We could clarify in the documentation that you can grid-search any
(hyper) parameter of a model,
but not parameters to fit?
Only the values returned by get_params() can be tuned.
Only "param_grid" will be searched, not "fit_params". "fit_params" can
contain only a single setting.
On 06/26/20
I don't think we'll be accepting a pull request adding this feature to
scikit-learn. It is too niche. But you should go ahead and modify the
search to operate over weightings for your own research. If you feel the
documentation can be clarified, a pull request there is welcome.
On 26 June 2017 at
Yes, I guess most users will be happy without using weights. Some will need
to use one single vector, but I am currently researching a weighting method
thus my need of evaluating multiple weight vectors.
I understand that it seems to be a very specific issue with a simple
workaround, most likely
Joel is right.
In fact, you usually don't want to tune a lot the sample weights: you may leave
them default, set them in order to balance classes, or fix them according to
some business rule.
That said, you can always run a couple of grid searchs changing that sample
weights and compare result
yes, trying multiple sample weightings is not supported by grid search
directly.
On 23 Jun 2017 6:36 pm, "Manuel Castejón Limas"
wrote:
> Dear Joel,
>
> I tried and removed the square brackets and now it works as expected *for
> a single* sample_weight vector:
>
> validator = GridSearchCV(my_Reg
Dear Joel,
I tried and removed the square brackets and now it works as expected *for a
single* sample_weight vector:
validator = GridSearchCV(my_Regressor,
param_grid={'number_of_hidden_neurons': range(4, 5),
'epochs': [50],
Dear Joel,
I'm just passing an iterable as I would do with any other sequence of
parameters to tune. In this case the list only has one element to use but
in general I ought to be able to pass a collection of vectors.
Anyway, I guess that that issue is not the cause of the problem.
El 23 jun. 2017
Hello Antonio,
Sure:
import sklearn
print(sklearn.__version__)
0.18.1
The error suggests that the fit function is expecting a split vector with
size 2/3*1000 but the whole vector (size 1000) is passed.
...
ValueError: Found a sample_weight array with shape (1000,) for an
input with shape (666,
why are you passing [my_sample_weights] rather than just my_sample_weights?
On 23 Jun 2017 7:49 am, "Julio Antonio Soto de Vicente"
wrote:
> Hi Manuel,
>
> Are you sure that you are using the latest version (or at least >0.17)?
> The code for splitting the sample weights in GridSearchCV has been
Hi Manuel,
Are you sure that you are using the latest version (or at least >0.17)? The
code for splitting the sample weights in GridSearchCV has been there for a
while now...
--
Julio
> El 22 jun 2017, a las 23:33, Manuel Castejón Limas
> escribió:
>
> Dear all,
> I posted the full question
Dear all,
I posted the full question on StackOverflow and as it contains some figures
I refer you to that post.
https://stackoverflow.com/questions/44661926/sample-
weight-parameter-shape-error-in-scikit-learn-gridsearchcv/44662285#44662285
I currently believe that this issue is a bug and I open
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