Struct google_api_proto::google::cloud::aiplatform::v1beta1::schema::trainingjob::definition::AutoMlForecastingInputs

pub struct AutoMlForecastingInputs {

    pub target_column: String,
    pub time_series_identifier_column: String,
    pub time_column: String,
    pub transformations: Vec<Transformation>,
    pub optimization_objective: String,
    pub train_budget_milli_node_hours: i64,
    pub weight_column: String,
    pub time_series_attribute_columns: Vec<String>,
    pub unavailable_at_forecast_columns: Vec<String>,
    pub available_at_forecast_columns: Vec<String>,
    pub data_granularity: Option<Granularity>,
    pub forecast_horizon: i64,
    pub context_window: i64,
    pub export_evaluated_data_items_config: Option<ExportEvaluatedDataItemsConfig>,
    pub quantiles: Vec<f64>,
    pub validation_options: String,
    pub additional_experiments: Vec<String>,
}

Fields

target_column: String

The name of the column that the model is to predict.

time_series_identifier_column: String

The name of the column that identifies the time series.

time_column: String

The name of the column that identifies time order in the time series.

transformations: Vec<Transformation>

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.

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.

train_budget_milli_node_hours: i64

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.

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.

time_series_attribute_columns: Vec<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.

unavailable_at_forecast_columns: Vec<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.

available_at_forecast_columns: Vec<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.

data_granularity: Option<Granularity>

Expected difference in time granularity between rows in the data.

forecast_horizon: i64

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.

context_window: i64

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.

export_evaluated_data_items_config: Option<ExportEvaluatedDataItemsConfig>

Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.

quantiles: Vec<f64>

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.

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

additional_experiments: Vec<String>

Additional experiment flags for the time series forcasting training.

Trait Implementations

impl Clone for AutoMlForecastingInputs

fn clone(&self) -> AutoMlForecastingInputs

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for AutoMlForecastingInputs

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for AutoMlForecastingInputs

fn default() -> Self

Returns the “default value” for a type.

impl Message for AutoMlForecastingInputs

fn encoded_len(&self) -> usize

Returns the encoded length of the message without a length delimiter.

fn clear(&mut self)

Clears the message, resetting all fields to their default.

fn encode(&self, buf: &mut impl BufMut) -> Result<(), EncodeError>

where Self: Sized,

Encodes the message to a buffer.

fn encode_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message to a newly allocated buffer.

fn encode_length_delimited( &self, buf: &mut impl BufMut, ) -> Result<(), EncodeError>

where Self: Sized,

Encodes the message with a length-delimiter to a buffer.

fn encode_length_delimited_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message with a length-delimiter to a newly allocated buffer.

fn decode(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes an instance of the message from a buffer.

fn decode_length_delimited(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes a length-delimited instance of the message from the buffer.

fn merge(&mut self, buf: impl Buf) -> Result<(), DecodeError>

where Self: Sized,

Decodes an instance of the message from a buffer, and merges it into self.

fn merge_length_delimited(&mut self, buf: impl Buf) -> Result<(), DecodeError>

where Self: Sized,

Decodes a length-delimited instance of the message from buffer, and merges it into self.

impl PartialEq for AutoMlForecastingInputs

fn eq(&self, other: &AutoMlForecastingInputs) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for AutoMlForecastingInputs