datasets/ogbgppa_dataset.py
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(and 21 similar)
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22 This dataset is directly adopted from Open Graph Benchmark, and originally curated by MoleculeNet. -
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35 Each molecule is annotated with the scaffold ID that the molecule is assigned to. -
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42 title={Open Graph Benchmark: Datasets for Machine Learning on Graphs}, -
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43 author={W. {Hu}, M. {Fey}, M. {Zitnik}, Y. {Dong}, H. {Ren}, B. {Liu}, M. {Catasta}, J. {Leskovec}}, -
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50 author={Z. {Wu}, B. {Ramsundar}, E. V {Feinberg}, J. {Gomes}, C. {Geniesse}, A. S {Pappu}, K. {Leswing}, V. {Pande}}, -
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70 def __init__(self, version=None, root_dir='data', download=False, split_scheme='official', random_split=False, -
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74 raise ValueError('Versioning for OGB-PPA is handled through the OGB package. Please set version=none.') -
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76 self.ogb_dataset = PygGraphPropPredDataset(name='ogbg-ppa', root=root_dir, transform=add_zeros) -
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83 self._y_type = 'float' # although the task is binary classification, the prediction target contains nan value, thus we need float -
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95 val_split_idx = random_index[len(split_idx['train']):len(split_idx['train'])+len(split_idx['valid'])] -
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96 test_split_idx = random_index[len(split_idx['train'])+len(split_idx['valid']):] -
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110 metadata_file_path = os.path.join(self.ogb_dataset.root, 'mapping', 'graphidx2speciesid.csv.gz') -
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113 self._metadata_array_wo_y = torch.from_numpy(df_np - df_np.min()).reshape(-1, 1).long() -
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114 self._metadata_array = torch.cat((self._metadata_array_wo_y, self.ogb_dataset.data.y), 1) -
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133 subgraph = GSN(dataset_name='ogbg-ppa', dataset_group='ogb', induced=True, id_type=self.id_type, -
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135 self.graphs_ptg, self.encoder_ids, self.d_id, self.d_degree = subgraph.preprocess(self.ogb_dataset.root) -
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146 self.num_edge_features = self.graphs_ptg[0].edge_features.shape[1] -
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168 - prediction_fn (function): A function that turns y_pred into predicted labels. -
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169 Only None is supported because OGB Evaluators accept binary logits -
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174 assert prediction_fn is None, "OGBHIVDataset.eval() does not support prediction_fn. Only binary logits accepted" -
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187 deg_inv = torch.where(deg > 0, 1. / torch.sqrt(deg), torch.zeros(deg.size())) -
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197 adj = _sym_normalize_adj(to_dense_adj(graph.edge_index, max_num_nodes=graph.x.size(0)).squeeze()) -
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206 target, source = graph.edge_index[0][edge], graph.edge_index[1][edge]
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- 2 E303: too many blank lines (2)
- 1 E203: whitespace before ':'