def test_entity(): #load model interpreter = Interpreter.load(model_location) duckling_extractors = {"ner_duckling", "ner_duckling_http"} #create dictionary of entity results entity_results = defaultdict(lambda: defaultdict(list)) #get extractors of the interpreter extractors = evaluate.get_entity_extractors(interpreter) #get entity predictions and tokens entity_predictions, tokens = evaluate.get_entity_predictions( interpreter, testing_data) # Create classification report if duckling_extractors.intersection(extractors): entity_predictions = evaluate.remove_duckling_entities( entity_predictions) extractors = evaluate.remove_duckling_extractors(extractors) if not extractors: return entity_results #get entity_targets entity_targets = evaluate.get_entity_targets(testing_data) #get aligned_prections aligned_predictions = evaluate.align_all_entity_predictions( entity_targets, entity_predictions, tokens, extractors) merged_targets = evaluate.merge_labels(aligned_predictions) merged_targets = evaluate.substitute_labels(merged_targets, "O", "no_entity") for extractor in extractors: merged_predictions = evaluate.merge_labels(aligned_predictions, extractor) merged_predictions = evaluate.substitute_labels( merged_predictions, "O", "no_entity") report, precision, f1, accuracy = evaluate.get_evaluation_metrics( merged_targets, merged_predictions) entity_results[extractor]["Accuracy"].append(accuracy) entity_results[extractor]["F1-score"].append(f1) entity_results[extractor]["Precision"].append(precision) print("entity_results: {}\n".format(entity_results), "Classification report: \n{}".format(report))
def test_label_replacement(): original_labels = ["O", "location"] target_labels = ["no_entity", "location"] assert substitute_labels(original_labels, "O", "no_entity") == target_labels
def test_label_replacement(): original_labels = ["O", "location"] target_labels = ["no_entity", "location"] assert substitute_labels(original_labels, "O", "no_entity") == target_labels