def analyze(self, event):
array_list = [
"lepJet_llpdnnx_-1_isLLP_QMU_QQMU",
"lepJet_llpdnnx_0_isLLP_QMU_QQMU",
"lepJet_llpdnnx_1_isLLP_QMU_QQMU",
"lepJet_llpdnnx_2_isLLP_QMU_QQMU", "dimuon_mass", "dimuon_deltaR",
"lepJet_pt", "lepJet_eta", "lepJet_deltaR", "MET_pt", "MET_phi",
"looseMuons_pt", "looseMuons_eta", "looseMuons_dxy",
"tightMuons_pt", "tightMuons_eta", "tightMuons_dxy"
]
data = pd.DataFrame(data={
"lepJet_llpdnnx_-1_isLLP_QMU_QQMU":
getattr(event, "lepJet_llpdnnx_-1_isLLP_QMU_QQMU"),
"lepJet_llpdnnx_0_isLLP_QMU_QQMU":
event.lepJet_llpdnnx_0_isLLP_QMU_QQMU,
"lepJet_llpdnnx_1_isLLP_QMU_QQMU":
event.lepJet_llpdnnx_1_isLLP_QMU_QQMU,
"lepJet_llpdnnx_2_isLLP_QMU_QQMU":
event.lepJet_llpdnnx_2_isLLP_QMU_QQMU,
"dimuon_mass":
event.dimuon_mass,
"dimuon_deltaR":
event.dimuon_deltaR,
"lepJet_pt":
event.lepJet_pt,
"lepJet_eta":
event.lepJet_eta,
"lepJet_deltaR":
event.lepJet_deltaR,
"MET_pt":
event.MET_pt,
"MET_phi":
event.MET_phi,
"looseMuons_pt":
event.looseMuons_pt,
"looseMuons_eta":
event.looseMuons_eta,
"looseMuons_dxy":
event.looseMuons_dxy,
"tightMuons_pt":
event.tightMuons_pt,
"tightMuons_eta":
event.tightMuons_eta,
"tightMuons_dxy":
event.tightMuons_dxy,
},
columns=array_list,
index=[0])
model = XGBClassifier()
booster = Booster()
#model._le = LabelEncoder().fit([1])
booster.load_model(self.modelPath)
booster.feature_names = array_list
model._Booster = booster
bdt_score = model.predict_proba(data)
setattr(event, "bdt_score", bdt_score[:, 1])
return True
def upload_xgb_to_memsql(xgb: Booster,
conn: Connection,
udf_name: str,
func=F.SIGMOID,
feature_names: List[str] = None,
allow_overwrite: bool = False) -> None:
if feature_names:
xgb.feature_names = feature_names
trees = split_trees(xgb.trees_to_dataframe())
sqls = [tree_to_func_def(udf_name, allow_overwrite, t) for t in trees]
sqls.append(
tree_to_main_func(udf_name, allow_overwrite, trees, xgb.feature_names,
func))
for s in sqls:
assert 1 == conn.query(s)
