expand ADWIN docs (#1334)

river/drift/adwin.py

@@ -6,7 +6,7 @@
 
 
 class ADWIN(DriftDetector):
-    r"""Adaptive Windowing method for concept drift detection.
+    r"""Adaptive Windowing method for concept drift detection[^1].
 
     ADWIN (ADaptive WINdowing) is a popular drift detection method with mathematical guarantees.
     ADWIN efficiently keeps a variable-length window of recent items; such that it holds that
@@ -22,18 +22,21 @@ class ADWIN(DriftDetector):
     delta
         Significance value.
     clock
-        How often ADWIN should check for change. 1 means every new data point, default is 32.
+        How often ADWIN should check for changes. 1 means every new data point, default is 32.
         Higher values speed up processing, but may also lead to increased delay in change
         detection.
     max_buckets
-        The maximum number of buckets of each size that ADWIN should keep before merging buckets
-        (default is 5).
+        The maximum number of buckets of each size that ADWIN should keep before merging buckets.
+        The idea of data buckets comes from the compression algorithm introduced in the ADWIN2,
+        the second iteration of the ADWIN algorithm presented in the original research paper.
+        This is the ADWIN version available in River.
     min_window_length
-        The minimum length of each subwindow (default is 5). Lower values may decrease delay in
-        change detection but may also lead to more false positives.
+        The minimum length allowed for a subwindow when checking for concept drift. Subwindows whose size
+        is smaller than this value will be ignored during concept drift evaluation. Lower values may
+        decrease delay in change detection but may also lead to more false positives.
     grace_period
         ADWIN does not perform any change detection until at least this many data points have
-        arrived (default is 10).
+        arrived.
 
     Examples
     --------
@@ -82,20 +85,23 @@ def _reset(self):
         )
 
     @property
-    def width(self):
+    def width(self) -> int:
         """Window size"""
         return self._helper.get_width()
 
     @property
-    def n_detections(self):
+    def n_detections(self) -> int:
+        """The total number of detected changes."""
         return self._helper.get_n_detections()
 
     @property
-    def variance(self):
+    def variance(self) -> float:
+        """The sample variance within the stored (adaptive) window."""
         return self._helper.get_variance()
 
     @property
-    def total(self):
+    def total(self) -> float:
+        """The sum of the stored elements."""
         return self._helper.get_total()
 
     @property