Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Add Optuna utilities to generate and run studies on single problem instance and multiple problem instances #227

Merged
merged 9 commits into from
Jun 11, 2024
Merged

Add Optuna utilities to generate and run studies on single problem instance and multiple problem instances #227

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
9 commits
Select commit Hold shift + click to select a range
4837db4
Update ParamsConstraintBuilder hyperparameters
nhuet Jun 10, 2024
8a26f7b
Add example of optuna tuning on multiple problem instances
nhuet Jun 10, 2024
5b81230
Put generic optuna study creation mechanism in a dedicated module
nhuet Jun 10, 2024
dfa0d01
Let TimedPercentilePruner class exists even if optuna not installed
nhuet Jun 10, 2024
438b0e6
Add a simple test for optuna generic functions
nhuet Jun 11, 2024
716bfff
Add options to generic optuna functions
nhuet Jun 11, 2024
68386a7
Remove particular solver instantiation for GPHH thanks to #229
nhuet Jun 11, 2024
5efdf7b
Add option to ResultStorage.get_best_solution_fit() to select only sa…
nhuet Jun 11, 2024
89b8311
Return fitness average instances even when pruning on cumulative fitness
nhuet Jun 11, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
26 changes: 6 additions & 20 deletions discrete_optimization/generic_rcpsp_tools/large_neighborhood_search_scheduling.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -327,9 +327,8 @@ def build_constraint_handler(rcpsp_problem: ANY_RCPSP, graph, **kwargs):
constraint_max_time_to_current_solution=False,
)
else:
params_0_cls = kwargs["params_0_cls"]
params_0_kwargs = kwargs["params_0_kwargs"]
params_0 = params_0_cls(**params_0_kwargs)
params_0 = ParamsConstraintBuilder(**params_0_kwargs)
if kwargs["params_1_kwargs"] is None:
params_1 = ParamsConstraintBuilder(
minus_delta_primary=6000,
Expand All @@ -339,9 +338,8 @@ def build_constraint_handler(rcpsp_problem: ANY_RCPSP, graph, **kwargs):
constraint_max_time_to_current_solution=False,
)
else:
params_1_cls = kwargs["params_1_cls"]
params_1_kwargs = kwargs["params_1_kwargs"]
params_1 = params_1_cls(**params_1_kwargs)
params_1 = ParamsConstraintBuilder(**params_1_kwargs)
params_list = [params_0, params_1]
constraint_handler = ConstraintHandlerScheduling(
problem=rcpsp_problem,
Expand All @@ -362,9 +360,8 @@ def build_constraint_handler(rcpsp_problem: ANY_RCPSP, graph, **kwargs):
constraint_max_time_to_current_solution=False,
)
else:
params_0_cls = kwargs["params_0_cls"]
params_0_kwargs = kwargs["params_0_kwargs"]
params_0 = params_0_cls(**params_0_kwargs)
params_0 = ParamsConstraintBuilder(**params_0_kwargs)
if kwargs["params_1_kwargs"] is None:
params_1 = ParamsConstraintBuilder(
minus_delta_primary=5000,
Expand All @@ -374,9 +371,8 @@ def build_constraint_handler(rcpsp_problem: ANY_RCPSP, graph, **kwargs):
constraint_max_time_to_current_solution=False,
)
else:
params_1_cls = kwargs["params_1_cls"]
params_1_kwargs = kwargs["params_1_kwargs"]
params_1 = params_1_cls(**params_1_kwargs)
params_1 = ParamsConstraintBuilder(**params_1_kwargs)
params_list = [params_0, params_1]
constraint_handler = NeighborRepairProblems(
problem=rcpsp_problem, params_list=params_list
Expand Down Expand Up @@ -517,21 +513,11 @@ class LargeNeighborhoodSearchScheduling(LNS_CP, SolverGenericRCPSP):
[ConstraintHandlerType.MIX_SUBPROBLEMS],
),
),
SubBrickHyperparameter(
name="params_0_cls",
choices=[ParamsConstraintBuilder],
default=ParamsConstraintBuilder,
),
SubBrickKwargsHyperparameter(
name="params_0_kwargs", subbrick_hyperparameter="params_0_cls"
),
SubBrickHyperparameter(
name="params_1_cls",
choices=[ParamsConstraintBuilder],
default=ParamsConstraintBuilder,
name="params_0_kwargs", subbrick_cls=ParamsConstraintBuilder
),
SubBrickKwargsHyperparameter(
name="params_1_kwargs", subbrick_hyperparameter="params_1_cls"
name="params_1_kwargs", subbrick_cls=ParamsConstraintBuilder
),
]

Expand Down
24 changes: 16 additions & 8 deletions discrete_optimization/generic_rcpsp_tools/neighbor_tools_rcpsp.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -70,14 +70,22 @@ def get_max_time_solution(solution: ANY_SOLUTION):

class ParamsConstraintBuilder(Hyperparametrizable):
hyperparameters = [
IntegerHyperparameter(name="minus_delta_primary", low=0, default=100),
IntegerHyperparameter(name="plus_delta_primary", low=0, default=100),
IntegerHyperparameter(name="minus_delta_secondary", low=0, default=0),
IntegerHyperparameter(name="plus_delta_secondary", low=0, default=0),
IntegerHyperparameter(name="minus_delta_primary_duration", default=5, low=0),
IntegerHyperparameter(name="plus_delta_primary_duration", default=5, low=0),
IntegerHyperparameter(name="minus_delta_secondary_duration", default=5, low=0),
IntegerHyperparameter(name="plus_delta_secondary_duration", default=5, low=0),
IntegerHyperparameter(name="minus_delta_primary", low=0, default=100, high=200),
IntegerHyperparameter(name="plus_delta_primary", low=0, default=100, high=200),
IntegerHyperparameter(name="minus_delta_secondary", low=0, default=0, high=10),
IntegerHyperparameter(name="plus_delta_secondary", low=0, default=0, high=10),
IntegerHyperparameter(
name="minus_delta_primary_duration", default=5, low=0, high=10
),
IntegerHyperparameter(
name="plus_delta_primary_duration", default=5, low=0, high=10
),
IntegerHyperparameter(
name="minus_delta_secondary_duration", default=5, low=0, high=10
),
IntegerHyperparameter(
name="plus_delta_secondary_duration", default=5, low=0, high=10
),
CategoricalHyperparameter(
name="constraint_max_time_to_current_solution",
choices=[True, False],
Expand Down
Empty file added discrete_optimization/generic_tools/optuna/__init__.py
View file
Open in desktop
Empty file.
5 changes: 5 additions & 0 deletions discrete_optimization/generic_tools/optuna/timed_percentile_pruner.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,11 @@
import optuna
except ImportError:
logger.warning("You should install optuna to use TimedPercentilePruner for optuna.")

class TimedPercentilePruner:
def __init__(self, **kwargs):
raise RuntimeError("Cannot be instantiated if optuna is not installed.")

else:
from optuna.pruners import BasePruner
from optuna.study._study_direction import StudyDirection
Expand Down
Loading