tests.test_initializers.test_initialize_grid_1() - Code Metrics - Inspection of "small fix for "6d14fbb1"" - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

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by Simon

created 2021-01-23 10:04 UTC

tests.test_initializers.test_initialize_grid_1() A

↳ Parent: tests.test_initializers

Complexity

Conditions

Size

Total Lines	11
Code Lines	7

Duplication

Lines	11
Ratio	100 %

Importance

Changes

Metric	Value
cc	1
eloc	7
nop	0
dl	11
loc	11
rs	10
c	0
b	0
f	0

import numpy as np
from gradient_free_optimizers import RandomSearchOptimizer


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


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


def test_initialize_warm_start_0():
    init = {
        "x1": 0,
    }

    initialize = {"warm_start": [init]}

    opt = RandomSearchOptimizer(search_space, initialize=initialize)
    opt.search(objective_function, n_iter=1)

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

    assert abs(opt.best_score) < 0.001


def test_initialize_warm_start_1():

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

    initialize = {"warm_start": [init]}

    opt = RandomSearchOptimizer(search_space, initialize=initialize)
    opt.search(objective_function, n_iter=1)

    assert opt.best_para == init


def test_initialize_warm_start_2():

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

    initialize = {"warm_start": [init], "random": 0, "vertices": 0, "grid": 0}

    opt = RandomSearchOptimizer(search_space, initialize=initialize)
    opt.search(objective_function, n_iter=1)

    assert opt.best_para == init


def test_initialize_vertices():
    initialize = {"vertices": 2}

    opt = RandomSearchOptimizer(search_space, initialize=initialize)
    opt.search(objective_function, n_iter=2)

    assert abs(opt.best_score) - 10000 < 0.001


def test_initialize_grid_0():

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

    opt = RandomSearchOptimizer(search_space, initialize=initialize)
    opt.search(objective_function, n_iter=1)

    assert abs(opt.best_score) < 0.001


def test_initialize_grid_1():

    search_space = {
        "x1": np.arange(-2, 3, 1),
    }

    initialize = {"grid": 1}

    opt = RandomSearchOptimizer(search_space, initialize=initialize)
    opt.search(objective_function, n_iter=1)

    assert abs(opt.best_score) - 1 < 0.001


1		import numpy as np
2		from gradient_free_optimizers import RandomSearchOptimizer
3
4
5		def objective_function(para):
6		score = -para["x1"] * para["x1"]
7		return score
8
9
10		search_space = {
11		"x1": np.arange(-100, 101, 1),
12		}
13
14
15		def test_initialize_warm_start_0():
16		init = {
17		"x1": 0,
18		}
19
20		initialize = {"warm_start": [init]}
21
22		opt = RandomSearchOptimizer(search_space, initialize=initialize)
23		opt.search(objective_function, n_iter=1)
24
25		# print("\nself.results \n", opt.results)
26
27		assert abs(opt.best_score) < 0.001
28
29
30	View Code Duplication	def test_initialize_warm_start_1():
		0 ignored issues – show Duplication introduced 2021-01-23 10:07 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
31		search_space = {
32		"x1": np.arange(-10, 10, 1),
33		}
34		init = {
35		"x1": -10,
36		}
37
38		initialize = {"warm_start": [init]}
39
40		opt = RandomSearchOptimizer(search_space, initialize=initialize)
41		opt.search(objective_function, n_iter=1)
42
43		assert opt.best_para == init
44
45
46	View Code Duplication	def test_initialize_warm_start_2():
		0 ignored issues – show Duplication introduced 2021-01-23 10:07 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
47		search_space = {
48		"x1": np.arange(-10, 10, 1),
49		}
50		init = {
51		"x1": -10,
52		}
53
54		initialize = {"warm_start": [init], "random": 0, "vertices": 0, "grid": 0}
55
56		opt = RandomSearchOptimizer(search_space, initialize=initialize)
57		opt.search(objective_function, n_iter=1)
58
59		assert opt.best_para == init
60
61
62		def test_initialize_vertices():
63		initialize = {"vertices": 2}
64
65		opt = RandomSearchOptimizer(search_space, initialize=initialize)
66		opt.search(objective_function, n_iter=2)
67
68		assert abs(opt.best_score) - 10000 < 0.001
69
70
71	View Code Duplication	def test_initialize_grid_0():
		0 ignored issues – show Duplication introduced 2020-10-07 11:59 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
72		search_space = {
73		"x1": np.arange(-1, 2, 1),
74		}
75		initialize = {"grid": 1}
76
77		opt = RandomSearchOptimizer(search_space, initialize=initialize)
78		opt.search(objective_function, n_iter=1)
79
80		assert abs(opt.best_score) < 0.001
81
82
83	View Code Duplication	def test_initialize_grid_1():
		0 ignored issues – show Duplication introduced 2020-10-07 11:59 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
84		search_space = {
85		"x1": np.arange(-2, 3, 1),
86		}
87
88		initialize = {"grid": 1}
89
90		opt = RandomSearchOptimizer(search_space, initialize=initialize)
91		opt.search(objective_function, n_iter=1)
92
93		assert abs(opt.best_score) - 1 < 0.001
94

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tests.test_initializers.test_initialize_grid_1() A

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