tests.test_results.test_attributes_results_8() - Code Metrics - Inspection of "update formating + renaming" - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

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by Simon

created 2020-10-07 11:55 UTC

tests.test_results.test_attributes_results_8() A

↳ Parent: tests.test_results

Complexity

Conditions

Size

Total Lines	26
Code Lines	18

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	18
nop	0
dl	0
loc	26
rs	9.5
c	0
b	0
f	0

import numpy as np
import pandas as pd
from gradient_free_optimizers import RandomSearchOptimizer


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


search_space = {
    "x1": np.arange(0, 100000, 0.1),
}


def test_attributes_results_0():
    opt = RandomSearchOptimizer(search_space)
    opt.search(objective_function, n_iter=100)

    assert isinstance(opt.results, pd.DataFrame)


def test_attributes_results_1():
    opt = RandomSearchOptimizer(search_space)
    opt.search(objective_function, n_iter=100)

    assert set(search_space.keys()) < set(opt.results.columns)


def test_attributes_results_2():
    opt = RandomSearchOptimizer(search_space)
    opt.search(objective_function, n_iter=100)

    assert "x1" in list(opt.results.columns)


def test_attributes_results_3():
    opt = RandomSearchOptimizer(search_space)
    opt.search(objective_function, n_iter=100)

    assert "score" in list(opt.results.columns)


def test_attributes_results_4():
    opt = RandomSearchOptimizer(search_space)
    opt.search(
        objective_function, n_iter=1, initialize={}, warm_start=[{"x1": 0}]
    )

    assert 0 in list(opt.results["x1"].values)


def test_attributes_results_5():
    opt = RandomSearchOptimizer(search_space)
    opt.search(
        objective_function, n_iter=1, initialize={}, warm_start=[{"x1": 10}]
    )

    assert 10 in list(opt.results["x1"].values)


def test_attributes_results_6():

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

    search_space = {
        "x1": np.arange(0, 10, 1),
    }

    opt = RandomSearchOptimizer(search_space)
    opt.search(
        objective_function, n_iter=20, initialize={"random": 1}, memory=False
    )

    x1_results = list(opt.results["x1"].values)

    print("\n x1_results \n", x1_results)

    assert len(set(x1_results)) < len(x1_results)


def test_attributes_results_7():

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

    search_space = {
        "x1": np.arange(0, 10, 1),
    }

    opt = RandomSearchOptimizer(search_space)
    opt.search(
        objective_function, n_iter=20, initialize={"random": 1}, memory=True
    )

    x1_results = list(opt.results["x1"].values)

    print("\n x1_results \n", x1_results)

    assert len(set(x1_results)) == len(x1_results)


def test_attributes_results_8():
    def objective_function(para):
        score = -para["x1"] * para["x1"]
        return score

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

    results = pd.DataFrame(np.arange(-10, 10, 1), columns=["x1"])
    results["score"] = 0

    opt = RandomSearchOptimizer(search_space)
    opt.search(
        objective_function,
        n_iter=100,
        initialize={},
        memory=True,
        memory_warm_start=results,
    )

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

    x1_results = list(opt.results["x1"].values)

    assert 10 == x1_results[0]



1		import numpy as np
2		import pandas as pd
3		from gradient_free_optimizers import RandomSearchOptimizer
4
5
6		def objective_function(para):
7		score = -para["x1"] * para["x1"]
8		return score
9
10
11		search_space = {
12		"x1": np.arange(0, 100000, 0.1),
13		}
14
15
16		def test_attributes_results_0():
17		opt = RandomSearchOptimizer(search_space)
18		opt.search(objective_function, n_iter=100)
19
20		assert isinstance(opt.results, pd.DataFrame)
21
22
23		def test_attributes_results_1():
24		opt = RandomSearchOptimizer(search_space)
25		opt.search(objective_function, n_iter=100)
26
27		assert set(search_space.keys()) < set(opt.results.columns)
28
29
30		def test_attributes_results_2():
31		opt = RandomSearchOptimizer(search_space)
32		opt.search(objective_function, n_iter=100)
33
34		assert "x1" in list(opt.results.columns)
35
36
37		def test_attributes_results_3():
38		opt = RandomSearchOptimizer(search_space)
39		opt.search(objective_function, n_iter=100)
40
41		assert "score" in list(opt.results.columns)
42
43
44		def test_attributes_results_4():
45		opt = RandomSearchOptimizer(search_space)
46		opt.search(
47		objective_function, n_iter=1, initialize={}, warm_start=[{"x1": 0}]
48		)
49
50		assert 0 in list(opt.results["x1"].values)
51
52
53		def test_attributes_results_5():
54		opt = RandomSearchOptimizer(search_space)
55		opt.search(
56		objective_function, n_iter=1, initialize={}, warm_start=[{"x1": 10}]
57		)
58
59		assert 10 in list(opt.results["x1"].values)
60
61
62	View Code Duplication	def test_attributes_results_6():
		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...
63		def objective_function(para):
64		score = -para["x1"] * para["x1"]
65		return score
66
67		search_space = {
68		"x1": np.arange(0, 10, 1),
69		}
70
71		opt = RandomSearchOptimizer(search_space)
72		opt.search(
73		objective_function, n_iter=20, initialize={"random": 1}, memory=False
74		)
75
76		x1_results = list(opt.results["x1"].values)
77
78		print("\n x1_results \n", x1_results)
79
80		assert len(set(x1_results)) < len(x1_results)
81
82
83	View Code Duplication	def test_attributes_results_7():
		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		def objective_function(para):
85		score = -para["x1"] * para["x1"]
86		return score
87
88		search_space = {
89		"x1": np.arange(0, 10, 1),
90		}
91
92		opt = RandomSearchOptimizer(search_space)
93		opt.search(
94		objective_function, n_iter=20, initialize={"random": 1}, memory=True
95		)
96
97		x1_results = list(opt.results["x1"].values)
98
99		print("\n x1_results \n", x1_results)
100
101		assert len(set(x1_results)) == len(x1_results)
102
103
104		def test_attributes_results_8():
105		def objective_function(para):
106		score = -para["x1"] * para["x1"]
107		return score
108
109		search_space = {
110		"x1": np.arange(-10, 11, 1),
111		}
112
113		results = pd.DataFrame(np.arange(-10, 10, 1), columns=["x1"])
114		results["score"] = 0
115
116		opt = RandomSearchOptimizer(search_space)
117		opt.search(
118		objective_function,
119		n_iter=100,
120		initialize={},
121		memory=True,
122		memory_warm_start=results,
123		)
124
125		print("\n opt.results \n", opt.results)
126
127		x1_results = list(opt.results["x1"].values)
128
129		assert 10 == x1_results[0]
130
131

SimonBlanke / Gradient-Free-Optimizers

Push — master ( 44bd28...268666 )

tests.test_results.test_attributes_results_8() A

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