📄 Source: GoogleCloudAiplatformV1FeatureStatsAnomaly.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 GoogleCloudAiplatformV1FeatureStatsAnomaly extends \Google\Model
{
/**
* This is the threshold used when detecting anomalies. The threshold can be
* changed by user, so this one might be different from ThresholdConfig.value.
*
* @var
*/
public $anomalyDetectionThreshold;
/**
* Path of the anomaly file for current feature values in Cloud Storage
* bucket. Format: gs:/anomalies. Example:
* gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary
* format with Protobuf message Anoamlies are stored as binary format with
* Protobuf message [tensorflow.metadata.v0.AnomalyInfo] (https://github.com/t
* ensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/anomalies.proto
* ).
*
* @var string
*/
public $anomalyUri;
/**
* Deviation from the current stats to baseline stats. 1. For categorical
* feature, the distribution distance is calculated by L-inifinity norm. 2.
* For numerical feature, the distribution distance is calculated by
* Jensen–Shannon divergence.
*
* @var
*/
public $distributionDeviation;
/**
* The end timestamp of window where stats were generated. For objectives
* where time window doesn't make sense (e.g. Featurestore Snapshot
* Monitoring), end_time indicates the timestamp of the data used to generate
* stats (e.g. timestamp we take snapshots for feature values).
*
* @var string
*/
public $endTime;
/**
* Feature importance score, only populated when cross-feature monitoring is
* enabled. For now only used to represent feature attribution score within
* range [0, 1] for
* ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW and
* ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT.
*
* @var
*/
public $score;
/**
* The start timestamp of window where stats were generated. For objectives
* where time window doesn't make sense (e.g. Featurestore Snapshot
* Monitoring), start_time is only used to indicate the monitoring intervals,
* so it always equals to (end_time - monitoring_interval).
*
* @var string
*/
public $startTime;
/**
* Path of the stats file for current feature values in Cloud Storage bucket.
* Format: gs:/stats. Example: gs://monitoring_bucket/feature_name/stats.
* Stats are stored as binary format with Protobuf message [tensorflow.metadat
* a.v0.FeatureNameStatistics](https://github.com/tensorflow/metadata/blob/mas
* ter/tensorflow_metadata/proto/v0/statistics.proto).
*
* @var string
*/
public $statsUri;
public function setAnomalyDetectionThreshold($anomalyDetectionThreshold)
{
$this->anomalyDetectionThreshold = $anomalyDetectionThreshold;
}
public function getAnomalyDetectionThreshold()
{
return $this->anomalyDetectionThreshold;
}
/**
* Path of the anomaly file for current feature values in Cloud Storage
* bucket. Format: gs:/anomalies. Example:
* gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary
* format with Protobuf message Anoamlies are stored as binary format with
* Protobuf message [tensorflow.metadata.v0.AnomalyInfo] (https://github.com/t
* ensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/anomalies.proto
* ).
*
* @param string $anomalyUri
*/
public function setAnomalyUri($anomalyUri)
{
$this->anomalyUri = $anomalyUri;
}
/**
* @return string
*/
public function getAnomalyUri()
{
return $this->anomalyUri;
}
public function setDistributionDeviation($distributionDeviation)
{
$this->distributionDeviation = $distributionDeviation;
}
public function getDistributionDeviation()
{
return $this->distributionDeviation;
}
/**
* The end timestamp of window where stats were generated. For objectives
* where time window doesn't make sense (e.g. Featurestore Snapshot
* Monitoring), end_time indicates the timestamp of the data used to generate
* stats (e.g. timestamp we take snapshots for feature values).
*
* @param string $endTime
*/
public function setEndTime($endTime)
{
$this->endTime = $endTime;
}
/**
* @return string
*/
public function getEndTime()
{
return $this->endTime;
}
public function setScore($score)
{
$this->score = $score;
}
public function getScore()
{
return $this->score;
}
/**
* The start timestamp of window where stats were generated. For objectives
* where time window doesn't make sense (e.g. Featurestore Snapshot
* Monitoring), start_time is only used to indicate the monitoring intervals,
* so it always equals to (end_time - monitoring_interval).
*
* @param string $startTime
*/
public function setStartTime($startTime)
{
$this->startTime = $startTime;
}
/**
* @return string
*/
public function getStartTime()
{
return $this->startTime;
}
/**
* Path of the stats file for current feature values in Cloud Storage bucket.
* Format: gs:/stats. Example: gs://monitoring_bucket/feature_name/stats.
* Stats are stored as binary format with Protobuf message [tensorflow.metadat
* a.v0.FeatureNameStatistics](https://github.com/tensorflow/metadata/blob/mas
* ter/tensorflow_metadata/proto/v0/statistics.proto).
*
* @param string $statsUri
*/
public function setStatsUri($statsUri)
{
$this->statsUri = $statsUri;
}
/**
* @return string
*/
public function getStatsUri()
{
return $this->statsUri;
}
}
// Adding a class alias for backwards compatibility with the previous class name.
class_alias(GoogleCloudAiplatformV1FeatureStatsAnomaly::class, 'Google_Service_Aiplatform_GoogleCloudAiplatformV1FeatureStatsAnomaly');
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