03_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 3 Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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.]]),\n",
       " array([5, 0, 4, ..., 4, 5, 6], dtype=int8))"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "try:\n",
    "    from sklearn.datasets import fetch_openml\n",
    "    mnist = fetch_openml('mnist_784', version=1, cache=True)\n",
    "    mnist.target = mnist.target.astype(np.int8) # fetch_openml() returns targets as strings\n",
    "except ImportError:\n",
    "    from sklearn.datasets import fetch_mldata\n",
    "    mnist = fetch_mldata('MNIST original')\n",
    "mnist[\"data\"], mnist[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((70000, 784), (70000,))"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y = mnist['data'], mnist['target']\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sort into training and testing instances\n",
    "def sort_by_target(mnist):\n",
    "    reorder_train = np.array(sorted([(target, i) for i, target in enumerate(mnist.target[:60000])]))[:, 1]\n",
    "    reorder_test = np.array(sorted([(target, i) for i, target in enumerate(mnist.target[60000:])]))[:, 1]\n",
    "    mnist.data[:60000] = mnist.data[reorder_train]\n",
    "    mnist.target[:60000] = mnist.target[reorder_train]\n",
    "    mnist.data[60000:] = mnist.data[reorder_test + 60000]\n",
    "    mnist.target[60000:] = mnist.target[reorder_test + 60000]\n",
    "    \n",
    "sort_by_target(mnist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label: 5\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Set seed for reproducibility\n",
    "np.random.seed(42)\n",
    "\n",
    "some_digit = X[36000]\n",
    "some_digit_image = some_digit.reshape(28, 28)\n",
    "plt.imshow(some_digit_image, cmap = plt.cm.binary,\n",
    "          interpolation='nearest');\n",
    "plt.axis('off');\n",
    "print(f'Label: {y[36000]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]\n",
    "# Shuffle so not all 0s, 1s, etc. are next to each other\n",
    "shuffle_index = np.random.permutation(60000)\n",
    "X_train, y_train = X_train[shuffle_index], y_train[shuffle_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Binary Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's create a binary classifier for the digit we saw above\n",
    "y_train_5 = (y_train == 5)\n",
    "y_test_5 = (y_test == 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hussain/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "# Classifier detects whether digit is a 5\n",
    "sgd_clfr = SGDClassifier(max_iter=5, random_state=42)\n",
    "sgd_clfr.fit(X_train, y_train_5)\n",
    "\n",
    "# Test on randomly chosen digit\n",
    "sgd_clfr.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hussain/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/hussain/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/hussain/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[53272,  1307],\n",
       "       [ 1077,  4344]])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "y_train_pred = cross_val_predict(sgd_clfr, X_train, y_train_5, cv=3)\n",
    "confusion_matrix(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.7687135020350381\n",
      "Recall: 0.801328168234643\n",
      "F1 score: 0.7846820809248555\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "\n",
    "# How many instances labelled positive are true positives\n",
    "print(f'Precision: {precision_score(y_train_5, y_train_pred)}')\n",
    "# How many positive instances we detected\n",
    "print(f'Recall: {recall_score(y_train_5, y_train_pred)}')\n",
    "# Harmonic mean of precision and recall\n",
    "print(f'F1 score: {f1_score(y_train_5, y_train_pred)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The F1 score is not always a good measure of model performance given your use case, since it favors classifiers with similar precision and recall, but in some cases one is more desirable or important than the other. \n",
    "\n",
    "We can control our precision and recall more carefully by outputting decision scores instead of classifications and changing the threshold which the scores use to make predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hussain/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/hussain/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/hussain/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ -434076.49813641, -1825667.15281624,  -767086.76186905,\n",
       "        -482514.55006702,  -466416.8082872 ])"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_scores = cross_val_predict(sgd_clfr, X_train, y_train_5, cv=3,\n",
    "                            method='decision_function')\n",
    "y_scores[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BwcT8J4b9J/dXyXp377ZuTaEjOihX0MJUTNHbXlx77bWcPHnSpcufOXMm48ePB2DKlCm8/PLLLl2+UtENo1l4y0LO5J1h0OxBJJxOqLR1ZWRYPaQaNaBx40pbjfIxWpiKKRiSaMuWLURGRp4dVFUpT9KzUU/m3jiXQ6cO0eO9HpU2OnnBSF1vvVUpi1c+SgvTBfTp04fExMIbtr300kv06NGDzp07849//OPs9FmzZtG5c2e6dOlydgDWr776il69etGtWzcGDhzIsWPHqjy/8m3D2g5j1d2rSEpP4tGlj7p8+UlJha8nT3b54pUPc9tBXB/55hGXjwPWtX5XXo993am2eXl5LF++/Ozo4kuXLmX37t388ssvGGMYMWIEP/zwA7Vr1+af//wnq1evpk6dOqSkpABwxRVXsHbtWkSEGTNm8OKLL/LKK6+49PsoVZbohtE83vdxnlv1HH0a92F8z/EuW/bf/249//yzyxapFODGhckuBbe92L9/P927d2fQoEGAVZiWLl1Kt27dAEhLS2P37t389ttvjBo1ijp16gAQGRkJQEJCAqNHj+bIkSOcOXOGFi1a2POFlM97+uqnWXd4HY8ufZS2tdsyuNXgi15mejpMnw6RkdCzpwtCKlWE2xYmZ3s2rlZwjOnUqVMMHz6cadOm8dBDD2GMYdKkSdxX7MrBqVOnlniriwkTJjBx4kRGjBjBihUrmDJlShV9A6XO5e/nz+yRs7niwysY+9lY9j+8n+rB1Su8PGOsY0u7d597WwulXEWPMZUiIiKCqVOn8vLLL5OTk0NMTAwffPDB2VuoJyYmcvz4cQYMGMAnn3xCcnIywNldeadOnaJRo0YAfPTRR/Z8CaUc6oXXY/bI2aRkpvDnJX+u8HKMgY4dYfFiaN1az8RTlUML0wV069aNLl26MHfuXAYPHswtt9xCnz596NSpE6NGjSI1NZVLL72UJ598kiuvvJIuXbowceJEwDoV/KabbqJfv35nd/MpZafejXszvsd43t/wPgt2LqjQMubPh23b4HV7dmgoH6G3vfAyug3VhWTnZtPp7U6EBoay4b4NZd5xuaj0dAgPh8BA+P33wlPFlXKG3vZCKVWi4IBgHu/7OL8d+415W+eV67MzZljPkyZpUVKVSwuTUj7mzq530qFuB/62/G+kZqc6/blJk6znIpfwKVUp3K4w2bVr0RvotlPO8Pfz55XBr7Dv5D7+tepfTn/ugw/gL38BP7f7q6G8jVv9iIWEhJCcnKx/YCvAGENycjIhev6uckJs61hu6XQLL6x+gVUHV5XZPi8PxoyBl16qgnDK57nVyQ85OTkkJCSQlZVlSyZPFxISQuPGjQkMDLQ7ivIAqdmptHmjDQ2rN2T9vetLPREiLQ3atIF//QvuvLNqMyrvUZ6TH9zqAtvAwEAdIUGpKlI9uDqTr5zMg4seZNXBVfRr1q/EdlOnwtGj0KpVFQdUPsutekxKqaqVdiaNNm+0oU1kG1beubLEXlPBpPx8vQmgqjg9XVwp5ZTwoHCe7PckPx78kVm/zTpvfnq69RwVpUVJVR0tTEr5uHu730uXel14ZMkjnM4+fc682bOt5+nTbQimfJZThUlEYkVkp4jEi8gTJcxvKiLfi8gGEdkkIkNdH1UpVRmC/IOYfu10Tmad5J24d86Zd/318NFH0LevTeGUTyqzMImIPzANGAJ0AMaKSIdizf4OfGKM6QaMAfR+lkp5kJ6NejKo5SBeW/saWbmFZ8XWrw933KG78VTVcqbH1BOIN8bsNcacAeYC1xVrY4AajtcRwGHXRVRKVYVJV0ziaNpRZm6cCcCrr8LLL9ubSfkmZwpTI+BQkfcJjmlFTQFuE5EEYBEwwSXplFJV5qrmV9GrUS9eXP0ihxJzefRR2LzZ7lTKFzlTmErqxBc/x3wsMNMY0xgYCswWkfOWLSL3ikiciMQlJSWVP61SqtKICJOumMS+k/u482VrgNf777c5lPJJzhSmBKBJkfeNOX9X3T3AJwDGmDVACHDeTYiMMdONMdHGmOi6detWLLFSqtJc2+5aOtTpwHfZz4Pk07u33YmUL3KmMK0D2ohICxEJwjq5ofhdxg4CAwBEJAqrMGmXSCkP4yd+DKnxBNTbQu8xP9gdR/moMguTMSYXGA8sAbZjnX23VUSeFpERjmaPAn8Ukd+AOcCdRkdiVcojPThgBBih962L7Y6ifJQOSaSUOs+IOSP4bt93HHjkALVDa9sdR3kBHZJIKVUhW7fCG2/AYz2eIT0nnfc3vG93JOWDtDAppc567z3461+hY50uXNnsSqb+PJWcvBy7Yykfo4VJKQVYNwP85BMYOhRq1oTH+j5GYmoi/938X7ujKR+jhUkpBUBcHBw5AjfeaL2PbR1L29pt+XDjh/YGUz5HC5NSCoD5863nXr2sZz/x45aOt/DDgR84dOpQ6R9UysW0MCmlAAgMhLp1oXXrwmm3dr4Vg2He1nn2BVM+RwuTUgqA556zbqFeVOvI1lzW4DK+2PGFPaGUT9LCpJQiJcU6+cGvhL8Isa1iWX1oNVuOb6n6YMonaWFSSjF+PHTpUvK8h3o9RFhgGC+sfqFqQymfpYVJKR+XmwuLF0N0Kdfk1wuvx11d72LelnkcTTtaciOlXEgLk1I+btkyOHkSBg8uvc2EXhPIyc/hgw0fVF0w5bO0MCnl4yZPtp6HDi29TdvabbmmxTVMXz+dvPy8qgmmfJYWJqV8XHKy9Vyz5oXb3df9Pg6cOsDieB11XFUuLUxK+bg5c+Cnn8puN7L9SOqF1dPdearSBdgdQCllr549nWsX6B/IjVE38uHGD8nIySA0MLRygymfpT0mpXzYxx/D0qXOt78h6gYyczNZtHtR5YVSPk8Lk1I+yhi47Tbr/kvOurL5ldQPr8/Hmz+uvGDK52lhUspHbXEM5JBXjpPsAvwCGBU1im/ivyErN6tygimfp4VJKR+1Zo31/Pzz5fvcsLbDyMrNYumecuwDVKoctDAp5aO+/x4aNIBOncr3uQEtBlC7Wm3mb5tfOcGUz9PCpJSPOnAArrkGRMr3uUD/QIa0GcL/dvyPlMyUygmnfJoWJqV81E8/wYwZFfvsY5c/RtqZNN74uRxnTijlJC1MSvkgY6znkJCKfb5TvU5c1fwq5m/X3XnK9bQwKeWDrrgCnnnm4pYxrM0wthzfQuLpRNeEUspBC5NSPiYuztqNF3qRAzcMaDEAgGV7l7kglVKFtDAp5WNef916vtBo4s7oUr8LDcIbsHD3wosPpVQRWpiU8jEffwxhYRAVdXHL8RM/hrYZypI9S8jJy3FNOKXQwqSUTzl1ynoeMMA1yxvedjins0+z6uAq1yxQKbQwKeVz/v1vePZZ1yxrYMuBBPkH6e485VJamJTyIRER8NBD5R/toTThQeFc0+Ia5m6Zq3e2VS6jhUkpH2EM9OsHX33l2uXe0fkOElMTdXeechktTEr5iO3bYdUqWL3atcsd2mYogX6BfLHjC9cuWPksLUxK+YjvvrOe//Qn1y43IiSCa9tdy3+3/Jd8k+/ahSufpIVJKR/x7bfQogU0b+76Zd/Q/gaOpx/nl8RfXL9w5XO0MCnlA3JyYMECiImpnOUPbzucIP8g5m2ZVzkrUD5FC5NSPmDTJuu5c+fKWX5ESARDWg/h022f6u48ddG0MCnlA7p3h82b4c47K28doy8dTWJqIqsPuvjsCuVztDAp5SM6doRq1Spv+de2u5ZqAdWYu2Vu5a1E+QSnCpOIxIrIThGJF5EnSmlzs4hsE5GtIvJf18ZUSlVUVhYMGQLLKnkQ8PCgcGJbx/L5js/Jzc+t3JUpr1ZmYRIRf2AaMAToAIwVkQ7F2rQBJgF9jTGXAo9UQlalVAWsWQPffGMVqMo2tuNYjqYd5YcDP1T+ypTXcqbH1BOIN8bsNcacAeYC1xVr80dgmjHmdwBjzHHXxlRKVdS330JAAPTvX/nrGtpmKCEBIfxv+/8qf2XKazlTmBoBh4q8T3BMK6ot0FZEVovIWhGJLWlBInKviMSJSFxSUlLFEiulymX5cujdG2rUqPx1hQWFEdMqhi92foEpuH+7UuXkTGGSEqYV/4kLANoAVwFjgRkiUvO8Dxkz3RgTbYyJrlu3bnmzKqXK6fRpWL8erryy6tZ5ffvrSTidwPoj66tupcqrOFOYEoAmRd43Bg6X0OZLY0yOMWYfsBOrUCmlbHT8uLULb9CgqlvntW2vxV/8dew8VWHOFKZ1QBsRaSEiQcAYYEGxNl8AVwOISB2sXXt7XRlUKVV+rVtbY+RVZY+pdmht+jfrz/xt83V3nqqQMguTMSYXGA8sAbYDnxhjtorI0yIywtFsCZAsItuA74G/GmOSKyu0Uso5qan2rPfmS29mZ/JONh3bZE8A5dGcuo7JGLPIGNPWGNPKGPNPx7TJxpgFjtfGGDPRGNPBGNPJGKNX2Clls8REqFULZs+u+nVf3/56AL6J/6bqV648no78oJSXWroU8vKgS5eqX3f98Pp0rtdZb7muKkQLk1JeatUqiIx03W3Uy2tk+5GsOriKfb/vsyeA8lhamJTyUqtWweWXg5R0wUcVuK3zbRgMX+/62p4AymNpYVLKCyUmwq5d0L69fRlaR7amVa1WLN271L4QyiNpYVLKC4WEQIMGcMMN9uaIbR3L9/u+Jzs3294gyqNoYVLKC9WuDYcPQ58+9uaIaRVDek46qw/pPZqU87QwKeVljIFHHoG9bnCJ+9UtribQL5Al8UvsjqI8iBYmpbzMgQPw73/Du+/ancS6R9MVTa/gmz16PZNynhYmpbzMasdes7Fj7c1RIKZVDJuObeJI6hG7oygPoYVJKS/z7bf2Xr9UXGxr6y44S/fo2XnKOVqYlPIi+fmweDHExIC/v91pLJ3rdaZ+eH3dnaecpoVJKS+ydy+cOgVDhtidpJCIMLjVYL7d8y15+Xl2x1EeQAuTUl6kdWtISYFRo+xOcq7YVrEkZybz65Ff7Y6iPIAWJqW8TGgoVKtmd4pzDWo1CEF0tHHlFC1MSnmJlBTo3Ru+/97uJOerE1qH6IbRLNmj1zOpsmlhUspLLF0KP//sfr2lAjGtYlibsJaTWSftjqLcnBYmpbzEokXWUEQ9etidpGQxrWPIM3ks37vc7ijKzWlhUsoL5OfDN99AbKz7nCZeXO/GvYkIjtDdeapMWpiU8gLr10NSknudJl5cgF8AA1oO4Jv4bzDG2B1HuTEtTEp5iWHDrAtr3Vlsq1gOnT7EjhM77I6i3JgWJqW8QI8e8PXXUKeO3UkuLKa1VTn1tHF1IVqYlPJwaWlwxEPGR20a0ZT2ddrrcSZ1QVqYlPJwCxZAw4awaZPdSZwT2yqWlQdWkpmTaXcU5aa0MCnl4RYtgrp1oWNHu5M4J6Z1DFm5Wfxw4Ae7oyg3pYVJKQ+Wnw9LllgnPfh5yG/zlc2uJMg/SHfnqVJ5yI+yUqokP/8MJ07A0KF2J3FetcBqDGk9hI9++0h356kSaWFSyoMtWQIi1oW1nuTubneTkpnC6kOr7Y6i3JAWJqU82F13wWefQa1adicpn4EtBxLsH8zi3YvtjqLckBYmpTxYs2YwcqTdKcovNDCUfs366XEmVSItTEp5qJUr4b334MwZu5NUzOCWg9matJWE0wl2R1FuRguTUh7q7bfh//4PAgLsTlIxBaNALN2z1OYkyt1oYVLKA+XkWCc+DB3qOaeJF9fpkk40CG+gu/PUeTz0R1op3/bVV3DyJAwfbneSihMRBrcazPK9y3W0cXUOLUxKeaAXXrCeBw60N8fF6te0H8mZyWxN2mp3FOVGtDAp5YEaN4ZevaBGDbuTXJxhbYcR4BfAfzb9x+4oyo1oYVLKA332Gfz4o90pLl798PoMbjWYOVvmkG/y7Y6j3IQWJqU8TFqa9RwYaG8OV7mpw00cPHWQLce32B1FuQmnCpOIxIrIThGJF5EnLtBulIgYEYl2XUSlVIGcHGjRAt580+4krjO41WDdnafOUWZhEhF/YBowBOgAjBWRDiW0qw48BPzs6pBKKct331mDtjZoYHcS12lYvSFDWg9h9qbZujtPAc71mHoC8caYvcaYM8Bc4LoS2j0DvAhkuTAVNDM2AAAZy0lEQVSfUqqIpY5rUa+5xt4crjaqwyiOph1l0zEPuduhqlTOFKZGwKEi7xMc084SkW5AE2PM1xdakIjcKyJxIhKXlJRU7rBK+TJj4NNPrWuXPG3Q1rJc08KqtCv3r7Q5iXIHzhQmKWHa2avhRMQPeA14tKwFGWOmG2OijTHRdevWdT6lUop16+DQIbjpJruTuF7jGo1pVL0RPyfqkQDlXGFKAJoUed8YOFzkfXWgI7BCRPYDvYEFegKEUq7VtKl1Ye2IEXYnqRxXNL2CFftX6CgQyqnCtA5oIyItRCQIGAMsKJhpjDlljKljjGlujGkOrAVGGGPiKiWxUj6qfn147DGoWdPuJJXjmhbXcCTtCDtO7LA7irJZmYXJGJMLjAeWANuBT4wxW0XkaRHx0v+7KeVeduyAuXMh04vvRB7TyhptfOHuhTYnUXYTu7rN0dHRJi5OO1VKOePRR+GNN+D4ce/tMQF0e7cbAX4BrPvjOrujKBcTkfXGGKcO8ejID0q5udxc+Phj6xYX3lyUAG6MupG4w3EcTj1cdmPltbQwKeXmVq6EY8fgjjvsTlL5Rra37hP/Tfw3NidRdtLCpJSbmzcPwsNhyBC7k1S+9nXaEx4UzppDa+yOomykhUkpN7d9u3WKeLVqdiepfP5+/twQdQNztszhVNYpu+Mom2hhUsrN/fADvPee3Smqzh8v+yPpOeks2Lmg7MbKK2lhUsqNZWaCCISG2p2k6lze5HIaVW/EFzu/sDuKsokWJqXc1PHj0LAhfPml3Umqlp/4cX3761m0exGp2al2x1E20MKklJuaNw9OnoRWrexOUvXGdhxLVm4Wn2z9xO4oygZamJRyU++/D9HR0LGj3Umq3uVNLqd5zeZ8udPHuosK0MKklFv69Vf47Te4/Xa7k9hDRBjZfiSL4xeTnJFsdxxVxbQwKeWGXnvNunZp3Di7k9jn1k63kpufy1e7vrI7iqpiWpiUckOPPgoffggREXYnsc9lDS6jQXgDFu1eZHcUVcUC7A6glDpf167Ww5eJCCPajWDWb7PIzs0mOCDY7kiqimiPSSk3kpoKt94KW7fancQ9xLSKITM3k7jDeicCX6KFSSk38sEH8N//QkaG3Uncw5XNryTQL5AvdujFtr5EC5NSbsIYeOst6N0bevSwO417iKwWycCWA5m3dR75Jt/uOKqKaGFSyk189x3s2gX33293EvcypuMYDp0+xC+Jv9gdRVURLUxKuYnnn4d69eDmm+1O4l6Gtx0OwPf7vrc5iaoqWpiUcgP5+XD55TBlCoSE2J3GvURWi6Rt7basPLDS7iiqimhhUsoN+PnBU0/Bn/5kdxL3NCpqFEv2LGF70na7o6gqoIVJKZvt2gWff271mlTJxvccT4BfADM3zrQ7iqoCWpiUstnkydaYeCkpdidxXw2qN+Dq5lfzvx3/wxhjdxxVybQwKWWjX3+1bm8xcSLUqWN3Gvc2puMYdqfsZk3CGrujqEqmhUkpG/397xAZCX/5i91J3N+NUTcS4BfA59s/tzuKqmRamJSyyZw5sHgxPP64bw/W6qyIkAhiWsUwb+s88vLz7I6jKpEWJqVskp5uPY8fb28OT3Jb59tIOJ3Ajwd/tDuKqkRamJSyyd13Q1YWhIbancRzXNv2WqoFVNNbrns5LUxKVbGjR+Gf/4ScHAjWOzmUS1hQGMPaDuPz7Z/r2HleTAuTUlVs4kR4+mk4eNDuJJ5pRNsRHEs/xs8JP9sdRVUSLUxKVaFly6yTHiZNgjZt7E7jma5rfx3hQeG8u/5du6OoSqKFSakqkpUFDzwArVvDE0/YncZz1QiuwR2d72DulrmcyDhhdxxVCbQwKVVFXnwRdu+GadN0oNaLNb7neLLzsnnux+fsjqIqgRYmparI8OHW6OGDB9udxPNF1Y1iXJdxvBP3DskZyXbHUS6mhUmpKnLZZfCPf9idwnv8ufefyczN1FPHvZAWJqUq2axZcNddkJZmdxLv0rleZ9rWbstn2z+zO4pyMS1MSlWi/fthwgTYsweqVbM7jXcREW7tdCvL9y1n/eH1dsdRLqSFSalKkpMDt95q3Wdp1izw97c7kfd5sMeDBPoF8tra1+yOolzIqcIkIrEislNE4kXkvBNdRWSiiGwTkU0islxEmrk+qlKe5a9/hZ9+ghkzoHlzu9N4p9qhtXm0z6N8vPljveDWi5RZmETEH5gGDAE6AGNFpEOxZhuAaGNMZ2A+8KKrgyrlSY4ds3pJDz8Mo0fbnca7PX7F40QER/DiT/pnx1sEONGmJxBvjNkLICJzgeuAbQUNjDHfF2m/FrjNlSGV8jT16sHGjdCggd1JvF/NkJrc2ulWPtz4Ieln0gkLCrM7krpIzuzKawQcKvI+wTGtNPcAiy8mlFKeas8eeOklMAaaNoXAQLsT+YYxHceQmZvJhxs/tDuKcgFnCpOUMM2U2FDkNiAaeKmU+feKSJyIxCUlJTmfUikPcOyYdfHs889bI4irqnNF0yu4qvlV/GPFPzidfdruOOoiOVOYEoAmRd43Bg4XbyQiA4EngRHGmOySFmSMmW6MiTbGRNetW7cieZVyS6mp1sgOR4/CokW6C6+qiQgvDXqJlMwUXv7pZbvjqIvkTGFaB7QRkRYiEgSMARYUbSAi3YB3sYrScdfHVMp9paRYPaUNG2DePOjVy+5Evim6YTQ3X3ozr6x5hZTMFLvjqItQZmEyxuQC44ElwHbgE2PMVhF5WkRGOJq9BIQDn4rIRhFZUMrilPI6a9fCpk3w6adWr0nZZ3L/yWTmZPLCqhfsjqIughhT4uGiShcdHW3i4uJsWbdSrpCRUXhb9OPH4ZJL7M2jLOO+GMfs32az8s6V9GvWz+44ykFE1htjop1pqyM/KFUBK1dCixaw2HH+qRYl9zFt6DTqhddj/OLx5Obn2h1HVYAWJqXK6aOPYNAgiIzUu9C6o/CgcF6PeZ1Nxzbx6dZP7Y6jKkALk1JOSk2FcePgzjvhiitgzRrrbrTK/YzqMIp2tdvx7I/Paq/JA2lhUspJ8+fDf/5j3VNp6VKoWdPuRKo0/n7+PHvNs2xL2qa9Jg+kJz8odQH5+bBrF7Rvb43msHkzdO5sdyrljHyTT4dpHQgNDOWne34iJEDvZ28nPflBKRfYswcGDrSuSzpyBES0KHkSP/Fj8pWT2XB0A3//7u92x1HloIVJqWLS02HyZLj0UoiLg1degfr17U6lKuKWTrdwS6dbmPrzVLYe32p3HOUkLUxKFZGeDlFR8MwzcOONsH07/OEPVm9JeabXYl4jNDCUR5Y8gl2HLlT5aGFSPu/gQeveSQBhYdZZdz/+CB9/DI0uNI6+8giXhF3C8wOfZ9neZby/4X274ygn6MkPyidlZcFXX8Hs2bBwoXXb84QEvVDWW+WbfAbMGsCvR35ly/1baBLRpOwPKZfSkx+UuoClS61jRjffDOvXw2OPQXy8FiVv5id+vD/ifXLzc3lkySN2x1Fl0MKkvFpeHvz0E0yaBAscQwt36gQjRlgF6uBB+Ne/rJv6Ke/WslZLJvaeyOfbP+eb+G/sjqMuQHflKa9jDLz7LixbBt9/b92Wwt8fHn8c/vlPu9MpO2XnZhM1LYo8k8eG+zYQWS3S7kg+ozy78gIqO4xSlcUY2L/fOqU7Lg5yc61Tu0Vg+nRITrZ6RrGxEBOjIzUoCA4IZt6oefSa0Yv7F97P3BvnInrKpdvRwqTcnjGQlGRd8NqnjzXtb3+Dt9+Gkyet94GB0K/IHQ6+/x5q1NDTvNX5ejTqwVNXPcXkFZOJqhPFlKum2B1JFaOFSbmF1FTreE/btlaRKThjbv9+2L27sAClpkJ4ODRvDqNHQ9euEB1tHTcKDi5cXkSEHd9CeYq/9/87205s46mVT9G8ZnPu7Hqn3ZFUEVqYVKXIzbV2pZ06ZT1OnICjR2HYMOvst2+/heefh8RE65GWZn1uxw5o186a9ttv0KwZjB1rTYuKsooWwL332vfdlOcTEd4Z9g5H045y/8L7ubzJ5bSt3dbuWMpBT35Q5zDG2v115ozVWykoLKdPW899+1q9ml27rMJSML/g8fbbMGCAdQbcddedv/zly+Gaa6wb7D3zjHUBa8GjSRPreJAeC1JV5UjqEaKmRdGyVktW3LmCGsE17I7ktfTkh2KMsR75+dZzwf+6z5yx/mdfML3gj3L16tb8U6esNkU/HxAAdeta848etS7UzMuz5uXlQUiItZsJYMsW6/bbeXmFj8hIa7cTwHffWfMLPpuXZ/1x7tXLmv/xx5Cdfe7nO3SAq6+2PvPcc1b+oo/+/a0D/unp8OCDVv7s7MLHHXfA7bfD4cNw1VWQmWl9h4LnV1+Fhx6CnTtLHrB0xgyrMKWlFd76ISLC2iatWxduuy5d4M03rXkREdb3btiwcCSFIUOsh1J2alC9AR9d/xGjPh3FDfNu4MsxXxIWFGZ3LGWMseXRvXt3c7EeeMCYkBBjgoOtR1CQMZGRhfPHji0oKYWPBg0K5w8bdv78tm0L5/fvf/78yy4rnN+t2/nzr7yycH7r1ufPHz68cH69eufPHzu2cH5o6Pnz773XmpeXVzjN39/6/mFhxvztb9b8tDRjmja1Mlx6qZW7Tx9j3n/fmp+SYsyYMcbcdZcx999vzMSJ1mdXr7bmnzplzKxZxixYYMzKlcZs3GjMvn3GZGRU9F9LKff1wa8fGJkiZsScESY3L9fuOF4JiDNO1geP7jFdc411IBwKz74KKXLLlZEjrVtfi4Cf37m9IbDGROvf35pe8IgsclnDww/DTTed+/mC3hJYN4wruEbGz896rlevcP6771o9EX//wkedOoXzFy2yekFFP1+rVuH8rVutdRb9fGioNc/Pz+oNBQSUfOZZWBgcOFD6tqtVC+bMKX1+jRpWz0opX3BXt7tIz0lnwuIJTFkxhWeuecbuSD5NjzEppZTD6Pmj+WrnV+wYv4OmETociCvpWHlKKVUBLw58ERFh1CejiE+JtzuOz9LCpJRSDs1qNuODER+wO2U3/T/sT+LpRLsj+SQtTEopVcTojqNZdvsyTmWfIvbjWJIzku2O5HO0MCmlVDHdG3ZnwZgF7E7ezY2f3Ei+ybc7kk/RwqSUUiUY0HIAbw59k5UHVvLw4oe1OFUhjz5dXCmlKtM93e5he9J2Xl37KgmpCbw7/F0uCdM7SlY27TEppVQpRISXB7/Ms1c/y5c7vqTz251JOJ1gdyyvp4VJKaUuQER4sv+T/HjXj/ye9TsDZg3g2z3f2h3Lq2lhUkopJ/Rt2pevx35NXn4eMf+J4eHFD5N2Js3uWF5JC5NSSjlpUKtBbLp/EzddehNTf5nK1R9dzf6T++2O5XW0MCmlVDmEBoYyb9Q8Pr/5c7YnbaftG20Z98U49v2+z+5oXkMLk1JKVcDIqJHE3RvHPd3uYc7mOXR4qwO3fHYLh1MP2x3N4+kgrkopdZG2J23n1TWv8p/N/yHf5DOgxQBuiLqBO7rcQZB/kN3x3EJ5BnHVwqSUUi6y48QOXlvzGkv3LmX/yf3UC6vHfd3vY3zP8dQNq1v2AryYFiallLKRMYbF8Yt5a91bLNy9EH/xp0ejHnS6pBM9Gvag4yUd6VK/C6GBoXZHrTJamJRSyk1sOLKB+dvms+LACnae2ElypjUorCC0qNWCqDpRRDeMJqZVDB0v6Uj14OplLNEzubwwiUgs8G/AH5hhjHm+2PxgYBbQHUgGRhtj9l9omVqYlFK+xhjD7pTdbD62mc3HN7MzeSdbjm9hW9K2s2PxNYtoRvs67YmsFkm9sHp0rd+VHo160DqytUcfr3JpYRIRf2AXMAhIANYBY40x24q0eQDobIz5k4iMAUYaY0ZfaLlamJRSypKUnsSqg6vYcWIHaxPXcjTtKMkZyRxNO0p6TvrZdqGBodQNrcslYZdQL7wekdUiqRVSi9rVahMWFEagXyCB/oHnPQf5B5U4L8g/yKn2InLR37E8hcmZQVx7AvHGmL2Ohc8FrgO2FWlzHTDF8Xo+8KaIiLFrP6FSSnmQumF1GRk18rzp+SafnSd28kviLyScTiAlM4WkjCSOpx8n8XQim49t5ves3zmdfbpS893e+XZmjZxVqesoypnC1Ag4VOR9AtCrtDbGmFwROQXUBk4UbSQi9wL3Ot6micjOioR2kToUy+fDdFtYdDtYdDtYdDs4zGZ2ndnMvtht0czZhs4UppL6cMV7Qs60wRgzHZjuxDornYjEOdut9Ha6LSy6HSy6HSy6HQpV9bZwZuSHBKBJkfeNgeKXNp9tIyIBQASQ4oqASimlfIszhWkd0EZEWohIEDAGWFCszQJgnOP1KOA7Pb6klFKqIsrclec4ZjQeWIJ1uvgHxpitIvI0EGeMWQC8D8wWkXisntKYygztIm6xS9FN6Law6Haw6Haw6HYoVKXbwrYLbJVSSqmS6OjiSiml3IoWJqWUUm7F4wqTiNwkIltFJF9EootMby4imSKy0fF4p8i87iKyWUTiRWSqOC5jFpFIEflWRHY7nms5poujXbyIbBKRy4osa5yj/W4RGVfWOuzYFo55kxxZdopITJHpsY5p8SLyRJHpLUTkZ8f3muc40QURCXa8j3fMb17RdVQFEZkiIolFfg6GVjSvK7eJp7Dz386VRGS/4/dxo4jEOaZV+u97aeuo4u/+gYgcF5EtRabZ9t0vtI5SGWM86gFEAe2AFUB0kenNgS2lfOYXoA/W9VaLgSGO6S8CTzhePwG84Hg91NFOgN7Az47pkcBex3Mtx+taF1qHTduiA/AbEAy0APZgnbji73jdEghytOng+MwnwBjH63eA+x2vHwDecbweA8yr6Dqq6OdjCvCXEqbbtk3s/p0px7az9d/Oxd9lP1Cn2LRK/30vbR1V/N37A5dR5O+hnd+9tHVc6OFxPSZjzHZjjNMjRohIA6CGMWaNsbbSLOB6x+zrgI8crz8qNn2WsawFajqWEwN8a4xJMcb8DnwLxJaxjkpzgW1xHTDXGJNtjNkHxGMNLXV2eCljzBlgLnCd438812ANJwXnb4uCbTQfGOBoX651uPabV4id28RTuOu/natUxe97aeuoMsaYHzj/OlI7v3tp6yiVxxWmMrQQkQ0islJE+jmmNcK6ALhAgmMaQD1jzBEAx/MlRT5TfBimRmVML20ddihv/trASWNMbrHp5yzLMb9guKnyrqMqjXfsMvigyK4UO7eJp/D0/EUZYKmIrBdrKDSomt/30tZhNzu/e7l/rpwZkqjKicgyoH4Js540xnxZyseOAE2NMcki0h34QkQuxcnhkopHKOUz5Z1+0Sq4LUrLU9J/RMrKX97vXNo6XOZC2wR4G3jGsc5ngFeAu7F3m3gKT89fVF9jzGERuQT4VkR2XKCt2/y+26Aqvnu5P+OWhckYM7ACn8kGsh2v14vIHqAtVnVuXKRp0SGVjolIA2PMEUfX8rhjemnDMCUAVxWbvqKMdVyUimwLLjyMVEnTT2B1rwMcPYCi7QuWlSDnDjdV3nW4jLPbRETeA752vLV7m3gCT89/ljHmsOP5uIj8D2s3ZVX8vpe2DrvZ+d3L/XPlNbvyRKSuWPeOQkRaAm2AvY4uZaqI9HYcB7gDKOhpFB1KaVyx6Xc4zibpDZxyLGcJMFhEajl2EQ0GlpSxDjssAMaIdfZYC6xt8QulDC/l2Ff8PdZwUnD+tihpuKlyraOSv+9ZxfZdjwQKzkyyc5t4Clv/7VxFRMJEpHrBa6zf0y1Uze97aeuwm53fvbR1lK6ssyPc7YH1xyYBq3d0zLGxAG4EtmKdSfQrcG2Rz0Rj/WDuAd6kcMSL2sByYLfjOdIxXYBpjvabOfeMt7uxDmrHA3eVtQ47toVj3pOOLDspcoYg1hkyuxzzniwyvSXWH9F44FMg2DE9xPE+3jG/ZUXXUUU/H7Md/2abHL8QDdxhm3jKw85/Oxd+h5aOvwO/Of4mPOmYXum/76Wto4q//xysQxs5jr8P99j53S+0jtIeOiSRUkopt+I1u/KUUkp5By1MSiml3IoWJqWUUm5FC5NSSim3ooVJKaWUW9HCpFQRIlJbCkcmPyqFI5WfFJFtlbC+q0Tk67JbnvOZFVJsNHnH9DtF5E3XpVPKHlqYlCrCGJNsjOlqjOmKNaL4a47XXYH8sj7vGAlCKXURtDAp5Tx/EXlPrHtgLRWRanC2B/OciKwEHnaMQvKZiKxzPPo62l1ZpDe2oWB0AiBcROaLyA4R+dhxRT0iMsDRbrNYA9IGFw8kIneJyC7HuvtW0XZQqlJpYVLKeW2AacaYS4GTWKONFKhpjLnSGPMK8G+snlYPR5sZjjZ/AR509MD6AZmO6d2AR7Du59QS6CsiIcBMYLQxphPWuJb3Fw3jGH7pKayCNMjxeaU8nhYmpZy3zxiz0fF6PdbNKQvMK/J6IPCmiGzEGhaphqN3tBp4VUQewipkBbfU+MUYk2CMyQc2OpbbzrG+XY42H2HdAK6oXsAKY0ySse6fNA+lvIDuD1fKedlFXucB1Yq8Ty/y2g/oY4zJ5FzPi8hCrPHo1opIwSjpxZcbQMm3CiiJjimmvI72mJRyvaXA+II3ItLV8dzKGLPZGPMCEAe0v8AydgDNRaS14/3twMpibX4GrnKcSRgI3OSqL6CUnbQwKeV6DwHRYt1FdxvwJ8f0R0Rki4j8hnV8aXFpCzDGZAF3AZ+KyGasMwLfKdbmCDAFWAMswxpVXymPp6OLK6WUcivaY1JKKeVWtDAppZRyK1qYlFJKuRUtTEoppdyKFiallFJuRQuTUkopt6KFSSmllFv5f453pkoeYIFUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "def plot_precision_recall_vs_thresholds(precisions, recalls, thresholds):\n",
    "    plt.plot(thresholds, precisions[:-1], 'b--', label='Precision')\n",
    "    plt.plot(thresholds, recalls[:-1], 'g-', label='Recall')\n",
    "    plt.xlabel('Threshold')\n",
    "    plt.legend(loc='best')\n",
    "    plt.ylim([0,1])\n",
    "\n",
    "precisions, recalls, thresholds = precision_recall_curve(y_train_5, y_scores)\n",
    "plot_precision_recall_vs_thresholds(precisions, recalls, thresholds)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n4ocXj2HqacUafiMJJEUomNlk4C4gFXjA3W9ttr0YeATID9vc4O7TD7dPhYJIclu4fic3/HkxyzZVxdZNGtOXK88awqi+OfTJ03WIKEQeCmaWCrwLnAeUAXOBqe6+tEmb+4G33f23ZjYWmO7uQw63X4WCSMdQWVPHfa+u4jevrDpk26NfOo0PjSqMoKquq72hEM8+3WnASndfHRb0JHAJsLRJGwf2zyjSA9gYx3pEJIF6dEvn+smjuX7yaNZsq2bOmgpu+9sKKqpr+fyDcwCYMm4Ad1w+jvRUDdqXLOLZU7gMmOzuXw6XPwec7u7XNmnTH/g70BPoDkxy9/kt7Osa4BqA4uLiCWvXro1LzSISf9ura/niQ3NYWFYZW3fpKQOZOKqQKeMG6EG5OGlvTyGe8dzS/9nmCTQVeNjdBwEXAY+Z2SE1ufv97l7i7iWFhepyinRkvbpn8Oy1H2DNLRfx7xOHA/DntzbwH08uYOiN06nYvS/iCru2eIZCGVDUZHkQh54eugp4CsDd3wCyAA3fKNIFmBk3XDia0lsv5l/Xf5hRfXMAmPCzf/DP5Vsirq7rimcozAVGmtlQM8sArgCmNWuzDjgXwMzGEIRCeRxrEpEkVNQrm79/eyI3XjgagC89PI8hNzzPXxfqMmOixS0U3L0euBZ4AVgGPOXuS8zsJ2Y2JWz2XeBqM1sIPAFc6R3twQkROWa+MnE4z3z97NjyN554myE3PM+80u0RVtW16OE1EUlKW6r2ct6dr1K1NxiY7+wRvXnoytM0vehRSoYLzSIiR61vXhaLbr6Av33rgwDMWlnBqB/O4NE3SiOtq7NTKIhIUhvdL481t1zENR8aBsBNzy5hyA3PU7W3LuLKOieFgogkPTPj+xeNYc4Pzo2tO+nmv3Pfq6toaOxYp8CTnUJBRDqMPrlZlN56MV+ZGPQabpmxnOHfn85d/3gv4so6D11oFpEOqaHR+fNbZVwXDuE9YXBPcrPSeODzJaRp2IxDJMPYRyIicZOaYnyypIgRfXK49+WV/GPZVgBG/GAGn5wwiNs/OS7iCjsmxamIdGjji3vywBdOZc0tF3H2iN4A/Gl+Gb/423I62pmQZKBQEJFOwcz445fP4OXvnQPAb19ZxdAbp3P7CwqHI6FQEJFOZWhBd1b+/EJOLsoH4N6Xg3D44kNzIq6sY1AoiEink5aawjNfP5tFN5/PpacMBODlFeU8/ua6iCtLfgoFEem08rLSufPyk5l53YcB+P5fFrNy666Iq0puCgUR6fSKe2fzgRHBqPyT7pzJx++dFXFFyUuhICJdwmNXncZvP3MKAAvW7+TZBRsirig5KRREpEswMy48sT+Pf/l0AK770yLqGxojrir5KBREpEs5a0QBpxTnU9vQyKd//2bU5SQdhYKIdDl//lowkc+c0u2sraiOuJrkolAQkS7p6x8eDsDE21/h1hnLNdpqSKEgIl3SdReM5vbLTgLgd6+uYtQPZ+jJZxQKItKFfbKkiCU/vgAIRl09945XI64oegoFEenSumemsfynkwFYva2avy7cGHFF0VIoiEiXl5Weyq+njgfgG0+8zbJNVRFXFB2FgogI8LFxA7jvcxMAuPCuf9HYRS88KxREREIXHN+PEwf2AGDY96d3yQvPCgURkSb+8rWzYq/PvvWfEVYSDYWCiEgTaakpLP1JcEfSxsq9DLnheR59ozTSmhJJoSAi0kx2RhrzfziJi0/sD8BNzy5hycbKiKtKDIWCiEgLeudkcu9nTuHOy8cBcOVDc7vExWeFgojIYXxifDBzW/mufUy+ayb76hsirii+FAoiIodhZqz8+YVkpafw7pbd3PvyqqhLiiuFgohIG9JSU5j/w/Mwg7tfeo9bpi+LuqS4iWsomNlkM1thZivN7IZW2lxuZkvNbImZPR7PekREjlb3zDTmfH8SAPfNXE31vvqIK4qPuIWCmaUC9wIXAmOBqWY2tlmbkcCNwNnufjzwrXjVIyLyfhXmZnLu6D4AXPXI3IiriY949hROA1a6+2p3rwWeBC5p1uZq4F533wHg7lvjWI+IyPv2wBdKAJi9enunvBspnqEwEFjfZLksXNfUKGCUmc0ys9lmNrmlHZnZNWY2z8zmlZeXx6lcEZG2mRn52ekAXHLvrIirOfbiGQrWwrrmsZoGjATOAaYCD5hZ/iFvcr/f3UvcvaSwsPCYFyoiciRm33guAIs3VLJ++56Iqzm24hkKZUBRk+VBQPOBysuAZ929zt3XACsIQkJEJGllpady5VlDAPjgbS+zYvOuaAs6huIZCnOBkWY21MwygCuAac3aPAN8GMDMCghOJ62OY00iIsfEzVOO5+MnDwDggv/uPA+1xS0U3L0euBZ4AVgGPOXuS8zsJ2Y2JWz2AlBhZkuBl4Hr3L0iXjWJiBxL/33FeIp7ZQMw6c5XO8VQ29bRvoiSkhKfN29e1GWIiADg7oz4wQwaGp3rJx/H184ZEXVJLTKz+e5e0lY7PdEsIvI+mBlv33QeALf9bQWl26ojruj9USiIiLxPeVnpXHpKcMf9r/+5MuJq3p+0w200s+8cbru733lsyxER6Zju+OQ4/vzWBv7vrTKun3wcffOyoi7pqLTVU8ht45+IiBCcRjpzWG8AzrjlpYirOXqH7Sm4+48TVYiISEf3+NWnM/TG6bhDY6OTktLSM7zJra3TR3cfbru7f/PYliMi0nGZGR89qT/PLdrEtt376NMBTyEdNhSA+QmpQkSkkxjZJxfYxMfueY03w6G2O5K2Th89kqhCREQ6g2+eO4Jf/eNdtlTto6HRSe1gp5DadUuqmRWa2S/NbLqZ/XP/v3gXJyLS0ZgZ543tC8DMdzveqM7tfU7hjwRDVQwFfgyUEoxtJCIizVz74eCp5gde63hDubU3FHq7+x+AOnd/1d2/BJwRx7pERDqscUXBDACzVlawu4NN29neUKgL/7vJzC42s/EEQ2GLiEgLLjyhHwD3dLAnnNsbCj8zsx7Ad4HvAQ8A345bVSIiHdxPP34CAL97dVXElRyZtm5JBcDdnwtfVhLOfyAiIq0ryMmMvd66ay99cjvGMwvtvfvokabTZJpZTzN7MH5liYh0fN/8SHDB+Q+vrYm4kvZr7+mjk9x95/4Fd98BjI9PSSIincOnTx8MwNPzyiKupP3aGwopZtZz/4KZ9aKdp55ERLqqfj2CU0YV1bURV9J+7f3Ffgfwupk9DThwOfDzuFUlItLJVOzeR+8m1xmSVbt6Cu7+KPBvwBagHLjU3R+LZ2EiIp3BB0cWALBg/c42WiaHI5l5rRdQ7e6/BsrNbGicahIR6TTG9s8D4JUVHWPIi/beffQj4D+BG8NV6cD/xKsoEZHO4vJTiwB4bPZaNlfujbiatrW3p/AJYApQDeDuG9HMayIibRpemMPA/G4A3Pty8j/d3N5QqHV3J7jIjJl1j19JIiKdy1+/8QEAZq3aFnElbWtvKDxlZvcB+WZ2NfAPgqEuRESkDdkZqQCMCa8vJLP2DnPxSzM7D6gCjgNucvcX41qZiEgnUR2OlLp0Y1XElbSt3Q+ghSHwIoCZpZrZZ9z9j3GrTESkk+jVPQOAwo7+nIKZ5ZnZjWZ2j5mdb4FrgdUED7CJiEg77eoAcyu01VN4DNgBvAF8GbgOyAAucfcFca5NRKRTaGh0AJZt6vinj4a5+4kAZvYAsA0odvddca9MRKSTSEs9cFJmb10DWempEVZzeG3dfbR/xjXcvQFYo0AQETly+2die3BWcg+j3VYojDOzqvDfLuCk/a/NLPn7QSIiSeK75x8HwIOvlUZbSBsOGwrunurueeG/XHdPa/K6zRtuzWyyma0ws5VmdsNh2l1mZm5mJUfzRYiIJLvBvbMB2LZ7Hz969p2Iq2ndkQyId0TMLBW4F7gQGAtMNbOxLbTLBb4JvBmvWkREopaemsKvPjUOgEfeWBtxNa2LWygApwEr3X21u9cCTwKXtNDup8BtQPKPFCUi8j58YvygqEtoUzxDYSCwvslyWbguxszGA0Xu/tzhdmRm15jZPDObV17eMYafFRFpydkjegOwYWdNxJW0LJ6hYC2s89hGsxTgV8B329qRu9/v7iXuXlJYWHgMSxQRSawx/ZJ7/KN4hkIZUNRkeRCwsclyLnAC8IqZlQJnANN0sVlEOrPSimoA3lq7I+JKWhbPUJgLjDSzoWaWAVwBTNu/0d0r3b3A3Ye4+xBgNjDF3efFsSYRkUhdeVYwaeVf3t4QcSUti1souHs9cC3wArAMeMrdl5jZT8xsSrw+V0QkmZ1cnA9Aca/siCtpWbtHST0a7j4dmN5s3U2ttD0nnrWIiCSD3XuDQfEefr2Um6ccH3E1h4rn6SMREWmmX4+sqEs4LIWCiEiC9Q7nV0hGCgURkQTLTEuJTdGZbBQKIiIJ1isngz21DdQ3NEZdyiEUCiIiCfbOhmCQ6WcWbGyjZeIpFEREEuzuqeMB6J2TfNcWFAoiIgm2fvseAHplKxRERLq8fXUNAIzokxNxJYdSKIiIJNiLy7YC8OTc9W20TDyFgohIgv3oY8F8Yz99bmnElRxKoSAikmBnDOsddQmtUiiIiESoel991CUcRKEgIhKBc0f3ASA1paX5yKKjUBARiUDV3joA3t2yK+JKDqZQEBGJwLhBwbwKtfXJNdSFQkFEJAKZ6cGv3xSdPhIRkf1j4XXPiOtcZ0dMoSAiEoEtVXsBWF2+O+JKDqZQEBGJwPhwrubR/fMiruRgCgURkQgsKqsEoKhnt4grOZhCQUQkArNXVwCwY09dxJUcTKEgIhKBM8OhLnpmp0dcycEUCiIiEXh9VdBTeGvdzogrOZhCQUQkAp8+vRiAy+97I+JKDqZQEBGJwNfOGR57vTi86JwMFAoiIhEwMz57RtBbeHDWmoirOUChICISkStODUKhfNe+iCs5QKEgIhKRoQXdSTGYMLhn1KXEKBRERCKyqKySRoexA5LnqWaFgohIRBasD25HveefKyOu5IC4hoKZTTazFWa20sxuaGH7d8xsqZktMrOXzGxwPOsREUkmV5xaBMCKzbtobPSIqwnELRTMLBW4F7gQGAtMNbOxzZq9DZS4+0nA08Bt8apHRCTZ9OgWPM1c29DI8s3JMQNbPHsKpwEr3X21u9cCTwKXNG3g7i+7+55wcTYwKI71iIgklZQU49uTRgFwy4xlEVcTiGcoDATWN1kuC9e15ipgRksbzOwaM5tnZvPKy8uPYYkiItHa/6xCZlpyXOKNZxUtzTHX4kkzM/ssUALc3tJ2d7/f3UvcvaSwsPAYligiEq39lxJG90uOO5DiOQ9cGVDUZHkQsLF5IzObBPwAmOjuyfMEh4hIAmzcWQPAzpraiCsJxLOnMBcYaWZDzSwDuAKY1rSBmY0H7gOmuPvWONYiIpKUKmuC+RTOG9sv4koCcQsFd68HrgVeAJYBT7n7EjP7iZlNCZvdDuQAfzKzBWY2rZXdiYh0Sq+vqiAtxTh1SHI81RzP00e4+3RgerN1NzV5PSmeny8ikuzeWLWN8cX5ZGfE9ddxuyXH5W4RkS5o555aFpZVUtQzO+pSYhQKIiIR2T86qllLN2tGQ6EgIhKR97buBuCyCcnz3K5CQUQkIs8u2ADAaUN7RVzJAQoFEZGIvLBkCwCpKTp9JCIioWQZIRUUCiIikXvkjdKoS4hRKIiIROSWS08E4Md/XRpxJQcoFEREIvKJ8QNJSzEuPrF/1KXEKBRERCLyyopy6hud5xdvYuuuvVGXAygUREQic1y/3Njr037+EovKdkZYTUChICISkaEF3Vnxs8mx5Sn3zKK2vjHCihQKIiKRykxLZc0tF5GdkQrAt59aEGk9CgURkYiZGX/9xgcAeH7RJkq3VUdWi0JBRCQJDC/MYcLgYE6Fc375CpV76iKpQ6EgIpIk/ueq02Ovp/5+diQ1KBRERJJEt4xU3v5/5wHEeg2JplAQEUkiC8PbUj8ypk8kn69QEBFJInPWbAfgiw/N5fYXlid8sDyFgohIErni1GJyM4P5mu99eRXXPDYvoZ+vUBARSSLFvbNZ/OMLmHXDRwBI9KjaCgURkSSUnR48zFYyJLEXnBUKIiJJ6M3w2sKEYoWCiEiXN+POlgBCAAAINElEQVSdTfTMTk/4rakKBRGRJLOjupYXl25h8gn9SEtN7K9phYKISJL5w2tr2FPbwJVnDU34ZysURESSyNKNVdw3cxVTxg04aL6FRFEoiIgkidJt1Xzp4bnkZ2dw85TjI6khLZJPFRGRmMZG59mFG7jp2SWkphhPXH0GvbpnRFKLQkFEJCI7qmt5fvEm/vjmOpZtqmJcUT73TB1PUa/syGpSKIiIJNhfF27kG0+8fcj6r04cxpKNVby5ZjuTT+hHTmbif0XH9RPNbDJwF5AKPODutzbbngk8CkwAKoBPuXtpPGsSEYnask1VLa7/9/95K/a6qqaOL30g8XcfxS0UzCwVuBc4DygD5prZNHdf2qTZVcAOdx9hZlcAvwA+Fa+aRESiUllTxwW/msnmqr3tar9kYxU3/nlxbDkj1fj3c4bTv0e3eJUIxLencBqw0t1XA5jZk8AlQNNQuAS4OXz9NHCPmZm7J3gIKBGR+KqqqWszEHp0SycjLbgpdOZ75bH1O6prqW90RvXL5TOnD45rnfEMhYHA+ibLZcDprbVx93ozqwR6A9uaNjKza4BrAIqLi+NVr4hI3BT1yqb01ouP6r2Ve+q48S+LKOoZ/wvQ8QwFa2Fd8x5Ae9rg7vcD9wOUlJSoFyEiXUqP7HR+85kJCfmseD68VgYUNVkeBGxsrY2ZpQE9gO1xrElERA4jnqEwFxhpZkPNLAO4ApjWrM004Avh68uAf+p6gohIdOJ2+ii8RnAt8ALBLakPuvsSM/sJMM/dpwF/AB4zs5UEPYQr4lWPiIi0La7PKbj7dGB6s3U3NXm9F/hkPGsQEZH204B4IiISo1AQEZEYhYKIiMQoFEREJMY62h2gZlYOrI26jmOogGZPcHdhOhYH6FgcoGNxwPs5FoPdvbCtRh0uFDobM5vn7iVR15EMdCwO0LE4QMfigEQcC50+EhGRGIWCiIjEKBSid3/UBSQRHYsDdCwO0LE4IO7HQtcUREQkRj0FERGJUSiIiEiMQiFBzGyyma0ws5VmdkML279jZkvNbJGZvWRm8Z1zL0JtHYsm7S4zMzezTnk7YnuOg5ldHn5fLDGzxxNdY6K04+ej2MxeNrO3w5+Ri6KoMxHM7EEz22pm77Sy3czs7vBYLTKzU45pAe6uf3H+RzB0+CpgGJABLATGNmvzYSA7fP1V4H+jrjuqYxG2ywVmArOBkqjrjuh7YiTwNtAzXO4Tdd0RHov7ga+Gr8cCpVHXHcfj8SHgFOCdVrZfBMwgmLnyDODNY/n56ikkxmnASndf7e61wJPAJU0buPvL7r4nXJxNMFNdZ9TmsQj9FLgNOPxM5x1Xe47D1cC97r4DwN23JrjGRGnPsXAgL3zdg0Nncew03H0mh5+B8hLgUQ/MBvLNrP+x+nyFQmIMBNY3WS4L17XmKoK/BDqjNo+FmY0Hitz9uUQWlmDt+Z4YBYwys1lmNtvMJiesusRqz7G4GfismZURzNHyjcSUlpSO9PfJEYnrJDsSYy2sa/FeYDP7LFACTIxrRdE57LEwsxTgV8CViSooIu35nkgjOIV0DkHP8V9mdoK774xzbYnWnmMxFXjY3e8wszMJZmw8wd0b419e0mn375OjoZ5CYpQBRU2WB9FC99fMJgE/AKa4+74E1ZZobR2LXOAE4BUzKyU4ZzqtE15sbs/3RBnwrLvXufsaYAVBSHQ27TkWVwFPAbj7G0AWweBwXVG7fp8cLYVCYswFRprZUDPLIJiLelrTBuEpk/sIAqGznjuGNo6Fu1e6e4G7D3H3IQTXV6a4+7xoyo2bNr8ngGcIbkDAzAoITietTmiVidGeY7EOOBfAzMYQhEJ5QqtMHtOAz4d3IZ0BVLr7pmO1c50+SgB3rzeza4EXCO60eNDdl5jZT4B57j4NuB3IAf5kZgDr3H1KZEXHSTuPRafXzuPwAnC+mS0FGoDr3L0iuqrjo53H4rvA783s2wSnSq708FaczsbMniA4ZVgQXkP5EZAO4O6/I7imchGwEtgDfPGYfn4nPa4iInIUdPpIRERiFAoiIhKjUBARkRiFgoiIxCgUREQkRqEgXZKZNZjZAjN7x8z+ZGbZx2CfJWZ292G2DzCzp9/v54jEk25JlS7JzHa7e074+o/AfHe/s8l2I/j56IrDKEgXpp6CCPwLGGFmQ8xsmZn9BngLKDKz883sDTN7K+xR7A+SU83sdTNbaGZzzCzXzM4xs+fC7RPDnsiCcA6A3HD/74Tbs8zsITNbHG7f/+TylWb2ZzP7m5m9Z2a3RXRMpItSKEiXZmZpwIXA4nDVcQTDEo8HqoEfApPc/RRgHvCdcCiG/wX+w93HAZOAmma7/h7wdXc/GfhgC9u/DuDuJxIM9vaImWWF204GPgWcCHzKzIoQSRCFgnRV3cxsAcEv+nXAH8L1a8Mx6iEYjG8sMCts+wVgMEFwbHL3uQDuXuXu9c32Pwu408y+CeS3sP0DwGPh+5cDawnGNgJ4KRwDai+wNPxMkYTQ2EfSVdWEf8XHhGNOVTddBbzo7lObtTuJNoYqdvdbzex5gjFqZocj4DadMKil4Y/3azpCbgP6OZUEUk9BpHWzgbPNbASAmWWb2ShgOTDAzE4N1+eGp6FizGy4uy92918Q9EZGN9v3TOAzYdtRQDHB0NgikVIoiLTC3csJJvt5wswWEYTE6HDKyE8BvzazhcCLBEM5N/Wt8HbXhQTXE5rPpPcbINXMFhNcn7iyE8+hIR2IbkkVEZEY9RRERCRGoSAiIjEKBRERiVEoiIhIjEJBRERiFAoiIhKjUBARkZj/D8GE4jKw84HyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(precisions, recalls);\n",
    "plt.xlabel('Precision')\n",
    "plt.ylabel('Recall')\n",
    "plt.title('Precision v. Recall');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create predictions corresponding to any threshold we like. For example, let's try to use the first precision and recall vs threshold plot above to find a threshold where precision and recall are more equal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-30000, 30000)"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_precision_recall_vs_thresholds(precisions, recalls, thresholds)\n",
    "plt.xlim([-30000,30000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.7864755604557148\n",
      "Recall: 0.7895222283711493\n"
     ]
    }
   ],
   "source": [
    "y_train_pred_adjusted = (y_scores > 10000)\n",
    "\n",
    "print(f'Precision: {precision_score(y_train_5, y_train_pred_adjusted)}')\n",
    "print(f'Recall: {recall_score(y_train_5, y_train_pred_adjusted)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We indeed see that precision and recall have come closer together."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC score: 0.9624496555967156\n"
     ]
    },
    {
     "data": {
      "image/png": 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wZ84c3nnnHZ555hmaNWvGP//5T6cj/YxK3oXyCn3sPpTHxj1H+PibTBZ9m/mTleonFnzNpDj2ZRfQpXlNUhJiufS82rRvXJ36aVV0jbeIyCnKzc1lwoQJPP3007Rr145Dhw6V+41lyotK3gU+/mYvf357PVv2HS3z2PaNq3PDRefQuVlNqsbFaH9yEZFytH79egYPHsyXX37JhAkTePjhh4mLi3M6VolU8pWMtZYlm/bzzpffs2bHwTJvDVovtQq9Wtflml/UoWXdFI3MRUTCpLCwkJ49e3L06FHeffddrrrqKqcjlUklX0m8tGwbv5/7danH9Muox4hfNaV57SSVuYhIBTl8+DBVq1YlNjaWV155hUaNGlGnTh2nY4VEJe+gFduy+N3ML9h6kmn4qy48m2pVYvlF/VQuOa8WdapVcSChiEh0++yzzxgyZAi33XYbDz74IB07dnQ60ilRyTvg861Z3PbiCo7kFf3k+aT4GF68tT0ZDVJ13bmIiIP8fj8TJ07kwQcfpEGDBlx22WVORzotKvkK8vLy7Tz09noKTrKD3KB2Dbj/6vN1MxURkUpg9+7d3HTTTXz44YcMHDiQ6dOnV9rV82VRyYfZwq9+YMS/V/3s+RiP4ZnftOOSFrUdSCUiIiXZvn07K1as4JlnnuG2225z9RoolXyYFPr8jHhpFR9u2PuT5//z2640rZWEV9PxIiKVRkFBAQsWLKB379506NCB7du3u3b0fjyVfDnbvv8ov5v5Bcu3Zh17Lj7Gw6w7OnFhvWoOJhMRkZPZtGkTQ4YMYeXKlXzxxRe0atUqIgoeVPLl6tXPd3Df7C9/8lyv1nWZOjjd1dM9IiKR6uWXX2bEiBHExsYye/ZsWrVq5XSkcqWSLwdz137H2NfW/uS5Jwan0zu9nkOJRESkLKNGjeKpp57i4osv5uWXX+acc85xOlK5U8mfgc2Z2Yz892q+2fPjrnQ1k+JY9LtLtZ2siEgl16ZNG/7whz/w+9//npiYyKzDyPxThdmWzGz+35JtvPTZ9mPP1UqOZ/YdnWhQPdHBZCIiUhJrLVOnTqVGjRrceOON3HbbbU5HCjuV/Cn69fOf8+nGzJ88N+36NvT8hTu2OBQRiUb79u3jlltuYf78+QwZMoQbb7zR6UgVQiUfom/3HOHaJ5eQW+g79lz/NvV5sOf5pFWtvHcgEhGJdh9//DE33HAD+/btY+rUqYwePdrpSBVGJR+CqR9+y+T3Nx57HBfjYf1DVxLj9TiYSkREyrJhwwa6d+9O8+bNeeedd0hPT3c6UoVSyZdi5bYsbn1hBYeP22P+/93yS+1SJyJSyeXm5lKlShXOO+88XnzxRfr06UNSUpLTsSqchqIl+P1bXzHgn8uOFXz382qz9ZGrVfAiIpXc7Nmzady4MatWBbYUv/HGG6Oy4EElf1KLvs38ycr5F275Jc/d/EttaCMiUonl5uYycuRI+vfvT4MGDSJm17ozoen6E7y8fDsPzPnq2OOtj1ytchcRqeS+/vprBg8ezFdffcVdd93FX//6V+LitChaJX+cB+Z8ycvLdwDgMbD+zz1U8CIiLvDmm2+yZ88eFixYQI8ePZyOU2kYa63TGU5Ju3bt7MqVK8v9de9+cx1vrtoFQM2keBb97hLtWiciUokdPHiQrVu3kpGRQVFREfv37+ess85yOla5M8assta2O52v1UgemPz+xmMFD7Dige4awYuIVGLLli1jyJAh+P1+Nm3aRFxcXEQW/JmK+oV3uw7kMPXDb4HACH7jX65SwYuIVFJ+v59HHnmELl26YIzhzTff1Ln3UkT1SP6HQ3kM/OcyAFISYvj8/u54PCp4EZHK6MiRI/Tr148PPviAQYMGMX36dKpVq+Z0rEotakveWsudr67h+0N5xHoNb43qrIIXEanEkpKSqF69Os8++yy33nqrZl1DELXT9W+s3Mnn27IA+NetF9GkVnRulCAiUpkVFBRw//33s23bNowxvP7669x2220q+BBFbcn/5Z3/AXBn9+Z0bFrD4TQiInKiTZs20alTJx555BHmzZvndBxXisrp+heXbuNIcLva69uf43AaERE50b///W/uuOMOYmNjmTNnDn369HE6kitF5Uj+z/PXA9Cj5dmcXS3B4TQiInK85557jptuuomMjAzWrVungj8DUTeSf33FDnz+wAZAd/do4XAaEREp5vP58Hq9DBo0iCNHjjB69GhiYqKupspVVI3kv/nhCPfM+hKAX3dsSFMtthMRcZy1lieeeIL27duTk5NDUlIS48aNU8GXg6gq+f8LTtMDjO3e3MEkIiICkJmZSa9evRg3bhz16tUjPz/f6UgRJWpK/s2VO1m8aR8A743rSo2keIcTiYhEt48++ojWrVvz/vvvM3XqVObOnUtaWprTsSJK1MyFFI/iM85JpcXZyQ6nERGJbtZa7r33XlJSUnj33XdJT093OlJEioqS/+ibvRwOXjL37K9P60Y+IiJSDrZv305KSgppaWnMmjWLtLQ0qlat6nSsiBUV0/VPfbTp2MeaphcRccasWbNIT09n7NixANSvX18FH2YRX/I7s3JYse0AAG8M7+hwGhGR6JObm8uIESMYMGAAzZo1449//KPTkaJGxJf8Q28HzsVfWC+F9o2rO5xGRCS6fPvtt7Rv357p06dz1113sWTJEpo2bep0rKgR0efkV27L4oP/7QFg9CW6ZE5EpKIlJQX2I1mwYAE9evRwOE30ieiR/B/mfg0EVtT3uPBsh9OIiESHgwcP8pe//AWfz0edOnVYt26dCt4hEVvy1lrW7z4MQN+Meg6nERGJDsuWLSM9PZ2HHnqIzz//HACPJ2KrptIL69+8MaaHMeYbY8wmY8y9J/n8OcaYj4wxa4wxXxhjri6v9162ef+xj3WnORGR8PL5fDz88MN06dIFj8fD4sWL6dhRi52dFraSN8Z4gWnAVcAFwBBjzAUnHPYg8Ia1NgMYDDxVXu//13cD94u/7PyziPHqp0gRkXAaOnQoDzzwANdddx1r1qzhoosucjqSEN6Fd+2BTdbaLQDGmNeA3sD6446xQErw42rA9+XxxjkFRcem6m/u1Kg8XlJERE7CWosxhqFDh3LxxRdzyy23YIxxOpYEhbPk6wE7j3u8CzjxR7s/Af8xxowBqgKXlccb/3fDXqyFC+qkcHHzmuXxkiIicpz8/Hzuu+8+ACZPnkzHjh01PV8JhXMe+2Q/ytkTHg8BXrDW1geuBl4yxvwskzFmmDFmpTFmZWZmZplv/MryHQB0P7/2KYcWEZHSffvtt3Tq1IkpU6ZQWFiItSd+a5fKIpwlvwtocNzj+vx8Ov424A0Aa+0yIAH42dDbWjvDWtvOWtuuVq1aZb7x0uCiu45NapxWcBERObmXXnqJNm3asHXrVubMmcM//vEPTc9XYuEs+RVAc2NMY2NMHIGFdfNOOGYH0B3AGHM+gZIve6heilXbDxz7uF0j7XAnIlJedu3axfDhw8nIyGDdunX06dPH6UhShrCdk7fWFhljRgPvAV7geWvt18aYPwMrrbXzgAnAM8aY3xKYyr/ZnuG8z3tf/wBAhybViYvRqnoRkTO1detWGjduTP369fn0009JT08nJiaiN0yNGGFtQWvtu9bac621Ta21fw0+94dgwWOtXW+t7WytbW2tTbfW/udM3/O7A7kAdNBUvYjIGbHWMmXKFFq0aMGrr74KQLt27VTwLhJx/0u98+VuAJrXTnY4iYiIe2VmZnLzzTfz7rvv0qtXL6644gqnI8lpiKj57Pwi37GPf9k4zcEkIiLu9fHHH9O6dWs++OADpk6dyty5c6lRQ7OjbhRRI/kvdx069nHt5AQHk4iIuFdWVhYpKSm8++67pKenOx1HzkBEjeRXBlfW92pd1+EkIiLusn37dt544w0A+vXrxxdffKGCjwARVfIfrA/cO/7CuillHCkiIsVmzZpFeno6o0aN4siRIwDExcU5nErKQ0SVfPFIvk1DnY8XESlLbm4uI0aMYMCAAZx77rksX76c5GQtWo4kEXNOPrfgx0V359fRSF5EpDT5+fl06NCBL774gt/97nf83//9n0bvEShiSn71jsAovmZSPEnxEfPHEhEJi/j4eH7961/TqlUrXR4XwSJmuv67g4FNcHIKihxOIiJSOR04cIBBgwbx4YcfAjBhwgQVfISLmJL/5JvAlve/0f3jRUR+ZunSpaSnpzN79mw2bdrkdBypIBFT8p9vywKgQVqiw0lERCoPn8/Hww8/TNeuXYmJiWHJkiUMHz7c6VhSQSKm5DOP5APQqal2ZRIRKTZ79mweeOABrrvuOlavXk379u2djiQVKCJWqB3KKTz2cYPqGsmLiGRmZlKrVi0GDBjAggULuPLKK3Xf9ygUESP5+V9+D0DDGol4PfpHLCLRKz8/n3HjxtGiRQt27NiBMYYePXqo4KNURIzkl2zaB8B5Z2sTBxGJXhs3bmTw4MGsWbOG0aNHU7t2bacjicMiouQ37A5sw3hh3WoOJxERccZLL73EHXfcQXx8PG+99Ra9e/d2OpJUAhFR8lv2HQWgXaPqDicREXHGBx98QNu2bfn3v/9NgwYNnI4jlYTrS95ae+zjprWrOphERKRirVq1ioSEBFq2bMk///lPYmNjiYlx/bd1KUeuX3j3v+BUfVyMh1pJ8Q6nEREJP7/fz+TJk+nYsSMTJkwAoEqVKip4+RnX/4v4Zs/hwAcWrR4VkYi3d+9ebrnlFt5991169+7Nc88953QkqcRcX/Jb9+UA0K9NPYeTiIiE14YNG7j00kvJysriySefZOTIkRrcSKlcP12/Pzuw051uLysika5JkyZccsklLF++nFGjRqngpUyuL/ns/MBd51KquH5SQkTkZ7Zt28agQYM4cOAAcXFxvPzyy7Ru3drpWOISri/5ldsC95FPSYh1OImISPmaOXMm6enpLFy4kK+++srpOOJCri/54vvI19fd50QkQuTk5DB8+HCuu+46WrRowZo1a+jSpYvTscSFXF3yfr89tld9wxoqeRGJDOPHj2fGjBncc889LF68mCZNmjgdSVzK1SeyD+YW4vNbUhJiSIj1Oh1HROS0WWs5evQoSUlJ/PGPf6R///5cfvnlTscSl3N1yR/JC9xiNlnn40XExQ4cOMDQoUPJysri/fffp06dOtSpU8fpWBIBXD1dn3W0AIBqVVTyIuJOS5YsIT09nblz53LVVVfpsjgpV64u+eLL59KqquRFxF18Ph9/+ctf6NatGzExMSxZsoS7774bj8fV35alknH1v6aDOYHpel0+JyJuk52dzbPPPsvAgQNZs2YN7du3dzqSRCCXn5MPjOQ1XS8ibvHf//6Xzp07U61aNT7//HNq1aqlKXoJG1eP5FfvCGyEU71qnMNJRERKl5+fz7hx4+jevTtPPPEEALVr11bBS1i5eiTvD95LPtbr6p9VRCTCbdy4kcGDB7NmzRrGjBnDnXfe6XQkiRKuLvldBwK73aWfk+pwEhGRk5s7dy433HAD8fHxzJ07l2uvvdbpSBJFXD0EPhC8hK5WUrzDSURETq5Zs2Z06dKFdevWqeClwrm65L/dmw1Ams7Ji0glsmrVKh588EEAWrZsyYIFC6hfv77DqSQaubrkY4L71icnuPqsg4hECL/fz+TJk+nYsSMvvvgimZmZTkeSKOfaks8t8FHkDyy8S45XyYuIs/bu3cs111zDhAkT6NmzJ+vWraNWrVpOx5Io59p23JedD0Cdagm6BEVEHOXz+bjkkkvYvHkz06ZN44477tD3JakUXFvyxbvdpSbqfLyIOKOwsBCv14vX62XSpEnUrVuXX/ziF07HEjnGtdP1+48GRvI6Hy8iTti2bRtdu3Zl6tSpAPTo0UMFL5WOa0u++A50+YU+h5OISLR58803SU9PZ/369dStW9fpOCIlcm3Jb9t3FIBayQkOJxGRaJGTk8OwYcMYOHAg5513HmvXrmXgwIFOxxIpkWtLvngrW61tEZGKsnr1ap5//nnuueceFi1aROPGjZ2OJFIq157Q3h+crm/bMM3hJCISyay1rFixgvbt23PxxRezceNGmjRp4nQskZC4diS/60AOAHVTqzicRERbyP0fAAAgAElEQVQiVVZWFv3796dDhw6sWrUKQAUvruLakfzyrVkA1EvVOXkRKX+LFy/m+uuvZ/fu3fztb38jIyPD6Ugip8y1I/mqcYGfT5LiYx1OIiKR5rHHHqNbt27ExsaydOlSJkyYgMfj2m+XEsVC+ldrjIkzxjQLd5hTUeT3A5CWqJIXkfIVHx9/7P7vv/zlL52OI3LaypyuN8b0BCYDcUBjY0w68Edrbd9whyuJtZZDuYEd75ITVPIicubmz5+P3+/n2muvZezYsQDamlZcL5SR/J+Bi4CDANbatYCjo/q8Qj+FPktcjIcqcV4no4iIy+Xn5zNu3Dh69erFlClTsNZijFHBS0QIpeQLrbUHT3jOhiNMqA7mBi6fKyjyOxlDRFxu48aNdOzYkSeeeII777yTBQsWqNwlooSyuv5/xpiBgMcY0xgYC3wW3lil258dKPkaVXVzGhE5Pdu2baNNmzYkJCQwb948evXq5XQkkXIXykh+NNAW8AOzgTwCRe+Y/KLAfvVH8oucjCEiLuQPLtpt1KgRf/zjH1m3bp0KXiJWKCV/pbX2HmttRvDXvcBV4Q5WmqP5gZL/ZSPtdicioVu5ciXp6el8/fXXANx9993Uq1fP4VQi4RNKyT94kuceKO8gpyI7OIJPjHPtXj4iUoH8fj+TJk2iU6dOHDx4kOzsbKcjiVSIElvSGHMl0AOoZ4yZfNynUghM3ZfJGNMDeALwAs9aax89yTEDgT8RWMy3zlp7fVmvm1sQGMlX1cp6ESnD3r17+c1vfsPChQvp06cPzz33HNWrV3c6lkiFKG0ovBf4isA5+K+Pe/4IcG9ZL2yM8QLTgMuBXcAKY8w8a+36445pDtwHdLbWHjDG1A4ldIEv8DNGfIxKXkRK9+STT/LRRx8xbdo07rjjDq2el6hSYslba9cAa4wxL1tr807jtdsDm6y1WwCMMa8BvYH1xx0zFJhmrT0QfM+9obzw9v2Bm9PExWibSRH5ucLCQnbu3EmTJk144IEHGDx4MBdccIHTsUQqXCgtWc8Y85ox5gtjzMbiX6F8HbDzuMe7gs8d71zgXGPMEmPMZ8Hp/Z8xxgwzxqw0xqzMzMw8Nk2/98jp/OwhIpFs69atdO3alUsvvZScnBzi4+NV8BK1Qin5F4D/BxgCq+rfAF4L4etONid24iY6MUBz4FfAEOBZY0zqz77I2hnW2nbW2na1atViU2Zg0UyretVCiCEi0eKNN94gPT2d9evXM3HiRBITE52OJOKoUEo+0Vr7HoC1drO19kHgkhC+bhfQ4LjH9YHvT3LMXGttobV2K/ANgdIvVZXYwEg+t9AXQgwRiXR5eXkMHTqUQYMGccEFF7B27VoGDhzodCwRx4VS8vkmsFJlszFmhDGmFxDKArkVQHNjTGNjTBwwGJh3wjFvEfyBwRhTk8D0/ZayXjgvWO6NayaFEENEIl1sbCzbtm3jvvvu49NPP6Vx48ZORxKpFEK50Py3QBJwJ/BXoBpwa1lfZK0tMsaMBt4jcAnd89bar40xfwZWWmvnBT93hTFmPeAD7rbW7i/rtQ8G70CXWkV3oBOJVtZann32WXr27EndunVZsGABMTHaO0PkeGWO5K21y621R6y1O6y1N1lrrwW2h/Li1tp3rbXnWmubWmv/GnzuD8GCxwaMt9ZeYK1tZa0N5Vw/K7ZmAZCikheJSllZWfTv359hw4bx9NNPA6jgRU6i1JI3xvzSGNMnOJWOMaalMeZfOHyDmnppVQCI1yV0IlFn8eLFpKenM3/+fB5//HEeeughpyOJVFoltqQx5hHgZeAGYKEx5gHgI2AdgXPnjskJ7nhXXXehE4kqs2fPplu3bsTFxbF06VImTJiAx6Mf9kVKUtr8Vm+gtbU21xhTncDK+NbW2m8qJlrJDgXPyVeN1/ScSDS55JJLuPPOO3nooYdISUlxOo5IpVfaj8B51tpcAGttFrChMhQ8/Lh3fZJKXiTivf322/To0YOCggLS0tKYMmWKCl4kRKWVfBNjzOzgrzlAo+Mez66ogCeyQJHf4jEQ69Ue1CKRKj8/n7Fjx3LttdeyZ88e9u3b53QkEdcpbSjc/4THT4YzSKj81gZ/RzeaEIlQ33zzDYMHD2bt2rWMHTuWxx57jPj4eKdjibhOaTeo+bAig4Qq2PGkJuryOZFIZK3l5ptvZufOnbz99ttcc801TkcScS3XndT2+wMtXzXOddFFpBSHDx/G4/GQlJTECy+8QFJSEvXqnXhPKxE5Fa679sQXHMrvPpTrcBIRKS8rVqygTZs2jBkzBoAWLVqo4EXKQcglb4ypHCfEgtP1DWtUdTaHiJwxv9/P448/TqdOnSgsLOT22293OpJIRCmz5I0x7Y0xXwLfBh+3Nsb8I+zJSuAPtnyazsmLuNrevXu5+uqrufvuu7n22mtZu3YtnTt3djqWSEQJZSQ/FbgG2A9grV1HaLeaDYvihXdx2tJWxNVycnL44osvePrpp5k5cyZpaWlORxKJOKGsXvNYa7efcLmaYzdyt8GWT9TCOxHXKSws5OWXX+Y3v/kNjRo1YvPmzVSpUsXpWCIRK5Th8E5jTHvAGmO8xphxwMYw5ypR8Ug+xqNr5EXcZOvWrXTp0oVbbrmFDz8MXKGrghcJr1BK/g5gPHAOsAfoEHzOEcGOJ1bT9SKu8frrr5Oens6GDRt4/fXXueyyy5yOJBIVQpnzLrLWDg57khAVT9fHaiQv4gr3338/jzzyCB06dODVV1+lUaNGTkcSiRqhlPwKY8w3wOvAbGvtkTBnKlWBz+IBPNrSVsQVikftDz30ELGxuipGpCKVWfLW2qbGmE7AYOAhY8xa4DVr7WthT3cSXo/BAnuO5Dnx9iJSBmstTz31FAcOHODBBx/k0ksv5dJLL3U6lkhUCunEtrV2qbX2TqANcBh4OaypSs8CwLlnJTsVQURKkJWVRb9+/Rg9ejSfffYZPp9jF+KICKFthpNkjLnBGPM28DmQCXQKe7ISHLtO3quFdyKVyeLFi0lPT+edd95h0qRJzJs3D6/X63QskagWyjn5r4C3gYnW2kVhzlMmCxi0GY5IZZKZmckVV1xB3bp1Wbp0Ke3atXM6kogQWsk3sdb6w54kRH4bWHgXr5IXcdyhQ4eoVq0atWrVYvbs2XTq1ImUlBSnY4lIUIlNaYyZFPxwljFm9om/KijfzxRP18fHaBpQxEnz5s2jadOmzJo1C4AePXqo4EUqmdJG8q8Hf3+yIoKE6th18l5dQifihLy8PH73u9/xj3/8g4yMDFq1auV0JBEpQYklb639PPjh+dbanxS9MWY08GE4g5WkyG+JQzveiThhw4YNDB48mHXr1jFu3DgeffRR4uMrx12oReTnQmnKW0/y3G3lHSRUPn9gJF/ks2UcKSLlbcWKFXz33XfMnz+fKVOmqOBFKrkSR/LGmEEENsBpfMI5+GTgYLiDlaT4xjQxmq4XqRCHDx9m5cqVXHrppdx0001cc801ui2siEuUdk7+cwL3kK8PTDvu+SPAmnCGKk3x+L1aFW2PKRJuK1asYPDgwWRmZrJ9+3bS0tJU8CIuUto5+a3AVuCDiotTNt1qViT8/H4/kyZN4v7776du3bosWLBA5S7iQqVN139ire1mjDnAjwNoCOxFY6211cOe7qQCUbweLbwTCYeioiJ69erFwoUL6devH88++6wKXsSlSpuuvyT4e82KCBIqjeRFwismJoaMjAx69+7N8OHDMbrjo4hrlTgcPm6XuwaA11rrAzoCw4GqFZDtpHILAze88KrkRcpNYWEh9957L8uWLQPg4YcfZsSIESp4EZcLZc77LcAaY5oC/wLOB14Ja6pSFO9ZX1BUaXbaFXG1LVu2cPHFF/PYY4+xcOFCp+OISDkKZe96v7W20BjTD/i7tXaqMcax1fXFUhO1ul7kTL3++usMGzYMYwxvvPEG1113ndORRKQchTKSLzLGXAfcBMwPPudYwx47J69bzYqckfnz5zN48GBatmzJ2rVrVfAiESjUHe8uIXCr2S3GmMbAq+GNVbLiZf5aeCdyevLy8gC46qqrmDFjBp988gmNGjVyNpSIhEWZJW+t/Qq4E1hpjDkP2Gmt/WvYk5WcB9COdyKnylrLtGnTOPfcc9m9ezder5ehQ4cSG6tTXyKRqsySN8Z0ATYBzwHPAxuNMZ3DHawk+cEFdzG6Tl4kZFlZWfTr14/Ro0fTqlUrYmJCWY4jIm4Xyn/pU4CrrbXrAYwx5wMvAe3CGawkscFz8bqETiQ0ixYt4vrrr2fPnj1MnjyZsWPH4tEPySJRIZSSjysueABr7f+MMXFhzBSSeN1qViQk06ZNIyEhgWXLltG2bVun44hIBQql5FcbY6YTGL0D3ICTN6gJnpP3aJMOkRLt2rWLwsJCGjduzPTp0/F4PCQnJzsdS0QqWCjD4RHAZuB3wD3AFgK73jlKs/UiJzd37lxat27NrbfeCkC1atVU8CJRqtSRvDGmFdAUmGOtnVgxkULjUcuL/EReXh533303Tz75JBkZGUyfPt3pSCLisBJH8saY+wlsaXsD8L4x5tYKS1WK4uvkNV0v8qOdO3fSoUMHnnzyScaNG8eyZcs499xznY4lIg4rbSR/A/ALa+1RY0wt4F0Cl9A5K9jyXpW8yDE1atQgLS2N+fPn07NnT6fjiEglUdo5+Xxr7VEAa21mGcdWmOKRvKkUaUScc/jwYe6++26ys7NJTEzkv//9rwpeRH6itJF8E2PM7ODHBmh63GOstf3CmqwMmq6XaPb5558zZMgQtm/fTteuXenVq5duCysiP1Nayfc/4fGT4QwSquJL6DRdL9HI7/czadIk7r//furWrcunn35Kp06dnI4lIpVUiSVvrf2wIoOcKnW8RKP77ruPiRMn0r9/f5555hnS0tKcjiQilZjrNrAuPicfq1vNShTx+Xx4vV7uuOMOmjVrxu23367peREpkyub0mO0d71Eh4KCAu655x769u2LtZZGjRoxdOhQFbyIhCTkkjfGxIczyKnQHegkGmzZsoUuXbowceJE6tatS2FhodORRMRlQrnVbHtjzJfAt8HHrY0x/wh7slJoFC+R7rXXXiMjI4ONGzcyc+ZM/vnPfxIX5/h9oUTEZUIZEk8FrgH2A1hr1wGXhDNUWWJU8hLBjhw5wvjx47nwwgtZu3Yt/fufeKGLiEhoQll457HWbj/hHKAvTHlC4vWq5CXybNiwgWbNmpGcnMwnn3xC48aNiYlx3dpYEalEQhnJ7zTGtAesMcZrjBkHbAxzrlJpJC+RxFrLk08+SXp6On/7298AaN68uQpeRM5YKCV/BzAeOAfYA3QIPlcmY0wPY8w3xphNxph7SzlugDHGGmPahfK6+7ILQjlMpNLbv38/ffv2ZcyYMXTv3p3bb7/d6UgiEkHKHCpYa/cCg0/1hY0xXmAacDmwC1hhjJlnrV1/wnHJwJ3A8lBf+5zqiacaR6TSWbZsGQMHDmTPnj1MnjyZcePG6dI4ESlXZZa8MeYZftyD5hhr7bAyvrQ9sMlauyX4Oq8BvYH1Jxz3f8BE4K5QAoNW10tkiIuLo1q1arz11lu0bdvW6TgiEoFCma7/APgw+GsJUBvID+Hr6gE7j3u8K/jcMcaYDKCBtXZ+aS9kjBlmjFlpjFkJgc1wRNxo165d/OMfgStQ27ZtyxdffKGCF5GwCWW6/vXjHxtjXgLeD+G1T1bFx2YEjDEeYApwcwgZZgAzAOLrNLcayYsbzZ07l1tvvZWCggL69etHvXr18GhjJxEJo9P5DtMYaBjCcbuABsc9rg98f9zjZOBC4GNjzDYCC/rmhbL4TreZFTfJy8tjzJgx9OnTh0aNGrF69Wrq1atX9heKiJyhUM7JH+DHEbgHyAJKXCl/nBVAc2NMY+A7Aov3ri/+pLX2EFDzuPf5GLjLWruyrBfWSF7cwlrLZZddxpIlSxg3bhyPPvoo8fGVZodoEYlwpZa8CSz1bU2gpAH8tviG7mWw1hYZY0YD7wFe4Hlr7dfGmD8DK6218043tEpeKrvi/0yMMYwZM4b77ruPnj17OpxKRKJNqSVvrbXGmDnW2tNaGWStfRd494Tn/lDCsb8K9XU1XS+V2aFDhxg+fDjdu3dn6NChDBo0yOlIIhKlQjkn/7kxpk3Yk5yC+BgtVpLKafny5WRkZDBz5kwOHz7sdBwRiXIltqUxpniUfzGBov/GGLPaGLPGGLO6YuKdnEbyUtn4/X4ee+wxLr74Yvx+P4sWLWLChAlOxxKRKFfadP3nQBugTwVlCZmuOpLK5rPPPuPee+/luuuuY8aMGaSmpjodSUSk1JI3ANbazRWUJWQayUtlsWPHDs455xw6derE0qVL6dChg7amFZFKo7SSr2WMGV/SJ621k8OQJyT6JipOKygo4MEHH+Tvf/87S5cupV27dnTs2NHpWCIiP1FayXuBJE6+c52jdAWdOGnLli0MHjyYFStWMGLECFq2bOl0JBGRkyqt5Hdba/9cYUlOgabrxSmvvfYaw4YNw+v1MnPmTPr37+90JBGREpV5Tr4y0khenLJp0yZatWrFK6+8QsOGoezuLCLinNLWqXevsBSnTC0vFWfdunV8/PHHANx333188sknKngRcYUSS95am1WRQU6FRvJSEay1PPnkk7Rv357f/va3WGvxer3ExJR5ywcRkUrBlVec65y8hNv+/fvp06cPY8aM4bLLLuM///mPruoQEddx5ZBEm+FIOH3//fe0b9+evXv3MmXKFMaOHauCFxFXcmXJ6xuuhFOdOnUYNGgQN9xwA23aVKrbNoiInBJXjok1XS/lbefOnfTs2ZNNmzZhjGHSpEkqeBFxPVeW/J7DeU5HkAjy1ltv0bp1az799FO++eYbp+OIiJQbV5Z8SoIrzzJIJZOXl8eoUaPo27cvTZo0YfXq1fTs2dPpWCIi5caVJZ+aGOd0BIkAEydO5KmnnmL8+PEsXbqU5s2bOx1JRKRcuXJIrDPycrqstWRlZVGjRg3uuusuOnfuTPfulXjfJxGRM+DKkbwW3snpOHToEEOGDKFTp04cPXqUxMREFbyIRDR3lrwrU4uTli9fTkZGBjNnzuTmm28mISHB6UgiImHnyrrUdfISKr/fz2OPPcbFF1+M3+9n0aJF3HfffXi9XqejiYiEnStLXnvXS6h8Ph9z586lb9++rF27lo4dOzodSUSkwrhy4Z3OyUtZ3n//fTIyMqhZsyYLFy4kOTlZM0AiEnVcOpLXN2s5uYKCAu6++26uuOIK/vKXvwCQkpKigheRqOTKkby+X8vJbN68mSFDhrBixQpGjBjBI4884nQkERFHubLkNZKXE/33v/+lT58+eL1eZs6cSf/+/Z2OJCLiOJdO1zudQCqbCy+8kMsvv5y1a9eq4EVEglxa8mp5gbVr13LLLbdQVFRE7dq1mTVrFg0bNnQ6lohIpeHKkrdOBxBHWWuZOnUqF110Ef/5z3/Ytm2b05FERColV5b89wdznY4gDtm3bx+9e/dm7NixXHHFFaxbt45mzZo5HUtEpFJy5cK7xjWrOh1BHDJgwACWLVvGE088wZgxY3RpnIhIKVxZ8vrGHl2Kiorw+XzEx8czZcoUADIyMhxOJSJS+blyul4VHz127tzJJZdcwvjx44FAuavgRURC48qS1+r66PDWW2/RunVr1q5dS6dOnZyOIyLiOi4teacTSDjl5uYyatQo+vbtS5MmTVizZg033HCD07FERFzHlSWvgXxk++677/jXv/7FhAkTWLp0qVbPi4icJi28k0rBWsuHH35I9+7dadasGZs2beKss85yOpaIiKtpJC+OO3ToEEOGDOHyyy9n/vz5ACp4EZFy4MqRvBbeRY7PPvuMIUOGsHPnTh5++GF69uzpdCQRkYjhypG8Ft5FhmnTptGlSxestSxatIj77rsPj8eV/yRFRColV35HNbpSPiI0bNiQfv36sXbtWjp27Oh0HBGRiOPOklfHu9bChQuZNm0aANdccw2vv/46qampDqcSEYlMrix5nZN3n4KCAu666y6uuuoqnnvuOQoLC52OJCIS8VxZ8up4d9m0aROdO3dm0qRJjBw5kiVLlhAbG+t0LBGRiKfV9RJWBw8epH379lhrmT17Nn379nU6kohI1HBlye/Pznc6gpShqKiImJgYUlNTmTp1Kl27duWcc85xOpaISFRx5XR9jaR4pyNIKdauXUurVq1YuHAhADfeeKMKXkTEAa4s+RivpusrI2stU6dO5aKLLuLw4cMkJiY6HUlEJKq5suR1nXzls2/fPnr37s3YsWO58sorWbduHV27dnU6lohIVHNnyavjK5158+bx3nvv8cQTTzB37lxq1qzpdCQRkajnyoV36vjKoaioiK+//prWrVtzyy230LVrV90WVkSkEtFIXk7Ljh07uOSSS+jSpQt79+7FGKOCFxGpZFxa8mp5J82ZM4f09HTWrVvH008/Te3atZ2OJCIiJ+HOknc6QJTy+/2MHDmSfv360bRpU9asWcMNN9zgdCwRESmBO0teI3lHeDweioqKmDBhAkuWLKFp06ZORxIRkVJo4Z2UylrLs88+S7t27cjIyGD69On6IUtExCXCOpI3xvQwxnxjjNlkjLn3JJ8fb4xZb4z5whjzoTGmYWivW/5Z5ecOHjzIoEGDGDZsGNOnTwc0iyIi4iZhK3ljjBeYBlwFXAAMMcZccMJha4B21tpfADOBiaG9dnkmlZNZtmwZ6enpzJkzh0cffZSnnnrK6UgiInKKwjld3x7YZK3dAmCMeQ3oDawvPsBa+9Fxx38G3BjKC2vHu/D6+OOPueyyy2jQoAGLFi2iQ4cOTkcSEZHTEM7p+nrAzuMe7wo+V5LbgAWhvLBG8uFhrQWgc+fOPPDAA6xZs0YFLyLiYuEs+ZNVsT3pgcbcCLQD/lbC54cZY1YaY1aWYz45zoIFC2jbti379+8nNjaWhx56iNTUVKdjiYjIGQhnye8CGhz3uD7w/YkHGWMuAx4ArrXWnvRG8dbaGdbadtbadsGvCUPc6FRQUMCECRO4+uqrKSoq4uDBg05HEhGRchLOkl8BNDfGNDbGxAGDgXnHH2CMyQCmEyj4vaG+sCq+fGzatInOnTszefJkRo4cyfLly3Xtu4hIBAnbwjtrbZExZjTwHuAFnrfWfm2M+TOw0lo7j8D0fBLwZnB0vsNae21Zr62BfPm4//772bx5M7Nnz6Zv375OxxERkXJmihdbuUV8neZ29nuf0vMXdZyO4krZ2dlkZ2dz9tlns3fvXvLy8jjnnHOcjiUiIiUwxqwqPl19qly6ra3TCdxpzZo1tG3blsGDB2OtpXbt2ip4EZEI5s6SdzqAy1hreeKJJ+jQoQNHjx7loYce0uJFEZEo4M6969VPIcvKyuLmm2/m7bffplevXjz//PPUrFnT6VgiIlIBXDmS11g+dDExMWzevJmpU6cyd+5cFbyISBTRSD4CFRUVMW3aNIYPH05KSgpr164lNjbW6VgiIlLBXDmSV8eXbPv27XTr1o1x48Yxc+ZMABW8iEiUcmXJHy0ocjpCpTRr1izS09P58ssveeWVV7jxxpDu9yMiIhHKlSWfWiXO6QiVzt/+9jcGDBhA8+bNWbNmDUOGDHE6koiIOEzn5CPEtddey4EDB/jTn/5EXJx+CBIREZeO5HWNd+Da9xkzZnDrrbdiraVFixY8/PDDKngRETnGlSXvifKOP3jwIIMGDWL48OHs3LmT3NxcpyOJiEgl5MqSN1G8vn7ZsmWkp6czZ84cHn30Ud577z0SExOdjiUiIpWQK8/JR+tIPjc3l759+5KYmMjixYu56KKLnI4kIiKVmCtLPtoG8pmZmdSoUYMqVaowb948WrRoQbVq1ZyOJSIilZwrp+s9UbTwbsGCBbRs2ZJJkyYB0L59exW8iIiExJUlHw0VX1BQwIQJE7j66qupU6cO11xzjdORRETEZVw5Xe+J8JPymzZtYvDgwaxatYpRo0bx+OOPk5CQ4HQsERFxGVeWfGRXPOzevZsdO3YwZ84c+vTp43QcERFxKXdO10fgOfns7GzeeOMNALp06cLWrVtV8CIickZcWvJOJyhfq1evpk2bNlx//fVs2bIFgKpVqzqcSkRE3M6dJe90gHJireXvf/87HTp0ICcnhw8//JAmTZo4HUtERCKEK8/JR8IldNZaBg4cyMyZM7n22mt5/vnnqVGjhtOxREQkgriy5COg4zHGcMUVV9CtWzdGjRoVkesMRETEWa4sebeO5IuKivjTn/5Ey5YtGTJkCEOHDnU6koiIRDBXnpMv9PmdjnDKtm/fTrdu3fjrX//KZ5995nQcERGJAq4cyVeJ8zod4ZTMmjWL22+/HZ/PxyuvvMKQIUOcjiQiIlHAlSN5N91qdtWqVQwYMIDmzZuzZs0aFbyIiFQYV5a8Gxw5cgSAtm3b8uabb7J48WKaNm3qcCoREYkmriz5yrzuzlrLjBkzaNiwIevWrQNgwIABxMXFOZxMRESijTtL3ukAJTh48CADBw5k+PDhtGvXjrPOOsvpSCIiEsXcWfKVsOWXLVtGeno6b731Fo899hgLFy7k7LPPdjqWiIhEMVeurq+M5s2bh8fjYfHixVx00UVOxxEREcFYa53OcEri6zS3X69bQ7PaSU5H4fvvv2f37t20bduWwsJCcnJyqFatmtOxREQkghhjVllr253O12q6/jS98847tG7dmuuvvx6fz0dsbKwKXkREKhV3lryD752fn89vf/tbrrnmGurWrcvcuXPxet21OY+IiEQHnZM/Bfv37+eKK65g9erVjBkzhokTJ5KQkOB0LBERkZNy50jeofn6tLQ0zj//fN566y2mTp2qghcRkUrNnSVfge915MgRRo4cyc6dO/F4PPz73/+md+/eFZhARETk9Liz5Cuo5T+58cEAAAsrSURBVFetWkWbNm2YPn06H330UcW8qYiISDlxZ8mHeSxvrWXKlCl07NiR3NxcPvroI37961+H9T1FRETKmytLPtwmT57M+PHjufrqq1m3bh1du3Z1OpKIiMgpc+Xq+nBN1xcUFBAXF8fQoUNJS0vjlltucWyRn4iIyJnSSB4oKirigQceoEOHDuTl5ZGSksKtt96qghcREVdzZcmXZ/du376dbt268fDDD5ORkYHf7y+/FxcREXGQK6fry8usWbP+f3v3H2RVXcZx/P1RERSNUhClNZERLLSViBhKBzPU1AYpB0VGURrJgdIGzWZgcIrKPwxzMlMDMgdtXCIYDUIZcwx/jqui/BAYTULGSCeN0GEUSJanP86Xum6X3XOXvXfvvft5zZyZ8/s8++yd++z5nrPfL1OmTKGlpYWmpiYmTpzY1SGZmZl1mpos8p3RjL5nzx5uuukmhgwZwsKFCxk0aFAnRGZmZlY9arPIH8CxGzZsoKGhgT59+vDQQw/Rr18/evTo0WmxmZmZVYtu80w+Ipg3bx4jRoxgxowZAAwYMMAF3szM6lZNFvlSbd++nYsvvpipU6cyevRoZs+e3dUhmZmZlV1NFvlSerxbvXo1w4YNY+nSpcyZM4cVK1bQv3//MkZnZmZWHWrzmXwJzfX9+vXj2GOPZfHixYwcObJ8QZmZmVWZGr2Tb9ubb77JrFmz2Lt3Lw0NDTQ3N7vAm5lZt1OTRb4ty5cvp7Gxkdtuu43169cDXTf+vJmZWVeqzSJfpGbv3r2b6dOnM3bsWBoaGnjxxRdpbGysfGxmZmZVojafyRep8hMmTGDp0qVce+21zJkzh169enVBZGZmZtVDEdHVMZSk53GD4++vrafvET0B2Lt3LwcddBBPP/0027ZtY9y4cV0coZmZWeeR9GJEjOjIsWVtrpd0nqRXJW2SNKPI9p6SFqXtz0kamPfcO3bsYNKkScycOROAM844wwXezMysQNmKvKSDgTuB84GhwERJQ1vtdhWwPSJOAn4O/DTPudeufonhw4fT1NRE7969OzNsMzOzulHOZ/IjgU0RsRlA0u+AccDGgn3GAbPT/BLgDkmKNp4htLz/LuePOZP+/fuzcuVKRo8eXZ7ozczMalw5m+s/CfytYHlrWld0n4jYA7wHHN3WSVt2bOOcr57HmjVrXODNzMzaUM47+WL/nN76Dj3PPki6Grg6Le5+ePkf1/ft2/cAw7M29AX+2dVBdAPOc/k5x+XnHJffyR09sJxFfitwfMFyA/DmfvbZKukQoA/wr9Ynioj5wHwASas6+pah5eMcV4bzXH7Ocfk5x+UnaVVHjy1nc/0LwGBJJ0o6FLgUWNZqn2XAlWl+PPDntp7Hm5mZWX5lu5OPiD2SrgEeAQ4G7omIDZJ+DKyKiGXAb4DfStpEdgd/abniMTMz627K2uNdRDwMPNxq3Q8K5ncBF5d42vmdEJq1zTmuDOe5/Jzj8nOOy6/DOa65Hu/MzMwsn9ocoMbMzMzaVbVFvpxd4lomR46vl7RR0jpJj0k6oSvirGXt5bhgv/GSQpLfUu6APHmWdEn6PG+Q1FTpGGtdju+LT0laKWl1+s64oCvirGWS7pH0tqT1+9kuSben38E6ScPbPWlEVN1E9qLeX4FBwKHAWmBoq32+DcxN85cCi7o67lqacub4LODwND/NOe78HKf9jgSeBJqBEV0dd61NOT/Lg4HVwCfS8jFdHXctTTlzPB+YluaHAlu6Ou5am4DRwHBg/X62XwCsIOtjZhTwXHvnrNY7+f92iRsR/wb2dYlbaBxwb5pfAoyRVKxzHSuu3RxHxMqI+CAtNpP1dWD55fkcA/wEmAPsqmRwdSRPnr8F3BkR2wEi4u0Kx1jr8uQ4gI+l+T78f78o1o6IeJIifcUUGAfcF5lm4OOSjmvrnNVa5MvSJa59RJ4cF7qK7C9Iy6/dHEv6HHB8RCyvZGB1Js9neQgwRNIzkpolnVex6OpDnhzPBi6XtJXsv6qurUxo3Uqp39vl/Re6A9BpXeLafuXOn6TLgRHAmWWNqP60mWNJB5GNvji5UgHVqTyf5UPImuy/TNYi9ZSkUyPi3TLHVi/y5HgisCAibpX0RbI+UE6NiL3lD6/bKLnuVeudfCld4tJWl7i2X3lyjKSzgVnAhRGxu0Kx1Yv2cnwkcCrwuKQtZM/Ylvnlu5Ll/b5YGhEfRsTrwKtkRd/yyZPjq4DfA0TEs0Avsn7trfPk+t4uVK1F3l3ill+7OU5NyfPICryfYZauzRxHxHsR0TciBkbEQLL3Hi6MiA73U91N5fm++APZi6RI6kvWfL+5olHWtjw5fgMYAyDpM2RF/p2KRln/lgFXpLfsRwHvRcRbbR1Qlc314S5xyy5njm8BjgAWp3ca34iIC7ss6BqTM8d2gHLm+RHgXEkbgRbg+xGxreuiri05c/w94NeSriNrQp7sG6/SSFpI9kipb3q34YdAD4CImEv2rsMFwCbgA+Cb7Z7TvwMzM7P6VK3N9WZmZnaAXOTNzMzqlIu8mZlZnXKRNzMzq1Mu8mZmZnXKRd6swiS1SFpTMA1sY9+B+xuRqsRrPp5GEFubunY9uQPnmCrpijQ/WdKAgm13SxrayXG+IGlYjmOmSzr8QK9tVo9c5M0qb2dEDCuYtlToupdFxGlkAzvdUurBETE3Iu5Li5OBAQXbpkTExk6J8n9x3kW+OKcDLvJmRbjIm1WBdMf+lKSX0vSlIvucIun5dPe/TtLgtP7ygvXzJB3czuWeBE5Kx45J43+/nMay7pnW35zGXl8n6Wdp3WxJN0gaTzaWwf3pmoelO/ARkqZJmlMQ82RJv+xgnM9SMPiGpF9JWqVsPPgfpXXfJftjY6WklWnduZKeTXlcLOmIdq5jVrdc5M0q77CCpvoH07q3gXMiYjgwAbi9yHFTgV9ExDCyIrs1dR86ATg9rW8BLmvn+mOBlyX1AhYAEyLis2Q9YE6TdBTwDeCUiGgEbio8OCKWAKvI7riHRcTOgs1LgIsKlicAizoY53lk3dHuMysiRgCNwJmSGiPidrK+u8+KiLNSl7U3AmenXK4Crm/nOmZ1qyq7tTWrcztToSvUA7gjPYNuIetbvbVngVmSGoAHIuI1SWOAzwMvpK6HDyP7g6GY+yXtBLaQDQN6MvB6RPwlbb8X+A5wB9nY9ndLegjIPQxuRLwjaXPqV/u1dI1n0nlLibM3WfepwwvWXyLparLvreOAocC6VseOSuufSdc5lCxvZt2Si7xZdbgO+AdwGlkL267WO0REk6TngK8Bj0iaQjb05L0RMTPHNS4rHPxG0tHFdkr9lI8kG2zkUuAa4Csl/CyLgEuAV4AHIyKUVdzccQJrgZuBO4GLJJ0I3AB8ISK2S1pANgBKawIejYiJJcRrVrfcXG9WHfoAb6WxtyeR3cV+hKRBwObURL2MrNn6MWC8pGPSPkdJOiHnNV8BBko6KS1PAp5Iz7D7RMTDZC+1FXvDfQfZULnFPAB8nWx88UVpXUlxRsSHZM3uo1JT/8eA94H3JPUHzt9PLM3A6ft+JkmHSyrWKmLWLbjIm1WHu4ArJTWTNdW/X2SfCcB6SWuATwP3pTfabwT+JGkd8ChZU3a7ImIX2ShWiyW9DOwF5pIVzOXpfE+QtTK0tgCYu+/Fu1bn3Q5sBE6IiOfTupLjTM/6bwVuiIi1wGpgA3AP2SOAfeYDKyStjIh3yN78X5iu00yWK7NuyaPQmZmZ1SnfyZuZmdUpF3kzM7M65SJvZmZWp1zkzczM6pSLvJmZWZ1ykTczM6tTLvJmZmZ1ykXezMysTv0Hf7xelL+y12sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_train_5, y_scores)\n",
    "\n",
    "def plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('ROC Curve')\n",
    "\n",
    "plt.figure(figsize=(8, 6));\n",
    "plot_roc_curve(fpr, tpr)\n",
    "\n",
    "print(f'ROC AUC score: {roc_auc_score(y_train_5, y_scores)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also try using a Random Forest classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "forest_clf = RandomForestClassifier(n_estimators=10, random_state=42)\n",
    "# Output probabilities \n",
    "y_probas_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3,\n",
    "                                   method='predict_proba')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC score: 0.9931243366003829\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use probability of positive class as score \n",
    "y_scores_forest = y_probas_forest[:,1] \n",
    "\n",
    "fpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train_5, y_scores_forest)\n",
    "\n",
    "plt.plot(fpr, tpr, \"b:\", linewidth=2, label=\"SGD\")\n",
    "plot_roc_curve(fpr_forest, tpr_forest, \"Random Forest\")\n",
    "plt.legend(loc=\"lower right\", fontsize=16);\n",
    "print(f'ROC AUC score: {roc_auc_score(y_train_5, y_scores_forest)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that Random Forest performs better than does our SGD classifier (when using the ROC AUC score as our metric)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiclass Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With multiple classes, two approaches still using binary classifiers is to have a classifier for every output (one-versus-all or OvA strategy) or a classifier for every pair of outputs (one-versus-one or OvO strategy). OvA is usually preferred, but for algorithms that don't scale well, OvO will run much faster since it only needs to be run on the subset of data for the two classes it's trying to distinguish."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hussain/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([5], dtype=int8)"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SGD classifier defaults to OvA given multinomial output\n",
    "sgd_clfr.fit(X_train, y_train)\n",
    "# Check whether it predicts 5\n",
    "sgd_clfr.predict([some_digit])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the scores to confirm that it's indeed using OvA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-311402.62954431, -363517.28355739, -446449.5306454 ,\n",
       "        -183226.61023518, -414337.15339485,  161855.74572176,\n",
       "        -452576.39616343, -471957.14962573, -518542.33997148,\n",
       "        -536774.63961222]])"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_digit_scores = sgd_clfr.decision_function([some_digit])\n",
    "some_digit_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check which class the highest score corresponds to\n",
    "max_score_idx = np.argmax(some_digit_scores)\n",
    "sgd_clfr.classes_[max_score_idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use an OvO strategy with the SGD classifier, or any other binary classifier we choose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45\n"
     ]
    }
   ],
   "source": [
    "from sklearn.multiclass import OneVsOneClassifier\n",
    "from sklearn.exceptions import ConvergenceWarning\n",
    "import warnings\n",
    "\n",
    "# Supress convergence warnings\n",
    "def fxn():\n",
    "    warnings.warn(\"convergence\", ConvergenceWarning)\n",
    "\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "    fxn()\n",
    "\n",
    "    ovo_clf = OneVsOneClassifier(SGDClassifier(max_iter=10, tol=1e-3))\n",
    "    ovo_clf.fit(X_train, y_train)\n",
    "    ovo_clf.predict([some_digit])\n",
    "    # Check that there are [(10 X 9) / 2] = 45 scores for each pair of classes\n",
    "    print(len(ovo_clf.estimators_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random Forests can deal with multiple classes without using OvA or OvO."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5], dtype=int8)"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_clf.fit(X_train, y_train)\n",
    "forest_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Error Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize a confusion matrix to see what kind of errors our classifier makes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hussain/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/hussain/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/hussain/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# First scale our data\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train.astype(np.float64))\n",
    "\n",
    "y_train_pred = cross_val_predict(sgd_clfr, X_train_scaled, y_train, cv=3)\n",
    "conf_matrix = confusion_matrix(y_train, y_train_pred)\n",
    "# Calculate number of instances of each class\n",
    "row_sums = conf_matrix.sum(axis=1, keepdims=True)\n",
    "# Divide each error count by size of true class to normalize matrix\n",
    "norm_conf_mx = conf_matrix / row_sums\n",
    "# Fill diagonal of matrix with 0s\n",
    "np.fill_diagonal(norm_conf_mx, 0)\n",
    "plt.matshow(norm_conf_mx, cmap=plt.cm.gray);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rows correspond to true classes and columns correspon to predictions, and the brighter an entry, the higher the error rate. We can see that 0s, 1s, and 6s have pretty good classifications, while 5s and 8s are prone to being misclassified. \n",
    "\n",
    "We can also look directly at what the digits that are being misclassified look like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_digits(instances, images_per_row=10, **options):\n",
    "    size = 28\n",
    "    images_per_row = min(len(instances), images_per_row)\n",
    "    images = [instance.reshape(size,size) for instance in instances]\n",
    "    n_rows = (len(instances) - 1) // images_per_row + 1\n",
    "    row_images = []\n",
    "    n_empty = n_rows * images_per_row - len(instances)\n",
    "    images.append(np.zeros((size, size * n_empty)))\n",
    "    for row in range(n_rows):\n",
    "        rimages = images[row * images_per_row : (row + 1) * images_per_row]\n",
    "        row_images.append(np.concatenate(rimages, axis=1))\n",
    "    image = np.concatenate(row_images, axis=0)\n",
    "    plt.imshow(image, cmap = plt.cm.binary, **options)\n",
    "    plt.axis(\"off\")\n",
    "\n",
    "def plot_misclassifications(class_a=3, class_b=5, images_per_row=5):\n",
    "    \n",
    "    #X_jk corresponds to class j being classified as class k\n",
    "    X_aa = X_train[(y_train == class_a) & (y_train_pred == class_a)]\n",
    "    X_ab = X_train[(y_train == class_a) & (y_train_pred == class_b)]\n",
    "    X_ba = X_train[(y_train == class_b) & (y_train_pred == class_a)]\n",
    "    X_bb = X_train[(y_train == class_b) & (y_train_pred == class_b)]\n",
    "    \n",
    "    plt.figure(figsize=(8,8))\n",
    "    plt.subplot(221); plot_digits(X_aa[:images_per_row**2], images_per_row)\n",
    "    plt.subplot(222); plot_digits(X_ab[:images_per_row**2], images_per_row)\n",
    "    plt.subplot(223); plot_digits(X_ba[:images_per_row**2], images_per_row)\n",
    "    plt.subplot(224); plot_digits(X_bb[:images_per_row**2], images_per_row)\n",
    "    \n",
    "plot_misclassifications()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_misclassifications(7, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_misclassifications(5, 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at these misclassifications and the types of errors our model is making can help us improve it further, by, for example, adding more examples of the misclassified digits or doing some image processing depending on how our algorithm works. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multilabel Classification\n",
    "\n",
    "There are also cases where our output can have multiple labels. Let's consider this type of problem with our handwritten digits dataset, although it will be a slightly artificial example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False,  True]])"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# Large/small label\n",
    "y_train_large = (y_train >= 7)\n",
    "# Odd/even label\n",
    "y_train_odd = (y_train % 2 == 1)\n",
    "# Combined label\n",
    "y_train_multilabel = np.c_[y_train_large, y_train_odd]\n",
    "\n",
    "knn_clf = KNeighborsClassifier()\n",
    "knn_clf.fit(X_train, y_train_multilabel)\n",
    "knn_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that our classifier predicted 5 was not large and was odd. Evaluating multilabel classifiers is more involved than evaluating our previous classifiers has been. One method is the average F1 score across labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Classification metrics can't handle a mix of multiclass and multilabel-indicator targets",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-205-b93cfc829971>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0my_train_knn_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mknn_clf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mf1_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train_knn_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train_multilabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'macro'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py\u001b[0m in \u001b[0;36mf1_score\u001b[0;34m(y_true, y_pred, labels, pos_label, average, sample_weight)\u001b[0m\n\u001b[1;32m    718\u001b[0m     return fbeta_score(y_true, y_pred, 1, labels=labels,\n\u001b[1;32m    719\u001b[0m                        \u001b[0mpos_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpos_label\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maverage\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 720\u001b[0;31m                        sample_weight=sample_weight)\n\u001b[0m\u001b[1;32m    721\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    722\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py\u001b[0m in \u001b[0;36mfbeta_score\u001b[0;34m(y_true, y_pred, beta, labels, pos_label, average, sample_weight)\u001b[0m\n\u001b[1;32m    832\u001b[0m                                                  \u001b[0maverage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maverage\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    833\u001b[0m                                                  \u001b[0mwarn_for\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'f-score'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 834\u001b[0;31m                                                  sample_weight=sample_weight)\n\u001b[0m\u001b[1;32m    835\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    836\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py\u001b[0m in \u001b[0;36mprecision_recall_fscore_support\u001b[0;34m(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight)\u001b[0m\n\u001b[1;32m   1029\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"beta should be >0 in the F-beta score\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1030\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1031\u001b[0;31m     \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1032\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1033\u001b[0m     \u001b[0mpresent_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munique_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     79\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m         raise ValueError(\"Classification metrics can't handle a mix of {0} \"\n\u001b[0;32m---> 81\u001b[0;31m                          \"and {1} targets\".format(type_true, type_pred))\n\u001b[0m\u001b[1;32m     82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m     \u001b[0;31m# We can't have more than one value on y_type => The set is no more needed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Classification metrics can't handle a mix of multiclass and multilabel-indicator targets"
     ]
    }
   ],
   "source": [
    "y_train_knn_pred = cross_val_predict(knn_clf, X_train_multilabel, y_train, cv=3)\n",
    "f1_score(y_train_knn_pred, y_train_multilabel, average='macro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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