def test_bad_interact():
cats = [1, 2]
interacter = OneHotCategoricalFeatureInteracter(cats)
with pytest.raises(ValueError, match='Expected '):
interacter.interact(np.array([
[1, 2, 3],
[4, 5, 6]
]), [1])
class SignedCodingFeatureExtractor(ContinuousFeatureExtractor):
"""
Signed-coding feature extractor. Forms a category from the conjunction of all state-feature signs and then places
the continuous feature vector into its associated category.
"""
def extract(
self,
state: GymState,
refit_scaler: bool
) -> np.ndarray:
"""
Extract state features.
:param state: State.
:param refit_scaler: Whether to refit the feature scaler before scaling the extracted features.
:return: State-feature vector.
"""
if self.state_category_interacter is None:
self.state_category_interacter = OneHotCategoricalFeatureInteracter([
OneHotCategory(*category_args)
for category_args in product(*([[True, False]] * state.observation.shape[0]))
])
# form the one-hot state category
state_category = OneHotCategory(*[
value < 0.0
for value in state.observation
])
# extract and encode feature values
raw_feature_values = super().extract(state, refit_scaler)
encoded_feature_values = self.state_category_interacter.interact(
np.array([raw_feature_values]),
[state_category]
)[0]
return encoded_feature_values
def __init__(
self
):
"""
Initialize the feature extractor.
"""
super().__init__()
self.state_category_interacter = None