def __init__(self,
model_fn=None,
input_fn=None,
predict_fn=None,
output_fn=None,
error_class=_errors.ClientError):
"""Default constructor. Wraps the any non default framework function in an error class to isolate
framework from user errors.
Args:
model_fn (fn): Function responsible to load the model.
input_fn (fn): Takes request data and de-serializes the data into an object for prediction.
predict_fn (fn): Function responsible for model predictions.
output_fn (fn): Function responsible to serialize the prediction for the response.
error_class (Exception): Error class used to separate framework and user errors.
"""
self._model = None
self._model_fn = _functions.error_wrapper(
model_fn, error_class) if model_fn else default_model_fn
self._input_fn = _functions.error_wrapper(
input_fn, error_class) if input_fn else default_input_fn
self._predict_fn = _functions.error_wrapper(
predict_fn, error_class) if predict_fn else default_predict_fn
self._output_fn = _functions.error_wrapper(
output_fn, error_class) if output_fn else default_output_fn
self._error_class = error_class
def __init__(
self,
model_fn=None,
input_fn=None,
predict_fn=None,
output_fn=None,
transform_fn=None,
error_class=_errors.ClientError,
):
"""Default constructor. Wraps the any non default framework function in an error class to
isolate framework from user errors.
Args:
model_fn (fn): Function responsible to load the model.
input_fn (fn): Takes request data and de-serializes the data into an object for
prediction.
predict_fn (fn): Function responsible for model predictions.
output_fn (fn): Function responsible to serialize the prediction for the response.
transform_fn (fn): Function responsible for taking input data and returning a prediction
as a serialized response. This function takes the place of ``input_fn``,
``predict_fn``, and ``output_fn``.
error_class (Exception): Error class used to separate framework and user errors.
"""
self._model = None
self._model_fn = (_functions.error_wrapper(model_fn, error_class)
if model_fn else default_model_fn)
if transform_fn and (input_fn or predict_fn or output_fn):
raise ValueError(
"Cannot use transform_fn implementation with input_fn, predict_fn, and/or output_fn"
)
if transform_fn is not None:
self._transform_fn = _functions.error_wrapper(
transform_fn, error_class)
else:
self._transform_fn = self._default_transform_fn
self._input_fn = (_functions.error_wrapper(input_fn, error_class)
if input_fn else default_input_fn)
self._predict_fn = (_functions.error_wrapper(predict_fn, error_class)
if predict_fn else default_predict_fn)
self._output_fn = (_functions.error_wrapper(output_fn, error_class)
if output_fn else default_output_fn)
self._error_class = error_class
def test_error_wrapper_exception():
with pytest.raises(NotImplementedError) as e:
_functions.error_wrapper(lambda x: x, NotImplementedError)(2, 3)
assert type(e.value.args[0]) == TypeError
def test_error_wrapper():
assert _functions.error_wrapper(lambda x: x * 10,
NotImplementedError)(3) == 30