Crate ndarray_glm
source ·Expand description
A rust library for performing GLM regression with data represented in
ndarrays.
The ndarray-linalg crate is used to allow
optimization of linear algebra operations with BLAS.
This crate is early alpha and may change rapidly. No guarantees can be made about the accuracy of the fits.
Feature summary:
- Linear, logistic, Poisson, and binomial regression (more to come)
- Generic over floating-point type
- L2 (ridge) regularization
- Statistical tests of fit result
- Alternative and custom link functions
Setting up BLAS backend
See the backend features of
ndarray-linalg
for a description of the available BLAS configuartions. You do not need to
include ndarray-linalg in your crate; simply provide the feature you need to
ndarray-glm and it will be forwarded to ndarray-linalg.
Examples using OpenBLAS are shown here. In principle you should also be able to use Netlib or Intel MKL, although these backends are untested.
System OpenBLAS (recommended)
Ensure that the development OpenBLAS library is installed on your system. In
Debian/Ubuntu, for instance, this means installing libopenblas-dev. Then, put the
following into your crate’s Cargo.toml:
ndarray = { version = "0.15", features = ["blas"]}
ndarray-glm = { version = "0.0.12", features = ["openblas-system"] }
Compile OpenBLAS from source
This option does not require OpenBLAS to be installed on your system, but the
initial compile time will be very long. Use the folling lines in your crate’s
Cargo.toml.
ndarray = { version = "0.15", features = ["blas"]}
ndarray-glm = { version = "0.0.12", features = ["openblas-static"] }
Examples:
Basic linear regression:
use ndarray_glm::{array, Linear, ModelBuilder};
let data_y = array![0.3, 1.3, 0.7];
let data_x = array![[0.1, 0.2], [-0.4, 0.1], [0.2, 0.4]];
let model = ModelBuilder::<Linear>::data(&data_y, &data_x).build().unwrap();
let fit = model.fit().unwrap();
// The result is a flat array with the first term as the intercept.
println!("Fit result: {}", fit.result);Data standardization and L2 regularization:
use ndarray_glm::{array, Linear, ModelBuilder, utility::standardize};
let data_y = array![0.3, 1.3, 0.7];
let data_x = array![[0.1, 0.2], [-0.4, 0.1], [0.2, 0.4]];
// The design matrix can optionally be standardized, where the mean of each independent
// variable is subtracted and each is then divided by the standard deviation of that variable.
let data_x = standardize(data_x);
let model = ModelBuilder::<Linear>::data(&data_y, &data_x).build().unwrap();
// L2 (ridge) regularization can be applied with l2_reg().
let fit = model.fit_options().l2_reg(1e-5).fit().unwrap();
println!("Fit result: {}", fit.result);Logistic regression with a non-canonical link function:
use ndarray_glm::{array, Logistic, logistic_link::Cloglog, ModelBuilder};
let data_y = array![true, false, false, true, true];
let data_x = array![[0.5, 0.2], [0.1, 0.3], [0.2, 0.6], [0.6, 0.3], [0.4, 0.4]];
let model = ModelBuilder::<Logistic<Cloglog>>::data(&data_y, &data_x).build().unwrap();
let fit = model.fit_options().l2_reg(1e-5).fit().unwrap();
println!("Fit result: {}", fit.result);Re-exports
pub use model::ModelBuilder;