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Posted to issues@flink.apache.org by GitBox <gi...@apache.org> on 2022/11/25 06:01:21 UTC
[GitHub] [flink-ml] yunfengzhou-hub commented on a diff in pull request #182: [FLINK-30160] Add Transformer for FValueTest
yunfengzhou-hub commented on code in PR #182:
URL: https://github.com/apache/flink-ml/pull/182#discussion_r1031974363
##########
flink-ml-lib/src/main/java/org/apache/flink/ml/stats/fvaluetest/FValueTest.java:
##########
@@ -0,0 +1,370 @@
+/*
Review Comment:
Let's add this class to the exception list in `test_ml_lib_completeness`.
##########
flink-ml-lib/src/test/java/org/apache/flink/ml/stats/FValueTestTest.java:
##########
@@ -0,0 +1,429 @@
+/*
+ * 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;
+
+import org.apache.flink.api.common.restartstrategy.RestartStrategies;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.ml.linalg.Vector;
+import org.apache.flink.ml.linalg.Vectors;
+import org.apache.flink.ml.stats.fvaluetest.FValueTest;
+import org.apache.flink.ml.util.TestUtils;
+import org.apache.flink.streaming.api.datastream.DataStream;
+import org.apache.flink.streaming.api.environment.ExecutionCheckpointingOptions;
+import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
+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.test.util.AbstractTestBase;
+import org.apache.flink.types.Row;
+
+import org.apache.commons.collections.IteratorUtils;
+import org.junit.Before;
+import org.junit.Rule;
+import org.junit.Test;
+import org.junit.rules.TemporaryFolder;
+
+import java.util.Arrays;
+import java.util.Comparator;
+import java.util.List;
+
+import static org.junit.Assert.assertArrayEquals;
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertFalse;
+import static org.junit.Assert.assertTrue;
+
+/** Tests the {@link FValueTest}. */
+public class FValueTestTest extends AbstractTestBase {
+ @Rule public final TemporaryFolder tempFolder = new TemporaryFolder();
+ private StreamTableEnvironment tEnv;
+ private Table denseInputTable;
+ private Table sparseInputTable;
+
+ private static final double EPS = 1.0e-5;
+ private static final List<Row> DENSE_INPUT_DATA =
+ Arrays.asList(
+ Row.of(
+ 0.19775997,
+ Vectors.dense(
+ 0.15266373,
+ 0.30235661,
+ 0.06203641,
+ 0.45986034,
+ 0.83525338,
+ 0.92699705)),
+ Row.of(
+ 0.66009772,
+ Vectors.dense(
+ 0.72698898,
+ 0.76849622,
+ 0.26920507,
+ 0.64402929,
+ 0.09337326,
+ 0.07968589)),
+ Row.of(
+ 0.80865842,
+ Vectors.dense(
+ 0.58961375,
+ 0.34334054,
+ 0.98887615,
+ 0.62647321,
+ 0.68177928,
+ 0.55225681)),
+ Row.of(
+ 0.34142582,
+ Vectors.dense(
+ 0.26886006,
+ 0.37325939,
+ 0.2229281,
+ 0.1864426,
+ 0.39064809,
+ 0.19316241)),
+ Row.of(
+ 0.84756607,
+ Vectors.dense(
+ 0.61091093,
+ 0.88280845,
+ 0.62233882,
+ 0.25311894,
+ 0.17993031,
+ 0.81640447)),
+ Row.of(
+ 0.53360225,
+ Vectors.dense(
+ 0.22537162,
+ 0.51685714,
+ 0.51849582,
+ 0.60037494,
+ 0.53262048,
+ 0.01331005)),
+ Row.of(
+ 0.90053371,
+ Vectors.dense(
+ 0.52409726,
+ 0.89588471,
+ 0.76990129,
+ 0.1228517,
+ 0.29587269,
+ 0.61202358)),
+ Row.of(
+ 0.78779561,
+ Vectors.dense(
+ 0.72613812,
+ 0.46349747,
+ 0.76911037,
+ 0.19163103,
+ 0.55786672,
+ 0.55077816)),
+ Row.of(
+ 0.51604647,
+ Vectors.dense(
+ 0.47222549,
+ 0.79188496,
+ 0.11524968,
+ 0.6813039,
+ 0.36233361,
+ 0.34420889)),
+ Row.of(
+ 0.35325637,
+ Vectors.dense(
+ 0.44951875,
+ 0.02694226,
+ 0.41524769,
+ 0.9222317,
+ 0.09120557,
+ 0.31512178)),
+ Row.of(
+ 0.51408926,
+ Vectors.dense(
+ 0.52802224,
+ 0.32806203,
+ 0.44891554,
+ 0.01633442,
+ 0.0970269,
+ 0.69258857)),
+ Row.of(
+ 0.84489897,
+ Vectors.dense(
+ 0.83594341,
+ 0.42432199,
+ 0.8487743,
+ 0.54679121,
+ 0.35410346,
+ 0.72724968)),
+ Row.of(
+ 0.55342816,
+ Vectors.dense(
+ 0.09385168,
+ 0.8928588,
+ 0.33625828,
+ 0.89183268,
+ 0.296849,
+ 0.30164829)),
+ Row.of(
+ 0.89405683,
+ Vectors.dense(
+ 0.80624061,
+ 0.83760997,
+ 0.63428133,
+ 0.3113273,
+ 0.02944858,
+ 0.39977732)),
+ Row.of(
+ 0.54588131,
+ Vectors.dense(
+ 0.51817346,
+ 0.00738845,
+ 0.77494778,
+ 0.8544712,
+ 0.13153282,
+ 0.28767364)),
+ Row.of(
+ 0.96038024,
+ Vectors.dense(
+ 0.32658881,
+ 0.90655956,
+ 0.99955954,
+ 0.77088429,
+ 0.04284752,
+ 0.96525111)),
+ Row.of(
+ 0.71349698,
+ Vectors.dense(
+ 0.97521246,
+ 0.2025168,
+ 0.67985305,
+ 0.46534506,
+ 0.92001748,
+ 0.72820735)),
+ Row.of(
+ 0.43456735,
+ Vectors.dense(
+ 0.24585653,
+ 0.01953996,
+ 0.70598881,
+ 0.77448287,
+ 0.4729746,
+ 0.80146736)),
+ Row.of(
+ 0.52462506,
+ Vectors.dense(
+ 0.17539792,
+ 0.72016934,
+ 0.3678759,
+ 0.53209295,
+ 0.29719397,
+ 0.37429151)),
+ Row.of(
+ 0.43074793,
+ Vectors.dense(
+ 0.72810013,
+ 0.39850784,
+ 0.1058295,
+ 0.39858265,
+ 0.52196395,
+ 0.1060125)));
+
+ private static final List<Row> SPARSE_INPUT_DATA =
+ Arrays.asList(
+ Row.of(4.6, Vectors.dense(6.0, 7.0, 0.0, 7.0, 6.0, 0.0, 0.0).toSparse()),
+ Row.of(6.6, Vectors.dense(0.0, 9.0, 6.0, 0.0, 5.0, 9.0, 0.0).toSparse()),
+ Row.of(5.1, Vectors.dense(0.0, 9.0, 3.0, 0.0, 5.0, 5.0, 0.0).toSparse()),
+ Row.of(7.6, Vectors.dense(0.0, 9.0, 8.0, 5.0, 6.0, 4.0, 0.0).toSparse()),
+ Row.of(9.0, Vectors.dense(8.0, 9.0, 6.0, 5.0, 4.0, 4.0, 0.0).toSparse()),
+ Row.of(
+ 9.0,
+ Vectors.dense(Double.NaN, 9.0, 6.0, 4.0, 0.0, 0.0, 0.0).toSparse()));
+
+ private static final Row EXPECTED_OUTPUT =
+ Row.of(
+ Vectors.dense(
+ 1.73658700e-02,
+ 1.49916659e-02,
+ 1.12697153e-04,
+ 4.26990301e-01,
+ 2.75911201e-01,
+ 1.93549275e-01),
+ new long[] {18, 18, 18, 18, 18, 18},
+ Vectors.dense(
+ 6.86260598,
+ 7.23175589,
+ 24.11424725,
+ 0.6605354,
+ 1.26266286,
+ 1.82421406));
+
+ private static final List<Row> EXPECTED_FLATTENED_OUTPUT =
+ Arrays.asList(
+ Row.of(0, 1.73658700e-02, 18, 6.86260598),
+ Row.of(1, 1.49916659e-02, 18, 7.23175589),
+ Row.of(2, 1.12697153e-04, 18, 24.11424725),
+ Row.of(3, 4.26990301e-01, 18, 0.6605354),
+ Row.of(4, 2.75911201e-01, 18, 1.26266286),
+ Row.of(5, 1.93549275e-01, 18, 1.82421406));
+
+ private static final Row EXPECTED_OUTPUT_SPARSE =
+ Row.of(
+ Vectors.dense(
+ Double.NaN,
+ 0.19167161,
+ 0.06506426,
+ 0.75183662,
+ 0.16111045,
+ 0.89090362,
+ Double.NaN),
+ new long[] {4, 4, 4, 4, 4, 4, 4},
+ Vectors.dense(
+ Double.NaN,
+ 2.46254817,
+ 6.37164347,
+ 0.1147488,
+ 2.94816821,
+ 0.02134755,
+ Double.NaN));
+
+ private static final List<Row> EXPECTED_FLATTENED_OUTPUT_SPARSE =
+ Arrays.asList(
+ Row.of(0, Double.NaN, 4, Double.NaN),
+ Row.of(1, 0.19167161, 4, 2.46254817),
+ Row.of(2, 0.06506426, 4, 6.37164347),
+ Row.of(3, 0.75183662, 4, 0.1147488),
+ Row.of(4, 0.16111045, 4, 2.94816821),
+ Row.of(5, 0.89090362, 4, 0.02134755),
+ Row.of(6, Double.NaN, 4, Double.NaN));
+
+ @Before
+ public void before() {
+ Configuration config = new Configuration();
+ config.set(ExecutionCheckpointingOptions.ENABLE_CHECKPOINTS_AFTER_TASKS_FINISH, true);
+ StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(config);
+ env.setParallelism(4);
+ env.enableCheckpointing(100);
+ env.setRestartStrategy(RestartStrategies.noRestart());
+ env.getConfig().enableObjectReuse();
+ tEnv = StreamTableEnvironment.create(env);
+ denseInputTable =
+ tEnv.fromDataStream(env.fromCollection(DENSE_INPUT_DATA)).as("label", "features");
+ sparseInputTable =
+ tEnv.fromDataStream(env.fromCollection(SPARSE_INPUT_DATA)).as("label", "features");
+ }
+
+ private static void verifyFlattenTransformationResult(Table output, List<Row> expected)
+ throws Exception {
+ StreamTableEnvironment tEnv =
+ (StreamTableEnvironment) ((TableImpl) output).getTableEnvironment();
+ DataStream<Row> outputDataStream = tEnv.toDataStream(output);
+ List<Row> results = IteratorUtils.toList(outputDataStream.executeAndCollect());
+ assertEquals(expected.size(), results.size());
+
+ results.sort(Comparator.comparing(r -> String.valueOf(r.getField(0))));
+ expected.sort(Comparator.comparing(r -> String.valueOf(r.getField(0))));
+
+ for (int i = 0; i < expected.size(); i++) {
+ assertEquals(expected.get(i).getArity(), results.get(i).getArity());
+ for (int j = 0; j < expected.get(i).getArity(); j++) {
+ assertEquals(
+ Double.valueOf(expected.get(i).getField(j).toString()),
+ Double.valueOf(results.get(i).getField(j).toString()),
+ EPS);
+ }
+ }
+ }
+
+ private static void verifyTransformationResult(Table output, Row expected) throws Exception {
+ StreamTableEnvironment tEnv =
+ (StreamTableEnvironment) ((TableImpl) output).getTableEnvironment();
+ DataStream<Row> outputDataStream = tEnv.toDataStream(output);
+ List<Row> results = IteratorUtils.toList(outputDataStream.executeAndCollect());
+ assertEquals(1, results.size());
+
+ Row result = results.get(0);
+ assertEquals(3, result.getArity());
+ compareVector((Vector) expected.getField(0), (Vector) result.getField(0));
+ assertArrayEquals((long[]) expected.getField(1), (long[]) result.getField(1));
+ compareVector((Vector) expected.getField(2), (Vector) result.getField(2));
+ }
+
+ private static void compareVector(Vector first, Vector second) {
Review Comment:
Let's update the implementations of `FValueTest` as those comments in `ANOVATest`.
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