[Example] GCMC with sampling #1296
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classicsong
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classicsong
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@classicsong The new RGCN example has been merged. You may check it out and adjust this PR accordingly. |
I c. will modify GCMC implementation accordingly. |
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@classicsong Hi, I tried your sampling-based GCMC model, yet there was an error: "AttributeError: module 'dgl' has no attribute 'in_subgraph'". I have installed dgl-4.0.2, but the error still exists |
You should use the nightly-build DGL |
Thanks. I reinstalled the nightly-build dgl used for CPU, but it didn't work. Does only the nightly-build dgl for GPU work? |
Did you install the dgl like: pip install --pre dgl |
I installed the dgl via command: pip install --pre dgl, but as you said , my platform is mac. I would reinstall the dgl from source. Thanks for your help |
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@classicsong i renstalled dgl from source and it worked. Thanks |
| def __init__(self, num_edges, minibatch_size, dataset): | ||
| self.num_edges = num_edges | ||
| self.minibatch_size = minibatch_size | ||
| self.seed = th.randperm(num_edges) |
| """ | ||
| def __init__(self, num_edges, minibatch_size, dataset): | ||
| self.num_edges = num_edges | ||
| self.minibatch_size = minibatch_size |
| self.minibatch_size = minibatch_size | ||
| self.seed = th.randperm(num_edges) | ||
| self.dataset = dataset | ||
| self.sample_idx = 0 |
| etype=str(possible_rating_value), | ||
| vtype=vtype, | ||
| card=(dataset.train_enc_graph.number_of_nodes(utype), | ||
| dataset.train_enc_graph.number_of_nodes(vtype)))) |
| seed_nodes[ntype] = g.nodes[ntype].data[dgl.NID] | ||
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| frontiner = dgl.in_subgraph(dataset.train_enc_graph, seed_nodes) | ||
| frontiner = dgl.to_block(frontiner, seed_nodes) |
| frontiner.srcnodes['user'].data['cj'] = \ | ||
| dataset.train_enc_graph.nodes['user'].data['cj'][frontiner.srcnodes['user'].data[dgl.NID]] | ||
| frontiner.dstnodes['movie'].data['ci'] = \ | ||
| dataset.train_enc_graph.nodes['movie'].data['ci'][frontiner.dstnodes['movie'].data[dgl.NID]] |
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The ci and cj will be calculated automatically by the GraphConv module if you plan to follow the new HeteroGraphConv approach. However, this means these normalizers will be calculated based on each sample rather than from the whole graph. Need some field tests to see the influence on the accuracy.
| args.gcn_agg_accum, | ||
| agg_act=self._act, | ||
| share_user_item_param=args.share_param, | ||
| device=dev_id) |
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You mention it's hard to unify the full graph and mini-batch versions. What is the problem here?
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For mini-batch, I suppose we always use mix_cpu_gpu implementation that is more relevant to large dataset. We can merge the two code maybe, but I think the code will be more complex to understand. As this is a baseline model, and some users from industry may borrow it, I want to make the multi-gpu/mix_cpu_gpu example code simple.
| enc_graph.number_of_nodes(vtype)))) | ||
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| g = dgl.hetero_from_relations(subg) | ||
| g = dgl.compact_graphs(g) |
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Yes. For eval, we also can use dataloader.
| self.reset_parameters() | ||
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| def partial_to(self, device): |
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Why not put all parameters into GPU?
Description
This PR contains a sampling-based GCMC using new sample APIs.
#957
Checklist
Please feel free to remove inapplicable items for your PR.
or have been fixed to be compatible with this change
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