This is a package to train pytorch model and evaluate the number significance for the H->mumu analysis
This project requires to use python3 and either conda or virtual environment. If you want to use a conda environment, install Anaconda or Miniconda before setting up. Below provides instructioon for setting up the virtual environment on lxplus with pip.
First checkout the code:
git clone set-url gitlab ssh://[email protected]:7999/wisc_atlas/HmumuTorch.git [-b your_branch]
cd HmumuTorch
Then,
source scripts/install.sh
After setting up the environment for the first time, you can return to this setup by doing source scripts/setup.sh
The core script is skim_ntuples.py. The script will apply the given skimming cuts to input samples and produce corresponding skimmed ntuples. You can run it locally for any specified input files. In H->mumu, it is more convenient and time-efficient to use submit_skim_ntuples.py which will find all of the input files specified in data/inputs_config.json and run the core script by submitting the condor jobs.
- The script is hard-coded currently, meaning one needs to directly modify the core script to change the variable calculation and the skimming cuts.
- The output files (skimmed ntuples) will be saved to the folder named
skimmed_ntuplesby default. submit_skim_ntuples.pywill only submit the jobs for those files that don't exist in the output folder.
python scripts/submit_skim_ntuples.py
or
python scripts/skim_ntuples.py [-i input_file_path] [-o output_file_path]
The script run_skim_ntuples.py will check if all of the outputs from last step exist and if the resulting number of events is correct.
python scripts/run_skim_ntuples.py [-c]
usage:
-c check the completeness only (without recovering the incomplete jobs)
-s skip checking the resulting number of events
Please be sure to do python run_skim_ntuples.py -c at least once before starting the BDT training exercise.
The whole ML task consists of training, applying the weights, optimizing the score boundaries for categorization, and calculating the number counting significances.
The training script train.py will train the model, and transform the output scores such that the unweighted signal distribution is flat. The detailed settings, including the preselections, training variables, hyperparameters, etc, are specified in the config file such as configs/training_config_FCN.json.
python scripts/train.py --help
usage: train.py [-h] [-c CONFIG] [-i INPUT_DIR] [-o OUTPUT_DIR]
[-r {two_jet,one_jet,zero_jet,VBF,all_jet}]
optional arguments:
-h, --help show this help message and exit
-c CONFIG, --config CONFIG
Region to process
-i INPUT_DIR, --input-dir INPUT_DIR
directory of training inputs
-o OUTPUT_DIR, --output-dir OUTPUT_DIR
directory for outputs
-r {two_jet,one_jet,zero_jet,VBF,all_jet}, --region {two_jet,one_jet,zero_jet,VBF,all_jet}
Region to process
Applying the trained model (as well as the score transformation) to the skimmed ntuples to get BDT scores for each event can be done by doing:
python scripts/apply.py --help
usage: apply.py [-h] [-ic INPUTCONFIG] [-c CONFIG CONFIG] [-i INPUT_DIR]
[-m MODEL_DIR] [-o OUTPUT_DIR]
[-r {two_jet,one_jet,zero_jet,all_jet}]
[-s SAMPLES [SAMPLES ...]]
optional arguments:
-h, --help show this help message and exit
-ic INPUTCONFIG, --inputConfig INPUTCONFIG
Input config
-c CONFIG CONFIG, --config CONFIG CONFIG
training config + apply config
-i INPUT_DIR, --input-dir INPUT_DIR
directory of training inputs
-m MODEL_DIR, --model-dir MODEL_DIR
directory of NN models
-o OUTPUT_DIR, --output-dir OUTPUT_DIR
directory for outputs
-r {two_jet,one_jet,zero_jet,all_jet}, --region {two_jet,one_jet,zero_jet,all_jet}
Region to process
-s SAMPLES [SAMPLES ...], --samples SAMPLES [SAMPLES ...]
apply only for specific samples
The script will take the settings specified in the training config file and the applying config file such as configs/apply_config.json.
categorization_1D.py will take the Higgs classifier scores of the samples and optimize the boundaries that give the best combined significance. categorization_2D.py, on the other hand, takes both the Higgs classifier scores and the VBF classifier scores of the samples and optimizes the 2D boundaries that give the best combined significance.
python categorization_1D.py [-r REGION] [-f NUMBER OF FOLDS] [-b NUMBER OF CATEGORIES] [-n NSCAN] [--floatB] [--minN minN] [--skip]
Usage:
-f, --fold Number of folds of the categorization optimization. Default is 1.
-b, --nbin Number of score categories
-n, --nscan Number of scans. Default is 100
--minN, minN is the minimum number of events required in the mass window. The default is 5.
--floatB To float the last score boundary, which means to veto the lowest score score events
--skip To skip the hadd step (if you have already merged signal and background samples)
python categorization_2D.py [-r REGION] [-f NUMBER OF FOLDS] [-b NUMBER OF CATEGORIES] [-b NUMBER OF ggF CATEGORIES] [-n NSCAN] [--floatB] [--minN minN] [--skip]
Usage:
-f, --fold Number of folds of the categorization optimization. Default is 1.
-b, --nbin Number of score categories
-n, --nscan Number of scans. Default is 100
--minN, minN is the minimum number of events required in the mass window. The default is 5.
--floatB To float the last score boundary, which means to veto the lowest BDT score events
--skip To skip the hadd step (if you have already merged signal and background samples)