SVM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%%\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'target_names', 'images', 'DESCR'])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#%%\n",
    "from sklearn.datasets import load_digits\n",
    "digits = load_digits()\n",
    "digits.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 8, 8)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#%%\n",
    "digits.images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEQAAABUCAYAAAA7xZEpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAABA1JREFUeJztm09oXFUUh7+frUUqJSATRVQyERSURhMZ3BQ0Ii11Fdy5kQSEbHSp4EbIssuIuLCIJCDirlikaAUJLkTpCBH/UCGWCYYIM7OIuKo0HBfT0Znzpn0vmXffvKH3g2HmHe675/Dj3Dv33fOuzIzI/9w16gDKRhTEEQVxREEcURBHFMQRBXGUUxDpCaSvkf5C2kJ6uSjX5RNEOgp8BnwO3AcsAx8jPV6I+9KtVKWTwHfACbrBSZeB7zF7J7T78mUI6Ba2k0U4L6MgV4Em8BbS3UhngOeB40U4L9+QAZCeAt6jkxV1oAVcx+y14K5LKYhH+hZYx+yD0K7KOGQ6GSLdg3Qc6U3gQWCtCNflFAReBf6kM5e8CJzG7HoRjsdjyBRIWTNkZERBHFEQRxTEcTRLI0lngXeBI8CHZnbudu0rlYpVq9UDB7O/v5+wtdvthK3ZbKb2NTMz03fdaDRot9uDHgv6SBVE0hHgfeA0sANckXTRzH691T3VapV6vZ4atGdvby9hW1tbS9hWV1dT+/L+a7VaphiyDJlngS0zu2Zm/wCfAguZeh9DsgjyEPBHz/XOTVsfkpYl1SXVW61WXvEVThZBBo27xGrOzM6bWc3MapOTk8NHNiKyTKo7wCM91w8Du8M63tzcTNjm5+cTtpWVldR7Z2dnhw3nP7JkyBXgMUnTko4BrwAXc4ugZKRmiJndkPQG8CWdv92PzOyX4JGNiEzrEDO7BFwKHEspyCRIHjQajb7rubm5RJvFxcWEbXp6OmHzc8bS0tJQsfUSl+6OKIgjCuKIgjhGNqlOTEwk2qyvr6feB7C9vd13PWiCPiwxQxxREEcUxBEFcRQ2qfon2UET6NTUVMI2aCvS2xYW8tuvihniiII4oiCOrGWIBvA3sA/cMLNsW9hjyEEm1RfMLFkkOSRZJ8KNjY2ELc8tQ08cMo6sghhwWdIPkpYHNbiTyhAAp8zsGeAl4HVJz/kGd1IZAjPbvfndlHSBTjXvm5CBdRlUrjhM3TgrqRki6V5JJ7q/gTPAz8EiGjFZMuQB4IKkbvtPzOyLoFGNkCx1mWvA0wXEUgri366jsKfdPBm0rZgXMUMcURBHFMQRBXEEebVbUgvYBipAbk/IjoP2PWVmqc8UQd91l1QPtXcSqu84ZBxREEdoQc6PW9/xvIwjDhlHFMQRRBBJZyX9JmlL0tsB+m9I+knSpqSDnzK4HWaW64fOu6y/A48Cx4AfgSdz9tEAKnnHbmZBMmSsT0+EECTT6YkhSS2LHJYQG0SZTk8MySkz25V0P/CVpKtmlksVIESGBDk90UtvWQTolkVyIYQgQU9PhC6L5D5kCjg9EbQsEpfujrhSdURBHFEQRxTEEQVxREEcURDHv12/8ieKHe4xAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%%\n",
    "X = digits.data\n",
    "y = digits.target\n",
    "\n",
    "\n",
    "p = np.random.randint(0, len(X), 25)\n",
    "\n",
    "\n",
    "samples = np.array(list(zip(X, y)))[p]\n",
    "for index, (data, label) in enumerate(samples):\n",
    "    plt.subplot(5, 5, index + 1)\n",
    "    plt.axis('on')\n",
    "    plt.imshow(data.reshape(8, 8), cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "    plt.title(label, color='red')\n",
    "    plt.show()\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  0.  5. 13.  9.  1.  0.  0.]\n",
      " [ 0.  0. 13. 15. 10. 15.  5.  0.]\n",
      " [ 0.  3. 15.  2.  0. 11.  8.  0.]\n",
      " [ 0.  4. 12.  0.  0.  8.  8.  0.]\n",
      " [ 0.  5.  8.  0.  0.  9.  8.  0.]\n",
      " [ 0.  4. 11.  0.  1. 12.  7.  0.]\n",
      " [ 0.  2. 14.  5. 10. 12.  0.  0.]\n",
      " [ 0.  0.  6. 13. 10.  0.  0.  0.]]\n"
     ]
    }
   ],
   "source": [
    "#%%\n",
    "print(digits.images[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc8df17ca20>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%%\n",
    "plt.matshow(digits.images[0], cmap=plt.cm.Greys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#%%\n",
    "digits.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#%%\n",
    "digits.target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%%\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%%\n",
    "from sklearn.svm import LinearSVC,SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%%\n",
    "svm = SVC(C=10,gamma=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.001, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#%%\n",
    "svm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9 1 4 ... 2 4 8]\n"
     ]
    }
   ],
   "source": [
    "#%%\n",
    "print(svm.predict(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9 1 4 ... 2 4 8]\n"
     ]
    }
   ],
   "source": [
    "#%%\n",
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 8 4 6 6 2 4 7 1 6 2 9 1 7 7 5 3 4 2 2 3 7 6 8 4 5 3 9 3 1 9 6 7 9 7 2 3\n",
      " 6 9 3 8 4 5 3 6 1 7 6 4 4 4 8 1 2 5 1 3 0 7 6 9 8 3 0 7 8 4 1 4 9 6 9 8 3\n",
      " 3 6 3 6 8 9 1 0 7 7 5 4 6 2 9 2 3 9 5 3 9 0 7 8 0 8 8 1 1 9 3 0 8 9 9 8 9\n",
      " 4 8 0 1 8 2 1 2 5 4 1 5 5 6 0 8 2 7 4 5 8 4 4 0 6 9 5 0 6 2 8 7 3 8 1 6 7\n",
      " 5 7 6 9 2 4 3 6 6 4 7 3 7 9 6 2 0 0 5 5 8 6 1 5 6 4 2 8 1 2 9 6 8 3 2 9 9\n",
      " 4 4 1 1 7 6 9 1 8 3 8 6 1 9 3 4 3 8 2 4 3 0 4 5 5 2 0 2 9 4 7 1 7 6 6 6 8\n",
      " 2 7 1 4 8 6 2 5 2 7 2 0 3 4 1 4 3 2 9 1 3 0 4 8 9 0 6 1 8 5 7 2 6 7 2 8 2\n",
      " 2 3 5 2 4 8 6 3 8 8 3 9 9 1 3 4 6 9 7 6 4 4 8 0 7 8 5 7 5 4 4 8 3 7 7 7 8\n",
      " 0 6 4 9 0 3 3 4 3 9 6 3 5 4 1 9 7 2 9 9 1 0 1 0 5 7 1 1 8 0 2 8 3 4 6 3 4\n",
      " 5 3 7 5 9 2 4 2 2 1 1 4 7 6 0 9 2 8 5 3 6 7 2 5 9 7 3 6 2 9 6 0 0 1 7 3 7\n",
      " 3 4 7 1 3 5 4 2 0 2 5 5 6 1 4 2 7 3 4 1 5 0 2 4 7 8 3 7 9 4 6 8 3 3 6 1 9\n",
      " 7 2 7 0 1 0 2 3 5 3 0 5 5 9 0 0 7 1 0 2 9 6 8 0 3 8 6 9 4 8 7 7 1 4 1 2 9\n",
      " 4 1 6 5 0 6]\n"
     ]
    }
   ],
   "source": [
    "#%%\n",
    "print(svm.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 8 4 6 6 2 4 7 1 6 2 9 1 7 7 5 3 4 2 2 3 7 6 8 4 5 3 9 3 1 9 6 7 9 7 2 3\n",
      " 6 9 3 8 4 5 3 6 1 7 6 4 4 4 8 1 2 5 1 3 0 7 6 9 8 3 0 7 8 4 1 4 9 6 9 8 3\n",
      " 3 6 3 6 8 9 1 0 7 9 5 4 6 2 9 2 3 9 5 3 9 0 7 8 0 8 8 1 1 9 3 0 8 9 9 8 9\n",
      " 4 8 0 1 8 2 1 2 5 4 1 5 5 6 0 8 2 7 4 5 8 4 4 0 6 9 5 0 6 2 8 7 3 8 1 6 7\n",
      " 5 7 6 9 2 4 3 6 6 4 7 3 7 9 6 2 0 0 5 5 8 6 1 5 6 4 2 8 1 2 9 6 8 3 2 9 9\n",
      " 4 4 1 1 7 6 9 1 8 3 8 6 1 9 3 4 3 8 2 4 3 0 4 5 5 2 0 2 9 4 7 1 7 6 6 6 8\n",
      " 2 7 1 4 8 6 2 5 2 7 2 0 3 4 1 4 3 2 9 1 3 0 4 8 9 0 6 1 8 5 7 2 6 7 2 8 2\n",
      " 2 3 5 2 4 8 6 3 8 8 3 9 9 1 3 4 6 9 7 6 4 4 8 0 7 8 5 7 5 4 4 8 3 7 7 7 8\n",
      " 0 6 4 3 0 3 3 4 3 9 6 3 5 4 1 9 7 2 9 9 1 0 1 0 5 7 1 1 8 0 2 8 3 4 6 3 4\n",
      " 5 3 7 5 9 2 4 2 2 1 1 4 7 6 0 9 2 8 5 3 6 7 2 5 9 7 3 6 2 9 6 0 0 1 7 3 7\n",
      " 3 4 7 1 3 5 4 2 0 2 5 5 6 1 4 2 7 3 4 1 5 0 2 4 7 8 3 7 9 4 6 8 3 3 6 1 9\n",
      " 7 2 7 0 1 0 2 3 5 3 0 5 5 9 0 0 7 1 0 2 9 6 8 0 3 6 6 9 4 8 7 7 1 4 1 2 9\n",
      " 4 1 6 5 0 6]\n"
     ]
    }
   ],
   "source": [
    "#%%\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#%%\n",
    "svm.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9933333333333333"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#%%\n",
    "svm.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/juliotorres/miniconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "/home/juliotorres/miniconda3/lib/python3.6/site-packages/sklearn/grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "#%%\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100]\n",
    "gammas = [0.001, 0.01, 0.1, 1, 10, 100]\n",
    "\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "\n",
    "param_grid = {'C': Cs, 'gamma' : gammas}\n",
    "grid_search = GridSearchCV(SVC(), param_grid, cv=5, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 36 candidates, totalling 180 fits\n",
      "[CV] C=0.001, gamma=0.001 ............................................\n",
      "[CV] ................................... C=0.001, gamma=0.001 -   0.3s\n",
      "[CV] C=0.001, gamma=0.001 ............................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.3s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................................... C=0.001, gamma=0.001 -   0.3s\n",
      "[CV] C=0.001, gamma=0.001 ............................................\n",
      "[CV] ................................... C=0.001, gamma=0.001 -   0.3s\n",
      "[CV] C=0.001, gamma=0.001 ............................................\n",
      "[CV] ................................... C=0.001, gamma=0.001 -   0.4s\n",
      "[CV] C=0.001, gamma=0.001 ............................................\n",
      "[CV] ................................... C=0.001, gamma=0.001 -   0.3s\n",
      "[CV] C=0.001, gamma=0.01 .............................................\n",
      "[CV] .................................... C=0.001, gamma=0.01 -   0.3s\n",
      "[CV] C=0.001, gamma=0.01 .............................................\n",
      "[CV] .................................... C=0.001, gamma=0.01 -   0.3s\n",
      "[CV] C=0.001, gamma=0.01 .............................................\n",
      "[CV] .................................... C=0.001, gamma=0.01 -   0.3s\n",
      "[CV] C=0.001, gamma=0.01 .............................................\n",
      "[CV] .................................... C=0.001, gamma=0.01 -   0.3s\n",
      "[CV] C=0.001, gamma=0.01 .............................................\n",
      "[CV] .................................... C=0.001, gamma=0.01 -   0.3s\n",
      "[CV] C=0.001, gamma=0.1 ..............................................\n",
      "[CV] ..................................... C=0.001, gamma=0.1 -   0.5s\n",
      "[CV] C=0.001, gamma=0.1 ..............................................\n",
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      "[CV] C=0.001, gamma=0.1 ..............................................\n",
      "[CV] ..................................... C=0.001, gamma=0.1 -   0.5s\n",
      "[CV] C=0.001, gamma=0.1 ..............................................\n",
      "[CV] ..................................... C=0.001, gamma=0.1 -   0.5s\n",
      "[CV] C=0.001, gamma=0.1 ..............................................\n",
      "[CV] ..................................... C=0.001, gamma=0.1 -   0.5s\n",
      "[CV] C=0.001, gamma=1 ................................................\n",
      "[CV] ....................................... C=0.001, gamma=1 -   0.5s\n",
      "[CV] C=0.001, gamma=1 ................................................\n",
      "[CV] ....................................... C=0.001, gamma=1 -   0.5s\n",
      "[CV] C=0.001, gamma=1 ................................................\n",
      "[CV] ....................................... C=0.001, gamma=1 -   0.5s\n",
      "[CV] C=0.001, gamma=1 ................................................\n",
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      "[CV] C=0.001, gamma=1 ................................................\n",
      "[CV] ....................................... C=0.001, gamma=1 -   0.5s\n",
      "[CV] C=0.001, gamma=10 ...............................................\n",
      "[CV] ...................................... C=0.001, gamma=10 -   0.5s\n",
      "[CV] C=0.001, gamma=10 ...............................................\n",
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      "[CV] C=0.001, gamma=10 ...............................................\n",
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      "[CV] C=0.001, gamma=10 ...............................................\n",
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      "[CV] C=0.001, gamma=10 ...............................................\n",
      "[CV] ...................................... C=0.001, gamma=10 -   0.5s\n",
      "[CV] C=0.001, gamma=100 ..............................................\n",
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      "[CV] C=0.001, gamma=100 ..............................................\n",
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      "[CV] C=0.001, gamma=100 ..............................................\n",
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      "[CV] C=0.001, gamma=100 ..............................................\n",
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      "[CV] C=0.001, gamma=100 ..............................................\n",
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      "[CV] C=0.01, gamma=0.001 .............................................\n",
      "[CV] .................................... C=0.01, gamma=0.001 -   0.4s\n",
      "[CV] C=0.01, gamma=0.001 .............................................\n",
      "[CV] .................................... C=0.01, gamma=0.001 -   0.4s\n",
      "[CV] C=0.01, gamma=0.001 .............................................\n",
      "[CV] .................................... C=0.01, gamma=0.001 -   0.4s\n",
      "[CV] C=0.01, gamma=0.001 .............................................\n",
      "[CV] .................................... C=0.01, gamma=0.001 -   0.4s\n",
      "[CV] C=0.01, gamma=0.001 .............................................\n",
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      "[CV] C=0.01, gamma=0.01 ..............................................\n",
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      "[CV] C=0.01, gamma=0.01 ..............................................\n",
      "[CV] ..................................... C=0.01, gamma=0.01 -   0.4s\n",
      "[CV] C=0.01, gamma=0.01 ..............................................\n",
      "[CV] ..................................... C=0.01, gamma=0.01 -   0.3s\n",
      "[CV] C=0.01, gamma=0.01 ..............................................\n",
      "[CV] ..................................... C=0.01, gamma=0.01 -   0.4s\n",
      "[CV] C=0.01, gamma=0.01 ..............................................\n",
      "[CV] ..................................... C=0.01, gamma=0.01 -   0.4s\n",
      "[CV] C=0.01, gamma=0.1 ...............................................\n",
      "[CV] ...................................... C=0.01, gamma=0.1 -   0.5s\n",
      "[CV] C=0.01, gamma=0.1 ...............................................\n",
      "[CV] ...................................... C=0.01, gamma=0.1 -   0.5s\n",
      "[CV] C=0.01, gamma=0.1 ...............................................\n",
      "[CV] ...................................... C=0.01, gamma=0.1 -   0.5s\n",
      "[CV] C=0.01, gamma=0.1 ...............................................\n",
      "[CV] ...................................... C=0.01, gamma=0.1 -   0.5s\n",
      "[CV] C=0.01, gamma=0.1 ...............................................\n",
      "[CV] ...................................... C=0.01, gamma=0.1 -   0.5s\n",
      "[CV] C=0.01, gamma=1 .................................................\n",
      "[CV] ........................................ C=0.01, gamma=1 -   0.5s\n",
      "[CV] C=0.01, gamma=1 .................................................\n",
      "[CV] ........................................ C=0.01, gamma=1 -   0.5s\n",
      "[CV] C=0.01, gamma=1 .................................................\n",
      "[CV] ........................................ C=0.01, gamma=1 -   0.5s\n",
      "[CV] C=0.01, gamma=1 .................................................\n",
      "[CV] ........................................ C=0.01, gamma=1 -   0.5s\n",
      "[CV] C=0.01, gamma=1 .................................................\n",
      "[CV] ........................................ C=0.01, gamma=1 -   0.5s\n",
      "[CV] C=0.01, gamma=10 ................................................\n",
      "[CV] ....................................... C=0.01, gamma=10 -   0.5s\n",
      "[CV] C=0.01, gamma=10 ................................................\n",
      "[CV] ....................................... C=0.01, gamma=10 -   0.5s\n",
      "[CV] C=0.01, gamma=10 ................................................\n",
      "[CV] ....................................... C=0.01, gamma=10 -   0.5s\n",
      "[CV] C=0.01, gamma=10 ................................................\n",
      "[CV] ....................................... C=0.01, gamma=10 -   0.5s\n",
      "[CV] C=0.01, gamma=10 ................................................\n",
      "[CV] ....................................... C=0.01, gamma=10 -   0.5s\n",
      "[CV] C=0.01, gamma=100 ...............................................\n",
      "[CV] ...................................... C=0.01, gamma=100 -   0.5s\n",
      "[CV] C=0.01, gamma=100 ...............................................\n",
      "[CV] ...................................... C=0.01, gamma=100 -   0.5s\n",
      "[CV] C=0.01, gamma=100 ...............................................\n",
      "[CV] ...................................... C=0.01, gamma=100 -   0.5s\n",
      "[CV] C=0.01, gamma=100 ...............................................\n",
      "[CV] ...................................... C=0.01, gamma=100 -   0.5s\n",
      "[CV] C=0.01, gamma=100 ...............................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ...................................... C=0.01, gamma=100 -   0.5s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] ..................................... C=0.1, gamma=0.001 -   0.3s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] ..................................... C=0.1, gamma=0.001 -   0.2s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] ..................................... C=0.1, gamma=0.001 -   0.2s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] ..................................... C=0.1, gamma=0.001 -   0.2s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] ..................................... C=0.1, gamma=0.001 -   0.2s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ...................................... C=0.1, gamma=0.01 -   0.4s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ...................................... C=0.1, gamma=0.01 -   0.3s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ...................................... C=0.1, gamma=0.01 -   0.4s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ...................................... C=0.1, gamma=0.01 -   0.4s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ...................................... C=0.1, gamma=0.01 -   0.3s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] ....................................... C=0.1, gamma=0.1 -   0.5s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] ....................................... C=0.1, gamma=0.1 -   0.5s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] ....................................... C=0.1, gamma=0.1 -   0.5s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] ....................................... C=0.1, gamma=0.1 -   0.5s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] ....................................... C=0.1, gamma=0.1 -   0.5s\n",
      "[CV] C=0.1, gamma=1 ..................................................\n",
      "[CV] ......................................... C=0.1, gamma=1 -   0.5s\n",
      "[CV] C=0.1, gamma=1 ..................................................\n",
      "[CV] ......................................... C=0.1, gamma=1 -   0.5s\n",
      "[CV] C=0.1, gamma=1 ..................................................\n",
      "[CV] ......................................... C=0.1, gamma=1 -   0.5s\n",
      "[CV] C=0.1, gamma=1 ..................................................\n",
      "[CV] ......................................... C=0.1, gamma=1 -   0.5s\n",
      "[CV] C=0.1, gamma=1 ..................................................\n",
      "[CV] ......................................... C=0.1, gamma=1 -   0.5s\n",
      "[CV] C=0.1, gamma=10 .................................................\n",
      "[CV] ........................................ C=0.1, gamma=10 -   0.5s\n",
      "[CV] C=0.1, gamma=10 .................................................\n",
      "[CV] ........................................ C=0.1, gamma=10 -   0.5s\n",
      "[CV] C=0.1, gamma=10 .................................................\n",
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      "[CV] C=0.1, gamma=10 .................................................\n",
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      "[CV] C=0.1, gamma=10 .................................................\n",
      "[CV] ........................................ C=0.1, gamma=10 -   0.5s\n",
      "[CV] C=0.1, gamma=100 ................................................\n",
      "[CV] ....................................... C=0.1, gamma=100 -   0.5s\n",
      "[CV] C=0.1, gamma=100 ................................................\n",
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      "[CV] C=0.1, gamma=100 ................................................\n",
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      "[CV] C=0.1, gamma=100 ................................................\n",
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      "[CV] C=0.1, gamma=100 ................................................\n",
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      "[CV] C=1, gamma=0.001 ................................................\n",
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      "[CV] C=1, gamma=0.001 ................................................\n",
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      "[CV] C=1, gamma=0.001 ................................................\n",
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      "[CV] C=1, gamma=0.001 ................................................\n",
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      "[CV] C=1, gamma=0.01 .................................................\n",
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      "[CV] C=1, gamma=0.01 .................................................\n",
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      "[CV] C=1, gamma=0.01 .................................................\n",
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      "[CV] C=1, gamma=0.01 .................................................\n",
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      "[CV] C=1, gamma=0.1 ..................................................\n",
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      "[CV] C=1, gamma=0.1 ..................................................\n",
      "[CV] ......................................... C=1, gamma=0.1 -   0.5s\n",
      "[CV] C=1, gamma=0.1 ..................................................\n",
      "[CV] ......................................... C=1, gamma=0.1 -   0.5s\n",
      "[CV] C=1, gamma=0.1 ..................................................\n",
      "[CV] ......................................... C=1, gamma=0.1 -   0.5s\n",
      "[CV] C=1, gamma=0.1 ..................................................\n",
      "[CV] ......................................... C=1, gamma=0.1 -   0.5s\n",
      "[CV] C=1, gamma=1 ....................................................\n",
      "[CV] ........................................... C=1, gamma=1 -   0.5s\n",
      "[CV] C=1, gamma=1 ....................................................\n",
      "[CV] ........................................... C=1, gamma=1 -   0.5s\n",
      "[CV] C=1, gamma=1 ....................................................\n",
      "[CV] ........................................... C=1, gamma=1 -   0.5s\n",
      "[CV] C=1, gamma=1 ....................................................\n",
      "[CV] ........................................... C=1, gamma=1 -   0.5s\n",
      "[CV] C=1, gamma=1 ....................................................\n",
      "[CV] ........................................... C=1, gamma=1 -   0.5s\n",
      "[CV] C=1, gamma=10 ...................................................\n",
      "[CV] .......................................... C=1, gamma=10 -   0.5s\n",
      "[CV] C=1, gamma=10 ...................................................\n",
      "[CV] .......................................... C=1, gamma=10 -   0.5s\n",
      "[CV] C=1, gamma=10 ...................................................\n",
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      "[CV] C=1, gamma=10 ...................................................\n",
      "[CV] .......................................... C=1, gamma=10 -   0.5s\n",
      "[CV] C=1, gamma=10 ...................................................\n",
      "[CV] .......................................... C=1, gamma=10 -   0.5s\n",
      "[CV] C=1, gamma=100 ..................................................\n",
      "[CV] ......................................... C=1, gamma=100 -   0.5s\n",
      "[CV] C=1, gamma=100 ..................................................\n",
      "[CV] ......................................... C=1, gamma=100 -   0.5s\n",
      "[CV] C=1, gamma=100 ..................................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ......................................... C=1, gamma=100 -   0.5s\n",
      "[CV] C=1, gamma=100 ..................................................\n",
      "[CV] ......................................... C=1, gamma=100 -   0.5s\n",
      "[CV] C=1, gamma=100 ..................................................\n",
      "[CV] ......................................... C=1, gamma=100 -   0.6s\n",
      "[CV] C=10, gamma=0.001 ...............................................\n",
      "[CV] ...................................... C=10, gamma=0.001 -   0.1s\n",
      "[CV] C=10, gamma=0.001 ...............................................\n",
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      "[CV] C=10, gamma=0.001 ...............................................\n",
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      "[CV] C=10, gamma=0.001 ...............................................\n",
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      "[CV] C=10, gamma=0.001 ...............................................\n",
      "[CV] ...................................... C=10, gamma=0.001 -   0.1s\n",
      "[CV] C=10, gamma=0.01 ................................................\n",
      "[CV] ....................................... C=10, gamma=0.01 -   0.4s\n",
      "[CV] C=10, gamma=0.01 ................................................\n",
      "[CV] ....................................... C=10, gamma=0.01 -   0.4s\n",
      "[CV] C=10, gamma=0.01 ................................................\n",
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      "[CV] C=10, gamma=0.01 ................................................\n",
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      "[CV] C=10, gamma=0.01 ................................................\n",
      "[CV] ....................................... C=10, gamma=0.01 -   0.4s\n",
      "[CV] C=10, gamma=0.1 .................................................\n",
      "[CV] ........................................ C=10, gamma=0.1 -   0.5s\n",
      "[CV] C=10, gamma=0.1 .................................................\n",
      "[CV] ........................................ C=10, gamma=0.1 -   0.6s\n",
      "[CV] C=10, gamma=0.1 .................................................\n",
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      "[CV] C=10, gamma=0.1 .................................................\n",
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      "[CV] C=10, gamma=0.1 .................................................\n",
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      "[CV] C=10, gamma=1 ...................................................\n",
      "[CV] .......................................... C=10, gamma=1 -   0.6s\n",
      "[CV] C=10, gamma=1 ...................................................\n",
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      "[CV] C=10, gamma=1 ...................................................\n",
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      "[CV] C=10, gamma=1 ...................................................\n",
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      "[CV] C=10, gamma=1 ...................................................\n",
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      "[CV] C=10, gamma=10 ..................................................\n",
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      "[CV] C=10, gamma=10 ..................................................\n",
      "[CV] ......................................... C=10, gamma=10 -   0.6s\n",
      "[CV] C=10, gamma=10 ..................................................\n",
      "[CV] ......................................... C=10, gamma=10 -   0.5s\n",
      "[CV] C=10, gamma=10 ..................................................\n",
      "[CV] ......................................... C=10, gamma=10 -   0.5s\n",
      "[CV] C=10, gamma=10 ..................................................\n",
      "[CV] ......................................... C=10, gamma=10 -   0.5s\n",
      "[CV] C=10, gamma=100 .................................................\n",
      "[CV] ........................................ C=10, gamma=100 -   0.5s\n",
      "[CV] C=10, gamma=100 .................................................\n",
      "[CV] ........................................ C=10, gamma=100 -   0.5s\n",
      "[CV] C=10, gamma=100 .................................................\n",
      "[CV] ........................................ C=10, gamma=100 -   0.5s\n",
      "[CV] C=10, gamma=100 .................................................\n",
      "[CV] ........................................ C=10, gamma=100 -   0.5s\n",
      "[CV] C=10, gamma=100 .................................................\n",
      "[CV] ........................................ C=10, gamma=100 -   0.5s\n",
      "[CV] C=100, gamma=0.001 ..............................................\n",
      "[CV] ..................................... C=100, gamma=0.001 -   0.1s\n",
      "[CV] C=100, gamma=0.001 ..............................................\n",
      "[CV] ..................................... C=100, gamma=0.001 -   0.1s\n",
      "[CV] C=100, gamma=0.001 ..............................................\n",
      "[CV] ..................................... C=100, gamma=0.001 -   0.1s\n",
      "[CV] C=100, gamma=0.001 ..............................................\n",
      "[CV] ..................................... C=100, gamma=0.001 -   0.1s\n",
      "[CV] C=100, gamma=0.001 ..............................................\n",
      "[CV] ..................................... C=100, gamma=0.001 -   0.1s\n",
      "[CV] C=100, gamma=0.01 ...............................................\n",
      "[CV] ...................................... C=100, gamma=0.01 -   0.4s\n",
      "[CV] C=100, gamma=0.01 ...............................................\n",
      "[CV] ...................................... C=100, gamma=0.01 -   0.4s\n",
      "[CV] C=100, gamma=0.01 ...............................................\n",
      "[CV] ...................................... C=100, gamma=0.01 -   0.4s\n",
      "[CV] C=100, gamma=0.01 ...............................................\n",
      "[CV] ...................................... C=100, gamma=0.01 -   0.4s\n",
      "[CV] C=100, gamma=0.01 ...............................................\n",
      "[CV] ...................................... C=100, gamma=0.01 -   0.4s\n",
      "[CV] C=100, gamma=0.1 ................................................\n",
      "[CV] ....................................... C=100, gamma=0.1 -   0.5s\n",
      "[CV] C=100, gamma=0.1 ................................................\n",
      "[CV] ....................................... C=100, gamma=0.1 -   0.6s\n",
      "[CV] C=100, gamma=0.1 ................................................\n",
      "[CV] ....................................... C=100, gamma=0.1 -   0.6s\n",
      "[CV] C=100, gamma=0.1 ................................................\n",
      "[CV] ....................................... C=100, gamma=0.1 -   0.6s\n",
      "[CV] C=100, gamma=0.1 ................................................\n",
      "[CV] ....................................... C=100, gamma=0.1 -   0.5s\n",
      "[CV] C=100, gamma=1 ..................................................\n",
      "[CV] ......................................... C=100, gamma=1 -   0.5s\n",
      "[CV] C=100, gamma=1 ..................................................\n",
      "[CV] ......................................... C=100, gamma=1 -   0.5s\n",
      "[CV] C=100, gamma=1 ..................................................\n",
      "[CV] ......................................... C=100, gamma=1 -   0.5s\n",
      "[CV] C=100, gamma=1 ..................................................\n",
      "[CV] ......................................... C=100, gamma=1 -   0.5s\n",
      "[CV] C=100, gamma=1 ..................................................\n",
      "[CV] ......................................... C=100, gamma=1 -   0.5s\n",
      "[CV] C=100, gamma=10 .................................................\n",
      "[CV] ........................................ C=100, gamma=10 -   0.5s\n",
      "[CV] C=100, gamma=10 .................................................\n",
      "[CV] ........................................ C=100, gamma=10 -   0.5s\n",
      "[CV] C=100, gamma=10 .................................................\n",
      "[CV] ........................................ C=100, gamma=10 -   0.5s\n",
      "[CV] C=100, gamma=10 .................................................\n",
      "[CV] ........................................ C=100, gamma=10 -   0.5s\n",
      "[CV] C=100, gamma=10 .................................................\n",
      "[CV] ........................................ C=100, gamma=10 -   0.5s\n",
      "[CV] C=100, gamma=100 ................................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ....................................... C=100, gamma=100 -   0.5s\n",
      "[CV] C=100, gamma=100 ................................................\n",
      "[CV] ....................................... C=100, gamma=100 -   0.5s\n",
      "[CV] C=100, gamma=100 ................................................\n",
      "[CV] ....................................... C=100, gamma=100 -   0.6s\n",
      "[CV] C=100, gamma=100 ................................................\n",
      "[CV] ....................................... C=100, gamma=100 -   0.5s\n",
      "[CV] C=100, gamma=100 ................................................\n",
      "[CV] ....................................... C=100, gamma=100 -   0.5s\n",
      "CPU times: user 1min 19s, sys: 118 ms, total: 1min 19s\n",
      "Wall time: 1min 19s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 180 out of 180 | elapsed:  1.3min finished\n"
     ]
    }
   ],
   "source": [
    "#%%\n",
    "%time _ = grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9933333333333333"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#%%\n",
    "grid_search.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'C': 10, 'gamma': 0.001}, 0.991833704528582)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#%%\n",
    "grid_search.best_params_, grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x7fc8d54f6160>,\n",
       "  <matplotlib.axis.YTick at 0x7fc8d54f19e8>,\n",
       "  <matplotlib.axis.YTick at 0x7fc8d54e99e8>,\n",
       "  <matplotlib.axis.YTick at 0x7fc8d5463630>,\n",
       "  <matplotlib.axis.YTick at 0x7fc8d5463b38>,\n",
       "  <matplotlib.axis.YTick at 0x7fc8d546b0f0>],\n",
       " <a list of 6 Text yticklabel objects>)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%%\n",
    "# We extract just the scores\n",
    "scores = [x[1] for x in grid_search.grid_scores_]\n",
    "scores = np.array(scores).reshape(6, 6)\n",
    "\n",
    "plt.matshow(scores)\n",
    "plt.xlabel('gamma')\n",
    "plt.ylabel('C')\n",
    "plt.colorbar()\n",
    "plt.xticks(np.arange(6), param_grid['gamma'])\n",
    "plt.yticks(np.arange(6), param_grid['C'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.966667\n"
     ]
    }
   ],
   "source": [
    "#%%\n",
    "from sklearn.datasets import load_digits\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "digits = load_digits()\n",
    "X, y = digits.data, digits.target\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n",
    "\n",
    "classifier = LinearSVC(random_state=0).fit(X_train, y_train)\n",
    "y_test_pred = classifier.predict(X_test)\n",
    "\n",
    "print(\"Accuracy: %f\" % classifier.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[42,  0,  0,  0,  1,  0,  0,  0,  0,  0],\n",
       "       [ 0, 36,  0,  0,  0,  0,  0,  0,  0,  1],\n",
       "       [ 0,  0, 38,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0, 44,  0,  1,  0,  0,  1,  0],\n",
       "       [ 0,  0,  0,  0, 55,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  1,  0,  0, 56,  1,  0,  0,  1],\n",
       "       [ 0,  0,  0,  0,  0,  1, 44,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0, 40,  0,  1],\n",
       "       [ 0,  2,  0,  0,  0,  1,  0,  0, 35,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0,  3, 45]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#%%\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'True label')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%%\n",
    "plt.imshow(confusion_matrix(y_test, y_test_pred), cmap=\"coolwarm\", interpolation=\"None\")\n",
    "plt.colorbar()\n",
    "plt.xlabel(\"Predicted label\")\n",
    "plt.xticks(range(10))\n",
    "plt.yticks(range(10))\n",
    "plt.ylabel(\"True label\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.98      0.99        43\n",
      "          1       0.95      0.97      0.96        37\n",
      "          2       0.97      1.00      0.99        38\n",
      "          3       1.00      0.96      0.98        46\n",
      "          4       0.98      1.00      0.99        55\n",
      "          5       0.95      0.95      0.95        59\n",
      "          6       0.98      0.98      0.98        45\n",
      "          7       1.00      0.98      0.99        41\n",
      "          8       0.90      0.92      0.91        38\n",
      "          9       0.94      0.94      0.94        48\n",
      "\n",
      "avg / total       0.97      0.97      0.97       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#%%\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}