sklearn：如何加速矢量化器（例如Tfidfvectorizer）

Question

徹底分析我的程序後，我已經能夠確定它正在被矢量化器放慢速度。

我正在處理文本數據，兩行簡單的tfidf單向量矢量化佔用代碼執行總時間的99.2％。

這裏是一個可運行的例子（這將下載一個3MB的培訓文件到您的磁盤，省略了urllib的零件在自己的樣品進行）：

##################################### 
# Loading Data 
##################################### 
import urllib 
from sklearn.feature_extraction.text import TfidfVectorizer 
import nltk.stem 
raw = urllib.urlopen("https://s3.amazonaws.com/hr-testcases/597/assets/trainingdata.txt").read() 
file = open("to_delete.txt","w").write(raw) 
### 
def extract_training(): 
    f = open("to_delete.txt") 
    N = int(f.readline()) 
    X = [] 
    y = [] 
    for i in xrange(N): 
     line = f.readline() 
     label,text = int(line[0]), line[2:] 
     X.append(text) 
     y.append(label) 
    return X,y 
X_train, y_train = extract_training()  
############################################# 
# Extending Tfidf to have only stemmed features 
############################################# 
english_stemmer = nltk.stem.SnowballStemmer('english') 

class StemmedTfidfVectorizer(TfidfVectorizer): 
    def build_analyzer(self): 
     analyzer = super(TfidfVectorizer, self).build_analyzer() 
     return lambda doc: (english_stemmer.stem(w) for w in analyzer(doc)) 

tfidf = StemmedTfidfVectorizer(min_df=1, stop_words='english', analyzer='word', ngram_range=(1,1)) 
############################################# 
# Line below takes 6-7 seconds on my machine 
############################################# 
Xv = tfidf.fit_transform(X_train) 

我試圖名單X_train轉換爲NP。陣列，但性能沒有差異。

Answer 1

不出所料，這是NLTK慢是：

>>> tfidf = StemmedTfidfVectorizer(min_df=1, stop_words='english', analyzer='word', ngram_range=(1,1)) 
>>> %timeit tfidf.fit_transform(X_train) 
1 loops, best of 3: 4.89 s per loop 
>>> tfidf = TfidfVectorizer(min_df=1, stop_words='english', analyzer='word', ngram_range=(1,1)) 
>>> %timeit tfidf.fit_transform(X_train) 
1 loops, best of 3: 415 ms per loop 

您可以通過使用更智能的實施雪球詞幹，例如，PyStemmer加快這：

>>> import Stemmer 
>>> english_stemmer = Stemmer.Stemmer('en') 
>>> class StemmedTfidfVectorizer(TfidfVectorizer): 
...  def build_analyzer(self): 
...   analyzer = super(TfidfVectorizer, self).build_analyzer() 
...   return lambda doc: english_stemmer.stemWords(analyzer(doc)) 
...  
>>> tfidf = StemmedTfidfVectorizer(min_df=1, stop_words='english', analyzer='word', ngram_range=(1,1)) 
>>> %timeit tfidf.fit_transform(X_train) 
1 loops, best of 3: 650 ms per loop 

NLTK是一個教學工具。它的設計很慢，因爲它的可讀性得到了優化。