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.



8623
8624
8625
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8623

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

Instance Attribute Details

#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) 


8255
8256
8257
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8255

def adjust_step_changes
  @adjust_step_changes
end

#auto_arima ⇒ Boolean Also known as: auto_arima?

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

Returns:

  • (Boolean) 


8261
8262
8263
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8261

def auto_arima
  @auto_arima
end

#auto_arima_max_order ⇒ Fixnum

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

Returns:

  • (Fixnum) 


8267
8268
8269
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8267

def auto_arima_max_order
  @auto_arima_max_order
end

#batch_size ⇒ Fixnum

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

Returns:

  • (Fixnum) 


8272
8273
8274
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8272

def batch_size
  @batch_size
end

#booster_type ⇒ String

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

Returns:

  • (String) 


8277
8278
8279
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8277

def booster_type
  @booster_type
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) 


8283
8284
8285
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8283

def calculate_p_values
  @calculate_p_values
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) 


8289
8290
8291
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8289

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) 


8296
8297
8298
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8296

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) 


8301
8302
8303
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8301

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) 


8306
8307
8308
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8306

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) 


8311
8312
8313
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8311

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) 


8316
8317
8318
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8316

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) 


8321
8322
8323
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8321

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) 


8333
8334
8335
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8333

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) 


8340
8341
8342
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8340

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) 


8345
8346
8347
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8345

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) 


8350
8351
8352
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8350

def decompose_time_series
  @decompose_time_series
end

#distance_type ⇒ String

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

Returns:

  • (String) 


8356
8357
8358
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8356

def distance_type
  @distance_type
end

#dropout ⇒ Float

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

Returns:

  • (Float) 


8361
8362
8363
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8361

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) 


8368
8369
8370
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8368

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) 


8374
8375
8376
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8374

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) 


8380
8381
8382
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8380

def feedback_type
  @feedback_type
end

#hidden_units ⇒ Array<Fixnum>

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

Returns:

  • (Array<Fixnum>) 


8385
8386
8387
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8385

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) 


8392
8393
8394
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8392

def holiday_region
  @holiday_region
end

#horizon ⇒ Fixnum

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

Returns:

  • (Fixnum) 


8397
8398
8399
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8397

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>) 


8402
8403
8404
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8402

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) 


8407
8408
8409
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8407

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) 


8413
8414
8415
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8413

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>) 


8418
8419
8420
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8418

def input_label_columns
  @input_label_columns
end

#integrated_gradients_num_steps ⇒ Fixnum

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

Returns:

  • (Fixnum) 


8423
8424
8425
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8423

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) 


8428
8429
8430
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8428

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) 


8434
8435
8436
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8434

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) 


8439
8440
8441
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8439

def kmeans_initialization_method
  @kmeans_initialization_method
end

#l1_regularization ⇒ Float

L1 regularization coefficient. Corresponds to the JSON property l1Regularization

Returns:

  • (Float) 


8444
8445
8446
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8444

def l1_regularization
  @l1_regularization
end

#l2_regularization ⇒ Float

L2 regularization coefficient. Corresponds to the JSON property l2Regularization

Returns:

  • (Float) 


8449
8450
8451
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8449

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>) 


8455
8456
8457
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8455

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) 


8460
8461
8462
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8460

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) 


8465
8466
8467
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8465

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) 


8470
8471
8472
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8470

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) 


8476
8477
8478
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8476

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) 


8481
8482
8483
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8481

def max_parallel_trials
  @max_parallel_trials
end

#max_time_series_length ⇒ Fixnum

Get truncated length by last n points in time series. Use separately from time_series_length_fraction and min_time_series_length. Corresponds to the JSON property maxTimeSeriesLength

Returns:

  • (Fixnum) 


8487
8488
8489
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8487

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) 


8492
8493
8494
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8492

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) 


8498
8499
8500
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8498

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) 


8503
8504
8505
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8503

def min_split_loss
  @min_split_loss
end

#min_time_series_length ⇒ Fixnum

Set fast trend ARIMA_PLUS model minimum training length. Use in pair with time_series_length_fraction. Corresponds to the JSON property minTimeSeriesLength

Returns:

  • (Fixnum) 


8509
8510
8511
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8509

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) 


8514
8515
8516
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8514

def min_tree_child_weight
  @min_tree_child_weight
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) 


8520
8521
8522
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8520

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:



8525
8526
8527
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8525

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) 


8530
8531
8532
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8530

def num_clusters
  @num_clusters
end

#num_factors ⇒ Fixnum

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

Returns:

  • (Fixnum) 


8535
8536
8537
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8535

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) 


8541
8542
8543
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8541

def num_parallel_tree
  @num_parallel_tree
end

#num_trials ⇒ Fixnum

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

Returns:

  • (Fixnum) 


8546
8547
8548
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8546

def num_trials
  @num_trials
end

#optimization_strategy ⇒ String

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

Returns:

  • (String) 


8551
8552
8553
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8551

def optimization_strategy
  @optimization_strategy
end

#preserve_input_structs ⇒ Boolean Also known as: preserve_input_structs?

Whether to preserve the input structs in output feature names. Suppose there is a struct A with field b. When false (default), the output feature name is A_b. When true, the output feature name is A.b. Corresponds to the JSON property preserveInputStructs

Returns:

  • (Boolean) 


8558
8559
8560
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8558

def preserve_input_structs
  @preserve_input_structs
end

#sampled_shapley_num_paths ⇒ Fixnum

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

Returns:

  • (Fixnum) 


8564
8565
8566
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8564

def sampled_shapley_num_paths
  @sampled_shapley_num_paths
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) 


8570
8571
8572
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8570

def subsample
  @subsample
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) 


8575
8576
8577
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8575

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) 


8580
8581
8582
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8580

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>) 


8585
8586
8587
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8585

def time_series_id_columns
  @time_series_id_columns
end

#time_series_length_fraction ⇒ Float

Get truncated length by fraction in time series. Corresponds to the JSON property timeSeriesLengthFraction

Returns:

  • (Float) 


8590
8591
8592
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8590

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) 


8595
8596
8597
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8595

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) 


8600
8601
8602
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8600

def tree_method
  @tree_method
end

#trend_smoothing_window_size ⇒ Fixnum

The smoothing window size for the trend component of the time series. Corresponds to the JSON property trendSmoothingWindowSize

Returns:

  • (Fixnum) 


8605
8606
8607
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8605

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) 


8610
8611
8612
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8610

def user_column
  @user_column
end

#wals_alpha ⇒ Float

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

Returns:

  • (Float) 


8615
8616
8617
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8615

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) 


8620
8621
8622
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8620

def warm_start
  @warm_start
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



8628
8629
8630
8631
8632
8633
8634
8635
8636
8637
8638
8639
8640
8641
8642
8643
8644
8645
8646
8647
8648
8649
8650
8651
8652
8653
8654
8655
8656
8657
8658
8659
8660
8661
8662
8663
8664
8665
8666
8667
8668
8669
8670
8671
8672
8673
8674
8675
8676
8677
8678
8679
8680
8681
8682
8683
8684
8685
8686
8687
8688
8689
8690
8691
8692
8693
8694
8695
8696
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8628

def update!(**args)
  @adjust_step_changes = args[:adjust_step_changes] if args.key?(:adjust_step_changes)
  @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)
  @batch_size = args[:batch_size] if args.key?(:batch_size)
  @booster_type = args[:booster_type] if args.key?(:booster_type)
  @calculate_p_values = args[:calculate_p_values] if args.key?(:calculate_p_values)
  @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)
  @hidden_units = args[:hidden_units] if args.key?(:hidden_units)
  @holiday_region = args[:holiday_region] if args.key?(:holiday_region)
  @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)
  @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_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_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_trials = args[:num_trials] if args.key?(:num_trials)
  @optimization_strategy = args[:optimization_strategy] if args.key?(:optimization_strategy)
  @preserve_input_structs = args[:preserve_input_structs] if args.key?(:preserve_input_structs)
  @sampled_shapley_num_paths = args[:sampled_shapley_num_paths] if args.key?(:sampled_shapley_num_paths)
  @subsample = args[:subsample] if args.key?(:subsample)
  @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)
  @wals_alpha = args[:wals_alpha] if args.key?(:wals_alpha)
  @warm_start = args[:warm_start] if args.key?(:warm_start)
end