* python/doc/source/benchmarks.inc.rst: dmosopt uses moocore now.

 * python/benchmarks/bench_hv.py: Update.
 * python/benchmarks/bench_ndsort.py: Update.

Author: MLopez-Ibanez

python/benchmarks/bench_hv.py

@@ -31,9 +31,6 @@
 ## https://github.com/secondmind-labs/trieste/issues/917
 # from trieste.acquisition.multi_objective import Pareto as trieste_Pareto
 # import tensorflow as tf
-## dmosopt is too slow to benchmark
-## https://github.com/dmosopt/dmosopt/issues/119
-# from dmosopt.hv_box_decomposition import compute_hypervolume_box_decomposition
 
 from moarchiving import get_mo_archive as moarch_get_mo_archive
 
@@ -93,7 +90,6 @@
         "fast_pareto": lambda z, ref=ref: fp_hv(
             z, ref_point=ref, assume_pareto=False
         ),
-        # "dmosopt": lambda z, ref=ref: compute_hypervolume_box_decomposition(z, ref_point=ref),
     }
     # Too slow to run.
     if dim >= 6:

python/benchmarks/bench_ndsort.py

@@ -20,8 +20,6 @@
 from paretoset import paretorank as paretoset_paretorank
 ## It cannot be installed: https://github.com/esa/pygmo2/issues/152
 # from pygmo import pareto_dominance as pg_pareto_dominance
-## dmosopt is 100-1000x slower, i.e., too slow.
-# from dmosopt.dda import dda_ens as dmosopt_paretorank
 
 files = {
     # range are the (start, stop, num) parameters for np.geomspace()
@@ -61,7 +59,6 @@ def get_dataset(name):
             "paretoset": lambda z: paretoset_paretorank(z, use_numba=True) - 1,
             "pymoo": lambda z, nds=pymoo_NDS(): nds.do(z, return_rank=True)[1],
             "desdeo": lambda z: desdeo_nds(z).argmax(axis=0),
-            # "dmosopt": lambda z: dmosopt_paretorank(z),
         },
         check=check_array_equal,
     )

python/doc/source/benchmarks.inc.rst

@@ -14,11 +14,7 @@ include `paretobench`_ because it is always slower than `BoTorch`_ for
 identifying nondominated points and more than 2000 times slower than `moocore`_
 for computing the hypervolume in 3D
 (https://github.com/electronsandstuff/ParetoBench/issues/57), which is too slow
-to include in the benchmarks.  Nondominated sorting and hypervolume computation
-in `dmosopt`_ are 100-1000 times lower than the equivalent functions in
-`moocore`_ even in 3D (See https://github.com/dmosopt/dmosopt/issues/118 and
-https://github.com/dmosopt/dmosopt/issues/119), which is too slow to include in
-the benchmarks.
+to include in the benchmarks.
 
 Not all packages provide the same functionality. For example, `pymoo`_ does not
 provide the :ref:`epsilon indicator <epsilon_metric>`. `BoTorch`_ and