fix: Resolve R1710 pylint inconsistent-return-statements errors

qlib/backtest/profit_attribution.py

@@ -221,6 +221,8 @@ def get_stock_group(stock_group_field_df, bench_stock_weight_df, group_method, g
                 group_n=group_n,
             )
         return new_stock_group_df
+    else:
+        raise ValueError(f"Unknown group_method: {group_method}. Expected 'category' or 'bins'.")
 
 
 def brinson_pa(

qlib/backtest/utils.py

@@ -222,6 +222,7 @@ def get(self, infra_name: str) -> Any:
             return getattr(self, infra_name)
         else:
             warnings.warn(f"infra {infra_name} is not found!")
+            return None
 
     def has(self, infra_name: str) -> bool:
         return infra_name in self.get_support_infra() and hasattr(self, infra_name)

qlib/contrib/data/utils/sepdf.py

@@ -72,6 +72,7 @@ def apply_each(self, method: str, skip_align=True, *args, **kwargs):
                 inplace = True
         if not inplace:
             return SepDataFrame(df_dict=df_dict, join=self.join, skip_align=skip_align)
+        return None
 
     def sort_index(self, *args, **kwargs):
         return self.apply_each("sort_index", True, *args, **kwargs)

qlib/contrib/model/pytorch_adarnn.py

@@ -711,6 +711,8 @@ def forward(self, source, target):
                 YX = torch.mean(kernels[batch_size:, :batch_size])
                 loss = torch.mean(XX + YY - XY - YX)
             return loss
+        else:
+            raise ValueError(f"Unknown kernel_type: {self.kernel_type}. Expected 'linear' or 'rbf'.")
 
 
 class Mine_estimator(nn.Module):

qlib/contrib/report/analysis_model/analysis_model_performance.py

@@ -337,5 +337,6 @@ def model_performance_graph(
 
     if show_notebook:
         BarGraph.show_graph_in_notebook(figure_list)
+        return None
     else:
         return figure_list

qlib/contrib/report/analysis_position/cumulative_return.py

@@ -269,5 +269,6 @@ def cumulative_return_graph(
     _figures = _get_figure_with_position(position, report_normal, label_data, start_date, end_date)
     if show_notebook:
         BaseGraph.show_graph_in_notebook(_figures)
+        return None
     else:
         return _figures

qlib/contrib/report/analysis_position/rank_label.py

@@ -124,5 +124,6 @@ def rank_label_graph(
     _figures = _get_figure_with_position(position, label_data, start_date, end_date)
     if show_notebook:
         ScatterGraph.show_graph_in_notebook(_figures)
+        return None
     else:
         return _figures

qlib/contrib/report/analysis_position/report.py

@@ -244,5 +244,6 @@ def report_graph(report_df: pd.DataFrame, show_notebook: bool = True) -> [list,
     fig_list = _report_figure(report_df)
     if show_notebook:
         BaseGraph.show_graph_in_notebook(fig_list)
+        return None
     else:
         return fig_list

qlib/contrib/report/analysis_position/risk_analysis.py

@@ -137,7 +137,7 @@ def _get_monthly_risk_analysis_figure(report_normal_df: pd.DataFrame) -> Iterabl
     # if report_normal_df is None and report_long_short_df is None:
     #     return []
     if report_normal_df is None:
-        return []
+        return
 
     # if report_normal_df is None:
     #     report_normal_df = pd.DataFrame(index=report_long_short_df.index)
@@ -293,5 +293,6 @@ def risk_analysis_graph(
     )
     if show_notebook:
         ScatterGraph.show_graph_in_notebook(_figure_list)
+        return None
     else:
         return _figure_list

qlib/contrib/report/analysis_position/score_ic.py

@@ -67,5 +67,6 @@ def score_ic_graph(pred_label: pd.DataFrame, show_notebook: bool = True, **kwarg
     ).figure
     if show_notebook:
         ScatterGraph.show_graph_in_notebook([_figure])
+        return None
     else:
         return (_figure,)

qlib/contrib/strategy/optimizer/optimizer.py

@@ -136,6 +136,8 @@ def _optimize(self, S: np.ndarray, r: Optional[np.ndarray] = None, w0: Optional[
                 warnings.warn("`r` is set but will not be used for `rp` portfolio")
             return self._optimize_rp(S, w0)
 
+        raise ValueError(f"Unknown optimization method: {self.method}")
+
     def _optimize_inv(self, S: np.ndarray) -> np.ndarray:
         """Inverse volatility"""
         vola = np.diag(S) ** 0.5

qlib/data/cache.py

@@ -1112,6 +1112,7 @@ def _dataset(
             )
             data.to_pickle(cache_file)
             return self.cache_to_origin_data(data, fields)
+        return None
 
 
 class DatasetURICache(DatasetCache):

qlib/data/data.py

@@ -1205,6 +1205,8 @@ def _uri(self, type, **kwargs):
             return Inst._uri(**kwargs)
         elif type == "feature":
             return DatasetD._uri(**kwargs)
+        else:
+            raise ValueError(f"Unknown type: {type}. Expected 'calendar', 'instrument', or 'feature'.")
 
     def features_uri(self, instruments, fields, start_time, end_time, freq, disk_cache=1):
         """features_uri

qlib/utils/index_data.py

@@ -505,6 +505,7 @@ def isna(self):
     def fillna(self, value=0.0, inplace: bool = False):
         if inplace:
             self.data = np.nan_to_num(self.data, nan=value)
+            return None
         else:
             return self.__class__(np.nan_to_num(self.data, nan=value), *self.indices)
 

qlib/workflow/exp.py

@@ -304,7 +304,7 @@ def _get_recorder(self, recorder_id=None, recorder_name=None):
                 raise ValueError(
                     "No valid recorder has been found, please make sure the input recorder id is correct."
                 ) from mlflow_exp
-        elif recorder_name is not None:
+        else:  # recorder_name is not None (guaranteed by assert above)
             logger.warning(
                 f"Please make sure the recorder name {recorder_name} is unique, we will only return the latest recorder if there exist several matched the given name."
             )

qlib/workflow/expm.py

@@ -384,7 +384,7 @@ def _get_exp(self, experiment_id=None, experiment_name=None):
                 raise ValueError(
                     "No valid experiment has been found, please make sure the input experiment id is correct."
                 ) from e
-        elif experiment_name is not None:
+        else:  # experiment_name is not None (guaranteed by assert above)
             try:
                 exp = self.client.get_experiment_by_name(experiment_name)
                 if exp is None or exp.lifecycle_stage.upper() == "DELETED":

qlib/workflow/online/update.py

@@ -233,7 +233,7 @@ def update(self, dataset: DatasetH = None, write: bool = True, ret_new: bool = F
             self.logger.info(
                 f"The data in {self.record.info['id']} are latest ({self.last_end}). No need to update to {self.to_date}."
             )
-            return
+            return None
 
         # load dataset
         if dataset is None:
@@ -246,6 +246,7 @@ def update(self, dataset: DatasetH = None, write: bool = True, ret_new: bool = F
             self.record.save_objects(**{self.fname: updated_data})
         if ret_new:
             return updated_data
+        return None
 
     @abstractmethod
     def get_update_data(self, dataset: Dataset) -> pd.DataFrame:

qlib/workflow/record_temp.py

@@ -226,13 +226,13 @@ def generate(self, *args, **kwargs):
                 pass  # continue to generating metrics
             else:
                 logger.info("The results has previously generated, Generation skipped.")
-                return
+                return None
 
         try:
             self.check()
         except FileNotFoundError:
             logger.warning("The dependent data does not exists. Generation skipped.")
-            return
+            return None
         artifact_dict = self._generate(*args, **kwargs)
         if isinstance(artifact_dict, dict):
             self.save(**artifact_dict)
@@ -320,7 +320,7 @@ def _generate(self, label: Optional[pd.DataFrame] = None, **kwargs):
             label = self.load("label.pkl")
         if label is None or not isinstance(label, pd.DataFrame) or label.empty:
             logger.warning(f"Empty label.")
-            return
+            return None
         ic, ric = calc_ic(pred.iloc[:, 0], label.iloc[:, self.label_col])
         metrics = {
             "IC": ic.mean(),