Test Suite

This section describes the test suite for the 5D Neural Network Interpolator.

Test Structure

The test suite is located in backend/tests/ and uses pytest as the testing framework.

Directory Structure

backend/tests/
├── __init__.py
├── conftest.py          # Pytest fixtures and configuration
├── test_data.py         # Tests for pydis_nn.data module
├── test_neuralnetwork.py # Tests for pydis_nn.neuralnetwork module
├── test_api.py          # Tests for FastAPI endpoints
└── fixtures/            # Test data files (if needed)

Running Tests

Install dev dependencies:

cd interpolator/backend
pip install -e ".[dev]"

Run all tests:

pytest tests/ -v

Run specific test file:

pytest tests/test_data.py -v

Run with coverage:

pytest tests/ -v --cov=pydis_nn --cov-report=html

View coverage report:

open htmlcov/index.html  # macOS
xdg-open htmlcov/index.html  # Linux

Test Coverage

The test suite includes:

Data Module Tests (17 tests)

  • Loading raw datasets

  • Dataset validation (5D requirement)

  • Missing value handling

  • Data splitting (train/val/test)

  • Feature standardization

  • Integration pipeline tests

Neural Network Tests (13 tests)

  • Model initialization

  • Training with and without validation data

  • Prediction functionality

  • R² score calculation

  • Early stopping behavior

  • Model state management

API Endpoint Tests (12 tests)

  • Health check endpoint

  • Dataset upload (valid/invalid files)

  • Model training endpoint

  • Prediction endpoint

  • Error handling

  • CORS configuration

  • State management

Test Fixtures

Common fixtures are defined in conftest.py:

  • sample_dataset: Generated 5D dataset (1000 samples)

  • sample_dataset_small: Small dataset for faster tests (100 samples)

  • temp_pkl_file: Temporary .pkl file with sample data

  • trained_model: Pre-trained NeuralNetwork instance

  • client: FastAPI TestClient instance

  • app_state_reset: Fixture to reset application state

Example Test

def test_load_dataset_valid(temp_pkl_file):
    """Test loading a valid 5D dataset."""
    from pydis_nn.data import load_dataset

    data = load_dataset(temp_pkl_file)
    assert data['X'].shape[1] == 5
    assert data['X'].shape[0] == data['y'].shape[0]

Testing Strategy

  • Unit Tests: Test individual functions and classes in isolation

  • Integration Tests: Test API endpoints with full request/response cycle

  • Fixtures: Reusable test data and setup/teardown logic

  • Coverage: Aim for >80% code coverage on critical paths

Continuous Integration

Tests are designed to run in CI/CD pipelines:

  • No external dependencies required

  • All tests use temporary files or in-memory data

  • Tests are deterministic (fixed random seeds)

  • Fast execution (< 30 seconds for full suite)