sklearn.model_selection.train_test_split 用法
sklearn之前没有接触过，以练代学了。常用的用法记录下来，这样才能慢慢总结。
- sklearn.model_selection.train_test_split 用法
在使用python做机械学习时候，为了制作训练数据（training samples）和测试数据（testing samples），常使用sklearn里面的
sklearn.model_selection.train_test_split模块。
train_test_split的使用方法：
sklearn.model_selection.train_test_split(*arrays, **options)arrays：分割对象同样长度的列表或者numpy arrays，矩阵。
test_size：两种指定方法。1：指定小数。小数范围在0.0~0.1之间，它代表test集占据的比例。2：指定整数。整数的大小必须在这个数据集个数范围内，总不能指定一个数超出了数据集的个数范围吧。要是test_size在没有指定的场合，可以通过train_size来指定。（两个是对应关系）。如果train_size也没有指定，那么默认值是0.25.
train_size：和test_size相似。
random_state:这是将分割的training和testing集合打乱的个数设定。如果不指定的话，也可以通过numpy.random来设定随机数。
shuffle和straify不常用。straify就是将数据分层。
train_test_split 用法举例：
这个数据集 4列（カラム），12行（レコード）。
>>> import pandas as pd
>>> from sklearn.model_selection import train_test_split
>>>
>>> namelist = pd.DataFrame({
... "name" : ["Suzuki", "Tanaka", "Yamada", "Watanabe", "Yamamoto",
... "Okada", "Ueda", "Inoue", "Hayashi", "Sato",
... "Hirayama", "Shimada"],
... "age": [30, 40, 55, 29, 41, 28, 42, 24, 33, 39, 49, 53],
... "department": ["HR", "Legal", "IT", "HR", "HR", "IT",
... "Legal", "Legal", "IT", "HR", "Legal", "Legal"],
... "attendance": [1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1]
... })
>>> print(namelist)
age attendance department name
0 30 1 HR Suzuki
1 40 1 Legal Tanaka
2 55 1 IT Yamada
3 29 0 HR Watanabe
4 41 1 HR Yamamoto
5 28 1 IT Okada
6 42 1 Legal Ueda
7 24 0 Legal Inoue
8 33 0 IT Hayashi
9 39 1 HR Sato
10 49 1 Legal Hirayama
11 53 1 Legal Shimada>>> namelist_train, namelist_test = train_test_split(namelist, test_size=0.3)
>>> print(namelist_train)
age attendance department name
10 49 1 Legal Hirayama
1 40 1 Legal Tanaka
7 24 0 Legal Inoue
2 55 1 IT Yamada
4 41 1 HR Yamamoto
3 29 0 HR Watanabe
9 39 1 HR Sato
6 42 1 Legal Ueda
>>> print(namelist_test)
age attendance department name
0 30 1 HR Suzuki
8 33 0 IT Hayashi
11 53 1 Legal Shimada
5 28 1 IT Okada接下来是将testing数据指定为具体数目。test_size=5。
>>> namelist_train, namelist_test = train_test_split(namelist, test_size=5)
>>> print(namelist_train)
age attendance department name
3 29 0 HR Watanabe
4 41 1 HR Yamamoto
6 42 1 Legal Ueda
1 40 1 Legal Tanaka
9 39 1 HR Sato
8 33 0 IT Hayashi
7 24 0 Legal Inoue
>>> print(namelist_test)
age attendance department name
2 55 1 IT Yamada
10 49 1 Legal Hirayama
5 28 1 IT Okada
11 53 1 Legal Shimada
0 30 1 HR Suzuki>>> namelist_train, namelist_test = train_test_split(namelist, test_size=None, train_size=0.5)
>>> print(namelist_train)
age attendance department name
5 28 1 IT Okada
2 55 1 IT Yamada
3 29 0 HR Watanabe
4 41 1 HR Yamamoto
10 49 1 Legal Hirayama
0 30 1 HR Suzuki
>>> print(namelist_test)
age attendance department name
6 42 1 Legal Ueda
7 24 0 Legal Inoue
9 39 1 HR Sato
11 53 1 Legal Shimada
8 33 0 IT Hayashi
1 40 1 Legal TanakaPython
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>>> namelist_train, namelist_test = train_test_split(namelist, shuffle=False)
>>> print(namelist_train)
age attendance department name
0 30 1 HR Suzuki
1 40 1 Legal Tanaka
2 55 1 IT Yamada
3 29 0 HR Watanabe
4 41 1 HR Yamamoto
5 28 1 IT Okada
6 42 1 Legal Ueda
7 24 0 Legal Inoue
8 33 0 IT Hayashi
>>> print(namelist_test)
age attendance department name
9 39 1 HR Sato
10 49 1 Legal Hirayama
11 53 1 Legal Shimada- summary
- train_test_split（arrays，options） arrays确定需要分割的对象，数据集。
- train_test_split（arrays，options） options确定需要分割的方法。例如比例，随机性，分层等。
- 日本人写的博客还是清楚，详细。以后除了中文博客，日文博客也应该多参考一下。
reference：
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