tests.test_optimizers.test_initializers.test_initialize_vertices() - Code Metrics - Inspection of "add new tests" - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

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created 2022-11-20 18:59 UTC

test_initialize_vertices() A

↳ Parent: tests.test_optimizers.test_initializers

Complexity

Conditions

Size

Total Lines	8
Code Lines	6

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	6
nop	1
dl	0
loc	8
rs	10
c	0
b	0
f	0

import pytest
import numpy as np

from ._parametrize import optimizers, optimizers_2


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


search_space = {
    "x1": np.arange(-100, 101, 1),
}


@pytest.mark.parametrize(*optimizers)
def test_initialize_warm_start_0(Optimizer):
    init = {
        "x1": 0,
    }

    initialize = {"warm_start": [init]}

    opt = Optimizer(search_space, initialize=initialize)
    opt.search(objective_function, n_iter=1)

    # print("\nself.results \n", opt.search_data)

    assert abs(opt.best_score) < 0.001


@pytest.mark.parametrize(*optimizers)

def test_initialize_warm_start_1(Optimizer):
    search_space = {
        "x1": np.arange(-10, 10, 1),
    }
    init = {
        "x1": -10,
    }

    initialize = {"warm_start": [init]}

    opt = Optimizer(search_space, initialize=initialize)
    opt.search(objective_function, n_iter=1)

    assert opt.best_para == init


@pytest.mark.parametrize(*optimizers)

def test_initialize_warm_start_2(Optimizer):
    search_space = {
        "x1": np.arange(-10, 10, 1),
    }
    init = {
        "x1": -10,
    }

    initialize = {"warm_start": [init], "random": 0, "vertices": 0, "grid": 0}

    opt = Optimizer(search_space, initialize=initialize)
    opt.search(objective_function, n_iter=1)

    assert opt.best_para == init


@pytest.mark.parametrize(*optimizers)
def test_initialize_vertices(Optimizer):
    initialize = {"vertices": 2}

    opt = Optimizer(search_space, initialize=initialize)
    opt.search(objective_function, n_iter=2)

    assert abs(opt.best_score) - 10000 < 0.001


@pytest.mark.parametrize(*optimizers)

def test_initialize_grid_0(Optimizer):
    search_space = {
        "x1": np.arange(-1, 2, 1),
    }
    initialize = {"grid": 1}

    opt = Optimizer(search_space, initialize=initialize)
    opt.search(objective_function, n_iter=1)

    assert abs(opt.best_score) < 0.001


@pytest.mark.parametrize(*optimizers)

def test_initialize_grid_1(Optimizer):
    search_space = {
        "x1": np.arange(-2, 3, 1),
    }

    initialize = {"grid": 1}

    opt = Optimizer(search_space, initialize=initialize)
    opt.search(objective_function, n_iter=1)

    assert abs(opt.best_score) - 1 < 0.001


@pytest.mark.parametrize(*optimizers)

@pytest.mark.parametrize(*optimizers_2)
def test_initialize_warm_start_twoOpt_0(Optimizer, Optimizer2):
    opt1 = Optimizer(search_space)
    opt1.search(objective_function, n_iter=1)

    opt2 = Optimizer2(search_space, initialize={"warm_start": [opt1.best_para]})
    opt2.search(objective_function, n_iter=20)

    assert opt1.best_score <= opt2.best_score


@pytest.mark.parametrize(*optimizers)

@pytest.mark.parametrize(*optimizers_2)
def test_initialize_warm_start_twoOpt_1(Optimizer, Optimizer2):
    opt1 = Optimizer(search_space)
    opt1.search(objective_function, n_iter=20)

    opt2 = Optimizer2(search_space, initialize={"warm_start": [opt1.best_para]})
    opt2.search(objective_function, n_iter=1)

    assert opt1.best_score <= opt2.best_score


1		import pytest
2		import numpy as np
3
4		from ._parametrize import optimizers, optimizers_2
5
6
7		def objective_function(para):
8		score = -para["x1"] * para["x1"]
9		return score
10
11
12		search_space = {
13		"x1": np.arange(-100, 101, 1),
14		}
15
16
17		@pytest.mark.parametrize(*optimizers)
18		def test_initialize_warm_start_0(Optimizer):
19		init = {
20		"x1": 0,
21		}
22
23		initialize = {"warm_start": [init]}
24
25		opt = Optimizer(search_space, initialize=initialize)
26		opt.search(objective_function, n_iter=1)
27
28		# print("\nself.results \n", opt.search_data)
29
30		assert abs(opt.best_score) < 0.001
31
32
33	View Code Duplication	@pytest.mark.parametrize(*optimizers)
		0 ignored issues – show Duplication introduced 2021-01-06 10:56 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
34		def test_initialize_warm_start_1(Optimizer):
35		search_space = {
36		"x1": np.arange(-10, 10, 1),
37		}
38		init = {
39		"x1": -10,
40		}
41
42		initialize = {"warm_start": [init]}
43
44		opt = Optimizer(search_space, initialize=initialize)
45		opt.search(objective_function, n_iter=1)
46
47		assert opt.best_para == init
48
49
50	View Code Duplication	@pytest.mark.parametrize(*optimizers)
		0 ignored issues – show Duplication introduced 2020-10-09 11:15 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
51		def test_initialize_warm_start_2(Optimizer):
52		search_space = {
53		"x1": np.arange(-10, 10, 1),
54		}
55		init = {
56		"x1": -10,
57		}
58
59		initialize = {"warm_start": [init], "random": 0, "vertices": 0, "grid": 0}
60
61		opt = Optimizer(search_space, initialize=initialize)
62		opt.search(objective_function, n_iter=1)
63
64		assert opt.best_para == init
65
66
67		@pytest.mark.parametrize(*optimizers)
68		def test_initialize_vertices(Optimizer):
69		initialize = {"vertices": 2}
70
71		opt = Optimizer(search_space, initialize=initialize)
72		opt.search(objective_function, n_iter=2)
73
74		assert abs(opt.best_score) - 10000 < 0.001
75
76
77	View Code Duplication	@pytest.mark.parametrize(*optimizers)
		0 ignored issues – show Duplication introduced 2022-11-20 19:03 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
78		def test_initialize_grid_0(Optimizer):
79		search_space = {
80		"x1": np.arange(-1, 2, 1),
81		}
82		initialize = {"grid": 1}
83
84		opt = Optimizer(search_space, initialize=initialize)
85		opt.search(objective_function, n_iter=1)
86
87		assert abs(opt.best_score) < 0.001
88
89
90	View Code Duplication	@pytest.mark.parametrize(*optimizers)
		0 ignored issues – show Duplication introduced 2022-11-20 19:03 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
91		def test_initialize_grid_1(Optimizer):
92		search_space = {
93		"x1": np.arange(-2, 3, 1),
94		}
95
96		initialize = {"grid": 1}
97
98		opt = Optimizer(search_space, initialize=initialize)
99		opt.search(objective_function, n_iter=1)
100
101		assert abs(opt.best_score) - 1 < 0.001
102
103
104	View Code Duplication	@pytest.mark.parametrize(*optimizers)
		0 ignored issues – show Duplication introduced 2022-11-20 19:03 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
105		@pytest.mark.parametrize(*optimizers_2)
106		def test_initialize_warm_start_twoOpt_0(Optimizer, Optimizer2):
107		opt1 = Optimizer(search_space)
108		opt1.search(objective_function, n_iter=1)
109
110		opt2 = Optimizer2(search_space, initialize={"warm_start": [opt1.best_para]})
111		opt2.search(objective_function, n_iter=20)
112
113		assert opt1.best_score <= opt2.best_score
114
115
116	View Code Duplication	@pytest.mark.parametrize(*optimizers)
		0 ignored issues – show Duplication introduced 2022-11-20 19:03 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
117		@pytest.mark.parametrize(*optimizers_2)
118		def test_initialize_warm_start_twoOpt_1(Optimizer, Optimizer2):
119		opt1 = Optimizer(search_space)
120		opt1.search(objective_function, n_iter=20)
121
122		opt2 = Optimizer2(search_space, initialize={"warm_start": [opt1.best_para]})
123		opt2.search(objective_function, n_iter=1)
124
125		assert opt1.best_score <= opt2.best_score
126

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Push — master ( e19d4b...e37ecf )

test_initialize_vertices() A

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