Class: Google::Apis::AiplatformV1::GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputs

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) ⇒ GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputs

Returns a new instance of GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputs.



24830
24831
24832
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24830

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

Instance Attribute Details

#additional_experiments ⇒ Array<String>

Additional experiment flags for the time series forcasting training. Corresponds to the JSON property additionalExperiments

Returns:

  • (Array<String>) 


24666
24667
24668
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24666

def additional_experiments
  @additional_experiments
end

#available_at_forecast_columns ⇒ Array<String>

Names of columns that are available and provided when a forecast is requested. These columns contain information for the given entity (identified by the time_series_identifier_column column) that is known at forecast. For example, predicted weather for a specific day. Corresponds to the JSON property availableAtForecastColumns

Returns:

  • (Array<String>) 


24674
24675
24676
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24674

def available_at_forecast_columns
  @available_at_forecast_columns
end

#context_window ⇒ Fixnum

The amount of time into the past training and prediction data is used for model training and prediction respectively. Expressed in number of units defined by the data_granularity field. Corresponds to the JSON property contextWindow

Returns:

  • (Fixnum) 


24681
24682
24683
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24681

def context_window
  @context_window
end

#data_granularity ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsGranularity

A duration of time expressed in time granularity units. Corresponds to the JSON property dataGranularity

Returns:



24686
24687
24688
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24686

def data_granularity
  @data_granularity
end

#enable_probabilistic_inference ⇒ Boolean Also known as: enable_probabilistic_inference?

If probabilistic inference is enabled, the model will fit a distribution that captures the uncertainty of a prediction. At inference time, the predictive distribution is used to make a point prediction that minimizes the optimization objective. For example, the mean of a predictive distribution is the point prediction that minimizes RMSE loss. If quantiles are specified, then the quantiles of the distribution are also returned. The optimization objective cannot be minimize-quantile-loss. Corresponds to the JSON property enableProbabilisticInference

Returns:

  • (Boolean) 


24697
24698
24699
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24697

def enable_probabilistic_inference
  @enable_probabilistic_inference
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:



24703
24704
24705
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24703

def export_evaluated_data_items_config
  @export_evaluated_data_items_config
end

#forecast_horizon ⇒ Fixnum

The amount of time into the future for which forecasted values for the target are returned. Expressed in number of units defined by the data_granularity field. Corresponds to the JSON property forecastHorizon

Returns:

  • (Fixnum) 


24710
24711
24712
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24710

def forecast_horizon
  @forecast_horizon
end

#hierarchy_config ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1SchemaTrainingjobDefinitionHierarchyConfig

Configuration that defines the hierarchical relationship of time series and parameters for hierarchical forecasting strategies. Corresponds to the JSON property hierarchyConfig

Returns:



24716
24717
24718
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24716

def hierarchy_config
  @hierarchy_config
end

#holiday_regions ⇒ Array<String>

The geographical region based on which the holiday effect is applied in modeling by adding holiday categorical array feature that include all holidays matching the date. This option only allowed when data_granularity is day. By default, holiday effect modeling is disabled. To turn it on, specify the holiday region using this option. Corresponds to the JSON property holidayRegions

Returns:

  • (Array<String>) 


24725
24726
24727
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24725

def holiday_regions
  @holiday_regions
end

#optimization_objective ⇒ String

Objective function the model is optimizing towards. The training process creates a model that optimizes the value of the objective function over the validation set. The supported optimization objectives: * "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). * "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE). * "minimize-wape-mae" - Minimize the combination of weighted absolute percentage error (WAPE) and mean-absolute-error (MAE). * " minimize-quantile-loss" - Minimize the quantile loss at the quantiles defined in quantiles. * "minimize-mape" - Minimize the mean absolute percentage error. Corresponds to the JSON property optimizationObjective

Returns:

  • (String) 


24740
24741
24742
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24740

def optimization_objective
  @optimization_objective
end

#quantiles ⇒ Array<Float>

Quantiles to use for minimize-quantile-loss optimization_objective, or for probabilistic inference. Up to 5 quantiles are allowed of values between 0 and 1, exclusive. Required if the value of optimization_objective is minimize- quantile-loss. Represents the percent quantiles to use for that objective. Quantiles must be unique. Corresponds to the JSON property quantiles

Returns:

  • (Array<Float>) 


24749
24750
24751
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24749

def quantiles
  @quantiles
end

#target_column ⇒ String

The name of the column that the Model is to predict values for. This column must be unavailable at forecast. Corresponds to the JSON property targetColumn

Returns:

  • (String) 


24755
24756
24757
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24755

def target_column
  @target_column
end

#time_column ⇒ String

The name of the column that identifies time order in the time series. This column must be available at forecast. Corresponds to the JSON property timeColumn

Returns:

  • (String) 


24761
24762
24763
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24761

def time_column
  @time_column
end

#time_series_attribute_columns ⇒ Array<String>

Column names that should be used as attribute columns. The value of these columns does not vary as a function of time. For example, store ID or item color. Corresponds to the JSON property timeSeriesAttributeColumns

Returns:

  • (Array<String>) 


24768
24769
24770
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24768

def time_series_attribute_columns
  @time_series_attribute_columns
end

#time_series_identifier_column ⇒ String

The name of the column that identifies the time series. Corresponds to the JSON property timeSeriesIdentifierColumn

Returns:

  • (String) 


24773
24774
24775
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24773

def time_series_identifier_column
  @time_series_identifier_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) 


24786
24787
24788
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24786

def train_budget_milli_node_hours
  @train_budget_milli_node_hours
end

#transformations ⇒ Array<Google::Apis::AiplatformV1::GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformation>

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:



24794
24795
24796
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24794

def transformations
  @transformations
end

#unavailable_at_forecast_columns ⇒ Array<String>

Names of columns that are unavailable when a forecast is requested. This column contains information for the given entity (identified by the time_series_identifier_column) that is unknown before the forecast For example, actual weather on a given day. Corresponds to the JSON property unavailableAtForecastColumns

Returns:

  • (Array<String>) 


24802
24803
24804
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24802

def unavailable_at_forecast_columns
  @unavailable_at_forecast_columns
end

#validation_options ⇒ String

Validation options for the data validation component. The available options are: * "fail-pipeline" - default, will validate against the validation and fail the pipeline if it fails. * "ignore-validation" - ignore the results of the validation and continue Corresponds to the JSON property validationOptions

Returns:

  • (String) 


24810
24811
24812
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24810

def validation_options
  @validation_options
end

#weight_column ⇒ 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 weightColumn

Returns:

  • (String) 


24819
24820
24821
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24819

def weight_column
  @weight_column
end

#window_config ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1SchemaTrainingjobDefinitionWindowConfig

Config that contains the strategy used to generate sliding windows in time series training. A window is a series of rows that comprise the context up to the time of prediction, and the horizon following. The corresponding row for each window marks the start of the forecast horizon. Each window is used as an input example for training/evaluation. Corresponds to the JSON property windowConfig

Returns:



24828
24829
24830
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24828

def window_config
  @window_config
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



24835
24836
24837
24838
24839
24840
24841
24842
24843
24844
24845
24846
24847
24848
24849
24850
24851
24852
24853
24854
24855
24856
24857
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24835

def update!(**args)
  @additional_experiments = args[:additional_experiments] if args.key?(:additional_experiments)
  @available_at_forecast_columns = args[:available_at_forecast_columns] if args.key?(:available_at_forecast_columns)
  @context_window = args[:context_window] if args.key?(:context_window)
  @data_granularity = args[:data_granularity] if args.key?(:data_granularity)
  @enable_probabilistic_inference = args[:enable_probabilistic_inference] if args.key?(:enable_probabilistic_inference)
  @export_evaluated_data_items_config = args[:export_evaluated_data_items_config] if args.key?(:export_evaluated_data_items_config)
  @forecast_horizon = args[:forecast_horizon] if args.key?(:forecast_horizon)
  @hierarchy_config = args[:hierarchy_config] if args.key?(:hierarchy_config)
  @holiday_regions = args[:holiday_regions] if args.key?(:holiday_regions)
  @optimization_objective = args[:optimization_objective] if args.key?(:optimization_objective)
  @quantiles = args[:quantiles] if args.key?(:quantiles)
  @target_column = args[:target_column] if args.key?(:target_column)
  @time_column = args[:time_column] if args.key?(:time_column)
  @time_series_attribute_columns = args[:time_series_attribute_columns] if args.key?(:time_series_attribute_columns)
  @time_series_identifier_column = args[:time_series_identifier_column] if args.key?(:time_series_identifier_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)
  @unavailable_at_forecast_columns = args[:unavailable_at_forecast_columns] if args.key?(:unavailable_at_forecast_columns)
  @validation_options = args[:validation_options] if args.key?(:validation_options)
  @weight_column = args[:weight_column] if args.key?(:weight_column)
  @window_config = args[:window_config] if args.key?(:window_config)
end