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 = "Credit Default 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 = "Credit Default Dataset SDK Tutorial" catalog = fb.Catalog.activate(catalog_name)

17:03:03 | INFO     | SDK version: 3.2.0.dev66
INFO    :featurebyte:SDK version: 3.2.0.dev66
17:03:03 | INFO     | No catalog activated.
INFO    :featurebyte:No catalog activated.
17:03:03 | INFO     | Using profile: staging
INFO    :featurebyte:Using profile: staging
17:03:03 | INFO     | Using configuration file at: /Users/gxav/.featurebyte/config.yaml
INFO    :featurebyte:Using configuration file at: /Users/gxav/.featurebyte/config.yaml
17:03:03 | INFO     | Active profile: staging (https://staging.featurebyte.com/api/v1)
INFO    :featurebyte:Active profile: staging (https://staging.featurebyte.com/api/v1)
17:03:03 | INFO     | SDK version: 3.2.0.dev66
INFO    :featurebyte:SDK version: 3.2.0.dev66
17:03:03 | INFO     | No catalog activated.
INFO    :featurebyte:No catalog activated.
17:03:03 | INFO     | Catalog activated: Credit Default Dataset SDK Tutorial
INFO    :featurebyte.api.catalog:Catalog activated: Credit Default 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	68ef63333e2dac375b630de7	40 features for Credit Default	40	DRAFT	False	0.0	0.0	[NEW_APPLICATION, CLIENT_PROFILE, BUREAU, INST...	[New Application, Client]	[New Application]	2025-10-15T09:02:51.346000

Get Feature List from Catalog¶

In [3]:

feature_list_name = "40 features for Credit Default"
simple_feature_list = catalog.get_feature_list(feature_list_name)

feature_list_name = "40 features for Credit Default" 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	68ef627ae645921d591abdd9	Applications up to March 2025	source_table	[307511, 3]	playground	2025-10-15T08:59:47.822000

In [5]:

# Get observation table: 'Applications up to March 2025'
training_observations = catalog.get_observation_table("Applications up to March 2025")

# Get observation table: 'Applications up to March 2025' training_observations = catalog.get_observation_table("Applications up to March 2025")

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 2:04.7 (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	68ef634bcf8a902261e4386a	40 features for Credit Default - TRAIN	playground	Applications up to March 2025	[307511, 43]	2025-10-15T09:05:07.959000

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