Class: Google::Apis::AiplatformV1::GoogleCloudAiplatformV1SchemaTrainingjobDefinitionTftForecastingInputs

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) ⇒ GoogleCloudAiplatformV1SchemaTrainingjobDefinitionTftForecastingInputs

Returns a new instance of GoogleCloudAiplatformV1SchemaTrainingjobDefinitionTftForecastingInputs.



24398
24399
24400
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24398

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>) 


24247
24248
24249
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24247

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>) 


24255
24256
24257
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24255

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) 


24262
24263
24264
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24262

def context_window
  @context_window
end

#data_granularity ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1SchemaTrainingjobDefinitionTftForecastingInputsGranularity

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

Returns:



24267
24268
24269
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24267

def data_granularity
  @data_granularity
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:



24272
24273
24274
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24272

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) 


24279
24280
24281
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24279

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:



24285
24286
24287
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24285

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>) 


24294
24295
24296
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24294

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) 


24309
24310
24311
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24309

def optimization_objective
  @optimization_objective
end

#quantiles ⇒ Array<Float>

Quantiles to use for minimize-quantile-loss optimization_objective. 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>) 


24317
24318
24319
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24317

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) 


24323
24324
24325
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24323

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) 


24329
24330
24331
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24329

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>) 


24336
24337
24338
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24336

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) 


24341
24342
24343
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24341

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) 


24354
24355
24356
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24354

def train_budget_milli_node_hours
  @train_budget_milli_node_hours
end

#transformations ⇒ Array<Google::Apis::AiplatformV1::GoogleCloudAiplatformV1SchemaTrainingjobDefinitionTftForecastingInputsTransformation>

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:



24362
24363
24364
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24362

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>) 


24370
24371
24372
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24370

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) 


24378
24379
24380
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24378

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. This column must be available at forecast. Corresponds to the JSON property weightColumn

Returns:

  • (String) 


24387
24388
24389
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24387

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:



24396
24397
24398
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24396

def window_config
  @window_config
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



24403
24404
24405
24406
24407
24408
24409
24410
24411
24412
24413
24414
24415
24416
24417
24418
24419
24420
24421
24422
24423
24424
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 24403

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)
  @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