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Hi, is there a way to provide an initial guess or starting point for the exploration? Should I just build a custom OptimizationProblem object with an observation already filled in? I tried doing this like the example using OptimizationProblem.from_json, but it seems that it won't take a dictionary, only strings or other objects.

I'm using minimize and I have variables named x and y. Both in the closed range [0, 10]. Is it possible to give a constraint in which y is larger than x?
I can implement it on my own by returning a large value in my function but is there a more correct way to do that?

Problem: Attempting to sample from a categorial variable, defined as a list containing np.arrays (see example below), generates an error in the np.random.choice() function used to draw a sample in function generate_samples_categorical() in ~/stats/categorical.py. The error message of the np.random.choice() function states that "a is not a 1-D variable".

control_handles_dict['Nc']['bounds_values'] = [ np.array([1.5]), np.array([2.0]), np.array([2.5]) ]

optimization_problem_parameters = [
    {
        "name": "x1",
        "category" : "categorical",
        "search_space" : {
            "values" : control_handles_dict['Nc']['bounds_values']
        }
    },
    {
        "name": "x2",
        "category" : "categorical",
        "search_space" : {
            "values" : control_handles_dict['Nc']['bounds_values']
        }
    },
]

some_obj_fun(**x):
    return x['x1'] + x['x2']

minimize(obj_fun, optimization_problem_parameters)

If one of the np.arrays() in control_handles_dict['Nc']['bounds_values'] is swapped for another data type, f.i.,

control_handles_dict['Nc']['bounds_values'] = [ 'a', np.array([2.0]), np.array([2.5]) ]

the error does not occur and np.random.choice() draws a sample from the above list.

Swapping numpy.random.choice() out for random.choices() in function generate_samples_categorical() in ~/stats/categorical.py resolves the issue.

import random

def generate_samples_categorical(values, probabilities, size=1):
    """Generate sample for categorical data with probability probabilities."""
    return random.choices(values, weights=probabilities, k=size)

Let me know if the above explanation is clear and thanks in advance.

Adding new optimizers is quite cumbersome now, since I have to modify a dictionary in your module to add them.

It would be better if minimize was also able to take optimizer directly, instead of a type.

Will probably make a PR at some point.

Hi,

Thank you so much for this package! Was looking for a hyperparameter optimizer that was both simple and easy to integrate and this just hit the nail on the head.

However, I have to access the underlying classes and methods to fit my use case via a small hack:

import benderopt.minimize as benderopt

benderopt.OptimizationProblem...

I suggest to allow more flexibility by adding these to __init__.py instead. The minimize point of access does not work for me, I imagine many people would like a slightly lower level access too.

If the decision is not to go in this direction I hope you keep the backdoor open 😛

Hi, first of all thank you for this excellent optimization package.

When setting wrong settings for an uniform parameter:

{ "name": "WestWWR", "category": "uniform", "search_space": { "low": 0.0, "high": 0.15, "step": 0.20} },

I get ValidationError: 'step' must be strictly superior than 'high'

Shouldn't it be the other way around ?
'high' must be strictly superior than 'step'

Hi, I tried to run the file minimizer.py, however, it raised an error:

Traceback (most recent call last):
  File "/home/zippingsugar/anaconda2/envs/HLSTS/lib/python2.7/site-packages/benderopt-1.3.2-py2.7.egg/benderopt/minimizer.py", line 44, in <module>
    f, optimization_problem_parameters=optimization_problem_parameters, number_of_evaluation=100)
  File "/home/zippingsugar/anaconda2/envs/HLSTS/lib/python2.7/site-packages/benderopt-1.3.2-py2.7.egg/benderopt/minimizer.py", line 19, in minimize
    optimization_problem = OptimizationProblem.from_list(optimization_problem_parameters)
  File "/home/zippingsugar/anaconda2/envs/HLSTS/lib/python2.7/site-packages/benderopt-1.3.2-py2.7.egg/benderopt/base/optimization_problem.py", line 259, in from_list
    return cls(parameters)
  File "/home/zippingsugar/anaconda2/envs/HLSTS/lib/python2.7/site-packages/benderopt-1.3.2-py2.7.egg/benderopt/base/optimization_problem.py", line 80, in __init__
    raise ValidationError(message="'parameters' must be a list of Parameter")
benderopt.validation.utils.ValidationError: 'parameters' must be a list of Parameter

I guess there were some modifications in the code and the example wasn't updated in time.

Oh, I realized this error might come from using python 2.7. I've modified several places and made it compatible with py2.7.

As part of my function (checking hyperparameters for NN training) I need to reset all global random number generators in python, which leads to optimizers suggesting the same value on subsequent passes.

Would be great if optimizers used their own generators.

I might create a PR in the future.