def test_exception_handling(self):
x0 = [5]
bounds = tuple([(0, 10) for i in x0])
options = {'generations': 2, 'pop_size': 4, 'trm_size': 1}
def fun_exception(x, *args):
raise Exception("Dummy Exception")
return self.fun(x, *args)
res = ga.minimize(fun_exception,
bounds,
x0=x0,
options=options,
workers=1)
def test_ga(self):
# Test norm()
x = [0, 2.5, 7.5, 10]
bounds = tuple([(0, 10) for i in x])
n = ga.norm(x, bounds)
self.assertTrue(
(np.abs(n - np.array([0, 0.25, 0.75, 1.0])) < 1e-12).all())
# Test x0 and elitism
options = {'generations': 10, 'mut_rate': 0.25}
opt1 = ga.minimize(self.fun, bounds, options=options)
self.assertEqual(opt1.fx, self.fun(opt1.x))
options = {'generations': 50, 'mut_rate': 0.01}
x0 = opt1.x
opt2 = ga.minimize(self.fun, bounds, x0=x0, options=options)
self.assertEqual(opt2.fx, self.fun(opt2.x))
self.assertLessEqual(opt2.fx, opt1.fx)
# Test small population size
options = {'generations': 2, 'pop_size': 4, 'trm_size': 1}
opt3 = ga.minimize(self.fun, bounds, x0=x0, options=options, workers=1)
# Test pop_size and trm_size re-adjusting
options = {'generations': 2, 'pop_size': 8}
opt4 = ga.minimize(self.fun, bounds, x0=x0, options=options, workers=2)
# Test convergence
options['generations'] = 100
options['mut_rate'] = 0.01
opt = ga.minimize(self.fun, bounds)
self.assertLess(opt.fx, 0.1)
# Test callback
options = {'generations': 5, 'pop_size': 8, 'trm_size': 1}
def cb(x, fx, ng):
print("Generation #{}".format(ng))
print("x = {}".format(x))
print("f(x) = {}".format(fx))
global fx_last
global x_last
fx_last = fx
x_last = x
opt = ga.minimize(self.fun,
bounds,
callback=cb,
options=options,
workers=2)
self.assertEqual(fx_last, opt.fx)
self.assertTrue((np.abs(x_last - opt.x) < 1e-10).all())
def test_args_passing_2workers(self):
x0 = tuple(np.random.random(5))
bounds = tuple([(-1, 5) for i in x0])
args = ['arg0_ok', 'arg1_ok']
options = {'generations': 3}
def fun_args_wrapper(x, *args):
arg0 = args[0]
arg1 = args[1]
self.assertEqual(arg0, 'arg0_ok')
self.assertEqual(arg1, 'arg1_ok')
return self.fun(x)
res = ga.minimize(fun_args_wrapper,
bounds,
x0=x0,
args=args,
options=options,
workers=2)
def test_exotic_pickling(self):
x0 = tuple(np.random.random(8))
bounds = tuple([(-1, 1) for i in x0])
options = {'generations': 3}
class FunWrapper:
def __init__(self, fun_to_apply):
self.fun_to_apply = fun_to_apply
args = [FunWrapper(np.fft.fft)]
def fun_to_pickle(x, *args):
"""Some exotic function to be pickled."""
fun_wrapper = args[0]
x = fun_wrapper.fun_to_apply(x)
return self.fun(x)
res = ga.minimize(fun_to_pickle,
bounds,
x0=x0,
args=args,
options=options,
workers=2)
def test_ga_1param(self):
x0 = [5]
bounds = tuple([(0, 10) for i in x0])
options = {'generations': 20, 'pop_size': 4, 'trm_size': 1}
res = ga.minimize(self.fun, bounds, x0=x0, options=options, workers=1)