Overloading of clone! That sounds like a really bad idea to me. It pretty much
means that we cannot enforce our api and our logic on these cases. That sounds
a very slippery slope to me.
Gaël
Sent from my phone. Please forgive brevity and mis spelling
On Feb 18, 2015, 12:33, at 12:33, Joel Nothman <joel.noth...@gmail.com> wrote:
>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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