使用scikit-learn进行特征选择

我是机器学习的新手.我正在使用Scikit Learn SVM准备我的数据进行分类.为了选择最好的功能,我使用了以下方法:

SelectKBest(chi2, k=10).fit_transform(A1, A2)

由于我的数据集由负值组成,因此出现以下错误:

ValueError                                Traceback (most recent call last)

/media/5804B87404B856AA/TFM_UC3M/test2_v.py in ()
----> 1 
      2 
      3 
      4 
      5 

/usr/local/lib/python2.6/dist-packages/sklearn/base.pyc in fit_transform(self, X, y,     **fit_params)
    427         else:
    428             # fit method of arity 2 (supervised transformation)

--> 429             return self.fit(X, y, **fit_params).transform(X)
    430 
    431 

/usr/local/lib/python2.6/dist-packages/sklearn/feature_selection/univariate_selection.pyc in fit(self, X, y)
    300         self._check_params(X, y)
    301 
--> 302         self.scores_, self.pvalues_ = self.score_func(X, y)
    303         self.scores_ = np.asarray(self.scores_)
    304         self.pvalues_ = np.asarray(self.pvalues_)

/usr/local/lib/python2.6/dist-  packages/sklearn/feature_selection/univariate_selection.pyc in chi2(X, y)
    190     X = atleast2d_or_csr(X)
    191     if np.any((X.data if issparse(X) else X) < 0):
--> 192         raise ValueError("Input X must be non-negative.")
    193 
    194     Y = LabelBinarizer().fit_transform(y)

ValueError: Input X must be non-negative.

有人能告诉我如何转换我的数据？