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Posted to issues@flink.apache.org by GitBox <gi...@apache.org> on 2022/11/24 07:01:06 UTC
[GitHub] [flink-ml] yunfengzhou-hub commented on a diff in pull request #180: [FLINK-30159] Add Transformer for ANOVATest
yunfengzhou-hub commented on code in PR #180:
URL: https://github.com/apache/flink-ml/pull/180#discussion_r1031101109
##########
flink-ml-python/pyflink/ml/lib/tests/test_ml_lib_completeness.py:
##########
@@ -140,6 +145,11 @@ def module(self):
from pyflink.ml.lib import stats
return stats
+ def exclude_java_stage(self) -> List[str]:
+ return [
+ "org.apache.flink.ml.stats.anovatest.ANOVATest",
Review Comment:
Is it possible to simplify it to `"anovatest.ANOVATest"` or `"ANOVATest"`? The test class has enough information to derive the root java package name.
##########
flink-ml-python/pyflink/ml/lib/tests/test_ml_lib_completeness.py:
##########
@@ -81,6 +83,9 @@ def module_name(self):
def module(self):
pass
+ def exclude_java_stage(self):
Review Comment:
Let's add a comment here, describing why we need this method and which java stages should be excluded.
##########
flink-ml-lib/src/main/java/org/apache/flink/ml/stats/anovatest/ANOVATest.java:
##########
@@ -0,0 +1,276 @@
+/*
+ * 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.anovatest;
+
+import org.apache.flink.api.common.functions.AggregateFunction;
+import org.apache.flink.api.common.functions.FlatMapFunction;
+import org.apache.flink.api.common.functions.MapFunction;
+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.ml.api.AlgoOperator;
+import org.apache.flink.ml.common.datastream.DataStreamUtils;
+import org.apache.flink.ml.common.param.HasFlatten;
+import org.apache.flink.ml.linalg.DenseVector;
+import org.apache.flink.ml.linalg.Vector;
+import org.apache.flink.ml.linalg.typeinfo.VectorTypeInfo;
+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.math3.distribution.FDistribution;
+
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.stream.IntStream;
+
+/**
+ * An AlgoOperator which implements the ANOVA test algorithm.
+ *
+ * <p>See <a href="https://en.wikipedia.org/wiki/Analysis_of_variance">Wikipedia</a> for more
+ * information on ANOVA test.
+ *
+ * <p>The input of this algorithm is a table containing a labelColumn of numerical type and a
+ * featuresColumn of vector type. Each index in the input vector represents a feature to be tested.
+ * By default, the output of this algorithm is a table containing a single row with the following
+ * columns, each of which has one value per feature.
+ *
+ * <ul>
+ * <li>"pValues": vector
+ * <li>"degreesOfFreedom": int array
+ * <li>"fValues": vector
+ * </ul>
+ *
+ * <p>The output of this algorithm can be flattened to multiple rows by setting {@link
+ * HasFlatten#FLATTEN} to true, which would contain the following columns:
+ *
+ * <ul>
+ * <li>"featureIndex": int
+ * <li>"pValue": double
+ * <li>"degreeOfFreedom": int
+ * <li>"fValues": double
+ * </ul>
+ */
+public class ANOVATest implements AlgoOperator<ANOVATest>, ANOVATestParams<ANOVATest> {
+
+ private final Map<Param<?>, Object> paramMap = new HashMap<>();
+
+ public ANOVATest() {
+ ParamUtils.initializeMapWithDefaultValues(paramMap, this);
+ }
+
+ @Override
+ public Table[] transform(Table... inputs) {
+ Preconditions.checkArgument(inputs.length == 1);
+
+ final String featuresCol = getFeaturesCol();
+ final String labelCol = getLabelCol();
+ StreamTableEnvironment tEnv =
+ (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment();
+
+ DataStream<Tuple2<Vector, Double>> inputData =
+ tEnv.toDataStream(inputs[0])
+ .map(
+ (MapFunction<Row, Tuple2<Vector, Double>>)
+ row -> {
+ Number number = (Number) row.getField(labelCol);
+ Preconditions.checkNotNull(
+ number, "Input data must contain label value.");
+ return new Tuple2<>(
+ ((Vector) row.getField(featuresCol)),
+ number.doubleValue());
+ })
+ .returns(Types.TUPLE(VectorTypeInfo.INSTANCE, Types.DOUBLE));
+ DataStream<List<Row>> streamWithANOVA =
+ DataStreamUtils.aggregate(inputData, new ANOVAAggregator());
+ return new Table[] {convertToTable(tEnv, streamWithANOVA, getFlatten())};
+ }
+
+ /** Computes the pValues and fValues of input features. */
Review Comment:
This function computes degrees of freedom as well.
##########
flink-ml-lib/src/test/java/org/apache/flink/ml/stats/ANOVATestTest.java:
##########
@@ -0,0 +1,411 @@
+/*
+ * 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.anovatest.ANOVATest;
+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 ANOVATest}. */
+public class ANOVATestTest 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(
+ 3,
+ Vectors.dense(
+ 0.85956061,
+ 0.1645695,
+ 0.48347596,
+ 0.92102727,
+ 0.42855644,
+ 0.05746009)),
+ Row.of(
+ 2,
+ Vectors.dense(
+ 0.92500743,
+ 0.65760154,
+ 0.13295284,
+ 0.53344893,
+ 0.8994776,
+ 0.24836496)),
+ Row.of(
+ 1,
+ Vectors.dense(
+ 0.03017182,
+ 0.07244715,
+ 0.87416449,
+ 0.55843035,
+ 0.91604736,
+ 0.63346045)),
+ Row.of(
+ 5,
+ Vectors.dense(
+ 0.28325261,
+ 0.36536881,
+ 0.09223386,
+ 0.37251258,
+ 0.34742278,
+ 0.70517077)),
+ Row.of(
+ 4,
+ Vectors.dense(
+ 0.64850904,
+ 0.04090877,
+ 0.21173176,
+ 0.00148992,
+ 0.13897166,
+ 0.21182539)),
+ Row.of(
+ 4,
+ Vectors.dense(
+ 0.02609493,
+ 0.44608735,
+ 0.23910531,
+ 0.95449222,
+ 0.90763182,
+ 0.8624905)),
+ Row.of(
+ 5,
+ Vectors.dense(
+ 0.09158744,
+ 0.97745235,
+ 0.41150139,
+ 0.45830467,
+ 0.52590925,
+ 0.29441554)),
+ Row.of(
+ 4,
+ Vectors.dense(
+ 0.97211594,
+ 0.1814442,
+ 0.30340642,
+ 0.17445413,
+ 0.52756958,
+ 0.02069296)),
+ Row.of(
+ 2,
+ Vectors.dense(
+ 0.06354593,
+ 0.63527231,
+ 0.49620335,
+ 0.0141264,
+ 0.62722219,
+ 0.63497507)),
+ Row.of(
+ 1,
+ Vectors.dense(
+ 0.10814149,
+ 0.8296426,
+ 0.51775217,
+ 0.57068344,
+ 0.54633305,
+ 0.12714921)),
+ Row.of(
+ 1,
+ Vectors.dense(
+ 0.72731796,
+ 0.94010124,
+ 0.45007811,
+ 0.87650674,
+ 0.53735565,
+ 0.49568415)),
+ Row.of(
+ 2,
+ Vectors.dense(
+ 0.41827208,
+ 0.85100628,
+ 0.38685271,
+ 0.60689503,
+ 0.21784097,
+ 0.91294433)),
+ Row.of(
+ 3,
+ Vectors.dense(
+ 0.65843656,
+ 0.5880859,
+ 0.18862706,
+ 0.856398,
+ 0.18029327,
+ 0.94851926)),
+ Row.of(
+ 4,
+ Vectors.dense(
+ 0.3841634,
+ 0.25138793,
+ 0.96746644,
+ 0.77048045,
+ 0.44685196,
+ 0.19813854)),
+ Row.of(
+ 5,
+ Vectors.dense(
+ 0.65982267,
+ 0.23024125,
+ 0.13598434,
+ 0.60144265,
+ 0.57848927,
+ 0.85623564)),
+ Row.of(
+ 1,
+ Vectors.dense(
+ 0.35764189,
+ 0.47623815,
+ 0.5459232,
+ 0.79508298,
+ 0.14462443,
+ 0.01802919)),
+ Row.of(
+ 5,
+ Vectors.dense(
+ 0.38532153,
+ 0.90614554,
+ 0.86629571,
+ 0.13988735,
+ 0.32062385,
+ 0.00179492)),
+ Row.of(
+ 3,
+ Vectors.dense(
+ 0.2142368,
+ 0.28306022,
+ 0.59481646,
+ 0.42567028,
+ 0.52207663,
+ 0.78082401)),
+ Row.of(
+ 1,
+ Vectors.dense(
+ 0.20788283,
+ 0.76861782,
+ 0.59595468,
+ 0.62103642,
+ 0.17781246,
+ 0.77655345)),
+ Row.of(
+ 1,
+ Vectors.dense(
+ 0.1751708,
+ 0.4547537,
+ 0.46187865,
+ 0.79781199,
+ 0.05104487,
+ 0.42406092)));
+
+ private static final List<Row> SPARSE_INPUT_DATA =
+ Arrays.asList(
+ Row.of(3, Vectors.dense(6.0, 7.0, 0.0, 7.0, 6.0, 0.0, 0.0).toSparse()),
+ Row.of(1, Vectors.dense(0.0, 9.0, 6.0, 0.0, 5.0, 9.0, 0.0).toSparse()),
+ Row.of(3, Vectors.dense(0.0, 9.0, 3.0, 0.0, 5.0, 5.0, 0.0).toSparse()),
+ Row.of(2, Vectors.dense(0.0, 9.0, 8.0, 5.0, 6.0, 4.0, 0.0).toSparse()),
+ Row.of(2, Vectors.dense(8.0, 9.0, 6.0, 5.0, 4.0, 4.0, 0.0).toSparse()),
+ Row.of(3, 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(
+ 0.64137831, 0.14830724, 0.69858474, 0.28038169, 0.86759161, 0.81608606),
+ new long[] {19, 19, 19, 19, 19, 19},
+ Vectors.dense(
+ 0.64110932, 1.98689258, 0.55499714, 1.40340562, 0.30881722, 0.3848595));
+
+ private static final List<Row> EXPECTED_FLATTENED_OUTPUT =
+ Arrays.asList(
+ Row.of(0, 0.64137831, 19, 0.64110932),
+ Row.of(1, 0.14830724, 19, 1.98689258),
+ Row.of(2, 0.69858474, 19, 0.55499714),
+ Row.of(3, 0.28038169, 19, 1.40340562),
+ Row.of(4, 0.86759161, 19, 0.30881722),
+ Row.of(5, 0.81608606, 19, 0.3848595));
+
+ private static final Row EXPECTED_OUTPUT_SPARSE =
+ Row.of(
+ Vectors.dense(
+ Double.NaN,
Review Comment:
Why would this value be `0.71554175` in Spark, but `NaN` in Flink?
##########
flink-ml-lib/src/test/java/org/apache/flink/ml/stats/ANOVATestTest.java:
##########
@@ -0,0 +1,411 @@
+/*
+ * 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.anovatest.ANOVATest;
+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 ANOVATest}. */
+public class ANOVATestTest 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;
Review Comment:
The EPS in Spark is 1e-6.
##########
flink-ml-lib/src/test/java/org/apache/flink/ml/stats/ANOVATestTest.java:
##########
@@ -0,0 +1,411 @@
+/*
+ * 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.anovatest.ANOVATest;
+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 ANOVATest}. */
+public class ANOVATestTest 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(
+ 3,
+ Vectors.dense(
+ 0.85956061,
+ 0.1645695,
+ 0.48347596,
+ 0.92102727,
+ 0.42855644,
+ 0.05746009)),
+ Row.of(
+ 2,
+ Vectors.dense(
+ 0.92500743,
+ 0.65760154,
+ 0.13295284,
+ 0.53344893,
+ 0.8994776,
+ 0.24836496)),
+ Row.of(
+ 1,
+ Vectors.dense(
+ 0.03017182,
+ 0.07244715,
+ 0.87416449,
+ 0.55843035,
+ 0.91604736,
+ 0.63346045)),
+ Row.of(
+ 5,
+ Vectors.dense(
+ 0.28325261,
+ 0.36536881,
+ 0.09223386,
+ 0.37251258,
+ 0.34742278,
+ 0.70517077)),
+ Row.of(
+ 4,
+ Vectors.dense(
+ 0.64850904,
+ 0.04090877,
+ 0.21173176,
+ 0.00148992,
+ 0.13897166,
+ 0.21182539)),
+ Row.of(
+ 4,
+ Vectors.dense(
+ 0.02609493,
+ 0.44608735,
+ 0.23910531,
+ 0.95449222,
+ 0.90763182,
+ 0.8624905)),
+ Row.of(
+ 5,
+ Vectors.dense(
+ 0.09158744,
+ 0.97745235,
+ 0.41150139,
+ 0.45830467,
+ 0.52590925,
+ 0.29441554)),
+ Row.of(
+ 4,
+ Vectors.dense(
+ 0.97211594,
+ 0.1814442,
+ 0.30340642,
+ 0.17445413,
+ 0.52756958,
+ 0.02069296)),
+ Row.of(
+ 2,
+ Vectors.dense(
+ 0.06354593,
+ 0.63527231,
+ 0.49620335,
+ 0.0141264,
+ 0.62722219,
+ 0.63497507)),
+ Row.of(
+ 1,
+ Vectors.dense(
+ 0.10814149,
+ 0.8296426,
+ 0.51775217,
+ 0.57068344,
+ 0.54633305,
+ 0.12714921)),
+ Row.of(
+ 1,
+ Vectors.dense(
+ 0.72731796,
+ 0.94010124,
+ 0.45007811,
+ 0.87650674,
+ 0.53735565,
+ 0.49568415)),
+ Row.of(
+ 2,
+ Vectors.dense(
+ 0.41827208,
+ 0.85100628,
+ 0.38685271,
+ 0.60689503,
+ 0.21784097,
+ 0.91294433)),
+ Row.of(
+ 3,
+ Vectors.dense(
+ 0.65843656,
+ 0.5880859,
+ 0.18862706,
+ 0.856398,
+ 0.18029327,
+ 0.94851926)),
+ Row.of(
+ 4,
+ Vectors.dense(
+ 0.3841634,
+ 0.25138793,
+ 0.96746644,
+ 0.77048045,
+ 0.44685196,
+ 0.19813854)),
+ Row.of(
+ 5,
+ Vectors.dense(
+ 0.65982267,
+ 0.23024125,
+ 0.13598434,
+ 0.60144265,
+ 0.57848927,
+ 0.85623564)),
+ Row.of(
+ 1,
+ Vectors.dense(
+ 0.35764189,
+ 0.47623815,
+ 0.5459232,
+ 0.79508298,
+ 0.14462443,
+ 0.01802919)),
+ Row.of(
+ 5,
+ Vectors.dense(
+ 0.38532153,
+ 0.90614554,
+ 0.86629571,
+ 0.13988735,
+ 0.32062385,
+ 0.00179492)),
+ Row.of(
+ 3,
+ Vectors.dense(
+ 0.2142368,
+ 0.28306022,
+ 0.59481646,
+ 0.42567028,
+ 0.52207663,
+ 0.78082401)),
+ Row.of(
+ 1,
+ Vectors.dense(
+ 0.20788283,
+ 0.76861782,
+ 0.59595468,
+ 0.62103642,
+ 0.17781246,
+ 0.77655345)),
+ Row.of(
+ 1,
+ Vectors.dense(
+ 0.1751708,
+ 0.4547537,
+ 0.46187865,
+ 0.79781199,
+ 0.05104487,
+ 0.42406092)));
+
+ private static final List<Row> SPARSE_INPUT_DATA =
+ Arrays.asList(
+ Row.of(3, Vectors.dense(6.0, 7.0, 0.0, 7.0, 6.0, 0.0, 0.0).toSparse()),
+ Row.of(1, Vectors.dense(0.0, 9.0, 6.0, 0.0, 5.0, 9.0, 0.0).toSparse()),
+ Row.of(3, Vectors.dense(0.0, 9.0, 3.0, 0.0, 5.0, 5.0, 0.0).toSparse()),
+ Row.of(2, Vectors.dense(0.0, 9.0, 8.0, 5.0, 6.0, 4.0, 0.0).toSparse()),
+ Row.of(2, Vectors.dense(8.0, 9.0, 6.0, 5.0, 4.0, 4.0, 0.0).toSparse()),
+ Row.of(3, 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(
+ 0.64137831, 0.14830724, 0.69858474, 0.28038169, 0.86759161, 0.81608606),
+ new long[] {19, 19, 19, 19, 19, 19},
+ Vectors.dense(
+ 0.64110932, 1.98689258, 0.55499714, 1.40340562, 0.30881722, 0.3848595));
+
+ private static final List<Row> EXPECTED_FLATTENED_OUTPUT =
+ Arrays.asList(
+ Row.of(0, 0.64137831, 19, 0.64110932),
+ Row.of(1, 0.14830724, 19, 1.98689258),
+ Row.of(2, 0.69858474, 19, 0.55499714),
+ Row.of(3, 0.28038169, 19, 1.40340562),
+ Row.of(4, 0.86759161, 19, 0.30881722),
+ Row.of(5, 0.81608606, 19, 0.3848595));
+
+ private static final Row EXPECTED_OUTPUT_SPARSE =
+ Row.of(
+ Vectors.dense(
+ Double.NaN,
+ 0.71554175,
+ 0.34278574,
+ 0.45824059,
+ 0.84633632,
+ 0.15673368,
+ Double.NaN),
+ new long[] {5, 5, 5, 5, 5, 5, 5},
+ Vectors.dense(
+ Double.NaN, 0.375, 1.5625, 1.02364865, 0.17647059, 3.66, Double.NaN));
+
+ private static final List<Row> EXPECTED_FLATTENED_OUTPUT_SPARSE =
+ Arrays.asList(
+ Row.of(0, Double.NaN, 5, Double.NaN),
+ Row.of(1, 0.71554175, 5, 0.375),
+ Row.of(2, 0.34278574, 5, 1.5625),
+ Row.of(3, 0.45824059, 5, 1.02364865),
+ Row.of(4, 0.84633632, 5, 0.17647059),
+ Row.of(5, 0.15673368, 5, 3.66),
+ Row.of(6, Double.NaN, 5, 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:
It might be enough to replace `compareVector(v1, v2)` with `assertArrayEquals(v1.toArray(), v2.toArray(), EPS)`.
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