def _generate_pfa_regressor(result, indep_vars, featurizer):
# Create mock SGDRegressor for sklearn_to_pfa
estimator = SGDRegressor()
estimator.intercept_ = [result['intercept']]
# NOTE: linearly dependent columns will be assigned 0
estimator.coef_ = [
result.get(c, {'coef': 0.})['coef'] for c in featurizer.columns
if c != 'intercept'
]
types = [(var['name'], var['type']['name']) for var in indep_vars]
return sklearn_to_pfa(estimator, types, featurizer.generate_pretty_pfa())
def get_SGDRegressor(self):
sr = SGDRegressor(fit_intercept = False)
sr.coef_ = self.coef_
sr.intercept_ = 0.
self.t_ = 0
return sr
