Method vote_proba

modAL/models/learners.py:598–629

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:

(self, X: modALinput, **predict_proba_kwargs)

        return prediction

    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


class CommitteeRegressor(BaseCommittee):