Java tutorial
/* * This file is part of ADDIS (Aggregate Data Drug Information System). * ADDIS is distributed from http://drugis.org/. * Copyright 2009 Gert van Valkenhoef, Tommi Tervonen. * Copyright 2010 Gert van Valkenhoef, Tommi Tervonen, Tijs Zwinkels, * Maarten Jacobs, Hanno Koeslag, Florin Schimbinschi, Ahmad Kamal, Daniel * Reid. * Copyright 2011 Gert van Valkenhoef, Ahmad Kamal, Daniel Reid, Florin * Schimbinschi. * Copyright 2012 Gert van Valkenhoef, Daniel Reid, Jol Kuiper, Wouter * Reckman. * Copyright 2013 Gert van Valkenhoef, Jol Kuiper. * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see <http://www.gnu.org/licenses/>. */ package org.drugis.addis.util.JSMAAintegration; import static org.junit.Assert.assertEquals; import java.util.ArrayList; import java.util.Arrays; import java.util.List; import org.apache.commons.math3.linear.Array2DRowRealMatrix; import org.apache.commons.math3.linear.ArrayRealVector; import junit.framework.AssertionFailedError; import fi.smaa.jsmaa.model.Alternative; import fi.smaa.jsmaa.model.Criterion; import fi.smaa.jsmaa.model.CriterionMeasurement; import fi.smaa.jsmaa.model.GaussianMeasurement; import fi.smaa.jsmaa.model.MultivariateGaussianCriterionMeasurement; import fi.smaa.jsmaa.model.RelativeGaussianCriterionMeasurement; import fi.smaa.jsmaa.model.RelativeLogitGaussianCriterionMeasurement; import fi.smaa.jsmaa.model.SMAAModel; import fi.smaa.jsmaa.simulator.SMAA2Simulation; public class NetworkBenefitRiskTestBase { private static final double[][] EXPECTED_RA = { { 0.0215, 0.2648, 0.3217, 0.3143, 0.0777 }, { 0.1496, 0.3248, 0.2519, 0.2249, 0.0488 }, { 0.6114, 0.1201, 0.0868, 0.0545, 0.1272 }, { 0.0758, 0.1342, 0.1931, 0.2389, 0.3580 }, { 0.1417, 0.1561, 0.1465, 0.1674, 0.3883 } }; private static final double[][] EXPECTED_CW = { { 0.08646901, 0.24112709, 0.22045918, 0.1362404, 0.10229834, 0.21340600 }, { 0.17593187, 0.12181948, 0.29181468, 0.1293235, 0.13949573, 0.14161473 }, { 0.17986517, 0.17659262, 0.09069475, 0.1618931, 0.19437357, 0.19658082 }, { 0.07495453, 0.31162260, 0.27471687, 0.1266365, 0.09423491, 0.11783455 }, { 0.18030730, 0.07638429, 0.27879293, 0.2584503, 0.11542176, 0.09064338 } }; private static final double[] EXPECTED_CF = { 0.1144, 0.5532, 0.9870, 0.5538, 0.6522 }; private static final double EPSILON_CW = 0.03; private static final double EPSILON_RA = 0.02; protected static CriterionMeasurement buildDiarrhea(final List<Alternative> alternatives) { checkAlternativeOrder(alternatives, true); final double mean = -2.189737; final double stdDev = 0.2074509; final double[] meanVector = new double[] { 0.0, 0.60820293, 0.14088424, 0.93203685, 0.09601785 }; final double[][] covMatrix = new double[][] { { 0.0, 0.0, 0.0, 0.0, 0.0 }, { 0.0, 0.05366822, 0.04117003, 0.03407600, 0.03788519 }, { 0.0, 0.04117003, 0.08108542, 0.03778130, 0.04324302 }, { 0.0, 0.03407600, 0.03778130, 0.05394404, 0.03338272 }, { 0.0, 0.03788519, 0.04324302, 0.03338272, 0.09859621 } }; return createMeasurement(mean, stdDev, meanVector, covMatrix, alternatives); } protected static CriterionMeasurement buildDizziness(final List<Alternative> alternatives) { checkAlternativeOrder(alternatives, true); final double mean = -2.229952; final double stdDev = 0.6103081; final double[] meanVector = new double[] { 0, 0.1203208, 0.6538699, -0.2203627, 1.1915229 }; final double[][] covMatrix = new double[][] { { 0.0, 0.0, 0.0, 0.0, 0.0 }, { 0.0, 0.09124249, 0.07387436, 0.08086186, 0.06457527 }, { 0.0, 0.07387436, 0.14080128, 0.10328788, 0.07030520 }, { 0.0, 0.08086186, 0.10328788, 0.14662864, 0.06920427 }, { 0.0, 0.06457527, 0.07030520, 0.06920427, 0.08590214 } }; return createMeasurement(mean, stdDev, meanVector, covMatrix, alternatives); } protected static CriterionMeasurement buildHAMD(final List<Alternative> alternatives) { checkAlternativeOrder(alternatives, true); final double mean = -0.1714358; final double stdDev = 0.1129926; final double[] meanVector = new double[] { 0.0, 0.4718129, 0.7258847, 0.6715258, 0.8211993 }; final double[][] covMatrix = new double[][] { { 0.0, 0.0, 0.0, 0.0, 0.0 }, { 0.0, 0.013445532, 0.010394690, 0.009881156, 0.010499559 }, { 0.0, 0.010394690, 0.023006616, 0.008196856, 0.010732709 }, { 0.0, 0.009881156, 0.008196856, 0.019023866, 0.009210099 }, { 0.0, 0.010499559, 0.010732709, 0.009210099, 0.019107243 } }; return createMeasurement(mean, stdDev, meanVector, covMatrix, alternatives); } protected static CriterionMeasurement buildHeadache(final List<Alternative> alternatives) { checkAlternativeOrder(alternatives, true); final double mean = -1.195991; final double stdDev = 0.2926644; final double[] meanVector = new double[] { 0.0, 0.1964591, 0.1263129, 0.2029933, -0.2254059 }; final double[][] covMatrix = new double[][] { { 0.0, 0.0, 0.0, 0.0, 0.0 }, { 0.0, 0.03519380, 0.02755068, 0.02189916, 0.02919822 }, { 0.0, 0.02755068, 0.04847513, 0.02374509, 0.03277411 }, { 0.0, 0.02189916, 0.02374509, 0.03259199, 0.02356638 }, { 0.0, 0.02919822, 0.03277411, 0.02356638, 0.05920276 } }; return createMeasurement(mean, stdDev, meanVector, covMatrix, alternatives); } protected static CriterionMeasurement buildInsomnia(final List<Alternative> alternatives) { checkAlternativeOrder(alternatives, true); final double mean = -2.607277; final double stdDev = 0.1905852; final double[] meanVector = new double[] { 0.0, 0.7978386, 0.7486351, 1.0663029, 0.9744811 }; final double[][] covMatrix = new double[][] { { 0.0, 0.0, 0.0, 0.0, 0.0 }, { 0.0, 0.04388258, 0.03516051, 0.02560003, 0.03283164 }, { 0.0, 0.03516051, 0.06255486, 0.02807280, 0.03474863 }, { 0.0, 0.02560003, 0.02807280, 0.05402063, 0.02592013 }, { 0.0, 0.03283164, 0.03474863, 0.02592013, 0.06738850 } }; return createMeasurement(mean, stdDev, meanVector, covMatrix, alternatives); } protected static CriterionMeasurement buildNausea(final List<Alternative> alternatives) { checkAlternativeOrder(alternatives, true); final double mean = -2.017171; final double stdDev = 0.1917053; final double[] meanVector = new double[] { 0.0, 0.3951561, 0.6442827, 0.6469638, 0.9771107 }; final double[][] covMatrix = new double[][] { { 0.0, 0.0, 0.0, 0.0, 0.0 }, { 0.0, 0.04167875, 0.03492591, 0.02952693, 0.03559971 }, { 0.0, 0.03492591, 0.05629741, 0.03102295, 0.03658295 }, { 0.0, 0.02952693, 0.03102295, 0.04396177, 0.02899487 }, { 0.0, 0.03559971, 0.03658295, 0.02899487, 0.05369960 } }; return createMeasurement(mean, stdDev, meanVector, covMatrix, alternatives); } private static RelativeLogitGaussianCriterionMeasurement createMeasurement(final double mean, final double stdDev, final double[] meanVector, final double[][] covMatrix, final List<Alternative> alternatives) { GaussianMeasurement baseline = new GaussianMeasurement(mean, stdDev); MultivariateGaussianCriterionMeasurement delta = new MultivariateGaussianCriterionMeasurement(alternatives); delta.setMeanVector(new ArrayRealVector(meanVector)); delta.setCovarianceMatrix(new Array2DRowRealMatrix(covMatrix)); RelativeGaussianCriterionMeasurement relative = new RelativeGaussianCriterionMeasurement(delta, baseline); RelativeLogitGaussianCriterionMeasurement incidence = new RelativeLogitGaussianCriterionMeasurement( relative); return incidence; } public NetworkBenefitRiskTestBase() { super(); } protected void checkResults(SMAAModel model, SMAA2Simulation simulation, double slack) { checkAlternativeOrder(model.getAlternatives(), false); assertEquals("Diarrhea", model.getCriteria().get(0).getName()); assertEquals("HAM-D Responders", model.getCriteria().get(2).getName()); // verify CWs & CFs for (int i = 0; i < model.getAlternatives().size(); ++i) { Alternative alt = model.getAlternatives().get(i); for (int j = 0; j < model.getCriteria().size(); ++j) { Criterion crit = model.getCriteria().get(j); final double actual = simulation.getResults().getCentralWeightVectors().get(alt).get(crit); final double expected = EXPECTED_CW[i][j]; if (Math.abs(actual - expected) > EPSILON_CW * slack) { throw new AssertionFailedError("Central weight for " + alt + " differs on " + crit + ": expected <" + expected + ">, actual <" + actual + ">"); } } final double actual = simulation.getResults().getConfidenceFactors().get(alt); final double expected = EXPECTED_CF[i]; if (Math.abs(actual - expected) > EPSILON_CW * slack) { throw new AssertionFailedError("Confidence factor for " + alt + " differs: expected <" + expected + ">, actual <" + actual + ">"); } } // verify RAs for (int i = 0; i < model.getAlternatives().size(); ++i) { for (int r = 0; r < model.getAlternatives().size(); ++r) { Alternative alt = model.getAlternatives().get(i); final double actual = simulation.getResults().getRankAcceptabilities().get(alt).get(r); final double expected = EXPECTED_RA[i][r]; if (Math.abs(actual - expected) > EPSILON_RA * slack) { throw new AssertionFailedError("Rank probability for " + alt + " differs on " + (r + 1) + ": expected <" + expected + ">, actual <" + actual + ">"); } } } } private static void checkAlternativeOrder(List<Alternative> alternatives, boolean placeboFirst) { List<String> expected; if (placeboFirst) { expected = Arrays.asList("Placebo", "Fluoxetine", "Paroxetine", "Sertraline", "Venlafaxine"); } else { expected = Arrays.asList("Fluoxetine", "Paroxetine", "Placebo", "Sertraline", "Venlafaxine"); } for (int i = 0; i < expected.size(); ++i) { assertEquals(expected.get(i), alternatives.get(i).getName()); } } protected static List<Alternative> movePlacebo(final List<Alternative> alternatives, final int fromIndex, final int toIndex) { List<Alternative> newAlts = new ArrayList<Alternative>(alternatives); Alternative placebo = newAlts.remove(fromIndex); assertEquals("Placebo", placebo.getName()); newAlts.add(toIndex, placebo); return newAlts; } }