9a62e98e14c1ad88b29baee3e5ba55cb45ac7aec,test/test_pipeline/test_classification.py,SimpleClassificationPipelineTest,test_predict_proba_batched_sparse,#SimpleClassificationPipelineTest#,512

Before Change

        cs = SimpleClassificationPipeline.get_hyperparameter_search_space(
            dataset_properties={"sparse": True})

        config = Configuration(cs,
                               values={"balancing:strategy": "none",
                                       "classifier:__choice__": "random_forest",
                                       "imputation:strategy": "mean",
                                       "one_hot_encoding:minimum_fraction": 0.01,
                                       "one_hot_encoding:use_minimum_fraction": "True",
                                       "preprocessor:__choice__": "no_preprocessing",
                                       "classifier:random_forest:bootstrap": "True",
                                       "classifier:random_forest:criterion": "gini",
                                       "classifier:random_forest:max_depth": "None",
                                       "classifier:random_forest:min_samples_split": 2,
                                       "classifier:random_forest:min_samples_leaf": 2,
                                       "classifier:random_forest:min_weight_fraction_leaf": 0.0,
                                       "classifier:random_forest:max_features": 0.5,
                                       "classifier:random_forest:max_leaf_nodes": "None",
                                       "classifier:random_forest:n_estimators": 100,
                                       "rescaling:__choice__": "min/max"})

        // Multiclass
        cls = SimpleClassificationPipeline(config)
        X_train, Y_train, X_test, Y_test = get_dataset(dataset="digits",
                                                       make_sparse=True)
        cls.fit(X_train, Y_train)
        X_test_ = X_test.copy()

After Change

        // Multilabel
        cls = SimpleClassificationPipeline(
            config=config, dataset_properties={"sparse": True,
                                               "multilabel": True})
        X_train, Y_train, X_test, Y_test = get_dataset(dataset="digits",
                                                       make_sparse=True)
        Y_train = np.array(list([(list([1 if i != y else 0 for i in range(10)]))

Instances