{ "metadata": { "name": "", "signature": "sha256:acab6cf23b286b47ee0bdb465729727fa612c952b0dc7c6159466efc66f75000" }, "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": [ "Supervised Learning In-Depth: SVMs and Random Forests" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are many machine learning algorithms available; here we'll go into brief detail on two of the most common and interesting ones: **Support Vector Machines (SVMs)** and **Random Forests**.\n", "\n", "By the end of this section you should:\n", "\n", "- have a qualitative idea of what problem Support Vector Machines are trying to solve\n", "- understand how decision trees work\n", "- understand how multiple decision trees are combined into *Random Forests*\n", "\n", "As before, we'll start by getting our notebook ready for interactive plotting:" ] }, { "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": 1 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Support Vector Machines" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Support Vector Machines (SVMs) are a powerful supervised learning algorithm used for **classification** or for **regression**. SVMs are a **discriminative** classifier: that is, they draw a boundary between clusters of data.\n", "\n", "Let's show a quick example of support vector classification. First we need to create a dataset:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.datasets.samples_generator import make_blobs\n", "X, y = make_blobs(n_samples=50, centers=2,\n", " random_state=0, cluster_std=0.60)\n", "plt.scatter(X[:, 0], X[:, 1], c=y, s=50);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we'll fit a Support Vector Machine Classifier to these points:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.svm import SVC # \"Support Vector Classifier\"\n", "clf = SVC(kernel='linear')\n", "clf.fit(X, y)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,\n", " kernel='linear', max_iter=-1, probability=False, random_state=None,\n", " shrinking=True, tol=0.001, verbose=False)" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "To better visualize what's happening here, let's create a quick convenience function that will plot SVM decision boundaries for us:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def plot_svc_decision_function(clf):\n", " \"\"\"Plot the decision function for a 2D SVC\"\"\"\n", " x = np.linspace(plt.xlim()[0], plt.xlim()[1], 30)\n", " y = np.linspace(plt.ylim()[0], plt.ylim()[1], 30)\n", " Y, X = np.meshgrid(y, x)\n", " P = np.zeros_like(X)\n", " for i, xi in enumerate(x):\n", " for j, yj in enumerate(y):\n", " P[i, j] = clf.decision_function([xi, yj])\n", " return plt.contour(X, Y, P, colors='k',\n", " levels=[-1, 0, 1],\n", " linestyles=['--', '-', '--'])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.scatter(X[:, 0], X[:, 1], c=y, s=50)\n", "plot_svc_decision_function(clf);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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odKFotfejUqWj0RzmkUcGMHfu54US3Df7fHU6HSEhIVbPnzlzhjfffNOqT7hu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\n9sGsWTQxGKiX47EAYJDJxBf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"text": [ "" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the dashed lines touch a couple of the points: these points are known as the \"support vectors\", and are stored in the ``support_vectors_`` attribute of the classifier:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.scatter(X[:, 0], X[:, 1], c=y, s=50)\n", "plot_svc_decision_function(clf)\n", "plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],\n", " s=200, facecolors='none');" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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qtTqji6Fv3768++67Zsdfu3aNS5cuZRntkNP9JENDQxkzZgwnT55EpVIxd+48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\nMmTIkCwtwsTERMa++iqrVq1CZTSiN5m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"text": [ "" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The unique thing about SVM is that only the support vectors matter: that is, if you moved any of the other points without letting them cross the decision boundaries, they would have no effect on the classification results! This can be seen interactively by running the ``fig_code/svm_gui.py`` script:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# %run fig_code/svm_gui.py" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above version uses a linear kernel; it is also possible to use *radial basis function* kernels as well as others." ] }, { "cell_type": "code", "collapsed": false, "input": [ "clf = SVC(kernel='rbf')\n", "clf.fit(X, y)\n", "plt.scatter(X[:, 0], X[:, 1], c=y, s=50)\n", "plot_svc_decision_function(clf)\n", "plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],\n", " s=200, facecolors='none');" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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yyM7OjujovG9I0tMqdO+R1157jY8//pjU1FS+/fZbAgICclfwHPUeyc/169fx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\nYWNlJVysrUUVEPVBjAfhBKIRiCEg1CBcq1QR3p0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"text": [ "" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The properties of SVMs make them extremely useful classifiers in practice.\n", "\n", "We'll leave SVMs for the time being and take a look at another powerful classifier." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Random Forests" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Random forests are an example of an *ensemble learner* built on decision trees. For this reason we'll first discuss decision trees themselves:" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Decision Trees" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we'll explore a class of algorithms based on Decision trees. Decision trees at their root (Ha!) are extremely intuitive. They encode a series of binary choices in a process that parallels how a person might classify things themselves, but using an information criterion to decide which question is most fruitful at each step. For example, if you wanted to create a guide to identifying an animal found in nature, you might ask the following series of questions:\n", "\n", "- Is the animal bigger or smaller than a meter long?\n", " + *bigger*: does the animal have horns?\n", " - *yes*: are the horns longer than ten centimeters?\n", " - *no*: is the animal wearing a collar\n", " + *smaller*: does the animal have two or four legs?\n", " - *two*: does the animal have wings?\n", " - *four*: does the animal have a bushy tail?\n", "\n", "and so on. This binary splitting of questions is the essence of a decision tree." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.tree import DecisionTreeClassifier" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "X, y = make_blobs(n_samples=300, centers=4,\n", " random_state=0, cluster_std=0.60)\n", "plt.scatter(X[:, 0], X[:, 1], c=y, s=50)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Frye/wZBBeQMUczjIni0bh729mYZojLAQmINwoWxCdKT/ePRoWrduTYLD4bHy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"text": [ "" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll start by defining a convenience function which allows us to plot the predicted value in the background:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def plot_estimator(estimator, X, y):\n", " estimator.fit(X, y)\n", " x_min, x_max = X[:, 0].min() - .1, X[:, 0].max() + .1\n", " y_min, y_max = X[:, 1].min() - .1, X[:, 1].max() + .1\n", " xx, yy = np.meshgrid(np.linspace(x_min, x_max, 50),\n", " np.linspace(y_min, y_max, 50))\n", " Z = estimator.predict(np.c_[xx.ravel(), yy.ravel()])\n", "\n", " # Put the result into a color plot\n", " Z = Z.reshape(xx.shape)\n", " plt.figure()\n", " plt.pcolormesh(xx, yy, Z, alpha=0.3)\n", "\n", " # Plot also the training points\n", " plt.scatter(X[:, 0], X[:, 1], c=y, s=50)\n", " plt.axis('tight')\n", " plt.axis('off')\n", " plt.tight_layout()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "clf = DecisionTreeClassifier(max_depth=10)\n", "plot_estimator(clf, X, y)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Fm5ylFUkSTkKSJCKbN8c3L6/Cl7A3JoZXP/uMXmYz/VF/7R8+downx47l40ce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\nkoh/HxEY8PNlzVGnh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"text": [ "" ] } ], "prompt_number": 12 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Random Forests" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One problem with decision trees is that they can end up **over-fitting** the data. They are such flexible models that, given a large depth, they can quickly memorize the inputs, which doesn't generalize well to previously unseen data. One way to get around this is to use many slightly different decision trees in concert. This is known as **Random Forests**, and is one of the more common techniques of **ensemble learning** (i.e. combining the results from several estimators." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.ensemble import RandomForestClassifier\n", "clf = RandomForestClassifier(n_estimators=10, random_state=0)\n", "plot_estimator(clf, X, y)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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tK9rkLK1IknASkiQR2aYNvvn5lX4Ie2Njeenjj+knywxG/bV/6OhRHp0wgQ8e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phbQ/qj7T4Oxq5E++wxp+eOIbheeSZs24+FpeUvmTcxZHOFv2Fi8+pEDJ8PXp82+Q0GZPP/Ym3T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\nYK+d40lpPyZNnwm3L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"text": [ "" ] } ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's difficult to see in this example, but for more complicated data, random forests can be a very powerful technique." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Exercise: Classifying Digits" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We previously saw the **hand-written digits** data. Let's use that here to test the efficacy of the SVM and Random Forest classifiers." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.datasets import load_digits\n", "digits = load_digits()\n", "digits.keys()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "['images', 'data', 'target_names', 'DESCR', 'target']" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "X = digits.data\n", "y = digits.target\n", "print(X.shape)\n", "print(y.shape)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(1797, 64)\n", "(1797,)\n" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "To remind us what we're looking at, we'll visualize the first few data points:" ] }, { "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": [ { "metadata": {}, "output_type": "display_data", "png": 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Dzblz57Bp0yY0NzcjGAzi4YcfxhtvvIElS5Zc99+ZiV8reuy0nJwcrF69GnPm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\nTWhubkYwGMSDDz6Is2fP4kc/+tF1/52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"text": [ "" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can quickly classify the digits using a decision tree as follows:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.cross_validation import train_test_split\n", "from sklearn import metrics\n", "\n", "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, random_state=0)\n", "clf = DecisionTreeClassifier(max_depth=5)\n", "clf.fit(Xtrain, ytrain)\n", "ypred = clf.predict(Xtest)\n", "\n", "plt.imshow(metrics.confusion_matrix(ypred, ytest),\n", " interpolation='nearest', cmap=plt.cm.binary)\n", "plt.colorbar()\n", "plt.xlabel(\"true label\")\n", "plt.ylabel(\"predicted label\");" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise\n", "1. Perform this classification task with ``sklearn.svm.SVC``. How does the choice of kernel affect the results?\n", "2. Perform this classification task with ``sklearn.ensemble.RandomForestClassifier``. How does the ``max_depth``, ``max_features``, and ``n_estimators`` affect the results?\n", "3. Try a few sets of parameters for each model and check the F1 score (``sklearn.metrics.f1_score``) on your results. What's the best F1 score you can reach?" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "# run this to load the solution\n", "# %load solutions/04_svm_rf.py" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 } ], "metadata": {} } ] }