tests.test_converter.get_idx_order() - Code Metrics - Inspection of "rename variables" - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( 80db5f...5e948f )

by Simon

created 2020-10-13 13:39 UTC

tests.test_converter.get_idx_order() A

↳ Parent: tests.test_converter

Complexity

Conditions

Size

Total Lines	2
Code Lines	2

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	2
nop	2
dl	0
loc	2
rs	10
c	0
b	0
f	0

import pytest
import numpy as np
import pandas as pd
from gradient_free_optimizers.converter import Converter


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])),
]


@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 = [
    (np.array([-10, 11]), np.array([0, 10])),
    (np.array([10, 11]), np.array([20, 10])),
    (np.array([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": np.array([-10])}, np.array([-10])),
    ({"x1": np.array([10])}, np.array([10])),
    ({"x1": np.array([0])}, np.array([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": np.array([-10]), "x2": np.array([11])}, np.array([-10, 11])),
    ({"x1": np.array([10]), "x2": np.array([11])}, np.array([10, 11])),
    ({"x1": np.array([0]), "x2": np.array([0])}, np.array([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)

    assert (value == 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): 0.1,
    (20, 10): 0.2,
    (10, 0): 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): 0.1,
    (20, 10): 0.2,
    (10, 0): 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 --- """


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


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

dataframe2memory_dict_test_para_0 = [
    (dataframe, memory_dict_0),
]


@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): 0.1,
    (20, 10): 0.2,
    (10, 0): 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]

    assert dataframe.equals(expected)


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

SimonBlanke / Gradient-Free-Optimizers

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