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_nca(self): n = self.X.shape[0] nca = NCA(max_iter=(100000 // n)) nca.fit(self.X, self.y) L = nca.components_ assert_array_almost_equal(L.T.dot(L), nca.get_mahalanobis_matrix())
def test_nca(self): n = self.X.shape[0] nca = NCA(max_iter=(100000//n)) nca.fit(self.X, self.y) L = nca.transformer_ assert_array_almost_equal(L.T.dot(L), nca.get_mahalanobis_matrix())
