org.apache.flink.ml.clustering.kmeans.KMeansModelData Maven / Gradle / Ivy
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package org.apache.flink.ml.clustering.kmeans;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.serialization.Encoder;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.common.typeutils.base.IntSerializer;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.connector.file.src.reader.SimpleStreamFormat;
import org.apache.flink.core.fs.FSDataInputStream;
import org.apache.flink.core.memory.DataInputViewStreamWrapper;
import org.apache.flink.core.memory.DataOutputViewStreamWrapper;
import org.apache.flink.ml.common.datastream.TableUtils;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.Vector;
import org.apache.flink.ml.linalg.typeinfo.DenseVectorSerializer;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
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.util.Preconditions;
import java.io.EOFException;
import java.io.IOException;
import java.io.OutputStream;
import java.util.Arrays;
import java.util.Random;
/**
* Model data of {@link KMeansModel} and {@link OnlineKMeansModel}.
*
* This class also provides methods to convert model data from Table to Datastream, and classes
* to save/load model data.
*/
public class KMeansModelData {
public DenseVector[] centroids;
/**
* The weight of the centroids. It is used when updating the model data in online training
* process.
*
*
KMeansModelData objects generated during {@link KMeans#fit(Table...)} also contains this
* field, so that it can be used as the initial model data of the online training process.
*/
public DenseVector weights;
public KMeansModelData(DenseVector[] centroids, DenseVector weights) {
Preconditions.checkArgument(centroids.length == weights.size());
this.centroids = centroids;
this.weights = weights;
}
public KMeansModelData() {}
/**
* Generates a Table containing a {@link KMeansModelData} instance with randomly generated
* centroids.
*
* @param tEnv The environment where to create the table.
* @param k The number of generated centroids.
* @param dim The size of generated centroids.
* @param weight The weight of the centroids.
* @param seed Random seed.
*/
public static Table generateRandomModelData(
StreamTableEnvironment tEnv, int k, int dim, double weight, long seed) {
StreamExecutionEnvironment env = TableUtils.getExecutionEnvironment(tEnv);
return tEnv.fromDataStream(
env.fromElements(1).map(new RandomCentroidsCreator(k, dim, weight, seed)));
}
private static class RandomCentroidsCreator implements MapFunction {
private final int k;
private final int dim;
private final double weight;
private final long seed;
private RandomCentroidsCreator(int k, int dim, double weight, long seed) {
this.k = k;
this.dim = dim;
this.weight = weight;
this.seed = seed;
}
@Override
public KMeansModelData map(Integer integer) {
DenseVector[] centroids = new DenseVector[k];
Random random = new Random(seed);
for (int i = 0; i < k; i++) {
centroids[i] = new DenseVector(dim);
for (int j = 0; j < dim; j++) {
centroids[i].values[j] = random.nextDouble();
}
}
DenseVector weights = new DenseVector(k);
Arrays.fill(weights.values, weight);
return new KMeansModelData(centroids, weights);
}
}
/**
* Converts the table model to a data stream.
*
* @param modelData The table model data.
* @return The data stream model data.
*/
public static DataStream getModelDataStream(Table modelData) {
StreamTableEnvironment tEnv =
(StreamTableEnvironment) ((TableImpl) modelData).getTableEnvironment();
return tEnv.toDataStream(modelData)
.map(
x ->
new KMeansModelData(
Arrays.stream(((Vector[]) x.getField(0)))
.map(Vector::toDense)
.toArray(DenseVector[]::new),
((Vector) x.getField(1)).toDense()));
}
/** Data encoder for {@link KMeansModelData}. */
public static class ModelDataEncoder implements Encoder {
private final DenseVectorSerializer serializer = new DenseVectorSerializer();
@Override
public void encode(KMeansModelData modelData, OutputStream outputStream)
throws IOException {
DataOutputViewStreamWrapper outputViewStreamWrapper =
new DataOutputViewStreamWrapper(outputStream);
IntSerializer.INSTANCE.serialize(modelData.centroids.length, outputViewStreamWrapper);
for (DenseVector denseVector : modelData.centroids) {
serializer.serialize(denseVector, new DataOutputViewStreamWrapper(outputStream));
}
serializer.serialize(modelData.weights, new DataOutputViewStreamWrapper(outputStream));
}
}
/** Data decoder for {@link KMeansModelData}. */
public static class ModelDataDecoder extends SimpleStreamFormat {
@Override
public Reader createReader(
Configuration config, FSDataInputStream inputStream) {
return new Reader() {
private final DenseVectorSerializer serializer = new DenseVectorSerializer();
@Override
public KMeansModelData read() throws IOException {
try {
DataInputViewStreamWrapper inputViewStreamWrapper =
new DataInputViewStreamWrapper(inputStream);
int numDenseVectors =
IntSerializer.INSTANCE.deserialize(inputViewStreamWrapper);
DenseVector[] centroids = new DenseVector[numDenseVectors];
for (int i = 0; i < numDenseVectors; i++) {
centroids[i] = serializer.deserialize(inputViewStreamWrapper);
}
DenseVector weights = serializer.deserialize(inputViewStreamWrapper);
return new KMeansModelData(centroids, weights);
} catch (EOFException e) {
return null;
}
}
@Override
public void close() throws IOException {
inputStream.close();
}
};
}
@Override
public TypeInformation getProducedType() {
return TypeInformation.of(KMeansModelData.class);
}
}
}