Skip to content

Python implementation of the Hydra algorithm for portfolio construction

License

Notifications You must be signed in to change notification settings

kw-corne/hydra-smac

Repository files navigation

Hydra-SMAC 0.1.11

A minimal Python re-implementation of Hydra.

Getting started

pip install hydra-smac

Example

For more information on how to use Scenario objects, please refer to the SMAC documentation.

from ConfigSpace import Configuration, ConfigurationSpace, Float
from hydrasmac import Hydra
from smac import Scenario

instances = ["a", "b", "c"]
features = {"a": [0.0], "b": [1.0], "c": [2.0]}

cs = ConfigurationSpace()
cs.add_hyperparameters(
    [
        Float("x", (1.0, 5.0)),
        Float("y", (1.0, 5.0)),
        Float("z", (1.0, 5.0)),
    ]
)


def target_function(config: Configuration, instance: str, seed: int = 0) -> float:
    config_dict = config.get_dictionary()
    x, y, z = config_dict["x"], config_dict["y"], config_dict["z"]

    if instance == "a" and x < 2.5 and y > 2.5 and z > 2.5:
        return 0.001

    if instance == "b" and y < 2.5 and x > 2.5 and z > 2.5:
        return 0.01

    if instance == "c" and z < 2.5 and y > 2.5 and x > 2.5:
        return 0.1

    return 1


scenario = Scenario(
    configspace=cs,
    instances=instances,
    instance_features=features,
    n_trials=500,
)

hydra = Hydra(
    scenario,
    target_function,
    hydra_iterations=3,
    smac_runs_per_iter=1,
    incumbents_added_per_iter=1,
    stop_early=True,
)

portfolio = hydra.optimize()
print("====== Resulting portfolio ======")
print(portfolio)
print("=================================")

About

Python implementation of the Hydra algorithm for portfolio construction

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages