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 <mcasl@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 :-)BestManolo
El 24 jun. 2017 20:05, "Julio Antonio Soto de Vicente" <julio@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.
--Julioyes, trying multiple sample weightings is not supported by grid search directly.
On 23 Jun 2017 6:36 pm, "Manuel Castejón Limas" <manuel.castejon@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.BestManolo
2017-06-23 1:17 GMT+02:00 Manuel CASTEJÓN LIMAS <mcasl@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.nothman@gmail.com> escribió:
why are you passing [my_sample_weights] rather than just my_sample_weights?
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