classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To support both python 2 and python 3\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"classification\"\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True):\n",
    "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'COL_NAMES': ['label', 'data'],\n",
       " 'DESCR': 'mldata.org dataset: mnist-original',\n",
       " 'data': 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]], dtype=uint8),\n",
       " 'target': array([ 0.,  0.,  0., ...,  9.,  9.,  9.])}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_mldata\n",
    "mnist = fetch_mldata('MNIST original')\n",
    "mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = mnist[\"data\"], mnist[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000, 784)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000,)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5b26a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "some_digit = X[36000]\n",
    "some_digit_image = some_digit.reshape(28,28)\n",
    "\n",
    "plt.imshow(some_digit_image, cmap=\n",
    "          matplotlib.cm.binary,\n",
    "          interpolation=\"nearest\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# EXTRA\n",
    "def plot_digits(instances, images_per_row=10, **options):\n",
    "    size = 28\n",
    "    images_per_row = min(len(instances), images_per_row)\n",
    "    images = [instance.reshape(size,size) for instance in instances]\n",
    "    n_rows = (len(instances) - 1) // images_per_row + 1\n",
    "    row_images = []\n",
    "    n_empty = n_rows * images_per_row - len(instances)\n",
    "    images.append(np.zeros((size, size * n_empty)))\n",
    "    for row in range(n_rows):\n",
    "        rimages = images[row * images_per_row : (row + 1) * images_per_row]\n",
    "        row_images.append(np.concatenate(rimages, axis=1))\n",
    "    image = np.concatenate(row_images, axis=0)\n",
    "    plt.imshow(image, cmap = matplotlib.cm.binary, **options)\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure more_digits_plot\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '.\\\\images\\\\classification\\\\more_digits_plot.png'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-93a5c63d36b3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mexample_images\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mr_\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m12000\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m600\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m13000\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m30600\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m600\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m30600\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m60000\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m590\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mplot_digits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexample_images\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimages_per_row\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0msave_fig\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"more_digits_plot\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-2-f8dad7e499f9>\u001b[0m in \u001b[0;36msave_fig\u001b[1;34m(fig_id, tight_layout)\u001b[0m\n\u001b[0;32m     26\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mtight_layout\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     27\u001b[0m         \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 28\u001b[1;33m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m300\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36msavefig\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    699\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0msavefig\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    700\u001b[0m     \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgcf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 701\u001b[1;33m     \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    702\u001b[0m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m   \u001b[1;31m# need this if 'transparent=True' to reset colors\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    703\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36msavefig\u001b[1;34m(self, fname, **kwargs)\u001b[0m\n\u001b[0;32m   1832\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_frameon\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mframeon\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1833\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1834\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1835\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1836\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mframeon\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[0;32m   2265\u001b[0m                 \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2266\u001b[0m                 \u001b[0mbbox_inches_restore\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_bbox_inches_restore\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2267\u001b[1;33m                 **kwargs)\n\u001b[0m\u001b[0;32m   2268\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2269\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mrestore_bbox\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[0;32m    510\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    511\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename_or_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 512\u001b[1;33m             \u001b[0mfilename_or_obj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename_or_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'wb'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    513\u001b[0m             \u001b[0mclose\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    514\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '.\\\\images\\\\classification\\\\more_digits_plot.png'"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1112bf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,9))\n",
    "example_images = np.r_[X[:12000:600], X[13000:30600:600], X[30600:60000:590]]\n",
    "plot_digits(example_images, images_per_row=10)\n",
    "save_fig(\"more_digits_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[36000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "shuffle_index = np.random.permutation(60000)\n",
    "X_train, y_train = X_train[shuffle_index], y_train[shuffle_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_5 = (y_train == 5)\n",
    "y_test_5 = (y_test == 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n",
       "       eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n",
       "       learning_rate='optimal', loss='hinge', max_iter=None, n_iter=None,\n",
       "       n_jobs=1, penalty='l2', power_t=0.5, random_state=42, shuffle=True,\n",
       "       tol=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 14,
     "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True], dtype=bool)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.9502 ,  0.96565,  0.96495])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "cross_val_score(sgd_clf, X_train, y_train_5, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9502\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.96565\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.96495\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.base import clone\n",
    "\n",
    "skfolds = StratifiedKFold(n_splits=3, random_state=42)\n",
    "\n",
    "for train_index, test_index in skfolds.split(X_train, y_train_5):\n",
    "    clone_clf = clone(sgd_clf)\n",
    "    X_train_folds = X_train[train_index]\n",
    "    y_train_folds = (y_train_5[train_index])\n",
    "    X_test_folds = X_train[test_index]\n",
    "    y_test_folds = (y_train_5[test_index])\n",
    "    \n",
    "    clone_clf.fit(X_train_folds, y_train_folds)\n",
    "    y_pred = clone_clf.predict(X_test_folds)\n",
    "    n_correct = sum(y_pred == y_test_folds)\n",
    "    print(n_correct / len(y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator\n",
    "\n",
    "class Never5Classifier(BaseEstimator):\n",
    "    def fit(self, X, y=None):\n",
    "        pass\n",
    "    def predict(self, X):\n",
    "        return np.zeros((len(X),1), dtype=bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "never_5_clf = Never5Classifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.909  ,  0.90715,  0.9128 ])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(never_5_clf, X_train, y_train_5, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "\n",
    "y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[53272,  1307],\n",
       "       [ 1077,  4344]], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_perfect_predictions = y_train_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[54579,     0],\n",
       "       [    0,  5421]], dtype=int64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train_5, y_train_perfect_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.76871350203503808"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.80132816823464303"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.801328168234643"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4344 / (4344 + 1077)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.78468208092485547"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores = sgd_clf.decision_function([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 161855.74572176])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_some_digit_pred = (y_scores > threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True], dtype=bool)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_some_digit_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False], dtype=bool)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = 200000\n",
    "y_some_digit_pred = (y_scores > threshold)\n",
    "y_some_digit_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    }
   ],
   "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": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_scores.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hack to work around issue #9589 introduced in Scikit-Learn 0.19.0\n",
    "if y_scores.ndim == 2:\n",
    "    y_scores = y_scores[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qgghCFARy6ttG1axZk/j4eGJiYihdujSdOnVi4sSJVKxYEScnJxYvXgxAYmIib731FiEhITg5ORESEsKnn35Kenr6de939uxZ+vTpQ3BwMM7OzgQHB9O5c2dSUowFzbI69f3dd98RGhqKq6srRYoUITw8nHlXx7uQ9anv/fv307ZtW3x8fHB1daVWrVosW7bsujZXP+vgwYO0bNkSDw8PSpUqxZAhQ27Knd8ppQgLCKPfI/3Y0G0D23ttp1FII5IsSczaPYuuv3Ul5LsQPl//OQfPHzQ7rk24fBk+/dToNf/3nzGGeccO4zT39u1G4ZYiLQojmy/USt16Gzs2s93Ysbdve60aNW7drmfPzHaR1w/JzRFHjhzB3t4eDw8PAFavXs0333zDhx9+yLJly6hcuTIWi4VmzZoxfvx4XnnlFZYuXUr37t35+OOPGThwYMZ7Xbhwgdq1azNz5kwGDBjAkiVL+PLLL0lLS7vlnOnTpk3j9ddfp2PHjixZsoRp06bxzDPPEBsbe8vMp06dom7duuzYsYMRI0Ywa9YsfHx8aNmyJUuXLr2pfdu2bXniiSeYP38+Tz/9NB9++CGTJ0/O4p1tR5ViVfij8x/80+sfPnviMyoWrUhsUizvrHyHB0c8yOOTH2f6rulY0vPu/gNbkZoKkycbM4INGmQ8vvYEW3AwVKliXj4hTKe1Nn2rUaOGvp09e/bc8jnj9+ystzFjMtuNGXP7tteqXv3W7Xr0yGy3dettY9/WTz/9pAG9b98+nZaWpmNjY/Xo0aO1nZ2dbtOmjdZa61KlSmlXV1cdHR193Wt//vlnDeg1a9Zcd/yTTz7Rjo6O+syZM1prrd9//31tZ2ent23bdsscH374oeaaH0Dfvn11tWrVbpu9QYMGukGDBhmPX3/9dW1vb68PHjyYccxisegHH3zwuve6+lkTJ0687v0qVaqkmzRpctvP1Pr2fw/ym1RLqp6wbYJ++penteMQR81gNIPRZb4ro/sv6a/3nd1ndsR8Ydq06/99+fhoPWGC1unpZicTIvcBW3U2aqTN96hvV36v7f327Hn7tteKjLx1u2t76dlZoeZOKlasiKOjI76+vvTp04eIiAgmTpyY8XytWrVumj512bJllCpVitq1a2OxWDK2pk2bkpaWxqYrg0qXL19OzZo1qVatWrbz1KxZk+3bt9O/f39WrFhB4o2L42Zh7dq11KpV67pr7fb29nTs2JHt27ffdL295dWpoq6oVKkSx6/ewltAONo70q1aN+Y9N4+oAVG8V+89inkU4/CFw/yw+QdCfwyl/az2rDu2Dp0P7hMxQ3IyRERkPn7mGWM94G7dZOpOIa5l84Xa1s2bN48tW7awb98+Ll++zM8//4yvr2/G88VvXHMOiImJ4dixYzg6Ol63PfLIIwCcP38+42tQUNBd5enSpQujRo3i77//plmzZvj6+tKuXTuOHj16y9fExsZmmbNYsWJorblw4cJ1x6/9/gCcnZ1JTk6+q5y2xN/dn0+e+ISD/Q8y/7n5dKrcCY1m7t651J9Un9AfQxkXOY6YyzFmR81TDg7G+rxg3Cw2e7ZcgxYiK1KoTVapUiXCw8OpUKECLi4uNz2f1RhnPz8/QkJC2LJlS5Zbq1atAChatChRUVF3lUcpRa9evdi8eTPnzp1j8uTJbN68medus1abr68vp0+fvun46dOnUUrdVJgLKw8nD9pUbMOUtlPY02cPA2oNwNvZm/3n99NzUU+KfVWMJlOa8E/0P2ZHzTX9+hkLZWzYYBTqL76A6GjI4q++EOIKKdQ2qHnz5hlLf4aHh9+0FS1aFICmTZuyefNmduzYcU+fU6RIEZ577jk6dOjAv//+e8t2DRo0YNOmTdf1uq1WKzNnzqRatWp4esoiFzcK9Q/l62Zfc+zVY3zZ+Evql6qPk70TKw6voNaEWgxaNYjYpFvfwGdrzpwxTmf/+CNs2QJXV6ytUQNkYTwhbk8KtQ2KiIigdu3aNGrUiG+++YaVK1eydOlSRowYQdOmTTOuK7/22muUKVOGxo0b891337Fq1SpmzZpFREQE8fHxWb53z549ef3115kzZw5r165l/PjxTJkyhaZNm94yz2uvvYaPjw9NmjRh+vTpLFq0iFatWnHgwAE+/fTTXPkZFBTeLt4MrDOQNS+u4eSAk3St2pVUayqfrvuUoG+C6L2ot83PerZz5/XFOCwsc81nIcSdSaG2QY6Ojvz+++/06NGDsWPH0qJFCyIiIpg8eTK1a9fOmBDFx8eHDRs20LZtWz7//HOaN2/O66+/joODwy0nTalTpw6RkZH06dOHJk2a8Omnn9KpU6fbDp8qUaIE69evJywsjN69e2cM51q8ePFdz3pWmBV1K8rENhP5839/Uq9kPZIsSYyOHE2Z78vQ7bdubIveZnbEu6I1TJx4/dCqUaPg33/lZjEh7obMTCZsRmH7e7Du2DoG/jGQv6P+BoxZz3548gdeqv6Sycmyr0kTWLHC2N++XcZDC3EtmZlMCBtXr1Q9Nr60kU0vbeLpik+TZEmi+8LuVBpZiak7p+bLYV0TJhgziV31+ONGLzotTYq0EPdKCrUQ+ZhSikeDHmVuh7l83uhz3Bzd2H12N53ndabl9JacSzxndkQA1q6FFi2MVayqVTPm5wZ49114+WXjDm8hxL2RQi2EDVBK8VbdtzjzxhkGNxiMi4MLSw8tpeGkhkSeyoW5bO9Cx47QoAFcnS32u++gfHlTIwlRoEihFsKGeDh58GHDD9n00iZKeJZg99ndhI8LZ9iGYabMI/7DD/DLL8Z+8eIwaxb06JHnMYQo0KRQC2GDqhSrwr+9/+XFqi8C8OaKN6k1vhY7z+zMswy7dsH//Z+xHxZmrB397LN59vFCFBpSqIWwUUVcizCx9URGtxxNoHsgkdGRVBtTjZcXvZwn1679/TP373FOHSFENkihFsKGKaXoFd6LyJ6R/K/K/9BaMyZyDGW/L8uIzSNy/PMsFnj1VeNmscBAWL8eUlLA3j7HP0oIcYUUaiEKgAe8HmDS05PY2XsnFYtW5FLKJfov7c8X67/I0WFcH34IU6aAu7sxaUmdOrKQhhC5TQq1EAVIpYBK7Omzh9cfex2At1e+zbOzn82RaUi7dYPPPoPYWFiw4L7fTgiRTVKohShglFJ81fQrJrSegJO9E7/u/ZUqo6swbec0rOnWe3rPNWvgp5+M/R9+MMZGCyHyhhRqk0yaNAmlVMbm5ORE2bJleffdd01fm7l06dK8+OKLGY+vZr3dmtQi/+lWrRu7++zmIf+HOBZ3jE7zOhE2MoyD5w/e1fuMHQsNGxr7ffoYS1UKIfKOFGqTzZ49m40bN7J48WKaNWvG0KFDGThwoNmxRAFRzrcc23puY3TL0RlrX1cdU5Vlh5Zl6/WRkdC3b+bjr77KpaBCiFvKVqFWSvkqpeYppS4rpY4ppV64RTtnpdRopdQZpVSsUmqhUuqBnI1csFStWpVatWrRpEkTRo4cSePGjZkwYQLp6elmRxMFhLODM73Ce3Hs1WO0KN+CxLREnpr+FB+v+fiOp8KDgow7vcGYr9vVNQ8CCyGuk90e9Y9AKhAIRACjlFJhWbR7BXgMqAyUAC4CP+RAzkKjevXqJCUlce5c5jjYI0eOEBERgb+/P87OzlStWpV58+bd9NodO3bQtm1b/Pz8cHV1pUKFCgwdOjTj+eXLl9OiRQuKFy+Om5sblSpV4uuvv8ZqvbfrlsK2eLt4s6jjIl559BWs2soHf35AhzkdiE+5eW3yqzeKBwbC8ePGECyZr1sIc9zxn55Syh1oD1TSWicA65VSC4DOwNs3NA8Bftdan7ny2l+Ab3I28jXZPsofi9rqD3Nu+MvRo0fx9vbGz88PgBMnTvDoo48SEBDA8OHD8ff3Z+bMmbRv35758+fTunVrADZv3kzDhg0pV64cw4cPJygoiIMHD7JzZ+ZMVYcPH6ZRo0b0798fFxcXtm7dyuDBgzl79iyff/55jn0PIv9SSvFt8295LOgxei3qxdy9c9kWvY0lLywh1N9YQnTIEGMY1rZtxgIbwcEmhxaikMvO78gPAlat9YFrju0AGmTRdgLwnVLqam86Alia1ZsqpXoCPQFKlix5N5kLFKvVisViIT4+nnnz5vHrr7/y7bffYn9lBonBgwejtWbNmjUZxbtZs2acOHGCDz74IKNQv/HGG/j5+bFp0ybc3NwAeOKJJ677rJevuVVXa029evVITU3lq6++4rPPPsPOTm5ZKCyeq/QcVYpVofWM1hyMPUjzac1Z1WUVL7Ury5o1RpsVK4xCLYQwV3YKtQdw4yDMOMAzi7YHgONAFGAFdgFZ3iOqtR4LjAUIDw+/py5pTvZkzVKxYsXrHvfp04d+19xWu2zZMlq0aIG3tzcWS+aiC82aNWPgwIFcunQJBwcHNmzYwMCBAzOKdFaio6MZPHgwy5Yt49SpU9e9X0xMDMWKFcvB70zkdxWLVmRT9000nNSQXTG7KDesBpycCTRj1CgZgiVEfpGdLlQC4HXDMS/g5gtbMApwAfwAd2Aut+hRC8O8efPYsmULS5YsoXHjxowcOZKff/454/mYmBh+/vlnHB0dr9uu3hl+/vx5Lly4QHp6OkFBQbf8nPT0dFq3bs2iRYsYNGgQq1atYsuWLbz33nsApg8JE+bwdfVlfbf1VKANuMRB+wg++vKCFGkh8pHs9KgPAA5KqfJa66sDMKsAu7NoWwV4T2sdC6CU+gEYopQqqrXOHyvc5zOVKlWiXLlygHGqunLlygwcOJD27dvj7u6On58f9erV46233sry9SVKlMBqtWJnZ0dUVNQtP+e///5j69atTJkyhU6dOmUcX7hwYc5+Q8Lm/DLZi/2D58H/noCQP1ng3YQO56ZSsWjFO79YCJHr7tij1lpfxugZD1FKuSul6gBtgClZNN8CdFFKeSulHIE+wCkp0tnj7OzMsGHDiImJYeTIkQA0b96cnTt3EhYWRnh4+E2bs7Mzbm5u1K1bl6lTp5KUlJTleycmJgLg6OiYcSwtLY1p06bl/jcm8jV7e/DwULxadhylfUoTGR1J9THVmbt3rtnRhBBkf3hWH8AViAFmAL211ruVUvWUUgnXtHsDSAYOAmeBFkDbHMxb4LVu3ZqaNWvy1VdfkZSUxJAhQ4iLi6N+/fpMnjyZNWvWMH/+fD755BO6deuW8bqvvvqK8+fP89hjjzFlyhRWr17NhAkT6N+/PwChoaGUKlWK9957jzlz5vDbb7/RpEkTs75NkY88+yxMngxfvVuOHS/vIOLhCJIsSbSf1Z7hG4ebHU+IQi9bhVprHau1flpr7a61Lqm1nn7l+Dqttcc17c5rrSO01gFaax+tdV2t9ebcCl9QffLJJ8TExDB69GhKlizJ1q1bqVKlCu+++y5NmjShd+/erFmz5rq7umvWrMmGDRsIDg6mf//+tGjRgmHDhmVct3ZycmL+/PkUK1aMLl260LdvX+rXr8/bb984wk4UBikpsG6dse/lBe3aGT1rL2cvJj89mc6VOwMwYPkAPlj9QY6uwCWEuDsqP/wDDA8P11u3br3l83v37iU0NDQPE4n8SP4e5JyqVWHHDvj+e7hy0uUmQ9cN5d1V7wIwsPZAvmzyZR4mFKLgU0pFaq3D79ROBs4KUcisW2cUaYB69W7d7p167zDiyREoFMP+GsaEbRPyJqAQ4jpSqIUoRJKSoH59Y//tt42e9e30faQvo1qOAqDf0n78fuj3XE4ohLiRFGohCom0NChbNvPxhx9m73W9wnvxco2XSbYk03ZmW3ac3pE7AYUQWZJCLUQh0acPREcb+ytXgotL9l/7Y8sfaRfajiRLEs2nNWfl4ZW5E1IIcRObKdT54aY3YR75878/KSlw8iQ4OsLvv8MN08DfkZ2yY2LriYQWDeV0wmkaT2nMZ+s+y52wQojr2EShdnR0vOVEHqJwSEpKum6yFnF3nJ1h8WL4+29o2vTe3sPbxZvInpG88dgbALy36j2++uurHEwphMiKTRTqgIAAoqKiSExMlJ5VIaO1JjExkaioKAICAsyOY3OsVmMDsLO7/9WwXB1dGdZ0GEMbGeucD/xjIJ+s/UT+XQqRi2y1ZMX4AAAgAElEQVRiKXgvL2NNkFOnTpGWlmZyGpHXHB0dCQwMzPh7ILLviy9g6VKYOhVKlcq593277tt4OnnSb2k/3l/9PheSLvBlky+xt7PPuQ8RQgA2UqjBKNbyH7UQ2bd5s3Fnt8UC+/fnbKEGY+iWm6MbPRb24JtN3xCfGs+Yp8aglMrZDxKikLOJU99CiLtz8SI8+qhRpF977d6vS99J12pdWdBxAU72TozbNo4vN8jsZULkNCnUQhRA7xozf1KyJHyWyzdntyjfgpnPzARg0OpBrDi8Inc/UIhCRgq1EAXMnDkwyphMjHHj7m689L16uuLTvP7Y61jSLbSa0YpVR1bl/ocKUUhIoRaiAImNhb59jf3vv8+9U95Z+bLJl3St2pVkSzLPzn6WYxeP5d2HC1GASaEWogApUgQ+/hhatjRmIstLdsqOca3G8WS5J4lNiuWZ2c+QbEnO2xBCFEBSqIUoQJSCnj1h4UJjfem8Zm9nz9R2UyntU5qtp7byytJX8j6EEAWMFGohCoATJ+DQoczHZo6Q8nX15dcOv+Js78zYbWN58483Sdfp5gUSwsZJoRbCxlks0KkTVKwIP/1kdhpD9eLVGd96PADD/hpG/Z/qcyHpgsmphLBNUqiFsHFPPQVr1xp3dzdoYHaaTJ0qd2Juh7n4uvqy4cQG2s1qhzXdanYsIWyOFGohbNj8+cZqWAALFkCZMubmuVHb0LZsfGkj/m7+/Hn0T8ZEjjE7khA2Rwq1EDZq+XJo29bYb9Hi7peuzCsP+j3IqJbGwO63VrzF8bjjJicSwrZIoRbCBiUkQLNmxn716vDbb+bmuZN2oe1oU6ENCakJtJrRisS0RLMjCWEzpFALYaPsrvzrXbUKHPL58jpKKca2Gktpn9LsPLOTj9d8bHYkIWyGFGohbJCHBxw7Bnv2gLe32WmyJ8A9gMlPTwbg8w2fs/jAYpMTCWEbpFALYUPOnIH0K0OSg4IgNNTcPHerfqn6vFn7TQA6zevEnrN7TE4kRP4nhVoIG5GWBs2bQ926cNyG78ca2ngorSu05mLyRWpPqM3B8wfNjiREviaFWggbMWgQbN8Op06Bn5/Zae6dnbJjervpNCnThLiUODrN60SqNdXsWELkW1KohbAB69fD118b+5Mmgbu7qXHum7uTO9PaTSPYK5jNUZtpNrUZadY0s2MJkS9JoRYin0tIgHr1wGqFN9+Ehg3NTpQz/N39WdBxAd7O3vx59E8G/D7A7EhC5EtSqIXI5wZcqV8ODsYSlgVJ1WJVWdZpGY52jozYMkKGbQmRBSnUQuRj27bBuHHG/u+/g5OTuXlyQ62gWoxvPR47ZccHf37AT//kk5VFhMgn8vk0CUIUbtWqwYQJcOlS/p0iNCd0qdKFxLREei/uTfeF3SnqVpRWFVqZHUuIfEF61ELkY0pBt27w6qtmJ8l9vWr0ond4b9J1uoyxFuIa2SrUSilfpdQ8pdRlpdQxpdQLt2lbXSm1VimVoJQ6o5R6JefiClE4/PMP7N9vdoq8pZTixxY/0i60HZdSLtFuZjvOXj5rdiwhTJfdHvWPQCoQCEQAo5RSYTc2UkoVBZYBYwA/oBywPGeiClE4xMUZC21UrAh//GF2mryllGJK2ymU9y3P/vP7eXb2s1jSLWbHEsJUdyzUSil3oD3wvtY6QWu9HlgAdM6i+QDgd631NK11itY6Xmu9N2cjC1Gwde9ufHVyMoZlFTZujm6s6LKCQPdA1hxbw6BVg9Bamx1LCNNkp0f9IGDVWh+45tgO4KYeNVALiFVK/aWUilFKLVRKlczqTZVSPZVSW5VSW8+eldNbQgCsWQNz5hj7CxaAi4u5ecxS0rskP7f9GXtlzxcbvmDyjslmRxLCNNkp1B5A3A3H4gDPLNoGAf8DXgFKAkeAGVm9qdZ6rNY6XGsd7u/vn/3EQhRQsbGZk5m8917metOFVdOyTRn91GgA/m/p/7E5arPJiYQwR3YKdQLgdcMxLyA+i7ZJwDyt9RatdTLwEVBbKWUjC/EJYZ4vvjC+li0L779vbpb8olu1brSt2Jb41Hg6zO5AQmqC2ZGEyHPZKdQHAAelVPlrjlUBdmfRdidw7cWkq/vq3uIJUTjEx8PkK2d3R44EZ2dz8+QXdsqOae2mUcGvAsfijtHo50YkpSWZHUuIPHXHQq21vgzMBYYopdyVUnWANsCULJr/BLRVSlVVSjkC7wPrtdYXczK0EAWNpyfs2gVTp0LTpmanyV9cHV35tcOvBLgHsDlqM70W9ZKby0Shkt3hWX0AVyAG45pzb631bqVUPaVUxrkorfUq4F1g8ZW25YBbjrkWQmTy94eICLNT5E9hAWH83ul3XB1cmbJzCuO3jTc7khB5JluFWmsdq7V+WmvtrrUuqbWefuX4Oq21xw1tR2mtH9BaF9Fat9Jan8iN4EIUBAsWQJ8+kCrLMd9R1WJVGfPUGAD6L+3P4gOLTU4kRN6QKUSFMMmFC9CzJ4waBbNmmZ3GNnSu0pkXq75IijWFp2Y8xT/R/5gdSYhcJ4VaCJMMGgRnzkCNGvD882ansR1jnhrDMw89A0CvRb1ItiSbnEiI3CWFWggT7N4NY8aAvT389JOx1rTIHid7J8a1GkdJ75JsObWFHgt7yM1lokCTQi1EHrNaoXdv42uvXvDww2Ynsj0+Lj4s6rgIFwcXpu6cyqTtk8yOJESukUItRB7r1w/WrYOAAPjkE7PT2K6HAx/m22bfAvDGH29w9OJRcwMJkUukUAuRh7SGpCRjnenJk6FIEbMT2bYeNXpQr2Q9YpNi6TC7A4lpiWZHEiLHSaEWIg8pZcw8tmQJNG9udhrbZ6fsmNNhDn6ufnK9WhRYUqiFyCNX64ebmxTpnBTgHsDyzstxdXBl+q7pMhmKKHCkUAuRB/bvBzs7WLvW7CQFU/Xi1RnVchQAr/7+KvvO7TM5kRA5Rwq1ELlMa3jnHWP/o4/MzVKQdanShWcfepbEtEQ6z+tMmjXN7EhC5Agp1ELksokTYd48Y//7783NUpAppRjz1BiCvYLZemorn6771OxIQuQIKdRC5KK9e6F/f2P/558hLMzcPAVdEdcijG9tXKMesmYIKw+vNDmREPdPCrUQuSQpyZgaNCkJOnc2NpH7mpZtyrt130Wj6TK/C9Hx0WZHEuK+SKEWIpcMHAg7d0L58vDjj2anKVw+evwj6gTX4VT8KdrPao813Wp2JCHumRRqIXJJ69YQHAy//AKenmanKVwc7ByY+9xcSniWYOPJjXy76VuzIwlxz6RQC5FLmjaFQ4egenWzkxROAe4BjG45GoD3Vr3HzjM7TU4kxL2RQi1EDjp3DhYuzHzs5GReFgGtKrSiR/UepFhT6PhrR5LSksyOJMRdk0ItRA6xWqF0aeOU93iZHCvfGN5sOBX8KrDn7B5e+/01s+MIcdekUAuRQ4YNg8uXjX2ZIjT/cHdyZ0b7GTjZOzEmcgzz9s4zO5IQd0UKtRA5YPNmeP99Y3/SJAgKMjWOuEG14tUY1mQYAAOWD5BT4MKmSKEW4j5duAAdO4LFAq+9Bv/7n9mJRFb61OxD5cDKHL14lGF/DTM7jhDZJoVaiPuQng4dOsDhw8bd3UOHmp1I3IqDnQPfNzfmcB26fqgs3CFshhRqIe7D6dPw33/g72/M5+3sbHYicTsNSjegU+VOJFuSafRzI3bH7DY7khB3JIVaiPtQogRs2waLFkHJkmanEdkxuuVoGpRqwKn4U7SY3oJLKZfMjiTEbUmhFuIeWCyZ+z4+8Mgj5mURd8fdyZ0lEUuoFFCJ43HH+WbjN2ZHEuK2pFALcZe0Nq5L9+0LKSlmpxH3ws3RjVEtRwHw1V9fsffsXpMTCXFrUqiFuEvDhxvXo6dOhagos9OIe1W3ZF3aVGjD5bTLNJ7SmITUBLMjCZElKdRC3IX16+HNN439SZOgTBlT44j7NKXtFML8wzgVf4p+S/qhtTY7khA3kUItRDadOWOc8rZa4Y03oG1bsxOJ++Xp7Mmkpyfh5ujG5B2TmbxjstmRhLiJFGohssFqhRdegOhoqFsXPvvM7EQip4SXCGdki5EA9F3SV4ZsiXxHCrUQ2fD117BqFQQGwsyZ4OhodiKRk7pU6ULEwxEkpiUSMTeCmMsxZkcSIoMUaiGyoWdPeO45mDHDGDstChalFKNajiLEJ4QdZ3bQakYrki3JZscSApBCLUS2+PjAL7/A44+bnUTkFk9nT9Z2XUuAewCbozbT9beuZkcSApBCLcQtbd8OnTpBgozaKTSCvIJY3mk5Lg4u/PLvL3yx/guzIwmRvUKtlPJVSs1TSl1WSh1TSr1wh/ZOSql9SqmTORNTiLx15gy0bg3TphnrTIvCo0qxKoxsMRI7ZcfbK9/m+7+/NzuSKOSy26P+EUgFAoEIYJRSKuw27QcCcjeGsEnJycbQqxMn4LHH4J13zE4k8lrXal35pqkxtegry15h/7n9JicShdkdC7VSyh1oD7yvtU7QWq8HFgCdb9E+BOgEyIJ/wuZoDd27w8aNxiIb8+aBi4vZqYQZXqn1Ci88bJw87DCnA5Z0yx1eIUTuyE6P+kHAqrU+cM2xHcCtetQ/AO8CSbd7U6VUT6XUVqXU1rNnz2YrrBC57ZtvjNPd7u7w22/GcCxReI1uOZrSPqXZeWYnY7aOMTuOKKSyU6g9gLgbjsUBnjc2VEq1BRy01vPu9KZa67Fa63Ctdbi/v3+2wgqRm1avNmYcA5g8GapWNTePMJ+nsydfN/0agDf+eIN95/aZnEgURtkp1AmA1w3HvID4aw9cOUX+JdA/Z6IJkbfq1IGuXWHoUGjf3uw0Ir9oF9qOiIcjSLYk89KCl7CmW82OJAqZ7BTqA4CDUqr8NceqADfOs1ceKA2sU0qdBuYCxZVSp5VSpe8/qhC5y8kJJkyAt94yO4nIb75/8nuKexTnrxN/8fHaj82OIwqZOxZqrfVljKI7RCnlrpSqA7QBptzQ9F8gGKh6ZesOnLmyfyInQwuRU06dMqYDPXjQeKyUsQlxLV9XXya2mQjAR2s+YurOqSYnEoVJdodn9QFcMYZczQB6a613K6XqKaUSALTWFq316asbEAukX3ks54pEvnPxIlSvDhYL9OpldhqR3zUv15wvGhsToHRf0J2/TvxlciJRWGSrUGutY7XWT2ut3bXWJbXW068cX6e19rjFa/7UWgflZFghckpaGjzzjDGxCcC4cebmEbZhYO2BdKvajRRrCq8se0XWrxZ5QqYQFYWO1tCjB6xcaTzetw/KljU3k7ANSilGtBiBn6sfW09t5f3V75sdSRQCUqhFoTNggDH8ys0NNm+GChXMTiRsiaujK981/w6AT9d9ytB1MreTyF1SqEWhsmMHfPutsT9+PNSsaW4eYZsiKkcwpe0UFIp3V73L2MixZkcSBZiD2QGEyEtVqkC/fsaNZB07mp1G2LJOlTtxKeUSfZf0pd+SfoT4hNCkbBOzY4kCSHrUolBIScnc/+EHmHLj4EIh7kGfmn0YUGsAaelptJvVjm3R28yOJAogKdSiwJs1Cx56CA4fNjuJKIiGNR1Gx0odSUhNoOX0lpxPPG92JFHASKEWBdr48fDCC0aR/u03s9OIgshO2fFTm5+oHFiZ0wmnaTm9JZdSLpkdSxQgUqhFgaQ1DB5sDMOyWuH99+HVV81OJQoqZwdnFnVcRLBXMH9H/c0Lv74gY6xFjpFCLQoci8Uo0B99BHZ2MHo0DBkiU4OK3BXsHcySiCV4OXux+OBiuRNc5Bgp1KJASU+Htm2NxTVcXWHePJkeVOSdSgGV+LHFjwC8vPhl5u2944q/QtyRFGpRoNjZwRNPgJ+fMfNY69ZmJxKFTcTDEbxVx1iCreeinnJzmbhvUqhFgWCxZO6/9hrs3QuPPWZeHlF4KaX4rNFn1AqqxbnEc3SY0wFLuuXOLxTiFqRQC5u3Zw9Urpy5VCWAv795eYSwU3ZMbD0RX1dfVh1ZxavLXpWby8Q9k0ItbNry5RAWZvSgv/zS7DRCZAr1D2X+c/Nxsnfixy0/8vn6z82OJGyUFGphs6ZPz7wG3awZfP+9uXmEuFG9UvWY1m4aAIPXDOZE3AmTEwlbJIVa2Byt4eOPISLCmBq0aVNYuNC4y1uI/OaZh56hQ1gHUq2pdJnfRSZDEXdNCrWwOa1awQcfGOOiv/8eli0DR0ezUwlxa0MbDSXQPZA/j/7JYxMe40zCGbMjCRsihVrYnKt3c8+dC/37y0QmIv8rU6QMG7pt4EG/B9lzdg9tZ7Yl2ZJsdixhI6RQi3xPa9i3L/Nx375w7Bg8/bR5mYS4W2V9y7KqyypKeJZg48mNvLTgJazpVrNjCRsghVrka3Fx8PzzULUqbNliHPPxgZIlzc0lxL14wOsBfu3wK64OrkzfNZ03lr9hdiRhA6RQi3wrMhKqVzeWqXR0hKgosxMJcf9qBdVi0QuLUCi+/ftbRm4ZaXYkkc9JoRb5TloavPQShIcby1NWrQrbtsmpblFwPBHyBMObDQeg75K+TNg2weREIj+TQi3ylaNH4dFHYeJE43HfvrBxI5Qvb2osIXLc/z36f3zeyJgEpcfCHvzx3x8mJxL5lRRqka84OcGRI+DtDYsWwYgR4OJidiohcp5SirfqvsUrj76CRtPx144cvXjU7FgiH5JCLUx3+DCkphr7JUoYS1MeOwYtW5qbS4i88HXTr2latinnk87TekZrGWMtbiKFWpjGYjEmLKlSBb77LvN4w4ZGj1qIwsDezp4pbadQ3KM4u2J28cTPT8hUo+I6UqiFKTZvhpo14ZVXICEB9u83O5EQ5glwD+Dv7n8TWjSUPWf3UPenuhy+cNjsWCKfkEIt8tSlS/B//we1asH27VCqFPz2G4wbZ3YyIcwV7B3Mhm4beOSBRzged5waY2swZ88cs2OJfEAKtcgzR49CaCj88APY2cHAgbB7t7EClkwDKgQUcS3Cwo4LqVuyLheTL/Ls7Gf5btN3d36hKNCkUIs8U7IkhIQYw68iI431o93dzU4lRP4S4B7A2hfX8kXjLwB49fdXmbFrhsmphJmkUItcc/w4dO4MBw8aj+3sYP582LDBuIFMCJE1pRRv1nmTb5p+A8CLv73I4gOLTU4lzCKFWuS4Cxfg3XehQgWYOhUGDcp8rmhRsLc3L5sQtuTVWq/SJ7wPqdZUnprxFF+s/wKttdmxRB6TQi1yzMWL8OGHULo0DB0Kycnw3HPwxRdmJxPCNiml+O7J73i7ztsAvL3ybZpNbUZsUqzJyUReylahVkr5KqXmKaUuK6WOKaVeuEW7gUqpf5VS8UqpI0qpgTkbV+RX8+YZ16CHDDHu7G7UyJj685dfjMIthLg3DnYODG08lDFPjcHX1Zc/Dv/Bo+Mf5a8Tf5kdTeSR7PaofwRSgUAgAhillArLop0CugBFgOZAP6XU8zkRVOQ/ly9n7leubDx+4glYtw5WrDCGYAkhckbPGj3Z3ms7VYtV5VDsIRr/3JidZ3aaHUvkgTsWaqWUO9AeeF9rnaC1Xg8sADrf2FZr/aXWepvW2qK13g/8BtTJ6dDCPFobRbhZM6hd23gMULYsHDoEK1dC3brmZhSioAr2Duavbn/xfKXnSbIk0eaXNhy5cMTsWCKXZadH/SBg1VofuObYDiCrHnUGpZQC6gG7b/F8T6XUVqXU1rNnz2Y3rzBJWhpMm2asD92kCSxfbhTmA9f8rQgJMS+fEIWFq6MrE1tPpGaJmhy9eJRKoyoxcstIucmsAMtOofYA4m44Fgd43uF1g6+8/09ZPam1Hqu1Dtdah/v7+2cjhjBDUhIMHw7lykGnTsZsYgEB8MkncOKEcWe3ECJvuTq6srzzctqHticxLZG+S/ry7OxnuZRyyexoIhdkp1AnAF43HPMC4m/1AqVUP4xr1S211in3Hk/kB0OHGmOiK1SAsWONla3eew98fc1OJkTh5ePiw5wOc5jUZhKeTp78uvdXwkaGsTlqs9nRRA7LTqE+ADgopcpfc6wKtz6l3Q14G2iktT55/xFFXjl0yBheVamSMRYawNUVvv7amKhkzx7o0UPWhxYiP/lf1f+xucdmHnngEU5eOsnjkx9nbORY0nW62dFEDlHZua6hlPoF0EB3oCqwBKittd59Q7sI4Gvgca313uyGCA8P11u3br2b3CKHHD8Os2fDrFnGilZXjR1rFGUhhG1Is6bRbUE3pu6cCsCDfg8yvtV46pWqZ3IycStKqUitdfid2mV3eFYfwBWIAWYAvbXWu5VS9ZRSCde0+wTwA7YopRKubKPvNrzIfWlpxt3ZpUrBG28YRdrdHbp0gT/+gG7dzE4ohLgbjvaOTH56MhNbT6S4R3EOnD9A/Un1eXH+i5xPPG92PHEfstWjzm3So859UVGweDF0727MuQ3GmOdNm+Cpp6BDB2jRAtzczM0phLh/KZYU3ln5DiM2jyAtPY0H/R5kWcQyQorI0Iz8JLs9ainUBVR6unGH9sKFsGRJ5mntTZuM1asADh+GwEBZwUqIgurfmH954dcX2BWzCz9XP35u+zMtyrcwO5a4IqdPfQsbER8PL70EwcFQowYMHmwUaRcXaNcOHB0z25YpI0VaiIKsUkAl1nZdS/NyzTmfdJ6W01vy4eoP5UYzGyOF2oZdvGj0lr/9NvOYhwcsWgSnTkFQEPTsCQsWwLlz8OuvxoQlQojCw8fFh4UdFzKk4RAUiiFrh9B6RmvWHF1jdjSRTXLq24ZcvAhLlxqLXWzcCNu2Gae47ewgNha8vY12CxcaRbpqVVDK3MxCiPxj6cGlPDP7GRLTEgHoXLkz3z/5PT4uPiYnK5zkGrWNu3ABEhKMU9hgzKHduPH1bRwdoWZNqF8fBgwAmeBNCHEnhy8c5rtN3/HD5h/QaIp5FOOX9r/QoHQDs6MVOlKobcj580bvODIycztyBF54wZhfGyA6GkqUMHrIr79uFO26deUasxDi3myL3kbPhT2JjI7EXtnzfv33GVhnIG6OMvQjr0ihzofi4mD3bqhSJbPAdusGP2UxG7qLC7RtC9OnZx5LS7v+ZjAhhLgflnQL76x4h682fgWAv5s/HzT4gN7hvbG3szc5XcGX3ULtkBdhCpvERFi1CvbuNVaXOnQIDh40xjIDrF0L9a5MFlS8uDF2uWpV4y7tGjWMG75CQ8Hhhj8dKdJCiJzkYOfAsKbDaPlgS9784022nNpC/6X9GbdtHJ83+pwnyz9pdkSB9KjvWno6nD5trBx19KhxivrwYaPgfvSR0ebMGShW7ObXOjvDQw/BsGHQqJFxLDHROG4vv7wKIUyktWb+vvn0X9qfqPgoFIru1bsztNFQ/Nz8zI5XIMmp77uUmmoU2NOnjevBV7cePeCBB4w2r70GI0aAxXLz6ytVgl27jH2toU0bY3rOihWhfHljCw6+uZcshBD5SWJaIp+u/ZTP1n8GQKB7IEMeH0LXql1xtJfTejlJTn1jFMz4eKMAXy28p08bhfeZZ4w2Bw9C7drGOOOs1KuXWahdXIwiXbSoUXRLl4aQEGMLDc18jVLG2GUhhLA1bo5ufNroU56r9BzdfutGZHQkvRb1Yty2cXz6xKc0KdMEJeM+85RN9agTEoxrvrGxmdv585n7w4eDz5XhgC+8AHPmGDdg3ahFC2PeazAKtL+/MRY5MNA4ZV28eObWuTM8+KDRNj7euE4syzwKIQoDS7qF2btn8+aKNzl5yVi1uLxved6v/z6dKneSgn2fbOrUd1BQuH7qqa3ExXHddvGisYjE4MFGuxUroEmTW7/Pvn1QoYKx36mTMbTJw8MoxMWLZxbh6tUzV4fS2uhx+/vLdWIhhMhKfEo8I7eMZPim4Zy5fAaAasWq8Xyl5+lUuRMlPEuYnNA22VShdnUN18nJWfeoe/WC0VcWyty1y+jh+vqCn5/x9dqtbVvjK8ClS+DkJL1fIYTIKZZ0C1//9TXfbPqGmMsxANgpO/o/0p8vm3yJk72TyQlti00V6pCQcD1w4Fa8vY1pMH18yNj385NJPYQQIj9JTEtkwf4FzNw9k/n75gPGKfEBjw2gc+XOuDvJf9rZYVOFOj/c9S2EEOLuLT6wmH5L+3H04lHAWASkc+XO9Kjeg4cDHzY3XD4nhVoIIUSeSLWmMnfvXL7/+3s2ntyYcbx2cG26V+tOx4c74uIg1yFvJIVaCCFEntsWvY3x28YzZecUElITAKgcWJnFLywmyCvI5HT5S3YLtaxHLYQQIsdUL16dkS1HEv16NBNbTyTEJ4SdZ3YS+mMoY7aOIV2nmx3R5kiPWgghRK45nXCalxa8xJKDSwAo6laUmiVqEvFwBG0qtsHDycPkhOaRU99CCCHyBa01c/bM4Y0/3uB43PGM476uvvSr2Y/u1bsT7B1sYkJzSKEWQgiRr2itOR53nGWHljFy60h2ntkJGGOxn3rwKd6p+w61gmqZnDLvSKEWQgiRb2mtWXtsLaO2jmLu3rmkpRvzPT9d8Wn61uzLEyFPYKcK9m1UUqiFEELYhNMJp/l4zcdM3D6RZEsyAMFewXSp0oXOlTtToWgFkxPmDinUQgghbMrxuOP89M9PTNoxKWMCFYAgryAalm7I82HP06J8iwKzGIgUaiGEEDYpXaez9thaJv4zkbl753I57XLGc48+8CjtQ9tTp2QdHnngERzsbHe1ZinUQgghbF66TmfH6R3M+HcGYyLHcCnlUsZz3s7e1ClZh/ah7WlZviWBHoEmJr17UqiFEEIUKOcSz7H8v+WsPrKaP4/9yaHYQ9c9H+wVzDMPPUPHSh0JLxGe70+RS6EWQghRoP0T/Q8L9i9g5ZGV7Diz47rednGP4pQpUoZg72CKuRfjyfJP0qRMk3xVvKVQCyGEKDS01vwd9Tczds1g1p5ZnE44fVObQPdAHvJ/iLJFylLerzyNyzSmWrFqphVvKfLK1dIAAAt2SURBVNRCCCEKJWu6lSMXj3Dy0kkOxR5i08lN/HH4j+tmRbvK19WXh/wfoqR3SYq6FqW4Z3Ee9HuQqsWqEuITkqtFXAq1EEIIcYXWmv8u/MfhC4fZc3YPq4+u5q8Tf3Eu8dwtX+Pj4kPFohXxdvamUUgjwgLCqFasGgHuAdjb2d93JinUQgghxG1orYmKj2LfuX1Ex0dzLvEcx+KOsefsHjZHbSYuJS7L19kre4p5FKO0T2l8XX3xdvHGx9kHX1dfgryCMo75ufrh5+aHt7M3ns6eN820lt1CbbsD0IQQQoj7oJQiyCsoy3WytdacuXyGDcc3cOLSCQ5fOMy26G3sP7+fc4nniIqPIio+KvufhcLL2QtvF2+8nb0p5lEs26/NVqFWSvkCE4D/b+9ug62q6jiOf38ioF1gvARCWok6moUzYtOLTEUyH9NJp2upVKMzlVPmCweb0qYx0imxUqc0NUfMfCA0E3UGzUc0SHFqMjPLRwJEQAfl3ssF4RqtXqx9bLM5TxfOhn3O/n1m9ujZa6291zp/1l53P5y1jwPWABeFEOZUySdgFvC1ZNVs4LuhCKftZmZmTZLExFET6flYz1Zpg5sHWbluJUt7l9K3sY/ejb30berjzfVv8vq61+PnjX2s2bCGtRvX0ruxl4HBAfo29b13lv7cm881XZdmz6h/CQwCE4ApwHxJz4YQns/kOwc4FTgECMDDwBLg+qZrZGZmVmAjho1g0h6TmLTHpKbLbP7vZvo39b83qC9Zu4SemVv/EVBNw4FaUhfQAxwcQhgAFkm6D/gKcGEm+1nAFSGEFUnZK4Cv44HazMxKbNguw+jevZvu3bsBmDJxStNlmzmjPhDYHEJ4KbXuWeCoKnknJ2npfJOrbVTSOcQzcIABSS82UZe8jCNe0i8rt7+87S9z28Htd/t3bvv3aSZTMwP1KCD76FsfMLqJvH3AKEnK3qcOIdwA3NBMJfMm6S/NPHnXqdz+8ra/zG0Ht9/tb4/2N/NW7gFgTGbdGGBdE3nHAAN+mMzMzGzbNDNQvwTsKumA1LpDgOyDZCTrDmkin5mZmTWh4UAdQlgP3A1cIqlL0uHAKcCtVbLfAsyQtLekvYALgJtbWN+8FOIS/E7k9pdXmdsObr/b3waampks+R31TcCxwFvAhSGEOZKOBB4IIYxK8gm4nP//jvpG/DtqMzOzbVaIKUTNzMysumbuUZuZmdlO4oHazMyswDpioJZ0hqR/SVov6dXk3nkl7TOSXpC0QdICSfuk0kZKuklSv6TVkmZktptL2Zy+gwMkbZR0W2b9dEnLku/mnuR5g0raWEnzkrRlkqbviLItau9ISbOTfayT9IykEzN5ShH7bdEofkXTKN5linW1vt6p/bxK26se6zs+/iGEtl6ID7gtAz5J/MNjb2DvJG0ccdKVLwC7AT8FFqfKXgYsBLqBjwKrgRPyLpvT9/BQUp/bUusmE3/vPpU4Gc0cYG4q/bfAHUnaEUmdJ+ddtkXt7QJmApOSuJ+c7HNS2WK/jd9fzfgVcakX77LFmkxfz7Ovbk/ZHNpd9Vhfhvjv9A7YguA9CXy1Rto5wJOpz13AO8BByefXgeNS6ZdW/qHlWTaH7+AM4E7igSw9UP8YmJP6vD/x5SqjkzoNAgem0m8FZuVZNud/C38HesoU+238nurGr12WSrzLFOtqfb0s/Zwax/oyxL+tL31LGgZ8Ahgv6RVJKyRdI2n3JMsWc4+H+JvwV4HJkrqBvag9N3kuZbevxVuTNAa4hPib9axsPV4l6XjUnsO9VhtaVTYXkiYk269MsNPxsd8OjeJXeJl4lyLWdfp6x/fzBsf6jo9/Ww/UxNduDgdOA44kvoLzUOD7SXq9ecpHpT5n0/Is22qXArNDCK9VSWvUhnp1zKtsy0kaDtwO/CaE8EITdeiU2G+rdqhjTVXiXZZY1+rrZejn9Y71HR//Qg/Ukh6XFGosi4iXGQCuDiGsCiGsAa4EPpusrzdP+UDqczYtz7JNa9R+SVOAY4CramyiURvq1TGvsk1pIvaVfLsQL8cNAuelNtHWsc9ZO9Sxqhrx7vhYN+jrbdvPh6Desb7j41/ogTqEMC2EoBrLESGEtcAKoNasLVvMPa74bu39geeTsquoPTd5LmVb2X5gGvFhmuWSVgPfBnok/bVGPfYDRhLnb280h3teZVvV9spMeLOJf233hBDeTW2irWOfs6HM318YdeJdhlhPo3Zfb9t+3qwGx/rOj38eN/135EK8Z/NnYE/ik3kLgUuTtPHESxE9xKfyLmfLJ/pmAU8k5Q5KgnJC3mVb2Pb3ARNTy8+Au4DxSfpkoJ94qagLuI0tn+icS3yqsws4nK2f6MylbAvbfz2wGBhVJa2jY9+C765m/Iq61Ip3GWJdr693ej9P1aXqsb4U8d/Zna8FwRsOXAv0Eh+d/wWwWyr9GOAF4qWTx0l+vpOkjSTOYd4PvAHMyGw7l7I5fhczST31naybDiwH1gP3AmNTaWOBe5K05cD0HVG2RW3dh/jX9UbiJajK8qUyxn4bvr+68Sva0ijeZYt1tq93aj/P7Kvmsb7T4++5vs3MzAqs0PeozczMys4DtZmZWYF5oDYzMyswD9RmZmYF5oHazMyswDxQm5mZFZgHarOc1ZkKNb0sTfLeLGnFTq4yAJJmJnXbtZXbayLftGS/01qxX7N215IOaGZ1HZb5PI/41p2ZqXWbdlhtzKyteKA2y1kIYXH6s6RNwJrs+u0laWQIwQO+WYfxpW+zApJ0qKSFkjZIelnSNzLpZyeXh6dK+p2kXuDpVPpRkh6VtE7SekkPSjo4s43jJf1JUp+kAUkvSrq4SnX2lTQ/ybNM0sXJW6zS2/qIpHmSeiW9I2mxpBOaaOd4SXMk9SdlbwH2GNKXZdbhPFCbFc8YYA7xJQenEF9EcJ2kT1fJezvwb+J7ei8EkHQS8ChxLuwvE+djHg0slPShJM9+wH3AUuB04HPE1wZ2VdnHPOAx4FTivM8/BM6qJEraC1hEfJPQecAXifMxz5d0YoO23g2cDHwvqcd/gKsblDErFV/6Niue0cC5IYQFAJL+CBwHnAksyOS9K4Twncy6nwNPhBBOqayQtABYAlwAnA98HBgBfDOE0J9ke6xGfa4IIfw6+f9HJB2d1KWybgbx7UKHhRBeSfZ3P/BP4EfAA9U2KulY4AjgzBDC3GT1g5IeAD5Yoy5mpeMzarPi2VAZpAGS+84vAx+uknde+kPy7uD9gdsl7VpZgA3AU8DUJOvfgHeBuZJOk7RnnfrMz3z+R6YuU4mv93slVefNxNcjTpE0psZ2DwM2A7/PrJ9bJa9ZaXmgNiuetVXWbSK+8zZrVeZzZcCdTRyI08vJwPsBkkH1eOIx4FZgtaSnJR1VZR9vN6jL2Cr1gPgqQhHPtqv5ALA2hPBuZv0bNfKblZIvfZu1t+zvkt9K/nsR8EiV/IPvFYxn7QskjQQOBy4h3leeFEJYM4Q6vA1MrLJ+YlK/7EBfsQroljQ8M1hPGMK+zTqeB2qzzvIi8QGxySGEWc0USC6tPyZpFHAvsC8wlIH6CeD8ZIBfCiBpGPHhsGdCCOtqlHsKGAb0sOXl7jOGsG+zjueB2qyDhBCCpG8B90oaAdxJHHQnAJ8ClocQrkx+7jUVuB94DRhHPAtfSbwHPRRXAWcDD0v6AdAPnAscCJxUp64PS1oE/ErSOOJ9+NOBg2uVMSsj36M26zAhhPuJg3AXcCPwIPAT4qXop5JszybplwEPAdcQf+Z1dAjhnSHubyXx6e3ngeuAu4j3rU8KIfyhQfHPE/9YuAy4g3jycN5Q9m/W6RRCw6l3zczMbCfxGbWZmVmBeaA2MzMrMA/UZmZmBeaB2szMrMA8UJuZmRWYB2ozM7MC80BtZmZWYB6ozczMCux/4IWiZwt07OQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abe00f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_precision_recall_vs_threshold(precisions, recalls, thresholds):\n",
    "    plt.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\", linewidth=2)\n",
    "    plt.plot(thresholds, recalls[:-1], \"g-\", label=\"Recall\", linewidth=2)\n",
    "    plt.xlabel(\"Threshold\", fontsize=16)\n",
    "    plt.legend(loc=\"upper left\", fontsize=16)\n",
    "    plt.ylim([0, 1])\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "plot_precision_recall_vs_threshold(precisions, recalls, thresholds)\n",
    "plt.xlim([-700000, 700000])\n",
    "#save_fig(\"precision_recall_vs_threshold_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y_train_pred == (y_scores > 0)).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred_90 = (y_scores > 70000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.86592051164915484"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_train_5, y_train_pred_90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.69931746910164172"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred_90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abcdfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_precision_vs_recall(precisions, recalls):\n",
    "    plt.plot(recalls, precisions, \"b-\", linewidth=2)\n",
    "    plt.xlabel(\"Recall\", fontsize=16)\n",
    "    plt.ylabel(\"Precision\", fontsize=16)\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plot_precision_vs_recall(precisions, recalls)\n",
    "#save_fig(\"precision_vs_recall_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bb734e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.xlabel('False Positive Rate', fontsize=16)\n",
    "    plt.ylabel('True Positive Rate', fontsize=16)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plot_roc_curve(fpr, tpr)\n",
    "#save_fig(\"roc_curve_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96244965559671547"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_train_5, y_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "forest_clf = RandomForestClassifier(random_state=42)\n",
    "y_probas_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3, \n",
    "                                    method=\"predict_proba\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores_forest = y_probas_forest[:, 1]\n",
    "fpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train_5, y_scores_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bb884a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, \"b:\", linewidth=2, label=\"SGD\")\n",
    "plot_roc_curve(fpr_forest, tpr_forest, \"Random Forest\")\n",
    "plt.legend(loc=\"lower right\", fontsize=16)\n",
    "#save_fig(\"roc_curve_comparison_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99312433660038291"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_train_5, y_scores_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n",
       "       eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n",
       "       learning_rate='optimal', loss='hinge', max_iter=None, n_iter=None,\n",
       "       n_jobs=1, penalty='l2', power_t=0.5, random_state=42, shuffle=True,\n",
       "       tol=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "some_digit_scores = sgd_clf.decision_function([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_digit_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(some_digit_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.classes_[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.multiclass import OneVsOneClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "ovo_clf = OneVsOneClassifier(SGDClassifier(random_state=42))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 5.])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ovo_clf.fit(X_train, y_train)\n",
    "ovo_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ovo_clf.estimators_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.1,  0. ,  0. ,  0.1,  0. ,  0.8,  0. ,  0. ,  0. ,  0. ]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_clf.predict_proba([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.84063187,  0.84899245,  0.86652998])"
      ]
     },
     "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": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_scaled = scaler.fit_transform(X_train.astype(np.float64))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.91011798,  0.90874544,  0.906636  ])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(sgd_clf, X_train_scaled, y_train, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "c:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = cross_val_predict(sgd_clf, X_train_scaled, y_train, cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "conf_mx = confusion_matrix(y_train, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5725,    3,   24,    9,   10,   49,   50,   10,   39,    4],\n",
       "       [   2, 6493,   43,   25,    7,   40,    5,   10,  109,    8],\n",
       "       [  51,   41, 5321,  104,   89,   26,   87,   60,  166,   13],\n",
       "       [  47,   46,  141, 5342,    1,  231,   40,   50,  141,   92],\n",
       "       [  19,   29,   41,   10, 5366,    9,   56,   37,   86,  189],\n",
       "       [  73,   45,   36,  193,   64, 4582,  111,   30,  193,   94],\n",
       "       [  29,   34,   44,    2,   42,   85, 5627,   10,   45,    0],\n",
       "       [  25,   24,   74,   32,   54,   12,    6, 5787,   15,  236],\n",
       "       [  52,  161,   73,  156,   10,  163,   61,   25, 5027,  123],\n",
       "       [  43,   35,   26,   92,  178,   28,    2,  223,   82, 5240]], dtype=int64)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_mx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(matrix):\n",
    "    \"\"\"If you prefer color and a colorbar\"\"\"\n",
    "    fig = plt.figure(figsize=(8,8))\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(matrix)\n",
    "    fig.colorbar(cax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x145c3f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(conf_mx, cmap=plt.cm.gray)\n",
    "#save_fig(\"confusion_matrix_plot\", tight_layout=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "row_sums = conf_mx.sum(axis=1, keepdims=True)\n",
    "norm_conf_mx = conf_mx / row_sums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bb7a518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.fill_diagonal(norm_conf_mx, 0)\n",
    "plt.matshow(norm_conf_mx, cmap=plt.cm.gray)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x145d36d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cl_a, cl_b = 3,5\n",
    "X_aa = X_train[(y_train == cl_a) & (y_train_pred == cl_a)]\n",
    "X_ab = X_train[(y_train == cl_a) & (y_train_pred == cl_b)]\n",
    "X_ba = X_train[(y_train == cl_b) & (y_train_pred == cl_a)]\n",
    "X_bb = X_train[(y_train == cl_b) & (y_train_pred == cl_b)]\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.subplot(221); plot_digits(X_aa[:25], images_per_row=5)\n",
    "plt.subplot(222); plot_digits(X_ab[:25], images_per_row=5)\n",
    "plt.subplot(223); plot_digits(X_ba[:25], images_per_row=5)\n",
    "plt.subplot(224); plot_digits(X_bb[:25], images_per_row=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "y_train_large = (y_train >= 7)\n",
    "y_train_odd = (y_train % 2 == 1)\n",
    "y_multilabel = np.c_[y_train_large, y_train_odd]\n",
    "\n",
    "knn_clf = KNeighborsClassifier()\n",
    "knn_clf.fit(X_train, y_multilabel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False,  True]], dtype=bool)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_knn_pred = cross_val_predict(knn_clf, X_train, y_train, cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96845540180280221"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_train, y_train_knn_pred, average=\"macro\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96858231602525713"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_train, y_train_knn_pred, average=\"weighted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise = np.random.randint(0, 100, (len(X_train), 784))\n",
    "X_train_mod = X_train + noise\n",
    "noise = np.random.randint(0, 100, (len(X_test), 784))\n",
    "X_test_mod = X_test + noise\n",
    "y_train_mod = X_train\n",
    "y_test_mod = X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot reshape array of size 1 into shape (28,28)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-95-6a85808ef01d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0msome_index\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m5500\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m121\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m \u001b[0mplot_digits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test_mod\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msome_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m122\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m \u001b[0mplot_digits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test_mod\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msome_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m#save_fig(\"noisy_digit_example_plot\")\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-8-a42fe1f80d92>\u001b[0m in \u001b[0;36mplot_digits\u001b[1;34m(instances, images_per_row, **options)\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0msize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m28\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mimages_per_row\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minstances\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimages_per_row\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[0mimages\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0minstance\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0minstance\u001b[0m \u001b[1;32min\u001b[0m \u001b[0minstances\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m     \u001b[0mn_rows\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minstances\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m//\u001b[0m \u001b[0mimages_per_row\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mrow_images\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-8-a42fe1f80d92>\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0msize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m28\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mimages_per_row\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minstances\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimages_per_row\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[0mimages\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0minstance\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0minstance\u001b[0m \u001b[1;32min\u001b[0m \u001b[0minstances\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m     \u001b[0mn_rows\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minstances\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m//\u001b[0m \u001b[0mimages_per_row\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mrow_images\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: cannot reshape array of size 1 into shape (28,28)"
     ]
    },
    {
     "data": {
      "image/png": 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A8fK1ldfO/rwD2A+cBL4HvGOO6joE/JxiFz75eqBct4LiBHuC4l68fwaG56iuh8vPbAHPAp9sG6cr26ts+1AZUrW1z8b22kyxt6q+NpcBbQFLK33voJh2/gnFeekFlXWXUUxDn6S49DHjDJ/vPTNL8m00ZkkOjVmSQ2OW5NCYJTk0ZkkOjVmSQ2OW5NCYJf0PDSJLCcnD3twAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cc45e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "some_index = 5500\n",
    "plt.subplot(121); plot_digits(X_test_mod[some_index])\n",
    "plt.subplot(122); plot_digits(y_test_mod[some_index])\n",
    "#save_fig(\"noisy_digit_example_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cefcc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_clf.fit(X_train_mod, y_train_mod)\n",
    "clean_digit = knn_clf.predict([X_test_mod[some_index]])\n",
    "plot_digits(clean_digit)\n",
    "#save_fig(\"cleaned_digit_example_plot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n",
      "[CV] n_neighbors=3, weights=uniform ..................................\n",
      "[CV]  n_neighbors=3, weights=uniform, score=0.9698458975426906, total=21.1min\n",
      "[CV] n_neighbors=3, weights=uniform ..................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 101.5min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_neighbors=3, weights=uniform, score=0.9714214297617064, total=21.7min\n",
      "[CV] n_neighbors=3, weights=uniform ..................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed: 206.1min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_neighbors=3, weights=uniform, score=0.9726666666666667, total=22.2min\n",
      "[CV] n_neighbors=3, weights=uniform ..................................\n",
      "[CV]  n_neighbors=3, weights=uniform, score=0.9728265399683255, total=20.7min\n",
      "[CV] n_neighbors=3, weights=uniform ..................................\n",
      "[CV]  n_neighbors=3, weights=uniform, score=0.9717405801933978, total=22.0min\n",
      "[CV] n_neighbors=3, weights=distance .................................\n",
      "[CV]  n_neighbors=3, weights=distance, score=0.9712619741774261, total=22.0min\n",
      "[CV] n_neighbors=3, weights=distance .................................\n",
      "[CV]  n_neighbors=3, weights=distance, score=0.9725879020163306, total=22.0min\n",
      "[CV] n_neighbors=3, weights=distance .................................\n",
      "[CV] .... n_neighbors=3, weights=distance, score=0.9745, total=21.9min\n",
      "[CV] n_neighbors=3, weights=distance .................................\n",
      "[CV]  n_neighbors=3, weights=distance, score=0.9743269150620989, total=22.0min\n",
      "[CV] n_neighbors=3, weights=distance .................................\n",
      "[CV]  n_neighbors=3, weights=distance, score=0.9724074691563854, total=22.0min\n",
      "[CV] n_neighbors=4, weights=uniform ..................................\n",
      "[CV]  n_neighbors=4, weights=uniform, score=0.9691795085381091, total=22.0min\n",
      "[CV] n_neighbors=4, weights=uniform ..................................\n",
      "[CV]  n_neighbors=4, weights=uniform, score=0.9698383602732877, total=22.0min\n",
      "[CV] n_neighbors=4, weights=uniform ..................................\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-97-bc2776abff41>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mknn_clf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mKNeighborsClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mgrid_search\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mknn_clf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparam_grid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mgrid_search\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[0;32m    637\u001b[0m                                   error_score=self.error_score)\n\u001b[0;32m    638\u001b[0m           for parameters, (train, test) in product(candidate_params,\n\u001b[1;32m--> 639\u001b[1;33m                                                    cv.split(X, y, groups)))\n\u001b[0m\u001b[0;32m    640\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    641\u001b[0m         \u001b[1;31m# if one choose to see train score, \"out\" will contain train score info\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m    777\u001b[0m             \u001b[1;31m# was dispatched. In particular this covers the edge\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    778\u001b[0m             \u001b[1;31m# case of Parallel used with an exhausted iterator.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 779\u001b[1;33m             \u001b[1;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    780\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    781\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    623\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    624\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 625\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    626\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    627\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    586\u001b[0m         \u001b[0mdispatch_timestamp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    587\u001b[0m         \u001b[0mcb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBatchCompletionCallBack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdispatch_timestamp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 588\u001b[1;33m         \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    589\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    590\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\externals\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    109\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    110\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 111\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    112\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    113\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\externals\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    330\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    331\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 332\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    333\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    334\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    130\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 131\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    133\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    130\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 131\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    133\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[1;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, error_score)\u001b[0m\n\u001b[0;32m    490\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mreturn_train_score\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    491\u001b[0m             train_scores = _score(estimator, X_train, y_train, scorer,\n\u001b[1;32m--> 492\u001b[1;33m                                   is_multimetric)\n\u001b[0m\u001b[0;32m    493\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    494\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mverbose\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_score\u001b[1;34m(estimator, X_test, y_test, scorer, is_multimetric)\u001b[0m\n\u001b[0;32m    521\u001b[0m     \"\"\"\n\u001b[0;32m    522\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mis_multimetric\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 523\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_multimetric_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    524\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    525\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0my_test\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_multimetric_score\u001b[1;34m(estimator, X_test, y_test, scorers)\u001b[0m\n\u001b[0;32m    551\u001b[0m             \u001b[0mscore\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    552\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 553\u001b[1;33m             \u001b[0mscore\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    554\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    555\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscore\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'item'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\metrics\\scorer.py\u001b[0m in \u001b[0;36m_passthrough_scorer\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m    242\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_passthrough_scorer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    243\u001b[0m     \u001b[1;34m\"\"\"Function that wraps estimator.score\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    245\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    246\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36mscore\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    347\u001b[0m         \"\"\"\n\u001b[0;32m    348\u001b[0m         \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 349\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    350\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    351\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\neighbors\\classification.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    143\u001b[0m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'csr'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    144\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 145\u001b[1;33m         \u001b[0mneigh_dist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mneigh_ind\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkneighbors\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    146\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    147\u001b[0m         \u001b[0mclasses_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\neighbors\\base.py\u001b[0m in \u001b[0;36mkneighbors\u001b[1;34m(self, X, n_neighbors, return_distance)\u001b[0m\n\u001b[0;32m    383\u001b[0m                 delayed(self._tree.query, check_pickle=False)(\n\u001b[0;32m    384\u001b[0m                     X[s], n_neighbors, return_distance)\n\u001b[1;32m--> 385\u001b[1;33m                 \u001b[1;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mgen_even_slices\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    386\u001b[0m             )\n\u001b[0;32m    387\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mreturn_distance\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m    777\u001b[0m             \u001b[1;31m# was dispatched. In particular this covers the edge\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    778\u001b[0m             \u001b[1;31m# case of Parallel used with an exhausted iterator.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 779\u001b[1;33m             \u001b[1;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    780\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    781\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    623\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    624\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 625\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    626\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    627\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    586\u001b[0m         \u001b[0mdispatch_timestamp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    587\u001b[0m         \u001b[0mcb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBatchCompletionCallBack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdispatch_timestamp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 588\u001b[1;33m         \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    589\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    590\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\externals\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    109\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    110\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 111\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    112\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    113\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\externals\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    330\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    331\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 332\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    333\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    334\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    130\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 131\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    133\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\zhuowu\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    130\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 131\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    133\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [{'weights': [\"uniform\", \"distance\"], 'n_neighbors': [3, 4, 5]}]\n",
    "\n",
    "knn_clf = KNeighborsClassifier()\n",
    "grid_search = GridSearchCV(knn_clf, param_grid, cv=5, verbose=3)\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.ndimage.interpolation import shift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "def shift_image(image, dx, dy):\n",
    "    image = image.reshape((28, 28))\n",
    "    shifted_image = shift(image, [dy, dx], cval=0, mode=\"constant\")\n",
    "    return shifted_image.reshape([-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d68e828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = X_train[1000]\n",
    "shifted_image_down = shift_image(image, 0, 5)\n",
    "shifted_image_left = shift_image(image, -5, 0)\n",
    "\n",
    "plt.figure(figsize=(12,3))\n",
    "plt.subplot(131)\n",
    "plt.title(\"Original\", fontsize=14)\n",
    "plt.imshow(image.reshape(28, 28), interpolation=\"nearest\", cmap=\"Greys\")\n",
    "plt.subplot(132)\n",
    "plt.title(\"Shifted down\", fontsize=14)\n",
    "plt.imshow(shifted_image_down.reshape(28, 28), interpolation=\"nearest\", cmap=\"Greys\")\n",
    "plt.subplot(133)\n",
    "plt.title(\"Shifted left\", fontsize=14)\n",
    "plt.imshow(shifted_image_left.reshape(28, 28), interpolation=\"nearest\", cmap=\"Greys\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_augmented = [image for image in X_train]\n",
    "y_train_augmented = [label for label in y_train]\n",
    "\n",
    "for dx, dy in ((1, 0), (-1, 0), (0, 1), (0, -1)):\n",
    "    for image, label in zip(X_train, y_train):\n",
    "        X_train_augmented.append(shift_image(image, dx, dy))\n",
    "        y_train_augmented.append(label)\n",
    "\n",
    "X_train_augmented = np.array(X_train_augmented)\n",
    "y_train_augmented = np.array(y_train_augmented)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "shuffle_idx = np.random.permutation(len(X_train_augmented))\n",
    "X_train_augmented = X_train_augmented[shuffle_idx]\n",
    "y_train_augmented = y_train_augmented[shuffle_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'GridSearchCV' object has no attribute 'best_params_'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-103-d0b40ad06e04>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mknn_clf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mKNeighborsClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mgrid_search\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'GridSearchCV' object has no attribute 'best_params_'"
     ]
    }
   ],
   "source": [
    "knn_clf = KNeighborsClassifier(**grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "TITANIC_PATH = os.path.join(\"datasets\", \"titanic\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def load_titanic_data(filename, titanic_path=TITANIC_PATH):\n",
    "    csv_path = os.path.join(titanic_path, filename)\n",
    "    return pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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