coderabbitai[bot]
commented
1 week ago Walkthrough
The changes involve simplifying the initialization logic for the element_types attribute in the TensorNet model and updating test methods for the SO3Net model. Specifically, element_types is now assigned directly without conditions, and the SO3Net test methods have been renamed and updated to include new parameters and assertions to better align with the model's functionality.
Changes
| File(s) | Change Summary |
|---|---|
src/matgl/models/_tensornet.py |
Simplified the initialization logic by directly assigning element_types without a conditional. |
tests/models/test_so3net.py |
Renamed test methods and updated model instantiation and assertion logic for the SO3Net model. |
Recent review details
**Configuration used: .coderabbit.yaml** **Review profile: CHILL**Commits
Files that changed from the base of the PR and between cfd4874aa886e520706ebcb5bb8d838711a1a3ca and 6aaad5135e52dea5b0e58a75cd74cdf7f0d153c4.Files selected for processing (2)
* src/matgl/models/_tensornet.py (1 hunks) * tests/models/test_so3net.py (1 hunks)Files skipped from review due to trivial changes (1)
* src/matgl/models/_tensornet.pyAdditional comments not posted (2)
tests/models/test_so3net.py (2)
`40-49`: **Verify the new parameter and assertion change in `test_model_intensive_reduce_atom_classification`.** The method now includes a new parameter `target_property="graph"` and the assertion checks for an output size of `1` instead of `2`. Ensure that this change aligns with the intended model behavior and output specifications.Verification successful
--- **Verified the new parameter and assertion change in `test_model_intensive_reduce_atom_classification`.** The `target_property` parameter being set to "graph" and the assertion checking for an output size of `1` align with the intended model behavior and output specifications. - `src/matgl/models/_so3net.py`: The `SO3Net` class handles the `target_property` parameter, including the "graph" value.--- Line range hint `51-57`: **Check consistency of test logic in `test_model_intensive_weighted_atom_classification`.** This test method has been renamed and possibly modified. Ensure that the test logic correctly reflects the model's capabilities and the intended classification task. The assertion checks that the output size is `1`, which should be verified against the model's expected behavior for the classification task.Scripts executed
The following scripts were executed for the analysis: --- Script: ```shell #!/bin/bash # Description: Verify that the new parameter and assertion are consistent with the model's expected behavior. # Test: Search for the model's documentation and usage of `target_property`. rg --type python $'SO3Net' | grep 'target_property' ``` Length of output: 79 --- --- Script: ```shell #!/bin/bash # Corrected description: Verify that the new parameter and assertion are consistent with the model's expected behavior. # Corrected test: Search for the model's documentation and usage of `target_property`. rg --type py 'target_property' | grep 'SO3Net' ``` Length of output: 190 --- --- Script: ```shell #!/bin/bash # Search for the implementation of the SO3Net model to understand the target_property parameter. rg --type py 'class SO3Net' ``` Length of output: 91 --- --- Script: ```shell #!/bin/bash # Search for the target_property parameter within the SO3Net class implementation. rg --type py 'target_property' src/matgl/models/_so3net.py ``` Length of output: 610Verification successful
--- **The test logic in `test_model_intensive_weighted_atom_classification` is consistent with similar tests in the codebase. The assertion that `torch.numel(output) == 1` aligns with the expected behavior for classification tasks.** - `tests/models/test_so3net.py`: Multiple instances of `assert torch.numel(output) == 1` for similar tests.Scripts executed
The following scripts were executed for the analysis: --- Script: ```shell #!/bin/bash # Description: Verify that the test logic and assertions are correct for the intended classification task. # Test: Search for similar tests in the codebase to compare the consistency of assertions. rg --type python $'assert' | grep 'torch.numel' ``` Length of output: 75 --- --- Script: ```shell #!/bin/bash # Description: Verify that the test logic and assertions are correct for the intended classification task. # Test: Search for similar tests in the codebase to compare the consistency of assertions. rg --type py $'assert' | grep 'torch.numel' ``` Length of output: 2951 --- --- Script: ```shell #!/bin/bash # Description: Extract the content of test_model_intensive_weighted_atom_classification from the test file. # Extract the complete function definition for test_model_intensive_weighted_atom_classification. ast-grep --lang python --pattern $'def test_model_intensive_weighted_atom_classification($_) { $$$ }' tests/models/test_so3net.py ``` Length of output: 622 --- --- Script: ```shell #!/bin/bash # Description: Extract the content of test_model_intensive_weighted_atom_classification from the test file. # Extract the complete function definition for test_model_intensive_weighted_atom_classification. ast-grep --lang python --pattern 'def test_model_intensive_weighted_atom_classification($_):' tests/models/test_so3net.py --after 20 ``` Length of output: 134 --- --- Script: ```shell #!/bin/bash # Description: Search for the function definition for test_model_intensive_weighted_atom_classification in the test file. # Search for the function definition directly in the file. grep -A 20 'def test_model_intensive_weighted_atom_classification' tests/models/test_so3net.py ``` Length of output: 1193
Summary
Improve SO3Net model class coverage and simplify TensorNet implementation
Checklist
ruff.mypy.duecredit@due.dcitedecorators to reference relevant papers by DOI (example)Tip: Install
pre-commithooks to auto-check types and linting before every commit: