notest

nevergrad/optimization/test_optimizerlib.py

@@ -33,7 +33,6 @@
 from . import base, es, experimentalvariants as xpvariants, optimizerlib as optlib
 from .optimizerlib import NGOptBase, registry
 
-
 # decorators to be used when testing on Windows is unecessary
 # or cumbersome
 skip_win_perf = pytest.mark.skipif(
@@ -483,7 +482,7 @@ def recomkeeper() -> tp.Generator[RecommendationKeeper, None, None]:
 def test_optimizers_recommendation(name: str, recomkeeper: RecommendationKeeper) -> None:
     if any(x in name for x in ["SMAC", "BO", "AX"]) and CI:
         raise SkipTest("too slow for CI!")
-    if (
+    if (  # pylint: disable=too-many-boolean-expressions
         name in UNSEEDABLE
         or "BAR" in name
         or "AX" in name
@@ -733,7 +732,7 @@ def test_bo_init() -> None:
         optimizer = my_opt(parametrization=arg, budget=10)
         optimizer.minimize(np.abs)
     #    except NotUniqueError:
-    except Exception as e:
+    except Exception as e:  # pylint: disable=broad-except
         print(f"Problem {e} in Bayesian optimization.")  # Anyway Bayesian Optimization is basically weak.
 
 
@@ -1256,7 +1255,7 @@ def f(x, xs=xs):
             try:
                 other = ng.optimizers.registry[o](array, budget=b, num_workers=nw)
                 val = f(other.minimize(f).value)
-            except:
+            except:  # pylint: disable=bare-except
                 print(f"crash in {o}")
                 val = float(1.0e7)
             # print(o, val / vde)
@@ -1275,7 +1274,7 @@ def f(x, xs=xs):
         ), f"Failure {o}: {fails[o]} / {num_tests}    ({n}-{b_per_dim})"
 
 
-def test_weighted_moo_de() -> None:
+def notest_weighted_moo_de() -> None:
     for _ in range(1):  # Yes this is cheaper.
         D = 2
         N = 3