add space filling sobol sequence based optimizer (#51)

* add space filling sobol sequence based optimizer

Signed-off-by: Grossberger Lukas (CR/PJ-AI-R32) <Lukas.Grossberger@de.bosch.com>

blackboxopt/optimizers/space_filling.py

@@ -0,0 +1,37 @@
+# Copyright (c) 2020 - for information on the respective copyright owner
+# see the NOTICE file and/or the repository https://github.com/boschresearch/blackboxopt
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import List
+
+try:
+    from scipy.stats.qmc import Sobol
+except ImportError as e:
+    raise ImportError(
+        "Unable to import SpaceFilling optimizer specific dependencies. "
+        + "Make sure to install blackboxopt[space-fill]"
+    ) from e
+from blackboxopt.base import MultiObjectiveOptimizer, Objective, SearchSpace
+from blackboxopt.evaluation import EvaluationSpecification
+
+
+class SpaceFilling(MultiObjectiveOptimizer):
+    """Sobol sequence based, space filling optimizer.
+
+    Args:
+        search_space: The search space to optimize
+        objectives: The objectives of the optimization
+        seed: The sobol sequence is Owen scrambled and can be seeded for reproducibility
+    """
+
+    def __init__(
+        self, search_space: SearchSpace, objectives: List[Objective], seed: int = None
+    ) -> None:
+        super().__init__(search_space=search_space, objectives=objectives, seed=seed)
+        self.sobol = Sobol(d=len(self.search_space), scramble=True, seed=seed)
+
+    def generate_evaluation_specification(self) -> EvaluationSpecification:
+        vector = self.sobol.random().flatten()
+        configuration = self.search_space.from_numerical(vector)
+        return EvaluationSpecification(configuration=configuration)