Embarrassingly obvious (in retrospect) ways to hack objective functions before you send them to optimization routines. See blog article for motivation and explanation
pip install embarrassingly
from embarrassingly.parallel import Parallel
import optuna
def pre_objective(worker, trial):
print('Hi this is worker ' + str(worker))
x = [trial.suggest_float('x' + str(i), 0, 1) for i in range(3)]
return x[0] + x[1] * x[2]
def test_optuna():
objective = Parallel(pre_objective, num_workers=7)
study = optuna.create_study()
study.optimize(objective, n_trials=15, n_jobs=7)
from scipy.optimize import shgo
from embarrassingly.underpromoted import plateaudinous, Underpromoted2d
bounds = [(-1 ,1) ,(-1 ,1)]
f = plateaudinous
res1 = shgo(func=f, bounds=bounds, n=8, iters=4, options={'minimize_every_iter': True, 'ftol': 0.1})
print('Minimum at '+str(res1.x))
f_tilde = Underpromoted2d(f, bounds=bounds, radius=0.05)
res1 = shgo(func=f_tilde, bounds=bounds, n=8, iters=4, options={'minimize_every_iter': True, 'ftol': 0.1})
print('Landed at '+str(res1.x))
See shy_shgo.py
def slow_and_pointless(x):
""" Example of a function with varying computation time """
r = np.linalg.norm(x)
quad = (0.5*0.5-r*r)/(0.5*0.5)
compute_time = max(0,0.5*quad+x[0])
time.sleep(compute_time)
return schwefel([1000*x[0],980*x[1]])[0]
# Save time by making it a "shy" objective function
bounds = [(-0.5, 0.5), (-0.5, 0.5)]
SAP = Shy(slow_and_pointless, bounds=bounds, t_unit=0.01, d_unit=0.3)
from scipy.optimize import minimize
res = scipy.optimize.shgo(func=SAP, bounds=bounds, n=8, iters=4, options={'minimize_every_iter': True, 'ftol': 0.1})