diff options
Diffstat (limited to 'mllib/src/test/java/org')
-rw-r--r-- | mllib/src/test/java/org/apache/spark/mllib/recommendation/JavaALSSuite.java | 32 |
1 files changed, 23 insertions, 9 deletions
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 b40f552e0d..b150334deb 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 @@ -19,7 +19,6 @@ package org.apache.spark.mllib.recommendation; import java.io.Serializable; import java.util.List; -import java.lang.Math; import org.junit.After; import org.junit.Assert; @@ -46,7 +45,7 @@ public class JavaALSSuite implements Serializable { System.clearProperty("spark.driver.port"); } - void validatePrediction(MatrixFactorizationModel model, int users, int products, int features, + static void validatePrediction(MatrixFactorizationModel model, int users, int products, int features, DoubleMatrix trueRatings, double matchThreshold, boolean implicitPrefs, DoubleMatrix truePrefs) { DoubleMatrix predictedU = new DoubleMatrix(users, features); List<scala.Tuple2<Object, double[]>> userFeatures = model.userFeatures().toJavaRDD().collect(); @@ -84,15 +83,15 @@ public class JavaALSSuite implements Serializable { for (int p = 0; p < products; ++p) { double prediction = predictedRatings.get(u, p); double truePref = truePrefs.get(u, p); - double confidence = 1.0 + /* alpha = */ 1.0 * trueRatings.get(u, p); + double confidence = 1.0 + /* alpha = */ 1.0 * Math.abs(trueRatings.get(u, p)); double err = confidence * (truePref - prediction) * (truePref - prediction); sqErr += err; - denom += 1.0; + denom += confidence; } } double rmse = Math.sqrt(sqErr / denom); Assert.assertTrue(String.format("Confidence-weighted RMSE=%2.4f above threshold of %2.2f", - rmse, matchThreshold), Math.abs(rmse) < matchThreshold); + rmse, matchThreshold), rmse < matchThreshold); } } @@ -103,7 +102,7 @@ public class JavaALSSuite implements Serializable { int users = 50; int products = 100; scala.Tuple3<List<Rating>, DoubleMatrix, DoubleMatrix> testData = ALSSuite.generateRatingsAsJavaList( - users, products, features, 0.7, false); + users, products, features, 0.7, false, false); JavaRDD<Rating> data = sc.parallelize(testData._1()); MatrixFactorizationModel model = ALS.train(data.rdd(), features, iterations); @@ -117,7 +116,7 @@ public class JavaALSSuite implements Serializable { int users = 100; int products = 200; scala.Tuple3<List<Rating>, DoubleMatrix, DoubleMatrix> testData = ALSSuite.generateRatingsAsJavaList( - users, products, features, 0.7, false); + users, products, features, 0.7, false, false); JavaRDD<Rating> data = sc.parallelize(testData._1()); @@ -134,7 +133,7 @@ public class JavaALSSuite implements Serializable { int users = 80; int products = 160; scala.Tuple3<List<Rating>, DoubleMatrix, DoubleMatrix> testData = ALSSuite.generateRatingsAsJavaList( - users, products, features, 0.7, true); + users, products, features, 0.7, true, false); JavaRDD<Rating> data = sc.parallelize(testData._1()); MatrixFactorizationModel model = ALS.trainImplicit(data.rdd(), features, iterations); @@ -148,7 +147,7 @@ public class JavaALSSuite implements Serializable { int users = 100; int products = 200; scala.Tuple3<List<Rating>, DoubleMatrix, DoubleMatrix> testData = ALSSuite.generateRatingsAsJavaList( - users, products, features, 0.7, true); + users, products, features, 0.7, true, false); JavaRDD<Rating> data = sc.parallelize(testData._1()); @@ -158,4 +157,19 @@ public class JavaALSSuite implements Serializable { .run(data.rdd()); validatePrediction(model, users, products, features, testData._2(), 0.4, true, testData._3()); } + + @Test + public void runImplicitALSWithNegativeWeight() { + int features = 2; + int iterations = 15; + int users = 80; + int products = 160; + scala.Tuple3<List<Rating>, DoubleMatrix, DoubleMatrix> testData = ALSSuite.generateRatingsAsJavaList( + users, products, features, 0.7, true, true); + + JavaRDD<Rating> data = sc.parallelize(testData._1()); + MatrixFactorizationModel model = ALS.trainImplicit(data.rdd(), features, iterations); + validatePrediction(model, users, products, features, testData._2(), 0.4, true, testData._3()); + } + } |