def _step_db(estimator: BaseEstimator, ids: Tuple):
estimator.is_fitted_ = True
# make a dictionary of parameters
pms = ()
estimator_params = estimator.get_params()
for key in sorted(estimator_params.keys()):
value = estimator_params[key]
if isinstance(value, Callable):
pms = pms + (key, value.__name__)
if value == "warn":
pms = pms + (key, 10)
# discard parameters which are not json serializable
try:
json.dumps(value)
pms = pms + (key, value)
except TypeError:
continue
query = (
json.dumps(estimator.train_),
json.dumps(estimator.features_),
json.dumps(pms),
json.dumps(ids),
)
entry = (
*query,
pickle.dumps(estimator),
)
return query, entry
def _load(self, transformer: BaseEstimator, ids: Tuple):
query, entry = _step_db(transformer, ids)
result = _load_from_db(self.database_, query, create_model_stmt,
query_model_stmt)
if result:
ids = ids + (result[0], )
transformer = pickle.loads(result[5])
transformer.is_fitted_ = True
return transformer, ids
else:
return None, None
