def test_update_no_improvement_count_errors(epoch_error, expected):
mod = TorchModelBase(tol=0.5)
mod.no_improvement_count = 5
mod.best_error = 1
mod.errors = []
mod._update_no_improvement_count_errors(epoch_error)
assert mod.no_improvement_count == expected
def test_build_validation_split(arg_count):
n_features = 2
n_examples = 10
validation_fraction = 0.2
expected_dev_size = int(n_examples * validation_fraction)
expected_train_size = n_examples - expected_dev_size
args = [np.ones((n_examples, n_features)) for _ in range(arg_count)]
train, dev = TorchModelBase._build_validation_split(
*args, validation_fraction=validation_fraction)
assert len(train) == arg_count
assert len(dev) == arg_count
assert all(x.shape == (expected_train_size, n_features) for x in train)
assert all(x.shape == (expected_dev_size, n_features) for x in dev)
def test_no_setting_of_missing_param():
mod = TorchModelBase(amsgrad=0.5)
with pytest.raises(ValueError):
mod.set_params(**{'NON_EXISTENT_PARAM': False})
def test_parameter_setting(param, expected):
mod = TorchModelBase(amsgrad=0.5)
mod.set_params(**{param: expected})
result = getattr(mod, param)
assert result == expected