Sorry about that oversight in the design! A common test to catch those
sorts of inconsistencies would be useful.
The biggest problem is that KernelDensity is not fundamentally a
classifier, regressor, or transformer, but a density estimator. When I
initially did the KDE pull request, I floated the
Fix here:
https://github.com/scikit-learn/scikit-learn/pull/3826
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Huh, I thought KernelDensity was a classifier, but apparently it is not.
You should be able to grid-search over "score" once the function call is
fixed to "score(X, y=None)".
Can you try adding that?
I am surprised there is no common test for that, but I guess this
estimator is too special in it
On Wed, Nov 5, 2014 at 1:52 PM, Kyle Kastner wrote:
> In addition to the y=None thing, KDE doesn't have a transform or predict
> method - and I don't think Pipeline supports score or score_samples.
>
That may have been the crucial thing I have missed :) -- Indeed KDE would
have to be at the end
In addition to the y=None thing, KDE doesn't have a transform or predict
method - and I don't think Pipeline supports score or score_samples. Maybe
someone can comment on this, but I don't think KDE is typically used in a
pipeline.
In this particular case the code *seems* reasonable (and I am surp
Hi José,
yes, there seems to be an inconsistency, KernelDensity.fit has signature
(self, X) and not (self, X, y=None) as is usually the case even if y is
never used, see
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/neighbors/kde.py#L113
I think the generally accepted way of re
Hi all,
Is the KernelDensity estimator compatible with pipelines? When I try
to use it inside one
pipe1 = make_pipeline(StandardScaler(with_mean=True, with_std=True),
KernelDensity(algorithm="auto",
kernel="gaussian", metric="euclidean"))
params = dict(kerneldens
Hi Jacob,
Thanks a lot for your detailed answer!
José Guilherme
On Tue, Oct 21, 2014 at 3:03 PM, Jacob Vanderplas
wrote:
> Hi Jose,
> The KDE implementation does work on multivariate data, and will in general
> work for multimodal data as well. There are two caveats to that:
>
> 1. In the skle
Hi Jose,
The KDE implementation does work on multivariate data, and will in general
work for multimodal data as well. There are two caveats to that:
1. In the sklearn implementation, the bandwidth must be the same across
each dimension. If this poses a problem for your data, the data can be
scaled
