def test_rosenbrock():
param_dict = {
'x': range(-10, 10),
'y': range(-10, 10),
}
a = 1
b = 100
x_opt = a
y_opt = a**2
def objfunc(args_list):
results = []
for hyper_par in args_list:
x = hyper_par['x']
y = hyper_par['y']
result = -(b * ((y - x**2)**2) + ((a - x)**2))
results.append(result)
return results
tuner = Tuner(param_dict, objfunc, conf_dict=dict(domain_size=100000))
results = tuner.run()
print('best hyper parameters:', results['best_params'])
print('best Accuracy:', results['best_objective'])
assert abs(results['best_params']['x'] - x_opt) <= 2
assert abs(results['best_params']['x'] - y_opt) <= 2
def test_six_hump():
def camel(x, y):
x2 = math.pow(x, 2)
x4 = math.pow(x, 4)
y2 = math.pow(y, 2)
return (4.0 - 2.1 * x2 +
(x4 / 3.0)) * x2 + x * y + (-4.0 + 4.0 * y2) * y2
param_dict = {
'x': uniform(-3, 3),
'y': uniform(-2, 2),
}
x_opt = 0.0898 # or -0;0898
y_opt = -0.7126 # or 0.7126
def objfunc(args_list):
results = []
for hyper_par in args_list:
x = hyper_par['x']
y = hyper_par['y']
result = -camel(x, y)
results.append(result)
return results
tuner = Tuner(param_dict, objfunc)
results = tuner.run()
print('best hyper parameters:', results['best_params'])
print('best objective:', results['best_objective'])
assert abs(results['best_params']['x']) - abs(x_opt) <= 0.1
assert abs(results['best_params']['y']) - abs(y_opt) <= 0.2
def test_tuner():
tuner_user = Tuner(param_dict, objectiveFunction)
results = tuner_user.run()
# max objective is 8, and minimum is 1
assert results['best_objective'] > 1