WiDS_workshop2018_part3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# PART III: MODEL EVALUATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import pandas as pd # package for data analysis, reading in the data, summarizing data, manipulating data\n",
    "import numpy as np # package for math operations\n",
    "import matplotlib.pyplot as plt # package for plotting\n",
    "from IPython.display import Image\n",
    "import itertools\n",
    "from sklearn import preprocessing, metrics, model_selection, linear_model, ensemble, svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seq_id</th>\n",
       "      <th>hubway_id</th>\n",
       "      <th>status</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_date</th>\n",
       "      <th>strt_statn</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_statn</th>\n",
       "      <th>bike_nr</th>\n",
       "      <th>subsc_type</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>check_out_hour</th>\n",
       "      <th>g1</th>\n",
       "      <th>g2</th>\n",
       "      <th>g3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>Closed</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7/28/2011 10:12:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:12:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00468</td>\n",
       "      <td>1</td>\n",
       "      <td>'97217</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>Closed</td>\n",
       "      <td>220.0</td>\n",
       "      <td>7/28/2011 10:21:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:25:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>1</td>\n",
       "      <td>'02215</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>1</td>\n",
       "      <td>45.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>Closed</td>\n",
       "      <td>56.0</td>\n",
       "      <td>7/28/2011 10:33:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:34:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00456</td>\n",
       "      <td>1</td>\n",
       "      <td>'02108</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>1</td>\n",
       "      <td>68.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>Closed</td>\n",
       "      <td>64.0</td>\n",
       "      <td>7/28/2011 10:35:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:36:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>1</td>\n",
       "      <td>'02116</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>Closed</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7/28/2011 10:37:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:37:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>1</td>\n",
       "      <td>'97214</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   seq_id  hubway_id  status  duration          start_date  strt_statn  \\\n",
       "0       1          8  Closed       9.0  7/28/2011 10:12:00        23.0   \n",
       "1       2          9  Closed     220.0  7/28/2011 10:21:00        23.0   \n",
       "2       3         10  Closed      56.0  7/28/2011 10:33:00        23.0   \n",
       "3       4         11  Closed      64.0  7/28/2011 10:35:00        23.0   \n",
       "4       5         12  Closed      12.0  7/28/2011 10:37:00        23.0   \n",
       "\n",
       "             end_date  end_statn bike_nr  subsc_type zip_code  birth_date  \\\n",
       "0  7/28/2011 10:12:00       23.0  B00468           1   '97217      1976.0   \n",
       "1  7/28/2011 10:25:00       23.0  B00554           1   '02215      1966.0   \n",
       "2  7/28/2011 10:34:00       23.0  B00456           1   '02108      1943.0   \n",
       "3  7/28/2011 10:36:00       23.0  B00554           1   '02116      1981.0   \n",
       "4  7/28/2011 10:37:00       23.0  B00554           1   '97214      1983.0   \n",
       "\n",
       "   gender   age  check_out_hour   g1   g2   g3  \n",
       "0       1  35.0              10  0.0  1.0  0.0  \n",
       "1       1  45.0              10  0.0  1.0  0.0  \n",
       "2       1  68.0              10  0.0  1.0  0.0  \n",
       "3       0  30.0              10  1.0  0.0  0.0  \n",
       "4       0  28.0              10  1.0  0.0  0.0  "
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read in the data\n",
    "X = pd.read_csv('hubway_data_subset.csv', index_col=0)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# MODEL EVALUATION\n",
    "* How well is my model doing - Receiver operating characteristic (ROC) curve\n",
    "* Over-fitting\n",
    "* How to choose model parameters - cross validation\n",
    "* How to choose model class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Dataset - Hubway Bike Rentals\n",
    "* Bike trips taken with variety of metrics collected on the trip\n",
    "* Classification task: classify each bike trip as taken by Registered vs. Casual user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "le = preprocessing.LabelEncoder() # specify parameters for encoder\n",
    "X['subsc_type'] = le.fit_transform(X['subsc_type']) # fit the model and transform the subscription type variable\n",
    "# test and train split\n",
    "# we will be using just two features for our task\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X[['duration', 'check_out_hour']], X['subsc_type'], test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# standardize the features\n",
    "scaler = preprocessing.StandardScaler().fit(X_train)\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# METRICS\n",
    "* Area under ROC curve (AUC)\n",
    "* Confusion matrix\n",
    "* For Kaggle competitions: note the metric you are evaluated on and optimize the performance on that specific metric!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Receiver operating characteristic (ROC) curve\n",
    "* Diagnostic ability of classifier\n",
    "* Plots true positive rate (TPR) against the false positive rate (FPR) at various threshold settings\n",
    "$$TPR = \\frac{TP}{TP + FN}$$\n",
    "$$FPR = \\frac{FP}{FP + TN}$$\n",
    "* Area under the ROC curve (AUC) - single number which reflects model's performance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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2zyqWGDb0S6oZwwHnnX05TwjBaKQlc2DefCz7uL3eQ4HtmDDpZhVS5G2uAlECBzq5ES58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9gK6vrynatRuBxGEtkKjb6HMzVJEGRU+UpvSygY2wVQhw5eCmWChN42l938anHz8HLJ4blAKl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8DxaBtMavu4wPOrr76Kt956S83DDz+MX/ziF6p94ZqA1rz44os4efKkXucvdwMATYpr0u4p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eAeNfzcIyMO8DdBo69+WO3mpHU7jhBHDkrO4y6g1eNN1DnpnT1ZWVP7ubPF/SQgqzLpS8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\n/wdLRAQ5fsqreAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 153,
     "metadata": {
      "image/png": {
       "height": 100,
       "width": 500
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='images/roc_thresholds.png', width = 500, height = 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Let's train model (SVM) and evalute its performance using ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f430ff054e0>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hIq1wio6eEZGVwCRVnZSTHZpckngIFo2AwhFQuDiEhkP5loGOyvjA/Plbue++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\nSJKaDApJUtMPul6U64nr1GIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4310058320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = svm.SVC()\n",
    "clf.fit(X_train, y_train)\n",
    "# obtain the learned decision function and evaluate it on the held-out data\n",
    "y_pred_scores = clf.decision_function(X_test)\n",
    "fpr, tpr, thresholds = metrics.roc_curve(y_test, y_pred_scores) # obtain false positive and true positive rates\n",
    "area_under_curve = metrics.auc(fpr, tpr)\n",
    "plt.plot(fpr, tpr, color='darkorange', label='ROC curve (area = %0.2f)' % area_under_curve) # plot ROC curve\n",
    "plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic')\n",
    "plt.legend(loc=\"lower right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Predicted label')"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Q0lNseqvSd0h6zs/Q/Mz5ivbX3zwOwN6H7MEeB767h9W5thMnffpYAGY8/xrL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\nZcyJ2MwsY/8ffFz2/KWuK+0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f430ff32940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = clf.predict(X_test) # predict class labels\n",
    "cnf_matrix = metrics.confusion_matrix(y_test, y_pred) # obtain confusion matrix\n",
    "plt.imshow(cnf_matrix) # plot the matrix\n",
    "plt.colorbar()\n",
    "plt.xticks([0, 1], ['Registered', 'Casual'], rotation=45)\n",
    "plt.yticks([0, 1], ['Registered', 'Casual'])\n",
    "width, height = cnf_matrix.shape\n",
    "for x in range(width):\n",
    "    for y in range(height):\n",
    "        plt.annotate(str(cnf_matrix[x][y]), xy=(y, x), \n",
    "                    horizontalalignment='center', verticalalignment='center', color=\"white\", fontsize = 25)    \n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#  Over-fitting and trade-off between model complexity\n",
    "* Model that corresponds too closely or exactly to a particular set of data\n",
    "* As a result model doesn't generalize well to additional data or predict future observations reliably\n",
    "* Important for Kaggle competitions and machine learning in general"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/1/19/Overfitting.svg/300px-Overfitting.svg.png\" width=\"300\" height=\"300\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url='https://upload.wikimedia.org/wikipedia/commons/thumb/1/19/Overfitting.svg/300px-Overfitting.svg.png',  width = 300, height = 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Let's fit a model, specifying parameters, and see if it over-fits. If the performance on the test (unseen data) is much worse than the performance on the training data, then there is over-fitting.\n",
    "For SVM (support vector machine) classifier, two parameters can be specified that affect complexity of the model:\n",
    "* The C parameter trades off misclassification of training examples against simplicity of the decision surface\n",
    "* Low C makes the decision surface smooth\n",
    "* High C aims at classifying all training examples correctly\n",
    "* The gamma parameter defines how far the influence of a single training example reaches\n",
    "* Low values meaning far and high values meaning close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.5, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_not_overfitted = svm.SVC(C=1, gamma=0.5)\n",
    "clf_not_overfitted.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC evaluated on training data = 0.75\n",
      "AUC evaluated on test data = 0.75\n"
     ]
    }
   ],
   "source": [
    "y_pred_scores_train = clf_not_overfitted.decision_function(X_train)\n",
    "fpr_train, tpr_train, thresholds_train = metrics.roc_curve(y_train, y_pred_scores_train) # obtain false positive and true positive rates\n",
    "area_under_curve_train = metrics.auc(fpr_train, tpr_train) # AUC\n",
    "print('AUC evaluated on training data = %0.2f' % area_under_curve_train)\n",
    "\n",
    "y_pred_scores_test = clf_not_overfitted.decision_function(X_test)\n",
    "fpr_test, tpr_test, thresholds_test = metrics.roc_curve(y_test, y_pred_scores_test) # obtain false positive and true positive rates\n",
    "area_under_curve_test = metrics.auc(fpr_test, tpr_test) # AUC\n",
    "print('AUC evaluated on test data = %0.2f' % area_under_curve_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Here AUC on training and test data are about the same, so there is likely no over-fitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Let's fit the same classifier (SVM) but with different model parameters and see if there is over-fitting. High C and gamma parameters are likely to result in over-fitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=100000, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=10, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_overfitted = svm.SVC(C=100000, gamma=10)\n",
    "clf_overfitted.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC evaluated on training data = 0.84\n",
      "AUC evaluated on test data = 0.71\n"
     ]
    }
   ],
   "source": [
    "y_pred_scores_train = clf_overfitted.decision_function(X_train)\n",
    "fpr_train, tpr_train, thresholds_train = metrics.roc_curve(y_train, y_pred_scores_train) # obtain false positive and true positive rates\n",
    "area_under_curve_train = metrics.auc(fpr_train, tpr_train) # AUC\n",
    "print('AUC evaluated on training data = %0.2f' % area_under_curve_train)\n",
    "\n",
    "y_pred_scores_test = clf_overfitted.decision_function(X_test)\n",
    "fpr_test, tpr_test, thresholds_test = metrics.roc_curve(y_test, y_pred_scores_test) # obtain false positive and true positive rates\n",
    "area_under_curve_test = metrics.auc(fpr_test, tpr_test) # AUC\n",
    "print('AUC evaluated on test data = %0.2f' % area_under_curve_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Here AUC on training data is much better than on test data, meaning that the model with these tuning parameters does not generalize well to unseen test data, so there is over-fitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Cross validation\n",
    "* Assess how the model will generalize to an independent dataset\n",
    "* Helps avoid over-fitting\n",
    "* Aids in choosing model tuning parameters such as the penalty parameters C and gamma in SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "1. Split data into training, validation and test sets\n",
    "2. Use **training set** to learn models under different tuning paramters ($\\lambda$)\n",
    "3. Check the performance of each model (for example by calculating AUC) on the **validation set**\n",
    "4. Choose model and associated tuning parameters that give highest performance on the **validation set**\n",
    "5. Evaluate the final generalization error of the model on the **test set**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# K-fold cross validation\n",
    "* Using a single validation set might be problematic\n",
    "* Could be overfitting on that validation set\n",
    "* Evaluate each model on **multiple** validation sets\n",
    "* However, drawing multiple training and validation sets randomly might mean that samples will appear only in training or only in validation sets\n",
    "* Solution: K-fold cross validation - splits data into $K$ uniformly sized chunks\n",
    "* Use $K-1$ chunks for training and the remaining chunk for validation\n",
    "* All observations are used for both training and validation\n",
    "* Each observation is used for validation exactly once\n",
    "* 5-fold cross validation and 10-fold cross validation are most common\n",
    "* Cross-validation error: model performance averaged over $K$ folds $$CV(\\lambda) = \\frac{1}{K}\\sum_{k=1}^K E_k(\\lambda)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sN/z8YF/nLQzv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Yf7zjisdBmHqzEOK03G4SqMw0qZcbgK47BSZRymo5xlF+Ow0mUc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\n+82wNOhNW/8r8Za1dolXr7hdHHj8b8vVz0T7/N4Hxes/uMOTP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7tF3H/j8N49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\n/z+0tWr7Hk2ljwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 166,
     "metadata": {
      "image/png": {
       "height": 600,
       "width": 800
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='images/kfold.png', width = 800, height = 600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We will fit several SVM classifiers with different C and gamma parameters. Each classifier will be evaluated using 5-fold cross validation. Performance will be measured using AUC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 25 candidates, totalling 125 fits\n",
      "The best parameters are {'C': 10000.0, 'gamma': 0.001} with a score of 0.75\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 125 out of 125 | elapsed: 99.8min finished\n"
     ]
    }
   ],
   "source": [
    "# this will probably take a long time to run\n",
    "C_range = np.logspace(-2, 10, 5) # choose the parameter range for C\n",
    "gamma_range = np.logspace(-9, 3, 5) # choose the parameter range for gamma\n",
    "param_grid = dict(gamma=gamma_range, C=C_range)\n",
    "cv = model_selection.KFold(n_splits=5) # choose 5-fold cross validation\n",
    "grid = model_selection.GridSearchCV(svm.SVC(), param_grid=param_grid, cv=cv, scoring = 'roc_auc', verbose=1) # evaluate every combo\n",
    "grid.fit(X_train, y_train)\n",
    "\n",
    "print(\"The best parameters are %s with a score of %0.2f\"\n",
    "      % (grid.best_params_, grid.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42ccc3a940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = grid.cv_results_['mean_test_score'].reshape(len(C_range),len(gamma_range))\n",
    "plt.imshow(scores, interpolation='nearest', cmap=plt.cm.hot)\n",
    "plt.xlabel('gamma')\n",
    "plt.ylabel('C')\n",
    "plt.xticks(np.arange(len(gamma_range)), gamma_range, rotation=45)\n",
    "plt.yticks(np.arange(len(C_range)), C_range)\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_label('Cross Validation AUC')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "What is the performance of the chosen model on the **test set**?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC evaluated on test data = 0.74\n"
     ]
    }
   ],
   "source": [
    "clf = svm.SVC(C = grid.best_params_['C'], gamma = grid.best_params_['gamma'])\n",
    "clf.fit(X_train, y_train)\n",
    "# obtain the learned decision function and evaluate it on the held-out data\n",
    "y_pred_scores = clf.decision_function(X_test)\n",
    "# obtain false positive and true positive rates\n",
    "fpr, tpr, thresholds = metrics.roc_curve(y_test, y_pred_scores)\n",
    "area_under_curve = metrics.auc(fpr, tpr)\n",
    "print('AUC evaluated on test data = %0.2f' % area_under_curve)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Choosing Model Classes\n",
    "* How to choose which model class to use\n",
    "* Logistic regression, SVM, Random forest, neural net\n",
    "* Depends on what you want to do with a model afterwards\n",
    "* Best prediction performance\n",
    "* Interpretability of learned model parameters\n",
    "* Uncertainty estimates/confidence intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"http://scikit-learn.org/stable/_static/ml_map.png\" width=\"800\" height=\"500\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url='http://scikit-learn.org/stable/_static/ml_map.png', width = 800, height = 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Schedule\n",
    "- **5:00pm-6:00pm** Dinner and Team Formation\n",
    "- **6:00pm-8:30pm** Datathon\n",
    "  - Register your team\n",
    "  - Download the data\n",
    "  - Perform exploratory data analysis\n",
    "  - Implement simple models\n",
    "  \n",
    "- **9:00pm-9:30pm** Teams progress report\n",
    "   - Each team gives a 5 minute report. Highlight important processes, insights and conclusions. \n",
    "   - Make at least 1 slide. "
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python [conda env:py36]",
   "language": "python",
   "name": "conda-env-py36-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
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