(size)
| 96 | return adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask |
| 97 | |
| 98 | def load_random_data(size): |
| 99 | |
| 100 | adj = sp.random(size, size, density=0.002) # density similar to cora |
| 101 | features = sp.random(size, 1000, density=0.015) |
| 102 | int_labels = np.random.randint(7, size=(size)) |
| 103 | labels = np.zeros((size, 7)) # Nx7 |
| 104 | labels[np.arange(size), int_labels] = 1 |
| 105 | |
| 106 | train_mask = np.zeros((size,)).astype(bool) |
| 107 | train_mask[np.arange(size)[0:int(size/2)]] = 1 |
| 108 | |
| 109 | val_mask = np.zeros((size,)).astype(bool) |
| 110 | val_mask[np.arange(size)[int(size/2):]] = 1 |
| 111 | |
| 112 | test_mask = np.zeros((size,)).astype(bool) |
| 113 | test_mask[np.arange(size)[int(size/2):]] = 1 |
| 114 | |
| 115 | y_train = np.zeros(labels.shape) |
| 116 | y_val = np.zeros(labels.shape) |
| 117 | y_test = np.zeros(labels.shape) |
| 118 | y_train[train_mask, :] = labels[train_mask, :] |
| 119 | y_val[val_mask, :] = labels[val_mask, :] |
| 120 | y_test[test_mask, :] = labels[test_mask, :] |
| 121 | |
| 122 | # sparse NxN, sparse NxF, norm NxC, ..., norm Nx1, ... |
| 123 | return adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask |
| 124 | |
| 125 | def sparse_to_tuple(sparse_mx): |
| 126 | """Convert sparse matrix to tuple representation.""" |
nothing calls this directly
no outgoing calls
no test coverage detected