def optimize(self, run_f, params):
search_tree = SearchSpace(params)
lb = search_tree.get_lb()
ub = search_tree.get_ub()
f = param_decorator(run_f, search_tree)
gs = RandomSearch(self.num_runs, lb, ub, self.sobol)
start = timeit.default_timer()
best_params, score = gs.optimize(f)
end = timeit.default_timer() - start
best_params = search_tree.transform(best_params)
Result = namedtuple('Result', ['params', 'score', 'time'])
return Result(best_params, score, end)
def optimize(self, run_f, params, parallel=False):
search_tree = SearchSpace(params)
lb = search_tree.get_lb()
ub = search_tree.get_ub()
f = Evaluator(run_f, search_tree)
algorithm = self.algorithm(lb, ub, parallel, *self.args, **self.kwargs)
start = timeit.default_timer()
best_params, score = algorithm.run(f)
end = timeit.default_timer() - start
best_params = search_tree.transform(best_params)
# Result = namedtuple('Result', ['params', 'score', 'time'])
return Result(best_params, score, end)
def optimize(self, run_f, params):
search_tree = SearchSpace(params)
lb = search_tree.get_lb()
ub = search_tree.get_ub()
f = param_decorator(run_f, search_tree)
pso = PSO(self.num_generations, self.num_particles, lb, ub, self.phi1,
self.phi2)
start = timeit.default_timer()
best_params, score = pso.optimize(f)
end = timeit.default_timer() - start
best_params = search_tree.transform(best_params)
Result = namedtuple('Result', ['params', 'score', 'time'])
return Result(best_params, score, end)
