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Version: 0.5

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_timestampuser_idlatlong
2017-04-06 00:50:31user_70946219640345.0033-93.4875
2017-05-08 16:07:51user_68795845205742.1938-85.5639
2017-06-15 19:33:18user_88424038724238.9021-88.6645

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