diff --git a/README.md b/README.md index 6580bfa..5157e1e 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ # HH->WWbb Run-3 analysis ----WORK IN PROGRESS---- -Install **bamboo analysis framework** with the instructions here: https://bamboo-hep.readthedocs.io/en/latest/install.html#fresh-install +This repository uses the **bamboo analysis framework**, you can install it via the instructions here: https://bamboo-hep.readthedocs.io/en/latest/install.html#fresh-install Then clone this repository in the parent directory containing the bamboo installation: @@ -13,14 +13,14 @@ Then clone this repository in the parent directory containing the bamboo install git clone https://github.com/cp3-llbb/HHtoWWbb_Run3.git && cd HHtoWWbb_Run3 ``` -Execute these each time you start from a clean shell: +Execute these each time you start from a clean shell on lxplus or any other machine with an cvmfs: ```bash source /cvmfs/sft.cern.ch/lcg/views/LCG_102/x86_64-centos7-gcc11-opt/setup.sh source (path to your bamboo installation)/bamboovenv/bin/activate export PYTHONPATH="${PYTHONPATH}:${PWD}/python/" ``` -and the followings before submitting to the batch system: +and the followings before submitting jobs to the batch system (HTCondor, Slurm, Dask and Spark are supported): ```bash voms-proxy-init --voms cms -rfc --valid 192:00 @@ -33,14 +33,25 @@ voms-proxy-init --voms cms -rfc --valid 192:00 --out ~/private/gridproxy/x509 export X509_USER_PROXY=$HOME/private/gridproxy/x509 ``` -Then plot various control regions via the following command line using batch (you can pass `--maxFiles 1` to use only 1 file from each sample for a test): +Then plot various control regions via the following command line using batch (you can pass `--maxFiles 1` to use only 1 file from each sample for a quick test): ```bash bambooRun -m python/controlPlotter.py config/analysis_2022.yml -o ./outputDir/ --distributed driver --envConfig config/cern.ini --eras combined -c
``` Instead of passing everytime `--envConfig config/cern.ini`, you can copy the content of that file to `~/.config/bamboorc`. -to produce plots, just execute: +Pass `--mvaSkim` to produce skims for MVA. as well. + +then to produce plots, just execute: ```sh -./scripts/plot_DL.sh +./scripts/plot_.sh +``` + +using the `parquet` output file that contains skims and the DNN.py file, you can perform machine learning applications; + +```sh +python DNN.py -s -o +``` + +Then passing `--mvaModels=` option, you can apply DNN score cuts on your analysis. diff --git a/config/analysis_2022.yml b/config/analysis_2022.yml index 078cefe..91431a2 100644 --- a/config/analysis_2022.yml +++ b/config/analysis_2022.yml @@ -17,7 +17,6 @@ samples: HH: era: "2022" type: "signal" - group: "HH" db: das:/GluGluToHHTo2B2VTo2L2Nu_node_SM_TuneCP5_PSWeights_13TeV-madgraph-pythia8/RunIIAutumn18NanoAODv7-Nano02Apr2020_102X_upgrade2018_realistic_v21-v1/NANOAODSIM cross-section: 1 branching-ratio: 0.027669886574221 @@ -28,7 +27,6 @@ samples: HH_EE: # using the same dataset era: "2022EE" type: "signal" - group: "HH" db: das:/GluGluToHHTo2B2VTo2L2Nu_node_SM_TuneCP5_PSWeights_13TeV-madgraph-pythia8/RunIIAutumn18NanoAODv7-Nano02Apr2020_102X_upgrade2018_realistic_v21-v1/NANOAODSIM cross-section: 1 branching-ratio: 0.027669886574221 @@ -307,12 +305,13 @@ samples: generated-events: genEventSumw split: 5 + # TW cross section from: https://cms-talk.web.cern.ch/t/new-jecs-for-run3-prompt-fg-eras/26529 TbarWplusto2l2nu: era: "2022" group: "TW" db: das:/TbarWplusto2L2Nu_TuneCP5_13p6TeV_powheg-pythia8/Run3Summer22NanoAODv11-126X_mcRun3_2022_realistic_v2-v1/NANOAODSIM type: mc - cross-section: 36.05 + cross-section: 4.668 # xsec(TW) / 2 (since t and tbar is included) x BR(W->l nu)^2 x 2 (since there are two W bosons) = 87.9 / 2 x 0.3259^2 x 2 generated-events: genEventSumw split: 3 @@ -321,16 +320,16 @@ samples: group: "TW" db: das:/TbarWplusto2L2Nu_TuneCP5_13p6TeV_powheg-pythia8/Run3Summer22EENanoAODv11-126X_mcRun3_2022_realistic_postEE_v1-v1/NANOAODSIM type: mc - cross-section: 3.605e+01 + cross-section: 4.668 generated-events: genEventSumw - split: 7 + split: 8 TbarWplustoLNu2Q: era: "2022" group: "TW" db: das:/TbarWplustoLNu2Q_TuneCP5_13p6TeV_powheg-pythia8/Run3Summer22NanoAODv11-126X_mcRun3_2022_realistic_v2-v1/NANOAODSIM type: mc - cross-section: 36.05 + cross-section: 19.31 # xsec(TW) / 2 (since t and tbar is included) x BR(W->l nu) x BR(W->qq) x 2 (since there are two W bosons) = 87.9 / 2 x 0.3259 x 0.6741 generated-events: genEventSumw split: 6 @@ -339,10 +338,45 @@ samples: group: "TW" db: das:/TbarWplustoLNu2Q_TuneCP5_13p6TeV_powheg-pythia8/Run3Summer22EENanoAODv11-126X_mcRun3_2022_realistic_postEE_v1-v1/NANOAODSIM type: mc - cross-section: 3.605e+01 + cross-section: 19.31 generated-events: genEventSumw split: 15 + TWminusto2l2nu: + era: "2022" + group: "TW" + db: das:/TWminusto2L2Nu_TuneCP5_13p6TeV_powheg-pythia8/Run3Summer22NanoAODv11-126X_mcRun3_2022_realistic_v2-v1/NANOAODSIM + type: mc + cross-section: 4.668 + generated-events: genEventSumw + split: 4 + + TWminusto2l2nu_EE: + era: "2022EE" + group: "TW" + db: das:/TWminusto2L2Nu_TuneCP5_13p6TeV_powheg-pythia8/Run3Summer22EENanoAODv11-126X_mcRun3_2022_realistic_postEE_v1-v1/NANOAODSIM + type: mc + cross-section: 4.668 + generated-events: genEventSumw + split: 8 + + TWminustoLNu2Q: + era: "2022" + group: "TW" + db: das:/TWminustoLNu2Q_TuneCP5_13p6TeV_powheg-pythia8/Run3Summer22NanoAODv11-126X_mcRun3_2022_realistic_v2-v1/NANOAODSIM + type: mc + cross-section: 19.31 + generated-events: genEventSumw + split: 6 + + TWminustoLNu2Q_EE: + era: "2022EE" + group: "TW" + db: das:/TWminustoLNu2Q_TuneCP5_13p6TeV_powheg-pythia8/Run3Summer22EENanoAODv11-126X_mcRun3_2022_realistic_postEE_v1-v1/NANOAODSIM + type: mc + cross-section: 19.31 + generated-events: genEventSumw + split: 15 plotIt: configuration: @@ -376,13 +410,13 @@ plotIt: fill-color: "#9FFF33" WJets: legend: WJets - fill-color: "#FF0000" + fill-color: "#FFC300" VV: legend: VV fill-color: "#C900FF" TW: legend: TW - fill-color: "#C900FF" + fill-color: "#FF0000" plotdefaults: normalized: False y-axis: Events diff --git a/config/dascache/TWminusto2l2nu.txt b/config/dascache/TWminusto2l2nu.txt new file mode 100644 index 0000000..9eb77cf --- /dev/null +++ b/config/dascache/TWminusto2l2nu.txt @@ -0,0 +1,5 @@ +# das:/TWminusto2L2Nu_TuneCP5_13p6TeV_powheg-pythia8/Run3Summer22NanoAODv11-126X_mcRun3_2022_realistic_v2-v1/NANOAODSIM +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22NanoAODv11/TWminusto2L2Nu_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_v2-v1/60000/2e392234-494e-4396-90d8-fd39800a3ad0.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22NanoAODv11/TWminusto2L2Nu_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_v2-v1/60000/5a5b2cce-b28f-4619-871b-7402d181bf89.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22NanoAODv11/TWminusto2L2Nu_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_v2-v1/60000/3a0c7e06-e9ed-4611-8d6d-47c4d4b82445.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22NanoAODv11/TWminusto2L2Nu_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_v2-v1/60000/455a9660-d79c-4908-87f9-c88adadac823.root \ No newline at end of file diff --git a/config/dascache/TWminusto2l2nu_EE.txt b/config/dascache/TWminusto2l2nu_EE.txt new file mode 100644 index 0000000..d8804ac --- /dev/null +++ b/config/dascache/TWminusto2l2nu_EE.txt @@ -0,0 +1,9 @@ +# das:/TWminusto2L2Nu_TuneCP5_13p6TeV_powheg-pythia8/Run3Summer22EENanoAODv11-126X_mcRun3_2022_realistic_postEE_v1-v1/NANOAODSIM +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22EENanoAODv11/TWminusto2L2Nu_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_postEE_v1-v1/80000/7e51dba3-6a7d-44c6-b19a-767e3aed65df.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22EENanoAODv11/TWminusto2L2Nu_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_postEE_v1-v1/80000/fcb39d43-55cd-4c5a-a4e3-3b435c5c2ad4.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22EENanoAODv11/TWminusto2L2Nu_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_postEE_v1-v1/80000/0e2b8c5e-34ab-4d95-acd6-1eea19cfdafc.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22EENanoAODv11/TWminusto2L2Nu_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_postEE_v1-v1/80000/d0fa196d-965f-4d08-b4f6-d9b72a6157ab.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22EENanoAODv11/TWminusto2L2Nu_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_postEE_v1-v1/80000/f8196550-7931-4e4e-94a8-21050c0a483e.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22EENanoAODv11/TWminusto2L2Nu_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_postEE_v1-v1/80000/4a1a6d21-b3e7-4681-92d9-59c0c234e58a.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22EENanoAODv11/TWminusto2L2Nu_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_postEE_v1-v1/80000/49d0b105-6b7f-4382-8054-2a63b5b0c8d0.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22EENanoAODv11/TWminusto2L2Nu_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_postEE_v1-v1/80000/4b2d99c5-f75a-408b-8078-3e844fce34c9.root \ No newline at end of file diff --git a/config/dascache/TWminustoLNu2Q.txt b/config/dascache/TWminustoLNu2Q.txt new file mode 100644 index 0000000..9d84857 --- /dev/null +++ b/config/dascache/TWminustoLNu2Q.txt @@ -0,0 +1,7 @@ +# das:/TWminustoLNu2Q_TuneCP5_13p6TeV_powheg-pythia8/Run3Summer22NanoAODv11-126X_mcRun3_2022_realistic_v2-v1/NANOAODSIM +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22NanoAODv11/TWminustoLNu2Q_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_v2-v1/40000/40ea1d1f-cf92-4961-84ed-38ebde8ba49b.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22NanoAODv11/TWminustoLNu2Q_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_v2-v1/40000/7ed217f6-6a88-4539-82b8-1cada7c03f87.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22NanoAODv11/TWminustoLNu2Q_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_v2-v1/40000/870b2636-bfec-4979-bd8e-e989c256bda3.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22NanoAODv11/TWminustoLNu2Q_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_v2-v1/40000/0554cc19-5d8d-4447-a636-6383abbfddcc.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22NanoAODv11/TWminustoLNu2Q_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_v2-v1/40000/ed7d93ef-4e5d-4604-a59e-923068cb02ef.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22NanoAODv11/TWminustoLNu2Q_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_v2-v1/40000/528256f1-094a-41c2-a41e-a5c3179ee7e7.root \ No newline at end of file diff --git a/config/dascache/TWminustoLNu2Q_EE.txt b/config/dascache/TWminustoLNu2Q_EE.txt new file mode 100644 index 0000000..807b1fa --- /dev/null +++ b/config/dascache/TWminustoLNu2Q_EE.txt @@ -0,0 +1,16 @@ +# das:/TWminustoLNu2Q_TuneCP5_13p6TeV_powheg-pythia8/Run3Summer22EENanoAODv11-126X_mcRun3_2022_realistic_postEE_v1-v1/NANOAODSIM +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22EENanoAODv11/TWminustoLNu2Q_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_postEE_v1-v1/60000/0d05f028-8f2c-4ee5-ba5e-02becfdfce52.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22EENanoAODv11/TWminustoLNu2Q_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_postEE_v1-v1/60000/9650eb1b-d347-40df-8d0c-5196b9172a06.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22EENanoAODv11/TWminustoLNu2Q_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_postEE_v1-v1/60000/40a9c47c-baff-421d-b18a-ed2ebcb616d1.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22EENanoAODv11/TWminustoLNu2Q_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_postEE_v1-v1/60000/d136b950-8cbd-44ad-a8e8-0613b560dc93.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22EENanoAODv11/TWminustoLNu2Q_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_postEE_v1-v1/60000/e32d5d60-3381-4df9-a780-eac2187eed8d.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22EENanoAODv11/TWminustoLNu2Q_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_postEE_v1-v1/60000/c4b062a7-8f44-4724-90d2-cc8bb3bdb028.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22EENanoAODv11/TWminustoLNu2Q_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_postEE_v1-v1/60000/befda636-ba50-4a84-9d0f-aaf95c2c448f.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22EENanoAODv11/TWminustoLNu2Q_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_postEE_v1-v1/60000/2cce1656-f3ae-4c1d-885c-21d47213c58a.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22EENanoAODv11/TWminustoLNu2Q_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_postEE_v1-v1/60000/7a813108-7a99-4bf3-92b3-4bd209f3f88a.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22EENanoAODv11/TWminustoLNu2Q_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_postEE_v1-v1/60000/2f3b555c-c35a-4312-9123-e5820c12748b.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22EENanoAODv11/TWminustoLNu2Q_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_postEE_v1-v1/60000/56b8dc60-b068-49ef-b709-3504a305a433.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22EENanoAODv11/TWminustoLNu2Q_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_postEE_v1-v1/60000/6c37faed-db9f-4cd1-8b50-95c9f943d03e.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22EENanoAODv11/TWminustoLNu2Q_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_postEE_v1-v1/60000/70ab972f-525c-4394-9d97-142c7f689f0f.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22EENanoAODv11/TWminustoLNu2Q_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_postEE_v1-v1/60000/235ee0c4-84ef-4acd-b3d2-3c0f4abb3cd6.root +root://cms-xrd-global.cern.ch///store/mc/Run3Summer22EENanoAODv11/TWminustoLNu2Q_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/126X_mcRun3_2022_realistic_postEE_v1-v1/60000/3ceb8058-6508-4712-846b-bed81c948bf8.root \ No newline at end of file diff --git a/python/DNN.ipynb b/python/DNN.ipynb index acec13a..69673bc 100644 --- a/python/DNN.ipynb +++ b/python/DNN.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 193, + "execution_count": 6, "id": "22512f4a", "metadata": {}, "outputs": [], @@ -11,6 +11,7 @@ "import optparse\n", "import yaml\n", "import importlib\n", + "import matplotlib\n", "import matplotlib.pyplot as plt\n", "from matplotlib.backends.backend_pdf import PdfPages\n", "import numpy as np\n", @@ -26,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 194, + "execution_count": 7, "id": "a5398a55", "metadata": {}, "outputs": [], @@ -39,12 +40,17 @@ "# help='output directory',\n", "# type='string')\n", "\n", + "# parser.add_option('-o',\n", + "# dest='outputDir',\n", + "# help='path to the output directory',\n", + "# type='string')\n", + "\n", "# (opt, args) = parser.parse_args()" ] }, { "cell_type": "code", - "execution_count": 195, + "execution_count": 8, "id": "1d7a4d05", "metadata": {}, "outputs": [ @@ -75,7 +81,7 @@ "\n", "# DNN hyperparameters #\n", "parameters = {\n", - " 'epochs' : 200,\n", + " 'epochs' : 10,\n", " 'lr' : 0.001,\n", " 'batch_size' : 256,\n", " 'n_layers' : 3,\n", @@ -104,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 196, + "execution_count": 9, "id": "ac770a37", "metadata": {}, "outputs": [ @@ -112,25 +118,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "Using skim file ../output/mvaSkim-invMleptons/results/DL_resolved.parquet\n", - "Using yaml file ../output/mvaSkim-invMleptons/plots.yml\n" + "Using skim file ./DL_resolved_ee.parquet\n" ] } ], "source": [ - "outputPath = '../output/mvaSkim-invMleptons'\n", - "yamlFile = os.path.join(outputPath,'plots.yml')\n", - "skimFile = os.path.join(outputPath,'results/DL_resolved.parquet')\n", - "# yamlFile = os.path.join(opt.outputPath,'plots.yml')\n", + "outputPath = './'\n", + "skimFile = os.path.join(outputPath,'DL_resolved_ee.parquet')\n", "# skimFile = os.path.join(opt.outputPath,'results/DL_resolved.parquet')\n", "\n", - "print(f'Using skim file {skimFile}')\n", - "print(f'Using yaml file {yamlFile}')" + "print(f'Using skim file {skimFile}')" ] }, { "cell_type": "code", - "execution_count": 197, + "execution_count": 10, "id": "100132f2", "metadata": {}, "outputs": [], @@ -141,55 +143,608 @@ }, { "cell_type": "code", - "execution_count": 198, + "execution_count": 11, + "id": "3f137d1f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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"execution_count": 198, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Add tag column #\n", - "df['tag'] = 'background'\n", - "df.loc[df.process.str.contains('HH'),['tag']] = 'HH'\n", - "df[\"tag\"].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 199, - "id": "fb21a819", - "metadata": {}, - "outputs": [], - "source": [ - "assert len(set(tags).intersection(set(pd.unique(df['tag'])))) == len(tags) # Just cross check to avoid mistakes" - ] - }, - { - "cell_type": "code", - "execution_count": 200, - "id": "68ad58a3", - "metadata": {}, - "outputs": [], - "source": [ - "# One-hot encoding is a way to convert a column to a format that is easier for machine learning applications\n", - "# Here I transform the tag (bkg or signal) column to binary and add it as new columns\n", - "\n", - "one_hot = pd.get_dummies(df['tag'], dtype=float)\n", - "df = pd.concat((df,one_hot),axis=1)" + "df" ] }, { "cell_type": "code", - "execution_count": 201, + "execution_count": 16, "id": "a3d130c9", "metadata": {}, "outputs": [ @@ -211,18 +766,31 @@ }, { "cell_type": "code", - "execution_count": 202, + "execution_count": 17, "id": "b5e7ae6a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/qc/rshk0r0n0zs37rx8rwj72r3h0000gp/T/ipykernel_14965/1692472565.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['event_weight'] = df.loc[:, 'weight']\n" + ] + } + ], "source": [ "# copy the weight column as event weight to calculate it later\n", - "df['event_weight'] = df['weight'].copy()" + "df['event_weight'] = df.loc[:, 'weight']" ] }, { "cell_type": "code", - "execution_count": 203, + "execution_count": 18, "id": "b0fdd3a3", "metadata": {}, "outputs": [], @@ -233,10 +801,23 @@ }, { "cell_type": "code", - "execution_count": 204, + "execution_count": 19, "id": "49ecea9a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/qc/rshk0r0n0zs37rx8rwj72r3h0000gp/T/ipykernel_14965/3698276694.py:5: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['training_weight'] = df['event_weight'].copy()\n" + ] + } + ], "source": [ "# copy the event weight column as training weight\n", "if 'training_weight' in df.columns:\n", @@ -247,7 +828,7 @@ }, { "cell_type": "code", - "execution_count": 205, + "execution_count": 20, "id": "d6dc6b85", "metadata": {}, "outputs": [ @@ -561,7 +1142,7 @@ "[570473 rows x 16 columns]" ] }, - "execution_count": 205, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -576,7 +1157,7 @@ }, { "cell_type": "code", - "execution_count": 206, + "execution_count": 21, "id": "ed89ab9d", "metadata": {}, "outputs": [], @@ -601,7 +1182,7 @@ }, { "cell_type": "code", - "execution_count": 207, + "execution_count": 22, "id": "c7d873e2", "metadata": {}, "outputs": [ @@ -611,12 +1192,12 @@ "text": [ "Using event weight\n", "On average, per batch the total weight is\n", - "\t... background : 7.702065268 [252.7 events]\n", - "\t... HH : 0.000667104 [3.3 events]\n", + "\t... background : 7.847116974 [252.65 events]\n", + "\t... HH : 0.000677314 [3.35 events]\n", "Using training weight\n", "On average, per batch the total weight is\n", - "\t... background : 249.463950433 [252.8 events]\n", - "\t... HH : 235.948694535 [3.2 events]\n" + "\t... background : 266.962269445 [252.2 events]\n", + "\t... HH : 280.232861050 [3.8 events]\n" ] } ], @@ -629,23 +1210,13 @@ }, { "cell_type": "code", - "execution_count": 208, + "execution_count": 23, "id": "259e26d6", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot the background and signal weights #\n", + "matplotlib.use('Agg') # to avoid \"\"\"qt.qpa.plugin: Could not find the Qt platform plugin \"xcb\" in \"\" \"\"\" error\n", "fig,axs = plt.subplots(figsize=(25,10),nrows=len(tags),ncols=2)\n", "fig.subplots_adjust(left=0.1, right=0.9, top=0.98, bottom=0.1, wspace=0.2,hspace=0.4)\n", "for irow,tag in enumerate(tags):\n", @@ -659,7 +1230,7 @@ }, { "cell_type": "code", - "execution_count": 209, + "execution_count": 24, "id": "24aafcff", "metadata": {}, "outputs": [ @@ -685,7 +1256,7 @@ }, { "cell_type": "code", - "execution_count": 210, + "execution_count": 25, "id": "85f60222", "metadata": {}, "outputs": [ @@ -712,7 +1283,7 @@ }, { "cell_type": "code", - "execution_count": 211, + "execution_count": 26, "id": "4c1ced49", "metadata": {}, "outputs": [], @@ -723,7 +1294,7 @@ }, { "cell_type": "code", - "execution_count": 212, + "execution_count": 27, "id": "edb8dc9e", "metadata": {}, "outputs": [], @@ -741,7 +1312,7 @@ }, { "cell_type": "code", - "execution_count": 213, + "execution_count": 28, "id": "0ea1e5a1", "metadata": {}, "outputs": [ @@ -798,7 +1369,7 @@ }, { "cell_type": "code", - "execution_count": 214, + "execution_count": 29, "id": "76336e63", "metadata": {}, "outputs": [], @@ -823,7 +1394,7 @@ }, { "cell_type": "code", - "execution_count": 215, + "execution_count": 30, "id": "3bd6ae33", "metadata": {}, "outputs": [], @@ -832,8 +1403,8 @@ "inputs = keras.Input(shape=(len(input_vars),), name=\"particles\")\n", "# Preprocessing layer\n", "from tensorflow.keras.layers.experimental import preprocessing\n", - "normalizer = preprocessing.Normalization(mean = train_df[input_vars].mean(axis=0),\n", - " variance = train_df[input_vars].var(axis=0),\n", + "normalizer = preprocessing.Normalization(mean = train_df[input_vars].mean(axis=0).to_numpy(),\n", + " variance = train_df[input_vars].var(axis=0).to_numpy(),\n", " name = 'Normalization')(inputs)\n", " # this layer does the preprocessing (x-mu)/std for each input\n", "# Dense (hidden) layers #\n", @@ -854,12 +1425,12 @@ " name = \"predictions\")(x)\n", "\n", "# Registering the model #\n", - "model = keras.Model(inputs=inputs, outputs=outputs)" + "model = tf.keras.Model(inputs=inputs, outputs=outputs)" ] }, { "cell_type": "code", - "execution_count": 216, + "execution_count": 31, "id": "85c2f07f", "metadata": {}, "outputs": [ @@ -867,15 +1438,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "40/40 [==============================] - 0s 353us/step\n", + "40/40 [==============================] - 0s 358us/step\n", "Input (after normalization) mean (should be close to 0)\n", - "[-1.1139421e-07 9.2654773e-09 1.7999174e-08 6.3836978e-08\n", - " -1.3721950e-08 1.4648467e-08 -1.9259394e-07 1.2650034e-08\n", - " 1.1470152e-08]\n", + "[-1.7825590e-07 1.7969347e-08 3.7409670e-08 4.0801550e-07\n", + " 8.6564480e-09 4.0541906e-08 -1.6428432e-07 -1.4097150e-08\n", + " 1.9639332e-08]\n", "Input (after normalization) variance (should be close to 1)\n", - "[0.99995416 0.9999578 0.9999702 0.99994487 0.9999524 0.99994314\n", - " 0.99994886 0.9999509 0.999967 ]\n", - "Model: \"model_11\"\n", + "[0.9999583 0.99994993 0.9999721 0.9999553 0.99994916 0.9999563\n", + " 0.99995637 0.9999578 0.9999714 ]\n", + "Model: \"model\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", @@ -886,18 +1457,18 @@ " \n", " dense_0 (Dense) (None, 64) 640 \n", " \n", - " batch_normalization_15 (Ba (None, 64) 256 \n", - " tchNormalization) \n", + " batch_normalization (Batch (None, 64) 256 \n", + " Normalization) \n", " \n", " dense_1 (Dense) (None, 64) 4160 \n", " \n", - " batch_normalization_16 (Ba (None, 64) 256 \n", - " tchNormalization) \n", + " batch_normalization_1 (Bat (None, 64) 256 \n", + " chNormalization) \n", " \n", " dense_2 (Dense) (None, 64) 4160 \n", " \n", - " batch_normalization_17 (Ba (None, 64) 256 \n", - " tchNormalization) \n", + " batch_normalization_2 (Bat (None, 64) 256 \n", + " chNormalization) \n", " \n", " predictions (Dense) (None, 2) 130 \n", " \n", @@ -910,7 +1481,7 @@ } ], "source": [ - "model_preprocess = keras.Model(inputs=inputs, outputs=normalizer)\n", + "model_preprocess = tf.keras.Model(inputs=inputs, outputs=normalizer)\n", "out_test = model_preprocess.predict(train_df[input_vars],batch_size=5000)\n", "print ('Input (after normalization) mean (should be close to 0)')\n", "print (out_test.mean(axis=0))\n", @@ -919,7 +1490,7 @@ "\n", "model.compile(\n", " #optimizer=keras.optimizers.RMSprop(),\n", - " optimizer=keras.optimizers.legacy.Adam(learning_rate=parameters['lr']), # Optimizer\n", + " optimizer='adam', # Optimizer\n", " # Loss function to minimize\n", " loss=keras.losses.CategoricalCrossentropy(),\n", " # List of metrics to monitor\n", @@ -935,7 +1506,7 @@ }, { "cell_type": "code", - "execution_count": 217, + "execution_count": 32, "id": "4e5028e2", "metadata": {}, "outputs": [], @@ -966,7 +1537,7 @@ }, { "cell_type": "code", - "execution_count": 218, + "execution_count": 33, "id": "10528faa", "metadata": { "scrolled": true @@ -976,68 +1547,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/200\n", - "781/781 - 1s - loss: 1.1070 - binary_accuracy: 0.6762 - auc_6: 0.7542 - precision_6: 0.6762 - recall_6: 0.6762 - val_loss: 1.0001 - val_binary_accuracy: 0.7527 - val_auc_6: 0.8462 - val_precision_6: 0.7527 - val_recall_6: 0.7527 - lr: 0.0010 - 1s/epoch - 2ms/step\n", - "Epoch 2/200\n", - "781/781 - 1s - loss: 0.8738 - binary_accuracy: 0.7704 - auc_6: 0.8688 - precision_6: 0.7704 - recall_6: 0.7704 - val_loss: 0.9000 - val_binary_accuracy: 0.7975 - val_auc_6: 0.8967 - val_precision_6: 0.7975 - val_recall_6: 0.7975 - lr: 0.0010 - 879ms/epoch - 1ms/step\n", - "Epoch 3/200\n", - "781/781 - 1s - loss: 0.8110 - binary_accuracy: 0.7848 - auc_6: 0.8908 - precision_6: 0.7848 - recall_6: 0.7848 - val_loss: 0.8488 - val_binary_accuracy: 0.7620 - val_auc_6: 0.8775 - val_precision_6: 0.7620 - val_recall_6: 0.7620 - lr: 0.0010 - 882ms/epoch - 1ms/step\n", - "Epoch 4/200\n", - "781/781 - 1s - loss: 0.7725 - binary_accuracy: 0.7881 - auc_6: 0.8967 - precision_6: 0.7881 - recall_6: 0.7881 - val_loss: 0.8783 - val_binary_accuracy: 0.7872 - val_auc_6: 0.8999 - val_precision_6: 0.7872 - val_recall_6: 0.7872 - lr: 0.0010 - 907ms/epoch - 1ms/step\n", - "Epoch 5/200\n", - "781/781 - 1s - loss: 0.7589 - binary_accuracy: 0.7915 - auc_6: 0.9017 - precision_6: 0.7915 - recall_6: 0.7915 - val_loss: 0.8755 - val_binary_accuracy: 0.7750 - val_auc_6: 0.8910 - val_precision_6: 0.7750 - val_recall_6: 0.7750 - lr: 0.0010 - 865ms/epoch - 1ms/step\n", - "Epoch 6/200\n", - "781/781 - 1s - loss: 0.7445 - binary_accuracy: 0.7881 - auc_6: 0.9017 - precision_6: 0.7881 - recall_6: 0.7881 - val_loss: 0.8714 - val_binary_accuracy: 0.8016 - val_auc_6: 0.9149 - val_precision_6: 0.8016 - val_recall_6: 0.8016 - lr: 0.0010 - 879ms/epoch - 1ms/step\n", - "Epoch 7/200\n", - "781/781 - 1s - loss: 0.7365 - binary_accuracy: 0.7913 - auc_6: 0.9056 - precision_6: 0.7913 - recall_6: 0.7913 - val_loss: 0.8649 - val_binary_accuracy: 0.7639 - val_auc_6: 0.8836 - val_precision_6: 0.7639 - val_recall_6: 0.7639 - lr: 0.0010 - 861ms/epoch - 1ms/step\n", - "Epoch 8/200\n", - "781/781 - 1s - loss: 0.7256 - binary_accuracy: 0.7914 - auc_6: 0.9064 - precision_6: 0.7914 - recall_6: 0.7914 - val_loss: 0.8378 - val_binary_accuracy: 0.7667 - val_auc_6: 0.8894 - val_precision_6: 0.7667 - val_recall_6: 0.7667 - lr: 0.0010 - 875ms/epoch - 1ms/step\n", - "Epoch 9/200\n", - "781/781 - 1s - loss: 0.7131 - binary_accuracy: 0.7931 - auc_6: 0.9085 - precision_6: 0.7931 - recall_6: 0.7931 - val_loss: 0.8589 - val_binary_accuracy: 0.7608 - val_auc_6: 0.8856 - val_precision_6: 0.7608 - val_recall_6: 0.7608 - lr: 0.0010 - 883ms/epoch - 1ms/step\n", - "Epoch 10/200\n", - "781/781 - 1s - loss: 0.7093 - binary_accuracy: 0.7928 - auc_6: 0.9090 - precision_6: 0.7928 - recall_6: 0.7928 - val_loss: 0.8576 - val_binary_accuracy: 0.7728 - val_auc_6: 0.8989 - val_precision_6: 0.7728 - val_recall_6: 0.7728 - lr: 0.0010 - 926ms/epoch - 1ms/step\n", - "Epoch 11/200\n", - "781/781 - 1s - loss: 0.7008 - binary_accuracy: 0.7986 - auc_6: 0.9131 - precision_6: 0.7986 - recall_6: 0.7986 - val_loss: 0.8475 - val_binary_accuracy: 0.7546 - val_auc_6: 0.8863 - val_precision_6: 0.7546 - val_recall_6: 0.7546 - lr: 0.0010 - 874ms/epoch - 1ms/step\n", - "Epoch 12/200\n", - "781/781 - 1s - loss: 0.6921 - binary_accuracy: 0.7936 - auc_6: 0.9107 - precision_6: 0.7936 - recall_6: 0.7936 - val_loss: 0.9328 - val_binary_accuracy: 0.8219 - val_auc_6: 0.9262 - val_precision_6: 0.8219 - val_recall_6: 0.8219 - lr: 0.0010 - 880ms/epoch - 1ms/step\n", - "Epoch 13/200\n", - "781/781 - 1s - loss: 0.6803 - binary_accuracy: 0.7984 - auc_6: 0.9144 - precision_6: 0.7984 - recall_6: 0.7984 - val_loss: 0.8749 - val_binary_accuracy: 0.7980 - val_auc_6: 0.9126 - val_precision_6: 0.7980 - val_recall_6: 0.7980 - lr: 0.0010 - 870ms/epoch - 1ms/step\n", - "Epoch 14/200\n", - "781/781 - 1s - loss: 0.6735 - binary_accuracy: 0.7977 - auc_6: 0.9146 - precision_6: 0.7977 - recall_6: 0.7977 - val_loss: 0.8490 - val_binary_accuracy: 0.7853 - val_auc_6: 0.9070 - val_precision_6: 0.7853 - val_recall_6: 0.7853 - lr: 0.0010 - 891ms/epoch - 1ms/step\n", - "Epoch 15/200\n", - "781/781 - 1s - loss: 0.6783 - binary_accuracy: 0.8015 - auc_6: 0.9172 - precision_6: 0.8015 - recall_6: 0.8015 - val_loss: 0.8594 - val_binary_accuracy: 0.7801 - val_auc_6: 0.8993 - val_precision_6: 0.7801 - val_recall_6: 0.7801 - lr: 0.0010 - 881ms/epoch - 1ms/step\n", - "Epoch 16/200\n", - "\n", - "Epoch 16: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n", - "781/781 - 1s - loss: 0.6676 - binary_accuracy: 0.8002 - auc_6: 0.9163 - precision_6: 0.8002 - recall_6: 0.8002 - val_loss: 0.8877 - val_binary_accuracy: 0.8346 - val_auc_6: 0.9364 - val_precision_6: 0.8346 - val_recall_6: 0.8346 - lr: 0.0010 - 859ms/epoch - 1ms/step\n", - "Epoch 17/200\n", - "781/781 - 1s - loss: 0.6030 - binary_accuracy: 0.8142 - auc_6: 0.9267 - precision_6: 0.8142 - recall_6: 0.8142 - val_loss: 0.8442 - val_binary_accuracy: 0.8033 - val_auc_6: 0.9205 - val_precision_6: 0.8033 - val_recall_6: 0.8033 - lr: 1.0000e-04 - 853ms/epoch - 1ms/step\n", - "Epoch 18/200\n", - "781/781 - 1s - loss: 0.5884 - binary_accuracy: 0.8114 - auc_6: 0.9255 - precision_6: 0.8114 - recall_6: 0.8114 - val_loss: 0.8483 - val_binary_accuracy: 0.8106 - val_auc_6: 0.9248 - val_precision_6: 0.8106 - val_recall_6: 0.8106 - lr: 1.0000e-04 - 853ms/epoch - 1ms/step\n", - "Epoch 19/200\n", - "781/781 - 1s - loss: 0.5860 - binary_accuracy: 0.8128 - auc_6: 0.9263 - precision_6: 0.8128 - recall_6: 0.8128 - val_loss: 0.8601 - val_binary_accuracy: 0.8115 - val_auc_6: 0.9257 - val_precision_6: 0.8115 - val_recall_6: 0.8115 - lr: 1.0000e-04 - 861ms/epoch - 1ms/step\n", - "Epoch 20/200\n", - "781/781 - 1s - loss: 0.5797 - binary_accuracy: 0.8168 - auc_6: 0.9287 - precision_6: 0.8168 - recall_6: 0.8168 - val_loss: 0.8610 - val_binary_accuracy: 0.8164 - val_auc_6: 0.9284 - val_precision_6: 0.8164 - val_recall_6: 0.8164 - lr: 1.0000e-04 - 845ms/epoch - 1ms/step\n", - "Epoch 21/200\n", - "781/781 - 1s - loss: 0.5681 - binary_accuracy: 0.8187 - auc_6: 0.9300 - precision_6: 0.8187 - recall_6: 0.8187 - val_loss: 0.8709 - val_binary_accuracy: 0.8139 - val_auc_6: 0.9269 - val_precision_6: 0.8139 - val_recall_6: 0.8139 - lr: 1.0000e-04 - 846ms/epoch - 1ms/step\n", - "Epoch 22/200\n", - "781/781 - 1s - loss: 0.5674 - binary_accuracy: 0.8188 - auc_6: 0.9300 - precision_6: 0.8188 - recall_6: 0.8188 - val_loss: 0.8763 - val_binary_accuracy: 0.8125 - val_auc_6: 0.9264 - val_precision_6: 0.8125 - val_recall_6: 0.8125 - lr: 1.0000e-04 - 847ms/epoch - 1ms/step\n", - "Epoch 23/200\n", - "781/781 - 1s - loss: 0.5616 - binary_accuracy: 0.8193 - auc_6: 0.9304 - precision_6: 0.8193 - recall_6: 0.8193 - val_loss: 0.8813 - val_binary_accuracy: 0.8186 - val_auc_6: 0.9299 - val_precision_6: 0.8186 - val_recall_6: 0.8186 - lr: 1.0000e-04 - 851ms/epoch - 1ms/step\n", - "Epoch 24/200\n", - "\n", - "Epoch 24: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n", - "781/781 - 1s - loss: 0.5623 - binary_accuracy: 0.8225 - auc_6: 0.9320 - precision_6: 0.8225 - recall_6: 0.8225 - val_loss: 0.8818 - val_binary_accuracy: 0.8112 - val_auc_6: 0.9259 - val_precision_6: 0.8112 - val_recall_6: 0.8112 - lr: 1.0000e-04 - 851ms/epoch - 1ms/step\n", - "Epoch 25/200\n", - "781/781 - 1s - loss: 0.5464 - binary_accuracy: 0.8147 - auc_6: 0.9286 - precision_6: 0.8147 - recall_6: 0.8147 - val_loss: 0.8852 - val_binary_accuracy: 0.8160 - val_auc_6: 0.9288 - val_precision_6: 0.8160 - val_recall_6: 0.8160 - lr: 1.0000e-05 - 850ms/epoch - 1ms/step\n", - "Epoch 26/200\n", - "781/781 - 1s - loss: 0.5478 - binary_accuracy: 0.8190 - auc_6: 0.9309 - precision_6: 0.8190 - recall_6: 0.8190 - val_loss: 0.8850 - val_binary_accuracy: 0.8162 - val_auc_6: 0.9286 - val_precision_6: 0.8162 - val_recall_6: 0.8162 - lr: 1.0000e-05 - 851ms/epoch - 1ms/step\n", - "Epoch 27/200\n", - "781/781 - 1s - loss: 0.5494 - binary_accuracy: 0.8215 - auc_6: 0.9323 - precision_6: 0.8215 - recall_6: 0.8215 - val_loss: 0.8895 - val_binary_accuracy: 0.8197 - val_auc_6: 0.9309 - val_precision_6: 0.8197 - val_recall_6: 0.8197 - lr: 1.0000e-05 - 869ms/epoch - 1ms/step\n", - "Epoch 28/200\n", - "Restoring model weights from the end of the best epoch: 8.\n", - "781/781 - 1s - loss: 0.5464 - binary_accuracy: 0.8237 - auc_6: 0.9332 - precision_6: 0.8237 - recall_6: 0.8237 - val_loss: 0.8909 - val_binary_accuracy: 0.8219 - val_auc_6: 0.9321 - val_precision_6: 0.8219 - val_recall_6: 0.8219 - lr: 1.0000e-05 - 842ms/epoch - 1ms/step\n", - "Epoch 28: early stopping\n" + "Epoch 1/10\n", + "781/781 - 2s - loss: 1.1140 - binary_accuracy: 0.6846 - auc: 0.7616 - precision: 0.6846 - recall: 0.6846 - val_loss: 0.9470 - val_binary_accuracy: 0.7473 - val_auc: 0.8397 - val_precision: 0.7473 - val_recall: 0.7473 - lr: 0.0010 - 2s/epoch - 2ms/step\n", + "Epoch 2/10\n", + "781/781 - 1s - loss: 0.8780 - binary_accuracy: 0.7746 - auc: 0.8755 - precision: 0.7746 - recall: 0.7746 - val_loss: 0.8463 - val_binary_accuracy: 0.7616 - val_auc: 0.8666 - val_precision: 0.7616 - val_recall: 0.7616 - lr: 0.0010 - 866ms/epoch - 1ms/step\n", + "Epoch 3/10\n", + "781/781 - 1s - loss: 0.8231 - binary_accuracy: 0.7829 - auc: 0.8887 - precision: 0.7829 - recall: 0.7829 - val_loss: 0.8505 - val_binary_accuracy: 0.7493 - val_auc: 0.8600 - val_precision: 0.7493 - val_recall: 0.7493 - lr: 0.0010 - 866ms/epoch - 1ms/step\n", + "Epoch 4/10\n", + "781/781 - 1s - loss: 0.8009 - binary_accuracy: 0.7831 - auc: 0.8943 - precision: 0.7831 - recall: 0.7831 - val_loss: 0.8316 - val_binary_accuracy: 0.7958 - val_auc: 0.9038 - val_precision: 0.7958 - val_recall: 0.7958 - lr: 0.0010 - 866ms/epoch - 1ms/step\n", + "Epoch 5/10\n", + "781/781 - 1s - loss: 0.7903 - binary_accuracy: 0.7856 - auc: 0.8973 - precision: 0.7856 - recall: 0.7856 - val_loss: 0.8017 - val_binary_accuracy: 0.7733 - val_auc: 0.8914 - val_precision: 0.7733 - val_recall: 0.7733 - lr: 0.0010 - 885ms/epoch - 1ms/step\n", + "Epoch 6/10\n", + "781/781 - 1s - loss: 0.7754 - binary_accuracy: 0.7866 - auc: 0.8995 - precision: 0.7866 - recall: 0.7866 - val_loss: 0.8351 - val_binary_accuracy: 0.7524 - val_auc: 0.8717 - val_precision: 0.7524 - val_recall: 0.7524 - lr: 0.0010 - 874ms/epoch - 1ms/step\n", + "Epoch 7/10\n", + "781/781 - 1s - loss: 0.7554 - binary_accuracy: 0.7923 - auc: 0.9041 - precision: 0.7923 - recall: 0.7923 - val_loss: 0.8089 - val_binary_accuracy: 0.8026 - val_auc: 0.9085 - val_precision: 0.8026 - val_recall: 0.8026 - lr: 0.0010 - 893ms/epoch - 1ms/step\n", + "Epoch 8/10\n", + "781/781 - 1s - loss: 0.7440 - binary_accuracy: 0.7929 - auc: 0.9055 - precision: 0.7929 - recall: 0.7929 - val_loss: 0.8199 - val_binary_accuracy: 0.7742 - val_auc: 0.8921 - val_precision: 0.7742 - val_recall: 0.7742 - lr: 0.0010 - 1s/epoch - 1ms/step\n", + "Epoch 9/10\n", + "781/781 - 1s - loss: 0.7359 - binary_accuracy: 0.7957 - auc: 0.9075 - precision: 0.7957 - recall: 0.7957 - val_loss: 0.8223 - val_binary_accuracy: 0.7870 - val_auc: 0.9025 - val_precision: 0.7870 - val_recall: 0.7870 - lr: 0.0010 - 965ms/epoch - 1ms/step\n", + "Epoch 10/10\n", + "781/781 - 1s - loss: 0.7329 - binary_accuracy: 0.7966 - auc: 0.9079 - precision: 0.7966 - recall: 0.7966 - val_loss: 0.8291 - val_binary_accuracy: 0.7862 - val_auc: 0.9003 - val_precision: 0.7862 - val_recall: 0.7862 - lr: 0.0010 - 865ms/epoch - 1ms/step\n" ] } ], @@ -1059,7 +1588,7 @@ }, { "cell_type": "code", - "execution_count": 219, + "execution_count": 34, "id": "d0f5ca52", "metadata": {}, "outputs": [ @@ -1070,16 +1599,6 @@ "Curves saved as loss_test1_odd.png\n", "printed history\n" ] - }, - { - "data": { - "image/png": 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", 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/guzela/Downloads/dnn/roc.py:80: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.\n", + " plt.show()\n" + ] }, { "name": "stdout", "output_type": "stream", "text": [ "Saved ROC as roc_test1_odd_HH.pdf\n", - "Best WP based on significance = 0.92426\n", + "Best WP based on significance = 0.95688\n", "ROC curve of binary classification of background node versus all the others\n" ] }, { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Saved ROC as roc_test1_odd_d_HH.pdf\n", - "Best WP based on significance = 2.50222\n", - "Multi roc curve for `output HH`\n" + "/Users/guzela/Downloads/dnn/roc.py:80: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.\n", + " plt.show()\n" ] }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "name": "stdout", "output_type": "stream", "text": [ + "Saved ROC as roc_test1_odd_d_HH.pdf\n", + "Best WP based on significance = 3.10005\n", + "Multi roc curve for `output HH`\n", "Saved multi ROC as multi_roc_test1_odd_output_HH.pdf\n", "Multi roc curve for `output background`\n" ] }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/guzela/Downloads/dnn/roc.py:110: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.\n", + " plt.show()\n", + "/Users/guzela/Downloads/dnn/roc.py:110: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.\n", + " plt.show()\n" + ] }, { "name": "stdout", @@ -1243,16 +1740,6 @@ "text": [ "Saved multi ROC as multi_roc_test1_odd_output_background.pdf\n" ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -1347,7 +1834,7 @@ }, { "cell_type": "code", - "execution_count": 222, + "execution_count": 37, "id": "2ea08ded", "metadata": { "scrolled": true @@ -1358,8 +1845,8 @@ "output_type": "stream", "text": [ "evaluate the model...\n", - "57/57 - 0s - loss: 0.8207 - binary_accuracy: 0.7640 - auc_6: 0.8875 - precision_6: 0.7640 - recall_6: 0.7640 - 103ms/epoch - 2ms/step\n", - "binary_accuracy: 76.40%\n" + "57/57 - 0s - loss: 0.8428 - binary_accuracy: 0.7850 - auc: 0.9002 - precision: 0.7850 - recall: 0.7850 - 107ms/epoch - 2ms/step\n", + "binary_accuracy: 78.50%\n" ] } ], @@ -1376,10 +1863,10 @@ }, { "cell_type": "code", - "execution_count": 223, + "execution_count": 38, "id": "10ea382f", "metadata": { - "scrolled": true + "scrolled": false }, "outputs": [ { @@ -1395,27 +1882,13 @@ "text": [ "INFO:tensorflow:Assets written to: model_test1_odd/assets\n" ] - }, - { - "ename": "TypeError", - "evalue": "Unable to serialize ak4bjet1_pt 91.592400\nak4bjet1_eta 0.001051\nak4bjet1_phi -0.000024\nleadingLepton_pt 77.542953\nleadingLepton_eta 0.000546\nleadingLepton_phi -0.008311\nsubleadingLepton_pt 41.223045\nsubleadingLepton_eta -0.001178\nsubleadingLepton_phi -0.003473\ndtype: float32 to JSON. Unrecognized type .", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn [223], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# save model and architecture to a single file\u001b[39;00m\n\u001b[1;32m 2\u001b[0m modelName \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msuffix\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msplit\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 3\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodelName\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSaved model to disk as \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodelName\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/keras/src/utils/traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 67\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m 68\u001b[0m \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[0;32m---> 70\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n\u001b[1;32m 71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n", - "File \u001b[0;32m/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/json/encoder.py:199\u001b[0m, in \u001b[0;36mJSONEncoder.encode\u001b[0;34m(self, o)\u001b[0m\n\u001b[1;32m 195\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m encode_basestring(o)\n\u001b[1;32m 196\u001b[0m \u001b[38;5;66;03m# This doesn't pass the iterator directly to ''.join() because the\u001b[39;00m\n\u001b[1;32m 197\u001b[0m \u001b[38;5;66;03m# exceptions aren't as detailed. The list call should be roughly\u001b[39;00m\n\u001b[1;32m 198\u001b[0m \u001b[38;5;66;03m# equivalent to the PySequence_Fast that ''.join() would do.\u001b[39;00m\n\u001b[0;32m--> 199\u001b[0m chunks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miterencode\u001b[49m\u001b[43m(\u001b[49m\u001b[43mo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_one_shot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 200\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(chunks, (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m)):\n\u001b[1;32m 201\u001b[0m chunks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(chunks)\n", - "File \u001b[0;32m/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/json/encoder.py:257\u001b[0m, in \u001b[0;36mJSONEncoder.iterencode\u001b[0;34m(self, o, _one_shot)\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 253\u001b[0m _iterencode \u001b[38;5;241m=\u001b[39m _make_iterencode(\n\u001b[1;32m 254\u001b[0m markers, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefault, _encoder, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindent, floatstr,\n\u001b[1;32m 255\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkey_separator, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitem_separator, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msort_keys,\n\u001b[1;32m 256\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mskipkeys, _one_shot)\n\u001b[0;32m--> 257\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_iterencode\u001b[49m\u001b[43m(\u001b[49m\u001b[43mo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mTypeError\u001b[0m: Unable to serialize ak4bjet1_pt 91.592400\nak4bjet1_eta 0.001051\nak4bjet1_phi -0.000024\nleadingLepton_pt 77.542953\nleadingLepton_eta 0.000546\nleadingLepton_phi -0.008311\nsubleadingLepton_pt 41.223045\nsubleadingLepton_eta -0.001178\nsubleadingLepton_phi -0.003473\ndtype: float32 to JSON. Unrecognized type ." - ] } ], "source": [ - "# save model and architecture to a single file\n", + "outputDir = opt.outputDir\n", "modelName = f\"model_{suffix}_{split}\"\n", - "model.save(modelName)\n", - "print(f\"Saved model to disk as {modelName}\")" + "model.save(outputDir+modelName)\n", + "print(f\"Saved model to {outputDir} as {modelName}\")" ] }, { diff --git a/python/DNN.py b/python/DNN.py new file mode 100644 index 0000000..5eb468f --- /dev/null +++ b/python/DNN.py @@ -0,0 +1,607 @@ +#!/usr/bin/env python +# coding: utf-8 + +# In[17]: + + +import os +import optparse +import yaml +import importlib +import matplotlib +import matplotlib.pyplot as plt +from matplotlib.backends.backend_pdf import PdfPages +import numpy as np +import pyarrow.parquet as pq +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import LabelEncoder, OneHotEncoder +import tensorflow as tf +from tensorflow import keras +from tensorflow.keras import layers +from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau + + +# In[18]: + + +usage = 'usage: %prog [options]' +parser = optparse.OptionParser(usage) + +parser.add_option('-s', '--skimFile', + dest='skimFile', + help='path to the skim file', + type='string') +parser.add_option('-o', + dest='outputDir', + help='path to the output directory', + type='string') + +(opt, args) = parser.parse_args() + + +# In[19]: + + +### Parameters of the training ### + +#split = "even" +split = "odd" +# split = even | odd -> on what split to train the model (will be in the name) +# -> you need one "odd" and one "even" models to be put inside bamboo + +suffix = 'test1' +# Suffix that will be added to the saved model (so multiple DNNs can be trained) + +quantile = 1.0 # We will repeat the part of the weights rightmost tail +# Eg : 0.95, means we take the 5% events on the right tail of training weight and repeat them +# 1.0 means no correction (to be used if you want to disable it) + +tags = ['HH','background'] + +# DNN hyperparameters # +parameters = { + 'epochs' : 200, + 'lr' : 0.001, + 'batch_size' : 256, + 'n_layers' : 3, + 'n_neurons' : 64, + 'hidden_activation' : 'relu', + 'output_activation' : 'softmax', + 'l2' : 1e-6, + 'dropout' : 0., + 'batch_norm' : True, +} +# Input variables +input_vars=[ + "ak4bjet1_pt", + "ak4bjet1_eta", + "ak4bjet1_phi", + "leadingLepton_pt", + "leadingLepton_eta", + "leadingLepton_phi", + "subleadingLepton_pt", + "subleadingLepton_eta", + "subleadingLepton_phi" + ] + +print(f'Using {len(input_vars)} input variables') + + +# In[20]: + + + + +print(f'Using skim file {opt.skimFile}') + + + +# In[21]: + + +# Load dataframe from parquet file +df = pd.read_parquet(opt.skimFile) + + +# In[22]: + + +# Add tag column # +df['tag'] = 'background' +df.loc[df.process.str.contains('HH'),['tag']] = 'HH' +df["tag"].unique() + + +# In[23]: + + +assert len(set(tags).intersection(set(pd.unique(df['tag'])))) == len(tags) # Just cross check to avoid mistakes + + +# In[24]: + + +# One-hot encoding is a way to convert a column to a format that is easier for machine learning applications +# Here I transform the tag (bkg or signal) column to binary and add it as new columns + +one_hot = pd.get_dummies(df['tag'], dtype=float) +df = pd.concat((df,one_hot),axis=1) + + +# In[25]: + + +Nevents_before_weight_cut = df.shape[0] +df = df[df.weight > 0] # remove events with negative weight +Nevents_after_weight_cut = df.shape[0] +Nevents_with_negative_weight = Nevents_before_weight_cut - Nevents_after_weight_cut +print(f"Number of events with negative weight is : {Nevents_with_negative_weight}") + + +# In[26]: + + +# copy the weight column as event weight to calculate it later +df['event_weight'] = df['weight'].copy() + + +# In[27]: + + +if (df['event_weight'] < 0).sum() > 0: + raise RuntimeError(f"There are {(df['event_weight'] < 0).sum()} events with negative event weight, this should not happen") + + +# In[28]: + + +# copy the event weight column as training weight +if 'training_weight' in df.columns: + del df['training_weight'] + +df['training_weight'] = df['event_weight'].copy() + + +# In[29]: + + +# and normalize the training weight +for tag in df.tag.unique(): + # training weight *= Nevents / sum of event weight + df.loc[df['tag']==tag,'training_weight'] *= df.shape[0] / df[df['tag']==tag]['event_weight'].sum() +df + + +# In[30]: + + +def checkBatches(df, label_column, batch_size=128, weight_column='weight'): + N_checks = 20 + labels = pd.unique(df[label_column]) + sum_label = {label:0. for label in labels} + N_label = {label:0 for label in labels} + for i in range(N_checks): + rnd_df = df.sample(batch_size) + for label in labels: + sum_label[label] += rnd_df[rnd_df[label_column]==label][weight_column].sum() + N_label[label] += rnd_df[rnd_df[label_column]==label][weight_column].shape[0] + + print (f'On average, per batch the total weight is') + for label in labels: + sum_label[label] /= N_checks + N_label[label] /= N_checks + print (f'\t... {label:20s}: {sum_label[label]:15.9f} [{N_label[label]} events]') + + +# In[31]: + + +print ('Using event weight') +checkBatches(df, label_column='tag', weight_column='event_weight', batch_size=parameters['batch_size']) +print ('Using training weight') +checkBatches(df, label_column='tag', weight_column='training_weight', batch_size=parameters['batch_size']) + + +# In[32]: + + +# Plot the background and signal weights # +matplotlib.use('Agg') # to avoid """qt.qpa.plugin: Could not find the Qt platform plugin "xcb" in "" """ error +fig,axs = plt.subplots(figsize=(25,10),nrows=len(tags),ncols=2) +fig.subplots_adjust(left=0.1, right=0.9, top=0.98, bottom=0.1, wspace=0.2,hspace=0.4) +for irow,tag in enumerate(tags): + for icol,column in enumerate(['event_weight','training_weight']): + axs[irow,icol].hist(df[df['tag']==tag][column],bins=100,color='b') + axs[irow,icol].set_title(f"Category = {tag}") + axs[irow,icol].set_xlabel(column) + axs[irow,icol].set_yscale('log') +fig.savefig("event_weights_A.pdf", dpi = 300) + + +# In[33]: + + +# Determine splitting variable # +split_var = np.abs(df['leadingLepton_phi'].copy()) +split_var *= 1e5 +split_var -= np.floor(split_var) +split_var = (split_var*1e1).astype(int) +split_var = split_var %2 == 0 +print (f'Even set has {df[split_var].shape[0]:10d} events [{df[split_var].shape[0]/df.shape[0]*100:5.2f}%]') +print (f'Odd set has {df[~split_var].shape[0]:10d} events [{df[~split_var].shape[0]/df.shape[0]*100:5.2f}%]') + + +# In[34]: + + +# Sets splitting # +print (f'Using split type {split}') +if split == 'even': + train_df = df[~split_var] # Trained on odd + test_df = df[split_var] # Evaluated on even +elif split == 'odd': + train_df = df[split_var] # Trained on even + test_df = df[~split_var] # Evaluated on odd +else: + raise RuntimeError(f'Split needs to be either odd or even, is {split}') + + +# In[35]: + + +# Randomize for training +train_df = train_df.sample(frac=1) + + +# In[36]: + + +# # Quantile corrections # +# # When an event has a large weight, it can imbalance a lot the training, still the weight might have a meaning +# # Idea : instead of 1 event with wi>>1, we use N copies of the event with wf = wi/N +# # From the point of view of the physics it does not matter, the total event weight sum of each process is the same +# # From the point of view of the DNN, we have split a tough nut to crack into several smaller ones + +# quantile_lim = train_df['training_weight'].quantile(0.99) +# print (f'{(1-quantile)*100:2.2f}% right quantile is when weight is at {quantile_lim}') +# print (' -> These events will be repeated and their learning weights reduced accordingly to avoid unstability') + + +# In[37]: + + +# # Select the events # +# idx_to_repeat = train_df['training_weight'] >= quantile_lim +# events_excess = train_df[idx_to_repeat].copy() +# saved_columns = train_df[['training_weight','process']].copy() +# # Compute multiplicative factor # +# factor = (events_excess['training_weight']/quantile_lim).values.astype(np.int32) +# # Correct the weights of events already in df # +# train_df.loc[idx_to_repeat,'training_weight'] /= factor +# # Add N-1 copies # +# arr_to_repeat = train_df[idx_to_repeat].values +# repetition = np.repeat(np.arange(arr_to_repeat.shape[0]), factor-1) +# df_repeated = pd.DataFrame(np.take(arr_to_repeat,repetition,axis=0),columns=train_df.columns) +# df_repeated = df_repeated.astype(train_df.dtypes.to_dict()) # otherwise dtypes are object +# train_df = pd.concat((train_df,df_repeated),axis=0,ignore_index=True).sample(frac=1).reset_index() # Add and randomize +# # Printout # +# print ('Changes per process in training set') +# for process in pd.unique(train_df['process']): +# N_before = saved_columns[saved_columns['process']==process].shape[0] +# N_after = train_df[train_df['process']==process].shape[0] +# if N_before != N_after: +# print (f"{process:20s}") +# print (f"... {N_before:6d} events [sum weight = {saved_columns[saved_columns['process']==process]['training_weight'].sum():14.6f}]",end=' -> ') +# print (f"{N_after:6d} events [sum weight = {train_df[train_df['process']==process]['training_weight'].sum():14.6f}]") +# print (f"Total entries : {saved_columns.shape[0]:14d} -> {train_df.shape[0]:14d}") +# print (f"Total event sum : {saved_columns['training_weight'].sum():14.6f} -> {train_df['training_weight'].sum():14.6f}") + +# Validation split # +train_df,val_df = train_test_split(train_df,test_size=0.3) + +# Printout # +print ('\nFinal sets') +print (f'Training set = {train_df.shape[0]} ({train_df.shape[0] / df.shape[0] * 100 :2.2f}%)') +print (f'Validation set = {val_df.shape[0]} ({val_df.shape[0] / df.shape[0] * 100 :2.2f}%)') +print (f'Testing set = {test_df.shape[0]} ({test_df.shape[0] / df.shape[0] * 100 :2.2f}%)') +print (f'Total set = {df.shape[0]}') + + +# In[38]: + + +# # Plot the background and signal weights # +# fig,axs = plt.subplots(figsize=(16,8),nrows=1,ncols=2) +# fig.subplots_adjust(left=0.1, right=0.9, top=0.96, bottom=0.1, wspace=0.2,hspace=0.3) + +# if split == 'even': +# axs[0].hist(df[~split_var]['training_weight'],bins=100,color='b') +# elif split == 'odd': +# axs[0].hist(df[split_var]['training_weight'],bins=100,color='b') +# axs[0].set_title("Before correction") +# axs[0].set_xlabel("Training weight") +# axs[0].set_yscale('log') +# axs[1].hist(train_df['training_weight'],bins=100,color='b') +# axs[1].set_title("After correction") +# axs[1].set_xlabel("Training weight") +# axs[1].set_yscale('log') +# fig.savefig("event_weights_C.pdf", dpi = 300) + + +# In[39]: + + +# Input layer # +inputs = keras.Input(shape=(len(input_vars),), name="particles") +# Preprocessing layer +from tensorflow.keras.layers.experimental import preprocessing +normalizer = preprocessing.Normalization(mean = train_df[input_vars].mean(axis=0), + variance = train_df[input_vars].var(axis=0), + name = 'Normalization')(inputs) + # this layer does the preprocessing (x-mu)/std for each input +# Dense (hidden) layers # +x = normalizer +for i in range(parameters['n_layers']): + x = layers.Dense(units = parameters['n_neurons'], + activation = parameters['hidden_activation'], + activity_regularizer = tf.keras.regularizers.l2(parameters['l2']), + name = f"dense_{i}")(x) + if parameters['batch_norm']: + x = layers.BatchNormalization()(x) + if parameters['dropout'] > 0.: + x = layers.Dropout(parameters['dropout'])(x) +# Output layer # +outputs = layers.Dense(units = 2, + activation = parameters['output_activation'], + activity_regularizer = tf.keras.regularizers.l2(parameters['l2']), + name = "predictions")(x) + +# Registering the model # +model = keras.Model(inputs=inputs, outputs=outputs) + + +# In[41]: + + +model_preprocess = keras.Model(inputs=inputs, outputs=normalizer) +out_test = model_preprocess.predict(train_df[input_vars],batch_size=5000) +print ('Input (after normalization) mean (should be close to 0)') +print (out_test.mean(axis=0)) +print ('Input (after normalization) variance (should be close to 1)') +print (out_test.var(axis=0)) + +model.compile( + #optimizer=keras.optimizers.RMSprop(), + optimizer='adam', # Optimizer + # Loss function to minimize + loss=keras.losses.CategoricalCrossentropy(), + # List of metrics to monitor + metrics=[keras.metrics.BinaryAccuracy(), + tf.keras.metrics.AUC(), + tf.keras.metrics.Precision(), + tf.keras.metrics.Recall()], + weighted_metrics=[] +) + +model.summary() + + +# In[42]: + + +# Callbacks # +early_stopping = EarlyStopping(monitor = 'val_loss', + min_delta = 0.001, + patience = 20, + verbose=1, + mode='min', + restore_best_weights=True) +# Stop the learning when val_loss stops increasing +# https://keras.io/api/callbacks/early_stopping/ + +reduce_plateau = ReduceLROnPlateau(monitor = 'val_loss', + factor = 0.1, + min_delta = 0.001, + patience = 8, + min_lr = 1e-8, + verbose=2, + mode='min') +# reduce LR if not improvement for some time +# https://keras.io/api/callbacks/reduce_lr_on_plateau/ +import History +importlib.reload(History) +loss_history = History.LossHistory() + + +# In[43]: + + +history = model.fit( + train_df[input_vars], + train_df[tags], + verbose=2, + batch_size=parameters['batch_size'], + epochs=parameters['epochs'], + sample_weight=train_df['training_weight'], + # We pass some validation for + # monitoring validation loss and metrics + # at the end of each epoch + validation_data=(val_df[input_vars],val_df[tags],val_df['training_weight']), + callbacks = [early_stopping, reduce_plateau, loss_history], +) + + +# In[44]: + + +History.PlotHistory(loss_history,params=parameters,outputName=f'loss_{suffix}_{split}.png') # giving an error i.e. +# Params is a dict of parameters with name and values +# used for plotting +print("printed history") + + +# In[45]: + + +# Produce output on the test set as new column # +output = model.predict(test_df[input_vars],batch_size=5000) + +output_tags = [f'output {tag}' for tag in tags] + # Here the batch_size arg is independent of the learning + # Default is 32, but it can become slow, by using large value it will just compute more values in parallel + # (more or less parallel, we are not using a GPU) +for output_tag in output_tags: + if output_tag in test_df.columns: + # If already output, need to remove to add again + # avoid issues in case you run this cell multiple times + del test_df[output_tag] + +test_df = pd.concat((test_df,pd.DataFrame(output,columns=output_tags,index=test_df.index)),axis=1) +# We add the output as a column, a bit messy, different ways, here I use a concatenation + +# Make the discriminator # +if 'd_HH' in test_df.columns: + del test_df['d_HH'] + +signal_idx = [i for i,tag in enumerate(tags) if 'HH' in tag] + +# d_HH = ln (P(HH) / (P(single H) + P(background))) + +#test_df['d_HH'] = pd.Series(np.ones(test_df.shape[0])) + +# Numerator # +num = pd.DataFrame((test_df[[output_tags[i] for i in range(len(tags)) if i in signal_idx]]).sum(axis=1)) +# Denominator # +den = pd.DataFrame(test_df[[output_tags[i] for i in range(len(tags)) if i not in signal_idx]].sum(axis=1)) +# Ln # +d_HH = np.log(num / den) +test_df['d_HH'] = d_HH + + +# In[46]: + + +print("ROC curves") +import roc +importlib.reload(roc) # Reload in case file has changed +for tag in tags: + print (f'ROC curve of binary classification of {tag} node versus all the others') + roc.rocAndSig(y_true = test_df[tag], + y_pred = test_df[f'output {tag}'], + w_roc = test_df['training_weight'], + w_sig = test_df['event_weight'], + show_significance = 'HH' in tag, + outputName = f'roc_{suffix}_{split}_{tag}.pdf') + +# Multiclassification ROC curves are a bit harder to interpret than binary classification +# Here I do one versus the rest, so each ROC curves shows how the DNN is able to classify +# one class (HH, single H or background) versus all the others, which is one projection on +# how to see the performances +# For HH I show the significance but more as an information, because using only the HH node +# means we do not use all the power of the multiclass (-> d_HH is for that) +print (f'ROC curve of binary classification of d_HH') +roc.rocAndSig(y_true = test_df['HH'], + y_pred = test_df['d_HH'], + w_roc = test_df['training_weight'], + w_sig = test_df['event_weight'], + show_significance = True, + outputName = f'roc_{suffix}_{split}_d_HH.pdf') + +# Tryign a new things, seeing the discrimination power of each node, class wise +for tag in tags: + print (f'Multi roc curve for `output {tag}`') + tags_order = [tag] + [t for t in tags if t != tag] + roc.multiRoc(outputs = [test_df[test_df['tag']==tag][f'output {tag}'] for tag in tags_order], + tags = tags_order, + weights = [test_df[test_df['tag']==tag]['training_weight'] for tag in tags_order], + title = f'Using node {tag}', + outputName = f'multi_roc_{suffix}_{split}_output_{tag}.pdf') + +fig,axs = plt.subplots(figsize=(6,12),nrows=len(tags)+1,ncols=2) +fig.subplots_adjust(left=0.1, right=0.9, top=0.98, bottom=0.1, wspace=0.3,hspace=0.5) + +tag_df = {tag:test_df[test_df['tag']==tag] for tag in tags} +colors = ['g','r','b'] + +# Manual binning so we can compute significance # +n_bins = 50 + +def get_bin_content(bins,y,w): + digitized = np.digitize(y,bins) + return np.array([w[digitized==i].sum() for i in range(1, len(bins))]) + +for irow,output_tag in enumerate(output_tags+['d_HH']): + for icol,weight in enumerate(['event_weight','training_weight']): + # Fill the bins myself # + bins = np.linspace(test_df[output_tag].min(),test_df[output_tag].max(),n_bins+1) + centers = (bins[1:]+bins[:-1])/2 + widths = np.diff(bins) + + tag_content = {tag:get_bin_content(bins,tag_df[tag][output_tag],tag_df[tag][weight])for tag in tags} + tag_cumsum_left = {tag:np.cumsum(tag_content[tag])/tag_content[tag].sum() for tag in tags} + tag_cumsum_right = {tag:np.cumsum(tag_content[tag][::-1])[::-1]/tag_content[tag].sum() for tag in tags} + # Need to integrate all the bins right of the DNN cut to get significance + #z_left = np.nan_to_num(np.sqrt(2*((cumsum_s_left+cumsum_b_left)*np.log(1+cumsum_s_left/cumsum_b_left)-cumsum_s_left))) + #z_right = np.nan_to_num(np.sqrt(2*((cumsum_s_right+cumsum_b_right)*np.log(1+cumsum_s_right/cumsum_b_right)-cumsum_s_right))) + #z_left /= z_left.max() + #z_right /= z_right.max() + for i,(tag,content) in enumerate(tag_content.items()): + axs[irow,icol].bar(x=centers,height=content,width=widths,fill=False,edgecolor=colors[i],label=tag) + #ax2=axs[irow,icol].twinx() + + #ax2.plot(centers,z_left,color='r',label='Significance (left of cut) [normed]') + #ax2.plot(centers,z_right,color='r',linestyle='--',label='Significance (right of cut) [normed]') + + #for i,tag in enumerate(tag_content.keys()): + # ax2.plot(centers,content,color=colors[i],linestyle='-',label=f'{tag} content (left of cut)') + # ax2.plot(centers,color=colors[i],linestyle='--',label=f'{tag} content (right of cut)') + + #ax2.set_yscale("log") + #ax2.set_ylim([0,1.4]) + #ax2.set_ylabel('Cumulative distribution') + #ax2.legend(loc='upper right') + + axs[irow,icol].set_title(f"Using {weight}") + axs[irow,icol].set_xlabel(output_tag) + axs[irow,icol].set_ylabel('Yield') + axs[irow,icol].set_ylim(1e-5,max([content.max() for content in tag_content.values()])*100) + axs[irow,icol].set_yscale('log') + axs[irow,icol].legend(loc='upper left') +fig.savefig(f"prediction_{suffix}_{split}.pdf", dpi = 100) + + +# In[47]: + + +# evaluate the model +print("evaluate the model...") +scores = model.evaluate(test_df[input_vars], + test_df[tags], + sample_weight = test_df['training_weight'], + batch_size = 5000, + verbose=2) +print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100)) + + +# In[48]: + + +# save model and architecture to a single file +outputDir = opt.outputDir +modelName = f"model_{suffix}_{split}" +model.save(outputDir+modelName) +print(f"Saved model to {outputDir} as {modelName}") + + +# In[ ]: + + + + + +# In[ ]: + + + + diff --git a/python/basePlotter.py b/python/basePlotter.py index 302f54e..ff92f07 100644 --- a/python/basePlotter.py +++ b/python/basePlotter.py @@ -17,14 +17,10 @@ def addArgs(self, parser): type=str, required=True, help='Channel to be selected between SL and DL') - parser.add_argument("--mvaSkim", - dest="mvaSkim", - action="store_true", - help="Produce skims for MVA") - parser.add_argument("--mvaEval", - dest="mvaEval", - action="store_true", - help="Evaulate DNN") + parser.add_argument("--mvaModels", + dest="mvaModels", + type=str, + help="Path to MVA models and Evaluate DNN") def prepareTree(self, tree, sample=None, sampleCfg=None, backend=None): def isMC(): @@ -53,10 +49,11 @@ def getNanoAODDescription(): groups = ["HLT_", "MET_", "RawMET_"] collections = ["nElectron", "nJet", "nMuon", "nFatJet", "nSubJet", "nTau"] - mcCollections = ["nGenDressedLepton", "nGenJet", "nGenPart"] + mcCollections = ["nGenDressedLepton", "nGenJet", "nGenPart", "nGenJetAK8", "nSubGenJetAK8"] varReaders = [] if isMC: varReaders.append(td.CalcCollectionsGroups(Jet=("pt", "mass"))) + varReaders.append(td.CalcCollectionsGroups(FatJet=("pt", "mass"))) varReaders.append(td.CalcCollectionsGroups(GenJet=("pt", "mass"))) varReaders.append(td.CalcCollectionsGroups(MET=("pt", "phi"))) return td.NanoAODDescription(groups=groups, collections=collections + mcCollections, systVariations=varReaders) @@ -92,17 +89,33 @@ def getNanoAODDescription(): noSel = noSel.refine('trigger', cut=[makeMultiPrimaryDatasetTriggerSelection( sample, self.triggersPerPrimaryDataset)]) + sources = ["Total"] + if sampleCfg['type'] == 'mc': JECTagDatabase = {"2022": "Winter22Run3_V2_MC", "2022EE": "Summer22EEPrompt22_V1_MC"} + JERTagDatabase = {"2022": "JR_Winter22Run3_V1_MC", + "2022EE": "Summer22EEPrompt22_JRV1_MC"} if era in JECTagDatabase.keys(): configureJets( variProxy = tree._Jet, jetType = "AK4PFPuppi", jec = JECTagDatabase[era], - # smear = JERTagDatabase['2022EE'], + smear = JERTagDatabase[era], # only for MC + jecLevels = "default", + jesUncertaintySources = sources, + mayWriteCache = self.args.distributed != "worker", + isMC = self.is_MC, + backend = backend, + uName = sample + ) + configureJets( + variProxy = tree._FatJet, + jetType = "AK8PFPuppi", + jec = JECTagDatabase[era], + smear = JERTagDatabase[era], # only for MC jecLevels = "default", - jesUncertaintySources = "All", + jesUncertaintySources = sources, mayWriteCache = self.args.distributed != "worker", isMC = self.is_MC, backend = backend, @@ -120,7 +133,18 @@ def getNanoAODDescription(): jetType = "AK4PFPuppi", jec = JECTagDatabase[era], jecLevels = "default", - jesUncertaintySources = "All", + jesUncertaintySources = sources, + mayWriteCache = self.args.distributed != "worker", + isMC = self.is_MC, + backend = backend, + uName = sample + ) + configureJets( + variProxy = tree._FatJet, + jetType = "AK8PFPuppi", + jec = JECTagDatabase[era], + jecLevels = "default", + jesUncertaintySources = sources, mayWriteCache = self.args.distributed != "worker", isMC = self.is_MC, backend = backend, @@ -145,11 +169,11 @@ def postProcess(self, taskList, config=None, workdir=None, resultsdir=None): eraMode, eras = self.args.eras if eras is None: eras = list(config["eras"].keys()) - # if plotList_cutflowreport: - # from bamboo.analysisutils import printCutFlowReports - # printCutFlowReports( - # config, plotList_cutflowreport, workdir=workdir, resultsdir=resultsdir, - # readCounters=self.readCounters, eras=(eraMode, eras), verbose=self.args.verbose) + if plotList_cutflowreport: + from bamboo.analysisutils import printCutFlowReports + printCutFlowReports( + config, plotList_cutflowreport, workdir=workdir, resultsdir=resultsdir, + readCounters=self.readCounters, eras=(eraMode, eras), verbose=self.args.verbose) if plotList_plotIt: from bamboo.analysisutils import writePlotIt, runPlotIt import os @@ -168,7 +192,7 @@ def postProcess(self, taskList, config=None, workdir=None, resultsdir=None): from bamboo.analysisutils import loadPlotIt p_config, samples, _, systematics, legend = loadPlotIt(config, [], eras=self.args.eras[1], workdir=workdir, resultsdir=resultsdir, readCounters=self.readCounters, vetoFileAttributes=self.__class__.CustomSampleAttributes) - if self.args.mvaSkim and skims: + if skims: from bamboo.analysisutils import loadPlotIt from bamboo.root import gbl import pandas as pd diff --git a/python/controlPlotter.py b/python/controlPlotter.py index 0d5b37d..94ec7f1 100644 --- a/python/controlPlotter.py +++ b/python/controlPlotter.py @@ -15,7 +15,7 @@ class controlPlotter(NanoBaseHHWWbb): def __init__(self, args): super(controlPlotter, self).__init__(args) self.channel = self.args.channel - self.mvaSkim = self.args.mvaSkim + self.mvaModels = self.args.mvaModels def definePlots(self, tree, noSel, sample=None, sampleCfg=None): plots = [] @@ -55,7 +55,12 @@ def definePlots(self, tree, noSel, sample=None, sampleCfg=None): DLresolvedEE_label = defs.labeler('DL resolved EE') DLresolvedMuMu_label = defs.labeler('DL resolved MuMu') DLresolvedEMu_label = defs.labeler('DL resolved EMu') - + + DLresolvedEEdnnCat1_label = defs.labeler('DL resolved EE DNN cat. 1') + DLresolvedEEdnnCat2_label = defs.labeler('DL resolved EE DNN cat. 2') + DLresolvedEEdnnCat3_label = defs.labeler('DL resolved EE DNN cat. 3') + DLresolvedEEdnnCat4_label = defs.labeler('DL resolved EE DNN cat. 4') + if self.channel == 'SL': # get SL selections SL_resolved, SL_resolved_e,\ @@ -75,34 +80,66 @@ def definePlots(self, tree, noSel, sample=None, sampleCfg=None): SLboostedMu_label = defs.labeler('SL boosted Mu') SLresolvedE_label = defs.labeler('SL resolved E') SLresolvedMu_label = defs.labeler('SL resolved Mu') + + # mva variables + mvaVars_DL_resolved = { + "weight": noSel.weight, + "ak4bjet1_pt": self.ak4BJets[0].pt, + "ak4bjet1_eta": self.ak4BJets[0].eta, + "ak4bjet1_phi": self.ak4BJets[0].phi, + # 'ak4jet1_pt': self.ak4Jets[0].pt, + # 'ak4jet1_eta': self.ak4Jets[0].eta, + # 'ak4jet1_phi': self.ak4Jets[0].phi, + # 'ak4jet2_pt': self.ak4Jets[1].pt, + # 'ak4jet2_eta': self.ak4Jets[1].eta, + # 'ak4jet2_phi': self.ak4Jets[1].phi, + "leadingLepton_pt": self.tightElectrons[0].pt, + "leadingLepton_eta": self.tightElectrons[0].eta, + "leadingLepton_phi": self.tightElectrons[0].phi, + "subleadingLepton_pt": self.tightElectrons[1].pt, + "subleadingLepton_eta": self.tightElectrons[1].eta, + "subleadingLepton_phi": self.tightElectrons[1].phi + } ############################################################################# - # Skims # + # MVA evaluation # ############################################################################# - if self.args.mvaSkim and self.channel == 'DL': - mvaVars_DL_resolved_ee = { - "weight": noSel.weight, - "ak4bjet1_pt": self.ak4BJets[0].pt, - "ak4bjet1_eta": self.ak4BJets[0].eta, - "ak4bjet1_phi": self.ak4BJets[0].phi, - 'ak4jet1_pt': self.ak4Jets[0].pt, - 'ak4jet1_eta': self.ak4Jets[0].eta, - 'ak4jet1_phi': self.ak4Jets[0].phi, - 'ak4jet2_pt': self.ak4Jets[1].pt, - 'ak4jet2_eta': self.ak4Jets[1].eta, - 'ak4jet2_phi': self.ak4Jets[1].phi, - "leadingLepton_pt": self.tightElectrons[0].pt, - "leadingLepton_eta": self.tightElectrons[0].eta, - "leadingLepton_phi": self.tightElectrons[0].phi, - "subleadingLepton_pt": self.tightElectrons[1].pt, - "subleadingLepton_eta": self.tightElectrons[1].eta, - "subleadingLepton_phi": self.tightElectrons[1].phi - } + if self.args.mvaModels and self.channel == 'DL': + mvaVars_DL_resolved.pop("weight", None) - plots.extend([ - Skim("DL_resolved_ee", mvaVars_DL_resolved_ee, DL_resolved_ee), - ]) + # import random + # split_var = random.randint(0,1) + split_var = 1 + + if split_var == 0: + model = self.args.mvaModels + "/model_test1_even/model.onnx" + elif split_var == 1: + model = self.args.mvaModels + "/model_test1_odd/model.onnx" + else: + print("ERROR: split_var is not 0 or 1") + exit(1) + dnn = op.mvaEvaluator(model, otherArgs = ("predictions")) + inputs = op.array('float', *[op.c_float(val) for val in mvaVars_DL_resolved.values()]) + output = dnn(inputs) + + # DNN cuts + DNNcat1 = DL_resolved_ee.refine("DNNcat1", cut = op.in_range(0.1, output[0], 0.6)) + DNNcat2 = DL_resolved_ee.refine("DNNcat2", cut = op.in_range(0.6, output[0], 0.8)) + DNNcat3 = DL_resolved_ee.refine("DNNcat3", cut = op.in_range(0.8, output[0], 0.92)) + DNNcat4 = DL_resolved_ee.refine("DNNcat4", cut = op.in_range(0.92, output[0], 1.0)) + + plots = [ + Plot.make1D("dnn_score", output[0], DL_resolved_ee, EqBin(40, 0, 1.)), + Plot.make1D("DL_resolved_InvM_ee_DNNcat1", op.invariant_mass(self.firstOSElEl[0].p4, self.firstOSElEl[1].p4), DNNcat1, EqBin( + 100, 0., 300.), title="InvM(ll)", xTitle="Invariant Mass of electrons (GeV/c^{2})", plotopts=DLresolvedEEdnnCat1_label), + Plot.make1D("DL_resolved_InvM_ee_DNNcat2", op.invariant_mass(self.firstOSElEl[0].p4, self.firstOSElEl[1].p4), DNNcat2, EqBin( + 100, 0., 300.), title="InvM(ll)", xTitle="Invariant Mass of electrons (GeV/c^{2})", plotopts=DLresolvedEEdnnCat2_label), + Plot.make1D("DL_resolved_InvM_ee_DNNcat3", op.invariant_mass(self.firstOSElEl[0].p4, self.firstOSElEl[1].p4), DNNcat3, EqBin( + 100, 0., 300.), title="InvM(ll)", xTitle="Invariant Mass of electrons (GeV/c^{2})", plotopts=DLresolvedEEdnnCat3_label), + Plot.make1D("DL_resolved_InvM_ee_DNNcat4", op.invariant_mass(self.firstOSElEl[0].p4, self.firstOSElEl[1].p4), DNNcat4, EqBin( + 100, 0., 300.), title="InvM(ll)", xTitle="Invariant Mass of electrons (GeV/c^{2})", plotopts=DLresolvedEEdnnCat4_label), + ] ############################################################################# # Plots # @@ -110,6 +147,11 @@ def definePlots(self, tree, noSel, sample=None, sampleCfg=None): if self.channel == 'DL': plots.extend([ + ######################################### + # Skims # + ######################################### + + Skim("DL_resolved_ee", mvaVars_DL_resolved, DL_resolved_ee), ######################################### ###### ###### diff --git a/python/definitions.py b/python/definitions.py index 0d51e91..b96644b 100644 --- a/python/definitions.py +++ b/python/definitions.py @@ -263,12 +263,12 @@ def defineObjects(self, tree): # clean jets wrt leptons if self.channel == 'DL': - self.cleanAk4Jets = cleaningWithRespectToLeadingLeptons(self.fakeElectrons, self.fakeMuons, 0.4) - self.cleanAk8Jets = cleaningWithRespectToLeadingLeptons(self.fakeElectrons, self.fakeMuons, 0.8) + self.cleanAk4Jets = cleaningWithRespectToLeadingLeptons(self.tightElectrons, self.tightMuons, 0.4) + self.cleanAk8Jets = cleaningWithRespectToLeadingLeptons(self.tightElectrons, self.tightMuons, 0.8) if self.channel == 'SL': - self.cleanAk4Jets = cleaningWithRespectToLeadingLepton(self.fakeElectrons, self.fakeMuons, 0.4) - self.cleanAk8Jets = cleaningWithRespectToLeadingLepton(self.fakeElectrons, self.fakeMuons, 0.8) + self.cleanAk4Jets = cleaningWithRespectToLeadingLepton(self.tightElectrons, self.tightMuons, 0.4) + self.cleanAk8Jets = cleaningWithRespectToLeadingLepton(self.tightElectrons, self.tightMuons, 0.8) self.ak4Jets = op.select(ak4JetsPreSel, self.cleanAk4Jets) self.ak4JetsByBtagScore = op.sort(self.ak4Jets, lambda j: -j.btagDeepFlavB)