class CrossSectionalModelLasso(CrossSectionalModelBase):
def fit(self, X, y, **kwargs):
if self.fit_intercept:
X = sm.add_constant(X)
try:
self.alpha = kwargs['alpha']
except:
raise Exception(
'cannot find alpha! please set the penalty of Lasso')
else:
self.model = OLS(y, X)
self.res = self.model.fit_regularized(alpha=self.alpha,
L1_wt=1,
**kwargs)
class CrossSectionalModelRidge(CrossSectionalModelLinear):
def __init__(self, jsonPath=None, paraDict={}):
self.parameter = paraDict
if jsonPath is not None:
with open(jsonPath, 'r') as f:
self.parameter = json.loads(f)
self.fit_intercept = self.parameter.get('fit_intercept', True)
self.model = None
def fit(self, X, y, **kwargs):
if self.fit_intercept:
X = sm.add_constant(X)
try:
self.alpha = self.parameter['alpha']
except:
raise Exception(
'cannot find alpha! please set the penalty of Ridge')
else:
self.model = OLS(y, X)
self.res = self.model.fit_regularized(alpha=self.alpha,
L1_wt=0,
**kwargs)
