This tool allows you to take a series of ROOT ntuples, signal & background, apply a lot of cuts automatically, and figure out the most optimal selections to maximize significance. It comes packed with a lot of features
- generator script to create, what we call, a supercuts file containing all the rules to tell the script what cuts to apply and on which branches
- cut script which will take your signal, background, and supercuts; run them all; and output a series of files with the appropriate event counts for all cuts provided
- optimization script which will take your signal counts and background counts; run them all; and output a sorted list of optimal cuts
- hash look up script to reverse-engineer the cut for a given hash when you supply the supercuts file
Note: as part of making the script run as fast as possible, I try to maintain a low memory profile. It will not store (or remember) the cut used to create a significance value. Instead, we compute a 32-bit hash which is used to encode the cuts, and a way to "decode" the hash is also provided.
Table of Contents generated with DocToc
- Major Dependencies
- Quick Start
- Documentation
- Authors
All other dependencies are listed in requirements.txt and can be installed in one line with pip install -r requirements.txt.
tl;dr - copy and paste, and off you go.
I use virtualenvwrapper to manage my python dependencies and workspace. I assume you have pip.
pip install virtualenvwrapper
echo "source /usr/local/bin/virtualenvwrapper.sh" >> ~/.bash_profile
source ~/.bash_profileand then at this point, you can set up and install:
mkvirtualenv optimization
workon optimization
pip install root-optimize
rooptimize -hStart a new environment with mkvirtualenv NameOfEnv and everytime you open a new shell, you just need to type workon NameOfEnv. Type workon alone to see a list of environments you've created already. Read the virtualenvwrapper docs for more information.
pip install root-optimize
rooptimize -hFirst, set up a virtual environment using python3 on the CVMFS-enabled machine and install the package:
python3 -m venv optimization
source optimization/bin/activate
pip install root-optimize
rooptimize -hwhich gets us a virtual environment (optimization) to work with. Lastly, all that's left when you log-in next time is
source optimization/bin/activate
rooptimize -hand you're good to go!
A straightforward example is simply just
rooptimize generate "Gtt_0L_a/fetch/data-optimizationTree/user.lgagnon:user.lgagnon.370101.Gtt.DAOD_SUSY10.e4049_s2608_r6765_r6282_p2411_tag_10_v1_output_xAOD.root-0.root"which will create a supercuts.json file for you to edit so that you can run the optimizations. As a more advanced example, I only wanted to generate a file using a subset of the branches in my file as well as setting some of them to be a fixed cut that I would configure, so I ran
rooptimize generate "Gtt_0L_a/fetch/data-optimizationTree/user.lgagnon:user.lgagnon.370101.Gtt.DAOD_SUSY10.e4049_s2608_r6765_r6282_p2411_tag_10_v1_output_xAOD.root-0.root" --fixedBranches multiplicity_topTag* -o dump.json -b -vv --skipBranches *_jet_rc*which will write branches that match multiplicity_topTag* to have a fixed cut when I eventually run optimize over it; and will also skip branches that match *_jet_rc* so they won't be considered at all for cuts.
After that, we just specify all of our ROOT files. The script takes advantage of TChain and *nix file handling, it will automatically handle multiple files specified either as a pattern or just explicitly writing them out. We will group every output by the filenames/treenames passed in.
rooptimize cut TA07_MBJ10V1/*_0L_a/fetch/data-optimizationTree/*.root --supercuts=supercuts_small.json -o cuts_0L_a -b
rooptimize cut TA07_MBJ10V1/*_0L_a/fetch/data-optimizationTree/*.root --supercuts=supercuts_small.json -o cuts_0L_b -b
rooptimize cut TA07_MBJ10V1/*_1L/fetch/data-optimizationTree/*.root --supercuts=supercuts_small.json -o cuts_1L -bWe use numpy and awkward-array in order to calculate the number of events passing a given cut. We will also attempt to parallelize the computations as much as possible.
After that, we just (at a bare minimum) specify the signal and bkgd json cut files. The following example takes the 0L_a files and calculates significances:
rooptimize optimize --signal 37* --bkgd 4* --searchDirectory=cuts_0L_a -b --o=significances_0L_aand this will automatically combine background and produce a significances file for each signal passed in.
When the optimizations have finished running, you'll want to take the given hash(es) and figure out what cut it corresponds to, you can do this with
rooptimize hash e31dcf5ba4786d9e8ffa9e642729a6b9 4e16fdc03c171913bc309d57739c7225 8fa0e0ab6bf6a957d545df68dba97a53 --supercuts=supercuts_small.jsonwhich will create outputHash/<hash>.json files detailing the cuts involved.
This is one of those pieces of python code we always want to run as fast as possible. Optimization should not take long. To figure out those dead-ends, I use snakeviz. The requirements.txt file contains this dependency. To run it, I first profile the code by running it:
python -m cProfile -o profiler.log rooptimize cut TA06_MBJ05/*_0L/fetch/data-optimizationTree/*.root --supercuts=supercuts.json -o cuts_0L -bthen I use the snakeviz script to help me visualize this
snakeviz profiler.logand I'm good to go.
See example_script.sh for an idea how how to run everything in order to produce a plot of significances.
rooptimizeor
rooptimize -husage: rooptimize [-h] [-a] {generate,cut,optimize,hash,summary} ...
Author: Giordon Stark. vX.Y.Z
positional arguments:
{generate,cut,optimize,hash,summary}
actions available
generate Write supercuts template
cut Apply the cuts
optimize Calculate significances for a series of computed
cuts
hash Translate hash to cut
summary Summarize Optimization Results
optional arguments:
-h, --help show this help message and exit
-a, --allhelp show this help message and all subcommand help
messages and exit
This is the top-level. You have no power here.
There is only one required position argument: the action. You can choose from
We also provide an optional argument -a, --allhelp which will print all the help documentation at once instead of just the top-level -h, --help.
Generate helps you quickly start. Given the ROOT ntuples, generate a supercuts.json template.
usage: rooptimize generate --signal=signal.root [..] --bkgd=bkgd.root [...] [options]| Variable | Type | Description |
|---|---|---|
| file | string | path to a root file containing an optimization ntuple to use |
| Variable | Type | Description |
|---|---|---|
| -h, --help | bool | display help message |
| -v, --verbose | count | enable more verbose output |
| --debug | bool | enable full-on debugging |
| -b, --batch | bool | enable batch mode for ROOT |
| --tree | string | ttree name in the ntuples |
| --eventWeight | string | event weight branch name |
| --o, --output | string | output json file to store generated supercuts file |
| --fixedBranches | strings | branches that should have a fixed cut |
| --skipBranches | strings | branches that should not have a cut (skip them) |
-
--globalMinValis just an aesthetic feature to make it easier to identify the "true" minimum of your ntuples. I often output -99.0 in case there is (for example) no 4th jet, or I could not calculate some substructure information, this allows me to automatically chop off the low end of a branch to get a better calculation of the percentiles -
--fixedBranchesand--skipBranchescan take a series of strings or a series of patterns--fixedBranches multiplicity_jet multiplicity_topTag_loose multiplicity_topTag_tight
or
--fixedBranches multiplicity_* pt_jet_rc8_1which aims to make life easier for all of us.
This script will generate a supercuts json file. See Supercuts File for more information.
Cut helps you by generating the cuts from a supercuts file and applying them to create an output file of counts. Process ROOT ntuples and apply cuts.
usage: rooptimize cut <file.root> ... [options]| Variable | Type | Description |
|---|---|---|
| files | string | path(s) to root files containing ntuples |
| Variable | Type | Description | Default |
|---|---|---|---|
| -h, --help | bool | display help message | False |
| -v, --verbose | count | enable more verbose output | 0 |
| --debug | bool | enable full-on debugging | False |
| -b, --batch | bool | enable batch mode for ROOT | False |
| --tree-pattern | string | patterns for ttree names in the files | * |
| --eventWeight | string | event weight branch name | event_weight |
| --supercuts | string | path to json dict of supercuts for generating cuts | supercuts.json |
| --o, --output | directory | output directory to store json files containing cuts | cuts |
| Variable | Type | Description |
|---|---|---|
| hash | 32-bit string | md5 hash of the cut |
| raw | integer | raw number of events passing cut |
| weighted | float | apply event weights to events passing cut |
Weighted events are applying the monte-carlo event weights that you specify. The calculation of significance is done for both the raw events and weighted events.
The output is a directory of json files which will look like
{
...
"09a130622e1e6345b83739b3527eccb1": {
"raw": 90909,
"weighted": 2.503
},
...
}This code will group your input files by filenames and tree names and will try its best to do its job to group your sample files.
Optimize helps you find your optimal cuts. Process cuts and determine significance.
usage: rooptimize optimize --signal=signal.root [..] --bkgd=bkgd.root [...] [options]Note: You can specify multiple backgrounds and multiple signals. Each signal will be run over separately and each background will be merged and treated as a single background.
| Variable | Type | Description |
|---|---|---|
| --signal | string | path(s) to json files containing signal cuts |
| --bkgd | string | path(s) to json files containing background cuts |
Note: this will search for files under the search_directory option, default is cuts to search for files produced by rooptimize cut.
| Variable | Type | Description | Default |
|---|---|---|---|
| -h, --help | bool | display help message | False |
| -v, --verbose | count | enable more verbose output | 0 |
| --debug | bool | enable full-on debugging | False |
| -b, --batch | bool | enable batch mode for ROOT | False |
| --searchDirectory | string | the directory that contains all cut.json files | 'cuts' |
| --bkgdUncertainty | float | bkgd sigma for calculating sig. | 0.3 |
| --bkgdStatUncertainty | float | bkgd statistical uncertainty for significance | 0.3 |
| --insignificance | int | min. number of events for non-zero sig. | 0.5 |
| --o, --output | string | output directory to store significances calculated | significances |
| -n, --max-num-hashes | int | maximum number of hashes to dump in the significance files | 25 |
| Variable | Type | Description |
|---|---|---|
| hash | 32-bit string | md5 hash of the cut |
| significance | float | calculated significance of the cut |
| yield | float | number of events passing the cut |
The output is a directory of json files which will look like
[
...
{
"hash": "7595976a84303a003f6a4a7458f12b8d",
"significance_raw": 7.643122000999725,
"significance_weighted": 18.34212454602254,
"yield_raw": { ... },
"yield_weighted": { ... }
},
...
]if a significance was calculated successfully or
[
...
{
"hash": "c911af35708dcdc51380ebbde81c9b1e",
"significance_raw": -3,
"significance_weighted": -3,
"yield_raw": { ... },
"yield_weighted": { ... }
},
{
"hash": "b383cea24037667ffb6136d670a33468",
"significance_raw": -2,
"significance_weighted": -2,
"yield_raw": { ... },
"yield_weighted": { ... }
},
{
"hash": "095414bacf1022f2c941cc6164b175a1",
"significance_raw": 9.421795580339449,
"significance_weighted": 20.37611073465684,
"yield_raw": { ... },
"yield_weighted": { ... }
},
...
]if the number of events in signal or background did not pass the --insignificance minimum threshold set. The significance will always be flagged as a negative number depending on the insignificance observed. The table below summarizes these cases:
| Sig. Value | What Happened |
|---|---|
| -1 | The signal was insignificant |
| -2 | The background was insignificant |
| -3 | There were not enough statistics in the background events |
Note that --max-num-hashes determines how many hashes you will actually see in these output files.
Hash to cut translation. Given a hash from optimization, dump the cuts associated with it.
usage: rooptimize hash <hash> [<hash> ...] [options]| Variable | Type | Description |
|---|---|---|
| hash (positional) | string | 32-bit hash(es) to decode as cuts. If --use-summary is flagged, you can pass in your summary.json file instead. |
| Variable | Type | Description | Default |
|---|---|---|---|
| -h, --help | bool | display help message | False |
| -v, --verbose | count | enable more verbose output | 0 |
| --debug | bool | enable full-on debugging | False |
| -b, --batch | bool | enable batch mode for ROOT | False |
| --supercuts | string | path to json dict of supercuts | supercuts.json |
| --o, --output | directory | output directory to store json files containing cuts | outputHash |
| --use-summary | bool | if enabled, you can pass in your summary.json file instead of a bunch of hashes | False |
The hash subcommand will create an output directory with multiple json files, one for each hash, containing details about the cut applied. Unlike a standard supercuts file, the hash will only output dictionaries of 4 elements
| Variable | Type | Description |
|---|---|---|
| branch | string | name of branch that cut was applied on |
| fixed | bool | whether the cut was from a fixed cut or a supercut |
| pivot | number | the value which we cut on, see signal_direction for more |
| signal_direction | string | ? = > or ? = <, cuts obey the rule value ? pivot |
Optimize results to summary json. Given the finished results of an optimization, produce a json summarizing it entirely.
usage: rooptimize summary [options]No required parameters
| Variable | Type | Description | Default |
|---|---|---|---|
| -h, --help | bool | display help message | False |
| -v, --verbose | count | enable more verbose output | 0 |
| --debug | bool | enable full-on debugging | False |
| -b, --batch | bool | enable batch mode for ROOT | False |
| --ncores | int | Number of cores to use for parallelization | |
| -o, --output | str | Name of output file to use | summary.json |
| --searchDirectory | str | The directory containing the significances produced from rooptimize optimize |
|
| -f, --fmtstr | str | format string for matching against signal filenames in config.json | "([a-zA-Z]+)(\d+)(\d+)_(\d+)" |
| -p, --interpretation | str | how to interpret the corresponding format string | "signal_type:gluino:stop:neutralino" |
The summary subcommand will create an output json file containing a list of dictionaries, one for each signal used in optimization. It will contain the following variables in each dictionary (assuming defaults):
| Variable | Type | Description |
|---|---|---|
| bkgd | float | Background yield |
| filename | str | signal filename used |
| hash | 32-bit string | md5 hash of the optimal cut |
| gluino | str | Mass of Gluino |
| neutralino | str | Mass of LSP |
| stop | str | Mass of Stop |
| signal_type | str | type of signal |
| ratio | float | Ratio of signal/bkgd |
| significance | float | Significance of optimal cut |
This will look something like:
[
...
{
"bkgd": 5.656293846714225,
"filename": "significances/Gtt_900_5000_400.json",
"hash": "dc41780c77207a9a5dcf6b97b0cac5ac",
"gluino": "900",
"neutralino": "400",
"stop": "5000",
"ratio": 79.33950854202577,
"signal": 448.76757396759103,
"signal_type": "Gtt",
"significance": 29.78897424015455
},
...
]
This is a potentially large JSON file that tells the optimize, hash, and generate commands the rules of your cuts.
- The
optimizecommand uses it to generate a series of cuts to apply to your ntuples, then hash these cuts and store them with their calculated significance. - The
hashcommand uses it to recompute the hash and find the cuts that match up to the hashes you need to decode. - The
generatecommand creates this file for you based on your ntuples to help you get started.
The file will always contain a list of objects (dictionaries) for each branch that you care about cutting on.
A fixed cut is a single cut on a single branch. This is like taking a partial derivative where you fix one thing and vary others. In this case, we fix a branch defined by a fixed cut.
| Key | Type | Description |
|---|---|---|
| selections | string | the various selections to apply for the cut |
| pivot | number | the value at which we cut (or pivot against) |
The simplest example is when we want to use a single fixed cut on a single branch. Your object will look like
[
...
{
"selections": "multiplicity_jet > {0}",
"pivot": 3,
},
...
]This says we would like a fixed cut on multiplicity_jet requiring that there are more than 3 jets (eg: the rule we obey is value > 3).
A supercut is our term for an object that generates more than 1 cut on the defined branch. A fixed cut will generate 1 cut, but a supercut can generate a boundless number of cuts.
| Key | Type | Description |
|---|---|---|
| selections | string | the various selections to apply for the cut |
| st3 | list | a list of [start, stop, step] values for each set of pivots |
| list | list | a list of [cut1, cut2, ..., cutN] values for each set of pivots |
Note: the direction in which cuts are generated can be controlled by running cuts in increasing values (start < stop, step > 0) or decreasing values (start > stop, step < 0).
There are two main examples we will provide to show the different cuts that could be generated.
[
...
{
"selections": "multiplicity_jet < {0}",
"st3": [
[2, 7, 2]
]
},
...
]This says we would like a supercut on multiplicity_jet where the pivot values are 2, 4, 6 obeying the rule that value < pivot. This supercut will generate 3 cuts:
value < 2value < 4value < 6
in that order.
[
...
{
"selections": "multiplicity_jet > {0}",
"st3": [
[3, 1, -1]
]
},
...
]This says we would like a supercut on multiplicity_jet where the pivot values are 3, 2 obeying the rule that value > pivot. This supercut will generate 2 cuts:
value > 3value > 2
in that order.
Here is an example supercuts.json file
[
{
"selections": "multiplicity_jet > {0}",
"st3": [
[2, 15, 1]
]
},
{
"selections": "multiplicity_jet_largeR > {0}",
"st3": [
[3, 1, -1]
]
},
{
"selections": "multiplicity_topTag_loose > {0}",
"pivot": [1]
}
]How do we interpret this? This file tells the code that there are 3 branches to apply cuts on: multiplicity_jet, multiplicity_jet_largeR, and multiplicity_topTag_loose. Each object {...} represents a branch. In order:
- This is a supercut. 13 cuts will be generated for
multiplicity_jetstarting from2to15in increments of1. This means the cut values (pivot) used will be2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14(inclusive start, exclusive end - adhere to python standards). Thesignal_directionspecifies where we expect the signal to be.>means to cut on the right so we only want to keep events withvalue > pivot. - This is a supercut.2 cuts will be generated for
multiplicity_jet_largeRstarting from3to1in incremenets of-1. This means the cut values (pivot) used will be3, 2(inclusive start, exclusive end - adhere to python standards). Thesignal_directionspecifies where we expect the signal to be.<means to cut on the left so we only want to keep events withvalue < pivot. - This is a fixed cut. 1 cut will be used for
multiplicity_topTag_loosewith apivot = 1andsignal_direction = >. This means we will only select events withvalue > 1always. Thepivotwill be fixed. One could also fix the cut by providingstart,stop,stepsuch that it only generates 1 cut, but the script will not identify this as a fixed cut for you when you look up thecutusing hash.
This supercuts file will generate 26 total cuts (13*2*1 = 26). Each cut will have an associated hash value and an associated significance which will be recorded to an output file when you run optimize.
If you wish to provide a fixed cut (the pivot does not change), you simply need to specify the pivot instead. Taking the example shown above, you might have something like
[
{
"selections": "multiplicity_jet >= {0}",
"pivot": [4]
},
{
"selections": "multiplicity_jet_largeR > {0}",
"st3": [
[3, 1, -1]
]
},
{
"selections": "multiplicity_topTag_loose > {0}",
"pivot": [1]
}
]which tells the code to always apply a cut of multiplicity_jet >= 4 always.
One can certainly provide more complicated selections involving multiple pivots and multiple branches. In fact, this makes our optimization increasingly more flexible and faster than any other code in existence. We use the formulate and numexpr packages to provide the parsing of the selection strings. This supports "standard" selection strings as well as those recognized by TCut/TFormula as well. Their documentation has examples of what you can do. You still need to specify placeholders for your pivots like below:
[
...
{
"selections": "(mass_jets_largeR_1 > {0} & mass_jets_largeR_2 > {0} & mass_jets_largeR_3 > {0}) >= {1}",
"st3": [
[50, 2000, 50],
[0, 4, 1],
]
},
...
]is an example of a perhaps more complicated selection that can be done. In this case, we are determining how many of the 3 leading jets pass a mass cut, but also applying a cut on that count. In this case, the {0} pivot placeholder refers to the first st^3 option: [50, 2000, 50] which is to vary the first pivot {0} from 50 GeV to 2 TeV in 50 GeV steps. The {1} pivot placeholder refers to the second st^3 option: [0, 4, 1] which is to vary the second pivot {1} from 0 to 4 in steps of 1. This will allow us to iterate over all possible values of pivots (the product of [50, 2000, 50] X [0, 4, 1]).