feat: Add comprehensive Python testing infrastructure with Poetry

[llbbl]

Add Python Testing Infrastructure

Summary

This PR sets up a complete testing infrastructure for the recommender systems evaluation project using Poetry as the package manager and pytest as the testing framework.

Changes Made

Package Management

• Poetry Configuration: Created pyproject.toml with Poetry as the package manager
• Dependency Migration: Migrated core dependencies from requirements.txt with flexible version constraints
• Development Dependencies: Added pytest, pytest-cov, and pytest-mock as dev dependencies

Testing Setup

• Directory Structure:

  • Created tests/ directory with unit/ and integration/ subdirectories
  • Added __init__.py files for proper Python package structure

• Pytest Configuration:

  • Test discovery patterns for multiple naming conventions
  • Coverage settings with 80% threshold
  • HTML and XML coverage report generation
  • Custom markers: unit, integration, slow
  • Strict marker enforcement

• Shared Fixtures (tests/conftest.py):

  • temp_dir: Temporary directory management
  • mock_config: Configuration dictionary for testing
  • sample_data_dir: Sample CSV files for data testing
  • sample_sparse_matrix: Sparse matrix for recommender testing
  • sample_dataframe: Pandas DataFrame fixture
  • mock_recommender: Mock recommender object
  • mock_evaluator: Mock evaluator object
  • mock_data_reader: Mock data reader
  • capture_logs: Log capture fixture
  • reset_random_seed: Automatic random seed reset
  • And more...

• Validation Tests: Created comprehensive tests to verify the infrastructure works correctly

Commands

• poetry run test - Run all tests
• poetry run tests - Alternative command (both work)
• All standard pytest options are available

Instructions for Running Tests

1. Install dependencies:

   poetry install

2. Run all tests:

   poetry run test

3. Run specific test file:

   poetry run pytest tests/test_file.py

4. Run with coverage:

   poetry run pytest --cov

5. Run only unit tests:

   poetry run pytest -m unit

Notes

Dependency Compatibility

The original requirements.txt contains legacy dependencies (TensorFlow 1.13.1, Keras 2.2.4, etc.) that require Python <=3.7. The testing infrastructure uses flexible version constraints to work with modern Python versions. For projects requiring the exact legacy versions:

1. Use a Python 3.7 virtual environment
2. Install from original requirements.txt: pip install -r requirements.txt
3. Or use Docker containers with appropriate base images

Coverage Configuration

• Coverage reports exclude test files, Cython builds, and virtual environments
• HTML reports are generated in htmlcov/
• XML reports for CI integration are generated as coverage.xml
• Default threshold is 80% but can be overridden with --cov-fail-under

Next Steps

Developers can now immediately start writing tests for the codebase. The infrastructure provides all necessary tools and configurations for comprehensive testing.