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Draft: Support log-uniform scale in search space definition #1708

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Draft: Support log-uniform scale in search space definition #1708

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8 changes: 8 additions & 0 deletions pkg/apis/controller/experiments/v1beta1/experiment_types.go
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Original file line number Diff line number Diff line change
Expand Up @@ -199,11 +199,19 @@ const (
ParameterTypeCategorical ParameterType = "categorical"
)

type ParameterDistribution string

const (
Uniform ParameterDistribution = "uniform"
LogUniform ParameterDistribution = "log-uniform"
)

type FeasibleSpace struct {
Max string `json:"max,omitempty"`
Min string `json:"min,omitempty"`
List []string `json:"list,omitempty"`
Step string `json:"step,omitempty"`
Distribution ParameterDistribution `json:"distribution,omitempty"`
}

// TrialTemplate describes structure of trial template
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13 changes: 10 additions & 3 deletions pkg/suggestion/v1beta1/goptuna/converter.go
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Expand Up @@ -122,9 +122,16 @@ func toGoptunaSearchSpace(parameters []*api_v1_beta1.ParameterSpec) (map[string]

stepstr := p.GetFeasibleSpace().GetStep()
if stepstr == "" {
searchSpace[p.Name] = goptuna.UniformDistribution{
High: high,
Low: low,
if p.GetFeasibleSpace().GetDistribution() == "uniform"{
searchSpace[p.Name] = goptuna.UniformDistribution{
High: high,
Low: low,
}
}else if p.GetFeasibleSpace().GetDistribution() == "log-uniform"{
searchSpace[p.Name] = goptuna.LogUniformDistribution{
High: high,
Low: low,
}
}
} else {
step, err := strconv.ParseFloat(stepstr, 64)
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14 changes: 10 additions & 4 deletions pkg/suggestion/v1beta1/hyperopt/base_service.py
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Expand Up @@ -62,10 +62,16 @@ def create_hyperopt_domain(self):
float(param.max),
float(param.step))
elif param.type == DOUBLE:
hyperopt_search_space[param.name] = hyperopt.hp.uniform(
param.name,
float(param.min),
float(param.max))
if param.distribution == "uniform":
hyperopt_search_space[param.name] = hyperopt.hp.uniform(
param.name,
float(param.min),
float(param.max))
elif param.distribution == "log-uniform":
hyperopt_search_space[param.name] = hyperopt.hp.loguniform(
param.name,
float(param.min),
float(param.max))
elif param.type == CATEGORICAL or param.type == DISCRETE:
hyperopt_search_space[param.name] = hyperopt.hp.choice(
param.name, param.list)
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21 changes: 13 additions & 8 deletions pkg/suggestion/v1beta1/internal/search_space.py
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Expand Up @@ -52,7 +52,8 @@ def convertParameter(p):
step = p.feasible_space.step
return HyperParameter.int(p.name, p.feasible_space.min, p.feasible_space.max, step)
elif p.parameter_type == api.DOUBLE:
return HyperParameter.double(p.name, p.feasible_space.min, p.feasible_space.max, p.feasible_space.step)
return HyperParameter.double(p.name, p.feasible_space.min, p.feasible_space.max, p.feasible_space.step,
p.feasible_space.distribution)
elif p.parameter_type == api.CATEGORICAL:
return HyperParameter.categorical(p.name, p.feasible_space.list)
elif p.parameter_type == api.DISCRETE:
Expand All @@ -63,34 +64,38 @@ def convertParameter(p):


class HyperParameter(object):
def __init__(self, name, type_, min_, max_, list_, step):
def __init__(self, name, type_, min_, max_, list_, step, distribution):
self.name = name
self.type = type_
self.min = min_
self.max = max_
self.list = list_
self.step = step
self.distribution = distribution

def __str__(self):
if self.type == INTEGER or self.type == DOUBLE:
if self.type == INTEGER:
return "HyperParameter(name: {}, type: {}, min: {}, max: {}, step: {})".format(
self.name, self.type, self.min, self.max, self.step)
elif self.type == DOUBLE:
return "HyperParameter(name: {}, type: {}, min: {}, max: {}, step: {}, distribution: {})".format(
self.name, self.type, self.min, self.max, self.step, self.distribution)
else:
return "HyperParameter(name: {}, type: {}, list: {})".format(
self.name, self.type, ", ".join(self.list))

@staticmethod
def int(name, min_, max_, step):
return HyperParameter(name, INTEGER, min_, max_, [], step)
return HyperParameter(name, INTEGER, min_, max_, [], step, None)

@staticmethod
def double(name, min_, max_, step):
return HyperParameter(name, DOUBLE, min_, max_, [], step)
def double(name, min_, max_, step, distribution):
return HyperParameter(name, DOUBLE, min_, max_, [], step, distribution)

@staticmethod
def categorical(name, lst):
return HyperParameter(name, CATEGORICAL, 0, 0, [str(e) for e in lst], 0)
return HyperParameter(name, CATEGORICAL, 0, 0, [str(e) for e in lst], 0, None)

@staticmethod
def discrete(name, lst):
return HyperParameter(name, DISCRETE, 0, 0, [str(e) for e in lst], 0)
return HyperParameter(name, DISCRETE, 0, 0, [str(e) for e in lst], 0, None)
7 changes: 6 additions & 1 deletion pkg/suggestion/v1beta1/optuna/service.py
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Expand Up @@ -151,7 +151,12 @@ def _get_optuna_search_space(self):
if param.type == INTEGER:
search_space[param.name] = optuna.distributions.IntUniformDistribution(int(param.min), int(param.max))
elif param.type == DOUBLE:
search_space[param.name] = optuna.distributions.UniformDistribution(float(param.min), float(param.max))
if param.distribution == "uniform":
search_space[param.name] = optuna.distributions.UniformDistribution(float(param.min),
float(param.max))
elif param.distribution == "log-uniform":
search_space[param.name] = optuna.distributions.LogUniformDistribution(float(param.min),
float(param.max))
elif param.type == CATEGORICAL or param.type == DISCRETE:
search_space[param.name] = optuna.distributions.CategoricalDistribution(param.list)
return search_space
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2 changes: 1 addition & 1 deletion pkg/suggestion/v1beta1/skopt/base_service.py
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Expand Up @@ -55,7 +55,7 @@ def create_optimizer(self):
int(param.min), int(param.max), name=param.name))
elif param.type == DOUBLE:
skopt_search_space.append(skopt.space.Real(
float(param.min), float(param.max), "log-uniform", name=param.name))
float(param.min), float(param.max), prior=param.distribution, name=param.name))
elif param.type == CATEGORICAL or param.type == DISCRETE:
skopt_search_space.append(
skopt.space.Categorical(param.list, name=param.name))
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5 changes: 5 additions & 0 deletions pkg/webhook/v1beta1/experiment/validator/validator.go
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Expand Up @@ -207,6 +207,11 @@ func (g *DefaultValidator) validateParameters(parameters []experimentsv1beta1.Pa
if param.FeasibleSpace.Max == "" && param.FeasibleSpace.Min == "" {
return fmt.Errorf("feasibleSpace.max or feasibleSpace.min must be specified for parameterType: %v in spec.parameters[%v]: %v", param.ParameterType, i, param)
}
if param.ParameterType == experimentsv1beta1.ParameterTypeDouble && param.FeasibleSpace.Distribution == ""{
return fmt.Errorf("feasibleSpace.distribution must be specified for parameterType: double in spec.parameters[%v]: %v", i, param)
}else if param.ParameterType == experimentsv1beta1.ParameterTypeDouble && param.FeasibleSpace.Distribution != "uniform" && param.FeasibleSpace.Distribution != "log-uniform"{
return fmt.Errorf("feasibleSpace.distribution must be one of 'uniform', 'log-uniform' for parameterType: double in spec.parameters[%v]: %v", i, param)
}

} else if param.ParameterType == experimentsv1beta1.ParameterTypeCategorical || param.ParameterType == experimentsv1beta1.ParameterTypeDiscrete {
if param.FeasibleSpace.Max != "" || param.FeasibleSpace.Min != "" || param.FeasibleSpace.Step != "" {
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