tests.test_optimizers.test_convex_convergence.test_convex_convergence_noSBOM() - Code Metrics - Inspection of "add args to stop random restart in test" - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

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by Simon

created 2020-10-09 11:13 UTC

test_convex_convergence_noSBOM() A

↳ Parent: tests.test_optimizers.test_convex_convergence

Complexity

Conditions

Size

Total Lines	28
Code Lines	22

Duplication

Lines	28
Ratio	100 %

Importance

Changes

Metric	Value
cc	2
eloc	22
nop	1
dl	28
loc	28
rs	9.352
c	0
b	0
f	0

import pytest
from tqdm import tqdm
import numpy as np

from ._parametrize import optimizers_noSBOM, optimizers_SBOM


@pytest.mark.parametrize(*optimizers_noSBOM)

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

    search_space = {"x1": np.arange(-33, 33, 1)}
    initialize = {"vertices": 2}

    n_opts = 33

    scores = []
    for rnd_st in tqdm(range(n_opts)):
        opt = Optimizer(search_space)
        opt.search(
            objective_function,
            n_iter=50,
            random_state=rnd_st,
            memory=False,
            verbosity={"print_results": False, "progress_bar": False},
            initialize=initialize,
        )

        scores.append(opt.best_score)
    score_mean = np.array(scores).mean()
    print("scores", scores)

    assert -500 < score_mean


@pytest.mark.parametrize(*optimizers_SBOM)

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

    search_space = {"x1": np.arange(-33, 33, 1)}
    initialize = {"vertices": 2}

    n_opts = 10

    scores = []
    for rnd_st in tqdm(range(n_opts)):
        opt = Optimizer(search_space)
        opt.search(
            objective_function,
            n_iter=30,
            random_state=rnd_st,
            memory=False,
            verbosity={"print_results": False, "progress_bar": False},
            initialize=initialize,
        )

        scores.append(opt.best_score)
    score_mean = np.array(scores).mean()
    print("scores", scores)

    assert -500 < score_mean



1		import pytest
2		from tqdm import tqdm
3		import numpy as np
4
5		from ._parametrize import optimizers_noSBOM, optimizers_SBOM
6
7
8	View Code Duplication	@pytest.mark.parametrize(*optimizers_noSBOM)
		0 ignored issues – show Duplication introduced 2020-10-09 11:15 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
9		def test_convex_convergence_noSBOM(Optimizer):
10		def objective_function(para):
11		score = -para["x1"] * para["x1"]
12		return score
13
14		search_space = {"x1": np.arange(-33, 33, 1)}
15		initialize = {"vertices": 2}
16
17		n_opts = 33
18
19		scores = []
20		for rnd_st in tqdm(range(n_opts)):
21		opt = Optimizer(search_space)
22		opt.search(
23		objective_function,
24		n_iter=50,
25		random_state=rnd_st,
26		memory=False,
27		verbosity={"print_results": False, "progress_bar": False},
28		initialize=initialize,
29		)
30
31		scores.append(opt.best_score)
32		score_mean = np.array(scores).mean()
33		print("scores", scores)
34
35		assert -500 < score_mean
36
37
38	View Code Duplication	@pytest.mark.parametrize(*optimizers_SBOM)
		0 ignored issues – show Duplication introduced 2020-10-09 11:15 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
39		def test_convex_convergence_SBOM(Optimizer):
40		def objective_function(para):
41		score = -para["x1"] * para["x1"]
42		return score
43
44		search_space = {"x1": np.arange(-33, 33, 1)}
45		initialize = {"vertices": 2}
46
47		n_opts = 10
48
49		scores = []
50		for rnd_st in tqdm(range(n_opts)):
51		opt = Optimizer(search_space)
52		opt.search(
53		objective_function,
54		n_iter=30,
55		random_state=rnd_st,
56		memory=False,
57		verbosity={"print_results": False, "progress_bar": False},
58		initialize=initialize,
59		)
60
61		scores.append(opt.best_score)
62		score_mean = np.array(scores).mean()
63		print("scores", scores)
64
65		assert -500 < score_mean
66
67

SimonBlanke / Gradient-Free-Optimizers

Push — master ( 268666...521b2e )

test_convex_convergence_noSBOM() A

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