12. Create feature list

Create a feature list¶

Feature list is a collection of features which can be used for machine learning model.

Let's take some of features we created and bundle them into a feature list.

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)

16:44:15 | INFO     | Using configuration file at: /Users/viktor/.featurebyte/config.yaml
16:44:15 | INFO     | Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
16:44:15 | INFO     | SDK version: 0.6.0.dev121
16:44:15 | INFO     | No catalog activated.
16:44:16 | INFO     | 10 feature lists, 59 features deployed
16:44:16 | INFO     | Using profile: tutorial
16:44:16 | INFO     | Using configuration file at: /Users/viktor/.featurebyte/config.yaml
16:44:16 | INFO     | Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
16:44:16 | INFO     | SDK version: 0.6.0.dev121
16:44:16 | INFO     | No catalog activated.
16:44:16 | INFO     | 10 feature lists, 59 features deployed
16:44:17 | 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	6564b93614db530858940f50	CUSTOMER_vs_OVERALL_item_TotalCost_across_prod...	FLOAT	DRAFT	False	[GROCERYINVOICE, INVOICEITEMS, GROCERYPRODUCT]	[INVOICEITEMS]	[customer]	[customer]	2023-11-27T15:44:00.351000
1	6564b91c69ee318ce739930c	CUSTOMER_Latest_invoice_Amount_Z_Score_to_invo...	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2023-11-27T15:43:32.311000
2	6564b8f0a69f1a9a43bdfd85	CUSTOMER_Std_of_invoice_Amount_28d	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2023-11-27T15:42:56.115000
3	6564b8f0a69f1a9a43bdfd84	CUSTOMER_Std_of_invoice_Amount_14d	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2023-11-27T15:42:54.994000
4	6564b8f0a69f1a9a43bdfd83	CUSTOMER_Avg_of_invoice_Amount_28d	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2023-11-27T15:42:53.425000
5	6564b8f0a69f1a9a43bdfd82	CUSTOMER_Avg_of_invoice_Amount_14d	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2023-11-27T15:42:52.201000
6	6564b8f0a69f1a9a43bdfd80	CUSTOMER_Count_of_invoice_28d	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2023-11-27T15:42:51.056000
7	6564b8f0a69f1a9a43bdfd7e	CUSTOMER_Count_of_invoice_14d	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2023-11-27T15:42:50.243000
8	6564b8f0a69f1a9a43bdfd7d	CUSTOMER_Latest_invoice_Amount	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2023-11-27T15:42:49.486000
9	6564b8c8e991187ee84de74e	CUSTOMER_Age_band	VARCHAR	DRAFT	False	[GROCERYCUSTOMER]	[GROCERYCUSTOMER]	[customer]	[customer]	2023-11-27T15:42:12.722000
10	6564b8c8e991187ee84de744	CUSTOMER_Age	INT	DRAFT	False	[GROCERYCUSTOMER]	[GROCERYCUSTOMER]	[customer]	[customer]	2023-11-27T15:42:05.100000

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_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" )

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
    ],
    name="Customer 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 ], name="Customer 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 0x16900c880>
 {
   'name': 'customer',
   'created_at': '2023-11-27T15:39:09.477000',
   'updated_at': '2023-11-27T15:39:19.968000',
   'description': None,
   'serving_names': [
     'GROCERYCUSTOMERGUID'
   ],
   'catalog_name': 'Grocery Dataset Tutorial'
 }]

In [6]:

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

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

Downloading table |████████████████████████████████████████| 10/10 [100%] in 0.1

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	GROCERYCUSTOMERGUID	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
0	2022-11-28 11:36:31	d4559f7d-eb28-42c6-b47d-847de24952c2	65-69	6.72	0	NaN	NaN	NaN	0.466220
1	2022-10-09 15:47:55	3f8c7c4c-f2c2-408e-a08e-622de3d3a0b9	55-59	12.28	0	NaN	NaN	NaN	0.558564
2	2022-09-14 15:42:42	35390325-8443-43c1-a934-18db923d9a47	15-19	10.02	0	NaN	NaN	-0.838057	0.872762
3	2022-12-26 18:39:46	4eb4ee84-ee13-4eec-9c26-61b6eb4ba35b	70-74	53.09	5	15.626000	19.000809	0.992653	0.713610
4	2022-12-06 08:47:43	e42fa5f3-7737-4c6a-9ef4-856f113e60bd	20-24	21.74	3	15.560000	6.681337	1.530644	0.657608
5	2022-11-09 12:14:40	8440debb-6abc-4adc-8c6c-749928141fd0	20-24	15.30	1	15.300000	0.000000	NaN	0.509610
6	2022-10-12 17:32:15	8a54e527-e9a4-47a9-a28f-8b3c6ecc02db	35-39	14.55	2	14.560000	0.010000	-0.049459	0.753250
7	2023-01-01 11:51:28	cea213d4-36e4-48c3-ae8d-c7a25911e11c	85-89	0.89	12	3.209167	4.154236	-0.530470	0.561260
8	2023-02-05 15:48:23	3b4f2821-b761-40e9-a32a-5f09685cc597	80-84	11.43	4	11.317500	4.526297	0.024855	0.761539
9	2023-03-10 16:15:46	91a64566-e212-4e36-8f23-c1f1f324a301	50-54	2.00	6	4.568333	2.832763	-0.743774	0.729546

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")

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

Done! |████████████████████████████████████████| 100% in 6.5s (0.16%/s)         
Loading Feature(s) |████████████████████████████████████████| 7/7 [100%] in 0.7s

Concepts in this tutorial¶

• More on feature lists

SDK Reference for¶

• Feature List
• FeatureList.save()

In [ ]: