tests.test_optimizers.test_convex_convergence.test_convex_convergence_popBased() - Code Metrics - Inspection of "small update in tree based opt." - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

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by Simon

created 2020-11-05 15:37 UTC

test_convex_convergence_popBased() A

↳ Parent: tests.test_optimizers.test_convex_convergence

Complexity

Conditions

Size

Total Lines	27
Code Lines	21

Duplication

Lines	27
Ratio	100 %

Importance

Changes

Metric	Value
cc	2
eloc	21
nop	1
dl	27
loc	27
rs	9.376
c	0
b	0
f	0

import pytest
from tqdm import tqdm
import numpy as np

from ._parametrize import (
    optimizers_singleOpt,
    optimizers_PopBased,
    optimizers_SBOM,
)


@pytest.mark.parametrize(*optimizers_singleOpt)

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

    search_space = {"x1": np.arange(-100, 101, 1)}
    initialize = {"vertices": 1}

    n_opts = 33

    scores = []
    for rnd_st in tqdm(range(n_opts)):
        opt = Optimizer(search_space)
        opt.search(
            objective_function,
            n_iter=100,
            random_state=rnd_st,
            memory=False,
            verbosity=False,
            initialize=initialize,
        )

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

    assert score_mean > -25


@pytest.mark.parametrize(*optimizers_PopBased)

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

    search_space = {"x1": np.arange(-100, 101, 1)}
    initialize = {"vertices": 2, "grid": 2}

    n_opts = 33

    scores = []
    for rnd_st in tqdm(range(n_opts)):
        opt = Optimizer(search_space)
        opt.search(
            objective_function,
            n_iter=80,
            random_state=rnd_st,
            memory=False,
            verbosity=False,
            initialize=initialize,
        )

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

    assert score_mean > -25


@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=False,
            initialize=initialize,
        )

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

    print("scores", scores)

    print("score_mean", score_mean)
    assert score_mean > -25
    # assert False



1		import pytest
2		from tqdm import tqdm
3		import numpy as np
4
5		from ._parametrize import (
6		optimizers_singleOpt,
7		optimizers_PopBased,
8		optimizers_SBOM,
9		)
10
11
12	View Code Duplication	@pytest.mark.parametrize(*optimizers_singleOpt)
		0 ignored issues – show Duplication introduced 2020-11-05 15:42 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
13		def test_convex_convergence_singleOpt(Optimizer):
14		def objective_function(para):
15		score = -para["x1"] * para["x1"]
16		return score
17
18		search_space = {"x1": np.arange(-100, 101, 1)}
19		initialize = {"vertices": 1}
20
21		n_opts = 33
22
23		scores = []
24		for rnd_st in tqdm(range(n_opts)):
25		opt = Optimizer(search_space)
26		opt.search(
27		objective_function,
28		n_iter=100,
29		random_state=rnd_st,
30		memory=False,
31		verbosity=False,
32		initialize=initialize,
33		)
34
35		scores.append(opt.best_score)
36		score_mean = np.array(scores).mean()
37
38		assert score_mean > -25
39
40
41	View Code Duplication	@pytest.mark.parametrize(*optimizers_PopBased)
		0 ignored issues – show Duplication introduced 2020-11-05 15:42 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
42		def test_convex_convergence_popBased(Optimizer):
43		def objective_function(para):
44		score = -para["x1"] * para["x1"]
45		return score
46
47		search_space = {"x1": np.arange(-100, 101, 1)}
48		initialize = {"vertices": 2, "grid": 2}
49
50		n_opts = 33
51
52		scores = []
53		for rnd_st in tqdm(range(n_opts)):
54		opt = Optimizer(search_space)
55		opt.search(
56		objective_function,
57		n_iter=80,
58		random_state=rnd_st,
59		memory=False,
60		verbosity=False,
61		initialize=initialize,
62		)
63
64		scores.append(opt.best_score)
65		score_mean = np.array(scores).mean()
66
67		assert score_mean > -25
68
69
70		@pytest.mark.parametrize(*optimizers_SBOM)
71		def test_convex_convergence_SBOM(Optimizer):
72		def objective_function(para):
73		score = -para["x1"] * para["x1"]
74		return score
75
76		search_space = {"x1": np.arange(-33, 33, 1)}
77		initialize = {"vertices": 2}
78
79		n_opts = 10
80
81		scores = []
82		for rnd_st in tqdm(range(n_opts)):
83		opt = Optimizer(search_space)
84		opt.search(
85		objective_function,
86		n_iter=30,
87		random_state=rnd_st,
88		memory=False,
89		verbosity=False,
90		initialize=initialize,
91		)
92
93		scores.append(opt.best_score)
94		score_mean = np.array(scores).mean()
95
96		print("scores", scores)
97
98		print("score_mean", score_mean)
99		assert score_mean > -25
100		# assert False
101
102

SimonBlanke / Gradient-Free-Optimizers

Push — master ( b0b7f3...e3d174 )

test_convex_convergence_popBased() A

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