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" <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.

--

Julio

El 24 jun 2017, a las 15:51, Joel Nothman <joel.nothman@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.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.

Best

Manolo

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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