def single_experiment(sigma, order, sigma_noise, experiment_type):
ename = '_'+experiment_type
Nside = 1024
EXP_NAME = '40sim_{}sides_{}noise_{}order_{}sigma{}'.format(
Nside, sigma_noise, order, sigma, ename)
x_raw_train, labels_raw_train, x_raw_std = experiment_helper.get_training_data(sigma, order)
x_raw_test, labels_test, _ = experiment_helper.get_testing_data(sigma, order, sigma_noise, x_raw_std)
ret = experiment_helper.data_preprossing(x_raw_train, labels_raw_train, x_raw_test, sigma_noise, feature_type=None)
features_train, labels_train, features_validation, labels_validation, features_test = ret
training = LabeledDatasetWithNoise(features_train, labels_train, end_level=sigma_noise)
validation = LabeledDataset(features_validation, labels_validation)
params = get_params(training.N, EXP_NAME, order, Nside, experiment_type)
model = models.deepsphere(**params)
# Cleanup before running again.
shutil.rmtree('summaries/{}/'.format(EXP_NAME), ignore_errors=True)
shutil.rmtree('checkpoints/{}/'.format(EXP_NAME), ignore_errors=True)
model.fit(training, validation)
error_validation = experiment_helper.model_error(model, features_validation, labels_validation)
print('The validation error is {}%'.format(error_validation * 100), flush=True)
error_test = experiment_helper.model_error(model, features_test, labels_test)
print('The testing error is {}%'.format(error_test * 100), flush=True)
return error_test
def single_experiment(sigma, order, sigma_noise, experiment_type, new,
n_neighbors):
ename = '_' + experiment_type
Nside = 1024
if Nside == 1024:
data_path = '/mnt/scratch/lts2/mdeff/deepsphere/data/same_psd/'
else:
data_path = 'data/same_psd/'
EXP_NAME = 'cosmo' if new else 'oldgraph'
EXP_NAME += '_{}sides_{}noise_{}order_{}sigma_{}neighbor{}_fold3'.format(
Nside, sigma_noise, order, sigma, n_neighbors, ename)
x_raw_train, labels_raw_train, x_raw_std = experiment_helper.get_training_data(
sigma, order, data_path=data_path)
x_raw_test, labels_test, _ = experiment_helper.get_testing_data(
sigma, order, sigma_noise, x_raw_std, data_path=data_path[:-9])
ret = experiment_helper.data_preprossing(x_raw_train,
labels_raw_train,
x_raw_test,
sigma_noise,
feature_type=None)
features_train, labels_train, features_validation, labels_validation, features_test = ret
training = LabeledDatasetWithNoise(features_train,
labels_train,
end_level=sigma_noise)
validation = LabeledDataset(features_validation, labels_validation)
# Cleanup before running again.
shutil.rmtree('summaries/{}/'.format(EXP_NAME), ignore_errors=True)
shutil.rmtree('checkpoints/{}/'.format(EXP_NAME), ignore_errors=True)
params = hyperparameters.get_params(training.N, EXP_NAME, order, Nside,
experiment_type)
model = models.deepsphere(**params, new=new, n_neighbors=n_neighbors)
accuracy_validation, loss_validation, loss_training, t_step, t_batch = model.fit(
training, validation)
print("inference time: ", t_batch / params["batch_size"])
error_validation = experiment_helper.model_error(
model, features_validation[:, :, np.newaxis], labels_validation)
print('The validation error is {}%'.format(error_validation * 100),
flush=True)
error_test = experiment_helper.model_error(model,
features_test[:, :, np.newaxis],
labels_test)
print('The testing error is {}%'.format(error_test * 100), flush=True)
return error_test, t_batch
def single_experiment(sigma, order, sigma_noise, experiment_type):
ename = '_'+experiment_type
Nside = 1024
data_path = '../../data/same_psd/'
EXP_NAME = '40sim_{}sides_{}noise_{}order_{}sigma{}'.format(
Nside, sigma_noise, order, sigma, ename)
x_raw_train, labels_raw_train, x_raw_std = experiment_helper.get_training_data(sigma, order, data_path=data_path)
x_raw_test, labels_test, _ = experiment_helper.get_testing_data(sigma, order, sigma_noise, x_raw_std, data_path=data_path[:-9])
ret = experiment_helper.data_preprossing(x_raw_train, labels_raw_train, x_raw_test, sigma_noise, feature_type=None)
features_train, labels_train, features_validation, labels_validation, features_test = ret
nx = Nside//order
nlevels = np.round(np.log2(nx)).astype(np.int)
index = build_index(nlevels).astype(np.int)
features_train = features_train[:, index]
features_validation = features_validation[:, index]
shuffle = np.random.permutation(len(features_test))
features_test = features_test[:, index]
features_test = features_test[shuffle]
labels_test = labels_test[shuffle]
training = LabeledDatasetWithNoise(features_train, labels_train, end_level=sigma_noise)
validation = LabeledDataset(features_validation, labels_validation)
# Better implementation, but it doesn't work for some reason.
# params = hyperparameters.get_params_CNN2D(training.N, EXP_NAME, order, Nside, experiment_type)
# model = Healpix2CNN(**params)
params = get_params(training.N, EXP_NAME, order, Nside, experiment_type)
model = models.cnn2d(**params)
# Cleanup before running again.
shutil.rmtree('summaries/{}/'.format(EXP_NAME), ignore_errors=True)
shutil.rmtree('checkpoints/{}/'.format(EXP_NAME), ignore_errors=True)
model.fit(training, validation)
error_validation = experiment_helper.model_error(model, features_validation, labels_validation)
print('The validation error is {}%'.format(error_validation * 100), flush=True)
error_test = experiment_helper.model_error(model, features_test, labels_test)
print('The testing error is {}%'.format(error_test * 100), flush=True)
return error_test
ret = train_test_split(x_raw,
x_psd,
labels,
test_size=2 * nclass - ntrain,
shuffle=True)
x_raw_train, x_raw_test, x_psd_train, x_psd_test, labels_train, labels_test = ret
print('Class 1 VS class 2')
print(' Training set: {} / {}'.format(np.sum(labels_train == 0),
np.sum(labels_train == 1)))
print(' Test set: {} / {}'.format(np.sum(labels_test == 0),
np.sum(labels_test == 1)))
clf = SVC(kernel='rbf')
clf.fit(x_raw_train, labels_train)
e_train = experiment_helper.model_error(clf, x_raw_train, labels_train)
e_test = experiment_helper.model_error(clf, x_raw_test, labels_test)
print('The training error is: {}%'.format(e_train * 100))
print('The testing error is: {}%'.format(e_test * 100))
clf = SVC(kernel='linear')
clf.fit(x_psd_train, labels_train)
e_train = experiment_helper.model_error(clf, x_psd_train, labels_train)
e_test = experiment_helper.model_error(clf, x_psd_test, labels_test)
print('The training error is: {}%'.format(e_train * 100))
print('The testing error is: {}%'.format(e_test * 100))
params = dict()
params['dir_name'] = EXP_NAME
# Types of layers.
params['conv'] = 'chebyshev5' # Graph convolution: chebyshev5 or monomials.