tests.test_search_spaces.test_search_space_0() - Code Metrics - Inspection of "remove class for ltm integration" - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( e85313...dd7f59 )

by Simon

created 2021-02-06 11:25 UTC

tests.test_search_spaces.test_search_space_0() A

↳ Parent: tests.test_search_spaces

Complexity

Conditions

Size

Total Lines	17
Code Lines	12

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	12
nop	0
dl	0
loc	17
rs	9.8
c	0
b	0
f	0

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.results(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.results(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.results(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.results(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.results(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 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.results(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

    search_space = {
        "x1": list(range(0, 100, 1)),
        "list1": [[1, 1, 1], [1, 2, 1], [1, 1, 2]],
    }

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

    assert isinstance(hyper.results(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		def test_search_space_0():
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, search_space, n_iter=15,
19		)
20		hyper.run()
21
22		assert isinstance(hyper.results(objective_function), pd.DataFrame)
23		assert hyper.best_para(objective_function)["x1"] in search_space["x1"]
24
25
26		def test_search_space_1():
27		def objective_function(opt):
28		score = -opt["x1"] * opt["x1"]
29		return score
30
31		search_space = {
32		"x1": list(np.arange(0, 0.003, 0.001)),
33		}
34
35		hyper = Hyperactive()
36		hyper.add_search(
37		objective_function, search_space, n_iter=15,
38		)
39		hyper.run()
40
41		assert isinstance(hyper.results(objective_function), pd.DataFrame)
42		assert hyper.best_para(objective_function)["x1"] in search_space["x1"]
43
44
45	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...
46		def objective_function(opt):
47		score = -opt["x1"] * opt["x1"]
48		return score
49
50		search_space = {
51		"x1": list(range(0, 100, 1)),
52		"str1": ["0", "1", "2"],
53		}
54
55		hyper = Hyperactive()
56		hyper.add_search(
57		objective_function, search_space, n_iter=15,
58		)
59		hyper.run()
60
61		assert isinstance(hyper.results(objective_function), pd.DataFrame)
62		assert hyper.best_para(objective_function)["str1"] in search_space["str1"]
63
64
65		def test_search_space_3():
66		def func1():
67		pass
68
69		def func2():
70		pass
71
72		def func3():
73		pass
74
75		def objective_function(opt):
76		score = -opt["x1"] * opt["x1"]
77		return score
78
79		search_space = {
80		"x1": list(range(0, 100, 1)),
81		"func1": [func1, func2, func3],
82		}
83
84		hyper = Hyperactive()
85		hyper.add_search(
86		objective_function, search_space, n_iter=15,
87		)
88		hyper.run()
89
90		assert isinstance(hyper.results(objective_function), pd.DataFrame)
91		assert (
92		hyper.best_para(objective_function)["func1"] in search_space["func1"]
93		)
94
95
96		def test_search_space_4():
97		class class1:
98		pass
99
100		class class2:
101		pass
102
103		class class3:
104		pass
105
106		def objective_function(opt):
107		score = -opt["x1"] * opt["x1"]
108		return score
109
110		search_space = {
111		"x1": list(range(0, 100, 1)),
112		"class1": [class1, class2, class3],
113		}
114
115		hyper = Hyperactive()
116		hyper.add_search(
117		objective_function, search_space, n_iter=15,
118		)
119		hyper.run()
120
121		assert isinstance(hyper.results(objective_function), pd.DataFrame)
122		assert (
123		hyper.best_para(objective_function)["class1"] in search_space["class1"]
124		)
125
126
127		def test_search_space_5():
128		class class1:
129		def __init__(self):
130		pass
131
132		class class2:
133		def __init__(self):
134		pass
135
136		class class3:
137		def __init__(self):
138		pass
139
140		def objective_function(opt):
141		score = -opt["x1"] * opt["x1"]
142		return score
143
144		search_space = {
145		"x1": list(range(0, 100, 1)),
146		"class1": [class1(), class2(), class3()],
147		}
148
149		hyper = Hyperactive()
150		hyper.add_search(
151		objective_function, search_space, n_iter=15,
152		)
153		hyper.run()
154
155		assert isinstance(hyper.results(objective_function), pd.DataFrame)
156		assert (
157		hyper.best_para(objective_function)["class1"] in search_space["class1"]
158		)
159
160
161	View Code Duplication	def test_search_space_6():
		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...
162		def objective_function(opt):
163		score = -opt["x1"] * opt["x1"]
164		return score
165
166		search_space = {
167		"x1": list(range(0, 100, 1)),
168		"list1": [[1, 1, 1], [1, 2, 1], [1, 1, 2]],
169		}
170
171		hyper = Hyperactive()
172		hyper.add_search(
173		objective_function, search_space, n_iter=15,
174		)
175		hyper.run()
176
177		assert isinstance(hyper.results(objective_function), pd.DataFrame)
178		assert (
179		hyper.best_para(objective_function)["list1"] in search_space["list1"]
180		)
181

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tests.test_search_spaces.test_search_space_0() A

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