feat: Add complete Python testing infrastructure with Poetry

[llbbl]

Add Complete Python Testing Infrastructure

Summary

This PR sets up a comprehensive testing infrastructure for the Python active learning project using Poetry as the package manager and pytest as the testing framework. The setup provides a ready-to-use testing environment where developers can immediately start writing tests.

Changes Made

Package Management

• Poetry Setup: Created pyproject.toml with Poetry configuration as the project's package manager
• Dependency Migration: Migrated existing dependencies from requirements.txt to Poetry
• Development Dependencies: Added pytest, pytest-cov, and pytest-mock as development dependencies

Testing Configuration

• pytest Configuration:

  • Set up test discovery patterns for test_*.py and *_test.py files
  • Configured coverage reporting with 80% threshold
  • Added HTML and XML coverage report generation
  • Enabled strict markers and comprehensive output formatting

• Coverage Configuration:

  • Configured source directories and exclusion patterns
  • Set up coverage thresholds and reporting formats
  • Excluded test files and virtual environments from coverage

Project Structure

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

Shared Fixtures (conftest.py)

• temp_dir: Creates temporary directories for test files
• mock_model: Provides mock PyTorch models
• sample_data: Generates sample data for testing
• mock_dataset: Creates mock datasets
• csv_data: Sets up temporary CSV files
• mock_config: Provides configuration objects
• reset_random_seeds: Ensures reproducible tests
• capture_stdout: Captures print output
• mock_file_operations: Mocks file I/O
• device: Provides PyTorch device configuration

Test Markers

• @pytest.mark.unit: For fast, isolated unit tests
• @pytest.mark.integration: For integration tests with external dependencies
• @pytest.mark.slow: For slow-running tests

Additional Setup

• Updated .gitignore with testing artifacts and Claude settings
• Created validation tests to verify the infrastructure works correctly
• Configured Poetry scripts for easy test execution

How to Use

Install Dependencies

poetry install

Run Tests

# Run all tests with coverage
poetry run pytest

# Or use the configured scripts
poetry run test
poetry run tests

# Run specific test types
poetry run pytest -m unit
poetry run pytest -m integration
poetry run pytest -m "not slow"

# Run tests in a specific directory
poetry run pytest tests/unit/

# Run with different verbosity
poetry run pytest -v  # verbose
poetry run pytest -q  # quiet

View Coverage Reports

• Terminal: Coverage is displayed after each test run
• HTML Report: Open htmlcov/index.html in a browser
• XML Report: Available at coverage.xml for CI integration

Notes

• The existing Python scripts contain module-level code that executes on import, which is why they're excluded from coverage requirements
• The 80% coverage threshold applies to new test code added to the project
• All pytest standard options are available when running tests
• The infrastructure is designed to support both unit and integration testing patterns

Next Steps

Developers can now:

1. Write unit tests in tests/unit/
2. Write integration tests in tests/integration/
3. Use the provided fixtures for common testing patterns
4. Run tests with coverage reporting to ensure code quality