TrainingOptions (BigQuery API v2 (Rev. 456) 1.25.0)

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

All Implemented Interfaces:

java.lang.Cloneable, java.util.Map<java.lang.String,java.lang.Object>
```
public final class TrainingOptions
extends com.google.api.client.json.GenericJson
```
Model definition for TrainingOptions.
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
  java.util.AbstractMap.SimpleEntry<K,V>, java.util.AbstractMap.SimpleImmutableEntry<K,V>
- Nested classes/interfaces inherited from interface java.util.Map
  java.util.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()`
`java.lang.String`	`getDataSplitColumn()` The column to split data with.
`java.lang.Double`	`getDataSplitEvalFraction()` The fraction of evaluation data over the whole input data.
`java.lang.String`	`getDataSplitMethod()` The data split type for training and evaluation, e.g.
`java.lang.String`	`getDistanceType()` Distance type for clustering models.
`java.lang.Boolean`	`getEarlyStop()` Whether to stop early when the loss doesn't improve significantly any more (compared to min_relative_progress).
`java.lang.Double`	`getInitialLearnRate()` Specifies the initial learning rate for the line search learn rate strategy.
`java.util.List<java.lang.String>`	`getInputLabelColumns()` Name of input label columns in training data.
`java.lang.String`	`getKmeansInitializationColumn()` The column used to provide the initial centroids for kmeans algorithm when kmeans_initialization_method is CUSTOM.
`java.lang.String`	`getKmeansInitializationMethod()` The method used to initialize the centroids for kmeans algorithm.
`java.lang.Double`	`getL1Regularization()` L1 regularization coefficient.
`java.lang.Double`	`getL2Regularization()` L2 regularization coefficient.
`java.util.Map<java.lang.String,java.lang.Double>`	`getLabelClassWeights()` Weights associated with each label class, for rebalancing the training data.
`java.lang.Double`	`getLearnRate()` Learning rate in training.
`java.lang.String`	`getLearnRateStrategy()` The strategy to determine learn rate for the current iteration.
`java.lang.String`	`getLossType()` Type of loss function used during training run.
`java.lang.Long`	`getMaxIterations()` The maximum number of iterations in training.
`java.lang.Double`	`getMinRelativeProgress()` When early_stop is true, stops training when accuracy improvement is less than 'min_relative_progress'.
`java.lang.String`	`getModelUri()` [Beta] Google Cloud Storage URI from which the model was imported.
`java.lang.Long`	`getNumClusters()` Number of clusters for clustering models.
`java.lang.String`	`getOptimizationStrategy()` Optimization strategy for training linear regression models.
`java.lang.Boolean`	`getWarmStart()` Whether to train a model from the last checkpoint.
`TrainingOptions`	`set(java.lang.String fieldName, java.lang.Object value)`
`TrainingOptions`	`setDataSplitColumn(java.lang.String dataSplitColumn)` The column to split data with.
`TrainingOptions`	`setDataSplitEvalFraction(java.lang.Double dataSplitEvalFraction)` The fraction of evaluation data over the whole input data.
`TrainingOptions`	`setDataSplitMethod(java.lang.String dataSplitMethod)` The data split type for training and evaluation, e.g.
`TrainingOptions`	`setDistanceType(java.lang.String distanceType)` Distance type for clustering models.
`TrainingOptions`	`setEarlyStop(java.lang.Boolean earlyStop)` Whether to stop early when the loss doesn't improve significantly any more (compared to min_relative_progress).
`TrainingOptions`	`setInitialLearnRate(java.lang.Double initialLearnRate)` Specifies the initial learning rate for the line search learn rate strategy.
`TrainingOptions`	`setInputLabelColumns(java.util.List<java.lang.String> inputLabelColumns)` Name of input label columns in training data.
`TrainingOptions`	`setKmeansInitializationColumn(java.lang.String kmeansInitializationColumn)` The column used to provide the initial centroids for kmeans algorithm when kmeans_initialization_method is CUSTOM.
`TrainingOptions`	`setKmeansInitializationMethod(java.lang.String kmeansInitializationMethod)` The method used to initialize the centroids for kmeans algorithm.
`TrainingOptions`	`setL1Regularization(java.lang.Double l1Regularization)` L1 regularization coefficient.
`TrainingOptions`	`setL2Regularization(java.lang.Double l2Regularization)` L2 regularization coefficient.
`TrainingOptions`	`setLabelClassWeights(java.util.Map<java.lang.String,java.lang.Double> labelClassWeights)` Weights associated with each label class, for rebalancing the training data.
`TrainingOptions`	`setLearnRate(java.lang.Double learnRate)` Learning rate in training.
`TrainingOptions`	`setLearnRateStrategy(java.lang.String learnRateStrategy)` The strategy to determine learn rate for the current iteration.
`TrainingOptions`	`setLossType(java.lang.String lossType)` Type of loss function used during training run.
`TrainingOptions`	`setMaxIterations(java.lang.Long maxIterations)` The maximum number of iterations in training.
`TrainingOptions`	`setMinRelativeProgress(java.lang.Double minRelativeProgress)` When early_stop is true, stops training when accuracy improvement is less than 'min_relative_progress'.
`TrainingOptions`	`setModelUri(java.lang.String modelUri)` [Beta] Google Cloud Storage URI from which the model was imported.
`TrainingOptions`	`setNumClusters(java.lang.Long numClusters)` Number of clusters for clustering models.
`TrainingOptions`	`setOptimizationStrategy(java.lang.String optimizationStrategy)` Optimization strategy for training linear regression models.
`TrainingOptions`	`setWarmStart(java.lang.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, get, getClassInfo, getUnknownKeys, put, putAll, remove, setUnknownKeys

Methods inherited from class java.util.AbstractMap
clear, containsKey, containsValue, equals, hashCode, 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
  - getDataSplitColumn
```
public java.lang.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(java.lang.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 java.lang.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(java.lang.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 java.lang.String getDataSplitMethod()
```
    The data split type for training and evaluation, e.g. RANDOM.
    
    Returns:
    
    value or null for none
  - setDataSplitMethod
```
public TrainingOptions setDataSplitMethod(java.lang.String dataSplitMethod)
```
    The data split type for training and evaluation, e.g. RANDOM.
    
    Parameters:
    
    dataSplitMethod - dataSplitMethod or null for none
  - getDistanceType
```
public java.lang.String getDistanceType()
```
    Distance type for clustering models.
    
    Returns:
    
    value or null for none
  - setDistanceType
```
public TrainingOptions setDistanceType(java.lang.String distanceType)
```
    Distance type for clustering models.
    
    Parameters:
    
    distanceType - distanceType or null for none
  - getEarlyStop
```
public java.lang.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(java.lang.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
  - getInitialLearnRate
```
public java.lang.Double getInitialLearnRate()
```
    Specifies the initial learning rate for the line search learn rate strategy.
    
    Returns:
    
    value or null for none
  - setInitialLearnRate
```
public TrainingOptions setInitialLearnRate(java.lang.Double initialLearnRate)
```
    Specifies the initial learning rate for the line search learn rate strategy.
    
    Parameters:
    
    initialLearnRate - initialLearnRate or null for none
  - getInputLabelColumns
```
public java.util.List<java.lang.String> getInputLabelColumns()
```
    Name of input label columns in training data.
    
    Returns:
    
    value or null for none
  - setInputLabelColumns
```
public TrainingOptions setInputLabelColumns(java.util.List<java.lang.String> inputLabelColumns)
```
    Name of input label columns in training data.
    
    Parameters:
    
    inputLabelColumns - inputLabelColumns or null for none
  - getKmeansInitializationColumn
```
public java.lang.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(java.lang.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 java.lang.String getKmeansInitializationMethod()
```
    The method used to initialize the centroids for kmeans algorithm.
    
    Returns:
    
    value or null for none
  - setKmeansInitializationMethod
```
public TrainingOptions setKmeansInitializationMethod(java.lang.String kmeansInitializationMethod)
```
    The method used to initialize the centroids for kmeans algorithm.
    
    Parameters:
    
    kmeansInitializationMethod - kmeansInitializationMethod or null for none
  - getL1Regularization
```
public java.lang.Double getL1Regularization()
```
    L1 regularization coefficient.
    
    Returns:
    
    value or null for none
  - setL1Regularization
```
public TrainingOptions setL1Regularization(java.lang.Double l1Regularization)
```
    L1 regularization coefficient.
    
    Parameters:
    
    l1Regularization - l1Regularization or null for none
  - getL2Regularization
```
public java.lang.Double getL2Regularization()
```
    L2 regularization coefficient.
    
    Returns:
    
    value or null for none
  - setL2Regularization
```
public TrainingOptions setL2Regularization(java.lang.Double l2Regularization)
```
    L2 regularization coefficient.
    
    Parameters:
    
    l2Regularization - l2Regularization or null for none
  - getLabelClassWeights
```
public java.util.Map<java.lang.String,java.lang.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(java.util.Map<java.lang.String,java.lang.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 java.lang.Double getLearnRate()
```
    Learning rate in training. Used only for iterative training algorithms.
    
    Returns:
    
    value or null for none
  - setLearnRate
```
public TrainingOptions setLearnRate(java.lang.Double learnRate)
```
    Learning rate in training. Used only for iterative training algorithms.
    
    Parameters:
    
    learnRate - learnRate or null for none
  - getLearnRateStrategy
```
public java.lang.String getLearnRateStrategy()
```
    The strategy to determine learn rate for the current iteration.
    
    Returns:
    
    value or null for none
  - setLearnRateStrategy
```
public TrainingOptions setLearnRateStrategy(java.lang.String learnRateStrategy)
```
    The strategy to determine learn rate for the current iteration.
    
    Parameters:
    
    learnRateStrategy - learnRateStrategy or null for none
  - getLossType
```
public java.lang.String getLossType()
```
    Type of loss function used during training run.
    
    Returns:
    
    value or null for none
  - setLossType
```
public TrainingOptions setLossType(java.lang.String lossType)
```
    Type of loss function used during training run.
    
    Parameters:
    
    lossType - lossType or null for none
  - getMaxIterations
```
public java.lang.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(java.lang.Long maxIterations)
```
    The maximum number of iterations in training. Used only for iterative training algorithms.
    
    Parameters:
    
    maxIterations - maxIterations or null for none
  - getMinRelativeProgress
```
public java.lang.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(java.lang.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
  - getModelUri
```
public java.lang.String getModelUri()
```
    [Beta] 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(java.lang.String modelUri)
```
    [Beta] Google Cloud Storage URI from which the model was imported. Only applicable for imported models.
    
    Parameters:
    
    modelUri - modelUri or null for none
  - getNumClusters
```
public java.lang.Long getNumClusters()
```
    Number of clusters for clustering models.
    
    Returns:
    
    value or null for none
  - setNumClusters
```
public TrainingOptions setNumClusters(java.lang.Long numClusters)
```
    Number of clusters for clustering models.
    
    Parameters:
    
    numClusters - numClusters or null for none
  - getOptimizationStrategy
```
public java.lang.String getOptimizationStrategy()
```
    Optimization strategy for training linear regression models.
    
    Returns:
    
    value or null for none
  - setOptimizationStrategy
```
public TrainingOptions setOptimizationStrategy(java.lang.String optimizationStrategy)
```
    Optimization strategy for training linear regression models.
    
    Parameters:
    
    optimizationStrategy - optimizationStrategy or null for none
  - getWarmStart
```
public java.lang.Boolean getWarmStart()
```
    Whether to train a model from the last checkpoint.
    
    Returns:
    
    value or null for none
  - setWarmStart
```
public TrainingOptions setWarmStart(java.lang.Boolean warmStart)
```
    Whether to train a model from the last checkpoint.
    
    Parameters:
    
    warmStart - warmStart or null for none
  - set
```
public TrainingOptions set(java.lang.String fieldName,
                           java.lang.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

BigQuery API v2 (revision 456)

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

getDataSplitColumn

setDataSplitColumn

getDataSplitEvalFraction

setDataSplitEvalFraction

getDataSplitMethod

setDataSplitMethod

getDistanceType

setDistanceType

getEarlyStop

setEarlyStop

getInitialLearnRate

setInitialLearnRate

getInputLabelColumns

setInputLabelColumns

getKmeansInitializationColumn

setKmeansInitializationColumn

getKmeansInitializationMethod

setKmeansInitializationMethod

getL1Regularization

setL1Regularization

getL2Regularization

setL2Regularization

getLabelClassWeights

setLabelClassWeights

getLearnRate

setLearnRate

getLearnRateStrategy

setLearnRateStrategy

getLossType

setLossType

getMaxIterations

setMaxIterations

getMinRelativeProgress

setMinRelativeProgress

getModelUri

setModelUri

getNumClusters

setNumClusters

getOptimizationStrategy

setOptimizationStrategy

getWarmStart

setWarmStart

set

clone