Class ActiveLearner

modAL/models/learners.py:20–180

This class is an model of a general classic (machine learning) active learning algorithm. Args: estimator: The estimator to be used in the active learning loop. query_strategy: Function providing the query strategy for the active learning loop, for instance, mod

class ActiveLearner(BaseLearner):
    """
    This class is an model of a general classic (machine learning) active learning algorithm.

    Args:
        estimator: The estimator to be used in the active learning loop.
        query_strategy: Function providing the query strategy for the active learning loop,
            for instance, modAL.uncertainty.uncertainty_sampling.
        X_training: Initial training samples, if available.
        y_training: Initial training labels corresponding to initial training samples.
        bootstrap_init: If initial training data is available, bootstrapping can be done during the first training.
            Useful when building Committee models with bagging.
        on_transformed: Whether to transform samples with the pipeline defined by the estimator
            when applying the query strategy.
        **fit_kwargs: keyword arguments.

    Attributes:
        estimator: The estimator to be used in the active learning loop.
        query_strategy: Function providing the query strategy for the active learning loop.
        X_training: If the model hasn't been fitted yet it is None, otherwise it contains the samples
            which the model has been trained on. If provided, the method fit() of estimator is called during __init__()
        y_training: The labels corresponding to X_training.

    Examples:

        >>> from sklearn.datasets import load_iris
        >>> from sklearn.ensemble import RandomForestClassifier
        >>> from modAL.models import ActiveLearner
        >>> iris = load_iris()
        >>> # give initial training examples
        >>> X_training = iris['data'][[0, 50, 100]]
        >>> y_training = iris['target'][[0, 50, 100]]
        >>>
        >>> # initialize active learner
        >>> learner = ActiveLearner(
        ...     estimator=RandomForestClassifier(),
        ...     X_training=X_training, y_training=y_training
        ... )
        >>>
        >>> # querying for labels
        >>> query_idx, query_sample = learner.query(iris['data'])
        >>>
        >>> # ...obtaining new labels from the Oracle...
        >>>
        >>> # teaching newly labelled examples
        >>> learner.teach(
        ...     X=iris['data'][query_idx].reshape(1, -1),
        ...     y=iris['target'][query_idx].reshape(1, )
        ... )
    """

    def __init__(self,
                 estimator: BaseEstimator,
                 query_strategy: Callable = uncertainty_sampling,
                 X_training: Optional[modALinput] = None,
                 y_training: Optional[modALinput] = None,
                 bootstrap_init: bool = False,
                 on_transformed: bool = False,