(self, label_index, unlabel_index, model=None, **kwargs)
return self.select_by_prediction_mat(label_index, unlabel_index, pred_unlab)
Select an instance and a batch of oracles to label it.
        The instance is selected by uncertainty, the oracles is
        selected by the difference between their
        labeling results and the majority vote results.

        Parameters
        ----------
        label_index: {list, np.ndarray, IndexCollection}
            The indexes of labeled samples.

        unlabel_index: {list, np.ndarray, IndexCollection}
            The indexes of unlabeled samples.

        Returns
        -------
        selected_instance: int
            The index of selected instance. Selected by uncertainty.

        selected_oracles: list
            The selected oracles for querying.
Select an instance and a batch of oracles to label it.
        The instance is selected by uncertainty, the oracles is
        selected by the difference between their
        labeling results and the majority vote results.
Select an instance and a batch of oracles to label it . The instance is selected by uncertainty the oracles is selected by the difference between their labeling results and the majority vote results .
def select(self, label_index, unlabel_index, model=None, **kwargs):
        """Select an instance and a batch of oracles to label it.
        The instance is selected by uncertainty, the oracles is
        selected by the difference between their
        labeling results and the majority vote results.

        Parameters
        ----------
        label_index: {list, np.ndarray, IndexCollection}
            The indexes of labeled samples.

        unlabel_index: {list, np.ndarray, IndexCollection}
            The indexes of unlabeled samples.

        Returns
        -------
        selected_instance: int
            The index of selected instance. Selected by uncertainty.

        selected_oracles: list
            The selected oracles for querying.
        """
        if model is None:
            model = LogisticRegression(solver='liblinear')
            model.fit(self.X[label_index], self.y[label_index])
        pred_unlab, _ = _get_proba_pred(self.X[unlabel_index], model)

        return self.select_by_prediction_mat(label_index, unlabel_index, pred_unlab)
def select ( self , label_index , unlabel_index , model = None , * * kwargs ) : if model is None : model = LogisticRegression ( solver = 'liblinear' ) model . fit ( self . X [ label_index ] , self . y [ label_index ] ) pred_unlab , _ = _get_proba_pred ( self . X [ unlabel_index ] , model ) return self . select_by_prediction_mat ( label_index , unlabel_index , pred_unlab )