TrainingOptions (BigQuery API v2-rev20251012-2.0.0)

java.lang.Object
- java.util.AbstractMap<String,Object>
- - com.google.api.client.util.GenericData
  - - com.google.api.client.json.GenericJson
    - - com.google.api.services.bigquery.model.TrainingOptions

All Implemented Interfaces:

Cloneable, Map<String,Object>
```
public final class TrainingOptions
extends com.google.api.client.json.GenericJson
```
Options used in model training.
This is the Java data model class that specifies how to parse/serialize into the JSON that is transmitted over HTTP when working with the BigQuery API. For a detailed explanation see: https://developers.google.com/api-client-library/java/google-http-java-client/json

Author:

Google, Inc.

Nested Class Summary
- Nested classes/interfaces inherited from class com.google.api.client.util.GenericData
  com.google.api.client.util.GenericData.Flags
- Nested classes/interfaces inherited from class java.util.AbstractMap
  AbstractMap.SimpleEntry<K,V>, AbstractMap.SimpleImmutableEntry<K,V>
- Nested classes/interfaces inherited from interface java.util.Map
  Map.Entry<K,V>

Constructor Summary

Constructors
Constructor and Description

TrainingOptions()

Constructors
Constructor and Description
`TrainingOptions()`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`TrainingOptions`	`clone()`
`String`	`getActivationFn()` Activation function of the neural nets.
`Boolean`	`getAdjustStepChanges()` If true, detect step changes and make data adjustment in the input time series.
`Boolean`	`getApproxGlobalFeatureContrib()` Whether to use approximate feature contribution method in XGBoost model explanation for global explain.
`Boolean`	`getAutoArima()` Whether to enable auto ARIMA or not.
`Long`	`getAutoArimaMaxOrder()` The max value of the sum of non-seasonal p and q.
`Long`	`getAutoArimaMinOrder()` The min value of the sum of non-seasonal p and q.
`Boolean`	`getAutoClassWeights()` Whether to calculate class weights automatically based on the popularity of each label.
`Long`	`getBatchSize()` Batch size for dnn models.
`String`	`getBoosterType()` Booster type for boosted tree models.
`Double`	`getBudgetHours()` Budget in hours for AutoML training.
`Boolean`	`getCalculatePValues()` Whether or not p-value test should be computed for this model.
`String`	`getCategoryEncodingMethod()` Categorical feature encoding method.
`Boolean`	`getCleanSpikesAndDips()` If true, clean spikes and dips in the input time series.
`String`	`getColorSpace()` Enums for color space, used for processing images in Object Table.
`Double`	`getColsampleBylevel()` Subsample ratio of columns for each level for boosted tree models.
`Double`	`getColsampleBynode()` Subsample ratio of columns for each node(split) for boosted tree models.
`Double`	`getColsampleBytree()` Subsample ratio of columns when constructing each tree for boosted tree models.
`String`	`getContributionMetric()` The contribution metric.
`String`	`getDartNormalizeType()` Type of normalization algorithm for boosted tree models using dart booster.
`String`	`getDataFrequency()` The data frequency of a time series.
`String`	`getDataSplitColumn()` The column to split data with.
`Double`	`getDataSplitEvalFraction()` The fraction of evaluation data over the whole input data.
`String`	`getDataSplitMethod()` The data split type for training and evaluation, e.g.
`Boolean`	`getDecomposeTimeSeries()` If true, perform decompose time series and save the results.
`List<String>`	`getDimensionIdColumns()` Optional.
`String`	`getDistanceType()` Distance type for clustering models.
`Double`	`getDropout()` Dropout probability for dnn models.
`Boolean`	`getEarlyStop()` Whether to stop early when the loss doesn't improve significantly any more (compared to min_relative_progress).
`Boolean`	`getEnableGlobalExplain()` If true, enable global explanation during training.
`String`	`getEndpointIdleTtl()` The idle TTL of the endpoint before the resources get destroyed.
`String`	`getFeedbackType()` Feedback type that specifies which algorithm to run for matrix factorization.
`Boolean`	`getFitIntercept()` Whether the model should include intercept during model training.
`Double`	`getForecastLimitLowerBound()` The forecast limit lower bound that was used during ARIMA model training with limits.
`Double`	`getForecastLimitUpperBound()` The forecast limit upper bound that was used during ARIMA model training with limits.
`List<Long>`	`getHiddenUnits()` Hidden units for dnn models.
`String`	`getHolidayRegion()` The geographical region based on which the holidays are considered in time series modeling.
`List<String>`	`getHolidayRegions()` A list of geographical regions that are used for time series modeling.
`Long`	`getHorizon()` The number of periods ahead that need to be forecasted.
`List<String>`	`getHparamTuningObjectives()` The target evaluation metrics to optimize the hyperparameters for.
`String`	`getHuggingFaceModelId()` The id of a Hugging Face model.
`Boolean`	`getIncludeDrift()` Include drift when fitting an ARIMA model.
`Double`	`getInitialLearnRate()` Specifies the initial learning rate for the line search learn rate strategy.
`List<String>`	`getInputLabelColumns()` Name of input label columns in training data.
`String`	`getInstanceWeightColumn()` Name of the instance weight column for training data.
`Long`	`getIntegratedGradientsNumSteps()` Number of integral steps for the integrated gradients explain method.
`String`	`getIsTestColumn()` Name of the column used to determine the rows corresponding to control and test.
`String`	`getItemColumn()` Item column specified for matrix factorization models.
`String`	`getKmeansInitializationColumn()` The column used to provide the initial centroids for kmeans algorithm when kmeans_initialization_method is CUSTOM.
`String`	`getKmeansInitializationMethod()` The method used to initialize the centroids for kmeans algorithm.
`Double`	`getL1RegActivation()` L1 regularization coefficient to activations.
`Double`	`getL1Regularization()` L1 regularization coefficient.
`Double`	`getL2Regularization()` L2 regularization coefficient.
`Map<String,Double>`	`getLabelClassWeights()` Weights associated with each label class, for rebalancing the training data.
`Double`	`getLearnRate()` Learning rate in training.
`String`	`getLearnRateStrategy()` The strategy to determine learn rate for the current iteration.
`String`	`getLossType()` Type of loss function used during training run.
`String`	`getMachineType()` The type of the machine used to deploy and serve the model.
`Long`	`getMaxIterations()` The maximum number of iterations in training.
`Long`	`getMaxParallelTrials()` Maximum number of trials to run in parallel.
`Long`	`getMaxReplicaCount()` The maximum number of machine replicas that will be deployed on an endpoint.
`Long`	`getMaxTimeSeriesLength()` The maximum number of time points in a time series that can be used in modeling the trend component of the time series.
`Long`	`getMaxTreeDepth()` Maximum depth of a tree for boosted tree models.
`Double`	`getMinAprioriSupport()` The apriori support minimum.
`Double`	`getMinRelativeProgress()` When early_stop is true, stops training when accuracy improvement is less than 'min_relative_progress'.
`Long`	`getMinReplicaCount()` The minimum number of machine replicas that will be always deployed on an endpoint.
`Double`	`getMinSplitLoss()` Minimum split loss for boosted tree models.
`Long`	`getMinTimeSeriesLength()` The minimum number of time points in a time series that are used in modeling the trend component of the time series.
`Long`	`getMinTreeChildWeight()` Minimum sum of instance weight needed in a child for boosted tree models.
`String`	`getModelGardenModelName()` The name of a Vertex model garden publisher model.
`String`	`getModelRegistry()` The model registry.
`String`	`getModelUri()` Google Cloud Storage URI from which the model was imported.
`ArimaOrder`	`getNonSeasonalOrder()` A specification of the non-seasonal part of the ARIMA model: the three components (p, d, q) are the AR order, the degree of differencing, and the MA order.
`Long`	`getNumClusters()` Number of clusters for clustering models.
`Long`	`getNumFactors()` Num factors specified for matrix factorization models.
`Long`	`getNumParallelTree()` Number of parallel trees constructed during each iteration for boosted tree models.
`Long`	`getNumPrincipalComponents()` Number of principal components to keep in the PCA model.
`Long`	`getNumTrials()` Number of trials to run this hyperparameter tuning job.
`String`	`getOptimizationStrategy()` Optimization strategy for training linear regression models.
`String`	`getOptimizer()` Optimizer used for training the neural nets.
`Double`	`getPcaExplainedVarianceRatio()` The minimum ratio of cumulative explained variance that needs to be given by the PCA model.
`String`	`getPcaSolver()` The solver for PCA.
`String`	`getReservationAffinityKey()` Corresponds to the label key of a reservation resource used by Vertex AI.
`String`	`getReservationAffinityType()` Specifies the reservation affinity type used to configure a Vertex AI resource.
`List<String>`	`getReservationAffinityValues()` Corresponds to the label values of a reservation resource used by Vertex AI.
`Long`	`getSampledShapleyNumPaths()` Number of paths for the sampled Shapley explain method.
`Boolean`	`getScaleFeatures()` If true, scale the feature values by dividing the feature standard deviation.
`Boolean`	`getStandardizeFeatures()` Whether to standardize numerical features.
`Double`	`getSubsample()` Subsample fraction of the training data to grow tree to prevent overfitting for boosted tree models.
`String`	`getTfVersion()` Based on the selected TF version, the corresponding docker image is used to train external models.
`String`	`getTimeSeriesDataColumn()` Column to be designated as time series data for ARIMA model.
`String`	`getTimeSeriesIdColumn()` The time series id column that was used during ARIMA model training.
`List<String>`	`getTimeSeriesIdColumns()` The time series id columns that were used during ARIMA model training.
`Double`	`getTimeSeriesLengthFraction()` The fraction of the interpolated length of the time series that's used to model the time series trend component.
`String`	`getTimeSeriesTimestampColumn()` Column to be designated as time series timestamp for ARIMA model.
`String`	`getTreeMethod()` Tree construction algorithm for boosted tree models.
`Long`	`getTrendSmoothingWindowSize()` Smoothing window size for the trend component.
`String`	`getUserColumn()` User column specified for matrix factorization models.
`List<String>`	`getVertexAiModelVersionAliases()` The version aliases to apply in Vertex AI model registry.
`Double`	`getWalsAlpha()` Hyperparameter for matrix factoration when implicit feedback type is specified.
`Boolean`	`getWarmStart()` Whether to train a model from the last checkpoint.
`String`	`getXgboostVersion()` User-selected XGBoost versions for training of XGBoost models.
`TrainingOptions`	`set(String fieldName, Object value)`
`TrainingOptions`	`setActivationFn(String activationFn)` Activation function of the neural nets.
`TrainingOptions`	`setAdjustStepChanges(Boolean adjustStepChanges)` If true, detect step changes and make data adjustment in the input time series.
`TrainingOptions`	`setApproxGlobalFeatureContrib(Boolean approxGlobalFeatureContrib)` Whether to use approximate feature contribution method in XGBoost model explanation for global explain.
`TrainingOptions`	`setAutoArima(Boolean autoArima)` Whether to enable auto ARIMA or not.
`TrainingOptions`	`setAutoArimaMaxOrder(Long autoArimaMaxOrder)` The max value of the sum of non-seasonal p and q.
`TrainingOptions`	`setAutoArimaMinOrder(Long autoArimaMinOrder)` The min value of the sum of non-seasonal p and q.
`TrainingOptions`	`setAutoClassWeights(Boolean autoClassWeights)` Whether to calculate class weights automatically based on the popularity of each label.
`TrainingOptions`	`setBatchSize(Long batchSize)` Batch size for dnn models.
`TrainingOptions`	`setBoosterType(String boosterType)` Booster type for boosted tree models.
`TrainingOptions`	`setBudgetHours(Double budgetHours)` Budget in hours for AutoML training.
`TrainingOptions`	`setCalculatePValues(Boolean calculatePValues)` Whether or not p-value test should be computed for this model.
`TrainingOptions`	`setCategoryEncodingMethod(String categoryEncodingMethod)` Categorical feature encoding method.
`TrainingOptions`	`setCleanSpikesAndDips(Boolean cleanSpikesAndDips)` If true, clean spikes and dips in the input time series.
`TrainingOptions`	`setColorSpace(String colorSpace)` Enums for color space, used for processing images in Object Table.
`TrainingOptions`	`setColsampleBylevel(Double colsampleBylevel)` Subsample ratio of columns for each level for boosted tree models.
`TrainingOptions`	`setColsampleBynode(Double colsampleBynode)` Subsample ratio of columns for each node(split) for boosted tree models.
`TrainingOptions`	`setColsampleBytree(Double colsampleBytree)` Subsample ratio of columns when constructing each tree for boosted tree models.
`TrainingOptions`	`setContributionMetric(String contributionMetric)` The contribution metric.
`TrainingOptions`	`setDartNormalizeType(String dartNormalizeType)` Type of normalization algorithm for boosted tree models using dart booster.
`TrainingOptions`	`setDataFrequency(String dataFrequency)` The data frequency of a time series.
`TrainingOptions`	`setDataSplitColumn(String dataSplitColumn)` The column to split data with.
`TrainingOptions`	`setDataSplitEvalFraction(Double dataSplitEvalFraction)` The fraction of evaluation data over the whole input data.
`TrainingOptions`	`setDataSplitMethod(String dataSplitMethod)` The data split type for training and evaluation, e.g.
`TrainingOptions`	`setDecomposeTimeSeries(Boolean decomposeTimeSeries)` If true, perform decompose time series and save the results.
`TrainingOptions`	`setDimensionIdColumns(List<String> dimensionIdColumns)` Optional.
`TrainingOptions`	`setDistanceType(String distanceType)` Distance type for clustering models.
`TrainingOptions`	`setDropout(Double dropout)` Dropout probability for dnn models.
`TrainingOptions`	`setEarlyStop(Boolean earlyStop)` Whether to stop early when the loss doesn't improve significantly any more (compared to min_relative_progress).
`TrainingOptions`	`setEnableGlobalExplain(Boolean enableGlobalExplain)` If true, enable global explanation during training.
`TrainingOptions`	`setEndpointIdleTtl(String endpointIdleTtl)` The idle TTL of the endpoint before the resources get destroyed.
`TrainingOptions`	`setFeedbackType(String feedbackType)` Feedback type that specifies which algorithm to run for matrix factorization.
`TrainingOptions`	`setFitIntercept(Boolean fitIntercept)` Whether the model should include intercept during model training.
`TrainingOptions`	`setForecastLimitLowerBound(Double forecastLimitLowerBound)` The forecast limit lower bound that was used during ARIMA model training with limits.
`TrainingOptions`	`setForecastLimitUpperBound(Double forecastLimitUpperBound)` The forecast limit upper bound that was used during ARIMA model training with limits.
`TrainingOptions`	`setHiddenUnits(List<Long> hiddenUnits)` Hidden units for dnn models.
`TrainingOptions`	`setHolidayRegion(String holidayRegion)` The geographical region based on which the holidays are considered in time series modeling.
`TrainingOptions`	`setHolidayRegions(List<String> holidayRegions)` A list of geographical regions that are used for time series modeling.
`TrainingOptions`	`setHorizon(Long horizon)` The number of periods ahead that need to be forecasted.
`TrainingOptions`	`setHparamTuningObjectives(List<String> hparamTuningObjectives)` The target evaluation metrics to optimize the hyperparameters for.
`TrainingOptions`	`setHuggingFaceModelId(String huggingFaceModelId)` The id of a Hugging Face model.
`TrainingOptions`	`setIncludeDrift(Boolean includeDrift)` Include drift when fitting an ARIMA model.
`TrainingOptions`	`setInitialLearnRate(Double initialLearnRate)` Specifies the initial learning rate for the line search learn rate strategy.
`TrainingOptions`	`setInputLabelColumns(List<String> inputLabelColumns)` Name of input label columns in training data.
`TrainingOptions`	`setInstanceWeightColumn(String instanceWeightColumn)` Name of the instance weight column for training data.
`TrainingOptions`	`setIntegratedGradientsNumSteps(Long integratedGradientsNumSteps)` Number of integral steps for the integrated gradients explain method.
`TrainingOptions`	`setIsTestColumn(String isTestColumn)` Name of the column used to determine the rows corresponding to control and test.
`TrainingOptions`	`setItemColumn(String itemColumn)` Item column specified for matrix factorization models.
`TrainingOptions`	`setKmeansInitializationColumn(String kmeansInitializationColumn)` The column used to provide the initial centroids for kmeans algorithm when kmeans_initialization_method is CUSTOM.
`TrainingOptions`	`setKmeansInitializationMethod(String kmeansInitializationMethod)` The method used to initialize the centroids for kmeans algorithm.
`TrainingOptions`	`setL1RegActivation(Double l1RegActivation)` L1 regularization coefficient to activations.
`TrainingOptions`	`setL1Regularization(Double l1Regularization)` L1 regularization coefficient.
`TrainingOptions`	`setL2Regularization(Double l2Regularization)` L2 regularization coefficient.
`TrainingOptions`	`setLabelClassWeights(Map<String,Double> labelClassWeights)` Weights associated with each label class, for rebalancing the training data.
`TrainingOptions`	`setLearnRate(Double learnRate)` Learning rate in training.
`TrainingOptions`	`setLearnRateStrategy(String learnRateStrategy)` The strategy to determine learn rate for the current iteration.
`TrainingOptions`	`setLossType(String lossType)` Type of loss function used during training run.
`TrainingOptions`	`setMachineType(String machineType)` The type of the machine used to deploy and serve the model.
`TrainingOptions`	`setMaxIterations(Long maxIterations)` The maximum number of iterations in training.
`TrainingOptions`	`setMaxParallelTrials(Long maxParallelTrials)` Maximum number of trials to run in parallel.
`TrainingOptions`	`setMaxReplicaCount(Long maxReplicaCount)` The maximum number of machine replicas that will be deployed on an endpoint.
`TrainingOptions`	`setMaxTimeSeriesLength(Long maxTimeSeriesLength)` The maximum number of time points in a time series that can be used in modeling the trend component of the time series.
`TrainingOptions`	`setMaxTreeDepth(Long maxTreeDepth)` Maximum depth of a tree for boosted tree models.
`TrainingOptions`	`setMinAprioriSupport(Double minAprioriSupport)` The apriori support minimum.
`TrainingOptions`	`setMinRelativeProgress(Double minRelativeProgress)` When early_stop is true, stops training when accuracy improvement is less than 'min_relative_progress'.
`TrainingOptions`	`setMinReplicaCount(Long minReplicaCount)` The minimum number of machine replicas that will be always deployed on an endpoint.
`TrainingOptions`	`setMinSplitLoss(Double minSplitLoss)` Minimum split loss for boosted tree models.
`TrainingOptions`	`setMinTimeSeriesLength(Long minTimeSeriesLength)` The minimum number of time points in a time series that are used in modeling the trend component of the time series.
`TrainingOptions`	`setMinTreeChildWeight(Long minTreeChildWeight)` Minimum sum of instance weight needed in a child for boosted tree models.
`TrainingOptions`	`setModelGardenModelName(String modelGardenModelName)` The name of a Vertex model garden publisher model.
`TrainingOptions`	`setModelRegistry(String modelRegistry)` The model registry.
`TrainingOptions`	`setModelUri(String modelUri)` Google Cloud Storage URI from which the model was imported.
`TrainingOptions`	`setNonSeasonalOrder(ArimaOrder nonSeasonalOrder)` A specification of the non-seasonal part of the ARIMA model: the three components (p, d, q) are the AR order, the degree of differencing, and the MA order.
`TrainingOptions`	`setNumClusters(Long numClusters)` Number of clusters for clustering models.
`TrainingOptions`	`setNumFactors(Long numFactors)` Num factors specified for matrix factorization models.
`TrainingOptions`	`setNumParallelTree(Long numParallelTree)` Number of parallel trees constructed during each iteration for boosted tree models.
`TrainingOptions`	`setNumPrincipalComponents(Long numPrincipalComponents)` Number of principal components to keep in the PCA model.
`TrainingOptions`	`setNumTrials(Long numTrials)` Number of trials to run this hyperparameter tuning job.
`TrainingOptions`	`setOptimizationStrategy(String optimizationStrategy)` Optimization strategy for training linear regression models.
`TrainingOptions`	`setOptimizer(String optimizer)` Optimizer used for training the neural nets.
`TrainingOptions`	`setPcaExplainedVarianceRatio(Double pcaExplainedVarianceRatio)` The minimum ratio of cumulative explained variance that needs to be given by the PCA model.
`TrainingOptions`	`setPcaSolver(String pcaSolver)` The solver for PCA.
`TrainingOptions`	`setReservationAffinityKey(String reservationAffinityKey)` Corresponds to the label key of a reservation resource used by Vertex AI.
`TrainingOptions`	`setReservationAffinityType(String reservationAffinityType)` Specifies the reservation affinity type used to configure a Vertex AI resource.
`TrainingOptions`	`setReservationAffinityValues(List<String> reservationAffinityValues)` Corresponds to the label values of a reservation resource used by Vertex AI.
`TrainingOptions`	`setSampledShapleyNumPaths(Long sampledShapleyNumPaths)` Number of paths for the sampled Shapley explain method.
`TrainingOptions`	`setScaleFeatures(Boolean scaleFeatures)` If true, scale the feature values by dividing the feature standard deviation.
`TrainingOptions`	`setStandardizeFeatures(Boolean standardizeFeatures)` Whether to standardize numerical features.
`TrainingOptions`	`setSubsample(Double subsample)` Subsample fraction of the training data to grow tree to prevent overfitting for boosted tree models.
`TrainingOptions`	`setTfVersion(String tfVersion)` Based on the selected TF version, the corresponding docker image is used to train external models.
`TrainingOptions`	`setTimeSeriesDataColumn(String timeSeriesDataColumn)` Column to be designated as time series data for ARIMA model.
`TrainingOptions`	`setTimeSeriesIdColumn(String timeSeriesIdColumn)` The time series id column that was used during ARIMA model training.
`TrainingOptions`	`setTimeSeriesIdColumns(List<String> timeSeriesIdColumns)` The time series id columns that were used during ARIMA model training.
`TrainingOptions`	`setTimeSeriesLengthFraction(Double timeSeriesLengthFraction)` The fraction of the interpolated length of the time series that's used to model the time series trend component.
`TrainingOptions`	`setTimeSeriesTimestampColumn(String timeSeriesTimestampColumn)` Column to be designated as time series timestamp for ARIMA model.
`TrainingOptions`	`setTreeMethod(String treeMethod)` Tree construction algorithm for boosted tree models.
`TrainingOptions`	`setTrendSmoothingWindowSize(Long trendSmoothingWindowSize)` Smoothing window size for the trend component.
`TrainingOptions`	`setUserColumn(String userColumn)` User column specified for matrix factorization models.
`TrainingOptions`	`setVertexAiModelVersionAliases(List<String> vertexAiModelVersionAliases)` The version aliases to apply in Vertex AI model registry.
`TrainingOptions`	`setWalsAlpha(Double walsAlpha)` Hyperparameter for matrix factoration when implicit feedback type is specified.
`TrainingOptions`	`setWarmStart(Boolean warmStart)` Whether to train a model from the last checkpoint.
`TrainingOptions`	`setXgboostVersion(String xgboostVersion)` User-selected XGBoost versions for training of XGBoost models.

Methods inherited from class com.google.api.client.json.GenericJson
getFactory, setFactory, toPrettyString, toString

Methods inherited from class com.google.api.client.util.GenericData
entrySet, equals, get, getClassInfo, getUnknownKeys, hashCode, put, putAll, remove, setUnknownKeys

Methods inherited from class java.util.AbstractMap
clear, containsKey, containsValue, isEmpty, keySet, size, values

Methods inherited from class java.lang.Object
finalize, getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface java.util.Map
compute, computeIfAbsent, computeIfPresent, forEach, getOrDefault, merge, putIfAbsent, remove, replace, replace, replaceAll

- Constructor Detail
  - TrainingOptions
```
public TrainingOptions()
```
- Method Detail
  - getActivationFn
```
public String getActivationFn()
```
    Activation function of the neural nets.
    
    Returns:
    
    value or null for none
  - setActivationFn
```
public TrainingOptions setActivationFn(String activationFn)
```
    Activation function of the neural nets.
    
    Parameters:
    
    activationFn - activationFn or null for none
  - getAdjustStepChanges
```
public Boolean getAdjustStepChanges()
```
    If true, detect step changes and make data adjustment in the input time series.
    
    Returns:
    
    value or null for none
  - setAdjustStepChanges
```
public TrainingOptions setAdjustStepChanges(Boolean adjustStepChanges)
```
    If true, detect step changes and make data adjustment in the input time series.
    
    Parameters:
    
    adjustStepChanges - adjustStepChanges or null for none
  - getApproxGlobalFeatureContrib
```
public Boolean getApproxGlobalFeatureContrib()
```
    Whether to use approximate feature contribution method in XGBoost model explanation for global explain.
    
    Returns:
    
    value or null for none
  - setApproxGlobalFeatureContrib
```
public TrainingOptions setApproxGlobalFeatureContrib(Boolean approxGlobalFeatureContrib)
```
    Whether to use approximate feature contribution method in XGBoost model explanation for global explain.
    
    Parameters:
    
    approxGlobalFeatureContrib - approxGlobalFeatureContrib or null for none
  - getAutoArima
```
public Boolean getAutoArima()
```
    Whether to enable auto ARIMA or not.
    
    Returns:
    
    value or null for none
  - setAutoArima
```
public TrainingOptions setAutoArima(Boolean autoArima)
```
    Whether to enable auto ARIMA or not.
    
    Parameters:
    
    autoArima - autoArima or null for none
  - getAutoArimaMaxOrder
```
public Long getAutoArimaMaxOrder()
```
    The max value of the sum of non-seasonal p and q.
    
    Returns:
    
    value or null for none
  - setAutoArimaMaxOrder
```
public TrainingOptions setAutoArimaMaxOrder(Long autoArimaMaxOrder)
```
    The max value of the sum of non-seasonal p and q.
    
    Parameters:
    
    autoArimaMaxOrder - autoArimaMaxOrder or null for none
  - getAutoArimaMinOrder
```
public Long getAutoArimaMinOrder()
```
    The min value of the sum of non-seasonal p and q.
    
    Returns:
    
    value or null for none
  - setAutoArimaMinOrder
```
public TrainingOptions setAutoArimaMinOrder(Long autoArimaMinOrder)
```
    The min value of the sum of non-seasonal p and q.
    
    Parameters:
    
    autoArimaMinOrder - autoArimaMinOrder or null for none
  - getAutoClassWeights
```
public Boolean getAutoClassWeights()
```
    Whether to calculate class weights automatically based on the popularity of each label.
    
    Returns:
    
    value or null for none
  - setAutoClassWeights
```
public TrainingOptions setAutoClassWeights(Boolean autoClassWeights)
```
    Whether to calculate class weights automatically based on the popularity of each label.
    
    Parameters:
    
    autoClassWeights - autoClassWeights or null for none
  - getBatchSize
```
public Long getBatchSize()
```
    Batch size for dnn models.
    
    Returns:
    
    value or null for none
  - setBatchSize
```
public TrainingOptions setBatchSize(Long batchSize)
```
    Batch size for dnn models.
    
    Parameters:
    
    batchSize - batchSize or null for none
  - getBoosterType
```
public String getBoosterType()
```
    Booster type for boosted tree models.
    
    Returns:
    
    value or null for none
  - setBoosterType
```
public TrainingOptions setBoosterType(String boosterType)
```
    Booster type for boosted tree models.
    
    Parameters:
    
    boosterType - boosterType or null for none
  - getBudgetHours
```
public Double getBudgetHours()
```
    Budget in hours for AutoML training.
    
    Returns:
    
    value or null for none
  - setBudgetHours
```
public TrainingOptions setBudgetHours(Double budgetHours)
```
    Budget in hours for AutoML training.
    
    Parameters:
    
    budgetHours - budgetHours or null for none
  - getCalculatePValues
```
public Boolean getCalculatePValues()
```
    Whether or not p-value test should be computed for this model. Only available for linear and logistic regression models.
    
    Returns:
    
    value or null for none
  - setCalculatePValues
```
public TrainingOptions setCalculatePValues(Boolean calculatePValues)
```
    Whether or not p-value test should be computed for this model. Only available for linear and logistic regression models.
    
    Parameters:
    
    calculatePValues - calculatePValues or null for none
  - getCategoryEncodingMethod
```
public String getCategoryEncodingMethod()
```
    Categorical feature encoding method.
    
    Returns:
    
    value or null for none
  - setCategoryEncodingMethod
```
public TrainingOptions setCategoryEncodingMethod(String categoryEncodingMethod)
```
    Categorical feature encoding method.
    
    Parameters:
    
    categoryEncodingMethod - categoryEncodingMethod or null for none
  - getCleanSpikesAndDips
```
public Boolean getCleanSpikesAndDips()
```
    If true, clean spikes and dips in the input time series.
    
    Returns:
    
    value or null for none
  - setCleanSpikesAndDips
```
public TrainingOptions setCleanSpikesAndDips(Boolean cleanSpikesAndDips)
```
    If true, clean spikes and dips in the input time series.
    
    Parameters:
    
    cleanSpikesAndDips - cleanSpikesAndDips or null for none
  - getColorSpace
```
public String getColorSpace()
```
    Enums for color space, used for processing images in Object Table. See more details at https://www.tensorflow.org/io/tutorials/colorspace.
    
    Returns:
    
    value or null for none
  - setColorSpace
```
public TrainingOptions setColorSpace(String colorSpace)
```
    Enums for color space, used for processing images in Object Table. See more details at https://www.tensorflow.org/io/tutorials/colorspace.
    
    Parameters:
    
    colorSpace - colorSpace or null for none
  - getColsampleBylevel
```
public Double getColsampleBylevel()
```
    Subsample ratio of columns for each level for boosted tree models.
    
    Returns:
    
    value or null for none
  - setColsampleBylevel
```
public TrainingOptions setColsampleBylevel(Double colsampleBylevel)
```
    Subsample ratio of columns for each level for boosted tree models.
    
    Parameters:
    
    colsampleBylevel - colsampleBylevel or null for none
  - getColsampleBynode
```
public Double getColsampleBynode()
```
    Subsample ratio of columns for each node(split) for boosted tree models.
    
    Returns:
    
    value or null for none
  - setColsampleBynode
```
public TrainingOptions setColsampleBynode(Double colsampleBynode)
```
    Subsample ratio of columns for each node(split) for boosted tree models.
    
    Parameters:
    
    colsampleBynode - colsampleBynode or null for none
  - getColsampleBytree
```
public Double getColsampleBytree()
```
    Subsample ratio of columns when constructing each tree for boosted tree models.
    
    Returns:
    
    value or null for none
  - setColsampleBytree
```
public TrainingOptions setColsampleBytree(Double colsampleBytree)
```
    Subsample ratio of columns when constructing each tree for boosted tree models.
    
    Parameters:
    
    colsampleBytree - colsampleBytree or null for none
  - getContributionMetric
```
public String getContributionMetric()
```
    The contribution metric. Applies to contribution analysis models. Allowed formats supported are for summable and summable ratio contribution metrics. These include expressions such as `SUM(x)` or `SUM(x)/SUM(y)`, where x and y are column names from the base table.
    
    Returns:
    
    value or null for none
  - setContributionMetric
```
public TrainingOptions setContributionMetric(String contributionMetric)
```
    The contribution metric. Applies to contribution analysis models. Allowed formats supported are for summable and summable ratio contribution metrics. These include expressions such as `SUM(x)` or `SUM(x)/SUM(y)`, where x and y are column names from the base table.
    
    Parameters:
    
    contributionMetric - contributionMetric or null for none
  - getDartNormalizeType
```
public String getDartNormalizeType()
```
    Type of normalization algorithm for boosted tree models using dart booster.
    
    Returns:
    
    value or null for none
  - setDartNormalizeType
```
public TrainingOptions setDartNormalizeType(String dartNormalizeType)
```
    Type of normalization algorithm for boosted tree models using dart booster.
    
    Parameters:
    
    dartNormalizeType - dartNormalizeType or null for none
  - getDataFrequency
```
public String getDataFrequency()
```
    The data frequency of a time series.
    
    Returns:
    
    value or null for none
  - setDataFrequency
```
public TrainingOptions setDataFrequency(String dataFrequency)
```
    The data frequency of a time series.
    
    Parameters:
    
    dataFrequency - dataFrequency or null for none
  - getDataSplitColumn
```
public String getDataSplitColumn()
```
    The column to split data with. This column won't be used as a feature. 1. When data_split_method is CUSTOM, the corresponding column should be boolean. The rows with true value tag are eval data, and the false are training data. 2. When data_split_method is SEQ, the first DATA_SPLIT_EVAL_FRACTION rows (from smallest to largest) in the corresponding column are used as training data, and the rest are eval data. It respects the order in Orderable data types: https://cloud.google.com/bigquery/docs/reference/standard-sql/data- types#data_type_properties
    
    Returns:
    
    value or null for none
  - setDataSplitColumn
```
public TrainingOptions setDataSplitColumn(String dataSplitColumn)
```
    The column to split data with. This column won't be used as a feature. 1. When data_split_method is CUSTOM, the corresponding column should be boolean. The rows with true value tag are eval data, and the false are training data. 2. When data_split_method is SEQ, the first DATA_SPLIT_EVAL_FRACTION rows (from smallest to largest) in the corresponding column are used as training data, and the rest are eval data. It respects the order in Orderable data types: https://cloud.google.com/bigquery/docs/reference/standard-sql/data- types#data_type_properties
    
    Parameters:
    
    dataSplitColumn - dataSplitColumn or null for none
  - getDataSplitEvalFraction
```
public Double getDataSplitEvalFraction()
```
    The fraction of evaluation data over the whole input data. The rest of data will be used as training data. The format should be double. Accurate to two decimal places. Default value is 0.2.
    
    Returns:
    
    value or null for none
  - setDataSplitEvalFraction
```
public TrainingOptions setDataSplitEvalFraction(Double dataSplitEvalFraction)
```
    The fraction of evaluation data over the whole input data. The rest of data will be used as training data. The format should be double. Accurate to two decimal places. Default value is 0.2.
    
    Parameters:
    
    dataSplitEvalFraction - dataSplitEvalFraction or null for none
  - getDataSplitMethod
```
public String getDataSplitMethod()
```
    The data split type for training and evaluation, e.g. RANDOM.
    
    Returns:
    
    value or null for none
  - setDataSplitMethod
```
public TrainingOptions setDataSplitMethod(String dataSplitMethod)
```
    The data split type for training and evaluation, e.g. RANDOM.
    
    Parameters:
    
    dataSplitMethod - dataSplitMethod or null for none
  - getDecomposeTimeSeries
```
public Boolean getDecomposeTimeSeries()
```
    If true, perform decompose time series and save the results.
    
    Returns:
    
    value or null for none
  - setDecomposeTimeSeries
```
public TrainingOptions setDecomposeTimeSeries(Boolean decomposeTimeSeries)
```
    If true, perform decompose time series and save the results.
    
    Parameters:
    
    decomposeTimeSeries - decomposeTimeSeries or null for none
  - getDimensionIdColumns
```
public List<String> getDimensionIdColumns()
```
    Optional. Names of the columns to slice on. Applies to contribution analysis models.
    
    Returns:
    
    value or null for none
  - setDimensionIdColumns
```
public TrainingOptions setDimensionIdColumns(List<String> dimensionIdColumns)
```
    Optional. Names of the columns to slice on. Applies to contribution analysis models.
    
    Parameters:
    
    dimensionIdColumns - dimensionIdColumns or null for none
  - getDistanceType
```
public String getDistanceType()
```
    Distance type for clustering models.
    
    Returns:
    
    value or null for none
  - setDistanceType
```
public TrainingOptions setDistanceType(String distanceType)
```
    Distance type for clustering models.
    
    Parameters:
    
    distanceType - distanceType or null for none
  - getDropout
```
public Double getDropout()
```
    Dropout probability for dnn models.
    
    Returns:
    
    value or null for none
  - setDropout
```
public TrainingOptions setDropout(Double dropout)
```
    Dropout probability for dnn models.
    
    Parameters:
    
    dropout - dropout or null for none
  - getEarlyStop
```
public Boolean getEarlyStop()
```
    Whether to stop early when the loss doesn't improve significantly any more (compared to min_relative_progress). Used only for iterative training algorithms.
    
    Returns:
    
    value or null for none
  - setEarlyStop
```
public TrainingOptions setEarlyStop(Boolean earlyStop)
```
    Whether to stop early when the loss doesn't improve significantly any more (compared to min_relative_progress). Used only for iterative training algorithms.
    
    Parameters:
    
    earlyStop - earlyStop or null for none
  - getEnableGlobalExplain
```
public Boolean getEnableGlobalExplain()
```
    If true, enable global explanation during training.
    
    Returns:
    
    value or null for none
  - setEnableGlobalExplain
```
public TrainingOptions setEnableGlobalExplain(Boolean enableGlobalExplain)
```
    If true, enable global explanation during training.
    
    Parameters:
    
    enableGlobalExplain - enableGlobalExplain or null for none
  - getEndpointIdleTtl
```
public String getEndpointIdleTtl()
```
    The idle TTL of the endpoint before the resources get destroyed. The default value is 6.5 hours.
    
    Returns:
    
    value or null for none
  - setEndpointIdleTtl
```
public TrainingOptions setEndpointIdleTtl(String endpointIdleTtl)
```
    The idle TTL of the endpoint before the resources get destroyed. The default value is 6.5 hours.
    
    Parameters:
    
    endpointIdleTtl - endpointIdleTtl or null for none
  - getFeedbackType
```
public String getFeedbackType()
```
    Feedback type that specifies which algorithm to run for matrix factorization.
    
    Returns:
    
    value or null for none
  - setFeedbackType
```
public TrainingOptions setFeedbackType(String feedbackType)
```
    Feedback type that specifies which algorithm to run for matrix factorization.
    
    Parameters:
    
    feedbackType - feedbackType or null for none
  - getFitIntercept
```
public Boolean getFitIntercept()
```
    Whether the model should include intercept during model training.
    
    Returns:
    
    value or null for none
  - setFitIntercept
```
public TrainingOptions setFitIntercept(Boolean fitIntercept)
```
    Whether the model should include intercept during model training.
    
    Parameters:
    
    fitIntercept - fitIntercept or null for none
  - getForecastLimitLowerBound
```
public Double getForecastLimitLowerBound()
```
    The forecast limit lower bound that was used during ARIMA model training with limits. To see more details of the algorithm: https://otexts.com/fpp2/limits.html
    
    Returns:
    
    value or null for none
  - setForecastLimitLowerBound
```
public TrainingOptions setForecastLimitLowerBound(Double forecastLimitLowerBound)
```
    The forecast limit lower bound that was used during ARIMA model training with limits. To see more details of the algorithm: https://otexts.com/fpp2/limits.html
    
    Parameters:
    
    forecastLimitLowerBound - forecastLimitLowerBound or null for none
  - getForecastLimitUpperBound
```
public Double getForecastLimitUpperBound()
```
    The forecast limit upper bound that was used during ARIMA model training with limits.
    
    Returns:
    
    value or null for none
  - setForecastLimitUpperBound
```
public TrainingOptions setForecastLimitUpperBound(Double forecastLimitUpperBound)
```
    The forecast limit upper bound that was used during ARIMA model training with limits.
    
    Parameters:
    
    forecastLimitUpperBound - forecastLimitUpperBound or null for none
  - getHiddenUnits
```
public List<Long> getHiddenUnits()
```
    Hidden units for dnn models.
    
    Returns:
    
    value or null for none
  - setHiddenUnits
```
public TrainingOptions setHiddenUnits(List<Long> hiddenUnits)
```
    Hidden units for dnn models.
    
    Parameters:
    
    hiddenUnits - hiddenUnits or null for none
  - getHolidayRegion
```
public String getHolidayRegion()
```
    The geographical region based on which the holidays are considered in time series modeling. If a valid value is specified, then holiday effects modeling is enabled.
    
    Returns:
    
    value or null for none
  - setHolidayRegion
```
public TrainingOptions setHolidayRegion(String holidayRegion)
```
    The geographical region based on which the holidays are considered in time series modeling. If a valid value is specified, then holiday effects modeling is enabled.
    
    Parameters:
    
    holidayRegion - holidayRegion or null for none
  - getHolidayRegions
```
public List<String> getHolidayRegions()
```
    A list of geographical regions that are used for time series modeling.
    
    Returns:
    
    value or null for none
  - setHolidayRegions
```
public TrainingOptions setHolidayRegions(List<String> holidayRegions)
```
    A list of geographical regions that are used for time series modeling.
    
    Parameters:
    
    holidayRegions - holidayRegions or null for none
  - getHorizon
```
public Long getHorizon()
```
    The number of periods ahead that need to be forecasted.
    
    Returns:
    
    value or null for none
  - setHorizon
```
public TrainingOptions setHorizon(Long horizon)
```
    The number of periods ahead that need to be forecasted.
    
    Parameters:
    
    horizon - horizon or null for none
  - getHparamTuningObjectives
```
public List<String> getHparamTuningObjectives()
```
    The target evaluation metrics to optimize the hyperparameters for.
    
    Returns:
    
    value or null for none
  - setHparamTuningObjectives
```
public TrainingOptions setHparamTuningObjectives(List<String> hparamTuningObjectives)
```
    The target evaluation metrics to optimize the hyperparameters for.
    
    Parameters:
    
    hparamTuningObjectives - hparamTuningObjectives or null for none
  - getHuggingFaceModelId
```
public String getHuggingFaceModelId()
```
    The id of a Hugging Face model. For example, `google/gemma-2-2b-it`.
    
    Returns:
    
    value or null for none
  - setHuggingFaceModelId
```
public TrainingOptions setHuggingFaceModelId(String huggingFaceModelId)
```
    The id of a Hugging Face model. For example, `google/gemma-2-2b-it`.
    
    Parameters:
    
    huggingFaceModelId - huggingFaceModelId or null for none
  - getIncludeDrift
```
public Boolean getIncludeDrift()
```
    Include drift when fitting an ARIMA model.
    
    Returns:
    
    value or null for none
  - setIncludeDrift
```
public TrainingOptions setIncludeDrift(Boolean includeDrift)
```
    Include drift when fitting an ARIMA model.
    
    Parameters:
    
    includeDrift - includeDrift or null for none
  - getInitialLearnRate
```
public Double getInitialLearnRate()
```
    Specifies the initial learning rate for the line search learn rate strategy.
    
    Returns:
    
    value or null for none
  - setInitialLearnRate
```
public TrainingOptions setInitialLearnRate(Double initialLearnRate)
```
    Specifies the initial learning rate for the line search learn rate strategy.
    
    Parameters:
    
    initialLearnRate - initialLearnRate or null for none
  - getInputLabelColumns
```
public List<String> getInputLabelColumns()
```
    Name of input label columns in training data.
    
    Returns:
    
    value or null for none
  - setInputLabelColumns
```
public TrainingOptions setInputLabelColumns(List<String> inputLabelColumns)
```
    Name of input label columns in training data.
    
    Parameters:
    
    inputLabelColumns - inputLabelColumns or null for none
  - getInstanceWeightColumn
```
public String getInstanceWeightColumn()
```
    Name of the instance weight column for training data. This column isn't be used as a feature.
    
    Returns:
    
    value or null for none
  - setInstanceWeightColumn
```
public TrainingOptions setInstanceWeightColumn(String instanceWeightColumn)
```
    Name of the instance weight column for training data. This column isn't be used as a feature.
    
    Parameters:
    
    instanceWeightColumn - instanceWeightColumn or null for none
  - getIntegratedGradientsNumSteps
```
public Long getIntegratedGradientsNumSteps()
```
    Number of integral steps for the integrated gradients explain method.
    
    Returns:
    
    value or null for none
  - setIntegratedGradientsNumSteps
```
public TrainingOptions setIntegratedGradientsNumSteps(Long integratedGradientsNumSteps)
```
    Number of integral steps for the integrated gradients explain method.
    
    Parameters:
    
    integratedGradientsNumSteps - integratedGradientsNumSteps or null for none
  - getIsTestColumn
```
public String getIsTestColumn()
```
    Name of the column used to determine the rows corresponding to control and test. Applies to contribution analysis models.
    
    Returns:
    
    value or null for none
  - setIsTestColumn
```
public TrainingOptions setIsTestColumn(String isTestColumn)
```
    Name of the column used to determine the rows corresponding to control and test. Applies to contribution analysis models.
    
    Parameters:
    
    isTestColumn - isTestColumn or null for none
  - getItemColumn
```
public String getItemColumn()
```
    Item column specified for matrix factorization models.
    
    Returns:
    
    value or null for none
  - setItemColumn
```
public TrainingOptions setItemColumn(String itemColumn)
```
    Item column specified for matrix factorization models.
    
    Parameters:
    
    itemColumn - itemColumn or null for none
  - getKmeansInitializationColumn
```
public String getKmeansInitializationColumn()
```
    The column used to provide the initial centroids for kmeans algorithm when kmeans_initialization_method is CUSTOM.
    
    Returns:
    
    value or null for none
  - setKmeansInitializationColumn
```
public TrainingOptions setKmeansInitializationColumn(String kmeansInitializationColumn)
```
    The column used to provide the initial centroids for kmeans algorithm when kmeans_initialization_method is CUSTOM.
    
    Parameters:
    
    kmeansInitializationColumn - kmeansInitializationColumn or null for none
  - getKmeansInitializationMethod
```
public String getKmeansInitializationMethod()
```
    The method used to initialize the centroids for kmeans algorithm.
    
    Returns:
    
    value or null for none
  - setKmeansInitializationMethod
```
public TrainingOptions setKmeansInitializationMethod(String kmeansInitializationMethod)
```
    The method used to initialize the centroids for kmeans algorithm.
    
    Parameters:
    
    kmeansInitializationMethod - kmeansInitializationMethod or null for none
  - getL1RegActivation
```
public Double getL1RegActivation()
```
    L1 regularization coefficient to activations.
    
    Returns:
    
    value or null for none
  - setL1RegActivation
```
public TrainingOptions setL1RegActivation(Double l1RegActivation)
```
    L1 regularization coefficient to activations.
    
    Parameters:
    
    l1RegActivation - l1RegActivation or null for none
  - getL1Regularization
```
public Double getL1Regularization()
```
    L1 regularization coefficient.
    
    Returns:
    
    value or null for none
  - setL1Regularization
```
public TrainingOptions setL1Regularization(Double l1Regularization)
```
    L1 regularization coefficient.
    
    Parameters:
    
    l1Regularization - l1Regularization or null for none
  - getL2Regularization
```
public Double getL2Regularization()
```
    L2 regularization coefficient.
    
    Returns:
    
    value or null for none
  - setL2Regularization
```
public TrainingOptions setL2Regularization(Double l2Regularization)
```
    L2 regularization coefficient.
    
    Parameters:
    
    l2Regularization - l2Regularization or null for none
  - getLabelClassWeights
```
public Map<String,Double> getLabelClassWeights()
```
    Weights associated with each label class, for rebalancing the training data. Only applicable for classification models.
    
    Returns:
    
    value or null for none
  - setLabelClassWeights
```
public TrainingOptions setLabelClassWeights(Map<String,Double> labelClassWeights)
```
    Weights associated with each label class, for rebalancing the training data. Only applicable for classification models.
    
    Parameters:
    
    labelClassWeights - labelClassWeights or null for none
  - getLearnRate
```
public Double getLearnRate()
```
    Learning rate in training. Used only for iterative training algorithms.
    
    Returns:
    
    value or null for none
  - setLearnRate
```
public TrainingOptions setLearnRate(Double learnRate)
```
    Learning rate in training. Used only for iterative training algorithms.
    
    Parameters:
    
    learnRate - learnRate or null for none
  - getLearnRateStrategy
```
public String getLearnRateStrategy()
```
    The strategy to determine learn rate for the current iteration.
    
    Returns:
    
    value or null for none
  - setLearnRateStrategy
```
public TrainingOptions setLearnRateStrategy(String learnRateStrategy)
```
    The strategy to determine learn rate for the current iteration.
    
    Parameters:
    
    learnRateStrategy - learnRateStrategy or null for none
  - getLossType
```
public String getLossType()
```
    Type of loss function used during training run.
    
    Returns:
    
    value or null for none
  - setLossType
```
public TrainingOptions setLossType(String lossType)
```
    Type of loss function used during training run.
    
    Parameters:
    
    lossType - lossType or null for none
  - getMachineType
```
public String getMachineType()
```
    The type of the machine used to deploy and serve the model.
    
    Returns:
    
    value or null for none
  - setMachineType
```
public TrainingOptions setMachineType(String machineType)
```
    The type of the machine used to deploy and serve the model.
    
    Parameters:
    
    machineType - machineType or null for none
  - getMaxIterations
```
public Long getMaxIterations()
```
    The maximum number of iterations in training. Used only for iterative training algorithms.
    
    Returns:
    
    value or null for none
  - setMaxIterations
```
public TrainingOptions setMaxIterations(Long maxIterations)
```
    The maximum number of iterations in training. Used only for iterative training algorithms.
    
    Parameters:
    
    maxIterations - maxIterations or null for none
  - getMaxParallelTrials
```
public Long getMaxParallelTrials()
```
    Maximum number of trials to run in parallel.
    
    Returns:
    
    value or null for none
  - setMaxParallelTrials
```
public TrainingOptions setMaxParallelTrials(Long maxParallelTrials)
```
    Maximum number of trials to run in parallel.
    
    Parameters:
    
    maxParallelTrials - maxParallelTrials or null for none
  - getMaxReplicaCount
```
public Long getMaxReplicaCount()
```
    The maximum number of machine replicas that will be deployed on an endpoint. The default value is equal to min_replica_count.
    
    Returns:
    
    value or null for none
  - setMaxReplicaCount
```
public TrainingOptions setMaxReplicaCount(Long maxReplicaCount)
```
    The maximum number of machine replicas that will be deployed on an endpoint. The default value is equal to min_replica_count.
    
    Parameters:
    
    maxReplicaCount - maxReplicaCount or null for none
  - getMaxTimeSeriesLength
```
public Long getMaxTimeSeriesLength()
```
    The maximum number of time points in a time series that can be used in modeling the trend component of the time series. Don't use this option with the `timeSeriesLengthFraction` or `minTimeSeriesLength` options.
    
    Returns:
    
    value or null for none
  - setMaxTimeSeriesLength
```
public TrainingOptions setMaxTimeSeriesLength(Long maxTimeSeriesLength)
```
    The maximum number of time points in a time series that can be used in modeling the trend component of the time series. Don't use this option with the `timeSeriesLengthFraction` or `minTimeSeriesLength` options.
    
    Parameters:
    
    maxTimeSeriesLength - maxTimeSeriesLength or null for none
  - getMaxTreeDepth
```
public Long getMaxTreeDepth()
```
    Maximum depth of a tree for boosted tree models.
    
    Returns:
    
    value or null for none
  - setMaxTreeDepth
```
public TrainingOptions setMaxTreeDepth(Long maxTreeDepth)
```
    Maximum depth of a tree for boosted tree models.
    
    Parameters:
    
    maxTreeDepth - maxTreeDepth or null for none
  - getMinAprioriSupport
```
public Double getMinAprioriSupport()
```
    The apriori support minimum. Applies to contribution analysis models.
    
    Returns:
    
    value or null for none
  - setMinAprioriSupport
```
public TrainingOptions setMinAprioriSupport(Double minAprioriSupport)
```
    The apriori support minimum. Applies to contribution analysis models.
    
    Parameters:
    
    minAprioriSupport - minAprioriSupport or null for none
  - getMinRelativeProgress
```
public Double getMinRelativeProgress()
```
    When early_stop is true, stops training when accuracy improvement is less than 'min_relative_progress'. Used only for iterative training algorithms.
    
    Returns:
    
    value or null for none
  - setMinRelativeProgress
```
public TrainingOptions setMinRelativeProgress(Double minRelativeProgress)
```
    When early_stop is true, stops training when accuracy improvement is less than 'min_relative_progress'. Used only for iterative training algorithms.
    
    Parameters:
    
    minRelativeProgress - minRelativeProgress or null for none
  - getMinReplicaCount
```
public Long getMinReplicaCount()
```
    The minimum number of machine replicas that will be always deployed on an endpoint. This value must be greater than or equal to 1. The default value is 1.
    
    Returns:
    
    value or null for none
  - setMinReplicaCount
```
public TrainingOptions setMinReplicaCount(Long minReplicaCount)
```
    The minimum number of machine replicas that will be always deployed on an endpoint. This value must be greater than or equal to 1. The default value is 1.
    
    Parameters:
    
    minReplicaCount - minReplicaCount or null for none
  - getMinSplitLoss
```
public Double getMinSplitLoss()
```
    Minimum split loss for boosted tree models.
    
    Returns:
    
    value or null for none
  - setMinSplitLoss
```
public TrainingOptions setMinSplitLoss(Double minSplitLoss)
```
    Minimum split loss for boosted tree models.
    
    Parameters:
    
    minSplitLoss - minSplitLoss or null for none
  - getMinTimeSeriesLength
```
public Long getMinTimeSeriesLength()
```
    The minimum number of time points in a time series that are used in modeling the trend component of the time series. If you use this option you must also set the `timeSeriesLengthFraction` option. This training option ensures that enough time points are available when you use `timeSeriesLengthFraction` in trend modeling. This is particularly important when forecasting multiple time series in a single query using `timeSeriesIdColumn`. If the total number of time points is less than the `minTimeSeriesLength` value, then the query uses all available time points.
    
    Returns:
    
    value or null for none
  - setMinTimeSeriesLength
```
public TrainingOptions setMinTimeSeriesLength(Long minTimeSeriesLength)
```
    The minimum number of time points in a time series that are used in modeling the trend component of the time series. If you use this option you must also set the `timeSeriesLengthFraction` option. This training option ensures that enough time points are available when you use `timeSeriesLengthFraction` in trend modeling. This is particularly important when forecasting multiple time series in a single query using `timeSeriesIdColumn`. If the total number of time points is less than the `minTimeSeriesLength` value, then the query uses all available time points.
    
    Parameters:
    
    minTimeSeriesLength - minTimeSeriesLength or null for none
  - getMinTreeChildWeight
```
public Long getMinTreeChildWeight()
```
    Minimum sum of instance weight needed in a child for boosted tree models.
    
    Returns:
    
    value or null for none
  - setMinTreeChildWeight
```
public TrainingOptions setMinTreeChildWeight(Long minTreeChildWeight)
```
    Minimum sum of instance weight needed in a child for boosted tree models.
    
    Parameters:
    
    minTreeChildWeight - minTreeChildWeight or null for none
  - getModelGardenModelName
```
public String getModelGardenModelName()
```
    The name of a Vertex model garden publisher model. Format is `publishers/{publisher}/models/{model}@{optional_version_id}`.
    
    Returns:
    
    value or null for none
  - setModelGardenModelName
```
public TrainingOptions setModelGardenModelName(String modelGardenModelName)
```
    The name of a Vertex model garden publisher model. Format is `publishers/{publisher}/models/{model}@{optional_version_id}`.
    
    Parameters:
    
    modelGardenModelName - modelGardenModelName or null for none
  - getModelRegistry
```
public String getModelRegistry()
```
    The model registry.
    
    Returns:
    
    value or null for none
  - setModelRegistry
```
public TrainingOptions setModelRegistry(String modelRegistry)
```
    The model registry.
    
    Parameters:
    
    modelRegistry - modelRegistry or null for none
  - getModelUri
```
public String getModelUri()
```
    Google Cloud Storage URI from which the model was imported. Only applicable for imported models.
    
    Returns:
    
    value or null for none
  - setModelUri
```
public TrainingOptions setModelUri(String modelUri)
```
    Google Cloud Storage URI from which the model was imported. Only applicable for imported models.
    
    Parameters:
    
    modelUri - modelUri or null for none
  - getNonSeasonalOrder
```
public ArimaOrder getNonSeasonalOrder()
```
    A specification of the non-seasonal part of the ARIMA model: the three components (p, d, q) are the AR order, the degree of differencing, and the MA order.
    
    Returns:
    
    value or null for none
  - setNonSeasonalOrder
```
public TrainingOptions setNonSeasonalOrder(ArimaOrder nonSeasonalOrder)
```
    A specification of the non-seasonal part of the ARIMA model: the three components (p, d, q) are the AR order, the degree of differencing, and the MA order.
    
    Parameters:
    
    nonSeasonalOrder - nonSeasonalOrder or null for none
  - getNumClusters
```
public Long getNumClusters()
```
    Number of clusters for clustering models.
    
    Returns:
    
    value or null for none
  - setNumClusters
```
public TrainingOptions setNumClusters(Long numClusters)
```
    Number of clusters for clustering models.
    
    Parameters:
    
    numClusters - numClusters or null for none
  - getNumFactors
```
public Long getNumFactors()
```
    Num factors specified for matrix factorization models.
    
    Returns:
    
    value or null for none
  - setNumFactors
```
public TrainingOptions setNumFactors(Long numFactors)
```
    Num factors specified for matrix factorization models.
    
    Parameters:
    
    numFactors - numFactors or null for none
  - getNumParallelTree
```
public Long getNumParallelTree()
```
    Number of parallel trees constructed during each iteration for boosted tree models.
    
    Returns:
    
    value or null for none
  - setNumParallelTree
```
public TrainingOptions setNumParallelTree(Long numParallelTree)
```
    Number of parallel trees constructed during each iteration for boosted tree models.
    
    Parameters:
    
    numParallelTree - numParallelTree or null for none
  - getNumPrincipalComponents
```
public Long getNumPrincipalComponents()
```
    Number of principal components to keep in the PCA model. Must be <= the number of features.
    
    Returns:
    
    value or null for none
  - setNumPrincipalComponents
```
public TrainingOptions setNumPrincipalComponents(Long numPrincipalComponents)
```
    Number of principal components to keep in the PCA model. Must be <= the number of features.
    
    Parameters:
    
    numPrincipalComponents - numPrincipalComponents or null for none
  - getNumTrials
```
public Long getNumTrials()
```
    Number of trials to run this hyperparameter tuning job.
    
    Returns:
    
    value or null for none
  - setNumTrials
```
public TrainingOptions setNumTrials(Long numTrials)
```
    Number of trials to run this hyperparameter tuning job.
    
    Parameters:
    
    numTrials - numTrials or null for none
  - getOptimizationStrategy
```
public String getOptimizationStrategy()
```
    Optimization strategy for training linear regression models.
    
    Returns:
    
    value or null for none
  - setOptimizationStrategy
```
public TrainingOptions setOptimizationStrategy(String optimizationStrategy)
```
    Optimization strategy for training linear regression models.
    
    Parameters:
    
    optimizationStrategy - optimizationStrategy or null for none
  - getOptimizer
```
public String getOptimizer()
```
    Optimizer used for training the neural nets.
    
    Returns:
    
    value or null for none
  - setOptimizer
```
public TrainingOptions setOptimizer(String optimizer)
```
    Optimizer used for training the neural nets.
    
    Parameters:
    
    optimizer - optimizer or null for none
  - getPcaExplainedVarianceRatio
```
public Double getPcaExplainedVarianceRatio()
```
    The minimum ratio of cumulative explained variance that needs to be given by the PCA model.
    
    Returns:
    
    value or null for none
  - setPcaExplainedVarianceRatio
```
public TrainingOptions setPcaExplainedVarianceRatio(Double pcaExplainedVarianceRatio)
```
    The minimum ratio of cumulative explained variance that needs to be given by the PCA model.
    
    Parameters:
    
    pcaExplainedVarianceRatio - pcaExplainedVarianceRatio or null for none
  - getPcaSolver
```
public String getPcaSolver()
```
    The solver for PCA.
    
    Returns:
    
    value or null for none
  - setPcaSolver
```
public TrainingOptions setPcaSolver(String pcaSolver)
```
    The solver for PCA.
    
    Parameters:
    
    pcaSolver - pcaSolver or null for none
  - getReservationAffinityKey
```
public String getReservationAffinityKey()
```
    Corresponds to the label key of a reservation resource used by Vertex AI. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
    
    Returns:
    
    value or null for none
  - setReservationAffinityKey
```
public TrainingOptions setReservationAffinityKey(String reservationAffinityKey)
```
    Corresponds to the label key of a reservation resource used by Vertex AI. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
    
    Parameters:
    
    reservationAffinityKey - reservationAffinityKey or null for none
  - getReservationAffinityType
```
public String getReservationAffinityType()
```
    Specifies the reservation affinity type used to configure a Vertex AI resource. The default value is `NO_RESERVATION`.
    
    Returns:
    
    value or null for none
  - setReservationAffinityType
```
public TrainingOptions setReservationAffinityType(String reservationAffinityType)
```
    Specifies the reservation affinity type used to configure a Vertex AI resource. The default value is `NO_RESERVATION`.
    
    Parameters:
    
    reservationAffinityType - reservationAffinityType or null for none
  - getReservationAffinityValues
```
public List<String> getReservationAffinityValues()
```
    Corresponds to the label values of a reservation resource used by Vertex AI. This must be the full resource name of the reservation or reservation block.
    
    Returns:
    
    value or null for none
  - setReservationAffinityValues
```
public TrainingOptions setReservationAffinityValues(List<String> reservationAffinityValues)
```
    Corresponds to the label values of a reservation resource used by Vertex AI. This must be the full resource name of the reservation or reservation block.
    
    Parameters:
    
    reservationAffinityValues - reservationAffinityValues or null for none
  - getSampledShapleyNumPaths
```
public Long getSampledShapleyNumPaths()
```
    Number of paths for the sampled Shapley explain method.
    
    Returns:
    
    value or null for none
  - setSampledShapleyNumPaths
```
public TrainingOptions setSampledShapleyNumPaths(Long sampledShapleyNumPaths)
```
    Number of paths for the sampled Shapley explain method.
    
    Parameters:
    
    sampledShapleyNumPaths - sampledShapleyNumPaths or null for none
  - getScaleFeatures
```
public Boolean getScaleFeatures()
```
    If true, scale the feature values by dividing the feature standard deviation. Currently only apply to PCA.
    
    Returns:
    
    value or null for none
  - setScaleFeatures
```
public TrainingOptions setScaleFeatures(Boolean scaleFeatures)
```
    If true, scale the feature values by dividing the feature standard deviation. Currently only apply to PCA.
    
    Parameters:
    
    scaleFeatures - scaleFeatures or null for none
  - getStandardizeFeatures
```
public Boolean getStandardizeFeatures()
```
    Whether to standardize numerical features. Default to true.
    
    Returns:
    
    value or null for none
  - setStandardizeFeatures
```
public TrainingOptions setStandardizeFeatures(Boolean standardizeFeatures)
```
    Whether to standardize numerical features. Default to true.
    
    Parameters:
    
    standardizeFeatures - standardizeFeatures or null for none
  - getSubsample
```
public Double getSubsample()
```
    Subsample fraction of the training data to grow tree to prevent overfitting for boosted tree models.
    
    Returns:
    
    value or null for none
  - setSubsample
```
public TrainingOptions setSubsample(Double subsample)
```
    Subsample fraction of the training data to grow tree to prevent overfitting for boosted tree models.
    
    Parameters:
    
    subsample - subsample or null for none
  - getTfVersion
```
public String getTfVersion()
```
    Based on the selected TF version, the corresponding docker image is used to train external models.
    
    Returns:
    
    value or null for none
  - setTfVersion
```
public TrainingOptions setTfVersion(String tfVersion)
```
    Based on the selected TF version, the corresponding docker image is used to train external models.
    
    Parameters:
    
    tfVersion - tfVersion or null for none
  - getTimeSeriesDataColumn
```
public String getTimeSeriesDataColumn()
```
    Column to be designated as time series data for ARIMA model.
    
    Returns:
    
    value or null for none
  - setTimeSeriesDataColumn
```
public TrainingOptions setTimeSeriesDataColumn(String timeSeriesDataColumn)
```
    Column to be designated as time series data for ARIMA model.
    
    Parameters:
    
    timeSeriesDataColumn - timeSeriesDataColumn or null for none
  - getTimeSeriesIdColumn
```
public String getTimeSeriesIdColumn()
```
    The time series id column that was used during ARIMA model training.
    
    Returns:
    
    value or null for none
  - setTimeSeriesIdColumn
```
public TrainingOptions setTimeSeriesIdColumn(String timeSeriesIdColumn)
```
    The time series id column that was used during ARIMA model training.
    
    Parameters:
    
    timeSeriesIdColumn - timeSeriesIdColumn or null for none
  - getTimeSeriesIdColumns
```
public List<String> getTimeSeriesIdColumns()
```
    The time series id columns that were used during ARIMA model training.
    
    Returns:
    
    value or null for none
  - setTimeSeriesIdColumns
```
public TrainingOptions setTimeSeriesIdColumns(List<String> timeSeriesIdColumns)
```
    The time series id columns that were used during ARIMA model training.
    
    Parameters:
    
    timeSeriesIdColumns - timeSeriesIdColumns or null for none
  - getTimeSeriesLengthFraction
```
public Double getTimeSeriesLengthFraction()
```
    The fraction of the interpolated length of the time series that's used to model the time series trend component. All of the time points of the time series are used to model the non-trend component. This training option accelerates modeling training without sacrificing much forecasting accuracy. You can use this option with `minTimeSeriesLength` but not with `maxTimeSeriesLength`.
    
    Returns:
    
    value or null for none
  - setTimeSeriesLengthFraction
```
public TrainingOptions setTimeSeriesLengthFraction(Double timeSeriesLengthFraction)
```
    The fraction of the interpolated length of the time series that's used to model the time series trend component. All of the time points of the time series are used to model the non-trend component. This training option accelerates modeling training without sacrificing much forecasting accuracy. You can use this option with `minTimeSeriesLength` but not with `maxTimeSeriesLength`.
    
    Parameters:
    
    timeSeriesLengthFraction - timeSeriesLengthFraction or null for none
  - getTimeSeriesTimestampColumn
```
public String getTimeSeriesTimestampColumn()
```
    Column to be designated as time series timestamp for ARIMA model.
    
    Returns:
    
    value or null for none
  - setTimeSeriesTimestampColumn
```
public TrainingOptions setTimeSeriesTimestampColumn(String timeSeriesTimestampColumn)
```
    Column to be designated as time series timestamp for ARIMA model.
    
    Parameters:
    
    timeSeriesTimestampColumn - timeSeriesTimestampColumn or null for none
  - getTreeMethod
```
public String getTreeMethod()
```
    Tree construction algorithm for boosted tree models.
    
    Returns:
    
    value or null for none
  - setTreeMethod
```
public TrainingOptions setTreeMethod(String treeMethod)
```
    Tree construction algorithm for boosted tree models.
    
    Parameters:
    
    treeMethod - treeMethod or null for none
  - getTrendSmoothingWindowSize
```
public Long getTrendSmoothingWindowSize()
```
    Smoothing window size for the trend component. When a positive value is specified, a center moving average smoothing is applied on the history trend. When the smoothing window is out of the boundary at the beginning or the end of the trend, the first element or the last element is padded to fill the smoothing window before the average is applied.
    
    Returns:
    
    value or null for none
  - setTrendSmoothingWindowSize
```
public TrainingOptions setTrendSmoothingWindowSize(Long trendSmoothingWindowSize)
```
    Smoothing window size for the trend component. When a positive value is specified, a center moving average smoothing is applied on the history trend. When the smoothing window is out of the boundary at the beginning or the end of the trend, the first element or the last element is padded to fill the smoothing window before the average is applied.
    
    Parameters:
    
    trendSmoothingWindowSize - trendSmoothingWindowSize or null for none
  - getUserColumn
```
public String getUserColumn()
```
    User column specified for matrix factorization models.
    
    Returns:
    
    value or null for none
  - setUserColumn
```
public TrainingOptions setUserColumn(String userColumn)
```
    User column specified for matrix factorization models.
    
    Parameters:
    
    userColumn - userColumn or null for none
  - getVertexAiModelVersionAliases
```
public List<String> getVertexAiModelVersionAliases()
```
    The version aliases to apply in Vertex AI model registry. Always overwrite if the version aliases exists in a existing model.
    
    Returns:
    
    value or null for none
  - setVertexAiModelVersionAliases
```
public TrainingOptions setVertexAiModelVersionAliases(List<String> vertexAiModelVersionAliases)
```
    The version aliases to apply in Vertex AI model registry. Always overwrite if the version aliases exists in a existing model.
    
    Parameters:
    
    vertexAiModelVersionAliases - vertexAiModelVersionAliases or null for none
  - getWalsAlpha
```
public Double getWalsAlpha()
```
    Hyperparameter for matrix factoration when implicit feedback type is specified.
    
    Returns:
    
    value or null for none
  - setWalsAlpha
```
public TrainingOptions setWalsAlpha(Double walsAlpha)
```
    Hyperparameter for matrix factoration when implicit feedback type is specified.
    
    Parameters:
    
    walsAlpha - walsAlpha or null for none
  - getWarmStart
```
public Boolean getWarmStart()
```
    Whether to train a model from the last checkpoint.
    
    Returns:
    
    value or null for none
  - setWarmStart
```
public TrainingOptions setWarmStart(Boolean warmStart)
```
    Whether to train a model from the last checkpoint.
    
    Parameters:
    
    warmStart - warmStart or null for none
  - getXgboostVersion
```
public String getXgboostVersion()
```
    User-selected XGBoost versions for training of XGBoost models.
    
    Returns:
    
    value or null for none
  - setXgboostVersion
```
public TrainingOptions setXgboostVersion(String xgboostVersion)
```
    User-selected XGBoost versions for training of XGBoost models.
    
    Parameters:
    
    xgboostVersion - xgboostVersion or null for none
  - set
```
public TrainingOptions set(String fieldName,
                           Object value)
```
    Overrides:
    
    set in class com.google.api.client.json.GenericJson
  - clone
```
public TrainingOptions clone()
```
    Overrides:
    
    clone in class com.google.api.client.json.GenericJson

Class TrainingOptions

Nested Class Summary

Nested classes/interfaces inherited from class com.google.api.client.util.GenericData

Nested classes/interfaces inherited from class java.util.AbstractMap

Nested classes/interfaces inherited from interface java.util.Map

Constructor Summary

Method Summary

Methods inherited from class com.google.api.client.json.GenericJson

Methods inherited from class com.google.api.client.util.GenericData

Methods inherited from class java.util.AbstractMap

Methods inherited from class java.lang.Object

Methods inherited from interface java.util.Map

Constructor Detail

TrainingOptions

Method Detail

getActivationFn

setActivationFn

getAdjustStepChanges

setAdjustStepChanges

getApproxGlobalFeatureContrib

setApproxGlobalFeatureContrib

getAutoArima

setAutoArima

getAutoArimaMaxOrder

setAutoArimaMaxOrder

getAutoArimaMinOrder

setAutoArimaMinOrder

getAutoClassWeights

setAutoClassWeights

getBatchSize

setBatchSize

getBoosterType

setBoosterType

getBudgetHours

setBudgetHours

getCalculatePValues

setCalculatePValues

getCategoryEncodingMethod

setCategoryEncodingMethod

getCleanSpikesAndDips

setCleanSpikesAndDips

getColorSpace

setColorSpace

getColsampleBylevel

setColsampleBylevel

getColsampleBynode

setColsampleBynode

getColsampleBytree

setColsampleBytree

getContributionMetric

setContributionMetric

getDartNormalizeType

setDartNormalizeType

getDataFrequency

setDataFrequency

getDataSplitColumn

setDataSplitColumn

getDataSplitEvalFraction

setDataSplitEvalFraction

getDataSplitMethod

setDataSplitMethod

getDecomposeTimeSeries

setDecomposeTimeSeries

getDimensionIdColumns

setDimensionIdColumns

getDistanceType

setDistanceType

getDropout

setDropout

getEarlyStop

setEarlyStop

getEnableGlobalExplain

setEnableGlobalExplain

getEndpointIdleTtl

setEndpointIdleTtl

getFeedbackType

setFeedbackType

getFitIntercept

setFitIntercept

getForecastLimitLowerBound