edu.cmu.tetrad.algcomparison.algorithm.pairwise.FaskPw Maven / Gradle / Ivy
package edu.cmu.tetrad.algcomparison.algorithm.pairwise;
import edu.cmu.tetrad.algcomparison.algorithm.Algorithm;
import edu.cmu.tetrad.algcomparison.utils.TakesExternalGraph;
import edu.cmu.tetrad.annotation.AlgType;
import edu.cmu.tetrad.annotation.Bootstrapping;
import edu.cmu.tetrad.data.DataModel;
import edu.cmu.tetrad.data.DataSet;
import edu.cmu.tetrad.data.DataType;
import edu.cmu.tetrad.data.SimpleDataLoader;
import edu.cmu.tetrad.graph.EdgeListGraph;
import edu.cmu.tetrad.graph.Graph;
import edu.cmu.tetrad.search.Fask;
import edu.cmu.tetrad.search.score.SemBicScore;
import edu.cmu.tetrad.search.test.IndTestFisherZ;
import edu.cmu.tetrad.util.Parameters;
import edu.cmu.tetrad.util.Params;
import edu.pitt.dbmi.algo.resampling.GeneralResamplingTest;
import java.util.ArrayList;
import java.util.List;
/**
* FASK-PW (pairwise).
*
* @author josephramsey
*/
@edu.cmu.tetrad.annotation.Algorithm(
name = "FASK-PW",
command = "fask-pw",
algoType = AlgType.orient_pairwise,
dataType = DataType.Continuous
)
@Bootstrapping
public class FaskPw implements Algorithm, TakesExternalGraph {
private static final long serialVersionUID = 23L;
private Algorithm algorithm;
private Graph externalGraph;
public FaskPw() {
}
public FaskPw(Algorithm algorithm) {
this.algorithm = algorithm;
}
@Override
public Graph search(DataModel dataModel, Parameters parameters) {
if (parameters.getInt(Params.NUMBER_RESAMPLING) < 1) {
if (this.externalGraph == null) {
this.externalGraph = this.algorithm.search(dataModel, parameters);
}
boolean precomputeCovariances = parameters.getBoolean(Params.PRECOMPUTE_COVARIANCES);
if (this.externalGraph == null) {
throw new IllegalArgumentException(
"This FASK-PW (pairwise) algorithm needs both data and a graph source as inputs; it \n"
+ "will orient the edges in the input graph using the data");
}
DataSet dataSet = SimpleDataLoader.getContinuousDataSet(dataModel);
Fask fask = new Fask(dataSet, new SemBicScore(dataSet, precomputeCovariances), new IndTestFisherZ(dataSet, 0.01));
fask.setAdjacencyMethod(Fask.AdjacencyMethod.EXTERNAL_GRAPH);
fask.setExternalGraph(this.externalGraph);
fask.setSkewEdgeThreshold(Double.POSITIVE_INFINITY);
return fask.search();
} else {
FaskPw rSkew = new FaskPw(this.algorithm);
if (this.externalGraph != null) {
rSkew.setExternalGraph(this.algorithm);
}
DataSet data = (DataSet) dataModel;
GeneralResamplingTest search = new GeneralResamplingTest(data, rSkew, parameters.getInt(Params.NUMBER_RESAMPLING), parameters.getDouble(Params.PERCENT_RESAMPLE_SIZE), parameters.getBoolean(Params.RESAMPLING_WITH_REPLACEMENT), parameters.getInt(Params.RESAMPLING_ENSEMBLE), parameters.getBoolean(Params.ADD_ORIGINAL_DATASET));
search.setParameters(parameters);
search.setVerbose(parameters.getBoolean(Params.VERBOSE));
return search.search();
}
}
@Override
public Graph getComparisonGraph(Graph graph) {
return new EdgeListGraph(graph);
}
@Override
public String getDescription() {
return "RSkew" + (this.algorithm != null ? " with initial graph from "
+ this.algorithm.getDescription() : "");
}
@Override
public DataType getDataType() {
return DataType.Continuous;
}
@Override
public List getParameters() {
List parameters = new ArrayList<>();
if (this.algorithm != null && !this.algorithm.getParameters().isEmpty()) {
parameters.addAll(this.algorithm.getParameters());
}
parameters.add(Params.VERBOSE);
parameters.add(Params.PRECOMPUTE_COVARIANCES);
return parameters;
}
@Override
public void setExternalGraph(Algorithm algorithm) {
if (algorithm == null) {
throw new IllegalArgumentException("This FASK-PW (pairwise) algorithm needs both data and a graph source as inputs; it \n"
+ "will orient the edges in the input graph using the data.");
}
this.algorithm = algorithm;
}
}