10. Derive Features from other Features

Derive feature from other features¶

Features can also be created based on other features.

In this tutorial we will create a feature that measures the Z-Score of the Latest invoice Amount for the customer compared to that customer's invoices over a 28d period. This feature is derived from 3 features we created previously: CUSTOMER_Latest_invoice_Amount, CUSTOMER_Avg_of_invoice_Amount_28d and CUSTOMER_Std_of_invoice_Amount_28d.

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)

10:51:19 | WARNING  | Service endpoint is inaccessible: http://featurebyte-server:8088/
10:51:19 | INFO     | Using profile: tutorial
10:51:20 | INFO     | Using configuration file at: /Users/gxav/.featurebyte/config.yaml
10:51:20 | INFO     | Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
10:51:20 | INFO     | SDK version: 2.0.1.dev67
10:51:20 | INFO     | No catalog activated.
10:51:20 | INFO     | Catalog activated: Grocery Dataset Tutorial
16:08:27 | INFO     | Using configuration file at: /Users/gxav/.featurebyte/config.yaml
16:08:27 | INFO     | Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
16:08:27 | WARNING  | Remote SDK version (1.1.0.dev7) is different from local (1.1.0.dev1). Update local SDK to avoid unexpected behavior.
16:08:27 | INFO     | No catalog activated.
16:08:27 | INFO     | Catalog activated: Grocery Dataset Tutorial

Get features we previously created and saved¶

In [2]:

customer_latest_invoice_amount = catalog.get_feature("CUSTOMER_Latest_invoice_Amount")
customer_avg_of_invoice_amount_28d = catalog.get_feature("CUSTOMER_Avg_of_invoice_Amount_28d")
customer_std_of_invoice_amount_28d = catalog.get_feature("CUSTOMER_Std_of_invoice_Amount_28d")

customer_latest_invoice_amount = catalog.get_feature("CUSTOMER_Latest_invoice_Amount") customer_avg_of_invoice_amount_28d = catalog.get_feature("CUSTOMER_Avg_of_invoice_Amount_28d") customer_std_of_invoice_amount_28d = catalog.get_feature("CUSTOMER_Std_of_invoice_Amount_28d")

Derive a z-score feature from saved features¶

In [3]:

customer_latest_invoice_amount_Z_score_to_invoice_amount_28d = (
    customer_latest_invoice_amount
    - customer_avg_of_invoice_amount_28d
) / customer_std_of_invoice_amount_28d
# Give a name to new feature
customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.name = \
"CUSTOMER_Latest_invoice_Amount_Z_Score_to_invoice_Amount_28d"

customer_latest_invoice_amount_Z_score_to_invoice_amount_28d = ( customer_latest_invoice_amount - customer_avg_of_invoice_amount_28d ) / customer_std_of_invoice_amount_28d # Give a name to new feature customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.name = \ "CUSTOMER_Latest_invoice_Amount_Z_Score_to_invoice_Amount_28d"

Preview feature¶

In [4]:

customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.primary_entity

customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.primary_entity

Out[4]:

[<featurebyte.api.entity.Entity at 0x323466bb0>
 {
   'name': 'customer',
   'created_at': '2024-12-18T02:40:54.536000',
   'updated_at': '2024-12-18T02:40:58.932000',
   'description': None,
   'serving_names': [
     'GROCERYCUSTOMERGUID'
   ],
   'catalog_name': 'Grocery Dataset Tutorial'
 }]

In [5]:

# 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 [6]:

# Preview CUSTOMER_Age
customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.preview(preview_table)

# Preview CUSTOMER_Age customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.preview(preview_table)

Out[6]:

	POINT_IN_TIME	GROCERYINVOICEITEMGUID	CUSTOMER_Latest_invoice_Amount_Z_Score_to_invoice_Amount_28d
0	2022-07-23 17:33:25	849454c5-6640-419d-871d-1f0895a1c3b4	NaN
1	2023-01-06 08:49:29	dad86e21-3af4-4b5b-8058-60f946b6dac5	0.016777
2	2023-06-06 16:13:44	bdc6b6bb-a23a-48c8-bb3a-622d9161c0e8	1.008156
3	2022-09-07 09:16:15	53eac49e-7ecd-4cb6-a1c0-38e3900efd7a	0.901901
4	2023-05-21 13:59:44	989643fe-1377-4f5f-8f38-f349a611da0c	-1.404144
5	2022-08-22 06:10:42	9267d0dd-9685-4667-8f06-1761abe73c4d	-0.820547
6	2023-03-07 17:53:55	cb478a4e-9266-4523-8ee0-e205881cc5f5	NaN
7	2022-12-30 16:42:05	4dd1487a-0379-4eef-b200-97ac1bb1164f	1.340493
8	2022-09-23 18:09:33	459a1b6e-1239-46d1-9e40-539c7e895483	-0.181665
9	2023-03-21 13:49:48	84225bbb-adb8-451e-98c0-897c83c2fad9	-0.063562

Save feature¶

In [7]:

customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.save()

customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.save()

Done! |████████████████████████████████████████| 100% in 6.1s (0.17%/s)         
Done! |████████████████████████████████████████| 100% in 6.1s (0.17%/s)         

Add description¶

In [8]:

# Add description
customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.update_description(
	"Z-Score of the Latest invoice Amount for the customer compared to customer invoices over a 28d period"
)

# Add description customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.update_description( "Z-Score of the Latest invoice Amount for the customer compared to customer invoices over a 28d period" )

Concepts in this tutorial¶

• Feature Transforms

SDK reference for¶

• Catalog.get_feature()