在 Python 中使用 GLM 回归模型执行交叉验证答案

【问题标题】：Perform cross-validation with GLM regression model in Python在 Python 中使用 GLM 回归模型执行交叉验证
【发布时间】：2022-01-09 20:47:14
【问题描述】：

如何使用 GLM 回归模型执行交叉验证？

我创建了一个 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，但是我找不到使用 sm.GLM 模型的方法。找到了多个使用 model = LogisticRegression() 的示例，但这不适用于我的数据。

代码如下：

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，我得到了错误。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

【问题讨论】：

标签： python regression cross-validation

【解决方案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)

【讨论】：