Add xopt tests

.github/workflows/unix-noax.yml

@@ -28,10 +28,12 @@ jobs:
     - shell: bash -l {0}
       name: Install dependencies
       run: |
-        conda install numpy pandas pytorch cpuonly -c pytorch
+        conda install -c conda-forge pytorch-cpu
+        conda install -c pytorch numpy pandas
         conda install -c conda-forge mpi4py mpich
         pip install .[test]
         pip install git+https://github.com/campa-consortium/gest-api.git
+        pip install git+https://github.com/xopt-org/xopt.git
         pip uninstall --yes ax-platform # Run without Ax
     - shell: bash -l {0}
       name: Run unit tests without Ax

.github/workflows/unix-openmpi.yml

@@ -33,6 +33,7 @@ jobs:
         conda install -c conda-forge mpi4py openmpi=5.*
         pip install .[test]
         pip install git+https://github.com/campa-consortium/gest-api.git
+        pip install --upgrade-strategy=only-if-needed git+https://github.com/xopt-org/xopt.git
     - shell: bash -l {0}
       name: Run unit tests with openMPI
       run: |

.github/workflows/unix.yml

@@ -33,6 +33,7 @@ jobs:
         conda install -c conda-forge mpi4py mpich
         pip install .[test]
         pip install git+https://github.com/campa-consortium/gest-api.git
+        pip install --upgrade-strategy=only-if-needed git+https://github.com/xopt-org/xopt.git
     - shell: bash -l {0}
       name: Run unit tests with MPICH
       run: |

tests/test_xopt_generators.py

@@ -0,0 +1,135 @@
+"""Tests Optimas with Xopt ExpectedImprovementGenerator generator."""
+
+import numpy as np
+
+from optimas.generators import ExternalGenerator
+from xopt.generators.bayesian.expected_improvement import (
+    ExpectedImprovementGenerator,
+)
+from xopt.generators.sequential.neldermead import NelderMeadGenerator
+
+from gest_api.vocs import VOCS
+from optimas.evaluators import FunctionEvaluator
+from optimas.explorations import Exploration
+
+
+def xtest(input_params, output_params):
+    """Single-objective callable test function"""
+    x1 = input_params["x1"]
+    x2 = input_params["x2"]
+    y1 = x2
+    output_params["y1"] = y1
+
+
+def rosenbrock(input_params, output_params):
+    """2D Rosenbrock function"""
+    x1 = input_params["x1"]
+    x2 = input_params["x2"]
+    y1 = 100 * (x2 - x1**2) ** 2 + (1 - x1) ** 2
+    output_params["y1"] = y1
+
+
+def test_xopt_EI():
+    """Test xopt ExpectedImprovementGenerator with a 2D function."""
+    initial_sample_size = 8
+
+    # Create vocs
+    vocs = VOCS(
+        variables={"x1": [0, 1.0], "x2": [0, 10.0]},
+        objectives={"y1": "MINIMIZE"},
+    )
+
+    gen = ExpectedImprovementGenerator(vocs=vocs)
+
+    # Create 4 initial points and ingest them
+    initial_points = [
+        {"x1": 0.2, "x2": 2.0, "y1": 2.0},
+        {"x1": 0.5, "x2": 5.0, "y1": 5.0},
+        {"x1": 0.7, "x2": 7.0, "y1": 7.0},
+        {"x1": 0.9, "x2": 9.0, "y1": 9.0},
+    ]
+    gen.ingest(initial_points)
+
+    # Create generator.
+    gen = ExternalGenerator(
+        ext_gen=gen,
+        vocs=vocs,
+        save_model=True,
+    )
+
+    # Create evaluator.
+    ev = FunctionEvaluator(function=xtest)
+
+    # Create exploration.
+    exp = Exploration(
+        generator=gen,
+        evaluator=ev,
+        max_evals=20,
+        sim_workers=4,
+        run_async=True,
+        exploration_dir_path="./tests_output/test_xopt_EI",
+    )
+
+    # Run exploration
+    exp.run()
+
+    if exp.is_manager:
+        H = exp.history
+        print(f"Completed {len(H)} simulations")
+        assert np.array_equal(H["y1"], H["x2"])
+
+
+def test_xopt_neldermead():
+    """Test xopt NelderMeadGenerator with a 2D function."""
+    initial_sample_size = 8
+
+    # Create vocs
+    vocs = VOCS(
+        variables={"x1": [0, 1.0], "x2": [0, 10.0]},
+        objectives={"y1": "MINIMIZE"},
+    )
+
+    gen = NelderMeadGenerator(vocs=vocs)
+
+    # Create 4 initial points and ingest them
+    initial_points = [
+        {"x1": -1.2, "x2": 1.0, "y1": 24.2},
+        {"x1": -1.0, "x2": 1.0, "y1": 4.0},
+        {"x1": -0.8, "x2": 0.8, "y1": 5.8},
+    ]
+    gen.ingest(initial_points)
+
+    # Create generator.
+    gen = ExternalGenerator(
+        ext_gen=gen,
+        vocs=vocs,
+        save_model=True,
+    )
+
+    # Create evaluator.
+    ev = FunctionEvaluator(function=rosenbrock)
+
+    # Create exploration.
+    exp = Exploration(
+        generator=gen,
+        evaluator=ev,
+        max_evals=30,
+        sim_workers=1,
+        run_async=True,
+        exploration_dir_path="./tests_output/test_xopt_neldermead",
+    )
+
+    # Run exploration
+    exp.run()
+
+    if exp.is_manager:
+        H = exp.history
+        print(f"Completed {len(H)} simulations")
+        initial_value = H["y1"][0]
+        best_value = H["y1"][np.argmin(H["y1"])]
+        assert best_value <= initial_value
+
+
+if __name__ == "__main__":
+    test_xopt_EI()
+    test_xopt_neldermead()