def test_model_accuracies_are_similar_before_and_after_caching(
kwik_e_mart_app_path):
# clear model cache
model_cache_path = MODEL_CACHE_PATH.format(app_path=kwik_e_mart_app_path)
try:
shutil.rmtree(MODEL_CACHE_PATH.format(app_path=kwik_e_mart_app_path))
except FileNotFoundError:
pass
# Make sure no cache exists
assert os.path.exists(model_cache_path) is False
nlp = NaturalLanguageProcessor(kwik_e_mart_app_path)
nlp.build(incremental=True)
nlp.dump()
intent_eval = nlp.domains["store_info"].intent_classifier.evaluate()
entity_eval = (nlp.domains["store_info"].intents["get_store_hours"].
entity_recognizer.evaluate())
intent_accuracy_no_cache = intent_eval.get_accuracy()
entity_accuracy_no_cache = entity_eval.get_accuracy()
example_cache = os.listdir(
MODEL_CACHE_PATH.format(app_path=kwik_e_mart_app_path))[0]
nlp = NaturalLanguageProcessor(kwik_e_mart_app_path)
nlp.load(example_cache)
# make sure cache exists
assert os.path.exists(model_cache_path) is True
intent_eval = nlp.domains["store_info"].intent_classifier.evaluate()
entity_eval = (nlp.domains["store_info"].intents["get_store_hours"].
entity_recognizer.evaluate())
intent_accuracy_cached = intent_eval.get_accuracy()
entity_accuracy_cached = entity_eval.get_accuracy()
assert intent_accuracy_no_cache == intent_accuracy_cached
assert entity_accuracy_no_cache == entity_accuracy_cached
def kwik_e_mart_nlp(kwik_e_mart_app_path):
"""Provides a built processor instance"""
nlp = NaturalLanguageProcessor(app_path=kwik_e_mart_app_path)
nlp.build()
nlp.dump()
return nlp
def home_assistant_nlp(home_assistant_app_path):
"""Provides a built processor instance"""
nlp = NaturalLanguageProcessor(app_path=home_assistant_app_path)
nlp.build()
nlp.dump()
return nlp
def food_ordering_nlp(food_ordering_app_path):
"""Provides a built processor instance"""
nlp = NaturalLanguageProcessor(app_path=food_ordering_app_path)
nlp.build()
nlp.dump()
return nlp