TrainingOptions (BigQuery API v2-rev20210529-1.31.0)

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 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
  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()`
`Boolean`	`getAdjustStepChanges()` If true, detect step changes and make data adjustment in the input time series.
`Boolean`	`getAutoArima()` Whether to enable auto ARIMA or not.
`Long`	`getAutoArimaMaxOrder()` The max value of non-seasonal p and q.
`Long`	`getBatchSize()` Batch size for dnn models.
`Boolean`	`getCleanSpikesAndDips()` If true, clean spikes and dips in the input time series.
`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.
`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).
`String`	`getFeedbackType()` Feedback type that specifies which algorithm to run for matrix factorization.
`List<Long>`	`getHiddenUnits()` Hidden units for dnn models.
`String`	`getHolidayRegion()` The geographical region based on which the holidays are considered in time series modeling.
`Long`	`getHorizon()` The number of periods ahead that need to be forecasted.
`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`	`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`	`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.
`Long`	`getMaxIterations()` The maximum number of iterations in training.
`Long`	`getMaxTreeDepth()` Maximum depth of a tree for boosted tree models.
`Double`	`getMinRelativeProgress()` When early_stop is true, stops training when accuracy improvement is less than 'min_relative_progress'.
`Double`	`getMinSplitLoss()` Minimum split loss for boosted tree models.
`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.
`String`	`getOptimizationStrategy()` Optimization strategy for training linear regression models.
`Boolean`	`getPreserveInputStructs()` Whether to preserve the input structs in output feature names.
`Double`	`getSubsample()` Subsample fraction of the training data to grow tree to prevent overfitting for boosted tree 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.
`String`	`getTimeSeriesTimestampColumn()` Column to be designated as time series timestamp for ARIMA model.
`String`	`getUserColumn()` User column specified for matrix factorization models.
`Double`	`getWalsAlpha()` Hyperparameter for matrix factoration when implicit feedback type is specified.
`Boolean`	`getWarmStart()` Whether to train a model from the last checkpoint.
`TrainingOptions`	`set(String fieldName, Object value)`
`TrainingOptions`	`setAdjustStepChanges(Boolean adjustStepChanges)` If true, detect step changes and make data adjustment in the input time series.
`TrainingOptions`	`setAutoArima(Boolean autoArima)` Whether to enable auto ARIMA or not.
`TrainingOptions`	`setAutoArimaMaxOrder(Long autoArimaMaxOrder)` The max value of non-seasonal p and q.
`TrainingOptions`	`setBatchSize(Long batchSize)` Batch size for dnn models.
`TrainingOptions`	`setCleanSpikesAndDips(Boolean cleanSpikesAndDips)` If true, clean spikes and dips in the input time series.
`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`	`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`	`setFeedbackType(String feedbackType)` Feedback type that specifies which algorithm to run for matrix factorization.
`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`	`setHorizon(Long horizon)` The number of periods ahead that need to be forecasted.
`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`	`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`	`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`	`setMaxIterations(Long maxIterations)` The maximum number of iterations in training.
`TrainingOptions`	`setMaxTreeDepth(Long maxTreeDepth)` Maximum depth of a tree for boosted tree models.
`TrainingOptions`	`setMinRelativeProgress(Double minRelativeProgress)` When early_stop is true, stops training when accuracy improvement is less than 'min_relative_progress'.
`TrainingOptions`	`setMinSplitLoss(Double minSplitLoss)` Minimum split loss for boosted tree models.
`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`	`setOptimizationStrategy(String optimizationStrategy)` Optimization strategy for training linear regression models.
`TrainingOptions`	`setPreserveInputStructs(Boolean preserveInputStructs)` Whether to preserve the input structs in output feature names.
`TrainingOptions`	`setSubsample(Double subsample)` Subsample fraction of the training data to grow tree to prevent overfitting for boosted tree 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`	`setTimeSeriesTimestampColumn(String timeSeriesTimestampColumn)` Column to be designated as time series timestamp for ARIMA model.
`TrainingOptions`	`setUserColumn(String userColumn)` User column specified for matrix factorization models.
`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.

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
  - 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
  - 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 non-seasonal p and q.
    
    Returns:
    
    value or null for none
  - setAutoArimaMaxOrder
```
public TrainingOptions setAutoArimaMaxOrder(Long autoArimaMaxOrder)
```
    The max value of non-seasonal p and q.
    
    Parameters:
    
    autoArimaMaxOrder - autoArimaMaxOrder 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - getPreserveInputStructs
```
public Boolean getPreserveInputStructs()
```
    Whether to preserve the input structs in output feature names. Suppose there is a struct A with field b. When false (default), the output feature name is A_b. When true, the output feature name is A.b.
    
    Returns:
    
    value or null for none
  - setPreserveInputStructs
```
public TrainingOptions setPreserveInputStructs(Boolean preserveInputStructs)
```
    Whether to preserve the input structs in output feature names. Suppose there is a struct A with field b. When false (default), the output feature name is A_b. When true, the output feature name is A.b.
    
    Parameters:
    
    preserveInputStructs - preserveInputStructs 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
  - 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
  - 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
  - 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
  - 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
  - set
```
public TrainingOptions set(String fieldName,
                           Object value)
```
    Overrides:
    
    set in class GenericJson
  - clone
```
public TrainingOptions clone()
```
    Overrides:
    
    clone in class 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

getAdjustStepChanges

setAdjustStepChanges

getAutoArima

setAutoArima

getAutoArimaMaxOrder

setAutoArimaMaxOrder

getBatchSize

setBatchSize

getCleanSpikesAndDips

setCleanSpikesAndDips

getDataFrequency

setDataFrequency

getDataSplitColumn

setDataSplitColumn

getDataSplitEvalFraction

setDataSplitEvalFraction

getDataSplitMethod

setDataSplitMethod

getDecomposeTimeSeries

setDecomposeTimeSeries

getDistanceType

setDistanceType

getDropout

setDropout

getEarlyStop

setEarlyStop

getFeedbackType

setFeedbackType

getHiddenUnits

setHiddenUnits

getHolidayRegion

setHolidayRegion

getHorizon

setHorizon

getIncludeDrift

setIncludeDrift

getInitialLearnRate

setInitialLearnRate

getInputLabelColumns

setInputLabelColumns

getItemColumn

setItemColumn

getKmeansInitializationColumn

setKmeansInitializationColumn

getKmeansInitializationMethod

setKmeansInitializationMethod

getL1Regularization

setL1Regularization

getL2Regularization

setL2Regularization

getLabelClassWeights

setLabelClassWeights

getLearnRate

setLearnRate

getLearnRateStrategy

setLearnRateStrategy

getLossType

setLossType

getMaxIterations

setMaxIterations

getMaxTreeDepth

setMaxTreeDepth

getMinRelativeProgress

setMinRelativeProgress

getMinSplitLoss