org.apache.flink.ml.classification.knn.KnnModelData Maven / Gradle / Ivy
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* to you under the Apache License, Version 2.0 (the
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* http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.flink.ml.classification.knn;
import org.apache.flink.api.common.serialization.Encoder;
import org.apache.flink.api.common.typeinfo.TypeInformation;
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.DataInputView;
import org.apache.flink.core.memory.DataInputViewStreamWrapper;
import org.apache.flink.core.memory.DataOutputView;
import org.apache.flink.core.memory.DataOutputViewStreamWrapper;
import org.apache.flink.ml.linalg.DenseMatrix;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.Matrix;
import org.apache.flink.ml.linalg.Vector;
import org.apache.flink.ml.linalg.typeinfo.DenseMatrixSerializer;
import org.apache.flink.ml.linalg.typeinfo.DenseVectorSerializer;
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 java.io.EOFException;
import java.io.IOException;
import java.io.OutputStream;
/**
* Model data of {@link KnnModel}.
*
* This class also provides methods to convert model data from Table to a data stream, and
* classes to save/load model data.
*/
public class KnnModelData {
public DenseMatrix packedFeatures;
public DenseVector featureNormSquares;
public DenseVector labels;
public KnnModelData() {}
public KnnModelData(
DenseMatrix packedFeatures, DenseVector featureNormSquares, DenseVector labels) {
this.packedFeatures = packedFeatures;
this.featureNormSquares = featureNormSquares;
this.labels = labels;
}
/**
* Converts the table model to a data stream.
*
* @param modelDataTable The table model data.
* @return The data stream model data.
*/
public static DataStream getModelDataStream(Table modelDataTable) {
StreamTableEnvironment tEnv =
(StreamTableEnvironment) ((TableImpl) modelDataTable).getTableEnvironment();
return tEnv.toDataStream(modelDataTable)
.map(
x ->
new KnnModelData(
((Matrix) x.getField(0)).toDense(),
((Vector) x.getField(1)).toDense(),
((Vector) x.getField(2)).toDense()));
}
/** Encoder for {@link KnnModelData}. */
public static class ModelDataEncoder implements Encoder {
private final DenseVectorSerializer serializer = new DenseVectorSerializer();
@Override
public void encode(KnnModelData modelData, OutputStream outputStream) throws IOException {
DataOutputView dataOutputView = new DataOutputViewStreamWrapper(outputStream);
DenseMatrixSerializer.INSTANCE.serialize(modelData.packedFeatures, dataOutputView);
serializer.serialize(modelData.featureNormSquares, dataOutputView);
serializer.serialize(modelData.labels, dataOutputView);
}
}
/** Decoder for {@link KnnModelData}. */
public static class ModelDataDecoder extends SimpleStreamFormat {
@Override
public Reader createReader(Configuration config, FSDataInputStream stream) {
return new Reader() {
private final DataInputView source = new DataInputViewStreamWrapper(stream);
private final DenseVectorSerializer serializer = new DenseVectorSerializer();
@Override
public KnnModelData read() throws IOException {
try {
DenseMatrix matrix = DenseMatrixSerializer.INSTANCE.deserialize(source);
DenseVector normSquares = serializer.deserialize(source);
DenseVector labels = serializer.deserialize(source);
return new KnnModelData(matrix, normSquares, labels);
} catch (EOFException e) {
return null;
}
}
@Override
public void close() throws IOException {
stream.close();
}
};
}
@Override
public TypeInformation getProducedType() {
return TypeInformation.of(KnnModelData.class);
}
}
}