def test_cropped_trial_epoch_scoring_none_x_test():
dataset_train = None
dataset_valid = None
predictions = np.array(
[
[[0.0, 0.0, 0.0, 0.0], [1.0, 1.0, 1.0, 1.0]],
[[1.0, 1.0, 1.0, 1.0], [0.0, 0.0, 0.0, 0.0]],
[[1.0, 1.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]],
[[1.0, 1.0, 1.0, 1.0], [0.0, 0.0, 0.0, 0.0]],
]
)
y_true = [torch.tensor([0, 0]), torch.tensor([1, 1])]
window_inds = [(
torch.tensor([0, 0]), # i_window_in_trials
[None], # won't be used
torch.tensor([4, 4]), # i_window_stops
),
(
torch.tensor([0, 0]), # i_window_in_trials
[None], # won't be used
torch.tensor([4, 4]), # i_window_stops
)]
cropped_trial_epoch_scoring = CroppedTrialEpochScoring("accuracy")
cropped_trial_epoch_scoring.initialize()
cropped_trial_epoch_scoring.y_preds_ = [
to_tensor(predictions[:2], device="cpu"),
to_tensor(predictions[2:], device="cpu"),
]
cropped_trial_epoch_scoring.y_trues_ = y_true
cropped_trial_epoch_scoring.window_inds_ = window_inds
mock_skorch_net = MockSkorchNet()
mock_skorch_net.callbacks_ = [(
"", cropped_trial_epoch_scoring)]
output = cropped_trial_epoch_scoring.on_epoch_end(
mock_skorch_net, dataset_train, dataset_valid
)
assert output is None
def test_cropped_trial_epoch_scoring():
dataset_train = None
# Definition of test cases
predictions_cases = [
# Exepected predictions classification results: [1, 0, 0, 0]
np.array(
[
[[0.2, 0.1, 0.1, 0.1], [0.8, 0.9, 0.9, 0.9]], # trial 0 preds
[[1.0, 1.0, 1.0, 1.0], [0.0, 0.0, 0.0, 0.0]], # trial 1 preds
[[1.0, 1.0, 1.0, 0.2], [0.0, 0.0, 0.0, 0.8]], # trial 2 preds
[[0.9, 0.8, 0.9, 1.0], [0.1, 0.2, 0.1, 0.0]], # trial 3 preds
]
),
# Expected predictions classification results: [1, 1, 1, 0]
np.array(
[
[[0.2, 0.1, 0.1, 0.1], [0.8, 0.9, 0.9, 0.9]],
[[0.0, 0.0, 0.0, 0.0], [1.0, 1.0, 1.0, 1.0]],
[[0.0, 0.0, 0.0, 0.2], [1.0, 1.0, 1.0, 0.8]],
[[0.9, 0.8, 0.9, 1.0], [0.1, 0.2, 0.1, 0.0]],
]
),
]
y_true_cases = [
[torch.tensor([0, 0]), torch.tensor([1, 1])],
[torch.tensor([1, 1]), torch.tensor([1, 1])],
]
expected_accuracies_cases = [0.25, 0.75]
window_inds = [(
torch.tensor([0,0]), # i_window_in_trials
[None],# won't be used
torch.tensor([4,4]), # i_window_stops
),(
torch.tensor([0,0]), # i_window_in_trials
[None],# won't be used
torch.tensor([4,4]), # i_window_stops
),]
for predictions, y_true, accuracy in zip(
predictions_cases, y_true_cases, expected_accuracies_cases
):
dataset_valid = create_from_X_y(
np.zeros((4, 1, 10)), np.concatenate(y_true),
window_size_samples=10, window_stride_samples=4, drop_last_window=False)
mock_skorch_net = MockSkorchNet()
cropped_trial_epoch_scoring = CroppedTrialEpochScoring(
"accuracy", on_train=False)
mock_skorch_net.callbacks = [(
"", cropped_trial_epoch_scoring)]
cropped_trial_epoch_scoring.initialize()
cropped_trial_epoch_scoring.y_preds_ = [
to_tensor(predictions[:2], device="cpu"),
to_tensor(predictions[2:], device="cpu"),
]
cropped_trial_epoch_scoring.y_trues_ = y_true
cropped_trial_epoch_scoring.window_inds_ = window_inds
cropped_trial_epoch_scoring.on_epoch_end(
mock_skorch_net, dataset_train, dataset_valid
)
np.testing.assert_almost_equal(
mock_skorch_net.history[0]["accuracy"], accuracy
)
