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materialsvirtuallab / matgl

Graph deep learning library for materials
BSD 3-Clause "New" or "Revised" License
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Increase more code coverage in _chgnet.py #261

Closed kenko911 closed 1 month ago

kenko911 commented 1 month ago

Summary

Increase more code coverage in _chgnet.py

Checklist

Tip: Install pre-commit hooks to auto-check types and linting before every commit:

pip install -U pre-commit
pre-commit install
coderabbitai[bot] commented 1 month ago

Walkthrough

The recent modifications involve enhancing the CHGNet class by introducing new parameters (readout_field, final_mlp_type) to its constructor in the test suite. Additionally, a new test function test_exceptions has been added to validate error handling capabilities, specifically for managing ValueError scenarios.

Changes

File Path Description of Changes
.../test_chgnet.py Added parameters readout_field, final_mlp_type to CHGNet constructor; introduced test_exceptions function for error handling.
Recent Review Details **Configuration used: .coderabbit.yaml** **Review profile: CHILL**
Commits Files that changed from the base of the PR and between ca6fddc649c7b149ab9dc43dadc11f2aa95d78f4 and ac92f1d19e04fecae2856504c39b4478818cf843.
Files selected for processing (1) * tests/models/test_chgnet.py (1 hunks)
Additional comments not posted (1)
tests/models/test_chgnet.py (1)
`45-47`: Ensure proper error message in `test_exceptions`. The test case `test_exceptions` is designed to catch a `ValueError` with a specific message. It's crucial to verify that the error message "Invalid activation type" is indeed implemented in the `CHGNet` class to ensure this test will behave as expected.
Verification successful --- The error message "Invalid activation type" is indeed implemented in the `CHGNet` class within the file `src/matgl/models/_chgnet.py`. This confirms that the test case `test_exceptions` in `test_chgnet.py` is correctly designed to catch this specific `ValueError`. - The error message is part of a formatted string that dynamically lists valid activation functions, which enhances the error message's usefulness by informing the user about acceptable values. **Conclusion:** The review comment concern has been verified as correct. The `CHGNet` class does implement the error message "Invalid activation type," ensuring that the associated test case will behave as expected.
Scripts Executed The following scripts were executed for the analysis: --- Script: ```shell #!/bin/bash # Description: Verify that the error message "Invalid activation type" is implemented in the CHGNet class. # Test: Search for the error message in the CHGNet class implementation. Expect: At least one occurrence. rg --type py 'Invalid activation type' src/models/chgnet.py ``` Length of output: 122 --- --- Script: ```shell #!/bin/bash # Description: Locate the file `chgnet.py` in the repository to confirm its existence and correct path. fd chgnet.py ``` Length of output: 70 --- --- Script: ```shell #!/bin/bash # Description: Verify that the error message "Invalid activation type" is implemented in the CHGNet class within the correct file path. # Test: Search for the error message in the CHGNet class implementation at the correct file path. Expect: At least one occurrence. rg --type py 'Invalid activation type' src/matgl/models/_chgnet.py ``` Length of output: 177
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