def saveTree(processPath,dictVar,vector,MVAVector=None,SF=1,nameTree="reducedTree"):
from root_numpy import array2root
i=0
for key in dictVar.keys():
if i == 0:
writeMode='recreate'
i=1
else:
writeMode='update'
v=(np.asarray(vector[:,dictVar[key]]))
name = key
if key == 'diphotonCandidate.M()':
name = 'Mgg'
elif key == 'dijetCandidate.M()':
name = 'Mjj'
#elif key == 'HHTagger2017':
# name = 'MVAOutput'
if SF != 1:
if key == 'weight':
v = (np.multiply(np.asarray(v),SF))
v.dtype = [(name.replace(".","").replace("(","").replace(")","").replace("/","_Over_").replace("_","").replace("Candidate",""), np.float64)]
array2root(v, processPath, nameTree, mode = writeMode)
if MVAVector is not None:
v=(np.asarray(MVAVector.ravel()))
v.dtype = [('MVAOutput', np.float64)]
array2root(v, processPath, nameTree, mode ='update')
def test_array2tree_fixed_length_arrays():
f = load(['fixed1.root', 'fixed2.root'])
a = rnp.root2array(f)
with temp() as tmp:
rnp.array2root(a, tmp.GetName(), mode='recreate')
a_conv = rnp.root2array(tmp.GetName())
assert_array_equal(a, a_conv)
def write_prediction_to_file(features,
scaler,
model,
filename='',
treename='',
branch=''):
data_out = features
features = features[:, 0:NDIM]
features = scaler.transform(features)
y_predict_all = model.predict(features) # normal numpy array
data_out = numpy.concatenate((data_out, y_predict_all), axis=1)
dtype = numpy.dtype([('layer', numpy.float32), ('n1', numpy.float32),
('n2', numpy.float32), ('n3', numpy.float32),
('n4', numpy.float32), ('n5', numpy.float32),
('n6', numpy.float32), ('un1', numpy.float32),
('un2', numpy.float32), ('un3', numpy.float32),
('un4', numpy.float32), ('un5', numpy.float32),
('un6', numpy.float32), ('dn1', numpy.float32),
('dn2', numpy.float32), ('dn3', numpy.float32),
('dn4', numpy.float32), ('dn5', numpy.float32),
('dn6', numpy.float32), ('nup', numpy.float32),
('ndown', numpy.float32), ('event', numpy.float32),
('rechitsum', numpy.float32),
('rechit', numpy.float32),
('rechit_dnn', numpy.float32)])
data_out = numpy.core.records.fromarrays(
data_out.transpose(), dtype=dtype) # structured numpy array
root_numpy.array2root(data_out,
filename,
treename=treename,
mode='recreate')
def writeOutPrediction(self, predicted, features, truth, weights,
outfilename, inputfile):
# predicted will be a list
spectator_branches = ['jet_pt', 'jet_eta']
from root_numpy import array2root
if inputfile[-5:] == 'djctd':
print(
"storing normed pt and eta... run on root files if you want something else for now"
)
spectators = features[0][:, 0:2].transpose()
else:
import uproot3 as uproot
print(inputfile)
urfile = uproot.open(inputfile)["deepntuplizer/tree"]
spectator_arrays = urfile.arrays(spectator_branches)
print(spectator_arrays)
spectators = [
spectator_arrays[a.encode()] for a in spectator_branches
]
out = np.core.records.fromarrays(
np.vstack(
(predicted[0].transpose(), truth[0].transpose(), spectators)),
names=
'prob_isB, prob_isBB, prob_isC,prob_isUDSG,isB, isBB, isC,isUDSG,jet_pt, jet_eta'
)
array2root(out, outfilename, 'tree')
def _predict(args):
data_iter = data_loader(args)
preds = model.predict(data_iter).asnumpy()
truths = data_iter.get_truths()
observers = data_iter.get_observers()
print(preds.shape, truths.shape, observers.shape)
pred_output = {}
for i, label in enumerate(data_iter._data_format.class_labels):
pred_output['class_%s' % label] = truths[:, i]
pred_output['score_%s' % label] = preds[:, i]
for i, obs in enumerate(data_iter._data_format.obs_vars):
pred_output[obs] = observers[:, i]
import pandas as pd
df = pd.DataFrame(pred_output)
if args.predict_output:
logging.info('Write prediction file to %s' % args.predict_output)
outdir = os.path.dirname(args.predict_output)
if not os.path.exists(outdir):
os.makedirs(outdir)
df.to_hdf(args.predict_output, 'Events', format='table')
from common.util import plotROC
plotROC(preds, truths, output=os.path.join(outdir, 'roc.pdf'))
from root_numpy import array2root
array2root(df.to_records(index=False),
filename=args.predict_output.rsplit('.', 1)[0] +
'.root',
treename='Events',
mode='RECREATE')
def writeOutPrediction(self, predicted, features, truth, weights,
outfilename, inputfile):
# predicted is a list
print("Prediction started")
from root_numpy import array2root
namesstring = 'prob_isPrompt, prob_isNonPrompt, prob_isFake, prob_lep_isFromSUSYandHF,' #prob_isFromSUSY, prob_isFromSUSYHF,'
for label in self.truth_branches:
namesstring += label + ', '
features_string = ', '.join(self.global_branches)
namesstring += features_string
out = np.core.records.fromarrays(np.vstack(
(predicted[0].transpose(), truth[0].transpose(),
features[0][:, :].transpose())),
names=namesstring)
# if one predicts on a DataCollection one has to change
# the file extension to .root
if not outfilename.endswith(".root"):
print("Predicted from a DataCollection make root files")
print("Note that outfiles files extensions must be adapted...")
filename, _ = os.path.splitext(outfilename)
outfilename = filename + ".root"
print("making {}".format(outfilename.split('/')[-1]))
array2root(out, outfilename, 'tree')
def save_data(self, metadata, data):
if self.treemaker.uses_arrays:
# Activate Joey's array saving code
dataframe_to_root(data,
self.path,
treename=self.treemaker.__name__,
mode='recreate')
else:
# Check we really aren't using arrays, otherwise we'll crash with a very uninformative message
for branch_name in data.columns:
if is_array_field(data, branch_name):
raise TypeError(
"Column %s is an array field, and you want to save to root. Either "
"(1) use MultipleRowExtractor-based minitrees; or "
"(2) add a uses_arrays=True attribute to the %s class; or "
"(3) use pickle as your minitree format." %
(branch_name, self.treemaker.__class__.__name__))
root_numpy.array2root(data.to_records(),
self.path,
treename=self.treemaker.__name__,
mode='recreate')
# Add metadata as JSON in a TNamed in the same ROOT file
bla = ROOT.TNamed('metadata', json.dumps(metadata))
minitree_f = ROOT.TFile(self.path, 'UPDATE')
bla.Write()
minitree_f.Close()
def run(name, source, quick=False):
print time.asctime(time.localtime()), "Filling BDT Branches"
branch_names = joblib.load("pickle/variables.pkl")
if quick == True:
signal = joblib.load('pickle/all_signalq.pkl')
clf = joblib.load("pickle/" + name + "quick.pkl")
else:
signal = joblib.load('pickle/all_signal.pkl')
clf = joblib.load("pickle/" + name + ".pkl")
# predict and write probability of each MC event being signal
bdt_MC_predicted = clf.predict_proba(signal)
bdt_MC_predicted.dtype = [('GradBoost_prob', np.float64)]
array2root((np.hsplit(bdt_MC_predicted,2)[1]), "/net/storage03/data/users/dlafferty/NTuples/SignalMC/2012/combined/Bs2phiphi_MC_2012_combined_corrected_TupleA_BDT.root", "DecayTree")
# predict and write probability of every data event being signal
all_data = root2array("/net/storage03/data/users/dlafferty/NTuples/data/2012/combined/Bs2phiphi_data_2012_corrected_TupleA_BDT.root", "DecayTree", branch_names)
all_data = rec2array(all_data)
bdt_data_predicted = clf.predict_proba(all_data)
bdt_data_predicted.dtype = [('GradBoost_prob', np.float64)]
array2root((np.hsplit(bdt_data_predicted,2)[1]), "/net/storage03/data/users/dlafferty/NTuples/data/2012/combined/Bs2phiphi_data_2012_corrected_TupleA_BDT.root", "DecayTree")
print time.asctime(time.localtime()), "Branches Filled!"
def _predict(self,
X,
features_names=None,
model_type=('classification', None)):
"""
Predict data
:param pandas.DataFrame X: data shape [n_samples, n_features]
:return: predicted values of shape n_samples
"""
self._check_fitted()
directory = self._create_tmp_directory()
try:
with tempfile.NamedTemporaryFile(mode="w",
suffix='.xml',
dir=directory,
delete=True) as file_xml:
file_xml.write(self.formula_xml)
file_xml.flush()
add_info = _AdditionalInformationPredict(directory,
file_xml.name,
features_names,
self._method_name,
model_type=model_type)
root_numpy.array2root(X.astype(numpy.float32).to_records(),
filename=add_info.filename,
treename=add_info.treename)
prediction = self._run_tmva_predict(add_info)
finally:
self._remove_tmp_directory(directory)
return prediction
def _fit(self,
X,
y,
sample_weight=None,
features_names=None,
model_type='classification'):
"""
Train the classifier
:param pandas.DataFrame X: data shape [n_samples, n_features]
:param list | numpy.array y: values - array-like of shape [n_samples]
:param list | numpy.array sample_weight: weight of events,
array-like of shape [n_samples] or None if all weights are equal
:return: self
"""
# saving data to 2 different root files.
directory = self._create_tmp_directory()
add_info = _AdditionalInformation(directory,
features_names,
model_type=model_type)
try:
X[add_info.weight_column] = sample_weight
X[add_info.target_column] = y
root_numpy.array2root(X.to_records(),
filename=add_info.filename,
treename=add_info.treename)
self._run_tmva_training(add_info)
finally:
self._remove_tmp_directory(directory)
return self
def test_array2tree_fixed_length_arrays():
f = load(['fixed1.root', 'fixed2.root'])
a = rnp.root2array(f)
with temp() as tmp:
rnp.array2root(a, tmp.GetName(), mode='recreate')
a_conv = rnp.root2array(tmp.GetName())
assert_array_equal(a, a_conv)
def writeOutPrediction(self, predicted, features, truth, weights, outfilename, inputfile):
# predicted will be a list
from root_numpy import array2root
out = np.core.records.fromarrays(np.vstack( (predicted[0].transpose(),truth[0].transpose(), features[0][:,0:2].transpose() ) ),
names='prob_isPrompt, prob_isNonPrompt, prob_isFake, lep_pt, lep_eta')
array2root(out, outfilename, 'tree')
def run(name, source, quick=False):
print time.asctime(time.localtime()), "Filling BDT Branches"
branch_names = joblib.load("pickle/variables.pkl")
if quick == True:
signal = joblib.load('pickle/all_signalq.pkl')
clf = joblib.load("pickle/" + name + "quick.pkl")
else:
signal = joblib.load('pickle/all_signal.pkl')
clf = joblib.load("pickle/" + name + ".pkl")
# predict and write probability of each MC event being signal
bdt_MC_predicted = clf.predict_proba(signal)
bdt_MC_predicted.dtype = [('GradBoost_prob', np.float64)]
array2root((
np.hsplit(bdt_MC_predicted, 2)[1]
), "/net/storage03/data/users/dlafferty/NTuples/SignalMC/2012/combined/Bs2phiphi_MC_2012_combined_corrected_TupleA_BDT.root",
"DecayTree")
# predict and write probability of every data event being signal
all_data = root2array(
"/net/storage03/data/users/dlafferty/NTuples/data/2012/combined/Bs2phiphi_data_2012_corrected_TupleA_BDT.root",
"DecayTree", branch_names)
all_data = rec2array(all_data)
bdt_data_predicted = clf.predict_proba(all_data)
bdt_data_predicted.dtype = [('GradBoost_prob', np.float64)]
array2root((
np.hsplit(bdt_data_predicted, 2)[1]
), "/net/storage03/data/users/dlafferty/NTuples/data/2012/combined/Bs2phiphi_data_2012_corrected_TupleA_BDT.root",
"DecayTree")
print time.asctime(time.localtime()), "Branches Filled!"
def SaveToRoot(self,df,path_output,output_name=None,out_idx=''):
# Get the unique samples as a list #
if output_name is None:
sample_list = list(df[parameters.split_name].unique())
# Loop over samples #
for sample in sample_list:
sample_df = df.loc[df[parameters.split_name]==sample] # We select the rows corresponding to this sample
# Remove tag and sample name (info in target as bool) #
sample_df = sample_df.drop('tag',axis=1)
sample_df = sample_df.drop('sample',axis=1)
# From df to numpy array with dtype #
sample_output = sample_df.to_records(index=False,column_dtypes='float64')
sample_output.dtype.names = parameters.make_dtype(sample_output.dtype.names)# because ( ) and . are an issue for root_numpy
sample_output_name = os.path.join(path_output,sample+out_idx+'.root')
# Save as root file #
array2root(sample_output,sample_output_name,mode='recreate')
logging.info('Output saved as : '+sample_output_name)
else:
# From df to numpy array with dtype #
full_output = df.to_records(index=False,column_dtypes='float64')
full_output.dtype.names = parameters.make_dtype(full_output.dtype.names)# because ( ) and . are an issue for root_numpy
full_output_name = os.path.join(path_output,output_name)
array2root(full_output,full_output_name,mode='recreate')
logging.info('Output saved as : '+full_output_name)
def writeOutPrediction(self, predicted, features, truth, weights,
outfilename, inputfile):
# predicted will be a list
print('writeout')
print('predicted', predicted[0].shape)
print('features', features[0].shape)
print('truth', truth[0].shape)
def unroll(a):
a = np.reshape(a,
[a.shape[0], a.shape[1] * a.shape[2], a.shape[3]])
return a
#unroll to event x vector
# first 100 are enough for now
parr = predicted[0][:100, ...] #unroll(predicted[0])
farr = features[0][:100, ...] #unroll(features[0])
tarr = truth[0][:100, ...] #unroll(truth[0])
from DeepJetCore.TrainData import TrainData
#use traindata as data storage
td = TrainData()
td._store([parr, farr, tarr], [], [])
td.writeToFile(outfilename)
return
from root_numpy import array2root
out = np.core.records.fromarrays(
[
parr[:, :, 0],
parr[:, :, 1],
parr[:, :, 2],
parr[:, :, 3],
parr[:, :, 4],
parr[:, :, 5],
parr[:, :, 6],
parr[:, :, 7],
parr[:, :, 9],
parr[:, :, 10],
tarr[:, :, 0],
tarr[:, :, 1],
tarr[:, :, 2],
tarr[:, :, 3],
tarr[:, :, 4],
tarr[:, :, 5],
tarr[:, :, 6],
tarr[:, :, 7],
farr[:, :, 0],
farr[:, :, 1],
farr[:, :, 2],
farr[:, :, 3],
farr[:, :, 4],
],
names=
'p_beta, p_posx, p_posy, p_ID0, p_ID1, p_ID2, p_dim1, p_dim2, p_ccoords1, p_coords2, t_mask, t_posx, t_posy, t_ID0, t_ID1, tID_2, t_dim1, t_dim2, f_r, f_g, f_b, f_x, f_y'
)
array2root(out, outfilename, 'tree')
'''
def write_tree(self):
array = []
all_branches = default_branches + ["label", "train_id"
] + self.additional_branches
for aux in all_branches:
print aux
a = self.append_arrays(aux)
if aux == "label" or aux == "train_id" or "dnn_score" in aux:
tree_name = aux
elif "evt_weight" in aux:
tree_name = "weight"
else:
tree_name = aux[:-1]
a_ = self.create_structured_array(a, tree_name)
array.append(a_)
for mva in self.mva_helpers.keys():
a = [
y for x in [
self.mva_helpers[mva]["prediction"]["train"],
self.mva_helpers[mva]["prediction"]["test"],
self.mva_helpers[mva]["prediction"]["data"]
] for y in x
]
a_ = self.create_structured_array(a, mva)
array.append(a_)
merged_array = rfn.merge_arrays(array, flatten=True, usemask=False)
self.output_root = self.output + ".root"
os.system("rm %s" % self.output_root)
root_numpy.array2root(merged_array, self.output_root, treename="t")
def test_drop_nonscalar_columns():
array = np.array([1, 2, 3])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
bool_matrix = np.array([[True, False, True], [True, True, True]])
dt = np.dtype([
('a', 'i4'),
('b', 'int64', array.shape),
('c', 'int64', matrix.shape),
('d', 'bool_'),
('e', 'bool_', matrix.shape)
])
arr = np.array([
(3, array, matrix, True, bool_matrix),
(2, array, matrix, False, bool_matrix)],
dtype=dt)
path = 'tmp.root'
array2root(arr, path, 'ntuple', mode='recreate')
df = read_root(path, flatten=False)
# the above line throws an error if flatten=True because nonscalar columns
# are dropped only after the flattening is applied. However, the flattening
# algorithm can not deal with arrays of more than one dimension.
assert(len(df.columns) == 2)
assert(np.all(df.index.values == np.array([0, 1])))
assert(np.all(df.a.values == np.array([3, 2])))
assert(np.all(df.d.values == np.array([True, False])))
os.remove(path)
def test_nonscalar_columns():
array = np.array([1, 2, 3], dtype=np.int64)
matrix = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int64)
bool_matrix = np.array([[True, False, True], [True, True, True]],
dtype=np.bool_)
dt = np.dtype([('a', 'i4'), ('b', 'int64', array.shape),
('c', 'int64', matrix.shape), ('d', 'bool_'),
('e', 'bool_', matrix.shape)])
arr = np.array([(3, array, matrix, True, bool_matrix),
(2, array, matrix, False, bool_matrix)],
dtype=dt)
reference_df = pd.DataFrame()
reference_df['a'] = np.array([3, 2], dtype=np.int32)
reference_df['b'] = to_object_array([array, array])
reference_df['c'] = to_object_array([matrix, matrix])
reference_df['d'] = np.array([True, False], dtype=np.bool_)
reference_df['e'] = to_object_array([bool_matrix, bool_matrix])
path = 'tmp.root'
array2root(arr, path, 'ntuple', mode='recreate')
df = read_root(path, flatten=False)
assert_frame_equal(df, reference_df)
os.remove(path)
def to_root(df, path, tree_key="default", mode='w', *kargs, **kwargs):
"""
Write DataFrame to a ROOT file.
Parameters
----------
path: string
File path to new ROOT file (will be overwritten)
tree_key: string
Name of tree that the DataFrame will be saved as
mode: string, {'w', 'a'}
Mode that the file should be opened in (default: 'w')
Notes
-----
Further *kargs and *kwargs are passed to root_numpy's array2root.
>>> df = DataFrame({'x': [1,2,3], 'y': [4,5,6]})
>>> df.to_root('test.root')
The DataFrame index will be saved as a branch called 'index'.
"""
if mode == 'a':
mode = 'update'
elif mode == 'w':
mode = 'recreate'
else:
raise ValueError('Unknown mode: {}. Must be "a" or "w".'.format(mode))
from root_numpy import array2root
arr = df.to_records()
array2root(arr, path, tree_key, mode=mode, *kargs, **kwargs)
def prepareOutput(outputD, ll, plotsD, rootFile, NN_MVA):
NN_Output = h5py.File("%sNN_Output_applied_%s.h5"%(outputD,ll), "r+")
mZ_x, mZ_y = (NN_Output['MET_GroundTruth'][:,0]), (NN_Output['MET_GroundTruth'][:,1])
a_x, a_y = (NN_Output['MET_Predictions'][:,0]), (NN_Output['MET_Predictions'][:,1])
mZ_r, mZ_phi = kar2pol(mZ_x, mZ_y)
mZ_r = NN_Output['Boson_Pt'][:]
a_r, a_phi = kar2pol(a_x, a_y)
NN_LongZ, NN_PerpZ = -np.cos(angularrange(np.add(a_phi,-mZ_phi)))*a_r, np.sin(angularrange(a_phi-mZ_phi))*a_r
#HDF5
dset = NN_MVA.create_dataset("NN_LongZ", dtype='d', data=NN_LongZ)
dset1 = NN_MVA.create_dataset("NN_PerpZ", dtype='d', data=NN_PerpZ)
dset2 = NN_MVA.create_dataset("NN_Phi", dtype='d', data=a_phi)
dset3 = NN_MVA.create_dataset("NN_Pt", dtype='d', data=a_r)
dset4 = NN_MVA.create_dataset("Boson_Pt", dtype='d', data=mZ_r)
dset5 = NN_MVA.create_dataset("NN_x", dtype='d', data=a_x)
dset6 = NN_MVA.create_dataset("NN_y", dtype='d', data=a_y)
dset7 = NN_MVA.create_dataset("Boson_x", dtype='d', data=mZ_x)
dset8 = NN_MVA.create_dataset("Boson_y", dtype='d', data=mZ_y)
dset9 = NN_MVA.create_dataset("Boson_Phi", dtype='d', data=mZ_phi)
NN_MVA.close()
#Root
#treename = ll+"_nominal/ntuple"
#Root_array = rnp.root2array(rootFile, treename=treename)
#print("shape Root_array", Root_array.shape)
NN_array = np.array([(a_r[i], a_phi[i], a_x[i], a_y[i], NN_LongZ[i], NN_PerpZ[i]) for i in range(len(a_r))],
dtype=[('NN_Pt', np.float32), ('NN_Phi', np.float32), ('NN_x', np.float32), ('NN_y', np.float32), ('NN_LongZ', np.float32), ('NN_PerpZ', np.float32)])
print("shape NN_array", NN_array.shape)
rnp.array2root(NN_array, "%s/NN_MVA_%s.root"%(outputDir,ll), mode='recreate')
def makePrediction(self, model, testdatacollection, outputDir, ident=''):
import numpy as np
from root_numpy import array2root
import os
outputDir = os.path.abspath(outputDir)
if len(ident) > 0:
ident = '_' + ident
self.__sourceroots = []
self.__predictroots = []
self.metrics = []
for i in range(len(testdatacollection.samples)):
sample = testdatacollection.samples[i]
originroot = testdatacollection.originRoots[i]
outrootfilename = os.path.basename(originroot).split(
'.')[0] + '_predict' + ident + '.root'
fullpath = testdatacollection.getSamplePath(sample)
td = testdatacollection.dataclass
td.readIn(fullpath)
truthclasses = td.getUsedTruth()
regressionclasses = td.regressiontargetclasses
formatstring = ','.join(
['prob_%s%s' % (i, ident) for i in truthclasses])
features = td.x
labels = td.y
#metric=model.evaluate(features, labels, batch_size=10000)
prediction = model.predict(features)
if isinstance(prediction, list):
######CHANGE FOR NEW FORMAT
formatstring += ','
formatstring += ','.join(
['reg_%s%s' % (i, ident) for i in regressionclasses])
all_write = np.concatenate(prediction, axis=1)
elif prediction.shape[1] == len(truthclasses):
all_write = prediction
else:
raise ValueError(
'Regression (2nd prediction output) can only have up to two values!'
)
all_write = np.core.records.fromarrays(np.transpose(all_write),
names=formatstring)
array2root(all_write,
outputDir + '/' + outrootfilename,
"tree",
mode="recreate")
#self.metrics.append(metric)
self.__sourceroots.append(originroot)
self.__predictroots.append(outputDir + '/' + outrootfilename)
print(formatstring)
print('\ncreated predition friend tree ' + outputDir + '/' +
outrootfilename + ' for ' + originroot)
def getArrayToRoot(rec_array, foutname, treename):
"""
Convert and return a tree into a numpy array
Inputs: TTree object
"""
from root_numpy import array2root
info('(getArrayToRoot) building tree %s in file %s' % (treename, foutname))
array2root(rec_array, foutname, treename)
def write_output_tree(allparticles, outputFile):
from root_numpy import array2root
out = np.core.records.fromarrays(
allparticles.transpose(),
names=
"is_reco, reco_posx, reco_posy, reco_e, is_true, true_posx, true_posy, true_e, true_id, n_true"
)
array2root(out, outputFile + ".root", 'tree')
def test_array2tree_charstar():
a = np.array([b'', b'a', b'ab', b'abc', b'xyz', b''],
dtype=[('string', 'S3')])
with temp() as tmp:
rnp.array2root(a, tmp.GetName(), mode='recreate')
a_conv = rnp.root2array(tmp.GetName())
assert_array_equal(a, a_conv)
def _saveAsROOT(self):
output = self.data.to_records(index=False, column_dtypes='float64')
output.dtype.names = [(name.replace('.', 'p').replace('(', '').replace(
')', '').replace('-', '_minus_').replace('*', '_times_'))
for name in output.dtype.names
] # root_numpy issues
array2root(output, self.save_path, mode='recreate')
print('Output saved as : ' + self.save_path)
def test_array2tree_charstar():
a = np.array([b'', b'a', b'ab', b'abc', b'xyz', b''],
dtype=[('string', 'S3')])
with temp() as tmp:
rnp.array2root(a, tmp.GetName(), mode='recreate')
a_conv = rnp.root2array(tmp.GetName())
assert_array_equal(a, a_conv)
def writeOutPrediction(self, predicted, features, truth, weights,
outfilename, inputfile):
# predicted will be a list
from root_numpy import array2root
out = np.core.records.fromarrays(predicted[0].transpose(),
names='prob_p, prob_np, prob_f')
array2root(out, outfilename, 'tree')
def add_to_rootfile(rootfile, new_branch, branch_name=None, overwrite=True):
"""Adds a new branch to a given root file.
.. warning:: Overwrite not working currently!
Parameters
----------
rootfile : root-dict
The ROOT-file where the data should be added
new_branch : numpy.array 1-D, list, root-dict
A one-dimensional numpy array that contains the data.
branch_name : str
The name of the branche resp. the name in the dtype of the array.
"""
from root_numpy import root2array, array2tree
from rootpy.io import root_open
rootfile = dev_tool.entries_to_str(rootfile)
new_branch = dev_tool.entries_to_str(new_branch)
branch_name = dev_tool.entries_to_str(branch_name)
# get the right parameters
# TODO: what does that if there? an assertion maybe?
write_mode = 'update'
branch_name = 'new_branch1' if branch_name is None else branch_name
if isinstance(rootfile, dict):
filename = rootfile.get('filenames')
treename = rootfile.get('treename')
new_branch = to_ndarray(new_branch)
# new_branch.dtype = [(branch_name, 'f8')]
# write to ROOT-file
write_to_root = False
if os.path.isfile(filename):
with root_open(filename, mode='a') as root_file:
tree = getattr(root_file, treename) # test
if not tree.has_branch(branch_name):
write_to_root = True
# array2tree(new_branch, tree=tree)
# f.write("", TObject.kOverwrite) # overwrite, does not create friends
else:
write_mode = 'recreate'
write_to_root = True
if write_to_root:
arr = np.core.records.fromarrays([new_branch], names=branch_name)
array2root(arr=arr,
filename=filename,
treename=treename,
mode=write_mode)
return 0
else:
return 1
def getHits_adu(self, runID, ohdu, adu_keV, save=False ):
print( 'image2hit' )
path = self.outPath + self.partial_id
simulatedImage_adu = self.getSimulatedImage_adu( adu_keV )
if not self.empty:
simulatedImage_adu += self.reconstructSCNImage_adu( runID, ohdu )
hits_adu = self.convertImage2Hits( simulatedImage_adu, save = True )
if save:
root_numpy.array2root(hits_adu, path, treename='hitSumm', mode='recreate')
return hits_adu
def save_file(data, pred, proba, filename, model):
data['isSignal'] = pred
print(filename)
data['probSignal'] = proba[:]
array2root(np.array(data.to_records()),
'OutputRoot/new_' + model + '_' + filename,
'nominal',
mode='recreate')
print('Save file as {}'.format('new_' + model + '_' + filename))
return
def test_to_root(folder,result_folder,output_root_folder,variables,is_signal,model_label,sample_list=[]):
if not os.path.isdir(output_root_folder+'/model_'+model_label): os.mkdir(output_root_folder+'/model_'+model_label)
if sample_list==[]:
print(" Empty sample list, will use full sample . . .")
##Read test sample
store = pd.HDFStore(result_folder+'test_score_'+model_label+'.h5')
df_test = store.select("df")
for n, a in enumerate(var):
back = np.array(df_test[a].loc[df_test[is_signal]==0].values, dtype=[(a, np.float64)])
sign = np.array(df_test[a].loc[df_test[is_signal]==1].values, dtype=[(a, np.float64)])
print(a," back: ", back)
print(a," sign: ", sign)
array2root(back, output_root_folder+'/model_'+model_label+'/test_bkg.root', mode='recreate' if n==0 else 'update')
array2root(sign, output_root_folder+'/model_'+model_label+'/test_sgn.root', mode='recreate' if n==0 else 'update')
print(" Signal and background root files written : ", output_root_folder+'/'+model_label+'/test_*.root')
else:
full_list = []
for sl in sample_list:
full_list += samples[sl]['files']
for sample in full_list:
##Read test sample
if not os.path.isfile(folder+sample+"_test.h5"):
print("!!!File ", folder+sample+"_test.h5", " does not exist! Continuing")
continue
store = pd.HDFStore(result_folder+sample+"_score_"+model_label+".h5")
df_test = store.select("df")
newFile = TFile(output_root_folder+'/model_'+model_label+'/'+sample+'.root', 'recreate')
newFile.cd()
for n, a in enumerate(var):
arr = np.array(df_test[a].values, dtype=[(a, np.float64)])
#print(a, " values: ", arr)
#array2root(arr, output_root_folder+'/model_'+model_label+'/'+sample+'.root', mode='update')#mode='recreate' if n==0 else 'update')
if n==0: skim = array2tree(arr)
else: array2tree(arr, tree=skim)#mode='recreate' if n==0 else 'update')
skim.Write()
##Recreate c_nEvents histogram
counter = TH1F("c_nEvents", "Event Counter", 1, 0., 1.)
counter.Sumw2()
##Fill counter histogram with the first entry of c_nEvents
counter.Fill(0., df_test["c_nEvents"].values[0])
##print("counter bin content: ", counter.GetBinContent(1))
counter.Write()
newFile.Close()
#counter.Delete()
print(" Root file written : ", output_root_folder+'/model_'+model_label+'/'+sample+'.root')
def CreateTestSample(path, **kwargs):
r"""Creates a :py:mod:`ROOT` file with toy data to be used for tests.
The output file contains one tree with **nevents** number of entries represented
by `nbranches` branches. Random numbers for each branch are drawn according to a
chisquare distribution with a mean indicated by the branch index. The name of
the output tree is given by **tree** and the branches are of the form
'branch_1', 'branch_2', ...
Numbers are generated using the :class:`numpy.random` module and the output file
is filled using the :func:`root_numpy.array2root` method.
If a file with the same name already exists it will be overwritten (can be
changed with the **overwrite** keyword argument). If **mkdir** is set to
``True`` (default: ``False``) directories in **path** with do not yet exist will
be created automatically.
:param path: path of output :py:mod:`ROOT` file
:type path: ``str``
:param \**kwargs: see below
:Keyword Arguments:
* **nevents** (``int``) -- number of events in the output tree (default:
10000)
* **nbranches** (``int``) -- number of branches (default: 10)
* **tree** (``int``) -- name of the output tree (default: 'tree')
* **overwrite** (``bool``) -- overwrite an existing file located at **path**
(default: ``True``)
* **mkdir** (``bool``) -- create non-existing directories in **path**
(default: ``False``)
"""
basedir = os.path.abspath(path)
if not basedir:
logger.error("Directory '{}' does not exist!".format(basedir))
raise IOError("Path not found!")
nevents = int(kwargs.get("nevents", 1e4))
nbranches = int(kwargs.get("nbranches", 10))
treename = kwargs.get("tree", "tree")
array = np.core.records.fromarrays(
np.transpose(
np.random.chisquare(range(1, nbranches + 1, 1),
size=(nevents, nbranches))),
names=",".join(
["branch_{}".format(i + 1) for i in range(nbranches)]),
)
rnp.array2root(array, path, treename=treename, mode="recreate")
if os.path.isfile(path):
logger.info("Created '{}'.".format(path))
def save_file(data, pred, proba, filename, model):
data['isSignal'] = pred
print(filename)
#for index in range(20):
# print "Proba {}".format(proba[index,0])
data['probSignal'] = proba[:, 0]
array2root(np.array(data.to_records()),
'OutputRoot/new_BDT_' + model + '_' + filename,
'nominal',
mode='recreate')
return
def add_branch(arr, bname, rfile, tname):
"""
Add the passed array to an existing TTree in an existing TFile
Args:
arr (numpy.array): 1D numpy array that will be stored under a new branch
bname (str): Branch name for the values
rfile (str): Filename to which the new branch should be added
tname (str): Name of the TTree to which the values should be added
"""
arr = np.array(arr, dtype=[(bname, np.find_common_type(arr, []))])
array2root(arr, rfile, treename=tname, mode='update')
def test_array2root():
a = np.array([
(12345, 2., 2.1, True),
(3, 4., 4.2, False),],
dtype=[
('x', np.int32),
('y', np.float32),
('z', np.float64),
('w', np.bool)])
tmp_fd, tmp_path = tempfile.mkstemp(suffix='.root')
rnp.array2root(a, tmp_path, mode='recreate')
os.close(tmp_fd)
os.remove(tmp_path)
def test_array2root():
a = np.array([
(12345, 2., 2.1, True),
(3, 4., 4.2, False),],
dtype=[
('x', np.int32),
('y', np.float32),
('z', np.float64),
('w', np.bool)])
with temp() as tmp:
rnp.array2root(a, tmp.GetName(), mode='recreate')
a_conv = rnp.root2array(tmp.GetName())
assert_array_equal(a, a_conv)
# extend the tree
rnp.array2root(a, tmp.GetName(), mode='update')
a_conv2 = rnp.root2array(tmp.GetName())
assert_array_equal(np.hstack([a, a]), a_conv2)
def test_array2root():
a = np.array([
(12345, 2., 2.1, True),
(3, 4., 4.2, False),],
dtype=[
('x', np.int32),
('y', np.float32),
('z', np.float64),
('w', np.bool)])
tmp_fd, tmp_path = tempfile.mkstemp(suffix='.root')
rnp.array2root(a, tmp_path, mode='recreate')
a_conv = rnp.root2array(tmp_path)
assert_array_equal(a, a_conv)
# extend the tree
rnp.array2root(a, tmp_path, mode='update')
a_conv2 = rnp.root2array(tmp_path)
assert_array_equal(np.hstack([a, a]), a_conv2)
os.close(tmp_fd)
os.remove(tmp_path)
def to_root(df, path, key='default', mode='w', *args, **kwargs):
"""
Write DataFrame to a ROOT file.
Parameters
----------
path: string
File path to new ROOT file (will be overwritten)
key: string
Name of tree that the DataFrame will be saved as
mode: string, {'w', 'a'}
Mode that the file should be opened in (default: 'w')
Notes
-----
Further *args and *kwargs are passed to root_numpy's array2root.
>>> df = DataFrame({'x': [1,2,3], 'y': [4,5,6]})
>>> df.to_root('test.root')
The DataFrame index will be saved as a branch called '__index__*',
where * is the name of the index in the original DataFrame
"""
if mode == 'a':
mode = 'update'
elif mode == 'w':
mode = 'recreate'
else:
raise ValueError('Unknown mode: {}. Must be "a" or "w".'.format(mode))
from root_numpy import array2root
# We don't want to modify the user's DataFrame here, so we make a shallow copy
df_ = df.copy(deep=False)
name = df_.index.name
if name is None:
# Handle the case where the index has no name
name = ''
df_['__index__' + name] = df_.index
arr = df_.to_records(index=False)
array2root(arr, path, key, mode=mode, *args, **kwargs)
def test_array2root():
a = np.array([
(12345, 2., 2.1, True),
(3, 4., 4.2, False),],
dtype=[
('x', np.int32),
('y', np.float32),
('z', np.float64),
('w', np.bool)])
with temp() as tmp:
rnp.array2root(a, tmp.GetName(), mode='recreate')
a_conv = rnp.root2array(tmp.GetName())
assert_array_equal(a, a_conv)
# extend the tree
rnp.array2root(a, tmp.GetName(), mode='update')
a_conv2 = rnp.root2array(tmp.GetName())
assert_array_equal(np.hstack([a, a]), a_conv2)
# write into subdirectory
tname = 'root/sub/tree'
rnp.array2root(a, tmp.GetName(), treename=tname, mode='update')
a_conv3 = rnp.root2array(tmp.GetName(), treename=tname)
assert_array_equal(a, a_conv3)
# try creating tree with conflicting name
assert_raises(IOError, rnp.array2root, a, tmp.GetName(),
treename='root/sub', mode='update')
# try creating subdirectory with conflicting name
assert_raises(IOError, rnp.array2root, a, tmp.GetName(),
treename='root/sub/tree/error', mode='update')
def save_data(self, metadata, data):
if self.treemaker.uses_arrays:
# Activate Joey's array saving code
dataframe_to_root(data, self.path, treename=self.treemaker.__name__, mode='recreate')
else:
# Check we really aren't using arrays, otherwise we'll crash with a very uninformative message
for branch_name in data.columns:
if is_array_field(data, branch_name):
raise TypeError("Column %s is an array field, and you want to save to root. Either "
"(1) use MultipleRowExtractor-based minitrees; or "
"(2) add a uses_arrays=True attribute to the %s class; or "
"(3) use pickle as your minitree format." % (branch_name,
self.treemaker.__class__.__name__))
root_numpy.array2root(data.to_records(), self.path,
treename=self.treemaker.__name__, mode='recreate')
# Add metadata as JSON in a TNamed in the same ROOT file
bla = ROOT.TNamed('metadata', json.dumps(metadata))
minitree_f = ROOT.TFile(self.path, 'UPDATE')
bla.Write()
minitree_f.Close()
def _fit(self, X, y, sample_weight=None, features_names=None, model_type='classification'):
"""
Train the classifier
:param pandas.DataFrame X: data shape [n_samples, n_features]
:param list | numpy.array y: values - array-like of shape [n_samples]
:param list | numpy.array sample_weight: weight of events,
array-like of shape [n_samples] or None if all weights are equal
:return: self
"""
# saving data to 2 different root files.
directory = self._create_tmp_directory()
add_info = _AdditionalInformation(directory, features_names, model_type=model_type)
try:
X[add_info.weight_column] = sample_weight
X[add_info.target_column] = y
root_numpy.array2root(X.to_records(), filename=add_info.filename,
treename=add_info.treename)
self._run_tmva_training(add_info)
finally:
self._remove_tmp_directory(directory)
return self
def _predict(self, X, features_names=None, model_type=('classification', None)):
"""
Predict data
:param pandas.DataFrame X: data shape [n_samples, n_features]
:return: predicted values of shape n_samples
"""
self._check_fitted()
directory = self._create_tmp_directory()
try:
with tempfile.NamedTemporaryFile(mode="w", suffix='.xml', dir=directory, delete=True) as file_xml:
file_xml.write(self.formula_xml)
file_xml.flush()
add_info = _AdditionalInformationPredict(directory, file_xml.name, features_names, self._method_name,
model_type=model_type)
root_numpy.array2root(X.astype(numpy.float32).to_records(), filename=add_info.filename,
treename=add_info.treename)
prediction = self._run_tmva_predict(add_info)
finally:
self._remove_tmp_directory(directory)
return prediction
def write_output(self,outfile_name,ROOT=True,pickle=False):
'''
Converts outputed event data into re-usable data format,
either ROOT or a pickled numpy record array
Input:
-outfile_name: string, name of outfile to write to (without extension)
-ROOT: bool, True to write a ROOT file
-pickle: bool, True to write a pickle file
'''
# build record array of outputed event dicts
# assume all dict's are the same structure
dt=[]
for item in self.events[0]:
if type(self.events[0][item])==numpy.ndarray:
dt+=[(item,type(self.events[0][item][0]),len(self.events[0][item]))]
else:
dt+=[(item,type(self.events[0][item]))]
dt=numpy.dtype(dt)
values=[tuple(each.values()) for each in self.events]
out=numpy.zeros((len(self.events),),dtype=dt)
out[:]=values
# convert record array to root tree, write to file
if ROOT:
if self.logging:
print "Creating file %s.root" % (outfile_name)
root_numpy.array2root(out,'%s.root' % (outfile_name))
# write record array to pickle file
if pickle:
if self.logging:
print "Creating file %s.pickle" % (outfile_name)
f=open('%s.pickle' % (outfile_name),'w')
pickle.dump(out,f)
f.close()
print "GAMMA2 done..." root['kappa'] = bcc['KAPPA'] print "KAPPA done..." root['size'] = bcc['SIZE'] print "SIZE done..." root['eps1'] = bcc["EPSILON"][0:,0] print "EPSILON 1 done..." root['eps2'] = bcc["EPSILON"][0:,1] print "EPSILON 2 done..." root["mag"] = bcc["TMAG"][0:,2] print "TMAG done..." root["teps1"] = bcc["TE"][0:,0] print "TEPS1 done..." root["teps2"] =bcc["TE"][0:,1] print "TEPS2 done..." root["tra"] = bcc["TRA"] print "TRA done..." root["tdec"] = bcc["TDEC"] print "TDEC done..." root["tsize"] = bcc["TSIZE"] print "TSIZE done..." root["mu"] = bcc["MU"] print "All Done !" array2root(root,output,'bcc')
from root_numpy import root2array, rec2array, array2root
bdt_file = '/lustre/cmswork/hh/mvas/xgboost/train_3CSVM_0.5sig_0.7bkg_weighted.pkl'
branch_names = ["H1_pT", "H2_pT",
"H1_dEta_abs", "H2_dEta_abs",
"H1_dPhi_abs", "H2_dPhi_abs"]
# compute bdt values
bdt = joblib.load(bdt_file)
for root_file in args.root_files:
print "processing {}".format(root_file)
# load vars data from ROOT
data = root2array(root_file, args.tree_name, branch_names)
data_bdt = bdt.predict_proba(rec2array(data[branch_names]))[:,1]
# save to ROOT file
data_bdt.dtype = [(args.bdt_name, np.float32)]
array2root(data_bdt, root_file, "tree")
range=(min_value, max_value), label='Signal SM', **hist_params)
areaSig = sum(np.diff(bins)*values)
#print areaBKG, " ",areaBKG2 ," ",areaSig
if n == 0 : plt.legend(loc='best')
plt.title(feature)
plt.savefig("Variables_"+subset+BKG+"_benchmarks_"+ext)
plt.clf()
"""
#################################################################################
### Define classifiers to test
traindataset, valdataset = train_test_split(dataset, random_state=11, train_size=0.50)
traindatasetmix, valdatasetmix = train_test_split(datasetmix, random_state=11, train_size=0.50)
#################################################################################
arr = valdatasetmix.to_records()
array2root(arr, outputCentral+"_AppliedToMixed"+typedata+".root" , 'tree', 'recreate')
arr = dataset.to_records()
array2root(arr, outputCentral+"_AppliedToPlain"+typedata+".root" , 'tree', 'recreate')
if typedata=="Data":
arr = dataset20.to_records()
array2root(arr, outputCentral+"_AppliedTo20pOfPlain"+typedata+".root" , 'tree', 'recreate')
#
for ii in range(0,3):
if ii==0 :
train= trainFeaturesplot
Var='All'
if ii==1 :
train= trainFeaturesObvious
Var='Mass'
if ii==2 :
import numpy as np
import root_numpy as rnp
import postprocessing.ttree
import ROOT
# Gets a TTree from a ROOT file.
signal_file = ROOT.TFile("ROOT_data/signal1MTraining.root")
signal_tree = signal_file.Get("h101;1")
# Converts the TTree to a NumPy structured array.
array = rnp.tree2array(signal_tree)
# Generates three new branches to be added to the array.
classifier_branch = postprocessing.ttree.name_classifier_branches(
np.random.rand(247015, 3), ['ClassA', 'ClassB', 'ClassC'])
# Attaches the new branches to the original array.
together = postprocessing.ttree.join_struct_arrays([array, classifier_branch])
# Outputs the total array as a ROOT file.
rnp.array2root(together, "ROOT_data/together.root")
import sys
import numpy as np
from root_numpy import array2root
print sys.argv[1]
# need to find out how many columns are in the file
f = open(sys.argv[1])
l = f.readline()
colcount = l.count(',')
f.close()
cols = np.linspace(1,colcount,colcount,dtype=int)
data = np.genfromtxt(sys.argv[1],delimiter=',',names=True,usecols=cols)
array2root(data, sys.argv[1].replace('.csv','.root'),'outputTree')
fname, "ttree", Output_variables, None, 0, nfiles_per_sample, skip_n_events, False, "weight"
)
Output_tree = rootnp.rec2array(Output_tree)
Output_tree_final = np.ndarray(
(Output_tree.shape[0],),
dtype=[
("Jet_flavour", float),
("TagVarCSV_vertexCategory", float),
("Jet_pt", float),
("Jet_eta", float),
("Jet_CSVIVF", float),
("BDTG", float),
],
) # , buffer = np.array([1,2,3,4,5]))
for idx, val in enumerate(BDTG):
Output_tree_final[idx][0] = Output_tree[idx][0]
Output_tree_final[idx][1] = Output_tree[idx][1]
Output_tree_final[idx][2] = Output_tree[idx][2]
Output_tree_final[idx][3] = Output_tree[idx][3]
Output_tree_final[idx][4] = Output_tree[idx][4]
Output_tree_final[idx][5] = BDTG[idx]
Output_tree_final = Output_tree_final.view(np.recarray)
tree = rootnp.array2root(
Output_tree_final, "trainPlusBDTG_CombinedSV" + category + "_" + flavor + ".root", "ttree", "recreate"
)
log.info("Output file dumped in trainPlusBDTG_CombinedSV" + category + "_" + flavor + ".root")
log.info("done")
if events[i]["event"]!=currentevent:
if cutType=="bumphunt":
candidates.sort(key=lambda x:events[x]["tarChisq"],reverse=False)
elif cutType=="vertexing":
candidates.sort(key=lambda x:events[x]["bscChisq"],reverse=False)
elif cutType=="none":
candidates.sort(key=lambda x:events[x]["tarChisq"],reverse=False)
else:
raise Exception("invalid cut type")
# ranked_candidates = sorted(candidates, key=lambda x:events[x][sortkey],reverse=highestBest)
rank=1
for j in candidates:
output[j]["nPass"]=len(candidates)
output[j]["rank"]=rank
rank+=1
del candidates[:]
currentevent = events[i]["event"]
if output[i]["cut"]!=0:
candidates.append(i)
if cutOutput:
output = output[output["cut"]!=0]
if onlyBest:
output = output[output["rank"]==1]
if onlyOnly:
output = output[output["nPass"]==1]
root_numpy.array2root(output,remainder[0],mode="recreate",treename="cut")
#newtree=root_numpy.array2tree(output)
#newtree.Scan()
def savedata(dt, dt_LE, basename, clf=None, dt_real=None):
# Get the data to write
dt_out = dt.data
labels = dt.treenames + dt.w_varnames + m_weightnames
# Also take care of the low energy data
dt_LE_out = dt_LE.data
# If we have real data, save as well
if dt_real != None:
dt_real_out = dt_real.data
# if a classifier is passed, then add that to the data field
if clf != None:
scores = clf.decision_function(dt.getDataNoWeight())
scores = scores.reshape((len(scores),1))
dt_out = np.concatenate((dt_out, scores),axis=1)
labels += ['score']
LE_scores = clf.decision_function(dt_LE.getDataNoWeight())
LE_scores = LE_scores.reshape((len(LE_scores),1))
dt_LE_out = np.concatenate((dt_LE_out, LE_scores),axis=1)
if dt_real != None:
real_scores = clf.decision_function(dt_real.getDataNoWeight())
real_scores = real_scores.reshape((len(real_scores),1))
dt_real_out = np.concatenate((dt_real_out,real_scores),1)
csl = ""
for i in range(len(labels)-1):
csl += labels[i] + ","
csl += labels[-1]
# Separate the data into signal and background
dt_out_sig = dt_out[ dt.targets > 0.5 ]
dt_out_bkg = dt_out[ dt.targets < 0.5 ]
# Turn into record array
dt_out_sig = np.rec.fromrecords(dt_out_sig, names=csl)
dt_out_bkg = np.rec.fromrecords(dt_out_bkg, names=csl)
dt_LE_out = np.rec.fromrecords(dt_LE_out, names=csl)
if dt_real != None:
dt_real_out = np.rec.fromrecords(dt_real_out, names=csl)
for wn in m_weightnames:
dt_out_sig[wn] *= dt.sf
dt_out_bkg[wn] *= dt.sf
dt_LE_out[wn] *= dt_LE.sf
# Convert directly to a root file
signame = 'processed_trees/' + basename + '_sig.root'
bkgname = 'processed_trees/' + basename + '_bkg.root'
dataname = 'processed_trees/' + basename + '_data.root'
array2root(dt_out_sig, signame, 'tree','recreate')
array2root(dt_out_bkg, bkgname, 'tree','recreate')
# Put LE sig into the same file
array2root(dt_LE_out, signame, 'tree')
# Save real data if added
if dt_real != None:
array2root(dt_real_out, dataname, 'tree','recreate')
def save_array(outputArray, outputName):
#array = np.savetxt(outputName+".txt", outputArray, fmt='%.4e',delimiter = "|")
outputString = str(outputName)
logging.info("Creating .Root file")
rnp.array2root(outputArray,outputString,treename='Training_Variables',mode='recreate')
# Convert to structured array
ntuple_list = df_ntuple.as_matrix().tolist()
ntuple_list = [tuple(a) for a in ntuple_list]
name_type_ref = [('BDT', 'f4'),
('Class', 'i4'),
('EventNumber', 'i4'),
('EventWeight', 'f4'),
('MET', 'f4'),
('Mtop', 'f4'),
('dPhiLBmin', 'f4'),
('dPhiVBB', 'f4'),
('dRBB', 'f4'),
('dYWH', 'f4'),
('mBB', 'f4'),
('mBBJ', 'f4'),
('mTW', 'f4'),
('nTags', 'i4'),
('nJ', 'i4'),
('pTB1', 'f4'),
('pTB2', 'f4'),
('pTJ3', 'f4'),
('pTV', 'f4'),
('sample', 'S15')]
ntuple_array = np.array(ntuple_list, dtype=name_type_ref)
# Write to ROOT file.
array2root(ntuple_array, '/Volumes/THUMB/VHbb-data/write/skl_BDT_results.root',
mode='recreate')
t = f.NStations
nentries = t.GetEntriesFast()
cut = np.zeros(nentries, dtype='bool')
for i in range(nentries):
t.GetEntry(i)
rootID = '%s_%s_%s' % (t.Run, t.Event, t.SubEvent)
if rootID in eventID:
cut[i] = True
f.Close()
## WRITE TO FILE ##
# Most likely composition
try:
values = np.zeros(nentries, dtype=[('comp', 'S1')])
values[cut] = d['llh_comp'][:]
root_numpy.array2root(values, outFile, 'llh_comp', 'recreate')
except ValueError:
print 'Length mismatch. Skipping...'
continue
# Most likely energy
values = np.zeros(nentries, dtype=[('energy', float)])
values[cut] = d['ML_energy'][:]
root_numpy.array2root(values, outFile, 'ML_energy')
# Likelihoods
keys = ['pLLH', 'hLLH', 'oLLH', 'fLLH']
for key in keys:
values = np.zeros(nentries, dtype=[('llh', float)])
values[cut] = d[key][:]
root_numpy.array2root(values, outFile, key)
pool_files.append(extfile)
nfiles_per_sample = None
X_val = rootnp.root2array(extfile.path,'tree',variables,None,0,nfiles_per_sample,args.testEvery,False,'weight')
X_val = rootnp.rec2array(X_val)
BDTG = clf_val.predict_proba(X_val)[:,1]
if (args.TMVAOut):
BDTG = [i*2-1 for i in BDTG]
Output_variables = ['flavour','vertexCategory','jetPt','jetEta']
Output_tree = rootnp.root2array(extfile.path,'tree',Output_variables,None,0,nfiles_per_sample,args.testEvery,False,'weight')
Output_tree = rootnp.rec2array(Output_tree)
Output_tree_final = np.ndarray((Output_tree.shape[0],),dtype=[('flavour', float), ('vertexCategory', float), ('jetPt', float), ('jetEta', float), ('BDTG', float)])#, buffer = np.array([1,2,3,4,5]))
for idx,val in enumerate(BDTG):
Output_tree_final[idx][0] = Output_tree[idx][0]
Output_tree_final[idx][1] = Output_tree[idx][1]
Output_tree_final[idx][2] = Output_tree[idx][2]
Output_tree_final[idx][3] = Output_tree[idx][3]
Output_tree_final[idx][4] = BDTG[idx]
Output_tree_final = Output_tree_final.view(np.recarray)
outname = 'trainPlusBDTG_CombinedSV%s_%s.root' % (category, flavor) if not args.batch else \
'%strainPlusBDTG_CombinedSV%s_%s.root' % (args.trainingTag, category, flavor)
outfile = os.path.join(dirpath, outname)
tree = rootnp.array2root(Output_tree_final, outfile, 'tree')
with io.root_open(outfile, 'update') as tout:
tout.WriteTObject(watermark, 'watermark')
tout.WriteTObject(codemark, 'codemark')
log.info('Output file dumped in %s' % outfile)
log.info('done')
def analyse(bdt_models, bdt_taggers, dnn_models, dnn_taggers, dnn_scaler, data_files):
# using bdt_model
# bdt_model.predict_proba(data)
# need to scale the data to use the dnn
# for i, v in enumerate(scaler.variables):
# data[v] = (data[v] - scaler.means[i]) / scaler.std[i]
# This can be done on an event by event basis too
# event.variable = event.variable - means[i]/ std[i]
# using dnn_model
# dnn_model.predict(data)[0]
# The variables have different names in data, so we need to map the variable names to something the
# models can use/ have the same names.
bdt_var_map = {'Aplanarity':'jetTrimX_aplanarity', 'EEC_C2_1':'jetTrimX_c2beta1', 'EEC_D2_1':'jetTrimX_d2beta1', 'Sphericity':'jetTrimX_sphericity', 'SPLIT12': 'jetTrimX_groosplit12', 'TauWTA1':'jetTrimX_grootau1','TauWTA2':'jetTrimX_grootau2', 'TauWTA2TauWTA1':'jetTrimX_grootau21', 'Mu12':'jetTrimX_mufilt', 'yfilt': 'jetTrimX_ysfilt', 'y':'jetTrimX_y', 'nTracks':'jetTrimX_ungrngtrk', 'PlanarFlow': 'jetTrimX_planarflow'}
# gotta get the order right for the variables
bdt_vars_1 = []
bdt_vars_2 = []
for b in bdt_taggers:
bdt_vars_1.append(bdt_var_map[b].replace('X','1'))
bdt_vars_2.append(bdt_var_map[b].replace('X','2'))
bdt_ivd = {}
for k, v in bdt_var_map.items():
bdt_ivd[v.replace('X','1')] = k
bdt_ivd[v.replace('X','2')] = k
dnn_var_map = {'aplanarity':'jetTrimX_aplanarity', 'eec_c2_1':'jetTrimX_c2beta1','eec_d2_1':'jetTrimX_d2beta1', 'sphericity':'jetTrimX_sphericity', 'split12': 'jetTrimX_groosplit12', 'tauwta1':'jetTrimX_grootau1','tauwta2':'jetTrimX_grootau2', 'tauwta2tauwta1':'jetTrimX_grootau21', 'mu12':'jetTrimX_mufilt', 'yfilt': 'jetTrimX_ysfilt', 'y':'jetTrimX_y', 'ntracks':'jetTrimX_ungrngtrk', 'planarflow':'jetTrimX_planarflow'}
# gotta get the order right for the variables
dnn_vars_1 = []
dnn_vars_2 = []
print 'dnn_taggers'
print dnn_taggers
for b in dnn_taggers:
dnn_vars_1.append(dnn_var_map[b].replace('X','1'))
dnn_vars_2.append(dnn_var_map[b].replace('X','2'))
dnn_ivd = {}
for k, v in dnn_var_map.items():
dnn_ivd[v.replace('X','1')] = k
dnn_ivd[v.replace('X','2')] = k
# read in the data so we can analyse it!
# gotta do it one at a time for each data file :(
for data_file in data_files:
# benchmark and try a few options
# first read in data using root2array
print data_file
data_arr = rn.root2rec(data_file)
# unfortunately, some of the values in the ntuples are NaNs!!!
# keep track of which ones these are...
nan_idx_tmp = np.empty([0])
no_values_tmp = np.empty([0])
for d in data_arr.dtype.names:
if d not in dnn_vars_1 and d not in dnn_vars_2:
continue
if np.any(np.isnan(data_arr[d])) or not np.all(np.isfinite(data_arr[d])):
if len(nan_idx_tmp) == 0:
nan_idx_tmp = np.asarray(np.where(np.isnan(data_arr[d])))[0]
else:
nan_idx_tmp = np.concatenate((nan_idx_tmp, np.asarray(np.where(np.isnan(data_arr[d])))[0]))
data_arr[d] = np.nan_to_num(data_arr[d])
if len(no_values_tmp) == 0:
no_values_tmp = np.asarray(np.where(data_arr[d]<-98))[0]
else:
no_values_tmp = np.concatenate((no_values_tmp, np.asarray(np.where(data_arr[d]<-98))[0]))
# concatenate
nan_idx = np.unique(nan_idx_tmp)
no_values = np.unique(no_values_tmp)
data_arr['jetTrim1_groosplit12'] = data_arr['jetTrim1_groosplit12']/1000.
data_arr['jetTrim2_groosplit12'] = data_arr['jetTrim2_groosplit12']/1000.
# get the columns for classifying for jet1
bdt_data = data_arr[bdt_vars_1]
dnn_data = data_arr[dnn_vars_1]
# get the columns for classifying for jet2
bdt_data_2 = data_arr[bdt_vars_2]
dnn_data_2 = data_arr[dnn_vars_2]
# do we have to rename the data fields?
# easy enough to rename if the fields
recs = []
bdt_data.dtype.names = [bdt_ivd[d] for d in bdt_data.dtype.names]
bdt_data_2.dtype.names = [bdt_ivd[d] for d in bdt_data_2.dtype.names]
for d in bdt_data.dtype.names:
if d != 'nTracks' and d.find('trk') == -1:
recs.append((d, 'float'))
else:
recs.append((d, 'int'))
#print np.any(np.isnan(bdt_data[d]))
#print np.all(np.isfinite(bdt_data[d]))
#print np.where(np.isnan(bdt_data[d]))
bdt_data_arr = bdt_data.view(np.float32).reshape(bdt_data.shape + (-1,))
bdt_data_arr_2 = bdt_data_2.view(np.float32).reshape(bdt_data_2.shape + (-1,))
bdt_proba = None
bdt_proba_2 = None
for m in bdt_models:
if bdt_proba is not None:
bdt_proba += m.predict_proba(bdt_data_arr)[:,1]
bdt_proba_2 += m.predict_proba(bdt_data_arr_2)[:,1]
else:
bdt_proba = m.predict_proba(bdt_data_arr)[:,1]
bdt_proba_2 = m.predict_proba(bdt_data_arr_2)[:,1]
# scale data
bdt_proba/=float(len(bdt_models))
bdt_proba_2/=float(len(bdt_models))
for i, v in enumerate(dnn_scaler.variables):
# reverse lookup for v
#print v
if v in dnn_var_map.keys():
#print 'found v in dnn_var_map'
#print dnn_var_map[v]
if dnn_var_map[v].replace('X','1') in dnn_data.dtype.names:
'''
print v
print 'means and std'
print dnn_scaler.means[i]
print dnn_scaler.std[i]
print np.mean(dnn_data[dnn_var_map[v].replace('X','1')])
'''
dnn_data[dnn_var_map[v].replace('X','1')] = (dnn_data[dnn_var_map[v].replace('X','1')] - dnn_scaler.means[i]) / dnn_scaler.std[i]
dnn_data_2[dnn_var_map[v].replace('X','2')] = (dnn_data_2[dnn_var_map[v].replace('X','2')] - dnn_scaler.means[i]) / dnn_scaler.std[i]
dnn_data.dtype.names = [dnn_ivd[d] for d in dnn_data.dtype.names]
dnn_data_2.dtype.names = [dnn_ivd[d] for d in dnn_data_2.dtype.names]
# do we have to rename the data fields to get this to work?
dnn_predictions = None
for m in dnn_models:
if dnn_predictions is not None:
dnn_predict1 = m.predict(dnn_data)[0]
dnn_predict1.dtype.names = ['jetTrim1_dnn']
dnn_predict2 = m.predict(dnn_data_2)[0]
dnn_predict2.dtype.names = ['jetTrim2_dnn']
for n in xrange(len(dnn_predict1['jetTrim1_dnn'])):
dnn_predictions['jetTrim1_dnn'][n] += dnn_predict1['jetTrim1_dnn'][n]
dnn_predictions_2['jetTrim2_dnn'][n] += dnn_predict2['jetTrim2_dnn'][n]
else:
dnn_predictions = m.predict(dnn_data)[0]
dnn_predictions.dtype.names = ['jetTrim1_dnn']
dnn_predictions_2 = m.predict(dnn_data_2)[0]
dnn_predictions_2.dtype.names = ['jetTrim2_dnn']
for n in xrange(len(dnn_predictions['jetTrim1_dnn'])):
dnn_predictions['jetTrim1_dnn'][n] /= float(len(dnn_models))
dnn_predictions_2['jetTrim2_dnn'][n] /= float(len(dnn_models))
# set all of the nan index ones to zero
for n in np.unique(np.concatenate((no_values, nan_idx))):#nan_idx:
dnn_predictions['jetTrim1_dnn'][n] = 0
dnn_predictions_2['jetTrim2_dnn'][n] = 0
bdt_proba[n] = 0
bdt_proba_2[n] = 0
data_arr['jetTrim1_groosplit12'] = data_arr['jetTrim1_groosplit12']*1000.
data_arr['jetTrim2_groosplit12']*=1000.
# have to do this annoying .copy() to be able to add the dtype.names to any
# arrays that come from a slice.
#bdt_ = X.copy().view(dtype=[(n, np.float64) for n in variables]).reshape(len(X))
# now add them to the data file and write it out
#data_scored = nf.append_fields(data_arr, names=['jetTrim1_bdt','jetTrim2_bdt','jetTrim1_dnn','jetTrim2_dnn'], data=[bdt_proba, bdt_proba_2, dnn_predictions, dnn_predictions_2], usemask = False)
data_scored = nf.append_fields(data_arr, names=['jetTrim1_bdt','jetTrim2_bdt','jetTrim1_dnn','jetTrim2_dnn'], data=[bdt_proba, bdt_proba_2, dnn_predictions, dnn_predictions_2], usemask = False)
rn.array2root(data_scored, data_file.replace('.root','_scored_nonTrk_avg_vTest.root'),'dibjet','recreate')
def table_to_root(table, filename, **kwargs):
"""Write a Table to a ROOT file
"""
import root_numpy
root_numpy.array2root(table.as_array(), filename, **kwargs)
