* The inference configuration parameter for the model container. *
* * @see AWS * API Documentation */ @Generated("com.amazonaws:aws-java-sdk-code-generator") public class ClarifyInferenceConfig implements Serializable, Cloneable, StructuredPojo { /** *
* 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]}'.
*
* 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.
*
* 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.
*
* The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to
* 6 MB.
*
* 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].
*
* 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'].
*
* 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'.
*
* 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"]
*
* 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.
*
* 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.
*
* 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.
*
* 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 is the JMESPath expression
* 'myfeatures', it extracts a list of features [1,2,3] from request data
* '{"myfeatures":[1,2,3]}'.
*/
public void setFeaturesAttribute(String featuresAttribute) {
this.featuresAttribute = 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 is the JMESPath expression
* 'myfeatures', it extracts a list of features [1,2,3] from request data
* '{"myfeatures":[1,2,3]}'.
*/
public String getFeaturesAttribute() {
return this.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 is the JMESPath expression
* 'myfeatures', it extracts a list of features [1,2,3] from request data
* '{"myfeatures":[1,2,3]}'.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ClarifyInferenceConfig withFeaturesAttribute(String featuresAttribute) {
setFeaturesAttribute(featuresAttribute);
return this;
}
/**
*
* 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 string '{"myfeatures":$features}' will format a list of features
* [1,2,3] into the record string '{"myfeatures":[1,2,3]}'. Required only when the
* model container input is in JSON Lines format.
*/
public void setContentTemplate(String contentTemplate) {
this.contentTemplate = contentTemplate;
}
/**
*
* A template string used to format a JSON record into an acceptable model container input. For example, a
* ContentTemplate string '{"myfeatures":$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 string '{"myfeatures":$features}' will format a list of
* features [1,2,3] into the record string '{"myfeatures":[1,2,3]}'. Required only
* when the model container input is in JSON Lines format.
*/
public String getContentTemplate() {
return this.contentTemplate;
}
/**
*
* A template string used to format a JSON record into an acceptable model container input. For example, a
* ContentTemplate string '{"myfeatures":$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 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.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ClarifyInferenceConfig withContentTemplate(String contentTemplate) {
setContentTemplate(contentTemplate);
return this;
}
/**
*
* 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 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.
*/
public void setMaxRecordCount(Integer maxRecordCount) {
this.maxRecordCount = 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 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.
*/
public Integer getMaxRecordCount() {
return this.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 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.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ClarifyInferenceConfig withMaxRecordCount(Integer maxRecordCount) {
setMaxRecordCount(maxRecordCount);
return this;
}
/**
*
* The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to
* 6 MB.
*
6 MB.
*/
public void setMaxPayloadInMB(Integer maxPayloadInMB) {
this.maxPayloadInMB = maxPayloadInMB;
}
/**
*
* The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to
* 6 MB.
*
6 MB.
*/
public Integer getMaxPayloadInMB() {
return this.maxPayloadInMB;
}
/**
*
* The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to
* 6 MB.
*
6 MB.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ClarifyInferenceConfig withMaxPayloadInMB(Integer maxPayloadInMB) {
setMaxPayloadInMB(maxPayloadInMB);
return this;
}
/**
* * 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].
*
* 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].
*/
public void setProbabilityIndex(Integer probabilityIndex) {
this.probabilityIndex = 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].
*
* 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].
*/
public Integer getProbabilityIndex() {
return this.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].
*
* 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].
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ClarifyInferenceConfig withProbabilityIndex(Integer probabilityIndex) {
setProbabilityIndex(probabilityIndex);
return this;
}
/**
*
* 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'].
*
* 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'].
*/
public void setLabelIndex(Integer labelIndex) {
this.labelIndex = 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'].
*
* 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'].
*/
public Integer getLabelIndex() {
return this.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'].
*
* 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'].
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ClarifyInferenceConfig withLabelIndex(Integer labelIndex) {
setLabelIndex(labelIndex);
return this;
}
/**
*
* 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'.
*
* Example: If the model container output of a single request is
* '{"predicted_label":1,"probability":0.6}', then set ProbabilityAttribute to
* 'probability'.
*/
public void setProbabilityAttribute(String probabilityAttribute) {
this.probabilityAttribute = 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'.
*
* Example: If the model container output of a single request is
* '{"predicted_label":1,"probability":0.6}', then set ProbabilityAttribute to
* 'probability'.
*/
public String getProbabilityAttribute() {
return this.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'.
*
* Example: If the model container output of a single request is
* '{"predicted_label":1,"probability":0.6}', then set ProbabilityAttribute to
* 'probability'.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ClarifyInferenceConfig withProbabilityAttribute(String probabilityAttribute) {
setProbabilityAttribute(probabilityAttribute);
return this;
}
/**
*
* 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"]
*
* 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"]
*/
public void setLabelAttribute(String labelAttribute) {
this.labelAttribute = 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"]
*
* 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"]
*/
public String getLabelAttribute() {
return this.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"]
*
* 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"]
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ClarifyInferenceConfig withLabelAttribute(String labelAttribute) {
setLabelAttribute(labelAttribute);
return this;
}
/**
*
* 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.
*
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.
*/
public java.util.List
* 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.
*
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.
*/
public void setLabelHeaders(java.util.Collection
* 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.
*
* NOTE: This method appends the values to the existing list (if any). Use * {@link #setLabelHeaders(java.util.Collection)} or {@link #withLabelHeaders(java.util.Collection)} if you want to * override the existing values. *
* * @param 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.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ClarifyInferenceConfig withLabelHeaders(String... labelHeaders) {
if (this.labelHeaders == null) {
setLabelHeaders(new java.util.ArrayList
* 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.
*
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.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ClarifyInferenceConfig withLabelHeaders(java.util.Collection
* 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.
*
InvokeEndpoint output. See the Response section under Invoke the endpoint in the Developer Guide for more information.
*/
public java.util.List
* 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.
*
InvokeEndpoint output. See the Response section under Invoke the endpoint in the Developer Guide for more information.
*/
public void setFeatureHeaders(java.util.Collection
* 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.
*
* NOTE: This method appends the values to the existing list (if any). Use * {@link #setFeatureHeaders(java.util.Collection)} or {@link #withFeatureHeaders(java.util.Collection)} if you want * to override the existing values. *
* * @param featureHeaders * The names of the features. If provided, these are included in the endpoint response payload to help * readability of theInvokeEndpoint output. See the Response section under Invoke the endpoint in the Developer Guide for more information.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ClarifyInferenceConfig withFeatureHeaders(String... featureHeaders) {
if (this.featureHeaders == null) {
setFeatureHeaders(new java.util.ArrayList
* 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.
*
InvokeEndpoint output. See the Response section under Invoke the endpoint in the Developer Guide for more information.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ClarifyInferenceConfig withFeatureHeaders(java.util.Collection
* 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 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.
* @see ClarifyFeatureType
*/
public java.util.List
* 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 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.
* @see ClarifyFeatureType
*/
public void setFeatureTypes(java.util.Collection
* 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.
*
* NOTE: This method appends the values to the existing list (if any). Use * {@link #setFeatureTypes(java.util.Collection)} or {@link #withFeatureTypes(java.util.Collection)} if you want to * override the existing values. *
* * @param 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.
* @return Returns a reference to this object so that method calls can be chained together.
* @see ClarifyFeatureType
*/
public ClarifyInferenceConfig withFeatureTypes(String... featureTypes) {
if (this.featureTypes == null) {
setFeatureTypes(new java.util.ArrayList
* 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 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.
* @return Returns a reference to this object so that method calls can be chained together.
* @see ClarifyFeatureType
*/
public ClarifyInferenceConfig withFeatureTypes(java.util.Collection
* 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 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.
* @return Returns a reference to this object so that method calls can be chained together.
* @see ClarifyFeatureType
*/
public ClarifyInferenceConfig withFeatureTypes(ClarifyFeatureType... featureTypes) {
java.util.ArrayList