如何在python中的sklearn中获取GridSearchCV中的选定功能

问题描述

我正在将recurive feature elimination with cross validation (rfecv)作为GridSearchCV的特征选择技术.

I am using recurive feature elimination with cross validation (rfecv) as the feature selection technique with GridSearchCV.

我的代码如下.

X = df[my_features_all]
y = df['gold_standard']

x_train, x_test, y_train, y_test = train_test_split(X, y, random_state=0)

k_fold = StratifiedKFold(n_splits=5, shuffle=True, random_state=0)

clf = RandomForestClassifier(random_state = 42, class_weight="balanced")

rfecv = RFECV(estimator=clf, step=1, cv=k_fold, scoring='roc_auc')

param_grid = {'estimator__n_estimators': [200, 500],
    'estimator__max_features': ['auto', 'sqrt', 'log2'],
    'estimator__max_depth' : [3,4,5]
    }

CV_rfc = GridSearchCV(estimator=rfecv, param_grid=param_grid, cv= k_fold, scoring = 'roc_auc', verbose=10, n_jobs = 5)
CV_rfc.fit(x_train, y_train)
print("Finished feature selection and parameter tuning")

现在，我想从上面的代码中获取optimal number of features和selected features.

Now, I want to get the optimal number of features and selected features from the above code.

为此，我运行了以下代码.

For that I ran the below code.

#feature selection results
print("Optimal number of features : %d" % rfecv.n_features_)
features=list(X.columns[rfecv.support_])
print(features)

但是，出现以下错误:AttributeError: 'RFECV' object has no attribute 'n_features_'.

However, I got the following error:AttributeError: 'RFECV' object has no attribute 'n_features_'.

还有其他获取这些详细信息的方法吗?

Is there any other way of getting these details?

如果需要，我很乐意提供更多详细信息.

I am happy to provide more details if needed.

推荐答案

您传递给GridSearchCV的对象rfecv不适合它.首先将其克隆，然后将这些克隆拟合至数据并评估超参数的所有不同组合.

The object rfecv that you passed to GridSearchCV is not fitted by it. It is first cloned and those clones are then fitted to data and evaluated for all the different combinations of hyperparameters.

因此，要访问最佳功能，您需要访问GridSearchCV的best_estimator_属性:-

So to access the best features, you would need to access the best_estimator_ attribute of the GridSearchCV:-

CV_rfc.fit(x_train, y_train)
print("Finished feature selection and parameter tuning")

print("Optimal number of features : %d" % rfecv.n_features_)
features=list(X.columns[CV_rfc.best_estimator_.support_])
print(features)

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