def main(args):
# Initialise
args, cfg = initialise(args)
# Initialise Keras backend
initialise_backend(args)
# Neural network-specific initialisation of the configuration dict
initialise_config(args, cfg)
# Keras import(s)
import keras.backend as K
from keras.models import load_model
# Project import(s)
from adversarial.models import classifier_model, adversary_model, combined_model, decorrelation_model
# Load data
data, features, _ = load_data(args.input + 'data.h5', test=True)
def meaningful_digits(number):
digits = 0
if number > 0:
digits = int(np.ceil(max(-np.log10(number), 0)))
pass
return '{l:.{d:d}f}'.format(d=digits, l=number)
# -- Adversarial neural network (ANN) scan
lambda_reg = 100.
lambda_regs = sorted([100.])
ann_vars = list()
lambda_strs = list()
for lambda_reg_ in lambda_regs:
lambda_str = meaningful_digits(lambda_reg_).replace('.', 'p')
lambda_strs.append(lambda_str)
ann_var_ = "ANN(#lambda={:s})".format(lambda_str.replace('p', '.'))
ann_vars.append(ann_var_)
pass
ann_var = ann_vars[lambda_regs.index(lambda_reg)]
print "ann_var"
print ann_var
# Tagger feature collection
# tagger_features = ['NN', ann_var]
tagger_features = ['NN', ann_var, 'MV2c10', 'XbbScoreHiggs']
# tagger_features = ['MV2c10']
# Add variables
# --------------------------------------------------------------------------
with Profile("Add variables"):
# NN
from run.adversarial.common import add_nn
with Profile("NN"):
classifier = load_model(
'models/adversarial/classifier/full/classifier.h5')
add_nn(data, classifier, 'NN')
pass
# ANN
with Profile("ANN"):
from adversarial.utils import DECORRELATION_VARIABLES
adversary = adversary_model(
gmm_dimensions=len(DECORRELATION_VARIABLES),
**cfg['adversary']['model'])
combined = combined_model(classifier, adversary,
**cfg['combined']['model'])
for ann_var_, lambda_str_ in zip(ann_vars, lambda_strs):
print "== Loading model for {}".format(ann_var_)
combined.load_weights(
'models/adversarial/combined/full/combined_lambda{}.h5'.
format(lambda_str_))
add_nn(data, classifier, ann_var_)
pass
pass
with Profile("MV2c10"):
data["MV2c10"] = pd.concat(
[data["MV2c10_discriminant_1"], data["MV2c10_discriminant_2"]],
axis=1).min(axis=1)
# Add MV2 and XbbScore here
# e.g. min(MV2_sj1, MV2_sj2)
# Remove unused variables
used_variables = set(tagger_features + ann_vars +
['mass', 'pt', 'npv', 'weight_test'])
unused_variables = [var for var in list(data) if var not in used_variables]
data.drop(columns=unused_variables)
gc.collect()
# Perform performance studies
perform_studies(data, args, tagger_features, ann_vars)
return 0
def main(args):
# Initialise
args, cfg = initialise(args)
# Initialise Keras backend
initialise_backend(args)
# Neural network-specific initialisation of the configuration dict
initialise_config(args, cfg)
# Keras import(s)
import keras.backend as K
from keras.models import load_model
# Project import(s)
from adversarial.models import classifier_model, adversary_model, combined_model, decorrelation_model
# Load data
data, features, _ = load_data(args.input + 'data.h5', test=True)
# Common definitions
# --------------------------------------------------------------------------
# -- k-nearest neighbour
kNN_var = 'D2-k#minusNN'
def meaningful_digits(number):
digits = 0
if number > 0:
digits = int(np.ceil(max(-np.log10(number), 0)))
pass
return '{l:.{d:d}f}'.format(d=digits, l=number)
# -- Adversarial neural network (ANN) scan
lambda_reg = 10.
lambda_regs = sorted([1., 3., 10.])
ann_vars = list()
lambda_strs = list()
for lambda_reg_ in lambda_regs:
lambda_str = meaningful_digits(lambda_reg_).replace('.', 'p')
lambda_strs.append(lambda_str)
ann_var_ = "ANN(#lambda={:s})".format(lambda_str.replace('p', '.'))
ann_vars.append(ann_var_)
pass
ann_var = ann_vars[lambda_regs.index(lambda_reg)]
# -- uBoost scan
uboost_eff = 92
uboost_ur = 0.3
uboost_urs = sorted([0., 0.01, 0.1, 0.3, 1.0])
uboost_var = 'uBoost(#alpha={:s})'.format(meaningful_digits(uboost_ur))
uboost_vars = [
'uBoost(#alpha={:s})'.format(meaningful_digits(ur))
for ur in uboost_urs
]
uboost_pattern = 'uboost_ur_{{:4.2f}}_te_{:.0f}_rel21_fixed'.format(
uboost_eff)
# Tagger feature collection
tagger_features = [
'Tau21', 'Tau21DDT', 'D2', kNN_var, 'D2', 'D2CSS', 'NN', ann_var,
'Adaboost', uboost_var
]
# Add variables
# --------------------------------------------------------------------------
with Profile("Add variables"):
# Tau21DDT
from run.ddt.common import add_ddt
add_ddt(data, path='models/ddt/ddt.pkl.gz')
# D2-kNN
from run.knn.common import add_knn, VAR as kNN_basevar, EFF as kNN_eff
print "k-NN base variable: {} (cp. {})".format(kNN_basevar, kNN_var)
add_knn(data,
newfeat=kNN_var,
path='models/knn/knn_{}_{}.pkl.gz'.format(
kNN_basevar, kNN_eff))
# D2-CSS
from run.css.common import add_css
add_css("D2", data)
# NN
from run.adversarial.common import add_nn
with Profile("NN"):
classifier = load_model(
'models/adversarial/classifier/full/classifier.h5')
add_nn(data, classifier, 'NN')
pass
# ANN
with Profile("ANN"):
from adversarial.utils import DECORRELATION_VARIABLES
adversary = adversary_model(
gmm_dimensions=len(DECORRELATION_VARIABLES),
**cfg['adversary']['model'])
combined = combined_model(classifier, adversary,
**cfg['combined']['model'])
for ann_var_, lambda_str_ in zip(ann_vars, lambda_strs):
print "== Loading model for {}".format(ann_var_)
combined.load_weights(
'models/adversarial/combined/full/combined_lambda{}.h5'.
format(lambda_str_))
add_nn(data, classifier, ann_var_)
pass
pass
# Adaboost/uBoost
with Profile("Adaboost/uBoost"):
from run.uboost.common import add_bdt
for var, ur in zip(uboost_vars, uboost_urs):
var = ('Adaboost' if ur == 0 else var)
path = 'models/uboost/' + uboost_pattern.format(ur).replace(
'.', 'p') + '.pkl.gz'
print "== Loading model for {}".format(var)
add_bdt(data, var, path)
pass
# Remove `Adaboost` from scan list
uboost_vars.pop(0)
pass
pass
# Remove unused variables
used_variables = set(tagger_features + ann_vars + uboost_vars +
['m', 'pt', 'npv', 'weight_test'])
unused_variables = [var for var in list(data) if var not in used_variables]
data.drop(columns=unused_variables)
gc.collect()
# Perform performance studies
perform_studies(data, args, tagger_features, ann_vars, uboost_vars)
return 0
def main(args):
# Initialise
args, cfg = initialise(args)
# Initialise Keras backend
initialise_backend(args)
# Neural network-specific initialisation of the configuration dict
initialise_config(args, cfg)
# Keras import(s)
import keras.backend as K
from keras.models import load_model
# Project import(s)
from adversarial.models import classifier_model, adversary_model, combined_model, decorrelation_model
# Load data
data, features, _ = load_data('data/' + args.input, test=True)
# Common definitions
# --------------------------------------------------------------------------
# -- k-nearest neighbour
#kNN_var = 'D2-k#minusNN'
#kNN_var = 'C1_02-knn'
#base_var = 'sub_jet_ntrk'
#kNN_var = base_var.replace('sub_jet_', '') + '-knn'
#base_var = ['lead_jet_ungrtrk500', 'sub_jet_ungrtrk500']
#kNN_var = [var.replace('jet', 'knn') for var in base_var]
base_var = 'jet_ungrtrk500'
kNN_var = base_var.replace('jet', 'knn')
#base_var = ['jet_ungrtrk500']
#kNN_var = [var.replace('jet', 'knn') for var in base_var]
#base_var = ['ntrk_sum']
#kNN_var = [var + '-knn' for var in base_var]
def meaningful_digits(number):
digits = 0
if number > 0:
digits = int(np.ceil(max(-np.log10(number), 0)))
pass
return '{l:.{d:d}f}'.format(d=digits, l=number)
"""
# -- Adversarial neural network (ANN) scan
lambda_reg = 10.
lambda_regs = sorted([1., 3., 10.])
å ham har jeg talt med løbende. For mange dage siden har vi talt om, om man kunne bruge grundlovsdag, og hvordan det ville hænge sammen med de frister, der er. In ann_vars = list()
lambda_strs = list()
for lambda_reg_ in lambda_regs:
lambda_str = meaningful_digits(lambda_reg_).replace('.', 'p')
lambda_strs.append(lambda_str)
ann_var_ = "ANN(#lambda={:s})".format(lambda_str.replace('p', '.'))
ann_vars.append(ann_var_)
pass
ann_var = ann_vars[lambda_regs.index(lambda_reg)]
# -- uBoost scan
uboost_eff = 92
uboost_ur = 0.3
uboost_urs = sorted([0., 0.01, 0.1, 0.3, 1.0])
uboost_var = 'uBoost(#alpha={:s})'.format(meaningful_digits(uboost_ur))
uboost_vars = ['uBoost(#alpha={:s})'.format(meaningful_digits(ur)) for ur in uboost_urs]
uboost_pattern = 'uboost_ur_{{:4.2f}}_te_{:.0f}_rel21_fixed'.format(uboost_eff)
"""
# Tagger feature collection
#tagger_features = ['Tau21','Tau21DDT', 'D2', kNN_var, 'D2', 'D2CSS', 'NN', ann_var, 'Adaboost', uboost_var]
#tagger_features = ['lead_jet_C1_02', kNN_var]
tagger_features = [
'lead_' + base_var, 'lead_' + kNN_var, 'sub_' + base_var,
'sub_' + kNN_var
]
#tagger_features = base_var + kNN_var
# Add variables
# --------------------------------------------------------------------------
with Profile("Add variables"):
#for i in range(len(base_var)):
from run.knn.common import add_knn, MODEL as sigModel, VAR as kNN_basevar, EFF as kNN_eff
print "k-NN base variable: {} (cp. {})".format(base_var, kNN_var)
add_knn(data,
newfeat='lead_' + kNN_var,
path='models/knn/knn_{}_{}_{}.pkl.gz'.format(
base_var, kNN_eff, sigModel))
add_knn(data,
newfeat='sub_' + kNN_var,
path='models/knn/knn_{}_{}_{}.pkl.gz'.format(
base_var, kNN_eff, sigModel))
# Remove unused variables
used_variables = set(tagger_features +
['lead_jet_m', 'lead_jet_pt', 'dijetmass', 'weight'])
unused_variables = [var for var in list(data) if var not in used_variables]
data.drop(columns=unused_variables)
gc.collect()
# Perform performance studies
perform_studies(data, args, tagger_features)
return 0