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+ Warmstarting SMACΒΆ
+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.
+We can communicate using the following objects:
+class TrialValue:
+ """Values of a trial.
+
+ Parameters
+ ----------
+ cost : float | list[float]
+ time : float, defaults to 0.0
+ status : StatusType, defaults to StatusType.SUCCESS
+ starttime : float, defaults to 0.0
+ endtime : float, defaults to 0.0
+ additional_info : dict[str, Any], defaults to {}
+ """
+
+class TrialInfo:
+ """Information about a trial.
+
+ Parameters
+ ----------
+ config : Configuration
+ instance : str | None, defaults to None
+ seed : int | None, defaults to None
+ budget : float | None, defaults to None
+ """
+Usage ExampleΒΆ
+See examples/1_basics/8_warmstart.py.
+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()
+For more details on ask and tell consult advanced_usage/5_ask_and_tell.
+
diff --git a/development/examples/1_basics/2_svm_cv.html b/development/examples/1_basics/2_svm_cv.html
index 5ffed220f..675c95073 100644
--- a/development/examples/1_basics/2_svm_cv.html
+++ b/development/examples/1_basics/2_svm_cv.html
@@ -204,6 +204,11 @@
User Priors over the Optimum
+