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article](https://www.microprediction.com/blog/robust-optimization) for motivation and explanation\n\n![](https://i.imgur.com/pvcS5AX.png)\n\n### Install \n\n    pip install embarrassingly \n\n### Example 1 : Parallel objective computation \n\nSee [optuna_parallel.py](https://github.com/microprediction/embarrassingly/blob/main/examples/optuna_parallel.py)\n\n    from embarrassingly.parallel import Parallel\n    import optuna\n\n    def pre_objective(worker, trial):\n        print('Hi this is worker ' + str(worker))\n        x = [trial.suggest_float('x' + str(i), 0, 1) for i in range(3)]\n        return x[0] + x[1] * x[2]\n    \n    def test_optuna():\n        objective = Parallel(pre_objective, num_workers=7)\n        study = optuna.create_study()\n        study.optimize(objective, n_trials=15, n_jobs=7)\n\n### Example 2 : Plateau finding\n\nSee [underpromoted_shgo.py](https://github.com/microprediction/embarrassingly/blob/main/examples/underpromoted_shgo.py)\n\n    from scipy.optimize import shgo\n    from embarrassingly.underpromoted import plateaudinous, Underpromoted2d\n    \n    bounds = [(-1 ,1) ,(-1 ,1)]\n    f = plateaudinous\n    res1 = shgo(func=f, bounds=bounds, n=8, iters=4, options={'minimize_every_iter': True, 'ftol': 0.1})\n    print('Minimum at '+str(res1.x))\n\n    f_tilde = Underpromoted2d(f, bounds=bounds, radius=0.05)\n    res1 = shgo(func=f_tilde, bounds=bounds, n=8, iters=4, options={'minimize_every_iter': True, 'ftol': 0.1})\n    print('Landed at '+str(res1.x))\n\n    \n\n### Example 3 : Expensive functions \n\nSee [shy_shgo.py](https://github.com/microprediction/embarrassingly/blob/main/examples/shy_shgo.py)\n\n    def slow_and_pointless(x):\n    \"\"\" Example of a function with varying computation time \"\"\"\n        r = np.linalg.norm(x)\n        quad = (0.5*0.5-r*r)/(0.5*0.5)\n        compute_time = max(0,0.5*quad+x[0])\n        time.sleep(compute_time)\n        return schwefel([1000*x[0],980*x[1]])[0]\n    \n    # Save time by making it a \"shy\" objective function\n    bounds = [(-0.5, 0.5), (-0.5, 0.5)]\n    SAP = Shy(slow_and_pointless, bounds=bounds, t_unit=0.01, d_unit=0.3)\n    from scipy.optimize import minimize\n    res = scipy.optimize.shgo(func=SAP, bounds=bounds, n=8, iters=4, options={'minimize_every_iter': True, 'ftol': 0.1})\n    \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmicroprediction%2Fembarrassingly","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmicroprediction%2Fembarrassingly","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmicroprediction%2Fembarrassingly/lists"}