| 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() |
|
|