tests.test_results.test_attributes_results_8() - Code Metrics - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

tests.test_results.test_attributes_results_8() A
last analyzed 2025-06-23 18:24 UTC

↳ Parent: tests.test_results

Complexity

Conditions

Size

Total Lines	20
Code Lines	15

Duplication

Lines	20
Ratio	100 %

Importance

Changes

Metric	Value
cc	2
eloc	15
nop	0
dl	20
loc	20
rs	9.65
c	0
b	0
f	0

import pytest
import numpy as np
import pandas as pd

from hyperactive import Hyperactive


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


search_space = {
    "x1": list(np.arange(0, 100, 1)),
}


def test_attributes_results_0():
    hyper = Hyperactive()
    hyper.add_search(objective_function, search_space, n_iter=100)
    hyper.run()

    assert isinstance(hyper.search_data(objective_function), pd.DataFrame)


def test_attributes_results_1():
    hyper = Hyperactive()
    hyper.add_search(objective_function, search_space, n_iter=100)
    hyper.run()

    assert set(search_space.keys()) < set(hyper.search_data(objective_function).columns)


def test_attributes_results_2():
    hyper = Hyperactive()
    hyper.add_search(objective_function, search_space, n_iter=100)
    hyper.run()

    assert "x1" in list(hyper.search_data(objective_function).columns)


def test_attributes_results_3():
    hyper = Hyperactive()
    hyper.add_search(objective_function, search_space, n_iter=100)
    hyper.run()

    assert "score" in list(hyper.search_data(objective_function).columns)


def test_attributes_results_4():
    hyper = Hyperactive()
    hyper.add_search(
        objective_function,
        search_space,
        n_iter=1,
        initialize={"warm_start": [{"x1": 0}]},
    )
    hyper.run()

    assert 0 in list(hyper.search_data(objective_function)["x1"].values)


def test_attributes_results_5():
    hyper = Hyperactive()
    hyper.add_search(
        objective_function,
        search_space,
        n_iter=1,
        initialize={"warm_start": [{"x1": 10}]},
    )
    hyper.run()

    print(
        "\n x1_results \n",
        list(hyper.search_data(objective_function)["x1"].values),
    )

    assert 10 in list(hyper.search_data(objective_function)["x1"].values)


def test_attributes_results_6():
    def objective_function(opt):
        score = -opt["x1"] * opt["x1"]
        return score

    search_space = {
        "x1": list(np.arange(0, 10, 1)),
    }

    hyper = Hyperactive()
    hyper.add_search(
        objective_function,
        search_space,
        n_iter=20,
        initialize={"random": 1},
        memory=False,
    )
    hyper.run()

    x1_results = list(hyper.search_data(objective_function)["x1"].values)

    print("\n x1_results \n", x1_results)

    assert len(set(x1_results)) < len(x1_results)


def test_attributes_results_7():

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

    search_space = {
        "x1": list(np.arange(0, 10, 1)),
    }

    hyper = Hyperactive()
    hyper.add_search(
        objective_function,
        search_space,
        n_iter=20,
    )
    hyper.run()

    search_data = hyper.search_data(objective_function)
    with pytest.raises(Exception) as e_info:
        search_data["eval_times"]


def test_attributes_results_8():

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

    search_space = {
        "x1": list(np.arange(0, 10, 1)),
    }

    hyper = Hyperactive()
    hyper.add_search(
        objective_function,
        search_space,
        n_iter=20,
    )
    hyper.run()

    search_data = hyper.search_data(objective_function)
    with pytest.raises(Exception) as e_info:
        search_data["iter_times"]


def test_attributes_results_9():
    def objective_function(opt):
        score = -opt["x1"] * opt["x1"]
        return score

    search_space = {
        "x1": list(np.arange(0, 10, 1)),
    }

    hyper = Hyperactive()
    hyper.add_search(
        objective_function,
        search_space,
        n_iter=20,
    )
    hyper.run()

    search_data = hyper.search_data(objective_function, times=True)
    search_data["iter_times"]
    search_data["eval_times"]


"""
def test_attributes_results_7():
    def objective_function(para):
        score = -para["x1"] * para["x1"]
        return score

    search_space = {
        "x1": np.arange(0, 10, 1),
    }

    opt = RandomSearchOptimizer(search_space)
    opt.search(
        objective_function, n_iter=20, initialize={"random": 1}, memory=True
    )

    x1_results = list(opt.results["x1"].values)

    print("\n x1_results \n", x1_results)

    assert len(set(x1_results)) == len(x1_results)


def test_attributes_results_8():
    def objective_function(para):
        score = -para["x1"] * para["x1"]
        return score

    search_space = {
        "x1": np.arange(-10, 11, 1),
    }

    results = pd.DataFrame(np.arange(-10, 10, 1), columns=["x1"])
    results["score"] = 0

    opt = RandomSearchOptimizer(search_space)
    opt.search(
        objective_function,
        n_iter=100,
        initialize={},
        memory=True,
        memory_warm_start=results,
    )

    print("\n opt.results \n", opt.results)

    x1_results = list(opt.results["x1"].values)

    assert 10 == x1_results[0]
"""


1		import pytest
2		import numpy as np
3		import pandas as pd
4
5		from hyperactive import Hyperactive
6
7
8		def objective_function(opt):
9		score = -opt["x1"] * opt["x1"]
10		return score
11
12
13		search_space = {
14		"x1": list(np.arange(0, 100, 1)),
15		}
16
17
18		def test_attributes_results_0():
19		hyper = Hyperactive()
20		hyper.add_search(objective_function, search_space, n_iter=100)
21		hyper.run()
22
23		assert isinstance(hyper.search_data(objective_function), pd.DataFrame)
24
25
26		def test_attributes_results_1():
27		hyper = Hyperactive()
28		hyper.add_search(objective_function, search_space, n_iter=100)
29		hyper.run()
30
31		assert set(search_space.keys()) < set(hyper.search_data(objective_function).columns)
32
33
34		def test_attributes_results_2():
35		hyper = Hyperactive()
36		hyper.add_search(objective_function, search_space, n_iter=100)
37		hyper.run()
38
39		assert "x1" in list(hyper.search_data(objective_function).columns)
40
41
42		def test_attributes_results_3():
43		hyper = Hyperactive()
44		hyper.add_search(objective_function, search_space, n_iter=100)
45		hyper.run()
46
47		assert "score" in list(hyper.search_data(objective_function).columns)
48
49
50		def test_attributes_results_4():
51		hyper = Hyperactive()
52		hyper.add_search(
53		objective_function,
54		search_space,
55		n_iter=1,
56		initialize={"warm_start": [{"x1": 0}]},
57		)
58		hyper.run()
59
60		assert 0 in list(hyper.search_data(objective_function)["x1"].values)
61
62
63		def test_attributes_results_5():
64		hyper = Hyperactive()
65		hyper.add_search(
66		objective_function,
67		search_space,
68		n_iter=1,
69		initialize={"warm_start": [{"x1": 10}]},
70		)
71		hyper.run()
72
73		print(
74		"\n x1_results \n",
75		list(hyper.search_data(objective_function)["x1"].values),
76		)
77
78		assert 10 in list(hyper.search_data(objective_function)["x1"].values)
79
80
81		def test_attributes_results_6():
82		def objective_function(opt):
83		score = -opt["x1"] * opt["x1"]
84		return score
85
86		search_space = {
87		"x1": list(np.arange(0, 10, 1)),
88		}
89
90		hyper = Hyperactive()
91		hyper.add_search(
92		objective_function,
93		search_space,
94		n_iter=20,
95		initialize={"random": 1},
96		memory=False,
97		)
98		hyper.run()
99
100		x1_results = list(hyper.search_data(objective_function)["x1"].values)
101
102		print("\n x1_results \n", x1_results)
103
104		assert len(set(x1_results)) < len(x1_results)
105
106
107	View Code Duplication	def test_attributes_results_7():
		0 ignored issues – show Duplication introduced 2022-05-04 15:29 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
108		def objective_function(opt):
109		score = -opt["x1"] * opt["x1"]
110		return score
111
112		search_space = {
113		"x1": list(np.arange(0, 10, 1)),
114		}
115
116		hyper = Hyperactive()
117		hyper.add_search(
118		objective_function,
119		search_space,
120		n_iter=20,
121		)
122		hyper.run()
123
124		search_data = hyper.search_data(objective_function)
125		with pytest.raises(Exception) as e_info:
126		search_data["eval_times"]
127
128
129	View Code Duplication	def test_attributes_results_8():
		0 ignored issues – show Duplication introduced 2022-05-04 15:29 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
130		def objective_function(opt):
131		score = -opt["x1"] * opt["x1"]
132		return score
133
134		search_space = {
135		"x1": list(np.arange(0, 10, 1)),
136		}
137
138		hyper = Hyperactive()
139		hyper.add_search(
140		objective_function,
141		search_space,
142		n_iter=20,
143		)
144		hyper.run()
145
146		search_data = hyper.search_data(objective_function)
147		with pytest.raises(Exception) as e_info:
148		search_data["iter_times"]
149
150
151		def test_attributes_results_9():
152		def objective_function(opt):
153		score = -opt["x1"] * opt["x1"]
154		return score
155
156		search_space = {
157		"x1": list(np.arange(0, 10, 1)),
158		}
159
160		hyper = Hyperactive()
161		hyper.add_search(
162		objective_function,
163		search_space,
164		n_iter=20,
165		)
166		hyper.run()
167
168		search_data = hyper.search_data(objective_function, times=True)
169		search_data["iter_times"]
170		search_data["eval_times"]
171
172
173		"""
174		def test_attributes_results_7():
175		def objective_function(para):
176		score = -para["x1"] * para["x1"]
177		return score
178
179		search_space = {
180		"x1": np.arange(0, 10, 1),
181		}
182
183		opt = RandomSearchOptimizer(search_space)
184		opt.search(
185		objective_function, n_iter=20, initialize={"random": 1}, memory=True
186		)
187
188		x1_results = list(opt.results["x1"].values)
189
190		print("\n x1_results \n", x1_results)
191
192		assert len(set(x1_results)) == len(x1_results)
193
194
195		def test_attributes_results_8():
196		def objective_function(para):
197		score = -para["x1"] * para["x1"]
198		return score
199
200		search_space = {
201		"x1": np.arange(-10, 11, 1),
202		}
203
204		results = pd.DataFrame(np.arange(-10, 10, 1), columns=["x1"])
205		results["score"] = 0
206
207		opt = RandomSearchOptimizer(search_space)
208		opt.search(
209		objective_function,
210		n_iter=100,
211		initialize={},
212		memory=True,
213		memory_warm_start=results,
214		)
215
216		print("\n opt.results \n", opt.results)
217
218		x1_results = list(opt.results["x1"].values)
219
220		assert 10 == x1_results[0]
221		"""
222

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tests.test_results.test_attributes_results_8() A last analyzed 2025-06-23 18:24 UTC

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tests.test_results.test_attributes_results_8() A
last analyzed 2025-06-23 18:24 UTC