{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 4 - Evaluation and Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We generate two inputs:\n",
    "* features – a matrix of input features\n",
    "* target – an array of target variables corresponding to those features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "features = rand(100,5)\n",
    "target = rand(100) > 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The holdout method\n",
    "\n",
    "We divide into a randomized training and test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/henrik/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:10: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "/Users/henrik/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:11: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
     ]
    }
   ],
   "source": [
    "N = features.shape[0]\n",
    "N_train = floor(0.7 * N)\n",
    "\n",
    "# Randomize index\n",
    "# Note: sometimes you want to retain the order in the dataset and skip this step\n",
    "# E.g. in the case of time-based datasets where you want to test on 'later' instances\n",
    "idx = random.permutation(N)\n",
    "\n",
    "# Split index\n",
    "idx_train = idx[:N_train]\n",
    "idx_test = idx[N_train:]\n",
    "\n",
    "# Break your data into training and testing subsets\n",
    "features_train = features[idx_train,:]\n",
    "target_train = target[idx_train]\n",
    "features_test = features[idx_test,:]\n",
    "target_test = target[idx_test]\n",
    "\n",
    "# Build, predict, evaluate (to be filled out)\n",
    "# model = train(features_train, target_train)\n",
    "# preds_test = predict(model, features_test)\n",
    "# accuracy = evaluate_acc(preds_test, target_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70, 5)\n",
      "(30, 5)\n",
      "(70,)\n",
      "(30,)\n"
     ]
    }
   ],
   "source": [
    "print features_train.shape\n",
    "print features_test.shape\n",
    "print target_train.shape\n",
    "print target_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-fold cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16 23 33 34 35 39 44 48 99]\n",
      "[ 3 10 29 37 45 52 54 62 68 83 98]\n",
      "[ 4 12 20 22 28 50 51 53 55 56 59 60 64 77 84 86 92 97]\n",
      "[ 6  7  9 11 18 27 41 49 57 61 70 73 80]\n",
      "[42 67 96]\n",
      "[30 63 66 78 90 91]\n",
      "[ 2  5 26 40 46 58 72 74 87 95]\n",
      "[ 0  8 14 24 32 36 38 43 85 88 89]\n",
      "[ 1 13 17 19 21 47 71 79 81 82 93 94]\n",
      "[15 25 31 65 69 75 76]\n"
     ]
    }
   ],
   "source": [
    "N = features.shape[0]\n",
    "K = 10 # number of folds\n",
    "\n",
    "preds_kfold = np.empty(N)\n",
    "folds = np.random.randint(0, K, size=N)\n",
    "\n",
    "for idx in np.arange(K):\n",
    "\n",
    "    # For each fold, break your data into training and testing subsets\n",
    "    features_train = features[folds != idx,:]\n",
    "    target_train = target[folds != idx]\n",
    "    features_test = features[folds == idx,:]\n",
    "    \n",
    "    # Print the indices in each fold, for inspection\n",
    "    print nonzero(folds == idx)[0]\n",
    "\n",
    "    # Build and predict for CV fold (to be filled out)\n",
    "    # model = train(features_train, target_train)\n",
    "    # preds_kfold[folds == idx] = predict(model, features_test)\n",
    "    \n",
    "# accuracy = evaluate_acc(preds_kfold, target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def roc_curve(true_labels, predicted_probs, n_points=100, pos_class=1):\n",
    "    thr = linspace(0,1,n_points)\n",
    "    tpr = zeros(n_points)\n",
    "    fpr = zeros(n_points)\n",
    "\n",
    "    pos = true_labels == pos_class\n",
    "    neg = logical_not(pos)\n",
    "    n_pos = count_nonzero(pos)\n",
    "    n_neg = count_nonzero(neg)\n",
    "      \n",
    "    for i,t in enumerate(thr):\n",
    "        tpr[i] = count_nonzero(logical_and(predicted_probs >= t, pos)) / n_pos\n",
    "        fpr[i] = count_nonzero(logical_and(predicted_probs >= t, neg)) / n_neg\n",
    "    \n",
    "    return fpr, tpr, thr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10595f150>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XH7vdbuF2u2c0WS0LBoXYvl2IxkYhdu0SYt06jmAAfJXX6xVerzfu+0Tc3CVJkqO92eHD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\n0P8DN1xo2SdDYJYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1058bdb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Randomly generated predictions should give us a diagonal ROC curve\n",
    "preds = rand(len(target))\n",
    "fpr, tpr, thr = roc_curve(target, preds, pos_class=True)\n",
    "plot(fpr, tpr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The area under the ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def auc(true_labels, predicted_labels, pos_class=1):\n",
    "    fpr, tpr, thr = roc_curve(true_labels, predicted_labels,\n",
    " pos_class=pos_class)\n",
    "    area = -trapz(tpr, x=fpr)\n",
    "    return area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auc(target, preds, pos_class=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-class classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d = pandas.read_csv(\"data/mnist_small.csv\")\n",
    "d_train = d[:int(0.8*len(d))]\n",
    "d_test = d[int(0.8*len(d)):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(d_train.drop('label', axis=1), d_train['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "preds = rf.predict(d_test.drop('label', axis=1))\n",
    "cm = confusion_matrix(d_test['label'], preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EF0CkMG0GECmhR14sgwNAic9F1z/44IMtwcHBleHh4Vdmzpx5rL6+3s1S/wgvACU+F10f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\na92hkfIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cf8b290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matshow(cm, cmap='Greys')\n",
    "colorbar()\n",
    "savefig(\"figures/figure-4.19.eps\", format='eps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The root-mean-square error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def rmse(true_values, predicted_values):\n",
    "    n = len(true_values)\n",
    "    residuals = 0\n",
    "    for i in range(n):\n",
    "        residuals += (true_values[i] - predicted_values[i])**2.\n",
    "    return np.sqrt(residuals/n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.26547176572681108"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse(rand(10), rand(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The R-squared error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def r2(true_values, predicted_values):\n",
    "    n = len(true_values)\n",
    "    mean = np.mean(true_values)\n",
    "    residuals = 0\n",
    "    total = 0\n",
    "    for i in range(n):\n",
    "        residuals += (true_values[i] - predicted_values[i])**2.\n",
    "        total += (true_values[i] - mean)**2.\n",
    "    return 1.0 - residuals/total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.98002618717502987"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2(arange(10)+rand(), arange(10)+rand(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grid search with kernel-SVM model\n",
    "\n",
    "Importing modules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading data and performang poor-mans feature engineering:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d = pandas.read_csv(\"data/titanic.csv\")\n",
    "\n",
    "# Target\n",
    "y = d[\"Survived\"]\n",
    "\n",
    "# Features\n",
    "X = d.drop([\"Survived\", \"PassengerId\", \"Cabin\",\"Ticket\",\"Name\", \"Fare\"], axis=1)\n",
    "X['Sex'] = map(lambda x: 1 if x==\"male\" else 0, X['Sex'])\n",
    "X['Embarked-Q'] = map(lambda x: 1 if x==\"Q\" else 0, X['Embarked'])\n",
    "X['Embarked-C'] = map(lambda x: 1 if x==\"C\" else 0, X['Embarked'])\n",
    "X['Embarked-S'] = map(lambda x: 1 if x==\"S\" else 0, X['Embarked'])\n",
    "X = X.drop([\"Embarked\", \"Sex\"], axis=1)\n",
    "X = X.fillna(-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Performing grid-search to find the optimal hyper-parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum = 0.674\n",
      "Tuning Parameters: (gamma = 0.01, C = 4.01)\n"
     ]
    }
   ],
   "source": [
    "# grid of (gamma, C) values to try \n",
    "gam_vec, cost_vec = np.meshgrid(np.linspace(0.01, 10, 11),\n",
    "                     np.linspace(0.01, 10, 11))\n",
    "\n",
    "AUC_all = [] # initialize empty array to store AUC results\n",
    "\n",
    "# set up cross-validation folds\n",
    "N = len(y)\n",
    "K = 10 # number of cross-validation folds\n",
    "folds = np.random.randint(0, K, size=N)\n",
    "\n",
    "# search over every value of the grid\n",
    "for param_ind in np.arange(len(gam_vec.ravel())):\n",
    "\n",
    "    # initialize cross-validation predictions\n",
    "    y_cv_pred = np.empty(N)\n",
    "\n",
    "    # loop through the cross-validation folds\n",
    "    for ii in np.arange(K):\n",
    "        # break your data into training and testing subsets\n",
    "        X_train = X.ix[folds != ii,:]\n",
    "        y_train = y.ix[folds != ii]\n",
    "        X_test = X.ix[folds == ii,:]\n",
    "\n",
    "        # build a model on the training set\n",
    "        model = SVC(gamma=gam_vec.ravel()[param_ind], C=cost_vec.ravel()[param_ind])\n",
    "        model.fit(X_train, y_train)\n",
    "\n",
    "        # generate and store model predictions on the testing set\n",
    "        y_cv_pred[folds == ii] = model.predict(X_test)\n",
    "\n",
    "    # evaluate the AUC of the predictions\n",
    "    AUC_all.append(roc_auc_score(y, y_cv_pred))\n",
    "\n",
    "indmax = np.argmax(AUC_all)\n",
    "print \"Maximum = %.3f\" % (np.max(AUC_all))\n",
    "print \"Tuning Parameters: (gamma = %.2f, C = %.2f)\" % (gam_vec.ravel()[indmax], cost_vec.ravel()[indmax])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the contours of the parameter performance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ff/3FwoULf5bd9vPPPy9cuHDhz5s3b94x0Ap/++23d62trR+yWKyKgoICP3nl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\nAAA9AqENAAB6BEIbAAD0yP8H2wuTQDJwp/QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a5a7610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "AUC_grid = np.array(AUC_all).reshape(gam_vec.shape)\n",
    "\n",
    "contourf(gam_vec, cost_vec, AUC_grid, 20, cmap='Greys')\n",
    "xlabel(\"kernel coefficient, gamma\")\n",
    "ylabel(\"penalty parameter, C\")\n",
    "colorbar()\n",
    "savefig(\"figures/figure-4.25.eps\", format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}