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NN_current_2.py
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#Loading the packages for running the networks
import os
import keras
import math
import sys
import matplotlib
import glob
import keras.backend as K
###time measurement
from timeit import default_timer as timer
###
from keras.models import Sequential, Model
from keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, EarlyStopping
#from keras.utils import plot_model
from keras.layers import Dense, Input, BatchNormalization, Dropout, Activation
from keras import metrics
from keras.optimizers import SGD, Adam
from keras.losses import binary_crossentropy
import sklearn as skl
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_curve, auc
from sklearn.preprocessing import StandardScaler
from sys import argv
from scipy import stats
#Loading the packages for handling the data
import uproot as ur
import pandas
import numpy as np
#Loading packages needed for plottting
import matplotlib.pyplot as plt
from matplotlib import rc
from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,AutoMinorLocator, MaxNLocator)
#Defining colours for the plots
#The colours were chosen using the xkcd guice
#color_tW = '#66FFFF'
color_tW = '#0066ff'
#color_tt = '#FF3333'
color_tt = '#990000'
color_sys = '#009900'
color_tW2 = '#02590f'
color_tt2 = '#FF6600'
# Color Codes for events
color_ttbar = '#FF3333'
color_zjets = '#FFCC66'
color_diboson = '#FFFF00'
colorST = '#999933'
my_path_to_data = '/cephfs/user/s6pinogg/PietBachelor/root_fixed_tZq/'
### If you want to sort the root files, make sure to run
### /cephfs/user/s6pinogg/PietBachelor/sort_tHq_loop
### in directory with your unsorted root files, it will put them in correct directories
data_signal = my_path_to_data + 'tZq/'
data_background_diboson = my_path_to_data + 'diboson/'
data_background_ttZ = my_path_to_data + 'ttV/ttZ/'
data_background_ttW = my_path_to_data + 'ttV/ttW/'
data_background_ttH = my_path_to_data + 'ttV/ttH/'
data_background_ttbar = my_path_to_data + 'ttbar/'
data_background_tt2l = my_path_to_data + 'tt_2l/'
data_background_tWZ = my_path_to_data + 'singleTop/tWZ/'
data_background_4top = my_path_to_data + 'singleTop/4_top/'
data_background_tchannel = my_path_to_data + 'singleTop/tchannel/'
data_background_tW = my_path_to_data + 'singleTop/tW/'
data_background_nBoson = my_path_to_data + 'nBoson/'
data_background_ZJets = my_path_to_data + 'ZJets/'
#------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#Setting up the output directories
output_path = '/cephfs/user/s6pinogg/PietBachelor/New_Samples/jobs/'
#output_path = './jobs/'
array_path = output_path + 'arrays/'
if not os.path.exists(output_path):
os.makedirs(output_path,exist_ok=True)
if not os.path.exists(array_path):
os.makedirs(array_path,exist_ok=True)
mysavedata = '/cephfs/user/s6pinogg/PietBachelor/Histo_Data/'
eventHistpath = '/cephfs/user/s6pinogg/PietBachelor/Histo_Event/'
if not os.path.exists(mysavedata):
os.makedirs(mysavedata,exist_ok=True)
if not os.path.exists(eventHistpath):
os.makedirs(eventHistpath,exist_ok=True)
#------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#This is the main class for the adversarial neural network setup
class neuralNetworkEnvironment(object):
def __init__(self):
#At the moment not may variables are passed to the class. You might want to change this
#A list of more general settings
self.variables = np.array(['m_b_jf','eta_jf','q_lW','eta_lW','pT_W','pT_lW','m_Z','eta_Z','dR_jf_Z','pT_jf','pT_jr','eta_jr','pT_Z','m_met','m_top','mT_W'])
# All possible Variables
#self.variables = np.array(['pt_lep1','eta_lep1','phi_lep1','E_lep1','charge_lep1','type_lep1',
# 'pt_lep2','eta_lep2','phi_lep2','E_lep2','charge_lep2','type_lep2',
# 'pt_lep3','eta_lep3','phi_lep3','E_lep3','charge_lep3','type_lep3',
# 'm_njets','m_nbjets','pt_bjet1','eta_bjet1','phi_bjet1','E_bjet1','mass_bjet1','m_nNoBjets,
# 'pt_jet1','eta_jet1','phi_jet1','E_jet1','mass_jet1','btagged_jet1',
# 'pt_jet2','eta_jet2','phi_jet2','E_jet2','mass_jet2','btagged_jet2',
# 'pt_jet3','eta_jet3','phi_jet3','E_jet3','mass_jet3','btagged_jet3',
# 'm_met','m_phi_met','m_sumet','m_min_diff_mass','m_b_jf','m_top',
# 'eta_jf','mT_W','q_lW','eta_lW','pT_W','pT_lW',
# 'm_Z','eta_Z','dR_jf_Z','pT_jf','eta_jr','pT_Z','pT_jr','pT_top','eta_top'])
#The seed is used to make sure that both the events and the labels are shuffeled the same way because they are not inherently connected.
self.seed = 250
#All information necessary for the input
#The exact data and targets are set later
self.output_job = None
self.sample_training = None
self.sample_validation = None
self.target_training = None
self.target_validation = None
#Dimension of the variable input used to define the size of the first layer
self.input_dimension = self.variables.shape
#These arrays are used to save loss and accuracy of the two networks
#That is also important to later be able to use the plotting software desired. matplotlib is not the best tool at all times
self.discriminator_history_array = []
self.discriminator_history_ttbar_array = []
self.model_history_array = []
self.discriminator_history = None
self.discriminator_history_ttbar = None
self.model = None
self.model_ttbar = None
self.network_input = None ###Change###
#Here are the definitions for the two models
#All information for the length of the training. Beware that epochs might only come into the pretraining
#Iterations are used for the adversarial part of the training
#If you want to make the training longer you want to change these numbers, there is no early stopping atm, feel free to add it
self.discriminator_epochs = 2015
self.batchSize = 628
#Setup of the networks, nodes and layers
self.discriminator_layers = 2
self.discriminator_nodes = 50
self.discriminator_nodes_ttbar = 128
#Setup of the networks, loss and optimisation
self.my_optimizer = 'Adam'
self.discriminator_lr = 0.1
self.discriminator_lr_adam = 1e-4
self.discriminator_lr_ttbar = 0.1
self.discriminator_momentum = 0.9
self.discriminator_optimizer = SGD(lr = self.discriminator_lr, momentum = self.discriminator_momentum)
self.discriminator_optimizer_ttbar = SGD(lr = self.discriminator_lr_ttbar, momentum = self.discriminator_momentum)
self.discriminator_optimizer_adam = Adam(lr = self.discriminator_lr_adam)
self.discriminator_dropout = 0.2
self.discriminator_loss = 'binary_crossentropy'
self.validation_fraction = 0.5
self.reduce_lr = ReduceLROnPlateau(monitor='val_binary_accuracy',factor = 0.8,patience=40,min_delta=1e-3,min_lr=1e-6,verbose=1)
self.early_stop = EarlyStopping(monitor='loss',min_delta=1e-6,patience=100,restore_best_weights=True,mode='min',verbose = 1)
if (self.my_optimizer== 'Adam'):
self.output_job = output_path + 'epochs_%i/Adam/layers_%i/nodes_%i/lr_%.2e/dropout_%.2f/valfrac_%.2f/' % (self.discriminator_epochs,self.discriminator_layers,self.discriminator_nodes,self.discriminator_lr_adam,self.discriminator_dropout,self.validation_fraction)
elif(self.my_optimizer=="SGD"):
self.output_job = output_path + 'epochs_%i/SGD/layers_%i/nodes_%i/lr_%.2e/momentum_%.2f/' % (self.discriminator_epochs,self.discriminator_layers,self.discriminator_nodes,self.discriminator_lr,self.discriminator_momentum)
self.output_lr = output_path + 'epochs_%i/' % (self.discriminator_epochs)
self.output_lrcurve = self.output_lr + 'Optimize/%.1e/'%(self.discriminator_momentum)
self.output_curve = self.output_lr + 'txtlr/'
if not os.path.exists(self.output_job):
os.makedirs(self.output_job,exist_ok=True)
if not os.path.exists(self.output_curve):
os.makedirs(self.output_curve,exist_ok=True)
if not os.path.exists(self.output_lrcurve):
os.makedirs(self.output_lrcurve,exist_ok=True)
#The following set of variables is used to evaluate the result
#fpr = false positive rate, tpr = true positive rate
self.tpr = 0. #true positive rate
self.fpr = 0. #false positive rate
self.threshold = 0.
self.auc = 0. #Area under the curve
## Tree Sample Names MC
self.signal_sample = "tHqLoop_nominal;1"
self.background_sample = "tHqLoop_nominal;1"
def read_root(self,pathtoMC,TreeName,BranchName):
first_iteration = True
SaveArray = np.array([])
for files in ur.iterate(pathtoMC + '*.root',TreeName,BranchName,outputtype = pandas.DataFrame):
files = files.to_numpy()
if (first_iteration==True):
SaveArray = files
else:
SaveArray = np.concatenate([SaveArray,files])
first_iteration = False
return SaveArray
def ConArrays(self,arrays):
conarray = np.concatenate(arrays)
return conarray
def initialize_sample(self):
### Signal
self.events_signal = self.read_root(data_signal,self.signal_sample,self.variables)
self.weight_signal = self.read_root(data_signal,self.signal_sample,'weight_nominal') * 139
self.weight_signal = np.absolute(self.weight_signal)
###Background
##Diboson
self.events_background_diboson = self.read_root(data_background_diboson,self.background_sample,self.variables)
self.weights_background_diboson = self.read_root(data_background_diboson,self.background_sample,'weight_nominal') * 139
##ttV
self.events_background_ttZ = self.read_root(data_background_ttZ,self.background_sample,self.variables)
self.weights_background_ttZ = self.read_root(data_background_ttZ,self.background_sample,'weight_nominal') * 139
self.events_background_ttW = self.read_root(data_background_ttW,self.background_sample,self.variables)
self.weights_background_ttW = self.read_root(data_background_ttW,self.background_sample,'weight_nominal') * 139
self.events_background_ttH = self.read_root(data_background_ttH,self.background_sample,self.variables)
self.weights_background_ttH = self.read_root(data_background_ttH,self.background_sample,'weight_nominal') * 139
self.events_background_ttV = self.ConArrays([self.events_background_ttZ,self.events_background_ttW,self.events_background_ttH])
self.weights_background_ttV = self.ConArrays([self.weights_background_ttZ,self.weights_background_ttW,self.weights_background_ttH])
## SingleTop
self.events_background_tWZ = self.read_root(data_background_tWZ,self.background_sample,self.variables)
self.weights_background_tWZ = self.read_root(data_background_tWZ,self.background_sample,'weight_nominal') * 139
self.events_background_4top = self.read_root(data_background_4top,self.background_sample,self.variables)
self.weights_background_4top = self.read_root(data_background_4top,self.background_sample,'weight_nominal') * 139
self.events_background_tchannel = self.read_root(data_background_tchannel,self.background_sample,self.variables)
self.weights_background_tchannel = self.read_root(data_background_tchannel,self.background_sample,'weight_nominal') * 139
self.events_background_tW = self.read_root(data_background_tW,self.background_sample,self.variables)
self.weights_background_tW = self.read_root(data_background_tW,self.background_sample,'weight_nominal') * 139
self.events_background_ST = self.ConArrays([self.events_background_tWZ,self.events_background_tW])
self.weights_background_ST = self.ConArrays([self.weights_background_tWZ,self.weights_background_tW])
## ttbar
self.events_background_ttbar = self.read_root(data_background_ttbar,self.background_sample,self.variables)
self.weights_background_ttbar = self.read_root(data_background_ttbar,self.background_sample,'weight_nominal') * 139
self.events_background_tt2l = self.read_root(data_background_tt2l,self.background_sample,self.variables)
self.weights_background_tt2l = self.read_root(data_background_tt2l,self.background_sample,'weight_nominal') * 139
self.events_background_ttbar_all = self.ConArrays([self.events_background_ttbar,self.events_background_tt2l])
self.weights_background_ttbar_all = self.ConArrays([self.weights_background_ttbar,self.weights_background_tt2l])
## Z plus Jets
self.events_background_ZJets = self.read_root(data_background_ZJets,self.background_sample,self.variables)
self.weights_background_ZJets = self.read_root(data_background_ZJets,self.background_sample,'weight_nominal') * 139
## All background put together
self.events_background = self.ConArrays([self.events_background_diboson,self.events_background_ttV,self.events_background_ST,self.events_background_ttbar_all])
self.weight_background = self.ConArrays([self.weights_background_diboson,self.weights_background_ttV,self.weights_background_ST,self.weights_background_ttbar_all])
self.weight_background = np.absolute(self.weight_background)
### Background for later use (Histogram etc)
self.events_background_ttZ_tWZ = np.concatenate([self.events_background_ttZ,self.events_background_tWZ])
self.events_background_ttZ_tWZ_ttH = np.concatenate([self.events_background_ttZ,self.events_background_tWZ,self.events_background_ttH])
self.events_background_ttbar_tW = np.concatenate([self.events_background_ttbar_all,self.events_background_tW])
## Check if the Event yields are correct
print('Events tZQ:' + '%.2f'%(self.weight_signal.sum()))
print('Events diboson:' + '%.2f'%self.weights_background_diboson.sum())
print('Events ttZ:' + '%.2f'%self.weights_background_ttZ.sum())
print('Events ttW:' + '%.2f'%self.weights_background_ttW.sum())
print('Events ttH:' + '%.2f'%self.weights_background_ttH.sum())
print('Events tWZ:' + '%.2f'%self.weights_background_tWZ.sum())
print('Events tW:' + '%.2f'%self.weights_background_tW.sum())
print('Events ttbar:' + '%.2f'%self.weights_background_ttbar.sum())
print('Events tt2_l:' + '%.2f'%self.weights_background_tt2l.sum())
print('Events ZJets:' + '%.2f'%self.weights_background_ZJets.sum())
#Reshaping the weights
self.weight_signal = np.reshape(self.weight_signal, (len(self.events_signal), 1))
self.weight_background = np.reshape(self.weight_background, (len(self.events_background), 1))
self.weights_background_ttbar_all = np.absolute(self.weights_background_ttbar_all)
self.weight_background_ttbar = np.reshape(self.weights_background_ttbar_all,(len(self.events_background_ttbar_all),1))
#Calculating the weight ratio to scale the signal weight up. This tries to take the high amount of background into account
self.weight_ratio = (self.weight_signal.sum())/ self.weight_background.sum()
self.weight_signal = self.weight_signal / self.weight_ratio
self.weight_signal_ttbar = self.weight_signal * (self.weights_background_ttbar_all.sum()/self.weight_signal.sum())
#Setting up the targets
#target combined is used to make sure the systematics are seen as signal for the first net in the combined training
self.target_signal = np.reshape([1 for x in range(len(self.events_signal))], (len(self.events_signal), 1))
self.target_background = np.reshape([0 for x in range(len(self.events_background))], (len(self.events_background), 1))
self.target_background_ttbar = np.reshape([0 for x in range(len(self.events_background_ttbar_all))], (len(self.events_background_ttbar_all), 1))
#The samples and corresponding targets are split into a sample for training and a sample for testing. Keep in mind that the same random seed should be used for both splits
self.sample_training, self.sample_validation = train_test_split(np.concatenate((self.events_signal, self.events_background)), test_size = self.validation_fraction, random_state = self.seed)
self.target_training, self.target_validation = train_test_split(np.concatenate((self.target_signal, self.target_background)), test_size = self.validation_fraction, random_state = self.seed)
# For Training ttbar seperately
self.sample_training_ttbar, self.sample_validation_ttbar = train_test_split(np.concatenate((self.events_signal, self.events_background_ttbar_all)), test_size = self.validation_fraction, random_state = self.seed)
self.target_training_ttbar, self.target_validation_ttbar = train_test_split(np.concatenate((self.target_signal, self.target_background_ttbar)), test_size = self.validation_fraction, random_state = self.seed)
#Splitting the weights
self.weight_training, self.weight_validation = train_test_split(np.concatenate((self.weight_signal, self.weight_background)), test_size = self.validation_fraction, random_state = self.seed)
self.weight_training_ttbar, self.weight_validation_ttbar = train_test_split(np.concatenate((self.weight_signal_ttbar, self.weight_background_ttbar)), test_size = self.validation_fraction, random_state = self.seed)
#Setting up a scaler
#A scaler makes sure that all variables are normalised to 1 and have the same order of magnitude for that reason
self.scaler_test = StandardScaler().fit(self.sample_training)
#self.scaler = StandardScaler()
self.sample_training = self.scaler_test.transform(self.sample_training)
self.sample_validation = self.scaler_test.transform(self.sample_validation)
self.scaler_test_ttbar = StandardScaler().fit(self.sample_training_ttbar)
self.sample_training_ttbar = self.scaler_test.transform(self.sample_training_ttbar)
self.sample_validation_ttbar = self.scaler_test.transform(self.sample_validation_ttbar)
#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
## Now we want to build our Neural Network
def My_DiscrimatorBuild(self):
self.model = Sequential()
self.model.add(Dense(self.discriminator_nodes,input_shape=(self.input_dimension),activation='elu'))
#self.model.add(Activation('elu'))
for layercount in range(self.discriminator_layers):
self.model.add(Dense(self.discriminator_nodes,activation = 'elu'))
self.model.add(BatchNormalization())
self.model.add(Dropout(self.discriminator_dropout))
self.model.add(Dense(1,activation='sigmoid'))
self.model.compile(loss=binary_crossentropy,metrics=['binary_accuracy'],weighted_metrics = [metrics.binary_accuracy],optimizer = self.discriminator_optimizer_adam)
#plot_model(self.model, to_file=self.output_job+'model.png')
self.model.summary()
def modelttbar(self):
self.model_ttbar = Sequential()
self.model_ttbar.add(Dense(self.discriminator_nodes_ttbar,input_shape=(self.input_dimension)))
self.model_ttbar.add(Activation('elu'))
for layercount in range(self.discriminator_layers - 1):
self.model_ttbar.add(Dense(self.discriminator_nodes_ttbar,activation = 'elu'))
self.model_ttbar.add(BatchNormalization())
self.model_ttbar.add(Dropout(self.discriminator_dropout))
self.model_ttbar.add(Dense(1,activation='sigmoid'))
self.model_ttbar.compile(loss=binary_crossentropy,metrics=['binary_accuracy'],weighted_metrics = [metrics.binary_accuracy],optimizer = self.discriminator_optimizer_ttbar)
def trainDiscriminator(self):
# With our Neural Network set up, we can now fit the training and test data
self.discriminator_history = self.model.fit(self.sample_training, self.target_training.ravel(), epochs=self.discriminator_epochs, batch_size = self.batchSize, sample_weight = self.weight_training.ravel(), validation_data = (self.sample_validation, self.target_validation, self.weight_validation.ravel()),callbacks=[self.reduce_lr,self.early_stop])
self.discriminator_history_array.append(self.discriminator_history)
print(self.discriminator_history.history.keys())
def trainttbar(self):
self.discriminator_history_ttbar = self.model_ttbar.fit(self.sample_training_ttbar, self.target_training_ttbar.ravel(), epochs=self.discriminator_epochs, batch_size = self.batchSize, sample_weight = self.weight_training_ttbar.ravel(), validation_data = (self.sample_validation_ttbar, self.target_validation_ttbar, self.weight_validation_ttbar.ravel()),callbacks=[self.reduce_lr,self.early_stop])
self.discriminator_history_ttbar_array.append(self.discriminator_history_ttbar)
print(self.discriminator_history_ttbar.history.keys())
def predictttbar (self):
self.model_prediction_ttbar = self.model_ttbar.predict(self.sample_validation_ttbar,batch_size = self.batchSize).ravel()
self.fpr_ttbar, self.tpr_ttbar, self.threshold_ttbar = roc_curve(self.target_validation_ttbar,self.model_prediction_ttbar)
self.auc_ttbar = auc(self.fpr_ttbar,self.tpr_ttbar)
print('AUC Test ttbar:',self.auc_ttbar)
self.pred_hist_ttbar_signal = self.model_ttbar.predict(self.scaler_test_ttbar.transform(self.events_signal),batch_size = self.batchSize).ravel()
self.pred_hist_ttbar_background = self.model_ttbar.predict(self.scaler_test_ttbar.transform(self.events_background_ttbar_all),batch_size = self.batchSize).ravel()
plt.hist(self.pred_hist_ttbar_signal, range=[0., 1.], linewidth = 2, bins=30, histtype="step", color='magenta',label="tZq",density = True)
plt.hist(self.pred_hist_ttbar_background, range=[0., 1.], linewidth = 2, bins=30, histtype="step", color='red',label=r"$t\bar{t}$",density = True)
plt.xlim(0,1)
plt.xlabel('NN output')
plt.legend()
plt.gcf().savefig(self.output_job + 'NNttbarseparation.png')
plt.gcf().clear()
plt.title('Receiver Operating Characteristic')
plt.plot(self.fpr_ttbar, self.tpr_ttbar, 'g--',color='blue', label='$AUC_{test}$ = %0.2f'% self.auc_ttbar)
plt.legend(loc='lower right')
plt.plot([0,1],[0,1],'r--')
plt.xlim([-0.,1.])
plt.ylim([-0.,1.])
plt.ylabel('True Positive Rate', fontsize='large')
plt.xlabel('False Positive Rate', fontsize='large')
plt.legend(frameon=False)
plt.gcf().savefig(self.output_job + 'roc_ttbar.png')
plt.gcf().clear()
ax = plt.subplot(111)
ax.ticklabel_format(style='sci', axis ='both', scilimits=(-2,2),useMathText=True)
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
plt.plot(self.discriminator_history_ttbar.history['binary_accuracy'])
plt.plot(self.discriminator_history_ttbar.history['val_binary_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('Epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.gcf().savefig(self.output_job + 'acc_ttbar.png')
plt.gcf().clear()
def predictModel(self):
# Predict Model on 'unknown' data and analyze predicition with i.e ROC Curve and AUC Value
self.model_prediction = self.model.predict(self.sample_training,batch_size=self.batchSize).ravel()
self.model_prediction_test = self.model.predict(self.sample_validation,batch_size=self.batchSize).ravel()
self.fpr, self.tpr, self.threshold = roc_curve(self.target_training, self.model_prediction)
self.fpr_test, self.tpr_test, self.threshold_test = roc_curve(self.target_validation, self.model_prediction_test)
self.auc = auc(self.fpr, self.tpr)
self.auc_test = auc(self.fpr_test, self.tpr_test)
print('Discriminator AUC Training:', self.auc)
print('Discriminator AUC Test:', self.auc_test)
def plotLosses(self):
ax = plt.subplot(111)
ax.ticklabel_format(style='sci', axis ='both', scilimits=(-1,2),useMathText=True)
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
plt.plot(self.discriminator_history.history['loss'])
plt.plot(self.discriminator_history.history['val_loss'])
plt.title('Discriminator Losses')
plt.ylabel('Loss',fontsize=12)
plt.xlabel('Epoch',fontsize = 12)
plt.legend(['train', 'test'], loc='upper left')
# plt.legend(loc="upper right", prop={'size' : 7})
plt.gcf().savefig(self.output_job + 'losses.png')
plt.gcf().clear()
def plotRoc(self):
plt.title('Receiver Operating Characteristic')
plt.plot(self.fpr, self.tpr, 'g--',color='blue', label='$AUC_{train}$ = %0.3f'% self.auc)
plt.plot(self.fpr_test, self.tpr_test, 'g--',color='orange', label='$AUC_{test}$ = %0.3f'% self.auc_test)
plt.legend(loc='lower right')
plt.plot([0,1],[0,1],'r--')
plt.xlim([-0.,1.])
plt.ylim([-0.,1.])
plt.ylabel('True Positive Rate', fontsize='large')
plt.xlabel('False Positive Rate', fontsize='large')
plt.legend(frameon=False)
#plt.show()
plt.gcf().savefig(self.output_job + 'roc.png')
plt.gcf().clear()
def plotSeparation(self):
self.signal_histo_test = []
self.background_histo_test = []
for i in range(len(self.sample_validation)):
if self.target_validation[i] == 1:
self.signal_histo_test.append(self.model_prediction_test[i])
if self.target_validation[i] == 0:
self.background_histo_test.append(self.model_prediction_test[i])
plt.hist(self.signal_histo_test, range=[0., 1.], linewidth = 2, bins=30, histtype="step",density = True,color=color_tW, label = "Signal")
plt.hist(self.background_histo_test, range=[0., 1.], linewidth = 2, bins=30, histtype="step", density = True, color=color_tt, label = "Background")
plt.legend()
plt.xlabel('Network response', horizontalalignment='left', fontsize='large')
plt.ylabel('Event fraction', fontsize='large')
plt.legend(frameon=False)
plt.gcf().savefig(self.output_job + 'separation_test.png')
plt.gcf().clear()
self.signal_histo = []
self.background_histo = []
for i in range(len(self.sample_training)):
if self.target_training[i] == 1:
self.signal_histo.append(self.model_prediction[i])
if self.target_training[i] == 0:
self.background_histo.append(self.model_prediction[i])
plt.hist(self.signal_histo, range=[0., 1.], linewidth = 2, bins=30, histtype="step",density = True,color=color_tW, label = "Signal")
plt.hist(self.background_histo, range=[0., 1.], linewidth = 2, bins=30, histtype="step", density = True, color=color_tt, label = "Background")
plt.legend()
plt.title('Separation Training')
plt.xlabel('Network response', horizontalalignment='left', fontsize='large')
plt.ylabel('Event fraction', fontsize='large')
plt.legend(frameon=False)
plt.gcf().savefig(self.output_job + 'separation_training.png')
plt.gcf().clear()
def plotWeightedAccuracy(self):
ax = plt.subplot(111)
ax.ticklabel_format(style='sci', axis ='both', scilimits=(-1,2),useMathText=True)
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
plt.plot(self.discriminator_history.history['weighted_binary_accuracy'])
plt.plot(self.discriminator_history.history['val_weighted_binary_accuracy'])
plt.title('model weighted accuracy')
plt.ylabel('weighted accuracy')
plt.xlabel('Epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.gcf().savefig(self.output_job + 'weighted_acc.png')
plt.gcf().clear()
def plotAccuracy(self):
ax = plt.subplot(111)
ax.ticklabel_format(style='sci', axis ='both', scilimits=(-2,2),useMathText=True)
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
plt.plot(self.discriminator_history.history['binary_accuracy'])
plt.plot(self.discriminator_history.history['val_binary_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('Epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.gcf().savefig(self.output_job + 'acc.png')
plt.gcf().clear()
def ParamstoTxt(self):
paramsfile = (self.output_job + 'params.txt')
file = open(paramsfile,'w')
file.write('We are using the %s optimizer at the moment' % (self.my_optimizer))
file.write('\n')
file.write('Number of epochs: %i' % (self.discriminator_epochs))
file.write('\n')
file.write('Batch Size: %i' % (self.batchSize))
file.write('\n')
file.write('Number of hidden layers: %i' % (self.discriminator_layers))
file.write('\n')
file.write('Number of nodes: %i' % (self.discriminator_nodes))
file.write('\n')
file.write('Dropout: %.2f' % (self.discriminator_dropout))
file.write('\n')
file.write('Validation fraction: %.2f' % (self.validation_fraction))
file.write('\n')
file.write('Learning rate:%.3e' % (self.discriminator_lr))
file.write('\n')
file.write('Momentum:%.2f' % (self.discriminator_momentum))
file.write('\n')
file.write('Loss after %i epochs: %.5e' % (self.discriminator_epochs,self.discriminator_history.history['loss'][-1]))
file.write('\n')
file.write('Validation Loss after %i epochs: %.5e' % (self.discriminator_epochs,self.discriminator_history.history['val_loss'][-1]))
file.write('\n')
file.write('Discriminator AUC:%.3f'%self.auc)
file.write('\n')
file.close()
file = open(self.output_curve + 'lr_%.1e_la_%i_n_%i.txt'%(self.discriminator_lr,self.discriminator_layers,self.discriminator_nodes),'w')
file.write('%.4e,%.4e,%.4e,%.4e,%.4e,%.4e,%.4e,%i,%i,%.4e'%(self.discriminator_history.history['loss'][-1],self.discriminator_history.history['val_loss'][-1],self.discriminator_lr,self.auc_test,self.discriminator_history.history['binary_accuracy'][-1],self.discriminator_history.history['val_binary_accuracy'][-1],self.discriminator_momentum,self.discriminator_nodes,self.discriminator_layers,self.auc))
file.close()
def plot_lr(self,filelist):
with open(self.output_lrcurve + 'plot_lr.txt','w') as self.outfile:
for fname in filelist:
with open(self.output_curve + fname,'r') as self.infile:
self.outfile.write(self.infile.read())
self.outfile.write('\n')
lr_list = []
val_loss_plot = []
loss_plot = []
auc_list = []
acc_list= []
val_acc_list= []
momentum_list= []
nodes = []
layers = []
auc_train_list=[]
self.outfile = self.output_lrcurve + 'plot_lr.txt'
results = open(self.outfile,'r')
for line in results:
dataline = line
data = dataline.split(',')
lr_list.append(float(data[2]))
val_loss_plot.append(float(data[1]))
loss_plot.append(float(data[0]))
auc_list.append(float(data[3]))
acc_list.append(float(data[4]))
val_acc_list.append(float(data[5]))
momentum_list.append(float(data[6]))
nodes.append(int(data[7]))
layers.append(int(data[8]))
auc_train_list.append(float(data[9]))
lr_list = np.array(lr_list)
val_loss_plot = np.array(val_loss_plot)
loss_plot = np.array(loss_plot)
auc_list = np.array(auc_list)
val_acc_list = np.array(val_acc_list)
acc_list = np.array(acc_list)
momentum_list = np.array(momentum_list)
nodes=np.array(nodes)
layers= np.array(layers)
auc_train=np.array(auc_train_list)
### Plot Lists
ax = plt.subplot(111)
ax.tick_params(direction='in')
ax.xaxis.set_minor_locator(AutoMinorLocator())
ax.yaxis.set_minor_locator(AutoMinorLocator())
ax.xaxis.set_ticks_position('both')
ax.yaxis.set_ticks_position('both')
ax.tick_params(direction='in',which='minor', length=2)
plt.plot(lr_list,loss_plot,color = color_tt,label='Training',marker = 'x',linestyle = 'None')
plt.plot(lr_list,val_loss_plot,color = color_tW,label = 'Test',marker = 'x',linestyle = 'None')
ax.ticklabel_format(style='sci', axis ='both', scilimits=(-2,2),useMathText = True)
ax.set_xscale("log", nonposx='clip')
plt.xlabel('Learning rate')
plt.ylabel('Loss')
plt.legend()
plt.gcf().savefig(self.output_lrcurve+'LRPlot.png')
plt.gcf().clear()
ax = plt.subplot(111)
ax.tick_params(direction='in')
ax.xaxis.set_minor_locator(AutoMinorLocator())
ax.yaxis.set_minor_locator(AutoMinorLocator())
ax.xaxis.set_ticks_position('both')
ax.yaxis.set_ticks_position('both')
ax.tick_params(direction='in',which='minor', length=2)
ax.ticklabel_format(style='sci', axis ='both', scilimits=(-2,2),useMathText = True)
ax.set_xscale("log", nonposx='clip')
plt.plot(lr_list,auc_list, color = 'navy', marker = 'x', linestyle = 'None',label='AUC Value')
plt.plot(lr_list,acc_list,color="red",marker='x',linestyle='None',label='Accuracy')
plt.legend()
plt.xlabel('Learning rate')
plt.gcf().savefig(self.output_lrcurve+'LRAucPlot.png')
plt.gcf().clear()
ax = plt.figure().gca()
ax.tick_params(direction='in')
ax.xaxis.set_minor_locator(AutoMinorLocator())
ax.yaxis.set_minor_locator(AutoMinorLocator())
ax.xaxis.set_ticks_position('both')
ax.yaxis.set_ticks_position('both')
ax.tick_params(direction='in',which='minor', length=2)
ax.ticklabel_format(style='sci', axis ='both', scilimits=(-2,2),useMathText = True)
plt.plot(layers,auc_list,color='navy',marker='x',linestyle='None',label='$AUC_{test}$')
plt.plot(layers,auc_train,color='orange',marker='x',linestyle='None',label='$AUC_{train}$')
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
plt.xlabel('Number hidden layers')
plt.legend()
#plt.title('Impact of # hidden layers')
plt.gcf().savefig(self.output_lrcurve+'layerimpact.png')
plt.gcf().clear()
ax = plt.figure().gca()
ax.tick_params(direction='in')
ax.xaxis.set_minor_locator(AutoMinorLocator())
ax.yaxis.set_minor_locator(AutoMinorLocator())
ax.xaxis.set_ticks_position('both')
ax.yaxis.set_ticks_position('both')
ax.tick_params(direction='in',which='minor', length=2)
ax.ticklabel_format(style='sci', axis ='both', scilimits=(-2,4),useMathText = True)
plt.plot(nodes,auc_list,color='navy',marker='x',linestyle='None',label='$AUC_{test}$')
plt.plot(nodes,auc_train,color='orange',marker='x',linestyle='None',label='$AUC_{train}$')
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
plt.xlabel('Number nodes in hidden layer')
plt.legend()
#plt.title('Impact of # nodes')
plt.gcf().savefig(self.output_lrcurve+'nodesimpact.png')
plt.gcf().clear()
def purityPlot(self):
(n_s,bins_s,patches_s) = plt.hist(self.signal_histo_test, range=[0., 1.], bins=50,density=True)
(n_b,bins_b,patches_b) = plt.hist(self.background_histo_test, range=[0., 1.], bins=50,density=True)
plt.gcf().clear()
nbins= np.linspace((bins_s[0]+bins_s[1])/2,(bins_s[-1]+bins_s[-2])/2,50)
self.purity = n_s/(n_s+n_b)
x = np.linspace(0,1,500)
y = x
ax = plt.subplot(111)
ax.tick_params(direction='in')
ax.xaxis.set_minor_locator(AutoMinorLocator())
ax.yaxis.set_minor_locator(AutoMinorLocator())
ax.xaxis.set_ticks_position('both')
ax.yaxis.set_ticks_position('both')
ax.tick_params(direction='in',which='minor', length=2)
plt.xlim(0,1)
plt.plot(x,y,color='r',linewidth=1)
plt.plot(nbins,self.purity,color='blue',marker='+',linestyle ='None',markersize=4.0)
plt.xlabel('Network Output')
plt.ylabel('Purity')
plt.gcf().savefig(self.output_job + 'purity.png')
plt.gcf().clear()
## Function to plot Histogram of Variables, with parameters s.t. you can hist any variable
def HistObject(self,Xaxisbins,Yaxisbins,range1,range2,bins,labelxaxis,savelabel,numbervariable):
self.hist_tZq = self.events_signal.transpose()
self.hist_diboson = self.events_background_diboson.transpose()
self.hist_ttbar_tW = self.events_background_ttbar_tW.transpose()
self.hist_ttZ_tWZ = self.events_background_ttZ_tWZ.transpose()
self.hist_ZJets = self.events_background_ZJets.transpose()
self.hist_tZq = np.clip(self.hist_tZq,None,range2)
self.hist_diboson = np.clip(self.hist_diboson,None,range2)
self.hist_ttbar_tW = np.clip(self.hist_ttbar_tW,None,range2)
self.hist_ttZ_tWZ = np.clip(self.hist_ttZ_tWZ,None,range2)
self.hist_ZJets = np.clip(self.hist_ZJets,None,range2)
ax = plt.subplot()
ax.ticklabel_format(style='sci', axis ='both', scilimits=(-4,4))
ax.xaxis.set_minor_locator(AutoMinorLocator())
ax.yaxis.set_minor_locator(AutoMinorLocator())
ax.tick_params(direction='in')
plt.locator_params(axis='x', nbins=Xaxisbins)
plt.locator_params(axis='y', nbins=Yaxisbins)
ax.xaxis.set_ticks_position('both')
ax.yaxis.set_ticks_position('both')
ax.tick_params(direction='in',which='minor', length=2)
plt.hist(self.hist_tZq[numbervariable], range=[range1, range2], linewidth = 2, bins=bins, histtype="step", color='magenta',label='tZq',density = True)
plt.hist(self.hist_diboson[numbervariable], range=[range1, range2], linewidth = 2, bins=bins, histtype="step", color=color_diboson,label='diboson',density = True)
plt.hist(self.hist_ttbar_tW[numbervariable], range=[range1, range2], linewidth = 2, bins=bins, histtype="step", color=color_ttbar,label=r'$t\bar{t}+tW$',density = True)
plt.hist(self.hist_ttZ_tWZ[numbervariable], range=[range1, range2], linewidth = 2, bins=bins, histtype="step", color=colorST,label=r'$ttZ+tWZ$',density = True)
#plt.hist(self.hist_ZJets[numbervariable], range=[range1, range2], linewidth = 2, bins=bins, histtype="step", color=color_zjets,label=r'$Z+Jets$',density = True)
plt.legend(frameon = False)
plt.xlim(range1,range2)
plt.xlabel(labelxaxis,horizontalalignment='right',x=1.0,fontsize=14)
plt.ylabel('Event density',va = 'top',y=0.87,labelpad=10,fontsize=13)
plt.gcf().savefig(mysavedata + savelabel +'.png')
plt.gcf().clear()
## Histogram of Neural Network output
def histPrediction(self,event,colors,eventlabel,savelabel):
ax = plt.subplot(111)
ax.tick_params(direction='in')
ax.xaxis.set_minor_locator(AutoMinorLocator())
ax.yaxis.set_minor_locator(AutoMinorLocator())
ax.xaxis.set_ticks_position('both')
ax.yaxis.set_ticks_position('both')
ax.tick_params(direction='in',which='minor', length=2)
self.pred_hist = self.model.predict(self.scaler_test.transform(event),batch_size = self.batchSize).ravel()
plt.hist(self.pred_hist, range=[0., 1.], linewidth = 2, bins=30, histtype="step", color=colors,label=eventlabel,density = True)
plt.xlim(0,1)
plt.xlabel('NN output', fontsize = 14)
plt.legend()
plt.gcf().savefig(eventHistpath + savelabel + '.png')
plt.gcf().clear()
def Runtime(self,start,stop):
file = open(self.output_job + 'params.txt','a')
file.write('Runtime of program: %.2f seconds' % (stop-start))
file.close()
start = timer()
training = neuralNetworkEnvironment()
training.initialize_sample()
### Model ttbar training tZq
#training.modelttbar()
#training.trainttbar()
#training.predictttbar()
### Model all background but ttbar training tZq
training.My_DiscrimatorBuild()
#training.trainDiscriminator()
#training.predictModel()
#training.plotLosses()
#training.plotRoc()
#training.plotSeparation()
#training.plotWeightedAccuracy()
#training.plotAccuracy()
#training.purityPlot()
#training.ParamstoTxt()
#plot_model(training.model, to_file=training.output_job + 'model.png',show_shapes=True,show_layer_names=False,rankdir='TB')
###
#training.histPrediction(training.events_signal,'magenta','tZq','tZq')
#training.histPrediction(training.events_background_diboson,color_diboson,'Diboson','diboson')
#training.histPrediction(training.events_background_ttbar_tW,color_ttbar,r'$t\bar{t} + tW','ttbar')
#training.histPrediction(training.events_background_ZJets,color_zjets,'Z+Jets','ZJets')
#training.histPrediction(training.events_background_ttZ_tWZ_ttH,colorST,r'$t\bar{t}Z + t\bar{t}H + tWZ$','ttvtthtwz')
#self.variables = np.array(['m_b_jf','eta_jf','q_lW','eta_lW','pT_W','pT_lW','m_Z','eta_Z','dR_jf_Z','pT_jf','pT_jr','eta_jr','pT_Z','m_met','m_top','mT_W'])
training.HistObject(10,5,0,800,16,'$m(bj_F)$','m_b_jf',0)
training.HistObject(10,5,0,5,15,'$\eta(j_f)$','eta_jf',1)
training.HistObject(5,10,-2.5,2.5,5,'$q(l^W)$','q_lW',2)
training.HistObject(6,3,0,3.,12,'$\eta(l^W)$','eta_lW',3)
training.HistObject(6,6,0,300.,11,'$p_T(W)$[GeV]','pT_W',4)
training.HistObject(10,4,0,200.,11,'$p_T(l^W)$[GeV]','pT_lW',5)
training.HistObject(6,5,60.,120.,25,'$m(ll)$[GeV]','m_Z',6)
training.HistObject(10,4,0,5.,11,'$\eta(Z)$','eta_Z',7)
training.HistObject(7,5,0,7.,16,'$\Delta R(j_f,Z)$','dR_jf_Z',8)
training.HistObject(6,5,0,300.,11,'$p_T(j_f)$[GeV]','pT_jf',9)
training.HistObject(10,6,0,200.,11,'$p_T(j_r)$[GeV]','pT_jr',10)
training.HistObject(10,6,0,5.,16,'$\eta(j_r)$','eta_jr',11)
training.HistObject(6,6,0,300.,11,'$p_T(Z)$[GeV]','pT_Z',12)
training.HistObject(6,6,0,600,31,'$E^{miss}$[GeV]','m_met',13)
training.HistObject(6,6,0,600,31,'$m_t$[GeV]','m_top',14)
training.HistObject(12,6,0,240,8,'$m_t$[GeV]','mT_W',15)
###
text_files = [f for f in os.listdir(training.output_curve) if f.endswith('.txt')]
training.plot_lr(text_files)
end = timer()
training.Runtime(start,end)