fix code style issue

Azure · Nov 28, 2024 · d8453cd · d8453cd
1 parent be35140
commit d8453cd
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Showing 6 changed files with 18 additions and 6 deletions.
diff --git a/...dalone-jobs/automl-forecasting-forecast-function/forecasting_script/forecasting_script.py b/...dalone-jobs/automl-forecasting-forecast-function/forecasting_script/forecasting_script.py
@@ -63,7 +63,9 @@ def run(mini_batch):
         # or at edges of time due to lags/rolling windows
         if target_column_name in X_test.columns:
             clean = X_test[
-            X_test[[target_column_name, predicted_column_name]].notnull().all(axis=1)
+                X_test[[target_column_name, predicted_column_name]]
+                .notnull()
+                .all(axis=1)
             ]
         else:
             clean = X_test[X_test[predicted_column_name].notnull()]

diff --git a/...n/jobs/automl-standalone-jobs/automl-forecasting-github-dau/helpers/forecasting_script.py b/...n/jobs/automl-standalone-jobs/automl-forecasting-github-dau/helpers/forecasting_script.py
@@ -42,7 +42,9 @@ def run(mini_batch):
             continue
 
         if target_column_name not in X_test.columns:
-            raise ValueError(f"Target column `{target_column_name}` not found in the test data, required for rolling forecast.")
+            raise ValueError(
+                f"Target column `{target_column_name}` not found in the test data, required for rolling forecast."
+            )
         y_test = X_test.pop(target_column_name).values
 
         # Make a rolling forecast, advancing the forecast origin by 1 period on each iteration through the test set

diff --git a/...toml-standalone-jobs/automl-forecasting-orange-juice-sales/forecast/forecasting_script.py b/...toml-standalone-jobs/automl-forecasting-orange-juice-sales/forecast/forecasting_script.py
@@ -63,7 +63,9 @@ def run(mini_batch):
         # or at edges of time due to lags/rolling windows
         if target_column_name in X_test.columns:
             clean = X_test[
-            X_test[[target_column_name, predicted_column_name]].notnull().all(axis=1)
+                X_test[[target_column_name, predicted_column_name]]
+                .notnull()
+                .all(axis=1)
             ]
         else:
             clean = X_test[X_test[predicted_column_name].notnull()]

diff --git a/...toml-standalone-jobs/automl-forecasting-recipes-univariate/forecast/forecasting_script.py b/...toml-standalone-jobs/automl-forecasting-recipes-univariate/forecast/forecasting_script.py
@@ -63,7 +63,9 @@ def run(mini_batch):
         # or at edges of time due to lags/rolling windows
         if target_column_name in X_test.columns:
             clean = X_test[
-            X_test[[target_column_name, predicted_column_name]].notnull().all(axis=1)
+                X_test[[target_column_name, predicted_column_name]]
+                .notnull()
+                .all(axis=1)
             ]
         else:
             clean = X_test[X_test[predicted_column_name].notnull()]

diff --git a/...jobs/automl-standalone-jobs/automl-forecasting-task-bike-share/forecast/rolling_script.py b/...jobs/automl-standalone-jobs/automl-forecasting-task-bike-share/forecast/rolling_script.py
@@ -39,7 +39,9 @@ def run(mini_batch):
             continue
 
         if target_column_name not in X_test.columns:
-            raise ValueError(f"Target column `{target_column_name}` not found in the test data, required for rolling forecast.")
+            raise ValueError(
+                f"Target column `{target_column_name}` not found in the test data, required for rolling forecast."
+            )
         y_test = X_test.pop(target_column_name).values
 
         # Make a rolling forecast, advancing the forecast origin by 1 period on each iteration through the test set

diff --git a/...toml-standalone-jobs/automl-forecasting-task-energy-demand/forecast/forecasting_script.py b/...toml-standalone-jobs/automl-forecasting-task-energy-demand/forecast/forecasting_script.py
@@ -63,7 +63,9 @@ def run(mini_batch):
         # or at edges of time due to lags/rolling windows
         if target_column_name in X_test.columns:
             clean = X_test[
-            X_test[[target_column_name, predicted_column_name]].notnull().all(axis=1)
+                X_test[[target_column_name, predicted_column_name]]
+                .notnull()
+                .all(axis=1)
             ]
         else:
             clean = X_test[X_test[predicted_column_name].notnull()]