Class ExplanationParameters

java.lang.Object
com.google.protobuf.AbstractMessageLite
com.google.protobuf.AbstractMessage
com.google.protobuf.GeneratedMessage
com.google.cloud.aiplatform.v1.ExplanationParameters
All Implemented Interfaces:
ExplanationParametersOrBuilder, com.google.protobuf.Message, com.google.protobuf.MessageLite, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, Serializable

@Generated public final class ExplanationParameters extends com.google.protobuf.GeneratedMessage implements ExplanationParametersOrBuilder
 Parameters to configure explaining for Model's predictions.
 
Protobuf type google.cloud.aiplatform.v1.ExplanationParameters
See Also:
  • Field Details

    • SAMPLED_SHAPLEY_ATTRIBUTION_FIELD_NUMBER

      public static final int SAMPLED_SHAPLEY_ATTRIBUTION_FIELD_NUMBER
      See Also:
    • INTEGRATED_GRADIENTS_ATTRIBUTION_FIELD_NUMBER

      public static final int INTEGRATED_GRADIENTS_ATTRIBUTION_FIELD_NUMBER
      See Also:
    • XRAI_ATTRIBUTION_FIELD_NUMBER

      public static final int XRAI_ATTRIBUTION_FIELD_NUMBER
      See Also:
    • EXAMPLES_FIELD_NUMBER

      public static final int EXAMPLES_FIELD_NUMBER
      See Also:
    • TOP_K_FIELD_NUMBER

      public static final int TOP_K_FIELD_NUMBER
      See Also:
    • OUTPUT_INDICES_FIELD_NUMBER

      public static final int OUTPUT_INDICES_FIELD_NUMBER
      See Also:
  • 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
    • getMethodCase

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

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

      public void writeTo(com.google.protobuf.CodedOutputStream output) throws IOException
      Specified by:
      writeTo in interface com.google.protobuf.MessageLite
      Overrides:
      writeTo in class com.google.protobuf.GeneratedMessage
      Throws:
      IOException
    • getSerializedSize

      public int getSerializedSize()
      Specified by:
      getSerializedSize in interface com.google.protobuf.MessageLite
      Overrides:
      getSerializedSize in class com.google.protobuf.GeneratedMessage
    • equals

      public boolean equals(Object obj)
      Specified by:
      equals in interface com.google.protobuf.Message
      Overrides:
      equals in class com.google.protobuf.AbstractMessage
    • hashCode

      public int hashCode()
      Specified by:
      hashCode in interface com.google.protobuf.Message
      Overrides:
      hashCode in class com.google.protobuf.AbstractMessage
    • parseFrom

      public static ExplanationParameters parseFrom(ByteBuffer data) throws com.google.protobuf.InvalidProtocolBufferException
      Throws:
      com.google.protobuf.InvalidProtocolBufferException
    • parseFrom

      public static ExplanationParameters parseFrom(ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException
      Throws:
      com.google.protobuf.InvalidProtocolBufferException
    • parseFrom

      public static ExplanationParameters parseFrom(com.google.protobuf.ByteString data) throws com.google.protobuf.InvalidProtocolBufferException
      Throws:
      com.google.protobuf.InvalidProtocolBufferException
    • parseFrom

      public static ExplanationParameters parseFrom(com.google.protobuf.ByteString data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException
      Throws:
      com.google.protobuf.InvalidProtocolBufferException
    • parseFrom

      public static ExplanationParameters parseFrom(byte[] data) throws com.google.protobuf.InvalidProtocolBufferException
      Throws:
      com.google.protobuf.InvalidProtocolBufferException
    • parseFrom

      public static ExplanationParameters parseFrom(byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException
      Throws:
      com.google.protobuf.InvalidProtocolBufferException
    • parseFrom

      public static ExplanationParameters parseFrom(InputStream input) throws IOException
      Throws:
      IOException
    • parseFrom

      public static ExplanationParameters parseFrom(InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws IOException
      Throws:
      IOException
    • parseDelimitedFrom

      public static ExplanationParameters parseDelimitedFrom(InputStream input) throws IOException
      Throws:
      IOException
    • parseDelimitedFrom

      public static ExplanationParameters parseDelimitedFrom(InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws IOException
      Throws:
      IOException
    • parseFrom

      public static ExplanationParameters parseFrom(com.google.protobuf.CodedInputStream input) throws IOException
      Throws:
      IOException
    • parseFrom

      public static ExplanationParameters parseFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws IOException
      Throws:
      IOException
    • newBuilderForType

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

      public static ExplanationParameters.Builder newBuilder()
    • newBuilder

      public static ExplanationParameters.Builder newBuilder(ExplanationParameters prototype)
    • toBuilder

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

      protected ExplanationParameters.Builder newBuilderForType(com.google.protobuf.AbstractMessage.BuilderParent parent)
      Overrides:
      newBuilderForType in class com.google.protobuf.AbstractMessage
    • getDefaultInstance

      public static ExplanationParameters getDefaultInstance()
    • parser

      public static com.google.protobuf.Parser<ExplanationParameters> parser()
    • getParserForType

      public com.google.protobuf.Parser<ExplanationParameters> getParserForType()
      Specified by:
      getParserForType in interface com.google.protobuf.Message
      Specified by:
      getParserForType in interface com.google.protobuf.MessageLite
      Overrides:
      getParserForType in class com.google.protobuf.GeneratedMessage
    • getDefaultInstanceForType

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