tests.test_optimizers.test_RandomRestartHillClimbingOptimizer() - Code Metrics - Inspection of "Merge branch 'master' into dev" - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

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created 2020-07-15 12:48 UTC

test_RandomRestartHillClimbingOptimizer() A

↳ Parent: tests.test_optimizers

Complexity

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Total Lines	10
Code Lines	8

Duplication

Lines	0
Ratio	0 %

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Metric	Value
cc	1
eloc	8
nop	0
dl	0
loc	10
rs	10
c	0
b	0
f	0

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

import numpy as np

from sklearn.datasets import load_iris
from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeClassifier

from hyperactive import Hyperactive

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


def model(para, X, y):
    dtc = DecisionTreeClassifier(
        max_depth=para["max_depth"], min_samples_split=para["min_samples_split"],
    )
    scores = cross_val_score(dtc, X, y, cv=2)

    return scores.mean()


search_space = {
    "max_depth": range(1, 21),
    "min_samples_split": range(2, 21),
}


def _base_test(search, opt_args={}, time=None):
    opt = Hyperactive(X, y, **opt_args)
    opt.add_search(**search)
    opt.run(time)


def test_HillClimbingOptimizer():
    search = {
        "model": model,
        "function_parameter": {"features": X, "target": y},
        "search_space": search_space,
        "n_iter": 15,
        "optimizer": "HillClimbing",
    }

    _base_test(search)


def test_StochasticHillClimbingOptimizer():
    search = {
        "model": model,
        "function_parameter": {"features": X, "target": y},
        "search_space": search_space,
        "n_iter": 15,
        "optimizer": "StochasticHillClimbing",
    }

    _base_test(search)


def test_TabuOptimizer():
    search = {
        "model": model,
        "function_parameter": {"features": X, "target": y},
        "search_space": search_space,
        "n_iter": 15,
        "optimizer": "TabuSearch",
    }

    _base_test(search)


def test_RandomSearchOptimizer():
    search = {
        "model": model,
        "function_parameter": {"features": X, "target": y},
        "search_space": search_space,
        "n_iter": 15,
        "optimizer": "RandomSearch",
    }

    _base_test(search)


def test_RandomRestartHillClimbingOptimizer():
    search = {
        "model": model,
        "function_parameter": {"features": X, "target": y},
        "search_space": search_space,
        "n_iter": 15,
        "optimizer": "RandomRestartHillClimbing",
    }

    _base_test(search)


def test_RandomAnnealingOptimizer():
    search = {
        "model": model,
        "function_parameter": {"features": X, "target": y},
        "search_space": search_space,
        "n_iter": 15,
        "optimizer": "RandomAnnealing",
    }

    _base_test(search)


def test_SimulatedAnnealingOptimizer():
    search = {
        "model": model,
        "function_parameter": {"features": X, "target": y},
        "search_space": search_space,
        "n_iter": 15,
        "optimizer": "SimulatedAnnealing",
    }

    _base_test(search)


def test_StochasticTunnelingOptimizer():
    search = {
        "model": model,
        "function_parameter": {"features": X, "target": y},
        "search_space": search_space,
        "n_iter": 15,
        "optimizer": "StochasticTunneling",
    }

    _base_test(search)


def test_ParallelTemperingOptimizer():
    search = {
        "model": model,
        "function_parameter": {"features": X, "target": y},
        "search_space": search_space,
        "n_iter": 15,
        "optimizer": "ParallelTempering",
    }

    _base_test(search)


def test_ParticleSwarmOptimizer():
    search = {
        "model": model,
        "function_parameter": {"features": X, "target": y},
        "search_space": search_space,
        "n_iter": 15,
        "optimizer": "ParticleSwarm",
    }

    _base_test(search)


def test_EvolutionStrategyOptimizer():
    search = {
        "model": model,
        "function_parameter": {"features": X, "target": y},
        "search_space": search_space,
        "n_iter": 15,
        "optimizer": "EvolutionStrategy",
    }

    _base_test(search)


def test_BayesianOptimizer():
    search = {
        "model": model,
        "function_parameter": {"features": X, "target": y},
        "search_space": search_space,
        "n_iter": 15,
        "optimizer": "Bayesian",
    }

    _base_test(search)


def test_TPE():
    search = {
        "model": model,
        "function_parameter": {"features": X, "target": y},
        "search_space": search_space,
        "n_iter": 15,
        "optimizer": "TPE",
    }

    _base_test(search)


def test_DecisionTreeOptimizer():
    search = {
        "model": model,
        "function_parameter": {"features": X, "target": y},
        "search_space": search_space,
        "n_iter": 15,
        "optimizer": "DecisionTree",
    }

    _base_test(search)


1			# Author: Simon Blanke
2			# Email: [email protected]
3			# License: MIT License
4
5			import numpy as np
6
7			from sklearn.datasets import load_iris
8			from sklearn.model_selection import cross_val_score
9			from sklearn.tree import DecisionTreeClassifier
10
11			from hyperactive import Hyperactive
12
13			data = load_iris()
14			X, y = data.data, data.target
15
16
17			def model(para, X, y):
18			dtc = DecisionTreeClassifier(
19			max_depth=para["max_depth"], min_samples_split=para["min_samples_split"],
20			)
21			scores = cross_val_score(dtc, X, y, cv=2)
22
23			return scores.mean()
24
25
26			search_space = {
27			"max_depth": range(1, 21),
28			"min_samples_split": range(2, 21),
29			}
30
31
32			def _base_test(search, opt_args={}, time=None):
33			opt = Hyperactive(X, y, **opt_args)
34			opt.add_search(**search)
35			opt.run(time)
36
37
38			def test_HillClimbingOptimizer():
39			search = {
40			"model": model,
41			"function_parameter": {"features": X, "target": y},
42			"search_space": search_space,
43			"n_iter": 15,
44			"optimizer": "HillClimbing",
45			}
46
47			_base_test(search)
48
49
50			def test_StochasticHillClimbingOptimizer():
51			search = {
52			"model": model,
53			"function_parameter": {"features": X, "target": y},
54			"search_space": search_space,
55			"n_iter": 15,
56			"optimizer": "StochasticHillClimbing",
57			}
58
59			_base_test(search)
60
61
62			def test_TabuOptimizer():
63			search = {
64			"model": model,
65			"function_parameter": {"features": X, "target": y},
66			"search_space": search_space,
67			"n_iter": 15,
68			"optimizer": "TabuSearch",
69			}
70
71			_base_test(search)
72
73
74			def test_RandomSearchOptimizer():
75			search = {
76			"model": model,
77			"function_parameter": {"features": X, "target": y},
78			"search_space": search_space,
79			"n_iter": 15,
80			"optimizer": "RandomSearch",
81			}
82
83			_base_test(search)
84
85
86			def test_RandomRestartHillClimbingOptimizer():
87			search = {
88			"model": model,
89			"function_parameter": {"features": X, "target": y},
90			"search_space": search_space,
91			"n_iter": 15,
92			"optimizer": "RandomRestartHillClimbing",
93			}
94
95			_base_test(search)
96
97
98			def test_RandomAnnealingOptimizer():
99			search = {
100			"model": model,
101			"function_parameter": {"features": X, "target": y},
102			"search_space": search_space,
103			"n_iter": 15,
104			"optimizer": "RandomAnnealing",
105			}
106
107			_base_test(search)
108
109
110			def test_SimulatedAnnealingOptimizer():
111			search = {
112			"model": model,
113			"function_parameter": {"features": X, "target": y},
114			"search_space": search_space,
115			"n_iter": 15,
116			"optimizer": "SimulatedAnnealing",
117			}
118
119			_base_test(search)
120
121
122			def test_StochasticTunnelingOptimizer():
123			search = {
124			"model": model,
125			"function_parameter": {"features": X, "target": y},
126			"search_space": search_space,
127			"n_iter": 15,
128			"optimizer": "StochasticTunneling",
129			}
130
131			_base_test(search)
132
133
134			def test_ParallelTemperingOptimizer():
135			search = {
136			"model": model,
137			"function_parameter": {"features": X, "target": y},
138			"search_space": search_space,
139			"n_iter": 15,
140			"optimizer": "ParallelTempering",
141			}
142
143			_base_test(search)
144
145
146			def test_ParticleSwarmOptimizer():
147			search = {
148			"model": model,
149			"function_parameter": {"features": X, "target": y},
150			"search_space": search_space,
151			"n_iter": 15,
152			"optimizer": "ParticleSwarm",
153			}
154
155			_base_test(search)
156
157
158			def test_EvolutionStrategyOptimizer():
159			search = {
160			"model": model,
161			"function_parameter": {"features": X, "target": y},
162			"search_space": search_space,
163			"n_iter": 15,
164			"optimizer": "EvolutionStrategy",
165			}
166
167			_base_test(search)
168
169
170			def test_BayesianOptimizer():
171			search = {
172			"model": model,
173			"function_parameter": {"features": X, "target": y},
174			"search_space": search_space,
175			"n_iter": 15,
176			"optimizer": "Bayesian",
177			}
178
179			_base_test(search)
180
181
182			def test_TPE():
183			search = {
184			"model": model,
185			"function_parameter": {"features": X, "target": y},
186			"search_space": search_space,
187			"n_iter": 15,
188			"optimizer": "TPE",
189			}
190
191			_base_test(search)
192
193
194			def test_DecisionTreeOptimizer():
195			search = {
196			"model": model,
197			"function_parameter": {"features": X, "target": y},
198			"search_space": search_space,
199			"n_iter": 15,
200			"optimizer": "DecisionTree",
201			}
202
203			_base_test(search)
204

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test_RandomRestartHillClimbingOptimizer() A

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