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 06/26/2017 03:17 AM, Joel Nothman wrote:
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 16:43, Manuel CASTEJÓN LIMAS <mc...@unileon.es
<mailto:mc...@unileon.es>> wrote:
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 not worthy of any programming
effort yet as there are more important issues to address.
I guess that adding a note on this behaviour on the documentation
could be great. If some parameters can be iterated and others are
not supported knowing it provides a more solid ground to the user
base.
I'm committed to spend a few hours studying the code. Should I be
successful I will come again with a pull request.
I'll cross my fingers :-)
Best
Manolo
El 24 jun. 2017 20:05, "Julio Antonio Soto de Vicente"
<ju...@esbet.es <mailto:ju...@esbet.es>> escribió:
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 results afterwards.
--
Julio
El 24 jun 2017, a las 15:51, Joel Nothman
<joel.noth...@gmail.com <mailto:joel.noth...@gmail.com>> escribió:
yes, trying multiple sample weightings is not supported by
grid search directly.
On 23 Jun 2017 6:36 pm, "Manuel Castejón Limas"
<manuel.caste...@gmail.com
<mailto:manuel.caste...@gmail.com>> 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_Regressor,
param_grid={'number_of_hidden_neurons': range(4, 5),
'epochs': [50], }, fit_params={'sample_weight':
my_sample_weights }, n_jobs=1, ) validator.fit(x, y)|
The problem now is that I want to try multiple trainings
with multiple sample_weight parameters, in the following
fashion:
|validator = GridSearchCV(my_Regressor,
param_grid={'number_of_hidden_neurons': range(4, 5),
'epochs': [50], 'sample_weight': [my_sample_weights,
my_sample_weights**2] , }, fit_params={}, n_jobs=1, )
validator.fit(x, y)|
But unfortunately it produces the same error again:
ValueError: Found a sample_weight array with shape
(1000,) for an input with shape (666, 1). sample_weight
cannot be broadcast.
I guess that the issue is that the sample__weight
parameter was not thought to be changed during the
tuning, was it?
Thank you all for your patience and support.
Best
Manolo
2017-06-23 1:17 GMT+02:00 Manuel CASTEJÓN LIMAS
<mc...@unileon.es <mailto:mc...@unileon.es>>:
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 1:04 a. m., "Joel Nothman"
<joel.noth...@gmail.com
<mailto:joel.noth...@gmail.com>> escribió:
why are you passing [my_sample_weights] rather
than just my_sample_weights?
_______________________________________________
scikit-learn mailing list
scikit-learn@python.org <mailto:scikit-learn@python.org>
https://mail.python.org/mailman/listinfo/scikit-learn
<https://mail.python.org/mailman/listinfo/scikit-learn>
_______________________________________________
scikit-learn mailing list
scikit-learn@python.org <mailto:scikit-learn@python.org>
https://mail.python.org/mailman/listinfo/scikit-learn
<https://mail.python.org/mailman/listinfo/scikit-learn>
_______________________________________________
scikit-learn mailing list
scikit-learn@python.org <mailto:scikit-learn@python.org>
https://mail.python.org/mailman/listinfo/scikit-learn
<https://mail.python.org/mailman/listinfo/scikit-learn>
_______________________________________________
scikit-learn mailing list
scikit-learn@python.org <mailto:scikit-learn@python.org>
https://mail.python.org/mailman/listinfo/scikit-learn
<https://mail.python.org/mailman/listinfo/scikit-learn>
_______________________________________________
scikit-learn mailing list
scikit-learn@python.org
https://mail.python.org/mailman/listinfo/scikit-learn
_______________________________________________
scikit-learn mailing list
scikit-learn@python.org
https://mail.python.org/mailman/listinfo/scikit-learn
