feat: Set up comprehensive testing infrastructure

[llbbl]

Set up comprehensive testing infrastructure

Summary

This PR establishes a complete testing infrastructure for the PyTorch Image Quality Assessment project using modern Python tooling and best practices.

Changes Made

Package Management

• Added Poetry configuration in pyproject.toml with proper dependency management
• Migrated from requirements.txt to Poetry for better dependency resolution and virtual environment handling
• Updated Python version requirement to ^3.9 for compatibility with modern packages

Testing Framework

• Configured pytest with comprehensive settings including:
  • Test discovery patterns for test_*.py and *_test.py files
  • Custom markers: unit, integration, slow for test categorization
  • Strict configuration options for robust testing
• Added pytest-cov for coverage reporting with multiple output formats
• Added pytest-mock for enhanced mocking capabilities

Directory Structure

tests/
├── __init__.py
├── conftest.py              # Shared fixtures and test utilities
├── unit/                    # Unit tests
│   └── __init__.py
├── integration/             # Integration tests
│   └── __init__.py
└── test_setup_validation.py # Infrastructure validation tests

Shared Testing Utilities

Created comprehensive fixtures in conftest.py:

• File system fixtures: temp_dir, sample_image_path
• Data fixtures: sample_image, sample_tensor, sample_patches, sample_labels
• Mock objects: mock_config, mock_dataset, mock_dataloader, mock_h5py_file, mock_yaml_config
• Environment setup: set_random_seeds for reproducible testing, mock_cuda for CI/CD

Coverage Configuration

• HTML reports generated in htmlcov/ directory
• XML reports for CI/CD integration (coverage.xml)
• Terminal output with missing line indicators
• Exclusion rules for common non-testable code patterns
• Configurable thresholds for coverage requirements

Development Workflow

• Poetry scripts: poetry run test and poetry run tests commands
• Marker-based testing: Run specific test categories with -m unit, -m integration, -m slow
• Coverage integration: Automatic coverage reporting with all test runs

Infrastructure Improvements

• Updated .gitignore with comprehensive rules for:
  • Python artifacts (__pycache__/, *.pyc, etc.)
  • Testing artifacts (.pytest_cache/, .coverage, htmlcov/)
  • Virtual environments and IDE files
  • Machine learning specific files (.h5, models/, logs/)
  • Claude Code configuration (.claude/)

Running Tests

Basic Usage

# Install dependencies
poetry install

# Run all tests
poetry run pytest

# Run tests without coverage
poetry run pytest --no-cov

# Run specific test categories
poetry run pytest -m unit
poetry run pytest -m integration
poetry run pytest -m slow

Coverage Reports

# Generate HTML coverage report
poetry run pytest --cov-report=html

# View coverage in terminal
poetry run pytest --cov-report=term-missing

Validation

The setup includes validation tests (tests/test_setup_validation.py) that verify:

• ✅ pytest is working correctly
• ✅ All fixtures are available and functional
• ✅ PyTorch and NumPy are properly installed
• ✅ File creation and mocking utilities work
• ✅ Coverage reporting is functional
• ✅ Test markers are properly configured

All validation tests pass, confirming the infrastructure is ready for development.

Dependencies Updated

Updated from legacy versions in requirements.txt to modern, compatible versions:

• Python: ^3.9 (increased from ^3.8 for scipy compatibility)
• Core dependencies: h5py, PyYAML, Pillow, numpy, scipy, torch
• Testing dependencies: pytest ^7.4.0, pytest-cov ^4.1.0, pytest-mock ^3.11.1

Notes

• The poetry.lock file ensures reproducible installations across environments
• The testing infrastructure is framework-agnostic and ready for immediate use
• Coverage thresholds can be adjusted in pyproject.toml as needed
• Additional fixtures can be easily added to conftest.py for project-specific needs

🤖 Generated with Claude Code