Creating Feature 4

In this topic, you will create and test the fourth feature, user_home_location. The feature outputs the lat(latitude) and long(longitude) of the user's home location. The transaction_distance_from_home feature, which you will define later, uses these outputs.

In your local feature repository, open the file features/batch_features/user_home_location.py. In the file, uncomment the following code, which is a definition of the Feature View.

from tecton import batch_feature_view, FilteredSource
from entities import user
from data_sources.customers import customers
from datetime import datetime, timedelta


@batch_feature_view(
    sources=[FilteredSource(customers)],
    entities=[user],
    mode="spark_sql",
    online=True,
    offline=True,
    feature_start_time=datetime(2017, 1, 1),
    batch_schedule=timedelta(days=1),
    ttl=timedelta(days=3650),
    description="User date of birth, entered at signup.",
    timestamp_field="signup_timestamp",
)
def user_home_location(customers):
    return f"""
        SELECT
            signup_timestamp,
            user_id,
            lat,
            long
        FROM
            {customers}
    """

In your terminal, run tecton apply to apply this Feature View to your workspace.

Testing the Feature View

Get the feature view from the workspace.

fv = ws.get_feature_view("user_home_location")

Call the run method of the feature view to get feature data for the timestamp range of 2022-01-01 to 2022-04-10, and display the generated feature values.

offline_features = fv.run(datetime(2017, 4, 1), datetime(2017, 7, 1)).to_spark().limit(10)
offline_features.show()

Sample Output:

signup_timestamp	user_id	lat	long
2017-04-06 00:50:31	user_709462196403	45.0033	-93.4875
2017-05-08 16:07:51	user_687958452057	42.1938	-85.5639
2017-06-15 19:33:18	user_884240387242	38.9021	-88.6645