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trainJets_cnn.py
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from __future__ import print_function
import numpy as np
np.random.seed(2671) # for reproducibility
import copy
from keras.datasets import mnist
from keras.models import Sequential, load_model
from keras.preprocessing.image import ImageDataGenerator
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D, AveragePooling2D
from keras.optimizers import SGD
from keras.utils import np_utils
from keras import backend as K
from keras.utils.layer_utils import print_summary
import sys
#from sklearn.metrics import confusion_matrix,classification_report, roc_curve, auc
import matplotlib.pyplot as plt
def second_largest(numbers):
count = 0
m1 = m2 = float('-inf')
for x in numbers:
count += 1
if x > m2:
if x >= m1:
m1, m2 = x, m1
else:
m2 = x
return m2 if count >= 2 else None
#print(sys.argv)
#ifname="array_ZH_MS55_ctauS100.npy"
#if len(sys.argv) > 1:
# ifname=sys.argv[1]
#X_signal = np.load("sig_array.npy")
X_bkg = np.load("array_DY_a2.npy")
X_sig = np.load("array_ZH_MS40_ctauS100.npy")
#X_bkg = np.load("array_DY_a2.npy")
#X_sig = np.load("array_ZH_MS40_ctauS10.npy")
#X_sig = np.load("array_ZH_MS40_ctauS10000.npy")
#X_sig = np.load("array_ZH_MS15_ctauS100.npy")
#X_sig = np.load(ifname)
#X_bkg = np.load("array_DY_b2.npy")
nToUse = min(X_bkg.shape[0],X_sig.shape[0])
maskSig = np.ones(X_sig.shape[0], dtype=bool)
maskSig[[i for i in range(nToUse,X_sig.shape[0])]] = False
X_sig_skim = X_sig[maskSig,...]
maskBkg = np.ones(X_bkg.shape[0], dtype=bool)
maskBkg[[i for i in range(nToUse,X_bkg.shape[0])]] = False
X_bkg_skim = X_bkg[maskBkg,...]
y_bkg = np.empty(shape=(nToUse,),dtype=np.int32)
y_bkg.fill(0)
y_sig = np.empty(shape=(nToUse,),dtype=np.int32)
y_sig.fill(1)
X_interLeave = np.empty(shape=(X_sig_skim.shape[0]+X_bkg_skim.shape[0],X_sig.shape[1],X_sig.shape[2],X_sig.shape[3]))
X_interLeave[0::2] = X_sig_skim
X_interLeave[1::2] = X_bkg_skim
Y_interLeave = np.empty(shape=(y_bkg.shape[0]+y_sig.shape[0],))
Y_interLeave[0::2] = y_sig
Y_interLeave[1::2] = y_bkg
testFraction = 1.0/6.0
nTest = int(testFraction*nToUse)
X_test = X_interLeave[:nTest]
y_test = Y_interLeave[:nTest]
X_train = X_interLeave[nTest:]
y_train = Y_interLeave[nTest:]
nb_classes = 2
batch_size = 100
nb_epoch = 50
data_augmentation = False
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
#print(Y_test[0])
#print(Y_test[1])
#print(X_test[0])
#print(X_test[1])
# input image dimensions
img_rows, img_cols = X_sig.shape[1], X_sig.shape[2]
if K.image_dim_ordering() == 'th':
#X_train = X_train.reshape(X_train.shape[0], X_signal.shape[3], img_rows, img_cols)
X_train = np.swapaxes(X_train,1,3)
X_train = np.swapaxes(X_train,2,3)
X_test = np.swapaxes(X_test,1,3)
X_test = np.swapaxes(X_test,2,3)
#X_test = X_test.reshape(X_test.shape[0], X_signal.shape[3], img_rows, img_cols)
input_shape = (X_sig.shape[3], img_rows, img_cols)
else:
#X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, X_signal.shape[3])
#X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, X_signal.shape[3])
input_shape = (img_rows, img_cols, X_sig.shape[3])
#X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
#X_train /= 255
#X_test /= 255
#print('X_train shape:', X_train.shape)
#print(X_train.shape[0], 'train samples')
#print(X_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
#Y_train = np_utils.to_categorical(y_train, nb_classes)
#Y_test = np_utils.to_categorical(y_test, nb_classes)
model = Sequential()
model.add(MaxPooling2D(pool_size=(2,2),input_shape=input_shape))
model.add(Convolution2D(100, 1,1,border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(70, 3,3, border_mode='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Convolution2D(50, 2,2, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(50, 2,2, border_mode='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
#model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adadelta',
metrics=['accuracy'])
model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
verbose=1, validation_split=1/12.0)
score = model.evaluate(X_test, Y_test, verbose=0)
Y_pred = model.predict(X_test)
#print(Y_pred.shape)
#print('Test score:', score[0])
#print('Test accuracy:', score[1])
#print(Y_test)
nToPrint = 10
nPrinted = 0
tryCut = 0.99
num_outputs = Y_pred.shape[1]
confusion_matrix = np.zeros((num_outputs,num_outputs),dtype=np.int32)
for i,y in enumerate(Y_test):
correct = np.argmax(y)
first = 0
if Y_pred[i][1] > tryCut: first = 1
#first = np.argmax(Y_pred[i])
confusion_matrix[correct, first] += 1
#if nPrinted < nToPrint and correct == 1 and first != 0:
# print(i,Y_pred[i])
# nPrinted += 1
model.save("jet_cnn.keras")
#print(ifname)
print(confusion_matrix)
print("using cut: %0.3f" % (tryCut,))
print("background rejection: %0.4f" % (float(confusion_matrix[0][0])/float(confusion_matrix[0][0]+confusion_matrix[0][1]),))
print("signal efficiency: %0.4f" % (float(confusion_matrix[1][1])/float(confusion_matrix[1][1]+confusion_matrix[1][0]),))