public final class TrainingOptions extends GenericJson
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
GenericData.Flags
AbstractMap.SimpleEntry<K,V>, AbstractMap.SimpleImmutableEntry<K,V>
Constructor and Description |
---|
TrainingOptions() |
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.
|
String |
getBoosterType()
Booster type for boosted tree models.
|
Boolean |
getCalculatePValues()
Whether or not p-value test should be computed for this model.
|
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 |
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.
|
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 |
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.
|
List<String> |
getHparamTuningObjectives()
The target evaluation metrics to optimize the hyperparameters for.
|
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.
|
Long |
getIntegratedGradientsNumSteps()
Number of integral steps for the integrated gradients explain method.
|
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 |
getMaxParallelTrials()
Maximum number of trials to run in parallel.
|
Long |
getMaxTimeSeriesLength()
Get truncated length by last n points in time series.
|
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.
|
Long |
getMinTimeSeriesLength()
Set fast trend ARIMA_PLUS model minimum training length.
|
Long |
getMinTreeChildWeight()
Minimum sum of instance weight needed in a child 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.
|
Long |
getNumParallelTree()
Number of parallel trees constructed during each iteration for boosted tree models.
|
Long |
getNumTrials()
Number of trials to run this hyperparameter tuning job.
|
String |
getOptimizationStrategy()
Optimization strategy for training linear regression models.
|
Boolean |
getPreserveInputStructs()
Whether to preserve the input structs in output feature names.
|
Long |
getSampledShapleyNumPaths()
Number of paths for the sampled Shapley explain method.
|
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.
|
Double |
getTimeSeriesLengthFraction()
Get truncated length by fraction in time series.
|
String |
getTimeSeriesTimestampColumn()
Column to be designated as time series timestamp for ARIMA model.
|
String |
getTreeMethod()
Tree construction algorithm for boosted tree models.
|
Long |
getTrendSmoothingWindowSize()
The smoothing window size for the trend component of the time series.
|
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 |
setBoosterType(String boosterType)
Booster type for boosted tree models.
|
TrainingOptions |
setCalculatePValues(Boolean calculatePValues)
Whether or not p-value test should be computed for this model.
|
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 |
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 |
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 |
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 |
setHparamTuningObjectives(List<String> hparamTuningObjectives)
The target evaluation metrics to optimize the hyperparameters for.
|
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 |
setIntegratedGradientsNumSteps(Long integratedGradientsNumSteps)
Number of integral steps for the integrated gradients explain method.
|
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 |
setMaxParallelTrials(Long maxParallelTrials)
Maximum number of trials to run in parallel.
|
TrainingOptions |
setMaxTimeSeriesLength(Long maxTimeSeriesLength)
Get truncated length by last n points in time series.
|
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 |
setMinTimeSeriesLength(Long minTimeSeriesLength)
Set fast trend ARIMA_PLUS model minimum training length.
|
TrainingOptions |
setMinTreeChildWeight(Long minTreeChildWeight)
Minimum sum of instance weight needed in a child 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 |
setNumParallelTree(Long numParallelTree)
Number of parallel trees constructed during each iteration for boosted tree models.
|
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 |
setPreserveInputStructs(Boolean preserveInputStructs)
Whether to preserve the input structs in output feature names.
|
TrainingOptions |
setSampledShapleyNumPaths(Long sampledShapleyNumPaths)
Number of paths for the sampled Shapley explain method.
|
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 |
setTimeSeriesLengthFraction(Double timeSeriesLengthFraction)
Get truncated length by fraction in time series.
|
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)
The smoothing window size for the trend component of the time series.
|
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.
|
getFactory, setFactory, toPrettyString, toString
entrySet, equals, get, getClassInfo, getUnknownKeys, hashCode, put, putAll, remove, setUnknownKeys
clear, containsKey, containsValue, isEmpty, keySet, size, values
finalize, getClass, notify, notifyAll, wait, wait, wait
compute, computeIfAbsent, computeIfPresent, forEach, getOrDefault, merge, putIfAbsent, remove, replace, replace, replaceAll
public Boolean getAdjustStepChanges()
null
for nonepublic TrainingOptions setAdjustStepChanges(Boolean adjustStepChanges)
adjustStepChanges
- adjustStepChanges or null
for nonepublic Boolean getAutoArima()
null
for nonepublic TrainingOptions setAutoArima(Boolean autoArima)
autoArima
- autoArima or null
for nonepublic Long getAutoArimaMaxOrder()
null
for nonepublic TrainingOptions setAutoArimaMaxOrder(Long autoArimaMaxOrder)
autoArimaMaxOrder
- autoArimaMaxOrder or null
for nonepublic Long getBatchSize()
null
for nonepublic TrainingOptions setBatchSize(Long batchSize)
batchSize
- batchSize or null
for nonepublic String getBoosterType()
null
for nonepublic TrainingOptions setBoosterType(String boosterType)
boosterType
- boosterType or null
for nonepublic Boolean getCalculatePValues()
null
for nonepublic TrainingOptions setCalculatePValues(Boolean calculatePValues)
calculatePValues
- calculatePValues or null
for nonepublic Boolean getCleanSpikesAndDips()
null
for nonepublic TrainingOptions setCleanSpikesAndDips(Boolean cleanSpikesAndDips)
cleanSpikesAndDips
- cleanSpikesAndDips or null
for nonepublic String getColorSpace()
null
for nonepublic TrainingOptions setColorSpace(String colorSpace)
colorSpace
- colorSpace or null
for nonepublic Double getColsampleBylevel()
null
for nonepublic TrainingOptions setColsampleBylevel(Double colsampleBylevel)
colsampleBylevel
- colsampleBylevel or null
for nonepublic Double getColsampleBynode()
null
for nonepublic TrainingOptions setColsampleBynode(Double colsampleBynode)
colsampleBynode
- colsampleBynode or null
for nonepublic Double getColsampleBytree()
null
for nonepublic TrainingOptions setColsampleBytree(Double colsampleBytree)
colsampleBytree
- colsampleBytree or null
for nonepublic String getDartNormalizeType()
null
for nonepublic TrainingOptions setDartNormalizeType(String dartNormalizeType)
dartNormalizeType
- dartNormalizeType or null
for nonepublic String getDataFrequency()
null
for nonepublic TrainingOptions setDataFrequency(String dataFrequency)
dataFrequency
- dataFrequency or null
for nonepublic String getDataSplitColumn()
null
for nonepublic TrainingOptions setDataSplitColumn(String dataSplitColumn)
dataSplitColumn
- dataSplitColumn or null
for nonepublic Double getDataSplitEvalFraction()
null
for nonepublic TrainingOptions setDataSplitEvalFraction(Double dataSplitEvalFraction)
dataSplitEvalFraction
- dataSplitEvalFraction or null
for nonepublic String getDataSplitMethod()
null
for nonepublic TrainingOptions setDataSplitMethod(String dataSplitMethod)
dataSplitMethod
- dataSplitMethod or null
for nonepublic Boolean getDecomposeTimeSeries()
null
for nonepublic TrainingOptions setDecomposeTimeSeries(Boolean decomposeTimeSeries)
decomposeTimeSeries
- decomposeTimeSeries or null
for nonepublic String getDistanceType()
null
for nonepublic TrainingOptions setDistanceType(String distanceType)
distanceType
- distanceType or null
for nonepublic Double getDropout()
null
for nonepublic TrainingOptions setDropout(Double dropout)
dropout
- dropout or null
for nonepublic Boolean getEarlyStop()
null
for nonepublic TrainingOptions setEarlyStop(Boolean earlyStop)
earlyStop
- earlyStop or null
for nonepublic Boolean getEnableGlobalExplain()
null
for nonepublic TrainingOptions setEnableGlobalExplain(Boolean enableGlobalExplain)
enableGlobalExplain
- enableGlobalExplain or null
for nonepublic String getFeedbackType()
null
for nonepublic TrainingOptions setFeedbackType(String feedbackType)
feedbackType
- feedbackType or null
for nonepublic List<Long> getHiddenUnits()
null
for nonepublic TrainingOptions setHiddenUnits(List<Long> hiddenUnits)
hiddenUnits
- hiddenUnits or null
for nonepublic String getHolidayRegion()
null
for nonepublic TrainingOptions setHolidayRegion(String holidayRegion)
holidayRegion
- holidayRegion or null
for nonepublic Long getHorizon()
null
for nonepublic TrainingOptions setHorizon(Long horizon)
horizon
- horizon or null
for nonepublic List<String> getHparamTuningObjectives()
null
for nonepublic TrainingOptions setHparamTuningObjectives(List<String> hparamTuningObjectives)
hparamTuningObjectives
- hparamTuningObjectives or null
for nonepublic Boolean getIncludeDrift()
null
for nonepublic TrainingOptions setIncludeDrift(Boolean includeDrift)
includeDrift
- includeDrift or null
for nonepublic Double getInitialLearnRate()
null
for nonepublic TrainingOptions setInitialLearnRate(Double initialLearnRate)
initialLearnRate
- initialLearnRate or null
for nonepublic List<String> getInputLabelColumns()
null
for nonepublic TrainingOptions setInputLabelColumns(List<String> inputLabelColumns)
inputLabelColumns
- inputLabelColumns or null
for nonepublic Long getIntegratedGradientsNumSteps()
null
for nonepublic TrainingOptions setIntegratedGradientsNumSteps(Long integratedGradientsNumSteps)
integratedGradientsNumSteps
- integratedGradientsNumSteps or null
for nonepublic String getItemColumn()
null
for nonepublic TrainingOptions setItemColumn(String itemColumn)
itemColumn
- itemColumn or null
for nonepublic String getKmeansInitializationColumn()
null
for nonepublic TrainingOptions setKmeansInitializationColumn(String kmeansInitializationColumn)
kmeansInitializationColumn
- kmeansInitializationColumn or null
for nonepublic String getKmeansInitializationMethod()
null
for nonepublic TrainingOptions setKmeansInitializationMethod(String kmeansInitializationMethod)
kmeansInitializationMethod
- kmeansInitializationMethod or null
for nonepublic Double getL1Regularization()
null
for nonepublic TrainingOptions setL1Regularization(Double l1Regularization)
l1Regularization
- l1Regularization or null
for nonepublic Double getL2Regularization()
null
for nonepublic TrainingOptions setL2Regularization(Double l2Regularization)
l2Regularization
- l2Regularization or null
for nonepublic Map<String,Double> getLabelClassWeights()
null
for nonepublic TrainingOptions setLabelClassWeights(Map<String,Double> labelClassWeights)
labelClassWeights
- labelClassWeights or null
for nonepublic Double getLearnRate()
null
for nonepublic TrainingOptions setLearnRate(Double learnRate)
learnRate
- learnRate or null
for nonepublic String getLearnRateStrategy()
null
for nonepublic TrainingOptions setLearnRateStrategy(String learnRateStrategy)
learnRateStrategy
- learnRateStrategy or null
for nonepublic String getLossType()
null
for nonepublic TrainingOptions setLossType(String lossType)
lossType
- lossType or null
for nonepublic Long getMaxIterations()
null
for nonepublic TrainingOptions setMaxIterations(Long maxIterations)
maxIterations
- maxIterations or null
for nonepublic Long getMaxParallelTrials()
null
for nonepublic TrainingOptions setMaxParallelTrials(Long maxParallelTrials)
maxParallelTrials
- maxParallelTrials or null
for nonepublic Long getMaxTimeSeriesLength()
null
for nonepublic TrainingOptions setMaxTimeSeriesLength(Long maxTimeSeriesLength)
maxTimeSeriesLength
- maxTimeSeriesLength or null
for nonepublic Long getMaxTreeDepth()
null
for nonepublic TrainingOptions setMaxTreeDepth(Long maxTreeDepth)
maxTreeDepth
- maxTreeDepth or null
for nonepublic Double getMinRelativeProgress()
null
for nonepublic TrainingOptions setMinRelativeProgress(Double minRelativeProgress)
minRelativeProgress
- minRelativeProgress or null
for nonepublic Double getMinSplitLoss()
null
for nonepublic TrainingOptions setMinSplitLoss(Double minSplitLoss)
minSplitLoss
- minSplitLoss or null
for nonepublic Long getMinTimeSeriesLength()
null
for nonepublic TrainingOptions setMinTimeSeriesLength(Long minTimeSeriesLength)
minTimeSeriesLength
- minTimeSeriesLength or null
for nonepublic Long getMinTreeChildWeight()
null
for nonepublic TrainingOptions setMinTreeChildWeight(Long minTreeChildWeight)
minTreeChildWeight
- minTreeChildWeight or null
for nonepublic String getModelUri()
null
for nonepublic TrainingOptions setModelUri(String modelUri)
modelUri
- modelUri or null
for nonepublic ArimaOrder getNonSeasonalOrder()
null
for nonepublic TrainingOptions setNonSeasonalOrder(ArimaOrder nonSeasonalOrder)
nonSeasonalOrder
- nonSeasonalOrder or null
for nonepublic Long getNumClusters()
null
for nonepublic TrainingOptions setNumClusters(Long numClusters)
numClusters
- numClusters or null
for nonepublic Long getNumFactors()
null
for nonepublic TrainingOptions setNumFactors(Long numFactors)
numFactors
- numFactors or null
for nonepublic Long getNumParallelTree()
null
for nonepublic TrainingOptions setNumParallelTree(Long numParallelTree)
numParallelTree
- numParallelTree or null
for nonepublic Long getNumTrials()
null
for nonepublic TrainingOptions setNumTrials(Long numTrials)
numTrials
- numTrials or null
for nonepublic String getOptimizationStrategy()
null
for nonepublic TrainingOptions setOptimizationStrategy(String optimizationStrategy)
optimizationStrategy
- optimizationStrategy or null
for nonepublic Boolean getPreserveInputStructs()
null
for nonepublic TrainingOptions setPreserveInputStructs(Boolean preserveInputStructs)
preserveInputStructs
- preserveInputStructs or null
for nonepublic Long getSampledShapleyNumPaths()
null
for nonepublic TrainingOptions setSampledShapleyNumPaths(Long sampledShapleyNumPaths)
sampledShapleyNumPaths
- sampledShapleyNumPaths or null
for nonepublic Double getSubsample()
null
for nonepublic TrainingOptions setSubsample(Double subsample)
subsample
- subsample or null
for nonepublic String getTimeSeriesDataColumn()
null
for nonepublic TrainingOptions setTimeSeriesDataColumn(String timeSeriesDataColumn)
timeSeriesDataColumn
- timeSeriesDataColumn or null
for nonepublic String getTimeSeriesIdColumn()
null
for nonepublic TrainingOptions setTimeSeriesIdColumn(String timeSeriesIdColumn)
timeSeriesIdColumn
- timeSeriesIdColumn or null
for nonepublic List<String> getTimeSeriesIdColumns()
null
for nonepublic TrainingOptions setTimeSeriesIdColumns(List<String> timeSeriesIdColumns)
timeSeriesIdColumns
- timeSeriesIdColumns or null
for nonepublic Double getTimeSeriesLengthFraction()
null
for nonepublic TrainingOptions setTimeSeriesLengthFraction(Double timeSeriesLengthFraction)
timeSeriesLengthFraction
- timeSeriesLengthFraction or null
for nonepublic String getTimeSeriesTimestampColumn()
null
for nonepublic TrainingOptions setTimeSeriesTimestampColumn(String timeSeriesTimestampColumn)
timeSeriesTimestampColumn
- timeSeriesTimestampColumn or null
for nonepublic String getTreeMethod()
null
for nonepublic TrainingOptions setTreeMethod(String treeMethod)
treeMethod
- treeMethod or null
for nonepublic Long getTrendSmoothingWindowSize()
null
for nonepublic TrainingOptions setTrendSmoothingWindowSize(Long trendSmoothingWindowSize)
trendSmoothingWindowSize
- trendSmoothingWindowSize or null
for nonepublic String getUserColumn()
null
for nonepublic TrainingOptions setUserColumn(String userColumn)
userColumn
- userColumn or null
for nonepublic Double getWalsAlpha()
null
for nonepublic TrainingOptions setWalsAlpha(Double walsAlpha)
walsAlpha
- walsAlpha or null
for nonepublic Boolean getWarmStart()
null
for nonepublic TrainingOptions setWarmStart(Boolean warmStart)
warmStart
- warmStart or null
for nonepublic TrainingOptions set(String fieldName, Object value)
set
in class GenericJson
public TrainingOptions clone()
clone
in class GenericJson
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