排除了召回相同的数据
if text == recalled_text:
continue
"""
# 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.
import os
import argparse
import numpy as np
# yapf: disable
parser = argparse.ArgumentParser()
parser.add_argument("--similar_text_pair", type=str, default='../data_set_96166/test_pair.csv',
help="The full path of similat pair file")
parser.add_argument("--recall_result_file", type=str,
default='../data_set_96166/recall_result_dir_sim/recall_result_init.txt',
help="The full path of recall result file")
parser.add_argument("--recall_num", type=int, default=10,
help="Most similair number of doc recalled from corpus per query")
args = parser.parse_args()
# yapf: enable
def recall(rs, N=10):
"""
Ratio of recalled Ground Truth at topN Recalled Docs
>>> rs = [[0, 0, 1], [0, 1, 0], [1, 0, 0]]
>>> recall(rs, N=1)
0.333333
>>> recall(rs, N=2)
>>> 0.6666667
>>> recall(rs, N=3)
>>> 1.0
Args:
rs: Iterator of recalled flag()
Returns:
Recall@N
"""
recall_flags = [np.sum(r[0:N]) for r in rs]
return np.mean(recall_flags)
if __name__ == "__main__":
text2similar = {}
with open(args.similar_text_pair, 'r', encoding='utf-8') as f:
for line in f:
text, similar_text = line.rstrip().split("\t")
text2similar[text] = similar_text
rs = []
with open(args.recall_result_file, 'r', encoding='utf-8') as f:
relevance_labels = []
for index, line in enumerate(f):
if index % args.recall_num == 0 and index != 0:
rs.append(relevance_labels)
relevance_labels = []
text, recalled_text, cosine_sim = line.rstrip().split("\t")
**if text == recalled_text:
continue**
if text2similar[text] == recalled_text:
relevance_labels.append(1)
else:
relevance_labels.append(0)
recall_N = []
recall_num = [1, 5, 10]
result = open('result.tsv', 'a')
res = []
for topN in recall_num:
R = round(100 * recall(rs, N=topN), 3)
recall_N.append(str(R))
for key, val in zip(recall_num, recall_N):
print('recall@{}={}'.format(key, val))
res.append(str(val))
result.write('\t'.join(res) + '\n')
# print("\t".join(recall_N))
