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



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.HparamSearchSpaces













All Implemented Interfaces:
Cloneable, Map<String,Object>




public final class HparamSearchSpaces
extends com.google.api.client.json.GenericJson
Hyperparameter search spaces. These should be a subset of training_options.

  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


HparamSearchSpaces() 









Method Summary

All Methods Instance Methods Concrete Methods 

Modifier and Type
Method and Description


HparamSearchSpaces
clone() 


StringHparamSearchSpace
getActivationFn()
Activation functions of neural network models.



IntHparamSearchSpace
getBatchSize()
Mini batch sample size.



StringHparamSearchSpace
getBoosterType()
Booster type for boosted tree models.



DoubleHparamSearchSpace
getColsampleBylevel()
Subsample ratio of columns for each level for boosted tree models.



DoubleHparamSearchSpace
getColsampleBynode()
Subsample ratio of columns for each node(split) for boosted tree models.



DoubleHparamSearchSpace
getColsampleBytree()
Subsample ratio of columns when constructing each tree for boosted tree models.



StringHparamSearchSpace
getDartNormalizeType()
Dart normalization type for boosted tree models.



DoubleHparamSearchSpace
getDropout()
Dropout probability for dnn model training and boosted tree models using dart booster.



IntArrayHparamSearchSpace
getHiddenUnits()
Hidden units for neural network models.



DoubleHparamSearchSpace
getL1Reg()
L1 regularization coefficient.



DoubleHparamSearchSpace
getL2Reg()
L2 regularization coefficient.



DoubleHparamSearchSpace
getLearnRate()
Learning rate of training jobs.



IntHparamSearchSpace
getMaxTreeDepth()
Maximum depth of a tree for boosted tree models.



DoubleHparamSearchSpace
getMinSplitLoss()
Minimum split loss for boosted tree models.



IntHparamSearchSpace
getMinTreeChildWeight()
Minimum sum of instance weight needed in a child for boosted tree models.



IntHparamSearchSpace
getNumClusters()
Number of clusters for k-means.



IntHparamSearchSpace
getNumFactors()
Number of latent factors to train on.



IntHparamSearchSpace
getNumParallelTree()
Number of parallel trees for boosted tree models.



StringHparamSearchSpace
getOptimizer()
Optimizer of TF models.



DoubleHparamSearchSpace
getSubsample()
Subsample the training data to grow tree to prevent overfitting for boosted tree models.



StringHparamSearchSpace
getTreeMethod()
Tree construction algorithm for boosted tree models.



DoubleHparamSearchSpace
getWalsAlpha()
Hyperparameter for matrix factoration when implicit feedback type is specified.



HparamSearchSpaces
set(String fieldName,
   Object value) 


HparamSearchSpaces
setActivationFn(StringHparamSearchSpace activationFn)
Activation functions of neural network models.



HparamSearchSpaces
setBatchSize(IntHparamSearchSpace batchSize)
Mini batch sample size.



HparamSearchSpaces
setBoosterType(StringHparamSearchSpace boosterType)
Booster type for boosted tree models.



HparamSearchSpaces
setColsampleBylevel(DoubleHparamSearchSpace colsampleBylevel)
Subsample ratio of columns for each level for boosted tree models.



HparamSearchSpaces
setColsampleBynode(DoubleHparamSearchSpace colsampleBynode)
Subsample ratio of columns for each node(split) for boosted tree models.



HparamSearchSpaces
setColsampleBytree(DoubleHparamSearchSpace colsampleBytree)
Subsample ratio of columns when constructing each tree for boosted tree models.



HparamSearchSpaces
setDartNormalizeType(StringHparamSearchSpace dartNormalizeType)
Dart normalization type for boosted tree models.



HparamSearchSpaces
setDropout(DoubleHparamSearchSpace dropout)
Dropout probability for dnn model training and boosted tree models using dart booster.



HparamSearchSpaces
setHiddenUnits(IntArrayHparamSearchSpace hiddenUnits)
Hidden units for neural network models.



HparamSearchSpaces
setL1Reg(DoubleHparamSearchSpace l1Reg)
L1 regularization coefficient.



HparamSearchSpaces
setL2Reg(DoubleHparamSearchSpace l2Reg)
L2 regularization coefficient.



HparamSearchSpaces
setLearnRate(DoubleHparamSearchSpace learnRate)
Learning rate of training jobs.



HparamSearchSpaces
setMaxTreeDepth(IntHparamSearchSpace maxTreeDepth)
Maximum depth of a tree for boosted tree models.



HparamSearchSpaces
setMinSplitLoss(DoubleHparamSearchSpace minSplitLoss)
Minimum split loss for boosted tree models.



HparamSearchSpaces
setMinTreeChildWeight(IntHparamSearchSpace minTreeChildWeight)
Minimum sum of instance weight needed in a child for boosted tree models.



HparamSearchSpaces
setNumClusters(IntHparamSearchSpace numClusters)
Number of clusters for k-means.



HparamSearchSpaces
setNumFactors(IntHparamSearchSpace numFactors)
Number of latent factors to train on.



HparamSearchSpaces
setNumParallelTree(IntHparamSearchSpace numParallelTree)
Number of parallel trees for boosted tree models.



HparamSearchSpaces
setOptimizer(StringHparamSearchSpace optimizer)
Optimizer of TF models.



HparamSearchSpaces
setSubsample(DoubleHparamSearchSpace subsample)
Subsample the training data to grow tree to prevent overfitting for boosted tree models.



HparamSearchSpaces
setTreeMethod(StringHparamSearchSpace treeMethod)
Tree construction algorithm for boosted tree models.



HparamSearchSpaces
setWalsAlpha(DoubleHparamSearchSpace walsAlpha)
Hyperparameter for matrix factoration when implicit feedback type is specified.







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





HparamSearchSpaces
public HparamSearchSpaces()









Method Detail





getActivationFn
public StringHparamSearchSpace getActivationFn()
Activation functions of neural network models.

Returns:
value or null for none








setActivationFn
public HparamSearchSpaces setActivationFn(StringHparamSearchSpace activationFn)
Activation functions of neural network models.

Parameters:
activationFn - activationFn or null for none








getBatchSize
public IntHparamSearchSpace getBatchSize()
Mini batch sample size.

Returns:
value or null for none








setBatchSize
public HparamSearchSpaces setBatchSize(IntHparamSearchSpace batchSize)
Mini batch sample size.

Parameters:
batchSize - batchSize or null for none








getBoosterType
public StringHparamSearchSpace getBoosterType()
Booster type for boosted tree models.

Returns:
value or null for none








setBoosterType
public HparamSearchSpaces setBoosterType(StringHparamSearchSpace boosterType)
Booster type for boosted tree models.

Parameters:
boosterType - boosterType or null for none








getColsampleBylevel
public DoubleHparamSearchSpace getColsampleBylevel()
Subsample ratio of columns for each level for boosted tree models.

Returns:
value or null for none








setColsampleBylevel
public HparamSearchSpaces setColsampleBylevel(DoubleHparamSearchSpace colsampleBylevel)
Subsample ratio of columns for each level for boosted tree models.

Parameters:
colsampleBylevel - colsampleBylevel or null for none








getColsampleBynode
public DoubleHparamSearchSpace getColsampleBynode()
Subsample ratio of columns for each node(split) for boosted tree models.

Returns:
value or null for none








setColsampleBynode
public HparamSearchSpaces setColsampleBynode(DoubleHparamSearchSpace colsampleBynode)
Subsample ratio of columns for each node(split) for boosted tree models.

Parameters:
colsampleBynode - colsampleBynode or null for none








getColsampleBytree
public DoubleHparamSearchSpace getColsampleBytree()
Subsample ratio of columns when constructing each tree for boosted tree models.

Returns:
value or null for none








setColsampleBytree
public HparamSearchSpaces setColsampleBytree(DoubleHparamSearchSpace colsampleBytree)
Subsample ratio of columns when constructing each tree for boosted tree models.

Parameters:
colsampleBytree - colsampleBytree or null for none








getDartNormalizeType
public StringHparamSearchSpace getDartNormalizeType()
Dart normalization type for boosted tree models.

Returns:
value or null for none








setDartNormalizeType
public HparamSearchSpaces setDartNormalizeType(StringHparamSearchSpace dartNormalizeType)
Dart normalization type for boosted tree models.

Parameters:
dartNormalizeType - dartNormalizeType or null for none








getDropout
public DoubleHparamSearchSpace getDropout()
Dropout probability for dnn model training and boosted tree models using dart booster.

Returns:
value or null for none








setDropout
public HparamSearchSpaces setDropout(DoubleHparamSearchSpace dropout)
Dropout probability for dnn model training and boosted tree models using dart booster.

Parameters:
dropout - dropout or null for none








getHiddenUnits
public IntArrayHparamSearchSpace getHiddenUnits()
Hidden units for neural network models.

Returns:
value or null for none








setHiddenUnits
public HparamSearchSpaces setHiddenUnits(IntArrayHparamSearchSpace hiddenUnits)
Hidden units for neural network models.

Parameters:
hiddenUnits - hiddenUnits or null for none








getL1Reg
public DoubleHparamSearchSpace getL1Reg()
L1 regularization coefficient.

Returns:
value or null for none








setL1Reg
public HparamSearchSpaces setL1Reg(DoubleHparamSearchSpace l1Reg)
L1 regularization coefficient.

Parameters:
l1Reg - l1Reg or null for none








getL2Reg
public DoubleHparamSearchSpace getL2Reg()
L2 regularization coefficient.

Returns:
value or null for none








setL2Reg
public HparamSearchSpaces setL2Reg(DoubleHparamSearchSpace l2Reg)
L2 regularization coefficient.

Parameters:
l2Reg - l2Reg or null for none








getLearnRate
public DoubleHparamSearchSpace getLearnRate()
Learning rate of training jobs.

Returns:
value or null for none








setLearnRate
public HparamSearchSpaces setLearnRate(DoubleHparamSearchSpace learnRate)
Learning rate of training jobs.

Parameters:
learnRate - learnRate or null for none








getMaxTreeDepth
public IntHparamSearchSpace getMaxTreeDepth()
Maximum depth of a tree for boosted tree models.

Returns:
value or null for none








setMaxTreeDepth
public HparamSearchSpaces setMaxTreeDepth(IntHparamSearchSpace maxTreeDepth)
Maximum depth of a tree for boosted tree models.

Parameters:
maxTreeDepth - maxTreeDepth or null for none








getMinSplitLoss
public DoubleHparamSearchSpace getMinSplitLoss()
Minimum split loss for boosted tree models.

Returns:
value or null for none








setMinSplitLoss
public HparamSearchSpaces setMinSplitLoss(DoubleHparamSearchSpace minSplitLoss)
Minimum split loss for boosted tree models.

Parameters:
minSplitLoss - minSplitLoss or null for none








getMinTreeChildWeight
public IntHparamSearchSpace getMinTreeChildWeight()
Minimum sum of instance weight needed in a child for boosted tree models.

Returns:
value or null for none








setMinTreeChildWeight
public HparamSearchSpaces setMinTreeChildWeight(IntHparamSearchSpace minTreeChildWeight)
Minimum sum of instance weight needed in a child for boosted tree models.

Parameters:
minTreeChildWeight - minTreeChildWeight or null for none








getNumClusters
public IntHparamSearchSpace getNumClusters()
Number of clusters for k-means.

Returns:
value or null for none








setNumClusters
public HparamSearchSpaces setNumClusters(IntHparamSearchSpace numClusters)
Number of clusters for k-means.

Parameters:
numClusters - numClusters or null for none








getNumFactors
public IntHparamSearchSpace getNumFactors()
Number of latent factors to train on.

Returns:
value or null for none








setNumFactors
public HparamSearchSpaces setNumFactors(IntHparamSearchSpace numFactors)
Number of latent factors to train on.

Parameters:
numFactors - numFactors or null for none








getNumParallelTree
public IntHparamSearchSpace getNumParallelTree()
Number of parallel trees for boosted tree models.

Returns:
value or null for none








setNumParallelTree
public HparamSearchSpaces setNumParallelTree(IntHparamSearchSpace numParallelTree)
Number of parallel trees for boosted tree models.

Parameters:
numParallelTree - numParallelTree or null for none








getOptimizer
public StringHparamSearchSpace getOptimizer()
Optimizer of TF models.

Returns:
value or null for none








setOptimizer
public HparamSearchSpaces setOptimizer(StringHparamSearchSpace optimizer)
Optimizer of TF models.

Parameters:
optimizer - optimizer or null for none








getSubsample
public DoubleHparamSearchSpace getSubsample()
Subsample the training data to grow tree to prevent overfitting for boosted tree models.

Returns:
value or null for none








setSubsample
public HparamSearchSpaces setSubsample(DoubleHparamSearchSpace subsample)
Subsample the training data to grow tree to prevent overfitting for boosted tree models.

Parameters:
subsample - subsample or null for none








getTreeMethod
public StringHparamSearchSpace getTreeMethod()
Tree construction algorithm for boosted tree models.

Returns:
value or null for none








setTreeMethod
public HparamSearchSpaces setTreeMethod(StringHparamSearchSpace treeMethod)
Tree construction algorithm for boosted tree models.

Parameters:
treeMethod - treeMethod or null for none








getWalsAlpha
public DoubleHparamSearchSpace getWalsAlpha()
Hyperparameter for matrix factoration when implicit feedback type is specified.

Returns:
value or null for none








setWalsAlpha
public HparamSearchSpaces setWalsAlpha(DoubleHparamSearchSpace walsAlpha)
Hyperparameter for matrix factoration when implicit feedback type is specified.

Parameters:
walsAlpha - walsAlpha or null for none








set
public HparamSearchSpaces set(String fieldName,
                              Object value)

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








clone
public HparamSearchSpaces 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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Field | 
Constr | 
Method


Detail: 
Field | 
Constr | 
Method






Copyright © 2011–2024 Google. All rights reserved.

Modifier and Type	Method and Description
`HparamSearchSpaces`	`clone()`
`StringHparamSearchSpace`	`getActivationFn()` Activation functions of neural network models.
`IntHparamSearchSpace`	`getBatchSize()` Mini batch sample size.
`StringHparamSearchSpace`	`getBoosterType()` Booster type for boosted tree models.
`DoubleHparamSearchSpace`	`getColsampleBylevel()` Subsample ratio of columns for each level for boosted tree models.
`DoubleHparamSearchSpace`	`getColsampleBynode()` Subsample ratio of columns for each node(split) for boosted tree models.
`DoubleHparamSearchSpace`	`getColsampleBytree()` Subsample ratio of columns when constructing each tree for boosted tree models.
`StringHparamSearchSpace`	`getDartNormalizeType()` Dart normalization type for boosted tree models.
`DoubleHparamSearchSpace`	`getDropout()` Dropout probability for dnn model training and boosted tree models using dart booster.
`IntArrayHparamSearchSpace`	`getHiddenUnits()` Hidden units for neural network models.
`DoubleHparamSearchSpace`	`getL1Reg()` L1 regularization coefficient.
`DoubleHparamSearchSpace`	`getL2Reg()` L2 regularization coefficient.
`DoubleHparamSearchSpace`	`getLearnRate()` Learning rate of training jobs.
`IntHparamSearchSpace`	`getMaxTreeDepth()` Maximum depth of a tree for boosted tree models.
`DoubleHparamSearchSpace`	`getMinSplitLoss()` Minimum split loss for boosted tree models.
`IntHparamSearchSpace`	`getMinTreeChildWeight()` Minimum sum of instance weight needed in a child for boosted tree models.
`IntHparamSearchSpace`	`getNumClusters()` Number of clusters for k-means.
`IntHparamSearchSpace`	`getNumFactors()` Number of latent factors to train on.
`IntHparamSearchSpace`	`getNumParallelTree()` Number of parallel trees for boosted tree models.
`StringHparamSearchSpace`	`getOptimizer()` Optimizer of TF models.
`DoubleHparamSearchSpace`	`getSubsample()` Subsample the training data to grow tree to prevent overfitting for boosted tree models.
`StringHparamSearchSpace`	`getTreeMethod()` Tree construction algorithm for boosted tree models.
`DoubleHparamSearchSpace`	`getWalsAlpha()` Hyperparameter for matrix factoration when implicit feedback type is specified.
`HparamSearchSpaces`	`set(String fieldName, Object value)`
`HparamSearchSpaces`	`setActivationFn(StringHparamSearchSpace activationFn)` Activation functions of neural network models.
`HparamSearchSpaces`	`setBatchSize(IntHparamSearchSpace batchSize)` Mini batch sample size.
`HparamSearchSpaces`	`setBoosterType(StringHparamSearchSpace boosterType)` Booster type for boosted tree models.
`HparamSearchSpaces`	`setColsampleBylevel(DoubleHparamSearchSpace colsampleBylevel)` Subsample ratio of columns for each level for boosted tree models.
`HparamSearchSpaces`	`setColsampleBynode(DoubleHparamSearchSpace colsampleBynode)` Subsample ratio of columns for each node(split) for boosted tree models.
`HparamSearchSpaces`	`setColsampleBytree(DoubleHparamSearchSpace colsampleBytree)` Subsample ratio of columns when constructing each tree for boosted tree models.
`HparamSearchSpaces`	`setDartNormalizeType(StringHparamSearchSpace dartNormalizeType)` Dart normalization type for boosted tree models.
`HparamSearchSpaces`	`setDropout(DoubleHparamSearchSpace dropout)` Dropout probability for dnn model training and boosted tree models using dart booster.
`HparamSearchSpaces`	`setHiddenUnits(IntArrayHparamSearchSpace hiddenUnits)` Hidden units for neural network models.
`HparamSearchSpaces`	`setL1Reg(DoubleHparamSearchSpace l1Reg)` L1 regularization coefficient.
`HparamSearchSpaces`	`setL2Reg(DoubleHparamSearchSpace l2Reg)` L2 regularization coefficient.
`HparamSearchSpaces`	`setLearnRate(DoubleHparamSearchSpace learnRate)` Learning rate of training jobs.
`HparamSearchSpaces`	`setMaxTreeDepth(IntHparamSearchSpace maxTreeDepth)` Maximum depth of a tree for boosted tree models.
`HparamSearchSpaces`	`setMinSplitLoss(DoubleHparamSearchSpace minSplitLoss)` Minimum split loss for boosted tree models.
`HparamSearchSpaces`	`setMinTreeChildWeight(IntHparamSearchSpace minTreeChildWeight)` Minimum sum of instance weight needed in a child for boosted tree models.
`HparamSearchSpaces`	`setNumClusters(IntHparamSearchSpace numClusters)` Number of clusters for k-means.
`HparamSearchSpaces`	`setNumFactors(IntHparamSearchSpace numFactors)` Number of latent factors to train on.
`HparamSearchSpaces`	`setNumParallelTree(IntHparamSearchSpace numParallelTree)` Number of parallel trees for boosted tree models.
`HparamSearchSpaces`	`setOptimizer(StringHparamSearchSpace optimizer)` Optimizer of TF models.
`HparamSearchSpaces`	`setSubsample(DoubleHparamSearchSpace subsample)` Subsample the training data to grow tree to prevent overfitting for boosted tree models.
`HparamSearchSpaces`	`setTreeMethod(StringHparamSearchSpace treeMethod)` Tree construction algorithm for boosted tree models.
`HparamSearchSpaces`	`setWalsAlpha(DoubleHparamSearchSpace walsAlpha)` Hyperparameter for matrix factoration when implicit feedback type is specified.