Vertex AI V1 API - Class Google::Cloud::AIPlatform::V1::ExplanationParameters (v0.51.0)

Version 0.51.0

• 0.58.0 (latest)
• 0.57.0
• 0.56.0
• 0.55.0
• 0.54.0
• 0.53.0
• 0.52.0
• 0.51.0
• 0.50.0
• 0.49.0
• 0.48.0
• 0.47.0
• 0.46.0
• 0.45.0
• 0.44.0
• 0.43.0
• 0.42.0
• 0.41.0
• 0.40.0
• 0.39.0
• 0.38.0
• 0.37.0
• 0.36.0
• 0.35.0
• 0.34.0
• 0.33.0
• 0.32.0
• 0.31.0
• 0.30.0
• 0.29.0
• 0.28.0
• 0.27.0
• 0.26.0
• 0.25.0
• 0.24.0
• 0.23.0
• 0.22.0
• 0.21.0
• 0.20.0
• 0.19.0
• 0.18.0
• 0.17.0
• 0.16.0
• 0.15.0
• 0.14.0
• 0.13.0
• 0.12.0
• 0.11.0
• 0.10.0
• 0.9.1
• 0.8.0
• 0.7.0
• 0.6.0
• 0.5.0
• 0.4.0
• 0.3.0
• 0.2.0
• 0.1.0

Reference documentation and code samples for the Vertex AI V1 API class Google::Cloud::AIPlatform::V1::ExplanationParameters.

Parameters to configure explaining for Model's predictions.

Inherits

• Object

Extended By

• Google::Protobuf::MessageExts::ClassMethods

Includes

• Google::Protobuf::MessageExts

Methods

#examples

def examples() -> ::Google::Cloud::AIPlatform::V1::Examples

Returns

• (::Google::Cloud::AIPlatform::V1::Examples) — Example-based explanations that returns the nearest neighbors from the provided dataset.

#examples=

def examples=(value) -> ::Google::Cloud::AIPlatform::V1::Examples

Parameter

• value (::Google::Cloud::AIPlatform::V1::Examples) — Example-based explanations that returns the nearest neighbors from the provided dataset.

Returns

• (::Google::Cloud::AIPlatform::V1::Examples) — Example-based explanations that returns the nearest neighbors from the provided dataset.

#integrated_gradients_attribution

def integrated_gradients_attribution() -> ::Google::Cloud::AIPlatform::V1::IntegratedGradientsAttribution

Returns

• (::Google::Cloud::AIPlatform::V1::IntegratedGradientsAttribution) — 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

#integrated_gradients_attribution=

def integrated_gradients_attribution=(value) -> ::Google::Cloud::AIPlatform::V1::IntegratedGradientsAttribution

Parameter

• value (::Google::Cloud::AIPlatform::V1::IntegratedGradientsAttribution) — 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

Returns

• (::Google::Cloud::AIPlatform::V1::IntegratedGradientsAttribution) — 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

#output_indices

def output_indices() -> ::Google::Protobuf::ListValue

Returns

• (::Google::Protobuf::ListValue) — 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).

#output_indices=

def output_indices=(value) -> ::Google::Protobuf::ListValue

Parameter

• value (::Google::Protobuf::ListValue) — 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).

Returns

• (::Google::Protobuf::ListValue) — 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).

#sampled_shapley_attribution

def sampled_shapley_attribution() -> ::Google::Cloud::AIPlatform::V1::SampledShapleyAttribution

Returns

• (::Google::Cloud::AIPlatform::V1::SampledShapleyAttribution) — 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.

#sampled_shapley_attribution=

def sampled_shapley_attribution=(value) -> ::Google::Cloud::AIPlatform::V1::SampledShapleyAttribution

Parameter

• value (::Google::Cloud::AIPlatform::V1::SampledShapleyAttribution) — 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.

Returns

• (::Google::Cloud::AIPlatform::V1::SampledShapleyAttribution) — 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.

#top_k

def top_k() -> ::Integer

Returns

• (::Integer) — 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.

#top_k=

def top_k=(value) -> ::Integer

Parameter

• value (::Integer) — 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.

Returns

• (::Integer) — 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.

#xrai_attribution

def xrai_attribution() -> ::Google::Cloud::AIPlatform::V1::XraiAttribution

Returns

• (::Google::Cloud::AIPlatform::V1::XraiAttribution) — 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.

#xrai_attribution=

def xrai_attribution=(value) -> ::Google::Cloud::AIPlatform::V1::XraiAttribution

Parameter

• value (::Google::Cloud::AIPlatform::V1::XraiAttribution) — 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.

Returns

• (::Google::Cloud::AIPlatform::V1::XraiAttribution) — 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.