Struct google_api_proto::google::cloud::bigquery::v2::model::HparamSearchSpaces

pub struct HparamSearchSpaces {

    pub learn_rate: Option<DoubleHparamSearchSpace>,
    pub l1_reg: Option<DoubleHparamSearchSpace>,
    pub l2_reg: Option<DoubleHparamSearchSpace>,
    pub num_clusters: Option<IntHparamSearchSpace>,
    pub num_factors: Option<IntHparamSearchSpace>,
    pub hidden_units: Option<IntArrayHparamSearchSpace>,
    pub batch_size: Option<IntHparamSearchSpace>,
    pub dropout: Option<DoubleHparamSearchSpace>,
    pub max_tree_depth: Option<IntHparamSearchSpace>,
    pub subsample: Option<DoubleHparamSearchSpace>,
    pub min_split_loss: Option<DoubleHparamSearchSpace>,
    pub wals_alpha: Option<DoubleHparamSearchSpace>,
    pub booster_type: Option<StringHparamSearchSpace>,
    pub num_parallel_tree: Option<IntHparamSearchSpace>,
    pub dart_normalize_type: Option<StringHparamSearchSpace>,
    pub tree_method: Option<StringHparamSearchSpace>,
    pub min_tree_child_weight: Option<IntHparamSearchSpace>,
    pub colsample_bytree: Option<DoubleHparamSearchSpace>,
    pub colsample_bylevel: Option<DoubleHparamSearchSpace>,
    pub colsample_bynode: Option<DoubleHparamSearchSpace>,
    pub activation_fn: Option<StringHparamSearchSpace>,
    pub optimizer: Option<StringHparamSearchSpace>,
}

Hyperparameter search spaces. These should be a subset of training_options.

Fields

learn_rate: Option<DoubleHparamSearchSpace>

Learning rate of training jobs.

l1_reg: Option<DoubleHparamSearchSpace>

L1 regularization coefficient.

l2_reg: Option<DoubleHparamSearchSpace>

L2 regularization coefficient.

num_clusters: Option<IntHparamSearchSpace>

Number of clusters for k-means.

num_factors: Option<IntHparamSearchSpace>

Number of latent factors to train on.

hidden_units: Option<IntArrayHparamSearchSpace>

Hidden units for neural network models.

batch_size: Option<IntHparamSearchSpace>

Mini batch sample size.

dropout: Option<DoubleHparamSearchSpace>

Dropout probability for dnn model training and boosted tree models using dart booster.

max_tree_depth: Option<IntHparamSearchSpace>

Maximum depth of a tree for boosted tree models.

subsample: Option<DoubleHparamSearchSpace>

Subsample the training data to grow tree to prevent overfitting for boosted tree models.

min_split_loss: Option<DoubleHparamSearchSpace>

Minimum split loss for boosted tree models.

wals_alpha: Option<DoubleHparamSearchSpace>

Hyperparameter for matrix factoration when implicit feedback type is specified.

booster_type: Option<StringHparamSearchSpace>

Booster type for boosted tree models.

num_parallel_tree: Option<IntHparamSearchSpace>

Number of parallel trees for boosted tree models.

dart_normalize_type: Option<StringHparamSearchSpace>

Dart normalization type for boosted tree models.

tree_method: Option<StringHparamSearchSpace>

Tree construction algorithm for boosted tree models.

min_tree_child_weight: Option<IntHparamSearchSpace>

Minimum sum of instance weight needed in a child for boosted tree models.

colsample_bytree: Option<DoubleHparamSearchSpace>

Subsample ratio of columns when constructing each tree for boosted tree models.

colsample_bylevel: Option<DoubleHparamSearchSpace>

Subsample ratio of columns for each level for boosted tree models.

colsample_bynode: Option<DoubleHparamSearchSpace>

Subsample ratio of columns for each node(split) for boosted tree models.

activation_fn: Option<StringHparamSearchSpace>

Activation functions of neural network models.

optimizer: Option<StringHparamSearchSpace>

Optimizer of TF models.

Trait Implementations

impl Clone for HparamSearchSpaces

fn clone(&self) -> HparamSearchSpaces

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for HparamSearchSpaces

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

Formats the value using the given formatter.

impl Default for HparamSearchSpaces

fn default() -> Self

Returns the “default value” for a type.

impl Message for HparamSearchSpaces

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 HparamSearchSpaces

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