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