https://github.com/frapac/logisticopttools.jl
A package to fit logistic regression in pure Julia
https://github.com/frapac/logisticopttools.jl
julia logistic-regression-algorithm optimization
Last synced: about 2 months ago
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A package to fit logistic regression in pure Julia
- Host: GitHub
- URL: https://github.com/frapac/logisticopttools.jl
- Owner: frapac
- License: mit
- Created: 2019-12-27T14:21:43.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-05-14T20:57:22.000Z (almost 5 years ago)
- Last Synced: 2025-01-20T19:54:53.467Z (3 months ago)
- Topics: julia, logistic-regression-algorithm, optimization
- Language: Julia
- Homepage:
- Size: 176 KB
- Stars: 1
- Watchers: 3
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# LogisticOptTools.jl
| **Build Status** |
|:----------------:|
| [![Build Status][build-img]][build-url] |
A Julia package to benchmark optimization solvers on
logistic regression problems.
* MIT license
* Install using `julia> ] add LogisticOptTools`
## Basic usage
Suppose you import LogisticOptTools in your REPL
```julia
julia> using LogisticOptTools
julia> const LOT = LogisticOptTools
```
Suppose you have available a matrix of features `X` and a vector of observations `y`,
and you want to fit a logistic model onto this data.
You could instantiate a new logistic model simply by typing
```julia
julia> logreg = LOT.LogisticRegressor(X, y,
fit_intercept=true,
penalty=LOT.L2Penalty(1.0))
```
and then fit the logistic regression with `Optim.jl`:
```julia
julia> p = LOT.nfeatures(logreg)
julia> x0 = zeros(p) ; algo = LBFGS()
julia> res = Optim.optimize(logreg, x0, algo)
# Fetch optimal parameters
julia> p_opt = res.minimizer
```
## Benchmarks
LogisticOptTools could use the different algorithms implemented in Optim.jl.
We depict in the following figure a comparison of three algorithms, when
fitting a logistic model on the `covtype` dataset (581,012 data, 54 features).
For an example on how to use other solvers, we have implemented
in `examples/tron.jl` a resolution of a logistic regression problem
with `tron`, a solver implemented [JSOSolvers.jl](https://github.com/JuliaSmoothOptimizers/JSOSolvers.jl/).
## Use-cases
### Import LIBSVM datasets
LogisticOptTools supports the `libsvm` format. Once a dataset downloaded
from the [website](https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html),
you could load it in the Julia REPL with
```julia
shell> ls .
covtype.binary.bz2
# Parse as Float64
julia> dataset = LOT.parse_libsvm("covtype.binary.bz2", Float64)
# Load as dense matrix
julia> X = LOT.to_dense(dataset)
julia> y = dataset.labels
```
You could load the dataset as a sparse matrix just by replacing
`LOT.to_dense` with `LOT.to_sparse`.
### Advanced usages
You could find in `examples/` a few examples on:
* optimizing the L2 penalty parameter with `Optim.jl`
* fitting a sparse regression (l0-l2 logistic regression) with JuMP and a MILP solver
[build-img]: https://travis-ci.org/frapac/LogisticOptTools.jl.svg?branch=master
[build-url]: https://travis-ci.org/frapac/LogisticOptTools.jl