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DDB_eval_keras.py
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import matplotlib as mpl
mpl.use('agg')
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, roc_auc_score, accuracy_score
import glob
import tqdm
import matplotlib.pyplot as plt
from matplotlib import rcParams
from matplotlib import rc
import pandas as pd
import sys
import imp
import os
import keras
from keras.layers import Input
try:
imp.find_module('setGPU')
import setGPU
except ImportError:
pass
import argparse
N = 60 # number of charged particles
N_sv = 5 # number of SVs
n_targets = 2 # number of classes
if os.path.isdir('/storage/group/gpu/bigdata/BumbleB'):
save_path = '/storage/group/gpu/bigdata/BumbleB/convert_20181121_ak8_80x_deepDoubleB_db_pf_cpf_sv_dl4jets_test/'
elif os.path.isdir('/eos/user/w/woodson/IN'):
save_path = '/eos/user/w/woodson/IN/convert_20181121_ak8_80x_deepDoubleB_db_pf_cpf_sv_dl4jets_test/'
spectators = ['fj_pt',
'fj_eta',
'fj_sdmass',
'fj_n_sdsubjets',
'fj_doubleb',
'fj_tau21',
'fj_tau32',
'npv',
'npfcands',
'ntracks',
'nsv']
params_0 = ['fj_jetNTracks',
'fj_nSV',
'fj_tau0_trackEtaRel_0',
'fj_tau0_trackEtaRel_1',
'fj_tau0_trackEtaRel_2',
'fj_tau1_trackEtaRel_0',
'fj_tau1_trackEtaRel_1',
'fj_tau1_trackEtaRel_2',
'fj_tau_flightDistance2dSig_0',
'fj_tau_flightDistance2dSig_1',
'fj_tau_vertexDeltaR_0',
'fj_tau_vertexEnergyRatio_0',
'fj_tau_vertexEnergyRatio_1',
'fj_tau_vertexMass_0',
'fj_tau_vertexMass_1',
'fj_trackSip2dSigAboveBottom_0',
'fj_trackSip2dSigAboveBottom_1',
'fj_trackSip2dSigAboveCharm_0',
'fj_trackSipdSig_0',
'fj_trackSipdSig_0_0',
'fj_trackSipdSig_0_1',
'fj_trackSipdSig_1',
'fj_trackSipdSig_1_0',
'fj_trackSipdSig_1_1',
'fj_trackSipdSig_2',
'fj_trackSipdSig_3',
'fj_z_ratio'
]
params_1 = ['pfcand_ptrel',
'pfcand_erel',
'pfcand_phirel',
'pfcand_etarel',
'pfcand_deltaR',
'pfcand_puppiw',
'pfcand_drminsv',
'pfcand_drsubjet1',
'pfcand_drsubjet2',
'pfcand_hcalFrac'
]
params_2 = ['track_ptrel',
'track_erel',
'track_phirel',
'track_etarel',
'track_deltaR',
'track_drminsv',
'track_drsubjet1',
'track_drsubjet2',
'track_dz',
'track_dzsig',
'track_dxy',
'track_dxysig',
'track_normchi2',
'track_quality',
'track_dptdpt',
'track_detadeta',
'track_dphidphi',
'track_dxydxy',
'track_dzdz',
'track_dxydz',
'track_dphidxy',
'track_dlambdadz',
'trackBTag_EtaRel',
'trackBTag_PtRatio',
'trackBTag_PParRatio',
'trackBTag_Sip2dVal',
'trackBTag_Sip2dSig',
'trackBTag_Sip3dVal',
'trackBTag_Sip3dSig',
'trackBTag_JetDistVal'
]
params_3 = ['sv_ptrel',
'sv_erel',
'sv_phirel',
'sv_etarel',
'sv_deltaR',
'sv_pt',
'sv_mass',
'sv_ntracks',
'sv_normchi2',
'sv_dxy',
'sv_dxysig',
'sv_d3d',
'sv_d3dsig',
'sv_costhetasvpv'
]
def main(args):
test_2_arrays = []
test_3_arrays = []
test_spec_arrays = []
target_test_arrays = []
for test_file in sorted(glob.glob(save_path + 'test_*_features_2.npy')):
test_2_arrays.append(np.load(test_file))
test_2 = np.concatenate(test_2_arrays)
for test_file in sorted(glob.glob(save_path + 'test_*_features_3.npy')):
test_3_arrays.append(np.load(test_file))
test_3 = np.concatenate(test_3_arrays)
for test_file in sorted(glob.glob(save_path + 'test_*_spectators_0.npy')):
test_spec_arrays.append(np.load(test_file))
test_spec = np.concatenate(test_spec_arrays)
for test_file in sorted(glob.glob(save_path + 'test_*_truth_0.npy')):
target_test_arrays.append(np.load(test_file))
target_test = np.concatenate(target_test_arrays)
del test_2_arrays
del test_3_arrays
del test_spec_arrays
del target_test_arrays
test_2 = np.swapaxes(test_2, 1, 2)
test_3 = np.swapaxes(test_3, 1, 2)
test_spec = np.swapaxes(test_spec, 1, 2)
print(test_2.shape)
print(test_3.shape)
print(target_test.shape)
print(test_spec.shape)
print(target_test.shape)
fj_pt = test_spec[:,0,0]
fj_eta = test_spec[:,1,0]
fj_sdmass = test_spec[:,2,0]
#no_undef = np.sum(target_test,axis=1) == 1
no_undef = fj_pt > -999 # no cut
min_pt = -999 #300
max_pt = 99999 #2000
min_eta = -999 # no cut
max_eta = 999 # no cut
min_msd = -999 #40
max_msd = 9999 #200
test_2 = test_2 [ (fj_sdmass > min_msd) & (fj_sdmass < max_msd) & (fj_eta > min_eta) & (fj_eta < max_eta) & (fj_pt > min_pt) & (fj_pt < max_pt) & no_undef]
test_3 = test_3 [ (fj_sdmass > min_msd) & (fj_sdmass < max_msd) & (fj_eta > min_eta) & (fj_eta < max_eta) & (fj_pt > min_pt) & (fj_pt < max_pt) & no_undef ]
test_spec = test_spec [ (fj_sdmass > min_msd) & (fj_sdmass < max_msd) & (fj_eta > min_eta) & (fj_eta < max_eta) & (fj_pt > min_pt) & (fj_pt < max_pt) & no_undef ]
target_test = target_test [ (fj_sdmass > min_msd) & (fj_sdmass < max_msd) & (fj_eta > min_eta) & (fj_eta < max_eta) & (fj_pt > min_pt) & (fj_pt < max_pt) & no_undef ]
test_2 = np.swapaxes(test_2, 1, 2)
test_3 = np.swapaxes(test_3, 1, 2)
test_spec = np.swapaxes(test_spec, 1, 2)
print(test_2.shape)
print(test_3.shape)
print(target_test.shape)
print(test_spec.shape)
print(target_test.shape)
#Convert two sets into two branch with one set in both and one set in only one (Use for this file)
test = test_2
params = params_2
test_sv = test_3
params_sv = params_3
batch_size = 1024
outdir = args.outdir
label = 'new'
prediction = np.array([])
from ddb import model_DeepDoubleXReference
keras_model = model_DeepDoubleXReference(inputs = [Input(shape=(N,len(params))),Input(shape=(N_sv,len(params_sv)))],
num_classes = n_targets, scale_hidden = 2,
hlf_input = None, datasets = ['cpf', 'sv'])
keras_model.load_weights('%s/keras_model_best.h5'%outdir)
keras_model.summary()
for j in tqdm.tqdm(range(0, target_test.shape[0], batch_size)):
out_test = keras_model.predict([test[j:j+batch_size],test_sv[j:j+batch_size]])
if j==0:
prediction = out_test
else:
prediction = np.concatenate((prediction, out_test),axis=0)
del out_test
np.save('%s/truth_%s.npy'%(outdir,label),target_test)
np.save('%s/prediction_%s.npy'%(outdir,label),prediction)
print(target_test)
print(prediction)
if __name__ == "__main__":
""" This is executed when run from the command line """
parser = argparse.ArgumentParser()
# Required positional arguments
parser.add_argument("outdir", help="Required output directory")
args = parser.parse_args()
main(args)