Quadratic Function#
Expand to copy examples/1_basics/9_additional_arguments.py (top right)
from functools import partial
from ConfigSpace import Configuration, ConfigurationSpace, Float
from matplotlib import pyplot as plt
from smac import RunHistory, Scenario
from smac.facade.hyperparameter_optimization_facade import (
HyperparameterOptimizationFacade as HPOFacade,
)
__copyright__ = "Copyright 2025, Leibniz University Hanover, Institute of AI"
__license__ = "3-clause BSD"
class AdditionalArgumentsClass:
def __init__(self, bias:int) -> None:
self.bias = bias
@property
def configspace(self) -> ConfigurationSpace:
cs = ConfigurationSpace(seed=0)
x = Float("x", (-5, 5), default=-5)
cs.add([x])
return cs
def train(self, config: Configuration, seed: int = 0) -> float:
"""Returns the y value of a quadratic function with a minimum we know to be at x=0."""
x = config["x"]
return x**2 + self.bias
class PartialFunctionClass:
@property
def configspace(self) -> ConfigurationSpace:
cs = ConfigurationSpace(seed=0)
x = Float("x", (-5, 5), default=-5)
cs.add([x])
return cs
def train(self, config: Configuration, seed: int = 0, bias:int=0) -> float:
"""Returns the y value of a quadratic function with a minimum we know to be at x=0."""
x = config["x"]
return x**2 + bias
def plot(runhistory: RunHistory, incumbent: Configuration, incumbent_cost:float, bias: int, incumbent_color:str, color:str) -> None:
# Get all configurations and costs
configs = [config["x"] for config in runhistory.get_configs()]
costs = [config.cost for config in runhistory.values()]
# Plot all trials
plt.scatter(configs, costs, c=color, alpha=0.4, zorder=9999, marker="o", label=f"Model with bias {bias}")
# Plot incumbent
plt.scatter(incumbent["x"], incumbent_cost, c=incumbent_color,s = 100, zorder=10000, marker="x", label=f"Incumbent with bias {bias}")
if __name__ == "__main__":
for model, bias, color, incumbent_color in [(AdditionalArgumentsClass(bias=2), 2, "green", "red"), (PartialFunctionClass(), -2, "blue", "orange")]:
# Scenario object specifying the optimization "environment"
seed = 0 if isinstance(model, AdditionalArgumentsClass) else 1
scenario = Scenario(model.configspace, deterministic=True, n_trials=100, seed=seed)
if isinstance(model, PartialFunctionClass):
model.train = partial(model.train, bias=-2)
# Now we use SMAC to find the best hyperparameters
smac = HPOFacade(
scenario,
model.train, # We pass the target function here
overwrite=True, # Overrides any previous results that are found that are inconsistent with the meta-data
)
incumbent_config = 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_config)
print(f"Incumbent cost: {incumbent_cost}")
# Let's plot it too
plot(smac.runhistory, incumbent_config, incumbent_cost, bias=bias, color=color, incumbent_color=incumbent_color)
plt.legend()
plt.show()
Description#
An example of adding additional arguments to the target function either using a class, or a partial function.
This example extends the quadratic function example at examples/1_basics/1_quadratic_function.py.
from functools import partial
from ConfigSpace import Configuration, ConfigurationSpace, Float
from matplotlib import pyplot as plt
from smac import RunHistory, Scenario
from smac.facade.hyperparameter_optimization_facade import (
HyperparameterOptimizationFacade as HPOFacade,
)
__copyright__ = "Copyright 2025, Leibniz University Hanover, Institute of AI"
__license__ = "3-clause BSD"
class AdditionalArgumentsClass:
def __init__(self, bias:int) -> None:
self.bias = bias
@property
def configspace(self) -> ConfigurationSpace:
cs = ConfigurationSpace(seed=0)
x = Float("x", (-5, 5), default=-5)
cs.add([x])
return cs
def train(self, config: Configuration, seed: int = 0) -> float:
"""Returns the y value of a quadratic function with a minimum we know to be at x=0."""
x = config["x"]
return x**2 + self.bias
class PartialFunctionClass:
@property
def configspace(self) -> ConfigurationSpace:
cs = ConfigurationSpace(seed=0)
x = Float("x", (-5, 5), default=-5)
cs.add([x])
return cs
def train(self, config: Configuration, seed: int = 0, bias:int=0) -> float:
"""Returns the y value of a quadratic function with a minimum we know to be at x=0."""
x = config["x"]
return x**2 + bias
def plot(runhistory: RunHistory, incumbent: Configuration, incumbent_cost:float, bias: int, incumbent_color:str, color:str) -> None:
# Get all configurations and costs
configs = [config["x"] for config in runhistory.get_configs()]
costs = [config.cost for config in runhistory.values()]
# Plot all trials
plt.scatter(configs, costs, c=color, alpha=0.4, zorder=9999, marker="o", label=f"Model with bias {bias}")
# Plot incumbent
plt.scatter(incumbent["x"], incumbent_cost, c=incumbent_color,s = 100, zorder=10000, marker="x", label=f"Incumbent with bias {bias}")
if __name__ == "__main__":
for model, bias, color, incumbent_color in [(AdditionalArgumentsClass(bias=2), 2, "green", "red"), (PartialFunctionClass(), -2, "blue", "orange")]:
# Scenario object specifying the optimization "environment"
seed = 0 if isinstance(model, AdditionalArgumentsClass) else 1
scenario = Scenario(model.configspace, deterministic=True, n_trials=100, seed=seed)
if isinstance(model, PartialFunctionClass):
model.train = partial(model.train, bias=-2)
# Now we use SMAC to find the best hyperparameters
smac = HPOFacade(
scenario,
model.train, # We pass the target function here
overwrite=True, # Overrides any previous results that are found that are inconsistent with the meta-data
)
incumbent_config = 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_config)
print(f"Incumbent cost: {incumbent_cost}")
# Let's plot it too
plot(smac.runhistory, incumbent_config, incumbent_cost, bias=bias, color=color, incumbent_color=incumbent_color)
plt.legend()
plt.show()