def _evaluate_sequence_tagger(model,
sentences: List[Sentence],
eval_mini_batch_size: int = 32,
embeddings_in_memory: bool = True,
out_path: Path = None) -> (dict, float):
with torch.no_grad():
eval_loss = 0
batch_no: int = 0
batches = [sentences[x:x + eval_mini_batch_size] for x in range(0, len(sentences), eval_mini_batch_size)]
metric = Metric('Evaluation')
lines: List[str] = []
for batch in batches:
batch_no += 1
tags, loss = model.forward_labels_and_loss(batch)
eval_loss += loss
for (sentence, sent_tags) in zip(batch, tags):
for (token, tag) in zip(sentence.tokens, sent_tags):
token: Token = token
token.add_tag_label('predicted', tag)
# append both to file for evaluation
eval_line = '{} {} {} {}\n'.format(token.text,
token.get_tag(model.tag_type).value, tag.value, tag.score)
lines.append(eval_line)
lines.append('\n')
for sentence in batch:
# make list of gold tags
gold_tags = [(tag.tag, str(tag)) for tag in sentence.get_spans(model.tag_type)]
# make list of predicted tags
predicted_tags = [(tag.tag, str(tag)) for tag in sentence.get_spans('predicted')]
# check for true positives, false positives and false negatives
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)
clear_embeddings(batch, also_clear_word_embeddings=not embeddings_in_memory)
eval_loss /= len(sentences)
if out_path is not None:
with open(out_path, "w", encoding='utf-8') as outfile:
outfile.write(''.join(lines))
return metric, eval_loss
def _evaluate_sentence_for_text_classification(metric: Metric,
available_labels: List[str],
predictions: List[str],
true_values: List[str]):
for label in available_labels:
if label in predictions and label in true_values:
metric.add_tp(label)
elif label in predictions and label not in true_values:
metric.add_fp(label)
elif label not in predictions and label in true_values:
metric.add_fn(label)
elif label not in predictions and label not in true_values:
metric.add_tn(label)
def test_metric_with_classes():
metric = Metric("Test")
metric.add_tp("class-1")
metric.add_tn("class-1")
metric.add_tn("class-1")
metric.add_fp("class-1")
metric.add_tp("class-2")
metric.add_tn("class-2")
metric.add_tn("class-2")
metric.add_fp("class-2")
for i in range(0, 10):
metric.add_tp("class-3")
for i in range(0, 90):
metric.add_fp("class-3")
metric.add_tp("class-4")
metric.add_tn("class-4")
metric.add_tn("class-4")
metric.add_fp("class-4")
print(metric)
assert metric.precision("class-1") == 0.5
assert metric.precision("class-2") == 0.5
assert metric.precision("class-3") == 0.1
assert metric.precision("class-4") == 0.5
assert metric.recall("class-1") == 1
assert metric.recall("class-2") == 1
assert metric.recall("class-3") == 1
assert metric.recall("class-4") == 1
assert metric.accuracy() == metric.micro_avg_accuracy()
assert metric.f_score() == metric.micro_avg_f_score()
assert metric.f_score("class-1") == 0.6666666666666666
assert metric.f_score("class-2") == 0.6666666666666666
assert metric.f_score("class-3") == 0.18181818181818182
assert metric.f_score("class-4") == 0.6666666666666666
assert metric.accuracy("class-1") == 0.75
assert metric.accuracy("class-2") == 0.75
assert metric.accuracy("class-3") == 0.1
assert metric.accuracy("class-4") == 0.75
assert metric.micro_avg_f_score() == 0.21848739495798317
assert metric.macro_avg_f_score() == 0.5454545454545454
assert metric.micro_avg_accuracy() == 0.16964285714285715
assert metric.macro_avg_accuracy() == 0.5875
assert metric.precision() == 0.12264150943396226
assert metric.recall() == 1
def _evaluate_text_classifier(
model: flair.nn.Model,
sentences: List[Sentence],
eval_mini_batch_size: int = 32,
embeddings_in_memory: bool = False) -> (dict, float):
with torch.no_grad():
eval_loss = 0
batches = [
sentences[x:x + eval_mini_batch_size]
for x in range(0, len(sentences), eval_mini_batch_size)
]
metric = Metric('Evaluation')
for batch in batches:
labels, loss = model.forward_labels_and_loss(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.add_tp(prediction)
else:
metric.add_fp(prediction)
for true_value in true_values:
if true_value not in predictions:
metric.add_fn(true_value)
else:
metric.add_tn(true_value)
eval_loss /= len(sentences)
return metric, eval_loss
def test_metric_with_classes():
metric = Metric("Test")
metric.add_tp("class-1")
metric.add_tn("class-1")
metric.add_tn("class-1")
metric.add_fp("class-1")
metric.add_tp("class-2")
metric.add_tn("class-2")
metric.add_tn("class-2")
metric.add_fp("class-2")
for i in range(0, 10):
metric.add_tp("class-3")
for i in range(0, 90):
metric.add_fp("class-3")
metric.add_tp("class-4")
metric.add_tn("class-4")
metric.add_tn("class-4")
metric.add_fp("class-4")
assert metric.precision("class-1") == 0.5
assert metric.precision("class-2") == 0.5
assert metric.precision("class-3") == 0.1
assert metric.precision("class-4") == 0.5
assert metric.recall("class-1") == 1
assert metric.recall("class-2") == 1
assert metric.recall("class-3") == 1
assert metric.recall("class-4") == 1
assert metric.accuracy() == metric.micro_avg_accuracy()
assert metric.f_score() == metric.micro_avg_f_score()
assert metric.f_score("class-1") == 0.6667
assert metric.f_score("class-2") == 0.6667
assert metric.f_score("class-3") == 0.1818
assert metric.f_score("class-4") == 0.6667
assert metric.accuracy("class-1") == 0.5
assert metric.accuracy("class-2") == 0.5
assert metric.accuracy("class-3") == 0.1
assert metric.accuracy("class-4") == 0.5
assert metric.micro_avg_f_score() == 0.2184
assert metric.macro_avg_f_score() == 0.5454749999999999
assert metric.micro_avg_accuracy() == 0.1226
assert metric.macro_avg_accuracy() == 0.4
assert metric.precision() == 0.1226
assert metric.recall() == 1
def test_metric_with_classes():
metric = Metric('Test')
metric.add_tp('class-1')
metric.add_tn('class-1')
metric.add_tn('class-1')
metric.add_fp('class-1')
metric.add_tp('class-2')
metric.add_tn('class-2')
metric.add_tn('class-2')
metric.add_fp('class-2')
for i in range(0, 10):
metric.add_tp('class-3')
for i in range(0, 90):
metric.add_fp('class-3')
metric.add_tp('class-4')
metric.add_tn('class-4')
metric.add_tn('class-4')
metric.add_fp('class-4')
assert(metric.precision('class-1') == 0.5)
assert(metric.precision('class-2') == 0.5)
assert(metric.precision('class-3') == 0.1)
assert(metric.precision('class-4') == 0.5)
assert(metric.recall('class-1') == 1)
assert(metric.recall('class-2') == 1)
assert(metric.recall('class-3') == 1)
assert(metric.recall('class-4') == 1)
assert(metric.accuracy() == metric.micro_avg_accuracy())
assert(metric.f_score() == metric.micro_avg_f_score())
assert(metric.f_score('class-1') == 0.6667)
assert(metric.f_score('class-2') == 0.6667)
assert(metric.f_score('class-3') == 0.1818)
assert(metric.f_score('class-4') == 0.6667)
assert(metric.accuracy('class-1') == 0.75)
assert(metric.accuracy('class-2') == 0.75)
assert(metric.accuracy('class-3') == 0.1)
assert(metric.accuracy('class-4') == 0.75)
assert(metric.micro_avg_f_score() == 0.2184)
assert(metric.macro_avg_f_score() == 0.4)
assert(metric.micro_avg_accuracy() == 0.1696)
assert(metric.macro_avg_accuracy() == 0.5875)
assert(metric.precision() == 0.1226)
assert(metric.recall() == 1)
def evaluate(gold_file: Path, pred_file: Path, match_func: Callable[[Tuple, List], Tuple]) -> Metric:
gold_annotations = read_annotations(gold_file)
pred_annotations = read_annotations(pred_file)
metric = Metric("Evaluation", beta=1)
copy_gold = copy_dict(gold_annotations)
for document_id, annotations in pred_annotations.items():
for pred_entry in annotations:
# Documents may not contain any gold entity!
if document_id in copy_gold:
matched_gold = match_func(pred_entry, copy_gold[document_id])
else:
matched_gold = None
if matched_gold:
# Assert same document and same entity type!
assert matched_gold[0] == pred_entry[0] and matched_gold[3] == pred_entry[3]
copy_gold[document_id].remove(matched_gold)
metric.add_tp(pred_entry[3])
else:
metric.add_fp(pred_entry[3])
copy_pred = copy_dict(pred_annotations)
for document_id, annotations in gold_annotations.items():
for gold_entry in annotations:
if document_id in copy_pred:
matched_pred = match_func(gold_entry, copy_pred[document_id])
else:
matched_pred = None
if not matched_pred:
metric.add_fn(gold_entry[3])
else:
# Assert same document and same entity type!
assert matched_pred[0] == gold_entry[0] and matched_pred[3] == gold_entry[3]
copy_pred[document_id].remove(matched_pred)
return metric
def evaluate(
self,
data_loader: DataLoader,
out_path: Path = None,
embeddings_storage_mode: str = "none",
eval_mode: EvalMode = EvalMode.Standard,
misspell_mode: MisspellingMode = MisspellingMode.Random,
misspelling_rate: float = 0.0,
char_vocab: set = {},
lut: dict = {},
cmx: np.array = None,
typos: dict = {},
correction_mode: CorrectionMode = CorrectionMode.NotSpecified,
eval_dict_name=None,
evaluation_metric: EvaluationMetric = EvaluationMetric.MICRO_F1_SCORE,
) -> (Result, float):
if type(out_path) == str:
out_path = Path(out_path)
from robust_ner.spellcheck import load_correction_dict, get_lang_from_corpus_name
if correction_mode == CorrectionMode.NotSpecified:
eval_dict = None
else:
eval_dict = load_correction_dict(eval_dict_name, log)
# note: use 'save_correction_dict' to re-generate a dictionary
lang = get_lang_from_corpus_name(eval_dict_name)
eval_params = {}
eval_params["eval_mode"] = eval_mode
eval_params["misspelling_rate"] = misspelling_rate
eval_params["misspell_mode"] = misspell_mode
eval_params["char_vocab"] = char_vocab
eval_params["lut"] = lut
eval_params["cmx"] = cmx
eval_params["typos"] = typos
eval_params["correction_mode"] = correction_mode
eval_params["lang"] = lang
eval_params["dictionary"] = eval_dict
with torch.no_grad():
eval_loss = 0
batch_no: int = 0
metric = Metric("Evaluation")
lines: List[str] = []
if self.use_crf:
transitions = self.transitions.detach().cpu().numpy()
else:
transitions = None
for batch in data_loader:
batch_no += 1
with torch.no_grad():
features = self.forward(batch, eval_params)
loss = self._calculate_loss(features, batch)
tags, _ = self._obtain_labels(
feature=features,
batch_sentences=batch,
transitions=transitions,
get_all_tags=False,
)
eval_loss += loss
for (sentence, sent_tags) in zip(batch, tags):
for (token, tag) in zip(sentence.tokens, sent_tags):
token: Token = token
token.add_tag("predicted", tag.value, tag.score)
# append both to file for evaluation
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:
# make list of gold tags
gold_tags = [(tag.tag, tag.text)
for tag in sentence.get_spans(self.tag_type)]
# make list of predicted tags
predicted_tags = [
(tag.tag, tag.text)
for tag in sentence.get_spans("predicted")
]
# check for true positives, false positives and false negatives
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 = (
f"\nMICRO_AVG: acc {metric.micro_avg_accuracy():.4f} - f1-score {metric.micro_avg_f_score():.4f}"
f"\nMACRO_AVG: acc {metric.macro_avg_accuracy():.4f} - f1-score {metric.macro_avg_f_score():.4f}"
)
for class_name in metric.get_classes():
detailed_result += (
f"\n{class_name:<10} tp: {metric.get_tp(class_name)} - fp: {metric.get_fp(class_name)} - "
f"fn: {metric.get_fn(class_name)} - tn: {metric.get_tn(class_name)} - precision: "
f"{metric.precision(class_name):.4f} - recall: {metric.recall(class_name):.4f} - "
f"accuracy: {metric.accuracy(class_name):.4f} - f1-score: "
f"{metric.f_score(class_name):.4f}")
if evaluation_metric == EvaluationMetric.MICRO_F1_SCORE:
main_score = metric.micro_avg_f_score()
elif evaluation_metric == EvaluationMetric.MACRO_F1_SCORE:
main_score = metric.macro_avg_f_score()
elif evaluation_metric == EvaluationMetric.MICRO_ACCURACY:
main_score = metric.micro_avg_accuracy()
elif evaluation_metric == EvaluationMetric.MACRO_ACCURACY:
main_score = metric.macro_avg_accuracy()
elif evaluation_metric == EvaluationMetric.MEAN_SQUARED_ERROR:
main_score = metric.mean_squared_error()
else:
log.error(f"unknown evaluation metric: {evaluation_metric}")
result = Result(
main_score=main_score,
log_line=
f"{metric.precision():.4f}\t{metric.recall():.4f}\t{main_score:.4f}",
log_header="PRECISION\tRECALL\tF1",
detailed_results=detailed_result,
)
return result, eval_loss
def _evaluate_with_span_F1(self, data_loader, embedding_storage_mode, mini_batch_size, out_path):
eval_loss = 0
batch_no: int = 0
metric = Metric("Evaluation", beta=self.beta)
lines: List[str] = []
y_true = []
y_pred = []
for batch in data_loader:
# predict for batch
loss = self.predict(batch,
embedding_storage_mode=embedding_storage_mode,
mini_batch_size=mini_batch_size,
label_name='predicted',
return_loss=True)
eval_loss += loss
batch_no += 1
for sentence in batch:
# make list of gold tags
gold_spans = sentence.get_spans(self.tag_type)
gold_tags = [(span.tag, repr(span)) for span in gold_spans]
# make list of predicted tags
predicted_spans = sentence.get_spans("predicted")
predicted_tags = [(span.tag, repr(span)) for span in predicted_spans]
# check for true positives, false positives and false negatives
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)
tags_gold = []
tags_pred = []
# also write to file in BIO format to use old conlleval script
if out_path:
for token in sentence:
# check if in gold spans
gold_tag = 'O'
for span in gold_spans:
if token in span:
gold_tag = 'B-' + span.tag if token == span[0] else 'I-' + span.tag
tags_gold.append(gold_tag)
predicted_tag = 'O'
# check if in predicted spans
for span in predicted_spans:
if token in span:
predicted_tag = 'B-' + span.tag if token == span[0] else 'I-' + span.tag
tags_pred.append(predicted_tag)
lines.append(f'{token.text} {gold_tag} {predicted_tag}\n')
lines.append('\n')
y_true.append(tags_gold)
y_pred.append(tags_pred)
if out_path:
with open(Path(out_path), "w", encoding="utf-8") as outfile:
outfile.write("".join(lines))
eval_loss /= batch_no
detailed_result = (
"\nResults:"
f"\n- F1-score (micro) {metric.micro_avg_f_score():.4f}"
f"\n- F1-score (macro) {metric.macro_avg_f_score():.4f}"
'\n\nBy class:'
)
for class_name in metric.get_classes():
detailed_result += (
f"\n{class_name:<10} tp: {metric.get_tp(class_name)} - fp: {metric.get_fp(class_name)} - "
f"fn: {metric.get_fn(class_name)} - precision: "
f"{metric.precision(class_name):.4f} - recall: {metric.recall(class_name):.4f} - "
f"f1-score: "
f"{metric.f_score(class_name):.4f}"
)
result = Result(
main_score=metric.micro_avg_f_score(),
log_line=f"{metric.precision():.4f}\t{metric.recall():.4f}\t{metric.micro_avg_f_score():.4f}",
log_header="PRECISION\tRECALL\tF1",
detailed_results=detailed_result,
)
return result, eval_loss
def evaluate(
self,
data_loader: DataLoader,
out_path: Path = None,
embedding_storage_mode: str = "none",
) -> (Result, float):
if type(out_path) == str:
out_path = Path(out_path)
metric = Metric("Evaluation", beta=self.beta)
parsing_metric = ParsingMetric()
lines: List[str] = []
eval_loss_arc = 0
eval_loss_rel = 0
for batch_idx, batch in enumerate(data_loader):
with torch.no_grad():
score_arc, score_rel = self.forward(batch)
loss_arc, loss_rel = self._calculate_loss(
score_arc, score_rel, batch)
arc_prediction, relation_prediction = self._obtain_labels_(
score_arc, score_rel)
parsing_metric(arc_prediction, relation_prediction, batch)
eval_loss_arc += loss_arc
eval_loss_rel += loss_rel
for (sentence, arcs, sent_tags) in zip(batch, arc_prediction,
relation_prediction):
for (token, arc, tag) in zip(sentence.tokens, arcs, sent_tags):
token: Token = token
token.add_tag_label("predicted", Label(tag))
token.add_tag_label("predicted_head_id", Label(str(arc)))
# append both to file for evaluation
eval_line = "{} {} {} {} {}\n".format(
token.text,
token.tags['dependency'].value,
str(token.head_id),
tag,
str(arc),
)
lines.append(eval_line)
lines.append("\n")
for sentence in batch:
# make list of gold tags
gold_tags = [
token.tags['dependency'].value for token in sentence.tokens
]
# make list of predicted tags
predicted_tags = [
tag.tag for tag in sentence.get_spans("predicted")
]
# check for true positives, false positives and false negatives
for tag_indx, predicted_tag in enumerate(predicted_tags):
if predicted_tag == gold_tags[tag_indx]:
metric.add_tp(tag)
else:
metric.add_fp(tag)
for tag_indx, label_tag in enumerate(gold_tags):
if label_tag != predicted_tags[tag_indx]:
metric.add_fn(tag)
else:
metric.add_tn(tag)
store_embeddings(batch, embedding_storage_mode)
eval_loss_arc /= len(data_loader)
eval_loss_rel /= len(data_loader)
if out_path is not None:
with open(out_path, "w", encoding="utf-8") as outfile:
outfile.write("".join(lines))
detailed_result = (
f"\nUAS : {parsing_metric.get_uas():.4f} - LAS : {parsing_metric.get_las():.4f}"
f"\neval loss rel : {eval_loss_rel:.4f} - eval loss arc : {eval_loss_arc:.4f}"
f"\nMICRO_AVG: acc {metric.micro_avg_accuracy()} - f1-score {metric.micro_avg_f_score()}"
f"\nMACRO_AVG: acc {metric.macro_avg_accuracy()} - f1-score {metric.macro_avg_f_score()}"
)
for class_name in metric.get_classes():
detailed_result += (
f"\n{class_name:<10} tp: {metric.get_tp(class_name)} - fp: {metric.get_fp(class_name)} - "
f"fn: {metric.get_fn(class_name)} - tn: {metric.get_tn(class_name)} - precision: "
f"{metric.precision(class_name):.4f} - recall: {metric.recall(class_name):.4f} - "
f"accuracy: {metric.accuracy(class_name):.4f} - f1-score: "
f"{metric.f_score(class_name):.4f}")
result = Result(
main_score=metric.micro_avg_f_score(),
log_line=
f"{metric.precision()}\t{metric.recall()}\t{metric.micro_avg_f_score()}",
log_header="PRECISION\tRECALL\tF1",
detailed_results=detailed_result,
)
return result, eval_loss_arc + eval_loss_rel
def evaluate(
self,
sentences: Dataset,
eval_mini_batch_size: int = 32,
embeddings_in_memory: bool = True,
out_path: Path = None,
) -> (Result, float):
with torch.no_grad():
eval_loss = 0
batch_no: int = 0
batch_loader = torch.utils.data.DataLoader(
sentences,
batch_size=eval_mini_batch_size,
shuffle=False,
num_workers=4,
collate_fn=list,
)
metric = Metric("Evaluation")
lines: List[str] = []
for batch in batch_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
token.add_tag_label("predicted", tag)
# append both to file for evaluation
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:
# make list of gold tags
gold_tags = [
(tag.tag, str(tag)) for tag in sentence.get_spans(self.tag_type)
]
# make list of predicted tags
predicted_tags = [
(tag.tag, str(tag)) for tag in sentence.get_spans("predicted")
]
# check for true positives, false positives and false negatives
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)
clear_embeddings(
batch, also_clear_word_embeddings=not embeddings_in_memory
)
eval_loss /= len(sentences)
if out_path is not None:
with open(out_path, "w", encoding="utf-8") as outfile:
outfile.write("".join(lines))
detailed_result = (
f"\nMICRO_AVG: acc {metric.micro_avg_accuracy()} - f1-score {metric.micro_avg_f_score()}"
f"\nMACRO_AVG: acc {metric.macro_avg_accuracy()} - f1-score {metric.macro_avg_f_score()}"
)
for class_name in metric.get_classes():
detailed_result += (
f"\n{class_name:<10} tp: {metric.get_tp(class_name)} - fp: {metric.get_fp(class_name)} - "
f"fn: {metric.get_fn(class_name)} - tn: {metric.get_tn(class_name)} - precision: "
f"{metric.precision(class_name):.4f} - recall: {metric.recall(class_name):.4f} - "
f"accuracy: {metric.accuracy(class_name):.4f} - f1-score: "
f"{metric.f_score(class_name):.4f}"
)
result = Result(
main_score=metric.micro_avg_f_score(),
log_line=f"{metric.precision()}\t{metric.recall()}\t{metric.micro_avg_f_score()}",
log_header="PRECISION\tRECALL\tF1",
detailed_results=detailed_result,
)
return result, eval_loss
def evaluate(
self,
sentences: List[Sentence],
eval_mini_batch_size: int = 32,
embeddings_in_memory: bool = False,
out_path: Path = None,
) -> (Result, float):
with torch.no_grad():
eval_loss = 0
batches = [
sentences[x:x + eval_mini_batch_size]
for x in range(0, len(sentences), eval_mini_batch_size)
]
metric = Metric("Evaluation")
lines: List[str] = []
for batch in batches:
labels, loss = self.forward_labels_and_loss(batch)
clear_embeddings(
batch, also_clear_word_embeddings=not embeddings_in_memory)
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)
eval_loss /= len(sentences)
detailed_result = (
f"\nMICRO_AVG: acc {metric.micro_avg_accuracy()} - f1-score {metric.micro_avg_f_score()}"
f"\nMACRO_AVG: acc {metric.macro_avg_accuracy()} - f1-score {metric.macro_avg_f_score()}"
)
for class_name in metric.get_classes():
detailed_result += (
f"\n{class_name:<10} tp: {metric.get_tp(class_name)} - fp: {metric.get_fp(class_name)} - "
f"fn: {metric.get_fn(class_name)} - tn: {metric.get_tn(class_name)} - precision: "
f"{metric.precision(class_name):.4f} - recall: {metric.recall(class_name):.4f} - "
f"accuracy: {metric.accuracy(class_name):.4f} - f1-score: "
f"{metric.f_score(class_name):.4f}")
result = Result(
main_score=metric.micro_avg_f_score(),
log_line=
f"{metric.precision()}\t{metric.recall()}\t{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,
sentences: Union[List[Sentence], Dataset],
out_path: Union[str, Path] = None,
embedding_storage_mode: str = "none",
mini_batch_size: int = 32,
num_workers: int = 8,
wsd_evaluation: bool = False,
**kwargs,
) -> (Result, float):
# read Dataset into data loader (if list of sentences passed, make Dataset first)
if not isinstance(sentences, Dataset):
sentences = SentenceDataset(sentences)
data_loader = DataLoader(sentences,
batch_size=mini_batch_size,
num_workers=num_workers)
eval_loss = 0
eval_count = 0
batch_no: int = 0
metric = Metric("Evaluation", beta=self.beta)
lines: List[str] = []
y_true = []
y_pred = []
for batch in data_loader:
# predict for batch
loss_and_count = self.predict(
batch,
embedding_storage_mode=embedding_storage_mode,
mini_batch_size=mini_batch_size,
label_name='predicted',
return_loss=True)
eval_loss += loss_and_count[0]
eval_count += loss_and_count[1]
batch_no += 1
for sentence in batch:
# make list of gold tags
gold_spans = sentence.get_spans(self.get_current_tag_type())
gold_tags = [(span.tag, repr(span)) for span in gold_spans]
# make list of predicted tags
predicted_spans = sentence.get_spans("predicted")
predicted_tags = [(span.tag, repr(span))
for span in predicted_spans]
# check for true positives, false positives and false negatives
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)
tags_gold = []
tags_pred = []
# also write to file in BIO format to use old conlleval script
if out_path:
for token in sentence:
# check if in gold spans
gold_tag = 'O'
for span in gold_spans:
if token in span:
gold_tag = 'B-' + span.tag if token == span[
0] else 'I-' + span.tag
tags_gold.append(gold_tag)
predicted_tag = 'O'
# check if in predicted spans
for span in predicted_spans:
if token in span:
predicted_tag = 'B-' + span.tag if token == span[
0] else 'I-' + span.tag
tags_pred.append(predicted_tag)
lines.append(
f'{token.text} {gold_tag} {predicted_tag}\n')
lines.append('\n')
y_true.append(tags_gold)
y_pred.append(tags_pred)
if out_path:
with open(Path(out_path), "w", encoding="utf-8") as outfile:
outfile.write("".join(lines))
detailed_result = (
"\nResults:"
f"\n- F1-score (micro) {metric.micro_avg_f_score():.4f}"
f"\n- F1-score (macro) {metric.macro_avg_f_score():.4f}"
'\n\nBy class:')
for class_name in metric.get_classes():
detailed_result += (
f"\n{class_name:<10} tp: {metric.get_tp(class_name)} - fp: {metric.get_fp(class_name)} - "
f"fn: {metric.get_fn(class_name)} - precision: "
f"{metric.precision(class_name):.4f} - recall: {metric.recall(class_name):.4f} - "
f"f1-score: "
f"{metric.f_score(class_name):.4f}")
result = Result(
main_score=metric.micro_avg_f_score(),
log_line=
f"{metric.precision():.4f}\t{metric.recall():.4f}\t{metric.micro_avg_f_score():.4f}",
log_header="PRECISION\tRECALL\tF1",
detailed_results=detailed_result,
)
return result, eval_loss / eval_count
from pathlib import Path
def evaluate(self,
data_loader,
out_path=None,
embedding_storage_mode="none"):
eval_loss = 0
batch_no: int = 0
metric = Metric("Evaluation", beta=1.0)
# lines: List[str] = []
for batch in data_loader:
batch_no += 1
loss, tags = _predict_batch(self.__models, batch)
eval_loss += loss
for (sentence, sent_tags) in zip(batch, tags):
for (token, tag) in zip(sentence.tokens, sent_tags):
token: Token = token
token.add_tag("predicted", tag.value, tag.score)
# append both to file for evaluation
# 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:
# make list of gold tags
gold_tags = [(tag.tag, tag.text)
for tag in sentence.get_spans(self.tag_type)]
# make list of predicted tags
predicted_tags = [(tag.tag, tag.text)
for tag in sentence.get_spans("predicted")]
# check for true positives, false positives and false negatives
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, embedding_storage_mode)
eval_loss /= batch_no
detailed_result = (
f"\nMICRO_AVG: acc {metric.micro_avg_accuracy()} - f1-score {metric.micro_avg_f_score()}"
f"\nMACRO_AVG: acc {metric.macro_avg_accuracy()} - f1-score {metric.macro_avg_f_score()}"
)
for class_name in metric.get_classes():
detailed_result += (
f"\n{class_name:<10} tp: {metric.get_tp(class_name)} - fp: {metric.get_fp(class_name)} - "
f"fn: {metric.get_fn(class_name)} - tn: {metric.get_tn(class_name)} - precision: "
f"{metric.precision(class_name):.4f} - recall: {metric.recall(class_name):.4f} - "
f"accuracy: {metric.accuracy(class_name):.4f} - f1-score: "
f"{metric.f_score(class_name):.4f}")
result = Result(
main_score=metric.micro_avg_f_score(),
log_line=
f"{metric.precision()}\t{metric.recall()}\t{metric.micro_avg_f_score()}",
log_header="PRECISION\tRECALL\tF1",
detailed_results=detailed_result,
)
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
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)
from __future__ import absolute_import
def eval_flair_spans(data, predicted_list, batch_size, out_path=None):
metric = Metric('Evaluation')
mini_batch_size = batch_size
batches = [
data[x:x + mini_batch_size]
for x in range(0, len(data), mini_batch_size)
]
lines: List[str] = []
word_counter = 0
for batch in batches:
for sentence in batch:
for token in sentence.tokens:
tag = Label(predicted_list[word_counter])
word_counter += 1
token.add_tag_label('predicted', tag)
# append both to file for evaluation
eval_line = '{} {} {} {}\n'.format(token.text,
token.get_tag('ner').value,
tag.value, tag.score)
lines.append(eval_line)
lines.append('\n')
for sentence in batch:
# make list of gold tags
gold_tags = [(tag.tag, str(tag))
for tag in sentence.get_spans('ner')]
# make list of predicted tags
predicted_tags = [(tag.tag, str(tag))
for tag in sentence.get_spans('predicted')]
# check for true positives, false positives and false negatives
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)
# add metrics scores at the beginning of the file
lines.insert(0, str(metric) + "\n\n")
if out_path is not None:
# create folder for json and corresponding output
if not os.path.exists(os.path.dirname(out_path)):
try:
os.makedirs(os.path.dirname(out_path))
except OSError as exc: # Guard against race condition
if exc.errno != errno.EEXIST:
raise
with open(out_path, "w", encoding='utf-8') as outfile:
outfile.write(''.join(lines))
#
# esnWrapper.model.output_activation = output_activation_training
return metric
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: List[str] = []
batch_count: int = 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 = (
f"\nMICRO_AVG: acc {metric.micro_avg_accuracy()} - f1-score {metric.micro_avg_f_score()}"
f"\nMACRO_AVG: acc {metric.macro_avg_accuracy()} - f1-score {metric.macro_avg_f_score()}"
)
for class_name in metric.get_classes():
detailed_result += (
f"\n{class_name:<10} tp: {metric.get_tp(class_name)} - fp: {metric.get_fp(class_name)} - "
f"fn: {metric.get_fn(class_name)} - tn: {metric.get_tn(class_name)} - precision: "
f"{metric.precision(class_name):.4f} - recall: {metric.recall(class_name):.4f} - "
f"accuracy: {metric.accuracy(class_name):.4f} - f1-score: "
f"{metric.f_score(class_name):.4f}")
result = Result(
main_score=metric.micro_avg_f_score(),
log_line=
f"{metric.precision()}\t{metric.recall()}\t{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",
prediction_mode: bool = False,
) -> (Result, float):
eval_loss = 0
batch_no = 0
data_loader.assign_embeddings()
if out_path is not None:
outfile = open(out_path, "w", encoding="utf-8")
if not self.binary:
metric = Metric("Evaluation")
with torch.no_grad():
for batch in data_loader:
batch_no += 1
scores = self.forward(batch, prediction_mode=prediction_mode)
loss = self._calculate_loss(scores, batch, self.mask)
eval_loss += loss
if self.binary:
pdb.set_trace()
result = Result(
main_score=LF1,
log_line=f"\nUF1: {UF1} - LF1 {LF1}",
log_header="PRECISION\tRECALL\tF1",
detailed_results=f"\nUF1: {UF1} - LF1 {LF1}",
)
else:
# if prediction_mode:
# eval_loss, metric=self.dependency_evaluate(data_loader,out_path=out_path,prediction_mode=prediction_mode)
# return eval_loss, metric
# else:
tags, _ = self._obtain_labels(scores, batch)
for (sentence, sent_tags) in zip(batch, tags):
for (token, tag) in zip(sentence.tokens, sent_tags):
token: Token = token
token.add_tag_label("predicted", tag)
# append both to file for evaluation
eval_line = "{} {} {} {}\n".format(
token.text,
token.get_tag(self.tag_type).value,
tag.value,
tag.score,
)
# lines.append(eval_line)
if out_path is not None:
outfile.write(eval_line)
# lines.append("\n")
if out_path is not None:
outfile.write("\n")
for sentence in batch:
# make list of gold tags
gold_tags = [
(tag.tag, str(tag))
for tag in sentence.get_spans(self.tag_type)
]
# make list of predicted tags
predicted_tags = [
(tag.tag, str(tag))
for tag in sentence.get_spans("predicted")
]
# check for true positives, false positives and false negatives
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)
eval_loss /= batch_no
if out_path is not None:
outfile.close()
detailed_result = (
f"\nMICRO_AVG: acc {metric.micro_avg_accuracy()} - f1-score {metric.micro_avg_f_score()}"
f"\nMACRO_AVG: acc {metric.macro_avg_accuracy()} - f1-score {metric.macro_avg_f_score()}"
)
for class_name in metric.get_classes():
detailed_result += (
f"\n{class_name:<10} tp: {metric.get_tp(class_name)} - fp: {metric.get_fp(class_name)} - "
f"fn: {metric.get_fn(class_name)} - tn: {metric.get_tn(class_name)} - precision: "
f"{metric.precision(class_name):.4f} - recall: {metric.recall(class_name):.4f} - "
f"accuracy: {metric.accuracy(class_name):.4f} - f1-score: "
f"{metric.f_score(class_name):.4f}")
result = Result(
main_score=metric.micro_avg_f_score(),
log_line=
f"{metric.precision()}\t{metric.recall()}\t{metric.micro_avg_f_score()}",
log_header="PRECISION\tRECALL\tF1",
detailed_results=detailed_result,
)
return result, eval_loss
def evaluate(
self,
data_loader: DataLoader,
out_path: Path = None,
embedding_storage_mode: str = "none",
) -> (Result, float):
if type(out_path) == str:
out_path = Path(out_path)
with torch.no_grad():
eval_loss = 0
batch_no: int = 0
metric = Metric("Evaluation", beta=self.beta)
lines: List[str] = []
if self.use_crf:
transitions = self.transitions.detach().cpu().numpy()
else:
transitions = None
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(
feature=features,
batch_sentences=batch,
transitions=transitions,
get_all_tags=False,
)
eval_loss += loss
for (sentence, sent_tags) in zip(batch, tags):
for (token, tag) in zip(sentence.tokens, sent_tags):
token: Token = token
token.add_tag("predicted", tag.value, tag.score)
# append both to file for evaluation
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:
# make list of gold tags
gold_tags = [
(tag.tag, tag.text) for tag in sentence.get_spans(self.tag_type)
]
# make list of predicted tags
predicted_tags = [
(tag.tag, tag.text) for tag in sentence.get_spans("predicted")
]
# check for true positives, false positives and false negatives
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, embedding_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 = (
f"\nMICRO_AVG: acc {metric.micro_avg_accuracy():.4f} - f1-score {metric.micro_avg_f_score():.4f}"
f"\nMACRO_AVG: acc {metric.macro_avg_accuracy():.4f} - f1-score {metric.macro_avg_f_score():.4f}"
)
for class_name in metric.get_classes():
detailed_result += (
f"\n{class_name:<10} tp: {metric.get_tp(class_name)} - fp: {metric.get_fp(class_name)} - "
f"fn: {metric.get_fn(class_name)} - tn: {metric.get_tn(class_name)} - precision: "
f"{metric.precision(class_name):.4f} - recall: {metric.recall(class_name):.4f} - "
f"accuracy: {metric.accuracy(class_name):.4f} - f1-score: "
f"{metric.f_score(class_name):.4f}"
)
result = Result(
main_score=metric.micro_avg_f_score(),
log_line=f"{metric.precision():.4f}\t{metric.recall():.4f}\t{metric.micro_avg_f_score():.4f}",
log_header="PRECISION\tRECALL\tF1",
detailed_results=detailed_result,
)
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)
