我正在尝试使用sklearn在NLP中的管道中添加词干。
from nltk.stem.snowball import FrenchStemmer
stop = stopwords.words('french')
stemmer = FrenchStemmer()
class StemmedCountVectorizer(CountVectorizer):
def __init__(self, stemmer):
super(StemmedCountVectorizer, self).__init__()
self.stemmer = stemmer
def build_analyzer(self):
analyzer = super(StemmedCountVectorizer, self).build_analyzer()
return lambda doc:(self.stemmer.stem(w) for w in analyzer(doc))
stem_vectorizer = StemmedCountVectorizer(stemmer)
text_clf = Pipeline([('vect', stem_vectorizer), ('tfidf', TfidfTransformer()), ('clf', SVC(kernel='linear', C=1)) ])
当将此管道与sklearn的CountVectorizer一起使用时,它可以工作。而且,如果我手动创建类似的功能,它也将起作用。
vectorizer = StemmedCountVectorizer(stemmer)
vectorizer.fit_transform(X)
tfidf_transformer = TfidfTransformer()
X_tfidf = tfidf_transformer.fit_transform(X_counts)
编辑:
如果我在IPython Notebook上尝试此管道,它将显示[*],并且什么也没有发生。当我查看我的终端时,它显示此错误:
Process PoolWorker-12:
Traceback (most recent call last):
File "C:\Anaconda2\lib\multiprocessing\process.py", line 258, in _bootstrap
self.run()
File "C:\Anaconda2\lib\multiprocessing\process.py", line 114, in run
self._target(*self._args, **self._kwargs)
File "C:\Anaconda2\lib\multiprocessing\pool.py", line 102, in worker
task = get()
File "C:\Anaconda2\lib\site-packages\sklearn\externals\joblib\pool.py", line 360, in get
return recv()
AttributeError: 'module' object has no attribute 'StemmedCountVectorizer'
例
这是完整的例子
from sklearn.pipeline import Pipeline
from sklearn import grid_search
from sklearn.svm import SVC
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from nltk.stem.snowball import FrenchStemmer
stemmer = FrenchStemmer()
analyzer = CountVectorizer().build_analyzer()
def stemming(doc):
return (stemmer.stem(w) for w in analyzer(doc))
X = ['le chat est beau', 'le ciel est nuageux', 'les gens sont gentils', 'Paris est magique', 'Marseille est tragique', 'JCVD est fou']
Y = [1,0,1,1,0,0]
text_clf = Pipeline([('vect', CountVectorizer()), ('tfidf', TfidfTransformer()), ('clf', SVC())])
parameters = { 'vect__analyzer': ['word', stemming]}
gs_clf = grid_search.GridSearchCV(text_clf, parameters, n_jobs=-1)
gs_clf.fit(X, Y)
如果从参数中删除词干,它将起作用,否则将不起作用。
更新:
问题似乎出在并行化过程中,因为删除 n_jobs = -1 时问题消失了。
最佳答案
您可以将可调用的analyzer
传递给CountVectorizer
构造函数以提供自定义分析器。这似乎为我工作。
from sklearn.feature_extraction.text import CountVectorizer
from nltk.stem.snowball import FrenchStemmer
stemmer = FrenchStemmer()
analyzer = CountVectorizer().build_analyzer()
def stemmed_words(doc):
return (stemmer.stem(w) for w in analyzer(doc))
stem_vectorizer = CountVectorizer(analyzer=stemmed_words)
print(stem_vectorizer.fit_transform(['Tu marches dans la rue']))
print(stem_vectorizer.get_feature_names())
打印输出:
(0, 4) 1
(0, 2) 1
(0, 0) 1
(0, 1) 1
(0, 3) 1
[u'dan', u'la', u'march', u'ru', u'tu']
关于python - 向CountVectorizer(sklearn)添加词干支持,我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/36182502/