def saveClassAttributes(clss, form, save_addr):
""" Save class attributes.
**Parameters**\n
clss: instance
Handle of the instance to be saved.
form: str
Format to save in ('h5'/'hdf5', 'mat', or 'dmp'/'dump').
save_addr: str
The address to save the attributes in.
"""
save_addr = u.appendformat(save_addr, form)
if form == 'mat':
sio.savemat(save_addr, clss.__dict__)
elif form in ('h5', 'hdf5'):
try:
dictdump.dicttoh5(clss.__dict__, save_addr)
except:
dio.save(save_addr, clss.__dict__, compression=None)
elif form in ('dmp', 'dump'):
fh = open(save_addr, 'wb')
pickle.dump(clss, fh)
fh.close()
else:
raise NotImplementedError
def saveClassAttributes(clss, form, save_addr):
""" Save class attributes.
:Parameters:
clss : instance
Handle of the instance to be saved.
form : str
Format to save in ('h5' or 'mat').
save_addr : str
The address to save the attributes in.
"""
save_addr = u.appendformat(save_addr, form)
if form == 'mat':
sio.savemat(save_addr, clss.__dict__)
elif form in ('h5', 'hdf5'):
try:
dictdump.dicttoh5(clss.__dict__, save_addr)
except:
dio.save(save_addr, clss.__dict__, compression=None)
else:
raise NotImplementedError
def test(net, data):
'''
Args:
-----
net: a trained keras Model instance
data: an OrderedDict containing all X, y, w ndarrays for all particles (both train and test), e.g.:
data = {
"X_jet_train" : X_jet_train,
"X_jet_test" : X_jet_test,
"X_photon_train" : X_photon_train,
"X_photon_test" : X_photon_test,
"y_train" : y_train,
"y_test" : y_test,
"w_train" : w_train,
"w_test" : w_test
}
Returns:
--------
yhat: numpy array of dim [n_ev, n_classes] with the net predictions on the test data
'''
yhat = net.predict([data['X_jet_test'], data['X_photon_test'], data['X_event_test']],
verbose = True, batch_size = 512)
io.save(open(os.path.join('output','yhat.h5'), 'wb'), yhat)
# -- example of other quantities that can be evaluated from yhat
#class_predictions = [np.argmax(ev) for ev in yhat])
return yhat
def save_pde_sol(self, fname, sol, t=None):
b, w = self.split_sol(sol)
data = {}
data['b'] = b
data['w'] = w
data['Ps'] = self.p
data['Es'] = self.setup
if t is not None:
data['t'] = t
dd.save(fname + '.hdf5', data, compression='blosc')
def save(self, path):
"""
Saves an instance of the class using :func:`deepdish.io.save`.
Parameters
----------
path : str
Output path to HDF5 file.
"""
io.save(path, self.save_to_dict())
def save(self, path):
"""
Saves an instance of the class using `deepdish.io.save`.
Parameters
----------
path : str
Output path to HDF5 file.
"""
io.save(path, self.save_to_dict())
def saveParmSet(fname, par, text=None, saveroot=False):
from deepdish.io import save
if saveroot:
from tlmModel import tlmModel, Es_normal
m = tlmModel(Es=Es_normal, Ps=par, Vs=None)
t, result = m.ode_integrate([0.9, 0.2, 0.2])
b, w, h = result.T[-1]
par['b'] = b
par['w'] = w
par['h'] = h
if text is not None:
par['text'] = text
save("./auto/" + fname + ".hdf5", par)
def test():
'''
'''
data = io.load(open('test_data.h5', 'rb'))
#data = remove_tau(data)
# -- Load scikit classifier
classifier = joblib.load('sklBDT_trk2.pkl')
# -- Get classifier predictions
yhat = classifier.predict_proba(data['X'])[:, 2]
io.save(open('yhat_test.h5', 'wb'), yhat)
def saveParmSet(fname, par, text=None, saveroot=False):
from deepdish.io import save
if saveroot:
from tlmModel import tlmModel, Es_normal
Es_normal['verbose'] = False
m = tlmModel(Es=Es_normal, Ps=par, Vs=None)
t, result = m.ode_integrate([0.9, 0.3, 0.3])
b, s1, s2 = result[-1]
par['b'] = b
par['s1'] = s1
par['s2'] = s2
print b, s1, s2
if text is not None:
par['text'] = text
save("./auto/" + fname + ".hdf5", par)
def pinv(x):
# This could be a numpy issue, since the same matrix works fine in Matlab.
# https://github.com/numpy/numpy/issues/1588
try:
res = np.linalg.pinv(x)
except np.linalg.LinAlgError as err:
n_retry = 0
while True:
try:
res = np.linalg.pinv(x + np.random.randn(*x.shape) * 1e-15)
break
except np.linalg.LinAlgError:
n_retry += 1
if n_retry == 3:
from deepdish import io as dio
dio.save('debug.hdf', dict(x=x))
raise err
return res
def main():
opts = parse_options()
inFile = opts.inputFile
tree = opts.treeName
df = root2pandas(inFile, tree)
# -- save a pandas df to hdf5 (better to first convert it back to ndarray, to be fair)
import deepdish.io as io
outFile = inFile.replace(".root", ".h5")
io.save(outFile, df)
# -- let's load it back in to make sure it actually worked!
new_df = io.load(outFile)
# -- check the shape again -- nice check to run every time you create a df
print "File check!"
print "(Number of events, Number of branches): ", new_df.shape
def main(MODEL_FILE):
print "Loading hdf5's..."
test_dict = io.load('./data/test_dict_IPConv_ntuple_'+ RUN_NAME +'.h5')
train_dict = io.load('./data/train_dict_IPConv_ntuple_'+ RUN_NAME +'.h5')
X_train = train_dict['X']
y_train = train_dict['y']
X_test = test_dict['X']
y_test = test_dict['y']
n_features = X_test.shape[2]
# this is a df
ip3d = test_dict['ip3d']
print 'Building model...'
if (MODEL_FILE == 'CRNN'):
graph = build_graph(n_features)
model = Sequential()
model.add(graph)
# remove Maxout for tensorflow
model.add(MaxoutDense(64, 5, input_shape=graph.nodes['dropout'].output_shape[1:]))
model.add(Dense(64))
elif (MODEL_FILE == 'RNN'):
model = Sequential()
model.add(Masking(mask_value=-999, input_shape=(N_TRACKS, n_features)))
model.add(GRU(25))#, input_shape=(N_TRACKS, n_features))) #GRU
model.add(Dropout(0.2)) #0.2
# remove Maxout for tensorflow
model.add(MaxoutDense(64, 5)) #, input_shape=graph.nodes['dropout'].output_shape[1:]))
model.add(Dense(64))
model.add(Dropout(0.4))
model.add(Highway(activation = 'relu'))
model.add(Dropout(0.3))
model.add(Dense(4))
model.add(Activation('softmax'))
print 'Compiling model...'
model.compile('adam', 'categorical_crossentropy')
model.summary()
print 'Training:'
try:
model.fit(X_train, y_train, batch_size=512,
callbacks = [
EarlyStopping(verbose=True, patience=20, monitor='val_loss'),
ModelCheckpoint(MODEL_FILE + RUN_NAME +'-progress', monitor='val_loss', verbose=True, save_best_only=True)
],
nb_epoch=100,
validation_split = 0.2,
show_accuracy=True)
except KeyboardInterrupt:
print 'Training ended early.'
# -- load in best network
model.load_weights(MODEL_FILE + RUN_NAME +'-progress')
if (SAVE_PROTOBUF):
print 'Saving protobuf'
# write out to a new directory called models
# the actual graph file is graph.pb
# the graph def is in the global session
import tensorflow as tf
import keras.backend.tensorflow_backend as tfbe
sess = tfbe._SESSION
saver = tf.train.Saver()
tf.train.write_graph(sess.graph_def, 'models/', 'graph.pb', as_text=False)
save_path = saver.save(sess, "./model-weights.ckpt")
print "Model saved in file: %s" % save_path
print saver.as_saver_def().filename_tensor_name
print saver.as_saver_def().restore_op_name
print model.get_output()
print 'Saving weights...'
model.save_weights('./weights/ip3d-replacement_' + MODEL_FILE + RUN_NAME +'.h5', overwrite=True)
json_string = model.to_json()
open(MODEL_FILE + RUN_NAME +'.json', 'w').write(json_string)
print 'Testing...'
yhat = model.predict(X_test, verbose = True, batch_size = 512)
io.save('yhat'+ RUN_NAME +'.h5', yhat)
print 'Plotting ROC...'
fg = plot_ROC(y_test, yhat, ip3d, MODEL_FILE)
#plt.show()
fg.savefig('./plots/roc' + MODEL_FILE + RUN_NAME +'.pdf')
def save_hdf_from_nc(fname):
from deepdish.io import save
data=conv_nc_to_dict(fname)
if fname.endswith('.nc'):
fname=fname[:-3]
save(fname+'.hdf5',data)
def makeCombi(inputDir, inputFile, outputDir, makeTrainingInput=False, sys=''):
print(str(inputDir+"/"+inputFile)+" start")
chain = TChain("ttbbLepJets/tree")
chain.Add(inputDir+"/"+inputFile)
data = False
if 'Data' in inputDir: data = True
ttbb = False
if 'ttbb' in inputDir: ttbb = True
if makeTrainingInput: ttbb = True
muon_ch = 0
muon_pt = 30.0
muon_eta = 2.1
electron_ch = 1
electron_pt = 35.0
electron_eta = 2.1
jet_pt = 30.0
jet_eta = 2.4
jet_CSV_tight = 0.9535
jetCombi = []
for i in xrange(chain.GetEntries()) :
chain.GetEntry(i)
lepton_SF = 1.0
jet_SF_CSV = 1.0
pdfweight = []
scaleweight = []
PUWeight = []
lepton_SF = []
jet_SF_CSV_30 = []
if not data:
for j in xrange((chain.lepton_SF).size()):
lepton_SF.append(float(chain.lepton_SF[j]))
for j in xrange((chain.jet_SF_CSV_30).size()):
jet_SF_CSV_30.append(float(chain.jet_SF_CSV_30[j]))
for j in xrange((chain.PUWeight).size()):
PUWeight.append(float(chain.PUWeight[j]))
if 'TT' in inputDir or 'tt' in inputDir:
for j in xrange((chain.scaleweight).size()):
scaleweight.append(float(chain.scaleweight[j]))
for j in xrange((chain.pdfweight).size()):
pdfweight.append(float(chain.pdfweight[j]))
MET_px = chain.MET*math.cos(chain.MET_phi)
MET_py = chain.MET*math.sin(chain.MET_phi)
nu = TLorentzVector(MET_px, MET_py, 0, chain.MET)
lep = TLorentzVector()
lep.SetPtEtaPhiE(chain.lepton_pT, chain.lepton_eta, chain.lepton_phi, chain.lepton_E)
passmuon = False
passelectron = False
passmuon = chain.channel == muon_ch and lep.Pt() > muon_pt and abs(lep.Eta()) < muon_eta
passelectron = chain.channel == electron_ch and lep.Pt() > electron_pt and abs(lep.Eta()) < electron_eta
if passmuon == False and passelectron == False:
continue
addbjet1 = TLorentzVector(0,0,0,0)
addbjet2 = TLorentzVector(0,0,0,0)
if ttbb:
addbjet1.SetPtEtaPhiE(chain.addbjet1_pt,chain.addbjet1_eta,chain.addbjet1_phi,chain.addbjet1_e)
addbjet2.SetPtEtaPhiE(chain.addbjet2_pt,chain.addbjet2_eta,chain.addbjet2_phi,chain.addbjet2_e)
njets = 0
nbjets = 0
addbjet1_matched = TLorentzVector(0,0,0,0)
addbjet2_matched = TLorentzVector(0,0,0,0)
for iJet in range(len(chain.jet_pT)):
jet = TLorentzVector()
jet.SetPtEtaPhiE(chain.jet_pT[iJet],chain.jet_eta[iJet],chain.jet_phi[iJet],chain.jet_E[iJet])
if not data :
if 'jecup' in sys:
jet *= chain.jet_JER_Nom[iJet] * chain.jet_JES_Up[iJet]
elif 'jecdown' in sys:
jet *= chain.jet_JER_Nom[iJet] * chain.jet_JES_Down[iJet]
elif 'jerup' in sys:
jet *= chain.jet_JER_Up[iJet]
elif 'jerdown' in sys:
jet *= chain.jet_JER_Down[iJet]
else:
jet *= chain.jet_JER_Nom[iJet]
if jet.Pt() < jet_pt or abs(jet.Eta()) > jet_eta: continue
njets += 1
if chain.jet_CSV[iJet] > jet_CSV_tight: nbjets += 1
if addbjet1.DeltaR(jet) < 0.4: addbjet1_matched = jet;
if addbjet2.DeltaR(jet) < 0.4: addbjet2_matched = jet;
if njets < 6 or nbjets < 3: continue
print("addbjet1: "+str(addbjet1.Pt())+" matched: "+str(addbjet1_matched.Pt()))
print("addbjet2: "+str(addbjet2.Pt())+" matched: "+str(addbjet2_matched.Pt()))
for j in range(len(chain.jet_pT)-1):
for k in range(j+1, len(chain.jet_pT)):
if chain.jet_CSV[j] > jet_CSV_tight and chain.jet_CSV[k] > jet_CSV_tight:
b1 = TLorentzVector()
b2 = TLorentzVector()
b1.SetPtEtaPhiE(chain.jet_pT[j], chain.jet_eta[j], chain.jet_phi[j], chain.jet_E[j])
b2.SetPtEtaPhiE(chain.jet_pT[k], chain.jet_eta[k], chain.jet_phi[k], chain.jet_E[k])
if not data :
if 'jecup' in sys:
b1 *= chain.jet_JER_Nom[j] * chain.jet_JES_Up[j]
b2 *= chain.jet_JER_Nom[k] * chain.jet_JES_Up[k]
elif 'jecdown' in sys:
b1 *= chain.jet_JER_Nom[j] * chain.jet_JES_Down[j]
b2 *= chain.jet_JER_Nom[k] * chain.jet_JES_Down[k]
elif 'jerup' in sys:
b1 *= chain.jet_JER_Up[j]
b2 *= chain.jet_JER_Up[k]
elif 'jerdown' in sys:
b1 *= chain.jet_JER_Down[j]
b2 *= chain.jet_JER_Down[k]
else :
b1 *= chain.jet_JER_Nom[j]
b2 *= chain.jet_JER_Nom[k]
if makeTrainingInput:
if (addbjet1_matched.DeltaR(b1) == 0 and addbjet2_matched.DeltaR(b2) == 0) or (addbjet2_matched.DeltaR(b1) == 0 and addbjet1_matched.DeltaR(b2) == 0):
signal = 1
else:
signal = 0
jetCombi.append([
signal,i,b1.DeltaR(b2),abs(b1.Eta()-b2.Eta()),b1.DeltaPhi(b2),
(b1+b2+nu).Pt(),(b1+b2+nu).Eta(),(b1+b2+nu).Phi(),(b1+b2+nu).M(),
(b1+b2+lep).Pt(),(b1+b2+lep).Eta(),(b1+b2+lep).Phi(),(b1+b2+lep).M(),
(b1+lep).Pt(),(b1+lep).Eta(),(b1+lep).Phi(),(b1+lep).M(),
(b2+lep).Pt(),(b2+lep).Eta(),(b2+lep).Phi(),(b2+lep).M(),
(b1+b2).Pt(),(b1+b2).Eta(),(b1+b2).Phi(),(b1+b2).M(),
chain.jet_CSV[j],chain.jet_CSV[k],
b1.Pt(),b2.Pt(),b1.Eta(),b2.Eta(),b1.Phi(),b2.Phi(),b1.E(),b2.E()
])
else:
jetCombi.append([
#Tree info
i, chain.channel, njets, nbjets,
chain.genweight, PUWeight,
lepton_SF, jet_SF_CSV_30, scaleweight, pdfweight,
lep.Pt(), lep.Eta(), lep.Phi(), lep.E(),
addbjet1.Pt(), addbjet1.Eta(), addbjet1.Phi(), addbjet1.E(),
addbjet2.Pt(), addbjet2.Eta(), addbjet2.Phi(), addbjet2.E(),
j,k,
#Deep learning variables
b1.DeltaR(b2),abs(b1.Eta()-b2.Eta()),b1.DeltaPhi(b2),
(b1+b2+nu).Pt(),(b1+b2+nu).Eta(),(b1+b2+nu).Phi(),(b1+b2+nu).M(),
(b1+b2+lep).Pt(),(b1+b2+lep).Eta(),(b1+b2+lep).Phi(),(b1+b2+lep).M(),
(b1+lep).Pt(),(b1+lep).Eta(),(b1+lep).Phi(),(b1+lep).M(),
(b2+lep).Pt(),(b2+lep).Eta(),(b2+lep).Phi(),(b2+lep).M(),
(b1+b2).Pt(),(b1+b2).Eta(),(b1+b2).Phi(),(b1+b2).M(),
chain.jet_CSV[j],chain.jet_CSV[k],
b1.Pt(),b2.Pt(),b1.Eta(),b2.Eta(),b1.Phi(),b2.Phi(),b1.E(),b2.E()
])
if makeTrainingInput:
combi = pd.DataFrame(jetCombi, columns=['signal', 'event']+ut.getVarlist())
else:
combi = pd.DataFrame(jetCombi, columns=
['event','channel','njets','nbjets',
'genWeight','PUWeight',
'lepton_SF','jet_SF_CSV_30', 'scaleweight', 'pdfweight',
'leptonPt','leptonEta','leptonPhi','leptonE',
'addbjet1_pt','addbjet1_eta','addbjet1_phi','addbjet1_e',
'addbjet2_pt','addbjet2_eta','addbjet2_phi','addbjet2_e',
'b1','b2',
] + ut.getVarlist())
tmp = inputFile[:-5]
if makeTrainingInput:
io.save(outputDir+"/array_train_ttbb.h5",combi)
else:
io.save(outputDir+"/array_"+tmp+".h5",combi)
print(str(inputDir+"/"+inputFile)+" end")
def _fit_and_score_ckpt(workdir=None,
checkpoint=True,
force_refresh=False,
**fit_and_score_kwargs):
"""Fit estimator and compute scores for a given dataset split.
This function wraps
:func:`sklearn:sklearn.model_selection._validation._fit_and_score`,
while also saving checkpoint files containing the estimator, paramters,
This is useful if fitting and scoring is costly or if it is being
performed within a large cross-validation experiment.
In avoid collisions with scores computed for other CV splits, this
function computes a hash from a nested dictionary containing all keyword
arguments as well as estimator parameters. It then saves the scores and
parameters in <hash>_params.h5 and the estimator itself in
<hash>_estimator.pkl
Parameters
----------
workdir : path-like object, default=None
A string or :term:`python:path-like-object` indicating the directory
in which to store checkpoint files
checkpoint : bool, default=True
If True, checkpoint the parameters, estimators, and scores.
force_refresh : bool, default=False
If True, recompute scores even if the checkpoint file already exists.
Otherwise, load scores from checkpoint files and return.
**fit_and_score_kwargs : kwargs
Key-word arguments passed to
:func:`sklearn:sklearn.model_selection._validation._fit_and_score`
Returns
-------
train_scores : dict of scorer name -> float
Score on training set (for all the scorers),
returned only if `return_train_score` is `True`.
test_scores : dict of scorer name -> float
Score on testing set (for all the scorers).
n_test_samples : int
Number of test samples.
fit_time : float
Time spent for fitting in seconds.
score_time : float
Time spent for scoring in seconds.
parameters : dict or None
The parameters that have been evaluated.
estimator : estimator object
The fitted estimator
"""
if not checkpoint:
return _fit_and_score(**fit_and_score_kwargs)
if workdir is None:
raise ValueError(
"If checkpoint is True, you must supply a working directory "
"through the ``workdir`` argument.")
estimator = fit_and_score_kwargs.pop("estimator", None)
estimator_params = _serialize_estimator_params(estimator.get_params())
all_params = {
"estimator_params": estimator_params,
"fit_and_score_kwargs": fit_and_score_kwargs,
}
cv_hash = hashlib.md5(
json.dumps(all_params, sort_keys=True, ensure_ascii=True,
default=str).encode()).hexdigest()
h5_file = os.path.join(workdir, cv_hash + "_params.h5")
pkl_file = os.path.join(workdir, cv_hash + "_estimator.pkl")
if not force_refresh and os.path.exists(h5_file):
ckpt_dict = ddio.load(h5_file)
scores = ckpt_dict["scores"]
if fit_and_score_kwargs.get("return_estimator", False):
with open(pkl_file, "rb") as fp:
estimator = pickle.load(fp)
scores.append(estimator)
return scores
else:
scores = _fit_and_score(estimator, **fit_and_score_kwargs)
os.makedirs(workdir, exist_ok=True)
if fit_and_score_kwargs.get("return_estimator", False):
estimator = scores[-1]
with open(pkl_file, "wb") as fp:
pickle.dump(estimator, fp)
ckpt_scores = scores[:-1]
if isinstance(estimator, Pipeline):
model = estimator.steps[-1]
else:
model = estimator
estimator_params = _serialize_estimator_params(
estimator.get_params())
fitted_params = {
"alpha_": getattr(model, "alpha_", None),
"alphas_": getattr(model, "alphas_", None),
"l1_ratio_": getattr(model, "l1_ratio_", None),
"mse_path_": getattr(model, "mse_path_", None),
"scoring_path_": getattr(model, "scoring_path_", None),
"intercept_": getattr(model, "intercept_", None),
"coef_": getattr(model, "coef_", None),
}
else:
estimator_params = None
fitted_params = None
ckpt_scores = scores
fit_and_score_kwargs.pop("X")
fit_and_score_kwargs.pop("y")
if "scorer" in fit_and_score_kwargs:
fit_and_score_kwargs["scorer"] = list(
fit_and_score_kwargs["scorer"].keys())
ckpt_dict = {
"scores": ckpt_scores,
"fit_and_score_kwargs": fit_and_score_kwargs,
"estimator_params": estimator_params,
"fitted_params": fitted_params,
}
ddio.save(h5_file, ckpt_dict)
return scores
def main(embed_size, normed, input_id, run_name):
configure_logging()
logger = logging.getLogger("RNNIP Training")
logger.info("Loading hdf5's")
test_dict = io.load(os.path.join('data', 'test_dict_' + input_id + '.h5'))
train_dict = io.load(os.path.join('data',
'train_dict_' + input_id + '.h5'))
X_train_stream0 = train_dict['grade']
X_train_stream1 = train_dict['X']
y_train = train_dict['y']
X_test_stream0 = test_dict['grade']
X_test_stream1 = test_dict['X']
y_test = test_dict['y']
ip3d = test_dict['ip3d']
logger.info('Building model')
model = build_model(X_train_stream0, X_train_stream1, embed_size, normed)
model.summary()
logger.info('Compiling model')
model.compile('adam', 'categorical_crossentropy', metrics=['accuracy'])
#-- if the pre-trained model exists, load it in, otherwise start from scratch
safe_mkdir('weights')
weights_file = os.path.join('weights', 'rnnip_' + run_name + '.h5')
try:
model.load_weights(weights_file)
logger.info('Loaded pre-trained model from ' + weights_file)
except IOError:
logger.info('No pre-trained model found in ' + weights_file)
logger.info('Training:')
try:
model.fit([X_train_stream0, X_train_stream1],
y_train,
batch_size=512,
callbacks=[
EarlyStopping(verbose=True,
patience=20,
monitor='val_loss'),
ModelCheckpoint(weights_file,
monitor='val_loss',
verbose=True,
save_best_only=True)
],
epochs=300,
validation_split=0.2)
except KeyboardInterrupt:
logger.info('Training ended early.')
# -- load in best network
logger.info('Loading best epoch')
model.load_weights(weights_file)
json_string = model.to_json()
safe_mkdir('json_models')
open(os.path.join('json_models', run_name + '.json'),
'w').write(json_string)
logger.info('Testing')
safe_mkdir('predictions')
yhat = model.predict([X_test_stream0, X_test_stream1],
verbose=True,
batch_size=10000)
io.save(os.path.join('predictions', 'yhat' + run_name + '.h5'), yhat)
logger.info('Plotting ROC')
plot_ROC(y_test, yhat, ip3d, run_name)
#'jet_trk_dPhi'] # more to be added in `process_data`
'jet_trk_phi'] # more to be added in `process_data`
cut_vars = ['jet_eta', 'jet_pt', 'jet_JVT', 'jet_aliveAfterOR'] # only necessary to remove bad jets
# -- load and process training set
print 'Loading training dataframe...'
trk_train = pup.root2panda(
'./data/Dan/NOtrkSel/train_NOtrkSel.root',
'bTag_AntiKt4EMTopoJets',
branches = track_inputs + cut_vars + ['jet_LabDr_HadF' , 'jet_ip3d_pu', 'jet_ip3d_pb', 'jet_ip3d_pc', 'jet_phi', 'jet_trk_theta']
)
print 'Processing training sample ...'
train_dict = process_data(trk_train, cut_vars, savevars=True)
del trk_train
io.save('./data/train_dict_IPConv_ntuple_MyTrkSel.h5', train_dict)
# -- load and process test set
print 'Loading test dataframe...'
trk_test = pup.root2panda(
'./data/Dan/NOtrkSel/test/user.dguest.8493098.Akt4EMTo._000013_NOtrkSel.root',
'bTag_AntiKt4EMTopoJets',
branches = track_inputs + cut_vars + ['jet_LabDr_HadF' , 'jet_ip3d_pu', 'jet_ip3d_pb', 'jet_ip3d_pc', 'jet_phi', 'jet_trk_theta']
)
print 'Processing test sample...'
test_dict = process_data(trk_test, cut_vars, savevars=False)
del trk_test
io.save('./data/test_dict_IPConv_ntuple_MyTrkSel.h5', test_dict)
help="Maximum number of tracks per event. \
If the event has fewer tracks, use padding; if is has more, only consider the first ntrk"
)
args = parser.parse_args()
print 'Loading dataframes...'
# -- currently only training and testing on one file each!
trk_train = pup.root2panda(os.path.join('data', 'train', args.train_files),
'bTag_AntiKt4EMTopoJets',
branches=track_inputs + jet_inputs)
trk_test = pup.root2panda(os.path.join('data', 'test', args.test_files),
'bTag_AntiKt4EMTopoJets',
branches=track_inputs + jet_inputs)
print 'Processing training sample ...'
train_dict = process_data(trk_train,
jet_inputs,
args.ntrk,
args.sort_by,
args.output,
savevars=True)
del trk_train
io.save(os.path.join('data', 'train_dict_' + args.output + '.h5'),
train_dict)
print 'Processing test sample...'
test_dict = process_data(trk_test, jet_inputs, args.ntrk, args.sort_by,
args.output)
del trk_test
io.save(os.path.join('data', 'test_dict_' + args.output + '.h5'),
test_dict)
def process(i, filepath, yaml_file):
'''
'''
import pandautils as pup
branches, training_vars, ip3d_training_vars, ipmp_training_vars = set_features(
yaml_file)
logger = logging.getLogger("ETL Service")
logger.info('Operating on {}'.format(filepath))
logger.info('Creating dataframe...')
df = pup.root2panda(filepath, 'bTag_AntiKt4EMTopoJets', branches=branches)
logger.info('Transforming variables...')
df = transformVars(df)
logger.info('Flattening df...')
df.drop(['PVx', 'PVy', 'PVz'], axis=1, inplace=True)
df_flat = pd.DataFrame({k: pup.flatten(c) for k, c in df.iteritems()})
del df
logger.info('Applying cuts...')
df_flat = apply_calojet_cuts(df_flat)
logger.info('Creating X, y, w, mv2c10...')
y = df_flat['jet_LabDr_HadF'].values
mv2c10 = df_flat['jet_mv2c10'].values
jet_pt = df_flat['jet_pt'].values
ip3d_vars = df_flat[ip3d_training_vars].values
ipmp_vars = df_flat[ipmp_training_vars].values
# -- slice df by only keeping the training variables
X = df_flat[training_vars].values
# -- Find weights by reweighting to the light distribution
# -- TO DO: pass the pt and eta columns directly, instead of passing their indices
pt_col = np.argwhere(np.array(training_vars) == 'jet_pt')[0][0]
eta_col = np.argwhere(np.array(training_vars) == 'abs(jet_eta)')[0][0]
#w = reweight_to_b(X, y, pt_col, eta_col)
w = reweight_to_l(X, y, pt_col, eta_col)
del df_flat
logger.info('Shuffling, splitting, scaling...')
ix = np.array(range(len(y)))
X_train, X_test, y_train, y_test, w_train, w_test, ix_train, ix_test, \
mv2c10_train, mv2c10_test, jet_pt_train, jet_pt_test, \
ip3d_vars_train, ip3d_vars_test, ipmp_vars_train, ipmp_vars_test = train_test_split(
X,
y,
w,
ix,
mv2c10,
jet_pt,
ip3d_vars,
ipmp_vars,
train_size=0.6)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
ip3d_vars_train = scaler.fit_transform(ip3d_vars_train)
ip3d_vars_test = scaler.transform(ip3d_vars_test)
ipmp_vars_train = scaler.fit_transform(ipmp_vars_train)
ipmp_vars_test = scaler.transform(ipmp_vars_test)
X_train, X_validate, y_train, y_validate, w_train, w_validate, ix_train, ix_validate, \
mv2c10_train, mv2c10_validate, jet_pt_train, jet_pt_validate, \
ip3d_vars_train, ip3d_vars_validate, ipmp_vars_train, ipmp_vars_validate = train_test_split(
X_train,
y_train,
w_train,
ix_train,
mv2c10_train,
jet_pt_train,
ip3d_vars_train,
ipmp_vars_train,
train_size=0.7)
train = {
'X': X_train,
'ip3d_vars': ip3d_vars_train,
'ipmp_vars': ipmp_vars_train,
'y': y_train,
'w': w_train,
'ix': ix_train,
'mv2c10': mv2c10_train,
'pt': jet_pt_train
}
test = {
'X': X_test,
'ip3d_vars': ip3d_vars_test,
'ipmp_vars': ipmp_vars_test,
'y': y_test,
'w': w_test,
'ix': ix_test,
'mv2c10': mv2c10_test,
'pt': jet_pt_test
}
validate = {
'X': X_validate,
'ip3d_vars': ip3d_vars_validate,
'ipmp_vars': ipmp_vars_validate,
'y': y_validate,
'w': w_validate,
'ix': ix_validate,
'mv2c10': mv2c10_validate,
'pt': jet_pt_validate
}
logger.info('Saving dictionaries to hdf5...')
hdf5_train_path = os.path.join('..', 'data',
'DL1-' + OUTNAME + str(i) + '-train-db.h5')
hdf5_test_path = os.path.join('..', 'data',
'DL1-' + OUTNAME + str(i) + '-test-db.h5')
hdf5_validate_path = os.path.join(
'..', 'data', 'DL1-' + OUTNAME + str(i) + '-validate-db.h5')
io.save(hdf5_train_path, train)
io.save(hdf5_test_path, test)
io.save(hdf5_validate_path, validate)
logger.debug('Saved hdf5 archives: {}, {}, {}'.format(
hdf5_train_path, hdf5_test_path, hdf5_validate_path))
return (y_train.shape[0], y_test.shape[0], y_validate.shape[0])
def save(self, fname):
with warnings.catch_warnings():
warnings.simplefilter('ignore', category=tables.NaturalNameWarning)
dio.save(fname, self.to_dict())
def integrate(prec_i,chi,beta,
Ps=defaultPs,
n=(512,512),l=(256.0,256.0),
Vs_initial="uniform",rhs="oz_EQK",
bc="periodic",it="pseudo_spectral",
first_time = 1000.0,tol=1.0e-8,add_noise=0.01,
fname="cont",verbose=True,
create_movie=False,
send_email=None, test=False):
import deepdish.io as dd
if send_email is not None:
import getpass
pswd = getpass.getpass('Password:'******'rhs':rhs,'n':n,'l':l,'bc':bc,'it':it,
'dt':0.1,'verbose':verbose,'analyze':False,'setPDE':True}
if Vs_initial.endswith(".dat",-4):
state = np.loadtxt(Vs_initial)
m = bwhModel(Es=Es,Ps=Ps,Vs=state)
fname = str(Vs_initial[:-4])
else:
m = bwhModel(Es=Es,Ps=Ps,Vs=None)
if Vs_initial=="uniform":
m.setup_initial_condition("uniform",p=prec_i,chi=chi,beta=beta)
else:
m.setup_initial_condition(Vs_initial)
m.setup['verbose']=verbose
yr=m.p['conv_T_to_t']
if test:
print("Test session:")
print(m.p)
print("Dimpar:",m.p['dimpar'])
print("chi=",chi,"beta=",beta)
print("fname:",fname,", fname type:",type(fname))
# sol = Vs_initial
if not test:
print("****Starting Integration****")
sol = m.integrate(m.initial_state,check_convergence=True,
max_time=first_time*yr,p=prec_i,chi=chi,beta=beta)
print("****Integration Finished****")
elif test:
sol = m.initial_state
b,w,h = m.split_state(sol)
dd.save(fname+".hdf5",{'p':prec_i,'chi':float(chi),'beta':float(beta),
'Ps_dimensional':m.p['dimpar'],
'n':n,'l':l,'state':sol,'test':test,
'b':b,'w':w,'h':h})
if send_email is not None:
try:
import smtplib
from socket import gaierror
server = smtplib.SMTP('smtp.gmail.com', 587)
server.ehlo()
server.starttls()
server.login(send_email, pswd)
msg = "\r\n".join([
"From: {}".format(send_email),
"To: {}".format(send_email),
"Subject: Drought bwh simulations finished",
"",
"Drought bwh simulations finished and saved to:", fname
])
server.sendmail(send_email, send_email, msg)
server.quit()
except gaierror:
pass
def main(file_name, run_name, n_tracks):
print "Loading hdf5's from ./data/test_dict" + file_name + ".h5 and ./data/train_dict" + file_name + ".h5"
test_dict = io.load(os.path.join('data', 'test_dict_' + file_name + '.h5'))
train_dict = io.load(os.path.join('data', 'train_dict_' + file_name + '.h5'))
X_train = train_dict['X']
y_train = train_dict['y']
X_test = test_dict['X']
y_test = test_dict['y']
n_features = X_test.shape[2]
# this is a df
ip3d = test_dict['ip3d']
print 'Building model...'
# -- for track grade as a normal input:
model = Sequential()
model.add(Masking(mask_value=-999, input_shape=(n_tracks, n_features)))
model.add(Dropout(0.2)) # dropping out before the GRU should help us reduce dependence on any specific input variable
# ^^ could be desirable when training on track hits in case one detector layer fails
model.add(GRU(25, return_sequences=False))
# model.add(Dropout(0.2))
model.add(Dense(4))
model.add(Activation('softmax'))
model.summary()
print 'Compiling model...'
model.compile('adam', 'categorical_crossentropy', metrics=["accuracy"])
# -- if the pre-trained model exists, load it in, otherwise start from scratch
try:
_weights_location = os.path.join('weights', 'ip3d-replacement_' + MODEL_FILE + '_' + run_name +'.h5')
model.load_weights(_weights_location)
print 'Loaded pre-trained model from ' + _weights_location
except IOError:
print 'No pre-trained model found in ' + _weights_location
print 'Training:'
try:
model.fit(X_train, y_train, batch_size=1024,
callbacks = [
EarlyStopping(verbose=True, patience=20, monitor='val_loss'),
ModelCheckpoint(MODEL_FILE + run_name +'-progress', monitor='val_loss', verbose=True, save_best_only=True)
],
nb_epoch=300,
validation_split = 0.2)
except KeyboardInterrupt:
print 'Training ended early.'
# -- load in best network
model.load_weights(MODEL_FILE + run_name +'-progress')
print 'Saving weights in ' + _weights_location
model.save_weights(_weights_location, overwrite=True)
json_string = model.to_json()
open(MODEL_FILE + run_name +'.json', 'w').write(json_string)
print 'Testing...'
yhat = model.predict(X_test, verbose = True, batch_size = 1024)
io.save('yhat'+ run_name +'.h5', yhat)
print 'Plotting ROC...'
fg_bl, fg_bc = plot_ROC(y_test, yhat, ip3d, run_name, MODEL_FILE)
def simdrought(prec_i,prec_f,delta_p,delta_year,chi,beta,
Ps=defaultPs,
n=(512,512),l=(256.0,256.0),
Vs_initial="uniform",rhs="oz_EQK",
bc="periodic",it="pseudo_spectral",
first_time = 100.0,tol=1.0e-8,add_noise=0.01,
fname="cont",verbose=True,
savefile=None,create_movie=False,
send_email=None):
import deepdish.io as dd
if send_email is not None:
import getpass
pswd = getpass.getpass('Password:'******'rhs':rhs,'n':n,'l':l,'bc':bc,'it':it,
'dt':0.1,'verbose':verbose,'analyze':False,'setPDE':True}
if type(Vs_initial)==str:
fname = fname+"_"+Vs_initial
prec_gradient_down = np.arange(prec_i,prec_f-delta_p,-delta_p)
time_span = np.arange(delta_year,len(prec_gradient_down)*delta_year+delta_year,delta_year)
m = bwhModel(Vs=None,Es=Es,Ps=Ps)
if Vs_initial=="uniform":
m.setup_initial_condition("uniform",p=prec_i,chi=chi,beta=beta)
else:
m.setup_initial_condition(Vs_initial)
m.setup['verbose']=verbose
yr=m.p['conv_T_to_t']
# Converging on the first solution using integration and then root
Vs_init = m.integrate(m.initial_state,check_convergence=True,
max_time=first_time*yr,p=prec_i,chi=chi,beta=beta)
# Es['rhs']="oz_EQK_relax"
# m = bwhModel(Vs=Vs_initial,Es=Es,Ps=Ps)
# m.setup['verbose']=verbose
Vs = Vs_init.copy()
b_sol = np.zeros((len(prec_gradient_down),n[0],n[1]))
w_sol = np.zeros((len(prec_gradient_down),n[0],n[1]))
h_sol = np.zeros((len(prec_gradient_down),n[0],n[1]))
if create_movie and savefile is not None:
savefile_base=savefile
for i,prec in enumerate(prec_gradient_down):
print("Integration for p =",prec)
if create_movie and savefile is not None:
savefile=savefile_base+"_p{:4.3f}".format(prec).replace(".","_")
b,w,h=m.split_state(Vs)
if add_noise is not None:
b=b+add_noise*np.random.random(size=b.shape)
w=w+add_noise*np.random.random(size=w.shape)
# h=h+add_noise*np.random.random(size=h.shape)
Vs = np.ravel((b,w,h))
Vs_new=m.integrate(initial_state=Vs,
max_time=delta_year*yr,step=yr,
check_convergence=False,
savefile=savefile,create_movie=False,
p=prec,chi=chi,beta=beta)
if rhs!="oz_EQK":
if m.converged_relaxation==False:
time,result=m.pseudo_spectral_integrate(initial_state=Vs,
finish=delta_year*yr,
step=yr,
p=prec,chi=chi,beta=beta)
Vs_new=result[-1]
b,w,h=m.split_state(Vs_new)
b_sol[i]=b
w_sol[i]=w
h_sol[i]=h
Vs = np.ravel((b,w,h))
dd.save(fname+".hdf5",{'p':prec_gradient_down,"T":time_span,
'chi':chi,'beta':beta,
'Ps_dimensional':m.p['dimpar'],
'n':n,'l':l,
'b':b_sol,
'w':w_sol,
'h':h_sol})
if send_email is not None:
try:
import smtplib
from socket import gaierror
server = smtplib.SMTP('smtp.gmail.com', 587)
server.ehlo()
server.starttls()
server.login(send_email, pswd)
msg = "\r\n".join([
"From: {}".format(send_email),
"To: {}".format(send_email),
"Subject: Drought bwh simulations finished",
"",
"Drought bwh simulations finished and saved to:", fname
])
server.sendmail(send_email, send_email, msg)
server.quit()
except gaierror:
pass
'jet_trk_nsplitPixHits', 'jet_trk_nSCTHits',
'jet_trk_nsharedSCTHits', 'jet_trk_expectBLayerHit'] # 2 more to be added in `process_data`
print 'Loading dataframes...'
# -- currently only training and testing on one file each!
trk_train = pup.root2panda(
'./data/train/*410000_00*.root',
'JetCollection',
branches = track_inputs + ['jet_truthflav' , 'jet_ip3d_pu', 'jet_ip3d_pb', 'jet_ip3d_pc', 'jet_phi', 'jet_trk_phi']
)
trk_test = pup.root2panda(
'./data/test/*410000*.root',
'JetCollection',
branches = track_inputs + ['jet_truthflav' , 'jet_ip3d_pu', 'jet_ip3d_pb', 'jet_ip3d_pc', 'jet_phi', 'jet_trk_phi']
)
print 'Processing training sample ...'
train_dict = process_data(trk_train, savevars=True)
del trk_train
io.save('./data/train_dict_IPConv.h5', train_dict)
print 'Processing test sample...'
test_dict = process_data(trk_test)
del trk_test
io.save('./data/test_dict_IPConv.h5', test_dict)
fig = plt.figure(figsize=(11.69, 8.27), dpi=100)
bins = np.linspace(
min(min(ttbar['NMuon']), min(qcd['NMuon']), min(wjets['NMuon'])),
max(max(ttbar['NMuon']), max(qcd['NMuon']), max(wjets['NMuon'])), 10)
_ = plt.hist(
[ttbar['NMuon'], qcd['NMuon'], wjets['NMuon']],
stacked=True,
label=[r'$t\overline{t}$', 'QCD', 'wjets'],
alpha=0.5,
histtype='stepfilled',
normed=False,
bins=bins,
weights=[ttbar['EventWeight'], qcd['EventWeight'], wjets['EventWeight']])
plt.xlabel('NMuon')
plt.ylabel('Number of Events')
plt.yscale('log')
plt.legend()
plt.plot()
plt.savefig('task3.pdf')
io.save('wj_nmuons.h5', wjets['NMuon'])
#new_df = io.load('wj_nmuons.h5')
#print new_df
pickle.dump(wjets['NMuon'], open('wj_nmuons.pkl', 'wb'))
#test = pickle.load(open('wj_nmuons.pkl', 'rb'))
#print test
def save(self, path):
d = self.save_to_dict()
d["name"] = self.name
io.save(path, d)
If the event has fewer tracks, use padding; if is has more, only consider the first ntrk")
parser.add_argument('--inputs',
default='grade',
help='one of: hits, grade')
args = parser.parse_args()
track_inputs, jet_inputs = generate_inputlist(args.inputs)
print 'Loading dataframes...'
# -- currently only training and testing on one file each!
trk_train = pup.root2panda(
os.path.join('data', 'train', args.train_files),
'bTag_AntiKt4EMTopoJets',
branches = track_inputs + jet_inputs
)
trk_test = pup.root2panda(
os.path.join('data', 'test', args.test_files),
'bTag_AntiKt4EMTopoJets',
branches = track_inputs + jet_inputs
)
print 'Processing training sample ...'
train_dict = process_data(trk_train, jet_inputs, args.ntrk, args.sort_by, args.output, args.inputs, savevars=True)
del trk_train
io.save(os.path.join('data', 'train_dict_' + args.output + '.h5'), train_dict)
print 'Processing test sample...'
test_dict = process_data(trk_test, jet_inputs, args.ntrk, args.sort_by, args.output, args.inputs,)
del trk_test
io.save(os.path.join('data', 'test_dict_' + args.output + '.h5'), test_dict)
def main(embed_size, normed, input_id, run_name):
configure_logging()
logger = logging.getLogger("RNNIP Training")
logger.info("Loading hdf5's")
test_dict = io.load(os.path.join('data', 'test_dict_' + input_id + '.h5'))
train_dict = io.load(os.path.join('data', 'train_dict_' + input_id + '.h5'))
X_train_stream0 = train_dict['grade']
X_train_stream1 = train_dict['X']
y_train = train_dict['y']
X_test_stream0 = test_dict['grade']
X_test_stream1 = test_dict['X']
y_test = test_dict['y']
ip3d = test_dict['ip3d']
logger.info('Building model')
model = build_model(X_train_stream0, X_train_stream1, embed_size, normed)
model.summary()
logger.info('Compiling model')
model.compile('adam', 'categorical_crossentropy', metrics=['accuracy'])
#-- if the pre-trained model exists, load it in, otherwise start from scratch
safe_mkdir('weights')
weights_file = os.path.join('weights', 'rnnip_' + run_name +'.h5')
try:
model.load_weights(weights_file)
logger.info('Loaded pre-trained model from ' + weights_file)
except IOError:
logger.info('No pre-trained model found in ' + weights_file)
logger.info('Training:')
try:
model.fit([X_train_stream0, X_train_stream1], y_train, batch_size=512,
callbacks = [
EarlyStopping(verbose=True, patience=20, monitor='val_loss'),
ModelCheckpoint(
weights_file,
monitor='val_loss', verbose=True, save_best_only=True
)
],
epochs=300,
validation_split = 0.2)
except KeyboardInterrupt:
logger.info('Training ended early.')
# -- load in best network
logger.info('Loading best epoch')
model.load_weights(weights_file)
json_string = model.to_json()
safe_mkdir('json_models')
open(os.path.join('json_models', run_name +'.json'), 'w').write(json_string)
logger.info('Testing')
safe_mkdir('predictions')
yhat = model.predict([X_test_stream0, X_test_stream1], verbose=True, batch_size=10000)
io.save(os.path.join('predictions', 'yhat'+ run_name +'.h5'), yhat)
logger.info('Plotting ROC')
plot_ROC(y_test, yhat, ip3d, run_name)
"""
return (
self.cells[tup]
for tup in self.cells
if self.cells[tup] != -1 and self.cell_distance(cell, tup)
)
def update(self, point, index):
"""updates the grid with the new point
Parameters
----------
point :
index :
Returns
-------
"""
self.cells[self.cellify(point)] = index
def __str__(self):
return self.cells.__str__()
if __name__ == "__main__":
bg = 255 - poisson_disk_background((640, 640), 12, 2)
dio.save("poisson_dense.h5", bg)
def main(inputfiles, treename, ftrain, max_n_pairs, exclude_list):
'''
Args:
-----
inputfiles: list of strings with the paths to root files
treename: string, name of the TTree that contains the branches
ftrain: float in range [0, 1], training fraction
max_n_pairs: int, maximum number of jet pairs to consider per event
exclude_list:
Returns:
--------
'''
# -- configure logging
utils.configure_logging()
logger = logging.getLogger('main')
# -- concatenate all files into a pandas df
short_filenames = [f.split('/')[-1] for f in inputfiles]
logger.info('Creating pandas dataframes from: {}'.format(short_filenames))
#df = pd.concat([pup.root2panda(f, treename) for f in inputfiles], ignore_index=True)
df_list = []
for f in inputfiles:
df_temp = pup.root2panda(f, treename)
df_temp['sample'] = f.split('/')[-1].split('.')[0]
df_list.append(df_temp)
df = pd.concat(df_list, ignore_index=True)
# -- remove events with more than one correct jet pair
# -- because that shouldn't happen and complicates the task
# -- of finding the correct jet pair
logger.info('Removing events with more than one correct jet pair')
keep = np.array([sum(yen) for yen in df['isCorrect'].values]) <= 1
df = df[keep].reset_index(drop=True)
# -- target
logger.info('Building one-hot target')
y = df['isCorrect'].values
# -- extract array of names of sample of origin
sample = df['sample'].values
# -- prepend 1 to all entries in y where there is no correct jet pair,
# -- 0 if there exists a correct jet pair already
# -- each entry in y will now have length (n_jet_pairs + 1)
y_long = np.array([
np.insert(yev, 0, 1) if sum(yev) == 0 else np.insert(yev, 0, 0)
for yev in y
])
# -- weights
logger.info('Extracting weights from event_weight')
w = df['event_weight'].values
del df['event_weight'], df['isCorrect'], df['sample']
df = df.drop(exclude_list, axis=1) # maybe in the future do something
# better with these variables instead of just removing them
# -- matrix of predictors
X = df.values
ix = range(X.shape[0])
varlist = df.columns.values.tolist()
# -- maximum number of jet pairs to consider in each event
# -- can be set to whatever number makes sense
#max_length = max([len(b) for b in df['Delta_eta_jb']]) + 1
max_length = max_n_pairs + 1
logger.info(
'The max number of jet pairs per event will be {}'.format(max_n_pairs))
X_train, X_test, y_train, y_test, w_train, w_test,\
sample_train, sample_test, ix_train, ix_test, scaler_list = shuffle_split_scale_pad(
X, y_long, w, sample, ix, ftrain, max_length
)
logger.info('Saving processed data as hdf5 in data/')
io.save(
os.path.join('data', 'train_dict.hdf5'), {
'X': X_train,
'y': y_train,
'w': w_train,
'ix': ix_train,
'vars': varlist,
'sample': sample_train.tolist(),
'scalers': scaler_list
})
io.save(
os.path.join('data', 'test_dict.hdf5'), {
'X': X_test,
'y': y_test,
'w': w_test,
'ix': ix_test,
'vars': varlist,
'sample': sample_test.tolist(),
'scalers': scaler_list
})
'jet_trk_expectBLayerHit'
] # 2 more to be added in `process_data`
print 'Loading dataframes...'
# -- currently only training and testing on one file each!
trk_train = pup.root2panda(
'./data/train/*410000_00*.root',
'JetCollection',
branches=track_inputs + [
'jet_truthflav', 'jet_ip3d_pu', 'jet_ip3d_pb', 'jet_ip3d_pc',
'jet_phi', 'jet_trk_phi'
])
trk_test = pup.root2panda(
'./data/test/*410000*.root',
'JetCollection',
branches=track_inputs + [
'jet_truthflav', 'jet_ip3d_pu', 'jet_ip3d_pb', 'jet_ip3d_pc',
'jet_phi', 'jet_trk_phi'
])
print 'Processing training sample ...'
train_dict = process_data(trk_train, savevars=True)
del trk_train
io.save('./data/train_dict_IPConv.h5', train_dict)
print 'Processing test sample...'
test_dict = process_data(trk_test)
del trk_test
io.save('./data/test_dict_IPConv.h5', test_dict)
def write_current_state(self):
"""
Write the current state of the sampler to disk.
The required information to reconstruct the state of the run are written
to an hdf5 file.
All but the most recent removed live point in the chain are removed from
the sampler to reduce memory usage.
This means it is necessary to not append the first live point to the
file if updating a previous checkpoint.
Parameters
----------
sampler: `dynesty.NestedSampler`
NestedSampler to write to disk.
"""
check_directory_exists_and_if_not_mkdir(self.outdir)
resume_file = '{}/{}_resume.h5'.format(self.outdir, self.label)
if os.path.isfile(resume_file):
saved = load(resume_file)
current_state = dict(
unit_cube_samples=np.vstack(
[saved['unit_cube_samples'], self.sampler.saved_u[1:]]),
physical_samples=np.vstack(
[saved['physical_samples'], self.sampler.saved_v[1:]]),
sample_likelihoods=np.concatenate(
[saved['sample_likelihoods'],
self.sampler.saved_logl[1:]]),
sample_log_volume=np.concatenate([
saved['sample_log_volume'], self.sampler.saved_logvol[1:]
]),
sample_log_weights=np.concatenate([
saved['sample_log_weights'], self.sampler.saved_logwt[1:]
]),
cumulative_log_evidence=np.concatenate([
saved['cumulative_log_evidence'],
self.sampler.saved_logz[1:]
]),
cumulative_log_evidence_error=np.concatenate([
saved['cumulative_log_evidence_error'],
self.sampler.saved_logzvar[1:]
]),
cumulative_information=np.concatenate([
saved['cumulative_information'], self.sampler.saved_h[1:]
]),
id=np.concatenate([saved['id'], self.sampler.saved_id[1:]]),
it=np.concatenate([saved['it'], self.sampler.saved_it[1:]]),
nc=np.concatenate([saved['nc'], self.sampler.saved_nc[1:]]),
boundidx=np.concatenate(
[saved['boundidx'], self.sampler.saved_boundidx[1:]]),
bounditer=np.concatenate(
[saved['bounditer'], self.sampler.saved_bounditer[1:]]),
scale=np.concatenate(
[saved['scale'], self.sampler.saved_scale[1:]]),
)
else:
current_state = dict(
unit_cube_samples=self.sampler.saved_u,
physical_samples=self.sampler.saved_v,
sample_likelihoods=self.sampler.saved_logl,
sample_log_volume=self.sampler.saved_logvol,
sample_log_weights=self.sampler.saved_logwt,
cumulative_log_evidence=self.sampler.saved_logz,
cumulative_log_evidence_error=self.sampler.saved_logzvar,
cumulative_information=self.sampler.saved_h,
id=self.sampler.saved_id,
it=self.sampler.saved_it,
nc=self.sampler.saved_nc,
boundidx=self.sampler.saved_boundidx,
bounditer=self.sampler.saved_bounditer,
scale=self.sampler.saved_scale,
)
current_state.update(ncall=self.sampler.ncall,
live_logl=self.sampler.live_logl,
iteration=self.sampler.it - 1,
live_u=self.sampler.live_u,
live_v=self.sampler.live_v,
nlive=self.sampler.nlive,
live_bound=self.sampler.live_bound,
live_it=self.sampler.live_it,
added_live=self.sampler.added_live)
weights = np.exp(current_state['sample_log_weights'] -
current_state['cumulative_log_evidence'][-1])
current_state[
'posterior'] = self.external_sampler.utils.resample_equal(
np.array(current_state['physical_samples']), weights)
save(resume_file, current_state)
self.sampler.saved_id = [self.sampler.saved_id[-1]]
self.sampler.saved_u = [self.sampler.saved_u[-1]]
self.sampler.saved_v = [self.sampler.saved_v[-1]]
self.sampler.saved_logl = [self.sampler.saved_logl[-1]]
self.sampler.saved_logvol = [self.sampler.saved_logvol[-1]]
self.sampler.saved_logwt = [self.sampler.saved_logwt[-1]]
self.sampler.saved_logz = [self.sampler.saved_logz[-1]]
self.sampler.saved_logzvar = [self.sampler.saved_logzvar[-1]]
self.sampler.saved_h = [self.sampler.saved_h[-1]]
self.sampler.saved_nc = [self.sampler.saved_nc[-1]]
self.sampler.saved_boundidx = [self.sampler.saved_boundidx[-1]]
self.sampler.saved_it = [self.sampler.saved_it[-1]]
self.sampler.saved_bounditer = [self.sampler.saved_bounditer[-1]]
self.sampler.saved_scale = [self.sampler.saved_scale[-1]]
def process(i, filepath, yaml_file, model_id):
'''
'''
import pandautils as pup
# -- load branches from yaml file
branches, training_vars, ip3d_training_vars, ipmp_training_vars = set_features(yaml_file)
logger = logging.getLogger("ETL Service")
# -- load root file to dataframe
logger.info('Operating on {}'.format(filepath))
logger.info('Creating dataframe...')
df = pup.root2panda(filepath, 'bTag_AntiKt4EMTopoJets', branches=branches)
# -- create MV2 input quantities, set default values
logger.info('Transforming variables...')
df = transformVars(df)
# -- flatten to jet-flat structure
logger.info('Flattening df...')
df.drop(['PVx', 'PVy', 'PVz'], axis=1, inplace=True)
df_flat = pd.DataFrame({k: pup.flatten(c) for k, c in df.iteritems()})
del df
# --apply standard cuts on AntiKT4EMTopoJets
logger.info('Applying cuts...')
df_flat = apply_calojet_cuts(df_flat)
# -- create numpy arrays for ML
logger.info('Creating X, y, w, mv2c10...')
y = df_flat['jet_LabDr_HadF'].values
mv2c10 = df_flat['jet_mv2c10'].values
jet_pt = df_flat['jet_pt'].values
ip3d_vars = df_flat[ip3d_training_vars].values
ipmp_vars = df_flat[ipmp_training_vars].values
# -- slice df by only keeping the training variables
X = df_flat[training_vars].values
# -- Find weights by reweighting to the light distribution
pteta = df_flat[['jet_pt', 'abs(jet_eta)']].values
w = reweight_to_l(pteta, y, pt_col=0, eta_col=1)
del df_flat, pteta
# -- shuffle data, split into train and test
logger.info('Shuffling, splitting, scaling...')
ix = np.array(range(len(y)))
X_train, X_test,\
y_train, y_test,\
w_train, w_test,\
ix_train, ix_test, \
mv2c10_train, mv2c10_test,\
jet_pt_train, jet_pt_test,\
ip3d_vars_train, ip3d_vars_test,\
ipmp_vars_train, ipmp_vars_test = train_test_split(
X, y, w, ix, mv2c10, jet_pt, ip3d_vars, ipmp_vars, train_size=0.6
)
# -- scale inputs to 0 mean, 1 std
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
ip3d_vars_train = scaler.fit_transform(ip3d_vars_train)
ip3d_vars_test = scaler.transform(ip3d_vars_test)
ipmp_vars_train = scaler.fit_transform(ipmp_vars_train)
ipmp_vars_test = scaler.transform(ipmp_vars_test)
# -- split the previously selected training data into train and validate
X_train, X_validate,\
y_train, y_validate,\
w_train, w_validate,\
ix_train, ix_validate,\
mv2c10_train, mv2c10_validate,\
jet_pt_train, jet_pt_validate,\
ip3d_vars_train, ip3d_vars_validate,\
ipmp_vars_train, ipmp_vars_validate = train_test_split(
X_train, y_train, w_train, ix_train, mv2c10_train, jet_pt_train, ip3d_vars_train, ipmp_vars_train, train_size=0.7
)
# -- assign train, test, validate data to dictionaries
train = {
'X' : X_train,
'ip3d_vars': ip3d_vars_train,
'ipmp_vars': ipmp_vars_train,
'y' : y_train,
'w' : w_train,
'ix': ix_train,
'mv2c10': mv2c10_train,
'pt': jet_pt_train
}
test = {
'X' : X_test,
'ip3d_vars': ip3d_vars_test,
'ipmp_vars': ipmp_vars_test,
'y' : y_test,
'w' : w_test,
'ix': ix_test,
'mv2c10': mv2c10_test,
'pt': jet_pt_test
}
validate = {
'X' : X_validate,
'ip3d_vars': ip3d_vars_validate,
'ipmp_vars': ipmp_vars_validate,
'y' : y_validate,
'w' : w_validate,
'ix': ix_validate,
'mv2c10': mv2c10_validate,
'pt': jet_pt_validate
}
# -- save dictionaries to hdf5
logger.info('Saving dictionaries to hdf5...')
hdf5_train_path = os.path.join('..', 'data', 'DL1-' + model_id + str(i) +'-train-db.h5')
hdf5_test_path = os.path.join('..', 'data', 'DL1-' + model_id + str(i) +'-test-db.h5')
hdf5_validate_path = os.path.join('..', 'data', 'DL1-' + model_id + str(i) +'-validate-db.h5')
io.save(hdf5_train_path, train)
io.save(hdf5_test_path, test)
io.save(hdf5_validate_path, validate)
logger.debug('Saved hdf5 archives: {}, {}, {}'. format(hdf5_train_path, hdf5_test_path, hdf5_validate_path))
return (y_train.shape[0], y_test.shape[0], y_validate.shape[0])
def process(i, filepath, yaml_file, model_id):
'''
'''
import pandautils as pup
# -- load branches from yaml file
branches, training_vars, ip3d_training_vars, ipmp_training_vars = set_features(
yaml_file)
logger = logging.getLogger("ETL Service")
# -- load root file to dataframe
logger.info('Operating on {}'.format(filepath))
logger.info('Creating dataframe...')
df = pup.root2panda(filepath, 'bTag_AntiKt4EMTopoJets', branches=branches)
# -- create MV2 input quantities, set default values
logger.info('Transforming variables...')
df = transformVars(df)
# -- flatten to jet-flat structure
logger.info('Flattening df...')
df.drop(['PVx', 'PVy', 'PVz'], axis=1, inplace=True)
df_flat = pd.DataFrame({k: pup.flatten(c) for k, c in df.iteritems()})
del df
# --apply standard cuts on AntiKT4EMTopoJets
logger.info('Applying cuts...')
df_flat = apply_calojet_cuts(df_flat)
# -- create numpy arrays for ML
logger.info('Creating X, y, w, mv2c10...')
y = df_flat['jet_LabDr_HadF'].values
mv2c10 = df_flat['jet_mv2c10'].values
jet_pt = df_flat['jet_pt'].values
ip3d_vars = df_flat[ip3d_training_vars].values
ipmp_vars = df_flat[ipmp_training_vars].values
# -- slice df by only keeping the training variables
X = df_flat[training_vars].values
# -- Find weights by reweighting to the light distribution
pteta = df_flat[['jet_pt', 'abs(jet_eta)']].values
w = reweight_to_l(pteta, y, pt_col=0, eta_col=1)
del df_flat, pteta
# -- shuffle data, split into train and test
logger.info('Shuffling, splitting, scaling...')
ix = np.array(range(len(y)))
X_train, X_test,\
y_train, y_test,\
w_train, w_test,\
ix_train, ix_test, \
mv2c10_train, mv2c10_test,\
jet_pt_train, jet_pt_test,\
ip3d_vars_train, ip3d_vars_test,\
ipmp_vars_train, ipmp_vars_test = train_test_split(
X, y, w, ix, mv2c10, jet_pt, ip3d_vars, ipmp_vars, train_size=0.6
)
# -- scale inputs to 0 mean, 1 std
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
ip3d_vars_train = scaler.fit_transform(ip3d_vars_train)
ip3d_vars_test = scaler.transform(ip3d_vars_test)
ipmp_vars_train = scaler.fit_transform(ipmp_vars_train)
ipmp_vars_test = scaler.transform(ipmp_vars_test)
# -- split the previously selected training data into train and validate
X_train, X_validate,\
y_train, y_validate,\
w_train, w_validate,\
ix_train, ix_validate,\
mv2c10_train, mv2c10_validate,\
jet_pt_train, jet_pt_validate,\
ip3d_vars_train, ip3d_vars_validate,\
ipmp_vars_train, ipmp_vars_validate = train_test_split(
X_train, y_train, w_train, ix_train, mv2c10_train, jet_pt_train, ip3d_vars_train, ipmp_vars_train, train_size=0.7
)
# -- assign train, test, validate data to dictionaries
train = {
'X': X_train,
'ip3d_vars': ip3d_vars_train,
'ipmp_vars': ipmp_vars_train,
'y': y_train,
'w': w_train,
'ix': ix_train,
'mv2c10': mv2c10_train,
'pt': jet_pt_train
}
test = {
'X': X_test,
'ip3d_vars': ip3d_vars_test,
'ipmp_vars': ipmp_vars_test,
'y': y_test,
'w': w_test,
'ix': ix_test,
'mv2c10': mv2c10_test,
'pt': jet_pt_test
}
validate = {
'X': X_validate,
'ip3d_vars': ip3d_vars_validate,
'ipmp_vars': ipmp_vars_validate,
'y': y_validate,
'w': w_validate,
'ix': ix_validate,
'mv2c10': mv2c10_validate,
'pt': jet_pt_validate
}
# -- save dictionaries to hdf5
logger.info('Saving dictionaries to hdf5...')
hdf5_train_path = os.path.join('..', 'data',
'DL1-' + model_id + str(i) + '-train-db.h5')
hdf5_test_path = os.path.join('..', 'data',
'DL1-' + model_id + str(i) + '-test-db.h5')
hdf5_validate_path = os.path.join(
'..', 'data', 'DL1-' + model_id + str(i) + '-validate-db.h5')
io.save(hdf5_train_path, train)
io.save(hdf5_test_path, test)
io.save(hdf5_validate_path, validate)
logger.debug('Saved hdf5 archives: {}, {}, {}'.format(
hdf5_train_path, hdf5_test_path, hdf5_validate_path))
return (y_train.shape[0], y_test.shape[0], y_validate.shape[0])
def main(inputfiles, treename='bTag_AntiKt2PV0TrackJets'):
configure_logging()
logger = logging.getLogger('ProcessTrackJetData')
# -- import root files into df
logger.info('Importing ROOT files into pandas dataframes')
df = pup.root2panda(
inputfiles,
treename,
branches=[
'jet_pt', 'jet_eta', 'jet_phi', 'jet_m', 'jet_ip2d_pu',
'jet_ip2d_pc', 'jet_ip2d_pb', 'jet_ip3d_pu', 'jet_ip3d_pc',
'jet_ip3d_pb', 'jet_sv1_vtx_x', 'jet_sv1_vtx_y', 'jet_sv1_vtx_z',
'jet_sv1_ntrkv', 'jet_sv1_m', 'jet_sv1_efc', 'jet_sv1_n2t',
'jet_sv1_sig3d', 'jet_jf_n2t', 'jet_jf_ntrkAtVx', 'jet_jf_nvtx',
'jet_jf_nvtx1t', 'jet_jf_m', 'jet_jf_efc', 'jet_jf_sig3d',
'jet_jf_deta', 'jet_jf_dphi', 'PVx', 'PVy', 'PVz',
'jet_aliveAfterOR', 'jet_aliveAfterORmu', 'jet_nConst',
'jet_LabDr_HadF'
])
# -- Insert default values, calculate MV2 variables from the branches in df
logger.info('Creating MV2 variables')
df = transformVars(df)
# -- Flatten from event-flat to jet-flat
# -- Before doing so, remove event-level variables such as PVx,y,z
logger.info('Flattening dataframe')
df.drop(['PVx', 'PVy', 'PVz'], axis=1, inplace=True)
df_flat = pd.DataFrame({k: pup.flatten(c) for k, c in df.iterkv()})
# -- apply eta, pt, OR cuts from b-tagging recommendations
logger.info('Applying cuts')
df_flat = applycuts(df_flat)
# -- build X, y, w
# -- target values
y = df_flat['jet_LabDr_HadF'].values
# -- slice df by only keeping the 24 variables for MV2 training
training_vars = [
'jet_pt', 'abs(jet_eta)', 'jet_ip2', 'jet_ip2_c', 'jet_ip2_cu',
'jet_ip3', 'jet_ip3_c', 'jet_ip3_cu', 'jet_sv1_ntrkv', 'jet_sv1_m',
'jet_sv1_efc', 'jet_sv1_n2t', 'jet_sv1_Lxy', 'jet_sv1_L3d',
'jet_sv1_sig3d', 'jet_sv1_dR', 'jet_jf_n2t', 'jet_jf_ntrkAtVx',
'jet_jf_nvtx', 'jet_jf_nvtx1t', 'jet_jf_m', 'jet_jf_efc', 'jet_jf_dR',
'jet_jf_sig3d'
]
X = df_flat[training_vars].as_matrix()
logger.info(
'2D pT and eta reweighting of charm and light to bottom distribution')
w = reweight_to_b(X, y)
X, y, w = remove_tau(X, y, w)
# -- turn classes 0, 4, 5, 15 to 0, 1, 2, 3
# le = LabelEncoder()
# y = le.fit_transform(y)
# -- randomly shuffle and split into train and test set
logger.info('Shuffling and splitting')
X_train, X_test, y_train, y_test, w_train, w_test = train_test_split(
X, y, w, train_size=0.6)
# -- save out to hdf5
logger.info('Saving data to hdf5')
io.save(open('train_data.h5', 'wb'), {
'X': X_train,
'y': y_train,
'w': w_train
})
io.save(open('test_data.h5', 'wb'), {
'X': X_test,
'y': y_test,
'w': w_test
})
def save(self, path):
d = self.save_to_dict()
d['name'] = self.name
io.save(path, d)
#use : $python n2a.py /path/[ntupleName].root
import numpy as np
from numpy.lib.recfunctions import stack_arrays
from ROOT import *
from root_numpy import tree2array
import glob
import pandas as pd
import deepdish.io as io
import sys
arg = sys.argv[1] #arg = ntuple.root
input_ntuple = TFile.Open(arg)
input_ntuple_tree = input_ntuple.Get('dnn_input')
input_ntuple_array = tree2array(input_ntuple_tree)
input_ntuple_df = pd.DataFrame(input_ntuple_array)
#a = [ntuple].root (delete path)
for a in arg.split('/'): continue
a = a.replace("ana","") #a = [ntupleName].root
a = a.replace(".root", ".h5") #a = [ntupleName].h5
#saved as [ntupleName].h5
io.save(a, input_ntuple_df)
def main(inputfiles, treename='bTag_AntiKt2PV0TrackJets'):
configure_logging()
logger = logging.getLogger('ProcessTrackJetData')
# -- import root files into df
logger.info('Importing ROOT files into pandas dataframes')
df = pup.root2panda(inputfiles, treename, branches = [
'jet_pt', 'jet_eta','jet_phi', 'jet_m', 'jet_ip2d_pu',
'jet_ip2d_pc', 'jet_ip2d_pb', 'jet_ip3d_pu', 'jet_ip3d_pc','jet_ip3d_pb',
'jet_sv1_vtx_x', 'jet_sv1_vtx_y', 'jet_sv1_vtx_z', 'jet_sv1_ntrkv',
'jet_sv1_m','jet_sv1_efc','jet_sv1_n2t','jet_sv1_sig3d',
'jet_jf_n2t','jet_jf_ntrkAtVx','jet_jf_nvtx','jet_jf_nvtx1t','jet_jf_m',
'jet_jf_efc','jet_jf_sig3d', 'jet_jf_deta', 'jet_jf_dphi', 'PVx', 'PVy', 'PVz',
'jet_aliveAfterOR', 'jet_aliveAfterORmu', 'jet_nConst', 'jet_LabDr_HadF'])
# -- Insert default values, calculate MV2 variables from the branches in df
logger.info('Creating MV2 variables')
df = transformVars(df)
# -- Flatten from event-flat to jet-flat
# -- Before doing so, remove event-level variables such as PVx,y,z
logger.info('Flattening dataframe')
df.drop(['PVx', 'PVy', 'PVz'], axis=1, inplace=True)
df_flat = pd.DataFrame({k: pup.flatten(c) for k, c in df.iterkv()})
# -- apply eta, pt, OR cuts from b-tagging recommendations
logger.info('Applying cuts')
df_flat = applycuts(df_flat)
# -- build X, y, w
# -- target values
y = df_flat['jet_LabDr_HadF'].values
# -- slice df by only keeping the 24 variables for MV2 training
training_vars = [
'jet_pt',
'abs(jet_eta)',
'jet_ip2',
'jet_ip2_c',
'jet_ip2_cu',
'jet_ip3',
'jet_ip3_c',
'jet_ip3_cu',
'jet_sv1_ntrkv',
'jet_sv1_m',
'jet_sv1_efc',
'jet_sv1_n2t',
'jet_sv1_Lxy',
'jet_sv1_L3d',
'jet_sv1_sig3d',
'jet_sv1_dR',
'jet_jf_n2t',
'jet_jf_ntrkAtVx',
'jet_jf_nvtx',
'jet_jf_nvtx1t',
'jet_jf_m',
'jet_jf_efc',
'jet_jf_dR',
'jet_jf_sig3d']
X = df_flat[training_vars].as_matrix()
logger.info('2D pT and eta reweighting of charm and light to bottom distribution')
w = reweight_to_b(X, y)
X, y, w = remove_tau(X, y, w)
# -- turn classes 0, 4, 5, 15 to 0, 1, 2, 3
# le = LabelEncoder()
# y = le.fit_transform(y)
# -- randomly shuffle and split into train and test set
logger.info('Shuffling and splitting')
X_train, X_test, y_train, y_test, w_train, w_test = train_test_split(X, y, w, train_size = 0.6)
# -- save out to hdf5
logger.info('Saving data to hdf5')
io.save(open('train_data.h5', 'wb'), {'X' : X_train, 'y' : y_train, 'w' : w_train})
io.save(open('test_data.h5', 'wb'), {'X' : X_test, 'y' : y_test, 'w' : w_test})
import matplotlib
import matplotlib.pyplot as plt
import cPickle as pickle
import deepdish.io as io
ttbar = root2array('ttbar.root')
qcd = root2array('qcd.root')
wjets = root2array('wjets.root')
matplotlib.rcParams.update({'font.size': 16})
fig = plt.figure(figsize=(11.69, 8.27), dpi=100)
bins = np.linspace(min(min(ttbar['NMuon']), min(qcd['NMuon']), min(wjets['NMuon'])), max(max(ttbar['NMuon']), max(qcd['NMuon']), max(wjets['NMuon'])), 10)
_ = plt.hist([ttbar['NMuon'], qcd['NMuon'], wjets['NMuon']], stacked=True, label=[r'$t\overline{t}$', 'QCD', 'wjets'], alpha = 0.5, histtype='stepfilled', normed=False, bins=bins, weights=[ttbar['EventWeight'], qcd['EventWeight'], wjets['EventWeight']])
plt.xlabel('NMuon')
plt.ylabel('Number of Events')
plt.yscale('log')
plt.legend()
plt.plot()
plt.savefig('task3.pdf')
io.save('wj_nmuons.h5', wjets['NMuon'])
#new_df = io.load('wj_nmuons.h5')
#print new_df
pickle.dump(wjets['NMuon'], open('wj_nmuons.pkl', 'wb'))
#test = pickle.load(open('wj_nmuons.pkl', 'rb'))
#print test