@inherit_docstring_from(QueryStrategy)
    def make_query(self):
        dataset = self.dataset
        self.model.train(dataset)

        unlabeled_entry_ids, X_pool = zip(*dataset.get_unlabeled_entries())

        if self.method == 'lc':  # least confident
            ask_id = np.argmin(
                np.max(self.model.predict_real(X_pool), axis=1)
            )

        elif self.method == 'sm':  # smallest margin
            dvalue = self.model.predict_real(X_pool)

            if np.shape(dvalue)[1] > 2:
                # Find 2 largest decision values
                dvalue = -(np.partition(-dvalue, 2, axis=1)[:, :2])

            margin = np.abs(dvalue[:, 0] - dvalue[:, 1])
            ask_id = np.argmin(margin)

        return unlabeled_entry_ids[ask_id]
dataset/ETHPy150Open ntucllab/libact/libact/query_strategies/uncertainty_sampling.py/UncertaintySampling.make_query/original