add CLUSTER dataset

docs/source/api/python/dgl.data.rst

@@ -54,6 +54,7 @@ Datasets for node classification/regression tasks
     WikiCSDataset
     FlickrDataset
     YelpDataset
+    CLUSTERDataset
 
 Edge Prediction Datasets
 ---------------------------------------

python/dgl/data/__init__.py

@@ -52,6 +52,7 @@
 from .tree import SST, SSTDataset
 from .tu import LegacyTUDataset, TUDataset
 from .utils import *
+from .cluster import CLUSTERDataset
 from .wikics import WikiCSDataset
 from .yelp import YelpDataset
 

python/dgl/data/cluster.py

@@ -0,0 +1,132 @@
+""" CLUSTERDataset for inductive learning. """
+import os
+
+from .dgl_dataset import DGLBuiltinDataset
+from .utils import _get_dgl_url, load_graphs
+
+
+class CLUSTERDataset(DGLBuiltinDataset):
+    r"""CLUSTER dataset for inductive semi-supervised clustering task.
+
+    Each graph contains 6 SBM clusters with sizes randomly selected between
+    [5, 35] and probabilities p = 0.55, q = 0.25. The graphs are of sizes 40
+    -190 nodes. Each node can take an input feature value in {0, 1, 2, ..., 6}
+    and value 1~6 corresponds to class 0~5 respectively, while value 0 means
+    that the class of the node is unknown. There is only one labeled node that
+    is randomly assigned to each community and most node features are set to 0.
+
+    Reference `<https://arxiv.org/pdf/2003.00982.pdf>`_
+
+    Statistics:
+
+    - Train examples: 10,000
+    - Valid examples: 1,000
+    - Test examples: 1,000
+    - Number of classes for each node: 6
+
+    Parameters
+    ----------
+    mode : str
+        Must be one of ('train', 'valid', 'test').
+        Default: 'train'
+    raw_dir : str
+        Raw file directory to download/contains the input data directory.
+        Default: ~/.dgl/
+    force_reload : bool
+        Whether to reload the dataset.
+        Default: False
+    verbose : bool
+        Whether to print out progress information.
+        Default: False
+    transform : callable, optional
+        A transform that takes in a :class:`~dgl.DGLGraph` object and returns
+        a transformed version. The :class:`~dgl.DGLGraph` object will be
+        transformed before every access.
+
+    Attributes
+    ----------
+    num_classes : int
+        Number of classes for each node
+
+    Examples
+    —-------
+    >>> from dgl.data import CLUSTERDataset
+    >>>
+    >>> trainset = CLUSTERDataset(mode='train')
+    >>>
+    >>> trainset.num_classes
+    6
+    >>> len(trainset)
+    10000
+    >>> trainset[0]
+    Graph(num_nodes=117, num_edges=4104,
+          ndata_schemes={'label': Scheme(shape=(), dtype=torch.int16),
+                         'feat': Scheme(shape=(), dtype=torch.int64)}
+          edata_schemes={'feat': Scheme(shape=(1,), dtype=torch.float32)})
+    """
+
+    def __init__(
+        self,
+        mode,
+        raw_dir=None,
+        force_reload=False,
+        verbose=False,
+        transform=None,
+    ):
+        self._url = _get_dgl_url("dataset/SBM_CLUSTER.zip")
+        self.mode = mode
+
+        super(CLUSTERDataset, self).__init__(
+            name="cluster",
+            url=self._url,
+            raw_dir=raw_dir,
+            force_reload=force_reload,
+            verbose=verbose,
+            transform=transform,
+        )
+
+    def process(self):
+        self.load()
+
+    def has_cache(self):
+        graph_path = os.path.join(
+            self.save_path, "CLUSTER_{}.bin".format(self.mode)
+        )
+        return os.path.exists(graph_path)
+
+    def load(self):
+        graph_path = os.path.join(
+            self.save_path, "CLUSTER_{}.bin".format(self.mode)
+        )
+        self._graphs, _ = load_graphs(graph_path)
+
+    @property
+    def num_classes(self):
+        r"""Number of classes for each node."""
+        return 6
+
+    def __len__(self):
+        r"""The number of examples in the dataset."""
+        return len(self._graphs)
+
+    def __getitem__(self, idx):
+        r"""Get the idx^th sample.
+
+        Parameters
+        ---------
+        idx : int
+            The sample index.
+
+        Returns
+        -------
+        :class:`dgl.DGLGraph`
+            graph structure, node features, node labels and edge features.
+
+            - ``ndata['feat']``: node features
+            - ``ndata['label']``: node labels
+            - ``edata['feat']``: edge features
+        """
+        if self._transform is None:
+            return self._graphs[idx]
+        else:
+            return self._transform(self._graphs[idx])

tests/python/common/data/test_data.py

@@ -364,6 +364,28 @@ def test_flickr():
     assert g2.num_edges() - g.num_edges() == g.num_nodes()
 
 
+@unittest.skipIf(
+    F._default_context_str == "gpu",
+    reason="Datasets don't need to be tested on GPU.",
+)
+@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Skip MXNet")
+def test_cluster():
+    mode_n_graphs = {
+        "train": 10000,
+        "valid": 1000,
+        "test": 1000,
+    }
+    transform = dgl.AddSelfLoop(allow_duplicate=True)
+    for mode, n_graphs in mode_n_graphs.items():
+        ds = data.CLUSTERDataset(mode=mode)
+        assert len(ds) == n_graphs, (len(ds), mode)
+        g1 = ds[0]
+        ds = data.CLUSTERDataset(mode=mode, transform=transform)
+        g2 = ds[0]
+        assert g2.num_edges() - g1.num_edges() == g1.num_nodes()
+        assert ds.num_classes == 6
+
+
 @unittest.skipIf(
     F._default_context_str == "gpu",
     reason="Datasets don't need to be tested on GPU.",