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inductive_train_test_split()
and split_graph()
functions
#8243
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ogawayuto
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Codecov Report
@@ Coverage Diff @@
## master #8243 +/- ##
==========================================
- Coverage 89.00% 88.40% -0.60%
==========================================
Files 475 476 +1
Lines 28841 28912 +71
==========================================
- Hits 25670 25561 -109
- Misses 3171 3351 +180
... and 37 files with indirect coverage changes 📣 Codecov offers a browser extension for seamless coverage viewing on GitHub. Try it in Chrome or Firefox today! |
ogawayuto
changed the title
inductive_train_test_split() and split_graph() functions from #8238
inductive_train_test_split() and split_graph() functions from
Oct 22, 2023
ogawayuto
changed the title
inductive_train_test_split() and split_graph() functions from
inductive_train_test_split() and split_graph() functions
Oct 22, 2023
rusty1s
changed the title
inductive_train_test_split() and split_graph() functions
Oct 22, 2023
inductive_train_test_split()
and split_graph()
functions
I'm ready for the pull request review ! |
…into inductive_train_test_split
…into inductive_train_test_split
for more information, see https://pre-commit.ci
…yuto/pytorch_geometric into inductive_train_test_split
…yuto/pytorch_geometric into inductive_train_test_split
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Hi,
This PR is based on the discussion in Discussion #8238. While it's possible to use masks and the subgraph() function to implement inductive node/link tasks, there's currently a lack of explicit functions for splitting data into training and testing sets specifically for inductive settings. Furthermore, when dealing with inductive link prediction, it's crucial to distinguish which edges are intended for training and testing. However, the subgraph() function doesn't provide information about test edges.
To address these limitations, I've introduced the inductive_train_test_split() function. This function streamlines the process of dividing a graph into a train graph and a test graph. It enables you to specify which edges should be allocated for training and testing, ensuring a clear separation of data for inductive tasks. It's also designed to be easily integrated with scikit-learn functions such as KFolds() or StratifiedKFolds().
This commit includes three main changes:
Modification of utils/__init__.py.
Creation of inductive_train_test_split.py.
Your reviews and comments on this contribution are greatly appreciated!