pub struct Dataset { /* private fields */ }
Expand description
Dataset used throughout LightGBM for training.
§Examples
§from mat
use lightgbm::Dataset;
let data = vec![vec![1.0, 0.1, 0.2, 0.1],
vec![0.7, 0.4, 0.5, 0.1],
vec![0.9, 0.8, 0.5, 0.1],
vec![0.2, 0.2, 0.8, 0.7],
vec![0.1, 0.7, 1.0, 0.9]];
let label = vec![0.0, 0.0, 0.0, 1.0, 1.0];
let dataset = Dataset::from_mat(data, label).unwrap();
§from file
use lightgbm::Dataset;
let dataset = Dataset::from_file(&"lightgbm-sys/lightgbm/examples/binary_classification/binary.train").unwrap();
Implementations§
Source§impl Dataset
impl Dataset
Sourcepub fn from_mat(data: Vec<Vec<f64>>, label: Vec<f32>) -> Result<Self>
pub fn from_mat(data: Vec<Vec<f64>>, label: Vec<f32>) -> Result<Self>
Create a new Dataset
from dense array in row-major order.
Example
use lightgbm::Dataset;
let data = vec![vec![1.0, 0.1, 0.2, 0.1],
vec![0.7, 0.4, 0.5, 0.1],
vec![0.9, 0.8, 0.5, 0.1],
vec![0.2, 0.2, 0.8, 0.7],
vec![0.1, 0.7, 1.0, 0.9]];
let label = vec![0.0, 0.0, 0.0, 1.0, 1.0];
let dataset = Dataset::from_mat(data, label).unwrap();
Sourcepub fn from_file(file_path: &str) -> Result<Self>
pub fn from_file(file_path: &str) -> Result<Self>
Create a new Dataset
from file.
file is tsv
.
<label>\t<value>\t<value>\t...
2 0.11 0.89 0.2
3 0.39 0.1 0.4
0 0.1 0.9 1.0
Example
use lightgbm::Dataset;
let dataset = Dataset::from_file(&"lightgbm-sys/lightgbm/examples/binary_classification/binary.train");
Trait Implementations§
Auto Trait Implementations§
impl Freeze for Dataset
impl RefUnwindSafe for Dataset
impl !Send for Dataset
impl !Sync for Dataset
impl Unpin for Dataset
impl UnwindSafe for Dataset
Blanket Implementations§
Source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere
T: ?Sized,
Source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more