWIvz67bgz1f2LUyhNVvNmfwuFjGr97Ldc/Esvo/xhfvp/mNuX/2IYZMiGHzJ8Yf4cZ3w7lmdJzTpYH2fGHe/zVj1KCujL85gptGJtKqUyYRl53jyaFdDJemDpl4+9gZfHsSqxY1dKkeNnwZwksj21187R+UT8RlGTw5JMKogw5ZDs1kVi10TdOse2ZGrGCeu9ioiZH88WsoT9x6FWPv7EfyaR86dk9l7J1XYLEoWrRNx9snn8E3xfHtl87Tnl+gOrl1gbHjwZWjKqjM5jcPeE4p1QnoC4wpwaSiVAx3Ip9C7kT16HvdWQ85YHlG154v5J63kJ8Huect1GmYC6LIPmesITqfbqNOmJGy2mpT5J63kJtlxeqlSD7uw9kEb1r3TXcab0qiN0f2GvnKsjKsxBzxo2GTnIvx+lxwf3r8FMs/DHPZ/Wnv1qASnLXsRTWfiGf5Bw2rpG7NjhXMcRfzC8ijS68zrP/GyOKdl2fh3FkvvGwF8ebnWbh15DFWLm5eI926Lsyu/uktCZVSp5RS2x0/pwMHuDR/e6kY7kQFOeyTTnlRLzTPQw5YHtAVof9jp3jzyp683qcXvkH5tO9/lpteOc63bzbnjSt68O0bzRk60cg2PHB0HF/9ozW/fNiIfiMTWPt2M4Y8G1O6fgmENc2mTedM9mwJ4pc19Zi5eh/xMT5kpltp3z2DzRvqlUuvMFkZVkNzzQHiY7wdmpls3hDssoZZ98yMWEvVdTPWxuGZnD3jzTP/2se7n/3G0y/vw26HTd+H8d7izcSf9CPjnI12nc+y+X+u9zqrk1sXgF1ZXDqqgip5JiciLYGewJYSPiufW5dZDljl1VWK/Rvq8cJPO/Grk88nY9qy/esQYnYFctNLx+l6/Rl2rarP0kmteeyTSJpEZDL2ayPhadSWIOqE5aCU8OnYtlhtihtePE6QEwcwX/98XppzhLn/bkbmOStL5zZm6dzGRryTo1n0TjhD7zpNr6vPEh3pz+L3mjith5JYOqcRS+c0cmgeY9HUJgy9K4le/dOIPuDH4vcaOz3frHtmRqyl4W6sFquibcd05r7VkYN7gxk1IZLbHzrGJ7Pb8tXHxrPDp1/exyez23LdzbH06ptM9OEgPl/Q2qludXPrqs5LSCq9aRWRQOArYLxSKq34587dunIvvg5tnEtyQiF3IrccsDyjW69ZNoEheVi9FF2GnOH49iD+WBZKl6FnAOh2Qwoxu4rua1QKvp8RzqCnTvLdu+Fc+0wsPW9JYtNHpf/xWG12Xp5zhB++CWHT2qJenW06Zzji9WXQiCTeGNOWFu2zaNLyfElSLlG4Dgbdmswbo1vTokPZmmbdMzNiLY9ueWJNTvQlKdGHg3uDAdi0MYy2HQu+9q07GD+fPO7PoBvj+M+k7rRoe44mzZzvU61Obl0KyFMWl46qoFKvKiJeGA3cp0qpZeU513Anyi7kTnSGzesLTHrcc8DyjO6JHYHkZFlQCo78WoeGbbKo0zCXqC1BABz9tQ6hxf7Y/vgqlI7XnMG/bj65WRZEjP+lc8+XdmsUz7x1jBNH/Fg2/9KGcORzJ1k4NbxovIoy3Z+cMXJCHAvfaeLQLHB+cs1RyvP3zIxYy6KisZ5J9uF0gi/hLYxGq/vlyZyILvCAuH/0ET6Z3cbh1lWg6+Nbc9y6QA9XAXCYwS4ADiil3inv+fZ8YeZLTQ13Ioti/ef1OX7IcB5y2wHLE7oidL0+hXdv7ILFpmgSkUmfuxNp0jmDlf9uiT0PbD6KEW9EXTwlJ8vCH1814NGFkQBc/Ug8n4xuh9VLcff0IyXG27n3OQbfmkz0AT9mrt4LwEdTmrLth2D6XXeGQ7sCLro/HdgewOx1e4mO9CP6gH+JeheY9F4U3fqlU6deHou27OaTd5qw7vNQ+l2XyqFd/qQkODT/CGT2+n1EH/AvU9Ose2ZGrGCeu9jcyR2Z+PoebF524mP9mPavLgD0HZDIoX11SUkyXLYid9dl5ue/En04kOjDQU41q5Vbl6rew9XKdOu6CvgZwxD2wn8n/3DkdC+ROlJf9bEMrozwPIJOtWRiqiWrOVkuzIjXFub6BEJ5yItP8LimJ9y66nVsqK754DaXyi67cnapbl1mUZluXb9QsnmsRqOp4VTnnlyN2vGg0WiqHzpppkajqdUohDx79d3WpRs5jUbjNtrIRqPR1F6UHq66RyXN/nqCSR37e1xzYuTvHtcEeKtNV1N0LQEBZReqAPbMTFN0axKmmPmUvqnGZfQzOY1GU+vRjZxGo6m1KIR8PfGg0WhqM3riQaPR1FqUnnjQaDS1HaUbOY1GU3up3hv0a1QjZ5Zxhxm6oY2zmTg1ymE4I6xe3IDlHzXi4Rdi+MuAVMNw5rk2hu4tSQTWzWN5KTnktn0Qwu4v6iNAaIfz/O2tWDbPacDuz+vjX99YA3D1cwm0GZhO7O/+bHilCVZvxU3TYqjXMofzaRZWPN2c2z88Vqp/hBl14OVtZ8pne/HytmO1KX5ZG8In7zbn4YnH6N0/laMHApj6vOHVcM3wRIKC81j+sWvJPc0wyDHDyCa8RQaT/rP74utG4Zl8Mqct9UOz3TKyMcvMp6JU555cZRrZ+IrIVhHZ5TCyebU855tl3GGWrj1PmPd6c0Zd243xIyK4aWSCw3AmnSev7+owXHEYztyaxKpPSs5ckR5vY/vHoYz85ggPrz2MssOBlUbesN4PJfHgqiM8uOoIbQYa3hDbFoQyfOYJ+k9IYMdnRkLNX2c0pO+Tp0tt4Myqg9wcYdLIzowZ1oMxw7pzWf9Uul5+lk490xl9Uw+sVkXL9hl4++Rz7YjTrPrUtYzAZhnkmGFkc/J4AE/d3Y+n7u7HuHv7kn3eyq8/NHTbyMYsM5+KoBTk28WloyqozJ5cNnCNUuqcI3nmLyKyRinlUn6iwsYdgMeMO8zSTTntTcppI69ZgeFMdlHDmVzhtlGnWP6xc8MZex7knbdgtRmJNQPD8jh70rvEshabIi/bQm6WYLUpzhz35ly8F837lJ5p1qw6AOF8ppEiyWZT2GzGwm6bd4E5TF6ecNtjcSxf2LicJi4+heL1kEHOlkDCmhZkzvWEkU1hul+ezKlYf48Y2ezdGlRCrO6b+VSU6jy7WplGNkopdc7x0stxuLydwSzjjsowBAkLz6ZNRCZ7ttThl7X1mfntPuJjXTOcCWqUx18eTWLO1R2Y2a8TPkF2Wl1tVOP2RSF8+Le2rHkhnPNnjVvZ98nTrHsxnN8/DKXX/cn8PDWMq55xnofMzDqwWBQzVuxk8eZt7NhUlz1b67JpXQgzVuwiPsaHjHQb7bueY/PG+mWLFYm3kgxyPKjZf0g8/1vXiKxMm9tGNiXGaoKZjysojOGqK0dVUKnP5ETECvwBtAVmKqXcN7Jx07jDbEMQX/98Xpp9mLmvNb/UcOY/DsOZOxPpdXUa0ZF+LJ5R1MDs/FkLR76rw+M/HsSnTj7LxzZn3zfB9Lw3hSvGJiICP78Txg9vNOb6yScJizjP/V8dBSBmqz+BYcYzu+VPNcPqpRj4j3gCQovu5TGzDux2YeywHgQE5fHyrEhatMtg6bxwls4zfs9xrx9h0fRmDLk9gV5XpRJ90J8ls5wP2SrTIMdTmjabnT79T/Pxe8YQ86uPW7llZFMSZpn5lE31nngotScnIiNcPVy9mFIqXynVA2gKXC4iXUoo48TIxvPGHWYaglhtdl6efZgfloewaV0xw5kIh+FMtC+DRiTzxtiSDWeObQqkbrMc/EPysXpB+yFpnNzuT0BoHhYriAW635XCqV1F03srBb/NbMgVYxPY9G4YV41PJGJ4Kn98FFKpdXCBjHQbu7fUpXf/1EJ1cM5RB34MuuU0b47rQMt2mTRpkeVUq6oNciqi2fvKJI5G1iE1xafI+xU1snE1Vk+a+ThDKdeOshCRoSJyUESOiMikUsoMEJGdjmf7/ytL09lwdamLx5dlh14UpVQq8CMw1NVzzDLuMM8QRPHM5GjDcGbBpf+Djnw2loXvhDsMTEo3XKnTJJe4nf7kZglKwfFfAwlpk825xIJO+KH1dQhtX/QLvPerYFoPSMe3rp28LEEsCrGUbJBjVh3UrZ9LQJDRa/T2yafnFanERBV4DNw/PoZF05sXqQO7kmppkOOuZv+hxlC1OBU1snGGWWY+zvDEcNUx0psJXA9EAHcXN6AXkWBgFjBMKdUZuL2s2EodrirlWWsdEWkA5CqlUkXEDxgMTHb1fLOMO8zS7dz7HINHJBMd6cfMbwsZzvwYTL9rz3Bod2CB4cyOQGav2UN05KWGK016ZNFh6Fk+HtYWixUads6i+10prP1HOIn7/RBR1Gmay5D/O3nxnNwsYe+yetzxcTQAvR9J4pvRLbB6KW6afqLS6qBegxwmvHUEi0UhFsXPa0LZ+oPRo+03OLlIHUTuCGLWqp0cO+hPdKTzTCamGeSYZGTj45tPzz7JzHi9U5H33TGyMcvMpyIYs6seaS4uB44opaIARGQJMBzYX6jMPcAypdQJ49oqsSzRyjSy6QZ8DFgxepBfKKX+7eycOlJf9ZFBlRGeRxAfn7ILlZOJ+3WqJahZqZbMMrLJT0r2uObmvHWk2d0zsvFr20S1enuUS2UP3PLqcSCp0FvvK6XeBxCR24ChSqlHHa/vB/oopcZeKCwi0zAmLTsDQcB0pdRCZ9d0eeJBRK4HxgKtgeuUUjEi8igQrZTaWNb5SqndQE9Xr6fRaGoO5Zg5TXLi1lWSSPFemA24DBgE+AG/ichmpdSh0i7oUh9TRO4FvgAOAS0xWlIwemXPu6Kh0WhqJwrXnse50BDGAoWn1psCcSWUWauUylBKJQE/Ad2dibo6kH4eeEwp9QxFc4luBnq4qKHRaGopysWjDLYB7USklYh4A3cBK4qVWQ5cLSI2EfEH+gAHnIm6OlxtB/xWwvvngDolvK/RaP4sKFAe2LKllMoTkbHAOoxR4gdKqX0i8oTj8zlKqQMishbYjWFSP18ptdeZrquNXBzQHjhe7P3+wNFy/B4ajaYW4qndDEqp1cDqYu/NKfZ6CjDFVU1Xh6vvA++KyJWO181E5AHgLWC2qxfTaDS1E08tBjYDl3pySqm3RKQusAHwBX7A2HD/tlJqponx1ShUTk7ZhcqJWUs9ph371RTd8S2vMEW31BQq1RCzlmWJd8lJGdzSzHd/fduFvavVFZeXkCilXhSR1zFWIluA/YU23Gs0mj8rCqgNjZwDBVzYP5Tv4Vg0Gk0NpTrbI7u6Ts7HsdI4BdiFMbORIiLTRcTXxPg0Gk21R1B2146qwNWe3GzgOuBRCpaS9APexNha8bDnQ9NoNDWGatyTc7WRux0YoZTaUOi9KBFJBL5CN3IazZ8XVTsmHjKAkyW8fxJwnvxLo9HUfmpBT+494J8i8qBSKgvAkS7pZcdnlUJNcusyw/nJ07H+OL8xmz8PA4HGHTK4Z8oREqP8+OLFNuRkWqjfNJv7px3GNyifqN+D+PKl1ti8FSPfPUSDlufJPGvl47HteWLhgUp1ATO1bj3sAAYQEJjLuH/up0WbcyglTHs1gn4DTtP7yiSiDgUx9WUjd+w1N8QRVCeP5Yubl6np5W1nyuK9eHmrAie06c14eOJxev81laMH/Jk60eGEdvNpgurmsfxjszIDQ8l766sHzjIDr7hwYOwPux44KSI/isiPGBtl/4aRA8plRMQqIjtEZFW5Aq1hbl1mOD95MtbUeG9++qgxz67czaT1O1F2YfvKUJZMastNLxznhXW76Dokhe/fNywCf5zXhIdnH+TGicfZ9ImR/HH9e824dszJSncBM61uTXAAA3j8+YP88WsIj4+4krF39iXltA+duqcy5s5+WCyKlhfduk6x6sumLmnm5giT7u/MmJu6M+amblx2tcMJrVc6o2/sjtVCISe0RFZ9aq4lIXYXjyrA2exqcrHjK4zNsdGOYwWwzPFZeRhHGRtqS6Kwo1RersUUty5P6u7dEkh6qvXia084P3k6Vnu+kHveQn4e5GRZqBuWQ2KUL236GCm5O1yVyq41Rrp0i02Re95CTpYFq81O0nEfzsZ707ZvWqXFewHz6tanUKyecQDzC8ijS68zrPva8LTIy7OQftYLL4ezlrePnbw8C7c+cIwVS5q57NZ1iROal8MJrVC8eXmWcjuhVYgL6+RcOaqAUn9zpdRDrh6uXkxEmgI3APPLG2hNdusCzzg/eTLW4EY5DHwsjlevuIxXLv8LfkH5dOx/lsbtM9nrcA/buTqE1FNGItDBo2P5/O9t+N8HTbj6gXi+ndKCvz13aZZhs+J1hufq1vMOYI3Dszh7xptnXt3He4s3M+6VfdjtwqaNYby3ZDMJcb5knLPRPiKNzT+WL9mm4YS2i8VbfmfHLxec0OozY8Vu4mN9jHi7ZbD5O9ed0CpKjd/W5UGmYaRtKjW3c21z6yqMu85Pnow186yVvRvq88rPf+BXJ58PR7fn969Dufutoyx7tRXr3m1Gl8EpWL2MMUbTzpk8880eAI5uqUOdsByUgo/GtMfqpbj5xWMENcgtco1aUbdu6lptdtp2TGfO5I4c3FuXxydGcsfD0Sya1ZalH7cEYNwr+1g0uw1DbomlV98Uog8HsmR+2W5dhhNad8MJbfZBWrTLLOqE9sZRFk1rxpA7Euh11VmiI/1ZMsu14XC5qcYTDy73YUXkIRFZLyKRIhJV+HDx/BuBRKXUH87K1Sa3rtKoqPOTJ2M99Esw9ZudJzAkD6uXotvQFKL/qENY2yyeXLSfCat202tYEqEtihrkKAXrZzRlyNMxrJvejOufiaH3zaf56aNLH2rXvLr1vANYUoIvSYk+HNxr9P5++S6MNh3TL35e4NYVwKAbT/HmC90Mt67mrrt1GU5odYo5oRVyg7v5NG8+3Z6W7ct2QqswNXG4WhgRmQhMxfBMbQl8A+wF6gMfuHitK4FhInIMWAJcIyKfuBpozXPrKp2KOj95MtbgJtkc3xFETpYFpeDwprqEtc0kPcn4w7bbjcbsinuLGlNvXdqAiIFn8K+bT06WxeECpsjJqjwXMGe4V7eedwA7k+zD6XhfwlsYjU6Py1M4EVXggzFy9FEWzW6DzWYv5NomZbp1XeqEdraYE9oJFk1rZriAOeK1m+jYJcq1oypwdbj6GDBKKbXUkdRuhlIqSkReBlq4IqCU+jvwdzB8E4EJSqn7XA20prl1meH85MlYW/Y8R/frk3n7hm5YbNC08zmuuDuBTZ824pdFxhCs25Bk+txeYIaUk2Vh21cNeXKRYZ404NE4PnyyA1YvY1lJja9bExzAAOZM7sjzb+zBZlPEn/Tjv//sbOgOSOTQvjqknDbq5MDuYGZ98Zvh1nXIuVtXvQY5TJhyBIsFwwltdQhbfzCepfYbnMKhPYWd0AKZ9e1OjkUGlOmEViGUQBVt2XIFl9y6RCQT6KiUOuHY5XCdUmqniLQFtiqlyvVks1Ajd6OzcjXNrcuUdEAmPa3VqZbMw9rQtfVz5UWlez7pz+asbzmbn+RW5fq0aKYa/32cS2WPPznxDydGNqbg6jO5eODCwqDjGPtWAdpSgUeOSqkfy2rgNBpNDcJDJg9m4Goj9z0wzPHzAuAdEfkB+BxjrZxGo/kzU40bOVefyY3C0SAqpeaIyBmMiYSvgLkmxabRaGoCtSFpplKqyKYMpdTnGL04jUajqbKZU1cotZETkV6uiiiltnsmHI1GUyOpiY0c8DtG6GX1QxWGR6JGo/mTUiN7ckCrSovCGTVoWYYZWAJMWNcEjG99tSm6Dx10aQNMufmocxtTdFVenudFg8y5Z/bT5c2FUTbK7qHFwTXxmZxSqriRtEaj0VxKFc6cukJlb9DXaDS1Ed3IaTSa2oxUUUJMV9CNnEajcR/dk9NoNLWVqsww4grlyoksIqEi0kdEfMourdFo/jTUgnxyQSLyBZAI/AqEO96fIyL/Mi+8ovQekMb8nw7w4S/7uWOMkefskX/EMXtDJBOnF0wGD7o1hZsfOV0+3Z8j+XDTAe4Y69B9MY7Z3x1k4vSCFN/l0X126gk+37WXuRsjL77niVi9vO1MW7qbmSt2Mmf1Du572ojv4YnHmLVyJ8+9dfhi2WuGJzL8gTjX4n37OJ/v3M3c7/YXivckszfsZ+K0Y4XiTebmRxJLUChg30dBfH1DY76+sTE/PhtKXjZsmxzMsqFN+Oamxmwc04DsNOMLn/CHD9/c1JiVtzYi7bgxsMhOE9Y90rDUlT7PTDnGku27mLNh38X3Hv57LLPX7WfCf6MLYh2RzPCHE0qSKBFPfQ/Gv7Cdz5avZtZHGy++FxiUw+tTNzHvsw28PnUTgYFGMtGILsnM/PB7ps39kcbhRpaRgMAcXnv7V5yNAT15vzxCNd676mpPbjJGw9aLoj6rq4BbXL2YiBwTkT0islNEfnc9TPPclGqSoxQ4XJpGdmbMsB6MGdady/o7XJp6pjP6ph5YraqQS9NpVn3qPD33xXi/rM+L97UtGu9l53jy2oiidXB7Cis/Lj2VUEaClf0L63DTV/HcsuoUKh+ivw2gyZXnuXlVHDevPEWdlrnsnmskz9z7YR0GvneaXs+mErnYyKG2a1Yw3R8/W+oSyQ1fhvDSyHbFYs3gySERRt12yMLbx87g25NZtdA13wRPfg++W9uclycWTTl1x72H2Lm9AY/dcy07tzfg9vuM/4xuuesIr798OR/Pi+CGm40G+u4HDvL5ovY4W4fvqfvlKapz0kxXG7lhwHil1E6KtscHgLKT0RdloFKqR3lzSpnlplSTHKUMirk02RwuTd6FXZqk3C5Ne7cEXRqvd/F4E1j+Qdnx2vMh/7xgz4O884J/w3zCrzqPxfEEuGGPbDLjjRcWmyL/vJCXJVhsirQTNjITrDS6vPS04nu3lhCrw/3K58Lv/0Q8yz9oWCXfg727QklP8yryXt+r4vlureGn+t3a5vS76hQA+XkWvL3z8fHNJy/PQqMmGYSEnmfvLuf/8XnyfrmNMmZXXTmqAlcbuXqUbD0YBOR7LpzSMctNqSY5Sl3AcGnayeLN29ix6YJLUwgzVuwiPsaHjHQb7bueY/PGirs0GfEGM2tdJAknHM5P3TP4bX2w0/MCwvLp8nAaXwwMZ8lVTfEOVIRfVdQn4vBXgTTtbwwIuj1+ll9fCWH/x3XodF862/8bTM9xqeWPdU09Zq45QHyMN5npVtp3z2TzBuexFsbs70FwvfOcSTYyAJ9J9qVuPaMR/+KTdjw9cSfDbzvKymWteeCx/Sxa0Knc+hW9Xx6jGg9XXZ1d3YbRm5vmeH0h3McxntG5igLWi4gC5iql3i9eoNxuXdXIAass3I31AoZLUw/DpWlWJC3aZRR1aXr9CIumN2PI7Qn0uiqV6IP+LJnVrALxNuLL2Y0c8R5n4dtNGHp3Epf1TzPq4N1LzWuyz1o4sdGf2zeexDvIzg/jGnB0eQBthhseB7tm10Gs0HqY8TqkUy43fhEPQPw2H/wb5oOCH8aHYrEpLp90Br/QsrsAS+c0YukcR6yTj7FoahOG3pVEr/5pRB/wY/F7zt3jK/N7UJioI8E8++RfAejSPYmUJF8EmPSvbeTlCfNndiH1jK9LWhW5Xx6jFsyu/gN4TUTmYTSMz4rI98D9wEvluN6VSqlewPXAGBHpX7yAc7cuE9yUapCjVHEMl6a6xVyajIfXsdF+DLrlNG+O60DLdu65NBWJ99YUXn+ytSPe85eUjfvVl6CmefjWt2PxghbXZZK4w7iPh78OIOZHf/76dtIljYpSsGt2XbqPPsvOGcH0fCqVNsMy2L+oziXXcDXWQbcm88bo1rTokEWTlpfGWhizvwepZ3ypF2LEUC/kPGfPFF+goLhr5EEWf9yBex6K5JMPOvLD+mYMu7X8e4HLc788RY1/JqeU+hW4AvAGjgKDgDigX3nSLCml4hz/JgJfA5e7eq5Zbko1yVEKSnJpSi3m0hTDounNDZcmh/uTXYlbLk0PTIwrFK+jDhT4luAoFdgkj9O7vMnLEpSCuN98qdsml9iffNkzrw6DZydi87v0237k6wCa/jULn7p28s4LYgGxQF5W+Z4njZwQx8J3mhSN1QWXKrO/B5s3NWLwUGOGdvDQE2z+pWivffDQE2z7rRHnznnj45OPsotx33zL/zSoPPfrz4DLi4GVUnuAByp6IREJACxKqXTHz9cB/3b1fLPclGqSoxQ4XJreOoLFYQX485pQtv5gPHvrNziZQ7sLuzQFMWvVTo4d9C/TpWnSjGi69Us34t22h0VTG7NuSagR764AUhK8C+rgu/1GvAf8L9Fp0D2HlkMyWXFLY8SmCOmUQ4c70/n6hibk5wjrHgpzlMvmin+nAEZDduTrQIZ8YCzb6PxQGt8/3QCrl+KvU5MujfW9KLr1S6dOvTwWbdnNJ+80Yd3nofS7LpVDu/wLxRrI7PX7iD7gT3QJsRbGk9+D51/ZRreeSdSpm8PCpWv55MOOfPlpe/7+6lauu+E4pxP8eOOVgv/ffXzyGDQ0hpeeM2Zkv/6iDS/+31Zyc4W3Xv2LqffLY3iolyYiQ4HpGOnb5iul/lNKub8Am4E7lVJLnWq66Nbl9Am2UirFBY3WGL03MBrXz5RSrzs7p47UV30sg8uMr9yYlWrJhLRQFn9zvpj2LHOGLg8d0KmWrG3NyVKWH3Wi7ELlZEv+etJUiltfXN8mzVTLUc+6VPbgq8+W6tYlIlbgEHAtEIsxF3C3Ump/CeU2AOeBD8pq5FztySXhvK0uM2mmUioK6O7i9TQaTU3CM/2Gy4EjjrYCEVkCDAf2Fyv3FIa/TMnd3GK42sgNLPbaC+gJPEn5Jh40Gk0tQyjXpEJosY0A7xdaZREOxBT6LBboU+RaIuEYGxCuwZONnFLqfyW8/Z2IRAGPAp+5oqPRaGoprjdySU42ApQ0bC6uPA14QSmVLy4+HnI3C8lO4JJlIBqN5k+E55aHxAKFF3Q2xVjFUZjewBJHAxcK/E1E8pRS35QmWuFGTkQCgfEU7V5qNJo/I55ZnbINaCcirYCTwF3APYULKKUuzuqIyEfAKmcNHLjYyIlIOkW7jQL4AxnAva5oaDSa2osnenJKqTwRGQusw5jM/EAptU9EnnB8Pqciuq725MYWe20HTgNblFJnKnJhjWvYMzLMEbaY4yL5UZd2ZReqACuP/2aK7o3hl3lc0x7jWnqr8gtXyjbxiuGhVVlKqdXA6mLvldi4KaUedEWzzEZORGxAAPDNhR0LGo1Gc5Fq7tZV5rYupVQeMAVj2YhGo9FcQo3fu4qxfcLz/XqNRlM7qAWpluYBb4tIc+APjAmHi5Rnk75Go6l91FhLQhH5AGOZyIXFvu+UUEzhwrYujUZTS6nmz+TK6sk9AEwCzNlxrNFoajyCMzeKqqesZ3ICoJQ67uyohDgB8xywapJblxmxgjnuT6GNs5m8JJL3N+5h7oY9DH/IyAD88KQYZq/dy4R3CjKWDLol6eLnJbF8fkNGXxPB6IERLJ9X1Jxm2Zwwbgy/jLMpxoBi/7YAxg7uxDN/60hctJGc8txZKy/f09ZpAhoz6ja0cTaTPzvA+xt2M3fdHoY/6KiDF2KYvWYPE6YeLVoHD5ZeB5URb4Wpxs/kXJl48FhoIhIsIktFJFJEDohIv/Kcb4YDVk1y6zIrVjDH/cmeL8z7v2aMGtSV8TdHcNPIRFp1yjR0h3YxdDtkOpy1kli1qGRnLaXsrPsslHe+PcB7G/az9bu6nIwyGq/TJ73Y8VMQDcILMvR+PTeMv78fxchJJ1m90Ih1ybTG3PFUfKnZsMyqW3ueMO/15oy6thvjR0Rw08gERx2k8+T1XR3uYo46uDWJVZ9UvruYJ6jps6vxIpLv7CjH9aYDa5VSHTHSLh0oT7BmOGDVJLcus2I14vW8+1NKojdH9hrJOrMyrMQc8aNhk5yL8V501nr8FMs/DCvdWUxBx14Z+PoprDbo0jed39YGAzDvX8146MWTRRovm02Rc95CdpYFm5fi1DFvkuO96NrvXKmxmlW3Kae9ObKveB1kF62DXOG2UadY/rGTOqikeCtMDe/JjQLuKOMoExGpg7GZfwGAUipHKZVa/pAL8IQDVk1y66qsWAvi9Zz7U1jTbNp0zmTPliDDWWv1PuJjfBzOWhls3lCv9JNF2Ls5kLQUK+ezhN+/r0tSnDdb1tclpHEOrTsX9a+4fWw8M55vzvJ5Ydz4YCILJ4dz30Tn69gro27DwrNpE5HJni11+GVtfWZ+u4/4WBfroAridRlVvS0JXVlCstLhyeAurTG2gn0oIt0xlqKMU0oVWY5SmltXafyZ3Loq21HKU+5Pvv75vDTnCHP/3YzMc1aWzm3M0rnGueMnR7PonXCG3nWaXlefJTrSn8XvNSn2ewu3jYnn5bvb4xuQT6uILKxWxefvNua1zw5dcr3WXbKYuuogAHs3B1I/LBcUTH6iFVYvxSOvxFKvQV6xa1watyfr1tc/n5dmH2bua80vrYP/OOrgzkR6XZ1GdKQfi2eEO9WrKnexUqnGs6tl9eQ8GboN6AXMVkr1xFhrN+mSC5bi1lUWfwa3rqqI9ZJ4y+n+ZLXZeXnOEX74JoRNa4tm0W/TOcOh68ugEUm8MaYtLdqX7Kx13d3JTF93gMnLDhEUnEfDZjkknPDmqWsjeLhPF5JOeTN+SARnEgv+31YKPp/emLvHn+Kz/zbmnglxDByRwsoFlz73MrNurTY7L88+zA/LQ9i0rlgdRDjqINqXQSOSeWNs6XVQWfFWhJr8TM6TA/lYIFYptcXxeilGo+cR/gxuXVURq6FbUfcnxTNvHePEET+Wzb+0lzryuZMsnBpetB5Uyc5aqUlG45V40ovf1tRj0G3JfLp7Nx9s2csHW/YS2jiHaev2U69hQQ9t4xch9B50lsDgfLKzLFgExKLIzrr0a29e3SqemRxt1MGCS3u+I5+NZeE74UXc1aqDu1i5qcbP5JwOV5VSrm77KhOlVLyIxIhIB6XUQQxbw+K5251ihgNWTXLrMitWMMf9qXPvcwy+NZnoA37MXL0XgI+mNGXbD8H0u+4Mh3YFXHQWO7A9gNnr9hId6Veis9Ybj7Um/YwNq03xxOsnCAx2Pt91PkvY+GUIry02hrM3j0rkjVFtsHnZeX5m9CXlzarbzr3PMXhEMtGRfsz8tlAd/BhMv2vPFHFXO7AjkNlr9hAdWbnuYp6gqnppruCSW5fHLibSA5iP4d8aBTzkLFWTduvCvFhNSrUkVnN0Vx6rOamWxMf1xyzlQWV7fqi5RW10263Lv0Ez1XGEa25dO94v3a3LLNxNf14ulFI7MdIXazSaWkI5jWwqnUpt5DQaTS1FN3IajaY2I5X42Ku86EZOo9G4Rw3PQqLRaDRlop/JaTSaWk2NTZpZLajGY/1LEI8tKyxENf72lIDKzSm7UAUwY6kHwIITv3hc87FOQzyuCSDe3mUXKq/mOQ99Z6vxn2n1b+Q0Gk31pgq3bLmCbuQ0Go376EZOo9HUVvRiYI1GU+sRe/Vt5XQjp9Fo3KOar5MzYzrQNMwy7jDFyMYEY5iLsf50gA9/2c8dYxyxesAgx9R4q/E927CgCS8P7snLg3qyYb6RrHPbqhBeHtSTR1tcybFdgRfLHt4WxD+v68lrN3Yn4ZiR8SPzrJV37utc6iIAL28705buZuaKncxZvYP7njZie3jiMWat3Mlzbx2+WPaa4YkMf8B5BuMiul/uZOby7cxZtZ37njLu/cMTopm1YjvPTT5YVHfkSZd0K0p1zgxcaY2ciHQQkZ2FjjQRGe/q+WYZd5hmZGOCMYzFohjzeiwv3deaxwZ2ZODNZxyxumeQY2q81fye/bQ4jJdW7uJf63awa2N9EqJ9Ce+QyZj3I2nfJ61oHc0LZ/TcA4x4/jg/LjJyw618tzk3jI0pNQFNbo4waWRnxgzrwZhh3bmsfypdLz9Lp57pjL6pB1aromX7DLx98rl2xGlWfeo8O3QR3Qe6MmZ4L8bc3IPLrj5D1784dIf1wmqlQPeWBFZ95loW5wpTU/PJeRJHDrkeACJiBU4CX7t6fmHjDsBjxh1m6e7dEkRY04LUOJ4whjFi9SkUaz36XnfWbYMcc+Ot3vesTa/0iwkqO/Q9y/a1IVz/ZMm9HqvDICfnvAWrzU7iMV/OxHvToW9aieUNhPOZRvopm01hsxl/6YXrNi9PuO2xOJYvbOyykU2pul52Q9fHofvoSZYvalIO3YpRnSceqmq4Ogg4Wh7PVrOMOyrXyMY9Yxgj1oIU10mnvKgXmue2QY658Vbve3ZoS13OnbGRnWVh9w/1SDlVei64v42JZeGktmxY0IRrHjzFsiktuGVC2V9hi0UxY8VOFm/exo5NddmztS6b1oUwY8Uu4mN8yEi30b7rOTZvrF+m1iW63+xg8a9b2PFrMHu21WXT+lBmfLOT+FiHbpd0Nm8MKZduuVEYi/ZdOaqAqpp4uAtYXNIHpRnZmGXcUblGNu4Zw5Qaq5sGOZUebzW6Z9c/GcvUe7vg459Ps04ZWK2l/yE275zBi8t3A3BwSx2Cw3JQCuaM7oDVS3HHS9HUbZB7yXl2uzB2WA8CgvJ4eVYkLdplsHReOEvnGWY1414/wqLpzRhyewK9rkol+qA/S2Y1c1oHF3Vv7mnozjxg6M5vytL5TQ3d/zvMondbMOS2+ALd2c3L1K0I1XlbV6X35ETEGxgGfFnS56UZ2Zhl3FHlRjblMIYxYi34IwptnEtyQqFYK2iQY2681fueXX1XAv9cvZNJS/cQEJxHw1ZZl5QpjlKw6t1m3PT0CVZMa87wZ0/Q95ZENn7YxOl5Gek2dm+pS+/+qQWxRhhesLHRfgy65TRvjutAy3aZNGlRdhyX6F5dkGS7TSeH7jE/Bt2cyJvjO5Zb11UurJOrqUY2ZnA9sF0plVCek8wy7qgSI5sKGsMYsWYXivUMm9fXKdCtoEGOufFW73uWlmQ0jsknfdi+NoQ+w8qe4d20tCHdrjlDQHA+OVlWxKIQC+SUYJBTt34uAUGGuY63Tz49r0glJqrAZ+H+8TEsmt68iJGNXUmZRjZ165WkWzDyuX/ccRa969B11IHdLvj4lscL3kVcHar+iYard1PKUNUZZhl3mGZkY4IxjD1fmPlSUyNWi2L95/U5fsiIwx2DHFPjreb3bNbjHTl3xgurl+Le144SEJzP9rUhfPZKa9JTvJj+UATNIjJ49pN9AGRnWfh1acOLr6979CSzHu+EzUsxakbkJfr1GuQw4a0jWCwKsSh+XhPK1h+MZ2/9BicXMbKJ3BHErFU7OXbQn+jIAKd1UK9hDhP+cwiLVSECP68NZeuPDt1ByRzaE0RKoo9Dtw6zVmzn2KEAog8GOpOtMNV54qGyjWz8gRigtVLqbFnl60h91UcGmR+YpzDDHEaZ9LDDlIwpgN2EnoKJ1KQsJFg8f882n1vB2fwkt4xsgoKbqp79x7lU9ueVz9d6I5tMwOSpHo1GU9lU555cjdrxoNFoqiEKyFeuHWUgIkNF5KCIHBGRSSV8fq+I7HYcv4pI97I09d5VjUbjNp7oyTk2CcwErgVigW0iskIpVdiEPhr4q1LqjIhcD7wP9HGmqxs5jUbjPp55tn85cEQpFQUgIkuA4cDFRk4p9Wuh8puBpmWJ6uGqRqNxm3KskwsVkd8LHaMKyYRjTExeINbxXmk8AqwpKzbdk9NoNO5Rvs33SU5mV0ua5S1RWUQGYjRyV5V1wT9nI2fGUg+TEKs5saq8PFN0xaf0vZ/uoHLMMch5pHmZfyPl5qvYjR7XBLi1+ZUe11R295coCSAuTCq4QCxQeD9bU+CS3FMi0g2YD1yvlEouS1QPVzUajduIUi4dZbANaCcirRzbP+8CVhS5jkhzYBlwv1LqkCux/Tl7chqNxnN4KFecUipPRMYC6wAr8IFSap+IPOH4fA7wCsZa21liZGrIK2txsW7kNBqNm3huX6pSajWwuth7cwr9/CjwaHk0dSOn0WjcpjrveNCNnEajcZ8qyjDiCrqR02g07qE8NrtqCjVqdrUmuWqZpfvMlGMs2b6LORv2XXzv4b/HMnvdfib8N7pAd0Qywx92PWWfGXUb2jibyZ8d4P0Nu5m7bg/DH4w34n0hhtlr9jBh6tEC3VuSLn7ucrwedi3zZB2smt+I8YO6Me6abqyab2RXXjylKc8M7spz13Xl3/d0JMWR6DNyWyDPDO7K8zd04VS0sQQn46yVf9/bsdQOklnfrwpTjY1sKrWRE5FnRGSfiOwVkcUi4uvquTXJVctM3Q1fhvDSyHbFdDN4ckiE4dbVIQtvHzuDb09m1cKGLmmaVbf2PGHe680ZdW03xo+I4KaRCbTqlEnEZek8eX1XR7yZRry3JrHqk3LE62HXMk/WwYlIP75b3JDJq/byzvrd/P5dPeKifBn+xCn++90epq7fw2WDUvlymrEjacXcJkx8/zD3vhDDukVGivYvp4dz69iTpbqAmfX9qigeWkJiCpVpSRgOPA30Vkp1wZgivsvV8ws7NOXlWjzqqpWeWrDg1hMuVabqbi1B1+HQ5HPB+emJeJZ/0LBC7leerNuU094c2Wckf8zKsBJzxI+GTbIv6vr42snLFW4bdYrlH4e57ChV2LXMiNd91zJP1kHsET/a9zyHj58dqw06901j69p6+AcV5NrLzrJcXN9v9TJcwLKzLFhtivhjPqTEe9O5X3qp1zDr+1VhqnFm4MoertoAPxGxAf6UsJq5NGqSq1al666px8w1B4iP8SYz3Ur77pls3uC6bmXUbVh4Nm0iMtmzpQ6/rK3PzG/3ER/r44g3g80b6pUzXs+6lnmyDpp3yGT/liDSHS5g278PJinOGIZ+OrkZo/7Sk5++DuWuCcY2zRFjTzLnhVasmt+Ivz2YwGdvNeOuCbEu1weY9/1yCQXYXTyqgMr0XT0pIm8DJ4AsYL1San3xcpXt1lUS7rpUVbbu0jmNWDrHoTv5GIumNmHoXUn06p9G9AE/Fr9XNa5aF/D1z+el2YeZ+1pzMs9ZWTq3MUvnGjGN/080i94JZ+idifS6Oo3oSD8Wz3C2J9sc1zJP1kHTdue5eXQcr97dCd+AfFpGZGJ1+KLe+0IM974Qw7IZTVjzYSPumhBLq86Z/Gel8Yx13+Yg6oUZZkVTn2yHzWbngVdOEFyCC1hxzPp+lYVQdUNRV6jM4Wo9jLQprYAmQICI3Fe8XGW7dTmjoi5V1UF30K3JvDG6NS06ZNGkZdW4agFYbXZenn2YH5aHsGldUV/RNhEZhm60L4NGJPPG2La0aO9qvJ51LfN0HQy++zRvr93D/321n8DgPBoXu7dX3ZzE5jVF60Mp+OrdcG4fF8sX7zTlzudi6D8iiW8/KJ+lpFnfL6fY7a4dVUBlDlcHA9FKqdNKqVyM/WdXuHpyTXLVqirdkRPiWPhOk6K6dsp0fjKvbhXPTI7mxBE/li24tBcx8tlYFr4TXsSpyvV4Peta5uk6OJtkDJJOn/Rm85r6XDU8ibiognm239fXI7xNUXvAH75sQK9rUgkMzic7y4LFQqkuYM4w6/tVKnq4epETQF+HmU0WMAj43dWTa5Krlqm670XRrV86derlsWjLbj55pwnrPg+l33WpHNrlX0g3kNnr9xF9wJ/oKnLV6tz7HINHJBMd6cfMb/cC8NGUpmz7MZh+154p4lR1YEcgs9fsITrSxXg97Frm6TqYMqo96WdsWG2Kx16PJjA4n1kTWxMX5YeIokHTHB5/M+pi+ewsCz9+GcornxmOXzeNOsWUx9pj87bzzMwjl+ib9f2qKNV5uFrZbl2vAncCecAO4FGlVKljCNPcumpSqiWLOTNjOtXSBWHPf/+/it3scU0wJ9XSlvz1pKkUt75kdf2bqH7tHnGp7Lrd/1fr3br+CfyzMq+p0WjMpuqWh7iC3tal0Wjc44JbVzVFN3IajcZtqvMzOd3IaTQa99GNnEajqbUowK4bOY1GU2vREw/uUVoaBnew55ddpiKYEKuym7NeWy/1MLAEBHhc847LhnlcE+DFI5fsgnSbx4dlekZIN3IajabWooD8KtrO4AK6kdNoNG6ijD1u1RTdyGk0GvfRw1WNRlNr0bOrGo2m1qN7chqNplajGznP0HtAGk/8+yRWi2LN4hC+mBnGI/+Io/fANKL2+zFlXAvAcFMKCs7nmwWuGXj0HpDGE6/FOXTr88WMMB55MY7eA9OJ2ufHlHHNy6377NQT9BmcRmqSjccHdQTwSKzPvn2cPoPPGrqDIxy6J+k98CxR+/yZMr6lQzfZoVu2OUxo42wmTo2iXoNclF1YvbgByz9qxMMvxPCXAakc3e/P28+1MXRvSSKwbh7LPyo7kaNZdQDm3DMvbztTPtuLl7cdq03xy9oQPnm3OQ9PPEbv/qkcPRDA1OcNE6FrhicSFJzH8o+blKkbEJjLuH/up0WbcyglTHs1gn4DTtP7yiSiDgUx9eUuhuYNcQTVyWP54ualam1Z0ICdX9RHBBq0P89NU05g81Fs+ziU3xeGYrEp2g5MY9CkU8T8HsDal5ti9VbcPP0Y9VvmcD7NytdPteCuj6I8t+JJKcg3aVmWB6hst65xDqeufSIyvjznmuHQdFHXDBewL+rz4r2tL772D8p3O1Ywx6XJLFcts+rArHuWmyNMGtmZMcN6MGZYdy7rn0rXy8/SqWc6o2/qgdWqaNk+A2+ffK4dcZpVn7qWsffx5w/yx68hPD7iSsbe2ZeU0z506p7KmDv7YbEoWrZNx9snn8E3nWLVl01L1UmL92Lbx6E8vPwQo9YeRNlh38p6HPstkEMb6vLY6oM8vu4gfR817BK3zG/ArbOiGTAhju2fGnXwy3thXDE6wfNLOrWRDYhIF+Ax4HKgO3CjiLRzflYBZjg0Feia4QIWWIKrlnuxGrqed2kyy1XLrDow656BcD7TiNdmU9gcvgyF6zcvT7jtsTiWL2zsUj34BeTRpdcZ1n1t+Fbk5VlIP+uFl8NhzdvHTl6ehVsfOMaKJc3K1LTnC3nnLdjzIDfLQlBYLts/DeGKJxKw+RjxBoQauQItXorc8xZyz1uw2BRnjnuTnuBFiz4ZLtZHOdCNHACdgM1KqUylVB7wP+AWV082w6GpQLeyXMDci7V0Xc+5NHnSVavkWN2vAzPvmcWimLFiJ4s3b2PHprrs2VqXTetCmLFiF/ExPmSk22jf9RybN9YvWwxoHJ7F2TPePPPqPt5bvJlxr+zDbhc2bQzjvSWbSYjzJeOcjfYRaWz+0XkPuU6jXPo+msh7V0UwvW8XfILyaX11OsnRvpzYFsiHt7Rj0V1tidtlZCu+4slEVr/YjG0fNqD3yCR+nNqYvz5zqlz14RrKmF115agCKvOZ3F7gdREJwUh//jdKSH9ebrcuNxyanOqa4gLmXqyl63rGpcnTrlolx+p+HZh5z+x2YeywHgQE5fHyrEhatMtg6bxwls4zftdxrx9h0fRmDLk9gV5XpRJ90J8ls5qVqme12WnbMZ05kztycG9dHp8YyR0PR7NoVluWftzS0HxlH4tmt2HILbH06ptC9OFAlsxvfYlW1lkrh76ry5j/7ce3Tj7LxrZizzf1UPlwPs3Kg8sOE7fbn2VPtWTM/w7QKCKLh5YdBuDE1gACG+ailLDsqRZYbYpB/3DZEdQ5ClQ1XgxcaT05pdQBYDKwAVgL7MJIg168nBO3Ls86NBXoVqELWDliLZduOV2azHDVcjnWctZBZdyzjHQbu7fUpXf/1ALdiHOGbrQfg245zZvjOtCyXSZNWmSVogJJCb4kJfpwcK/Ro/zluzDadCwwjW7dIQ2Ak8cDGHTjKd58oRst2p6jSfNLh5THNgUS3DSHgJB8rF7QYUgqsX8EENQol45DziIC4d0zEQtkphR6TKDglxlhXPVUAj+/G0b/8fF0ufkM28rxHLRM8u2uHVVApU48KKUWKKV6KaX6AynAYVfPNcOhqUC3kl3AKhhrmboVdmkyx1XLaaxu1IFZ96xu/VwCgoz/d7198ul5RSoxUQVGNfePj2HR9OZF6sGuxGk9nEn24XS8L+EtjEarx+UpnIgqSAowcvRRFs1ug81mL1S3gk8J96xOk1xO7vQnN0tQCo79GkRo2/O0v/Ysx34LBCA5yof8XMG/fsFs5+6v6tN2YBp+dfPJPW9BxHAByyunC1ipKFWtLQkrdQmJiDRUSiWKSHNgBNDP1XPNcGi6qGuGC9jMY3Trd85wU/p9H4vebsS6JSFuxQrmuDSZ5aplVh2Ydc/qNchhwltHsFgUYlH8vCaUrT8Yvdp+g5OL1EPkjiBmrdrJsYP+REc6z2QyZ3JHnn9jDzabIv6kH//9Z2dDc0Aih/bVIeW0EfuB3cHM+uI3og8HEn0o6BKd8B6ZdBx6lgU3dcBiU4RFZNHzrmREYNULzXh/aAcsXophU05cHNLnZgl7ltXj7o+PAtDn4dN8Nbqlsaxk2nGWbiuzul2jGq+Tq2y3rp+BECAXeFYptdFZ+TpSX/WxDPZ8IGb9zmakhRKTUi15mfP/m061BBLoeU2ASb+ZkWophoO7z7vn1mUNVX39bnCp7PqMhbXerevqyryeRqOpDHTSTI1GU5vRG/Q1Gk1tRgFKb+vSaDS1FuVImunKUQYiMlREDorIERGZVMLnIiLvOj7fLSK9ytLUPTmNRuM2ygPDVRGxAjOBa4FYYJuIrFBK7S9U7HqgnePoA8x2/Fsquien0WjcxzM9ucuBI0qpKKVUDrAEGF6szHBgoTLYDASLiNMtPdW6J5fOmaTv7F8ed6FoKJBkQgjl03X9P7Oqj9f1RyhVH6uZuudM0DVDE1jXyhTdFi6rlkI6Z9Z9p5a6un3CV0QKb+d8Xyn1vuPncCCm0GexXNpLK6lMOFDqptxq3cgppVzKFSQiv5ux9kbr1qxYa5puTYrVGUqpoR6SKmm9XvGugytliqCHqxqNproQCxTOdtAUKJ5FwJUyRdCNnEajqS5sA9qJSCsR8QbuAlYUK7MCGOmYZe0LnFVKOc0fVa2Hq+Xg/bKLaN1qpKl1zdM0U9dUlFJ5IjIWWAdYgQ+UUvtE5AnH53OA1Rhp2o4AmcBDZelW6t5VjUajqWz0cFWj0dRqdCOn0WhqNTW+kStrG0gFNT8QkUQR2esJPYdmMxH5QUQOONzKxnlI11dEtorILofuq57QLaRvFZEdIrLKg5rHRGSPiOwstmbKHc1gEVkqIpGOOnY5V6ETzQ6OGC8caeV1mXOi/Yzjfu0VkcUi4ush3Qo74tValFI19sB4OHkUaA14Y6RUj/CAbn+gF7DXg7E2Bno5fg4CDnkoVgECHT97AVuAvh6M+1ngM2CVBzWPAaEe/i58DDzq+NkbCDbhuxYPtPCAVjgQDfg5Xn8BPOgB3S4YXir+GJOK3wHtPFkPNfGo6T05V7aBlBul1E8Y6dk9hlLqlFJqu+PndOAAxpfdXV2llLqwxt7LcXhkNklEmgI3APM9oWcWIlIH4z+mBQBKqRylVKqHLzMIOKqUcmUHjivYAD8RsWE0Sp5wlXHLEa+2UtMbudK2eFRrRKQl0BOj1+UJPauI7AQSgQ1KKY/oAtOA5wFPJ+dXwHoR+cPhzuYurYHTwIeOofV8EfF0et67gMWeEFJKnQTeBk5gbEc6q5TyRNrfvUB/EQkREX+MpRalW4n9SajpjVy5t3hUNSISCHwFjFdKpXlCUymVr5TqgbH6+3KHkbdbiMiNQKJS6g93tUrgSqVUL4yMEmNEpL+bejaMxwuzlVI9gQzAI89nARwLU4cBX3pIrx7GiKMV0AQIEJH73NVVLjri/dmo6Y1cubd4VCUi4oXRwH2qlFrmaX3HEO1HwBN7Ca8EhonIMYzHANeIyCce0EUpFef4NxH4GuOxgzvEArGFerBLMRo9T3E9sF0pleAhvcFAtFLqtFIqF1gGXOEJYeWGI15tpaY3cq5sA6kWiIhgPDM6oJR6x4O6DUQk2PGzH8YfUKS7ukqpvyulmiqlWmLU6/dKKbd7GyISICJBF34GrsMYZrkTazwQIyIdHG8NAvY7OaW83I2HhqoOTgB9RcTf8b0YhPGM1m1EpKHj3wuOeJ6Mu0ZSo7d1qVK2gbirKyKLgQFAqIjEAv9USi1wU/ZK4H5gj+P5GcA/lFKr3dRtDHzsSDhoAb5QSnlsuYcJhAFfG3/b2IDPlFJrPaD7FPCp4z+7KFzY7uMKjmdb1wKPe0IPQCm1RUSWAtsxhpM78NxWrK9E5IIj3hil1BkP6dZY9LYujUZTq6npw1WNRqNxim7kNBpNrUY3chqNplajGzmNRlOr0Y2cRqOp1ehG7k+EiNwmIqrQ6wdFxHVvKc/GskpEPnLy+QARUSLiqgsUIvKjiMxwM66WjutWmhGMxlx0I1fFiMhHjj8qJSK5IhIlIm+bsPeyJD7H2PfpEo4USRNMjEej8Tg1ejFwLeI7jIXCXsDVGFk/AoAnixd0ZK3IVx5Y4KiUygKy3NXRaKozuidXPchWSsUrpWKUUp8BnwI3A4jIvxxJEB8UkaNANsaG7roi8r4juWe6iPyv+BBLREaKyHERyXQkvQwr9vklw1URuUFEtohIlogki8hKMRJz/ohhRDzlQs+z0DlXOK6fKSInRWS2I/3Rhc/9HT3WcyKSICL/KG8FOTJrLBaRWEds+0SkpF0NNhGZLiJnHMcUEbEU0vEWkckOnQwR2SYiQ8obj6bmoBu56kkWRq/uAq2Ae4Dbge4YDd23GGmlbsRI2/QT8L2INAYQkT7ARxjbhXoAK4F/O7uoiAwFlmNksbgMGIiRk8yCsQ8y1qHR2HEgIl2B9Rh7hrs7yvUAPigk/TbG1qhbMfZp9sTI/1YefDG2Qd0IdAamA3NFZFCxcvc64u2HsRVrFDC+0OcfAn/FqM+uGMk2V4pI93LGo6kpVHXWzj/7gdEQrSr0+nIgCfjc8fpfGPsQwwqVuQY4hyOzbKH3dwLPO37+DCO3XOHP5xu3/OLrB4FzhV5vApY4ifUYMKHYewuBBcXe64GR8qohEIjRKN9b6PNAIBX4yMm1Bjg0Ss0gjJEdZX6h1z9iZFyWQu+9hJGhBKANRm685sV0vgFmOX5u6bhu76r+bujDM4d+Jlc9GOoYNtowenDLMTacXyBWFU3zcxlGNtnTjo3uF/DF+EMGI0vsymLX+Q14xEkcPTEa3fJwGdBWRO4s9N6FoNpgeGN6O64NgFLqnIjsKc9FHAkIJgF3YvRgfRy6PxYrulk5WisHvwGvOYbPvRyx7S9Wbz7A9+WJR1Nz0I1c9eAnjGFVLhCnjBxjhcko9toCJGBMUhTnQiLOkhKKmoEFo4f43xI+Owl0KOH9ijABeA4YB+zB6Mm+gdFbdBULRi/tLxh1XRg9AVNL0Y1c9SBTKXWkHOW3Y0wi2JVSUaWU2Q/0LfZe8dfF2YHxzGxeKZ/nYKS0Kh5L59LiF5EjGA1KX4wUSBfyyHXBMCFylauAlUqpRQ4NAdpjDHsL00dEpFBvri/GfxxpIrIDo/FvpJT6oRzX1tRg9MRDzeQ7jOdny0XkejGShvYTkVdF5ELv7l1gsIj8XUTaichjlG1q8jpwu4j8n4hEiEhnMazz/B2fHwOuFpHwQot0J2OkXJ8jIj1FpK2I3Cgic8EYmmIkC50sIteKSGeMSYnijWVZHAIGichVItIRmIExIVOcJsA0MewEbwMm4uhlKqUOYcxcfyTGwujWItJbRCaIyIhyxqOpIehGrgbi6KX8DeM50jzgIIatXQcc6d+VUpsxnr89CezGmPX8Vxm6qzEawusxenX/w5hhvWBk8wpGuvmjGMYxKKV2Y8yUtnSU3wW8iTGcvsAE4AeMVOc/YGQC/qmcv/b/AVuBNY5zMzAarOJ8itGAbsGomwUUHUo/hDHD+hZGBuVVjvg95cKlqWbopJkajaZWo3tyGo2mVqMbOY1GU6vRjZxGo6nV6EZOo9HUanQjp9FoajW6kdNoNLUa3chpNJpajW7kNBpNreb/AcxqDVUenSHjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rc('font', size=10)  # extra code\n",
    "ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred,\n",
    "                                        normalize=\"true\", values_format=\".0%\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_weight = (y_train_pred != y_train)\n",
    "plt.rc('font', size=10)  # extra code\n",
    "ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred,\n",
    "                                        sample_weight=sample_weight,\n",
    "                                        normalize=\"true\", values_format=\".0%\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's put all plots in a couple of figures for the book:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code – this cell generates and saves Figure 3–9\n",
    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(9, 4))\n",
    "plt.rc('font', size=9)\n",
    "ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred, ax=axs[0])\n",
    "axs[0].set_title(\"Confusion matrix\")\n",
    "plt.rc('font', size=10)\n",
    "ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred, ax=axs[1],\n",
    "                                        normalize=\"true\", values_format=\".0%\")\n",
    "axs[1].set_title(\"CM normalized by row\")\n",
    "save_fig(\"confusion_matrix_plot_1\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code – this cell generates and saves Figure 3–10\n",
    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(9, 4))\n",
    "plt.rc('font', size=10)\n",
    "ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred, ax=axs[0],\n",
    "                                        sample_weight=sample_weight,\n",
    "                                        normalize=\"true\", values_format=\".0%\")\n",
    "axs[0].set_title(\"Errors normalized by row\")\n",
    "ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred, ax=axs[1],\n",
    "                                        sample_weight=sample_weight,\n",
    "                                        normalize=\"pred\", values_format=\".0%\")\n",
    "axs[1].set_title(\"Errors normalized by column\")\n",
    "save_fig(\"confusion_matrix_plot_2\")\n",
    "plt.show()\n",
    "plt.rc('font', size=14)  # make fonts great again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "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)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code – this cell generates and saves Figure 3–11\n",
    "size = 5\n",
    "pad = 0.2\n",
    "plt.figure(figsize=(size, size))\n",
    "for images, (label_col, label_row) in [(X_ba, (0, 0)), (X_bb, (1, 0)),\n",
    "                                       (X_aa, (0, 1)), (X_ab, (1, 1))]:\n",
    "    for idx, image_data in enumerate(images[:size*size]):\n",
    "        x = idx % size + label_col * (size + pad)\n",
    "        y = idx // size + label_row * (size + pad)\n",
    "        plt.imshow(image_data.reshape(28, 28), cmap=\"binary\",\n",
    "                   extent=(x, x + 1, y, y + 1))\n",
    "plt.xticks([size / 2, size + pad + size / 2], [str(cl_a), str(cl_b)])\n",
    "plt.yticks([size / 2, size + pad + size / 2], [str(cl_b), str(cl_a)])\n",
    "plt.plot([size + pad / 2, size + pad / 2], [0, 2 * size + pad], \"k:\")\n",
    "plt.plot([0, 2 * size + pad], [size + pad / 2, size + pad / 2], \"k:\")\n",
    "plt.axis([0, 2 * size + pad, 0, 2 * size + pad])\n",
    "plt.xlabel(\"Predicted label\")\n",
    "plt.ylabel(\"True label\")\n",
    "save_fig(\"error_analysis_digits_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: there are several other ways you could code a plot like this one, but it's a bit hard to get the axis labels right:\n",
    "* using [nested GridSpecs](https://matplotlib.org/stable/gallery/subplots_axes_and_figures/gridspec_nested.html)\n",
    "* merging all the digits in each block into a single image (then using 2×2 subplots). For example:\n",
    "    ```python\n",
    "    X_aa[:25].reshape(5, 5, 28, 28).transpose(0, 2, 1, 3).reshape(5 * 28, 5 * 28)\n",
    "    ```\n",
    "* using [subfigures](https://matplotlib.org/stable/gallery/subplots_axes_and_figures/subfigures.html) (since Matplotlib 3.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multilabel Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "y_train_large = (y_train >= '7')\n",
    "y_train_odd = (y_train.astype('int8') % 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": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False,  True]])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: the following cell may take a few minutes to run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.976410265560605"
      ]
     },
     "execution_count": 82,
     "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": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9778357403921755"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extra code – shows that we get a negligible performance improvement when we\n",
    "#              set average=\"weighted\" because the classes are already pretty\n",
    "#              well balanced.\n",
    "f1_score(y_multilabel, y_train_knn_pred, average=\"weighted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ClassifierChain(base_estimator=SVC(), cv=3, random_state=42)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.multioutput import ClassifierChain\n",
    "\n",
    "chain_clf = ClassifierChain(SVC(), cv=3, random_state=42)\n",
    "chain_clf.fit(X_train[:2000], y_multilabel[:2000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1.]])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multioutput Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)  # to make this code example reproducible\n",
    "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": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extra code – this cell generates and saves Figure 3–12\n",
    "plt.subplot(121); plot_digit(X_test_mod[0])\n",
    "plt.subplot(122); plot_digit(y_test_mod[0])\n",
    "save_fig(\"noisy_digit_example_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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": [
    "knn_clf = KNeighborsClassifier()\n",
    "knn_clf.fit(X_train_mod, y_train_mod)\n",
    "clean_digit = knn_clf.predict([X_test_mod[0]])\n",
    "plot_digit(clean_digit)\n",
    "save_fig(\"cleaned_digit_example_plot\")  # extra code – saves Figure 3–13\n",
    "plt.show()"
   ]
  },
  {
   "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": [
    "Exercise: _Try to build a classifier for the MNIST dataset that achieves over 97% accuracy on the test set. Hint: the `KNeighborsClassifier` works quite well for this task; you just need to find good hyperparameter values (try a grid search on the `weights` and `n_neighbors` hyperparameters)._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start with a simple K-Nearest Neighbors classifier and measure its performance on the test set. This will be our baseline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9688"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf = KNeighborsClassifier()\n",
    "knn_clf.fit(X_train, y_train)\n",
    "baseline_accuracy = knn_clf.score(X_test, y_test)\n",
    "baseline_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! A regular KNN classifier with the default hyperparameters is already very close to our goal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see if tuning the hyperparameters can help. To speed up the search, let's train only on the first 10,000 images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid=[{'n_neighbors': [3, 4, 5, 6],\n",
       "                          'weights': ['uniform', 'distance']}])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [{'weights': [\"uniform\", \"distance\"], 'n_neighbors': [3, 4, 5, 6]}]\n",
    "\n",
    "knn_clf = KNeighborsClassifier()\n",
    "grid_search = GridSearchCV(knn_clf, param_grid, cv=5)\n",
    "grid_search.fit(X_train[:10_000], y_train[:10_000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 4, 'weights': 'distance'}"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9441999999999998"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The score dropped, but that was expected since we only trained on 10,000 images. So let's take the best model and train it again on the full training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9714"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_.fit(X_train, y_train)\n",
    "tuned_accuracy = grid_search.score(X_test, y_test)\n",
    "tuned_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We reached our goal of 97% accuracy! 🥳"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Augmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: _Write a function that can shift an MNIST image in any direction (left, right, up, or down) by one pixel. You can use the `shift()` function from the `scipy.ndimage` module. For example, `shift(image, [2, 1], cval=0)` shifts the image two pixels down and one pixel to the right. Then, for each image in the training set, create four shifted copies (one per direction) and add them to the training set. Finally, train your best model on this expanded training set and measure its accuracy on the test set. You should observe that your model performs even better now! This technique of artificially growing the training set is called _data augmentation_ or _training set expansion_._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try augmenting the MNIST dataset by adding slightly shifted versions of each image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.ndimage import shift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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": "markdown",
   "metadata": {},
   "source": [
    "Let's see if it works:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = X_train[1000]  # some random digit to demo\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\")\n",
    "plt.imshow(image.reshape(28, 28),\n",
    "           interpolation=\"nearest\", cmap=\"Greys\")\n",
    "plt.subplot(132)\n",
    "plt.title(\"Shifted down\")\n",
    "plt.imshow(shifted_image_down.reshape(28, 28),\n",
    "           interpolation=\"nearest\", cmap=\"Greys\")\n",
    "plt.subplot(133)\n",
    "plt.title(\"Shifted left\")\n",
    "plt.imshow(shifted_image_left.reshape(28, 28),\n",
    "           interpolation=\"nearest\", cmap=\"Greys\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks good! Now let's create an augmented training set by shifting every image left, right, up and down by one pixel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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": "markdown",
   "metadata": {},
   "source": [
    "Let's shuffle the augmented training set, or else all shifted images will be grouped together:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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": "markdown",
   "metadata": {},
   "source": [
    "Now let's train the model using the best hyperparameters we found in the previous exercise:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_clf = KNeighborsClassifier(**grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=4, weights='distance')"
      ]
     },
     "execution_count": 100,
     "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 a few minutes to run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9763"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "augmented_accuracy = knn_clf.score(X_test, y_test)\n",
    "augmented_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By simply augmenting the data, we've got a 0.5% accuracy boost. Perhaps it does not sound so impressive, but it actually means that the error rate dropped significantly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "error_rate_change = -17%\n"
     ]
    }
   ],
   "source": [
    "error_rate_change = (1 - augmented_accuracy) / (1 - tuned_accuracy) - 1\n",
    "print(f\"error_rate_change = {error_rate_change:.0%}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The error rate dropped quite a bit thanks to data augmentation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Tackle the Titanic dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: _Tackle the Titanic dataset. A great place to start is on [Kaggle](https://www.kaggle.com/c/titanic). Alternatively, you can download the data from https://homl.info/titanic.tgz and unzip this tarball like you did for the housing data in Chapter 2. This will give you two CSV files: _train.csv_ and _test.csv_ which you can load using `pandas.read_csv()`. The goal is to train a classifier that can predict the `Survived` column based on the other columns._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's fetch the data and load it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import tarfile\n",
    "import urllib.request\n",
    "\n",
    "def load_titanic_data():\n",
    "    tarball_path = Path(\"datasets/titanic.tgz\")\n",
    "    if not tarball_path.is_file():\n",
    "        Path(\"datasets\").mkdir(parents=True, exist_ok=True)\n",
    "        url = \"https://github.com/ageron/data/raw/main/titanic.tgz\"\n",
    "        urllib.request.urlretrieve(url, tarball_path)\n",
    "        with tarfile.open(tarball_path) as titanic_tarball:\n",
    "            titanic_tarball.extractall(path=\"datasets\")\n",
    "    return [pd.read_csv(Path(\"datasets/titanic\") / filename)\n",
    "            for filename in (\"train.csv\", \"test.csv\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, test_data = load_titanic_data()"
   ]
  },
  {
   "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 on 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": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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": 105,
     "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": [
    "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 explicitly set the `PassengerId` column as the index column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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": 107,
   "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": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.0"
      ]
     },
     "execution_count": 108,
     "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": 109,
   "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": 109,
     "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": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 110,
     "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": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    491\n",
       "1    216\n",
       "2    184\n",
       "Name: Pclass, dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Pclass\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Sex\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 113,
     "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": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\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": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: the `sparse` hyperparameter below was renamed to `sparse_output`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_pipeline = Pipeline([\n",
    "        (\"ordinal_encoder\", OrdinalEncoder()),    \n",
    "        (\"imputer\", SimpleImputer(strategy=\"most_frequent\")),\n",
    "        (\"cat_encoder\", OneHotEncoder(sparse_output=False)),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's join the numerical and categorical pipelines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.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": 118,
   "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": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = preprocess_pipeline.fit_transform(train_data)\n",
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's not forget to get the labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = train_data[\"Survived\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now ready to train a classifier. Let's start with a `RandomForestClassifier`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = preprocess_pipeline.transform(test_data)\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 expected by Kaggle), then upload it and hope for the best. But wait! We can do better than hope. Why don't we use cross-validation to have an idea of how good our model is?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8137578027465668"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8249313358302123"
      ]
     },
     "execution_count": 123,
     "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": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))\n",
    "plt.plot([1]*10, svm_scores, \".\")\n",
    "plt.plot([2]*10, forest_scores, \".\")\n",
    "plt.boxplot([svm_scores, forest_scores], labels=(\"SVM\", \"Random Forest\"))\n",
    "plt.ylabel(\"Accuracy\")\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": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AgeBucket</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.576923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>0.362745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30.0</th>\n",
       "      <td>0.423256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45.0</th>\n",
       "      <td>0.404494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60.0</th>\n",
       "      <td>0.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75.0</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Survived\n",
       "AgeBucket          \n",
       "0.0        0.576923\n",
       "15.0       0.362745\n",
       "30.0       0.423256\n",
       "45.0       0.404494\n",
       "60.0       0.240000\n",
       "75.0       1.000000"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"AgeBucket\"] = train_data[\"Age\"] // 15 * 15\n",
    "train_data[[\"AgeBucket\", \"Survived\"]].groupby(['AgeBucket']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RelativesOnboard</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.303538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.552795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.578431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.724138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Survived\n",
       "RelativesOnboard          \n",
       "0                 0.303538\n",
       "1                 0.552795\n",
       "2                 0.578431\n",
       "3                 0.724138\n",
       "4                 0.200000\n",
       "5                 0.136364\n",
       "6                 0.333333\n",
       "7                 0.000000\n",
       "10                0.000000"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"RelativesOnboard\"] = train_data[\"SibSp\"] + train_data[\"Parch\"]\n",
    "train_data[[\"RelativesOnboard\", \"Survived\"]].groupby(\n",
    "    ['RelativesOnboard']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Spam classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise: _Build a spam classifier (a more challenging exercise):_\n",
    "\n",
    "* _Download examples of spam and ham from [Apache SpamAssassin's public datasets](https://homl.info/spamassassin)._\n",
    "* _Unzip the datasets and familiarize yourself with the data format._\n",
    "* _Split the datasets into a training set and a test set._\n",
    "* _Write a data preparation pipeline to convert each email into a feature vector. Your preparation pipeline should transform an email into a (sparse) vector that indicates the presence or absence of each possible word. For example, if all emails only ever contain four words, \"Hello,\" \"how,\" \"are,\" \"you,\" then the email \"Hello you Hello Hello you\" would be converted into a vector [1, 0, 0, 1] (meaning [“Hello\" is present, \"how\" is absent, \"are\" is absent, \"you\" is present]), or [3, 0, 0, 2] if you prefer to count the number of occurrences of each word._\n",
    "\n",
    "_You may want to add hyperparameters to your preparation pipeline to control whether or not to strip off email headers, convert each email to lowercase, remove punctuation, replace all URLs with \"URL,\" replace all numbers with \"NUMBER,\" or even perform _stemming_ (i.e., trim off word endings; there are Python libraries available to do this)._\n",
    "\n",
    "_Finally, try out several classifiers and see if you can build a great spam classifier, with both high recall and high precision._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tarfile\n",
    "\n",
    "def fetch_spam_data():\n",
    "    spam_root = \"https://spamassassin.apache.org/old/publiccorpus/\"\n",
    "    ham_url = spam_root + \"20030228_easy_ham.tar.bz2\"\n",
    "    spam_url = spam_root + \"20030228_spam.tar.bz2\"\n",
    "\n",
    "    spam_path = Path() / \"datasets\" / \"spam\"\n",
    "    spam_path.mkdir(parents=True, exist_ok=True)\n",
    "    for dir_name, tar_name, url in ((\"easy_ham\", \"ham\", ham_url),\n",
    "                                    (\"spam\", \"spam\", spam_url)):\n",
    "        if not (spam_path / dir_name).is_dir():\n",
    "            path = (spam_path / tar_name).with_suffix(\".tar.bz2\")\n",
    "            print(\"Downloading\", 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()\n",
    "    return [spam_path / dir_name for dir_name in (\"easy_ham\", \"spam\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "ham_dir, spam_dir = fetch_spam_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's load all the emails:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "ham_filenames = [f for f in sorted(ham_dir.iterdir()) if len(f.name) > 20]\n",
    "spam_filenames = [f for f in sorted(spam_dir.iterdir()) if len(f.name) > 20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2500"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ham_filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500"
      ]
     },
     "execution_count": 131,
     "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": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "import email\n",
    "import email.policy\n",
    "\n",
    "def load_email(filepath):\n",
    "    with open(filepath, \"rb\") as f:\n",
    "        return email.parser.BytesParser(policy=email.policy.default).parse(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "ham_emails = [load_email(filepath) for filepath in ham_filenames]\n",
    "spam_emails = [load_email(filepath) for filepath 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": 134,
   "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",
      "https://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 https://docs.yahoo.com/info/terms/\n"
     ]
    }
   ],
   "source": [
    "print(ham_emails[1].get_content().strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "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",
      "https://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",
      "https://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": 136,
   "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",
    "        multipart = \", \".join([get_email_structure(sub_email)\n",
    "                               for sub_email in payload])\n",
    "        return f\"multipart({multipart})\"\n",
    "    else:\n",
    "        return email.get_content_type()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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": 138,
   "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": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structures_counter(ham_emails).most_common()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "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": 139,
     "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": 140,
   "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": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Life Insurance - Why Pay More?'"
      ]
     },
     "execution_count": 141,
     "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": 142,
   "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,\n",
    "                                                    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": 143,
   "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": 144,
   "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": 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",
      "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": 146,
   "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": 147,
   "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! We will use the Natural Language Toolkit ([NLTK](https://www.nltk.org/)):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "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": [
    "import nltk\n",
    "\n",
    "stemmer = nltk.PorterStemmer()\n",
    "for word in (\"Computations\", \"Computation\", \"Computing\", \"Computed\", \"Compute\",\n",
    "             \"Compulsive\"):\n",
    "    print(word, \"=>\", stemmer.stem(word))"
   ]
  },
  {
   "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:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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",
    "# 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": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['github.com', 'https://youtu.be/7Pq-S557XQU?t=3m32s']\n"
     ]
    }
   ],
   "source": [
    "import urlextract # may require an Internet connection to download root domain\n",
    "                  # names\n",
    "\n",
    "url_extractor = urlextract.URLExtract()\n",
    "some_text = \"Will it detect github.com and https://youtu.be/7Pq-S557XQU?t=3m32s\"\n",
    "print(url_extractor.find_urls(some_text))"
   ]
  },
  {
   "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": 151,
   "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,\n",
    "                 remove_punctuation=True, replace_urls=True,\n",
    "                 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": 152,
   "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": 152,
     "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": 153,
   "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\n",
    "                            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)),\n",
    "                          shape=(len(X), self.vocabulary_size + 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<3x11 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 20 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 154,
     "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": 155,
   "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]])"
      ]
     },
     "execution_count": 155,
     "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": 156,
   "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": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab_transformer.vocabulary_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now ready to train our first spam classifier! Let's transform the whole dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "preprocess_pipeline = Pipeline([\n",
    "    (\"email_to_wordcount\", EmailToWordCounterTransformer()),\n",
    "    (\"wordcount_to_vector\", WordCounterToVectorTransformer()),\n",
    "])\n",
    "\n",
    "X_train_transformed = preprocess_pipeline.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.985"
      ]
     },
     "execution_count": 158,
     "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(max_iter=1000, random_state=42)\n",
    "score = cross_val_score(log_clf, X_train_transformed, y_train, cv=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": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 96.88%\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(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(f\"Precision: {precision_score(y_test, y_pred):.2%}\")\n",
    "print(f\"Recall: {recall_score(y_test, y_pred):.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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