yunfengzhou-hub commented on code in PR #172: URL: https://github.com/apache/flink-ml/pull/172#discussion_r1023411005
########## docs/content/docs/operators/feature/robustscaler.md: ########## @@ -0,0 +1,208 @@ +--- +title: "Robust Scaler" +weight: 1 +type: docs +aliases: +- /operators/feature/robustscaler.html +--- + +<!-- +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. +--> + +## Robust Scaler + +An Estimator which scales features using statistics that are robust to outliers. Review Comment: The description here should stand for the algorithm as a whole, including both `RobustScaler` and `RobustScalerModel`. Thus it might be improper to say "Estimator" here. ########## 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: Would it be better if we add the method `void axpy(double a, Vector x, Vector y)` and do `Vector`-to-`DenseVector` conversions in this method if it is the best practice? -- This is an automated message from the Apache Git Service. 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