com.google.cloud.aiplatform.v1beta1

Interface ExplanationOrBuilder

All Superinterfaces:

com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder

All Known Implementing Classes:

Explanation, Explanation.Builder

public interface ExplanationOrBuilder
extends com.google.protobuf.MessageOrBuilder

Method Summary

Modifier and Type	Method and Description
Attribution	getAttributions(int index) Output only.
int	getAttributionsCount() Output only.
List<Attribution>	getAttributionsList() Output only.
AttributionOrBuilder	getAttributionsOrBuilder(int index) Output only.
List<? extends AttributionOrBuilder>	getAttributionsOrBuilderList() Output only.
Neighbor	getNeighbors(int index) Output only.
int	getNeighborsCount() Output only.
List<Neighbor>	getNeighborsList() Output only.
NeighborOrBuilder	getNeighborsOrBuilder(int index) Output only.
List<? extends NeighborOrBuilder>	getNeighborsOrBuilderList() Output only.

Methods inherited from interface com.google.protobuf.MessageOrBuilder

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

Methods inherited from interface com.google.protobuf.MessageLiteOrBuilder

isInitialized

Method Detail

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.v1beta1.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.v1beta1.ExplanationParameters.top_k],
 the attributions are sorted by
 [instance_output_value][Attributions.instance_output_value] in descending
 order. If
 [ExplanationParameters.output_indices][google.cloud.aiplatform.v1beta1.ExplanationParameters.output_indices]
 is specified, the attributions are stored by
 [Attribution.output_index][google.cloud.aiplatform.v1beta1.Attribution.output_index]
 in the same order as they appear in the output_indices.
 

repeated .google.cloud.aiplatform.v1beta1.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.v1beta1.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.v1beta1.ExplanationParameters.top_k],
 the attributions are sorted by
 [instance_output_value][Attributions.instance_output_value] in descending
 order. If
 [ExplanationParameters.output_indices][google.cloud.aiplatform.v1beta1.ExplanationParameters.output_indices]
 is specified, the attributions are stored by
 [Attribution.output_index][google.cloud.aiplatform.v1beta1.Attribution.output_index]
 in the same order as they appear in the output_indices.
 

repeated .google.cloud.aiplatform.v1beta1.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.v1beta1.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.v1beta1.ExplanationParameters.top_k],
 the attributions are sorted by
 [instance_output_value][Attributions.instance_output_value] in descending
 order. If
 [ExplanationParameters.output_indices][google.cloud.aiplatform.v1beta1.ExplanationParameters.output_indices]
 is specified, the attributions are stored by
 [Attribution.output_index][google.cloud.aiplatform.v1beta1.Attribution.output_index]
 in the same order as they appear in the output_indices.
 

repeated .google.cloud.aiplatform.v1beta1.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.v1beta1.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.v1beta1.ExplanationParameters.top_k],
 the attributions are sorted by
 [instance_output_value][Attributions.instance_output_value] in descending
 order. If
 [ExplanationParameters.output_indices][google.cloud.aiplatform.v1beta1.ExplanationParameters.output_indices]
 is specified, the attributions are stored by
 [Attribution.output_index][google.cloud.aiplatform.v1beta1.Attribution.output_index]
 in the same order as they appear in the output_indices.
 

repeated .google.cloud.aiplatform.v1beta1.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.v1beta1.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.v1beta1.ExplanationParameters.top_k],
 the attributions are sorted by
 [instance_output_value][Attributions.instance_output_value] in descending
 order. If
 [ExplanationParameters.output_indices][google.cloud.aiplatform.v1beta1.ExplanationParameters.output_indices]
 is specified, the attributions are stored by
 [Attribution.output_index][google.cloud.aiplatform.v1beta1.Attribution.output_index]
 in the same order as they appear in the output_indices.
 

repeated .google.cloud.aiplatform.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.Neighbor neighbors = 2 [(.google.api.field_behavior) = OUTPUT_ONLY];