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 2025-08-17 14:42 UTC

↳ Parent: tests.test_search_spaces

Complexity

Conditions

Size

Total Lines	21
Code Lines	14

Duplication

Lines	19
Ratio	90.48 %

Importance

Changes

Metric	Value
eloc	14
dl	19
loc	21
rs	9.7
c	0
b	0
f	0
cc	1
nop	0

"""Test module for search space functionality."""

import sys

import numpy as np
import pandas as pd
import pytest

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():

    """Test search space with integer range."""

    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():

    """Test search space with float range."""

    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():

    """Test search space with numpy float range."""

    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():
    """Test search space with function objects."""

    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():
    """Test search space with class objects."""

    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():
    """Test search space with initialized class instances."""

    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():
    """Test search space with mixed parameter types."""

    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		"""Test module for search space functionality."""
2
3		import sys
4
5		import numpy as np
6		import pandas as pd
7		import pytest
8
9		from hyperactive import Hyperactive
10
11		if sys.platform.startswith("win"):
12		pytest.skip("skip these tests for windows", allow_module_level=True)
13
14
15	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...
16		"""Test search space with integer range."""
17
18		def objective_function(opt):
19		score = -opt["x1"] * opt["x1"]
20		return score
21
22		search_space = {
23		"x1": list(range(0, 3, 1)),
24		}
25
26		hyper = Hyperactive()
27		hyper.add_search(
28		objective_function,
29		search_space,
30		n_iter=15,
31		)
32		hyper.run()
33
34		assert isinstance(hyper.search_data(objective_function), pd.DataFrame)
35		assert hyper.best_para(objective_function)["x1"] in search_space["x1"]
36
37
38	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...
39		"""Test search space with float range."""
40
41		def objective_function(opt):
42		score = -opt["x1"] * opt["x1"]
43		return score
44
45		search_space = {
46		"x1": list(np.arange(0, 0.003, 0.001)),
47		}
48
49		hyper = Hyperactive()
50		hyper.add_search(
51		objective_function,
52		search_space,
53		n_iter=15,
54		)
55		hyper.run()
56
57		assert isinstance(hyper.search_data(objective_function), pd.DataFrame)
58		assert hyper.best_para(objective_function)["x1"] in search_space["x1"]
59
60
61	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...
62		"""Test search space with numpy float range."""
63
64		def objective_function(opt):
65		score = -opt["x1"] * opt["x1"]
66		return score
67
68		search_space = {
69		"x1": list(range(0, 100, 1)),
70		"str1": ["0", "1", "2"],
71		}
72
73		hyper = Hyperactive()
74		hyper.add_search(
75		objective_function,
76		search_space,
77		n_iter=15,
78		)
79		hyper.run()
80
81		assert isinstance(hyper.search_data(objective_function), pd.DataFrame)
82		assert hyper.best_para(objective_function)["str1"] in search_space["str1"]
83
84
85		def test_search_space_3():
86		"""Test search space with function objects."""
87
88		def func1():
89		pass
90
91		def func2():
92		pass
93
94		def func3():
95		pass
96
97		def objective_function(opt):
98		score = -opt["x1"] * opt["x1"]
99		return score
100
101		search_space = {
102		"x1": list(range(0, 100, 1)),
103		"func1": [func1, func2, func3],
104		}
105
106		hyper = Hyperactive()
107		hyper.add_search(
108		objective_function,
109		search_space,
110		n_iter=15,
111		)
112		hyper.run()
113
114		assert isinstance(hyper.search_data(objective_function), pd.DataFrame)
115		assert hyper.best_para(objective_function)["func1"] in search_space["func1"]
116
117
118		def test_search_space_4():
119		"""Test search space with class objects."""
120
121		class class1:
122		pass
123
124		class class2:
125		pass
126
127		class class3:
128		pass
129
130		def objective_function(opt):
131		score = -opt["x1"] * opt["x1"]
132		return score
133
134		search_space = {
135		"x1": list(range(0, 100, 1)),
136		"class1": [class1, class2, class3],
137		}
138
139		hyper = Hyperactive()
140		hyper.add_search(
141		objective_function,
142		search_space,
143		n_iter=15,
144		)
145		hyper.run()
146
147		assert isinstance(hyper.search_data(objective_function), pd.DataFrame)
148		assert hyper.best_para(objective_function)["class1"] in search_space["class1"]
149
150
151		def test_search_space_5():
152		"""Test search space with initialized class instances."""
153
154		class class1:
155		def __init__(self):
156		pass
157
158		class class2:
159		def __init__(self):
160		pass
161
162		class class3:
163		def __init__(self):
164		pass
165
166		def class_f1():
167		return class1
168
169		def class_f2():
170		return class2
171
172		def class_f3():
173		return class3
174
175		def objective_function(opt):
176		score = -opt["x1"] * opt["x1"]
177		return score
178
179		search_space = {
180		"x1": list(range(0, 100, 1)),
181		"class1": [class_f1, class_f2, class_f3],
182		}
183
184		hyper = Hyperactive()
185		hyper.add_search(
186		objective_function,
187		search_space,
188		n_iter=15,
189		)
190		hyper.run()
191
192		assert isinstance(hyper.search_data(objective_function), pd.DataFrame)
193		assert hyper.best_para(objective_function)["class1"] in search_space["class1"]
194
195
196		def test_search_space_6():
197		"""Test search space with mixed parameter types."""
198
199		def objective_function(opt):
200		score = -opt["x1"] * opt["x1"]
201		return score
202
203		def list_f1():
204		return [0, 1]
205
206		def list_f2():
207		return [1, 0]
208
209		search_space = {
210		"x1": list(range(0, 100, 1)),
211		"list1": [list_f1, list_f2],
212		}
213
214		hyper = Hyperactive()
215		hyper.add_search(
216		objective_function,
217		search_space,
218		n_iter=15,
219		)
220		hyper.run()
221
222		assert isinstance(hyper.search_data(objective_function), pd.DataFrame)
223		assert hyper.best_para(objective_function)["list1"] in search_space["list1"]
224

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tests.test_search_spaces.test_search_space_0() A last analyzed 2025-08-17 14:42 UTC

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tests.test_search_spaces.test_search_space_0() A
last analyzed 2025-08-17 14:42 UTC