tests_old.test_search_spaces - Code Metrics - Inspection of "V5 api design" - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Pull Request — master (#101)

by Simon

created 2025-04-12 10:21 UTC

tests_old.test_search_spaces A

↳ Parent: Project

Complexity

Total Complexity

Size/Duplication

Total Lines	202
Duplicated Lines	28.71 %

Importance

Changes

Metric	Value
eloc	138
dl	58
loc	202
rs	10
c	0
b	0
f	0
wmc	7

7 Functions

Rating	Name	Duplication	Size	Complexity
A	test_search_space_4()	0	29	1
A	test_search_space_1()	19	19	1
A	test_search_space_2()	20	20	1
A	test_search_space_6()	0	26	1
A	test_search_space_0()	19	19	1
A	test_search_space_3()	0	29	1
A	test_search_space_5()	0	41	1

How to fix Duplicated Code

import numpy as np
import pandas as pd

from hyperactive import Hyperactive


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

SimonBlanke / Hyperactive

Pull Request — master (#101)

tests_old.test_search_spaces A

Complexity

Size/Duplication

Importance

7 Functions

How to fix Duplicated Code

Duplicated Code

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