Testing Suite
==============

This section outlines the testing suite for the 5D Neural Network Interpolator application. All tests are located in the ``backend/tests/`` folder and use the pytest framework.

Overview
--------

The testing suite is organised into separate test files, each focusing on a specific component of the application. A ``conftest.py`` file provides shared test fixtures and data to ensure consistent test setup across all test files.

Running the Tests
-----------------

To run the entire test suite:

.. code-block:: bash

   cd backend
   pytest tests/ -v

To run a specific test file:

.. code-block:: bash

   pytest tests/test_api.py -v

Test Files
----------

test_api.py
~~~~~~~~~~~

Tests for the FastAPI backend endpoints.

**Purpose:**
This module validates all API endpoints and their error handling.

**Test Coverage:**

- **Health Check Endpoint** (``TestHealthEndpoint``)
  - Verifies the health endpoint returns a success status

- **File Upload Endpoint** (``TestUploadEndpoint``)
  - Tests uploading valid pickle files
  - Validates rejection of invalid file types
  - Tests handling of missing file parameters

- **Training Endpoint** (``TestTrainEndpoint``)
  - Tests that training fails without uploaded data
  - Validates successful training with uploaded data
  - Checks response structure and metrics (MSE, R², training time)

- **Prediction Endpoint** (``TestPredictEndpoint``)
  - Tests that prediction fails without a trained model
  - Validates successful prediction with a trained model
  - Tests input validation (correct number of inputs)

- **Reset Endpoint** (``TestResetEndpoint``)
  - Verifies the reset endpoint clears application state

**Key Features:**
- Uses FastAPI's ``TestClient`` for HTTP request testing
- Creates temporary pickle files for testing
- Ensures test isolation by clearing application state between tests


test_data.py
~~~~~~~~~~~~

Tests for the ``DataLoader`` class.

**Purpose:**
This module validates data loading, preprocessing, and validation functionality.

**Test Coverage:**

- **DataLoader Initialization** (``TestDataLoaderInitialization``)
  - Tests correct initialization with file paths
  - Verifies scaler creation

- **Dataset Loading** (``TestDataLoaderLoadDataset``)
  - Tests loading valid pickle files
  - Validates data structure and column names (x1-x5, y)
  - Checks that loaded data matches original format

- **Data Inspection and Validation** (``TestDataLoaderInspectData``)
  - Tests data inspection with valid datasets
  - Validates error handling for missing features
  - Tests error handling for missing target columns
  - Tests handling of missing values (NaN)
  - Validates correct train/validation/test split ratios (70%/15%/15%)
  - Verifies feature standardization (mean ~0, std ~1)

**Key Features:**
- Tests the new dictionary-based dataset format (X, y, metadata)
- Validates data splitting and preprocessing
- Ensures proper error handling for invalid data


test_neuralnetwork.py
~~~~~~~~~~~~~~~~~~~~~

Tests for the ``NeuralNetwork`` class.

**Purpose:**
This module validates neural network initialization, training, evaluation, and prediction.

**Test Coverage:**

- **Initialization** (``TestNeuralNetworkInitialization``)
  - Tests initialization with default parameters
  - Tests initialization with custom parameters (layers, learning rate, epochs)
  - Validates model architecture (input/output layers, hidden layers)

- **Training** (``TestNeuralNetworkTraining``)
  - Tests that training completes without errors
  - Validates loss history recording
  - Checks that losses are positive numbers

- **Evaluation** (``TestNeuralNetworkEvaluation``)
  - Tests that evaluation returns MSE and R² metrics
  - Validates metric types and ranges
  - Checks that predictions are stored

- **Prediction** (``TestNeuralNetworkPrediction``)
  - Tests that prediction returns numeric values
  - Validates predictions vary with different inputs
  - Tests input validation

**Key Features:**
- Tests PyTorch model creation and architecture
- Validates training and evaluation workflows
- Ensures predictions are numeric and reasonable


test_plotter.py
~~~~~~~~~~~~~~~

Tests for the ``Plotter`` class.

**Purpose:**
This module validates plot generation and file saving functionality.

**Test Coverage:**

- **Learning Curve Plotting** (``TestPlotterLearningCurve``)
  - Tests creation of learning curve plots
  - Tests plotting without validation history
  - Tests handling of empty history

- **Predictions vs Actual Plotting** (``TestPlotterPredictionsVsActual``)
  - Tests creation of predictions vs actual plots
  - Tests handling of different array shapes (1D, 2D)
  - Validates file creation and size

- **Error Handling** (``TestPlotterErrorHandling``)
  - Tests handling of invalid file paths
  - Validates graceful error handling

**Key Features:**
- Tests matplotlib plot generation
- Validates file saving functionality
- Ensures proper error handling for invalid paths


test_logger.py
~~~~~~~~~~~~~~~

Tests for the ``Logger`` class.

**Purpose:**
This module validates logging functionality.

**Test Coverage:**

- **Logger Initialization** (``TestLogger``)
  - Tests that Logger can be initialized
  - Validates logger instance creation

- **Logging Methods**
  - Tests ``info`` method
  - Tests ``error`` method
  - Tests ``warning`` method

**Key Features:**
- Simple validation of logging interface
- Ensures all logging methods are callable


conftest.py
~~~~~~~~~~~

Shared test fixtures and data.

**Purpose:**
This file provides reusable test fixtures and sample data for all test files.

**Key Features:**
- Creates temporary pickle files with sample data
- Provides sample training/validation/test data splits
- Creates temporary output directories for plot testing
- Ensures consistent test setup across all test files

Test Structure
--------------

The test suite follows a clear structure:

- Each test file focuses on a single component
- Test classes group related test methods
- Descriptive test method names explain what is being tested
- Fixtures in ``conftest.py`` provide reusable test data
- Tests use pytest's assertion syntax for clarity

Best Practices
--------------

The test suite follows these best practices:

- **Isolation:** Each test is independent and doesn't rely on other tests
- **Fixtures:** Shared test data is provided through pytest fixtures
- **Clear Names:** Test methods have descriptive names explaining their purpose
- **Error Handling:** Tests validate both success and error cases
- **Temporary Files:** Tests use temporary files that are cleaned up automatically
- **State Management:** Application state is cleared between tests to prevent interference