def evaluate(self,
sentences: List[Sentence],
eval_class_metrics: bool = False,
mini_batch_size: int = 32,
embeddings_in_memory: bool = False,
metric_name: str = 'MICRO_AVG') -> (dict, float):
"""
Evaluates the model with the given list of sentences.
:param sentences: the list of sentences
:param eval_class_metrics: boolean indicating whether to print class metrics or not
:param mini_batch_size: the mini batch size to use
:param embeddings_in_memory: boolean value indicating, if embeddings should be kept in memory or not
:param metric_name: the name of the metrics to compute
:return: list of metrics, and the loss
"""
with torch.no_grad():
eval_loss = 0
batches = [
sentences[x:x + mini_batch_size]
for x in range(0, len(sentences), mini_batch_size)
]
metric = Metric(metric_name)
for batch in batches:
scores = self.model.forward(batch)
labels = self.model.obtain_labels(scores)
loss = self.model.calculate_loss(scores, batch)
clear_embeddings(
batch, also_clear_word_embeddings=not embeddings_in_memory)
eval_loss += loss
for predictions, true_values in zip(
[[label.value for label in sent_labels]
for sent_labels in labels],
[sentence.get_label_names() for sentence in batch]):
for prediction in predictions:
if prediction in true_values:
metric.tp()
if eval_class_metrics: metric.tp(prediction)
else:
metric.fp()
if eval_class_metrics: metric.fp(prediction)
for true_value in true_values:
if true_value not in predictions:
metric.fn()
if eval_class_metrics: metric.fn(true_value)
else:
metric.tn()
if eval_class_metrics: metric.tn(true_value)
eval_loss /= len(sentences)
return metric, eval_loss
def evaluate(self,
evaluation: List[Sentence],
out_path=None,
evaluation_method: str = 'F1',
eval_batch_size: int = 32,
embeddings_in_memory: bool = True):
batch_no: int = 0
batches = [
evaluation[x:x + eval_batch_size]
for x in range(0, len(evaluation), eval_batch_size)
]
metric = Metric('')
lines: List[str] = []
for batch in batches:
batch_no += 1
scores, tag_seq = self.model._predict_scores_batch(batch)
predicted_ids = tag_seq
all_tokens = []
for sentence in batch:
all_tokens.extend(sentence.tokens)
for (token, score, predicted_id) in zip(all_tokens, scores,
predicted_ids):
token: Token = token
# get the predicted tag
predicted_value = self.model.tag_dictionary.get_item_for_index(
predicted_id)
token.add_tag('predicted', predicted_value, score)
for sentence in batch:
# add predicted tags
for token in sentence.tokens:
predicted_tag: Label = token.get_tag('predicted')
# append both to file for evaluation
eval_line = '{} {} {}\n'.format(
token.text,
token.get_tag(self.model.tag_type).value,
predicted_tag.value)
lines.append(eval_line)
lines.append('\n')
# make list of gold tags
gold_tags = [
str(tag) for tag in sentence.get_spans(self.model.tag_type)
]
# make list of predicted tags
predicted_tags = [
str(tag) for tag in sentence.get_spans('predicted')
]
# check for true positives, false positives and false negatives
for prediction in predicted_tags:
if prediction in gold_tags:
metric.tp()
else:
metric.fp()
for gold in gold_tags:
if gold not in predicted_tags:
metric.fn()
if not embeddings_in_memory:
self.clear_embeddings_in_batch(batch)
if out_path is not None:
test_tsv = os.path.join(out_path, "test.tsv")
with open(test_tsv, "w", encoding='utf-8') as outfile:
outfile.write(''.join(lines))
if evaluation_method == 'accuracy':
score = metric.accuracy()
return score, metric
if evaluation_method == 'F1':
score = metric.f_score()
return score, metric
def evaluate(self, evaluation: List[Sentence], out_path=None, evaluation_method: str = 'F1',
embeddings_in_memory: bool = True):
tp: int = 0
fp: int = 0
batch_no: int = 0
mini_batch_size = 32
batches = [evaluation[x:x + mini_batch_size] for x in
range(0, len(evaluation), mini_batch_size)]
metric = Metric('')
lines: List[str] = []
for batch in batches:
batch_no += 1
self.model.embeddings.embed(batch)
for sentence in batch:
sentence: Sentence = sentence
# Step 3. Run our forward pass.
score, tag_seq = self.model.predict_scores(sentence)
# Step 5. Compute predictions
predicted_id = tag_seq
for (token, pred_id) in zip(sentence.tokens, predicted_id):
token: Token = token
# get the predicted tag
predicted_tag = self.model.tag_dictionary.get_item_for_index(pred_id)
token.add_tag('predicted', predicted_tag)
# get the gold tag
gold_tag = token.get_tag(self.model.tag_type)
# append both to file for evaluation
eval_line = token.text + ' ' + gold_tag + ' ' + predicted_tag + "\n"
# positives
if predicted_tag != '':
# true positives
if predicted_tag == gold_tag:
metric.tp()
# false positive
if predicted_tag != gold_tag:
metric.fp()
# negatives
if predicted_tag == '':
# true negative
if predicted_tag == gold_tag:
metric.tn()
# false negative
if predicted_tag != gold_tag:
metric.fn()
lines.append(eval_line)
lines.append('\n')
if not embeddings_in_memory:
self.clear_embeddings_in_batch(batch)
if out_path is not None:
test_tsv = os.path.join(out_path, "test.tsv")
with open(test_tsv, "w", encoding='utf-8') as outfile:
outfile.write(''.join(lines))
if evaluation_method == 'span-F1':
# get the eval script
eval_script = cached_path('https://s3.eu-central-1.amazonaws.com/alan-nlp/resources/scripts/conll03_eval_script.pl', cache_dir='scripts')
os.chmod(eval_script, 0o777)
eval_data = ''.join(lines)
p = run(eval_script, stdout=PIPE, input=eval_data, encoding='utf-8')
main_result = p.stdout
print(main_result)
main_result = main_result.split('\n')[1]
# parse the result file
main_result = re.sub(';', ' ', main_result)
main_result = re.sub('precision', 'p', main_result)
main_result = re.sub('recall', 'r', main_result)
main_result = re.sub('accuracy', 'acc', main_result)
f_score = float(re.findall(r'\d+\.\d+$', main_result)[0])
return f_score, metric._fp, main_result
if evaluation_method == 'accuracy':
score = metric.accuracy()
return score, metric._fp, str(score)
if evaluation_method == 'F1':
score = metric.f_score()
return score, metric._fp, str(metric)
