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-
(::Google::Cloud::AIPlatform::V1::Examples) — Example-based explanations that returns the nearest neighbors from the
provided dataset.
Note: The following fields are mutually exclusive:
examples,sampled_shapley_attribution,integrated_gradients_attribution,xrai_attribution. If a field in that set is populated, all other fields in the set will automatically be cleared.
#examples=
def examples=(value) -> ::Google::Cloud::AIPlatform::V1::Examples-
value (::Google::Cloud::AIPlatform::V1::Examples) — Example-based explanations that returns the nearest neighbors from the
provided dataset.
Note: The following fields are mutually exclusive:
examples,sampled_shapley_attribution,integrated_gradients_attribution,xrai_attribution. If a field in that set is populated, all other fields in the set will automatically be cleared.
-
(::Google::Cloud::AIPlatform::V1::Examples) — Example-based explanations that returns the nearest neighbors from the
provided dataset.
Note: The following fields are mutually exclusive:
examples,sampled_shapley_attribution,integrated_gradients_attribution,xrai_attribution. If a field in that set is populated, all other fields in the set will automatically be cleared.
#integrated_gradients_attribution
def integrated_gradients_attribution() -> ::Google::Cloud::AIPlatform::V1::IntegratedGradientsAttribution-
(::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
Note: The following fields are mutually exclusive:
integrated_gradients_attribution,sampled_shapley_attribution,xrai_attribution,examples. If a field in that set is populated, all other fields in the set will automatically be cleared.
#integrated_gradients_attribution=
def integrated_gradients_attribution=(value) -> ::Google::Cloud::AIPlatform::V1::IntegratedGradientsAttribution-
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
Note: The following fields are mutually exclusive:
integrated_gradients_attribution,sampled_shapley_attribution,xrai_attribution,examples. If a field in that set is populated, all other fields in the set will automatically be cleared.
-
(::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
Note: The following fields are mutually exclusive:
integrated_gradients_attribution,sampled_shapley_attribution,xrai_attribution,examples. If a field in that set is populated, all other fields in the set will automatically be cleared.
#output_indices
def output_indices() -> ::Google::Protobuf::ListValue-
(::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-
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).
-
(::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-
(::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.
Note: The following fields are mutually exclusive:
sampled_shapley_attribution,integrated_gradients_attribution,xrai_attribution,examples. If a field in that set is populated, all other fields in the set will automatically be cleared.
#sampled_shapley_attribution=
def sampled_shapley_attribution=(value) -> ::Google::Cloud::AIPlatform::V1::SampledShapleyAttribution-
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.
Note: The following fields are mutually exclusive:
sampled_shapley_attribution,integrated_gradients_attribution,xrai_attribution,examples. If a field in that set is populated, all other fields in the set will automatically be cleared.
-
(::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.
Note: The following fields are mutually exclusive:
sampled_shapley_attribution,integrated_gradients_attribution,xrai_attribution,examples. If a field in that set is populated, all other fields in the set will automatically be cleared.
#top_k
def top_k() -> ::Integer- (::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- 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.
- (::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-
(::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.
Note: The following fields are mutually exclusive:
xrai_attribution,sampled_shapley_attribution,integrated_gradients_attribution,examples. If a field in that set is populated, all other fields in the set will automatically be cleared.
#xrai_attribution=
def xrai_attribution=(value) -> ::Google::Cloud::AIPlatform::V1::XraiAttribution-
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.
Note: The following fields are mutually exclusive:
xrai_attribution,sampled_shapley_attribution,integrated_gradients_attribution,examples. If a field in that set is populated, all other fields in the set will automatically be cleared.
-
(::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.
Note: The following fields are mutually exclusive:
xrai_attribution,sampled_shapley_attribution,integrated_gradients_attribution,examples. If a field in that set is populated, all other fields in the set will automatically be cleared.