From e1f4de4a7d15d4ca4b5c64ff929ac3980f5d706f Mon Sep 17 00:00:00 2001 From: Feynman Liang Date: Thu, 27 Aug 2015 18:46:41 +0100 Subject: [SPARK-10257] [MLLIB] Removes Guava from all spark.mllib Java tests * Replaces instances of `Lists.newArrayList` with `Arrays.asList` * Replaces `commons.lang.StringUtils` over `com.google.collections.Strings` * Replaces `List` interface over `ArrayList` implementations This PR along with #8445 #8446 #8447 completely removes all `com.google.collections.Lists` dependencies within mllib's Java tests. Author: Feynman Liang Closes #8451 from feynmanliang/SPARK-10257. --- .../JavaStreamingLogisticRegressionSuite.java | 10 ++++---- .../mllib/clustering/JavaGaussianMixtureSuite.java | 4 ++-- .../spark/mllib/clustering/JavaKMeansSuite.java | 9 ++++---- .../mllib/clustering/JavaStreamingKMeansSuite.java | 10 ++++---- .../apache/spark/mllib/feature/JavaTfIdfSuite.java | 19 ++++++++------- .../spark/mllib/feature/JavaWord2VecSuite.java | 6 ++--- .../spark/mllib/fpm/JavaAssociationRulesSuite.java | 5 ++-- .../apache/spark/mllib/fpm/JavaFPGrowthSuite.java | 17 +++++++------- .../spark/mllib/linalg/JavaVectorsSuite.java | 5 ++-- .../spark/mllib/random/JavaRandomRDDsSuite.java | 27 +++++++++++----------- .../spark/mllib/recommendation/JavaALSSuite.java | 5 ++-- .../regression/JavaIsotonicRegressionSuite.java | 7 +++--- .../JavaStreamingLinearRegressionSuite.java | 10 ++++---- .../spark/mllib/stat/JavaStatisticsSuite.java | 11 +++++---- 14 files changed, 71 insertions(+), 74 deletions(-) (limited to 'mllib') diff --git a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaStreamingLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaStreamingLogisticRegressionSuite.java index 55787f8606..c9e5ee22f3 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaStreamingLogisticRegressionSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaStreamingLogisticRegressionSuite.java @@ -18,11 +18,11 @@ package org.apache.spark.mllib.classification; import java.io.Serializable; +import java.util.Arrays; import java.util.List; import scala.Tuple2; -import com.google.common.collect.Lists; import org.junit.After; import org.junit.Before; import org.junit.Test; @@ -60,16 +60,16 @@ public class JavaStreamingLogisticRegressionSuite implements Serializable { @Test @SuppressWarnings("unchecked") public void javaAPI() { - List trainingBatch = Lists.newArrayList( + List trainingBatch = Arrays.asList( new LabeledPoint(1.0, Vectors.dense(1.0)), new LabeledPoint(0.0, Vectors.dense(0.0))); JavaDStream training = - attachTestInputStream(ssc, Lists.newArrayList(trainingBatch, trainingBatch), 2); - List> testBatch = Lists.newArrayList( + attachTestInputStream(ssc, Arrays.asList(trainingBatch, trainingBatch), 2); + List> testBatch = Arrays.asList( new Tuple2(10, Vectors.dense(1.0)), new Tuple2(11, Vectors.dense(0.0))); JavaPairDStream test = JavaPairDStream.fromJavaDStream( - attachTestInputStream(ssc, Lists.newArrayList(testBatch, testBatch), 2)); + attachTestInputStream(ssc, Arrays.asList(testBatch, testBatch), 2)); StreamingLogisticRegressionWithSGD slr = new StreamingLogisticRegressionWithSGD() .setNumIterations(2) .setInitialWeights(Vectors.dense(0.0)); diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaGaussianMixtureSuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaGaussianMixtureSuite.java index 467a7a69e8..123f78da54 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaGaussianMixtureSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaGaussianMixtureSuite.java @@ -18,9 +18,9 @@ package org.apache.spark.mllib.clustering; import java.io.Serializable; +import java.util.Arrays; import java.util.List; -import com.google.common.collect.Lists; import org.junit.After; import org.junit.Before; import org.junit.Test; @@ -48,7 +48,7 @@ public class JavaGaussianMixtureSuite implements Serializable { @Test public void runGaussianMixture() { - List points = Lists.newArrayList( + List points = Arrays.asList( Vectors.dense(1.0, 2.0, 6.0), Vectors.dense(1.0, 3.0, 0.0), Vectors.dense(1.0, 4.0, 6.0) diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java index 31676e6402..ad06676c72 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java @@ -18,6 +18,7 @@ package org.apache.spark.mllib.clustering; import java.io.Serializable; +import java.util.Arrays; import java.util.List; import org.junit.After; @@ -25,8 +26,6 @@ import org.junit.Before; import org.junit.Test; import static org.junit.Assert.*; -import com.google.common.collect.Lists; - import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.mllib.linalg.Vector; @@ -48,7 +47,7 @@ public class JavaKMeansSuite implements Serializable { @Test public void runKMeansUsingStaticMethods() { - List points = Lists.newArrayList( + List points = Arrays.asList( Vectors.dense(1.0, 2.0, 6.0), Vectors.dense(1.0, 3.0, 0.0), Vectors.dense(1.0, 4.0, 6.0) @@ -67,7 +66,7 @@ public class JavaKMeansSuite implements Serializable { @Test public void runKMeansUsingConstructor() { - List points = Lists.newArrayList( + List points = Arrays.asList( Vectors.dense(1.0, 2.0, 6.0), Vectors.dense(1.0, 3.0, 0.0), Vectors.dense(1.0, 4.0, 6.0) @@ -90,7 +89,7 @@ public class JavaKMeansSuite implements Serializable { @Test public void testPredictJavaRDD() { - List points = Lists.newArrayList( + List points = Arrays.asList( Vectors.dense(1.0, 2.0, 6.0), Vectors.dense(1.0, 3.0, 0.0), Vectors.dense(1.0, 4.0, 6.0) diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaStreamingKMeansSuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaStreamingKMeansSuite.java index 3b0e879eec..d644766d1e 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaStreamingKMeansSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaStreamingKMeansSuite.java @@ -18,11 +18,11 @@ package org.apache.spark.mllib.clustering; import java.io.Serializable; +import java.util.Arrays; import java.util.List; import scala.Tuple2; -import com.google.common.collect.Lists; import org.junit.After; import org.junit.Before; import org.junit.Test; @@ -60,16 +60,16 @@ public class JavaStreamingKMeansSuite implements Serializable { @Test @SuppressWarnings("unchecked") public void javaAPI() { - List trainingBatch = Lists.newArrayList( + List trainingBatch = Arrays.asList( Vectors.dense(1.0), Vectors.dense(0.0)); JavaDStream training = - attachTestInputStream(ssc, Lists.newArrayList(trainingBatch, trainingBatch), 2); - List> testBatch = Lists.newArrayList( + attachTestInputStream(ssc, Arrays.asList(trainingBatch, trainingBatch), 2); + List> testBatch = Arrays.asList( new Tuple2(10, Vectors.dense(1.0)), new Tuple2(11, Vectors.dense(0.0))); JavaPairDStream test = JavaPairDStream.fromJavaDStream( - attachTestInputStream(ssc, Lists.newArrayList(testBatch, testBatch), 2)); + attachTestInputStream(ssc, Arrays.asList(testBatch, testBatch), 2)); StreamingKMeans skmeans = new StreamingKMeans() .setK(1) .setDecayFactor(1.0) diff --git a/mllib/src/test/java/org/apache/spark/mllib/feature/JavaTfIdfSuite.java b/mllib/src/test/java/org/apache/spark/mllib/feature/JavaTfIdfSuite.java index fbc26167ce..8a320afa4b 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/feature/JavaTfIdfSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/feature/JavaTfIdfSuite.java @@ -18,14 +18,13 @@ package org.apache.spark.mllib.feature; import java.io.Serializable; -import java.util.ArrayList; +import java.util.Arrays; import java.util.List; import org.junit.After; import org.junit.Assert; import org.junit.Before; import org.junit.Test; -import com.google.common.collect.Lists; import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; @@ -50,10 +49,10 @@ public class JavaTfIdfSuite implements Serializable { // The tests are to check Java compatibility. HashingTF tf = new HashingTF(); @SuppressWarnings("unchecked") - JavaRDD> documents = sc.parallelize(Lists.newArrayList( - Lists.newArrayList("this is a sentence".split(" ")), - Lists.newArrayList("this is another sentence".split(" ")), - Lists.newArrayList("this is still a sentence".split(" "))), 2); + JavaRDD> documents = sc.parallelize(Arrays.asList( + Arrays.asList("this is a sentence".split(" ")), + Arrays.asList("this is another sentence".split(" ")), + Arrays.asList("this is still a sentence".split(" "))), 2); JavaRDD termFreqs = tf.transform(documents); termFreqs.collect(); IDF idf = new IDF(); @@ -70,10 +69,10 @@ public class JavaTfIdfSuite implements Serializable { // The tests are to check Java compatibility. HashingTF tf = new HashingTF(); @SuppressWarnings("unchecked") - JavaRDD> documents = sc.parallelize(Lists.newArrayList( - Lists.newArrayList("this is a sentence".split(" ")), - Lists.newArrayList("this is another sentence".split(" ")), - Lists.newArrayList("this is still a sentence".split(" "))), 2); + JavaRDD> documents = sc.parallelize(Arrays.asList( + Arrays.asList("this is a sentence".split(" ")), + Arrays.asList("this is another sentence".split(" ")), + Arrays.asList("this is still a sentence".split(" "))), 2); JavaRDD termFreqs = tf.transform(documents); termFreqs.collect(); IDF idf = new IDF(2); diff --git a/mllib/src/test/java/org/apache/spark/mllib/feature/JavaWord2VecSuite.java b/mllib/src/test/java/org/apache/spark/mllib/feature/JavaWord2VecSuite.java index fb7afe8c64..e13ed07e28 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/feature/JavaWord2VecSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/feature/JavaWord2VecSuite.java @@ -18,11 +18,11 @@ package org.apache.spark.mllib.feature; import java.io.Serializable; +import java.util.Arrays; import java.util.List; import scala.Tuple2; -import com.google.common.collect.Lists; import com.google.common.base.Strings; import org.junit.After; import org.junit.Assert; @@ -51,8 +51,8 @@ public class JavaWord2VecSuite implements Serializable { public void word2Vec() { // The tests are to check Java compatibility. String sentence = Strings.repeat("a b ", 100) + Strings.repeat("a c ", 10); - List words = Lists.newArrayList(sentence.split(" ")); - List> localDoc = Lists.newArrayList(words, words); + List words = Arrays.asList(sentence.split(" ")); + List> localDoc = Arrays.asList(words, words); JavaRDD> doc = sc.parallelize(localDoc); Word2Vec word2vec = new Word2Vec() .setVectorSize(10) diff --git a/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaAssociationRulesSuite.java b/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaAssociationRulesSuite.java index d7c2cb3ae2..2bef7a8609 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaAssociationRulesSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaAssociationRulesSuite.java @@ -17,17 +17,16 @@ package org.apache.spark.mllib.fpm; import java.io.Serializable; +import java.util.Arrays; import org.junit.After; import org.junit.Before; import org.junit.Test; -import com.google.common.collect.Lists; import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.mllib.fpm.FPGrowth.FreqItemset; - public class JavaAssociationRulesSuite implements Serializable { private transient JavaSparkContext sc; @@ -46,7 +45,7 @@ public class JavaAssociationRulesSuite implements Serializable { public void runAssociationRules() { @SuppressWarnings("unchecked") - JavaRDD> freqItemsets = sc.parallelize(Lists.newArrayList( + JavaRDD> freqItemsets = sc.parallelize(Arrays.asList( new FreqItemset(new String[] {"a"}, 15L), new FreqItemset(new String[] {"b"}, 35L), new FreqItemset(new String[] {"a", "b"}, 12L) diff --git a/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaFPGrowthSuite.java b/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaFPGrowthSuite.java index 9ce2c52dca..154f75d75e 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaFPGrowthSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaFPGrowthSuite.java @@ -18,13 +18,12 @@ package org.apache.spark.mllib.fpm; import java.io.Serializable; -import java.util.ArrayList; +import java.util.Arrays; import java.util.List; import org.junit.After; import org.junit.Before; import org.junit.Test; -import com.google.common.collect.Lists; import static org.junit.Assert.*; import org.apache.spark.api.java.JavaRDD; @@ -48,13 +47,13 @@ public class JavaFPGrowthSuite implements Serializable { public void runFPGrowth() { @SuppressWarnings("unchecked") - JavaRDD> rdd = sc.parallelize(Lists.newArrayList( - Lists.newArrayList("r z h k p".split(" ")), - Lists.newArrayList("z y x w v u t s".split(" ")), - Lists.newArrayList("s x o n r".split(" ")), - Lists.newArrayList("x z y m t s q e".split(" ")), - Lists.newArrayList("z".split(" ")), - Lists.newArrayList("x z y r q t p".split(" "))), 2); + JavaRDD> rdd = sc.parallelize(Arrays.asList( + Arrays.asList("r z h k p".split(" ")), + Arrays.asList("z y x w v u t s".split(" ")), + Arrays.asList("s x o n r".split(" ")), + Arrays.asList("x z y m t s q e".split(" ")), + Arrays.asList("z".split(" ")), + Arrays.asList("x z y r q t p".split(" "))), 2); FPGrowthModel model = new FPGrowth() .setMinSupport(0.5) diff --git a/mllib/src/test/java/org/apache/spark/mllib/linalg/JavaVectorsSuite.java b/mllib/src/test/java/org/apache/spark/mllib/linalg/JavaVectorsSuite.java index 1421067dc6..77c8c6274f 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/linalg/JavaVectorsSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/linalg/JavaVectorsSuite.java @@ -18,11 +18,10 @@ package org.apache.spark.mllib.linalg; import java.io.Serializable; +import java.util.Arrays; import scala.Tuple2; -import com.google.common.collect.Lists; - import org.junit.Test; import static org.junit.Assert.*; @@ -37,7 +36,7 @@ public class JavaVectorsSuite implements Serializable { @Test public void sparseArrayConstruction() { @SuppressWarnings("unchecked") - Vector v = Vectors.sparse(3, Lists.>newArrayList( + Vector v = Vectors.sparse(3, Arrays.asList( new Tuple2(0, 2.0), new Tuple2(2, 3.0))); assertArrayEquals(new double[]{2.0, 0.0, 3.0}, v.toArray(), 0.0); diff --git a/mllib/src/test/java/org/apache/spark/mllib/random/JavaRandomRDDsSuite.java b/mllib/src/test/java/org/apache/spark/mllib/random/JavaRandomRDDsSuite.java index fcc13c00cb..33d81b1e95 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/random/JavaRandomRDDsSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/random/JavaRandomRDDsSuite.java @@ -17,7 +17,8 @@ package org.apache.spark.mllib.random; -import com.google.common.collect.Lists; +import java.util.Arrays; + import org.apache.spark.api.java.JavaRDD; import org.junit.Assert; import org.junit.After; @@ -51,7 +52,7 @@ public class JavaRandomRDDsSuite { JavaDoubleRDD rdd1 = uniformJavaRDD(sc, m); JavaDoubleRDD rdd2 = uniformJavaRDD(sc, m, p); JavaDoubleRDD rdd3 = uniformJavaRDD(sc, m, p, seed); - for (JavaDoubleRDD rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) { + for (JavaDoubleRDD rdd: Arrays.asList(rdd1, rdd2, rdd3)) { Assert.assertEquals(m, rdd.count()); } } @@ -64,7 +65,7 @@ public class JavaRandomRDDsSuite { JavaDoubleRDD rdd1 = normalJavaRDD(sc, m); JavaDoubleRDD rdd2 = normalJavaRDD(sc, m, p); JavaDoubleRDD rdd3 = normalJavaRDD(sc, m, p, seed); - for (JavaDoubleRDD rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) { + for (JavaDoubleRDD rdd: Arrays.asList(rdd1, rdd2, rdd3)) { Assert.assertEquals(m, rdd.count()); } } @@ -79,7 +80,7 @@ public class JavaRandomRDDsSuite { JavaDoubleRDD rdd1 = logNormalJavaRDD(sc, mean, std, m); JavaDoubleRDD rdd2 = logNormalJavaRDD(sc, mean, std, m, p); JavaDoubleRDD rdd3 = logNormalJavaRDD(sc, mean, std, m, p, seed); - for (JavaDoubleRDD rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) { + for (JavaDoubleRDD rdd: Arrays.asList(rdd1, rdd2, rdd3)) { Assert.assertEquals(m, rdd.count()); } } @@ -93,7 +94,7 @@ public class JavaRandomRDDsSuite { JavaDoubleRDD rdd1 = poissonJavaRDD(sc, mean, m); JavaDoubleRDD rdd2 = poissonJavaRDD(sc, mean, m, p); JavaDoubleRDD rdd3 = poissonJavaRDD(sc, mean, m, p, seed); - for (JavaDoubleRDD rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) { + for (JavaDoubleRDD rdd: Arrays.asList(rdd1, rdd2, rdd3)) { Assert.assertEquals(m, rdd.count()); } } @@ -107,7 +108,7 @@ public class JavaRandomRDDsSuite { JavaDoubleRDD rdd1 = exponentialJavaRDD(sc, mean, m); JavaDoubleRDD rdd2 = exponentialJavaRDD(sc, mean, m, p); JavaDoubleRDD rdd3 = exponentialJavaRDD(sc, mean, m, p, seed); - for (JavaDoubleRDD rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) { + for (JavaDoubleRDD rdd: Arrays.asList(rdd1, rdd2, rdd3)) { Assert.assertEquals(m, rdd.count()); } } @@ -122,7 +123,7 @@ public class JavaRandomRDDsSuite { JavaDoubleRDD rdd1 = gammaJavaRDD(sc, shape, scale, m); JavaDoubleRDD rdd2 = gammaJavaRDD(sc, shape, scale, m, p); JavaDoubleRDD rdd3 = gammaJavaRDD(sc, shape, scale, m, p, seed); - for (JavaDoubleRDD rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) { + for (JavaDoubleRDD rdd: Arrays.asList(rdd1, rdd2, rdd3)) { Assert.assertEquals(m, rdd.count()); } } @@ -138,7 +139,7 @@ public class JavaRandomRDDsSuite { JavaRDD rdd1 = uniformJavaVectorRDD(sc, m, n); JavaRDD rdd2 = uniformJavaVectorRDD(sc, m, n, p); JavaRDD rdd3 = uniformJavaVectorRDD(sc, m, n, p, seed); - for (JavaRDD rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) { + for (JavaRDD rdd: Arrays.asList(rdd1, rdd2, rdd3)) { Assert.assertEquals(m, rdd.count()); Assert.assertEquals(n, rdd.first().size()); } @@ -154,7 +155,7 @@ public class JavaRandomRDDsSuite { JavaRDD rdd1 = normalJavaVectorRDD(sc, m, n); JavaRDD rdd2 = normalJavaVectorRDD(sc, m, n, p); JavaRDD rdd3 = normalJavaVectorRDD(sc, m, n, p, seed); - for (JavaRDD rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) { + for (JavaRDD rdd: Arrays.asList(rdd1, rdd2, rdd3)) { Assert.assertEquals(m, rdd.count()); Assert.assertEquals(n, rdd.first().size()); } @@ -172,7 +173,7 @@ public class JavaRandomRDDsSuite { JavaRDD rdd1 = logNormalJavaVectorRDD(sc, mean, std, m, n); JavaRDD rdd2 = logNormalJavaVectorRDD(sc, mean, std, m, n, p); JavaRDD rdd3 = logNormalJavaVectorRDD(sc, mean, std, m, n, p, seed); - for (JavaRDD rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) { + for (JavaRDD rdd: Arrays.asList(rdd1, rdd2, rdd3)) { Assert.assertEquals(m, rdd.count()); Assert.assertEquals(n, rdd.first().size()); } @@ -189,7 +190,7 @@ public class JavaRandomRDDsSuite { JavaRDD rdd1 = poissonJavaVectorRDD(sc, mean, m, n); JavaRDD rdd2 = poissonJavaVectorRDD(sc, mean, m, n, p); JavaRDD rdd3 = poissonJavaVectorRDD(sc, mean, m, n, p, seed); - for (JavaRDD rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) { + for (JavaRDD rdd: Arrays.asList(rdd1, rdd2, rdd3)) { Assert.assertEquals(m, rdd.count()); Assert.assertEquals(n, rdd.first().size()); } @@ -206,7 +207,7 @@ public class JavaRandomRDDsSuite { JavaRDD rdd1 = exponentialJavaVectorRDD(sc, mean, m, n); JavaRDD rdd2 = exponentialJavaVectorRDD(sc, mean, m, n, p); JavaRDD rdd3 = exponentialJavaVectorRDD(sc, mean, m, n, p, seed); - for (JavaRDD rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) { + for (JavaRDD rdd: Arrays.asList(rdd1, rdd2, rdd3)) { Assert.assertEquals(m, rdd.count()); Assert.assertEquals(n, rdd.first().size()); } @@ -224,7 +225,7 @@ public class JavaRandomRDDsSuite { JavaRDD rdd1 = gammaJavaVectorRDD(sc, shape, scale, m, n); JavaRDD rdd2 = gammaJavaVectorRDD(sc, shape, scale, m, n, p); JavaRDD rdd3 = gammaJavaVectorRDD(sc, shape, scale, m, n, p, seed); - for (JavaRDD rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) { + for (JavaRDD rdd: Arrays.asList(rdd1, rdd2, rdd3)) { Assert.assertEquals(m, rdd.count()); Assert.assertEquals(n, rdd.first().size()); } diff --git a/mllib/src/test/java/org/apache/spark/mllib/recommendation/JavaALSSuite.java b/mllib/src/test/java/org/apache/spark/mllib/recommendation/JavaALSSuite.java index af688c504c..271dda4662 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/recommendation/JavaALSSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/recommendation/JavaALSSuite.java @@ -18,12 +18,12 @@ package org.apache.spark.mllib.recommendation; import java.io.Serializable; +import java.util.ArrayList; import java.util.List; import scala.Tuple2; import scala.Tuple3; -import com.google.common.collect.Lists; import org.jblas.DoubleMatrix; import org.junit.After; import org.junit.Assert; @@ -56,8 +56,7 @@ public class JavaALSSuite implements Serializable { double matchThreshold, boolean implicitPrefs, DoubleMatrix truePrefs) { - List> localUsersProducts = - Lists.newArrayListWithCapacity(users * products); + List> localUsersProducts = new ArrayList(users * products); for (int u=0; u < users; ++u) { for (int p=0; p < products; ++p) { localUsersProducts.add(new Tuple2(u, p)); diff --git a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaIsotonicRegressionSuite.java b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaIsotonicRegressionSuite.java index d38fc91ace..32c2f4f339 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaIsotonicRegressionSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaIsotonicRegressionSuite.java @@ -18,11 +18,12 @@ package org.apache.spark.mllib.regression; import java.io.Serializable; +import java.util.ArrayList; +import java.util.Arrays; import java.util.List; import scala.Tuple3; -import com.google.common.collect.Lists; import org.junit.After; import org.junit.Assert; import org.junit.Before; @@ -36,7 +37,7 @@ public class JavaIsotonicRegressionSuite implements Serializable { private transient JavaSparkContext sc; private List> generateIsotonicInput(double[] labels) { - List> input = Lists.newArrayList(); + ArrayList> input = new ArrayList(labels.length); for (int i = 1; i <= labels.length; i++) { input.add(new Tuple3(labels[i-1], (double) i, 1d)); @@ -77,7 +78,7 @@ public class JavaIsotonicRegressionSuite implements Serializable { IsotonicRegressionModel model = runIsotonicRegression(new double[]{1, 2, 3, 3, 1, 6, 7, 8, 11, 9, 10, 12}); - JavaDoubleRDD testRDD = sc.parallelizeDoubles(Lists.newArrayList(0.0, 1.0, 9.5, 12.0, 13.0)); + JavaDoubleRDD testRDD = sc.parallelizeDoubles(Arrays.asList(0.0, 1.0, 9.5, 12.0, 13.0)); List predictions = model.predict(testRDD).collect(); Assert.assertTrue(predictions.get(0) == 1d); diff --git a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaStreamingLinearRegressionSuite.java b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaStreamingLinearRegressionSuite.java index 899c4ea607..dbf6488d41 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaStreamingLinearRegressionSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaStreamingLinearRegressionSuite.java @@ -18,11 +18,11 @@ package org.apache.spark.mllib.regression; import java.io.Serializable; +import java.util.Arrays; import java.util.List; import scala.Tuple2; -import com.google.common.collect.Lists; import org.junit.After; import org.junit.Before; import org.junit.Test; @@ -59,16 +59,16 @@ public class JavaStreamingLinearRegressionSuite implements Serializable { @Test @SuppressWarnings("unchecked") public void javaAPI() { - List trainingBatch = Lists.newArrayList( + List trainingBatch = Arrays.asList( new LabeledPoint(1.0, Vectors.dense(1.0)), new LabeledPoint(0.0, Vectors.dense(0.0))); JavaDStream training = - attachTestInputStream(ssc, Lists.newArrayList(trainingBatch, trainingBatch), 2); - List> testBatch = Lists.newArrayList( + attachTestInputStream(ssc, Arrays.asList(trainingBatch, trainingBatch), 2); + List> testBatch = Arrays.asList( new Tuple2(10, Vectors.dense(1.0)), new Tuple2(11, Vectors.dense(0.0))); JavaPairDStream test = JavaPairDStream.fromJavaDStream( - attachTestInputStream(ssc, Lists.newArrayList(testBatch, testBatch), 2)); + attachTestInputStream(ssc, Arrays.asList(testBatch, testBatch), 2)); StreamingLinearRegressionWithSGD slr = new StreamingLinearRegressionWithSGD() .setNumIterations(2) .setInitialWeights(Vectors.dense(0.0)); diff --git a/mllib/src/test/java/org/apache/spark/mllib/stat/JavaStatisticsSuite.java b/mllib/src/test/java/org/apache/spark/mllib/stat/JavaStatisticsSuite.java index eb4e369862..4795809e47 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/stat/JavaStatisticsSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/stat/JavaStatisticsSuite.java @@ -19,7 +19,8 @@ package org.apache.spark.mllib.stat; import java.io.Serializable; -import com.google.common.collect.Lists; +import java.util.Arrays; + import org.junit.After; import org.junit.Before; import org.junit.Test; @@ -50,8 +51,8 @@ public class JavaStatisticsSuite implements Serializable { @Test public void testCorr() { - JavaRDD x = sc.parallelize(Lists.newArrayList(1.0, 2.0, 3.0, 4.0)); - JavaRDD y = sc.parallelize(Lists.newArrayList(1.1, 2.2, 3.1, 4.3)); + JavaRDD x = sc.parallelize(Arrays.asList(1.0, 2.0, 3.0, 4.0)); + JavaRDD y = sc.parallelize(Arrays.asList(1.1, 2.2, 3.1, 4.3)); Double corr1 = Statistics.corr(x, y); Double corr2 = Statistics.corr(x, y, "pearson"); @@ -61,7 +62,7 @@ public class JavaStatisticsSuite implements Serializable { @Test public void kolmogorovSmirnovTest() { - JavaDoubleRDD data = sc.parallelizeDoubles(Lists.newArrayList(0.2, 1.0, -1.0, 2.0)); + JavaDoubleRDD data = sc.parallelizeDoubles(Arrays.asList(0.2, 1.0, -1.0, 2.0)); KolmogorovSmirnovTestResult testResult1 = Statistics.kolmogorovSmirnovTest(data, "norm"); KolmogorovSmirnovTestResult testResult2 = Statistics.kolmogorovSmirnovTest( data, "norm", 0.0, 1.0); @@ -69,7 +70,7 @@ public class JavaStatisticsSuite implements Serializable { @Test public void chiSqTest() { - JavaRDD data = sc.parallelize(Lists.newArrayList( + JavaRDD data = sc.parallelize(Arrays.asList( new LabeledPoint(0.0, Vectors.dense(0.1, 2.3)), new LabeledPoint(1.0, Vectors.dense(1.5, 5.1)), new LabeledPoint(0.0, Vectors.dense(2.4, 8.1)))); -- cgit v1.2.3