tidy up dbstream

docs/releases/unreleased.md

@@ -5,6 +5,10 @@
 - Added `bandit.BayesUCB`.
 - Added `bandit.evaluate_offline`, for evaluating bandits on historical (logged) data.
 
+## cluster
+
+- `DBStream` will now only recluster on demand, rather than at every call to `learn_one`.
+
 ## compat
 
 - The `predict_many` method scikit-learn models wrapped with `compat.convert_sklearn_to_river` raised an exception if the model had not been fitted on any data yet. Instead, default predictions will be produced, which is consistent with the rest of River.
@@ -35,9 +39,9 @@
 ## tree
 
 - Expose the `min_branch_fraction` parameter to avoid splits where most of the data goes to a single branch. Affects
-classification trees.
+ classification trees.
 - Added the `max_share_to_split` parameter to Hoeffding Tree classifiers. This parameters avoids splitting when the majority
-class has most of the data.
+ class has most of the data.
 
 ## utils
 

river/cluster/dbstream.py

@@ -75,7 +75,6 @@ class DBSTREAM(base.Clusterer):
  relative to the area cover by micro clusters. This parameter is used to determine
  whether a micro cluster or a shared density is weak.
 
-
  Attributes
  ----------
  n_clusters
@@ -111,11 +110,13 @@ class DBSTREAM(base.Clusterer):
  ... [4, 1.5], [4, 2.25], [4, 2.5], [4, 3], [4, 3.25], [4, 3.5]
  ... ]
 
- >>> dbstream = cluster.DBSTREAM(clustering_threshold = 1.5,
- ... fading_factor = 0.05,
- ... cleanup_interval = 4,
- ... intersection_factor = 0.5,
- ... minimum_weight = 1)
+ >>> dbstream = cluster.DBSTREAM(
+ ... clustering_threshold=1.5,
+ ... fading_factor=0.05,
+ ... cleanup_interval=4,
+ ... intersection_factor=0.5,
+ ... minimum_weight=1
+ ... )
 
  >>> for x, _ in stream.iter_array(X):
  ... dbstream = dbstream.learn_one(x)
@@ -128,6 +129,7 @@ class DBSTREAM(base.Clusterer):
 
  >>> dbstream._n_clusters
  2
+
  """
 
  def __init__(
@@ -148,9 +150,9 @@ def __init__(
  self.minimum_weight = minimum_weight
 
  self._n_clusters: int = 0
- self._clusters: typing.Dict[int, "DBSTREAMMicroCluster"] = {}
+ self._clusters: typing.Dict[int, DBSTREAMMicroCluster] = {}
  self._centers: typing.Dict = {}
- self._micro_clusters: typing.Dict[int, "DBSTREAMMicroCluster"] = {}
+ self._micro_clusters: typing.Dict[int, DBSTREAMMicroCluster] = {}
 
  self.s: dict[int, dict[int, float]] = {}
  self.s_t: dict[int, dict[int, float]] = {}
@@ -405,7 +407,7 @@ def n_clusters(self) -> int:
  return self._n_clusters
 
  @property
- def clusters(self) -> typing.Dict[int, "DBSTREAMMicroCluster"]:
+ def clusters(self) -> typing.Dict[int, DBSTREAMMicroCluster]:
  self._recluster()
  return self._clusters
 
@@ -415,7 +417,7 @@ def centers(self) -> typing.Dict:
  return self._centers
 
  @property
- def micro_clusters(self) -> typing.Dict[int, "DBSTREAMMicroCluster"]:
+ def micro_clusters(self) -> typing.Dict[int, DBSTREAMMicroCluster]:
  return self._micro_clusters