adding experimental design search

deephyper/search/hps/__init__.py

@@ -2,9 +2,11 @@
 
 .. warning:: All search algorithms are MAXIMIZING the objective function. If you want to MINIMIZE the objective function, you have to return the negative of you objective.
 """
+
 from deephyper.search.hps._cbo import CBO, AMBS
+from deephyper.search.hps._eds import ExperimentalDesignSearch
 
-__all__ = ["CBO", "AMBS"]
+__all__ = ["CBO", "AMBS", "ExperimentalDesignSearch"]
 
 try:
     from deephyper.search.hps._mpi_dbo import MPIDistributedBO  # noqa: F401

deephyper/search/hps/_eds.py

@@ -0,0 +1,55 @@
+from deephyper.search.hps import CBO
+
+
+class ExperimentalDesignSearch(CBO):
+    """Centralized Experimental Design Search. It follows a manager-workers architecture where the manager runs the sampling process and workers execute parallel evaluations of the black-box function.
+
+    Example Usage:
+
+        >>> max_evals = 100
+        >>> search = ExperimentalDesignSearch(problem, evaluator, n_points=max_evals, design="grid")
+        >>> results = search.search(max_evals=100)
+
+    Args:
+        problem (HpProblem): Hyperparameter problem describing the search space to explore.
+        evaluator (Evaluator): An ``Evaluator`` instance responsible of distributing the tasks.
+        random_state (int, optional): Random seed. Defaults to ``None``.
+        log_dir (str, optional): Log directory where search's results are saved. Defaults to ``"."``.
+        verbose (int, optional): Indicate the verbosity level of the search. Defaults to ``0``.
+        n_points (int, optional): Number of points to sample. Defaults to ``None``.
+        design (str, optional): Experimental design to use. Defaults to ``"random"``.
+            - ``"random"`` for uniform random numbers.
+            - ``"sobol"`` for a Sobol' sequence.
+            - ``"halton"`` for a Halton sequence.
+            - ``"hammersly"`` for a Hammersly sequence.
+            - ``"lhs"`` for a latin hypercube sequence.
+            - ``"grid"`` for a uniform grid sequence.
+        initial_points (list, optional): List of initial points to evaluate. Defaults to ``None``.
+    """
+
+    def __init__(
+        self,
+        problem,
+        evaluator,
+        random_state: int = None,
+        log_dir: str = ".",
+        verbose: int = 0,
+        n_points: int = None,
+        design: str = "random",
+        initial_points=None,
+    ):
+        if n_points is None:
+            raise ValueError(
+                "n_points must be specified for the ExperimentalDesignSearch."
+            )
+        super().__init__(
+            problem,
+            evaluator,
+            random_state,
+            log_dir,
+            verbose,
+            n_initial_points=n_points,
+            initial_points=initial_points,
+            initial_point_generator=design,
+            surrogate_model="DUMMY",
+        )