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_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)
def test_classification_learners(self): """ Tests creating classification learners from the classification.py file :return: a list of learners, i.e. WekaClassifier objects """ lrn = [] num_exceptions = 0 try: lrn.append(c.logistic()) lrn.append(c.j48()) lrn.append(c.rep_tree()) lrn.append(c.random_forest()) lrn.append(c.random_tree()) lrn.append(c.rules_zeror()) lrn.append(c.rules_jrip()) lrn.append(c.ibk()) lrn.append(c.k_star()) lrn.append(c.naive_bayes()) lrn.append(c.multilayer_perceptron()) lrn.append(c.smo()) except Exception, e: num_exceptions += 1 print "Exception: " + str(e)