Continue an Optimization#
Expand to copy examples/1_basics/5_continue.py (top right)
from __future__ import annotations
from ConfigSpace import Configuration, ConfigurationSpace, Float
from smac import Callback
from smac import HyperparameterOptimizationFacade as HPOFacade
from smac import Scenario
from smac.main.smbo import SMBO
from smac.runhistory import TrialInfo, TrialValue
__copyright__ = "Copyright 2025, Leibniz University Hanover, Institute of AI"
__license__ = "3-clause BSD"
class StopCallback(Callback):
def __init__(self, stop_after: int):
self._stop_after = stop_after
def on_tell_end(self, smbo: SMBO, info: TrialInfo, value: TrialValue) -> bool | None:
"""Called after the stats are updated and the trial is added to the runhistory. Optionally, returns false
to gracefully stop the optimization.
"""
if smbo.runhistory.finished == self._stop_after:
return False
return None
class QuadraticFunction:
@property
def configspace(self) -> ConfigurationSpace:
cs = ConfigurationSpace(seed=0)
x = Float("x", (-5, 5), default=-5)
cs.add([x])
return cs
def train(self, config: Configuration, seed: int = 0) -> float:
"""Returns the y value of a quadratic function with a minimum at x=0."""
x = config["x"]
return x * x
if __name__ == "__main__":
model = QuadraticFunction()
# Scenario object specifying the optimization "environment"
scenario = Scenario(model.configspace, deterministic=True, n_trials=50)
stop_after = 10
# Now we use SMAC to find the best hyperparameters
smac = HPOFacade(
scenario,
model.train, # We pass the target function here
callbacks=[StopCallback(stop_after=stop_after)],
overwrite=True, # Overrides any previous results that are found that are inconsistent with the meta-data
)
incumbent = smac.optimize()
assert smac.runhistory.finished == stop_after
# Now, we want to continue the optimization
# Make sure, we don't overwrite the last run
smac2 = HPOFacade(
scenario,
model.train,
overwrite=False,
)
# Check whether we get the same incumbent
assert smac.intensifier.get_incumbent() == smac2.intensifier.get_incumbent()
assert smac2.runhistory.finished == stop_after
# And now we finish the optimization
incumbent2 = smac2.optimize()
default_cost = smac.validate(model.configspace.get_default_configuration())
print(f"Default cost: {default_cost}")
incumbent_cost = smac.validate(incumbent)
print(f"Incumbent cost of first run: {incumbent_cost}")
incumbent_cost = smac2.validate(incumbent2)
print(f"Incumbent cost of continued run: {incumbent_cost}")
Description#
SMAC can also be continued from a previous run. To do so, it reads in old files (derived from scenario's name, output_directory and seed) and sets the corresponding components. In this example, an optimization of a simple quadratic function is continued.
First, after creating a scenario with 50 trials, we run SMAC with overwrite=True. This will
overwrite any previous runs (in case the example was called before). We use a custom callback to artificially stop
this first optimization after 10 trials.
Second, we again run the SMAC optimization using the same scenario, but this time with overwrite=False. As
there already is a previous run with the same meta data, this run will be continued until the 50 trials are reached.
from __future__ import annotations
from ConfigSpace import Configuration, ConfigurationSpace, Float
from smac import Callback
from smac import HyperparameterOptimizationFacade as HPOFacade
from smac import Scenario
from smac.main.smbo import SMBO
from smac.runhistory import TrialInfo, TrialValue
__copyright__ = "Copyright 2025, Leibniz University Hanover, Institute of AI"
__license__ = "3-clause BSD"
class StopCallback(Callback):
def __init__(self, stop_after: int):
self._stop_after = stop_after
def on_tell_end(self, smbo: SMBO, info: TrialInfo, value: TrialValue) -> bool | None:
"""Called after the stats are updated and the trial is added to the runhistory. Optionally, returns false
to gracefully stop the optimization.
"""
if smbo.runhistory.finished == self._stop_after:
return False
return None
class QuadraticFunction:
@property
def configspace(self) -> ConfigurationSpace:
cs = ConfigurationSpace(seed=0)
x = Float("x", (-5, 5), default=-5)
cs.add([x])
return cs
def train(self, config: Configuration, seed: int = 0) -> float:
"""Returns the y value of a quadratic function with a minimum at x=0."""
x = config["x"]
return x * x
if __name__ == "__main__":
model = QuadraticFunction()
# Scenario object specifying the optimization "environment"
scenario = Scenario(model.configspace, deterministic=True, n_trials=50)
stop_after = 10
# Now we use SMAC to find the best hyperparameters
smac = HPOFacade(
scenario,
model.train, # We pass the target function here
callbacks=[StopCallback(stop_after=stop_after)],
overwrite=True, # Overrides any previous results that are found that are inconsistent with the meta-data
)
incumbent = smac.optimize()
assert smac.runhistory.finished == stop_after
# Now, we want to continue the optimization
# Make sure, we don't overwrite the last run
smac2 = HPOFacade(
scenario,
model.train,
overwrite=False,
)
# Check whether we get the same incumbent
assert smac.intensifier.get_incumbent() == smac2.intensifier.get_incumbent()
assert smac2.runhistory.finished == stop_after
# And now we finish the optimization
incumbent2 = smac2.optimize()
default_cost = smac.validate(model.configspace.get_default_configuration())
print(f"Default cost: {default_cost}")
incumbent_cost = smac.validate(incumbent)
print(f"Incumbent cost of first run: {incumbent_cost}")
incumbent_cost = smac2.validate(incumbent2)
print(f"Incumbent cost of continued run: {incumbent_cost}")