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.

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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)

16:11:44 | WARNING  | Service endpoint is inaccessible: http://featurebyte-server:8088
16:11:44 | INFO     | Using profile: 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¶

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catalog.list_features()
catalog.list_features()

Out[2]:

	id	name	dtype	readiness	online_enabled	tables	primary_tables	entities	primary_entities	created_at
0	666957cd3fab5208644858b2	CUSTOMER_Mean_vector_of_item_product_ProductGr...	FLOAT	DRAFT	False	[GROCERYINVOICE, INVOICEITEMS, GROCERYPRODUCT]	[GROCERYINVOICE, INVOICEITEMS]	[customer]	[customer]	2024-06-12T08:11:35.077000
1	6669578ded0c9d417ba58fff	CUSTOMER_vs_OVERALL_item_TotalCost_across_prod...	FLOAT	DRAFT	False	[GROCERYINVOICE, INVOICEITEMS, GROCERYPRODUCT]	[INVOICEITEMS]	[customer]	[customer]	2024-06-12T08:08:56.428000
2	6669577ca1b61f71af4710cd	CUSTOMER_Latest_invoice_Amount_Z_Score_to_invo...	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2024-06-12T08:08:34.434000
3	6669575033eb5cd5aebc1ff0	CUSTOMER_Std_of_invoice_Amount_28d	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2024-06-12T08:08:09.891000
4	6669575033eb5cd5aebc1fef	CUSTOMER_Std_of_invoice_Amount_14d	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2024-06-12T08:08:09.341000
5	6669575033eb5cd5aebc1fee	CUSTOMER_Avg_of_invoice_Amount_28d	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2024-06-12T08:08:08.818000
6	6669575033eb5cd5aebc1fed	CUSTOMER_Avg_of_invoice_Amount_14d	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2024-06-12T08:08:08.270000
7	6669575033eb5cd5aebc1fec	CUSTOMER_Count_of_invoice_28d	INT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2024-06-12T08:08:07.750000
8	6669575033eb5cd5aebc1feb	CUSTOMER_Count_of_invoice_14d	INT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2024-06-12T08:08:07.361000
9	6669575033eb5cd5aebc1fea	CUSTOMER_Latest_invoice_Amount	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2024-06-12T08:08:06.967000
10	6669575033eb5cd5aebc1fe6	CUSTOMER_x_PRODUCTGROUP_Sum_of_item_TotalCost_28d	FLOAT	DRAFT	False	[GROCERYINVOICE, INVOICEITEMS, GROCERYPRODUCT]	[INVOICEITEMS]	[customer, productgroup]	[customer, productgroup]	2024-06-12T08:08:06.426000
11	6669575033eb5cd5aebc1fe5	CUSTOMER_x_PRODUCTGROUP_Sum_of_item_TotalCost_14d	FLOAT	DRAFT	False	[GROCERYINVOICE, INVOICEITEMS, GROCERYPRODUCT]	[INVOICEITEMS]	[customer, productgroup]	[customer, productgroup]	2024-06-12T08:08:05.758000
12	6669575033eb5cd5aebc1fe9	CUSTOMER_x_PRODUCTGROUP_Time_Since_Latest_Time...	FLOAT	DRAFT	False	[GROCERYINVOICE, INVOICEITEMS, GROCERYPRODUCT]	[INVOICEITEMS]	[customer, productgroup]	[customer, productgroup]	2024-06-12T08:08:05.091000
13	666957381ecbdd152339ded8	CUSTOMER_Age_band	VARCHAR	DRAFT	False	[GROCERYCUSTOMER]	[GROCERYCUSTOMER]	[customer]	[customer]	2024-06-12T08:07:33.012000
14	666957381ecbdd152339dece	CUSTOMER_Age	INT	DRAFT	False	[GROCERYCUSTOMER]	[GROCERYCUSTOMER]	[customer]	[customer]	2024-06-12T08:07:26.610000

Get features from catalog¶

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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¶

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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¶

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# 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 0x176a74c00>
 {
   'name': 'customer',
   'created_at': '2024-06-12T08:05:47.417000',
   'updated_at': '2024-06-12T08:05:50.497000',
   'description': None,
   'serving_names': [
     'GROCERYCUSTOMERGUID'
   ],
   'catalog_name': 'Grocery Dataset Tutorial'
 },
 <featurebyte.api.entity.Entity at 0x1769d41c0>
 {
   'name': 'productgroup',
   'created_at': '2024-06-12T08:05:48.244000',
   'updated_at': '2024-06-12T08:05:51.678000',
   'description': None,
   'serving_names': [
     'PRODUCTGROUP'
   ],
   'catalog_name': 'Grocery Dataset Tutorial'
 }]

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# 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")

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# 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	2023-05-28 19:27:14	15973b2f-2256-4caa-b65b-cbbfdff0905b	80-84	4.17	1.0	4.170	0.000000	NaN	0.603565	NaN	1106.468611
1	2023-02-07 11:04:26	fd1caae1-77e6-4667-8c83-df13f05bf2f5	20-24	36.35	1.0	36.350	0.000000	-0.218570	0.736515	NaN	360.113056
2	2022-09-18 18:52:36	ac7edfb5-63ed-49fb-9b89-76b0288ed2f8	35-39	95.05	6.0	34.825	27.195796	2.357059	0.705695	16.63	99.261667
3	2023-03-31 18:50:00	213ef7d3-c27b-43e0-bc0a-57d6c7c254b0	45-49	53.11	1.0	53.110	0.000000	NaN	0.840879	8.00	269.623333
4	2022-12-26 15:01:07	264f79fd-c24a-47cc-8a68-fe3753a4d74b	40-44	39.25	1.0	39.250	0.000000	-0.153150	0.836482	6.28	73.213056
5	2023-04-11 17:23:57	6084f39f-9d2c-4111-b1cc-502e1559c0c0	30-34	19.70	2.0	18.490	1.210000	1.000000	0.733969	6.00	197.481944
6	2022-12-10 21:08:26	77d02174-f1e1-41c1-9fb9-01c6246b0009	75-79	31.73	1.0	31.730	0.000000	NaN	0.794200	NaN	NaN
7	2022-08-17 19:13:52	40a07ca4-a991-4d21-b5cf-74ee61220f96	75-79	34.27	NaN	NaN	NaN	1.658670	0.576912	NaN	343.933611
8	2023-03-17 11:15:09	1b627a25-7eb4-4f61-b243-c93db487bff0	60-64	9.82	5.0	23.380	21.979151	-0.564107	0.655646	1.67	140.312500
9	2023-05-05 08:00:42	57ca0770-eb8b-4769-8e67-eb1b7cc0a934	35-39	8.47	2.0	14.795	6.325000	-0.508099	0.697578	NaN	1097.448611

Save feature list¶

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# 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 0.4s

Concepts in this tutorial¶

More on feature lists