linting

Author: frederikhoengaard

python/src/lazylearn/ingestion/ingestion_pipeline_steps/interpreter_step.py

@@ -87,7 +87,11 @@ def numeric_test(types: list):
         :return: True if column is numeric, False otherwise
         """
         return all(
-            [item == float or item == int for item in set(types) if item is not None]
+            [
+                item == float or item == int
+                for item in set(types)
+                if item is not None  # noqa
+            ]
         )
 
     @staticmethod
@@ -125,17 +129,23 @@ def id_check(self, types, values):
         :param values:
         :return:
         """
-        return all([item == int for item in set(types) if item is not None]) and len(
+        return all(
+            [item == int for item in set(types) if item is not None]
+        ) and len(  # noqa
             set(values)
-        ) == len(self.df)
+        ) == len(
+            self.df
+        )
 
     @staticmethod
     def build_type_collections(column_type_map):
         collections = {}
 
         for data_type in ["datetime", "numeric", "categorical"]:
             collections[data_type] = [
-                col for col in column_type_map if column_type_map[col] == data_type
+                col
+                for col in column_type_map
+                if column_type_map[col] == data_type  # noqa
             ]
 
         return collections

python/src/lazylearn/preprocessing/time/date_processor.py

@@ -20,7 +20,9 @@ def date_processor(dataset: Dataset) -> Dataset:
         dataset.df[f"{date_column}_week"] = (
             dataset.df[date_column].dt.isocalendar().week
         )
-        dataset.df[f"{date_column}_day"] = dataset.df[date_column].dt.isocalendar().day
+        dataset.df[f"{date_column}_day"] = (
+            dataset.df[date_column].dt.isocalendar().day
+        )  # noqa
 
         new_categorical_cols.append(f"{date_column}_year")
         new_categorical_cols.append(f"{date_column}_month")

python/src/lazylearn/regression/models/randomforest/randomforest.py

@@ -1,6 +1,5 @@
 from models.models import Dataset
 from pipeline.pipeline import RegressionPipeline
-from sklearn.ensemble import RandomForestRegressor
 
 
 class RandomForestRegressionPipeline(RegressionPipeline):