|
import json |
|
import argparse |
|
import sys |
|
from rouge import Rouge |
|
import numpy as np |
|
import jieba |
|
|
|
def rouge_score(data): |
|
""" |
|
compute rouge score |
|
Args: |
|
data (list of dict including reference and candidate): |
|
Returns: |
|
res (dict of list of scores): rouge score |
|
""" |
|
rouge_name = ["rouge-1", "rouge-2", "rouge-l"] |
|
item_name = ["f", "p", "r"] |
|
|
|
res = {} |
|
for name1 in rouge_name: |
|
for name2 in item_name: |
|
res["%s-%s"%(name1, name2)] = [] |
|
for tmp_data in data: |
|
origin_candidate = tmp_data['candidate'] |
|
origin_reference = tmp_data['reference'] |
|
assert isinstance(origin_candidate, str) |
|
if not isinstance(origin_reference, list): |
|
origin_reference = [origin_reference] |
|
|
|
tmp_res = [] |
|
for r in origin_reference: |
|
tmp_res.append(Rouge().get_scores(refs=r, hyps=origin_candidate)[0]) |
|
|
|
for name1 in rouge_name: |
|
for name2 in item_name: |
|
res["%s-%s"%(name1, name2)].append(max([tr[name1][name2] for tr in tmp_res])) |
|
|
|
for name1 in rouge_name: |
|
for name2 in item_name: |
|
res["%s-%s"%(name1, name2)] = np.mean(res["%s-%s"%(name1, name2)]) |
|
return res |
|
|
|
def load_file(filename): |
|
data = [] |
|
with open(filename, "r") as f: |
|
for line in f.readlines(): |
|
data.append(json.loads(line)) |
|
f.close() |
|
return data |
|
|
|
def proline(line): |
|
return " ".join([w for w in jieba.cut("".join(line.strip().split()))]) |
|
|
|
|
|
def compute(golden_file, pred_file, return_dict=True): |
|
golden_data = load_file(golden_file) |
|
pred_data = load_file(pred_file) |
|
|
|
if len(golden_data) != len(pred_data): |
|
raise RuntimeError("Wrong Predictions") |
|
|
|
eval_data = [{"reference": proline(g["summary"]), "candidate": proline(p["summary"])} for g, p in zip(golden_data, pred_data)] |
|
return rouge_score(eval_data) |
|
|
|
def main(): |
|
argv = sys.argv |
|
print("预测结果:{}, 测试集: {}".format(argv[1], argv[2])) |
|
print(compute(argv[2], argv[1])) |
|
|
|
|
|
if __name__ == '__main__': |
|
main() |
|
|