Copyright | (c) 2013-2023 Brendan Hay |
---|---|
License | Mozilla Public License, v. 2.0. |
Maintainer | Brendan Hay |
Stability | auto-generated |
Portability | non-portable (GHC extensions) |
Safe Haskell | Safe-Inferred |
Language | Haskell2010 |
Synopsis
- data ClarifyInferenceConfig = ClarifyInferenceConfig' {
- contentTemplate :: Maybe Text
- featureHeaders :: Maybe (NonEmpty Text)
- featureTypes :: Maybe (NonEmpty ClarifyFeatureType)
- featuresAttribute :: Maybe Text
- labelAttribute :: Maybe Text
- labelHeaders :: Maybe (NonEmpty Text)
- labelIndex :: Maybe Natural
- maxPayloadInMB :: Maybe Natural
- maxRecordCount :: Maybe Natural
- probabilityAttribute :: Maybe Text
- probabilityIndex :: Maybe Natural
- newClarifyInferenceConfig :: ClarifyInferenceConfig
- clarifyInferenceConfig_contentTemplate :: Lens' ClarifyInferenceConfig (Maybe Text)
- clarifyInferenceConfig_featureHeaders :: Lens' ClarifyInferenceConfig (Maybe (NonEmpty Text))
- clarifyInferenceConfig_featureTypes :: Lens' ClarifyInferenceConfig (Maybe (NonEmpty ClarifyFeatureType))
- clarifyInferenceConfig_featuresAttribute :: Lens' ClarifyInferenceConfig (Maybe Text)
- clarifyInferenceConfig_labelAttribute :: Lens' ClarifyInferenceConfig (Maybe Text)
- clarifyInferenceConfig_labelHeaders :: Lens' ClarifyInferenceConfig (Maybe (NonEmpty Text))
- clarifyInferenceConfig_labelIndex :: Lens' ClarifyInferenceConfig (Maybe Natural)
- clarifyInferenceConfig_maxPayloadInMB :: Lens' ClarifyInferenceConfig (Maybe Natural)
- clarifyInferenceConfig_maxRecordCount :: Lens' ClarifyInferenceConfig (Maybe Natural)
- clarifyInferenceConfig_probabilityAttribute :: Lens' ClarifyInferenceConfig (Maybe Text)
- clarifyInferenceConfig_probabilityIndex :: Lens' ClarifyInferenceConfig (Maybe Natural)
Documentation
data ClarifyInferenceConfig Source #
The inference configuration parameter for the model container.
See: newClarifyInferenceConfig
smart constructor.
ClarifyInferenceConfig' | |
|
Instances
newClarifyInferenceConfig :: ClarifyInferenceConfig Source #
Create a value of ClarifyInferenceConfig
with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:contentTemplate:ClarifyInferenceConfig'
, clarifyInferenceConfig_contentTemplate
- A template string used to format a JSON record into an acceptable model
container input. For example, a ContentTemplate
string
'{"myfeatures":$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.
$sel:featureHeaders:ClarifyInferenceConfig'
, clarifyInferenceConfig_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.
$sel:featureTypes:ClarifyInferenceConfig'
, clarifyInferenceConfig_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.
$sel:featuresAttribute:ClarifyInferenceConfig'
, clarifyInferenceConfig_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]}'
.
$sel:labelAttribute:ClarifyInferenceConfig'
, clarifyInferenceConfig_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"]
$sel:labelHeaders:ClarifyInferenceConfig'
, clarifyInferenceConfig_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.
$sel:labelIndex:ClarifyInferenceConfig'
, clarifyInferenceConfig_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']
.
$sel:maxPayloadInMB:ClarifyInferenceConfig'
, clarifyInferenceConfig_maxPayloadInMB
- The maximum payload size (MB) allowed of a request from the explainer to
the model container. Defaults to 6
MB.
$sel:maxRecordCount:ClarifyInferenceConfig'
, clarifyInferenceConfig_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.
$sel:probabilityAttribute:ClarifyInferenceConfig'
, clarifyInferenceConfig_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'
.
$sel:probabilityIndex:ClarifyInferenceConfig'
, clarifyInferenceConfig_probabilityIndex
- A zero-based index used to extract a probability value (score) or list
from model container output in CSV format. If this value is not
provided, the entire model container output will be treated as a
probability value (score) or list.
Example for a single class model: If the model container output
consists of a string-formatted prediction label followed by its
probability: '1,0.6'
, set ProbabilityIndex
to 1
to select the
probability value 0.6
.
Example for a multiclass model: If the model container output
consists of a string-formatted prediction label followed by its
probability:
'"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"'
, set
ProbabilityIndex
to 1
to select the probability values
[0.1,0.6,0.3]
.
clarifyInferenceConfig_contentTemplate :: Lens' ClarifyInferenceConfig (Maybe Text) Source #
A template string used to format a JSON record into an acceptable model
container input. For example, a ContentTemplate
string
'{"myfeatures":$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_featureHeaders :: Lens' ClarifyInferenceConfig (Maybe (NonEmpty Text)) Source #
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_featureTypes :: Lens' ClarifyInferenceConfig (Maybe (NonEmpty ClarifyFeatureType)) Source #
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_featuresAttribute :: Lens' ClarifyInferenceConfig (Maybe Text) Source #
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_labelAttribute :: Lens' ClarifyInferenceConfig (Maybe Text) Source #
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_labelHeaders :: Lens' ClarifyInferenceConfig (Maybe (NonEmpty Text)) Source #
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_labelIndex :: Lens' ClarifyInferenceConfig (Maybe Natural) Source #
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_maxPayloadInMB :: Lens' ClarifyInferenceConfig (Maybe Natural) Source #
The maximum payload size (MB) allowed of a request from the explainer to
the model container. Defaults to 6
MB.
clarifyInferenceConfig_maxRecordCount :: Lens' ClarifyInferenceConfig (Maybe Natural) Source #
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_probabilityAttribute :: Lens' ClarifyInferenceConfig (Maybe Text) Source #
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_probabilityIndex :: Lens' ClarifyInferenceConfig (Maybe Natural) Source #
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]
.