Part 10 - Advanced Scoring.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics.scorer import SCORERS\n",
    "print(SCORERS.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.datasets import load_digits\n",
    "from sklearn.cross_validation import train_test_split\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "digits = load_digits()\n",
    "X, y = digits.data, digits.target == 3\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "param_grid = {'C': 10. ** np.arange(-3, 3), 'gamma': 10. ** np.arange(-3, 3)}\n",
    "grid = GridSearchCV(SVC(), param_grid=param_grid)\n",
    "grid.fit(X_train, y_train)\n",
    "print(grid.best_params_)\n",
    "print(grid.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import cross_val_score\n",
    "cross_val_score(SVC(), X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.dummy import DummyClassifier\n",
    "cross_val_score(DummyClassifier(\"most_frequent\"), X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "\n",
    "for gamma in [.01, .1, 1]:\n",
    "    plt.xlabel(\"FPR\")\n",
    "    plt.ylabel(\"TPR\")\n",
    "    svm = SVC(gamma=gamma).fit(X_train, y_train)\n",
    "    decision_function = svm.decision_function(X_test)\n",
    "    fpr, tpr, _ = roc_curve(y_test, decision_function)\n",
    "    acc = svm.score(X_test, y_test)\n",
    "    auc = roc_auc_score(y_test, svm.decision_function(X_test))\n",
    "    plt.plot(fpr, tpr, label=\"acc:%.2f auc:%.2f\" % (acc, auc))\n",
    "    print()\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics.scorer import accuracy_scorer\n",
    "\n",
    "def my_super_scoring(est, X, y):\n",
    "\treturn accuracy_scorer(est, X, y) - np.mean(est.coef_ != 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "y = digits.target\n",
    "grid = GridSearchCV(LinearSVC(C=.01, dual=False), param_grid={'penalty' : ['l1', 'l2']}, scoring=my_super_scoring)\n",
    "grid.fit(X, y)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics.scorer import make_scorer\n",
    "\n",
    "def scoring_function(y_true, y_pred):\n",
    "\treturn (np.abs(y_true - y_pred) < 3).mean()\n",
    "    \n",
    "tolerant_scoring = make_scorer(scoring_function)\n",
    "print(cross_val_score(LinearSVC(random_state=0), X, y))\n",
    "print(cross_val_score(LinearSVC(random_state=0), X, y, scoring=tolerant_scoring))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import fbeta_score\n",
    "beta_scoring = make_scorer(fbeta_score, beta=1.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}