org.apache.flink.ml.clustering.kmeans.KMeansModel Maven / Gradle / Ivy

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 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
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 *     http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.flink.ml.clustering.kmeans;

import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.api.java.typeutils.RowTypeInfo;
import org.apache.flink.ml.api.Model;
import org.apache.flink.ml.clustering.kmeans.KMeansModelData.ModelDataDecoder;
import org.apache.flink.ml.common.broadcast.BroadcastUtils;
import org.apache.flink.ml.common.datastream.TableUtils;
import org.apache.flink.ml.common.distance.DistanceMeasure;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.Vector;
import org.apache.flink.ml.linalg.VectorWithNorm;
import org.apache.flink.ml.param.Param;
import org.apache.flink.ml.util.ParamUtils;
import org.apache.flink.ml.util.ReadWriteUtils;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.api.internal.TableImpl;
import org.apache.flink.types.Row;
import org.apache.flink.util.Preconditions;

import org.apache.commons.lang3.ArrayUtils;

import java.io.IOException;
import java.util.Collections;
import java.util.HashMap;
import java.util.Map;

/** A Model which clusters data into k clusters using the model data computed by {@link KMeans}. */
public class KMeansModel implements Model, KMeansModelParams {
    private final Map, Object> paramMap = new HashMap<>();
    private Table modelDataTable;

    public KMeansModel() {
        ParamUtils.initializeMapWithDefaultValues(paramMap, this);
    }

    @Override
    public KMeansModel setModelData(Table... inputs) {
        Preconditions.checkArgument(inputs.length == 1);
        modelDataTable = inputs[0];
        return this;
    }

    @Override
    public Table[] getModelData() {
        return new Table[] {modelDataTable};
    }

    @Override
    @SuppressWarnings("unchecked")
    public Table[] transform(Table... inputs) {
        Preconditions.checkArgument(inputs.length == 1);

        StreamTableEnvironment tEnv =
                (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment();
        DataStream modelDataStream =
                KMeansModelData.getModelDataStream(modelDataTable);

        RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema());
        RowTypeInfo outputTypeInfo =
                new RowTypeInfo(
                        ArrayUtils.addAll(inputTypeInfo.getFieldTypes(), Types.INT),
                        ArrayUtils.addAll(inputTypeInfo.getFieldNames(), getPredictionCol()));
        final String broadcastModelKey = "broadcastModelKey";
        DataStream predictionResult =
                BroadcastUtils.withBroadcastStream(
                        Collections.singletonList(tEnv.toDataStream(inputs[0])),
                        Collections.singletonMap(broadcastModelKey, modelDataStream),
                        inputList -> {
                            DataStream inputData = inputList.get(0);
                            return inputData.map(
                                    new PredictLabelFunction(
                                            broadcastModelKey,
                                            getFeaturesCol(),
                                            DistanceMeasure.getInstance(getDistanceMeasure()),
                                            getK()),
                                    outputTypeInfo);
                        });

        return new Table[] {tEnv.fromDataStream(predictionResult)};
    }

    /** A utility function used for prediction. */
    private static class PredictLabelFunction extends RichMapFunction {

        private final String broadcastModelKey;

        private final String featuresCol;

        private final DistanceMeasure distanceMeasure;

        private final int k;

        private VectorWithNorm[] centroids;

        public PredictLabelFunction(
                String broadcastModelKey,
                String featuresCol,
                DistanceMeasure distanceMeasure,
                int k) {
            this.broadcastModelKey = broadcastModelKey;
            this.featuresCol = featuresCol;
            this.distanceMeasure = distanceMeasure;
            this.k = k;
        }

        @Override
        public Row map(Row dataPoint) {
            if (centroids == null) {
                KMeansModelData modelData =
                        (KMeansModelData)
                                getRuntimeContext().getBroadcastVariable(broadcastModelKey).get(0);
                Preconditions.checkArgument(modelData.centroids.length <= k);
                centroids = new VectorWithNorm[modelData.centroids.length];
                for (int i = 0; i < modelData.centroids.length; i++) {
                    centroids[i] = new VectorWithNorm(modelData.centroids[i]);
                }
            }
            DenseVector point = ((Vector) dataPoint.getField(featuresCol)).toDense();
            int closestCentroidId =
                    distanceMeasure.findClosest(centroids, new VectorWithNorm(point));
            return Row.join(dataPoint, Row.of(closestCentroidId));
        }
    }

    @Override
    public Map, Object> getParamMap() {
        return paramMap;
    }

    @Override
    public void save(String path) throws IOException {
        ReadWriteUtils.saveModelData(
                KMeansModelData.getModelDataStream(modelDataTable),
                path,
                new KMeansModelData.ModelDataEncoder());
        ReadWriteUtils.saveMetadata(this, path);
    }

    // TODO: Add INFO level logging.
    public static KMeansModel load(StreamTableEnvironment tEnv, String path) throws IOException {
        Table modelDataTable = ReadWriteUtils.loadModelData(tEnv, path, new ModelDataDecoder());
        KMeansModel model = ReadWriteUtils.loadStageParam(path);
        return model.setModelData(modelDataTable);
    }
}