Class: Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputs
- Inherits:
-
Object
- Object
- Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputs
- 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
-
#additional_experiments ⇒ Array<String>
Additional experiment flags for the time series forcasting training.
-
#available_at_forecast_columns ⇒ Array<String>
Names of columns that are available and provided when a forecast is requested.
-
#context_window ⇒ Fixnum
The amount of time into the past training and prediction data is used for model training and prediction respectively.
-
#data_granularity ⇒ Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputsGranularity
A duration of time expressed in time granularity units.
-
#export_evaluated_data_items_config ⇒ Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionExportEvaluatedDataItemsConfig
Configuration for exporting test set predictions to a BigQuery table.
-
#forecast_horizon ⇒ Fixnum
The amount of time into the future for which forecasted values for the target are returned.
-
#hierarchy_config ⇒ Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionHierarchyConfig
Configuration that defines the hierarchical relationship of time series and parameters for hierarchical forecasting strategies.
-
#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.
-
#optimization_objective ⇒ String
Objective function the model is optimizing towards.
-
#quantiles ⇒ Array<Float>
Quantiles to use for minimize-quantile-loss
optimization_objective
. -
#target_column ⇒ String
The name of the column that the Model is to predict values for.
-
#time_column ⇒ String
The name of the column that identifies time order in the time series.
-
#time_series_attribute_columns ⇒ Array<String>
Column names that should be used as attribute columns.
-
#time_series_identifier_column ⇒ String
The name of the column that identifies the time series.
-
#train_budget_milli_node_hours ⇒ Fixnum
Required.
-
#transformations ⇒ Array<Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputsTransformation>
Each transformation will apply transform function to given input column.
-
#unavailable_at_forecast_columns ⇒ Array<String>
Names of columns that are unavailable when a forecast is requested.
-
#validation_options ⇒ String
Validation options for the data validation component.
-
#weight_column ⇒ String
Column name that should be used as the weight column.
-
#window_config ⇒ Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionWindowConfig
Config that contains the strategy used to generate sliding windows in time series training.
Instance Method Summary collapse
-
#initialize(**args) ⇒ GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputs
constructor
A new instance of GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputs.
-
#update!(**args) ⇒ Object
Update properties of this object.
Constructor Details
#initialize(**args) ⇒ GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputs
Returns a new instance of GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputs.
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30429 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
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30278 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
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30286 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
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30293 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
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30298 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
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30303 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
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30310 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
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30316 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
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30325 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
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30340 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
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30348 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
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30354 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
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30360 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
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30367 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
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30372 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
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30385 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
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30393 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
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30401 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
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30409 def @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
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30418 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
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30427 def window_config @window_config end |
Instance Method Details
#update!(**args) ⇒ Object
Update properties of this object
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 30434 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 |