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PicoAOD BDT inference (#209)
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* bdt inference

* style: pre-commit fixes

* update

* style: pre-commit fixes

* update

* style: pre-commit fixes

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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@jmduarte @pre-commit-ci
jmduarte and pre-commit-ci[bot] authored Aug 16, 2024
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272 changes: 272 additions & 0 deletions src/HH4b/boosted/PicoAODBDTInference.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import xgboost as xgb\n",
"from coffea import nanoevents\n",
"from coffea.nanoevents.methods.base import NanoEventsArray\n",
"import awkward as ak\n",
"import vector\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import mplhep as hep\n",
"import uproot\n",
"\n",
"plt.style.use(hep.style.CMS)\n",
"\n",
"\n",
"def to_np_array(ak_array, max_n=2, pad=0):\n",
" return ak.fill_none(ak.pad_none(ak_array, max_n, clip=True, axis=-1), pad).to_numpy()\n",
"\n",
"\n",
"file_name = \"/ceph/cms/store/user/woodson/boosted/GluGluToHHTo4B_cHHH1_UL16_preVFP/picoAOD.root\"\n",
"# file_name = \"/ceph/cms/store/user/woodson/boosted/GluGluToHHTo4B_cHHH1_UL16_postVFP/picoAOD.root\"\n",
"# file_name = \"/ceph/cms/store/user/woodson/boosted/GluGluToHHTo4B_cHHH1_UL17/picoAOD.chunk0.root\"\n",
"# file_name = \"/ceph/cms/store/user/woodson/boosted/GluGluToHHTo4B_cHHH1_UL17/picoAOD.chunk1.root\"\n",
"# file_name = \"/ceph/cms/store/user/woodson/boosted/GluGluToHHTo4B_cHHH1_UL18/picoAOD.chunk0.root\"\n",
"# file_name = \"/ceph/cms/store/user/woodson/boosted/GluGluToHHTo4B_cHHH1_UL18/picoAOD.chunk1.root\"\n",
"model_fname = \"src/HH4b/boosted/bdt_trainings_run2/model_xgboost_training_weights_qcd_and_ttbar_Run2_bdt_enhanced_v8p2/trained_bdt.model\"\n",
"bdt_model = xgb.XGBClassifier()\n",
"bdt_model.load_model(fname=model_fname)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"events = nanoevents.NanoEventsFactory.from_root(\n",
" file_name,\n",
" schemaclass=nanoevents.NanoAODSchema,\n",
").events()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sorted_by_bbtag = ak.argsort(\n",
" events[\"FatJet\"][\"particleNetMD_Xbb\"]\n",
" / (events[\"FatJet\"][\"particleNetMD_Xbb\"] + events[\"FatJet\"][\"particleNetMD_QCD\"]),\n",
" ascending=False,\n",
" axis=-1,\n",
")\n",
"fatjets_sorted = events[\"FatJet\"][sorted_by_bbtag]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fatjet_xbb = to_np_array(fatjets_sorted[\"particleNetMD_Xbb\"], max_n=2, pad=0)\n",
"fatjet_qcd = to_np_array(fatjets_sorted[\"particleNetMD_QCD\"], max_n=2, pad=0)\n",
"fatjet_txbb = fatjet_xbb / (fatjet_xbb + fatjet_qcd)\n",
"fatjet_qcdb = to_np_array(fatjets_sorted[\"particleNetMD_QCDb\"], max_n=2, pad=0)\n",
"fatjet_qcdbb = to_np_array(fatjets_sorted[\"particleNetMD_QCDbb\"], max_n=2, pad=0)\n",
"fatjet_qcdothers = to_np_array(fatjets_sorted[\"particleNetMD_QCDothers\"], max_n=2, pad=0)\n",
"fatjet_pnetmass = to_np_array(fatjets_sorted[\"particleNet_mass\"], max_n=2, pad=0)\n",
"\n",
"fatjet_pt = to_np_array(fatjets_sorted[\"pt\"], max_n=2, pad=0)\n",
"fatjet_eta = to_np_array(fatjets_sorted[\"eta\"], max_n=2, pad=0)\n",
"fatjet_phi = to_np_array(fatjets_sorted[\"phi\"], max_n=2, pad=0)\n",
"fatjet_msd = to_np_array(fatjets_sorted[\"msoftdrop\"], max_n=2, pad=0)\n",
"fatjet_tau2 = to_np_array(fatjets_sorted[\"tau2\"], max_n=2, pad=0)\n",
"fatjet_tau3 = to_np_array(fatjets_sorted[\"tau3\"], max_n=2, pad=0)\n",
"\n",
"mask = (\n",
" (fatjet_pt[:, 0] > 300)\n",
" & (fatjet_pt[:, 1] > 300)\n",
" & (fatjet_msd[:, 0] > 40)\n",
" & (fatjet_pnetmass[:, 1] > 50)\n",
" & (fatjet_txbb[:, 0] > 0.8)\n",
" & (np.abs(fatjet_eta[:, 0]) < 2.4)\n",
" & (np.abs(fatjet_eta[:, 1]) < 2.4)\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"h1 = vector.array(\n",
" {\"pt\": fatjet_pt[:, 0], \"phi\": fatjet_phi[:, 0], \"eta\": fatjet_eta[:, 0], \"M\": fatjet_msd[:, 0]}\n",
")\n",
"h2 = vector.array(\n",
" {\"pt\": fatjet_pt[:, 1], \"phi\": fatjet_phi[:, 1], \"eta\": fatjet_eta[:, 1], \"M\": fatjet_msd[:, 1]}\n",
")\n",
"hh = h1 + h2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_events = pd.DataFrame(\n",
" {\n",
" # dihiggs system\n",
" \"HHPt\": hh.pt,\n",
" \"HHeta\": hh.eta,\n",
" \"HHmass\": hh.mass,\n",
" # met in the event\n",
" \"MET\": events[\"PuppiMET\"][\"pt\"].to_numpy(),\n",
" # fatjet tau32\n",
" \"H1T32\": fatjet_tau3[:, 0] / fatjet_tau2[:, 0],\n",
" \"H2T32\": fatjet_tau3[:, 1] / fatjet_tau2[:, 1],\n",
" # fatjet mass\n",
" \"H1Mass\": fatjet_msd[:, 0],\n",
" # fatjet kinematics\n",
" \"H1Pt\": fatjet_pt[:, 0],\n",
" \"H1eta\": fatjet_eta[:, 0],\n",
" # xbb\n",
" \"H1Xbb\": fatjet_txbb[:, 0],\n",
" \"H1QCDb\": fatjet_qcdb[:, 0],\n",
" \"H1QCDbb\": fatjet_qcdbb[:, 0],\n",
" \"H1QCDothers\": fatjet_qcdothers[:, 0],\n",
" \"H2Pt\": fatjet_pt[:, 1],\n",
" # ratios\n",
" \"H1Pt_HHmass\": fatjet_pt[:, 0] / hh.mass,\n",
" \"H2Pt_HHmass\": fatjet_pt[:, 1] / hh.mass,\n",
" \"H2Pt/H1Pt\": fatjet_pt[:, 1] / fatjet_pt[:, 0],\n",
" }\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_events[\"bdt_score\"] = bdt_model.predict_proba(df_events)[:, 1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"bdt_fail = 0.03\n",
"bdt_bin1 = 0.43\n",
"bdt_bin2 = 0.11\n",
"xbb_bin1 = 0.98\n",
"xbb_bin2 = 0.95\n",
"\n",
"mask_bin1 = mask & (fatjet_txbb[:, 1] > xbb_bin1) & (df_events[\"bdt_score\"] > bdt_bin1)\n",
"mask_bin2 = (\n",
" mask\n",
" & ~mask_bin1\n",
" & (\n",
" ((fatjet_txbb[:, 1] > xbb_bin1) & (df_events[\"bdt_score\"] > bdt_bin2))\n",
" | ((fatjet_txbb[:, 1] > xbb_bin2) & (df_events[\"bdt_score\"] > bdt_bin1))\n",
" )\n",
")\n",
"mask_bin3 = (\n",
" mask\n",
" & ~mask_bin1\n",
" & ~mask_bin2\n",
" & (fatjet_txbb[:, 1] > xbb_bin2)\n",
" & (df_events[\"bdt_score\"] > bdt_fail)\n",
")\n",
"mask_fail = mask & ~mask_bin1 & ~mask_bin2 & ~mask_bin3 & (df_events[\"bdt_score\"] > bdt_fail)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_events[\"bdt_bin\"] = np.zeros(len(df_events))\n",
"df_events.loc[mask_bin1, \"bdt_bin\"] = 1\n",
"df_events.loc[mask_bin2, \"bdt_bin\"] = 2\n",
"df_events.loc[mask_bin3, \"bdt_bin\"] = 3\n",
"df_events.loc[mask_fail, \"bdt_bin\"] = 0\n",
"df_events.loc[~mask, \"bdt_bin\"] = -1\n",
"df_events.loc[~mask, \"bdt_score\"] = -1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# make 18 subfigures\n",
"plt.figure()\n",
"fig, axs = plt.subplots(3, 6, figsize=(40, 20), sharey=True)\n",
"for i, col in enumerate(df_events.columns):\n",
" if i > 17:\n",
" continue\n",
" ax = axs[i // 6, i % 6]\n",
" ax.hist(df_events[col][mask], bins=50, histtype=\"step\")\n",
" ax.set_xlabel(col)\n",
" if i % 6 == 0:\n",
" ax.set_ylabel(\"Events\")\n",
" ax.set_yscale(\"log\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with uproot.open(file_name) as f:\n",
" arrays = f[\"Events\"].arrays()\n",
" with uproot.recreate(file_name.replace(\".root\", \".withBDT.root\")) as f_out:\n",
" f_out[\"Events\"] = {field: arrays[field] for field in arrays.fields} | {\n",
" \"bdt_score\": df_events[\"bdt_score\"].to_numpy()\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure()\n",
"plt.scatter(df_events[\"bdt_score\"], fatjet_txbb[:, 1], c=df_events[\"bdt_bin\"])\n",
"plt.xlim(0, 1)\n",
"plt.ylim(0.93, 1)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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