def test_history_best_so_far(self):
input_data = [0.5, 0.6, 0.4, 0.7, 0.3, 0.2]
output_data = [0.5, 0.5, 0.4, 0.4, 0.3, 0.2]
obj = Objective()
obj.set_history(input_data)
best_history = obj.get_history_bestsofar()
assert best_history == output_data
def search(_dataset):
'''
Search the best hyper-paramers for the given dataset Using ZOOpt
:param _dataset: the given dataset
:return: (best hyper-parameters，performance of the best hyper-parameters)
'''
global dataset
dataset = _dataset
dim = Dimension(
19, [[16, 32], [1, 8], [1, 1], [1, 1], [16, 32], [1, 8],
[1, 1], [1, 1], [0, 1], [1, 8], [1, 10], [0, 1], [1, 8], [1, 10],
[40, 50], [30, 40], [20, 30], [10, 20], [0.0001, 0.001]],
[
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False, True
])
obj = Objective(eval, dim)
# perform optimization
global round
round = 0
solution = Opt.min(obj, Parameter(budget=BUDGET))
# print result
solution.print_solution()
plt.plot(obj.get_history_bestsofar())
plt.savefig('figure.png')
return (solution.get_x(), solution.get_value())
import numpy as np
import matplotlib.pyplot as plt
from zoopt import Dimension, Objective, Parameter, Opt
def ackley(solution):
x = solution.get_x()
bias = 0.2
value = -20 * np.exp(-0.2 * np.sqrt(sum([(i - bias) * (i - bias) for i in x]) / len(x))) - \
np.exp(sum([np.cos(2.0*np.pi*(i-bias)) for i in x]) / len(x)) + 20.0 + np.e
return value
dim = 100 # dimension
obj = Objective(ackley, Dimension(dim, [[-1, 1]] * dim, [True] * dim))
# perform optimization
solution = Opt.min(obj, Parameter(budget=100 * dim))
# print result
solution.print_solution()
plt.plot(obj.get_history_bestsofar())
plt.savefig('figure.png')
