tests.test_optimizers.test_backend_api.objective_function() - Code Metrics - Inspection of "Merge branch 'master' into dev" - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

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by Simon

created 2022-10-22 18:36 UTC

objective_function() A

↳ Parent: tests.test_optimizers.test_backend_api

Complexity

Conditions

Size

Total Lines	2
Code Lines	2

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	2
nop	1
dl	0
loc	2
rs	10
c	0
b	0
f	0

import pytest
import random
import numpy as np

from gradient_free_optimizers.optimizers.core_optimizer.converter import Converter

from ._parametrize import optimizers


def objective_function(para):
    return -(para["x1"] + para["x1"])


search_space1 = {
    "x1": np.array([1]),
    "x2": np.arange(-10, 10, 1),
}

search_space2 = {
    "x1": np.arange(-10, 10, 1),
    "x2": np.array([1]),
}

search_space3 = {
    "x1": np.arange(-10, 10, 1),
    "x2": np.array([1]),
    "x3": np.array([1]),
}

search_space4 = {
    "x1": np.arange(-10, 10, 1),
    "x2": np.array([1]),
    "x3": np.array([1]),
    "x4": np.array([1]),
}


objective_para = (
    "search_space",
    [
        (search_space1),
        (search_space2),
        (search_space3),
        (search_space4),
    ],
)


@pytest.mark.parametrize(*objective_para)
@pytest.mark.parametrize(*optimizers)
def test_backend_api_0(Optimizer, search_space):
    opt = Optimizer(search_space)

    conv = Converter(search_space)

    n_inits = len(opt.init.init_positions_l)

    for _ in range(n_inits):
        pos = opt.init_pos()
        value = conv.position2value(pos)
        para = conv.value2para(value)
        score = objective_function(para)
        opt.evaluate(score)

    opt.finish_initialization()

    for _ in range(20):
        pos = opt.iterate()
        value = conv.position2value(pos)
        para = conv.value2para(value)
        score = objective_function(para)
        opt.evaluate(score)


1			import pytest
2			import random
3			import numpy as np
4
5			from gradient_free_optimizers.optimizers.core_optimizer.converter import Converter
6
7			from ._parametrize import optimizers
8
9
10			def objective_function(para):
11			return -(para["x1"] + para["x1"])
12
13
14			search_space1 = {
15			"x1": np.array([1]),
16			"x2": np.arange(-10, 10, 1),
17			}
18
19			search_space2 = {
20			"x1": np.arange(-10, 10, 1),
21			"x2": np.array([1]),
22			}
23
24			search_space3 = {
25			"x1": np.arange(-10, 10, 1),
26			"x2": np.array([1]),
27			"x3": np.array([1]),
28			}
29
30			search_space4 = {
31			"x1": np.arange(-10, 10, 1),
32			"x2": np.array([1]),
33			"x3": np.array([1]),
34			"x4": np.array([1]),
35			}
36
37
38			objective_para = (
39			"search_space",
40			[
41			(search_space1),
42			(search_space2),
43			(search_space3),
44			(search_space4),
45			],
46			)
47
48
49			@pytest.mark.parametrize(*objective_para)
50			@pytest.mark.parametrize(*optimizers)
51			def test_backend_api_0(Optimizer, search_space):
52			opt = Optimizer(search_space)
53
54			conv = Converter(search_space)
55
56			n_inits = len(opt.init.init_positions_l)
57
58			for _ in range(n_inits):
59			pos = opt.init_pos()
60			value = conv.position2value(pos)
61			para = conv.value2para(value)
62			score = objective_function(para)
63			opt.evaluate(score)
64
65			opt.finish_initialization()
66
67			for _ in range(20):
68			pos = opt.iterate()
69			value = conv.position2value(pos)
70			para = conv.value2para(value)
71			score = objective_function(para)
72			opt.evaluate(score)
73

SimonBlanke / Gradient-Free-Optimizers

Push — master ( 047469...b66fc0 )

objective_function() A

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