Add option to redirect logs to file in a FunctionEvaluator

Author: AngelFP

optimas/evaluators/function_evaluator.py

@@ -20,15 +20,23 @@ class FunctionEvaluator(Evaluator):
         using this option, the current working directory inside the ``function``
         will be changed to the corresponding evaluation directory.
         By default, ``False``.
+    redirect_logs_to_file : bool
+        Whether to redirect the logs (stdout and stderr) of the evaluation
+        function to a file (log.out and log.err). This can be useful to keep the
+        logs of the exploration clean, preventing many processes from writing to the
+        terminal at once. If enabled, `create_evaluation_dirs` will be set to `True`.
 
     """
 
     def __init__(
-        self, function: Callable, create_evaluation_dirs: bool = False
+        self, function: Callable, create_evaluation_dirs: bool = False, redirect_logs_to_file: bool = False
     ) -> None:
         super().__init__(sim_function=run_function)
         self.function = function
         self._create_evaluation_dirs = create_evaluation_dirs
+        self._redirect_logs_to_file = redirect_logs_to_file
+        if self._redirect_logs_to_file:
+            self._create_evaluation_dirs = True
 
     def get_sim_specs(
         self,
@@ -43,6 +51,7 @@ def get_sim_specs(
         )
         # Add evaluation function to sim_specs.
         sim_specs["user"]["evaluation_func"] = self.function
+        sim_specs["user"]["redirect_logs_to_file"] = self._redirect_logs_to_file
         return sim_specs
 
     def get_libe_specs(self) -> Dict:

optimas/sim_functions.py

@@ -1,4 +1,5 @@
 """Contains the definition of the simulation functions given to libEnsemble."""
+from contextlib import redirect_stderr, redirect_stdout
 
 import jinja2
 import numpy as np
@@ -122,6 +123,7 @@ def run_function(H, persis_info, sim_specs, libE_info):
     else:
         user_specs = sim_specs["user"]
     evaluation_func = user_specs["evaluation_func"]
+    redirect_logs_to_file = user_specs["redirect_logs_to_file"]
 
     # Prepare the array that is returned to libE
     libE_output = np.zeros(1, dtype=sim_specs["out"])
@@ -130,7 +132,16 @@ def run_function(H, persis_info, sim_specs, libE_info):
             libE_output[name].fill(np.nan)
 
     # Run evaluation.
-    evaluation_func(input_values, libE_output[0])
+    if redirect_logs_to_file:
+        with (
+            open("log.out", "w") as stdout_file,
+            open("log.err", "w") as stderr_file,
+            redirect_stdout(stdout_file),
+            redirect_stderr(stderr_file),
+        ):
+            evaluation_func(input_values, libE_output[0])
+    else:
+        evaluation_func(input_values, libE_output[0])
     calc_status = WORKER_DONE
 
     # If required, fail when the objectives are NaN.