生成K-fold交叉验证数据集

import pandas as pd

from sklearn import model_selection

if __name__ == "__main__":

　　

　　# Training data is in a CSV file called train.csv

　　df = pd.read_csv("train.csv")

　　# we create a new column called kfold and fill it with -1

　　df["kfold"] = -1

　　# the next step is to randomize the rows of the data

　　df = df.sample(frac=1).reset_index(drop=True)

　　# initiate the kfold class from model_selection module

　　kf = model_selection.KFold(n_splits=5)

　　# fill the new kfold column

　　for fold, (trn_, val_) in enumerate(kf.split(X=df)):

　　　　df.loc[val_, 'kfold'] = fold

　　　　# save the new csv with kfold column

　　df.to_csv("train_folds.csv", index=False)

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import pandas as pd
from sklearn.datasets import make_regression
from sklearn import model_selection
if __name__ == '__main__':
    X,y = make_regression(n_samples=15000,n_features=8,n_targets=1)
    df = pd.DataFrame(X,columns=[f"f_{i}" for i in range(X.shape[1])])
    df.loc[:, "target"] = y
    df["kfold"] = -1
    df = df.sample(frac=1).reset_index(drop=True)
    kf = model_selection.KFold(n_splits=5)
    for f, (t_,v_) in enumerate(kf.split(X=df)):
        df.loc[v_, "kfold"] = f

    print(df)