1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
|
import numpy as np
from numpy.testing import assert_array_equal
from sklearn.utils._unique import attach_unique, cached_unique
from sklearn.utils.validation import check_array
def test_attach_unique_attaches_unique_to_array():
arr = np.array([1, 2, 2, 3, 4, 4, 5])
arr_ = attach_unique(arr)
assert_array_equal(arr_.dtype.metadata["unique"], np.array([1, 2, 3, 4, 5]))
assert_array_equal(arr_, arr)
def test_cached_unique_returns_cached_unique():
my_dtype = np.dtype(np.float64, metadata={"unique": np.array([1, 2])})
arr = np.array([1, 2, 2, 3, 4, 4, 5], dtype=my_dtype)
assert_array_equal(cached_unique(arr), np.array([1, 2]))
def test_attach_unique_not_ndarray():
"""Test that when not np.ndarray, we don't touch the array."""
arr = [1, 2, 2, 3, 4, 4, 5]
arr_ = attach_unique(arr)
assert arr_ is arr
def test_attach_unique_returns_view():
"""Test that attach_unique returns a view of the array."""
arr = np.array([1, 2, 2, 3, 4, 4, 5])
arr_ = attach_unique(arr)
assert arr_.base is arr
def test_attach_unique_return_tuple():
"""Test return_tuple argument of the function."""
arr = np.array([1, 2, 2, 3, 4, 4, 5])
arr_tuple = attach_unique(arr, return_tuple=True)
assert isinstance(arr_tuple, tuple)
assert len(arr_tuple) == 1
assert_array_equal(arr_tuple[0], arr)
arr_single = attach_unique(arr, return_tuple=False)
assert isinstance(arr_single, np.ndarray)
assert_array_equal(arr_single, arr)
def test_check_array_keeps_unique():
"""Test that check_array keeps the unique metadata."""
arr = np.array([[1, 2, 2, 3, 4, 4, 5]])
arr_ = attach_unique(arr)
arr_ = check_array(arr_)
assert_array_equal(arr_.dtype.metadata["unique"], np.array([1, 2, 3, 4, 5]))
assert_array_equal(arr_, arr)
|
