Class: Google::Apis::AiplatformV1::GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputs

Inherits:
Object
  • Object
show all
Includes:
Core::Hashable, Core::JsonObjectSupport
Defined in:
lib/google/apis/aiplatform_v1/classes.rb,
lib/google/apis/aiplatform_v1/representations.rb,
lib/google/apis/aiplatform_v1/representations.rb

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(**args) ⇒ GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputs

Returns a new instance of GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputs.



43020
43021
43022
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 43020

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>) 


42932
42933
42934
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 42932

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) 


42939
42940
42941
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 42939

def disable_early_stopping
  @disable_early_stopping
end

#export_evaluated_data_items_config ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1SchemaTrainingjobDefinitionExportEvaluatedDataItemsConfig

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

Returns:



42945
42946
42947
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 42945

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) 


42962
42963
42964
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 42962

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) 


42968
42969
42970
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 42968

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) 


42974
42975
42976
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 42974

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) 


42983
42984
42985
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 42983

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) 


42988
42989
42990
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 42988

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) 


43001
43002
43003
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 43001

def train_budget_milli_node_hours
  @train_budget_milli_node_hours
end

#transformations ⇒ Array<Google::Apis::AiplatformV1::GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformation>

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:



43009
43010
43011
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 43009

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) 


43018
43019
43020
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 43018

def weight_column_name
  @weight_column_name
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



43025
43026
43027
43028
43029
43030
43031
43032
43033
43034
43035
43036
43037
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 43025

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