def duckling_interpreter(component_builder, tmpdir_factory):
conf = RasaNLUModelConfig(
{"pipeline": [{"name": "DucklingHTTPExtractor"}]}
)
return utilities.interpreter_for(
component_builder,
data="./data/examples/rasa/demo-rasa.json",
path=tmpdir_factory.mktemp("projects").strpath,
config=conf)
def test_example_component(component_builder, tmpdir_factory):
conf = RasaNLUModelConfig(
{"pipeline": [{
"name": "tests.nlu.example_component.MyComponent"
}]})
interpreter = utilities.interpreter_for(
component_builder,
data="./data/examples/rasa/demo-rasa.json",
path=tmpdir_factory.mktemp("projects").strpath,
config=conf)
r = interpreter.parse("test")
assert r is not None
def test_interpreter(pipeline_template, component_builder, tmpdir):
test_data = "data/examples/rasa/demo-rasa.json"
_conf = utilities.base_test_conf(pipeline_template)
_conf["data"] = test_data
td = training_data.load_data(test_data)
interpreter = utilities.interpreter_for(
component_builder, "data/examples/rasa/demo-rasa.json", tmpdir.strpath, _conf
)
texts = ["good bye", "i am looking for an indian spot"]
for text in texts:
result = interpreter.parse(text, time=None)
assert result["text"] == text
assert not result["intent"]["name"] or result["intent"]["name"] in td.intents
assert result["intent"]["confidence"] >= 0
# Ensure the model doesn't detect entity types that are not present
# Models on our test data set are not stable enough to
# require the exact entities to be found
for entity in result["entities"]:
assert entity["entity"] in td.entities
