def run(Emin, Emax, BDT=False):
if BDT:
figureName = 'pred_BDT_' + str(int(Emin / 1000)) + '-' + str(
int(Emax / 1000))
else:
figureName = 'pred_DNN_' + str(int(Emin / 1000)) + '-' + str(
int(Emax / 1000))
if os.path.isfile(figureName + '.png'): return
train_e = getParticleSet(
'/home/drozd/analysis/fraction1/data_train_elecs.npy', -1, Emax, BDT)
train_p = getParticleSet(
'/home/drozd/analysis/fraction1/data_train_prots.npy', -1, Emax, BDT)
train = np.concatenate((train_e, train_p))
np.random.shuffle(train)
X_train = train[:, 0:-1]
Y_train = train[:, -1]
ghost = plt.figure()
plt.hist(train_e[:, 30], 50, histtype='step', label='e', color='green')
plt.hist(train_p[:, 30], 50, histtype='step', label='p', color='red')
plt.title(str(int(Emin / 1000)) + ' - ' + str(int(Emax / 1000)))
plt.xlabel('Binned normalised energy')
plt.legend(loc='best')
plt.savefig(figureName.replace('pred_', 'spectre_'))
plt.close(ghost)
del train_e, train_p, train
val_e = getParticleSet(
'/home/drozd/analysis/fraction1/data_validate_elecs_1.npy', Emin, Emax,
BDT)
val_p = getParticleSet(
'/home/drozd/analysis/fraction1/data_validate_prots_1.npy', Emin, Emax,
BDT)
val = np.concatenate((val_e, val_p))
np.random.shuffle(val)
X_val = val[:, 0:-1]
Y_val = val[:, -1]
del val_e, val_p, val
print(
str(int(Emin / 1000)) + '-' + str(int(Emax / 1000)) + ': Training on ',
X_train.shape[0], ' events')
if BDT:
model = GradientBoostingClassifier(n_estimators=100,
learning_rate=0.1,
max_depth=3,
min_samples_leaf=0.0001)
model.fit(X_train, Y_train)
predictions = model.predict_proba(X_val)[:, 1]
else:
model = Sequential()
model.add(
Dense(300,
input_shape=(X_train.shape[1], ),
kernel_initializer='he_uniform',
activation='relu'))
model.add(Dropout(0.1))
model.add(
Dense(150, kernel_initializer='he_uniform', activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(70, kernel_initializer='he_uniform',
activation='relu'))
model.add(Dropout(0.1))
model.add(
Dense(1, kernel_initializer='he_uniform', activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['binary_accuracy'])
rdlronplt = ReduceLROnPlateau(monitor='loss', patience=3, min_lr=0.001)
#~ earl = EarlyStopping(monitor='loss',min_delta=0.0001,patience=5)
callbacks = [rdlronplt]
history = model.fit(X_train,
Y_train,
batch_size=150,
epochs=75,
verbose=0,
callbacks=callbacks,
validation_data=(X_val, Y_val))
predictions = model.predict(X_val)
elecs_p, prots_p = getClassifierScore(Y_val, predictions)
Nbins_plt = 50
binList = [x / Nbins_plt for x in range(0, Nbins_plt + 1)]
fig = plt.figure()
plt.hist(elecs_p,
bins=binList,
label='e',
alpha=0.7,
histtype='step',
color='green')
plt.hist(prots_p,
bins=binList,
label='p',
alpha=0.7,
histtype='step',
color='red')
plt.xlabel('Classifier score')
plt.ylabel('Number of events')
plt.title(
str(int(Emin / 1000)) + ' GeV - ' + str(int(Emax / 1000)) + ' GeV')
plt.legend(loc='upper center')
plt.yscale('log')
plt.savefig(figureName)
plt.close(fig)
arr_e = np.load('/home/drozd/analysis/fraction1/data_test_elecs_1.npy')
X_e = _normalise(arr_e[:, 0:-2])
Y_e = arr_e[:, -1]
arr_e = np.concatenate((X_e, Y_e.reshape((Y_e.shape[0], 1))), axis=1)
del X_e, Y_e
arr_p = np.load('/home/drozd/analysis/fraction1/data_test_prots_1.npy')
X_p = _normalise(arr_p[:, 0:-2])
Y_p = arr_p[:, -1]
arr_p = np.concatenate((X_p, Y_p.reshape((Y_p.shape[0], 1))), axis=1)
del X_p, Y_p
tst = np.concatenate((arr_e, arr_p))
np.random.shuffle(tst)
del arr_e, arr_p
X_tst = tst[:, 0:-1]
Y_tst = tst[:, -1]
del tst
if BDT:
pred_tst = model.predict_proba(X_tst)[:, 1]
else:
pred_tst = model.predict(X_tst)
del X_tst
elecs_pd, prots_pd = getClassifierScore(Y_tst, pred_tst)
fig = plt.figure()
plt.hist(elecs_pd,
bins=binList,
label='e',
alpha=0.7,
histtype='step',
color='green')
plt.hist(prots_pd,
bins=binList,
label='p',
alpha=0.7,
histtype='step',
color='red')
plt.xlabel('Classifier score')
plt.ylabel('Number of events')
plt.title('Full validation range, limited train range \n' +
str(int(Emin / 1000)) + ' GeV - ' + str(int(Emax / 1000)) +
' GeV')
plt.legend(loc='upper center')
plt.yscale('log')
plt.savefig(figureName.replace('pred', 'full'))
plt.close(fig)
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X_train, Y_train, test_size = 0.4, random_state = 5)
from keras import regularizers
from keras import backend as K
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, BatchNormalization
from keras.layers.advanced_activations import LeakyReLU
from keras.callbacks import EarlyStopping
epochs = 80
batch_size = 64
model = Sequential()
model.add(Dense(64, input_shape = (X_train.shape[1], )))
model.add(BatchNormalization(axis = 1, momentum=0.99, epsilon=0.001))
model.add(Activation('sigmoid'))
model.add(Dropout(0.4))
model.add(Dense(64))
model.add(BatchNormalization(axis = 1, momentum=0.99, epsilon=0.001))
model.add(Activation('sigmoid'))
model.add(Dropout(0.4))
model.add(Dense(64))
model.add(BatchNormalization(axis = 1, momentum=0.99, epsilon=0.001))
model.add(Activation('sigmoid'))
model.add(Dropout(0.4))
model.add(Dense(64))
