org.apache.flink.ml.classification.naivebayes.NaiveBayesModelData 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.naivebayes;
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.typeinfo.Types;
import org.apache.flink.api.common.typeutils.base.DoubleSerializer;
import org.apache.flink.api.common.typeutils.base.MapSerializer;
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.linalg.DenseVector;
import org.apache.flink.ml.linalg.Vector;
import org.apache.flink.ml.linalg.typeinfo.DenseVectorSerializer;
import org.apache.flink.ml.linalg.typeinfo.DenseVectorTypeInfo;
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 java.io.EOFException;
import java.io.IOException;
import java.io.OutputStream;
import java.util.HashMap;
import java.util.Map;
/**
* Model data of {@link NaiveBayesModel}.
*
* This class also provides methods to convert model data from Table to Datastream, and classes
* to save/load model data.
*/
public class NaiveBayesModelData {
private static final Map> fields;
static {
fields = new HashMap<>();
fields.put(
"theta",
Types.OBJECT_ARRAY(Types.OBJECT_ARRAY(Types.MAP(Types.DOUBLE, Types.DOUBLE))));
fields.put("piArray", DenseVectorTypeInfo.INSTANCE);
fields.put("labels", DenseVectorTypeInfo.INSTANCE);
}
public static final TypeInformation TYPE_INFO =
Types.POJO(NaiveBayesModelData.class, fields);
/**
* Log of class conditional probabilities, whose dimension is C (number of classes) by D (number
* of features).
*/
public Map[][] theta;
/** Log of class priors, whose dimension is C (number of classes). */
public DenseVector piArray;
/** Value of labels. */
public DenseVector labels;
public NaiveBayesModelData(
Map[][] theta, DenseVector piArray, DenseVector labels) {
this.theta = theta;
this.piArray = piArray;
this.labels = labels;
}
public NaiveBayesModelData() {}
/**
* 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(
(MapFunction)
row ->
new NaiveBayesModelData(
(Map[][]) row.getField(0),
((Vector) row.getField(1)).toDense(),
((Vector) row.getField(2)).toDense()),
TYPE_INFO);
}
/** Data encoder for the {@link NaiveBayesModelData}. */
public static class ModelDataEncoder implements Encoder {
private final DenseVectorSerializer serializer = new DenseVectorSerializer();
@Override
public void encode(NaiveBayesModelData modelData, OutputStream outputStream)
throws IOException {
DataOutputViewStreamWrapper outputViewStreamWrapper =
new DataOutputViewStreamWrapper(outputStream);
MapSerializer mapSerializer =
new MapSerializer<>(DoubleSerializer.INSTANCE, DoubleSerializer.INSTANCE);
serializer.serialize(modelData.labels, outputViewStreamWrapper);
serializer.serialize(modelData.piArray, outputViewStreamWrapper);
outputViewStreamWrapper.writeInt(modelData.theta.length);
outputViewStreamWrapper.writeInt(modelData.theta[0].length);
for (Map[] maps : modelData.theta) {
for (Map map : maps) {
mapSerializer.serialize(map, outputViewStreamWrapper);
}
}
}
}
/** Data decoder for the {@link NaiveBayesModelData}. */
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 NaiveBayesModelData read() throws IOException {
try {
DataInputViewStreamWrapper inputViewStreamWrapper =
new DataInputViewStreamWrapper(inputStream);
MapSerializer mapSerializer =
new MapSerializer<>(
DoubleSerializer.INSTANCE, DoubleSerializer.INSTANCE);
DenseVector labels = serializer.deserialize(inputViewStreamWrapper);
DenseVector piArray = serializer.deserialize(inputViewStreamWrapper);
int featureSize = inputViewStreamWrapper.readInt();
int numLabels = inputViewStreamWrapper.readInt();
Map[][] theta = new HashMap[numLabels][featureSize];
for (int i = 0; i < featureSize; i++) {
for (int j = 0; j < numLabels; j++) {
theta[i][j] = mapSerializer.deserialize(inputViewStreamWrapper);
}
}
return new NaiveBayesModelData(theta, piArray, labels);
} catch (EOFException e) {
return null;
}
}
@Override
public void close() throws IOException {
inputStream.close();
}
};
}
@Override
public TypeInformation getProducedType() {
return TYPE_INFO;
}
}
}