public Boolean call(Tuple2<Tuple3<String, String, String>, LogStatistics> s) { Tuple3<String, String, String> t3 = s._1; return (t3._1() != null); // exclude Tuple3(null,null,null) } });
@SuppressWarnings("unchecked") @Test public void cogroup3() { JavaPairRDD<String, String> categories = sc.parallelizePairs(Arrays.asList( new Tuple2<>("Apples", "Fruit"), new Tuple2<>("Oranges", "Fruit"), new Tuple2<>("Oranges", "Citrus") )); JavaPairRDD<String, Integer> prices = sc.parallelizePairs(Arrays.asList( new Tuple2<>("Oranges", 2), new Tuple2<>("Apples", 3) )); JavaPairRDD<String, Integer> quantities = sc.parallelizePairs(Arrays.asList( new Tuple2<>("Oranges", 21), new Tuple2<>("Apples", 42) )); JavaPairRDD<String, Tuple3<Iterable<String>, Iterable<Integer>, Iterable<Integer>>> cogrouped = categories.cogroup(prices, quantities); assertEquals("[Fruit, Citrus]", Iterables.toString(cogrouped.lookup("Oranges").get(0)._1())); assertEquals("[2]", Iterables.toString(cogrouped.lookup("Oranges").get(0)._2())); assertEquals("[42]", Iterables.toString(cogrouped.lookup("Apples").get(0)._3())); cogrouped.collect(); }
@SuppressWarnings("unchecked") @Test public void cogroup3() { JavaPairRDD<String, String> categories = sc.parallelizePairs(Arrays.asList( new Tuple2<>("Apples", "Fruit"), new Tuple2<>("Oranges", "Fruit"), new Tuple2<>("Oranges", "Citrus") )); JavaPairRDD<String, Integer> prices = sc.parallelizePairs(Arrays.asList( new Tuple2<>("Oranges", 2), new Tuple2<>("Apples", 3) )); JavaPairRDD<String, Integer> quantities = sc.parallelizePairs(Arrays.asList( new Tuple2<>("Oranges", 21), new Tuple2<>("Apples", 42) )); JavaPairRDD<String, Tuple3<Iterable<String>, Iterable<Integer>, Iterable<Integer>>> cogrouped = categories.cogroup(prices, quantities); assertEquals("[Fruit, Citrus]", Iterables.toString(cogrouped.lookup("Oranges").get(0)._1())); assertEquals("[2]", Iterables.toString(cogrouped.lookup("Oranges").get(0)._2())); assertEquals("[42]", Iterables.toString(cogrouped.lookup("Apples").get(0)._3())); cogrouped.collect(); }
@SuppressWarnings("unchecked") @Test public void cogroup3() { JavaPairRDD<String, String> categories = sc.parallelizePairs(Arrays.asList( new Tuple2<>("Apples", "Fruit"), new Tuple2<>("Oranges", "Fruit"), new Tuple2<>("Oranges", "Citrus") )); JavaPairRDD<String, Integer> prices = sc.parallelizePairs(Arrays.asList( new Tuple2<>("Oranges", 2), new Tuple2<>("Apples", 3) )); JavaPairRDD<String, Integer> quantities = sc.parallelizePairs(Arrays.asList( new Tuple2<>("Oranges", 21), new Tuple2<>("Apples", 42) )); JavaPairRDD<String, Tuple3<Iterable<String>, Iterable<Integer>, Iterable<Integer>>> cogrouped = categories.cogroup(prices, quantities); assertEquals("[Fruit, Citrus]", Iterables.toString(cogrouped.lookup("Oranges").get(0)._1())); assertEquals("[2]", Iterables.toString(cogrouped.lookup("Oranges").get(0)._2())); assertEquals("[42]", Iterables.toString(cogrouped.lookup("Apples").get(0)._3())); cogrouped.collect(); }
@Override public Filter[] pushFilters(Filter[] filters) { Tuple3<String, Filter[], Filter[]> tuple3 = new FilterExpressionCompiler().pushFilters(filters); whereClause = tuple3._1(); pushedFilters = tuple3._3(); return tuple3._2(); }
@Override public void write(final Kryo kryo, final Output output, final Tuple3<A, B, C> tuple3) { kryo.writeClassAndObject(output, tuple3._1()); kryo.writeClassAndObject(output, tuple3._2()); kryo.writeClassAndObject(output, tuple3._3()); }
extracted.filter((Tuple2<Tuple3<String, String, String>, LogStatistics> s) -> { Tuple3<String, String, String> t3 = s._1; return (t3._1() != null); // exclude Tuple3(null,null,null) });
private void createEdge(List<WordToken> tokens, Tuple3<Object, Object, String> edge) { WordToken govenor = tokens.get(getInt(edge._1())); WordToken dependent = tokens.get(getInt(edge._2())); String type = edge._3(); createdependency(govenor, dependent, type); }
private void createEdge(List<WordToken> tokens, Tuple3<Object, Object, String> edge) { WordToken govenor = tokens.get(getInt(edge._1())); WordToken dependent = tokens.get(getInt(edge._2())); String type = edge._3(); createdependency(govenor, dependent, type); }
@Override public void write(final Kryo kryo, final Output output, final Tuple3<A, B, C> tuple3) { kryo.writeClassAndObject(output, tuple3._1()); kryo.writeClassAndObject(output, tuple3._2()); kryo.writeClassAndObject(output, tuple3._3()); }
@Test public void runImplicitALSUsingStaticMethods() { int features = 1; int iterations = 15; int users = 80; int products = 160; Tuple3<List<Rating>, double[], double[]> testData = ALSSuite.generateRatingsAsJava(users, products, features, 0.7, true, false); JavaRDD<Rating> data = jsc.parallelize(testData._1()); MatrixFactorizationModel model = ALS.trainImplicit(data.rdd(), features, iterations); validatePrediction(model, users, products, testData._2(), 0.4, true, testData._3()); }
@Test public void runImplicitALSUsingStaticMethods() { int features = 1; int iterations = 15; int users = 80; int products = 160; Tuple3<List<Rating>, double[], double[]> testData = ALSSuite.generateRatingsAsJava(users, products, features, 0.7, true, false); JavaRDD<Rating> data = jsc.parallelize(testData._1()); MatrixFactorizationModel model = ALS.trainImplicit(data.rdd(), features, iterations); validatePrediction(model, users, products, testData._2(), 0.4, true, testData._3()); }
@Test public void runImplicitALSUsingStaticMethods() { int features = 1; int iterations = 15; int users = 80; int products = 160; Tuple3<List<Rating>, double[], double[]> testData = ALSSuite.generateRatingsAsJava(users, products, features, 0.7, true, false); JavaRDD<Rating> data = jsc.parallelize(testData._1()); MatrixFactorizationModel model = ALS.trainImplicit(data.rdd(), features, iterations); validatePrediction(model, users, products, testData._2(), 0.4, true, testData._3()); }
@Test public void runALSUsingStaticMethods() { int features = 1; int iterations = 15; int users = 50; int products = 100; Tuple3<List<Rating>, double[], double[]> testData = ALSSuite.generateRatingsAsJava(users, products, features, 0.7, false, false); JavaRDD<Rating> data = jsc.parallelize(testData._1()); MatrixFactorizationModel model = ALS.train(data.rdd(), features, iterations); validatePrediction(model, users, products, testData._2(), 0.3, false, testData._3()); }
@Test public void runALSUsingStaticMethods() { int features = 1; int iterations = 15; int users = 50; int products = 100; Tuple3<List<Rating>, double[], double[]> testData = ALSSuite.generateRatingsAsJava(users, products, features, 0.7, false, false); JavaRDD<Rating> data = jsc.parallelize(testData._1()); MatrixFactorizationModel model = ALS.train(data.rdd(), features, iterations); validatePrediction(model, users, products, testData._2(), 0.3, false, testData._3()); }
@Test public void runALSUsingStaticMethods() { int features = 1; int iterations = 15; int users = 50; int products = 100; Tuple3<List<Rating>, double[], double[]> testData = ALSSuite.generateRatingsAsJava(users, products, features, 0.7, false, false); JavaRDD<Rating> data = jsc.parallelize(testData._1()); MatrixFactorizationModel model = ALS.train(data.rdd(), features, iterations); validatePrediction(model, users, products, testData._2(), 0.3, false, testData._3()); }
@Test public void runALSUsingConstructor() { int features = 2; int iterations = 15; int users = 100; int products = 200; Tuple3<List<Rating>, double[], double[]> testData = ALSSuite.generateRatingsAsJava(users, products, features, 0.7, false, false); JavaRDD<Rating> data = jsc.parallelize(testData._1()); MatrixFactorizationModel model = new ALS().setRank(features) .setIterations(iterations) .run(data); validatePrediction(model, users, products, testData._2(), 0.3, false, testData._3()); }
@Test public void runALSUsingConstructor() { int features = 2; int iterations = 15; int users = 100; int products = 200; Tuple3<List<Rating>, double[], double[]> testData = ALSSuite.generateRatingsAsJava(users, products, features, 0.7, false, false); JavaRDD<Rating> data = jsc.parallelize(testData._1()); MatrixFactorizationModel model = new ALS().setRank(features) .setIterations(iterations) .run(data); validatePrediction(model, users, products, testData._2(), 0.3, false, testData._3()); }
@Test public void runALSUsingConstructor() { int features = 2; int iterations = 15; int users = 100; int products = 200; Tuple3<List<Rating>, double[], double[]> testData = ALSSuite.generateRatingsAsJava(users, products, features, 0.7, false, false); JavaRDD<Rating> data = jsc.parallelize(testData._1()); MatrixFactorizationModel model = new ALS().setRank(features) .setIterations(iterations) .run(data); validatePrediction(model, users, products, testData._2(), 0.3, false, testData._3()); }