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.



11937
11938
11939
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11937

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) 


11356
11357
11358
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11356

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) 


11361
11362
11363
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11361

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) 


11368
11369
11370
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11368

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) 


11374
11375
11376
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11374

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) 


11380
11381
11382
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11380

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) 


11385
11386
11387
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11385

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) 


11391
11392
11393
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11391

def auto_class_weights
  @auto_class_weights
end

#batch_size ⇒ Fixnum

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

Returns:

  • (Fixnum) 


11397
11398
11399
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11397

def batch_size
  @batch_size
end

#booster_type ⇒ String

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

Returns:

  • (String) 


11402
11403
11404
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11402

def booster_type
  @booster_type
end

#budget_hours ⇒ Float

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

Returns:

  • (Float) 


11407
11408
11409
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11407

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) 


11413
11414
11415
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11413

def calculate_p_values
  @calculate_p_values
end

#category_encoding_method ⇒ String

Categorical feature encoding method. Corresponds to the JSON property categoryEncodingMethod

Returns:

  • (String) 


11419
11420
11421
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11419

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) 


11424
11425
11426
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11424

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) 


11431
11432
11433
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11431

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) 


11436
11437
11438
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11436

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) 


11441
11442
11443
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11441

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) 


11446
11447
11448
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11446

def colsample_bytree
  @colsample_bytree
end

#contribution_metric ⇒ String

The contribution metric. Applies to contribution analysis models. Allowed formats supported are for summable and summable ratio contribution metrics. These include expressions such as SUM(x) or SUM(x)/SUM(y), where x and y are column names from the base table. Corresponds to the JSON property contributionMetric

Returns:

  • (String) 


11454
11455
11456
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11454

def contribution_metric
  @contribution_metric
end

#dart_normalize_type ⇒ String

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

Returns:

  • (String) 


11459
11460
11461
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11459

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) 


11464
11465
11466
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11464

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) 


11476
11477
11478
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11476

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) 


11483
11484
11485
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11483

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) 


11488
11489
11490
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11488

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) 


11493
11494
11495
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11493

def decompose_time_series
  @decompose_time_series
end

#dimension_id_columns ⇒ Array<String>

Optional. Names of the columns to slice on. Applies to contribution analysis models. Corresponds to the JSON property dimensionIdColumns

Returns:

  • (Array<String>) 


11500
11501
11502
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11500

def dimension_id_columns
  @dimension_id_columns
end

#distance_type ⇒ String

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

Returns:

  • (String) 


11505
11506
11507
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11505

def distance_type
  @distance_type
end

#dropout ⇒ Float

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

Returns:

  • (Float) 


11510
11511
11512
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11510

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) 


11517
11518
11519
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11517

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) 


11523
11524
11525
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11523

def enable_global_explain
  @enable_global_explain
end

#endpoint_idle_ttl ⇒ String

The idle TTL of the endpoint before the resources get destroyed. The default value is 6.5 hours. Corresponds to the JSON property endpointIdleTtl

Returns:

  • (String) 


11530
11531
11532
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11530

def endpoint_idle_ttl
  @endpoint_idle_ttl
end

#feedback_type ⇒ String

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

Returns:

  • (String) 


11535
11536
11537
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11535

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) 


11540
11541
11542
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11540

def fit_intercept
  @fit_intercept
end

#forecast_limit_lower_bound ⇒ Float

The forecast limit lower bound that was used during ARIMA model training with limits. To see more details of the algorithm: https://otexts.com/fpp2/limits. html Corresponds to the JSON property forecastLimitLowerBound

Returns:

  • (Float) 


11548
11549
11550
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11548

def forecast_limit_lower_bound
  @forecast_limit_lower_bound
end

#forecast_limit_upper_bound ⇒ Float

The forecast limit upper bound that was used during ARIMA model training with limits. Corresponds to the JSON property forecastLimitUpperBound

Returns:

  • (Float) 


11554
11555
11556
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11554

def forecast_limit_upper_bound
  @forecast_limit_upper_bound
end

#hidden_units ⇒ Array<Fixnum>

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

Returns:

  • (Array<Fixnum>) 


11559
11560
11561
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11559

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) 


11566
11567
11568
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11566

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>) 


11571
11572
11573
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11571

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) 


11576
11577
11578
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11576

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>) 


11581
11582
11583
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11581

def hparam_tuning_objectives
  @hparam_tuning_objectives
end

#hugging_face_model_id ⇒ String

The id of a Hugging Face model. For example, google/gemma-2-2b-it. Corresponds to the JSON property huggingFaceModelId

Returns:

  • (String) 


11586
11587
11588
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11586

def hugging_face_model_id
  @hugging_face_model_id
end

#include_drift ⇒ Boolean Also known as: include_drift?

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

Returns:

  • (Boolean) 


11591
11592
11593
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11591

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) 


11597
11598
11599
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11597

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>) 


11602
11603
11604
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11602

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) 


11608
11609
11610
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11608

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) 


11613
11614
11615
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11613

def integrated_gradients_num_steps
  @integrated_gradients_num_steps
end

#is_test_column ⇒ String

Name of the column used to determine the rows corresponding to control and test. Applies to contribution analysis models. Corresponds to the JSON property isTestColumn

Returns:

  • (String) 


11619
11620
11621
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11619

def is_test_column
  @is_test_column
end

#item_column ⇒ String

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

Returns:

  • (String) 


11624
11625
11626
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11624

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) 


11630
11631
11632
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11630

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) 


11635
11636
11637
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11635

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) 


11640
11641
11642
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11640

def l1_reg_activation
  @l1_reg_activation
end

#l1_regularization ⇒ Float

L1 regularization coefficient. Corresponds to the JSON property l1Regularization

Returns:

  • (Float) 


11645
11646
11647
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11645

def l1_regularization
  @l1_regularization
end

#l2_regularization ⇒ Float

L2 regularization coefficient. Corresponds to the JSON property l2Regularization

Returns:

  • (Float) 


11650
11651
11652
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11650

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>) 


11656
11657
11658
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11656

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) 


11661
11662
11663
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11661

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) 


11666
11667
11668
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11666

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) 


11671
11672
11673
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11671

def loss_type
  @loss_type
end

#machine_type ⇒ String

The type of the machine used to deploy and serve the model. Corresponds to the JSON property machineType

Returns:

  • (String) 


11676
11677
11678
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11676

def machine_type
  @machine_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) 


11682
11683
11684
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11682

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) 


11687
11688
11689
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11687

def max_parallel_trials
  @max_parallel_trials
end

#max_replica_count ⇒ Fixnum

The maximum number of machine replicas that will be deployed on an endpoint. The default value is equal to min_replica_count. Corresponds to the JSON property maxReplicaCount

Returns:

  • (Fixnum) 


11693
11694
11695
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11693

def max_replica_count
  @max_replica_count
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) 


11700
11701
11702
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11700

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) 


11705
11706
11707
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11705

def max_tree_depth
  @max_tree_depth
end

#min_apriori_support ⇒ Float

The apriori support minimum. Applies to contribution analysis models. Corresponds to the JSON property minAprioriSupport

Returns:

  • (Float) 


11710
11711
11712
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11710

def min_apriori_support
  @min_apriori_support
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) 


11716
11717
11718
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11716

def min_relative_progress
  @min_relative_progress
end

#min_replica_count ⇒ Fixnum

The minimum number of machine replicas that will be always deployed on an endpoint. This value must be greater than or equal to 1. The default value is 1. Corresponds to the JSON property minReplicaCount

Returns:

  • (Fixnum) 


11723
11724
11725
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11723

def min_replica_count
  @min_replica_count
end

#min_split_loss ⇒ Float

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

Returns:

  • (Float) 


11728
11729
11730
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11728

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) 


11740
11741
11742
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11740

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) 


11745
11746
11747
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11745

def min_tree_child_weight
  @min_tree_child_weight
end

#model_garden_model_name ⇒ String

The name of a Vertex model garden publisher model. Format is publishers/ publisher/models/model@optional_version_id`. Corresponds to the JSON propertymodelGardenModelName`

Returns:

  • (String) 


11751
11752
11753
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11751

def model_garden_model_name
  @model_garden_model_name
end

#model_registry ⇒ String

The model registry. Corresponds to the JSON property modelRegistry

Returns:

  • (String) 


11756
11757
11758
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11756

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) 


11762
11763
11764
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11762

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:



11767
11768
11769
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11767

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) 


11772
11773
11774
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11772

def num_clusters
  @num_clusters
end

#num_factors ⇒ Fixnum

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

Returns:

  • (Fixnum) 


11777
11778
11779
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11777

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) 


11783
11784
11785
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11783

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) 


11789
11790
11791
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11789

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) 


11794
11795
11796
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11794

def num_trials
  @num_trials
end

#optimization_strategy ⇒ String

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

Returns:

  • (String) 


11799
11800
11801
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11799

def optimization_strategy
  @optimization_strategy
end

#optimizer ⇒ String

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

Returns:

  • (String) 


11804
11805
11806
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11804

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) 


11810
11811
11812
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11810

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) 


11815
11816
11817
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11815

def pca_solver
  @pca_solver
end

#reservation_affinity_key ⇒ String

Corresponds to the label key of a reservation resource used by Vertex AI. To target a SPECIFIC_RESERVATION by name, use compute.googleapis.com/reservation- name as the key and specify the name of your reservation as its value. Corresponds to the JSON property reservationAffinityKey

Returns:

  • (String) 


11822
11823
11824
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11822

def reservation_affinity_key
  @reservation_affinity_key
end

#reservation_affinity_type ⇒ String

Specifies the reservation affinity type used to configure a Vertex AI resource. The default value is NO_RESERVATION. Corresponds to the JSON property reservationAffinityType

Returns:

  • (String) 


11828
11829
11830
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11828

def reservation_affinity_type
  @reservation_affinity_type
end

#reservation_affinity_values ⇒ Array<String>

Corresponds to the label values of a reservation resource used by Vertex AI. This must be the full resource name of the reservation or reservation block. Corresponds to the JSON property reservationAffinityValues

Returns:

  • (Array<String>) 


11834
11835
11836
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11834

def reservation_affinity_values
  @reservation_affinity_values
end

#sampled_shapley_num_paths ⇒ Fixnum

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

Returns:

  • (Fixnum) 


11839
11840
11841
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11839

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) 


11845
11846
11847
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11845

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) 


11851
11852
11853
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11851

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) 


11858
11859
11860
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11858

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) 


11864
11865
11866
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11864

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) 


11869
11870
11871
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11869

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) 


11874
11875
11876
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11874

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>) 


11879
11880
11881
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11879

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) 


11889
11890
11891
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11889

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) 


11894
11895
11896
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11894

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) 


11899
11900
11901
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11899

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) 


11908
11909
11910
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11908

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) 


11913
11914
11915
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11913

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>) 


11919
11920
11921
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11919

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) 


11924
11925
11926
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11924

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) 


11929
11930
11931
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11929

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) 


11935
11936
11937
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11935

def xgboost_version
  @xgboost_version
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



11942
11943
11944
11945
11946
11947
11948
11949
11950
11951
11952
11953
11954
11955
11956
11957
11958
11959
11960
11961
11962
11963
11964
11965
11966
11967
11968
11969
11970
11971
11972
11973
11974
11975
11976
11977
11978
11979
11980
11981
11982
11983
11984
11985
11986
11987
11988
11989
11990
11991
11992
11993
11994
11995
11996
11997
11998
11999
12000
12001
12002
12003
12004
12005
12006
12007
12008
12009
12010
12011
12012
12013
12014
12015
12016
12017
12018
12019
12020
12021
12022
12023
12024
12025
12026
12027
12028
12029
12030
12031
12032
12033
12034
12035
12036
12037
12038
12039
12040
12041
12042
12043
12044
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11942

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)
  @contribution_metric = args[:contribution_metric] if args.key?(:contribution_metric)
  @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)
  @dimension_id_columns = args[:dimension_id_columns] if args.key?(:dimension_id_columns)
  @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)
  @endpoint_idle_ttl = args[:endpoint_idle_ttl] if args.key?(:endpoint_idle_ttl)
  @feedback_type = args[:feedback_type] if args.key?(:feedback_type)
  @fit_intercept = args[:fit_intercept] if args.key?(:fit_intercept)
  @forecast_limit_lower_bound = args[:forecast_limit_lower_bound] if args.key?(:forecast_limit_lower_bound)
  @forecast_limit_upper_bound = args[:forecast_limit_upper_bound] if args.key?(:forecast_limit_upper_bound)
  @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)
  @hugging_face_model_id = args[:hugging_face_model_id] if args.key?(:hugging_face_model_id)
  @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)
  @is_test_column = args[:is_test_column] if args.key?(:is_test_column)
  @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)
  @machine_type = args[:machine_type] if args.key?(:machine_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_replica_count = args[:max_replica_count] if args.key?(:max_replica_count)
  @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_apriori_support = args[:min_apriori_support] if args.key?(:min_apriori_support)
  @min_relative_progress = args[:min_relative_progress] if args.key?(:min_relative_progress)
  @min_replica_count = args[:min_replica_count] if args.key?(:min_replica_count)
  @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_garden_model_name = args[:model_garden_model_name] if args.key?(:model_garden_model_name)
  @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)
  @reservation_affinity_key = args[:reservation_affinity_key] if args.key?(:reservation_affinity_key)
  @reservation_affinity_type = args[:reservation_affinity_type] if args.key?(:reservation_affinity_type)
  @reservation_affinity_values = args[:reservation_affinity_values] if args.key?(:reservation_affinity_values)
  @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