ndarray-glm

Rust library for solving linear, logistic, and generalized linear models through iteratively reweighted least squares, using the ndarray-linalg module.

Status

This package is in early alpha and the interface is likely to undergo many changes. Functionality may change from one release to the next.

The regression algorithm uses iteratively re-weighted least squares (IRLS) with a step-halving procedure applied when the next iteration of guesses does not increase the likelihood.

At the moment the master branch of ndarray-linalg along with blas-src = 0.6 is required to pass CI, but this precludes publishing new versions of this crate. For the time being you may see some version mismatches in the instructions below as a result.

Prerequisites

fortran and BLAS must be installed: sudo apt update && sudo apt install gfortran libblas-dev

To use the OpenBLAS backend, install also libopenblas-dev and use this crate with the "openblas-src" feature.

Example

To use in your crate, add the following to the Cargo.toml:

ndarray = { version = "0.13", features = ["blas"]} blas-src = { version = "0.4", default-features = false, features = ["openblas"] } ndarray-glm = { version = "0.0.3", features = ["openblas-static"] }

An example for linear regression is shown below.

``` rust use ndarray::array; use ndarray_glm::{linear::Linear, model::ModelBuilder, standardize::standardize};

// define some test data let datay = array![0.3, 1.3, 0.7]; let datax = 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 datax = standardize(datax); // The model is generic over floating point type for the independent data variables, and // the type will be inferred from the type of the arrays passed to data(). // L2 regularization can be applied with l2reg(). let model = ModelBuilder::::data(&datay, &datax).l2reg(1e-5).build()?; let fit = model.fit()?; // Currently the result is a simple array of the MLE estimators, including the intercept term. println!("Fit result: {}", fit.result); ```

For logistic regression, the y array data must be boolean, and for Poisson regression it must be an unsigned integer.

Custom non-canonical link functions can be defined by the user, although the interface is not particularly ergonomic. See tests/custom_link.rs for examples.

Features

• [X] Linear regression
• [X] Logistic regression
• [X] Generalized linear model IRLS
• [X] Linear offsets
• [X] Allow non-float domain types
• [X] L2 (ridge) Regularization
• [ ] L1 (lasso) Regularization
• [X] Generic over floating point type
• [ ] Other exponential family distributions
  • [X] Poisson
  • [X] Binomial (nightly only)
  • [ ] Exponential
  • [ ] Gamma (which effectively reduces to exponential with an arbitrary dispersion parameter)
  • [ ] Inverse Gaussian
  • [ ] ...
• [X] Option for data standardization/normalization
• [ ] Weighted and correlated regressions
  • [ ] Weight the covariance matrix with point-by-point error bars
  • [ ] Allow for off-diagonal correlations between points
  • [ ] Fix likelihood functions for weighted and/or correlated case
  • [ ] Re-visit the tolerance conditions for termination in these instances.
• [X] Non-canonical link functions
• [ ] Goodness-of-fit tests
  • [X] Likelihood ratio test
  • [ ] Log-likelihood difference from saturated model (deviance analysis)
  • [X] Akaike and Bayesian information criteria
  • [ ] generalized R^2?

TODO

• [ ] Generalize GLM interface to allow multi-parameter fits like a gamma distribution. This would demand other sufficient statistics besides y (e.g. y^2 for Gaussian w/ variance, log(y) for gamma). It might be worth putting off until const generics.
• [ ] Per-parameter test statistic (may require re-minimization)
• [ ] Calculate/estimate dispersion parameter from the data
• [ ] More rigorous convergence tests and options for termination
• [ ] Logging system with configurable levels

Reference

The author's notes on generalized linear models summarize many of the relevant concepts and provide some additional references.