python – 使用Spearman与Sklearn KNN的相关性进行模式匹配

我试图使用包含在用户定义的度量中的Spearman相关性来查找Scikit-learn中的最近邻居.出于某种原因,它仅在我的训练数据中的列数为5且k = 5时才有效.对于任何其他组合(例如,列数= 8和k = 6),它会给我以下错误. (此处列车和测试装置有4列,k = 4).如果我使用Pearson进行相关,它的效果非常好.有谁知道为什么会发生这种情况或如何解决它？谢谢.

from scipy.stats import spearmanr
def spearmancorr(x,y):
    rho,pval = spearmanr(x,y,axis=0)
    return rho * (-1)

from sklearn.neighbors import NearestNeighbors
nbrs = NearestNeighbors(n_neighbors=4,algorithm='ball_tree',metric=spearmancorr)
nbrs.fit(train)
dist,ind = nbrs.kneighbors(test)

SystemError                               Traceback (most recent call last)
<ipython-input-11-f04b508b1263> in <module>()
      5 for i in range(1):
      6     nbrs = NearestNeighbors(n_neighbors=4,metric=spearmancorr)
----> 7     nbrs.fit(train)
      8     dist,ind = nbrs.kneighbors(test)
      9     print "for: " + funcs[i]

C:UsersAppDataLocalEnthoughtCanopyUserlibsite-packagessklearnneighborsbase.pyc in fit(self,X,y)
    797             or [n_samples,n_samples] if metric='precomputed'.
    798         """
--> 799         return self._fit(X)

C:UsersAppDataLocalEnthoughtCanopyUserlibsite-packagessklearnneighborsbase.pyc in _fit(self,X)
    238             self._tree = BallTree(X,self.leaf_size,239                                   metric=self.effective_metric_,--> 240                                   **self.effective_metric_params_)
    241         elif self._fit_method == 'kd_tree':
    242             self._tree = KDTree(X,SystemError: NULL result without error in PyObject_Call