PR comments

contrib/hamilton/contrib/user/zilto/nixtla_statsforecast/__init__.py

@@ -125,7 +125,7 @@ def best_model_per_series(cross_validation_evaluation: pd.DataFrame) -> pd.Serie
 def inference_predictions(
     forecaster: StatsForecast,
     inference_forecast_steps: int = 12,
-    inference_confidence_percentile: list[float] = [90.0],  # noqa
+    inference_confidence_percentile: list[float] = [90.0],  # noqa: B006
 ) -> pd.DataFrame:
     """Infer values using the training harness. Fitted models aren't stored
 

contrib/setup.py

@@ -10,7 +10,7 @@
 try:
     with open("README.md") as readme_file:
         readme = readme_file.read()
-except Exception:
+except FileNotFoundError:
     warnings.warn("README.md not found")  # noqa
     readme = None
 

examples/LLM_Workflows/image_telephone/streamlit.py

@@ -403,7 +403,6 @@ def explore_display():
         image_urls_to_display = image_urls[0 : len(projection)]
         if len(image_urls_to_display) != len(projection):
             image_url_length = len(image_urls_to_display)
-            # for i in range(len(projection) - len(image_urls_to_display)):
             image_urls_to_display.append(image_urls[image_url_length - 1])
         embedding_path_plot(projection, image_urls_to_display, selected_entry, prompt_path)
         # highlight_point(projection, selected_entry)

examples/LLM_Workflows/knowledge_retrieval/summarize_text.py

@@ -56,7 +56,7 @@ def pdf_text(pdf_path: pd.Series) -> pd.Series:
     :return: Series of strings of the PDFs' contents
     """
     _pdf_text = []
-    for _, file_path in pdf_path.items():
+    for _i, file_path in pdf_path.items():
         # creating a pdf reader object
         reader = PdfReader(file_path)
         text = ""

examples/due_date_probabilities/probability_estimation.py

@@ -125,10 +125,9 @@ def raw_probabilities(raw_data: str) -> pd.DataFrame:
 
 def resampled(raw_probabilities: pd.DataFrame) -> List[int]:
     sample_data = []
-    for _, row in raw_probabilities.iterrows():
+    for _idx, row in raw_probabilities.iterrows():
         count = row.probability * 1000
-        for _i in range(int(count)):
-            sample_data.append(row.days)
+        sample_data.extend([row.days] * int(count))
     return sample_data
 
 

hamilton/execution/state.py

@@ -307,7 +307,7 @@ def realize_parameterized_group(
             for dependency in new_task.base_dependencies:
                 new_dependencies[dependency] = []
                 if dependency in task_names_in_group:
-                    for _, name_map in name_maps.items():
+                    for _group_name, name_map in name_maps.items():
                         new_dependencies[dependency].append(name_map[dependency])
                 else:
                     new_dependencies[dependency].append(dependency)