e81142f0869321cd8792e13bfc4f6fb85d9f1efc,libact/query_strategies/uncertainty_sampling.py,UncertaintySampling,make_query,#UncertaintySampling#,62

Before Change

            // therefore, total time complexity is O(NK) + O(NK) + O(NK) + O(N) = O(NK)
            ask_id = np.argmax(1 - np.max(self.model.predict_real(X_pool), 1))

        elif self.method == "sm":  // smallest margin
            // time complexity analysis:
            // O(NK) + O(N)
            prob = self.model.predict_real(X_pool)
            min_margin = np.inf
            for j in range(len(prob)):
                m1_id = np.argmax(prob[j])
                m2_id = np.argmax(np.delete(prob[j], m1_id))
                margin = prob[j][m1_id] - prob[j][m2_id]
                if margin < min_margin:
                    min_margin = margin
                    ask_id = j

        else:
            raise ValueError(
                "Invalid method "%s" (available choices: ("lc", "sm", "le")"
                % self.method

After Change

                np.max(np.abs(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:
                ind = [0, 1]

Instances