def main(args):
utils.configure_colored_logging(args.loglevel)
pre_load = args.pre_load
_endpoints = read_endpoints(args.endpoints)
router = DataRouter(
args.path,
args.max_training_processes,
args.response_log,
args.emulate,
args.storage,
model_server=_endpoints.model,
wait_time_between_pulls=args.wait_time_between_pulls
)
if pre_load:
logger.debug('Preloading....')
if 'all' in pre_load:
pre_load = router.project_store.keys()
router._pre_load(pre_load)
rasa = RasaNLU(
router,
args.loglevel,
args.write,
args.num_threads,
get_token(cmdline_args.token),
args.cors,
default_config_path=args.config
)
logger.info('Started http server on port %s' % args.port)
rasa.app.run('0.0.0.0', args.port)
def main(args):
utils.configure_colored_logging(args.loglevel)
pre_load = args.pre_load
_endpoints = read_endpoints(args.endpoints)
router = DataRouter(args.path,
args.max_training_processes,
args.response_log,
args.emulate,
args.storage,
model_server=_endpoints.model,
wait_time_between_pulls=args.wait_time_between_pulls)
if pre_load:
logger.debug('Preloading....')
if 'all' in pre_load:
pre_load = router.project_store.keys()
router._pre_load(pre_load)
rasa = RasaNLU(router,
args.loglevel,
args.write,
args.num_threads,
get_token(cmdline_args.token),
args.cors,
default_config_path=args.config)
logger.info('Started http server on port %s' % args.port)
rasa.app.run('0.0.0.0', args.port)
def main():
parser = create_argument_parser()
cmdline_args = parser.parse_args()
utils.configure_colored_logging(cmdline_args.loglevel)
if cmdline_args.mode == "crossvalidation":
# TODO: move parsing into sub parser
# manual check argument dependency
if cmdline_args.model is not None:
parser.error("Crossvalidation will train a new model "
"- do not specify external model.")
if cmdline_args.config is None:
parser.error("Crossvalidation will train a new model "
"you need to specify a model configuration.")
nlu_config = config.load(cmdline_args.config)
data = training_data.load_data(cmdline_args.data)
data = drop_intents_below_freq(data, cutoff=5)
results, entity_results = run_cv_evaluation(
data, int(cmdline_args.folds), nlu_config)
logger.info("CV evaluation (n={})".format(cmdline_args.folds))
if any(results):
logger.info("Intent evaluation results")
return_results(results.train, "train")
return_results(results.test, "test")
if any(entity_results):
logger.info("Entity evaluation results")
return_entity_results(entity_results.train, "train")
return_entity_results(entity_results.test, "test")
elif cmdline_args.mode == "evaluation":
run_evaluation(cmdline_args.data,
cmdline_args.model,
cmdline_args.report,
cmdline_args.successes,
cmdline_args.errors,
cmdline_args.confmat,
cmdline_args.histogram)
logger.info("Finished evaluation")
def main():
parser = create_argument_parser()
cmdline_args = parser.parse_args()
utils.configure_colored_logging(cmdline_args.loglevel)
if cmdline_args.mode == "crossvalidation":
# TODO: move parsing into sub parser
# manual check argument dependency
if cmdline_args.model is not None:
parser.error("Crossvalidation will train a new model "
"- do not specify external model.")
if cmdline_args.config is None:
parser.error("Crossvalidation will train a new model "
"you need to specify a model configuration.")
nlu_config = config.load(cmdline_args.config)
data = training_data.load_data(cmdline_args.data)
data = drop_intents_below_freq(data, cutoff=5)
results, entity_results = cross_validate(
data, int(cmdline_args.folds), nlu_config)
logger.info("CV evaluation (n={})".format(cmdline_args.folds))
if any(results):
logger.info("Intent evaluation results")
return_results(results.train, "train")
return_results(results.test, "test")
if any(entity_results):
logger.info("Entity evaluation results")
return_entity_results(entity_results.train, "train")
return_entity_results(entity_results.test, "test")
elif cmdline_args.mode == "evaluation":
run_evaluation(cmdline_args.data,
cmdline_args.model,
cmdline_args.report,
cmdline_args.successes,
cmdline_args.errors,
cmdline_args.confmat,
cmdline_args.histogram)
logger.info("Finished evaluation")
params.get('project',
RasaNLUModelConfig.DEFAULT_PROJECT_NAME),
params.get('model')
)
return simplejson.dumps(response)
except Exception as e:
request.setResponseCode(500)
logger.exception(e)
return simplejson.dumps({"error": "{}".format(e)})
if __name__ == '__main__':
# Running as standalone python application
cmdline_args = create_argument_parser().parse_args()
utils.configure_colored_logging(cmdline_args.loglevel)
pre_load = cmdline_args.pre_load
_endpoints = read_endpoints(cmdline_args.endpoints)
router = DataRouter(
cmdline_args.path,
cmdline_args.max_training_processes,
cmdline_args.response_log,
cmdline_args.emulate,
cmdline_args.storage,
model_server=_endpoints.model,
wait_time_between_pulls=cmdline_args.wait_time_between_pulls
)
if pre_load:
logger.debug('Preloading....')
response = self.data_router.unload_model(
params.get('project', RasaNLUModelConfig.DEFAULT_PROJECT_NAME),
params.get('model')
)
return simplejson.dumps(response)
except Exception as e:
request.setResponseCode(500)
logger.exception(e)
return simplejson.dumps({"error": "{}".format(e)})
if __name__ == '__main__':
# Running as standalone python application
cmdline_args = create_argument_parser().parse_args()
utils.configure_colored_logging(cmdline_args.loglevel)
router = DataRouter(cmdline_args.path,
cmdline_args.max_training_processes,
cmdline_args.response_log)
rasa = RasaNLU(
router,
cmdline_args.loglevel,
cmdline_args.write,
cmdline_args.num_threads,
cmdline_args.token,
cmdline_args.cors,
default_config_path=cmdline_args.config
)
logger.info('Started http server on port %s' % cmdline_args.port)
