zhipeng93 commented on code in PR #142: URL: https://github.com/apache/flink-ml/pull/142#discussion_r950988727
########## flink-ml-lib/src/main/java/org/apache/flink/ml/feature/maxabsscaler/MaxAbsScalerModel.java: ########## @@ -0,0 +1,169 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.flink.ml.feature.maxabsscaler; + +import org.apache.flink.api.common.functions.RichMapFunction; +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.linalg.BLAS; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.Vector; +import org.apache.flink.ml.linalg.typeinfo.VectorTypeInfo; +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 transforms data using the model data computed by {@link MaxAbsScaler}. */ +public class MaxAbsScalerModel + implements Model<MaxAbsScalerModel>, MaxAbsScalerParams<MaxAbsScalerModel> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + private Table modelDataTable; + + public MaxAbsScalerModel() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public MaxAbsScalerModel setModelData(Table... inputs) { + 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<Row> data = tEnv.toDataStream(inputs[0]); + DataStream<MaxAbsScalerModelData> maxAbsScalerModel = + MaxAbsScalerModelData.getModelDataStream(modelDataTable); + + final String broadcastModelKey = "broadcastModelKey"; + RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema()); + RowTypeInfo outputTypeInfo = + new RowTypeInfo( + ArrayUtils.addAll(inputTypeInfo.getFieldTypes(), VectorTypeInfo.INSTANCE), + ArrayUtils.addAll(inputTypeInfo.getFieldNames(), getOutputCol())); + + DataStream<Row> output = + BroadcastUtils.withBroadcastStream( + Collections.singletonList(data), + Collections.singletonMap(broadcastModelKey, maxAbsScalerModel), + inputList -> { + DataStream input = inputList.get(0); + return input.map( + new PredictOutputFunction(broadcastModelKey, getInputCol()), + outputTypeInfo); + }); + + return new Table[] {tEnv.fromDataStream(output)}; + } + + @Override + public Map<Param<?>, Object> getParamMap() { + return paramMap; + } + + @Override + public void save(String path) throws IOException { + ReadWriteUtils.saveMetadata(this, path); + ReadWriteUtils.saveModelData( + MaxAbsScalerModelData.getModelDataStream(modelDataTable), + path, + new MaxAbsScalerModelData.ModelDataEncoder()); + } + + /** + * Loads model data from path. + * + * @param tEnv Stream table environment. + * @param path Model path. + * @return MaxAbsScalerModel model. + */ + public static MaxAbsScalerModel load(StreamTableEnvironment tEnv, String path) + throws IOException { + MaxAbsScalerModel model = ReadWriteUtils.loadStageParam(path); + + Table modelDataTable = + ReadWriteUtils.loadModelData( + tEnv, path, new MaxAbsScalerModelData.ModelDataDecoder()); + return model.setModelData(modelDataTable); + } + + /** This operator loads model data and predicts result. */ Review Comment: nit: This operator --> This function. ########## flink-ml-lib/src/main/java/org/apache/flink/ml/feature/maxabsscaler/MaxAbsScaler.java: ########## @@ -0,0 +1,201 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.flink.ml.feature.maxabsscaler; + +import org.apache.flink.api.common.functions.MapFunction; +import org.apache.flink.api.common.state.ListState; +import org.apache.flink.api.common.state.ListStateDescriptor; +import org.apache.flink.iteration.operator.OperatorStateUtils; +import org.apache.flink.ml.api.Estimator; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.SparseVector; +import org.apache.flink.ml.linalg.Vector; +import org.apache.flink.ml.linalg.typeinfo.DenseVectorTypeInfo; +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.runtime.state.StateInitializationContext; +import org.apache.flink.runtime.state.StateSnapshotContext; +import org.apache.flink.streaming.api.datastream.DataStream; +import org.apache.flink.streaming.api.operators.AbstractStreamOperator; +import org.apache.flink.streaming.api.operators.BoundedOneInput; +import org.apache.flink.streaming.api.operators.OneInputStreamOperator; +import org.apache.flink.streaming.runtime.streamrecord.StreamRecord; +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 java.io.IOException; +import java.util.HashMap; +import java.util.Map; + +/** + * An Estimator which implements the MaxAbsScaler algorithm. This algorithm rescales feature values + * to the range [-1, 1] by dividing through the largest maximum absolute value in each feature. It + * does not shift/center the data and thus does not destroy any sparsity. + */ +public class MaxAbsScaler + implements Estimator<MaxAbsScaler, MaxAbsScalerModel>, MaxAbsScalerParams<MaxAbsScaler> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + + public MaxAbsScaler() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public MaxAbsScalerModel fit(Table... inputs) { + Preconditions.checkArgument(inputs.length == 1); + final String inputCol = getInputCol(); + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment(); + + DataStream<Vector> inputData = + tEnv.toDataStream(inputs[0]) + .map( + (MapFunction<Row, Vector>) + value -> ((Vector) value.getField(inputCol))); + + DataStream<Vector> maxAbsValues = + inputData + .transform( + "reduceInEachPartition", + inputData.getType(), + new MaxAbsReduceFunctionOperator()) + .transform( + "reduceInFinalPartition", + inputData.getType(), + new MaxAbsReduceFunctionOperator()) + .setParallelism(1); + + DataStream<MaxAbsScalerModelData> modelData = + maxAbsValues.map( + (MapFunction<Vector, MaxAbsScalerModelData>) + vector -> new MaxAbsScalerModelData((DenseVector) vector)); + + MaxAbsScalerModel model = + new MaxAbsScalerModel().setModelData(tEnv.fromDataStream(modelData)); + ReadWriteUtils.updateExistingParams(model, getParamMap()); + return model; + } + + /** + * A stream operator to compute the maximum absolute values in each partition of the input + * bounded data stream. + */ + private static class MaxAbsReduceFunctionOperator extends AbstractStreamOperator<Vector> + implements OneInputStreamOperator<Vector, Vector>, BoundedOneInput { + private ListState<DenseVector> maxAbsState; + private DenseVector maxAbsVector; + + @Override + public void endInput() { + if (maxAbsVector != null) { + output.collect(new StreamRecord<>(maxAbsVector)); + } + } + + @Override + public void processElement(StreamRecord<Vector> streamRecord) { + Vector currentValue = streamRecord.getValue(); + if (currentValue == null) { + throw new RuntimeException("Input column data cannot be null."); + } + + if (maxAbsVector == null) { + int vecSize = currentValue.size(); + maxAbsVector = new DenseVector(vecSize); + if (currentValue instanceof DenseVector) { + double[] values = ((DenseVector) currentValue).values; + for (int i = 0; i < currentValue.size(); ++i) { + maxAbsVector.values[i] = Math.abs(values[i]); + } + } else { + int[] indices = ((SparseVector) currentValue).indices; + double[] values = ((SparseVector) currentValue).values; + + for (int i = 0; i < indices.length; ++i) { + maxAbsVector.values[indices[i]] = + Math.max(maxAbsVector.values[indices[i]], Math.abs(values[i])); + } + } + } else { + Preconditions.checkArgument( + currentValue.size() == maxAbsVector.size(), + "CurrentValue should has same size with maxVector."); + + if (currentValue instanceof DenseVector) { + double[] values = ((DenseVector) currentValue).values; + for (int i = 0; i < currentValue.size(); ++i) { + maxAbsVector.values[i] = + Math.max(maxAbsVector.values[i], Math.abs(values[i])); + } + } else if (currentValue instanceof SparseVector) { + int[] indices = ((SparseVector) currentValue).indices; + double[] values = ((SparseVector) currentValue).values; + + for (int i = 0; i < indices.length; ++i) { + maxAbsVector.values[indices[i]] = + Math.max(maxAbsVector.values[indices[i]], Math.abs(values[i])); + } + } else { + throw new IllegalArgumentException( Review Comment: nit: This line could be removed because the Vector could only be SparseVector or DenseVector for now. ########## flink-ml-lib/src/main/java/org/apache/flink/ml/feature/maxabsscaler/MaxAbsScalerModel.java: ########## @@ -0,0 +1,169 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.flink.ml.feature.maxabsscaler; + +import org.apache.flink.api.common.functions.RichMapFunction; +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.linalg.BLAS; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.Vector; +import org.apache.flink.ml.linalg.typeinfo.VectorTypeInfo; +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 transforms data using the model data computed by {@link MaxAbsScaler}. */ +public class MaxAbsScalerModel + implements Model<MaxAbsScalerModel>, MaxAbsScalerParams<MaxAbsScalerModel> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + private Table modelDataTable; + + public MaxAbsScalerModel() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public MaxAbsScalerModel setModelData(Table... inputs) { + 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<Row> data = tEnv.toDataStream(inputs[0]); + DataStream<MaxAbsScalerModelData> maxAbsScalerModel = + MaxAbsScalerModelData.getModelDataStream(modelDataTable); + + final String broadcastModelKey = "broadcastModelKey"; + RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema()); + RowTypeInfo outputTypeInfo = + new RowTypeInfo( + ArrayUtils.addAll(inputTypeInfo.getFieldTypes(), VectorTypeInfo.INSTANCE), + ArrayUtils.addAll(inputTypeInfo.getFieldNames(), getOutputCol())); + + DataStream<Row> output = + BroadcastUtils.withBroadcastStream( + Collections.singletonList(data), + Collections.singletonMap(broadcastModelKey, maxAbsScalerModel), + inputList -> { + DataStream input = inputList.get(0); + return input.map( + new PredictOutputFunction(broadcastModelKey, getInputCol()), + outputTypeInfo); + }); + + return new Table[] {tEnv.fromDataStream(output)}; + } + + @Override + public Map<Param<?>, Object> getParamMap() { + return paramMap; + } + + @Override + public void save(String path) throws IOException { + ReadWriteUtils.saveMetadata(this, path); + ReadWriteUtils.saveModelData( + MaxAbsScalerModelData.getModelDataStream(modelDataTable), + path, + new MaxAbsScalerModelData.ModelDataEncoder()); + } + + /** + * Loads model data from path. + * + * @param tEnv Stream table environment. + * @param path Model path. + * @return MaxAbsScalerModel model. + */ + public static MaxAbsScalerModel load(StreamTableEnvironment tEnv, String path) + throws IOException { + MaxAbsScalerModel model = ReadWriteUtils.loadStageParam(path); + + Table modelDataTable = + ReadWriteUtils.loadModelData( + tEnv, path, new MaxAbsScalerModelData.ModelDataDecoder()); + return model.setModelData(modelDataTable); + } + + /** This operator loads model data and predicts result. */ + private static class PredictOutputFunction extends RichMapFunction<Row, Row> { + private final String inputCol; + private final String broadcastKey; + private DenseVector scaleVector; + + public PredictOutputFunction(String broadcastKey, String inputCol) { + this.broadcastKey = broadcastKey; + this.inputCol = inputCol; + } + + @Override + public Row map(Row row) { + if (scaleVector == null) { + MaxAbsScalerModelData maxAbsScalerModelData = + (MaxAbsScalerModelData) + getRuntimeContext().getBroadcastVariable(broadcastKey).get(0); + scaleVector = maxAbsScalerModelData.maxVector; + + for (int i = 0; i < scaleVector.size(); ++i) { + if (scaleVector.values[i] != 0) { + scaleVector.values[i] = 1.0 / scaleVector.values[i]; + } else { + scaleVector.values[i] = 1.0; + } + } + } + + if (row.getFieldAs(inputCol) == null) { Review Comment: Shall we remove Line#159 to Line#161 and assume that the `inputCol` field of the row is never null? We need to make sure that the behavior of the existing implementations are consistent. Please refer to MinMaxScalerModel, StringIndexerModel and other existing algorithms. ########## flink-ml-lib/src/main/java/org/apache/flink/ml/feature/maxabsscaler/MaxAbsScaler.java: ########## @@ -0,0 +1,201 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.flink.ml.feature.maxabsscaler; + +import org.apache.flink.api.common.functions.MapFunction; +import org.apache.flink.api.common.state.ListState; +import org.apache.flink.api.common.state.ListStateDescriptor; +import org.apache.flink.iteration.operator.OperatorStateUtils; +import org.apache.flink.ml.api.Estimator; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.SparseVector; +import org.apache.flink.ml.linalg.Vector; +import org.apache.flink.ml.linalg.typeinfo.DenseVectorTypeInfo; +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.runtime.state.StateInitializationContext; +import org.apache.flink.runtime.state.StateSnapshotContext; +import org.apache.flink.streaming.api.datastream.DataStream; +import org.apache.flink.streaming.api.operators.AbstractStreamOperator; +import org.apache.flink.streaming.api.operators.BoundedOneInput; +import org.apache.flink.streaming.api.operators.OneInputStreamOperator; +import org.apache.flink.streaming.runtime.streamrecord.StreamRecord; +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 java.io.IOException; +import java.util.HashMap; +import java.util.Map; + +/** + * An Estimator which implements the MaxAbsScaler algorithm. This algorithm rescales feature values + * to the range [-1, 1] by dividing through the largest maximum absolute value in each feature. It + * does not shift/center the data and thus does not destroy any sparsity. + */ +public class MaxAbsScaler + implements Estimator<MaxAbsScaler, MaxAbsScalerModel>, MaxAbsScalerParams<MaxAbsScaler> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + + public MaxAbsScaler() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public MaxAbsScalerModel fit(Table... inputs) { + Preconditions.checkArgument(inputs.length == 1); + final String inputCol = getInputCol(); + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment(); + + DataStream<Vector> inputData = + tEnv.toDataStream(inputs[0]) + .map( + (MapFunction<Row, Vector>) + value -> ((Vector) value.getField(inputCol))); + + DataStream<Vector> maxAbsValues = + inputData + .transform( + "reduceInEachPartition", + inputData.getType(), + new MaxAbsReduceFunctionOperator()) + .transform( + "reduceInFinalPartition", + inputData.getType(), + new MaxAbsReduceFunctionOperator()) + .setParallelism(1); + + DataStream<MaxAbsScalerModelData> modelData = + maxAbsValues.map( + (MapFunction<Vector, MaxAbsScalerModelData>) + vector -> new MaxAbsScalerModelData((DenseVector) vector)); + + MaxAbsScalerModel model = + new MaxAbsScalerModel().setModelData(tEnv.fromDataStream(modelData)); + ReadWriteUtils.updateExistingParams(model, getParamMap()); + return model; + } + + /** + * A stream operator to compute the maximum absolute values in each partition of the input + * bounded data stream. + */ + private static class MaxAbsReduceFunctionOperator extends AbstractStreamOperator<Vector> + implements OneInputStreamOperator<Vector, Vector>, BoundedOneInput { + private ListState<DenseVector> maxAbsState; + private DenseVector maxAbsVector; + + @Override + public void endInput() { + if (maxAbsVector != null) { + output.collect(new StreamRecord<>(maxAbsVector)); + } + } + + @Override + public void processElement(StreamRecord<Vector> streamRecord) { + Vector currentValue = streamRecord.getValue(); + if (currentValue == null) { + throw new RuntimeException("Input column data cannot be null."); + } + + if (maxAbsVector == null) { + int vecSize = currentValue.size(); + maxAbsVector = new DenseVector(vecSize); + if (currentValue instanceof DenseVector) { + double[] values = ((DenseVector) currentValue).values; + for (int i = 0; i < currentValue.size(); ++i) { + maxAbsVector.values[i] = Math.abs(values[i]); + } + } else { + int[] indices = ((SparseVector) currentValue).indices; + double[] values = ((SparseVector) currentValue).values; + + for (int i = 0; i < indices.length; ++i) { + maxAbsVector.values[indices[i]] = + Math.max(maxAbsVector.values[indices[i]], Math.abs(values[i])); + } + } + } else { + Preconditions.checkArgument( + currentValue.size() == maxAbsVector.size(), + "CurrentValue should has same size with maxVector."); Review Comment: nit: How about update the error message as: `The training data should all have same dimensions`. ########## flink-ml-lib/src/main/java/org/apache/flink/ml/feature/maxabsscaler/MaxAbsScaler.java: ########## @@ -0,0 +1,201 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.flink.ml.feature.maxabsscaler; + +import org.apache.flink.api.common.functions.MapFunction; +import org.apache.flink.api.common.state.ListState; +import org.apache.flink.api.common.state.ListStateDescriptor; +import org.apache.flink.iteration.operator.OperatorStateUtils; +import org.apache.flink.ml.api.Estimator; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.SparseVector; +import org.apache.flink.ml.linalg.Vector; +import org.apache.flink.ml.linalg.typeinfo.DenseVectorTypeInfo; +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.runtime.state.StateInitializationContext; +import org.apache.flink.runtime.state.StateSnapshotContext; +import org.apache.flink.streaming.api.datastream.DataStream; +import org.apache.flink.streaming.api.operators.AbstractStreamOperator; +import org.apache.flink.streaming.api.operators.BoundedOneInput; +import org.apache.flink.streaming.api.operators.OneInputStreamOperator; +import org.apache.flink.streaming.runtime.streamrecord.StreamRecord; +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 java.io.IOException; +import java.util.HashMap; +import java.util.Map; + +/** + * An Estimator which implements the MaxAbsScaler algorithm. This algorithm rescales feature values + * to the range [-1, 1] by dividing through the largest maximum absolute value in each feature. It + * does not shift/center the data and thus does not destroy any sparsity. + */ +public class MaxAbsScaler + implements Estimator<MaxAbsScaler, MaxAbsScalerModel>, MaxAbsScalerParams<MaxAbsScaler> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + + public MaxAbsScaler() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public MaxAbsScalerModel fit(Table... inputs) { + Preconditions.checkArgument(inputs.length == 1); + final String inputCol = getInputCol(); + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment(); + + DataStream<Vector> inputData = + tEnv.toDataStream(inputs[0]) + .map( + (MapFunction<Row, Vector>) + value -> ((Vector) value.getField(inputCol))); + + DataStream<Vector> maxAbsValues = + inputData + .transform( + "reduceInEachPartition", + inputData.getType(), + new MaxAbsReduceFunctionOperator()) + .transform( + "reduceInFinalPartition", + inputData.getType(), + new MaxAbsReduceFunctionOperator()) + .setParallelism(1); + + DataStream<MaxAbsScalerModelData> modelData = + maxAbsValues.map( + (MapFunction<Vector, MaxAbsScalerModelData>) + vector -> new MaxAbsScalerModelData((DenseVector) vector)); + + MaxAbsScalerModel model = + new MaxAbsScalerModel().setModelData(tEnv.fromDataStream(modelData)); + ReadWriteUtils.updateExistingParams(model, getParamMap()); + return model; + } + + /** + * A stream operator to compute the maximum absolute values in each partition of the input + * bounded data stream. + */ + private static class MaxAbsReduceFunctionOperator extends AbstractStreamOperator<Vector> + implements OneInputStreamOperator<Vector, Vector>, BoundedOneInput { + private ListState<DenseVector> maxAbsState; + private DenseVector maxAbsVector; + + @Override + public void endInput() { + if (maxAbsVector != null) { + output.collect(new StreamRecord<>(maxAbsVector)); + } + } + + @Override + public void processElement(StreamRecord<Vector> streamRecord) { + Vector currentValue = streamRecord.getValue(); + if (currentValue == null) { + throw new RuntimeException("Input column data cannot be null."); + } + + if (maxAbsVector == null) { + int vecSize = currentValue.size(); + maxAbsVector = new DenseVector(vecSize); + if (currentValue instanceof DenseVector) { + double[] values = ((DenseVector) currentValue).values; + for (int i = 0; i < currentValue.size(); ++i) { + maxAbsVector.values[i] = Math.abs(values[i]); + } + } else { + int[] indices = ((SparseVector) currentValue).indices; + double[] values = ((SparseVector) currentValue).values; + + for (int i = 0; i < indices.length; ++i) { + maxAbsVector.values[indices[i]] = + Math.max(maxAbsVector.values[indices[i]], Math.abs(values[i])); + } + } + } else { + Preconditions.checkArgument( + currentValue.size() == maxAbsVector.size(), + "CurrentValue should has same size with maxVector."); + + if (currentValue instanceof DenseVector) { + double[] values = ((DenseVector) currentValue).values; + for (int i = 0; i < currentValue.size(); ++i) { + maxAbsVector.values[i] = + Math.max(maxAbsVector.values[i], Math.abs(values[i])); + } + } else if (currentValue instanceof SparseVector) { + int[] indices = ((SparseVector) currentValue).indices; + double[] values = ((SparseVector) currentValue).values; + + for (int i = 0; i < indices.length; ++i) { + maxAbsVector.values[indices[i]] = + Math.max(maxAbsVector.values[indices[i]], Math.abs(values[i])); + } + } else { + throw new IllegalArgumentException( + "Input column type must be SparseVector or DenseVector. "); + } + } + } + + @Override + public void initializeState(StateInitializationContext context) throws Exception { + super.initializeState(context); + maxAbsState = + context.getOperatorStateStore() + .getListState( + new ListStateDescriptor<>( + "maxState", DenseVectorTypeInfo.INSTANCE)); + + OperatorStateUtils.getUniqueElement(maxAbsState, "maxState") Review Comment: nit: maxAbsState ########## flink-ml-lib/src/main/java/org/apache/flink/ml/feature/maxabsscaler/MaxAbsScaler.java: ########## @@ -0,0 +1,201 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.flink.ml.feature.maxabsscaler; + +import org.apache.flink.api.common.functions.MapFunction; +import org.apache.flink.api.common.state.ListState; +import org.apache.flink.api.common.state.ListStateDescriptor; +import org.apache.flink.iteration.operator.OperatorStateUtils; +import org.apache.flink.ml.api.Estimator; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.SparseVector; +import org.apache.flink.ml.linalg.Vector; +import org.apache.flink.ml.linalg.typeinfo.DenseVectorTypeInfo; +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.runtime.state.StateInitializationContext; +import org.apache.flink.runtime.state.StateSnapshotContext; +import org.apache.flink.streaming.api.datastream.DataStream; +import org.apache.flink.streaming.api.operators.AbstractStreamOperator; +import org.apache.flink.streaming.api.operators.BoundedOneInput; +import org.apache.flink.streaming.api.operators.OneInputStreamOperator; +import org.apache.flink.streaming.runtime.streamrecord.StreamRecord; +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 java.io.IOException; +import java.util.HashMap; +import java.util.Map; + +/** + * An Estimator which implements the MaxAbsScaler algorithm. This algorithm rescales feature values + * to the range [-1, 1] by dividing through the largest maximum absolute value in each feature. It + * does not shift/center the data and thus does not destroy any sparsity. + */ +public class MaxAbsScaler + implements Estimator<MaxAbsScaler, MaxAbsScalerModel>, MaxAbsScalerParams<MaxAbsScaler> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + + public MaxAbsScaler() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public MaxAbsScalerModel fit(Table... inputs) { + Preconditions.checkArgument(inputs.length == 1); + final String inputCol = getInputCol(); + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment(); + + DataStream<Vector> inputData = + tEnv.toDataStream(inputs[0]) + .map( + (MapFunction<Row, Vector>) + value -> ((Vector) value.getField(inputCol))); + + DataStream<Vector> maxAbsValues = + inputData + .transform( + "reduceInEachPartition", + inputData.getType(), + new MaxAbsReduceFunctionOperator()) + .transform( + "reduceInFinalPartition", + inputData.getType(), + new MaxAbsReduceFunctionOperator()) + .setParallelism(1); + + DataStream<MaxAbsScalerModelData> modelData = + maxAbsValues.map( + (MapFunction<Vector, MaxAbsScalerModelData>) + vector -> new MaxAbsScalerModelData((DenseVector) vector)); + + MaxAbsScalerModel model = + new MaxAbsScalerModel().setModelData(tEnv.fromDataStream(modelData)); + ReadWriteUtils.updateExistingParams(model, getParamMap()); + return model; + } + + /** + * A stream operator to compute the maximum absolute values in each partition of the input + * bounded data stream. + */ + private static class MaxAbsReduceFunctionOperator extends AbstractStreamOperator<Vector> + implements OneInputStreamOperator<Vector, Vector>, BoundedOneInput { + private ListState<DenseVector> maxAbsState; + private DenseVector maxAbsVector; + + @Override + public void endInput() { + if (maxAbsVector != null) { + output.collect(new StreamRecord<>(maxAbsVector)); + } + } + + @Override + public void processElement(StreamRecord<Vector> streamRecord) { + Vector currentValue = streamRecord.getValue(); + if (currentValue == null) { + throw new RuntimeException("Input column data cannot be null."); + } + + if (maxAbsVector == null) { + int vecSize = currentValue.size(); + maxAbsVector = new DenseVector(vecSize); + if (currentValue instanceof DenseVector) { Review Comment: How about we simplify the code as: ``` maxAbsVector = maxAbsVector == null? new DenseVector(currentValue.size): maxAbsVector ``` and remove the code from Line#121 to Line#139? ########## flink-ml-lib/src/test/java/org/apache/flink/ml/feature/MaxAbsScalerTest.java: ########## @@ -0,0 +1,286 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.flink.ml.feature; + +import org.apache.flink.api.common.functions.MapFunction; +import org.apache.flink.api.common.restartstrategy.RestartStrategies; +import org.apache.flink.configuration.Configuration; +import org.apache.flink.ml.feature.maxabsscaler.MaxAbsScaler; +import org.apache.flink.ml.feature.maxabsscaler.MaxAbsScalerModel; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.Vector; +import org.apache.flink.ml.linalg.Vectors; +import org.apache.flink.ml.util.ReadWriteUtils; +import org.apache.flink.ml.util.TestUtils; +import org.apache.flink.streaming.api.datastream.DataStream; +import org.apache.flink.streaming.api.environment.ExecutionCheckpointingOptions; +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.types.Row; + +import org.apache.commons.collections.IteratorUtils; +import org.apache.commons.lang3.exception.ExceptionUtils; +import org.junit.Before; +import org.junit.Rule; +import org.junit.Test; +import org.junit.rules.TemporaryFolder; + +import java.util.ArrayList; +import java.util.Arrays; +import java.util.Collections; +import java.util.List; + +import static org.apache.flink.test.util.TestBaseUtils.compareResultCollections; +import static org.junit.Assert.assertEquals; + +/** Tests {@link MaxAbsScaler} and {@link MaxAbsScalerModel}. */ +public class MaxAbsScalerTest { + @Rule public final TemporaryFolder tempFolder = new TemporaryFolder(); + private StreamTableEnvironment tEnv; + private StreamExecutionEnvironment env; + + private Table trainDataTable; + private Table predictDataTable; + private Table trainSparseDataTable; + private Table predictSparseDataTable; + + private static final List<Row> TRAIN_DATA = + new ArrayList<>( + Arrays.asList( + Row.of(Vectors.dense(0.0, 3.0, 0.0)), + Row.of(Vectors.dense(2.1, 0.0, 0.0)), + Row.of(Vectors.dense(4.1, 5.1, 0.0)), + Row.of(Vectors.dense(6.1, 8.1, 0.0)), + Row.of(Vectors.dense(200, -400, 0.0)))); + + private static final List<Row> PREDICT_DATA = + new ArrayList<>( + Arrays.asList( + Row.of(Vectors.dense(150.0, 90.0, 1.0)), + Row.of(Vectors.dense(50.0, 40.0, 1.0)), + Row.of(Vectors.dense(100.0, 50.0, 0.5)))); + + private static final List<Row> TRAIN_SPARSE_DATA = + new ArrayList<>( + Arrays.asList( + Row.of(Vectors.sparse(4, new int[] {1, 3}, new double[] {4.0, 3.0})), + Row.of(Vectors.sparse(4, new int[] {0, 2}, new double[] {2.0, -6.0})), + Row.of(Vectors.sparse(4, new int[] {1, 2}, new double[] {1.0, 3.0})), + Row.of(Vectors.sparse(4, new int[] {0, 1}, new double[] {2.0, 8.0})), + Row.of(Vectors.sparse(4, new int[] {1, 3}, new double[] {1.0, 5.0})))); + + private static final List<Row> PREDICT_SPARSE_DATA = + new ArrayList<>( + Arrays.asList( + Row.of(Vectors.sparse(4, new int[] {0, 1}, new double[] {2.0, 4.0})), + Row.of(Vectors.sparse(4, new int[] {0, 2}, new double[] {1.0, 3.0})), + Row.of(Vectors.sparse(4, new int[] {}, new double[] {})), + Row.of(Vectors.sparse(4, new int[] {1, 3}, new double[] {1.0, 2.0})))); + + private static final List<Vector> EXPECTED_DATA = + new ArrayList<>( + Arrays.asList( + Vectors.dense(0.25, 0.1, 1.0), + Vectors.dense(0.5, 0.125, 0.5), + Vectors.dense(0.75, 0.225, 1.0))); + + private static final List<Vector> EXPECTED_SPARSE_DATA = + new ArrayList<>( + Arrays.asList( + Vectors.sparse(4, new int[] {0, 1}, new double[] {1.0, 0.5}), + Vectors.sparse(4, new int[] {0, 2}, new double[] {0.5, 0.5}), + Vectors.sparse(4, new int[] {}, new double[] {}), + Vectors.sparse(4, new int[] {1, 3}, new double[] {0.125, 0.4}))); + + @Before + public void before() { + Configuration config = new Configuration(); + config.set(ExecutionCheckpointingOptions.ENABLE_CHECKPOINTS_AFTER_TASKS_FINISH, true); + + env = StreamExecutionEnvironment.getExecutionEnvironment(config); + env.setParallelism(4); + env.enableCheckpointing(100); + env.setRestartStrategy(RestartStrategies.noRestart()); + tEnv = StreamTableEnvironment.create(env); + + trainDataTable = tEnv.fromDataStream(env.fromCollection(TRAIN_DATA)).as("input"); + predictDataTable = tEnv.fromDataStream(env.fromCollection(PREDICT_DATA)).as("input"); + + trainSparseDataTable = + tEnv.fromDataStream(env.fromCollection(TRAIN_SPARSE_DATA)).as("input"); + predictSparseDataTable = + tEnv.fromDataStream(env.fromCollection(PREDICT_SPARSE_DATA)).as("input"); + } + + private static void verifyPredictionResult(Table output, String outputCol, boolean isSparse) + throws Exception { + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) output).getTableEnvironment(); + + DataStream<Vector> stream = + tEnv.toDataStream(output) + .map((MapFunction<Row, Vector>) row -> row.getFieldAs(outputCol)); + + List<Vector> result = IteratorUtils.toList(stream.executeAndCollect()); + if (isSparse) { + compareResultCollections( Review Comment: Shall we make expectedData an argument of `verifyPredictionResult` function? ########## flink-ml-lib/src/test/java/org/apache/flink/ml/feature/MaxAbsScalerTest.java: ########## @@ -0,0 +1,286 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.flink.ml.feature; + +import org.apache.flink.api.common.functions.MapFunction; +import org.apache.flink.api.common.restartstrategy.RestartStrategies; +import org.apache.flink.configuration.Configuration; +import org.apache.flink.ml.feature.maxabsscaler.MaxAbsScaler; +import org.apache.flink.ml.feature.maxabsscaler.MaxAbsScalerModel; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.Vector; +import org.apache.flink.ml.linalg.Vectors; +import org.apache.flink.ml.util.ReadWriteUtils; +import org.apache.flink.ml.util.TestUtils; +import org.apache.flink.streaming.api.datastream.DataStream; +import org.apache.flink.streaming.api.environment.ExecutionCheckpointingOptions; +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.types.Row; + +import org.apache.commons.collections.IteratorUtils; +import org.apache.commons.lang3.exception.ExceptionUtils; +import org.junit.Before; +import org.junit.Rule; +import org.junit.Test; +import org.junit.rules.TemporaryFolder; + +import java.util.ArrayList; +import java.util.Arrays; +import java.util.Collections; +import java.util.List; + +import static org.apache.flink.test.util.TestBaseUtils.compareResultCollections; +import static org.junit.Assert.assertEquals; + +/** Tests {@link MaxAbsScaler} and {@link MaxAbsScalerModel}. */ +public class MaxAbsScalerTest { + @Rule public final TemporaryFolder tempFolder = new TemporaryFolder(); + private StreamTableEnvironment tEnv; + private StreamExecutionEnvironment env; + + private Table trainDataTable; + private Table predictDataTable; + private Table trainSparseDataTable; + private Table predictSparseDataTable; + + private static final List<Row> TRAIN_DATA = + new ArrayList<>( + Arrays.asList( + Row.of(Vectors.dense(0.0, 3.0, 0.0)), + Row.of(Vectors.dense(2.1, 0.0, 0.0)), + Row.of(Vectors.dense(4.1, 5.1, 0.0)), + Row.of(Vectors.dense(6.1, 8.1, 0.0)), + Row.of(Vectors.dense(200, -400, 0.0)))); + + private static final List<Row> PREDICT_DATA = + new ArrayList<>( + Arrays.asList( + Row.of(Vectors.dense(150.0, 90.0, 1.0)), + Row.of(Vectors.dense(50.0, 40.0, 1.0)), + Row.of(Vectors.dense(100.0, 50.0, 0.5)))); + + private static final List<Row> TRAIN_SPARSE_DATA = + new ArrayList<>( + Arrays.asList( + Row.of(Vectors.sparse(4, new int[] {1, 3}, new double[] {4.0, 3.0})), + Row.of(Vectors.sparse(4, new int[] {0, 2}, new double[] {2.0, -6.0})), + Row.of(Vectors.sparse(4, new int[] {1, 2}, new double[] {1.0, 3.0})), + Row.of(Vectors.sparse(4, new int[] {0, 1}, new double[] {2.0, 8.0})), + Row.of(Vectors.sparse(4, new int[] {1, 3}, new double[] {1.0, 5.0})))); + + private static final List<Row> PREDICT_SPARSE_DATA = + new ArrayList<>( + Arrays.asList( + Row.of(Vectors.sparse(4, new int[] {0, 1}, new double[] {2.0, 4.0})), + Row.of(Vectors.sparse(4, new int[] {0, 2}, new double[] {1.0, 3.0})), + Row.of(Vectors.sparse(4, new int[] {}, new double[] {})), + Row.of(Vectors.sparse(4, new int[] {1, 3}, new double[] {1.0, 2.0})))); + + private static final List<Vector> EXPECTED_DATA = + new ArrayList<>( + Arrays.asList( + Vectors.dense(0.25, 0.1, 1.0), + Vectors.dense(0.5, 0.125, 0.5), + Vectors.dense(0.75, 0.225, 1.0))); + + private static final List<Vector> EXPECTED_SPARSE_DATA = + new ArrayList<>( + Arrays.asList( + Vectors.sparse(4, new int[] {0, 1}, new double[] {1.0, 0.5}), + Vectors.sparse(4, new int[] {0, 2}, new double[] {0.5, 0.5}), + Vectors.sparse(4, new int[] {}, new double[] {}), + Vectors.sparse(4, new int[] {1, 3}, new double[] {0.125, 0.4}))); + + @Before + public void before() { + Configuration config = new Configuration(); + config.set(ExecutionCheckpointingOptions.ENABLE_CHECKPOINTS_AFTER_TASKS_FINISH, true); + + env = StreamExecutionEnvironment.getExecutionEnvironment(config); + env.setParallelism(4); + env.enableCheckpointing(100); + env.setRestartStrategy(RestartStrategies.noRestart()); + tEnv = StreamTableEnvironment.create(env); + + trainDataTable = tEnv.fromDataStream(env.fromCollection(TRAIN_DATA)).as("input"); + predictDataTable = tEnv.fromDataStream(env.fromCollection(PREDICT_DATA)).as("input"); + + trainSparseDataTable = + tEnv.fromDataStream(env.fromCollection(TRAIN_SPARSE_DATA)).as("input"); + predictSparseDataTable = + tEnv.fromDataStream(env.fromCollection(PREDICT_SPARSE_DATA)).as("input"); + } + + private static void verifyPredictionResult(Table output, String outputCol, boolean isSparse) + throws Exception { + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) output).getTableEnvironment(); + + DataStream<Vector> stream = + tEnv.toDataStream(output) + .map((MapFunction<Row, Vector>) row -> row.getFieldAs(outputCol)); + + List<Vector> result = IteratorUtils.toList(stream.executeAndCollect()); + if (isSparse) { + compareResultCollections( + MaxAbsScalerTest.EXPECTED_SPARSE_DATA, result, TestUtils::compare); + } else { + compareResultCollections(MaxAbsScalerTest.EXPECTED_DATA, result, TestUtils::compare); + } + } + + @Test + public void testParam() { + MaxAbsScaler maxAbsScaler = new MaxAbsScaler(); + assertEquals("input", maxAbsScaler.getInputCol()); + assertEquals("output", maxAbsScaler.getOutputCol()); + + maxAbsScaler.setInputCol("test_input").setOutputCol("test_output"); + assertEquals("test_input", maxAbsScaler.getInputCol()); + assertEquals("test_output", maxAbsScaler.getOutputCol()); + } + + @Test + public void testOutputSchema() { + MaxAbsScaler maxAbsScaler = + new MaxAbsScaler().setInputCol("test_input").setOutputCol("test_output"); + + MaxAbsScalerModel model = maxAbsScaler.fit(trainDataTable.as("test_input")); + Table output = model.transform(predictDataTable.as("test_input"))[0]; + assertEquals( + Arrays.asList("test_input", "test_output"), + output.getResolvedSchema().getColumnNames()); + } + + @Test + public void testFitAndPredict() throws Exception { + MaxAbsScaler maxAbsScaler = new MaxAbsScaler(); + MaxAbsScalerModel maxAbsScalerModel = maxAbsScaler.fit(trainDataTable); + Table output = maxAbsScalerModel.transform(predictDataTable)[0]; + verifyPredictionResult(output, maxAbsScaler.getOutputCol(), false); + } + + @Test + public void testFitDataWithNullValue() { + List<Row> trainData = + new ArrayList<>( + Arrays.asList( + Row.of(Vectors.dense(0.0, 3.0)), + Row.of(Vectors.dense(2.1, 0.0)), + Row.of((Object) null), + Row.of(Vectors.dense(6.1, 8.1)), + Row.of(Vectors.dense(200, 400)))); + + Table trainDataWithInvalidData = + tEnv.fromDataStream(env.fromCollection(trainData)).as("input"); + + try { + MaxAbsScaler maxAbsScaler = new MaxAbsScaler(); + MaxAbsScalerModel maxAbsScalerModel = maxAbsScaler.fit(trainDataWithInvalidData); + IteratorUtils.toList( + tEnv.toDataStream(maxAbsScalerModel.getModelData()[0]).executeAndCollect()); Review Comment: Shall we add `fail()` next line? For now, the test passes even we do not get into the exception. ########## flink-ml-lib/src/test/java/org/apache/flink/ml/feature/MaxAbsScalerTest.java: ########## @@ -0,0 +1,286 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.flink.ml.feature; + +import org.apache.flink.api.common.functions.MapFunction; +import org.apache.flink.api.common.restartstrategy.RestartStrategies; +import org.apache.flink.configuration.Configuration; +import org.apache.flink.ml.feature.maxabsscaler.MaxAbsScaler; +import org.apache.flink.ml.feature.maxabsscaler.MaxAbsScalerModel; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.Vector; +import org.apache.flink.ml.linalg.Vectors; +import org.apache.flink.ml.util.ReadWriteUtils; +import org.apache.flink.ml.util.TestUtils; +import org.apache.flink.streaming.api.datastream.DataStream; +import org.apache.flink.streaming.api.environment.ExecutionCheckpointingOptions; +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.types.Row; + +import org.apache.commons.collections.IteratorUtils; +import org.apache.commons.lang3.exception.ExceptionUtils; +import org.junit.Before; +import org.junit.Rule; +import org.junit.Test; +import org.junit.rules.TemporaryFolder; + +import java.util.ArrayList; +import java.util.Arrays; +import java.util.Collections; +import java.util.List; + +import static org.apache.flink.test.util.TestBaseUtils.compareResultCollections; +import static org.junit.Assert.assertEquals; + +/** Tests {@link MaxAbsScaler} and {@link MaxAbsScalerModel}. */ +public class MaxAbsScalerTest { + @Rule public final TemporaryFolder tempFolder = new TemporaryFolder(); + private StreamTableEnvironment tEnv; + private StreamExecutionEnvironment env; + + private Table trainDataTable; + private Table predictDataTable; + private Table trainSparseDataTable; + private Table predictSparseDataTable; + + private static final List<Row> TRAIN_DATA = + new ArrayList<>( + Arrays.asList( + Row.of(Vectors.dense(0.0, 3.0, 0.0)), + Row.of(Vectors.dense(2.1, 0.0, 0.0)), + Row.of(Vectors.dense(4.1, 5.1, 0.0)), + Row.of(Vectors.dense(6.1, 8.1, 0.0)), + Row.of(Vectors.dense(200, -400, 0.0)))); + + private static final List<Row> PREDICT_DATA = + new ArrayList<>( + Arrays.asList( + Row.of(Vectors.dense(150.0, 90.0, 1.0)), + Row.of(Vectors.dense(50.0, 40.0, 1.0)), + Row.of(Vectors.dense(100.0, 50.0, 0.5)))); + + private static final List<Row> TRAIN_SPARSE_DATA = + new ArrayList<>( + Arrays.asList( + Row.of(Vectors.sparse(4, new int[] {1, 3}, new double[] {4.0, 3.0})), + Row.of(Vectors.sparse(4, new int[] {0, 2}, new double[] {2.0, -6.0})), + Row.of(Vectors.sparse(4, new int[] {1, 2}, new double[] {1.0, 3.0})), + Row.of(Vectors.sparse(4, new int[] {0, 1}, new double[] {2.0, 8.0})), + Row.of(Vectors.sparse(4, new int[] {1, 3}, new double[] {1.0, 5.0})))); + + private static final List<Row> PREDICT_SPARSE_DATA = + new ArrayList<>( + Arrays.asList( + Row.of(Vectors.sparse(4, new int[] {0, 1}, new double[] {2.0, 4.0})), + Row.of(Vectors.sparse(4, new int[] {0, 2}, new double[] {1.0, 3.0})), + Row.of(Vectors.sparse(4, new int[] {}, new double[] {})), + Row.of(Vectors.sparse(4, new int[] {1, 3}, new double[] {1.0, 2.0})))); + + private static final List<Vector> EXPECTED_DATA = + new ArrayList<>( + Arrays.asList( + Vectors.dense(0.25, 0.1, 1.0), + Vectors.dense(0.5, 0.125, 0.5), + Vectors.dense(0.75, 0.225, 1.0))); + + private static final List<Vector> EXPECTED_SPARSE_DATA = + new ArrayList<>( + Arrays.asList( + Vectors.sparse(4, new int[] {0, 1}, new double[] {1.0, 0.5}), + Vectors.sparse(4, new int[] {0, 2}, new double[] {0.5, 0.5}), + Vectors.sparse(4, new int[] {}, new double[] {}), + Vectors.sparse(4, new int[] {1, 3}, new double[] {0.125, 0.4}))); + + @Before + public void before() { + Configuration config = new Configuration(); + config.set(ExecutionCheckpointingOptions.ENABLE_CHECKPOINTS_AFTER_TASKS_FINISH, true); + + env = StreamExecutionEnvironment.getExecutionEnvironment(config); + env.setParallelism(4); + env.enableCheckpointing(100); + env.setRestartStrategy(RestartStrategies.noRestart()); + tEnv = StreamTableEnvironment.create(env); + + trainDataTable = tEnv.fromDataStream(env.fromCollection(TRAIN_DATA)).as("input"); + predictDataTable = tEnv.fromDataStream(env.fromCollection(PREDICT_DATA)).as("input"); + + trainSparseDataTable = + tEnv.fromDataStream(env.fromCollection(TRAIN_SPARSE_DATA)).as("input"); + predictSparseDataTable = + tEnv.fromDataStream(env.fromCollection(PREDICT_SPARSE_DATA)).as("input"); + } + + private static void verifyPredictionResult(Table output, String outputCol, boolean isSparse) + throws Exception { + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) output).getTableEnvironment(); + + DataStream<Vector> stream = + tEnv.toDataStream(output) + .map((MapFunction<Row, Vector>) row -> row.getFieldAs(outputCol)); + + List<Vector> result = IteratorUtils.toList(stream.executeAndCollect()); + if (isSparse) { + compareResultCollections( + MaxAbsScalerTest.EXPECTED_SPARSE_DATA, result, TestUtils::compare); + } else { + compareResultCollections(MaxAbsScalerTest.EXPECTED_DATA, result, TestUtils::compare); + } + } + + @Test + public void testParam() { + MaxAbsScaler maxAbsScaler = new MaxAbsScaler(); + assertEquals("input", maxAbsScaler.getInputCol()); + assertEquals("output", maxAbsScaler.getOutputCol()); + + maxAbsScaler.setInputCol("test_input").setOutputCol("test_output"); + assertEquals("test_input", maxAbsScaler.getInputCol()); + assertEquals("test_output", maxAbsScaler.getOutputCol()); + } + + @Test + public void testOutputSchema() { + MaxAbsScaler maxAbsScaler = + new MaxAbsScaler().setInputCol("test_input").setOutputCol("test_output"); + + MaxAbsScalerModel model = maxAbsScaler.fit(trainDataTable.as("test_input")); + Table output = model.transform(predictDataTable.as("test_input"))[0]; + assertEquals( + Arrays.asList("test_input", "test_output"), + output.getResolvedSchema().getColumnNames()); + } + + @Test + public void testFitAndPredict() throws Exception { + MaxAbsScaler maxAbsScaler = new MaxAbsScaler(); + MaxAbsScalerModel maxAbsScalerModel = maxAbsScaler.fit(trainDataTable); + Table output = maxAbsScalerModel.transform(predictDataTable)[0]; + verifyPredictionResult(output, maxAbsScaler.getOutputCol(), false); + } + + @Test + public void testFitDataWithNullValue() { + List<Row> trainData = + new ArrayList<>( + Arrays.asList( + Row.of(Vectors.dense(0.0, 3.0)), + Row.of(Vectors.dense(2.1, 0.0)), + Row.of((Object) null), + Row.of(Vectors.dense(6.1, 8.1)), + Row.of(Vectors.dense(200, 400)))); + + Table trainDataWithInvalidData = + tEnv.fromDataStream(env.fromCollection(trainData)).as("input"); + + try { + MaxAbsScaler maxAbsScaler = new MaxAbsScaler(); + MaxAbsScalerModel maxAbsScalerModel = maxAbsScaler.fit(trainDataWithInvalidData); + IteratorUtils.toList( + tEnv.toDataStream(maxAbsScalerModel.getModelData()[0]).executeAndCollect()); + } catch (Exception e) { + assertEquals( + "Input column data cannot be null.", + ExceptionUtils.getRootCause(e).getMessage()); + } + } + + @Test + public void testTransformDataWithNullValue() throws Exception { Review Comment: Probably we should not allow transforming invalid data. ########## flink-ml-lib/src/main/java/org/apache/flink/ml/feature/maxabsscaler/MaxAbsScaler.java: ########## @@ -0,0 +1,201 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.flink.ml.feature.maxabsscaler; + +import org.apache.flink.api.common.functions.MapFunction; +import org.apache.flink.api.common.state.ListState; +import org.apache.flink.api.common.state.ListStateDescriptor; +import org.apache.flink.iteration.operator.OperatorStateUtils; +import org.apache.flink.ml.api.Estimator; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.SparseVector; +import org.apache.flink.ml.linalg.Vector; +import org.apache.flink.ml.linalg.typeinfo.DenseVectorTypeInfo; +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.runtime.state.StateInitializationContext; +import org.apache.flink.runtime.state.StateSnapshotContext; +import org.apache.flink.streaming.api.datastream.DataStream; +import org.apache.flink.streaming.api.operators.AbstractStreamOperator; +import org.apache.flink.streaming.api.operators.BoundedOneInput; +import org.apache.flink.streaming.api.operators.OneInputStreamOperator; +import org.apache.flink.streaming.runtime.streamrecord.StreamRecord; +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 java.io.IOException; +import java.util.HashMap; +import java.util.Map; + +/** + * An Estimator which implements the MaxAbsScaler algorithm. This algorithm rescales feature values + * to the range [-1, 1] by dividing through the largest maximum absolute value in each feature. It + * does not shift/center the data and thus does not destroy any sparsity. + */ +public class MaxAbsScaler + implements Estimator<MaxAbsScaler, MaxAbsScalerModel>, MaxAbsScalerParams<MaxAbsScaler> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + + public MaxAbsScaler() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public MaxAbsScalerModel fit(Table... inputs) { + Preconditions.checkArgument(inputs.length == 1); + final String inputCol = getInputCol(); + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment(); + + DataStream<Vector> inputData = + tEnv.toDataStream(inputs[0]) + .map( + (MapFunction<Row, Vector>) + value -> ((Vector) value.getField(inputCol))); + + DataStream<Vector> maxAbsValues = + inputData + .transform( + "reduceInEachPartition", + inputData.getType(), + new MaxAbsReduceFunctionOperator()) + .transform( + "reduceInFinalPartition", + inputData.getType(), + new MaxAbsReduceFunctionOperator()) + .setParallelism(1); + + DataStream<MaxAbsScalerModelData> modelData = + maxAbsValues.map( + (MapFunction<Vector, MaxAbsScalerModelData>) + vector -> new MaxAbsScalerModelData((DenseVector) vector)); + + MaxAbsScalerModel model = + new MaxAbsScalerModel().setModelData(tEnv.fromDataStream(modelData)); + ReadWriteUtils.updateExistingParams(model, getParamMap()); + return model; + } + + /** + * A stream operator to compute the maximum absolute values in each partition of the input + * bounded data stream. + */ + private static class MaxAbsReduceFunctionOperator extends AbstractStreamOperator<Vector> + implements OneInputStreamOperator<Vector, Vector>, BoundedOneInput { + private ListState<DenseVector> maxAbsState; + private DenseVector maxAbsVector; + + @Override + public void endInput() { + if (maxAbsVector != null) { + output.collect(new StreamRecord<>(maxAbsVector)); + } + } + + @Override + public void processElement(StreamRecord<Vector> streamRecord) { + Vector currentValue = streamRecord.getValue(); + if (currentValue == null) { + throw new RuntimeException("Input column data cannot be null."); + } + + if (maxAbsVector == null) { + int vecSize = currentValue.size(); + maxAbsVector = new DenseVector(vecSize); + if (currentValue instanceof DenseVector) { + double[] values = ((DenseVector) currentValue).values; + for (int i = 0; i < currentValue.size(); ++i) { + maxAbsVector.values[i] = Math.abs(values[i]); + } + } else { + int[] indices = ((SparseVector) currentValue).indices; + double[] values = ((SparseVector) currentValue).values; + + for (int i = 0; i < indices.length; ++i) { + maxAbsVector.values[indices[i]] = + Math.max(maxAbsVector.values[indices[i]], Math.abs(values[i])); + } + } + } else { + Preconditions.checkArgument( + currentValue.size() == maxAbsVector.size(), + "CurrentValue should has same size with maxVector."); + + if (currentValue instanceof DenseVector) { + double[] values = ((DenseVector) currentValue).values; + for (int i = 0; i < currentValue.size(); ++i) { + maxAbsVector.values[i] = + Math.max(maxAbsVector.values[i], Math.abs(values[i])); + } + } else if (currentValue instanceof SparseVector) { + int[] indices = ((SparseVector) currentValue).indices; + double[] values = ((SparseVector) currentValue).values; + + for (int i = 0; i < indices.length; ++i) { + maxAbsVector.values[indices[i]] = + Math.max(maxAbsVector.values[indices[i]], Math.abs(values[i])); + } + } else { + throw new IllegalArgumentException( + "Input column type must be SparseVector or DenseVector. "); + } + } + } + + @Override + public void initializeState(StateInitializationContext context) throws Exception { + super.initializeState(context); + maxAbsState = + context.getOperatorStateStore() + .getListState( + new ListStateDescriptor<>( + "maxState", DenseVectorTypeInfo.INSTANCE)); Review Comment: nit: maxAbsState -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: issues-unsubscr...@flink.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
