Note
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Continue an Optimization¶
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.
[INFO][abstract_initial_design.py:147] Using 10 initial design configurations and 0 additional configurations.
[INFO][abstract_intensifier.py:305] Using only one seed for deterministic scenario.
[INFO][abstract_intensifier.py:515] Added config f09c3b as new incumbent because there are no incumbents yet.
[INFO][abstract_intensifier.py:594] Added config bec0fc and rejected config f09c3b as incumbent because it is not better than the incumbents on 1 instances:
[INFO][abstract_intensifier.py:594] Added config a34626 and rejected config bec0fc as incumbent because it is not better than the incumbents on 1 instances:
[INFO][abstract_intensifier.py:594] Added config f72805 and rejected config a34626 as incumbent because it is not better than the incumbents on 1 instances:
[INFO][smbo.py:224] A callback returned False. Abort is requested.
[INFO][smbo.py:332] Shutting down because the stop flag was set.
[INFO][abstract_initial_design.py:147] Using 10 initial design configurations and 0 additional configurations.
[INFO][smbo.py:497] Continuing from previous run.
[INFO][abstract_intensifier.py:287] Added existing seed 209652396 from runhistory to the intensifier.
[INFO][abstract_intensifier.py:305] Using only one seed for deterministic scenario.
[INFO][abstract_intensifier.py:594] Added config d7ecca and rejected config f72805 as incumbent because it is not better than the incumbents on 1 instances:
[INFO][abstract_intensifier.py:594] Added config 10bb50 and rejected config d7ecca as incumbent because it is not better than the incumbents on 1 instances:
[INFO][abstract_intensifier.py:594] Added config 6a2c79 and rejected config 10bb50 as incumbent because it is not better than the incumbents on 1 instances:
[INFO][abstract_intensifier.py:594] Added config 4380b1 and rejected config 6a2c79 as incumbent because it is not better than the incumbents on 1 instances:
[INFO][abstract_intensifier.py:594] Added config 15dfc6 and rejected config 4380b1 as incumbent because it is not better than the incumbents on 1 instances:
[INFO][abstract_intensifier.py:594] Added config b9f3bd and rejected config 15dfc6 as incumbent because it is not better than the incumbents on 1 instances:
[INFO][abstract_intensifier.py:594] Added config c714f5 and rejected config b9f3bd as incumbent because it is not better than the incumbents on 1 instances:
[INFO][abstract_intensifier.py:594] Added config 2da694 and rejected config c714f5 as incumbent because it is not better than the incumbents on 1 instances:
[INFO][smbo.py:319] Finished 50 trials.
[INFO][smbo.py:327] Configuration budget is exhausted:
[INFO][smbo.py:328] --- Remaining wallclock time: inf
[INFO][smbo.py:329] --- Remaining cpu time: inf
[INFO][smbo.py:330] --- Remaining trials: 0
[INFO][abstract_intensifier.py:305] Using only one seed for deterministic scenario.
Default cost: 25.0
Incumbent cost of first run: 0.09616130553975616
[INFO][abstract_intensifier.py:305] Using only one seed for deterministic scenario.
Incumbent cost of continued run: 0.001512079916069889
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 2021, AutoML.org Freiburg-Hannover"
__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_hyperparameters([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}")
Total running time of the script: (0 minutes 1.546 seconds)
