Functions
def	fac0 (jeta)

def	fac1 (jeta)

def	fac2 (jeta)

def	propweights (y_true)

Variables
	_ = plt.plot(lims, lims)

list	a0 = [0.973, 0.998, 0.992, 0.965 ]
	marinas correction form More...

list	a1 = [0, -0.318, -0.261, -0.406]

list	a2 = [0, 0, 0.047, 0.089]

	alpha

	args = parser1.parse_args(args=[])

	bins

list	cols_to_minmax = ['t_delta', 't_hmaxNearP','t_emaxNearP', 't_hAnnular', 't_eAnnular','t_pt','t_nVtx','t_ieta','t_eHcal10', 't_eHcal30','t_rhoh','t_eHcal_x']

	data = df.values

	default

	df = pd.read_pickle(fName)

tuple	dop = (X_test[:,16]/X_test[:,13])

tuple	eop = (X_test[:,15]/X_test[:,13])

	fName = args.input

	help

	history = model.fit(X_train,Y_train , batch_size=5000, epochs=50, validation_split=0.2, verbose=1,sample_weight=propweights(Y_train))

list	keya = []

	label

list	lims = [0, 200]

	loc

	loss

tuple	marinascorr = X_test[:,15]*mcorrval

list	maxa = []

tuple	mcorrval = vec0(abs(X_test[:,17])) + vec1(abs(X_test[:,17]))eopdop( 1 + vec2(abs(X_test[:,17]))dop)

	meany = np.mean(Y_train)

list	mina = []

	model = Sequential()

	modv = args.modelv

int	ntest = 20000

	optimizer

	parser1 = argparse.ArgumentParser()

	pmom = X_test[:,13]

	preds = model.predict(X_test[:,0:12])

	range

	stdy = np.std(Y_train)

	targets = data[:,12]

int	testindx = data.shape[0] - ntest

	uncorrected = X_test[:,15]

	vec0 = np.vectorize(fac0)

	vec1 = np.vectorize(fac1)

	vec2 = np.vectorize(fac2)

	X_test = data[testindx:,:]

	X_train = data[:testindx,0:12]

	Y_train = data[:testindx,12]

Function Documentation

◆ fac0()

def isotrackTrainRegressorNoOptimization.fac0 ( jeta )

Definition at line 135 of file isotrackTrainRegressorNoOptimization.py.

References createfilelist.int.

 def fac0(jeta):
     PU_IETA_1 = 9
     PU_IETA_2 = 16
     PU_IETA_3 = 25
     idx = (int(jeta >= PU_IETA_1) + int(jeta >= PU_IETA_2) + int(jeta >= PU_IETA_3))
     return a0[idx]

◆ fac1()

def isotrackTrainRegressorNoOptimization.fac1 ( jeta )

Definition at line 141 of file isotrackTrainRegressorNoOptimization.py.

References createfilelist.int.

 def fac1(jeta):
     PU_IETA_1 = 9
     PU_IETA_2 = 16
     PU_IETA_3 = 25
     idx = (int(jeta >= PU_IETA_1) + int(jeta >= PU_IETA_2) + int(jeta >= PU_IETA_3))
     return a1[idx]

◆ fac2()

def isotrackTrainRegressorNoOptimization.fac2 ( jeta )

Definition at line 147 of file isotrackTrainRegressorNoOptimization.py.

References createfilelist.int.

 def fac2(jeta):
     PU_IETA_1 = 9
     PU_IETA_2 = 16
     PU_IETA_3 = 25
     idx = (int(jeta >= PU_IETA_1) + int(jeta >= PU_IETA_2) + int(jeta >= PU_IETA_3))
     return a2[idx]
 
 
 # In[44]:
 
 

◆ propweights()

def isotrackTrainRegressorNoOptimization.propweights ( y_true )

Definition at line 174 of file isotrackTrainRegressorNoOptimization.py.

References funct.abs().

 def propweights(y_true):
     weight = np.copy(y_true)
     weight[abs(y_true - meany) > 0] = 1.90*abs(y_true - meany)/stdy  #1.25
 #    weight[abs(y_true - meany) > stdy] = 1.75*abs((weight[abs(y_true - meany) > stdy]) - meany)/(stdy)
     weight[abs(y_true - meany) < stdy] =  1
     print ("wieght : ", weight)
     return weight
 
 
 # In[46]:
 
 

Variable Documentation

◆ _

isotrackTrainRegressorNoOptimization._ = plt.plot(lims, lims)

private

Definition at line 270 of file isotrackTrainRegressorNoOptimization.py.

◆ a0

list isotrackTrainRegressorNoOptimization.a0 = [0.973, 0.998, 0.992, 0.965 ]

marinas correction form

Definition at line 132 of file isotrackTrainRegressorNoOptimization.py.

◆ a1

list isotrackTrainRegressorNoOptimization.a1 = [0, -0.318, -0.261, -0.406]

Definition at line 133 of file isotrackTrainRegressorNoOptimization.py.

◆ a2

list isotrackTrainRegressorNoOptimization.a2 = [0, 0, 0.047, 0.089]

Definition at line 134 of file isotrackTrainRegressorNoOptimization.py.

◆ alpha

isotrackTrainRegressorNoOptimization.alpha

Definition at line 230 of file isotrackTrainRegressorNoOptimization.py.

◆ args

isotrackTrainRegressorNoOptimization.args = parser1.parse_args(args=[])

Definition at line 43 of file isotrackTrainRegressorNoOptimization.py.

◆ bins

isotrackTrainRegressorNoOptimization.bins

Definition at line 230 of file isotrackTrainRegressorNoOptimization.py.

◆ cols_to_minmax

list isotrackTrainRegressorNoOptimization.cols_to_minmax = ['t_delta', 't_hmaxNearP','t_emaxNearP', 't_hAnnular', 't_eAnnular','t_pt','t_nVtx','t_ieta','t_eHcal10', 't_eHcal30','t_rhoh','t_eHcal_x']

Definition at line 90 of file isotrackTrainRegressorNoOptimization.py.

◆ data

isotrackTrainRegressorNoOptimization.data = df.values

Definition at line 96 of file isotrackTrainRegressorNoOptimization.py.

◆ default

isotrackTrainRegressorNoOptimization.default

Definition at line 40 of file isotrackTrainRegressorNoOptimization.py.

◆ df

isotrackTrainRegressorNoOptimization.df = pd.read_pickle(fName)

Definition at line 59 of file isotrackTrainRegressorNoOptimization.py.

◆ dop

tuple isotrackTrainRegressorNoOptimization.dop = (X_test[:,16]/X_test[:,13])

Definition at line 164 of file isotrackTrainRegressorNoOptimization.py.

◆ eop

tuple isotrackTrainRegressorNoOptimization.eop = (X_test[:,15]/X_test[:,13])

Definition at line 163 of file isotrackTrainRegressorNoOptimization.py.

◆ fName

isotrackTrainRegressorNoOptimization.fName = args.input

Definition at line 45 of file isotrackTrainRegressorNoOptimization.py.

◆ help

isotrackTrainRegressorNoOptimization.help

Definition at line 40 of file isotrackTrainRegressorNoOptimization.py.

◆ history

isotrackTrainRegressorNoOptimization.history = model.fit(X_train,Y_train , batch_size=5000, epochs=50, validation_split=0.2, verbose=1,sample_weight=propweights(Y_train))

Definition at line 206 of file isotrackTrainRegressorNoOptimization.py.