def test_should_be_deserializable_when_fitted_without_slots(self):
# Given
dataset = {
"language": "en",
"intents": {
"intent1": {
"utterances": [{
"data": [{
"text":
"This is an utterance without "
"slots"
}]
}]
}
},
"entities": {}
}
shared = self.get_shared_data(dataset)
slot_filler = CRFSlotFiller(**shared)
slot_filler.fit(dataset, intent="intent1")
slot_filler.persist(self.tmp_file_path)
loaded_slot_filler = CRFSlotFiller.from_path(self.tmp_file_path,
**shared)
# When
slots = loaded_slot_filler.get_slots(
"This is an utterance without slots")
# Then
self.assertListEqual([], slots)
def test_should_be_serializable_before_fit(self):
# Given
features_factories = [{
"factory_name": ShapeNgramFactory.name,
"args": {
"n": 1
},
"offsets": [0]
}, {
"factory_name": IsDigitFactory.name,
"args": {},
"offsets": [-1, 0]
}]
config = CRFSlotFillerConfig(
tagging_scheme=TaggingScheme.BILOU,
feature_factory_configs=features_factories)
slot_filler = CRFSlotFiller(config)
# When
slot_filler.persist(self.tmp_file_path)
# Then
metadata_path = self.tmp_file_path / "metadata.json"
self.assertJsonContent(metadata_path, {"unit_name": "crf_slot_filler"})
expected_slot_filler_dict = {
"crf_model_file": None,
"language_code": None,
"config": config.to_dict(),
"intent": None,
"slot_name_mapping": None,
}
slot_filler_path = self.tmp_file_path / "slot_filler.json"
self.assertJsonContent(slot_filler_path, expected_slot_filler_dict)
def test_should_get_slots_after_deserialization(self):
# Given
dataset_stream = io.StringIO("""
---
type: intent
name: MakeTea
utterances:
- make me [number_of_cups:snips/number](one) cup of tea
- i want [number_of_cups] cups of tea please
- can you prepare [number_of_cups] cups of tea ?""")
dataset = Dataset.from_yaml_files("en", [dataset_stream]).json
intent = "MakeTea"
shared = self.get_shared_data(dataset)
shared[RANDOM_STATE] = 42
slot_filler = CRFSlotFiller(**shared)
slot_filler.fit(dataset, intent)
slot_filler.persist(self.tmp_file_path)
deserialized_slot_filler = CRFSlotFiller.from_path(
self.tmp_file_path, **shared)
# When
slots = deserialized_slot_filler.get_slots("make me two cups of tea")
# Then
expected_slots = [
unresolved_slot(match_range={
START: 8,
END: 11
},
value='two',
entity='snips/number',
slot_name='number_of_cups')
]
self.assertListEqual(expected_slots, slots)
def test_should_be_serializable(self):
# Given
features_factories = [{
"factory_name": ShapeNgramFactory.name,
"args": {
"n": 1
},
"offsets": [0]
}, {
"factory_name": IsDigitFactory.name,
"args": {},
"offsets": [-1, 0]
}]
config = CRFSlotFillerConfig(
tagging_scheme=TaggingScheme.BILOU,
feature_factory_configs=features_factories)
dataset = SAMPLE_DATASET
slot_filler = CRFSlotFiller(config)
intent = "dummy_intent_1"
slot_filler.fit(dataset, intent=intent)
# When
slot_filler.persist(self.tmp_file_path)
# Then
metadata_path = self.tmp_file_path / "metadata.json"
self.assertJsonContent(metadata_path, {"unit_name": "crf_slot_filler"})
expected_crf_file = Path(slot_filler.crf_model.modelfile.name).name
self.assertTrue((self.tmp_file_path / expected_crf_file).exists())
expected_feature_factories = [{
"factory_name": ShapeNgramFactory.name,
"args": {
"n": 1,
"language_code": "en"
},
"offsets": [0]
}, {
"factory_name": IsDigitFactory.name,
"args": {},
"offsets": [-1, 0]
}]
expected_config = CRFSlotFillerConfig(
tagging_scheme=TaggingScheme.BILOU,
feature_factory_configs=expected_feature_factories)
expected_slot_filler_dict = {
"crf_model_file": expected_crf_file,
"language_code": "en",
"config": expected_config.to_dict(),
"intent": intent,
"slot_name_mapping": {
"dummy_slot_name": "dummy_entity_1",
"dummy_slot_name2": "dummy_entity_2",
"dummy_slot_name3": "dummy_entity_2",
}
}
slot_filler_path = self.tmp_file_path / "slot_filler.json"
self.assertJsonContent(slot_filler_path, expected_slot_filler_dict)
def test_should_get_slots_after_deserialization(self):
# Given
dataset = BEVERAGE_DATASET
config = CRFSlotFillerConfig(random_seed=42)
intent = "MakeTea"
slot_filler = CRFSlotFiller(config)
slot_filler.fit(dataset, intent)
slot_filler.persist(self.tmp_file_path)
custom_entity_parser = slot_filler.custom_entity_parser
builtin_entity_parser = slot_filler.builtin_entity_parser
deserialized_slot_filler = CRFSlotFiller.from_path(
self.tmp_file_path,
custom_entity_parser=custom_entity_parser,
builtin_entity_parser=builtin_entity_parser)
# When
slots = deserialized_slot_filler.get_slots("make me two cups of tea")
# Then
expected_slots = [
unresolved_slot(match_range={
START: 8,
END: 11
},
value='two',
entity='snips/number',
slot_name='number_of_cups')
]
self.assertListEqual(expected_slots, slots)
def test_should_be_serializable_when_fitted_without_slots(self):
# Given
features_factories = [
{
"factory_name": ShapeNgramFactory.name,
"args": {"n": 1},
"offsets": [0]
},
{
"factory_name": IsDigitFactory.name,
"args": {},
"offsets": [-1, 0]
}
]
config = CRFSlotFillerConfig(
tagging_scheme=TaggingScheme.BILOU,
feature_factory_configs=features_factories)
dataset = {
"language": "en",
"intents": {
"intent1": {
"utterances": [
{
"data": [
{
"text": "This is an utterance without "
"slots"
}
]
}
]
}
},
"entities": {}
}
slot_filler = CRFSlotFiller(config, **self.get_shared_data(dataset))
slot_filler.fit(dataset, intent="intent1")
# When
slot_filler.persist(self.tmp_file_path)
# Then
metadata_path = self.tmp_file_path / "metadata.json"
self.assertJsonContent(metadata_path, {"unit_name": "crf_slot_filler"})
self.assertIsNone(slot_filler.crf_model)
def test_crfsuite_files_modes_should_be_644(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""")
dataset = Dataset.from_yaml_files("en", [dataset_stream]).json
# When
slot_filler = CRFSlotFiller().fit(dataset, "MakeTea")
slot_filler.persist(self.tmp_file_path)
# Then
crfmodel_file = str(self.tmp_file_path / CRF_MODEL_FILENAME)
filemode = oct(os.stat(crfmodel_file).st_mode & 0o0777)
self.assertEqual(oct(0o644), filemode)
def test_should_be_serializable(self):
# Given
dataset_stream = io.StringIO("""
---
type: intent
name: my_intent
utterances:
- this is [slot1:entity1](my first entity)
- this is [slot2:entity2](second_entity)""")
dataset = Dataset.from_yaml_files("en", [dataset_stream]).json
features_factories = [{
"factory_name": ShapeNgramFactory.name,
"args": {
"n": 1
},
"offsets": [0]
}, {
"factory_name": IsDigitFactory.name,
"args": {},
"offsets": [-1, 0]
}]
config = CRFSlotFillerConfig(
tagging_scheme=TaggingScheme.BILOU,
feature_factory_configs=features_factories)
shared = self.get_shared_data(dataset)
slot_filler = CRFSlotFiller(config, **shared)
intent = "my_intent"
slot_filler.fit(dataset, intent=intent)
# When
slot_filler.persist(self.tmp_file_path)
# Then
metadata_path = self.tmp_file_path / "metadata.json"
self.assertJsonContent(metadata_path, {"unit_name": "crf_slot_filler"})
self.assertTrue((self.tmp_file_path / CRF_MODEL_FILENAME).exists())
expected_feature_factories = [{
"factory_name": ShapeNgramFactory.name,
"args": {
"n": 1,
"language_code": "en"
},
"offsets": [0]
}, {
"factory_name": IsDigitFactory.name,
"args": {},
"offsets": [-1, 0]
}]
expected_config = CRFSlotFillerConfig(
tagging_scheme=TaggingScheme.BILOU,
feature_factory_configs=expected_feature_factories)
expected_slot_filler_dict = {
"crf_model_file": CRF_MODEL_FILENAME,
"language_code": "en",
"config": expected_config.to_dict(),
"intent": intent,
"slot_name_mapping": {
"slot1": "entity1",
"slot2": "entity2",
}
}
slot_filler_path = self.tmp_file_path / "slot_filler.json"
self.assertJsonContent(slot_filler_path, expected_slot_filler_dict)
