def log_metric(self,
metric: Metric,
dataset_name: str,
log_class_metrics=False):
log.info(
"{0:<4}: f-score {1:.4f} - acc {2:.4f} - tp {3} - fp {4} - fn {5} - tn {6}"
.format(dataset_name, metric.f_score(), metric.accuracy(),
metric.get_tp(), metric.get_fp(), metric.get_fn(),
metric.get_tn()))
if log_class_metrics:
for cls in metric.get_classes():
log.info(
"{0:<4}: f-score {1:.4f} - acc {2:.4f} - tp {3} - fp {4} - fn {5} - tn {6}"
.format(cls, metric.f_score(cls), metric.accuracy(cls),
metric.get_tp(cls), metric.get_fp(cls),
metric.get_fn(cls), metric.get_tn(cls)))
def evaluate(self,
data_loader: DataLoader,
out_path: Path = None,
embeddings_storage_mode: str = 'cpu') -> (Result, float):
with torch.no_grad():
eval_loss = 0
metric = Metric('Evaluation')
lines = []
batch_count = 0
for batch in data_loader:
batch_count += 1
(labels, loss) = self.forward_labels_and_loss(batch)
eval_loss += loss
sentences_for_batch = [
sent.to_plain_string() for sent in batch
]
confidences_for_batch = [[
label.score for label in sent_labels
] for sent_labels in labels]
predictions_for_batch = [[
label.value for label in sent_labels
] for sent_labels in labels]
true_values_for_batch = [
sentence.get_label_names() for sentence in batch
]
available_labels = self.label_dictionary.get_items()
for (sentence, confidence, prediction, true_value) in zip(
sentences_for_batch, confidences_for_batch,
predictions_for_batch, true_values_for_batch):
eval_line = '{}\t{}\t{}\t{}\n'.format(
sentence, true_value, prediction, confidence)
lines.append(eval_line)
for (predictions_for_sentence,
true_values_for_sentence) in zip(predictions_for_batch,
true_values_for_batch):
for label in available_labels:
if ((label in predictions_for_sentence)
and (label in true_values_for_sentence)):
metric.add_tp(label)
elif ((label in predictions_for_sentence)
and (label not in true_values_for_sentence)):
metric.add_fp(label)
elif ((label not in predictions_for_sentence)
and (label in true_values_for_sentence)):
metric.add_fn(label)
elif ((label not in predictions_for_sentence)
and (label not in true_values_for_sentence)):
metric.add_tn(label)
store_embeddings(batch, embeddings_storage_mode)
eval_loss /= batch_count
detailed_result = ''.join([
'\nMICRO_AVG: acc ', '{}'.format(metric.micro_avg_accuracy()),
' - f1-score ', '{}'.format(metric.micro_avg_f_score()),
'\nMACRO_AVG: acc ', '{}'.format(metric.macro_avg_accuracy()),
' - f1-score ', '{}'.format(metric.macro_avg_f_score())
])
for class_name in metric.get_classes():
detailed_result += ''.join([
'\n', '{:<10}'.format(class_name), ' tp: ',
'{}'.format(metric.get_tp(class_name)), ' - fp: ',
'{}'.format(metric.get_fp(class_name)), ' - fn: ',
'{}'.format(metric.get_fn(class_name)), ' - tn: ',
'{}'.format(metric.get_tn(class_name)), ' - precision: ',
'{:.4f}'.format(metric.precision(class_name)),
' - recall: ', '{:.4f}'.format(metric.recall(class_name)),
' - accuracy: ', '{:.4f}'.format(
metric.accuracy(class_name)), ' - f1-score: ',
'{:.4f}'.format(metric.f_score(class_name))
])
result = Result(main_score=metric.micro_avg_f_score(),
log_line=''.join([
'{}'.format(metric.precision()), '\t',
'{}'.format(metric.recall()), '\t',
'{}'.format(metric.micro_avg_f_score())
]),
log_header='PRECISION\tRECALL\tF1',
detailed_results=detailed_result)
if (out_path is not None):
with open(out_path, 'w', encoding='utf-8') as outfile:
outfile.write(''.join(lines))
return (result, eval_loss)
def evaluate(self,
data_loader: DataLoader,
out_path: Path = None,
embeddings_storage_mode: str = 'cpu') -> (Result, float):
with torch.no_grad():
eval_loss = 0
batch_no = 0
metric = Metric('Evaluation')
lines = []
for batch in data_loader:
batch_no += 1
with torch.no_grad():
features = self.forward(batch)
loss = self._calculate_loss(features, batch)
(tags, _) = self._obtain_labels(features, batch)
eval_loss += loss
for (sentence, sent_tags) in zip(batch, tags):
for (token, tag) in zip(sentence.tokens, sent_tags):
token = token
token.add_tag_label('predicted', tag)
eval_line = '{} {} {} {}\n'.format(
token.text,
token.get_tag(self.tag_type).value, tag.value,
tag.score)
lines.append(eval_line)
lines.append('\n')
for sentence in batch:
gold_tags = [(tag.tag, str(tag))
for tag in sentence.get_spans(self.tag_type)]
predicted_tags = [
(tag.tag, str(tag))
for tag in sentence.get_spans('predicted')
]
for (tag, prediction) in predicted_tags:
if ((tag, prediction) in gold_tags):
metric.add_tp(tag)
else:
metric.add_fp(tag)
for (tag, gold) in gold_tags:
if ((tag, gold) not in predicted_tags):
metric.add_fn(tag)
else:
metric.add_tn(tag)
store_embeddings(batch, embeddings_storage_mode)
eval_loss /= batch_no
if (out_path is not None):
with open(out_path, 'w', encoding='utf-8') as outfile:
outfile.write(''.join(lines))
detailed_result = ''.join([
'\nMICRO_AVG: acc ', '{}'.format(metric.micro_avg_accuracy()),
' - f1-score ', '{}'.format(metric.micro_avg_f_score()),
'\nMACRO_AVG: acc ', '{}'.format(metric.macro_avg_accuracy()),
' - f1-score ', '{}'.format(metric.macro_avg_f_score())
])
for class_name in metric.get_classes():
detailed_result += ''.join([
'\n', '{:<10}'.format(class_name), ' tp: ',
'{}'.format(metric.get_tp(class_name)), ' - fp: ',
'{}'.format(metric.get_fp(class_name)), ' - fn: ',
'{}'.format(metric.get_fn(class_name)), ' - tn: ',
'{}'.format(metric.get_tn(class_name)), ' - precision: ',
'{:.4f}'.format(metric.precision(class_name)),
' - recall: ', '{:.4f}'.format(metric.recall(class_name)),
' - accuracy: ', '{:.4f}'.format(
metric.accuracy(class_name)), ' - f1-score: ',
'{:.4f}'.format(metric.f_score(class_name))
])
result = Result(main_score=metric.micro_avg_f_score(),
log_line=''.join([
'{}'.format(metric.precision()), '\t',
'{}'.format(metric.recall()), '\t',
'{}'.format(metric.micro_avg_f_score())
]),
log_header='PRECISION\tRECALL\tF1',
detailed_results=detailed_result)
return (result, eval_loss)
