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133 lines
4.4 KiB
Python
133 lines
4.4 KiB
Python
from rasa.nlu.emulators.luis import LUISEmulator
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def test_luis_request():
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em = LUISEmulator()
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norm = em.normalise_request_json({"text": ["arb text"]})
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assert norm == {"text": "arb text", "time": None}
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def test_luis_response():
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em = LUISEmulator()
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data = {
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"text": "I want italian food",
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"intent": {"name": "restaurant_search", "confidence": 0.737014589341683},
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"intent_ranking": [
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{"confidence": 0.737014589341683, "name": "restaurant_search"},
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{"confidence": 0.11605464483122209, "name": "goodbye"},
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{"confidence": 0.08816417744097163, "name": "greet"},
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{"confidence": 0.058766588386123204, "name": "affirm"},
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],
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"entities": [
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{
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"entity": "cuisine",
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"value": "italian",
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"role": "roleCuisine",
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"extractor": "SpacyEntityExtractor",
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"start": 7,
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"end": 14,
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}
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],
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}
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norm = em.normalise_response_json(data)
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assert norm == {
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"query": data["text"],
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"prediction": {
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"normalizedQuery": data["text"],
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"topIntent": "restaurant_search",
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"intents": {
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"restaurant_search": {"score": 0.737014589341683},
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"goodbye": {"score": 0.11605464483122209},
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"greet": {"score": 0.08816417744097163},
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"affirm": {"score": 0.058766588386123204},
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},
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"entities": {
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"roleCuisine": ["italian"],
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"$instance": {
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"roleCuisine": [
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{
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"role": "roleCuisine",
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"type": "cuisine",
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"text": "italian",
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"startIndex": 7,
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"length": len("italian"),
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"score": None,
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"modelType": "SpacyEntityExtractor",
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}
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]
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},
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},
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},
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}
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def test_luis_response_without_role():
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em = LUISEmulator()
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data = {
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"text": "I want italian food",
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"intent": {"name": "restaurant_search", "confidence": 0.737014589341683},
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"intent_ranking": [
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{"confidence": 0.737014589341683, "name": "restaurant_search"}
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],
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"entities": [{"entity": "cuisine", "value": "italian"}],
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}
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norm = em.normalise_response_json(data)
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assert norm == {
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"query": data["text"],
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"prediction": {
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"normalizedQuery": data["text"],
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"topIntent": "restaurant_search",
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"intents": {"restaurant_search": {"score": 0.737014589341683}},
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"entities": {
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"cuisine": ["italian"],
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"$instance": {
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"cuisine": [
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{
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"role": None,
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"type": "cuisine",
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"text": "italian",
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"startIndex": None,
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"length": None,
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"score": None,
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"modelType": None,
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}
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]
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},
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},
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},
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}
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def test_luis_response_without_intent_ranking():
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em = LUISEmulator()
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data = {
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"text": "I want italian food",
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"intent": {"name": "restaurant_search", "confidence": 0.737014589341683},
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"entities": [{"entity": "cuisine", "value": "italian", "role": "roleCuisine"}],
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}
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norm = em.normalise_response_json(data)
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assert norm == {
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"query": data["text"],
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"prediction": {
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"normalizedQuery": data["text"],
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"topIntent": "restaurant_search",
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"intents": {"restaurant_search": {"score": 0.737014589341683}},
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"entities": {
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"roleCuisine": ["italian"],
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"$instance": {
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"roleCuisine": [
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{
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"role": "roleCuisine",
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"type": "cuisine",
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"text": "italian",
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"startIndex": None,
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"length": None,
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"score": None,
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"modelType": None,
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}
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]
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},
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},
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},
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}
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