Space search dictionary: only float parameter space is ineffective for small integer ranges
In notebook http://optunity.readthedocs.org/en/latest/notebooks/notebooks/sklearn-automated-classification.html?highlight=forest, there is an example how to use optunity with scikit-learn for RandomForestClassifier:
Scikit-learn classifiers require integer values for some parameters, but optunity always return floats. In your example, you postprocess optunity parameters:
model = RandomForestClassifier(
n_estimators=int(n_estimators),
max_features=int(max_features))
but in case of small range it can be very ineffective. Given a range [2, 5](we want to try values 2, 3, 4, 5 for degree), we are testing a lot of values which will be converted to the same point. It is much worse in case of [0, 1] range (turn on/off some feature). Therefore, I would like to ask for support for integer range searches, or for advice how to design experiments more efficiently. Thank you very much
We plan to provide facilities for explicit post-processing of inputs (*) and specifying minimally relevant differences per post-processed feature, which would address issues like yours. I will notify you when this functionality becomes available in the master branch.
(*) e.g. converting float to int, exponentiation, ...
Mr Claesenm, I am using optunity package to tune the number of hidden neurons in a One layer Perceptron. I will need that optunity ( using PSO) retruns an integer. So far, I am doing int(round(number)),in order to get an integer. Is there any support for integer range searches. Thank you so much !
At the moment we have no native support for integer ranges, but this will be incorporated in a near-future release. For now, rounding is the only solution. I will update this issue once the new functionality is available.
Hi, @claesenm is the feature available currently?
I found only one similar feature, "constraints" in docs, can we use them for the task of optimization values from integer space [2, 5]? https://optunity.readthedocs.io/en/latest/user/constraints.html
Thanks