169 lines
5.6 KiB
Python
169 lines
5.6 KiB
Python
"""
|
|
Tests for the birch clustering algorithm.
|
|
"""
|
|
|
|
from scipy import sparse
|
|
import numpy as np
|
|
import pytest
|
|
|
|
from sklearn.cluster.tests.common import generate_clustered_data
|
|
from sklearn.cluster import Birch
|
|
from sklearn.cluster import AgglomerativeClustering
|
|
from sklearn.datasets import make_blobs
|
|
from sklearn.exceptions import ConvergenceWarning
|
|
from sklearn.linear_model import ElasticNet
|
|
from sklearn.metrics import pairwise_distances_argmin, v_measure_score
|
|
|
|
from sklearn.utils._testing import assert_almost_equal
|
|
from sklearn.utils._testing import assert_array_equal
|
|
from sklearn.utils._testing import assert_array_almost_equal
|
|
from sklearn.utils._testing import assert_warns
|
|
|
|
|
|
def test_n_samples_leaves_roots():
|
|
# Sanity check for the number of samples in leaves and roots
|
|
X, y = make_blobs(n_samples=10)
|
|
brc = Birch()
|
|
brc.fit(X)
|
|
n_samples_root = sum([sc.n_samples_ for sc in brc.root_.subclusters_])
|
|
n_samples_leaves = sum([sc.n_samples_ for leaf in brc._get_leaves()
|
|
for sc in leaf.subclusters_])
|
|
assert n_samples_leaves == X.shape[0]
|
|
assert n_samples_root == X.shape[0]
|
|
|
|
|
|
def test_partial_fit():
|
|
# Test that fit is equivalent to calling partial_fit multiple times
|
|
X, y = make_blobs(n_samples=100)
|
|
brc = Birch(n_clusters=3)
|
|
brc.fit(X)
|
|
brc_partial = Birch(n_clusters=None)
|
|
brc_partial.partial_fit(X[:50])
|
|
brc_partial.partial_fit(X[50:])
|
|
assert_array_almost_equal(brc_partial.subcluster_centers_,
|
|
brc.subcluster_centers_)
|
|
|
|
# Test that same global labels are obtained after calling partial_fit
|
|
# with None
|
|
brc_partial.set_params(n_clusters=3)
|
|
brc_partial.partial_fit(None)
|
|
assert_array_equal(brc_partial.subcluster_labels_, brc.subcluster_labels_)
|
|
|
|
|
|
def test_birch_predict():
|
|
# Test the predict method predicts the nearest centroid.
|
|
rng = np.random.RandomState(0)
|
|
X = generate_clustered_data(n_clusters=3, n_features=3,
|
|
n_samples_per_cluster=10)
|
|
|
|
# n_samples * n_samples_per_cluster
|
|
shuffle_indices = np.arange(30)
|
|
rng.shuffle(shuffle_indices)
|
|
X_shuffle = X[shuffle_indices, :]
|
|
brc = Birch(n_clusters=4, threshold=1.)
|
|
brc.fit(X_shuffle)
|
|
centroids = brc.subcluster_centers_
|
|
assert_array_equal(brc.labels_, brc.predict(X_shuffle))
|
|
nearest_centroid = pairwise_distances_argmin(X_shuffle, centroids)
|
|
assert_almost_equal(v_measure_score(nearest_centroid, brc.labels_), 1.0)
|
|
|
|
|
|
def test_n_clusters():
|
|
# Test that n_clusters param works properly
|
|
X, y = make_blobs(n_samples=100, centers=10)
|
|
brc1 = Birch(n_clusters=10)
|
|
brc1.fit(X)
|
|
assert len(brc1.subcluster_centers_) > 10
|
|
assert len(np.unique(brc1.labels_)) == 10
|
|
|
|
# Test that n_clusters = Agglomerative Clustering gives
|
|
# the same results.
|
|
gc = AgglomerativeClustering(n_clusters=10)
|
|
brc2 = Birch(n_clusters=gc)
|
|
brc2.fit(X)
|
|
assert_array_equal(brc1.subcluster_labels_, brc2.subcluster_labels_)
|
|
assert_array_equal(brc1.labels_, brc2.labels_)
|
|
|
|
# Test that the wrong global clustering step raises an Error.
|
|
clf = ElasticNet()
|
|
brc3 = Birch(n_clusters=clf)
|
|
with pytest.raises(ValueError):
|
|
brc3.fit(X)
|
|
|
|
# Test that a small number of clusters raises a warning.
|
|
brc4 = Birch(threshold=10000.)
|
|
assert_warns(ConvergenceWarning, brc4.fit, X)
|
|
|
|
|
|
def test_sparse_X():
|
|
# Test that sparse and dense data give same results
|
|
X, y = make_blobs(n_samples=100, centers=10)
|
|
brc = Birch(n_clusters=10)
|
|
brc.fit(X)
|
|
|
|
csr = sparse.csr_matrix(X)
|
|
brc_sparse = Birch(n_clusters=10)
|
|
brc_sparse.fit(csr)
|
|
|
|
assert_array_equal(brc.labels_, brc_sparse.labels_)
|
|
assert_array_almost_equal(brc.subcluster_centers_,
|
|
brc_sparse.subcluster_centers_)
|
|
|
|
|
|
def check_branching_factor(node, branching_factor):
|
|
subclusters = node.subclusters_
|
|
assert branching_factor >= len(subclusters)
|
|
for cluster in subclusters:
|
|
if cluster.child_:
|
|
check_branching_factor(cluster.child_, branching_factor)
|
|
|
|
|
|
def test_branching_factor():
|
|
# Test that nodes have at max branching_factor number of subclusters
|
|
X, y = make_blobs()
|
|
branching_factor = 9
|
|
|
|
# Purposefully set a low threshold to maximize the subclusters.
|
|
brc = Birch(n_clusters=None, branching_factor=branching_factor,
|
|
threshold=0.01)
|
|
brc.fit(X)
|
|
check_branching_factor(brc.root_, branching_factor)
|
|
brc = Birch(n_clusters=3, branching_factor=branching_factor,
|
|
threshold=0.01)
|
|
brc.fit(X)
|
|
check_branching_factor(brc.root_, branching_factor)
|
|
|
|
# Raises error when branching_factor is set to one.
|
|
brc = Birch(n_clusters=None, branching_factor=1, threshold=0.01)
|
|
with pytest.raises(ValueError):
|
|
brc.fit(X)
|
|
|
|
|
|
def check_threshold(birch_instance, threshold):
|
|
"""Use the leaf linked list for traversal"""
|
|
current_leaf = birch_instance.dummy_leaf_.next_leaf_
|
|
while current_leaf:
|
|
subclusters = current_leaf.subclusters_
|
|
for sc in subclusters:
|
|
assert threshold >= sc.radius
|
|
current_leaf = current_leaf.next_leaf_
|
|
|
|
|
|
def test_threshold():
|
|
# Test that the leaf subclusters have a threshold lesser than radius
|
|
X, y = make_blobs(n_samples=80, centers=4)
|
|
brc = Birch(threshold=0.5, n_clusters=None)
|
|
brc.fit(X)
|
|
check_threshold(brc, 0.5)
|
|
|
|
brc = Birch(threshold=5.0, n_clusters=None)
|
|
brc.fit(X)
|
|
check_threshold(brc, 5.)
|
|
|
|
|
|
def test_birch_n_clusters_long_int():
|
|
# Check that birch supports n_clusters with np.int64 dtype, for instance
|
|
# coming from np.arange. #16484
|
|
X, _ = make_blobs(random_state=0)
|
|
n_clusters = np.int64(5)
|
|
Birch(n_clusters=n_clusters).fit(X)
|