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public interface ExplanationParametersOrBuilder extends MessageOrBuilderImplements
MessageOrBuilderMethods
getExamples()
public abstract Examples getExamples()Example-based explanations that returns the nearest neighbors from the provided dataset.
.google.cloud.aiplatform.v1beta1.Examples examples = 7;
| Type | Description |
| Examples | The examples. |
getExamplesOrBuilder()
public abstract ExamplesOrBuilder getExamplesOrBuilder()Example-based explanations that returns the nearest neighbors from the provided dataset.
.google.cloud.aiplatform.v1beta1.Examples examples = 7;
| Type | Description |
| ExamplesOrBuilder |
getIntegratedGradientsAttribution()
public abstract 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.v1beta1.IntegratedGradientsAttribution integrated_gradients_attribution = 2;
| Type | Description |
| IntegratedGradientsAttribution | The integratedGradientsAttribution. |
getIntegratedGradientsAttributionOrBuilder()
public abstract 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.v1beta1.IntegratedGradientsAttribution integrated_gradients_attribution = 2;
| Type | Description |
| IntegratedGradientsAttributionOrBuilder |
getMethodCase()
public abstract ExplanationParameters.MethodCase getMethodCase()| Type | Description |
| ExplanationParameters.MethodCase |
getOutputIndices()
public abstract ListValue getOutputIndices()If populated, only returns attributions that have 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 indices of outputs. If neither top_k nor output_indeices 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;
| Type | Description |
| ListValue | The outputIndices. |
getOutputIndicesOrBuilder()
public abstract ListValueOrBuilder getOutputIndicesOrBuilder()If populated, only returns attributions that have 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 indices of outputs. If neither top_k nor output_indeices 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;
| Type | Description |
| ListValueOrBuilder |
getSampledShapleyAttribution()
public abstract 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.v1beta1.SampledShapleyAttribution sampled_shapley_attribution = 1;
| Type | Description |
| SampledShapleyAttribution | The sampledShapleyAttribution. |
getSampledShapleyAttributionOrBuilder()
public abstract 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.v1beta1.SampledShapleyAttribution sampled_shapley_attribution = 1;
| Type | Description |
| SampledShapleyAttributionOrBuilder |
getTopK()
public abstract 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;
| Type | Description |
| int | The topK. |
getXraiAttribution()
public abstract 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.v1beta1.XraiAttribution xrai_attribution = 3;
| Type | Description |
| XraiAttribution | The xraiAttribution. |
getXraiAttributionOrBuilder()
public abstract 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.v1beta1.XraiAttribution xrai_attribution = 3;
| Type | Description |
| XraiAttributionOrBuilder |
hasExamples()
public abstract boolean hasExamples()Example-based explanations that returns the nearest neighbors from the provided dataset.
.google.cloud.aiplatform.v1beta1.Examples examples = 7;
| Type | Description |
| boolean | Whether the examples field is set. |
hasIntegratedGradientsAttribution()
public abstract 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.v1beta1.IntegratedGradientsAttribution integrated_gradients_attribution = 2;
| Type | Description |
| boolean | Whether the integratedGradientsAttribution field is set. |
hasOutputIndices()
public abstract boolean hasOutputIndices()If populated, only returns attributions that have 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 indices of outputs. If neither top_k nor output_indeices 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;
| Type | Description |
| boolean | Whether the outputIndices field is set. |
hasSampledShapleyAttribution()
public abstract 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.v1beta1.SampledShapleyAttribution sampled_shapley_attribution = 1;
| Type | Description |
| boolean | Whether the sampledShapleyAttribution field is set. |
hasXraiAttribution()
public abstract 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.v1beta1.XraiAttribution xrai_attribution = 3;
| Type | Description |
| boolean | Whether the xraiAttribution field is set. |