def test_train_load_predict_loop(
default_model_storage: ModelStorage,
default_execution_context: ExecutionContext,
mitie_model: MitieModel,
mitie_tokenizer: MitieTokenizer,
):
resource = Resource("mitie_classifier")
component = MitieIntentClassifier.create(
MitieIntentClassifier.get_default_config(),
default_model_storage,
resource,
default_execution_context,
)
training_data = rasa.shared.nlu.training_data.loading.load_data(
"data/examples/rasa/demo-rasa.yml")
# Tokenize message as classifier needs that
mitie_tokenizer.process_training_data(training_data)
component.train(training_data, mitie_model)
component = MitieIntentClassifier.load(
MitieIntentClassifier.get_default_config(),
default_model_storage,
resource,
default_execution_context,
)
test_message = Message({TEXT: "hi"})
mitie_tokenizer.process([test_message])
component.process([test_message], mitie_model)
assert test_message.data[INTENT][INTENT_NAME_KEY] == "greet"
assert test_message.data[INTENT][PREDICTED_CONFIDENCE_KEY] > 0
def test_mitie_featurizer(
create: Callable[[Dict[Text, Any]], MitieFeaturizer],
mitie_model: MitieModel,
mitie_tokenizer: MitieTokenizer,
):
featurizer = create({"alias": "mitie_featurizer"})
sentence = "Hey how are you today"
message = Message(data={TEXT: sentence})
mitie_tokenizer.process([message])
tokens = message.get(TOKENS_NAMES[TEXT])
seq_vec, sen_vec = featurizer.features_for_tokens(
tokens, mitie_model.word_feature_extractor)
expected = np.array([
0.00000000e00, -5.12735510e00, 4.39929873e-01, -5.60760403e00,
-8.26445103e00
])
expected_cls = np.array(
[0.0, -4.4551446, 0.26073121, -1.46632245, -1.84205751])
assert 6 == len(seq_vec) + len(sen_vec)
assert np.allclose(seq_vec[0][:5], expected, atol=1e-5)
assert np.allclose(sen_vec[-1][:5], expected_cls, atol=1e-5)
def test_load_from_untrained(
default_model_storage: ModelStorage,
default_execution_context: ExecutionContext,
mitie_model: MitieModel,
mitie_tokenizer: MitieTokenizer,
):
resource = Resource("some_resource")
component = MitieIntentClassifier.load(
MitieIntentClassifier.get_default_config(),
default_model_storage,
resource,
default_execution_context,
)
test_message = Message({TEXT: "hi"})
mitie_tokenizer.process([test_message])
component.process([test_message], mitie_model)
assert test_message.data[INTENT] == {"name": None, "confidence": 0.0}
def test_load_from_untrained_but_with_resource_existing(
default_model_storage: ModelStorage,
default_execution_context: ExecutionContext,
mitie_model: MitieModel,
mitie_tokenizer: MitieTokenizer,
):
resource = Resource("some_resource")
with default_model_storage.write_to(resource):
# This makes sure the directory exists but the model file itself doesn't
pass
component = MitieIntentClassifier.load(
MitieIntentClassifier.get_default_config(),
default_model_storage,
resource,
default_execution_context,
)
test_message = Message({TEXT: "hi"})
mitie_tokenizer.process([test_message])
component.process([test_message], mitie_model)
assert test_message.data[INTENT] == {"name": None, "confidence": 0.0}