Struct google_api_proto::google::cloud::automl::v1beta1::TablesModelMetadata

pub struct TablesModelMetadata {
    pub target_column_spec: Option<ColumnSpec>,
    pub input_feature_column_specs: Vec<ColumnSpec>,
    pub optimization_objective: String,
    pub tables_model_column_info: Vec<TablesModelColumnInfo>,
    pub train_budget_milli_node_hours: i64,
    pub train_cost_milli_node_hours: i64,
    pub disable_early_stopping: bool,
    pub additional_optimization_objective_config: Option<AdditionalOptimizationObjectiveConfig>,
}

Model metadata specific to AutoML Tables.

Fields

target_column_spec: Option<ColumnSpec>

Column spec of the dataset’s primary table’s column the model is predicting. Snapshotted when model creation started. Only 3 fields are used: name - May be set on CreateModel, if it’s not then the ColumnSpec corresponding to the current target_column_spec_id of the dataset the model is trained from is used. If neither is set, CreateModel will error. display_name - Output only. data_type - Output only.

input_feature_column_specs: Vec<ColumnSpec>

Column specs of the dataset’s primary table’s columns, on which the model is trained and which are used as the input for predictions. The

[target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec] as well as, according to dataset’s state upon model creation,

[weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id], and

[ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id] must never be included here.

Only 3 fields are used:

• name - May be set on CreateModel, if set only the columns specified are used, otherwise all primary table’s columns (except the ones listed above) are used for the training and prediction input.

• display_name - Output only.

• data_type - Output only.

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).

tables_model_column_info: Vec<TablesModelColumnInfo>

Output only. Auxiliary information for each of the input_feature_column_specs with respect to this particular model.

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.

train_cost_milli_node_hours: i64

Output only. The actual training cost of the model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget.

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.

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 TablesModelMetadata

fn clone(&self) -> TablesModelMetadata

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for TablesModelMetadata

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

Formats the value using the given formatter.

impl Default for TablesModelMetadata

fn default() -> Self

Returns the “default value” for a type.

impl Message for TablesModelMetadata

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 TablesModelMetadata

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