def test_windows_fixed_length_cropped(lazy_loadable_dataset):
"""Test fixed length windowing on cropped data.
Cropping raw data changes the `first_samp` attribute of the Raw object, and
so it is important to test this is taken into account by the windowers.
"""
tmin, tmax = 100, 120
ds = copy.deepcopy(lazy_loadable_dataset)
ds.datasets[0].raw.annotations.crop(tmin, tmax)
crop_ds = copy.deepcopy(lazy_loadable_dataset)
crop_transform = Preprocessor('crop', tmin=tmin, tmax=tmax)
preprocess(crop_ds, [crop_transform])
# Extract windows
sfreq = ds.datasets[0].raw.info['sfreq']
tmin_samples, tmax_samples = int(tmin * sfreq), int(tmax * sfreq)
windows1 = create_fixed_length_windows(
concat_ds=ds, start_offset_samples=tmin_samples,
stop_offset_samples=tmax_samples, window_size_samples=100,
window_stride_samples=100, drop_last_window=True)
windows2 = create_fixed_length_windows(
concat_ds=crop_ds, start_offset_samples=0,
stop_offset_samples=None, window_size_samples=100,
window_stride_samples=100, drop_last_window=True)
assert (windows1[0][0] == windows2[0][0]).all()
def test_epochs_kwargs(lazy_loadable_dataset):
picks = ['ch0']
on_missing = 'warning'
flat = {'eeg': 3e-6}
reject = {'eeg': 43e-6}
windows = create_windows_from_events(
concat_ds=lazy_loadable_dataset, trial_start_offset_samples=0,
trial_stop_offset_samples=0, window_size_samples=100,
window_stride_samples=100, drop_last_window=False, picks=picks,
on_missing=on_missing, flat=flat, reject=reject)
epochs = windows.datasets[0].windows
assert epochs.ch_names == picks
assert epochs.reject == reject
assert epochs.flat == flat
windows = create_fixed_length_windows(
concat_ds=lazy_loadable_dataset, start_offset_samples=0,
stop_offset_samples=None, window_size_samples=100,
window_stride_samples=100, drop_last_window=False, picks=picks,
on_missing=on_missing, flat=flat, reject=reject)
epochs = windows.datasets[0].windows
assert epochs.ch_names == picks
assert epochs.reject == reject
assert epochs.flat == flat
def test_fixed_length_windows_preload_false(lazy_loadable_dataset):
windows = create_fixed_length_windows(
concat_ds=lazy_loadable_dataset, start_offset_samples=0,
stop_offset_samples=100, window_size_samples=100,
window_stride_samples=100, drop_last_window=False, preload=False)
assert all([not ds.windows.preload for ds in windows.datasets])
def test_drop_bad_windows(concat_ds_targets, drop_bad_windows, preload):
concat_ds, _ = concat_ds_targets
windows_from_events = create_windows_from_events(
concat_ds=concat_ds,
trial_start_offset_samples=0,
trial_stop_offset_samples=0,
window_size_samples=100,
window_stride_samples=100,
drop_last_window=False,
preload=preload,
drop_bad_windows=drop_bad_windows)
windows_fixed_length = create_fixed_length_windows(
concat_ds=concat_ds,
start_offset_samples=0,
stop_offset_samples=1000,
window_size_samples=1000,
window_stride_samples=1000,
drop_last_window=False,
preload=preload,
drop_bad_windows=drop_bad_windows)
assert (windows_from_events.datasets[0].windows._bad_dropped ==
drop_bad_windows)
assert (windows_fixed_length.datasets[0].windows._bad_dropped ==
drop_bad_windows)
def test_fixed_length_windower_n_jobs(lazy_loadable_dataset):
longer_dataset = BaseConcatDataset([lazy_loadable_dataset.datasets[0]] * 8)
windows = [create_fixed_length_windows(
concat_ds=longer_dataset, start_offset_samples=0,
stop_offset_samples=None, window_size_samples=100,
window_stride_samples=100, drop_last_window=True, preload=True,
n_jobs=n_jobs) for n_jobs in [1, 2]]
assert windows[0].description.equals(windows[1].description)
for ds1, ds2 in zip(windows[0].datasets, windows[1].datasets):
# assert ds1.windows == ds2.windows # Runs locally, fails in CI
assert np.allclose(ds1.windows.get_data(), ds2.windows.get_data())
assert pd.Series(ds1.windows.info).to_json() == \
pd.Series(ds2.windows.info).to_json()
assert ds1.description.equals(ds2.description)
assert np.array_equal(ds1.y, ds2.y)
assert np.array_equal(ds1.crop_inds, ds2.crop_inds)
def test_fixed_length_windower(start_offset_samples, window_size_samples,
window_stride_samples, drop_last_window,
mapping):
rng = np.random.RandomState(42)
info = mne.create_info(ch_names=['0', '1'], sfreq=50, ch_types='eeg')
data = rng.randn(2, 1000)
raw = mne.io.RawArray(data=data, info=info)
desc = pd.Series({'pathological': True, 'gender': 'M', 'age': 48})
base_ds = BaseDataset(raw, desc, target_name="age")
concat_ds = BaseConcatDataset([base_ds])
if window_size_samples is None:
window_size_samples = base_ds.raw.n_times
stop_offset_samples = data.shape[1] - start_offset_samples
epochs_ds = create_fixed_length_windows(
concat_ds,
start_offset_samples=start_offset_samples,
stop_offset_samples=stop_offset_samples,
window_size_samples=window_size_samples,
window_stride_samples=window_stride_samples,
drop_last_window=drop_last_window,
mapping=mapping)
if mapping is not None:
assert base_ds.target == 48
assert all(epochs_ds.datasets[0].windows.metadata['target'] == 0)
epochs_data = epochs_ds.datasets[0].windows.get_data()
idxs = np.arange(start_offset_samples,
stop_offset_samples - window_size_samples + 1,
window_stride_samples)
if not drop_last_window and idxs[
-1] != stop_offset_samples - window_size_samples:
idxs = np.append(idxs, stop_offset_samples - window_size_samples)
assert len(idxs) == epochs_data.shape[0], (
'Number of epochs different than expected')
assert window_size_samples == epochs_data.shape[2], (
'Window size different than expected')
for j, idx in enumerate(idxs):
np.testing.assert_allclose(base_ds.raw.get_data()[:, idx:idx +
window_size_samples],
epochs_data[j, :],
err_msg=f'Epochs different for epoch {j}')
fake_descrition = pd.Series(
data=[target, train_or_eval],
index=["target", "session"])
base_ds = BaseDataset(raw, fake_descrition, target_name="target")
datasets.append(base_ds)
dataset = BaseConcatDataset(datasets)
return dataset
dataset = fake_regression_dataset(
n_fake_recs=5, n_fake_chs=21, fake_sfreq=100, fake_duration_s=60)
windows_dataset = create_fixed_length_windows(
dataset,
start_offset_samples=0,
stop_offset_samples=0,
window_size_samples=input_window_samples,
window_stride_samples=n_preds_per_input,
drop_last_window=False,
drop_bad_windows=True,
)
splits = windows_dataset.split("session")
train_set = splits["train"]
valid_set = splits["eval"]
regressor = EEGRegressor(
model,
cropped=True,
criterion=CroppedLoss,
criterion__loss_function=torch.nn.functional.mse_loss,
optimizer=torch.optim.AdamW,
base_ds = BaseDataset(raw, fake_descrition, target_name="target")
datasets.append(base_ds)
dataset = BaseConcatDataset(datasets)
return dataset
dataset = fake_regression_dataset(n_fake_recs=5,
n_fake_chs=21,
fake_sfreq=100,
fake_duration_s=60)
windows_dataset = create_fixed_length_windows(
dataset,
start_offset_samples=0,
stop_offset_samples=None,
supercrop_size_samples=input_time_length,
supercrop_stride_samples=n_preds_per_input,
drop_samples=False,
drop_bad_windows=True,
)
splits = windows_dataset.split("session")
train_set = splits["train"]
valid_set = splits["eval"]
regressor = EEGRegressor(
model,
cropped=True,
criterion=CroppedLoss,
criterion__loss_function=torch.nn.functional.mse_loss,
optimizer=torch.optim.AdamW,
