Xio*_*g89 5 python arrays python-itertools scikit-learn one-hot-encoding
我想为一组DNA序列生成一个热编码.例如,序列ACGTCCA可以以转置方式如下表示.但是下面的代码将以水平方式生成一个热编码,我希望它以垂直形式.谁能帮我?
ACGTCCA
1000001 - A
0100110 - C
0010000 - G
0001000 - T
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示例代码:
from sklearn.preprocessing import OneHotEncoder
import itertools
# two example sequences
seqs = ["ACGTCCA","CGGATTG"]
# split sequences to tokens
tokens_seqs = [seq.split("\\") for seq in seqs]
# convert list of of token-lists to one flat list of tokens
# and then create a dictionary that maps word to id of word,
# like {A: 1, B: 2} here
all_tokens = itertools.chain.from_iterable(tokens_seqs)
word_to_id = {token: idx for idx, token in enumerate(set(all_tokens))}
# convert token lists to token-id lists, e.g. [[1, 2], [2, 2]] here
token_ids = [[word_to_id[token] for token in tokens_seq] for tokens_seq in tokens_seqs]
# convert list of token-id lists to one-hot representation
vec = OneHotEncoder(n_values=len(word_to_id))
X = vec.fit_transform(token_ids)
print X.toarray()
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但是,代码给了我输出:
[[ 0. 1.]
[ 1. 0.]]
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预期产量:
[[1. 0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 1. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
[0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 1. 0. 0. 0. 0. 1. 1. 0.]]
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def one_hot_encode(seq):
mapping = dict(zip("ACGT", range(4)))
seq2 = [mapping[i] for i in seq]
return np.eye(4)[seq2]
one_hot_encode("AACGT")
## Output:
array([[1., 0., 0., 0.],
[1., 0., 0., 0.],
[0., 1., 0., 0.],
[0., 0., 1., 0.],
[0., 0., 0., 1.]])
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我建议以稍微更手动的方式进行:
import numpy as np
seqs = ["ACGTCCA","CGGATTG"]
CHARS = 'ACGT'
CHARS_COUNT = len(CHARS)
maxlen = max(map(len, seqs))
res = np.zeros((len(seqs), CHARS_COUNT * maxlen), dtype=np.uint8)
for si, seq in enumerate(seqs):
seqlen = len(seq)
arr = np.chararray((seqlen,), buffer=seq)
for ii, char in enumerate(CHARS):
res[si][ii*seqlen:(ii+1)*seqlen][arr == char] = 1
print res
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这会给你你想要的结果:
[[1 0 0 0 0 0 1 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0]
[0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 1 1 0]]
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