package edu.cmu.tetrad.algcomparison.algorithm.multi;

import edu.cmu.tetrad.algcomparison.algorithm.MultiDataSetAlgorithm;
import edu.cmu.tetrad.algcomparison.algorithm.oracle.pattern.Fges;
import edu.cmu.tetrad.algcomparison.score.BdeuScore;
import edu.cmu.tetrad.algcomparison.utils.HasKnowledge;
import edu.cmu.tetrad.annotation.AlgType;
import edu.cmu.tetrad.data.*;
import edu.cmu.tetrad.graph.EdgeListGraph;
import edu.cmu.tetrad.graph.Graph;
import edu.cmu.tetrad.search.BdeuScoreImages;
import edu.cmu.tetrad.search.SearchGraphUtils;
import edu.cmu.tetrad.util.Parameters;
import edu.pitt.dbmi.algo.resampling.GeneralResamplingTest;
import edu.pitt.dbmi.algo.resampling.ResamplingEdgeEnsemble;

import java.util.ArrayList;
import java.util.Collections;
import java.util.List;

/**
 * Wraps the IMaGES algorithm for discrete variables.
 * </p>
 * Requires that the parameter 'randomSelectionSize' be set to indicate how many
 * datasets should be taken at a time (randomly). This cannot given multiple
 * values.
 *
 * @author jdramsey
 */
@edu.cmu.tetrad.annotation.Algorithm(
        name = "IMaGES Discrete",
        command = "imgs_disc",
        algoType = AlgType.forbid_latent_common_causes
)
public class ImagesBDeu implements MultiDataSetAlgorithm, HasKnowledge {

    static final long serialVersionUID = 23L;
    private IKnowledge knowledge = new Knowledge2();

    public ImagesBDeu() {
    }

    @Override
    public Graph search(List<DataModel> dataSets, Parameters parameters) {
    	if (parameters.getInt("numberResampling") < 1) {
            BdeuScoreImages score = new BdeuScoreImages(dataSets);
            score.setSamplePrior(parameters.getDouble("samplePrior"));
            score.setStructurePrior(parameters.getDouble("structurePrior"));
            edu.cmu.tetrad.search.Fges search = new edu.cmu.tetrad.search.Fges(score);
            search.setFaithfulnessAssumed(true);
            search.setKnowledge(knowledge);

            return search.search();
        } else {
            ImagesBDeu imagesBDeu = new ImagesBDeu();

            List<DataSet> datasets = new ArrayList<>();

            for (DataModel dataModel : dataSets) {
                datasets.add((DataSet) dataModel);
            }
            GeneralResamplingTest search = new GeneralResamplingTest(datasets, imagesBDeu, parameters.getInt("numberResampling"));
            search.setKnowledge(knowledge);

            search.setPercentResampleSize(parameters.getDouble("percentResampleSize"));
            search.setResamplingWithReplacement(parameters.getBoolean("resamplingWithReplacement"));
            
            ResamplingEdgeEnsemble edgeEnsemble = ResamplingEdgeEnsemble.Highest;
            switch (parameters.getInt("resamplingEnsemble", 1)) {
                case 0:
                    edgeEnsemble = ResamplingEdgeEnsemble.Preserved;
                    break;
                case 1:
                    edgeEnsemble = ResamplingEdgeEnsemble.Highest;
                    break;
                case 2:
                    edgeEnsemble = ResamplingEdgeEnsemble.Majority;
            }
            search.setEdgeEnsemble(edgeEnsemble);
            search.setAddOriginalDataset(parameters.getBoolean("addOriginalDataset"));
            
            search.setParameters(parameters);
            search.setVerbose(parameters.getBoolean("verbose"));
            return search.search();
        }
    }

    @Override
    public Graph search(DataModel dataSet, Parameters parameters) {
        if (parameters.getInt("numberResampling") < 1) {
            return search(Collections.singletonList((DataModel) DataUtils.getDiscreteDataSet(dataSet)), parameters);
        } else {
            ImagesBDeu imagesBDeu = new ImagesBDeu();

            List<DataSet> dataSets = Collections.singletonList(DataUtils.getContinuousDataSet(dataSet));
            GeneralResamplingTest search = new GeneralResamplingTest(dataSets, imagesBDeu, parameters.getInt("numberResampling"));
            search.setKnowledge(knowledge);

            search.setPercentResampleSize(parameters.getDouble("percentResampleSize"));
            search.setResamplingWithReplacement(parameters.getBoolean("resamplingWithReplacement"));
            
            ResamplingEdgeEnsemble edgeEnsemble = ResamplingEdgeEnsemble.Highest;
            switch (parameters.getInt("resamplingEnsemble", 1)) {
                case 0:
                    edgeEnsemble = ResamplingEdgeEnsemble.Preserved;
                    break;
                case 1:
                    edgeEnsemble = ResamplingEdgeEnsemble.Highest;
                    break;
                case 2:
                    edgeEnsemble = ResamplingEdgeEnsemble.Majority;
            }
            search.setEdgeEnsemble(edgeEnsemble);
            search.setAddOriginalDataset(parameters.getBoolean("addOriginalDataset"));
            
            search.setParameters(parameters);
            search.setVerbose(parameters.getBoolean("verbose"));
            return search.search();
        }
    }

    @Override
    public Graph getComparisonGraph(Graph graph) {
        return SearchGraphUtils.patternForDag(new EdgeListGraph(graph));
    }

    @Override
    public String getDescription() {
        return "IMaGES for discrete variables (using the BDeu score)";
    }

    @Override
    public DataType getDataType() {
        return DataType.Discrete;
    }

    @Override
    public List<String> getParameters() {
        List<String> parameters = new Fges(new BdeuScore(), false).getParameters();
        parameters.add("numRuns");
        parameters.add("randomSelectionSize");
        // Resampling
        parameters.add("numberResampling");
        parameters.add("percentResampleSize");
        parameters.add("resamplingWithReplacement");
        parameters.add("resamplingEnsemble");
        parameters.add("addOriginalDataset");
        parameters.add("verbose");

        return parameters;
    }

    @Override
    public IKnowledge getKnowledge() {
        return knowledge;
    }

    @Override
    public void setKnowledge(IKnowledge knowledge) {
        this.knowledge = knowledge;
    }
}
