style: 💄 pylint

basicts/losses/losses.py

@@ -8,13 +8,13 @@
 def l1_loss(prediction: torch.Tensor, target: torch._tensor, size_average: Optional[bool] = None, reduce: Optional[bool] = None, reduction: str = "mean") -> torch.Tensor:
     """unmasked mae."""
 
-    return F.l1_loss(prediction, target)
+    return F.l1_loss(prediction, target, size_average=size_average, reduce=reduce, reduction=reduction)
 
 
 def l2_loss(prediction: torch.Tensor, target: torch.Tensor, size_average: Optional[bool] = None, reduce: Optional[bool] = None, reduction: str = "mean") -> torch.Tensor:
     """unmasked mse"""
 
-    return F.mse_loss(prediction, target)
+    return F.mse_loss(prediction, target, size_average=size_average, reduce=reduce, reduction=reduction)
 
 
 def masked_mae(prediction: torch.Tensor, target: torch.Tensor, null_val: float = np.nan) -> torch.Tensor:

basicts/utils/m4.py

@@ -38,7 +38,7 @@ class M4Dataset:
     values: np.ndarray
 
     @staticmethod
-    def load(info_file_path: str = None, data: np.array = None) -> 'M4Dataset':
+    def load(info_file_path: str = None, data: np.array = None) -> "M4Dataset":
         """
         Load cached dataset.
 
@@ -165,20 +165,20 @@ def group_count(group_name):
             return len(np.where(self.test_set.groups == group_name)[0])
 
         weighted_score = {}
-        for g in ['Yearly', 'Quarterly', 'Monthly']:
+        for g in ["Yearly", "Quarterly", "Monthly"]:
             weighted_score[g] = scores[g] * group_count(g)
             scores_summary[g] = scores[g]
 
         others_score = 0
         others_count = 0
-        for g in ['Weekly', 'Daily', 'Hourly']:
+        for g in ["Weekly", "Daily", "Hourly"]:
             others_score += scores[g] * group_count(g)
             others_count += group_count(g)
-        weighted_score['Others'] = others_score
-        scores_summary['Others'] = others_score / others_count
+        weighted_score["Others"] = others_score
+        scores_summary["Others"] = others_score / others_count
 
         average = np.sum(list(weighted_score.values())) / len(self.test_set.groups)
-        scores_summary['Average'] = average
+        scores_summary["Average"] = average
 
         return scores_summary
 
@@ -187,7 +187,8 @@ def m4_summary(save_dir, project_dir):
     """Summary evaluation for M4 dataset.
 
     Args:
-        save_dir (str): Directory where prediction results are saved. All "{save_dir}/M4_{seasonal pattern}.npy" should exist. Seasonal patterns = ["Yearly", "Quarterly", "Monthly", "Weekly", "Daily", "Hourly"]
+        save_dir (str): Directory where prediction results are saved. All "{save_dir}/M4_{seasonal pattern}.npy" should exist.
+                        Seasonal patterns = ["Yearly", "Quarterly", "Monthly", "Weekly", "Daily", "Hourly"]
         project_dir (str): Project directory. The M4 raw data should be in "{project_dir}/datasets/raw_data/M4".
     """
     seasonal_patterns = ["Yearly", "Quarterly", "Monthly", "Weekly", "Daily", "Hourly"] # the order cannot be changed
@@ -205,16 +206,16 @@ def build_cache(files: str) -> None:
                 values = row.values
                 timeseries_dict[m4id] = values[~np.isnan(values)]
         return np.array(list(timeseries_dict.values()), dtype=object)
-    
+
     print("Building cache for M4 dataset...")
     # read prediction and ground truth
     prediction = []
     for seasonal_pattern in seasonal_patterns:
         prediction.extend(np.load(save_dir + "/M4_{0}.npy".format(seasonal_pattern)))
     prediction = np.array(prediction, dtype=object)
-    train_values = build_cache('*-train.csv')
-    test_values = build_cache('*-test.csv')
-    print("Summarizing M4 dataset...")        
+    train_values = build_cache("*-train.csv")
+    test_values = build_cache("*-test.csv")
+    print("Summarizing M4 dataset...")
     summary = M4Summary(info_file_path, train_values, test_values, data_dir + "/submission-Naive2.csv")
-    results = pd.DataFrame(summary.evaluate(prediction), index=['SMAPE', 'OWA'])
+    results = pd.DataFrame(summary.evaluate(prediction), index=["SMAPE", "OWA"])
     return results