def vote_entropy(committee: BaseCommittee, X: modALinput,
**predict_proba_kwargs) -> np.ndarray:
"""
Calculates the vote entropy for the Committee. First it computes the predictions of X for each learner in the
Committee, then calculates the probability distribution of the votes. The entropy of this distribution is the vote
entropy of the Committee, which is returned.
Args:
committee: The :class:`modAL.models.BaseCommittee` instance for which the vote entropy is to be calculated.
X: The data for which the vote entropy is to be calculated.
**predict_proba_kwargs: Keyword arguments for the :meth:`predict_proba` of the Committee.
Returns:
Vote entropy of the Committee for the samples in X.
"""
n_learners = len(committee)
try:
votes = committee.vote(X, **predict_proba_kwargs)
except NotFittedError:
return np.zeros(shape=(X.shape[0], ))
p_vote = np.zeros(shape=(X.shape[0], len(committee.classes_)))
entr = np.zeros(shape=(X.shape[0], ))
for vote_idx, vote in enumerate(votes):
vote_counter = Counter(vote)
for class_idx, class_label in enumerate(committee.classes_):
p_vote[vote_idx,
class_idx] = vote_counter[class_label] / n_learners
entr[vote_idx] = entropy(p_vote[vote_idx])
return entr
def vote_entropy(committee: BaseCommittee, X: modALinput,
**predict_proba_kwargs) -> np.ndarray:
n_learners = len(committee)
try:
votes = committee.vote(X, **predict_proba_kwargs)
except NotFittedError:
return np.zeros(shape=(X.shape[0], ))
p_vote = np.zeros(shape=(X.shape[0], len(committee.classes_)))
entr = np.zeros(shape=(X.shape[0], ))
for vote_idx, vote in enumerate(votes):
vote_counter = Counter(vote)
for class_idx, class_label in enumerate(committee.classes_):
p_vote[vote_idx,
class_idx] = vote_counter[class_label] / n_learners
entr[vote_idx] = entropy(p_vote[vote_idx])
return entr