Google Cloud Ai Platform V1 Client - Class ExplanationParameters (0.27.0)

Reference documentation and code samples for the Google Cloud Ai Platform V1 Client class ExplanationParameters.

Parameters to configure explaining for Model's predictions.

Generated from protobuf message google.cloud.aiplatform.v1.ExplanationParameters

Namespace

Google \ Cloud \ AIPlatform \ V1

Methods

__construct

Constructor.

Parameters
NameDescription
data array

Optional. Data for populating the Message object.

↳ sampled_shapley_attribution Google\Cloud\AIPlatform\V1\SampledShapleyAttribution

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.

↳ integrated_gradients_attribution Google\Cloud\AIPlatform\V1\IntegratedGradientsAttribution

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

↳ xrai_attribution Google\Cloud\AIPlatform\V1\XraiAttribution

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.

↳ examples Google\Cloud\AIPlatform\V1\Examples

Example-based explanations that returns the nearest neighbors from the provided dataset.

↳ top_k int

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.

↳ output_indices Google\Protobuf\ListValue

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_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).

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.

Returns
TypeDescription
Google\Cloud\AIPlatform\V1\SampledShapleyAttribution|null

hasSampledShapleyAttribution

setSampledShapleyAttribution

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.

Parameter
NameDescription
var Google\Cloud\AIPlatform\V1\SampledShapleyAttribution
Returns
TypeDescription
$this

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

Returns
TypeDescription
Google\Cloud\AIPlatform\V1\IntegratedGradientsAttribution|null

hasIntegratedGradientsAttribution

setIntegratedGradientsAttribution

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

Parameter
NameDescription
var Google\Cloud\AIPlatform\V1\IntegratedGradientsAttribution
Returns
TypeDescription
$this

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.

Returns
TypeDescription
Google\Cloud\AIPlatform\V1\XraiAttribution|null

hasXraiAttribution

setXraiAttribution

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.

Parameter
NameDescription
var Google\Cloud\AIPlatform\V1\XraiAttribution
Returns
TypeDescription
$this

getExamples

Example-based explanations that returns the nearest neighbors from the provided dataset.

Returns
TypeDescription
Google\Cloud\AIPlatform\V1\Examples|null

hasExamples

setExamples

Example-based explanations that returns the nearest neighbors from the provided dataset.

Parameter
NameDescription
var Google\Cloud\AIPlatform\V1\Examples
Returns
TypeDescription
$this

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.

Returns
TypeDescription
int

setTopK

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.

Parameter
NameDescription
var int
Returns
TypeDescription
$this

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_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).

Returns
TypeDescription
Google\Protobuf\ListValue|null

hasOutputIndices

clearOutputIndices

setOutputIndices

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_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).

Parameter
NameDescription
var Google\Protobuf\ListValue
Returns
TypeDescription
$this

getMethod

Returns
TypeDescription
string