def eval(out_file, src_file, tgt_file, isDIn=False, num_pairs=500):
"""
Given a filename, calculate the metric scores for that prediction file
isDin: boolean value to check whether input file is DirectIn.txt
"""
pairs = []
with open(src_file, 'r') as infile:
for line in infile:
line = line.strip('\r\n')
pair = {}
pair['tokenized_sentence'] = line[:-1]
pairs.append(pair)
with open(tgt_file, "r") as infile:
cnt = 0
for line in infile:
line = line.strip('\n')
pairs[cnt]['tokenized_question'] = line[:-1]
cnt += 1
output = []
with open(out_file, 'r') as infile:
for line in infile:
line = line[:-1]
output.append(line)
for idx, pair in enumerate(pairs):
pair['prediction'] = output[idx]
## eval
from eval import QGEvalCap
import json
from json import encoder
encoder.FLOAT_REPR = lambda o: format(o, '.4f')
res = defaultdict(lambda: [])
gts = defaultdict(lambda: [])
for pair in pairs[:]:
key = pair['tokenized_sentence']
res[key] = [pair['prediction'].encode('utf-8')]
## gts
gts[key].append(pair['tokenized_question'].encode('utf-8'))
QGEval = QGEvalCap(gts, res)
return QGEval.evaluate()
def eval(out_file, src_file, tgt_file, isDIn=False, num_pairs=500):
"""
Given a filename, calculate the metric scores for that prediction file
isDin: boolean value to check whether input file is DirectIn.txt
"""
pairs = []
with open(src_file, 'r') as infile:
for line in infile:
pair = {}
pair['tokenized_sentence'] = line[:-1].strip().lower()
pairs.append(pair)
with open(tgt_file, "r") as infile:
cnt = 0
for line in infile:
pairs[cnt]['tokenized_question'] = line[:-1].strip()
cnt += 1
output = []
with open(out_file, 'r') as infile:
for line in infile:
line = fix_tokenization(line[:-1].strip()).lower()
output.append(line)
for idx, pair in enumerate(pairs):
pair['prediction'] = output[idx]
# eval
from eval import QGEvalCap
import json
from json import encoder
encoder.FLOAT_REPR = lambda o: format(o, '.4f')
res = defaultdict(lambda: [])
gts = defaultdict(lambda: [])
for pair in pairs[:]:
#Le dict pair contient 3 clef: tokenized_sentence, tokenized_question et prediction
key = pair['tokenized_sentence']
res[key] = [pair['prediction'].encode('utf-8')]
# gts
gts[key].append(pair['tokenized_question'].encode('utf-8'))
#print("\n")
#print("TEXTE:{}\nREPONSE:{}\n".format(key.split("[sep]")[0], key.split("[sep]")[1]))
#print("PREDICTION:",res[key])
#print("\nSQUAD QUESTION:",gts[key])
#print("\n")
#print([key for key in pair.keys()])
#print("GTS:{} et RES: {}".format(gts[key], res[key]))
QGEval = QGEvalCap(gts, res)
return QGEval.evaluate()
def eval(out_file, src_file, tgt_file, isDIn = False, num_pairs = 500):
"""
Given a filename, calculate the metric scores for that prediction file
isDin: boolean value to check whether input file is DirectIn.txt
"""
pairs = []
with open(src_file, 'r') as infile:
for line in infile:
pair = {}
pair['tokenized_sentence'] = line[:-1]
pairs.append(pair)
with open(tgt_file, "r") as infile:
cnt = 0
for line in infile:
pairs[cnt]['tokenized_question'] = line[:-1]
cnt += 1
output = []
with open(out_file, 'r') as infile:
for line in infile:
line = line[:-1]
output.append(line)
for idx, pair in enumerate(pairs):
pair['prediction'] = output[idx]
## eval
from eval import QGEvalCap
import json
from json import encoder
encoder.FLOAT_REPR = lambda o: format(o, '.4f')
res = defaultdict(lambda: [])
gts = defaultdict(lambda: [])
for pair in pairs[:]:
key = pair['tokenized_sentence']
res[key] = [pair['prediction'].encode('utf-8')]
## gts
gts[key].append(pair['tokenized_question'].encode('utf-8'))
QGEval = QGEvalCap(gts, res)
return QGEval.evaluate()
def eval_squad(out_file, simplified_para_file, para_file, src_file, tgt_file, n_best, isDIn = False, num_pairs = 500):
"""
Given a filename, calculate the metric scores for that prediction file
isDin: boolean value to check whether input file is DirectIn.txt
"""
simplified_paragraphs =[]
with open(simplified_para_file, 'r') as infile:
for line in infile:
simplified_paragraphs.append(line[:-1])
predictions = []
with open(out_file, 'r') as infile:
for i in range(len(simplified_paragraphs)):
j = n_best
array = []
while j > 0:
line = infile.readline()
array.append(line[:-1])
j -= 1
predictions.append(array)
assert(len(simplified_paragraphs) == len(predictions))
# Mapping between paragraphs and predictions
para_prediciton_map = {}
for i in range(len(simplified_paragraphs)):
para_prediciton_map[simplified_paragraphs[i]] = predictions[i]
paragraphs =[]
with open(para_file, 'r') as infile:
for line in infile:
paragraphs.append(line[:-1])
sentences =[]
with open(src_file, 'r') as infile:
for line in infile:
sentences.append(line[:-1])
questions =[]
with open(tgt_file, 'r') as infile:
for line in infile:
questions.append(line[:-1])
pairs = defaultdict(lambda: {'sentences':set(), 'questions':[], 'predictions':[]})
for paragraph, sentence, question in zip(paragraphs, sentences, questions):
pairs[paragraph]['sentences'].add(sentence)
pairs[paragraph]['questions'].append(question)
pairs[paragraph]['predictions'] = para_prediciton_map[paragraph]
# Clean up redundant predictions
for paragraph in pairs.keys():
while len(pairs[paragraph]['sentences']) != len(pairs[paragraph]['predictions']):
del pairs[paragraph]['predictions'][-1]
## eval
from eval import QGEvalCap
import json
from json import encoder
encoder.FLOAT_REPR = lambda o: format(o, '.4f')
res = defaultdict(lambda: [])
gts = defaultdict(lambda: [])
for paragraph in pairs.keys():
for prediction in pairs[paragraph]['predictions']:
res[paragraph].append(prediction.encode('utf-8'))
## gts
for question in pairs[paragraph]['questions']:
gts[paragraph].append(question.encode('utf-8'))
QGEval = QGEvalCap(gts, res)
return QGEval.evaluate()
def eval(out_file, src_file, tgt_file, isDIn=False, num_pairs=500):
"""
Given a filename, calculate the metric scores for that prediction file
isDin: boolean value to check whether input file is DirectIn.txt
"""
#pairs:リスト、サイズはlen(sentence)、中身はpair
#pair:sentence,question_target,question_predictが辞書の形で格納されている。
pairs = []
with open(src_file, 'r') as infile:
for line in infile:
pair = {}
pair['tokenized_sentence'] = line[:-1]
pairs.append(pair)
with open(tgt_file, "r") as infile:
cnt = 0
for line in infile:
pairs[cnt]['tokenized_question'] = line[:-1]
cnt += 1
output = []
with open(out_file, 'r') as infile:
for line in infile:
line = line[:-1]
output.append(line)
for idx, pair in enumerate(pairs):
pair['prediction'] = output[idx]
## eval
from eval import QGEvalCap
import json
from json import encoder
encoder.FLOAT_REPR = lambda o: format(o, '.4f')
#pair:sentence,prediction,tokenized_question
#res:key:sentence,value:prediction
#gts:key:sentence,value:question
#ただし、gtsの方は同じsentenceについてはquestionを一つのsentenceに与える
#また、一つの文につき一つのpredictしか評価していない。->10000文の内4000文は評価していない。
#全ての文を評価する。(同じsrcでも答えが違っている場合はanswerを使うものは違っている可能性があるから)
#文と疑問詞句が一致しているものをtargetとする
if 0:
target_dict = defaultdict(lambda: [])
predict_dict = defaultdict(str)
for i, pair in enumerate(pairs):
s = pair["tokenized_sentence"]
t = pair['tokenized_question'].encode('utf-8')
p = pair['prediction'].encode('utf-8')
target_dict[s].append(t)
predict_dict[(p, s)] = s
gts = {
i: target_dict[predict_dict[(p["prediction"],
p["tokenized_sentence"])]]
for i, p in enumerate(pairs)
}
res = {i: p["prediction"] for i, p in enumerate(pairs)}
if 0:
res = defaultdict(lambda: [])
gts = defaultdict(lambda: [])
for i, pair in enumerate(pairs[:]):
key = pair['tokenized_sentence']
res[key] = [pair['prediction'].encode('utf-8')]
gts[key].append(pair['tokenized_question'].encode('utf-8'))
print(list(res.items())[0:5])
print(list(gts.items())[0:5])
#正しいもののみをペアにする
if 1:
target_dict = defaultdict(lambda: [])
predict_dict = defaultdict(str)
for i, pair in enumerate(pairs):
s = pair["tokenized_sentence"]
t = pair['tokenized_question'].encode('utf-8')
p = pair['prediction'].encode('utf-8')
target_dict[s].append(t)
predict_dict[(p, s)] = s
pairs = [p for p in pairs if "<UNK>" not in p["prediction"]]
gts = {i: [p["tokenized_question"]] for i, p in enumerate(pairs)}
res = {i: [p["prediction"]] for i, p in enumerate(pairs)}
#print(len(pairs))
print("size of items:{}".format(len(res.items())))
QGEval = QGEvalCap(gts, res)
return QGEval.evaluate()
