Struct TensorForestParams

pub struct TensorForestParams {

    pub leaf_type: i32,
    pub stats_type: i32,
    pub collection_type: i32,
    pub pruning_type: Option<SplitPruningConfig>,
    pub finish_type: Option<SplitFinishConfig>,
    pub num_trees: i32,
    pub max_nodes: i32,
    pub num_features: i32,
    pub inequality_test_type: i32,
    pub is_regression: bool,
    pub drop_final_class: bool,
    pub collate_examples: bool,
    pub checkpoint_stats: bool,
    pub use_running_stats_method: bool,
    pub initialize_average_splits: bool,
    pub inference_tree_paths: bool,
    pub num_outputs: i32,
    pub num_splits_to_consider: Option<DepthDependentParam>,
    pub split_after_samples: Option<DepthDependentParam>,
    pub dominate_fraction: Option<DepthDependentParam>,
    pub min_split_samples: Option<DepthDependentParam>,
    pub graph_dir: String,
    pub num_select_features: i32,
    pub num_classes_to_track: i32,
}

Fields

leaf_type: i32

———— Types that control training subsystems —— //

stats_type: i32

collection_type: i32

pruning_type: Option<SplitPruningConfig>

finish_type: Option<SplitFinishConfig>

num_trees: i32

——— Parameters that can’t change by definition ————— //

max_nodes: i32

num_features: i32

inequality_test_type: i32

is_regression: bool

Some booleans controlling execution

drop_final_class: bool

collate_examples: bool

checkpoint_stats: bool

use_running_stats_method: bool

initialize_average_splits: bool

inference_tree_paths: bool

num_outputs: i32

Number of classes (classification) or targets (regression)

num_splits_to_consider: Option<DepthDependentParam>

——— Parameters that could be depth-dependent ————— //

split_after_samples: Option<DepthDependentParam>

dominate_fraction: Option<DepthDependentParam>

min_split_samples: Option<DepthDependentParam>

graph_dir: String

——— Parameters for experimental features ––––––––––– //

num_select_features: i32

num_classes_to_track: i32

When using a FixedSizeSparseClassificationGrowStats, keep track of this many classes.

Implementations

impl TensorForestParams

pub fn leaf_type(&self) -> LeafModelType

Returns the enum value of leaf_type, or the default if the field is set to an invalid enum value.

pub fn set_leaf_type(&mut self, value: LeafModelType)

Sets leaf_type to the provided enum value.

pub fn stats_type(&self) -> StatsModelType

Returns the enum value of stats_type, or the default if the field is set to an invalid enum value.

pub fn set_stats_type(&mut self, value: StatsModelType)

Sets stats_type to the provided enum value.

pub fn collection_type(&self) -> SplitCollectionType

Returns the enum value of collection_type, or the default if the field is set to an invalid enum value.

pub fn set_collection_type(&mut self, value: SplitCollectionType)

Sets collection_type to the provided enum value.

pub fn inequality_test_type(&self) -> Type

Returns the enum value of inequality_test_type, or the default if the field is set to an invalid enum value.

pub fn set_inequality_test_type(&mut self, value: Type)

Sets inequality_test_type to the provided enum value.

Trait Implementations

impl Clone for TensorForestParams

fn clone(&self) -> TensorForestParams

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for TensorForestParams

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

Formats the value using the given formatter.

impl Default for TensorForestParams

fn default() -> Self

Returns the “default value” for a type.

impl Message for TensorForestParams

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<B>(&self, buf: &mut B) -> Result<(), EncodeError>

where B: BufMut, Self: Sized,

Encodes the message to a buffer.

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

where B: BufMut, Self: Sized,

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

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

where B: Buf, Self: Default,

Decodes an instance of the message from a buffer.

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

where B: Buf, Self: Default,

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

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

where B: Buf, Self: Sized,

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

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

where B: Buf, Self: Sized,

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

impl PartialEq for TensorForestParams

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

Tests for self and other values to be equal, and is used by ==.

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

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

impl StructuralPartialEq for TensorForestParams