[Python] FEAST Feature Store - What is event_timestamp in entity_df parameter of...

What is event_timestamp in entity_df parameter of FeatureStore.get_historical_features method?

Are they dummy random timestamp just to create data? And what does ensure point-in-time correctness mean?

• get_historical_features

This method joins historical feature data from one or more feature views to an entity dataframe by using a time travel join. Each feature view is joined to the entity dataframe using all entities configured for the respective feature view.

Parameters

entity_df (Union[pd.DataFrame, str]) – An entity dataframe is a collection of rows containing all entity columns (e.g., customer_id, driver_id) on which features need to be joined, as well as a event_timestamp column used to ensure point-in-time correctness. Either a Pandas DataFrame can be provided or a string SQL query. The query must be of a format supported by the configured offline store (e.g., BigQuery):
​

Example code from Quickstart - Generating training data.

entity_df = pd.DataFrame.from_dict(
{
# entity's join key -> entity values
"driver_id": [1001, 1002, 1003],
# "event_timestamp" (reserved key) -> timestamps
"event_timestamp": [ # <-------- Are these random timestamp?
datetime(2021, 4, 12, 10, 59, 42),
datetime(2021, 4, 12, 8, 12, 10),
datetime(2021, 4, 12, 16, 40, 26),
],
# (optional) label name -> label values. Feast does not process these
"label_driver_reported_satisfaction": [1, 5, 3],
# values we're using for an on-demand transformation
"val_to_add": [1, 2, 3],
"val_to_add_2": [10, 20, 30],
}
)

store = FeatureStore(repo_path=".")

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()



Documentation Feature retrieval - Event timestamp says:

The timestamp on which an event occurred, as found in a feature view's data source. The event timestamp describes the event time at which a feature was observed or generated.

Event timestamps are used during point-in-time joins to ensure that the latest feature values are joined from feature views onto entity rows. Event timestamps are also used to ensure that old feature values aren't served to models during online serving.
​

However, datetime(2021, 4, 12, 10, 59, 42) for driver_id 1001 does not exist in the data source.

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