def test_data_recovery():
train_sample, _, _ = get_dummy_sample()
X = train_sample[0][0]
datum = Datum(X=X, y=train_sample[0][1])
datum = TransformSignal(identity)(TransformFFT(identity)(datum))
assert_almost_equal(datum.X.numpy(), X, decimal=3)
def test_delay_signal():
train_sample, _, _ = get_dummy_sample()
transf = TransformSignal(delay_signal, transforms_args)
X = train_sample[0][0]
datum = Datum(X=X, y=train_sample[0][1])
datum = transf(datum)
value_cutoff = int(np.round(transforms_args["magnitude"] * X.shape[1]))
assert_array_equal(X[:, -value_cutoff:], datum.X[:, :value_cutoff])
assert_array_equal(X[:, :-value_cutoff], datum.X[:, value_cutoff:])
def test_noise_addition():
transforms_args["magnitude"] = 0.1
train_sample, _, _ = get_dummy_sample()
transf = TransformSignal(add_noise_to_signal, transforms_args)
X = train_sample[0][0]
scale = torch.mean(torch.abs(X)).item()
datum = Datum(X=X, y=train_sample[0][1])
datum = transf(datum)
remains = (datum.X - X) / (transforms_args["magnitude"] * 0.1)
assert_almost_equal(np.mean(remains.numpy()) / scale, 0, 1)
assert_almost_equal(np.var(remains.numpy()) / (scale * scale), 1, 1)
def test_mask_along_axis_nonrandom():
params_masking_nonrandom_test_1 = {
"mask_value": 0.0,
"mask_start": 5,
"mask_end": 10,
"axis": 2,
}
params_masking_nonrandom_test_2 = {
"mask_value": 0.0,
"mask_start": 4,
"mask_end": 25,
"axis": 1,
}
train_sample, _, _ = get_dummy_sample()
datum = Datum(X=train_sample[0][0], y=train_sample[0][1])
datum_with_tr = Datum(X=train_sample[0][0], y=train_sample[0][1])
datum_spec = TransformFFT(identity)(datum)
datum_spec_with_tr = TransformFFT(
mask_along_axis, params_masking_nonrandom_test_1)(datum_with_tr)
datum_spec_with_tr = TransformFFT(
mask_along_axis, params_masking_nonrandom_test_2)(datum_spec_with_tr)
img = datum_spec.X[0, :, :, 0].numpy()
img_with_zeros = datum_with_tr.X[0, :, :, 0].numpy()
# first, asserting masked transform contains at least
# one row with zeros.
line_has_zeros = np.all((img_with_zeros == 0.0), axis=0)
column_has_zeros = np.all((img_with_zeros == 0.0), axis=1)
lines_with_zeros = [
i for i in range(len(line_has_zeros)) if line_has_zeros[i]
]
columns_with_zeros = [
i for i in range(len(column_has_zeros)) if column_has_zeros[i]
]
assert (lines_with_zeros == list(range(5, 10)))
assert (columns_with_zeros == list(range(4, 25)))
# Second, asserting the equality of img
# and img_with_zeros on other elements
where_equal = [
(round(img[i, j], 5) == round(img_with_zeros[i, j], 5))
for i in range(img.shape[0]) for j in range(img.shape[1])
if ((j not in lines_with_zeros) and (i not in columns_with_zeros))
]
assert (all(where_equal))
def test_randaugment():
train_sample, _, _ = get_dummy_sample()
subset_aug_sample, subset_aug_labels = get_sample(train_sample,
[["randaugment"]],
sample_size=1,
random_state=1)
transforms_args["train_sample"] = train_sample
transforms_args["label_index_dict"] = create_label_index_dict(
subset_aug_sample, subset_aug_labels)
transf = TransformSignal(randaugment, transforms_args)
X = train_sample[0][0]
datum = Datum(X=X, y=train_sample[0][1])
datum = transf(datum)
def test_signal_addition():
train_sample, _, _ = get_dummy_sample()
subset_aug_sample, subset_aug_labels = get_sample(train_sample,
[["merge_two_signals"]],
sample_size=1,
random_state=1)
transforms_args["train_sample"] = train_sample
transforms_args["label_index_dict"] = create_label_index_dict(
subset_aug_sample, subset_aug_labels)
transf = TransformSignal(merge_two_signals, transforms_args)
X = train_sample[0][0]
datum = Datum(X=X, y=train_sample[0][1])
datum = transf(datum)
assert_array_almost_equal(X, datum.X, 5)
def test_add_noise_to_signal_with_proba():
transforms_args["magnitude"] = 0.1
train_sample, _, _ = get_dummy_sample()
transf = TransformSignal(add_noise_to_signal_with_proba, transforms_args)
random.seed(1) # returns 0.13
X = train_sample[0][0]
datum = Datum(X=X, y=train_sample[0][1])
datum = transf(datum)
assert_almost_equal(datum.X.numpy(), X)
random.seed(0) # return 0.88
datum = transf(datum)
scale = torch.mean(torch.abs(X)).item()
remains = (datum.X - X) / (transforms_args["magnitude"] * 0.1)
assert_almost_equal(np.mean(remains.numpy()) / scale, 0, 1)
assert_almost_equal(np.var(remains.numpy()) / (scale * scale), 1, 1)
def test_add_noise_to_signal_only_one_signal():
transforms_args["magnitude"] = 0.1
train_sample, _, _ = get_dummy_sample()
transf = TransformSignal(add_noise_to_signal_only_one_signal,
transforms_args)
random.seed(1) # returns 0.1
X = train_sample[0][0]
datum = Datum(X=X, y=train_sample[0][1])
datum = transf(datum)
assert_almost_equal(datum.X[1, :].numpy(), X[1, :])
random.seed(0) # returns 0.8
X = train_sample[0][0]
datum = Datum(X=X, y=train_sample[0][1])
datum = transf(datum)
assert_almost_equal(datum.X[0, :].numpy(), X[0, :])
def test_dummy_sleepstagernet():
train_sample, valid_sample, test_sample = get_dummy_sample()
dummy_sleepstagernet(train_sample, valid_sample, test_sample)
plot_result(saving_params)
assert (True)
def test_dummy_handcrafted_features():
train_sample, valid_sample, test_sample = get_dummy_sample()
dummy_handcrafted_features(train_sample, valid_sample, test_sample)
plot_result(saving_params)
assert (True)
def test_dummy_shallownet_with_transf():
train_sample, valid_sample, test_sample = get_dummy_sample()
dummy_shallownet_with_transf(train_sample, valid_sample, test_sample)
plot_result(saving_params)
assert (True)
def test_dummy_sleepstagernet():
train_sample, valid_sample, test_sample = get_dummy_sample()
dummy_sleepstagernet_with_randaugment(train_sample, valid_sample,
test_sample)
