结合使用statsmodel估计和scikit-learn交叉验证，是否可能？

问题描述

我将此问题发布到了Cross Validated论坛，后来意识到可能是可以在stackoverlfow中找到合适的受众。

I posted this question to Cross Validated forum and later realized may be this would find appropriate audience in stackoverlfow instead.

我正在寻找一种可以使用从python statsmodel获取的 fit 对象（结果），以馈入scikit-learn cross_validation方法的 cross_val_score 吗？
附加链接表明可能，但我没有成功。

I am looking for a way I can use the fit object (result) ontained from python statsmodel to feed into cross_val_score of scikit-learn cross_validation method?The attached link suggests that it may be possible but I have not succeeded.

我遇到以下错误

推荐答案

实际上，您不能直接在 statsmodels 上使用 cross_val_score 对象，因为接口不同：在statsmodels中

Indeed, you cannot use cross_val_score directly on statsmodels objects, because of different interface: in statsmodels

• 训练数据直接传递到构造函数中


• 一个单独的对象包含模型估计的结果

但是，您可以编写一个简单的包装器来制作 statsmodels 对象看起来像 sklearn 估算器：

However, you can write a simple wrapper to make statsmodels objects look like sklearn estimators:

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

此类包含正确的 fit 和预测方法，并且可以与 sklearn 一起使用，例如交叉验证或包含在管道中。像这里：

This class contains correct fit and predict methods, and can be used with sklearn, e.g. cross-validated or included into a pipeline. Like here:

from sklearn.datasets import make_regression
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LinearRegression

X, y = make_regression(random_state=1, n_samples=300, noise=100)

print(cross_val_score(SMWrapper(sm.OLS), X, y, scoring='r2'))
print(cross_val_score(LinearRegression(), X, y, scoring='r2'))

您可以看到两个模型的输出相同，因为它们都是OLS模型，并且以相同的方式进行交叉验证。

You can see that the output of two models is identical, because they are both OLS models, cross-validated in the same way.

[0.28592315 0.37367557 0.47972639]
[0.28592315 0.37367557 0.47972639]

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