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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
Closed

Should mixture models have a clusterer-compatible interface #10336

jnothman opened this issue Dec 18, 2017 · 6 comments
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@jnothman
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@jnothman jnothman commented Dec 18, 2017

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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@spinicist spinicist commented Jan 11, 2018

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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@amueller amueller commented May 22, 2018

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

@Eight1911
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@Eight1911 Eight1911 commented May 22, 2018

I would like to work on this!

@amueller
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@amueller amueller commented May 24, 2018

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

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

@Eight1911 Mind if I take a look at this?

@haoranShu
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@haoranShu haoranShu commented Jun 15, 2018

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

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