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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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{ | ||
"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 | ||
} |