Package com.google.cloud.aiplatform.v1

Class ExplanationParameters.Builder

    • Method Detail

      • getDescriptor

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

      • internalGetFieldAccessorTable

        protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
        Specified by:
        internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessageV3.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.GeneratedMessageV3.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.GeneratedMessageV3.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

      • clone

        public ExplanationParameters.Builder clone()
        Specified by:
        clone in interface com.google.protobuf.Message.Builder
        Specified by:
        clone in interface com.google.protobuf.MessageLite.Builder
        Overrides:
        clone in class com.google.protobuf.GeneratedMessageV3.Builder<ExplanationParameters.Builder>

      • clearField

        public ExplanationParameters.Builder clearField​(com.google.protobuf.Descriptors.FieldDescriptor field)
        Specified by:
        clearField in interface com.google.protobuf.Message.Builder
        Overrides:
        clearField in class com.google.protobuf.GeneratedMessageV3.Builder<ExplanationParameters.Builder>

      • clearOneof

        public ExplanationParameters.Builder clearOneof​(com.google.protobuf.Descriptors.OneofDescriptor oneof)
        Specified by:
        clearOneof in interface com.google.protobuf.Message.Builder
        Overrides:
        clearOneof in class com.google.protobuf.GeneratedMessageV3.Builder<ExplanationParameters.Builder>

      • setRepeatedField

        public ExplanationParameters.Builder setRepeatedField​(com.google.protobuf.Descriptors.FieldDescriptor field,
                                                      int index,
                                                      Object value)
        Specified by:
        setRepeatedField in interface com.google.protobuf.Message.Builder
        Overrides:
        setRepeatedField in class com.google.protobuf.GeneratedMessageV3.Builder<ExplanationParameters.Builder>

      • addRepeatedField

        public ExplanationParameters.Builder addRepeatedField​(com.google.protobuf.Descriptors.FieldDescriptor field,
                                                      Object value)
        Specified by:
        addRepeatedField in interface com.google.protobuf.Message.Builder
        Overrides:
        addRepeatedField in class com.google.protobuf.GeneratedMessageV3.Builder<ExplanationParameters.Builder>

      • isInitialized

        public final boolean isInitialized()
        Specified by:
        isInitialized in interface com.google.protobuf.MessageLiteOrBuilder
        Overrides:
        isInitialized in class com.google.protobuf.GeneratedMessageV3.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

      • 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;

      • 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;

      • 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

      • setUnknownFields

        public final ExplanationParameters.Builder setUnknownFields​(com.google.protobuf.UnknownFieldSet unknownFields)
        Specified by:
        setUnknownFields in interface com.google.protobuf.Message.Builder
        Overrides:
        setUnknownFields in class com.google.protobuf.GeneratedMessageV3.Builder<ExplanationParameters.Builder>

      • mergeUnknownFields

        public final ExplanationParameters.Builder mergeUnknownFields​(com.google.protobuf.UnknownFieldSet unknownFields)
        Specified by:
        mergeUnknownFields in interface com.google.protobuf.Message.Builder
        Overrides:
        mergeUnknownFields in class com.google.protobuf.GeneratedMessageV3.Builder<ExplanationParameters.Builder>