chi squared selectKbest bad input shape error - python-2.7

Question

I'm a little new to scikit and ML. I'm trying to train an Adaboost classifier for one vs Rest classification. I'm using the following code
# To Read Training data set
test = pd.read_csv("train.csv", header=0, delimiter=",", \
quoting=1, error_bad_lines=False)
num_reviews = len(test["text"])
clean_train_reviews = []
catlist=[]
for i in xrange(0,num_reviews):
data=processText(test["text"][i])
data1=test["category"][i]
clean_train_reviews.append(data)
catlist.append(data1.split('.'))
# To read test dataset
test = pd.read_csv("test.csv", header=0, delimiter=",", \
quoting=1, error_bad_lines=False)
num_reviews = len(test["text"])
clean_test_reviews = []
for i in xrange(0,num_reviews):
data=processText(test["text"][i])
clean_test_reviews.append(data)
X_test=np.array(clean_test_reviews)
lb = preprocessing.MultiLabelBinarizer()
Y = lb.fit_transform(catlist)
classifier = Pipeline([
('vectorizer', CountVectorizer(ngram_range=(1,2), max_features=1500,min_df=4)),
('tfidf', TfidfTransformer()),
('chi2', SelectKBest(chi2, k=200)),
('clf', OneVsRestClassifier(AdaBoostClassifier()))])
classifier.fit(clean_train_reviews, Y)
predicted = classifier.predict(X_test)
I use a pipeline, where text is inserted as clean_train_reviews and Y is the class (multi-Label, N = 10). Textual features are extracted in the pipeline using TfidfVectorizer() and selected using Chi squared feature selection method. Adaboost classifiers give: ValueError: bad input shape (1000, 10)
File "<ipython-input-10-9dbc8b18e6b8>", line 1, in <module>
runfile('C:/Users/Administrator/Desktop/nincymiss/adaboost.py', wdir='C:/Users/Administrator/Desktop/nincymiss')
File "C:\Python27\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 601, in runfile
execfile(filename, namespace)
File "C:\Python27\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 66, in execfile
exec(compile(scripttext, filename, 'exec'), glob, loc)
File "C:/Users/Administrator/Desktop/nincymiss/adaboost.py", line 179, in <module>
classifier.fit(clean_train_reviews, Y)
File "C:\Python27\lib\site-packages\sklearn\pipeline.py", line 164, in fit
Xt, fit_params = self._pre_transform(X, y, **fit_params)
File "C:\Python27\lib\site-packages\sklearn\pipeline.py", line 145, in _pre_transform
Xt = transform.fit_transform(Xt, y, **fit_params_steps[name])
File "C:\Python27\lib\site-packages\sklearn\base.py", line 458, in fit_transform
return self.fit(X, y, **fit_params).transform(X)
File "C:\Python27\lib\site-packages\sklearn\feature_selection\univariate_selection.py", line 322, in fit
X, y = check_X_y(X, y, ['csr', 'csc'])
File "C:\Python27\lib\site-packages\sklearn\utils\validation.py", line 515, in check_X_y
y = column_or_1d(y, warn=True)
File "C:\Python27\lib\site-packages\sklearn\utils\validation.py", line 551, in column_or_1d
raise ValueError("bad input shape {0}".format(shape))
ValueError: bad input shape (1000, 10)

Answer 1

This is because feature selection does not work as you'd expect for multilabel problems. You can try the following which will select the 'best' features for each label separately.
classifier = Pipeline([
('vectorizer', CountVectorizer(ngram_range=(1,2), max_features=1500, min_df=4)),
('tfidf', TfidfTransformer()),
('chi2', SelectKBest(chi2, k=200)),
('clf', AdaBoostClassifier())])
clf = OneVsRestClassifier(classifier)