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 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( 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(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)