Datasets:
CZLC
/

Modalities:
Tabular
Text
Formats:
json
Languages:
Czech
Libraries:
Datasets
pandas
License:
File size: 5,040 Bytes
40b0307
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
#!/usr/bin/env python3
#
# This file is part of SumeCzech corpus <http://hdl.handle.net/11234/1-2615>.
#
# Copyright 2018 Institute of Formal and Applied Linguistics, Faculty of
# Mathematics and Physics, Charles University in Prague, Czech Republic.
#
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.

import re

class RougeRaw:
    """Compute RougeRAW-1, RougeRAW-2, RougeRAW-L metrics."""

    class FScore:
        """F1 score representation."""
        def __init__(self, correct, gold, system):
            self.p = correct / system if system else 0.
            self.r = correct / gold if gold else 0.
            self.f = 2 * correct / (system + gold) if system + gold else 0.

    def _rouge_n(self, n, gold_words, system_words):
        """Compute Rouge-n for given words."""
        def n_grams(n, words):
            ngrams = {}
            total = 0
            for i in range(len(words) - n + 1):
                ngram = "\t".join(words[i:i + n])
                ngrams[ngram] = 1 + ngrams.get(ngram, 0)
                total += 1
            return ngrams, total

        gold_ngrams, gold_total = n_grams(n, gold_words)
        system_ngrams, system_total = n_grams(n, system_words)

        intersection = 0
        for ngram in system_ngrams:
            intersection += min(system_ngrams[ngram], gold_ngrams.get(ngram, 0))

        return self.FScore(intersection, gold_total, system_total)

    def _rouge_l(self, gold_words, system_words):
        """Compute Rouge-L for given words."""
        lcs = [[0] * len(system_words) for _ in gold_words]
        for r in range(len(gold_words)):
            for s in range(len(system_words)):
                if gold_words[r] == system_words[s]:
                    lcs[r][s] = 1 + (lcs[r - 1][s - 1] if r and s else 0)
                lcs[r][s] = max(lcs[r][s], lcs[r - 1][s] if r else 0)
                lcs[r][s] = max(lcs[r][s], lcs[r][s - 1] if s else 0)

        return self.FScore(lcs[-1][-1], len(gold_words), len(system_words))

    def _tokenize(self, text):
        """Tokenize given text."""
        return re.sub(r"\s+", " ", re.sub(r"\b", " ", text, re.UNICODE), re.UNICODE).strip().split(" ")

    def document(self, gold, system):
        """Compute RougeRAW-1, RougeRAW-2, RougeRAW-L for given documents.

        Each document should be a string.
        """

        assert isinstance(gold, str) and isinstance(system, str), "Expected string arguments"

        lc_gold_words = [word.lower() for word in self._tokenize(gold)]
        lc_system_words = [word.lower() for word in self._tokenize(system)]

        return {
            "1": self._rouge_n(1, lc_gold_words, lc_system_words),
            "2": self._rouge_n(2, lc_gold_words, lc_system_words),
            "L": self._rouge_l(lc_gold_words, lc_system_words),
        }

    def corpus(self, gold, system):
        """Compute RougeRAW-1, RougeRAW-2, RougeRAW-L for given corpora.

        Each corpus should be a collection of documents, each document a string.
        """

        assert isinstance(gold, list) and isinstance(system, list), "Expected list arguments"
        assert len(gold) == len(system), "Given corpora should be of the same length"

        rouge = {key: self.FScore(0, 0, 0) for key in ["1", "2", "L"]}

        if len(gold):
            for gold_document, system_document in zip(gold, system):
                for key, value in self.document(gold_document, system_document).items():
                    rouge[key].p += value.p
                    rouge[key].r += value.r
                    rouge[key].f += value.f

            for key in rouge:
                rouge[key].p /= len(gold)
                rouge[key].r /= len(gold)
                rouge[key].f /= len(gold)

        return rouge


if __name__ == "__main__":
    import argparse
    import json

    parser = argparse.ArgumentParser()
    parser.add_argument("gold", type=str, help="Gold jsonl file path")
    parser.add_argument("system", type=str, help="System jsonl output file")
    parser.add_argument("field", type=str, help="Which jsonl field to compare")
    args = parser.parse_args()

    gold = []
    with open(args.gold, "r", encoding="utf-8") as gold_file:
        for gold_line in gold_file:
            gold.append(json.loads(gold_line)[args.field])

    system = []
    with open(args.system, "r", encoding="utf-8") as system_file:
        for system_line in system_file:
            system.append(json.loads(system_line)[args.field])

    rouge = RougeRaw().corpus(gold, system)
    print("  RougeRAW-1      RougeRAW-2      RougeRAW-L")
    print("  P    R    F     P    R    F     P    R    F")
    for metric in ["1", "2", "L"]:
        print("{:04.1f} {:04.1f} {:04.1f}{}".format(
            100 * rouge[metric].p,
            100 * rouge[metric].r,
            100 * rouge[metric].f,
            "\n" if metric == "L" else "  "), end="")