yunfengzhou-hub commented on code in PR #82: URL: https://github.com/apache/flink-ml/pull/82#discussion_r846883248
########## flink-ml-lib/src/main/java/org/apache/flink/ml/feature/bucketizer/Bucketizer.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.bucketizer; + +import org.apache.flink.api.common.functions.FlatMapFunction; +import org.apache.flink.api.common.typeinfo.BasicTypeInfo; +import org.apache.flink.api.common.typeinfo.TypeInformation; +import org.apache.flink.api.java.typeutils.RowTypeInfo; +import org.apache.flink.ml.api.Transformer; +import org.apache.flink.ml.common.datastream.TableUtils; +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.Collector; +import org.apache.flink.util.Preconditions; + +import org.apache.commons.lang3.ArrayUtils; + +import java.io.IOException; +import java.util.Arrays; +import java.util.HashMap; +import java.util.Map; + +/** + * Bucketizer is a transformer that maps multiple columns of continuous features to multiple columns + * of discrete features, i.e., buckets IDs. + */ +public class Bucketizer implements Transformer<Bucketizer>, BucketizerParams<Bucketizer> { + private final Map<Param<?>, Object> paramMap = new HashMap<>(); + + public Bucketizer() { + ParamUtils.initializeMapWithDefaultValues(paramMap, this); + } + + @Override + public Table[] transform(Table... inputs) { + Preconditions.checkArgument(inputs.length == 1); + String[] inputCols = getInputCols(); + String[] outputCols = getOutputCols(); + Double[][] splitsArray = getSplitsArray(); + + Preconditions.checkArgument(inputCols.length == outputCols.length); + Preconditions.checkArgument(inputCols.length == splitsArray.length); + for (Double[] splits : splitsArray) { + Preconditions.checkArgument( + splits.length >= 3, + "Illegal value for " + + BucketizerParams.SPLITS_ARRAY + + ". See param " + + BucketizerParams.SPLITS_ARRAY + + " for details."); + for (int j = 1; j < splits.length; j++) { + Preconditions.checkArgument( + splits[j] > splits[j - 1], + "Illegal value for " + + BucketizerParams.SPLITS_ARRAY + + ". See param " + + BucketizerParams.SPLITS_ARRAY + + " for details."); + } + } + + StreamTableEnvironment tEnv = + (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment(); + + RowTypeInfo inputTypeInfo = TableUtils.getRowTypeInfo(inputs[0].getResolvedSchema()); + TypeInformation<?>[] outputTypes = new TypeInformation[outputCols.length]; + Arrays.fill(outputTypes, BasicTypeInfo.INT_TYPE_INFO); + RowTypeInfo outputTypeInfo = + new RowTypeInfo( + ArrayUtils.addAll(inputTypeInfo.getFieldTypes(), outputTypes), + ArrayUtils.addAll(inputTypeInfo.getFieldNames(), getOutputCols())); + + DataStream<Row> result = + tEnv.toDataStream(inputs[0]) + .flatMap( + new FindBucketFunction(inputCols, splitsArray, getHandleInvalid()), + outputTypeInfo); + return new Table[] {tEnv.fromDataStream(result)}; + } + + /** Finds the bucket index for each continuous feature of an input data point. */ + private static class FindBucketFunction implements FlatMapFunction<Row, Row> { + private final String[] inputCols; + private final String handleInvalid; + private final Double[][] splitsArray; + + public FindBucketFunction( + String[] inputCols, Double[][] splitsArray, String handleInvalid) { + this.inputCols = inputCols; + this.splitsArray = splitsArray; + this.handleInvalid = handleInvalid; + } + + @Override + public void flatMap(Row value, Collector<Row> out) { + Row outputRow = new Row(inputCols.length); + + for (int i = 0; i < inputCols.length; i++) { + double feature = ((Number) value.getField(inputCols[i])).doubleValue(); Review Comment: I mean if we set `handleInvalid` as `SKIP` or `KEEP`, the invalid non-number input would still cause the operator to behave as if `handleInvalid` is `ERROR`. Is this the expected behavior? -- 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]
