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pythonscipyscikit-learnsparse-matrix

sklearn GMM raises "ValueError: setting an array element with a sequence." on sparse matrix

I am attempting to cluster a set of data points that are represented as a sparse scipy matrix, X. That is,

>>> print type(X)
    <class 'scipy.sparse.csr.csr_matrix'>
>>> print X.shape
    (57, 1038)
>>> print X[0]
  (0, 223)  0.471313296962
  (0, 420)  0.621222153695
  (0, 1030) 0.442688836467
  (0, 124)  0.442688836467

When I feed this matrix into an sklearn.mixture.GMM model, however, it raises the following ValueError: 

 File "/Library/Python/2.7/site-packages/sklearn/mixture/gmm.py", line 423, in fit
      X = np.asarray(X, dtype=np.float)
 File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/numpy/core/numeric.py", line 235, in asarray
     return array(a, dtype, copy=False, order=order)
 ValueError: setting an array element with a sequence.

However, I have been able to make the sklearn.cluster.KMeans model work perfectly on the same sparse matrix X.

Some other hopefully useful info: scipy version = 0.11.0, sklearn version = 0.14.1

Any ideas on what is going wrong? Thanks in advance!

Solution

  • GMMs don't support sparse matrix input, while KMeans does. If an estimator supports sparse matrices, this is always explicitly stated in the docstring for the relevant method.