def main(queryfile: str, features: Optional[str] = None, halving: Optional[bool] = False, save: Optional[bool] = True):
service = GridSearchService()
models = ModelService()
with open(queryfile, 'r') as f:
query = json.load(f)
# if save:
# models.clear_parameters(query)
# else:
# logging.info("Results will not be saved [--no-save]")
search_models = models.query_models(query)
models.clear_parameters(query)
logging.info("[i] {} models to train".format(len(search_models)))
for i, m in enumerate(search_models):
# if m.parameters:
# logging.info("==[{}/{}]== MODEL: {} {} {} {} ==> SKIP".format(i+1, len(search_models), m.symbol, m.dataset, m.target, m.pipeline))
# continue # Skip this as search has already been performed
logging.info("==[{}/{}]== MODEL: {} {} {} {} =====".format(i+1, len(search_models), m.symbol, m.dataset, m.target, m.pipeline))
mp = service.create_parameters_search(m, split=0.7, features=features)
logging.info("[{}] Start grid search".format(get_timestamp()))
mp = service.grid_search(m, mp, sync=True, verbose=1, n_jobs=8, halving=halving, save=save)
logging.info("[{}] End grid search".format(get_timestamp()))
def model_index(features: Optional[bool] = True,
parameters: Optional[bool] = True,
tests: Optional[bool] = True,
query: Optional[dict] = Body(...),
service: ModelService = Depends(ModelService)):
result = {}
if features:
result['features'] = service.clear_features(query)
if parameters:
result['parameters'] = service.clear_parameters(query)
if tests:
result['tests'] = service.clear_tests(query)
return result