📄 Source: GoogleCloudAiplatformV1Attribution.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 GoogleCloudAiplatformV1Attribution extends \Google\Collection
{
protected $collection_key = 'outputIndex';
/**
* Output only. Error of feature_attributions caused by approximation used in
* the explanation method. Lower value means more precise attributions. * For
* Sampled Shapley attribution, increasing path_count might reduce the error.
* * For Integrated Gradients attribution, increasing step_count might reduce
* the error. * For XRAI attribution, increasing step_count might reduce the
* error. See [this introduction](/vertex-ai/docs/explainable-ai/overview) for
* more information.
*
* @var
*/
public $approximationError;
/**
* Output only. Model predicted output if the input instance is constructed
* from the baselines of all the features defined in
* ExplanationMetadata.inputs. The field name of the output is determined by
* the key in ExplanationMetadata.outputs. If the Model's predicted output has
* multiple dimensions (rank > 1), this is the value in the output located by
* output_index. If there are multiple baselines, their output values are
* averaged.
*
* @var
*/
public $baselineOutputValue;
/**
* Output only. Attributions of each explained feature. Features are extracted
* from the prediction instances according to explanation metadata for inputs.
* The value is a struct, whose keys are the name of the feature. The values
* are how much the feature in the instance contributed to the predicted
* result. The format of the value is determined by the feature's input
* format: * If the feature is a scalar value, the attribution value is a
* floating number. * If the feature is an array of scalar values, the
* attribution value is an array. * If the feature is a struct, the
* attribution value is a struct. The keys in the attribution value struct are
* the same as the keys in the feature struct. The formats of the values in
* the attribution struct are determined by the formats of the values in the
* feature struct. The ExplanationMetadata.feature_attributions_schema_uri
* field, pointed to by the ExplanationSpec field of the
* Endpoint.deployed_models object, points to the schema file that describes
* the features and their attribution values (if it is populated).
*
* @var array
*/
public $featureAttributions;
/**
* Output only. Model predicted output on the corresponding explanation
* instance. The field name of the output is determined by the key in
* ExplanationMetadata.outputs. If the Model predicted output has multiple
* dimensions, this is the value in the output located by output_index.
*
* @var
*/
public $instanceOutputValue;
/**
* Output only. The display name of the output identified by output_index. For
* example, the predicted class name by a multi-classification Model. This
* field is only populated iff the Model predicts display names as a separate
* field along with the explained output. The predicted display name must has
* the same shape of the explained output, and can be located using
* output_index.
*
* @var string
*/
public $outputDisplayName;
/**
* Output only. The index that locates the explained prediction output. If the
* prediction output is a scalar value, output_index is not populated. If the
* prediction output has multiple dimensions, the length of the output_index
* list is the same as the number of dimensions of the output. The i-th
* element in output_index is the element index of the i-th dimension of the
* output vector. Indices start from 0.
*
* @var int[]
*/
public $outputIndex;
/**
* Output only. Name of the explain output. Specified as the key in
* ExplanationMetadata.outputs.
*
* @var string
*/
public $outputName;
public function setApproximationError($approximationError)
{
$this->approximationError = $approximationError;
}
public function getApproximationError()
{
return $this->approximationError;
}
public function setBaselineOutputValue($baselineOutputValue)
{
$this->baselineOutputValue = $baselineOutputValue;
}
public function getBaselineOutputValue()
{
return $this->baselineOutputValue;
}
/**
* Output only. Attributions of each explained feature. Features are extracted
* from the prediction instances according to explanation metadata for inputs.
* The value is a struct, whose keys are the name of the feature. The values
* are how much the feature in the instance contributed to the predicted
* result. The format of the value is determined by the feature's input
* format: * If the feature is a scalar value, the attribution value is a
* floating number. * If the feature is an array of scalar values, the
* attribution value is an array. * If the feature is a struct, the
* attribution value is a struct. The keys in the attribution value struct are
* the same as the keys in the feature struct. The formats of the values in
* the attribution struct are determined by the formats of the values in the
* feature struct. The ExplanationMetadata.feature_attributions_schema_uri
* field, pointed to by the ExplanationSpec field of the
* Endpoint.deployed_models object, points to the schema file that describes
* the features and their attribution values (if it is populated).
*
* @param array $featureAttributions
*/
public function setFeatureAttributions($featureAttributions)
{
$this->featureAttributions = $featureAttributions;
}
/**
* @return array
*/
public function getFeatureAttributions()
{
return $this->featureAttributions;
}
public function setInstanceOutputValue($instanceOutputValue)
{
$this->instanceOutputValue = $instanceOutputValue;
}
public function getInstanceOutputValue()
{
return $this->instanceOutputValue;
}
/**
* Output only. The display name of the output identified by output_index. For
* example, the predicted class name by a multi-classification Model. This
* field is only populated iff the Model predicts display names as a separate
* field along with the explained output. The predicted display name must has
* the same shape of the explained output, and can be located using
* output_index.
*
* @param string $outputDisplayName
*/
public function setOutputDisplayName($outputDisplayName)
{
$this->outputDisplayName = $outputDisplayName;
}
/**
* @return string
*/
public function getOutputDisplayName()
{
return $this->outputDisplayName;
}
/**
* Output only. The index that locates the explained prediction output. If the
* prediction output is a scalar value, output_index is not populated. If the
* prediction output has multiple dimensions, the length of the output_index
* list is the same as the number of dimensions of the output. The i-th
* element in output_index is the element index of the i-th dimension of the
* output vector. Indices start from 0.
*
* @param int[] $outputIndex
*/
public function setOutputIndex($outputIndex)
{
$this->outputIndex = $outputIndex;
}
/**
* @return int[]
*/
public function getOutputIndex()
{
return $this->outputIndex;
}
/**
* Output only. Name of the explain output. Specified as the key in
* ExplanationMetadata.outputs.
*
* @param string $outputName
*/
public function setOutputName($outputName)
{
$this->outputName = $outputName;
}
/**
* @return string
*/
public function getOutputName()
{
return $this->outputName;
}
}
// Adding a class alias for backwards compatibility with the previous class name.
class_alias(GoogleCloudAiplatformV1Attribution::class, 'Google_Service_Aiplatform_GoogleCloudAiplatformV1Attribution');
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