在 Hyperband 中通过总预算指定试验次数

展开复制 examples/2_multi_fidelity/3_specify_HB_via_total_budget.py (右上角)

from __future__ import annotations

import numpy as np
from ConfigSpace import Configuration, ConfigurationSpace, Float
from matplotlib import pyplot as plt

from smac import MultiFidelityFacade, RunHistory, Scenario
from smac.intensifier.hyperband_utils import get_n_trials_for_hyperband_multifidelity

__copyright__ = "Copyright 2025, Leibniz University Hanover, Institute of AI"
__license__ = "3-clause BSD"


class QuadraticFunction:
    max_budget = 500

    @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, budget: float | None = None) -> float:
        """Returns the y value of a quadratic function with a minimum we know to be at x=0."""
        x = config["x"]

        if budget is None:
            multiplier = 1
        else:
            multiplier = 1 + budget / self.max_budget

        return x**2 * multiplier


def plot(runhistory: RunHistory, incumbent: Configuration) -> None:
    plt.figure()

    # Plot ground truth
    x = list(np.linspace(-5, 5, 100))
    y = [xi * xi for xi in x]
    plt.plot(x, y)

    # Plot all trials
    for k, v in runhistory.items():
        config = runhistory.get_config(k.config_id)
        x = config["x"]
        y = v.cost  # type: ignore
        plt.scatter(x, y, c="blue", alpha=0.1, zorder=9999, marker="o")

    # Plot incumbent
    plt.scatter(incumbent["x"], incumbent["x"] * incumbent["x"], c="red", zorder=10000, marker="x")

    plt.show()


if __name__ == "__main__":
    model = QuadraticFunction()

    min_budget = 10  # minimum budget per trial
    max_budget = 500  # maximum budget per trial
    eta = 3  # standard HB parameter influencing the number of stages

    # Let's calculate how many trials we need to exhaust the total optimization budget (in terms of
    # fidelity units)
    n_trials = get_n_trials_for_hyperband_multifidelity(
        total_budget=10000,  # this is the total optimization budget we specify in terms of fidelity units
        min_budget=min_budget,  # This influences the Hyperband rounds, minimum budget per trial
        max_budget=max_budget,  # This influences the Hyperband rounds, maximum budget per trial
        eta=eta,  # This influences the Hyperband rounds
        print_summary=True,
    )

    # Scenario object specifying the optimization "environment"
    scenario = Scenario(
        model.configspace, deterministic=True, n_trials=n_trials, min_budget=min_budget, max_budget=max_budget
    )

    # Now we use SMAC to find the best hyperparameters
    smac = MultiFidelityFacade(
        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
        intensifier=MultiFidelityFacade.get_intensifier(scenario=scenario, eta=eta),
    )

    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}")

    # Let's plot it too
    plot(smac.runhistory, incumbent)

描述

本示例使用了一个虚拟函数，但说明了如何在 Hyperband 中设置优化，以便您可以通过保真度单位的总预算来指定。

在 Hyperband 中，SMAC 通常会计算典型的 Hyperband 回合。如果一个回合没有用尽试验次数，则会开始下一个回合。您可以不预先指定试验次数，而是指定保真度单位的总预算，然后让 SMAC 计算这相当于多少次试验。

代码运行

from __future__ import annotations

import numpy as np
from ConfigSpace import Configuration, ConfigurationSpace, Float
from matplotlib import pyplot as plt

from smac import MultiFidelityFacade, RunHistory, Scenario
from smac.intensifier.hyperband_utils import get_n_trials_for_hyperband_multifidelity

__copyright__ = "Copyright 2025, Leibniz University Hanover, Institute of AI"
__license__ = "3-clause BSD"


class QuadraticFunction:
    max_budget = 500

    @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, budget: float | None = None) -> float:
        """Returns the y value of a quadratic function with a minimum we know to be at x=0."""
        x = config["x"]

        if budget is None:
            multiplier = 1
        else:
            multiplier = 1 + budget / self.max_budget

        return x**2 * multiplier


def plot(runhistory: RunHistory, incumbent: Configuration) -> None:
    plt.figure()

    # Plot ground truth
    x = list(np.linspace(-5, 5, 100))
    y = [xi * xi for xi in x]
    plt.plot(x, y)

    # Plot all trials
    for k, v in runhistory.items():
        config = runhistory.get_config(k.config_id)
        x = config["x"]
        y = v.cost  # type: ignore
        plt.scatter(x, y, c="blue", alpha=0.1, zorder=9999, marker="o")

    # Plot incumbent
    plt.scatter(incumbent["x"], incumbent["x"] * incumbent["x"], c="red", zorder=10000, marker="x")

    plt.show()


if __name__ == "__main__":
    model = QuadraticFunction()

    min_budget = 10  # minimum budget per trial
    max_budget = 500  # maximum budget per trial
    eta = 3  # standard HB parameter influencing the number of stages

    # Let's calculate how many trials we need to exhaust the total optimization budget (in terms of
    # fidelity units)
    n_trials = get_n_trials_for_hyperband_multifidelity(
        total_budget=10000,  # this is the total optimization budget we specify in terms of fidelity units
        min_budget=min_budget,  # This influences the Hyperband rounds, minimum budget per trial
        max_budget=max_budget,  # This influences the Hyperband rounds, maximum budget per trial
        eta=eta,  # This influences the Hyperband rounds
        print_summary=True,
    )

    # Scenario object specifying the optimization "environment"
    scenario = Scenario(
        model.configspace, deterministic=True, n_trials=n_trials, min_budget=min_budget, max_budget=max_budget
    )

    # Now we use SMAC to find the best hyperparameters
    smac = MultiFidelityFacade(
        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
        intensifier=MultiFidelityFacade.get_intensifier(scenario=scenario, eta=eta),
    )

    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}")

    # Let's plot it too
    plot(smac.runhistory, incumbent)