add stemming support to CountVectorizer (sklearn)
<p>I'm trying to add stemming to my pipeline in NLP with sklearn.</p>

<pre><code>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)) ])
</code></pre>

<p>When using this pipeline with the CountVectorizer of sklearn it works. And if I create manually the features like this it works also.<br/></p>

<pre><code>vectorizer = StemmedCountVectorizer(stemmer)
vectorizer.fit_transform(X)
tfidf_transformer = TfidfTransformer()
X_tfidf = tfidf_transformer.fit_transform(X_counts)
</code></pre>

<p><strong>EDIT</strong>:</p>

<p>If I try this pipeline on my IPython Notebook it displays the [*] and nothing happens. When I look at my terminal, it gives this error :<br/></p>

<pre><code>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'
</code></pre>

<p><em>Example</em></p>

<p>Here is the complete example</p>

<pre><code>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)
</code></pre>

<p>If you remove stemming from the parameters it works otherwise it doesn't work.</p>

<p><strong>UPDATE</strong>:</p>

<p>The problem seems to be in the parallelization process because when removing <strong>n_jobs=-1</strong> the problem disappear.</p>