tests.test_convex_convergence.test_EnsembleOptimizer_convergence() - Code Metrics - Inspection of "prototype of new experimental sb-optimizer + testi..." - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

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by Simon

created 2020-09-01 15:32 UTC

test_EnsembleOptimizer_convergence() A

↳ Parent: tests.test_convex_convergence

Complexity

Conditions

Size

Total Lines	18
Code Lines	15

Duplication

Lines	18
Ratio	100 %

Importance

Changes

Metric	Value
cc	2
eloc	15
nop	0
dl	18
loc	18
rs	9.65
c	0
b	0
f	0

import numpy as np
from tqdm import tqdm

from gradient_free_optimizers import (
    HillClimbingOptimizer,
    StochasticHillClimbingOptimizer,
    TabuOptimizer,
    RandomSearchOptimizer,
    RandomRestartHillClimbingOptimizer,
    RandomAnnealingOptimizer,
    SimulatedAnnealingOptimizer,
    ParallelTemperingOptimizer,
    ParticleSwarmOptimizer,
    EvolutionStrategyOptimizer,
    BayesianOptimizer,
    TreeStructuredParzenEstimators,
    DecisionTreeOptimizer,
    EnsembleOptimizer,
)


def objective_function(pos_new):
    score = -pos_new[0] * pos_new[0]
    return score


search_space = [np.arange(-100, 100, 1)]
initialize = {"vertices": 2}

n_opts = 33
n_iter = 50
min_score_accept = -500


def test_HillClimbingOptimizer_convergence():

    scores = []
    for rnd_st in tqdm(range(n_opts)):
        opt = HillClimbingOptimizer(search_space)
        opt.search(
            objective_function,
            n_iter=n_iter,
            random_state=rnd_st,
            memory=False,
            print_results=False,
            progress_bar=False,
            initialize=initialize,
        )

        scores.append(opt.best_score)

    score_mean = np.array(scores).mean()
    assert min_score_accept < score_mean


def test_StochasticHillClimbingOptimizer_convergence():

    scores = []
    for rnd_st in tqdm(range(n_opts)):
        opt = StochasticHillClimbingOptimizer(search_space)
        opt.search(
            objective_function,
            n_iter=n_iter,
            random_state=rnd_st,
            memory=False,
            print_results=False,
            progress_bar=False,
            initialize=initialize,
        )

        scores.append(opt.best_score)

    score_mean = np.array(scores).mean()
    assert min_score_accept < score_mean


def test_TabuOptimizer_convergence():

    scores = []
    for rnd_st in tqdm(range(n_opts)):
        opt = TabuOptimizer(search_space)
        opt.search(
            objective_function,
            n_iter=n_iter,
            random_state=rnd_st,
            memory=False,
            print_results=False,
            progress_bar=False,
            initialize=initialize,
        )

        scores.append(opt.best_score)

    score_mean = np.array(scores).mean()
    assert min_score_accept < score_mean


def test_RandomSearchOptimizer_convergence():

    scores = []
    for rnd_st in tqdm(range(n_opts)):
        opt = RandomSearchOptimizer(search_space)
        opt.search(
            objective_function,
            n_iter=n_iter,
            random_state=rnd_st,
            memory=False,
            print_results=False,
            progress_bar=False,
            initialize=initialize,
        )

        scores.append(opt.best_score)

    score_mean = np.array(scores).mean()
    assert min_score_accept < score_mean


def test_RandomRestartHillClimbingOptimizer_convergence():

    scores = []
    for rnd_st in tqdm(range(n_opts)):
        opt = RandomRestartHillClimbingOptimizer(search_space)
        opt.search(
            objective_function,
            n_iter=n_iter,
            random_state=rnd_st,
            memory=False,
            print_results=False,
            progress_bar=False,
            initialize=initialize,
        )

        scores.append(opt.best_score)

    score_mean = np.array(scores).mean()
    assert min_score_accept < score_mean


def test_RandomAnnealingOptimizer_convergence():

    scores = []
    for rnd_st in tqdm(range(n_opts)):
        opt = RandomAnnealingOptimizer(search_space)
        opt.search(
            objective_function,
            n_iter=n_iter,
            random_state=rnd_st,
            memory=False,
            print_results=False,
            progress_bar=False,
            initialize=initialize,
        )

        scores.append(opt.best_score)

    score_mean = np.array(scores).mean()
    assert min_score_accept < score_mean


def test_SimulatedAnnealingOptimizer_convergence():

    scores = []
    for rnd_st in tqdm(range(n_opts)):
        opt = SimulatedAnnealingOptimizer(search_space)
        opt.search(
            objective_function,
            n_iter=n_iter,
            random_state=rnd_st,
            memory=False,
            print_results=False,
            progress_bar=False,
            initialize=initialize,
        )

        scores.append(opt.best_score)

    score_mean = np.array(scores).mean()
    assert min_score_accept < score_mean


def test_ParallelTemperingOptimizer_convergence():

    scores = []
    for rnd_st in tqdm(range(n_opts)):
        opt = ParallelTemperingOptimizer(search_space)
        opt.search(
            objective_function,
            n_iter=n_iter,
            random_state=rnd_st,
            memory=False,
            print_results=False,
            progress_bar=False,
            initialize=initialize,
        )

        scores.append(opt.best_score)

    score_mean = np.array(scores).mean()
    assert min_score_accept < score_mean


def test_ParticleSwarmOptimizer_convergence():

    scores = []
    for rnd_st in tqdm(range(n_opts)):
        opt = ParticleSwarmOptimizer(search_space)
        opt.search(
            objective_function,
            n_iter=n_iter,
            random_state=rnd_st,
            memory=False,
            print_results=False,
            progress_bar=False,
            initialize=initialize,
        )

        scores.append(opt.best_score)

    score_mean = np.array(scores).mean()
    assert min_score_accept < score_mean


def test_EvolutionStrategyOptimizer_convergence():

    scores = []
    for rnd_st in tqdm(range(n_opts)):
        opt = EvolutionStrategyOptimizer(search_space)
        opt.search(
            objective_function,
            n_iter=n_iter,
            random_state=rnd_st,
            memory=False,
            print_results=False,
            progress_bar=False,
            initialize=initialize,
        )

        scores.append(opt.best_score)

    score_mean = np.array(scores).mean()
    assert min_score_accept < score_mean


def test_BayesianOptimizer_convergence():

    scores = []
    for rnd_st in tqdm(range(n_opts)):
        opt = BayesianOptimizer(search_space)
        opt.search(
            objective_function,
            n_iter=int(n_iter / 2),
            random_state=rnd_st,
            memory=False,
            print_results=False,
            progress_bar=False,
            initialize=initialize,
        )

        scores.append(opt.best_score)

    score_mean = np.array(scores).mean()
    assert min_score_accept < score_mean


def test_TreeStructuredParzenEstimators_convergence():

    scores = []
    for rnd_st in tqdm(range(n_opts)):
        opt = TreeStructuredParzenEstimators(search_space)
        opt.search(
            objective_function,
            n_iter=int(n_iter / 2),
            random_state=rnd_st,
            memory=False,
            print_results=False,
            progress_bar=False,
            initialize=initialize,
        )

        scores.append(opt.best_score)

    score_mean = np.array(scores).mean()
    assert min_score_accept < score_mean


def test_DecisionTreeOptimizer_convergence():

    scores = []
    for rnd_st in tqdm(range(n_opts)):
        opt = DecisionTreeOptimizer(search_space)
        opt.search(
            objective_function,
            n_iter=int(n_iter / 2),
            random_state=rnd_st,
            memory=False,
            print_results=False,
            progress_bar=False,
            initialize=initialize,
        )

        scores.append(opt.best_score)

    score_mean = np.array(scores).mean()
    assert min_score_accept < score_mean


def test_EnsembleOptimizer_convergence():

    scores = []
    for rnd_st in tqdm(range(n_opts)):
        opt = EnsembleOptimizer(search_space)
        opt.search(
            objective_function,
            n_iter=int(n_iter / 2),
            random_state=rnd_st,
            memory=False,
            print_results=False,
            progress_bar=False,
            initialize=initialize,
        )

        scores.append(opt.best_score)

    score_mean = np.array(scores).mean()
    assert min_score_accept < score_mean


1		import numpy as np
2		from tqdm import tqdm
3
4		from gradient_free_optimizers import (
5		HillClimbingOptimizer,
6		StochasticHillClimbingOptimizer,
7		TabuOptimizer,
8		RandomSearchOptimizer,
9		RandomRestartHillClimbingOptimizer,
10		RandomAnnealingOptimizer,
11		SimulatedAnnealingOptimizer,
12		ParallelTemperingOptimizer,
13		ParticleSwarmOptimizer,
14		EvolutionStrategyOptimizer,
15		BayesianOptimizer,
16		TreeStructuredParzenEstimators,
17		DecisionTreeOptimizer,
18		EnsembleOptimizer,
19		)
20
21
22		def objective_function(pos_new):
23		score = -pos_new[0] * pos_new[0]
24		return score
25
26
27		search_space = [np.arange(-100, 100, 1)]
28		initialize = {"vertices": 2}
29
30		n_opts = 33
31		n_iter = 50
32		min_score_accept = -500
33
34
35	View Code Duplication	def test_HillClimbingOptimizer_convergence():
		0 ignored issues – show Duplication introduced 2020-08-30 11:11 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
36		scores = []
37		for rnd_st in tqdm(range(n_opts)):
38		opt = HillClimbingOptimizer(search_space)
39		opt.search(
40		objective_function,
41		n_iter=n_iter,
42		random_state=rnd_st,
43		memory=False,
44		print_results=False,
45		progress_bar=False,
46		initialize=initialize,
47		)
48
49		scores.append(opt.best_score)
50
51		score_mean = np.array(scores).mean()
52		assert min_score_accept < score_mean
53
54
55	View Code Duplication	def test_StochasticHillClimbingOptimizer_convergence():
		0 ignored issues – show Duplication introduced 2020-08-30 11:11 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
56		scores = []
57		for rnd_st in tqdm(range(n_opts)):
58		opt = StochasticHillClimbingOptimizer(search_space)
59		opt.search(
60		objective_function,
61		n_iter=n_iter,
62		random_state=rnd_st,
63		memory=False,
64		print_results=False,
65		progress_bar=False,
66		initialize=initialize,
67		)
68
69		scores.append(opt.best_score)
70
71		score_mean = np.array(scores).mean()
72		assert min_score_accept < score_mean
73
74
75	View Code Duplication	def test_TabuOptimizer_convergence():
		0 ignored issues – show Duplication introduced 2020-08-30 11:11 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
76		scores = []
77		for rnd_st in tqdm(range(n_opts)):
78		opt = TabuOptimizer(search_space)
79		opt.search(
80		objective_function,
81		n_iter=n_iter,
82		random_state=rnd_st,
83		memory=False,
84		print_results=False,
85		progress_bar=False,
86		initialize=initialize,
87		)
88
89		scores.append(opt.best_score)
90
91		score_mean = np.array(scores).mean()
92		assert min_score_accept < score_mean
93
94
95	View Code Duplication	def test_RandomSearchOptimizer_convergence():
		0 ignored issues – show Duplication introduced 2020-08-30 11:11 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
96		scores = []
97		for rnd_st in tqdm(range(n_opts)):
98		opt = RandomSearchOptimizer(search_space)
99		opt.search(
100		objective_function,
101		n_iter=n_iter,
102		random_state=rnd_st,
103		memory=False,
104		print_results=False,
105		progress_bar=False,
106		initialize=initialize,
107		)
108
109		scores.append(opt.best_score)
110
111		score_mean = np.array(scores).mean()
112		assert min_score_accept < score_mean
113
114
115	View Code Duplication	def test_RandomRestartHillClimbingOptimizer_convergence():
		0 ignored issues – show Duplication introduced 2020-08-30 11:11 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
116		scores = []
117		for rnd_st in tqdm(range(n_opts)):
118		opt = RandomRestartHillClimbingOptimizer(search_space)
119		opt.search(
120		objective_function,
121		n_iter=n_iter,
122		random_state=rnd_st,
123		memory=False,
124		print_results=False,
125		progress_bar=False,
126		initialize=initialize,
127		)
128
129		scores.append(opt.best_score)
130
131		score_mean = np.array(scores).mean()
132		assert min_score_accept < score_mean
133
134
135	View Code Duplication	def test_RandomAnnealingOptimizer_convergence():
		0 ignored issues – show Duplication introduced 2020-08-30 11:11 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
136		scores = []
137		for rnd_st in tqdm(range(n_opts)):
138		opt = RandomAnnealingOptimizer(search_space)
139		opt.search(
140		objective_function,
141		n_iter=n_iter,
142		random_state=rnd_st,
143		memory=False,
144		print_results=False,
145		progress_bar=False,
146		initialize=initialize,
147		)
148
149		scores.append(opt.best_score)
150
151		score_mean = np.array(scores).mean()
152		assert min_score_accept < score_mean
153
154
155	View Code Duplication	def test_SimulatedAnnealingOptimizer_convergence():
		0 ignored issues – show Duplication introduced 2020-08-30 11:11 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
156		scores = []
157		for rnd_st in tqdm(range(n_opts)):
158		opt = SimulatedAnnealingOptimizer(search_space)
159		opt.search(
160		objective_function,
161		n_iter=n_iter,
162		random_state=rnd_st,
163		memory=False,
164		print_results=False,
165		progress_bar=False,
166		initialize=initialize,
167		)
168
169		scores.append(opt.best_score)
170
171		score_mean = np.array(scores).mean()
172		assert min_score_accept < score_mean
173
174
175	View Code Duplication	def test_ParallelTemperingOptimizer_convergence():
		0 ignored issues – show Duplication introduced 2020-08-30 11:11 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
176		scores = []
177		for rnd_st in tqdm(range(n_opts)):
178		opt = ParallelTemperingOptimizer(search_space)
179		opt.search(
180		objective_function,
181		n_iter=n_iter,
182		random_state=rnd_st,
183		memory=False,
184		print_results=False,
185		progress_bar=False,
186		initialize=initialize,
187		)
188
189		scores.append(opt.best_score)
190
191		score_mean = np.array(scores).mean()
192		assert min_score_accept < score_mean
193
194
195	View Code Duplication	def test_ParticleSwarmOptimizer_convergence():
		0 ignored issues – show Duplication introduced 2020-08-30 11:11 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
196		scores = []
197		for rnd_st in tqdm(range(n_opts)):
198		opt = ParticleSwarmOptimizer(search_space)
199		opt.search(
200		objective_function,
201		n_iter=n_iter,
202		random_state=rnd_st,
203		memory=False,
204		print_results=False,
205		progress_bar=False,
206		initialize=initialize,
207		)
208
209		scores.append(opt.best_score)
210
211		score_mean = np.array(scores).mean()
212		assert min_score_accept < score_mean
213
214
215	View Code Duplication	def test_EvolutionStrategyOptimizer_convergence():
		0 ignored issues – show Duplication introduced 2020-08-30 11:11 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
216		scores = []
217		for rnd_st in tqdm(range(n_opts)):
218		opt = EvolutionStrategyOptimizer(search_space)
219		opt.search(
220		objective_function,
221		n_iter=n_iter,
222		random_state=rnd_st,
223		memory=False,
224		print_results=False,
225		progress_bar=False,
226		initialize=initialize,
227		)
228
229		scores.append(opt.best_score)
230
231		score_mean = np.array(scores).mean()
232		assert min_score_accept < score_mean
233
234
235	View Code Duplication	def test_BayesianOptimizer_convergence():
		0 ignored issues – show Duplication introduced 2020-08-30 11:11 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
236		scores = []
237		for rnd_st in tqdm(range(n_opts)):
238		opt = BayesianOptimizer(search_space)
239		opt.search(
240		objective_function,
241		n_iter=int(n_iter / 2),
242		random_state=rnd_st,
243		memory=False,
244		print_results=False,
245		progress_bar=False,
246		initialize=initialize,
247		)
248
249		scores.append(opt.best_score)
250
251		score_mean = np.array(scores).mean()
252		assert min_score_accept < score_mean
253
254
255	View Code Duplication	def test_TreeStructuredParzenEstimators_convergence():
		0 ignored issues – show Duplication introduced 2020-08-30 11:11 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
256		scores = []
257		for rnd_st in tqdm(range(n_opts)):
258		opt = TreeStructuredParzenEstimators(search_space)
259		opt.search(
260		objective_function,
261		n_iter=int(n_iter / 2),
262		random_state=rnd_st,
263		memory=False,
264		print_results=False,
265		progress_bar=False,
266		initialize=initialize,
267		)
268
269		scores.append(opt.best_score)
270
271		score_mean = np.array(scores).mean()
272		assert min_score_accept < score_mean
273
274
275	View Code Duplication	def test_DecisionTreeOptimizer_convergence():
		0 ignored issues – show Duplication introduced 2020-08-30 11:11 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
276		scores = []
277		for rnd_st in tqdm(range(n_opts)):
278		opt = DecisionTreeOptimizer(search_space)
279		opt.search(
280		objective_function,
281		n_iter=int(n_iter / 2),
282		random_state=rnd_st,
283		memory=False,
284		print_results=False,
285		progress_bar=False,
286		initialize=initialize,
287		)
288
289		scores.append(opt.best_score)
290
291		score_mean = np.array(scores).mean()
292		assert min_score_accept < score_mean
293
294
295	View Code Duplication	def test_EnsembleOptimizer_convergence():
		0 ignored issues – show Duplication introduced 2020-09-01 15:33 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
296		scores = []
297		for rnd_st in tqdm(range(n_opts)):
298		opt = EnsembleOptimizer(search_space)
299		opt.search(
300		objective_function,
301		n_iter=int(n_iter / 2),
302		random_state=rnd_st,
303		memory=False,
304		print_results=False,
305		progress_bar=False,
306		initialize=initialize,
307		)
308
309		scores.append(opt.best_score)
310
311		score_mean = np.array(scores).mean()
312		assert min_score_accept < score_mean
313

SimonBlanke / Gradient-Free-Optimizers

Push — master ( 300275...433e71 )

test_EnsembleOptimizer_convergence() A

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