def test_feature_set_db(self):
name = "stocks_test"
stocks_set = fs.FeatureSet(
name, entities=[Entity("ticker", ValueType.STRING)])
fs.preview(
stocks_set,
stocks,
)
stocks_set.save()
db = mlrun.get_run_db()
sets = db.list_feature_sets(self.project_name, name)
assert len(sets) == 1, "bad number of results"
feature_set = fs.get_feature_set(name, self.project_name)
assert feature_set.metadata.name == name, "bad feature set response"
fs.ingest(stocks_set, stocks)
with pytest.raises(mlrun.errors.MLRunPreconditionFailedError):
fs.delete_feature_set(name, self.project_name)
stocks_set.purge_targets()
fs.delete_feature_set(name, self.project_name)
sets = db.list_feature_sets(self.project_name, name)
assert not sets, "Feature set should be deleted"
def test_feature_set_db(self):
name = "stocks_test"
stocks_set = fs.FeatureSet(name, entities=[Entity("ticker", ValueType.STRING)])
fs.infer_metadata(
stocks_set, stocks,
)
stocks_set.save()
db = mlrun.get_run_db()
sets = db.list_feature_sets(self.project_name, name)
assert len(sets) == 1, "bad number of results"
feature_set = fs.get_feature_set(name, self.project_name)
assert feature_set.metadata.name == name, "bad feature set response"
fs.delete_feature_set(name, self.project_name)
sets = db.list_feature_sets(self.project_name, name)
assert not sets, "Feature set should be deleted"