Can you illustrate your use-case Michael?
On 06/25/2015 02:14 PM, Joel Nothman wrote:
As much as possible, parameters to a model should be specified to the
class constructor, not methods, even if application is there. This has
been the scikit-learn design for a while in order to enable things
like grid search and a bare-bones pipeline implementation. So external
projects adding additional args to predict may not have been correctly
designed (and it's easy enough for you to inherit from their model to
fix the problem by moving that param to the constructor). It may be
valid to add a param to predict when: (a) it is data-dependent, e.g. a
parallel array to the feature array; or (b) it modifies the output
format of predict, e.g. to return error as in GaussianProcess. Any
uses of this are fundamentally custom approaches for which Pipeline
already has reduced utility, but I can see how forwarding kwargs may
give a minor convenience.
On 26 June 2015 at 03:35, Michael Kneier <michael.kne...@gmail.com
<mailto:michael.kne...@gmail.com>> wrote:
As far as I know, there aren't any estimators with predict kwargs.
This doesn't mean that engineers aren't writing their own
estimators, which may need kwargs. To me, one of sklearns great
strengths is its pipeline, and extending its functionality to
allow for more flexible estimator methods seems like a good thing.
Perusing the sklearn github, it seems that there are is demand for
extending the parameters pipeline accepts.
If the yourself and the community prefer not to, that's fine. I
think it is worth discussion though.
On Wed, Jun 24, 2015 at 2:47 PM, Joel Nothman
<joel.noth...@gmail.com <mailto:joel.noth...@gmail.com>> wrote:
What estimators have predict with multiple args? Without
support for same in cross validation routines and scorers,
isn't t easier to write this functionality in custom code as
you need it, leaving the predictor off the Pipeline?
On 25 June 2015 at 06:06, Michael Kneier
<michael.kne...@gmail.com <mailto:michael.kne...@gmail.com>>
wrote:
Hi all,
It doesn't look like pipelines currently support passing
kwargs to their estimators' predict method. I think it
would be great to add this functionality, but I want to
get your thoughts before I open a PR.
Thanks,
Mike
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