fix data-preparation lint issues

src/functions/data-preparation.py

@@ -7,17 +7,16 @@
 import time
 
 
-
-
 def data_prepare(context):
     # Set AWS environment variables:
     _set_envars(context)
 
-
     region = sagemaker.Session().boto_region_name
     sm_client = boto3.client("sagemaker")
     boto_session = boto3.Session(region_name=region)
-    sagemaker_session = sagemaker.session.Session(boto_session=boto_session, sagemaker_client=sm_client)
+    sagemaker_session = sagemaker.session.Session(
+        boto_session=boto_session, sagemaker_client=sm_client
+    )
     role = os.environ["SAGEMAKER_ROLE"]
     bucket_prefix = "payment-classification"
     s3_bucket = sagemaker_session.default_bucket()
@@ -74,7 +73,9 @@ def data_prepare(context):
     feature_group_name = "feature-group-payment-classification"
     record_identifier_feature_name = "identifier"
 
-    feature_group = FeatureGroup(name=feature_group_name, sagemaker_session=sagemaker_session)
+    feature_group = FeatureGroup(
+        name=feature_group_name, sagemaker_session=sagemaker_session
+    )
 
     featurestore_runtime = boto_session.client(
         service_name="sagemaker-featurestore-runtime", region_name=region
@@ -98,7 +99,7 @@ def data_prepare(context):
 
     status = feature_group.describe().get("FeatureGroupStatus")
 
-    if status!='Created':
+    if status != "Created":
         feature_group.create(
             s3_uri=f"s3://{s3_bucket}/{bucket_prefix}",
             record_identifier_name=record_identifier_feature_name,
@@ -137,7 +138,9 @@ def get_feature_store_values():
         )
         feature_store_resp["identifier"] = feature_store_resp["identifier"].astype(int)
         feature_store_resp["count"] = feature_store_resp["count"].astype(int)
-        feature_store_resp["mean_amount"] = feature_store_resp["mean_amount"].astype(float)
+        feature_store_resp["mean_amount"] = feature_store_resp["mean_amount"].astype(
+            float
+        )
         feature_store_resp["EventTime"] = feature_store_resp["EventTime"].astype(float)
         feature_store_resp = feature_store_resp.sort_values(by="identifier")
 
@@ -146,8 +149,12 @@ def get_feature_store_values():
     feature_store_resp = get_feature_store_values()
 
     feature_store_data = pd.DataFrame()
-    feature_store_data["mean_amount"] = data.groupby(["transaction_category"]).mean()["amount"]
-    feature_store_data["count"] = data.groupby(["transaction_category"]).count()["amount"]
+    feature_store_data["mean_amount"] = data.groupby(["transaction_category"]).mean()[
+        "amount"
+    ]
+    feature_store_data["count"] = data.groupby(["transaction_category"]).count()[
+        "amount"
+    ]
     feature_store_data["identifier"] = feature_store_data.index
     feature_store_data["EventTime"] = time.time()
 
@@ -157,7 +164,9 @@ def get_feature_store_values():
         .apply(lambda x: np.average(x["mean_amount"], weights=x["count"]))
     )
     feature_store_data["count"] = (
-        pd.concat([feature_store_resp, feature_store_data]).groupby("identifier").sum()["count"]
+        pd.concat([feature_store_resp, feature_store_data])
+        .groupby("identifier")
+        .sum()["count"]
     )
 
     feature_group.ingest(data_frame=feature_store_data, max_workers=3, wait=True)
@@ -168,7 +177,9 @@ def get_feature_store_values():
         feature_store_data, values=["mean_amount"], index=["identifier"]
     ).T.add_suffix("_dist")
     additional_features_columns = list(additional_features.columns)
-    data = pd.concat([data, pd.DataFrame(columns=additional_features_columns, dtype=object)])
+    data = pd.concat(
+        [data, pd.DataFrame(columns=additional_features_columns, dtype=object)]
+    )
     data[additional_features_columns] = additional_features.values[0]
     for col in additional_features_columns:
         data[col] = abs(data[col] - data["amount"])