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.



9173
9174
9175
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9173

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) 


8684
8685
8686
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8684

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) 


8689
8690
8691
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8689

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) 


8696
8697
8698
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8696

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) 


8702
8703
8704
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8702

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) 


8708
8709
8710
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8708

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) 


8713
8714
8715
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8713

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) 


8719
8720
8721
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8719

def auto_class_weights
  @auto_class_weights
end

#batch_size ⇒ Fixnum

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

Returns:

  • (Fixnum) 


8725
8726
8727
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8725

def batch_size
  @batch_size
end

#booster_type ⇒ String

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

Returns:

  • (String) 


8730
8731
8732
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8730

def booster_type
  @booster_type
end

#budget_hours ⇒ Float

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

Returns:

  • (Float) 


8735
8736
8737
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8735

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) 


8741
8742
8743
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8741

def calculate_p_values
  @calculate_p_values
end

#category_encoding_method ⇒ String

Categorical feature encoding method. Corresponds to the JSON property categoryEncodingMethod

Returns:

  • (String) 


8747
8748
8749
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8747

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) 


8752
8753
8754
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8752

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) 


8759
8760
8761
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8759

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) 


8764
8765
8766
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8764

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) 


8769
8770
8771
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8769

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) 


8774
8775
8776
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8774

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) 


8779
8780
8781
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8779

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) 


8784
8785
8786
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8784

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) 


8796
8797
8798
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8796

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) 


8803
8804
8805
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8803

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) 


8808
8809
8810
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8808

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) 


8813
8814
8815
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8813

def decompose_time_series
  @decompose_time_series
end

#distance_type ⇒ String

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

Returns:

  • (String) 


8819
8820
8821
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8819

def distance_type
  @distance_type
end

#dropout ⇒ Float

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

Returns:

  • (Float) 


8824
8825
8826
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8824

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) 


8831
8832
8833
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8831

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) 


8837
8838
8839
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8837

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) 


8843
8844
8845
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8843

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) 


8848
8849
8850
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8848

def fit_intercept
  @fit_intercept
end

#hidden_units ⇒ Array<Fixnum>

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

Returns:

  • (Array<Fixnum>) 


8854
8855
8856
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8854

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) 


8861
8862
8863
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8861

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>) 


8866
8867
8868
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8866

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) 


8871
8872
8873
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8871

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>) 


8876
8877
8878
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8876

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) 


8881
8882
8883
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8881

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) 


8887
8888
8889
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8887

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>) 


8892
8893
8894
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8892

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) 


8898
8899
8900
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8898

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) 


8903
8904
8905
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8903

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) 


8908
8909
8910
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8908

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) 


8914
8915
8916
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8914

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) 


8919
8920
8921
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8919

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) 


8924
8925
8926
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8924

def l1_reg_activation
  @l1_reg_activation
end

#l1_regularization ⇒ Float

L1 regularization coefficient. Corresponds to the JSON property l1Regularization

Returns:

  • (Float) 


8929
8930
8931
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8929

def l1_regularization
  @l1_regularization
end

#l2_regularization ⇒ Float

L2 regularization coefficient. Corresponds to the JSON property l2Regularization

Returns:

  • (Float) 


8934
8935
8936
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8934

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>) 


8940
8941
8942
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8940

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) 


8945
8946
8947
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8945

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) 


8950
8951
8952
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8950

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) 


8955
8956
8957
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8955

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) 


8961
8962
8963
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8961

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) 


8966
8967
8968
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8966

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) 


8973
8974
8975
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8973

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) 


8978
8979
8980
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8978

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) 


8984
8985
8986
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8984

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) 


8989
8990
8991
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 8989

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) 


9001
9002
9003
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9001

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) 


9006
9007
9008
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9006

def min_tree_child_weight
  @min_tree_child_weight
end

#model_registry ⇒ String

The model registry. Corresponds to the JSON property modelRegistry

Returns:

  • (String) 


9011
9012
9013
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9011

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) 


9017
9018
9019
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9017

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:



9022
9023
9024
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9022

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) 


9027
9028
9029
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9027

def num_clusters
  @num_clusters
end

#num_factors ⇒ Fixnum

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

Returns:

  • (Fixnum) 


9032
9033
9034
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9032

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) 


9038
9039
9040
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9038

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) 


9044
9045
9046
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9044

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) 


9049
9050
9051
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9049

def num_trials
  @num_trials
end

#optimization_strategy ⇒ String

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

Returns:

  • (String) 


9054
9055
9056
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9054

def optimization_strategy
  @optimization_strategy
end

#optimizer ⇒ String

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

Returns:

  • (String) 


9059
9060
9061
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9059

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) 


9065
9066
9067
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9065

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) 


9070
9071
9072
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9070

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) 


9075
9076
9077
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9075

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) 


9081
9082
9083
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9081

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) 


9087
9088
9089
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9087

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) 


9094
9095
9096
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9094

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) 


9100
9101
9102
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9100

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) 


9105
9106
9107
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9105

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) 


9110
9111
9112
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9110

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>) 


9115
9116
9117
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9115

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) 


9125
9126
9127
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9125

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) 


9130
9131
9132
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9130

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) 


9135
9136
9137
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9135

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) 


9144
9145
9146
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9144

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) 


9149
9150
9151
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9149

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>) 


9155
9156
9157
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9155

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) 


9160
9161
9162
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9160

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) 


9165
9166
9167
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9165

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) 


9171
9172
9173
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9171

def xgboost_version
  @xgboost_version
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



9178
9179
9180
9181
9182
9183
9184
9185
9186
9187
9188
9189
9190
9191
9192
9193
9194
9195
9196
9197
9198
9199
9200
9201
9202
9203
9204
9205
9206
9207
9208
9209
9210
9211
9212
9213
9214
9215
9216
9217
9218
9219
9220
9221
9222
9223
9224
9225
9226
9227
9228
9229
9230
9231
9232
9233
9234
9235
9236
9237
9238
9239
9240
9241
9242
9243
9244
9245
9246
9247
9248
9249
9250
9251
9252
9253
9254
9255
9256
9257
9258
9259
9260
9261
9262
9263
9264
9265
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 9178

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