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)