Class DeepActiveLearner

modAL/models/learners.py:183–296

This class is an model of a general deep active learning algorithm. Differences to the classical ActiveLearner are: - Data is no member variable of the DeepActiveLearner class - Misses the initial add/train data methods, therefore always trains on new data - Uses dif

class DeepActiveLearner(BaseLearner):
    """
    This class is an model of a general deep active learning algorithm.
    Differences to the classical ActiveLearner are:
        - Data is no member variable of the DeepActiveLearner class
        - Misses the initial add/train data methods, therefore always trains on new data
        - Uses different interfaces to sklearn in some functions

    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.
        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.
    """

    def __init__(self,
                 estimator: BaseEstimator,
                 query_strategy: Callable = uncertainty_sampling,
                 on_transformed: bool = False,
                 **fit_kwargs
                 ) -> None:
        # TODO: Check if given query strategy works for Deep Learning
        super().__init__(estimator, query_strategy, on_transformed, **fit_kwargs)

        self.estimator.initialize()

    def fit(self, X: modALinput, y: modALinput, bootstrap: bool = False, **fit_kwargs) -> 'BaseLearner':
        """
        Interface for the fit method of the predictor. Fits the predictor to the supplied data.

        Args:
            X: The samples to be fitted.
            y: The corresponding labels.
            bootstrap: If true, trains the estimator on a set bootstrapped from X.
                Useful for building Committee models with bagging.
            **fit_kwargs: Keyword arguments to be passed to the fit method of the predictor.

        Returns:
            self
        """
        return self._fit_on_new(X, y, bootstrap=bootstrap, **fit_kwargs)

    def teach(self, X: modALinput, y: modALinput, warm_start: bool = True, bootstrap: bool = False, **fit_kwargs) -> None:
        """
        Trains the predictor with the passed data (warm_start decides if params are resetted or not). 

        Args:
            X: The new samples for which the labels are supplied by the expert.
            y: Labels corresponding to the new instances in X.
            warm_start: If False, the model parameters are resetted and the training starts from zero, 
                otherwise the pre trained model is kept and further trained.
            bootstrap: If True, training is done on a bootstrapped dataset. Useful for building Committee models