tests.test_optimizers.test_random_state - Code Metrics - Inspection of "Merge branch 'master' into dev" - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Push — master ( 047469...b66fc0 )

by Simon

created 2022-10-22 18:36 UTC

tests.test_optimizers.test_random_state A

↳ Parent: Project

Complexity

Total Complexity

Size/Duplication

Total Lines	139
Duplicated Lines	47.48 %

Importance

Changes

Metric	Value
eloc	89
dl	66
loc	139
rs	10
c	0
b	0
f	0
wmc	6

6 Functions

Rating	Name	Duplication	Size	Complexity
A	objective_function()	0	3	1
A	test_random_state_2()	21	21	1
A	test_random_state_1()	0	18	1
A	test_random_state_0()	21	21	1
A	test_no_random_state_0()	0	15	1
A	test_random_state_direct()	24	24	1

How to fix Duplicated Code

import pytest
import time
import numpy as np
import pandas as pd


from ._parametrize import optimizers_non_deterministic as optimizers
from surfaces.test_functions import AckleyFunction
from gradient_free_optimizers import DirectAlgorithm

ackkley_function = AckleyFunction()


def objective_function(para):
    score = -(para["x0"] * para["x0"] + para["x1"] * para["x1"])
    return score


search_space = {
    "x0": np.arange(-75, 100, 1),
    "x1": np.arange(-100, 75, 1),
}

err = 0.000001

n_iter = 10
n_random = 2

n_last = n_iter - n_random


@pytest.mark.parametrize(*optimizers)

def test_random_state_0(Optimizer):
    opt0 = Optimizer(search_space, initialize={"random": n_random}, random_state=1)
    opt0.search(
        ackkley_function,
        n_iter=n_iter,
    )

    opt1 = Optimizer(search_space, initialize={"random": n_random}, random_state=1)
    opt1.search(
        ackkley_function,
        n_iter=n_iter,
    )

    print("\n opt0.search_data \n", opt0.search_data)
    print("\n opt1.search_data \n", opt1.search_data)

    n_last_scores0 = list(opt0.search_data["score"].values)[-n_last:]
    n_last_scores1 = list(opt1.search_data["score"].values)[-n_last:]

    assert abs(np.sum(n_last_scores0) - np.sum(n_last_scores1)) < err


@pytest.mark.parametrize(*optimizers)
def test_random_state_1(Optimizer):
    opt0 = Optimizer(search_space, initialize={"random": n_random}, random_state=10)
    opt0.search(
        ackkley_function,
        n_iter=n_iter,
    )

    opt1 = Optimizer(search_space, initialize={"random": n_random}, random_state=10)
    opt1.search(
        ackkley_function,
        n_iter=n_iter,
    )

    n_last_scores0 = list(opt0.search_data["score"].values)[-n_last:]
    n_last_scores1 = list(opt1.search_data["score"].values)[-n_last:]

    assert abs(np.sum(n_last_scores0) - np.sum(n_last_scores1)) < err


@pytest.mark.parametrize(*optimizers)

def test_random_state_2(Optimizer):
    opt0 = Optimizer(search_space, initialize={"random": n_random}, random_state=1)
    opt0.search(
        ackkley_function,
        n_iter=n_iter,
    )

    opt1 = Optimizer(search_space, initialize={"random": n_random}, random_state=10)
    opt1.search(
        ackkley_function,
        n_iter=n_iter,
    )

    print("\n opt0.search_data \n", opt0.search_data)
    print("\n opt1.search_data \n", opt1.search_data)

    n_last_scores0 = list(opt0.search_data["score"].values)[-n_last:]
    n_last_scores1 = list(opt1.search_data["score"].values)[-n_last:]

    assert abs(np.sum(n_last_scores0) - np.sum(n_last_scores1)) > err


def test_random_state_direct():

    opt0 = DirectAlgorithm(
        search_space, initialize={"random": n_random}, random_state=1
    )
    opt0.search(
        ackkley_function,
        n_iter=n_iter,
    )

    opt1 = DirectAlgorithm(
        search_space, initialize={"random": n_random}, random_state=10
    )
    opt1.search(
        ackkley_function,
        n_iter=n_iter,
    )

    print("\n opt0.search_data \n", opt0.search_data)
    print("\n opt1.search_data \n", opt1.search_data)

    n_last_scores0 = list(opt0.search_data["score"].values)[-n_last:]
    n_last_scores1 = list(opt1.search_data["score"].values)[-n_last:]

    assert abs(np.sum(n_last_scores0) - np.sum(n_last_scores1)) < err


@pytest.mark.parametrize(*optimizers)
def test_no_random_state_0(Optimizer):
    opt0 = Optimizer(search_space, initialize={"random": n_random})
    opt0.search(ackkley_function, n_iter=n_iter)

    opt1 = Optimizer(search_space, initialize={"random": n_random})
    opt1.search(ackkley_function, n_iter=n_iter)

    print("\n opt0.search_data \n", opt0.search_data)
    print("\n opt1.search_data \n", opt1.search_data)

    n_last_scores0 = list(opt0.search_data["score"].values)[-n_last:]
    n_last_scores1 = list(opt1.search_data["score"].values)[-n_last:]

    assert abs(np.sum(n_last_scores0) - np.sum(n_last_scores1)) > err


1		import pytest
2		import time
3		import numpy as np
4		import pandas as pd
5
6
7		from ._parametrize import optimizers_non_deterministic as optimizers
8		from surfaces.test_functions import AckleyFunction
9		from gradient_free_optimizers import DirectAlgorithm
10
11		ackkley_function = AckleyFunction()
12
13
14		def objective_function(para):
15		score = -(para["x0"] * para["x0"] + para["x1"] * para["x1"])
16		return score
17
18
19		search_space = {
20		"x0": np.arange(-75, 100, 1),
21		"x1": np.arange(-100, 75, 1),
22		}
23
24		err = 0.000001
25
26		n_iter = 10
27		n_random = 2
28
29		n_last = n_iter - n_random
30
31
32	View Code Duplication	@pytest.mark.parametrize(*optimizers)
		0 ignored issues – show Duplication introduced 2021-03-21 10:54 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
33		def test_random_state_0(Optimizer):
34		opt0 = Optimizer(search_space, initialize={"random": n_random}, random_state=1)
35		opt0.search(
36		ackkley_function,
37		n_iter=n_iter,
38		)
39
40		opt1 = Optimizer(search_space, initialize={"random": n_random}, random_state=1)
41		opt1.search(
42		ackkley_function,
43		n_iter=n_iter,
44		)
45
46		print("\n opt0.search_data \n", opt0.search_data)
47		print("\n opt1.search_data \n", opt1.search_data)
48
49		n_last_scores0 = list(opt0.search_data["score"].values)[-n_last:]
50		n_last_scores1 = list(opt1.search_data["score"].values)[-n_last:]
51
52		assert abs(np.sum(n_last_scores0) - np.sum(n_last_scores1)) < err
53
54
55		@pytest.mark.parametrize(*optimizers)
56		def test_random_state_1(Optimizer):
57		opt0 = Optimizer(search_space, initialize={"random": n_random}, random_state=10)
58		opt0.search(
59		ackkley_function,
60		n_iter=n_iter,
61		)
62
63		opt1 = Optimizer(search_space, initialize={"random": n_random}, random_state=10)
64		opt1.search(
65		ackkley_function,
66		n_iter=n_iter,
67		)
68
69		n_last_scores0 = list(opt0.search_data["score"].values)[-n_last:]
70		n_last_scores1 = list(opt1.search_data["score"].values)[-n_last:]
71
72		assert abs(np.sum(n_last_scores0) - np.sum(n_last_scores1)) < err
73
74
75	View Code Duplication	@pytest.mark.parametrize(*optimizers)
		0 ignored issues – show Duplication introduced 2021-03-21 10:54 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
76		def test_random_state_2(Optimizer):
77		opt0 = Optimizer(search_space, initialize={"random": n_random}, random_state=1)
78		opt0.search(
79		ackkley_function,
80		n_iter=n_iter,
81		)
82
83		opt1 = Optimizer(search_space, initialize={"random": n_random}, random_state=10)
84		opt1.search(
85		ackkley_function,
86		n_iter=n_iter,
87		)
88
89		print("\n opt0.search_data \n", opt0.search_data)
90		print("\n opt1.search_data \n", opt1.search_data)
91
92		n_last_scores0 = list(opt0.search_data["score"].values)[-n_last:]
93		n_last_scores1 = list(opt1.search_data["score"].values)[-n_last:]
94
95		assert abs(np.sum(n_last_scores0) - np.sum(n_last_scores1)) > err
96
97
98	View Code Duplication	def test_random_state_direct():
		0 ignored issues – show Duplication introduced 2021-03-21 10:54 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
99		opt0 = DirectAlgorithm(
100		search_space, initialize={"random": n_random}, random_state=1
101		)
102		opt0.search(
103		ackkley_function,
104		n_iter=n_iter,
105		)
106
107		opt1 = DirectAlgorithm(
108		search_space, initialize={"random": n_random}, random_state=10
109		)
110		opt1.search(
111		ackkley_function,
112		n_iter=n_iter,
113		)
114
115		print("\n opt0.search_data \n", opt0.search_data)
116		print("\n opt1.search_data \n", opt1.search_data)
117
118		n_last_scores0 = list(opt0.search_data["score"].values)[-n_last:]
119		n_last_scores1 = list(opt1.search_data["score"].values)[-n_last:]
120
121		assert abs(np.sum(n_last_scores0) - np.sum(n_last_scores1)) < err
122
123
124		@pytest.mark.parametrize(*optimizers)
125		def test_no_random_state_0(Optimizer):
126		opt0 = Optimizer(search_space, initialize={"random": n_random})
127		opt0.search(ackkley_function, n_iter=n_iter)
128
129		opt1 = Optimizer(search_space, initialize={"random": n_random})
130		opt1.search(ackkley_function, n_iter=n_iter)
131
132		print("\n opt0.search_data \n", opt0.search_data)
133		print("\n opt1.search_data \n", opt1.search_data)
134
135		n_last_scores0 = list(opt0.search_data["score"].values)[-n_last:]
136		n_last_scores1 = list(opt1.search_data["score"].values)[-n_last:]
137
138		assert abs(np.sum(n_last_scores0) - np.sum(n_last_scores1)) > err
139

SimonBlanke / Gradient-Free-Optimizers

Push — master ( 047469...b66fc0 )

tests.test_optimizers.test_random_state A

Complexity

Size/Duplication

Importance

6 Functions

How to fix Duplicated Code

Duplicated Code

Duplication Side-by-Side

Filter issues like