----- Mail original -----
> De: "Jacob Vanderplas" <jake...@cs.washington.edu>
> À: scikit-learn-general@lists.sourceforge.net
> Envoyé: Dimanche 7 Juillet 2013 19:10:38
> Objet: [Scikit-learn-general] Defining a Density Estimation Interface
> Hi,
> I've been working on a big rewrite of the Ball Tree and KD Tree in
> sklearn.neighbors [0], and one of the enhancements is a fast Kernel
> Density estimation routine. As part of the PR, I've created a
> KernelDensity class to wrap this functionality. For the initial pass
> at the interface, I've used the same method names used in
> sklearn.mixture.GMM, which (I believe) is the only other density
> estimation routine we currently have. In particular, I've defined
> these methods:
> - fit(X) -- fit the model
> - eval(X) -- compute the log-probability (i.e. normalized density)
> under the model at positions X
> - score(X) -- compute the log-likelihood of a set of data X under the
> model
> - sample(n_samples) -- draw random samples from the underlying
> density model
> Olivier suggested that perhaps ``eval`` is too generic a name, and
> should instead be something more specific (logprobability?
> loglikelihood? predict_loglikelihood? something else?)
Sounds good to me. As a matter of taste, I like `log_likelihood`, which would
be a synonym of `eval` in that case (as a second choice, log_density rather
than log_probability) ?
Bertrand
> I think this would be a good time to discuss what we'd like for a
> general interface for density estimators within scikit-learn. A
> common interface would have the advantage that several density
> estimators could be used together within a general Bayesian
> generative classification routine (I've created a proof-of-concept
> of this estimator at [1]). Note that any change to the above method
> names would require the current GMM interface to be modified.
> Let me know if you have thoughts on this,
> Jake
> [0] https://github.com/scikit-learn/scikit-learn/pull/1732
> [1] https://gist.github.com/jakevdp/5891921
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