415 lines
14 KiB
Python
415 lines
14 KiB
Python
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"""
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Tests for DBSCAN clustering algorithm
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"""
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import pickle
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import numpy as np
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import warnings
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from scipy.spatial import distance
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from scipy import sparse
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import pytest
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from sklearn.utils._testing import assert_array_equal
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from sklearn.neighbors import NearestNeighbors
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from sklearn.cluster import DBSCAN
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from sklearn.cluster import dbscan
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from sklearn.cluster.tests.common import generate_clustered_data
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from sklearn.metrics.pairwise import pairwise_distances
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n_clusters = 3
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X = generate_clustered_data(n_clusters=n_clusters)
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def test_dbscan_similarity():
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# Tests the DBSCAN algorithm with a similarity array.
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# Parameters chosen specifically for this task.
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eps = 0.15
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min_samples = 10
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# Compute similarities
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D = distance.squareform(distance.pdist(X))
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D /= np.max(D)
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# Compute DBSCAN
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core_samples, labels = dbscan(
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D, metric="precomputed", eps=eps, min_samples=min_samples
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)
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# number of clusters, ignoring noise if present
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n_clusters_1 = len(set(labels)) - (1 if -1 in labels else 0)
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assert n_clusters_1 == n_clusters
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db = DBSCAN(metric="precomputed", eps=eps, min_samples=min_samples)
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labels = db.fit(D).labels_
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n_clusters_2 = len(set(labels)) - int(-1 in labels)
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assert n_clusters_2 == n_clusters
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def test_dbscan_feature():
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# Tests the DBSCAN algorithm with a feature vector array.
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# Parameters chosen specifically for this task.
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# Different eps to other test, because distance is not normalised.
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eps = 0.8
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min_samples = 10
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metric = "euclidean"
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# Compute DBSCAN
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# parameters chosen for task
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core_samples, labels = dbscan(X, metric=metric, eps=eps, min_samples=min_samples)
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# number of clusters, ignoring noise if present
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n_clusters_1 = len(set(labels)) - int(-1 in labels)
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assert n_clusters_1 == n_clusters
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db = DBSCAN(metric=metric, eps=eps, min_samples=min_samples)
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labels = db.fit(X).labels_
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n_clusters_2 = len(set(labels)) - int(-1 in labels)
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assert n_clusters_2 == n_clusters
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def test_dbscan_sparse():
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core_sparse, labels_sparse = dbscan(sparse.lil_matrix(X), eps=0.8, min_samples=10)
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core_dense, labels_dense = dbscan(X, eps=0.8, min_samples=10)
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assert_array_equal(core_dense, core_sparse)
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assert_array_equal(labels_dense, labels_sparse)
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@pytest.mark.parametrize("include_self", [False, True])
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def test_dbscan_sparse_precomputed(include_self):
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D = pairwise_distances(X)
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nn = NearestNeighbors(radius=0.9).fit(X)
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X_ = X if include_self else None
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D_sparse = nn.radius_neighbors_graph(X=X_, mode="distance")
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# Ensure it is sparse not merely on diagonals:
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assert D_sparse.nnz < D.shape[0] * (D.shape[0] - 1)
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core_sparse, labels_sparse = dbscan(
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D_sparse, eps=0.8, min_samples=10, metric="precomputed"
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)
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core_dense, labels_dense = dbscan(D, eps=0.8, min_samples=10, metric="precomputed")
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assert_array_equal(core_dense, core_sparse)
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assert_array_equal(labels_dense, labels_sparse)
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def test_dbscan_sparse_precomputed_different_eps():
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# test that precomputed neighbors graph is filtered if computed with
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# a radius larger than DBSCAN's eps.
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lower_eps = 0.2
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nn = NearestNeighbors(radius=lower_eps).fit(X)
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D_sparse = nn.radius_neighbors_graph(X, mode="distance")
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dbscan_lower = dbscan(D_sparse, eps=lower_eps, metric="precomputed")
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higher_eps = lower_eps + 0.7
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nn = NearestNeighbors(radius=higher_eps).fit(X)
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D_sparse = nn.radius_neighbors_graph(X, mode="distance")
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dbscan_higher = dbscan(D_sparse, eps=lower_eps, metric="precomputed")
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assert_array_equal(dbscan_lower[0], dbscan_higher[0])
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assert_array_equal(dbscan_lower[1], dbscan_higher[1])
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@pytest.mark.parametrize("use_sparse", [True, False])
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@pytest.mark.parametrize("metric", ["precomputed", "minkowski"])
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def test_dbscan_input_not_modified(use_sparse, metric):
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# test that the input is not modified by dbscan
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X = np.random.RandomState(0).rand(10, 10)
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X = sparse.csr_matrix(X) if use_sparse else X
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X_copy = X.copy()
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dbscan(X, metric=metric)
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if use_sparse:
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assert_array_equal(X.toarray(), X_copy.toarray())
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else:
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assert_array_equal(X, X_copy)
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def test_dbscan_no_core_samples():
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rng = np.random.RandomState(0)
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X = rng.rand(40, 10)
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X[X < 0.8] = 0
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for X_ in [X, sparse.csr_matrix(X)]:
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db = DBSCAN(min_samples=6).fit(X_)
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assert_array_equal(db.components_, np.empty((0, X_.shape[1])))
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assert_array_equal(db.labels_, -1)
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assert db.core_sample_indices_.shape == (0,)
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def test_dbscan_callable():
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# Tests the DBSCAN algorithm with a callable metric.
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# Parameters chosen specifically for this task.
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# Different eps to other test, because distance is not normalised.
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eps = 0.8
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min_samples = 10
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# metric is the function reference, not the string key.
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metric = distance.euclidean
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# Compute DBSCAN
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# parameters chosen for task
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core_samples, labels = dbscan(
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X, metric=metric, eps=eps, min_samples=min_samples, algorithm="ball_tree"
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)
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# number of clusters, ignoring noise if present
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n_clusters_1 = len(set(labels)) - int(-1 in labels)
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assert n_clusters_1 == n_clusters
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db = DBSCAN(metric=metric, eps=eps, min_samples=min_samples, algorithm="ball_tree")
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labels = db.fit(X).labels_
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n_clusters_2 = len(set(labels)) - int(-1 in labels)
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assert n_clusters_2 == n_clusters
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def test_dbscan_metric_params():
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# Tests that DBSCAN works with the metrics_params argument.
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eps = 0.8
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min_samples = 10
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p = 1
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# Compute DBSCAN with metric_params arg
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with warnings.catch_warnings(record=True) as warns:
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db = DBSCAN(
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metric="minkowski",
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metric_params={"p": p},
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eps=eps,
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p=None,
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min_samples=min_samples,
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algorithm="ball_tree",
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).fit(X)
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assert not warns, warns[0].message
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core_sample_1, labels_1 = db.core_sample_indices_, db.labels_
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# Test that sample labels are the same as passing Minkowski 'p' directly
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db = DBSCAN(
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metric="minkowski", eps=eps, min_samples=min_samples, algorithm="ball_tree", p=p
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).fit(X)
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core_sample_2, labels_2 = db.core_sample_indices_, db.labels_
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assert_array_equal(core_sample_1, core_sample_2)
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assert_array_equal(labels_1, labels_2)
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# Minkowski with p=1 should be equivalent to Manhattan distance
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db = DBSCAN(
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metric="manhattan", eps=eps, min_samples=min_samples, algorithm="ball_tree"
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).fit(X)
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core_sample_3, labels_3 = db.core_sample_indices_, db.labels_
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assert_array_equal(core_sample_1, core_sample_3)
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assert_array_equal(labels_1, labels_3)
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with pytest.warns(
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SyntaxWarning,
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match=(
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"Parameter p is found in metric_params. "
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"The corresponding parameter from __init__ "
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"is ignored."
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),
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):
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# Test that checks p is ignored in favor of metric_params={'p': <val>}
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db = DBSCAN(
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metric="minkowski",
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metric_params={"p": p},
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eps=eps,
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p=p + 1,
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min_samples=min_samples,
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algorithm="ball_tree",
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).fit(X)
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core_sample_4, labels_4 = db.core_sample_indices_, db.labels_
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assert_array_equal(core_sample_1, core_sample_4)
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assert_array_equal(labels_1, labels_4)
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def test_dbscan_balltree():
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# Tests the DBSCAN algorithm with balltree for neighbor calculation.
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eps = 0.8
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min_samples = 10
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D = pairwise_distances(X)
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core_samples, labels = dbscan(
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D, metric="precomputed", eps=eps, min_samples=min_samples
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)
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# number of clusters, ignoring noise if present
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n_clusters_1 = len(set(labels)) - int(-1 in labels)
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assert n_clusters_1 == n_clusters
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db = DBSCAN(p=2.0, eps=eps, min_samples=min_samples, algorithm="ball_tree")
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labels = db.fit(X).labels_
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n_clusters_2 = len(set(labels)) - int(-1 in labels)
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assert n_clusters_2 == n_clusters
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db = DBSCAN(p=2.0, eps=eps, min_samples=min_samples, algorithm="kd_tree")
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labels = db.fit(X).labels_
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n_clusters_3 = len(set(labels)) - int(-1 in labels)
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assert n_clusters_3 == n_clusters
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db = DBSCAN(p=1.0, eps=eps, min_samples=min_samples, algorithm="ball_tree")
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labels = db.fit(X).labels_
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n_clusters_4 = len(set(labels)) - int(-1 in labels)
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assert n_clusters_4 == n_clusters
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db = DBSCAN(leaf_size=20, eps=eps, min_samples=min_samples, algorithm="ball_tree")
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labels = db.fit(X).labels_
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n_clusters_5 = len(set(labels)) - int(-1 in labels)
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assert n_clusters_5 == n_clusters
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def test_input_validation():
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# DBSCAN.fit should accept a list of lists.
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X = [[1.0, 2.0], [3.0, 4.0]]
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DBSCAN().fit(X) # must not raise exception
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def test_pickle():
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obj = DBSCAN()
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s = pickle.dumps(obj)
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assert type(pickle.loads(s)) == obj.__class__
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def test_boundaries():
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# ensure min_samples is inclusive of core point
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core, _ = dbscan([[0], [1]], eps=2, min_samples=2)
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assert 0 in core
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# ensure eps is inclusive of circumference
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core, _ = dbscan([[0], [1], [1]], eps=1, min_samples=2)
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assert 0 in core
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core, _ = dbscan([[0], [1], [1]], eps=0.99, min_samples=2)
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assert 0 not in core
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def test_weighted_dbscan(global_random_seed):
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# ensure sample_weight is validated
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with pytest.raises(ValueError):
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dbscan([[0], [1]], sample_weight=[2])
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with pytest.raises(ValueError):
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dbscan([[0], [1]], sample_weight=[2, 3, 4])
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# ensure sample_weight has an effect
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assert_array_equal([], dbscan([[0], [1]], sample_weight=None, min_samples=6)[0])
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assert_array_equal([], dbscan([[0], [1]], sample_weight=[5, 5], min_samples=6)[0])
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assert_array_equal([0], dbscan([[0], [1]], sample_weight=[6, 5], min_samples=6)[0])
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assert_array_equal(
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[0, 1], dbscan([[0], [1]], sample_weight=[6, 6], min_samples=6)[0]
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)
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# points within eps of each other:
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assert_array_equal(
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[0, 1], dbscan([[0], [1]], eps=1.5, sample_weight=[5, 1], min_samples=6)[0]
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)
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# and effect of non-positive and non-integer sample_weight:
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assert_array_equal(
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[], dbscan([[0], [1]], sample_weight=[5, 0], eps=1.5, min_samples=6)[0]
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)
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assert_array_equal(
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[0, 1], dbscan([[0], [1]], sample_weight=[5.9, 0.1], eps=1.5, min_samples=6)[0]
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)
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assert_array_equal(
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[0, 1], dbscan([[0], [1]], sample_weight=[6, 0], eps=1.5, min_samples=6)[0]
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)
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assert_array_equal(
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[], dbscan([[0], [1]], sample_weight=[6, -1], eps=1.5, min_samples=6)[0]
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)
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# for non-negative sample_weight, cores should be identical to repetition
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rng = np.random.RandomState(global_random_seed)
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sample_weight = rng.randint(0, 5, X.shape[0])
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core1, label1 = dbscan(X, sample_weight=sample_weight)
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assert len(label1) == len(X)
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X_repeated = np.repeat(X, sample_weight, axis=0)
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core_repeated, label_repeated = dbscan(X_repeated)
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core_repeated_mask = np.zeros(X_repeated.shape[0], dtype=bool)
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core_repeated_mask[core_repeated] = True
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core_mask = np.zeros(X.shape[0], dtype=bool)
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core_mask[core1] = True
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assert_array_equal(np.repeat(core_mask, sample_weight), core_repeated_mask)
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# sample_weight should work with precomputed distance matrix
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D = pairwise_distances(X)
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core3, label3 = dbscan(D, sample_weight=sample_weight, metric="precomputed")
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assert_array_equal(core1, core3)
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assert_array_equal(label1, label3)
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# sample_weight should work with estimator
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est = DBSCAN().fit(X, sample_weight=sample_weight)
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core4 = est.core_sample_indices_
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label4 = est.labels_
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assert_array_equal(core1, core4)
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assert_array_equal(label1, label4)
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||
|
|
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||
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est = DBSCAN()
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|
label5 = est.fit_predict(X, sample_weight=sample_weight)
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|
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core5 = est.core_sample_indices_
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assert_array_equal(core1, core5)
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assert_array_equal(label1, label5)
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||
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assert_array_equal(label1, est.labels_)
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||
|
|
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||
|
|
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|
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@pytest.mark.parametrize("algorithm", ["brute", "kd_tree", "ball_tree"])
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|
|
def test_dbscan_core_samples_toy(algorithm):
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|
|
X = [[0], [2], [3], [4], [6], [8], [10]]
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|
|
n_samples = len(X)
|
||
|
|
|
||
|
|
# Degenerate case: every sample is a core sample, either with its own
|
||
|
|
# cluster or including other close core samples.
|
||
|
|
core_samples, labels = dbscan(X, algorithm=algorithm, eps=1, min_samples=1)
|
||
|
|
assert_array_equal(core_samples, np.arange(n_samples))
|
||
|
|
assert_array_equal(labels, [0, 1, 1, 1, 2, 3, 4])
|
||
|
|
|
||
|
|
# With eps=1 and min_samples=2 only the 3 samples from the denser area
|
||
|
|
# are core samples. All other points are isolated and considered noise.
|
||
|
|
core_samples, labels = dbscan(X, algorithm=algorithm, eps=1, min_samples=2)
|
||
|
|
assert_array_equal(core_samples, [1, 2, 3])
|
||
|
|
assert_array_equal(labels, [-1, 0, 0, 0, -1, -1, -1])
|
||
|
|
|
||
|
|
# Only the sample in the middle of the dense area is core. Its two
|
||
|
|
# neighbors are edge samples. Remaining samples are noise.
|
||
|
|
core_samples, labels = dbscan(X, algorithm=algorithm, eps=1, min_samples=3)
|
||
|
|
assert_array_equal(core_samples, [2])
|
||
|
|
assert_array_equal(labels, [-1, 0, 0, 0, -1, -1, -1])
|
||
|
|
|
||
|
|
# It's no longer possible to extract core samples with eps=1:
|
||
|
|
# everything is noise.
|
||
|
|
core_samples, labels = dbscan(X, algorithm=algorithm, eps=1, min_samples=4)
|
||
|
|
assert_array_equal(core_samples, [])
|
||
|
|
assert_array_equal(labels, np.full(n_samples, -1.0))
|
||
|
|
|
||
|
|
|
||
|
|
def test_dbscan_precomputed_metric_with_degenerate_input_arrays():
|
||
|
|
# see https://github.com/scikit-learn/scikit-learn/issues/4641 for
|
||
|
|
# more details
|
||
|
|
X = np.eye(10)
|
||
|
|
labels = DBSCAN(eps=0.5, metric="precomputed").fit(X).labels_
|
||
|
|
assert len(set(labels)) == 1
|
||
|
|
|
||
|
|
X = np.zeros((10, 10))
|
||
|
|
labels = DBSCAN(eps=0.5, metric="precomputed").fit(X).labels_
|
||
|
|
assert len(set(labels)) == 1
|
||
|
|
|
||
|
|
|
||
|
|
def test_dbscan_precomputed_metric_with_initial_rows_zero():
|
||
|
|
# sample matrix with initial two row all zero
|
||
|
|
ar = np.array(
|
||
|
|
[
|
||
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||
|
|
[0.0, 0.0, 0.0, 0.0, 0.1, 0.0, 0.0],
|
||
|
|
[0.0, 0.0, 0.0, 0.0, 0.1, 0.0, 0.0],
|
||
|
|
[0.0, 0.0, 0.1, 0.1, 0.0, 0.0, 0.3],
|
||
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1],
|
||
|
|
[0.0, 0.0, 0.0, 0.0, 0.3, 0.1, 0.0],
|
||
|
|
]
|
||
|
|
)
|
||
|
|
matrix = sparse.csr_matrix(ar)
|
||
|
|
labels = DBSCAN(eps=0.2, metric="precomputed", min_samples=2).fit(matrix).labels_
|
||
|
|
assert_array_equal(labels, [-1, -1, 0, 0, 0, 1, 1])
|