Issues in _test_memory_warm_start_n_jobs.py (master) - Issues in master - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

Issues (61)

_test_memory_warm_start_n_jobs.py (3 issues)

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Duplication 3

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Informational 3

import time
import pytest
import numpy as np
import pandas as pd

from hyperactive import Hyperactive

############################## create search spaces
size = 1000

dim_full = list(range(0, size))
dim_cat = list(range(round(size / 3)))
dim_10 = list(range(round(size ** 0.1)))

search_space_0 = {
    "x1": dim_full,
}

search_space_1 = {
    "x1": dim_cat,
    "x2": list(range(3)),
}

search_space_2 = {
    "x1": dim_10,
    "x2": dim_10,
    "x3": dim_10,
    "x4": dim_10,
    "x5": dim_10,
    "x6": dim_10,
    "x7": dim_10,
    "x8": dim_10,
    "x9": dim_10,
    "x10": dim_10,
}

search_space_3 = {
    "x1": dim_10,
    "x2": dim_10,
    "x3": dim_10,
    "x4": dim_10,
    "x5": dim_10,
    "x6": dim_10,
    "x7": dim_10,
    "x8": dim_10,
    "x9": dim_10,
    "x10": dim_10,
    "x11": [1],
    "x12": [1],
    "x13": [1],
    "x14": [1],
    "x15": [1],
}

search_space_4 = {
    "x1": dim_cat,
    "str1": ["0", "1", "2"],
}


def func1():
    pass


def func2():
    pass


def func3():
    pass


search_space_5 = {
    "x1": dim_cat,
    "func1": [func1, func2, func3],
}


class class1:
    pass


class class2:
    pass


class class3:
    pass


def wr_func_1():
    return class1


def wr_func_2():
    return class2


def wr_func_3():
    return class3


search_space_6 = {
    "x1": dim_cat,
    "class_1": [wr_func_1, wr_func_2, wr_func_3],
}


class class1_o:
    def __init__(self):
        pass


class class2_o:
    def __init__(self):
        pass


class class3_o:
    def __init__(self):
        pass


def wr_func_1():
    return class1_o()


def wr_func_2():
    return class2_o()


def wr_func_3():
    return class3_o()


search_space_7 = {
    "x1": dim_cat,
    "class_obj_1": [wr_func_1, wr_func_2, wr_func_3],
}


def wr_func_1():
    return [1, 0, 0]


def wr_func_2():
    return [0, 1, 0]


def wr_func_3():
    return [0, 0, 1]


search_space_8 = {
    "x1": dim_cat,
    "list_1": [wr_func_1, wr_func_2, wr_func_3],
}


def wr_func_1():
    return np.array([1, 0, 0])


def wr_func_2():
    return np.array([0, 1, 0])


def wr_func_3():
    return np.array([0, 0, 1])


search_space_9 = {
    "x1": dim_cat,
    "array_1": [wr_func_1, wr_func_2, wr_func_3],
}


def wr_func_1():
    return pd.DataFrame(np.array([1, 0, 0]))


def wr_func_2():
    return pd.DataFrame(np.array([0, 1, 0]))


def wr_func_3():
    return pd.DataFrame(np.array([0, 0, 1]))


search_space_10 = {
    "x1": dim_cat,
    "df_1": [wr_func_1, wr_func_2, wr_func_3],
}

search_space_list = [
    (search_space_0),
    (search_space_1),
    (search_space_2),
    (search_space_3),
    (search_space_4),
    (search_space_5),
    (search_space_6),
    (search_space_7),
    (search_space_8),
    (search_space_9),
    (search_space_10),
]

############################## start tests


def objective_function(opt):
    time.sleep(0.003)
    return 0


@pytest.mark.parametrize("search_space", search_space_list)

def test_memory_warm_start_0(search_space):
    n_iter = 1500

    c_time = time.perf_counter()
    hyper0 = Hyperactive(distribution="pathos")
    hyper0.add_search(
        objective_function, search_space, n_iter=n_iter, n_jobs=2, memory=True
    )
    hyper0.run()
    d_time_1 = time.perf_counter() - c_time

    search_data0 = hyper0.search_data(objective_function)

    c_time = time.perf_counter()
    hyper1 = Hyperactive()
    hyper1.add_search(
        objective_function,
        search_space,
        n_iter=n_iter,
        memory_warm_start=search_data0,
        memory=True,
    )
    hyper1.run()
    d_time_2 = time.perf_counter() - c_time

    d_time_frac = d_time_1 / d_time_2

    print("\n d_time_1 ", d_time_1)
    print("\n d_time_2 ", d_time_2)

    assert d_time_frac > 1.5


@pytest.mark.parametrize("search_space", search_space_list)

def test_memory_warm_start_1(search_space):
    n_iter = 1500

    c_time = time.perf_counter()
    hyper0 = Hyperactive()
    hyper0.add_search(objective_function, search_space, n_iter=n_iter, memory=True)
    hyper0.run()
    d_time_1 = time.perf_counter() - c_time

    search_data0 = hyper0.search_data(objective_function)

    c_time = time.perf_counter()
    hyper1 = Hyperactive(distribution="pathos")
    hyper1.add_search(
        objective_function,
        search_space,
        n_iter=n_iter,
        n_jobs=2,
        memory=True,
        memory_warm_start=search_data0,
    )
    hyper1.run()
    d_time_2 = time.perf_counter() - c_time

    d_time_frac = d_time_1 / d_time_2

    print("\n d_time_1 ", d_time_1)
    print("\n d_time_2 ", d_time_2)

    assert d_time_frac > 1.5


@pytest.mark.parametrize("search_space", search_space_list)

def test_memory_warm_start_1(search_space):
    n_iter = 1500

    c_time = time.perf_counter()
    hyper0 = Hyperactive(distribution="pathos")
    hyper0.add_search(
        objective_function, search_space, n_iter=n_iter, n_jobs=2, memory=True
    )
    hyper0.run()
    d_time_1 = time.perf_counter() - c_time

    search_data0 = hyper0.search_data(objective_function)

    c_time = time.perf_counter()
    hyper1 = Hyperactive(distribution="pathos")
    hyper1.add_search(
        objective_function,
        search_space,
        n_iter=n_iter,
        n_jobs=2,
        memory=True,
        memory_warm_start=search_data0,
    )
    hyper1.run()
    d_time_2 = time.perf_counter() - c_time

    d_time_frac = d_time_1 / d_time_2

    print("\n d_time_1 ", d_time_1)
    print("\n d_time_2 ", d_time_2)

    assert d_time_frac > 1.5


1		import time
2		import pytest
3		import numpy as np
4		import pandas as pd
5
6		from hyperactive import Hyperactive
7
8		############################## create search spaces
9		size = 1000
10
11		dim_full = list(range(0, size))
12		dim_cat = list(range(round(size / 3)))
13		dim_10 = list(range(round(size ** 0.1)))
14
15		search_space_0 = {
16		"x1": dim_full,
17		}
18
19		search_space_1 = {
20		"x1": dim_cat,
21		"x2": list(range(3)),
22		}
23
24		search_space_2 = {
25		"x1": dim_10,
26		"x2": dim_10,
27		"x3": dim_10,
28		"x4": dim_10,
29		"x5": dim_10,
30		"x6": dim_10,
31		"x7": dim_10,
32		"x8": dim_10,
33		"x9": dim_10,
34		"x10": dim_10,
35		}
36
37		search_space_3 = {
38		"x1": dim_10,
39		"x2": dim_10,
40		"x3": dim_10,
41		"x4": dim_10,
42		"x5": dim_10,
43		"x6": dim_10,
44		"x7": dim_10,
45		"x8": dim_10,
46		"x9": dim_10,
47		"x10": dim_10,
48		"x11": [1],
49		"x12": [1],
50		"x13": [1],
51		"x14": [1],
52		"x15": [1],
53		}
54
55		search_space_4 = {
56		"x1": dim_cat,
57		"str1": ["0", "1", "2"],
58		}
59
60
61		def func1():
62		pass
63
64
65		def func2():
66		pass
67
68
69		def func3():
70		pass
71
72
73		search_space_5 = {
74		"x1": dim_cat,
75		"func1": [func1, func2, func3],
76		}
77
78
79		class class1:
80		pass
81
82
83		class class2:
84		pass
85
86
87		class class3:
88		pass
89
90
91		def wr_func_1():
92		return class1
93
94
95		def wr_func_2():
96		return class2
97
98
99		def wr_func_3():
100		return class3
101
102
103		search_space_6 = {
104		"x1": dim_cat,
105		"class_1": [wr_func_1, wr_func_2, wr_func_3],
106		}
107
108
109		class class1_o:
110		def __init__(self):
111		pass
112
113
114		class class2_o:
115		def __init__(self):
116		pass
117
118
119		class class3_o:
120		def __init__(self):
121		pass
122
123
124		def wr_func_1():
125		return class1_o()
126
127
128		def wr_func_2():
129		return class2_o()
130
131
132		def wr_func_3():
133		return class3_o()
134
135
136		search_space_7 = {
137		"x1": dim_cat,
138		"class_obj_1": [wr_func_1, wr_func_2, wr_func_3],
139		}
140
141
142		def wr_func_1():
143		return [1, 0, 0]
144
145
146		def wr_func_2():
147		return [0, 1, 0]
148
149
150		def wr_func_3():
151		return [0, 0, 1]
152
153
154		search_space_8 = {
155		"x1": dim_cat,
156		"list_1": [wr_func_1, wr_func_2, wr_func_3],
157		}
158
159
160		def wr_func_1():
161		return np.array([1, 0, 0])
162
163
164		def wr_func_2():
165		return np.array([0, 1, 0])
166
167
168		def wr_func_3():
169		return np.array([0, 0, 1])
170
171
172		search_space_9 = {
173		"x1": dim_cat,
174		"array_1": [wr_func_1, wr_func_2, wr_func_3],
175		}
176
177
178		def wr_func_1():
179		return pd.DataFrame(np.array([1, 0, 0]))
180
181
182		def wr_func_2():
183		return pd.DataFrame(np.array([0, 1, 0]))
184
185
186		def wr_func_3():
187		return pd.DataFrame(np.array([0, 0, 1]))
188
189
190		search_space_10 = {
191		"x1": dim_cat,
192		"df_1": [wr_func_1, wr_func_2, wr_func_3],
193		}
194
195		search_space_list = [
196		(search_space_0),
197		(search_space_1),
198		(search_space_2),
199		(search_space_3),
200		(search_space_4),
201		(search_space_5),
202		(search_space_6),
203		(search_space_7),
204		(search_space_8),
205		(search_space_9),
206		(search_space_10),
207		]
208
209		############################## start tests
210
211
212		def objective_function(opt):
213		time.sleep(0.003)
214		return 0
215
216
217	View Code Duplication	@pytest.mark.parametrize("search_space", search_space_list)
		0 ignored issues – show Duplication introduced 2022-03-16 12:55 UTC by Report Bug Copy Issue Report Show Similar Issues like this This code seems to be duplicated in your project. Loading history...
218		def test_memory_warm_start_0(search_space):
219		n_iter = 1500
220
221		c_time = time.perf_counter()
222		hyper0 = Hyperactive(distribution="pathos")
223		hyper0.add_search(
224		objective_function, search_space, n_iter=n_iter, n_jobs=2, memory=True
225		)
226		hyper0.run()
227		d_time_1 = time.perf_counter() - c_time
228
229		search_data0 = hyper0.search_data(objective_function)
230
231		c_time = time.perf_counter()
232		hyper1 = Hyperactive()
233		hyper1.add_search(
234		objective_function,
235		search_space,
236		n_iter=n_iter,
237		memory_warm_start=search_data0,
238		memory=True,
239		)
240		hyper1.run()
241		d_time_2 = time.perf_counter() - c_time
242
243		d_time_frac = d_time_1 / d_time_2
244
245		print("\n d_time_1 ", d_time_1)
246		print("\n d_time_2 ", d_time_2)
247
248		assert d_time_frac > 1.5
249
250
251	View Code Duplication	@pytest.mark.parametrize("search_space", search_space_list)
		0 ignored issues – show Duplication introduced 2022-03-16 12:55 UTC by Report Bug Copy Issue Report Show Similar Issues like this This code seems to be duplicated in your project. Loading history...
252		def test_memory_warm_start_1(search_space):
253		n_iter = 1500
254
255		c_time = time.perf_counter()
256		hyper0 = Hyperactive()
257		hyper0.add_search(objective_function, search_space, n_iter=n_iter, memory=True)
258		hyper0.run()
259		d_time_1 = time.perf_counter() - c_time
260
261		search_data0 = hyper0.search_data(objective_function)
262
263		c_time = time.perf_counter()
264		hyper1 = Hyperactive(distribution="pathos")
265		hyper1.add_search(
266		objective_function,
267		search_space,
268		n_iter=n_iter,
269		n_jobs=2,
270		memory=True,
271		memory_warm_start=search_data0,
272		)
273		hyper1.run()
274		d_time_2 = time.perf_counter() - c_time
275
276		d_time_frac = d_time_1 / d_time_2
277
278		print("\n d_time_1 ", d_time_1)
279		print("\n d_time_2 ", d_time_2)
280
281		assert d_time_frac > 1.5
282
283
284	View Code Duplication	@pytest.mark.parametrize("search_space", search_space_list)
		0 ignored issues – show Duplication introduced 2022-03-16 12:55 UTC by Report Bug Copy Issue Report Show Similar Issues like this This code seems to be duplicated in your project. Loading history...
285		def test_memory_warm_start_1(search_space):
286		n_iter = 1500
287
288		c_time = time.perf_counter()
289		hyper0 = Hyperactive(distribution="pathos")
290		hyper0.add_search(
291		objective_function, search_space, n_iter=n_iter, n_jobs=2, memory=True
292		)
293		hyper0.run()
294		d_time_1 = time.perf_counter() - c_time
295
296		search_data0 = hyper0.search_data(objective_function)
297
298		c_time = time.perf_counter()
299		hyper1 = Hyperactive(distribution="pathos")
300		hyper1.add_search(
301		objective_function,
302		search_space,
303		n_iter=n_iter,
304		n_jobs=2,
305		memory=True,
306		memory_warm_start=search_data0,
307		)
308		hyper1.run()
309		d_time_2 = time.perf_counter() - c_time
310
311		d_time_frac = d_time_1 / d_time_2
312
313		print("\n d_time_1 ", d_time_1)
314		print("\n d_time_2 ", d_time_2)
315
316		assert d_time_frac > 1.5
317

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Issues (61)

_test_memory_warm_start_n_jobs.py (3 issues)

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