tests.test_results.test_attributes_results_2() - Code Metrics - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

tests.test_results.test_attributes_results_2() A
last analyzed 2025-08-17 14:42 UTC

↳ Parent: tests.test_results

Complexity

Conditions

Size

Total Lines	7
Code Lines	5

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	5
dl	0
loc	7
rs	10
c	0
b	0
f	0
cc	1
nop	0

"""Test module for results functionality."""

import numpy as np
import pandas as pd
import pytest

from hyperactive import Hyperactive


def objective_function(opt):
    """Return simple quadratic objective function for results testing."""
    score = -opt["x1"] * opt["x1"]
    return score


search_space = {
    "x1": list(np.arange(0, 100, 1)),
}


def test_attributes_results_0():
    """Test search data returns pandas DataFrame."""
    hyper = Hyperactive()
    hyper.add_search(objective_function, search_space, n_iter=100)
    hyper.run()

    assert isinstance(hyper.search_data(objective_function), pd.DataFrame)


def test_attributes_results_1():
    """Test search data contains search space columns."""
    hyper = Hyperactive()
    hyper.add_search(objective_function, search_space, n_iter=100)
    hyper.run()

    assert set(search_space.keys()) < set(hyper.search_data(objective_function).columns)


def test_attributes_results_2():
    """Test search data contains x1 column."""
    hyper = Hyperactive()
    hyper.add_search(objective_function, search_space, n_iter=100)
    hyper.run()

    assert "x1" in list(hyper.search_data(objective_function).columns)


def test_attributes_results_3():
    """Test search data contains score column."""
    hyper = Hyperactive()
    hyper.add_search(objective_function, search_space, n_iter=100)
    hyper.run()

    assert "score" in list(hyper.search_data(objective_function).columns)


def test_attributes_results_4():
    """Test warm start initialization with specific value."""
    hyper = Hyperactive()
    hyper.add_search(
        objective_function,
        search_space,
        n_iter=1,
        initialize={"warm_start": [{"x1": 0}]},
    )
    hyper.run()

    assert 0 in list(hyper.search_data(objective_function)["x1"].values)


def test_attributes_results_5():
    """Test warm start initialization with different value."""
    hyper = Hyperactive()
    hyper.add_search(
        objective_function,
        search_space,
        n_iter=1,
        initialize={"warm_start": [{"x1": 10}]},
    )
    hyper.run()

    print(
        "\n x1_results \n",
        list(hyper.search_data(objective_function)["x1"].values),
    )

    assert 10 in list(hyper.search_data(objective_function)["x1"].values)


def test_attributes_results_6():
    """Test memory disabled allows duplicate search space points."""

    def objective_function(opt):
        score = -opt["x1"] * opt["x1"]
        return score

    search_space = {
        "x1": list(np.arange(0, 10, 1)),
    }

    hyper = Hyperactive()
    hyper.add_search(
        objective_function,
        search_space,
        n_iter=20,
        initialize={"random": 1},
        memory=False,
    )
    hyper.run()

    x1_results = list(hyper.search_data(objective_function)["x1"].values)

    print("\n x1_results \n", x1_results)

    assert len(set(x1_results)) < len(x1_results)


def test_attributes_results_7():

    """Test search data without times parameter excludes timing columns."""

    def objective_function(opt):
        score = -opt["x1"] * opt["x1"]
        return score

    search_space = {
        "x1": list(np.arange(0, 10, 1)),
    }

    hyper = Hyperactive()
    hyper.add_search(
        objective_function,
        search_space,
        n_iter=20,
    )
    hyper.run()

    search_data = hyper.search_data(objective_function)
    with pytest.raises(Exception):
        search_data["eval_times"]


def test_attributes_results_8():

    """Test search data without times parameter excludes iteration timing."""

    def objective_function(opt):
        score = -opt["x1"] * opt["x1"]
        return score

    search_space = {
        "x1": list(np.arange(0, 10, 1)),
    }

    hyper = Hyperactive()
    hyper.add_search(
        objective_function,
        search_space,
        n_iter=20,
    )
    hyper.run()

    search_data = hyper.search_data(objective_function)
    with pytest.raises(Exception):
        search_data["iter_times"]


def test_attributes_results_9():
    """Test search data with times parameter includes timing columns."""

    def objective_function(opt):
        score = -opt["x1"] * opt["x1"]
        return score

    search_space = {
        "x1": list(np.arange(0, 10, 1)),
    }

    hyper = Hyperactive()
    hyper.add_search(
        objective_function,
        search_space,
        n_iter=20,
    )
    hyper.run()

    search_data = hyper.search_data(objective_function, times=True)
    search_data["iter_times"]
    search_data["eval_times"]


"""
def test_attributes_results_7():
    def objective_function(para):
        score = -para["x1"] * para["x1"]
        return score

    search_space = {
        "x1": np.arange(0, 10, 1),
    }

    opt = RandomSearchOptimizer(search_space)
    opt.search(
        objective_function, n_iter=20, initialize={"random": 1}, memory=True
    )

    x1_results = list(opt.results["x1"].values)

    print("\n x1_results \n", x1_results)

    assert len(set(x1_results)) == len(x1_results)


def test_attributes_results_8():
    def objective_function(para):
        score = -para["x1"] * para["x1"]
        return score

    search_space = {
        "x1": np.arange(-10, 11, 1),
    }

    results = pd.DataFrame(np.arange(-10, 10, 1), columns=["x1"])
    results["score"] = 0

    opt = RandomSearchOptimizer(search_space)
    opt.search(
        objective_function,
        n_iter=100,
        initialize={},
        memory=True,
        memory_warm_start=results,
    )

    print("\n opt.results \n", opt.results)

    x1_results = list(opt.results["x1"].values)

    assert 10 == x1_results[0]
"""


1		"""Test module for results functionality."""
2
3		import numpy as np
4		import pandas as pd
5		import pytest
6
7		from hyperactive import Hyperactive
8
9
10		def objective_function(opt):
11		"""Return simple quadratic objective function for results testing."""
12		score = -opt["x1"] * opt["x1"]
13		return score
14
15
16		search_space = {
17		"x1": list(np.arange(0, 100, 1)),
18		}
19
20
21		def test_attributes_results_0():
22		"""Test search data returns pandas DataFrame."""
23		hyper = Hyperactive()
24		hyper.add_search(objective_function, search_space, n_iter=100)
25		hyper.run()
26
27		assert isinstance(hyper.search_data(objective_function), pd.DataFrame)
28
29
30		def test_attributes_results_1():
31		"""Test search data contains search space columns."""
32		hyper = Hyperactive()
33		hyper.add_search(objective_function, search_space, n_iter=100)
34		hyper.run()
35
36		assert set(search_space.keys()) < set(hyper.search_data(objective_function).columns)
37
38
39		def test_attributes_results_2():
40		"""Test search data contains x1 column."""
41		hyper = Hyperactive()
42		hyper.add_search(objective_function, search_space, n_iter=100)
43		hyper.run()
44
45		assert "x1" in list(hyper.search_data(objective_function).columns)
46
47
48		def test_attributes_results_3():
49		"""Test search data contains score column."""
50		hyper = Hyperactive()
51		hyper.add_search(objective_function, search_space, n_iter=100)
52		hyper.run()
53
54		assert "score" in list(hyper.search_data(objective_function).columns)
55
56
57		def test_attributes_results_4():
58		"""Test warm start initialization with specific value."""
59		hyper = Hyperactive()
60		hyper.add_search(
61		objective_function,
62		search_space,
63		n_iter=1,
64		initialize={"warm_start": [{"x1": 0}]},
65		)
66		hyper.run()
67
68		assert 0 in list(hyper.search_data(objective_function)["x1"].values)
69
70
71		def test_attributes_results_5():
72		"""Test warm start initialization with different value."""
73		hyper = Hyperactive()
74		hyper.add_search(
75		objective_function,
76		search_space,
77		n_iter=1,
78		initialize={"warm_start": [{"x1": 10}]},
79		)
80		hyper.run()
81
82		print(
83		"\n x1_results \n",
84		list(hyper.search_data(objective_function)["x1"].values),
85		)
86
87		assert 10 in list(hyper.search_data(objective_function)["x1"].values)
88
89
90		def test_attributes_results_6():
91		"""Test memory disabled allows duplicate search space points."""
92
93		def objective_function(opt):
94		score = -opt["x1"] * opt["x1"]
95		return score
96
97		search_space = {
98		"x1": list(np.arange(0, 10, 1)),
99		}
100
101		hyper = Hyperactive()
102		hyper.add_search(
103		objective_function,
104		search_space,
105		n_iter=20,
106		initialize={"random": 1},
107		memory=False,
108		)
109		hyper.run()
110
111		x1_results = list(hyper.search_data(objective_function)["x1"].values)
112
113		print("\n x1_results \n", x1_results)
114
115		assert len(set(x1_results)) < len(x1_results)
116
117
118	View Code Duplication	def test_attributes_results_7():
		0 ignored issues – show Duplication introduced 2022-05-04 15:29 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
119		"""Test search data without times parameter excludes timing columns."""
120
121		def objective_function(opt):
122		score = -opt["x1"] * opt["x1"]
123		return score
124
125		search_space = {
126		"x1": list(np.arange(0, 10, 1)),
127		}
128
129		hyper = Hyperactive()
130		hyper.add_search(
131		objective_function,
132		search_space,
133		n_iter=20,
134		)
135		hyper.run()
136
137		search_data = hyper.search_data(objective_function)
138		with pytest.raises(Exception):
139		search_data["eval_times"]
140
141
142	View Code Duplication	def test_attributes_results_8():
		0 ignored issues – show Duplication introduced 2022-05-04 15:29 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
143		"""Test search data without times parameter excludes iteration timing."""
144
145		def objective_function(opt):
146		score = -opt["x1"] * opt["x1"]
147		return score
148
149		search_space = {
150		"x1": list(np.arange(0, 10, 1)),
151		}
152
153		hyper = Hyperactive()
154		hyper.add_search(
155		objective_function,
156		search_space,
157		n_iter=20,
158		)
159		hyper.run()
160
161		search_data = hyper.search_data(objective_function)
162		with pytest.raises(Exception):
163		search_data["iter_times"]
164
165
166		def test_attributes_results_9():
167		"""Test search data with times parameter includes timing columns."""
168
169		def objective_function(opt):
170		score = -opt["x1"] * opt["x1"]
171		return score
172
173		search_space = {
174		"x1": list(np.arange(0, 10, 1)),
175		}
176
177		hyper = Hyperactive()
178		hyper.add_search(
179		objective_function,
180		search_space,
181		n_iter=20,
182		)
183		hyper.run()
184
185		search_data = hyper.search_data(objective_function, times=True)
186		search_data["iter_times"]
187		search_data["eval_times"]
188
189
190		"""
191		def test_attributes_results_7():
192		def objective_function(para):
193		score = -para["x1"] * para["x1"]
194		return score
195
196		search_space = {
197		"x1": np.arange(0, 10, 1),
198		}
199
200		opt = RandomSearchOptimizer(search_space)
201		opt.search(
202		objective_function, n_iter=20, initialize={"random": 1}, memory=True
203		)
204
205		x1_results = list(opt.results["x1"].values)
206
207		print("\n x1_results \n", x1_results)
208
209		assert len(set(x1_results)) == len(x1_results)
210
211
212		def test_attributes_results_8():
213		def objective_function(para):
214		score = -para["x1"] * para["x1"]
215		return score
216
217		search_space = {
218		"x1": np.arange(-10, 11, 1),
219		}
220
221		results = pd.DataFrame(np.arange(-10, 10, 1), columns=["x1"])
222		results["score"] = 0
223
224		opt = RandomSearchOptimizer(search_space)
225		opt.search(
226		objective_function,
227		n_iter=100,
228		initialize={},
229		memory=True,
230		memory_warm_start=results,
231		)
232
233		print("\n opt.results \n", opt.results)
234
235		x1_results = list(opt.results["x1"].values)
236
237		assert 10 == x1_results[0]
238		"""
239

SimonBlanke / Hyperactive

tests.test_results.test_attributes_results_2() A last analyzed 2025-08-17 14:42 UTC

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tests.test_results.test_attributes_results_2() A
last analyzed 2025-08-17 14:42 UTC