WARNING:root:_list_feature_views will make breaking changes. Please use _list_batch_feature_views instead. _list_feature_views will behave like _list_all_feature_views in the future.

# setup a feature store
feast init feature_store
cd feature_store/feature_repo

feast apply

In [1]: from datetime import datetime
   ...: import pandas as pd
   ...: 
   ...: from feast import FeatureStore
   ...: 
   ...: # Note: see https://docs.feast.dev/getting-started/concepts/feature-retrieval for 
   ...: # more details on how to retrieve for all entities in the offline store instead
   ...: entity_df = pd.DataFrame.from_dict(
   ...:     {
   ...:         # entity's join key -> entity values
   ...:         "driver_id": [1001, 1002, 1003],
   ...:         # "event_timestamp" (reserved key) -> timestamps
   ...:         "event_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()

# warning triggered
WARNING:root:_list_feature_views will make breaking changes. Please use _list_batch_feature_views instead. _list_feature_views will behave like _list_all_feature_views in the future.