def test_seed_reproducability(self):
rds = np.random.RandomState(55)
dataset = MNISTRegressionDataset(random_state=rds)
data_test_1 = dataset.generate_meta_test_data(n_tasks=2,
n_samples_context=5,
n_samples_test=10)
data_train_1 = dataset.generate_meta_train_data(n_tasks=5,
n_samples=20)
rds = np.random.RandomState(55)
dataset = MNISTRegressionDataset(random_state=rds)
data_test_2 = dataset.generate_meta_test_data(n_tasks=2,
n_samples_context=5,
n_samples_test=10)
data_train_2 = dataset.generate_meta_train_data(n_tasks=5,
n_samples=20)
for test_tuple_1, test_tuple_2 in zip(data_test_1, data_test_2):
for data_array_1, data_array_2 in zip(test_tuple_1, test_tuple_2):
assert np.array_equal(data_array_1, data_array_2)
for train_tuple_1, train_tuple_2 in zip(data_train_1, data_train_2):
for data_array_1, data_array_2 in zip(train_tuple_1,
train_tuple_2):
assert np.array_equal(data_array_1, data_array_2)
def test_output_shapes_generate_train(self):
rds = np.random.RandomState(123)
dataset = MNISTRegressionDataset(random_state=rds)
for n_tasks in [24, 2]:
for n_samples in [1, 85]:
data_test = dataset.generate_meta_train_data(
n_tasks=n_tasks, n_samples=n_samples)
assert len(data_test) == n_tasks
for (x_train, t_train) in data_test:
assert x_train.shape[0] == t_train.shape[0]
assert x_train.shape[1] == 2