ValueError                                Traceback (most recent call last)
<ipython-input-2-304908356f18> in <module>()
      2 from sklearn.preprocessing import KBinsDiscretizer
      3 X = np.array([0.05, 0.05, 0.95]).reshape(-1, 1)
----> 4 KBinsDiscretizer(n_bins=10, encode='ordinal', strategy='quantile').fit_transform(X)
      5 """
      6 Xdi = transformer.inverse_transform(Xd)

/home/sandro/code/scikit-learn/sklearn/base.py in fit_transform(self, X, y, **fit_params)
    461         if y is None:
    462             # fit method of arity 1 (unsupervised transformation)
--> 463             return self.fit(X, **fit_params).transform(X)
    464         else:
    465             # fit method of arity 2 (supervised transformation)

/home/sandro/code/scikit-learn/sklearn/preprocessing/_discretization.py in transform(self, X)
    259             atol = 1.e-8
    260             eps = atol + rtol * np.abs(Xt[:, jj])
--> 261             Xt[:, jj] = np.digitize(Xt[:, jj] + eps, bin_edges[jj][1:])
    262         np.clip(Xt, 0, self.n_bins_ - 1, out=Xt)
    263 

ValueError: bins must be monotonically increasing or decreasing

System:
   machine: Linux-4.15.0-45-generic-x86_64-with-Ubuntu-16.04-xenial
executable: /home/sandro/.virtualenvs/scikit-learn/bin/python
    python: 3.5.2 (default, Nov 23 2017, 16:37:01)  [GCC 5.4.0 20160609]

BLAS:
    macros: 
cblas_libs: cblas
  lib_dirs: 

Python deps:
       pip: 10.0.1
setuptools: 39.1.0
   sklearn: 0.21.dev0
    Cython: 0.28.5
     scipy: 1.1.0
    pandas: 0.23.4
     numpy: 1.15.2