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.



12311
12312
12313
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12311

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) 


11730
11731
11732
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11730

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) 


11735
11736
11737
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11735

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) 


11742
11743
11744
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11742

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) 


11748
11749
11750
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11748

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) 


11754
11755
11756
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11754

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) 


11759
11760
11761
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11759

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) 


11765
11766
11767
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11765

def auto_class_weights
  @auto_class_weights
end

#batch_size ⇒ Fixnum

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

Returns:

  • (Fixnum) 


11771
11772
11773
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11771

def batch_size
  @batch_size
end

#booster_type ⇒ String

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

Returns:

  • (String) 


11776
11777
11778
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11776

def booster_type
  @booster_type
end

#budget_hours ⇒ Float

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

Returns:

  • (Float) 


11781
11782
11783
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11781

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) 


11787
11788
11789
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11787

def calculate_p_values
  @calculate_p_values
end

#category_encoding_method ⇒ String

Categorical feature encoding method. Corresponds to the JSON property categoryEncodingMethod

Returns:

  • (String) 


11793
11794
11795
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11793

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) 


11798
11799
11800
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11798

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) 


11805
11806
11807
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11805

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) 


11810
11811
11812
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11810

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) 


11815
11816
11817
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11815

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) 


11820
11821
11822
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11820

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) 


11828
11829
11830
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11828

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) 


11833
11834
11835
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11833

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) 


11838
11839
11840
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11838

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) 


11850
11851
11852
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11850

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) 


11857
11858
11859
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11857

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) 


11862
11863
11864
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11862

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) 


11867
11868
11869
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11867

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>) 


11874
11875
11876
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11874

def dimension_id_columns
  @dimension_id_columns
end

#distance_type ⇒ String

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

Returns:

  • (String) 


11879
11880
11881
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11879

def distance_type
  @distance_type
end

#dropout ⇒ Float

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

Returns:

  • (Float) 


11884
11885
11886
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11884

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) 


11891
11892
11893
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11891

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) 


11897
11898
11899
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11897

def enable_global_explain
  @enable_global_explain
end

#endpoint_idle_ttl ⇒ String

The idle TTL of the endpoint before the resources get destroyed. The default value is 6.5 hours. Corresponds to the JSON property endpointIdleTtl

Returns:

  • (String) 


11904
11905
11906
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11904

def endpoint_idle_ttl
  @endpoint_idle_ttl
end

#feedback_type ⇒ String

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

Returns:

  • (String) 


11909
11910
11911
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11909

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) 


11914
11915
11916
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11914

def fit_intercept
  @fit_intercept
end

#forecast_limit_lower_bound ⇒ Float

The forecast limit lower bound that was used during ARIMA model training with limits. To see more details of the algorithm: https://otexts.com/fpp2/limits. html Corresponds to the JSON property forecastLimitLowerBound

Returns:

  • (Float) 


11922
11923
11924
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11922

def forecast_limit_lower_bound
  @forecast_limit_lower_bound
end

#forecast_limit_upper_bound ⇒ Float

The forecast limit upper bound that was used during ARIMA model training with limits. Corresponds to the JSON property forecastLimitUpperBound

Returns:

  • (Float) 


11928
11929
11930
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11928

def forecast_limit_upper_bound
  @forecast_limit_upper_bound
end

#hidden_units ⇒ Array<Fixnum>

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

Returns:

  • (Array<Fixnum>) 


11933
11934
11935
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11933

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) 


11940
11941
11942
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11940

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>) 


11945
11946
11947
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11945

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) 


11950
11951
11952
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11950

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>) 


11955
11956
11957
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11955

def hparam_tuning_objectives
  @hparam_tuning_objectives
end

#hugging_face_model_id ⇒ String

The id of a Hugging Face model. For example, google/gemma-2-2b-it. Corresponds to the JSON property huggingFaceModelId

Returns:

  • (String) 


11960
11961
11962
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11960

def hugging_face_model_id
  @hugging_face_model_id
end

#include_drift ⇒ Boolean Also known as: include_drift?

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

Returns:

  • (Boolean) 


11965
11966
11967
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11965

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) 


11971
11972
11973
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11971

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>) 


11976
11977
11978
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11976

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) 


11982
11983
11984
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11982

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) 


11987
11988
11989
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11987

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) 


11993
11994
11995
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11993

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) 


11998
11999
12000
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 11998

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) 


12004
12005
12006
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12004

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) 


12009
12010
12011
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12009

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) 


12014
12015
12016
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12014

def l1_reg_activation
  @l1_reg_activation
end

#l1_regularization ⇒ Float

L1 regularization coefficient. Corresponds to the JSON property l1Regularization

Returns:

  • (Float) 


12019
12020
12021
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12019

def l1_regularization
  @l1_regularization
end

#l2_regularization ⇒ Float

L2 regularization coefficient. Corresponds to the JSON property l2Regularization

Returns:

  • (Float) 


12024
12025
12026
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12024

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>) 


12030
12031
12032
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12030

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) 


12035
12036
12037
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12035

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) 


12040
12041
12042
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12040

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) 


12045
12046
12047
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12045

def loss_type
  @loss_type
end

#machine_type ⇒ String

The type of the machine used to deploy and serve the model. Corresponds to the JSON property machineType

Returns:

  • (String) 


12050
12051
12052
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12050

def machine_type
  @machine_type
end

#max_iterations ⇒ Fixnum

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

Returns:

  • (Fixnum) 


12056
12057
12058
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12056

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) 


12061
12062
12063
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12061

def max_parallel_trials
  @max_parallel_trials
end

#max_replica_count ⇒ Fixnum

The maximum number of machine replicas that will be deployed on an endpoint. The default value is equal to min_replica_count. Corresponds to the JSON property maxReplicaCount

Returns:

  • (Fixnum) 


12067
12068
12069
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12067

def max_replica_count
  @max_replica_count
end

#max_time_series_length ⇒ Fixnum

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

Returns:

  • (Fixnum) 


12074
12075
12076
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12074

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) 


12079
12080
12081
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12079

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) 


12084
12085
12086
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12084

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) 


12090
12091
12092
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12090

def min_relative_progress
  @min_relative_progress
end

#min_replica_count ⇒ Fixnum

The minimum number of machine replicas that will be always deployed on an endpoint. This value must be greater than or equal to 1. The default value is 1. Corresponds to the JSON property minReplicaCount

Returns:

  • (Fixnum) 


12097
12098
12099
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12097

def min_replica_count
  @min_replica_count
end

#min_split_loss ⇒ Float

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

Returns:

  • (Float) 


12102
12103
12104
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12102

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) 


12114
12115
12116
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12114

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) 


12119
12120
12121
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12119

def min_tree_child_weight
  @min_tree_child_weight
end

#model_garden_model_name ⇒ String

The name of a Vertex model garden publisher model. Format is publishers/ publisher/models/model@optional_version_id`. Corresponds to the JSON propertymodelGardenModelName`

Returns:

  • (String) 


12125
12126
12127
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12125

def model_garden_model_name
  @model_garden_model_name
end

#model_registry ⇒ String

The model registry. Corresponds to the JSON property modelRegistry

Returns:

  • (String) 


12130
12131
12132
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12130

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) 


12136
12137
12138
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12136

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:



12141
12142
12143
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12141

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) 


12146
12147
12148
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12146

def num_clusters
  @num_clusters
end

#num_factors ⇒ Fixnum

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

Returns:

  • (Fixnum) 


12151
12152
12153
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12151

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) 


12157
12158
12159
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12157

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) 


12163
12164
12165
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12163

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) 


12168
12169
12170
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12168

def num_trials
  @num_trials
end

#optimization_strategy ⇒ String

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

Returns:

  • (String) 


12173
12174
12175
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12173

def optimization_strategy
  @optimization_strategy
end

#optimizer ⇒ String

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

Returns:

  • (String) 


12178
12179
12180
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12178

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) 


12184
12185
12186
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12184

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) 


12189
12190
12191
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12189

def pca_solver
  @pca_solver
end

#reservation_affinity_key ⇒ String

Corresponds to the label key of a reservation resource used by Vertex AI. To target a SPECIFIC_RESERVATION by name, use compute.googleapis.com/reservation- name as the key and specify the name of your reservation as its value. Corresponds to the JSON property reservationAffinityKey

Returns:

  • (String) 


12196
12197
12198
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12196

def reservation_affinity_key
  @reservation_affinity_key
end

#reservation_affinity_type ⇒ String

Specifies the reservation affinity type used to configure a Vertex AI resource. The default value is NO_RESERVATION. Corresponds to the JSON property reservationAffinityType

Returns:

  • (String) 


12202
12203
12204
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12202

def reservation_affinity_type
  @reservation_affinity_type
end

#reservation_affinity_values ⇒ Array<String>

Corresponds to the label values of a reservation resource used by Vertex AI. This must be the full resource name of the reservation or reservation block. Corresponds to the JSON property reservationAffinityValues

Returns:

  • (Array<String>) 


12208
12209
12210
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12208

def reservation_affinity_values
  @reservation_affinity_values
end

#sampled_shapley_num_paths ⇒ Fixnum

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

Returns:

  • (Fixnum) 


12213
12214
12215
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12213

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) 


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

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) 


12225
12226
12227
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12225

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) 


12232
12233
12234
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12232

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) 


12238
12239
12240
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12238

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) 


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

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) 


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

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>) 


12253
12254
12255
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12253

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) 


12263
12264
12265
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12263

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) 


12268
12269
12270
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12268

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) 


12273
12274
12275
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12273

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) 


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

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) 


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

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>) 


12293
12294
12295
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12293

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) 


12298
12299
12300
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12298

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) 


12303
12304
12305
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12303

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) 


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

def xgboost_version
  @xgboost_version
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



12316
12317
12318
12319
12320
12321
12322
12323
12324
12325
12326
12327
12328
12329
12330
12331
12332
12333
12334
12335
12336
12337
12338
12339
12340
12341
12342
12343
12344
12345
12346
12347
12348
12349
12350
12351
12352
12353
12354
12355
12356
12357
12358
12359
12360
12361
12362
12363
12364
12365
12366
12367
12368
12369
12370
12371
12372
12373
12374
12375
12376
12377
12378
12379
12380
12381
12382
12383
12384
12385
12386
12387
12388
12389
12390
12391
12392
12393
12394
12395
12396
12397
12398
12399
12400
12401
12402
12403
12404
12405
12406
12407
12408
12409
12410
12411
12412
12413
12414
12415
12416
12417
12418
 
# File 'lib/google/apis/bigquery_v2/classes.rb', line 12316

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