我将这个问题发布到了Cross Validated论坛，后来意识到可能在stackoverflow中会找到更合适的受众。
我正在寻找一种方法，可以使用从Python statsmodels得出的fit对象（结果），将其输入到scikit-learn交叉验证方法的cross_val_score中？附带的链接表明这可能是可能的，但我没有成功。
我遇到了以下错误：
评估器应该成为实现'fit'方法的评估器 传递了statsmodels.discrete.discrete_model.BinaryResultsWrapper对象于0x7fa6e801c590
确实，您不能直接在statsmodels对象上使用cross_val_score，因为它们的接口不同：
但是，您可以编写一个简单的包装器，使statsmodels对象看起来像sklearn评估器：
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)
这个类包含正确的fit和predict方法，并且可以与sklearn一起使用，例如交叉验证或包含到管道中。就像这样：
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'))
你可以看到两个模型的输出是相同的，因为它们都是OLS模型，并以相同的方式进行了交叉验证。
[0.28592315 0.37367557 0.47972639]
[0.28592315 0.37367557 0.47972639]
参照David的建议(但是出现了错误，提示缺少函数get_parameters)以及scikit learn文档，我编写了以下线性回归包装器。它具有与sklearn.linear_model.LinearRegression相同的接口，并且还有summary()函数，该函数提供有关p值、R2和其他统计信息的信息，就像statsmodels.OLS一样。
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() )
使用示例：
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)
如果想要显示列的名称，可以调用以下方法：
model.fit(X_train, y_train, column_names=cols)
要在交叉验证中使用它：
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
为了参考，如果您使用 statsmodels 公式API 和/或使用 fit_regularized 方法，您可以按照以下方式修改 @David Dale 的包装器类。
import pandas as pd
from sklearn.base import BaseEstimator, RegressorMixin
from statsmodels.formula.api import glm as glm_sm
# This is an example wrapper for statsmodels GLM
class SMWrapper(BaseEstimator, RegressorMixin):
def __init__(self, family, formula, alpha, L1_wt):
self.family = family
self.formula = formula
self.alpha = alpha
self.L1_wt = L1_wt
self.model = None
self.result = None
def fit(self, X, y):
data = pd.concat([pd.DataFrame(X), pd.Series(y)], axis=1)
data.columns = X.columns.tolist() + ['y']
self.model = glm_sm(self.formula, data, family=self.family)
self.result = self.model.fit_regularized(alpha=self.alpha, L1_wt=self.L1_wt, refit=True)
return self.result
def predict(self, X):
return self.result.predict(X)
虽然我认为这不是技术上的scikit-learn，但有一个名为pmdarima（链接到PyPi上的pmdarima包）的软件包，它封装了statsmodel并提供了类似于scikit-learn的接口。