def _validate_schema(feature_columns, cat_names, cont_names, schema=None):
_uses_feature_columns = feature_columns is not None
_uses_explicit_schema = (cat_names is not None) or (cont_names is not None)
cat_tag_names = schema.select_by_tag([Tags.CATEGORICAL
]).column_names if schema else []
cont_tag_names = schema.select_by_tag([Tags.CONTINUOUS
]).column_names if schema else []
_uses_dataset_schema = cat_tag_names or cont_tag_names
if _uses_feature_columns and _uses_explicit_schema:
raise ValueError(
"Passed `feature_column`s and explicit column names, must be one or the other"
)
elif _uses_feature_columns:
return get_dataset_schema_from_feature_columns(feature_columns)
elif _uses_explicit_schema:
cat_names = cat_names or []
cont_names = cont_names or []
return cat_names, cont_names
elif _uses_dataset_schema:
cat_tag_names = cat_tag_names or []
cont_tag_names = cont_tag_names or []
return cat_tag_names, cont_tag_names
else:
raise ValueError(
"Must either pass a list of TensorFlow `feature_column`s "
"or explicit `cat_name` and `cont_name` column name lists.")
def _validate_schema(feature_columns, cat_names, cont_names):
_uses_feature_columns = feature_columns is not None
_uses_explicit_schema = (cat_names is not None) or (cont_names is not None)
if _uses_feature_columns and _uses_explicit_schema:
raise ValueError(
"Passed `feature_column`s and explicit column names, must be one or the other"
)
elif _uses_feature_columns:
return get_dataset_schema_from_feature_columns(feature_columns)
elif _uses_explicit_schema:
cat_names = cat_names or []
cont_names = cont_names or []
return cat_names, cont_names
else:
raise ValueError(
"Must either pass a list of TensorFlow `feature_column`s "
"or explicit `cat_name` and `cont_name` column name lists.")
