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
|
# ===================================================
# UMAP Fit and Transform Operations Test cases
# (not really fitting anywhere else)
# ===================================================
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
from sklearn.metrics import adjusted_rand_score
from sklearn.preprocessing import normalize
from nose.tools import assert_equal, assert_less, assert_raises
from numpy.testing import assert_array_equal
from umap import UMAP
import numpy as np
# Transform isn't stable under batching; hard to opt out of this.
# @SkipTest
# def test_scikit_learn_compatibility():
# check_estimator(UMAP)
# This test is currently to expensive to run when turning
# off numba JITting to detect coverage.
# @SkipTest
# def test_umap_regression_supervision(): # pragma: no cover
# boston = load_boston()
# data = boston.data
# embedding = UMAP(n_neighbors=10,
# min_dist=0.01,
# target_metric='euclidean',
# random_state=42).fit_transform(data, boston.target)
#
# Umap Clusterability
def test_blobs_cluster():
data, labels = make_blobs(n_samples=500, n_features=10, centers=5)
embedding = UMAP().fit_transform(data)
assert_equal(adjusted_rand_score(labels, KMeans(5).fit_predict(embedding)), 1.0)
# Multi-components Layout
def test_multi_component_layout():
data, labels = make_blobs(
100, 2, centers=5, cluster_std=0.5, center_box=[-20, 20], random_state=42
)
true_centroids = np.empty((labels.max() + 1, data.shape[1]), dtype=np.float64)
for label in range(labels.max() + 1):
true_centroids[label] = data[labels == label].mean(axis=0)
true_centroids = normalize(true_centroids, norm="l2")
embedding = UMAP(n_neighbors=4).fit_transform(data)
embed_centroids = np.empty((labels.max() + 1, data.shape[1]), dtype=np.float64)
embed_labels = KMeans(n_clusters=5).fit_predict(embedding)
for label in range(embed_labels.max() + 1):
embed_centroids[label] = data[embed_labels == label].mean(axis=0)
embed_centroids = normalize(embed_centroids, norm="l2")
error = np.sum((true_centroids - embed_centroids) ** 2)
assert_less(error, 15.0, msg="Multi component embedding to far astray")
# ---------------
# Umap Transform
# --------------
def test_bad_transform_data(nn_data):
u = UMAP().fit([[1, 1, 1, 1]])
assert_raises(ValueError, u.transform, [[0, 0, 0, 0]])
# Transform Stability
# -------------------
def test_umap_transform_embedding_stability(iris, iris_selection):
"""Test that transforming data does not alter the learned embeddings
Issue #217 describes how using transform to embed new data using a
trained UMAP transformer causes the fitting embedding matrix to change
in cases when the new data has the same number of rows as the original
training data.
"""
data = iris.data[iris_selection]
fitter = UMAP(n_neighbors=10, min_dist=0.01, random_state=42).fit(data)
original_embedding = fitter.embedding_.copy()
# The important point is that the new data has the same number of rows
# as the original fit data
new_data = np.random.random(data.shape)
_ = fitter.transform(new_data)
assert_array_equal(
original_embedding,
fitter.embedding_,
"Transforming new data changed the original embeddings",
)
# Example from issue #217
a = np.random.random((1000, 10))
b = np.random.random((1000, 5))
umap = UMAP()
u1 = umap.fit_transform(a[:, :5])
u1_orig = u1.copy()
assert_array_equal(u1_orig, umap.embedding_)
_ = umap.transform(b)
assert_array_equal(u1_orig, umap.embedding_)
|
