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.



11147
11148
11149
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11147

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

Instance Attribute Details

#activation_fnString

Activation function of the neural nets. Corresponds to the JSON property activationFn

Returns:

  • (String)


10633
10634
10635
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10633

def activation_fn
  @activation_fn
end

#adjust_step_changesBoolean 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)


10638
10639
10640
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10638

def adjust_step_changes
  @adjust_step_changes
end

#approx_global_feature_contribBoolean 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)


10645
10646
10647
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10645

def approx_global_feature_contrib
  @approx_global_feature_contrib
end

#auto_arimaBoolean Also known as: auto_arima?

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

Returns:

  • (Boolean)


10651
10652
10653
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10651

def auto_arima
  @auto_arima
end

#auto_arima_max_orderFixnum

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

Returns:

  • (Fixnum)


10657
10658
10659
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10657

def auto_arima_max_order
  @auto_arima_max_order
end

#auto_arima_min_orderFixnum

The min value of the sum of non-seasonal p and q. Corresponds to the JSON property autoArimaMinOrder

Returns:

  • (Fixnum)


10662
10663
10664
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10662

def auto_arima_min_order
  @auto_arima_min_order
end

#auto_class_weightsBoolean 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)


10668
10669
10670
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10668

def auto_class_weights
  @auto_class_weights
end

#batch_sizeFixnum

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

Returns:

  • (Fixnum)


10674
10675
10676
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10674

def batch_size
  @batch_size
end

#booster_typeString

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

Returns:

  • (String)


10679
10680
10681
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10679

def booster_type
  @booster_type
end

#budget_hoursFloat

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

Returns:

  • (Float)


10684
10685
10686
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10684

def budget_hours
  @budget_hours
end

#calculate_p_valuesBoolean 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)


10690
10691
10692
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10690

def calculate_p_values
  @calculate_p_values
end

#category_encoding_methodString

Categorical feature encoding method. Corresponds to the JSON property categoryEncodingMethod

Returns:

  • (String)


10696
10697
10698
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10696

def category_encoding_method
  @category_encoding_method
end

#clean_spikes_and_dipsBoolean 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)


10701
10702
10703
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10701

def clean_spikes_and_dips
  @clean_spikes_and_dips
end

#color_spaceString

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)


10708
10709
10710
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10708

def color_space
  @color_space
end

#colsample_bylevelFloat

Subsample ratio of columns for each level for boosted tree models. Corresponds to the JSON property colsampleBylevel

Returns:

  • (Float)


10713
10714
10715
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10713

def colsample_bylevel
  @colsample_bylevel
end

#colsample_bynodeFloat

Subsample ratio of columns for each node(split) for boosted tree models. Corresponds to the JSON property colsampleBynode

Returns:

  • (Float)


10718
10719
10720
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10718

def colsample_bynode
  @colsample_bynode
end

#colsample_bytreeFloat

Subsample ratio of columns when constructing each tree for boosted tree models. Corresponds to the JSON property colsampleBytree

Returns:

  • (Float)


10723
10724
10725
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10723

def colsample_bytree
  @colsample_bytree
end

#contribution_metricString

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)


10731
10732
10733
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10731

def contribution_metric
  @contribution_metric
end

#dart_normalize_typeString

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

Returns:

  • (String)


10736
10737
10738
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10736

def dart_normalize_type
  @dart_normalize_type
end

#data_frequencyString

The data frequency of a time series. Corresponds to the JSON property dataFrequency

Returns:

  • (String)


10741
10742
10743
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10741

def data_frequency
  @data_frequency
end

#data_split_columnString

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)


10753
10754
10755
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10753

def data_split_column
  @data_split_column
end

#data_split_eval_fractionFloat

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)


10760
10761
10762
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10760

def data_split_eval_fraction
  @data_split_eval_fraction
end

#data_split_methodString

The data split type for training and evaluation, e.g. RANDOM. Corresponds to the JSON property dataSplitMethod

Returns:

  • (String)


10765
10766
10767
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10765

def data_split_method
  @data_split_method
end

#decompose_time_seriesBoolean Also known as: decompose_time_series?

If true, perform decompose time series and save the results. Corresponds to the JSON property decomposeTimeSeries

Returns:

  • (Boolean)


10770
10771
10772
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10770

def decompose_time_series
  @decompose_time_series
end

#dimension_id_columnsArray<String>

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

Returns:

  • (Array<String>)


10777
10778
10779
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10777

def dimension_id_columns
  @dimension_id_columns
end

#distance_typeString

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

Returns:

  • (String)


10782
10783
10784
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10782

def distance_type
  @distance_type
end

#dropoutFloat

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

Returns:

  • (Float)


10787
10788
10789
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10787

def dropout
  @dropout
end

#early_stopBoolean 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)


10794
10795
10796
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10794

def early_stop
  @early_stop
end

#enable_global_explainBoolean Also known as: enable_global_explain?

If true, enable global explanation during training. Corresponds to the JSON property enableGlobalExplain

Returns:

  • (Boolean)


10800
10801
10802
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10800

def enable_global_explain
  @enable_global_explain
end

#feedback_typeString

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

Returns:

  • (String)


10806
10807
10808
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10806

def feedback_type
  @feedback_type
end

#fit_interceptBoolean Also known as: fit_intercept?

Whether the model should include intercept during model training. Corresponds to the JSON property fitIntercept

Returns:

  • (Boolean)


10811
10812
10813
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10811

def fit_intercept
  @fit_intercept
end

#hidden_unitsArray<Fixnum>

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

Returns:

  • (Array<Fixnum>)


10817
10818
10819
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10817

def hidden_units
  @hidden_units
end

#holiday_regionString

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)


10824
10825
10826
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10824

def holiday_region
  @holiday_region
end

#holiday_regionsArray<String>

A list of geographical regions that are used for time series modeling. Corresponds to the JSON property holidayRegions

Returns:

  • (Array<String>)


10829
10830
10831
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10829

def holiday_regions
  @holiday_regions
end

#horizonFixnum

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

Returns:

  • (Fixnum)


10834
10835
10836
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10834

def horizon
  @horizon
end

#hparam_tuning_objectivesArray<String>

The target evaluation metrics to optimize the hyperparameters for. Corresponds to the JSON property hparamTuningObjectives

Returns:

  • (Array<String>)


10839
10840
10841
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10839

def hparam_tuning_objectives
  @hparam_tuning_objectives
end

#include_driftBoolean Also known as: include_drift?

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

Returns:

  • (Boolean)


10844
10845
10846
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10844

def include_drift
  @include_drift
end

#initial_learn_rateFloat

Specifies the initial learning rate for the line search learn rate strategy. Corresponds to the JSON property initialLearnRate

Returns:

  • (Float)


10850
10851
10852
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10850

def initial_learn_rate
  @initial_learn_rate
end

#input_label_columnsArray<String>

Name of input label columns in training data. Corresponds to the JSON property inputLabelColumns

Returns:

  • (Array<String>)


10855
10856
10857
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10855

def input_label_columns
  @input_label_columns
end

#instance_weight_columnString

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)


10861
10862
10863
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10861

def instance_weight_column
  @instance_weight_column
end

#integrated_gradients_num_stepsFixnum

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

Returns:

  • (Fixnum)


10866
10867
10868
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10866

def integrated_gradients_num_steps
  @integrated_gradients_num_steps
end

#is_test_columnString

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)


10872
10873
10874
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10872

def is_test_column
  @is_test_column
end

#item_columnString

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

Returns:

  • (String)


10877
10878
10879
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10877

def item_column
  @item_column
end

#kmeans_initialization_columnString

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)


10883
10884
10885
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10883

def kmeans_initialization_column
  @kmeans_initialization_column
end

#kmeans_initialization_methodString

The method used to initialize the centroids for kmeans algorithm. Corresponds to the JSON property kmeansInitializationMethod

Returns:

  • (String)


10888
10889
10890
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10888

def kmeans_initialization_method
  @kmeans_initialization_method
end

#l1_reg_activationFloat

L1 regularization coefficient to activations. Corresponds to the JSON property l1RegActivation

Returns:

  • (Float)


10893
10894
10895
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10893

def l1_reg_activation
  @l1_reg_activation
end

#l1_regularizationFloat

L1 regularization coefficient. Corresponds to the JSON property l1Regularization

Returns:

  • (Float)


10898
10899
10900
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10898

def l1_regularization
  @l1_regularization
end

#l2_regularizationFloat

L2 regularization coefficient. Corresponds to the JSON property l2Regularization

Returns:

  • (Float)


10903
10904
10905
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10903

def l2_regularization
  @l2_regularization
end

#label_class_weightsHash<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>)


10909
10910
10911
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10909

def label_class_weights
  @label_class_weights
end

#learn_rateFloat

Learning rate in training. Used only for iterative training algorithms. Corresponds to the JSON property learnRate

Returns:

  • (Float)


10914
10915
10916
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10914

def learn_rate
  @learn_rate
end

#learn_rate_strategyString

The strategy to determine learn rate for the current iteration. Corresponds to the JSON property learnRateStrategy

Returns:

  • (String)


10919
10920
10921
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10919

def learn_rate_strategy
  @learn_rate_strategy
end

#loss_typeString

Type of loss function used during training run. Corresponds to the JSON property lossType

Returns:

  • (String)


10924
10925
10926
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10924

def loss_type
  @loss_type
end

#max_iterationsFixnum

The maximum number of iterations in training. Used only for iterative training algorithms. Corresponds to the JSON property maxIterations

Returns:

  • (Fixnum)


10930
10931
10932
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10930

def max_iterations
  @max_iterations
end

#max_parallel_trialsFixnum

Maximum number of trials to run in parallel. Corresponds to the JSON property maxParallelTrials

Returns:

  • (Fixnum)


10935
10936
10937
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10935

def max_parallel_trials
  @max_parallel_trials
end

#max_time_series_lengthFixnum

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)


10942
10943
10944
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10942

def max_time_series_length
  @max_time_series_length
end

#max_tree_depthFixnum

Maximum depth of a tree for boosted tree models. Corresponds to the JSON property maxTreeDepth

Returns:

  • (Fixnum)


10947
10948
10949
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10947

def max_tree_depth
  @max_tree_depth
end

#min_apriori_supportFloat

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

Returns:

  • (Float)


10952
10953
10954
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10952

def min_apriori_support
  @min_apriori_support
end

#min_relative_progressFloat

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)


10958
10959
10960
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10958

def min_relative_progress
  @min_relative_progress
end

#min_split_lossFloat

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

Returns:

  • (Float)


10963
10964
10965
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10963

def min_split_loss
  @min_split_loss
end

#min_time_series_lengthFixnum

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)


10975
10976
10977
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10975

def min_time_series_length
  @min_time_series_length
end

#min_tree_child_weightFixnum

Minimum sum of instance weight needed in a child for boosted tree models. Corresponds to the JSON property minTreeChildWeight

Returns:

  • (Fixnum)


10980
10981
10982
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10980

def min_tree_child_weight
  @min_tree_child_weight
end

#model_registryString

The model registry. Corresponds to the JSON property modelRegistry

Returns:

  • (String)


10985
10986
10987
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10985

def model_registry
  @model_registry
end

#model_uriString

Google Cloud Storage URI from which the model was imported. Only applicable for imported models. Corresponds to the JSON property modelUri

Returns:

  • (String)


10991
10992
10993
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10991

def model_uri
  @model_uri
end

#non_seasonal_orderGoogle::Apis::BigqueryV2::ArimaOrder

Arima order, can be used for both non-seasonal and seasonal parts. Corresponds to the JSON property nonSeasonalOrder



10996
10997
10998
# File 'lib/google/apis/bigquery_v2/classes.rb', line 10996

def non_seasonal_order
  @non_seasonal_order
end

#num_clustersFixnum

Number of clusters for clustering models. Corresponds to the JSON property numClusters

Returns:

  • (Fixnum)


11001
11002
11003
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11001

def num_clusters
  @num_clusters
end

#num_factorsFixnum

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

Returns:

  • (Fixnum)


11006
11007
11008
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11006

def num_factors
  @num_factors
end

#num_parallel_treeFixnum

Number of parallel trees constructed during each iteration for boosted tree models. Corresponds to the JSON property numParallelTree

Returns:

  • (Fixnum)


11012
11013
11014
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11012

def num_parallel_tree
  @num_parallel_tree
end

#num_principal_componentsFixnum

Number of principal components to keep in the PCA model. Must be <= the number of features. Corresponds to the JSON property numPrincipalComponents

Returns:

  • (Fixnum)


11018
11019
11020
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11018

def num_principal_components
  @num_principal_components
end

#num_trialsFixnum

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

Returns:

  • (Fixnum)


11023
11024
11025
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11023

def num_trials
  @num_trials
end

#optimization_strategyString

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

Returns:

  • (String)


11028
11029
11030
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11028

def optimization_strategy
  @optimization_strategy
end

#optimizerString

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

Returns:

  • (String)


11033
11034
11035
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11033

def optimizer
  @optimizer
end

#pca_explained_variance_ratioFloat

The minimum ratio of cumulative explained variance that needs to be given by the PCA model. Corresponds to the JSON property pcaExplainedVarianceRatio

Returns:

  • (Float)


11039
11040
11041
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11039

def pca_explained_variance_ratio
  @pca_explained_variance_ratio
end

#pca_solverString

The solver for PCA. Corresponds to the JSON property pcaSolver

Returns:

  • (String)


11044
11045
11046
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11044

def pca_solver
  @pca_solver
end

#sampled_shapley_num_pathsFixnum

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

Returns:

  • (Fixnum)


11049
11050
11051
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11049

def sampled_shapley_num_paths
  @sampled_shapley_num_paths
end

#scale_featuresBoolean 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)


11055
11056
11057
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11055

def scale_features
  @scale_features
end

#standardize_featuresBoolean Also known as: standardize_features?

Whether to standardize numerical features. Default to true. Corresponds to the JSON property standardizeFeatures

Returns:

  • (Boolean)


11061
11062
11063
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11061

def standardize_features
  @standardize_features
end

#subsampleFloat

Subsample fraction of the training data to grow tree to prevent overfitting for boosted tree models. Corresponds to the JSON property subsample

Returns:

  • (Float)


11068
11069
11070
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11068

def subsample
  @subsample
end

#tf_versionString

Based on the selected TF version, the corresponding docker image is used to train external models. Corresponds to the JSON property tfVersion

Returns:

  • (String)


11074
11075
11076
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11074

def tf_version
  @tf_version
end

#time_series_data_columnString

Column to be designated as time series data for ARIMA model. Corresponds to the JSON property timeSeriesDataColumn

Returns:

  • (String)


11079
11080
11081
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11079

def time_series_data_column
  @time_series_data_column
end

#time_series_id_columnString

The time series id column that was used during ARIMA model training. Corresponds to the JSON property timeSeriesIdColumn

Returns:

  • (String)


11084
11085
11086
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11084

def time_series_id_column
  @time_series_id_column
end

#time_series_id_columnsArray<String>

The time series id columns that were used during ARIMA model training. Corresponds to the JSON property timeSeriesIdColumns

Returns:

  • (Array<String>)


11089
11090
11091
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11089

def time_series_id_columns
  @time_series_id_columns
end

#time_series_length_fractionFloat

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)


11099
11100
11101
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11099

def time_series_length_fraction
  @time_series_length_fraction
end

#time_series_timestamp_columnString

Column to be designated as time series timestamp for ARIMA model. Corresponds to the JSON property timeSeriesTimestampColumn

Returns:

  • (String)


11104
11105
11106
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11104

def time_series_timestamp_column
  @time_series_timestamp_column
end

#tree_methodString

Tree construction algorithm for boosted tree models. Corresponds to the JSON property treeMethod

Returns:

  • (String)


11109
11110
11111
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11109

def tree_method
  @tree_method
end

#trend_smoothing_window_sizeFixnum

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)


11118
11119
11120
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11118

def trend_smoothing_window_size
  @trend_smoothing_window_size
end

#user_columnString

User column specified for matrix factorization models. Corresponds to the JSON property userColumn

Returns:

  • (String)


11123
11124
11125
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11123

def user_column
  @user_column
end

#vertex_ai_model_version_aliasesArray<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>)


11129
11130
11131
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11129

def vertex_ai_model_version_aliases
  @vertex_ai_model_version_aliases
end

#wals_alphaFloat

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

Returns:

  • (Float)


11134
11135
11136
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11134

def wals_alpha
  @wals_alpha
end

#warm_startBoolean Also known as: warm_start?

Whether to train a model from the last checkpoint. Corresponds to the JSON property warmStart

Returns:

  • (Boolean)


11139
11140
11141
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11139

def warm_start
  @warm_start
end

#xgboost_versionString

User-selected XGBoost versions for training of XGBoost models. Corresponds to the JSON property xgboostVersion

Returns:

  • (String)


11145
11146
11147
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11145

def xgboost_version
  @xgboost_version
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



11152
11153
11154
11155
11156
11157
11158
11159
11160
11161
11162
11163
11164
11165
11166
11167
11168
11169
11170
11171
11172
11173
11174
11175
11176
11177
11178
11179
11180
11181
11182
11183
11184
11185
11186
11187
11188
11189
11190
11191
11192
11193
11194
11195
11196
11197
11198
11199
11200
11201
11202
11203
11204
11205
11206
11207
11208
11209
11210
11211
11212
11213
11214
11215
11216
11217
11218
11219
11220
11221
11222
11223
11224
11225
11226
11227
11228
11229
11230
11231
11232
11233
11234
11235
11236
11237
11238
11239
11240
11241
11242
11243
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11152

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