From: Jérôme Benoit Date: Sun, 21 Jun 2026 18:04:17 +0000 (+0200) Subject: style(quickadapter): wrap long lines X-Git-Url: https://git.piment-noir.org/?a=commitdiff_plain;h=9a9cd98001bf000cbf9dab2525697dfb8a6f1b18;p=freqai-strategies.git style(quickadapter): wrap long lines --- diff --git a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py index 4841aa7..233abb4 100644 --- a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py +++ b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py @@ -381,7 +381,9 @@ class QuickAdapterRegressorV3(BaseRegressionModel): unfiltered_df: pd.DataFrame, ) -> None: if not unfiltered_df.index.is_unique: - raise ValueError("unfiltered_df.index must be unique for causal split guards") + raise ValueError( + "unfiltered_df.index must be unique for causal split guards" + ) if not filtered_dataframe.index.isin(unfiltered_df.index).all(): raise ValueError( "filtered_dataframe.index must be a subset of unfiltered_df.index" @@ -395,7 +397,9 @@ class QuickAdapterRegressorV3(BaseRegressionModel): QuickAdapterRegressorV3._validate_index_alignment( filtered_dataframe, unfiltered_df ) - positions = pd.Series(np.arange(len(unfiltered_df), dtype=np.int64), index=unfiltered_df.index) + positions = pd.Series( + np.arange(len(unfiltered_df), dtype=np.int64), index=unfiltered_df.index + ) return positions.loc[filtered_dataframe.index] @staticmethod @@ -1593,9 +1597,7 @@ class QuickAdapterRegressorV3(BaseRegressionModel): known_at_train.to_numpy(dtype=np.int64) < first_test_position ) else: - _log_known_at_none_once( - dk.pair, "train_test_split causal guard" - ) + _log_known_at_none_once(dk.pair, "train_test_split causal guard") train_features, train_labels, train_weights = ( QuickAdapterRegressorV3._filter_train_by_mask( train_features, @@ -1992,7 +1994,9 @@ class QuickAdapterRegressorV3(BaseRegressionModel): ) if known_at_index is not None: known_at_train = known_at_index.iloc[train_idx] - keep_mask = known_at_train.to_numpy(dtype=np.int64) < first_test_position + keep_mask = ( + known_at_train.to_numpy(dtype=np.int64) < first_test_position + ) train_features, train_labels, train_weights = ( QuickAdapterRegressorV3._filter_train_by_mask( train_features, @@ -2003,9 +2007,7 @@ class QuickAdapterRegressorV3(BaseRegressionModel): ) ) else: - _log_known_at_none_once( - dk.pair, "timeseries_split causal guard" - ) + _log_known_at_none_once(dk.pair, "timeseries_split causal guard") train_weights = sanitize_and_renormalize( train_weights, logger=logger, context="timeseries_split:train" diff --git a/quickadapter/user_data/strategies/QuickAdapterV3.py b/quickadapter/user_data/strategies/QuickAdapterV3.py index 1a5af3f..3f01dd3 100644 --- a/quickadapter/user_data/strategies/QuickAdapterV3.py +++ b/quickadapter/user_data/strategies/QuickAdapterV3.py @@ -911,7 +911,9 @@ class QuickAdapterV3(IStrategy): dataframe[label_col] = label_data.series if label_data.known_at_index is not None: - dataframe[label_known_at_column_name(label_col)] = label_data.known_at_index + dataframe[label_known_at_column_name(label_col)] = ( + label_data.known_at_index + ) label_weight_col = label_weight_column_name(label_col) if is_weighting_active: diff --git a/quickadapter/user_data/strategies/Utils.py b/quickadapter/user_data/strategies/Utils.py index 925ab90..67735ae 100644 --- a/quickadapter/user_data/strategies/Utils.py +++ b/quickadapter/user_data/strategies/Utils.py @@ -3136,9 +3136,7 @@ def _validate_optuna_label_best_params( } if not isinstance(best_params, dict): if logger is not None: - logger.warning( - f"[{pair}] Ignoring Optuna label best-params: not a dict" - ) + logger.warning(f"[{pair}] Ignoring Optuna label best-params: not a dict") return None schema_version = best_params.get("schema_version") if schema_version is None: