(normalization="AugNormAdj", porting_to_torch=True, data_path=datadir)
| 125 | |
| 126 | |
| 127 | def load_reddit_data(normalization="AugNormAdj", porting_to_torch=True, data_path=datadir): |
| 128 | adj, features, y_train, y_val, y_test, train_index, val_index, test_index = loadRedditFromNPZ(data_path) |
| 129 | labels = np.zeros(adj.shape[0]) |
| 130 | labels[train_index] = y_train |
| 131 | labels[val_index] = y_val |
| 132 | labels[test_index] = y_test |
| 133 | adj = adj + adj.T + sp.eye(adj.shape[0]) |
| 134 | train_adj = adj[train_index, :][:, train_index] |
| 135 | degree = np.sum(train_adj, axis=1) |
| 136 | |
| 137 | features = torch.FloatTensor(np.array(features)) |
| 138 | features = (features-features.mean(dim=0))/features.std(dim=0) |
| 139 | train_features = torch.index_select(features, 0, torch.LongTensor(train_index)) |
| 140 | if not porting_to_torch: |
| 141 | features = features.numpy() |
| 142 | train_features = train_features.numpy() |
| 143 | |
| 144 | adj_normalizer = fetch_normalization(normalization) |
| 145 | adj = adj_normalizer(adj) |
| 146 | train_adj = adj_normalizer(train_adj) |
| 147 | |
| 148 | if porting_to_torch: |
| 149 | train_adj = sparse_mx_to_torch_sparse_tensor(train_adj).float() |
| 150 | labels = torch.LongTensor(labels) |
| 151 | adj = sparse_mx_to_torch_sparse_tensor(adj).float() |
| 152 | degree = torch.LongTensor(degree) |
| 153 | train_index = torch.LongTensor(train_index) |
| 154 | val_index = torch.LongTensor(val_index) |
| 155 | test_index = torch.LongTensor(test_index) |
| 156 | learning_type = "inductive" |
| 157 | return adj, train_adj, features, train_features, labels, train_index, val_index, test_index, degree, learning_type |
| 158 | |
| 159 | |
| 160 |
no test coverage detected