[Distributed] Partition MovieLens dataset
#8815
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rusty1s
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Distributed] Partition MovieLens dataset
| train_data, val_data, test_data = T.RandomLinkSplit( | ||
| num_val=0.1, | ||
| num_test=0.1, | ||
| neg_sampling_ratio=0.0, | ||
| edge_types=[edge_type], | ||
| rev_edge_types=[('movie', 'rev_rates', 'user')], | ||
| )(dataset[0]) |
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Looks like we drop the adjustment of message passing here computed in train_data, val_data and test_data. This is not necessarily a blocker (since I don't necessarily see a good way to fix this for now), but something you should be aware of. Currently, you would like information.
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Sorry, I didn't get that. What do you mean by "we drop the adjustment of message passing here computed in train_data, val_data and test_data"?
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The edge_index is different across different splits for link prediction tasks (in order to not leak information).
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I see. What about the solution used in the temporal_link_pred example?: temporal_link_pred.py #L27 Maybe we can use that one instead?
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Yeah, using temporal sampling is usually a good option to resolve this.
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