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_nlu_engine_should_raise_error_with_bytes_input(self):
# Given
bytes_input = b"brew me an espresso"
engine = SnipsNLUEngine().fit(BEVERAGE_DATASET)
# When / Then
with self.assertRaises(TypeError) as cm:
engine.parse(bytes_input)
message = str(cm.exception.args[0])
self.assertTrue("Expected unicode but received" in message)
def test_should_handle_empty_dataset(self):
# Given
dataset = validate_and_format_dataset(get_empty_dataset(LANGUAGE_EN))
engine = SnipsNLUEngine().fit(dataset)
# When
result = engine.parse("hello world")
# Then
self.assertEqual(empty_result("hello world"), result)
def test_nlu_engine_should_train_and_parse_in_all_languages(self):
# Given
text = "brew me an espresso"
for language in get_all_languages():
dataset = deepcopy(BEVERAGE_DATASET)
dataset[LANGUAGE] = language
engine = SnipsNLUEngine()
# When / Then
msg = "Could not fit engine in '%s'" % language
with self.fail_if_exception(msg):
engine = engine.fit(dataset)
msg = "Could not parse in '%s'" % language
with self.fail_if_exception(msg):
engine.parse(text)
def test_should_be_serializable_when_empty(self):
# Given
nlu_engine = SnipsNLUEngine()
# When
engine_dict = nlu_engine.to_dict()
# Then
expected_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__
}
self.assertDictEqual(expected_dict, engine_dict)
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_synonyms_should_point_to_base_value(self, mocked_deter_parse,
mocked_proba_parse):
# Given
dataset = {
"snips_nlu_version": "1.1.1",
"intents": {
"dummy_intent_1": {
"utterances": [
{
"data": [
{
"text": "dummy_1",
"entity": "dummy_entity_1",
"slot_name": "dummy_slot_name"
}
]
}
]
}
},
"entities": {
"dummy_entity_1": {
"use_synonyms": True,
"automatically_extensible": False,
"data": [
{
"value": "dummy1",
"synonyms": [
"dummy1",
"dummy1_bis"
]
}
]
}
},
"language": "en"
}
text = "dummy1_bis"
mocked_proba_parser_intent = intent_classification_result(
"dummy_intent_1", 1.0)
mocked_proba_parser_slots = [
unresolved_slot(match_range=(0, 10), value="dummy1_bis",
entity="dummy_entity_1",
slot_name="dummy_slot_name")]
mocked_deter_parse.return_value = empty_result(text)
mocked_proba_parse.return_value = parsing_result(
text, mocked_proba_parser_intent, mocked_proba_parser_slots)
engine = SnipsNLUEngine().fit(dataset)
# When
result = engine.parse(text)
# Then
expected_slot = {
RES_MATCH_RANGE: {
"start": 0,
"end": 10
},
RES_RAW_VALUE: "dummy1_bis",
RES_VALUE: {
"kind": "Custom",
"value": "dummy1"
},
RES_ENTITY: "dummy_entity_1",
RES_SLOT_NAME: "dummy_slot_name"
}
expected_result = parsing_result(
text, intent=mocked_proba_parser_intent, slots=[expected_slot])
self.assertEqual(expected_result, result)
def test_should_handle_keyword_entities(self, mocked_regex_parse,
mocked_crf_parse):
# Given
dataset = {
"snips_nlu_version": "1.1.1",
"intents": {
"dummy_intent_1": {
"utterances": [
{
"data": [
{
"text": "dummy_1",
"entity": "dummy_entity_1",
"slot_name": "dummy_slot_name"
},
{
"text": " dummy_2",
"entity": "dummy_entity_2",
"slot_name": "other_dummy_slot_name"
}
]
}
]
}
},
"entities": {
"dummy_entity_1": {
"use_synonyms": True,
"automatically_extensible": False,
"data": [
{
"value": "dummy1",
"synonyms": [
"dummy1",
"dummy1_bis"
]
},
{
"value": "dummy2",
"synonyms": [
"dummy2",
"dummy2_bis"
]
}
]
},
"dummy_entity_2": {
"use_synonyms": False,
"automatically_extensible": True,
"data": [
{
"value": "dummy2",
"synonyms": [
"dummy2"
]
}
]
}
},
"language": "en"
}
text = "dummy_3 dummy_4"
mocked_crf_intent = intent_classification_result("dummy_intent_1", 1.0)
mocked_crf_slots = [unresolved_slot(match_range=(0, 7),
value="dummy_3",
entity="dummy_entity_1",
slot_name="dummy_slot_name"),
unresolved_slot(match_range=(8, 15),
value="dummy_4",
entity="dummy_entity_2",
slot_name="other_dummy_slot_name")]
mocked_regex_parse.return_value = empty_result(text)
mocked_crf_parse.return_value = parsing_result(
text, mocked_crf_intent, mocked_crf_slots)
engine = SnipsNLUEngine()
# When
engine = engine.fit(dataset)
result = engine.parse(text)
# Then
expected_slot = custom_slot(unresolved_slot(
match_range=(8, 15), value="dummy_4", entity="dummy_entity_2",
slot_name="other_dummy_slot_name"))
expected_result = parsing_result(text, intent=mocked_crf_intent,
slots=[expected_slot])
self.assertEqual(expected_result, result)
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)
def test_should_retrain_only_non_trained_subunits(self):
# Given
class TestIntentParserConfig(ProcessingUnitConfig):
unit_name = "test_intent_parser"
def to_dict(self):
return {"unit_name": self.unit_name}
@classmethod
def from_dict(cls, obj_dict):
return TestIntentParserConfig()
class TestIntentParser(IntentParser):
unit_name = "test_intent_parser"
config_type = TestIntentParserConfig
def __init__(self, config):
super(TestIntentParser, self).__init__(config)
self.sub_unit_1 = dict(fitted=False, calls=0)
self.sub_unit_2 = dict(fitted=False, calls=0)
def fit(self, dataset, force_retrain):
if force_retrain:
self.sub_unit_1["fitted"] = True
self.sub_unit_1["calls"] += 1
self.sub_unit_2["fitted"] = True
self.sub_unit_2["calls"] += 1
else:
if not self.sub_unit_1["fitted"]:
self.sub_unit_1["fitted"] = True
self.sub_unit_1["calls"] += 1
if not self.sub_unit_2["fitted"]:
self.sub_unit_2["fitted"] = True
self.sub_unit_2["calls"] += 1
return self
@property
def fitted(self):
return self.sub_unit_1["fitted"] and \
self.sub_unit_2["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):
conf = cls.config_type()
return TestIntentParser(conf)
register_processing_unit(TestIntentParser)
intent_parser_config = TestIntentParserConfig()
nlu_engine_config = NLUEngineConfig([intent_parser_config])
nlu_engine = SnipsNLUEngine(nlu_engine_config)
intent_parser = TestIntentParser(intent_parser_config)
intent_parser.sub_unit_1.update(dict(fitted=True, calls=0))
nlu_engine.intent_parsers.append(intent_parser)
# When
nlu_engine.fit(SAMPLE_DATASET, force_retrain=False)
# Then
self.assertDictEqual(dict(fitted=True, calls=0),
intent_parser.sub_unit_1)
self.assertDictEqual(dict(fitted=True, calls=1),
intent_parser.sub_unit_2)
def test_synonyms_should_not_collide_when_remapped_to_base_value(
self, mocked_proba_parse):
# Given
# Given
dataset = {
"intents": {
"intent1": {
"utterances": [
{
"data": [
{
"text": "value",
"entity": "entity1",
"slot_name": "slot1"
}
]
}
]
}
},
"entities": {
"entity1": {
"data": [
{
"value": "a",
"synonyms": [
"favorïte"
]
},
{
"value": "b",
"synonyms": [
"favorite"
]
}
],
"use_synonyms": True,
"automatically_extensible": False,
"parser_threshold": 1.0
}
},
"language": "en",
}
mocked_proba_parser_intent = intent_classification_result(
"intent1", 1.0)
# pylint: disable=unused-argument
def mock_proba_parse(text, intents):
slots = [unresolved_slot(match_range=(0, len(text)), value=text,
entity="entity1", slot_name="slot1")]
return parsing_result(
text, mocked_proba_parser_intent, slots)
mocked_proba_parse.side_effect = mock_proba_parse
config = NLUEngineConfig([ProbabilisticIntentParserConfig()])
engine = SnipsNLUEngine(config).fit(dataset)
# When
result1 = engine.parse("favorite")
result2 = engine.parse("favorïte")
# Then
expected_slot1 = {
RES_MATCH_RANGE: {
"start": 0,
"end": 8
},
RES_RAW_VALUE: "favorite",
RES_VALUE: {
"kind": "Custom",
"value": "b"
},
RES_ENTITY: "entity1",
RES_SLOT_NAME: "slot1"
}
expected_slot2 = {
RES_MATCH_RANGE: {
"start": 0,
"end": 8
},
RES_RAW_VALUE: "favorïte",
RES_VALUE: {
"kind": "Custom",
"value": "a"
},
RES_ENTITY: "entity1",
RES_SLOT_NAME: "slot1"
}
expected_result1 = parsing_result(
"favorite", intent=mocked_proba_parser_intent,
slots=[expected_slot1])
expected_result2 = parsing_result(
"favorïte", intent=mocked_proba_parser_intent,
slots=[expected_slot2])
self.assertEqual(expected_result1, result1)
self.assertEqual(expected_result2, result2)
def test_synonyms_should_point_to_base_value(self, mocked_proba_parse):
# Given
dataset = {
"intents": {
"dummy_intent_1": {
"utterances": [
{
"data": [
{
"text": "dummy_1",
"entity": "dummy_entity_1",
"slot_name": "dummy_slot_name"
}
]
}
]
}
},
"entities": {
"dummy_entity_1": {
"use_synonyms": True,
"automatically_extensible": False,
"data": [
{
"value": "dummy1",
"synonyms": [
"dummy1",
"dummy1_bis"
]
}
],
"parser_threshold": 1.0
}
},
"language": "en"
}
text = "dummy1_bis"
mocked_proba_parser_intent = intent_classification_result(
"dummy_intent_1", 1.0)
mocked_proba_parser_slots = [
unresolved_slot(match_range=(0, 10), value="dummy1_bis",
entity="dummy_entity_1",
slot_name="dummy_slot_name")]
mocked_proba_parse.return_value = parsing_result(
text, mocked_proba_parser_intent, mocked_proba_parser_slots)
config = NLUEngineConfig([ProbabilisticIntentParserConfig()])
engine = SnipsNLUEngine(config).fit(dataset)
# When
result = engine.parse(text)
# Then
expected_slot = {
RES_MATCH_RANGE: {
"start": 0,
"end": 10
},
RES_RAW_VALUE: "dummy1_bis",
RES_VALUE: {
"kind": "Custom",
"value": "dummy1"
},
RES_ENTITY: "dummy_entity_1",
RES_SLOT_NAME: "dummy_slot_name"
}
expected_result = parsing_result(
text, intent=mocked_proba_parser_intent, slots=[expected_slot])
self.assertEqual(expected_result, result)
def test_should_handle_keyword_entities(self, mocked_regex_parse,
mocked_crf_parse):
# Given
dataset = {
"intents": {
"dummy_intent_1": {
"utterances": [
{
"data": [
{
"text": "dummy_1",
"entity": "dummy_entity_1",
"slot_name": "dummy_slot_name"
},
{
"text": " dummy_2",
"entity": "dummy_entity_2",
"slot_name": "other_dummy_slot_name"
}
]
}
]
}
},
"entities": {
"dummy_entity_1": {
"use_synonyms": True,
"automatically_extensible": False,
"data": [
{
"value": "dummy1",
"synonyms": [
"dummy1",
"dummy1_bis"
]
},
{
"value": "dummy2",
"synonyms": [
"dummy2",
"dummy2_bis"
]
}
],
"parser_threshold": 1.0
},
"dummy_entity_2": {
"use_synonyms": False,
"automatically_extensible": True,
"data": [
{
"value": "dummy2",
"synonyms": [
"dummy2"
]
}
],
"parser_threshold": 1.0
}
},
"language": "en"
}
text = "dummy_3 dummy_4"
mocked_crf_intent = intent_classification_result("dummy_intent_1", 1.0)
mocked_crf_slots = [unresolved_slot(match_range=(0, 7),
value="dummy_3",
entity="dummy_entity_1",
slot_name="dummy_slot_name"),
unresolved_slot(match_range=(8, 15),
value="dummy_4",
entity="dummy_entity_2",
slot_name="other_dummy_slot_name")]
mocked_regex_parse.return_value = empty_result(text)
mocked_crf_parse.return_value = parsing_result(
text, mocked_crf_intent, mocked_crf_slots)
engine = SnipsNLUEngine()
# When
engine = engine.fit(dataset)
result = engine.parse(text)
# Then
expected_slot = custom_slot(unresolved_slot(
match_range=(8, 15), value="dummy_4", entity="dummy_entity_2",
slot_name="other_dummy_slot_name"))
expected_result = parsing_result(text, intent=mocked_crf_intent,
slots=[expected_slot])
self.assertEqual(expected_result, result)
def test_should_use_parsers_sequentially(self):
# Given
input_text = "hello world"
intent = intent_classification_result(
intent_name='dummy_intent_1', probability=0.7)
slots = [unresolved_slot(match_range=(6, 11),
value='world',
entity='mocked_entity',
slot_name='mocked_slot_name')]
class FirstIntentParserConfig(ProcessingUnitConfig):
unit_name = "first_intent_parser"
def to_dict(self):
return {"unit_name": self.unit_name}
@classmethod
def from_dict(cls, obj_dict):
return FirstIntentParserConfig()
class FirstIntentParser(IntentParser):
unit_name = "first_intent_parser"
config_type = FirstIntentParserConfig
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 persist(self, path):
path = Path(path)
path.mkdir()
with (path / "metadata.json").open(mode="w") as f:
f.write(json_string({"unit_name": self.unit_name}))
@classmethod
def from_path(cls, path, **shared):
cfg = cls.config_type()
return cls(cfg)
class SecondIntentParserConfig(ProcessingUnitConfig):
unit_name = "second_intent_parser"
def to_dict(self):
return {"unit_name": self.unit_name}
@classmethod
def from_dict(cls, obj_dict):
return SecondIntentParserConfig()
class SecondIntentParser(IntentParser):
unit_name = "second_intent_parser"
config_type = SecondIntentParserConfig
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):
if text == input_text:
return parsing_result(text, intent, slots)
return empty_result(text)
def persist(self, path):
path = Path(path)
path.mkdir()
with (path / "metadata.json").open(mode="w") as f:
f.write(json_string({"unit_name": self.unit_name}))
@classmethod
def from_path(cls, path, **shared):
cfg = cls.config_type()
return cls(cfg)
register_processing_unit(FirstIntentParser)
register_processing_unit(SecondIntentParser)
mocked_dataset_metadata = {
"language_code": "en",
"entities": {
"mocked_entity": {
"automatically_extensible": True,
"utterances": dict()
}
},
"slot_name_mappings": {
"dummy_intent_1": {
"mocked_slot_name": "mocked_entity"
}
}
}
config = NLUEngineConfig([FirstIntentParserConfig(),
SecondIntentParserConfig()])
engine = SnipsNLUEngine(config).fit(SAMPLE_DATASET)
# pylint:disable=protected-access
engine._dataset_metadata = mocked_dataset_metadata
# pylint:enable=protected-access
# When
parse = engine.parse(input_text)
# Then
expected_slots = [custom_slot(s) for s in slots]
expected_parse = parsing_result(input_text, intent, expected_slots)
self.assertDictEqual(expected_parse, parse)
def test_should_be_deserializable_from_dir(
self, mocked_builtin_entity_parser, mocked_custom_entity_parser):
# Given
mocked_builtin_entity_parser.from_path = MagicMock()
mocked_custom_entity_parser.from_path = MagicMock()
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": [
"test_intent_parser1",
"test_intent_parser2",
],
"builtin_entity_parser": "builtin_entity_parser",
"custom_entity_parser": "custom_entity_parser",
"model_version": snips_nlu.__model_version__,
"training_package_version": snips_nlu.__version__
}
self.tmp_file_path.mkdir()
parser1_path = self.tmp_file_path / "test_intent_parser1"
parser1_path.mkdir()
parser2_path = self.tmp_file_path / "test_intent_parser2"
parser2_path.mkdir()
(self.tmp_file_path / "resources").mkdir()
self.writeJsonContent(self.tmp_file_path / "nlu_engine.json",
engine_dict)
self.writeJsonContent(parser1_path / "metadata.json",
{"unit_name": "test_intent_parser1"})
self.writeJsonContent(parser2_path / "metadata.json",
{"unit_name": "test_intent_parser2"})
# When
engine = SnipsNLUEngine.from_path(self.tmp_file_path)
# 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)
mocked_custom_entity_parser.from_path.assert_called_once_with(
self.tmp_file_path / "custom_entity_parser")
mocked_builtin_entity_parser.from_path.assert_called_once_with(
self.tmp_file_path / "builtin_entity_parser")
def test_should_retrain_only_non_trained_subunits(self):
# Given
class TestIntentParserConfig(ProcessingUnitConfig):
unit_name = "test_intent_parser"
def to_dict(self):
return {"unit_name": self.unit_name}
@classmethod
def from_dict(cls, obj_dict):
return TestIntentParserConfig()
def get_required_resources(self):
return None
class TestIntentParser(IntentParser):
unit_name = "test_intent_parser"
config_type = TestIntentParserConfig
def __init__(self, config):
super(TestIntentParser, self).__init__(config)
self.sub_unit_1 = dict(fitted=False, calls=0)
self.sub_unit_2 = dict(fitted=False, calls=0)
def fit(self, dataset, force_retrain):
if force_retrain:
self.sub_unit_1["fitted"] = True
self.sub_unit_1["calls"] += 1
self.sub_unit_2["fitted"] = True
self.sub_unit_2["calls"] += 1
else:
if not self.sub_unit_1["fitted"]:
self.sub_unit_1["fitted"] = True
self.sub_unit_1["calls"] += 1
if not self.sub_unit_2["fitted"]:
self.sub_unit_2["fitted"] = True
self.sub_unit_2["calls"] += 1
return self
@property
def fitted(self):
return self.sub_unit_1["fitted"] and \
self.sub_unit_2["fitted"]
def parse(self, text, intents):
return empty_result(text)
def persist(self, path):
path = Path(path)
path.mkdir()
with (path / "metadata.json").open(mode="w") as f:
f.write(json_string({"unit_name": self.unit_name}))
@classmethod
def from_path(cls, path):
cfg = cls.config_type()
return cls(cfg)
register_processing_unit(TestIntentParser)
intent_parser_config = TestIntentParserConfig()
nlu_engine_config = NLUEngineConfig([intent_parser_config])
nlu_engine = SnipsNLUEngine(nlu_engine_config)
intent_parser = TestIntentParser(intent_parser_config)
intent_parser.sub_unit_1.update(dict(fitted=True, calls=0))
nlu_engine.intent_parsers.append(intent_parser)
# When
nlu_engine.fit(SAMPLE_DATASET, force_retrain=False)
# Then
self.assertDictEqual(dict(fitted=True, calls=0),
intent_parser.sub_unit_1)
self.assertDictEqual(dict(fitted=True, calls=1),
intent_parser.sub_unit_2)
def test_should_be_serializable_into_zip(self):
# Given
register_processing_unit(TestIntentParser1)
register_processing_unit(TestIntentParser2)
parser1_config = TestIntentParser1Config()
parser2_config = TestIntentParser2Config()
parsers_configs = [parser1_config, parser2_config]
config = NLUEngineConfig(parsers_configs)
engine = SnipsNLUEngine(config).fit(BEVERAGE_DATASET)
# When
engine.persist(self.tmp_file_path)
# Then
parser1_config = TestIntentParser1Config()
parser2_config = TestIntentParser2Config()
parsers_configs = [parser1_config, parser2_config]
expected_engine_config = NLUEngineConfig(parsers_configs)
expected_engine_dict = {
"unit_name": "nlu_engine",
"dataset_metadata": {
"language_code": "en",
"entities": {
"Temperature": {
"automatically_extensible": True,
"utterances": {
"boiling": "hot",
"Boiling": "hot",
"cold": "cold",
"Cold": "cold",
"hot": "hot",
"Hot": "hot",
"iced": "cold",
"Iced": "cold"
}
}
},
"slot_name_mappings": {
"MakeCoffee": {
"number_of_cups": "snips/number"
},
"MakeTea": {
"beverage_temperature": "Temperature",
"number_of_cups": "snips/number"
}
},
},
"config": expected_engine_config.to_dict(),
"intent_parsers": [
"test_intent_parser1",
"test_intent_parser2"
],
"model_version": snips_nlu.__model_version__,
"training_package_version": snips_nlu.__version__
}
self.assertJsonContent(self.tmp_file_path / "nlu_engine.json",
expected_engine_dict)
self.assertJsonContent(
self.tmp_file_path / "test_intent_parser1" / "metadata.json",
{"unit_name": "test_intent_parser1"})
self.assertJsonContent(
self.tmp_file_path / "test_intent_parser2" / "metadata.json",
{"unit_name": "test_intent_parser2"})
def test_should_use_parsers_sequentially(self):
# Given
input_text = "hello world"
intent = intent_classification_result(
intent_name='dummy_intent_1', probability=0.7)
slots = [unresolved_slot(match_range=(6, 11),
value='world',
entity='mocked_entity',
slot_name='mocked_slot_name')]
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):
conf = cls.config_type()
return TestIntentParser1(conf)
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):
if text == input_text:
return parsing_result(text, intent, slots)
return empty_result(text)
def to_dict(self):
return {
"unit_name": self.unit_name,
}
@classmethod
def from_dict(cls, unit_dict):
conf = cls.config_type()
return TestIntentParser2(conf)
register_processing_unit(TestIntentParser1)
register_processing_unit(TestIntentParser2)
mocked_dataset_metadata = {
"language_code": "en",
"entities": {
"mocked_entity": {
"automatically_extensible": True,
"utterances": dict()
}
},
"slot_name_mappings": {
"dummy_intent_1": {
"mocked_slot_name": "mocked_entity"
}
}
}
config = NLUEngineConfig([TestIntentParser1Config(),
TestIntentParser2Config()])
engine = SnipsNLUEngine(config).fit(SAMPLE_DATASET)
# pylint:disable=protected-access
engine._dataset_metadata = mocked_dataset_metadata
# pylint:enable=protected-access
# When
parse = engine.parse(input_text)
# Then
expected_slots = [custom_slot(s) for s in slots]
expected_parse = parsing_result(input_text, intent, expected_slots)
self.assertDictEqual(expected_parse, parse)
