But the missing implementation of LogUniformRange for hpbandster still causes problems.

This code snipet produces numbers in the range from 10 to 1000 instead of [10^-3, 10]. This could be fixed by changing https://github.com/allegroai/clearml/blob/master/clearml/automation/parameters.py#L168 :

Now:
values = [v*step_size for v in range(0, int(steps))]
Should be:
values = [self.min_value + v * step_size for v in range(0, int(steps))]

I've tested it locally and it behaves as expected. Also, it would allow for negative values which aren't supported at the moment.

What do you think? Thanks for your feedback!

GreasyLeopard35
I can update that the fix to UniformIntegerParameterRange should be pushed with tomorrows release 🙂
(which would fix in turn LogUniformParameterRange)

Yup, I think that's it! 🙂

Look here AgitatedDove14 :
https://github.com/allegroai/clearml/blob/master/clearml/automation/hpbandster/bandster.py#L356

There is no implementation for LogUniformParameterRange, but since it is an instance of UniformParameterRange (by inheritance), this method will return values between [-3, .., 1] for my example. It should either raise an Exception or return [0.001, ..., 1].

GreasyLeopard35 from the implementation:
https://github.com/allegroai/clearml/blob/fcad50b6266f445424a1f1fb361f5a4bc5c7f6a3/clearml/automation/parameters.py#L215
Which basically returns the "self.base" (default) 10 to the power of the selected value:
10**-3 = 0.001
So how would I get a negative value ?

But the missing implementation of LogUniformRange for hpbandster still causes problems.

wdym?

Moreover, the LogUniformParameterRange is not implemented for hpbanster optimizer and results in a range from values [-3, 1] since LogUniformParameterRange inherits from UniformParameterRange. See https://github.com/allegroai/clearml/blob/master/clearml/automation/hpbandster/bandster.py#L355

Awesome!

GreasyLeopard35 I think you are on to something, I think UniformParameterRange just misses a min value:
https://github.com/allegroai/clearml/blob/fcad50b6266f445424a1f1fb361f5a4bc5c7f6a3/clearml/automation/parameters.py#L168
Should be:
[self.min_value + v*step_size for v in range(0, int(steps))]

Hi AgitatedDove14 ,
The get_value() method works fine. The issue is in to_list(), which calls super().to_list(), which in turn returns a list starting at 0 (thus only positive values). My suggested modification to http://UniformParameterRange.to _list() would return a list starting at self.min_value (which could be negative) instead.

` from clearml.automation.parameters import LogUniformParameterRange
sampler = LogUniformParameterRange(name='test', min_value=-3.0, max_value=1.0, step_size=0.5)
sampler.to_list()

Out[2]:
[{'test': 1.0},
{'test': 3.1622776601683795},
{'test': 10.0},
{'test': 31.622776601683793},
{'test': 100.0},
{'test': 316.22776601683796},
{'test': 1000.0},
{'test': 3162.2776601683795}] `

from clearml.automation.parameters import LogUniformParameterRange
sampler = LogUniformParameterRange(name='test', min_value=-3.0, max_value=1.0, step_size=0.5)
http://sampler.to _list()

Hmm GreasyLeopard35 can you specify the range you are passing to the HPO, as well as the type of optimization class ? (grid/random/optuna etc.)