07 - Feature Union.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More process building tools!\n",
    "----------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use multiple feature extractors (on the same data), concatenate results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_union, make_pipeline\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_20newsgroups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "news = fetch_20newsgroups()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data, y = news.data, news.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.bincount(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "data_train, data_test, y_train, y_test = train_test_split(data, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "char_and_word = make_union(CountVectorizer(analyzer=\"char\"),\n",
    "                           CountVectorizer(analyzer=\"word\"))\n",
    "\n",
    "text_pipe = make_pipeline(char_and_word, LinearSVC(dual=False))\n",
    "param_grid = {'linearsvc__C': 10. ** np.arange(-3, 3)}\n",
    "\n",
    "grid = GridSearchCV(text_pipe, param_grid=param_grid, cv=5, verbose=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "grid.fit(data_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "param_grid = {'featureunion__countvectorizer-1__ngram_range': [(1, 3), (1, 5), (2, 5)],\n",
    "              'featureunion__countvectorizer-2__ngram_range': [(1, 1), (1, 2), (2, 2)],\n",
    "              'linearsvc__C': 10. ** np.arange(-3, 3)}"
   ]
  }
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