我想使用单词以及一些其他功能(例如,有链接)在文本上构建分类模型
tweets = ['this tweet has a link htt://link','this one does not','this one does http://link.net']
我使用sklearn来获取文本数据的稀疏矩阵
tfidf_vectorizer = TfidfVectorizer(max_df=0.90, max_features=200000,
min_df=0.1, stop_words='english',
use_idf=True, ntlk.tokenize,ngram_range=(1,2))
tfidf_matrix = tfidf_vectorizer.fit_transform(tweets)
我想为其添加列以支持文本数据的其他功能。我试过了:
import scipy as sc
all_data = sc.hstack((tfidf_matrix, [1,0,1]))
这给了我这样的数据:
array([ <3x8 sparse matrix of type '<type 'numpy.float64'>'
with 10 stored elements in Compressed Sparse Row format>,
1, 1, 0], dtype=object)
当我将此数据框提供给模型时:
`from sklearn.naive_bayes import MultinomialNB
clf = MultinomialNB().fit(all_data, y)`
我收到了追溯错误:
`Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Anaconda\lib\site- packages\spyderlib\widgets\externalshell\sitecustomize.py", line 580, in runfile
execfile(filename, namespace)
File "C:/Users/c/Desktop/features.py", line 157, in <module>
clf = MultinomialNB().fit(all_data, y)
File "C:\Anaconda\lib\site-packages\sklearn\naive_bayes.py", line 302, in fit
_, n_features = X.shape
ValueError:需要多于1个值来解包`
编辑:数据的形状
`tfidf_matrix.shape
(100, 2)
all_data.shape
(100L,)`
我可以将列直接追加到稀疏矩阵吗?如果没有,我应该如何将数据转换为可以支持此格式的格式?我担心除了稀疏矩阵之外的其他东西会增加内存占用。
答案 0 :(得分:11)
“我可以将直接列添加到稀疏矩阵吗?” - 是的你可能应该这样做,因为解包(使用todense
或toarray
)很容易导致大型语料库中的内存爆炸。
import numpy as np
import scipy as sp
from sklearn.feature_extraction.text import TfidfVectorizer
tweets = ['this tweet has a link htt://link','this one does not','this one does http://link.net']
tfidf_vectorizer = TfidfVectorizer()
tfidf_matrix = tfidf_vectorizer.fit_transform(tweets)
print tfidf_matrix.shape
(3,10)
new_column = np.array([[1],[0],[1]])
print new_column.shape
(3,1)
final = sp.sparse.hstack((tfidf_matrix, new_column))
print final.shape
(3,11)
答案 1 :(得分:1)
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
tweets = ['this tweet has a link htt://link','this one does not','this one does http://link.net']
tfidf_vectorizer = TfidfVectorizer()
tfidf_matrix = tfidf_vectorizer.fit_transform(tweets)
dense = tfidf_matrix.todense()
print dense.shape
newCol = [[1],[0],[1]]
allData = np.append(dense, newCol, 1)
print allData.shape
答案 2 :(得分:0)
这是正确的格式:
all_data = sc.hstack([tfidf_matrix, sc.csr_matrix([1,0,1]).T], 'csr')