Upgrade notebooks to TensorFlow 1.0.0

08_dimensionality_reduction.ipynb

@@ -2,28 +2,40 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "**Chapter 8 – Dimensionality Reduction**"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "_This notebook contains all the sample code and solutions to the exercices in chapter 8._"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# Setup"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:"
    ]
@@ -32,7 +44,9 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -69,7 +83,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# Projection methods\n",
     "Build 3D dataset:"
@@ -79,7 +96,9 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -97,7 +116,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Mean normalize the data:"
    ]
@@ -106,7 +128,9 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -115,7 +139,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Apply PCA to reduce to 2D."
    ]
@@ -124,7 +151,9 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -136,7 +165,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Recover the 3D points projected on the plane (PCA 2D subspace)."
    ]
@@ -145,7 +177,9 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -154,7 +188,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Utility class to draw 3D arrows (copied from http://stackoverflow.com/questions/11140163)"
    ]
@@ -163,7 +200,9 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -184,7 +223,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Express the plane as a function of x and y."
    ]
@@ -193,7 +235,9 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -210,7 +254,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Plot the 3D dataset, the plane and the projections on that plane."
    ]
@@ -219,7 +266,9 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -263,7 +312,9 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -284,7 +335,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "PCA using SVD decomposition"
    ]
@@ -293,7 +347,9 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -312,7 +368,9 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -323,7 +381,9 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -334,7 +394,9 @@
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -345,7 +407,9 @@
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -359,7 +423,9 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -370,7 +436,9 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -381,7 +449,9 @@
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -392,7 +462,9 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -403,7 +475,9 @@
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -414,7 +488,9 @@
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -425,7 +501,9 @@
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -436,7 +514,9 @@
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -447,7 +527,9 @@
    "cell_type": "code",
    "execution_count": 23,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -458,7 +540,9 @@
    "cell_type": "code",
    "execution_count": 24,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -467,7 +551,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# Manifold learning\n",
     "Swiss roll:"
@@ -477,7 +564,9 @@
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -489,7 +578,9 @@
    "cell_type": "code",
    "execution_count": 26,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -515,7 +606,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -542,7 +635,9 @@
    "cell_type": "code",
    "execution_count": 28,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -623,7 +718,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# PCA"
    ]
@@ -632,7 +730,9 @@
    "cell_type": "code",
    "execution_count": 29,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -698,7 +798,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# MNIST compression"
    ]
@@ -707,7 +810,9 @@
    "cell_type": "code",
    "execution_count": 30,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -725,7 +830,9 @@
    "cell_type": "code",
    "execution_count": 31,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -741,7 +848,9 @@
    "cell_type": "code",
    "execution_count": 32,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -754,7 +863,9 @@
    "cell_type": "code",
    "execution_count": 33,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -765,7 +876,9 @@
    "cell_type": "code",
    "execution_count": 34,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -780,7 +893,9 @@
    "cell_type": "code",
    "execution_count": 35,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -802,9 +917,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -823,7 +940,9 @@
    "cell_type": "code",
    "execution_count": 37,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -842,7 +961,9 @@
    "cell_type": "code",
    "execution_count": 38,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -853,7 +974,9 @@
    "cell_type": "code",
    "execution_count": 39,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -869,7 +992,9 @@
    "cell_type": "code",
    "execution_count": 40,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -880,7 +1005,9 @@
    "cell_type": "code",
    "execution_count": 41,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -891,7 +1018,9 @@
    "cell_type": "code",
    "execution_count": 42,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -906,7 +1035,9 @@
    "cell_type": "code",
    "execution_count": 43,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -921,13 +1052,13 @@
    "cell_type": "code",
    "execution_count": 44,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
-    "from sklearn.decomposition import RandomizedPCA\n",
-    "\n",
-    "rnd_pca = RandomizedPCA(n_components=154, random_state=42)\n",
+    "rnd_pca = PCA(n_components=154, random_state=42, svd_solver=\"randomized\")\n",
     "X_reduced = rnd_pca.fit_transform(X_mnist)"
    ]
   },
@@ -935,7 +1066,9 @@
    "cell_type": "code",
    "execution_count": 45,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -945,7 +1078,7 @@
     "    print(\"n_components =\", n_components)\n",
     "    regular_pca = PCA(n_components=n_components)\n",
     "    inc_pca = IncrementalPCA(n_components=154, batch_size=500)\n",
-    "    rnd_pca = RandomizedPCA(n_components=154, random_state=42)\n",
+    "    rnd_pca = PCA(n_components=154, random_state=42, svd_solver=\"randomized\")\n",
     "\n",
     "    for pca in (regular_pca, inc_pca, rnd_pca):\n",
     "        t1 = time.time()\n",
@@ -956,7 +1089,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# Kernel PCA"
    ]
@@ -965,7 +1101,9 @@
    "cell_type": "code",
    "execution_count": 46,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1003,7 +1141,9 @@
    "cell_type": "code",
    "execution_count": 47,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1029,7 +1169,9 @@
    "cell_type": "code",
    "execution_count": 48,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1047,7 +1189,9 @@
    "cell_type": "code",
    "execution_count": 49,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1073,7 +1217,9 @@
    "cell_type": "code",
    "execution_count": 50,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1087,7 +1233,9 @@
    "cell_type": "code",
    "execution_count": 51,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1099,7 +1247,9 @@
    "cell_type": "code",
    "execution_count": 52,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1108,7 +1258,7 @@
     "sizes = [1000, 10000, 20000, 30000, 40000, 50000, 70000, 100000, 200000, 500000]\n",
     "for n_samples in sizes:\n",
     "    X = rnd.randn(n_samples, 5)\n",
-    "    pca = RandomizedPCA(n_components = 2, random_state=42)\n",
+    "    pca = PCA(n_components = 2, random_state=42, svd_solver=\"randomized\")\n",
     "    t1 = time.time()\n",
     "    pca.fit(X)\n",
     "    t2 = time.time()\n",
@@ -1129,9 +1279,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 53,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1140,7 +1292,7 @@
     "sizes = [1000, 2000, 3000, 4000, 5000, 6000]\n",
     "for n_features in sizes:\n",
     "    X = rnd.randn(2000, n_features)\n",
-    "    pca = RandomizedPCA(n_components = 2, random_state=42)\n",
+    "    pca = PCA(n_components = 2, random_state=42, svd_solver=\"randomized\")\n",
     "    t1 = time.time()\n",
     "    pca.fit(X)\n",
     "    t2 = time.time()\n",
@@ -1161,16 +1313,21 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# LLE"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 54,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1194,16 +1351,21 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# MDS, Isomap and t-SNE"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 55,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1214,9 +1376,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 56,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1227,9 +1391,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 57,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1240,9 +1406,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 58,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1256,9 +1424,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 59,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -1283,7 +1453,9 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "source": [
     "# Exercise solutions"
@@ -1291,7 +1463,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "**Coming soon**"
    ]
@@ -1300,7 +1475,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": []
@@ -1322,7 +1499,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.5.2+"
   },
   "nav_menu": {
    "height": "352px",