DBSTREAM: fix microcluster labeling (#1339)

* Fix microcluster labeling

* Add tests

* Update unreleased.md

* Fix linter errors

* Fix imports order

docs/releases/unreleased.md

@@ -14,7 +14,8 @@ River's mini-batch methods now support pandas v2. In particular, River conforms
   - Addition of the `-` sign before the `fading_factor` in accordance with the algorithm 2 proposed by Hashler and Bolanos (2016) to allow clusters with low weights to be removed.
   - The new `micro_cluster` is added with the key derived from the maximum key of the existing micro clusters. If the set of micro clusters is still empty (`len = 0`), a new micro cluster is added with key 0.
   - `cluster_is_up_to_date` is set to `True` at the end of the `self._recluster()` function.
-  - shared density graph update timestamps are initialized with the current timestamp value
+  - Shared density graph update timestamps are initialized with the current timestamp value
+  - `neighbour_neighbours` are appended correctly to the `seed_set` when generating cluster labels
 
 ## datasets
 

river/cluster/dbstream.py

@@ -337,7 +337,7 @@ def _generate_labels(self, weighted_adjacency_list):
                         )
                         # add new neighbors to seed set
                         for neighbor_neighbor in neighbor_neighbors:
-                            if labels[neighbor_neighbor] is not None:
+                            if labels[neighbor_neighbor] is None:
                                 seed_set.append(neighbor_neighbor)
 
         return labels

river/cluster/test_dbstream.py

@@ -0,0 +1,120 @@
+from __future__ import annotations
+
+import pytest
+
+from river.cluster import DBSTREAM
+
+
+@pytest.fixture
+def dbstream():
+    return DBSTREAM(
+        fading_factor=0.001, clustering_threshold=1, cleanup_interval=1, intersection_factor=0.05
+    )
+
+
+def assert_micro_cluster_properties(cluster, center, last_update=None):
+    assert cluster.center == pytest.approx(center)
+    if last_update is not None:
+        assert cluster.last_update == last_update
+
+
+def test_cluster_formation_and_cleanup(dbstream: DBSTREAM):
+    X = [
+        {1: 1},
+        {1: 3},
+        {1: 3},
+        {1: 3},
+        {1: 5},
+        {1: 7},
+        {1: 9},
+        {1: 11},
+        {1: 11},
+        {1: 13},
+        {1: 11},
+        {1: 15},
+        {1: 17},
+    ]
+
+    for x in X:
+        dbstream.learn_one(x)
+
+    assert len(dbstream._micro_clusters) == 3
+    assert_micro_cluster_properties(dbstream.micro_clusters[1], center={1: 3}, last_update=3)
+    assert_micro_cluster_properties(dbstream.micro_clusters[5], center={1: 11}, last_update=10)
+    assert_micro_cluster_properties(dbstream.micro_clusters[7], center={1: 17}, last_update=12)
+
+
+def test_with_two_micro_clusters(dbstream: DBSTREAM):
+    # First micro-cluster
+    dbstream.learn_one({1: 1, 2: 1})
+    for _ in range(25):
+        dbstream.learn_one({1: 1.7, 2: 1.7})
+
+    # Second micro-cluster
+    dbstream.learn_one({1: 3, 2: 3})
+    for _ in range(25):
+        dbstream.learn_one({1: 2.3, 2: 2.3})
+
+    # Points in the middle of two micro-clusters
+    for _ in range(5):
+        dbstream.learn_one({1: 2, 2: 2})
+
+    assert len(dbstream._micro_clusters) == 2
+    assert_micro_cluster_properties(
+        dbstream.micro_clusters[0], center={1: 1.597322, 2: 1.597322}, last_update=56
+    )
+    assert_micro_cluster_properties(
+        dbstream.micro_clusters[1], center={1: 2.402677, 2: 2.402677}, last_update=56
+    )
+
+    assert dbstream.s == {0: {1: 3.995844478090532}}
+    assert dbstream.s_t == {0: {1: 56}}
+
+    dbstream._recluster()
+    assert len(dbstream.clusters) == 1
+    assert_micro_cluster_properties(dbstream.clusters[0], center={1: 2.003033, 2: 2.003033})
+
+
+def test_density_graph_with_three_micro_clusters(dbstream: DBSTREAM):
+    # First micro-cluster
+    dbstream.learn_one({1: 1, 2: 1})
+    for _ in range(25):
+        dbstream.learn_one({1: 1.7, 2: 1.7})
+
+    # Second micro-cluster
+    dbstream.learn_one({1: 3, 2: 3})
+    for _ in range(25):
+        dbstream.learn_one({1: 2.3, 2: 2.3})
+
+    # Points in the middle of first and second micro-clusters
+    for _ in range(5):
+        dbstream.learn_one({1: 2, 2: 2})
+
+    # Third micro-cluster
+    dbstream.learn_one({1: 4, 2: 4})
+    for _ in range(25):
+        dbstream.learn_one({1: 3.3, 2: 3.3})
+
+    # Points in the middle of second and third micro-clusters
+    for _ in range(4):
+        dbstream.learn_one({1: 3, 2: 3})
+
+    assert len(dbstream._micro_clusters) == 3
+
+    assert_micro_cluster_properties(
+        dbstream.micro_clusters[0], center={1: 1.597322, 2: 1.597322}, last_update=56
+    )
+    assert_micro_cluster_properties(
+        dbstream.micro_clusters[1], center={1: 2.461654, 2: 2.461654}, last_update=86
+    )
+    assert_micro_cluster_properties(
+        dbstream.micro_clusters[2], center={1: 3.430485, 2: 3.430485}, last_update=86
+    )
+
+    assert dbstream.s[0] == pytest.approx({1: 3.995844})
+    assert dbstream.s[1] == pytest.approx({2: 2.997921})
+    assert dbstream.s_t == {0: {1: 56}, 1: {2: 86}}
+
+    dbstream._recluster()
+    assert len(dbstream.clusters) == 1
+    assert_micro_cluster_properties(dbstream.clusters[0], center={1: 2.489894, 2: 2.489894})