public static interface ClarifyInferenceConfig.Builder extends SdkPojo, CopyableBuilder<ClarifyInferenceConfig.Builder,ClarifyInferenceConfig>
| Modifier and Type | Method and Description |
|---|---|
ClarifyInferenceConfig.Builder |
contentTemplate(String contentTemplate)
A template string used to format a JSON record into an acceptable model container input.
|
ClarifyInferenceConfig.Builder |
featureHeaders(Collection<String> featureHeaders)
The names of the features.
|
ClarifyInferenceConfig.Builder |
featureHeaders(String... featureHeaders)
The names of the features.
|
ClarifyInferenceConfig.Builder |
featuresAttribute(String featuresAttribute)
Provides the JMESPath expression to extract the features from a model container input in JSON Lines format.
|
ClarifyInferenceConfig.Builder |
featureTypes(ClarifyFeatureType... featureTypes)
A list of data types of the features (optional).
|
ClarifyInferenceConfig.Builder |
featureTypes(Collection<ClarifyFeatureType> featureTypes)
A list of data types of the features (optional).
|
ClarifyInferenceConfig.Builder |
featureTypesWithStrings(Collection<String> featureTypes)
A list of data types of the features (optional).
|
ClarifyInferenceConfig.Builder |
featureTypesWithStrings(String... featureTypes)
A list of data types of the features (optional).
|
ClarifyInferenceConfig.Builder |
labelAttribute(String labelAttribute)
A JMESPath expression used to locate the list of label headers in the model container output.
|
ClarifyInferenceConfig.Builder |
labelHeaders(Collection<String> labelHeaders)
For multiclass classification problems, the label headers are the names of the classes.
|
ClarifyInferenceConfig.Builder |
labelHeaders(String... labelHeaders)
For multiclass classification problems, the label headers are the names of the classes.
|
ClarifyInferenceConfig.Builder |
labelIndex(Integer labelIndex)
A zero-based index used to extract a label header or list of label headers from model container output in CSV
format.
|
ClarifyInferenceConfig.Builder |
maxPayloadInMB(Integer maxPayloadInMB)
The maximum payload size (MB) allowed of a request from the explainer to the model container.
|
ClarifyInferenceConfig.Builder |
maxRecordCount(Integer maxRecordCount)
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.
|
ClarifyInferenceConfig.Builder |
probabilityAttribute(String probabilityAttribute)
A JMESPath expression used to extract the probability (or score) from the model container output if the model
container is in JSON Lines format.
|
ClarifyInferenceConfig.Builder |
probabilityIndex(Integer probabilityIndex)
A zero-based index used to extract a probability value (score) or list from model container output in CSV
format.
|
equalsBySdkFields, sdkFieldscopyapplyMutation, buildClarifyInferenceConfig.Builder featuresAttribute(String featuresAttribute)
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]}'.
featuresAttribute - 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]}'.ClarifyInferenceConfig.Builder contentTemplate(String contentTemplate)
A template string used to format a JSON record into an acceptable model container input. For example, a
ContentTemplate string '{"myfeatures":$features}' will format a list of features
[1,2,3] into the record string '{"myfeatures":[1,2,3]}'. Required only when the
model container input is in JSON Lines format.
contentTemplate - A template string used to format a JSON record into an acceptable model container input. For example,
a ContentTemplate string '{"myfeatures":$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.ClarifyInferenceConfig.Builder maxRecordCount(Integer maxRecordCount)
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.
maxRecordCount - 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.ClarifyInferenceConfig.Builder maxPayloadInMB(Integer maxPayloadInMB)
The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to
6 MB.
maxPayloadInMB - The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults
to 6 MB.ClarifyInferenceConfig.Builder probabilityIndex(Integer probabilityIndex)
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].
probabilityIndex - 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].
ClarifyInferenceConfig.Builder labelIndex(Integer labelIndex)
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'].
labelIndex - 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'].
ClarifyInferenceConfig.Builder probabilityAttribute(String probabilityAttribute)
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'.
probabilityAttribute - 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'.
ClarifyInferenceConfig.Builder labelAttribute(String labelAttribute)
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"]
labelAttribute - 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"]
ClarifyInferenceConfig.Builder labelHeaders(Collection<String> labelHeaders)
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.
labelHeaders - 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.ClarifyInferenceConfig.Builder labelHeaders(String... labelHeaders)
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.
labelHeaders - 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.ClarifyInferenceConfig.Builder featureHeaders(Collection<String> featureHeaders)
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.
featureHeaders - 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.ClarifyInferenceConfig.Builder featureHeaders(String... featureHeaders)
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.
featureHeaders - 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.ClarifyInferenceConfig.Builder featureTypesWithStrings(Collection<String> featureTypes)
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.
featureTypes - 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.ClarifyInferenceConfig.Builder featureTypesWithStrings(String... featureTypes)
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.
featureTypes - 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.ClarifyInferenceConfig.Builder featureTypes(Collection<ClarifyFeatureType> featureTypes)
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.
featureTypes - 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.ClarifyInferenceConfig.Builder featureTypes(ClarifyFeatureType... featureTypes)
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.
featureTypes - 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.Copyright © 2022. All rights reserved.