def test_classification_models(self): """ Tests building classification models using provided learners :return: True if all tests pass """ lrn_arr = self.test_classification_learners() for lrn in lrn_arr: try: base_dir = normpath(dirname(__file__)) + '/../' weka_dir = normpath(join(base_dir, 'weka')) data_dir = normpath(join(weka_dir, 'data')) # f = data_dir + os.sep + 'cpu.with.vendor.arff' # numeric target, with both numeric and nominal features f = data_dir + os.sep + 'breast-cancer.arff' # nominal target fi = open(f, 'r') classification_dataset = fi.read() fi.close() data = ut.import_dataset_from_arff(classification_dataset) lrn.buildClassifier(data) data_new = lrn.applyClassifier(data) self.assertIsNotNone(data_new['targetPredicted']) except Exception, e: print "Exception: " + str(e)
def convert_dataset_from_orange_to_scikit(dataset): """Converts dataset from an Orange Data Table to scikit Bunch format :param dataset: :return: """ arff_str = to_arff_string(dataset).getvalue() dataset_new = u.import_dataset_from_arff(arff_str) return dataset_new