test_sbom_parameter.test_smbo_para() - Code Metrics - Inspection of "rename to sequence model based optimization (as it..." - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

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by Simon

created 2020-11-05 15:53 UTC

test_sbom_parameter.test_smbo_para() A

↳ Parent: test_sbom_parameter

Complexity

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Total Lines	17
Code Lines	14

Duplication

Lines	0
Ratio	0 %

Importance

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Metric	Value
cc	2
eloc	14
nop	2
dl	0
loc	17
rs	9.7
c	0
b	0
f	0

# Author: Simon Blanke
# Email: [email protected]
# License: MIT License

import pytest
import numpy as np

from gradient_free_optimizers import (
    BayesianOptimizer,
    TreeStructuredParzenEstimators,
    DecisionTreeOptimizer,
    EnsembleOptimizer,
)


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


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


sbom_para = [
    ({"warm_start_smbo": None}),
    ({"warm_start_smbo": None}),
    ({"rand_rest_p": 0}),
    ({"rand_rest_p": 0.5}),
    ({"rand_rest_p": 1}),
    ({"rand_rest_p": 10}),
]


pytest_wrapper = ("para", sbom_para)

optimizers_sbom = (
    "Optimizer",
    [
        (BayesianOptimizer),
        (TreeStructuredParzenEstimators),
        (DecisionTreeOptimizer),
        (EnsembleOptimizer),
    ],
)


@pytest.mark.parametrize(*optimizers_sbom)
@pytest.mark.parametrize(*pytest_wrapper)
def test_smbo_para(Optimizer, para):
    opt = Optimizer(search_space, **para)
    opt.search(
        objective_function,
        n_iter=10,
        memory=False,
        verbosity=False,
        initialize={"vertices": 2},
    )

    for optimizer in opt.optimizers:
        para_key = list(para.keys())[0]
        para_value = getattr(optimizer, para_key)

        assert para_value == para[para_key]


1			# Author: Simon Blanke
2			# Email: [email protected]
3			# License: MIT License
4
5			import pytest
6			import numpy as np
7
8			from gradient_free_optimizers import (
9			BayesianOptimizer,
10			TreeStructuredParzenEstimators,
11			DecisionTreeOptimizer,
12			EnsembleOptimizer,
13			)
14
15
16			def objective_function(para):
17			score = -para["x1"] * para["x1"]
18			return score
19
20
21			search_space = {"x1": np.arange(-10, 11, 1)}
22
23
24			sbom_para = [
25			({"warm_start_smbo": None}),
26			({"warm_start_smbo": None}),
27			({"rand_rest_p": 0}),
28			({"rand_rest_p": 0.5}),
29			({"rand_rest_p": 1}),
30			({"rand_rest_p": 10}),
31			]
32
33
34			pytest_wrapper = ("para", sbom_para)
35
36			optimizers_sbom = (
37			"Optimizer",
38			[
39			(BayesianOptimizer),
40			(TreeStructuredParzenEstimators),
41			(DecisionTreeOptimizer),
42			(EnsembleOptimizer),
43			],
44			)
45
46
47			@pytest.mark.parametrize(*optimizers_sbom)
48			@pytest.mark.parametrize(*pytest_wrapper)
49			def test_smbo_para(Optimizer, para):
50			opt = Optimizer(search_space, **para)
51			opt.search(
52			objective_function,
53			n_iter=10,
54			memory=False,
55			verbosity=False,
56			initialize={"vertices": 2},
57			)
58
59			for optimizer in opt.optimizers:
60			para_key = list(para.keys())[0]
61			para_value = getattr(optimizer, para_key)
62
63			assert para_value == para[para_key]
64

SimonBlanke / Gradient-Free-Optimizers

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test_sbom_parameter.test_smbo_para() A

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