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Adding CMA-ES (both inside MVRSM and separate) and Hospital simulator…
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expensiveoptimbenchmark/problems/DockerHospitalBenchmark.py
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,62 @@ | ||
| from .base import BaseProblem | ||
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| import subprocess | ||
| import platform | ||
| import numpy as np | ||
| import os | ||
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| class DockerHospitalBenchmarkProblem(BaseProblem): | ||
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| def __init__(self, name, d, lbs, ubs, vartype, direction, errval): | ||
| self.name = name | ||
| self.d = d | ||
| self.lb = np.asarray(lbs) | ||
| self.ub = np.asarray(ubs) | ||
| self.vt = np.asarray(vartype) | ||
| self.direction = 1 if direction == "min" else -1 | ||
| self.errval = errval | ||
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| if os.path.exists("./evaluate.sh"): | ||
| self.evalCommand = ["./evaluate.sh"] | ||
| elif os.path.exists("/evaluate.sh"): | ||
| self.evalCommand = ["/evaluate.sh"] | ||
| else: | ||
| # Note: experiment should be ran with sudo under linux to allow this command to work, | ||
| # or under an user in the docker group. | ||
| self.evalCommand = ["docker", "run", "--rm", "mrebolle/r-geccoc:Track1", "-c"] | ||
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| def evaluate(self, xs): | ||
| concatenatedCandidate = ",".join(["%.8f" % x if xvt == 'cont' else "%i" % x for (x, xvt) in zip(xs, self.vt)]) | ||
| parsedCandidate = f"Rscript objfun.R \"{concatenatedCandidate}\"" | ||
| cmd = f"{self.evalCommand + [parsedCandidate]}" | ||
| print(f"Running '{cmd}'") | ||
| # res = subprocess.check_output(cmd, shell=True) | ||
| res = subprocess.check_output(self.evalCommand + [parsedCandidate]) | ||
| reslast = res.strip().split(b"\n")[-1] | ||
| print(f"Result: {res}. Objective: {reslast}") | ||
| try: | ||
| return self.direction * float(reslast) | ||
| except: | ||
| return self.errval | ||
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| def lbs(self): | ||
| return self.lb | ||
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| def ubs(self): | ||
| return self.ub | ||
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| def vartype(self): | ||
| return self.vt | ||
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| def dims(self): | ||
| return self.d | ||
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| def __str__(self): | ||
| return f"Hospital()" | ||
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| Hospital = DockerHospitalBenchmarkProblem("hospital", 29, | ||
| np.array([ 6, 7, 3, 3, 3, 5, 3, 3, 25, 17, 2, 1, 0.25, 0.05, 0.07, 0.005, 0.07, 1e-04, 0.08, 0.25, 0.08, 0.5, 1e-6, 2, 1e-6, 1e-6, 1, 2, 0.5 ]), | ||
| np.array([14, 13, 7, 9, 7, 9, 5, 7, 35, 25, 5, 7, 2 , 0.15, 0.11, 0.02 , 0.13, 0.002, 0.12, 0.35, 0.12, 0.9, 0.01, 4, 1.1, 0.0625, 2, 5, 0.75]), | ||
| ['cont'] * 29, "min", 10000.0) | ||
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| dockerhospitalsimbenches = [Hospital] |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,74 @@ | ||
| import numpy as np | ||
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| from cma import BoundPenalty, BoundTransform, fmin, CMAOptions | ||
| from ..utils import Monitor, Binarizer | ||
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| def optimize_CMA(problem, max_evals, bound_h='transform', binarize_categorical=False, log=None): | ||
| d = problem.dims() | ||
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| vartypes = problem.vartype() | ||
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| if binarize_categorical: | ||
| raise Exception("While sort-of implemented, this implementation has issues. Please do not enable binarization.") | ||
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| # Bounds | ||
| lb = problem.lbs() | ||
| ub = problem.ubs() | ||
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| # Generate initial point, round the integers. | ||
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| if binarize_categorical: | ||
| b = Binarizer(problem.vartype()[perm] == 'cat', lb, ub) | ||
| x0 = b.binarize(x0) | ||
| # Get new bounds. | ||
| lb = b.lbs() | ||
| ub = b.ubs() | ||
| # All new variables introduced by the binarized are 'integer'. | ||
| # print(f"Binarization introduced {b.dout - b.din} new variables.") | ||
| vartypes = vartypes[self.origin_mapping] | ||
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| cmascale = (ub - lb) | ||
| x0 = (0.5*(ub-lb) + lb) / cmascale | ||
| not_continuous_variables = vartypes != "cont" | ||
| x0[not_continuous_variables] = np.round(x0[not_continuous_variables]) | ||
| sigma0 = 1/4 | ||
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| mon = Monitor(f"CMAES{'/cbinarized' if binarize_categorical else ''}/{bound_h}", problem, log=log) | ||
| def f(x): | ||
| x = x * cmascale | ||
| if binarize_categorical: | ||
| xred = b.unbinarize(x) | ||
| else: | ||
| xred = x | ||
| x = x[vartypes != "cont"] | ||
| # print(xred) | ||
| mon.commit_start_eval() | ||
| r = problem.evaluate(xred) | ||
| mon.commit_end_eval(xred, r) | ||
| return r | ||
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| # lb, ub, max_evals | ||
| opts = CMAOptions() | ||
| opts['bounds'] = [list(lb / cmascale), list(ub / cmascale)] | ||
| opts['BoundaryHandler'] = BoundPenalty if bound_h == "penalty" else BoundTransform | ||
| opts['maxfevals'] = max_evals | ||
| opts['verbose'] = 0 # Supress printing! | ||
| opts['integer_variables'] = [i for i,t in enumerate(vartypes) if t != 'cont'] | ||
| mon.start() | ||
| res = fmin(f, x0 / cmascale, sigma0, options=opts) | ||
| mon.end() | ||
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| # """ | ||
| # - `res[0]` (`xopt`) -- best evaluated solution | ||
| # - `res[1]` (`fopt`) -- respective function value | ||
| # - `res[2]` (`evalsopt`) -- respective number of function evaluations | ||
| # - `res[3]` (`evals`) -- number of overall conducted objective function evaluations | ||
| # - `res[4]` (`iterations`) -- number of overall conducted iterations | ||
| # - `res[5]` (`xmean`) -- mean of the final sample distribution | ||
| # - `res[6]` (`stds`) -- effective stds of the final sample distribution | ||
| # - `res[-3]` (`stop`) -- termination condition(s) in a dictionary | ||
| # - `res[-2]` (`cmaes`) -- class `CMAEvolutionStrategy` instance | ||
| # - `res[-1]` (`logger`) -- class `CMADataLogger` instance | ||
| # """ | ||
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| return mon.best_x, mon.best_fitness, mon |
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