如何使用 Leave One Group Out 作为特征选择的交叉验证？

Question

我有 16 个 csv 文件，每个文件包含大约 11250 行，其中包含 19 个特征和一列标签。我想将 Leave One Group Out 作为功能选择算法（如顺序前向选择和互信息）的交叉验证。我不知道如何实现这种交叉验证技术。以下代码如下：

from mlxtend.feature_selection import SequentialFeatureSelector as SFS
from sklearn.feature_selection import SelectKBest, mutual_info_classif
from sklearn.ensemble import RandomForestClassifier as rfc
from sklearn.linear_model import LogisticRegression
from numpy import array  

import pandas as pd
import matplotlib.pyplot as plt
import glob

#################
#Reading cvs file
#################

print("Reading multiple csv files")
print("")
csv_files = glob.iglob('D:/Project/csvfiles/*')
dataframe = pd.DataFrame()

print("Merging all the csv files")
print("")
#append all files together
for file in csv_files:
            df_temp = pd.read_csv(file)
            dataframe = dataframe.append(df_temp, ignore_index=True)

print("Dataframe is saved")
print("")
# dataframe.to_csv("Dataset.csv")

X = dataframe.drop(['Unnamed: 0', 'Labels'], axis=1)
y = dataframe['Labels']

#############################
# Class for Feature Selection
#############################

class FeatureSelection:
    def __init__(self, dataframe, target):
        self.dataframe = dataframe
        self.target = target

    ###########################
    # Normalizing the dataframe
    ###########################

    def normalization(self):
        print("Performing Normalization")
        print("")
        for column in self.dataframe.columns:
            self.dataframe[column] = (self.dataframe[column]-self.dataframe[column].min()) / (self.dataframe[column].max() - self.dataframe[column].min())
        print("Normalization Completed")
        print("")

    ##############################
    # Sequential Forward Selection
    ##############################

    def sequential_forward_selection(self):
        print("Performing Sequential Forward Selection")
        print("")
        sfs = SFS(rfc(n_jobs=-1),
                k_features='best', 
                forward=True, 
                floating=False, 
                verbose=2,
                scoring='accuracy',  # sklearn classifiers
                cv=5)

        sfs = sfs.fit(self.dataframe, self.target)

        print('Dictionary: ',sfs.get_metric_dict())
        print('')

        from mlxtend.plotting import plot_sequential_feature_selection as plot_sfs
        fig1 = plot_sfs(sfs.get_metric_dict(confidence_interval=0.95), kind='std_err')

        plt.title('Sequential Forward Selection (with normalization)')
        plt.grid()
        plt.show()

        print('Best Features {} with Index number {}:'.format(sfs.k_feature_names_, sfs.k_feature_idx_))
        print('')

        df = pd.DataFrame.from_dict(sfs.get_metric_dict()).T
        print(df[["feature_idx","avg_score"]] )

        print("Sequential Forward Selection completed")
        print("")

    ####################
    # Mutual Information
    ####################

    def mutual_information(self):
        print("Performing Mutual Information")
        print("")
        mic = SelectKBest(score_func=mutual_info_classif, k=15)
        mic.fit(self.dataframe, self.target)
        feature_MI_score = pd.Series(mic.scores_, index=self.dataframe.columns)
        print(feature_MI_score.sort_values(ascending=False))
        feature_MI_score.sort_values(ascending=False).plot.bar(figsize=(10, 8))
        plt.show()
        print("")
        print("Mutual Information completed")
        print("")

if __name__ == "__main__":
    featureselection = FeatureSelection(X, y)
    featureselection.normalization()
    featureselection.sequential_forward_selection()
    featureselection.mutual_information()

我的任务是使用 Leave One Group Out 作为交叉验证，它首先将 1 个数据集作为测试数据，其余 15 个作为训练数据。然后它应该继续前进，直到所有数据集都变成测试数据并从中找到最好的特征。任何帮助表示赞赏。