Examples with MultiDataSetAlgorithm edu.cmu.tetrad.algcomparison.algorithm.MultiDataSetAlgorithm used on opensource projects

Example 1 with MultiDataSetAlgorithm

use of edu.cmu.tetrad.algcomparison.algorithm.MultiDataSetAlgorithm in project tetrad by cmu-phil.

the class GeneralAlgorithmRunner method execute.

// ============================PUBLIC METHODS==========================//
@Override
public void execute() {
    List<Graph> graphList = new ArrayList<>();
    int i = 0;
    if (getDataModelList().isEmpty()) {
        if (getSourceGraph() != null) {
            Algorithm algo = getAlgorithm();
            if (algo instanceof HasKnowledge) {
                ((HasKnowledge) algo).setKnowledge(getKnowledge());
            }
            graphList.add(algo.search(null, parameters));
        } else {
            throw new IllegalArgumentException("The parent boxes did not include any datasets or graphs. Try opening\n" + "the editors for those boxes and loading or simulating them.");
        }
    } else {
        if (getAlgorithm() instanceof MultiDataSetAlgorithm) {
            for (int k = 0; k < parameters.getInt("numRuns"); k++) {
                List<DataSet> dataSets = getDataModelList().stream().map(e -> (DataSet) e).collect(Collectors.toCollection(ArrayList::new));
                if (dataSets.size() < parameters.getInt("randomSelectionSize")) {
                    throw new IllegalArgumentException("Sorry, the 'random selection size' is greater than " + "the number of data sets.");
                }
                Collections.shuffle(dataSets);
                List<DataModel> sub = new ArrayList<>();
                for (int j = 0; j < parameters.getInt("randomSelectionSize"); j++) {
                    sub.add(dataSets.get(j));
                }
                Algorithm algo = getAlgorithm();
                if (algo instanceof HasKnowledge) {
                    ((HasKnowledge) algo).setKnowledge(getKnowledge());
                }
                graphList.add(((MultiDataSetAlgorithm) algo).search(sub, parameters));
            }
        } else if (getAlgorithm() instanceof ClusterAlgorithm) {
            for (int k = 0; k < parameters.getInt("numRuns"); k++) {
                getDataModelList().forEach(dataModel -> {
                    if (dataModel instanceof ICovarianceMatrix) {
                        ICovarianceMatrix dataSet = (ICovarianceMatrix) dataModel;
                        graphList.add(algorithm.search(dataSet, parameters));
                    } else if (dataModel instanceof DataSet) {
                        DataSet dataSet = (DataSet) dataModel;
                        if (!dataSet.isContinuous()) {
                            throw new IllegalArgumentException("Sorry, you need a continuous dataset for a cluster algorithm.");
                        }
                        graphList.add(algorithm.search(dataSet, parameters));
                    }
                });
            }
        } else {
            getDataModelList().forEach(data -> {
                IKnowledge knowledgeFromData = data.getKnowledge();
                if (!(knowledgeFromData == null || knowledgeFromData.getVariables().isEmpty())) {
                    this.knowledge = knowledgeFromData;
                }
                Algorithm algo = getAlgorithm();
                if (algo instanceof HasKnowledge) {
                    ((HasKnowledge) algo).setKnowledge(getKnowledge());
                }
                DataType algDataType = algo.getDataType();
                if (data.isContinuous() && (algDataType == DataType.Continuous || algDataType == DataType.Mixed)) {
                    graphList.add(algo.search(data, parameters));
                } else if (data.isDiscrete() && (algDataType == DataType.Discrete || algDataType == DataType.Mixed)) {
                    graphList.add(algo.search(data, parameters));
                } else if (data.isMixed() && algDataType == DataType.Mixed) {
                    graphList.add(algo.search(data, parameters));
                } else {
                    throw new IllegalArgumentException("The type of data changed; try opening up the search editor and " + "running the algorithm there.");
                }
            });
        }
    }
    if (getKnowledge().getVariablesNotInTiers().size() < getKnowledge().getVariables().size()) {
        for (Graph graph : graphList) {
            SearchGraphUtils.arrangeByKnowledgeTiers(graph, getKnowledge());
        }
    } else {
        for (Graph graph : graphList) {
            GraphUtils.circleLayout(graph, 225, 200, 150);
        }
    }
    this.graphList = graphList;
}

Example 2 with MultiDataSetAlgorithm

use of edu.cmu.tetrad.algcomparison.algorithm.MultiDataSetAlgorithm 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;
            }
        }
    }
}

Example 3 with MultiDataSetAlgorithm

use of edu.cmu.tetrad.algcomparison.algorithm.MultiDataSetAlgorithm in project tetrad by cmu-phil.

the class Comparison 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;
            }
        }
    }
}