Package com.google.cloud.aiplatform.v1

Interface ExplanationParametersOrBuilder

All Superinterfaces:
com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder
All Known Implementing Classes:
ExplanationParameters, ExplanationParameters.Builder
@Generated public interface ExplanationParametersOrBuilder extends com.google.protobuf.MessageOrBuilder

  • Method Summary

    Modifier and Type
    Method
    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.MessageLiteOrBuilder

    isInitialized

    Methods inherited from interface com.google.protobuf.MessageOrBuilder

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

  • Method Details

    • 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()