tests.test_optimizers.test_early_stop.test_early_stop_6() - Code Metrics - Inspection of "Merge branch 'master' of https://github.com/SimonB..." - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( 8df986...d057bb )

by Simon

created 2021-09-03 05:40 UTC

test_early_stop_6() B

↳ Parent: tests.test_optimizers.test_early_stop

Complexity

Conditions

Size

Total Lines	56
Code Lines	45

Duplication

Lines	56
Ratio	100 %

Importance

Changes

Metric	Value
cc	1
eloc	45
nop	1
dl	56
loc	56
rs	8.8
c	0
b	0
f	0

How to fix Long Method

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 ._parametrize import optimizers


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


search_space = {
    "x1": np.arange(0, 10, 0.1),
}


@pytest.mark.parametrize(*optimizers)
def test_early_stop_0(Optimizer):
    early_stopping = {
        "n_iter_no_change": 5,
        "tol_abs": 0.1,
        "tol_rel": 0.1,
    }

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


@pytest.mark.parametrize(*optimizers)
def test_early_stop_1(Optimizer):
    early_stopping = {
        "n_iter_no_change": 5,
        "tol_abs": None,
        "tol_rel": 5,
    }

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


@pytest.mark.parametrize(*optimizers)
def test_early_stop_2(Optimizer):
    early_stopping = {
        "n_iter_no_change": 5,
        "tol_abs": 0.1,
        "tol_rel": None,
    }

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


@pytest.mark.parametrize(*optimizers)

def test_early_stop_3(Optimizer):
    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 = Optimizer(search_space, initialize={"warm_start": [{"x1": 0}]})
    opt.search(
        objective_function,
        n_iter=100000,
        early_stopping=early_stopping,
    )
    search_data = opt.results
    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)


@pytest.mark.parametrize(*optimizers)

def test_early_stop_4(Optimizer):
    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": 0.1,
        "tol_rel": None,
    }

    start1 = {"x1": 0}
    start2 = {"x1": 0.1}
    start3 = {"x1": 0.2}
    start4 = {"x1": 0.3}
    start5 = {"x1": 0.4}

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


@pytest.mark.parametrize(*optimizers)

def test_early_stop_5(Optimizer):
    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": 0.1,
        "tol_rel": None,
    }

    start1 = {"x1": 0}
    start2 = {"x1": 0.09}
    start3 = {"x1": 0.20}

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

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


@pytest.mark.parametrize(*optimizers)

def test_early_stop_6(Optimizer):
    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 = Optimizer(search_space, initialize={"warm_start": warm_start_l})
    opt.search(
        objective_function,
        n_iter=n_iter,
        early_stopping=early_stopping,
    )
    search_data = opt.results
    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


@pytest.mark.parametrize(*optimizers)

def test_early_stop_7(Optimizer):
    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 = Optimizer(search_space, initialize={"warm_start": warm_start_l})
    opt.search(
        objective_function,
        n_iter=n_iter,
        early_stopping=early_stopping,
    )
    search_data = opt.results
    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
8		from ._parametrize import optimizers
9
10
11		def objective_function(para):
12		score = -para["x1"] * para["x1"]
13		return score
14
15
16		search_space = {
17		"x1": np.arange(0, 10, 0.1),
18		}
19
20
21		@pytest.mark.parametrize(*optimizers)
22		def test_early_stop_0(Optimizer):
23		early_stopping = {
24		"n_iter_no_change": 5,
25		"tol_abs": 0.1,
26		"tol_rel": 0.1,
27		}
28
29		opt = Optimizer(search_space, initialize={"warm_start": [{"x1": 0}]})
30		opt.search(
31		objective_function,
32		n_iter=1000,
33		early_stopping=early_stopping,
34		)
35
36
37		@pytest.mark.parametrize(*optimizers)
38		def test_early_stop_1(Optimizer):
39		early_stopping = {
40		"n_iter_no_change": 5,
41		"tol_abs": None,
42		"tol_rel": 5,
43		}
44
45		opt = Optimizer(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		@pytest.mark.parametrize(*optimizers)
54		def test_early_stop_2(Optimizer):
55		early_stopping = {
56		"n_iter_no_change": 5,
57		"tol_abs": 0.1,
58		"tol_rel": None,
59		}
60
61		opt = Optimizer(search_space, initialize={"warm_start": [{"x1": 0}]})
62		opt.search(
63		objective_function,
64		n_iter=1000,
65		early_stopping=early_stopping,
66		)
67
68
69	View Code Duplication	@pytest.mark.parametrize(*optimizers)
		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...
70		def test_early_stop_3(Optimizer):
71		def objective_function(para):
72		score = -para["x1"] * para["x1"]
73		return score
74
75		search_space = {
76		"x1": np.arange(0, 100, 0.1),
77		}
78
79		n_iter_no_change = 5
80		early_stopping = {
81		"n_iter_no_change": n_iter_no_change,
82		}
83
84		opt = Optimizer(search_space, initialize={"warm_start": [{"x1": 0}]})
85		opt.search(
86		objective_function,
87		n_iter=100000,
88		early_stopping=early_stopping,
89		)
90		search_data = opt.results
91		n_performed_iter = len(search_data)
92
93		print("\n n_performed_iter \n", n_performed_iter)
94		print("\n n_iter_no_change \n", n_iter_no_change)
95
96		assert n_performed_iter == (n_iter_no_change + 1)
97
98
99	View Code Duplication	@pytest.mark.parametrize(*optimizers)
		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...
100		def test_early_stop_4(Optimizer):
101		def objective_function(para):
102		return para["x1"]
103
104		search_space = {
105		"x1": np.arange(0, 100, 0.1),
106		}
107
108		n_iter_no_change = 5
109		early_stopping = {
110		"n_iter_no_change": 5,
111		"tol_abs": 0.1,
112		"tol_rel": None,
113		}
114
115		start1 = {"x1": 0}
116		start2 = {"x1": 0.1}
117		start3 = {"x1": 0.2}
118		start4 = {"x1": 0.3}
119		start5 = {"x1": 0.4}
120
121		warm_start_l = [
122		start1,
123		start1,
124		start1,
125		start1,
126		start1,
127		start2,
128		start2,
129		start2,
130		start3,
131		start3,
132		start3,
133		start4,
134		start4,
135		start4,
136		start5,
137		start5,
138		start5,
139		]
140		n_iter = len(warm_start_l)
141
142		opt = Optimizer(search_space, initialize={"warm_start": warm_start_l})
143		opt.search(
144		objective_function,
145		n_iter=n_iter,
146		early_stopping=early_stopping,
147		)
148		search_data = opt.results
149		n_performed_iter = len(search_data)
150
151		print("\n n_performed_iter \n", n_performed_iter)
152		print("\n n_iter_no_change \n", n_iter_no_change)
153
154		assert n_performed_iter == n_iter
155
156
157	View Code Duplication	@pytest.mark.parametrize(*optimizers)
		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...
158		def test_early_stop_5(Optimizer):
159		def objective_function(para):
160		return para["x1"]
161
162		search_space = {
163		"x1": np.arange(0, 100, 0.01),
164		}
165
166		n_iter_no_change = 5
167		early_stopping = {
168		"n_iter_no_change": n_iter_no_change,
169		"tol_abs": 0.1,
170		"tol_rel": None,
171		}
172
173		start1 = {"x1": 0}
174		start2 = {"x1": 0.09}
175		start3 = {"x1": 0.20}
176
177		warm_start_l = [
178		start1,
179		start1,
180		start1,
181		start1,
182		start1,
183		start2,
184		start2,
185		start2,
186		start3,
187		start3,
188		start3,
189		]
190		n_iter = len(warm_start_l)
191
192		opt = Optimizer(search_space, initialize={"warm_start": warm_start_l})
193		opt.search(
194		objective_function,
195		n_iter=n_iter,
196		early_stopping=early_stopping,
197		)
198		search_data = opt.results
199		n_performed_iter = len(search_data)
200
201		print("\n n_performed_iter \n", n_performed_iter)
202		print("\n n_iter_no_change \n", n_iter_no_change)
203
204		assert n_performed_iter == (n_iter_no_change + 1)
205
206
207	View Code Duplication	@pytest.mark.parametrize(*optimizers)
		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...
208		def test_early_stop_6(Optimizer):
209		def objective_function(para):
210		return para["x1"]
211
212		search_space = {
213		"x1": np.arange(0, 100, 0.01),
214		}
215
216		n_iter_no_change = 5
217		early_stopping = {
218		"n_iter_no_change": 5,
219		"tol_abs": None,
220		"tol_rel": 10,
221		}
222
223		start1 = {"x1": 1}
224		start2 = {"x1": 1.1}
225		start3 = {"x1": 1.22}
226		start4 = {"x1": 1.35}
227		start5 = {"x1": 1.48}
228
229		warm_start_l = [
230		start1,
231		start1,
232		start1,
233		start1,
234		start1,
235		start2,
236		start2,
237		start2,
238		start3,
239		start3,
240		start3,
241		start4,
242		start4,
243		start4,
244		start5,
245		start5,
246		start5,
247		]
248		n_iter = len(warm_start_l)
249
250		opt = Optimizer(search_space, initialize={"warm_start": warm_start_l})
251		opt.search(
252		objective_function,
253		n_iter=n_iter,
254		early_stopping=early_stopping,
255		)
256		search_data = opt.results
257		n_performed_iter = len(search_data)
258
259		print("\n n_performed_iter \n", n_performed_iter)
260		print("\n n_iter_no_change \n", n_iter_no_change)
261
262		assert n_performed_iter == n_iter
263
264
265	View Code Duplication	@pytest.mark.parametrize(*optimizers)
		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...
266		def test_early_stop_7(Optimizer):
267		def objective_function(para):
268		return para["x1"]
269
270		search_space = {
271		"x1": np.arange(0, 100, 0.01),
272		}
273
274		n_iter_no_change = 5
275		early_stopping = {
276		"n_iter_no_change": n_iter_no_change,
277		"tol_abs": None,
278		"tol_rel": 10,
279		}
280
281		start1 = {"x1": 1}
282		start2 = {"x1": 1.09}
283		start3 = {"x1": 1.20}
284
285		warm_start_l = [
286		start1,
287		start1,
288		start1,
289		start1,
290		start1,
291		start2,
292		start2,
293		start2,
294		start3,
295		start3,
296		start3,
297		]
298		n_iter = len(warm_start_l)
299
300		opt = Optimizer(search_space, initialize={"warm_start": warm_start_l})
301		opt.search(
302		objective_function,
303		n_iter=n_iter,
304		early_stopping=early_stopping,
305		)
306		search_data = opt.results
307		n_performed_iter = len(search_data)
308
309		print("\n n_performed_iter \n", n_performed_iter)
310		print("\n n_iter_no_change \n", n_iter_no_change)
311
312		assert n_performed_iter == (n_iter_no_change + 1)
313

SimonBlanke / Gradient-Free-Optimizers

Push — master ( 8df986...d057bb )

test_early_stop_6() B

Complexity

Size

Duplication

Importance

How to fix Long Method

Long Method

Duplication Side-by-Side

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