Merge pull request #122 from Helmholtz-AI-Energy/maintenance/enhanced…

…_tests

Maintenance/enhanced tests

.github/workflows/python-test.yml

@@ -35,7 +35,7 @@ jobs:
                 coverage xml
                 genbadge coverage -i coverage.xml -o coverage.svg
             - name: Verify Changed files
-              uses: tj-actions/verify-changed-files@v16
+              uses: tj-actions/verify-changed-files@v19
               id: verify-changed-files
               with:
                 files: coverage.svg

README.md

@@ -41,16 +41,20 @@ next generation using the fitness values of all candidates it evaluated and rece
 It was already successfully applied in several accepted scientific publications. Applications include grid load
 forecasting, remote sensing, and structural molecular biology:
 
-> D. Coquelin, R. Sedona, M. Riedel, and M. Götz. **Evolutionary Optimization of Neural Architectures in Remote Sensing
-> Classification Problems**. IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium,
-> pp. 1587-1590 (2021). https://doi.org/10.1109/IGARSS47720.2021.9554309
-
 > O. Taubert, F. von der Lehr, A. Bazarova, et al. **RNA contact prediction by data efficient deep learning**. Commun
 > Biol 6, 913 (2023). https://doi.org/10.1038/s42003-023-05244-9
 
 > D. Coquelin, K. Flügel, M. Weiel, et al. **Harnessing Orthogonality to Train Low-Rank Neural Networks**. arXiv
 > preprint (2023). https://doi.org/10.48550/arXiv.2401.08505
 
+> Y. Funk, M. Götz, & H. Anzt. **Prediction of optimal solvers for sparse linear systems using deep learning**.
+> Proceedings of the 2022 SIAM Conference on Parallel Processing for Scientific Computing (pp. 14-24). Society for
+> Industrial and Applied Mathematics (2022). https://doi.org/10.1137/1.9781611977141.2
+
+> D. Coquelin, R. Sedona, M. Riedel, and M. Götz. **Evolutionary Optimization of Neural Architectures in Remote Sensing
+> Classification Problems**. IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium,
+> pp. 1587-1590 (2021). https://doi.org/10.1109/IGARSS47720.2021.9554309
+
 ## In more technical terms
 
 ``Propulate`` is a massively parallel evolutionary hyperparameter optimizer based on the island model with asynchronous

propulate/__init__.py

@@ -1,14 +1,12 @@
-# -*- coding: utf-8 -*-
-from pkg_resources import DistributionNotFound, get_distribution
+from importlib.metadata import PackageNotFoundError, version
 
 try:
     # Change here if project is renamed and does not equal the package name
-    dist_name = __name__
-    __version__ = get_distribution(dist_name).version
-except DistributionNotFound:
+    __version__ = version(__name__)
+except PackageNotFoundError:
     __version__ = "unknown"
 finally:
-    del get_distribution, DistributionNotFound
+    del version, PackageNotFoundError
 
 from . import propagators
 from .islands import Islands

tests/test_cmaes.py

@@ -1,56 +1,36 @@
 import pathlib
 import random
-from typing import Tuple
 
 import pytest
 
 from propulate import Propulator
-from propulate.propagators import BasicCMA, CMAPropagator
+from propulate.propagators import ActiveCMA, BasicCMA, CMAPropagator
 from propulate.utils.benchmark_functions import get_function_search_space
 
 
-@pytest.fixture(
-    params=[
-        ("rosenbrock", 0.0),
-        ("step", -25.0),
-        ("quartic", 0.0),
-        ("rastrigin", 0.0),
-        ("griewank", 0.0),
-        ("schwefel", 0.0),
-        ("bisphere", 0.0),
-        ("birastrigin", 0.0),
-        ("bukin", 0.0),
-        ("eggcrate", -1.0),
-        ("himmelblau", 0.0),
-        ("keane", 0.6736675),
-        ("leon", 0.0),
-        ("sphere", 0.0),  # (fname, expected)
-    ]
-)
-def function_parameters(request):
-    """Define benchmark function parameter sets as used in tests."""
+@pytest.fixture(params=[BasicCMA(), ActiveCMA()])
+def cma_adapter(request):
+    """Iterate over CMA adapters (basic and active)."""
     return request.param
 
 
-def test_cmaes(
-    function_parameters: Tuple[str, float], mpi_tmp_path: pathlib.Path
-) -> None:
+def test_cmaes_basic(cma_adapter, mpi_tmp_path: pathlib.Path) -> None:
     """
-    Test Propulator to optimize a benchmark function using a CMA-ES propagator.
+    Test Propulator to optimize a benchmark function using CMA-ES propagators.
 
     This test is run both sequentially and in parallel.
 
     Parameters
     ----------
-    function_parameters : Tuple[str, float]
-        The tuple containing each function name along with its global minimum.
+    cma_adapter : CMAAdapter
+        The CMA adapter used, either basic or active.
     mpi_tmp_path : pathlib.Path
         The temporary checkpoint directory.
     """
     rng = random.Random(42)  # Separate random number generator for optimization.
-    function, limits = get_function_search_space(function_parameters[0])
+    function, limits = get_function_search_space("sphere")
     # Set up evolutionary operator.
-    adapter = BasicCMA()
+    adapter = cma_adapter
     propagator = CMAPropagator(adapter, limits, rng=rng)
 
     # Set up Propulator performing actual optimization.