功能选择过程中出错

Question

我正在尝试为多标签分类进行特征选择。我提取了模型将被训练到X中的特征。模型测试是在同一个X上完成的。我正在使用Pipeline并选择最好的100个特征-

#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)

但我得到了以下错误-

Traceback (most recent call last):
  File "C:Users50004182Documents\callee.py", line 10, in <module
>
    combine.combine_main(dict_ids,inv_dict_ids,noOfIDs)
  File "C:Users50004182Documentscombine.py", line 201, in combi
ne_main
    clf.fit(X, Y)
  File "C:Python34libsite-packagessklearnmulticlass.py", line 287, in fit
    for i, column in enumerate(columns))
  File "C:Python34libsite-packagessklearnexternalsjoblibparallel.py", lin
e 804, in __call__
    while self.dispatch_one_batch(iterator):
  File "C:Python34libsite-packagessklearnexternalsjoblibparallel.py", lin
e 662, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:Python34libsite-packagessklearnexternalsjoblibparallel.py", lin
e 570, in _dispatch
    job = ImmediateComputeBatch(batch)
  File "C:Python34libsite-packagessklearnexternalsjoblibparallel.py", lin
e 183, in __init__
    self.results = batch()
  File "C:Python34libsite-packagessklearnexternalsjoblibparallel.py", lin
e 72, in __call__
    return [func(*args, **kwargs) for func, args, kwargs in self.items]
  File "C:Python34libsite-packagessklearnexternalsjoblibparallel.py", lin
e 72, in <listcomp>
    return [func(*args, **kwargs) for func, args, kwargs in self.items]
  File "C:Python34libsite-packagessklearnmulticlass.py", line 74, in _fit_b
inary
    estimator.fit(X, y)
  File "C:Python34libsite-packagessklearnpipeline.py", line 164, in fit
    Xt, fit_params = self._pre_transform(X, y, **fit_params)
  File "C:Python34libsite-packagessklearnpipeline.py", line 145, in _pre_tr
ansform
    Xt = transform.fit_transform(Xt, y, **fit_params_steps[name])
  File "C:Python34libsite-packagessklearnbase.py", line 458, in fit_transfo
rm
    return self.fit(X, y, **fit_params).transform(X)
  File "C:Python34libsite-packagessklearnfeature_selectionunivariate_selec
tion.py", line 331, in fit
    self.scores_, self.pvalues_ = self.score_func(X, y)
  File "C:Python34libsite-packagessklearnfeature_selectionunivariate_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

因此，在与@JamieBull和@Joker进行了上述评论中的调试会话之后。我们想出的解决方案是：

确保类型正确（原始字符串）

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

首先使用VarianceThreshold删除chi2之前的常量（0）列。

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