Spatially-Encouraged Spectral Clustering, a method of discovering clusters/deriving labels for spatially-referenced data with attribute/labels attached.
Home Page: https://osf.io/jh483/
License: BSD 3-Clause "New" or "Revised" License
In [1]: import sklearn
In [2]: sklearn.__version__
Out[2]: '0.23.1'
In [3]: sklearn.__file__
Out[3]: '/Users/knaaptime/anaconda3/envs/toss/lib/python3.8/site-packages/sklearn/__init__.py'
In [4]: import spenc
/Users/knaaptime/anaconda3/envs/toss/lib/python3.8/site-packages/sklearn/utils/deprecation.py:143: FutureWarning: The sklearn.cluster.spectral module is deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.cluster. Anything that cannot be imported from sklearn.cluster is now part of the private API.
warnings.warn(message, FutureWarning)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-4-b10ad84211d1> in <module>
----> 1 import spenc
~/anaconda3/envs/toss/lib/python3.8/site-packages/spenc/__init__.py in <module>
----> 1 from .abstracts import SPENC
~/anaconda3/envs/toss/lib/python3.8/site-packages/spenc/abstracts.py in <module>
15 from warnings import warn as Warn
16
---> 17 class SPENC(clust.SpectralClustering):
18 def __init__(self, n_clusters=8, eigen_solver=None, random_state=None,
19 n_init=10, gamma=1., affinity='rbf', n_neighbors=10,
~/anaconda3/envs/toss/lib/python3.8/site-packages/spenc/abstracts.py in SPENC()
455
456 def score(self, X, W, labels=None, delta=.5,
--> 457 attribute_score=skm.calinski_harabaz_score,
458 spatial_score=boundary_fraction,
459 attribute_kw = dict(),
AttributeError: module 'sklearn.metrics' has no attribute 'calinski_harabaz_score'
I guess scikit caught this typo and corrected it in version 0.23 without warning? It's a breaking change for spenc that causes import failure (can be solved by downgrading to sklearn 0.22
the fix is to change attribute_score=skm.calinski_harabaz_score to attribute_score=skm.calinski_harabasz_score (with the sz)
Usually, you should be able to pass an affinity matrix with -1 to indicate that a connection cannot be made, and two observations cannot be put in the same cluster.
But, because of the way sklearn.manifold.spectral_embedding works, it computes a normalized laplacian, so any negative weights become nan.
i'd like to get as much of our stack as possible available directly through conda. The only requirement is that all deps for each package must also be available on conda... so i need spenc on conda-forge before we can get geosnap there. Assuming you're cool with that, conda-forge wants a license.txt in the repo
When importing this package using scikit-learn v1.0.2, several errors are raised about missing names. Slight corrections appear to fix these issues.
When you pass
spenc.SPENC(n_clusters=k, affinity='precomputed').fit(affinity, W)spenc ignores W altogether, since it treats the precomputed affinity matrix as if it's already filtered by W.
This is not intended. I'd instead always intended in setting attribute_affinity_, then mixing that with spatial_affinity_ to get affinity_matrix_, on which the clustering is based.
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