Further adapt benchmark paraneters to reduce runtime

Author: Hrovatin

benchmarks/domains/__init__.py

@@ -12,10 +12,11 @@
     hartmann_tl_inverted_noise_benchmark,
 )
 from benchmarks.domains.michalewicz_tl_noise import michalewicz_tl_noise_benchmark
-from benchmarks.domains.synthetic_2C1D_1C import synthetic_2C1D_1C_benchmark
+
+# from benchmarks.domains.synthetic_2C1D_1C import synthetic_2C1D_1C_benchmark
 
 BENCHMARKS: list[Benchmark] = [
-    synthetic_2C1D_1C_benchmark,
+    # synthetic_2C1D_1C_benchmark,
     arylhalides_tl_substance_benchmark,
     direct_arylation_tl_temp_benchmark,
     hartmann_tl_inverted_noise_benchmark,

benchmarks/domains/easom_tl_noise.py

@@ -158,10 +158,10 @@ def easom_tl_noise(settings: ConvergenceBenchmarkSettings) -> pd.DataFrame:
     results = []
 
     def sample_initial_data():
-        p = 0.0005
+        p = 0.0001
         upsample_max_thr = 0.5
         n_upsample_max = 3
-        return pd.concat(
+        data_sub = pd.concat(
             [
                 # Sample specific fraction of initial data
                 initial_data.sample(frac=p),
@@ -171,6 +171,7 @@ def sample_initial_data():
                 ).sample(n=n_upsample_max),
             ]
         )
+        return data_sub
 
     results.append(
         simulate_scenarios(

benchmarks/domains/michalewicz_tl_noise.py

@@ -144,10 +144,10 @@ def michalewicz_tl_noise(settings: ConvergenceBenchmarkSettings) -> pd.DataFrame
     results = []
 
     def sample_initial_data():
-        p = 0.005
+        p = 0.0005
         upsample_max_thr = 3
         n_upsample_max = 3
-        return pd.concat(
+        data_sub = pd.concat(
             [
                 # Sample specific fraction of initial data
                 initial_data.sample(frac=p),
@@ -157,6 +157,7 @@ def sample_initial_data():
                 ).sample(n=n_upsample_max),
             ]
         )
+        return data_sub
 
     results.append(
         simulate_scenarios(