[GitHub] [flink-ml] taosiyuan163 commented on a diff in pull request #132: [FLINK-28571] Add AlgoOperator for Chi-squared test

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

taosiyuan163 commented on code in PR #132:
URL: https://github.com/apache/flink-ml/pull/132#discussion_r930018268


##########
flink-ml-lib/src/main/java/org/apache/flink/ml/stats/chisqtest/ChiSqTest.java:
##########
@@ -0,0 +1,492 @@
+/*
+ * 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.stats.chisqtest;
+
+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.typeinfo.TypeInformation;
+import org.apache.flink.api.common.typeinfo.Types;
+import org.apache.flink.api.java.tuple.Tuple2;
+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.ml.api.AlgoOperator;
+import org.apache.flink.ml.common.broadcast.BroadcastUtils;
+import org.apache.flink.ml.common.datastream.DataStreamUtils;
+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.streaming.api.datastream.SingleOutputStreamOperator;
+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.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 org.apache.commons.math3.distribution.ChiSquaredDistribution;
+
+import java.io.IOException;
+import java.math.BigDecimal;
+import java.math.RoundingMode;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.HashMap;
+import java.util.HashSet;
+import java.util.List;
+import java.util.Map;
+import java.util.Optional;
+import java.util.function.Function;
+import java.util.stream.Collectors;
+
+/**
+ * Chi-square test of independence of variables in a contingency table. This Transformer computes
+ * the chi-square statistic,p-value,and dof(number of degrees of freedom) for every feature in the
+ * contingency table, which constructed from the `observed` for each categorical values. All label
+ * and feature values must be categorical.
+ *
+ * <p>See: http://en.wikipedia.org/wiki/Chi-squared_test.
+ */
+public class ChiSqTest implements AlgoOperator<ChiSqTest>, ChiSqTestParams<ChiSqTest> {
+
+    final String bcCategoricalMarginsKey = "bcCategoricalMarginsKey";
+    final String bcLabelMarginsKey = "bcLabelMarginsKey";
+
+    private final Map<Param<?>, Object> paramMap = new HashMap<>();
+
+    public ChiSqTest() {
+        ParamUtils.initializeMapWithDefaultValues(paramMap, this);
+    }
+
+    @Override
+    public Table[] transform(Table... inputs) {
+
+        final String[] inputCols = getInputCols();
+        String labelCol = getLabelCol();
+
+        StreamTableEnvironment tEnv =
+                (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment();
+
+        SingleOutputStreamOperator<Tuple3<String, Object, Object>> colAndFeatureAndLabel =
+                tEnv.toDataStream(inputs[0])
+                        .flatMap(new ExtractColAndFeatureAndLabel(inputCols, labelCol));
+
+        // compute the observed frequencies
+        DataStream<Tuple4<String, Object, Object, Long>> observedFreq =
+                DataStreamUtils.mapPartition(
+                        colAndFeatureAndLabel.keyBy(Tuple3::hashCode),
+                        new GenerateObservedFrequencies());
+
+        SingleOutputStreamOperator<Tuple4<String, Object, Object, Long>> filledObservedFreq =

Review Comment:
   > How about we just compute the `distinct labels` and postpone the `fill` operation to Line#199, e.g., `DataStream<Tuple3<String, Double, Integer>> categoricalStatistics =...`?
   > 
   > Using `parallellism=1` for computing all data is not efficient usually.
   
   Hi @zhipeng93 , thanks for the comments.If we need to adjust this method block,colud consider the following?
   
   1. Typically, developers use discretized data as input,therefore,`GenerateObservedFrequencies `will reduce large amounts of input data before calling `FillZeroFunc`.
   
   2. `FilledObservedFreq `represents the contingency table ,maybe it can be output as a result in the future? Refer to pandas `crosstab `in Python.



-- 
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-unsubscribe@flink.apache.org

For queries about this service, please contact Infrastructure at:
users@infra.apache.org