Class Committee

modAL/models/learners.py:438–629

This class is an abstract model of a committee-based active learning algorithm. Args: learner_list: A list of ActiveLearners forming the Committee. query_strategy: Query strategy function. Committee supports disagreement-based query strategies from :mod:`modAL.di

class Committee(BaseCommittee):
    """
    This class is an abstract model of a committee-based active learning algorithm.
    Args:
        learner_list: A list of ActiveLearners forming the Committee.
        query_strategy: Query strategy function. Committee supports disagreement-based query strategies from
            :mod:`modAL.disagreement`, but uncertainty-based ones from :mod:`modAL.uncertainty` are also supported.
        on_transformed: Whether to transform samples with the pipeline defined by each learner's estimator
            when applying the query strategy.
    Attributes:
        classes_: Class labels known by the Committee.
        n_classes_: Number of classes known by the Committee.
    Examples:
        >>> from sklearn.datasets import load_iris
        >>> from sklearn.neighbors import KNeighborsClassifier
        >>> from sklearn.ensemble import RandomForestClassifier
        >>> from modAL.models import ActiveLearner, Committee
        >>>
        >>> iris = load_iris()
        >>>
        >>> # initialize ActiveLearners
        >>> learner_1 = ActiveLearner(
        ...     estimator=RandomForestClassifier(),
        ...     X_training=iris['data'][[0, 50, 100]], y_training=iris['target'][[0, 50, 100]]
        ... )
        >>> learner_2 = ActiveLearner(
        ...     estimator=KNeighborsClassifier(n_neighbors=3),
        ...     X_training=iris['data'][[1, 51, 101]], y_training=iris['target'][[1, 51, 101]]
        ... )
        >>>
        >>> # initialize the Committee
        >>> committee = Committee(
        ...     learner_list=[learner_1, learner_2]
        ... )
        >>>
        >>> # querying for labels
        >>> query_idx, query_sample = committee.query(iris['data'])
        >>>
        >>> # ...obtaining new labels from the Oracle...
        >>>
        >>> # teaching newly labelled examples
        >>> committee.teach(
        ...     X=iris['data'][query_idx].reshape(1, -1),
        ...     y=iris['target'][query_idx].reshape(1, )
        ... )
    """
    def __init__(self, learner_list: List[ActiveLearner], query_strategy: Callable = vote_entropy_sampling,
                 on_transformed: bool = False) -> None:
        super().__init__(learner_list, query_strategy, on_transformed)
        self._set_classes()

    def _set_classes(self):
        """
        Checks the known class labels by each learner, merges the labels and returns a mapping which maps the learner's
        classes to the complete label list.
        """
        # assemble the list of known classes from each learner
        try: