13. Create Feature List

Create a feature list¶

A feature list is an essential component in machine learning, comprising a collection of features that are used to train models. Let's compile a feature list using some of the features we've created.

For additional features:

• Visit the 'Learn by example' section for a variety of features tailored to different entities and signals.
• Check out the 'Bring Your Own Transformer' tutorials to learn about integrating Large Language Models (LLMs) within the FeatureByte ecosystem.

For those in an enterprise setting, explore 'Ideate Features with FeatureByte Copilot' which adopts an agentic approach to ideate features tailored to your use case.

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)

14:30:17 | INFO     | SDK version: 3.0.1.dev45
INFO    :featurebyte:SDK version: 3.0.1.dev45
14:30:17 | INFO     | No catalog activated.
INFO    :featurebyte:No catalog activated.
14:30:17 | INFO     | Using profile: tutorial
INFO    :featurebyte:Using profile: tutorial
14:30:17 | INFO     | Using configuration file at: /Users/gxav/.featurebyte/config.yaml
INFO    :featurebyte:Using configuration file at: /Users/gxav/.featurebyte/config.yaml
14:30:17 | INFO     | Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
INFO    :featurebyte:Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
14:30:17 | INFO     | SDK version: 3.0.1.dev45
INFO    :featurebyte:SDK version: 3.0.1.dev45
14:30:17 | INFO     | No catalog activated.
INFO    :featurebyte:No catalog activated.
14:30:17 | INFO     | Catalog activated: Grocery Dataset Tutorial
INFO    :featurebyte.api.catalog:Catalog activated: Grocery Dataset Tutorial
16:11:44 | INFO     | Using configuration file at: /Users/gxav/.featurebyte/config.yaml
16:11:44 | INFO     | Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
16:11:44 | WARNING  | Remote SDK version (1.1.0.dev7) is different from local (1.1.0.dev1). Update local SDK to avoid unexpected behavior.
16:11:44 | INFO     | No catalog activated.
16:11:44 | INFO     | Catalog activated: Grocery Dataset Tutorial

List all features we created so far¶

In [2]:

catalog.list_features()

catalog.list_features()

Out[2]:

	id	name	dtype	readiness	online_enabled	tables	primary_tables	entities	primary_entities	created_at
0	683d4463b4cfc2806a63dcb2	CUSTOMER_Mean_vector_of_item_product_ProductGr...	FLOAT	DRAFT	False	[GROCERYINVOICE, INVOICEITEMS, GROCERYPRODUCT]	[GROCERYINVOICE, INVOICEITEMS]	[customer]	[customer]	2025-06-02T06:29:21.284000
1	683d44313a504320cf0dbb86	CUSTOMER_vs_OVERALL_item_TotalCost_across_prod...	FLOAT	DRAFT	False	[GROCERYINVOICE, INVOICEITEMS, GROCERYPRODUCT]	[INVOICEITEMS]	[customer]	[customer]	2025-06-02T06:27:10.713000
2	683d43364b16d208b726da64	CUSTOMER_Latest_invoice_Amount_Z_Score_to_invo...	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2025-06-02T06:22:51.361000
3	683d42ebc133ec5ab4a622c8	CUSTOMER_Std_of_invoice_Amount_28d	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2025-06-02T06:21:49.200000
4	683d42ebc133ec5ab4a622c7	CUSTOMER_Std_of_invoice_Amount_14d	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2025-06-02T06:21:48.870000
5	683d42ebc133ec5ab4a622c6	CUSTOMER_Avg_of_invoice_Amount_28d	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2025-06-02T06:21:48.540000
6	683d42ebc133ec5ab4a622c5	CUSTOMER_Avg_of_invoice_Amount_14d	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2025-06-02T06:21:48.194000
7	683d42ebc133ec5ab4a622c4	CUSTOMER_Count_of_invoice_28d	INT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2025-06-02T06:21:47.877000
8	683d42ebc133ec5ab4a622c3	CUSTOMER_Count_of_invoice_14d	INT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2025-06-02T06:21:47.619000
9	683d42ebc133ec5ab4a622c2	CUSTOMER_Latest_invoice_Amount	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2025-06-02T06:21:47.232000
10	683d42ebc133ec5ab4a622be	CUSTOMER_x_PRODUCTGROUP_Sum_of_item_TotalCost_28d	FLOAT	DRAFT	False	[GROCERYINVOICE, INVOICEITEMS, GROCERYPRODUCT]	[INVOICEITEMS]	[customer, productgroup]	[customer, productgroup]	2025-06-02T06:21:46.932000
11	683d42ebc133ec5ab4a622bd	CUSTOMER_x_PRODUCTGROUP_Sum_of_item_TotalCost_14d	FLOAT	DRAFT	False	[GROCERYINVOICE, INVOICEITEMS, GROCERYPRODUCT]	[INVOICEITEMS]	[customer, productgroup]	[customer, productgroup]	2025-06-02T06:21:46.567000
12	683d42ebc133ec5ab4a622c1	CUSTOMER_x_PRODUCTGROUP_Time_Since_Latest_Time...	FLOAT	DRAFT	False	[GROCERYINVOICE, INVOICEITEMS, GROCERYPRODUCT]	[INVOICEITEMS]	[customer, productgroup]	[customer, productgroup]	2025-06-02T06:21:46.025000
13	683d426c59a4af633ab188d6	CUSTOMER_Age_band	VARCHAR	DRAFT	False	[GROCERYCUSTOMER]	[GROCERYCUSTOMER]	[customer]	[customer]	2025-06-02T06:20:46.476000
14	683d426c59a4af633ab188cc	CUSTOMER_Age	INT	DRAFT	False	[GROCERYCUSTOMER]	[GROCERYCUSTOMER]	[customer]	[customer]	2025-06-02T06:20:39.849000

Get features from catalog¶

In [3]:

customer_age_band = catalog.get_feature("CUSTOMER_Age_band")
customer_latest_invoice_amount = catalog.get_feature("CUSTOMER_Latest_invoice_Amount")
customer_count_of_invoice_14d = catalog.get_feature("CUSTOMER_Count_of_invoice_14d")
customer_avg_of_invoice_amount_14d = catalog.get_feature("CUSTOMER_Avg_of_invoice_Amount_14d")
customer_std_of_invoice_amount_14d = catalog.get_feature("CUSTOMER_Std_of_invoice_Amount_14d")
customer_latest_invoice_amount_Z_score_to_invoice_amount_28d = catalog.get_feature(
    "CUSTOMER_Latest_invoice_Amount_Z_Score_to_invoice_Amount_28d"
)
customer_vs_overall_item_totalcost_across_product_productgroups_26w = catalog.get_feature(
    "CUSTOMER_vs_OVERALL_item_TotalCost_across_product_ProductGroups_26w"
)
customer_x_productgroup_sum_of_item_totalcost_14d = \
catalog.get_feature("CUSTOMER_x_PRODUCTGROUP_Sum_of_item_TotalCost_14d")
customer_x_productgroup_time_since_latest_timestamp = \
catalog.get_feature("CUSTOMER_x_PRODUCTGROUP_Time_Since_Latest_Timestamp")

customer_age_band = catalog.get_feature("CUSTOMER_Age_band") customer_latest_invoice_amount = catalog.get_feature("CUSTOMER_Latest_invoice_Amount") customer_count_of_invoice_14d = catalog.get_feature("CUSTOMER_Count_of_invoice_14d") customer_avg_of_invoice_amount_14d = catalog.get_feature("CUSTOMER_Avg_of_invoice_Amount_14d") customer_std_of_invoice_amount_14d = catalog.get_feature("CUSTOMER_Std_of_invoice_Amount_14d") customer_latest_invoice_amount_Z_score_to_invoice_amount_28d = catalog.get_feature( "CUSTOMER_Latest_invoice_Amount_Z_Score_to_invoice_Amount_28d" ) customer_vs_overall_item_totalcost_across_product_productgroups_26w = catalog.get_feature( "CUSTOMER_vs_OVERALL_item_TotalCost_across_product_ProductGroups_26w" ) customer_x_productgroup_sum_of_item_totalcost_14d = \ catalog.get_feature("CUSTOMER_x_PRODUCTGROUP_Sum_of_item_TotalCost_14d") customer_x_productgroup_time_since_latest_timestamp = \ catalog.get_feature("CUSTOMER_x_PRODUCTGROUP_Time_Since_Latest_Timestamp")

Create feature list¶

In [4]:

simple_feature_list = fb.FeatureList(
    [
        customer_age_band,
        customer_latest_invoice_amount,
        customer_count_of_invoice_14d,
        customer_avg_of_invoice_amount_14d,
        customer_std_of_invoice_amount_14d,
        customer_latest_invoice_amount_Z_score_to_invoice_amount_28d,
        customer_vs_overall_item_totalcost_across_product_productgroups_26w,
        customer_x_productgroup_sum_of_item_totalcost_14d,
        customer_x_productgroup_time_since_latest_timestamp,
    ],
    name="Customer x ProductGroup Simple FeatureList",
)

simple_feature_list = fb.FeatureList( [ customer_age_band, customer_latest_invoice_amount, customer_count_of_invoice_14d, customer_avg_of_invoice_amount_14d, customer_std_of_invoice_amount_14d, customer_latest_invoice_amount_Z_score_to_invoice_amount_28d, customer_vs_overall_item_totalcost_across_product_productgroups_26w, customer_x_productgroup_sum_of_item_totalcost_14d, customer_x_productgroup_time_since_latest_timestamp, ], name="Customer x ProductGroup Simple FeatureList", )

Preview feature list¶

In [5]:

# Check the primary entity of the feature list
simple_feature_list.primary_entity

# Check the primary entity of the feature list simple_feature_list.primary_entity

Out[5]:

[<featurebyte.api.entity.Entity at 0x16bd0cef0>
 {
   '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 0x17b3dcf40>
 {
   '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 [6]:

# 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 [7]:

# Preview simple_feature_list
simple_feature_list.preview(preview_table)

# Preview simple_feature_list simple_feature_list.preview(preview_table)

Out[7]:

	POINT_IN_TIME	GROCERYINVOICEITEMGUID	CUSTOMER_Age_band	CUSTOMER_Latest_invoice_Amount	CUSTOMER_Count_of_invoice_14d	CUSTOMER_Avg_of_invoice_Amount_14d	CUSTOMER_Std_of_invoice_Amount_14d	CUSTOMER_Latest_invoice_Amount_Z_Score_to_invoice_Amount_28d	CUSTOMER_vs_OVERALL_item_TotalCost_across_product_ProductGroups_26w	CUSTOMER_x_PRODUCTGROUP_Sum_of_item_TotalCost_14d	CUSTOMER_x_PRODUCTGROUP_Time_Since_Latest_Timestamp
0	2022-07-23 15:32:29	d87d65b8-4f78-41cc-8bd3-0064f83fe4fb	50-54	2.50	8.0	14.246250	14.128027	-1.067855	0.804626	5.00	7.715000
1	2023-02-28 10:28:24	59b63729-d448-4496-8f36-de26a91e2310	80-84	183.32	2.0	144.770000	38.550000	1.000000	0.728330	19.10	334.700556
2	2023-05-25 05:21:12	cf670b7a-c6bf-4598-b0c0-400378b9cab6	20-24	2.99	6.0	4.273333	3.607879	-0.393856	0.493392	0.99	269.470000
3	2023-04-26 19:36:57	f867935a-d33a-43d1-b3bc-02c539769836	75-79	1.00	8.0	5.066250	8.702607	-0.613917	0.816872	NaN	8904.007778
4	2023-01-06 14:38:32	e63c0f14-3530-49e9-b73e-f92594e82663	40-44	22.94	1.0	22.940000	0.000000	1.000000	0.770818	2.79	315.693056
5	2023-06-09 16:38:32	6acb20fd-605d-4982-aa39-77054f08103c	30-34	0.88	5.0	15.198000	19.734034	-1.014358	0.627731	NaN	965.136389
6	2023-03-20 15:08:44	6f5299d0-fa38-4707-8108-1b66805d84e5	20-24	35.00	4.0	35.472500	17.705779	-0.146625	0.393753	2.99	241.958889
7	2023-05-04 15:15:25	099cb405-5b2d-4dba-9071-a157ff0dbadc	55-59	24.16	2.0	14.825000	9.335000	1.494143	0.610841	0.79	219.637222
8	2022-11-01 14:32:22	8687e2a4-7f97-4442-873c-5c52d74404f8	40-44	1.43	6.0	14.155000	15.361576	-0.623232	0.706009	3.64	284.610278
9	2023-06-02 14:24:28	0154e4b4-25a4-4276-af72-2826bbc64c31	25-29	3.46	NaN	NaN	NaN	NaN	0.774300	NaN	745.476667

Save feature list¶

In [8]:

# Save feature list
simple_feature_list.save()
# Add description
simple_feature_list.update_description("Simple feature list for the customer x productgroup engagement")

# Save feature list simple_feature_list.save() # Add description simple_feature_list.update_description("Simple feature list for the customer x productgroup engagement")

Done! |████████████████████████████████████████| 100% in 6.1s (0.17%/s)         
Done! |████████████████████████████████████████| 100% in 6.1s (0.17%/s)         
Loading Feature(s) |████████████████████████████████████████| 9/9 [100%] in 0.2s
Done! |████████████████████████████████████████| 100% in 6.1s (0.17%/s)         
Done! |████████████████████████████████████████| 100% in 6.1s (0.17%/s)         
Loading Feature(s) |████████████████████████████████████████| 9/9 [100%] in 0.4s

Concepts in this tutorial¶

• More on feature lists

SDK Reference for¶

• Feature List
• FeatureList.save()