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mjbommar/isotonic_test_case_20150129.json

Last active August 29, 2015 14:14
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Test case for Isotonic Regression regression in fit vs. fit_transform
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isotonic_test_case_20150129.json
{"nbformat_minor": 0, "cells": [{"execution_count": 1, "cell_type": "code", "source": "# Imports\nimport matplotlib.pyplot as plt\nimport numpy\nimport pandas\nimport scipy\nimport sklearn\nimport sklearn.isotonic\nimport sys", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"source": "## Version strings", "cell_type": "markdown", "metadata": {}}, {"execution_count": 2, "cell_type": "code", "source": "print(sys.version)\nprint(sklearn.__version__)", "outputs": [{"output_type": "stream", "name": "stdout", "text": "2.7.3 (default, Mar 13 2014, 11:03:55) \n[GCC 4.7.2]\n0.16.dev\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "## Generate samples with and without ties", "cell_type": "markdown", "metadata": {}}, {"execution_count": 3, "cell_type": "code", "source": "# Sample with x ties\ndata_with_ties = pandas.DataFrame()\ndata_with_ties[\"feature\"] = [0, 0, 1, 2, 3]\ndata_with_ties[\"target\"] = [0.1, 0.05, 0.15, 0.2, 0.35]\n\n# Sample without x ties\ndata_without_ties = pandas.DataFrame()\ndata_without_ties[\"feature\"] = [0, 1, 2, 3, 4]\ndata_without_ties[\"target\"] = [0.1, 0.05, 0.15, 0.2, 0.35]\n", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"source": "## Failure test case with ties", "cell_type": "markdown", "metadata": {}}, {"execution_count": 4, "cell_type": "code", "source": "%matplotlib inline\n\n# Fit and then transform the model\nisotonic = sklearn.isotonic.IsotonicRegression()\nisotonic.fit(data_with_ties.loc[:, \"feature\"],\n data_with_ties.loc[:, \"target\"])\nresults_fit = isotonic.transform(data_with_ties.loc[:, \"feature\"])\n\n# fit_transform in one go\nisotonic = sklearn.isotonic.IsotonicRegression()\nresults_fit_transform = isotonic.fit_transform(data_with_ties.loc[:, \"feature\"],\n data_with_ties.loc[:, \"target\"])\n\n# Describe data and output norm\nprint(\"Norm: {0}\".format(numpy.linalg.norm(results_fit - results_fit_transform)))\nprint(\"fit, transform\")\nprint(pandas.DataFrame(results_fit).describe())\nprint(\"fit_transform\")\nprint(pandas.DataFrame(results_fit_transform).describe())\n\n# Plot\nf = plt.figure()\nplt.scatter(data_with_ties.loc[:, \"feature\"],\n data_with_ties.loc[:, \"target\"],\n color=\"red\", alpha=0.25)\nplt.scatter(data_with_ties.loc[:, \"feature\"],\n results_fit,\n color=\"green\", alpha=0.25)\nplt.scatter(data_with_ties.loc[:, \"feature\"],\n results_fit_transform,\n color=\"blue\", alpha=0.25)\n_ = plt.legend((\"Actual\", \"fit, transform\", \"fit_transform\"),\n loc=\"best\")", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Norm: 0.111803398875\nfit, transform\n 0\ncount 5.000000\nmean 0.140000\nstd 0.147479\nmin 0.000000\n25% 0.000000\n50% 0.150000\n75% 0.200000\nmax 0.350000\nfit_transform\n 0\ncount 5.000000\nmean 0.170000\nstd 0.115109\nmin 0.050000\n25% 0.100000\n50% 0.150000\n75% 0.200000\nmax 0.350000\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x46dd9d0>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "## Success case without ties", "cell_type": "markdown", "metadata": {}}, {"execution_count": 5, "cell_type": "code", "source": "%matplotlib inline\n\n# Fit and then transform the model\nisotonic = sklearn.isotonic.IsotonicRegression()\nisotonic.fit(data_without_ties.loc[:, \"feature\"],\n data_without_ties.loc[:, \"target\"])\nresults_fit = isotonic.transform(data_without_ties.loc[:, \"feature\"])\n\n# fit_transform in one go\nisotonic = sklearn.isotonic.IsotonicRegression()\nresults_fit_transform = isotonic.fit_transform(data_without_ties.loc[:, \"feature\"],\n data_without_ties.loc[:, \"target\"])\n\n# Describe data and output norm\nprint(\"Norm: {0}\".format(numpy.linalg.norm(results_fit - results_fit_transform)))\nprint(\"fit, transform\")\nprint(pandas.DataFrame(results_fit).describe())\nprint(\"fit_transform\")\nprint(pandas.DataFrame(results_fit_transform).describe())\n\n# Plot\nf = plt.figure()\nplt.scatter(data_without_ties.loc[:, \"feature\"],\n data_without_ties.loc[:, \"target\"],\n color=\"red\", alpha=0.25)\nplt.scatter(data_without_ties.loc[:, \"feature\"],\n results_fit,\n color=\"green\", alpha=0.25)\nplt.scatter(data_without_ties.loc[:, \"feature\"],\n results_fit_transform,\n color=\"blue\", alpha=0.25)\n_ = plt.legend((\"Actual\", \"fit, transform\", \"fit_transform\"),\n loc=\"best\")", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Norm: 0.0\nfit, transform\n 0\ncount 5.000000\nmean 0.170000\nstd 0.113743\nmin 0.075000\n25% 0.075000\n50% 0.150000\n75% 0.200000\nmax 0.350000\nfit_transform\n 0\ncount 5.000000\nmean 0.170000\nstd 0.113743\nmin 0.075000\n25% 0.075000\n50% 0.150000\n75% 0.200000\nmax 0.350000\n"}, {"output_type": "display_data", "data": {"image/png": 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