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import argparse
import logging
import re
from datetime import datetime
import os
import numpy as np
import torch
from nltk import bleu, meteor
from rouge_score.rouge_scorer import RougeScorer
from tqdm import tqdm
from src.distinct_n.distinct_n.metrics import distinct_n_corpus_level as distinct_n
from inductor import BartInductor, CometInductor
FILES = {
'amie-yago2': 'data/RE-datasets/AMIE-yago2.txt',
'rules-yago2': 'data/RE-datasets/RuLES-yago2.txt',
"openrule155": "data/OpenRule155.txt",
'fewrel': 'data/RE/fewrel-5.txt',
'semeval': 'data/RE/semeval-5.txt',
'TREx': 'data/RE/trex-5.txt',
'nyt10': 'data/RE/nyt10-5.txt',
'google-re': 'data/RE/google-re-5.txt',
'wiki80': 'data/RE/wiki80-5.txt',
}
if not os.path.exists('logs/'):
os.mkdir('logs/')
logging.basicConfig(
filename='logs/evaluation-{}.log'.format(str(datetime.now())),
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO)
logger = logging.getLogger(__name__)
def print_config(config):
config = vars(config)
logger.info("**************** MODEL CONFIGURATION ****************")
for key in sorted(config.keys()):
val = config[key]
keystr = "{}".format(key) + (" " * (25 - len(key)))
logger.info("{} --> {}".format(keystr, val))
logger.info("**************** MODEL CONFIGURATION ****************")
scorer = RougeScorer(['rougeL'], use_stemmer=True)
def rouge(references, hypothesis):
scores = []
for reference in references:
scores.append(
scorer.score(
reference,
hypothesis)['rougeL'][2]
)
return max(scores)
class RelationExtractionEvaluator(object):
def __init__(self, args):
self.args = args
if self.args.inductor == 'rule':
self.inductor = BartInductor(
group_beam=self.args.group_beam,
continue_pretrain_instance_generator=self.args.mlm_training,
continue_pretrain_hypo_generator=self.args.bart_training,
if_then=self.args.if_then,
)
elif self.args.inductor == 'comet':
self.inductor = CometInductor()
def clean(self, text):
segments = text.split('<mask>')
if len(segments) == 3 and segments[2].startswith('.'):
return '<mask>'.join(segments[:2]) + '<mask>.'
else:
return text
def clean_references(self, texts):
for i, text in enumerate(texts):
if text.endswith(" ."):
texts[i] = text.replace(" .", ".")
return texts
def self_bleu(self, hypothesis):
bleus = []
for i in range(len(hypothesis)):
bleus.append(bleu(
hypothesis[:i] + hypothesis[i + 1:],
hypothesis[i],
weights=(0.5, 0.5)))
ret = np.mean(bleus)
return ret
def evaluate(self, task):
with torch.no_grad():
self.metrics = {
"bleu-4": [],
"bleu-3": [],
"bleu-2": [],
"bleu-1": [],
"METEOR": [],
"ROUGE-L": [],
"self-BLEU-2": [],
}
with open(FILES[task], 'r', encoding='utf-8') as file:
data = file.readlines()
with tqdm(total=len(data)) as pbar:
for row in data:
pbar.update(1)
row = row.strip().split('\t')
inputs, head, tail, relations = row[0], row[1], row[2], row[3]
inputs = inputs.strip()
if relations.startswith('[') and relations.endswith(']'):
inputs = re.sub("<A>|<B>", "<mask>", inputs)
references = [relation.replace('<A>', '<mask>').replace('<B>', '<mask>').lower().strip() for relation in eval(relations)]
else:
references = [relations.replace('[X]', '<mask>').replace('[Y]', '<mask>').lower().strip()]
references = self.clean_references(references)
hypothesis = self.inductor.generate(inputs, k=10, topk=10)
logger.info("***********Input************")
logger.info(inputs)
logger.info("*********Hypothesis*********")
for i, hypo in enumerate(hypothesis):
hypothesis[i] = self.clean(hypo.lower().strip())
logger.info(hypo)
logger.info("****************************")
logger.info("*********References*********")
logger.info(references)
logger.info("****************************")
if len(hypothesis) == 0:
for k in self.metrics.keys():
if k != 'self-BLEU-2':
self.metrics[k].append(0.)
else:
for hypo in hypothesis:
try:
self.metrics['bleu-4'].append(
bleu(
[reference.split() for reference in references],
hypo.split(),
weights=(0.25, 0.25, 0.25, 0.25)
)
)
except Exception:
logger.warning("Skip bleu-4 in example: {}".format(inputs))
pass
try:
self.metrics['bleu-3'].append(
bleu(
[reference.split() for reference in references],
hypo.split(),
weights=(1 / 3, ) * 3
)
)
except Exception:
logger.warning("Skip bleu-3 in example: {}".format(inputs))
pass
try:
self.metrics['bleu-2'].append(
bleu(
[reference.split() for reference in references],
hypo.split(),
weights=(0.5, 0.5)
)
)
except Exception:
logger.warning("Skip bleu-2 in example: {}".format(inputs))
pass
try:
self.metrics['bleu-1'].append(
bleu(
[reference.split() for reference in references],
hypo.split(),
weights=(1.0, )
)
)
except Exception:
logger.warning("Skip bleu-1 in example: {}".format(inputs))
pass
try:
self.metrics['METEOR'].append(
meteor(
references,
hypo,
)
)
except:
logger.warning("Skip METEOR in example: {}".format(inputs))
pass
try:
self.metrics['ROUGE-L'].append(
rouge(
references,
hypo,
)
)
except:
logger.warning("Skip ROUGE-L in example: {}".format(inputs))
pass
try:
self.metrics['self-BLEU-2'].append(
self.self_bleu(
hypothesis,
)
)
except:
logger.warning("Skip self-bleu-2 in example: {}.".format(inputs))
pass
# break
self.print(task, self.metrics)
def print(self, task, metrics):
logger.info("Task: {}".format(str(task)))
for k, v in metrics.items():
logger.info("{}: {}".format(k, str(np.mean(v))))
logger.info("*******************************************************")
logger.info("*******************************************************")
logger.info("*******************************************************")
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--inductor", type=str, default='rule')
parser.add_argument("--group_beam", type=bool, default=False)
parser.add_argument("--mlm_training", type=bool, default=False)
parser.add_argument("--bart_training", type=bool, default=False)
parser.add_argument("--if_then", type=bool, default=False)
parser.add_argument("--task", type=str, default='openrule155')
args = parser.parse_args()
print_config(args)
evaluator = RelationExtractionEvaluator(args)
evaluator.evaluate(args.task)
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