def _validate_data_checks(self, data_checks):
"""Validate data_checks parameter.
Arguments:
data_checks (DataChecks, list(Datacheck), str, None): Input to validate. If not of the right type,
raise an exception.
Returns:
An instance of DataChecks used to perform checks before search.
"""
if isinstance(data_checks, DataChecks):
return data_checks
elif isinstance(data_checks, list):
return AutoMLDataChecks(data_checks)
elif isinstance(data_checks, str):
if data_checks == "auto":
return DefaultDataChecks(
problem_type=self.problem_type,
objective=self.objective,
n_splits=self.data_splitter.get_n_splits())
elif data_checks == "disabled":
return EmptyDataChecks()
else:
raise ValueError(
"If data_checks is a string, it must be either 'auto' or 'disabled'. "
f"Received '{data_checks}'.")
elif data_checks is None:
return EmptyDataChecks()
else:
return DataChecks(data_checks)
def test_empty_data_checks(input_type, X_y_binary):
X, y = X_y_binary
if input_type != "np":
X = pd.DataFrame(X)
y = pd.Series(y)
if input_type == "ww":
X = ww.DataTable(X)
y = ww.DataColumn(y)
data_checks = EmptyDataChecks()
assert data_checks.validate(X, y) == {"warnings": [], "errors": []}