def test_iris(self): lfda = LFDA(k=2, n_components=2) lfda.fit(self.iris_points, self.iris_labels) csep = class_separation(lfda.transform(self.iris_points), self.iris_labels) self.assertLess(csep, 0.15) # Sanity checks for learned matrices. self.assertEqual(lfda.get_mahalanobis_matrix().shape, (4, 4)) self.assertEqual(lfda.components_.shape, (2, 4))
def test_iris(self): lfda = LFDA(k=2, num_dims=2) lfda.fit(self.iris_points, self.iris_labels) csep = class_separation(lfda.transform(self.iris_points), self.iris_labels) self.assertLess(csep, 0.15) # Sanity checks for learned matrices. self.assertEqual(lfda.get_mahalanobis_matrix().shape, (4, 4)) self.assertEqual(lfda.transformer_.shape, (2, 4))
def test_lfda(self): lfda = LFDA(k=2, n_components=2) lfda.fit(self.X, self.y) L = lfda.components_ assert_array_almost_equal(L.T.dot(L), lfda.get_mahalanobis_matrix())
def main(params): initialize_results_dir(params.get('results_dir')) backup_params(params, params.get('results_dir')) print('>>> loading data...') X_train, y_train, X_test, y_test = LoaderFactory().create( name=params.get('dataset'), root=params.get('dataset_dir'), random=True, seed=params.getint('split_seed'))() print('<<< data loaded') print('>>> computing psd matrix...') if params.get('algorithm') == 'identity': psd_matrix = np.identity(X_train.shape[1], dtype=X_train.dtype) elif params.get('algorithm') == 'nca': nca = NCA(init='auto', verbose=True, random_state=params.getint('algorithm_seed')) nca.fit(X_train, y_train) psd_matrix = nca.get_mahalanobis_matrix() elif params.get('algorithm') == 'lmnn': lmnn = LMNN(init='auto', verbose=True, random_state=params.getint('algorithm_seed')) lmnn.fit(X_train, y_train) psd_matrix = lmnn.get_mahalanobis_matrix() elif params.get('algorithm') == 'itml': itml = ITML_Supervised(verbose=True, random_state=params.getint('algorithm_seed')) itml.fit(X_train, y_train) psd_matrix = itml.get_mahalanobis_matrix() elif params.get('algorithm') == 'lfda': lfda = LFDA() lfda.fit(X_train, y_train) psd_matrix = lfda.get_mahalanobis_matrix() elif params.get('algorithm') == 'arml': learner = TripleLearner( optimizer=params.get('optimizer'), optimizer_params={ 'lr': params.getfloat('lr'), 'momentum': params.getfloat('momentum'), 'weight_decay': params.getfloat('weight_decay'), }, criterion=params.get('criterion'), criterion_params={'calibration': params.getfloat('calibration')}, n_epochs=params.getint('n_epochs'), batch_size=params.getint('batch_size'), random_initialization=params.getboolean('random_initialization', fallback=False), update_triple=params.getboolean('update_triple', fallback=False), device=params.get('device'), seed=params.getint('learner_seed')) psd_matrix = learner(X_train, y_train, n_candidate_mins=params.getint('n_candidate_mins', fallback=1)) else: raise Exception('unsupported algorithm') print('<<< psd matrix got') np.savetxt(os.path.join(params.get('results_dir'), 'psd_matrix.txt'), psd_matrix)
def test_lfda(self): lfda = LFDA(k=2, num_dims=2) lfda.fit(self.X, self.y) L = lfda.transformer_ assert_array_almost_equal(L.T.dot(L), lfda.get_mahalanobis_matrix())
def test_lfda(self): lfda = LFDA(k=2, num_dims=2) lfda.fit(self.X, self.y) L = lfda.transformer_ assert_array_almost_equal(L.T.dot(L), lfda.get_mahalanobis_matrix())