1182 lines
38 KiB
Python
1182 lines
38 KiB
Python
|
import sys
|
||
|
|
from io import StringIO
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import pytest
|
||
|
|
import scipy.sparse as sp
|
||
|
|
from numpy.testing import assert_allclose
|
||
|
|
from scipy.optimize import check_grad
|
||
|
|
from scipy.spatial.distance import pdist, squareform
|
||
|
|
|
||
|
|
from sklearn import config_context
|
||
|
|
from sklearn.datasets import make_blobs
|
||
|
|
from sklearn.exceptions import EfficiencyWarning
|
||
|
|
|
||
|
|
# mypy error: Module 'sklearn.manifold' has no attribute '_barnes_hut_tsne'
|
||
|
|
from sklearn.manifold import ( # type: ignore
|
||
|
|
TSNE,
|
||
|
|
_barnes_hut_tsne,
|
||
|
|
)
|
||
|
|
from sklearn.manifold._t_sne import (
|
||
|
|
_gradient_descent,
|
||
|
|
_joint_probabilities,
|
||
|
|
_joint_probabilities_nn,
|
||
|
|
_kl_divergence,
|
||
|
|
_kl_divergence_bh,
|
||
|
|
trustworthiness,
|
||
|
|
)
|
||
|
|
from sklearn.manifold._utils import _binary_search_perplexity
|
||
|
|
from sklearn.metrics.pairwise import (
|
||
|
|
cosine_distances,
|
||
|
|
manhattan_distances,
|
||
|
|
pairwise_distances,
|
||
|
|
)
|
||
|
|
from sklearn.neighbors import NearestNeighbors, kneighbors_graph
|
||
|
|
from sklearn.utils import check_random_state
|
||
|
|
from sklearn.utils._testing import (
|
||
|
|
assert_almost_equal,
|
||
|
|
assert_array_almost_equal,
|
||
|
|
assert_array_equal,
|
||
|
|
ignore_warnings,
|
||
|
|
skip_if_32bit,
|
||
|
|
)
|
||
|
|
from sklearn.utils.fixes import CSR_CONTAINERS, LIL_CONTAINERS
|
||
|
|
|
||
|
|
x = np.linspace(0, 1, 10)
|
||
|
|
xx, yy = np.meshgrid(x, x)
|
||
|
|
X_2d_grid = np.hstack(
|
||
|
|
[
|
||
|
|
xx.ravel().reshape(-1, 1),
|
||
|
|
yy.ravel().reshape(-1, 1),
|
||
|
|
]
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def test_gradient_descent_stops():
|
||
|
|
# Test stopping conditions of gradient descent.
|
||
|
|
class ObjectiveSmallGradient:
|
||
|
|
def __init__(self):
|
||
|
|
self.it = -1
|
||
|
|
|
||
|
|
def __call__(self, _, compute_error=True):
|
||
|
|
self.it += 1
|
||
|
|
return (10 - self.it) / 10.0, np.array([1e-5])
|
||
|
|
|
||
|
|
def flat_function(_, compute_error=True):
|
||
|
|
return 0.0, np.ones(1)
|
||
|
|
|
||
|
|
# Gradient norm
|
||
|
|
old_stdout = sys.stdout
|
||
|
|
sys.stdout = StringIO()
|
||
|
|
try:
|
||
|
|
_, error, it = _gradient_descent(
|
||
|
|
ObjectiveSmallGradient(),
|
||
|
|
np.zeros(1),
|
||
|
|
0,
|
||
|
|
n_iter=100,
|
||
|
|
n_iter_without_progress=100,
|
||
|
|
momentum=0.0,
|
||
|
|
learning_rate=0.0,
|
||
|
|
min_gain=0.0,
|
||
|
|
min_grad_norm=1e-5,
|
||
|
|
verbose=2,
|
||
|
|
)
|
||
|
|
finally:
|
||
|
|
out = sys.stdout.getvalue()
|
||
|
|
sys.stdout.close()
|
||
|
|
sys.stdout = old_stdout
|
||
|
|
assert error == 1.0
|
||
|
|
assert it == 0
|
||
|
|
assert "gradient norm" in out
|
||
|
|
|
||
|
|
# Maximum number of iterations without improvement
|
||
|
|
old_stdout = sys.stdout
|
||
|
|
sys.stdout = StringIO()
|
||
|
|
try:
|
||
|
|
_, error, it = _gradient_descent(
|
||
|
|
flat_function,
|
||
|
|
np.zeros(1),
|
||
|
|
0,
|
||
|
|
n_iter=100,
|
||
|
|
n_iter_without_progress=10,
|
||
|
|
momentum=0.0,
|
||
|
|
learning_rate=0.0,
|
||
|
|
min_gain=0.0,
|
||
|
|
min_grad_norm=0.0,
|
||
|
|
verbose=2,
|
||
|
|
)
|
||
|
|
finally:
|
||
|
|
out = sys.stdout.getvalue()
|
||
|
|
sys.stdout.close()
|
||
|
|
sys.stdout = old_stdout
|
||
|
|
assert error == 0.0
|
||
|
|
assert it == 11
|
||
|
|
assert "did not make any progress" in out
|
||
|
|
|
||
|
|
# Maximum number of iterations
|
||
|
|
old_stdout = sys.stdout
|
||
|
|
sys.stdout = StringIO()
|
||
|
|
try:
|
||
|
|
_, error, it = _gradient_descent(
|
||
|
|
ObjectiveSmallGradient(),
|
||
|
|
np.zeros(1),
|
||
|
|
0,
|
||
|
|
n_iter=11,
|
||
|
|
n_iter_without_progress=100,
|
||
|
|
momentum=0.0,
|
||
|
|
learning_rate=0.0,
|
||
|
|
min_gain=0.0,
|
||
|
|
min_grad_norm=0.0,
|
||
|
|
verbose=2,
|
||
|
|
)
|
||
|
|
finally:
|
||
|
|
out = sys.stdout.getvalue()
|
||
|
|
sys.stdout.close()
|
||
|
|
sys.stdout = old_stdout
|
||
|
|
assert error == 0.0
|
||
|
|
assert it == 10
|
||
|
|
assert "Iteration 10" in out
|
||
|
|
|
||
|
|
|
||
|
|
def test_binary_search():
|
||
|
|
# Test if the binary search finds Gaussians with desired perplexity.
|
||
|
|
random_state = check_random_state(0)
|
||
|
|
data = random_state.randn(50, 5)
|
||
|
|
distances = pairwise_distances(data).astype(np.float32)
|
||
|
|
desired_perplexity = 25.0
|
||
|
|
P = _binary_search_perplexity(distances, desired_perplexity, verbose=0)
|
||
|
|
P = np.maximum(P, np.finfo(np.double).eps)
|
||
|
|
mean_perplexity = np.mean(
|
||
|
|
[np.exp(-np.sum(P[i] * np.log(P[i]))) for i in range(P.shape[0])]
|
||
|
|
)
|
||
|
|
assert_almost_equal(mean_perplexity, desired_perplexity, decimal=3)
|
||
|
|
|
||
|
|
|
||
|
|
def test_binary_search_underflow():
|
||
|
|
# Test if the binary search finds Gaussians with desired perplexity.
|
||
|
|
# A more challenging case than the one above, producing numeric
|
||
|
|
# underflow in float precision (see issue #19471 and PR #19472).
|
||
|
|
random_state = check_random_state(42)
|
||
|
|
data = random_state.randn(1, 90).astype(np.float32) + 100
|
||
|
|
desired_perplexity = 30.0
|
||
|
|
P = _binary_search_perplexity(data, desired_perplexity, verbose=0)
|
||
|
|
perplexity = 2 ** -np.nansum(P[0, 1:] * np.log2(P[0, 1:]))
|
||
|
|
assert_almost_equal(perplexity, desired_perplexity, decimal=3)
|
||
|
|
|
||
|
|
|
||
|
|
def test_binary_search_neighbors():
|
||
|
|
# Binary perplexity search approximation.
|
||
|
|
# Should be approximately equal to the slow method when we use
|
||
|
|
# all points as neighbors.
|
||
|
|
n_samples = 200
|
||
|
|
desired_perplexity = 25.0
|
||
|
|
random_state = check_random_state(0)
|
||
|
|
data = random_state.randn(n_samples, 2).astype(np.float32, copy=False)
|
||
|
|
distances = pairwise_distances(data)
|
||
|
|
P1 = _binary_search_perplexity(distances, desired_perplexity, verbose=0)
|
||
|
|
|
||
|
|
# Test that when we use all the neighbors the results are identical
|
||
|
|
n_neighbors = n_samples - 1
|
||
|
|
nn = NearestNeighbors().fit(data)
|
||
|
|
distance_graph = nn.kneighbors_graph(n_neighbors=n_neighbors, mode="distance")
|
||
|
|
distances_nn = distance_graph.data.astype(np.float32, copy=False)
|
||
|
|
distances_nn = distances_nn.reshape(n_samples, n_neighbors)
|
||
|
|
P2 = _binary_search_perplexity(distances_nn, desired_perplexity, verbose=0)
|
||
|
|
|
||
|
|
indptr = distance_graph.indptr
|
||
|
|
P1_nn = np.array(
|
||
|
|
[
|
||
|
|
P1[k, distance_graph.indices[indptr[k] : indptr[k + 1]]]
|
||
|
|
for k in range(n_samples)
|
||
|
|
]
|
||
|
|
)
|
||
|
|
assert_array_almost_equal(P1_nn, P2, decimal=4)
|
||
|
|
|
||
|
|
# Test that the highest P_ij are the same when fewer neighbors are used
|
||
|
|
for k in np.linspace(150, n_samples - 1, 5):
|
||
|
|
k = int(k)
|
||
|
|
topn = k * 10 # check the top 10 * k entries out of k * k entries
|
||
|
|
distance_graph = nn.kneighbors_graph(n_neighbors=k, mode="distance")
|
||
|
|
distances_nn = distance_graph.data.astype(np.float32, copy=False)
|
||
|
|
distances_nn = distances_nn.reshape(n_samples, k)
|
||
|
|
P2k = _binary_search_perplexity(distances_nn, desired_perplexity, verbose=0)
|
||
|
|
assert_array_almost_equal(P1_nn, P2, decimal=2)
|
||
|
|
idx = np.argsort(P1.ravel())[::-1]
|
||
|
|
P1top = P1.ravel()[idx][:topn]
|
||
|
|
idx = np.argsort(P2k.ravel())[::-1]
|
||
|
|
P2top = P2k.ravel()[idx][:topn]
|
||
|
|
assert_array_almost_equal(P1top, P2top, decimal=2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_binary_perplexity_stability():
|
||
|
|
# Binary perplexity search should be stable.
|
||
|
|
# The binary_search_perplexity had a bug wherein the P array
|
||
|
|
# was uninitialized, leading to sporadically failing tests.
|
||
|
|
n_neighbors = 10
|
||
|
|
n_samples = 100
|
||
|
|
random_state = check_random_state(0)
|
||
|
|
data = random_state.randn(n_samples, 5)
|
||
|
|
nn = NearestNeighbors().fit(data)
|
||
|
|
distance_graph = nn.kneighbors_graph(n_neighbors=n_neighbors, mode="distance")
|
||
|
|
distances = distance_graph.data.astype(np.float32, copy=False)
|
||
|
|
distances = distances.reshape(n_samples, n_neighbors)
|
||
|
|
last_P = None
|
||
|
|
desired_perplexity = 3
|
||
|
|
for _ in range(100):
|
||
|
|
P = _binary_search_perplexity(distances.copy(), desired_perplexity, verbose=0)
|
||
|
|
P1 = _joint_probabilities_nn(distance_graph, desired_perplexity, verbose=0)
|
||
|
|
# Convert the sparse matrix to a dense one for testing
|
||
|
|
P1 = P1.toarray()
|
||
|
|
if last_P is None:
|
||
|
|
last_P = P
|
||
|
|
last_P1 = P1
|
||
|
|
else:
|
||
|
|
assert_array_almost_equal(P, last_P, decimal=4)
|
||
|
|
assert_array_almost_equal(P1, last_P1, decimal=4)
|
||
|
|
|
||
|
|
|
||
|
|
def test_gradient():
|
||
|
|
# Test gradient of Kullback-Leibler divergence.
|
||
|
|
random_state = check_random_state(0)
|
||
|
|
|
||
|
|
n_samples = 50
|
||
|
|
n_features = 2
|
||
|
|
n_components = 2
|
||
|
|
alpha = 1.0
|
||
|
|
|
||
|
|
distances = random_state.randn(n_samples, n_features).astype(np.float32)
|
||
|
|
distances = np.abs(distances.dot(distances.T))
|
||
|
|
np.fill_diagonal(distances, 0.0)
|
||
|
|
X_embedded = random_state.randn(n_samples, n_components).astype(np.float32)
|
||
|
|
|
||
|
|
P = _joint_probabilities(distances, desired_perplexity=25.0, verbose=0)
|
||
|
|
|
||
|
|
def fun(params):
|
||
|
|
return _kl_divergence(params, P, alpha, n_samples, n_components)[0]
|
||
|
|
|
||
|
|
def grad(params):
|
||
|
|
return _kl_divergence(params, P, alpha, n_samples, n_components)[1]
|
||
|
|
|
||
|
|
assert_almost_equal(check_grad(fun, grad, X_embedded.ravel()), 0.0, decimal=5)
|
||
|
|
|
||
|
|
|
||
|
|
def test_trustworthiness():
|
||
|
|
# Test trustworthiness score.
|
||
|
|
random_state = check_random_state(0)
|
||
|
|
|
||
|
|
# Affine transformation
|
||
|
|
X = random_state.randn(100, 2)
|
||
|
|
assert trustworthiness(X, 5.0 + X / 10.0) == 1.0
|
||
|
|
|
||
|
|
# Randomly shuffled
|
||
|
|
X = np.arange(100).reshape(-1, 1)
|
||
|
|
X_embedded = X.copy()
|
||
|
|
random_state.shuffle(X_embedded)
|
||
|
|
assert trustworthiness(X, X_embedded) < 0.6
|
||
|
|
|
||
|
|
# Completely different
|
||
|
|
X = np.arange(5).reshape(-1, 1)
|
||
|
|
X_embedded = np.array([[0], [2], [4], [1], [3]])
|
||
|
|
assert_almost_equal(trustworthiness(X, X_embedded, n_neighbors=1), 0.2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_trustworthiness_n_neighbors_error():
|
||
|
|
"""Raise an error when n_neighbors >= n_samples / 2.
|
||
|
|
|
||
|
|
Non-regression test for #18567.
|
||
|
|
"""
|
||
|
|
regex = "n_neighbors .+ should be less than .+"
|
||
|
|
rng = np.random.RandomState(42)
|
||
|
|
X = rng.rand(7, 4)
|
||
|
|
X_embedded = rng.rand(7, 2)
|
||
|
|
with pytest.raises(ValueError, match=regex):
|
||
|
|
trustworthiness(X, X_embedded, n_neighbors=5)
|
||
|
|
|
||
|
|
trust = trustworthiness(X, X_embedded, n_neighbors=3)
|
||
|
|
assert 0 <= trust <= 1
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("method", ["exact", "barnes_hut"])
|
||
|
|
@pytest.mark.parametrize("init", ("random", "pca"))
|
||
|
|
def test_preserve_trustworthiness_approximately(method, init):
|
||
|
|
# Nearest neighbors should be preserved approximately.
|
||
|
|
random_state = check_random_state(0)
|
||
|
|
n_components = 2
|
||
|
|
X = random_state.randn(50, n_components).astype(np.float32)
|
||
|
|
tsne = TSNE(
|
||
|
|
n_components=n_components,
|
||
|
|
init=init,
|
||
|
|
random_state=0,
|
||
|
|
method=method,
|
||
|
|
n_iter=700,
|
||
|
|
learning_rate="auto",
|
||
|
|
)
|
||
|
|
X_embedded = tsne.fit_transform(X)
|
||
|
|
t = trustworthiness(X, X_embedded, n_neighbors=1)
|
||
|
|
assert t > 0.85
|
||
|
|
|
||
|
|
|
||
|
|
def test_optimization_minimizes_kl_divergence():
|
||
|
|
"""t-SNE should give a lower KL divergence with more iterations."""
|
||
|
|
random_state = check_random_state(0)
|
||
|
|
X, _ = make_blobs(n_features=3, random_state=random_state)
|
||
|
|
kl_divergences = []
|
||
|
|
for n_iter in [250, 300, 350]:
|
||
|
|
tsne = TSNE(
|
||
|
|
n_components=2,
|
||
|
|
init="random",
|
||
|
|
perplexity=10,
|
||
|
|
learning_rate=100.0,
|
||
|
|
n_iter=n_iter,
|
||
|
|
random_state=0,
|
||
|
|
)
|
||
|
|
tsne.fit_transform(X)
|
||
|
|
kl_divergences.append(tsne.kl_divergence_)
|
||
|
|
assert kl_divergences[1] <= kl_divergences[0]
|
||
|
|
assert kl_divergences[2] <= kl_divergences[1]
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("method", ["exact", "barnes_hut"])
|
||
|
|
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
|
||
|
|
def test_fit_transform_csr_matrix(method, csr_container):
|
||
|
|
# TODO: compare results on dense and sparse data as proposed in:
|
||
|
|
# https://github.com/scikit-learn/scikit-learn/pull/23585#discussion_r968388186
|
||
|
|
# X can be a sparse matrix.
|
||
|
|
rng = check_random_state(0)
|
||
|
|
X = rng.randn(50, 2)
|
||
|
|
X[(rng.randint(0, 50, 25), rng.randint(0, 2, 25))] = 0.0
|
||
|
|
X_csr = csr_container(X)
|
||
|
|
tsne = TSNE(
|
||
|
|
n_components=2,
|
||
|
|
init="random",
|
||
|
|
perplexity=10,
|
||
|
|
learning_rate=100.0,
|
||
|
|
random_state=0,
|
||
|
|
method=method,
|
||
|
|
n_iter=750,
|
||
|
|
)
|
||
|
|
X_embedded = tsne.fit_transform(X_csr)
|
||
|
|
assert_allclose(trustworthiness(X_csr, X_embedded, n_neighbors=1), 1.0, rtol=1.1e-1)
|
||
|
|
|
||
|
|
|
||
|
|
def test_preserve_trustworthiness_approximately_with_precomputed_distances():
|
||
|
|
# Nearest neighbors should be preserved approximately.
|
||
|
|
random_state = check_random_state(0)
|
||
|
|
for i in range(3):
|
||
|
|
X = random_state.randn(80, 2)
|
||
|
|
D = squareform(pdist(X), "sqeuclidean")
|
||
|
|
tsne = TSNE(
|
||
|
|
n_components=2,
|
||
|
|
perplexity=2,
|
||
|
|
learning_rate=100.0,
|
||
|
|
early_exaggeration=2.0,
|
||
|
|
metric="precomputed",
|
||
|
|
random_state=i,
|
||
|
|
verbose=0,
|
||
|
|
n_iter=500,
|
||
|
|
init="random",
|
||
|
|
)
|
||
|
|
X_embedded = tsne.fit_transform(D)
|
||
|
|
t = trustworthiness(D, X_embedded, n_neighbors=1, metric="precomputed")
|
||
|
|
assert t > 0.95
|
||
|
|
|
||
|
|
|
||
|
|
def test_trustworthiness_not_euclidean_metric():
|
||
|
|
# Test trustworthiness with a metric different from 'euclidean' and
|
||
|
|
# 'precomputed'
|
||
|
|
random_state = check_random_state(0)
|
||
|
|
X = random_state.randn(100, 2)
|
||
|
|
assert trustworthiness(X, X, metric="cosine") == trustworthiness(
|
||
|
|
pairwise_distances(X, metric="cosine"), X, metric="precomputed"
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"method, retype",
|
||
|
|
[
|
||
|
|
("exact", np.asarray),
|
||
|
|
("barnes_hut", np.asarray),
|
||
|
|
*[("barnes_hut", csr_container) for csr_container in CSR_CONTAINERS],
|
||
|
|
],
|
||
|
|
)
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"D, message_regex",
|
||
|
|
[
|
||
|
|
([[0.0], [1.0]], ".* square distance matrix"),
|
||
|
|
([[0.0, -1.0], [1.0, 0.0]], ".* positive.*"),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_bad_precomputed_distances(method, D, retype, message_regex):
|
||
|
|
tsne = TSNE(
|
||
|
|
metric="precomputed",
|
||
|
|
method=method,
|
||
|
|
init="random",
|
||
|
|
random_state=42,
|
||
|
|
perplexity=1,
|
||
|
|
)
|
||
|
|
with pytest.raises(ValueError, match=message_regex):
|
||
|
|
tsne.fit_transform(retype(D))
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
|
||
|
|
def test_exact_no_precomputed_sparse(csr_container):
|
||
|
|
tsne = TSNE(
|
||
|
|
metric="precomputed",
|
||
|
|
method="exact",
|
||
|
|
init="random",
|
||
|
|
random_state=42,
|
||
|
|
perplexity=1,
|
||
|
|
)
|
||
|
|
with pytest.raises(TypeError, match="sparse"):
|
||
|
|
tsne.fit_transform(csr_container([[0, 5], [5, 0]]))
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
|
||
|
|
def test_high_perplexity_precomputed_sparse_distances(csr_container):
|
||
|
|
# Perplexity should be less than 50
|
||
|
|
dist = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0]])
|
||
|
|
bad_dist = csr_container(dist)
|
||
|
|
tsne = TSNE(metric="precomputed", init="random", random_state=42, perplexity=1)
|
||
|
|
msg = "3 neighbors per samples are required, but some samples have only 1"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
tsne.fit_transform(bad_dist)
|
||
|
|
|
||
|
|
|
||
|
|
@ignore_warnings(category=EfficiencyWarning)
|
||
|
|
@pytest.mark.parametrize("sparse_container", CSR_CONTAINERS + LIL_CONTAINERS)
|
||
|
|
def test_sparse_precomputed_distance(sparse_container):
|
||
|
|
"""Make sure that TSNE works identically for sparse and dense matrix"""
|
||
|
|
random_state = check_random_state(0)
|
||
|
|
X = random_state.randn(100, 2)
|
||
|
|
|
||
|
|
D_sparse = kneighbors_graph(X, n_neighbors=100, mode="distance", include_self=True)
|
||
|
|
D = pairwise_distances(X)
|
||
|
|
assert sp.issparse(D_sparse)
|
||
|
|
assert_almost_equal(D_sparse.toarray(), D)
|
||
|
|
|
||
|
|
tsne = TSNE(
|
||
|
|
metric="precomputed", random_state=0, init="random", learning_rate="auto"
|
||
|
|
)
|
||
|
|
Xt_dense = tsne.fit_transform(D)
|
||
|
|
|
||
|
|
Xt_sparse = tsne.fit_transform(sparse_container(D_sparse))
|
||
|
|
assert_almost_equal(Xt_dense, Xt_sparse)
|
||
|
|
|
||
|
|
|
||
|
|
def test_non_positive_computed_distances():
|
||
|
|
# Computed distance matrices must be positive.
|
||
|
|
def metric(x, y):
|
||
|
|
return -1
|
||
|
|
|
||
|
|
# Negative computed distances should be caught even if result is squared
|
||
|
|
tsne = TSNE(metric=metric, method="exact", perplexity=1)
|
||
|
|
X = np.array([[0.0, 0.0], [1.0, 1.0]])
|
||
|
|
with pytest.raises(ValueError, match="All distances .*metric given.*"):
|
||
|
|
tsne.fit_transform(X)
|
||
|
|
|
||
|
|
|
||
|
|
def test_init_ndarray():
|
||
|
|
# Initialize TSNE with ndarray and test fit
|
||
|
|
tsne = TSNE(init=np.zeros((100, 2)), learning_rate="auto")
|
||
|
|
X_embedded = tsne.fit_transform(np.ones((100, 5)))
|
||
|
|
assert_array_equal(np.zeros((100, 2)), X_embedded)
|
||
|
|
|
||
|
|
|
||
|
|
def test_init_ndarray_precomputed():
|
||
|
|
# Initialize TSNE with ndarray and metric 'precomputed'
|
||
|
|
# Make sure no FutureWarning is thrown from _fit
|
||
|
|
tsne = TSNE(
|
||
|
|
init=np.zeros((100, 2)),
|
||
|
|
metric="precomputed",
|
||
|
|
learning_rate=50.0,
|
||
|
|
)
|
||
|
|
tsne.fit(np.zeros((100, 100)))
|
||
|
|
|
||
|
|
|
||
|
|
def test_pca_initialization_not_compatible_with_precomputed_kernel():
|
||
|
|
# Precomputed distance matrices cannot use PCA initialization.
|
||
|
|
tsne = TSNE(metric="precomputed", init="pca", perplexity=1)
|
||
|
|
with pytest.raises(
|
||
|
|
ValueError,
|
||
|
|
match='The parameter init="pca" cannot be used with metric="precomputed".',
|
||
|
|
):
|
||
|
|
tsne.fit_transform(np.array([[0.0], [1.0]]))
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
|
||
|
|
def test_pca_initialization_not_compatible_with_sparse_input(csr_container):
|
||
|
|
# Sparse input matrices cannot use PCA initialization.
|
||
|
|
tsne = TSNE(init="pca", learning_rate=100.0, perplexity=1)
|
||
|
|
with pytest.raises(TypeError, match="PCA initialization.*"):
|
||
|
|
tsne.fit_transform(csr_container([[0, 5], [5, 0]]))
|
||
|
|
|
||
|
|
|
||
|
|
def test_n_components_range():
|
||
|
|
# barnes_hut method should only be used with n_components <= 3
|
||
|
|
tsne = TSNE(n_components=4, method="barnes_hut", perplexity=1)
|
||
|
|
with pytest.raises(ValueError, match="'n_components' should be .*"):
|
||
|
|
tsne.fit_transform(np.array([[0.0], [1.0]]))
|
||
|
|
|
||
|
|
|
||
|
|
def test_early_exaggeration_used():
|
||
|
|
# check that the ``early_exaggeration`` parameter has an effect
|
||
|
|
random_state = check_random_state(0)
|
||
|
|
n_components = 2
|
||
|
|
methods = ["exact", "barnes_hut"]
|
||
|
|
X = random_state.randn(25, n_components).astype(np.float32)
|
||
|
|
for method in methods:
|
||
|
|
tsne = TSNE(
|
||
|
|
n_components=n_components,
|
||
|
|
perplexity=1,
|
||
|
|
learning_rate=100.0,
|
||
|
|
init="pca",
|
||
|
|
random_state=0,
|
||
|
|
method=method,
|
||
|
|
early_exaggeration=1.0,
|
||
|
|
n_iter=250,
|
||
|
|
)
|
||
|
|
X_embedded1 = tsne.fit_transform(X)
|
||
|
|
tsne = TSNE(
|
||
|
|
n_components=n_components,
|
||
|
|
perplexity=1,
|
||
|
|
learning_rate=100.0,
|
||
|
|
init="pca",
|
||
|
|
random_state=0,
|
||
|
|
method=method,
|
||
|
|
early_exaggeration=10.0,
|
||
|
|
n_iter=250,
|
||
|
|
)
|
||
|
|
X_embedded2 = tsne.fit_transform(X)
|
||
|
|
|
||
|
|
assert not np.allclose(X_embedded1, X_embedded2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_n_iter_used():
|
||
|
|
# check that the ``n_iter`` parameter has an effect
|
||
|
|
random_state = check_random_state(0)
|
||
|
|
n_components = 2
|
||
|
|
methods = ["exact", "barnes_hut"]
|
||
|
|
X = random_state.randn(25, n_components).astype(np.float32)
|
||
|
|
for method in methods:
|
||
|
|
for n_iter in [251, 500]:
|
||
|
|
tsne = TSNE(
|
||
|
|
n_components=n_components,
|
||
|
|
perplexity=1,
|
||
|
|
learning_rate=0.5,
|
||
|
|
init="random",
|
||
|
|
random_state=0,
|
||
|
|
method=method,
|
||
|
|
early_exaggeration=1.0,
|
||
|
|
n_iter=n_iter,
|
||
|
|
)
|
||
|
|
tsne.fit_transform(X)
|
||
|
|
|
||
|
|
assert tsne.n_iter_ == n_iter - 1
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
|
||
|
|
def test_answer_gradient_two_points(csr_container):
|
||
|
|
# Test the tree with only a single set of children.
|
||
|
|
#
|
||
|
|
# These tests & answers have been checked against the reference
|
||
|
|
# implementation by LvdM.
|
||
|
|
pos_input = np.array([[1.0, 0.0], [0.0, 1.0]])
|
||
|
|
pos_output = np.array(
|
||
|
|
[[-4.961291e-05, -1.072243e-04], [9.259460e-05, 2.702024e-04]]
|
||
|
|
)
|
||
|
|
neighbors = np.array([[1], [0]])
|
||
|
|
grad_output = np.array(
|
||
|
|
[[-2.37012478e-05, -6.29044398e-05], [2.37012478e-05, 6.29044398e-05]]
|
||
|
|
)
|
||
|
|
_run_answer_test(pos_input, pos_output, neighbors, grad_output, csr_container)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
|
||
|
|
def test_answer_gradient_four_points(csr_container):
|
||
|
|
# Four points tests the tree with multiple levels of children.
|
||
|
|
#
|
||
|
|
# These tests & answers have been checked against the reference
|
||
|
|
# implementation by LvdM.
|
||
|
|
pos_input = np.array([[1.0, 0.0], [0.0, 1.0], [5.0, 2.0], [7.3, 2.2]])
|
||
|
|
pos_output = np.array(
|
||
|
|
[
|
||
|
|
[6.080564e-05, -7.120823e-05],
|
||
|
|
[-1.718945e-04, -4.000536e-05],
|
||
|
|
[-2.271720e-04, 8.663310e-05],
|
||
|
|
[-1.032577e-04, -3.582033e-05],
|
||
|
|
]
|
||
|
|
)
|
||
|
|
neighbors = np.array([[1, 2, 3], [0, 2, 3], [1, 0, 3], [1, 2, 0]])
|
||
|
|
grad_output = np.array(
|
||
|
|
[
|
||
|
|
[5.81128448e-05, -7.78033454e-06],
|
||
|
|
[-5.81526851e-05, 7.80976444e-06],
|
||
|
|
[4.24275173e-08, -3.69569698e-08],
|
||
|
|
[-2.58720939e-09, 7.52706374e-09],
|
||
|
|
]
|
||
|
|
)
|
||
|
|
_run_answer_test(pos_input, pos_output, neighbors, grad_output, csr_container)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
|
||
|
|
def test_skip_num_points_gradient(csr_container):
|
||
|
|
# Test the kwargs option skip_num_points.
|
||
|
|
#
|
||
|
|
# Skip num points should make it such that the Barnes_hut gradient
|
||
|
|
# is not calculated for indices below skip_num_point.
|
||
|
|
# Aside from skip_num_points=2 and the first two gradient rows
|
||
|
|
# being set to zero, these data points are the same as in
|
||
|
|
# test_answer_gradient_four_points()
|
||
|
|
pos_input = np.array([[1.0, 0.0], [0.0, 1.0], [5.0, 2.0], [7.3, 2.2]])
|
||
|
|
pos_output = np.array(
|
||
|
|
[
|
||
|
|
[6.080564e-05, -7.120823e-05],
|
||
|
|
[-1.718945e-04, -4.000536e-05],
|
||
|
|
[-2.271720e-04, 8.663310e-05],
|
||
|
|
[-1.032577e-04, -3.582033e-05],
|
||
|
|
]
|
||
|
|
)
|
||
|
|
neighbors = np.array([[1, 2, 3], [0, 2, 3], [1, 0, 3], [1, 2, 0]])
|
||
|
|
grad_output = np.array(
|
||
|
|
[
|
||
|
|
[0.0, 0.0],
|
||
|
|
[0.0, 0.0],
|
||
|
|
[4.24275173e-08, -3.69569698e-08],
|
||
|
|
[-2.58720939e-09, 7.52706374e-09],
|
||
|
|
]
|
||
|
|
)
|
||
|
|
_run_answer_test(
|
||
|
|
pos_input, pos_output, neighbors, grad_output, csr_container, False, 0.1, 2
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def _run_answer_test(
|
||
|
|
pos_input,
|
||
|
|
pos_output,
|
||
|
|
neighbors,
|
||
|
|
grad_output,
|
||
|
|
csr_container,
|
||
|
|
verbose=False,
|
||
|
|
perplexity=0.1,
|
||
|
|
skip_num_points=0,
|
||
|
|
):
|
||
|
|
distances = pairwise_distances(pos_input).astype(np.float32)
|
||
|
|
args = distances, perplexity, verbose
|
||
|
|
pos_output = pos_output.astype(np.float32)
|
||
|
|
neighbors = neighbors.astype(np.int64, copy=False)
|
||
|
|
pij_input = _joint_probabilities(*args)
|
||
|
|
pij_input = squareform(pij_input).astype(np.float32)
|
||
|
|
grad_bh = np.zeros(pos_output.shape, dtype=np.float32)
|
||
|
|
|
||
|
|
P = csr_container(pij_input)
|
||
|
|
|
||
|
|
neighbors = P.indices.astype(np.int64)
|
||
|
|
indptr = P.indptr.astype(np.int64)
|
||
|
|
|
||
|
|
_barnes_hut_tsne.gradient(
|
||
|
|
P.data, pos_output, neighbors, indptr, grad_bh, 0.5, 2, 1, skip_num_points=0
|
||
|
|
)
|
||
|
|
assert_array_almost_equal(grad_bh, grad_output, decimal=4)
|
||
|
|
|
||
|
|
|
||
|
|
def test_verbose():
|
||
|
|
# Verbose options write to stdout.
|
||
|
|
random_state = check_random_state(0)
|
||
|
|
tsne = TSNE(verbose=2, perplexity=4)
|
||
|
|
X = random_state.randn(5, 2)
|
||
|
|
|
||
|
|
old_stdout = sys.stdout
|
||
|
|
sys.stdout = StringIO()
|
||
|
|
try:
|
||
|
|
tsne.fit_transform(X)
|
||
|
|
finally:
|
||
|
|
out = sys.stdout.getvalue()
|
||
|
|
sys.stdout.close()
|
||
|
|
sys.stdout = old_stdout
|
||
|
|
|
||
|
|
assert "[t-SNE]" in out
|
||
|
|
assert "nearest neighbors..." in out
|
||
|
|
assert "Computed conditional probabilities" in out
|
||
|
|
assert "Mean sigma" in out
|
||
|
|
assert "early exaggeration" in out
|
||
|
|
|
||
|
|
|
||
|
|
def test_chebyshev_metric():
|
||
|
|
# t-SNE should allow metrics that cannot be squared (issue #3526).
|
||
|
|
random_state = check_random_state(0)
|
||
|
|
tsne = TSNE(metric="chebyshev", perplexity=4)
|
||
|
|
X = random_state.randn(5, 2)
|
||
|
|
tsne.fit_transform(X)
|
||
|
|
|
||
|
|
|
||
|
|
def test_reduction_to_one_component():
|
||
|
|
# t-SNE should allow reduction to one component (issue #4154).
|
||
|
|
random_state = check_random_state(0)
|
||
|
|
tsne = TSNE(n_components=1, perplexity=4)
|
||
|
|
X = random_state.randn(5, 2)
|
||
|
|
X_embedded = tsne.fit(X).embedding_
|
||
|
|
assert np.all(np.isfinite(X_embedded))
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("method", ["barnes_hut", "exact"])
|
||
|
|
@pytest.mark.parametrize("dt", [np.float32, np.float64])
|
||
|
|
def test_64bit(method, dt):
|
||
|
|
# Ensure 64bit arrays are handled correctly.
|
||
|
|
random_state = check_random_state(0)
|
||
|
|
|
||
|
|
X = random_state.randn(10, 2).astype(dt, copy=False)
|
||
|
|
tsne = TSNE(
|
||
|
|
n_components=2,
|
||
|
|
perplexity=2,
|
||
|
|
learning_rate=100.0,
|
||
|
|
random_state=0,
|
||
|
|
method=method,
|
||
|
|
verbose=0,
|
||
|
|
n_iter=300,
|
||
|
|
init="random",
|
||
|
|
)
|
||
|
|
X_embedded = tsne.fit_transform(X)
|
||
|
|
effective_type = X_embedded.dtype
|
||
|
|
|
||
|
|
# tsne cython code is only single precision, so the output will
|
||
|
|
# always be single precision, irrespectively of the input dtype
|
||
|
|
assert effective_type == np.float32
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("method", ["barnes_hut", "exact"])
|
||
|
|
def test_kl_divergence_not_nan(method):
|
||
|
|
# Ensure kl_divergence_ is computed at last iteration
|
||
|
|
# even though n_iter % n_iter_check != 0, i.e. 1003 % 50 != 0
|
||
|
|
random_state = check_random_state(0)
|
||
|
|
|
||
|
|
X = random_state.randn(50, 2)
|
||
|
|
tsne = TSNE(
|
||
|
|
n_components=2,
|
||
|
|
perplexity=2,
|
||
|
|
learning_rate=100.0,
|
||
|
|
random_state=0,
|
||
|
|
method=method,
|
||
|
|
verbose=0,
|
||
|
|
n_iter=503,
|
||
|
|
init="random",
|
||
|
|
)
|
||
|
|
tsne.fit_transform(X)
|
||
|
|
|
||
|
|
assert not np.isnan(tsne.kl_divergence_)
|
||
|
|
|
||
|
|
|
||
|
|
def test_barnes_hut_angle():
|
||
|
|
# When Barnes-Hut's angle=0 this corresponds to the exact method.
|
||
|
|
angle = 0.0
|
||
|
|
perplexity = 10
|
||
|
|
n_samples = 100
|
||
|
|
for n_components in [2, 3]:
|
||
|
|
n_features = 5
|
||
|
|
degrees_of_freedom = float(n_components - 1.0)
|
||
|
|
|
||
|
|
random_state = check_random_state(0)
|
||
|
|
data = random_state.randn(n_samples, n_features)
|
||
|
|
distances = pairwise_distances(data)
|
||
|
|
params = random_state.randn(n_samples, n_components)
|
||
|
|
P = _joint_probabilities(distances, perplexity, verbose=0)
|
||
|
|
kl_exact, grad_exact = _kl_divergence(
|
||
|
|
params, P, degrees_of_freedom, n_samples, n_components
|
||
|
|
)
|
||
|
|
|
||
|
|
n_neighbors = n_samples - 1
|
||
|
|
distances_csr = (
|
||
|
|
NearestNeighbors()
|
||
|
|
.fit(data)
|
||
|
|
.kneighbors_graph(n_neighbors=n_neighbors, mode="distance")
|
||
|
|
)
|
||
|
|
P_bh = _joint_probabilities_nn(distances_csr, perplexity, verbose=0)
|
||
|
|
kl_bh, grad_bh = _kl_divergence_bh(
|
||
|
|
params,
|
||
|
|
P_bh,
|
||
|
|
degrees_of_freedom,
|
||
|
|
n_samples,
|
||
|
|
n_components,
|
||
|
|
angle=angle,
|
||
|
|
skip_num_points=0,
|
||
|
|
verbose=0,
|
||
|
|
)
|
||
|
|
|
||
|
|
P = squareform(P)
|
||
|
|
P_bh = P_bh.toarray()
|
||
|
|
assert_array_almost_equal(P_bh, P, decimal=5)
|
||
|
|
assert_almost_equal(kl_exact, kl_bh, decimal=3)
|
||
|
|
|
||
|
|
|
||
|
|
@skip_if_32bit
|
||
|
|
def test_n_iter_without_progress():
|
||
|
|
# Use a dummy negative n_iter_without_progress and check output on stdout
|
||
|
|
random_state = check_random_state(0)
|
||
|
|
X = random_state.randn(100, 10)
|
||
|
|
for method in ["barnes_hut", "exact"]:
|
||
|
|
tsne = TSNE(
|
||
|
|
n_iter_without_progress=-1,
|
||
|
|
verbose=2,
|
||
|
|
learning_rate=1e8,
|
||
|
|
random_state=0,
|
||
|
|
method=method,
|
||
|
|
n_iter=351,
|
||
|
|
init="random",
|
||
|
|
)
|
||
|
|
tsne._N_ITER_CHECK = 1
|
||
|
|
tsne._EXPLORATION_N_ITER = 0
|
||
|
|
|
||
|
|
old_stdout = sys.stdout
|
||
|
|
sys.stdout = StringIO()
|
||
|
|
try:
|
||
|
|
tsne.fit_transform(X)
|
||
|
|
finally:
|
||
|
|
out = sys.stdout.getvalue()
|
||
|
|
sys.stdout.close()
|
||
|
|
sys.stdout = old_stdout
|
||
|
|
|
||
|
|
# The output needs to contain the value of n_iter_without_progress
|
||
|
|
assert "did not make any progress during the last -1 episodes. Finished." in out
|
||
|
|
|
||
|
|
|
||
|
|
def test_min_grad_norm():
|
||
|
|
# Make sure that the parameter min_grad_norm is used correctly
|
||
|
|
random_state = check_random_state(0)
|
||
|
|
X = random_state.randn(100, 2)
|
||
|
|
min_grad_norm = 0.002
|
||
|
|
tsne = TSNE(min_grad_norm=min_grad_norm, verbose=2, random_state=0, method="exact")
|
||
|
|
|
||
|
|
old_stdout = sys.stdout
|
||
|
|
sys.stdout = StringIO()
|
||
|
|
try:
|
||
|
|
tsne.fit_transform(X)
|
||
|
|
finally:
|
||
|
|
out = sys.stdout.getvalue()
|
||
|
|
sys.stdout.close()
|
||
|
|
sys.stdout = old_stdout
|
||
|
|
|
||
|
|
lines_out = out.split("\n")
|
||
|
|
|
||
|
|
# extract the gradient norm from the verbose output
|
||
|
|
gradient_norm_values = []
|
||
|
|
for line in lines_out:
|
||
|
|
# When the computation is Finished just an old gradient norm value
|
||
|
|
# is repeated that we do not need to store
|
||
|
|
if "Finished" in line:
|
||
|
|
break
|
||
|
|
|
||
|
|
start_grad_norm = line.find("gradient norm")
|
||
|
|
if start_grad_norm >= 0:
|
||
|
|
line = line[start_grad_norm:]
|
||
|
|
line = line.replace("gradient norm = ", "").split(" ")[0]
|
||
|
|
gradient_norm_values.append(float(line))
|
||
|
|
|
||
|
|
# Compute how often the gradient norm is smaller than min_grad_norm
|
||
|
|
gradient_norm_values = np.array(gradient_norm_values)
|
||
|
|
n_smaller_gradient_norms = len(
|
||
|
|
gradient_norm_values[gradient_norm_values <= min_grad_norm]
|
||
|
|
)
|
||
|
|
|
||
|
|
# The gradient norm can be smaller than min_grad_norm at most once,
|
||
|
|
# because in the moment it becomes smaller the optimization stops
|
||
|
|
assert n_smaller_gradient_norms <= 1
|
||
|
|
|
||
|
|
|
||
|
|
def test_accessible_kl_divergence():
|
||
|
|
# Ensures that the accessible kl_divergence matches the computed value
|
||
|
|
random_state = check_random_state(0)
|
||
|
|
X = random_state.randn(50, 2)
|
||
|
|
tsne = TSNE(
|
||
|
|
n_iter_without_progress=2, verbose=2, random_state=0, method="exact", n_iter=500
|
||
|
|
)
|
||
|
|
|
||
|
|
old_stdout = sys.stdout
|
||
|
|
sys.stdout = StringIO()
|
||
|
|
try:
|
||
|
|
tsne.fit_transform(X)
|
||
|
|
finally:
|
||
|
|
out = sys.stdout.getvalue()
|
||
|
|
sys.stdout.close()
|
||
|
|
sys.stdout = old_stdout
|
||
|
|
|
||
|
|
# The output needs to contain the accessible kl_divergence as the error at
|
||
|
|
# the last iteration
|
||
|
|
for line in out.split("\n")[::-1]:
|
||
|
|
if "Iteration" in line:
|
||
|
|
_, _, error = line.partition("error = ")
|
||
|
|
if error:
|
||
|
|
error, _, _ = error.partition(",")
|
||
|
|
break
|
||
|
|
assert_almost_equal(tsne.kl_divergence_, float(error), decimal=5)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("method", ["barnes_hut", "exact"])
|
||
|
|
def test_uniform_grid(method):
|
||
|
|
"""Make sure that TSNE can approximately recover a uniform 2D grid
|
||
|
|
|
||
|
|
Due to ties in distances between point in X_2d_grid, this test is platform
|
||
|
|
dependent for ``method='barnes_hut'`` due to numerical imprecision.
|
||
|
|
|
||
|
|
Also, t-SNE is not assured to converge to the right solution because bad
|
||
|
|
initialization can lead to convergence to bad local minimum (the
|
||
|
|
optimization problem is non-convex). To avoid breaking the test too often,
|
||
|
|
we re-run t-SNE from the final point when the convergence is not good
|
||
|
|
enough.
|
||
|
|
"""
|
||
|
|
seeds = range(3)
|
||
|
|
n_iter = 500
|
||
|
|
for seed in seeds:
|
||
|
|
tsne = TSNE(
|
||
|
|
n_components=2,
|
||
|
|
init="random",
|
||
|
|
random_state=seed,
|
||
|
|
perplexity=50,
|
||
|
|
n_iter=n_iter,
|
||
|
|
method=method,
|
||
|
|
learning_rate="auto",
|
||
|
|
)
|
||
|
|
Y = tsne.fit_transform(X_2d_grid)
|
||
|
|
|
||
|
|
try_name = "{}_{}".format(method, seed)
|
||
|
|
try:
|
||
|
|
assert_uniform_grid(Y, try_name)
|
||
|
|
except AssertionError:
|
||
|
|
# If the test fails a first time, re-run with init=Y to see if
|
||
|
|
# this was caused by a bad initialization. Note that this will
|
||
|
|
# also run an early_exaggeration step.
|
||
|
|
try_name += ":rerun"
|
||
|
|
tsne.init = Y
|
||
|
|
Y = tsne.fit_transform(X_2d_grid)
|
||
|
|
assert_uniform_grid(Y, try_name)
|
||
|
|
|
||
|
|
|
||
|
|
def assert_uniform_grid(Y, try_name=None):
|
||
|
|
# Ensure that the resulting embedding leads to approximately
|
||
|
|
# uniformly spaced points: the distance to the closest neighbors
|
||
|
|
# should be non-zero and approximately constant.
|
||
|
|
nn = NearestNeighbors(n_neighbors=1).fit(Y)
|
||
|
|
dist_to_nn = nn.kneighbors(return_distance=True)[0].ravel()
|
||
|
|
assert dist_to_nn.min() > 0.1
|
||
|
|
|
||
|
|
smallest_to_mean = dist_to_nn.min() / np.mean(dist_to_nn)
|
||
|
|
largest_to_mean = dist_to_nn.max() / np.mean(dist_to_nn)
|
||
|
|
|
||
|
|
assert smallest_to_mean > 0.5, try_name
|
||
|
|
assert largest_to_mean < 2, try_name
|
||
|
|
|
||
|
|
|
||
|
|
def test_bh_match_exact():
|
||
|
|
# check that the ``barnes_hut`` method match the exact one when
|
||
|
|
# ``angle = 0`` and ``perplexity > n_samples / 3``
|
||
|
|
random_state = check_random_state(0)
|
||
|
|
n_features = 10
|
||
|
|
X = random_state.randn(30, n_features).astype(np.float32)
|
||
|
|
X_embeddeds = {}
|
||
|
|
n_iter = {}
|
||
|
|
for method in ["exact", "barnes_hut"]:
|
||
|
|
tsne = TSNE(
|
||
|
|
n_components=2,
|
||
|
|
method=method,
|
||
|
|
learning_rate=1.0,
|
||
|
|
init="random",
|
||
|
|
random_state=0,
|
||
|
|
n_iter=251,
|
||
|
|
perplexity=29.5,
|
||
|
|
angle=0,
|
||
|
|
)
|
||
|
|
# Kill the early_exaggeration
|
||
|
|
tsne._EXPLORATION_N_ITER = 0
|
||
|
|
X_embeddeds[method] = tsne.fit_transform(X)
|
||
|
|
n_iter[method] = tsne.n_iter_
|
||
|
|
|
||
|
|
assert n_iter["exact"] == n_iter["barnes_hut"]
|
||
|
|
assert_allclose(X_embeddeds["exact"], X_embeddeds["barnes_hut"], rtol=1e-4)
|
||
|
|
|
||
|
|
|
||
|
|
def test_gradient_bh_multithread_match_sequential():
|
||
|
|
# check that the bh gradient with different num_threads gives the same
|
||
|
|
# results
|
||
|
|
|
||
|
|
n_features = 10
|
||
|
|
n_samples = 30
|
||
|
|
n_components = 2
|
||
|
|
degrees_of_freedom = 1
|
||
|
|
|
||
|
|
angle = 3
|
||
|
|
perplexity = 5
|
||
|
|
|
||
|
|
random_state = check_random_state(0)
|
||
|
|
data = random_state.randn(n_samples, n_features).astype(np.float32)
|
||
|
|
params = random_state.randn(n_samples, n_components)
|
||
|
|
|
||
|
|
n_neighbors = n_samples - 1
|
||
|
|
distances_csr = (
|
||
|
|
NearestNeighbors()
|
||
|
|
.fit(data)
|
||
|
|
.kneighbors_graph(n_neighbors=n_neighbors, mode="distance")
|
||
|
|
)
|
||
|
|
P_bh = _joint_probabilities_nn(distances_csr, perplexity, verbose=0)
|
||
|
|
kl_sequential, grad_sequential = _kl_divergence_bh(
|
||
|
|
params,
|
||
|
|
P_bh,
|
||
|
|
degrees_of_freedom,
|
||
|
|
n_samples,
|
||
|
|
n_components,
|
||
|
|
angle=angle,
|
||
|
|
skip_num_points=0,
|
||
|
|
verbose=0,
|
||
|
|
num_threads=1,
|
||
|
|
)
|
||
|
|
for num_threads in [2, 4]:
|
||
|
|
kl_multithread, grad_multithread = _kl_divergence_bh(
|
||
|
|
params,
|
||
|
|
P_bh,
|
||
|
|
degrees_of_freedom,
|
||
|
|
n_samples,
|
||
|
|
n_components,
|
||
|
|
angle=angle,
|
||
|
|
skip_num_points=0,
|
||
|
|
verbose=0,
|
||
|
|
num_threads=num_threads,
|
||
|
|
)
|
||
|
|
|
||
|
|
assert_allclose(kl_multithread, kl_sequential, rtol=1e-6)
|
||
|
|
assert_allclose(grad_multithread, grad_multithread)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"metric, dist_func",
|
||
|
|
[("manhattan", manhattan_distances), ("cosine", cosine_distances)],
|
||
|
|
)
|
||
|
|
@pytest.mark.parametrize("method", ["barnes_hut", "exact"])
|
||
|
|
def test_tsne_with_different_distance_metrics(metric, dist_func, method):
|
||
|
|
"""Make sure that TSNE works for different distance metrics"""
|
||
|
|
|
||
|
|
if method == "barnes_hut" and metric == "manhattan":
|
||
|
|
# The distances computed by `manhattan_distances` differ slightly from those
|
||
|
|
# computed internally by NearestNeighbors via the PairwiseDistancesReduction
|
||
|
|
# Cython code-based. This in turns causes T-SNE to converge to a different
|
||
|
|
# solution but this should not impact the qualitative results as both
|
||
|
|
# methods.
|
||
|
|
# NOTE: it's probably not valid from a mathematical point of view to use the
|
||
|
|
# Manhattan distance for T-SNE...
|
||
|
|
# TODO: re-enable this test if/when `manhattan_distances` is refactored to
|
||
|
|
# reuse the same underlying Cython code NearestNeighbors.
|
||
|
|
# For reference, see:
|
||
|
|
# https://github.com/scikit-learn/scikit-learn/pull/23865/files#r925721573
|
||
|
|
pytest.xfail(
|
||
|
|
"Distance computations are different for method == 'barnes_hut' and metric"
|
||
|
|
" == 'manhattan', but this is expected."
|
||
|
|
)
|
||
|
|
|
||
|
|
random_state = check_random_state(0)
|
||
|
|
n_components_original = 3
|
||
|
|
n_components_embedding = 2
|
||
|
|
X = random_state.randn(50, n_components_original).astype(np.float32)
|
||
|
|
X_transformed_tsne = TSNE(
|
||
|
|
metric=metric,
|
||
|
|
method=method,
|
||
|
|
n_components=n_components_embedding,
|
||
|
|
random_state=0,
|
||
|
|
n_iter=300,
|
||
|
|
init="random",
|
||
|
|
learning_rate="auto",
|
||
|
|
).fit_transform(X)
|
||
|
|
X_transformed_tsne_precomputed = TSNE(
|
||
|
|
metric="precomputed",
|
||
|
|
method=method,
|
||
|
|
n_components=n_components_embedding,
|
||
|
|
random_state=0,
|
||
|
|
n_iter=300,
|
||
|
|
init="random",
|
||
|
|
learning_rate="auto",
|
||
|
|
).fit_transform(dist_func(X))
|
||
|
|
assert_array_equal(X_transformed_tsne, X_transformed_tsne_precomputed)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("method", ["exact", "barnes_hut"])
|
||
|
|
def test_tsne_n_jobs(method):
|
||
|
|
"""Make sure that the n_jobs parameter doesn't impact the output"""
|
||
|
|
random_state = check_random_state(0)
|
||
|
|
n_features = 10
|
||
|
|
X = random_state.randn(30, n_features)
|
||
|
|
X_tr_ref = TSNE(
|
||
|
|
n_components=2,
|
||
|
|
method=method,
|
||
|
|
perplexity=25.0,
|
||
|
|
angle=0,
|
||
|
|
n_jobs=1,
|
||
|
|
random_state=0,
|
||
|
|
init="random",
|
||
|
|
learning_rate="auto",
|
||
|
|
).fit_transform(X)
|
||
|
|
X_tr = TSNE(
|
||
|
|
n_components=2,
|
||
|
|
method=method,
|
||
|
|
perplexity=25.0,
|
||
|
|
angle=0,
|
||
|
|
n_jobs=2,
|
||
|
|
random_state=0,
|
||
|
|
init="random",
|
||
|
|
learning_rate="auto",
|
||
|
|
).fit_transform(X)
|
||
|
|
|
||
|
|
assert_allclose(X_tr_ref, X_tr)
|
||
|
|
|
||
|
|
|
||
|
|
def test_tsne_with_mahalanobis_distance():
|
||
|
|
"""Make sure that method_parameters works with mahalanobis distance."""
|
||
|
|
random_state = check_random_state(0)
|
||
|
|
n_samples, n_features = 300, 10
|
||
|
|
X = random_state.randn(n_samples, n_features)
|
||
|
|
default_params = {
|
||
|
|
"perplexity": 40,
|
||
|
|
"n_iter": 250,
|
||
|
|
"learning_rate": "auto",
|
||
|
|
"init": "random",
|
||
|
|
"n_components": 3,
|
||
|
|
"random_state": 0,
|
||
|
|
}
|
||
|
|
|
||
|
|
tsne = TSNE(metric="mahalanobis", **default_params)
|
||
|
|
msg = "Must provide either V or VI for Mahalanobis distance"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
tsne.fit_transform(X)
|
||
|
|
|
||
|
|
precomputed_X = squareform(pdist(X, metric="mahalanobis"), checks=True)
|
||
|
|
X_trans_expected = TSNE(metric="precomputed", **default_params).fit_transform(
|
||
|
|
precomputed_X
|
||
|
|
)
|
||
|
|
|
||
|
|
X_trans = TSNE(
|
||
|
|
metric="mahalanobis", metric_params={"V": np.cov(X.T)}, **default_params
|
||
|
|
).fit_transform(X)
|
||
|
|
assert_allclose(X_trans, X_trans_expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("perplexity", (20, 30))
|
||
|
|
def test_tsne_perplexity_validation(perplexity):
|
||
|
|
"""Make sure that perplexity > n_samples results in a ValueError"""
|
||
|
|
|
||
|
|
random_state = check_random_state(0)
|
||
|
|
X = random_state.randn(20, 2)
|
||
|
|
est = TSNE(
|
||
|
|
learning_rate="auto",
|
||
|
|
init="pca",
|
||
|
|
perplexity=perplexity,
|
||
|
|
random_state=random_state,
|
||
|
|
)
|
||
|
|
msg = "perplexity must be less than n_samples"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
est.fit_transform(X)
|
||
|
|
|
||
|
|
|
||
|
|
def test_tsne_works_with_pandas_output():
|
||
|
|
"""Make sure that TSNE works when the output is set to "pandas".
|
||
|
|
|
||
|
|
Non-regression test for gh-25365.
|
||
|
|
"""
|
||
|
|
pytest.importorskip("pandas")
|
||
|
|
with config_context(transform_output="pandas"):
|
||
|
|
arr = np.arange(35 * 4).reshape(35, 4)
|
||
|
|
TSNE(n_components=2).fit_transform(arr)
|