Hyperactive returns integer parameters as float in best_para when search space has both int and float parameters about hyperactive

[SimonBlanke]

After this problem was fixed in GFO I tested the same thing for Hyperactive. The same error occurred so I merged the pull request #35, added some small fixes and added the test.

Thanks to @0liu for fixing this bug.

[SimonBlanke]

Hello @0liu,

I am grateful for your continuing interest in Hyperactive, but we should focus on identifying the error first. I took the code from issue 15 of GFO and converted it to Hyperactive to validate your concerns about this problem:

from hyperactive import Hyperactive

search_space = dict(my_para=range(100, 200, 10))

def obj_func(para):
    score = 1
    my_para = para["my_para"]
    for i in range(my_para):
        pass
    return score

hyper = Hyperactive()
hyper.add_search(obj_func, search_space, n_iter=100)
hyper.run()

This small test script runs fine. Could you provide a small but complete example to replicate the problem you are encountering?

[0liu]

Hi Simon,

I missed the key information in this issue's description but mentioned it in my PR: This happens when search space contains both integer and float type parameters. In this way, numpy will convert integer parameter value to float. I updated the title and first post.

By your example, if we have two parameters for the obj_func:

from hyperactive import Hyperactive

search_space = dict(my_para=range(100, 200, 10), my_para_2=[0.1, 0.2, 0.3])

def obj_func(para):
    score = 1
    my_para = para["my_para"]
    for i in range(my_para):
        pass
    return score

hyper = Hyperactive()
hyper.add_search(obj_func, search_space, n_iter=100)
hyper.run()

The results are:

Results: 'obj_func'
Best score: 1
Best parameter set:
'my_para' : 130.0
'my_para_2' : 0.3
Best iteration: 0

We can see my_para in the returned best para is 130.0 instead of 130. This is fine for human reading or manual post-processing, but in an automated process, if passing the returned results to evaluate model on validation / test data sets, there could be a problem. That's why I thought it's more like a API design concern instead of a bug.

[SimonBlanke]

Hello @0liu,

now I see! Thank you for the explanation. This should be classified as a bug. But I think this bug originates in GFO. I reopened your original issue in GFO, because the error occurs only when the search space contains int and float. We missed this in the original issue.

GFO also has the int to float converted parameter, if you extract the best parameter with opt.best_para. Therefore I think those problems (one within the objective function and one with .best_para) are related and both originate in GFO.

When we fix the problem in GFO and the problem persists in Hyperactive, then we continue our efforts here. Until then we should focus on fixing the problem in GFO.