TypeError：ufunc'add'不包含签名匹配类型的循环

  • Z时代
  • 2024-01-10
  • 分类：问答

我正在创建单词表示该句子的包。然后将句子中存在的单词与文件“

vectors.txt”进行比较，以获取其嵌入矢量。在获取句子中每个单词的向量之后，我将句子中单词的向量取平均值。这是我的代码：

import nltk
import numpy as np
from nltk import FreqDist
from nltk.corpus import brown
news = brown.words(categories='news') 
news_sents = brown.sents(categories='news')
fdist = FreqDist(w.lower() for w in news) 
vocabulary = [word for word, _ in fdist.most_common(10)] 
num_sents = len(news_sents)
def averageEmbeddings(sentenceTokens, embeddingLookupTable):
    listOfEmb=[]
    for token in sentenceTokens:
        embedding = embeddingLookupTable[token] 
        listOfEmb.append(embedding)
return sum(np.asarray(listOfEmb)) / float(len(listOfEmb))
embeddingVectors = {}
with open("D:\\Embedding\\vectors.txt") as file: 
    for line in file:
       (key, *val) = line.split()
       embeddingVectors[key] = val
for i in range(num_sents): 
    features = {}
    for word in vocabulary: 
        features[word] = int(word in news_sents[i])        
    print(features) 
    print(list(features.values()))  
sentenceTokens = [] 
for key, value in features.items(): 
    if value == 1:
       sentenceTokens.append(key)
sentenceTokens.remove(".")    
print(sentenceTokens)        
print(averageEmbeddings(sentenceTokens, embeddingVectors))
print(features.keys())

不知道为什么，但是我得到这个错误：

TypeError                                 Traceback (most recent call last)
<ipython-input-4-643ccd012438> in <module>()
 39     sentenceTokens.remove(".")
 40     print(sentenceTokens)
---> 41     print(averageEmbeddings(sentenceTokens, embeddingVectors))
 42 
 43 print(features.keys())
<ipython-input-4-643ccd012438> in averageEmbeddings(sentenceTokens, embeddingLookupTable)
 18         listOfEmb.append(embedding)
 19 
---> 20     return sum(np.asarray(listOfEmb)) / float(len(listOfEmb))
 21 
 22 embeddingVectors = {}
TypeError: ufunc 'add' did not contain a loop with signature matching types dtype('<U9') dtype('<U9') dtype('<U9')

PS嵌入向量如下所示：

the 0.011384 0.010512 -0.008450 -0.007628 0.000360 -0.010121 0.004674 -0.000076 
of 0.002954 0.004546 0.005513 -0.004026 0.002296 -0.016979 -0.011469 -0.009159 
and 0.004691 -0.012989 -0.003122 0.004786 -0.002907 0.000526 -0.006146 -0.003058 
one 0.014722 -0.000810 0.003737 -0.001110 -0.011229 0.001577 -0.007403 -0.005355 
in -0.001046 -0.008302 0.010973 0.009608 0.009494 -0.008253 0.001744 0.003263

使用np.sum后，出现此错误：

TypeError                                 Traceback (most recent call last)
<ipython-input-13-8a7edbb9d946> in <module>()
 40     sentenceTokens.remove(".")
 41     print(sentenceTokens)
---> 42     print(averageEmbeddings(sentenceTokens, embeddingVectors))
 43 
 44 print(features.keys())
<ipython-input-13-8a7edbb9d946> in averageEmbeddings(sentenceTokens, embeddingLookupTable)
 18         listOfEmb.append(embedding)
 19 
---> 20     return np.sum(np.asarray(listOfEmb)) / float(len(listOfEmb))
 21 
 22 embeddingVectors = {}
C:\Anaconda3\lib\site-packages\numpy\core\fromnumeric.py in sum(a, axis, dtype, out, keepdims)
   1829     else:
   1830         return _methods._sum(a, axis=axis, dtype=dtype,
-> 1831                              out=out, keepdims=keepdims)
   1832 
   1833
C:\Anaconda3\lib\site-packages\numpy\core\_methods.py in _sum(a, axis, dtype, out, keepdims)
 30 
 31 def _sum(a, axis=None, dtype=None, out=None, keepdims=False):
---> 32     return umr_sum(a, axis, dtype, out, keepdims)
 33 
 34 def _prod(a, axis=None, dtype=None, out=None, keepdims=False):
TypeError: cannot perform reduce with flexible type

回答：

您有一个numpy的字符串数组，而不是浮点数。这就是dtype('<U9')一个小端编码的unicode字符串，最多9个字符。

尝试：

return sum(np.asarray(listOfEmb, dtype=float)) / float(len(listOfEmb))

但是，这里根本不需要numpy。您真的可以做到：

return sum(float(embedding) for embedding in listOfEmb) / len(listOfEmb)

或者，如果您真的想使用numpy。

return np.asarray(listOfEmb, dtype=float).mean()

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