Class: Google::Apis::BigqueryV2::TrainingOptions

Inherits:
Object
  • Object
show all
Includes:
Core::Hashable, Core::JsonObjectSupport
Defined in:
lib/google/apis/bigquery_v2/classes.rb,
lib/google/apis/bigquery_v2/representations.rb,
lib/google/apis/bigquery_v2/representations.rb

Overview

Options used in model training.

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(**args) ⇒ TrainingOptions

Returns a new instance of TrainingOptions.



10877
10878
10879
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10877

def initialize(**args)
   update!(**args)
end

Instance Attribute Details

#activation_fn ⇒ String

Activation function of the neural nets. Corresponds to the JSON property activationFn

Returns:

  • (String) 


10388
10389
10390
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10388

def activation_fn
  @activation_fn
end

#adjust_step_changes ⇒ Boolean Also known as: adjust_step_changes?

If true, detect step changes and make data adjustment in the input time series. Corresponds to the JSON property adjustStepChanges

Returns:

  • (Boolean) 


10393
10394
10395
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10393

def adjust_step_changes
  @adjust_step_changes
end

#approx_global_feature_contrib ⇒ Boolean Also known as: approx_global_feature_contrib?

Whether to use approximate feature contribution method in XGBoost model explanation for global explain. Corresponds to the JSON property approxGlobalFeatureContrib

Returns:

  • (Boolean) 


10400
10401
10402
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10400

def approx_global_feature_contrib
  @approx_global_feature_contrib
end

#auto_arima ⇒ Boolean Also known as: auto_arima?

Whether to enable auto ARIMA or not. Corresponds to the JSON property autoArima

Returns:

  • (Boolean) 


10406
10407
10408
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10406

def auto_arima
  @auto_arima
end

#auto_arima_max_order ⇒ Fixnum

The max value of the sum of non-seasonal p and q. Corresponds to the JSON property autoArimaMaxOrder

Returns:

  • (Fixnum) 


10412
10413
10414
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10412

def auto_arima_max_order
  @auto_arima_max_order
end

#auto_arima_min_order ⇒ Fixnum

The min value of the sum of non-seasonal p and q. Corresponds to the JSON property autoArimaMinOrder

Returns:

  • (Fixnum) 


10417
10418
10419
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10417

def auto_arima_min_order
  @auto_arima_min_order
end

#auto_class_weights ⇒ Boolean Also known as: auto_class_weights?

Whether to calculate class weights automatically based on the popularity of each label. Corresponds to the JSON property autoClassWeights

Returns:

  • (Boolean) 


10423
10424
10425
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10423

def auto_class_weights
  @auto_class_weights
end

#batch_size ⇒ Fixnum

Batch size for dnn models. Corresponds to the JSON property batchSize

Returns:

  • (Fixnum) 


10429
10430
10431
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10429

def batch_size
  @batch_size
end

#booster_type ⇒ String

Booster type for boosted tree models. Corresponds to the JSON property boosterType

Returns:

  • (String) 


10434
10435
10436
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10434

def booster_type
  @booster_type
end

#budget_hours ⇒ Float

Budget in hours for AutoML training. Corresponds to the JSON property budgetHours

Returns:

  • (Float) 


10439
10440
10441
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10439

def budget_hours
  @budget_hours
end

#calculate_p_values ⇒ Boolean Also known as: calculate_p_values?

Whether or not p-value test should be computed for this model. Only available for linear and logistic regression models. Corresponds to the JSON property calculatePValues

Returns:

  • (Boolean) 


10445
10446
10447
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10445

def calculate_p_values
  @calculate_p_values
end

#category_encoding_method ⇒ String

Categorical feature encoding method. Corresponds to the JSON property categoryEncodingMethod

Returns:

  • (String) 


10451
10452
10453
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10451

def category_encoding_method
  @category_encoding_method
end

#clean_spikes_and_dips ⇒ Boolean Also known as: clean_spikes_and_dips?

If true, clean spikes and dips in the input time series. Corresponds to the JSON property cleanSpikesAndDips

Returns:

  • (Boolean) 


10456
10457
10458
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10456

def clean_spikes_and_dips
  @clean_spikes_and_dips
end

#color_space ⇒ String

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

Returns:

  • (String) 


10463
10464
10465
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10463

def color_space
  @color_space
end

#colsample_bylevel ⇒ Float

Subsample ratio of columns for each level for boosted tree models. Corresponds to the JSON property colsampleBylevel

Returns:

  • (Float) 


10468
10469
10470
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10468

def colsample_bylevel
  @colsample_bylevel
end

#colsample_bynode ⇒ Float

Subsample ratio of columns for each node(split) for boosted tree models. Corresponds to the JSON property colsampleBynode

Returns:

  • (Float) 


10473
10474
10475
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10473

def colsample_bynode
  @colsample_bynode
end

#colsample_bytree ⇒ Float

Subsample ratio of columns when constructing each tree for boosted tree models. Corresponds to the JSON property colsampleBytree

Returns:

  • (Float) 


10478
10479
10480
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10478

def colsample_bytree
  @colsample_bytree
end

#dart_normalize_type ⇒ String

Type of normalization algorithm for boosted tree models using dart booster. Corresponds to the JSON property dartNormalizeType

Returns:

  • (String) 


10483
10484
10485
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10483

def dart_normalize_type
  @dart_normalize_type
end

#data_frequency ⇒ String

The data frequency of a time series. Corresponds to the JSON property dataFrequency

Returns:

  • (String) 


10488
10489
10490
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10488

def data_frequency
  @data_frequency
end

#data_split_column ⇒ String

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 Corresponds to the JSON property dataSplitColumn

Returns:

  • (String) 


10500
10501
10502
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10500

def data_split_column
  @data_split_column
end

#data_split_eval_fraction ⇒ Float

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. Corresponds to the JSON property dataSplitEvalFraction

Returns:

  • (Float) 


10507
10508
10509
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10507

def data_split_eval_fraction
  @data_split_eval_fraction
end

#data_split_method ⇒ String

The data split type for training and evaluation, e.g. RANDOM. Corresponds to the JSON property dataSplitMethod

Returns:

  • (String) 


10512
10513
10514
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10512

def data_split_method
  @data_split_method
end

#decompose_time_series ⇒ Boolean Also known as: decompose_time_series?

If true, perform decompose time series and save the results. Corresponds to the JSON property decomposeTimeSeries

Returns:

  • (Boolean) 


10517
10518
10519
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10517

def decompose_time_series
  @decompose_time_series
end

#distance_type ⇒ String

Distance type for clustering models. Corresponds to the JSON property distanceType

Returns:

  • (String) 


10523
10524
10525
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10523

def distance_type
  @distance_type
end

#dropout ⇒ Float

Dropout probability for dnn models. Corresponds to the JSON property dropout

Returns:

  • (Float) 


10528
10529
10530
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10528

def dropout
  @dropout
end

#early_stop ⇒ Boolean Also known as: early_stop?

Whether to stop early when the loss doesn't improve significantly any more ( compared to min_relative_progress). Used only for iterative training algorithms. Corresponds to the JSON property earlyStop

Returns:

  • (Boolean) 


10535
10536
10537
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10535

def early_stop
  @early_stop
end

#enable_global_explain ⇒ Boolean Also known as: enable_global_explain?

If true, enable global explanation during training. Corresponds to the JSON property enableGlobalExplain

Returns:

  • (Boolean) 


10541
10542
10543
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10541

def enable_global_explain
  @enable_global_explain
end

#feedback_type ⇒ String

Feedback type that specifies which algorithm to run for matrix factorization. Corresponds to the JSON property feedbackType

Returns:

  • (String) 


10547
10548
10549
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10547

def feedback_type
  @feedback_type
end

#fit_intercept ⇒ Boolean Also known as: fit_intercept?

Whether the model should include intercept during model training. Corresponds to the JSON property fitIntercept

Returns:

  • (Boolean) 


10552
10553
10554
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10552

def fit_intercept
  @fit_intercept
end

#hidden_units ⇒ Array<Fixnum>

Hidden units for dnn models. Corresponds to the JSON property hiddenUnits

Returns:

  • (Array<Fixnum>) 


10558
10559
10560
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10558

def hidden_units
  @hidden_units
end

#holiday_region ⇒ String

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. Corresponds to the JSON property holidayRegion

Returns:

  • (String) 


10565
10566
10567
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10565

def holiday_region
  @holiday_region
end

#holiday_regions ⇒ Array<String>

A list of geographical regions that are used for time series modeling. Corresponds to the JSON property holidayRegions

Returns:

  • (Array<String>) 


10570
10571
10572
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10570

def holiday_regions
  @holiday_regions
end

#horizon ⇒ Fixnum

The number of periods ahead that need to be forecasted. Corresponds to the JSON property horizon

Returns:

  • (Fixnum) 


10575
10576
10577
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10575

def horizon
  @horizon
end

#hparam_tuning_objectives ⇒ Array<String>

The target evaluation metrics to optimize the hyperparameters for. Corresponds to the JSON property hparamTuningObjectives

Returns:

  • (Array<String>) 


10580
10581
10582
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10580

def hparam_tuning_objectives
  @hparam_tuning_objectives
end

#include_drift ⇒ Boolean Also known as: include_drift?

Include drift when fitting an ARIMA model. Corresponds to the JSON property includeDrift

Returns:

  • (Boolean) 


10585
10586
10587
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10585

def include_drift
  @include_drift
end

#initial_learn_rate ⇒ Float

Specifies the initial learning rate for the line search learn rate strategy. Corresponds to the JSON property initialLearnRate

Returns:

  • (Float) 


10591
10592
10593
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10591

def initial_learn_rate
  @initial_learn_rate
end

#input_label_columns ⇒ Array<String>

Name of input label columns in training data. Corresponds to the JSON property inputLabelColumns

Returns:

  • (Array<String>) 


10596
10597
10598
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10596

def input_label_columns
  @input_label_columns
end

#instance_weight_column ⇒ String

Name of the instance weight column for training data. This column isn't be used as a feature. Corresponds to the JSON property instanceWeightColumn

Returns:

  • (String) 


10602
10603
10604
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10602

def instance_weight_column
  @instance_weight_column
end

#integrated_gradients_num_steps ⇒ Fixnum

Number of integral steps for the integrated gradients explain method. Corresponds to the JSON property integratedGradientsNumSteps

Returns:

  • (Fixnum) 


10607
10608
10609
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10607

def integrated_gradients_num_steps
  @integrated_gradients_num_steps
end

#item_column ⇒ String

Item column specified for matrix factorization models. Corresponds to the JSON property itemColumn

Returns:

  • (String) 


10612
10613
10614
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10612

def item_column
  @item_column
end

#kmeans_initialization_column ⇒ String

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

Returns:

  • (String) 


10618
10619
10620
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10618

def kmeans_initialization_column
  @kmeans_initialization_column
end

#kmeans_initialization_method ⇒ String

The method used to initialize the centroids for kmeans algorithm. Corresponds to the JSON property kmeansInitializationMethod

Returns:

  • (String) 


10623
10624
10625
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10623

def kmeans_initialization_method
  @kmeans_initialization_method
end

#l1_reg_activation ⇒ Float

L1 regularization coefficient to activations. Corresponds to the JSON property l1RegActivation

Returns:

  • (Float) 


10628
10629
10630
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10628

def l1_reg_activation
  @l1_reg_activation
end

#l1_regularization ⇒ Float

L1 regularization coefficient. Corresponds to the JSON property l1Regularization

Returns:

  • (Float) 


10633
10634
10635
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10633

def l1_regularization
  @l1_regularization
end

#l2_regularization ⇒ Float

L2 regularization coefficient. Corresponds to the JSON property l2Regularization

Returns:

  • (Float) 


10638
10639
10640
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10638

def l2_regularization
  @l2_regularization
end

#label_class_weights ⇒ Hash<String,Float>

Weights associated with each label class, for rebalancing the training data. Only applicable for classification models. Corresponds to the JSON property labelClassWeights

Returns:

  • (Hash<String,Float>) 


10644
10645
10646
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10644

def label_class_weights
  @label_class_weights
end

#learn_rate ⇒ Float

Learning rate in training. Used only for iterative training algorithms. Corresponds to the JSON property learnRate

Returns:

  • (Float) 


10649
10650
10651
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10649

def learn_rate
  @learn_rate
end

#learn_rate_strategy ⇒ String

The strategy to determine learn rate for the current iteration. Corresponds to the JSON property learnRateStrategy

Returns:

  • (String) 


10654
10655
10656
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10654

def learn_rate_strategy
  @learn_rate_strategy
end

#loss_type ⇒ String

Type of loss function used during training run. Corresponds to the JSON property lossType

Returns:

  • (String) 


10659
10660
10661
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10659

def loss_type
  @loss_type
end

#max_iterations ⇒ Fixnum

The maximum number of iterations in training. Used only for iterative training algorithms. Corresponds to the JSON property maxIterations

Returns:

  • (Fixnum) 


10665
10666
10667
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10665

def max_iterations
  @max_iterations
end

#max_parallel_trials ⇒ Fixnum

Maximum number of trials to run in parallel. Corresponds to the JSON property maxParallelTrials

Returns:

  • (Fixnum) 


10670
10671
10672
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10670

def max_parallel_trials
  @max_parallel_trials
end

#max_time_series_length ⇒ Fixnum

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. Corresponds to the JSON property maxTimeSeriesLength

Returns:

  • (Fixnum) 


10677
10678
10679
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10677

def max_time_series_length
  @max_time_series_length
end

#max_tree_depth ⇒ Fixnum

Maximum depth of a tree for boosted tree models. Corresponds to the JSON property maxTreeDepth

Returns:

  • (Fixnum) 


10682
10683
10684
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10682

def max_tree_depth
  @max_tree_depth
end

#min_relative_progress ⇒ Float

When early_stop is true, stops training when accuracy improvement is less than 'min_relative_progress'. Used only for iterative training algorithms. Corresponds to the JSON property minRelativeProgress

Returns:

  • (Float) 


10688
10689
10690
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10688

def min_relative_progress
  @min_relative_progress
end

#min_split_loss ⇒ Float

Minimum split loss for boosted tree models. Corresponds to the JSON property minSplitLoss

Returns:

  • (Float) 


10693
10694
10695
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10693

def min_split_loss
  @min_split_loss
end

#min_time_series_length ⇒ Fixnum

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. Corresponds to the JSON property minTimeSeriesLength

Returns:

  • (Fixnum) 


10705
10706
10707
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10705

def min_time_series_length
  @min_time_series_length
end

#min_tree_child_weight ⇒ Fixnum

Minimum sum of instance weight needed in a child for boosted tree models. Corresponds to the JSON property minTreeChildWeight

Returns:

  • (Fixnum) 


10710
10711
10712
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10710

def min_tree_child_weight
  @min_tree_child_weight
end

#model_registry ⇒ String

The model registry. Corresponds to the JSON property modelRegistry

Returns:

  • (String) 


10715
10716
10717
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10715

def model_registry
  @model_registry
end

#model_uri ⇒ String

Google Cloud Storage URI from which the model was imported. Only applicable for imported models. Corresponds to the JSON property modelUri

Returns:

  • (String) 


10721
10722
10723
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10721

def model_uri
  @model_uri
end

#non_seasonal_order ⇒ Google::Apis::BigqueryV2::ArimaOrder

Arima order, can be used for both non-seasonal and seasonal parts. Corresponds to the JSON property nonSeasonalOrder

Returns:



10726
10727
10728
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10726

def non_seasonal_order
  @non_seasonal_order
end

#num_clusters ⇒ Fixnum

Number of clusters for clustering models. Corresponds to the JSON property numClusters

Returns:

  • (Fixnum) 


10731
10732
10733
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10731

def num_clusters
  @num_clusters
end

#num_factors ⇒ Fixnum

Num factors specified for matrix factorization models. Corresponds to the JSON property numFactors

Returns:

  • (Fixnum) 


10736
10737
10738
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10736

def num_factors
  @num_factors
end

#num_parallel_tree ⇒ Fixnum

Number of parallel trees constructed during each iteration for boosted tree models. Corresponds to the JSON property numParallelTree

Returns:

  • (Fixnum) 


10742
10743
10744
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10742

def num_parallel_tree
  @num_parallel_tree
end

#num_principal_components ⇒ Fixnum

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

Returns:

  • (Fixnum) 


10748
10749
10750
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10748

def num_principal_components
  @num_principal_components
end

#num_trials ⇒ Fixnum

Number of trials to run this hyperparameter tuning job. Corresponds to the JSON property numTrials

Returns:

  • (Fixnum) 


10753
10754
10755
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10753

def num_trials
  @num_trials
end

#optimization_strategy ⇒ String

Optimization strategy for training linear regression models. Corresponds to the JSON property optimizationStrategy

Returns:

  • (String) 


10758
10759
10760
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10758

def optimization_strategy
  @optimization_strategy
end

#optimizer ⇒ String

Optimizer used for training the neural nets. Corresponds to the JSON property optimizer

Returns:

  • (String) 


10763
10764
10765
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10763

def optimizer
  @optimizer
end

#pca_explained_variance_ratio ⇒ Float

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

Returns:

  • (Float) 


10769
10770
10771
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10769

def pca_explained_variance_ratio
  @pca_explained_variance_ratio
end

#pca_solver ⇒ String

The solver for PCA. Corresponds to the JSON property pcaSolver

Returns:

  • (String) 


10774
10775
10776
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10774

def pca_solver
  @pca_solver
end

#sampled_shapley_num_paths ⇒ Fixnum

Number of paths for the sampled Shapley explain method. Corresponds to the JSON property sampledShapleyNumPaths

Returns:

  • (Fixnum) 


10779
10780
10781
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10779

def sampled_shapley_num_paths
  @sampled_shapley_num_paths
end

#scale_features ⇒ Boolean Also known as: scale_features?

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

Returns:

  • (Boolean) 


10785
10786
10787
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10785

def scale_features
  @scale_features
end

#standardize_features ⇒ Boolean Also known as: standardize_features?

Whether to standardize numerical features. Default to true. Corresponds to the JSON property standardizeFeatures

Returns:

  • (Boolean) 


10791
10792
10793
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10791

def standardize_features
  @standardize_features
end

#subsample ⇒ Float

Subsample fraction of the training data to grow tree to prevent overfitting for boosted tree models. Corresponds to the JSON property subsample

Returns:

  • (Float) 


10798
10799
10800
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10798

def subsample
  @subsample
end

#tf_version ⇒ String

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

Returns:

  • (String) 


10804
10805
10806
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10804

def tf_version
  @tf_version
end

#time_series_data_column ⇒ String

Column to be designated as time series data for ARIMA model. Corresponds to the JSON property timeSeriesDataColumn

Returns:

  • (String) 


10809
10810
10811
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10809

def time_series_data_column
  @time_series_data_column
end

#time_series_id_column ⇒ String

The time series id column that was used during ARIMA model training. Corresponds to the JSON property timeSeriesIdColumn

Returns:

  • (String) 


10814
10815
10816
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10814

def time_series_id_column
  @time_series_id_column
end

#time_series_id_columns ⇒ Array<String>

The time series id columns that were used during ARIMA model training. Corresponds to the JSON property timeSeriesIdColumns

Returns:

  • (Array<String>) 


10819
10820
10821
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10819

def time_series_id_columns
  @time_series_id_columns
end

#time_series_length_fraction ⇒ Float

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. Corresponds to the JSON property timeSeriesLengthFraction

Returns:

  • (Float) 


10829
10830
10831
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10829

def time_series_length_fraction
  @time_series_length_fraction
end

#time_series_timestamp_column ⇒ String

Column to be designated as time series timestamp for ARIMA model. Corresponds to the JSON property timeSeriesTimestampColumn

Returns:

  • (String) 


10834
10835
10836
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10834

def time_series_timestamp_column
  @time_series_timestamp_column
end

#tree_method ⇒ String

Tree construction algorithm for boosted tree models. Corresponds to the JSON property treeMethod

Returns:

  • (String) 


10839
10840
10841
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10839

def tree_method
  @tree_method
end

#trend_smoothing_window_size ⇒ Fixnum

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. Corresponds to the JSON property trendSmoothingWindowSize

Returns:

  • (Fixnum) 


10848
10849
10850
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10848

def trend_smoothing_window_size
  @trend_smoothing_window_size
end

#user_column ⇒ String

User column specified for matrix factorization models. Corresponds to the JSON property userColumn

Returns:

  • (String) 


10853
10854
10855
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10853

def user_column
  @user_column
end

#vertex_ai_model_version_aliases ⇒ Array<String>

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

Returns:

  • (Array<String>) 


10859
10860
10861
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10859

def vertex_ai_model_version_aliases
  @vertex_ai_model_version_aliases
end

#wals_alpha ⇒ Float

Hyperparameter for matrix factoration when implicit feedback type is specified. Corresponds to the JSON property walsAlpha

Returns:

  • (Float) 


10864
10865
10866
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10864

def wals_alpha
  @wals_alpha
end

#warm_start ⇒ Boolean Also known as: warm_start?

Whether to train a model from the last checkpoint. Corresponds to the JSON property warmStart

Returns:

  • (Boolean) 


10869
10870
10871
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10869

def warm_start
  @warm_start
end

#xgboost_version ⇒ String

User-selected XGBoost versions for training of XGBoost models. Corresponds to the JSON property xgboostVersion

Returns:

  • (String) 


10875
10876
10877
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10875

def xgboost_version
  @xgboost_version
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



10882
10883
10884
10885
10886
10887
10888
10889
10890
10891
10892
10893
10894
10895
10896
10897
10898
10899
10900
10901
10902
10903
10904
10905
10906
10907
10908
10909
10910
10911
10912
10913
10914
10915
10916
10917
10918
10919
10920
10921
10922
10923
10924
10925
10926
10927
10928
10929
10930
10931
10932
10933
10934
10935
10936
10937
10938
10939
10940
10941
10942
10943
10944
10945
10946
10947
10948
10949
10950
10951
10952
10953
10954
10955
10956
10957
10958
10959
10960
10961
10962
10963
10964
10965
10966
10967
10968
10969
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10882

def update!(**args)
  @activation_fn = args[:activation_fn] if args.key?(:activation_fn)
  @adjust_step_changes = args[:adjust_step_changes] if args.key?(:adjust_step_changes)
  @approx_global_feature_contrib = args[:approx_global_feature_contrib] if args.key?(:approx_global_feature_contrib)
  @auto_arima = args[:auto_arima] if args.key?(:auto_arima)
  @auto_arima_max_order = args[:auto_arima_max_order] if args.key?(:auto_arima_max_order)
  @auto_arima_min_order = args[:auto_arima_min_order] if args.key?(:auto_arima_min_order)
  @auto_class_weights = args[:auto_class_weights] if args.key?(:auto_class_weights)
  @batch_size = args[:batch_size] if args.key?(:batch_size)
  @booster_type = args[:booster_type] if args.key?(:booster_type)
  @budget_hours = args[:budget_hours] if args.key?(:budget_hours)
  @calculate_p_values = args[:calculate_p_values] if args.key?(:calculate_p_values)
  @category_encoding_method = args[:category_encoding_method] if args.key?(:category_encoding_method)
  @clean_spikes_and_dips = args[:clean_spikes_and_dips] if args.key?(:clean_spikes_and_dips)
  @color_space = args[:color_space] if args.key?(:color_space)
  @colsample_bylevel = args[:colsample_bylevel] if args.key?(:colsample_bylevel)
  @colsample_bynode = args[:colsample_bynode] if args.key?(:colsample_bynode)
  @colsample_bytree = args[:colsample_bytree] if args.key?(:colsample_bytree)
  @dart_normalize_type = args[:dart_normalize_type] if args.key?(:dart_normalize_type)
  @data_frequency = args[:data_frequency] if args.key?(:data_frequency)
  @data_split_column = args[:data_split_column] if args.key?(:data_split_column)
  @data_split_eval_fraction = args[:data_split_eval_fraction] if args.key?(:data_split_eval_fraction)
  @data_split_method = args[:data_split_method] if args.key?(:data_split_method)
  @decompose_time_series = args[:decompose_time_series] if args.key?(:decompose_time_series)
  @distance_type = args[:distance_type] if args.key?(:distance_type)
  @dropout = args[:dropout] if args.key?(:dropout)
  @early_stop = args[:early_stop] if args.key?(:early_stop)
  @enable_global_explain = args[:enable_global_explain] if args.key?(:enable_global_explain)
  @feedback_type = args[:feedback_type] if args.key?(:feedback_type)
  @fit_intercept = args[:fit_intercept] if args.key?(:fit_intercept)
  @hidden_units = args[:hidden_units] if args.key?(:hidden_units)
  @holiday_region = args[:holiday_region] if args.key?(:holiday_region)
  @holiday_regions = args[:holiday_regions] if args.key?(:holiday_regions)
  @horizon = args[:horizon] if args.key?(:horizon)
  @hparam_tuning_objectives = args[:hparam_tuning_objectives] if args.key?(:hparam_tuning_objectives)
  @include_drift = args[:include_drift] if args.key?(:include_drift)
  @initial_learn_rate = args[:initial_learn_rate] if args.key?(:initial_learn_rate)
  @input_label_columns = args[:input_label_columns] if args.key?(:input_label_columns)
  @instance_weight_column = args[:instance_weight_column] if args.key?(:instance_weight_column)
  @integrated_gradients_num_steps = args[:integrated_gradients_num_steps] if args.key?(:integrated_gradients_num_steps)
  @item_column = args[:item_column] if args.key?(:item_column)
  @kmeans_initialization_column = args[:kmeans_initialization_column] if args.key?(:kmeans_initialization_column)
  @kmeans_initialization_method = args[:kmeans_initialization_method] if args.key?(:kmeans_initialization_method)
  @l1_reg_activation = args[:l1_reg_activation] if args.key?(:l1_reg_activation)
  @l1_regularization = args[:l1_regularization] if args.key?(:l1_regularization)
  @l2_regularization = args[:l2_regularization] if args.key?(:l2_regularization)
  @label_class_weights = args[:label_class_weights] if args.key?(:label_class_weights)
  @learn_rate = args[:learn_rate] if args.key?(:learn_rate)
  @learn_rate_strategy = args[:learn_rate_strategy] if args.key?(:learn_rate_strategy)
  @loss_type = args[:loss_type] if args.key?(:loss_type)
  @max_iterations = args[:max_iterations] if args.key?(:max_iterations)
  @max_parallel_trials = args[:max_parallel_trials] if args.key?(:max_parallel_trials)
  @max_time_series_length = args[:max_time_series_length] if args.key?(:max_time_series_length)
  @max_tree_depth = args[:max_tree_depth] if args.key?(:max_tree_depth)
  @min_relative_progress = args[:min_relative_progress] if args.key?(:min_relative_progress)
  @min_split_loss = args[:min_split_loss] if args.key?(:min_split_loss)
  @min_time_series_length = args[:min_time_series_length] if args.key?(:min_time_series_length)
  @min_tree_child_weight = args[:min_tree_child_weight] if args.key?(:min_tree_child_weight)
  @model_registry = args[:model_registry] if args.key?(:model_registry)
  @model_uri = args[:model_uri] if args.key?(:model_uri)
  @non_seasonal_order = args[:non_seasonal_order] if args.key?(:non_seasonal_order)
  @num_clusters = args[:num_clusters] if args.key?(:num_clusters)
  @num_factors = args[:num_factors] if args.key?(:num_factors)
  @num_parallel_tree = args[:num_parallel_tree] if args.key?(:num_parallel_tree)
  @num_principal_components = args[:num_principal_components] if args.key?(:num_principal_components)
  @num_trials = args[:num_trials] if args.key?(:num_trials)
  @optimization_strategy = args[:optimization_strategy] if args.key?(:optimization_strategy)
  @optimizer = args[:optimizer] if args.key?(:optimizer)
  @pca_explained_variance_ratio = args[:pca_explained_variance_ratio] if args.key?(:pca_explained_variance_ratio)
  @pca_solver = args[:pca_solver] if args.key?(:pca_solver)
  @sampled_shapley_num_paths = args[:sampled_shapley_num_paths] if args.key?(:sampled_shapley_num_paths)
  @scale_features = args[:scale_features] if args.key?(:scale_features)
  @standardize_features = args[:standardize_features] if args.key?(:standardize_features)
  @subsample = args[:subsample] if args.key?(:subsample)
  @tf_version = args[:tf_version] if args.key?(:tf_version)
  @time_series_data_column = args[:time_series_data_column] if args.key?(:time_series_data_column)
  @time_series_id_column = args[:time_series_id_column] if args.key?(:time_series_id_column)
  @time_series_id_columns = args[:time_series_id_columns] if args.key?(:time_series_id_columns)
  @time_series_length_fraction = args[:time_series_length_fraction] if args.key?(:time_series_length_fraction)
  @time_series_timestamp_column = args[:time_series_timestamp_column] if args.key?(:time_series_timestamp_column)
  @tree_method = args[:tree_method] if args.key?(:tree_method)
  @trend_smoothing_window_size = args[:trend_smoothing_window_size] if args.key?(:trend_smoothing_window_size)
  @user_column = args[:user_column] if args.key?(:user_column)
  @vertex_ai_model_version_aliases = args[:vertex_ai_model_version_aliases] if args.key?(:vertex_ai_model_version_aliases)
  @wals_alpha = args[:wals_alpha] if args.key?(:wals_alpha)
  @warm_start = args[:warm_start] if args.key?(:warm_start)
  @xgboost_version = args[:xgboost_version] if args.key?(:xgboost_version)
end