[python] added f-strings to python-package/lightgbm/engine.py (#4258)

python-package/lightgbm/engine.py

@@ -149,12 +149,12 @@ def train(params, train_set, num_boost_round=100,
     for alias in _ConfigAliases.get("num_iterations"):
         if alias in params:
             num_boost_round = params.pop(alias)
-            _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
+            _log_warning(f"Found `{alias}` in params. Will use it instead of argument")
     params["num_iterations"] = num_boost_round
     for alias in _ConfigAliases.get("early_stopping_round"):
         if alias in params:
             early_stopping_rounds = params.pop(alias)
-            _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
+            _log_warning(f"Found `{alias}` in params. Will use it instead of argument")
     params["early_stopping_round"] = early_stopping_rounds
     first_metric_only = params.get('first_metric_only', False)
 
@@ -198,7 +198,7 @@ def train(params, train_set, num_boost_round=100,
             if valid_names is not None and len(valid_names) > i:
                 name_valid_sets.append(valid_names[i])
             else:
-                name_valid_sets.append('valid_' + str(i))
+                name_valid_sets.append(f'valid_{i}')
     # process callbacks
     if callbacks is None:
         callbacks = set()
@@ -383,7 +383,7 @@ def _agg_cv_result(raw_results, eval_train_metric=False):
     for one_result in raw_results:
         for one_line in one_result:
             if eval_train_metric:
-                key = "{} {}".format(one_line[0], one_line[1])
+                key = f"{one_line[0]} {one_line[1]}"
             else:
                 key = one_line[1]
             metric_type[key] = one_line[3]
@@ -532,12 +532,12 @@ def cv(params, train_set, num_boost_round=100,
         params['objective'] = 'none'
     for alias in _ConfigAliases.get("num_iterations"):
         if alias in params:
-            _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
+            _log_warning(f"Found `{alias}` in params. Will use it instead of argument")
             num_boost_round = params.pop(alias)
     params["num_iterations"] = num_boost_round
     for alias in _ConfigAliases.get("early_stopping_round"):
         if alias in params:
-            _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
+            _log_warning(f"Found `{alias}` in params. Will use it instead of argument")
             early_stopping_rounds = params.pop(alias)
     params["early_stopping_round"] = early_stopping_rounds
     first_metric_only = params.get('first_metric_only', False)
@@ -597,8 +597,8 @@ def cv(params, train_set, num_boost_round=100,
         cvfolds.update(fobj=fobj)
         res = _agg_cv_result(cvfolds.eval_valid(feval), eval_train_metric)
         for _, key, mean, _, std in res:
-            results[key + '-mean'].append(mean)
-            results[key + '-stdv'].append(std)
+            results[f'{key}-mean'].append(mean)
+            results[f'{key}-stdv'].append(std)
         try:
             for cb in callbacks_after_iter:
                 cb(callback.CallbackEnv(model=cvfolds,