Struct google_api_proto::google::cloud::aiplatform::v1::schema::trainingjob::definition::AutoMlTablesInputs

pub struct AutoMlTablesInputs {
    pub prediction_type: String,
    pub target_column: String,
    pub transformations: Vec<Transformation>,
    pub optimization_objective: String,
    pub train_budget_milli_node_hours: i64,
    pub disable_early_stopping: bool,
    pub weight_column_name: String,
    pub export_evaluated_data_items_config: Option<ExportEvaluatedDataItemsConfig>,
    pub additional_experiments: Vec<String>,
    pub additional_optimization_objective_config: Option<AdditionalOptimizationObjectiveConfig>,
}

Fields

prediction_type: String

The type of prediction the Model is to produce. “classification” - Predict one out of multiple target values is picked for each row. “regression” - Predict a value based on its relation to other values. This type is available only to columns that contain semantically numeric values, i.e. integers or floating point number, even if stored as e.g. strings.

target_column: String

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

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 maximizes/minimizes the value of the objective function over the validation set.

The supported optimization objectives depend on the prediction type. If the field is not set, a default objective function is used.

classification (binary): “maximize-au-roc” (default) - Maximize the area under the receiver operating characteristic (ROC) curve. “minimize-log-loss” - Minimize log loss. “maximize-au-prc” - Maximize the area under the precision-recall curve. “maximize-precision-at-recall” - Maximize precision for a specified recall value. “maximize-recall-at-precision” - Maximize recall for a specified precision value.

classification (multi-class): “minimize-log-loss” (default) - Minimize log loss.

regression: “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).

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.

disable_early_stopping: bool

Use the entire training budget. This disables the early stopping feature. By default, the early stopping feature is enabled, which means that AutoML Tables might stop training before the entire training budget has been used.

weight_column_name: 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.

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.

additional_experiments: Vec<String>

Additional experiment flags for the Tables training pipeline.

additional_optimization_objective_config: Option<AdditionalOptimizationObjectiveConfig>

Additional optimization objective configuration. Required for maximize-precision-at-recall and maximize-recall-at-precision, otherwise unused.

Trait Implementations

impl Clone for AutoMlTablesInputs

fn clone(&self) -> AutoMlTablesInputs

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for AutoMlTablesInputs

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

Formats the value using the given formatter.

impl Default for AutoMlTablesInputs

fn default() -> Self

Returns the “default value” for a type.

impl Message for AutoMlTablesInputs

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 AutoMlTablesInputs

fn eq(&self, other: &AutoMlTablesInputs) -> 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 AutoMlTablesInputs