Warning: This is an experimental feature. It's intended for early testing and feedback, and could change without warnings in future releases.
On demand feature views allows data scientists to use existing features and request time data (features only available at request time) to transform and create new features. Users define python transformation logic which is executed in both historical retrieval and online retrieval paths.
Currently, these transformations are executed locally. This is fine for online serving, but does not scale well offline.
This enables data scientists to easily impact the online feature retrieval path. For example, a data scientist could
Call get_historical_features to generate a training dataframe
Iterate in notebook on feature engineering in Pandas
Copy transformation logic into on demand feature views and commit to a dev branch of the feature repository
There are new CLI commands:
feast on-demand-feature-views list lists all registered on demand feature view after feast apply is run
feast on-demand-feature-views describe [NAME] describes the definition of an on demand feature view
See for an example on how to use on demand feature views.
We register RequestSource inputs and the transform in on_demand_feature_view:
And then to retrieve historical or online features, we can call this in a feature service or reference individual features:
get_historical_features (on a small dataset) that the transformation gives expected output over historical dataVerify with get_online_features on dev branch that the transformation correctly outputs online features
Submit a pull request to the staging / prod branches which impact production traffic
from feast import Field, RequestSource
from feast.on_demand_feature_view import on_demand_feature_view
from feast.types import Float64, Int64
import pandas as pd
# Define a request data source which encodes features / information only
# available at request time (e.g. part of the user initiated HTTP request)
input_request = RequestSource(
name="vals_to_add",
schema=[
Field(name='val_to_add', dtype=Int64),
Field(name='val_to_add_2', dtype=Int64)
]
)
# Use the input data and feature view features to create new features
@on_demand_feature_view(
sources=[
driver_hourly_stats_view,
input_request
],
schema=[
Field(name='conv_rate_plus_val1', dtype=Float64),
Field(name='conv_rate_plus_val2', dtype=Float64)
]
)
def transformed_conv_rate(features_df: pd.DataFrame) -> pd.DataFrame:
df = pd.DataFrame()
df['conv_rate_plus_val1'] = (features_df['conv_rate'] + features_df['val_to_add'])
df['conv_rate_plus_val2'] = (features_df['conv_rate'] + features_df['val_to_add_2'])
return dftraining_df = store.get_historical_features(
entity_df=entity_df,
features=[
"driver_hourly_stats:conv_rate",
"driver_hourly_stats:acc_rate",
"driver_hourly_stats:avg_daily_trips",
"transformed_conv_rate:conv_rate_plus_val1",
"transformed_conv_rate:conv_rate_plus_val2",
],
).to_df()