F/tests

.github/workflows/ci.yml

@@ -0,0 +1,30 @@
+name: CI
+
+on:
+  push:
+    branches: ["main", "master"]
+  pull_request:
+    branches: ["main", "master"]
+
+jobs:
+  tests:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.10"
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install torch --extra-index-url https://download.pytorch.org/whl/cpu
+          pip install -e .
+          pip install pytest
+
+      - name: Run tests
+        run: pytest

README.md

@@ -32,6 +32,18 @@ cd vbll
 pip install -e .
 ```
 
+## Testing
+
+Install the test dependencies and run the suite with `pytest`:
+
+```bash
+python -m pip install --upgrade pip
+pip install torch --extra-index-url https://download.pytorch.org/whl/cpu
+pip install -e .
+pip install pytest
+pytest
+```
+
 ## Usage and Tutorials
 Documentation is available [here](https://vbll.readthedocs.io/en/latest/). 
 
@@ -55,4 +67,3 @@ If you find VBLL useful in your research, please consider citing our [paper](htt
 }
 ```
 
-

pyproject.toml

@@ -10,6 +10,9 @@ python = "^3.9"
 torch = ">= 1.6.0"
 numpy = ">= 1.21.0"
 
+[tool.poetry.group.dev.dependencies]
+pytest = "^7.0.0"
+
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"

pytest.ini

@@ -0,0 +1,21 @@
+[tool:pytest]
+testpaths = tests
+python_files = test_*.py
+python_classes = Test*
+python_functions = test_*
+addopts = 
+    -v
+    --tb=short
+    --strict-markers
+    --disable-warnings
+    --color=yes
+    --durations=10
+markers =
+    slow: marks tests as slow (deselect with '-m "not slow"')
+    gpu: marks tests that require GPU
+    integration: marks integration tests
+    unit: marks unit tests
+filterwarnings =
+    ignore::UserWarning
+    ignore::DeprecationWarning
+    ignore::FutureWarning

tests/README.md

@@ -0,0 +1,49 @@
+# VBLL Test Suite
+
+This directory contains comprehensive tests for the VBLL (Variational Bayesian Last Layers) library.
+
+## Test Structure
+
+### Core Test Files
+
+- **`test_distributions.py`** - Tests for distribution classes (`Normal`, `DenseNormal`, `DenseNormalPrec`, `LowRankNormal`)
+- **`test_classification.py`** - Tests for classification layers (`DiscClassification`, `tDiscClassification`, `HetClassification`)
+- **`test_regression.py`** - Tests for regression layers (`Regression`, `tRegression`, `HetRegression`)
+- **`test_integration.py`** - End-to-end integration tests
+
+### Configuration Files
+
+- **`conftest.py`** - Pytest fixtures and configuration
+- **`__init__.py`** - Test package initialization
+
+## Running Tests
+
+### Basic Test Execution
+
+```bash
+# Run all tests
+pytest
+
+# Run with verbose output
+pytest -v
+
+# Run specific test file
+pytest tests/test_distributions.py
+
+# Run specific test class
+pytest tests/test_classification.py::TestDiscClassification
+
+# Run specific test function
+pytest tests/test_distributions.py::TestNormal::test_initialization
+```
+
+## Adding New Tests
+
+When adding new tests:
+
+1. **Follow naming conventions** - Use `test_` prefix for functions
+2. **Use appropriate markers** - Mark tests as `@pytest.mark.slow`, `@pytest.mark.gpu`, etc.
+3. **Add docstrings** - Explain what the test validates
+4. **Use fixtures** - Leverage existing fixtures for common setup
+5. **Test edge cases** - Include boundary conditions and error cases
+6. **Verify numerical stability** - Check for NaN, inf, and extreme values

tests/__init__.py

@@ -0,0 +1,2 @@
+# Test package for VBLL
+

tests/conftest.py

@@ -0,0 +1,142 @@
+"""
+Pytest configuration and shared fixtures for VBLL tests.
+"""
+import pytest
+import torch
+import numpy as np
+from typing import Tuple, Dict, Any
+
+
+@pytest.fixture
+def device():
+    """Get the device for testing (CPU or GPU if available)."""
+    return torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+@pytest.fixture
+def seed():
+    """Set random seed for reproducible tests."""
+    torch.manual_seed(42)
+    np.random.seed(42)
+    return 42
+
+
+@pytest.fixture
+def small_batch():
+    """Small batch size for quick tests."""
+    return 32
+
+
+@pytest.fixture
+def medium_batch():
+    """Medium batch size for more comprehensive tests."""
+    return 128
+
+
+@pytest.fixture
+def small_features():
+    """Small feature dimension for quick tests."""
+    return 10
+
+
+@pytest.fixture
+def medium_features():
+    """Medium feature dimension for more comprehensive tests."""
+    return 50
+
+
+@pytest.fixture
+def small_outputs():
+    """Small output dimension for quick tests."""
+    return 5
+
+
+@pytest.fixture
+def medium_outputs():
+    """Medium output dimension for more comprehensive tests."""
+    return 20
+
+
+@pytest.fixture
+def sample_data_small(small_batch, small_features, small_outputs, device):
+    """Generate small sample data for testing."""
+    x = torch.randn(small_batch, small_features, device=device)
+    y_class = torch.randint(0, small_outputs, (small_batch,), device=device)
+    y_reg = torch.randn(small_batch, small_outputs, device=device)
+    return {
+        'x': x,
+        'y_classification': y_class,
+        'y_regression': y_reg,
+        'batch_size': small_batch,
+        'input_features': small_features,
+        'output_features': small_outputs
+    }
+
+
+@pytest.fixture
+def sample_data_medium(medium_batch, medium_features, medium_outputs, device):
+    """Generate medium sample data for testing."""
+    x = torch.randn(medium_batch, medium_features, device=device)
+    y_class = torch.randint(0, medium_outputs, (medium_batch,), device=device)
+    y_reg = torch.randn(medium_batch, medium_outputs, device=device)
+    return {
+        'x': x,
+        'y_classification': y_class,
+        'y_regression': y_reg,
+        'batch_size': medium_batch,
+        'input_features': medium_features,
+        'output_features': medium_outputs
+    }
+
+
+@pytest.fixture
+def classification_params():
+    """Common parameters for classification layers."""
+    return {
+        'regularization_weight': 0.1,
+        'prior_scale': 1.0,
+        'wishart_scale': 1.0,
+        'dof': 2.0,
+        'return_ood': False
+    }
+
+@pytest.fixture
+def het_classification_params():
+    """Common parameters for hetclassification layers."""
+    return {
+        'regularization_weight': 0.1,
+        'prior_scale': 1.0,
+        'noise_prior_scale': 2.0,
+        'return_ood': False
+    }
+
+
+@pytest.fixture
+def regression_params():
+    """Common parameters for regression layers."""
+    return {
+        'regularization_weight': 0.1,
+        'prior_scale': 1.0,
+        'wishart_scale': 1e-2,
+        'dof': 1.0
+    }
+
+@pytest.fixture
+def het_regression_params():
+    """Common parameters for heteroscedastic regression layers."""
+    return {
+        'regularization_weight': 0.1,
+        'prior_scale': 1.0,
+        'noise_prior_scale': 1e-2,
+    }
+
+@pytest.fixture
+def parameterizations():
+    """Available covariance parameterizations."""
+    return ['diagonal', 'dense', 'lowrank']
+
+
+@pytest.fixture
+def softmax_bounds():
+    """Available softmax bounds for classification."""
+    return ['jensen', 'semimontecarlo', 'reduced_kn']