def fit(self, dataset, force_retrain=True):
"""Fit the slot filler
Args:
dataset (dict): A valid Snips dataset
force_retrain (bool, optional): If *False*, will not retrain intent
classifier and slot fillers when they are already fitted.
Default to *True*.
Returns:
:class:`ProbabilisticIntentParser`: The same instance, trained
"""
dataset = validate_and_format_dataset(dataset)
intents = list(dataset[INTENTS])
if self.intent_classifier is None:
self.intent_classifier = build_processing_unit(
self.config.intent_classifier_config)
if force_retrain or not self.intent_classifier.fitted:
self.intent_classifier.fit(dataset)
if self.slot_fillers is None:
self.slot_fillers = dict()
for intent_name in intents:
# We need to copy the slot filler config as it may be mutated
if self.slot_fillers.get(intent_name) is None:
slot_filler_config = deepcopy(self.config.slot_filler_config)
self.slot_fillers[intent_name] = build_processing_unit(
slot_filler_config)
if force_retrain or not self.slot_fillers[intent_name].fitted:
self.slot_fillers[intent_name].fit(dataset, intent_name)
return self
def fit(self, dataset, force_retrain=True):
"""Fit the slot filler
Args:
dataset (dict): A valid Snips dataset
force_retrain (bool, optional): If *False*, will not retrain intent
classifier and slot fillers when they are already fitted.
Default to *True*.
Returns:
:class:`ProbabilisticIntentParser`: The same instance, trained
"""
logger.info("Fitting probabilistic intent parser...")
dataset = validate_and_format_dataset(dataset)
intents = list(dataset[INTENTS])
if self.intent_classifier is None:
self.intent_classifier = build_processing_unit(
self.config.intent_classifier_config)
if force_retrain or not self.intent_classifier.fitted:
self.intent_classifier.fit(dataset)
if self.slot_fillers is None:
self.slot_fillers = dict()
slot_fillers_start = datetime.now()
for intent_name in intents:
# We need to copy the slot filler config as it may be mutated
if self.slot_fillers.get(intent_name) is None:
slot_filler_config = deepcopy(self.config.slot_filler_config)
self.slot_fillers[intent_name] = build_processing_unit(
slot_filler_config)
if force_retrain or not self.slot_fillers[intent_name].fitted:
self.slot_fillers[intent_name].fit(dataset, intent_name)
logger.debug("Fitted slot fillers in %s",
elapsed_since(slot_fillers_start))
return self
def fit(self, dataset, force_retrain=True):
"""Fit the NLU engine
Args:
dataset (dict): A valid Snips dataset
force_retrain (bool, optional): If *False*, will not retrain intent
parsers when they are already fitted. Default to *True*.
Returns:
The same object, trained.
"""
logger.info("Fitting NLU engine...")
dataset = validate_and_format_dataset(dataset)
self._dataset_metadata = _get_dataset_metadata(dataset)
if self.config is None:
language = self._dataset_metadata["language_code"]
self.config = self.config_type.from_dict(DEFAULT_CONFIGS[language])
parsers = []
for parser_config in self.config.intent_parsers_configs:
# Re-use existing parsers to allow pre-training
recycled_parser = None
for parser in self.intent_parsers:
if parser.unit_name == parser_config.unit_name:
recycled_parser = parser
break
if recycled_parser is None:
recycled_parser = build_processing_unit(parser_config)
if force_retrain or not recycled_parser.fitted:
recycled_parser.fit(dataset, force_retrain)
parsers.append(recycled_parser)
self.intent_parsers = parsers
return self
def fit(self, dataset, force_retrain=True):
"""Fit the NLU engine
Args:
dataset (dict): A valid Snips dataset
force_retrain (bool, optional): If *False*, will not retrain intent
parsers when they are already fitted. Default to *True*.
Returns:
The same object, trained.
"""
dataset = validate_and_format_dataset(dataset)
self._dataset_metadata = _get_dataset_metadata(dataset)
if self.config is None:
language = self._dataset_metadata["language_code"]
self.config = self.config_type.from_dict(DEFAULT_CONFIGS[language])
parsers = []
for parser_config in self.config.intent_parsers_configs:
# Re-use existing parsers to allow pre-training
recycled_parser = None
for parser in self.intent_parsers:
if parser.unit_name == parser_config.unit_name:
recycled_parser = parser
break
if recycled_parser is None:
recycled_parser = build_processing_unit(parser_config)
if force_retrain or not recycled_parser.fitted:
recycled_parser.fit(dataset, force_retrain)
parsers.append(recycled_parser)
self.intent_parsers = parsers
return self