# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List

import numpy as np
from sklearn.metrics import roc_curve


def compute_eer(labels: np.ndarray, scores: np.ndarray) -> List[float]:
    '''
    Compute EER and return score threshold.
    '''
    fpr, tpr, threshold = roc_curve(y_true=labels, y_score=scores)
    fnr = 1 - tpr
    eer_threshold = threshold[np.nanargmin(np.absolute((fnr - fpr)))]
    eer = fpr[np.nanargmin(np.absolute((fnr - fpr)))]
    return eer, eer_threshold