[Alpha] On demand feature view

Warning: This is an experimental feature. It's intended for early testing and feedback, and could change without warnings in future releases.
Overview
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.
Why use on demand feature views?
This enables data scientists to easily impact the online feature retrieval path. For example, a data scientist could
1.Call get_historical_features to generate a training dataframe
2.Iterate in notebook on feature engineering in Pandas
3.Copy transformation logic into on demand feature views and commit to a dev branch of the feature repository
4.Verify with get_historical_features (on a small dataset) that the transformation gives expected output over historical data
5.Verify with get_online_features on dev branch that the transformation correctly outputs online features
6.Submit a pull request to the staging / prod branches which impact production traffic
CLI
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
Example
See https://github.com/feast-dev/on-demand-feature-views-demo for an example on how to use on demand feature views.
Registering transformations
We register RequestSource inputs and the transform in on_demand_feature_view:
from feast import Field, RequestSource
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 df
Feature retrieval
The on demand feature view's name is the function name (i.e. transformed_conv_rate).
And then to retrieve historical or online features, we can call this in a feature service or reference individual features:
training_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()
Last modified 24d ago