Updated gitignore

.gitignore

@@ -1,10 +1,15 @@
 venv/
+.venv/
 __pycache__/
 .env
 .ipynb_checkpoints
 *ipynb
 .vscode/
 .DS_Store
+.ruff_cache/
+.python-version
+.profile_app.python
+*pstats
 
 eval-queue/
 eval-results/

src/tools/collections.py

@@ -17,7 +17,7 @@ intervals = {
 }
 
 
-def filter_by_type_and_size(df, model_type, size_interval):
+def _filter_by_type_and_size(df, model_type, size_interval):
     """Filter DataFrame by model type and parameter size interval."""
     type_emoji = model_type.value.symbol[0]
     filtered_df = df[df[AutoEvalColumn.model_type_symbol.name] == type_emoji]
@@ -26,7 +26,7 @@ def filter_by_type_and_size(df, model_type, size_interval):
     return filtered_df.loc[mask]
 
 
-def add_models_to_collection(collection, models, model_type, size):
+def _add_models_to_collection(collection, models, model_type, size):
     """Add best models to the collection and update positions."""
     cur_len_collection = len(collection.items)
     for ix, model in enumerate(models, start=1):
@@ -58,12 +58,12 @@ def update_collections(df: DataFrame):
         if not model_type.value.name:
             continue
         for size, interval in intervals.items():
-            filtered_df = filter_by_type_and_size(df, model_type, interval)
+            filtered_df = _filter_by_type_and_size(df, model_type, interval)
             best_models = list(
                 filtered_df.sort_values(AutoEvalColumn.average.name, ascending=False)[AutoEvalColumn.dummy.name][:10]
             )
             print(model_type.value.symbol, size, best_models[:10])
-            add_models_to_collection(collection, best_models, model_type, size)
+            _add_models_to_collection(collection, best_models, model_type, size)
             cur_best_models.extend(best_models)
 
     # Cleanup