如何在python中解决将来的警告->%（min_groups，self.n_splits）），warning）？

Question

当我在我的程序中运行mean_acc（）方法时，有（min_groups，self.n_splits）），警告）错误。。。

def mean_acc():
    models = [
        RandomForestClassifier(n_estimators=200, max_depth=3, random_state=0),
        LinearSVC(),
        MultinomialNB(),
        LogisticRegression(random_state=0)]
    CV = 6
    cv_df = pd.DataFrame(index=range(CV * len(models)))
    entries = []
    for model in models:
        model_name = model.__class__.__name__
        accuracies = cross_val_score(model, features, labels, scoring='accuracy', cv=CV)
        for fold_idx, accuracy in enumerate(accuracies):
            entries.append((model_name, fold_idx, accuracy))
    cv_df = pd.DataFrame(entries, columns=['model_name', 'fold_idx', 'accuracy'])

    print(cv_df.groupby('model_name').accuracy.mean())

这些是我用mean_acc（）方法运行程序时显示的错误。我能知道如何解决下面这些错误吗？请帮助我看一下我上面的代码，这些代码导致了这些错误，谢谢！！！

 % (min_groups, self.n_splits)), Warning)
C:\Users\L31307\PycharmProjects\FYP\venv\lib\site-packages\sklearn\model_selection\_split.py:626: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.
  % (min_groups, self.n_splits)), Warning)
C:\Users\L31307\PycharmProjects\FYP\venv\lib\site-packages\sklearn\model_selection\_split.py:626: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.
  % (min_groups, self.n_splits)), Warning)
C:\Users\L31307\PycharmProjects\FYP\venv\lib\site-packages\sklearn\model_selection\_split.py:626: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.
  % (min_groups, self.n_splits)), Warning)
C:\Users\L31307\PycharmProjects\FYP\venv\lib\site-packages\sklearn\linear_model\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
  FutureWarning)
C:\Users\L31307\PycharmProjects\FYP\venv\lib\site-packages\sklearn\linear_model\logistic.py:459: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.
  "this warning.", FutureWarning)
C:\Users\L31307\PycharmProjects\FYP\venv\lib\site-packages\sklearn\linear_model\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
  FutureWarning)
C:\Users\L31307\PycharmProjects\FYP\venv\lib\site-packages\sklearn\linear_model\logistic.py:459: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.
  "this warning.", FutureWarning)
C:\Users\L31307\PycharmProjects\FYP\venv\lib\site-packages\sklearn\linear_model\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
  FutureWarning)
C:\Users\L31307\PycharmProjects\FYP\venv\lib\site-packages\sklearn\linear_model\logistic.py:459: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.
  "this warning.", FutureWarning)
C:\Users\L31307\PycharmProjects\FYP\venv\lib\site-packages\sklearn\linear_model\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
  FutureWarning)
C:\Users\L31307\PycharmProjects\FYP\venv\lib\site-packages\sklearn\linear_model\logistic.py:459: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.
  "this warning.", FutureWarning)

Answer 1

如果您使用的Logistic回归模型的惩罚系数为l1，则可以使用solver='liblinear'

我的代码示例：

logistic_regression_model=LogisticRegression(penalty='l1',dual=False,max_iter=110, solver='liblinear')

Answer 2

如果要忽略它，请在顶部的代码中添加以下内容：

import warnings
warnings.filterwarnings("ignore", category=FutureWarning)

否则指定解算器为：

LogisticRegression(solver='lbfgs')

Source：

solver : str, {‘newton-cg’, ‘lbfgs’, ‘liblinear’, ‘sag’, ‘saga’}, default: ‘liblinear’.
Algorithm to use in the optimization problem.

For small datasets, ‘liblinear’ is a good choice, whereas ‘sag’ and ‘saga’ are faster for large ones.
For multiclass problems, only ‘newton-cg’, ‘sag’, ‘saga’ and ‘lbfgs’ handle multinomial loss; ‘liblinear’ is limited to one-versus-rest schemes.
‘newton-cg’, ‘lbfgs’ and ‘sag’ only handle L2 penalty, whereas ‘liblinear’ and ‘saga’ handle L1 penalty.