def validate_dataset(model, split, tokenizer, topk=1):
assert split in ('dev', 'test')
dataloader = get_dataloader('xlnet', split, tokenizer, bwd=False, \
batch_size=16, num_workers=16)
em, f1, count = 0, 0, 0
model.start_n_top = topk
model.end_n_top = topk
model.eval()
for batch in dataloader:
input_ids, attention_mask, token_type_ids, input_tokens_no_unk, answers = batch
input_ids = input_ids.cuda(device=device)
attention_mask = attention_mask.cuda(device=device)
token_type_ids = token_type_ids.cuda(device=device)
with torch.no_grad():
outputs = model(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids)
start_index = outputs[1]
end_index = outputs[3].view(-1, model.end_n_top,
model.start_n_top).permute([0, 2, 1])[:, :,
0]
for i, answer in enumerate(answers):
preds = []
for k in range(model.start_n_top):
pred_tokens = input_tokens_no_unk[i][
start_index[i][k]:end_index[i][k] + 1]
preds.append(tokenizer.convert_tokens_to_string(pred_tokens))
norm_preds_tokens = [
norm_tokenizer.basic_tokenizer.tokenize(pred) for pred in preds
]
norm_preds = [
norm_tokenizer.convert_tokens_to_string(norm_pred_tokens)
for norm_pred_tokens in norm_preds_tokens
]
norm_answer_tokens = [
norm_tokenizer.basic_tokenizer.tokenize(ans) for ans in answer
]
norm_answer = [
norm_tokenizer.convert_tokens_to_string(ans_tokens)
for ans_tokens in norm_answer_tokens
]
em += max(
metric_max_over_ground_truths(exact_match_score, norm_pred,
norm_answer)
for norm_pred in norm_preds)
f1 += max(
metric_max_over_ground_truths(f1_score, norm_pred, norm_answer)
for norm_pred in norm_preds)
count += 1
del dataloader
return em, f1, count
def evaluate_q_types(dataset, predictions):
q_one_grams = {}
q_two_grams = {}
for article in dataset:
for paragraph in article['paragraphs']:
for qa in paragraph['qas']:
qa_split = qa['question'].split()
first_word = qa_split[0]
first_2_words = first_word + " " + qa_split[1]
if first_word not in q_one_grams:
q_one_grams[first_word] = {'f1': 0.0, 'em': 0.0, 'count': 0}
if first_2_words not in q_two_grams:
q_two_grams[first_2_words] = {'f1': 0.0, 'em': 0.0, 'count': 0}
q_one_grams[first_word]['count'] += 1
q_two_grams[first_2_words]['count'] += 1
if qa['id'] not in predictions:
continue
ground_truths = list(map(lambda x: x['text'], qa['answers']))
prediction = predictions[qa['id']]
em = metric_max_over_ground_truths(exact_match_score, prediction, ground_truths)
f1 = metric_max_over_ground_truths(f1_score, prediction, ground_truths)
q_one_grams[first_word]['f1'] += f1
q_one_grams[first_word]['em'] += em
q_two_grams[first_2_words]['f1'] += f1
q_two_grams[first_2_words]['em'] += em
results_1 = {}
for key in q_one_grams:
val = q_one_grams[key]
results_1[key] = {'f1':100.0 * val['f1'] / val['count'], 'em':100.0 * val['em'] / val['count'], 'count': val['count']}
sorted_results_1 = sorted(results_1.items(), key=lambda (x,y): y['count'], reverse=True)
results_2 = {}
for key in q_two_grams:
val = q_two_grams[key]
results_2[key] = {'f1':100.0 * val['f1'] / val['count'], 'em':100.0 * val['em'] / val['count'], 'count': val['count']}
sorted_results_2 = sorted(results_2.items(), key=lambda (x,y): y['count'], reverse=True)
return {'one grams': sorted_results_1[:20], 'two grams': sorted_results_2[:20]}
def evaluation_devresult(pre_result,target_result):
'''
function:
QA evaluation
'''
f1 = exact_match = total = 0
for i in range(len(pre_result)):
total += 1
prediction = pre_result[i]
ground_truths = target_result[i]
exact_match += evaluate.metric_max_over_ground_truths(evaluate.exact_match_score, prediction, ground_truths)
f1 += evaluate.metric_max_over_ground_truths(evaluate.f1_score, prediction, ground_truths)
exact_match = exact_match / total
f1 = f1 / total
return exact_match,f1
def validate_dataset(model, split, tokenizer, topk=1, prefix=None):
assert split in ('dev', 'test')
fwd_dataloader = get_dataloader('bert', split, tokenizer, bwd=False, \
batch_size=16, num_workers=16, prefix=prefix)
bwd_dataloader = get_dataloader('bert', split, tokenizer, bwd=True, \
batch_size=16, num_workers=16, prefix=prefix)
em, f1, count = 0, 0, 0
model.eval()
for fwd_batch, bwd_batch in zip(fwd_dataloader, bwd_dataloader):
# FWD
input_ids, attention_mask, token_type_ids, margin_mask, fwd_input_tokens_no_unks, answers = fwd_batch
input_ids = input_ids.cuda(device=device)
attention_mask = attention_mask.cuda(device=device)
token_type_ids = token_type_ids.cuda(device=device)
margin_mask = margin_mask.cuda(device=device)
with torch.no_grad():
outputs = model(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids)
start_logits, end_logits = outputs[0], outputs[1]
start_logits += margin_mask
end_logits += margin_mask
start_logits = start_logits.cpu().clone()
fwd_end_logits = end_logits.cpu().clone()
start_probs = start_logits #softmax(start_logits, dim=1)
fwd_start_probs, fwd_start_index = start_probs.topk(topk * 5, dim=1)
# BWD
input_ids, attention_mask, token_type_ids, margin_mask, bwd_input_tokens_no_unks, answers = bwd_batch
input_ids = input_ids.cuda(device=device)
attention_mask = attention_mask.cuda(device=device)
token_type_ids = token_type_ids.cuda(device=device)
margin_mask = margin_mask.cuda(device=device)
with torch.no_grad():
outputs = model(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids)
start_logits, end_logits = outputs[0], outputs[1]
start_logits += margin_mask
end_logits += margin_mask
start_logits = start_logits.cpu().clone()
bwd_end_logits = end_logits.cpu().clone()
start_probs = start_logits #softmax(start_logits, dim=1)
bwd_start_probs, bwd_start_index = start_probs.topk(topk * 5, dim=1)
# FWD-BWD
for i, answer in enumerate(answers):
preds, probs = [], []
for n in range(topk * 5):
# FWD
start_prob = fwd_start_probs[i][n].item()
start_ind = fwd_start_index[i][n].item()
beam_end_logits = fwd_end_logits[i].clone().unsqueeze(0)
end_probs = beam_end_logits #softmax(beam_end_logits, dim=1)
end_probs[0, :start_ind] += -1e10
end_probs[0, start_ind + 20:] += -1e10
end_probs, end_index = end_probs.topk(topk * 5, dim=1)
# topk*topk combination
for m in range(topk * 5):
end_prob = end_probs[0][m].item()
end_ind = end_index[0][m].item()
prob = start_prob + end_prob # log prob i.e. logits
span_tokens = fwd_input_tokens_no_unks[i][
start_ind:end_ind + 1]
pred = convert_tokens_to_string(span_tokens)
if pred == tokenizer.sep_token or pred == '':
pass
elif pred and pred not in preds:
probs.append(prob)
preds.append(pred)
elif pred and pred in preds:
pred_idx = preds.index(pred)
if prob > probs[pred_idx]:
probs[pred_idx] = prob
#probs[preds.index(pred)] += prob
else:
pass
# BWD
start_prob = bwd_start_probs[i][n].item()
start_ind = bwd_start_index[i][n].item()
beam_end_logits = bwd_end_logits[i].clone().unsqueeze(0)
end_probs = beam_end_logits #softmax(beam_end_logits, dim=1)
end_probs[0, :start_ind] += -1e10
end_probs[0, start_ind + 20:] += -1e10
end_probs, end_index = end_probs.topk(topk * 5, dim=1)
end_ind = end_index[0][0]
# topk*topk combination
for m in range(topk * 5):
end_prob = end_probs[0][m].item()
end_ind = end_index[0][m].item()
prob = start_prob + end_prob # log prob i.e. logits
span_tokens = bwd_input_tokens_no_unks[i][
start_ind:end_ind + 1]
pred = convert_tokens_to_string(span_tokens)
if pred == tokenizer.sep_token or pred == '':
pass
elif pred and pred not in preds:
probs.append(prob)
preds.append(pred)
elif pred and pred in preds:
pred_idx = pred.index(pred)
if prob > probs[pred_idx]:
probs[pred_idx] = prob
#probs[preds.index(pred)] += prob
else:
pass
count += 1
if len(preds) > 0:
sorted_probs_preds = list(reversed(sorted(zip(probs, preds))))
probs, preds = map(list, zip(*sorted_probs_preds))
probs, preds = probs[:topk], preds[:topk]
norm_preds_tokens = [
norm_tokenizer.basic_tokenizer.tokenize(pred)
for pred in preds
]
norm_preds = [
norm_tokenizer.convert_tokens_to_string(norm_pred_tokens)
for norm_pred_tokens in norm_preds_tokens
]
norm_answer_tokens = [
norm_tokenizer.basic_tokenizer.tokenize(ans)
for ans in answer
]
norm_answer = [
norm_tokenizer.convert_tokens_to_string(ans_tokens)
for ans_tokens in norm_answer_tokens
]
em += max(
metric_max_over_ground_truths(exact_match_score, norm_pred,
norm_answer)
for norm_pred in norm_preds)
f1 += max(
metric_max_over_ground_truths(f1_score, norm_pred,
norm_answer)
for norm_pred in norm_preds)
del fwd_dataloader, bwd_dataloader
return em, f1, count
def validate_dataset(model, split, tokenizer, dataset, topk=1):
assert split in ('dev', 'test')
fwd_dataloader = get_dataloader('bert', split, tokenizer, bwd=False, \
batch_size=16, num_workers=16, prefix=dataset)
bwd_dataloader = get_dataloader('bert', split, tokenizer, bwd=True, \
batch_size=16, num_workers=16, prefix=dataset)
em, f1, count = 0, 0, 0
model.eval()
for fwd_batch, bwd_batch in zip(fwd_dataloader, bwd_dataloader):
# Forward
input_ids, attention_mask, token_type_ids, fwd_input_tokens_no_unk, answers = fwd_batch
input_ids = input_ids.cuda(device=device)
attention_mask = attention_mask.cuda(device=device)
token_type_ids = token_type_ids.cuda(device=device)
with torch.no_grad():
outputs = model(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids)
start_logits, end_logits = outputs[0], outputs[1]
start_probs = softmax(start_logits, dim=1)
end_probs = softmax(end_logits, dim=1)
fwd_start_probs, fwd_start_index = start_probs.topk(topk, dim=1)
fwd_end_probs, fwd_end_index = end_probs.topk(topk, dim=1)
# Backward
input_ids, attention_mask, token_type_ids, bwd_input_tokens_no_unk, answers = bwd_batch
input_ids = input_ids.cuda(device=device)
attention_mask = attention_mask.cuda(device=device)
token_type_ids = token_type_ids.cuda(device=device)
with torch.no_grad():
outputs = model(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids)
start_logits, end_logits = outputs[0], outputs[1]
start_probs = softmax(start_logits, dim=1)
end_probs = softmax(end_logits, dim=1)
bwd_start_probs, bwd_start_index = start_probs.topk(topk, dim=1)
bwd_end_probs, bwd_end_index = end_probs.topk(topk, dim=1)
for i, answer in enumerate(answers):
preds = []
if topk <= 1:
span_tokens = fwd_input_tokens_no_unk[i][
fwd_start_index[i][0]:fwd_end_index[i][0] + 1]
preds.append(tokenizer.convert_tokens_to_string(span_tokens))
span_tokens = bwd_input_tokens_no_unk[i][
bwd_start_index[i][0]:bwd_end_index[i][0] + 1]
preds.append(tokenizer.convert_tokens_to_string(span_tokens))
else:
joint_probs, joint_index = (
fwd_start_probs[i].unsqueeze(1) *
fwd_end_probs[i].unsqueeze(0)).view(topk * topk).topk(topk)
for n in range(topk):
smap = joint_index[n] // topk
emap = joint_index[n] - smap * topk
span_tokens = fwd_input_tokens_no_unk[i][
fwd_start_index[i][smap]:fwd_end_index[i][emap] + 1]
preds.append(
tokenizer.convert_tokens_to_string(span_tokens))
joint_probs, joint_index = (
bwd_start_probs[i].unsqueeze(1) *
bwd_end_probs[i].unsqueeze(0)).view(topk * topk).topk(topk)
for n in range(topk):
smap = joint_index[n] // topk
emap = joint_index[n] - smap * topk
span_tokens = bwd_input_tokens_no_unk[i][
bwd_start_index[i][smap]:bwd_end_index[i][emap] + 1]
preds.append(
tokenizer.convert_tokens_to_string(span_tokens))
norm_preds_tokens = [
norm_tokenizer.basic_tokenizer.tokenize(pred) for pred in preds
]
norm_preds = [
norm_tokenizer.convert_tokens_to_string(norm_pred_tokens)
for norm_pred_tokens in norm_preds_tokens
]
norm_answer_tokens = [
norm_tokenizer.basic_tokenizer.tokenize(ans) for ans in answer
]
norm_answer = [
norm_tokenizer.convert_tokens_to_string(ans_tokens)
for ans_tokens in norm_answer_tokens
]
em += max(
metric_max_over_ground_truths(exact_match_score, norm_pred,
norm_answer)
for norm_pred in norm_preds)
f1 += max(
metric_max_over_ground_truths(f1_score, norm_pred, norm_answer)
for norm_pred in norm_preds)
count += 1
del fwd_dataloader, bwd_dataloader
return em, f1, count
def validate_dataset(model, split, tokenizer, topk=5):
assert split in ('dev', 'test')
dataloader = get_dataloader('xlnet', split, tokenizer, bwd=False, \
batch_size=8, num_workers=8)
em, f1, count = 0, 0, 0
model.start_n_top = topk
model.end_n_top = topk
model.eval()
for batch in dataloader:
batch = (*(tensor.cuda(device) for tensor in batch[:-2]), *batch[-2:])
input_ids, attention_mask, token_type_ids, cls_index, input_tokens_no_unk, answers = batch
with torch.no_grad():
outputs = model(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
cls_index=cls_index)
start_index = outputs[1]
end_index = outputs[3][:, :, 0]
op_types = outputs[4]
for i, answer in enumerate(answers):
preds = []
for k in range(model.start_n_top):
op_type = op_types[i][k].argmax().item()
if op_type == 0:
pred_tokens = input_tokens_no_unk[i][
start_index[i][k]:end_index[i][k] + 1]
pred = tokenizer.convert_tokens_to_string(pred_tokens)
elif op_type == 1:
pred = arithmetic_op(tokenizer,
num_match_re,
input_tokens_no_unk[i],
start_index[i][k],
end_index[i][k],
plus=True)
elif op_type == 2:
pred = arithmetic_op(tokenizer,
num_match_re,
input_tokens_no_unk[i],
start_index[i][k],
end_index[i][k],
plus=False)
elif op_type == 3:
pred = date_duration_op(tokenizer,
date_re,
dur_re,
tn,
input_tokens_no_unk[i],
start_index[i][k],
end_index[i][k],
plus=True)
elif op_type == 4:
pred = date_duration_op(tokenizer,
date_re,
dur_re,
tn,
input_tokens_no_unk[i],
start_index[i][k],
end_index[i][k],
plus=False)
preds.append(pred)
norm_preds_tokens = [
norm_tokenizer.basic_tokenizer.tokenize(pred) for pred in preds
]
norm_preds = [
norm_tokenizer.convert_tokens_to_string(norm_pred_tokens)
for norm_pred_tokens in norm_preds_tokens
]
norm_answer_tokens = [
norm_tokenizer.basic_tokenizer.tokenize(ans) for ans in answer
]
norm_answer = [
norm_tokenizer.convert_tokens_to_string(ans_tokens)
for ans_tokens in norm_answer_tokens
]
em += max(
metric_max_over_ground_truths(exact_match_score, norm_pred,
norm_answer)
for norm_pred in norm_preds)
f1 += max(
metric_max_over_ground_truths(f1_score, norm_pred, norm_answer)
for norm_pred in norm_preds)
count += 1
del dataloader
return em, f1, count
