目的：将数组或矩阵分割为随机的训练和测试子集。
语法格式
sklearn.model_selection.train_test_split(*arrays, test_size=None, train_size=None, random_state=None, shuffle=True, stratify=None)
几个常用参数解释：
- *arrays：允许输入列表、numpy数组、scipy-sparse矩阵或pandas数据框架。
- test_size: float or int。接受0-1之间数值，表示test数据集的占比；接受整数值，表示训练集的绝对数量；默认值为None，表示总数据集减train数据集的size；如果train_size也为None，将设置为0.25
- train_size: train数据集size。默认值为None，总数据集减test数据集的size
- random_state: 随机种子，随便给个整数就行。目的是每次执行生成的数据集一致，方便重复
- shuffle: 布尔值，表示是否在分割前对完整数据集顺序洗牌。默认值为True。如果shuffle=False，则stratify必须为None
- stratify: 默认值为None。如果不是None，则以分层方式分割数据，并将其用作类标签。
返回包括train-test的list, length=2 * len(arrays)
代码示例
import pandas as pd
from sklearn.model_selection import train_test_split
import numpy as np
d1 = [[3,"negative",2],[4,"negative",6],[11,"positive",0],[12,"positive",2],
[14,"positive",2],[3,"positive",5],[7,"negative",7],[7,"negative",6],
[1,"positive",2],[5,"negative",2]]
df1 = pd.DataFrame(d1, columns=["xuhao","result","value"])
print(df1,"\n")
#
idx = np.arange(0, len(df1))
idx_train, idx_test = train_test_split(idx, test_size=0.6) #idx也可直接替换为df1
print(idx_train)
print(idx_test,"\n")
#
idx_train, idx_test = train_test_split(
idx, stratify=df1['result'], test_size=0.6
)
print(idx_train)
print(idx_test)输出结果
xuhao result value
0 3 negative 2
1 4 negative 6
2 11 positive 0
3 12 positive 2
4 14 positive 2
5 3 positive 5
6 7 negative 7
7 7 negative 6
8 1 positive 2
9 5 negative 2
[9 7 6 3]
[2 4 8 5 1 0]
[5 0 4 6]
[3 7 1 2 8 9]可以看到如果不加stratify选项，会存在抽取的train数据集对应的result列中三个样本都是negative；如果加上stratify=df1['result']，在提取train和test时，总是会按照result分类平均分配各样本。
