Method query

modAL/models/base.py:297–331

Finds the n_instances most informative point in the data provided by calling the query_strategy function. Args: X_pool: Pool of unlabeled instances to retrieve most informative instances from return_metrics: boolean to indicate, if the corresponding query me

(self, X_pool, return_metrics: bool = False, *query_args, **query_kwargs)

        return data_hstack([learner.transform_without_estimating(X) for learner in self.learner_list])

    def query(self, X_pool, return_metrics: bool = False, *query_args, **query_kwargs) -> Union[Tuple, modALinput]:
        """
        Finds the n_instances most informative point in the data provided by calling the query_strategy function.

        Args:
            X_pool: Pool of unlabeled instances to retrieve most informative instances from
            return_metrics: boolean to indicate, if the corresponding query metrics should be (not) returned
            *query_args: The arguments for the query strategy. For instance, in the case of
                :func:`~modAL.disagreement.max_disagreement_sampling`, it is the pool of samples from which the query.
                strategy should choose instances to request labels.
            **query_kwargs: Keyword arguments for the query strategy function.

        Returns:
            Return value of the query_strategy function. Should be the indices of the instances from the pool chosen to
            be labelled and the instances themselves. Can be different in other cases, for instance only the instance to
            be labelled upon query synthesis.
            query_metrics: returns also the corresponding metrics, if return_metrics == True
        """

        try:
            query_result, query_metrics = self.query_strategy(
                self, X_pool, *query_args, **query_kwargs)

        except:
            query_metrics = None
            query_result = self.query_strategy(
                self, X_pool, *query_args, **query_kwargs)

        if return_metrics:
            if query_metrics is None: 
                warnings.warn(
                "The selected query strategy doesn't support return_metrics")
            return query_result, retrieve_rows(X_pool, query_result), query_metrics
        else:
            return query_result, retrieve_rows(X_pool, query_result)

    def rebag(self, **fit_kwargs) -> None:
        """