add pbar support for hyperopt

bofire/benchmarks/hyperopt.py

@@ -1,6 +1,7 @@
 from typing import Optional
 
 import pandas as pd
+from tqdm import tqdm
 
 import bofire.surrogates.api as surrogates
 from bofire.benchmarks.benchmark import Benchmark
@@ -15,6 +16,7 @@ def __init__(
         training_data: pd.DataFrame,
         folds: int,
         random_state: Optional[int] = None,
+        show_progress_bar: bool = False,
     ) -> None:
         super().__init__()
         if surrogate_data.hyperconfig is None:
@@ -24,6 +26,7 @@ def __init__(
         self.folds = folds
         self.results = None
         self.random_state = random_state
+        self.show_progress_bar = show_progress_bar
 
     @property
     def domain(self) -> Domain:
@@ -34,7 +37,11 @@ def target_metric(self):
         return self.surrogate_data.hyperconfig.target_metric  # type: ignore
 
     def _f(self, candidates: pd.DataFrame) -> pd.DataFrame:
-        for i, candidate in candidates.iterrows():
+        for i, candidate in tqdm(
+            candidates.iterrows(),
+            total=candidates.shape[0],
+            disable=self.show_progress_bar is False,
+        ):
             self.surrogate_data.update_hyperparameters(candidate)
             surrogate = surrogates.map(self.surrogate_data)
             _, cv_test, _ = surrogate.cross_validate(  # type: ignore

bofire/runners/hyperoptimize.py

@@ -48,6 +48,9 @@ def sample(domain):
         training_data=training_data,
         folds=folds,
         random_state=random_state,
+        show_progress_bar=True
+        if surrogate_data.hyperconfig.hyperstrategy == "FractionalFactorialStrategy"
+        else False,
     )
 
     if surrogate_data.hyperconfig.hyperstrategy == "FractionalFactorialStrategy":