{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Chapter 7: Nonlinear Models ###" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from __future__ import division\n", "import pandas as pd\n", "import numpy as np\n", "import scipy as sp\n", "import matplotlib.pyplot as plt\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.metrics import mean_squared_error\n", "from sklearn.feature_selection import SelectKBest\n", "from sklearn.feature_selection import f_regression\n", "from sklearn.feature_selection import chi2\n", "from sklearn.cluster import MeanShift\n", "from scipy.interpolate import LSQUnivariateSpline\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# Data comes from ISLR::Wage - written using write.csv(Wage, \"wage.csv\", row.names=FALSE)\n", "wage_df = pd.read_csv(\"../data/Wage.csv\")\n", "wage_df.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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yearagesexmaritlraceeducationregionjobclasshealthhealth_inslogwagewage
0 2006 18 1. Male 1. Never Married 1. White 1. < HS Grad 2. Middle Atlantic 1. Industrial 1. <=Good 2. No 4.318063 75.043154
1 2004 24 1. Male 1. Never Married 1. White 4. College Grad 2. Middle Atlantic 2. Information 2. >=Very Good 2. No 4.255273 70.476020
2 2003 45 1. Male 2. Married 1. White 3. Some College 2. Middle Atlantic 1. Industrial 1. <=Good 1. Yes 4.875061 130.982177
3 2003 43 1. Male 2. Married 3. Asian 4. College Grad 2. Middle Atlantic 2. Information 2. >=Very Good 1. Yes 5.041393 154.685293
4 2005 50 1. Male 4. Divorced 1. White 2. HS Grad 2. Middle Atlantic 2. Information 1. <=Good 1. Yes 4.318063 75.043154
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5 rows \u00d7 12 columns

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" ], "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ " year age sex maritl race education \\\n", "0 2006 18 1. Male 1. Never Married 1. White 1. < HS Grad \n", "1 2004 24 1. Male 1. Never Married 1. White 4. College Grad \n", "2 2003 45 1. Male 2. Married 1. White 3. Some College \n", "3 2003 43 1. Male 2. Married 3. Asian 4. College Grad \n", "4 2005 50 1. Male 4. Divorced 1. White 2. HS Grad \n", "\n", " region jobclass health health_ins logwage \\\n", "0 2. Middle Atlantic 1. Industrial 1. <=Good 2. No 4.318063 \n", "1 2. Middle Atlantic 2. Information 2. >=Very Good 2. No 4.255273 \n", "2 2. Middle Atlantic 1. Industrial 1. <=Good 1. Yes 4.875061 \n", "3 2. Middle Atlantic 2. Information 2. >=Very Good 1. Yes 5.041393 \n", "4 2. Middle Atlantic 2. Information 1. <=Good 1. Yes 4.318063 \n", "\n", " wage \n", "0 75.043154 \n", "1 70.476020 \n", "2 130.982177 \n", "3 154.685293 \n", "4 75.043154 \n", "\n", "[5 rows x 12 columns]" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# factorize non-numeric variables\n", "wage_df[\"sex\"] = pd.factorize(wage_df[\"sex\"])[0]\n", "wage_df[\"maritl\"] = pd.factorize(wage_df[\"maritl\"])[0]\n", "wage_df[\"race\"] = pd.factorize(wage_df[\"race\"])[0]\n", "wage_df[\"education\"] = pd.factorize(wage_df[\"education\"])[0]\n", "wage_df[\"region\"] = pd.factorize(wage_df[\"region\"])[0]\n", "wage_df[\"health\"] = pd.factorize(wage_df[\"health\"])[0]\n", "wage_df[\"health_ins\"] = pd.factorize(wage_df[\"health_ins\"])[0]\n", "wage_df.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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yearagesexmaritlraceeducationregionjobclasshealthhealth_inslogwagewage
0 2006 18 0 0 0 0 0 1. Industrial 0 0 4.318063 75.043154
1 2004 24 0 0 0 1 0 2. Information 1 0 4.255273 70.476020
2 2003 45 0 1 0 2 0 1. Industrial 0 1 4.875061 130.982177
3 2003 43 0 1 1 1 0 2. Information 1 1 5.041393 154.685293
4 2005 50 0 2 0 3 0 2. Information 0 1 4.318063 75.043154
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5 rows \u00d7 12 columns

\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ " year age sex maritl race education region jobclass health \\\n", "0 2006 18 0 0 0 0 0 1. Industrial 0 \n", "1 2004 24 0 0 0 1 0 2. Information 1 \n", "2 2003 45 0 1 0 2 0 1. Industrial 0 \n", "3 2003 43 0 1 1 1 0 2. Information 1 \n", "4 2005 50 0 2 0 3 0 2. Information 0 \n", "\n", " health_ins logwage wage \n", "0 0 4.318063 75.043154 \n", "1 0 4.255273 70.476020 \n", "2 1 4.875061 130.982177 \n", "3 1 5.041393 154.685293 \n", "4 1 4.318063 75.043154 \n", "\n", "[5 rows x 12 columns]" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Polynomials ###\n", "\n", "We attempt to build a model to predict wage given a degree 4 polynomial over age. In R the polynomial features are created using the poly() function. As pointed out in [this post from Dave Moore](http://davmre.github.io/python/2013/12/15/orthogonal_poly/) the method creates orthogonal polynomial features, which are different from simple powers of the original feature. Code for the poly() function shown below is a straight copy from the ortho_poly_fit() function code from this page." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def poly(x, degree = 1):\n", " n = degree + 1\n", " x = np.asarray(x).flatten()\n", " if(degree >= len(np.unique(x))):\n", " print(\"'degree' must be less than number of unique points\")\n", " return\n", " xbar = np.mean(x)\n", " x = x - xbar\n", " X = np.fliplr(np.vander(x, n))\n", " q, r = np.linalg.qr(X)\n", "\n", " z = np.diag(np.diag(r))\n", " raw = np.dot(q, z)\n", " \n", " norm2 = np.sum(raw ** 2, axis=0)\n", " alpha = (np.sum((raw ** 2) * np.reshape(x,(-1,1)), axis=0) / norm2 + xbar)[:degree]\n", " Z = raw / np.sqrt(norm2)\n", " return Z, norm2, alpha\n", "\n", "X = poly(wage_df[\"age\"].values, 4)[0]\n", "X[0:5, 1:]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "array([[-0.0386248 , 0.05590873, -0.07174058, 0.08672985],\n", " [-0.0291326 , 0.02629807, -0.01454995, -0.00259928],\n", " [ 0.00409008, -0.01450655, -0.00013318, 0.01448009],\n", " [ 0.00092602, -0.0148314 , 0.00451367, 0.01265751],\n", " [ 0.01200024, -0.00981585, -0.01113663, 0.01021146]])" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "y = wage_df[\"wage\"].values\n", "reg = LinearRegression()\n", "reg.fit(X, y)\n", "print \"Intercepts:\", reg.intercept_\n", "print \"Coefficients:\", reg.coef_\n", "ax = wage_df.plot(x=\"age\", y=\"wage\", style=\"o\")\n", "ax.set_ylabel(\"wage\")\n", "# The poly() method cannot return features for a single X value, so we have\n", "# to plot the raw predictions.\n", "age = wage_df[\"age\"].values\n", "ypred = reg.predict(X)\n", "polyline = np.poly1d(np.polyfit(age, ypred, 4))\n", "xs = range(int(np.min(age)), int(np.max(age)))\n", "ys = polyline(xs) \n", "ax.plot(xs, ys, 'r', linewidth=2.5)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Intercepts: -6287691072.04\n", "Coefficients: [ 3.44391030e+11 4.47067854e+02 -4.78315807e+02 1.25521688e+02\n", " -7.79111830e+01]\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "[]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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k008/5eKL1Q+xRtvAubi7BtNWTkb834EgJiaC2lp79HMWEGE6dnrQ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\nOoI1ISDIeA8V7Blji4A3TcfTkIkKzUL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By0kLhxaGm83BbRmanNyL3r3NEaHz+NnPfktB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"text": [ "" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can repeat this experiment with manually calculated powers of the age feature as follows. Even though the parameters are different, the resulting linear model appears to be the same." ] }, { "cell_type": "code", "collapsed": false, "input": [ "x1 = wage_df[\"age\"].values\n", "x2 = np.power(x1, 2)\n", "x3 = np.power(x1, 3)\n", "x4 = np.power(x1, 4)\n", "X = np.vstack((x1, x2, x3, x4)).T\n", "X[0:5, :]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "array([[ 18, 324, 5832, 104976],\n", " [ 24, 576, 13824, 331776],\n", " [ 45, 2025, 91125, 4100625],\n", " [ 43, 1849, 79507, 3418801],\n", " [ 50, 2500, 125000, 6250000]])" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "y = wage_df[\"wage\"].values\n", "reg = LinearRegression()\n", "reg.fit(X, y)\n", "print \"Intercepts:\", reg.intercept_\n", "print \"Coefficients:\", reg.coef_\n", "ax = wage_df.plot(x=\"age\", y=\"wage\", style=\"o\")\n", "ax.set_ylabel(\"wage\")\n", "xs = range(int(np.min(x1)), int(np.max(x1)))\n", "ys = [reg.predict([x, x**2, x**3, x**4]) for x in xs]\n", "ax.plot(xs, ys, 'r', linewidth=2.5)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Intercepts: -184.154179774\n", "Coefficients: [ 2.12455205e+01 -5.63859313e-01 6.81068771e-03 -3.20383037e-05]\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "[]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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51D5gVcvU1nYCnGol86pDteIIVFVjh5u6pbi43ELr7t1lwGu2Wq946bSeNzZ2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"text": [ "" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "R's summary() function applied to a linear model returns p-values for each coefficient, which enables one to guage the significance of each feature. Scikit-Learn does not provide anything similar, but it does provide methods for feature selection which provides p-values and scores. Based on p-values and scores below, it appears that the 4th degree feature is the least important of the four. However, R reports a p-value of 0.05 for the 4th power." ] }, { "cell_type": "code", "collapsed": false, "input": [ "selector = SelectKBest(score_func=f_regression, k=10)\n", "selector.fit(X, y)\n", "(selector.scores_, selector.pvalues_)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "(array([ 119.31172277, 77.71495649, 46.35938463, 25.11432639]),\n", " array([ 2.90077768e-27, 1.97798773e-18, 1.18437983e-11,\n", " 5.71655064e-07]))" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The p-values observed from a linear model's summary() output only works as an indicator of feature importance if a single feature is being used. For multiple features, R advises the use of ANOVA. It turns out that the SelectKBest feature selector is [based on ANOVA](http://scikit-learn.org/stable/auto_examples/feature_selection_pipeline.html). The R example manually built models based on different feature sets then applied ANOVA to the models - there is no direct analog to that here.\n", "\n", "### Polynomial Logistic Regression ###\n", "\n", "Fitting a Logistic Regression model to a binary response variable. The response variable is \"high-earners\" where the salary > 250K." ] }, { "cell_type": "code", "collapsed": false, "input": [ "X = np.vstack((x1, x2, x3)).T\n", "y = wage_df[\"wage\"].map(lambda w: 1 if w > 250 else 0).values\n", "reg = LogisticRegression()\n", "reg.fit(X, y)\n", "print \"Intercepts:\", reg.intercept_\n", "print \"Coefficients:\", reg.coef_\n", "ypred = reg.predict(X)\n", "print \"MSE:\", mean_squared_error(y, ypred)\n", "\n", "# Plot the predicted probability of being a high-earner over age range\n", "ages = wage_df[\"age\"].values\n", "xs = range(np.min(ages), np.max(ages))\n", "ys = [reg.predict_proba(np.array([x, x*x, x*x*x]))[0][0] for x in xs]\n", "plt.plot(xs, ys)\n", "plt.xlabel(\"age\")\n", "plt.ylabel(\"p(wage > 250k)\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Intercepts: [-0.00721825]\n", "Coefficients: [[ -9.77916678e-02 -8.45456184e-04 2.46527805e-05]]\n", "MSE: 0.0263333333333\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Splines ###\n", "\n", "Splines is a curve approximated by a series of straight lines. The breaks in the straight lines are called knots. The idea is to fit multiple linear models to these ranges, one each for each straight line segment. Scipy offers the UnivariateSpline but it requires manual knot selection. The code snippet below uses Mean-Shift clustering to automatically derive the knots based on this [thread on StackOverflow](http://stackoverflow.com/questions/8719754/scipy-interpolate-univariatespline-not-smoothing-regardless-of-parameters)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "ages = wage_df[\"age\"].values\n", "wages = wage_df[\"wage\"].values\n", "X = np.vstack((ages, wages)).T\n", "# cluster points to find the knot location\n", "msc = MeanShift()\n", "msc.fit(X)\n", "knots = msc.cluster_centers_[:, 0]\n", "\n", "# fit a spline over the points\n", "spl = LSQUnivariateSpline(ages, wages, knots)\n", "xs = range(np.min(ages), np.max(ages))\n", "ys = spl(xs)\n", "\n", "# plot the points and the spline\n", "ax = wage_df.plot(x=\"age\", y=\"wage\", style=\"o\")\n", "ax.set_ylabel(\"wage\")\n", "ax.plot(xs, ys, 'r', linewidth=2.5)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "[]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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bp/6HDEmwiX3ALJaprGwJ2MVKxl2HbMfhr6jGCidxS15esYnW/fuLgDcstV71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\nhZF88s705CXxQebw5o8F08Vg5aSYQwPDSefgtA1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yy+DQIQKBAL974Q267B/DNzjCEbbQ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"text": [ "" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generalized Additive Models ###\n", "\n", "Generalized Additive Models are not yet available under Scikit-Learn. It was one of the projects proposed for GSoC 2014 but as far as I know it did not get approved. StatsModel has an untested implementation. Since it is not available in Python, I decided to skip it for now." ] } ], "metadata": {} } ] }