def save(self) -> dict:
model_desc = super().save()
model_desc['internal_model'] = self.impl.__class__.__module__ + "." + self.impl.__class__.__name__
model_desc['desc'] = encode(self.impl)
model_desc['sklearn_version'] = sklearn_version
model_desc['trained_time'] = self.trained_time
model_desc['weight'] = self.impl.coef_.tolist()
return model_desc
def encode_formula(formula):
str_repr = encode(formula)
return {
'desc': str_repr,
'formula_type':
formula.__class__.__module__ + "." + formula.__class__.__name__,
'dependency': formula.fields,
'window': formula.window
}
def save(self) -> dict:
if self.__class__.__module__ == '__main__':
alpha_logger.warning(
"model is defined in a main module. The model_name may not be correct."
)
model_desc = dict(model_name=self.__class__.__module__ + "." +
self.__class__.__name__,
language='python',
saved_time=arrow.now().format("YYYY-MM-DD HH:mm:ss"),
features=list(self.features),
trained_time=self.trained_time,
desc=self.model_encode(),
formulas=encode(self.formulas),
fit_target=encode(self.fit_target),
internal_model=self.impl.__class__.__module__ + "." +
self.impl.__class__.__name__)
return model_desc
def save(self):
return dict(name=self.name,
base_universe=self.base_universe,
exclude_universe=self.exclude_universe,
special_codes=self.special_codes,
filter_cond=encode(self.filter_cond))
def __eq__(self, rhs):
return self.model_encode() == rhs.model_encode() \
and self.trained_time == rhs.trained_time \
and list_eq(self.features, rhs.features) \
and encode(self.formulas) == encode(rhs.formulas) \
and encode(self.fit_target) == encode(rhs.fit_target)
def model_encode(self):
return encode(self.impl)