def test_two_filters(self): """ Tests saturation_filter (normal and prune) and classification_filter """ import cf_noise_detection.library as l import cf_weka.classification as c learner = c.j48() data = ut.load_UCI_dataset("iris") inp_dict = {'data': data, 'satur_type': 'normal'} out_dict = l.saturation_filter(inp_dict, None) self.assertGreaterEqual(len(out_dict.keys()), 1) inp_dict = {'data': data, 'satur_type': 'prune'} out_dict = l.saturation_filter(inp_dict, None) self.assertGreaterEqual(len(out_dict.keys()), 1) inp_dict = { 'learner': learner, 'data': data, 'timeout': 60.0, 'k_folds': 10 } out_dict = l.classification_filter(inp_dict, None) self.assertGreaterEqual(len(out_dict.keys()), 1)
def test_two_filters(self): """ Tests saturation_filter (normal and prune) and classification_filter """ import cf_noise_detection.library as l import cf_weka.classification as c learner = c.j48() data = ut.load_UCI_dataset("iris") inp_dict = {'data': data, 'satur_type': 'normal'} out_dict = l.saturation_filter(inp_dict, None) self.assertGreaterEqual(len(out_dict.keys()), 1) inp_dict = {'data': data, 'satur_type': 'prune'} out_dict = l.saturation_filter(inp_dict, None) self.assertGreaterEqual(len(out_dict.keys()), 1) inp_dict = {'learner': learner, 'data': data, 'timeout': 60.0, 'k_folds': 10} out_dict = l.classification_filter(inp_dict, None) self.assertGreaterEqual(len(out_dict.keys()), 1)
def test_add_class_noise(self): import cf_noise_detection.library as l data = ut.load_UCI_dataset("iris") inp_dict = {'data': data, 'noise_level': 10.0, 'rnd_seed': 1} out_dict = l.add_class_noise(inp_dict) self.assertGreaterEqual(len(out_dict["noise_inds"]), 1)
def test_regression_models(self): """ Tests building regression models using provided learners""" num_exceptions = 0 lrn_arr = self.test_regression_learners() for lrn in lrn_arr: try: regression_dataset = ut.load_UCI_dataset("boston") ev.build_classifier(lrn, regression_dataset) except Exception, e: num_exceptions += 1 print "Exception: " + str(e)
def test_classification_models(self): """ Tests building classification models using provided learners""" num_exceptions = 0 lrn_arr = self.test_classification_learners() for lrn in lrn_arr: try: classification_dataset = ut.load_UCI_dataset("iris") ev.build_classifier(lrn, classification_dataset) except Exception as e: num_exceptions += 1 print("Exception: " + str(e)) self.assertIs(num_exceptions, 0)
def test_noise_rank(self): """Tests noise rank widget """ import cf_noise_detection.library as l import cf_weka.classification as c learner = c.j48() data = ut.load_UCI_dataset("iris") inp_dict = {'learner': learner, 'data': data, 'timeout': 60.0, 'k_folds': 10} out_dict = l.classification_filter(inp_dict, None) inp_dict = {'noise': [out_dict['noise_dict']], 'data': data} out_dict = l.noiserank(inp_dict) self.assertGreaterEqual(len(out_dict['allnoise']), 1)
def test_noise_rank(self): """Tests noise rank widget """ import cf_noise_detection.library as l import cf_weka.classification as c learner = c.j48() data = ut.load_UCI_dataset("iris") inp_dict = { 'learner': learner, 'data': data, 'timeout': 60.0, 'k_folds': 10 } out_dict = l.classification_filter(inp_dict, None) inp_dict = {'noise': [out_dict['noise_dict']], 'data': data} out_dict = l.noiserank(inp_dict) self.assertGreaterEqual(len(out_dict['allnoise']), 1)