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"]
]
}