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
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
|
import os
import pytest
import numpy as np
from scipy import sparse
# Making Random Seed as a fixture in case it would be
# needed in tests for random states
@pytest.fixture
def seed():
return 189212 # 0b101110001100011100
np.random.seed(189212)
@pytest.fixture
def spatial_data():
sp_data = np.random.randn(10, 20)
# Add some all zero graph_data for corner case test
sp_data = np.vstack([sp_data, np.zeros((2, 20))]).astype(np.float32, order="C")
return sp_data
@pytest.fixture
def binary_data():
bin_data = np.random.choice(a=[False, True], size=(10, 20), p=[0.66, 1 - 0.66])
# Add some all zero graph_data for corner case test
bin_data = np.vstack([bin_data, np.zeros((2, 20), dtype="bool")])
return bin_data
@pytest.fixture
def sparse_spatial_data(spatial_data, binary_data):
sp_sparse_data = sparse.csr_matrix(spatial_data * binary_data, dtype=np.float32)
sp_sparse_data.sort_indices()
return sp_sparse_data
@pytest.fixture
def sparse_binary_data(binary_data):
bin_sparse_data = sparse.csr_matrix(binary_data)
bin_sparse_data.sort_indices()
return bin_sparse_data
@pytest.fixture
def nn_data():
nndata = np.random.uniform(0, 1, size=(1000, 5))
# Add some all zero graph_data for corner case test
nndata = np.vstack([nndata, np.zeros((2, 5))])
return nndata
@pytest.fixture
def sparse_nn_data():
return sparse.random(1000, 50, density=0.5, format="csr")
@pytest.fixture
def cosine_hang_data():
this_dir = os.path.dirname(os.path.abspath(__file__))
data_path = os.path.join(this_dir, "test_data/cosine_hang.npy")
return np.load(data_path)
@pytest.fixture
def cosine_near_duplicates_data():
this_dir = os.path.dirname(os.path.abspath(__file__))
data_path = os.path.join(this_dir, "test_data/cosine_near_duplicates.npy")
return np.load(data_path)
@pytest.fixture
def small_data():
return np.random.uniform(40, 5, size=(20, 5))
@pytest.fixture
def sparse_small_data():
# Too low dim might cause more than one empty row,
# which might decrease the computed performance
return sparse.random(40, 32, density=0.5, format="csr")
@pytest.fixture
def update_data():
np.random.seed(12345)
xs_orig = np.random.uniform(0, 1, size=(1000, 5))
xs_fresh = np.random.uniform(0, 1, size=xs_orig.shape)
xs_fresh_small = np.random.uniform(0, 1, size=(100, xs_orig.shape[1]))
xs_for_complete_update = np.random.uniform(0, 1, size=xs_orig.shape)
updates = [
(xs_orig, None, None, None),
(xs_orig, xs_fresh, None, None),
(xs_orig, None, xs_for_complete_update, list(range(xs_orig.shape[0]))),
(xs_orig, None, -xs_orig[0:50:2], list(range(0, 50, 2))),
(xs_orig, None, -xs_orig[0:500:2], list(range(0, 500, 2))),
(xs_orig, xs_fresh, xs_for_complete_update, list(range(xs_orig.shape[0]))),
(xs_orig, xs_fresh_small, -xs_orig[0:50:2], list(range(0, 50, 2))),
(xs_orig, xs_fresh, -xs_orig[0:500:2], list(range(0, 500, 2))),
]
return updates
|
