wehub-resource-sync
90 lines
2.5 KiB
Python
90 lines
2.5 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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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# Unless 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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import argparse
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import numpy as np
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--similar_text_pair",
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type=str,
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default="",
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help="The full path of similar pair file",
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)
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parser.add_argument(
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"--recall_result_file",
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type=str,
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default="",
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help="The full path of recall result file",
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)
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parser.add_argument(
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"--recall_num",
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type=int,
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default=10,
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help="Most similar number of doc recalled from corpus per query",
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)
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args = parser.parse_args()
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def recall(rs, N=10):
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"""
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Ratio of recalled Ground Truth at topN Recalled Docs
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>>> rs = [[0, 0, 1], [0, 1, 0], [1, 0, 0]]
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>>> recall(rs, N=1)
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0.333333
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>>> recall(rs, N=2)
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>>> 0.6666667
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>>> recall(rs, N=3)
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>>> 1.0
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Args:
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rs: Iterator of recalled flag()
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Returns:
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Recall@N
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"""
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recall_flags = [np.sum(r[0:N]) for r in rs]
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return np.mean(recall_flags)
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if __name__ == "__main__":
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text2similar = {}
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with open(args.similar_text_pair, "r", encoding="utf-8") as f:
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for line in f:
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text, similar_text = line.rstrip().split("\t")
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text2similar[text] = similar_text
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rs = []
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with open(args.recall_result_file, "r", encoding="utf-8") as f:
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relevance_labels = []
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for index, line in enumerate(f):
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text, recalled_text, cosine_sim = line.rstrip().split("\t")
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if text == recalled_text:
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continue
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if text2similar[text] == recalled_text:
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relevance_labels.append(1)
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else:
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relevance_labels.append(0)
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if (index + 1) % args.recall_num == 0:
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rs.append(relevance_labels)
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relevance_labels = []
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recall_N = []
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for topN in (10, 50):
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R = round(100 * recall(rs, N=topN), 3)
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recall_N.append(str(R))
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print("\t".join(recall_N))
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