test_performance - Code Metrics - Inspection of "extended tests for local testing" - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( a4a5a4...600197 )

by Simon

created 2019-09-14 14:57 UTC

test_performance A

↳ Parent: Project

Complexity

Total Complexity

Size/Duplication

Total Lines	363
Duplicated Lines	0 %

Importance

Changes

Metric	Value
wmc	12
eloc	269
dl	0
loc	363
rs	10
c	0
b	0
f	0

12 Functions

Rating	Name	Size	Complexity
A	test_RandomSearchOptimizer()	26	1
A	test_RandomAnnealingOptimizer()	26	1
A	test_TabuOptimizer()	26	1
A	test_ParallelTemperingOptimizer()	26	1
A	test_RandomRestartHillClimbingOptimizer()	26	1
A	test_BayesianOptimizer()	26	1
A	test_EvolutionStrategyOptimizer()	26	1
A	test_SimulatedAnnealingOptimizer()	26	1
A	test_StochasticTunnelingOptimizer()	26	1
A	test_ParticleSwarmOptimizer()	26	1
A	test_StochasticHillClimbingOptimizer()	26	1
A	test_HillClimbingOptimizer()	26	1

# Author: Simon Blanke
# Email: [email protected]
# License: MIT License

from sklearn.datasets import load_iris

data = load_iris()
X = data.data
y = data.target

n_iter_0 = 0
n_iter_1 = 33
random_state = 0
cv = 2
n_jobs = 2

search_config = {
    "sklearn.tree.DecisionTreeClassifier": {
        "criterion": ["gini", "entropy"],
        "max_depth": range(1, 21),
        "min_samples_split": range(2, 21),
        "min_samples_leaf": range(1, 21),
    }
}

warm_start = {"sklearn.tree.DecisionTreeClassifier": {"max_depth": [1]}}


def test_HillClimbingOptimizer():
    from hyperactive import HillClimbingOptimizer

    opt0 = HillClimbingOptimizer(
        search_config,
        n_iter_0,
        random_state=random_state,
        verbosity=1,
        cv=cv,
        n_jobs=1,
        warm_start=warm_start,
    )
    opt0.fit(X, y)

    opt1 = HillClimbingOptimizer(
        search_config,
        n_iter_1,
        random_state=random_state,
        verbosity=1,
        cv=cv,
        n_jobs=n_jobs,
        warm_start=warm_start,
    )
    opt1.fit(X, y)

    assert opt0.score_best < opt1.score_best


def test_StochasticHillClimbingOptimizer():
    from hyperactive import StochasticHillClimbingOptimizer

    opt0 = StochasticHillClimbingOptimizer(
        search_config,
        n_iter_0,
        random_state=random_state,
        verbosity=0,
        cv=cv,
        n_jobs=1,
        warm_start=warm_start,
    )
    opt0.fit(X, y)

    opt1 = StochasticHillClimbingOptimizer(
        search_config,
        n_iter_1,
        random_state=random_state,
        verbosity=0,
        cv=cv,
        n_jobs=n_jobs,
        warm_start=warm_start,
    )
    opt1.fit(X, y)

    assert opt0.score_best < opt1.score_best


def test_TabuOptimizer():
    from hyperactive import TabuOptimizer

    opt0 = TabuOptimizer(
        search_config,
        n_iter_0,
        random_state=random_state,
        verbosity=0,
        cv=cv,
        n_jobs=1,
        warm_start=warm_start,
    )
    opt0.fit(X, y)

    opt1 = TabuOptimizer(
        search_config,
        n_iter_1,
        random_state=random_state,
        verbosity=0,
        cv=cv,
        n_jobs=n_jobs,
        warm_start=warm_start,
    )
    opt1.fit(X, y)

    assert opt0.score_best < opt1.score_best


def test_RandomSearchOptimizer():
    from hyperactive import RandomSearchOptimizer

    opt0 = RandomSearchOptimizer(
        search_config,
        n_iter_0,
        random_state=random_state,
        verbosity=0,
        cv=cv,
        n_jobs=1,
        warm_start=warm_start,
    )
    opt0.fit(X, y)

    opt1 = RandomSearchOptimizer(
        search_config,
        n_iter_1,
        random_state=random_state,
        verbosity=0,
        cv=cv,
        n_jobs=n_jobs,
        warm_start=warm_start,
    )
    opt1.fit(X, y)

    assert opt0.score_best < opt1.score_best


def test_RandomRestartHillClimbingOptimizer():
    from hyperactive import RandomRestartHillClimbingOptimizer

    opt0 = RandomRestartHillClimbingOptimizer(
        search_config,
        n_iter_0,
        random_state=random_state,
        verbosity=0,
        cv=cv,
        n_jobs=1,
        warm_start=warm_start,
    )
    opt0.fit(X, y)

    opt1 = RandomRestartHillClimbingOptimizer(
        search_config,
        n_iter_1,
        random_state=random_state,
        verbosity=0,
        cv=cv,
        n_jobs=n_jobs,
        warm_start=warm_start,
    )
    opt1.fit(X, y)

    assert opt0.score_best < opt1.score_best


def test_RandomAnnealingOptimizer():
    from hyperactive import RandomAnnealingOptimizer

    opt0 = RandomAnnealingOptimizer(
        search_config,
        n_iter_0,
        random_state=random_state,
        verbosity=0,
        cv=cv,
        n_jobs=1,
        warm_start=warm_start,
    )
    opt0.fit(X, y)

    opt1 = RandomAnnealingOptimizer(
        search_config,
        n_iter_1,
        random_state=random_state,
        verbosity=0,
        cv=cv,
        n_jobs=n_jobs,
        warm_start=warm_start,
    )
    opt1.fit(X, y)

    assert opt0.score_best < opt1.score_best


def test_SimulatedAnnealingOptimizer():
    from hyperactive import SimulatedAnnealingOptimizer

    opt0 = SimulatedAnnealingOptimizer(
        search_config,
        n_iter_0,
        random_state=random_state,
        verbosity=0,
        cv=cv,
        n_jobs=1,
        warm_start=warm_start,
    )
    opt0.fit(X, y)

    opt1 = SimulatedAnnealingOptimizer(
        search_config,
        n_iter_1,
        random_state=random_state,
        verbosity=0,
        cv=cv,
        n_jobs=n_jobs,
        warm_start=warm_start,
    )
    opt1.fit(X, y)

    assert opt0.score_best < opt1.score_best


def test_StochasticTunnelingOptimizer():
    from hyperactive import StochasticTunnelingOptimizer

    opt0 = StochasticTunnelingOptimizer(
        search_config,
        n_iter_0,
        random_state=random_state,
        verbosity=0,
        cv=cv,
        n_jobs=1,
        warm_start=warm_start,
    )
    opt0.fit(X, y)

    opt1 = StochasticTunnelingOptimizer(
        search_config,
        n_iter_1,
        random_state=random_state,
        verbosity=0,
        cv=cv,
        n_jobs=n_jobs,
        warm_start=warm_start,
    )
    opt1.fit(X, y)

    assert opt0.score_best < opt1.score_best


def test_ParallelTemperingOptimizer():
    from hyperactive import ParallelTemperingOptimizer

    opt0 = ParallelTemperingOptimizer(
        search_config,
        n_iter_0,
        random_state=random_state,
        verbosity=0,
        cv=cv,
        n_jobs=1,
        warm_start=warm_start,
    )
    opt0.fit(X, y)

    opt1 = ParallelTemperingOptimizer(
        search_config,
        n_iter_1,
        random_state=random_state,
        verbosity=0,
        cv=cv,
        n_jobs=n_jobs,
        warm_start=warm_start,
    )
    opt1.fit(X, y)

    assert opt0.score_best < opt1.score_best


def test_ParticleSwarmOptimizer():
    from hyperactive import ParticleSwarmOptimizer

    opt0 = ParticleSwarmOptimizer(
        search_config,
        n_iter_0,
        random_state=random_state,
        verbosity=0,
        cv=cv,
        n_jobs=1,
        warm_start=warm_start,
    )
    opt0.fit(X, y)

    opt1 = ParticleSwarmOptimizer(
        search_config,
        n_iter_1,
        random_state=random_state,
        verbosity=0,
        cv=cv,
        n_jobs=n_jobs,
        warm_start=warm_start,
    )
    opt1.fit(X, y)

    assert opt0.score_best < opt1.score_best


def test_EvolutionStrategyOptimizer():
    from hyperactive import EvolutionStrategyOptimizer

    opt0 = EvolutionStrategyOptimizer(
        search_config,
        n_iter_0,
        random_state=random_state,
        verbosity=0,
        cv=cv,
        n_jobs=1,
        warm_start=warm_start,
    )
    opt0.fit(X, y)

    opt1 = EvolutionStrategyOptimizer(
        search_config,
        n_iter_1,
        random_state=random_state,
        verbosity=0,
        cv=cv,
        n_jobs=n_jobs,
        warm_start=warm_start,
    )
    opt1.fit(X, y)

    assert opt0.score_best < opt1.score_best


def test_BayesianOptimizer():
    from hyperactive import BayesianOptimizer

    opt0 = BayesianOptimizer(
        search_config,
        n_iter_0,
        random_state=random_state,
        verbosity=0,
        cv=cv,
        n_jobs=1,
        warm_start=warm_start,
    )
    opt0.fit(X, y)

    opt1 = BayesianOptimizer(
        search_config,
        n_iter_1,
        random_state=random_state,
        verbosity=0,
        cv=cv,
        n_jobs=n_jobs,
        warm_start=warm_start,
    )
    opt1.fit(X, y)

    assert opt0.score_best < opt1.score_best


1			# Author: Simon Blanke
2			# Email: [email protected]
3			# License: MIT License
4
5			from sklearn.datasets import load_iris
6
7			data = load_iris()
8			X = data.data
9			y = data.target
10
11			n_iter_0 = 0
12			n_iter_1 = 33
13			random_state = 0
14			cv = 2
15			n_jobs = 2
16
17			search_config = {
18			"sklearn.tree.DecisionTreeClassifier": {
19			"criterion": ["gini", "entropy"],
20			"max_depth": range(1, 21),
21			"min_samples_split": range(2, 21),
22			"min_samples_leaf": range(1, 21),
23			}
24			}
25
26			warm_start = {"sklearn.tree.DecisionTreeClassifier": {"max_depth": [1]}}
27
28
29			def test_HillClimbingOptimizer():
30			from hyperactive import HillClimbingOptimizer
31
32			opt0 = HillClimbingOptimizer(
33			search_config,
34			n_iter_0,
35			random_state=random_state,
36			verbosity=1,
37			cv=cv,
38			n_jobs=1,
39			warm_start=warm_start,
40			)
41			opt0.fit(X, y)
42
43			opt1 = HillClimbingOptimizer(
44			search_config,
45			n_iter_1,
46			random_state=random_state,
47			verbosity=1,
48			cv=cv,
49			n_jobs=n_jobs,
50			warm_start=warm_start,
51			)
52			opt1.fit(X, y)
53
54			assert opt0.score_best < opt1.score_best
55
56
57			def test_StochasticHillClimbingOptimizer():
58			from hyperactive import StochasticHillClimbingOptimizer
59
60			opt0 = StochasticHillClimbingOptimizer(
61			search_config,
62			n_iter_0,
63			random_state=random_state,
64			verbosity=0,
65			cv=cv,
66			n_jobs=1,
67			warm_start=warm_start,
68			)
69			opt0.fit(X, y)
70
71			opt1 = StochasticHillClimbingOptimizer(
72			search_config,
73			n_iter_1,
74			random_state=random_state,
75			verbosity=0,
76			cv=cv,
77			n_jobs=n_jobs,
78			warm_start=warm_start,
79			)
80			opt1.fit(X, y)
81
82			assert opt0.score_best < opt1.score_best
83
84
85			def test_TabuOptimizer():
86			from hyperactive import TabuOptimizer
87
88			opt0 = TabuOptimizer(
89			search_config,
90			n_iter_0,
91			random_state=random_state,
92			verbosity=0,
93			cv=cv,
94			n_jobs=1,
95			warm_start=warm_start,
96			)
97			opt0.fit(X, y)
98
99			opt1 = TabuOptimizer(
100			search_config,
101			n_iter_1,
102			random_state=random_state,
103			verbosity=0,
104			cv=cv,
105			n_jobs=n_jobs,
106			warm_start=warm_start,
107			)
108			opt1.fit(X, y)
109
110			assert opt0.score_best < opt1.score_best
111
112
113			def test_RandomSearchOptimizer():
114			from hyperactive import RandomSearchOptimizer
115
116			opt0 = RandomSearchOptimizer(
117			search_config,
118			n_iter_0,
119			random_state=random_state,
120			verbosity=0,
121			cv=cv,
122			n_jobs=1,
123			warm_start=warm_start,
124			)
125			opt0.fit(X, y)
126
127			opt1 = RandomSearchOptimizer(
128			search_config,
129			n_iter_1,
130			random_state=random_state,
131			verbosity=0,
132			cv=cv,
133			n_jobs=n_jobs,
134			warm_start=warm_start,
135			)
136			opt1.fit(X, y)
137
138			assert opt0.score_best < opt1.score_best
139
140
141			def test_RandomRestartHillClimbingOptimizer():
142			from hyperactive import RandomRestartHillClimbingOptimizer
143
144			opt0 = RandomRestartHillClimbingOptimizer(
145			search_config,
146			n_iter_0,
147			random_state=random_state,
148			verbosity=0,
149			cv=cv,
150			n_jobs=1,
151			warm_start=warm_start,
152			)
153			opt0.fit(X, y)
154
155			opt1 = RandomRestartHillClimbingOptimizer(
156			search_config,
157			n_iter_1,
158			random_state=random_state,
159			verbosity=0,
160			cv=cv,
161			n_jobs=n_jobs,
162			warm_start=warm_start,
163			)
164			opt1.fit(X, y)
165
166			assert opt0.score_best < opt1.score_best
167
168
169			def test_RandomAnnealingOptimizer():
170			from hyperactive import RandomAnnealingOptimizer
171
172			opt0 = RandomAnnealingOptimizer(
173			search_config,
174			n_iter_0,
175			random_state=random_state,
176			verbosity=0,
177			cv=cv,
178			n_jobs=1,
179			warm_start=warm_start,
180			)
181			opt0.fit(X, y)
182
183			opt1 = RandomAnnealingOptimizer(
184			search_config,
185			n_iter_1,
186			random_state=random_state,
187			verbosity=0,
188			cv=cv,
189			n_jobs=n_jobs,
190			warm_start=warm_start,
191			)
192			opt1.fit(X, y)
193
194			assert opt0.score_best < opt1.score_best
195
196
197			def test_SimulatedAnnealingOptimizer():
198			from hyperactive import SimulatedAnnealingOptimizer
199
200			opt0 = SimulatedAnnealingOptimizer(
201			search_config,
202			n_iter_0,
203			random_state=random_state,
204			verbosity=0,
205			cv=cv,
206			n_jobs=1,
207			warm_start=warm_start,
208			)
209			opt0.fit(X, y)
210
211			opt1 = SimulatedAnnealingOptimizer(
212			search_config,
213			n_iter_1,
214			random_state=random_state,
215			verbosity=0,
216			cv=cv,
217			n_jobs=n_jobs,
218			warm_start=warm_start,
219			)
220			opt1.fit(X, y)
221
222			assert opt0.score_best < opt1.score_best
223
224
225			def test_StochasticTunnelingOptimizer():
226			from hyperactive import StochasticTunnelingOptimizer
227
228			opt0 = StochasticTunnelingOptimizer(
229			search_config,
230			n_iter_0,
231			random_state=random_state,
232			verbosity=0,
233			cv=cv,
234			n_jobs=1,
235			warm_start=warm_start,
236			)
237			opt0.fit(X, y)
238
239			opt1 = StochasticTunnelingOptimizer(
240			search_config,
241			n_iter_1,
242			random_state=random_state,
243			verbosity=0,
244			cv=cv,
245			n_jobs=n_jobs,
246			warm_start=warm_start,
247			)
248			opt1.fit(X, y)
249
250			assert opt0.score_best < opt1.score_best
251
252
253			def test_ParallelTemperingOptimizer():
254			from hyperactive import ParallelTemperingOptimizer
255
256			opt0 = ParallelTemperingOptimizer(
257			search_config,
258			n_iter_0,
259			random_state=random_state,
260			verbosity=0,
261			cv=cv,
262			n_jobs=1,
263			warm_start=warm_start,
264			)
265			opt0.fit(X, y)
266
267			opt1 = ParallelTemperingOptimizer(
268			search_config,
269			n_iter_1,
270			random_state=random_state,
271			verbosity=0,
272			cv=cv,
273			n_jobs=n_jobs,
274			warm_start=warm_start,
275			)
276			opt1.fit(X, y)
277
278			assert opt0.score_best < opt1.score_best
279
280
281			def test_ParticleSwarmOptimizer():
282			from hyperactive import ParticleSwarmOptimizer
283
284			opt0 = ParticleSwarmOptimizer(
285			search_config,
286			n_iter_0,
287			random_state=random_state,
288			verbosity=0,
289			cv=cv,
290			n_jobs=1,
291			warm_start=warm_start,
292			)
293			opt0.fit(X, y)
294
295			opt1 = ParticleSwarmOptimizer(
296			search_config,
297			n_iter_1,
298			random_state=random_state,
299			verbosity=0,
300			cv=cv,
301			n_jobs=n_jobs,
302			warm_start=warm_start,
303			)
304			opt1.fit(X, y)
305
306			assert opt0.score_best < opt1.score_best
307
308
309			def test_EvolutionStrategyOptimizer():
310			from hyperactive import EvolutionStrategyOptimizer
311
312			opt0 = EvolutionStrategyOptimizer(
313			search_config,
314			n_iter_0,
315			random_state=random_state,
316			verbosity=0,
317			cv=cv,
318			n_jobs=1,
319			warm_start=warm_start,
320			)
321			opt0.fit(X, y)
322
323			opt1 = EvolutionStrategyOptimizer(
324			search_config,
325			n_iter_1,
326			random_state=random_state,
327			verbosity=0,
328			cv=cv,
329			n_jobs=n_jobs,
330			warm_start=warm_start,
331			)
332			opt1.fit(X, y)
333
334			assert opt0.score_best < opt1.score_best
335
336
337			def test_BayesianOptimizer():
338			from hyperactive import BayesianOptimizer
339
340			opt0 = BayesianOptimizer(
341			search_config,
342			n_iter_0,
343			random_state=random_state,
344			verbosity=0,
345			cv=cv,
346			n_jobs=1,
347			warm_start=warm_start,
348			)
349			opt0.fit(X, y)
350
351			opt1 = BayesianOptimizer(
352			search_config,
353			n_iter_1,
354			random_state=random_state,
355			verbosity=0,
356			cv=cv,
357			n_jobs=n_jobs,
358			warm_start=warm_start,
359			)
360			opt1.fit(X, y)
361
362			assert opt0.score_best < opt1.score_best
363

SimonBlanke / Hyperactive

Push — master ( a4a5a4...600197 )

test_performance A

Complexity

Size/Duplication

Importance

12 Functions

Duplication Side-by-Side

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