📄 Source: GoogleCloudAiplatformV1ExplanationParameters.php
<?php
/*
* Copyright 2014 Google Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License"); you may not
* use this file except in compliance with the License. You may obtain a copy of
* the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations under
* the License.
*/
namespace Google\Service\Aiplatform;
class GoogleCloudAiplatformV1ExplanationParameters extends \Google\Collection
{
protected $collection_key = 'outputIndices';
protected $examplesType = GoogleCloudAiplatformV1Examples::class;
protected $examplesDataType = '';
protected $integratedGradientsAttributionType = GoogleCloudAiplatformV1IntegratedGradientsAttribution::class;
protected $integratedGradientsAttributionDataType = '';
/**
* 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).
*
* @var array[]
*/
public $outputIndices;
protected $sampledShapleyAttributionType = GoogleCloudAiplatformV1SampledShapleyAttribution::class;
protected $sampledShapleyAttributionDataType = '';
/**
* 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.
*
* @var int
*/
public $topK;
protected $xraiAttributionType = GoogleCloudAiplatformV1XraiAttribution::class;
protected $xraiAttributionDataType = '';
/**
* Example-based explanations that returns the nearest neighbors from the
* provided dataset.
*
* @param GoogleCloudAiplatformV1Examples $examples
*/
public function setExamples(GoogleCloudAiplatformV1Examples $examples)
{
$this->examples = $examples;
}
/**
* @return GoogleCloudAiplatformV1Examples
*/
public function getExamples()
{
return $this->examples;
}
/**
* 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
*
* @param GoogleCloudAiplatformV1IntegratedGradientsAttribution $integratedGradientsAttribution
*/
public function setIntegratedGradientsAttribution(GoogleCloudAiplatformV1IntegratedGradientsAttribution $integratedGradientsAttribution)
{
$this->integratedGradientsAttribution = $integratedGradientsAttribution;
}
/**
* @return GoogleCloudAiplatformV1IntegratedGradientsAttribution
*/
public function getIntegratedGradientsAttribution()
{
return $this->integratedGradientsAttribution;
}
/**
* 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).
*
* @param array[] $outputIndices
*/
public function setOutputIndices($outputIndices)
{
$this->outputIndices = $outputIndices;
}
/**
* @return array[]
*/
public function getOutputIndices()
{
return $this->outputIndices;
}
/**
* 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.
*
* @param GoogleCloudAiplatformV1SampledShapleyAttribution $sampledShapleyAttribution
*/
public function setSampledShapleyAttribution(GoogleCloudAiplatformV1SampledShapleyAttribution $sampledShapleyAttribution)
{
$this->sampledShapleyAttribution = $sampledShapleyAttribution;
}
/**
* @return GoogleCloudAiplatformV1SampledShapleyAttribution
*/
public function getSampledShapleyAttribution()
{
return $this->sampledShapleyAttribution;
}
/**
* 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.
*
* @param int $topK
*/
public function setTopK($topK)
{
$this->topK = $topK;
}
/**
* @return int
*/
public function getTopK()
{
return $this->topK;
}
/**
* 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.
*
* @param GoogleCloudAiplatformV1XraiAttribution $xraiAttribution
*/
public function setXraiAttribution(GoogleCloudAiplatformV1XraiAttribution $xraiAttribution)
{
$this->xraiAttribution = $xraiAttribution;
}
/**
* @return GoogleCloudAiplatformV1XraiAttribution
*/
public function getXraiAttribution()
{
return $this->xraiAttribution;
}
}
// Adding a class alias for backwards compatibility with the previous class name.
class_alias(GoogleCloudAiplatformV1ExplanationParameters::class, 'Google_Service_Aiplatform_GoogleCloudAiplatformV1ExplanationParameters');
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