yunfengzhou-hub commented on a change in pull request #28: URL: https://github.com/apache/flink-ml/pull/28#discussion_r753930171
########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/common/param/HasL2.java ########## @@ -0,0 +1,42 @@ +/* + * 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.common.param; + +import org.apache.flink.ml.param.DoubleParam; +import org.apache.flink.ml.param.Param; +import org.apache.flink.ml.param.ParamValidators; +import org.apache.flink.ml.param.WithParams; + +/** Interface for the shared L2 regularization param. */ +public interface HasL2<T> extends WithParams<T> { + Param<Double> L_2 = + new DoubleParam( + "l2", + "The L2-regularized parameter. The default value is 0.", Review comment: We might need to establish a unified practice of whether to describe the default value (or accepted values in enum params). We can discuss it offline. ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/classification/linear/LogisticGradient.java ########## @@ -0,0 +1,97 @@ +/* + * 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.classification.linear; + +import org.apache.flink.api.java.tuple.Tuple2; +import org.apache.flink.api.java.tuple.Tuple3; +import org.apache.flink.ml.common.linalg.BLAS; + +import java.io.Serializable; + +/** Logistic gradient. */ Review comment: Shall we have a more detailed description for this class? It might be difficult to infer the functionality of this class by reading this javadoc. ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/common/param/HasBatchSize.java ########## @@ -0,0 +1,43 @@ +/* + * 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.common.param; + +import org.apache.flink.ml.param.IntParam; +import org.apache.flink.ml.param.Param; +import org.apache.flink.ml.param.ParamValidators; +import org.apache.flink.ml.param.WithParams; + +/** Interface for the shared batchSize param. */ +public interface HasBatchSize<T> extends WithParams<T> { + + Param<Integer> BATCH_SIZE = + new IntParam( + "batchSize", + "batch size of training algorithms. The default value is 100.", Review comment: A better style would be make descriptions start with Uppercase letter and ends with full stop. ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/classification/linear/LogisticRegressionModel.java ########## @@ -0,0 +1,220 @@ +/* + * 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.classification.linear; + +import org.apache.flink.api.common.eventtime.WatermarkStrategy; +import org.apache.flink.api.common.functions.RichMapFunction; +import org.apache.flink.api.common.typeinfo.BasicTypeInfo; +import org.apache.flink.api.common.typeinfo.PrimitiveArrayTypeInfo; +import org.apache.flink.api.common.typeinfo.TypeInformation; +import org.apache.flink.api.connector.source.Source; +import org.apache.flink.api.java.tuple.Tuple2; +import org.apache.flink.api.java.typeutils.RowTypeInfo; +import org.apache.flink.connector.file.sink.FileSink; +import org.apache.flink.connector.file.src.FileSource; +import org.apache.flink.core.fs.Path; +import org.apache.flink.ml.api.core.Model; +import org.apache.flink.ml.classification.linear.LogisticRegressionModelData.LogisticRegressionModelDataEncoder; +import org.apache.flink.ml.classification.linear.LogisticRegressionModelData.LogisticRegressionModelDataStreamFormat; +import org.apache.flink.ml.common.broadcast.BroadcastUtils; +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.streaming.api.functions.sink.filesystem.bucketassigners.BasePathBucketAssigner; +import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.OnCheckpointRollingPolicy; +import org.apache.flink.streaming.api.operators.AbstractUdfStreamOperator; +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 java.io.IOException; +import java.util.Collections; +import java.util.HashMap; +import java.util.Map; + +/** This class implements {@link Model} for {@link LogisticRegression}. */ +public class LogisticRegressionModel + implements Model<LogisticRegressionModel>, + LogisticRegressionModelParams<LogisticRegressionModel> { + + private Map<Param<?>, Object> paramMap; + + private Table model; + + public LogisticRegressionModel(Map<Param<?>, Object> paramMap) { + this.paramMap = paramMap; + ParamUtils.initializeMapWithDefaultValues(this.paramMap, this); + } + + public LogisticRegressionModel() { + this(new HashMap<>()); Review comment: Maybe the default constructor should be implemented like in `Kmeans`? ``` ParamUtils.initializeMapWithDefaultValues(paramMap, this); ``` ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/common/param/HasEpsilon.java ########## @@ -0,0 +1,43 @@ +/* + * 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.common.param; + +import org.apache.flink.ml.param.DoubleParam; +import org.apache.flink.ml.param.Param; +import org.apache.flink.ml.param.ParamValidators; +import org.apache.flink.ml.param.WithParams; + +/** Interface for the shared epsilon param. */ +public interface HasEpsilon<T> extends WithParams<T> { + + Param<Double> EPSILON = + new DoubleParam( + "epsilon", + "Convergence tolerance for iterative algorithms. The default value is 0.1", + 0.1, Review comment: Shall we make the default value same as either both Alink `HasEpsilon` or Spark `HasTol`? I think it could be better to adopt one of the existing practices. ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/classification/linear/LogisticRegressionModel.java ########## @@ -0,0 +1,220 @@ +/* + * 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.classification.linear; + +import org.apache.flink.api.common.eventtime.WatermarkStrategy; +import org.apache.flink.api.common.functions.RichMapFunction; +import org.apache.flink.api.common.typeinfo.BasicTypeInfo; +import org.apache.flink.api.common.typeinfo.PrimitiveArrayTypeInfo; +import org.apache.flink.api.common.typeinfo.TypeInformation; +import org.apache.flink.api.connector.source.Source; +import org.apache.flink.api.java.tuple.Tuple2; +import org.apache.flink.api.java.typeutils.RowTypeInfo; +import org.apache.flink.connector.file.sink.FileSink; +import org.apache.flink.connector.file.src.FileSource; +import org.apache.flink.core.fs.Path; +import org.apache.flink.ml.api.core.Model; +import org.apache.flink.ml.classification.linear.LogisticRegressionModelData.LogisticRegressionModelDataEncoder; +import org.apache.flink.ml.classification.linear.LogisticRegressionModelData.LogisticRegressionModelDataStreamFormat; +import org.apache.flink.ml.common.broadcast.BroadcastUtils; +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.streaming.api.functions.sink.filesystem.bucketassigners.BasePathBucketAssigner; +import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.OnCheckpointRollingPolicy; +import org.apache.flink.streaming.api.operators.AbstractUdfStreamOperator; +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 java.io.IOException; +import java.util.Collections; +import java.util.HashMap; +import java.util.Map; + +/** This class implements {@link Model} for {@link LogisticRegression}. */ +public class LogisticRegressionModel + implements Model<LogisticRegressionModel>, + LogisticRegressionModelParams<LogisticRegressionModel> { + + private Map<Param<?>, Object> paramMap; + + private Table model; + + public LogisticRegressionModel(Map<Param<?>, Object> paramMap) { + this.paramMap = paramMap; + ParamUtils.initializeMapWithDefaultValues(this.paramMap, this); + } + + public LogisticRegressionModel() { + this(new HashMap<>()); + } + + @Override + public Map<Param<?>, Object> getParamMap() { + return paramMap; + } + + @Override + public void save(String path) throws IOException { + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) model).getTableEnvironment(); + String dataPath = ReadWriteUtils.getDataPath(path); + FileSink<LogisticRegressionModelData> sink = + FileSink.forRowFormat(new Path(dataPath), new LogisticRegressionModelDataEncoder()) + .withRollingPolicy(OnCheckpointRollingPolicy.build()) + .withBucketAssigner(new BasePathBucketAssigner<>()) + .build(); + ReadWriteUtils.saveMetadata(this, path); + tEnv.toDataStream(model) + .map(x -> (LogisticRegressionModelData) x.getField(0)) + .sinkTo(sink) + .setParallelism(1); + } + + public static LogisticRegressionModel load(StreamExecutionEnvironment env, String path) + throws IOException { + StreamTableEnvironment tEnv = StreamTableEnvironment.create(env); + Source<LogisticRegressionModelData, ?, ?> source = + FileSource.forRecordStreamFormat( + new LogisticRegressionModelDataStreamFormat(), + ReadWriteUtils.getDataPaths(path)) + .build(); + LogisticRegressionModel model = ReadWriteUtils.loadStageParam(path); + DataStream<LogisticRegressionModelData> modelData = + env.fromSource(source, WatermarkStrategy.noWatermarks(), "modelData"); + model.setModelData(tEnv.fromDataStream(modelData)); + return model; + } + + @Override + public LogisticRegressionModel setModelData(Table... inputs) { + model = inputs[0]; + return this; + } + + @Override + public Table[] getModelData() { + return new Table[] {model}; + } + + @Override + public Table[] transform(Table... inputs) { + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment(); + + DataStream<Row> data = tEnv.toDataStream(inputs[0]); + final String broadcastModelName = "broadcastModel"; + DataStream<LogisticRegressionModelData> modelData = + tEnv.toDataStream(model).map(x -> (LogisticRegressionModelData) x.getField(0)); + DataStream<Row> predictResult = + BroadcastUtils.withBroadcastStream( + Collections.singletonList(data), + Collections.singletonMap(broadcastModelName, modelData), + inputList -> { + DataStream inputData = inputList.get(0); + return inputData.transform( + "predict", + new RowTypeInfo( + new TypeInformation[] { + PrimitiveArrayTypeInfo + .DOUBLE_PRIMITIVE_ARRAY_TYPE_INFO, + BasicTypeInfo.DOUBLE_TYPE_INFO, + PrimitiveArrayTypeInfo + .DOUBLE_PRIMITIVE_ARRAY_TYPE_INFO + }, + new String[] { + getFeaturesCol(), + getPredictionCol(), + getPredictionDetailCol() + }), + new PredictOp( + new PredictOneRecord( + getFeaturesCol(), broadcastModelName))); + }); + + return new Table[] {tEnv.fromDataStream(predictResult)}; + } + + /** A utility operator used for prediction. */ + private static class PredictOp extends AbstractUdfStreamOperator<Row, RichMapFunction<Row, Row>> + implements OneInputStreamOperator<Row, Row> { + public PredictOp(RichMapFunction<Row, Row> userFunction) { + super(userFunction); + } + + @Override + public void processElement(StreamRecord<Row> streamRecord) throws Exception { + output.collect(new StreamRecord<>(userFunction.map(streamRecord.getValue()))); + } + } + + /** A utility function used to predict one input record. */ + private static class PredictOneRecord extends RichMapFunction<Row, Row> { + + String broadcastModelName; + Review comment: It might be better to reduce the empty lines here. ########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/classification/linear/LogisticRegression.java ########## @@ -0,0 +1,594 @@ +/* + * 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.classification.linear; + +import org.apache.flink.api.common.functions.FlatMapFunction; +import org.apache.flink.api.common.functions.RichMapFunction; +import org.apache.flink.api.common.state.ListState; +import org.apache.flink.api.common.state.ListStateDescriptor; +import org.apache.flink.api.common.typeinfo.BasicTypeInfo; +import org.apache.flink.api.common.typeinfo.PrimitiveArrayTypeInfo; +import org.apache.flink.api.common.typeinfo.TypeInformation; +import org.apache.flink.api.java.tuple.Tuple2; +import org.apache.flink.api.java.tuple.Tuple3; +import org.apache.flink.api.java.typeutils.TupleTypeInfo; +import org.apache.flink.iteration.DataStreamList; +import org.apache.flink.iteration.IterationBody; +import org.apache.flink.iteration.IterationBodyResult; +import org.apache.flink.iteration.IterationConfig; +import org.apache.flink.iteration.IterationConfig.OperatorLifeCycle; +import org.apache.flink.iteration.IterationListener; +import org.apache.flink.iteration.Iterations; +import org.apache.flink.iteration.ReplayableDataStreamList; +import org.apache.flink.iteration.operator.OperatorStateUtils; +import org.apache.flink.ml.api.core.Estimator; +import org.apache.flink.ml.common.broadcast.BroadcastUtils; +import org.apache.flink.ml.common.datastream.AllReduceUtils; +import org.apache.flink.ml.common.datastream.DataStreamUtils; +import org.apache.flink.ml.common.linalg.BLAS; +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.datastream.SingleOutputStreamOperator; +import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; +import org.apache.flink.streaming.api.operators.AbstractStreamOperator; +import org.apache.flink.streaming.api.operators.AbstractUdfStreamOperator; +import org.apache.flink.streaming.api.operators.BoundedMultiInput; +import org.apache.flink.streaming.api.operators.BoundedOneInput; +import org.apache.flink.streaming.api.operators.OneInputStreamOperator; +import org.apache.flink.streaming.api.operators.TwoInputStreamOperator; +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.util.Collector; +import org.apache.flink.util.OutputTag; +import org.apache.flink.util.Preconditions; + +import org.apache.commons.collections.IteratorUtils; + +import java.io.IOException; +import java.util.ArrayList; +import java.util.Arrays; +import java.util.Collections; +import java.util.HashMap; +import java.util.List; +import java.util.Map; +import java.util.Random; + +/** This class implements methods to train a logistic regression model. */ +public class LogisticRegression + implements Estimator<LogisticRegression, LogisticRegressionModel>, + LogisticRegressionParams<LogisticRegression> { + + Map<Param<?>, Object> paramMap; + + private static final OutputTag<Tuple2<double[], double[]>> MODEL_OUTPUT = + new OutputTag<Tuple2<double[], double[]>>("MODEL_OUTPUT") {}; + + public LogisticRegression(Map<Param<?>, Object> paramMap) { + this.paramMap = paramMap; + ParamUtils.initializeMapWithDefaultValues(this.paramMap, this); + } + + public LogisticRegression() { + this(new HashMap<>()); + } + + @Override + public Map<Param<?>, Object> getParamMap() { + return paramMap; + } + + @Override + public void save(String path) throws IOException { + ReadWriteUtils.saveMetadata(this, path); + } + + public static LogisticRegression load(StreamExecutionEnvironment env, String path) + throws IOException { + return ReadWriteUtils.loadStageParam(path); + } + + @Override + @SuppressWarnings("unchecked") + public LogisticRegressionModel fit(Table... inputs) { + Preconditions.checkArgument(inputs.length == 1); + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment(); + + DataStream<Tuple3<Double, Double, double[]>> trainData = + tEnv.toDataStream(inputs[0]) + .map( + row -> + Tuple3.of( + getWeightCol() == null + ? new Double(1.0) + : (Double) row.getField(getWeightCol()), + (Double) row.getField(getLabelCol()), + (double[]) row.getField(getFeaturesCol()))) + .returns( + new TupleTypeInfo<>( + BasicTypeInfo.DOUBLE_TYPE_INFO, + BasicTypeInfo.DOUBLE_TYPE_INFO, + PrimitiveArrayTypeInfo.DOUBLE_PRIMITIVE_ARRAY_TYPE_INFO)); + + DataStream<Double> labelValues = DataStreamUtils.distinct(trainData.map(x -> x.f1)); + + final String broadcastLabelsName = "broadcastLabels"; + trainData = + BroadcastUtils.withBroadcastStream( + Collections.singletonList(trainData), + Collections.singletonMap(broadcastLabelsName, labelValues), + inputList -> { + DataStream data = inputList.get(0); + return data.transform( + "preprocess", + new TupleTypeInfo<>( + BasicTypeInfo.DOUBLE_TYPE_INFO, + BasicTypeInfo.DOUBLE_TYPE_INFO, + TypeInformation.of(double[].class)), + new PreprocessDataOp( + new PreprocessOneRecord(broadcastLabelsName))); + }); + + DataStream<double[]> initModel = + trainData + .transform( + "getInitModel", + TypeInformation.of(double[].class), + new GetInitModel()) + .returns(TypeInformation.of(double[].class)); + + DataStream<Tuple2<double[], double[]>> modelAndLossCurve = train(trainData, initModel); + + DataStream<LogisticRegressionModelData> modelData = + modelAndLossCurve + .connect(labelValues) + .transform( + "composeModelData", + TypeInformation.of(LogisticRegressionModelData.class), + new ComposeModelDataOp()) + .setParallelism(1); + + LogisticRegressionModel model = + new LogisticRegressionModel(new HashMap<>()) + .setModelData(tEnv.fromDataStream(modelData)); + ReadWriteUtils.updateExistingParams(model, paramMap); + return model; + } + + /** Pre-processes the training data. */ + private static class PreprocessDataOp + extends AbstractUdfStreamOperator< + Tuple3<Double, Double, double[]>, + RichMapFunction< + Tuple3<Double, Double, double[]>, Tuple3<Double, Double, double[]>>> + implements OneInputStreamOperator< + Tuple3<Double, Double, double[]>, Tuple3<Double, Double, double[]>> { + public PreprocessDataOp( + RichMapFunction<Tuple3<Double, Double, double[]>, Tuple3<Double, Double, double[]>> + userFunction) { + super(userFunction); + } + + @Override + public void processElement(StreamRecord<Tuple3<Double, Double, double[]>> streamRecord) + throws Exception { + streamRecord.replace(userFunction.map(streamRecord.getValue())); + output.collect(streamRecord); + } + } + + /** Pre-processes the training data. */ + private static class PreprocessOneRecord + extends RichMapFunction< + Tuple3<Double, Double, double[]>, Tuple3<Double, Double, double[]>> { + + String broadcastName; + double[] labelValues; + + public PreprocessOneRecord(String broadcastName) { + this.broadcastName = broadcastName; + } + + @Override + public Tuple3<Double, Double, double[]> map(Tuple3<Double, Double, double[]> value) { + if (labelValues == null) { + List<Double> labelList = getRuntimeContext().getBroadcastVariable(broadcastName); + labelValues = labelList.stream().mapToDouble(Double::doubleValue).toArray(); + } + value.f1 = getLabel(labelValues, value.f1); + return value; + } + + private double getLabel(double[] labels, double label) { + if (Math.abs(label - labels[0]) < 1e-7) { + return 1.; + } else { + return -1.; + } + } + } + + /** + * Gets initialized model, note that the parallelism is same as the input train data, not one. + */ + private static class GetInitModel extends AbstractStreamOperator<double[]> + implements OneInputStreamOperator<Tuple3<Double, Double, double[]>, double[]>, + BoundedOneInput { + + int dim = 0; + + @Override + public void endInput() { + output.collect(new StreamRecord<>(new double[dim])); + } + + @Override + public void processElement(StreamRecord<Tuple3<Double, Double, double[]>> streamRecord) { + dim = Math.max(dim, streamRecord.getValue().f2.length); + } + } + + /** + * Trains a machine learning model on the input data and initialized model, return the trained + * model and loss curve. + * + * @param trainData the training data + * @param initModel the init model + * @return the trained model and loss during the training + */ + private DataStream<Tuple2<double[], double[]>> train( + DataStream<Tuple3<Double, Double, double[]>> trainData, + DataStream<double[]> initModel) { + LogisticGradient logisticGradient = new LogisticGradient(getL2()); + + DataStreamList resultList = + Iterations.iterateBoundedStreamsUntilTermination( + DataStreamList.of(initModel), + ReplayableDataStreamList.notReplay(trainData), + IterationConfig.newBuilder() + .setOperatorLifeCycle(OperatorLifeCycle.ALL_ROUND) + .build(), + new TrainIterationBody( + logisticGradient, + getBatchSize(), + getLearningRate(), + getMaxIter(), + getEpsilon())); + + return resultList.get(0); + } + + /** The iteration implementation for training process. */ + private static class TrainIterationBody implements IterationBody { + + private final LogisticGradient logisticGradient; + + private final int batchSize; + + private final double learningRate; + + private final int maxIter; + + private final double epsilon; + + public TrainIterationBody( + LogisticGradient logisticGradient, + int batchSize, + double learningRate, + int maxIter, + double epsilon) { + this.logisticGradient = logisticGradient; + this.batchSize = batchSize; + this.learningRate = learningRate; + this.maxIter = maxIter; + this.epsilon = epsilon; + } + + @Override + public IterationBodyResult process( + DataStreamList variableStreams, DataStreamList dataStreams) { + DataStream<double[]> initModelOrGradientsAndWeightAndLoss = variableStreams.get(0); + DataStream<Tuple3<Double, Double, double[]>> trainData = dataStreams.get(0); + SingleOutputStreamOperator<double[]> gradientAndWeightAndLoss = + trainData + .connect(initModelOrGradientsAndWeightAndLoss) + .transform( + "updateModelAndComputeGradients", + TypeInformation.of(double[].class), + new CacheDataAndUpdateModelAndComputeGradient( + logisticGradient, + batchSize, + learningRate, + maxIter, + epsilon)); + + DataStreamList outputs = + IterationBody.forEachRound( + DataStreamList.of(gradientAndWeightAndLoss), + input -> { + DataStream<double[]> feedback = + AllReduceUtils.allReduce(input.get(0)); + return DataStreamList.of(feedback); + }); + DataStream<Integer> terminatation = Review comment: a small typo here. -- This is an automated message from the Apache Git Service. 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