tests.test_search_spaces.test_search_space_0() - Code Metrics - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

tests.test_search_spaces.test_search_space_0() A
last analyzed 2025-06-23 18:24 UTC

↳ Parent: tests.test_search_spaces

Complexity

Conditions

Size

Total Lines	19
Code Lines	14

Duplication

Lines	19
Ratio	100 %

Importance

Changes

Metric	Value
cc	1
eloc	14
nop	0
dl	19
loc	19
rs	9.7
c	0
b	0
f	0

import sys, pytest
import numpy as np
import pandas as pd

from hyperactive import Hyperactive


if sys.platform.startswith("win"):
    pytest.skip("skip these tests for windows", allow_module_level=True)


def test_search_space_0():

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

    search_space = {
        "x1": list(range(0, 3, 1)),
    }

    hyper = Hyperactive()
    hyper.add_search(
        objective_function,
        search_space,
        n_iter=15,
    )
    hyper.run()

    assert isinstance(hyper.search_data(objective_function), pd.DataFrame)
    assert hyper.best_para(objective_function)["x1"] in search_space["x1"]


def test_search_space_1():

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

    search_space = {
        "x1": list(np.arange(0, 0.003, 0.001)),
    }

    hyper = Hyperactive()
    hyper.add_search(
        objective_function,
        search_space,
        n_iter=15,
    )
    hyper.run()

    assert isinstance(hyper.search_data(objective_function), pd.DataFrame)
    assert hyper.best_para(objective_function)["x1"] in search_space["x1"]


def test_search_space_2():

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

    search_space = {
        "x1": list(range(0, 100, 1)),
        "str1": ["0", "1", "2"],
    }

    hyper = Hyperactive()
    hyper.add_search(
        objective_function,
        search_space,
        n_iter=15,
    )
    hyper.run()

    assert isinstance(hyper.search_data(objective_function), pd.DataFrame)
    assert hyper.best_para(objective_function)["str1"] in search_space["str1"]


def test_search_space_3():
    def func1():
        pass

    def func2():
        pass

    def func3():
        pass

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

    search_space = {
        "x1": list(range(0, 100, 1)),
        "func1": [func1, func2, func3],
    }

    hyper = Hyperactive()
    hyper.add_search(
        objective_function,
        search_space,
        n_iter=15,
    )
    hyper.run()

    assert isinstance(hyper.search_data(objective_function), pd.DataFrame)
    assert hyper.best_para(objective_function)["func1"] in search_space["func1"]


def test_search_space_4():
    class class1:
        pass

    class class2:
        pass

    class class3:
        pass

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

    search_space = {
        "x1": list(range(0, 100, 1)),
        "class1": [class1, class2, class3],
    }

    hyper = Hyperactive()
    hyper.add_search(
        objective_function,
        search_space,
        n_iter=15,
    )
    hyper.run()

    assert isinstance(hyper.search_data(objective_function), pd.DataFrame)
    assert hyper.best_para(objective_function)["class1"] in search_space["class1"]


def test_search_space_5():
    class class1:
        def __init__(self):
            pass

    class class2:
        def __init__(self):
            pass

    class class3:
        def __init__(self):
            pass

    def class_f1():
        return class1

    def class_f2():
        return class2

    def class_f3():
        return class3

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

    search_space = {
        "x1": list(range(0, 100, 1)),
        "class1": [class_f1, class_f2, class_f3],
    }

    hyper = Hyperactive()
    hyper.add_search(
        objective_function,
        search_space,
        n_iter=15,
    )
    hyper.run()

    assert isinstance(hyper.search_data(objective_function), pd.DataFrame)
    assert hyper.best_para(objective_function)["class1"] in search_space["class1"]


def test_search_space_6():
    def objective_function(opt):
        score = -opt["x1"] * opt["x1"]
        return score

    def list_f1():
        return [0, 1]

    def list_f2():
        return [1, 0]

    search_space = {
        "x1": list(range(0, 100, 1)),
        "list1": [list_f1, list_f2],
    }

    hyper = Hyperactive()
    hyper.add_search(
        objective_function,
        search_space,
        n_iter=15,
    )
    hyper.run()

    assert isinstance(hyper.search_data(objective_function), pd.DataFrame)
    assert hyper.best_para(objective_function)["list1"] in search_space["list1"]


1		import sys, pytest
2		import numpy as np
3		import pandas as pd
4
5		from hyperactive import Hyperactive
6
7
8		if sys.platform.startswith("win"):
9		pytest.skip("skip these tests for windows", allow_module_level=True)
10
11
12	View Code Duplication	def test_search_space_0():
		0 ignored issues – show Duplication introduced 2022-03-16 12:55 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
13		def objective_function(opt):
14		score = -opt["x1"] * opt["x1"]
15		return score
16
17		search_space = {
18		"x1": list(range(0, 3, 1)),
19		}
20
21		hyper = Hyperactive()
22		hyper.add_search(
23		objective_function,
24		search_space,
25		n_iter=15,
26		)
27		hyper.run()
28
29		assert isinstance(hyper.search_data(objective_function), pd.DataFrame)
30		assert hyper.best_para(objective_function)["x1"] in search_space["x1"]
31
32
33	View Code Duplication	def test_search_space_1():
		0 ignored issues – show Duplication introduced 2022-03-16 12:55 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
34		def objective_function(opt):
35		score = -opt["x1"] * opt["x1"]
36		return score
37
38		search_space = {
39		"x1": list(np.arange(0, 0.003, 0.001)),
40		}
41
42		hyper = Hyperactive()
43		hyper.add_search(
44		objective_function,
45		search_space,
46		n_iter=15,
47		)
48		hyper.run()
49
50		assert isinstance(hyper.search_data(objective_function), pd.DataFrame)
51		assert hyper.best_para(objective_function)["x1"] in search_space["x1"]
52
53
54	View Code Duplication	def test_search_space_2():
		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...
55		def objective_function(opt):
56		score = -opt["x1"] * opt["x1"]
57		return score
58
59		search_space = {
60		"x1": list(range(0, 100, 1)),
61		"str1": ["0", "1", "2"],
62		}
63
64		hyper = Hyperactive()
65		hyper.add_search(
66		objective_function,
67		search_space,
68		n_iter=15,
69		)
70		hyper.run()
71
72		assert isinstance(hyper.search_data(objective_function), pd.DataFrame)
73		assert hyper.best_para(objective_function)["str1"] in search_space["str1"]
74
75
76		def test_search_space_3():
77		def func1():
78		pass
79
80		def func2():
81		pass
82
83		def func3():
84		pass
85
86		def objective_function(opt):
87		score = -opt["x1"] * opt["x1"]
88		return score
89
90		search_space = {
91		"x1": list(range(0, 100, 1)),
92		"func1": [func1, func2, func3],
93		}
94
95		hyper = Hyperactive()
96		hyper.add_search(
97		objective_function,
98		search_space,
99		n_iter=15,
100		)
101		hyper.run()
102
103		assert isinstance(hyper.search_data(objective_function), pd.DataFrame)
104		assert hyper.best_para(objective_function)["func1"] in search_space["func1"]
105
106
107		def test_search_space_4():
108		class class1:
109		pass
110
111		class class2:
112		pass
113
114		class class3:
115		pass
116
117		def objective_function(opt):
118		score = -opt["x1"] * opt["x1"]
119		return score
120
121		search_space = {
122		"x1": list(range(0, 100, 1)),
123		"class1": [class1, class2, class3],
124		}
125
126		hyper = Hyperactive()
127		hyper.add_search(
128		objective_function,
129		search_space,
130		n_iter=15,
131		)
132		hyper.run()
133
134		assert isinstance(hyper.search_data(objective_function), pd.DataFrame)
135		assert hyper.best_para(objective_function)["class1"] in search_space["class1"]
136
137
138		def test_search_space_5():
139		class class1:
140		def __init__(self):
141		pass
142
143		class class2:
144		def __init__(self):
145		pass
146
147		class class3:
148		def __init__(self):
149		pass
150
151		def class_f1():
152		return class1
153
154		def class_f2():
155		return class2
156
157		def class_f3():
158		return class3
159
160		def objective_function(opt):
161		score = -opt["x1"] * opt["x1"]
162		return score
163
164		search_space = {
165		"x1": list(range(0, 100, 1)),
166		"class1": [class_f1, class_f2, class_f3],
167		}
168
169		hyper = Hyperactive()
170		hyper.add_search(
171		objective_function,
172		search_space,
173		n_iter=15,
174		)
175		hyper.run()
176
177		assert isinstance(hyper.search_data(objective_function), pd.DataFrame)
178		assert hyper.best_para(objective_function)["class1"] in search_space["class1"]
179
180
181		def test_search_space_6():
182		def objective_function(opt):
183		score = -opt["x1"] * opt["x1"]
184		return score
185
186		def list_f1():
187		return [0, 1]
188
189		def list_f2():
190		return [1, 0]
191
192		search_space = {
193		"x1": list(range(0, 100, 1)),
194		"list1": [list_f1, list_f2],
195		}
196
197		hyper = Hyperactive()
198		hyper.add_search(
199		objective_function,
200		search_space,
201		n_iter=15,
202		)
203		hyper.run()
204
205		assert isinstance(hyper.search_data(objective_function), pd.DataFrame)
206		assert hyper.best_para(objective_function)["list1"] in search_space["list1"]
207

SimonBlanke / Hyperactive

tests.test_search_spaces.test_search_space_0() A last analyzed 2025-06-23 18:24 UTC

Complexity

Size

Duplication

Importance

Duplication Side-by-Side

Filter issues like

tests.test_search_spaces.test_search_space_0() A
last analyzed 2025-06-23 18:24 UTC