def test_should_fit_and_parse_empty_intent(self):
# Given
dataset = {
"intents": {
"dummy_intent": {
"utterances": [
{
"data": [
{
"text": " "
}
]
}
]
}
},
"language": "en",
"entities": dict()
}
slot_filler = CRFSlotFiller(**self.get_shared_data(dataset))
# When
slot_filler.fit(dataset, "dummy_intent")
slot_filler.get_slots("ya")
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_compute_features(self):
# Given
features_factories = [
{
"factory_name": NgramFactory.name,
"args": {
"n": 1,
"use_stemming": False,
"common_words_gazetteer_name": None
},
"offsets": [0],
"drop_out": 0.3
},
]
slot_filler_config = CRFSlotFillerConfig(
feature_factory_configs=features_factories, random_seed=40)
slot_filler = CRFSlotFiller(slot_filler_config)
tokens = tokenize("foo hello world bar", LANGUAGE_EN)
dataset = validate_and_format_dataset(SAMPLE_DATASET)
slot_filler.fit(dataset, intent="dummy_intent_1")
# When
features_with_drop_out = slot_filler.compute_features(tokens, True)
# Then
expected_features = [
{"ngram_1": "foo"},
{},
{"ngram_1": "world"},
{},
]
self.assertListEqual(expected_features, features_with_drop_out)
def test_should_get_sub_builtin_slots(self):
# Given
dataset_stream = io.StringIO("""
---
type: intent
name: PlanBreak
utterances:
- 'I want to leave from [start:snips/datetime](tomorrow) until
[end:snips/datetime](next thursday)'
- find me something from [start](9am) to [end](12pm)
- I need a break from [start](2pm) until [end](4pm)
- Can you suggest something from [start](april 4th) until [end](april 6th) ?
- Book me a trip from [start](this friday) to [end](next tuesday)""")
dataset = Dataset.from_yaml_files("en", [dataset_stream]).json
config = CRFSlotFillerConfig(random_seed=42)
intent = "PlanBreak"
slot_filler = CRFSlotFiller(config,
**self.get_shared_data(dataset))
slot_filler.fit(dataset, intent)
# When
slots = slot_filler.get_slots("Find me a plan from 5pm to 6pm")
# Then
expected_slots = [
unresolved_slot(match_range={START: 20, END: 23},
value="5pm",
entity="snips/datetime",
slot_name="start"),
unresolved_slot(match_range={START: 27, END: 30},
value="6pm",
entity="snips/datetime",
slot_name="end")
]
self.assertListEqual(expected_slots, slots)
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_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(self):
# Given
dataset_stream = io.StringIO("""
---
type: intent
name: MakeTea
utterances:
- make me [number_of_cups:snips/number](five) cups of tea
- please I want [number_of_cups](two) cups of tea""")
dataset = Dataset.from_yaml_files("en", [dataset_stream]).json
shared = self.get_shared_data(dataset)
shared[RANDOM_STATE] = 42
slot_filler = CRFSlotFiller(**shared)
intent = "MakeTea"
slot_filler.fit(dataset, intent)
# When
slots = 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(slots, expected_slots)
def test_should_not_use_crf_when_dataset_with_no_slots(self):
# Given
dataset = {
"language": "en",
"intents": {
"intent1": {
"utterances": [{
"data": [{
"text":
"This is an utterance without "
"slots"
}]
}]
}
},
"entities": {}
}
slot_filler = CRFSlotFiller(**self.get_shared_data(dataset))
mock_compute_features = MagicMock()
slot_filler.compute_features = mock_compute_features
# When
slot_filler.fit(dataset, "intent1")
slots = slot_filler.get_slots("This is an utterance without slots")
# Then
mock_compute_features.assert_not_called()
self.assertListEqual([], slots)
def test_should_get_builtin_slots(self):
# Given
dataset = validate_and_format_dataset(WEATHER_DATASET)
config = CRFSlotFillerConfig(random_seed=42)
intent = "SearchWeatherForecast"
slot_filler = CRFSlotFiller(config)
slot_filler.fit(dataset, intent)
# When
slots = slot_filler.get_slots("Give me the weather at 9p.m. in Paris")
# Then
expected_slots = [
unresolved_slot(match_range={
START: 20,
END: 28
},
value='at 9p.m.',
entity='snips/datetime',
slot_name='datetime'),
unresolved_slot(match_range={
START: 32,
END: 37
},
value='Paris',
entity='weather_location',
slot_name='location')
]
self.assertListEqual(expected_slots, slots)
def test_should_get_builtin_slots(self):
# Given
dataset_stream = io.StringIO("""
---
type: intent
name: GetWeather
utterances:
- what is the weather [datetime:snips/datetime](at 9pm)
- what's the weather in [location:weather_location](berlin)
- What's the weather in [location](tokyo) [datetime](this weekend)?
- Can you tell me the weather [datetime] please ?
- what is the weather forecast [datetime] in [location](paris)""")
dataset = Dataset.from_yaml_files("en", [dataset_stream]).json
config = CRFSlotFillerConfig(random_seed=42)
intent = "GetWeather"
slot_filler = CRFSlotFiller(config, **self.get_shared_data(dataset))
slot_filler.fit(dataset, intent)
# When
slots = slot_filler.get_slots("Give me the weather at 9pm in Paris")
# Then
expected_slots = [
unresolved_slot(match_range={START: 20, END: 26},
value='at 9pm',
entity='snips/datetime',
slot_name='datetime'),
unresolved_slot(match_range={START: 30, END: 35},
value='Paris',
entity='weather_location',
slot_name='location')
]
self.assertListEqual(expected_slots, slots)
def test_should_be_serializable(self, mock_serialize_crf_model):
# Given
mock_serialize_crf_model.return_value = "mocked_crf_model_data"
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 = validate_and_format_dataset(SAMPLE_DATASET)
slot_filler = CRFSlotFiller(config)
intent = "dummy_intent_1"
slot_filler.fit(dataset, intent=intent)
# When
actual_slot_filler_dict = slot_filler.to_dict()
# Then
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 = {
"unit_name": "crf_slot_filler",
"crf_model_data": "mocked_crf_model_data",
"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",
}
}
self.assertDictEqual(actual_slot_filler_dict,
expected_slot_filler_dict)
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_should_compute_features(self):
# Given
features_factories = [
{
"factory_name": NgramFactory.name,
"args": {
"n": 1,
"use_stemming": False,
"common_words_gazetteer_name": None
},
"offsets": [0],
"drop_out": 0.3
},
]
slot_filler_config = CRFSlotFillerConfig(
feature_factory_configs=features_factories, random_seed=40)
tokens = tokenize("foo hello world bar", LANGUAGE_EN)
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
shared = self.get_shared_data(dataset,
CustomEntityParserUsage.WITHOUT_STEMS)
slot_filler = CRFSlotFiller(slot_filler_config, **shared)
slot_filler.fit(dataset, intent="my_intent")
# When
features_with_drop_out = slot_filler.compute_features(tokens, True)
# Then
expected_features = [
{
"ngram_1": "foo"
},
{},
{
"ngram_1": "world"
},
{},
]
self.assertListEqual(expected_features, features_with_drop_out)
def test_should_fit_and_parse_with_non_ascii_tags(self):
# Given
inputs = ("string%s" % i for i in range(10))
utterances = [{
DATA: [{
TEXT: string,
ENTITY: "non_ascìi_entïty",
SLOT_NAME: "non_ascìi_slöt"
}]
} for string in inputs]
# When
naughty_dataset = {
"intents": {
"naughty_intent": {
"utterances": utterances
}
},
"entities": {
"non_ascìi_entïty": {
"use_synonyms": False,
"automatically_extensible": True,
"data": []
}
},
"language": "en",
"snips_nlu_version": "0.0.1"
}
naughty_dataset = validate_and_format_dataset(naughty_dataset)
# Then
with self.fail_if_exception("Naughty string make NLU crash"):
slot_filler = CRFSlotFiller()
slot_filler.fit(naughty_dataset, "naughty_intent")
slots = slot_filler.get_slots("string0")
expected_slot = {
"entity": "non_ascìi_entïty",
"range": {
"start": 0,
"end": 7
},
"slotName": u"non_ascìi_slöt",
"value": u"string0"
}
self.assertListEqual([expected_slot], slots)
def test_should_get_slots(self):
# Given
dataset = validate_and_format_dataset(BEVERAGE_DATASET)
config = CRFSlotFillerConfig(random_seed=42)
intent = "MakeTea"
slot_filler = CRFSlotFiller(config)
slot_filler.fit(dataset, intent)
# When
slots = 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(slots, expected_slots)
def test_refit(self):
# Given
dataset_stream = io.StringIO("""
---
type: intent
name: my_intent
utterances:
- this is [entity1](my first entity)""")
dataset = Dataset.from_yaml_files("en", [dataset_stream]).json
updated_dataset_stream = io.StringIO("""
---
type: intent
name: my_intent
utterances:
- this is [entity1](my first entity)
- this is [entity1](my first entity) again""")
updated_dataset = Dataset.from_yaml_files(
"en", [updated_dataset_stream]).json
config = CRFSlotFillerConfig(feature_factory_configs=[
{
"args": {
"common_words_gazetteer_name": "top_10000_words_stemmed",
"use_stemming": True,
"n": 1
},
"factory_name": "ngram",
"offsets": [-2, -1, 0, 1, 2]
},
])
# When
slot_filler = CRFSlotFiller(config).fit(dataset, "my_intent")
# Then
slot_filler.fit(updated_dataset, "my_intent")
def test_training_should_be_reproducible(self):
# Given
random_state = 42
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_filler1 = CRFSlotFiller(random_state=random_state)
slot_filler1.fit(dataset, "MakeTea")
slot_filler2 = CRFSlotFiller(random_state=random_state)
slot_filler2.fit(dataset, "MakeTea")
# Then
self.assertDictEqual(slot_filler1.crf_model.state_features_,
slot_filler2.crf_model.state_features_)
self.assertDictEqual(slot_filler1.crf_model.transition_features_,
slot_filler2.crf_model.transition_features_)
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)
