tests.test_api.test_early_stop.test_early_stop_4() - Code Metrics - Inspection of "V5 API rework" - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Pull Request — master (#110)

unknown

created 2025-05-03 22:21 UTC

tests.test_api.test_early_stop.test_early_stop_4() B

↳ Parent: tests.test_api.test_early_stop

Complexity

Conditions

Size

Total Lines	62
Code Lines	49

Duplication

Lines	62
Ratio	100 %

Importance

Changes

Metric	Value
cc	1
eloc	49
nop	0
dl	62
loc	62
rs	8.669
c	0
b	0
f	0

How to fix Long Method

import time
import pytest
import numpy as np

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


class Experiment(BaseExperiment):
    def objective_function(self, para):
        score = -para["x0"] * para["x0"]
        return score


experiment = Experiment()
search_space = {
    "x0": list(np.arange(0, 100000, 0.1)),
}
search_config = SearchConfig(
    x0=list(np.arange(0, 100000, 0.1)),
)


def test_early_stop_0():
    early_stopping = {
        "n_iter_no_change": 5,
        "tol_abs": 0.1,
        "tol_rel": 0.1,
    }

    hyper = RandomSearchOptimizer()
    hyper.add_search(
        experiment,
        search_config,
        n_iter=1000,
        initialize={"warm_start": [{"x0": 0}]},
        early_stopping=early_stopping,
    )
    hyper.run()


def test_early_stop_1():
    early_stopping = {
        "n_iter_no_change": 5,
        "tol_abs": None,
        "tol_rel": 5,
    }

    hyper = RandomSearchOptimizer()
    hyper.add_search(
        experiment,
        search_config,
        n_iter=1000,
        initialize={"warm_start": [{"x0": 0}]},
        early_stopping=early_stopping,
    )
    hyper.run()

    hyper = RandomSearchOptimizer()
    hyper.add_search(
        experiment,
        search_config,
        n_iter=1000,
        initialize={"warm_start": [{"x0": 0}]},
        early_stopping=early_stopping,
    )
    hyper.run()


def test_early_stop_3():
    def objective_function(para):
        score = -para["x0"] * para["x0"]
        return score

    search_space = {
        "x0": list(np.arange(0, 100, 0.1)),
    }

    n_iter_no_change = 5
    early_stopping = {
        "n_iter_no_change": n_iter_no_change,
    }

    hyper = RandomSearchOptimizer()
    hyper.add_search(
        experiment,
        search_config,
        n_iter=100000,
        initialize={"warm_start": [{"x0": 0}]},
        early_stopping=early_stopping,
    )
    hyper.run()

    search_data = hyper.search_data(experiment)
    n_performed_iter = len(search_data)

    print("\n n_performed_iter \n", n_performed_iter)
    print("\n n_iter_no_change \n", n_iter_no_change)

    assert n_performed_iter == (n_iter_no_change + 1)


def test_early_stop_4():

    class Experiment(BaseExperiment):
        def objective_function(self, para):
            return para["x0"]

    experiment = Experiment()

    search_config = SearchConfig(
        x0=list(np.arange(0, 100, 0.01)),
    )

    n_iter_no_change = 5
    early_stopping = {
        "n_iter_no_change": 5,
        "tol_abs": 1,
        "tol_rel": None,
    }

    start1 = {"x0": 0}
    start2 = {"x0": 1}
    start3 = {"x0": 2}
    start4 = {"x0": 3}
    start5 = {"x0": 4}

    warm_start_l = [
        start1,
        start1,
        start1,
        start1,
        start1,
        start2,
        start2,
        start2,
        start3,
        start3,
        start3,
        start4,
        start4,
        start4,
        start5,
        start5,
        start5,
    ]
    n_iter = len(warm_start_l)

    hyper = RandomSearchOptimizer()
    hyper.add_search(
        experiment,
        search_config,
        n_iter=n_iter,
        initialize={"warm_start": warm_start_l},
        early_stopping=early_stopping,
    )
    hyper.run()

    search_data = hyper.search_data(experiment)
    n_performed_iter = len(search_data)

    print("\n n_performed_iter \n", n_performed_iter)
    print("\n n_iter_no_change \n", n_iter_no_change)

    assert n_performed_iter == n_iter


def test_early_stop_5():

    class Experiment(BaseExperiment):
        def objective_function(self, para):
            return para["x0"]

    experiment = Experiment()

    search_config = SearchConfig(
        x0=list(np.arange(0, 100, 0.01)),
    )

    n_iter_no_change = 5
    early_stopping = {
        "n_iter_no_change": n_iter_no_change,
        "tol_abs": 10,
        "tol_rel": None,
    }

    start1 = {"x0": 0}
    start2 = {"x0": 9}
    start3 = {"x0": 20}

    warm_start_l = [
        start1,
        start1,
        start1,
        start1,
        start1,
        start2,
        start2,
        start2,
        start3,
        start3,
        start3,
    ]
    n_iter = len(warm_start_l)

    hyper = RandomSearchOptimizer()
    hyper.add_search(
        experiment,
        search_config,
        n_iter=n_iter,
        initialize={"warm_start": warm_start_l},
        early_stopping=early_stopping,
    )
    hyper.run()

    search_data = hyper.search_data(experiment)
    n_performed_iter = len(search_data)

    print("\n n_performed_iter \n", n_performed_iter)
    print("\n n_iter_no_change \n", n_iter_no_change)

    assert n_performed_iter == (n_iter_no_change + 1)


def test_early_stop_6():

    class Experiment(BaseExperiment):
        def objective_function(self, para):
            return para["x0"]

    experiment = Experiment()

    search_config = SearchConfig(
        x0=list(np.arange(0, 100, 0.01)),
    )

    n_iter_no_change = 5
    early_stopping = {
        "n_iter_no_change": 5,
        "tol_abs": None,
        "tol_rel": 10,
    }

    start1 = {"x0": 1}
    start2 = {"x0": 1.1}
    start3 = {"x0": 1.22}
    start4 = {"x0": 1.35}
    start5 = {"x0": 1.48}

    warm_start_l = [
        start1,
        start1,
        start1,
        start1,
        start1,
        start2,
        start2,
        start2,
        start3,
        start3,
        start3,
        start4,
        start4,
        start4,
        start5,
        start5,
        start5,
    ]
    n_iter = len(warm_start_l)

    hyper = RandomSearchOptimizer()
    hyper.add_search(
        experiment,
        search_config,
        n_iter=n_iter,
        initialize={"warm_start": warm_start_l},
        early_stopping=early_stopping,
    )
    hyper.run()

    search_data = hyper.search_data(experiment)
    n_performed_iter = len(search_data)

    print("\n n_performed_iter \n", n_performed_iter)
    print("\n n_iter_no_change \n", n_iter_no_change)

    assert n_performed_iter == n_iter


def test_early_stop_7():

    def objective_function(para):
        return para["x0"]

    experiment = Experiment()

    search_config = SearchConfig(
        x0=list(np.arange(0, 100, 0.01)),
    )

    n_iter_no_change = 5
    early_stopping = {
        "n_iter_no_change": n_iter_no_change,
        "tol_abs": None,
        "tol_rel": 10,
    }

    start1 = {"x0": 1}
    start2 = {"x0": 1.09}
    start3 = {"x0": 1.20}

    warm_start_l = [
        start1,
        start1,
        start1,
        start1,
        start1,
        start2,
        start2,
        start2,
        start3,
        start3,
        start3,
    ]
    n_iter = len(warm_start_l)

    hyper = RandomSearchOptimizer()
    hyper.add_search(
        experiment,
        search_config,
        n_iter=n_iter,
        initialize={"warm_start": warm_start_l},
        early_stopping=early_stopping,
    )
    hyper.run()

    search_data = hyper.search_data(experiment)
    n_performed_iter = len(search_data)

    print("\n n_performed_iter \n", n_performed_iter)
    print("\n n_iter_no_change \n", n_iter_no_change)

    assert n_performed_iter == (n_iter_no_change + 1)


1		import time
2		import pytest
3		import numpy as np
4
5		from hyperactive.optimizers import RandomSearchOptimizer, 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, para):
12		score = -para["x0"] * para["x0"]
13		return score
14
15
16		experiment = Experiment()
17		search_space = {
18		"x0": list(np.arange(0, 100000, 0.1)),
19		}
20		search_config = SearchConfig(
21		x0=list(np.arange(0, 100000, 0.1)),
22		)
23
24
25		def test_early_stop_0():
26		early_stopping = {
27		"n_iter_no_change": 5,
28		"tol_abs": 0.1,
29		"tol_rel": 0.1,
30		}
31
32		hyper = RandomSearchOptimizer()
33		hyper.add_search(
34		experiment,
35		search_config,
36		n_iter=1000,
37		initialize={"warm_start": [{"x0": 0}]},
38		early_stopping=early_stopping,
39		)
40		hyper.run()
41
42
43		def test_early_stop_1():
44		early_stopping = {
45		"n_iter_no_change": 5,
46		"tol_abs": None,
47		"tol_rel": 5,
48		}
49
50		hyper = RandomSearchOptimizer()
51		hyper.add_search(
52		experiment,
53		search_config,
54		n_iter=1000,
55		initialize={"warm_start": [{"x0": 0}]},
56		early_stopping=early_stopping,
57		)
58		hyper.run()
59
60		hyper = RandomSearchOptimizer()
61		hyper.add_search(
62		experiment,
63		search_config,
64		n_iter=1000,
65		initialize={"warm_start": [{"x0": 0}]},
66		early_stopping=early_stopping,
67		)
68		hyper.run()
69
70
71		def test_early_stop_3():
72		def objective_function(para):
73		score = -para["x0"] * para["x0"]
74		return score
75
76		search_space = {
77		"x0": list(np.arange(0, 100, 0.1)),
78		}
79
80		n_iter_no_change = 5
81		early_stopping = {
82		"n_iter_no_change": n_iter_no_change,
83		}
84
85		hyper = RandomSearchOptimizer()
86		hyper.add_search(
87		experiment,
88		search_config,
89		n_iter=100000,
90		initialize={"warm_start": [{"x0": 0}]},
91		early_stopping=early_stopping,
92		)
93		hyper.run()
94
95		search_data = hyper.search_data(experiment)
96		n_performed_iter = len(search_data)
97
98		print("\n n_performed_iter \n", n_performed_iter)
99		print("\n n_iter_no_change \n", n_iter_no_change)
100
101		assert n_performed_iter == (n_iter_no_change + 1)
102
103
104	View Code Duplication	def test_early_stop_4():
		0 ignored issues – show Duplication introduced 2025-04-01 14:52 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
105		class Experiment(BaseExperiment):
106		def objective_function(self, para):
107		return para["x0"]
108
109		experiment = Experiment()
110
111		search_config = SearchConfig(
112		x0=list(np.arange(0, 100, 0.01)),
113		)
114
115		n_iter_no_change = 5
116		early_stopping = {
117		"n_iter_no_change": 5,
118		"tol_abs": 1,
119		"tol_rel": None,
120		}
121
122		start1 = {"x0": 0}
123		start2 = {"x0": 1}
124		start3 = {"x0": 2}
125		start4 = {"x0": 3}
126		start5 = {"x0": 4}
127
128		warm_start_l = [
129		start1,
130		start1,
131		start1,
132		start1,
133		start1,
134		start2,
135		start2,
136		start2,
137		start3,
138		start3,
139		start3,
140		start4,
141		start4,
142		start4,
143		start5,
144		start5,
145		start5,
146		]
147		n_iter = len(warm_start_l)
148
149		hyper = RandomSearchOptimizer()
150		hyper.add_search(
151		experiment,
152		search_config,
153		n_iter=n_iter,
154		initialize={"warm_start": warm_start_l},
155		early_stopping=early_stopping,
156		)
157		hyper.run()
158
159		search_data = hyper.search_data(experiment)
160		n_performed_iter = len(search_data)
161
162		print("\n n_performed_iter \n", n_performed_iter)
163		print("\n n_iter_no_change \n", n_iter_no_change)
164
165		assert n_performed_iter == n_iter
166
167
168	View Code Duplication	def test_early_stop_5():
		0 ignored issues – show Duplication introduced 2025-04-12 08:48 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
169		class Experiment(BaseExperiment):
170		def objective_function(self, para):
171		return para["x0"]
172
173		experiment = Experiment()
174
175		search_config = SearchConfig(
176		x0=list(np.arange(0, 100, 0.01)),
177		)
178
179		n_iter_no_change = 5
180		early_stopping = {
181		"n_iter_no_change": n_iter_no_change,
182		"tol_abs": 10,
183		"tol_rel": None,
184		}
185
186		start1 = {"x0": 0}
187		start2 = {"x0": 9}
188		start3 = {"x0": 20}
189
190		warm_start_l = [
191		start1,
192		start1,
193		start1,
194		start1,
195		start1,
196		start2,
197		start2,
198		start2,
199		start3,
200		start3,
201		start3,
202		]
203		n_iter = len(warm_start_l)
204
205		hyper = RandomSearchOptimizer()
206		hyper.add_search(
207		experiment,
208		search_config,
209		n_iter=n_iter,
210		initialize={"warm_start": warm_start_l},
211		early_stopping=early_stopping,
212		)
213		hyper.run()
214
215		search_data = hyper.search_data(experiment)
216		n_performed_iter = len(search_data)
217
218		print("\n n_performed_iter \n", n_performed_iter)
219		print("\n n_iter_no_change \n", n_iter_no_change)
220
221		assert n_performed_iter == (n_iter_no_change + 1)
222
223
224	View Code Duplication	def test_early_stop_6():
		0 ignored issues – show Duplication introduced 2022-03-16 12:55 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
225		class Experiment(BaseExperiment):
226		def objective_function(self, para):
227		return para["x0"]
228
229		experiment = Experiment()
230
231		search_config = SearchConfig(
232		x0=list(np.arange(0, 100, 0.01)),
233		)
234
235		n_iter_no_change = 5
236		early_stopping = {
237		"n_iter_no_change": 5,
238		"tol_abs": None,
239		"tol_rel": 10,
240		}
241
242		start1 = {"x0": 1}
243		start2 = {"x0": 1.1}
244		start3 = {"x0": 1.22}
245		start4 = {"x0": 1.35}
246		start5 = {"x0": 1.48}
247
248		warm_start_l = [
249		start1,
250		start1,
251		start1,
252		start1,
253		start1,
254		start2,
255		start2,
256		start2,
257		start3,
258		start3,
259		start3,
260		start4,
261		start4,
262		start4,
263		start5,
264		start5,
265		start5,
266		]
267		n_iter = len(warm_start_l)
268
269		hyper = RandomSearchOptimizer()
270		hyper.add_search(
271		experiment,
272		search_config,
273		n_iter=n_iter,
274		initialize={"warm_start": warm_start_l},
275		early_stopping=early_stopping,
276		)
277		hyper.run()
278
279		search_data = hyper.search_data(experiment)
280		n_performed_iter = len(search_data)
281
282		print("\n n_performed_iter \n", n_performed_iter)
283		print("\n n_iter_no_change \n", n_iter_no_change)
284
285		assert n_performed_iter == n_iter
286
287
288	View Code Duplication	def test_early_stop_7():
		0 ignored issues – show Duplication introduced 2025-04-01 14:52 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
289		def objective_function(para):
290		return para["x0"]
291
292		experiment = Experiment()
293
294		search_config = SearchConfig(
295		x0=list(np.arange(0, 100, 0.01)),
296		)
297
298		n_iter_no_change = 5
299		early_stopping = {
300		"n_iter_no_change": n_iter_no_change,
301		"tol_abs": None,
302		"tol_rel": 10,
303		}
304
305		start1 = {"x0": 1}
306		start2 = {"x0": 1.09}
307		start3 = {"x0": 1.20}
308
309		warm_start_l = [
310		start1,
311		start1,
312		start1,
313		start1,
314		start1,
315		start2,
316		start2,
317		start2,
318		start3,
319		start3,
320		start3,
321		]
322		n_iter = len(warm_start_l)
323
324		hyper = RandomSearchOptimizer()
325		hyper.add_search(
326		experiment,
327		search_config,
328		n_iter=n_iter,
329		initialize={"warm_start": warm_start_l},
330		early_stopping=early_stopping,
331		)
332		hyper.run()
333
334		search_data = hyper.search_data(experiment)
335		n_performed_iter = len(search_data)
336
337		print("\n n_performed_iter \n", n_performed_iter)
338		print("\n n_iter_no_change \n", n_iter_no_change)
339
340		assert n_performed_iter == (n_iter_no_change + 1)
341

SimonBlanke / Hyperactive

Pull Request — master (#110)

tests.test_api.test_early_stop.test_early_stop_4() B

Complexity

Size

Duplication

Importance

How to fix Long Method

Long Method

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