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[GSOC] optuna suggestion service logic update #2446

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[GSOC] optuna suggestion service logic update #2446

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d4ae140
unit test fixed
shashank-iitbhu Sep 22, 2024
99ecbcd
Update pkg/suggestion/v1beta1/hyperopt/base_service.py
shashank-iitbhu Sep 22, 2024
fd460b6
comment fixed
shashank-iitbhu Sep 22, 2024
6b18e9b
initial logic update
shashank-iitbhu Oct 20, 2024
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12 changes: 9 additions & 3 deletions pkg/suggestion/v1beta1/hyperopt/base_service.py
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Original file line number Diff line number Diff line change
Expand Up @@ -80,9 +80,14 @@ def create_hyperopt_domain(self):
param.name, float(param.min), float(param.max)
)
else:
hyperopt_search_space[param.name] = hyperopt.hp.uniform(
param.name, float(param.min), float(param.max)
)
if param.type == INTEGER:
hyperopt_search_space[param.name] = hyperopt.hp.uniformint(
param.name, float(param.min), float(param.max)
)
else:
hyperopt_search_space[param.name] = hyperopt.hp.uniform(
param.name, float(param.min), float(param.max)
)
elif param.distribution == api_pb2.LOG_UNIFORM:
# Log-uniform distribution: used for parameters that vary exponentially.
# We convert min and max to their logarithmic scale using math.log, because
Expand All @@ -106,6 +111,7 @@ def create_hyperopt_domain(self):
# min and max, and sigma as (max - min) / 6. This is based on the assumption
# that 99.7% of the values in a normal distribution fall within ±3 sigma.
mu = (float(param.min) + float(param.max)) / 2
# We consider the normal distribution based on the range of ±3 sigma.
sigma = (float(param.max) - float(param.min)) / 6

if param.step:
Expand Down
37 changes: 31 additions & 6 deletions pkg/suggestion/v1beta1/optuna/base_service.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,7 @@

import optuna

from pkg.apis.manager.v1beta1.python import api_pb2
from pkg.suggestion.v1beta1.internal.constant import (
CATEGORICAL,
DISCRETE,
Expand Down Expand Up @@ -110,13 +111,37 @@ def _get_optuna_search_space(self):
search_space = {}
for param in self.search_space.params:
if param.type == INTEGER:
search_space[param.name] = optuna.distributions.IntDistribution(
int(param.min), int(param.max)
)
if param.distribution == api_pb2.UNIFORM or param.distribution is None:
if param.step:
search_space[param.name] = optuna.distributions.IntDistribution(
int(param.min), int(param.max), False, param.step
)
else:
search_space[param.name] = optuna.distributions.IntDistribution(
int(param.min), int(param.max)
)
if param.distribution == api_pb2.LOG_UNIFORM:
search_space[param.name] = optuna.distributions.IntDistribution(
int(param.min), int(param.max), True, param.step
)
elif param.type == DOUBLE:
search_space[param.name] = optuna.distributions.FloatDistribution(
float(param.min), float(param.max)
)
if param.distribution == api_pb2.UNIFORM or param.distribution is None:
if param.step:
search_space[param.name] = (
optuna.distributions.FloatDistribution(
int(param.min), int(param.max), False, param.step
)
)
else:
search_space[param.name] = (
optuna.distributions.FloatDistribution(
int(param.min), int(param.max)
)
)
if param.distribution == api_pb2.LOG_UNIFORM:
search_space[param.name] = optuna.distributions.FloatDistribution(
int(param.min), int(param.max), True, param.step
)
elif param.type == CATEGORICAL or param.type == DISCRETE:
search_space[param.name] = optuna.distributions.CategoricalDistribution(
param.list
Expand Down
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