mllib/util/JavaMLUtilsSuite.java

0001 /*
0002  * Licensed to the Apache Software Foundation (ASF) under one or more
0003  * contributor license agreements.  See the NOTICE file distributed with
0004  * this work for additional information regarding copyright ownership.
0005  * The ASF licenses this file to You under the Apache License, Version 2.0
0006  * (the "License"); you may not use this file except in compliance with
0007  * the License.  You may obtain a copy of the License at
0008  *
0009  *    http://www.apache.org/licenses/LICENSE-2.0
0010  *
0011  * Unless required by applicable law or agreed to in writing, software
0012  * distributed under the License is distributed on an "AS IS" BASIS,
0013  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
0014  * See the License for the specific language governing permissions and
0015  * limitations under the License.
0016  */
0017
0018 package org.apache.spark.mllib.util;
0019
0020 import java.util.Arrays;
0021 import java.util.Collections;
0022
0023 import org.junit.Assert;
0024 import org.junit.Test;
0025
0026 import org.apache.spark.SharedSparkSession;
0027 import org.apache.spark.mllib.linalg.*;
0028 import org.apache.spark.mllib.regression.LabeledPoint;
0029 import org.apache.spark.sql.Dataset;
0030 import org.apache.spark.sql.Row;
0031 import org.apache.spark.sql.RowFactory;
0032 import org.apache.spark.sql.types.DataTypes;
0033 import org.apache.spark.sql.types.Metadata;
0034 import org.apache.spark.sql.types.StructField;
0035 import org.apache.spark.sql.types.StructType;
0036
0037 public class JavaMLUtilsSuite extends SharedSparkSession {
0038
0039   @Test
0040   public void testConvertVectorColumnsToAndFromML() {
0041     Vector x = Vectors.dense(2.0);
0042     Dataset<Row> dataset = spark.createDataFrame(
0043       Collections.singletonList(new LabeledPoint(1.0, x)), LabeledPoint.class
0044     ).select("label", "features");
0045     Dataset<Row> newDataset1 = MLUtils.convertVectorColumnsToML(dataset);
0046     Row new1 = newDataset1.first();
0047     Assert.assertEquals(RowFactory.create(1.0, x.asML()), new1);
0048     Row new2 = MLUtils.convertVectorColumnsToML(dataset, "features").first();
0049     Assert.assertEquals(new1, new2);
0050     Row old1 = MLUtils.convertVectorColumnsFromML(newDataset1).first();
0051     Assert.assertEquals(RowFactory.create(1.0, x), old1);
0052   }
0053
0054   @Test
0055   public void testConvertMatrixColumnsToAndFromML() {
0056     Matrix x = Matrices.dense(2, 1, new double[]{1.0, 2.0});
0057     StructType schema = new StructType(new StructField[]{
0058       new StructField("label", DataTypes.DoubleType, false, Metadata.empty()),
0059       new StructField("features", new MatrixUDT(), false, Metadata.empty())
0060     });
0061     Dataset<Row> dataset = spark.createDataFrame(
0062       Arrays.asList(
0063         RowFactory.create(1.0, x)),
0064       schema);
0065
0066     Dataset<Row> newDataset1 = MLUtils.convertMatrixColumnsToML(dataset);
0067     Row new1 = newDataset1.first();
0068     Assert.assertEquals(RowFactory.create(1.0, x.asML()), new1);
0069     Row new2 = MLUtils.convertMatrixColumnsToML(dataset, "features").first();
0070     Assert.assertEquals(new1, new2);
0071     Row old1 = MLUtils.convertMatrixColumnsFromML(newDataset1).first();
0072     Assert.assertEquals(RowFactory.create(1.0, x), old1);
0073   }
0074 }