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.presentation; import java.io.IOException; import java.io.OutputStream; import java.text.Normalizer; import java.text.Normalizer.Form; import java.util.Locale; import java.util.regex.Pattern; import org.apache.commons.math3.linear.RealMatrix; import org.codehaus.jackson.JsonGenerationException; import org.codehaus.jackson.JsonNode; import org.codehaus.jackson.map.JsonMappingException; import org.codehaus.jackson.map.ObjectMapper; import org.codehaus.jackson.node.ArrayNode; import org.codehaus.jackson.node.ObjectNode; import org.drugis.addis.entities.Entity; import org.drugis.addis.entities.OutcomeMeasure; import org.drugis.addis.entities.OutcomeMeasure.Direction; import org.drugis.addis.entities.analysis.BenefitRiskAnalysis; import org.drugis.addis.util.JSMAAintegration.AbstractBenefitRiskSMAAFactory; import fi.smaa.jsmaa.model.BetaMeasurement; import fi.smaa.jsmaa.model.Criterion; import fi.smaa.jsmaa.model.CriterionMeasurement; import fi.smaa.jsmaa.model.FullJointMeasurements; import fi.smaa.jsmaa.model.GaussianMeasurement; import fi.smaa.jsmaa.model.ImpactMatrix; import fi.smaa.jsmaa.model.Interval; import fi.smaa.jsmaa.model.Measurement; import fi.smaa.jsmaa.model.MultivariateGaussianCriterionMeasurement; import fi.smaa.jsmaa.model.PerCriterionMeasurements; import fi.smaa.jsmaa.model.RelativeGaussianCriterionMeasurement; import fi.smaa.jsmaa.model.RelativeLogitGaussianCriterionMeasurement; import fi.smaa.jsmaa.model.SMAAModel; public class SMAASerializer<Alternative extends Entity, AnalysisType extends BenefitRiskAnalysis<Alternative>> { private SMAAModel d_model; private BenefitRiskAnalysis<Alternative> d_analysis; private AbstractBenefitRiskSMAAFactory<Alternative> d_factory; private ObjectMapper d_mapper; private static final Pattern NONLATIN = Pattern.compile("[^\\w-]"); private static final Pattern WHITESPACE = Pattern.compile("[\\s]"); public static String toSlug(String input) { String nowhitespace = WHITESPACE.matcher(input).replaceAll("-"); String normalized = Normalizer.normalize(nowhitespace, Form.NFD); String slug = NONLATIN.matcher(normalized).replaceAll(""); return slug.toLowerCase(Locale.ENGLISH); } public SMAASerializer(SMAAModel model, AnalysisType a, AbstractBenefitRiskSMAAFactory<Alternative> smaaFactory) { d_model = model; d_analysis = a; d_factory = smaaFactory; d_mapper = new ObjectMapper(); } public JsonNode getRootNode() { ObjectNode rootNode = (ObjectNode) d_mapper.createObjectNode(); rootNode.put("title", d_analysis.getName()); insertCriteria(d_mapper, rootNode); insertAlternatives(d_mapper, rootNode); // Add PerfomanceTable FullJointMeasurements m = d_model.getMeasurements(); ArrayNode performancesNode = (ArrayNode) d_mapper.createArrayNode(); if (m instanceof ImpactMatrix) { insertMeasurements(d_mapper, m, performancesNode); } if (m instanceof PerCriterionMeasurements) { insertPerCriterionMeasurement(d_mapper, m, performancesNode); } rootNode.put("performanceTable", performancesNode); rootNode.put("preferences", d_mapper.createObjectNode()); return rootNode; } public void serialize(OutputStream stream) { try { d_mapper.writerWithDefaultPrettyPrinter().writeValue(stream, getRootNode()); } catch (JsonGenerationException e) { e.printStackTrace(); } catch (JsonMappingException e) { e.printStackTrace(); } catch (IOException e) { e.printStackTrace(); } } private void insertPerCriterionMeasurement(ObjectMapper mapper, FullJointMeasurements m, ArrayNode performancesNode) { PerCriterionMeasurements measurements = (PerCriterionMeasurements) m; for (Criterion criterion : measurements.getCriteria()) { ObjectNode measurementNode = (ObjectNode) mapper.createObjectNode(); measurementNode.put("criterion", toSlug(criterion.getName())); ObjectNode performanceNode = (ObjectNode) mapper.createObjectNode(); CriterionMeasurement criterionMeasurement = measurements.getCriterionMeasurement(criterion); Class<? extends CriterionMeasurement> measurementType = criterionMeasurement.getClass(); if (measurementType.equals(RelativeLogitGaussianCriterionMeasurement.class) || measurementType.equals(RelativeGaussianCriterionMeasurement.class)) { ObjectNode parameterNode = (ObjectNode) mapper.createObjectNode(); String type = measurementType.equals(RelativeLogitGaussianCriterionMeasurement.class) ? "relative-logit-normal" : "relative-normal"; performanceNode.put("type", type); GaussianMeasurement baseline; MultivariateGaussianCriterionMeasurement relativeMeasurement; CriterionMeasurement measurement = measurementType.cast(criterionMeasurement); if (measurementType.equals(RelativeLogitGaussianCriterionMeasurement.class)) { RelativeLogitGaussianCriterionMeasurement tmp = (RelativeLogitGaussianCriterionMeasurement) measurement; baseline = tmp.getGaussianMeasurement().getBaselineMeasurement(); relativeMeasurement = tmp.getGaussianMeasurement().getRelativeMeasurement(); } else { RelativeGaussianCriterionMeasurement tmp = (RelativeGaussianCriterionMeasurement) measurement; baseline = tmp.getBaselineMeasurement(); relativeMeasurement = tmp.getRelativeMeasurement(); } // Add baseline ObjectNode baselineNode = (ObjectNode) mapper.createObjectNode(); baselineNode.put("type", "dnorm"); baselineNode.put("name", toSlug(d_analysis.getBaseline().getLabel())); baselineNode.put("mu", baseline.getMean()); baselineNode.put("sigma", baseline.getStDev()); parameterNode.put("baseline", baselineNode); // Add relative ObjectNode relativeNode = (ObjectNode) mapper.createObjectNode(); relativeNode.put("type", "dmnorm"); ObjectNode relativeMuNode = (ObjectNode) mapper.createObjectNode(); ObjectNode relativeCovNode = (ObjectNode) mapper.createObjectNode(); ArrayNode relativeCovRowNames = (ArrayNode) mapper.createArrayNode(); ArrayNode relativeCovColNames = (ArrayNode) mapper.createArrayNode(); for (int i = 0; i < relativeMeasurement.getAlternatives().size(); ++i) { String alternative = toSlug(relativeMeasurement.getAlternatives().get(i).getName()); relativeCovRowNames.add(alternative); relativeCovColNames.add(alternative); relativeMuNode.put(alternative, relativeMeasurement.getMeanVector().getEntry(i)); } relativeCovNode.put("colnames", relativeCovColNames); relativeCovNode.put("rownames", relativeCovRowNames); ArrayNode relativeCovDataNode = (ArrayNode) mapper.createArrayNode(); RealMatrix covarianceMatrix = relativeMeasurement.getCovarianceMatrix(); for (int i = 0; i < covarianceMatrix.getRowDimension(); ++i) { ArrayNode row = (ArrayNode) mapper.createArrayNode(); for (int j = 0; j < covarianceMatrix.getRowDimension(); ++j) { row.add(covarianceMatrix.getRow(i)[j]); } relativeCovDataNode.add(row); } relativeCovNode.put("data", relativeCovDataNode); relativeNode.put("mu", relativeMuNode); relativeNode.put("cov", relativeCovNode); parameterNode.put("relative", relativeNode); performanceNode.put("parameters", parameterNode); } measurementNode.put("performance", performanceNode); performancesNode.add(measurementNode); } } private void insertMeasurements(ObjectMapper mapper, FullJointMeasurements m, ArrayNode performancesNode) { ImpactMatrix impactMatrix = (ImpactMatrix) m; for (Criterion criterion : impactMatrix.getCriteria()) { for (fi.smaa.jsmaa.model.Alternative alternative : impactMatrix.getAlternatives()) { ObjectNode measurementNode = (ObjectNode) mapper.createObjectNode(); measurementNode.put("alternative", toSlug(alternative.getName())); measurementNode.put("criterion", toSlug(criterion.getName())); ObjectNode performanceNode = (ObjectNode) mapper.createObjectNode(); Measurement measurement = impactMatrix.getMeasurement(criterion, alternative); if (measurement instanceof BetaMeasurement) { BetaMeasurement betaMeasurement = (BetaMeasurement) measurement; performanceNode.put("type", "dbeta"); ObjectNode parameters = (ObjectNode) mapper.createObjectNode(); parameters.put("alpha", betaMeasurement.getAlpha()); parameters.put("beta", betaMeasurement.getBeta()); performanceNode.put("parameters", parameters); } if (measurement instanceof GaussianMeasurement) { GaussianMeasurement gaussianMeasurement = (GaussianMeasurement) measurement; performanceNode.put("type", "dnorm"); ObjectNode parameters = (ObjectNode) mapper.createObjectNode(); parameters.put("mu", gaussianMeasurement.getMean()); parameters.put("sigma", gaussianMeasurement.getStDev()); performanceNode.put("parameters", parameters); } measurementNode.put("performance", performanceNode); performancesNode.add(measurementNode); } } } private void insertAlternatives(ObjectMapper mapper, ObjectNode rootNode) { // Add Alternatives ObjectNode alternativesNode = (ObjectNode) mapper.createObjectNode(); for (fi.smaa.jsmaa.model.Alternative alternative : d_model.getAlternatives()) { ObjectNode alternativeNode = (ObjectNode) mapper.createObjectNode(); alternativeNode.put("title", alternative.getName()); alternativesNode.put(toSlug(alternative.getName()), alternativeNode); } rootNode.put("alternatives", alternativesNode); } private void insertCriteria(ObjectMapper mapper, ObjectNode rootNode) { // Add Criteria ObjectNode criteriaNode = (ObjectNode) mapper.createObjectNode(); for (OutcomeMeasure criterion : d_analysis.getCriteria()) { ObjectNode criterionNode = (ObjectNode) mapper.createObjectNode(); criterionNode.put("title", criterion.getLabel()); ObjectNode pvfNode = (ObjectNode) mapper.createObjectNode(); pvfNode.put("direction", criterion.getDirection() == Direction.HIGHER_IS_BETTER ? "increasing" : "decreasing"); pvfNode.put("type", "linear"); ArrayNode scaleRangeNode = mapper.createArrayNode(); Interval scale = d_factory.getCriterion(criterion).getScale(); scaleRangeNode.add(scale.getStart()); scaleRangeNode.add(scale.getEnd()); pvfNode.put("range", scaleRangeNode); criterionNode.put("pvf", pvfNode); criteriaNode.put(toSlug(criterion.getName()), criterionNode); } rootNode.put("criteria", criteriaNode); } }