datasets/rotated_mnist_dataset.py
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20 def __init__(self, version=None, root_dir='data', download=False, split_scheme='official', random_split=False, -
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24 self.ogb_dataset = PyGRotatedMNISTDataset(name='RotatedMNIST', root=root_dir) -
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31 self._y_type = 'float' # although the task is binary classification, the prediction target contains nan value, thus we need float -
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40 #https://www.dropbox.com/s/zulrcyh846w9maw/RotatedMNIST_group_expired.zip?dl=1 -
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41 metadata_file_path = os.path.join(self.ogb_dataset.raw_dir, 'RotatedMNIST_group.npy') -
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44 'https://www.dropbox.com/s/xv6bx7ihqmeuv80/RotatedMNIST_group.zip?dl=1', self.ogb_dataset.raw_dir) -
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48 self._metadata_array_wo_y = torch.from_numpy(np.load(metadata_file_path)).reshape(-1, 1).long() -
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50 torch.unsqueeze(self.ogb_dataset.data.y, dim=1)), 1) -
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57 val_split_idx = random_index[int(4 / 6 * dataset_size):int(5 / 6 * dataset_size)] -
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62 train_val_group_idx, test_group_idx = torch.tensor(train_val_group_idx), torch.tensor(test_group_idx) -
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65 split_idx = torch.zeros(len(torch.squeeze(self._metadata_array_wo_y)), dtype=torch.bool) -
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67 split_idx += (torch.squeeze(self._metadata_array_wo_y) == idx) -
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70 train_val_split_idx, test_split_idx = split_idx(train_val_group_idx), split_idx(test_group_idx) -
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72 train_val_split_idx = torch.arange(dataset_size, dtype=torch.int64)[train_val_split_idx] -
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75 train_split_idx = train_val_split_idx[random_index[:int(4 / 5 * train_val_sets_size)]] -
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76 val_split_idx = train_val_split_idx[random_index[int(4 / 5 * train_val_sets_size):]] -
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100 subgraph = GSN(dataset_name='RotatedMNIST', dataset_group='MNIST', induced=True, id_type=self.id_type, -
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102 self.graphs_ptg, self.encoder_ids, self.d_id, self.d_degree = subgraph.preprocess(self.ogb_dataset.root) -
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113 self.num_edge_features = self.graphs_ptg[0].edge_features.shape[1] -
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136 - prediction_fn (function): A function that turns y_pred into predicted labels. -
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137 Only None is supported because OGB Evaluators accept binary logits -
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142 assert prediction_fn is None, "OGBPCBADataset.eval() does not support prediction_fn. Only binary logits accepted" -
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154 deg_inv = torch.where(deg > 0, 1. / torch.sqrt(deg), torch.zeros(deg.size())) -
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164 adj = _sym_normalize_adj(to_dense_adj(graph.edge_index, max_num_nodes=graph.x.size(0)).squeeze())
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- 3 E303: too many blank lines (2)
- 1 E265: block comment should start with '# '
- 1 W293: blank line contains whitespace