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 'FeatureByte Copilot'. This tool automatically suggests and evaluates features based on the semantics of your data and the specifics of 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)

15:34:17 | INFO     | SDK version: 1.0.2.dev46
15:34:17 | INFO     | No catalog activated.
15:34:17 | INFO     | Using profile: tutorial
15:34:17 | INFO     | Using configuration file at: /Users/gxav/.featurebyte/config.yaml
15:34:17 | INFO     | Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
15:34:17 | INFO     | SDK version: 1.0.2.dev46
15:34:17 | INFO     | No catalog activated.
15:34:17 | 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	662b58875827fd6e8d7545fe	CUSTOMER_Mean_vector_of_item_product_ProductGr...	FLOAT	DRAFT	False	[GROCERYINVOICE, INVOICEITEMS, GROCERYPRODUCT]	[GROCERYINVOICE, INVOICEITEMS]	[customer]	[customer]	2024-04-26T07:34:09.434000
1	662b586bf010e567696bfd3b	CUSTOMER_vs_OVERALL_item_TotalCost_across_prod...	FLOAT	DRAFT	False	[GROCERYINVOICE, INVOICEITEMS, GROCERYPRODUCT]	[INVOICEITEMS]	[customer]	[customer]	2024-04-26T07:32:08.909000
2	662b5858869e92ad5091c274	CUSTOMER_Latest_invoice_Amount_Z_Score_to_invo...	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2024-04-26T07:31:44.888000
3	662b581ada3f1a887399f993	CUSTOMER_Std_of_invoice_Amount_28d	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2024-04-26T07:31:10.389000
4	662b581ada3f1a887399f992	CUSTOMER_Std_of_invoice_Amount_14d	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2024-04-26T07:31:09.360000
5	662b581ada3f1a887399f991	CUSTOMER_Avg_of_invoice_Amount_28d	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2024-04-26T07:31:08.355000
6	662b581ada3f1a887399f990	CUSTOMER_Avg_of_invoice_Amount_14d	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2024-04-26T07:31:07.332000
7	662b581ada3f1a887399f98e	CUSTOMER_Count_of_invoice_28d	INT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2024-04-26T07:31:06.365000
8	662b581ada3f1a887399f98c	CUSTOMER_Count_of_invoice_14d	INT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2024-04-26T07:31:05.648000
9	662b581ada3f1a887399f98b	CUSTOMER_Latest_invoice_Amount	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2024-04-26T07:31:04.914000
10	662b5819da3f1a887399f986	CUSTOMER_x_PRODUCTGROUP_Sum_of_item_TotalCost_28d	FLOAT	DRAFT	False	[GROCERYINVOICE, INVOICEITEMS, GROCERYPRODUCT]	[INVOICEITEMS]	[customer, productgroup]	[customer, productgroup]	2024-04-26T07:31:03.919000
11	662b5819da3f1a887399f984	CUSTOMER_x_PRODUCTGROUP_Sum_of_item_TotalCost_14d	FLOAT	DRAFT	False	[GROCERYINVOICE, INVOICEITEMS, GROCERYPRODUCT]	[INVOICEITEMS]	[customer, productgroup]	[customer, productgroup]	2024-04-26T07:31:02.590000
12	662b5819da3f1a887399f98a	CUSTOMER_x_PRODUCTGROUP_Time_Since_Latest_Time...	FLOAT	DRAFT	False	[GROCERYINVOICE, INVOICEITEMS, GROCERYPRODUCT]	[INVOICEITEMS]	[customer, productgroup]	[customer, productgroup]	2024-04-26T07:31:01.264000
13	662b57fc1bdd8afa8a9be631	CUSTOMER_Age_band	VARCHAR	DRAFT	False	[GROCERYCUSTOMER]	[GROCERYCUSTOMER]	[customer]	[customer]	2024-04-26T07:30:20.276000
14	662b57fc1bdd8afa8a9be627	CUSTOMER_Age	INT	DRAFT	False	[GROCERYCUSTOMER]	[GROCERYCUSTOMER]	[customer]	[customer]	2024-04-26T07:30:12.892000

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 0x2824d8180>
 {
   'name': 'customer',
   'created_at': '2024-04-26T07:28:15.128000',
   'updated_at': '2024-04-26T07:28:24.021000',
   'description': None,
   'serving_names': [
     'GROCERYCUSTOMERGUID'
   ],
   'catalog_name': 'Grocery Dataset Tutorial'
 },
 <featurebyte.api.entity.Entity at 0x1104afa00>
 {
   'name': 'productgroup',
   'created_at': '2024-04-26T07:28:17.313000',
   'updated_at': '2024-04-26T07:28:27.128000',
   '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	2022-11-03 14:39:00	9faf5936-d4bb-4709-a530-c1624ec003a5	50-54	63.65	2	46.9650	16.685000	2.039603	0.677794	0.0	6314.067778
1	2022-08-16 15:37:21	6781acc6-652d-4867-a138-2d8adb278886	60-64	1.50	1	1.5000	0.000000	-0.849703	0.722791	0.0	NaN
2	2022-10-05 14:30:03	fc4769cf-41d6-4fb0-9cfc-fe30502cfa18	35-39	14.57	2	7.9150	6.655000	-0.153076	0.741273	0.0	360.176111
3	2022-11-18 10:07:49	ae2ccf38-4e5d-4c76-b1e5-04f12307e45b	60-64	9.24	3	28.6700	33.102335	-0.586120	0.731057	0.0	NaN
4	2023-03-13 21:11:43	cfc620a2-0054-4b4b-99d5-040cf87cfe2d	15-19	4.29	2	3.1400	1.150000	-0.799217	0.544831	0.0	414.501389
5	2023-03-19 15:22:18	dcdb7c98-dd62-4287-b432-bfe3a2317ebc	20-24	85.74	2	60.1600	25.580000	0.958202	0.855608	9.0	163.990000
6	2023-02-15 21:10:22	5daf8edb-8625-4653-aaa0-9ac03df92017	70-74	24.04	2	35.8850	11.845000	-1.168187	0.614021	0.0	1705.355000
7	2022-11-09 18:07:20	37122b82-478b-4d9e-b236-69629b592c0b	25-29	69.93	1	69.9300	0.000000	-1.000000	0.805024	0.0	4448.626944
8	2023-02-27 13:27:47	b807e05c-ff1c-4fb3-a760-e0e8ce29c859	35-39	4.80	4	10.8925	7.927192	-0.512459	0.760363	1.0	316.402222
9	2023-04-19 15:25:05	8a60f455-aff1-4b4e-8c63-9ab89df2715d	70-74	3.39	1	3.3900	0.000000	-1.327036	0.754857	0.0	503.857500

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 3.1s (0.32%/s)         
Done! |████████████████████████████████████████| 100% in 6.2s (0.16%/s)         
Loading Feature(s) |████████████████████████████████████████| 9/9 [100%] in 0.6s

Concepts in this tutorial¶

• More on feature lists

SDK Reference for¶

• Feature List
• FeatureList.save()

In [ ]: