# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================

"""
postprocess.
"""
import os
import argparse
import numpy as np
from src.ms_utils import calculate_auc
from mindspore import context, load_checkpoint


def softmax(x):
    t_max = np.max(x, axis=1, keepdims=True)  # returns max of each row and keeps same dims
    e_x = np.exp(x - t_max)  # subtracts each row with its max value
    t_sum = np.sum(e_x, axis=1, keepdims=True)  # returns sum of each row and keeps same dims
    f_x = e_x / t_sum
    return f_x

def score_model(preds, test_pos, test_neg, weight, bias):
    """
    Score the model on the test set edges in each epoch.
    Args:
        epoch (LongTensor): Training epochs.

    Returns:
        auc(Float32): AUC result.
        f1(Float32): F1-Score result.
    """
    score_positive_edges = 
np.array(test_pos, dtype=np.int32)