Error during feature selection

Question

I am trying to do feature selection for multilabel classification.I extracted the features on which model will be trained into X. Model testing is done on the same X. I am using Pipeline and selecting best 100 features-

#arrFinal contains all the features and the labels. Last 16 columns are labels and features are from 1 to 521. 17th column from the last is not taken
X=np.array(arrFinal[:,1:-17])
Xtest=np.array(X)
Y=np.array(arrFinal[:,522:]).astype(int)
clf = Pipeline([('chi2', SelectKBest(chi2, k=100)),('rbf',SVC())])
clf = OneVsRestClassifier(clf)
clf.fit(X, Y)
ans=clf.predict(X_test)

But I am getting the following error-

Traceback (most recent call last):
  File "C:\Users\50004182\Documents\\callee.py", line 10, in <module
>
    combine.combine_main(dict_ids,inv_dict_ids,noOfIDs)
  File "C:\Users\50004182\Documents\combine.py", line 201, in combi
ne_main
    clf.fit(X, Y)
  File "C:\Python34\lib\site-packages\sklearn\multiclass.py", line 287, in fit
    for i, column in enumerate(columns))
  File "C:\Python34\lib\site-packages\sklearn\externals\joblib\parallel.py", lin
e 804, in __call__
    while self.dispatch_one_batch(iterator):
  File "C:\Python34\lib\site-packages\sklearn\externals\joblib\parallel.py", lin
e 662, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Python34\lib\site-packages\sklearn\externals\joblib\parallel.py", lin
e 570, in _dispatch
    job = ImmediateComputeBatch(batch)
  File "C:\Python34\lib\site-packages\sklearn\externals\joblib\parallel.py", lin
e 183, in __init__
    self.results = batch()
  File "C:\Python34\lib\site-packages\sklearn\externals\joblib\parallel.py", lin
e 72, in __call__
    return [func(*args, **kwargs) for func, args, kwargs in self.items]
  File "C:\Python34\lib\site-packages\sklearn\externals\joblib\parallel.py", lin
e 72, in <listcomp>
    return [func(*args, **kwargs) for func, args, kwargs in self.items]
  File "C:\Python34\lib\site-packages\sklearn\multiclass.py", line 74, in _fit_b
inary
    estimator.fit(X, y)
  File "C:\Python34\lib\site-packages\sklearn\pipeline.py", line 164, in fit
    Xt, fit_params = self._pre_transform(X, y, **fit_params)
  File "C:\Python34\lib\site-packages\sklearn\pipeline.py", line 145, in _pre_tr
ansform
    Xt = transform.fit_transform(Xt, y, **fit_params_steps[name])
  File "C:\Python34\lib\site-packages\sklearn\base.py", line 458, in fit_transfo
rm
    return self.fit(X, y, **fit_params).transform(X)
  File "C:\Python34\lib\site-packages\sklearn\feature_selection\univariate_selec
tion.py", line 331, in fit
    self.scores_, self.pvalues_ = self.score_func(X, y)
  File "C:\Python34\lib\site-packages\sklearn\feature_selection\univariate_selec
tion.py", line 213, in chi2
    if np.any((X.data if issparse(X) else X) < 0):
TypeError: unorderable types: numpy.ndarray() < int()

Answer 1

So, after a debugging session in the comments above with @JamieBull and @Joker. The solution we came up with was:

Ensure type is correct (originally string)

X=np.array(arrFinal[:,1:-17]).astype(np.float64)
Xtest=np.array(X)
Y=np.array(arrFinal[:,522:]).astype(int)

First use VarianceThreshold to remove constant (0) columns prior to chi2.

clf = Pipeline([
      ('vt', VarianceThreshold()),
      ('chi2', SelectKBest(chi2, k=100)),
      ('rbf',SVC())
])
clf = OneVsRestClassifier(clf)
clf.fit(X, Y)
ans=clf.predict(X_test)