[Dataset] Chameleon

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

@@ -56,6 +56,7 @@ Datasets for node classification/regression tasks
     YelpDataset
     PATTERNDataset
     CLUSTERDataset
+    ChameleonDataset
 
 Edge Prediction Datasets
 ---------------------------------------

python/dgl/data/__init__.py

@@ -54,6 +54,7 @@
 from .utils import *
 from .cluster import CLUSTERDataset
 from .pattern import PATTERNDataset
+from .wiki_network import ChameleonDataset
 from .wikics import WikiCSDataset
 from .yelp import YelpDataset
 

python/dgl/data/dgl_dataset.py

@@ -6,7 +6,6 @@
 import abc
 import hashlib
 import os
-import sys
 import traceback
 
 from ..utils import retry_method_with_fix
@@ -221,6 +220,15 @@ def _get_hash(self):
         hash_func.update(str(self._hash_key).encode("utf-8"))
         return hash_func.hexdigest()[:8]
 
+    def _get_hash_url_suffix(self):
+        """Get the suffix based on the hash value of the url."""
+        if self._url is None:
+            return ""
+        else:
+            hash_func = hashlib.sha1()
+            hash_func.update(str(self._url).encode("utf-8"))
+            return "_" + hash_func.hexdigest()[:8]
+
     @property
     def url(self):
         r"""Get url to download the raw dataset."""
@@ -241,7 +249,9 @@ def raw_path(self):
         r"""Directory contains the input data files.
         By default raw_path = os.path.join(self.raw_dir, self.name)
         """
-        return os.path.join(self.raw_dir, self.name)
+        return os.path.join(
+            self.raw_dir, self.name + self._get_hash_url_suffix()
+        )
 
     @property
     def save_dir(self):

python/dgl/data/wiki_network.py

@@ -0,0 +1,182 @@
+"""
+Wikipedia page-page networks on the chameleon topic.
+"""
+import os
+
+import numpy as np
+
+from ..convert import graph
+from .dgl_dataset import DGLBuiltinDataset
+from .utils import _get_dgl_url
+
+
+class WikiNetworkDataset(DGLBuiltinDataset):
+    r"""Wikipedia page-page networks from `Multi-scale Attributed Node
+    Embedding <https://arxiv.org/abs/1909.13021>`__
+
+    Parameters
+    ----------
+    name : str
+        Name of the dataset.
+    raw_dir : str
+        Raw file directory to store the processed data.
+    force_reload : bool
+        Whether to always generate the data from scratch rather than load a
+        cached version.
+    verbose : bool
+        Whether to print progress information.
+    transform : callable
+        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.
+    """
+
+    def __init__(self, name, raw_dir, force_reload, verbose, transform):
+        url = _get_dgl_url(f"dataset/{name}.zip")
+        super(WikiNetworkDataset, self).__init__(
+            name=name,
+            url=url,
+            raw_dir=raw_dir,
+            force_reload=force_reload,
+            verbose=verbose,
+            transform=transform,
+        )
+
+    def process(self):
+        """Load and process the data."""
+        try:
+            import torch
+        except ImportError:
+            raise ModuleNotFoundError(
+                "This dataset requires PyTorch to be the backend."
+            )
+
+        # Process node features and labels.
+        with open(f"{self.raw_path}/out1_node_feature_label.txt", "r") as f:
+            data = f.read().split("\n")[1:-1]
+        features = [
+            [float(v) for v in r.split("\t")[1].split(",")] for r in data
+        ]
+        features = torch.tensor(features, dtype=torch.float)
+        labels = [int(r.split("\t")[2]) for r in data]
+        self._num_classes = max(labels) + 1
+        labels = torch.tensor(labels, dtype=torch.long)
+
+        # Process graph structure.
+        with open(f"{self.raw_path}/out1_graph_edges.txt", "r") as f:
+            data = f.read().split("\n")[1:-1]
+            data = [[int(v) for v in r.split("\t")] for r in data]
+        dst, src = torch.tensor(data, dtype=torch.long).t().contiguous()
+
+        self._g = graph((src, dst), num_nodes=features.size(0))
+        self._g.ndata["feat"] = features
+        self._g.ndata["label"] = labels
+
+        # Process 10 train/val/test node splits.
+        train_masks, val_masks, test_masks = [], [], []
+        for i in range(10):
+            filepath = f"{self.raw_path}/{self.name}_split_0.6_0.2_{i}.npz"
+            f = np.load(filepath)
+            train_masks += [torch.from_numpy(f["train_mask"])]
+            val_masks += [torch.from_numpy(f["val_mask"])]
+            test_masks += [torch.from_numpy(f["test_mask"])]
+        self._g.ndata["train_mask"] = torch.stack(train_masks, dim=1).bool()
+        self._g.ndata["val_mask"] = torch.stack(val_masks, dim=1).bool()
+        self._g.ndata["test_mask"] = torch.stack(test_masks, dim=1).bool()
+
+    def has_cache(self):
+        return os.path.exists(self.raw_path)
+
+    def load(self):
+        self.process()
+
+    def __getitem__(self, idx):
+        assert idx == 0, "This dataset has only one graph."
+        if self._transform is None:
+            return self._g
+        else:
+            return self._transform(self._g)
+
+    def __len__(self):
+        return 1
+
+    @property
+    def num_classes(self):
+        return self._num_classes
+
+
+class ChameleonDataset(WikiNetworkDataset):
+    """Wikipedia page-page network on chameleons from `Multi-scale Attributed
+    Node Embedding <https://arxiv.org/abs/1909.13021>`__ and later processed by
+    `Geom-GCN: Geometric Graph Convolutional Networks
+    <https://arxiv.org/abs/2002.05287>`
+
+    Nodes represent articles from the English Wikipedia, edges reflect mutual
+    links between them. Node features indicate the presence of particular nouns
+    in the articles. The nodes were classified into 5 classes in terms of their
+    average monthly traffic.
+
+    Statistics:
+
+    - Nodes: 2277
+    - Edges: 36101
+    - Number of Classes: 5
+    - 10 splits with 60/20/20 train/val/test ratio
+
+        - Train: 1092
+        - Val: 729
+        - Test: 456
+
+    Parameters
+    ----------
+    raw_dir : str, optional
+        Raw file directory to store the processed data. Default: ~/.dgl/
+    force_reload : bool, optional
+        Whether to always generate the data from scratch rather than load a
+        cached version. Default: False
+    verbose : bool, optional
+        Whether to print progress information. Default: True
+    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. Default: None
+
+    Attributes
+    ----------
+    num_classes : int
+        Number of node classes
+
+    Notes
+    -----
+    The graph does not come with edges for both directions.
+
+    Examples
+    --------
+
+    >>> from dgl.data import ChameleonDataset
+    >>> dataset = ChameleonDataset()
+    >>> g = dataset[0]
+    >>> num_classes = dataset.num_classes
+
+    >>> # get node features
+    >>> feat = g.ndata["feat"]
+
+    >>> # get data split
+    >>> train_mask = g.ndata["train_mask"]
+    >>> val_mask = g.ndata["val_mask"]
+    >>> test_mask = g.ndata["test_mask"]
+
+    >>> # get labels
+    >>> label = g.ndata['label']
+    """
+
+    def __init__(
+        self, raw_dir=None, force_reload=False, verbose=True, transform=None
+    ):
+        super(ChameleonDataset, self).__init__(
+            name="chameleon",
+            raw_dir=raw_dir,
+            force_reload=force_reload,
+            verbose=verbose,
+            transform=transform,
+        )

tests/python/common/data/test_wiki_network.py

@@ -0,0 +1,23 @@
+import unittest
+
+import backend as F
+
+import dgl
+
+
+@unittest.skipIf(
+    F._default_context_str == "gpu",
+    reason="Datasets don't need to be tested on GPU.",
+)
+@unittest.skipIf(
+    dgl.backend.backend_name != "pytorch", reason="only supports pytorch"
+)
+def test_chameleon():
+    transform = dgl.AddSelfLoop(allow_duplicate=True)
+
+    # chameleon
+    g = dgl.data.ChameleonDataset(force_reload=True)[0]
+    assert g.num_nodes() == 2277
+    assert g.num_edges() == 36101
+    g2 = dgl.data.ChameleonDataset(force_reload=True, transform=transform)[0]
+    assert g2.num_edges() - g.num_edges() == g.num_nodes()