Warmstarting SMAC#
Expand to copy examples/1_basics/8_warmstart.py (top right)
from __future__ import annotations
from smac.scenario import Scenario
from smac.facade import HyperparameterOptimizationFacade
from ConfigSpace import Configuration, ConfigurationSpace, Float
from smac.runhistory.dataclasses import TrialValue, TrialInfo
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 evaluate(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__":
SEED = 12345
task = Rosenbrock2D()
# Previous evaluations
# X vectors need to be connected to the configuration space
configurations = [
Configuration(task.configspace, {'x0':1, 'x1':2}),
Configuration(task.configspace, {'x0':-1, 'x1':3}),
Configuration(task.configspace, {'x0':5, 'x1':5}),
]
costs = [task.evaluate(c, seed=SEED) for c in configurations]
# Define optimization problem and budget
scenario = Scenario(task.configspace, deterministic=False, n_trials=30)
intensifier = HyperparameterOptimizationFacade.get_intensifier(scenario, max_config_calls=1)
smac = HyperparameterOptimizationFacade(
scenario,
task.evaluate,
intensifier=intensifier,
overwrite=True,
# Modify the initial design to use our custom initial design
initial_design=HyperparameterOptimizationFacade.get_initial_design(
scenario,
n_configs=0, # Do not use the default initial design
additional_configs=configurations # Use the configurations previously evaluated as initial design
# This only passes the configurations but not the cost!
# So in order to actually use the custom, pre-evaluated initial design
# we need to tell those trials, like below.
)
)
# Convert previously evaluated configurations into TrialInfo and TrialValue instances to pass to SMAC
trial_infos = [TrialInfo(config=c, seed=SEED) for c in configurations]
trial_values = [TrialValue(cost=c) for c in costs]
# Warmstart SMAC with the trial information and values
for info, value in zip(trial_infos, trial_values):
smac.tell(info, value)
# Optimize as usual
smac.optimize()
Description#
With the ask and tell interface, we can support warmstarting SMAC. We can communicate rich
information about the previous trials to SMAC using TrialInfo and TrialValue instances.
For more details on ask and tell consult the info page ask-and-tell.
from __future__ import annotations
from smac.scenario import Scenario
from smac.facade import HyperparameterOptimizationFacade
from ConfigSpace import Configuration, ConfigurationSpace, Float
from smac.runhistory.dataclasses import TrialValue, TrialInfo
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 evaluate(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__":
SEED = 12345
task = Rosenbrock2D()
# Previous evaluations
# X vectors need to be connected to the configuration space
configurations = [
Configuration(task.configspace, {'x0':1, 'x1':2}),
Configuration(task.configspace, {'x0':-1, 'x1':3}),
Configuration(task.configspace, {'x0':5, 'x1':5}),
]
costs = [task.evaluate(c, seed=SEED) for c in configurations]
# Define optimization problem and budget
scenario = Scenario(task.configspace, deterministic=False, n_trials=30)
intensifier = HyperparameterOptimizationFacade.get_intensifier(scenario, max_config_calls=1)
smac = HyperparameterOptimizationFacade(
scenario,
task.evaluate,
intensifier=intensifier,
overwrite=True,
# Modify the initial design to use our custom initial design
initial_design=HyperparameterOptimizationFacade.get_initial_design(
scenario,
n_configs=0, # Do not use the default initial design
additional_configs=configurations # Use the configurations previously evaluated as initial design
# This only passes the configurations but not the cost!
# So in order to actually use the custom, pre-evaluated initial design
# we need to tell those trials, like below.
)
)
# Convert previously evaluated configurations into TrialInfo and TrialValue instances to pass to SMAC
trial_infos = [TrialInfo(config=c, seed=SEED) for c in configurations]
trial_values = [TrialValue(cost=c) for c in costs]
# Warmstart SMAC with the trial information and values
for info, value in zip(trial_infos, trial_values):
smac.tell(info, value)
# Optimize as usual
smac.optimize()