tests.test_api.test_random_state - 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)

unknown

created 2025-05-03 22:16 UTC

tests.test_api.test_random_state A

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Complexity

Total Complexity

Size/Duplication

Total Lines	202
Duplicated Lines	57.92 %

Importance

Changes

Metric	Value
eloc	142
dl	117
loc	202
rs	10
c	0
b	0
f	0
wmc	8

7 Functions

Rating	Name	Duplication	Size	Complexity
A	test_random_state_n_jobs_1()	21	21	1
A	test_random_state_2()	25	25	1
A	test_random_state_n_jobs_2()	21	21	1
A	test_random_state_0()	25	25	1
A	test_no_random_state_0()	0	23	1
A	test_random_state_1()	25	25	1
A	test_random_state_n_jobs_0()	0	24	1

1 Method

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

How to fix Duplicated Code

import numpy as np

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"] + opt["x2"] * opt["x2"])
        return score


experiment = Experiment()


search_config = SearchConfig(
    x1=list(np.arange(-1000, 1000, 0.1)),
    x2=list(np.arange(-1000, 1000, 0.1)),
)


err = 0.001


def test_random_state_n_jobs_0():
    n_jobs = 2

    hyper = HillClimbingOptimizer()
    hyper.add_search(
        experiment,
        search_config,
        n_iter=5,
        initialize={"random": 1},
        random_state=1,
        n_jobs=n_jobs,
    )
    hyper.run()

    results = hyper.search_data(experiment)

    no_dup = results.drop_duplicates(subset=list(search_config.keys()))
    print("no_dup", no_dup)
    print("results", results)

    print(int(len(results) / n_jobs))
    print(len(no_dup))

    assert int(len(results) / n_jobs) != len(no_dup)


def test_random_state_n_jobs_1():

    n_jobs = 3

    hyper = HillClimbingOptimizer()
    hyper.add_search(
        experiment,
        search_config,
        n_iter=5,
        initialize={"random": 1},
        random_state=1,
        n_jobs=n_jobs,
    )
    hyper.run()

    results = hyper.search_data(experiment)

    no_dup = results.drop_duplicates(subset=list(search_config.keys()))
    print("no_dup", no_dup)
    print("results", results)

    assert int(len(results) / n_jobs) != len(no_dup)


def test_random_state_n_jobs_2():

    n_jobs = 4

    hyper = HillClimbingOptimizer()
    hyper.add_search(
        experiment,
        search_config,
        n_iter=5,
        initialize={"random": 1},
        random_state=1,
        n_jobs=n_jobs,
    )
    hyper.run()

    results = hyper.search_data(experiment)

    no_dup = results.drop_duplicates(subset=list(search_config.keys()))
    print("no_dup", no_dup)
    print("results", results)

    assert int(len(results) / n_jobs) != len(no_dup)


def test_random_state_0():

    hyper0 = HillClimbingOptimizer()
    hyper0.add_search(
        experiment,
        search_config,
        n_iter=10,
        initialize={"random": 1},
        random_state=1,
    )
    hyper0.run()

    hyper1 = HillClimbingOptimizer()
    hyper1.add_search(
        experiment,
        search_config,
        n_iter=10,
        initialize={"random": 1},
        random_state=1,
    )
    hyper1.run()

    best_score0 = hyper0.best_score(experiment)
    best_score1 = hyper1.best_score(experiment)

    assert abs(best_score0 - best_score1) < err


def test_random_state_1():

    hyper0 = HillClimbingOptimizer()
    hyper0.add_search(
        experiment,
        search_config,
        n_iter=10,
        initialize={"random": 1},
        random_state=10,
    )
    hyper0.run()

    hyper1 = HillClimbingOptimizer()
    hyper1.add_search(
        experiment,
        search_config,
        n_iter=10,
        initialize={"random": 1},
        random_state=10,
    )
    hyper1.run()

    best_score0 = hyper0.best_score(experiment)
    best_score1 = hyper1.best_score(experiment)

    assert abs(best_score0 - best_score1) < err


def test_random_state_2():

    hyper0 = HillClimbingOptimizer()
    hyper0.add_search(
        experiment,
        search_config,
        n_iter=10,
        initialize={"random": 1},
        random_state=1,
    )
    hyper0.run()

    hyper1 = HillClimbingOptimizer()
    hyper1.add_search(
        experiment,
        search_config,
        n_iter=10,
        initialize={"random": 1},
        random_state=10,
    )
    hyper1.run()

    best_score0 = hyper0.best_score(experiment)
    best_score1 = hyper1.best_score(experiment)

    assert abs(best_score0 - best_score1) > err


def test_no_random_state_0():
    hyper0 = HillClimbingOptimizer()
    hyper0.add_search(
        experiment,
        search_config,
        n_iter=10,
        initialize={"random": 1},
    )
    hyper0.run()

    hyper1 = HillClimbingOptimizer()
    hyper1.add_search(
        experiment,
        search_config,
        n_iter=10,
        initialize={"random": 1},
    )
    hyper1.run()

    best_score0 = hyper0.best_score(experiment)
    best_score1 = hyper1.best_score(experiment)

    assert abs(best_score0 - best_score1) > err


1		import numpy as np
2
3		from hyperactive.optimizers import HillClimbingOptimizer
4		from hyperactive.experiment import BaseExperiment
5		from hyperactive.search_config import SearchConfig
6
7
8		class Experiment(BaseExperiment):
9		def objective_function(self, opt):
10		score = -(opt["x1"] * opt["x1"] + opt["x2"] * opt["x2"])
11		return score
12
13
14		experiment = Experiment()
15
16
17		search_config = SearchConfig(
18		x1=list(np.arange(-1000, 1000, 0.1)),
19		x2=list(np.arange(-1000, 1000, 0.1)),
20		)
21
22
23		err = 0.001
24
25
26		def test_random_state_n_jobs_0():
27		n_jobs = 2
28
29		hyper = HillClimbingOptimizer()
30		hyper.add_search(
31		experiment,
32		search_config,
33		n_iter=5,
34		initialize={"random": 1},
35		random_state=1,
36		n_jobs=n_jobs,
37		)
38		hyper.run()
39
40		results = hyper.search_data(experiment)
41
42		no_dup = results.drop_duplicates(subset=list(search_config.keys()))
43		print("no_dup", no_dup)
44		print("results", results)
45
46		print(int(len(results) / n_jobs))
47		print(len(no_dup))
48
49		assert int(len(results) / n_jobs) != len(no_dup)
50
51
52	View Code Duplication	def test_random_state_n_jobs_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...
53		n_jobs = 3
54
55		hyper = HillClimbingOptimizer()
56		hyper.add_search(
57		experiment,
58		search_config,
59		n_iter=5,
60		initialize={"random": 1},
61		random_state=1,
62		n_jobs=n_jobs,
63		)
64		hyper.run()
65
66		results = hyper.search_data(experiment)
67
68		no_dup = results.drop_duplicates(subset=list(search_config.keys()))
69		print("no_dup", no_dup)
70		print("results", results)
71
72		assert int(len(results) / n_jobs) != len(no_dup)
73
74
75	View Code Duplication	def test_random_state_n_jobs_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...
76		n_jobs = 4
77
78		hyper = HillClimbingOptimizer()
79		hyper.add_search(
80		experiment,
81		search_config,
82		n_iter=5,
83		initialize={"random": 1},
84		random_state=1,
85		n_jobs=n_jobs,
86		)
87		hyper.run()
88
89		results = hyper.search_data(experiment)
90
91		no_dup = results.drop_duplicates(subset=list(search_config.keys()))
92		print("no_dup", no_dup)
93		print("results", results)
94
95		assert int(len(results) / n_jobs) != len(no_dup)
96
97
98	View Code Duplication	def test_random_state_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...
99		hyper0 = HillClimbingOptimizer()
100		hyper0.add_search(
101		experiment,
102		search_config,
103		n_iter=10,
104		initialize={"random": 1},
105		random_state=1,
106		)
107		hyper0.run()
108
109		hyper1 = HillClimbingOptimizer()
110		hyper1.add_search(
111		experiment,
112		search_config,
113		n_iter=10,
114		initialize={"random": 1},
115		random_state=1,
116		)
117		hyper1.run()
118
119		best_score0 = hyper0.best_score(experiment)
120		best_score1 = hyper1.best_score(experiment)
121
122		assert abs(best_score0 - best_score1) < err
123
124
125	View Code Duplication	def test_random_state_1():
		0 ignored issues – show Duplication introduced 2021-03-12 14:38 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
126		hyper0 = HillClimbingOptimizer()
127		hyper0.add_search(
128		experiment,
129		search_config,
130		n_iter=10,
131		initialize={"random": 1},
132		random_state=10,
133		)
134		hyper0.run()
135
136		hyper1 = HillClimbingOptimizer()
137		hyper1.add_search(
138		experiment,
139		search_config,
140		n_iter=10,
141		initialize={"random": 1},
142		random_state=10,
143		)
144		hyper1.run()
145
146		best_score0 = hyper0.best_score(experiment)
147		best_score1 = hyper1.best_score(experiment)
148
149		assert abs(best_score0 - best_score1) < err
150
151
152	View Code Duplication	def test_random_state_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...
153		hyper0 = HillClimbingOptimizer()
154		hyper0.add_search(
155		experiment,
156		search_config,
157		n_iter=10,
158		initialize={"random": 1},
159		random_state=1,
160		)
161		hyper0.run()
162
163		hyper1 = HillClimbingOptimizer()
164		hyper1.add_search(
165		experiment,
166		search_config,
167		n_iter=10,
168		initialize={"random": 1},
169		random_state=10,
170		)
171		hyper1.run()
172
173		best_score0 = hyper0.best_score(experiment)
174		best_score1 = hyper1.best_score(experiment)
175
176		assert abs(best_score0 - best_score1) > err
177
178
179		def test_no_random_state_0():
180		hyper0 = HillClimbingOptimizer()
181		hyper0.add_search(
182		experiment,
183		search_config,
184		n_iter=10,
185		initialize={"random": 1},
186		)
187		hyper0.run()
188
189		hyper1 = HillClimbingOptimizer()
190		hyper1.add_search(
191		experiment,
192		search_config,
193		n_iter=10,
194		initialize={"random": 1},
195		)
196		hyper1.run()
197
198		best_score0 = hyper0.best_score(experiment)
199		best_score1 = hyper1.best_score(experiment)
200
201		assert abs(best_score0 - best_score1) > err
202

SimonBlanke / Hyperactive

Pull Request — master (#110)

tests.test_api.test_random_state A

Complexity

Size/Duplication

Importance

7 Functions

1 Method

How to fix Duplicated Code

Duplicated Code

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