feat: set up complete Python testing infrastructure

[llbbl]

Set up Python Testing Infrastructure

Summary

This PR establishes a complete testing infrastructure for the ML project using Poetry as the package manager and pytest as the testing framework.

Changes Made

Package Management

• Set up Poetry as the package manager with pyproject.toml configuration
• Migrated dependencies from requirements.txt to Poetry format with proper version constraints
• Added testing dependencies: pytest, pytest-cov, pytest-mock
• Fixed Python version constraint to >=3.8,<3.12 for TensorFlow compatibility

Testing Configuration

• Configured pytest with comprehensive settings in pyproject.toml:
  • Test discovery patterns for test_*.py and *_test.py files
  • Coverage reporting with 80% threshold requirement
  • HTML and XML coverage report generation
  • Custom markers: unit, integration, slow
  • Strict configuration and marker validation

Directory Structure

• Created testing directories:

  tests/
  ├── __init__.py
  ├── conftest.py           # Shared fixtures
  ├── unit/
  │   └── __init__.py
  ├── integration/
  │   └── __init__.py
  └── test_infrastructure.py  # Validation tests
  

Shared Testing Fixtures

• Created conftest.py with ML/AI-specific fixtures:
  • temp_dir, temp_file - Temporary filesystem utilities
  • mock_wandb - Mock Weights & Biases logging
  • mock_tensorflow - Mock TensorFlow imports
  • mock_huggingface_hub - Mock Hugging Face Hub operations
  • mock_dvc - Mock DVC operations
  • sample_data - Sample ML training/test data
  • sample_params, sample_config_file - Configuration utilities

Development Commands

• Set up Poetry scripts:
  • poetry run test - Run all tests
  • poetry run tests - Alternative command
  • Both commands support all standard pytest options

Project Configuration

• Updated .gitignore with testing patterns:
  • Test artifacts: .pytest_cache/, .coverage, htmlcov/
  • Python artifacts: __pycache__/, *.pyc, virtual environments
  • IDE and OS files
  • Claude Code settings: .claude/*

Validation

• Created infrastructure validation tests that verify:
  • Python version compatibility (3.8+)
  • Testing framework imports work correctly
  • Project directory structure exists
  • Shared fixtures are available and functional
  • Custom pytest markers work
  • Temporary file/directory utilities work

Testing Instructions

Running Tests

# Install dependencies
poetry install

# Run all tests
poetry run test

# Run tests with specific options
poetry run test -v                    # Verbose output
poetry run test --no-cov             # Skip coverage
poetry run test -m unit              # Only unit tests
poetry run test -k "infrastructure"   # Tests matching pattern

Coverage Reports

• Terminal output: Shows coverage percentage and missing lines
• HTML report: Generated in htmlcov/ directory
• XML report: Generated as coverage.xml for CI/CD integration

Writing New Tests

1. Unit tests: Place in tests/unit/
2. Integration tests: Place in tests/integration/
3. Use shared fixtures: Available from conftest.py
4. Mark tests: Use @pytest.mark.unit, @pytest.mark.integration, @pytest.mark.slow

Dependencies

Production Dependencies (from requirements.txt)

• dvc[gdrive]==2.10.2 - Data version control
• wandb==0.12.19 - Experiment tracking
• tensorflow==2.8 - ML framework
• typer==0.4.1 - CLI framework
• docopt==0.6.2 - Command line parsing
• huggingface-hub - Model hub integration

Testing Dependencies (new)

• pytest ^7.4.0 - Testing framework
• pytest-cov ^4.1.0 - Coverage reporting
• pytest-mock ^3.11.0 - Mocking utilities

Notes

• Python version constraint set to >=3.8,<3.12 due to TensorFlow compatibility requirements
• Coverage threshold set to 80% - can be adjusted in pyproject.toml
• Testing infrastructure only - no actual unit tests for the codebase are included
• Ready for development - developers can immediately start writing tests using the provided fixtures and configuration

Future Improvements

• Add CI/CD integration with coverage reporting
• Set up automated testing on pull requests
• Add performance testing framework for ML model benchmarking
• Integrate with code quality tools (black, flake8, mypy)