def test_seed_class_splitter():
dataset_transform = ClassSplitter(shuffle=True,
num_train_per_class=5, num_test_per_class=5)
dataset = Sinusoid(10, num_tasks=1000, noise_std=0.1,
dataset_transform=dataset_transform)
dataset.seed(1)
expected_train_inputs = np.array([1.08565437,-1.56211897,4.62078213,-2.03870077,0.76977846])
expected_train_targets = np.array([-0.00309463,-1.37650356,-0.9346262,-0.1031986,-0.4698061])
expected_test_inputs = np.array([-2.48340416,3.75388738,-3.15504396,0.09898378,0.32922559])
expected_test_targets = np.array([0.73113509,0.91773121,1.86656819,-1.61885041,-1.52508997])
task = dataset[0]
train_dataset, test_dataset = task['train'], task['test']
assert len(train_dataset) == 5
assert len(test_dataset) == 5
for i, (train_input, train_target) in enumerate(train_dataset):
assert np.isclose(train_input, expected_train_inputs[i])
assert np.isclose(train_target, expected_train_targets[i])
for i, (test_input, test_target) in enumerate(test_dataset):
assert np.isclose(test_input, expected_test_inputs[i])
assert np.isclose(test_target, expected_test_targets[i])
def sinusoid(shots, shuffle=True, test_shots=None, seed=None, **kwargs):
"""Helper function to create a meta-dataset for the Sinusoid toy dataset.
Parameters
----------
shots : int
Number of (training) examples in each task. This corresponds to `k` in
`k-shot` classification.
shuffle : bool (default: `True`)
Shuffle the examples when creating the tasks.
test_shots : int, optional
Number of test examples in each task. If `None`, then the number of test
examples is equal to the number of training examples in each task.
seed : int, optional
Random seed to be used in the meta-dataset.
kwargs
Additional arguments passed to the `Sinusoid` class.
See also
--------
`torchmeta.toy.Sinusoid` : Meta-dataset for the Sinusoid toy dataset.
"""
if 'num_samples_per_task' in kwargs:
warnings.warn(
'Both arguments `shots` and `num_samples_per_task` were '
'set in the helper function for the number of samples in each task. '
'Ignoring the argument `shots`.',
stacklevel=2)
if test_shots is not None:
shots = kwargs['num_samples_per_task'] - test_shots
if shots <= 0:
raise ValueError(
'The argument `test_shots` ({0}) is greater '
'than the number of samples per task ({1}). Either use the '
'argument `shots` instead of `num_samples_per_task`, or '
'increase the value of `num_samples_per_task`.'.format(
test_shots, kwargs['num_samples_per_task']))
else:
shots = kwargs['num_samples_per_task'] // 2
if test_shots is None:
test_shots = shots
dataset = Sinusoid(num_samples_per_task=shots + test_shots, **kwargs)
dataset = ClassSplitter(dataset,
shuffle=shuffle,
num_train_per_class=shots,
num_test_per_class=test_shots)
dataset.seed(seed)
return dataset