weibozhao commented on a change in pull request #54: URL: https://github.com/apache/flink-ml/pull/54#discussion_r829014907
########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/minmaxscaler/MinMaxScalerModel.java ########## @@ -0,0 +1,181 @@ +/* + * 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.minmaxscaler; + +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.DenseVector; +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.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.table.runtime.typeutils.ExternalTypeInfo; +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 do a minMax scaler operation using the model data computed by {@link MinMaxScaler}. + */ +public class MinMaxScalerModel + implements Model<MinMaxScalerModel>, MinMaxScalerParams<MinMaxScalerModel> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + private Table modelDataTable; + + public MinMaxScalerModel() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public MinMaxScalerModel 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<MinMaxScalerModelData> minMaxScalerModel = + MinMaxScalerModelData.getModelDataStream(modelDataTable); + final String broadcastModelKey = "broadcastModelKey"; + RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema()); + RowTypeInfo outputTypeInfo = + new RowTypeInfo( + ArrayUtils.addAll( + inputTypeInfo.getFieldTypes(), + ExternalTypeInfo.of(DenseVector.class)), + ArrayUtils.addAll(inputTypeInfo.getFieldNames(), getPredictionCol())); + DataStream<Row> output = + BroadcastUtils.withBroadcastStream( + Collections.singletonList(data), + Collections.singletonMap(broadcastModelKey, minMaxScalerModel), + inputList -> { + DataStream input = inputList.get(0); + return input.map( + new PredictOutputFunction( + broadcastModelKey, + getMax(), + getMin(), + getFeaturesCol()), + 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( + MinMaxScalerModelData.getModelDataStream(modelDataTable), + path, + new MinMaxScalerModelData.ModelDataEncoder()); + } + + /** + * Loads model data from path. + * + * @param env Stream execution environment. + * @param path Model path. + * @return MinMaxScalerModel model. + */ + public static MinMaxScalerModel load(StreamExecutionEnvironment env, String path) + throws IOException { + StreamTableEnvironment tEnv = StreamTableEnvironment.create(env); + MinMaxScalerModel model = ReadWriteUtils.loadStageParam(path); + DataStream<MinMaxScalerModelData> modelData = + ReadWriteUtils.loadModelData( + env, path, new MinMaxScalerModelData.ModelDataDecoder()); + return model.setModelData(tEnv.fromDataStream(modelData)); + } + + /** This operator loads model data and predicts result. */ + private static class PredictOutputFunction extends RichMapFunction<Row, Row> { + private final String featureCol; + private MinMaxScalerModelData minMaxScalerModelData; + private final double upperBound; + private final double lowerBound; + private final String broadcastKey; + private DenseVector maxVector; + private DenseVector minVector; + + public PredictOutputFunction( + String broadcastKey, double upperBound, double lowerBound, String featureCol) { + this.upperBound = upperBound; + this.lowerBound = lowerBound; + this.broadcastKey = broadcastKey; + this.featureCol = featureCol; + } + + @Override + public Row map(Row row) { + if (minMaxScalerModelData == null) { + minMaxScalerModelData = + (MinMaxScalerModelData) + getRuntimeContext().getBroadcastVariable(broadcastKey).get(0); + maxVector = minMaxScalerModelData.maxVector; + minVector = minMaxScalerModelData.minVector; + } + DenseVector feature = (DenseVector) row.getField(featureCol); + DenseVector outputVector = new DenseVector(maxVector.size()); + if (feature != null) { + for (int i = 0; i < maxVector.size(); ++i) { + if ((minVector.values[i] - maxVector.values[i]) != 0.0) { + outputVector.values[i] = + (feature.values[i] - minVector.values[i]) + / (maxVector.values[i] - minVector.values[i]) Review comment: I think spanVector maybe not very good. 1. In Spark and alink, maxVector and minVector are saved for modelData. 2. If we spanVector, code may be not clear as minVector and maxVector used. ########## File path: flink-ml-lib/src/test/java/org/apache/flink/ml/feature/MinMaxScalerTest.java ########## @@ -0,0 +1,208 @@ +/* + * 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.minmaxscaler.MinMaxScaler; +import org.apache.flink.ml.feature.minmaxscaler.MinMaxScalerModel; +import org.apache.flink.ml.feature.minmaxscaler.MinMaxScalerModelData; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.Vectors; +import org.apache.flink.ml.util.ReadWriteUtils; +import org.apache.flink.ml.util.StageTestUtils; +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.DataTypes; +import org.apache.flink.table.api.Schema; +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.junit.Assert; +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.junit.Assert.assertEquals; + +/** Tests {@link MinMaxScaler} and {@link MinMaxScalerModel}. */ +public class MinMaxScalerTest { + @Rule public final TemporaryFolder tempFolder = new TemporaryFolder(); + private StreamExecutionEnvironment env; + private StreamTableEnvironment tEnv; + private Table trainDataTable; + private Table predictDataTable; + private static final 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(Vectors.dense(4.1, 5.1)), + Row.of(Vectors.dense(6.1, 8.1)), + Row.of(Vectors.dense(200, 300)))); + private static final List<Row> predictRows = + new ArrayList<>(Collections.singletonList(Row.of(Vectors.dense(150.0, 90.0)))); + + @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); + Schema schema = Schema.newBuilder().column("f0", DataTypes.of(DenseVector.class)).build(); + DataStream<Row> dataStream = env.fromCollection(trainData); + trainDataTable = tEnv.fromDataStream(dataStream, schema).as("features"); + DataStream<Row> predDataStream = env.fromCollection(predictRows); + predictDataTable = tEnv.fromDataStream(predDataStream, schema).as("features"); + } + + private static void verifyPredictionResult(Table output, String outputCol, DenseVector expected) + throws Exception { + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) output).getTableEnvironment(); + DataStream<DenseVector> stream = + tEnv.toDataStream(output) + .map( + (MapFunction<Row, DenseVector>) + row -> (DenseVector) row.getField(outputCol)); + List<DenseVector> result = IteratorUtils.toList(stream.executeAndCollect()); + assertEquals(1, result.size()); + assertEquals(expected, result.get(0)); + } + + @Test + public void testParam() { + MinMaxScaler minMaxScaler = new MinMaxScaler(); + assertEquals("features", minMaxScaler.getFeaturesCol()); + assertEquals(1.0, minMaxScaler.getMax(), 0.0001); + assertEquals(0.0, minMaxScaler.getMin(), 0.0001); + assertEquals("prediction", minMaxScaler.getPredictionCol()); + minMaxScaler + .setFeaturesCol("test_features") + .setMax(4.0) + .setMin(1.0) + .setPredictionCol("test_output"); + assertEquals("test_features", minMaxScaler.getFeaturesCol()); + assertEquals(1.0, minMaxScaler.getMin(), 0.0001); + assertEquals(4.0, minMaxScaler.getMax(), 0.0001); + assertEquals("test_output", minMaxScaler.getPredictionCol()); + } + + @Test + public void testFeaturePredictionParam() { + MinMaxScaler minMaxScaler = + new MinMaxScaler() + .setMin(1.0) + .setMax(4.0) + .setFeaturesCol("test_features") + .setPredictionCol("test_output"); + MinMaxScalerModel model = minMaxScaler.fit(trainDataTable.as("test_features")); + Table output = model.transform(predictDataTable.as("test_features"))[0]; + assertEquals( + Arrays.asList("test_features", "test_output"), + output.getResolvedSchema().getColumnNames()); + } + + @Test + public void testFewerDistinctPointsThanCluster() throws Exception { Review comment: Kmeans and knn user this name already, I just take it from other ut. ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/minmaxscaler/MinMaxScaler.java ########## @@ -0,0 +1,205 @@ +/* + * 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.minmaxscaler; + +import org.apache.flink.api.common.functions.MapFunction; +import org.apache.flink.api.common.functions.RichMapPartitionFunction; +import org.apache.flink.api.common.state.ListState; +import org.apache.flink.api.common.state.ListStateDescriptor; +import org.apache.flink.ml.api.Estimator; +import org.apache.flink.ml.common.datastream.DataStreamUtils; +import org.apache.flink.ml.linalg.DenseVector; +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.environment.StreamExecutionEnvironment; +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.Collector; +import org.apache.flink.util.Preconditions; + +import java.io.IOException; +import java.util.HashMap; +import java.util.Iterator; +import java.util.Map; + +/** + * An Estimator which implements the MinMaxScaler algorithm. + * + * <p>See https://en.wikipedia.org/wiki/Feature_scaling#Rescaling_(min-max_normalization). + */ +public class MinMaxScaler + implements Estimator<MinMaxScaler, MinMaxScalerModel>, MinMaxScalerParams<MinMaxScaler> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + + public MinMaxScaler() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public MinMaxScalerModel fit(Table... inputs) { + Preconditions.checkArgument(inputs.length == 1); + final String featureCol = getFeaturesCol(); + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment(); + DataStream<DenseVector> features = + tEnv.toDataStream(inputs[0]) + .map( + (MapFunction<Row, DenseVector>) + value -> (DenseVector) value.getField(featureCol)); + DataStream<DenseVector> minMaxValues = + features.transform( + "reduceInEachPartition", + features.getType(), + new MinMaxReduceFunctionOperator()) + .transform( + "reduceInFinalPartition", + features.getType(), + new MinMaxReduceFunctionOperator()) + .setParallelism(1); + DataStream<MinMaxScalerModelData> modelData = + DataStreamUtils.mapPartition( + minMaxValues, + new RichMapPartitionFunction<DenseVector, MinMaxScalerModelData>() { + @Override + public void mapPartition( + Iterable<DenseVector> values, + Collector<MinMaxScalerModelData> out) { + Iterator<DenseVector> iter = values.iterator(); + DenseVector minVector = iter.next(); + DenseVector maxVector = iter.next(); + out.collect(new MinMaxScalerModelData(minVector, maxVector)); + } + }); + + MinMaxScalerModel model = + new MinMaxScalerModel().setModelData(tEnv.fromDataStream(modelData)); + ReadWriteUtils.updateExistingParams(model, getParamMap()); + return model; + } + + /** + * A stream operator to compute the min and max values in each partition of the input bounded + * data stream. + */ + private static class MinMaxReduceFunctionOperator extends AbstractStreamOperator<DenseVector> + implements OneInputStreamOperator<DenseVector, DenseVector>, BoundedOneInput { + private ListState<DenseVector> minState; + private ListState<DenseVector> maxState; + + private DenseVector minVector; + private DenseVector maxVector; + + @Override + public void endInput() { + if (minVector != null) { + output.collect(new StreamRecord<>(minVector)); + } + if (maxVector != null) { + output.collect(new StreamRecord<>(maxVector)); + } + } + + @Override + public void processElement(StreamRecord<DenseVector> streamRecord) { + DenseVector currentValue = streamRecord.getValue(); + if (minVector == null) { + int vecSize = currentValue.size(); + minVector = new DenseVector(vecSize); + maxVector = new DenseVector(vecSize); + System.arraycopy(currentValue.values, 0, minVector.values, 0, vecSize); + System.arraycopy(currentValue.values, 0, maxVector.values, 0, vecSize); + + } else { + for (int i = 0; i < currentValue.size(); ++i) { + minVector.values[i] = Math.min(minVector.values[i], currentValue.values[i]); Review comment: As I Know, blas has no function like minVector or maxVector. If user blas1 function(minVector is blas1 function), loop may not be avoided, too. ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/minmaxscaler/MinMaxScalerModel.java ########## @@ -0,0 +1,181 @@ +/* + * 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.minmaxscaler; + +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.DenseVector; +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.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.table.runtime.typeutils.ExternalTypeInfo; +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 do a minMax scaler operation using the model data computed by {@link MinMaxScaler}. + */ +public class MinMaxScalerModel + implements Model<MinMaxScalerModel>, MinMaxScalerParams<MinMaxScalerModel> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + private Table modelDataTable; + + public MinMaxScalerModel() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public MinMaxScalerModel 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<MinMaxScalerModelData> minMaxScalerModel = + MinMaxScalerModelData.getModelDataStream(modelDataTable); + final String broadcastModelKey = "broadcastModelKey"; + RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema()); + RowTypeInfo outputTypeInfo = + new RowTypeInfo( + ArrayUtils.addAll( + inputTypeInfo.getFieldTypes(), + ExternalTypeInfo.of(DenseVector.class)), + ArrayUtils.addAll(inputTypeInfo.getFieldNames(), getPredictionCol())); + DataStream<Row> output = + BroadcastUtils.withBroadcastStream( + Collections.singletonList(data), + Collections.singletonMap(broadcastModelKey, minMaxScalerModel), + inputList -> { + DataStream input = inputList.get(0); + return input.map( + new PredictOutputFunction( + broadcastModelKey, + getMax(), + getMin(), + getFeaturesCol()), + 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( + MinMaxScalerModelData.getModelDataStream(modelDataTable), + path, + new MinMaxScalerModelData.ModelDataEncoder()); + } + + /** + * Loads model data from path. + * + * @param env Stream execution environment. + * @param path Model path. + * @return MinMaxScalerModel model. + */ + public static MinMaxScalerModel load(StreamExecutionEnvironment env, String path) + throws IOException { + StreamTableEnvironment tEnv = StreamTableEnvironment.create(env); + MinMaxScalerModel model = ReadWriteUtils.loadStageParam(path); + DataStream<MinMaxScalerModelData> modelData = + ReadWriteUtils.loadModelData( + env, path, new MinMaxScalerModelData.ModelDataDecoder()); + return model.setModelData(tEnv.fromDataStream(modelData)); + } + + /** This operator loads model data and predicts result. */ + private static class PredictOutputFunction extends RichMapFunction<Row, Row> { + private final String featureCol; + private MinMaxScalerModelData minMaxScalerModelData; + private final double upperBound; + private final double lowerBound; + private final String broadcastKey; + private DenseVector maxVector; + private DenseVector minVector; + + public PredictOutputFunction( + String broadcastKey, double upperBound, double lowerBound, String featureCol) { + this.upperBound = upperBound; + this.lowerBound = lowerBound; + this.broadcastKey = broadcastKey; + this.featureCol = featureCol; + } + + @Override + public Row map(Row row) { + if (minMaxScalerModelData == null) { + minMaxScalerModelData = + (MinMaxScalerModelData) + getRuntimeContext().getBroadcastVariable(broadcastKey).get(0); + maxVector = minMaxScalerModelData.maxVector; + minVector = minMaxScalerModelData.minVector; + } + DenseVector feature = (DenseVector) row.getField(featureCol); + DenseVector outputVector = new DenseVector(maxVector.size()); + if (feature != null) { + for (int i = 0; i < maxVector.size(); ++i) { Review comment: There is a judge about max-min==0 exists in the loop, then we can not use blas here. If we want to use blas, we need another loop to prepare data. I think it is more expensive. ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/minmaxscaler/MinMaxScaler.java ########## @@ -0,0 +1,205 @@ +/* + * 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.minmaxscaler; + +import org.apache.flink.api.common.functions.MapFunction; +import org.apache.flink.api.common.functions.RichMapPartitionFunction; +import org.apache.flink.api.common.state.ListState; +import org.apache.flink.api.common.state.ListStateDescriptor; +import org.apache.flink.ml.api.Estimator; +import org.apache.flink.ml.common.datastream.DataStreamUtils; +import org.apache.flink.ml.linalg.DenseVector; +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.environment.StreamExecutionEnvironment; +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.Collector; +import org.apache.flink.util.Preconditions; + +import java.io.IOException; +import java.util.HashMap; +import java.util.Iterator; +import java.util.Map; + +/** + * An Estimator which implements the MinMaxScaler algorithm. + * + * <p>See https://en.wikipedia.org/wiki/Feature_scaling#Rescaling_(min-max_normalization). + */ +public class MinMaxScaler + implements Estimator<MinMaxScaler, MinMaxScalerModel>, MinMaxScalerParams<MinMaxScaler> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + + public MinMaxScaler() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public MinMaxScalerModel fit(Table... inputs) { + Preconditions.checkArgument(inputs.length == 1); + final String featureCol = getFeaturesCol(); + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment(); + DataStream<DenseVector> features = + tEnv.toDataStream(inputs[0]) + .map( + (MapFunction<Row, DenseVector>) + value -> (DenseVector) value.getField(featureCol)); + DataStream<DenseVector> minMaxValues = + features.transform( + "reduceInEachPartition", + features.getType(), + new MinMaxReduceFunctionOperator()) + .transform( + "reduceInFinalPartition", + features.getType(), + new MinMaxReduceFunctionOperator()) + .setParallelism(1); + DataStream<MinMaxScalerModelData> modelData = + DataStreamUtils.mapPartition( + minMaxValues, + new RichMapPartitionFunction<DenseVector, MinMaxScalerModelData>() { + @Override + public void mapPartition( + Iterable<DenseVector> values, + Collector<MinMaxScalerModelData> out) { + Iterator<DenseVector> iter = values.iterator(); + DenseVector minVector = iter.next(); + DenseVector maxVector = iter.next(); + out.collect(new MinMaxScalerModelData(minVector, maxVector)); + } + }); + + MinMaxScalerModel model = + new MinMaxScalerModel().setModelData(tEnv.fromDataStream(modelData)); + ReadWriteUtils.updateExistingParams(model, getParamMap()); + return model; + } + + /** + * A stream operator to compute the min and max values in each partition of the input bounded + * data stream. + */ + private static class MinMaxReduceFunctionOperator extends AbstractStreamOperator<DenseVector> + implements OneInputStreamOperator<DenseVector, DenseVector>, BoundedOneInput { + private ListState<DenseVector> minState; + private ListState<DenseVector> maxState; + + private DenseVector minVector; + private DenseVector maxVector; + + @Override + public void endInput() { + if (minVector != null) { + output.collect(new StreamRecord<>(minVector)); + } + if (maxVector != null) { + output.collect(new StreamRecord<>(maxVector)); + } + } + + @Override + public void processElement(StreamRecord<DenseVector> streamRecord) { + DenseVector currentValue = streamRecord.getValue(); + if (minVector == null) { + int vecSize = currentValue.size(); + minVector = new DenseVector(vecSize); + maxVector = new DenseVector(vecSize); + System.arraycopy(currentValue.values, 0, minVector.values, 0, vecSize); + System.arraycopy(currentValue.values, 0, maxVector.values, 0, vecSize); + + } else { + for (int i = 0; i < currentValue.size(); ++i) { + minVector.values[i] = Math.min(minVector.values[i], currentValue.values[i]); Review comment: As I Know, blas has no function like minVector or maxVector. If user blas1 function(minVector is like blas1 function), loop may not be avoided, too. ########## File path: flink-ml-lib/src/test/java/org/apache/flink/ml/feature/MinMaxScalerTest.java ########## @@ -0,0 +1,208 @@ +/* + * 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.minmaxscaler.MinMaxScaler; +import org.apache.flink.ml.feature.minmaxscaler.MinMaxScalerModel; +import org.apache.flink.ml.feature.minmaxscaler.MinMaxScalerModelData; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.Vectors; +import org.apache.flink.ml.util.ReadWriteUtils; +import org.apache.flink.ml.util.StageTestUtils; +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.DataTypes; +import org.apache.flink.table.api.Schema; +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.junit.Assert; +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.junit.Assert.assertEquals; + +/** Tests {@link MinMaxScaler} and {@link MinMaxScalerModel}. */ +public class MinMaxScalerTest { + @Rule public final TemporaryFolder tempFolder = new TemporaryFolder(); + private StreamExecutionEnvironment env; + private StreamTableEnvironment tEnv; + private Table trainDataTable; + private Table predictDataTable; + private static final 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(Vectors.dense(4.1, 5.1)), + Row.of(Vectors.dense(6.1, 8.1)), + Row.of(Vectors.dense(200, 300)))); + private static final List<Row> predictRows = + new ArrayList<>(Collections.singletonList(Row.of(Vectors.dense(150.0, 90.0)))); + + @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); + Schema schema = Schema.newBuilder().column("f0", DataTypes.of(DenseVector.class)).build(); + DataStream<Row> dataStream = env.fromCollection(trainData); + trainDataTable = tEnv.fromDataStream(dataStream, schema).as("features"); + DataStream<Row> predDataStream = env.fromCollection(predictRows); + predictDataTable = tEnv.fromDataStream(predDataStream, schema).as("features"); + } + + private static void verifyPredictionResult(Table output, String outputCol, DenseVector expected) + throws Exception { + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) output).getTableEnvironment(); + DataStream<DenseVector> stream = + tEnv.toDataStream(output) + .map( + (MapFunction<Row, DenseVector>) + row -> (DenseVector) row.getField(outputCol)); + List<DenseVector> result = IteratorUtils.toList(stream.executeAndCollect()); + assertEquals(1, result.size()); + assertEquals(expected, result.get(0)); + } + + @Test + public void testParam() { + MinMaxScaler minMaxScaler = new MinMaxScaler(); + assertEquals("features", minMaxScaler.getFeaturesCol()); + assertEquals(1.0, minMaxScaler.getMax(), 0.0001); + assertEquals(0.0, minMaxScaler.getMin(), 0.0001); + assertEquals("prediction", minMaxScaler.getPredictionCol()); + minMaxScaler + .setFeaturesCol("test_features") + .setMax(4.0) + .setMin(1.0) + .setPredictionCol("test_output"); + assertEquals("test_features", minMaxScaler.getFeaturesCol()); + assertEquals(1.0, minMaxScaler.getMin(), 0.0001); + assertEquals(4.0, minMaxScaler.getMax(), 0.0001); + assertEquals("test_output", minMaxScaler.getPredictionCol()); + } + + @Test + public void testFeaturePredictionParam() { + MinMaxScaler minMaxScaler = + new MinMaxScaler() + .setMin(1.0) + .setMax(4.0) + .setFeaturesCol("test_features") + .setPredictionCol("test_output"); + MinMaxScalerModel model = minMaxScaler.fit(trainDataTable.as("test_features")); + Table output = model.transform(predictDataTable.as("test_features"))[0]; + assertEquals( + Arrays.asList("test_features", "test_output"), + output.getResolvedSchema().getColumnNames()); + } + + @Test + public void testFewerDistinctPointsThanCluster() throws Exception { Review comment: Kmeans and knn use this name already, I just take it from other ut. ########## File path: flink-ml-lib/src/test/java/org/apache/flink/ml/feature/MinMaxScalerTest.java ########## @@ -0,0 +1,208 @@ +/* + * 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.minmaxscaler.MinMaxScaler; +import org.apache.flink.ml.feature.minmaxscaler.MinMaxScalerModel; +import org.apache.flink.ml.feature.minmaxscaler.MinMaxScalerModelData; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.Vectors; +import org.apache.flink.ml.util.ReadWriteUtils; +import org.apache.flink.ml.util.StageTestUtils; +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.DataTypes; +import org.apache.flink.table.api.Schema; +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.junit.Assert; +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.junit.Assert.assertEquals; + +/** Tests {@link MinMaxScaler} and {@link MinMaxScalerModel}. */ +public class MinMaxScalerTest { + @Rule public final TemporaryFolder tempFolder = new TemporaryFolder(); + private StreamExecutionEnvironment env; + private StreamTableEnvironment tEnv; + private Table trainDataTable; + private Table predictDataTable; + private static final 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(Vectors.dense(4.1, 5.1)), + Row.of(Vectors.dense(6.1, 8.1)), + Row.of(Vectors.dense(200, 300)))); + private static final List<Row> predictRows = + new ArrayList<>(Collections.singletonList(Row.of(Vectors.dense(150.0, 90.0)))); + + @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); + Schema schema = Schema.newBuilder().column("f0", DataTypes.of(DenseVector.class)).build(); + DataStream<Row> dataStream = env.fromCollection(trainData); + trainDataTable = tEnv.fromDataStream(dataStream, schema).as("features"); + DataStream<Row> predDataStream = env.fromCollection(predictRows); + predictDataTable = tEnv.fromDataStream(predDataStream, schema).as("features"); + } + + private static void verifyPredictionResult(Table output, String outputCol, DenseVector expected) + throws Exception { + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) output).getTableEnvironment(); + DataStream<DenseVector> stream = + tEnv.toDataStream(output) + .map( + (MapFunction<Row, DenseVector>) + row -> (DenseVector) row.getField(outputCol)); + List<DenseVector> result = IteratorUtils.toList(stream.executeAndCollect()); + assertEquals(1, result.size()); + assertEquals(expected, result.get(0)); + } + + @Test + public void testParam() { + MinMaxScaler minMaxScaler = new MinMaxScaler(); + assertEquals("features", minMaxScaler.getFeaturesCol()); + assertEquals(1.0, minMaxScaler.getMax(), 0.0001); + assertEquals(0.0, minMaxScaler.getMin(), 0.0001); + assertEquals("prediction", minMaxScaler.getPredictionCol()); + minMaxScaler + .setFeaturesCol("test_features") + .setMax(4.0) + .setMin(1.0) + .setPredictionCol("test_output"); + assertEquals("test_features", minMaxScaler.getFeaturesCol()); + assertEquals(1.0, minMaxScaler.getMin(), 0.0001); + assertEquals(4.0, minMaxScaler.getMax(), 0.0001); + assertEquals("test_output", minMaxScaler.getPredictionCol()); + } + + @Test + public void testFeaturePredictionParam() { + MinMaxScaler minMaxScaler = + new MinMaxScaler() + .setMin(1.0) + .setMax(4.0) + .setFeaturesCol("test_features") + .setPredictionCol("test_output"); + MinMaxScalerModel model = minMaxScaler.fit(trainDataTable.as("test_features")); + Table output = model.transform(predictDataTable.as("test_features"))[0]; + assertEquals( + Arrays.asList("test_features", "test_output"), + output.getResolvedSchema().getColumnNames()); + } + + @Test + public void testFewerDistinctPointsThanCluster() throws Exception { Review comment: OK，I got it. I will refine it later. ########## File path: flink-ml-lib/src/test/java/org/apache/flink/ml/feature/MinMaxScalerTest.java ########## @@ -0,0 +1,208 @@ +/* + * 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.minmaxscaler.MinMaxScaler; +import org.apache.flink.ml.feature.minmaxscaler.MinMaxScalerModel; +import org.apache.flink.ml.feature.minmaxscaler.MinMaxScalerModelData; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.Vectors; +import org.apache.flink.ml.util.ReadWriteUtils; +import org.apache.flink.ml.util.StageTestUtils; +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.DataTypes; +import org.apache.flink.table.api.Schema; +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.junit.Assert; +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.junit.Assert.assertEquals; + +/** Tests {@link MinMaxScaler} and {@link MinMaxScalerModel}. */ +public class MinMaxScalerTest { + @Rule public final TemporaryFolder tempFolder = new TemporaryFolder(); + private StreamExecutionEnvironment env; + private StreamTableEnvironment tEnv; + private Table trainDataTable; + private Table predictDataTable; + private static final 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(Vectors.dense(4.1, 5.1)), + Row.of(Vectors.dense(6.1, 8.1)), + Row.of(Vectors.dense(200, 300)))); + private static final List<Row> predictRows = Review comment: OK ########## File path: flink-ml-lib/src/test/java/org/apache/flink/ml/feature/MinMaxScalerTest.java ########## @@ -0,0 +1,208 @@ +/* + * 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.minmaxscaler.MinMaxScaler; +import org.apache.flink.ml.feature.minmaxscaler.MinMaxScalerModel; +import org.apache.flink.ml.feature.minmaxscaler.MinMaxScalerModelData; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.Vectors; +import org.apache.flink.ml.util.ReadWriteUtils; +import org.apache.flink.ml.util.StageTestUtils; +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.DataTypes; +import org.apache.flink.table.api.Schema; +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.junit.Assert; +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.junit.Assert.assertEquals; + +/** Tests {@link MinMaxScaler} and {@link MinMaxScalerModel}. */ +public class MinMaxScalerTest { + @Rule public final TemporaryFolder tempFolder = new TemporaryFolder(); + private StreamExecutionEnvironment env; + private StreamTableEnvironment tEnv; + private Table trainDataTable; + private Table predictDataTable; + private static final 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(Vectors.dense(4.1, 5.1)), + Row.of(Vectors.dense(6.1, 8.1)), + Row.of(Vectors.dense(200, 300)))); + private static final List<Row> predictRows = + new ArrayList<>(Collections.singletonList(Row.of(Vectors.dense(150.0, 90.0)))); + + @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); + Schema schema = Schema.newBuilder().column("f0", DataTypes.of(DenseVector.class)).build(); + DataStream<Row> dataStream = env.fromCollection(trainData); + trainDataTable = tEnv.fromDataStream(dataStream, schema).as("features"); + DataStream<Row> predDataStream = env.fromCollection(predictRows); + predictDataTable = tEnv.fromDataStream(predDataStream, schema).as("features"); + } + + private static void verifyPredictionResult(Table output, String outputCol, DenseVector expected) + throws Exception { + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) output).getTableEnvironment(); + DataStream<DenseVector> stream = + tEnv.toDataStream(output) + .map( + (MapFunction<Row, DenseVector>) + row -> (DenseVector) row.getField(outputCol)); + List<DenseVector> result = IteratorUtils.toList(stream.executeAndCollect()); + assertEquals(1, result.size()); + assertEquals(expected, result.get(0)); + } + + @Test + public void testParam() { + MinMaxScaler minMaxScaler = new MinMaxScaler(); + assertEquals("features", minMaxScaler.getFeaturesCol()); + assertEquals(1.0, minMaxScaler.getMax(), 0.0001); + assertEquals(0.0, minMaxScaler.getMin(), 0.0001); + assertEquals("prediction", minMaxScaler.getPredictionCol()); Review comment: OK ########## File path: flink-ml-lib/src/test/java/org/apache/flink/ml/feature/MinMaxScalerTest.java ########## @@ -0,0 +1,208 @@ +/* + * 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.minmaxscaler.MinMaxScaler; +import org.apache.flink.ml.feature.minmaxscaler.MinMaxScalerModel; +import org.apache.flink.ml.feature.minmaxscaler.MinMaxScalerModelData; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.Vectors; +import org.apache.flink.ml.util.ReadWriteUtils; +import org.apache.flink.ml.util.StageTestUtils; +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.DataTypes; +import org.apache.flink.table.api.Schema; +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.junit.Assert; +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.junit.Assert.assertEquals; + +/** Tests {@link MinMaxScaler} and {@link MinMaxScalerModel}. */ +public class MinMaxScalerTest { + @Rule public final TemporaryFolder tempFolder = new TemporaryFolder(); + private StreamExecutionEnvironment env; + private StreamTableEnvironment tEnv; + private Table trainDataTable; + private Table predictDataTable; + private static final 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(Vectors.dense(4.1, 5.1)), + Row.of(Vectors.dense(6.1, 8.1)), + Row.of(Vectors.dense(200, 300)))); + private static final List<Row> predictRows = + new ArrayList<>(Collections.singletonList(Row.of(Vectors.dense(150.0, 90.0)))); + + @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); + Schema schema = Schema.newBuilder().column("f0", DataTypes.of(DenseVector.class)).build(); + DataStream<Row> dataStream = env.fromCollection(trainData); + trainDataTable = tEnv.fromDataStream(dataStream, schema).as("features"); + DataStream<Row> predDataStream = env.fromCollection(predictRows); + predictDataTable = tEnv.fromDataStream(predDataStream, schema).as("features"); + } + + private static void verifyPredictionResult(Table output, String outputCol, DenseVector expected) + throws Exception { + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) output).getTableEnvironment(); + DataStream<DenseVector> stream = + tEnv.toDataStream(output) + .map( + (MapFunction<Row, DenseVector>) + row -> (DenseVector) row.getField(outputCol)); + List<DenseVector> result = IteratorUtils.toList(stream.executeAndCollect()); + assertEquals(1, result.size()); + assertEquals(expected, result.get(0)); + } + + @Test + public void testParam() { + MinMaxScaler minMaxScaler = new MinMaxScaler(); + assertEquals("features", minMaxScaler.getFeaturesCol()); + assertEquals(1.0, minMaxScaler.getMax(), 0.0001); + assertEquals(0.0, minMaxScaler.getMin(), 0.0001); + assertEquals("prediction", minMaxScaler.getPredictionCol()); + minMaxScaler + .setFeaturesCol("test_features") + .setMax(4.0) + .setMin(1.0) + .setPredictionCol("test_output"); + assertEquals("test_features", minMaxScaler.getFeaturesCol()); + assertEquals(1.0, minMaxScaler.getMin(), 0.0001); + assertEquals(4.0, minMaxScaler.getMax(), 0.0001); + assertEquals("test_output", minMaxScaler.getPredictionCol()); + } + + @Test + public void testFeaturePredictionParam() { + MinMaxScaler minMaxScaler = + new MinMaxScaler() + .setMin(1.0) + .setMax(4.0) + .setFeaturesCol("test_features") + .setPredictionCol("test_output"); + MinMaxScalerModel model = minMaxScaler.fit(trainDataTable.as("test_features")); + Table output = model.transform(predictDataTable.as("test_features"))[0]; + assertEquals( + Arrays.asList("test_features", "test_output"), + output.getResolvedSchema().getColumnNames()); + } + + @Test + public void testFewerDistinctPointsThanCluster() throws Exception { + MinMaxScaler minMaxScaler = new MinMaxScaler(); + MinMaxScalerModel model = minMaxScaler.fit(predictDataTable); + Table result = model.transform(predictDataTable)[0]; + verifyPredictionResult(result, minMaxScaler.getPredictionCol(), Vectors.dense(0.5, 0.5)); + } + + @Test + public void testFitAndPredict() throws Exception { + MinMaxScaler minMaxScaler = new MinMaxScaler(); + MinMaxScalerModel minMaxScalerModel = minMaxScaler.fit(trainDataTable); + Table output = minMaxScalerModel.transform(predictDataTable)[0]; + verifyPredictionResult(output, minMaxScaler.getPredictionCol(), Vectors.dense(0.75, 0.3)); + } + + @Test + public void testSaveLoadAndPredict() throws Exception { + MinMaxScaler minMaxScaler = new MinMaxScaler(); + MinMaxScaler loadedMinMaxScaler = + StageTestUtils.saveAndReload( + env, minMaxScaler, tempFolder.newFolder().getAbsolutePath()); + MinMaxScalerModel minMaxScalerModel = loadedMinMaxScaler.fit(trainDataTable); + minMaxScalerModel = + StageTestUtils.saveAndReload( + env, minMaxScalerModel, tempFolder.newFolder().getAbsolutePath()); + assertEquals( + Arrays.asList("minVector", "maxVector"), + minMaxScalerModel.getModelData()[0].getResolvedSchema().getColumnNames()); + Table output = minMaxScalerModel.transform(predictDataTable)[0]; + verifyPredictionResult(output, minMaxScaler.getPredictionCol(), Vectors.dense(0.75, 0.3)); + } + + @Test + public void testModelSaveLoadAndPredict() throws Exception { Review comment: OK ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/minmaxscaler/MinMaxScaler.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.minmaxscaler; + +import org.apache.flink.api.common.functions.MapFunction; +import org.apache.flink.api.common.functions.RichMapPartitionFunction; +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.common.datastream.DataStreamUtils; +import org.apache.flink.ml.linalg.DenseVector; +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.environment.StreamExecutionEnvironment; +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.Collector; +import org.apache.flink.util.Preconditions; + +import java.io.IOException; +import java.util.HashMap; +import java.util.Iterator; +import java.util.Map; + +/** + * An Estimator which implements the MinMaxScaler algorithm. + * + * <p>See https://en.wikipedia.org/wiki/Feature_scaling#Rescaling_(min-max_normalization). Review comment: OK ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/minmaxscaler/MinMaxScaler.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.minmaxscaler; + +import org.apache.flink.api.common.functions.MapFunction; +import org.apache.flink.api.common.functions.RichMapPartitionFunction; +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.common.datastream.DataStreamUtils; +import org.apache.flink.ml.linalg.DenseVector; +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.environment.StreamExecutionEnvironment; +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.Collector; +import org.apache.flink.util.Preconditions; + +import java.io.IOException; +import java.util.HashMap; +import java.util.Iterator; +import java.util.Map; + +/** + * An Estimator which implements the MinMaxScaler algorithm. + * + * <p>See https://en.wikipedia.org/wiki/Feature_scaling#Rescaling_(min-max_normalization). + */ +public class MinMaxScaler + implements Estimator<MinMaxScaler, MinMaxScalerModel>, MinMaxScalerParams<MinMaxScaler> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + + public MinMaxScaler() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public MinMaxScalerModel fit(Table... inputs) { + Preconditions.checkArgument(inputs.length == 1); + final String featureCol = getFeaturesCol(); + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment(); + DataStream<DenseVector> features = + tEnv.toDataStream(inputs[0]) + .map( + (MapFunction<Row, DenseVector>) + value -> (DenseVector) value.getField(featureCol)); + DataStream<DenseVector> minMaxValues = + features.transform( + "reduceInEachPartition", + features.getType(), + new MinMaxReduceFunctionOperator()) + .transform( + "reduceInFinalPartition", + features.getType(), + new MinMaxReduceFunctionOperator()) + .setParallelism(1); + DataStream<MinMaxScalerModelData> modelData = + DataStreamUtils.mapPartition( + minMaxValues, + new RichMapPartitionFunction<DenseVector, MinMaxScalerModelData>() { + @Override + public void mapPartition( + Iterable<DenseVector> values, + Collector<MinMaxScalerModelData> out) { + Iterator<DenseVector> iter = values.iterator(); + DenseVector minVector = iter.next(); + DenseVector maxVector = iter.next(); + out.collect(new MinMaxScalerModelData(minVector, maxVector)); + } + }); + + MinMaxScalerModel model = + new MinMaxScalerModel().setModelData(tEnv.fromDataStream(modelData)); + ReadWriteUtils.updateExistingParams(model, getParamMap()); + return model; + } + + /** + * A stream operator to compute the min and max values in each partition of the input bounded + * data stream. + */ + private static class MinMaxReduceFunctionOperator extends AbstractStreamOperator<DenseVector> + implements OneInputStreamOperator<DenseVector, DenseVector>, BoundedOneInput { + private ListState<DenseVector> minState; + private ListState<DenseVector> maxState; + + private DenseVector minVector; + private DenseVector maxVector; + + @Override + public void endInput() { + if (minVector != null) { Review comment: OK ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/minmaxscaler/MinMaxScalerModel.java ########## @@ -0,0 +1,181 @@ +/* + * 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.minmaxscaler; + +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.DenseVector; +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.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.table.runtime.typeutils.ExternalTypeInfo; +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 do a minMax scaler operation using the model data computed by {@link MinMaxScaler}. + */ +public class MinMaxScalerModel + implements Model<MinMaxScalerModel>, MinMaxScalerParams<MinMaxScalerModel> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + private Table modelDataTable; + + public MinMaxScalerModel() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public MinMaxScalerModel 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<MinMaxScalerModelData> minMaxScalerModel = + MinMaxScalerModelData.getModelDataStream(modelDataTable); + final String broadcastModelKey = "broadcastModelKey"; + RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema()); + RowTypeInfo outputTypeInfo = + new RowTypeInfo( + ArrayUtils.addAll( + inputTypeInfo.getFieldTypes(), + ExternalTypeInfo.of(DenseVector.class)), + ArrayUtils.addAll(inputTypeInfo.getFieldNames(), getPredictionCol())); + DataStream<Row> output = + BroadcastUtils.withBroadcastStream( + Collections.singletonList(data), + Collections.singletonMap(broadcastModelKey, minMaxScalerModel), + inputList -> { + DataStream input = inputList.get(0); + return input.map( + new PredictOutputFunction( + broadcastModelKey, + getMax(), + getMin(), + getFeaturesCol()), + 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( + MinMaxScalerModelData.getModelDataStream(modelDataTable), + path, + new MinMaxScalerModelData.ModelDataEncoder()); + } + + /** + * Loads model data from path. + * + * @param env Stream execution environment. + * @param path Model path. + * @return MinMaxScalerModel model. + */ + public static MinMaxScalerModel load(StreamExecutionEnvironment env, String path) + throws IOException { + StreamTableEnvironment tEnv = StreamTableEnvironment.create(env); + MinMaxScalerModel model = ReadWriteUtils.loadStageParam(path); + DataStream<MinMaxScalerModelData> modelData = + ReadWriteUtils.loadModelData( + env, path, new MinMaxScalerModelData.ModelDataDecoder()); + return model.setModelData(tEnv.fromDataStream(modelData)); + } + + /** This operator loads model data and predicts result. */ + private static class PredictOutputFunction extends RichMapFunction<Row, Row> { + private final String featureCol; + private MinMaxScalerModelData minMaxScalerModelData; + private final double upperBound; + private final double lowerBound; + private final String broadcastKey; + private DenseVector maxVector; + private DenseVector minVector; + + public PredictOutputFunction( + String broadcastKey, double upperBound, double lowerBound, String featureCol) { + this.upperBound = upperBound; + this.lowerBound = lowerBound; + this.broadcastKey = broadcastKey; + this.featureCol = featureCol; + } + + @Override + public Row map(Row row) { + if (minMaxScalerModelData == null) { + minMaxScalerModelData = + (MinMaxScalerModelData) + getRuntimeContext().getBroadcastVariable(broadcastKey).get(0); + maxVector = minMaxScalerModelData.maxVector; + minVector = minMaxScalerModelData.minVector; + } + DenseVector feature = (DenseVector) row.getField(featureCol); + DenseVector outputVector = new DenseVector(maxVector.size()); + if (feature != null) { + for (int i = 0; i < maxVector.size(); ++i) { Review comment: If featureValue[i] != maxVector[i], above code may not get the same result as original code. In read world, data for train and predict are different, featureValue[i] != maxVector[i] is existed. ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/minmaxscaler/MinMaxScalerModel.java ########## @@ -0,0 +1,181 @@ +/* + * 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.minmaxscaler; + +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.DenseVector; +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.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.table.runtime.typeutils.ExternalTypeInfo; +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 do a minMax scaler operation using the model data computed by {@link MinMaxScaler}. + */ +public class MinMaxScalerModel + implements Model<MinMaxScalerModel>, MinMaxScalerParams<MinMaxScalerModel> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + private Table modelDataTable; + + public MinMaxScalerModel() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public MinMaxScalerModel 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<MinMaxScalerModelData> minMaxScalerModel = + MinMaxScalerModelData.getModelDataStream(modelDataTable); + final String broadcastModelKey = "broadcastModelKey"; + RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema()); + RowTypeInfo outputTypeInfo = + new RowTypeInfo( + ArrayUtils.addAll( + inputTypeInfo.getFieldTypes(), + ExternalTypeInfo.of(DenseVector.class)), + ArrayUtils.addAll(inputTypeInfo.getFieldNames(), getPredictionCol())); + DataStream<Row> output = + BroadcastUtils.withBroadcastStream( + Collections.singletonList(data), + Collections.singletonMap(broadcastModelKey, minMaxScalerModel), + inputList -> { + DataStream input = inputList.get(0); + return input.map( + new PredictOutputFunction( + broadcastModelKey, + getMax(), + getMin(), + getFeaturesCol()), + 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( + MinMaxScalerModelData.getModelDataStream(modelDataTable), + path, + new MinMaxScalerModelData.ModelDataEncoder()); + } + + /** + * Loads model data from path. + * + * @param env Stream execution environment. + * @param path Model path. + * @return MinMaxScalerModel model. + */ + public static MinMaxScalerModel load(StreamExecutionEnvironment env, String path) + throws IOException { + StreamTableEnvironment tEnv = StreamTableEnvironment.create(env); + MinMaxScalerModel model = ReadWriteUtils.loadStageParam(path); + DataStream<MinMaxScalerModelData> modelData = + ReadWriteUtils.loadModelData( + env, path, new MinMaxScalerModelData.ModelDataDecoder()); + return model.setModelData(tEnv.fromDataStream(modelData)); + } + + /** This operator loads model data and predicts result. */ + private static class PredictOutputFunction extends RichMapFunction<Row, Row> { + private final String featureCol; + private MinMaxScalerModelData minMaxScalerModelData; + private final double upperBound; + private final double lowerBound; + private final String broadcastKey; + private DenseVector maxVector; + private DenseVector minVector; + + public PredictOutputFunction( + String broadcastKey, double upperBound, double lowerBound, String featureCol) { + this.upperBound = upperBound; + this.lowerBound = lowerBound; + this.broadcastKey = broadcastKey; + this.featureCol = featureCol; + } + + @Override + public Row map(Row row) { + if (minMaxScalerModelData == null) { + minMaxScalerModelData = + (MinMaxScalerModelData) + getRuntimeContext().getBroadcastVariable(broadcastKey).get(0); + maxVector = minMaxScalerModelData.maxVector; + minVector = minMaxScalerModelData.minVector; + } + DenseVector feature = (DenseVector) row.getField(featureCol); + DenseVector outputVector = new DenseVector(maxVector.size()); + if (feature != null) { + for (int i = 0; i < maxVector.size(); ++i) { Review comment: When maxVector[i] == minVector[i], If featureValue[i] != maxVector[i], above code may not get the same result as original code. In read world, data for train and predict are different, featureValue[i] != maxVector[i] is existed. ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/minmaxscaler/MinMaxScalerModel.java ########## @@ -0,0 +1,181 @@ +/* + * 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.minmaxscaler; + +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.DenseVector; +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.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.table.runtime.typeutils.ExternalTypeInfo; +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 do a minMax scaler operation using the model data computed by {@link MinMaxScaler}. + */ +public class MinMaxScalerModel + implements Model<MinMaxScalerModel>, MinMaxScalerParams<MinMaxScalerModel> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + private Table modelDataTable; + + public MinMaxScalerModel() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public MinMaxScalerModel 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<MinMaxScalerModelData> minMaxScalerModel = + MinMaxScalerModelData.getModelDataStream(modelDataTable); + final String broadcastModelKey = "broadcastModelKey"; + RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema()); + RowTypeInfo outputTypeInfo = + new RowTypeInfo( + ArrayUtils.addAll( + inputTypeInfo.getFieldTypes(), + ExternalTypeInfo.of(DenseVector.class)), + ArrayUtils.addAll(inputTypeInfo.getFieldNames(), getPredictionCol())); + DataStream<Row> output = + BroadcastUtils.withBroadcastStream( + Collections.singletonList(data), + Collections.singletonMap(broadcastModelKey, minMaxScalerModel), + inputList -> { + DataStream input = inputList.get(0); + return input.map( + new PredictOutputFunction( + broadcastModelKey, + getMax(), + getMin(), + getFeaturesCol()), + 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( + MinMaxScalerModelData.getModelDataStream(modelDataTable), + path, + new MinMaxScalerModelData.ModelDataEncoder()); + } + + /** + * Loads model data from path. + * + * @param env Stream execution environment. + * @param path Model path. + * @return MinMaxScalerModel model. + */ + public static MinMaxScalerModel load(StreamExecutionEnvironment env, String path) + throws IOException { + StreamTableEnvironment tEnv = StreamTableEnvironment.create(env); + MinMaxScalerModel model = ReadWriteUtils.loadStageParam(path); + DataStream<MinMaxScalerModelData> modelData = + ReadWriteUtils.loadModelData( + env, path, new MinMaxScalerModelData.ModelDataDecoder()); + return model.setModelData(tEnv.fromDataStream(modelData)); + } + + /** This operator loads model data and predicts result. */ + private static class PredictOutputFunction extends RichMapFunction<Row, Row> { + private final String featureCol; + private MinMaxScalerModelData minMaxScalerModelData; + private final double upperBound; + private final double lowerBound; + private final String broadcastKey; + private DenseVector maxVector; + private DenseVector minVector; + + public PredictOutputFunction( + String broadcastKey, double upperBound, double lowerBound, String featureCol) { + this.upperBound = upperBound; + this.lowerBound = lowerBound; + this.broadcastKey = broadcastKey; + this.featureCol = featureCol; + } + + @Override + public Row map(Row row) { + if (minMaxScalerModelData == null) { + minMaxScalerModelData = + (MinMaxScalerModelData) + getRuntimeContext().getBroadcastVariable(broadcastKey).get(0); + maxVector = minMaxScalerModelData.maxVector; + minVector = minMaxScalerModelData.minVector; + } + DenseVector feature = (DenseVector) row.getField(featureCol); + DenseVector outputVector = new DenseVector(maxVector.size()); + if (feature != null) { + for (int i = 0; i < maxVector.size(); ++i) { + if ((minVector.values[i] - maxVector.values[i]) != 0.0) { + outputVector.values[i] = + (feature.values[i] - minVector.values[i]) + / (maxVector.values[i] - minVector.values[i]) Review comment: val scaleArray = Array.tabulate(numFeatures) { i => val range = originalMax(i) - originalMin(i) ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/minmaxscaler/MinMaxScalerModel.java ########## @@ -0,0 +1,181 @@ +/* + * 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.minmaxscaler; + +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.DenseVector; +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.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.table.runtime.typeutils.ExternalTypeInfo; +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 do a minMax scaler operation using the model data computed by {@link MinMaxScaler}. + */ +public class MinMaxScalerModel + implements Model<MinMaxScalerModel>, MinMaxScalerParams<MinMaxScalerModel> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + private Table modelDataTable; + + public MinMaxScalerModel() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public MinMaxScalerModel 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<MinMaxScalerModelData> minMaxScalerModel = + MinMaxScalerModelData.getModelDataStream(modelDataTable); + final String broadcastModelKey = "broadcastModelKey"; + RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema()); + RowTypeInfo outputTypeInfo = + new RowTypeInfo( + ArrayUtils.addAll( + inputTypeInfo.getFieldTypes(), + ExternalTypeInfo.of(DenseVector.class)), + ArrayUtils.addAll(inputTypeInfo.getFieldNames(), getPredictionCol())); + DataStream<Row> output = + BroadcastUtils.withBroadcastStream( + Collections.singletonList(data), + Collections.singletonMap(broadcastModelKey, minMaxScalerModel), + inputList -> { + DataStream input = inputList.get(0); + return input.map( + new PredictOutputFunction( + broadcastModelKey, + getMax(), + getMin(), + getFeaturesCol()), + 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( + MinMaxScalerModelData.getModelDataStream(modelDataTable), + path, + new MinMaxScalerModelData.ModelDataEncoder()); + } + + /** + * Loads model data from path. + * + * @param env Stream execution environment. + * @param path Model path. + * @return MinMaxScalerModel model. + */ + public static MinMaxScalerModel load(StreamExecutionEnvironment env, String path) + throws IOException { + StreamTableEnvironment tEnv = StreamTableEnvironment.create(env); + MinMaxScalerModel model = ReadWriteUtils.loadStageParam(path); + DataStream<MinMaxScalerModelData> modelData = + ReadWriteUtils.loadModelData( + env, path, new MinMaxScalerModelData.ModelDataDecoder()); + return model.setModelData(tEnv.fromDataStream(modelData)); + } + + /** This operator loads model data and predicts result. */ + private static class PredictOutputFunction extends RichMapFunction<Row, Row> { + private final String featureCol; + private MinMaxScalerModelData minMaxScalerModelData; + private final double upperBound; + private final double lowerBound; + private final String broadcastKey; + private DenseVector maxVector; + private DenseVector minVector; + + public PredictOutputFunction( + String broadcastKey, double upperBound, double lowerBound, String featureCol) { + this.upperBound = upperBound; + this.lowerBound = lowerBound; + this.broadcastKey = broadcastKey; + this.featureCol = featureCol; + } + + @Override + public Row map(Row row) { + if (minMaxScalerModelData == null) { + minMaxScalerModelData = + (MinMaxScalerModelData) + getRuntimeContext().getBroadcastVariable(broadcastKey).get(0); + maxVector = minMaxScalerModelData.maxVector; + minVector = minMaxScalerModelData.minVector; + } + DenseVector feature = (DenseVector) row.getField(featureCol); + DenseVector outputVector = new DenseVector(maxVector.size()); + if (feature != null) { + for (int i = 0; i < maxVector.size(); ++i) { + if ((minVector.values[i] - maxVector.values[i]) != 0.0) { + outputVector.values[i] = + (feature.values[i] - minVector.values[i]) + / (maxVector.values[i] - minVector.values[i]) Review comment: In spark, maxVector and minVector are saved for modelData. The scaleArray is computed before transform. I will do it as spark done. ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/minmaxscaler/MinMaxScalerModel.java ########## @@ -0,0 +1,181 @@ +/* + * 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.minmaxscaler; + +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.DenseVector; +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.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.table.runtime.typeutils.ExternalTypeInfo; +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 do a minMax scaler operation using the model data computed by {@link MinMaxScaler}. + */ +public class MinMaxScalerModel + implements Model<MinMaxScalerModel>, MinMaxScalerParams<MinMaxScalerModel> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + private Table modelDataTable; + + public MinMaxScalerModel() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public MinMaxScalerModel 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<MinMaxScalerModelData> minMaxScalerModel = + MinMaxScalerModelData.getModelDataStream(modelDataTable); + final String broadcastModelKey = "broadcastModelKey"; + RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema()); + RowTypeInfo outputTypeInfo = + new RowTypeInfo( + ArrayUtils.addAll( + inputTypeInfo.getFieldTypes(), + ExternalTypeInfo.of(DenseVector.class)), + ArrayUtils.addAll(inputTypeInfo.getFieldNames(), getPredictionCol())); + DataStream<Row> output = + BroadcastUtils.withBroadcastStream( + Collections.singletonList(data), + Collections.singletonMap(broadcastModelKey, minMaxScalerModel), + inputList -> { + DataStream input = inputList.get(0); + return input.map( + new PredictOutputFunction( + broadcastModelKey, + getMax(), + getMin(), + getFeaturesCol()), + 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( + MinMaxScalerModelData.getModelDataStream(modelDataTable), + path, + new MinMaxScalerModelData.ModelDataEncoder()); + } + + /** + * Loads model data from path. + * + * @param env Stream execution environment. + * @param path Model path. + * @return MinMaxScalerModel model. + */ + public static MinMaxScalerModel load(StreamExecutionEnvironment env, String path) + throws IOException { + StreamTableEnvironment tEnv = StreamTableEnvironment.create(env); + MinMaxScalerModel model = ReadWriteUtils.loadStageParam(path); + DataStream<MinMaxScalerModelData> modelData = + ReadWriteUtils.loadModelData( + env, path, new MinMaxScalerModelData.ModelDataDecoder()); + return model.setModelData(tEnv.fromDataStream(modelData)); + } + + /** This operator loads model data and predicts result. */ + private static class PredictOutputFunction extends RichMapFunction<Row, Row> { + private final String featureCol; + private MinMaxScalerModelData minMaxScalerModelData; + private final double upperBound; + private final double lowerBound; + private final String broadcastKey; + private DenseVector maxVector; + private DenseVector minVector; + + public PredictOutputFunction( + String broadcastKey, double upperBound, double lowerBound, String featureCol) { + this.upperBound = upperBound; + this.lowerBound = lowerBound; + this.broadcastKey = broadcastKey; + this.featureCol = featureCol; + } + + @Override + public Row map(Row row) { + if (minMaxScalerModelData == null) { + minMaxScalerModelData = + (MinMaxScalerModelData) + getRuntimeContext().getBroadcastVariable(broadcastKey).get(0); + maxVector = minMaxScalerModelData.maxVector; + minVector = minMaxScalerModelData.minVector; + } + DenseVector feature = (DenseVector) row.getField(featureCol); + DenseVector outputVector = new DenseVector(maxVector.size()); + if (feature != null) { + for (int i = 0; i < maxVector.size(); ++i) { Review comment: When maxVector[i] == minVector[i], If featureValue[i] != maxVector[i], above code may not get the same result as original code. In real world, data for train and predict are different, featureValue[i] != maxVector[i] is existed. ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/minmaxscaler/MinMaxScalerModel.java ########## @@ -0,0 +1,181 @@ +/* + * 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.minmaxscaler; + +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.DenseVector; +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.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.table.runtime.typeutils.ExternalTypeInfo; +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 do a minMax scaler operation using the model data computed by {@link MinMaxScaler}. + */ +public class MinMaxScalerModel + implements Model<MinMaxScalerModel>, MinMaxScalerParams<MinMaxScalerModel> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + private Table modelDataTable; + + public MinMaxScalerModel() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public MinMaxScalerModel 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<MinMaxScalerModelData> minMaxScalerModel = + MinMaxScalerModelData.getModelDataStream(modelDataTable); + final String broadcastModelKey = "broadcastModelKey"; + RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema()); + RowTypeInfo outputTypeInfo = + new RowTypeInfo( + ArrayUtils.addAll( + inputTypeInfo.getFieldTypes(), + ExternalTypeInfo.of(DenseVector.class)), + ArrayUtils.addAll(inputTypeInfo.getFieldNames(), getPredictionCol())); + DataStream<Row> output = + BroadcastUtils.withBroadcastStream( + Collections.singletonList(data), + Collections.singletonMap(broadcastModelKey, minMaxScalerModel), + inputList -> { + DataStream input = inputList.get(0); + return input.map( + new PredictOutputFunction( + broadcastModelKey, + getMax(), + getMin(), + getFeaturesCol()), + 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( + MinMaxScalerModelData.getModelDataStream(modelDataTable), + path, + new MinMaxScalerModelData.ModelDataEncoder()); + } + + /** + * Loads model data from path. + * + * @param env Stream execution environment. + * @param path Model path. + * @return MinMaxScalerModel model. + */ + public static MinMaxScalerModel load(StreamExecutionEnvironment env, String path) + throws IOException { + StreamTableEnvironment tEnv = StreamTableEnvironment.create(env); + MinMaxScalerModel model = ReadWriteUtils.loadStageParam(path); + DataStream<MinMaxScalerModelData> modelData = + ReadWriteUtils.loadModelData( + env, path, new MinMaxScalerModelData.ModelDataDecoder()); + return model.setModelData(tEnv.fromDataStream(modelData)); + } + + /** This operator loads model data and predicts result. */ + private static class PredictOutputFunction extends RichMapFunction<Row, Row> { + private final String featureCol; + private MinMaxScalerModelData minMaxScalerModelData; + private final double upperBound; + private final double lowerBound; + private final String broadcastKey; + private DenseVector maxVector; + private DenseVector minVector; + + public PredictOutputFunction( + String broadcastKey, double upperBound, double lowerBound, String featureCol) { + this.upperBound = upperBound; + this.lowerBound = lowerBound; + this.broadcastKey = broadcastKey; + this.featureCol = featureCol; + } + + @Override + public Row map(Row row) { + if (minMaxScalerModelData == null) { + minMaxScalerModelData = + (MinMaxScalerModelData) + getRuntimeContext().getBroadcastVariable(broadcastKey).get(0); + maxVector = minMaxScalerModelData.maxVector; + minVector = minMaxScalerModelData.minVector; + } + DenseVector feature = (DenseVector) row.getField(featureCol); + DenseVector outputVector = new DenseVector(maxVector.size()); + if (feature != null) { + for (int i = 0; i < maxVector.size(); ++i) { Review comment: When maxVector[i] == minVector[i], If featureValue[i] != maxVector[i], above code may not get the same result as original code. In real world, data for train and predict may be different, featureValue[i] != maxVector[i] is existed. ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/minmaxscaler/MinMaxScalerModel.java ########## @@ -0,0 +1,181 @@ +/* + * 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.minmaxscaler; + +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.DenseVector; +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.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.table.runtime.typeutils.ExternalTypeInfo; +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 do a minMax scaler operation using the model data computed by {@link MinMaxScaler}. + */ +public class MinMaxScalerModel + implements Model<MinMaxScalerModel>, MinMaxScalerParams<MinMaxScalerModel> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + private Table modelDataTable; + + public MinMaxScalerModel() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public MinMaxScalerModel 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<MinMaxScalerModelData> minMaxScalerModel = + MinMaxScalerModelData.getModelDataStream(modelDataTable); + final String broadcastModelKey = "broadcastModelKey"; + RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema()); + RowTypeInfo outputTypeInfo = + new RowTypeInfo( + ArrayUtils.addAll( + inputTypeInfo.getFieldTypes(), + ExternalTypeInfo.of(DenseVector.class)), + ArrayUtils.addAll(inputTypeInfo.getFieldNames(), getPredictionCol())); + DataStream<Row> output = + BroadcastUtils.withBroadcastStream( + Collections.singletonList(data), + Collections.singletonMap(broadcastModelKey, minMaxScalerModel), + inputList -> { + DataStream input = inputList.get(0); + return input.map( + new PredictOutputFunction( + broadcastModelKey, + getMax(), + getMin(), + getFeaturesCol()), + 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( + MinMaxScalerModelData.getModelDataStream(modelDataTable), + path, + new MinMaxScalerModelData.ModelDataEncoder()); + } + + /** + * Loads model data from path. + * + * @param env Stream execution environment. + * @param path Model path. + * @return MinMaxScalerModel model. + */ + public static MinMaxScalerModel load(StreamExecutionEnvironment env, String path) + throws IOException { + StreamTableEnvironment tEnv = StreamTableEnvironment.create(env); + MinMaxScalerModel model = ReadWriteUtils.loadStageParam(path); + DataStream<MinMaxScalerModelData> modelData = + ReadWriteUtils.loadModelData( + env, path, new MinMaxScalerModelData.ModelDataDecoder()); + return model.setModelData(tEnv.fromDataStream(modelData)); + } + + /** This operator loads model data and predicts result. */ + private static class PredictOutputFunction extends RichMapFunction<Row, Row> { + private final String featureCol; + private MinMaxScalerModelData minMaxScalerModelData; + private final double upperBound; + private final double lowerBound; + private final String broadcastKey; + private DenseVector maxVector; + private DenseVector minVector; + + public PredictOutputFunction( + String broadcastKey, double upperBound, double lowerBound, String featureCol) { + this.upperBound = upperBound; + this.lowerBound = lowerBound; + this.broadcastKey = broadcastKey; + this.featureCol = featureCol; + } + + @Override + public Row map(Row row) { + if (minMaxScalerModelData == null) { + minMaxScalerModelData = + (MinMaxScalerModelData) + getRuntimeContext().getBroadcastVariable(broadcastKey).get(0); + maxVector = minMaxScalerModelData.maxVector; + minVector = minMaxScalerModelData.minVector; + } + DenseVector feature = (DenseVector) row.getField(featureCol); + DenseVector outputVector = new DenseVector(maxVector.size()); + if (feature != null) { Review comment: OK, I have add exception. ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/minmaxscaler/MinMaxScalerModel.java ########## @@ -0,0 +1,181 @@ +/* + * 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.minmaxscaler; + +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.DenseVector; +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.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.table.runtime.typeutils.ExternalTypeInfo; +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 do a minMax scaler operation using the model data computed by {@link MinMaxScaler}. + */ +public class MinMaxScalerModel + implements Model<MinMaxScalerModel>, MinMaxScalerParams<MinMaxScalerModel> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + private Table modelDataTable; + + public MinMaxScalerModel() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public MinMaxScalerModel 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<MinMaxScalerModelData> minMaxScalerModel = + MinMaxScalerModelData.getModelDataStream(modelDataTable); + final String broadcastModelKey = "broadcastModelKey"; + RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema()); + RowTypeInfo outputTypeInfo = + new RowTypeInfo( + ArrayUtils.addAll( + inputTypeInfo.getFieldTypes(), + ExternalTypeInfo.of(DenseVector.class)), + ArrayUtils.addAll(inputTypeInfo.getFieldNames(), getPredictionCol())); + DataStream<Row> output = + BroadcastUtils.withBroadcastStream( + Collections.singletonList(data), + Collections.singletonMap(broadcastModelKey, minMaxScalerModel), + inputList -> { + DataStream input = inputList.get(0); + return input.map( + new PredictOutputFunction( + broadcastModelKey, + getMax(), + getMin(), + getFeaturesCol()), + 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( + MinMaxScalerModelData.getModelDataStream(modelDataTable), + path, + new MinMaxScalerModelData.ModelDataEncoder()); + } + + /** + * Loads model data from path. + * + * @param env Stream execution environment. + * @param path Model path. + * @return MinMaxScalerModel model. + */ + public static MinMaxScalerModel load(StreamExecutionEnvironment env, String path) + throws IOException { + StreamTableEnvironment tEnv = StreamTableEnvironment.create(env); + MinMaxScalerModel model = ReadWriteUtils.loadStageParam(path); + DataStream<MinMaxScalerModelData> modelData = + ReadWriteUtils.loadModelData( + env, path, new MinMaxScalerModelData.ModelDataDecoder()); + return model.setModelData(tEnv.fromDataStream(modelData)); + } + + /** This operator loads model data and predicts result. */ + private static class PredictOutputFunction extends RichMapFunction<Row, Row> { + private final String featureCol; + private MinMaxScalerModelData minMaxScalerModelData; + private final double upperBound; + private final double lowerBound; + private final String broadcastKey; + private DenseVector maxVector; + private DenseVector minVector; + + public PredictOutputFunction( + String broadcastKey, double upperBound, double lowerBound, String featureCol) { + this.upperBound = upperBound; + this.lowerBound = lowerBound; + this.broadcastKey = broadcastKey; + this.featureCol = featureCol; + } + + @Override + public Row map(Row row) { + if (minMaxScalerModelData == null) { + minMaxScalerModelData = + (MinMaxScalerModelData) + getRuntimeContext().getBroadcastVariable(broadcastKey).get(0); + maxVector = minMaxScalerModelData.maxVector; + minVector = minMaxScalerModelData.minVector; + } + DenseVector feature = (DenseVector) row.getField(featureCol); + DenseVector outputVector = new DenseVector(maxVector.size()); + if (feature != null) { + for (int i = 0; i < maxVector.size(); ++i) { Review comment: I don‘t think so. If featureValue[i] != maxVector[i], predict data may have mo problem. For train data defines the rule and we can't constrain predict data follow this rule. For example, in train data one feature is people's age, all feature values in the train data is 20 years old. If predict data has a sample which feature is 21 years old which will be scaled to 0.5*(max - min). -- 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
