def _test(config: Config, model: AttentionNestedNERModel):
"""
:param config: Config file pass to test_one_sentence, find_entities, evaluate functions.
:param model: model after load parameters.
:return:
"""
# initialize the confusion matrix.
config.metric_dicts = [{"TP": 0, "FP": 0, "FN": 0} for i in range(len(config.labels))]
# start test.
config.result_file = open(config.output_path, "w")
for test_index in range(len(config.test_data)):
output_sent(config.result_file, config.test_str[test_index])
word_ids = config.test_data[test_index]
gt_labels = config.test_label[test_index]
gt_entities = []
for nested_level_index in range(len(gt_labels)): # all nested levels.
# for nested_level_index in range(config.max_nested_level): # todo attention this gt label!
output_level(config.result_file, nested_level_index, config.bio_labels, gt_labels[nested_level_index], "gt")
gt_entities, added_flag = process_one_nested_level_predict_result(config, gt_labels[nested_level_index],
gt_entities)
if added_flag is False:
break
predict_candidates = _test_one_sentence(config, model, word_ids)
evaluate(config, predict_candidates, gt_entities)
output_summary(config.result_file, config.test_str[test_index], config.labels, predict_candidates, gt_entities)
# print result
print(get_metrics(config))
config.result_file.close()
return
def _test(config: Config, model: AttentionNestedNERModel):
"""
:param config: Config file pass to test_one_sentence, find_entities, evaluate functions.
:param model: model after load parameters.
:return:
"""
# initialize the confusion matrix.
metric_dicts = []
one_metric_dicts = [{"TP": 0, "FP": 0, "FN": 0} for i in range(len(config.labels))]
for i in range(config.max_nested_level):
metric_dicts.append(deepcopy(one_metric_dicts))
config.metric_dicts = metric_dicts
# start test.
# config.result_file = open(config.output_path, "w")
for test_index in range(len(config.test_data)):
# output_sent(config.result_file, config.test_str[test_index])
word_ids = config.test_data[test_index]
gt_labels = config.test_label[test_index]
gt_entities = [[] for i in range(config.max_nested_level)]
for nested_level_index in range(len(gt_labels)): # all nested levels.
one_layer_entity = process_one_nested_level_predict_result_layer_by_layer(config, gt_labels[
nested_level_index])
gt_entities[nested_level_index].extend(one_layer_entity)
predict_candidates = _test_one_sentence_layer_by_layer(config, model, word_ids)
evaluate_layer_by_layer(config, predict_candidates, gt_entities)
# output_summary(config.result_file, config.test_str[test_index], config.labels, predict_candidates, gt_entities)
# print result
for i in range(config.max_nested_level):
print("Layer: ", i + 1)
print(get_metrics_one_layer(config, config.metric_dicts[i]))
# config.result_file.close()
return