[fix] Change f-string to format in base_task.py

Author: nabenabe0928

autoPyTorch/api/base_task.py

@@ -852,9 +852,10 @@ def _search(
         time_left_for_modelfit = int(max(0, total_walltime_limit - elapsed_time))
         if func_eval_time_limit_secs is None or func_eval_time_limit_secs > time_left_for_modelfit:
             self._logger.warning(
-                'Time limit for a single run is higher than total time '
-                'limit. Capping the limit for a single run to the total '
-                'time given to SMAC (%f)' % time_left_for_modelfit
+                "Time limit for a single run exceeded total time limit. "
+                "Cap the limit for a single run to the total time given to SMAC ({})".format(
+                    time_left_for_modelfit
+                )
             )
             func_eval_time_limit_secs = time_left_for_modelfit
 
@@ -863,10 +864,8 @@ def _search(
         if num_models < 2:
             func_eval_time_limit_secs = time_left_for_modelfit // 2
             self._logger.warning(
-                "Capping the func_eval_time_limit_secs to {} to have "
-                "time for a least 2 models to ensemble.".format(
-                    func_eval_time_limit_secs
-                )
+                "Cap the func_eval_time_limit_secs to {} to guarantee at least"
+                "two model-trainings".format(func_eval_time_limit_secs)
             )
 
         # ============> Run dummy predictions
@@ -879,7 +878,7 @@ def _search(
 
         if enable_traditional_pipeline:
             if STRING_TO_TASK_TYPES[self.task_type] in REGRESSION_TASKS:
-                self._logger.warning("Traditional Pipeline is not enabled for regression. Skipping...")
+                self._logger.warning("Regression tasks are not supported by traditional pipelines. Skip...")
             else:
                 traditional_task_name = 'runTraditional'
                 self._stopwatch.start_task(traditional_task_name)
@@ -906,9 +905,9 @@ def _search(
                                  "is no time left. Try increasing the value "
                                  "of time_left_for_this_task.")
         elif self.ensemble_size <= 0:
-            self._logger.info("Not starting ensemble builder as ensemble size is 0")
+            self._logger.info("Skip ensemble builder as the provided ensemble size is 0.")
         else:
-            self._logger.info("Starting ensemble")
+            self._logger.info("Start building ensemble.")
             ensemble_task_name = 'ensemble'
             self._stopwatch.start_task(ensemble_task_name)
             proc_ensemble = EnsembleBuilderManager(
@@ -939,9 +938,9 @@ def _search(
         elapsed_time = self._stopwatch.wall_elapsed(experiment_task_name)
         time_left_for_smac = max(0, total_walltime_limit - elapsed_time)
 
-        self._logger.info("Starting SMAC with %5.2f sec time left" % time_left_for_smac)
+        self._logger.info("Start SMAC with {:.2f} [sec] time left".format(time_left_for_smac))
         if time_left_for_smac <= 0:
-            self._logger.warning(" Not starting SMAC because there is no time left")
+            self._logger.warning("Skip SMAC because there is no time left")
         else:
 
             _proc_smac = AutoMLSMBO(
@@ -984,43 +983,43 @@ def _search(
                     with open(trajectory_filename, 'w') as fh:
                         json.dump(saveable_trajectory, fh)
                 except Exception as e:
-                    self._logger.warning(f"Cannot save {trajectory_filename} due to {e}...")
+                    self._logger.warning("Cannot save {} due to {}".format(trajectory_filename, e))
             except Exception as e:
                 self._logger.exception(str(e))
                 raise
         # Wait until the ensemble process is finished to avoid shutting down
         # while the ensemble builder tries to access the data
-        self._logger.info("Starting Shutdown")
+        self._logger.info("Start shutdown")
 
         if proc_ensemble is not None:
             self.ensemble_performance_history = list(proc_ensemble.history)
 
             if len(proc_ensemble.futures) > 0:
                 # Also add ensemble runs that did not finish within smac time
                 # and add them into the ensemble history
-                self._logger.info("Ensemble script still running, waiting for it to finish.")
+                self._logger.info("Wait until all the ensemble processes finish")
                 result = proc_ensemble.futures.pop().result()
                 if result:
                     ensemble_history, _, _, _ = result
                     self.ensemble_performance_history.extend(ensemble_history)
-                self._logger.info("Ensemble script finished, continue shutdown.")
+                self._logger.info("Finish all the ensemble processes and start shutdown")
 
             # save the ensemble performance history file
             if len(self.ensemble_performance_history) > 0:
                 pd.DataFrame(self.ensemble_performance_history).to_json(
                     os.path.join(self._backend.internals_directory, 'ensemble_history.json'))
 
-        self._logger.info("Closing the dask infrastructure")
+        self._logger.info("Close the dask infrastructure")
         self._close_dask_client()
-        self._logger.info("Finished closing the dask infrastructure")
+        self._logger.info("Finish closing the dask infrastructure")
 
         if load_models:
-            self._logger.info("Loading models...")
+            self._logger.info("Load models")
             self._load_models()
-            self._logger.info("Finished loading models...")
+            self._logger.info("Finish loading models")
 
         # Clean up the logger
-        self._logger.info("Starting to clean up the logger")
+        self._logger.info("Clean up the logger")
         self._clean_logger()
 
         return self
@@ -1294,10 +1293,11 @@ def _print_debug_info_to_log(self) -> None:
         Prints to the log file debug information about the current estimator
         """
         assert self._logger is not None
-        self._logger.debug("Starting to print environment information")
-        self._logger.debug('  Python version: %s', sys.version.split('\n'))
-        self._logger.debug('  System: %s', platform.system())
-        self._logger.debug('  Machine: %s', platform.machine())
-        self._logger.debug('  Platform: %s', platform.platform())
+        self._logger.debug("Display environment information")
+        self._logger.debug("  Python version: {}".format(sys.version.split('\n')))
+        self._logger.debug("  System: {}".format(platform.system()))
+        self._logger.debug("  Machine: {}".format(platform.machine()))
+        self._logger.debug("  Platform: {}".format(platform.platform()))
+
         for key, value in vars(self).items():
-            self._logger.debug(f"\t{key}->{value}")
+            self._logger.debug("\t{}->{}".format(key, value))