CH2/templates/05_sklearn.metrics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## sklearn.metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "документация: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import cross_validation, datasets, linear_model, metrics \n",
    "from matplotlib.colors import ListedColormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: pylab import has clobbered these variables: ['colors']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Генерация датасетов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clf_data, clf_target = datasets.make_classification(n_features = 2, n_informative = 2, n_classes = 2, \n",
    "                                                    n_redundant = 0, n_clusters_per_class = 1, \n",
    "                                                    random_state = 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reg_data, reg_target = datasets.make_regression(n_features = 2, n_informative = 1, n_targets = 1, \n",
    "                                                noise = 5., random_state = 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f8f89b5ed90>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FfivwEbSPTJ5c+X7ZC7Np0yYZMOAh6dfvAcumxLlcLrn11rvF4agrUVHtJCam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\nP7pwTCeGReKsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f8c3f4110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ListedColormap(['red', 'blue'])\n",
    "pylab.scatter(map(lambda x: x[0], clf_data), map(lambda x: x[1], clf_data), c = clf_target, cmap = colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f8f89aa37d0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VClMbfsnaT76OoSFTn/0UG43VYC2gBt/ci70bZLufGGFVyJERuP/+uVF9fJw1\nf/zP2b3b8lTBj02ate88zfqpRul0qeYv4cNLofuavzRIo/bdYhB1ZgepZa4rc8FohK90L+zTCPC+\n98Hhw433zVAeIatBLskFxvMWSJWaip/SPtKdVl1EfvnL8PtnpAUxy10ak2rnzmPvHjVKZ4OCv4R/\nGufPh5tumlvrh0x0fM9D0Eti6oo8BtIijKUoAgV/af1pbI4qAAsXZqIFMY9BLw55DKR5apQuMgV/\nCf803nMPHDvWeL+FC4NBXxlIKOcx6MUhr4E0h8M/CkcNvjKrufU0yVbLPmS8eInKasO3pEO9fWRw\nGY8qGS+eSCoU/EVESkhdPUVEJJSCv4hICSn4F4EmSSkFnWaJkoJ/3vUzzFVRJFcqFbj99uyOZpZ8\nUoNvnvUzSUreJoIpuakpuO46OHSocbvmwpF21OBbdL0Oc83DnAhyUu10NQd+KOdoZonWQMHfzH7b\nzH5gZsfN7IKm2z5qZrvMbKeZXVa3/QIze8rMfmRmnxnk9Uuv12GumhMhV8JOV00ZRzNLtAat+T8N\nvAv4dv1GM1sOXA0sB94JfM7Maj9B/gpY6+7nAeeZ2eoBy1BevY7t15wIuRJ2uiA/UzhItg0U/N39\nh+6+C2jOLV0BfNndj7n7DLALuMjMzgAWu/vW6v2+CFw5SBlKr5dJUvI6EUxJNZ+ukRH4kz/RXDgS\njQUxPe9S4Lt115+vbjsGPFe3/bnqdhnE+PjcAN5q3oM1a+DSSzUnQk4Mcro09YW007Hmb2aPVXP0\ntcvT1b+/mUQBy65lr8x23TU7df9MYqJ5iUw/pyvLC91INnSs+bv7qj6e93ngrLrrZ1a3tdre0vr1\n60/+Pzk5yeTkZB/FyaeWvTLbddes79FT6wK6dm1QfVSwLwW9Bcpnenqa6enpnh4TST9/M3sc+LC7\nP1G9fj7wJeCtBGmdx4DXu7ub2feADwJbgf8KfNbdH27xvKXt59+yC/8TLzJ+4dmt+/Zv3RpU9/bt\nm719bCxoE1i5MrkdkNToLSCx9/M3syvN7FngbcDfm9m3ANx9B/AgsAN4CPhAXRT/Q2Aj8CNgV6vA\nX3Yte2Vu2dO+u6Z69JSe3gLSDY3wzai+a/6gVU5Eb4GS03z+OdfyA9zNJ1tdPUpPb4HyUvAvgJYf\nYH2yRaQFBX8RkRLSxG55ommWpU9660g/FPyzQCNypE9660i/lPZJ286dsGIFHD48u02TtZdOP004\n/SznIOXho3nYAAAFk0lEQVSgtE/WNP8+n5qaG/hB0yyXTL+1d83QPbgyp8wU/JPS/AnfsCHortkc\n+EEjckpkkPV1NJhrMGVPmSn4JyHsE37TTbAgZGqlhQs1zXKJDFJ71wzd/dOidvFN6Sz1ap/w+uTs\n0NDcatvChbB9OyxfnmjxJD2D1t41Q3d/Wn0kZ2bKcwxV809C2Cf8+HG4557GatsXvqDAXzJR1N41\nQ3fvlDJTb5/ktJqSQSN1Bb0N0lDk+Y80wjdr9AkXyZSifiQV/LOkqO+ymOmwifRO/fyzoux9yvqk\nwyYSH9X846ZhmH3RYRPpn2r+WaBhmH3RYROJl4J/3NSnrC86bCLxUvCPm4Zh9kWHTSReyvknRd1W\n+qLDJtI7dfUUESkhNfiKiEgoBX8RkRJS8BcRKSEFfxGRElLwFxEpIQV/EZESUvAXESmhgYK/mf22\nmf3AzI6b2QV125eZ2Stmtq16+VzdbReY2VNm9iMz+8wgry8iIv0ZtOb/NPAu4Nsht/3Y3S+oXj5Q\nt/2vgLXufh5wnpmtHrAMuTU9PZ12EWJT5H0D7V/eFX3/ujFQ8Hf3H7r7LiBsJNmcbWZ2BrDY3bdW\nN30RuHKQMuRZkd+ARd430P7lXdH3rxtx5vwnqimfx83s7dVtS4Hn6u7zXHWbiIgkaEGnO5jZY8Br\n6zcBDtzq7t9s8bD/B5zt7nurbQH/xczOH7i0IiISiUgmdjOzx4EPufu2drcTfCk87u7Lq9uvAS52\n93/f4nGa1U1EpA+dJnbrWPPvwckXMrMlwC/c/YSZvQ44F/iJu79kZvvM7CJgK/Bvgc+2esJOhRcR\nkf4M2tXzSjN7Fngb8Pdm9q3qTf8CeMrMtgEPAte7+0vV2/4Q2Aj8CNjl7g8PUgYREeldpufzFxGR\neGR6hK+Z/Scz+19mtt3MHq52FS0MM/u0me00syfN7KtmNpZ2maLUahBg3pnZ5Wb2THWg4kfSLk+U\nzGyjmb1gZk+lXZaomdmZZvYPZva/zexpM/tg2mWKkpktNLPvV+Pl02a2ru39s1zzN7NF7r6/+v+N\nwPmtGofzyMwuBf6h2jZyJ+Du/tG0yxUVM3sDcALYAHy4VYeAPDGzeQQpy3cQdGDYClzj7s+kWrCI\nVLtl7we+6O5vSrs8UapWHs9w9yfNbBHwBHBFUc4dgJmd4u6vmNl84H8AH3T3LWH3zXTNvxb4q04l\nCCSF4e6b3b22T98DzkyzPFHrMAgwry4iaKva7e5HgS8DV6Rcpsi4+3eAvWmXIw7u/jN3f7L6/35g\nJwUbZ+Tur1T/XUjQoadl7T7TwR/AzG43s58C1wKfSLs8MboO+FbHe0nalgLP1l3XQMUcMrMJ4M3A\n99MtSbTMbJ6ZbQd+BjxWN5vCHKkHfzN7rDrRW+3ydPXvbwK4+x+7+9nAl4Ab0y1t7zrtX/U+twJH\n3X1TikXtSzf7J5Il1ZTP3wI3NWUXcs/dT7j7CoIswlvbDa6Nsp9/X9x9VZd33QQ8BKyPrzTR67R/\nZvZe4DeAf5lIgSLWw/kriueBs+uun1ndJjlgZgsIAv9/dve/S7s8cXH3X1YH114O7Ai7T+o1/3bM\n7Ny6q1cS5OgKw8wuB/4D8Fvufjjt8sSsKHn/rcC51WnLh4FrgG+kXKaoGcU5X83uA3a4+z1pFyRq\nZrbEzE6r/j8KrAJaNmZnvbfP3wLnETT07gbe7+7/lG6pomNmu4Bh4OfVTd9rmv4618zsSuDPgSXA\nS8CT7v7OdEs1uOqX9j0ElaeN7n5nykWKjJltAiaBVwMvAOvc/QupFioiZvbrwH8nmIreq5ePFWWg\nqZn9GvAAwftyHvAVd7+j5f2zHPxFRCQemU77iIhIPBT8RURKSMFfRKSEFPxFREpIwV9EpIQU/EVE\nSkjBX0SkhBT8RURK6P8DMVSKardloRoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f89b811d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.scatter(map(lambda x:x[1], reg_data), reg_target, color = 'r')\n",
    "pylab.scatter(map(lambda x:x[0], reg_data), reg_target, color = 'b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clf_train_data, clf_test_data, clf_train_labels, clf_test_labels = cross_validation.train_test_split(clf_data, clf_target,\n",
    "                                                                                     test_size = 0.3, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reg_train_data, reg_test_data, reg_train_labels, reg_test_labels = cross_validation.train_test_split(reg_data, reg_target,\n",
    "                                                                                     test_size = 0.3, random_state = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Метрики качества в задачах классификации"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Обучение модели классификации"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classifier = linear_model.SGDClassifier(loss = 'log', random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "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='log', n_iter=5, n_jobs=1,\n",
       "       penalty='l2', power_t=0.5, random_state=1, shuffle=True, verbose=0,\n",
       "       warm_start=False)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(clf_train_data, clf_train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predictions = classifier.predict(clf_test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "probability_predictions = classifier.predict_proba(clf_test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 0 0 1 0 0 0 1 0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1 0 0 0 1 1]\n"
     ]
    }
   ],
   "source": [
    "print clf_test_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 0 0 1 0 1 0 1 0 0 1 1 0 1 0 0 0 1 1 0 0 1 1 1 0 0 0 1 1]\n"
     ]
    }
   ],
   "source": [
    "print predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30, 2)\n"
     ]
    }
   ],
   "source": [
    "print probability_predictions.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.83333333333333337"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum([1. if pair[0] == pair[1] else 0. for pair in zip(clf_test_labels, predictions)])/len(clf_test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.83333333333333337"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(clf_test_labels, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[12  2]\n",
      " [ 3 13]]\n"
     ]
    }
   ],
   "source": [
    "matrix = metrics.confusion_matrix(clf_test_labels, predictions)\n",
    "print matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum([1 if pair[0] == pair[1] else 0 for pair in zip(clf_test_labels, predictions)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix.diagonal().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### precision "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.80000000000000004"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.precision_score(clf_test_labels, predictions, pos_label = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8666666666666667"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.precision_score(clf_test_labels, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8571428571428571"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.recall_score(clf_test_labels, predictions, pos_label = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8125"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.recall_score(clf_test_labels, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.82758620689655182"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.f1_score(clf_test_labels, predictions, pos_label = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.83870967741935487"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.f1_score(clf_test_labels, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.80      0.86      0.83        14\n",
      "          1       0.87      0.81      0.84        16\n",
      "\n",
      "avg / total       0.84      0.83      0.83        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print metrics.classification_report(clf_test_labels, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fpr, tpr, _ = metrics.roc_curve(clf_test_labels, probability_predictions[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f2a22c48cd0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LvLi42Dds2FD72plnnlm7nddff72fffbZW733+OOP93vuuac2jjvvvLPeGBv6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\nICISU0oAIiIx9f8BdO/t1jgBYH4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2a22e7fd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(fpr, tpr, label = 'linear model')\n",
    "pylab.plot([0, 1], [0, 1], '--', color = 'grey', label = 'random')\n",
    "pylab.xlim([-0.05, 1.05])\n",
    "pylab.ylim([-0.05, 1.05])\n",
    "pylab.xlabel('False Positive Rate')\n",
    "pylab.ylabel('True Positive Rate')\n",
    "pylab.title('ROC curve')\n",
    "pylab.legend(loc = \"lower right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ROC AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8348214285714286"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.roc_auc_score(clf_test_labels, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9017857142857143"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.roc_auc_score(clf_test_labels, probability_predictions[:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PR AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.88958333333333328"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.average_precision_score(clf_test_labels, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### log_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3284088910578731"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.log_loss(clf_test_labels, probability_predictions[:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Метрики качества в задачах регрессии"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Обучение регрессионной модели "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "regressor = linear_model.SGDRegressor(random_state = 1, n_iter = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDRegressor(alpha=0.0001, average=False, epsilon=0.1, eta0=0.01,\n",
       "       fit_intercept=True, l1_ratio=0.15, learning_rate='invscaling',\n",
       "       loss='squared_loss', n_iter=20, penalty='l2', power_t=0.25,\n",
       "       random_state=1, shuffle=True, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(reg_train_data, reg_train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "reg_predictions = regressor.predict(reg_test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   2.67799047    7.06525927  -56.43389936   10.08001896  -22.46817716\n",
      "  -19.27471232   59.44372825  -21.60494574   32.54682713  -41.89798772\n",
      "  -18.16390935   32.75688783   31.04095773    2.39589626   -5.04783924\n",
      "  -70.20925097   86.69034305   18.50402992   32.31573461 -101.81138022\n",
      "   15.14628858   29.49813932   97.282674     25.88034991  -41.63332253\n",
      "  -92.11198201   86.7177122     2.13250832  -20.24967575  -27.32511755]\n"
     ]
    }
   ],
   "source": [
    "print reg_test_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -1.46503565   5.75776789 -50.13234306   5.05646094 -24.09370893\n",
      "  -8.34831546  61.77254998 -21.98350565  30.65112022 -39.25972497\n",
      " -17.19337022  30.94178225  26.98820076  -6.08321732  -3.46551    -78.9843398\n",
      "  84.80190097  14.80638314  22.91302375 -89.63572717  14.5954632\n",
      "  31.64431951  95.81031534  21.5037679  -43.1101736  -95.06972123\n",
      "  86.70086546   0.47837761 -16.44594704 -22.72581879]\n"
     ]
    }
   ],
   "source": [
    "print reg_predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### mean absolute error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.748761311885298"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_absolute_error(reg_test_labels, reg_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### mean squared error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.114925597460914"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_squared_error(reg_test_labels, reg_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### root mean squared error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.9106950218335603"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sqrt(metrics.mean_squared_error(reg_test_labels, reg_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### r2 score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.98931761505469495"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(reg_test_labels, reg_predictions)"
   ]
  }
 ],
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