examples/ch14/snippets_ipynb/.ipynb_checkpoints/14_06-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 14.6 Case Study: Unsupervised Machine Learning, Part 1—Dimensionality Reduction \n",
    "### Loading the Digits Dataset\n",
    "\n",
    "**We added `%matplotlib inline` to enable Matplotlib in this notebook.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from sklearn.datasets import load_digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = load_digits()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a `TSNE` Estimator for Dimensionality Reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsne = TSNE(n_components=2, random_state=11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transforming the Digits Dataset’s Features into Two Dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_data = tsne.fit_transform(digits.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the Reduced Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dots = plt.scatter(reduced_data[:, 0], reduced_data[:, 1],\n",
    "                    c='black')\n",
    "                    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the Reduced Data with Different Colors for Each Digit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dots = plt.scatter(reduced_data[:, 0], reduced_data[:, 1],\n",
    "     c=digits.target, cmap=plt.cm.get_cmap('nipy_spectral_r', 10))\n",
    "\n",
    "colorbar = plt.colorbar(dots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This placeholder cell was added because we had to combine \n",
    "# the sections snippets 9-10 for the visualization to work in Jupyter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* As an experiment, you might want to investigate Matplotlib’s **`Axes3D`**, which provides **<em>x</em>**-, **<em>y</em>**- and **<em>z</em>**-axes for plotting in three-dimensional graphs. \n",
    "\n",
    "<!-- ![Color Digits dataset clustering scatterplot after TSNE dimensionality reduction to two dimensions](./ch14images/digits_black.png \"Color Digits dataset clustering scatterplot after TSNE dimensionality reduction to two dimensions\") -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code for visualizing in 3D:\n",
    "# You might instead want to run this from the command line using \n",
    "# the provided digits3d.py file, because the diagram in JupyterLab\n",
    "# is not interactive\n",
    "tsne3 = TSNE(n_components=3, random_state=11)\n",
    "reduced_data3 = tsne3.fit_transform(digits.data) \n",
    "figure = plt.figure(figsize=(7, 5))\n",
    "import mpl_toolkits.mplot3d.axes3d as axes3d\n",
    "axes = figure.add_subplot(projection='3d')\n",
    "dots = axes.scatter(reduced_data3[:, 0], reduced_data3[:, 1], reduced_data3[:, 2], \n",
    "    c=digits.target, cmap=plt.cm.get_cmap('nipy_spectral_r', 10))\n",
    "colorbar = plt.colorbar(dots)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# More Info \n",
    "* See **video** Lesson 14 in [**Python Fundamentals LiveLessons** on Safari Online Learning](https://learning.oreilly.com/videos/python-fundamentals/9780135917411)\n",
    "* See **book** Chapter 14 in [**Python for Programmers** on Safari Online Learning](https://learning.oreilly.com/library/view/python-for-programmers/9780135231364/), or see **book** Chapter 15 in **Intro to Python for Computer Science and Data Science**\n",
    "* Interested in a print book? Check out:\n",
    "\n",
    "| Python for Programmers | Intro to Python for Computer<br>Science and Data Science\n",
    "| :------ | :------\n",
    "| <a href=\"https://amzn.to/2VvdnxE\"><img alt=\"Python for Programmers cover\" src=\"../images/PyFPCover.png\" width=\"150\" border=\"1\"/></a> | <a href=\"https://amzn.to/2LiDCmt\"><img alt=\"Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud\" src=\"../images/IntroToPythonCover.png\" width=\"159\" border=\"1\"></a>\n",
    "\n",
    ">Please **do not** purchase both books&mdash;our professional book **_Python for Programmers_** is a subset of our college textbook **_Intro to Python for Computer Science and Data Science_**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##########################################################################\n",
    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
    "#                                                                        #\n",
    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
    "# best efforts in preparing the book. These efforts include the          #\n",
    "# development, research, and testing of the theories and programs        #\n",
    "# to determine their effectiveness. The authors and publisher make       #\n",
    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
    "# programs or to the documentation contained in these books. The authors #\n",
    "# and publisher shall not be liable in any event for incidental or       #\n",
    "# consequential damages in connection with, or arising out of, the       #\n",
    "# furnishing, performance, or use of these programs.                     #\n",
    "##########################################################################\n"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}