def test_relative_positioning_sampler(windows_ds, same_rec_neg):
tau_pos, tau_neg = 2000, 3000
n_examples = 100
sampler = RelativePositioningSampler(
windows_ds.get_metadata(), tau_pos=tau_pos, tau_neg=tau_neg,
n_examples=n_examples, tau_max=None, same_rec_neg=same_rec_neg,
random_state=33)
pairs = [pair for pair in sampler]
pairs_df = pd.DataFrame(pairs, columns=['win_ind1', 'win_ind2', 'y'])
pairs_df['diff'] = pairs_df.apply(
lambda x: abs(windows_ds[int(x['win_ind1'])][2][1] -
windows_ds[int(x['win_ind2'])][2][1]), axis=1)
pairs_df['same_rec'] = pairs_df.apply(
lambda x: (find_dataset_ind(windows_ds, int(x['win_ind1'])) ==
find_dataset_ind(windows_ds, int(x['win_ind2']))), axis=1)
assert len(pairs) == n_examples == len(sampler)
assert all(pairs_df.loc[pairs_df['y'] == 1, 'diff'] <= tau_pos)
if same_rec_neg:
assert all(pairs_df.loc[pairs_df['y'] == 0, 'diff'] >= tau_neg)
assert all(pairs_df['same_rec'] == same_rec_neg)
else:
assert all(pairs_df.loc[pairs_df['y'] == 0, 'same_rec'] == False) # noqa: E712
assert all(pairs_df.loc[pairs_df['y'] == 1, 'same_rec'] == True) # noqa: E712
assert abs(np.diff(pairs_df['y'].value_counts())) < 20
def test_relative_positioning_sampler_presample(windows_ds):
tau_pos, tau_neg = 2000, 3000
n_examples = 100
sampler = RelativePositioningSampler(
windows_ds.get_metadata(), tau_pos=tau_pos, tau_neg=tau_neg,
n_examples=n_examples, tau_max=None, same_rec_neg=True,
random_state=33)
sampler.presample()
assert hasattr(sampler, 'examples')
assert len(sampler.examples) == n_examples
pairs = [pair for pair in sampler]
pairs2 = [pair for pair in sampler]
assert np.array_equal(sampler.examples, pairs)
assert np.array_equal(sampler.examples, pairs2)
#
# The samplers also control the number of pairs to be sampled (defined with
# `n_examples`). This number can be large to help regularize the pretext task
# training, for instance 2,000 pairs per recording as in [1]_. Here, we use a
# lower number of 250 pairs per recording to reduce training time.
#
from braindecode.samplers.ssl import RelativePositioningSampler
tau_pos, tau_neg = int(sfreq * 60), int(sfreq * 15 * 60)
n_examples_train = 250 * len(splitted['train'].datasets)
n_examples_valid = 250 * len(splitted['valid'].datasets)
n_examples_test = 250 * len(splitted['test'].datasets)
train_sampler = RelativePositioningSampler(
splitted['train'].get_metadata(), tau_pos=tau_pos, tau_neg=tau_neg,
n_examples=n_examples_train, same_rec_neg=True, random_state=random_state)
valid_sampler = RelativePositioningSampler(
splitted['valid'].get_metadata(), tau_pos=tau_pos, tau_neg=tau_neg,
n_examples=n_examples_valid, same_rec_neg=True,
random_state=random_state).presample()
test_sampler = RelativePositioningSampler(
splitted['test'].get_metadata(), tau_pos=tau_pos, tau_neg=tau_neg,
n_examples=n_examples_test, same_rec_neg=True,
random_state=random_state).presample()
######################################################################
# Creating the model
# ------------------
#