* Expand test coverage with comprehensive test suites
Add extensive test coverage for previously untested modules:
- test_utils.py: Comprehensive tests for utility functions (setup_logger, log, RBF,
parse_device, load_hdf5_parallel, PairedDataset, collate_paired_sequences)
- test_glider.py: Complete test suite for graph-based link prediction module
(get_dim, densify, compute_X_normalized, scoring functions, GLIDE algorithms)
- test_loading.py: Tests for parallel HDF5 data loading with LoadingPool,
including edge cases, error handling, and integration tests
- test_language_model.py: Expanded from 2 to 13 test methods, adding coverage
for lm_embed, embed_from_fasta with various edge cases and validations
These additions significantly improve test coverage for:
- dscript/utils.py (167 lines, previously untested)
- dscript/glider.py (346 lines, previously untested)
- dscript/loading.py (92 lines, previously untested)
- dscript/language_model.py (minimal coverage expanded)
Total new test methods: ~200+ assertions across 4 test modules
* Add comprehensive tests for command modules and worker functions
Create four new test modules to expand coverage of previously untested code:
1. test_extract_3di.py (19 test methods, ~370 lines)
- Tests for 3Di sequence extraction from PDB/CIF files
- Argument parsing, file filtering, FASTA output validation
- Integration tests for full workflow
- Covers dscript/commands/extract_3di.py (~58 lines)
2. test_par_writer.py (24 test methods, ~400 lines)
- Tests for parallel prediction writer process
- TSV output writing, threshold filtering, contact map storage
- HDF5 contact map dataset handling
- Progress tracking and data type validation
- Covers dscript/commands/par_writer.py (~40 lines)
3. test_main.py (24 test methods, ~320 lines)
- Tests for CLI entry point and argument parsing
- CitationAction class testing
- All subcommand registration and invocation
- Version and help flag handling
- Integration tests for command dispatch
- Covers dscript/__main__.py (~87 lines, increasing from ~85% to ~95%)
4. test_load_worker.py (23 test methods, ~330 lines)
- Direct unit tests for HDF5 loading worker function
- Queue handling, data type conversion, memory sharing
- Error handling for corrupted/missing files
- Multi-dimensional array support
- Covers dscript/load_worker.py (~25 lines, previously only indirect coverage)
Total additions:
- ~1,420 lines of new test code
- 90+ test methods with comprehensive assertions
- ~210 lines of source code now directly tested
- Addresses high-priority gaps identified in coverage analysis
These tests complement the existing suite and focus on command-line
interface components and parallel processing infrastructure.
* Fix linting issues and apply code formatting
- Remove unused variables flagged by ruff
- Apply ruff formatting to all test files
- Ensure all pre-commit hooks pass
Changes:
- test_loading.py: Remove unused 'f' variable
- test_main.py: Remove unused 'fake_out' and 'output' variables
- test_utils.py: Remove unused 'log_file' variable and tmp_path param
- Applied ruff formatting to maintain code style consistency
* Fix test_load_worker.py hanging issue in CI
Rewrote test_load_worker.py to prevent CI hangs that occurred when
tests called the blocking worker function directly. The worker function
_hdf5_load_partial_func runs in an infinite loop waiting on a queue,
which caused tests to hang indefinitely.
Changes:
- Created run_worker_with_timeout() helper that wraps worker execution
in a daemon thread with configurable timeout (default 5 seconds)
- Modified all tests to use this helper and assert successful completion
- Changed queue operations from blocking get() to non-blocking get_nowait()
- Reduced test count from 23 to 16 focused tests
- Added documentation noting worker is primarily tested via LoadingPool
This should resolve the CI timeout issue where tests hung at 43% completion.
* Rewrite test_language_model.py to use mocks instead of real model
The original tests were calling the real language model which:
- Downloads/loads pretrained model weights (slow, can fail)
- Runs actual neural network inference (resource intensive)
- Causes test failures when model files aren't available
Changes:
- Rewrote unit tests to mock get_pretrained() function
- Mock model returns realistic tensor shapes but doesn't load weights
- Tests are now fast, reliable, and don't require model files
- Moved real model tests to TestLanguageModelIntegration class
- Marked integration tests with @pytest.mark.slow so they can be skipped
- Removed unnecessary loguru import that caused import errors
- Removed problematic setup.py install step from setup_class
This should fix the 4 failing tests reported by CI.
* fix failing tests
* Update .github/workflows/autorun-tests.yml
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Update .github/workflows/autorun-tests.yml
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---------
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
