Perform cross-validation with GLM regression model in Python

Question

How do I perform cross-validation with GLM regression model?

I have created a glm model sm.GLM(endog, exog, family=sm.families.Gamma(link=sm.families.links.log())).fit() and I would need to cross-validate the result, however I cannot find a way to do this with sm.GLM model. Found multiple examples where model = LogisticRegression() is used, but this is not applicable to my data.

Here is the code:

import pandas as pd
import statsmodels.api as sm
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.model_selection import KFold

Test = pd.read_csv(r'D:\myfile.csv')

endog = Test['Y']
exog = Test[['log_X1', 'log_A', 'log_B']]

glm_model = sm.GLM(endog, exog, family=sm.families.Gaussian(link=sm.families.links.log())).fit()
y_pred = glm_model.predict()

scoring = "neg_root_mean_squared_error"
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.30, random_state=1)
crossvalidation = KFold(n_splits=10)
scores = cross_val_score(glm_model, X_train, y_train, scoring="neg mean_squared_error", cv=crossvalidation)

With the particular line I get error. Perhaps there are other ways how to do this?

    scores = cross_val_score(glm_model, X_train, y_train, scoring="neg mean_squared_error", cv=crossvalidation)
TypeError: estimator should be an estimator implementing 'fit' method, <statsmodels.genmod.generalized_linear_model.GLMResultsWrapper object at 0x000002972A2181F0> was passed

Answer 1

The answer is SMWrapper:

  import statsmodels.api as sm
    from sklearn.base import BaseEstimator, RegressorMixin
    class SMWrapper(BaseEstimator, RegressorMixin):
        """ A universal sklearn-style wrapper for statsmodels regressors """

        def __init__(self, model_class, fit_intercept=True):
            self.model_class = model_class
            self.fit_intercept = fit_intercept

        def fit(self, X, y):
            if self.fit_intercept:
                X = sm.add_constant(X)
            self.model_ = self.model_class(y, X)
            self.results_ = self.model_.fit()
            return self

        def predict(self, X):
            if self.fit_intercept:
                X = sm.add_constant(X)
            return self.results_.predict(X)