def test_train_load_predict_loop(
default_model_storage: ModelStorage,
default_execution_context: ExecutionContext,
mitie_model: MitieModel,
):
resource = Resource("mitie_classifier")
component = MitieIntentClassifierGraphComponent.create(
MitieIntentClassifierGraphComponent.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")
tokenizer = MitieTokenizer()
# Tokenize message as classifier needs that
tokenizer.train(training_data)
component.train(training_data, mitie_model)
component = MitieIntentClassifierGraphComponent.load(
MitieIntentClassifierGraphComponent.get_default_config(),
default_model_storage,
resource,
default_execution_context,
)
test_message = Message({TEXT: "hi"})
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_custom_intent_symbol(text, expected_tokens):
component_config = {
"intent_tokenization_flag": True,
"intent_split_symbol": "+"
}
tk = MitieTokenizer(component_config)
message = Message(text)
message.set(INTENT, text)
tk.train(TrainingData([message]))
assert [t.text
for t in message.get(TOKENS_NAMES[INTENT])] == expected_tokens