def GenerateSimpleModelCollections(MC_path,
weight_path=None,
input_config_file=None):
mcolls = []
global_max_epochs = 100
global_hyperparams = {'number_layers': 2, 'number_neurons': 128}
# some shortcuts
nonperiodic_variables_default_0j = [
"PFMET", "Z1Pt", "Z2Pt", "Z1Mass", "Z2Mass", "ZZPt", "ZZEta",
"ZZMass", "ZZMassErr"
]
nonperiodic_variables_default_1j = nonperiodic_variables_default_0j + [
"nCleanedJetsPt30BTagged_bTagSF"
]
nonperiodic_variables_default = nonperiodic_variables_default_0j + [
"nCleanedJetsPt30", "nCleanedJetsPt30BTagged_bTagSF"
]
periodic_variables_default = ["ZZPhi"]
def nonperiodic_variables_jet(num):
retval = []
for i in range(num):
retval.append("JetPt[JetPt|{:d}]".format(i))
retval.append("JetEta[JetPt|{:d}]".format(i))
return retval
def periodic_variables_jet(num):
retval = []
for i in range(num):
retval.append("JetPhi[JetPt|{:d}]".format(i))
return retval
def nonperiodic_variables_extra_lep(num):
retval = []
for i in range(num):
retval.append("ExtraLepPt[ExtraLepPt|{:d}]".format(i))
retval.append("ExtraLepEta[ExtraLepPt|{:d}]".format(i))
return retval
def periodic_variables_extra_lep(num):
retval = []
for i in range(num):
retval.append("ExtraLepPhi[ExtraLepPt|{:d}]".format(i))
return retval
MELA_2j_variables = [
"D_VBF2j_ggH_ME", "D_ZHh_ggH_ME", "D_WHh_ZHh_ME", "D_WHh_ggH_ME",
"D_VBF2j_WHh_ME", "D_VBF2j_ZHh_ME"
]
MELA_1j_variables = ["D_VBF1j_ggH_ME"]
# ------------------------
# define the categories for the classifier
#VBF_cat = Category("VBF", {MC_path + "VBFH125/ZZ4lAnalysis.root": cuts.mZZ_cut})
VBF_cat = Category(
"VBF", {MC_path + "VBFH125/ZZ4lAnalysis.root": cuts.no_cut})
# mode that is fully differential in the number of jets
ep = DiscriminantEndpiece("2|1|0j")
ep_comp = DiscriminantEndpieceComponent(
name="2j",
public_name="2j",
component_cut=lambda row: row["nCleanedJetsPt30"] >=
2, # and row["ZZMass"] > 118. and row["ZZMass"] < 130.,
nonperiodic_columns=nonperiodic_variables_default +
MELA_2j_variables + nonperiodic_variables_jet(2),
periodic_columns=periodic_variables_default +
periodic_variables_jet(2),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
ep_comp = DiscriminantEndpieceComponent(
name="1j",
public_name="1j",
component_cut=lambda row: row["nCleanedJetsPt30"] ==
1, # and row["ZZMass"] > 118. and row["ZZMass"] < 130.,
nonperiodic_columns=nonperiodic_variables_default +
MELA_1j_variables + nonperiodic_variables_jet(1),
periodic_columns=periodic_variables_default +
periodic_variables_jet(1),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
ep_comp = DiscriminantEndpieceComponent(
name="0j",
public_name="0j",
component_cut=lambda row: row["nCleanedJetsPt30"] ==
0, # and row["ZZMass"] > 118. and row["ZZMass"] < 130.,
nonperiodic_columns=nonperiodic_variables_default_0j,
periodic_columns=[],
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
VBF_cat.add_endpiece(ep)
# mode that is inclusive in the number of jets
ep = DiscriminantEndpiece("210j")
ep_comp = DiscriminantEndpieceComponent(
name="210j",
public_name="",
component_cut=lambda row:
True, #row["ZZMass"] > 118. and row["ZZMass"] < 130.,
nonperiodic_columns=nonperiodic_variables_default +
nonperiodic_variables_jet(2),
periodic_columns=periodic_variables_default +
periodic_variables_jet(2),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
VBF_cat.add_endpiece(ep)
# ------------------------------------
# define the categories for the classifier
#ggH_cat = Category("ggH", {MC_path + "ggH125/ZZ4lAnalysis.root": cuts.mZZ_cut})
ggH_cat = Category("ggH",
{MC_path + "ggH125/ZZ4lAnalysis.root": cuts.no_cut})
# mode that is fully differential in the number of jets
ep = DiscriminantEndpiece("2|1|0j")
ep_comp = DiscriminantEndpieceComponent(
name="2j",
public_name="2j",
component_cut=lambda row: row["nCleanedJetsPt30"] >=
2, # and row["ZZMass"] > 118. and row["ZZMass"] < 130.,
nonperiodic_columns=nonperiodic_variables_default +
MELA_2j_variables + nonperiodic_variables_jet(2),
periodic_columns=periodic_variables_default +
periodic_variables_jet(2),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
ep_comp = DiscriminantEndpieceComponent(
name="1j",
public_name="1j",
component_cut=lambda row: row["nCleanedJetsPt30"] ==
1, # and row["ZZMass"] > 118. and row["ZZMass"] < 130.,
nonperiodic_columns=nonperiodic_variables_default_1j +
MELA_1j_variables + nonperiodic_variables_jet(1),
periodic_columns=periodic_variables_default +
periodic_variables_jet(1),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
ep_comp = DiscriminantEndpieceComponent(
name="0j",
public_name="0j",
component_cut=lambda row: row["nCleanedJetsPt30"] ==
0, # and row["ZZMass"] > 118. and row["ZZMass"] < 130.,
nonperiodic_columns=nonperiodic_variables_default_0j,
periodic_columns=[],
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
ggH_cat.add_endpiece(ep)
# mode that is inclusive in the number of jets
ep = DiscriminantEndpiece("210j")
ep_comp = DiscriminantEndpieceComponent(
name="210j",
public_name="",
component_cut=lambda row: row[
"ZZMass"] > 0, #row["ZZMass"] > 118. and row["ZZMass"] < 130.,
nonperiodic_columns=nonperiodic_variables_default +
nonperiodic_variables_jet(2),
periodic_columns=periodic_variables_default +
periodic_variables_jet(2),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
ggH_cat.add_endpiece(ep)
# ------------------------------------
# define the categories for the classifier
WHh_cat = Category(
"WHh", {
MC_path + "WplusH125/ZZ4lAnalysis.root": cuts.WHhadr_cut,
MC_path + "WminusH125/ZZ4lAnalysis.root": cuts.WHhadr_cut
})
# mode that is inclusive in the number of jets
ep = DiscriminantEndpiece("210j")
ep_comp = DiscriminantEndpieceComponent(
name="210j",
public_name="",
component_cut=lambda row: row[
"ZZMass"] > 0, #row["ZZMass"] > 118. and row["ZZMass"] < 130.,
nonperiodic_columns=nonperiodic_variables_default +
nonperiodic_variables_jet(2),
periodic_columns=periodic_variables_default +
periodic_variables_jet(2),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
WHh_cat.add_endpiece(ep)
# ------------------------------------
# define the categories for the classifier
ZHh_cat = Category(
"ZHh", {MC_path + "ZH125/ZZ4lAnalysis.root": cuts.ZHhadr_cut})
# mode that is inclusive in the number of jets
ep = DiscriminantEndpiece("210j")
ep_comp = DiscriminantEndpieceComponent(
name="210j",
public_name="",
component_cut=lambda row: row[
"ZZMass"] > 0, #row["ZZMass"] > 118. and row["ZZMass"] < 130.,
nonperiodic_columns=nonperiodic_variables_default +
nonperiodic_variables_jet(2),
periodic_columns=periodic_variables_default +
periodic_variables_jet(2),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
ZHh_cat.add_endpiece(ep)
# ------------------------------------
# define the categories for the classifier
ZHl_cat = Category(
"ZHl", {MC_path + "ZH125/ZZ4lAnalysis.root": cuts.ZHlept_cut})
# mode that is inclusive in the number of jets
ep = DiscriminantEndpiece("210j")
ep_comp = DiscriminantEndpieceComponent(
name="210j",
public_name="",
component_cut=lambda row: row[
"ZZMass"] > 0, #row["ZZMass"] > 118. and row["ZZMass"] < 130.,
nonperiodic_columns=nonperiodic_variables_default +
nonperiodic_variables_extra_lep(2) + nonperiodic_variables_jet(2),
periodic_columns=periodic_variables_default +
periodic_variables_extra_lep(2) + periodic_variables_jet(2),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
ZHl_cat.add_endpiece(ep)
# ------------------------------------
# define the categories for the classifier
WHl_cat = Category(
"WHl", {
MC_path + "WplusH125/ZZ4lAnalysis.root": cuts.WHlept_cut,
MC_path + "WminusH125/ZZ4lAnalysis.root": cuts.WHlept_cut
})
# mode that is inclusive in the number of jets
ep = DiscriminantEndpiece("210j")
ep_comp = DiscriminantEndpieceComponent(
name="210j",
public_name="",
component_cut=lambda row: row[
"ZZMass"] > 0, #row["ZZMass"] > 118. and row["ZZMass"] < 130.,
nonperiodic_columns=nonperiodic_variables_default +
nonperiodic_variables_extra_lep(2) + nonperiodic_variables_jet(2),
periodic_columns=periodic_variables_default +
periodic_variables_extra_lep(2) + periodic_variables_jet(2),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
WHl_cat.add_endpiece(ep)
# ------------------------------------
# define the categories for the classifier
ZHMET_cat = Category(
"ZHMET", {MC_path + "ZH125/ZZ4lAnalysis.root": cuts.ZHMET_cut})
# mode that is inclusive in the number of jets
ep = DiscriminantEndpiece("210j")
ep_comp = DiscriminantEndpieceComponent(
name="210j",
public_name="",
component_cut=lambda row: row[
"ZZMass"] > 0, #row["ZZMass"] > 118. and row["ZZMass"] < 130.,
nonperiodic_columns=nonperiodic_variables_default +
nonperiodic_variables_jet(2),
periodic_columns=periodic_variables_default +
periodic_variables_jet(2),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
ZHMET_cat.add_endpiece(ep)
# ------------------------------------
# define the categories for the classifier
ttHh_cat = Category(
"ttHh", {MC_path + "ttH125/ZZ4lAnalysis.root": cuts.ttHhadr_cut})
# mode that is inclusive in the number of jets
ep = DiscriminantEndpiece("210j")
ep_comp = DiscriminantEndpieceComponent(
name="210j",
public_name="",
component_cut=lambda row: row[
"ZZMass"] > 0, #row["ZZMass"] > 118. and row["ZZMass"] < 130.,
nonperiodic_columns=nonperiodic_variables_default +
nonperiodic_variables_jet(2) + nonperiodic_variables_extra_lep(2),
periodic_columns=periodic_variables_default +
periodic_variables_jet(2) + periodic_variables_extra_lep(2),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
ttHh_cat.add_endpiece(ep)
# ------------------------------------
# define the categories for the classifier
ttHl_cat = Category(
"ttHl", {MC_path + "ttH125/ZZ4lAnalysis.root": cuts.ttHlept_cut})
# mode that is inclusive in the number of jets
ep = DiscriminantEndpiece("210j")
ep_comp = DiscriminantEndpieceComponent(
name="210j",
public_name="",
component_cut=lambda row: row[
"ZZMass"] > 0, #row["ZZMass"] > 118. and row["ZZMass"] < 130.,
nonperiodic_columns=nonperiodic_variables_default +
nonperiodic_variables_extra_lep(2) + nonperiodic_variables_jet(2),
periodic_columns=periodic_variables_default +
periodic_variables_extra_lep(2) + periodic_variables_jet(2),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
ttHl_cat.add_endpiece(ep)
# now make all combinations between those categories and add them to the model collections (do it manually now, automatized later)
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(VBF_cat, ggH_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(WHh_cat, ggH_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHh_cat, ggH_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(WHh_cat, ZHh_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(VBF_cat, WHh_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(VBF_cat, ZHh_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(WHl_cat, ggH_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(WHl_cat, VBF_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(WHl_cat, WHh_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(WHl_cat, ZHh_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(WHl_cat, ZHl_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(WHl_cat, ZHMET_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(WHl_cat, ttHh_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(WHl_cat, ttHl_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHh_cat, ZHl_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHh_cat, ZHMET_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHh_cat, ttHh_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHh_cat, ttHl_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHl_cat, ggH_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHl_cat, VBF_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHl_cat, WHh_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHl_cat, ZHMET_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHl_cat, ttHh_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHl_cat, ttHl_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHMET_cat, ggH_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHMET_cat, VBF_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHMET_cat, WHh_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHMET_cat, ttHh_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHMET_cat, ttHl_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ttHh_cat, ggH_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ttHh_cat, VBF_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ttHh_cat, WHh_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ttHh_cat, ttHl_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ttHl_cat, ggH_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ttHl_cat, VBF_cat),
input_config_file=input_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ttHl_cat, WHh_cat),
input_config_file=input_config_file))
return mcolls
def GenerateSimpleModelCollections(MC_path,
weight_path=None,
input_config_file=None,
hyperparam_config_file=None,
mass_point=125.0):
mcolls = []
global_max_epochs = 100
global_hyperparams = {'number_layers': 2, 'number_neurons': 128}
# some shortcuts concerning the default variables that are used for category pairings in case nothing else is specified
nonperiodic_variables_default_0j = [
"PFMET", "Z1Pt", "Z2Pt", "Z1Mass", "Z2Mass", "ZZPt", "ZZEta",
"ZZMass", "ZZMassErr"
]
nonperiodic_variables_default_1j = nonperiodic_variables_default_0j + [
"nCleanedJetsPt30BTagged_bTagSF"
]
nonperiodic_variables_default = nonperiodic_variables_default_0j + [
"nCleanedJetsPt30", "nCleanedJetsPt30BTagged_bTagSF"
]
periodic_variables_default = ["ZZPhi"]
def nonperiodic_variables_jet(num):
retval = []
for i in range(num):
retval.append("JetPt[JetPt|{:d}]".format(i))
retval.append("JetEta[JetPt|{:d}]".format(i))
return retval
def periodic_variables_jet(num):
retval = []
for i in range(num):
retval.append("JetPhi[JetPt|{:d}]".format(i))
return retval
def nonperiodic_variables_extra_lep(num):
retval = []
for i in range(num):
retval.append("ExtraLepPt[ExtraLepPt|{:d}]".format(i))
retval.append("ExtraLepEta[ExtraLepPt|{:d}]".format(i))
return retval
def periodic_variables_extra_lep(num):
retval = []
for i in range(num):
retval.append("ExtraLepPhi[ExtraLepPt|{:d}]".format(i))
return retval
MELA_2j_variables = [
"D_VBF2j_ggH_ME", "D_ZHh_ggH_ME", "D_WHh_ZHh_ME", "D_WHh_ggH_ME",
"D_VBF2j_WHh_ME", "D_VBF2j_ZHh_ME"
]
MELA_1j_variables = ["D_VBF1j_ggH_ME"]
# translation dictionaries that link Higgs mass points to the corresponding training files
mass_point_translation = {
120.0: "120",
124.0: "124",
125.0: "125",
126.0: "126",
130.0: "130"
}
mass_point_suffix = mass_point_translation[mass_point]
# ------------------------
# define the categories for the classifier
VBF_cat = Category(
"VBF", {
MC_path + "VBFH" + mass_point_suffix + "/ZZ4lAnalysis.root":
cuts.no_cut
})
# mode that is fully differential in the number of jets
ep = DiscriminantEndpiece("2|1|0j")
ep_comp = DiscriminantEndpieceComponent(
name="2j",
public_name="2j",
component_cut=lambda row: row["nCleanedJetsPt30"] >= 2,
nonperiodic_columns=nonperiodic_variables_default +
MELA_2j_variables + nonperiodic_variables_jet(2),
periodic_columns=periodic_variables_default +
periodic_variables_jet(2),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
ep_comp = DiscriminantEndpieceComponent(
name="1j",
public_name="1j",
component_cut=lambda row: row["nCleanedJetsPt30"] == 1,
nonperiodic_columns=nonperiodic_variables_default +
MELA_1j_variables + nonperiodic_variables_jet(1),
periodic_columns=periodic_variables_default +
periodic_variables_jet(1),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
ep_comp = DiscriminantEndpieceComponent(
name="0j",
public_name="0j",
component_cut=lambda row: row["nCleanedJetsPt30"] == 0,
nonperiodic_columns=nonperiodic_variables_default_0j,
periodic_columns=[],
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
VBF_cat.add_endpiece(ep)
# mode that is inclusive in the number of jets
ep = DiscriminantEndpiece("210j")
ep_comp = DiscriminantEndpieceComponent(
name="210j",
public_name="",
component_cut=lambda row: True,
nonperiodic_columns=nonperiodic_variables_default +
nonperiodic_variables_jet(2),
periodic_columns=periodic_variables_default +
periodic_variables_jet(2),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
VBF_cat.add_endpiece(ep)
# ------------------------------------
# define the categories for the classifier
ggH_cat = Category(
"ggH", {
MC_path + "ggH" + mass_point_suffix + "/ZZ4lAnalysis.root":
cuts.no_cut
})
# mode that is fully differential in the number of jets
ep = DiscriminantEndpiece("2|1|0j")
ep_comp = DiscriminantEndpieceComponent(
name="2j",
public_name="2j",
component_cut=lambda row: row["nCleanedJetsPt30"] >= 2,
nonperiodic_columns=nonperiodic_variables_default +
MELA_2j_variables + nonperiodic_variables_jet(2),
periodic_columns=periodic_variables_default +
periodic_variables_jet(2),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
ep_comp = DiscriminantEndpieceComponent(
name="1j",
public_name="1j",
component_cut=lambda row: row["nCleanedJetsPt30"] == 1,
nonperiodic_columns=nonperiodic_variables_default_1j +
MELA_1j_variables + nonperiodic_variables_jet(1),
periodic_columns=periodic_variables_default +
periodic_variables_jet(1),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
ep_comp = DiscriminantEndpieceComponent(
name="0j",
public_name="0j",
component_cut=lambda row: row["nCleanedJetsPt30"] == 0,
nonperiodic_columns=nonperiodic_variables_default_0j,
periodic_columns=[],
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
ggH_cat.add_endpiece(ep)
# mode that is inclusive in the number of jets
ep = DiscriminantEndpiece("210j")
ep_comp = DiscriminantEndpieceComponent(
name="210j",
public_name="",
component_cut=lambda row: True,
nonperiodic_columns=nonperiodic_variables_default +
nonperiodic_variables_jet(2),
periodic_columns=periodic_variables_default +
periodic_variables_jet(2),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
ggH_cat.add_endpiece(ep)
# ------------------------------------
# define the categories for the classifier
WHh_cat = Category(
"WHh", {
MC_path + "WplusH" + mass_point_suffix + "/ZZ4lAnalysis.root":
cuts.WHhadr_cut,
MC_path + "WminusH" + mass_point_suffix + "/ZZ4lAnalysis.root":
cuts.WHhadr_cut
})
# mode that is inclusive in the number of jets
ep = DiscriminantEndpiece("210j")
ep_comp = DiscriminantEndpieceComponent(
name="210j",
public_name="",
component_cut=lambda row: True,
nonperiodic_columns=nonperiodic_variables_default +
nonperiodic_variables_jet(2),
periodic_columns=periodic_variables_default +
periodic_variables_jet(2),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
WHh_cat.add_endpiece(ep)
# ------------------------------------
# define the categories for the classifier
ZHh_cat = Category(
"ZHh", {
MC_path + "ZH" + mass_point_suffix + "/ZZ4lAnalysis.root":
cuts.ZHhadr_cut
})
# mode that is inclusive in the number of jets
ep = DiscriminantEndpiece("210j")
ep_comp = DiscriminantEndpieceComponent(
name="210j",
public_name="",
component_cut=lambda row: True,
nonperiodic_columns=nonperiodic_variables_default +
nonperiodic_variables_jet(2),
periodic_columns=periodic_variables_default +
periodic_variables_jet(2),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
ZHh_cat.add_endpiece(ep)
# ------------------------------------
# define the categories for the classifier
ZHl_cat = Category(
"ZHl", {
MC_path + "ZH" + mass_point_suffix + "/ZZ4lAnalysis.root":
cuts.ZHlept_cut
})
# mode that is inclusive in the number of jets
ep = DiscriminantEndpiece("210j")
ep_comp = DiscriminantEndpieceComponent(
name="210j",
public_name="",
component_cut=lambda row: True,
nonperiodic_columns=nonperiodic_variables_default +
nonperiodic_variables_extra_lep(2) + nonperiodic_variables_jet(2),
periodic_columns=periodic_variables_default +
periodic_variables_extra_lep(2) + periodic_variables_jet(2),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
ZHl_cat.add_endpiece(ep)
# ------------------------------------
# define the categories for the classifier
WHl_cat = Category(
"WHl", {
MC_path + "WplusH" + mass_point_suffix + "/ZZ4lAnalysis.root":
cuts.WHlept_cut,
MC_path + "WminusH" + mass_point_suffix + "/ZZ4lAnalysis.root":
cuts.WHlept_cut
})
# mode that is inclusive in the number of jets
ep = DiscriminantEndpiece("210j")
ep_comp = DiscriminantEndpieceComponent(
name="210j",
public_name="",
component_cut=lambda row: True,
nonperiodic_columns=nonperiodic_variables_default +
nonperiodic_variables_extra_lep(2) + nonperiodic_variables_jet(2),
periodic_columns=periodic_variables_default +
periodic_variables_extra_lep(2) + periodic_variables_jet(2),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
WHl_cat.add_endpiece(ep)
# ------------------------------------
# define the categories for the classifier
ZHMET_cat = Category(
"ZHMET", {
MC_path + "ZH" + mass_point_suffix + "/ZZ4lAnalysis.root":
cuts.ZHMET_cut
})
# mode that is inclusive in the number of jets
ep = DiscriminantEndpiece("210j")
ep_comp = DiscriminantEndpieceComponent(
name="210j",
public_name="",
component_cut=lambda row: True,
nonperiodic_columns=nonperiodic_variables_default +
nonperiodic_variables_jet(2),
periodic_columns=periodic_variables_default +
periodic_variables_jet(2),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
ZHMET_cat.add_endpiece(ep)
# ------------------------------------
# define the categories for the classifier
ttHh_cat = Category(
"ttHh", {
MC_path + "ttH" + mass_point_suffix + "/ZZ4lAnalysis.root":
cuts.ttHhadr_cut
})
# mode that is inclusive in the number of jets
ep = DiscriminantEndpiece("210j")
ep_comp = DiscriminantEndpieceComponent(
name="210j",
public_name="",
component_cut=lambda row: True,
nonperiodic_columns=nonperiodic_variables_default +
nonperiodic_variables_jet(2) + nonperiodic_variables_extra_lep(2),
periodic_columns=periodic_variables_default +
periodic_variables_jet(2) + periodic_variables_extra_lep(2),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
ttHh_cat.add_endpiece(ep)
# ------------------------------------
# define the categories for the classifier
ttHl_cat = Category(
"ttHl", {
MC_path + "ttH" + mass_point_suffix + "/ZZ4lAnalysis.root":
cuts.ttHlept_cut
})
# mode that is inclusive in the number of jets
ep = DiscriminantEndpiece("210j")
ep_comp = DiscriminantEndpieceComponent(
name="210j",
public_name="",
component_cut=lambda row: True,
nonperiodic_columns=nonperiodic_variables_default +
nonperiodic_variables_extra_lep(2) + nonperiodic_variables_jet(2),
periodic_columns=periodic_variables_default +
periodic_variables_extra_lep(2) + periodic_variables_jet(2),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
ttHl_cat.add_endpiece(ep)
# ------------------------------------
# for training on Z+X only
ZX_datastream = {MC_path + "AllData/ZZ4lAnalysis.root": cuts.no_cut}
ZX_cat = Category("ZX",
{MC_path + "ZX/ZZ4lAnalysis.root": cuts.no_cut},
datastream_unmixed=ZX_datastream)
# mode that is inclusive in the number of jets
ep = DiscriminantEndpiece("210j")
ep_comp = DiscriminantEndpieceComponent(
name="210j",
public_name="",
component_cut=lambda row: True,
nonperiodic_columns=nonperiodic_variables_default +
nonperiodic_variables_extra_lep(2) + nonperiodic_variables_jet(2),
periodic_columns=periodic_variables_default +
periodic_variables_extra_lep(2) + periodic_variables_jet(2),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
ZX_cat.add_endpiece(ep)
# ------------------------------------
# for training on Z+X only
qq_cat = Category("qq",
{MC_path + "ZZTo4l/ZZ4lAnalysis.root": cuts.no_cut})
# mode that is inclusive in the number of jets
ep = DiscriminantEndpiece("210j")
ep_comp = DiscriminantEndpieceComponent(
name="210j",
public_name="",
component_cut=lambda row: True,
nonperiodic_columns=nonperiodic_variables_default +
nonperiodic_variables_extra_lep(2) + nonperiodic_variables_jet(2),
periodic_columns=periodic_variables_default +
periodic_variables_extra_lep(2) + periodic_variables_jet(2),
model_basetype=SimpleModel,
model_hyperparams=global_hyperparams,
preprocessor_basetype=FlexiblePCAWhiteningPreprocessor)
ep.add_component(ep_comp)
qq_cat.add_endpiece(ep)
# now make all combinations between those categories and add them to the model collections (do it manually now, automatized later)
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(VBF_cat, ggH_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(WHh_cat, ggH_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHh_cat, ggH_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(WHh_cat, ZHh_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(VBF_cat, WHh_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(VBF_cat, ZHh_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(WHl_cat, ggH_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(WHl_cat, VBF_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(WHl_cat, WHh_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(WHl_cat, ZHh_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(WHl_cat, ZHl_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(WHl_cat, ZHMET_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(WHl_cat, ttHh_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(WHl_cat, ttHl_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHh_cat, ZHl_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHh_cat, ZHMET_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHh_cat, ttHh_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHh_cat, ttHl_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHl_cat, ggH_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHl_cat, VBF_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHl_cat, WHh_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHl_cat, ZHMET_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHl_cat, ttHh_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHl_cat, ttHl_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHMET_cat, ggH_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHMET_cat, VBF_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHMET_cat, WHh_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHMET_cat, ttHh_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHMET_cat, ttHl_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ttHh_cat, ggH_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ttHh_cat, VBF_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ttHh_cat, WHh_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ttHh_cat, ttHl_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ttHl_cat, ggH_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ttHl_cat, VBF_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ttHl_cat, WHh_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
# pairings of Z+X category with the signals
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ggH_cat, ZX_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(VBF_cat, ZX_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHh_cat, ZX_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHl_cat, ZX_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(WHh_cat, ZX_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(WHl_cat, ZX_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHMET_cat, ZX_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ttHh_cat, ZX_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ttHl_cat, ZX_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
# pairings of qq category with the signals
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ggH_cat, qq_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(VBF_cat, qq_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHh_cat, qq_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHl_cat, qq_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(WHh_cat, qq_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(WHl_cat, qq_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZHMET_cat, qq_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ttHh_cat, qq_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ttHl_cat, qq_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
# the two background categories among themselves
mcolls.append(
ModelCollection.from_discriminant_endpieces(
*Category.match(ZX_cat, qq_cat),
input_config_file=input_config_file,
hyperparam_config_file=hyperparam_config_file))
return mcolls