sklearn.cluster.KMeans.java Source code

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Here is the source code for sklearn.cluster.KMeans.java

Source

/*
 * Copyright (c) 2015 Villu Ruusmann
 *
 * This file is part of JPMML-SkLearn
 *
 * JPMML-SkLearn is free software: you can redistribute it and/or modify
 * it under the terms of the GNU Affero General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * JPMML-SkLearn is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU Affero General Public License for more details.
 *
 * You should have received a copy of the GNU Affero General Public License
 * along with JPMML-SkLearn.  If not, see <http://www.gnu.org/licenses/>.
 */
package sklearn.cluster;

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

import com.google.common.collect.HashMultiset;
import com.google.common.collect.Multiset;
import org.dmg.pmml.Array;
import org.dmg.pmml.CompareFunction;
import org.dmg.pmml.ComparisonMeasure;
import org.dmg.pmml.FieldName;
import org.dmg.pmml.MiningFunction;
import org.dmg.pmml.Output;
import org.dmg.pmml.SquaredEuclidean;
import org.dmg.pmml.clustering.Cluster;
import org.dmg.pmml.clustering.ClusteringField;
import org.dmg.pmml.clustering.ClusteringModel;
import org.jpmml.converter.Feature;
import org.jpmml.converter.ModelUtil;
import org.jpmml.converter.PMMLUtil;
import org.jpmml.converter.Schema;
import org.jpmml.converter.ValueUtil;
import org.jpmml.converter.clustering.ClusteringModelUtil;
import org.jpmml.sklearn.ClassDictUtil;
import org.jpmml.sklearn.MatrixUtil;
import sklearn.Clusterer;

public class KMeans extends Clusterer {

    public KMeans(String module, String name) {
        super(module, name);
    }

    @Override
    public int getNumberOfFeatures() {
        int[] shape = getClusterCentersShape();

        return shape[1];
    }

    @Override
    public ClusteringModel encodeModel(Schema schema) {
        int[] shape = getClusterCentersShape();

        int numberOfClusters = shape[0];
        int numberOfFeatures = shape[1];

        List<? extends Number> clusterCenters = getClusterCenters();
        List<Integer> labels = getLabels();

        Multiset<Integer> labelCounts = HashMultiset.create();

        if (labels != null) {
            labelCounts.addAll(labels);
        }

        List<Cluster> clusters = new ArrayList<>();

        for (int i = 0; i < numberOfClusters; i++) {
            Array array = PMMLUtil
                    .createRealArray(MatrixUtil.getRow(clusterCenters, numberOfClusters, numberOfFeatures, i));

            Cluster cluster = new Cluster().setId(String.valueOf(i))
                    .setSize((labelCounts.size() > 0 ? labelCounts.count(i) : null)).setArray(array);

            clusters.add(cluster);
        }

        List<Feature> features = schema.getFeatures();

        List<ClusteringField> clusteringFields = ClusteringModelUtil.createClusteringFields(features);

        ComparisonMeasure comparisonMeasure = new ComparisonMeasure(ComparisonMeasure.Kind.DISTANCE)
                .setCompareFunction(CompareFunction.ABS_DIFF).setMeasure(new SquaredEuclidean());

        Output output = ClusteringModelUtil.createOutput(FieldName.create("Cluster"), clusters);

        ClusteringModel clusteringModel = new ClusteringModel(MiningFunction.CLUSTERING,
                ClusteringModel.ModelClass.CENTER_BASED, numberOfClusters, ModelUtil.createMiningSchema(schema),
                comparisonMeasure, clusteringFields, clusters).setOutput(output);

        return clusteringModel;
    }

    public List<? extends Number> getClusterCenters() {
        return (List) ClassDictUtil.getArray(this, "cluster_centers_");
    }

    public List<Integer> getLabels() {
        return ValueUtil.asIntegers((List) ClassDictUtil.getArray(this, "labels_"));
    }

    private int[] getClusterCentersShape() {
        return ClassDictUtil.getShape(this, "cluster_centers_", 2);
    }
}