Google Cloud Ai Platform V1 Client - Class Attribution (0.22.0)

Version 0.22.0

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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

Namespace

Google \ Cloud \ AIPlatform \ V1

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