8. Create Lookup Feature

Create Lookup features¶

We've learned how to define a target and materialize data using observation tables. Now, let's dive into basic feature engineering.

The most straightforward features we can craft with FeatureByte are known as lookup features. These are either direct columns taken from the source table or simple computations that don't require any aggregations.

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)

16:07:20 | WARNING  | Service endpoint is inaccessible: http://featurebyte-server:8088
16:07:20 | INFO     | Using profile: tutorial
16:07:20 | INFO     | Using configuration file at: /Users/gxav/.featurebyte/config.yaml
16:07:20 | INFO     | Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
16:07:20 | WARNING  | Remote SDK version (1.1.0.dev7) is different from local (1.1.0.dev1). Update local SDK to avoid unexpected behavior.
16:07:20 | INFO     | No catalog activated.
16:07:20 | INFO     | Catalog activated: Grocery Dataset Tutorial

In [2]:

# Get view from GROCERYCUSTOMER scd table.
grocerycustomer_view = catalog.get_view("GROCERYCUSTOMER")

# Get view from GROCERYCUSTOMER scd table. grocerycustomer_view = catalog.get_view("GROCERYCUSTOMER")

Create Lookup feature¶

In [3]:

# Create lookup feature from DateOfBirth column for customer entity.
customer_dateofbirth = grocerycustomer_view["DateOfBirth"].as_feature("CUSTOMER_DateOfBirth")

# Create lookup feature from DateOfBirth column for customer entity. customer_dateofbirth = grocerycustomer_view["DateOfBirth"].as_feature("CUSTOMER_DateOfBirth")

Derive Age at the point-in-time of the request observation¶

In [4]:

# Derive Age from the point-in-time and the date of birth.
customer_age = (
    (fb.RequestColumn.point_in_time() - customer_dateofbirth).dt.day / 365.25
).floor()
# Name feature
customer_age.name = "CUSTOMER_Age"

# Derive Age from the point-in-time and the date of birth. customer_age = ( (fb.RequestColumn.point_in_time() - customer_dateofbirth).dt.day / 365.25 ).floor() # Name feature customer_age.name = "CUSTOMER_Age"

In [5]:

# Transform age into a 5 year age band.
customer_age_band = (
    ((customer_age + 1) / 5).ceil() - 1
) * 5
customer_age_band = (
    customer_age_band.astype(str)
     + "-" + (customer_age_band + 4).astype(str)
)
# Name feature
customer_age_band.name = "CUSTOMER_Age_band"

# Transform age into a 5 year age band. customer_age_band = ( ((customer_age + 1) / 5).ceil() - 1 ) * 5 customer_age_band = ( customer_age_band.astype(str) + "-" + (customer_age_band + 4).astype(str) ) # Name feature customer_age_band.name = "CUSTOMER_Age_band"

Preview feature¶

We will use observation table we created in previous tutorial here.

In [6]:

# Check the primary entity of the feature'
customer_age.primary_entity

# Check the primary entity of the feature' customer_age.primary_entity

Out[6]:

[<featurebyte.api.entity.Entity at 0x13a78b0c0>
 {
   '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'
 }]

In [7]:

# 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 [8]:

# Preview CUSTOMER_Age
customer_age.preview(preview_table)

# Preview CUSTOMER_Age customer_age.preview(preview_table)

Out[8]:

	POINT_IN_TIME	GROCERYINVOICEITEMGUID	CUSTOMER_Age
0	2022-08-17 19:13:52	40a07ca4-a991-4d21-b5cf-74ee61220f96	78
1	2022-12-10 21:08:26	77d02174-f1e1-41c1-9fb9-01c6246b0009	76
2	2023-04-11 17:23:57	6084f39f-9d2c-4111-b1cc-502e1559c0c0	32
3	2022-09-18 18:52:36	ac7edfb5-63ed-49fb-9b89-76b0288ed2f8	38
4	2023-03-31 18:50:00	213ef7d3-c27b-43e0-bc0a-57d6c7c254b0	45
5	2023-02-07 11:04:26	fd1caae1-77e6-4667-8c83-df13f05bf2f5	22
6	2023-05-05 08:00:42	57ca0770-eb8b-4769-8e67-eb1b7cc0a934	36
7	2023-03-17 11:15:09	1b627a25-7eb4-4f61-b243-c93db487bff0	62
8	2022-12-26 15:01:07	264f79fd-c24a-47cc-8a68-fe3753a4d74b	44
9	2023-05-28 19:27:14	15973b2f-2256-4caa-b65b-cbbfdff0905b	80

In [9]:

# Preview CUSTOMER_Age_band
customer_age_band.preview(preview_table)

# Preview CUSTOMER_Age_band customer_age_band.preview(preview_table)

Out[9]:

	POINT_IN_TIME	GROCERYINVOICEITEMGUID	CUSTOMER_Age_band
0	2022-08-17 19:13:52	40a07ca4-a991-4d21-b5cf-74ee61220f96	75-79
1	2022-12-10 21:08:26	77d02174-f1e1-41c1-9fb9-01c6246b0009	75-79
2	2023-04-11 17:23:57	6084f39f-9d2c-4111-b1cc-502e1559c0c0	30-34
3	2022-09-18 18:52:36	ac7edfb5-63ed-49fb-9b89-76b0288ed2f8	35-39
4	2023-03-31 18:50:00	213ef7d3-c27b-43e0-bc0a-57d6c7c254b0	45-49
5	2023-02-07 11:04:26	fd1caae1-77e6-4667-8c83-df13f05bf2f5	20-24
6	2023-05-05 08:00:42	57ca0770-eb8b-4769-8e67-eb1b7cc0a934	35-39
7	2023-03-17 11:15:09	1b627a25-7eb4-4f61-b243-c93db487bff0	60-64
8	2022-12-26 15:01:07	264f79fd-c24a-47cc-8a68-fe3753a4d74b	40-44
9	2023-05-28 19:27:14	15973b2f-2256-4caa-b65b-cbbfdff0905b	80-84

In [10]:

# Save features to catalog 
customer_age.save()
customer_age_band.save()

# Save features to catalog customer_age.save() customer_age_band.save()

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

In [11]:

# Add description
customer_age.update_description("Age of the customer.")
customer_age_band.update_description("Age Band of the customer.")

# Add description customer_age.update_description("Age of the customer.") customer_age_band.update_description("Age Band of the customer.")

Check feature definition files (same as definition files we discussed in target tutorial)¶

In [12]:

customer_age.definition

customer_age.definition

Out[12]:

# Generated by SDK version: 1.1.0.dev7
from bson import ObjectId
from featurebyte import SCDTable
from featurebyte.api.request_column import RequestColumn


# scd_table name: "GROCERYCUSTOMER"
scd_table = SCDTable.get_by_id(ObjectId("666956c28080c62d0dc616df"))
scd_view = scd_table.get_view(
    view_mode="manual",
    drop_column_names=["record_available_at", "CurrentRecord"],
    column_cleaning_operations=[],
)
grouped = scd_view.as_features(
    column_names=["DateOfBirth"],
    feature_names=["CUSTOMER_DateOfBirth"],
    offset=None,
)
feat = grouped["CUSTOMER_DateOfBirth"]
request_col = RequestColumn.point_in_time()
feat_1 = ((request_col - feat).dt.day / 365.25).floor()
feat_1.name = "CUSTOMER_Age"
output = feat_1
output.save(_id=ObjectId("666957381ecbdd152339dece"))

In [13]:

customer_age_band.definition

customer_age_band.definition

Out[13]:

# Generated by SDK version: 1.1.0.dev7
from bson import ObjectId
from featurebyte import SCDTable
from featurebyte.api.request_column import RequestColumn


# scd_table name: "GROCERYCUSTOMER"
scd_table = SCDTable.get_by_id(ObjectId("666956c28080c62d0dc616df"))
scd_view = scd_table.get_view(
    view_mode="manual",
    drop_column_names=["record_available_at", "CurrentRecord"],
    column_cleaning_operations=[],
)
grouped = scd_view.as_features(
    column_names=["DateOfBirth"],
    feature_names=["CUSTOMER_DateOfBirth"],
    offset=None,
)
feat = grouped["CUSTOMER_DateOfBirth"]
request_col = RequestColumn.point_in_time()
feat_1 = ((request_col - feat).dt.day / 365.25).floor()
feat_1.name = "CUSTOMER_Age"
feat_2 = ((((feat_1 + 1) / 5).ceil() - 1) * 5) + 4
feat_3 = ((((feat_1 + 1) / 5).ceil() - 1) * 5).astype(str) + "-"
feat_4 = feat_3 + feat_2.astype(str)
feat_4.name = "CUSTOMER_Age_band"
output = feat_4
output.save(_id=ObjectId("666957381ecbdd152339ded8"))

Concepts in this tutorial¶

• More on features
• More on lookup features
• More about point-in-time

SDK reference for¶

• Feature
• ViewColumn.as_feature()
• RequestColumn.point_in_time
• Feature.preview()
• Feature.save()