[Model] Add HGAT#213
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gyzhou2000
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Jul 2, 2024
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| # out_dict={} | ||
| # for node_type, _ in x_dict.items(): | ||
| # out_dict[node_type]=[] | ||
| # for edge_type, edge_index in edge_index_dict.items(): | ||
| # src_type, _, dst_type = edge_type | ||
| # src = edge_index[0,:] | ||
| # dst = edge_index[1,:] | ||
| # message = unsorted_segment_sum(tlx.gather(x_dict[src_type],src),dst,num_nodes_dict[dst_type]) | ||
| # out_dict[dst_type].append(message) | ||
| # for node_type, outs in out_dict.items(): | ||
| # aggr_out = tlx.reduce_sum(outs,axis=0) | ||
| # out_dict[node_type]=tlx.relu(aggr_out) No newline at end of file |
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| data = HeteroGraph() | ||
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| node_types = ['documents', 'topics', 'words'] | ||
| for i, node_type in enumerate(node_types): | ||
| x = sp.load_npz(osp.join(self.raw_dir, f'features_{i}.npz')) | ||
| data[node_type].x = tlx.convert_to_tensor(x.todense(), dtype=tlx.float32) | ||
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| y = np.load(osp.join(self.raw_dir, 'labels.npy')) | ||
| y = np.argmax(y,axis=1) | ||
| data['documents'].y = tlx.convert_to_tensor(y, dtype=tlx.int64) | ||
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| split = np.load(osp.join(self.raw_dir, 'train_val_test_idx.npz')) | ||
| for name in ['train', 'val', 'test']: | ||
| idx = split[f'{name}_idx'] | ||
| mask = np.zeros(data['documents'].num_nodes, dtype=np.bool_) | ||
| mask[idx] = True | ||
| data['documents'][f'{name}_mask'] = tlx.convert_to_tensor(mask, dtype=tlx.bool) | ||
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| s = {} | ||
| N_m = data['documents'].num_nodes | ||
| N_d = data['topics'].num_nodes | ||
| N_a = data['words'].num_nodes | ||
| s['documents'] = (0, N_m) | ||
| s['topics'] = (N_m, N_m + N_d) | ||
| s['words'] = (N_m + N_d, N_m + N_d + N_a) | ||
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| A = sp.load_npz(osp.join(self.raw_dir, 'adjM.npz')).tocsr() | ||
| for src, dst in product(node_types, node_types): | ||
| A_sub = A[s[src][0]:s[src][1], s[dst][0]:s[dst][1]].tocoo() | ||
| if A_sub.nnz > 0: | ||
| row = tlx.convert_to_tensor(A_sub.row, dtype=tlx.int64) | ||
| col = tlx.convert_to_tensor(A_sub.col, dtype=tlx.int64) | ||
| data[src, dst].edge_index = tlx.stack([row, col], axis=0) | ||
| print(src+"____"+dst) | ||
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| if self.pre_transform is not None: | ||
| data = self.pre_transform(data) | ||
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| self.save_data(self.collate([data]), self.processed_paths[0]) |
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修改成从作者提供的url下载数据集，然后处理成heterograph
| from gammagl.data import (HeteroGraph, InMemoryDataset, download_url, | ||
| extract_zip) | ||
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| class OHSUMED(InMemoryDataset): |
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写一个test文件，参考gammagl/datasets/路径下的其他测试文件
gyzhou2000
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Jul 4, 2024
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Add a new model: HGAT
corresponding with two conv layer, HINConv and HGATConv
and a new method to convert the Heterogeneous graph dict to homogeneous graph matrix: heter_homo_mutual_convert