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A Julia package to benchmark optimization solvers on logistic regression problems.

- MIT license
- Install using
`julia> ] add LogisticOptTools`

Suppose you import LogisticOptTools in your REPL

```
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> logreg = LOT.LogisticRegressor(X, y,
fit_intercept=true,
penalty=LOT.L2Penalty(1.0))
```

and then fit the logistic regression with `Optim.jl`

:

```
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
```

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.

LogisticOptTools supports the `libsvm`

format. Once a dataset downloaded
from the website,
you could load it in the Julia REPL with

```
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`

.

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