📄 Source: GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputs.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 GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputs extends \Google\Collection
{
protected $collection_key = 'transformations';
/**
* Additional experiment flags for the Tables training pipeline.
*
* @var string[]
*/
public $additionalExperiments;
/**
* Use the entire training budget. This disables the early stopping feature.
* By default, the early stopping feature is enabled, which means that AutoML
* Tables might stop training before the entire training budget has been used.
*
* @var bool
*/
public $disableEarlyStopping;
protected $exportEvaluatedDataItemsConfigType = GoogleCloudAiplatformV1SchemaTrainingjobDefinitionExportEvaluatedDataItemsConfig::class;
protected $exportEvaluatedDataItemsConfigDataType = '';
/**
* Objective function the model is optimizing towards. The training process
* creates a model that maximizes/minimizes the value of the objective
* function over the validation set. The supported optimization objectives
* depend on the prediction type. If the field is not set, a default objective
* function is used. classification (binary): "maximize-au-roc" (default) -
* Maximize the area under the receiver operating characteristic (ROC) curve.
* "minimize-log-loss" - Minimize log loss. "maximize-au-prc" - Maximize the
* area under the precision-recall curve. "maximize-precision-at-recall" -
* Maximize precision for a specified recall value. "maximize-recall-at-
* precision" - Maximize recall for a specified precision value.
* classification (multi-class): "minimize-log-loss" (default) - Minimize log
* loss. regression: "minimize-rmse" (default) - Minimize root-mean-squared
* error (RMSE). "minimize-mae" - Minimize mean-absolute error (MAE).
* "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
*
* @var string
*/
public $optimizationObjective;
/**
* Required when optimization_objective is "maximize-recall-at-precision".
* Must be between 0 and 1, inclusive.
*
* @var float
*/
public $optimizationObjectivePrecisionValue;
/**
* Required when optimization_objective is "maximize-precision-at-recall".
* Must be between 0 and 1, inclusive.
*
* @var float
*/
public $optimizationObjectiveRecallValue;
/**
* The type of prediction the Model is to produce. "classification" - Predict
* one out of multiple target values is picked for each row. "regression" -
* Predict a value based on its relation to other values. This type is
* available only to columns that contain semantically numeric values, i.e.
* integers or floating point number, even if stored as e.g. strings.
*
* @var string
*/
public $predictionType;
/**
* The column name of the target column that the model is to predict.
*
* @var string
*/
public $targetColumn;
/**
* Required. The train budget of creating this model, expressed in milli node
* hours i.e. 1,000 value in this field means 1 node hour. The training cost
* of the model will not exceed this budget. The final cost will be attempted
* to be close to the budget, though may end up being (even) noticeably
* smaller - at the backend's discretion. This especially may happen when
* further model training ceases to provide any improvements. If the budget is
* set to a value known to be insufficient to train a model for the given
* dataset, the training won't be attempted and will error. The train budget
* must be between 1,000 and 72,000 milli node hours, inclusive.
*
* @var string
*/
public $trainBudgetMilliNodeHours;
protected $transformationsType = GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformation::class;
protected $transformationsDataType = 'array';
/**
* Column name that should be used as the weight column. Higher values in this
* column give more importance to the row during model training. The column
* must have numeric values between 0 and 10000 inclusively; 0 means the row
* is ignored for training. If weight column field is not set, then all rows
* are assumed to have equal weight of 1.
*
* @var string
*/
public $weightColumnName;
/**
* Additional experiment flags for the Tables training pipeline.
*
* @param string[] $additionalExperiments
*/
public function setAdditionalExperiments($additionalExperiments)
{
$this->additionalExperiments = $additionalExperiments;
}
/**
* @return string[]
*/
public function getAdditionalExperiments()
{
return $this->additionalExperiments;
}
/**
* Use the entire training budget. This disables the early stopping feature.
* By default, the early stopping feature is enabled, which means that AutoML
* Tables might stop training before the entire training budget has been used.
*
* @param bool $disableEarlyStopping
*/
public function setDisableEarlyStopping($disableEarlyStopping)
{
$this->disableEarlyStopping = $disableEarlyStopping;
}
/**
* @return bool
*/
public function getDisableEarlyStopping()
{
return $this->disableEarlyStopping;
}
/**
* Configuration for exporting test set predictions to a BigQuery table. If
* this configuration is absent, then the export is not performed.
*
* @param GoogleCloudAiplatformV1SchemaTrainingjobDefinitionExportEvaluatedDataItemsConfig $exportEvaluatedDataItemsConfig
*/
public function setExportEvaluatedDataItemsConfig(GoogleCloudAiplatformV1SchemaTrainingjobDefinitionExportEvaluatedDataItemsConfig $exportEvaluatedDataItemsConfig)
{
$this->exportEvaluatedDataItemsConfig = $exportEvaluatedDataItemsConfig;
}
/**
* @return GoogleCloudAiplatformV1SchemaTrainingjobDefinitionExportEvaluatedDataItemsConfig
*/
public function getExportEvaluatedDataItemsConfig()
{
return $this->exportEvaluatedDataItemsConfig;
}
/**
* Objective function the model is optimizing towards. The training process
* creates a model that maximizes/minimizes the value of the objective
* function over the validation set. The supported optimization objectives
* depend on the prediction type. If the field is not set, a default objective
* function is used. classification (binary): "maximize-au-roc" (default) -
* Maximize the area under the receiver operating characteristic (ROC) curve.
* "minimize-log-loss" - Minimize log loss. "maximize-au-prc" - Maximize the
* area under the precision-recall curve. "maximize-precision-at-recall" -
* Maximize precision for a specified recall value. "maximize-recall-at-
* precision" - Maximize recall for a specified precision value.
* classification (multi-class): "minimize-log-loss" (default) - Minimize log
* loss. regression: "minimize-rmse" (default) - Minimize root-mean-squared
* error (RMSE). "minimize-mae" - Minimize mean-absolute error (MAE).
* "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
*
* @param string $optimizationObjective
*/
public function setOptimizationObjective($optimizationObjective)
{
$this->optimizationObjective = $optimizationObjective;
}
/**
* @return string
*/
public function getOptimizationObjective()
{
return $this->optimizationObjective;
}
/**
* Required when optimization_objective is "maximize-recall-at-precision".
* Must be between 0 and 1, inclusive.
*
* @param float $optimizationObjectivePrecisionValue
*/
public function setOptimizationObjectivePrecisionValue($optimizationObjectivePrecisionValue)
{
$this->optimizationObjectivePrecisionValue = $optimizationObjectivePrecisionValue;
}
/**
* @return float
*/
public function getOptimizationObjectivePrecisionValue()
{
return $this->optimizationObjectivePrecisionValue;
}
/**
* Required when optimization_objective is "maximize-precision-at-recall".
* Must be between 0 and 1, inclusive.
*
* @param float $optimizationObjectiveRecallValue
*/
public function setOptimizationObjectiveRecallValue($optimizationObjectiveRecallValue)
{
$this->optimizationObjectiveRecallValue = $optimizationObjectiveRecallValue;
}
/**
* @return float
*/
public function getOptimizationObjectiveRecallValue()
{
return $this->optimizationObjectiveRecallValue;
}
/**
* The type of prediction the Model is to produce. "classification" - Predict
* one out of multiple target values is picked for each row. "regression" -
* Predict a value based on its relation to other values. This type is
* available only to columns that contain semantically numeric values, i.e.
* integers or floating point number, even if stored as e.g. strings.
*
* @param string $predictionType
*/
public function setPredictionType($predictionType)
{
$this->predictionType = $predictionType;
}
/**
* @return string
*/
public function getPredictionType()
{
return $this->predictionType;
}
/**
* The column name of the target column that the model is to predict.
*
* @param string $targetColumn
*/
public function setTargetColumn($targetColumn)
{
$this->targetColumn = $targetColumn;
}
/**
* @return string
*/
public function getTargetColumn()
{
return $this->targetColumn;
}
/**
* Required. The train budget of creating this model, expressed in milli node
* hours i.e. 1,000 value in this field means 1 node hour. The training cost
* of the model will not exceed this budget. The final cost will be attempted
* to be close to the budget, though may end up being (even) noticeably
* smaller - at the backend's discretion. This especially may happen when
* further model training ceases to provide any improvements. If the budget is
* set to a value known to be insufficient to train a model for the given
* dataset, the training won't be attempted and will error. The train budget
* must be between 1,000 and 72,000 milli node hours, inclusive.
*
* @param string $trainBudgetMilliNodeHours
*/
public function setTrainBudgetMilliNodeHours($trainBudgetMilliNodeHours)
{
$this->trainBudgetMilliNodeHours = $trainBudgetMilliNodeHours;
}
/**
* @return string
*/
public function getTrainBudgetMilliNodeHours()
{
return $this->trainBudgetMilliNodeHours;
}
/**
* Each transformation will apply transform function to given input column.
* And the result will be used for training. When creating transformation for
* BigQuery Struct column, the column should be flattened using "." as the
* delimiter.
*
* @param GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformation[] $transformations
*/
public function setTransformations($transformations)
{
$this->transformations = $transformations;
}
/**
* @return GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformation[]
*/
public function getTransformations()
{
return $this->transformations;
}
/**
* Column name that should be used as the weight column. Higher values in this
* column give more importance to the row during model training. The column
* must have numeric values between 0 and 10000 inclusively; 0 means the row
* is ignored for training. If weight column field is not set, then all rows
* are assumed to have equal weight of 1.
*
* @param string $weightColumnName
*/
public function setWeightColumnName($weightColumnName)
{
$this->weightColumnName = $weightColumnName;
}
/**
* @return string
*/
public function getWeightColumnName()
{
return $this->weightColumnName;
}
}
// Adding a class alias for backwards compatibility with the previous class name.
class_alias(GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputs::class, 'Google_Service_Aiplatform_GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputs');
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