Row 6091

Row ID: 6091 | Dataset Entry | Axioma AXP Content Repository

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The idea of the MetaPath2Vec example \[[https://github.com/pyg-team/pytorch\_geometric/blob/master/examples/hetero/metapath2vec.py](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/hetero/metapath2vec.py)\]:

1. On `train()` we try to reach the best embeddings for ALL of the nodes (not only ones from `data["author"].y_index`). It's the unsupervised part, you don't need any labels here.

2. On `test()` we evaluate how author nodes with known labels can be classified by logistic regression \[`model.test(z[train_perm], y[train_perm], z[test_perm], y[test_perm], max_iter=150)`\] using their embedding we got on the 1 step \[`z = model('author', batch=data['author'].y_index.to(device))`\] (supervised part).

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text The idea of the MetaPath2Vec example \[[https://github.com/pyg-team/pytorch\_geometric/blob/master/examples/hetero/metapath2vec.py](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/hetero/metapath2vec.py)\]: 1. On `train()` we try to reach the best embeddings for ALL of the nodes (not only ones from `data["author"].y_index`). It's the unsupervised part, you don't need any labels here. 2. On `test()` we evaluate how author nodes with known labels can be classified by logistic …
label r/pytorch
dataType comment
communityName r/pytorch
datetime 2024-05-07
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{
  "text": "The idea of the MetaPath2Vec example \\[[https://github.com/pyg-team/pytorch\\_geometric/blob/master/examples/hetero/metapath2vec.py](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/hetero/metapath2vec.py)\\]:\n\n1. On `train()` we try to reach the best embeddings for ALL of the nodes (not only ones from `data[\"author\"].y_index`). It's the unsupervised part, you don't need any labels here.\n\n2. On `test()` we evaluate how author nodes with known labels can be classified by logistic regression \\[`model.test(z[train_perm], y[train_perm], z[test_perm], y[test_perm], max_iter=150)`\\] using their embedding we got on the 1 step \\[`z = model('author', batch=data['author'].y_index.to(device))`\\] (supervised part).",
  "label": "r/pytorch",
  "dataType": "comment",
  "communityName": "r/pytorch",
  "datetime": "2024-05-07",
  "username_encoded": "Z0FBQUFBQm5LakwySmlzUTk4c3htRENKVlk3NHc0NlZCRGtVYXVSS1o0c1BQNkoyWnVycDU1aDZOSXpSb2g5VktCZjBSam94aTdRUWEwU3FMWnlaWGFacFNUZXVTczZzX1E9PQ==",
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}
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