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.



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# File 'lib/google/apis/bigquery_v2/classes.rb', line 11127

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) 


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

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) 


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

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) 


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

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10631

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10637

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10642

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10648

def auto_class_weights
  @auto_class_weights
end

#batch_size ⇒ Fixnum

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

Returns:

  • (Fixnum) 


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

def batch_size
  @batch_size
end

#booster_type ⇒ String

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

Returns:

  • (String) 


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

def booster_type
  @booster_type
end

#budget_hours ⇒ Float

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

Returns:

  • (Float) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10664

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) 


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

def calculate_p_values
  @calculate_p_values
end

#category_encoding_method ⇒ String

Categorical feature encoding method. Corresponds to the JSON property categoryEncodingMethod

Returns:

  • (String) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10676

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10681

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) 


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

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) 


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

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10698

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10703

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) 


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

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) 


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

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) 


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

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) 


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

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10740

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10745

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) 


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

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>) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10757

def dimension_id_columns
  @dimension_id_columns
end

#distance_type ⇒ String

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

Returns:

  • (String) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10762

def distance_type
  @distance_type
end

#dropout ⇒ Float

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

Returns:

  • (Float) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10767

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) 


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

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) 


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

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) 


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

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) 


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

def fit_intercept
  @fit_intercept
end

#hidden_units ⇒ Array<Fixnum>

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

Returns:

  • (Array<Fixnum>) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10797

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) 


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

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>) 


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

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) 


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

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>) 


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

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10824

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) 


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

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>) 


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

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) 


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

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10846

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10852

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10857

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10863

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10868

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10873

def l1_reg_activation
  @l1_reg_activation
end

#l1_regularization ⇒ Float

L1 regularization coefficient. Corresponds to the JSON property l1Regularization

Returns:

  • (Float) 


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

def l1_regularization
  @l1_regularization
end

#l2_regularization ⇒ Float

L2 regularization coefficient. Corresponds to the JSON property l2Regularization

Returns:

  • (Float) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10883

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>) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10889

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10894

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10899

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10904

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10910

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10915

def max_parallel_trials
  @max_parallel_trials
end

#max_time_series_length ⇒ Fixnum

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

Returns:

  • (Fixnum) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10922

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10927

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10932

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10938

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10943

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10955

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10960

def min_tree_child_weight
  @min_tree_child_weight
end

#model_registry ⇒ String

The model registry. Corresponds to the JSON property modelRegistry

Returns:

  • (String) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10965

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10971

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:



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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10976

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10981

def num_clusters
  @num_clusters
end

#num_factors ⇒ Fixnum

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

Returns:

  • (Fixnum) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10986

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10992

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 10998

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 11003

def num_trials
  @num_trials
end

#optimization_strategy ⇒ String

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

Returns:

  • (String) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 11008

def optimization_strategy
  @optimization_strategy
end

#optimizer ⇒ String

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

Returns:

  • (String) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 11013

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 11019

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 11024

def pca_solver
  @pca_solver
end

#sampled_shapley_num_paths ⇒ Fixnum

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

Returns:

  • (Fixnum) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 11029

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 11035

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 11041

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 11048

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 11054

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 11059

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 11064

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>) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 11069

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 11079

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 11084

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 11089

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 11098

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 11103

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>) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 11109

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 11114

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 11119

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) 


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# File 'lib/google/apis/bigquery_v2/classes.rb', line 11125

def xgboost_version
  @xgboost_version
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



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# File 'lib/google/apis/bigquery_v2/classes.rb', line 11132

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)
  @feedback_type = args[:feedback_type] if args.key?(:feedback_type)
  @fit_intercept = args[:fit_intercept] if args.key?(:fit_intercept)
  @hidden_units = args[:hidden_units] if args.key?(:hidden_units)
  @holiday_region = args[:holiday_region] if args.key?(:holiday_region)
  @holiday_regions = args[:holiday_regions] if args.key?(:holiday_regions)
  @horizon = args[:horizon] if args.key?(:horizon)
  @hparam_tuning_objectives = args[:hparam_tuning_objectives] if args.key?(:hparam_tuning_objectives)
  @include_drift = args[:include_drift] if args.key?(:include_drift)
  @initial_learn_rate = args[:initial_learn_rate] if args.key?(:initial_learn_rate)
  @input_label_columns = args[:input_label_columns] if args.key?(:input_label_columns)
  @instance_weight_column = args[:instance_weight_column] if args.key?(:instance_weight_column)
  @integrated_gradients_num_steps = args[:integrated_gradients_num_steps] if args.key?(:integrated_gradients_num_steps)
  @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)
  @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_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_split_loss = args[:min_split_loss] if args.key?(:min_split_loss)
  @min_time_series_length = args[:min_time_series_length] if args.key?(:min_time_series_length)
  @min_tree_child_weight = args[:min_tree_child_weight] if args.key?(:min_tree_child_weight)
  @model_registry = args[:model_registry] if args.key?(:model_registry)
  @model_uri = args[:model_uri] if args.key?(:model_uri)
  @non_seasonal_order = args[:non_seasonal_order] if args.key?(:non_seasonal_order)
  @num_clusters = args[:num_clusters] if args.key?(:num_clusters)
  @num_factors = args[:num_factors] if args.key?(:num_factors)
  @num_parallel_tree = args[:num_parallel_tree] if args.key?(:num_parallel_tree)
  @num_principal_components = args[:num_principal_components] if args.key?(:num_principal_components)
  @num_trials = args[:num_trials] if args.key?(:num_trials)
  @optimization_strategy = args[:optimization_strategy] if args.key?(:optimization_strategy)
  @optimizer = args[:optimizer] if args.key?(:optimizer)
  @pca_explained_variance_ratio = args[:pca_explained_variance_ratio] if args.key?(:pca_explained_variance_ratio)
  @pca_solver = args[:pca_solver] if args.key?(:pca_solver)
  @sampled_shapley_num_paths = args[:sampled_shapley_num_paths] if args.key?(:sampled_shapley_num_paths)
  @scale_features = args[:scale_features] if args.key?(:scale_features)
  @standardize_features = args[:standardize_features] if args.key?(:standardize_features)
  @subsample = args[:subsample] if args.key?(:subsample)
  @tf_version = args[:tf_version] if args.key?(:tf_version)
  @time_series_data_column = args[:time_series_data_column] if args.key?(:time_series_data_column)
  @time_series_id_column = args[:time_series_id_column] if args.key?(:time_series_id_column)
  @time_series_id_columns = args[:time_series_id_columns] if args.key?(:time_series_id_columns)
  @time_series_length_fraction = args[:time_series_length_fraction] if args.key?(:time_series_length_fraction)
  @time_series_timestamp_column = args[:time_series_timestamp_column] if args.key?(:time_series_timestamp_column)
  @tree_method = args[:tree_method] if args.key?(:tree_method)
  @trend_smoothing_window_size = args[:trend_smoothing_window_size] if args.key?(:trend_smoothing_window_size)
  @user_column = args[:user_column] if args.key?(:user_column)
  @vertex_ai_model_version_aliases = args[:vertex_ai_model_version_aliases] if args.key?(:vertex_ai_model_version_aliases)
  @wals_alpha = args[:wals_alpha] if args.key?(:wals_alpha)
  @warm_start = args[:warm_start] if args.key?(:warm_start)
  @xgboost_version = args[:xgboost_version] if args.key?(:xgboost_version)
end