The overloading of get_params and set_params becomes more complex in #1769.
I have also found cases (of helper meta-estimators / wrappers) that require
the overloading of clone behaviour, though this is not yet supported.
On 18 February 2015 at 18:14, Gael Varoquaux <gael.varoqu...@normalesup.org>
wrote:
> > This might be a very silly question, but I am wondering why set_params
> > and get_params are methods and not functions.
> > They are not overloaded anywhere in sklearn, and the ability to overload
> > them seems likely to cause trouble.
>
> They are overloaded. get_params is overloaded in the pipeline. set_params
> is overloaded in the SGG (but that does not seem a worth while use, and I
> wouldn't mind removing it).
>
> > We could even go one step further, and instead of using set_params,
> > directly build the subestimator behavior into "clone".
> > The only application for get_params that we currently have afaik is
> > est_with_params = clone(est).set_params(**params)
> > If we instead did
> > est_with_params = clone_with_params(est, **params)
>
> I have been using set_params in my own interactive work, to modify params
> on the fly. It wouldn't be the end of the world if the method goes away,
> as long as we can still assign values to attributes.
>
> If we can get rid of the overrides of set_params and get_params in our
> codebase, I am not opposed to the plan.
>
> That said, I think that the OP was really careless, and get_params /
> set_params are not that tricky. We've had issues like this previously was
> people not implementing proper __init__. The most important thing is to
> capture these API errors and give a good error message.
>
> > we could use __init__ to set the parameters directly, which would allow
> > us to do input validation in __init__.
>
> The logic for not putting input validation in __init__ is that it is
> legit to do:
>
> clf = Blah(param1=1)
> clf.param1 = 2
> clf.fit(...)
>
>
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