forked from mindspore-Ecosystem/mindspore
100 lines
3.2 KiB
Python
100 lines
3.2 KiB
Python
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# less required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Evaluation for NAML"""
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import os
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import argparse
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import numpy as np
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from sklearn.metrics import roc_auc_score
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parser = argparse.ArgumentParser(description="")
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parser.add_argument("--result_path", type=str, default="", help="Device id")
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parser.add_argument("--label_path", type=str, default="", help="output file name.")
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args = parser.parse_args()
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def AUC(y_true, y_pred):
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return roc_auc_score(y_true, y_pred)
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def MRR(y_true, y_pred):
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index = np.argsort(y_pred)[::-1]
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y_true = np.take(y_true, index)
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score = y_true / (np.arange(len(y_true)) + 1)
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return np.sum(score) / np.sum(y_true)
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def DCG(y_true, y_pred, n):
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index = np.argsort(y_pred)[::-1]
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y_true = np.take(y_true, index[:n])
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score = (2 ** y_true - 1) / np.log2(np.arange(len(y_true)) + 2)
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return np.sum(score)
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def nDCG(y_true, y_pred, n):
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return DCG(y_true, y_pred, n) / DCG(y_true, y_true, n)
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class NAMLMetric:
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"""
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Metric method
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"""
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def __init__(self):
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super(NAMLMetric, self).__init__()
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self.AUC_list = []
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self.MRR_list = []
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self.nDCG5_list = []
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self.nDCG10_list = []
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def clear(self):
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"""Clear the internal evaluation result."""
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self.AUC_list = []
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self.MRR_list = []
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self.nDCG5_list = []
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self.nDCG10_list = []
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def update(self, predict, y_true):
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predict = predict.flatten()
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y_true = y_true.flatten()
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self.AUC_list.append(AUC(y_true, predict))
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self.MRR_list.append(MRR(y_true, predict))
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self.nDCG5_list.append(nDCG(y_true, predict, 5))
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self.nDCG10_list.append(nDCG(y_true, predict, 10))
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def eval(self):
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auc = np.mean(self.AUC_list)
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print('AUC:', auc)
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print('MRR:', np.mean(self.MRR_list))
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print('nDCG@5:', np.mean(self.nDCG5_list))
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print('nDCG@10:', np.mean(self.nDCG10_list))
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return auc
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def get_metric(result_path, label_path, metric):
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"""get accuracy"""
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result_files = os.listdir(result_path)
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for file in result_files:
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result_file = os.path.join(result_path, file)
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pred = np.fromfile(result_file, dtype=np.float32)
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label_file = os.path.join(label_path, file)
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label = np.fromfile(label_file, dtype=np.int32)
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if np.nan in pred:
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continue
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metric.update(pred, label)
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auc = metric.eval()
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return auc
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if __name__ == "__main__":
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naml_metric = NAMLMetric()
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get_metric(args.result_path, args.label_path, naml_metric)
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