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Ask-and-Tell¶
This examples show how to use the Ask-and-Tell interface.
[INFO][abstract_initial_design.py:147] Using 20 initial design configurations and 0 additional configurations.
[INFO][abstract_intensifier.py:516] Added config 6ccff1 as new incumbent because there are no incumbents yet.
[INFO][abstract_intensifier.py:595] Added config 075f19 and rejected config 6ccff1 as incumbent because it is not better than the incumbents on 1 instances:
[INFO][abstract_intensifier.py:595] Added config efdd3f and rejected config 075f19 as incumbent because it is not better than the incumbents on 1 instances:
[INFO][abstract_intensifier.py:595] Added config 6f36d7 and rejected config efdd3f as incumbent because it is not better than the incumbents on 1 instances:
[INFO][abstract_intensifier.py:595] Added config 16407a and rejected config 6f36d7 as incumbent because it is not better than the incumbents on 1 instances:
[INFO][smbo.py:320] Finished 50 trials.
[INFO][abstract_intensifier.py:595] Added config 434d71 and rejected config 16407a as incumbent because it is not better than the incumbents on 1 instances:
[INFO][smbo.py:320] Finished 100 trials.
[INFO][smbo.py:328] Configuration budget is exhausted:
[INFO][smbo.py:329] --- Remaining wallclock time: inf
[INFO][smbo.py:330] --- Remaining cpu time: inf
[INFO][smbo.py:331] --- Remaining trials: 0
Default cost: 16916.0
Incumbent cost: 0.4924238375584856
from ConfigSpace import Configuration, ConfigurationSpace, Float
from smac import HyperparameterOptimizationFacade, Scenario
from smac.runhistory.dataclasses import TrialValue
__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
__license__ = "3-clause BSD"
class Rosenbrock2D:
@property
def configspace(self) -> ConfigurationSpace:
cs = ConfigurationSpace(seed=0)
x0 = Float("x0", (-5, 10), default=-3)
x1 = Float("x1", (-5, 10), default=-4)
cs.add([x0, x1])
return cs
def train(self, config: Configuration, seed: int = 0) -> float:
"""The 2-dimensional Rosenbrock function as a toy model.
The Rosenbrock function is well know in the optimization community and
often serves as a toy problem. It can be defined for arbitrary
dimensions. The minimium is always at x_i = 1 with a function value of
zero. All input parameters are continuous. The search domain for
all x's is the interval [-5, 10].
"""
x1 = config["x0"]
x2 = config["x1"]
cost = 100.0 * (x2 - x1**2.0) ** 2.0 + (1 - x1) ** 2.0
return cost
if __name__ == "__main__":
model = Rosenbrock2D()
# Scenario object
scenario = Scenario(model.configspace, deterministic=False, n_trials=100)
intensifier = HyperparameterOptimizationFacade.get_intensifier(
scenario,
max_config_calls=1, # We basically use one seed per config only
)
# Now we use SMAC to find the best hyperparameters
smac = HyperparameterOptimizationFacade(
scenario,
model.train,
intensifier=intensifier,
overwrite=True,
)
# We can ask SMAC which trials should be evaluated next
for _ in range(10):
info = smac.ask()
assert info.seed is not None
cost = model.train(info.config, seed=info.seed)
value = TrialValue(cost=cost, time=0.5)
smac.tell(info, value)
# After calling ask+tell, we can still optimize
# Note: SMAC will optimize the next 90 trials because 10 trials already have been evaluated
incumbent = smac.optimize()
# Get cost of default configuration
default_cost = smac.validate(model.configspace.get_default_configuration())
print(f"Default cost: {default_cost}")
# Let's calculate the cost of the incumbent
incumbent_cost = smac.validate(incumbent)
print(f"Incumbent cost: {incumbent_cost}")
Total running time of the script: (0 minutes 3.980 seconds)