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)
