在绘制树状图以获取Spearmanr相关性时出错

时间:2020-01-21 10:19:04

标签: python matplotlib machine-learning scipy data-science

绘制 spearmanr 相关性的树图时出现错误。 下面是我正在使用的代码

corr = np.round(scipy.stats.spearmanr(full_data[list_of_continous]).correlation, 4)
corr_condensed = hc.distance.squareform(1-corr)
z = hc.linkage(corr_condensed, method='average')
fig = plt.figure(figsize=(20,20))
dendrogram = hc.dendrogram(z, labels=full_data[list_of_continous].columns, orientation='left', leaf_font_size=30)
plt.show()

以下是我得到的错误:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-10-9873c0be8dc7> in <module>()
      1 corr = np.round(scipy.stats.spearmanr(full_data[list_of_continous]).correlation, 4)
----> 2 corr_condensed = hc.distance.squareform(1-corr)
      3 z = hc.linkage(corr_condensed, method='average')
      4 fig = plt.figure(figsize=(20,20))
      5 dendrogram = hc.dendrogram(z, labels=full_data[list_of_continous].columns, orientation='left', leaf_font_size=30)

/usr/local/anaconda/lib/python3.6/site-packages/scipy/spatial/distance.py in squareform(X, force, checks)
   1844             raise ValueError('The matrix argument must be square.')
   1845         if checks:
-> 1846             is_valid_dm(X, throw=True, name='X')
   1847 
   1848         # One-side of the dimensions is set here.

/usr/local/anaconda/lib/python3.6/site-packages/scipy/spatial/distance.py in is_valid_dm(D, tol, throw, name, warning)
   1920                 if name:
   1921                     raise ValueError(('Distance matrix \'%s\' must be '
-> 1922                                      'symmetric.') % name)
   1923                 else:
   1924                     raise ValueError('Distance matrix must be symmetric.')

ValueError: Distance matrix 'X' must be symmetric.

1 个答案:

答案 0 :(得分:0)

变量corr可能具有nan值,可能会使它变形。
试试:

corr = np.nan_to_num(corr)

更新:

跳过

    corr_condensed = hc.distance.squareform(1-corr)

对我来说没有任何错误。

所以

corr = np.round(scipy.stats.spearmanr(full_data[list_of_continous]).correlation, 4)
z = hc.linkage(corr, method='average')
fig = plt.figure(figsize=(20,20))
dendrogram = hc.dendrogram(z, labels=full_data[list_of_continous].columns, orientation='left', leaf_font_size=30)
plt.show()

也应该为您工作。