async def test_use_of_interface(): importer = TrainingDataImporter() functions_to_test = [ lambda: importer.get_config(), lambda: importer.get_stories(), lambda: importer.get_nlu_data(), lambda: importer.get_domain(), ] for f in functions_to_test: with pytest.raises(NotImplementedError): await f()
async def _train_core_with_validated_data( file_importer: TrainingDataImporter, output: Text, train_path: Optional[Text] = None, fixed_model_name: Optional[Text] = None, additional_arguments: Optional[Dict] = None, ) -> Optional[Text]: """Train Core with validated training and config data.""" import rasa.core.train with ExitStack() as stack: if train_path: # If the train path was provided, do nothing on exit. _train_path = train_path else: # Otherwise, create a temp train path and clean it up on exit. _train_path = stack.enter_context( TempDirectoryPath(tempfile.mkdtemp())) # normal (not compare) training print_color("Training Core model...", color=bcolors.OKBLUE) # bf mod domain, config = await asyncio.gather( file_importer.get_domain(), file_importer.get_core_config() #.get_config() ) # /bf mod await rasa.core.train( domain_file=domain, training_resource=file_importer, output_path=os.path.join(_train_path, DEFAULT_CORE_SUBDIRECTORY_NAME), policy_config=config, additional_arguments=additional_arguments, ) print_color("Core model training completed.", color=bcolors.OKBLUE) if train_path is None: # Only Core was trained. new_fingerprint = await model.model_fingerprint(file_importer) return model.package_model( fingerprint=new_fingerprint, output_directory=output, train_path=_train_path, fixed_model_name=fixed_model_name, model_prefix="core-", ) return _train_path