Class: Google::Apis::AiplatformV1::GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputs

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) ⇒ GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputs

Returns a new instance of GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputs.



26612
26613
26614
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 26612

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>) 


26461
26462
26463
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 26461

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>) 


26469
26470
26471
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 26469

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) 


26476
26477
26478
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 26476

def context_window
  @context_window
end

#data_granularity ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsGranularity

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

Returns:



26481
26482
26483
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 26481

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:



26486
26487
26488
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 26486

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) 


26493
26494
26495
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 26493

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:



26499
26500
26501
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 26499

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>) 


26508
26509
26510
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 26508

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) 


26523
26524
26525
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 26523

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>) 


26531
26532
26533
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 26531

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) 


26537
26538
26539
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 26537

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) 


26543
26544
26545
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 26543

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>) 


26550
26551
26552
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 26550

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) 


26555
26556
26557
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 26555

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) 


26568
26569
26570
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 26568

def train_budget_milli_node_hours
  @train_budget_milli_node_hours
end

#transformations ⇒ Array<Google::Apis::AiplatformV1::GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsTransformation>

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:



26576
26577
26578
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 26576

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>) 


26584
26585
26586
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 26584

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) 


26592
26593
26594
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 26592

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) 


26601
26602
26603
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 26601

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:



26610
26611
26612
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 26610

def window_config
  @window_config
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



26617
26618
26619
26620
26621
26622
26623
26624
26625
26626
26627
26628
26629
26630
26631
26632
26633
26634
26635
26636
26637
26638
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 26617

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