[GraphBolt] add abstract Dataset (#5926)

python/dgl/graphbolt/__init__.py

@@ -9,6 +9,7 @@ from .graph_storage import *
 from .itemset import *
 from .minibatch_sampler import *
 from .feature_store import *
+from .dataset import *
 from .subgraph_sampler import *
 
 

python/dgl/graphbolt/dataset.py

+"""GraphBolt Dataset."""
+
+from .feature_store import FeatureStore
+from .itemset import ItemSet, ItemSetDict
+
+__all__ = ["Dataset"]
+
+
+class Dataset:
+    """An abstract dataset.
+
+    Dataset provides abstraction for accessing the data required for training.
+    The data abstraction could be a native CPU memory block, a shared memory
+    block, a file handle of an opened file on disk, a service that provides
+    the API to access the data e.t.c. There are 3 primary components in the
+    dataset: *Train-Validation-Test Set*, *Feature Storage*, *Graph Topology*.
+
+    *Train-Validation-Test Set*:
+    The training-validation-testing (TVT) set which is used to train the neural
+    networks. We calculate the embeddings based on their respective features
+    and the graph structure, and then utilize the embeddings to optimize the
+    neural network parameters.
+
+    *Feature Storage*:
+    A key-value store which stores node/edge/graph features.
+
+    *Graph Topology*:
+    Graph topology is used by the subgraph sampling algorithm to
+    generate a subgraph.
+    """
+
+    def train_set(self) -> ItemSet or ItemSetDict:
+        """Return the training set."""
+        raise NotImplementedError
+
+    def validation_set(self) -> ItemSet or ItemSetDict:
+        """Return the validation set."""
+        raise NotImplementedError
+
+    def test_set(self) -> ItemSet or ItemSetDict:
+        """Return the test set."""
+        raise NotImplementedError
+
+    def graph(self) -> object:
+        """Return the graph."""
+        raise NotImplementedError
+
+    def feature(self) -> FeatureStore:
+        """Return the feature."""
+        raise NotImplementedError

tests/python/pytorch/graphbolt/test_dataset.py

+import pytest
+from dgl import graphbolt as gb
+
+
+def test_Dataset():
+    dataset = gb.Dataset()
+    with pytest.raises(NotImplementedError):
+        _ = dataset.train_set()
+    with pytest.raises(NotImplementedError):
+        _ = dataset.validation_set()
+    with pytest.raises(NotImplementedError):
+        _ = dataset.test_set()
+    with pytest.raises(NotImplementedError):
+        _ = dataset.graph()
+    with pytest.raises(NotImplementedError):
+        _ = dataset.feature()