📄 Source: GoogleCloudDatalabelingV1beta1EvaluationJob.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\DataLabeling;
class GoogleCloudDatalabelingV1beta1EvaluationJob extends \Google\Collection
{
public const STATE_STATE_UNSPECIFIED = 'STATE_UNSPECIFIED';
/**
* The job is scheduled to run at the configured interval. You can pause or
* delete the job. When the job is in this state, it samples prediction input
* and output from your model version into your BigQuery table as predictions
* occur.
*/
public const STATE_SCHEDULED = 'SCHEDULED';
/**
* The job is currently running. When the job runs, Data Labeling Service does
* several things: 1. If you have configured your job to use Data Labeling
* Service for ground truth labeling, the service creates a Dataset and a
* labeling task for all data sampled since the last time the job ran. Human
* labelers provide ground truth labels for your data. Human labeling may take
* hours, or even days, depending on how much data has been sampled. The job
* remains in the `RUNNING` state during this time, and it can even be running
* multiple times in parallel if it gets triggered again (for example 24 hours
* later) before the earlier run has completed. When human labelers have
* finished labeling the data, the next step occurs. If you have configured
* your job to provide your own ground truth labels, Data Labeling Service
* still creates a Dataset for newly sampled data, but it expects that you
* have already added ground truth labels to the BigQuery table by this time.
* The next step occurs immediately. 2. Data Labeling Service creates an
* Evaluation by comparing your model version's predictions with the ground
* truth labels. If the job remains in this state for a long time, it
* continues to sample prediction data into your BigQuery table and will run
* again at the next interval, even if it causes the job to run multiple times
* in parallel.
*/
public const STATE_RUNNING = 'RUNNING';
/**
* The job is not sampling prediction input and output into your BigQuery
* table and it will not run according to its schedule. You can resume the
* job.
*/
public const STATE_PAUSED = 'PAUSED';
/**
* The job has this state right before it is deleted.
*/
public const STATE_STOPPED = 'STOPPED';
protected $collection_key = 'attempts';
/**
* Required. Name of the AnnotationSpecSet describing all the labels that your
* machine learning model outputs. You must create this resource before you
* create an evaluation job and provide its name in the following format:
* "projects/{project_id}/annotationSpecSets/{annotation_spec_set_id}"
*
* @var string
*/
public $annotationSpecSet;
protected $attemptsType = GoogleCloudDatalabelingV1beta1Attempt::class;
protected $attemptsDataType = 'array';
/**
* Output only. Timestamp of when this evaluation job was created.
*
* @var string
*/
public $createTime;
/**
* Required. Description of the job. The description can be up to 25,000
* characters long.
*
* @var string
*/
public $description;
protected $evaluationJobConfigType = GoogleCloudDatalabelingV1beta1EvaluationJobConfig::class;
protected $evaluationJobConfigDataType = '';
/**
* Required. Whether you want Data Labeling Service to provide ground truth
* labels for prediction input. If you want the service to assign human
* labelers to annotate your data, set this to `true`. If you want to provide
* your own ground truth labels in the evaluation job's BigQuery table, set
* this to `false`.
*
* @var bool
*/
public $labelMissingGroundTruth;
/**
* Required. The [AI Platform Prediction model version](/ml-
* engine/docs/prediction-overview) to be evaluated. Prediction input and
* output is sampled from this model version. When creating an evaluation job,
* specify the model version in the following format:
* "projects/{project_id}/models/{model_name}/versions/{version_name}" There
* can only be one evaluation job per model version.
*
* @var string
*/
public $modelVersion;
/**
* Output only. After you create a job, Data Labeling Service assigns a name
* to the job with the following format:
* "projects/{project_id}/evaluationJobs/ {evaluation_job_id}"
*
* @var string
*/
public $name;
/**
* Required. Describes the interval at which the job runs. This interval must
* be at least 1 day, and it is rounded to the nearest day. For example, if
* you specify a 50-hour interval, the job runs every 2 days. You can provide
* the schedule in [crontab format](/scheduler/docs/configuring/cron-job-
* schedules) or in an [English-like
* format](/appengine/docs/standard/python/config/cronref#schedule_format).
* Regardless of what you specify, the job will run at 10:00 AM UTC. Only the
* interval from this schedule is used, not the specific time of day.
*
* @var string
*/
public $schedule;
/**
* Output only. Describes the current state of the job.
*
* @var string
*/
public $state;
/**
* Required. Name of the AnnotationSpecSet describing all the labels that your
* machine learning model outputs. You must create this resource before you
* create an evaluation job and provide its name in the following format:
* "projects/{project_id}/annotationSpecSets/{annotation_spec_set_id}"
*
* @param string $annotationSpecSet
*/
public function setAnnotationSpecSet($annotationSpecSet)
{
$this->annotationSpecSet = $annotationSpecSet;
}
/**
* @return string
*/
public function getAnnotationSpecSet()
{
return $this->annotationSpecSet;
}
/**
* Output only. Every time the evaluation job runs and an error occurs, the
* failed attempt is appended to this array.
*
* @param GoogleCloudDatalabelingV1beta1Attempt[] $attempts
*/
public function setAttempts($attempts)
{
$this->attempts = $attempts;
}
/**
* @return GoogleCloudDatalabelingV1beta1Attempt[]
*/
public function getAttempts()
{
return $this->attempts;
}
/**
* Output only. Timestamp of when this evaluation job was created.
*
* @param string $createTime
*/
public function setCreateTime($createTime)
{
$this->createTime = $createTime;
}
/**
* @return string
*/
public function getCreateTime()
{
return $this->createTime;
}
/**
* Required. Description of the job. The description can be up to 25,000
* characters long.
*
* @param string $description
*/
public function setDescription($description)
{
$this->description = $description;
}
/**
* @return string
*/
public function getDescription()
{
return $this->description;
}
/**
* Required. Configuration details for the evaluation job.
*
* @param GoogleCloudDatalabelingV1beta1EvaluationJobConfig $evaluationJobConfig
*/
public function setEvaluationJobConfig(GoogleCloudDatalabelingV1beta1EvaluationJobConfig $evaluationJobConfig)
{
$this->evaluationJobConfig = $evaluationJobConfig;
}
/**
* @return GoogleCloudDatalabelingV1beta1EvaluationJobConfig
*/
public function getEvaluationJobConfig()
{
return $this->evaluationJobConfig;
}
/**
* Required. Whether you want Data Labeling Service to provide ground truth
* labels for prediction input. If you want the service to assign human
* labelers to annotate your data, set this to `true`. If you want to provide
* your own ground truth labels in the evaluation job's BigQuery table, set
* this to `false`.
*
* @param bool $labelMissingGroundTruth
*/
public function setLabelMissingGroundTruth($labelMissingGroundTruth)
{
$this->labelMissingGroundTruth = $labelMissingGroundTruth;
}
/**
* @return bool
*/
public function getLabelMissingGroundTruth()
{
return $this->labelMissingGroundTruth;
}
/**
* Required. The [AI Platform Prediction model version](/ml-
* engine/docs/prediction-overview) to be evaluated. Prediction input and
* output is sampled from this model version. When creating an evaluation job,
* specify the model version in the following format:
* "projects/{project_id}/models/{model_name}/versions/{version_name}" There
* can only be one evaluation job per model version.
*
* @param string $modelVersion
*/
public function setModelVersion($modelVersion)
{
$this->modelVersion = $modelVersion;
}
/**
* @return string
*/
public function getModelVersion()
{
return $this->modelVersion;
}
/**
* Output only. After you create a job, Data Labeling Service assigns a name
* to the job with the following format:
* "projects/{project_id}/evaluationJobs/ {evaluation_job_id}"
*
* @param string $name
*/
public function setName($name)
{
$this->name = $name;
}
/**
* @return string
*/
public function getName()
{
return $this->name;
}
/**
* Required. Describes the interval at which the job runs. This interval must
* be at least 1 day, and it is rounded to the nearest day. For example, if
* you specify a 50-hour interval, the job runs every 2 days. You can provide
* the schedule in [crontab format](/scheduler/docs/configuring/cron-job-
* schedules) or in an [English-like
* format](/appengine/docs/standard/python/config/cronref#schedule_format).
* Regardless of what you specify, the job will run at 10:00 AM UTC. Only the
* interval from this schedule is used, not the specific time of day.
*
* @param string $schedule
*/
public function setSchedule($schedule)
{
$this->schedule = $schedule;
}
/**
* @return string
*/
public function getSchedule()
{
return $this->schedule;
}
/**
* Output only. Describes the current state of the job.
*
* Accepted values: STATE_UNSPECIFIED, SCHEDULED, RUNNING, PAUSED, STOPPED
*
* @param self::STATE_* $state
*/
public function setState($state)
{
$this->state = $state;
}
/**
* @return self::STATE_*
*/
public function getState()
{
return $this->state;
}
}
// Adding a class alias for backwards compatibility with the previous class name.
class_alias(GoogleCloudDatalabelingV1beta1EvaluationJob::class, 'Google_Service_DataLabeling_GoogleCloudDatalabelingV1beta1EvaluationJob');
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