ML framework workflow - jniedzie/SVJanalysis_wiki GitHub Wiki

The ML framework allows to train different ML models (BDT, NN, NAE, ...), evaluate them and plot performance plots. The code is located in the MLFramework directory. Before running any code in this directory, you'll need to create a conda environment with python 3 and install several modules. Refer to instructions in Creating SVJ virtual environment.

Preparing input data

The ML FW takes PFNanoAODSuper as input. These are PFNanoAOD ROOT files, with 2 additional trees, one containing the cut flow information and the other containing the process cross-section. You can find more information in Framework-workflow.

Preparing config file

See instructions in Preparing config file.

Training

See instructions in Training.

Producing evaluation objects

See instructions in Evaluation.

Plotting and analyzing results

See instructions in Analysis and plotting tools.

Adding new architecture to the framework

In case you want to extend the ML framework adding a new architecture, follow instructions here: