在Python中使用GLM回归模型进行交叉验证

Question

如何使用GLM回归模型进行交叉验证？

我已经创建了一个glm模型sm.GLM(endog, exog, family=sm.families.Gamma(link=sm.families.links.log())).fit()，我需要交叉验证结果，但我找不到用sm.GLM模型来实现这一点的方法。找到了多个使用model = LogisticRegression()的示例，但这不适用于我的数据。

这是代码：

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)

对于特定的行，我会出错。也许还有其他方法可以做到这一点？

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

答案是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)