tests.test_early_stop.objective_function() - Code Metrics - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

tests.test_early_stop.objective_function() A
last analyzed 2025-06-28 09:03 UTC

↳ Parent: tests.test_early_stop

Complexity

Conditions

Size

Total Lines	3
Code Lines	3

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	3
nop	1
dl	0
loc	3
rs	10
c	0
b	0
f	0

import time
import pytest
import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeClassifier
from gradient_free_optimizers import (
    RandomSearchOptimizer,
    HillClimbingOptimizer,
)


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


search_space = {
    "x1": 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,
    }

    opt = HillClimbingOptimizer(search_space, initialize={"warm_start": [{"x1": 0}]})
    opt.search(
        objective_function,
        n_iter=1000,
        early_stopping=early_stopping,
    )


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

    opt = HillClimbingOptimizer(search_space, initialize={"warm_start": [{"x1": 0}]})
    opt.search(
        objective_function,
        n_iter=1000,
        early_stopping=early_stopping,
    )


def test_early_stop_2():
    early_stopping = {
        "n_iter_no_change": 5,
        "tol_abs": 0.1,
        "tol_rel": None,
    }

    opt = HillClimbingOptimizer(search_space, initialize={"warm_start": [{"x1": 0}]})
    opt.search(
        objective_function,
        n_iter=1000,
        early_stopping=early_stopping,
    )


def test_early_stop_3():

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

    search_space = {
        "x1": np.arange(0, 100, 0.1),
    }

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

    opt = HillClimbingOptimizer(search_space, initialize={"warm_start": [{"x1": 0}]})
    opt.search(
        objective_function,
        n_iter=100000,
        early_stopping=early_stopping,
    )
    search_data = opt.search_data
    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():

    def objective_function(para):
        return para["x1"]

    search_space = {
        "x1": np.arange(0, 100, 0.1),
    }

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

    start1 = {"x1": 0}
    start2 = {"x1": 1}
    start3 = {"x1": 2}
    start4 = {"x1": 3}
    start5 = {"x1": 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)

    opt = HillClimbingOptimizer(search_space, initialize={"warm_start": warm_start_l})
    opt.search(
        objective_function,
        n_iter=n_iter,
        early_stopping=early_stopping,
    )
    search_data = opt.search_data
    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():

    def objective_function(para):
        return para["x1"]

    search_space = {
        "x1": 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 = {"x1": 0}
    start2 = {"x1": 9}
    start3 = {"x1": 20}

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

    opt = HillClimbingOptimizer(search_space, initialize={"warm_start": warm_start_l})
    opt.search(
        objective_function,
        n_iter=n_iter,
        early_stopping=early_stopping,
    )
    search_data = opt.search_data
    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():

    def objective_function(para):
        return para["x1"]

    search_space = {
        "x1": 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 = {"x1": 1}
    start2 = {"x1": 1.1}
    start3 = {"x1": 1.22}
    start4 = {"x1": 1.35}
    start5 = {"x1": 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)

    opt = HillClimbingOptimizer(search_space, initialize={"warm_start": warm_start_l})
    opt.search(
        objective_function,
        n_iter=n_iter,
        early_stopping=early_stopping,
    )
    search_data = opt.search_data
    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["x1"]

    search_space = {
        "x1": 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 = {"x1": 1}
    start2 = {"x1": 1.09}
    start3 = {"x1": 1.20}

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

    opt = HillClimbingOptimizer(search_space, initialize={"warm_start": warm_start_l})
    opt.search(
        objective_function,
        n_iter=n_iter,
        early_stopping=early_stopping,
    )
    search_data = opt.search_data
    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		from sklearn.datasets import load_breast_cancer
5		from sklearn.model_selection import cross_val_score
6		from sklearn.tree import DecisionTreeClassifier
7		from gradient_free_optimizers import (
8		RandomSearchOptimizer,
9		HillClimbingOptimizer,
10		)
11
12
13		def objective_function(para):
14		score = -para["x1"] * para["x1"]
15		return score
16
17
18		search_space = {
19		"x1": np.arange(0, 100000, 0.1),
20		}
21
22
23		def test_early_stop_0():
24		early_stopping = {
25		"n_iter_no_change": 5,
26		"tol_abs": 0.1,
27		"tol_rel": 0.1,
28		}
29
30		opt = HillClimbingOptimizer(search_space, initialize={"warm_start": [{"x1": 0}]})
31		opt.search(
32		objective_function,
33		n_iter=1000,
34		early_stopping=early_stopping,
35		)
36
37
38		def test_early_stop_1():
39		early_stopping = {
40		"n_iter_no_change": 5,
41		"tol_abs": None,
42		"tol_rel": 5,
43		}
44
45		opt = HillClimbingOptimizer(search_space, initialize={"warm_start": [{"x1": 0}]})
46		opt.search(
47		objective_function,
48		n_iter=1000,
49		early_stopping=early_stopping,
50		)
51
52
53		def test_early_stop_2():
54		early_stopping = {
55		"n_iter_no_change": 5,
56		"tol_abs": 0.1,
57		"tol_rel": None,
58		}
59
60		opt = HillClimbingOptimizer(search_space, initialize={"warm_start": [{"x1": 0}]})
61		opt.search(
62		objective_function,
63		n_iter=1000,
64		early_stopping=early_stopping,
65		)
66
67
68	View Code Duplication	def test_early_stop_3():
		0 ignored issues – show Duplication introduced 2021-09-03 05:42 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
69		def objective_function(para):
70		score = -para["x1"] * para["x1"]
71		return score
72
73		search_space = {
74		"x1": np.arange(0, 100, 0.1),
75		}
76
77		n_iter_no_change = 5
78		early_stopping = {
79		"n_iter_no_change": n_iter_no_change,
80		}
81
82		opt = HillClimbingOptimizer(search_space, initialize={"warm_start": [{"x1": 0}]})
83		opt.search(
84		objective_function,
85		n_iter=100000,
86		early_stopping=early_stopping,
87		)
88		search_data = opt.search_data
89		n_performed_iter = len(search_data)
90
91		print("\n n_performed_iter \n", n_performed_iter)
92		print("\n n_iter_no_change \n", n_iter_no_change)
93
94		assert n_performed_iter == (n_iter_no_change + 1)
95
96
97	View Code Duplication	def test_early_stop_4():
		0 ignored issues – show Duplication introduced 2021-08-29 06:03 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
98		def objective_function(para):
99		return para["x1"]
100
101		search_space = {
102		"x1": np.arange(0, 100, 0.1),
103		}
104
105		n_iter_no_change = 5
106		early_stopping = {
107		"n_iter_no_change": 5,
108		"tol_abs": 1,
109		"tol_rel": None,
110		}
111
112		start1 = {"x1": 0}
113		start2 = {"x1": 1}
114		start3 = {"x1": 2}
115		start4 = {"x1": 3}
116		start5 = {"x1": 4}
117
118		warm_start_l = [
119		start1,
120		start1,
121		start1,
122		start1,
123		start1,
124		start2,
125		start2,
126		start2,
127		start3,
128		start3,
129		start3,
130		start4,
131		start4,
132		start4,
133		start5,
134		start5,
135		start5,
136		]
137		n_iter = len(warm_start_l)
138
139		opt = HillClimbingOptimizer(search_space, initialize={"warm_start": warm_start_l})
140		opt.search(
141		objective_function,
142		n_iter=n_iter,
143		early_stopping=early_stopping,
144		)
145		search_data = opt.search_data
146		n_performed_iter = len(search_data)
147
148		print("\n n_performed_iter \n", n_performed_iter)
149		print("\n n_iter_no_change \n", n_iter_no_change)
150
151		assert n_performed_iter == n_iter
152
153
154	View Code Duplication	def test_early_stop_5():
		0 ignored issues – show Duplication introduced 2021-08-29 06:03 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
155		def objective_function(para):
156		return para["x1"]
157
158		search_space = {
159		"x1": np.arange(0, 100, 0.01),
160		}
161
162		n_iter_no_change = 5
163		early_stopping = {
164		"n_iter_no_change": n_iter_no_change,
165		"tol_abs": 10,
166		"tol_rel": None,
167		}
168
169		start1 = {"x1": 0}
170		start2 = {"x1": 9}
171		start3 = {"x1": 20}
172
173		warm_start_l = [
174		start1,
175		start1,
176		start1,
177		start1,
178		start1,
179		start2,
180		start2,
181		start2,
182		start3,
183		start3,
184		start3,
185		]
186		n_iter = len(warm_start_l)
187
188		opt = HillClimbingOptimizer(search_space, initialize={"warm_start": warm_start_l})
189		opt.search(
190		objective_function,
191		n_iter=n_iter,
192		early_stopping=early_stopping,
193		)
194		search_data = opt.search_data
195		n_performed_iter = len(search_data)
196
197		print("\n n_performed_iter \n", n_performed_iter)
198		print("\n n_iter_no_change \n", n_iter_no_change)
199
200		assert n_performed_iter == (n_iter_no_change + 1)
201
202
203	View Code Duplication	def test_early_stop_6():
		0 ignored issues – show Duplication introduced 2021-08-29 06:03 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
204		def objective_function(para):
205		return para["x1"]
206
207		search_space = {
208		"x1": np.arange(0, 100, 0.01),
209		}
210
211		n_iter_no_change = 5
212		early_stopping = {
213		"n_iter_no_change": 5,
214		"tol_abs": None,
215		"tol_rel": 10,
216		}
217
218		start1 = {"x1": 1}
219		start2 = {"x1": 1.1}
220		start3 = {"x1": 1.22}
221		start4 = {"x1": 1.35}
222		start5 = {"x1": 1.48}
223
224		warm_start_l = [
225		start1,
226		start1,
227		start1,
228		start1,
229		start1,
230		start2,
231		start2,
232		start2,
233		start3,
234		start3,
235		start3,
236		start4,
237		start4,
238		start4,
239		start5,
240		start5,
241		start5,
242		]
243		n_iter = len(warm_start_l)
244
245		opt = HillClimbingOptimizer(search_space, initialize={"warm_start": warm_start_l})
246		opt.search(
247		objective_function,
248		n_iter=n_iter,
249		early_stopping=early_stopping,
250		)
251		search_data = opt.search_data
252		n_performed_iter = len(search_data)
253
254		print("\n n_performed_iter \n", n_performed_iter)
255		print("\n n_iter_no_change \n", n_iter_no_change)
256
257		assert n_performed_iter == n_iter
258
259
260	View Code Duplication	def test_early_stop_7():
		0 ignored issues – show Duplication introduced 2021-08-29 06:03 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
261		def objective_function(para):
262		return para["x1"]
263
264		search_space = {
265		"x1": np.arange(0, 100, 0.01),
266		}
267
268		n_iter_no_change = 5
269		early_stopping = {
270		"n_iter_no_change": n_iter_no_change,
271		"tol_abs": None,
272		"tol_rel": 10,
273		}
274
275		start1 = {"x1": 1}
276		start2 = {"x1": 1.09}
277		start3 = {"x1": 1.20}
278
279		warm_start_l = [
280		start1,
281		start1,
282		start1,
283		start1,
284		start1,
285		start2,
286		start2,
287		start2,
288		start3,
289		start3,
290		start3,
291		]
292		n_iter = len(warm_start_l)
293
294		opt = HillClimbingOptimizer(search_space, initialize={"warm_start": warm_start_l})
295		opt.search(
296		objective_function,
297		n_iter=n_iter,
298		early_stopping=early_stopping,
299		)
300		search_data = opt.search_data
301		n_performed_iter = len(search_data)
302
303		print("\n n_performed_iter \n", n_performed_iter)
304		print("\n n_iter_no_change \n", n_iter_no_change)
305
306		assert n_performed_iter == (n_iter_no_change + 1)
307

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tests.test_early_stop.objective_function() A last analyzed 2025-06-28 09:03 UTC

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tests.test_early_stop.objective_function() A
last analyzed 2025-06-28 09:03 UTC