days until card expires example

Author: davitbzh

Diff for: docs/user_guides/fs/feature_view/helper-columns.md

@@ -11,17 +11,17 @@ Hopsworks Feature Store provides a functionality to define two types of helper c
 
 ## Inference Helper columns
 `inference_helper_columns` are a list of feature names that are not used for training the model itself but are used for extra information during online or batch inference. 
-For example computing [on-demand feature](../../../concepts/fs/feature_group/on_demand_feature.md) like distance between previous and current place of transaction `loc_delta_t_minus_1` in credit card fraud detection system.
-Feature `loc_delta_t_minus_1` will be computed using previous transaction coordinates `longitude` and `latitude` that needs to fetched from the feature store and compared to the new transaction coordinates that arrives at inference application. 
-In this use case `longitude` and `latitude` are `inference_helper_columns`. They are not used for training but are necessary for computing [on-demand feature](../../../concepts/fs/feature_group/on_demand_feature.md) `loc_delta_t_minus_1`.
+For example computing [on-demand feature](../../../concepts/fs/feature_group/on_demand_feature.md) like days left until credit card is valid at the time of transaction `days_until_valid` in credit card fraud detection system.
+Feature `days_until_valid` will be computed using credit cart valid through date `cc_valid_through` that needs to fetched from the feature store and compared to the new transaction date that arrives at 
+inference application. In this use case `cc_valid_through` are `inference_helper_column`. It is not used for training but is necessary for computing [on-demand feature](../../../concepts/fs/feature_group/on_demand_feature.md) `days_until_valid`.
 
 === "Python"
 
     !!! example "Define inference columns for feature views."
         ```python
         # define query object 
         query = label_fg.select("fraud_label")\
-                        .join(trans_fg.select(["amount", "loc_delta_t_minus_1", "longitude", "latitude", "category"])) 
+                        .join(trans_fg.select(["amount", "days_until_valid", "cc_valid_through", "category"])) 
         
         # define feature view with helper columns
         feature_view = fs.get_or_create_feature_view(
@@ -30,7 +30,7 @@ In this use case `longitude` and `latitude` are `inference_helper_columns`. They
             query=query,
             labels=["fraud_label"],
             transformation_functions=transformation_functions,
-            inference_helper_columns=["longitude", "latitude"],
+            inference_helper_columns=["cc_valid_through"],
         )
         ```
 
@@ -45,7 +45,7 @@ When retrieving data for model inference, helper columns will be omitted. Howeve
         ```python
 
         # import feature functions
-        from feature_functions import location_delta, time_delta
+        from feature_functions import time_delta
         
         # Fetch feature view object  
         feature_view = fs.get_feature_view(
@@ -54,15 +54,10 @@ When retrieving data for model inference, helper columns will be omitted. Howeve
         )
 
         # Fetch feature data for batch inference with helper columns
-        df = feature_view.get_batch_data(start_time=start_time, end_time=end_time, inference_helpers=True)
-        df['longitude_prev'] = df['longitude'].shift(-1)
-        df['latitute_prev'] = df['latitute'].shift(-1)
+        df = feature_view.get_batch_data(start_time=start_time, end_time=end_time, inference_helpers=True, event_time=True)
 
         # compute location delta
-        df['loc_delta_t_minus_1'] = df.apply(lambda row: location_delta(row['longitude'], 
-                                                                        row['latitute'],
-                                                                        row['longitude_prev'], 
-                                                                        row['latitute_prev']), axis=1)
+        df['days_until_valid'] = df.apply(lambda row: time_delta(row['cc_valid_through'], row['transaction_date']), axis=1)
 
         # prepare datatame for prediction
         df = df[[f.name for f in feature_view.features if not (f.label or f.inference_helper_column or f.training_helper_column)]]
@@ -75,7 +70,7 @@ When retrieving data for model inference, helper columns will be omitted. Howeve
     !!! example "Fetch inference helper column values and compute on-demand features during online inference."
         ```python
 
-        from feature_functions import location_delta, time_delta
+        from feature_functions import time_delta
         
         # Fetch feature view object  
         feature_view = fs.get_feature_view(
@@ -91,22 +86,17 @@ When retrieving data for model inference, helper columns will be omitted. Howeve
 
         # here cc_num, longitute and lattitude are provided as parameters to the application
         cc_num = ...
-        longitude = ...
-        latitute = ...
+        transaction_date = ...
         
         # get previous transaction location of this credit card
         inference_helper = feature_view.get_inference_helper({"cc_num": cc_num}, return_type="dict")
 
         # compute location delta 
-        loc_delta_t_minus_1 = location_delta(longitude, 
-                                             latitute, 
-                                             inference_helper['longitude'], 
-                                             inference_helper['latitute'])
-
+        days_until_valid = time_delta(transaction_date, inference_helper['cc_valid_through'])
 
         # Now get assembled feature vector for prediction
         feature_vector = feature_view.get_feature_vector({"cc_num": cc_num}, 
-                                                          passed_features={"loc_delta_t_minus_1": loc_delta_t_minus_1}
+                                                          passed_features={"days_until_valid": days_until_valid}
                                                          )
         ```
 
@@ -121,7 +111,7 @@ For example one might want to use feature like `category` of the purchased produ
         ```python
         # define query object 
         query = label_fg.select("fraud_label")\
-                        .join(trans_fg.select(["amount", "loc_delta_t_minus_1", "longitude", "latitude", "category"])) 
+                        .join(trans_fg.select(["amount", "days_until_valid", "cc_valid_through", "category"])) 
         
         # define feature view with helper columns
         feature_view = fs.get_or_create_feature_view(