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embarrassingly
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robust optimization
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embarrassingly
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# embarrassingly Embarrassingly obvious (in retrospect) ways to hack objective functions before you send them to optimization routines. See [blog article](https://www.microprediction.com/blog/robust-optimization) for motivation and explanation  ### Install pip install embarrassingly ### Example 1 : Parallel objective computation See [optuna_parallel.py](https://github.com/microprediction/embarrassingly/blob/main/examples/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](https://github.com/microprediction/embarrassingly/blob/main/examples/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](https://github.com/microprediction/embarrassingly/blob/main/examples/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})
