HH Inference Tools

Cloning the repository

This repository uses submodules, so you should clone it recursively via

# ssh (recommended)
git clone --recursive ssh://[email protected]:7999/hh/tools/inference.git

# or

# https (discouraged)
git clone --recursive https://gitlab.cern.ch/hh/tools/inference.git

Environment and software setup

1. Source the setup

The setup.sh script will install (and compile if necessary) all relevant software packages.

cd inference
source setup.sh

When the software is set up for the first time, this might take a few moments to complete.

By default, the setup script makes a few assumptions on where software is installed, outputs are stored, etc, but this is fully configurable. To do so, run

source setup.sh some_name

where the value of some_name is your choice, and the script interactively guides you through the quick setup process. To use the same configuration the next time, make sure to use the same value you passed before. Internally, a file .setups/some_name.sh is created which contains export statements line by line that you can be update anytime.

Integrating the datacards_run2 repository

Datacards for the combination are stored in the (protected) datacards_run2 repository. Some of the inference tools (especially the automated workflows) can directly inspect and use these datacards without much configuration. You only have to set the environment variable DHI_DATACARDS_RUN2 to the location of your local checkout. If you decided to use a configurable setup (see above), you will be asked for the value of this variable interactively. For instance, you could clone the repository first to a location of your choice,

git clone --recursive ssh://[email protected]:7999/hh/results/datacards_run2.git /your/path

(note that using ssh://... is recommended), and then use /your/path for the DHI_DATACARDS_RUN2 variable later on.

After that, you can use the names of the HH channels in the --datacards parameters of the inference tasks, e.g. --datacards bbww or --datacards bbww,bbbb to get results of the latest bbWW or bbWW+bbbb channels, respectively.

Reinstalling software

In case you want to reinstall the software stack or combine from scratch, prepend DHI_REINSTALL_SOFTWARE=1 or DHI_REINSTALL_COMBINE=1 to the source commend, e.g.

DHI_REINSTALL_SOFTWARE=1 source setup.sh some_name

2. Let law index your tasks and their parameters

law index --verbose

While indexing always sounds somewhat cumbersome, the law index file is just a human-readable file summarizing your tasks, the corresponding python modules, and their parameters. Law uses that file only to accelerate the autocompletion of the command line interface.

You should see:

indexing tasks in 1 module(s)
loading module 'dhi.tasks', done

module 'law.contrib.cms.tasks', 1 task(s):
    - law.cms.BundleCMSSW

module 'law.contrib.git', 1 task(s):
    - law.git.BundleGitRepository

module 'dhi.tasks.combine', 3 task(s):
    - InputDatacards
    - CreateWorkspace
    - CombineDatacards

module 'dhi.tasks.remote', 3 task(s):
    - BundleCMSSW
    - BundleRepo
    - BundleSoftware

module 'dhi.tasks.snapshot', 1 task(s):
    - Snapshot

module 'dhi.tasks.limits', 9 task(s):
    - UpperLimits
    - UpperLimitsGrid
    - PlotUpperLimitsAtPoint
    - PlotUpperLimits
    - PlotUpperLimits2D
    - PlotMultipleUpperLimitsByModel
    - PlotMultipleUpperLimits
    - MergeUpperLimits
    - MergeUpperLimitsGrid

module 'dhi.tasks.likelihoods', 5 task(s):
    - LikelihoodScan
    - PlotLikelihoodScan
    - PlotMultipleLikelihoodScansByModel
    - PlotMultipleLikelihoodScans
    - MergeLikelihoodScan

module 'dhi.tasks.significances', 4 task(s):
    - SignificanceScan
    - PlotSignificanceScan
    - PlotMultipleSignificanceScans
    - MergeSignificanceScan

module 'dhi.tasks.pulls_impacts', 4 task(s):
    - PullsAndImpacts
    - PlotPullsAndImpacts
    - PlotMultiplePullsAndImpacts
    - MergePullsAndImpacts

module 'dhi.tasks.postfit', 4 task(s):
    - FitDiagnostics
    - PlotDistributionsAndTables
    - PlotPostfitSOverB
    - PlotNuisanceLikelihoodScans

module 'dhi.tasks.gof', 4 task(s):
    - GoodnessOfFit
    - PlotMultipleGoodnessOfFits
    - PlotGoodnessOfFit
    - MergeGoodnessOfFit

module 'dhi.tasks.eft', 4 task(s):
    - EFTBenchmarkLimits
    - PlotEFTBenchmarkLimits
    - PlotMultipleEFTBenchmarkLimits
    - MergeEFTBenchmarkLimits

module 'dhi.tasks.test', 1 task(s):
    - test.TestPlots

module 'dhi.tasks.studies.model_selection', 3 task(s):
    - study.PlotMorphingScales
    - study.PlotMorphedDiscriminant
    - study.PlotStatErrorScan

module 'dhi.tasks.studies.model_plots', 1 task(s):
    - study.PlotSignalEnhancement

module 'dhi.tasks.exclusion', 2 task(s):
    - PlotExclusionAndBestFit
    - PlotExclusionAndBestFit2D

written 50 task(s) to index file '/your/path/inference/.law/index'

You can type

law run <tab><tab>

to see the list of available tasks in your shell, and

law run SomeTask <tab><tab>

to list all parameters of SomeTask.

Now you are done with the setup and can start running the statistical inference!

Configurable postfit plots

Postfit plots are not yet covered by the tasks listed above but will be provided in the future. In the meantime, you can use a separate set of scripts that allow to create fully configurable postfit plots for your analysis channel. For more info, see the dedicated README.

For developers

Code checks and CI

The code in this project follows a rather relaxed, yet PEP8 compliant style that is defined in .flake8. A linting process is triggered on each push event to the repository in a CI pipeline. However, you can also run these checks in your local environment by running

./lint.sh

in the root directory of the project, or by setting up your editor accordingly (recommended).

In addition to simple code checks, the CI is also configured to perform the standard project setup (including the CMSSW setup and compilation of combine), as well as to run specific tests tasks whose output plots will be visible right in GitLab UI. For more info, see this talk.

Documentation

The documentation is hosted at cern.ch/cms-hh/tools/inference.

It is built with MkDocs using the material theme and support for PyMdown extensions. Developing and building the documentation locally requires docker and a valid login at the CERN GitLab container registry.

To login, run

docker login gitlab-registry.cern.ch

and type your CERN username and password. Then, to build the documentation, run

./docs/docker/run.sh build

which creates a directory docs/site/ containing static HTML pages. To start a server to browse the pages, run

./docs/docker/run.sh serve

and open your webbrowser at http://localhost:8000. By default, all pages are automatically rebuilt and reloaded when a source file is updated.