@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class ClarifyInferenceConfig extends Object implements Serializable, Cloneable, StructuredPojo
The inference configuration parameter for the model container.
| Constructor and Description |
|---|
ClarifyInferenceConfig() |
| Modifier and Type | Method and Description |
|---|---|
ClarifyInferenceConfig |
clone() |
boolean |
equals(Object obj) |
String |
getContentTemplate()
A template string used to format a JSON record into an acceptable model container input.
|
List<String> |
getFeatureHeaders()
The names of the features.
|
String |
getFeaturesAttribute()
Provides the JMESPath expression to extract the features from a model container input in JSON Lines format.
|
List<String> |
getFeatureTypes()
A list of data types of the features (optional).
|
String |
getLabelAttribute()
A JMESPath expression used to locate the list of label headers in the model container output.
|
List<String> |
getLabelHeaders()
For multiclass classification problems, the label headers are the names of the classes.
|
Integer |
getLabelIndex()
A zero-based index used to extract a label header or list of label headers from model container output in CSV
format.
|
Integer |
getMaxPayloadInMB()
The maximum payload size (MB) allowed of a request from the explainer to the model container.
|
Integer |
getMaxRecordCount()
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.
|
String |
getProbabilityAttribute()
A JMESPath expression used to extract the probability (or score) from the model container output if the model
container is in JSON Lines format.
|
Integer |
getProbabilityIndex()
A zero-based index used to extract a probability value (score) or list from model container output in CSV format.
|
int |
hashCode() |
void |
marshall(ProtocolMarshaller protocolMarshaller)
Marshalls this structured data using the given
ProtocolMarshaller. |
void |
setContentTemplate(String contentTemplate)
A template string used to format a JSON record into an acceptable model container input.
|
void |
setFeatureHeaders(Collection<String> featureHeaders)
The names of the features.
|
void |
setFeaturesAttribute(String featuresAttribute)
Provides the JMESPath expression to extract the features from a model container input in JSON Lines format.
|
void |
setFeatureTypes(Collection<String> featureTypes)
A list of data types of the features (optional).
|
void |
setLabelAttribute(String labelAttribute)
A JMESPath expression used to locate the list of label headers in the model container output.
|
void |
setLabelHeaders(Collection<String> labelHeaders)
For multiclass classification problems, the label headers are the names of the classes.
|
void |
setLabelIndex(Integer labelIndex)
A zero-based index used to extract a label header or list of label headers from model container output in CSV
format.
|
void |
setMaxPayloadInMB(Integer maxPayloadInMB)
The maximum payload size (MB) allowed of a request from the explainer to the model container.
|
void |
setMaxRecordCount(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.
|
void |
setProbabilityAttribute(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.
|
void |
setProbabilityIndex(Integer probabilityIndex)
A zero-based index used to extract a probability value (score) or list from model container output in CSV format.
|
String |
toString()
Returns a string representation of this object.
|
ClarifyInferenceConfig |
withContentTemplate(String contentTemplate)
A template string used to format a JSON record into an acceptable model container input.
|
ClarifyInferenceConfig |
withFeatureHeaders(Collection<String> featureHeaders)
The names of the features.
|
ClarifyInferenceConfig |
withFeatureHeaders(String... featureHeaders)
The names of the features.
|
ClarifyInferenceConfig |
withFeaturesAttribute(String featuresAttribute)
Provides the JMESPath expression to extract the features from a model container input in JSON Lines format.
|
ClarifyInferenceConfig |
withFeatureTypes(ClarifyFeatureType... featureTypes)
A list of data types of the features (optional).
|
ClarifyInferenceConfig |
withFeatureTypes(Collection<String> featureTypes)
A list of data types of the features (optional).
|
ClarifyInferenceConfig |
withFeatureTypes(String... featureTypes)
A list of data types of the features (optional).
|
ClarifyInferenceConfig |
withLabelAttribute(String labelAttribute)
A JMESPath expression used to locate the list of label headers in the model container output.
|
ClarifyInferenceConfig |
withLabelHeaders(Collection<String> labelHeaders)
For multiclass classification problems, the label headers are the names of the classes.
|
ClarifyInferenceConfig |
withLabelHeaders(String... labelHeaders)
For multiclass classification problems, the label headers are the names of the classes.
|
ClarifyInferenceConfig |
withLabelIndex(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 |
withMaxPayloadInMB(Integer maxPayloadInMB)
The maximum payload size (MB) allowed of a request from the explainer to the model container.
|
ClarifyInferenceConfig |
withMaxRecordCount(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 |
withProbabilityAttribute(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 |
withProbabilityIndex(Integer probabilityIndex)
A zero-based index used to extract a probability value (score) or list from model container output in CSV format.
|
public void setFeaturesAttribute(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]}'.public String getFeaturesAttribute()
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 ClarifyInferenceConfig withFeaturesAttribute(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]}'.public void setContentTemplate(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.public String getContentTemplate()
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 ClarifyInferenceConfig withContentTemplate(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.public void setMaxRecordCount(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.public Integer getMaxRecordCount()
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 ClarifyInferenceConfig withMaxRecordCount(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.public void setMaxPayloadInMB(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.public Integer getMaxPayloadInMB()
The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to
6 MB.
6 MB.public ClarifyInferenceConfig withMaxPayloadInMB(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.public void setProbabilityIndex(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].
public Integer getProbabilityIndex()
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 ClarifyInferenceConfig withProbabilityIndex(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].
public void setLabelIndex(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'].
public Integer getLabelIndex()
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 ClarifyInferenceConfig withLabelIndex(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'].
public void setProbabilityAttribute(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'.
public String getProbabilityAttribute()
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 ClarifyInferenceConfig withProbabilityAttribute(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'.
public void setLabelAttribute(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"]
public String getLabelAttribute()
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 ClarifyInferenceConfig withLabelAttribute(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"]
public List<String> getLabelHeaders()
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(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.public ClarifyInferenceConfig withLabelHeaders(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.
NOTE: This method appends the values to the existing list (if any). Use
setLabelHeaders(java.util.Collection) or withLabelHeaders(java.util.Collection) if you want to
override the existing values.
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.public ClarifyInferenceConfig withLabelHeaders(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.public List<String> getFeatureHeaders()
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(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.public ClarifyInferenceConfig withFeatureHeaders(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.
NOTE: This method appends the values to the existing list (if any). Use
setFeatureHeaders(java.util.Collection) or withFeatureHeaders(java.util.Collection) if you want
to override the existing values.
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.public ClarifyInferenceConfig withFeatureHeaders(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.public List<String> getFeatureTypes()
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.ClarifyFeatureTypepublic void setFeatureTypes(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.ClarifyFeatureTypepublic ClarifyInferenceConfig withFeatureTypes(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.
NOTE: This method appends the values to the existing list (if any). Use
setFeatureTypes(java.util.Collection) or withFeatureTypes(java.util.Collection) if you want to
override the existing values.
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.ClarifyFeatureTypepublic ClarifyInferenceConfig withFeatureTypes(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.ClarifyFeatureTypepublic ClarifyInferenceConfig withFeatureTypes(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.ClarifyFeatureTypepublic String toString()
toString in class ObjectObject.toString()public ClarifyInferenceConfig clone()
public void marshall(ProtocolMarshaller protocolMarshaller)
StructuredPojoProtocolMarshaller.marshall in interface StructuredPojoprotocolMarshaller - Implementation of ProtocolMarshaller used to marshall this object's data.