训练测试拆分一个句子列表
[英]Train test split a list of sentence
问题描述
I have a list of sentences.
我有一个句子列表。 I want to randomly separate into 80% and 20%, it looks like this:我想随机分成80%和20%，看起来是这样的：
['Hi.',
'Hi.',
'Run!',
'Wow!',
'Wow!',
'Fire!',
'Help!',
'Help!',
'Stop!',
'Wait!',
'Go on.',
'Hello!',
'I ran.',
'I see.',
'I see.',
'I try.',
'I won!',...]
I was thinking using a mask我在考虑用面膜
import random
mask = [0] * 4000 + [1] * 16000
random.shuffle(mask)
But it is not like a data frame.但它不像数据框。 and I tried我试过
percent=80
bol_mask =[random.randrange(100) < percent for i in range(100)]
Cant really apply boolean to sentences不能真正将布尔值应用于句子
Also the separation mask must be kept, and will later apply to another list in German, which is the corresponding translation.此外，分隔掩码必须保留，稍后将应用于另一个德语列表，即相应的翻译。
it looks like this它看起来像这样
array([[ 553, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0],
[3430, 1114, 6, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0],
[1115, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0],
[3431, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0],
[3432, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0],
[2459, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0],
[3433, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0],
[1533, 3434, 6, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0],
[2460, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 394, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0]],
dtype=int32)
My question is how to apply mask to a list of sentences?我的问题是如何将掩码应用于句子列表？ and keep the same split and apply to the corresponding ndarray?并保持相同的拆分并应用于相应的ndarray？
2 个解决方案
解决方案1
0 已采纳 2020-10-13 19:24:21
If using scikit-learn is an option, you can just use train_test_split method as the following:如果使用scikit-learn是一个选项，您可以使用train_test_split方法如下：
>>> from sklearn.model_selection import train_test_split
>>> print(x)
>>> x
['Hi.', 'Hi.', 'Run!', 'Wow!', 'Wow!', 'Fire!', 'Help!', 'Help!', 'Stop!', 'Wait!']
>>> len(x)
10
>>> x1
array([[ 553, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0],
[3430, 1114, 6, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0],
[1115, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0],
[3431, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0],
[3432, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0],
[2459, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0],
[3433, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0],
[1533, 3434, 6, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0],
[2460, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 394, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0]])
>>> x1.shape
(10, 20)
#assuming x, x1 have same length train test split should work fine.
>>> train, test, train_german, test_german = train_test_split(x,x1, test_size=0.2, shuffle=True)
>>> len(train)
8
>>> len(test)
2
>>> len(train_german)
8
>>> len(test)
2
解决方案2
0 2020-10-13 20:10:54
Actually I've solved it my self.其实我自己已经解决了。
bol_mask =[random.randrange(100) < 80 for i in range(20000)]
inv_mask = np.invert(bol_mask)
Eng_train =np.array(Eng)[bol_mask]
Eng_test =np.array(Eng)[inv_mask]
German_train = padded[bol_mask]
German_test = padded[inv_mask]
Thanks Grayrigel, an accept for your effort in helping感谢 Grayrigel，接受您的帮助
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