embarrassingly 0.0.6

Pimp your objective function for faster, robust optimization

embarrassingly

Embarrassingly obvious (in retrospect) ways to hack objective functions before you send them to optimization routines. See blog article for motivation and explanation

Install

pip install embarrassingly 

Example 1 : Parallel objective computation

See optuna_parallel.py

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)

Example 2 : Plateau finding

See underpromoted_shgo.py

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))

Example 3 : Expensive functions

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})