weibozhao commented on code in PR #83: URL: https://github.com/apache/flink-ml/pull/83#discussion_r887422996
########## flink-ml-lib/src/main/java/org/apache/flink/ml/classification/logisticregression/OnlineLogisticRegression.java: ########## @@ -0,0 +1,434 @@ +/* + * 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.FilterFunction; +import org.apache.flink.api.common.functions.MapFunction; +import org.apache.flink.api.common.functions.ReduceFunction; +import org.apache.flink.api.common.state.ListState; +import org.apache.flink.api.common.state.ListStateDescriptor; +import org.apache.flink.api.common.typeinfo.TypeInformation; +import org.apache.flink.api.java.typeutils.ObjectArrayTypeInfo; +import org.apache.flink.iteration.DataStreamList; +import org.apache.flink.iteration.IterationBody; +import org.apache.flink.iteration.IterationBodyResult; +import org.apache.flink.iteration.Iterations; +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.linalg.SparseVector; +import org.apache.flink.ml.linalg.Vector; +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.streaming.api.datastream.DataStream; +import org.apache.flink.streaming.api.operators.AbstractStreamOperator; +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.types.Row; +import org.apache.flink.util.Preconditions; + +import org.apache.commons.collections.IteratorUtils; + +import java.io.IOException; +import java.util.Arrays; +import java.util.HashMap; +import java.util.List; +import java.util.Map; + +/** + * An Estimator which implements the FTRL-Proximal online learning algorithm proposed by H. Brendan + * McMahan et al. + * + * <p>See <a href="https://doi.org/10.1145/2487575.2488200";>H. Brendan McMahan et al., Ad click + * prediction: a view from the trenches.</a> + */ +public class OnlineLogisticRegression + implements Estimator<OnlineLogisticRegression, OnlineLogisticRegressionModel>, + OnlineLogisticRegressionParams<OnlineLogisticRegression> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + private Table initModelDataTable; + + public OnlineLogisticRegression() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + @SuppressWarnings("unchecked") + public OnlineLogisticRegressionModel fit(Table... inputs) { + Preconditions.checkArgument(inputs.length == 1); + + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment(); + DataStream<LogisticRegressionModelData> modelDataStream = + LogisticRegressionModelData.getModelDataStream(initModelDataTable); + + DataStream<Row> points = + tEnv.toDataStream(inputs[0]) + .map(new FeaturesExtractor(getFeaturesCol(), getLabelCol())); + + DataStream<DenseVector> initModelData = + modelDataStream.map( + (MapFunction<LogisticRegressionModelData, DenseVector>) + value -> value.coefficient); Review Comment: 1. The init model is a offline model. Adding a version to the offline model is just to keep the offline and online model have the same format and they can share the same modelData processing function. In fact, version has no meaning in offline model. 2. The online model version is bigger than the init model version has no meaning. If an online algorithm is running and producing models one by one, we just want to keep the i+1 model's version is bigger then the i model's version and the init model is a special one. -- 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
