tests.test_convex_convergence.test_TabuOptimizer_convergence() - Code Metrics - Inspection of "update optimizer arguments for better performance" - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

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by Simon

created 2020-08-30 11:07 UTC

test_TabuOptimizer_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,
)


optimizer_dict = {
    "HillClimbing": HillClimbingOptimizer,
    "StochasticHillClimbing": StochasticHillClimbingOptimizer,
    "TabuSearch": TabuOptimizer,
    "RandomSearch": RandomSearchOptimizer,
    "RandomRestartHillClimbing": RandomRestartHillClimbingOptimizer,
    "RandomAnnealing": RandomAnnealingOptimizer,
    "SimulatedAnnealing": SimulatedAnnealingOptimizer,
    "ParallelTempering": ParallelTemperingOptimizer,
    "ParticleSwarm": ParticleSwarmOptimizer,
    "EvolutionStrategy": EvolutionStrategyOptimizer,
    "Bayesian": BayesianOptimizer,
    "TreeStructured": TreeStructuredParzenEstimators,
    "DecisionTree": DecisionTreeOptimizer,
}


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


search_space = [np.arange(-100, 100, 1)]
initialize = {"grid": 4, "random": 2, "vertices": 4}

n_opts = 100
n_iter = 50
min_score_accept = -100


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=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_TreeStructuredParzenEstimators_convergence():

    scores = []
    for rnd_st in tqdm(range(n_opts)):
        opt = TreeStructuredParzenEstimators(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_DecisionTreeOptimizer_convergence():

    scores = []
    for rnd_st in tqdm(range(n_opts)):
        opt = DecisionTreeOptimizer(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


test_DecisionTreeOptimizer_convergence()


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

SimonBlanke / Gradient-Free-Optimizers

Push — master ( 4ef66c...afb360 )

test_TabuOptimizer_convergence() A

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