def load_config(user_path: str, default_path: tuple) -> dict:
"""
Quickly extract either configuration given by user and fall back to package default if no user
config given.
Args:
user_path: path to YAML file
default_path: tuple were to find the resource and name of resource
Returns:
dictionary built from YAML
"""
logger = get_logger()
stream = None
if user_path is None:
print(default_path[0], default_path[1])
stream = resource_stream(default_path[0], default_path[1])
else:
if not exists(user_path):
logger_string = f"The file {user_path} does not exist."
logger.fatal(logger_string)
stream = open(user_path)
return yaml.load(stream, yaml.FullLoader)
class AnalyzerManager:
"""
Manager class handling analysis and systematic objects
"""
def __init__(self, ana_class, database, case, typean, doperiodbyperiod, *args):
self.ana_class = ana_class
self.database = database
self.case = case
self.typean = typean
self.doperiodbyperiod = doperiodbyperiod
# Additional arguments to be forwarded to the analyzers
self.add_args = args
self.logger = get_logger()
self.analyzers = []
self.after_burner = None
self.is_initialized = False
def calc_sigeff_steps(num_steps, df_sig, name, multiclass_labels):
logger = get_logger()
if multiclass_labels is None:
ns_left = int(num_steps / 10) - 1
ns_right = num_steps - ns_left
x_axis_left = np.linspace(0., 0.49, ns_left)
x_axis_right = np.linspace(0.5, 1.0, ns_right)
x_axis = np.concatenate((x_axis_left, x_axis_right))
else:
x_axis = np.linspace(0, 0.4, num_steps)
if df_sig.empty:
logger.error("In division denominator is empty")
eff_array = [0] * num_steps
eff_err_array = [0] * num_steps
return eff_array, eff_err_array, x_axis
num_tot_cand = len(df_sig)
eff_array = []
eff_err_array = []
if multiclass_labels is not None:
for thr0 in x_axis:
for thr1 in x_axis:
mlsel_multi0 = 'y_test_prob' + name + multiclass_labels[
0] + ' <= ' + str(thr0)
mlsel_multi1 = 'y_test_prob' + name + multiclass_labels[
1] + ' >= ' + str(thr1)
mlsel_multi = mlsel_multi0 + ' and ' + mlsel_multi1
num_sel_cand = len(df_sig.query(mlsel_multi))
eff, err_eff = calc_eff(num_sel_cand, num_tot_cand)
eff_array.append(eff)
eff_err_array.append(err_eff)
else:
for thr in x_axis:
num_sel_cand = len(
df_sig[df_sig['y_test_prob' + name].values >= thr])
eff, err_eff = calc_eff(num_sel_cand, num_tot_cand)
eff_array.append(eff)
eff_err_array.append(err_eff)
return eff_array, eff_err_array, x_axis
def calc_bkg(df_bkg, name, num_step, fit_region, bin_width, sig_region):
"""
Estimate the number of background candidates under the signal peak. This is obtained
from real data with a fit of the sidebands of the invariant mass distribution.
"""
logger = get_logger()
x_axis = np.linspace(0, 1.00, num_step)
bkg_array = []
bkg_err_array = []
num_bins = (fit_region[1] - fit_region[0]) / bin_width
num_bins = int(round(num_bins))
bin_width = (fit_region[1] - fit_region[0]) / num_bins
logger.debug("To fit the bkg an exponential function is used")
for thr in x_axis:
bkg = 0.
bkg_err = 0.
hmass = TH1F('hmass', '', num_bins, fit_region[0], fit_region[1])
bkg_sel_mask = df_bkg['y_test_prob' + name].values >= thr
sel_mass_array = df_bkg[bkg_sel_mask]['inv_mass_ML'].values
if len(sel_mass_array) > 5:
for mass_value in np.nditer(sel_mass_array):
hmass.Fill(mass_value)
fit = hmass.Fit('expo', 'Q', '', fit_region[0], fit_region[1])
if int(fit) == 0:
fit_func = hmass.GetFunction('expo')
bkg = fit_func.Integral(sig_region[0],
sig_region[1]) / bin_width
bkg_err = fit_func.IntegralError(sig_region[0],
sig_region[1]) / bin_width
del fit_func
bkg_array.append(bkg)
bkg_err_array.append(bkg_err)
del hmass
return bkg_array, bkg_err_array, x_axis
def calc_sigeff_steps(num_steps, df_sig, name):
logger = get_logger()
ns_left = int(num_steps / 10) - 1
ns_right = num_steps - ns_left
x_axis_left = np.linspace(0., 0.49, ns_left)
x_axis_right = np.linspace(0.5, 1.0, ns_right)
x_axis = np.concatenate((x_axis_left, x_axis_right))
if df_sig.empty:
logger.error("In division denominator is empty")
eff_array = [0] * num_steps
eff_err_array = [0] * num_steps
return eff_array, eff_err_array, x_axis
num_tot_cand = len(df_sig)
eff_array = []
eff_err_array = []
for thr in x_axis:
num_sel_cand = len(df_sig[df_sig['y_test_prob' + name].values >= thr])
eff, err_eff = calc_eff(num_sel_cand, num_tot_cand)
eff_array.append(eff)
eff_err_array.append(err_eff)
return eff_array, eff_err_array, x_axis
def getclf_xgboost(model_config):
logger = get_logger()
logger.debug("Load xgboost models")
if "xgboost" not in model_config:
logger.debug("No xgboost models found")
return [], []
classifiers = []
names = []
for c in model_config["xgboost"]:
try:
model = getattr(templates_xgboost, c)(model_config["xgboost"][c])
classifiers.append(model)
names.append(c)
logger.info("Added xgboost model %s", c)
except AttributeError:
logger.critical("Could not load xgboost model %s", c)
return classifiers, names
def make_and_fill(self, binx, namex, biny=None, namey=None):
"""
Makes histogram and fills them based on their axis titles
"""
h = None
if namey:
# Check that column exists
if namex not in self.source_dataframe:
get_logger().warning(
"Columns %s for X axis does not exist in dataframe, skipping histogram",
namex)
return
if namey not in self.source_dataframe:
get_logger().warning(
"Columns %s for Y axis does not exist in dataframe, skipping histogram",
namey)
return
h_name = f"hVal_{namex}_vs_{namey}{self.collection_tag}"
h_tit = f" ; {namex} ; {namey}"
h = makefill2dhist(self.source_dataframe, h_name, binx, biny,
namex, namey)
h.SetTitle(h_tit)
else:
# Check that column exists
if namex not in self.source_dataframe:
get_logger().warning(
"Columns %s for X axis does not exist in dataframe, skipping histogram",
namex)
return
h_name = f"hVal_{namex}{self.collection_tag}"
h_tit = f" ; {namex} ; Entries"
h = makefill1dhist(self.source_dataframe, h_name, h_tit, binx,
namex)
if self.verbose:
get_logger().info("Filling histogram %s", h.GetName())
self.histograms.append(h)
def prep_mlsamples(df_sig, df_bkg, namesig, nevt_sig, nevt_bkg, test_frac, rnd_splt):
logger = get_logger()
if nevt_sig > len(df_sig):
logger.warning("There are not enough signal events")
if nevt_bkg > len(df_bkg):
logger.warning("There are not enough background events")
nevt_sig = min(len(df_sig), nevt_sig)
nevt_bkg = min(len(df_bkg), nevt_bkg)
logger.info("Used number of signal events is %d", nevt_sig)
logger.info("Used number of background events is %d", nevt_bkg)
df_sig = df_sig[:nevt_sig]
df_bkg = df_bkg[:nevt_bkg]
df_sig[namesig] = 1
df_bkg[namesig] = 0
df_ml = pd.DataFrame()
df_ml = pd.concat([df_sig, df_bkg])
df_ml_train, df_ml_test = train_test_split(df_ml, test_size=test_frac, random_state=rnd_splt)
logger.info("%d events for training and %d for testing", len(df_ml_train), len(df_ml_test))
return df_ml_train, df_ml_test
def do_gridsearch(names, classifiers, param_grid, refit_arr, x_train, y_train_,
cv_, ncores):
logger = get_logger()
grid_search_models_ = []
grid_search_bests_ = []
list_scores_ = []
for _, clf, param_cv, refit in zip(names, classifiers, param_grid,
refit_arr):
grid_search = GridSearchCV(clf,
param_cv,
cv=cv_,
refit=refit,
scoring='neg_mean_squared_error',
n_jobs=ncores)
grid_search_model = grid_search.fit(x_train, y_train_)
cvres = grid_search.cv_results_
for mean_score, params in zip(cvres["mean_test_score"],
cvres["params"]):
logger.info(np.sqrt(-mean_score), params)
list_scores_.append(pd.DataFrame(cvres))
grid_search_best = grid_search.best_estimator_.fit(x_train, y_train_)
grid_search_models_.append(grid_search_model)
grid_search_bests_.append(grid_search_best)
return grid_search_models_, grid_search_bests_, list_scores_
def create_mlsamples(
df_sig,
df_bkg,
sel_opt_sig,
main_dict,
sel_bkg,
rnd_shuffle, # pylint: disable=too-many-arguments
var_signal,
var_training,
nevt_sig,
nevt_bkg,
test_frac,
rnd_splt):
df_sig = filter_df_cand(df_sig, main_dict, sel_opt_sig)
df_bkg = df_bkg.query(sel_bkg)
df_sig = shuffle(df_sig, random_state=rnd_shuffle)
df_bkg = shuffle(df_bkg, random_state=rnd_shuffle)
df_ml_train, df_ml_test = prep_mlsamples(df_sig, df_bkg, var_signal,
nevt_sig, nevt_bkg, test_frac,
rnd_splt)
df_sig_train, df_bkg_train = split_df_sigbkg(df_ml_train, var_signal)
df_sig_test, df_bkg_test = split_df_sigbkg(df_ml_test, var_signal)
logger = get_logger()
logger.info("Events for ml train %d and test %d", len(df_ml_train),
len(df_ml_test))
logger.info("Events for signal train %d and test %d", len(df_sig_train),
len(df_sig_test))
logger.info("Events for bkg train %d and test %d", len(df_bkg_train),
len(df_bkg_test))
x_train = df_ml_train[var_training]
y_train = df_ml_train[var_signal]
x_test = df_ml_test[var_training]
y_test = df_ml_test[var_signal]
return df_ml_train, df_ml_test, df_sig_train, df_bkg_train, df_sig_test, df_bkg_test, \
x_train, y_train, x_test, y_test
def assert_model_config(self): # pylint: disable=R0912
"""
Validate and return the configuration for ml models
Args:
path: path to configuration YAML
run_config: Run configuration since loading some models can depend on that, e.g.
if run_config["activate_keras"] == 0 the keras config does not need
to be checked and loaded.
"""
logger = get_logger()
logger.debug("Check sanity of user configs")
user_config = {}
if isinstance(self.model_config_input, str):
user_config = parse_yaml(
os.path.expanduser(self.model_config_input))
elif isinstance(self.model_config_input, dict):
user_config = self.model_config_input
# At this point the asserted_config dict is just the one with defaults
asserted_config = Configuration.get_meta_config("models")[
self.run_config["mltype"]]
user_config = user_config.get(self.run_config["mltype"], {})
# Could probably merged with the former loop, however, would like to see whether there are
# e.g. typos. Because steering a run wanting keras - but writing kras - could cost a lot of
# time when it needs to be done again.
if self.run_config["mltype"] in self.run_config["activate_models"]:
for backend, model in \
self.run_config["activate_models"][self.run_config["mltype"]].items():
if backend not in asserted_config:
logger.critical("Unknown backend %s.", backend)
if model is None:
logger.critical("No models specified for backend %s.",
backend)
for name, activate in model.items():
if name not in asserted_config[backend]:
logger.critical("Unknown model %s for backend %s.",
name, backend)
if name in asserted_config[backend]:
if activate is None or not isinstance(activate, bool):
logger.critical("Activation value of model %s for backend %s " \
"must be specified as boolean value.", name, backend)
asserted_config[backend][name]["activate"] = activate
# Pop deactivated models
for backend in list(asserted_config.keys()):
for model in list(asserted_config[backend].keys()):
if not asserted_config[backend][model]["activate"]:
del asserted_config[backend][model]
else:
asserted_config[backend][model] = asserted_config[backend][
model]["default"]
if backend in user_config and model in user_config[backend]:
if len(user_config[backend][model]) != len(
asserted_config[backend][model]):
logger.critical(
"Parameter list for %s model %s differs",
backend, model)
for u in asserted_config[backend][model]:
asserted_config[backend][model][u] = \
user_config[backend][model].get(u,
asserted_config[backend][model][u])
self.model_config = asserted_config
from machine_learning_hep.models import fit, savemodels, test, apply, decisionboundaries
from machine_learning_hep.models import importanceplotall
from machine_learning_hep.mlperformance import cross_validation_mse, cross_validation_mse_continuous
from machine_learning_hep.mlperformance import plot_cross_validation_mse, plot_learning_curves
# from machine_learning_hep.mlperformance import confusion, plot_overtraining
from machine_learning_hep.mlperformance import precision_recall
from machine_learning_hep.grid_search import do_gridsearch, read_grid_dict, perform_plot_gridsearch
from machine_learning_hep.logger import get_logger
from machine_learning_hep.optimization import study_signif
from machine_learning_hep.efficiency import study_eff
DATA_PREFIX = os.path.expanduser("~/.machine_learning_hep")
def doclassification_regression(run_config, data, model_config, case, binmin,
binmax): # pylint: disable=too-many-locals, too-many-statements, too-many-branches
logger = get_logger()
logger.info("Start classification_regression run")
mltype = run_config['mltype']
mlsubtype = run_config['mlsubtype']
loadsampleoption = run_config['loadsampleoption']
rnd_shuffle = run_config['rnd_shuffle']
nevt_sig = run_config['nevt_sig']
nevt_bkg = run_config['nevt_bkg']
test_frac = run_config['test_frac']
rnd_splt = run_config['rnd_splt']
docorrelation = run_config['docorrelation']
dostandard = run_config['dostandard']
dopca = run_config['dopca']
dotraining = run_config['dotraining']
dotesting = run_config['dotesting']
def assert_run_config(self):
"""
Validate and return the configuration for run
Args:
path: path to configuration YAML
"""
logger = get_logger()
logger.debug("Check sanity of user configs")
user_run_config = {}
if isinstance(self.run_config_input, str):
user_run_config = parse_yaml(
os.path.expanduser(self.run_config_input))
elif isinstance(self.run_config_input, dict):
user_run_config = self.run_config_input
# At this point the asserted_config dict is just the one with defaults
run_config = Configuration.get_meta_config("run")
asserted_config = {k: run_config[k]["default"] for k in run_config}
choices_config = {
k: run_config[k]["choices"]
for k in run_config if "choices" in run_config[k]
}
depends_config = {
k: run_config[k]["depends"]
for k in run_config if "depends" in run_config[k]
}
types_config = {
k: run_config[k]["type_as"]
for k in run_config if "type_as" in run_config[k]
}
# Check for unknown parameters and abort since running entire machinery with wrong
# setting (e.g. 'dotaining' instead of 'dotraining' might happen just by accident)
# could be just overhead.
for k in user_run_config:
if k not in asserted_config:
logger.critical("Unkown parameter %s in config", k)
elif user_run_config[k] is None:
logger.critical("Missing value for parameter %s in config", k)
# Replace all defaults if user specified parameter
for k in asserted_config:
asserted_config[k] = user_run_config.get(k, asserted_config[k])
# If parameter is already set, check if consistent
if k in choices_config and asserted_config[
k] not in choices_config[k]:
logger.critical(
"Invalid value %s for parameter %s. Must be one of %s",
str(user_run_config[k]), k, str(choices_config[k]))
if k in types_config:
check_types = [type(t) for t in types_config[k]]
if not isinstance(asserted_config[k], tuple(check_types)):
logger.critical(
"Invalid value type %s of parameter %s. Must be of type %s",
str(type(asserted_config[k])), k, str(check_types))
# Can so far only depend on one parameter, change to combination
# of parameters. Do we need to check for circular dependencies?
for k in depends_config:
if (asserted_config[depends_config[k]["parameter"]]
== depends_config[k]["value"]
and asserted_config[k] != depends_config[k]["set"]):
asserted_config[k] = depends_config[k]["set"]
logger.info(
"Parameter %s = %s enforced since it is required for %s == %s",
k, str(depends_config[k]["set"]),
str(depends_config[k]["parameter"]),
str(depends_config[k]["value"]))
self.run_config = asserted_config
def make_asymm_y_errors(*args):
if len(args) % 2 != 0:
get_logger().fatal(
"Need an even number ==> ((low, up) * n_central) of errors")
return [[0, 0, args[i], args[i + 1]] for i in range(0, len(args), 2)]
def signal_func(sgnfunc):
if sgnfunc != "kGaus":
get_logger().fatal("Unknown signal fit function %s", sgnfunc)
return "[0]/(sqrt(2.*pi))/[2]*(exp(-(x-[1])*(x-[1])/2./[2]/[2]))"
def calc_systematic_mesonratio(errnum_list, errden_list, n_bins, justfd=-99):
"""
Returns a list of total errors taking into account the defined correlations
Propagation uncertainties defined for Ds(MB or mult) / D0(MB or mult).
Check if applicable to your situation
"""
tot_list = [[0., 0., 0., 0.] for _ in range(n_bins)]
if n_bins != len(list(errnum_list.errors.values())[0]) or \
n_bins != len(list(errden_list.errors.values())[0]):
get_logger().fatal("Number of bins and number of errors mismatch, %i vs. %i vs. %i", \
n_bins, len(list(errnum_list.errors.values())[0]), \
len(list(errden_list.errors.values())[0]))
listimpl = ["yield", "cut", "pid", "feeddown_mult", "feeddown_mult_spectra", "trigger", \
"multiplicity_interval", "multiplicity_weights", "track", "ptshape", \
"feeddown_NB", "sigmav0", "branching_ratio"]
j = 0
for (_, errnum), (_, errden) in zip(errnum_list.errors.items(),
errden_list.errors.items()):
for i in range(n_bins):
if errnum_list.names[j] not in listimpl:
get_logger().fatal("Unknown systematic name: %s",
errnum_list.names[j])
if errnum_list.names[j] != errden_list.names[j]:
get_logger().fatal("Names not in same order: %s vs %s", \
errnum_list.names[j], errden_list.names[j])
for nb in range(len(tot_list[i])):
if errnum_list.names[j] == "yield" and justfd is not True:
#Uncorrelated
tot_list[i][nb] += errnum[i][nb] * errnum[i][nb] + errden[
i][nb] * errden[i][nb]
elif errnum_list.names[j] == "cut" and justfd is not True:
#Uncorrelated
tot_list[i][nb] += errnum[i][nb] * errnum[i][nb] + errden[
i][nb] * errden[i][nb]
elif errnum_list.names[j] == "pid" and justfd is not True:
#Correlated, assign difference
diff = abs(errnum[i][nb] - errden[i][nb])
tot_list[i][nb] += diff * diff
elif errnum_list.names[
j] == "feeddown_mult_spectra" and justfd is not False:
#Fully correlated
ynum = errnum_list.errors["feeddown_NB"][i][4]
yden = errden_list.errors["feeddown_NB"][i][4]
#Relative uncertainties stored, make absolute
ynuml = ynum - ynum * errnum[i][2]
ydenl = yden - yden * errden[i][2]
ynumh = ynum + ynum * errnum[i][3]
ydenh = yden + yden * errden[i][3]
rat = [ynuml / ydenl, ynum / yden, ynumh / ydenh]
minsys = min(rat)
maxsys = max(rat)
if nb == 2:
tot_list[i][nb] += (rat[1] - minsys) * (
rat[1] - minsys) / (rat[1] * rat[1])
if nb == 3:
tot_list[i][nb] += (maxsys - rat[1]) * (
maxsys - rat[1]) / (rat[1] * rat[1])
elif errnum_list.names[
j] == "feeddown_mult" and justfd is not False:
#Spectra here, skip ratio systematic
pass
elif errnum_list.names[j] == "trigger" and justfd is not True:
#Correlated, do nothing
pass
elif errnum_list.names[
j] == "feeddown_NB" and justfd is not False:
#Fully correlated under assumption central Fc value stays within Nb syst
ynum = errnum[i][4]
yden = errden[i][4]
#Absolute uncertainties stored
ynuml = ynum - errnum[i][2]
ydenl = yden - errden[i][2]
ynumh = ynum + errnum[i][3]
ydenh = yden + errden[i][3]
rat = [ynuml / ydenl, ynum / yden, ynumh / ydenh]
minsys = min(rat)
maxsys = max(rat)
if nb == 2:
tot_list[i][nb] += (rat[1] - minsys) * (
rat[1] - minsys) / (rat[1] * rat[1])
if nb == 3:
tot_list[i][nb] += (maxsys - rat[1]) * (
maxsys - rat[1]) / (rat[1] * rat[1])
elif errnum_list.names[
j] == "multiplicity_weights" and justfd is not True:
#Correlated, assign difference
diff = abs(errnum[i][nb] - errden[i][nb])
tot_list[i][nb] += diff * diff
elif errnum_list.names[j] == "track" and justfd is not True:
#Correlated, assign difference
diff = abs(errnum[i][nb] - errden[i][nb])
tot_list[i][nb] += diff * diff
elif errnum_list.names[j] == "ptshape" and justfd is not True:
#Uncorrelated
tot_list[i][nb] += errnum[i][nb] * errnum[i][nb] + errden[
i][nb] * errden[i][nb]
elif errnum_list.names[
j] == "multiplicity_interval" and justfd is not True:
#NB: Assuming ratio: 3prongs over 2prongs here! 2prong part cancels
#We use 1/3 of systematic of numerator
tot_list[i][nb] += errnum[i][nb] * errnum[i][nb] / 9
elif errnum_list.names[j] == "sigmav0" and justfd is not True:
#Correlated and usually not plotted in boxes, do nothing
pass
elif errnum_list.names[
j] == "branching_ratio" and justfd is not True:
#Uncorrelated, but usually not plotted in boxes, so pass
pass
j = j + 1
tot_list = np.sqrt(tot_list)
return tot_list
def calc_systematic_mesondoubleratio(errnum_list1, errnum_list2, errden_list1, \
errden_list2, n_bins, dropbins=None, justfd=-99):
"""
Returns a list of total errors taking into account the defined correlations
Propagation uncertainties defined for Lc/D0_mult-i / Lc/D0_mult-j.
Check if applicable to your situation
"""
tot_list = [[0., 0., 0., 0.] for _ in range(n_bins)]
if n_bins != len(list(errnum_list1.errors.values())[0]) or \
n_bins != len(list(errden_list1.errors.values())[0]):
if dropbins is None:
get_logger().fatal("Number of bins and number of errors mismatch, %i vs. %i vs. %i", \
n_bins, len(list(errnum_list1.errors.values())[0]), \
len(list(errden_list1.errors.values())[0]))
listimpl = ["yield", "cut", "pid", "feeddown_mult", "feeddown_mult_spectra", "trigger", \
"multiplicity_interval", "multiplicity_weights", "track", "ptshape", \
"feeddown_NB", "sigmav0", "branching_ratio"]
j = 0
for (_, errnum1), (_, errnum2), (_, errden1), (_, errden2) in zip(errnum_list1.errors.items(), \
errnum_list2.errors.items(), \
errden_list1.errors.items(), \
errden_list2.errors.items()):
for i in range(n_bins):
inum = i
iden = i
if dropbins is not None:
inum = dropbins[0][i]
iden = dropbins[1][i]
if errnum_list1.names[j] not in listimpl:
get_logger().fatal("Unknown systematic name: %s",
errnum_list1.names[j])
if errnum_list1.names[j] != errden_list2.names[j]:
get_logger().fatal("Names not in same order: %s vs %s", \
errnum_list1.names[j], errden_list2.names[j])
for nb in range(len(tot_list[i])):
if errnum_list1.names[j] == "yield" and justfd is not True:
#Uncorrelated
tot_list[i][nb] += errnum1[inum][nb] * errnum1[inum][nb] + \
errnum2[inum][nb] * errnum2[inum][nb] + \
errden1[iden][nb] * errden1[iden][nb] + \
errden2[iden][nb] * errden2[iden][nb]
elif errnum_list1.names[j] == "cut" and justfd is not True:
#Uncorrelated
tot_list[i][nb] += errnum1[inum][nb] * errnum1[inum][nb] + \
errnum2[inum][nb] * errnum2[inum][nb] + \
errden1[iden][nb] * errden1[iden][nb] + \
errden2[iden][nb] * errden2[iden][nb]
elif errnum_list1.names[j] == "pid" and justfd is not True:
#Correlated, do nothing
pass
elif errnum_list1.names[
j] == "feeddown_mult_spectra" and justfd is not False:
#Correlated, do nothing
pass
elif errnum_list1.names[
j] == "feeddown_mult" and justfd is not False:
#Correlated, do nothing
pass
elif errnum_list1.names[j] == "trigger" and justfd is not True:
#Correlated, do nothing
pass
elif errnum_list1.names[
j] == "feeddown_NB" and justfd is not False:
#Correlated, do nothing
pass
elif errnum_list1.names[
j] == "multiplicity_weights" and justfd is not True:
#Correlated, do nothing
pass
elif errnum_list1.names[j] == "track" and justfd is not True:
#Correlated, do nothing
pass
elif errnum_list1.names[j] == "ptshape" and justfd is not True:
#Uncorrelated
tot_list[i][nb] += errnum1[inum][nb] * errnum1[inum][nb] + \
errnum2[inum][nb] * errnum2[inum][nb] + \
errden1[iden][nb] * errden1[iden][nb] + \
errden2[iden][nb] * errden2[iden][nb]
elif errnum_list1.names[
j] == "multiplicity_interval" and justfd is not True:
#NB: Assuming ratio: 3prongs over 2prongs here! 2prong part cancels
#We use 1/3 of systematic of numerator
tot_list[i][
nb] += errden1[iden][nb] * errden1[iden][nb] / 9
elif errnum_list1.names[j] == "sigmav0" and justfd is not True:
#Correlated and usually not plotted in boxes, do nothing
pass
elif errnum_list1.names[
j] == "branching_ratio" and justfd is not True:
#Uncorrelated, but usually not plotted in boxes, so pass
pass
j = j + 1
tot_list = np.sqrt(tot_list)
return tot_list
def __init__(self, data_param, case):
self.logger = get_logger()
self.logger.info("DnnOptimizer::Init\nCase: %s", case)
# Dataset config
self.grid_phi = data_param["grid_phi"]
self.grid_z = data_param["grid_z"]
self.grid_r = data_param["grid_r"]
self.selopt_input = data_param["selopt_input"]
self.selopt_output = data_param["selopt_output"]
self.opt_train = data_param["opt_train"]
self.opt_predout = data_param["opt_predout"]
self.nameopt_predout = data_param["nameopt_predout"]
self.dim_input = sum(self.opt_train)
self.dim_output = sum(self.opt_predout)
self.use_scaler = data_param["use_scaler"]
# Directories
self.dirmodel = data_param["dirmodel"]
self.dirval = data_param["dirval"]
self.diroutflattree = data_param["diroutflattree"]
train_dir = data_param["dirinput_bias"] if data_param["train_bias"] \
else data_param["dirinput_nobias"]
test_dir = data_param["dirinput_bias"] if data_param["test_bias"] \
else data_param["dirinput_nobias"]
apply_dir = data_param["dirinput_bias"] if data_param["apply_bias"] \
else data_param["dirinput_nobias"]
self.dirinput_train = "%s/SC-%d-%d-%d/" % \
(train_dir, self.grid_z, self.grid_r, self.grid_phi)
self.dirinput_test = "%s/SC-%d-%d-%d/" % \
(test_dir, self.grid_z, self.grid_r, self.grid_phi)
self.dirinput_apply = "%s/SC-%d-%d-%d/" % \
(apply_dir, self.grid_z, self.grid_r, self.grid_phi)
# DNN config
self.filters = data_param["filters"]
self.pooling = data_param["pooling"]
self.batch_size = data_param["batch_size"]
self.shuffle = data_param["shuffle"]
self.depth = data_param["depth"]
self.batch_normalization = data_param["batch_normalization"]
self.dropout = data_param["dropout"]
self.epochs = data_param["epochs"]
self.lossfun = data_param["lossfun"]
self.metrics = data_param["metrics"]
self.adamlr = data_param["adamlr"]
self.params = {'phi_slice': self.grid_phi,
'r_row' : self.grid_r,
'z_col' : self.grid_z,
'batch_size': self.batch_size,
'shuffle': self.shuffle,
'opt_train' : self.opt_train,
'opt_predout' : self.opt_predout,
'selopt_input' : self.selopt_input,
'selopt_output' : self.selopt_output,
'use_scaler': self.use_scaler}
self.suffix = "phi%d_r%d_z%d_filter%d_poo%d_drop%.2f_depth%d_batch%d_scaler%d" % \
(self.grid_phi, self.grid_r, self.grid_z, self.filters, self.pooling,
self.dropout, self.depth, self.batch_normalization, self.use_scaler)
self.suffix = "%s_useSCMean%d_useSCFluc%d" % \
(self.suffix, self.opt_train[0], self.opt_train[1])
self.suffix = "%s_pred_doR%d_dophi%d_doz%d" % \
(self.suffix, self.opt_predout[0], self.opt_predout[1], self.opt_predout[2])
self.suffix_ds = "phi%d_r%d_z%d" % \
(self.grid_phi, self.grid_r, self.grid_z)
if not os.path.isdir("plots"):
os.makedirs("plots")
if not os.path.isdir(self.dirmodel):
os.makedirs(self.dirmodel)
if not os.path.isdir(self.dirval):
os.makedirs(self.dirval)
self.logger.info("I am processing the configuration %s", self.suffix)
if self.dim_output > 1:
self.logger.fatal("YOU CAN PREDICT ONLY 1 DISTORSION. The sum of opt_predout == 1")
self.logger.info("Inputs active for training: (SCMean, SCFluctuations)=(%d, %d)",
self.opt_train[0], self.opt_train[1])
self.maxrandomfiles = data_param["maxrandomfiles"]
self.range_mean_index = data_param["range_mean_index"]
self.indices_events_means_train = None
self.partition = None
self.total_events = 0
gROOT.SetStyle("Plain")
gROOT.SetBatch()
def calc_systematic_multovermb(errnum_list, errden_list, n_bins, justfd=-99):
"""
Returns a list of total errors taking into account the defined correlations
Propagation uncertainties defined for Ds(mult) / Ds(MB). Check if applicable to your situation
"""
tot_list = [[0., 0., 0., 0.] for _ in range(n_bins)]
if n_bins != len(list(errnum_list.errors.values())[0]) or \
n_bins != len(list(errden_list.errors.values())[0]):
get_logger().fatal("Number of bins and number of errors mismatch, %i vs. %i vs. %i", \
n_bins, len(list(errnum_list.errors.values())[0]), \
len(list(errden_list.errors.values())[0]))
listimpl = ["yield", "cut", "pid", "feeddown_mult", "feeddown_mult_spectra", "trigger", \
"multiplicity_interval", "multiplicity_weights", "track", "ptshape", \
"feeddown_NB", "sigmav0", "branching_ratio"]
j = 0
for (_, errnum), (_, errden) in zip(errnum_list.errors.items(),
errden_list.errors.items()):
for i in range(n_bins):
if errnum_list.names[j] not in listimpl:
get_logger().fatal("Unknown systematic name: %s",
errnum_list.names[j])
if errnum_list.names[j] != errden_list.names[j]:
get_logger().fatal("Names not in same order: %s vs %s", \
errnum.names[j], errden.names[j])
for nb in range(len(tot_list[i])):
if errnum_list.names[j] == "yield" and justfd is not True:
#Partially correlated, take largest
tot_list[i][nb] += max(errnum[i][nb], errden[i][nb]) \
* max(errnum[i][nb], errden[i][nb])
elif errnum_list.names[j] == "cut" and justfd is not True:
#Partially correlated, take largest
tot_list[i][nb] += max(errnum[i][nb], errden[i][nb]) \
* max(errnum[i][nb], errden[i][nb])
elif errnum_list.names[j] == "pid" and justfd is not True:
#Correlated, do nothing
pass
elif errnum_list.names[
j] == "feeddown_mult" and justfd is not False:
#Assign directly from multiplicity case, no syst for MB
tot_list[i][nb] += errnum[i][nb] * errnum[i][nb]
elif errnum_list.names[
j] == "feeddown_mult_spectra" and justfd is not False:
#Ratio here, skip spectra syst
pass
elif errnum_list.names[j] == "trigger" and justfd is not True:
#Assign directly from multiplicity case, no syst for MB
tot_list[i][nb] += errnum[i][nb] * errnum[i][nb]
elif errnum_list.names[
j] == "multiplicity_interval" and justfd is not True:
#FD: estimated using 7TeV strategy directly for ratio
tot_list[i][nb] += errnum[i][nb] * errnum[i][nb]
elif errnum_list.names[
j] == "multiplicity_weights" and justfd is not True:
#Uncorrelated
tot_list[i][nb] += errnum[i][nb] * errnum[i][nb] + errden[
i][nb] * errden[i][nb]
elif errnum_list.names[j] == "track" and justfd is not True:
#Correlated, do nothing
pass
elif errnum_list.names[j] == "ptshape" and justfd is not True:
#Correlated, assign difference
diff = abs(errnum[i][nb] - errden[i][nb])
tot_list[i][nb] += diff * diff
elif errnum_list.names[
j] == "feeddown_NB" and justfd is not False:
#Correlated, do nothing
pass
elif errnum_list.names[j] == "sigmav0" and justfd is not True:
#Correlated and usually not plotted in boxes, do nothing
pass
elif errnum_list.names[
j] == "branching_ratio" and justfd is not True:
#Correlated and usually not plotted in boxes, do nothing
pass
j = j + 1
tot_list = np.sqrt(tot_list)
return tot_list
def main(): # pylint: disable=too-many-locals, too-many-statements, too-many-branches
"""
Main plotting function
"""
gROOT.SetBatch(True)
# pylint: disable=unused-variable
parser = argparse.ArgumentParser()
parser.add_argument("--database-analysis",
"-d",
dest="database_analysis",
help="analysis database to be used",
required=True)
parser.add_argument("--analysis",
"-a",
dest="type_ana",
help="choose type of analysis",
required=True)
parser.add_argument("--input",
"-i",
dest="input_file",
help="results input file",
required=True)
args = parser.parse_args()
typean = args.type_ana
shape = typean[len("jet_"):]
print("Shape:", shape)
file_in = args.input_file
with open(args.database_analysis, "r") as file_db:
data_param = yaml.safe_load(file_db)
case = list(data_param.keys())[0]
datap = data_param[case]
logger = get_logger()
i_cut = file_in.rfind("/")
rootpath = file_in[:i_cut]
# plotting
# LaTeX string
p_latexnhadron = datap["analysis"][typean]["latexnamehadron"]
p_latexbin2var = datap["analysis"][typean]["latexbin2var"]
v_varshape_latex = datap["analysis"][typean]["var_shape_latex"]
# first variable (hadron pt)
lpt_finbinmin = datap["analysis"][typean]["sel_an_binmin"]
lpt_finbinmax = datap["analysis"][typean]["sel_an_binmax"]
var1ranges = lpt_finbinmin.copy()
var1ranges.append(lpt_finbinmax[-1])
# second variable (jet pt)
v_var2_binning = datap["analysis"][typean]["var_binning2"] # name
lvar2_binmin_reco = datap["analysis"][typean].get("sel_binmin2_reco", None)
lvar2_binmax_reco = datap["analysis"][typean].get("sel_binmax2_reco", None)
p_nbin2_reco = len(lvar2_binmin_reco) # number of reco bins
lvar2_binmin_gen = datap["analysis"][typean].get("sel_binmin2_gen", None)
lvar2_binmax_gen = datap["analysis"][typean].get("sel_binmax2_gen", None)
p_nbin2_gen = len(lvar2_binmin_gen) # number of gen bins
var2ranges_reco = lvar2_binmin_reco.copy()
var2ranges_reco.append(lvar2_binmax_reco[-1])
var2binarray_reco = array(
"d",
var2ranges_reco) # array of bin edges to use in histogram constructors
var2ranges_gen = lvar2_binmin_gen.copy()
var2ranges_gen.append(lvar2_binmax_gen[-1])
var2binarray_gen = array(
"d",
var2ranges_gen) # array of bin edges to use in histogram constructors
# observable (z, shape,...)
v_varshape_binning = datap["analysis"][typean][
"var_binningshape"] # name (reco)
v_varshape_binning_gen = datap["analysis"][typean][
"var_binningshape_gen"] # name (gen)
lvarshape_binmin_reco = \
datap["analysis"][typean].get("sel_binminshape_reco", None)
lvarshape_binmax_reco = \
datap["analysis"][typean].get("sel_binmaxshape_reco", None)
p_nbinshape_reco = len(lvarshape_binmin_reco) # number of reco bins
lvarshape_binmin_gen = \
datap["analysis"][typean].get("sel_binminshape_gen", None)
lvarshape_binmax_gen = \
datap["analysis"][typean].get("sel_binmaxshape_gen", None)
p_nbinshape_gen = len(lvarshape_binmin_gen) # number of gen bins
varshaperanges_reco = lvarshape_binmin_reco.copy()
varshaperanges_reco.append(lvarshape_binmax_reco[-1])
varshapebinarray_reco = array(
"d", varshaperanges_reco
) # array of bin edges to use in histogram constructors
varshaperanges_gen = lvarshape_binmin_gen.copy()
varshaperanges_gen.append(lvarshape_binmax_gen[-1])
varshapebinarray_gen = array(
"d", varshaperanges_gen
) # array of bin edges to use in histogram constructors
file_results = TFile.Open(file_in)
if not file_results:
logger.fatal(make_message_notfound(file_in))
ibin2 = 1
suffix = "%s_%g_%g" % (v_var2_binning, lvar2_binmin_gen[ibin2],
lvar2_binmax_gen[ibin2])
# HF data
nameobj = "%s_hf_data_%d_stat" % (shape, ibin2)
hf_data_stat = file_results.Get(nameobj)
if not hf_data_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_hf_data_%d_syst" % (shape, ibin2)
hf_data_syst = file_results.Get(nameobj)
if not hf_data_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# HF PYTHIA
nameobj = "%s_hf_pythia_%d_stat" % (shape, ibin2)
hf_pythia_stat = file_results.Get(nameobj)
if not hf_pythia_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
# HF ratio
nameobj = "%s_hf_ratio_%d_stat" % (shape, ibin2)
hf_ratio_stat = file_results.Get(nameobj)
if not hf_ratio_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_hf_ratio_%d_syst" % (shape, ibin2)
hf_ratio_syst = file_results.Get(nameobj)
if not hf_ratio_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# inclusive data
nameobj = "%s_incl_data_%d_stat" % (shape, ibin2)
incl_data_stat = file_results.Get(nameobj)
if not incl_data_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_incl_data_%d_syst" % (shape, ibin2)
incl_data_syst = file_results.Get(nameobj)
if not incl_data_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# inclusive PYTHIA
nameobj = "%s_incl_pythia_%d_stat" % (shape, ibin2)
incl_pythia_stat = file_results.Get(nameobj)
if not incl_pythia_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_incl_pythia_%d_syst" % (shape, ibin2)
incl_pythia_syst = file_results.Get(nameobj)
if not incl_pythia_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# inclusive ratio
nameobj = "%s_incl_ratio_%d_stat" % (shape, ibin2)
incl_ratio_stat = file_results.Get(nameobj)
if not incl_ratio_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_incl_ratio_%d_syst" % (shape, ibin2)
incl_ratio_syst = file_results.Get(nameobj)
if not incl_ratio_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# quark PYTHIA
nameobj = "%s_quark_pythia_%d_stat" % (shape, ibin2)
quark_pythia_stat = file_results.Get(nameobj)
if not quark_pythia_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_quark_pythia_%d_syst" % (shape, ibin2)
quark_pythia_syst = file_results.Get(nameobj)
if not quark_pythia_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# gluon PYTHIA
nameobj = "%s_gluon_pythia_%d_stat" % (shape, ibin2)
gluon_pythia_stat = file_results.Get(nameobj)
if not gluon_pythia_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_gluon_pythia_%d_syst" % (shape, ibin2)
gluon_pythia_syst = file_results.Get(nameobj)
if not gluon_pythia_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# plot the results with systematic uncertainties and models
size_can = [800, 800]
offsets_axes = [0.8, 1.1]
margins_can = [0.1, 0.13, 0.1, 0.03]
size_thg = 0.05
offset_thg = 0.85
gStyle.SetErrorX(0) # do not plot horizontal error bars of histograms
fontsize = 0.035
opt_leg_g = "FP"
opt_plot_g = "2"
list_new = [] # list to avoid loosing objects created in loops
# labels
x_latex = 0.16
y_latex_top = 0.83
y_step = 0.055
title_x = v_varshape_latex
title_y = "(1/#it{N}_{jet}) d#it{N}/d%s" % v_varshape_latex
title_full = ";%s;%s" % (title_x, title_y)
title_full_ratio = ";%s;data/MC: ratio of %s" % (title_x, title_y)
text_alice = "#bf{ALICE} Preliminary, pp, #sqrt{#it{s}} = 13 TeV"
text_alice_sim = "#bf{ALICE} Simulation, pp, #sqrt{#it{s}} = 13 TeV"
text_pythia = "PYTHIA 8 (Monash)"
text_pythia_split = "#splitline{PYTHIA 8}{(Monash)}"
text_jets = "charged jets, anti-#it{k}_{T}, #it{R} = 0.4"
text_ptjet = "%g #leq %s < %g GeV/#it{c}, #left|#it{#eta}_{jet}#right| #leq 0.5" % (
lvar2_binmin_reco[ibin2], p_latexbin2var, lvar2_binmax_reco[ibin2])
text_pth = "%g #leq #it{p}_{T}^{%s} < %g GeV/#it{c}, #left|#it{y}_{%s}#right| #leq 0.8" % (
lpt_finbinmin[0], p_latexnhadron,
min(lpt_finbinmax[-1], lvar2_binmax_reco[ibin2]), p_latexnhadron)
text_ptcut = "#it{p}_{T, incl. ch. jet}^{leading track} #geq 5.33 GeV/#it{c}"
text_ptcut_sim = "#it{p}_{T, incl. ch. jet}^{leading h^{#pm}} #geq 5.33 GeV/#it{c} (varied)"
text_sd = "Soft Drop (#it{z}_{cut} = 0.1, #it{#beta} = 0)"
title_thetag = "#it{#theta}_{g} = #it{R}_{g}/#it{R}"
radius_jet = 0.4
# colour and marker indeces
c_hf_data = 0
c_incl_data = 1
c_hf_mc = 2
c_incl_mc = 6
c_quark_mc = 5
c_gluon_mc = 0
# markers
m_hf_data = get_marker(0)
m_incl_data = get_marker(1)
m_hf_mc = get_marker(0, 2)
m_incl_mc = get_marker(1, 2)
m_quark_mc = get_marker(2)
m_gluon_mc = get_marker(3)
# make the horizontal error bars smaller
if shape == "nsd":
for gr in [
hf_data_syst, incl_data_syst, hf_ratio_syst, incl_ratio_syst,
incl_pythia_syst, quark_pythia_syst, gluon_pythia_syst
]:
for i in range(gr.GetN()):
gr.SetPointEXlow(i, 0.1)
gr.SetPointEXhigh(i, 0.1)
# data, HF and inclusive
hf_data_syst_cl = hf_data_syst.Clone()
leg_pos = [.72, .75, .85, .85]
list_obj = [hf_data_syst, incl_data_syst, hf_data_stat, incl_data_stat]
labels_obj = ["%s-tagged" % p_latexnhadron, "inclusive", "", ""]
colours = [
get_colour(i, j) for i, j in zip((c_hf_data, c_incl_data, c_hf_data,
c_incl_data), (2, 2, 1, 1))
]
markers = [m_hf_data, m_incl_data, m_hf_data, m_incl_data]
y_margin_up = 0.46
y_margin_down = 0.05
cshape_data, list_obj_data_new = make_plot("cshape_data_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, margins_y=[y_margin_down, y_margin_up], margins_c=margins_can, \
title=title_full)
for gr, c in zip((hf_data_syst, incl_data_syst), (c_hf_data, c_incl_data)):
gr.SetMarkerColor(get_colour(c))
list_obj_data_new[0].SetTextSize(fontsize)
if shape == "nsd":
hf_data_syst.GetXaxis().SetNdivisions(5)
# Draw a line through the points.
if shape == "nsd":
for h in (hf_data_stat, incl_data_stat):
h_line = h.Clone(h.GetName() + "_line")
h_line.SetLineStyle(2)
h_line.Draw("l hist same")
list_new.append(h_line)
cshape_data.Update()
if shape == "rg":
# plot the theta_g axis
gr_frame = hf_data_syst
axis_rg = gr_frame.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_data.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_data.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_data = []
for text_latex in [
text_alice, text_jets, text_ptjet, text_pth, text_ptcut, text_sd
]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_data.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_data.Update()
cshape_data.SaveAs("%s/%s_data_%s.pdf" % (rootpath, shape, suffix))
# data and PYTHIA, HF
leg_pos = [.72, .65, .85, .85]
list_obj = [hf_data_syst_cl, hf_data_stat, hf_pythia_stat]
labels_obj = ["data", "", text_pythia_split]
colours = [
get_colour(i, j)
for i, j in zip((c_hf_data, c_hf_data, c_hf_mc), (2, 1, 1))
]
markers = [m_hf_data, m_hf_data, m_hf_mc]
y_margin_up = 0.4
y_margin_down = 0.05
cshape_data_mc_hf, list_obj_data_mc_hf_new = make_plot("cshape_data_mc_hf_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, margins_y=[y_margin_down, y_margin_up], margins_c=margins_can, \
title=title_full)
for gr, c in zip([hf_data_syst_cl], [c_hf_data]):
gr.SetMarkerColor(get_colour(c))
leg_data_mc_hf = list_obj_data_mc_hf_new[0]
leg_data_mc_hf.SetHeader("%s-tagged" % p_latexnhadron)
leg_data_mc_hf.SetTextSize(fontsize)
if shape == "nsd":
hf_data_syst_cl.GetXaxis().SetNdivisions(5)
#axis_nsd = hf_data_syst_cl.GetHistogram().GetXaxis()
#x1 = axis_nsd.GetBinLowEdge(1)
#x2 = axis_nsd.GetBinUpEdge(axis_nsd.GetNbins())
#axis_nsd.Set(5, x1, x2)
#for ibin in range(axis_nsd.GetNbins()):
# axis_nsd.SetBinLabel(ibin + 1, "%d" % ibin)
#axis_nsd.SetNdivisions(5)
cshape_data_mc_hf.Update()
if shape == "rg":
# plot the theta_g axis
axis_rg = hf_data_stat.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_data_mc_hf.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_data_mc_hf.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_data_mc_hf = []
for text_latex in [text_alice, text_jets, text_ptjet, text_pth, text_sd]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_data_mc_hf.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_data_mc_hf.Update()
cshape_data_mc_hf.SaveAs("%s/%s_data_mc_hf_%s.pdf" %
(rootpath, shape, suffix))
# data and PYTHIA, inclusive
#leg_pos = [.68, .65, .85, .85]
list_obj = [
incl_data_syst, incl_pythia_syst, incl_data_stat, incl_pythia_stat
]
labels_obj = ["data", text_pythia_split]
colours = [
get_colour(i, j) for i, j in zip((c_incl_data, c_incl_mc, c_incl_data,
c_incl_mc), (2, 2, 1, 1))
]
markers = [m_incl_data, m_incl_mc, m_incl_data, m_incl_mc]
y_margin_up = 0.4
y_margin_down = 0.05
cshape_data_mc_incl, list_obj_data_mc_incl_new = make_plot("cshape_data_mc_incl_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, margins_y=[y_margin_down, y_margin_up], margins_c=margins_can, \
title=title_full)
for gr, c in zip([incl_data_syst, incl_pythia_syst],
[c_incl_data, c_incl_mc]):
gr.SetMarkerColor(get_colour(c))
leg_data_mc_incl = list_obj_data_mc_incl_new[0]
leg_data_mc_incl.SetHeader("inclusive")
leg_data_mc_incl.SetTextSize(fontsize)
if shape == "nsd":
incl_data_syst.GetXaxis().SetNdivisions(5)
cshape_data_mc_incl.Update()
if shape == "rg":
# plot the theta_g axis
axis_rg = incl_data_stat.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_data_mc_incl.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_data_mc_incl.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_data_mc_incl = []
for text_latex in [text_alice, text_jets, text_ptjet, text_ptcut, text_sd]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_data_mc_incl.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_data_mc_incl.Update()
cshape_data_mc_incl.SaveAs("%s/%s_data_mc_incl_%s.pdf" %
(rootpath, shape, suffix))
# Ratios data/MC, HF and inclusive
line_1 = TLine(lvarshape_binmin_reco[0], 1, lvarshape_binmax_reco[-1], 1)
line_1.SetLineStyle(9)
line_1.SetLineColor(1)
line_1.SetLineWidth(3)
#leg_pos = [.72, .7, .85, .85] # with header
leg_pos = [.72, .75, .85, .85] # without header
list_obj = [
hf_ratio_syst, line_1, incl_ratio_syst, hf_ratio_stat, incl_ratio_stat
]
labels_obj = ["%s-tagged" % p_latexnhadron, "inclusive"]
colours = [
get_colour(i, j) for i, j in zip((c_hf_data, c_incl_data, c_hf_data,
c_incl_data), (2, 2, 1, 1))
]
markers = [m_hf_data, m_incl_data, m_hf_data, m_incl_data]
y_margin_up = 0.52
y_margin_down = 0.05
if shape == "nsd":
y_margin_up = 0.22
cshape_ratio, list_obj_ratio_new = make_plot("cshape_ratio_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, margins_y=[y_margin_down, y_margin_up], margins_c=margins_can, \
title=title_full_ratio)
cshape_ratio.Update()
for gr, c in zip((hf_ratio_syst, incl_ratio_syst),
(c_hf_data, c_incl_data)):
gr.SetMarkerColor(get_colour(c))
leg_ratio = list_obj_ratio_new[0]
leg_ratio.SetTextSize(fontsize)
#leg_ratio.SetHeader("data/MC")
if shape == "nsd":
hf_ratio_syst.GetXaxis().SetNdivisions(5)
cshape_ratio.Update()
if shape == "rg":
# plot the theta_g axis
gr_frame = hf_ratio_syst
axis_rg = gr_frame.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_ratio.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_ratio.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_ratio = []
for text_latex in [
text_alice, text_jets, text_ptjet, text_pth, text_ptcut, text_sd,
text_pythia
]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_ratio.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_ratio.Update()
cshape_ratio.SaveAs("%s/%s_ratio_%s.pdf" % (rootpath, shape, suffix))
# PYTHIA, HF, inclusive, quark, gluon
incl_pythia_syst_cl = incl_pythia_syst.Clone()
y_min_h, y_max_h = get_y_window_his([
hf_pythia_stat, incl_pythia_stat, quark_pythia_stat, gluon_pythia_stat
])
y_min_g, y_max_g = get_y_window_gr(
[incl_pythia_syst, quark_pythia_syst, gluon_pythia_syst])
y_min = min(y_min_h, y_min_g)
y_max = max(y_max_h, y_max_g)
y_margin_up = 0.46
y_margin_down = 0.05
y_min_plot, y_max_plot = get_plot_range(y_min, y_max, y_margin_down,
y_margin_up)
#leg_pos = [.6, .65, .75, .85]
leg_pos = [.72, .55, .85, .85]
list_obj = [
incl_pythia_syst, quark_pythia_syst, gluon_pythia_syst, hf_pythia_stat,
incl_pythia_stat, quark_pythia_stat, gluon_pythia_stat
]
labels_obj = ["inclusive", "quark", "gluon", "%s-tagged" % p_latexnhadron]
colours = [
get_colour(i, j)
for i, j in zip((c_incl_mc, c_quark_mc, c_gluon_mc, c_hf_mc, c_incl_mc,
c_quark_mc, c_gluon_mc), (2, 2, 2, 1, 1, 1, 1))
]
markers = [
m_incl_mc, m_quark_mc, m_gluon_mc, m_hf_mc, m_incl_mc, m_quark_mc,
m_gluon_mc
]
y_margin_up = 0.46
y_margin_down = 0.05
cshape_mc, list_obj_mc_new = make_plot("cshape_mc_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, range_y=[y_min_plot, y_max_plot], margins_c=margins_can, \
title=title_full)
cshape_mc.Update()
for gr, c in zip((incl_pythia_syst, quark_pythia_syst, gluon_pythia_syst),
(c_incl_mc, c_quark_mc, c_gluon_mc)):
gr.SetMarkerColor(get_colour(c))
leg_mc = list_obj_mc_new[0]
leg_mc.SetTextSize(fontsize)
leg_mc.SetHeader(text_pythia_split)
if shape == "nsd":
incl_pythia_syst.GetXaxis().SetNdivisions(5)
cshape_mc.Update()
if shape == "rg":
# plot the theta_g axis
axis_rg = hf_pythia_stat.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_mc.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_mc.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_mc = []
for text_latex in [
text_alice_sim, text_jets, text_ptjet, text_pth, text_ptcut_sim,
text_sd
]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_mc.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_mc.Update()
cshape_mc.SaveAs("%s/%s_mc_%s.pdf" % (rootpath, shape, suffix))
# PYTHIA, HF, quark, gluon
#leg_pos = [.6, .65, .75, .85]
leg_pos = [.72, .61, .85, .85]
list_obj = [
quark_pythia_syst, gluon_pythia_syst, hf_pythia_stat,
quark_pythia_stat, gluon_pythia_stat
]
labels_obj = ["quark", "gluon", "%s-tagged" % p_latexnhadron]
colours = [
get_colour(i, j)
for i, j in zip((c_quark_mc, c_gluon_mc, c_hf_mc, c_quark_mc,
c_gluon_mc), (2, 2, 1, 1, 1))
]
markers = [m_quark_mc, m_gluon_mc, m_hf_mc, m_quark_mc, m_gluon_mc]
y_margin_up = 0.46
y_margin_down = 0.05
cshape_mc, list_obj_mc_new = make_plot("cshape_mc_qgd_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, range_y=[y_min_plot, y_max_plot], margins_c=margins_can, \
title=title_full)
cshape_mc.Update()
for gr, c in zip((quark_pythia_syst, gluon_pythia_syst),
(c_quark_mc, c_gluon_mc)):
gr.SetMarkerColor(get_colour(c))
leg_mc = list_obj_mc_new[0]
leg_mc.SetTextSize(fontsize)
leg_mc.SetHeader(text_pythia_split)
if shape == "nsd":
quark_pythia_syst.GetXaxis().SetNdivisions(5)
cshape_mc.Update()
if shape == "rg":
# plot the theta_g axis
axis_rg = hf_pythia_stat.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_mc.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_mc.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_mc = []
for text_latex in [
text_alice_sim, text_jets, text_ptjet, text_pth, text_ptcut_sim,
text_sd
]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_mc.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_mc.Update()
cshape_mc.SaveAs("%s/%s_mc_qgd_%s.pdf" % (rootpath, shape, suffix))
# PYTHIA, HF, inclusive
#leg_pos = [.6, .65, .75, .85]
leg_pos = [.72, .67, .85, .85]
list_obj = [incl_pythia_syst_cl, incl_pythia_stat, hf_pythia_stat]
labels_obj = ["inclusive", "", "%s-tagged" % p_latexnhadron]
colours = [
get_colour(i, j)
for i, j in zip((c_incl_mc, c_incl_mc, c_hf_mc), (2, 1, 1))
]
markers = [m_incl_mc, m_incl_mc, m_hf_mc]
y_margin_up = 0.46
y_margin_down = 0.05
cshape_mc, list_obj_mc_new = make_plot("cshape_mc_id_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, range_y=[y_min_plot, y_max_plot], margins_c=margins_can, \
title=title_full)
# Draw a line through the points.
if shape == "nsd":
for h in (incl_pythia_stat, hf_pythia_stat):
h_line = h.Clone(h.GetName() + "_line")
h_line.SetLineStyle(2)
h_line.Draw("l hist same")
list_new.append(h_line)
cshape_mc.Update()
incl_pythia_syst_cl.SetMarkerColor(get_colour(c_incl_mc))
leg_mc = list_obj_mc_new[0]
leg_mc.SetTextSize(fontsize)
leg_mc.SetHeader(text_pythia_split)
if shape == "nsd":
incl_pythia_syst_cl.GetXaxis().SetNdivisions(5)
cshape_mc.Update()
if shape == "rg":
# plot the theta_g axis
axis_rg = hf_pythia_stat.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_mc.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_mc.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_mc = []
for text_latex in [
text_alice_sim, text_jets, text_ptjet, text_pth, text_ptcut_sim,
text_sd
]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_mc.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_mc.Update()
cshape_mc.SaveAs("%s/%s_mc_id_%s.pdf" % (rootpath, shape, suffix))
# data inclusive vs PYTHIA, quark, gluon
#leg_pos = [.6, .65, .75, .85]
#leg_pos = [.72, .55, .85, .85]
leg_pos = [.6, .7, .85, .85]
list_obj = [
incl_data_syst, quark_pythia_syst, gluon_pythia_syst, incl_data_stat,
quark_pythia_stat, gluon_pythia_stat
]
labels_obj = ["inclusive (data)", "quark (PYTHIA 8)", "gluon (PYTHIA 8)"]
colours = [
get_colour(i, j)
for i, j in zip((c_incl_data, c_quark_mc, c_gluon_mc, c_incl_data,
c_quark_mc, c_gluon_mc), (2, 2, 2, 1, 1, 1))
]
markers = [
m_incl_data, m_quark_mc, m_gluon_mc, m_incl_data, m_quark_mc,
m_gluon_mc
]
y_margin_up = 0.3
y_margin_down = 0.05
cshape_mc, list_obj_mc_new = make_plot("cshape_mc_data_iqg" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, margins_y=[y_margin_down, y_margin_up], margins_c=margins_can, \
title=title_full)
for gr, c in zip((incl_data_syst, quark_pythia_syst, gluon_pythia_syst),
(c_incl_data, c_quark_mc, c_gluon_mc)):
gr.SetMarkerColor(get_colour(c))
leg_mc = list_obj_mc_new[0]
leg_mc.SetTextSize(fontsize)
cshape_mc.Update()
cshape_mc.SaveAs("%s/%s_data_i_mc_qg_%s.pdf" % (rootpath, shape, suffix))
def reset_input(self, dataframe, tag):
self.source_dataframe = dataframe
self.collection_tag = tag
if self.verbose:
get_logger().info("Resetting ValidationCollection with tag '%s'",
self.collection_tag)
def do_entire_analysis(data_config: dict, data_param: dict, data_param_overwrite: dict, # pylint: disable=too-many-locals, too-many-statements, too-many-branches
data_model: dict, run_param: dict, clean: bool):
# Disable any graphical stuff. No TCanvases opened and shown by default
gROOT.SetBatch(True)
logger = get_logger()
logger.info("Do analysis chain")
# If we are here we are interested in the very first key in the parameters database
case = list(data_param.keys())[0]
# Update database accordingly if needed
update_config(data_param, data_config, data_param_overwrite)
dodownloadalice = data_config["download"]["alice"]["activate"]
doconversionmc = data_config["conversion"]["mc"]["activate"]
doconversiondata = data_config["conversion"]["data"]["activate"]
domergingmc = data_config["merging"]["mc"]["activate"]
domergingdata = data_config["merging"]["data"]["activate"]
doskimmingmc = data_config["skimming"]["mc"]["activate"]
doskimmingdata = data_config["skimming"]["data"]["activate"]
domergingperiodsmc = data_config["mergingperiods"]["mc"]["activate"]
domergingperiodsdata = data_config["mergingperiods"]["data"]["activate"]
doml = data_config["ml_study"]["activate"]
docorrelation = data_config["ml_study"]['docorrelation']
dotraining = data_config["ml_study"]['dotraining']
dotesting = data_config["ml_study"]['dotesting']
doapplytodatamc = data_config["ml_study"]['doapplytodatamc']
docrossvalidation = data_config["ml_study"]['docrossvalidation']
dolearningcurve = data_config["ml_study"]['dolearningcurve']
doroc = data_config["ml_study"]['doroc']
doroctraintest = data_config["ml_study"]['doroctraintest']
doboundary = data_config["ml_study"]['doboundary']
doimportance = data_config["ml_study"]['doimportance']
doimportanceshap = data_config["ml_study"]['doimportanceshap']
dogridsearch = data_config["ml_study"]['dogridsearch']
dobayesianopt = data_config["ml_study"]['dobayesianopt']
doefficiencyml = data_config["ml_study"]['doefficiency']
dosignifopt = data_config["ml_study"]['dosignifopt']
doscancuts = data_config["ml_study"]["doscancuts"]
doplotdistr = data_config["ml_study"]["doplotdistr"]
doapplydata = data_config["mlapplication"]["data"]["doapply"]
doapplymc = data_config["mlapplication"]["mc"]["doapply"]
domergeapplydata = data_config["mlapplication"]["data"]["domergeapply"]
domergeapplymc = data_config["mlapplication"]["mc"]["domergeapply"]
docontinueapplydata = data_config["mlapplication"]["data"]["docontinueafterstop"]
docontinueapplymc = data_config["mlapplication"]["mc"]["docontinueafterstop"]
dohistomassmc = data_config["analysis"]["mc"]["histomass"]
dohistomassdata = data_config["analysis"]["data"]["histomass"]
doefficiency = data_config["analysis"]["mc"]["efficiency"]
doresponse = data_config["analysis"]["mc"]["response"]
dofeeddown = data_config["analysis"]["mc"]["feeddown"]
dounfolding = data_config["analysis"]["mc"]["dounfolding"]
dojetsystematics = data_config["analysis"]["data"]["dojetsystematics"]
dofit = data_config["analysis"]["dofit"]
doeff = data_config["analysis"]["doeff"]
docross = data_config["analysis"]["docross"]
doplotsval = data_config["analysis"]["doplotsval"]
doplots = data_config["analysis"]["doplots"]
dosyst = data_config["analysis"]["dosyst"]
dosystprob = data_config["systematics"]["cutvar"]["activate"]
do_syst_prob_mass = data_config["systematics"]["cutvar"]["probvariationmass"]
do_syst_prob_eff = data_config["systematics"]["cutvar"]["probvariationeff"]
do_syst_prob_fit = data_config["systematics"]["cutvar"]["probvariationfit"]
do_syst_prob_cross = data_config["systematics"]["cutvar"]["probvariationcross"]
dosystptshape = data_config["systematics"]["mcptshape"]["activate"]
doanaperperiod = data_config["analysis"]["doperperiod"]
typean = data_config["analysis"]["type"]
dojetstudies = data_config["analysis"]["dojetstudies"]
dirpklmc = data_param[case]["multi"]["mc"]["pkl"]
dirpklevtcounter_allmc = data_param[case]["multi"]["mc"]["pkl_evtcounter_all"]
dirpklskmc = data_param[case]["multi"]["mc"]["pkl_skimmed"]
dirpklmlmc = data_param[case]["multi"]["mc"]["pkl_skimmed_merge_for_ml"]
dirpklmltotmc = data_param[case]["multi"]["mc"]["pkl_skimmed_merge_for_ml_all"]
dirpkldata = data_param[case]["multi"]["data"]["pkl"]
dirpklevtcounter_alldata = data_param[case]["multi"]["data"]["pkl_evtcounter_all"]
dirpklskdata = data_param[case]["multi"]["data"]["pkl_skimmed"]
dirpklmldata = data_param[case]["multi"]["data"]["pkl_skimmed_merge_for_ml"]
dirpklmltotdata = data_param[case]["multi"]["data"]["pkl_skimmed_merge_for_ml_all"]
dirpklskdecmc = data_param[case]["mlapplication"]["mc"]["pkl_skimmed_dec"]
dirpklskdec_mergedmc = data_param[case]["mlapplication"]["mc"]["pkl_skimmed_decmerged"]
dirpklskdecdata = data_param[case]["mlapplication"]["data"]["pkl_skimmed_dec"]
dirpklskdec_mergeddata = data_param[case]["mlapplication"]["data"]["pkl_skimmed_decmerged"]
dirresultsdata = data_param[case]["analysis"][typean]["data"]["results"]
dirresultsmc = data_param[case]["analysis"][typean]["mc"]["results"]
dirresultsdatatot = data_param[case]["analysis"][typean]["data"]["resultsallp"]
dirresultsmctot = data_param[case]["analysis"][typean]["mc"]["resultsallp"]
binminarray = data_param[case]["ml"]["binmin"]
binmaxarray = data_param[case]["ml"]["binmax"]
raahp = data_param[case]["ml"]["opt"]["raahp"]
mltype = data_param[case]["ml"]["mltype"]
training_vars = data_param[case]["variables"]["var_training"]
mlout = data_param[case]["ml"]["mlout"]
mlplot = data_param[case]["ml"]["mlplot"]
proc_type = data_param[case]["analysis"][typean]["proc_type"]
#creating folder if not present
counter = 0
if doconversionmc is True:
counter = counter + checkdirlist(dirpklmc)
if doconversiondata is True:
counter = counter + checkdirlist(dirpkldata)
if doskimmingmc is True:
checkdirlist(dirpklskmc)
counter = counter + checkdir(dirpklevtcounter_allmc)
if doskimmingdata is True:
counter = counter + checkdirlist(dirpklskdata)
counter = counter + checkdir(dirpklevtcounter_alldata)
if domergingmc is True:
counter = counter + checkdirlist(dirpklmlmc)
if domergingdata is True:
counter = counter + checkdirlist(dirpklmldata)
if domergingperiodsmc is True:
counter = counter + checkdir(dirpklmltotmc)
if domergingperiodsdata is True:
counter = counter + checkdir(dirpklmltotdata)
if doml is True:
counter = counter + checkdir(mlout)
counter = counter + checkdir(mlplot)
if docontinueapplymc is False:
if doapplymc is True:
counter = counter + checkdirlist(dirpklskdecmc)
if domergeapplymc is True:
counter = counter + checkdirlist(dirpklskdec_mergedmc)
if docontinueapplydata is False:
if doapplydata is True:
counter = counter + checkdirlist(dirpklskdecdata)
if domergeapplydata is True:
counter = counter + checkdirlist(dirpklskdec_mergeddata)
if dohistomassmc is True:
counter = counter + checkdirlist(dirresultsmc)
counter = counter + checkdir(dirresultsmctot)
if dohistomassdata is True:
counter = counter + checkdirlist(dirresultsdata)
counter = counter + checkdir(dirresultsdatatot)
if counter < 0:
sys.exit()
# check and create directories
if doconversionmc is True:
checkmakedirlist(dirpklmc)
if doconversiondata is True:
checkmakedirlist(dirpkldata)
if doskimmingmc is True:
checkmakedirlist(dirpklskmc)
checkmakedir(dirpklevtcounter_allmc)
if doskimmingdata is True:
checkmakedirlist(dirpklskdata)
checkmakedir(dirpklevtcounter_alldata)
if domergingmc is True:
checkmakedirlist(dirpklmlmc)
if domergingdata is True:
checkmakedirlist(dirpklmldata)
if domergingperiodsmc is True:
checkmakedir(dirpklmltotmc)
if domergingperiodsdata is True:
checkmakedir(dirpklmltotdata)
if doml is True:
checkmakedir(mlout)
checkmakedir(mlplot)
if docontinueapplymc is False:
if doapplymc is True:
checkmakedirlist(dirpklskdecmc)
if domergeapplymc is True:
checkmakedirlist(dirpklskdec_mergedmc)
if docontinueapplydata is False:
if doapplydata is True:
checkmakedirlist(dirpklskdecdata)
if domergeapplydata is True:
checkmakedirlist(dirpklskdec_mergeddata)
if dohistomassmc is True:
checkmakedirlist(dirresultsmc)
checkmakedir(dirresultsmctot)
if dohistomassdata is True:
checkmakedirlist(dirresultsdata)
checkmakedir(dirresultsdatatot)
proc_class = Processer
ana_class = Analyzer
syst_class = Systematics
if proc_type == "Dhadrons":
print("Using new feature for Dhadrons")
proc_class = ProcesserDhadrons
ana_class = AnalyzerDhadrons
if proc_type == "Dhadrons_mult":
print("Using new feature for Dhadrons_mult")
proc_class = ProcesserDhadrons_mult
ana_class = AnalyzerDhadrons_mult
if proc_type == "Dhadrons_jet":
print("Using new feature for Dhadrons_jet")
proc_class = ProcesserDhadrons_jet
ana_class = AnalyzerJet
mymultiprocessmc = MultiProcesser(case, proc_class, data_param[case], typean, run_param, "mc")
mymultiprocessdata = MultiProcesser(case, proc_class, data_param[case], typean, run_param,\
"data")
ana_mgr = AnalyzerManager(ana_class, data_param[case], case, typean, doanaperperiod)
# Has to be done always period-by-period
syst_mgr = AnalyzerManager(syst_class, data_param[case], case, typean, True, run_param)
#perform the analysis flow
if dodownloadalice == 1:
subprocess.call("../cplusutilities/Download.sh")
if doconversionmc == 1:
mymultiprocessmc.multi_unpack_allperiods()
if doconversiondata == 1:
mymultiprocessdata.multi_unpack_allperiods()
if doskimmingmc == 1:
mymultiprocessmc.multi_skim_allperiods()
if doskimmingdata == 1:
mymultiprocessdata.multi_skim_allperiods()
if domergingmc == 1:
mymultiprocessmc.multi_mergeml_allperiods()
if domergingdata == 1:
mymultiprocessdata.multi_mergeml_allperiods()
if domergingperiodsmc == 1:
mymultiprocessmc.multi_mergeml_allinone()
if domergingperiodsdata == 1:
mymultiprocessdata.multi_mergeml_allinone()
if doml is True:
index = 0
for binmin, binmax in zip(binminarray, binmaxarray):
myopt = Optimiser(data_param[case], case, typean,
data_model[mltype], binmin, binmax,
raahp[index], training_vars[index])
if docorrelation is True:
myopt.do_corr()
if dotraining is True:
myopt.do_train()
if dotesting is True:
myopt.do_test()
if doapplytodatamc is True:
myopt.do_apply()
if docrossvalidation is True:
myopt.do_crossval()
if dolearningcurve is True:
myopt.do_learningcurve()
if doroc is True:
myopt.do_roc()
if doroctraintest is True:
myopt.do_roc_train_test()
if doplotdistr is True:
myopt.do_plot_model_pred()
if doimportance is True:
myopt.do_importance()
if doimportanceshap is True:
myopt.do_importance_shap()
if dogridsearch is True:
myopt.do_grid()
if dobayesianopt is True:
myopt.do_bayesian_opt()
if doboundary is True:
myopt.do_boundary()
if doefficiencyml is True:
myopt.do_efficiency()
if dosignifopt is True:
myopt.do_significance()
if doscancuts is True:
myopt.do_scancuts()
index = index + 1
if doapplydata is True:
mymultiprocessdata.multi_apply_allperiods()
if doapplymc is True:
mymultiprocessmc.multi_apply_allperiods()
if domergeapplydata is True:
mymultiprocessdata.multi_mergeapply_allperiods()
if domergeapplymc is True:
mymultiprocessmc.multi_mergeapply_allperiods()
if dohistomassmc is True:
mymultiprocessmc.multi_histomass()
if dohistomassdata is True:
# After-burner in case of a mult analysis to obtain "correctionsweight.root"
# for merged-period data
# pylint: disable=fixme
# FIXME Can only be run here because result directories are constructed when histomass
# is run. If this step was independent, histomass would always complain that the
# result directory already exists.
mymultiprocessdata.multi_histomass()
if doefficiency is True:
mymultiprocessmc.multi_efficiency()
if doresponse is True:
mymultiprocessmc.multi_response()
# Collect all desired analysis steps
analyze_steps = []
if dofit is True:
analyze_steps.append("fit")
if dosyst is True:
analyze_steps.append("yield_syst")
if doeff is True:
analyze_steps.append("efficiency")
if dojetstudies is True:
if dofit is False:
analyze_steps.append("fit")
if doeff is False:
analyze_steps.append("efficiency")
analyze_steps.append("sideband_sub")
if dofeeddown is True:
analyze_steps.append("feeddown")
if dounfolding is True:
analyze_steps.append("unfolding")
analyze_steps.append("unfolding_closure")
if dojetsystematics is True:
analyze_steps.append("jetsystematics")
if docross is True:
analyze_steps.append("makenormyields")
if doplots is True:
analyze_steps.append("plotternormyields")
if doplotsval is True:
analyze_steps.append("plottervalidation")
# Now do the analysis
ana_mgr.analyze(*analyze_steps)
ml_syst_steps = []
if dosystprob is True:
if do_syst_prob_mass:
ml_syst_steps.append("ml_cutvar_mass")
if do_syst_prob_eff:
ml_syst_steps.append("ml_cutvar_eff")
if do_syst_prob_fit:
ml_syst_steps.append("ml_cutvar_fit")
if do_syst_prob_cross:
ml_syst_steps.append("ml_cutvar_cross")
if dosystptshape is True:
ml_syst_steps.append("mcptshape")
syst_mgr.analyze(*ml_syst_steps)
# Delete per-period results.
if clean:
print("Cleaning")
if doanaperperiod:
print("Per-period analysis enabled. Skipping.")
else:
if not delete_dirlist(dirresultsmc + dirresultsdata):
print("Error: Failed to complete cleaning.")
print("Done")
def efficiency_cutscan(
dataframe_,
mylistvariables_,
modelname_,
threshold, # pylint: disable=too-many-statements
output_,
suffix_,
plot_options_=None):
plot_type_name = "eff_cut_scan"
plot_options = {}
if isinstance(plot_options_, dict):
plot_options = plot_options_.get(plot_type_name, {})
selml = "y_test_prob%s>%s" % (modelname_, threshold)
dataframe_ = dataframe_.query(selml)
fig = plt.figure(figsize=(60, 25))
gs = GridSpec(3, int(len(mylistvariables_) / 3 + 1))
axes = [fig.add_subplot(gs[i]) for i in range(len(mylistvariables_))]
# Available cut options
cut_options = ["lt", "st", "abslt", "absst"]
for i, var_tuple in enumerate(mylistvariables_):
var = var_tuple[0]
vardir = var_tuple[1]
axes[i].set_xlabel(var, fontsize=30)
axes[i].set_ylabel("entries (normalised)", fontsize=30)
axes[i].tick_params(labelsize=20)
axes[i].set_yscale('log')
axes[i].set_ylim(0.1, 1.5)
values = dataframe_[var].values
if "abs" in vardir:
cen = var_tuple[2] if len(var_tuple) > 2 else None
if cen is None:
get_logger().error("Absolute cut chosen for %s. " \
"However, no central value provided", var)
continue
values = np.array([abs(v - cen) for v in values])
nbinscan = 100
minv, maxv = values.min(), values.max()
if var in plot_options and "xlim" in plot_options[var]:
minv = plot_options[var]["xlim"][0]
maxv = plot_options[var]["xlim"][1]
else:
minv = values.min()
maxv = values.max()
_, bina = np.histogram(values, range=(minv, maxv), bins=nbinscan)
widthbin = (maxv - minv) / (float)(nbinscan)
width = np.diff(bina)
center = (bina[:-1] + bina[1:]) / 2
den = len(values)
ratios = deque()
if vardir not in cut_options:
get_logger().error("Please choose cut option from %s. " \
"Your current setting for variable %s is %s", str(cut_options), vardir, var)
continue
if "lt" in vardir:
for ibin in range(nbinscan):
values = values[values > minv + widthbin * ibin]
num = len(values)
eff = float(num) / float(den)
ratios.append(eff)
else:
for ibin in range(nbinscan, 0, -1):
values = values[values < minv + widthbin * ibin]
num = len(values)
eff = float(num) / float(den)
ratios.appendleft(eff)
lbl = f'prob > {threshold}'
axes[i].bar(center, ratios, align='center', width=width, label=lbl)
axes[i].legend(fontsize=30)
plotname = join(output_,
f"variables_effscan_prob{threshold}_{suffix_}.png")
plt.savefig(plotname, bbox_inches='tight')
plt.savefig(plotname, bbox_inches='tight')
def write(self):
for i in self.histograms:
if self.verbose:
get_logger().info("Writing histogram %s", i.GetName())
i.Write()
def __init__(self, database: dict, ana_type: str, file_data_name: str,
file_mc_name: str):
"""
Initialize MLFitParsFactory
Args:
database: dictionary of the entire analysis database
ana_type: specifying the analysis within the database to be done
file_data_name: file path where to find data histograms to fit
file_mc_name: file path where to find MC histograms to fit
"""
self.logger = get_logger()
ana_config = database["analysis"][ana_type]
self.prob_cut_fin = database["mlapplication"]["probcutoptimal"]
# File config
self.file_data_name = file_data_name
self.file_mc_name = file_mc_name
# Binning
self.bin1_name = database["var_binning"]
self.bins1_edges_low = ana_config["sel_an_binmin"]
self.bins1_edges_up = ana_config["sel_an_binmax"]
self.n_bins1 = len(self.bins1_edges_low)
self.bin2_name = ana_config["var_binning2"]
self.bin2_gen_name = ana_config["var_binning2_gen"]
self.bins2_edges_low = ana_config["sel_binmin2"]
self.bins2_edges_up = ana_config["sel_binmax2"]
self.n_bins2 = len(self.bins2_edges_low)
self.bin_matching = ana_config["binning_matching"]
bineff = ana_config["usesinglebineff"]
self.bins2_int_bin = bineff if bineff is not None else 0
# Fit method flags
self.init_fits_from = ana_config["init_fits_from"]
self.sig_func_name = ana_config["sgnfunc"]
self.bkg_func_name = ana_config["bkgfunc"]
self.fit_range_low = ana_config["massmin"]
self.fit_range_up = ana_config["massmax"]
self.likelihood = ana_config["dolikelihood"]
self.rebin = ana_config["rebin"]
try:
iter(self.rebin[0])
except TypeError:
self.rebin = [self.rebin for _ in range(self.n_bins2)]
# Initial fit parameters
self.mean = ana_config["masspeak"]
self.fix_mean = ana_config["FixedMean"]
self.use_user_mean = ana_config["SetInitialGaussianMean"]
self.sigma = ana_config["sigmaarray"]
self.fix_sigma = ana_config["SetFixGaussianSigma"]
self.use_user_sigma = ana_config["SetInitialGaussianSigma"]
self.max_rel_sigma_diff = ana_config["MaxPercSigmaDeviation"]
self.n_sigma_sideband = ana_config["exclude_nsigma_sideband"]
self.n_sigma_signal = ana_config["nsigma_signal"]
self.rel_sigma_bound = ana_config["MaxPercSigmaDeviation"]
# Second peak flags
self.include_sec_peak = ana_config.get("includesecpeak",
[False] * self.n_bins1)
try:
iter(self.include_sec_peak[0])
except TypeError:
self.include_sec_peak = [
self.include_sec_peak for _ in range(self.n_bins2)
]
self.sec_mean = ana_config[
"masssecpeak"] if self.include_sec_peak else None
self.fix_sec_mean = ana_config.get("fix_masssecpeak",
[False] * self.n_bins1)
try:
iter(self.fix_sec_mean[0])
except TypeError:
self.fix_sec_mean = [
self.fix_sec_mean for _ in range(self.n_bins2)
]
self.sec_sigma = ana_config[
"widthsecpeak"] if self.include_sec_peak else None
self.fix_sec_sigma = ana_config[
"fix_widthsecpeak"] if self.include_sec_peak else None
# Reflections flag
self.include_reflections = ana_config.get("include_reflection", False)
# Is this a trigger weighted histogram?
self.apply_weights = ana_config["triggersel"]["weighttrig"]
# Systematics
self.syst_pars = ana_config.get("systematics", {})
self.syst_init_sigma_from = None
if self.syst_pars:
self.syst_init_sigma_from = self.syst_pars.get(
"init_sigma_from", "central")
if not isinstance(self.syst_init_sigma_from, list):
self.syst_init_sigma_from = [self.syst_init_sigma_from
] * self.n_bins1
if not isinstance(self.syst_init_sigma_from[0], list):
self.syst_init_sigma_from = [self.syst_init_sigma_from
] * self.n_bins2
def preparesample(self):
logger = get_logger()
print("prepare sample")
self.df_data = pd.read_pickle(self.f_reco_data)
self.df_mc = pd.read_pickle(self.f_reco_mc)
self.df_mcgen = pd.read_pickle(self.f_gen_mc)
self.df_mcgen = self.df_mcgen.query(self.p_presel_gen_eff)
arraydf = [self.df_data, self.df_mc]
self.df_mc = seldf_singlevar(self.df_mc, self.v_bin, self.p_binmin,
self.p_binmax)
self.df_mcgen = seldf_singlevar(self.df_mcgen, self.v_bin,
self.p_binmin, self.p_binmax)
self.df_data = seldf_singlevar(self.df_data, self.v_bin, self.p_binmin,
self.p_binmax)
self.df_sig, self.df_bkg = arraydf[self.p_tagsig], arraydf[
self.p_tagbkg]
self.df_sig = seldf_singlevar(self.df_sig, self.v_bin, self.p_binmin,
self.p_binmax)
self.df_bkg = seldf_singlevar(self.df_bkg, self.v_bin, self.p_binmin,
self.p_binmax)
self.df_sig = self.df_sig.query(self.s_selsigml)
self.df_bkg = self.df_bkg.query(self.s_selbkgml)
self.df_bkg["ismcsignal"] = 0
self.df_bkg["ismcprompt"] = 0
self.df_bkg["ismcfd"] = 0
self.df_bkg["ismcbkg"] = 0
if self.p_nsig > len(self.df_sig):
logger.warning("There are not enough signal events")
if self.p_nbkg > len(self.df_bkg):
logger.warning("There are not enough background events")
self.p_nsig = min(len(self.df_sig), self.p_nsig)
self.p_nbkg = min(len(self.df_bkg), self.p_nbkg)
logger.info("Used number of signal events is %d", self.p_nsig)
logger.info("Used number of background events is %d", self.p_nbkg)
self.df_ml = pd.DataFrame()
self.df_sig = shuffle(self.df_sig, random_state=self.rnd_shuffle)
self.df_bkg = shuffle(self.df_bkg, random_state=self.rnd_shuffle)
self.df_sig = self.df_sig[:self.p_nsig]
self.df_bkg = self.df_bkg[:self.p_nbkg]
self.df_sig[self.v_sig] = 1
self.df_bkg[self.v_sig] = 0
self.df_ml = pd.concat([self.df_sig, self.df_bkg])
self.df_mltrain, self.df_mltest = train_test_split(self.df_ml, \
test_size=self.test_frac, random_state=self.rnd_splt)
self.df_mltrain = self.df_mltrain.reset_index(drop=True)
self.df_mltest = self.df_mltest.reset_index(drop=True)
self.df_sigtrain, self.df_bkgtrain = split_df_sigbkg(
self.df_mltrain, self.v_sig)
self.df_sigtest, self.df_bkgtest = split_df_sigbkg(
self.df_mltest, self.v_sig)
logger.info("Nev ml train %d and test %d", len(self.df_mltrain),
len(self.df_mltest))
logger.info("Nev signal train %d and test %d", len(self.df_sigtrain),
len(self.df_sigtest))
logger.info("Nev bkg train %d and test %d", len(self.df_bkgtrain),
len(self.df_bkgtest))
self.df_xtrain = self.df_mltrain[self.v_train]
self.df_ytrain = self.df_mltrain[self.v_sig]
self.df_xtest = self.df_mltest[self.v_train]
self.df_ytest = self.df_mltest[self.v_sig]
class Optimiser:
#Class Attribute
species = "optimiser"
def __init__(self, data_param, case, model_config, grid_config, binmin,
binmax, raahp):
self.logger = get_logger()
dirmcml = data_param["multi"]["mc"]["pkl_skimmed_merge_for_ml_all"]
dirdataml = data_param["multi"]["data"]["pkl_skimmed_merge_for_ml_all"]
dirdatatotsample = data_param["multi"]["data"]["pkl_evtcounter_all"]
self.v_bin = data_param["var_binning"]
#directory
self.dirmlout = data_param["ml"]["mlout"]
self.dirmlplot = data_param["ml"]["mlplot"]
#ml file names
self.n_reco = data_param["files_names"]["namefile_reco"]
self.n_reco = self.n_reco.replace(".pkl", "_%s%d_%d.pkl" % (self.v_bin, binmin, binmax))
self.n_evt = data_param["files_names"]["namefile_evt"]
self.n_gen = data_param["files_names"]["namefile_gen"]
self.n_gen = self.n_gen.replace(".pkl", "_%s%d_%d.pkl" % (self.v_bin, binmin, binmax))
self.n_treetest = data_param["files_names"]["treeoutput"]
self.n_reco_applieddata = data_param["files_names"]["namefile_reco_applieddata"]
self.n_reco_appliedmc = data_param["files_names"]["namefile_reco_appliedmc"]
# ml files
self.f_gen_mc = os.path.join(dirmcml, self.n_gen)
self.f_reco_mc = os.path.join(dirmcml, self.n_reco)
self.f_evt_mc = os.path.join(dirmcml, self.n_evt)
self.f_reco_data = os.path.join(dirdataml, self.n_reco)
self.f_evt_data = os.path.join(dirdataml, self.n_evt)
self.f_evttotsample_data = os.path.join(dirdatatotsample, self.n_evt)
self.f_reco_applieddata = os.path.join(self.dirmlout, self.n_reco_applieddata)
self.f_reco_appliedmc = os.path.join(self.dirmlout, self.n_reco_appliedmc)
#variables
self.v_all = data_param["variables"]["var_all"]
self.v_train = data_param["variables"]["var_training"]
self.v_bound = data_param["variables"]["var_boundaries"]
self.v_sig = data_param["variables"]["var_signal"]
self.v_invmass = data_param["variables"]["var_inv_mass"]
self.v_cuts = data_param["variables"].get("var_cuts", [])
self.v_corrx = data_param["variables"]["var_correlation"][0]
self.v_corry = data_param["variables"]["var_correlation"][1]
self.v_isstd = data_param["bitmap_sel"]["var_isstd"]
self.v_ismcsignal = data_param["bitmap_sel"]["var_ismcsignal"]
self.v_ismcprompt = data_param["bitmap_sel"]["var_ismcprompt"]
self.v_ismcfd = data_param["bitmap_sel"]["var_ismcfd"]
self.v_ismcbkg = data_param["bitmap_sel"]["var_ismcbkg"]
#parameters
self.p_case = case
self.p_nbkg = data_param["ml"]["nbkg"]
self.p_nsig = data_param["ml"]["nsig"]
self.p_tagsig = data_param["ml"]["sampletagforsignal"]
self.p_tagbkg = data_param["ml"]["sampletagforbkg"]
self.p_binmin = binmin
self.p_binmax = binmax
self.p_npca = None
self.p_mltype = data_param["ml"]["mltype"]
self.p_nkfolds = data_param["ml"]["nkfolds"]
self.p_ncorescross = data_param["ml"]["ncorescrossval"]
self.rnd_shuffle = data_param["ml"]["rnd_shuffle"]
self.rnd_splt = data_param["ml"]["rnd_splt"]
self.test_frac = data_param["ml"]["test_frac"]
self.p_plot_options = data_param["variables"].get("plot_options", {})
self.p_dofullevtmerge = data_param["dofullevtmerge"]
#dataframes
self.df_mc = None
self.df_mcgen = None
self.df_data = None
self.df_sig = None
self.df_bkg = None
self.df_ml = None
self.df_mltest = None
self.df_mltrain = None
self.df_sigtrain = None
self.df_sigtest = None
self.df_bkgtrain = None
self.df_bktest = None
self.df_xtrain = None
self.df_ytrain = None
self.df_xtest = None
self.df_ytest = None
#selections
self.s_selbkgml = data_param["ml"]["sel_bkgml"]
self.s_selsigml = data_param["ml"]["sel_sigml"]
#model param
self.db_model = model_config
self.p_class = None
self.p_classname = None
self.p_trainedmod = None
self.s_suffix = None
#config files
self.c_gridconfig = grid_config
#significance
self.f_fonll = data_param["ml"]["opt"]["filename_fonll"]
self.p_fonllband = data_param["ml"]["opt"]["fonll_pred"]
self.p_fragf = data_param["ml"]["opt"]["FF"]
self.p_sigmamb = data_param["ml"]["opt"]["sigma_MB"]
self.p_taa = data_param["ml"]["opt"]["Taa"]
self.p_br = data_param["ml"]["opt"]["BR"]
self.p_fprompt = data_param["ml"]["opt"]["f_prompt"]
self.p_bkgfracopt = data_param["ml"]["opt"]["bkg_data_fraction"]
self.p_nstepsign = data_param["ml"]["opt"]["num_steps"]
self.p_savefit = data_param["ml"]["opt"]["save_fit"]
self.p_nevtml = None
self.p_nevttot = None
self.p_presel_gen_eff = data_param["analysis"]["presel_gen_eff"]
self.p_mass_fit_lim = data_param["analysis"]['mass_fit_lim']
self.p_bin_width = data_param["analysis"]['bin_width']
self.p_num_bins = int(round((self.p_mass_fit_lim[1] - self.p_mass_fit_lim[0]) / \
self.p_bin_width))
self.p_mass = data_param["mass"]
self.p_raahp = raahp
self.preparesample()
self.loadmodels()
self.create_suffix()
self.df_evt_data = None
self.df_evttotsample_data = None
self.f_reco_applieddata = \
self.f_reco_applieddata.replace(".pkl", "%s.pkl" % self.s_suffix)
self.f_reco_appliedmc = \
self.f_reco_appliedmc.replace(".pkl", "%s.pkl" % self.s_suffix)
def calc_bkg(df_bkg, name, num_steps, fit_region, bkg_func, bin_width,
sig_region, save_fit, out_dir, pt_lims, multiclass_labels):
"""
Estimate the number of background candidates under the signal peak. This is obtained
from real data with a fit of the sidebands of the invariant mass distribution.
"""
logger = get_logger()
if multiclass_labels is None:
ns_left = int(num_steps / 10) - 1
ns_right = num_steps - ns_left
x_axis_left = np.linspace(0., 0.49, ns_left)
x_axis_right = np.linspace(0.5, 1.0, ns_right)
x_axis = np.concatenate((x_axis_left, x_axis_right))
else:
x_axis = np.linspace(0, 0.4, num_steps)
bkg_array = []
bkg_err_array = []
num_bins = (fit_region[1] - fit_region[0]) / bin_width
num_bins = int(round(num_bins))
bin_width = (fit_region[1] - fit_region[0]) / num_bins
if save_fit:
logger.debug("Saving bkg fits to file")
pt_min = pt_lims[0]
pt_max = pt_lims[1]
out_file = TFile(
f'{out_dir}/bkg_fits_{name}_pt{pt_min:.1f}_{pt_max:.1f}.root',
'recreate')
out_file.cd()
logger.debug("To fit the bkg a %s function is used", bkg_func)
if multiclass_labels is not None:
for thr0 in x_axis:
for thr1 in x_axis:
bkg = 0.
bkg_err = 0.
hmass = TH1F(f'hmass_{thr0:.5f}_{thr1:.5f}', '', num_bins,
fit_region[0], fit_region[1])
mlsel_multi0 = 'y_test_prob' + name + multiclass_labels[
0] + ' <= ' + str(thr0)
mlsel_multi1 = 'y_test_prob' + name + multiclass_labels[
1] + ' >= ' + str(thr1)
mlsel_multi = mlsel_multi0 + ' and ' + mlsel_multi1
sel_mass_array = df_bkg.query(mlsel_multi)['inv_mass'].values
if len(sel_mass_array) > 5:
for mass_value in np.nditer(sel_mass_array):
hmass.Fill(mass_value)
fit = hmass.Fit(bkg_func, 'Q', '', fit_region[0],
fit_region[1])
if save_fit:
hmass.Write()
if int(fit) == 0:
fit_func = hmass.GetFunction(bkg_func)
bkg = fit_func.Integral(sig_region[0],
sig_region[1]) / bin_width
bkg_err = fit_func.IntegralError(
sig_region[0], sig_region[1]) / bin_width
del fit_func
elif save_fit:
hmass.Write()
bkg_array.append(bkg)
bkg_err_array.append(bkg_err)
del hmass
else:
for thr in x_axis:
bkg = 0.
bkg_err = 0.
hmass = TH1F(f'hmass_{thr:.5f}', '', num_bins, fit_region[0],
fit_region[1])
bkg_sel_mask = df_bkg['y_test_prob' + name].values >= thr
sel_mass_array = df_bkg[bkg_sel_mask]['inv_mass'].values
if len(sel_mass_array) > 5:
for mass_value in np.nditer(sel_mass_array):
hmass.Fill(mass_value)
fit = hmass.Fit(bkg_func, 'Q', '', fit_region[0],
fit_region[1])
if save_fit:
hmass.Write()
if int(fit) == 0:
fit_func = hmass.GetFunction(bkg_func)
bkg = fit_func.Integral(sig_region[0],
sig_region[1]) / bin_width
bkg_err = fit_func.IntegralError(sig_region[0],
sig_region[1]) / bin_width
del fit_func
elif save_fit:
hmass.Write()
bkg_array.append(bkg)
bkg_err_array.append(bkg_err)
del hmass
if save_fit:
out_file.Close()
return bkg_array, bkg_err_array, x_axis
def filter_df_cand(dataframe, main_dict, sel_opt):
'''Filter a dataframe looking at the type of candidate.
It works both for bitmap and old selection method.
In 'database_ml_parameters.yml' only one between old_sel and bitmap_sel must have 'use: True'
Implemented selection options:
- 'mc_signal' -> select MC signal
- 'mc_signal_prompt' -> select only prompt MC signal
- 'mc_signal_FD' -> select only feed-down MC signal
- 'mc_bkg' -> select MC background
- 'presel_track_pid' -> select candidates satisfing PID and track pre-selections
- 'sel_std_analysis' -> select candidates fulfilling the std analysis selections
Args:
dataframe: pandas dataframe to filter
main_dict: dictionary of parameters loaded from 'database_ml_parameters.yml'
sel_opt: selection option (string)
Return:
df_selected: filtered pandas dataframe
'''
logger = get_logger()
bitmap_dict = main_dict['bitmap_sel']
old_dict = main_dict['old_sel']
use_bitmap = bitmap_dict['use']
use_old = old_dict['use']
if use_bitmap == use_old:
logger.critical(
"One and only one of the selection method have to be used, i.e. with "
"'use' flag set to True")
if use_bitmap:
logger.debug("Using bitmap selection")
if sel_opt == 'mc_signal':
sel_bits = bitmap_dict['mcsignal_on_off']
elif sel_opt == 'mc_signal_prompt':
sel_bits = bitmap_dict['mcsignal_prompt_on_off']
elif sel_opt == 'mc_signal_FD':
sel_bits = bitmap_dict['mcsignal_feed_on_off']
elif sel_opt == 'mc_bkg':
sel_bits = bitmap_dict['mcbkg_on_off']
elif sel_opt == 'presel_track_pid':
sel_bits = bitmap_dict['preseltrack_pid_on_off']
elif sel_opt == 'sel_std_analysis':
sel_bits = bitmap_dict['std_analysis_on_off']
else:
logger.critical("Wrong selection option!")
logger.debug("Candidates before selection: %d", len(dataframe))
df_selected = filter_bit_df(dataframe, bitmap_dict['var_sel'],
sel_bits)
logger.debug("Candidates after %s selection: %d", sel_opt,
len(df_selected))
if use_old:
logger.debug("Using old selection")
if sel_opt == 'mc_signal':
sel_string = old_dict['mc_signal']
elif sel_opt == 'mc_signal_prompt':
sel_string = old_dict['mc_signal_prompt']
elif sel_opt == 'mc_signal_FD':
sel_string = old_dict['mc_signal_FD']
elif sel_opt == 'mc_bkg':
sel_string = old_dict['mc_bkg']
elif sel_opt == 'presel_track_pid':
sel_string = old_dict['presel_track_pid']
elif sel_opt == 'sel_std_analysis':
sel_string = old_dict['sel_std_analysis']
else:
logger.critical("Wrong selection option!")
logger.debug("Candidates before selection: %d", len(dataframe))
df_selected = dataframe.query(sel_string)
logger.debug("Candidates after %s selection: %d", sel_opt,
len(df_selected))
return df_selected
# HF specific imports
from machine_learning_hep.logger import get_logger
# pylint: disable=import-error, no-name-in-module
from ROOT import gStyle
# pylint: disable=too-few-public-methods
class WorkflowBase:
"""
Base class for all workflows related classes including systematics
"""
species = "workflow_base"
def __init__(self, datap, case, typean, period=None):
self.logger = get_logger()
self.datap = datap
self.case = case
self.typean = typean
self.period = period
@staticmethod
def loadstyle():
gStyle.SetOptStat(0)
gStyle.SetOptStat(0000)
gStyle.SetPalette(1)
gStyle.SetNumberContours(100)
gStyle.SetCanvasColor(0)
gStyle.SetFrameFillColor(0)
@staticmethod
