Examples with IKnowledge edu.cmu.tetrad.data.IKnowledge used on opensource projects

Example 36 with IKnowledge

use of edu.cmu.tetrad.data.IKnowledge in project tetrad by cmu-phil.

the class MbSearchEditor method layoutByKnowledge.

public void layoutByKnowledge() {
    GraphWorkbench resultWorkbench = getWorkbench();
    Graph graph = resultWorkbench.getGraph();
    IKnowledge knowledge = (IKnowledge) getAlgorithmRunner().getParams().get("knowledge", new Knowledge2());
    SearchGraphUtils.arrangeByKnowledgeTiers(graph, knowledge);
// resultWorkbench.setGraph(graph);
}

Example 37 with IKnowledge

use of edu.cmu.tetrad.data.IKnowledge 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 38 with IKnowledge

use of edu.cmu.tetrad.data.IKnowledge in project tetrad by cmu-phil.

the class MbFanSearchRunner 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() {
    int pcDepth = getParams().getInt("depth", -1);
    Mbfs mbfs = new Mbfs(getIndependenceTest(), pcDepth);
    Parameters params = getParams();
    if (params instanceof Parameters) {
        mbfs.setAggressivelyPreventCycles(params.getBoolean("aggressivelyPreventCycles", false));
    }
    IKnowledge knowledge = (IKnowledge) getParams().get("knowledge", new Knowledge2());
    mbfs.setKnowledge(knowledge);
    String targetName = getParams().getString("targetName", null);
    Graph graph = mbfs.search(targetName);
    setResultGraph(graph);
    if (getSourceGraph() != null) {
        GraphUtils.arrangeBySourceGraph(graph, getSourceGraph());
    } else if (knowledge.isDefaultToKnowledgeLayout()) {
        SearchGraphUtils.arrangeByKnowledgeTiers(graph, knowledge);
    } else {
        GraphUtils.circleLayout(graph, 200, 200, 150);
    }
    this.mbfs = mbfs;
}

Example 39 with IKnowledge

use of edu.cmu.tetrad.data.IKnowledge in project tetrad by cmu-phil.

the class MbfsRunner 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() {
    // int pcDepth = ((Parameters) getParameters()).getMaxIndegree();
    // Mbfs mbfs = new Mbfs(getIndependenceTest(), pcDepth);
    // Parameters params = getParameters();
    // if (params instanceof Parameters) {
    // mbfs.setAggressivelyPreventCycles(((Parameters) params)
    // .isAggressivelyPreventCycles());
    // }
    IKnowledge knowledge = (IKnowledge) getParams().get("knowledge", new Knowledge2());
    // mbfs.setKnowledge(knowledge);
    String targetName = getParams().getString("targetName", null);
    // Graph searchGraph = mbfs.search(targetName);
    // setResultGraph(searchGraph);
    DataSet dataSet = (DataSet) getDataModelList().get(0);
    SemBicScore score = new SemBicScore(new CovarianceMatrixOnTheFly(dataSet));
    score.setPenaltyDiscount(getParams().getDouble("alpha", 0.001));
    FgesMb search = new FgesMb(score);
    search.setFaithfulnessAssumed(true);
    Graph searchGraph = search.search(dataSet.getVariable(targetName));
    if (getSourceGraph() != null) {
        GraphUtils.arrangeBySourceGraph(searchGraph, getSourceGraph());
    } else if (knowledge.isDefaultToKnowledgeLayout()) {
        SearchGraphUtils.arrangeByKnowledgeTiers(searchGraph, knowledge);
    } else {
        GraphUtils.circleLayout(searchGraph, 200, 200, 150);
    }
    // this.mbfs = mbfs;
    setResultGraph(searchGraph);
}

Example 40 with IKnowledge

use of edu.cmu.tetrad.data.IKnowledge in project tetrad by cmu-phil.

the class ForbiddenGraphModel method createKnowledge.

private void createKnowledge(Parameters params) {
    IKnowledge knwl = getKnowledge();
    if (knwl == null) {
        return;
    }
    knwl.clear();
    List<String> varNames = getVarNames();
    getKnowledgeBoxInput().getVariableNames().stream().filter(e -> !e.startsWith("E_")).forEach(e -> {
        varNames.add(e);
        knwl.addVariable(e);
    });
    if (resultGraph == null) {
        throw new NullPointerException("I couldn't find a parent graph.");
    }
    List<Node> nodes = resultGraph.getNodes();
    int numOfNodes = nodes.size();
    for (int i = 0; i < numOfNodes; i++) {
        for (int j = i + 1; j < numOfNodes; j++) {
            Node n1 = nodes.get(i);
            Node n2 = nodes.get(j);
            if (n1.getName().startsWith("E_") || n2.getName().startsWith("E_")) {
                continue;
            }
            Edge edge = resultGraph.getEdge(n1, n2);
            if (edge != null && edge.isDirected()) {
                knwl.setForbidden(edge.getNode2().getName(), edge.getNode1().getName());
            }
        }
    }
}