<filename>humpday/optimizers/axcube.py<gh_stars>1-10
from ax import optimize
from logging import CRITICAL
from ax.utils.common.logger import get_logger
rt = get_logger('ax')
rt.setLevel(CRITICAL)
import warnings
warnings.filterwarnings("ignore")
from funcy import print_durations
from humpday.objectives.classic import CLASSIC_OBJECTIVES


def ax_cube(objective, n_trials, n_dim, with_count=False, method=None):
    global feval_count
    feval_count = 0
    rt = get_logger('ax')
    rt.setLevel(CRITICAL)

    def evaluation_func(prms):
        global feval_count
        feval_count += 1
        return objective([prms["u" + str(i)] for i in range(n_dim)])

    parameters = [{
        "name": "u" + str(i),
        "type": "range",
        "bounds": [0.0, 1.0],
    } for i in range(n_dim)]
    best_parameters, best_values, experiment, model = optimize(parameters=parameters,
                                                               evaluation_function=evaluation_func,
                                                               minimize=True,
                                                               total_trials=n_trials)
    best_x = [ best_parameters['u'+str(i)] for i in range(n_dim) ]
    best_val = best_values[0]['objective']
    return (best_val, best_x, feval_count) if with_count else (best_val, best_x)


def ax_default_cube(objective, n_trials, n_dim, with_count=False):
    return ax_cube(objective=objective, n_trials=n_trials, n_dim=n_dim, with_count=with_count)


AX_OPTIMIZERS = [ ax_default_cube ]

@print_durations()
def demo():
    for objective in CLASSIC_OBJECTIVES:
        print(' ')
        print(objective.__name__)
        for optimizer in AX_OPTIMIZERS:
            print(optimizer(objective, n_trials=250, n_dim=6, with_count=True))


if __name__=='__main__':
    demo()
<filename>humpday/optimizers/axcube.py<gh_stars>1-10
from ax import optimize
from logging import CRITICAL
from ax.utils.common.logger import get_logger
rt = get_logger('ax')
rt.setLevel(CRITICAL)
import warnings
warnings.filterwarnings("ignore")
from funcy import print_durations
from humpday.objectives.classic import CLASSIC_OBJECTIVES


def ax_cube(objective, n_trials, n_dim, with_count=False, method=None):
    global feval_count
    feval_count = 0
    rt = get_logger('ax')
    rt.setLevel(CRITICAL)

    def evaluation_func(prms):
        global feval_count
        feval_count += 1
        return objective([prms["u" + str(i)] for i in range(n_dim)])

    parameters = [{
        "name": "u" + str(i),
        "type": "range",
        "bounds": [0.0, 1.0],
    } for i in range(n_dim)]
    best_parameters, best_values, experiment, model = optimize(parameters=parameters,
                                                               evaluation_function=evaluation_func,
                                                               minimize=True,
                                                               total_trials=n_trials)
    best_x = [ best_parameters['u'+str(i)] for i in range(n_dim) ]
    best_val = best_values[0]['objective']
    return (best_val, best_x, feval_count) if with_count else (best_val, best_x)


def ax_default_cube(objective, n_trials, n_dim, with_count=False):
    return ax_cube(objective=objective, n_trials=n_trials, n_dim=n_dim, with_count=with_count)


AX_OPTIMIZERS = [ ax_default_cube ]

@print_durations()
def demo():
    for objective in CLASSIC_OBJECTIVES:
        print(' ')
        print(objective.__name__)
        for optimizer in AX_OPTIMIZERS:
            print(optimizer(objective, n_trials=250, n_dim=6, with_count=True))


if __name__=='__main__':
    demo()