- Run PID task on grid with RunAnalysisAODVertexingHFPIDsyst.C passing a yaml configuration file for your desired data sample
- Run getdataframesfromroot.py script to convert trees to pandas dataframe and store them in files:
python3 getdataframesfromroot.py input_root_file name_TDirectoryFile output_directory - Binary classification with BTD (using interpret library): test_BDTinterpret.py
- Multi-class classification with SVM using the scikit-learn library and with the xgboost library: test_multiclass.py
- One vs. rest classification with xgboost classifier using the scikit-learn library for the one vs. rest classification: test_BDTOneVsRest.py
- dE/dx vs. p representation in 2D plots (both data or MC):
python3 plot_hist2d.py --mc (--data) name_directory_with_files
where name_directory_with_files is a directory that should contain the data or MC files
- plot purity of tagged samples (from MC truth only):
python3 plot_purity.py name_directory_with_files_MC
where name_directory_with_files_MC is a directory that should contain the MC files
- Grid search for one vs. rest classifier with xgboost binary classifiers:
python3 grid_search_OvR.py name_directory_with_files
- Grid search for multi-class classifier with xgboost:
python3 grid_search_multiclass.py name_directory_with_files
in both cases name_directory_with_files is a directory that should contain the files for the grid search (either data or MC)
- Training and testing for one vs. rest classifier with xgboost binary classifiers:
python3 OvsR_classifier_train_test.py name_directory_with_files
where name_directory_with_files is a directory that should contain the data or MC files
- Data: LHC17pq_cent
- MC (general purpose): LHC17l3b_cent
- MC (with injected nuclei): LHC18b5a_cent
git config --global user.name "<Firstname> <Lastname>"git config --global user.email <your-email-address>git config --global user.github <your-github-username>