com.google.cloud.aiplatform.v1

Interface ExplanationParametersOrBuilder

All Superinterfaces:

com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder

All Known Implementing Classes:

ExplanationParameters, ExplanationParameters.Builder

public interface ExplanationParametersOrBuilder
extends com.google.protobuf.MessageOrBuilder

Method Summary

Modifier and Type	Method and Description
Examples	getExamples() Example-based explanations that returns the nearest neighbors from the provided dataset.
ExamplesOrBuilder	getExamplesOrBuilder() Example-based explanations that returns the nearest neighbors from the provided dataset.
IntegratedGradientsAttribution	getIntegratedGradientsAttribution() An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure.
IntegratedGradientsAttributionOrBuilder	getIntegratedGradientsAttributionOrBuilder() An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure.
ExplanationParameters.MethodCase	getMethodCase()
com.google.protobuf.ListValue	getOutputIndices() If populated, only returns attributions that have [output_index][google.cloud.aiplatform.v1.Attribution.output_index] contained in output_indices.
com.google.protobuf.ListValueOrBuilder	getOutputIndicesOrBuilder() If populated, only returns attributions that have [output_index][google.cloud.aiplatform.v1.Attribution.output_index] contained in output_indices.
SampledShapleyAttribution	getSampledShapleyAttribution() An attribution method that approximates Shapley values for features that contribute to the label being predicted.
SampledShapleyAttributionOrBuilder	getSampledShapleyAttributionOrBuilder() An attribution method that approximates Shapley values for features that contribute to the label being predicted.
int	getTopK() If populated, returns attributions for top K indices of outputs (defaults to 1).
XraiAttribution	getXraiAttribution() An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure.
XraiAttributionOrBuilder	getXraiAttributionOrBuilder() An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure.
boolean	hasExamples() Example-based explanations that returns the nearest neighbors from the provided dataset.
boolean	hasIntegratedGradientsAttribution() An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure.
boolean	hasOutputIndices() If populated, only returns attributions that have [output_index][google.cloud.aiplatform.v1.Attribution.output_index] contained in output_indices.
boolean	hasSampledShapleyAttribution() An attribution method that approximates Shapley values for features that contribute to the label being predicted.
boolean	hasXraiAttribution() An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure.

Methods inherited from interface com.google.protobuf.MessageOrBuilder

findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof

Methods inherited from interface com.google.protobuf.MessageLiteOrBuilder

isInitialized

Method Detail

hasSampledShapleyAttribution

boolean hasSampledShapleyAttribution()

 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.
 

.google.cloud.aiplatform.v1.SampledShapleyAttribution sampled_shapley_attribution = 1;

Returns:

Whether the sampledShapleyAttribution field is set.

getSampledShapleyAttribution

SampledShapleyAttribution getSampledShapleyAttribution()

 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.
 

.google.cloud.aiplatform.v1.SampledShapleyAttribution sampled_shapley_attribution = 1;

Returns:

The sampledShapleyAttribution.

getSampledShapleyAttributionOrBuilder

SampledShapleyAttributionOrBuilder getSampledShapleyAttributionOrBuilder()

 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.
 

.google.cloud.aiplatform.v1.SampledShapleyAttribution sampled_shapley_attribution = 1;

hasIntegratedGradientsAttribution

boolean hasIntegratedGradientsAttribution()

 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
 

.google.cloud.aiplatform.v1.IntegratedGradientsAttribution integrated_gradients_attribution = 2;

Returns:

Whether the integratedGradientsAttribution field is set.

getIntegratedGradientsAttribution

IntegratedGradientsAttribution getIntegratedGradientsAttribution()

 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
 

.google.cloud.aiplatform.v1.IntegratedGradientsAttribution integrated_gradients_attribution = 2;

Returns:

The integratedGradientsAttribution.

getIntegratedGradientsAttributionOrBuilder

IntegratedGradientsAttributionOrBuilder getIntegratedGradientsAttributionOrBuilder()

 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
 

.google.cloud.aiplatform.v1.IntegratedGradientsAttribution integrated_gradients_attribution = 2;

hasXraiAttribution

boolean hasXraiAttribution()

 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.
 

.google.cloud.aiplatform.v1.XraiAttribution xrai_attribution = 3;

Returns:

Whether the xraiAttribution field is set.

getXraiAttribution

XraiAttribution getXraiAttribution()

 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.
 

.google.cloud.aiplatform.v1.XraiAttribution xrai_attribution = 3;

Returns:

The xraiAttribution.

getXraiAttributionOrBuilder

XraiAttributionOrBuilder getXraiAttributionOrBuilder()

 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.
 

.google.cloud.aiplatform.v1.XraiAttribution xrai_attribution = 3;

hasExamples

boolean hasExamples()

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

.google.cloud.aiplatform.v1.Examples examples = 7;

Returns:

Whether the examples field is set.

getExamples

Examples getExamples()

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

.google.cloud.aiplatform.v1.Examples examples = 7;

Returns:

The examples.

getExamplesOrBuilder

ExamplesOrBuilder getExamplesOrBuilder()

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

.google.cloud.aiplatform.v1.Examples examples = 7;

getTopK

int getTopK()

 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.
 

int32 top_k = 4;

Returns:

The topK.

hasOutputIndices

boolean hasOutputIndices()

 If populated, only returns attributions that have
 [output_index][google.cloud.aiplatform.v1.Attribution.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][google.cloud.aiplatform.v1.ExplanationParameters.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 output_indices = 5;

Returns:

Whether the outputIndices field is set.

getOutputIndices

com.google.protobuf.ListValue getOutputIndices()

 If populated, only returns attributions that have
 [output_index][google.cloud.aiplatform.v1.Attribution.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][google.cloud.aiplatform.v1.ExplanationParameters.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 output_indices = 5;

Returns:

The outputIndices.

getOutputIndicesOrBuilder

com.google.protobuf.ListValueOrBuilder getOutputIndicesOrBuilder()

 If populated, only returns attributions that have
 [output_index][google.cloud.aiplatform.v1.Attribution.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][google.cloud.aiplatform.v1.ExplanationParameters.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 output_indices = 5;

getMethodCase

ExplanationParameters.MethodCase getMethodCase()