def test_duckling_patching():
entities = [[
{
"start": 37,
"end": 56,
"value": "near Alexanderplatz",
"entity": "location",
"extractor": "ner_crf"
},
{
"start": 57,
"end": 64,
"value": "tonight",
"entity": "Time",
"extractor": "ner_duckling_http"
}
]]
patched = [[
{
"start": 37,
"end": 56,
"value": "near Alexanderplatz",
"entity": "location",
"extractor": "ner_crf"
}
]]
assert remove_duckling_entities(entities) == patched
def test_duckling_patching():
entities = [[
{
"start": 37,
"end": 56,
"value": "near Alexanderplatz",
"entity": "location",
"extractor": "ner_crf"
},
{
"start": 57,
"end": 64,
"value": "tonight",
"entity": "Time",
"extractor": "ner_duckling"
}
]]
patched = [[
{
"start": 37,
"end": 56,
"value": "near Alexanderplatz",
"entity": "location",
"extractor": "ner_crf"
}
]]
assert remove_duckling_entities(entities) == patched
def run_evaluation(data_path, model,
report_folder=None,
successes_filename=None,
errors_filename='errors.json',
confmat_filename=None,
intent_hist_filename=None,
component_builder=None): # pragma: no cover
"""Evaluate intent classification and entity extraction."""
# get the metadata config from the package data
if isinstance(model, Interpreter):
interpreter = model
else:
interpreter = Interpreter.load(model, component_builder)
test_data = training_data.load_data(data_path,
interpreter.model_metadata.language)
extractors = get_entity_extractors(interpreter)
entity_predictions, tokens = get_entity_predictions(interpreter,
test_data)
if duckling_extractors.intersection(extractors):
entity_predictions = remove_duckling_entities(entity_predictions)
extractors = remove_duckling_extractors(extractors)
result = {
"intent_evaluation": None,
"entity_evaluation": None
}
if report_folder:
utils.create_dir(report_folder)
if is_intent_classifier_present(interpreter):
intent_targets = get_intent_targets(test_data)
intent_results = get_intent_predictions(
intent_targets, interpreter, test_data)
logger.info("Intent evaluation results:")
result['intent_evaluation'] = evaluate_intents(intent_results,
report_folder,
successes_filename,
errors_filename,
confmat_filename,
intent_hist_filename)
if extractors:
entity_targets = get_entity_targets(test_data)
logger.info("Entity evaluation results:")
result['entity_evaluation'] = evaluate_entities(entity_targets,
entity_predictions,
tokens,
extractors,
report_folder)
return result
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))