lightweight pdata cleaning/processing/plotting/ML training library for use with an ATLAS BSM dihiggs search
Project description
shml
: routines to automate machine learning experiments for a X -> SH -> bbyy search
This module aims to provide a set of functions that, when composed, can run a pipeline capable of:
- going from
.root
files toparquet
files viauproot
andawkward
- constructing useful kinematic quantites for training
- applying a chosen or manual preselection
- configuring any additional processing, e.g. weight normalization, feature scaling
- access event data that's prepared for
pytorch
usingshml.torch_dataset.EventDataset
still to do:
- infra to run ml experiments in a GPU or CPU environment via
pytorch-lightining
Usage
To see currently usable implemented functionality, check the examples
folder.
Install
For preprocessing only:
python3 -m pip install shml
For ML extras (pytorch
, plotting):
python3 -m pip install shml[ml]
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