Class TrainingOptions

Options used in model training.

Inheritance

object

TrainingOptions

Implements

IDirectResponseSchema

Inherited Members

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Namespace: Google.Apis.Bigquery.v2.Data

Assembly: Google.Apis.Bigquery.v2.dll

Syntax

public class TrainingOptions : IDirectResponseSchema

Properties

ActivationFn

Activation function of the neural nets.

Declaration

[JsonProperty("activationFn")]
public virtual string ActivationFn { get; set; }

Property Value

Type	Description
string

AdjustStepChanges

If true, detect step changes and make data adjustment in the input time series.

Declaration

[JsonProperty("adjustStepChanges")]
public virtual bool? AdjustStepChanges { get; set; }

Property Value

Type	Description
bool?

ApproxGlobalFeatureContrib

Whether to use approximate feature contribution method in XGBoost model explanation for global explain.

Declaration

[JsonProperty("approxGlobalFeatureContrib")]
public virtual bool? ApproxGlobalFeatureContrib { get; set; }

Property Value

Type	Description
bool?

AutoArima

Whether to enable auto ARIMA or not.

Declaration

[JsonProperty("autoArima")]
public virtual bool? AutoArima { get; set; }

Property Value

Type	Description
bool?

AutoArimaMaxOrder

The max value of the sum of non-seasonal p and q.

Declaration

[JsonProperty("autoArimaMaxOrder")]
public virtual long? AutoArimaMaxOrder { get; set; }

Property Value

Type	Description
long?

AutoArimaMinOrder

The min value of the sum of non-seasonal p and q.

Declaration

[JsonProperty("autoArimaMinOrder")]
public virtual long? AutoArimaMinOrder { get; set; }

Property Value

Type	Description
long?

AutoClassWeights

Whether to calculate class weights automatically based on the popularity of each label.

Declaration

[JsonProperty("autoClassWeights")]
public virtual bool? AutoClassWeights { get; set; }

Property Value

Type	Description
bool?

BatchSize

Batch size for dnn models.

Declaration

[JsonProperty("batchSize")]
public virtual long? BatchSize { get; set; }

Property Value

Type	Description
long?

BoosterType

Booster type for boosted tree models.

Declaration

[JsonProperty("boosterType")]
public virtual string BoosterType { get; set; }

Property Value

Type	Description
string

BudgetHours

Budget in hours for AutoML training.

Declaration

[JsonProperty("budgetHours")]
public virtual double? BudgetHours { get; set; }

Property Value

Type	Description
double?

CalculatePValues

Whether or not p-value test should be computed for this model. Only available for linear and logistic regression models.

Declaration

[JsonProperty("calculatePValues")]
public virtual bool? CalculatePValues { get; set; }

Property Value

Type	Description
bool?

CategoryEncodingMethod

Categorical feature encoding method.

Declaration

[JsonProperty("categoryEncodingMethod")]
public virtual string CategoryEncodingMethod { get; set; }

Property Value

Type	Description
string

CleanSpikesAndDips

If true, clean spikes and dips in the input time series.

Declaration

[JsonProperty("cleanSpikesAndDips")]
public virtual bool? CleanSpikesAndDips { get; set; }

Property Value

Type	Description
bool?

ColorSpace

Enums for color space, used for processing images in Object Table. See more details at https://www.tensorflow.org/io/tutorials/colorspace.

Declaration

[JsonProperty("colorSpace")]
public virtual string ColorSpace { get; set; }

Property Value

Type	Description
string

ColsampleBylevel

Subsample ratio of columns for each level for boosted tree models.

Declaration

[JsonProperty("colsampleBylevel")]
public virtual double? ColsampleBylevel { get; set; }

Property Value

Type	Description
double?

ColsampleBynode

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

Declaration

[JsonProperty("colsampleBynode")]
public virtual double? ColsampleBynode { get; set; }

Property Value

Type	Description
double?

ColsampleBytree

Subsample ratio of columns when constructing each tree for boosted tree models.

Declaration

[JsonProperty("colsampleBytree")]
public virtual double? ColsampleBytree { get; set; }

Property Value

Type	Description
double?

DartNormalizeType

Type of normalization algorithm for boosted tree models using dart booster.

Declaration

[JsonProperty("dartNormalizeType")]
public virtual string DartNormalizeType { get; set; }

Property Value

Type	Description
string

DataFrequency

The data frequency of a time series.

Declaration

[JsonProperty("dataFrequency")]
public virtual string DataFrequency { get; set; }

Property Value

Type	Description
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

Declaration

[JsonProperty("dataSplitColumn")]
public virtual string DataSplitColumn { get; set; }

Property Value

Type	Description
string

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.

Declaration

[JsonProperty("dataSplitEvalFraction")]
public virtual double? DataSplitEvalFraction { get; set; }

Property Value

Type	Description
double?

DataSplitMethod

The data split type for training and evaluation, e.g. RANDOM.

Declaration

[JsonProperty("dataSplitMethod")]
public virtual string DataSplitMethod { get; set; }

Property Value

Type	Description
string

DecomposeTimeSeries

If true, perform decompose time series and save the results.

Declaration

[JsonProperty("decomposeTimeSeries")]
public virtual bool? DecomposeTimeSeries { get; set; }

Property Value

Type	Description
bool?

DistanceType

Distance type for clustering models.

Declaration

[JsonProperty("distanceType")]
public virtual string DistanceType { get; set; }

Property Value

Type	Description
string

Dropout

Dropout probability for dnn models.

Declaration

[JsonProperty("dropout")]
public virtual double? Dropout { get; set; }

Property Value

Type	Description
double?

ETag

The ETag of the item.

Declaration

public virtual string ETag { get; set; }

Property Value

Type	Description
string

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.

Declaration

[JsonProperty("earlyStop")]
public virtual bool? EarlyStop { get; set; }

Property Value

Type	Description
bool?

EnableGlobalExplain

If true, enable global explanation during training.

Declaration

[JsonProperty("enableGlobalExplain")]
public virtual bool? EnableGlobalExplain { get; set; }

Property Value

Type	Description
bool?

FeedbackType

Feedback type that specifies which algorithm to run for matrix factorization.

Declaration

[JsonProperty("feedbackType")]
public virtual string FeedbackType { get; set; }

Property Value

Type	Description
string

FitIntercept

Whether the model should include intercept during model training.

Declaration

[JsonProperty("fitIntercept")]
public virtual bool? FitIntercept { get; set; }

Property Value

Type	Description
bool?

HiddenUnits

Hidden units for dnn models.

Declaration

[JsonProperty("hiddenUnits")]
public virtual IList<long?> HiddenUnits { get; set; }

Property Value

Type	Description
IList<long?>

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.

Declaration

[JsonProperty("holidayRegion")]
public virtual string HolidayRegion { get; set; }

Property Value

Type	Description
string

HolidayRegions

A list of geographical regions that are used for time series modeling.

Declaration

[JsonProperty("holidayRegions")]
public virtual IList<string> HolidayRegions { get; set; }

Property Value

Type	Description
IList<string>

Horizon

The number of periods ahead that need to be forecasted.

Declaration

[JsonProperty("horizon")]
public virtual long? Horizon { get; set; }

Property Value

Type	Description
long?

HparamTuningObjectives

The target evaluation metrics to optimize the hyperparameters for.

Declaration

[JsonProperty("hparamTuningObjectives")]
public virtual IList<string> HparamTuningObjectives { get; set; }

Property Value

Type	Description
IList<string>

IncludeDrift

Include drift when fitting an ARIMA model.

Declaration

[JsonProperty("includeDrift")]
public virtual bool? IncludeDrift { get; set; }

Property Value

Type	Description
bool?

InitialLearnRate

Specifies the initial learning rate for the line search learn rate strategy.

Declaration

[JsonProperty("initialLearnRate")]
public virtual double? InitialLearnRate { get; set; }

Property Value

Type	Description
double?

InputLabelColumns

Name of input label columns in training data.

Declaration

[JsonProperty("inputLabelColumns")]
public virtual IList<string> InputLabelColumns { get; set; }

Property Value

Type	Description
IList<string>

InstanceWeightColumn

Name of the instance weight column for training data. This column isn't be used as a feature.

Declaration

[JsonProperty("instanceWeightColumn")]
public virtual string InstanceWeightColumn { get; set; }

Property Value

Type	Description
string

IntegratedGradientsNumSteps

Number of integral steps for the integrated gradients explain method.

Declaration

[JsonProperty("integratedGradientsNumSteps")]
public virtual long? IntegratedGradientsNumSteps { get; set; }

Property Value

Type	Description
long?

ItemColumn

Item column specified for matrix factorization models.

Declaration

[JsonProperty("itemColumn")]
public virtual string ItemColumn { get; set; }

Property Value

Type	Description
string

KmeansInitializationColumn

The column used to provide the initial centroids for kmeans algorithm when kmeans_initialization_method is CUSTOM.

Declaration

[JsonProperty("kmeansInitializationColumn")]
public virtual string KmeansInitializationColumn { get; set; }

Property Value

Type	Description
string

KmeansInitializationMethod

The method used to initialize the centroids for kmeans algorithm.

Declaration

[JsonProperty("kmeansInitializationMethod")]
public virtual string KmeansInitializationMethod { get; set; }

Property Value

Type	Description
string

L1RegActivation

L1 regularization coefficient to activations.

Declaration

[JsonProperty("l1RegActivation")]
public virtual double? L1RegActivation { get; set; }

Property Value

Type	Description
double?

L1Regularization

L1 regularization coefficient.

Declaration

[JsonProperty("l1Regularization")]
public virtual double? L1Regularization { get; set; }

Property Value

Type	Description
double?

L2Regularization

L2 regularization coefficient.

Declaration

[JsonProperty("l2Regularization")]
public virtual double? L2Regularization { get; set; }

Property Value

Type	Description
double?

LabelClassWeights

Weights associated with each label class, for rebalancing the training data. Only applicable for classification models.

Declaration

[JsonProperty("labelClassWeights")]
public virtual IDictionary<string, double?> LabelClassWeights { get; set; }

Property Value

Type	Description
IDictionary<string, double?>

LearnRate

Learning rate in training. Used only for iterative training algorithms.

Declaration

[JsonProperty("learnRate")]
public virtual double? LearnRate { get; set; }

Property Value

Type	Description
double?

LearnRateStrategy

The strategy to determine learn rate for the current iteration.

Declaration

[JsonProperty("learnRateStrategy")]
public virtual string LearnRateStrategy { get; set; }

Property Value

Type	Description
string

LossType

Type of loss function used during training run.

Declaration

[JsonProperty("lossType")]
public virtual string LossType { get; set; }

Property Value

Type	Description
string

MaxIterations

The maximum number of iterations in training. Used only for iterative training algorithms.

Declaration

[JsonProperty("maxIterations")]
public virtual long? MaxIterations { get; set; }

Property Value

Type	Description
long?

MaxParallelTrials

Maximum number of trials to run in parallel.

Declaration

[JsonProperty("maxParallelTrials")]
public virtual long? MaxParallelTrials { get; set; }

Property Value

Type	Description
long?

MaxTimeSeriesLength

The maximum number of time points in a time series that can be used in modeling the trend component of the time series. Don't use this option with the timeSeriesLengthFraction or minTimeSeriesLength options.

Declaration

[JsonProperty("maxTimeSeriesLength")]
public virtual long? MaxTimeSeriesLength { get; set; }

Property Value

Type	Description
long?

MaxTreeDepth

Maximum depth of a tree for boosted tree models.

Declaration

[JsonProperty("maxTreeDepth")]
public virtual long? MaxTreeDepth { get; set; }

Property Value

Type	Description
long?

MinRelativeProgress

When early_stop is true, stops training when accuracy improvement is less than 'min_relative_progress'. Used only for iterative training algorithms.

Declaration

[JsonProperty("minRelativeProgress")]
public virtual double? MinRelativeProgress { get; set; }

Property Value

Type	Description
double?

MinSplitLoss

Minimum split loss for boosted tree models.

Declaration

[JsonProperty("minSplitLoss")]
public virtual double? MinSplitLoss { get; set; }

Property Value

Type	Description
double?

MinTimeSeriesLength

The minimum number of time points in a time series that are used in modeling the trend component of the time series. If you use this option you must also set the timeSeriesLengthFraction option. This training option ensures that enough time points are available when you use timeSeriesLengthFraction in trend modeling. This is particularly important when forecasting multiple time series in a single query using timeSeriesIdColumn. If the total number of time points is less than the minTimeSeriesLength value, then the query uses all available time points.

Declaration

[JsonProperty("minTimeSeriesLength")]
public virtual long? MinTimeSeriesLength { get; set; }

Property Value

Type	Description
long?

MinTreeChildWeight

Minimum sum of instance weight needed in a child for boosted tree models.

Declaration

[JsonProperty("minTreeChildWeight")]
public virtual long? MinTreeChildWeight { get; set; }

Property Value

Type	Description
long?

ModelRegistry

The model registry.

Declaration

[JsonProperty("modelRegistry")]
public virtual string ModelRegistry { get; set; }

Property Value

Type	Description
string

ModelUri

Google Cloud Storage URI from which the model was imported. Only applicable for imported models.

Declaration

[JsonProperty("modelUri")]
public virtual string ModelUri { get; set; }

Property Value

Type	Description
string

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.

Declaration

[JsonProperty("nonSeasonalOrder")]
public virtual ArimaOrder NonSeasonalOrder { get; set; }

Property Value

Type	Description
ArimaOrder

NumClusters

Number of clusters for clustering models.

Declaration

[JsonProperty("numClusters")]
public virtual long? NumClusters { get; set; }

Property Value

Type	Description
long?

NumFactors

Num factors specified for matrix factorization models.

Declaration

[JsonProperty("numFactors")]
public virtual long? NumFactors { get; set; }

Property Value

Type	Description
long?

NumParallelTree

Number of parallel trees constructed during each iteration for boosted tree models.

Declaration

[JsonProperty("numParallelTree")]
public virtual long? NumParallelTree { get; set; }

Property Value

Type	Description
long?

NumPrincipalComponents

Number of principal components to keep in the PCA model. Must be <= the number of features.

Declaration

[JsonProperty("numPrincipalComponents")]
public virtual long? NumPrincipalComponents { get; set; }

Property Value

Type	Description
long?

NumTrials

Number of trials to run this hyperparameter tuning job.

Declaration

[JsonProperty("numTrials")]
public virtual long? NumTrials { get; set; }

Property Value

Type	Description
long?

OptimizationStrategy

Optimization strategy for training linear regression models.

Declaration

[JsonProperty("optimizationStrategy")]
public virtual string OptimizationStrategy { get; set; }

Property Value

Type	Description
string

Optimizer

Optimizer used for training the neural nets.

Declaration

[JsonProperty("optimizer")]
public virtual string Optimizer { get; set; }

Property Value

Type	Description
string

PcaExplainedVarianceRatio

The minimum ratio of cumulative explained variance that needs to be given by the PCA model.

Declaration

[JsonProperty("pcaExplainedVarianceRatio")]
public virtual double? PcaExplainedVarianceRatio { get; set; }

Property Value

Type	Description
double?

PcaSolver

The solver for PCA.

Declaration

[JsonProperty("pcaSolver")]
public virtual string PcaSolver { get; set; }

Property Value

Type	Description
string

SampledShapleyNumPaths

Number of paths for the sampled Shapley explain method.

Declaration

[JsonProperty("sampledShapleyNumPaths")]
public virtual long? SampledShapleyNumPaths { get; set; }

Property Value

Type	Description
long?

ScaleFeatures

If true, scale the feature values by dividing the feature standard deviation. Currently only apply to PCA.

Declaration

[JsonProperty("scaleFeatures")]
public virtual bool? ScaleFeatures { get; set; }

Property Value

Type	Description
bool?

StandardizeFeatures

Whether to standardize numerical features. Default to true.

Declaration

[JsonProperty("standardizeFeatures")]
public virtual bool? StandardizeFeatures { get; set; }

Property Value

Type	Description
bool?

Subsample

Subsample fraction of the training data to grow tree to prevent overfitting for boosted tree models.

Declaration

[JsonProperty("subsample")]
public virtual double? Subsample { get; set; }

Property Value

Type	Description
double?

TfVersion

Based on the selected TF version, the corresponding docker image is used to train external models.

Declaration

[JsonProperty("tfVersion")]
public virtual string TfVersion { get; set; }

Property Value

Type	Description
string

TimeSeriesDataColumn

Column to be designated as time series data for ARIMA model.

Declaration

[JsonProperty("timeSeriesDataColumn")]
public virtual string TimeSeriesDataColumn { get; set; }

Property Value

Type	Description
string

TimeSeriesIdColumn

The time series id column that was used during ARIMA model training.

Declaration

[JsonProperty("timeSeriesIdColumn")]
public virtual string TimeSeriesIdColumn { get; set; }

Property Value

Type	Description
string

TimeSeriesIdColumns

The time series id columns that were used during ARIMA model training.

Declaration

[JsonProperty("timeSeriesIdColumns")]
public virtual IList<string> TimeSeriesIdColumns { get; set; }

Property Value

Type	Description
IList<string>

TimeSeriesLengthFraction

The fraction of the interpolated length of the time series that's used to model the time series trend component. All of the time points of the time series are used to model the non-trend component. This training option accelerates modeling training without sacrificing much forecasting accuracy. You can use this option with minTimeSeriesLength but not with maxTimeSeriesLength.

Declaration

[JsonProperty("timeSeriesLengthFraction")]
public virtual double? TimeSeriesLengthFraction { get; set; }

Property Value

Type	Description
double?

TimeSeriesTimestampColumn

Column to be designated as time series timestamp for ARIMA model.

Declaration

[JsonProperty("timeSeriesTimestampColumn")]
public virtual string TimeSeriesTimestampColumn { get; set; }

Property Value

Type	Description
string

TreeMethod

Tree construction algorithm for boosted tree models.

Declaration

[JsonProperty("treeMethod")]
public virtual string TreeMethod { get; set; }

Property Value

Type	Description
string

TrendSmoothingWindowSize

Smoothing window size for the trend component. When a positive value is specified, a center moving average smoothing is applied on the history trend. When the smoothing window is out of the boundary at the beginning or the end of the trend, the first element or the last element is padded to fill the smoothing window before the average is applied.

Declaration

[JsonProperty("trendSmoothingWindowSize")]
public virtual long? TrendSmoothingWindowSize { get; set; }

Property Value

Type	Description
long?

UserColumn

User column specified for matrix factorization models.

Declaration

[JsonProperty("userColumn")]
public virtual string UserColumn { get; set; }

Property Value

Type	Description
string

VertexAiModelVersionAliases

The version aliases to apply in Vertex AI model registry. Always overwrite if the version aliases exists in a existing model.

Declaration

[JsonProperty("vertexAiModelVersionAliases")]
public virtual IList<string> VertexAiModelVersionAliases { get; set; }

Property Value

Type	Description
IList<string>

WalsAlpha

Hyperparameter for matrix factoration when implicit feedback type is specified.

Declaration

[JsonProperty("walsAlpha")]
public virtual double? WalsAlpha { get; set; }

Property Value

Type	Description
double?

WarmStart

Whether to train a model from the last checkpoint.

Declaration

[JsonProperty("warmStart")]
public virtual bool? WarmStart { get; set; }

Property Value

Type	Description
bool?

XgboostVersion

User-selected XGBoost versions for training of XGBoost models.

Declaration

[JsonProperty("xgboostVersion")]
public virtual string XgboostVersion { get; set; }

Property Value

Type	Description
string

Implements

IDirectResponseSchema