{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "import warnings\n", "from sklearn.utils import ConvergenceWarning\n", "warnings.simplefilter(\"ignore\", RuntimeWarning)\n", "warnings.simplefilter(\"ignore\", ConvergenceWarning)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Build You a Machine Learning\n", "\n", "G iven some inputs $x_1, \\ldots, x_n \\in \\mathbb{R}^m$, and corresponding outputs $y_1, \\ldots, y_n \\in \\mathbb{R}$, find a function $f: \\mathbb{R}^n \\to \\mathbb{R}$ so that\n", "\n", "\$$\n", "f(x) \\approx y\n", "\$$\n", "for all $(x, y)$ drawn from the same distribution as $(x_j, y_j)$.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Definitions!\n", "\n", "The points $x_j$ are *features* and corresponding $y_j$ are *labels*. Together, they are called the *training set*, and since we love matrices and notation gets hellish without them,\n", "\n", "\$$\n", "X = (x_1, \\ldots, x_n) = \\left( \\begin{array}{ccc} \n", "x_1^1 & \\ldots & x_1^m \\\\\n", "\\vdots & & \\vdots \\\\\n", "x_n^1 & \\ldots & x_n^m\n", "\\end{array} \\right)\n", "\$$\n", "\n", "Also, \n", "$$\\mathbf{y} = (y_1, \\ldots, y_n)$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Some practical notes:\n", "\n", "* You don't have to use the features you are given. The literature will sometimes call the $X$ you use for model building *basis functions*. Another word to google about this is *feature selection*.\n", "* There is a lot to be said about *overfitting*.\n", "* You probably want to reserve some data to use as a *test set*, and maybe some more for *cross validation*." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## More notes\n", "\n", "* *Regression* tasks mean the labels are real valued. *Classification* tasks have categorical labels.\n", "* A task is *supervised* if you have labels, *unsupervised* if you don't, and *semi-supervised* if you have some, but not tons." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Linear Regression\n", "$$f(\\mathbf{x}) \\approx y \\Rightarrow \\mathbf{x} \\cdot \\mathbf{w} \\approx y$$\n", "\n", "For **reasons** we will choose the vector $\\mathbf{w}$ that minimizes\n", "$$\n", "cost(\\mathbf{w}) = \\| X\\mathbf{w} - \\mathbf{y} \\|^2\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Deriving an exact least squares solution:\n", "\n", "\\begin{eqnarray}\n", "&& X\\mathbf{w} = \\mathbf{y} \\\\\n", "&\\Rightarrow& X^T X \\mathbf{w} = X^T \\mathbf{y} \\\\\n", "&\\Rightarrow& \\mathbf{w} = (X^TX)^{-1} X^T \\mathbf{y}\n", "\\end{eqnarray}\n", "\n", "The best part is that this is almost true!\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Examples!\n", "\n", "Let's generate some random data for *supervised regression*:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-1.09805639 0.42003036 -2.48283096 0.44664483 0.06329607]\n" ] } ], "source": [ "import numpy as np\n", "DIM = 5\n", "COEFS = np.random.randn(DIM)\n", "\n", "def generate_feature(x, weights):\n", " feature = [w * x ** idx for idx, w in enumerate(weights)]\n", " return feature\n", "\n", "def generate_features_and_labels(X, weights, noise=0):\n", " features = np.array([generate_feature(x, weights) for x in X])\n", " features += noise * np.random.randn(*features.shape)\n", " labels = features.sum(axis=1)\n", " return features, labels\n", "\n", "print(COEFS)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Model Building\n", "\n", "[Scikit-learn](http://scikit-learn.org/) makes our lives easy:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "y = -1.80 * x + -0.71\n" ] } ], "source": [ "from sklearn.linear_model import LinearRegression\n", "x = np.random.random(50)\n", "features, y = generate_features_and_labels(x, COEFS, noise=0.05)\n", "\n", "reg = LinearRegression().fit(x.reshape(-1, 1), y)\n", "print(\"y = {:.2f} * x + {:.2f}\".format(reg.coef_[0], reg.intercept_))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "y = -1.80 * x + -0.71\n" ] } ], "source": [ "from talk_utils import Matrix\n", "\n", "# Manually add intercept\n", "A = Matrix([list(x), [1 for _ in x]]).T\n", "b = Matrix([list(y)]).T\n", "\n", "w = (A.T * A).inverse() * A.T * b\n", "print(\"y = {:.2f} * x + {:.2f}\".format(w.vals[0][0], w.vals[1][0]))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Plotting with pyplot\n", "\n", "We use the model to make a prediction and then plot that prediction:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import seaborn # makes plots pretty\n", "from talk_utils import plotter\n", "\n", "t = np.linspace(x.min(), x.max())\n", "plotter(t, reg.predict(t.reshape(-1, 1)), x, y)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Linear regression can also be curvy!\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": 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7eigFGzdjJCTW+fiGzu4mEgoU3CLiU44hwyn997+IenIBcWNHU/jiKxBWt181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\nyy+A+s+DLk2PgltEpBkrv34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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "new_dim = 4 * DIM\n", "high_dim_features, _ = generate_features_and_labels(x, np.ones(new_dim))\n", "\n", "overfit_reg = LinearRegression().fit(high_dim_features, y)\n", "\n", "t_transformed, _ = generate_features_and_labels(t, np.ones(new_dim))\n", "overfit_preds = overfit_reg.predict(t_transformed)\n", "plotter(t, overfit_preds, x, y)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Other forms of regression!\n", "\n", "### Ridge regression: \n", "Instead of minimizing \n", "$$\n", "cost(\\mathbf{w}) = \\| X\\mathbf{w} - \\mathbf{y} \\|^2\n", "$$\n", "we will minimize \n", "$$\n", "cost(\\mathbf{w}) = \\| X\\mathbf{w} - \\mathbf{y} \\|^2 + \\alpha \\|\\mathbf{w} \\|^2\n", "$$\n", "\n", "* Intuitively: we want to penalize complexity, so having a big weight incurs a penalty\n", "* Notice we now also have to choose $\\alpha$\n", "* This is also called $L^2$ *regularization*" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "array([ 0. , -0.41349154, -0.58080439, -0.44419022, -0.27891466,\n", " -0.15459739, -0.07354935, -0.02405877, 0.00500718, 0.02155159,\n", " 0.03063587, 0.03534176, 0.03748795, 0.03812214, 0.03783417,\n", " 0.03694787, 0.0356356 , 0.03398576, 0.03204177, 0.02982429])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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L57hFRERMRMEtIiJiIgpuERERE1Fwi4iImIiCW0RExEQU3CIiIiai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\nAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.linear_model import RidgeCV\n", "\n", "ridge_reg = RidgeCV().fit(high_dim_features, y)\n", "\n", "ridge_preds = ridge_reg.predict(t_transformed)\n", "\n", "plotter(t, ridge_preds, x, y)\n", "ridge_reg.coef_" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Lasso regression: \n", "Instead of minimizing \n", "\n", "We will minimize \n", "$$\n", "cost(\\mathbf{w}) = \\| X\\mathbf{w} - \\mathbf{y} \\|^2 + \\alpha \\|\\mathbf{w} \\|^1\n", "$$\n", "\n", "* This encourages sparse weights for awesome, easy reasons\n", "* We still have to choose $\\alpha$\n", "* This is also called $L^1$ *regularization*" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.965889563709\n", "0.966076370214\n", "0.978404025746\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.linear_model import LassoCV\n", "\n", "lasso_reg = LassoCV()\n", "lasso_reg.fit(high_dim_features, y)\n", "\n", "lasso_preds = lasso_reg.predict(t_transformed)\n", "\n", "plotter(t, lasso_preds, x, y)\n", "print(lasso_reg.score(high_dim_features, y))\n", "print(ridge_reg.score(high_dim_features, y))\n", "print(overfit_reg.score(high_dim_features, y))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Classification!\n", "\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": 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3OoxGI44dXg3WlA6FsiVie4+GWCy2qafX63Hs8G8AlwOVX3v0iBlarVEB96vN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\n4eOZF3Dt0zz8eeYcj/eVl5eHbV+Ow6fvhkClqj2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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.datasets import load_iris\n", "import matplotlib.pyplot as plt\n", "\n", "CMAP = plt.cm.viridis\n", "iris = load_iris()\n", "X = iris.data[:, :2]\n", "_, ax = plt.subplots()\n", "ax.scatter(X[:, 0], X[:, 1], c=iris.target, cmap=CMAP, s=40);" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "from sklearn.metrics import accuracy_score\n", "\n", "def visualize_classifier(classifier_cls):\n", " clf = classifier_cls().fit(X, iris.target)\n", " x_min, y_min = X.min(axis=0) - 1\n", " x_max, y_max = X.max(axis=0) + 1\n", " xx, yy = np.meshgrid(np.linspace(x_min, x_max, 500),\n", " np.linspace(y_min, y_max, 500))\n", " Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", "\n", " _, ax = plt.subplots()\n", " ax.contourf(xx, yy, Z, cmap=CMAP, alpha=0.8)\n", "\n", " # Plot also the training points\n", " ax.scatter(X[:, 0], X[:, 1], c=iris.target, cmap=CMAP, s=40)\n", " plt.xlim(xx.min(), xx.max())\n", " plt.ylim(yy.min(), yy.max())\n", " print(\"Accuracy is {:.1%}\".format(accuracy_score(clf.predict(X), iris.target)))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy is 76.7%\n" ] }, { "data": { "image/png": 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+Y6Zp8eJrUVZfnbhFX1hdwObtsXPaY6y+RtZLFiITJDErw2ZaYhYkkrOOndxH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\nAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.linear_model import LogisticRegression\n", "\n", "visualize_classifier(LogisticRegression)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy is 83.3%\n" ] }, { "data": { "image/png": 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oi2oK+WjXhe0RNlwn6yULMRUkRWmKzcUUJdMyON52kLAnVkWpqKyCyuLxBTGA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\nJWFCCCFEIJSECSGEEIFQEiaEEEIE8v8BR+vV2d0C9zUAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "\n", "visualize_classifier(KNeighborsClassifier)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy is 82.7%\n" ] }, { "data": { "image/png": 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RugfaOXXkIAuVC/WBbfCfH+TdPb/mpg2fzm7n0tDr7aL18FEKIkXD29oGjsFqm7Kimiz2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\nl+8AAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.svm import SVC\n", "\n", "visualize_classifier(SVC)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy is 78.0%\n" ] }, { "data": { "image/png": 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xBTGAYNjPrkN/xPIboEJRZSXLa8ZeEMIb6GffkS1YAQt0i9LKWpZUXz7YHooE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\n1A6zIx0SJPTlDiGjYq3il3oBIHf5Ctg+/xucC13Qzs+D6HZ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.naive_bayes import GaussianNB\n", "\n", "visualize_classifier(GaussianNB)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy is 92.7%\n" ] }, { "data": { "image/png": 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VCEb8nOg6RNwfBVXBU5TL2pXXoWWwWAaALzhOd/dRYoEIqqqSU5LPmqZrUc8t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\nEK54YPH6MemBhVFLVOFcPlmF1m4nlCw7vB0u+Ko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YhImIiDTCJExERKQRJmEiIiKNMAkT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\nRESkESZhIiIijfw/2P40LjliqCcAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.tree import DecisionTreeClassifier\n", "\n", "visualize_classifier(DecisionTreeClassifier)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy is 92.0%\n" ] }, { "data": { "image/png": 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RuvbvpThaNtFnYPQ41gqLukq5ZSyEmF3yTFjMacd6D1ISKk/Z5rG8nD7Zn1P/nt6DFEdK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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "\n", "visualize_classifier(RandomForestClassifier)" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 1 }