zhipeng93 commented on code in PR #237: URL: https://github.com/apache/flink-ml/pull/237#discussion_r1222525949
########## flink-ml-lib/src/main/java/org/apache/flink/ml/classification/logisticregression/LogisticRegressionWithFtrl.java: ########## @@ -0,0 +1,380 @@ +/* + * 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.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.tuple.Tuple2; +import org.apache.flink.api.java.tuple.Tuple3; +import org.apache.flink.ml.api.Estimator; +import org.apache.flink.ml.common.datastream.DataStreamUtils; +import org.apache.flink.ml.common.feature.LabeledLargePointWithWeight; +import org.apache.flink.ml.common.lossfunc.BinaryLogisticLoss; +import org.apache.flink.ml.common.lossfunc.LossFunc; +import org.apache.flink.ml.common.ps.training.IterationStageList; +import org.apache.flink.ml.common.ps.training.ProcessStage; +import org.apache.flink.ml.common.ps.training.PullStage; +import org.apache.flink.ml.common.ps.training.PushStage; +import org.apache.flink.ml.common.ps.training.SerializableConsumer; +import org.apache.flink.ml.common.ps.training.TrainingContext; +import org.apache.flink.ml.common.ps.training.TrainingUtils; +import org.apache.flink.ml.common.updater.FTRL; +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.runtime.state.StateInitializationContext; +import org.apache.flink.runtime.state.StateSnapshotContext; +import org.apache.flink.runtime.util.ResettableIterator; +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 org.apache.flink.types.Row; +import org.apache.flink.util.Preconditions; +import org.apache.flink.util.function.SerializableFunction; +import org.apache.flink.util.function.SerializableSupplier; + +import it.unimi.dsi.fastutil.longs.Long2DoubleOpenHashMap; +import it.unimi.dsi.fastutil.longs.LongOpenHashSet; +import org.apache.commons.collections.IteratorUtils; + +import java.io.IOException; +import java.util.ArrayList; +import java.util.Arrays; +import java.util.HashMap; +import java.util.Iterator; +import java.util.List; +import java.util.Map; + +/** + * An Estimator which implements the large scale logistic regression algorithm using FTRL optimizer. + * + * <p>See https://en.wikipedia.org/wiki/Logistic_regression. + */ +public class LogisticRegressionWithFtrl Review Comment: We have aggreed to go with option-2 since it is more intuitive and simpler for users. -- 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
