fix: typo for WideSampler

Author: rememberYou

pyrdf2vec/samplers/wide.py

@@ -10,6 +10,30 @@
 
 @attr.s
 class WideSampler(Sampler):
+    """Wide sampling node-centric sampling strategy which gives priority to
+    walks containing edges with the highest degree of predicates and
+    objects. The degree of a predicate and an object being defined by the
+    number of predicates and objects present in its neighborhood, but also by
+    their number of occurrence in a Knowledge Graph.
+
+    Attributes:
+        _is_support_remote: True if the sampling strategy can be used with a
+            remote Knowledge Graph, False Otherwise
+            Defaults to False.
+        _random_state: The random state to use to keep random determinism with
+            the sampling strategy.
+            Defaults to None.
+        _vertices_deg: The degree of the vertices.
+            Defaults to {}.
+        _visited: Tags vertices that appear at the max depth or of which all
+            their children are tagged.
+            Defaults to set.
+        inverse: True if the inverse algorithm must be used, False otherwise.
+            Defaults to False.
+        split: True if the split algorithm must be used, False otherwise.
+            Defaults to False.
+
+    """
 
     _pred_degs: DefaultDict[str, int] = attr.ib(
         init=False, repr=False, factory=lambda: defaultdict(dict)
@@ -24,14 +48,16 @@ class WideSampler(Sampler):
     )
 
     def fit(self, kg: KG) -> None:
-        """Since the weights are uniform, this function does nothing.
+        """Fits the sampling strategy by couting the number of available
+        neighbors for each vertex, but also by counting the number of
+        occurrence that a predicate and an object appears in the Knowledge
+        Graph.
 
         Args:
             kg: The Knowledge Graph.
 
         """
         super().fit(kg)
-
         for vertex in kg._vertices:
             if vertex.predicate:
                 self._neighbor_counts[vertex.name] = len(
@@ -53,10 +79,14 @@ def get_weight(self, hop: Hop) -> float:
         """Gets the weight of a hop in the Knowledge Graph.
 
         Args:
-            hop: The hop (pred, obj) to get the weight.
-
+            hop: The hop of a vertex in a (predicate, object) form to get the
+                weight.
         Returns:
-            The weight for a given hop.
+            The weight of a given hop.
+
+        Raises:
+            ValueError: If there is an attempt to access the weight of a hop
+                without the sampling strategy having been trained.
 
         """
         if not (self._pred_degs and self._obj_degs and self._neighbor_counts):