Class: Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputs

Inherits:
Object
  • Object
show all
Includes:
Core::Hashable, Core::JsonObjectSupport
Defined in:
lib/google/apis/aiplatform_v1beta1/classes.rb,
lib/google/apis/aiplatform_v1beta1/representations.rb,
lib/google/apis/aiplatform_v1beta1/representations.rb

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(**args) ⇒ GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputs

Returns a new instance of GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputs.



24699
24700
24701
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 24699

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

Instance Attribute Details

#additional_experiments ⇒ Array<String>

Additional experiment flags for the Tables training pipeline. Corresponds to the JSON property additionalExperiments

Returns:

  • (Array<String>) 


24611
24612
24613
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 24611

def additional_experiments
  @additional_experiments
end

#disable_early_stopping ⇒ Boolean Also known as: disable_early_stopping?

Use the entire training budget. This disables the early stopping feature. By default, the early stopping feature is enabled, which means that AutoML Tables might stop training before the entire training budget has been used. Corresponds to the JSON property disableEarlyStopping

Returns:

  • (Boolean) 


24618
24619
24620
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 24618

def disable_early_stopping
  @disable_early_stopping
end

#export_evaluated_data_items_config ⇒ Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionExportEvaluatedDataItemsConfig

Configuration for exporting test set predictions to a BigQuery table. Corresponds to the JSON property exportEvaluatedDataItemsConfig

Returns:



24624
24625
24626
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 24624

def export_evaluated_data_items_config
  @export_evaluated_data_items_config
end

#optimization_objective ⇒ String

Objective function the model is optimizing towards. The training process creates a model that maximizes/minimizes the value of the objective function over the validation set. The supported optimization objectives depend on the prediction type. If the field is not set, a default objective function is used. classification (binary): "maximize-au-roc" (default) - Maximize the area under the receiver operating characteristic (ROC) curve. "minimize-log-loss" - Minimize log loss. "maximize-au-prc" - Maximize the area under the precision- recall curve. "maximize-precision-at-recall" - Maximize precision for a specified recall value. "maximize-recall-at-precision" - Maximize recall for a specified precision value. classification (multi-class): "minimize-log-loss" ( default) - Minimize log loss. regression: "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE). "minimize-mae" - Minimize mean-absolute error ( MAE). "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE). Corresponds to the JSON property optimizationObjective

Returns:

  • (String) 


24641
24642
24643
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 24641

def optimization_objective
  @optimization_objective
end

#optimization_objective_precision_value ⇒ Float

Required when optimization_objective is "maximize-recall-at-precision". Must be between 0 and 1, inclusive. Corresponds to the JSON property optimizationObjectivePrecisionValue

Returns:

  • (Float) 


24647
24648
24649
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 24647

def optimization_objective_precision_value
  @optimization_objective_precision_value
end

#optimization_objective_recall_value ⇒ Float

Required when optimization_objective is "maximize-precision-at-recall". Must be between 0 and 1, inclusive. Corresponds to the JSON property optimizationObjectiveRecallValue

Returns:

  • (Float) 


24653
24654
24655
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 24653

def optimization_objective_recall_value
  @optimization_objective_recall_value
end

#prediction_type ⇒ String

The type of prediction the Model is to produce. "classification" - Predict one out of multiple target values is picked for each row. "regression" - Predict a value based on its relation to other values. This type is available only to columns that contain semantically numeric values, i.e. integers or floating point number, even if stored as e.g. strings. Corresponds to the JSON property predictionType

Returns:

  • (String) 


24662
24663
24664
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 24662

def prediction_type
  @prediction_type
end

#target_column ⇒ String

The column name of the target column that the model is to predict. Corresponds to the JSON property targetColumn

Returns:

  • (String) 


24667
24668
24669
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 24667

def target_column
  @target_column
end

#train_budget_milli_node_hours ⇒ Fixnum

Required. The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The training cost of the model will not exceed this budget. The final cost will be attempted to be close to the budget, though may end up being (even) noticeably smaller - at the backend's discretion. This especially may happen when further model training ceases to provide any improvements. If the budget is set to a value known to be insufficient to train a model for the given dataset, the training won't be attempted and will error. The train budget must be between 1,000 and 72,000 milli node hours, inclusive. Corresponds to the JSON property trainBudgetMilliNodeHours

Returns:

  • (Fixnum) 


24680
24681
24682
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 24680

def train_budget_milli_node_hours
  @train_budget_milli_node_hours
end

#transformations ⇒ Array<Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformation>

Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter. Corresponds to the JSON property transformations

Returns:



24688
24689
24690
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 24688

def transformations
  @transformations
end

#weight_column_name ⇒ String

Column name that should be used as the weight column. Higher values in this column give more importance to the row during model training. The column must have numeric values between 0 and 10000 inclusively; 0 means the row is ignored for training. If weight column field is not set, then all rows are assumed to have equal weight of 1. Corresponds to the JSON property weightColumnName

Returns:

  • (String) 


24697
24698
24699
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 24697

def weight_column_name
  @weight_column_name
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



24704
24705
24706
24707
24708
24709
24710
24711
24712
24713
24714
24715
24716
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 24704

def update!(**args)
  @additional_experiments = args[:additional_experiments] if args.key?(:additional_experiments)
  @disable_early_stopping = args[:disable_early_stopping] if args.key?(:disable_early_stopping)
  @export_evaluated_data_items_config = args[:export_evaluated_data_items_config] if args.key?(:export_evaluated_data_items_config)
  @optimization_objective = args[:optimization_objective] if args.key?(:optimization_objective)
  @optimization_objective_precision_value = args[:optimization_objective_precision_value] if args.key?(:optimization_objective_precision_value)
  @optimization_objective_recall_value = args[:optimization_objective_recall_value] if args.key?(:optimization_objective_recall_value)
  @prediction_type = args[:prediction_type] if args.key?(:prediction_type)
  @target_column = args[:target_column] if args.key?(:target_column)
  @train_budget_milli_node_hours = args[:train_budget_milli_node_hours] if args.key?(:train_budget_milli_node_hours)
  @transformations = args[:transformations] if args.key?(:transformations)
  @weight_column_name = args[:weight_column_name] if args.key?(:weight_column_name)
end