Struct google_bigquery2::TrainingOptions

pub struct TrainingOptions {
    pub optimization_strategy: Option<String>,
    pub max_iterations: Option<String>,
    pub early_stop: Option<bool>,
    pub initial_learn_rate: Option<f64>,
    pub data_split_column: Option<String>,
    pub num_clusters: Option<String>,
    pub warm_start: Option<bool>,
    pub input_label_columns: Option<Vec<String>>,
    pub loss_type: Option<String>,
    pub learn_rate_strategy: Option<String>,
    pub data_split_eval_fraction: Option<f64>,
    pub data_split_method: Option<String>,
    pub distance_type: Option<String>,
    pub label_class_weights: Option<HashMap<String, f64>>,
    pub l1_regularization: Option<f64>,
    pub l2_regularization: Option<f64>,
    pub model_uri: Option<String>,
    pub min_relative_progress: Option<f64>,
    pub learn_rate: Option<f64>,
}

There is no detailed description.

This type is not used in any activity, and only used as part of another schema.

Fields

optimization_strategy: Option<String>

Optimization strategy for training linear regression models.

max_iterations: Option<String>

The maximum number of iterations in training. Used only for iterative training algorithms.

early_stop: Option<bool>

Whether to stop early when the loss doesn't improve significantly any more (compared to min_relative_progress). Used only for iterative training algorithms.

initial_learn_rate: Option<f64>

Specifies the initial learning rate for the line search learn rate strategy.

data_split_column: Option<String>

The column to split data with. This column won't be used as a feature.

1. When data_split_method is CUSTOM, the corresponding column should be boolean. The rows with true value tag are eval data, and the false are training data.
2. When data_split_method is SEQ, the first DATA_SPLIT_EVAL_FRACTION rows (from smallest to largest) in the corresponding column are used as training data, and the rest are eval data. It respects the order in Orderable data types: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#data-type-properties

num_clusters: Option<String>

[Beta] Number of clusters for clustering models.

warm_start: Option<bool>

Whether to train a model from the last checkpoint.

input_label_columns: Option<Vec<String>>

Name of input label columns in training data.

loss_type: Option<String>

Type of loss function used during training run.

learn_rate_strategy: Option<String>

The strategy to determine learn rate for the current iteration.

data_split_eval_fraction: Option<f64>

The fraction of evaluation data over the whole input data. The rest of data will be used as training data. The format should be double. Accurate to two decimal places. Default value is 0.2.

data_split_method: Option<String>

The data split type for training and evaluation, e.g. RANDOM.

distance_type: Option<String>

[Beta] Distance type for clustering models.

label_class_weights: Option<HashMap<String, f64>>

Weights associated with each label class, for rebalancing the training data. Only applicable for classification models.

l1_regularization: Option<f64>

L1 regularization coefficient.

l2_regularization: Option<f64>

L2 regularization coefficient.

model_uri: Option<String>

[Beta] Google Cloud Storage URI from which the model was imported. Only applicable for imported models.

min_relative_progress: Option<f64>

When early_stop is true, stops training when accuracy improvement is less than 'min_relative_progress'. Used only for iterative training algorithms.

learn_rate: Option<f64>

Learning rate in training. Used only for iterative training algorithms.

Trait Implementations

impl Part for TrainingOptions

impl Default for TrainingOptions

fn default() -> TrainingOptions

Returns the "default value" for a type.

impl Clone for TrainingOptions

fn clone(&self) -> TrainingOptions

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for TrainingOptions

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

Formats the value using the given formatter.

impl Serialize for TrainingOptions

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error> where
    __S: Serializer, 

Serialize this value into the given Serde serializer.

impl<'de> Deserialize<'de> for TrainingOptions

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
    __D: Deserializer<'de>, 

Deserialize this value from the given Serde deserializer.