DRACO-MLfoy

Collection of Machine learning frameworks for DNNs, Regressions, Adversaries, CNNs and Others (DRACO)

Refer to READMEs in the corresponding directories for specific information!

For readability there is a README for each step (this here is only for an overview!):

• preprocessing
• training
• variables
• variable plotting

ATTENTION

When working with the combination of

• ROOT.__version__ = 6.14/04
• keras.__version__ = 2.2.4
• pandas.__version__ = 0.23.4 segmentation violations appear when opening hdf-files with pandas.read_hdf() or pandas.HDFStore(). To circumvent this, first import everything related to ROOT, only then start importing things from keras.

Package-requirements

• CMSSW_9_4_9 or newer
• uproot pip install --user uproot
• TensorFlow backend for KERAS export KERAS_BACKEND=tensorflow

Workflow in a nutshell

1. Preprocess input data:
   • to create input features you need data in the ntuple format, these get converted into hdf5 dataframes
   • create a list of input features in variable_sets/ (look at the README for structural advice)
   • adjust settings in preprocessing/root2pandas/preprocessing.py, check README
   • execute it on the NAF (tested with CMSSW_9_4_9)
   • this creates one dataset for each event category you specified in preprocessing.py
2. Setup training:
   • move to directory train_scripts/ and create a config script for your training (for simple DNN training adjust train_scripts/train_template.py)
   • look at the README for further instructions
3. Execute training
   • execute the trainig script with your jet-tag category as option (further parser options described in README)
   • after completion of the script you can look at results in the specified output directory

preprocessing

collection of scripts for preprocessing of ntuples files to hdf5 files as input for DRACOs

• root2pandas: generate hdf5 files from ntuples + MEM files

train_scripts

collection of top-level scripts for training

pyrootsOfTheCaribbean

collection of scripts for plotting

• plot_configs: collection of scripts for configuring plots

variable_sets

lists of input variables used for dnn trainings

DRACO_Frameworks

collections of frameworks for training and evaluation of different machine learning frameworks