[GraphBolt] Add functional name into docstring (#6739)

python/dgl/graphbolt/base.py

@@ -104,6 +104,8 @@ class CopyTo(IterDataPipe):
     """DataPipe that transfers each element yielded from the previous DataPipe
     to the given device.
 
+    Functional name: :obj:`copy_to`.
+
     This is equivalent to
 
     .. code:: python

python/dgl/graphbolt/feature_fetcher.py

@@ -16,7 +16,30 @@ __all__ = [
 
 @functional_datapipe("fetch_feature")
 class FeatureFetcher(MiniBatchTransformer):
-    """A feature fetcher used to fetch features for node/edge in graphbolt."""
+    """A feature fetcher used to fetch features for node/edge in graphbolt.
+
+    Functional name: :obj:`fetch_feature`.
+
+    Parameters
+    ----------
+    datapipe : DataPipe
+        The datapipe.
+    feature_store : FeatureStore
+        A storage for features, support read and update.
+    node_feature_keys : List[str] or Dict[str, List[str]]
+        Node features keys indicates the node features need to be read.
+        - If `node_features` is a list: It means the graph is homogeneous
+        graph, and the 'str' inside are feature names.
+        - If `node_features` is a dictionary: The keys should be node type
+        and the values are lists of feature names.
+    edge_feature_keys : List[str] or Dict[str, List[str]]
+        Edge features name indicates the edge features need to be read.
+        - If `edge_features` is a list: It means the graph is homogeneous
+        graph, and the 'str' inside are feature names.
+        - If `edge_features` is a dictionary: The keys are edge types,
+        following the format 'str:str:str', and the values are lists of
+        feature names.
+    """
 
     def __init__(
         self,
@@ -25,29 +48,6 @@ class FeatureFetcher(MiniBatchTransformer):
         node_feature_keys=None,
         edge_feature_keys=None,
     ):
-        """
-        Initlization for a feature fetcher.
-
-        Parameters
-        ----------
-        datapipe : DataPipe
-            The datapipe.
-        feature_store : FeatureStore
-            A storage for features, support read and update.
-        node_feature_keys : List[str] or Dict[str, List[str]]
-            Node features keys indicates the node features need to be read.
-            - If `node_features` is a list: It means the graph is homogeneous
-            graph, and the 'str' inside are feature names.
-            - If `node_features` is a dictionary: The keys should be node type
-            and the values are lists of feature names.
-        edge_feature_keys : List[str] or Dict[str, List[str]]
-            Edge features name indicates the edge features need to be read.
-            - If `edge_features` is a list: It means the graph is homogeneous
-            graph, and the 'str' inside are feature names.
-            - If `edge_features` is a dictionary: The keys are edge types,
-            following the format 'str:str:str', and the values are lists of
-            feature names.
-        """
         super().__init__(datapipe, self._read)
         self.feature_store = feature_store
         self.node_feature_keys = node_feature_keys

python/dgl/graphbolt/impl/in_subgraph_sampler.py

@@ -15,6 +15,8 @@ __all__ = ["InSubgraphSampler"]
 class InSubgraphSampler(SubgraphSampler):
     """Sample the subgraph induced on the inbound edges of the given nodes.
 
+    Functional name: :obj:`sample_in_subgraph`.
+
     In-subgraph sampler is responsible for sampling a subgraph from given data,
     returning an induced subgraph along with compacted information.
 

python/dgl/graphbolt/impl/neighbor_sampler.py

@@ -20,6 +20,8 @@ __all__ = ["NeighborSampler", "LayerNeighborSampler"]
 class NeighborSampler(SubgraphSampler):
     """Sample neighbor edges from a graph and return a subgraph.
 
+    Functional name: :obj:`sample_neighbor`.
+
     Neighbor sampler is responsible for sampling a subgraph from given data. It
     returns an induced subgraph along with compacted information. In the
     context of a node classification task, the neighbor sampler directly
@@ -183,6 +185,8 @@ class NeighborSampler(SubgraphSampler):
 class LayerNeighborSampler(NeighborSampler):
     """Sample layer neighbor edges from a graph and return a subgraph.
 
+    Functional name: :obj:`sample_layer_neighbor`.
+
     Sampler that builds computational dependency of node representations via
     labor sampling for multilayer GNN from the NeurIPS 2023 paper
     `Layer-Neighbor Sampling -- Defusing Neighborhood Explosion in GNNs

python/dgl/graphbolt/impl/uniform_negative_sampler.py

@@ -12,6 +12,8 @@ class UniformNegativeSampler(NegativeSampler):
     """Sample negative destination nodes for each source node based on a uniform
     distribution.
 
+    Functional name: :obj:`sample_uniform_negative`.
+
     It's important to note that the term 'negative' refers to false negatives,
     indicating that the sampled pairs are not ensured to be absent in the graph.
     For each edge ``(u, v)``, it is supposed to generate `negative_ratio` pairs

python/dgl/graphbolt/minibatch_transformer.py

@@ -14,23 +14,24 @@ __all__ = [
 
 @functional_datapipe("transform")
 class MiniBatchTransformer(Mapper):
-    """A mini-batch transformer used to manipulate mini-batch"""
+    """A mini-batch transformer used to manipulate mini-batch.
+
+    Functional name: :obj:`transform`.
+
+    Parameters
+    ----------
+    datapipe : DataPipe
+        The datapipe.
+    transformer:
+        The function applied to each minibatch which is responsible for
+        transforming the minibatch.
+    """
 
     def __init__(
         self,
         datapipe,
         transformer,
     ):
-        """
-        Initlization for a subgraph transformer.
-        Parameters
-        ----------
-        datapipe : DataPipe
-            The datapipe.
-        transformer:
-            The function applied to each minibatch which is responsible for
-            transforming the minibatch.
-        """
         super().__init__(datapipe, self._transformer)
         self.transformer = transformer
 
@@ -44,17 +45,18 @@ class MiniBatchTransformer(Mapper):
 
 @functional_datapipe("to_dgl")
 class DGLMiniBatchConverter(Mapper):
-    """Convert a graphbolt mini-batch to a dgl mini-batch."""
+    """Convert a graphbolt mini-batch to a dgl mini-batch.
+
+    Functional name: :obj:`to_dgl`.
+
+    Parameters
+    ----------
+    datapipe : DataPipe
+        The datapipe.
+    """
 
     def __init__(
         self,
         datapipe,
     ):
-        """
-        Initlization for a subgraph transformer.
-        Parameters
-        ----------
-        datapipe : DataPipe
-            The datapipe.
-        """
         super().__init__(datapipe, MiniBatch.to_dgl)

python/dgl/graphbolt/negative_sampler.py

@@ -16,6 +16,15 @@ class NegativeSampler(MiniBatchTransformer):
     """
     A negative sampler used to generate negative samples and return
     a mix of positive and negative samples.
+
+    Functional name: :obj:`sample_negative`.
+
+    Parameters
+    ----------
+    datapipe : DataPipe
+        The datapipe.
+    negative_ratio : int
+        The proportion of negative samples to positive samples.
     """
 
     def __init__(
@@ -23,16 +32,6 @@ class NegativeSampler(MiniBatchTransformer):
         datapipe,
         negative_ratio,
     ):
-        """
-        Initlization for a negative sampler.
-
-        Parameters
-        ----------
-        datapipe : DataPipe
-            The datapipe.
-        negative_ratio : int
-            The proportion of negative samples to positive samples.
-        """
         super().__init__(datapipe, self._sample)
         assert negative_ratio > 0, "Negative_ratio should be positive Integer."
         self.negative_ratio = negative_ratio

python/dgl/graphbolt/subgraph_sampler.py

@@ -17,20 +17,20 @@ __all__ = [
 @functional_datapipe("sample_subgraph")
 class SubgraphSampler(MiniBatchTransformer):
     """A subgraph sampler used to sample a subgraph from a given set of nodes
-    from a larger graph."""
+    from a larger graph.
+
+    Functional name: :obj:`sample_subgraph`.
+
+    Parameters
+    ----------
+    datapipe : DataPipe
+        The datapipe.
+    """
 
     def __init__(
         self,
         datapipe,
     ):
-        """
-        Initlization for a subgraph sampler.
-
-        Parameters
-        ----------
-        datapipe : DataPipe
-            The datapipe.
-        """
         super().__init__(datapipe, self._sample)
 
     def _sample(self, minibatch):