Merge pull request freqtrade#10173 from freqtrade/fix/mutable_defaults

Fix mutable defaults, enable bugbear ruff rule also for freqAI code

Author: robcaulk

freqtrade/freqai/RL/BaseEnvironment.py

@@ -46,19 +46,20 @@ class BaseEnvironment(gym.Env):
 
     def __init__(
         self,
-        df: DataFrame = DataFrame(),
-        prices: DataFrame = DataFrame(),
-        reward_kwargs: dict = {},
+        *,
+        df: DataFrame,
+        prices: DataFrame,
+        reward_kwargs: dict,
         window_size=10,
         starting_point=True,
         id: str = "baseenv-1",  # noqa: A002
         seed: int = 1,
-        config: dict = {},
+        config: dict,
         live: bool = False,
         fee: float = 0.0015,
         can_short: bool = False,
         pair: str = "",
-        df_raw: DataFrame = DataFrame(),
+        df_raw: DataFrame,
     ):
         """
         Initializes the training/eval environment.

freqtrade/freqai/RL/BaseReinforcementLearningModel.py

@@ -488,7 +488,7 @@ def make_env(
     seed: int,
     train_df: DataFrame,
     price: DataFrame,
-    env_info: dict[str, Any] = {},
+    env_info: dict[str, Any],
 ) -> Callable:
     """
     Utility function for multiprocessed env.

freqtrade/freqai/data_kitchen.py

@@ -214,7 +214,7 @@ def filter_features(
         self,
         unfiltered_df: DataFrame,
         training_feature_list: list,
-        label_list: list = list(),
+        label_list: list | None = None,
         training_filter: bool = True,
     ) -> tuple[DataFrame, DataFrame]:
         """
@@ -244,7 +244,7 @@ def filter_features(
             # we don't care about total row number (total no. datapoints) in training, we only care
             # about removing any row with NaNs
             # if labels has multiple columns (user wants to train multiple modelEs), we detect here
-            labels = unfiltered_df.filter(label_list, axis=1)
+            labels = unfiltered_df.filter(label_list or [], axis=1)
             drop_index_labels = pd.isnull(labels).any(axis=1)
             drop_index_labels = (
                 drop_index_labels.replace(True, 1).replace(False, 0).infer_objects(copy=False)
@@ -654,8 +654,8 @@ def get_pair_data_for_features(
         pair: str,
         tf: str,
         strategy: IStrategy,
-        corr_dataframes: dict = {},
-        base_dataframes: dict = {},
+        corr_dataframes: dict,
+        base_dataframes: dict,
         is_corr_pairs: bool = False,
     ) -> DataFrame:
         """
@@ -773,10 +773,10 @@ def populate_features(
     def use_strategy_to_populate_indicators(  # noqa: C901
         self,
         strategy: IStrategy,
-        corr_dataframes: dict = {},
-        base_dataframes: dict = {},
+        corr_dataframes: dict[str, DataFrame] | None = None,
+        base_dataframes: dict[str, dict[str, DataFrame]] | None = None,
         pair: str = "",
-        prediction_dataframe: DataFrame = pd.DataFrame(),
+        prediction_dataframe: DataFrame | None = None,
         do_corr_pairs: bool = True,
     ) -> DataFrame:
         """
@@ -793,6 +793,10 @@ def use_strategy_to_populate_indicators(  # noqa: C901
         :return:
         dataframe: DataFrame = dataframe containing populated indicators
         """
+        if not corr_dataframes:
+            corr_dataframes = {}
+        if not base_dataframes:
+            base_dataframes = {}
 
         # check if the user is using the deprecated populate_any_indicators function
         new_version = inspect.getsource(strategy.populate_any_indicators) == (
@@ -822,7 +826,7 @@ def use_strategy_to_populate_indicators(  # noqa: C901
                 if tf not in corr_dataframes[p]:
                     corr_dataframes[p][tf] = pd.DataFrame()
 
-        if not prediction_dataframe.empty:
+        if prediction_dataframe is not None and not prediction_dataframe.empty:
             dataframe = prediction_dataframe.copy()
             base_dataframes[self.config["timeframe"]] = dataframe.copy()
         else:

freqtrade/freqai/freqai_interface.py

@@ -618,7 +618,7 @@ def extract_data_and_train_model(
         )
 
         unfiltered_dataframe = dk.use_strategy_to_populate_indicators(
-            strategy, corr_dataframes, base_dataframes, pair
+            strategy, corr_dataframes=corr_dataframes, base_dataframes=base_dataframes, pair=pair
         )
 
         trained_timestamp = new_trained_timerange.stopts

freqtrade/freqai/torch/PyTorchModelTrainer.py

@@ -25,7 +25,7 @@ def __init__(
         criterion: nn.Module,
         device: str,
         data_convertor: PyTorchDataConvertor,
-        model_meta_data: dict[str, Any] = {},
+        model_meta_data: dict[str, Any] | None = None,
         window_size: int = 1,
         tb_logger: Any = None,
         **kwargs,
@@ -45,6 +45,8 @@ def __init__(
         :param n_epochs: The maximum number batches to use for evaluation.
         :param batch_size: The size of the batches to use during training.
         """
+        if model_meta_data is None:
+            model_meta_data = {}
         self.model = model
         self.optimizer = optimizer
         self.criterion = criterion

pyproject.toml

@@ -287,8 +287,6 @@ max-complexity = 12
 [tool.ruff.lint.per-file-ignores]
 "freqtrade/freqai/**/*.py" = [
   "S311",  # Standard pseudo-random generators are not suitable for cryptographic purposes
-  "B006",  # Bugbear - mutable default argument
-  "B008",  # bugbear - Do not perform function calls in argument defaults
 ]
 "tests/**/*.py" = [
   "S101",  # allow assert in tests

tests/freqai/test_freqai_datakitchen.py

@@ -150,7 +150,9 @@ def test_get_pair_data_for_features_with_prealoaded_data(mocker, freqai_conf):
     freqai.dd.load_all_pair_histories(timerange, freqai.dk)
 
     _, base_df = freqai.dd.get_base_and_corr_dataframes(timerange, "LTC/BTC", freqai.dk)
-    df = freqai.dk.get_pair_data_for_features("LTC/BTC", "5m", strategy, base_dataframes=base_df)
+    df = freqai.dk.get_pair_data_for_features(
+        "LTC/BTC", "5m", strategy, {}, base_dataframes=base_df
+    )
 
     assert df is base_df["5m"]
     assert not df.empty
@@ -170,7 +172,9 @@ def test_get_pair_data_for_features_without_preloaded_data(mocker, freqai_conf):
     freqai.dd.load_all_pair_histories(timerange, freqai.dk)
 
     base_df = {"5m": pd.DataFrame()}
-    df = freqai.dk.get_pair_data_for_features("LTC/BTC", "5m", strategy, base_dataframes=base_df)
+    df = freqai.dk.get_pair_data_for_features(
+        "LTC/BTC", "5m", strategy, {}, base_dataframes=base_df
+    )
 
     assert df is not base_df["5m"]
     assert not df.empty