def test_train_selector(pipeline, component_builder, tmpdir):
# use data that include some responses
training_data = load_data("data/examples/rasa/demo-rasa.md")
training_data_responses = load_data(
"data/examples/rasa/demo-rasa-responses.md")
training_data = training_data.merge(training_data_responses)
nlu_config = RasaNLUModelConfig({"language": "en", "pipeline": pipeline})
trainer = Trainer(nlu_config)
trainer.train(training_data)
persisted_path = trainer.persist(tmpdir)
assert trainer.pipeline
loaded = Interpreter.load(persisted_path, component_builder)
parsed = loaded.parse("hello")
assert loaded.pipeline
assert parsed is not None
assert (parsed.get("response_selector").get("all_retrieval_intents")) == [
"chitchat"
]
assert (parsed.get("response_selector").get("default").get("response").get(
"intent_response_key")) is not None
assert (parsed.get("response_selector").get("default").get("response").get(
"response_templates")) is not None
ranking = parsed.get("response_selector").get("default").get("ranking")
assert ranking is not None
for rank in ranking:
assert rank.get("confidence") is not None
assert rank.get("intent_response_key") is not None
def test_train_selector(pipeline, component_builder, tmpdir):
# use data that include some responses
td = load_data("data/examples/rasa/demo-rasa.md")
td_responses = load_data("data/examples/rasa/demo-rasa-responses.md")
td = td.merge(td_responses)
nlu_config = RasaNLUModelConfig({"language": "en", "pipeline": pipeline})
trainer = Trainer(nlu_config)
trainer.train(td)
persisted_path = trainer.persist(tmpdir)
assert trainer.pipeline
loaded = Interpreter.load(persisted_path, component_builder)
parsed = loaded.parse("hello")
assert loaded.pipeline
assert parsed is not None
assert (parsed.get(RESPONSE_SELECTOR_PROPERTY_NAME).get("default").get(
"full_retrieval_intent")) is not None
ranking = parsed.get(RESPONSE_SELECTOR_PROPERTY_NAME).get("default").get(
"ranking")
assert ranking is not None
for rank in ranking:
assert rank.get("name") is not None
assert rank.get("confidence") is not None
assert rank.get("full_retrieval_intent") is not None
def test_train_response_selector(component_builder, tmpdir):
td = load_data("data/examples/rasa/demo-rasa.md")
td_responses = load_data("data/examples/rasa/demo-rasa-responses.md")
td = td.merge(td_responses)
td.fill_response_phrases()
nlu_config = config.load(
"sample_configs/config_embedding_intent_response_selector.yml"
)
trainer = Trainer(nlu_config)
trainer.train(td)
persisted_path = trainer.persist(tmpdir)
assert trainer.pipeline
loaded = Interpreter.load(persisted_path, component_builder)
assert loaded.pipeline
assert loaded.parse("hello") is not None
assert loaded.parse("Hello today is Monday, again!") is not None
def test_train_selector(pipeline, component_builder, tmpdir):
# use data that include some responses
td = load_data("data/examples/rasa/demo-rasa.md")
td_responses = load_data("data/examples/rasa/demo-rasa-responses.md")
td = td.merge(td_responses)
td.fill_response_phrases()
nlu_config = RasaNLUModelConfig({"language": "en", "pipeline": pipeline})
trainer = Trainer(nlu_config)
trainer.train(td)
persisted_path = trainer.persist(tmpdir)
assert trainer.pipeline
loaded = Interpreter.load(persisted_path, component_builder)
assert loaded.pipeline
assert loaded.parse("hello") is not None
