tests.test_api.test_results - Code Metrics - Inspection of "V5 api design" - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Pull Request — master (#101)

by Simon

created 2025-04-01 15:00 UTC

tests.test_api.test_results A

↳ Parent: Project

Complexity

Total Complexity

Size/Duplication

Total Lines	179
Duplicated Lines	35.75 %

Importance

Changes

Metric	Value
eloc	117
dl	64
loc	179
rs	10
c	0
b	0
f	0
wmc	13

10 Functions

Rating	Name	Duplication	Size	Complexity
A	test_attributes_results_5()	0	16	1
A	test_attributes_results_8()	20	20	2
A	test_attributes_results_3()	0	6	1
A	test_attributes_results_7()	20	20	2
A	test_attributes_results_4()	0	11	1
A	test_attributes_results_1()	0	6	1
A	test_attributes_results_0()	0	6	1
A	test_attributes_results_9()	0	20	1
A	test_attributes_results_2()	0	6	1
A	test_attributes_results_6()	24	24	1

1 Method

Rating	Name	Duplication	Size	Complexity
A	Experiment.objective_function()	0	3	1

How to fix Duplicated Code

import pytest
import numpy as np
import pandas as pd

from hyperactive.optimizers import HillClimbingOptimizer
from hyperactive.experiment import BaseExperiment
from hyperactive.search_config import SearchConfig


class Experiment(BaseExperiment):
    def objective_function(self, opt):
        score = -opt["x1"] * opt["x1"]
        return score


experiment = Experiment()

search_config = SearchConfig(
    x1=list(np.arange(0, 100, 1)),
)


def test_attributes_results_0():
    hyper = HillClimbingOptimizer()
    hyper.add_search(experiment, search_config, n_iter=100)
    hyper.run()

    assert isinstance(hyper.search_data(experiment), pd.DataFrame)


def test_attributes_results_1():
    hyper = HillClimbingOptimizer()
    hyper.add_search(experiment, search_config, n_iter=100)
    hyper.run()

    assert set(search_config.keys()) < set(hyper.search_data(experiment).columns)


def test_attributes_results_2():
    hyper = HillClimbingOptimizer()
    hyper.add_search(experiment, search_config, n_iter=100)
    hyper.run()

    assert "x1" in list(hyper.search_data(experiment).columns)


def test_attributes_results_3():
    hyper = HillClimbingOptimizer()
    hyper.add_search(experiment, search_config, n_iter=100)
    hyper.run()

    assert "score" in list(hyper.search_data(experiment).columns)


def test_attributes_results_4():
    hyper = HillClimbingOptimizer()
    hyper.add_search(
        experiment,
        search_config,
        n_iter=1,
        initialize={"warm_start": [{"x1": 0}]},
    )
    hyper.run()

    assert 0 in list(hyper.search_data(experiment)["x1"].values)


def test_attributes_results_5():
    hyper = HillClimbingOptimizer()
    hyper.add_search(
        experiment,
        search_config,
        n_iter=1,
        initialize={"warm_start": [{"x1": 10}]},
    )
    hyper.run()

    print(
        "\n x1_results \n",
        list(hyper.search_data(experiment)["x1"].values),
    )

    assert 10 in list(hyper.search_data(experiment)["x1"].values)


def test_attributes_results_6():

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

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

    hyper = HillClimbingOptimizer()
    hyper.add_search(
        experiment,
        search_config,
        n_iter=20,
        initialize={"random": 1},
        memory=False,
    )
    hyper.run()

    x1_results = list(hyper.search_data(experiment)["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(opt):
        score = -opt["x1"] * opt["x1"]
        return score

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

    hyper = HillClimbingOptimizer()
    hyper.add_search(
        experiment,
        search_config,
        n_iter=20,
    )
    hyper.run()

    search_data = hyper.search_data(experiment)
    with pytest.raises(Exception) as e_info:
        search_data["eval_times"]


def test_attributes_results_8():

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

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

    hyper = HillClimbingOptimizer()
    hyper.add_search(
        experiment,
        search_config,
        n_iter=20,
    )
    hyper.run()

    search_data = hyper.search_data(experiment)
    with pytest.raises(Exception) as e_info:
        search_data["iter_times"]


def test_attributes_results_9():
    def objective_function(opt):
        score = -opt["x1"] * opt["x1"]
        return score

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

    hyper = HillClimbingOptimizer()
    hyper.add_search(
        experiment,
        search_config,
        n_iter=20,
    )
    hyper.run()

    search_data = hyper.search_data(experiment, times=True)
    search_data["iter_times"]
    search_data["eval_times"]


"""
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(
        experiment, 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(
        experiment,
        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 pytest
2		import numpy as np
3		import pandas as pd
4
5		from hyperactive.optimizers import HillClimbingOptimizer
6		from hyperactive.experiment import BaseExperiment
7		from hyperactive.search_config import SearchConfig
8
9
10		class Experiment(BaseExperiment):
11		def objective_function(self, opt):
12		score = -opt["x1"] * opt["x1"]
13		return score
14
15
16		experiment = Experiment()
17
18		search_config = SearchConfig(
19		x1=list(np.arange(0, 100, 1)),
20		)
21
22
23		def test_attributes_results_0():
24		hyper = HillClimbingOptimizer()
25		hyper.add_search(experiment, search_config, n_iter=100)
26		hyper.run()
27
28		assert isinstance(hyper.search_data(experiment), pd.DataFrame)
29
30
31		def test_attributes_results_1():
32		hyper = HillClimbingOptimizer()
33		hyper.add_search(experiment, search_config, n_iter=100)
34		hyper.run()
35
36		assert set(search_config.keys()) < set(hyper.search_data(experiment).columns)
37
38
39		def test_attributes_results_2():
40		hyper = HillClimbingOptimizer()
41		hyper.add_search(experiment, search_config, n_iter=100)
42		hyper.run()
43
44		assert "x1" in list(hyper.search_data(experiment).columns)
45
46
47		def test_attributes_results_3():
48		hyper = HillClimbingOptimizer()
49		hyper.add_search(experiment, search_config, n_iter=100)
50		hyper.run()
51
52		assert "score" in list(hyper.search_data(experiment).columns)
53
54
55		def test_attributes_results_4():
56		hyper = HillClimbingOptimizer()
57		hyper.add_search(
58		experiment,
59		search_config,
60		n_iter=1,
61		initialize={"warm_start": [{"x1": 0}]},
62		)
63		hyper.run()
64
65		assert 0 in list(hyper.search_data(experiment)["x1"].values)
66
67
68		def test_attributes_results_5():
69		hyper = HillClimbingOptimizer()
70		hyper.add_search(
71		experiment,
72		search_config,
73		n_iter=1,
74		initialize={"warm_start": [{"x1": 10}]},
75		)
76		hyper.run()
77
78		print(
79		"\n x1_results \n",
80		list(hyper.search_data(experiment)["x1"].values),
81		)
82
83		assert 10 in list(hyper.search_data(experiment)["x1"].values)
84
85
86	View Code Duplication	def test_attributes_results_6():
		0 ignored issues – show Duplication introduced 2022-05-04 15:29 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
87		def objective_function(opt):
88		score = -opt["x1"] * opt["x1"]
89		return score
90
91		search_space = {
92		"x1": list(np.arange(0, 10, 1)),
93		}
94
95		hyper = HillClimbingOptimizer()
96		hyper.add_search(
97		experiment,
98		search_config,
99		n_iter=20,
100		initialize={"random": 1},
101		memory=False,
102		)
103		hyper.run()
104
105		x1_results = list(hyper.search_data(experiment)["x1"].values)
106
107		print("\n x1_results \n", x1_results)
108
109		assert len(set(x1_results)) < len(x1_results)
110
111
112	View Code Duplication	def test_attributes_results_7():
		0 ignored issues – show Duplication introduced 2025-04-01 15:02 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
113		def objective_function(opt):
114		score = -opt["x1"] * opt["x1"]
115		return score
116
117		search_space = {
118		"x1": list(np.arange(0, 10, 1)),
119		}
120
121		hyper = HillClimbingOptimizer()
122		hyper.add_search(
123		experiment,
124		search_config,
125		n_iter=20,
126		)
127		hyper.run()
128
129		search_data = hyper.search_data(experiment)
130		with pytest.raises(Exception) as e_info:
131		search_data["eval_times"]
132
133
134	View Code Duplication	def test_attributes_results_8():
		0 ignored issues – show Duplication introduced 2025-04-01 15:02 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
135		def objective_function(opt):
136		score = -opt["x1"] * opt["x1"]
137		return score
138
139		search_space = {
140		"x1": list(np.arange(0, 10, 1)),
141		}
142
143		hyper = HillClimbingOptimizer()
144		hyper.add_search(
145		experiment,
146		search_config,
147		n_iter=20,
148		)
149		hyper.run()
150
151		search_data = hyper.search_data(experiment)
152		with pytest.raises(Exception) as e_info:
153		search_data["iter_times"]
154
155
156		def test_attributes_results_9():
157		def objective_function(opt):
158		score = -opt["x1"] * opt["x1"]
159		return score
160
161		search_space = {
162		"x1": list(np.arange(0, 10, 1)),
163		}
164
165		hyper = HillClimbingOptimizer()
166		hyper.add_search(
167		experiment,
168		search_config,
169		n_iter=20,
170		)
171		hyper.run()
172
173		search_data = hyper.search_data(experiment, times=True)
174		search_data["iter_times"]
175		search_data["eval_times"]
176
177
178		"""
179		def test_attributes_results_7():
180		def objective_function(para):
181		score = -para["x1"] * para["x1"]
182		return score
183
184		search_space = {
185		"x1": np.arange(0, 10, 1),
186		}
187
188		opt = RandomSearchOptimizer(search_space)
189		opt.search(
190		experiment, n_iter=20, initialize={"random": 1}, memory=True
191		)
192
193		x1_results = list(opt.results["x1"].values)
194
195		print("\n x1_results \n", x1_results)
196
197		assert len(set(x1_results)) == len(x1_results)
198
199
200		def test_attributes_results_8():
201		def objective_function(para):
202		score = -para["x1"] * para["x1"]
203		return score
204
205		search_space = {
206		"x1": np.arange(-10, 11, 1),
207		}
208
209		results = pd.DataFrame(np.arange(-10, 10, 1), columns=["x1"])
210		results["score"] = 0
211
212		opt = RandomSearchOptimizer(search_space)
213		opt.search(
214		experiment,
215		n_iter=100,
216		initialize={},
217		memory=True,
218		memory_warm_start=results,
219		)
220
221		print("\n opt.results \n", opt.results)
222
223		x1_results = list(opt.results["x1"].values)
224
225		assert 10 == x1_results[0]
226		"""
227

SimonBlanke / Hyperactive

Pull Request — master (#101)

tests.test_api.test_results A

Complexity

Size/Duplication

Importance

10 Functions

1 Method

How to fix Duplicated Code

Duplicated Code

Duplication Side-by-Side

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