06-model-evaluation-and-improvement.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "from preamble import *\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['legend.numpoints'] = 1\n",
    "mpl.rcParams['savefig.bbox'] = \"tight\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model evaluation and improvement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.88"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# create a synthetic dataset\n",
    "X, y = make_blobs(random_state=0)\n",
    "# split data and labels into a training and a test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "# Instantiate a model and fit it to the training set\n",
    "logreg = LogisticRegression().fit(X_train, y_train)\n",
    "# evaluate the model on the test set\n",
    "logreg.score(X_test, y_test)\n",
    "# we predicted the correct class on 88% of the samples in X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(12, 2))\n",
    "plt.title(\"cross_validation\")\n",
    "axes = plt.gca()\n",
    "axes.set_frame_on(False)\n",
    "\n",
    "n_folds = 5\n",
    "n_samples = 25\n",
    "\n",
    "n_samples_per_fold = n_samples / float(n_folds)\n",
    "\n",
    "\n",
    "for i in range(n_folds):\n",
    "    colors = [\"w\"] * n_folds\n",
    "    colors[i] = \"grey\"\n",
    "    plt.barh(bottom=range(n_folds), width=[n_samples_per_fold - 0.1] * n_folds,\n",
    "             left=i * n_samples_per_fold, height=.6, color=colors, hatch=\"//\")\n",
    "axes.invert_yaxis()\n",
    "axes.set_xlim(0, n_samples + 1)\n",
    "plt.ylabel(\"CV iterations\")\n",
    "plt.xlabel(\"Data points\")\n",
    "plt.xticks(np.arange(n_samples_per_fold / 2., n_samples, n_samples_per_fold), [\"Fold %d\" % x for x in range(1, n_folds + 1)])\n",
    "plt.yticks(np.arange(n_folds) + .3, [\"Split %d\" % x for x in range(1, n_folds + 1)])\n",
    "plt.legend(['Training data', 'Test data'], loc=(1.05, 0.4), frameon=False);\n",
    "plt.savefig(\"images/06_cross_validation2.png\")\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validation in scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cross-validation scores:  [ 0.961  0.922  0.958]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "iris = load_iris()\n",
    "logreg = LogisticRegression()\n",
    "\n",
    "scores = cross_val_score(logreg, iris.data, iris.target)\n",
    "print(\"cross-validation scores: \", scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.   ,  0.967,  0.933,  0.9  ,  1.   ])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = cross_val_score(logreg, iris.data, iris.target, cv=5)\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96000000000000019"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benefits of cross-validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stratified K-Fold cross-validation and other strategies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "print(iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "# FIXME HIDE! FIXME LEGEND\n",
    "fig, both_axes = plt.subplots(2, 1, figsize=(12, 5))\n",
    "#plt.title(\"cross_validation_not_stratified\")\n",
    "axes = both_axes[0]\n",
    "axes.set_title(\"Standard cross-validation with sorted class labels\")\n",
    "\n",
    "axes.set_frame_on(False)\n",
    "\n",
    "n_folds = 3\n",
    "n_samples = 150\n",
    "\n",
    "n_samples_per_fold = n_samples / float(n_folds)\n",
    "\n",
    "\n",
    "for i in range(n_folds):\n",
    "    colors = [\"w\"] * n_folds\n",
    "    colors[i] = \"grey\"\n",
    "    axes.barh(bottom=range(n_folds), width=[n_samples_per_fold - 1] * n_folds, left=i * n_samples_per_fold, height=.6, color=colors, hatch=\"//\")\n",
    "    \n",
    "axes.barh(bottom=[n_folds] * n_folds, width=[n_samples_per_fold - 1] * n_folds, left=np.arange(3) * n_samples_per_fold, height=.6, color=\"w\")\n",
    "\n",
    "axes.invert_yaxis()\n",
    "axes.set_xlim(0, n_samples + 1)\n",
    "axes.set_ylabel(\"CV iterations\")\n",
    "axes.set_xlabel(\"Data points\")\n",
    "axes.set_xticks(np.arange(n_samples_per_fold / 2., n_samples, n_samples_per_fold))\n",
    "axes.set_xticklabels([\"Fold %d\" % x for x in range(1, n_folds + 1)])\n",
    "axes.set_yticks(np.arange(n_folds + 1) + .3)\n",
    "axes.set_yticklabels([\"Split %d\" % x for x in range(1, n_folds + 1)] + [\"Class label\"])\n",
    "for i in range(3):\n",
    "    axes.text((i + .5) * n_samples_per_fold, 3.5, \"Class %d\" % i, horizontalalignment=\"center\")\n",
    "\n",
    "\n",
    "ax = both_axes[1]\n",
    "ax.set_title(\"Stratified Cross-validation\")\n",
    "ax.set_frame_on(False)\n",
    "ax.invert_yaxis()\n",
    "ax.set_xlim(0, n_samples + 1)\n",
    "ax.set_ylabel(\"CV iterations\")\n",
    "ax.set_xlabel(\"Data points\")\n",
    "\n",
    "ax.set_yticks(np.arange(n_folds + 1) + .3)\n",
    "ax.set_yticklabels([\"Split %d\" % x for x in range(1, n_folds + 1)] + [\"Class label\"]);\n",
    "\n",
    "n_subsplit = n_samples_per_fold / 3.\n",
    "for i in range(n_folds):\n",
    "    test_bars = ax.barh(bottom=[i] * n_folds, width=[n_subsplit - 1] * n_folds, left=np.arange(n_folds) * n_samples_per_fold + i * n_subsplit, height=.6, color=\"grey\", hatch=\"//\")\n",
    "\n",
    "w = 2 * n_subsplit - 1\n",
    "ax.barh(bottom=[0] * n_folds, width=[w] * n_folds, left=np.arange(n_folds) * n_samples_per_fold + (0 + 1) * n_subsplit, height=.6, color=\"w\", hatch=\"//\")\n",
    "ax.barh(bottom=[1] * (n_folds + 1), width=[w / 2., w, w, w / 2.], left=np.maximum(0, np.arange(n_folds + 1) * n_samples_per_fold - n_subsplit), height=.6, color=\"w\", hatch=\"//\")\n",
    "training_bars = ax.barh(bottom=[2] * n_folds, width=[w] * n_folds, left=np.arange(n_folds) * n_samples_per_fold , height=.6, color=\"w\", hatch=\"//\")\n",
    "\n",
    "\n",
    "    \n",
    "ax.barh(bottom=[n_folds] * n_folds, width=[n_samples_per_fold - 1] * n_folds, left=np.arange(n_folds) * n_samples_per_fold, height=.6, color=\"w\")\n",
    "\n",
    "for i in range(3):\n",
    "    ax.text((i + .5) * n_samples_per_fold, 3.5, \"Class %d\" % i, horizontalalignment=\"center\")\n",
    "ax.set_ylim(4, -0.1)\n",
    "plt.legend([training_bars[0], test_bars[0]], ['Training data', 'Test data'], loc=(1.05, 1), frameon=False);\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.savefig(\"images/06_stratified_kfold.png\")\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More control over cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "kfold = KFold(n_folds=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.   ,  0.933,  0.433,  0.967,  0.433])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(logreg, iris.data, iris.target, cv=kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kfold = KFold(n_folds=3)\n",
    "cross_val_score(logreg, iris.data, iris.target, cv=kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.9 ,  0.96,  0.96])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kfold = KFold(n_folds=3, shuffle=True, random_state=0)\n",
    "cross_val_score(logreg, iris.data, iris.target, cv=kfold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Leave-One-Out cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of cv iterations:  150\n",
      "mean accuracy:  0.953333333333\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import LeaveOneOut\n",
    "loo = LeaveOneOut()\n",
    "scores = cross_val_score(logreg, iris.data, iris.target, cv=loo)\n",
    "print(\"number of cv iterations: \", len(scores))\n",
    "print(\"mean accuracy: \", scores.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shuffle-Split cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "# FIXME HIDE THIS\n",
    "\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "plt.figure(figsize=(10, 2))\n",
    "plt.title(\"ShuffleSplit with 10 points, train_size=5, test_size=2, n_iter=4\")\n",
    "\n",
    "axes = plt.gca()\n",
    "axes.set_frame_on(False)\n",
    "\n",
    "n_folds = 10\n",
    "n_samples = 10\n",
    "n_iter = 4\n",
    "n_samples_per_fold = 1\n",
    "\n",
    "ss = ShuffleSplit(n_iter=4, train_size=5, test_size=2, random_state=43)\n",
    "mask = np.zeros((n_iter, n_samples))\n",
    "for i, (train, test) in enumerate(ss.split(range(10))):\n",
    "    mask[i, train] = 1\n",
    "    mask[i, test] = 2\n",
    "\n",
    "\n",
    "for i in range(n_folds):\n",
    "    # test is grey\n",
    "    colors = [\"grey\"  if x == 2 else \"white\" for x in mask[:, i]]\n",
    "    # not selected has no hatch\n",
    "    \n",
    "    boxes = axes.barh(bottom=range(n_iter), width=[1 - 0.1] * n_iter, left=i * n_samples_per_fold, height=.6, color=colors, hatch=\"//\")\n",
    "    for j in np.where(mask[:, i] == 0)[0]:\n",
    "        boxes[j].set_hatch(\"\")\n",
    "\n",
    "axes.invert_yaxis()\n",
    "axes.set_xlim(0, n_samples + 1)\n",
    "axes.set_ylabel(\"CV iterations\")\n",
    "axes.set_xlabel(\"Data points\")\n",
    "axes.set_xticks(np.arange(n_samples) + .5)\n",
    "axes.set_xticklabels(np.arange(1, n_samples + 1))\n",
    "axes.set_yticks(np.arange(n_iter) + .3)\n",
    "axes.set_yticklabels([\"Split %d\" % x for x in range(1, n_iter + 1)]);\n",
    "# legend hacked for this random state\n",
    "plt.legend([boxes[1], boxes[0], boxes[2]], [\"Training set\", \"Test set\", \"Not selected\"], loc=(1, .3));\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"images/06_shuffle_split.png\")\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.947,  0.92 ,  0.893,  0.973,  0.893,  0.973,  0.933,  0.96 ,\n",
       "        0.853,  0.907])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import ShuffleSplit\n",
    "shuffle_split = ShuffleSplit(test_size=.5, train_size=.5, n_iter=10)\n",
    "cross_val_score(logreg, iris.data, iris.target, cv=shuffle_split)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validation with groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "# FIXME HIDE THIS\n",
    "from sklearn.model_selection import LabelKFold\n",
    "labels = [0, 0, 0, 1, 1, 1, 1, 2, 2, 3, 3, 3]\n",
    "\n",
    "plt.figure(figsize=(10, 2))\n",
    "plt.title(\"LabelKFold\")\n",
    "\n",
    "axes = plt.gca()\n",
    "axes.set_frame_on(False)\n",
    "\n",
    "n_folds = 12\n",
    "n_samples = 12\n",
    "n_iter = 3\n",
    "n_samples_per_fold = 1\n",
    "\n",
    "cv = LabelKFold(n_folds=3)\n",
    "mask = np.zeros((n_iter, n_samples))\n",
    "for i, (train, test) in enumerate(cv.split(range(12), labels=labels)):\n",
    "    mask[i, train] = 1\n",
    "    mask[i, test] = 2\n",
    "\n",
    "\n",
    "for i in range(n_folds):\n",
    "    # test is grey\n",
    "    colors = [\"grey\"  if x == 2 else \"white\" for x in mask[:, i]]\n",
    "    # not selected has no hatch\n",
    "    \n",
    "    boxes = axes.barh(bottom=range(n_iter), width=[1 - 0.1] * n_iter, left=i * n_samples_per_fold, height=.6, color=colors, hatch=\"//\")\n",
    "    for j in np.where(mask[:, i] == 0)[0]:\n",
    "        boxes[j].set_hatch(\"\")\n",
    "        \n",
    "axes.barh(bottom=[n_iter] * n_folds, width=[1 - 0.1] * n_folds, left=np.arange(n_folds) * n_samples_per_fold, height=.6, color=\"w\")\n",
    "\n",
    "for i in range(12):\n",
    "    axes.text((i + .5) * n_samples_per_fold, 3.5, \"%d\" % labels[i], horizontalalignment=\"center\")\n",
    "#ax.set_ylim(4, -0.1)\n",
    "        \n",
    "axes.invert_yaxis()\n",
    "axes.set_xlim(0, n_samples + 1)\n",
    "axes.set_ylabel(\"CV iterations\")\n",
    "axes.set_xlabel(\"Data points\")\n",
    "axes.set_xticks(np.arange(n_samples) + .5)\n",
    "axes.set_xticklabels(np.arange(1, n_samples + 1))\n",
    "axes.set_yticks(np.arange(n_iter) + .3)\n",
    "axes.set_yticklabels([\"Split %d\" % x for x in range(1, n_iter + 1)] + [\"labels\"]);\n",
    "# legend hacked for this random state\n",
    "plt.legend([boxes[1], boxes[0], boxes[2]], [\"Training set\", \"Test set\"], loc=(1, .3));\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"images/06_label_kfold.png\")\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.75 ,  0.8  ,  0.667])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import LabelKFold\n",
    "# create synthetic dataset\n",
    "X, y = make_blobs(n_samples=12, random_state=0)\n",
    "# assume the first three samples belong to the same group, then the next four etc.\n",
    "labels = [0, 0, 0, 1, 1, 1, 1, 2, 2, 3, 3, 3]\n",
    "cross_val_score(logreg, X, y, labels, cv=LabelKFold(n_folds=3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grid Search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple Grid-Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of training set: 112   size of test set: 38\n",
      "best score:  0.973684210526\n",
      "best parameters:  {'C': 100, 'gamma': 0.001}\n"
     ]
    }
   ],
   "source": [
    "# naive grid search implementation\n",
    "from sklearn.svm import SVC\n",
    "X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=0)\n",
    "print(\"Size of training set: %d   size of test set: %d\" % (X_train.shape[0], X_test.shape[0]))\n",
    "\n",
    "best_score = 0\n",
    "\n",
    "for gamma in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "    for C in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "        # for each combination of parameters\n",
    "        # train an SVC\n",
    "        svm = SVC(gamma=gamma, C=C)\n",
    "        svm.fit(X_train, y_train)\n",
    "        # evaluate the SVC on the test set \n",
    "        score = svm.score(X_test, y_test)\n",
    "        # if we got a better score, store the score and parameters\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            best_parameters = {'C': C, 'gamma': gamma}\n",
    "            \n",
    "print(\"best score: \", best_score)\n",
    "print(\"best parameters: \", best_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97368421052631582"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The danger of overfitting the parameters and the validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(15, 1))\n",
    "axis = plt.gca()\n",
    "bars = axis.barh([0, 0, 0], [11.9, 2.9, 4.9], left=[0, 12, 15], color=['white', 'grey', 'grey'], hatch=\"//\")\n",
    "bars[2].set_hatch(r\"\")\n",
    "axis.set_yticks(())\n",
    "axis.set_frame_on(False)\n",
    "axis.set_ylim(-.1, .8)\n",
    "axis.set_xlim(-0.1, 20.1)\n",
    "axis.set_xticks([6, 13.3, 17.5])\n",
    "axis.set_xticklabels([\"training set\", \"validation set\", \"test set\"], fontdict={'fontsize': 20});\n",
    "axis.tick_params(length=0, labeltop=True, labelbottom=False)\n",
    "axis.text(6, -.3, \"Model fitting\", fontdict={'fontsize': 13}, horizontalalignment=\"center\")\n",
    "axis.text(13.3, -.3, \"Parameter selection\", fontdict={'fontsize': 13}, horizontalalignment=\"center\")\n",
    "axis.text(17.5, -.3, \"Evaluation\", fontdict={'fontsize': 13}, horizontalalignment=\"center\")\n",
    "plt.savefig(\"images/06_threefold_split.png\")\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of training set: 84   size of validation set: 28   size of test set: 38\n",
      "best score on validation set:  0.964285714286\n",
      "best parameters:  {'C': 10, 'gamma': 0.001}\n",
      "test set score with best parameters:  0.921052631579\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "# split data into train+validation set and test set\n",
    "X_trainval, X_test, y_trainval, y_test = train_test_split(iris.data, iris.target, random_state=0)\n",
    "# split train+validation set into training and validation set\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(X_trainval, y_trainval, random_state=1)\n",
    "\n",
    "print(\"Size of training set: %d   size of validation set: %d   size of test set: %d\" % (X_train.shape[0], X_valid.shape[0], X_test.shape[0]))\n",
    "best_score = 0\n",
    "\n",
    "for gamma in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "    for C in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "        # for each combination of parameters\n",
    "        # train an SVC\n",
    "        svm = SVC(gamma=gamma, C=C)\n",
    "        svm.fit(X_train, y_train)\n",
    "        # evaluate the SVC on the test set \n",
    "        score = svm.score(X_valid, y_valid)\n",
    "        # if we got a better score, store the score and parameters\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            best_parameters = {'C': C, 'gamma': gamma}\n",
    "\n",
    "# rebuild a model on the combined training and validation set, and evaluate it on the test set\n",
    "svm = SVC(**best_parameters)\n",
    "svm.fit(X_trainval, y_trainval)\n",
    "test_score = svm.score(X_test, y_test)\n",
    "print(\"best score on validation set: \", best_score)\n",
    "print(\"best parameters: \", best_parameters)\n",
    "print(\"test set score with best parameters: \", test_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grid-search with cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma=0.01, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reference: manual_grid_search_cv\n",
    "for gamma in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "    for C in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "        # for each combination of parameters\n",
    "        # train an SVC\n",
    "        svm = SVC(gamma=gamma, C=C)\n",
    "        # perform cross-validation\n",
    "        scores = cross_val_score(svm, X_trainval, y_trainval, cv=5)\n",
    "        # compute mean cross-validation accuracy\n",
    "        score = np.mean(scores)\n",
    "        # if we got a better score, store the score and parameters\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            best_parameters = {'C': C, 'gamma': gamma}\n",
    "# rebuild a model on the combined training and validation set\n",
    "svm = SVC(**best_parameters)\n",
    "svm.fit(X_trainval, y_trainval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "              'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}\n",
    "grid_search = GridSearchCV(SVC(), param_grid, cv=5)\n",
    "grid_search.fit(X_trainval, y_trainval)\n",
    "scores = grid_search.grid_scores_[15:]\n",
    "\n",
    "best = np.argmax([x.mean_validation_score for x in scores])\n",
    "plt.figure(figsize=(10, 3))\n",
    "plt.xlim(-1, len(scores))\n",
    "plt.ylim(0, 1.1)\n",
    "for i, score in enumerate(scores):\n",
    "    marker_cv, = plt.plot([i] * 5, score.cv_validation_scores, '^', c='gray', markersize=5, alpha=.5)\n",
    "    marker_mean, = plt.plot(i, score.mean_validation_score, 'v', c='none', alpha=1, markersize=10)\n",
    "    if i == best:\n",
    "        marker_best, = plt.plot(i, score.mean_validation_score, 'o', c='red', fillstyle=\"none\", alpha=1, markersize=20, markeredgewidth=3)\n",
    "\n",
    "plt.xticks(range(len(scores)), [str(score.parameters).strip(\"{}\").replace(\"'\", \"\") for score in scores], rotation=90);\n",
    "plt.ylabel(\"validation accuracy\")\n",
    "plt.xlabel(\"parameter settings\")\n",
    "plt.legend([marker_cv, marker_mean, marker_best], [\"cv accuracy\", \"mean accuracy\", \"best parameter setting\"], loc=(1.05, .4))\n",
    "plt.savefig(\"images/06_cross_val_selection.png\")\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "hide_input": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10, 3))\n",
    "axes = plt.gca()\n",
    "axes.yaxis.set_visible(False)\n",
    "axes.xaxis.set_visible(False)\n",
    "axes.set_frame_on(False)\n",
    "#axes.invert_yaxis()\n",
    "def draw(ax, text, start, target=None):\n",
    "    if target is not None:\n",
    "        patchB = target.get_bbox_patch()\n",
    "        end = target.get_position()\n",
    "    else:\n",
    "        end = start\n",
    "        patchB = None\n",
    "    annotation = ax.annotate(text, end, start, xycoords='axes pixels', textcoords='axes pixels', size=20,\n",
    "                arrowprops=dict(arrowstyle=\"-|>\", fc=\"w\", ec=\"k\", patchB=patchB,\n",
    "                               connectionstyle=\"arc3,rad=0.0\"),\n",
    "                bbox=dict(boxstyle=\"round\", fc=\"w\"), horizontalalignment=\"center\", verticalalignment=\"center\")\n",
    "    plt.draw()\n",
    "    return annotation\n",
    "\n",
    "step = 100\n",
    "grr = 400\n",
    "\n",
    "final_evaluation = draw(axes, \"final evaluation\", (5 * step, grr - 3 * step))\n",
    "retrained_model = draw(axes, \"retrained model\", (3 * step, grr - 3 * step), final_evaluation)\n",
    "best_parameters = draw(axes, \"best parameters\", (.5 * step, grr - 3 * step), retrained_model)\n",
    "cross_validation = draw(axes, \"cross validation\", (.5 * step, grr - 2 * step), best_parameters)\n",
    "parameters = draw(axes, \"parameter grid\", (0.0, grr - 0), cross_validation)\n",
    "training_data = draw(axes, \"training data\", (2 * step, grr - step), cross_validation)\n",
    "draw(axes, \"training data\", (2 * step, grr - step), retrained_model)\n",
    "test_data = draw(axes, \"test data\", (5 * step, grr - step), final_evaluation)\n",
    "draw(axes, \"data set\", (3.5 * step, grr - 0.0), training_data)\n",
    "data_set = draw(axes, \"data set\", (3.5 * step, grr - 0.0), test_data)\n",
    "plt.ylim(0, 1)\n",
    "plt.xlim(0, 1.5)\n",
    "plt.savefig(\"images/06_grid_search_overview.png\")\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': [0.001, 0.01, 0.1, 1, 10, 100], 'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "              'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}\n",
    "param_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "grid_search = GridSearchCV(SVC(), param_grid, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100], 'gamma': [0.001, 0.01, 0.1, 1, 10, 100]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=0)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97368421052631582"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 100, 'gamma': 0.01}\n",
      "0.973214285714\n"
     ]
    }
   ],
   "source": [
    "print(grid_search.best_params_)\n",
    "print(grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma=0.01, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing the result of cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[mean: 0.36607, std: 0.01137, params: {'C': 0.001, 'gamma': 0.001},\n",
       " mean: 0.36607, std: 0.01137, params: {'C': 0.001, 'gamma': 0.01},\n",
       " mean: 0.36607, std: 0.01137, params: {'C': 0.001, 'gamma': 0.1},\n",
       " mean: 0.36607, std: 0.01137, params: {'C': 0.001, 'gamma': 1},\n",
       " mean: 0.36607, std: 0.01137, params: {'C': 0.001, 'gamma': 10},\n",
       " mean: 0.36607, std: 0.01137, params: {'C': 0.001, 'gamma': 100},\n",
       " mean: 0.36607, std: 0.01137, params: {'C': 0.01, 'gamma': 0.001},\n",
       " mean: 0.36607, std: 0.01137, params: {'C': 0.01, 'gamma': 0.01},\n",
       " mean: 0.36607, std: 0.01137, params: {'C': 0.01, 'gamma': 0.1},\n",
       " mean: 0.36607, std: 0.01137, params: {'C': 0.01, 'gamma': 1},\n",
       " mean: 0.36607, std: 0.01137, params: {'C': 0.01, 'gamma': 10},\n",
       " mean: 0.36607, std: 0.01137, params: {'C': 0.01, 'gamma': 100},\n",
       " mean: 0.36607, std: 0.01137, params: {'C': 0.1, 'gamma': 0.001},\n",
       " mean: 0.69643, std: 0.01333, params: {'C': 0.1, 'gamma': 0.01},\n",
       " mean: 0.91964, std: 0.04442, params: {'C': 0.1, 'gamma': 0.1},\n",
       " mean: 0.95536, std: 0.03981, params: {'C': 0.1, 'gamma': 1},\n",
       " mean: 0.36607, std: 0.01137, params: {'C': 0.1, 'gamma': 10},\n",
       " mean: 0.36607, std: 0.01137, params: {'C': 0.1, 'gamma': 100},\n",
       " mean: 0.69643, std: 0.01333, params: {'C': 1, 'gamma': 0.001},\n",
       " mean: 0.92857, std: 0.04278, params: {'C': 1, 'gamma': 0.01},\n",
       " mean: 0.96429, std: 0.03405, params: {'C': 1, 'gamma': 0.1},\n",
       " mean: 0.94643, std: 0.03251, params: {'C': 1, 'gamma': 1},\n",
       " mean: 0.91964, std: 0.06507, params: {'C': 1, 'gamma': 10},\n",
       " mean: 0.50893, std: 0.04666, params: {'C': 1, 'gamma': 100},\n",
       " mean: 0.92857, std: 0.04278, params: {'C': 10, 'gamma': 0.001},\n",
       " mean: 0.96429, std: 0.03405, params: {'C': 10, 'gamma': 0.01},\n",
       " mean: 0.96429, std: 0.01793, params: {'C': 10, 'gamma': 0.1},\n",
       " mean: 0.93750, std: 0.04556, params: {'C': 10, 'gamma': 1},\n",
       " mean: 0.91964, std: 0.06507, params: {'C': 10, 'gamma': 10},\n",
       " mean: 0.56250, std: 0.04966, params: {'C': 10, 'gamma': 100},\n",
       " mean: 0.96429, std: 0.03405, params: {'C': 100, 'gamma': 0.001},\n",
       " mean: 0.97321, std: 0.02234, params: {'C': 100, 'gamma': 0.01},\n",
       " mean: 0.95536, std: 0.04983, params: {'C': 100, 'gamma': 0.1},\n",
       " mean: 0.94643, std: 0.05199, params: {'C': 100, 'gamma': 1},\n",
       " mean: 0.91964, std: 0.06507, params: {'C': 100, 'gamma': 10},\n",
       " mean: 0.56250, std: 0.04966, params: {'C': 100, 'gamma': 100}]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.grid_scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x7f12ce1992e8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T7Az3zulyacZfl8XQ3hn0HxxAt+7efUoCpGc6aR1trpxuFW0hPdNz8KjLu/qx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\nqP8HuRihjlUM1YEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12ceb247b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = [score.mean_validation_score for score in grid_search.grid_scores_]\n",
    "scores = np.array(scores).reshape(6, 6)\n",
    "\n",
    "# plot the mean cross-validation scores\n",
    "mglearn.tools.heatmap(scores, xlabel='gamma', ylabel='C', xticklabels=param_grid['gamma'],\n",
    "                      yticklabels=param_grid['C'], cmap=\"viridis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gridsearch_failures\n"
     ]
    },
    {
     "data": {
      "image/png": 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4mAO7hHE0/wSphfnYtIMvEvdxdVTMactPio5jTeK+M6p70p5dViK7mwivaDNjJvry3w0l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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12ce0fb978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(13, 5))\n",
    "\n",
    "param_grid_linear = {'C': np.linspace(1, 2, 6),\n",
    "                     'gamma':  np.linspace(1, 2, 6)}\n",
    "\n",
    "param_grid_one_log = {'C': np.linspace(1, 2, 6),\n",
    "                     'gamma':  np.logspace(-3, 2, 6)}\n",
    "\n",
    "param_grid_range = {'C': np.logspace(-3, 2, 6),\n",
    "                     'gamma':  np.logspace(-7, -2, 6)}\n",
    "\n",
    "for param_grid, ax in zip([param_grid_linear, param_grid_one_log,\n",
    "                           param_grid_range], axes):\n",
    "    grid_search = GridSearchCV(SVC(), param_grid, cv=5)\n",
    "    grid_search.fit(X_train, y_train)\n",
    "    scores = [score.mean_validation_score for score in grid_search.grid_scores_]\n",
    "    scores = np.array(scores).reshape(6, 6)\n",
    "\n",
    "    # plot the mean cross-validation scores\n",
    "    scores_image = mglearn.tools.heatmap(scores, xlabel='gamma', ylabel='C', xticklabels=param_grid['gamma'],\n",
    "                                         yticklabels=param_grid['C'], cmap=\"viridis\", ax=ax)\n",
    "    \n",
    "plt.colorbar(scores_image, ax=axes.tolist())\n",
    "print(\"gridsearch_failures\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using different cross-validation strategies with grid-search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nested cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores:  [ 0.967  1.     0.967  0.967  1.   ]\n",
      "Mean cross-validation score:  0.98\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(GridSearchCV(SVC(), param_grid, cv=5), iris.data, iris.target, cv=5)\n",
    "print(\"Cross-validation scores: \", scores)\n",
    "print(\"Mean cross-validation score: \", scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def nested_cv(X, y, inner_cv, outer_cv, Classifier, parameter_grid):\n",
    "    outer_scores = []\n",
    "    # for each split of the data in the outer cross-validation\n",
    "    # (split method returns indices)\n",
    "    for training_samples, test_samples in outer_cv.split(X, y):\n",
    "        # find best parameter using inner cross-validation:\n",
    "        best_parms = {}\n",
    "        best_score = -np.inf\n",
    "        # iterate over parameters\n",
    "        for parameters in parameter_grid:\n",
    "            # accumulate score over inner splits\n",
    "            cv_scores = []\n",
    "            # iterate over inner cross-validation\n",
    "            for inner_train, inner_test in inner_cv.split(X[training_samples], y[training_samples]):\n",
    "                # build classifier given parameters and training data\n",
    "                clf = Classifier(**parameters)\n",
    "                clf.fit(X[inner_train], y[inner_train])\n",
    "                # evaluate on inner test set\n",
    "                score = clf.score(X[inner_test], y[inner_test])\n",
    "                cv_scores.append(score)\n",
    "            # compute mean score over inner folds\n",
    "            mean_score = np.mean(cv_scores)\n",
    "            if mean_score > best_score:\n",
    "                # if better than so far, remember parameters\n",
    "                best_score = mean_score\n",
    "                best_params = parameters\n",
    "        # build classifier on best parameters using outer training set\n",
    "        clf = Classifier(**best_params)\n",
    "        clf.fit(X[training_samples], y[training_samples])\n",
    "        # evaluate \n",
    "        outer_scores.append(clf.score(X[test_samples], y[test_samples]))\n",
    "    return outer_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.96666666666666667, 1.0, 0.96666666666666667, 0.96666666666666667, 1.0]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import ParameterGrid, StratifiedKFold\n",
    "nested_cv(iris.data, iris.target, StratifiedKFold(5), StratifiedKFold(5), SVC, ParameterGrid(param_grid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Parallelizing cross-validation and grid-search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation Metrics and scoring\n",
    "### Keep the end-goal in mind"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metrics for binary classification\n",
    "#### Kinds of Errors\n",
    "#### Imbalanced datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "\n",
    "digits = load_digits()\n",
    "y = digits.target == 9\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    digits.data, y, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted labels: [False]\n",
      "score: 0.895556\n"
     ]
    }
   ],
   "source": [
    "from sklearn.dummy import DummyClassifier\n",
    "dummy_majority = DummyClassifier(strategy='most_frequent').fit(X_train, y_train)\n",
    "pred_most_frequent = dummy_majority.predict(X_test)\n",
    "print(\"predicted labels: %s\" % np.unique(pred_most_frequent))\n",
    "print(\"score: %f\" % dummy_majority.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9177777777777778"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "tree = DecisionTreeClassifier(max_depth=2).fit(X_train, y_train)\n",
    "pred_tree = tree.predict(X_test)\n",
    "tree.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dummy score: 0.824444\n",
      "logreg score: 0.977778\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "dummy = DummyClassifier().fit(X_train, y_train)\n",
    "pred_dummy = dummy.predict(X_test)\n",
    "print(\"dummy score: %f\" % dummy.score(X_test, y_test))\n",
    "\n",
    "logreg = LogisticRegression(C=0.1).fit(X_train, y_train)\n",
    "pred_logreg = logreg.predict(X_test)\n",
    "print(\"logreg score: %f\" % logreg.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Confusion matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[401   2]\n",
      " [  8  39]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion = confusion_matrix(y_test, pred_logreg)\n",
    "print(confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hdxB6uxXUeMLeji9M5KywjsdgOzYZPCkoBaLGdNddd+GPP/7A22+/nRtEHZPZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12cde02eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"confusion_matrix\")\n",
    "plt.text(0.45, .6, confusion[0, 0], size=70, horizontalalignment='right')\n",
    "plt.text(0.45, .1, confusion[1, 0], size=70, horizontalalignment='right')\n",
    "plt.text(.95, .6, confusion[0, 1], size=70, horizontalalignment='right')\n",
    "plt.text(.95, 0.1, confusion[1, 1], size=70, horizontalalignment='right')\n",
    "plt.xticks([.25, .75], [\"predicted 'not 4'\", \"predicted '4'\"], size=20)\n",
    "plt.yticks([.25, .75], [\"true '4'\", \"true 'not 4'\"], size=20);\n",
    "plt.plot([.5, .5], [0, 1], '--', c='k')\n",
    "plt.plot([0, 1], [.5, .5], '--', c='k')\n",
    "\n",
    "plt.xlim(0, 1)\n",
    "plt.ylim(0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ik6ZzzjmXI0+azrmC8GfPulLkvWerMe8964qZP3vWFSvvPVuDSaowpDvDHzp0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\nnHM58qTpnHPO5ej/A4YM1By6N4tYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12cddf4b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"binary_confusion_matrix_tp_fp\")\n",
    "plt.text(0.45, .6, \"TN\", size=100, horizontalalignment='right')\n",
    "plt.text(0.45, .1, \"FN\", size=100, horizontalalignment='right')\n",
    "plt.text(.95, .6, \"FP\", size=100, horizontalalignment='right')\n",
    "plt.text(.95, 0.1, \"TP\", size=100, horizontalalignment='right')\n",
    "plt.xticks([.25, .75], [\"predicted negative\", \"predicted positive\"], size=15)\n",
    "plt.yticks([.25, .75], [\"positive class\", \"negative class\"], size=15);\n",
    "plt.plot([.5, .5], [0, 1], '--', c='k')\n",
    "plt.plot([0, 1], [.5, .5], '--', c='k')\n",
    "\n",
    "plt.xlim(0, 1)\n",
    "plt.ylim(0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most frequent class:\n",
      "[[403   0]\n",
      " [ 47   0]]\n",
      "\n",
      "Dummy model:\n",
      "[[367  36]\n",
      " [ 40   7]]\n",
      "\n",
      "Decision tree:\n",
      "[[390  13]\n",
      " [ 24  23]]\n",
      "\n",
      "Logistic Regression\n",
      "[[401   2]\n",
      " [  8  39]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Most frequent class:\")\n",
    "print(confusion_matrix(y_test, pred_most_frequent))\n",
    "print(\"\\nDummy model:\")\n",
    "print(confusion_matrix(y_test, pred_dummy))\n",
    "print(\"\\nDecision tree:\")\n",
    "print(confusion_matrix(y_test, pred_tree))\n",
    "print(\"\\nLogistic Regression\")\n",
    "print(confusion_matrix(y_test, pred_logreg))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Relation to accuracy\n",
    "\\begin{equation}\n",
    "\\text{Accuracy} = \\frac{\\text{TP} + \\text{TN}}{\\text{TP} + \\text{TN} + \\text{FP} + \\text{FN}}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Precision, recall and f-score\n",
    "\\begin{equation}\n",
    "\\text{Precision} = \\frac{\\text{TP}}{\\text{TP} + \\text{FP}}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hide_input": false
   },
   "source": [
    "\\begin{equation}\n",
    "\\text{Recall} = \\frac{\\text{TP}}{\\text{TP} + \\text{FN}}\n",
    "\\end{equation}\n",
    "\\begin{equation}\n",
    "\\text{F} = 2 \\cdot \\frac{\\text{precision} \\cdot \\text{recall}}{\\text{precision} + \\text{recall}}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1 score most frequent: 0.00\n",
      "f1 score dummy: 0.16\n",
      "f1 score tree: 0.55\n",
      "f1 score: 0.89\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/andy/checkout/scikit-learn/sklearn/metrics/classification.py:1117: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "print(\"f1 score most frequent: %.2f\" % f1_score(y_test, pred_most_frequent))\n",
    "print(\"f1 score dummy: %.2f\" % f1_score(y_test, pred_dummy))\n",
    "print(\"f1 score tree: %.2f\" % f1_score(y_test, pred_tree))\n",
    "print(\"f1 score: %.2f\" % f1_score(y_test, pred_logreg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "   not nine       0.90      1.00      0.94       403\n",
      "       nine       0.00      0.00      0.00        47\n",
      "\n",
      "avg / total       0.80      0.90      0.85       450\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/andy/checkout/scikit-learn/sklearn/metrics/classification.py:1117: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, pred_most_frequent,\n",
    "                            target_names=[\"not nine\", \"nine\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "   not nine       0.90      0.91      0.91       403\n",
      "       nine       0.16      0.15      0.16        47\n",
      "\n",
      "avg / total       0.82      0.83      0.83       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred_dummy,\n",
    "                            target_names=[\"not nine\", \"nine\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "   not nine       0.98      1.00      0.99       403\n",
      "       nine       0.95      0.83      0.89        47\n",
      "\n",
      "avg / total       0.98      0.98      0.98       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred_logreg,\n",
    "                            target_names=[\"not nine\", \"nine\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Taking uncertainty into account"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "from mglearn.datasets import make_blobs\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X, y = make_blobs(n_samples=(400, 50), centers=2,\n",
    "                  cluster_std=[7.0, 2], random_state=22)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "\n",
    "fig, axes = plt.subplots(2, 3, figsize=(15, 8))\n",
    "plt.suptitle(\"decision_threshold\")\n",
    "axes[0, 0].set_title(\"training data\")\n",
    "axes[0, 0].scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=mglearn.cm2)\n",
    "\n",
    "svc = SVC(gamma=.05).fit(X_train, y_train)\n",
    "axes[0, 1].set_title(\"decision with threshold 0\")\n",
    "axes[0, 1].scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=mglearn.cm2)\n",
    "mglearn.tools.plot_2d_scores(svc, X_train,\n",
    "                             function=\"decision_function\", alpha=.7, ax=axes[0, 1])\n",
    "mglearn.tools.plot_2d_separator(svc, X_train, linewidth=3, ax=axes[0, 1])\n",
    "axes[0, 2].set_title(\"decision with threshold -0.8\")\n",
    "axes[0, 2].scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=mglearn.cm2)\n",
    "mglearn.tools.plot_2d_separator(svc, X_train, linewidth=3, ax=axes[0, 2],\n",
    "                                threshold=-.8)\n",
    "mglearn.tools.plot_2d_scores(svc, X_train,\n",
    "                             function=\"decision_function\", alpha=.7, ax=axes[0, 2])\n",
    "\n",
    "\n",
    "#axes[1, 0].scatter(X_train[mask, 0], np.zeros(bla), c=y_train[mask], cmap=mglearn.cm2, alpha=.1, s=100)\n",
    "axes[1, 0].set_visible(False)\n",
    "\n",
    "mask = np.abs(X_train[:, 1] - 7) < 5\n",
    "bla = np.sum(mask)\n",
    "\n",
    "line = np.linspace(X_train.min(), X_train.max(), 100)\n",
    "axes[1, 1].set_title(\"Cross-section with threshold 0\")\n",
    "axes[1, 1].plot(line, svc.decision_function(np.c_[line, 10 * np.ones(100)]), c='k')\n",
    "contour = (svc.decision_function(np.c_[line, 10 * np.ones(100)]) > 0).reshape(1, -1).repeat(10, axis=0)\n",
    "axes[1, 1].contourf(line, np.linspace(-1.5, 1.5, 10), contour, alpha=0.2, cmap=mglearn.cm2)\n",
    "axes[1, 1].scatter(X_train[mask, 0], np.zeros(bla), c=y_train[mask], cmap=mglearn.cm2, alpha=.1, s=100)\n",
    "axes[1, 1].set_xlim(X_train.min(), X_train.max())\n",
    "axes[1, 1].set_ylim(-1.5, 1.5)\n",
    "axes[1, 1].set_xticks(())\n",
    "axes[1, 1].set_ylabel(\"Decision value\")\n",
    "\n",
    "contour2 = (svc.decision_function(np.c_[line, 10 * np.ones(100)]) > -.8).reshape(1, -1).repeat(10, axis=0)\n",
    "axes[1, 2].set_title(\"Cross-section with threshold -0.8\")\n",
    "axes[1, 2].contourf(line, np.linspace(-1.5, 1.5, 10), contour2, alpha=0.2, cmap=mglearn.cm2)\n",
    "axes[1, 2].scatter(X_train[mask, 0], np.zeros(bla), c=y_train[mask], cmap=mglearn.cm2, alpha=.1, s=100)\n",
    "axes[1, 2].plot(line, svc.decision_function(np.c_[line, 10 * np.ones(100)]), c='k')\n",
    "axes[1, 2].set_xlim(X_train.min(), X_train.max())\n",
    "axes[1, 2].set_ylim(-1.5, 1.5);\n",
    "axes[1, 2].set_xticks(())\n",
    "axes[1, 2].set_ylabel(\"Decision value\")\n",
    "plt.savefig(\"images/06_decision_threshold.png\")\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.97      0.89      0.93       104\n",
      "          1       0.35      0.67      0.46         9\n",
      "\n",
      "avg / total       0.92      0.88      0.89       113\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, svc.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_pred_lower_threshold = svc.decision_function(X_test) > -.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.82      0.90       104\n",
      "          1       0.32      1.00      0.49         9\n",
      "\n",
      "avg / total       0.95      0.83      0.87       113\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_lower_threshold))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Precision-Recall curves and ROC curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "precision, recall, thresholds = precision_recall_curve(y_test,\n",
    "                                                       svc.decision_function(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f12cddfea20>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bEyL2+1q0Jk2Cnj2hVi03RUd+XnyxZK5EF7Y1CKA+kBLwOBVol0/5W4HpIY3I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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12cde352b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a similar dataset as before, but with more samples to get a smoother curve\n",
    "X, y = make_blobs(n_samples=(4000, 500), centers=2, cluster_std=[7.0, 2], random_state=22)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "\n",
    "svc = SVC(gamma=.05).fit(X_train, y_train)\n",
    "\n",
    "precision, recall, thresholds = precision_recall_curve(\n",
    "    y_test, svc.decision_function(X_test))\n",
    "# find threshold closest to zero:\n",
    "close_zero = np.argmin(np.abs(thresholds))\n",
    "plt.plot(precision[close_zero], recall[close_zero], 'o', markersize=10,\n",
    "         label=\"threshold zero\", fillstyle=\"none\", c='k', mew=2)\n",
    "\n",
    "plt.plot(precision, recall, label=\"precision recall curve\")\n",
    "plt.xlabel(\"precision\")\n",
    "plt.ylabel(\"recall\")\n",
    "plt.title(\"precision_recall_curve\");\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Rhd2oiWlupUQhRDW388RO2jVoV+HlnjgBzzwDnTsbC/dd0aULjBlT4acTVcx1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\nCMdxHKcg7iAcx3GcgvwflEt6yNXe128AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12cdcc2860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=100, random_state=0, max_features=2)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# RandomForestClassifier has predict_proba, but not decision_function\n",
    "precision_rf, recall_rf, thresholds_rf = precision_recall_curve(\n",
    "    y_test, rf.predict_proba(X_test)[:, 1])\n",
    "\n",
    "plt.plot(precision, recall, label=\"svc\")\n",
    "\n",
    "plt.plot(precision[close_zero], recall[close_zero], 'o', markersize=10,\n",
    "         label=\"threshold zero svc\", fillstyle=\"none\", c='k', mew=2)\n",
    "\n",
    "plt.plot(precision_rf, recall_rf, label=\"rf\")\n",
    "\n",
    "close_default_rf = np.argmin(np.abs(thresholds_rf - 0.5))\n",
    "plt.plot(precision_rf[close_default_rf], recall_rf[close_default_rf], '^', markersize=10,\n",
    "         label=\"threshold 0.5 rf\", fillstyle=\"none\", c='k', mew=2)\n",
    "plt.xlabel(\"precision\")\n",
    "plt.ylabel(\"recall\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.title(\"precision_recall_comparison\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score of random forest: 0.609756\n",
      "f1_score of svc: 0.655870\n"
     ]
    }
   ],
   "source": [
    "print(\"f1_score of random forest: %f\" % f1_score(y_test, rf.predict(X_test)))\n",
    "print(\"f1_score of svc: %f\" % f1_score(y_test, svc.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average precision of random forest: 0.665737\n",
      "average precision of svc: 0.662636\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import average_precision_score\n",
    "ap_rf = average_precision_score(y_test, rf.predict_proba(X_test)[:, 1])\n",
    "ap_svc = average_precision_score(y_test, svc.decision_function(X_test))\n",
    "print(\"average precision of random forest: %f\" % ap_rf)\n",
    "print(\"average precision of svc: %f\" % ap_svc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Receiver Operating Characteristics (ROC) and AUC\n",
    "\\begin{equation}\n",
    "\\text{FPR} = \\frac{\\text{FP}}{\\text{FP} + \\text{TN}}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f12cd9293c8>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SHdW1MhXJQUREki9eDWI/kdFbD2SfI4BPosvu7hmhR3gwlsNqELpBTkSkcZ32RjldXhIR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\niEggJQgREQmkBCEiIoGUIEREJND/AehmUHt99dvCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12cde592e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, svc.decision_function(X_test))\n",
    "\n",
    "plt.plot(fpr, tpr, label=\"ROC Curve\")\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR (recall)\")\n",
    "plt.title(\"roc_curve\");\n",
    "# find threshold closest to zero:\n",
    "close_zero = np.argmin(np.abs(thresholds))\n",
    "plt.plot(fpr[close_zero], tpr[close_zero], 'o', markersize=10,\n",
    "         label=\"threshold zero\", fillstyle=\"none\", c='k', mew=2)\n",
    "plt.legend(loc=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f12cd91aeb8>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eqw899FCRbQwZMkSff/55VVXds2ePDh8+XJs0aaJxcXE6c+bMEvfdoEEDXbt2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\nJQhjjDEeWYIwxhjj0f8HRqzjYiOepisAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12cde2ab70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "fpr_rf, tpr_rf, thresholds_rf = roc_curve(y_test, rf.predict_proba(X_test)[:, 1])\n",
    "\n",
    "plt.plot(fpr, tpr, label=\"ROC Curve SVC\")\n",
    "plt.plot(fpr_rf, tpr_rf, label=\"ROC Curve RF\")\n",
    "\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR (recall)\")\n",
    "plt.title(\"roc_curve_comparison\");\n",
    "plt.plot(fpr[close_zero], tpr[close_zero], 'o', markersize=10,\n",
    "         label=\"threshold zero SVC\", fillstyle=\"none\", c='k', mew=2)\n",
    "close_default_rf = np.argmin(np.abs(thresholds_rf - 0.5))\n",
    "plt.plot(fpr_rf[close_default_rf], tpr[close_default_rf], '^', markersize=10,\n",
    "         label=\"threshold 0.5 RF\", fillstyle=\"none\", c='k', mew=2)\n",
    "\n",
    "plt.legend(loc=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC for Random Forest: 0.936695\n",
      "AUC for SVC: 0.916294\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "rf_auc = roc_auc_score(y_test, rf.predict_proba(X_test)[:, 1])\n",
    "svc_auc = roc_auc_score(y_test, svc.decision_function(X_test))\n",
    "print(\"AUC for Random Forest: %f\" % rf_auc)\n",
    "print(\"AUC for SVC: %f\" % svc_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gamma = 1.00  accuracy = 0.90  AUC = 0.50\n",
      "gamma = 0.05  accuracy = 0.90  AUC = 0.90\n",
      "gamma = 0.01  accuracy = 0.90  AUC = 1.00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f12cd963588>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QZ+dy3xdIRzn/ce4cDB0Kd9wBe/ZY4z17mlFDu3bO5yZEbvz888/c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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12cd9632b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = digits.target == 9\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    digits.data, y, random_state=0)\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "for gamma in [1, 0.05, 0.01]:\n",
    "    svc = SVC(gamma=gamma).fit(X_train, y_train)\n",
    "    accuracy = svc.score(X_test, y_test)\n",
    "    auc = roc_auc_score(y_test, svc.decision_function(X_test))\n",
    "    fpr, tpr, _ = roc_curve(y_test , svc.decision_function(X_test))\n",
    "    print(\"gamma = %.02f  accuracy = %.02f  AUC = %.02f\" % (gamma, accuracy, auc))\n",
    "    plt.plot(fpr, tpr, label=\"gamma=%.03f\" % gamma, linewidth=4)\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR\")\n",
    "plt.xlim(-0.01, 1)\n",
    "plt.ylim(0, 1.02)\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-class classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.953\n",
      "confusion matrix:\n",
      "[[37  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 39  0  0  0  0  2  0  2  0]\n",
      " [ 0  0 41  3  0  0  0  0  0  0]\n",
      " [ 0  0  1 43  0  0  0  0  0  1]\n",
      " [ 0  0  0  0 38  0  0  0  0  0]\n",
      " [ 0  1  0  0  0 47  0  0  0  0]\n",
      " [ 0  0  0  0  0  0 52  0  0  0]\n",
      " [ 0  1  0  1  1  0  0 45  0  0]\n",
      " [ 0  3  1  0  0  0  0  0 43  1]\n",
      " [ 0  0  0  1  0  1  0  0  1 44]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    digits.data, digits.target, random_state=0)\n",
    "lr = LogisticRegression().fit(X_train, y_train)\n",
    "pred = lr.predict(X_test)\n",
    "print(\"accuracy: %0.3f\" % accuracy_score(y_test, pred))\n",
    "print(\"confusion matrix:\")\n",
    "print(confusion_matrix(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zLEKRGAzDNX369GHXrl2sW7euTAth2rRp9OnThwsvvJBdu3Yxbty4JEaZOEKR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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12ce23c4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores_image = mglearn.tools.heatmap(confusion_matrix(y_test, pred), xlabel='Predicted label', ylabel='True label',\n",
    "                                     xticklabels=digits.target_names, yticklabels=digits.target_names,\n",
    "                                     cmap=plt.cm.gray_r, fmt=\"%d\")    \n",
    "plt.title(\"Confusion matrix\")\n",
    "plt.gca().invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00        37\n",
      "          1       0.89      0.91      0.90        43\n",
      "          2       0.95      0.93      0.94        44\n",
      "          3       0.90      0.96      0.92        45\n",
      "          4       0.97      1.00      0.99        38\n",
      "          5       0.98      0.98      0.98        48\n",
      "          6       0.96      1.00      0.98        52\n",
      "          7       1.00      0.94      0.97        48\n",
      "          8       0.93      0.90      0.91        48\n",
      "          9       0.96      0.94      0.95        47\n",
      "\n",
      "avg / total       0.95      0.95      0.95       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "micro average f1 score: 0.953333\n",
      "macro average f1 score: 0.954000\n"
     ]
    }
   ],
   "source": [
    "print(\"micro average f1 score: %f\" % f1_score(y_test, pred, average=\"micro\"))\n",
    "print(\"macro average f1 score: %f\" % f1_score(y_test, pred, average=\"macro\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using evaluation metrics in model selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default scoring  [ 0.9  0.9  0.9]\n",
      "explicit accuracy scoring  [ 0.9  0.9  0.9]\n",
      "AUC scoring  [ 0.994  0.99   0.996]\n"
     ]
    }
   ],
   "source": [
    "# default scoring for classification is accuracy\n",
    "print(\"default scoring \", cross_val_score(SVC(), digits.data, digits.target == 9))\n",
    "# providing scoring=\"accuracy\" doesn't change the results\n",
    "explicit_accuracy =  cross_val_score(SVC(), digits.data, digits.target == 9, scoring=\"accuracy\")\n",
    "print(\"explicit accuracy scoring \", explicit_accuracy)\n",
    "roc_auc =  cross_val_score(SVC(), digits.data, digits.target == 9, scoring=\"roc_auc\")\n",
    "print(\"AUC scoring \", roc_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid-Search with accuracy\n",
      "Best parameters: {'gamma': 0.0001}\n",
      "Best cross-validation score (accuracy)): 0.970304380104\n",
      "Test set AUC: 0.992\n",
      "Test set accuracy 0.973: \n",
      "\n",
      "Grid-Search with AUC\n",
      "Best parameters: {'gamma': 0.01}\n",
      "Best cross-validation score (AUC): 0.997467845028\n",
      "Test set AUC: 1.000\n",
      "Test set accuracy 1.000: \n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    digits.data, digits.target == 9, random_state=0)\n",
    "\n",
    "# we provide a somewhat bad grid to illustrate the point:\n",
    "param_grid = {'gamma': [0.0001, 0.01, 0.1, 1, 10]}\n",
    "# using the default scoring of accuracy:\n",
    "grid = GridSearchCV(SVC(), param_grid=param_grid)\n",
    "grid.fit(X_train, y_train)\n",
    "print(\"Grid-Search with accuracy\")\n",
    "print(\"Best parameters:\", grid.best_params_)\n",
    "print(\"Best cross-validation score (accuracy)):\", grid.best_score_)\n",
    "print(\"Test set AUC: %.3f\" % roc_auc_score(y_test, grid.decision_function(X_test)))\n",
    "print(\"Test set accuracy %.3f: \" % grid.score(X_test, y_test))\n",
    "\n",
    "# using AUC scoring instead:\n",
    "grid = GridSearchCV(SVC(), param_grid=param_grid, scoring=\"roc_auc\")\n",
    "grid.fit(X_train, y_train)\n",
    "print(\"\\nGrid-Search with AUC\")\n",
    "print(\"Best parameters:\", grid.best_params_)\n",
    "print(\"Best cross-validation score (AUC):\", grid.best_score_)\n",
    "print(\"Test set AUC: %.3f\" % roc_auc_score(y_test, grid.decision_function(X_test)))\n",
    "print(\"Test set accuracy %.3f: \" % grid.score(X_test, y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['accuracy', 'adjusted_rand_score', 'average_precision', 'f1', 'f1_macro', 'f1_micro', 'f1_samples', 'f1_weighted', 'log_loss', 'mean_absolute_error', 'mean_squared_error', 'median_absolute_error', 'precision', 'precision_macro', 'precision_micro', 'precision_samples', 'precision_weighted', 'r2', 'recall', 'recall_macro', 'recall_micro', 'recall_samples', 'recall_weighted', 'roc_auc']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics.scorer import SCORERS\n",
    "print(sorted(SCORERS.keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary and outlook"
   ]
  }
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