weibozhao commented on code in PR #142: URL: https://github.com/apache/flink-ml/pull/142#discussion_r945550050
########## flink-ml-lib/src/main/java/org/apache/flink/ml/feature/maxabsscaler/MaxAbsScaler.java: ########## @@ -0,0 +1,201 @@ +/* + * 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.maxabsscaler; + +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.api.common.typeinfo.TypeInformation; +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.runtime.state.StateSnapshotContext; +import org.apache.flink.streaming.api.datastream.DataStream; +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.Map; + +/** + * An Estimator which implements the MaxAbsScaler algorithm. This algorithm rescales feature values + * to the range [-1, 1] by dividing through the largest maximum absolute value in each feature. It + * does not shift/center the data and thus does not destroy any sparsity. + */ +public class MaxAbsScaler + implements Estimator<MaxAbsScaler, MaxAbsScalerModel>, MaxAbsScalerParams<MaxAbsScaler> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + + public MaxAbsScaler() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public MaxAbsScalerModel fit(Table... inputs) { + Preconditions.checkArgument(inputs.length == 1); + final String inputCol = getInputCol(); + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment(); + DataStream<Vector> inputData = + tEnv.toDataStream(inputs[0]) + .map( + (MapFunction<Row, Vector>) + value -> ((Vector) value.getField(inputCol))); + DataStream<Vector> maxAbsValues = + inputData + .transform( + "reduceInEachPartition", + inputData.getType(), + new MaxAbsReduceFunctionOperator()) + .transform( + "reduceInFinalPartition", + inputData.getType(), + new MaxAbsReduceFunctionOperator()) + .setParallelism(1); Review Comment: I think `DataStreamUtils.reduce` is not suit here. When the input table only has one vector and this vector has some negative values, the output data will not be correct. -- 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]
