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Should mixture models have a clusterer-compatible interface #10336

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jnothman opened this issue Dec 18, 2017 · 6 comments · Fixed by #11281
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Should mixture models have a clusterer-compatible interface #10336

jnothman opened this issue Dec 18, 2017 · 6 comments · Fixed by #11281
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@jnothman
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Mixture models are currently a bit different. They are basically clusterers, except they are probabilistic, and are applied to inductive problems unlike many clusterers. But they are unlike clusterers in API:

  • they have an n_components parameter, with identical purpose to n_clusters
  • they do not store the labels_ of the training data
  • they do not have a fit_predict method

And they are almost entirely documented separately.

Should we make the MMs more like clusterers?
@spinicist
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In my opinion, yes.

I wanted to compare K-Means, GMM and HDBSCAN and was very disappointed that GMM does not have a fit_predict method. The HDBSCAN examples use fit_predict, so I was expecting GMM to have the same interface.

@amueller
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I think we should add fit_predict at least. I wouldn't rename n_components.

@Eight1911
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I would like to work on this!

@amueller
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@Eight1911 go for it. It is probably relatively simple but maybe not entirely trivial.

@g-walsh
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g-walsh commented Jun 15, 2018

@Eight1911 Mind if I take a look at this?

@haoranShu
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@Eight1911 Do you mind if I jump in as well?

@haoranShu haoranShu mentioned this issue Jun 15, 2018
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jnothman pushed a commit that referenced this issue Jul 3, 2018
@haoranShu @jnothman
ENH adding fit_predict to mixture models (#11281)
c303ed8 
Fixes #10336
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[MRG + 1] #10336 adding fit_predict to mixture models haoranShu/scikit-learn
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@jnothman @amueller @spinicist @Eight1911 @g-walsh @haoranShu