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by Simon

created 2025-05-26 05:49 UTC

test_p_accept() A

↳ Parent: tests.test_parameters.test_stochastic_hill_climbing

Complexity

Conditions

Size

Total Lines	32
Code Lines	24

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	24
nop	0
dl	0
loc	32
rs	9.304
c	0
b	0
f	0

# Author: Simon Blanke
# Email: [email protected]
# License: MIT License

import pytest
import numpy as np

from gradient_free_optimizers import StochasticHillClimbingOptimizer


n_iter = 1000


def objective_function(para):
    score = -para["x1"] * para["x1"]
    return score


search_space = {
    "x1": np.arange(0, 10, 1),
}


def test_p_accept():
    p_accept_low = 0.5
    p_accept_high = 1

    epsilon = 1 / np.inf

    opt = StochasticHillClimbingOptimizer(
        search_space,
        p_accept=p_accept_low,
        epsilon=epsilon,
        initialize={"random": 1},
    )
    opt.search(objective_function, n_iter=n_iter)
    n_transitions_low = opt.n_transitions

    opt = StochasticHillClimbingOptimizer(
        search_space,
        p_accept=p_accept_high,
        epsilon=epsilon,
        initialize={"random": 1},
    )
    opt.search(objective_function, n_iter=n_iter)
    n_transitions_high = opt.n_transitions

    print("\n n_transitions_low", n_transitions_low)
    print("\n n_transitions_high", n_transitions_high)

    lower_bound = int(n_iter * p_accept_low)
    lower_bound -= lower_bound * 0.1
    higher_bound = n_iter

    assert lower_bound < n_transitions_low < n_transitions_high < higher_bound


1			# Author: Simon Blanke
2			# Email: [email protected]
3			# License: MIT License
4
5			import pytest
6			import numpy as np
7
8			from gradient_free_optimizers import StochasticHillClimbingOptimizer
9
10
11			n_iter = 1000
12
13
14			def objective_function(para):
15			score = -para["x1"] * para["x1"]
16			return score
17
18
19			search_space = {
20			"x1": np.arange(0, 10, 1),
21			}
22
23
24			def test_p_accept():
25			p_accept_low = 0.5
26			p_accept_high = 1
27
28			epsilon = 1 / np.inf
29
30			opt = StochasticHillClimbingOptimizer(
31			search_space,
32			p_accept=p_accept_low,
33			epsilon=epsilon,
34			initialize={"random": 1},
35			)
36			opt.search(objective_function, n_iter=n_iter)
37			n_transitions_low = opt.n_transitions
38
39			opt = StochasticHillClimbingOptimizer(
40			search_space,
41			p_accept=p_accept_high,
42			epsilon=epsilon,
43			initialize={"random": 1},
44			)
45			opt.search(objective_function, n_iter=n_iter)
46			n_transitions_high = opt.n_transitions
47
48			print("\n n_transitions_low", n_transitions_low)
49			print("\n n_transitions_high", n_transitions_high)
50
51			lower_bound = int(n_iter * p_accept_low)
52			lower_bound -= lower_bound * 0.1
53			higher_bound = n_iter
54
55			assert lower_bound < n_transitions_low < n_transitions_high < higher_bound
56

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