Struct GoogleCloudAiplatformV1ExplanationParameters

pub struct GoogleCloudAiplatformV1ExplanationParameters {
    pub top_k: Option<i32>,
    pub examples: Option<GoogleCloudAiplatformV1Examples>,
    pub integrated_gradients_attribution: Option<GoogleCloudAiplatformV1IntegratedGradientsAttribution>,
    pub sampled_shapley_attribution: Option<GoogleCloudAiplatformV1SampledShapleyAttribution>,
    pub xrai_attribution: Option<GoogleCloudAiplatformV1XraiAttribution>,
    pub output_indices: Option<Vec<Value>>,
}

Parameters to configure explaining for Model’s predictions.

This type is not used in any activity, and only used as part of another schema.

Fields

top_k: Option<i32>

If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.

examples: Option<GoogleCloudAiplatformV1Examples>

Example-based explanations that returns the nearest neighbors from the provided dataset.

integrated_gradients_attribution: Option<GoogleCloudAiplatformV1IntegratedGradientsAttribution>

An attribution method that computes Aumann-Shapley values taking advantage of the model’s fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365

sampled_shapley_attribution: Option<GoogleCloudAiplatformV1SampledShapleyAttribution>

An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.

xrai_attribution: Option<GoogleCloudAiplatformV1XraiAttribution>

An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model’s fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.

output_indices: Option<Vec<Value>>

If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it’s explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).

Trait Implementations

impl Clone for GoogleCloudAiplatformV1ExplanationParameters

fn clone(&self) -> GoogleCloudAiplatformV1ExplanationParameters

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for GoogleCloudAiplatformV1ExplanationParameters

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

Formats the value using the given formatter.

impl Default for GoogleCloudAiplatformV1ExplanationParameters

fn default() -> GoogleCloudAiplatformV1ExplanationParameters

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for GoogleCloudAiplatformV1ExplanationParameters

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for GoogleCloudAiplatformV1ExplanationParameters

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl Part for GoogleCloudAiplatformV1ExplanationParameters