diff --git "a/datumbox_datumbox-framework-0.8.2.json" "b/datumbox_datumbox-framework-0.8.2.json" new file mode 100644--- /dev/null +++ "b/datumbox_datumbox-framework-0.8.2.json" @@ -0,0 +1,30644 @@ +[ + { + "task_id": "datumbox_datumbox-framework-0.8.2__05ca32e_d8b5d1e__BernoulliNaiveBayesTest_1fff4d33", + "project_name": "datumbox_datumbox-framework-0.8.2", + "git_clone_url": "https://github.com/datumbox/datumbox-framework.git", + "rev1": "05ca32efa4fd656be9c3954d1f2c9a8f43401c40", + "rev2": "d8b5d1e09a4c69baef55ddb966e75937b54d918e", + "rev1_date": "2016-12-23T23:11:16Z", + "rev2_date": "2016-12-24T00:59:48Z", + "git_diff_url": "https://github.com/datumbox/datumbox-framework/compare/05ca32efa4fd656be9c3954d1f2c9a8f43401c40...d8b5d1e09a4c69baef55ddb966e75937b54d918e", + "test_file": "datumbox-framework-core/src/test/java/com/datumbox/framework/core/machinelearning/classification/BernoulliNaiveBayesTest.java", + "test_changes": [ + { + "change_id": "218_datumbox_datumbox-framework-0.8.2_05ca32e_d8b5d1e_BernoulliNaiveBayesTest", + "test_sign": "com/datumbox/framework/core/machinelearning/classification/BernoulliNaiveBayesTest.testKFoldCrossValidation:()V", + "class": "BernoulliNaiveBayesTest", + "method": "testKFoldCrossValidation", + "module": "datumbox-framework-core", + "junit_selector": "com.datumbox.framework.core.machinelearning.classification.BernoulliNaiveBayesTest#testKFoldCrossValidation", + "old_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"testKFoldCrossValidation\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.carsNumeric(conf);\n Dataframe trainingData = data[0];\n data[1].close();\n ClassificationMetrics vm = new KFoldValidator<>(ClassificationMetrics.class, conf, k).validate(trainingData, new BernoulliNaiveBayes.TrainingParameters());\n double expResult = 0.6631318681318682;\n double result = vm.getMacroF1();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n trainingData.close();\n}", + "new_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"testKFoldCrossValidation\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.carsNumeric(conf);\n Dataframe trainingData = data[0];\n data[1].close();\n ClassificationMetrics vm = new Validator<>(ClassificationMetrics.class, conf).validate(new KFoldSplitter(k).split(trainingData), new BernoulliNaiveBayes.TrainingParameters());\n double expResult = 0.6631318681318682;\n double result = vm.getMacroF1();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n trainingData.close();\n}", + "old_test_file_path": [ + "datumbox-framework-core/src/test/java/com/datumbox/framework/core/machinelearning/classification/BernoulliNaiveBayesTest.java" + ], + "old_test_lnum": [ + "89-108" + ], + "new_test_lnum": [ + "90-110" + ], + "old_production_code": "", + 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"java_version": 11 + }, + { + "task_id": "datumbox_datumbox-framework-0.8.2__05ca32e_d8b5d1e__BinarizedNaiveBayesTest_1fff4d33", + "project_name": "datumbox_datumbox-framework-0.8.2", + "git_clone_url": "https://github.com/datumbox/datumbox-framework.git", + "rev1": "05ca32efa4fd656be9c3954d1f2c9a8f43401c40", + "rev2": "d8b5d1e09a4c69baef55ddb966e75937b54d918e", + "rev1_date": "2016-12-23T23:11:16Z", + "rev2_date": "2016-12-24T00:59:48Z", + "git_diff_url": "https://github.com/datumbox/datumbox-framework/compare/05ca32efa4fd656be9c3954d1f2c9a8f43401c40...d8b5d1e09a4c69baef55ddb966e75937b54d918e", + "test_file": "datumbox-framework-core/src/test/java/com/datumbox/framework/core/machinelearning/classification/BinarizedNaiveBayesTest.java", + "test_changes": [ + { + "change_id": "224_datumbox_datumbox-framework-0.8.2_05ca32e_d8b5d1e_BinarizedNaiveBayesTest", + "test_sign": "com/datumbox/framework/core/machinelearning/classification/BinarizedNaiveBayesTest.testKFoldCrossValidation:()V", + 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Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.carsNumeric(conf);\n Dataframe trainingData = data[0];\n data[1].close();\n ClassificationMetrics vm = new Validator<>(ClassificationMetrics.class, conf).validate(new KFoldSplitter(k).split(trainingData), new BinarizedNaiveBayes.TrainingParameters());\n double expResult = 0.6631318681318682;\n double result = vm.getMacroF1();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n trainingData.close();\n}", + "old_test_file_path": [ + "datumbox-framework-core/src/test/java/com/datumbox/framework/core/machinelearning/classification/BinarizedNaiveBayesTest.java" + ], + "old_test_lnum": [ + "89-108" + ], + "new_test_lnum": [ + "90-110" + ], + "old_production_code": "", + "new_production_code": "public VM validate(Iterable dataSplits, TrainingParameters trainingParameters) {\n AbstractModeler modeler = MLBuilder.create(trainingParameters, conf);\n List validationMetricsList = new LinkedList<>();\n for 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Datasets.carsNumeric(conf);\n Dataframe trainingData = data[0];\n data[1].close();\n SupportVectorMachine.TrainingParameters param = new SupportVectorMachine.TrainingParameters();\n param.getSvmParameter().kernel_type = svm_parameter.LINEAR;\n ClassificationMetrics vm = new KFoldValidator<>(ClassificationMetrics.class, conf, k).validate(trainingData, param);\n double expResult = 0.5861704961704961;\n double result = vm.getMacroF1();\n Assert.assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n trainingData.close();\n}", + "new_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"testKFoldCrossValidation\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.carsNumeric(conf);\n Dataframe trainingData = data[0];\n data[1].close();\n SupportVectorMachine.TrainingParameters param = new SupportVectorMachine.TrainingParameters();\n param.getSvmParameter().kernel_type = svm_parameter.LINEAR;\n 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expResult = 1.0;\n double result = vm.getPurity();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n trainingData.close();\n}", + "new_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"testKFoldCrossValidation\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.gaussianClusters(conf);\n Dataframe trainingData = data[0];\n data[1].close();\n GaussianDPMM.TrainingParameters param = new GaussianDPMM.TrainingParameters();\n param.setAlpha(0.01);\n param.setMaxIterations(100);\n param.setInitializationMethod(GaussianDPMM.TrainingParameters.Initialization.ONE_CLUSTER_PER_RECORD);\n param.setKappa0(0);\n param.setNu0(1);\n param.setMu0(new double[] { 0.0, 0.0 });\n param.setPsi0(new double[][] { { 1.0, 0.0 }, { 0.0, 1.0 } });\n ClusteringMetrics vm = new Validator<>(ClusteringMetrics.class, conf).validate(new KFoldSplitter(k).split(trainingData), param);\n double expResult = 1.0;\n double result 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data = Datasets.heartDiseaseClusters(conf);\n Dataframe trainingData = data[0];\n data[1].close();\n DummyXYMinMaxNormalizer df = MLBuilder.create(new DummyXYMinMaxNormalizer.TrainingParameters(), conf);\n df.fit_transform(trainingData);\n HierarchicalAgglomerative.TrainingParameters param = new HierarchicalAgglomerative.TrainingParameters();\n param.setDistanceMethod(HierarchicalAgglomerative.TrainingParameters.Distance.EUCLIDIAN);\n param.setLinkageMethod(HierarchicalAgglomerative.TrainingParameters.Linkage.COMPLETE);\n param.setMinClustersThreshold(2);\n param.setMaxDistanceThreshold(Double.MAX_VALUE);\n ClusteringMetrics vm = new KFoldValidator<>(ClusteringMetrics.class, conf, k).validate(trainingData, param);\n df.denormalize(trainingData);\n double expResult = 0.7666666666666667;\n double result = vm.getPurity();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n df.close();\n trainingData.close();\n}", + "new_test_code": "@Test\npublic void 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testKFoldCrossValidation() {\n logger.info(\"testKFoldCrossValidation\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.housingNumerical(conf);\n Dataframe trainingData = data[0];\n data[1].close();\n DummyXYMinMaxNormalizer df = MLBuilder.create(new DummyXYMinMaxNormalizer.TrainingParameters(), conf);\n df.fit_transform(trainingData);\n PCA.TrainingParameters featureSelectorParameters = new PCA.TrainingParameters();\n featureSelectorParameters.setMaxDimensions(trainingData.xColumnSize() - 1);\n featureSelectorParameters.setWhitened(false);\n featureSelectorParameters.setVariancePercentageThreshold(0.99999995);\n PCA featureSelector = MLBuilder.create(featureSelectorParameters, conf);\n featureSelector.fit_transform(trainingData);\n featureSelector.close();\n NLMS.TrainingParameters param = new NLMS.TrainingParameters();\n param.setTotalIterations(500);\n param.setL1(0.001);\n param.setL2(0.001);\n LinearRegressionMetrics vm = new KFoldValidator<>(LinearRegressionMetrics.class, conf, k).validate(trainingData, param);\n df.denormalize(trainingData);\n double expResult = 0.7748106446239166;\n double result = vm.getRSquare();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n df.close();\n trainingData.close();\n}", + "new_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"testKFoldCrossValidation\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.housingNumerical(conf);\n Dataframe trainingData = data[0];\n data[1].close();\n DummyXYMinMaxNormalizer df = MLBuilder.create(new DummyXYMinMaxNormalizer.TrainingParameters(), conf);\n df.fit_transform(trainingData);\n PCA.TrainingParameters featureSelectorParameters = new PCA.TrainingParameters();\n featureSelectorParameters.setMaxDimensions(trainingData.xColumnSize() - 1);\n featureSelectorParameters.setWhitened(false);\n 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HashMap<>();\n try {\n dataset.put(\"negative\", this.getClass().getClassLoader().getResource(\"datasets/sentimentAnalysis.neg.txt\").toURI());\n dataset.put(\"positive\", this.getClass().getClassLoader().getResource(\"datasets/sentimentAnalysis.pos.txt\").toURI());\n } catch (UncheckedIOException | URISyntaxException ex) {\n logger.warn(\"Unable to download datasets, skipping test.\");\n throw new RuntimeException(ex);\n }\n UniqueWordSequenceExtractor wsExtractor = new UniqueWordSequenceExtractor(new UniqueWordSequenceExtractor.Parameters());\n Dataframe trainingData = Dataframe.Builder.parseTextFiles(dataset, wsExtractor, conf);\n LatentDirichletAllocation.TrainingParameters trainingParameters = new LatentDirichletAllocation.TrainingParameters();\n trainingParameters.setMaxIterations(15);\n trainingParameters.setAlpha(0.01);\n trainingParameters.setBeta(0.01);\n trainingParameters.setK(25);\n LatentDirichletAllocation lda = MLBuilder.create(trainingParameters, conf);\n 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Datasets.carsNumeric(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n String dbName = this.getClass().getSimpleName();\n MultinomialNaiveBayes instance = new MultinomialNaiveBayes(dbName, conf);\n MultinomialNaiveBayes.TrainingParameters param = new MultinomialNaiveBayes.TrainingParameters();\n param.setMultiProbabilityWeighted(true);\n MultinomialNaiveBayes.ValidationMetrics vm = instance.kFoldCrossValidation(trainingData, param, k);\n double expResult = 0.6631318681318682;\n double result = vm.getMacroF1();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n instance.delete();\n trainingData.delete();\n}", + "new_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"kFoldCrossValidation\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.carsNumeric(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n String dbName = this.getClass().getSimpleName();\n 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Datasets.winesOrdinal(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n String dbName = this.getClass().getSimpleName();\n DummyXMinMaxNormalizer df = new DummyXMinMaxNormalizer(dbName, conf);\n df.fit_transform(trainingData, new DummyXMinMaxNormalizer.TrainingParameters());\n OrdinalRegression instance = new OrdinalRegression(dbName, conf);\n OrdinalRegression.TrainingParameters param = new OrdinalRegression.TrainingParameters();\n param.setTotalIterations(100);\n param.setL2(0.001);\n OrdinalRegression.ValidationMetrics vm = instance.kFoldCrossValidation(trainingData, param, k);\n df.denormalize(trainingData);\n double expResult = 0.9823403146614675;\n double result = vm.getMacroF1();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n df.delete();\n instance.delete();\n trainingData.delete();\n}", + "new_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"kFoldCrossValidation\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.winesOrdinal(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n String dbName = this.getClass().getSimpleName();\n DummyXMinMaxNormalizer df = new DummyXMinMaxNormalizer(dbName, conf);\n df.fit_transform(trainingData, new DummyXMinMaxNormalizer.TrainingParameters());\n OrdinalRegression instance = new OrdinalRegression(dbName, conf);\n OrdinalRegression.TrainingParameters param = new OrdinalRegression.TrainingParameters();\n param.setTotalIterations(100);\n param.setL2(0.001);\n ClassifierValidator.ValidationMetrics vm = instance.kFoldCrossValidation(trainingData, param, k);\n df.denormalize(trainingData);\n double expResult = 0.9823403146614675;\n double result = vm.getMacroF1();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n df.delete();\n instance.delete();\n trainingData.delete();\n}", + "old_test_file_path": [ + 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Constants.DOUBLE_ACCURACY_HIGH);\n instance.delete();\n trainingData.delete();\n}", + "old_test_file_path": [ + "datumbox-framework-core/src/test/java/com/datumbox/framework/core/machinelearning/clustering/GaussianDPMMTest.java" + ], + "old_test_lnum": [ + "87-121" + ], + "new_test_lnum": [ + "88-122" + ], + "old_production_code": "@Override\npublic void delete() {\n kb().delete();\n}", + "new_production_code": "@Override\npublic void delete() {\n knowledgeBase.delete();\n}", + "focal_file_path": "datumbox-framework-core/src/main/java/com/datumbox/framework/core/machinelearning/common/abstracts/AbstractTrainer.java", + "focal_method_sign": "com/datumbox/framework/core/machinelearning/common/abstracts/AbstractTrainer.delete:()V", + "focal_all_deps_scored": [ + { + "dep_id": 2, + "method_sign": "com/datumbox/framework/core/machinelearning/common/abstracts/AbstractTrainer.delete:()V", + "file_path_old": 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PCA.TrainingParameters();\n featureSelectorParameters.setMaxDimensions(trainingData.xColumnSize() - 1);\n featureSelectorParameters.setWhitened(false);\n featureSelectorParameters.setVariancePercentageThreshold(0.99999995);\n featureSelector.fit_transform(trainingData, featureSelectorParameters);\n featureSelector.delete();\n NLMS instance = new NLMS(dbName, conf);\n NLMS.TrainingParameters param = new NLMS.TrainingParameters();\n param.setTotalIterations(500);\n param.setL1(0.001);\n param.setL2(0.001);\n NLMS.ValidationMetrics vm = instance.kFoldCrossValidation(trainingData, param, k);\n df.denormalize(trainingData);\n double expResult = 0.7748106446239166;\n double result = vm.getRSquare();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n df.delete();\n instance.delete();\n trainingData.delete();\n}", + "new_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"kFoldCrossValidation\");\n Configuration conf = 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Datasets.regressionMixed(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n String dbName = this.getClass().getSimpleName();\n DummyXYMinMaxNormalizer df = new DummyXYMinMaxNormalizer(dbName, conf);\n df.fit_transform(trainingData, new DummyXYMinMaxNormalizer.TrainingParameters());\n MatrixLinearRegression instance = new MatrixLinearRegression(dbName, conf);\n MatrixLinearRegression.TrainingParameters param = new MatrixLinearRegression.TrainingParameters();\n LinearRegressionValidator.ValidationMetrics vm = instance.kFoldCrossValidation(trainingData, param, k);\n df.denormalize(trainingData);\n double expResult = 1;\n double result = vm.getRSquare();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n df.delete();\n instance.delete();\n trainingData.delete();\n}", + "new_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"kFoldCrossValidation\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] 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Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.housingNumerical(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n String dbName = this.getClass().getSimpleName();\n DummyXYMinMaxNormalizer df = new DummyXYMinMaxNormalizer(dbName, conf);\n df.fit_transform(trainingData, new DummyXYMinMaxNormalizer.TrainingParameters());\n PCA featureSelector = new PCA(dbName, conf);\n PCA.TrainingParameters featureSelectorParameters = new PCA.TrainingParameters();\n featureSelectorParameters.setMaxDimensions(trainingData.xColumnSize() - 1);\n featureSelectorParameters.setWhitened(false);\n featureSelectorParameters.setVariancePercentageThreshold(0.99999995);\n featureSelector.fit_transform(trainingData, featureSelectorParameters);\n featureSelector.delete();\n NLMS instance = new NLMS(dbName, conf);\n NLMS.TrainingParameters param = new NLMS.TrainingParameters();\n param.setTotalIterations(500);\n param.setL1(0.001);\n param.setL2(0.001);\n 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OrdinalRegression(dbName, conf);\n OrdinalRegression.TrainingParameters param = new OrdinalRegression.TrainingParameters();\n param.setTotalIterations(100);\n param.setL2(0.001);\n OrdinalRegression.ValidationMetrics vm = instance.kFoldCrossValidation(trainingData, param, k);\n df.denormalize(trainingData);\n double expResult = 0.9823403146614675;\n double result = vm.getMacroF1();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n df.delete();\n instance.delete();\n trainingData.delete();\n}", + "new_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"kFoldCrossValidation\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.winesOrdinal(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n String dbName = this.getClass().getSimpleName();\n DummyXMinMaxNormalizer df = new DummyXMinMaxNormalizer(dbName, conf);\n df.fit_transform(trainingData, new 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param, k);\n df.denormalize(trainingData);\n double expResult = 0.7557492507492508;\n double result = vm.getMacroF1();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n df.delete();\n instance.delete();\n trainingData.delete();\n}", + "new_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"kFoldCrossValidation\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.carsNumeric(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n String dbName = this.getClass().getSimpleName();\n XMinMaxNormalizer df = new XMinMaxNormalizer(dbName, conf);\n df.fit_transform(trainingData, new XMinMaxNormalizer.TrainingParameters());\n SoftMaxRegression instance = new SoftMaxRegression(dbName, conf);\n SoftMaxRegression.TrainingParameters param = new SoftMaxRegression.TrainingParameters();\n param.setTotalIterations(30);\n param.setL1(0.0001);\n param.setL2(0.0001);\n ClassifierValidator.ValidationMetrics vm = instance.kFoldCrossValidation(trainingData, param, k);\n df.denormalize(trainingData);\n double expResult = 0.7557492507492508;\n double result = vm.getMacroF1();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n df.delete();\n instance.delete();\n trainingData.delete();\n}", + "old_test_file_path": [ + "datumbox-framework-core/src/test/java/com/datumbox/framework/core/machinelearning/classification/SoftMaxRegressionTest.java" + ], + "old_test_lnum": [ + "106-141" + ], + "new_test_lnum": [ + "107-142" + ], + "old_production_code": "public void denormalize(Dataframe data) {\n logger.info(\"denormalize()\");\n kb().load();\n _denormalize(data);\n}", + "new_production_code": "public void denormalize(Dataframe data) {\n logger.info(\"denormalize()\");\n knowledgeBase.load();\n _denormalize(data);\n}", + "focal_file_path": 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Configuration conf = Configuration.getConfiguration();\n Dataframe[] data = Datasets.recommenderSystemFood(conf);\n Dataframe trainingData = data[0];\n Dataframe validationData = data[1];\n String dbName = this.getClass().getSimpleName();\n CollaborativeFiltering instance = new CollaborativeFiltering(dbName, conf);\n CollaborativeFiltering.TrainingParameters param = new CollaborativeFiltering.TrainingParameters();\n param.setSimilarityMethod(CollaborativeFiltering.TrainingParameters.SimilarityMeasure.PEARSONS_CORRELATION);\n instance.fit(trainingData, param);\n instance.close();\n instance = new CollaborativeFiltering(dbName, conf);\n CollaborativeFiltering.ValidationMetrics vm = instance.validate(validationData);\n Map expResult = new HashMap<>();\n expResult.put(\"pitta\", 4.686394033077408);\n expResult.put(\"burger\", 4.68408210680137);\n expResult.put(\"pizza\", 4.6194430718558745);\n expResult.put(\"chocolate\", 4.580630241051733);\n expResult.put(\"potato\", 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dbName = this.getClass().getSimpleName();\n DummyXYMinMaxNormalizer df = new DummyXYMinMaxNormalizer(dbName, conf);\n df.fit_transform(trainingData, new DummyXYMinMaxNormalizer.TrainingParameters());\n PCA featureSelector = new PCA(dbName, conf);\n PCA.TrainingParameters featureSelectorParameters = new PCA.TrainingParameters();\n featureSelectorParameters.setMaxDimensions(trainingData.xColumnSize() - 1);\n featureSelectorParameters.setWhitened(false);\n featureSelectorParameters.setVariancePercentageThreshold(0.99999995);\n featureSelector.fit_transform(trainingData, featureSelectorParameters);\n featureSelector.delete();\n NLMS instance = new NLMS(dbName, conf);\n NLMS.TrainingParameters param = new NLMS.TrainingParameters();\n param.setTotalIterations(500);\n param.setL1(0.001);\n param.setL2(0.001);\n NLMS.ValidationMetrics vm = instance.kFoldCrossValidation(trainingData, param, k);\n df.denormalize(trainingData);\n double expResult = 0.7748106446239166;\n double result = vm.getRSquare();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n df.delete();\n instance.delete();\n trainingData.delete();\n}", + "new_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"kFoldCrossValidation\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.housingNumerical(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n String dbName = this.getClass().getSimpleName();\n DummyXYMinMaxNormalizer df = new DummyXYMinMaxNormalizer(dbName, conf);\n df.fit_transform(trainingData, new DummyXYMinMaxNormalizer.TrainingParameters());\n PCA featureSelector = new PCA(dbName, conf);\n PCA.TrainingParameters featureSelectorParameters = new PCA.TrainingParameters();\n featureSelectorParameters.setMaxDimensions(trainingData.xColumnSize() - 1);\n featureSelectorParameters.setWhitened(false);\n featureSelectorParameters.setVariancePercentageThreshold(0.99999995);\n 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param, k);\n double expResult = 1.0;\n double result = vm.getPurity();\n Assert.assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n instance.delete();\n trainingData.delete();\n}", + "new_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"validate\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.multinomialClusters(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n String dbName = this.getClass().getSimpleName();\n MultinomialDPMM instance = new MultinomialDPMM(dbName, conf);\n MultinomialDPMM.TrainingParameters param = new MultinomialDPMM.TrainingParameters();\n param.setAlpha(0.01);\n param.setMaxIterations(100);\n param.setInitializationMethod(MultinomialDPMM.TrainingParameters.Initialization.ONE_CLUSTER_PER_RECORD);\n param.setAlphaWords(1);\n ClusteringMetrics vm = instance.kFoldCrossValidation(trainingData, param, k);\n double expResult = 1.0;\n double result = 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Datasets.featureSelectorCategorical(conf, 1000);\n Dataframe trainingData = data[0];\n Dataframe validationData = data[1];\n String dbName = this.getClass().getSimpleName();\n MutualInformation.TrainingParameters param = new MutualInformation.TrainingParameters();\n param.setRareFeatureThreshold(2);\n param.setMaxFeatures(5);\n param.setIgnoringNumericalFeatures(false);\n MutualInformation instance = new MutualInformation(dbName, conf, param);\n instance.fit_transform(trainingData);\n instance.close();\n instance = new MutualInformation(dbName, conf);\n instance.transform(validationData);\n Set expResult = new HashSet<>(Arrays.asList(\"high_paid\", \"has_boat\", \"has_luxury_car\", \"has_butler\", \"has_pool\"));\n Set result = trainingData.getXDataTypes().keySet();\n assertEquals(expResult, result);\n instance.delete();\n trainingData.delete();\n validationData.delete();\n}", + "new_test_code": "@Test\npublic void testSelectFeatures() {\n logger.info(\"selectFeatures\");\n 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"change_id": "273_datumbox_datumbox-framework-0.8.2_97531c2_8281722_ModelerTest", + "test_sign": "com/datumbox/framework/applications/datamodeling/ModelerTest.testTrainAndValidate:()V", + "class": "ModelerTest", + "method": "testTrainAndValidate", + "module": "datumbox-framework-applications", + "junit_selector": "com.datumbox.framework.applications.datamodeling.ModelerTest#testTrainAndValidate", + "old_test_code": "@Test\npublic void testTrainAndValidate() {\n logger.info(\"testTrainAndValidate\");\n Configuration conf = Configuration.getConfiguration();\n Dataframe[] data = Datasets.carsNumeric(conf);\n Dataframe trainingData = data[0];\n Dataframe validationData = data[1];\n String dbName = this.getClass().getSimpleName();\n Modeler.TrainingParameters trainingParameters = new Modeler.TrainingParameters();\n trainingParameters.setModelerClass(MultinomialNaiveBayes.class);\n MultinomialNaiveBayes.TrainingParameters modelTrainingParameters = new 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foldSize = n / k;\n Integer[] ids = new Integer[n];\n int j = 0;\n for (Integer rId : dataset.index()) {\n ids[j] = rId;\n ++j;\n }\n PHPMethods.shuffle(ids);\n Class aClass = null;\n try {\n String className = trainingParameters.getClass().getCanonicalName();\n aClass = (Class) Class.forName(className.substring(0, className.lastIndexOf('.')));\n } catch (ClassNotFoundException e) {\n throw new IllegalArgumentException(e);\n }\n AbstractModeler modeler = Trainable.newInstance(aClass, \"kfold_\" + RandomGenerator.getThreadLocalRandomUnseeded().nextLong(), conf, trainingParameters);\n List validationMetricsList = new LinkedList<>();\n for (int fold = 0; fold < k; ++fold) {\n logger.info(\"Kfold {}\", fold);\n FlatDataList foldTrainingIds = new FlatDataList(new ArrayList<>(n - foldSize));\n FlatDataList foldValidationIds = new FlatDataList(new ArrayList<>(foldSize));\n for (int i = 0; i < n; ++i) {\n boolean isInValidationFoldRange = false;\n if (fold * foldSize <= i && i < (fold + 1) * foldSize) {\n isInValidationFoldRange = true;\n }\n if (isInValidationFoldRange) {\n foldValidationIds.add(ids[i]);\n } else {\n foldTrainingIds.add(ids[i]);\n }\n }\n if (k == 1) {\n foldTrainingIds = foldValidationIds;\n }\n Dataframe trainingData = dataset.getSubset(foldTrainingIds);\n modeler.fit(trainingData);\n trainingData.delete();\n Dataframe validationData = dataset.getSubset(foldValidationIds);\n modeler.predict(validationData);\n VM entrySample = ValidationMetrics.newInstance(vmClass, validationData);\n validationData.delete();\n validationMetricsList.add(entrySample);\n }\n modeler.delete();\n VM avgValidationMetrics = ValidationMetrics.newInstance(vmClass, validationMetricsList);\n return avgValidationMetrics;\n}", + "new_production_code": "@Override\npublic VM validate(Dataframe dataset, TrainingParameters trainingParameters) {\n int n = dataset.size();\n if (k <= 0 || n <= k) {\n throw new IllegalArgumentException(\"Invalid number of folds.\");\n }\n int foldSize = n / k;\n Integer[] ids = new Integer[n];\n int j = 0;\n for (Integer rId : dataset.index()) {\n ids[j] = rId;\n ++j;\n }\n PHPMethods.shuffle(ids);\n AbstractModeler modeler = Trainable.newInstance(trainingParameters, \"kfold_\" + RandomGenerator.getThreadLocalRandomUnseeded().nextLong(), conf);\n List validationMetricsList = new LinkedList<>();\n for (int fold = 0; fold < k; ++fold) {\n logger.info(\"Kfold {}\", fold);\n FlatDataList foldTrainingIds = new FlatDataList(new ArrayList<>(n - foldSize));\n FlatDataList foldValidationIds = new FlatDataList(new ArrayList<>(foldSize));\n for (int i = 0; i < n; ++i) {\n boolean isInValidationFoldRange = false;\n if (fold * foldSize <= i && i < (fold + 1) * foldSize) {\n isInValidationFoldRange = true;\n }\n if (isInValidationFoldRange) {\n foldValidationIds.add(ids[i]);\n } else {\n foldTrainingIds.add(ids[i]);\n }\n }\n if (k == 1) {\n foldTrainingIds = foldValidationIds;\n }\n Dataframe trainingData = dataset.getSubset(foldTrainingIds);\n 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"com/datumbox/framework/core/machinelearning/ensemblelearning/BootstrapAggregatingTest.testKFoldCrossValidation:()V", + "class": "BootstrapAggregatingTest", + "method": "testKFoldCrossValidation", + "module": "datumbox-framework-core", + "junit_selector": "com.datumbox.framework.core.machinelearning.ensemblelearning.BootstrapAggregatingTest#testKFoldCrossValidation", + "old_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"validate\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.carsNumeric(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n BootstrapAggregating.TrainingParameters param = new BootstrapAggregating.TrainingParameters();\n param.setMaxWeakClassifiers(5);\n param.setWeakClassifierClass(MultinomialNaiveBayes.class);\n MultinomialNaiveBayes.TrainingParameters trainingParameters = new MultinomialNaiveBayes.TrainingParameters();\n 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"257_datumbox_datumbox-framework-0.8.2_b8da962_b1e75b4_BinarizedNaiveBayesTest", + "test_sign": "com/datumbox/framework/core/machinelearning/classification/BinarizedNaiveBayesTest.testKFoldCrossValidation:()V", + "class": "BinarizedNaiveBayesTest", + "method": "testKFoldCrossValidation", + "module": "datumbox-framework-core", + "junit_selector": "com.datumbox.framework.core.machinelearning.classification.BinarizedNaiveBayesTest#testKFoldCrossValidation", + "old_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"validate\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.carsNumeric(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n String dbName = this.getClass().getSimpleName();\n BinarizedNaiveBayes instance = new BinarizedNaiveBayes(dbName, conf);\n BinarizedNaiveBayes.TrainingParameters param = new BinarizedNaiveBayes.TrainingParameters();\n ClassificationMetrics vm = 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"254_datumbox_datumbox-framework-0.8.2_b8da962_b1e75b4_MaximumEntropyTest", + "test_sign": "com/datumbox/framework/core/machinelearning/classification/MaximumEntropyTest.testKFoldCrossValidation:()V", + "class": "MaximumEntropyTest", + "method": "testKFoldCrossValidation", + "module": "datumbox-framework-core", + "junit_selector": "com.datumbox.framework.core.machinelearning.classification.MaximumEntropyTest#testKFoldCrossValidation", + "old_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"validate\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.carsNumeric(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n String dbName = this.getClass().getSimpleName();\n MaximumEntropy instance = new MaximumEntropy(dbName, conf);\n MaximumEntropy.TrainingParameters param = new MaximumEntropy.TrainingParameters();\n param.setTotalIterations(10);\n ClassificationMetrics vm = instance.kFoldCrossValidation(trainingData, param, k);\n double expResult = 0.6051098901098901;\n double result = vm.getMacroF1();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n instance.delete();\n trainingData.delete();\n}", + "new_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"validate\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.carsNumeric(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n MaximumEntropy.TrainingParameters param = new MaximumEntropy.TrainingParameters();\n param.setTotalIterations(10);\n ClassificationMetrics vm = new KFoldValidator<>(ClassificationMetrics.class, conf, k).validate(trainingData, param);\n double expResult = 0.6051098901098901;\n double result = vm.getMacroF1();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n trainingData.delete();\n}", + "old_test_file_path": [ + 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conf);\n List validationMetricsList = new LinkedList<>();\n for (int fold = 0; fold < k; ++fold) {\n logger.info(\"Kfold {}\", fold);\n FlatDataList foldTrainingIds = new FlatDataList(new ArrayList<>(n - foldSize));\n FlatDataList foldValidationIds = new FlatDataList(new ArrayList<>(foldSize));\n for (int i = 0; i < n; ++i) {\n boolean isInValidationFoldRange = false;\n if (fold * foldSize <= i && i < (fold + 1) * foldSize) {\n isInValidationFoldRange = true;\n }\n if (isInValidationFoldRange) {\n foldValidationIds.add(ids[i]);\n } else {\n foldTrainingIds.add(ids[i]);\n }\n }\n if (k == 1) {\n foldTrainingIds = foldValidationIds;\n }\n Dataframe trainingData = dataset.getSubset(foldTrainingIds);\n modeler.fit(trainingData, (AbstractTrainer.AbstractTrainingParameters) trainingParameters);\n trainingData.delete();\n Dataframe validationData = dataset.getSubset(foldValidationIds);\n modeler.predict(validationData);\n VM entrySample = ValidationMetrics.newInstance(vmClass, 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OrdinalRegression.TrainingParameters param = new OrdinalRegression.TrainingParameters();\n param.setTotalIterations(100);\n param.setL2(0.001);\n ClassificationMetrics vm = instance.kFoldCrossValidation(trainingData, param, k);\n df.denormalize(trainingData);\n double expResult = 0.9823403146614675;\n double result = vm.getMacroF1();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n df.delete();\n instance.delete();\n trainingData.delete();\n}", + "new_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"validate\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.winesOrdinal(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n String dbName = this.getClass().getSimpleName();\n DummyXMinMaxNormalizer df = new DummyXMinMaxNormalizer(dbName, conf);\n df.fit_transform(trainingData, new DummyXMinMaxNormalizer.TrainingParameters());\n OrdinalRegression.TrainingParameters param = new 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vm.getMacroF1();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n df.delete();\n instance.delete();\n trainingData.delete();\n}", + "new_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"validate\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.carsNumeric(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n String dbName = this.getClass().getSimpleName();\n XMinMaxNormalizer df = new XMinMaxNormalizer(dbName, conf);\n df.fit_transform(trainingData, new XMinMaxNormalizer.TrainingParameters());\n SoftMaxRegression.TrainingParameters param = new SoftMaxRegression.TrainingParameters();\n param.setTotalIterations(30);\n param.setL1(0.0001);\n param.setL2(0.0001);\n ClassificationMetrics vm = new KFoldValidator<>(ClassificationMetrics.class, conf, k).validate(trainingData, param);\n df.denormalize(trainingData);\n double expResult = 0.7557492507492508;\n double result = 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"old_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"validate\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.gaussianClusters(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n String dbName = this.getClass().getSimpleName();\n GaussianDPMM instance = new GaussianDPMM(dbName, conf);\n GaussianDPMM.TrainingParameters param = new GaussianDPMM.TrainingParameters();\n param.setAlpha(0.01);\n param.setMaxIterations(100);\n param.setInitializationMethod(GaussianDPMM.TrainingParameters.Initialization.ONE_CLUSTER_PER_RECORD);\n param.setKappa0(0);\n param.setNu0(1);\n param.setMu0(new double[] { 0.0, 0.0 });\n param.setPsi0(new double[][] { { 1.0, 0.0 }, { 0.0, 1.0 } });\n ClusteringMetrics vm = instance.kFoldCrossValidation(trainingData, param, k);\n double expResult = 1.0;\n double result = vm.getPurity();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n instance.delete();\n trainingData.delete();\n}", + "new_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"validate\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.gaussianClusters(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n GaussianDPMM.TrainingParameters param = new GaussianDPMM.TrainingParameters();\n param.setAlpha(0.01);\n param.setMaxIterations(100);\n param.setInitializationMethod(GaussianDPMM.TrainingParameters.Initialization.ONE_CLUSTER_PER_RECORD);\n param.setKappa0(0);\n param.setNu0(1);\n param.setMu0(new double[] { 0.0, 0.0 });\n param.setPsi0(new double[][] { { 1.0, 0.0 }, { 0.0, 1.0 } });\n ClusteringMetrics vm = new KFoldValidator<>(ClusteringMetrics.class, conf, k).validate(trainingData, param);\n double expResult = 1.0;\n double result = vm.getPurity();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n trainingData.delete();\n}", + "old_test_file_path": [ + "datumbox-framework-core/src/test/java/com/datumbox/framework/core/machinelearning/clustering/GaussianDPMMTest.java" + ], + "old_test_lnum": [ + "84-118" + ], + "new_test_lnum": [ + "86-117" + ], + "old_production_code": "", + "new_production_code": "@Override\npublic VM validate(Dataframe dataset, TrainingParameters trainingParameters) {\n int n = dataset.size();\n if (k <= 0 || n <= k) {\n throw new IllegalArgumentException(\"Invalid number of folds.\");\n }\n int foldSize = n / k;\n Integer[] ids = new Integer[n];\n int j = 0;\n for (Integer rId : dataset.index()) {\n ids[j] = rId;\n ++j;\n }\n PHPMethods.shuffle(ids);\n Class aClass = null;\n try {\n String className = trainingParameters.getClass().getCanonicalName();\n aClass = (Class) Class.forName(className.substring(0, className.lastIndexOf('.')));\n } catch (ClassNotFoundException e) {\n throw new IllegalArgumentException(e);\n }\n AbstractModeler modeler = Trainable.newInstance(aClass, \"kfold_\" + System.nanoTime(), conf);\n List validationMetricsList = new LinkedList<>();\n for (int fold = 0; fold < k; ++fold) {\n logger.info(\"Kfold {}\", fold);\n FlatDataList foldTrainingIds = new FlatDataList(new ArrayList<>(n - foldSize));\n FlatDataList foldValidationIds = new FlatDataList(new ArrayList<>(foldSize));\n for (int i = 0; i < n; ++i) {\n boolean isInValidationFoldRange = false;\n if (fold * foldSize <= i && i < (fold + 1) * foldSize) {\n isInValidationFoldRange = true;\n }\n if (isInValidationFoldRange) {\n foldValidationIds.add(ids[i]);\n } else {\n foldTrainingIds.add(ids[i]);\n }\n }\n if (k == 1) {\n foldTrainingIds = foldValidationIds;\n }\n Dataframe trainingData = dataset.getSubset(foldTrainingIds);\n modeler.fit(trainingData, (AbstractTrainer.AbstractTrainingParameters) trainingParameters);\n trainingData.delete();\n Dataframe validationData = dataset.getSubset(foldValidationIds);\n modeler.predict(validationData);\n VM entrySample = ValidationMetrics.newInstance(vmClass, validationData);\n validationData.delete();\n validationMetricsList.add(entrySample);\n }\n modeler.delete();\n VM avgValidationMetrics = ValidationMetrics.newInstance(vmClass, validationMetricsList);\n return avgValidationMetrics;\n}", + "focal_file_path": "datumbox-framework-core/src/main/java/com/datumbox/framework/core/machinelearning/modelselection/validators/KFoldValidator.java", + "focal_method_sign": "com/datumbox/framework/core/machinelearning/modelselection/validators/KFoldValidator.validate:(Lcom/datumbox/framework/common/dataobjects/Dataframe;Lcom/datumbox/framework/core/machinelearning/common/interfaces/TrainingParameters;)Lcom/datumbox/framework/core/machinelearning/common/interfaces/ValidationMetrics", + "focal_all_deps_scored": [ + { + "dep_id": 0, + "method_sign": 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"datumbox-framework-core/src/main/java/com/datumbox/framework/core/machinelearning/modelselection/validators/KFoldValidator.java", + "source": "production", + "is_test_dependency": false, + "is_refactoring_only": false + } + ], + "variant_result": { + "old_on_old": "pass", + "old_on_new": "error", + "new_on_old": "error", + "new_on_new": "pass" + } + } + ], + "version": "0.8.2", + "java_version": 11 + }, + { + "task_id": "datumbox_datumbox-framework-0.8.2__b8da962_b1e75b4__HierarchicalAgglomerativeTest_1fff4d33", + "project_name": "datumbox_datumbox-framework-0.8.2", + "git_clone_url": "https://github.com/datumbox/datumbox-framework.git", + "rev1": "b8da9620fcd6b75abc772f5e1306f0a4bf33a6e4", + "rev2": "b1e75b47d24f4300afdfd596fbd5a46faa666589", + "rev1_date": "2016-12-18T20:23:35Z", + "rev2_date": "2016-12-18T22:01:33Z", + "git_diff_url": "https://github.com/datumbox/datumbox-framework/compare/b8da9620fcd6b75abc772f5e1306f0a4bf33a6e4...b1e75b47d24f4300afdfd596fbd5a46faa666589", + "test_file": "datumbox-framework-core/src/test/java/com/datumbox/framework/core/machinelearning/clustering/HierarchicalAgglomerativeTest.java", + "test_changes": [ + { + "change_id": "247_datumbox_datumbox-framework-0.8.2_b8da962_b1e75b4_HierarchicalAgglomerativeTest", + "test_sign": "com/datumbox/framework/core/machinelearning/clustering/HierarchicalAgglomerativeTest.testKFoldCrossValidation:()V", + "class": "HierarchicalAgglomerativeTest", + "method": "testKFoldCrossValidation", + "module": "datumbox-framework-core", + "junit_selector": "com.datumbox.framework.core.machinelearning.clustering.HierarchicalAgglomerativeTest#testKFoldCrossValidation", + "old_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"validate\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.heartDiseaseClusters(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n String dbName = this.getClass().getSimpleName();\n DummyXYMinMaxNormalizer df = new DummyXYMinMaxNormalizer(dbName, conf);\n df.fit_transform(trainingData, new DummyXYMinMaxNormalizer.TrainingParameters());\n HierarchicalAgglomerative instance = new HierarchicalAgglomerative(dbName, conf);\n HierarchicalAgglomerative.TrainingParameters param = new HierarchicalAgglomerative.TrainingParameters();\n param.setDistanceMethod(HierarchicalAgglomerative.TrainingParameters.Distance.EUCLIDIAN);\n param.setLinkageMethod(HierarchicalAgglomerative.TrainingParameters.Linkage.COMPLETE);\n param.setMinClustersThreshold(2);\n param.setMaxDistanceThreshold(Double.MAX_VALUE);\n ClusteringMetrics vm = instance.kFoldCrossValidation(trainingData, param, k);\n df.denormalize(trainingData);\n double expResult = 0.7666666666666667;\n double result = vm.getPurity();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n df.delete();\n instance.delete();\n trainingData.delete();\n}", + "new_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"validate\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.heartDiseaseClusters(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n String dbName = this.getClass().getSimpleName();\n DummyXYMinMaxNormalizer df = new DummyXYMinMaxNormalizer(dbName, conf);\n df.fit_transform(trainingData, new DummyXYMinMaxNormalizer.TrainingParameters());\n HierarchicalAgglomerative.TrainingParameters param = new HierarchicalAgglomerative.TrainingParameters();\n param.setDistanceMethod(HierarchicalAgglomerative.TrainingParameters.Distance.EUCLIDIAN);\n param.setLinkageMethod(HierarchicalAgglomerative.TrainingParameters.Linkage.COMPLETE);\n param.setMinClustersThreshold(2);\n param.setMaxDistanceThreshold(Double.MAX_VALUE);\n ClusteringMetrics vm = new KFoldValidator<>(ClusteringMetrics.class, conf, k).validate(trainingData, param);\n df.denormalize(trainingData);\n double expResult = 0.7666666666666667;\n double result = vm.getPurity();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n df.delete();\n trainingData.delete();\n}", + "old_test_file_path": [ + "datumbox-framework-core/src/test/java/com/datumbox/framework/core/machinelearning/clustering/HierarchicalAgglomerativeTest.java" + ], + "old_test_lnum": [ + "97-138" + ], + "new_test_lnum": [ + "99-138" + ], + "old_production_code": "", + "new_production_code": "@Override\npublic VM validate(Dataframe dataset, TrainingParameters trainingParameters) {\n int n = dataset.size();\n if (k <= 0 || n <= k) {\n throw new IllegalArgumentException(\"Invalid number of folds.\");\n }\n int foldSize = n / k;\n Integer[] ids = new Integer[n];\n int j = 0;\n for (Integer rId : dataset.index()) {\n ids[j] = rId;\n ++j;\n }\n PHPMethods.shuffle(ids);\n Class aClass = null;\n try {\n String className = trainingParameters.getClass().getCanonicalName();\n aClass = (Class) Class.forName(className.substring(0, className.lastIndexOf('.')));\n } catch (ClassNotFoundException e) {\n throw new IllegalArgumentException(e);\n }\n AbstractModeler modeler = Trainable.newInstance(aClass, \"kfold_\" + System.nanoTime(), conf);\n List validationMetricsList = new LinkedList<>();\n for (int fold = 0; fold < k; ++fold) {\n logger.info(\"Kfold {}\", fold);\n FlatDataList foldTrainingIds = new FlatDataList(new ArrayList<>(n - foldSize));\n FlatDataList foldValidationIds = new FlatDataList(new ArrayList<>(foldSize));\n for (int i = 0; i < n; ++i) {\n boolean isInValidationFoldRange = false;\n if (fold * foldSize <= i && i < (fold + 1) * foldSize) {\n isInValidationFoldRange = true;\n }\n if (isInValidationFoldRange) {\n foldValidationIds.add(ids[i]);\n } else {\n foldTrainingIds.add(ids[i]);\n }\n }\n if (k == 1) {\n foldTrainingIds = foldValidationIds;\n }\n Dataframe trainingData = dataset.getSubset(foldTrainingIds);\n modeler.fit(trainingData, (AbstractTrainer.AbstractTrainingParameters) trainingParameters);\n trainingData.delete();\n Dataframe 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testKFoldCrossValidation() {\n logger.info(\"validate\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.heartDiseaseClusters(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n String dbName = this.getClass().getSimpleName();\n DummyXYMinMaxNormalizer df = new DummyXYMinMaxNormalizer(dbName, conf);\n df.fit_transform(trainingData, new DummyXYMinMaxNormalizer.TrainingParameters());\n Kmeans.TrainingParameters param = new Kmeans.TrainingParameters();\n param.setK(2);\n param.setMaxIterations(200);\n param.setInitializationMethod(Kmeans.TrainingParameters.Initialization.FORGY);\n param.setDistanceMethod(Kmeans.TrainingParameters.Distance.EUCLIDIAN);\n param.setWeighted(false);\n param.setCategoricalGamaMultiplier(1.0);\n param.setSubsetFurthestFirstcValue(2.0);\n ClusteringMetrics vm = new KFoldValidator<>(ClusteringMetrics.class, conf, k).validate(trainingData, param);\n df.denormalize(trainingData);\n double expResult = 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"old_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"validate\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.carsNumeric(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n String dbName = this.getClass().getSimpleName();\n Adaboost instance = new Adaboost(dbName, conf);\n Adaboost.TrainingParameters param = new Adaboost.TrainingParameters();\n param.setMaxWeakClassifiers(5);\n param.setWeakClassifierClass(MultinomialNaiveBayes.class);\n MultinomialNaiveBayes.TrainingParameters trainingParameters = new MultinomialNaiveBayes.TrainingParameters();\n trainingParameters.setMultiProbabilityWeighted(true);\n param.setWeakClassifierTrainingParameters(trainingParameters);\n ClassificationMetrics vm = instance.kFoldCrossValidation(trainingData, param, k);\n double expResult = 0.6923992673992675;\n double result = vm.getMacroF1();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n instance.delete();\n trainingData.delete();\n}", + "new_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"validate\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.carsNumeric(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n Adaboost.TrainingParameters param = new Adaboost.TrainingParameters();\n param.setMaxWeakClassifiers(5);\n param.setWeakClassifierClass(MultinomialNaiveBayes.class);\n MultinomialNaiveBayes.TrainingParameters trainingParameters = new MultinomialNaiveBayes.TrainingParameters();\n trainingParameters.setMultiProbabilityWeighted(true);\n param.setWeakClassifierTrainingParameters(trainingParameters);\n ClassificationMetrics vm = new KFoldValidator<>(ClassificationMetrics.class, conf, k).validate(trainingData, param);\n ;\n double expResult = 0.6923992673992675;\n double result = vm.getMacroF1();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n trainingData.delete();\n}", + "old_test_file_path": [ + "datumbox-framework-core/src/test/java/com/datumbox/framework/core/machinelearning/ensemblelearning/AdaboostTest.java" + ], + "old_test_lnum": [ + "118-153" + ], + "new_test_lnum": [ + "119-150" + ], + "old_production_code": "", + "new_production_code": "@Override\npublic VM validate(Dataframe dataset, TrainingParameters trainingParameters) {\n int n = dataset.size();\n if (k <= 0 || n <= k) {\n throw new IllegalArgumentException(\"Invalid number of folds.\");\n }\n int foldSize = n / k;\n Integer[] ids = new Integer[n];\n int j = 0;\n for (Integer rId : dataset.index()) {\n ids[j] = rId;\n ++j;\n }\n PHPMethods.shuffle(ids);\n Class aClass = null;\n try {\n String className = trainingParameters.getClass().getCanonicalName();\n aClass = (Class) Class.forName(className.substring(0, className.lastIndexOf('.')));\n } catch (ClassNotFoundException e) {\n throw new IllegalArgumentException(e);\n }\n AbstractModeler modeler = Trainable.newInstance(aClass, \"kfold_\" + System.nanoTime(), conf);\n List validationMetricsList = new LinkedList<>();\n for (int fold = 0; fold < k; ++fold) {\n logger.info(\"Kfold {}\", fold);\n FlatDataList foldTrainingIds = new FlatDataList(new ArrayList<>(n - foldSize));\n FlatDataList foldValidationIds = new FlatDataList(new ArrayList<>(foldSize));\n for (int i = 0; i < n; ++i) {\n boolean isInValidationFoldRange = false;\n if (fold * foldSize <= i && i < (fold + 1) * foldSize) {\n isInValidationFoldRange = true;\n }\n if (isInValidationFoldRange) {\n foldValidationIds.add(ids[i]);\n } else {\n foldTrainingIds.add(ids[i]);\n }\n }\n if (k == 1) {\n foldTrainingIds = foldValidationIds;\n }\n Dataframe trainingData = dataset.getSubset(foldTrainingIds);\n modeler.fit(trainingData, (AbstractTrainer.AbstractTrainingParameters) trainingParameters);\n trainingData.delete();\n Dataframe validationData = dataset.getSubset(foldValidationIds);\n modeler.predict(validationData);\n VM 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Datasets.carsNumeric(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n String dbName = this.getClass().getSimpleName();\n BootstrapAggregating instance = new BootstrapAggregating(dbName, conf);\n BootstrapAggregating.TrainingParameters param = new BootstrapAggregating.TrainingParameters();\n param.setMaxWeakClassifiers(5);\n param.setWeakClassifierClass(MultinomialNaiveBayes.class);\n MultinomialNaiveBayes.TrainingParameters trainingParameters = new MultinomialNaiveBayes.TrainingParameters();\n trainingParameters.setMultiProbabilityWeighted(true);\n param.setWeakClassifierTrainingParameters(trainingParameters);\n ClassificationMetrics vm = instance.kFoldCrossValidation(trainingData, param, k);\n double expResult = 0.6609432234432234;\n double result = vm.getMacroF1();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n instance.delete();\n trainingData.delete();\n}", + "new_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"validate\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.carsNumeric(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n BootstrapAggregating.TrainingParameters param = new BootstrapAggregating.TrainingParameters();\n param.setMaxWeakClassifiers(5);\n param.setWeakClassifierClass(MultinomialNaiveBayes.class);\n MultinomialNaiveBayes.TrainingParameters trainingParameters = new MultinomialNaiveBayes.TrainingParameters();\n trainingParameters.setMultiProbabilityWeighted(true);\n param.setWeakClassifierTrainingParameters(trainingParameters);\n ClassificationMetrics vm = new KFoldValidator<>(ClassificationMetrics.class, conf, k).validate(trainingData, param);\n double expResult = 0.6609432234432234;\n double result = vm.getMacroF1();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n trainingData.delete();\n}", + "old_test_file_path": [ + "datumbox-framework-core/src/test/java/com/datumbox/framework/core/machinelearning/ensemblelearning/BootstrapAggregatingTest.java" + ], + "old_test_lnum": [ + "114-149" + ], + "new_test_lnum": [ + "115-147" + ], + "old_production_code": "", + "new_production_code": "@Override\npublic VM validate(Dataframe dataset, TrainingParameters trainingParameters) {\n int n = dataset.size();\n if (k <= 0 || n <= k) {\n throw new IllegalArgumentException(\"Invalid number of folds.\");\n }\n int foldSize = n / k;\n Integer[] ids = new Integer[n];\n int j = 0;\n for (Integer rId : dataset.index()) {\n ids[j] = rId;\n ++j;\n }\n PHPMethods.shuffle(ids);\n Class aClass = null;\n try {\n String className = trainingParameters.getClass().getCanonicalName();\n aClass = (Class) Class.forName(className.substring(0, className.lastIndexOf('.')));\n } catch (ClassNotFoundException e) {\n throw new IllegalArgumentException(e);\n }\n AbstractModeler modeler = Trainable.newInstance(aClass, \"kfold_\" + System.nanoTime(), conf);\n List validationMetricsList = new LinkedList<>();\n for (int fold = 0; fold < k; ++fold) {\n logger.info(\"Kfold {}\", fold);\n FlatDataList foldTrainingIds = new FlatDataList(new ArrayList<>(n - foldSize));\n FlatDataList foldValidationIds = new FlatDataList(new ArrayList<>(foldSize));\n for (int i = 0; i < n; ++i) {\n boolean isInValidationFoldRange = false;\n if (fold * foldSize <= i && i < (fold + 1) * foldSize) {\n isInValidationFoldRange = true;\n }\n if (isInValidationFoldRange) {\n foldValidationIds.add(ids[i]);\n } else {\n foldTrainingIds.add(ids[i]);\n }\n }\n if (k == 1) {\n foldTrainingIds = foldValidationIds;\n }\n Dataframe trainingData = dataset.getSubset(foldTrainingIds);\n modeler.fit(trainingData, (AbstractTrainer.AbstractTrainingParameters) trainingParameters);\n trainingData.delete();\n Dataframe validationData = dataset.getSubset(foldValidationIds);\n modeler.predict(validationData);\n VM entrySample = ValidationMetrics.newInstance(vmClass, validationData);\n validationData.delete();\n validationMetricsList.add(entrySample);\n }\n modeler.delete();\n VM avgValidationMetrics = ValidationMetrics.newInstance(vmClass, validationMetricsList);\n return avgValidationMetrics;\n}", + "focal_file_path": "datumbox-framework-core/src/main/java/com/datumbox/framework/core/machinelearning/modelselection/validators/KFoldValidator.java", + "focal_method_sign": "com/datumbox/framework/core/machinelearning/modelselection/validators/KFoldValidator.validate:(Lcom/datumbox/framework/common/dataobjects/Dataframe;Lcom/datumbox/framework/core/machinelearning/common/interfaces/TrainingParameters;)Lcom/datumbox/framework/core/machinelearning/common/interfaces/ValidationMetrics", + "focal_all_deps_scored": [ + { + "dep_id": 0, + "method_sign": 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"https://github.com/datumbox/datumbox-framework/compare/b8da9620fcd6b75abc772f5e1306f0a4bf33a6e4...b1e75b47d24f4300afdfd596fbd5a46faa666589", + "test_file": "datumbox-framework-core/src/test/java/com/datumbox/framework/core/machinelearning/regression/MatrixLinearRegressionTest.java", + "test_changes": [ + { + "change_id": "243_datumbox_datumbox-framework-0.8.2_b8da962_b1e75b4_MatrixLinearRegressionTest", + "test_sign": "com/datumbox/framework/core/machinelearning/regression/MatrixLinearRegressionTest.testKFoldCrossValidation:()V", + "class": "MatrixLinearRegressionTest", + "method": "testKFoldCrossValidation", + "module": "datumbox-framework-core", + "junit_selector": "com.datumbox.framework.core.machinelearning.regression.MatrixLinearRegressionTest#testKFoldCrossValidation", + "old_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"validate\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.regressionMixed(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n String dbName = this.getClass().getSimpleName();\n DummyXYMinMaxNormalizer df = new DummyXYMinMaxNormalizer(dbName, conf);\n df.fit_transform(trainingData, new DummyXYMinMaxNormalizer.TrainingParameters());\n MatrixLinearRegression instance = new MatrixLinearRegression(dbName, conf);\n MatrixLinearRegression.TrainingParameters param = new MatrixLinearRegression.TrainingParameters();\n LinearRegressionMetrics vm = instance.kFoldCrossValidation(trainingData, param, k);\n df.denormalize(trainingData);\n double expResult = 1;\n double result = vm.getRSquare();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n df.delete();\n instance.delete();\n trainingData.delete();\n}", + "new_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"validate\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.regressionMixed(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n String dbName = this.getClass().getSimpleName();\n DummyXYMinMaxNormalizer df = new DummyXYMinMaxNormalizer(dbName, conf);\n df.fit_transform(trainingData, new DummyXYMinMaxNormalizer.TrainingParameters());\n MatrixLinearRegression.TrainingParameters param = new MatrixLinearRegression.TrainingParameters();\n LinearRegressionMetrics vm = new KFoldValidator<>(LinearRegressionMetrics.class, conf, k).validate(trainingData, param);\n df.denormalize(trainingData);\n double expResult = 1;\n double result = vm.getRSquare();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n df.delete();\n trainingData.delete();\n}", + "old_test_file_path": [ + "datumbox-framework-core/src/test/java/com/datumbox/framework/core/machinelearning/regression/MatrixLinearRegressionTest.java" + ], + "old_test_lnum": [ + "97-131" + ], + "new_test_lnum": [ + "98-130" + ], + "old_production_code": "", + 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foldValidationIds = new FlatDataList(new ArrayList<>(foldSize));\n for (int i = 0; i < n; ++i) {\n boolean isInValidationFoldRange = false;\n if (fold * foldSize <= i && i < (fold + 1) * foldSize) {\n isInValidationFoldRange = true;\n }\n if (isInValidationFoldRange) {\n foldValidationIds.add(ids[i]);\n } else {\n foldTrainingIds.add(ids[i]);\n }\n }\n if (k == 1) {\n foldTrainingIds = foldValidationIds;\n }\n Dataframe trainingData = dataset.getSubset(foldTrainingIds);\n modeler.fit(trainingData, (AbstractTrainer.AbstractTrainingParameters) trainingParameters);\n trainingData.delete();\n Dataframe validationData = dataset.getSubset(foldValidationIds);\n modeler.predict(validationData);\n VM entrySample = ValidationMetrics.newInstance(vmClass, validationData);\n validationData.delete();\n validationMetricsList.add(entrySample);\n }\n modeler.delete();\n VM avgValidationMetrics = ValidationMetrics.newInstance(vmClass, validationMetricsList);\n return avgValidationMetrics;\n}", + 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"256_datumbox_datumbox-framework-0.8.2_b8da962_b1e75b4_NLMSTest", + "test_sign": "com/datumbox/framework/core/machinelearning/regression/NLMSTest.testKFoldCrossValidation:()V", + "class": "NLMSTest", + "method": "testKFoldCrossValidation", + "module": "datumbox-framework-core", + "junit_selector": "com.datumbox.framework.core.machinelearning.regression.NLMSTest#testKFoldCrossValidation", + "old_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"validate\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.housingNumerical(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n String dbName = this.getClass().getSimpleName();\n DummyXYMinMaxNormalizer df = new DummyXYMinMaxNormalizer(dbName, conf);\n df.fit_transform(trainingData, new DummyXYMinMaxNormalizer.TrainingParameters());\n PCA featureSelector = new PCA(dbName, conf);\n PCA.TrainingParameters featureSelectorParameters = new PCA.TrainingParameters();\n featureSelectorParameters.setMaxDimensions(trainingData.xColumnSize() - 1);\n featureSelectorParameters.setWhitened(false);\n featureSelectorParameters.setVariancePercentageThreshold(0.99999995);\n featureSelector.fit_transform(trainingData, featureSelectorParameters);\n featureSelector.delete();\n NLMS instance = new NLMS(dbName, conf);\n NLMS.TrainingParameters param = new NLMS.TrainingParameters();\n param.setTotalIterations(500);\n param.setL1(0.001);\n param.setL2(0.001);\n LinearRegressionMetrics vm = instance.kFoldCrossValidation(trainingData, param, k);\n df.denormalize(trainingData);\n double expResult = 0.7748106446239166;\n double result = vm.getRSquare();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n df.delete();\n instance.delete();\n trainingData.delete();\n}", + "new_test_code": "@Test\npublic void testKFoldCrossValidation() {\n logger.info(\"validate\");\n Configuration conf = Configuration.getConfiguration();\n int k = 5;\n Dataframe[] data = Datasets.housingNumerical(conf);\n Dataframe trainingData = data[0];\n data[1].delete();\n String dbName = this.getClass().getSimpleName();\n DummyXYMinMaxNormalizer df = new DummyXYMinMaxNormalizer(dbName, conf);\n df.fit_transform(trainingData, new DummyXYMinMaxNormalizer.TrainingParameters());\n PCA featureSelector = new PCA(dbName, conf);\n PCA.TrainingParameters featureSelectorParameters = new PCA.TrainingParameters();\n featureSelectorParameters.setMaxDimensions(trainingData.xColumnSize() - 1);\n featureSelectorParameters.setWhitened(false);\n featureSelectorParameters.setVariancePercentageThreshold(0.99999995);\n featureSelector.fit_transform(trainingData, featureSelectorParameters);\n featureSelector.delete();\n NLMS.TrainingParameters param = new NLMS.TrainingParameters();\n param.setTotalIterations(500);\n param.setL1(0.001);\n param.setL2(0.001);\n LinearRegressionMetrics vm = new KFoldValidator<>(LinearRegressionMetrics.class, conf, 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"com.datumbox.framework.core.machinelearning.topicmodeling.LatentDirichletAllocationTest#testValidate", + "old_test_code": "@Test\npublic void testValidate() {\n logger.info(\"validate\");\n Configuration conf = Configuration.getConfiguration();\n String dbName = this.getClass().getSimpleName();\n Map dataset = new HashMap<>();\n try {\n dataset.put(\"negative\", this.getClass().getClassLoader().getResource(\"datasets/sentimentAnalysis.neg.txt\").toURI());\n dataset.put(\"positive\", this.getClass().getClassLoader().getResource(\"datasets/sentimentAnalysis.pos.txt\").toURI());\n } catch (UncheckedIOException | URISyntaxException ex) {\n logger.warn(\"Unable to download datasets, skipping test.\");\n throw new RuntimeException(ex);\n }\n UniqueWordSequenceExtractor wsExtractor = new UniqueWordSequenceExtractor(new UniqueWordSequenceExtractor.Parameters());\n Dataframe trainingData = Dataframe.Builder.parseTextFiles(dataset, wsExtractor, conf);\n LatentDirichletAllocation lda = new LatentDirichletAllocation(dbName, conf);\n LatentDirichletAllocation.TrainingParameters trainingParameters = new LatentDirichletAllocation.TrainingParameters();\n trainingParameters.setMaxIterations(15);\n trainingParameters.setAlpha(0.01);\n trainingParameters.setBeta(0.01);\n trainingParameters.setK(25);\n lda.fit(trainingData, trainingParameters);\n lda.predict(trainingData);\n Dataframe reducedTrainingData = new Dataframe(conf);\n for (Record r : trainingData) {\n reducedTrainingData.add(new Record(r.getYPredictedProbabilities(), r.getY()));\n }\n SoftMaxRegression smr = new SoftMaxRegression(dbName, conf);\n SoftMaxRegression.TrainingParameters tp = new SoftMaxRegression.TrainingParameters();\n tp.setLearningRate(1.0);\n tp.setTotalIterations(50);\n ClassificationMetrics vm = smr.kFoldCrossValidation(reducedTrainingData, tp, 1);\n double expResult = 0.6843125117743629;\n double result = vm.getMacroF1();\n assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH);\n smr.delete();\n lda.delete();\n reducedTrainingData.delete();\n trainingData.delete();\n}", + "new_test_code": "@Test\npublic void testValidate() {\n logger.info(\"validate\");\n Configuration conf = Configuration.getConfiguration();\n String dbName = this.getClass().getSimpleName();\n Map dataset = new HashMap<>();\n try {\n dataset.put(\"negative\", this.getClass().getClassLoader().getResource(\"datasets/sentimentAnalysis.neg.txt\").toURI());\n dataset.put(\"positive\", this.getClass().getClassLoader().getResource(\"datasets/sentimentAnalysis.pos.txt\").toURI());\n } catch (UncheckedIOException | URISyntaxException ex) {\n logger.warn(\"Unable to download datasets, skipping test.\");\n throw new RuntimeException(ex);\n }\n UniqueWordSequenceExtractor wsExtractor = new UniqueWordSequenceExtractor(new UniqueWordSequenceExtractor.Parameters());\n Dataframe trainingData = Dataframe.Builder.parseTextFiles(dataset, wsExtractor, conf);\n LatentDirichletAllocation lda = new 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OrdinalRegression.TrainingParameters();\n param.setTotalIterations(100);\n param.setL2(0.001);\n OrdinalRegression instance = MLBuilder.create(param, configuration);\n trainingData = Dataframe.Builder.load(datasetName, configuration);\n instance.fit(trainingData);\n instance.save(storageName);\n df.denormalize(trainingData);\n trainingData.delete();\n instance.close();\n df.close();\n df = MLBuilder.load(DummyXMinMaxNormalizer.class, storageName, configuration);\n instance = MLBuilder.load(OrdinalRegression.class, storageName, configuration);\n df.transform(validationData);\n instance.predict(validationData);\n df.denormalize(validationData);\n Map expResult = new HashMap<>();\n Map result = new HashMap<>();\n for (Map.Entry e : validationData.entries()) {\n Integer rId = e.getKey();\n Record r = e.getValue();\n expResult.put(rId, r.getY());\n result.put(rId, r.getYPredicted());\n }\n assertEquals(expResult, result);\n df.delete();\n instance.delete();\n validationData.close();\n}", + 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df.denormalize(trainingData);\n trainingData.close();\n instance.close();\n df.close();\n df = MLBuilder.load(DummyXYMinMaxNormalizer.class, storageName, configuration);\n instance = MLBuilder.load(MultinomialNaiveBayes.class, storageName, configuration);\n df.transform(validationData);\n instance.predict(validationData);\n df.denormalize(validationData);\n Map expResult = new HashMap<>();\n Map result = new HashMap<>();\n for (Map.Entry e : validationData.entries()) {\n Integer rId = e.getKey();\n Record r = e.getValue();\n expResult.put(rId, r.getY());\n result.put(rId, r.getYPredicted());\n }\n assertEquals(expResult, result);\n df.delete();\n instance.delete();\n validationData.close();\n}", + "new_test_code": "@Test\npublic void testPredict() {\n logger.info(\"testPredict\");\n Configuration configuration = Configuration.getConfiguration();\n Dataframe[] data = Datasets.carsCategorical(configuration);\n Dataframe trainingData = data[0];\n Dataframe validationData = data[1];\n 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