Question

我正在使用test_train_split中的sklearn模块生成用于培训和测试的随机数据集组合。我有一个指向原始数据集的文件路径列表。我还想知道数据是如何被洗牌的，或者以相同的方式对文件路径列表进行洗牌，以便能够在洗牌/拆分数据集可用后跟踪文件路径？

Answer 1

如果您在random_state和test_train_split中指定相同的shuffle，您将获得相同的订单。

请参阅下面的代码段进行演示。

from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
import random

X = list()
y = list()
files = list()

random_state = 42
test_size = 0.9
n = 100

for i in range(n):
    X.append(i)
    y.append(i + random.random())
    files.append('file_{0:02d}.csv'.format(i))

X_train, X_test, y_train, y_test = train_test_split(X,
                                                y,
                                                test_size=test_size,
                                                random_state=random_state)
X_shuffle = shuffle(X, random_state=random_state)
y_shuffle = shuffle(y, random_state=random_state)
file_shuffle = shuffle(files, random_state=random_state)

print(X_train)
print(X_shuffle[int(n * test_size):])
print(y_shuffle[int(n * test_size):])
print(file_shuffle[int(n * test_size):])
print(X_train == X_shuffle[int(n * test_size):])

[91, 74, 86, 82, 20, 60, 71, 14, 92, 51]
[91, 74, 86, 82, 20, 60, 71, 14, 92, 51]
[91.64119581793204, 74.77493553783724, 86.62410189510936, 82.40452263996107, 20.22784747831378, 60.913989700418675, 71.1940538438253, 14.644282494118647, 92.97808337955185, 51.289858815186356]
['file_91.csv', 'file_74.csv', 'file_86.csv', 'file_82.csv', 'file_20.csv', 'file_60.csv', 'file_71.csv', 'file_14.csv', 'file_92.csv', 'file_51.csv']

True

sklearn测试列车拆分 - 获取原始列表的文件名索引

1 个答案: