tests.test_api.test_search_spaces - Code Metrics - Inspection of "V5 API rework" - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — master (#110)

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created 2025-05-03 22:16 UTC

tests.test_api.test_search_spaces A

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Complexity

Total Complexity

Size/Duplication

Total Lines	186
Duplicated Lines	24.73 %

Importance

Changes

Metric	Value
eloc	124
dl	46
loc	186
rs	10
c	0
b	0
f	0
wmc	8

7 Functions

Rating	Name	Duplication	Size	Complexity
A	test_search_space_3()	0	25	1
A	test_search_space_0()	15	15	1
A	test_search_space_2()	16	16	1
A	test_search_space_4()	0	25	1
A	test_search_space_1()	15	15	1
A	test_search_space_5()	0	37	1
A	test_search_space_6()	0	23	1

1 Method

Rating	Name	Duplication	Size	Complexity
A	Experiment.objective_function()	0	3	1

How to fix Duplicated Code

import numpy as np
import pandas as pd

from hyperactive.optimizers import HillClimbingOptimizer
from hyperactive.experiment import BaseExperiment
from hyperactive.search_config import SearchConfig


class Experiment(BaseExperiment):
    def objective_function(self, opt):
        score = -opt["x1"] * opt["x1"]
        return score


experiment = Experiment()


def test_search_space_0():

    search_config = SearchConfig(
        x1=list(np.arange(0, 3, 1)),
    )

    hyper = HillClimbingOptimizer()
    hyper.add_search(
        experiment,
        search_config,
        n_iter=15,
    )
    hyper.run()

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


def test_search_space_1():

    search_config = SearchConfig(
        x1=list(np.arange(0, 0.003, 0.001)),
    )

    hyper = HillClimbingOptimizer()
    hyper.add_search(
        experiment,
        search_config,
        n_iter=15,
    )
    hyper.run()

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


def test_search_space_2():

    search_config = SearchConfig(
        x1=list(np.arange(0, 100, 1)),
        str1=["0", "1", "2"],
    )

    hyper = HillClimbingOptimizer()
    hyper.add_search(
        experiment,
        search_config,
        n_iter=15,
    )
    hyper.run()

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


def test_search_space_3():
    def func1():
        pass

    def func2():
        pass

    def func3():
        pass

    search_config = SearchConfig(
        x1=list(np.arange(0, 100, 1)),
        func1=[func1, func2, func3],
    )

    hyper = HillClimbingOptimizer()
    hyper.add_search(
        experiment,
        search_config,
        n_iter=15,
    )
    hyper.run()

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


def test_search_space_4():
    class class1:
        pass

    class class2:
        pass

    class class3:
        pass

    search_config = SearchConfig(
        x1=list(np.arange(0, 100, 1)),
        class1=[class1, class2, class3],
    )

    hyper = HillClimbingOptimizer()
    hyper.add_search(
        experiment,
        search_config,
        n_iter=15,
    )
    hyper.run()

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

    search_config = SearchConfig(
        x1=list(np.arange(0, 100, 1)),
        class1=[class_f1, class_f2, class_f3],
    )

    hyper = HillClimbingOptimizer()
    hyper.add_search(
        experiment,
        search_config,
        n_iter=15,
    )
    hyper.run()

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


def test_search_space_6():

    def list_f1():
        return [0, 1]

    def list_f2():
        return [1, 0]

    search_config = SearchConfig(
        x1=list(np.arange(0, 100, 1)),
        list1=[list_f1, list_f2],
    )

    hyper = HillClimbingOptimizer()
    hyper.add_search(
        experiment,
        search_config,
        n_iter=15,
    )
    hyper.run()

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


1		import numpy as np
2		import pandas as pd
3
4		from hyperactive.optimizers import HillClimbingOptimizer
5		from hyperactive.experiment import BaseExperiment
6		from hyperactive.search_config import SearchConfig
7
8
9		class Experiment(BaseExperiment):
10		def objective_function(self, opt):
11		score = -opt["x1"] * opt["x1"]
12		return score
13
14
15		experiment = Experiment()
16
17
18	View Code Duplication	def test_search_space_0():
		0 ignored issues – show Duplication introduced 2025-04-01 14:52 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
19		search_config = SearchConfig(
20		x1=list(np.arange(0, 3, 1)),
21		)
22
23		hyper = HillClimbingOptimizer()
24		hyper.add_search(
25		experiment,
26		search_config,
27		n_iter=15,
28		)
29		hyper.run()
30
31		assert isinstance(hyper.search_data(experiment), pd.DataFrame)
32		assert hyper.best_para(experiment)["x1"] in search_config["x1"]
33
34
35	View Code Duplication	def test_search_space_1():
		0 ignored issues – show Duplication introduced 2025-04-01 14:52 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
36		search_config = SearchConfig(
37		x1=list(np.arange(0, 0.003, 0.001)),
38		)
39
40		hyper = HillClimbingOptimizer()
41		hyper.add_search(
42		experiment,
43		search_config,
44		n_iter=15,
45		)
46		hyper.run()
47
48		assert isinstance(hyper.search_data(experiment), pd.DataFrame)
49		assert hyper.best_para(experiment)["x1"] in search_config["x1"]
50
51
52	View Code Duplication	def test_search_space_2():
		0 ignored issues – show Duplication introduced 2025-04-01 14:52 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
53		search_config = SearchConfig(
54		x1=list(np.arange(0, 100, 1)),
55		str1=["0", "1", "2"],
56		)
57
58		hyper = HillClimbingOptimizer()
59		hyper.add_search(
60		experiment,
61		search_config,
62		n_iter=15,
63		)
64		hyper.run()
65
66		assert isinstance(hyper.search_data(experiment), pd.DataFrame)
67		assert hyper.best_para(experiment)["str1"] in search_config["str1"]
68
69
70		def test_search_space_3():
71		def func1():
72		pass
73
74		def func2():
75		pass
76
77		def func3():
78		pass
79
80		search_config = SearchConfig(
81		x1=list(np.arange(0, 100, 1)),
82		func1=[func1, func2, func3],
83		)
84
85		hyper = HillClimbingOptimizer()
86		hyper.add_search(
87		experiment,
88		search_config,
89		n_iter=15,
90		)
91		hyper.run()
92
93		assert isinstance(hyper.search_data(experiment), pd.DataFrame)
94		assert hyper.best_para(experiment)["func1"] in search_config["func1"]
95
96
97		def test_search_space_4():
98		class class1:
99		pass
100
101		class class2:
102		pass
103
104		class class3:
105		pass
106
107		search_config = SearchConfig(
108		x1=list(np.arange(0, 100, 1)),
109		class1=[class1, class2, class3],
110		)
111
112		hyper = HillClimbingOptimizer()
113		hyper.add_search(
114		experiment,
115		search_config,
116		n_iter=15,
117		)
118		hyper.run()
119
120		assert isinstance(hyper.search_data(experiment), pd.DataFrame)
121		assert hyper.best_para(experiment)["class1"] in search_config["class1"]
122
123
124		def test_search_space_5():
125		class class1:
126		def __init__(self):
127		pass
128
129		class class2:
130		def __init__(self):
131		pass
132
133		class class3:
134		def __init__(self):
135		pass
136
137		def class_f1():
138		return class1
139
140		def class_f2():
141		return class2
142
143		def class_f3():
144		return class3
145
146		search_config = SearchConfig(
147		x1=list(np.arange(0, 100, 1)),
148		class1=[class_f1, class_f2, class_f3],
149		)
150
151		hyper = HillClimbingOptimizer()
152		hyper.add_search(
153		experiment,
154		search_config,
155		n_iter=15,
156		)
157		hyper.run()
158
159		assert isinstance(hyper.search_data(experiment), pd.DataFrame)
160		assert hyper.best_para(experiment)["class1"] in search_config["class1"]
161
162
163		def test_search_space_6():
164
165		def list_f1():
166		return [0, 1]
167
168		def list_f2():
169		return [1, 0]
170
171		search_config = SearchConfig(
172		x1=list(np.arange(0, 100, 1)),
173		list1=[list_f1, list_f2],
174		)
175
176		hyper = HillClimbingOptimizer()
177		hyper.add_search(
178		experiment,
179		search_config,
180		n_iter=15,
181		)
182		hyper.run()
183
184		assert isinstance(hyper.search_data(experiment), pd.DataFrame)
185		assert hyper.best_para(experiment)["list1"] in search_config["list1"]
186

SimonBlanke / Hyperactive

Pull Request — master (#110)

tests.test_api.test_search_spaces A

Complexity

Size/Duplication

Importance

7 Functions

1 Method

How to fix Duplicated Code

Duplicated Code

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