Struct ClarifyInferenceConfig

#[non_exhaustive]
pub struct ClarifyInferenceConfig {
    pub features_attribute: Option<String>,
    pub content_template: Option<String>,
    pub max_record_count: Option<i32>,
    pub max_payload_in_mb: Option<i32>,
    pub probability_index: Option<i32>,
    pub label_index: Option<i32>,
    pub probability_attribute: Option<String>,
    pub label_attribute: Option<String>,
    pub label_headers: Option<Vec<String>>,
    pub feature_headers: Option<Vec<String>>,
    pub feature_types: Option<Vec<ClarifyFeatureType>>,
}

The inference configuration parameter for the model container.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

features_attribute: Option<String>

Provides the JMESPath expression to extract the features from a model container input in JSON Lines format. For example, if FeaturesAttribute is the JMESPath expression 'myfeatures', it extracts a list of features \[1,2,3\] from request data '{"myfeatures":\[1,2,3\]}'.

content_template: Option<String>

A template string used to format a JSON record into an acceptable model container input. For example, a ContentTemplate string '{"myfeatures":$features}' will format a list of features \[1,2,3\] into the record string '{"myfeatures":\[1,2,3\]}'. Required only when the model container input is in JSON Lines format.

max_record_count: Option<i32>

The maximum number of records in a request that the model container can process when querying the model container for the predictions of a synthetic dataset. A record is a unit of input data that inference can be made on, for example, a single line in CSV data. If MaxRecordCount is 1, the model container expects one record per request. A value of 2 or greater means that the model expects batch requests, which can reduce overhead and speed up the inferencing process. If this parameter is not provided, the explainer will tune the record count per request according to the model container's capacity at runtime.

max_payload_in_mb: Option<i32>

The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to 6 MB.

probability_index: Option<i32>

A zero-based index used to extract a probability value (score) or list from model container output in CSV format. If this value is not provided, the entire model container output will be treated as a probability value (score) or list.

Example for a single class model: If the model container output consists of a string-formatted prediction label followed by its probability: '1,0.6', set ProbabilityIndex to 1 to select the probability value 0.6.

Example for a multiclass model: If the model container output consists of a string-formatted prediction label followed by its probability: '"\[\'cat\',\'dog\',\'fish\'\]","\[0.1,0.6,0.3\]"', set ProbabilityIndex to 1 to select the probability values \[0.1,0.6,0.3\].

label_index: Option<i32>

A zero-based index used to extract a label header or list of label headers from model container output in CSV format.

Example for a multiclass model: If the model container output consists of label headers followed by probabilities: '"\[\'cat\',\'dog\',\'fish\'\]","\[0.1,0.6,0.3\]"', set LabelIndex to 0 to select the label headers \['cat','dog','fish'\].

probability_attribute: Option<String>

A JMESPath expression used to extract the probability (or score) from the model container output if the model container is in JSON Lines format.

Example: If the model container output of a single request is '{"predicted_label":1,"probability":0.6}', then set ProbabilityAttribute to 'probability'.

label_attribute: Option<String>

A JMESPath expression used to locate the list of label headers in the model container output.

Example: If the model container output of a batch request is '{"labels":\["cat","dog","fish"\],"probability":\[0.6,0.3,0.1\]}', then set LabelAttribute to 'labels' to extract the list of label headers \["cat","dog","fish"\]

label_headers: Option<Vec<String>>

For multiclass classification problems, the label headers are the names of the classes. Otherwise, the label header is the name of the predicted label. These are used to help readability for the output of the InvokeEndpoint API. See the response section under Invoke the endpoint in the Developer Guide for more information. If there are no label headers in the model container output, provide them manually using this parameter.

feature_headers: Option<Vec<String>>

The names of the features. If provided, these are included in the endpoint response payload to help readability of the InvokeEndpoint output. See the Response section under Invoke the endpoint in the Developer Guide for more information.

feature_types: Option<Vec<ClarifyFeatureType>>

A list of data types of the features (optional). Applicable only to NLP explainability. If provided, FeatureTypes must have at least one 'text' string (for example, \['text'\]). If FeatureTypes is not provided, the explainer infers the feature types based on the baseline data. The feature types are included in the endpoint response payload. For additional information see the response section under Invoke the endpoint in the Developer Guide for more information.

Implementations

impl ClarifyInferenceConfig

pub fn features_attribute(&self) -> Option<&str>

Provides the JMESPath expression to extract the features from a model container input in JSON Lines format. For example, if FeaturesAttribute is the JMESPath expression 'myfeatures', it extracts a list of features \[1,2,3\] from request data '{"myfeatures":\[1,2,3\]}'.

pub fn content_template(&self) -> Option<&str>

A template string used to format a JSON record into an acceptable model container input. For example, a ContentTemplate string '{"myfeatures":$features}' will format a list of features \[1,2,3\] into the record string '{"myfeatures":\[1,2,3\]}'. Required only when the model container input is in JSON Lines format.

pub fn max_record_count(&self) -> Option<i32>

The maximum number of records in a request that the model container can process when querying the model container for the predictions of a synthetic dataset. A record is a unit of input data that inference can be made on, for example, a single line in CSV data. If MaxRecordCount is 1, the model container expects one record per request. A value of 2 or greater means that the model expects batch requests, which can reduce overhead and speed up the inferencing process. If this parameter is not provided, the explainer will tune the record count per request according to the model container's capacity at runtime.

pub fn max_payload_in_mb(&self) -> Option<i32>

The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to 6 MB.

pub fn probability_index(&self) -> Option<i32>

A zero-based index used to extract a probability value (score) or list from model container output in CSV format. If this value is not provided, the entire model container output will be treated as a probability value (score) or list.

Example for a single class model: If the model container output consists of a string-formatted prediction label followed by its probability: '1,0.6', set ProbabilityIndex to 1 to select the probability value 0.6.

Example for a multiclass model: If the model container output consists of a string-formatted prediction label followed by its probability: '"\[\'cat\',\'dog\',\'fish\'\]","\[0.1,0.6,0.3\]"', set ProbabilityIndex to 1 to select the probability values \[0.1,0.6,0.3\].

pub fn label_index(&self) -> Option<i32>

A zero-based index used to extract a label header or list of label headers from model container output in CSV format.

Example for a multiclass model: If the model container output consists of label headers followed by probabilities: '"\[\'cat\',\'dog\',\'fish\'\]","\[0.1,0.6,0.3\]"', set LabelIndex to 0 to select the label headers \['cat','dog','fish'\].

pub fn probability_attribute(&self) -> Option<&str>

A JMESPath expression used to extract the probability (or score) from the model container output if the model container is in JSON Lines format.

Example: If the model container output of a single request is '{"predicted_label":1,"probability":0.6}', then set ProbabilityAttribute to 'probability'.

pub fn label_attribute(&self) -> Option<&str>

A JMESPath expression used to locate the list of label headers in the model container output.

Example: If the model container output of a batch request is '{"labels":\["cat","dog","fish"\],"probability":\[0.6,0.3,0.1\]}', then set LabelAttribute to 'labels' to extract the list of label headers \["cat","dog","fish"\]

pub fn label_headers(&self) -> &[String]

For multiclass classification problems, the label headers are the names of the classes. Otherwise, the label header is the name of the predicted label. These are used to help readability for the output of the InvokeEndpoint API. See the response section under Invoke the endpoint in the Developer Guide for more information. If there are no label headers in the model container output, provide them manually using this parameter.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .label_headers.is_none().

pub fn feature_headers(&self) -> &[String]

The names of the features. If provided, these are included in the endpoint response payload to help readability of the InvokeEndpoint output. See the Response section under Invoke the endpoint in the Developer Guide for more information.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .feature_headers.is_none().

pub fn feature_types(&self) -> &[ClarifyFeatureType]

A list of data types of the features (optional). Applicable only to NLP explainability. If provided, FeatureTypes must have at least one 'text' string (for example, \['text'\]). If FeatureTypes is not provided, the explainer infers the feature types based on the baseline data. The feature types are included in the endpoint response payload. For additional information see the response section under Invoke the endpoint in the Developer Guide for more information.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .feature_types.is_none().

impl ClarifyInferenceConfig

pub fn builder() -> ClarifyInferenceConfigBuilder

Creates a new builder-style object to manufacture ClarifyInferenceConfig.

Trait Implementations

impl Clone for ClarifyInferenceConfig

fn clone(&self) -> ClarifyInferenceConfig

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for ClarifyInferenceConfig

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl PartialEq for ClarifyInferenceConfig

fn eq(&self, other: &ClarifyInferenceConfig) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for ClarifyInferenceConfig