def vote_proba(self, X: modALinput, **predict_proba_kwargs) -> Any:
"""
Predicts the probabilities of the classes for each sample and each learner.
Args:
X: The samples for which class probabilities are to be calculated.
**predict_proba_kwargs: Keyword arguments for the :meth:`predict_proba` of the learners.
Returns:
Probabilities of each class for each learner and each instance.
"""
# get dimensions
n_samples = X.shape[0]
n_learners = len(self.learner_list)
proba = np.zeros(shape=(n_samples, n_learners, self.n_classes_))
# checking if the learners in the Committee know the same set of class labels
if check_class_labels(*[learner.estimator for learner in self.learner_list]):
# known class labels are the same for each learner
# probability prediction is straightforward
for learner_idx, learner in enumerate(self.learner_list):
proba[:, learner_idx, :] = learner.predict_proba(X, **predict_proba_kwargs)
else:
for learner_idx, learner in enumerate(self.learner_list):
proba[:, learner_idx, :] = check_class_proba(
proba=learner.predict_proba(X, **predict_proba_kwargs),
known_labels=learner.estimator.classes_,
all_labels=self.classes_
)
return proba
def vote_proba(self, X: modALinput, **predict_proba_kwargs) -> Any:
# get dimensions
n_samples = X.shape[0]
n_learners = len(self.learner_list)
proba = np.zeros(shape=(n_samples, n_learners, self.n_classes_))
# checking if the learners in the Committee know the same set of class labels
if check_class_labels(
*[learner.estimator for learner in self.learner_list]):
# known class labels are the same for each learner
# probability prediction is straightforward
for learner_idx, learner in enumerate(self.learner_list):
proba[:, learner_idx, :] = learner.predict_proba(
X, **predict_proba_kwargs)
else:
for learner_idx, learner in enumerate(self.learner_list):
proba[:, learner_idx, :] = check_class_proba(
proba=learner.predict_proba(X, **predict_proba_kwargs),
known_labels=learner.estimator.classes_,
all_labels=self.classes_)
return proba
def vote_proba(self, X, **predict_proba_kwargs):
"""
Predicts the probabilities of the classes for each sample and each learner.
Parameters
----------
X: numpy.ndarray of shape (n_samples, n_features)
The samples for which class probabilities are to be calculated.
predict_proba_kwargs: keyword arguments
Keyword arguments for the .predict_proba() method of the learners.
Returns
-------
vote_proba: numpy.ndarray of shape (n_samples, n_learners, n_classes)
Probabilities of each class for each learner and each instance.
"""
check_array(X, ensure_2d=True)
# get dimensions
n_samples = X.shape[0]
n_learners = len(self._learner_list)
proba = np.zeros(shape=(n_samples, n_learners, self.n_classes_))
# checking if the learners in the Committee know the same set of class labels
if check_class_labels(
*[learner._predictor for learner in self._learner_list]):
# known class labels are the same for each learner
# probability prediction is straightforward
for learner_idx, learner in enumerate(self._learner_list):
proba[:, learner_idx, :] = learner.predict_proba(
X, **predict_proba_kwargs)
else:
for learner_idx, learner in enumerate(self._learner_list):
proba[:, learner_idx, :] = check_class_proba(
proba=learner.predict_proba(X, **predict_proba_kwargs),
known_labels=learner._predictor.classes_,
all_labels=self.classes_)
return proba
def vote_proba(self, X, **predict_proba_kwargs):
"""
Predicts the probabilities of the classes for each sample and each learner.
Parameters
----------
X: numpy.ndarray of shape (n_samples, n_features)
The samples for which class probabilities are to be calculated.
predict_proba_kwargs: keyword arguments
Keyword arguments for the .predict_proba() method of the learners.
Returns
-------
vote_proba: numpy.ndarray of shape (n_samples, n_learners, n_classes)
Probabilities of each class for each learner and each instance.
"""
check_array(X, ensure_2d=True)
# get dimensions
n_samples = X.shape[0]
n_learners = len(self._learner_list)
proba = np.zeros(shape=(n_samples, n_learners, self.n_classes_))
# checking if the learners in the Committee know the same set of class labels
if check_class_labels(*[learner._predictor for learner in self._learner_list]):
# known class labels are the same for each learner
# probability prediction is straightforward
for learner_idx, learner in enumerate(self._learner_list):
proba[:, learner_idx, :] = learner.predict_proba(X, **predict_proba_kwargs)
else:
for learner_idx, learner in enumerate(self._learner_list):
proba[:, learner_idx, :] = check_class_proba(
proba=learner.predict_proba(X, **predict_proba_kwargs),
known_labels=learner._predictor.classes_,
all_labels=self.classes_
)
return proba