Package com.google.cloud.aiplatform.v1

Class ExplanationParameters.Builder

java.lang.Object
com.google.protobuf.AbstractMessageLite.Builder
com.google.protobuf.AbstractMessage.Builder<BuilderT>
com.google.protobuf.GeneratedMessage.Builder<ExplanationParameters.Builder>
com.google.cloud.aiplatform.v1.ExplanationParameters.Builder
All Implemented Interfaces:
ExplanationParametersOrBuilder, com.google.protobuf.Message.Builder, com.google.protobuf.MessageLite.Builder, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, Cloneable
Enclosing class:
ExplanationParameters
public static final class ExplanationParameters.Builder extends com.google.protobuf.GeneratedMessage.Builder<ExplanationParameters.Builder> implements ExplanationParametersOrBuilder
 Parameters to configure explaining for Model's predictions.
Protobuf type google.cloud.aiplatform.v1.ExplanationParameters

  • Method Details

    • getDescriptor

      public static final com.google.protobuf.Descriptors.Descriptor getDescriptor()

    • internalGetFieldAccessorTable

      protected com.google.protobuf.GeneratedMessage.FieldAccessorTable internalGetFieldAccessorTable()
      Specified by:
      internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessage.Builder<ExplanationParameters.Builder>

    • clear

      public ExplanationParameters.Builder clear()
      Specified by:
      clear in interface com.google.protobuf.Message.Builder
      Specified by:
      clear in interface com.google.protobuf.MessageLite.Builder
      Overrides:
      clear in class com.google.protobuf.GeneratedMessage.Builder<ExplanationParameters.Builder>

    • getDescriptorForType

      public com.google.protobuf.Descriptors.Descriptor getDescriptorForType()
      Specified by:
      getDescriptorForType in interface com.google.protobuf.Message.Builder
      Specified by:
      getDescriptorForType in interface com.google.protobuf.MessageOrBuilder
      Overrides:
      getDescriptorForType in class com.google.protobuf.GeneratedMessage.Builder<ExplanationParameters.Builder>

    • getDefaultInstanceForType

      public ExplanationParameters getDefaultInstanceForType()
      Specified by:
      getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuilder
      Specified by:
      getDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilder

    • build

      public ExplanationParameters build()
      Specified by:
      build in interface com.google.protobuf.Message.Builder
      Specified by:
      build in interface com.google.protobuf.MessageLite.Builder

    • buildPartial

      public ExplanationParameters buildPartial()
      Specified by:
      buildPartial in interface com.google.protobuf.Message.Builder
      Specified by:
      buildPartial in interface com.google.protobuf.MessageLite.Builder

    • mergeFrom

      public ExplanationParameters.Builder mergeFrom(com.google.protobuf.Message other)
      Specified by:
      mergeFrom in interface com.google.protobuf.Message.Builder
      Overrides:
      mergeFrom in class com.google.protobuf.AbstractMessage.Builder<ExplanationParameters.Builder>

    • mergeFrom

      public ExplanationParameters.Builder mergeFrom(ExplanationParameters other)

    • isInitialized

      public final boolean isInitialized()
      Specified by:
      isInitialized in interface com.google.protobuf.MessageLiteOrBuilder
      Overrides:
      isInitialized in class com.google.protobuf.GeneratedMessage.Builder<ExplanationParameters.Builder>

    • mergeFrom

      public ExplanationParameters.Builder mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws IOException
      Specified by:
      mergeFrom in interface com.google.protobuf.Message.Builder
      Specified by:
      mergeFrom in interface com.google.protobuf.MessageLite.Builder
      Overrides:
      mergeFrom in class com.google.protobuf.AbstractMessage.Builder<ExplanationParameters.Builder>
      Throws:
      IOException

    • getMethodCase

      public ExplanationParameters.MethodCase getMethodCase()
      Specified by:
      getMethodCase in interface ExplanationParametersOrBuilder

    • clearMethod

      public ExplanationParameters.Builder clearMethod()

    • hasSampledShapleyAttribution

      public 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;
      Specified by:
      hasSampledShapleyAttribution in interface ExplanationParametersOrBuilder
      Returns:
      Whether the sampledShapleyAttribution field is set.

    • getSampledShapleyAttribution

      public 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;
      Specified by:
      getSampledShapleyAttribution in interface ExplanationParametersOrBuilder
      Returns:
      The sampledShapleyAttribution.

    • setSampledShapleyAttribution

      public ExplanationParameters.Builder setSampledShapleyAttribution(SampledShapleyAttribution value)
       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;

    • setSampledShapleyAttribution

      public ExplanationParameters.Builder setSampledShapleyAttribution(SampledShapleyAttribution.Builder builderForValue)
       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;

    • mergeSampledShapleyAttribution

      public ExplanationParameters.Builder mergeSampledShapleyAttribution(SampledShapleyAttribution value)
       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;

    • clearSampledShapleyAttribution

      public ExplanationParameters.Builder clearSampledShapleyAttribution()
       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;

    • getSampledShapleyAttributionBuilder

      public SampledShapleyAttribution.Builder getSampledShapleyAttributionBuilder()
       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;

    • getSampledShapleyAttributionOrBuilder

      public 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;
      Specified by:
      getSampledShapleyAttributionOrBuilder in interface ExplanationParametersOrBuilder

    • hasIntegratedGradientsAttribution

      public 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;
      Specified by:
      hasIntegratedGradientsAttribution in interface ExplanationParametersOrBuilder
      Returns:
      Whether the integratedGradientsAttribution field is set.

    • getIntegratedGradientsAttribution

      public 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;
      Specified by:
      getIntegratedGradientsAttribution in interface ExplanationParametersOrBuilder
      Returns:
      The integratedGradientsAttribution.

    • setIntegratedGradientsAttribution

      public ExplanationParameters.Builder setIntegratedGradientsAttribution(IntegratedGradientsAttribution value)
       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;

    • setIntegratedGradientsAttribution

      public ExplanationParameters.Builder setIntegratedGradientsAttribution(IntegratedGradientsAttribution.Builder builderForValue)
       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;

    • mergeIntegratedGradientsAttribution

      public ExplanationParameters.Builder mergeIntegratedGradientsAttribution(IntegratedGradientsAttribution value)
       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;

    • clearIntegratedGradientsAttribution

      public ExplanationParameters.Builder clearIntegratedGradientsAttribution()
       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;

    • getIntegratedGradientsAttributionBuilder

      public IntegratedGradientsAttribution.Builder getIntegratedGradientsAttributionBuilder()
       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;

    • getIntegratedGradientsAttributionOrBuilder

      public 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;
      Specified by:
      getIntegratedGradientsAttributionOrBuilder in interface ExplanationParametersOrBuilder

    • hasXraiAttribution

      public 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;
      Specified by:
      hasXraiAttribution in interface ExplanationParametersOrBuilder
      Returns:
      Whether the xraiAttribution field is set.

    • getXraiAttribution

      public 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;
      Specified by:
      getXraiAttribution in interface ExplanationParametersOrBuilder
      Returns:
      The xraiAttribution.

    • setXraiAttribution

      public ExplanationParameters.Builder setXraiAttribution(XraiAttribution value)
       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;

    • setXraiAttribution

      public ExplanationParameters.Builder setXraiAttribution(XraiAttribution.Builder builderForValue)
       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;

    • mergeXraiAttribution

      public ExplanationParameters.Builder mergeXraiAttribution(XraiAttribution value)
       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;

    • clearXraiAttribution

      public ExplanationParameters.Builder clearXraiAttribution()
       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;

    • getXraiAttributionBuilder

      public XraiAttribution.Builder getXraiAttributionBuilder()
       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;

    • getXraiAttributionOrBuilder

      public 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;
      Specified by:
      getXraiAttributionOrBuilder in interface ExplanationParametersOrBuilder

    • hasExamples

      public boolean hasExamples()
       Example-based explanations that returns the nearest neighbors from the
 provided dataset.
      .google.cloud.aiplatform.v1.Examples examples = 7;
      Specified by:
      hasExamples in interface ExplanationParametersOrBuilder
      Returns:
      Whether the examples field is set.

    • getExamples

      public Examples getExamples()
       Example-based explanations that returns the nearest neighbors from the
 provided dataset.
      .google.cloud.aiplatform.v1.Examples examples = 7;
      Specified by:
      getExamples in interface ExplanationParametersOrBuilder
      Returns:
      The examples.

    • setExamples

      public ExplanationParameters.Builder setExamples(Examples value)
       Example-based explanations that returns the nearest neighbors from the
 provided dataset.
      .google.cloud.aiplatform.v1.Examples examples = 7;

    • setExamples

      public ExplanationParameters.Builder setExamples(Examples.Builder builderForValue)
       Example-based explanations that returns the nearest neighbors from the
 provided dataset.
      .google.cloud.aiplatform.v1.Examples examples = 7;

    • mergeExamples

      public ExplanationParameters.Builder mergeExamples(Examples value)
       Example-based explanations that returns the nearest neighbors from the
 provided dataset.
      .google.cloud.aiplatform.v1.Examples examples = 7;

    • clearExamples

      public ExplanationParameters.Builder clearExamples()
       Example-based explanations that returns the nearest neighbors from the
 provided dataset.
      .google.cloud.aiplatform.v1.Examples examples = 7;

    • getExamplesBuilder

      public Examples.Builder getExamplesBuilder()
       Example-based explanations that returns the nearest neighbors from the
 provided dataset.
      .google.cloud.aiplatform.v1.Examples examples = 7;

    • getExamplesOrBuilder

      public ExamplesOrBuilder getExamplesOrBuilder()
       Example-based explanations that returns the nearest neighbors from the
 provided dataset.
      .google.cloud.aiplatform.v1.Examples examples = 7;
      Specified by:
      getExamplesOrBuilder in interface ExplanationParametersOrBuilder

    • getTopK

      public 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;
      Specified by:
      getTopK in interface ExplanationParametersOrBuilder
      Returns:
      The topK.

    • setTopK

      public ExplanationParameters.Builder setTopK(int value)
       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;
      Parameters:
      value - The topK to set.
      Returns:
      This builder for chaining.

    • clearTopK

      public ExplanationParameters.Builder clearTopK()
       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:
      This builder for chaining.

    • hasOutputIndices

      public 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;
      Specified by:
      hasOutputIndices in interface ExplanationParametersOrBuilder
      Returns:
      Whether the outputIndices field is set.

    • getOutputIndices

      public 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;
      Specified by:
      getOutputIndices in interface ExplanationParametersOrBuilder
      Returns:
      The outputIndices.

    • setOutputIndices

      public ExplanationParameters.Builder setOutputIndices(com.google.protobuf.ListValue value)
       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;

    • setOutputIndices

      public ExplanationParameters.Builder setOutputIndices(com.google.protobuf.ListValue.Builder builderForValue)
       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;

    • mergeOutputIndices

      public ExplanationParameters.Builder mergeOutputIndices(com.google.protobuf.ListValue value)
       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;

    • clearOutputIndices

      public ExplanationParameters.Builder clearOutputIndices()
       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;

    • getOutputIndicesBuilder

      public com.google.protobuf.ListValue.Builder getOutputIndicesBuilder()
       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;

    • getOutputIndicesOrBuilder

      public 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;
      Specified by:
      getOutputIndicesOrBuilder in interface ExplanationParametersOrBuilder