热启动 SMAC#
展开以复制 examples/1_basics/8_warmstart.py (右上角)
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()
描述#
通过问询接口，我们可以支持热启动 SMAC。我们可以使用 TrialInfo 和 TrialValue 实例将之前试验的丰富信息传达给 SMAC。有关问询接口的更多详细信息，请查阅问询接口信息页面。
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()