Package com.google.cloud.aiplatform.v1

Interface ExplanationOrBuilder

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

  • Method Details

    • getAttributionsList

      List<Attribution> getAttributionsList()
       Output only. Feature attributions grouped by predicted outputs.

 For Models that predict only one output, such as regression Models that
 predict only one score, there is only one attibution that explains the
 predicted output. For Models that predict multiple outputs, such as
 multiclass Models that predict multiple classes, each element explains one
 specific item.
 [Attribution.output_index][google.cloud.aiplatform.v1.Attribution.output_index]
 can be used to identify which output this attribution is explaining.

 By default, we provide Shapley values for the predicted class. However,
 you can configure the explanation request to generate Shapley values for
 any other classes too. For example, if a model predicts a probability of
 `0.4` for approving a loan application, the model's decision is to reject
 the application since `p(reject) = 0.6 > p(approve) = 0.4`, and the default
 Shapley values would be computed for rejection decision and not approval,
 even though the latter might be the positive class.

 If users set
 [ExplanationParameters.top_k][google.cloud.aiplatform.v1.ExplanationParameters.top_k],
 the attributions are sorted by
 [instance_output_value][google.cloud.aiplatform.v1.Attribution.instance_output_value]
 in descending order. If
 [ExplanationParameters.output_indices][google.cloud.aiplatform.v1.ExplanationParameters.output_indices]
 is specified, the attributions are stored by
 [Attribution.output_index][google.cloud.aiplatform.v1.Attribution.output_index]
 in the same order as they appear in the output_indices.
      repeated .google.cloud.aiplatform.v1.Attribution attributions = 1 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getAttributions

      Attribution getAttributions(int index)
       Output only. Feature attributions grouped by predicted outputs.

 For Models that predict only one output, such as regression Models that
 predict only one score, there is only one attibution that explains the
 predicted output. For Models that predict multiple outputs, such as
 multiclass Models that predict multiple classes, each element explains one
 specific item.
 [Attribution.output_index][google.cloud.aiplatform.v1.Attribution.output_index]
 can be used to identify which output this attribution is explaining.

 By default, we provide Shapley values for the predicted class. However,
 you can configure the explanation request to generate Shapley values for
 any other classes too. For example, if a model predicts a probability of
 `0.4` for approving a loan application, the model's decision is to reject
 the application since `p(reject) = 0.6 > p(approve) = 0.4`, and the default
 Shapley values would be computed for rejection decision and not approval,
 even though the latter might be the positive class.

 If users set
 [ExplanationParameters.top_k][google.cloud.aiplatform.v1.ExplanationParameters.top_k],
 the attributions are sorted by
 [instance_output_value][google.cloud.aiplatform.v1.Attribution.instance_output_value]
 in descending order. If
 [ExplanationParameters.output_indices][google.cloud.aiplatform.v1.ExplanationParameters.output_indices]
 is specified, the attributions are stored by
 [Attribution.output_index][google.cloud.aiplatform.v1.Attribution.output_index]
 in the same order as they appear in the output_indices.
      repeated .google.cloud.aiplatform.v1.Attribution attributions = 1 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getAttributionsCount

      int getAttributionsCount()
       Output only. Feature attributions grouped by predicted outputs.

 For Models that predict only one output, such as regression Models that
 predict only one score, there is only one attibution that explains the
 predicted output. For Models that predict multiple outputs, such as
 multiclass Models that predict multiple classes, each element explains one
 specific item.
 [Attribution.output_index][google.cloud.aiplatform.v1.Attribution.output_index]
 can be used to identify which output this attribution is explaining.

 By default, we provide Shapley values for the predicted class. However,
 you can configure the explanation request to generate Shapley values for
 any other classes too. For example, if a model predicts a probability of
 `0.4` for approving a loan application, the model's decision is to reject
 the application since `p(reject) = 0.6 > p(approve) = 0.4`, and the default
 Shapley values would be computed for rejection decision and not approval,
 even though the latter might be the positive class.

 If users set
 [ExplanationParameters.top_k][google.cloud.aiplatform.v1.ExplanationParameters.top_k],
 the attributions are sorted by
 [instance_output_value][google.cloud.aiplatform.v1.Attribution.instance_output_value]
 in descending order. If
 [ExplanationParameters.output_indices][google.cloud.aiplatform.v1.ExplanationParameters.output_indices]
 is specified, the attributions are stored by
 [Attribution.output_index][google.cloud.aiplatform.v1.Attribution.output_index]
 in the same order as they appear in the output_indices.
      repeated .google.cloud.aiplatform.v1.Attribution attributions = 1 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getAttributionsOrBuilderList

      List<? extends AttributionOrBuilder> getAttributionsOrBuilderList()
       Output only. Feature attributions grouped by predicted outputs.

 For Models that predict only one output, such as regression Models that
 predict only one score, there is only one attibution that explains the
 predicted output. For Models that predict multiple outputs, such as
 multiclass Models that predict multiple classes, each element explains one
 specific item.
 [Attribution.output_index][google.cloud.aiplatform.v1.Attribution.output_index]
 can be used to identify which output this attribution is explaining.

 By default, we provide Shapley values for the predicted class. However,
 you can configure the explanation request to generate Shapley values for
 any other classes too. For example, if a model predicts a probability of
 `0.4` for approving a loan application, the model's decision is to reject
 the application since `p(reject) = 0.6 > p(approve) = 0.4`, and the default
 Shapley values would be computed for rejection decision and not approval,
 even though the latter might be the positive class.

 If users set
 [ExplanationParameters.top_k][google.cloud.aiplatform.v1.ExplanationParameters.top_k],
 the attributions are sorted by
 [instance_output_value][google.cloud.aiplatform.v1.Attribution.instance_output_value]
 in descending order. If
 [ExplanationParameters.output_indices][google.cloud.aiplatform.v1.ExplanationParameters.output_indices]
 is specified, the attributions are stored by
 [Attribution.output_index][google.cloud.aiplatform.v1.Attribution.output_index]
 in the same order as they appear in the output_indices.
      repeated .google.cloud.aiplatform.v1.Attribution attributions = 1 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getAttributionsOrBuilder

      AttributionOrBuilder getAttributionsOrBuilder(int index)
       Output only. Feature attributions grouped by predicted outputs.

 For Models that predict only one output, such as regression Models that
 predict only one score, there is only one attibution that explains the
 predicted output. For Models that predict multiple outputs, such as
 multiclass Models that predict multiple classes, each element explains one
 specific item.
 [Attribution.output_index][google.cloud.aiplatform.v1.Attribution.output_index]
 can be used to identify which output this attribution is explaining.

 By default, we provide Shapley values for the predicted class. However,
 you can configure the explanation request to generate Shapley values for
 any other classes too. For example, if a model predicts a probability of
 `0.4` for approving a loan application, the model's decision is to reject
 the application since `p(reject) = 0.6 > p(approve) = 0.4`, and the default
 Shapley values would be computed for rejection decision and not approval,
 even though the latter might be the positive class.

 If users set
 [ExplanationParameters.top_k][google.cloud.aiplatform.v1.ExplanationParameters.top_k],
 the attributions are sorted by
 [instance_output_value][google.cloud.aiplatform.v1.Attribution.instance_output_value]
 in descending order. If
 [ExplanationParameters.output_indices][google.cloud.aiplatform.v1.ExplanationParameters.output_indices]
 is specified, the attributions are stored by
 [Attribution.output_index][google.cloud.aiplatform.v1.Attribution.output_index]
 in the same order as they appear in the output_indices.
      repeated .google.cloud.aiplatform.v1.Attribution attributions = 1 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getNeighborsList

      List<Neighbor> getNeighborsList()
       Output only. List of the nearest neighbors for example-based explanations.

 For models deployed with the examples explanations feature enabled, the
 attributions field is empty and instead the neighbors field is populated.
      repeated .google.cloud.aiplatform.v1.Neighbor neighbors = 2 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getNeighbors

      Neighbor getNeighbors(int index)
       Output only. List of the nearest neighbors for example-based explanations.

 For models deployed with the examples explanations feature enabled, the
 attributions field is empty and instead the neighbors field is populated.
      repeated .google.cloud.aiplatform.v1.Neighbor neighbors = 2 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getNeighborsCount

      int getNeighborsCount()
       Output only. List of the nearest neighbors for example-based explanations.

 For models deployed with the examples explanations feature enabled, the
 attributions field is empty and instead the neighbors field is populated.
      repeated .google.cloud.aiplatform.v1.Neighbor neighbors = 2 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getNeighborsOrBuilderList

      List<? extends NeighborOrBuilder> getNeighborsOrBuilderList()
       Output only. List of the nearest neighbors for example-based explanations.

 For models deployed with the examples explanations feature enabled, the
 attributions field is empty and instead the neighbors field is populated.
      repeated .google.cloud.aiplatform.v1.Neighbor neighbors = 2 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getNeighborsOrBuilder

      NeighborOrBuilder getNeighborsOrBuilder(int index)
       Output only. List of the nearest neighbors for example-based explanations.

 For models deployed with the examples explanations feature enabled, the
 attributions field is empty and instead the neighbors field is populated.
      repeated .google.cloud.aiplatform.v1.Neighbor neighbors = 2 [(.google.api.field_behavior) = OUTPUT_ONLY];