Class: Google::Apis::BigqueryV2::TrainingOptions

Inherits:
Object
  • Object
show all
Includes:
Core::Hashable, Core::JsonObjectSupport
Defined in:
generated/google/apis/bigquery_v2/classes.rb,
generated/google/apis/bigquery_v2/representations.rb,
generated/google/apis/bigquery_v2/representations.rb

Instance Attribute Summary collapse

Instance Method Summary collapse

Methods included from Core::JsonObjectSupport

#to_json

Methods included from Core::Hashable

process_value, #to_h

Constructor Details

#initialize(**args) ⇒ TrainingOptions

Returns a new instance of TrainingOptions



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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5703

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

Instance Attribute Details

#data_split_column ⇒ String

The column to split data with. This column won't be used as a feature.

  1. When data_split_method is CUSTOM, the corresponding column should be boolean. The rows with true value tag are eval data, and the false are training data.
  2. When data_split_method is SEQ, the first DATA_SPLIT_EVAL_FRACTION rows (from smallest to largest) in the corresponding column are used as training data, and the rest are eval data. It respects the order in Orderable data types: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#data- type-properties Corresponds to the JSON property dataSplitColumn

Returns:

  • (String) 


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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5587

def data_split_column
  @data_split_column
end

#data_split_eval_fraction ⇒ Float

The fraction of evaluation data over the whole input data. The rest of data will be used as training data. The format should be double. Accurate to two decimal places. Default value is 0.2. Corresponds to the JSON property dataSplitEvalFraction

Returns:

  • (Float) 


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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5595

def data_split_eval_fraction
  @data_split_eval_fraction
end

#data_split_method ⇒ String

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

Returns:

  • (String) 


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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5600

def data_split_method
  @data_split_method
end

#distance_type ⇒ String

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

Returns:

  • (String) 


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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5605

def distance_type
  @distance_type
end

#early_stop ⇒ Boolean Also known as: early_stop?

Whether to stop early when the loss doesn't improve significantly any more (compared to min_relative_progress). Used only for iterative training algorithms. Corresponds to the JSON property earlyStop

Returns:

  • (Boolean) 


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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5612

def early_stop
  @early_stop
end

#initial_learn_rate ⇒ Float

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

Returns:

  • (Float) 


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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5619

def initial_learn_rate
  @initial_learn_rate
end

#input_label_columns ⇒ Array<String>

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

Returns:

  • (Array<String>) 


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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5624

def input_label_columns
  @input_label_columns
end

#kmeans_initialization_column ⇒ String

The column used to provide the initial centroids for kmeans algorithm when kmeans_initialization_method is CUSTOM. Corresponds to the JSON property kmeansInitializationColumn

Returns:

  • (String) 


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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5630

def kmeans_initialization_column
  @kmeans_initialization_column
end

#kmeans_initialization_method ⇒ String

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

Returns:

  • (String) 


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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5635

def kmeans_initialization_method
  @kmeans_initialization_method
end

#l1_regularization ⇒ Float

L1 regularization coefficient. Corresponds to the JSON property l1Regularization

Returns:

  • (Float) 


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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5640

def l1_regularization
  @l1_regularization
end

#l2_regularization ⇒ Float

L2 regularization coefficient. Corresponds to the JSON property l2Regularization

Returns:

  • (Float) 


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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5645

def l2_regularization
  @l2_regularization
end

#label_class_weights ⇒ Hash<String,Float>

Weights associated with each label class, for rebalancing the training data. Only applicable for classification models. Corresponds to the JSON property labelClassWeights

Returns:

  • (Hash<String,Float>) 


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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5651

def label_class_weights
  @label_class_weights
end

#learn_rate ⇒ Float

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

Returns:

  • (Float) 


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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5656

def learn_rate
  @learn_rate
end

#learn_rate_strategy ⇒ String

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

Returns:

  • (String) 


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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5661

def learn_rate_strategy
  @learn_rate_strategy
end

#loss_type ⇒ String

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

Returns:

  • (String) 


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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5666

def loss_type
  @loss_type
end

#max_iterations ⇒ Fixnum

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

Returns:

  • (Fixnum) 


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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5672

def max_iterations
  @max_iterations
end

#min_relative_progress ⇒ Float

When early_stop is true, stops training when accuracy improvement is less than 'min_relative_progress'. Used only for iterative training algorithms. Corresponds to the JSON property minRelativeProgress

Returns:

  • (Float) 


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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5679

def min_relative_progress
  @min_relative_progress
end

#model_uri ⇒ String

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

Returns:

  • (String) 


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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5685

def model_uri
  @model_uri
end

#num_clusters ⇒ Fixnum

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

Returns:

  • (Fixnum) 


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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5690

def num_clusters
  @num_clusters
end

#optimization_strategy ⇒ String

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

Returns:

  • (String) 


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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5695

def optimization_strategy
  @optimization_strategy
end

#warm_start ⇒ Boolean Also known as: warm_start?

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

Returns:

  • (Boolean) 


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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5700

def warm_start
  @warm_start
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5708

def update!(**args)
  @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)
  @distance_type = args[:distance_type] if args.key?(:distance_type)
  @early_stop = args[:early_stop] if args.key?(:early_stop)
  @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)
  @kmeans_initialization_column = args[:kmeans_initialization_column] if args.key?(:kmeans_initialization_column)
  @kmeans_initialization_method = args[:kmeans_initialization_method] if args.key?(:kmeans_initialization_method)
  @l1_regularization = args[:l1_regularization] if args.key?(:l1_regularization)
  @l2_regularization = args[:l2_regularization] if args.key?(:l2_regularization)
  @label_class_weights = args[:label_class_weights] if args.key?(:label_class_weights)
  @learn_rate = args[:learn_rate] if args.key?(:learn_rate)
  @learn_rate_strategy = args[:learn_rate_strategy] if args.key?(:learn_rate_strategy)
  @loss_type = args[:loss_type] if args.key?(:loss_type)
  @max_iterations = args[:max_iterations] if args.key?(:max_iterations)
  @min_relative_progress = args[:min_relative_progress] if args.key?(:min_relative_progress)
  @model_uri = args[:model_uri] if args.key?(:model_uri)
  @num_clusters = args[:num_clusters] if args.key?(:num_clusters)
  @optimization_strategy = args[:optimization_strategy] if args.key?(:optimization_strategy)
  @warm_start = args[:warm_start] if args.key?(:warm_start)
end