def generate_saved_dataset(): store = FeatureStore(repo_path=".") entity_df = pd.read_parquet(path="data/loan_table.parquet") fs = store.get_feature_service("credit_score_v1") job = store.get_historical_features(entity_df=entity_df, features=fs,) store.create_saved_dataset( from_=job, name="my_training_ds", storage=SavedDatasetFileStorage(path="my_training_ds.parquet"), feature_service=fs, profiler=credit_profiler, )
def run_demo(): store = FeatureStore(repo_path=".") print("--- Historical features (from saved dataset) ---") ds = store.get_saved_dataset("my_training_ds") print(ds.to_df()) print("\n--- Online features ---") features = store.get_online_features( features=store.get_feature_service("credit_score_v3"), entity_rows=[ {"zipcode": 30721, "dob_ssn": "19530219_5179", "transaction_amt": 1023} ], ).to_dict() for key, value in sorted(features.items()): print(key, " : ", value)