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

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

• preprocessing
• training
• variables
• variable plotting

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.

• CMSSW_9_4_9 or newer
• uproot pip install --user uproot==3.2.7
• pip install --user uproot-methods==0.2.6
• pip install --user awkward==0.4.2
• TensorFlow backend for KERAS export KERAS_BACKEND=tensorflow

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

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

• root2pandas: generate hdf5 files from ntuples + MEM files

collection of top-level scripts for training

collection of scripts for plotting

• plot_configs: collection of scripts for configuring plots

lists of input variables used for dnn trainings

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