johannbrehmer/madminer: v0.2.0

New features: Nuisance parameters to model systematic uncertainties, currently only from PDF / scale reweighting and for SALLY / SALLINO. New "score" mode for ensemble Fisher information calculation. No need for the Pythia / Delphes patch anymore: DelphesProcessor can extract the event weights from the LHE file. Additional (non-morphing) benchmarks can be defined in MadMiner without breaking or resetting the morphing. DelphesProcessor allows k factors. New function plot_distributions() to plot distributions of observables and systematic error bands. Option to save full pyTorch models. Option to limit the training sample size. Dynamic binning for 2D histograms. Breaking / API changes: DelphesProcessor.add_sample() replaces DelphesProcessor.add_hepmc_sample(). MadMiner.set_morphing() replaces MadMiner.set_benchmarks_from_morphing(). Documentation and examples: New toy example. Updated docs. Bug fixes: Fixed critical bug in the evaluation of the score with ensemble methods, which led to wrong results for the detector-level Fisher information. Fixed bug in the calculation of the covariance of truth-type Fisher information matrices. Fixed gradient clipping. Many small bug fixes. Internal changes: MadMiner file format changed to include systematics / nuisance parameter data. Significant speed-up of training for CARL, ROLR, and ALICE estimators. No unnecessary running of Delphes in DelphesProcessor. Restructured logging, now the user is responsible for setting up handlers. Renamed some submodules in madminer.utils.interfaces. Removed unnecessary static functions in MadMiner. Rewrote profiling algorithm