org.apache.flink.ml.feature.onehotencoder.OneHotEncoderModel 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.feature.onehotencoder;
import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.typeutils.RowTypeInfo;
import org.apache.flink.ml.api.Model;
import org.apache.flink.ml.common.broadcast.BroadcastUtils;
import org.apache.flink.ml.common.datastream.TableUtils;
import org.apache.flink.ml.common.param.HasHandleInvalid;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.ml.linalg.typeinfo.SparseVectorTypeInfo;
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.List;
import java.util.Map;
import java.util.function.Function;
/**
* A Model which encodes data into one-hot format using the model data computed by {@link
* OneHotEncoder}.
*
* The `keep` and `skip` option of {@link HasHandleInvalid} is not supported in {@link
* OneHotEncoderParams}.
*/
public class OneHotEncoderModel
implements Model, OneHotEncoderParams {
private final Map, Object> paramMap = new HashMap<>();
private Table modelDataTable;
public OneHotEncoderModel() {
ParamUtils.initializeMapWithDefaultValues(paramMap, this);
}
@Override
public Table[] transform(Table... inputs) {
final String[] inputCols = getInputCols();
final String[] outputCols = getOutputCols();
final boolean dropLast = getDropLast();
final String broadcastModelKey = "OneHotModelStream";
Preconditions.checkArgument(getHandleInvalid().equals(ERROR_INVALID));
Preconditions.checkArgument(inputs.length == 1);
Preconditions.checkArgument(inputCols.length == outputCols.length);
RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema());
RowTypeInfo outputTypeInfo =
new RowTypeInfo(
ArrayUtils.addAll(
inputTypeInfo.getFieldTypes(),
Collections.nCopies(
outputCols.length, SparseVectorTypeInfo.INSTANCE)
.toArray(new TypeInformation[0])),
ArrayUtils.addAll(inputTypeInfo.getFieldNames(), outputCols));
StreamTableEnvironment tEnv =
(StreamTableEnvironment) ((TableImpl) modelDataTable).getTableEnvironment();
DataStream input = tEnv.toDataStream(inputs[0]);
DataStream> modelStream =
OneHotEncoderModelData.getModelDataStream(modelDataTable);
Function>, DataStream> function =
dataStreams -> {
DataStream stream = dataStreams.get(0);
return stream.map(
new GenerateOutputsFunction(inputCols, dropLast, broadcastModelKey),
outputTypeInfo);
};
DataStream output =
BroadcastUtils.withBroadcastStream(
Collections.singletonList(input),
Collections.singletonMap(broadcastModelKey, modelStream),
function);
Table outputTable = tEnv.fromDataStream(output);
return new Table[] {outputTable};
}
@Override
public void save(String path) throws IOException {
ReadWriteUtils.saveMetadata(this, path);
ReadWriteUtils.saveModelData(
OneHotEncoderModelData.getModelDataStream(modelDataTable),
path,
new OneHotEncoderModelData.ModelDataEncoder());
}
public static OneHotEncoderModel load(StreamTableEnvironment tEnv, String path)
throws IOException {
OneHotEncoderModel model = ReadWriteUtils.loadStageParam(path);
Table modelDataTable =
ReadWriteUtils.loadModelData(
tEnv, path, new OneHotEncoderModelData.ModelDataStreamFormat());
return model.setModelData(modelDataTable);
}
@Override
public Map, Object> getParamMap() {
return paramMap;
}
@Override
public OneHotEncoderModel setModelData(Table... inputs) {
Preconditions.checkArgument(inputs.length == 1);
modelDataTable = inputs[0];
return this;
}
@Override
public Table[] getModelData() {
return new Table[] {modelDataTable};
}
private static class GenerateOutputsFunction extends RichMapFunction {
private final String[] inputCols;
private final boolean dropLast;
private final String broadcastModelKey;
private List> model = null;
public GenerateOutputsFunction(
String[] inputCols, boolean dropLast, String broadcastModelKey) {
this.inputCols = inputCols;
this.dropLast = dropLast;
this.broadcastModelKey = broadcastModelKey;
}
@Override
public Row map(Row row) {
if (model == null) {
model = getRuntimeContext().getBroadcastVariable(broadcastModelKey);
}
int[] categorySizes = new int[model.size()];
int offset = dropLast ? 0 : 1;
for (Tuple2 tup : model) {
categorySizes[tup.f0] = tup.f1 + offset;
}
Row result = new Row(categorySizes.length);
for (int i = 0; i < categorySizes.length; i++) {
Number number = (Number) row.getField(inputCols[i]);
Preconditions.checkArgument(
number.intValue() == number.doubleValue(),
String.format("Value %s cannot be parsed as indexed integer.", number));
int idx = number.intValue();
if (idx == categorySizes[i]) {
result.setField(i, Vectors.sparse(categorySizes[i], new int[0], new double[0]));
} else {
result.setField(
i,
Vectors.sparse(categorySizes[i], new int[] {idx}, new double[] {1.0}));
}
}
return Row.join(row, result);
}
}
}