def test_should_parse_after_deserialization(self):
# Given
dataset = BEVERAGE_DATASET
engine = SnipsNLUEngine().fit(dataset)
input_ = "Give me 3 cups of hot tea please"
# When
engine_dict = engine.to_dict()
deserialized_engine = SnipsNLUEngine.from_dict(engine_dict)
result = deserialized_engine.parse(input_)
# Then
msg = "SnipsNLUEngine dict should be json serializable to utf-8"
with self.fail_if_exception(msg):
json.dumps(engine_dict).encode("utf-8")
expected_slots = [
resolved_slot({
START: 8,
END: 9
}, '3', {
'kind': 'Number',
'value': 3.0
}, 'snips/number', 'number_of_cups'),
custom_slot(
unresolved_slot({
START: 18,
END: 21
}, 'hot', 'Temperature', 'beverage_temperature'))
]
self.assertEqual(result[RES_INPUT], input_)
self.assertEqual(result[RES_INTENT][RES_INTENT_NAME], 'MakeTea')
self.assertListEqual(result[RES_SLOTS], expected_slots)
def test_should_parse_after_deserialization_from_dir(self):
# Given
dataset = BEVERAGE_DATASET
engine = SnipsNLUEngine().fit(dataset)
input_ = "Give me 3 cups of hot tea please"
# When
engine.persist(self.tmp_file_path)
deserialized_engine = SnipsNLUEngine.from_path(self.tmp_file_path)
result = deserialized_engine.parse(input_)
# Then
expected_slots = [
resolved_slot({
START: 8,
END: 9
}, "3", {
"kind": "Number",
"value": 3.0
}, "snips/number", "number_of_cups"),
custom_slot(
unresolved_slot({
START: 18,
END: 21
}, "hot", "Temperature", "beverage_temperature"))
]
self.assertEqual(result[RES_INPUT], input_)
self.assertEqual(result[RES_INTENT][RES_INTENT_NAME], "MakeTea")
self.assertListEqual(result[RES_SLOTS], expected_slots)
def test_should_parse_after_deserialization(self):
# Given
dataset = BEVERAGE_DATASET
engine = SnipsNLUEngine().fit(dataset)
input_ = "Give me 3 cups of hot tea please"
# When
engine_dict = engine.to_dict()
deserialized_engine = SnipsNLUEngine.from_dict(engine_dict)
result = deserialized_engine.parse(input_)
# Then
msg = "SnipsNLUEngine dict should be json serializable to utf-8"
with self.fail_if_exception(msg):
json.dumps(engine_dict).encode("utf-8")
expected_slots = [
resolved_slot({START: 8, END: 9}, '3',
{'kind': 'Number', 'value': 3.0},
'snips/number', 'number_of_cups'),
custom_slot(
unresolved_slot({START: 18, END: 21}, 'hot', 'Temperature',
'beverage_temperature'))
]
self.assertEqual(result[RES_INPUT], input_)
self.assertEqual(result[RES_INTENT][RES_INTENT_NAME], 'MakeTea')
self.assertListEqual(result[RES_SLOTS], expected_slots)
def test_should_parse_after_deserialization_from_dir(self):
# Given
dataset_stream = io.StringIO("""
---
type: intent
name: MakeTea
utterances:
- make me a [beverage_temperature:Temperature](hot) cup of tea
- make me [number_of_cups:snips/number](five) tea cups
- i want [number_of_cups] cups of [beverage_temperature](boiling hot) tea pls
- can you prepare [number_of_cups] cup of [beverage_temperature](cold) tea ?
---
type: intent
name: MakeCoffee
utterances:
- make me [number_of_cups:snips/number](one) cup of coffee please
- brew [number_of_cups] cups of coffee
- can you prepare [number_of_cups] cup of coffee""")
dataset = Dataset.from_yaml_files("en", [dataset_stream]).json
shared = self.get_shared_data(dataset)
engine = SnipsNLUEngine(**shared).fit(dataset)
text = "Give me 3 cups of hot tea please"
# When
engine.persist(self.tmp_file_path)
deserialized_engine = SnipsNLUEngine.from_path(self.tmp_file_path)
result = deserialized_engine.parse(text)
# Then
expected_slots = [
resolved_slot({
START: 8,
END: 9
}, "3", {
"kind": "Number",
"value": 3.0
}, "snips/number", "number_of_cups"),
custom_slot(
unresolved_slot({
START: 18,
END: 21
}, "hot", "Temperature", "beverage_temperature"))
]
self.assertEqual(result[RES_INPUT], text)
self.assertEqual(result[RES_INTENT][RES_INTENT_NAME], "MakeTea")
self.assertListEqual(result[RES_SLOTS], expected_slots)