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
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
|
import numpy as np
import pytest
from numpy.testing import assert_array_equal
from seaborn import algorithms as algo
@pytest.fixture
def random():
np.random.seed(sum(map(ord, "test_algorithms")))
def test_bootstrap(random):
"""Test that bootstrapping gives the right answer in dumb cases."""
a_ones = np.ones(10)
n_boot = 5
out1 = algo.bootstrap(a_ones, n_boot=n_boot)
assert_array_equal(out1, np.ones(n_boot))
out2 = algo.bootstrap(a_ones, n_boot=n_boot, func=np.median)
assert_array_equal(out2, np.ones(n_boot))
def test_bootstrap_length(random):
"""Test that we get a bootstrap array of the right shape."""
a_norm = np.random.randn(1000)
out = algo.bootstrap(a_norm)
assert len(out) == 10000
n_boot = 100
out = algo.bootstrap(a_norm, n_boot=n_boot)
assert len(out) == n_boot
def test_bootstrap_range(random):
"""Test that bootstrapping a random array stays within the right range."""
a_norm = np.random.randn(1000)
amin, amax = a_norm.min(), a_norm.max()
out = algo.bootstrap(a_norm)
assert amin <= out.min()
assert amax >= out.max()
def test_bootstrap_multiarg(random):
"""Test that bootstrap works with multiple input arrays."""
x = np.vstack([[1, 10] for i in range(10)])
y = np.vstack([[5, 5] for i in range(10)])
def f(x, y):
return np.vstack((x, y)).max(axis=0)
out_actual = algo.bootstrap(x, y, n_boot=2, func=f)
out_wanted = np.array([[5, 10], [5, 10]])
assert_array_equal(out_actual, out_wanted)
def test_bootstrap_axis(random):
"""Test axis kwarg to bootstrap function."""
x = np.random.randn(10, 20)
n_boot = 100
out_default = algo.bootstrap(x, n_boot=n_boot)
assert out_default.shape == (n_boot,)
out_axis = algo.bootstrap(x, n_boot=n_boot, axis=0)
assert out_axis.shape, (n_boot, x.shape[1])
def test_bootstrap_seed(random):
"""Test that we can get reproducible resamples by seeding the RNG."""
data = np.random.randn(50)
seed = 42
boots1 = algo.bootstrap(data, seed=seed)
boots2 = algo.bootstrap(data, seed=seed)
assert_array_equal(boots1, boots2)
def test_bootstrap_ols(random):
"""Test bootstrap of OLS model fit."""
def ols_fit(X, y):
XtXinv = np.linalg.inv(np.dot(X.T, X))
return XtXinv.dot(X.T).dot(y)
X = np.column_stack((np.random.randn(50, 4), np.ones(50)))
w = [2, 4, 0, 3, 5]
y_noisy = np.dot(X, w) + np.random.randn(50) * 20
y_lownoise = np.dot(X, w) + np.random.randn(50)
n_boot = 500
w_boot_noisy = algo.bootstrap(X, y_noisy,
n_boot=n_boot,
func=ols_fit)
w_boot_lownoise = algo.bootstrap(X, y_lownoise,
n_boot=n_boot,
func=ols_fit)
assert w_boot_noisy.shape == (n_boot, 5)
assert w_boot_lownoise.shape == (n_boot, 5)
assert w_boot_noisy.std() > w_boot_lownoise.std()
def test_bootstrap_units(random):
"""Test that results make sense when passing unit IDs to bootstrap."""
data = np.random.randn(50)
ids = np.repeat(range(10), 5)
bwerr = np.random.normal(0, 2, 10)
bwerr = bwerr[ids]
data_rm = data + bwerr
seed = 77
boots_orig = algo.bootstrap(data_rm, seed=seed)
boots_rm = algo.bootstrap(data_rm, units=ids, seed=seed)
assert boots_rm.std() > boots_orig.std()
def test_bootstrap_arglength():
"""Test that different length args raise ValueError."""
with pytest.raises(ValueError):
algo.bootstrap(np.arange(5), np.arange(10))
def test_bootstrap_string_func():
"""Test that named numpy methods are the same as the numpy function."""
x = np.random.randn(100)
res_a = algo.bootstrap(x, func="mean", seed=0)
res_b = algo.bootstrap(x, func=np.mean, seed=0)
assert np.array_equal(res_a, res_b)
res_a = algo.bootstrap(x, func="std", seed=0)
res_b = algo.bootstrap(x, func=np.std, seed=0)
assert np.array_equal(res_a, res_b)
with pytest.raises(AttributeError):
algo.bootstrap(x, func="not_a_method_name")
def test_bootstrap_reproducibility(random):
"""Test that bootstrapping uses the internal random state."""
data = np.random.randn(50)
boots1 = algo.bootstrap(data, seed=100)
boots2 = algo.bootstrap(data, seed=100)
assert_array_equal(boots1, boots2)
random_state1 = np.random.RandomState(200)
boots1 = algo.bootstrap(data, seed=random_state1)
random_state2 = np.random.RandomState(200)
boots2 = algo.bootstrap(data, seed=random_state2)
assert_array_equal(boots1, boots2)
with pytest.warns(UserWarning):
# Deprecated, remove when removing random_seed
boots1 = algo.bootstrap(data, random_seed=100)
boots2 = algo.bootstrap(data, random_seed=100)
assert_array_equal(boots1, boots2)
def test_nanaware_func_auto(random):
x = np.random.normal(size=10)
x[0] = np.nan
boots = algo.bootstrap(x, func="mean")
assert not np.isnan(boots).any()
def test_nanaware_func_warning(random):
x = np.random.normal(size=10)
x[0] = np.nan
with pytest.warns(UserWarning, match="Data contain nans but"):
boots = algo.bootstrap(x, func="ptp")
assert np.isnan(boots).any()
|
