Chapter 2 - Unsupervised Transformers.ipynb

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.datasets import load_digits\n",
      "from sklearn.cross_validation import train_test_split\n",
      "import numpy as np\n",
      "np.set_printoptions(suppress=True)\n",
      "\n",
      "digits = load_digits()\n",
      "X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Removing mean and scaling variance\n",
      "==================================="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.preprocessing import StandardScaler"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "1) Instantiate the model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scaler = StandardScaler()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "2) Fit using only the data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scaler.fit(X_train)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "3) `transform` the data (not `predict`)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_train_scaled = scaler.transform(X_train)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_train.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_train_scaled.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The transformed version of the data has the mean removed:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_train_scaled.mean(axis=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_train_scaled.std(axis=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_test_transformed = scaler.transform(X_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Principal Component Analysis\n",
      "============================="
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "0) Import the model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.decomposition import PCA"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "1) Instantiate the model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pca = PCA(n_components=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "2) Fit to training data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pca.fit(X_train)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "3) Transform to lower-dimensional representation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(X_train.shape)\n",
      "X_pca = pca.transform(X_train)\n",
      "X_pca.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Visualize\n",
      "----------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y_train)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
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     "cell_type": "code",
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     "input": [],
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