Examples with Parameters edu.cmu.tetrad.util.Parameters used on opensource projects

Example 86 with Parameters

use of edu.cmu.tetrad.util.Parameters in project tetrad by cmu-phil.

the class TsGFciRunner method execute.

// =================PUBLIC METHODS OVERRIDING ABSTRACT=================//
/**
 * Executes the algorithm, producing (at least) a result workbench. Must be
 * implemented in the extending class.
 */
// public void execute() {
// IKnowledge knowledge = getParameters().getKnowledge();
// Parameters searchParams = getParameters();
// 
// Parameters params = (Parameters) searchParams;
// 
// Graph graph;
// 
// if (getIndependenceTest() instanceof IndTestDSep) {
// GFci gfci = new GFci(getIndependenceTest());
// graph = gfci.search();
// } else {
// GFci fci = new GFci(getIndependenceTest());
// fci.setKnowledge(knowledge);
// fci.setCompleteRuleSetUsed(params.isCompleteRuleSetUsed());
// fci.setMaxPathLength(params.getMaxReachablePathLength());
// fci.setMaxIndegree(params.getMaxIndegree());
// double penaltyDiscount = params.getPenaltyDiscount();
// 
// fci.setCorrErrorsAlpha(penaltyDiscount);
// fci.setSamplePrior(params.getSamplePrior());
// fci.setStructurePrior(params.getStructurePrior());
// fci.setCompleteRuleSetUsed(false);
// fci.setFaithfulnessAssumed(params.isFaithfulnessAssumed());
// graph = fci.search();
// }
// 
// if (getSourceGraph() != null) {
// GraphUtils.arrangeBySourceGraph(graph, getSourceGraph());
// } else if (knowledge.isDefaultToKnowledgeLayout()) {
// SearchGraphUtils.arrangeByKnowledgeTiers(graph, knowledge);
// } else {
// GraphUtils.circleLayout(graph, 200, 200, 150);
// }
// 
// setResultGraph(graph);
// }
/**
 * Executes the algorithm, producing (at least) a result workbench. Must be
 * implemented in the extending class.
 */
public void execute() {
    Object model = getDataModel();
    if (model == null && getSourceGraph() != null) {
        model = getSourceGraph();
    }
    if (model == null) {
        throw new RuntimeException("Data source is unspecified. You may need to double click all your data boxes, \n" + "then click Save, and then right click on them and select Propagate Downstream. \n" + "The issue is that we use a seed to simulate from IM's, so your data is not saved to \n" + "file when you save the session. It can, however, be recreated from the saved seed.");
    }
    Parameters params = getParams();
    double penaltyDiscount = params.getDouble("penaltyDiscount", 4);
    if (model instanceof Graph) {
        GraphScore gesScore = new GraphScore((Graph) model);
        IndependenceTest test = new IndTestDSep((Graph) model);
        gfci = new TsGFci(test, gesScore);
        gfci.setKnowledge((IKnowledge) getParams().get("knowledge", new Knowledge2()));
        gfci.setVerbose(true);
    } else {
        if (model instanceof DataSet) {
            DataSet dataSet = (DataSet) model;
            if (dataSet.isContinuous()) {
                SemBicScore gesScore = new SemBicScore(new CovarianceMatrixOnTheFly((DataSet) model));
                // SemBicScore2 gesScore = new SemBicScore2(new CovarianceMatrixOnTheFly((DataSet) model));
                // SemGpScore gesScore = new SemGpScore(new CovarianceMatrixOnTheFly((DataSet) model));
                // SvrScore gesScore = new SvrScore((DataSet) model);
                gesScore.setPenaltyDiscount(penaltyDiscount);
                System.out.println("Score done");
                IndependenceTest test = new IndTestDSep((Graph) model);
                gfci = new TsGFci(test, gesScore);
                gfci.setKnowledge((IKnowledge) getParams().get("knowledge", new Knowledge2()));
            } else // else if (dataSet.isDiscrete()) {
            // double samplePrior = ((Parameters) getParameters()).getSamplePrior();
            // double structurePrior = ((Parameters) getParameters()).getStructurePrior();
            // BDeuScore score = new BDeuScore(dataSet);
            // score.setSamplePrior(samplePrior);
            // score.setStructurePrior(structurePrior);
            // gfci = new GFci(score);
            // }
            {
                throw new IllegalStateException("Data set must either be continuous or discrete.");
            }
        } else if (model instanceof ICovarianceMatrix) {
            SemBicScore gesScore = new SemBicScore((ICovarianceMatrix) model);
            gesScore.setPenaltyDiscount(penaltyDiscount);
            gesScore.setPenaltyDiscount(penaltyDiscount);
            IndependenceTest test = new IndTestDSep((Graph) model);
            gfci = new TsGFci(test, gesScore);
            gfci.setKnowledge((IKnowledge) getParams().get("knowledge", new Knowledge2()));
        } else if (model instanceof DataModelList) {
            DataModelList list = (DataModelList) model;
            for (DataModel dataModel : list) {
                if (!(dataModel instanceof DataSet || dataModel instanceof ICovarianceMatrix)) {
                    throw new IllegalArgumentException("Need a combination of all continuous data sets or " + "covariance matrices, or else all discrete data sets, or else a single initialGraph.");
                }
            }
            if (list.size() != 1) {
                throw new IllegalArgumentException("FGES takes exactly one data set, covariance matrix, or initialGraph " + "as input. For multiple data sets as input, use IMaGES.");
            }
            if (allContinuous(list)) {
                double penalty = params.getDouble("penaltyDiscount", 4);
                SemBicScoreImages fgesScore = new SemBicScoreImages(list);
                fgesScore.setPenaltyDiscount(penalty);
                IndependenceTest test = new IndTestDSep((Graph) model);
                gfci = new TsGFci(test, fgesScore);
                gfci.setKnowledge((IKnowledge) getParams().get("knowledge", new Knowledge2()));
            } else // else if (allDiscrete(list)) {
            // double structurePrior = ((Parameters) getParameters()).getStructurePrior();
            // double samplePrior = ((Parameters) getParameters()).getSamplePrior();
            // 
            // BdeuScoreImages fgesScore = new BdeuScoreImages(list);
            // fgesScore.setSamplePrior(samplePrior);
            // fgesScore.setStructurePrior(structurePrior);
            // 
            // gfci = new GFci(fgesScore);
            // }
            {
                throw new IllegalArgumentException("Data must be either all discrete or all continuous.");
            }
        } else {
            System.out.println("No viable input.");
        }
    }
    // gfci.setInitialGraph(initialGraph);
    // gfci.setKnowledge(getParameters().getKnowledge());
    // gfci.setNumPatternsToStore(params.getNumPatternsToSave());
    gfci.setVerbose(true);
    // gfci.setHeuristicSpeedup(true);
    // gfci.setMaxIndegree(3);
    gfci.setFaithfulnessAssumed(params.getBoolean("faithfulnessAssumed", true));
    Graph graph = gfci.search();
    if (getSourceGraph() != null) {
        GraphUtils.arrangeBySourceGraph(graph, getSourceGraph());
    } else if (((IKnowledge) getParams().get("knowledge", new Knowledge2())).isDefaultToKnowledgeLayout()) {
        SearchGraphUtils.arrangeByKnowledgeTiers(graph, (IKnowledge) getParams().get("knowledge", new Knowledge2()));
    } else {
        GraphUtils.circleLayout(graph, 200, 200, 150);
    }
    setResultGraph(graph);
// this.topGraphs = new ArrayList<>(gfci.getTopGraphs());
// 
// if (topGraphs.isEmpty()) {
// 
// topGraphs.add(new ScoredGraph(getResultGraph(), Double.NaN));
// }
// 
// setIndex(topGraphs.size() - 1);
}

Example 87 with Parameters

use of edu.cmu.tetrad.util.Parameters in project tetrad by cmu-phil.

the class MeasurementSimulatorParams method serializableInstance.

/**
 * Generates a simple exemplar of this class to test serialization.
 */
public static MeasurementSimulatorParams serializableInstance() {
    MeasurementSimulatorParams params = new MeasurementSimulatorParams(new Parameters());
    params.setHistory(new GeneHistory(BasalInitializer.serializableInstance(), BooleanGlassFunction.serializableInstance()));
    return params;
}

Example 88 with Parameters

use of edu.cmu.tetrad.util.Parameters in project tetrad by cmu-phil.

the class CcdRunner2 method getIndependenceTest.

public IndependenceTest getIndependenceTest() {
    Object dataModel = getDataModel();
    if (dataModel == null) {
        dataModel = getSourceGraph();
    }
    Parameters params = getParams();
    IndTestType testType = null;
    Parameters _params = params;
    testType = (IndTestType) _params.get("indTestType", IndTestType.FISHER_Z);
    return new IndTestChooser().getTest(dataModel, params, testType);
}

Example 89 with Parameters

use of edu.cmu.tetrad.util.Parameters in project tetrad by cmu-phil.

the class CfciRunner method execute.

// =================PUBLIC METHODS OVERRIDING ABSTRACT=================//
/**
 * Executes the algorithm, producing (at least) a result workbench. Must be
 * implemented in the extending class.
 */
public void execute() {
    IKnowledge knowledge = (IKnowledge) getParams().get("knowledge", new Knowledge2());
    Parameters searchParams = getParams();
    Parameters params = searchParams;
    Cfci cfci = new Cfci(getIndependenceTest());
    cfci.setKnowledge(knowledge);
    cfci.setDepth(params.getInt("depth", -1));
    Graph graph = cfci.search();
    if (getSourceGraph() != null) {
        GraphUtils.arrangeBySourceGraph(graph, getSourceGraph());
    } else if (knowledge.isDefaultToKnowledgeLayout()) {
        SearchGraphUtils.arrangeByKnowledgeTiers(graph, knowledge);
    } else {
        GraphUtils.circleLayout(graph, 200, 200, 150);
    }
    setResultGraph(graph);
}

Example 90 with Parameters

use of edu.cmu.tetrad.util.Parameters in project tetrad by cmu-phil.

the class TimeoutComparison method doRun.

private void doRun(List<AlgorithmSimulationWrapper> algorithmSimulationWrappers, List<AlgorithmWrapper> algorithmWrappers, List<SimulationWrapper> simulationWrappers, Statistics statistics, int numGraphTypes, double[][][][] allStats, Run run) {
    System.out.println();
    System.out.println("Run " + (run.getRunIndex() + 1));
    System.out.println();
    AlgorithmSimulationWrapper algorithmSimulationWrapper = algorithmSimulationWrappers.get(run.getAlgSimIndex());
    AlgorithmWrapper algorithmWrapper = algorithmSimulationWrapper.getAlgorithmWrapper();
    SimulationWrapper simulationWrapper = algorithmSimulationWrapper.getSimulationWrapper();
    DataModel data = simulationWrapper.getDataModel(run.getRunIndex());
    Graph trueGraph = simulationWrapper.getTrueGraph(run.getRunIndex());
    System.out.println((run.getAlgSimIndex() + 1) + ". " + algorithmWrapper.getDescription() + " simulationWrapper: " + simulationWrapper.getDescription());
    long start = System.currentTimeMillis();
    Graph out;
    try {
        Algorithm algorithm = algorithmWrapper.getAlgorithm();
        Simulation simulation = simulationWrapper.getSimulation();
        if (algorithm instanceof HasKnowledge && simulation instanceof HasKnowledge) {
            ((HasKnowledge) algorithm).setKnowledge(((HasKnowledge) simulation).getKnowledge());
        }
        if (algorithmWrapper.getAlgorithm() instanceof ExternalAlgorithm) {
            ExternalAlgorithm external = (ExternalAlgorithm) algorithmWrapper.getAlgorithm();
            external.setSimulation(simulationWrapper.getSimulation());
            external.setPath(resultsPath);
            external.setSimIndex(simulationWrappers.indexOf(simulationWrapper));
        }
        if (algorithm instanceof MultiDataSetAlgorithm) {
            List<Integer> indices = new ArrayList<>();
            int numDataModels = simulationWrapper.getSimulation().getNumDataModels();
            for (int i = 0; i < numDataModels; i++) {
                indices.add(i);
            }
            Collections.shuffle(indices);
            List<DataModel> dataModels = new ArrayList<>();
            int randomSelectionSize = algorithmWrapper.getAlgorithmSpecificParameters().getInt("randomSelectionSize");
            for (int i = 0; i < Math.min(numDataModels, randomSelectionSize); i++) {
                dataModels.add(simulationWrapper.getSimulation().getDataModel(indices.get(i)));
            }
            Parameters _params = algorithmWrapper.getAlgorithmSpecificParameters();
            out = ((MultiDataSetAlgorithm) algorithm).search(dataModels, _params);
        } else {
            DataModel dataModel = copyData ? data.copy() : data;
            Parameters _params = algorithmWrapper.getAlgorithmSpecificParameters();
            out = algorithm.search(dataModel, _params);
        }
    } catch (Exception e) {
        System.out.println("Could not run " + algorithmWrapper.getDescription());
        e.printStackTrace();
        return;
    }
    int simIndex = simulationWrappers.indexOf(simulationWrapper) + 1;
    int algIndex = algorithmWrappers.indexOf(algorithmWrapper) + 1;
    long stop = System.currentTimeMillis();
    long elapsed = stop - start;
    saveGraph(resultsPath, out, run.getRunIndex(), simIndex, algIndex, algorithmWrapper, elapsed);
    if (trueGraph != null) {
        out = GraphUtils.replaceNodes(out, trueGraph.getNodes());
    }
    if (algorithmWrapper.getAlgorithm() instanceof ExternalAlgorithm) {
        ExternalAlgorithm extAlg = (ExternalAlgorithm) algorithmWrapper.getAlgorithm();
        extAlg.setSimIndex(simulationWrappers.indexOf(simulationWrapper));
        extAlg.setSimulation(simulationWrapper.getSimulation());
        extAlg.setPath(resultsPath);
        elapsed = extAlg.getElapsedTime(data, simulationWrapper.getSimulationSpecificParameters());
    }
    Graph[] est = new Graph[numGraphTypes];
    Graph comparisonGraph;
    if (this.comparisonGraph == ComparisonGraph.true_DAG) {
        comparisonGraph = new EdgeListGraph(trueGraph);
    } else if (this.comparisonGraph == ComparisonGraph.Pattern_of_the_true_DAG) {
        comparisonGraph = SearchGraphUtils.patternForDag(new EdgeListGraph(trueGraph));
    } else if (this.comparisonGraph == ComparisonGraph.PAG_of_the_true_DAG) {
        comparisonGraph = new DagToPag(new EdgeListGraph(trueGraph)).convert();
    } else {
        throw new IllegalArgumentException("Unrecognized graph type.");
    }
    // Graph comparisonGraph = trueGraph == null ? null : algorithmSimulationWrapper.getComparisonGraph(trueGraph);
    est[0] = out;
    graphTypeUsed[0] = true;
    if (data.isMixed()) {
        est[1] = getSubgraph(out, true, true, data);
        est[2] = getSubgraph(out, true, false, data);
        est[3] = getSubgraph(out, false, false, data);
        graphTypeUsed[1] = true;
        graphTypeUsed[2] = true;
        graphTypeUsed[3] = true;
    }
    Graph[] truth = new Graph[numGraphTypes];
    truth[0] = comparisonGraph;
    if (data.isMixed() && comparisonGraph != null) {
        truth[1] = getSubgraph(comparisonGraph, true, true, data);
        truth[2] = getSubgraph(comparisonGraph, true, false, data);
        truth[3] = getSubgraph(comparisonGraph, false, false, data);
    }
    if (comparisonGraph != null) {
        for (int u = 0; u < numGraphTypes; u++) {
            if (!graphTypeUsed[u]) {
                continue;
            }
            int statIndex = -1;
            for (Statistic _stat : statistics.getStatistics()) {
                statIndex++;
                if (_stat instanceof ParameterColumn) {
                    continue;
                }
                double stat;
                if (_stat instanceof ElapsedTime) {
                    stat = elapsed / 1000.0;
                } else {
                    stat = _stat.getValue(truth[u], est[u]);
                }
                allStats[u][run.getAlgSimIndex()][statIndex][run.getRunIndex()] = stat;
            }
        }
    }
}