ID-Trainer
A simple config-based tool for high-energy-physics machine learning tasks.
Full documentation and instructions to use are available here: https://akapoorcern.github.io/ID-Trainer/
| Currently supports |
|
| Binary-classification (currently using XGBoost and DNN) |
Examples: DY vs ttbar, DY prompt vs DY fake, good electrons vs bad electrons |
| Multi-sample classification (currently using XGBoost and DNN) |
Examples: DY vs (ttbar and QCD) |
| Multi-class classification (currently using XGBoost and DNN) |
Examples: DY vs ttbar vs QCD, , good photons vs bad photons |
| Salient features: |
| Parallel reading of root files (using DASK) |
| Runs on flat ntuples (even NanoAODs) out of the box |
| Adding multiple MVAs is very trivial (Subject to available computing power) |
| Cross-section and pt-eta reweighting can be handled together |
| Multi-Sample training possible |
| Multi-Class training possible |
| Ability to customize thresholds |
| What will be the output of the trainer: |
| Feature distributions |
| Statistics in training and testing |
| ROCs, loss plots, MVA scores |
| Confusion Matrices |
| Correlation plots |
| Trained models (h5/pb for DNN / pkl for XGBoost) |
Optional outputs
- Threshold values of scores for chosen working points
- Efficiency vs pT and Efficiency vs eta plots for all classes
- Reweighting plots for pT and eta
- Comparison of new ID performance with benchmark ID flags
