Using statsmodel estimations with scikit-learn cross validation, is it possible?

Question

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

I am looking for a way I can use

Answer 1

Following the suggestion of David (which gave me an error, complaining about missing function get_parameters) and the scikit learn documentation, I created the following wrapper for a linear regression. It has the same interface of sklearn.linear_model.LinearRegression but in addition has also the function summary(), which gives the info about p-values, R2 and other statistics, as in statsmodels.OLS.

import statsmodels.api as sm
from sklearn.base import BaseEstimator, RegressorMixin
import pandas as pd
import numpy as np

from sklearn.utils.multiclass import check_classification_targets
from sklearn.utils.validation import check_X_y, check_is_fitted, check_array
from sklearn.utils.multiclass import unique_labels
from sklearn.utils.estimator_checks import check_estimator



class MyLinearRegression(BaseEstimator, RegressorMixin):
    def __init__(self, fit_intercept=True):

        self.fit_intercept = fit_intercept


    """
    Parameters
    ------------
    column_names: list
            It is an optional value, such that this class knows 
            what is the name of the feature to associate to 
            each column of X. This is useful if you use the method
            summary(), so that it can show the feature name for each
            coefficient
    """ 
    def fit(self, X, y, column_names=() ):

        if self.fit_intercept:
            X = sm.add_constant(X)

        # Check that X and y have correct shape
        X, y = check_X_y(X, y)


        self.X_ = X
        self.y_ = y

        if len(column_names) != 0:
            cols = column_names.copy()
            cols = list(cols)
            X = pd.DataFrame(X)
            cols = column_names.copy()
            cols.insert(0,'intercept')
            print('X ', X)
            X.columns = cols

        self.model_ = sm.OLS(y, X)
        self.results_ = self.model_.fit()
        return self



    def predict(self, X):
        # Check is fit had been called
        check_is_fitted(self, 'model_')

        # Input validation
        X = check_array(X)

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


    def get_params(self, deep = False):
        return {'fit_intercept':self.fit_intercept}


    def summary(self):
        print(self.results_.summary() )

Example of use:

cols = ['feature1','feature2']
X_train = df_train[cols].values
X_test = df_test[cols].values
y_train = df_train['label']
y_test = df_test['label']
model = MyLinearRegression()
model.fit(X_train, y_train)
model.summary()
model.predict(X_test)

If you want to show the names of the columns, you can call

model.fit(X_train, y_train, column_names=cols)

To use it in cross_validation:

from sklearn.model_selection import cross_val_score
scores = cross_val_score(MyLinearRegression(), X_train, y_train, cv=10, scoring='neg_mean_squared_error')
scores