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import json
import argparse
import sys
import numpy as np
import jieba
import nltk
from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
from nltk import ngrams
def bleu(data):
"""
compute rouge score
Args:
data (list of dict including reference and candidate):
Returns:
res (dict of list of scores): rouge score
"""
res = {}
for i in range(1, 5):
res["sentence-bleu-%d"%i] = []
res["corpus-bleu-%d"%i] = nltk.translate.bleu_score.corpus_bleu([[d["reference"].strip().split()] for d in data], [d["candidate"].strip().split() for d in data], weights=tuple([1./i for j in range(i)]))
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]
for i in range(1, 5):
res["sentence-bleu-%d"%i].append(sentence_bleu(references=[r.strip().split() for r in origin_reference], hypothesis=origin_candidate.strip().split(), weights=tuple([1./i for j in range(i)])))
for key in res:
if "sentence" in key:
res[key] = np.mean(res[key])
return res
def distinct(eval_data):
result = {}
for i in range(1, 5):
all_ngram, all_ngram_num = {}, 0.
for k, tmp_data in enumerate(eval_data):
ngs = ["_".join(c) for c in ngrams(tmp_data["candidate"].strip().split(), i)]
all_ngram_num += len(ngs)
for s in ngs:
if s in all_ngram:
all_ngram[s] += 1
else:
all_ngram[s] = 1
result["distinct-%d"%i] = len(all_ngram) / float(all_ngram_num)
return result
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["plot"]), "candidate": proline(p["plot"])} for g, p in zip(golden_data, pred_data)]
res = bleu(eval_data)
res.update(distinct(eval_data))
for key in res:
res[key] = "_"
return res
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()
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