def __init__(self, probs, labels=None):
"""The discrete individual perturbation to a feature variable.
Parameters
----------
probs: np.ndarray
the probabilities to change from a value of a category to another
value.
"""
self._initialization()
self.basecoreperturbation = BaseCoreSubstitutionArray(probs, labels)
class DiscreteIndPerturbation(BasePerturbation):
"""Discrete perturbation of a discrete feature variable."""
_categorytype = "feature"
_perturbtype = "discrete"
def __init__(self, probs, labels=None):
"""The discrete individual perturbation to a feature variable.
Parameters
----------
probs: np.ndarray
the probabilities to change from a value of a category to another
value.
"""
self._initialization()
self.basecoreperturbation = BaseCoreSubstitutionArray(probs, labels)
# if np.all(probs.sum(1) != 1):
# raise TypeError("Not correct probs input.")
# if probs.shape[0] != probs.shape[1]:
# raise IndexError("Probs is noot a square matrix.")
# self.probs = probs.cumsum(1)
# self.labels = np.arange(len(probs)) if labels is None else labels
def apply2features(self, feature, k=None):
"""Apply perturbation to features.
Parameters
----------
features: np.ndarray or others
the element features collection to be perturbed.
k: int (default=None)
the perturbation indices.
Returns
-------
features: np.ndarray or others
the element features collection perturbated.
"""
## Prepare inputs
# categories = np.unique(feature)
# if len(categories) > len(self.probs):
# msg = "Not matching dimension between probs and features."
# raise IndexError(msg)
k = self._format_k_perturb(k)
logi_int = check_int(k)
if logi_int:
k = [k]
if len(feature.shape) == 1:
feature = feature.reshape((len(feature), 1))
## Compute each change
features_p = self.basecoreperturbation.apply(feature, k)
# features_p = np.zeros((len(feature), 1, len(k)))
# for i_k in k:
# for i in xrange(len(feature)):
# r = np.random.random()
# idx = np.where(feature[i] == self.labels)[0]
# idx2 = np.where(self.probs[idx] > r)[0][0]
# features_p[i, 0, i_k] = self.labels[idx2]
if logi_int:
features_p = features_p[:, :, 0]
return features_p
