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(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_be_deserializable_when_empty(self):
# Given
engine_dict = {
"unit_name": "nlu_engine",
"dataset_metadata": None,
"config": None,
"intent_parsers": [],
"model_version": snips_nlu.__model_version__,
"training_package_version": snips_nlu.__version__
}
# When
engine = SnipsNLUEngine.from_dict(engine_dict)
# Then
self.assertFalse(engine.fitted)
def test_should_be_deserializable_when_empty(self):
# Given
engine_dict = {
"unit_name": "nlu_engine",
"dataset_metadata": None,
"config": None,
"intent_parsers": [],
"model_version": snips_nlu.version.__model_version__,
"training_package_version": snips_nlu.__version__
}
# When
engine = SnipsNLUEngine.from_dict(engine_dict)
# Then
self.assertFalse(engine.fitted)
def test_should_be_deserializable(self):
# When
class TestIntentParser1Config(ProcessingUnitConfig):
unit_name = "test_intent_parser1"
def to_dict(self):
return {"unit_name": self.unit_name}
@classmethod
def from_dict(cls, obj_dict):
return TestIntentParser1Config()
class TestIntentParser1(IntentParser):
unit_name = "test_intent_parser1"
config_type = TestIntentParser1Config
def fit(self, dataset, force_retrain):
self._fitted = True
return self
@property
def fitted(self):
return hasattr(self, '_fitted') and self._fitted
def parse(self, text, intents):
return empty_result(text)
def to_dict(self):
return {
"unit_name": self.unit_name,
}
@classmethod
def from_dict(cls, unit_dict):
config = cls.config_type()
return TestIntentParser1(config)
class TestIntentParser2Config(ProcessingUnitConfig):
unit_name = "test_intent_parser2"
def to_dict(self):
return {"unit_name": self.unit_name}
@classmethod
def from_dict(cls, obj_dict):
return TestIntentParser2Config()
class TestIntentParser2(IntentParser):
unit_name = "test_intent_parser2"
config_type = TestIntentParser2Config
def fit(self, dataset, force_retrain):
self._fitted = True
return self
@property
def fitted(self):
return hasattr(self, '_fitted') and self._fitted
def parse(self, text, intents):
return empty_result(text)
def to_dict(self):
return {
"unit_name": self.unit_name,
}
@classmethod
def from_dict(cls, unit_dict):
config = cls.config_type()
return TestIntentParser2(config)
register_processing_unit(TestIntentParser1)
register_processing_unit(TestIntentParser2)
dataset_metadata = {
"language_code": "en",
"entities": {
"Temperature": {
"automatically_extensible": True,
"utterances": {
"boiling": "hot",
"cold": "cold",
"hot": "hot",
"iced": "cold"
}
}
},
"slot_name_mappings": {
"MakeCoffee": {
"number_of_cups": "snips/number"
},
"MakeTea": {
"beverage_temperature": "Temperature",
"number_of_cups": "snips/number"
}
},
}
parser1_config = TestIntentParser1Config()
parser2_config = TestIntentParser2Config()
engine_config = NLUEngineConfig([parser1_config, parser2_config])
engine_dict = {
"unit_name":
"nlu_engine",
"dataset_metadata":
dataset_metadata,
"config":
engine_config.to_dict(),
"intent_parsers": [
{
"unit_name": "test_intent_parser1"
},
{
"unit_name": "test_intent_parser2"
},
],
"model_version":
snips_nlu.__model_version__,
"training_package_version":
snips_nlu.__version__
}
engine = SnipsNLUEngine.from_dict(engine_dict)
# Then
parser1_config = TestIntentParser1Config()
parser2_config = TestIntentParser2Config()
expected_engine_config = NLUEngineConfig(
[parser1_config, parser2_config]).to_dict()
# pylint:disable=protected-access
self.assertDictEqual(engine._dataset_metadata, dataset_metadata)
# pylint:enable=protected-access
self.assertDictEqual(engine.config.to_dict(), expected_engine_config)
def test_should_be_deserializable(self):
# When
class TestIntentParser1Config(ProcessingUnitConfig):
unit_name = "test_intent_parser1"
def to_dict(self):
return {"unit_name": self.unit_name}
@classmethod
def from_dict(cls, obj_dict):
return TestIntentParser1Config()
class TestIntentParser1(IntentParser):
unit_name = "test_intent_parser1"
config_type = TestIntentParser1Config
def fit(self, dataset, force_retrain):
self._fitted = True
return self
@property
def fitted(self):
return hasattr(self, '_fitted') and self._fitted
def parse(self, text, intents):
return empty_result(text)
def to_dict(self):
return {
"unit_name": self.unit_name,
}
@classmethod
def from_dict(cls, unit_dict):
config = cls.config_type()
return TestIntentParser1(config)
class TestIntentParser2Config(ProcessingUnitConfig):
unit_name = "test_intent_parser2"
def to_dict(self):
return {"unit_name": self.unit_name}
@classmethod
def from_dict(cls, obj_dict):
return TestIntentParser2Config()
class TestIntentParser2(IntentParser):
unit_name = "test_intent_parser2"
config_type = TestIntentParser2Config
def fit(self, dataset, force_retrain):
self._fitted = True
return self
@property
def fitted(self):
return hasattr(self, '_fitted') and self._fitted
def parse(self, text, intents):
return empty_result(text)
def to_dict(self):
return {
"unit_name": self.unit_name,
}
@classmethod
def from_dict(cls, unit_dict):
config = cls.config_type()
return TestIntentParser2(config)
register_processing_unit(TestIntentParser1)
register_processing_unit(TestIntentParser2)
dataset_metadata = {
"language_code": "en",
"entities": {
"Temperature": {
"automatically_extensible": True,
"utterances": {
"boiling": "hot",
"cold": "cold",
"hot": "hot",
"iced": "cold"
}
}
},
"slot_name_mappings": {
"MakeCoffee": {
"number_of_cups": "snips/number"
},
"MakeTea": {
"beverage_temperature": "Temperature",
"number_of_cups": "snips/number"
}
},
}
parser1_config = TestIntentParser1Config()
parser2_config = TestIntentParser2Config()
engine_config = NLUEngineConfig([parser1_config, parser2_config])
engine_dict = {
"unit_name": "nlu_engine",
"dataset_metadata": dataset_metadata,
"config": engine_config.to_dict(),
"intent_parsers": [
{"unit_name": "test_intent_parser1"},
{"unit_name": "test_intent_parser2"},
],
"model_version": snips_nlu.version.__model_version__,
"training_package_version": snips_nlu.__version__
}
engine = SnipsNLUEngine.from_dict(engine_dict)
# Then
parser1_config = TestIntentParser1Config()
parser2_config = TestIntentParser2Config()
expected_engine_config = NLUEngineConfig(
[parser1_config, parser2_config]).to_dict()
# pylint:disable=protected-access
self.assertDictEqual(engine._dataset_metadata, dataset_metadata)
# pylint:enable=protected-access
self.assertDictEqual(engine.config.to_dict(), expected_engine_config)
