def validate_official(args, data_loader, model, global_stats, offsets, texts,
answers):
"""Run one full official validation. Uses exact spans and same
exact match/F1 score computation as in the SQuAD script.
Extra arguments:
offsets: The character start/end indices for the tokens in each context.
texts: Map of qid --> raw text of examples context (matches offsets).
answers: Map of qid --> list of accepted answers.
"""
clean_id_file = open(os.path.join(DATA_DIR, "clean_qids.txt"), "w+")
eval_time = utils.Timer()
f1 = utils.AverageMeter()
exact_match = utils.AverageMeter()
# Run through examples
examples = 0
bad_examples = 0
for ex in data_loader:
ex_id, batch_size = ex[-1], ex[0].size(0)
chosen_offset = ex[-2]
pred_s, pred_e, _ = model.predict(ex)
for i in range(batch_size):
if pred_s[i][0] >= len(offsets[ex_id[i]]) or pred_e[i][0] >= len(
offsets[ex_id[i]]):
bad_examples += 1
continue
if args.use_sentence_selector:
s_offset = chosen_offset[i][pred_s[i][0]][0]
e_offset = chosen_offset[i][pred_e[i][0]][1]
else:
s_offset = offsets[ex_id[i]][pred_s[i][0]][0]
e_offset = offsets[ex_id[i]][pred_e[i][0]][1]
prediction = texts[ex_id[i]][s_offset:e_offset]
# Compute metrics
ground_truths = answers[ex_id[i]]
exact_match.update(
utils.metric_max_over_ground_truths(utils.exact_match_score,
prediction, ground_truths))
f1.update(
utils.metric_max_over_ground_truths(utils.f1_score, prediction,
ground_truths))
f1_example = utils.metric_max_over_ground_truths(
utils.f1_score, prediction, ground_truths)
if f1_example != 0:
clean_id_file.write(ex_id + "\n")
examples += batch_size
clean_id_file.close()
logger.info('dev valid official: Epoch = %d | EM = %.2f | ' %
(global_stats['epoch'], exact_match.avg * 100) +
'F1 = %.2f | examples = %d | valid time = %.2f (s)' %
(f1.avg * 100, examples, eval_time.time()))
logger.info('Bad Offset Examples during official eval: %d' % bad_examples)
return {'exact_match': exact_match.avg * 100, 'f1': f1.avg * 100}
def validate_official(args,
data_loader,
model,
global_stats,
offsets,
texts,
answers,
mode="dev"):
"""Run one full official validation. Uses exact spans and same
exact match/F1 score computation as in the SQuAD script.
Extra arguments:
offsets: The character start/end indices for the tokens in each context.
texts: Map of qid --> raw text of examples context (matches offsets).
answers: Map of qid --> list of accepted answers.
"""
eval_time = utils.Timer()
f1 = utils.AverageMeter()
exact_match = utils.AverageMeter()
# Run through examples
examples = 0
for ex in data_loader:
ex_id, batch_size = ex[-1], ex[0].size(0)
pred_s, pred_e, _ = model.predict(ex)
for i in range(batch_size):
s_offset = offsets[ex_id[i]][pred_s[i][0]][0]
e_offset = offsets[ex_id[i]][pred_e[i][0]][1]
prediction = texts[ex_id[i]][s_offset:e_offset]
# Compute metrics
ground_truths = answers[ex_id[i]]
exact_match.update(
utils.metric_max_over_ground_truths(utils.exact_match_score,
prediction, ground_truths))
f1.update(
utils.metric_max_over_ground_truths(utils.f1_score, prediction,
ground_truths))
examples += batch_size
logger.info(mode + ' valid official: Epoch = %d | EM = %.2f | ' %
(global_stats['epoch'], exact_match.avg * 100) +
'F1 = %.2f | examples = %d | valid time = %.2f (s)' %
(f1.avg * 100, examples, eval_time.time()))
return {'exact_match': exact_match.avg * 100, 'f1': f1.avg * 100}
def evaluate(dataset_file, prediction_file, regex=False):
print("-" * 50)
print("Dataset: %s" % dataset_file)
print("Predictions: %s" % prediction_file)
answers = []
for line in open(args.dataset):
data = json.loads(line)
answer = [normalize(a) for a in data["answer"]]
answers.append(answer)
predictions = []
with open(prediction_file) as f:
for line in f:
data = json.loads(line)
prediction = normalize(data[0]["span"])
predictions.append(prediction)
exact_match = 0
for i in range(len(predictions)):
match_fn = regex_match_score if regex else exact_match_score
exact_match += metric_max_over_ground_truths(match_fn, predictions[i],
answers[i])
total = len(predictions)
exact_match = 100.0 * exact_match / total
print({"exact_match": exact_match})
def evaluate(dataset_file, prediction_file, regex=False):
print('-' * 50)
print('Dataset: %s' % dataset_file)
print('Predictions: %s' % prediction_file)
answers = []
for line in open(args.dataset):
data = json.loads(line)
answer = [normalize(a) for a in data['answer']]
answers.append(answer)
predictions = []
with open(prediction_file) as f:
for line in f:
data = json.loads(line)
prediction = normalize(data[0]['span'])
predictions.append(prediction)
exact_match = 0
for i in range(len(predictions)):
match_fn = regex_match_score if regex else exact_match_score
exact_match += metric_max_over_ground_truths(
match_fn, predictions[i], answers[i]
)
total = len(predictions)
exact_match = 100.0 * exact_match / total
print({'exact_match': exact_match})
def get_rank(prediction_, answer_, use_regex_=False):
for rank_, entry in enumerate(prediction_):
if use_regex_:
match_fn = regex_match_score
else:
match_fn = exact_match_score
exact_match = metric_max_over_ground_truths(match_fn, normalize(entry['span']), answer_)
if exact_match:
return rank_ + 1
return 1000
def validate_official(args, data_loader, model, global_stats,
offsets, texts, answers):
"""Run one full official validation. Uses exact spans and same
exact match/F1 score computation as in the SQuAD script.
Extra arguments:
offsets: The character start/end indices for the tokens in each context.
texts: Map of qid --> raw text of examples context (matches offsets).
answers: Map of qid --> list of accepted answers.
"""
eval_time = utils.Timer()
f1 = utils.AverageMeter()
exact_match = utils.AverageMeter()
# Run through examples
examples = 0
for ex in data_loader:
ex_id, batch_size = ex[-1], ex[0].size(0)
pred_s, pred_e, _ = model.predict(ex)
for i in range(batch_size):
s_offset = offsets[ex_id[i]][pred_s[i][0]][0]
e_offset = offsets[ex_id[i]][pred_e[i][0]][1]
prediction = texts[ex_id[i]][s_offset:e_offset]
# Compute metrics
ground_truths = answers[ex_id[i]]
exact_match.update(utils.metric_max_over_ground_truths(
utils.exact_match_score, prediction, ground_truths))
f1.update(utils.metric_max_over_ground_truths(
utils.f1_score, prediction, ground_truths))
examples += batch_size
logger.info('dev valid official: Epoch = %d | EM = %.2f | ' %
(global_stats['epoch'], exact_match.avg * 100) +
'F1 = %.2f | examples = %d | valid time = %.2f (s)' %
(f1.avg * 100, examples, eval_time.time()))
return {'exact_match': exact_match.avg * 100, 'f1': f1.avg * 100}
def compute_expected_metric(args, data_loader, model, global_stats, offsets,
texts, answers):
scores = {}
preds = {}
for ex in data_loader:
ex_id, batch_size = ex[-1], ex[0].size(0)
chosen_offset = ex[-2]
tup, score_s, score_e = model.predict_probs(ex)
pred_s, pred_e, _ = tup
for i in range(batch_size):
if args.use_sentence_selector:
s_offset = chosen_offset[i][pred_s[i][0]][0]
e_offset = chosen_offset[i][pred_e[i][0]][1]
else:
s_offset = offsets[ex_id[i]][pred_s[i][0]][0]
e_offset = offsets[ex_id[i]][pred_e[i][0]][1]
prediction = texts[ex_id[i]][s_offset:e_offset]
ground_truths = answers[ex_id[i]]
beam = get_y_pred_beam(score_s[i].numpy(), score_e[i].numpy(),
BEAM_SIZE)
total_prob = sum(x[2] for x in beam)
score = 0.0
for (start, end, prob) in beam:
if args.use_sentence_selector:
s_offset = chosen_offset[i][start][0]
e_offset = chosen_offset[i][end][1]
else:
s_offset = offsets[ex_id[i]][start][0]
e_offset = offsets[ex_id[i]][end][1]
phrase = texts[ex_id[i]][s_offset:e_offset]
cur_f1 = utils.metric_max_over_ground_truths(
utils.f1_score, prediction, ground_truths)
score += prob / total_prob * cur_f1
scores[ex_id[i]] = score
preds[ex_id[i]] = prediction
return scores, preds
def validate_adversarial(args, model, global_stats, mode="dev"):
# create dataloader for dev sets, load thier jsons, integrate the function
for idx, dataset_file in enumerate(args.adv_dev_json):
predictions = {}
logger.info("Validating Adversarial Dataset %s" % dataset_file)
exs = utils.load_data(args, args.adv_dev_file[idx])
logger.info('Num dev examples = %d' % len(exs))
## Create dataloader
dev_dataset = reader_data.ReaderDataset(exs,
model,
single_answer=False)
if args.sort_by_len:
dev_sampler = reader_data.SortedBatchSampler(dev_dataset.lengths(),
args.test_batch_size,
shuffle=False)
else:
dev_sampler = torch.utils.data.sampler.SequentialSampler(
dev_dataset)
if args.use_sentence_selector:
dev_batcher = reader_vector.sentence_batchifier(
model, single_answer=False)
#batching_function = dev_batcher.batchify
batching_function = reader_vector.batchify
else:
batching_function = reader_vector.batchify
dev_loader = torch.utils.data.DataLoader(
dev_dataset,
batch_size=args.test_batch_size,
sampler=dev_sampler,
num_workers=args.data_workers,
collate_fn=batching_function,
pin_memory=args.cuda,
)
texts = utils.load_text(dataset_file)
offsets = {ex['id']: ex['offsets'] for ex in exs}
answers = utils.load_answers(dataset_file)
eval_time = utils.Timer()
f1 = utils.AverageMeter()
exact_match = utils.AverageMeter()
examples = 0
bad_examples = 0
for ex in dev_loader:
ex_id, batch_size = ex[-1], ex[0].size(0)
chosen_offset = ex[-2]
pred_s, pred_e, _ = model.predict(ex)
for i in range(batch_size):
if pred_s[i][0] >= len(
offsets[ex_id[i]]) or pred_e[i][0] >= len(
offsets[ex_id[i]]):
bad_examples += 1
continue
if args.use_sentence_selector:
s_offset = chosen_offset[i][pred_s[i][0]][0]
e_offset = chosen_offset[i][pred_e[i][0]][1]
else:
s_offset = offsets[ex_id[i]][pred_s[i][0]][0]
e_offset = offsets[ex_id[i]][pred_e[i][0]][1]
prediction = texts[ex_id[i]][s_offset:e_offset]
if args.select_k > 1:
prediction = ""
offset_subset = chosen_offset[i][pred_s[i][0]:pred_e[i][0]]
for enum_, o in enumerate(offset_subset):
prediction += texts[ex_id[i]][o[0]:o[1]] + " "
prediction = prediction.strip()
predictions[ex_id[i]] = prediction
ground_truths = answers[ex_id[i]]
exact_match.update(
utils.metric_max_over_ground_truths(
utils.exact_match_score, prediction, ground_truths))
f1.update(
utils.metric_max_over_ground_truths(
utils.f1_score, prediction, ground_truths))
examples += batch_size
logger.info(
'dev valid official for dev file %s : Epoch = %d | EM = %.2f | ' %
(dataset_file, global_stats['epoch'], exact_match.avg * 100) +
'F1 = %.2f | examples = %d | valid time = %.2f (s)' %
(f1.avg * 100, examples, eval_time.time()))
orig_f1_score = 0.0
orig_exact_match_score = 0.0
adv_f1_scores = {} # Map from original ID to F1 score
adv_exact_match_scores = {
} # Map from original ID to exact match score
adv_ids = {}
all_ids = set() # Set of all original IDs
f1 = exact_match = 0
dataset = json.load(open(dataset_file))['data']
for article in dataset:
for paragraph in article['paragraphs']:
for qa in paragraph['qas']:
orig_id = qa['id'].split('-')[0]
all_ids.add(orig_id)
if qa['id'] not in predictions:
message = 'Unanswered question ' + qa[
'id'] + ' will receive score 0.'
# logger.info(message)
continue
ground_truths = list(
map(lambda x: x['text'], qa['answers']))
prediction = predictions[qa['id']]
cur_exact_match = utils.metric_max_over_ground_truths(
utils.exact_match_score, prediction, ground_truths)
cur_f1 = utils.metric_max_over_ground_truths(
utils.f1_score, prediction, ground_truths)
if orig_id == qa['id']:
# This is an original example
orig_f1_score += cur_f1
orig_exact_match_score += cur_exact_match
if orig_id not in adv_f1_scores:
# Haven't seen adversarial example yet, so use original for adversary
adv_ids[orig_id] = orig_id
adv_f1_scores[orig_id] = cur_f1
adv_exact_match_scores[orig_id] = cur_exact_match
else:
# This is an adversarial example
if (orig_id not in adv_f1_scores
or adv_ids[orig_id] == orig_id
or adv_f1_scores[orig_id] > cur_f1):
# Always override if currently adversary currently using orig_id
adv_ids[orig_id] = qa['id']
adv_f1_scores[orig_id] = cur_f1
adv_exact_match_scores[orig_id] = cur_exact_match
orig_f1 = 100.0 * orig_f1_score / len(all_ids)
orig_exact_match = 100.0 * orig_exact_match_score / len(all_ids)
adv_exact_match = 100.0 * sum(
adv_exact_match_scores.values()) / len(all_ids)
adv_f1 = 100.0 * sum(adv_f1_scores.values()) / len(all_ids)
logger.info(
"For the file %s Original Exact Match : %.4f ; Original F1 : : %.4f | "
% (dataset_file, orig_exact_match, orig_f1) +
"Adversarial Exact Match : %.4f ; Adversarial F1 : : %.4f " %
(adv_exact_match, adv_f1))
def validate_official(args, data_loader, model, global_stats, offsets, texts,
questions, answers):
"""Run one full official validation. Uses exact spans and same
exact match/F1 score computation as in the SQuAD script.
Extra arguments:
offsets: The character start/end indices for the tokens in each context.
texts: Map of qid --> raw text of examples context (matches offsets).
answers: Map of qid --> list of accepted answers.
"""
eval_time = utils.Timer()
f1 = utils.AverageMeter()
exact_match = utils.AverageMeter()
# Run through examples
examples = 0
em_false = {} # cid -> (context, [(qid, question, answer)...])
predictions = {} # qid -> prediction
for ex in data_loader:
ex_id, batch_size = ex[-1], ex[0].size(0)
pred_s, pred_e, _ = model.predict(ex)
for i in range(batch_size):
s_offset = offsets[ex_id[i]][pred_s[i][0]][0]
e_offset = offsets[ex_id[i]][pred_e[i][0]][1]
prediction = texts[ex_id[i]][1][s_offset:e_offset]
cid = texts[ex_id[i]][0]
predictions[ex_id[i]] = prediction
# Compute metrics
ground_truths = answers[ex_id[i]]
em_score = utils.metric_max_over_ground_truths(
utils.exact_match_score, prediction, ground_truths)
if em_score < 1:
if cid not in em_false:
em_false[cid] = {
'text':
texts[ex_id[i]][1],
'qa': [{
'qid': ex_id[i],
'question': questions[ex_id[i]],
'answers': answers[ex_id[i]],
'prediction': prediction
}]
}
else:
em_false[cid]['qa'].append({
'qid': ex_id[i],
'question': questions[ex_id[i]],
'answers': answers[ex_id[i]],
'prediction': prediction
})
exact_match.update(em_score)
f1.update(
utils.metric_max_over_ground_truths(utils.f1_score, prediction,
ground_truths))
examples += batch_size
logger.info('dev valid official: Epoch = %d | EM = %.2f | ' %
(global_stats['epoch'], exact_match.avg * 100) +
'F1 = %.2f | examples = %d | valid time = %.2f (s)' %
(f1.avg * 100, examples, eval_time.time()))
return {
'exact_match': exact_match.avg * 100,
'f1': f1.avg * 100
}, em_false, predictions
question = data['question']
questions.append(question)
answer = [normalize(a) for a in data['answer']]
answers.append(answer)
doc_ids_ans = []
prediction_file = os.path.join(args.prediction_dir, PREDICTION_FILE)
for line, answer in zip(open(prediction_file, encoding=ENCODING), answers):
data = json.loads(line)
if len(data):
prediction = normalize(data[0]['span'])
id_ans = data[0]['doc_id']
else:
prediction = ''
id_ans = ''
exact_match = metric_max_over_ground_truths(regex_match_score,
prediction, answer)
doc_ids_ans.append((id_ans, exact_match))
query_doc_dict = {}
for line in extract_lines(args.log_file, 'question_d:', ' ]'):
question, sec_strings = line.split(', query:')
start_index = sec_strings.index('doc_ids:') + len('doc_ids:')
end_index = sec_strings.index(', doc_scores:')
doc_id_strings = sec_strings[start_index:end_index]
top_doc_ids = ast.literal_eval(doc_id_strings)
query_doc_dict[question.strip()] = top_doc_ids
ranks = []
right_ranks = []
for question, id_ans in zip(questions, doc_ids_ans):
doc_id, ans = id_ans
