Reduce number of replicates to speed up benchmark time

Author: Hrovatin

benchmarks/domains/kernel_presets/arylhalides_tl_substance.py

@@ -40,6 +40,8 @@ def arylhalides_tl_substance(settings: ConvergenceBenchmarkSettings) -> pd.DataF
     ]
     Optimal Output: 68.24812709999999
     """
+    print("arylhalides_tl_substance")
+
     objective, searchspace, searchspace_nontl, initial_data, lookup = space_data()
 
     recommender_botorch_preset = TwoPhaseMetaRecommender(
@@ -127,7 +129,7 @@ def arylhalides_tl_substance(settings: ConvergenceBenchmarkSettings) -> pd.DataF
 benchmark_config = ConvergenceBenchmarkSettings(
     batch_size=2,
     n_doe_iterations=10,
-    n_mc_iterations=50,
+    n_mc_iterations=10,
 )
 
 arylhalides_tl_substance_benchmark = ConvergenceBenchmark(

benchmarks/domains/kernel_presets/direct_arylation_tl_temp.py

@@ -47,6 +47,8 @@ def direct_arylation_tl_temp(settings: ConvergenceBenchmarkSettings) -> pd.DataF
     ]
     Optimal Output: 100.0
     """
+    print("direct_arylation_tl_temp")
+
     objective, searchspace, searchspace_nontl, initial_data, lookup = space_data()
 
     recommender_botorch_preset = TwoPhaseMetaRecommender(
@@ -134,7 +136,7 @@ def direct_arylation_tl_temp(settings: ConvergenceBenchmarkSettings) -> pd.DataF
 benchmark_config = ConvergenceBenchmarkSettings(
     batch_size=2,
     n_doe_iterations=10,
-    n_mc_iterations=50,
+    n_mc_iterations=10,
 )
 
 direct_arylation_tl_temp_benchmark = ConvergenceBenchmark(

benchmarks/domains/kernel_presets/easom_tl_noise.py

@@ -37,6 +37,8 @@ def easom_tl_noise(settings: ConvergenceBenchmarkSettings) -> pd.DataFrame:
     ]
     Optimal Output: 0.9462931105452647
     """
+    print(easom_tl_noise)
+
     objective, searchspace, searchspace_nontl, initial_data, lookup = space_data()
 
     recommender_botorch_preset = TwoPhaseMetaRecommender(
@@ -139,7 +141,7 @@ def sample_initial_data():
 benchmark_config = ConvergenceBenchmarkSettings(
     batch_size=2,
     n_doe_iterations=10,
-    n_mc_iterations=50,
+    n_mc_iterations=10,
 )
 
 easom_tl_noise_benchmark = ConvergenceBenchmark(

benchmarks/domains/kernel_presets/hartmann_tl_inverted_noise.py

@@ -39,6 +39,8 @@ def hartmann_tl_inverted_noise(settings: ConvergenceBenchmarkSettings) -> pd.Dat
     ]
     Optimal Output: 2.999716768817375
     """
+    print("hartmann_tl_inverted_noise")
+
     objective, searchspace, searchspace_nontl, initial_data, lookup = space_data()
 
     recommender_botorch_preset = TwoPhaseMetaRecommender(
@@ -126,7 +128,7 @@ def hartmann_tl_inverted_noise(settings: ConvergenceBenchmarkSettings) -> pd.Dat
 benchmark_config = ConvergenceBenchmarkSettings(
     batch_size=2,
     n_doe_iterations=10,
-    n_mc_iterations=50,
+    n_mc_iterations=10,
 )
 
 hartmann_tl_inverted_noise_benchmark = ConvergenceBenchmark(

benchmarks/domains/kernel_presets/michalewicz_tl_noise.py

@@ -41,6 +41,8 @@ def michalewicz_tl_noise(settings: ConvergenceBenchmarkSettings) -> pd.DataFrame
     ]
     Optimal Output: 3.418800985955677
     """
+    print("michalewicz_tl_noise")
+
     objective, searchspace, searchspace_nontl, initial_data, lookup = space_data()
 
     recommender_botorch_preset = TwoPhaseMetaRecommender(
@@ -143,7 +145,7 @@ def sample_initial_data():
 benchmark_config = ConvergenceBenchmarkSettings(
     batch_size=2,
     n_doe_iterations=10,
-    n_mc_iterations=50,
+    n_mc_iterations=10,
 )
 
 michalewicz_tl_noise_benchmark = ConvergenceBenchmark(