StatsModel的GLM不适用于ScikitLearn的RandomizedSearch

Question

When I tried to test out different hyperparameter options using GLM 
with a tweedie via RandomizedSearchCV, I got this error:


/opt/anaconda/lib/python3.6/site- 
packages/statsmodels/genmod/families/family.py in __init__(self, 
link, var_power)
1361             link = L.log()
1362         super(Tweedie, self).__init__(
-> 1363             link=link, variance=V.Power(power=var_power * 
1.))

1365     def _resid_dev(self, endog, mu):

TypeError: unsupported operand type(s) for *: 'NoneType' and 'float'

******************************************************************
Note: this is part of a pipeline implementation. 

When I tried to do the pipeline by itself, the code was working as 
expected.


I tried tuning the other parameters in the pipeline, and got the 
same error.

#############################################
Background code - Given that statsmodel was not a native model in 
scitlearn, I first created the transformer before using it in the 
pipeline
#############################################

class GLM(BaseEstimator, RegressorMixin):
    def __init__(self, tweedie_power=1.5, intercept=True):
        self._power = tweedie_power
        self._intercept = intercept

    def fit(self, X, y=None):
        tweedie = sm.families.Tweedie(var_power=self._power)
        if self._intercept:
            X["intercept"] = 1
        self.model = sm.GLM(y, X, family=tweedie, maxiter=1000).fit()
        self.params_ = self.model.params
        self.standard_errors_ = self.model.bse
        self.pvalues_ = self.model.pvalues
        return self

    def predict(self, X):
        if self._intercept:
            X["intercept"] = 1
        y = self.model.predict(X)
        return y

＃

在使用RandomizedSearchCV之前，如下所示的管道已按预期＃功能运行，并且GLM不会返回任何错误。

＃

steps = [("copy", Copy()),
         ("credit_fix", CreditScoreHistoricalNoHitFix()),
         ("glm", GLM(tweedie_power=1.1))]

pipe = Pipeline(steps)
pipe.fit(X_train, y_train)
y_pred = pipe.predict(X_test)

＃

在将RandomizedSearchCV添加为下面的超参数调整之后，这就是我开始在 init （（自我，链接，＃var_power）

#   1361             link = L.log()
#   1362         super(Tweedie, self).__init__(
#-> 1363             link=link, variance=V.Power(power=var_power * 
1.))
#   1364 
#   1365     def _resid_dev(self, endog, mu):

#TypeError: unsupported operand type(s) for *: 'NoneType' and 
'float'

＃

pipe = Pipeline(steps)

hyperparameters = {
    "glm__tweedie_power": [1.5, 1.6]
 }

model = RandomizedSearchCV(pipe, hyperparameters)

model.fit(X_train, y_train)
y_pred = pipe.predict(X_test)