Reference documentation and code samples for the Google Cloud Ai Platform V1 Client class Attribution.
Attribution that explains a particular prediction output.
Generated from protobuf message google.cloud.aiplatform.v1.Attribution
Methods
__construct
Constructor.
| Parameters | |
|---|---|
| Name | Description |
data |
array
Optional. Data for populating the Message object. |
↳ baseline_output_value |
float
Output only. Model predicted output if the input instance is constructed from the baselines of all the features defined in ExplanationMetadata.inputs. The field name of the output is determined by the key in ExplanationMetadata.outputs. If the Model's predicted output has multiple dimensions (rank > 1), this is the value in the output located by output_index. If there are multiple baselines, their output values are averaged. |
↳ instance_output_value |
float
Output only. Model predicted output on the corresponding explanation instance. The field name of the output is determined by the key in ExplanationMetadata.outputs. If the Model predicted output has multiple dimensions, this is the value in the output located by output_index. |
↳ feature_attributions |
Google\Protobuf\Value
Output only. Attributions of each explained feature. Features are extracted from the prediction instances according to explanation metadata for inputs. The value is a struct, whose keys are the name of the feature. The values are how much the feature in the instance contributed to the predicted result. The format of the value is determined by the feature's input format: * If the feature is a scalar value, the attribution value is a floating number. * If the feature is an array of scalar values, the attribution value is an array. * If the feature is a struct, the attribution value is a struct. The keys in the attribution value struct are the same as the keys in the feature struct. The formats of the values in the attribution struct are determined by the formats of the values in the feature struct. The ExplanationMetadata.feature_attributions_schema_uri field, pointed to by the ExplanationSpec field of the Endpoint.deployed_models object, points to the schema file that describes the features and their attribution values (if it is populated). |
↳ output_index |
array
Output only. The index that locates the explained prediction output. If the prediction output is a scalar value, output_index is not populated. If the prediction output has multiple dimensions, the length of the output_index list is the same as the number of dimensions of the output. The i-th element in output_index is the element index of the i-th dimension of the output vector. Indices start from 0. |
↳ output_display_name |
string
Output only. The display name of the output identified by output_index. For example, the predicted class name by a multi-classification Model. This field is only populated iff the Model predicts display names as a separate field along with the explained output. The predicted display name must has the same shape of the explained output, and can be located using output_index. |
↳ approximation_error |
float
Output only. Error of feature_attributions caused by approximation used in the explanation method. Lower value means more precise attributions. * For Sampled Shapley attribution, increasing path_count might reduce the error. * For Integrated Gradients attribution, increasing step_count might reduce the error. * For XRAI attribution, increasing step_count might reduce the error. See this introduction for more information. |
↳ output_name |
string
Output only. Name of the explain output. Specified as the key in ExplanationMetadata.outputs. |
getBaselineOutputValue
Output only. Model predicted output if the input instance is constructed from the baselines of all the features defined in ExplanationMetadata.inputs.
The field name of the output is determined by the key in ExplanationMetadata.outputs. If the Model's predicted output has multiple dimensions (rank > 1), this is the value in the output located by output_index. If there are multiple baselines, their output values are averaged.
| Returns | |
|---|---|
| Type | Description |
float | |
setBaselineOutputValue
Output only. Model predicted output if the input instance is constructed from the baselines of all the features defined in ExplanationMetadata.inputs.
The field name of the output is determined by the key in ExplanationMetadata.outputs. If the Model's predicted output has multiple dimensions (rank > 1), this is the value in the output located by output_index. If there are multiple baselines, their output values are averaged.
| Parameter | |
|---|---|
| Name | Description |
var |
float
|
| Returns | |
|---|---|
| Type | Description |
$this | |
getInstanceOutputValue
Output only. Model predicted output on the corresponding explanation instance. The field name of the output is determined by the key in ExplanationMetadata.outputs.
If the Model predicted output has multiple dimensions, this is the value in the output located by output_index.
| Returns | |
|---|---|
| Type | Description |
float | |
setInstanceOutputValue
Output only. Model predicted output on the corresponding explanation instance. The field name of the output is determined by the key in ExplanationMetadata.outputs.
If the Model predicted output has multiple dimensions, this is the value in the output located by output_index.
| Parameter | |
|---|---|
| Name | Description |
var |
float
|
| Returns | |
|---|---|
| Type | Description |
$this | |
getFeatureAttributions
Output only. Attributions of each explained feature. Features are extracted from the prediction instances according to explanation metadata for inputs.
The value is a struct, whose keys are the name of the feature. The values are how much the feature in the instance contributed to the predicted result. The format of the value is determined by the feature's input format:
- If the feature is a scalar value, the attribution value is a floating number.
- If the feature is an array of scalar values, the attribution value is an array.
- If the feature is a struct, the attribution value is a struct. The keys in the attribution value struct are the same as the keys in the feature struct. The formats of the values in the attribution struct are determined by the formats of the values in the feature struct. The ExplanationMetadata.feature_attributions_schema_uri field, pointed to by the ExplanationSpec field of the Endpoint.deployed_models object, points to the schema file that describes the features and their attribution values (if it is populated).
| Returns | |
|---|---|
| Type | Description |
Google\Protobuf\Value|null | |
hasFeatureAttributions
clearFeatureAttributions
setFeatureAttributions
Output only. Attributions of each explained feature. Features are extracted from the prediction instances according to explanation metadata for inputs.
The value is a struct, whose keys are the name of the feature. The values are how much the feature in the instance contributed to the predicted result. The format of the value is determined by the feature's input format:
- If the feature is a scalar value, the attribution value is a floating number.
- If the feature is an array of scalar values, the attribution value is an array.
- If the feature is a struct, the attribution value is a struct. The keys in the attribution value struct are the same as the keys in the feature struct. The formats of the values in the attribution struct are determined by the formats of the values in the feature struct. The ExplanationMetadata.feature_attributions_schema_uri field, pointed to by the ExplanationSpec field of the Endpoint.deployed_models object, points to the schema file that describes the features and their attribution values (if it is populated).
| Parameter | |
|---|---|
| Name | Description |
var |
Google\Protobuf\Value
|
| Returns | |
|---|---|
| Type | Description |
$this | |
getOutputIndex
Output only. The index that locates the explained prediction output.
If the prediction output is a scalar value, output_index is not populated. If the prediction output has multiple dimensions, the length of the output_index list is the same as the number of dimensions of the output. The i-th element in output_index is the element index of the i-th dimension of the output vector. Indices start from 0.
| Returns | |
|---|---|
| Type | Description |
Google\Protobuf\Internal\RepeatedField | |
setOutputIndex
Output only. The index that locates the explained prediction output.
If the prediction output is a scalar value, output_index is not populated. If the prediction output has multiple dimensions, the length of the output_index list is the same as the number of dimensions of the output. The i-th element in output_index is the element index of the i-th dimension of the output vector. Indices start from 0.
| Parameter | |
|---|---|
| Name | Description |
var |
int[]
|
| Returns | |
|---|---|
| Type | Description |
$this | |
getOutputDisplayName
Output only. The display name of the output identified by output_index. For example, the predicted class name by a multi-classification Model.
This field is only populated iff the Model predicts display names as a separate field along with the explained output. The predicted display name must has the same shape of the explained output, and can be located using output_index.
| Returns | |
|---|---|
| Type | Description |
string | |
setOutputDisplayName
Output only. The display name of the output identified by output_index. For example, the predicted class name by a multi-classification Model.
This field is only populated iff the Model predicts display names as a separate field along with the explained output. The predicted display name must has the same shape of the explained output, and can be located using output_index.
| Parameter | |
|---|---|
| Name | Description |
var |
string
|
| Returns | |
|---|---|
| Type | Description |
$this | |
getApproximationError
Output only. Error of feature_attributions caused by approximation used in the explanation method. Lower value means more precise attributions.
- For Sampled Shapley attribution, increasing path_count might reduce the error.
- For Integrated Gradients attribution, increasing step_count might reduce the error.
- For XRAI attribution, increasing step_count might reduce the error. See this introduction for more information.
| Returns | |
|---|---|
| Type | Description |
float | |
setApproximationError
Output only. Error of feature_attributions caused by approximation used in the explanation method. Lower value means more precise attributions.
- For Sampled Shapley attribution, increasing path_count might reduce the error.
- For Integrated Gradients attribution, increasing step_count might reduce the error.
- For XRAI attribution, increasing step_count might reduce the error. See this introduction for more information.
| Parameter | |
|---|---|
| Name | Description |
var |
float
|
| Returns | |
|---|---|
| Type | Description |
$this | |
getOutputName
Output only. Name of the explain output. Specified as the key in ExplanationMetadata.outputs.
| Returns | |
|---|---|
| Type | Description |
string | |
setOutputName
Output only. Name of the explain output. Specified as the key in ExplanationMetadata.outputs.
| Parameter | |
|---|---|
| Name | Description |
var |
string
|
| Returns | |
|---|---|
| Type | Description |
$this | |