3 unstable releases
0.7.0 | Oct 16, 2023 |
---|---|
0.6.1 | Dec 3, 2022 |
0.6.0 | Jun 15, 2022 |
#940 in Machine learning
225KB
4K
SLoC
Follow the regularized leader
linfa-ftrl
provides a pure Rust implementations of follow the regularized leader, proximal, model.
The Big Picture
linfa-ftrl
is a crate in the linfa
ecosystem, an effort to create a toolkit for classical Machine Learning implemented in pure Rust, akin to Python's scikit-learn
.
Current state
The linfa-ftrl
crate provides Follow The Regularized Leader - Proximal model with L1 and L2 regularization from Logistic Regression, and primarily used for CTR prediction. It actively stores z and n values, needed to calculate weights.
Without L1 and L2 regularization, it is identical to online gradient descent.
See also:
Examples
There is a usage example in the examples/
directory. To run, use:
$ cargo run --example winequality
Show source code
use linfa::prelude::*;
use linfa::dataset::{AsSingleTargets, Records};
use linfa_ftrl::{Ftrl, Result};
use rand::{rngs::SmallRng, SeedableRng};
// load Winequality dataset
let (train, valid) = linfa_datasets::winequality()
.map_targets(|v| if *v > 6 { true } else { false })
.split_with_ratio(0.9);
let params = Ftrl::params()
.alpha(0.005)
.beta(1.0)
.l1_ratio(0.005)
.l2_ratio(1.0);
let valid_params = params.clone().check_unwrap();
let mut model = Ftrl::new(valid_params, train.nfeatures());
// Bootstrap each row from the train dataset to imitate online nature of the data flow
let mut rng = SmallRng::seed_from_u64(42);
let mut row_iter = train.bootstrap_samples(1, &mut rng);
for _ in 0..train.nsamples() {
let b_dataset = row_iter.next().unwrap();
model = params.fit_with(Some(model), &b_dataset)?;
}
let val_predictions = model.predict(&valid);
println!("valid log loss {:?}", val_predictions.log_loss(&valid.as_single_targets().to_vec())?);
# Result::Ok(())
Dependencies
~6MB
~116K SLoC