[Feature] Add Unifews model and related layers

examples/unifews/.gitignore

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examples/unifews/convert_data.py

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+"""Data conversion helpers for UNIFEWS.
+
+These are example-local utilities for converting OGB/PyG datasets
+into the format expected by the data processor. Not intended as
+shared GammaGL utilities.
+"""
+
+import os
+import argparse
+import json
+import numpy as np
+import scipy.sparse as sp
+import gammagl.datasets as ggl_ds
+import gammagl.transforms as ggl_t
+import gammagl.utils as ggl_utils
+
+from gammagl.utils.data_processor import DataProcess, edgeidx2adj
+
+
+class DataProcess_OGB(DataProcess):
+    def __init__(self, name, dataset_root, path='../data/', rrz=0.5, seed=0):
+        super().__init__(name, path=path, rrz=rrz, seed=seed)
+        self.dataset_root = dataset_root
+
+    @property
+    def n_train(self):
+        if self.idx_train is None:
+            self.input(['labels'])
+        return len(self.idx_train)
+
+    def fetch(self):
+        dataset = ggl_ds.OGB(self.name, root=self.dataset_root)
+        split_idx = dataset.get_idx_split()
+        idx_train, idx_val, idx_test = split_idx["train"], split_idx["valid"], split_idx["test"]
+        graph, labels = dataset[0]
+
+        row, col = graph.edge_index[0].numpy(), graph.edge_index[1].numpy()
+        row, col = np.concatenate([row, col], axis=0), np.concatenate([col, row], axis=0)
+        deg = np.bincount(row)
+        idx_zero = np.where(deg == 0)[0]
+        if len(idx_zero) > 0:
+            print(f"Warning: removing {len(idx_zero)} isolated nodes: {idx_zero}!")
+
+        nodelst = deg.nonzero()[0]
+        idxnew = np.full(graph.num_nodes, -1)
+        idxnew[nodelst] = np.arange(len(nodelst))
+        self._n = len(nodelst)
+        row, col = idxnew[row], idxnew[col]
+        self.adj_matrix = edgeidx2adj(row, col, self.n)
+        self._m = self.adj_matrix.nnz
+
+        self.attr_matrix = graph.x.numpy()[nodelst]
+        assert (labels.ndim == 2 and labels.shape[1] == 1) or labels.ndim == 1, "label shape error"
+        self.labels = labels.numpy().flatten()[nodelst]
+
+        self.idx_train = idxnew[idx_train]
+        self.idx_train = self.idx_train[self.idx_train > -1]
+        self.idx_val = idxnew[idx_val]
+        self.idx_val = self.idx_val[self.idx_val > -1]
+        self.idx_test = idxnew[idx_test]
+        self.idx_test = self.idx_test[self.idx_test > -1]
+
+
+class DataProcess_PyGFlickr(DataProcess):
+    def __init__(self, name, raw_root, path='../data/', rrz=0.5, seed=0):
+        super().__init__(name, path=path, rrz=rrz, seed=seed)
+        self.raw_root = raw_root
+
+    def fetch(self):
+        f = np.load(os.path.join(self.raw_root, 'adj_full.npz'))
+        self.adj_matrix = sp.csr_matrix((f['data'], f['indices'], f['indptr']), f['shape'])
+        self.to_undirected()
+        self.attr_matrix = np.load(os.path.join(self.raw_root, 'feats.npy'), allow_pickle=True)
+
+        ys = [-1] * self.attr_matrix.shape[0]
+        with open(os.path.join(self.raw_root, 'class_map.json')) as f:
+            class_map = json.load(f)
+            for key, item in class_map.items():
+                ys[int(key)] = item
+        self.labels = np.array(ys).flatten()
+        if self.labels.min() < 0:
+            print(f"Warning: negative label: {self.labels.min()}")
+
+        with open(os.path.join(self.raw_root, 'role.json')) as f:
+            role = json.load(f)
+        self.idx_train = np.array(role['tr'])
+        self.idx_val = np.array(role['va'])
+        self.idx_test = np.array(role['te'])
+
+
+class DataProcess_PyG(DataProcess):
+    def __init__(self, name, dataset_root, path='../data/', rrz=0.5, seed=0):
+        super().__init__(name, path=path, rrz=rrz, seed=seed)
+        self.dataset_root = dataset_root
+
+    def fetch(self):
+        dataset = ggl_ds.Coauthor(root=self.dataset_root, name='CS', transform=ggl_t.ToUndirected())
+        graph = dataset[0]
+        degree = ggl_utils.degree(graph.edge_index[0], graph.num_nodes).numpy()
+        idx_zero = np.where(degree == 0)[0]
+        if len(idx_zero) > 0:
+            print(f"Warning: removing {len(idx_zero)} isolated nodes: {idx_zero}!")
+
+        self._n = graph.num_nodes
+        self.adj_matrix = edgeidx2adj(graph.edge_index[0].numpy(), graph.edge_index[1].numpy(), self.n)
+        self._m = self.adj_matrix.nnz
+        self.attr_matrix = graph.x.numpy()
+        self.labels = graph.y.numpy().flatten()
+        if self.labels.min() < 0:
+            print(f"Warning: negative label: {self.labels.min()}")
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Convert OGB/PyG datasets for UNIFEWS')
+    parser.add_argument('--dataset', type=str, default='ogbn-papers100M', choices=['ogbn-papers100M', 'flickr', 'cs'])
+    parser.add_argument('--dataset-root', type=str, required=True, help='Root directory for the source dataset')
+    parser.add_argument('--output-path', type=str, default='./data/', help='Output directory')
+    args = parser.parse_args()
+
+    if args.dataset.startswith('ogbn'):
+        ds = DataProcess_OGB(args.dataset, dataset_root=args.dataset_root, path=args.output_path)
+    elif args.dataset == 'flickr':
+        ds = DataProcess_PyGFlickr(args.dataset, raw_root=args.dataset_root, path=args.output_path)
+    elif args.dataset == 'cs':
+        ds = DataProcess_PyG(args.dataset, dataset_root=args.dataset_root, path=args.output_path)
+    else:
+        raise ValueError(f"Unknown dataset: {args.dataset}")
+
+    ds.fetch()
+    ds.calculate(['deg', 'idx_train'])
+    print(ds)
+    os.makedirs(os.path.join(ds.path, ds.name), exist_ok=False)
+    ds.output(['adjtxt', 'adjnpz', 'adjl', 'attr_matrix', 'deg', 'labels', 'attribute'])