9. Create Window Aggregate Features

Create window aggregate features¶

Next feature type we will consider is window aggregate feature. These are features generated by aggregating data within specific time frame.

In [1]:

import featurebyte as fb

# Set your profile to the tutorial environment
fb.use_profile("tutorial")

catalog_name = "Grocery Dataset Tutorial"
catalog = fb.Catalog.activate(catalog_name)

import featurebyte as fb # Set your profile to the tutorial environment fb.use_profile("tutorial") catalog_name = "Grocery Dataset Tutorial" catalog = fb.Catalog.activate(catalog_name)

14:21:29 | INFO     | SDK version: 3.0.1.dev45
INFO    :featurebyte:SDK version: 3.0.1.dev45
14:21:29 | INFO     | No catalog activated.
INFO    :featurebyte:No catalog activated.
14:21:29 | INFO     | Using profile: tutorial
INFO    :featurebyte:Using profile: tutorial
14:21:29 | INFO     | Using configuration file at: /Users/gxav/.featurebyte/config.yaml
INFO    :featurebyte:Using configuration file at: /Users/gxav/.featurebyte/config.yaml
14:21:29 | INFO     | Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
INFO    :featurebyte:Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
14:21:29 | INFO     | SDK version: 3.0.1.dev45
INFO    :featurebyte:SDK version: 3.0.1.dev45
14:21:29 | INFO     | No catalog activated.
INFO    :featurebyte:No catalog activated.
14:21:30 | INFO     | Catalog activated: Grocery Dataset Tutorial
INFO    :featurebyte.api.catalog:Catalog activated: Grocery Dataset Tutorial
16:07:43 | INFO     | Using configuration file at: /Users/gxav/.featurebyte/config.yaml
16:07:43 | INFO     | Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
16:07:43 | WARNING  | Remote SDK version (1.1.0.dev7) is different from local (1.1.0.dev1). Update local SDK to avoid unexpected behavior.
16:07:43 | INFO     | No catalog activated.
16:07:43 | INFO     | Catalog activated: Grocery Dataset Tutorial

In [2]:

# Set desired windows
windows = ['14d', '28d']

# Set desired windows windows = ['14d', '28d']

Do window aggregation from INVOICEITEMS¶

Let's start with some aggregations from the items view and create features for the interaction between Customer and Product Group.

In [3]:

# Get view from GROCERYPRODUCT dimension table.
groceryproduct_view = catalog.get_view("GROCERYPRODUCT")
# Get view from INVOICEITEMS item table.
invoiceitems_view = catalog.get_view("INVOICEITEMS")
# Join GROCERYPRODUCT view to INVOICEITEMS view.
invoiceitems_view = invoiceitems_view.join(groceryproduct_view, rsuffix="")

# Get view from GROCERYPRODUCT dimension table. groceryproduct_view = catalog.get_view("GROCERYPRODUCT") # Get view from INVOICEITEMS item table. invoiceitems_view = catalog.get_view("INVOICEITEMS") # Join GROCERYPRODUCT view to INVOICEITEMS view. invoiceitems_view = invoiceitems_view.join(groceryproduct_view, rsuffix="")

In [4]:

# Group GROCERYINVOICE view by customer entity (GroceryCustomerGuid) and productgroup entity (ProductGroup).
invoiceitems_view_by_customer_x_productgroup = invoiceitems_view.groupby(
    ['GroceryCustomerGuid', 'ProductGroup']
)

# Group GROCERYINVOICE view by customer entity (GroceryCustomerGuid) and productgroup entity (ProductGroup). invoiceitems_view_by_customer_x_productgroup = invoiceitems_view.groupby( ['GroceryCustomerGuid', 'ProductGroup'] )

In [5]:

# Get Sum of TotalCost for the customer x productgroup over time.
customer_productgroup_sum_of_totalcost_14d_28d = \
invoiceitems_view_by_customer_x_productgroup.aggregate_over(
    "TotalCost", method="sum",
    feature_names=[
        "CUSTOMER_x_PRODUCTGROUP_Sum_of_item_TotalCost"
        + "_" + w for w in windows
    ],
    windows=windows
)

# Get Sum of TotalCost for the customer x productgroup over time. customer_productgroup_sum_of_totalcost_14d_28d = \ invoiceitems_view_by_customer_x_productgroup.aggregate_over( "TotalCost", method="sum", feature_names=[ "CUSTOMER_x_PRODUCTGROUP_Sum_of_item_TotalCost" + "_" + w for w in windows ], windows=windows )

In [6]:

# Get Latest Interaction between Customer and ProductGroup
customer_x_productgroup_latest_timestamp = \
invoiceitems_view_by_customer_x_productgroup.aggregate_over(
    "Timestamp", method="latest",
    feature_names=["CUSTOMER_x_PRODUCTGROUP_Latest_Timestamp"],
    windows=[None]
)["CUSTOMER_x_PRODUCTGROUP_Latest_Timestamp"]

# Get Latest Interaction between Customer and ProductGroup customer_x_productgroup_latest_timestamp = \ invoiceitems_view_by_customer_x_productgroup.aggregate_over( "Timestamp", method="latest", feature_names=["CUSTOMER_x_PRODUCTGROUP_Latest_Timestamp"], windows=[None] )["CUSTOMER_x_PRODUCTGROUP_Latest_Timestamp"]

In [7]:

# Create recency feature: Time Since Latest Interaction between Customer and Product Group
customer_x_productgroup_time_since_latest_timestamp = (
    fb.RequestColumn.point_in_time()
    - customer_x_productgroup_latest_timestamp
).dt.hour
customer_x_productgroup_time_since_latest_timestamp.name = \
"CUSTOMER_x_PRODUCTGROUP_Time_Since_Latest_Timestamp"

# Create recency feature: Time Since Latest Interaction between Customer and Product Group customer_x_productgroup_time_since_latest_timestamp = ( fb.RequestColumn.point_in_time() - customer_x_productgroup_latest_timestamp ).dt.hour customer_x_productgroup_time_since_latest_timestamp.name = \ "CUSTOMER_x_PRODUCTGROUP_Time_Since_Latest_Timestamp"

Do window aggregation from GROCERYINVOICE¶

Now, let's do some aggregations on the invoices view for the Customer entity.

In [8]:

# Get view from GROCERYINVOICE event table.
groceryinvoice_view = catalog.get_view("GROCERYINVOICE")

# Get view from GROCERYINVOICE event table. groceryinvoice_view = catalog.get_view("GROCERYINVOICE")

In [9]:

# Group GROCERYINVOICE view by customer entity (GroceryCustomerGuid).
groceryinvoice_view_by_customer = groceryinvoice_view.groupby(['GroceryCustomerGuid'])

# Group GROCERYINVOICE view by customer entity (GroceryCustomerGuid). groceryinvoice_view_by_customer = groceryinvoice_view.groupby(['GroceryCustomerGuid'])

In [10]:

# Get Latest invoice Amount for the customer
customer_latest_invoice_amount = groceryinvoice_view_by_customer.aggregate_over(
    "Amount", method="latest",
    feature_names=["CUSTOMER_Latest_invoice_Amount"],
    windows=[None]
)["CUSTOMER_Latest_invoice_Amount"]

# Get Latest invoice Amount for the customer customer_latest_invoice_amount = groceryinvoice_view_by_customer.aggregate_over( "Amount", method="latest", feature_names=["CUSTOMER_Latest_invoice_Amount"], windows=[None] )["CUSTOMER_Latest_invoice_Amount"]

In [11]:

# Get Count of invoices for the customer
customer_count_of_invoice_14d_28d = groceryinvoice_view_by_customer.aggregate_over(
    value_column=None,
    method="count",
    feature_names=[
        "CUSTOMER_Count_of_invoice"
        + "_" + w for w in windows
    ],
    windows=windows
)

# Get Count of invoices for the customer customer_count_of_invoice_14d_28d = groceryinvoice_view_by_customer.aggregate_over( value_column=None, method="count", feature_names=[ "CUSTOMER_Count_of_invoice" + "_" + w for w in windows ], windows=windows )

In [12]:

# Get Avg of Amount for the customer over time.
customer_avg_of_invoice_amount_14d_28d = groceryinvoice_view_by_customer.aggregate_over(
    "Amount", method="avg",
    feature_names=[
        "CUSTOMER_Avg_of_invoice_Amount"
        + "_" + w for w in windows
    ],
    windows=windows
)

# Get Avg of Amount for the customer over time. customer_avg_of_invoice_amount_14d_28d = groceryinvoice_view_by_customer.aggregate_over( "Amount", method="avg", feature_names=[ "CUSTOMER_Avg_of_invoice_Amount" + "_" + w for w in windows ], windows=windows )

In [13]:

# Get Std of Amount for the customer over time.
customer_std_of_invoice_amount_14d_28d = groceryinvoice_view_by_customer.aggregate_over(
    "Amount", method="std",
    feature_names=[
        "CUSTOMER_Std_of_invoice_Amount"
        + "_" + w for w in windows
    ],
    windows=windows
)

# Get Std of Amount for the customer over time. customer_std_of_invoice_amount_14d_28d = groceryinvoice_view_by_customer.aggregate_over( "Amount", method="std", feature_names=[ "CUSTOMER_Std_of_invoice_Amount" + "_" + w for w in windows ], windows=windows )

Preview a feature group¶

For convenience, we can create a feature group to preview/save all features we just created.

In [14]:

feature_group = fb.FeatureGroup([
    customer_x_productgroup_time_since_latest_timestamp,
    customer_productgroup_sum_of_totalcost_14d_28d,
    customer_latest_invoice_amount,
    customer_count_of_invoice_14d_28d,
    customer_avg_of_invoice_amount_14d_28d,
    customer_std_of_invoice_amount_14d_28d,
])

feature_group = fb.FeatureGroup([ customer_x_productgroup_time_since_latest_timestamp, customer_productgroup_sum_of_totalcost_14d_28d, customer_latest_invoice_amount, customer_count_of_invoice_14d_28d, customer_avg_of_invoice_amount_14d_28d, customer_std_of_invoice_amount_14d_28d, ])

In [15]:

# Check the primary entity of the feature group. It should be the interaction Customer x ProductGroup.
feature_group.primary_entity

# Check the primary entity of the feature group. It should be the interaction Customer x ProductGroup. feature_group.primary_entity

Out[15]:

[<featurebyte.api.entity.Entity at 0x17511a520>
 {
   'name': 'customer',
   'created_at': '2025-06-02T06:17:26.675000',
   'updated_at': '2025-06-02T06:17:28.925000',
   'description': None,
   'serving_names': [
     'GROCERYCUSTOMERGUID'
   ],
   'catalog_name': 'Grocery Dataset Tutorial'
 },
 <featurebyte.api.entity.Entity at 0x17511a5c0>
 {
   'name': 'productgroup',
   'created_at': '2025-06-02T06:17:27.207000',
   'updated_at': '2025-06-02T06:17:29.690000',
   'description': None,
   'serving_names': [
     'PRODUCTGROUP'
   ],
   'catalog_name': 'Grocery Dataset Tutorial'
 }]

In [16]:

# Get observation table: 'Preview Table with 10 items'
preview_table = catalog.get_observation_table("Preview Table with 10 items")

# Get observation table: 'Preview Table with 10 items' preview_table = catalog.get_observation_table("Preview Table with 10 items")

In [17]:

# Preview feature_group
feature_group.preview(preview_table)

# Preview feature_group feature_group.preview(preview_table)

Out[17]:

	POINT_IN_TIME	GROCERYINVOICEITEMGUID	CUSTOMER_x_PRODUCTGROUP_Time_Since_Latest_Timestamp	CUSTOMER_x_PRODUCTGROUP_Sum_of_item_TotalCost_14d	CUSTOMER_x_PRODUCTGROUP_Sum_of_item_TotalCost_28d	CUSTOMER_Latest_invoice_Amount	CUSTOMER_Count_of_invoice_14d	CUSTOMER_Count_of_invoice_28d	CUSTOMER_Avg_of_invoice_Amount_14d	CUSTOMER_Avg_of_invoice_Amount_28d	CUSTOMER_Std_of_invoice_Amount_14d	CUSTOMER_Std_of_invoice_Amount_28d
0	2022-07-23 15:32:29	d87d65b8-4f78-41cc-8bd3-0064f83fe4fb	7.715000	5.00	5.00	2.50	8.0	15.0	14.246250	17.746667	14.128027	14.277847
1	2023-05-25 05:21:12	cf670b7a-c6bf-4598-b0c0-400378b9cab6	269.470000	0.99	2.62	2.99	6.0	9.0	4.273333	4.196667	3.607879	3.063727
2	2023-02-28 10:28:24	59b63729-d448-4496-8f36-de26a91e2310	334.700556	19.10	19.10	183.32	2.0	2.0	144.770000	144.770000	38.550000	38.550000
3	2023-04-26 19:36:57	f867935a-d33a-43d1-b3bc-02c539769836	8904.007778	NaN	NaN	1.00	8.0	15.0	5.066250	5.500667	8.702607	7.331070
4	2023-01-06 14:38:32	e63c0f14-3530-49e9-b73e-f92594e82663	315.693056	2.79	2.79	22.94	1.0	2.0	22.940000	13.675000	0.000000	9.265000
5	2023-06-09 16:38:32	6acb20fd-605d-4982-aa39-77054f08103c	965.136389	NaN	NaN	0.88	5.0	7.0	15.198000	23.201429	19.734034	22.005472
6	2023-03-20 15:08:44	6f5299d0-fa38-4707-8108-1b66805d84e5	241.958889	2.99	2.99	35.00	4.0	8.0	35.472500	37.061250	17.705779	14.057970
7	2023-05-04 15:15:25	099cb405-5b2d-4dba-9071-a157ff0dbadc	219.637222	0.79	0.79	24.16	2.0	5.0	14.825000	11.268000	9.335000	8.628357
8	2023-06-02 14:24:28	0154e4b4-25a4-4276-af72-2826bbc64c31	745.476667	NaN	NaN	3.46	NaN	NaN	NaN	NaN	NaN	NaN
9	2022-11-01 14:32:22	8687e2a4-7f97-4442-873c-5c52d74404f8	284.610278	3.64	11.29	1.43	6.0	15.0	14.155000	11.654667	15.361576	16.405865

Save features into catalog¶

With feature groups we can do it in one call.

In [18]:

feature_group.save()

feature_group.save()

Done! |████████████████████████████████████████| 100% in 9.1s (0.11%/s)         
Done! |████████████████████████████████████████| 100% in 6.2s (0.16%/s)         
Loading Feature(s) |████████████████████████████████████████| 10/10 [100%] in 0.
Done! |████████████████████████████████████████| 100% in 9.1s (0.11%/s)         
Done! |████████████████████████████████████████| 100% in 6.1s (0.17%/s)         
Loading Feature(s) |████████████████████████████████████████| 10/10 [100%] in 0.

Add description¶

In [19]:

# Add description
customer_x_productgroup_time_since_latest_timestamp.update_description(
    "Time Since Latest interaction between the customer and the product group"
)

# Add description customer_x_productgroup_time_since_latest_timestamp.update_description( "Time Since Latest interaction between the customer and the product group" )

In [20]:

# Add description
customer_productgroup_sum_of_totalcost_14d = \
customer_productgroup_sum_of_totalcost_14d_28d["CUSTOMER_x_PRODUCTGROUP_Sum_of_item_TotalCost_14d"]
customer_productgroup_sum_of_totalcost_14d.update_description(
    "Total spent by the customer on the product group over a 14d period."
)
customer_productgroup_sum_of_totalcost_28d = \
customer_productgroup_sum_of_totalcost_14d_28d["CUSTOMER_x_PRODUCTGROUP_Sum_of_item_TotalCost_28d"]
customer_productgroup_sum_of_totalcost_28d.update_description(
    "Total spent by the customer on the product group over a 28d period."
)

# Add description customer_productgroup_sum_of_totalcost_14d = \ customer_productgroup_sum_of_totalcost_14d_28d["CUSTOMER_x_PRODUCTGROUP_Sum_of_item_TotalCost_14d"] customer_productgroup_sum_of_totalcost_14d.update_description( "Total spent by the customer on the product group over a 14d period." ) customer_productgroup_sum_of_totalcost_28d = \ customer_productgroup_sum_of_totalcost_14d_28d["CUSTOMER_x_PRODUCTGROUP_Sum_of_item_TotalCost_28d"] customer_productgroup_sum_of_totalcost_28d.update_description( "Total spent by the customer on the product group over a 28d period." )

In [21]:

# Add description
customer_latest_invoice_amount.update_description("Latest invoice Amount for the customer")

# Add description customer_latest_invoice_amount.update_description("Latest invoice Amount for the customer")

In [22]:

# Add description
customer_count_of_invoice_14d = customer_count_of_invoice_14d_28d["CUSTOMER_Count_of_invoice_14d"]
customer_count_of_invoice_14d.update_description(
    "Count of invoice for the customer over a 14d period."
)
customer_count_of_invoice_28d = customer_count_of_invoice_14d_28d["CUSTOMER_Count_of_invoice_28d"]
customer_count_of_invoice_28d.update_description(
    "Count of invoice for the customer over a 28d period."
)

# Add description customer_count_of_invoice_14d = customer_count_of_invoice_14d_28d["CUSTOMER_Count_of_invoice_14d"] customer_count_of_invoice_14d.update_description( "Count of invoice for the customer over a 14d period." ) customer_count_of_invoice_28d = customer_count_of_invoice_14d_28d["CUSTOMER_Count_of_invoice_28d"] customer_count_of_invoice_28d.update_description( "Count of invoice for the customer over a 28d period." )

In [23]:

# Add description
customer_avg_of_invoice_amount_14d = customer_avg_of_invoice_amount_14d_28d["CUSTOMER_Avg_of_invoice_Amount_14d"]
customer_avg_of_invoice_amount_14d.update_description(
    "Avg of invoice Amount for the customer over a 14d period."
)
customer_avg_of_invoice_amount_28d = customer_avg_of_invoice_amount_14d_28d["CUSTOMER_Avg_of_invoice_Amount_28d"]
customer_avg_of_invoice_amount_28d.update_description(
    "Avg of invoice Amount for the customer over a 28d period."
)

# Add description customer_avg_of_invoice_amount_14d = customer_avg_of_invoice_amount_14d_28d["CUSTOMER_Avg_of_invoice_Amount_14d"] customer_avg_of_invoice_amount_14d.update_description( "Avg of invoice Amount for the customer over a 14d period." ) customer_avg_of_invoice_amount_28d = customer_avg_of_invoice_amount_14d_28d["CUSTOMER_Avg_of_invoice_Amount_28d"] customer_avg_of_invoice_amount_28d.update_description( "Avg of invoice Amount for the customer over a 28d period." )

In [24]:

# Add description
customer_std_of_invoice_amount_14d = customer_std_of_invoice_amount_14d_28d["CUSTOMER_Std_of_invoice_Amount_14d"]
customer_std_of_invoice_amount_14d.update_description(
    "Std of invoice Amount for the customer over a 14d period."
)
customer_std_of_invoice_amount_28d = customer_std_of_invoice_amount_14d_28d["CUSTOMER_Std_of_invoice_Amount_28d"]
customer_std_of_invoice_amount_28d.update_description(
    "Std of invoice Amount for the customer over a 28d period."
)

# Add description customer_std_of_invoice_amount_14d = customer_std_of_invoice_amount_14d_28d["CUSTOMER_Std_of_invoice_Amount_14d"] customer_std_of_invoice_amount_14d.update_description( "Std of invoice Amount for the customer over a 14d period." ) customer_std_of_invoice_amount_28d = customer_std_of_invoice_amount_14d_28d["CUSTOMER_Std_of_invoice_Amount_28d"] customer_std_of_invoice_amount_28d.update_description( "Std of invoice Amount for the customer over a 28d period." )

Concepts in this tutorial¶

• Aggregate Features
• Window Aggregate Features

SDK reference for¶

• View.groupby()
• Groupby.aggregate_over()
• List of aggregate functions
• FeatureGroup
• FeatureGroup.save()

In [ ]: