{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Auto Miles Per Gallon Data Experiments\n", "\n", "This notebook runs the experiments and generates the figures on the [auto mpg data](https://archive.ics.uci.edu/ml/datasets/auto+mpg). We are working with a subset of the data created by: \n", " \n", " 1. selecting a subset of columns to suit our problem setting: three continuous (mpg, acceleration, and horsepower) and three categorical attributes (cylinders, model year, and origin) \n", " 1. removing incomplete records\n", " \n", "This notebook generates the results and figures related to the AutoMPG experiments" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.colors as mcolors\n", "import matplotlib.pyplot as plt\n", "\n", "# clean up notebook output by removing warnings about future changes\n", "import warnings\n", "warnings.simplefilter(action='ignore', category=FutureWarning)\n", "\n", "# our code packaged for easy use\n", "import detect_simpsons_paradox as dsp" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " mpg cylinders horsepower acceleration model year origin\n", "0 18.0 8 130.0 12.0 70 1\n", "1 15.0 8 165.0 11.5 70 1\n", "2 18.0 8 150.0 11.0 70 1\n", "3 16.0 8 150.0 12.0 70 1\n", "4 17.0 8 140.0 10.5 70 1" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# import the prepared copy of the data\n", "auto_df = pd.read_csv('../../../data/auto2.csv')\n", "auto_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From examining the above, we know that the integer columns are the group-by variables and the float type variables are the continuous attributes. The detector function will automatically " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['cylinders', 'model year', 'origin']\n" ] } ], "source": [ "groupbyAttrs = auto_df.select_dtypes(include=['int64'])\n", "groupbyAttrs_labels = list(groupbyAttrs)\n", "print(groupbyAttrs_labels)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['mpg', 'horsepower', 'acceleration']\n" ] } ], "source": [ "continuousAttrs = auto_df.select_dtypes(include=['float64'])\n", "continuousAttrs_labels = list(continuousAttrs)\n", "print(continuousAttrs_labels)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Results\n", "\n", "Now we can run our algorithm and print out the results after a little bit of post-processing to improve readability." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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allCorrattr1attr2reverseCorrgroupbyAttrsubgroup
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" ], "text/plain": [ " allCorr attr1 attr2 reverseCorr groupbyAttr subgroup\n", "1 0.423329 mpg acceleration -0.818873 cylinders 3\n", "3 0.423329 mpg acceleration -0.341214 cylinders 6\n", "4 0.423329 mpg acceleration -0.050545 model year 75\n", "5 0.423329 mpg acceleration -0.051280 model year 79\n", "0 -0.778427 mpg horsepower 0.620807 cylinders 3\n", "2 -0.778427 mpg horsepower 0.013135 cylinders 6" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# run detection algorithm\n", "result_df = dsp.detect_simpsons_paradox(auto_df)\n", "\n", "# Map attribute index to attribute name\n", "result_df['attr1'] = result_df['attr1'].map(lambda x:continuousAttrs_labels[x])\n", "result_df['attr2'] = result_df['attr2'].map(lambda x:continuousAttrs_labels[x])\n", "# sort for easy reading\n", "result_df = result_df.sort_values(['attr1', 'attr2'], ascending=[1, 1])\n", "\n", "# data frames print neatly in notebooks \n", "result_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting\n", "\n", "We plot all data in scatter plots based on each group by attribute, for each pair of candidate attributes. For each plot we add the overall trendline and the trend line for each occurence of Simpson's Paradox. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[8 4 6 3 5]\n", "[70 71 72 73 74 75 76 77 78 79 80 81 82]\n" ] } ], "source": [ "print(auto_df.cylinders.unique())\n", "print(auto_df['model year'].unique())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "colors = {'3':'red', '4':'blue', '5':'purple', '6':'black','8':'green'}\n", "markers = {'3':'x', '4':'o', '5':'s','6':'*','8':'d'}\n", "\n", "#plt.scatter(auto_df['mpg'], auto_df['acceleration'], c=auto_df['cylinders'].apply(lambda x: colors[str(x)]))\n", "for i in range(len(auto_df['mpg'])):\n", " plt.scatter(auto_df['mpg'][i], auto_df['acceleration'][i], c=colors[str(auto_df['cylinders'][i])], marker=markers[str(auto_df['cylinders'][i])], label=auto_df['cylinders'][i])\n", "\n", "plt.xlabel('mpg', fontsize=24)\n", "plt.ylabel('acceleration', fontsize=24)\n", "plt.xticks(fontsize = 20)\n", "plt.yticks(fontsize = 20)\n", "\n", "#import matplotlib.patches as mpatches\n", "#red_patch = mpatches.Patch(color='red', label='3')\n", "#green_patch = mpatches.Patch(color='blue', label='4')\n", "#purple_patch = mpatches.Patch(color='purple', label='5')\n", "#black_patch = mpatches.Patch(color='black', label='6')\n", "#green_patch = mpatches.Patch(color='green', label='8')\n", "#plt.legend(handles=[red_patch, green_patch, blue_patch,black_patch,orange_patch])\n", "\n", "from collections import OrderedDict\n", "handles, labels = plt.gca().get_legend_handles_labels()\n", "by_label = OrderedDict(zip(labels, handles))\n", "plt.legend(by_label.values(), by_label.keys(), prop={'size':15})\n", "\n", "# Add correlation line\n", "axes = plt.gca()\n", "x = auto_df['mpg']\n", "y = auto_df['acceleration']\n", "\n", "m, b = np.polyfit(x, y, 1)\n", "X_plot = np.linspace(axes.get_xlim()[0],axes.get_xlim()[1],100)\n", "plt.plot(X_plot, m*X_plot + b, '--',color='black')\n", "\n", "cylinder3 = auto_df[auto_df['cylinders'] ==3]\n", "cylinder6 = auto_df[auto_df['cylinders'] ==6]\n", "x1 = cylinder3['mpg']\n", "y1 = cylinder3['acceleration']\n", "\n", "m1, b1 = np.polyfit(x1, y1, 1)\n", "#print(axes.get_xlim()[0])\n", "#print(axes.get_xlim()[1])\n", "X_plot1 = np.linspace(5,48,100)\n", "plt.plot(X_plot1, m1*X_plot1 + b1, '-', color='red')\n", "\n", "x2 = cylinder6['mpg']\n", "y2 = cylinder6['acceleration']\n", "\n", "m, b = np.polyfit(x2, y2, 1)\n", "X_plot = np.linspace(5,48,100)\n", "plt.plot(X_plot, m*X_plot + b, '-', color='black')\n", "\n", "plt.show()\n", "\n", "#fig.savefig('auto1.jpg')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "colors = {'70':'coral', '71':'blue', '72':'purple', '73':'orange','74':'green', '75':'black', '76':'grey','77':'gold', '78':'lightgreen','79':'red', '80':'cyan', '81':'skyblue','82':'pink'}\n", "markers = {'70':'x', '71':'o', '72':'s','73':'*','74':'d', '75':'v', '76':'^','77':'<', '78':'>','79':'1', '80':'2', '81':'3','82':'4'}\n", "\n", "#plt.scatter(auto_df['mpg'], auto_df['acceleration'], c=auto_df['cylinders'].apply(lambda x: colors[str(x)]))\n", "for i in range(len(auto_df['mpg'])):\n", " plt.scatter(auto_df['mpg'][i], auto_df['acceleration'][i], c=colors[str(auto_df['model year'][i])], marker=markers[str(auto_df['model year'][i])], label=auto_df['model year'][i])\n", "\n", "\n", "#plt.scatter(auto_df['mpg'], auto_df['acceleration'], c=auto_df['model year'].apply(lambda x: colors[str(x)]))\n", "\n", "plt.xlabel('mpg', fontsize=24)\n", "plt.ylabel('acceleration', fontsize=24)\n", "plt.xticks(fontsize = 20)\n", "plt.yticks(fontsize = 20)\n", "#import matplotlib.patches as mpatches\n", "#patch1 = mpatches.Patch(color='coral', label='70')\n", "#patch2 = mpatches.Patch(color='blue', label='71')\n", "#patch3 = mpatches.Patch(color='purple', label='72')\n", "#patch4 = mpatches.Patch(color='orange', label='73')\n", "#patch5 = mpatches.Patch(color='green', label='74')\n", "#patch6 = mpatches.Patch(color='black', label='75')\n", "#patch7 = mpatches.Patch(color='grey', label='76')\n", "#patch8 = mpatches.Patch(color='gold', label='77')\n", "#patch9 = mpatches.Patch(color='lightgreen', label='78')\n", "#patch10 = mpatches.Patch(color='red', label='79')\n", "#patch11 = mpatches.Patch(color='cyan', label='80')\n", "#patch12 = mpatches.Patch(color='skyblue', label='81')\n", "#patch13 = mpatches.Patch(color='pink', label='82')\n", "\n", "#plt.legend(handles=[patch1, patch2, patch3,patch4,patch5,patch6, patch7, patch8,patch9,patch10,patch11, patch12, patch13])\n", "from collections import OrderedDict\n", "handles, labels = plt.gca().get_legend_handles_labels()\n", "by_label = OrderedDict(zip(labels, handles))\n", "plt.legend(by_label.values(), by_label.keys(), prop={'size':15})\n", "\n", "\n", "# Add correlation line\n", "axes = plt.gca()\n", "x = auto_df['mpg']\n", "y = auto_df['acceleration']\n", "\n", "m, b = np.polyfit(x, y, 1)\n", "X_plot = np.linspace(axes.get_xlim()[0],axes.get_xlim()[1],100)\n", "plt.plot(X_plot, m*X_plot + b, '--',color='black')\n", "\n", "cylinder3 = auto_df[auto_df['model year'] ==75]\n", "cylinder6 = auto_df[auto_df['model year'] ==79]\n", "x1 = cylinder3['mpg']\n", "y1 = cylinder3['acceleration']\n", "\n", "m1, b1 = np.polyfit(x1, y1, 1)\n", "#print(axes.get_xlim()[0])\n", "#print(axes.get_xlim()[1])\n", "X_plot1 = np.linspace(5,48,100)\n", "plt.plot(X_plot1, m1*X_plot1 + b1, '-', color='red')\n", "\n", "x2 = cylinder6['mpg']\n", "y2 = cylinder6['acceleration']\n", "\n", "m, b = np.polyfit(x2, y2, 1)\n", "X_plot = np.linspace(5,48,100)\n", "plt.plot(X_plot, m*X_plot + b, '-', color='black')\n", "\n", "plt.show()\n", "\n", "#fig.savefig('auto2.jpg')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "colors = {'3':'red', '4':'blue', '5':'purple', '6':'black','8':'green'}\n", "markers = {'3':'x', '4':'o', '5':'s','6':'*','8':'d'}\n", "\n", "for i in range(len(auto_df['mpg'])):\n", " plt.scatter(auto_df['mpg'][i], auto_df['horsepower'][i], c=colors[str(auto_df['cylinders'][i])], marker=markers[str(auto_df['cylinders'][i])], label=auto_df['cylinders'][i])\n", "\n", "#plt.scatter(auto_df['mpg'], auto_df['horsepower'], c=auto_df['cylinders'].apply(lambda x: colors[str(x)]))\n", "\n", "plt.xlabel('mpg', fontsize=24)\n", "plt.ylabel('horsepower', fontsize=24)\n", "plt.xticks(fontsize = 20)\n", "plt.yticks(fontsize = 20)\n", "#import matplotlib.patches as mpatches\n", "#red_patch = mpatches.Patch(color='red', label='3')\n", "#green_patch = mpatches.Patch(color='blue', label='4')\n", "#purple_patch = mpatches.Patch(color='purple', label='5')\n", "#black_patch = mpatches.Patch(color='black', label='6')\n", "#green_patch = mpatches.Patch(color='green', label='8')\n", "#plt.legend(handles=[red_patch, green_patch, blue_patch,black_patch,orange_patch])\n", "from collections import OrderedDict\n", "handles, labels = plt.gca().get_legend_handles_labels()\n", "by_label = OrderedDict(zip(labels, handles))\n", "plt.legend(by_label.values(), by_label.keys(), prop={'size':15}, loc = 1)\n", "\n", "\n", "# Add correlation line\n", "axes = plt.gca()\n", "x = auto_df['mpg']\n", "y = auto_df['horsepower']\n", "\n", "m, b = np.polyfit(x, y, 1)\n", "X_plot = np.linspace(axes.get_xlim()[0],axes.get_xlim()[1],100)\n", "plt.plot(X_plot, m*X_plot + b, '--',color='black')\n", "\n", "cylinder3 = auto_df[auto_df['cylinders'] ==3]\n", "cylinder6 = auto_df[auto_df['cylinders'] ==6]\n", "x1 = cylinder3['mpg']\n", "y1 = cylinder3['horsepower']\n", "\n", "m1, b1 = np.polyfit(x1, y1, 1)\n", "#print(axes.get_xlim()[0])\n", "#print(axes.get_xlim()[1])\n", "X_plot1 = np.linspace(5,48,100)\n", "plt.plot(X_plot1, m1*X_plot1 + b1, '-', color='red')\n", "\n", "x2 = cylinder6['mpg']\n", "y2 = cylinder6['horsepower']\n", "\n", "m, b = np.polyfit(x2, y2, 1)\n", "X_plot = np.linspace(5,48,100)\n", "plt.plot(X_plot, m*X_plot + b, '-', color='black')\n", "\n", "plt.show()\n", "\n", "#fig.savefig('auto3.jpg')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11+" } }, "nbformat": 4, "nbformat_minor": 2 }