(self, label_index, unlabel_index, eval_cost=False, model=None, **kwargs)
return self.select_by_prediction_mat(label_index, unlabel_index, pred_unlab,
                                             n_neighbors=n_neighbors, eval_cost=eval_cost)
Query from oracles. Return the index of selected instance and oracle.

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

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

        eval_cost: bool, optional (default=False)
            To evaluate the cost of oracles or use the cost provided by oracles.

        model: object, optional (default=None)
            Current classification model, should have the 'predict_proba' method for probabilistic output.
            If not provided, LogisticRegression with default parameters implemented by sklearn will be used.

        n_neighbors: int, optional (default=10)
            How many neighbors of the selected instance will be used
            to evaluate the oracles.

        Returns
        -------
        selected_instance: int
            The index of selected instance.

        selected_oracle: int or str
            The index of selected oracle.
            If a list is given, the index of oracle will be returned.
            If a Oracles object is given, the oracle name will be returned.
Query from oracles. Return the index of selected instance and oracle.
Query from oracles . Return the index of selected instance and oracle .
def select(self, label_index, unlabel_index, eval_cost=False, model=None, **kwargs):
        """Query from oracles. Return the index of selected instance and oracle.

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

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

        eval_cost: bool, optional (default=False)
            To evaluate the cost of oracles or use the cost provided by oracles.

        model: object, optional (default=None)
            Current classification model, should have the 'predict_proba' method for probabilistic output.
            If not provided, LogisticRegression with default parameters implemented by sklearn will be used.

        n_neighbors: int, optional (default=10)
            How many neighbors of the selected instance will be used
            to evaluate the oracles.

        Returns
        -------
        selected_instance: int
            The index of selected instance.

        selected_oracle: int or str
            The index of selected oracle.
            If a list is given, the index of oracle will be returned.
            If a Oracles object is given, the oracle name will be returned.
        """

        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)

        n_neighbors = min(kwargs.pop('n_neighbors', 10), len(self._ini_ind) - 1)
        return self.select_by_prediction_mat(label_index, unlabel_index, pred_unlab,
                                             n_neighbors=n_neighbors, eval_cost=eval_cost)
def select ( self , label_index , unlabel_index , eval_cost = False , 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 ) n_neighbors = min ( kwargs . pop ( 'n_neighbors' , 10 ) , len ( self . _ini_ind ) - 1 ) return self . select_by_prediction_mat ( label_index , unlabel_index , pred_unlab , n_neighbors = n_neighbors , eval_cost = eval_cost )