📄 Source: GoogleCloudAiplatformV1StudySpecConvexAutomatedStoppingSpec.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 GoogleCloudAiplatformV1StudySpecConvexAutomatedStoppingSpec extends \Google\Model
{
/**
* The hyper-parameter name used in the tuning job that stands for learning
* rate. Leave it blank if learning rate is not in a parameter in tuning. The
* learning_rate is used to estimate the objective value of the ongoing trial.
*
* @var string
*/
public $learningRateParameterName;
/**
* Steps used in predicting the final objective for early stopped trials. In
* general, it's set to be the same as the defined steps in training / tuning.
* If not defined, it will learn it from the completed trials. When use_steps
* is false, this field is set to the maximum elapsed seconds.
*
* @var string
*/
public $maxStepCount;
/**
* The minimal number of measurements in a Trial. Early-stopping checks will
* not trigger if less than min_measurement_count+1 completed trials or
* pending trials with less than min_measurement_count measurements. If not
* defined, the default value is 5.
*
* @var string
*/
public $minMeasurementCount;
/**
* Minimum number of steps for a trial to complete. Trials which do not have a
* measurement with step_count > min_step_count won't be considered for early
* stopping. It's ok to set it to 0, and a trial can be early stopped at any
* stage. By default, min_step_count is set to be one-tenth of the
* max_step_count. When use_elapsed_duration is true, this field is set to the
* minimum elapsed seconds.
*
* @var string
*/
public $minStepCount;
/**
* ConvexAutomatedStoppingSpec by default only updates the trials that needs
* to be early stopped using a newly trained auto-regressive model. When this
* flag is set to True, all stopped trials from the beginning are potentially
* updated in terms of their `final_measurement`. Also, note that the training
* logic of autoregressive models is different in this case. Enabling this
* option has shown better results and this may be the default option in the
* future.
*
* @var bool
*/
public $updateAllStoppedTrials;
/**
* This bool determines whether or not the rule is applied based on
* elapsed_secs or steps. If use_elapsed_duration==false, the early stopping
* decision is made according to the predicted objective values according to
* the target steps. If use_elapsed_duration==true, elapsed_secs is used
* instead of steps. Also, in this case, the parameters max_num_steps and
* min_num_steps are overloaded to contain max_elapsed_seconds and
* min_elapsed_seconds.
*
* @var bool
*/
public $useElapsedDuration;
/**
* The hyper-parameter name used in the tuning job that stands for learning
* rate. Leave it blank if learning rate is not in a parameter in tuning. The
* learning_rate is used to estimate the objective value of the ongoing trial.
*
* @param string $learningRateParameterName
*/
public function setLearningRateParameterName($learningRateParameterName)
{
$this->learningRateParameterName = $learningRateParameterName;
}
/**
* @return string
*/
public function getLearningRateParameterName()
{
return $this->learningRateParameterName;
}
/**
* Steps used in predicting the final objective for early stopped trials. In
* general, it's set to be the same as the defined steps in training / tuning.
* If not defined, it will learn it from the completed trials. When use_steps
* is false, this field is set to the maximum elapsed seconds.
*
* @param string $maxStepCount
*/
public function setMaxStepCount($maxStepCount)
{
$this->maxStepCount = $maxStepCount;
}
/**
* @return string
*/
public function getMaxStepCount()
{
return $this->maxStepCount;
}
/**
* The minimal number of measurements in a Trial. Early-stopping checks will
* not trigger if less than min_measurement_count+1 completed trials or
* pending trials with less than min_measurement_count measurements. If not
* defined, the default value is 5.
*
* @param string $minMeasurementCount
*/
public function setMinMeasurementCount($minMeasurementCount)
{
$this->minMeasurementCount = $minMeasurementCount;
}
/**
* @return string
*/
public function getMinMeasurementCount()
{
return $this->minMeasurementCount;
}
/**
* Minimum number of steps for a trial to complete. Trials which do not have a
* measurement with step_count > min_step_count won't be considered for early
* stopping. It's ok to set it to 0, and a trial can be early stopped at any
* stage. By default, min_step_count is set to be one-tenth of the
* max_step_count. When use_elapsed_duration is true, this field is set to the
* minimum elapsed seconds.
*
* @param string $minStepCount
*/
public function setMinStepCount($minStepCount)
{
$this->minStepCount = $minStepCount;
}
/**
* @return string
*/
public function getMinStepCount()
{
return $this->minStepCount;
}
/**
* ConvexAutomatedStoppingSpec by default only updates the trials that needs
* to be early stopped using a newly trained auto-regressive model. When this
* flag is set to True, all stopped trials from the beginning are potentially
* updated in terms of their `final_measurement`. Also, note that the training
* logic of autoregressive models is different in this case. Enabling this
* option has shown better results and this may be the default option in the
* future.
*
* @param bool $updateAllStoppedTrials
*/
public function setUpdateAllStoppedTrials($updateAllStoppedTrials)
{
$this->updateAllStoppedTrials = $updateAllStoppedTrials;
}
/**
* @return bool
*/
public function getUpdateAllStoppedTrials()
{
return $this->updateAllStoppedTrials;
}
/**
* This bool determines whether or not the rule is applied based on
* elapsed_secs or steps. If use_elapsed_duration==false, the early stopping
* decision is made according to the predicted objective values according to
* the target steps. If use_elapsed_duration==true, elapsed_secs is used
* instead of steps. Also, in this case, the parameters max_num_steps and
* min_num_steps are overloaded to contain max_elapsed_seconds and
* min_elapsed_seconds.
*
* @param bool $useElapsedDuration
*/
public function setUseElapsedDuration($useElapsedDuration)
{
$this->useElapsedDuration = $useElapsedDuration;
}
/**
* @return bool
*/
public function getUseElapsedDuration()
{
return $this->useElapsedDuration;
}
}
// Adding a class alias for backwards compatibility with the previous class name.
class_alias(GoogleCloudAiplatformV1StudySpecConvexAutomatedStoppingSpec::class, 'Google_Service_Aiplatform_GoogleCloudAiplatformV1StudySpecConvexAutomatedStoppingSpec');
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