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_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
actual_slot_filler_dict = slot_filler.to_dict()
# Then
expected_slot_filler_dict = {
"unit_name": "crf_slot_filler",
"crf_model_data": None,
"language_code": None,
"config": config.to_dict(),
"intent": None,
"slot_name_mapping": None,
}
self.assertDictEqual(actual_slot_filler_dict,
expected_slot_filler_dict)
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_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_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_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
config = CRFSlotFillerConfig(random_seed=42)
intent = "MakeTea"
shared = self.get_shared_data(dataset)
slot_filler = CRFSlotFiller(config, **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_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
config = CRFSlotFillerConfig(random_seed=42)
shared = self.get_shared_data(dataset)
slot_filler = CRFSlotFiller(config, **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_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_fitting_should_be_reproducible_after_serialization(self):
# Given
dataset = BEVERAGE_DATASET
validated_dataset = validate_and_format_dataset(dataset)
seed1 = 666
seed2 = 42
config = ProbabilisticIntentParserConfig(
intent_classifier_config=LogRegIntentClassifierConfig(
random_seed=seed1),
slot_filler_config=CRFSlotFillerConfig(random_seed=seed2))
parser = ProbabilisticIntentParser(config)
parser_dict = parser.to_dict()
# When
fitted_parser_1 = ProbabilisticIntentParser.from_dict(parser_dict).fit(
validated_dataset)
fitted_parser_2 = ProbabilisticIntentParser.from_dict(parser_dict).fit(
validated_dataset)
# Then
feature_weights_1 = fitted_parser_1.slot_fillers[
"MakeTea"].crf_model.state_features_
feature_weights_2 = fitted_parser_2.slot_fillers[
"MakeTea"].crf_model.state_features_
self.assertEqual(feature_weights_1, feature_weights_2)
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_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 __init__(self, intent_classifier_config=None, slot_filler_config=None):
if intent_classifier_config is None:
from snips_nlu.pipeline.configs import LogRegIntentClassifierConfig
intent_classifier_config = LogRegIntentClassifierConfig()
if slot_filler_config is None:
from snips_nlu.pipeline.configs import CRFSlotFillerConfig
slot_filler_config = CRFSlotFillerConfig()
self.intent_classifier_config = get_processing_unit_config(
intent_classifier_config)
self.slot_filler_config = get_processing_unit_config(
slot_filler_config)
def test_should_be_deserializable_before_fit(self):
# Given
features_factories = [
{
"factory_name": ShapeNgramFactory.name,
"args": {"n": 1},
"offsets": [0]
},
{
"factory_name": IsDigitFactory.name,
"args": {},
"offsets": [-1, 0]
}
]
slot_filler_config = CRFSlotFillerConfig(
feature_factory_configs=features_factories)
slot_filler_dict = {
"unit_name": "crf_slot_filler",
"crf_model_file": None,
"language_code": None,
"intent": None,
"slot_name_mapping": None,
"config": slot_filler_config.to_dict()
}
metadata = {"unit_name": "crf_slot_filler"}
self.tmp_file_path.mkdir()
self.writeJsonContent(self.tmp_file_path / "metadata.json", metadata)
self.writeJsonContent(self.tmp_file_path / "slot_filler.json",
slot_filler_dict)
# When
slot_filler = CRFSlotFiller.from_path(self.tmp_file_path)
# Then
expected_features_factories = [
{
"factory_name": ShapeNgramFactory.name,
"args": {"n": 1},
"offsets": [0]
},
{
"factory_name": IsDigitFactory.name,
"args": {},
"offsets": [-1, 0]
}
]
expected_language = None
expected_config = CRFSlotFillerConfig(
feature_factory_configs=expected_features_factories)
expected_intent = None
expected_slot_name_mapping = None
expected_crf_model = None
self.assertEqual(slot_filler.crf_model, expected_crf_model)
self.assertEqual(slot_filler.language, expected_language)
self.assertEqual(slot_filler.intent, expected_intent)
self.assertEqual(slot_filler.slot_name_mapping,
expected_slot_name_mapping)
self.assertDictEqual(expected_config.to_dict(),
slot_filler.config.to_dict())
def test_should_be_deserializable_before_fit(self,
mock_deserialize_crf_model):
# Given
mock_deserialize_crf_model.return_value = None
features_factories = [
{
"factory_name": ShapeNgramFactory.name,
"args": {"n": 1},
"offsets": [0]
},
{
"factory_name": IsDigitFactory.name,
"args": {},
"offsets": [-1, 0]
}
]
slot_filler_config = CRFSlotFillerConfig(
feature_factory_configs=features_factories)
slot_filler_dict = {
"unit_name": "crf_slot_filler",
"crf_model_data": None,
"language_code": None,
"intent": None,
"slot_name_mapping": None,
"config": slot_filler_config.to_dict()
}
# When
slot_filler = CRFSlotFiller.from_dict(slot_filler_dict)
# Then
expected_features_factories = [
{
"factory_name": ShapeNgramFactory.name,
"args": {"n": 1},
"offsets": [0]
},
{
"factory_name": IsDigitFactory.name,
"args": {},
"offsets": [-1, 0]
}
]
expected_language = None
expected_config = CRFSlotFillerConfig(
feature_factory_configs=expected_features_factories)
expected_intent = None
expected_slot_name_mapping = None
expected_crf_model = None
self.assertEqual(slot_filler.crf_model, expected_crf_model)
self.assertEqual(slot_filler.language, expected_language)
self.assertEqual(slot_filler.intent, expected_intent)
self.assertEqual(slot_filler.slot_name_mapping,
expected_slot_name_mapping)
self.assertDictEqual(expected_config.to_dict(),
slot_filler.config.to_dict())
def test_probabilistic_intent_parser_config(self):
# Given
config_dict = {
"unit_name": "probabilistic_intent_parser",
"intent_classifier_config":
LogRegIntentClassifierConfig().to_dict(),
"slot_filler_config": CRFSlotFillerConfig().to_dict(),
}
# When
config = ProbabilisticIntentParserConfig.from_dict(config_dict)
serialized_config = config.to_dict()
# Then
self.assertDictEqual(config_dict, serialized_config)
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_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_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_fitting_should_be_reproducible_after_serialization(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
---
type: intent
name: MakeCoffee
utterances:
- make me [number_of_cups:snips/number](one) cup of coffee please
- brew [number_of_cups] cups of coffee""")
dataset = Dataset.from_yaml_files("en", [dataset_stream]).json
seed1 = 666
seed2 = 42
config = ProbabilisticIntentParserConfig(
intent_classifier_config=LogRegIntentClassifierConfig(
random_seed=seed1),
slot_filler_config=CRFSlotFillerConfig(random_seed=seed2))
shared = self.get_shared_data(dataset)
parser = ProbabilisticIntentParser(config, **shared)
parser.persist(self.tmp_file_path)
# When
fitted_parser_1 = ProbabilisticIntentParser.from_path(
self.tmp_file_path, **shared).fit(dataset)
fitted_parser_2 = ProbabilisticIntentParser.from_path(
self.tmp_file_path, **shared).fit(dataset)
# Then
feature_weights_1 = fitted_parser_1.slot_fillers[
"MakeTea"].crf_model.state_features_
feature_weights_2 = fitted_parser_2.slot_fillers[
"MakeTea"].crf_model.state_features_
self.assertEqual(feature_weights_1, feature_weights_2)
def test_crf_slot_filler_config(self):
# Given
feature_factories = [{
"args": {
"common_words_gazetteer_name": None,
"use_stemming": True,
"n": 1
},
"factory_name": "get_ngram_fn",
"offsets": [-2, -1, 0, 1, 2]
}, {
"args": {},
"factory_name": "is_digit",
"offsets": [-1, 0, 1]
}]
config_dict = {
"unit_name":
"crf_slot_filler",
"feature_factory_configs":
feature_factories,
"tagging_scheme":
2,
"crf_args": {
"c1": .2,
"c2": .3,
"algorithm": "lbfgs"
},
"exhaustive_permutations_threshold":
42,
"data_augmentation_config":
SlotFillerDataAugmentationConfig().to_dict(),
"random_seed":
43
}
# When
config = CRFSlotFillerConfig.from_dict(config_dict)
serialized_config = config.to_dict()
# Then
self.assertDictEqual(config_dict, serialized_config)
def test_should_be_serializable_before_fitting(self):
# Given
parser = ProbabilisticIntentParser()
# When
parser.persist(self.tmp_file_path)
# Then
expected_parser_dict = {
"config": {
"unit_name": "probabilistic_intent_parser",
"slot_filler_config": CRFSlotFillerConfig().to_dict(),
"intent_classifier_config":
LogRegIntentClassifierConfig().to_dict()
},
"slot_fillers": []
}
metadata = {"unit_name": "probabilistic_intent_parser"}
self.assertJsonContent(self.tmp_file_path / "metadata.json", metadata)
self.assertJsonContent(self.tmp_file_path / "intent_parser.json",
expected_parser_dict)
def test_should_parse_top_intents(self):
# Given
dataset_stream = io.StringIO("""
---
type: intent
name: intent1
utterances:
- "[entity1](foo) bar"
---
type: intent
name: intent2
utterances:
- foo bar [entity2](baz)
---
type: intent
name: intent3
utterances:
- foz for [entity3](baz)""")
dataset = Dataset.from_yaml_files("en", [dataset_stream]).json
classifier_config = LogRegIntentClassifierConfig(random_seed=42)
slot_filler_config = CRFSlotFillerConfig(random_seed=42)
parser_config = ProbabilisticIntentParserConfig(
classifier_config, slot_filler_config)
parser = ProbabilisticIntentParser(parser_config)
parser.fit(dataset)
text = "foo bar baz"
# When
results = parser.parse(text, top_n=2)
intents = [res[RES_INTENT][RES_INTENT_NAME] for res in results]
entities = [[s[RES_VALUE] for s in res[RES_SLOTS]] for res in results]
# Then
expected_intents = ["intent2", "intent1"]
expected_entities = [["baz"], ["foo"]]
self.assertListEqual(expected_intents, intents)
self.assertListEqual(expected_entities, entities)
def test_should_be_serializable_before_fitting(self):
# Given
parser = ProbabilisticIntentParser()
# When
actual_parser_dict = parser.to_dict()
# Then
expected_parser_dict = {
"unit_name": "probabilistic_intent_parser",
"config": {
"unit_name":
"probabilistic_intent_parser",
"slot_filler_config":
CRFSlotFillerConfig().to_dict(),
"intent_classifier_config":
LogRegIntentClassifierConfig().to_dict()
},
"intent_classifier": None,
"slot_fillers": dict(),
}
self.assertDictEqual(actual_parser_dict, expected_parser_dict)
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_should_parse_with_filter(self):
dataset_stream = io.StringIO("""
---
type: intent
name: intent1
utterances:
- "[slot1:entity1](foo) bar"
---
type: intent
name: intent2
utterances:
- foo bar [slot2:entity2](baz)
---
type: intent
name: intent3
utterances:
- foz for [slot3:entity3](baz)""")
dataset = Dataset.from_yaml_files("en", [dataset_stream]).json
classifier_config = LogRegIntentClassifierConfig(random_seed=42)
slot_filler_config = CRFSlotFillerConfig(random_seed=42)
parser_config = ProbabilisticIntentParserConfig(
classifier_config, slot_filler_config)
parser = ProbabilisticIntentParser(parser_config)
parser.fit(dataset)
text = "foo bar baz"
# When
result = parser.parse(text, intents=["intent1", "intent3"])
# Then
expected_slots = [unresolved_slot((0, 3), "foo", "entity1", "slot1")]
self.assertEqual("intent1", result[RES_INTENT][RES_INTENT_NAME])
self.assertEqual(expected_slots, result[RES_SLOTS])
def test_should_be_deserializable(self):
# Given
language = LANGUAGE_EN
feature_factories = [{
"factory_name": ShapeNgramFactory.name,
"args": {
"n": 1,
"language_code": language
},
"offsets": [0]
}, {
"factory_name": IsDigitFactory.name,
"args": {},
"offsets": [-1, 0]
}]
slot_filler_config = CRFSlotFillerConfig(
feature_factory_configs=feature_factories)
slot_filler_dict = {
"unit_name": "crf_slot_filler",
"crf_model_file": "foobar.crfsuite",
"language_code": "en",
"intent": "dummy_intent_1",
"slot_name_mapping": {
"dummy_intent_1": {
"dummy_slot_name": "dummy_entity_1",
}
},
"config": slot_filler_config.to_dict()
}
metadata = {"unit_name": "crf_slot_filler"}
self.tmp_file_path.mkdir()
self.writeJsonContent(self.tmp_file_path / "metadata.json", metadata)
self.writeJsonContent(self.tmp_file_path / "slot_filler.json",
slot_filler_dict)
self.writeFileContent(self.tmp_file_path / "foobar.crfsuite",
"foo bar")
# When
slot_filler = CRFSlotFiller.from_path(self.tmp_file_path)
# Then
expected_language = LANGUAGE_EN
expected_feature_factories = [{
"factory_name": ShapeNgramFactory.name,
"args": {
"n": 1,
"language_code": language
},
"offsets": [0]
}, {
"factory_name": IsDigitFactory.name,
"args": {},
"offsets": [-1, 0]
}]
expected_config = CRFSlotFillerConfig(
feature_factory_configs=expected_feature_factories)
expected_intent = "dummy_intent_1"
expected_slot_name_mapping = {
"dummy_intent_1": {
"dummy_slot_name": "dummy_entity_1",
}
}
self.assertEqual(slot_filler.language, expected_language)
self.assertEqual(slot_filler.intent, expected_intent)
self.assertEqual(slot_filler.slot_name_mapping,
expected_slot_name_mapping)
self.assertDictEqual(expected_config.to_dict(),
slot_filler.config.to_dict())
crf_path = Path(slot_filler.crf_model.modelfile.name)
self.assertFileContent(crf_path, "foo bar")
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)
def test_should_be_deserializable(self, mock_deserialize_crf_model):
# Given
language = LANGUAGE_EN
mock_deserialize_crf_model.return_value = None
feature_factories = [
{
"factory_name": ShapeNgramFactory.name,
"args": {"n": 1, "language_code": language},
"offsets": [0]
},
{
"factory_name": IsDigitFactory.name,
"args": {},
"offsets": [-1, 0]
}
]
slot_filler_config = CRFSlotFillerConfig(
feature_factory_configs=feature_factories)
slot_filler_dict = {
"unit_name": "crf_slot_filler",
"crf_model_data": "mocked_crf_model_data",
"language_code": "en",
"intent": "dummy_intent_1",
"slot_name_mapping": {
"dummy_intent_1": {
"dummy_slot_name": "dummy_entity_1",
}
},
"config": slot_filler_config.to_dict()
}
# When
slot_filler = CRFSlotFiller.from_dict(slot_filler_dict)
# Then
mock_deserialize_crf_model.assert_called_once_with(
"mocked_crf_model_data")
expected_language = LANGUAGE_EN
expected_feature_factories = [
{
"factory_name": ShapeNgramFactory.name,
"args": {"n": 1, "language_code": language},
"offsets": [0]
},
{
"factory_name": IsDigitFactory.name,
"args": {},
"offsets": [-1, 0]
}
]
expected_config = CRFSlotFillerConfig(
feature_factory_configs=expected_feature_factories)
expected_intent = "dummy_intent_1"
expected_slot_name_mapping = {
"dummy_intent_1": {
"dummy_slot_name": "dummy_entity_1",
}
}
self.assertEqual(slot_filler.language, expected_language)
self.assertEqual(slot_filler.intent, expected_intent)
self.assertEqual(slot_filler.slot_name_mapping,
expected_slot_name_mapping)
self.assertDictEqual(expected_config.to_dict(),
slot_filler.config.to_dict())
def test_augment_slots(self):
# Given
language = LANGUAGE_EN
text = "Find me a flight before 10pm and after 8pm"
tokens = tokenize(text, language)
missing_slots = {"start_date", "end_date"}
tags = ['O' for _ in tokens]
def mocked_sequence_probability(_, tags_):
tags_1 = [
'O', 'O', 'O', 'O',
'%sstart_date' % BEGINNING_PREFIX,
'%sstart_date' % INSIDE_PREFIX, 'O',
'%send_date' % BEGINNING_PREFIX,
'%send_date' % INSIDE_PREFIX
]
tags_2 = [
'O', 'O', 'O', 'O',
'%send_date' % BEGINNING_PREFIX,
'%send_date' % INSIDE_PREFIX, 'O',
'%sstart_date' % BEGINNING_PREFIX,
'%sstart_date' % INSIDE_PREFIX
]
tags_3 = ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']
tags_4 = [
'O', 'O', 'O', 'O', 'O', 'O', 'O',
'%sstart_date' % BEGINNING_PREFIX,
'%sstart_date' % INSIDE_PREFIX
]
tags_5 = [
'O', 'O', 'O', 'O', 'O', 'O', 'O',
'%send_date' % BEGINNING_PREFIX,
'%send_date' % INSIDE_PREFIX
]
tags_6 = [
'O', 'O', 'O', 'O',
'%sstart_date' % BEGINNING_PREFIX,
'%sstart_date' % INSIDE_PREFIX, 'O', 'O', 'O'
]
tags_7 = [
'O', 'O', 'O', 'O',
'%send_date' % BEGINNING_PREFIX,
'%send_date' % INSIDE_PREFIX, 'O', 'O', 'O'
]
tags_8 = [
'O', 'O', 'O', 'O',
'%sstart_date' % BEGINNING_PREFIX,
'%sstart_date' % INSIDE_PREFIX, 'O',
'%sstart_date' % BEGINNING_PREFIX,
'%sstart_date' % INSIDE_PREFIX
]
tags_9 = [
'O', 'O', 'O', 'O',
'%send_date' % BEGINNING_PREFIX,
'%send_date' % INSIDE_PREFIX, 'O',
'%send_date' % BEGINNING_PREFIX,
'%send_date' % INSIDE_PREFIX
]
if tags_ == tags_1:
return 0.6
elif tags_ == tags_2:
return 0.8
elif tags_ == tags_3:
return 0.2
elif tags_ == tags_4:
return 0.2
elif tags_ == tags_5:
return 0.99
elif tags_ == tags_6:
return 0.0
elif tags_ == tags_7:
return 0.0
elif tags_ == tags_8:
return 0.5
elif tags_ == tags_9:
return 0.5
else:
raise ValueError("Unexpected tag sequence: %s" % tags_)
slot_filler_config = CRFSlotFillerConfig(random_seed=42)
slot_filler = CRFSlotFiller(config=slot_filler_config)
slot_filler.language = LANGUAGE_EN
slot_filler.intent = "intent1"
slot_filler.slot_name_mapping = {
"start_date": "snips/datetime",
"end_date": "snips/datetime",
}
# pylint:disable=protected-access
slot_filler._get_sequence_probability = MagicMock(
side_effect=mocked_sequence_probability)
# pylint:enable=protected-access
slot_filler.compute_features = MagicMock(return_value=None)
# When
# pylint: disable=protected-access
augmented_slots = slot_filler._augment_slots(text, tokens, tags,
missing_slots)
# pylint: enable=protected-access
# Then
expected_slots = [
unresolved_slot(value='after 8pm',
match_range={
START: 33,
END: 42
},
entity='snips/datetime',
slot_name='end_date')
]
self.assertListEqual(augmented_slots, expected_slots)