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>:
> 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>
> escribió:
>
>> why are you passing [my_sample_weights] rather than just
>> my_sample_weights?
>>
>>
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