def test_emulators_can_handle_missing_data(): from rasa_nlu.emulators.luis import LUISEmulator em = LUISEmulator() norm = em.normalise_response_json( {"text": "this data doesn't contain an intent result"}) assert norm["topScoringIntent"] is None assert norm["intents"] == []
def test_luis_response(): from rasa_nlu.emulators.luis import LUISEmulator em = LUISEmulator() data = { "text": "I want italian food", "intent": "inform", "entities": [{ "entity": "cuisine", "value": "italian" }] } norm = em.normalise_response_json(data) assert norm == { "query": data["text"], "topScoringIntent": { "intent": "inform", "score": None }, "entities": [{ "entity": e["value"], "type": e["entity"], "startIndex": None, "endIndex": None, "score": None } for e in data["entities"]] }
def test_emulators_can_handle_missing_data(): from rasa_nlu.emulators.luis import LUISEmulator em = LUISEmulator() norm = em.normalise_response_json({ "text": "this data doesn't contain an intent result"}) assert norm["topScoringIntent"] is None assert norm["intents"] == []
def test_luis_response(): from rasa_nlu.emulators.luis import LUISEmulator em = LUISEmulator() data = { "text": "I want italian food", "intent": { "name": "restaurant_search", "confidence": 0.737014589341683 }, "intent_ranking": [{ "confidence": 0.737014589341683, "name": "restaurant_search" }, { "confidence": 0.11605464483122209, "name": "goodbye" }, { "confidence": 0.08816417744097163, "name": "greet" }, { "confidence": 0.058766588386123204, "name": "affirm" }], "entities": [{ "entity": "cuisine", "value": "italian" }] } norm = em.normalise_response_json(data) assert norm == { "query": data["text"], "topScoringIntent": { "intent": "restaurant_search", "score": 0.737014589341683 }, "intents": [{ "intent": "restaurant_search", "score": 0.737014589341683 }, { "intent": "goodbye", "score": 0.11605464483122209 }, { "intent": "greet", "score": 0.08816417744097163 }, { "intent": "affirm", "score": 0.058766588386123204 }], "entities": [{ "entity": e["value"], "type": e["entity"], "startIndex": None, "endIndex": None, "score": None } for e in data["entities"]] }
def test_luis_response(): from rasa_nlu.emulators.luis import LUISEmulator em = LUISEmulator() data = { "text": "I want italian food", "intent": {"name": "restaurant_search", "confidence": 0.737014589341683}, "intent_ranking": [ { "confidence": 0.737014589341683, "name": "restaurant_search" }, { "confidence": 0.11605464483122209, "name": "goodbye" }, { "confidence": 0.08816417744097163, "name": "greet" }, { "confidence": 0.058766588386123204, "name": "affirm" } ], "entities": [{"entity": "cuisine", "value": "italian"}] } norm = em.normalise_response_json(data) assert norm == { "query": data["text"], "topScoringIntent": { "intent": "restaurant_search", "score": 0.737014589341683 }, "intents": [ { "intent": "restaurant_search", "score": 0.737014589341683 }, { "intent": "goodbye", "score": 0.11605464483122209 }, { "intent": "greet", "score": 0.08816417744097163 }, { "intent": "affirm", "score": 0.058766588386123204 } ], "entities": [ { "entity": e["value"], "type": e["entity"], "startIndex": None, "endIndex": None, "score": None } for e in data["entities"] ] }