Question

对于以下scikit-learn函数：train_test_split()：

是否可以告诉功能设置数据分割？

或换句话说：

我可以告诉功能GET http://testsymf/css/style.css net::ERR_ABORTED应该在分割点的左侧还是右侧，<VirtualHost *:80> DocumentRoot "C:\xampp\htdocs\test3\web\app_dev.php" ServerName testsymf <Directory /xampp/htdocs/test3/web> AllowOverride All Order Allow,Deny Allow from All </Directory>应该在右侧？

（并且拆分确实以这种方式工作 - 或者只是输入数据的任意行，直到达到分割比率？）

如果无法告诉函数应该采取哪些数据进行培训和测试：是否有可用于此用例的等效替代方案？

Answer 1

来自Scikit Learn文档：将数组或矩阵拆分为随机训练和测试子集..

>>> import numpy as np
>>> from sklearn.model_selection import train_test_split
>>> X, y = np.arange(10).reshape((5, 2)), range(5)
>>> X
array([[0, 1],
       [2, 3],
       [4, 5],
       [6, 7],
       [8, 9]])
>>> list(y)
[0, 1, 2, 3, 4]

>>> X_train, X_test, y_train, y_test = train_test_split(
...     X, y, test_size=0.33, random_state=42)
...
>>> X_train
array([[4, 5],
       [0, 1],
       [6, 7]])
>>> y_train
[2, 0, 3]
>>> X_test
array([[2, 3],
       [8, 9]])
>>> y_test
[1, 4]

你也可以关掉洗牌：

>>> train_test_split(y, shuffle=False)
[[0, 1, 2], [3, 4]]

Answer 2

使用KFold的解决方案如下：

import numpy as np
from sklearn.model_selection import KFold

X = np.arange(20).reshape((10, 2))
y = np.arange(20)

print(X)
print(y)
kf = KFold(n_splits=10)
for train_index, test_index in kf.split(X):
    print("TRAIN size: {0:5d} from: {1:5d} to: {2:5d}".format(train_index.size, train_index[0], train_index[train_index.size - 1]))
    print("TEST  size: {0:5d} from: {1:5d} to: {2:5d}".format(test_index.size, test_index[0], test_index[test_index.size - 1]))
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]

结果：

 [[ 0  1]
 [ 2  3]
 [ 4  5]
 [ 6  7]
 [ 8  9]
 [10 11]
 [12 13]
 [14 15]
 [16 17]
 [18 19]]
[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19]
--
TRAIN size:     9 from:     1 to:     9
TEST  size:     1 from:     0 to:     0
--
TRAIN size:     9 from:     0 to:     9
TEST  size:     1 from:     1 to:     1
--
TRAIN size:     9 from:     0 to:     9
TEST  size:     1 from:     2 to:     2
--
TRAIN size:     9 from:     0 to:     9
TEST  size:     1 from:     3 to:     3
--
TRAIN size:     9 from:     0 to:     9
TEST  size:     1 from:     4 to:     4
--
TRAIN size:     9 from:     0 to:     9
TEST  size:     1 from:     5 to:     5
--
TRAIN size:     9 from:     0 to:     9
TEST  size:     1 from:     6 to:     6
--
TRAIN size:     9 from:     0 to:     9
TEST  size:     1 from:     7 to:     7
--
TRAIN size:     9 from:     0 to:     9
TEST  size:     1 from:     8 to:     8
--
TRAIN size:     9 from:     0 to:     8
TEST  size:     1 from:     9 to:     9

Python Sklearn train_test_split（）：如何设置哪些数据用于培训？

2 个答案: