i'm stuck on how to give it a range of integers to check for the number of topics, and not use floats. any suggestions beyond casting the number with int()?
adding explicit support for integers is on the to-do list currently, but admittedly not very high
if you have use cases where casting to int() is problematic for some reason we would be interested to hear about those!
the only foreseeable case i have is possibly wasting an evaluation on an integer that's previously been tried
Yeah, that's a good point. I'm playing with the idea of specifying an 'equivalence distance' (could be a callable) for each hyperparameter that would prevent that stuff from happening. This would probably be a fairly clean fix, and solves a variety of requests from our users in one go.
this is my question : http://stats.stackexchange.com/questions/177220/libsvm-parameter-tuning
i mean optunity doesnt support c#
Is there an example of optunity use with "neural net" passage? https://github.com/IndicoDataSolutions/Passage
Hi there. How to tune (by using optunity.maximize_structured with search space) a sklearn ML model without any cross validation on specific x_train and x_test data (which means I should be able to determine which data set is for training and which one for testing) ? I would appreciate a simple and clear example.
Thanks
Thanks
hi..i would like to ask if matlab wrapper supports python's aggregator "mean_and_list"? I cannot find a way to return the individual fold errors for further statistical processing (e.g. confidence interv etc.) [except maybe hacking global variables inside objective function or writing to files which is not very nice or fast;)]
@Arch111 you can do this by making your own objective function, e.g.
x_train = foo
x_test = bar
def create_objfun(your list of hyperpars):
train with x_train
predict with x_test
return score
x_train = foo
x_test = bar
def create_objfun(your list of hyperpars):
train with x_train
predict with x_test
return score
also x2;) is strata = [[all_indices_class_1], [all_indices_class_2],....,....,[all_indices_class_N]] the 'manual' way for str.sampling in cross_validate ?
I have a question regarding how maximize or maximize_structured methods work and converge. Every time i run Maximize_structured with the same constraints and conditions, the method seem to return different optimized parameters
Thanks
@claesenm are you here?
I have a function which takes a numpy array as parameter, can I optimize it using Optunity?
It looks like this:
def target(x):
return np.sum(xx - np.cos(2math.pi*x)) + np.prod(x.shape)
return np.sum(xx - np.cos(2math.pi*x)) + np.prod(x.shape)
it's a test function for global optimization
I don't want to fix the dimension because I will test it with different dimension numbers and I would need to change many lines in my code