tests.test_converter.equal_dictKeysValues() - Code Metrics - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

tests.test_converter.equal_dictKeysValues() A
last analyzed 2025-08-06 16:49 UTC

↳ Parent: tests.test_converter

Complexity

Conditions

Size

Total Lines	8
Code Lines	7

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	4
eloc	7
nop	2
dl	0
loc	8
rs	10
c	0
b	0
f	0

import pytest
import numpy as np
import pandas as pd

from gradient_free_optimizers.optimizers.core_optimizer import Converter
from gradient_free_optimizers._result import Result


def equal_arraysInList(list1, list2):
    return all((e1 == e2).all() for e1, e2 in zip(list1, list2))


def equal_dictKeysValues(dict1, dict2):
    if len(dict1.keys()) != len(dict2.keys()):
        return False
    for key1 in dict1.keys:
        if dict1[key1] != dict2[key1]:
            return False

    return True


def get_idx_order(list1, list2):
    return [idx for o in list1 for idx, name in enumerate(list2) if o == name]


def reorder(list1, idx_list):
    return [list1[i] for i in idx_list]


def unordered_dict_workaround(conv, order):
    # workaround for doing this test with unordered dicts

    idx_order = get_idx_order(order, conv.para_names)
    search_space_values_reordered = reorder(conv.search_space_values, idx_order)
    para_names_reordered = reorder(conv.para_names, idx_order)

    conv.search_space_values = search_space_values_reordered
    conv.para_names = para_names_reordered

    return conv


######### test position2value #########


position2value_test_para_0 = [
    (np.array([0]), np.array([-10])),
    (np.array([20]), np.array([10])),
    (np.array([10]), np.array([0])),
    (None, None),
]


@pytest.mark.parametrize("test_input,expected", position2value_test_para_0)
def test_position2value_0(test_input, expected):
    search_space = {
        "x1": np.arange(-10, 11, 1),
    }

    conv = Converter(search_space)
    value = conv.position2value(test_input)

    assert value == expected


position2value_test_para_1 = [
    (np.array([0, 0]), np.array([-10, 0])),
    (np.array([20, 0]), np.array([10, 0])),
    (np.array([10, 10]), np.array([0, 10])),
]


@pytest.mark.parametrize("test_input,expected", position2value_test_para_1)
def test_position2value_1(test_input, expected):
    search_space = {
        "x1": np.arange(-10, 11, 1),
        "x2": np.arange(0, 11, 1),
    }

    conv = Converter(search_space)

    order = ["x1", "x2"]
    conv = unordered_dict_workaround(conv, order)

    value = conv.position2value(test_input)

    assert (value == expected).all()


######### test value2position #########


value2position_test_para_0 = [
    (np.array([-10]), np.array([0])),
    (np.array([10]), np.array([20])),
    (np.array([0]), np.array([10])),
]


@pytest.mark.parametrize("test_input,expected", value2position_test_para_0)
def test_value2position_0(test_input, expected):
    search_space = {
        "x1": np.arange(-10, 11, 1),
    }

    conv = Converter(search_space)
    position = conv.value2position(test_input)

    assert position == expected


value2position_test_para_1 = [
    ([-10, 11], np.array([0, 10])),
    ([10, 11], np.array([20, 10])),
    ([0, 0], np.array([10, 0])),
]


@pytest.mark.parametrize("test_input,expected", value2position_test_para_1)
def test_value2position_1(test_input, expected):
    search_space = {
        "x1": np.arange(-10, 11, 1),
        "x2": np.arange(0, 11, 1),
    }

    conv = Converter(search_space)
    order = ["x1", "x2"]
    conv = unordered_dict_workaround(conv, order)
    position = conv.value2position(test_input)

    assert (position == expected).all()


######### test value2para #########


value2para_test_para_0 = [
    (np.array([-10]), {"x1": np.array([-10])}),
    (np.array([10]), {"x1": np.array([10])}),
    (np.array([0]), {"x1": np.array([0])}),
]


@pytest.mark.parametrize("test_input,expected", value2para_test_para_0)
def test_value2para_0(test_input, expected):
    search_space = {
        "x1": np.arange(-10, 11, 1),
    }

    conv = Converter(search_space)
    para = conv.value2para(test_input)

    assert para == expected


value2para_test_para_1 = [
    (np.array([-10, 11]), {"x1": np.array([-10]), "x2": np.array([11])}),
    (np.array([10, 11]), {"x1": np.array([10]), "x2": np.array([11])}),
    (np.array([0, 0]), {"x1": np.array([0]), "x2": np.array([0])}),
]


@pytest.mark.parametrize("test_input,expected", value2para_test_para_1)
def test_value2para_1(test_input, expected):
    search_space = {
        "x1": np.arange(-10, 11, 1),
        "x2": np.arange(0, 11, 1),
    }

    conv = Converter(search_space)
    order = ["x1", "x2"]
    conv = unordered_dict_workaround(conv, order)
    para = conv.value2para(test_input)

    assert para == expected


######### test para2value #########


para2value_test_para_0 = [
    ({"x1": -10}, [-10]),
    ({"x1": 10}, [10]),
    ({"x1": 0}, [0]),
]


@pytest.mark.parametrize("test_input,expected", para2value_test_para_0)
def test_para2value_0(test_input, expected):
    search_space = {
        "x1": np.arange(-10, 11, 1),
    }

    conv = Converter(search_space)
    value = conv.para2value(test_input)

    assert value == expected


para2value_test_para_1 = [
    ({"x1": -10, "x2": 11}, [-10, 11]),
    ({"x1": 10, "x2": 11}, [10, 11]),
    ({"x1": 0, "x2": 0}, [0, 0]),
]


@pytest.mark.parametrize("test_input,expected", para2value_test_para_1)
def test_para2value_1(test_input, expected):
    search_space = {
        "x1": np.arange(-10, 11, 1),
        "x2": np.arange(0, 11, 1),
    }

    conv = Converter(search_space)
    order = ["x1", "x2"]
    conv = unordered_dict_workaround(conv, order)
    value = conv.para2value(test_input)

    print("value", type(value))
    print("expected", type(expected))

    assert (np.array(value) == np.array(expected)).all()


######### test values2positions #########


values_0 = [
    np.array([-10]),
    np.array([10]),
    np.array([0]),
]

positions_0 = [
    np.array([0]),
    np.array([20]),
    np.array([10]),
]


values_1 = [
    np.array([-10]),
    np.array([10]),
    np.array([0]),
    np.array([-10]),
    np.array([10]),
    np.array([0]),
    np.array([-10]),
    np.array([10]),
    np.array([0]),
]

positions_1 = [
    np.array([0]),
    np.array([20]),
    np.array([10]),
    np.array([0]),
    np.array([20]),
    np.array([10]),
    np.array([0]),
    np.array([20]),
    np.array([10]),
]


values2positions_test_para_0 = [
    (values_0, positions_0),
    (values_1, positions_1),
]


@pytest.mark.parametrize("test_input,expected", values2positions_test_para_0)
def test_values2positions_0(test_input, expected):
    search_space = {
        "x1": np.arange(-10, 11, 1),
    }

    conv = Converter(search_space)
    positions = conv.values2positions(test_input)

    assert positions == expected


values_0 = [
    np.array([-10, 10]),
    np.array([10, 10]),
    np.array([0, 0]),
]

positions_0 = [
    np.array([0, 10]),
    np.array([20, 10]),
    np.array([10, 0]),
]


values_1 = [
    np.array([-10, 10]),
    np.array([10, 10]),
    np.array([0, 0]),
    np.array([-10, 10]),
    np.array([10, 10]),
    np.array([0, 0]),
    np.array([-10, 10]),
    np.array([10, 10]),
    np.array([0, 0]),
]

positions_1 = [
    np.array([0, 10]),
    np.array([20, 10]),
    np.array([10, 0]),
    np.array([0, 10]),
    np.array([20, 10]),
    np.array([10, 0]),
    np.array([0, 10]),
    np.array([20, 10]),
    np.array([10, 0]),
]


values2positions_test_para_1 = [
    (values_0, positions_0),
    (values_1, positions_1),
]


@pytest.mark.parametrize("test_input,expected", values2positions_test_para_1)
def test_values2positions_1(test_input, expected):
    search_space = {
        "x1": np.arange(-10, 11, 1),
        "x2": np.arange(0, 11, 1),
    }

    conv = Converter(search_space)
    order = ["x1", "x2"]
    conv = unordered_dict_workaround(conv, order)
    positions = conv.values2positions(test_input)

    assert equal_arraysInList(positions, expected)


""" --- test positions2values --- """

values_0 = [
    np.array([-10]),
    np.array([10]),
    np.array([0]),
]

positions_0 = [
    np.array([0]),
    np.array([20]),
    np.array([10]),
]


values_1 = [
    np.array([-10]),
    np.array([10]),
    np.array([0]),
    np.array([-10]),
    np.array([10]),
    np.array([0]),
    np.array([-10]),
    np.array([10]),
    np.array([0]),
]


positions_1 = [
    np.array([0]),
    np.array([20]),
    np.array([10]),
    np.array([0]),
    np.array([20]),
    np.array([10]),
    np.array([0]),
    np.array([20]),
    np.array([10]),
]


positions2values_test_para_0 = [
    (positions_0, values_0),
    (positions_1, values_1),
]


@pytest.mark.parametrize("test_input,expected", positions2values_test_para_0)
def test_positions2values_0(test_input, expected):
    search_space = {
        "x1": np.arange(-10, 11, 1),
    }

    conv = Converter(search_space)
    values = conv.positions2values(test_input)

    assert values == expected


values_0 = [
    np.array([-10, 10]),
    np.array([10, 10]),
    np.array([0, 0]),
]

positions_0 = [
    np.array([0, 10]),
    np.array([20, 10]),
    np.array([10, 0]),
]


values_1 = [
    np.array([-10, 10]),
    np.array([10, 10]),
    np.array([0, 0]),
    np.array([-10, 10]),
    np.array([10, 10]),
    np.array([0, 0]),
    np.array([-10, 10]),
    np.array([10, 10]),
    np.array([0, 0]),
]

positions_1 = [
    np.array([0, 10]),
    np.array([20, 10]),
    np.array([10, 0]),
    np.array([0, 10]),
    np.array([20, 10]),
    np.array([10, 0]),
    np.array([0, 10]),
    np.array([20, 10]),
    np.array([10, 0]),
]


positions2values_test_para_1 = [
    (positions_0, values_0),
    (positions_1, values_1),
]


@pytest.mark.parametrize("test_input,expected", positions2values_test_para_1)
def test_positions2values_1(test_input, expected):
    search_space = {
        "x1": np.arange(-10, 11, 1),
        "x2": np.arange(0, 11, 1),
    }

    conv = Converter(search_space)
    order = ["x1", "x2"]
    conv = unordered_dict_workaround(conv, order)
    values = conv.positions2values(test_input)

    assert equal_arraysInList(values, expected)


""" --- test positions_scores2memory_dict --- """


positions_0 = [
    np.array([0, 10]),
    np.array([20, 10]),
    np.array([10, 0]),
]

scores_0 = [0.1, 0.2, 0.3]


memory_dict_0 = {
    (0, 10): Result(0.1, {}),
    (20, 10): Result(0.2, {}),
    (10, 0): Result(0.3, {}),
}

positions_scores2memory_dict_test_para_0 = [
    ((positions_0, scores_0), memory_dict_0),
    # ((positions_1, scores_1), values_1),
]


@pytest.mark.parametrize(
    "test_input,expected", positions_scores2memory_dict_test_para_0
)
def test_positions_scores2memory_dict_0(test_input, expected):
    search_space = {
        "x1": np.arange(-10, 11, 1),
        "x2": np.arange(0, 11, 1),
    }

    conv = Converter(search_space)
    order = ["x1", "x2"]
    conv = unordered_dict_workaround(conv, order)
    memory_dict = conv.positions_scores2memory_dict(*test_input)

    assert memory_dict == expected


""" --- test memory_dict2positions_scores --- """


positions_0 = [
    np.array([0, 10]),
    np.array([20, 10]),
    np.array([10, 0]),
]

scores_0 = [0.1, 0.2, 0.3]


memory_dict_0 = {
    (0, 10): Result(0.1, {}),
    (20, 10): Result(0.2, {}),
    (10, 0): Result(0.3, {}),
}

memory_dict2positions_scores_test_para_0 = [
    (memory_dict_0, (positions_0, scores_0)),
]


@pytest.mark.parametrize(
    "test_input,expected", memory_dict2positions_scores_test_para_0
)
def test_memory_dict2positions_scores_0(test_input, expected):
    search_space = {
        "x1": np.arange(-10, 11, 1),
        "x2": np.arange(0, 11, 1),
    }

    conv = Converter(search_space)
    order = ["x1", "x2"]
    conv = unordered_dict_workaround(conv, order)
    positions, scores = conv.memory_dict2positions_scores(test_input)

    idx_order = get_idx_order(scores, expected[1])
    scores_reordered = reorder(scores, idx_order)
    positions_reordered = reorder(positions, idx_order)

    assert equal_arraysInList(positions_reordered, expected[0])
    assert scores_reordered == expected[1]


""" --- test dataframe2memory_dict --- """


dataframe0 = pd.DataFrame(
    [[-10, 10, 0.1], [10, 10, 0.2], [0, 0, 0.3]], columns=["x1", "x2", "score"]
)

dataframe1 = pd.DataFrame(
    [[-10, 0.1], [10, 0.2], [0, 0.3]], columns=["x1", "score"]
)

memory_dict_0 = {
    (0, 10): Result(0.1, {}),
    (20, 10): Result(0.2, {}),
    (10, 0): Result(0.3, {}),
}

dataframe2memory_dict_test_para_0 = [
    (dataframe0, memory_dict_0),
    (dataframe1, {}),
]


@pytest.mark.parametrize(
    "test_input,expected", dataframe2memory_dict_test_para_0
)
def test_dataframe2memory_dict_0(test_input, expected):
    search_space = {
        "x1": np.arange(-10, 11, 1),
        "x2": np.arange(0, 11, 1),
    }

    conv = Converter(search_space)
    order = ["x1", "x2"]
    conv = unordered_dict_workaround(conv, order)
    memory_dict = conv.dataframe2memory_dict(test_input)

    assert memory_dict == expected


""" --- test memory_dict2dataframe --- """


dataframe = pd.DataFrame(
    [[-10, 10, 0.1], [10, 10, 0.2], [0, 0, 0.3]], columns=["x1", "x2", "score"]
)


memory_dict_0 = {
    (0, 10): Result(0.1, {}),
    (20, 10): Result(0.2, {}),
    (10, 0): Result(0.3, {}),
}

memory_dict2dataframe_test_para_0 = [
    (memory_dict_0, dataframe),
]


@pytest.mark.parametrize(
    "test_input,expected", memory_dict2dataframe_test_para_0
)
def test_memory_dict2dataframe_0(test_input, expected):
    search_space = {
        "x1": np.arange(-10, 11, 1),
        "x2": np.arange(0, 11, 1),
    }

    conv = Converter(search_space)
    order = ["x1", "x2"]
    conv = unordered_dict_workaround(conv, order)
    dataframe = conv.memory_dict2dataframe(test_input)

    dataframe.sort_values("score", inplace=True)
    expected.sort_values("score", inplace=True)

    dataframe.reset_index(drop=True, inplace=True)
    expected.reset_index(drop=True, inplace=True)

    dataframe = dataframe[expected.columns]

    dataframe[dataframe.select_dtypes(include=["number"]).columns] = (
        dataframe.select_dtypes(include=["number"]).astype("int")
    )
    expected[expected.select_dtypes(include=["number"]).columns] = (
        expected.select_dtypes(include=["number"]).astype("int")
    )
    assert dataframe.equals(expected)


1			import pytest
2			import numpy as np
3			import pandas as pd
4
5			from gradient_free_optimizers.optimizers.core_optimizer import Converter
6			from gradient_free_optimizers._result import Result
7
8
9			def equal_arraysInList(list1, list2):
10			return all((e1 == e2).all() for e1, e2 in zip(list1, list2))
11
12
13			def equal_dictKeysValues(dict1, dict2):
14			if len(dict1.keys()) != len(dict2.keys()):
15			return False
16			for key1 in dict1.keys:
17			if dict1[key1] != dict2[key1]:
18			return False
19
20			return True
21
22
23			def get_idx_order(list1, list2):
24			return [idx for o in list1 for idx, name in enumerate(list2) if o == name]
25
26
27			def reorder(list1, idx_list):
28			return [list1[i] for i in idx_list]
29
30
31			def unordered_dict_workaround(conv, order):
32			# workaround for doing this test with unordered dicts
33
34			idx_order = get_idx_order(order, conv.para_names)
35			search_space_values_reordered = reorder(conv.search_space_values, idx_order)
36			para_names_reordered = reorder(conv.para_names, idx_order)
37
38			conv.search_space_values = search_space_values_reordered
39			conv.para_names = para_names_reordered
40
41			return conv
42
43
44			######### test position2value #########
45
46
47			position2value_test_para_0 = [
48			(np.array([0]), np.array([-10])),
49			(np.array([20]), np.array([10])),
50			(np.array([10]), np.array([0])),
51			(None, None),
52			]
53
54
55			@pytest.mark.parametrize("test_input,expected", position2value_test_para_0)
56			def test_position2value_0(test_input, expected):
57			search_space = {
58			"x1": np.arange(-10, 11, 1),
59			}
60
61			conv = Converter(search_space)
62			value = conv.position2value(test_input)
63
64			assert value == expected
65
66
67			position2value_test_para_1 = [
68			(np.array([0, 0]), np.array([-10, 0])),
69			(np.array([20, 0]), np.array([10, 0])),
70			(np.array([10, 10]), np.array([0, 10])),
71			]
72
73
74			@pytest.mark.parametrize("test_input,expected", position2value_test_para_1)
75			def test_position2value_1(test_input, expected):
76			search_space = {
77			"x1": np.arange(-10, 11, 1),
78			"x2": np.arange(0, 11, 1),
79			}
80
81			conv = Converter(search_space)
82
83			order = ["x1", "x2"]
84			conv = unordered_dict_workaround(conv, order)
85
86			value = conv.position2value(test_input)
87
88			assert (value == expected).all()
89
90
91			######### test value2position #########
92
93
94			value2position_test_para_0 = [
95			(np.array([-10]), np.array([0])),
96			(np.array([10]), np.array([20])),
97			(np.array([0]), np.array([10])),
98			]
99
100
101			@pytest.mark.parametrize("test_input,expected", value2position_test_para_0)
102			def test_value2position_0(test_input, expected):
103			search_space = {
104			"x1": np.arange(-10, 11, 1),
105			}
106
107			conv = Converter(search_space)
108			position = conv.value2position(test_input)
109
110			assert position == expected
111
112
113			value2position_test_para_1 = [
114			([-10, 11], np.array([0, 10])),
115			([10, 11], np.array([20, 10])),
116			([0, 0], np.array([10, 0])),
117			]
118
119
120			@pytest.mark.parametrize("test_input,expected", value2position_test_para_1)
121			def test_value2position_1(test_input, expected):
122			search_space = {
123			"x1": np.arange(-10, 11, 1),
124			"x2": np.arange(0, 11, 1),
125			}
126
127			conv = Converter(search_space)
128			order = ["x1", "x2"]
129			conv = unordered_dict_workaround(conv, order)
130			position = conv.value2position(test_input)
131
132			assert (position == expected).all()
133
134
135			######### test value2para #########
136
137
138			value2para_test_para_0 = [
139			(np.array([-10]), {"x1": np.array([-10])}),
140			(np.array([10]), {"x1": np.array([10])}),
141			(np.array([0]), {"x1": np.array([0])}),
142			]
143
144
145			@pytest.mark.parametrize("test_input,expected", value2para_test_para_0)
146			def test_value2para_0(test_input, expected):
147			search_space = {
148			"x1": np.arange(-10, 11, 1),
149			}
150
151			conv = Converter(search_space)
152			para = conv.value2para(test_input)
153
154			assert para == expected
155
156
157			value2para_test_para_1 = [
158			(np.array([-10, 11]), {"x1": np.array([-10]), "x2": np.array([11])}),
159			(np.array([10, 11]), {"x1": np.array([10]), "x2": np.array([11])}),
160			(np.array([0, 0]), {"x1": np.array([0]), "x2": np.array([0])}),
161			]
162
163
164			@pytest.mark.parametrize("test_input,expected", value2para_test_para_1)
165			def test_value2para_1(test_input, expected):
166			search_space = {
167			"x1": np.arange(-10, 11, 1),
168			"x2": np.arange(0, 11, 1),
169			}
170
171			conv = Converter(search_space)
172			order = ["x1", "x2"]
173			conv = unordered_dict_workaround(conv, order)
174			para = conv.value2para(test_input)
175
176			assert para == expected
177
178
179			######### test para2value #########
180
181
182			para2value_test_para_0 = [
183			({"x1": -10}, [-10]),
184			({"x1": 10}, [10]),
185			({"x1": 0}, [0]),
186			]
187
188
189			@pytest.mark.parametrize("test_input,expected", para2value_test_para_0)
190			def test_para2value_0(test_input, expected):
191			search_space = {
192			"x1": np.arange(-10, 11, 1),
193			}
194
195			conv = Converter(search_space)
196			value = conv.para2value(test_input)
197
198			assert value == expected
199
200
201			para2value_test_para_1 = [
202			({"x1": -10, "x2": 11}, [-10, 11]),
203			({"x1": 10, "x2": 11}, [10, 11]),
204			({"x1": 0, "x2": 0}, [0, 0]),
205			]
206
207
208			@pytest.mark.parametrize("test_input,expected", para2value_test_para_1)
209			def test_para2value_1(test_input, expected):
210			search_space = {
211			"x1": np.arange(-10, 11, 1),
212			"x2": np.arange(0, 11, 1),
213			}
214
215			conv = Converter(search_space)
216			order = ["x1", "x2"]
217			conv = unordered_dict_workaround(conv, order)
218			value = conv.para2value(test_input)
219
220			print("value", type(value))
221			print("expected", type(expected))
222
223			assert (np.array(value) == np.array(expected)).all()
224
225
226			######### test values2positions #########
227
228
229			values_0 = [
230			np.array([-10]),
231			np.array([10]),
232			np.array([0]),
233			]
234
235			positions_0 = [
236			np.array([0]),
237			np.array([20]),
238			np.array([10]),
239			]
240
241
242			values_1 = [
243			np.array([-10]),
244			np.array([10]),
245			np.array([0]),
246			np.array([-10]),
247			np.array([10]),
248			np.array([0]),
249			np.array([-10]),
250			np.array([10]),
251			np.array([0]),
252			]
253
254			positions_1 = [
255			np.array([0]),
256			np.array([20]),
257			np.array([10]),
258			np.array([0]),
259			np.array([20]),
260			np.array([10]),
261			np.array([0]),
262			np.array([20]),
263			np.array([10]),
264			]
265
266
267			values2positions_test_para_0 = [
268			(values_0, positions_0),
269			(values_1, positions_1),
270			]
271
272
273			@pytest.mark.parametrize("test_input,expected", values2positions_test_para_0)
274			def test_values2positions_0(test_input, expected):
275			search_space = {
276			"x1": np.arange(-10, 11, 1),
277			}
278
279			conv = Converter(search_space)
280			positions = conv.values2positions(test_input)
281
282			assert positions == expected
283
284
285			values_0 = [
286			np.array([-10, 10]),
287			np.array([10, 10]),
288			np.array([0, 0]),
289			]
290
291			positions_0 = [
292			np.array([0, 10]),
293			np.array([20, 10]),
294			np.array([10, 0]),
295			]
296
297
298			values_1 = [
299			np.array([-10, 10]),
300			np.array([10, 10]),
301			np.array([0, 0]),
302			np.array([-10, 10]),
303			np.array([10, 10]),
304			np.array([0, 0]),
305			np.array([-10, 10]),
306			np.array([10, 10]),
307			np.array([0, 0]),
308			]
309
310			positions_1 = [
311			np.array([0, 10]),
312			np.array([20, 10]),
313			np.array([10, 0]),
314			np.array([0, 10]),
315			np.array([20, 10]),
316			np.array([10, 0]),
317			np.array([0, 10]),
318			np.array([20, 10]),
319			np.array([10, 0]),
320			]
321
322
323			values2positions_test_para_1 = [
324			(values_0, positions_0),
325			(values_1, positions_1),
326			]
327
328
329			@pytest.mark.parametrize("test_input,expected", values2positions_test_para_1)
330			def test_values2positions_1(test_input, expected):
331			search_space = {
332			"x1": np.arange(-10, 11, 1),
333			"x2": np.arange(0, 11, 1),
334			}
335
336			conv = Converter(search_space)
337			order = ["x1", "x2"]
338			conv = unordered_dict_workaround(conv, order)
339			positions = conv.values2positions(test_input)
340
341			assert equal_arraysInList(positions, expected)
342
343
344			""" --- test positions2values --- """
345
346			values_0 = [
347			np.array([-10]),
348			np.array([10]),
349			np.array([0]),
350			]
351
352			positions_0 = [
353			np.array([0]),
354			np.array([20]),
355			np.array([10]),
356			]
357
358
359			values_1 = [
360			np.array([-10]),
361			np.array([10]),
362			np.array([0]),
363			np.array([-10]),
364			np.array([10]),
365			np.array([0]),
366			np.array([-10]),
367			np.array([10]),
368			np.array([0]),
369			]
370
371
372			positions_1 = [
373			np.array([0]),
374			np.array([20]),
375			np.array([10]),
376			np.array([0]),
377			np.array([20]),
378			np.array([10]),
379			np.array([0]),
380			np.array([20]),
381			np.array([10]),
382			]
383
384
385			positions2values_test_para_0 = [
386			(positions_0, values_0),
387			(positions_1, values_1),
388			]
389
390
391			@pytest.mark.parametrize("test_input,expected", positions2values_test_para_0)
392			def test_positions2values_0(test_input, expected):
393			search_space = {
394			"x1": np.arange(-10, 11, 1),
395			}
396
397			conv = Converter(search_space)
398			values = conv.positions2values(test_input)
399
400			assert values == expected
401
402
403			values_0 = [
404			np.array([-10, 10]),
405			np.array([10, 10]),
406			np.array([0, 0]),
407			]
408
409			positions_0 = [
410			np.array([0, 10]),
411			np.array([20, 10]),
412			np.array([10, 0]),
413			]
414
415
416			values_1 = [
417			np.array([-10, 10]),
418			np.array([10, 10]),
419			np.array([0, 0]),
420			np.array([-10, 10]),
421			np.array([10, 10]),
422			np.array([0, 0]),
423			np.array([-10, 10]),
424			np.array([10, 10]),
425			np.array([0, 0]),
426			]
427
428			positions_1 = [
429			np.array([0, 10]),
430			np.array([20, 10]),
431			np.array([10, 0]),
432			np.array([0, 10]),
433			np.array([20, 10]),
434			np.array([10, 0]),
435			np.array([0, 10]),
436			np.array([20, 10]),
437			np.array([10, 0]),
438			]
439
440
441			positions2values_test_para_1 = [
442			(positions_0, values_0),
443			(positions_1, values_1),
444			]
445
446
447			@pytest.mark.parametrize("test_input,expected", positions2values_test_para_1)
448			def test_positions2values_1(test_input, expected):
449			search_space = {
450			"x1": np.arange(-10, 11, 1),
451			"x2": np.arange(0, 11, 1),
452			}
453
454			conv = Converter(search_space)
455			order = ["x1", "x2"]
456			conv = unordered_dict_workaround(conv, order)
457			values = conv.positions2values(test_input)
458
459			assert equal_arraysInList(values, expected)
460
461
462			""" --- test positions_scores2memory_dict --- """
463
464
465			positions_0 = [
466			np.array([0, 10]),
467			np.array([20, 10]),
468			np.array([10, 0]),
469			]
470
471			scores_0 = [0.1, 0.2, 0.3]
472
473
474			memory_dict_0 = {
475			(0, 10): Result(0.1, {}),
476			(20, 10): Result(0.2, {}),
477			(10, 0): Result(0.3, {}),
478			}
479
480			positions_scores2memory_dict_test_para_0 = [
481			((positions_0, scores_0), memory_dict_0),
482			# ((positions_1, scores_1), values_1),
483			]
484
485
486			@pytest.mark.parametrize(
487			"test_input,expected", positions_scores2memory_dict_test_para_0
488			)
489			def test_positions_scores2memory_dict_0(test_input, expected):
490			search_space = {
491			"x1": np.arange(-10, 11, 1),
492			"x2": np.arange(0, 11, 1),
493			}
494
495			conv = Converter(search_space)
496			order = ["x1", "x2"]
497			conv = unordered_dict_workaround(conv, order)
498			memory_dict = conv.positions_scores2memory_dict(*test_input)
499
500			assert memory_dict == expected
501
502
503			""" --- test memory_dict2positions_scores --- """
504
505
506			positions_0 = [
507			np.array([0, 10]),
508			np.array([20, 10]),
509			np.array([10, 0]),
510			]
511
512			scores_0 = [0.1, 0.2, 0.3]
513
514
515			memory_dict_0 = {
516			(0, 10): Result(0.1, {}),
517			(20, 10): Result(0.2, {}),
518			(10, 0): Result(0.3, {}),
519			}
520
521			memory_dict2positions_scores_test_para_0 = [
522			(memory_dict_0, (positions_0, scores_0)),
523			]
524
525
526			@pytest.mark.parametrize(
527			"test_input,expected", memory_dict2positions_scores_test_para_0
528			)
529			def test_memory_dict2positions_scores_0(test_input, expected):
530			search_space = {
531			"x1": np.arange(-10, 11, 1),
532			"x2": np.arange(0, 11, 1),
533			}
534
535			conv = Converter(search_space)
536			order = ["x1", "x2"]
537			conv = unordered_dict_workaround(conv, order)
538			positions, scores = conv.memory_dict2positions_scores(test_input)
539
540			idx_order = get_idx_order(scores, expected[1])
541			scores_reordered = reorder(scores, idx_order)
542			positions_reordered = reorder(positions, idx_order)
543
544			assert equal_arraysInList(positions_reordered, expected[0])
545			assert scores_reordered == expected[1]
546
547
548			""" --- test dataframe2memory_dict --- """
549
550
551			dataframe0 = pd.DataFrame(
552			[[-10, 10, 0.1], [10, 10, 0.2], [0, 0, 0.3]], columns=["x1", "x2", "score"]
553			)
554
555			dataframe1 = pd.DataFrame(
556			[[-10, 0.1], [10, 0.2], [0, 0.3]], columns=["x1", "score"]
557			)
558
559			memory_dict_0 = {
560			(0, 10): Result(0.1, {}),
561			(20, 10): Result(0.2, {}),
562			(10, 0): Result(0.3, {}),
563			}
564
565			dataframe2memory_dict_test_para_0 = [
566			(dataframe0, memory_dict_0),
567			(dataframe1, {}),
568			]
569
570
571			@pytest.mark.parametrize(
572			"test_input,expected", dataframe2memory_dict_test_para_0
573			)
574			def test_dataframe2memory_dict_0(test_input, expected):
575			search_space = {
576			"x1": np.arange(-10, 11, 1),
577			"x2": np.arange(0, 11, 1),
578			}
579
580			conv = Converter(search_space)
581			order = ["x1", "x2"]
582			conv = unordered_dict_workaround(conv, order)
583			memory_dict = conv.dataframe2memory_dict(test_input)
584
585			assert memory_dict == expected
586
587
588			""" --- test memory_dict2dataframe --- """
589
590
591			dataframe = pd.DataFrame(
592			[[-10, 10, 0.1], [10, 10, 0.2], [0, 0, 0.3]], columns=["x1", "x2", "score"]
593			)
594
595
596			memory_dict_0 = {
597			(0, 10): Result(0.1, {}),
598			(20, 10): Result(0.2, {}),
599			(10, 0): Result(0.3, {}),
600			}
601
602			memory_dict2dataframe_test_para_0 = [
603			(memory_dict_0, dataframe),
604			]
605
606
607			@pytest.mark.parametrize(
608			"test_input,expected", memory_dict2dataframe_test_para_0
609			)
610			def test_memory_dict2dataframe_0(test_input, expected):
611			search_space = {
612			"x1": np.arange(-10, 11, 1),
613			"x2": np.arange(0, 11, 1),
614			}
615
616			conv = Converter(search_space)
617			order = ["x1", "x2"]
618			conv = unordered_dict_workaround(conv, order)
619			dataframe = conv.memory_dict2dataframe(test_input)
620
621			dataframe.sort_values("score", inplace=True)
622			expected.sort_values("score", inplace=True)
623
624			dataframe.reset_index(drop=True, inplace=True)
625			expected.reset_index(drop=True, inplace=True)
626
627			dataframe = dataframe[expected.columns]
628
629			dataframe[dataframe.select_dtypes(include=["number"]).columns] = (
630			dataframe.select_dtypes(include=["number"]).astype("int")
631			)
632			expected[expected.select_dtypes(include=["number"]).columns] = (
633			expected.select_dtypes(include=["number"]).astype("int")
634			)
635			assert dataframe.equals(expected)
636

SimonBlanke / Gradient-Free-Optimizers

tests.test_converter.equal_dictKeysValues() A last analyzed 2025-08-06 16:49 UTC

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tests.test_converter.equal_dictKeysValues() A
last analyzed 2025-08-06 16:49 UTC