[GitHub] [flink-ml] weibozhao commented on a diff in pull request #86: [FLINK-27294] Add Transformer for BinaryClassificationEvaluator

[GitBox <gi...@apache.org>]

weibozhao commented on code in PR #86:
URL: https://github.com/apache/flink-ml/pull/86#discussion_r867591071


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flink-ml-lib/src/main/java/org/apache/flink/ml/evaluation/binaryeval/BinaryClassificationEvaluator.java:
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@@ -0,0 +1,783 @@
+/*
+ * 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.evaluation.binaryeval;
+
+import org.apache.flink.api.common.functions.MapFunction;
+import org.apache.flink.api.common.functions.MapPartitionFunction;
+import org.apache.flink.api.common.functions.RichFlatMapFunction;
+import org.apache.flink.api.common.functions.RichMapFunction;
+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.api.java.tuple.Tuple3;
+import org.apache.flink.api.java.tuple.Tuple4;
+import org.apache.flink.api.java.typeutils.RowTypeInfo;
+import org.apache.flink.api.scala.typeutils.Types;
+import org.apache.flink.iteration.operator.OperatorStateUtils;
+import org.apache.flink.ml.api.AlgoOperator;
+import org.apache.flink.ml.common.broadcast.BroadcastUtils;
+import org.apache.flink.ml.common.datastream.DataStreamUtils;
+import org.apache.flink.ml.linalg.DenseVector;
+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.api.operators.StreamMap;
+import org.apache.flink.streaming.api.watermark.Watermark;
+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.io.Serializable;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.Comparator;
+import java.util.HashMap;
+import java.util.Iterator;
+import java.util.LinkedList;
+import java.util.List;
+import java.util.Map;
+import java.util.Random;
+
+import static org.apache.flink.runtime.blob.BlobWriter.LOG;
+
+/**
+ * Calculates the evaluation metrics for binary classification. The input data has columns
+ * rawPrediction, label and an optional weight column. The rawPrediction can be of type double
+ * (binary 0/1 prediction, or probability of label 1) or of type vector (length-2 vector of raw
+ * predictions, scores, or label probabilities). The output may contain different metrics which will
+ * be defined by parameter MetricsNames. See @BinaryClassificationEvaluatorParams.
+ */
+public class BinaryClassificationEvaluator
+        implements AlgoOperator<BinaryClassificationEvaluator>,
+                BinaryClassificationEvaluatorParams<BinaryClassificationEvaluator> {
+    private final Map<Param<?>, Object> paramMap = new HashMap<>();
+    private static final int NUM_SAMPLE_FOR_RANGE_PARTITION = 100;
+
+    public BinaryClassificationEvaluator() {
+        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<Tuple3<Double, Boolean, Double>> evalData =
+                tEnv.toDataStream(inputs[0])
+                        .map(new ParseSample(getLabelCol(), getRawPredictionCol(), getWeightCol()));
+        final String boundaryRangeKey = "boundaryRange";
+        final String partitionSummariesKey = "partitionSummaries";
+
+        DataStream<Tuple4<Double, Boolean, Double, Integer>> evalDataWithTaskId =
+                BroadcastUtils.withBroadcastStream(
+                        Collections.singletonList(evalData),
+                        Collections.singletonMap(boundaryRangeKey, getBoundaryRange(evalData)),
+                        inputList -> {
+                            DataStream input = inputList.get(0);
+                            return input.map(new AppendTaskId(boundaryRangeKey));
+                        });
+
+        /* Repartition the evaluated data by range. */
+        evalDataWithTaskId =
+                evalDataWithTaskId.partitionCustom((chunkId, numPartitions) -> chunkId, x -> x.f3);
+
+        /* Sorts local data by score.*/
+        evalData =
+                DataStreamUtils.mapPartition(
+                        evalDataWithTaskId,
+                        new MapPartitionFunction<
+                                Tuple4<Double, Boolean, Double, Integer>,
+                                Tuple3<Double, Boolean, Double>>() {
+                            @Override
+                            public void mapPartition(
+                                    Iterable<Tuple4<Double, Boolean, Double, Integer>> values,
+                                    Collector<Tuple3<Double, Boolean, Double>> out) {
+                                List<Tuple3<Double, Boolean, Double>> bufferedData =
+                                        new LinkedList<>();
+                                for (Tuple4<Double, Boolean, Double, Integer> t4 : values) {
+                                    bufferedData.add(Tuple3.of(t4.f0, t4.f1, t4.f2));

Review Comment:
   I think there is no way to avoid sorting action.



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