yunfengzhou-hub commented on a change in pull request #37: URL: https://github.com/apache/flink-ml/pull/37#discussion_r765568922
########## File path: flink-ml-lib/src/test/java/org/apache/flink/ml/feature/OneHotEncoderTest.java ########## @@ -0,0 +1,282 @@ +/* + * 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; + +import org.apache.flink.api.common.restartstrategy.RestartStrategies; +import org.apache.flink.api.java.tuple.Tuple2; +import org.apache.flink.configuration.Configuration; +import org.apache.flink.ml.feature.onehotencoder.OneHotEncoder; +import org.apache.flink.ml.feature.onehotencoder.OneHotEncoderModel; +import org.apache.flink.ml.feature.onehotencoder.OneHotEncoderModelData; +import org.apache.flink.ml.linalg.Vector; +import org.apache.flink.ml.linalg.Vectors; +import org.apache.flink.ml.util.ReadWriteUtils; +import org.apache.flink.ml.util.StageTestUtils; +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.types.Row; +import org.apache.flink.util.CloseableIterator; + +import org.junit.Assert; +import org.junit.Before; +import org.junit.Rule; +import org.junit.Test; +import org.junit.rules.TemporaryFolder; + +import java.util.Arrays; +import java.util.HashMap; +import java.util.List; +import java.util.Map; + +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 OneHotEncoder and OneHotEncoderModel. */ +public class OneHotEncoderTest { + @Rule public final TemporaryFolder tempFolder = new TemporaryFolder(); + + private StreamExecutionEnvironment env; + private StreamTableEnvironment tEnv; + private Table trainTable; + private Table predictTable; + private Map<Double, Vector>[] expectedOutput; + private OneHotEncoder estimator; + + @Before + public void before() { + Configuration config = new Configuration(); + config.set(ExecutionCheckpointingOptions.ENABLE_CHECKPOINTS_AFTER_TASKS_FINISH, true); + env = StreamExecutionEnvironment.getExecutionEnvironment(config); + env.setParallelism(4); + env.enableCheckpointing(100); + env.setRestartStrategy(RestartStrategies.noRestart()); + tEnv = StreamTableEnvironment.create(env); + + List<Row> trainData = Arrays.asList(Row.of(0.0), Row.of(1.0), Row.of(2.0), Row.of(0.0)); + + trainTable = tEnv.fromDataStream(env.fromCollection(trainData)).as("input"); + + List<Row> predictData = Arrays.asList(Row.of(0.0), Row.of(1.0), Row.of(2.0)); + + predictTable = tEnv.fromDataStream(env.fromCollection(predictData)).as("input"); + + expectedOutput = + new HashMap[] { + new HashMap<Double, Vector>() { + { + put(0.0, Vectors.sparse(2, new int[] {0}, new double[] {1.0})); + put(1.0, Vectors.sparse(2, new int[] {1}, new double[] {1.0})); + put(2.0, Vectors.sparse(2, new int[0], new double[0])); + } + } + }; + + estimator = new OneHotEncoder().setInputCols("input").setOutputCols("output"); + } + + /** + * Executes a given table and collect its results. Results are returned as a map array. Each + * element in the array is a map corresponding to a input column whose key is the original value + * in the input column, value is the one-hot encoding result of that value. + * + * @param table A table to be executed and to have its result collected + * @param inputCols Name of the input columns + * @param outputCols Name of the output columns containing one-hot encoding result + * @return An array of map containing the collected results for each input column + */ + private static Map<Double, Vector>[] executeAndCollect( + Table table, String[] inputCols, String[] outputCols) { + Map<Double, Vector>[] maps = new HashMap[inputCols.length]; + for (int i = 0; i < inputCols.length; i++) { + maps[i] = new HashMap<>(); + } + for (CloseableIterator<Row> it = table.execute().collect(); it.hasNext(); ) { + Row row = it.next(); + for (int i = 0; i < inputCols.length; i++) { + maps[i].put( + ((Number) row.getField(inputCols[i])).doubleValue(), + (Vector) row.getField(outputCols[i])); + } + } + return maps; + } + + @Test + public void testParam() { + OneHotEncoder estimator = new OneHotEncoder(); + + assertTrue(estimator.getDropLast()); + + estimator.setInputCols("test_input").setOutputCols("test_output").setDropLast(false); + + assertArrayEquals(new String[] {"test_input"}, estimator.getInputCols()); + assertArrayEquals(new String[] {"test_output"}, estimator.getOutputCols()); + assertFalse(estimator.getDropLast()); + + OneHotEncoderModel model = new OneHotEncoderModel(); + + assertTrue(model.getDropLast()); + + model.setInputCols("test_input").setOutputCols("test_output").setDropLast(false); + + assertArrayEquals(new String[] {"test_input"}, model.getInputCols()); + assertArrayEquals(new String[] {"test_output"}, model.getOutputCols()); + assertFalse(model.getDropLast()); + } + + @Test + public void testFitAndPredict() { + OneHotEncoderModel model = estimator.fit(trainTable); + Table outputTable = model.transform(predictTable)[0]; + Map<Double, Vector>[] actualOutput = + executeAndCollect(outputTable, model.getInputCols(), model.getOutputCols()); + assertArrayEquals(expectedOutput, actualOutput); + } + + @Test + public void testDropLast() { + estimator.setDropLast(false); + + expectedOutput = + new HashMap[] { + new HashMap<Double, Vector>() { + { + put(0.0, Vectors.sparse(3, new int[] {0}, new double[] {1.0})); + put(1.0, Vectors.sparse(3, new int[] {1}, new double[] {1.0})); + put(2.0, Vectors.sparse(3, new int[] {2}, new double[] {1.0})); + } + } + }; + + OneHotEncoderModel model = estimator.fit(trainTable); + Table outputTable = model.transform(predictTable)[0]; + Map<Double, Vector>[] actualOutput = + executeAndCollect(outputTable, model.getInputCols(), model.getOutputCols()); + assertArrayEquals(expectedOutput, actualOutput); + } + + @Test + public void testInputDataType() { + List<Row> trainData = Arrays.asList(Row.of(0), Row.of(1), Row.of(2), Row.of(0)); + + trainTable = tEnv.fromDataStream(env.fromCollection(trainData)).as("input"); + + List<Row> predictData = Arrays.asList(Row.of(0), Row.of(1), Row.of(2)); + predictTable = tEnv.fromDataStream(env.fromCollection(predictData)).as("input"); + + expectedOutput = + new HashMap[] { + new HashMap<Double, Vector>() { + { + put(0.0, Vectors.sparse(2, new int[] {0}, new double[] {1.0})); + put(1.0, Vectors.sparse(2, new int[] {1}, new double[] {1.0})); + put(2.0, Vectors.sparse(2, new int[0], new double[0])); + } + } + }; + + OneHotEncoderModel model = estimator.fit(trainTable); + Table outputTable = model.transform(predictTable)[0]; + Map<Double, Vector>[] actualOutput = + executeAndCollect(outputTable, model.getInputCols(), model.getOutputCols()); + assertArrayEquals(expectedOutput, actualOutput); + } + + @Test + public void testNonIntegerDouble() { + List<Row> trainData = Arrays.asList(Row.of(0.5), Row.of(1.0), Row.of(2.0), Row.of(0.0)); + + trainTable = tEnv.fromDataStream(env.fromCollection(trainData)).as("input"); + OneHotEncoderModel model = estimator.fit(trainTable); + Table outputTable = model.transform(predictTable)[0]; + try { + outputTable.execute().collect().next(); + Assert.fail("Expected IllegalArgumentException"); + } catch (Exception e) { + Throwable exception = e; + while (exception.getCause() != null) { + exception = exception.getCause(); + } + assertEquals(IllegalArgumentException.class, exception.getClass()); + assertEquals("Value 0.5 cannot be parsed as indexed integer.", exception.getMessage()); + } + } + + @Test + public void testNonIntegerDouble2() { Review comment: OK. 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