# Beyond Tikhonov: Faster Learning withSelf-Concordant Losses via Iterative Regularization

## Installing Cyanure

You need Cyanure to run the experiments. If you don't want to install Cyanure, you can comment `from cyanure import BinaryClassifier` in `spline_logistic.py` and run `learning_rate` with `cyan=False`. 

Create a new environment, extract `cyanure.zip`. Then install the package with:
```
python setup_cyanure_mkl.py install
```

You may need a recent version of CMake or GCC compiler. This was tested on Ubuntu 18.04. 

## Running the experiments

You may want to configure the path at which the experiments' results will be saved. You can set the `scratch_dir` variable in `gscfunc/config.py`.  

You can run the experiments with:
```
python spline_{logistic,ls}.py
```

You may use less seeds or less regularization if you have less computational power. 

## Plots 

Refer to the Jupyter notebooks for the plots. 
