zhipeng93 commented on a change in pull request #28: URL: https://github.com/apache/flink-ml/pull/28#discussion_r771192152
########## File path: flink-ml-lib/src/main/java/org/apache/flink/ml/classification/logisticregression/LogisticRegressionModel.java ########## @@ -0,0 +1,173 @@ +/* + * 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.logisticregression; + +import org.apache.flink.api.common.functions.RichMapFunction; +import org.apache.flink.api.common.typeinfo.BasicTypeInfo; +import org.apache.flink.api.common.typeinfo.TypeInformation; +import org.apache.flink.api.java.tuple.Tuple2; +import org.apache.flink.api.java.typeutils.RowTypeInfo; +import org.apache.flink.ml.api.Model; +import org.apache.flink.ml.common.broadcast.BroadcastUtils; +import org.apache.flink.ml.common.datastream.TableUtils; +import org.apache.flink.ml.linalg.BLAS; +import org.apache.flink.ml.linalg.DenseVector; +import org.apache.flink.ml.linalg.Vectors; +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.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 classifies data using the model data computed by {@link LogisticRegression}. */ +public class LogisticRegressionModel + implements Model<LogisticRegressionModel>, + LogisticRegressionModelParams<LogisticRegressionModel> { + + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + + private Table modelDataTable; + + public LogisticRegressionModel() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public Map<Param<?>, Object> getParamMap() { + return paramMap; + } + + @Override + public void save(String path) throws IOException { + ReadWriteUtils.saveMetadata(this, path); + ReadWriteUtils.saveModelData( + LogisticRegressionModelData.getModelDataStream(modelDataTable), + path, + new LogisticRegressionModelData.ModelDataEncoder()); + } + + public static LogisticRegressionModel load(StreamExecutionEnvironment env, String path) + throws IOException { + StreamTableEnvironment tEnv = StreamTableEnvironment.create(env); + LogisticRegressionModel model = ReadWriteUtils.loadStageParam(path); + DataStream<LogisticRegressionModelData> modelData = + ReadWriteUtils.loadModelData( + env, path, new LogisticRegressionModelData.ModelDataDecoder()); + return model.setModelData(tEnv.fromDataStream(modelData)); + } + + @Override + public LogisticRegressionModel 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> inputStream = tEnv.toDataStream(inputs[0]); + final String broadcastModelKey = "broadcastModelKey"; + DataStream<LogisticRegressionModelData> modelDataStream = + LogisticRegressionModelData.getModelDataStream(modelDataTable); + RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema()); + RowTypeInfo outputTypeInfo = + new RowTypeInfo( + ArrayUtils.addAll( + inputTypeInfo.getFieldTypes(), + BasicTypeInfo.DOUBLE_TYPE_INFO, + TypeInformation.of(DenseVector.class)), + ArrayUtils.addAll( + inputTypeInfo.getFieldNames(), + getPredictionCol(), + getRawPredictionCol())); + DataStream<Row> predictionResult = + BroadcastUtils.withBroadcastStream( + Collections.singletonList(inputStream), + Collections.singletonMap(broadcastModelKey, modelDataStream), + inputList -> { + DataStream inputData = inputList.get(0); + return inputData.map( + new PredictLabelFunction(broadcastModelKey, getFeaturesCol()), + outputTypeInfo); + }); + return new Table[] {tEnv.fromDataStream(predictionResult)}; + } + + /** A utility function used for prediction. */ + private static class PredictLabelFunction extends RichMapFunction<Row, Row> { + + private final String broadcastModelKey; + + private final String featuresCol; + + private DenseVector coefficient; + + public PredictLabelFunction(String broadcastModelKey, String featuresCol) { + this.broadcastModelKey = broadcastModelKey; + this.featuresCol = featuresCol; + } + + @Override + public Row map(Row dataPoint) { + if (coefficient == null) { + LogisticRegressionModelData modelData = + (LogisticRegressionModelData) + getRuntimeContext().getBroadcastVariable(broadcastModelKey).get(0); + coefficient = modelData.coefficient; + } + DenseVector features = (DenseVector) dataPoint.getField(featuresCol); + Tuple2<Double, DenseVector> predictionResult = predictRaw(features, coefficient); + return Row.join(dataPoint, Row.of(predictionResult.f0, predictionResult.f1)); + } + } + + /** + * The main logic that predicts one input record. + * + * @param feature The input feature. + * @param coefficient The model parameters. + * @return The prediction label and the raw probabilities. + */ + private static Tuple2<Double, DenseVector> predictRaw( + DenseVector feature, DenseVector coefficient) { Review comment: It may not work with multi-classes case. In that case, we probably need to refactor this method since the the `coefficient` here is supposed to be a `matrix`. We could do this later. -- 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
