chore: import upstream snapshot with attribution

2026-07-13 13:35:51 +08:00
commit c36a561cd8
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+""" PATTERNDataset for inductive learning. """
+import os
+
+from .dgl_dataset import DGLBuiltinDataset
+from .utils import _get_dgl_url, load_graphs
+
+
+class PATTERNDataset(DGLBuiltinDataset):
+    r"""PATTERN dataset for graph pattern recognition task.
+
+    Each graph G contains 5 communities with sizes randomly selected between [5, 35].
+    The SBM of each community is p = 0.5, q = 0.35, and the node features on G are
+    generated with a uniform random distribution with a vocabulary of size 3, i.e. {0, 1, 2}.
+    Then randomly generate 100 patterns P composed of 20 nodes with intra-probability :math:`p_P` = 0.5
+    and extra-probability :math:`q_P` = 0.5 (i.e. 50% of nodes in P are connected to G). The node features
+    for P are also generated as a random signal with values {0, 1, 2}. The graphs are of sizes
+    44-188 nodes. The output node labels have value 1 if the node belongs to P and value 0 if it is in G.
+
+    Reference `<https://arxiv.org/pdf/2003.00982.pdf>`_
+
+    Statistics:
+
+    - Train examples: 10,000
+    - Valid examples: 2,000
+    - Test examples: 2,000
+    - Number of classes for each node: 2
+
+    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 PATTERNDataset
+    >>> data = PATTERNDataset(mode='train')
+    >>> data.num_classes
+    2
+    >>> len(trainset)
+    10000
+    >>> data[0]
+    Graph(num_nodes=108, num_edges=4884, ndata_schemes={'feat': Scheme(shape=(), dtype=torch.int64), 'label': Scheme(shape=(), dtype=torch.int16)}
+    edata_schemes={'feat': Scheme(shape=(1,), dtype=torch.float32)})
+    """
+
+    def __init__(
+        self,
+        mode="train",
+        raw_dir=None,
+        force_reload=False,
+        verbose=False,
+        transform=None,
+    ):
+        assert mode in ["train", "valid", "test"]
+        self.mode = mode
+        _url = _get_dgl_url("dataset/SBM_PATTERN.zip")
+
+        super(PATTERNDataset, self).__init__(
+            name="pattern",
+            url=_url,
+            raw_dir=raw_dir,
+            force_reload=force_reload,
+            verbose=verbose,
+            transform=transform,
+        )
+
+    def process(self):
+        self.load()
+
+    @property
+    def graph_path(self):
+        return os.path.join(
+            self.save_path, "SBM_PATTERN_{}.bin".format(self.mode)
+        )
+
+    def has_cache(self):
+        return os.path.exists(self.graph_path)
+
+    def load(self):
+        self._graphs, _ = load_graphs(self.graph_path)
+
+    @property
+    def num_classes(self):
+        r"""Number of classes for each node."""
+        return 2
+
+    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])