Method teach

modAL/models/learners.py:160–180

Adds X and y to the known training data and retrains the predictor with the augmented dataset. Args: X: The new samples for which the labels are supplied by the expert. y: Labels corresponding to the new instances in X. bootstrap: If True, traini

(self, X: modALinput, y: modALinput, bootstrap: bool = False, only_new: bool = False, **fit_kwargs)

        return self._fit_to_known(bootstrap=bootstrap, **fit_kwargs)

    def teach(self, X: modALinput, y: modALinput, bootstrap: bool = False, only_new: bool = False, **fit_kwargs) -> None:
        """
        Adds X and y to the known training data and retrains the predictor with the augmented dataset.

        Args:
            X: The new samples for which the labels are supplied by the expert.
            y: Labels corresponding to the new instances in X.
            bootstrap: If True, training is done on a bootstrapped dataset. Useful for building Committee models
                with bagging.
            only_new: If True, the model is retrained using only X and y, ignoring the previously provided examples.
                Useful when working with models where the .fit() method doesn't retrain the model from scratch (e. g. in
                tensorflow or keras).
            **fit_kwargs: Keyword arguments to be passed to the fit method of the predictor.
        """
        if not only_new:
            self._add_training_data(X, y)
            self._fit_to_known(bootstrap=bootstrap, **fit_kwargs)
        else:
            check_X_y(X, y, accept_sparse=True, ensure_2d=False, allow_nd=True, multi_output=True, dtype=None,
                      force_all_finite=self.force_all_finite)
            self._fit_on_new(X, y, bootstrap=bootstrap, **fit_kwargs)


class DeepActiveLearner(BaseLearner):