def load(cls, model_desc: dict):
obj_layout = cls()
obj_layout.features = model_desc['features']
obj_layout.formulas = decode(model_desc['formulas'])
obj_layout.trained_time = model_desc['trained_time']
obj_layout.impl = cls.model_decode(model_desc['desc'])
if 'fit_target' in model_desc:
obj_layout.fit_target = decode(model_desc['fit_target'])
else:
obj_layout.fit_target = None
return obj_layout
def load(cls, universe_desc: dict):
name = universe_desc['name']
base_universe = universe_desc['base_universe']
exclude_universe = universe_desc['exclude_universe']
special_codes = universe_desc['special_codes']
filter_cond = decode(universe_desc['filter_cond'])
return cls(name=name,
base_universe=base_universe,
exclude_universe=exclude_universe,
special_codes=special_codes,
filter_cond=filter_cond)
def load(cls, model_desc: dict):
obj_layout = cls()
obj_layout.features = model_desc['features']
obj_layout.trained_time = model_desc['trained_time']
if LooseVersion(sklearn_version) < LooseVersion(model_desc['sklearn_version']):
alpha_logger.warning('Current sklearn version {0} is lower than the model version {1}. '
'Loaded model may work incorrectly.'.format(
sklearn_version, model_desc['sklearn_version']))
obj_layout.impl = decode(model_desc['desc'])
return obj_layout
def decode_formula(str_repr):
formula = decode(str_repr)
return formula
def model_decode(cls, model_desc):
return decode(model_desc)