tests.test_initializers - Code Metrics - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

tests.test_initializers A
last analyzed 2025-06-23 18:24 UTC

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Complexity

Total Complexity

Size/Duplication

Total Lines	123
Duplicated Lines	26.83 %

Importance

Changes

Metric	Value
eloc	79
dl	33
loc	123
rs	10
c	0
b	0
f	0
wmc	7

7 Functions

Rating	Name	Duplication	Size	Complexity
A	objective_function()	0	3	1
A	test_initialize_all_0()	0	15	1
A	test_initialize_grid_1()	17	17	1
A	test_initialize_warm_start_0()	0	17	1
A	test_initialize_grid_0()	16	16	1
A	test_initialize_vertices()	0	13	1
A	test_initialize_warm_start_1()	0	20	1

How to fix Duplicated Code

import numpy as np
from hyperactive import Hyperactive


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


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


def test_initialize_warm_start_0():
    init = {
        "x1": 0,
    }

    initialize = {"warm_start": [init]}

    hyper = Hyperactive()
    hyper.add_search(
        objective_function,
        search_space,
        n_iter=1,
        initialize=initialize,
    )
    hyper.run()

    assert abs(hyper.best_score(objective_function)) < 0.001


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

    initialize = {"warm_start": [init]}

    hyper = Hyperactive()
    hyper.add_search(
        objective_function,
        search_space,
        n_iter=1,
        initialize=initialize,
    )
    hyper.run()

    assert hyper.best_para(objective_function) == init


def test_initialize_vertices():
    initialize = {"vertices": 2}

    hyper = Hyperactive()
    hyper.add_search(
        objective_function,
        search_space,
        n_iter=2,
        initialize=initialize,
    )
    hyper.run()

    assert abs(hyper.best_score(objective_function)) - 10000 < 0.001


def test_initialize_grid_0():

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

    hyper = Hyperactive()
    hyper.add_search(
        objective_function,
        search_space,
        n_iter=1,
        initialize=initialize,
    )
    hyper.run()

    assert abs(hyper.best_score(objective_function)) < 0.001


def test_initialize_grid_1():

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

    initialize = {"grid": 1}

    hyper = Hyperactive()
    hyper.add_search(
        objective_function,
        search_space,
        n_iter=1,
        initialize=initialize,
    )
    hyper.run()

    assert abs(hyper.best_score(objective_function)) - 1 < 0.001


def test_initialize_all_0():
    search_space = {
        "x1": list(np.arange(-2, 3, 1)),
    }

    initialize = {"grid": 100, "vertices": 100, "random": 100}

    hyper = Hyperactive()
    hyper.add_search(
        objective_function,
        search_space,
        n_iter=300,
        initialize=initialize,
    )
    hyper.run()


1		import numpy as np
2		from hyperactive import Hyperactive
3
4
5		def objective_function(opt):
6		score = -opt["x1"] * opt["x1"]
7		return score
8
9
10		search_space = {
11		"x1": list(np.arange(-100, 101, 1)),
12		}
13
14
15		def test_initialize_warm_start_0():
16		init = {
17		"x1": 0,
18		}
19
20		initialize = {"warm_start": [init]}
21
22		hyper = Hyperactive()
23		hyper.add_search(
24		objective_function,
25		search_space,
26		n_iter=1,
27		initialize=initialize,
28		)
29		hyper.run()
30
31		assert abs(hyper.best_score(objective_function)) < 0.001
32
33
34		def test_initialize_warm_start_1():
35		search_space = {
36		"x1": list(np.arange(-10, 10, 1)),
37		}
38		init = {
39		"x1": -10,
40		}
41
42		initialize = {"warm_start": [init]}
43
44		hyper = Hyperactive()
45		hyper.add_search(
46		objective_function,
47		search_space,
48		n_iter=1,
49		initialize=initialize,
50		)
51		hyper.run()
52
53		assert hyper.best_para(objective_function) == init
54
55
56		def test_initialize_vertices():
57		initialize = {"vertices": 2}
58
59		hyper = Hyperactive()
60		hyper.add_search(
61		objective_function,
62		search_space,
63		n_iter=2,
64		initialize=initialize,
65		)
66		hyper.run()
67
68		assert abs(hyper.best_score(objective_function)) - 10000 < 0.001
69
70
71	View Code Duplication	def test_initialize_grid_0():
		0 ignored issues – show Duplication introduced 2021-01-11 15:36 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
72		search_space = {
73		"x1": list(np.arange(-1, 2, 1)),
74		}
75		initialize = {"grid": 1}
76
77		hyper = Hyperactive()
78		hyper.add_search(
79		objective_function,
80		search_space,
81		n_iter=1,
82		initialize=initialize,
83		)
84		hyper.run()
85
86		assert abs(hyper.best_score(objective_function)) < 0.001
87
88
89	View Code Duplication	def test_initialize_grid_1():
		0 ignored issues – show Duplication introduced 2021-01-11 15:36 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
90		search_space = {
91		"x1": list(np.arange(-2, 3, 1)),
92		}
93
94		initialize = {"grid": 1}
95
96		hyper = Hyperactive()
97		hyper.add_search(
98		objective_function,
99		search_space,
100		n_iter=1,
101		initialize=initialize,
102		)
103		hyper.run()
104
105		assert abs(hyper.best_score(objective_function)) - 1 < 0.001
106
107
108		def test_initialize_all_0():
109		search_space = {
110		"x1": list(np.arange(-2, 3, 1)),
111		}
112
113		initialize = {"grid": 100, "vertices": 100, "random": 100}
114
115		hyper = Hyperactive()
116		hyper.add_search(
117		objective_function,
118		search_space,
119		n_iter=300,
120		initialize=initialize,
121		)
122		hyper.run()
123

SimonBlanke / Hyperactive

tests.test_initializers A last analyzed 2025-06-23 18:24 UTC

Complexity

Size/Duplication

Importance

7 Functions

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tests.test_initializers A
last analyzed 2025-06-23 18:24 UTC