Class: Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputs

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) ⇒ GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputs

Returns a new instance of GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputs.



30315
30316
30317
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30315

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>) 


30164
30165
30166
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30164

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>) 


30172
30173
30174
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30172

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) 


30179
30180
30181
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30179

def context_window
  @context_window
end

#data_granularity ⇒ Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputsGranularity

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

Returns:



30184
30185
30186
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30184

def data_granularity
  @data_granularity
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:



30189
30190
30191
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30189

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) 


30196
30197
30198
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30196

def forecast_horizon
  @forecast_horizon
end

#hierarchy_config ⇒ Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionHierarchyConfig

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

Returns:



30202
30203
30204
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30202

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>) 


30211
30212
30213
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30211

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) 


30226
30227
30228
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30226

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>) 


30234
30235
30236
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30234

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) 


30240
30241
30242
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30240

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) 


30246
30247
30248
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30246

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>) 


30253
30254
30255
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30253

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) 


30258
30259
30260
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30258

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) 


30271
30272
30273
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30271

def train_budget_milli_node_hours
  @train_budget_milli_node_hours
end

#transformations ⇒ Array<Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputsTransformation>

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:



30279
30280
30281
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30279

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>) 


30287
30288
30289
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30287

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) 


30295
30296
30297
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30295

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) 


30304
30305
30306
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30304

def weight_column
  @weight_column
end

#window_config ⇒ Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionWindowConfig

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:



30313
30314
30315
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30313

def window_config
  @window_config
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



30320
30321
30322
30323
30324
30325
30326
30327
30328
30329
30330
30331
30332
30333
30334
30335
30336
30337
30338
30339
30340
30341
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30320

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