{ "metadata": { "celltoolbar": "Slideshow", "name": "", "signature": "sha256:77da6630cf7f19852eecb99aef8e8d1e781a82562da7416237f1273c611a8753" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline\n", "import sklearn" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# Visualizes how a classifier would classify each point in a grid\n", "# http://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html\n", "\n", "from matplotlib.colors import ListedColormap\n", "def decision_boundary(clf, X, Y):\n", " h = .02 # step size in the mesh\n", "\n", " # Create color maps\n", " cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n", " cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n", "\n", " # Plot the decision boundary. For that, we will assign a color to each\n", " # point in the mesh [x_min, m_max] x[y_min, y_max].\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.arange(x_min, x_max, h),\n", " np.arange(y_min, y_max, h))\n", " Z = clf.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, cmap=cmap_light)\n", "\n", " # Plot also the training points\n", " plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=cmap_bold)\n", " plt.xlim(xx.min(), xx.max())\n", " plt.ylim(yy.min(), yy.max())\n", "\n", " plt.show()" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "def plot_test_train(clf, Xtrain, Ytrain, Xtest):\n", " plt.prism() # this sets a nice color map\n", " plt.scatter(Xtest[:, 0], Xtest[:, 1], c=clf.predict(Xtest), marker='^')\n", " plt.scatter(Xtrain[:, 0], Xtrain[:, 1], c=Ytrain)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Machine Learning with scikit-learn\n", "## Introduction to Hacking | Spring 2014\n", "## David Dohan" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# [Installing sklearn](http://scikit-learn.org/stable/install.html)\n", "\n", "sklearn requires depends on numpy and scipy. \n", "\n", " pip install numpy scipy scikit-learn\n", " \n", "It's also helpful to have matplotlib\n", " \n", " pip install matplotlib\n", " \n", "Installation on a Mac can be tricky. Take a look at [here](http://scikit-learn.org/stable/install.html) for more details." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# sklearn documentation\n", "\n", "### sklearn is one of the best documented projects out there.\n", "\n", "### If you need help, there's plenty available:\n", "\n", "+ [Tutorial](http://scikit-learn.org/stable/tutorial/)\n", "+ [Documentation](http://scikit-learn.org/)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# An Overview of Machine Learning\n", "\n", "### ML can be roughly broken down into a few areas:\n", "\n", "+ ## Supervised learning\n", " + ### Classification\n", " + ### Regression\n", "+ ## Unsupervised learning\n", " + ### Clustering\n", " + ### Dimensionality reduction" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Example data: MNIST\n", "\n", "We'll use the MNIST digit dataset for classification" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.datasets import load_digits\n", "digits = load_digits()\n", "print(\"images shape: %s\" % str(digits.images.shape))\n", "print(\"targets shape: %s\" % str(digits.target.shape))\n", "digit_X = digits.images.reshape(-1, 64) # Reshape 8x8 images to length 64 vectors\n", "digit_Y = digits.target # Get labels" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "images shape: (1797, 8, 8)\n", "targets shape: (1797,)\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.matshow(digits.images[0], cmap=plt.cm.Greys);" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Example data: Iris\n", "\n", "We'll also use the iris dataset, which consists of data from three types of irises (Setosa, Versicolour, and Virginica). Features are measurements of\n", "\n", "+ Sepal Length\n", "+ Sepal Width\n", "+ Petal Length\n", "+ Petal Width." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.datasets import load_iris\n", "iris = load_iris()\n", "print iris.data.shape" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(150, 4)\n" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Example data: Iris\n", "\n", "![Iris Setosa](figures/iris_setosa.jpg)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Representing the Data\n", "\n", "### If we have $n$ examples, each with $d$ features, then the data is:\n", "\n", "+ ### $X$: an $n \\times d$ matrix in which each row is an example\n", "+ ### $Y$: a length $n$ vector" ] }, { "cell_type": "code", "collapsed": false, "input": [ "IX = iris.data # Get features\n", "IY = iris.target # Get labels\n", "print(\"X shape: {}\".format(IX.shape))\n", "print(\"Example features:\\n {}\".format(IX[:5]))\n", "print(\"Labels:\\n {}\".format(IY[:70]))" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "X shape: (150, 4)\n", "Example features:\n", " [[ 5.1 3.5 1.4 0.2]\n", " [ 4.9 3. 1.4 0.2]\n", " [ 4.7 3.2 1.3 0.2]\n", " [ 4.6 3.1 1.5 0.2]\n", " [ 5. 3.6 1.4 0.2]]\n", "Labels:\n", " [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]\n" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Classification\n", "\n", "+ ## [K Nearest Neighbors](http://scikit-learn.org/stable/modules/neighbors.html)\n", "+ ## [Decision Tree](http://scikit-learn.org/stable/modules/tree.html)\n", "+ ## [Random Forests](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)\n", "+ ## [Support Vector Machines](http://scikit-learn.org/stable/modules/svm.html)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Getting started: Classification with [K-Nearest Neighbors](http://scikit-learn.org/stable/modules/neighbors.html)\n", "\n", "First we generate sample data:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.datasets import make_blobs\n", "BX, BY = make_blobs(cluster_std=1.6, random_state=9)\n", "plt.scatter(BX[:, 0], BX[:, 1], c=BY);plt.show()" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Jiw4OGIErwEKgukbDmMGDmVy9Olt27sTFxSXn/EkTJzJ+/HhMJhPtWrfm92XL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\nHTp0eKAbqw9TWFgYYWFhOa8/+OAD2/W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"text": [ "" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Now training a simple KNN classifier" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.neighbors import KNeighborsClassifier\n", "\n", "knn = KNeighborsClassifier(n_neighbors=1)\n", "\n", "knn.fit(BX, BY)\n", "decision_boundary(knn, BX, BY)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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yBMa/jaRHpDOz/0xm/D2DcjXLWa0NIedEoDs5P29vVo0aRY9589DKMiaVih8/\n+ABvD49cnceRw/x+j6szL0Gf3/d8+fJJ9u5di1qtoW3bgZQsWTFf53NGx349hvm6GbyBSiA/L3Ni\n2wkR6HYiAt0FdGnQgKvLlnE9NRV/H59cLx/rLGGenYJ+D2fP7mfq1B5I0usoFDfZuPFpZs7cg79/\n1QKtI9esfCFU660l83Im1AZkUF5W4tEsd50JwXrERVEXodNoKO/n57JrgTua7777FINhNrI8DYvl\nM/T6N/nll3n2LqvAvRz6MtquWpgMmn4ail0txtMhT9u7rEJL/PYXcq7QO7eHzMx0IODuY1kO4Nat\ni/YrKCdsME2x45CO+Ff25/hfx/Fp4UOHZR1wKyI2h7cXmwX63Llz+eCDD0hMTLy7YbTgWOwd5omJ\nl1m6dAxxcVHUqNGIV16Zgbu7l11ryqlWrXoSFzcWg2EZkIFOF0qrVnPsXdbj2XDOee32tandvrbN\nzi/knE0CPSYmhm3btlGhQgVbnF6wAnuHeWbmTSZMaE9q6kAslneIj19CbOxzTJnyOwqFwq615UT3\n7iMwGDLYtu05VCoNzz03nqef7mPvsoRCziaBPmrUKGbNmkXPnj1tcXohn+wd5pB1UVGvL4PF8jEA\nJlMzLl4sRWrqdXx8/O1c3ZMplUr69v2Ivn0/sncpT+bEd4SajCa2fbmN6H+jqRRYieA3glGqxKW/\nx7F6oP/222+UK1eOunXrZnvcpLB7P2RBtWoRFBho7VIEK7ly5TRLl44hKekatWu3ZNCg6eh0+ZvJ\noFJpkOV0QAYUgAGTKZOFC9+iaFFfnn/+A0qXrvbY18uyzKVLR8jISKVy5UZ4eBTNVz2C45FlmZn9\nZnI6/TRSNwndGh0n955k9KrRTvEpzhoidkYQuTMyx8fnaT304OBg4uLiHno+NDSUadOm8ccff+Dt\n7U2lSpX4559/8PX1fbBRsR663eS2d56Scp0RIxqQkTEeaIpGM4c6dRSMG5e3/z+TSUKt1mIySYwb\n15Zr16pgNHZAqZwOGLFYpqJQROHmtpC5c8Px83t4WVyLxczMmf2JjDyKSlUapTKKKVN+p1y5Wnmq\nKS8sFgtpaQl4ehaz3q5BtuCEvfOoY1HsXrObjOQMdm/ZjXROAi2QCdpKWubum0upyqXsXaZd2GQ9\n9G3btj3y+VOnTnHp0iXq1asHwJUrV2jUqBHh4eGUzMOdi4J15WWo5eTJP7FYmgPvAGA0ruLYsaIY\njQY0Gl0uctYnAAAgAElEQVSOz3PmzB7mzBlAWloMvr7VGDt2DZ9+uo1ffpnN1as7OHHiJnr9FqA+\nsgwGwzV27/6e3r3HP3SunTu/JTIyHoMhkqzf9K9YsOBNZs36O9fvLy+ioo4TGtqLjIybgJG33lpK\nixb2H8ZyBad3nya0TyjSMAnSAA1Z/8UAbqD0UiJlSnas0LFZdcildu3aXL9+bxfySpUqcfjwYTHL\nxU6sMVaeFdqp3BsauQkoUCpVOT5HenoS06Y9h17/DdCVGzd+YMqUZ/nyy3O88MJkAAYProhef39P\nV4fFYn7k+eLiLmAwdODeb3oX4uMn5/at5YnFYiE0tBepqZ8CLwHHWbSoI5UrN8Tfv0qB1JBjTtg7\n/z70e6T5Egwga0nRKqD4RIH8vIxqtYqiHkUpXb20vct0WDa9ulBYxrkcxZ1NIqy5WUT9+l0oWjQe\ntXoI8BU6XWe6dh2JSpXzvsDlyydRKqsBz5L1IzcAk8mD+Ph787Y7dRqMTvcysBVYjEaziubN+z7y\nfBUr1kOn+xlIAWSUymVUqFA/z+8xN9LS4snISCcrzAHqoVK1ICrqWIG07+r0t/RQ5vYDd+Bd8Frt\nRfF+xalzrg5Ttk5BrRG3zzyOTb8zFy86+I0WTq4gZqu4uRVhxoy/+e23uSQkHKJu3XcJChqUq3P4\n+PhjMl0gK4B9gGuYzdcf2OQ4JGQCHh7e7N07B09Pb1588XfKlKl+9+sWi4U9e34gPv4iFSrUo23b\nDmzfXhGVyhsfHx/efXezdd7wE3h6Fgck4ARQF0jFYjmGn5+DzXZxwt45QOverVn3wToM3xggHbRf\nanlzwZs07tHY3qU5BbFJtBNxhOmGeWE0Gli48A0OH94DtEWh+JPevd+mT58xOXq9LMvMnv0iJ09G\nIUnt0Wp/ITi4F716jSQz8yZ+fuVz9Ykhv/bsWcOXX76DStUCi+UY7dqF8Nprswus/Sdy0jCHrP/r\nddPX8efKP1Fr1Tw/6nnavdLO3mU5jCddFBWB7sCcNcDvl5x8jYkTg0lLU2A2J+DnV5I331xIzZpt\ncnyOixcP88knIbcvgroBN1CrK/HVV5fw8vJ90sttIi7uPFFRx/DzK0/Vqk3tUgPg1OEt5J5NZrkI\ntuEKAf5fS5aMIjGxBxZLKGDixo3enD0bnqtAv3UrBZUqgKwwByiOSuVDRkaq3QLd37+qfVdWFEEu\nPIIIdAfgikF+x5Ur/2KxjCJrhowGSepOVNShXJ2jUqWGwDngW+AZlMpleHt7PXKOeqEgwlx4DBHo\nduLKIX6/ChUCSUz8EbO5MSCh1f5E5cpdcnUOT89iTJq0hf/973USE0cRENCAkSM3F+i4ucMQYS5k\noxD+RtiPvUPcYrEQHv4LN27EUK1aM6pXb27zNl9/fS4xMc+QlFQVozEJna4EiYmx3LqVQpEiPjk+\nT8WK9Zg/P9yGlTo4EeRCDoiLojZm7xC/w2KxMHNmP06fjsZkaopS+SsDBnzEM8+8afO2TSYjM2f2\nIzIyBaNxMGr1DkqWPMqsWXvRarPGxQ2GDK5ePYOXly8lSohVOh8gwly4TVwUtQNHCfH7RUbu5PTp\n0+j1R8i6w3IUK1fWITh4iM2HLvT6dE6d2obZfB3wwGR6keTkpzlzZjd16wZz+fJJJk/uhsnkg8kU\nS8eOg3n11ZlWr+Ps2f2sXTsHvT6Ddu1CaN/+Vce++U0EuZBLItCtyBGD/I60tAQUihrcu12+ErKs\nRK9Pz/HQhyzL7Nz5LSdO7KFEidL07Dk6R6+1WMwoFCqyFuaArAukbndv7Z87dxA3b04CXgOS2b69\nBQ0atKN+/Wdy9R6zExV1jKlTe2AwTANKEB09DknS06XLcKu1YVUizIU8EIGeT44c4verVu1pLJa3\ngW1AK5TKuZQqVT1Xy86uWjWBbdu2YjAMRa0OZ//+NsyevR83tyLZvs7Ly5caNVpz9uzLGI1voFTu\nwM0tlqeeagXA9euRQL/bRxfDbO7ElSuRVg30HTu+w2B4B3gdAIOhBJs2vX030GVZZvv2ZRw69CfF\nivkREjKO4sXLWq39wiw+Kp6ti7eSmZFJq+daERgklsq2FbFSfB5Zc72UglCiRAXGjPmRokWHolQW\npXz5P/joo19yPORgNpvYvHkeBsPvwFBMpqWkppbk2LEtT3ytQqFg3Lg1tGtXlgoVJtOkSTTTp+/E\nzc3zdm1PAT/dPjoVlWobZco8lbc3mk0NcP9iX8YH3vuaNVNZseJzjhzpyo4dRRgzpgVpaYkPnScq\n6jhr107il19mkJx8zao13uVCvfOE6ATGNB/DJnkTf1X8i+kvTufgzwezfU34L+GMaDaCt+q9xbrp\n67BYLAVUrfMTPfRccKYAf5Q6dTrw9dcXkWU512PHWcMjMnBnz08F4IPRaMjR63U6D4YMmfvIr40e\n/S1TpnTDbP4Mk+kKbdu+TIMGuZva+CQdO77KX3+1xWAoCpREp/uY3r0n3v36xo3zkaRjQAUsFtDr\nozh48CeCg4fePSYiYifTp4dgNA5BqbzGhg1NmTPngOjJZ+OPJX+gf1GPPCvrQp5UW2L1xNU069Ps\nkcdH7IxgwfAFSMsk8IXf3voNhULBc+Oey1W7mTczSY1PxS/AD7W28MRc4XmneeTsIf4oebkQqNHo\nqF+/JydPvozR+D4KxSGUyr3UqfNFnmqwWCzs2vUt0dGRlC//FAsXRhAbexYvL19Klaqcp3Nmp1y5\nWkyduo2ff56PXp9JUNDMB9Ywl2Uz9+5EBVl2f2j53pUrJyFJXwB9MZshI2MUGzd+zsCBM6xXqAv1\nzgEMegMWv/t62H5ku5757nW7kd6X4Pbfc8PnBnYO35mrQP9r+V8sG7EMpY8SjVnDhF8nUKWxgy1t\nbCMi0B/BFUPcGkaOXMG3337IyZPD8fUtw5Ahf+Hjk/udY2RZ5rPPXuXo0fMYDN3R6ZZz9OhORo1a\nadNZJxUr1mfUqG8f+bW2bV/l77/7I0kfARGo1Ztp1GjKA8dkZKQB96ZUWiwVuHXrrPUKdLEwB2j5\nXEu299mOFChBKdC9pyOoX9Bjj3f3cEeRoEDm9tS8eNB55HwjlSunr7B83HKMh4xQHQzrDEzrM42v\no75Gqcz/CLPJaGL5mOXsWb0HtZuavuP70nlo53yf11pEoN9HBHn2dDoP3njjs3yf5/r1Cxw58geS\ndAHwwGB4j6NHq3Lt2rkHlswtSIMHz6Vo0RkcPjyVokV9GThwO35+AQ8c06JFDzZv/gCDYSlwA612\nHk8//aVd6nUWNVrUYNQ3o/h+2vcYMgy0DWmbbW+7y7AubG++Hb1Fj+wro52v5YWlL+S4vcsnL6Nq\npYI7P0bPQ+brmaTfSMe7hHc+3w38MOkHdp3YhXRQgiT47rnv8C3tm+3yvrIsc+HQBdIS06jcqDI+\npXJ+Q11uFfpAFyFe8PT6dFSq4sCdjabdUal80etv2q0mlUpNv34f0a/f49c179v3IyRJz99/d0Kt\ndqN//8lWH+t3RQ27NaRht4Y5OrZkpZLM3j+b35f8juGagVbrWvFUq5xfIC9VuRSWQxa4AfgCB0Cl\nUOFZ3DNvxd8nPTmdv1b9hbROyvqgVgEMIw0c3HLwsYEuyzKfvfoZR3YfQVVFheWYhQm/TqBGixr5\nrudRCmWgixC3r7Jla+LhYcZgmIbF0g+lMgx3dz3lyjn2dDaVSs2gQTMYNMiKY+b3CwtxyWGX3CpZ\nqSQvT385T6+t0rgKnQd1ZmudrahrqTEfNzNi5QiUqnvDLYmXEzm88TAqjYqnn3s6R2Ev6SUmtJ9A\npiYTzgO3V0xWnVfh7fP4nv+h3w5x5PgRDKcMWTsw/QbzX53Pl//a5pNdoQp0EeSOQaPRMWXK73z+\n+TCuXl1C2bI1efvtP+4uAyA4NpNkIj0pawjj/qB0FAOmDqBt/7bcuHKD8rXLU7zsvT2NL5+8zMSO\nEzF3M6NIV7Bm2hpm75+Nj3/2wyBn950lRZMCK4A+wCHgGrjvdefZ8Gcf+7r4S/GYW5uzwhwgGFIu\npeT3LT6WSwe6CPCCYTabCAubTnj4Vry8ijFo0BQqV87+I3aJEhWYMqVgto0TrGffun0sGrIIWSPj\n7uHOh798SOWG1p+VlF8BgQEEBAY89PzyCcvJ/CQTbt8gbBpl4qdZPzF43uBszyfLMqiA1sAu4BdQ\nfqVk8qHJFCtd7LGvq9SgEqoFKkzjTFAGFF8pKNvIdtNcbfLn9fPPP6dmzZrUrl2bsWPH2qKJbDnb\nTT/O7MiRTQweXIWff/4fV65U5PTpbkya9AxxcRfsXZpgZfGX4lk0bBHSTgljgpG0WWlM6z0Ni9kx\nbvzRp+ufeBNSamIq1L732FzHTHJi8hPPXb15dTzTPFGNVkE0aI5qqNWxFuVqlcv2dYFBgfQZ3gdV\ndRXaMlp8l/jywXcf5Oj95IXVe+g7duxg/fr1nDhxAo1GQ0JCgrWbeCQR4AXv/Plw5s17DUlaAQQA\n7wEXMZlCOHToV7p3H23fAp2RA4+jR5+IRtVMBfVvP9EP9O/pSYlLeWBYo6DFXYgjtE8oCWcTUOvU\nDFsyjJZ9Wz7y2EYdGxE/NR5ptQTpoJuvo/H7T96AWuehY9qOaaycuJJr869Ro1ENXvj4hRxNs+39\nQW86D+1MRkoGxcsWt+kwldUDffHixYwfPx6NJmshphIlSjzhFfkjgtx+wsPXI0lvcvcuEBYBXVAo\nWqNWa7N5peOTZZmff57Jhg0LkGUzHToMZsCAT60yl9lZ+ZX3w3zcDMlAMeAUyJkynr75n0GSH6F9\nQol/NR75PRnphMTi4MVUqFOBcjUf7j33m9iP1PdS2VNxD0qtku6jutP25bY5aqdoyaK889U7earR\nw9sDD2+PJx+YT1YP9HPnzvH333/z4Ycf4ubmxpw5c2jc+OG/gJPC7vVCgmrVIigw5zMcRIg7Bnf3\nIqhUlzDfvaEyFpBwc9tOixaz7VhZ/u3YsYJff/0eg2EHoGXbthfw9p5Pr16F91NHpQaVCB4QzJ/1\n/kRZX4n5gJmhXw5F62a/P976W3oSziYgvydnrUZRD5QdlVw4dOGRga7WqBm+aDjDFz28yqYsy2xe\nuJkty7agVCnp814fgl4Osv2byEbEzggid0bm+Pg8BXpwcDBxcXEPPR8aGorJZCI5OZkDBw5w6NAh\n+vbty8WLFx86dlJI7kNZBLljadfuNTZtasqtW8MwmwNQKudRu3Yzhg37iqJFS9q7vHw5cGALBsN4\nIGu+sMEwiQMH5hRMoOdw2CXzZiYXDl1A46ahWrNqBTLjZND0QbR+vjWJlxMpP688/lX9bd5mdrTu\nWtQ6NdJxKWsoSA/yMZnig3M/BPTHkj/48csfMXxtAD0sfXUpHl4eNO3V1PqF51BgUOADq1OGTc7+\n5yJPgb5t27bHfm3x4sX06dMHgCZNmqBUKrlx4wa+vvbZnV2wHR+fUsyZE862bV9x61YyTZv+TK1a\nbexdllV4eRVDoTjLvc1hzuLt/fjZDAUt/lI8EzpMQCotISfLBJQJ4JONnxRIb7lyo8pUbuQYM1uU\nSiXDvx7Ook6LUHZUwjFo0LQBtdvXfvKL/2PH2h0YZhugRdZjaZLEjrAddg303LL6kEuvXr3Yvn07\nbdu25ezZs0iSlO8wFz1zx+XjU4qQkI/tXYbVREefYPbsAcTHR6BQuKNSXQZ0aDQ/MWDAXwVXyBN6\n6V+O+JK0N9KQx8lghqg+UWz5fAs9P+hZcDU6iBYhLahQpwIXDl2g2OBi1G5fO09rArl5uMH9czji\nwd3d/bHHOyKrB/prr73Ga6+9Rp06ddBqtaxcuTJf5xNhLhQUgyGDyZO7kZ7+KdAfWf4GpXIcffqM\noXXrg5QsWcneJd4VdzEOefLtjw8qMAYbiTkVY9+i7KjsU2Up+1T+5nf3G9OP0D6hSDES6EH3lY5e\nO3pZqcKCYfVA12g0rFq1yirnEmEuFKRr185iNvsAg24/Mxy1ehl16wZbNcwlKZM///ya5OQ4atZs\nRcOGXXN9jsr1K5P8TTLm/5khA3Q/6qjxsm3WByksarauyZStU9j5/U5UahUdd3fM9x+Jguawd4qK\nMBesKTn5GidP/olG40aDBl0fuW2el5cfJlMs91Z2SsFsjsHLy89qdRiNBiZM6MC1ayWQpMZs3foO\nISH/0qPHyIcPzmbYZehnQ5nSfQrXyl7DkmmhyfNN6PB6B6vVWVg50vWBvHDIQBdh7npiY/8lOfka\nAQGBeHvb9t6E/4qJiWDixI5YLK2BZLy9pzBz5u6HNrj29S3HM88M448/nsZi6YRS+Rft2g3E3996\nmyMcObKJ69dVSNKvgAKDYSA//liLZ599L1dz3L18vZi5ZyY3Ym6gcdPYdElWwXk4VKCLIHdN3377\nIdu2LUetrorF8i9jx64lMDCowNpfunQMGRkTyVrAQ8ZkGsL69fN44YUpDx378sufUr9+O2JiIihb\ntif16nWyai2ZmTeB8mRNmgYog9lsxGw2olQ+YiOHbHrpSqWSEhUK9o+j4NgcKtAF13PmzB7+/PNH\nJCkCSSoObGPOnJdYtuyKTXcnul9S0jWgye1HCkymJiQkHH7s8XXqdKBOHdsMXwQGBiHL7wNhQGPU\n6ulUq9YRjSbnu/IIwuM4xH3MYjEt1xUXdx5oCdy50aMjmZmJSFJmgdVQu3YrNJpZQCZwHZ3uS+rW\nbVVg7d+vRIkKTJy4nrJlZ+Pp2Yb69dMZM+YHu9TiTCxmC8nXkjEajFY976kdp3izxpu8WORFJnSc\nQFJsklXPX9BED12wOlmWuXjxMKmp8fj4lAZ2ADFkLeC1hqJFA9DpbL+uxR2vvDKD5ORXOXbMB4VC\nSefOo2nbdmCBtf9f1as3Z/78cLu172yijkXxaa9PyczIBAMM+2oYrfrn/w9yQnQCM/vOxPCtAVrC\n+ZnnmdpzKvMPzbdC1fZh10AXvXLXI8syCxcOJTz8T1SqalgsR2nTph87dtRGrS6JWq1n7NjfCrQm\nnc6DcePWYDJJKJUqlEpVjl8bHx9FWNgMUlOTaNasM+3bv1ZgQ0V3OfAKjLZmsVj4tNenpE1LgxeB\nE/Blxy+p2rhqvpcdOLv/bNb65rdnjcqhMlfnXeXqmatON13xDrsFughz13T06BbCw/djMJwEigBb\nCA8fzpIll0hNjadEiYp225kotytApqTEMXZsSzIyBiPLbTh9eiZJSXGEhEywUYXCf6UlpJGZnpkV\n5gB1QdVcRfSJ6HwHumdxT4wRRjCRlYRRgAy//u9X3lr8Vv4KtxMx5CJYVXz8JWS5JVlhDtCRtLTL\neHj44OlpvzWz82L//jAkKRhZzpoNYzA0ZcOGFiLQbUyWZQ6sO8C1s9coU6MMGIFjZC2+lQKWYxb8\nJub/3oA6HeugzdCib6XPWr9lHTAAkhOevOGFo3KIi6KC66hUqQEKxWayxswBluDvX98p1xG3WMzI\n8v29ei2ybKfdecJc7xOt0WBkyYglvF7tdUY0GcGJbSeyhuyGLmTRjEWsubmGLyZ/Qc2gmmiDtbj3\ncEdXT0e7fu2o0jj/9wZc/OciNerVQBmnBB9gEegidTRo2+Cxr5FlmfSk9CfujGQvoocu5FpGRhrr\n1k3nypWL1KjRgJ49R6NWZ21oUqNGC0JCRvHjj7VQKr3x9CzC2LEbrda2Xn+L9evnERsbRc2ajQkO\nHmqzPxZNm/Zm7drpmEyBwFPodFPo0CH7vSeFhxkNRtRa9UPXHpaMWML+mP1I6yVSz6cy68VZvPv1\nuxzcdBDprARFwDDOwOkqp5m4fiJp8Wn4lfezyp2cm7/YzA/Tf8jam+VfYBYoTUqC3gqiy1tdHvma\n84fOM/256WSmZqLWqhn9/WjqdaqXbTsmycSWhVuIOR9D1bpV6fh6R5suc6yQ5XsLhBYUhULB2rUF\n3qxgBSaTxNixbbh27SlMpk5otauoU8ebsWPXPHBcZuZNbt1Kpnjxsrm6CPmktsePb0dsbABGY3t0\nupU0axbI229/ZZXzP0pMTATffTeZ1NQbNG3amV69Rlvt/eSJE10cTb6WzIx+M4g6EIXaXc2QBUNo\nN6jd3a8PKjWIzMOZcHsfCuX7Stqnt2fvkb1kht+b1ur2lBufrvuU8rXLW6UuQ4aBV0u+iumUKWtP\nlg+ASGjUqREjV4xE6/7wtRZJLzG0ylBufX4L+gC7QddHx+enPn/sXboWi4Up3adwnvNIz0jownQ0\nrNyQkSsescxDDvVV9CW7yHa+z8GCXZ07d5CEBAMm03LgRSTpF44f30Zy8rUHjnN398LPr7xVw+/M\nmT1cv67HaPwBeAODYQt79/7ArVspVmvjvwICAhk/fi0zZvxFnz5j7BvmTmbOy3OIbhmNnClj3G/k\nm/HfcP7Q+btf1xTRZAXqbapYFf5V/FFdU8FSIBEUCxS4Gd0oXa201eq6lXwLpacya2y+J/AO8Dcc\nzzjOF8O/eORrEqISMHuYs8IcoDWoaqq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"text": [ "" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Let's try a larger k" ] }, { "cell_type": "code", "collapsed": false, "input": [ "knn = KNeighborsClassifier(n_neighbors=10)\n", "\n", "knn.fit(BX, BY)\n", "decision_boundary(knn, BX, BY)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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yr2RCTUAG5RUlbk3c7F1WoSWmLToJnUbDQd+hYm2PfLJy5ecYDDOR5SlYLF+g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\nAUznTE/cHFvKlFB4K+4NbLuC0l2JlGm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"text": [ "" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Applying to Iris\n", "\n", "Applying the same technique to Iris is simple:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "X, Y = sklearn.utils.shuffle(IX, IY)\n", "\n", "for k in [1,3,5]:\n", " knn = KNeighborsClassifier(n_neighbors=k)\n", " for n in [10, 50, 100]:\n", " knn.fit(X[:n], Y[:n])\n", " print(\"{} {}: {}\".format(k, n, knn.score(X[n:], Y[n:])))\n", " print" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1 10: 0.85\n", "1 50: 0.93\n", "1 100: 0.96\n", "\n", "3 10: 0.8\n", "3 50: 0.92\n", "3 100: 0.96\n", "\n", "5 10: 0.664285714286\n", "5 50: 0.95\n", "5 100: 0.98\n", "\n" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## $$\\text{The training set should always be separate from test set.}$$\n", "\n", "# $$\\text{That's worth repeating}$$\n", "\n", "## $$\\text{trainset} \\cap \\text{testset} = \\emptyset$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Avoiding overfitting\n", "\n", "+ Overfitting: fitting a model to details of your training data that don't generalize.\n", "+ Testing on a held out test set gives an estimate of generalization error\n", "+ Ideally, one has a \"gold standard\" test set (separate from validation set) that is never used for fitting parameters\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# sklearn makes it easy\n", "\n", "## Test/Train split:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.cross_validation import train_test_split\n", "\n", "# Test on 1/3 of data\n", "X_train, X_test, Y_train, Y_test = train_test_split(IX, IY, test_size=0.33)\n", "\n", "knn = KNeighborsClassifier(n_neighbors=3)\n", "knn.fit(X_train, Y_train)\n", "knn.score(X_test, Y_test)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "0.95999999999999996" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# [KFold Cross validation](http://scikit-learn.org/stable/modules/cross_validation.html)\n", "\n", "This method splits the data into folds so that every example is held out in the test set exactly once." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.cross_validation import KFold\n", "from sklearn.metrics import accuracy_score\n", "\n", "def score(clf, X, Y, folds=2, verbose=False, metric=accuracy_score):\n", " predictions = np.zeros(len(Y))\n", " for i, (train, test) in enumerate(KFold(len(X), n_folds=folds, shuffle=True)):\n", " clf.fit(X[train], Y[train])\n", " predictions[test] = clf.predict(X[test])\n", " if verbose:\n", " print(\"Fold {}: {}\".format(i + 1, accuracy_score(Y[test], predictions[test])))\n", " if metric:\n", " return metric(Y, predictions)\n", " return Y, predictions" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# [KFold Cross validation](http://scikit-learn.org/stable/modules/cross_validation.html)\n", "\n", "We can use such a method to test out different parameter settings" ] }, { "cell_type": "code", "collapsed": false, "input": [ "for k in range(1, 10, 2):\n", " acc = score(KNeighborsClassifier(n_neighbors=k), IX, IY, folds=30)\n", " print(\"{}: {}\".format(k, acc))" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1: 0.96\n", "3: 0.96\n", "5: 0.966666666667\n", "7: 0.966666666667" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "9: 0.966666666667\n" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "On the dataset of 150 examples, this does 5 splits of 120 training/30 testing examples" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# [Random Forests](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)\n", "\n", "Random forests are an ensemble technique: they combine many decision trees\n", "\n", "Decision trees classify an instance by repeatedly branching on features until they reach a labelled node" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn import tree\n", "\n", "dt = tree.DecisionTreeClassifier()\n", "dt.fit(BX, BY)\n", "decision_boundary(dt, BX, BY)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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TkJLikqD2vceWOhYS4hKe2naN5jVwTXZFPUYN4aA9rsWnow/lfco/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\nhovbRJwAAAAASUVORK5CYII=\n", "text": [ "" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# [Random Forests](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)\n", "\n", "Random forests train many decision trees on subsets of features and data and combine the individual predictions" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.ensemble import RandomForestClassifier\n", "\n", "df = RandomForestClassifier(n_estimators=10)\n", "df.fit(BX, BY)\n", "decision_boundary(df, BX, BY)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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PPz4+/g8eBwdPfebxNknopUqVIiIi4sHjiIgISttgcYfBaCQ8NhZ3Z2c0SiXhFgsVydhn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\nQ/9S2KMwdTvVtekMj6xISUhhx5IdJN1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"text": [ "" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# [Support Vector Machines](http://scikit-learn.org/stable/modules/svm.html)\n", "\n", "SVMs attempt to find a hyperplane that separates different classes. In 2d, this is just a line." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn import svm\n", "\n", "clf = svm.SVC(kernel='linear')\n", "clf.fit(BX, BY)\n", "decision_boundary(clf, BX, BY)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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phF9lP47/fRzPlp50XNoRJ1exOby92CzQ586dy6hRo0hMTKRUqVK2akawgYIK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\nD4Oc23eOFE0KLAd6A4eAWHD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"text": [ "" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# [Support Vector Machines](http://scikit-learn.org/stable/modules/svm.html)\n", "\n", "Things get trickier when your data isn't linearly separable. For example:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.datasets import make_circles\n", "CX, CY = make_circles(factor=0.5, noise=0.2, random_state=1)\n", "clf = svm.SVC(kernel='linear')\n", "clf.fit(CX, CY)\n", "decision_boundary(clf, CX, CY)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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mu09dTjnAt1S+amE/8OyzvdHrRwJHgM3o9Yto3Dj9JOzv35zXXluEt/ebFC3a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\nDk3kkAxEOpCClLAdyarlrxK1cyUTjQk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"text": [ "" ] } ], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# SVM Kernels\n", "\n", "SVMs get around this by projecting data into a space where it is linearly separable." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Illustrate linearSVM on Circle dataset\n", "clf = svm.SVC(kernel='rbf') # rbf is the default kernel type\n", "clf.fit(CX, CY)\n", "decision_boundary(clf, CX, CY)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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SgEr7N3LmzN40V6Czp6SkON57rwkxMS9hsw3m6tWFXL7cmQkTfsmTy7z26BGK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\n8i9Po9nMe8uW8fP+/Xi5uTG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"text": [ "" ] } ], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# SVM Kernels\n", "\n", "The default rbf kernel is a safe bet in most cases" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Illustrate linearSVM on Circle dataset\n", "clf = svm.SVC()\n", "clf.fit(BX, BY)\n", "decision_boundary(clf, BX, BY)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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HjyWpOHfvXrFdQVlhhe/7rQa0wr+MPyf/Ool3Q29aLm2JSz6xOXxu5OYiq9UC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\nP2+3YsWGqNWRaLXTkaTmqFSLKFWqZp4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"text": [ "" ] } ], "prompt_number": 20 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Multiclass classification" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "While SVMs are generally a safe default, they are not ideal for data with many classes.\n", "\n", "If there are many classes, consider using alternative classifiers such as random forests." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Inspecting results: Metrics\n", "Aggregate classification accuracy is a coarse measure of performance. sklearn provides many metrics to get a better sense of what's going on." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn import metrics\n", "clf = svm.SVC()\n", "y, pred = score(clf, IX, IY, metric=None)\n", "print(metrics.classification_report(y, pred))\n", "print(metrics.confusion_matrix(y, pred))" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " precision recall f1-score support\n", "\n", " 0 1.00 1.00 1.00 50\n", " 1 0.92 0.98 0.95 50\n", " 2 0.98 0.92 0.95 50\n", "\n", "avg / total 0.97 0.97 0.97 150\n", "\n", "[[50 0 0]\n", " [ 0 49 1]\n", " [ 0 4 46]]\n" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "clf = svm.SVC()\n", "y, pred = score(clf, digit_X, digit_Y, folds=10, metric=None)\n", "print(metrics.classification_report(y, pred))\n", "print(metrics.confusion_matrix(y, pred))" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " precision recall f1-score support\n", "\n", " 0 1.00 0.54 0.71 178\n", " 1 1.00 0.45 0.62 182\n", " 2 1.00 0.46 0.63 177\n", " 3 0.33 0.74 0.45 183\n", " 4 1.00 0.52 0.68 181\n", " 5 0.25 0.76 0.37 182\n", " 6 1.00 0.66 0.79 181\n", " 7 1.00 0.49 0.65 179\n", " 8 1.00 0.16 0.27 174\n", " 9 0.45 0.59 0.51 180\n", "\n", "avg / total 0.80 0.54 0.57 1797\n", "\n", "[[ 97 0 0 21 0 44 0 0 0 16]\n", " [ 0 81 0 34 0 51 0 0 0 16]\n", " [ 0 0 81 48 0 40 0 0 0 8]\n", " [ 0 0 0 135 0 45 0 0 0 3]\n", " [ 0 0 0 34 94 45 0 0 0 8]\n", " [ 0 0 0 31 0 138 0 0 0 13]\n", " [ 0 0 0 22 0 23 119 0 0 17]\n", " [ 0 0 0 23 0 55 0 87 0 14]\n", " [ 0 0 0 40 0 71 0 0 27 36]\n", " [ 0 0 0 23 0 50 0 0 0 107]]\n" ] } ], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "clf = svm.SVC(kernel='linear') #This is a case where a different kernel helps\n", "y, pred = score(clf, digit_X, digit_Y, folds=10, metric=None)\n", "print(metrics.classification_report(y, pred))\n", "print(metrics.confusion_matrix(y, pred))" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " precision recall f1-score support\n", "\n", " 0 1.00 0.99 1.00 178\n", " 1 0.96 0.98 0.97 182\n", " 2 0.99 1.00 1.00 177\n", " 3 0.98 0.97 0.98 183\n", " 4 0.98 0.99 0.99 181\n", " 5 0.96 0.98 0.97 182\n", " 6 1.00 0.98 0.99 181\n", " 7 0.98 0.98 0.98 179\n", " 8 0.95 0.94 0.94 174\n", " 9 0.97 0.96 0.96 180\n", "\n", "avg / total 0.98 0.98 0.98 1797\n", "\n", "[[177 0 0 0 1 0 0 0 0 0]\n", " [ 0 179 0 0 0 0 0 0 3 0]\n", " [ 0 0 177 0 0 0 0 0 0 0]\n", " [ 0 0 0 178 0 2 0 0 3 0]\n", " [ 0 0 0 0 180 0 0 1 0 0]\n", " [ 0 0 0 1 0 178 0 0 0 3]\n", " [ 0 1 0 0 1 0 178 0 1 0]\n", " [ 0 0 0 0 1 1 0 176 0 1]\n", " [ 0 6 1 0 1 1 0 1 163 1]\n", " [ 0 0 0 2 0 3 0 1 2 172]]\n" ] } ], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# [Linear Regression](http://scikit-learn.org/stable/modules/linear_model.html)\n", "\n", "Regression attempts to predict continuous values rather than discrete labels.\n", "\n", "sklearn has many different [many different types of regression](http://scikit-learn.org/stable/modules/linear_model.html), but they all follow the fit/predict paradigm.\n", "\n", "This example uses a dataset of house prices:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.datasets import load_boston\n", "data = load_boston()\n", "HX = data['data']\n", "HY = data['target']" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "prompt_number": 24 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# [Linear Regression](http://scikit-learn.org/stable/modules/linear_model.html)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print data.DESCR[:1200]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Boston House Prices dataset\n", "\n", "Notes\n", "------\n", "Data Set Characteristics: \n", "\n", " :Number of Instances: 506 \n", "\n", " :Number of Attributes: 13 numeric/categorical predictive\n", " \n", " :Median Value (attribute 14) is usually the target\n", "\n", " :Attribute Information (in order):\n", " - CRIM per capita crime rate by town\n", " - ZN proportion of residential land zoned for lots over 25,000 sq.ft.\n", " - INDUS proportion of non-retail business acres per town\n", " - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n", " - NOX nitric oxides concentration (parts per 10 million)\n", " - RM average number of rooms per dwelling\n", " - AGE proportion of owner-occupied units built prior to 1940\n", " - DIS weighted distances to five Boston employment centres\n", " - RAD index of accessibility to radial highways\n", " - TAX full-value property-tax rate per $10,000\n", " - PTRATIO pupil-teacher ratio by town\n", " - B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n", " - LSTAT % lower status of the population\n", " - MEDV Median value of owner-occupied homes in $1000's\n", "\n", " \n" ] } ], "prompt_number": 25 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# [Linear Regression](http://scikit-learn.org/stable/modules/linear_model.html)\n", "Let's try vanilla linear regression:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn import linear_model as lm\n", "\n", "y, pred = score(lm.LinearRegression(), HX, HY, folds=10, metric=None)\n", "print(metrics.mean_squared_error(y, pred))" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "24.2456385287\n" ] } ], "prompt_number": 26 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "To put this in context, take a look at the price distribution:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.hist(HY);plt.show()" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 27 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Clustering with [K-means](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html)\n", "\n", "Clustering is used to assign observations to a group, or cluster.\n", "\n", "KMeans discovers clusters by iterating two steps:\n", "+ Assign training data to the nearest cluster center\n", "+ Recalculate cluster centers by averaging all points belonging to the cluster\n", " \n", "" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Clustering with [K-means](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Example discovered cluster centers\n", "from sklearn import cluster\n", "km = cluster.KMeans(n_clusters=3)\n", "Y_hat = km.fit(BX).labels_\n", "\n", "plt.scatter(BX[:,0], BX[:,1], c=BY, alpha=0.4)\n", "mu = km.cluster_centers_\n", "plt.scatter(mu[:,0], mu[:,1], s=100, c=np.unique(Y_hat))\n", "plt.show()" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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pUmJjY5HLb+3nSJKEJEm3/d69io2N5Vs//KHT7zuaSJLE0RNHuVh1AaWnEi1a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\n5Ela6usJj4lhfkbGqOzxW61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"text": [ "" ] } ], "prompt_number": 28 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Dimensionality reduction: [PCA](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html)\n", "Principal Component Analysis (PCA) is a technique to maintain as much information as possible while reducing the number of dimensions in the data.\n", "\n", "\n", "![](figures/pca.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Dimensionality reduction: [PCA](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html)\n", "\n", "PCA can also be used to visualize datasets. Consider visualizing the digit dataset (64 dimensions) in 2d." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.decomposition import RandomizedPCA\n", "pca = RandomizedPCA(n_components=2)\n", "proj = pca.fit_transform(digits.data)\n", "plt.scatter(proj[:, 0], proj[:, 1], c=digits.target)\n", "plt.colorbar();plt.show()" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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pr1M1cyCsRrRN59d3aN5QdM1ZLrVoXE/5+fnyeMIF68u8x1y53cmy2xxy2JyC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\nFp9++ilXrlzh22+/xWdJQG+0AvMopBJPFxupkwfl8+CYV4dwEZ8unO/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9Xtp07kBOko3Q\n13viXLGLww+9S+NdL3Pu5dXk7TtPvW8mcHTI+2XasuN6NuLxUnHpCB5/cBzl4srdk3L98/eoXbv2\n/ds0/wH4PeFWCQkJHDly5DeP/zPc6p9AxYR4dPt+UP8jgnb3Zo4eO0pmRjqs+xQ8Hrh4Gha+AUkp\n3LhwDp/PR25uLvFVa/DW7NnkZNwkxwtvfrGGpJq1yI1OxP3cHAiJUGNXr1+A7ExY9Ca+y2fxVah6\n9wHiK4HJCKkN4dRsNS315x5XPhd4HWr1f3zQbBk0mo9y+QtEo6daqwh0BvW1P/Vtfb72wOBC+NYN\ny2xA7nGa14zk4wXzUPY+gbL/ObT7nlIjAbIOltJwo8k+hC17P8afBqPP3crz61Oo0doAN7+/WxHr\n1g7wOPB5oTDHpU71CXcyhUiLlhR3MaN8PpoDDRBs/ICZOWh0LanY0EbjAVGkbcjkxMbb9J1WEcxC\neatg0sLT9aFQA/OsMNkCw5w6DP4mtmgVZlq0vK3XcxMYpoMgjfqbM9wEOq2XMSm32L5lLT6fj8cf\nf5ZBg6bjdDbmHB6KgGigqcNHTGgUt9KzGDhwIF7vReBnr/gatNo8ZsyYwv79W0itV5uTIz6i6OIt\n0r/cQ+6GY/SKrceCl2fz2nRV48/Ly2Pz9l3oO7ekID6Jkjwn/aKOMK7STm48/hYXZnxJykcjOTn+\nU6L6N0Kj0xI3vDXV5w2lYNsRHunrolnN9Tw5tj8TJjxLg0YNqVgliaHDHuPCzatU/fAxwtqnkvLx\nKFxZdyg6m44zPRedzURQk8oEN6vCjkYvcOSx/8PeWUdJcWV//FPt3uNuDDrA4O7uGtwheIAkBAIh\nggUIkBA0SAghSIKFIEFD0ODu7q7j1jPdfX9/FDssv2R3s7tk5Zz9nvPOTHXfJ1Wv6var++6937ns\nrfQhBce2w796EYJ7Vuenbf/du/2/F/9Kzqv/mQL+ATx48IBy1WuQ4gxCcrJJu3oBqdoYovLCuaNw\n9gi4c9SQ1dtX8DGbSHz4gBYdOrHOZYDR8yHhMbxeCwZPRNm5Hs25o3iqN4Fjv8C7n8Gy2bB7A5it\naNNT8OgMMH8r+AfD8M5q/cYdYcFkTOjIshaB2A5w/TtwJWLWpFGjQjF27dlLZlYmGIOgQC8cDz9n\n4qHSPLySxqoxV7h5KIGbZiFQAx9mKfwSV5LF36/m+PHj9O3ejWauNCK0CmEaYYDLiie8PtrEs1gz\nH1CLdLblePFqQcxmRCrhyj4FtjAwBsKT4+ABrdNBgH8SdfqGcXpbGpf3uynqeITxkZd6ooYNnFdg\nvBVueeHjTNAHGtAm5TBcJzxSYCFaYnRQxuXhii/s7A5dftTw8w0TOUC21kvPeYWp1C6MU1uf8Nlr\nR+ng9nBZYKcDjAosyIL5FtjfE0JmG8hAITMjCK/7MmBCywAUZmPTatHb7ez45ZfcMOPdu3fTpEkr\n0tPTMRpNLFo0j7Zt2wKQkpJCn0H92blzF4HBQcydOpMqVaq8dM8MHz6crw78iLg9eLKyaRV3m68/\nU3NKHD0F9fvaqHzra7bnHQjPMhCznvxj2+DJyKbovm9ZPl394Tx5Fqq0gNC3WuBOyeTOot1ojXrq\nPvwSjU6LeLxsi+hHTlI6flXjSD1xk4iu1QjvVp0bk9dz/4dDFPi4HfmGNAXgdLsZvF2xFW+//Tb/\nqXhVpoBZ0vN3yQ5UFvwvu9W/A6GhoVw8cZy9e/ei1Wpp1KwZOSYT+AfBR7OhfTlYdw5CI+HaBdI7\nlCcpKYl9+/fDvG2g00FQGDTrCod2IDWa4vlpFXz9uZovdUx/+GYnnDqA8f4tOuZkc1NR2N2lKl6v\nByUkAll7FgxGqNaYrM6VaVcznMfPNpASnUOe2NJ0aNOc40cOs33zj9g1JjKzHuLOuE9a9PsMyPcB\nYnVA6z5osw8Rdvow4RovPmGhDH3rbYoUL4supBLp+PCd18NnukzOe0CDBu/dDXjs+UiJG8zG0x9T\nB3jsgatpmbh5iot1kDga9SX9IjAJT/JCHiV7Wf7BA3Bb8ZCEM9DLQQWiBI4BGx1Q+vnd+MAD8x9n\n84MdGj7PieFO9/A0B+bawPcRtFlv4uATCwfPHiMjI4OGLWtQub3qHF+iQRAhsRa6305lrgviktQg\nh7tamFQOIqZBclY2FquWHEMI2W7VrulhFgbDt6zbtp5y5cqpjLbPodPp8HoVRN7D5crm9dcHULhw\nYYoWLYrD4WD5om8B1Ta5Y8cO5s6dS7FixahUqRIAcxfMR1soCHdqJqGtK+Dz6HZu2067moM2+eRN\nNEku5s+dz8Cx73Htsx8JblKKAKcnV9bPFzDqiZug2gt1Tgs3ZmzmWOvPCe9UhQfL96N1CzqnDc4/\npnv7zty//YDT7eYRHhrKI42Wy2NWkXnjMelXH5J2+AY+DXsyYcIEAgMDufM8A1bnTp0oUKDAq3xs\n/u1wYfzbQq8K/7SD2F/A5s2bpWDBgpIvXz6ZOHHiS9/9gd3+W9C6UxfRl64iBIQIb40Xild44ct6\nTsSaJ79s27ZNfMMjhci8QqW6wooj6l+DUZTAMMFgF+L6CJXnC36FhfwlBB9/CdRapIjWLD1BBoCY\ntVqhTZ8X7f/yWLDY5LVOXV4a088//ywhVqsMe+6z2hREr+iEiCaC3ihsuqzWP+sVa+nKMmPGDMnO\nzhaL3V9ouEflveqSLhqfGIkr7xS/ILMozmhRDDahS6bog6tLA5DmIFEKssyGzLAgZqwCH4uGIIGx\nAkUE9glsFQU/aa5Hguw2cZotEqdBIhXEDnLSiZxwIhV0iFNBfEA22V/kE5hoQQabkERfxKTVyuef\nf57L1XT79m2xOc0y924dlcvqWX0xO3SyxqrmFdhpR6wghYP14qNDtjxvd70dMSsILBeYIdBIwsPz\n/SaHVM2azQS+lud0DaIok6RDh56/khsw+E3xLxAp+XvVE5+oEBk/6RPxer2i1enEEGgXe9FIqX52\nitj9jbJ4BvLLWqRkUcQRbBKDzSwrVq2U1NRUicofK4F1i4ne1yoWM/LtF8jhTUjFMkh0mzK5Pqbx\nc3qJPtAh4d2rizkmUDQmveicFgnvUlX8CkTKpE8nS4kKZURvNIjebBR9gF3Cu1YTY7BTNEa9oNeK\nNTxAorvXEI1RL84yseJXIb/Y/Xzk1KlTf8wD83fiVegLQCbLoN9VXkV/f4iN1ePxMHDgQLZs2cL5\n8+dZtmwZFy5c+CO6+oeRmprKs2fPXolJYvH8eXQpVxxfnQbD0mlw+TScP65+uWUlGY8fUbdpcxIf\nP4LqTaB+W+heA1ISYflhJDMdzBbwpEBEE6i3E66cgTQXT8pO4lyZSSzWWbkMiMcDG5bC9rWqHXdM\nX4gpQJCf70tjOnv2LHncbv6Uq6g44BYPusc7ULxuCHseyqooEJmXHzdtxmz3ISM1EYKf+9HqLHh9\nynHtUBrpTzIxue+g0enUVbXrGTrgtAKL7dDeCIPMMMycjompNNQ9RcMXwDSgElALYQI79YFk6+0k\nWwpz27cEd0RDBjVpkWKmbgr0NcI5J/Q3QZc0OJIDm7Jh4vOd/7puC31792bw4MFs/WkrPv52YmKi\n8HiyGVpsN5+3OsY7hXdhzRbapUPzTOiZDSXCFHR+eYjQG6j/fBXc1ADhFg062pOfNynPJlIT7rHt\nN2yOaWkZwAsuKpFgUlMzXpK5cOECS5Z/R9nDH1Nofi/KHhjNuPHjefbsGeWqViT0tQq4Hidze/52\nokd1ZPAUG637KJQoCg6dMG/mbNq0as20mdNx5WSTcfI2Rp0er83J21Oc1O+s5dpdX56df0jKmdsk\n7LvI5Y9XI64cHPFRaEwGqhwYR/XTn5F+8T62moUY//lksurGUD9zCZWPT8TryuHJ1lP4Vy9M3Qfz\nqHlmCjkuF08PXUHrMJN28T6ZD5PIyMqg36AB//Az8Z+If6WN9Q9RrIcPHyZfvnzExMSg1+tp3749\n69at+yO6+rshIvR98y38Q0IIyxNL5Tr1SE5O/qfaNJvNLJg9i4QH93A9fczqpUux9K6DtX4Myug+\nSKeBcDQNfrwAO9dBeAxUbwz3bqoKtnUv+GoTVI6GnfVUcj+9FSrMgLiBUHgQOeU+Z4feTj6gSWYG\nvN8NulWD6xfxS3rEB8PefWlMBQoU4JZOh+v58UXAabVwYN9uqtSui2HiW/D4PuzaQM6O9fxy8x4e\nnRF84uD8c//G5Ctwbw8arEw2gybdi8eVDXs6kxNQhp8Bl8Cfk30oQGFtEt/YvAiZwA4gAjACE7D7\nOklKM0PZyaQ3OwFlJuJByy3pRh6Nlu4mCNfCeIuaq6BZCrRLhdI1a3GgaDFSooM5euEEsaGh9B/U\nk7YT8tB3QXHKNAzCkenh1g/3afnIxcAsD3WAGyZY1BbG1BDMRh0P0PDoebrcex64l+4lFugENASa\nZGYyZODAX81xjx5tsFiGAQeAXVgso+nevfVLMo8ePcKZNxS9U/05M4X5IVY95apVol/3XvifScaT\nmMHdhbtInbcXd5aBRwkK36zWEhhRlE6dOvHFnNnMXLGQQuvfpvRP72P0d/B6uy4M6TOC5cs2cefW\nQ8IUO/uqfMShxhNxJ2US0b4SN6ZuxPUwkf3VR3Np5AoiXq/JwzWHSX2ayL0fDpF06AqS7UZRFAJq\nFMbr8fJLmRHofSwE1i9O+pUH4PFS9eB46tz4ggo/fciRo0dIS0v7+x+I/1C40f6u8irwh9hY7927\nR2RkZO5xREQEhw4deklm9OjRuf/XqFGDGjVq/BFD+RUWLPiapXsOkrP9HljtHBvThzfeGcq3C+b/\npryIcP78eZU3vlgxzGYza9eupefAQaQ8fUKlGrX4fskiEhIS6DFgENdv3KBMqVKcP3kwHlf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Nfrlfnz54spJFyYtU6Ys1HwDxKCw1XiwHMirD8n6A1ir1pPzP4B8u67w+Std4ZIjZq1hE5vqsmv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\n5J8LDAws0Dr38OHDV+2mqbn9REW1IzFxOlZrVcCAh0ciUVFPX/MaX19fX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"text": [ "" ] } ], "prompt_number": 29 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Dimensionality reduction: [PCA](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html)\n", "\n", "Or the iris dataset (projecting 4 dimensions to 2)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.decomposition import RandomizedPCA\n", "pca = RandomizedPCA(n_components=2)\n", "proj = pca.fit_transform(IX)\n", "plt.scatter(proj[:, 0], proj[:, 1], c=IY)\n", "plt.colorbar();plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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o1q3bE8cJhYInSRKRvSOJSTiKf1d/zq0+h/qChj3b9zx2W35RuHXrFlUqVCBS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\nps3u0P3792fChAloNBqKi4s5ePAgU6dOrfa81Sb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"text": [ "" ] } ], "prompt_number": 30 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Dimensionality reduction: [Manifold learning](http://scikit-learn.org/stable/modules/manifold.html)\n", "\n", "Manifold learning is another approach to mapping data to a lower dimensional space.\n", "\n", "There are many different types of manifold learning in sklearn. In practice, PCA is a safe bet." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.manifold import Isomap\n", "iso = Isomap(n_neighbors=5, n_components=2)\n", "proj = iso.fit_transform(digits.data)\n", "plt.scatter(proj[:, 0], proj[:, 1], c=digits.target)\n", "plt.colorbar();plt.show()" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Q4NvX0anLuDJ9A/atArEJ8QHA+7UOnH97JXK9gbaXv0BpbU7B+ZscbPwuwcte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\nA5/n4N49MLoPdK0O6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"text": [ "" ] } ], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "#from sklearn.manifold import MDS\n", "#mds = MDS()\n", "#proj = mds.fit_transform(digit_X)\n", "#plt.scatter(proj[:, 0], proj[:, 1], c=digit_Y)\n", "#plt.colorbar();plt.show()" ], "language": "python", "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "prompt_number": 32 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Choosing parameters: [Grid Search](http://scikit-learn.org/stable/modules/grid_search.html)\n", "\n", "Grid search is a method for systematically testing model performance with various parameter settings\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.grid_search import GridSearchCV\n", "\n", "param_grid = [\n", " {'C': [1, 10], 'kernel': ['linear']},\n", " {'C': [1, 10], 'gamma': [0.001, 0.0001], 'kernel': ['rbf']},\n", " ]\n", "\n", "gs = GridSearchCV(svm.SVC(), param_grid)\n", "gs.fit(digit_X, digit_Y) # Let's try it on MNIST\n", "print(gs.best_params_)\n", "print(gs.grid_scores_)\n", "\n", "gs.fit(IX, IY) # Different settings work better for the iris dataset\n", "print(gs.best_params_)\n", "print(gs.grid_scores_)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "{'kernel': 'rbf', 'C': 1, 'gamma': 0.001}\n", "[mean: 0.98164, std: 0.00136, params: {'kernel': 'linear', 'C': 1}, mean: 0.98164, std: 0.00136, params: {'kernel': 'linear', 'C': 10}, mean: 0.99110, std: 0.00284, params: {'kernel': 'rbf', 'C': 1, 'gamma': 0.001}, mean: 0.97051, std: 0.00799, params: {'kernel': 'rbf', 'C': 1, 'gamma': 0.0001}, mean: 0.99054, std: 0.00208, params: {'kernel': 'rbf', 'C': 10, 'gamma': 0.001}, mean: 0.98497, std: 0.00491, params: {'kernel': 'rbf', 'C': 10, 'gamma': 0.0001}]\n", "{'kernel': 'linear', 'C': 1}\n", "[mean: 0.98000, std: 0.01633, params: {'kernel': 'linear', 'C': 1}, mean: 0.96667, std: 0.00943, params: {'kernel': 'linear', 'C': 10}, mean: 0.52000, std: 0.14514, params: {'kernel': 'rbf', 'C': 1, 'gamma': 0.001}, mean: 0.32000, std: 0.00000, params: {'kernel': 'rbf', 'C': 1, 'gamma': 0.0001}, mean: 0.90000, std: 0.01633, params: {'kernel': 'rbf', 'C': 10, 'gamma': 0.001}, mean: 0.52000, std: 0.14514, params: {'kernel': 'rbf', 'C': 10, 'gamma': 0.0001}]\n" ] } ], "prompt_number": 33 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Combining models: Pipelines\n", "\n", "We can combine many models together in a `pipeline`. The final step in the pipeline is a classifier, and all other steps are feature transforms\n", "\n", "A simple pipeline could use PCA to project the data into 3 dimensions before classifying with an SVM " ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.pipeline import Pipeline\n", "\n", "pca = RandomizedPCA(n_components=16)\n", "clf = svm.SVC(kernel='linear')\n", "\n", "pipeline = Pipeline(steps=[('PCA', pca), ('SVM', clf)])\n", "\n", "# fit/predict work in the same way as other classifiers\n", "# pipeline.fit(X[train], Y[train])\n", "# pipeline.predict(X[test], Y[test])\n", "\n", "score(pipeline, digit_X, digit_Y, folds=10)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 34, "text": [ "0.96215915414579856" ] } ], "prompt_number": 34 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Putting it all together: Facial recognition" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Putting it all together: Image classification" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Putting it all together: Working with Text" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Resources\n", "\n", "+ ### [The sklearn flowchart](http://scikit-learn.org/dev/tutorial/machine_learning_map/index.html)\n", "\n", "+ ### Tutorials\n", " + [Scientific computing in Python](https://scipy-lectures.github.io/index.html)\n", " + [Scipy2013 sklearn tutorial](http://nbviewer.ipython.org/github/glouppe/tutorial-sklearn-scipy2013/tree/master/rendered_notebooks/)\n", " + [KMeans](http://nbviewer.ipython.org/github/temporaer/tutorial_ml_gkbionics/blob/master/2%20-%20KMeans.ipynb)\n", "+ ### Courses\n", " + COS402/424\n", " + ORF350\n", "+ ### Misc. internet resources\n", " + [Metacademy](http://www.metacademy.org/)\n", " + [Metaoptimize - ML stackoverflow](http://metaoptimize.com/qa/)\n", " + [ML subreddit](http://www.reddit.com/r/machinelearning)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Attributions\n", "\n", "This presentation has been pieced together from my own knowledge and tutorials such as the [Scipy2013 sklearn tutorial](http://nbviewer.ipython.org/github/glouppe/tutorial-sklearn-scipy2013/tree/master/rendered_notebooks/) mentioned above (particularly for some visualizations and the eigenfaces example). Other sources include [Andreas Mueller's excellent sklearn presentation](https://amueller.github.io/sklearn_tutorial/) and the many tutorials available in the sklearn documentation." ] } ], "metadata": {} } ] }