Struct google_api_proto::google::cloud::aiplatform::v1::Attribution

pub struct Attribution {
    pub baseline_output_value: f64,
    pub instance_output_value: f64,
    pub feature_attributions: Option<Value>,
    pub output_index: Vec<i32>,
    pub output_display_name: String,
    pub approximation_error: f64,
    pub output_name: String,
}

Attribution that explains a particular prediction output.

Fields

baseline_output_value: f64

Output only. Model predicted output if the input instance is constructed from the baselines of all the features defined in [ExplanationMetadata.inputs][google.cloud.aiplatform.v1.ExplanationMetadata.inputs]. The field name of the output is determined by the key in [ExplanationMetadata.outputs][google.cloud.aiplatform.v1.ExplanationMetadata.outputs].

If the Model’s predicted output has multiple dimensions (rank > 1), this is the value in the output located by [output_index][google.cloud.aiplatform.v1.Attribution.output_index].

If there are multiple baselines, their output values are averaged.

instance_output_value: f64

Output only. Model predicted output on the corresponding [explanation instance][ExplainRequest.instances]. The field name of the output is determined by the key in [ExplanationMetadata.outputs][google.cloud.aiplatform.v1.ExplanationMetadata.outputs].

If the Model predicted output has multiple dimensions, this is the value in the output located by [output_index][google.cloud.aiplatform.v1.Attribution.output_index].

feature_attributions: Option<Value>

Output only. Attributions of each explained feature. Features are extracted from the [prediction instances][google.cloud.aiplatform.v1.ExplainRequest.instances] according to [explanation metadata for inputs][google.cloud.aiplatform.v1.ExplanationMetadata.inputs].

The value is a struct, whose keys are the name of the feature. The values are how much the feature in the [instance][google.cloud.aiplatform.v1.ExplainRequest.instances] 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][google.protobuf.Value.number_value].

• If the feature is an array of scalar values, the attribution value is an [array][google.protobuf.Value.list_value].

• If the feature is a struct, the attribution value is a [struct][google.protobuf.Value.struct_value]. 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][google.cloud.aiplatform.v1.ExplanationMetadata.feature_attributions_schema_uri] field, pointed to by the [ExplanationSpec][google.cloud.aiplatform.v1.ExplanationSpec] field of the [Endpoint.deployed_models][google.cloud.aiplatform.v1.Endpoint.deployed_models] object, points to the schema file that describes the features and their attribution values (if it is populated).

output_index: Vec<i32>

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][google.cloud.aiplatform.v1.Attribution.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: f64

Output only. Error of [feature_attributions][google.cloud.aiplatform.v1.Attribution.feature_attributions] caused by approximation used in the explanation method. Lower value means more precise attributions.

• For Sampled Shapley [attribution][google.cloud.aiplatform.v1.ExplanationParameters.sampled_shapley_attribution], increasing [path_count][google.cloud.aiplatform.v1.SampledShapleyAttribution.path_count] might reduce the error.
• For Integrated Gradients [attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution], increasing [step_count][google.cloud.aiplatform.v1.IntegratedGradientsAttribution.step_count] might reduce the error.
• For [XRAI attribution][google.cloud.aiplatform.v1.ExplanationParameters.xrai_attribution], increasing [step_count][google.cloud.aiplatform.v1.XraiAttribution.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][google.cloud.aiplatform.v1.ExplanationMetadata.outputs].

Trait Implementations

impl Clone for Attribution

fn clone(&self) -> Attribution

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for Attribution

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for Attribution

fn default() -> Self

Returns the “default value” for a type.

impl Message for Attribution

fn encoded_len(&self) -> usize

Returns the encoded length of the message without a length delimiter.

fn clear(&mut self)

Clears the message, resetting all fields to their default.

fn encode<B>(&self, buf: &mut B) -> Result<(), EncodeError>

where B: BufMut, Self: Sized,

Encodes the message to a buffer.

fn encode_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message to a newly allocated buffer.

fn encode_length_delimited<B>(&self, buf: &mut B) -> Result<(), EncodeError>

where B: BufMut, Self: Sized,

Encodes the message with a length-delimiter to a buffer.

fn encode_length_delimited_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message with a length-delimiter to a newly allocated buffer.

fn decode<B>(buf: B) -> Result<Self, DecodeError>

where B: Buf, Self: Default,

Decodes an instance of the message from a buffer.

fn decode_length_delimited<B>(buf: B) -> Result<Self, DecodeError>

where B: Buf, Self: Default,

Decodes a length-delimited instance of the message from the buffer.

fn merge<B>(&mut self, buf: B) -> Result<(), DecodeError>

where B: Buf, Self: Sized,

Decodes an instance of the message from a buffer, and merges it into self.

fn merge_length_delimited<B>(&mut self, buf: B) -> Result<(), DecodeError>

where B: Buf, Self: Sized,

Decodes a length-delimited instance of the message from buffer, and merges it into self.

impl PartialEq for Attribution

fn eq(&self, other: &Attribution) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for Attribution