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-rw-r--r-- | mllib/src/test/java/org/apache/spark/ml/feature/JavaPCASuite.java | 114 |
1 files changed, 114 insertions, 0 deletions
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaPCASuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaPCASuite.java new file mode 100644 index 0000000000..5cf43fec6f --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaPCASuite.java @@ -0,0 +1,114 @@ +/* + * 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.spark.ml.feature; + +import java.io.Serializable; +import java.util.List; + +import scala.Tuple2; + +import com.google.common.collect.Lists; +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.function.Function; +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.mllib.linalg.distributed.RowMatrix; +import org.apache.spark.mllib.linalg.Matrix; +import org.apache.spark.mllib.linalg.Vector; +import org.apache.spark.mllib.linalg.Vectors; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.SQLContext; + +public class JavaPCASuite implements Serializable { + private transient JavaSparkContext jsc; + private transient SQLContext sqlContext; + + @Before + public void setUp() { + jsc = new JavaSparkContext("local", "JavaPCASuite"); + sqlContext = new SQLContext(jsc); + } + + @After + public void tearDown() { + jsc.stop(); + jsc = null; + } + + public static class VectorPair implements Serializable { + private Vector features = Vectors.dense(0.0); + private Vector expected = Vectors.dense(0.0); + + public void setFeatures(Vector features) { + this.features = features; + } + + public Vector getFeatures() { + return this.features; + } + + public void setExpected(Vector expected) { + this.expected = expected; + } + + public Vector getExpected() { + return this.expected; + } + } + + @Test + public void testPCA() { + List<Vector> points = Lists.newArrayList( + Vectors.sparse(5, new int[]{1, 3}, new double[]{1.0, 7.0}), + Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0), + Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0) + ); + JavaRDD<Vector> dataRDD = jsc.parallelize(points, 2); + + RowMatrix mat = new RowMatrix(dataRDD.rdd()); + Matrix pc = mat.computePrincipalComponents(3); + JavaRDD<Vector> expected = mat.multiply(pc).rows().toJavaRDD(); + + JavaRDD<VectorPair> featuresExpected = dataRDD.zip(expected).map( + new Function<Tuple2<Vector, Vector>, VectorPair>() { + public VectorPair call(Tuple2<Vector, Vector> pair) { + VectorPair featuresExpected = new VectorPair(); + featuresExpected.setFeatures(pair._1()); + featuresExpected.setExpected(pair._2()); + return featuresExpected; + } + } + ); + + DataFrame df = sqlContext.createDataFrame(featuresExpected, VectorPair.class); + PCAModel pca = new PCA() + .setInputCol("features") + .setOutputCol("pca_features") + .setK(3) + .fit(df); + List<Row> result = pca.transform(df).select("pca_features", "expected").toJavaRDD().collect(); + for (Row r : result) { + Assert.assertEquals(r.get(1), r.get(0)); + } + } +} |