I'm using feast to create a Feature store for my MLOPs project. However when I execute the command:
# Retrieve training data
training_df = fs.get_historical_features(
entity_df=orders,
features=[
"driver_stats:conv_rate",
"driver_stats:acc_rate",
"driver_stats:avg_daily_trips",
],
).to_df()
Then get the following error:
feast.errors.FeatureViewNotFoundException: Feature view driver_stats does not exist
I have redefined the Features in the features.py file as follows:
from datetime import timedelta
from feast import FeatureView, Field
from feast.stream_feature_view import stream_feature_view
from feast.types import Float32, Int32
from pyspark.sql import DataFrame
from data_sources import driver_stats_batch_source, driver_stats_stream_source
from entities import driver
driver_stats_view = FeatureView(
name="driver_stats",
description="driver features",
entities=[driver],
ttl=timedelta(days=36500),
schema=[
Field(name="conv_rate", dtype=Float32),
Field(name="acc_rate", dtype=Float32),
Field(name="avg_daily_trips", dtype=Int32),
],
online=True,
source=driver_stats_batch_source,
tags={},
owner="[email protected]",
)
I use feast[redis]=0.29.0.