AffinityPropagation creates 3d array of cluster centers on rare occasions #9612
Description
Description
Just stumbled upon a rare combination of training data and preference value that causes the model to save its cluster centers as a 3d ndarray instead of expected 2d.
Steps/Code to Reproduce
import numpy as np
from sklearn.cluster.affinity_propagation_ import AffinityPropagation
train_data = np.array([[-1., 1.], [1., -1.]])
model = AffinityPropagation(preference=-10).fit(train_data)
model.cluster_centers_yields
array([[[-1., 1.], [ 1., -1.]]]) # 3d!!
and
model.predict(train_data)leads to
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "/Users/jsamoocha/.virtualenvs/coach/lib/python2.7/site-packages/sklearn/cluster/affinity_propagation_.py", line 324, in predict
return pairwise_distances_argmin(X, self.cluster_centers_)
File "/Users/jsamoocha/.virtualenvs/coach/lib/python2.7/site-packages/sklearn/metrics/pairwise.py", line 464, in pairwise_distances_argmin
metric_kwargs)[0]
File "/Users/jsamoocha/.virtualenvs/coach/lib/python2.7/site-packages/sklearn/metrics/pairwise.py", line 339, in pairwise_distances_argmin_min
X, Y = check_pairwise_arrays(X, Y)
File "/Users/jsamoocha/.virtualenvs/coach/lib/python2.7/site-packages/sklearn/metrics/pairwise.py", line 111, in check_pairwise_arrays
warn_on_dtype=warn_on_dtype, estimator=estimator)
File "/Users/jsamoocha/.virtualenvs/coach/lib/python2.7/site-packages/sklearn/utils/validation.py", line 405, in check_array
% (array.ndim, estimator_name))
ValueError: Found array with dim 3. check_pairwise_arrays expected <= 2.
When using slightly different values for preference (e.g. 0 or -20), or slightly different training data (e.g. [[-1, 1], [1, -0.9]]), cluster centers are stored correctly as 2d ndarray.
Expected Results
Cluster centers to be stored as 2d ndarray, as in normal cases.
Versions
Darwin-15.6.0-x86_64-i386-64bit
('Python', '2.7.13 (default, Jul 18 2017, 09:16:53) \n[GCC 4.2.1 Compatible Apple LLVM 8.0.0 (clang-800.0.42.1)]')
('NumPy', '1.13.1')
('SciPy', '0.19.1')
('Scikit-Learn', '0.18.2')