===============================
QGML Testing and Validation Guide
===============================

This section provides comprehensive documentation for testing and validating QGML framework components.

Overview
========

The QGML testing framework ensures:

* **Mathematical Correctness**: Quantum state computations and geometric properties
* **Numerical Stability**: Robust behavior across different parameter ranges
* **Performance Validation**: Efficient computation and memory usage
* **Integration Reliability**: Seamless component interactions

Test Categories
===============

Integration Tests
-----------------

End-to-end validation of complete workflows:

.. toctree::
   :maxdepth: 2

   integration_tests
   dimensional_consistency
   backend_validation

Unit Tests
----------

Individual component validation:

.. toctree::
   :maxdepth: 2

   core_components
   learning_components
   analysis_components

Validation Tests
----------------

Mathematical and numerical correctness:

.. toctree::
   :maxdepth: 2

   quantum_state_validation
   geometric_properties
   numerical_stability

Running Tests
=============

Basic Test Execution
--------------------

.. code-block:: bash

   # Run all tests
   pytest

   # Run specific test category
   pytest tests/test_integration/

   # Run with verbose output
   pytest -v

   # Run with coverage report
   pytest --cov=qgml --cov-report=html

Test Configuration
------------------

.. code-block:: python

   # pytest.ini configuration
   [tool:pytest]
   testpaths = tests
   python_files = test_*.py
   python_classes = Test*
   python_functions = test_*
   addopts = -v --tb=short

Continuous Integration
======================

GitHub Actions
--------------

Automated testing on every commit:

.. code-block:: yaml

   name: QGML Tests
   on: [push, pull_request]
   jobs:
     test:
       runs-on: ubuntu-latest
       strategy:
         matrix:
           python-version: [3.8, 3.9, 3.10, 3.11]
       steps:
       - uses: actions/checkout@v2
       - name: Set up Python
         uses: actions/setup-python@v2
         with:
           python-version: ${{ matrix.python-version }}
       - name: Install dependencies
         run: |
           pip install -e .
           pip install pytest pytest-cov
       - name: Run tests
         run: pytest --cov=qgml

Performance Benchmarking
========================

Benchmark Suite
---------------

Comprehensive performance testing:

.. code-block:: python

   # benchmarks/performance_tests.py
   import time
   import torch
   from qgml.learning.supervised_trainer import SupervisedMatrixTrainer

   def benchmark_training_time():
       """Benchmark training performance."""
       trainer = SupervisedMatrixTrainer(N=8, D=3)
       X = torch.randn(1000, 3)
       y = torch.randn(1000)
       
       start_time = time.time()
       trainer.fit(X, y, n_epochs=100)
       end_time = time.time()
       
       return end_time - start_time

Memory Profiling
----------------

Memory usage analysis:

.. code-block:: python

   # tests/test_memory.py
   import memory_profiler
   from qgml.geometry.quantum_geometry_trainer import QuantumGeometryTrainer

   @memory_profiler.profile
   def test_memory_usage():
       """Profile memory usage during training."""
       trainer = QuantumGeometryTrainer(N=16, D=4)
       # Training code here

Troubleshooting
===============

Common Issues
-------------

**Import Errors**
   Ensure QGML is properly installed and Python path is correct.

**Backend Issues**
   Check PyTorch/JAX installation and backend configuration.

**Memory Issues**
   Reduce batch size or Hilbert space dimension for large models.

**Numerical Instability**
   Adjust learning rates and regularization parameters.

Debug Mode
----------

.. code-block:: python

   # Enable debug logging
   import logging
   logging.basicConfig(level=logging.DEBUG)

   # Run tests with debug output
   pytest -s --log-cli-level=DEBUG

Test Data
=========

Synthetic Data
--------------

Standard test datasets for validation:

.. code-block:: python

   from qgml.utils.data_generation import (
       generate_spiral_data,
       generate_sphere_data,
       generate_hypercube_data
   )

   # Generate test datasets
   X_spiral = generate_spiral_data(n_points=200, noise=0.1)
   X_sphere = generate_sphere_data(n_points=150, radius=1.0)
   X_hypercube = generate_hypercube_data(n_points=100, dim=3)

Real Data Integration
---------------------

Integration with real-world datasets:

* **Genomics Data**: Chromosomal instability analysis
* **Performance Benchmarks**: Backend comparison data
* **Validation Datasets**: Standard ML benchmarks

Best Practices
==============

Test Design
-----------

1. **Isolation**: Tests should not depend on each other
2. **Determinism**: Use fixed random seeds for reproducible results
3. **Coverage**: Aim for >90% code coverage
4. **Performance**: Keep test execution time reasonable

Test Documentation
------------------

1. **Clear Purpose**: Document what each test validates
2. **Expected Behavior**: Specify expected outcomes
3. **Edge Cases**: Test boundary conditions and error cases
4. **Performance Expectations**: Include timing and memory benchmarks

Contributing
============

Adding New Tests
----------------

1. Follow naming convention: `test_<component>_<functionality>.py`
2. Include comprehensive docstrings
3. Add to continuous integration pipeline
4. Update documentation

Test Review Process
-------------------

1. **Code Review**: All test code must be reviewed
2. **Coverage Analysis**: Ensure adequate test coverage
3. **Performance Impact**: Verify tests don't slow down CI
4. **Documentation Updates**: Update test documentation

See Also
========

* :doc:`../api/core` - Core API documentation
* :doc:`../user_guide/installation` - Installation guide
* :doc:`../examples/quickstart` - Quickstart tutorial