Struct rusoto_sagemaker::DataProcessing
source · [−]pub struct DataProcessing {
pub input_filter: Option<String>,
pub join_source: Option<String>,
pub output_filter: Option<String>,
}
Expand description
The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records.
Fields
input_filter: Option<String>
A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the InputFilter
parameter to exclude fields, such as an ID column, from the input. If you want Amazon SageMaker to pass the entire input dataset to the algorithm, accept the default value $
.
Examples: "$"
, "$[1:]"
, "$.features"
join_source: Option<String>
Specifies the source of the data to join with the transformed data. The valid values are None
and Input
. The default value is None
, which specifies not to join the input with the transformed data. If you want the batch transform job to join the original input data with the transformed data, set JoinSource
to Input
. You can specify OutputFilter
as an additional filter to select a portion of the joined dataset and store it in the output file.
For JSON or JSONLines objects, such as a JSON array, Amazon SageMaker adds the transformed data to the input JSON object in an attribute called SageMakerOutput
. The joined result for JSON must be a key-value pair object. If the input is not a key-value pair object, Amazon SageMaker creates a new JSON file. In the new JSON file, and the input data is stored under the SageMakerInput
key and the results are stored in SageMakerOutput
.
For CSV data, Amazon SageMaker takes each row as a JSON array and joins the transformed data with the input by appending each transformed row to the end of the input. The joined data has the original input data followed by the transformed data and the output is a CSV file.
For information on how joining in applied, see Workflow for Associating Inferences with Input Records.
output_filter: Option<String>
A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch transform job. If you want Amazon SageMaker to store the entire input dataset in the output file, leave the default value, $
. If you specify indexes that aren't within the dimension size of the joined dataset, you get an error.
Examples: "$"
, "$[0,5:]"
, "$['id','SageMakerOutput']"
Trait Implementations
sourceimpl Clone for DataProcessing
impl Clone for DataProcessing
sourcefn clone(&self) -> DataProcessing
fn clone(&self) -> DataProcessing
Returns a copy of the value. Read more
1.0.0 · sourcefn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
Performs copy-assignment from source
. Read more
sourceimpl Debug for DataProcessing
impl Debug for DataProcessing
sourceimpl Default for DataProcessing
impl Default for DataProcessing
sourcefn default() -> DataProcessing
fn default() -> DataProcessing
Returns the “default value” for a type. Read more
sourceimpl<'de> Deserialize<'de> for DataProcessing
impl<'de> Deserialize<'de> for DataProcessing
sourcefn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
Deserialize this value from the given Serde deserializer. Read more
sourceimpl PartialEq<DataProcessing> for DataProcessing
impl PartialEq<DataProcessing> for DataProcessing
sourcefn eq(&self, other: &DataProcessing) -> bool
fn eq(&self, other: &DataProcessing) -> bool
This method tests for self
and other
values to be equal, and is used
by ==
. Read more
sourcefn ne(&self, other: &DataProcessing) -> bool
fn ne(&self, other: &DataProcessing) -> bool
This method tests for !=
.
sourceimpl Serialize for DataProcessing
impl Serialize for DataProcessing
impl StructuralPartialEq for DataProcessing
Auto Trait Implementations
impl RefUnwindSafe for DataProcessing
impl Send for DataProcessing
impl Sync for DataProcessing
impl Unpin for DataProcessing
impl UnwindSafe for DataProcessing
Blanket Implementations
sourceimpl<T> BorrowMut<T> for T where
T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
const: unstable · sourcefn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
sourceimpl<T> Instrument for T
impl<T> Instrument for T
sourcefn instrument(self, span: Span) -> Instrumented<Self>
fn instrument(self, span: Span) -> Instrumented<Self>
sourcefn in_current_span(self) -> Instrumented<Self>
fn in_current_span(self) -> Instrumented<Self>
sourceimpl<T> ToOwned for T where
T: Clone,
impl<T> ToOwned for T where
T: Clone,
type Owned = T
type Owned = T
The resulting type after obtaining ownership.
sourcefn clone_into(&self, target: &mut T)
fn clone_into(&self, target: &mut T)
toowned_clone_into
)Uses borrowed data to replace owned data, usually by cloning. Read more
sourceimpl<T> WithSubscriber for T
impl<T> WithSubscriber for T
sourcefn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self> where
S: Into<Dispatch>,
fn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self> where
S: Into<Dispatch>,
Attaches the provided Subscriber
to this type, returning a
WithDispatch
wrapper. Read more
sourcefn with_current_subscriber(self) -> WithDispatch<Self>
fn with_current_subscriber(self) -> WithDispatch<Self>
Attaches the current default Subscriber
to this type, returning a
WithDispatch
wrapper. Read more