chore: import upstream snapshot with attribution

easygraph/datasets/hypergraph/loadDeepSetDatasets.py

@@ -0,0 +1,78 @@
+import os.path as osp
+
+import numpy as np
+import scipy.sparse as sp
+import torch
+
+from torch_geometric.data import Data
+from torch_sparse import coalesce
+
+
+__all__ = ["load_line_expansion_dataset"]
+
+
+def load_line_expansion_dataset(
+    path=None, dataset="cocitation-cora", train_percent=0.5
+):
+    # load edges, features, and labels.
+    print("Loading {} dataset...".format(dataset))
+
+    file_name = f"{dataset}.content"
+    p2idx_features_labels = osp.join(path, dataset, file_name)
+    idx_features_labels = np.genfromtxt(p2idx_features_labels, dtype=np.dtype(str))
+    # features = np.array(idx_features_labels[:, 1:-1])
+    features = sp.csr_matrix(idx_features_labels[:, 1:-1], dtype=np.float32)
+    #     labels = encode_onehot(idx_features_labels[:, -1])
+    labels = torch.LongTensor(idx_features_labels[:, -1].astype(float))
+
+    print("load features")
+
+    # build graph
+    idx = np.array(idx_features_labels[:, 0], dtype=np.int32)
+    idx_map = {j: i for i, j in enumerate(idx)}
+
+    file_name = f"{dataset}.edges"
+    p2edges_unordered = osp.join(path, dataset, file_name)
+    edges_unordered = np.genfromtxt(p2edges_unordered, dtype=np.int32)
+
+    edges = np.array(
+        list(map(idx_map.get, edges_unordered.flatten())), dtype=np.int32
+    ).reshape(edges_unordered.shape)
+
+    print("load edges")
+
+    # From adjacency matrix to edge_list
+    edge_index = edges.T
+    #     ipdb.set_trace()
+    assert edge_index[0].max() == edge_index[1].min() - 1
+
+    # check if values in edge_index is consecutive. i.e. no missing value for node_id/he_id.
+    assert len(np.unique(edge_index)) == edge_index.max() + 1
+
+    num_nodes = edge_index[0].max() + 1
+    num_he = edge_index[1].max() - num_nodes + 1
+    edge_index = np.hstack((edge_index, edge_index[::-1, :]))
+
+    # build torch data class
+    data = Data(
+        x=torch.FloatTensor(np.array(features[:num_nodes].todense())),
+        edge_index=torch.LongTensor(edge_index),
+        y=labels[:num_nodes],
+    )
+
+    # used user function to override the default function.
+    # the following will also sort the edge_index and remove duplicates.
+    total_num_node_id_he_id = len(np.unique(edge_index))
+    data.edge_index, data.edge_attr = coalesce(
+        data.edge_index, None, total_num_node_id_he_id, total_num_node_id_he_id
+    )
+    n_x = num_nodes
+    #     n_x = n_expanded
+    num_class = len(np.unique(labels[:num_nodes].numpy()))
+    data.n_x = n_x
+    # add parameters to attribute
+
+    data.train_percent = train_percent
+    data.num_hyperedges = num_he
+
+    return data