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"""
This is testing the equivalence between some estimators with internal nearest
neighbors computations, and the corresponding pipeline versions with
KNeighborsTransformer or RadiusNeighborsTransformer to precompute the
neighbors.
"""
import numpy as np
from sklearn.utils._testing import assert_array_almost_equal
from sklearn.cluster.tests.common import generate_clustered_data
from sklearn.datasets import make_blobs
from sklearn.pipeline import make_pipeline
from sklearn.base import clone
from sklearn.neighbors import KNeighborsTransformer
from sklearn.neighbors import RadiusNeighborsTransformer
from sklearn.cluster import DBSCAN
from sklearn.cluster import SpectralClustering
from sklearn.neighbors import KNeighborsRegressor
from sklearn.neighbors import RadiusNeighborsRegressor
from sklearn.neighbors import LocalOutlierFactor
from sklearn.manifold import SpectralEmbedding
from sklearn.manifold import Isomap
from sklearn.manifold import TSNE
def test_spectral_clustering():
# Test chaining KNeighborsTransformer and SpectralClustering
n_neighbors = 5
X, _ = make_blobs(random_state=0)
# compare the chained version and the compact version
est_chain = make_pipeline(
KNeighborsTransformer(n_neighbors=n_neighbors, mode="connectivity"),
SpectralClustering(
n_neighbors=n_neighbors, affinity="precomputed", random_state=42
),
)
est_compact = SpectralClustering(
n_neighbors=n_neighbors, affinity="nearest_neighbors", random_state=42
)
labels_compact = est_compact.fit_predict(X)
labels_chain = est_chain.fit_predict(X)
assert_array_almost_equal(labels_chain, labels_compact)
def test_spectral_embedding():
# Test chaining KNeighborsTransformer and SpectralEmbedding
n_neighbors = 5
n_samples = 1000
centers = np.array(
[
[0.0, 5.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 4.0, 0.0, 0.0],
[1.0, 0.0, 0.0, 5.0, 1.0],
]
)
S, true_labels = make_blobs(
n_samples=n_samples, centers=centers, cluster_std=1.0, random_state=42
)
# compare the chained version and the compact version
est_chain = make_pipeline(
KNeighborsTransformer(n_neighbors=n_neighbors, mode="connectivity"),
SpectralEmbedding(
n_neighbors=n_neighbors, affinity="precomputed", random_state=42
),
)
est_compact = SpectralEmbedding(
n_neighbors=n_neighbors, affinity="nearest_neighbors", random_state=42
)
St_compact = est_compact.fit_transform(S)
St_chain = est_chain.fit_transform(S)
assert_array_almost_equal(St_chain, St_compact)
def test_dbscan():
# Test chaining RadiusNeighborsTransformer and DBSCAN
radius = 0.3
n_clusters = 3
X = generate_clustered_data(n_clusters=n_clusters)
# compare the chained version and the compact version
est_chain = make_pipeline(
RadiusNeighborsTransformer(radius=radius, mode="distance"),
DBSCAN(metric="precomputed", eps=radius),
)
est_compact = DBSCAN(eps=radius)
labels_chain = est_chain.fit_predict(X)
labels_compact = est_compact.fit_predict(X)
assert_array_almost_equal(labels_chain, labels_compact)
def test_isomap():
# Test chaining KNeighborsTransformer and Isomap with
# neighbors_algorithm='precomputed'
algorithm = "auto"
n_neighbors = 10
X, _ = make_blobs(random_state=0)
X2, _ = make_blobs(random_state=1)
# compare the chained version and the compact version
est_chain = make_pipeline(
KNeighborsTransformer(
n_neighbors=n_neighbors, algorithm=algorithm, mode="distance"
),
Isomap(n_neighbors=n_neighbors, metric="precomputed"),
)
est_compact = Isomap(n_neighbors=n_neighbors, neighbors_algorithm=algorithm)
Xt_chain = est_chain.fit_transform(X)
Xt_compact = est_compact.fit_transform(X)
assert_array_almost_equal(Xt_chain, Xt_compact)
Xt_chain = est_chain.transform(X2)
Xt_compact = est_compact.transform(X2)
assert_array_almost_equal(Xt_chain, Xt_compact)
def test_tsne():
# Test chaining KNeighborsTransformer and TSNE
n_iter = 250
perplexity = 5
n_neighbors = int(3.0 * perplexity + 1)
rng = np.random.RandomState(0)
X = rng.randn(20, 2)
for metric in ["minkowski", "sqeuclidean"]:
# compare the chained version and the compact version
est_chain = make_pipeline(
KNeighborsTransformer(
n_neighbors=n_neighbors, mode="distance", metric=metric
),
TSNE(
init="random",
metric="precomputed",
perplexity=perplexity,
method="barnes_hut",
random_state=42,
n_iter=n_iter,
),
)
est_compact = TSNE(
init="random",
metric=metric,
perplexity=perplexity,
n_iter=n_iter,
method="barnes_hut",
random_state=42,
)
Xt_chain = est_chain.fit_transform(X)
Xt_compact = est_compact.fit_transform(X)
assert_array_almost_equal(Xt_chain, Xt_compact)
def test_lof_novelty_false():
# Test chaining KNeighborsTransformer and LocalOutlierFactor
n_neighbors = 4
rng = np.random.RandomState(0)
X = rng.randn(40, 2)
# compare the chained version and the compact version
est_chain = make_pipeline(
KNeighborsTransformer(n_neighbors=n_neighbors, mode="distance"),
LocalOutlierFactor(
metric="precomputed",
n_neighbors=n_neighbors,
novelty=False,
contamination="auto",
),
)
est_compact = LocalOutlierFactor(
n_neighbors=n_neighbors, novelty=False, contamination="auto"
)
pred_chain = est_chain.fit_predict(X)
pred_compact = est_compact.fit_predict(X)
assert_array_almost_equal(pred_chain, pred_compact)
def test_lof_novelty_true():
# Test chaining KNeighborsTransformer and LocalOutlierFactor
n_neighbors = 4
rng = np.random.RandomState(0)
X1 = rng.randn(40, 2)
X2 = rng.randn(40, 2)
# compare the chained version and the compact version
est_chain = make_pipeline(
KNeighborsTransformer(n_neighbors=n_neighbors, mode="distance"),
LocalOutlierFactor(
metric="precomputed",
n_neighbors=n_neighbors,
novelty=True,
contamination="auto",
),
)
est_compact = LocalOutlierFactor(
n_neighbors=n_neighbors, novelty=True, contamination="auto"
)
pred_chain = est_chain.fit(X1).predict(X2)
pred_compact = est_compact.fit(X1).predict(X2)
assert_array_almost_equal(pred_chain, pred_compact)
def test_kneighbors_regressor():
# Test chaining KNeighborsTransformer and classifiers/regressors
rng = np.random.RandomState(0)
X = 2 * rng.rand(40, 5) - 1
X2 = 2 * rng.rand(40, 5) - 1
y = rng.rand(40, 1)
n_neighbors = 12
radius = 1.5
# We precompute more neighbors than necessary, to have equivalence between
# k-neighbors estimator after radius-neighbors transformer, and vice-versa.
factor = 2
k_trans = KNeighborsTransformer(n_neighbors=n_neighbors, mode="distance")
k_trans_factor = KNeighborsTransformer(
n_neighbors=int(n_neighbors * factor), mode="distance"
)
r_trans = RadiusNeighborsTransformer(radius=radius, mode="distance")
r_trans_factor = RadiusNeighborsTransformer(
radius=int(radius * factor), mode="distance"
)
k_reg = KNeighborsRegressor(n_neighbors=n_neighbors)
r_reg = RadiusNeighborsRegressor(radius=radius)
test_list = [
(k_trans, k_reg),
(k_trans_factor, r_reg),
(r_trans, r_reg),
(r_trans_factor, k_reg),
]
for trans, reg in test_list:
# compare the chained version and the compact version
reg_compact = clone(reg)
reg_precomp = clone(reg)
reg_precomp.set_params(metric="precomputed")
reg_chain = make_pipeline(clone(trans), reg_precomp)
y_pred_chain = reg_chain.fit(X, y).predict(X2)
y_pred_compact = reg_compact.fit(X, y).predict(X2)
assert_array_almost_equal(y_pred_chain, y_pred_compact)
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