245 lines
8.4 KiB
Python
245 lines
8.4 KiB
Python
"""
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Tests for the birch clustering algorithm.
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"""
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from scipy import sparse
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import numpy as np
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import pytest
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from sklearn.cluster.tests.common import generate_clustered_data
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from sklearn.cluster import Birch
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from sklearn.cluster import AgglomerativeClustering
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from sklearn.datasets import make_blobs
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from sklearn.exceptions import ConvergenceWarning
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from sklearn.metrics import pairwise_distances_argmin, v_measure_score
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from sklearn.utils._testing import assert_array_equal
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from sklearn.utils._testing import assert_allclose
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def test_n_samples_leaves_roots(global_random_seed, global_dtype):
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# Sanity check for the number of samples in leaves and roots
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X, y = make_blobs(n_samples=10, random_state=global_random_seed)
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X = X.astype(global_dtype, copy=False)
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brc = Birch()
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brc.fit(X)
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n_samples_root = sum([sc.n_samples_ for sc in brc.root_.subclusters_])
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n_samples_leaves = sum(
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[sc.n_samples_ for leaf in brc._get_leaves() for sc in leaf.subclusters_]
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)
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assert n_samples_leaves == X.shape[0]
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assert n_samples_root == X.shape[0]
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def test_partial_fit(global_random_seed, global_dtype):
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# Test that fit is equivalent to calling partial_fit multiple times
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X, y = make_blobs(n_samples=100, random_state=global_random_seed)
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X = X.astype(global_dtype, copy=False)
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brc = Birch(n_clusters=3)
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brc.fit(X)
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brc_partial = Birch(n_clusters=None)
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brc_partial.partial_fit(X[:50])
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brc_partial.partial_fit(X[50:])
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assert_allclose(brc_partial.subcluster_centers_, brc.subcluster_centers_)
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# Test that same global labels are obtained after calling partial_fit
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# with None
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brc_partial.set_params(n_clusters=3)
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brc_partial.partial_fit(None)
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assert_array_equal(brc_partial.subcluster_labels_, brc.subcluster_labels_)
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def test_birch_predict(global_random_seed, global_dtype):
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# Test the predict method predicts the nearest centroid.
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rng = np.random.RandomState(global_random_seed)
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X = generate_clustered_data(n_clusters=3, n_features=3, n_samples_per_cluster=10)
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X = X.astype(global_dtype, copy=False)
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# n_samples * n_samples_per_cluster
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shuffle_indices = np.arange(30)
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rng.shuffle(shuffle_indices)
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X_shuffle = X[shuffle_indices, :]
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brc = Birch(n_clusters=4, threshold=1.0)
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brc.fit(X_shuffle)
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# Birch must preserve inputs' dtype
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assert brc.subcluster_centers_.dtype == global_dtype
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assert_array_equal(brc.labels_, brc.predict(X_shuffle))
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centroids = brc.subcluster_centers_
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nearest_centroid = brc.subcluster_labels_[
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pairwise_distances_argmin(X_shuffle, centroids)
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]
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assert_allclose(v_measure_score(nearest_centroid, brc.labels_), 1.0)
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def test_n_clusters(global_random_seed, global_dtype):
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# Test that n_clusters param works properly
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X, y = make_blobs(n_samples=100, centers=10, random_state=global_random_seed)
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X = X.astype(global_dtype, copy=False)
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brc1 = Birch(n_clusters=10)
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brc1.fit(X)
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assert len(brc1.subcluster_centers_) > 10
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assert len(np.unique(brc1.labels_)) == 10
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# Test that n_clusters = Agglomerative Clustering gives
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# the same results.
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gc = AgglomerativeClustering(n_clusters=10)
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brc2 = Birch(n_clusters=gc)
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brc2.fit(X)
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assert_array_equal(brc1.subcluster_labels_, brc2.subcluster_labels_)
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assert_array_equal(brc1.labels_, brc2.labels_)
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# Test that a small number of clusters raises a warning.
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brc4 = Birch(threshold=10000.0)
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with pytest.warns(ConvergenceWarning):
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brc4.fit(X)
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def test_sparse_X(global_random_seed, global_dtype):
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# Test that sparse and dense data give same results
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X, y = make_blobs(n_samples=100, centers=10, random_state=global_random_seed)
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X = X.astype(global_dtype, copy=False)
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brc = Birch(n_clusters=10)
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brc.fit(X)
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csr = sparse.csr_matrix(X)
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brc_sparse = Birch(n_clusters=10)
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brc_sparse.fit(csr)
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# Birch must preserve inputs' dtype
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assert brc_sparse.subcluster_centers_.dtype == global_dtype
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assert_array_equal(brc.labels_, brc_sparse.labels_)
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assert_allclose(brc.subcluster_centers_, brc_sparse.subcluster_centers_)
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def test_partial_fit_second_call_error_checks():
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# second partial fit calls will error when n_features is not consistent
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# with the first call
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X, y = make_blobs(n_samples=100)
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brc = Birch(n_clusters=3)
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brc.partial_fit(X, y)
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msg = "X has 1 features, but Birch is expecting 2 features"
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with pytest.raises(ValueError, match=msg):
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brc.partial_fit(X[:, [0]], y)
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def check_branching_factor(node, branching_factor):
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subclusters = node.subclusters_
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assert branching_factor >= len(subclusters)
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for cluster in subclusters:
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if cluster.child_:
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check_branching_factor(cluster.child_, branching_factor)
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def test_branching_factor(global_random_seed, global_dtype):
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# Test that nodes have at max branching_factor number of subclusters
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X, y = make_blobs(random_state=global_random_seed)
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X = X.astype(global_dtype, copy=False)
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branching_factor = 9
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# Purposefully set a low threshold to maximize the subclusters.
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brc = Birch(n_clusters=None, branching_factor=branching_factor, threshold=0.01)
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brc.fit(X)
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check_branching_factor(brc.root_, branching_factor)
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brc = Birch(n_clusters=3, branching_factor=branching_factor, threshold=0.01)
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brc.fit(X)
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check_branching_factor(brc.root_, branching_factor)
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def check_threshold(birch_instance, threshold):
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"""Use the leaf linked list for traversal"""
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current_leaf = birch_instance.dummy_leaf_.next_leaf_
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while current_leaf:
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subclusters = current_leaf.subclusters_
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for sc in subclusters:
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assert threshold >= sc.radius
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current_leaf = current_leaf.next_leaf_
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def test_threshold(global_random_seed, global_dtype):
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# Test that the leaf subclusters have a threshold lesser than radius
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X, y = make_blobs(n_samples=80, centers=4, random_state=global_random_seed)
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X = X.astype(global_dtype, copy=False)
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brc = Birch(threshold=0.5, n_clusters=None)
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brc.fit(X)
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check_threshold(brc, 0.5)
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brc = Birch(threshold=5.0, n_clusters=None)
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brc.fit(X)
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check_threshold(brc, 5.0)
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def test_birch_n_clusters_long_int():
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# Check that birch supports n_clusters with np.int64 dtype, for instance
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# coming from np.arange. #16484
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X, _ = make_blobs(random_state=0)
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n_clusters = np.int64(5)
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Birch(n_clusters=n_clusters).fit(X)
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def test_feature_names_out():
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"""Check `get_feature_names_out` for `Birch`."""
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X, _ = make_blobs(n_samples=80, n_features=4, random_state=0)
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brc = Birch(n_clusters=4)
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brc.fit(X)
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n_clusters = brc.subcluster_centers_.shape[0]
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names_out = brc.get_feature_names_out()
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assert_array_equal([f"birch{i}" for i in range(n_clusters)], names_out)
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def test_transform_match_across_dtypes(global_random_seed):
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X, _ = make_blobs(n_samples=80, n_features=4, random_state=global_random_seed)
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brc = Birch(n_clusters=4, threshold=1.1)
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Y_64 = brc.fit_transform(X)
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Y_32 = brc.fit_transform(X.astype(np.float32))
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assert_allclose(Y_64, Y_32, atol=1e-6)
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def test_subcluster_dtype(global_dtype):
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X = make_blobs(n_samples=80, n_features=4, random_state=0)[0].astype(
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global_dtype, copy=False
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)
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brc = Birch(n_clusters=4)
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assert brc.fit(X).subcluster_centers_.dtype == global_dtype
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def test_both_subclusters_updated():
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"""Check that both subclusters are updated when a node a split, even when there are
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duplicated data points. Non-regression test for #23269.
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"""
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X = np.array(
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[
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[-2.6192791, -1.5053215],
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[-2.9993038, -1.6863596],
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[-2.3724914, -1.3438171],
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[-2.336792, -1.3417323],
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[-2.4089134, -1.3290224],
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[-2.3724914, -1.3438171],
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[-3.364009, -1.8846745],
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[-2.3724914, -1.3438171],
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[-2.617677, -1.5003285],
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[-2.2960556, -1.3260119],
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[-2.3724914, -1.3438171],
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[-2.5459878, -1.4533926],
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[-2.25979, -1.3003055],
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[-2.4089134, -1.3290224],
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[-2.3724914, -1.3438171],
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[-2.4089134, -1.3290224],
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[-2.5459878, -1.4533926],
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[-2.3724914, -1.3438171],
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[-2.9720619, -1.7058647],
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[-2.336792, -1.3417323],
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[-2.3724914, -1.3438171],
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],
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dtype=np.float32,
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)
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# no error
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Birch(branching_factor=5, threshold=1e-5, n_clusters=None).fit(X)
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