{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 9 - Scaling ML Workflows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": [
    "### Polynomial features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (linear):\t0.95 (+/- 0.12)\n",
      "Accuracy (nonlinear):\t0.98 (+/- 0.09)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "linear_classifier = LogisticRegression()\n",
    "linear_scores = cross_val_score(linear_classifier, \\\n",
    "iris.data, iris.target, cv=10) #2\n",
    "print \"Accuracy (linear):\\t%0.2f (+/- %0.2f)\" % \\\n",
    "(linear_scores.mean(), linear_scores.std() * 2)\n",
    "\n",
    "\n",
    "pol = PolynomialFeatures(degree=2)\n",
    "nonlinear_data = pol.fit_transform(iris.data)\n",
    "\n",
    "nonlinear_classifier = LogisticRegression()\n",
    "nonlinear_scores = cross_val_score(nonlinear_classifier, \\\n",
    "nonlinear_data, iris.target, cv=10)\n",
    "print \"Accuracy (nonlinear):\\t%0.2f (+/- %0.2f)\" % \\\n",
    "(nonlinear_scores.mean(), nonlinear_scores.std() * 2)\n"
   ]
  }
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