13. Compute Historical Feature Values

Compute historical feature values¶

Historical feature values are needed to train and test Machine Learning models.

Let's take the feature list we just created and compute feature values for a given observation table.

In [1]:

import featurebyte as fb

# Set your profile to the tutorial environment
fb.use_profile("tutorial")

catalog_name = "Loan Applications Dataset SDK Tutorial"
catalog = fb.Catalog.activate(catalog_name)

import featurebyte as fb # Set your profile to the tutorial environment fb.use_profile("tutorial") catalog_name = "Loan Applications Dataset SDK Tutorial" catalog = fb.Catalog.activate(catalog_name)

14:11:26 | INFO     | SDK version: 3.0.1.dev45
INFO    :featurebyte:SDK version: 3.0.1.dev45
14:11:26 | INFO     | No catalog activated.
INFO    :featurebyte:No catalog activated.
14:11:26 | INFO     | Using profile: tutorial
INFO    :featurebyte:Using profile: tutorial
14:11:27 | INFO     | Using configuration file at: /Users/gxav/.featurebyte/config.yaml
INFO    :featurebyte:Using configuration file at: /Users/gxav/.featurebyte/config.yaml
14:11:27 | INFO     | Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
INFO    :featurebyte:Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
14:11:27 | INFO     | SDK version: 3.0.1.dev45
INFO    :featurebyte:SDK version: 3.0.1.dev45
14:11:27 | INFO     | No catalog activated.
INFO    :featurebyte:No catalog activated.
14:11:27 | INFO     | Catalog activated: Loan Applications Dataset SDK Tutorial
INFO    :featurebyte.api.catalog:Catalog activated: Loan Applications Dataset SDK Tutorial
16:12:25 | WARNING  | Remote SDK version (1.1.0.dev7) is different from local (1.1.0.dev1). Update local SDK to avoid unexpected behavior.
16:12:25 | INFO     | No catalog activated.
16:12:25 | INFO     | Catalog activated: Grocery Dataset Tutorial

List feature lists in Catalog¶

In [2]:

catalog.list_feature_lists()

catalog.list_feature_lists()

Out[2]:

	id	name	num_feature	status	deployed	readiness_frac	online_frac	tables	entities	primary_entity	created_at
0	683d3c6ea3c52cb2862eea26	40 features for Loan Applications	40	DRAFT	False	0.0	0.0	[NEW_APPLICATION, CLIENT_PROFILE, BUREAU, INST...	[New Application, Client]	[New Application]	2025-06-02T05:53:57.964000

Get Feature List from Catalog¶

In [3]:

feature_list_name = "40 features for Loan Applications"
simple_feature_list = catalog.get_feature_list(feature_list_name)

feature_list_name = "40 features for Loan Applications" simple_feature_list = catalog.get_feature_list(feature_list_name)

Loading Feature(s) |████████████████████████████████████████| 40/40 [100%] in 0.

Get an observation table¶

In [4]:

# List observation tables
catalog.list_observation_tables()

# List observation tables catalog.list_observation_tables()

Out[4]:

	id	name	type	shape	feature_store_name	created_at
0	683d3bde07274be4537267b1	Applications up to Sept 2024 with Loan Defaults	source_table	[303270, 3]	playground	2025-06-02T05:51:37.364000

In [5]:

# Get observation table: 'Applications up to Sept 2024 with Loan Defaults'
training_observations = catalog.get_observation_table("Applications up to Sept 2024 with Loan Defaults")

# Get observation table: 'Applications up to Sept 2024 with Loan Defaults' training_observations = catalog.get_observation_table("Applications up to Sept 2024 with Loan Defaults")

Compute historical features¶

In [6]:

# Create training data
training_data_table = simple_feature_list.compute_historical_feature_table(
    training_observations,
    historical_feature_table_name=f"{feature_list_name} - TRAIN",
)

# Create training data training_data_table = simple_feature_list.compute_historical_feature_table( training_observations, historical_feature_table_name=f"{feature_list_name} - TRAIN", )

Done! |████████████████████████████████████████| 100% in 2:02.4 (0.01%/s)       
Done! |████████████████████████████████████████| 100% in 36.4s (0.03%/s)        

In [7]:

# List historical feature tables from catalog
catalog.list_historical_feature_tables()

# List historical feature tables from catalog catalog.list_historical_feature_tables()

Out[7]:

	id	name	feature_store_name	observation_table_name	shape	created_at
0	683d3c98954f0aa89942addd	40 features for Loan Applications - TRAIN	playground	Applications up to Sept 2024 with Loan Defaults	[303270, 43]	2025-06-02T05:56:31.688000

Concepts in this tutorial¶

• More on Historical feature serving

SDK reference for¶

• Historical feature table
• FeatureList.compute historical feature table()
• FeatureList.compute_historical_features() to compute directly a data frame