{ "metadata": { "name": "", "signature": "sha256:6e0bd432ae8373d06007ffd140037e99723999175b22ce0f808aafad27259ef9" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook was put together by [Jake Vanderplas](http://www.vanderplas.com) for PyCon 2014. Source and license info is on [GitHub](https://github.com/jakevdp/sklearn_pycon2014/)." ] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Part 1: Some Background" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this section we'll go through some preliminary topics and helpful background for the content in this tutorial.\n", "\n", "By the end of this section you should:\n", "\n", "- Know what sort of tasks qualify as Machine Learning problems.\n", "- See some simple examples of machine learning\n", "- Know the basics of creating and manipulating numpy arrays.\n", "- Know the basics of scatter plots in matplotlib." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "What is Machine Learning?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this section we will begin to explore the basic principles of machine learning.\n", "Machine Learning is about building programs with **tunable parameters** (typically an\n", "array of floating point values) that are adjusted automatically so as to improve\n", "their behavior by **adapting to previously seen data.**\n", "\n", "Machine Learning can be considered a subfield of **Artificial Intelligence** since those\n", "algorithms can be seen as building blocks to make computers learn to behave more\n", "intelligently by somehow **generalizing** rather that just storing and retrieving data items\n", "like a database system would do.\n", "\n", "We'll take a look at two very simple machine learning tasks here.\n", "The first is a **classification** task: the figure shows a\n", "collection of two-dimensional data, colored according to two different class\n", "labels. A classification algorithm may be used to draw a dividing boundary\n", "between the two clusters of points:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Start matplotlib inline mode, so figures will appear in the notebook\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# Import the example plot from the figures directory\n", "from fig_code import plot_sgd_separator\n", "plot_sgd_separator()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This may seem like a trivial task, but it is a simple version of a very important concept.\n", "By drawing this separating line, we have learned a model which can **generalize** to new\n", "data: if you were to drop another point onto the plane which is unlabeled, this algorithm\n", "could now **predict** whether it's a blue or a red point.\n", "\n", "If you'd like to see the source code used to generate this, you can either open the\n", "code in the `figures` directory, or you can load the code using the `%load` magic command:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Uncomment the %load command to load the contents of the file\n", "# %load fig_code/sgd_separator.py" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next simple task we'll look at is a **regression** task: a simple best-fit line\n", "to a set of data:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from fig_code import plot_linear_regression\n", "plot_linear_regression()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, this is an example of fitting a model to data, such that the model can make\n", "generalizations about new data. The model has been **learned** from the training\n", "data, and can be used to predict the result of test data:\n", "here, we might be given an x-value, and the model would\n", "allow us to predict the y value. Again, this might seem like a trivial problem,\n", "but it is a basic example of a type of operation that is fundamental to\n", "machine learning tasks." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Numpy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Manipulating `numpy` arrays is an important part of doing machine learning\n", "(or, really, any type of scientific computation) in python. This will likely\n", "be review for most: we'll quickly go through some of the most important features." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "\n", "# Generating a random array\n", "X = np.random.random((3, 5)) # a 3 x 5 array\n", "\n", "print X" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 0.68554084 0.99481945 0.42171778 0.2980198 0.788365 ]\n", " [ 0.91414655 0.26728696 0.59262316 0.05796566 0.73325553]\n", " [ 0.31650997 0.26602885 0.46244526 0.62143656 0.23170365]]\n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# Accessing elements\n", "\n", "# get a single element\n", "print X[0, 0]\n", "\n", "# get a row\n", "print X[1]\n", "\n", "# get a column\n", "print X[:, 1]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.685540838679\n", "[ 0.91414655 0.26728696 0.59262316 0.05796566 0.73325553]\n", "[ 0.99481945 0.26728696 0.26602885]\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "# Transposing an array\n", "print X.T" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 0.68554084 0.91414655 0.31650997]\n", " [ 0.99481945 0.26728696 0.26602885]\n", " [ 0.42171778 0.59262316 0.46244526]\n", " [ 0.2980198 0.05796566 0.62143656]\n", " [ 0.788365 0.73325553 0.23170365]]\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "# Turning a row vector into a column vector\n", "y = np.linspace(0, 12, 5)\n", "print y\n", "\n", "# make into a column vector\n", "print y[:, np.newaxis]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 0. 3. 6. 9. 12.]\n", "[[ 0.]\n", " [ 3.]\n", " [ 6.]\n", " [ 9.]\n", " [ 12.]]\n" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is much, much more to know, but these few operations are fundamental to what we'll\n", "do during this tutorial." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Scipy Sparse Matrices" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We won't make very much use of these in this tutorial, but sparse matrices are very nice\n", "in some situations. For example, in some machine learning tasks, especially those associated\n", "with textual analysis, the data may be mostly zeros. Storing all these zeros is very\n", "inefficient. We can create and manipulate sparse matrices as follows:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Create a random array with a lot of zeros\n", "X = np.random.random((10, 5))\n", "print X" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 0.94531988 0.24380585 0.28827154 0.81048153 0.75609912]\n", " [ 0.41189305 0.91508148 0.11583652 0.78611657 0.28910284]\n", " [ 0.57837054 0.88415398 0.69852079 0.03052359 0.69401941]\n", " [ 0.57988757 0.84273914 0.83461578 0.28921739 0.64017248]\n", " [ 0.76197598 0.50129526 0.94714542 0.45829698 0.37543524]\n", " [ 0.95940386 0.67520976 0.52125365 0.93214266 0.17329138]\n", " [ 0.32179395 0.55854669 0.66216461 0.70072992 0.60990773]\n", " [ 0.83447821 0.08467047 0.65538539 0.77651973 0.02608172]\n", " [ 0.53401371 0.18526802 0.53457061 0.58878805 0.79437564]\n", " [ 0.58140691 0.35953142 0.36719677 0.16835714 0.12890584]]\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "X[X < 0.7] = 0\n", "print X" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 0.94531988 0. 0. 0.81048153 0.75609912]\n", " [ 0. 0.91508148 0. 0.78611657 0. ]\n", " [ 0. 0.88415398 0. 0. 0. ]\n", " [ 0. 0.84273914 0.83461578 0. 0. ]\n", " [ 0.76197598 0. 0.94714542 0. 0. ]\n", " [ 0.95940386 0. 0. 0.93214266 0. ]\n", " [ 0. 0. 0. 0.70072992 0. ]\n", " [ 0.83447821 0. 0. 0.77651973 0. ]\n", " [ 0. 0. 0. 0. 0.79437564]\n", " [ 0. 0. 0. 0. 0. ]]\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "from scipy import sparse\n", "\n", "# turn X into a csr (Compressed-Sparse-Row) matrix\n", "X_csr = sparse.csr_matrix(X)\n", "print X_csr" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " (0, 0)\t0.945319879873\n", " (0, 3)\t0.810481526417\n", " (0, 4)\t0.756099123544\n", " (1, 1)\t0.915081477351\n", " (1, 3)\t0.786116574635\n", " (2, 1)\t0.884153979327\n", " (3, 1)\t0.842739141962\n", " (3, 2)\t0.834615776369\n", " (4, 0)\t0.761975977392\n", " (4, 2)\t0.947145416297\n", " (5, 0)\t0.959403863631\n", " (5, 3)\t0.932142657865\n", " (6, 3)\t0.700729919386\n", " (7, 0)\t0.834478214188\n", " (7, 3)\t0.776519731559\n", " (8, 4)\t0.79437564315\n" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "# convert the sparse matrix to a dense array\n", "print X_csr.toarray()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 0.94531988 0. 0. 0.81048153 0.75609912]\n", " [ 0. 0.91508148 0. 0.78611657 0. ]\n", " [ 0. 0.88415398 0. 0. 0. ]\n", " [ 0. 0.84273914 0.83461578 0. 0. ]\n", " [ 0.76197598 0. 0.94714542 0. 0. ]\n", " [ 0.95940386 0. 0. 0.93214266 0. ]\n", " [ 0. 0. 0. 0.70072992 0. ]\n", " [ 0.83447821 0. 0. 0.77651973 0. ]\n", " [ 0. 0. 0. 0. 0.79437564]\n", " [ 0. 0. 0. 0. 0. ]]\n" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "# Sparse matrices support linear algebra:\n", "y = np.random.random(X_csr.shape[1])\n", "z1 = X_csr.dot(y)\n", "z2 = X.dot(y)\n", "np.allclose(z1, z2)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "True" ] } ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The CSR representation can be very efficient for computations, but it is not\n", "as good for adding elements. For that, the LIL (List-In-List) representation\n", "is better:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Create an empty LIL matrix and add some items\n", "X_lil = sparse.lil_matrix((5, 5))\n", "\n", "for i, j in np.random.randint(0, 5, (15, 2)):\n", " X_lil[i, j] = i + j\n", "\n", "print X_lil\n", "print X_lil.toarray()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " (0, 4)\t4.0\n", " (1, 0)\t1.0\n", " (1, 2)\t3.0\n", " (1, 4)\t5.0\n", " (2, 0)\t2.0\n", " (2, 3)\t5.0\n", " (3, 0)\t3.0\n", " (3, 1)\t4.0\n", " (3, 2)\t5.0\n", " (3, 4)\t7.0\n", " (4, 0)\t4.0\n", " (4, 1)\t5.0\n", " (4, 4)\t8.0\n", "[[ 0. 0. 0. 0. 4.]\n", " [ 1. 0. 3. 0. 5.]\n", " [ 2. 0. 0. 5. 0.]\n", " [ 3. 4. 5. 0. 7.]\n", " [ 4. 5. 0. 0. 8.]]\n" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Often, once an LIL matrix is created, it is useful to convert it to a CSR format\n", "(many scikit-learn algorithms require CSR or CSC format)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "X_csr = X_lil.tocsr()\n", "print X_csr" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " (0, 4)\t4.0\n", " (1, 0)\t1.0\n", " (1, 2)\t3.0\n", " (1, 4)\t5.0\n", " (2, 0)\t2.0\n", " (2, 3)\t5.0\n", " (3, 0)\t3.0\n", " (3, 1)\t4.0\n", " (3, 2)\t5.0\n", " (3, 4)\t7.0\n", " (4, 0)\t4.0\n", " (4, 1)\t5.0\n", " (4, 4)\t8.0\n" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are several other sparse formats that can be useful for various problems:\n", "\n", "- `CSC` (compressed sparse column)\n", "- `BSR` (block sparse row)\n", "- `COO` (coordinate)\n", "- `DIA` (diagonal)\n", "- `DOK` (dictionary of keys)\n", "\n", "The ``scipy.sparse`` submodule also has a lot of functions for sparse matrices\n", "including linear algebra, sparse solvers, graph algorithms, and much more." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Matplotlib" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another important part of machine learning is visualization of data. The most common\n", "tool for this in Python is `matplotlib`. It is an extremely flexible package, but\n", "we will go over some basics here.\n", "\n", "First, something special to IPython notebook. We can turn on the \"IPython inline\" mode,\n", "which will make plots show up inline in the notebook." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "# plotting a line\n", "\n", "x = np.linspace(0, 10, 100)\n", "plt.plot(x, np.sin(x));" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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YM2cO5syZAwBYtmwZIiIi0LFjR0ycOBGLddgmunatrCdu0sTjT20JycnSOMsK9UjST2kp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\nckQ2s0VHq46EjMj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"text": [ "" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "# scatter-plot points\n", "\n", "x = np.random.normal(size=500)\n", "y = np.random.normal(size=500)\n", "plt.scatter(x, y);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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n+pIDpCz35MKFC9P0c/fu3fv6h196qT8tlgoExtFiqcYOHbrf97ydO3eq92wa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"text": [ "" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "# showing images\n", "x = np.linspace(1, 12, 100)\n", "y = x[:, np.newaxis]\n", "\n", "im = y * np.sin(x) * np.cos(y)\n", "print(im.shape)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(100, 100)\n" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "# imshow - note that origin is at the top-left!\n", "plt.imshow(im);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Da+iRxZ6wdkKhM73NC0lbja0ElSbLhdeKIFafhpe8/wuiJXT8b5Os9RjwWuS12rPLrj2+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\nZzcNtIOj3TtUTxT0PXDO+qCDfJUntlYtPYcCX5zyonJN+S5AB3rnsC4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"text": [ "" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "# Contour plot - note that origin here is at the bottom-left!\n", "plt.contour(im);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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8/xrVfyJS2FHEU+ZfuwYcdpH3Hxa5IEvk5hNEZr0mUlsVWF+B4vWKbFoq8vwt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\nK1qwT4TyQuUciL3LUjMMA/7YFu47aHm1ppCyrApKfJIP2VI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