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from umap import UMAP
from sklearn.datasets import make_blobs
from sklearn.metrics import pairwise_distances
import numpy as np
import scipy.sparse
try:
# works for sklearn>=0.22
from sklearn.manifold import trustworthiness
except ImportError:
# this is to comply with requirements (scikit-learn>=0.20)
# More recent versions of sklearn have exposed trustworthiness
# in top level module API
# see: https://github.com/scikit-learn/scikit-learn/pull/15337
from sklearn.manifold.t_sne import trustworthiness
# ===================================================
# UMAP Trustworthiness Test cases
# ===================================================
def test_umap_sparse_trustworthiness(sparse_test_data):
embedding = UMAP(n_neighbors=10, n_epochs=100).fit_transform(sparse_test_data[:100])
trust = trustworthiness(sparse_test_data[:100].toarray(), embedding, n_neighbors=10)
assert (
trust >= 0.88
), "Insufficiently trustworthy embedding for sparse test dataset: {}".format(trust)
def test_umap_trustworthiness_fast_approx(nn_data):
data = nn_data[:50]
embedding = UMAP(
n_neighbors=10,
min_dist=0.01,
random_state=42,
n_epochs=100,
force_approximation_algorithm=True,
).fit_transform(data)
trust = trustworthiness(data, embedding, n_neighbors=10)
assert (
trust >= 0.8
), "Insufficiently trustworthy embedding for" "nn dataset: {}".format(trust)
def test_umap_trustworthiness_random_init(nn_data):
data = nn_data[:50]
embedding = UMAP(
n_neighbors=10, min_dist=0.01, random_state=42, n_epochs=100, init="random"
).fit_transform(data)
trust = trustworthiness(data, embedding, n_neighbors=10)
assert (
trust >= 0.8
), "Insufficiently trustworthy embedding for" "nn dataset: {}".format(trust)
def test_supervised_umap_trustworthiness():
data, labels = make_blobs(50, cluster_std=0.5, random_state=42)
embedding = UMAP(
n_neighbors=10, min_dist=0.01, random_state=42, n_epochs=100
).fit_transform(data, labels)
trust = trustworthiness(data, embedding, n_neighbors=10)
assert (
trust >= 0.95
), "Insufficiently trustworthy embedding for" "blobs dataset: {}".format(trust)
def test_semisupervised_umap_trustworthiness():
data, labels = make_blobs(50, cluster_std=0.5, random_state=42)
labels[10:30] = -1
embedding = UMAP(
n_neighbors=10, min_dist=0.01, random_state=42, n_epochs=100
).fit_transform(data, labels)
trust = trustworthiness(data, embedding, n_neighbors=10)
assert (
trust >= 0.95
), "Insufficiently trustworthy embedding for" "blobs dataset: {}".format(trust)
def test_metric_supervised_umap_trustworthiness():
data, labels = make_blobs(50, cluster_std=0.5, random_state=42)
embedding = UMAP(
n_neighbors=10,
min_dist=0.01,
target_metric="l1",
target_weight=0.8,
n_epochs=100,
random_state=42,
).fit_transform(data, labels)
trust = trustworthiness(data, embedding, n_neighbors=10)
assert (
trust >= 0.95
), "Insufficiently trustworthy embedding for" "blobs dataset: {}".format(trust)
def test_string_metric_supervised_umap_trustworthiness():
data, labels = make_blobs(50, cluster_std=0.5, random_state=42)
labels = np.array(["this", "that", "other"])[labels]
embedding = UMAP(
n_neighbors=10,
min_dist=0.01,
target_metric="string",
target_weight=0.8,
n_epochs=100,
random_state=42,
).fit_transform(data, labels)
trust = trustworthiness(data, embedding, n_neighbors=10)
assert (
trust >= 0.95
), "Insufficiently trustworthy embedding for" "blobs dataset: {}".format(trust)
def test_discrete_metric_supervised_umap_trustworthiness():
data, labels = make_blobs(50, cluster_std=0.5, random_state=42)
embedding = UMAP(
n_neighbors=10,
min_dist=0.01,
target_metric="ordinal",
target_weight=0.8,
n_epochs=100,
random_state=42,
).fit_transform(data, labels)
trust = trustworthiness(data, embedding, n_neighbors=10)
assert (
trust >= 0.95
), "Insufficiently trustworthy embedding for" "blobs dataset: {}".format(trust)
def test_count_metric_supervised_umap_trustworthiness():
data, labels = make_blobs(50, cluster_std=0.5, random_state=42)
labels = (labels ** 2) + 2 * labels
embedding = UMAP(
n_neighbors=10,
min_dist=0.01,
target_metric="count",
target_weight=0.8,
n_epochs=100,
random_state=42,
).fit_transform(data, labels)
trust = trustworthiness(data, embedding, n_neighbors=10)
assert (
trust >= 0.95
), "Insufficiently trustworthy embedding for" "blobs dataset: {}".format(trust)
def test_sparse_precomputed_metric_umap_trustworthiness():
data, labels = make_blobs(50, cluster_std=0.5, random_state=42)
dmat = scipy.sparse.csr_matrix(pairwise_distances(data))
embedding = UMAP(
n_neighbors=10,
min_dist=0.01,
random_state=42,
n_epochs=100,
metric="precomputed",
).fit_transform(dmat)
trust = trustworthiness(data, embedding, n_neighbors=10)
assert (
trust >= 0.75
), "Insufficiently trustworthy embedding for" "nn dataset: {}".format(trust)
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