yunfengzhou-hub commented on code in PR #172: URL: https://github.com/apache/flink-ml/pull/172#discussion_r1024682602
########## flink-ml-lib/src/main/java/org/apache/flink/ml/feature/robustscaler/RobustScalerModel.java: ########## @@ -0,0 +1,179 @@ +/* + * 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.robustscaler; + +import org.apache.flink.api.common.functions.RichMapFunction; +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.Vector; +import org.apache.flink.ml.linalg.typeinfo.VectorTypeInfo; +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.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.Arrays; +import java.util.Collections; +import java.util.HashMap; +import java.util.Map; + +/** A Model which transforms data using the model data computed by {@link RobustScaler}. */ +public class RobustScalerModel + implements Model<RobustScalerModel>, RobustScalerModelParams<RobustScalerModel> { + + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + private Table modelDataTable; + + public RobustScalerModel() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @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]); + + RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema()); + RowTypeInfo outputTypeInfo = + new RowTypeInfo( + ArrayUtils.addAll(inputTypeInfo.getFieldTypes(), VectorTypeInfo.INSTANCE), + ArrayUtils.addAll(inputTypeInfo.getFieldNames(), getOutputCol())); + final String broadcastModelKey = "broadcastModelKey"; + DataStream<RobustScalerModelData> modelDataStream = + RobustScalerModelData.getModelDataStream(modelDataTable); + + DataStream<Row> output = + BroadcastUtils.withBroadcastStream( + Collections.singletonList(inputStream), + Collections.singletonMap(broadcastModelKey, modelDataStream), + inputList -> { + DataStream inputData = inputList.get(0); + return inputData.map( + new PredictOutputFunction( + broadcastModelKey, + getInputCol(), + getWithCentering(), + getWithScaling()), + outputTypeInfo); + }); + + return new Table[] {tEnv.fromDataStream(output)}; + } + + /** This operator loads model data and predicts result. */ + private static class PredictOutputFunction extends RichMapFunction<Row, Row> { + private final String broadcastModelKey; + private final String inputCol; + private final boolean withCentering; + private final boolean withScaling; + + private DenseVector medians; + private DenseVector scales; + + public PredictOutputFunction( + String broadcastModelKey, + String inputCol, + boolean withCentering, + boolean withScaling) { + this.broadcastModelKey = broadcastModelKey; + this.inputCol = inputCol; + this.withCentering = withCentering; + this.withScaling = withScaling; + } + + @Override + public Row map(Row row) throws Exception { + if (medians == null) { + RobustScalerModelData modelData = + (RobustScalerModelData) + getRuntimeContext().getBroadcastVariable(broadcastModelKey).get(0); + medians = modelData.medians; + scales = + new DenseVector( + Arrays.stream(modelData.ranges.values) + .map(range -> range == 0 ? 0 : 1 / range) + .toArray()); + } + DenseVector outputVec = ((Vector) row.getField(inputCol)).clone().toDense(); + Preconditions.checkState( + medians.size() == outputVec.size(), + "Number of features must be %s but got %s.", + medians.size(), + outputVec.size()); + + if (withCentering) { + BLAS.axpy(-1, medians, outputVec); Review Comment: According to our offline discussion, I think that we should avoid allocating/deallocating memories in BLAS operations. Thus the current implementation is acceptable to me. -- 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
