To regenerate the benchmark

1. First, reproduce the environment:

conda env create -f environment.yml

And install my scikit-learn's fork branch containing both LMNN and NCA: https://github.com/wdevazelhes/scikit-learn/tree/ncalmnn (Clone https://github.com/wdevazelhes/scikit-learn, install it in editable mode and checkout to ncalmnn branch)

2. Then, reproduce the results:

cd benchmarks
python bench.py bench_gs.cfg

3. Once the results are generated (it may take a while), plot the results with the help of plot_bench.py: For instance, open an ipython terminal in the benchmark folder, and type:

cd bench20171204-XXX2
plot_bench(algos=('scaler + knn', 'scaler + lmnn + knn', 'scaler + nca + knn'), 
           min_score=0.8)
cd ../bench20171201-XXX1
plot_bench(algos=('scaler + pca + knn', 'scaler + lmnn + knn', 'scaler + nca + knn'), 
           min_score=0.2)