[Dataset] Change Cora Split (#1583)

* Use new version of Cora

* Fix import

docs/source/api/python/data.rst

@@ -49,13 +49,6 @@ Citation Network dataset
     :members: __getitem__, __len__
 
 
-Cora Citation Network dataset
-```````````````````````````````````
-
-.. autoclass:: CoraDataset
-    :members: __getitem__, __len__
-
-
 CoraFull dataset
 ```````````````````````````````````
 

docs/source/features/dataset.rst

@@ -8,7 +8,7 @@ Dataset (Temporary)
     +================+==========================================================+===========+===============+===============+============================================+===========+========+
     |BitcoinOTC      |BitcoinOTC()                                              |        136|        6005.00|       21209.98|                                            |h          |True    |
     +----------------+----------------------------------------------------------+-----------+---------------+---------------+--------------------------------------------+-----------+--------+
-    |Cora            |CoraDataset()                                             |          1|        2708.00|       10556.00|train_mask, val_mask, test_mask, label, feat|           |False   |
+    |Cora            |CitationGraphDataset('cora')                              |          1|        2708.00|       10556.00|train_mask, val_mask, test_mask, label, feat|           |False   |
     +----------------+----------------------------------------------------------+-----------+---------------+---------------+--------------------------------------------+-----------+--------+
     |Citeseer        |CitationGraphDataset('citeseer')                          |          1|        3327.00|        9228.00|train_mask, val_mask, test_mask, label, feat|           |False   |
     +----------------+----------------------------------------------------------+-----------+---------------+---------------+--------------------------------------------+-----------+--------+

docs/source/gen_dataset_stat.py

@@ -10,11 +10,11 @@ from dgl.data.gdelt import GDELT
 from dgl.data.icews18 import ICEWS18
 from dgl.data.qm7b import QM7b
 # from dgl.data.qm9 import QM9
-from dgl.data import CitationGraphDataset, CoraDataset, PPIDataset, RedditDataset, TUDataset
+from dgl.data import CitationGraphDataset, PPIDataset, RedditDataset, TUDataset
 
 ds_list = {
     "BitcoinOTC": "BitcoinOTC()",
-    "Cora": "CoraDataset()",
+    "Cora": "CitationGraphDataset('cora')",
     "Citeseer": "CitationGraphDataset('citeseer')",
     "PubMed": "CitationGraphDataset('pubmed')",
     "QM7b": "QM7b()",

examples/pytorch/sign/dataset.py

@@ -22,8 +22,8 @@ def load_dataset(name):
             data = RedditDataset(self_loop=True)
             g = data.graph
         else:
-            from dgl.data import CoraDataset
-            data = CoraDataset()
+            from dgl.data import CitationGraphDataset
+            data = CitationGraphDataset('cora')
             g = dgl.DGLGraph(data.graph)
         train_mask = data.train_mask
         val_mask = data.val_mask

python/dgl/data/__init__.py

@@ -2,7 +2,7 @@
 from __future__ import absolute_import
 
 from . import citation_graph as citegrh
-from .citation_graph import CoraBinary, CitationGraphDataset, CoraDataset
+from .citation_graph import CoraBinary, CitationGraphDataset
 from .minigc import *
 from .tree import *
 from .utils import *

python/dgl/data/citation_graph.py

@@ -16,7 +16,7 @@ from ..graph import DGLGraph
 from ..graph import batch as graph_batch
 
 _urls = {
-    'cora' : 'dataset/cora_raw.zip',
+    'cora_v2' : 'dataset/cora_v2.zip',
     'citeseer' : 'dataset/citeseer.zip',
     'pubmed' : 'dataset/pubmed.zip',
     'cora_binary' : 'dataset/cora_binary.zip',
@@ -29,16 +29,22 @@ def _pickle_load(pkl_file):
         return pkl.load(pkl_file)
 
 class CitationGraphDataset(object):
-    r"""The citation graph dataset, including citeseer and pubmeb.
+    r"""The citation graph dataset, including cora, citeseer and pubmeb.
     Nodes mean authors and edges mean citation relationships.
 
     Parameters
     -----------
     name: str
-      name can be 'citeseer' or 'pubmed'.
+      name can be 'cora', 'citeseer' or 'pubmed'.
     """
     def __init__(self, name):
-        assert name.lower() in ['citeseer', 'pubmed']
+        assert name.lower() in ['cora', 'citeseer', 'pubmed']
+
+        # Previously we use the pre-processing in pygcn (https://github.com/tkipf/pygcn)
+        # for Cora, which is slightly different from the one used in the GCN paper
+        if name.lower() == 'cora':
+            name = 'cora_v2'
+
         self.name = name
         self.dir = get_download_dir()
         self.zip_file_path='{}/{}.zip'.format(self.dir, name)
@@ -157,7 +163,7 @@ def _sample_mask(idx, l):
     return mask
 
 def load_cora():
-    data = CoraDataset()
+    data = CitationGraphDataset('cora')
     return data
 
 def load_citeseer():
@@ -353,67 +359,6 @@ class CoraBinary(object):
         batched_labels = np.concatenate(labels, axis=0)
         return batched_graphs, batched_pmpds, batched_labels
 
-class CoraDataset(object):
-    r"""Cora citation network dataset. Nodes mean author and edges mean citation
-    relationships.
-    """
-    def __init__(self):
-        self.name = 'cora'
-        self.dir = get_download_dir()
-        self.zip_file_path='{}/{}.zip'.format(self.dir, self.name)
-        download(_get_dgl_url(_urls[self.name]), path=self.zip_file_path)
-        extract_archive(self.zip_file_path,
-                        '{}/{}'.format(self.dir, self.name))
-        self._load()
-
-    def _load(self):
-        idx_features_labels = np.genfromtxt("{}/cora/cora.content".
-                                            format(self.dir),
-                                            dtype=np.dtype(str))
-        features = sp.csr_matrix(idx_features_labels[:, 1:-1],
-                                 dtype=np.float32)
-        labels = _encode_onehot(idx_features_labels[:, -1])
-        self.num_labels = labels.shape[1]
-
-        # build graph
-        idx = np.asarray(idx_features_labels[:, 0], dtype=np.int32)
-        idx_map = {j: i for i, j in enumerate(idx)}
-        edges_unordered = np.genfromtxt("{}/cora/cora.cites".format(self.dir),
-                                        dtype=np.int32)
-        edges = np.asarray(list(map(idx_map.get, edges_unordered.flatten())),
-                           dtype=np.int32).reshape(edges_unordered.shape)
-        adj = sp.coo_matrix((np.ones(edges.shape[0]),
-                             (edges[:, 0], edges[:, 1])),
-                            shape=(labels.shape[0], labels.shape[0]),
-                            dtype=np.float32)
-
-        # build symmetric adjacency matrix
-        adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)
-        self.graph = nx.from_scipy_sparse_matrix(adj, create_using=nx.DiGraph())
-
-        features = _normalize(features)
-        self.features = np.asarray(features.todense())
-        self.labels = np.where(labels)[1]
-
-        self.train_mask = _sample_mask(range(140), labels.shape[0])
-        self.val_mask = _sample_mask(range(200, 500), labels.shape[0])
-        self.test_mask = _sample_mask(range(500, 1500), labels.shape[0])
-    
-    def __getitem__(self, idx):
-        assert idx == 0, "This dataset has only one graph"
-        g = DGLGraph(self.graph)
-        g.ndata['train_mask'] = self.train_mask
-        g.ndata['val_mask'] = self.val_mask
-        g.ndata['test_mask'] = self.test_mask
-        g.ndata['label'] = self.labels
-        g.ndata['feat'] = self.features
-        return g
-    
-    def __len__(self):
-        return 1
-
-
-
 def _normalize(mx):
     """Row-normalize sparse matrix"""
     rowsum = np.asarray(mx.sum(1))
@@ -430,4 +375,3 @@ def _encode_onehot(labels):
     labels_onehot = np.asarray(list(map(classes_dict.get, labels)),
                                dtype=np.int32)
     return labels_onehot
-