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)

16:41:57 | INFO     | Using configuration file at: /Users/viktor/.featurebyte/config.yaml
16:41:57 | INFO     | Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
16:41:57 | INFO     | SDK version: 0.6.0.dev121
16:41:57 | INFO     | No catalog activated.
16:41:58 | INFO     | 11 feature lists, 66 features deployed
16:41:58 | INFO     | Using profile: tutorial
16:41:58 | INFO     | Using configuration file at: /Users/viktor/.featurebyte/config.yaml
16:41:58 | INFO     | Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
16:41:58 | INFO     | SDK version: 0.6.0.dev121
16:41:58 | INFO     | No catalog activated.
16:41:58 | INFO     | 11 feature lists, 66 features deployed
16:41:59 | 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 0x105412f40>
 {
   '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 [7]:

# 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 [8]:

# Preview CUSTOMER_Age
customer_age.preview(preview_table)

# Preview CUSTOMER_Age customer_age.preview(preview_table)

Out[8]:

	POINT_IN_TIME	GROCERYCUSTOMERGUID	CUSTOMER_Age
0	2022-11-28 11:36:31	d4559f7d-eb28-42c6-b47d-847de24952c2	66
1	2022-10-09 15:47:55	3f8c7c4c-f2c2-408e-a08e-622de3d3a0b9	59
2	2022-09-14 15:42:42	35390325-8443-43c1-a934-18db923d9a47	19
3	2022-12-26 18:39:46	4eb4ee84-ee13-4eec-9c26-61b6eb4ba35b	70
4	2022-12-06 08:47:43	e42fa5f3-7737-4c6a-9ef4-856f113e60bd	24
5	2022-11-09 12:14:40	8440debb-6abc-4adc-8c6c-749928141fd0	22
6	2022-10-12 17:32:15	8a54e527-e9a4-47a9-a28f-8b3c6ecc02db	39
7	2023-01-01 11:51:28	cea213d4-36e4-48c3-ae8d-c7a25911e11c	85
8	2023-02-05 15:48:23	3b4f2821-b761-40e9-a32a-5f09685cc597	83
9	2023-03-10 16:15:46	91a64566-e212-4e36-8f23-c1f1f324a301	54

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	GROCERYCUSTOMERGUID	CUSTOMER_Age_band
0	2022-11-28 11:36:31	d4559f7d-eb28-42c6-b47d-847de24952c2	65-69
1	2022-10-09 15:47:55	3f8c7c4c-f2c2-408e-a08e-622de3d3a0b9	55-59
2	2022-09-14 15:42:42	35390325-8443-43c1-a934-18db923d9a47	15-19
3	2022-12-26 18:39:46	4eb4ee84-ee13-4eec-9c26-61b6eb4ba35b	70-74
4	2022-12-06 08:47:43	e42fa5f3-7737-4c6a-9ef4-856f113e60bd	20-24
5	2022-11-09 12:14:40	8440debb-6abc-4adc-8c6c-749928141fd0	20-24
6	2022-10-12 17:32:15	8a54e527-e9a4-47a9-a28f-8b3c6ecc02db	35-39
7	2023-01-01 11:51:28	cea213d4-36e4-48c3-ae8d-c7a25911e11c	85-89
8	2023-02-05 15:48:23	3b4f2821-b761-40e9-a32a-5f09685cc597	80-84
9	2023-03-10 16:15:46	91a64566-e212-4e36-8f23-c1f1f324a301	50-54

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 6.5s (0.16%/s)         
Done! |████████████████████████████████████████| 100% in 6.5s (0.16%/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: 0.6.0.dev121
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("6564b7eabeba6c193e0fe3bb"))
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("6564b8c8e991187ee84de744"))

In [13]:

customer_age_band.definition

customer_age_band.definition

Out[13]:

# Generated by SDK version: 0.6.0.dev121
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("6564b7eabeba6c193e0fe3bb"))
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("6564b8c8e991187ee84de74e"))

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()

In [ ]: