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.



12800
12801
12802
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12800

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) 


12219
12220
12221
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12219

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) 


12224
12225
12226
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12224

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) 


12231
12232
12233
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12231

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) 


12237
12238
12239
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12237

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) 


12243
12244
12245
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12243

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) 


12248
12249
12250
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12248

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) 


12254
12255
12256
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12254

def auto_class_weights
  @auto_class_weights
end

#batch_size ⇒ Fixnum

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

Returns:

  • (Fixnum) 


12260
12261
12262
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12260

def batch_size
  @batch_size
end

#booster_type ⇒ String

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

Returns:

  • (String) 


12265
12266
12267
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12265

def booster_type
  @booster_type
end

#budget_hours ⇒ Float

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

Returns:

  • (Float) 


12270
12271
12272
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12270

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) 


12276
12277
12278
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12276

def calculate_p_values
  @calculate_p_values
end

#category_encoding_method ⇒ String

Categorical feature encoding method. Corresponds to the JSON property categoryEncodingMethod

Returns:

  • (String) 


12282
12283
12284
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12282

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) 


12287
12288
12289
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12287

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) 


12294
12295
12296
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12294

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) 


12299
12300
12301
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12299

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) 


12304
12305
12306
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12304

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) 


12309
12310
12311
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12309

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) 


12317
12318
12319
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12317

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) 


12322
12323
12324
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12322

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) 


12327
12328
12329
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12327

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) 


12339
12340
12341
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12339

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) 


12346
12347
12348
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12346

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) 


12351
12352
12353
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12351

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) 


12356
12357
12358
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12356

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>) 


12363
12364
12365
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12363

def dimension_id_columns
  @dimension_id_columns
end

#distance_type ⇒ String

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

Returns:

  • (String) 


12368
12369
12370
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12368

def distance_type
  @distance_type
end

#dropout ⇒ Float

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

Returns:

  • (Float) 


12373
12374
12375
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12373

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) 


12380
12381
12382
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12380

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) 


12386
12387
12388
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12386

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) 


12393
12394
12395
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12393

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) 


12398
12399
12400
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12398

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) 


12403
12404
12405
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12403

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) 


12411
12412
12413
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12411

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) 


12417
12418
12419
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12417

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>) 


12422
12423
12424
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12422

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) 


12429
12430
12431
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12429

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>) 


12434
12435
12436
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12434

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) 


12439
12440
12441
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12439

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>) 


12444
12445
12446
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12444

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) 


12449
12450
12451
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12449

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) 


12454
12455
12456
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12454

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) 


12460
12461
12462
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12460

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>) 


12465
12466
12467
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12465

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) 


12471
12472
12473
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12471

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) 


12476
12477
12478
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12476

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) 


12482
12483
12484
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12482

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) 


12487
12488
12489
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12487

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) 


12493
12494
12495
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12493

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) 


12498
12499
12500
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12498

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) 


12503
12504
12505
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12503

def l1_reg_activation
  @l1_reg_activation
end

#l1_regularization ⇒ Float

L1 regularization coefficient. Corresponds to the JSON property l1Regularization

Returns:

  • (Float) 


12508
12509
12510
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12508

def l1_regularization
  @l1_regularization
end

#l2_regularization ⇒ Float

L2 regularization coefficient. Corresponds to the JSON property l2Regularization

Returns:

  • (Float) 


12513
12514
12515
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12513

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>) 


12519
12520
12521
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12519

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) 


12524
12525
12526
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12524

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) 


12529
12530
12531
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12529

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) 


12534
12535
12536
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12534

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) 


12539
12540
12541
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12539

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) 


12545
12546
12547
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12545

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) 


12550
12551
12552
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12550

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) 


12556
12557
12558
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12556

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) 


12563
12564
12565
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12563

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) 


12568
12569
12570
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12568

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) 


12573
12574
12575
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12573

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) 


12579
12580
12581
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12579

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) 


12586
12587
12588
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12586

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) 


12591
12592
12593
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12591

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) 


12603
12604
12605
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12603

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) 


12608
12609
12610
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12608

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) 


12614
12615
12616
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12614

def model_garden_model_name
  @model_garden_model_name
end

#model_registry ⇒ String

The model registry. Corresponds to the JSON property modelRegistry

Returns:

  • (String) 


12619
12620
12621
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12619

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) 


12625
12626
12627
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12625

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:



12630
12631
12632
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12630

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) 


12635
12636
12637
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12635

def num_clusters
  @num_clusters
end

#num_factors ⇒ Fixnum

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

Returns:

  • (Fixnum) 


12640
12641
12642
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12640

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) 


12646
12647
12648
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12646

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) 


12652
12653
12654
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12652

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) 


12657
12658
12659
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12657

def num_trials
  @num_trials
end

#optimization_strategy ⇒ String

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

Returns:

  • (String) 


12662
12663
12664
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12662

def optimization_strategy
  @optimization_strategy
end

#optimizer ⇒ String

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

Returns:

  • (String) 


12667
12668
12669
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12667

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) 


12673
12674
12675
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12673

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) 


12678
12679
12680
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12678

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) 


12685
12686
12687
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12685

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) 


12691
12692
12693
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12691

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>) 


12697
12698
12699
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12697

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) 


12702
12703
12704
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12702

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) 


12708
12709
12710
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12708

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) 


12714
12715
12716
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12714

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) 


12721
12722
12723
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12721

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) 


12727
12728
12729
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12727

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) 


12732
12733
12734
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12732

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) 


12737
12738
12739
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12737

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>) 


12742
12743
12744
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12742

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) 


12752
12753
12754
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12752

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) 


12757
12758
12759
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12757

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) 


12762
12763
12764
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12762

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) 


12771
12772
12773
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12771

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) 


12776
12777
12778
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12776

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>) 


12782
12783
12784
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12782

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) 


12787
12788
12789
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12787

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) 


12792
12793
12794
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12792

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) 


12798
12799
12800
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12798

def xgboost_version
  @xgboost_version
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



12805
12806
12807
12808
12809
12810
12811
12812
12813
12814
12815
12816
12817
12818
12819
12820
12821
12822
12823
12824
12825
12826
12827
12828
12829
12830
12831
12832
12833
12834
12835
12836
12837
12838
12839
12840
12841
12842
12843
12844
12845
12846
12847
12848
12849
12850
12851
12852
12853
12854
12855
12856
12857
12858
12859
12860
12861
12862
12863
12864
12865
12866
12867
12868
12869
12870
12871
12872
12873
12874
12875
12876
12877
12878
12879
12880
12881
12882
12883
12884
12885
12886
12887
12888
12889
12890
12891
12892
12893
12894
12895
12896
12897
12898
12899
12900
12901
12902
12903
12904
12905
12906
12907
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12805

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