rs-example.md

A Complete Rust Example

To run this example, add the following code to your Cargo.toml file.

[dependencies]
forust-ml = "0.4.8"
polars = "0.28"
reqwest = { version = "0.11", features = ["blocking"] }

The following is a runable example using polars for data processing. The actual data manipulation can be performed with any tool, the only vital part, is the data be in column major format.

use forust_ml::{GradientBooster, Matrix};
use polars::prelude::*;
use reqwest::blocking::Client;
use std::error::Error;
use std::io::Cursor;

fn main() -> Result<(), Box<dyn Error>> {
    let data: Vec<u8> = Client::new()
        .get("https://raw.githubusercontent.com/mwaskom/seaborn-data/master/titanic.csv")
        .send()?
        .text()?
        .bytes()
        .collect();

    let df = CsvReader::new(Cursor::new(data))
        .has_header(true)
        .finish()?
        .select(["survived", "pclass", "age", "sibsp", "parch", "fare"])?;

    // Get data in column major format...
    let id_vars: Vec<&str> = Vec::new();
    let mdf = df.melt(id_vars, ["pclass", "age", "sibsp", "parch", "fare"])?;

    let data: Vec<f64> = mdf
        .select_at_idx(1)
        .expect("Invalid column")
        .f64()?
        .into_iter()
        .map(|v| v.unwrap_or(f64::NAN))
        .collect();
    let y: Vec<f64> = df
        .column("survived")?
        .cast(&DataType::Float64)?
        .f64()?
        .into_iter()
        .map(|v| v.unwrap_or(f64::NAN))
        .collect();

    // Create Matrix from ndarray.
    let matrix = Matrix::new(&data, y.len(), 5);

    // Create booster.
    // To provide parameters generate a default booster, and then use
    // the relevant `set_` methods for any parameters you would like to
    // adjust.
    let mut model = GradientBooster::default().set_learning_rate(0.3);
    model.fit_unweighted(&matrix, &y, None)?;

    // Predict output.
    println!("{:?} ...", &model.predict(&matrix, true)[0..10]);
    Ok(())
}

[-1.275806741323322, 0.9178487278986722, -1.4758225567638874, 1.0830510996747762, -1.7252372093498707, -1.4195771454833448, -0.27499967138282955, -0.9451315118931234, -0.08839774504303932, 1.374593096319586] ...

We first read in the data, and then, generate a contiguous matrix, that is used for training the booster. At this point, we can then instantiate out gradient booster, using the default parameters. These can be adjusted using the relevant set_ methods, for any parameters of interest (see here for all such methods).