{ "metadata": { "name": "02A_representation_of_data" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Representation and Visualization of Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Machine learning is about creating models from data: for that reason, we'll start by\n", "discussing how data can be represented in order to be understood by the computer. Along\n", "with this, we'll build on our matplotlib examples from the previous section and show some\n", "examples of how to visualize data.\n", "\n", "By the end of this section you should:\n", "\n", "- Know the internal data representation of scikit-learn.\n", "- Know how to use scikit-learn's dataset loaders to load example data.\n", "- Know how to turn image & text data into data matrices for learning.\n", "- Know how to use matplotlib to help visualize different types of data." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Data in scikit-learn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Data in scikit-learn, with very few exceptions, is assumed to be stored as a\n", "**two-dimensional array**, of size `[n_samples, n_features]`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Most machine learning algorithms implemented in scikit-learn expect data to be stored in a\n", "**two-dimensional array or matrix**. The arrays can be\n", "either ``numpy`` arrays, or in some cases ``scipy.sparse`` matrices.\n", "The size of the array is expected to be `[n_samples, n_features]`\n", "\n", "- **n_samples:** The number of samples: each sample is an item to process (e.g. classify).\n", " A sample can be a document, a picture, a sound, a video, an astronomical object,\n", " a row in database or CSV file,\n", " or whatever you can describe with a fixed set of quantitative traits.\n", "- **n_features:** The number of features or distinct traits that can be used to describe each\n", " item in a quantitative manner. Features are generally real-valued, but may be boolean or\n", " discrete-valued in some cases.\n", "\n", "The number of features must be fixed in advance. However it can be very high dimensional\n", "(e.g. millions of features) with most of them being zeros for a given sample. This is a case\n", "where `scipy.sparse` matrices can be useful, in that they are\n", "much more memory-efficient than numpy arrays." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "A Simple Example: the Iris Dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As an example of a simple dataset, we're going to take a look at the iris data stored by scikit-learn.\n", "The data consists of measurements of three different species of irises. There are three species of iris\n", "in the dataset, which we can picture here:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.core.display import Image, display\n", "display(Image(filename='figures/iris_setosa.jpg'))\n", "print \"Iris Setosa\\n\"\n", "\n", "display(Image(filename='figures/iris_versicolor.jpg'))\n", "print \"Iris Versicolor\\n\"\n", "\n", "display(Image(filename='figures/iris_virginica.jpg'))\n", "print \"Iris Virginica\"" ], "language": "python", "metadata": {}, "outputs": [ { "jpeg": 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T3Mjj93NOUgsM9KrBuaeCSa+sR0s3zpAuFQ25wNucnvVSfRrqHJKhh7Vt2lt5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\nnb5qngXNV0XJq3EuKw6mNxWXip7QYIq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HB+4waiiwuCwBIJUsfQdQfp/Sjsx2tGlAe5ooJxtLq3KKPPjz35FAfFd1zMFI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\nWlJnW5EIgFHOKhvrgKhCnnFfA2xMk80Iv7nJPNC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mg6Z4s0pPD2uskFoH/wBC1Zk3S6c2OAD1MecZXI+vFfM/iPwTrHhOSbTNQ0Qw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naQdh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\nwrKSN6b5lY+N9eiOlWcd21uRECFKZ5GO544Fdf8AC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"output_type": "display_data", "text": [ "" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Iris Virginica\n" ] } ], "prompt_number": 0 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Quick Question:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**If we want to design an algorithm to recognize iris species, what might the data be?**\n", "\n", "Remember: we need a 2D array of size `[n_samples x n_features]`.\n", "\n", "- What would the `n_samples` refer to?\n", "\n", "- What might the `n_features` refer to?\n", "\n", "Remember that there must be a **fixed** number of features for each sample, and feature\n", "number ``i`` must be a similar kind of quantity for each sample." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Loading the Iris Data with Scikit-learn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Scikit-learn has a very straightforward set of data on these iris species. The data consist of\n", "the following:\n", "\n", "- Features in the Iris dataset:\n", "\n", " 1. sepal length in cm\n", " 2. sepal width in cm\n", " 3. petal length in cm\n", " 4. petal width in cm\n", "\n", "- Target classes to predict:\n", "\n", " 1. Iris Setosa\n", " 2. Iris Versicolour\n", " 3. Iris Virginica" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "``scikit-learn`` embeds a copy of the iris CSV file along with a helper function to load it into numpy arrays:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.datasets import load_iris\n", "iris = load_iris()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The resulting dataset is a ``Bunch`` object: you can see what's available using\n", "the method ``keys()``:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "iris.keys()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 4, "text": [ "['target_names', 'data', 'target', 'DESCR', 'feature_names']" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The features of each sample flower are stored in the ``data`` attribute of the dataset:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "n_samples, n_features = iris.data.shape\n", "print n_samples\n", "print n_features\n", "print iris.data[0]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "150\n", "4\n", "[ 5.1 3.5 1.4 0.2]\n" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The information about the class of each sample is stored in the ``target`` attribute of the dataset:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print iris.data.shape\n", "print iris.target.shape" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(150, 4)\n", "(150,)\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "print iris.target" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n", " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", " 2 2]\n" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The names of the classes are stored in the last attribute, namely ``target_names``:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print iris.target_names" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "['setosa' 'versicolor' 'virginica']\n" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This data is four dimensional, but we can visualize two of the dimensions\n", "at a time using a simple scatter-plot. Again, we'll start by enabling\n", "pylab inline mode:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# note: this also imports numpy as np, imports matplotlib.pyplot as plt, and others\n", "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n", "For more information, type 'help(pylab)'.\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "x_index = 0\n", "y_index = 1\n", "\n", "# this formatter will label the colorbar with the correct target names\n", "formatter = plt.FuncFormatter(lambda i, *args: iris.target_names[int(i)])\n", "\n", "plt.scatter(iris.data[:, x_index], iris.data[:, y_index], c=iris.target)\n", "plt.colorbar(ticks=[0, 1, 2], format=formatter)\n", "plt.xlabel(iris.feature_names[x_index])\n", "plt.ylabel(iris.feature_names[y_index])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 10, "text": [ "" ] }, { "output_type": "display_data", "png": 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//PPP1/K4vtVrC0ymHNLT0+04e8dbu3EtMaOjcfNyw9nNmbjxsazbuK7Y9OuT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\nyKZNm6RmcE3xru4tXr5e8tO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LDF65ckX+NniwhNaqJa2bNZPt27ffUjynT5+W+7t1k7qB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\nXxsbG3To0AG3bt0CoDzH7uTkVK0MH/3l0i5f9wLt3r0bEyZMUPseH33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} ], "prompt_number": 8 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Quick Exercise:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Change** `x_index` **and** `y_index` **in the above script\n", "and find a combination of two parameters\n", "which maximally separate the three classes.**\n", "\n", "This exercise is a preview of **dimensionality reduction**, which we'll see later." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Other Available Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Scikit-learn makes available a host of datasets for testing learning algorithms.\n", "They come in three flavors:\n", "\n", "- **Packaged Data:** these small datasets are packaged with the scikit-learn installation,\n", " and can be downloaded using the tools in ``sklearn.datasets.load_*``\n", "- **Downloadable Data:** these larger datasets are available for download, and scikit-learn\n", " includes tools which streamline this process. These tools can be found in\n", " ``sklearn.datasets.fetch_*``\n", "- **Generated Data:** there are several datasets which are generated from models based on a\n", " random seed. These are available in the ``sklearn.datasets.make_*``\n", "\n", "You can explore the available dataset loaders, fetchers, and generators using IPython's\n", "tab-completion functionality. After importing the ``datasets`` submodule from ``sklearn``,\n", "type\n", "\n", " datasets.load_\n", "\n", "or\n", "\n", " datasets.fetch_\n", "\n", "or\n", "\n", " datasets.make_\n", "\n", "to see a list of available functions." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn import datasets" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data downloaded using the ``fetch_`` scripts are stored locally,\n", "within a subdirectory of your home directory.\n", "You can use the following to determine where it is:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.datasets import get_data_home\n", "get_data_home()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 12, "text": [ "'/Users/ogrisel/scikit_learn_data'" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "!ls $HOME/scikit_learn_data/" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "20news-bydate.pkz cal_housing.pkz \u001b[34mlfw_home\u001b[m\u001b[m \u001b[34mmldata\u001b[m\u001b[m olivetti.pkz species_coverage.pkz\r\n" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Be warned: many of these datasets are quite large, and can take a long time to download!\n", "(especially on Conference wifi).\n", "\n", "If you start a download within the IPython notebook\n", "and you want to kill it, you can use ipython's \"kernel interrupt\" feature, available in the menu or using\n", "the shortcut ``Ctrl-m i``.\n", "\n", "You can press ``Ctrl-m h`` for a list of all ``ipython`` keyboard shortcuts." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Loading Digits Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we'll take a look at another dataset, one where we have to put a bit\n", "more thought into how to represent the data. We can explore the data in\n", "a similar manner as above:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.datasets import load_digits\n", "digits = load_digits()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "digits.keys()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 15, "text": [ "['images', 'data', 'target_names', 'DESCR', 'target']" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "n_samples, n_features = digits.data.shape\n", "print (n_samples, n_features)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(1797, 64)\n" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "print digits.data[0]\n", "print digits.target" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 0. 0. 5. 13. 9. 1. 0. 0. 0. 0. 13. 15. 10. 15. 5.\n", " 0. 0. 3. 15. 2. 0. 11. 8. 0. 0. 4. 12. 0. 0. 8.\n", " 8. 0. 0. 5. 8. 0. 0. 9. 8. 0. 0. 4. 11. 0. 1.\n", " 12. 7. 0. 0. 2. 14. 5. 10. 12. 0. 0. 0. 0. 6. 13.\n", " 10. 0. 0. 0.]\n", "[0 1 2 ..., 8 9 8]\n" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The target here is just the digit represented by the data. The data is an array of\n", "length 64... but what does this data mean?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There's a clue in the fact that we have two versions of the data array:\n", "``data`` and ``images``. Let's take a look at them:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print digits.data.shape\n", "print digits.images.shape" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(1797, 64)\n", "(1797, 8, 8)\n" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that they're related by a simple reshaping:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print np.all(digits.images.reshape((1797, 64)) == digits.data)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "True\n" ] } ], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Aside... numpy and memory efficiency:*\n", "\n", "*You might wonder whether duplicating the data is a problem. In this case, the memory\n", "overhead is very small. Even though the arrays are different shapes, they point to the\n", "same memory block, which we can see by doing a bit of digging into the guts of numpy:*" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print digits.data.__array_interface__['data']\n", "print digits.images.__array_interface__['data']" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(4434440192, False)\n", "(4434440192, False)\n" ] } ], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": {}, "source": [ "*The long integer here is a memory address: the fact that the two are the same tells\n", "us that the two arrays are looking at the same data.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's visualize the data. It's little bit more involved than the simple scatter-plot\n", "we used above, but we can do it rather quickly." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# set up the figure\n", "fig = plt.figure(figsize=(6, 6)) # figure size in inches\n", "fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)\n", "\n", "# plot the digits: each image is 8x8 pixels\n", "for i in range(64):\n", " ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])\n", " ax.imshow(digits.images[i], cmap=plt.cm.binary, interpolation='nearest')\n", " \n", " # label the image with the target value\n", " ax.text(0, 7, str(digits.target[i]))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "png": 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WNDU1IRgMYsmSJXj99dexbNmyG/6dmfi1osdOS01Nxdq1azFv3jwMGTIE4XAY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\nwoGPiIh8hQMfERH5Cgc+IiLylf8HYwGtAoXEvwsAAAAASUVORK5CYII=\n" } ], "prompt_number": 20 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see now what the features mean. Each feature is a real-valued quantity representing the\n", "darkness of a pixel in an 8x8 image of a hand-written digit.\n", "\n", "Even though each sample has data that is inherently two-dimensional, the data matrix flattens\n", "this 2D data into a **single vector**, which can be contained in one **row** of the data matrix." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Generated Data: the S-Curve" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One dataset often used as an example of a simple nonlinear dataset is the S-cure:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.datasets import make_s_curve\n", "data, colors = make_s_curve(n_samples=1000)\n", "print(data.shape)\n", "print(colors.shape)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(1000, 3)\n", "(1000,)\n" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "from mpl_toolkits.mplot3d import Axes3D\n", "ax = plt.axes(projection='3d')\n", "ax.scatter(data[:, 0], data[:, 1], data[:, 2], c=colors)\n", "ax.view_init(10, -60)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/usr/local/lib/python2.7/site-packages/mpl_toolkits/__init__.py:2: UserWarning: Module argparse was already imported from /usr/local/Cellar/python/2.7.5/Frameworks/Python.framework/Versions/2.7/lib/python2.7/argparse.pyc, but /usr/local/lib/python2.7/site-packages is being added to sys.path\n", " __import__('pkg_resources').declare_namespace(__name__)\n" ] }, { "output_type": "display_data", "png": 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zBeBCEGIRBBWXawqC8CyBgEIwOJ7cXA+aFoUojqS83Mprry1nwIALSUzsjSRZ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\nt91sJ8NNWlqaU8+UL51ECMGqV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} ], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example is typically used with an unsupervised learning method called Locally\n", "Linear Embedding. We'll explore unsupervised learning in detail later in the tutorial." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Exercise: working with the faces dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we'll take a moment for you to explore the datasets yourself.\n", "Later on we'll be using the Olivetti faces dataset.\n", "Take a moment to fetch the data (about 1.4MB), and visualize the faces.\n", "You can copy the code used to visualize the digits above, and modify it for this data." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.datasets import fetch_olivetti_faces" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "# fetch the faces data\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "# Use a script like above to plot the faces image data.\n", "# hint: plt.cm.bone is a good colormap for this data\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 25 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Solution:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%load solutions/02A_faces_plot.py" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 26 } ], "metadata": {} } ] }