jiangxin369 commented on code in PR #158: URL: https://github.com/apache/flink-ml/pull/158#discussion_r980656357
########## flink-ml-lib/src/main/java/org/apache/flink/ml/feature/variancethresholdselector/VarianceThresholdSelectorModel.java: ########## @@ -0,0 +1,172 @@ +/* + * 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.variancethresholdselector; + +import org.apache.flink.api.common.functions.RichMapFunction; +import org.apache.flink.api.common.typeinfo.TypeInformation; +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.DenseVector; +import org.apache.flink.ml.linalg.Vector; +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.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.Collections; +import java.util.HashMap; +import java.util.Map; + +/** + * A Model which removes low-variance data using the model data computed by {@link + * VarianceThresholdSelector}. + */ +public class VarianceThresholdSelectorModel + implements Model<VarianceThresholdSelectorModel>, + VarianceThresholdSelectorParams<VarianceThresholdSelectorModel> { + + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + private Table modelDataTable; + + public VarianceThresholdSelectorModel() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public VarianceThresholdSelectorModel setModelData(Table... inputs) { + Preconditions.checkArgument(inputs.length == 1); + modelDataTable = inputs[0]; + return this; + } + + @Override + public Table[] getModelData() { + return new Table[] {modelDataTable}; + } + + @Override + public Map<Param<?>, Object> getParamMap() { + return paramMap; + } + + @Override + public void save(String path) throws IOException { + ReadWriteUtils.saveMetadata(this, path); + ReadWriteUtils.saveModelData( + VarianceThresholdSelectorModelData.getModelDataStream(modelDataTable), + path, + new VarianceThresholdSelectorModelData.ModelDataEncoder()); + } + + public static VarianceThresholdSelectorModel load(StreamTableEnvironment tEnv, String path) + throws IOException { + VarianceThresholdSelectorModel model = ReadWriteUtils.loadStageParam(path); + Table modelDataTable = + ReadWriteUtils.loadModelData( + tEnv, path, new VarianceThresholdSelectorModelData.ModelDataDecoder()); + return model.setModelData(modelDataTable); + } + + @Override + public Table[] transform(Table... inputs) { + Preconditions.checkArgument(inputs.length == 1); + + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment(); + DataStream<Row> data = tEnv.toDataStream(inputs[0]); + DataStream<VarianceThresholdSelectorModelData> varianceThresholdSelectorModel = + VarianceThresholdSelectorModelData.getModelDataStream(modelDataTable); + + final String broadcastModelKey = "broadcastModelKey"; + RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema()); + RowTypeInfo outputTypeInfo = + new RowTypeInfo( + ArrayUtils.addAll( + inputTypeInfo.getFieldTypes(), + TypeInformation.of(DenseVector.class)), + ArrayUtils.addAll(inputTypeInfo.getFieldNames(), getOutputCol())); + + DataStream<Row> output = + BroadcastUtils.withBroadcastStream( + Collections.singletonList(data), + Collections.singletonMap(broadcastModelKey, varianceThresholdSelectorModel), + inputList -> { + DataStream input = inputList.get(0); + return input.map( + new PredictOutputFunction(getFeaturesCol(), broadcastModelKey), + 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 featureCol; + private final String broadcastKey; + private int expectedNumOfFeatures; + private int[] indices = null; + + public PredictOutputFunction(String featureCol, String broadcastKey) { + this.featureCol = featureCol; + this.broadcastKey = broadcastKey; + } + + @Override + public Row map(Row row) { + if (indices == null) { + VarianceThresholdSelectorModelData varianceThresholdSelectorModelData = + (VarianceThresholdSelectorModelData) + getRuntimeContext().getBroadcastVariable(broadcastKey).get(0); + expectedNumOfFeatures = varianceThresholdSelectorModelData.numOfFeatures; + indices = varianceThresholdSelectorModelData.indices; + } + + if (indices.length == 0) { + return Row.join(row, Row.of(Vectors.dense())); + } else { + DenseVector inputVec = ((Vector) row.getField(featureCol)).toDense(); + Preconditions.checkArgument( Review Comment: Good catch, I'll move it. -- This is an automated message from the Apache Git Service. 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