zhipeng93 commented on a change in pull request #54: URL: https://github.com/apache/flink-ml/pull/54#discussion_r828785312
########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/minmaxscaler/MinMaxScaler.java ########## @@ -0,0 +1,197 @@ +/* + * 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 max vectors 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() { + output.collect(new StreamRecord<>(minVector)); + 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]); + maxVector.values[i] = Math.max(maxVector.values[i], currentValue.values[i]); + } + } + } + + @Override + @SuppressWarnings("unchecked") + public void initializeState(StateInitializationContext context) throws Exception { + super.initializeState(context); + minState = + context.getOperatorStateStore() + .getListState( + new ListStateDescriptor<>( + "minVectorState", Review comment: nit: minVectorState -> minState ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/minmaxscaler/MinMaxScaler.java ########## @@ -0,0 +1,197 @@ +/* + * 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 max vectors 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() { + output.collect(new StreamRecord<>(minVector)); + 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]); + maxVector.values[i] = Math.max(maxVector.values[i], currentValue.values[i]); + } + } + } + + @Override + @SuppressWarnings("unchecked") + public void initializeState(StateInitializationContext context) throws Exception { + super.initializeState(context); + minState = + context.getOperatorStateStore() + .getListState( + new ListStateDescriptor<>( + "minVectorState", + getOperatorConfig() + .getTypeSerializerIn( + 0, getClass().getClassLoader()))); + maxState = + context.getOperatorStateStore() + .getListState( + new ListStateDescriptor<>( + "maxVectorState", Review comment: ditto. ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/minmaxscaler/MinMaxScaler.java ########## @@ -0,0 +1,197 @@ +/* + * 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 max vectors in each partition of the input bounded data Review comment: min max vectors --> ...min and max values ########## File path: flink-ml-lib/src/test/java/org/apache/flink/ml/feature/MinMaxScalerTest.java ########## @@ -0,0 +1,206 @@ +/* + * 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 trainData; + private Table predictData; + private static final List<Row> trainRows = + 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(trainRows); + trainData = tEnv.fromDataStream(dataStream, schema).as("features"); + DataStream<Row> predDataStream = env.fromCollection(predictRows); + predictData = tEnv.fromDataStream(predDataStream, schema).as("features"); + } + + private static void verifyPredictionResult(Table output, String outputCol) 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()); + for (DenseVector t2 : result) { + assertEquals(Vectors.dense(0.75, 0.3), t2); + } + } + + @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("output", minMaxScaler.getOutputCol()); + minMaxScaler + .setFeaturesCol("test_features") + .setMax(4.0) + .setMIN(0.0) + .setOutputCol("test_output"); + assertEquals("test_features", minMaxScaler.getFeaturesCol()); + assertEquals(0.0, minMaxScaler.getMIN(), 0.0001); + assertEquals(4.0, minMaxScaler.getMax(), 0.0001); + assertEquals("test_output", minMaxScaler.getOutputCol()); + } + + @Test + public void testFeaturePredictionParam() { + MinMaxScaler minMaxScaler = + new MinMaxScaler() + .setMIN(0.0) + .setMax(4.0) + .setFeaturesCol("test_features") + .setOutputCol("test_output"); + MinMaxScalerModel model = minMaxScaler.fit(trainData.as("test_features")); + Table output = model.transform(predictData.as("test_features"))[0]; + assertEquals( + Arrays.asList("test_features", "test_output"), + output.getResolvedSchema().getColumnNames()); + } + + @Test + public void testFewerDistinctPointsThanCluster() { Review comment: Could you also verify the execution result here? ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/minmaxscaler/MinMaxScaler.java ########## @@ -0,0 +1,197 @@ +/* + * 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 max vectors 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() { + output.collect(new StreamRecord<>(minVector)); Review comment: If there is no data in one partition, it throw a NPE here. ########## 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 PredictLabelFunction( + 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 PredictLabelFunction extends RichMapFunction<Row, Row> { Review comment: Is `PredictOutputFunction` a better name for `PredictLabelFunction`, since there is no label here? ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/feature/minmaxscaler/MinMaxScalerModelData.java ########## @@ -0,0 +1,120 @@ +/* + * 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.serialization.Encoder; +import org.apache.flink.api.common.typeinfo.TypeInformation; +import org.apache.flink.configuration.Configuration; +import org.apache.flink.connector.file.src.reader.SimpleStreamFormat; +import org.apache.flink.core.fs.FSDataInputStream; +import org.apache.flink.core.memory.DataInputView; +import org.apache.flink.core.memory.DataInputViewStreamWrapper; +import org.apache.flink.core.memory.DataOutputView; +import org.apache.flink.core.memory.DataOutputViewStreamWrapper; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.typeinfo.DenseVectorSerializer; +import org.apache.flink.streaming.api.datastream.DataStream; +import org.apache.flink.table.api.Table; +import org.apache.flink.table.api.bridge.java.StreamTableEnvironment; +import org.apache.flink.table.api.internal.TableImpl; + +import java.io.EOFException; +import java.io.IOException; +import java.io.OutputStream; + +/** + * Model data of {@link MinMaxScalerModel}. + * + * <p>This class also provides methods to convert model data from Table to a data stream, and + * classes to save/load model data. + */ +public class MinMaxScalerModelData { + public DenseVector minVector; + + public DenseVector maxVector; + + public MinMaxScalerModelData() {} + + public MinMaxScalerModelData(DenseVector minVector, DenseVector maxVector) { + this.minVector = minVector; + this.maxVector = maxVector; + } + + /** + * Converts the table model to a data stream. + * + * @param modelDataTable The table model data. + * @return The data stream model data. + */ + public static DataStream<MinMaxScalerModelData> getModelDataStream(Table modelDataTable) { + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) modelDataTable).getTableEnvironment(); + return tEnv.toDataStream(modelDataTable) + .map( + x -> + new MinMaxScalerModelData( + (DenseVector) x.getField(0), (DenseVector) x.getField(1))); + } + + /** Encoder for {@link MinMaxScalerModelData}. */ + public static class ModelDataEncoder implements Encoder<MinMaxScalerModelData> { + @Override + public void encode(MinMaxScalerModelData minMaxScalerModelData, OutputStream outputStream) + throws IOException { + DataOutputView dataOutputView = new DataOutputViewStreamWrapper(outputStream); + DenseVectorSerializer.INSTANCE.serialize( + minMaxScalerModelData.minVector, dataOutputView); + DenseVectorSerializer.INSTANCE.serialize( + minMaxScalerModelData.maxVector, dataOutputView); + } + } + + /** Decoder for {@link MinMaxScalerModelData}. */ + public static class ModelDataDecoder extends SimpleStreamFormat<MinMaxScalerModelData> { + @Override + public Reader<MinMaxScalerModelData> createReader( + Configuration config, FSDataInputStream stream) { + return new Reader<MinMaxScalerModelData>() { + + private final DataInputView source = new DataInputViewStreamWrapper(stream); Review comment: nit: this could be intialized in `read()`. -- 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: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
