def dump_to_data_file(data): if isinstance(data, str): data_string = data else: data_string = utils.json_to_string(data) return utils.create_temporary_file(data_string, "_training_data")
def evaluate( self, data: Text, project: Optional[Text] = None, model: Optional[Text] = None ) -> Dict[Text, Any]: """Perform a model evaluation.""" project = project or RasaNLUModelConfig.DEFAULT_PROJECT_NAME model = model or None file_name = utils.create_temporary_file(data, "_training_data") if project not in self.project_store: raise InvalidProjectError("Project {} could not be found".format(project)) model_name = self.project_store[project]._dynamic_load_model(model) self.project_store[project]._loader_lock.acquire() try: if not self.project_store[project]._models.get(model_name): interpreter = self.project_store[project]._interpreter_for_model( model_name ) self.project_store[project]._models[model_name] = interpreter finally: self.project_store[project]._loader_lock.release() return run_evaluation( data_path=file_name, model=self.project_store[project]._models[model_name], errors_filename=None, )
def test_url_data_format(): data = """ { "rasa_nlu_data": { "entity_synonyms": [ { "value": "nyc", "synonyms": ["New York City", "nyc", "the big apple"] } ], "common_examples" : [ { "text": "show me flights to New York City", "intent": "unk", "entities": [ { "entity": "destination", "start": 19, "end": 32, "value": "NYC" } ] } ] } }""" fname = utils.create_temporary_file(data.encode("utf-8"), suffix="_tmp_training_data.json", mode="w+b") data = utils.read_json_file(fname) assert data is not None validate_rasa_nlu_data(data)
def evaluate(self, data: Text, project: Optional[Text] = None, model: Optional[Text] = None) -> Deferred: """Perform a model evaluation.""" logger.debug("Evaluation request received for " "project '{}' and model '{}'.".format(project, model)) if self._worker_processes <= self._current_worker_processes: raise MaxWorkerProcessError project = project or RasaNLUModelConfig.DEFAULT_PROJECT_NAME data_path = utils.create_temporary_file(data, "_training_data") if project not in self.project_store: raise InvalidProjectError("Project '{}' could not " "be found.".format(project)) model = model or self.project_store[project]._dynamic_load_model(model) if model == FALLBACK_MODEL_NAME: raise UnsupportedModelError("No model in project '{}' to " "evaluate.".format(project)) model_path = os.path.join(self.project_store[project]._path, model) def evaluation_callback(result): logger.debug("Evaluation was successful") self._current_worker_processes -= 1 self.project_store[project].current_worker_processes -= 1 return result def evaluation_errback(failure): logger.warning(failure) self._current_worker_processes -= 1 self.project_store[project].current_worker_processes -= 1 return failure logger.debug("New evaluation queued.") self._current_worker_processes += 1 self.project_store[project].current_worker_processes += 1 result = self.pool.submit(run_evaluation, data_path, model_path, errors_filename=None) result = deferred_from_future(result) result.addCallback(evaluation_callback) result.addErrback(evaluation_errback) return result
def test_emojis_in_tmp_file(): test_data = """ data: - one ππ― π©πΏβπ»π¨πΏβπ» - two Β£ (?u)\\b\\w+\\b f\u00fcr """ test_file = utils.create_temporary_file(test_data) with io.open(test_file, mode="r", encoding="utf-8") as f: content = f.read() actual = rasa.utils.io.read_yaml(content) assert actual["data"][0] == "one ππ― π©πΏβπ»π¨πΏβπ»" assert actual["data"][1] == "two Β£ (?u)\\b\\w+\\b fΓΌr"
async def evaluate( self, data: Text, project: Optional[Text] = None, model: Optional[Text] = None ) -> Dict: """Perform a model evaluation.""" logger.debug( "Evaluation request received for " "project '{}' and model '{}'.".format(project, model) ) if self._worker_processes <= self._current_worker_processes: raise MaxWorkerProcessError project = project or RasaNLUModelConfig.DEFAULT_PROJECT_NAME data_path = utils.create_temporary_file(data, "_training_data") if project not in self.project_store: raise InvalidProjectError( "Project '{}' could not be found.".format(project) ) model = model or self.project_store[project]._dynamic_load_model(model) if model == FALLBACK_MODEL_NAME: raise UnsupportedModelError( "No model in project '{}' to evaluate.".format(project) ) model_path = os.path.join(self.project_store[project]._path, model) logger.debug("New evaluation queued.") self._current_worker_processes += 1 self.project_store[project].current_worker_processes += 1 loop = asyncio.get_event_loop() task = loop.run_in_executor(self.pool, run_evaluation, data_path, model_path) try: return await task except Exception as e: logger.warning(e) self.project_store[project].status = STATUS_FAILED self.project_store[project].error_message = str(e) raise finally: self._current_worker_processes -= 1 self.project_store[project].current_worker_processes -= 1
async def download_file_from_url(url: Text) -> Text: """Download a story file from a url and persists it into a temp file. Returns the file path of the temp file that contains the downloaded content.""" from rasa.nlu import utils as nlu_utils if not nlu_utils.is_url(url): raise InvalidURL(url) async with aiohttp.ClientSession() as session: async with session.get(url, raise_for_status=True) as resp: filename = nlu_utils.create_temporary_file(await resp.read(), mode="w+b") return filename
async def load_data_from_endpoint( data_endpoint: EndpointConfig, language: Optional[Text] = "en" ) -> "TrainingData": """Load training data from a URL.""" if not utils.is_url(data_endpoint.url): raise requests.exceptions.InvalidURL(data_endpoint.url) try: response = await data_endpoint.request("get") response.raise_for_status() temp_data_file = utils.create_temporary_file(response.content, mode="w+b") training_data = _load(temp_data_file, language) return training_data except Exception as e: logger.warning("Could not retrieve training data from URL:\n{}".format(e))