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TrainingOptions (BigQuery API v2-rev20190423-1.28.0)











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com.google.api.services.bigquery.model
Class TrainingOptions



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
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
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() 









Method Summary

All Methods Instance Methods Concrete Methods 

Modifier and Type
Method and Description


TrainingOptions
clone() 


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.



String
getDistanceType()
[Beta] Distance type for clustering models.



Boolean
getEarlyStop()
Whether to stop early when the loss doesn't improve significantly any more (compared to
 min_relative_progress).



Double
getInitialLearnRate()
Specifies the initial learning rate for line search to start at.



List<String>
getInputLabelColumns()
Name of input label columns in training data.



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 learning rate.



String
getLossType()
Type of loss function used during training run.



Long
getMaxIterations()
The maximum number of iterations in training.



Double
getMinRelativeProgress()
When early_stop is true, stops training when accuracy improvement is less than
 'min_relative_progress'.



Long
getNumClusters()
[Beta] Number of clusters for clustering models.



Boolean
getWarmStart()
Whether to train a model from the last checkpoint.



TrainingOptions
set(String fieldName,
   Object value) 


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
setDistanceType(String distanceType)
[Beta] Distance type for clustering models.



TrainingOptions
setEarlyStop(Boolean earlyStop)
Whether to stop early when the loss doesn't improve significantly any more (compared to
 min_relative_progress).



TrainingOptions
setInitialLearnRate(Double initialLearnRate)
Specifies the initial learning rate for line search to start at.



TrainingOptions
setInputLabelColumns(List<String> inputLabelColumns)
Name of input label columns in training data.



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 learning rate.



TrainingOptions
setLossType(String lossType)
Type of loss function used during training run.



TrainingOptions
setMaxIterations(Long maxIterations)
The maximum number of iterations in training.



TrainingOptions
setMinRelativeProgress(Double minRelativeProgress)
When early_stop is true, stops training when accuracy improvement is less than
 'min_relative_progress'.



TrainingOptions
setNumClusters(Long numClusters)
[Beta] Number of clusters for clustering models.



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, 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 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








getDistanceType
public String getDistanceType()
[Beta] Distance type for clustering models.

Returns:
value or null for none








setDistanceType
public TrainingOptions setDistanceType(String distanceType)
[Beta] Distance type for clustering models.

Parameters:
distanceType - distanceType 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).

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).

Parameters:
earlyStop - earlyStop or null for none








getInitialLearnRate
public Double getInitialLearnRate()
Specifies the initial learning rate for line search to start at.

Returns:
value or null for none








setInitialLearnRate
public TrainingOptions setInitialLearnRate(Double initialLearnRate)
Specifies the initial learning rate for line search to start at.

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








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.

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.

Parameters:
labelClassWeights - labelClassWeights or null for none








getLearnRate
public Double getLearnRate()
Learning rate in training.

Returns:
value or null for none








setLearnRate
public TrainingOptions setLearnRate(Double learnRate)
Learning rate in training.

Parameters:
learnRate - learnRate or null for none








getLearnRateStrategy
public String getLearnRateStrategy()
The strategy to determine learning rate.

Returns:
value or null for none








setLearnRateStrategy
public TrainingOptions setLearnRateStrategy(String learnRateStrategy)
The strategy to determine learning rate.

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.

Returns:
value or null for none








setMaxIterations
public TrainingOptions setMaxIterations(Long maxIterations)
The maximum number of iterations in training.

Parameters:
maxIterations - maxIterations or null for none








getMinRelativeProgress
public Double getMinRelativeProgress()
When early_stop is true, stops training when accuracy improvement is less than
 'min_relative_progress'.

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'.

Parameters:
minRelativeProgress - minRelativeProgress or null for none








getNumClusters
public Long getNumClusters()
[Beta] Number of clusters for clustering models.

Returns:
value or null for none








setNumClusters
public TrainingOptions setNumClusters(Long numClusters)
[Beta] Number of clusters for clustering models.

Parameters:
numClusters - numClusters 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 com.google.api.client.json.GenericJson








clone
public TrainingOptions clone()

Overrides:
clone in class com.google.api.client.json.GenericJson














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Overview
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Summary: 
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Copyright © 2011–2019 Google. All rights reserved.

Modifier and Type	Method and Description
`TrainingOptions`	`clone()`
`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.
`String`	`getDistanceType()` [Beta] Distance type for clustering models.
`Boolean`	`getEarlyStop()` Whether to stop early when the loss doesn't improve significantly any more (compared to min_relative_progress).
`Double`	`getInitialLearnRate()` Specifies the initial learning rate for line search to start at.
`List<String>`	`getInputLabelColumns()` Name of input label columns in training data.
`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 learning rate.
`String`	`getLossType()` Type of loss function used during training run.
`Long`	`getMaxIterations()` The maximum number of iterations in training.
`Double`	`getMinRelativeProgress()` When early_stop is true, stops training when accuracy improvement is less than 'min_relative_progress'.
`Long`	`getNumClusters()` [Beta] Number of clusters for clustering models.
`Boolean`	`getWarmStart()` Whether to train a model from the last checkpoint.
`TrainingOptions`	`set(String fieldName, Object value)`
`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`	`setDistanceType(String distanceType)` [Beta] Distance type for clustering models.
`TrainingOptions`	`setEarlyStop(Boolean earlyStop)` Whether to stop early when the loss doesn't improve significantly any more (compared to min_relative_progress).
`TrainingOptions`	`setInitialLearnRate(Double initialLearnRate)` Specifies the initial learning rate for line search to start at.
`TrainingOptions`	`setInputLabelColumns(List<String> inputLabelColumns)` Name of input label columns in training data.
`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 learning rate.
`TrainingOptions`	`setLossType(String lossType)` Type of loss function used during training run.
`TrainingOptions`	`setMaxIterations(Long maxIterations)` The maximum number of iterations in training.
`TrainingOptions`	`setMinRelativeProgress(Double minRelativeProgress)` When early_stop is true, stops training when accuracy improvement is less than 'min_relative_progress'.
`TrainingOptions`	`setNumClusters(Long numClusters)` [Beta] Number of clusters for clustering models.
`TrainingOptions`	`setWarmStart(Boolean warmStart)` Whether to train a model from the last checkpoint.