def __init__(self):
# event-level features included -- target '1' = leading top; '0' = leading antitop
hpl_text_dicts = hpl.text_dicts()
self.text_dicts = hpl_text_dicts['dnn']
self.processlabel_args = hpl_text_dicts['samples']
self.text_args = {
'fontsize': 18,
'ha': 'left',
'va': 'top',
'transform': None
}
self.verbose = True
self.history = None
self.date = datetime.date.today().strftime('%d%b%Y')
self.hep_data = None
self.dnn_data = None
self.df = None # set later
self.model = None # set later
self.fpr = None # set later
self.tpr = None # set later
self.accuracy = {'mean': 0, 'std': 0} # set later
self.metadata = {} # set later
self.train_data = {} # set later
self.test_data = {} # set later
self.rejections = [] # set later
self.rejection = {} # set later
self.percentile = 75
self.output = 'data/DNN/' # set later
self.dnn_name = "ttbar_DNN" # name to access in lwtnn ('variables.json')
self.image_format = 'png'
self.test_scores = [] # set later
self.train_scores = [] # set later
self.verbose_level = "INFO"
## NN parameters -- set by config file
self.features = []
self.batch_size = 32
self.nb_epoch = 1
self.loss = 'binary_crossentropy' # preferred for binary classification
self.optimizer = 'adam' # old: 'sgd'; change to 'adam' per Riccardo di Sipio
self.metrics = ['accuracy']
self.init = 'normal' #'uniform'
self.input_dim = len(self.features)
self.nHiddenLayers = 1
self.nNodes = [5 for _ in range(self.nHiddenLayers)]
self.activation = 'elu'
self.elu_alpha = 1.0
self.kfold_splits = 2
self.earlystopping = {
'monitor': 'loss',
'min_delta': 0.0001,
'patience': 5,
'verbose': self.verbose,
'mode': 'auto'
}
return
def __init__(self):
"""Initialize class for each sample you want systematics"""
self.sampleName = ''
self.variable = ''
self.outpath = "./"
self.binning = 10
self.rebin = 1
self.x_labels = hpl.text_dicts()['variables']
self.plotSystematics = True
return
def initialize(self):
"""Initialize some things."""
HepPlotter.initialize(self)
self.sampleTypes = {
'background': [],
'signal': [],
'data': [],
'systematic': []
} # systematic is for plotting single systematic uncertainties
self.labels = hpl.text_dicts()
self.systematics = OrderedDict()
self.uncertainty_handles = []
self.uncertainty_labels = []
return
action='store',
dest='outpath',
default='examples/hepPlotter/',
help='Directory for storing output plots')
results = parser.parse_args()
listOfFiles = results.listOfFiles
listOfHists = results.listOfHists
outpath = results.outpath
files = open(listOfFiles, "r").readlines()
histograms = open(listOfHists, "r").readlines()
betterColors = hpt.betterColors()['linecolors']
x_labels = hpl.text_dicts()[
'variables'] # labels based on histogram plotted (e.g., 'Jet pT')
labels = hpl.text_dicts()[
'samples'] # labels based on filetype being plotted (e.g., 'ttbar')
numberOfHists = 0
# Access data -- assumes you are plotting histograms from multiple sources in one figure
for hi, histogram in enumerate(histograms):
histogram = histogram.strip('\n')
histogramName = histogram.split("h_")[1].split("_pre")[0]
print " :: Plotting " + histogram + "\n"
## setup histogram
hist = HepPlotter("histogram", 1)
hist.ratio_plot = False # plot a ratio of things [Data/MC]
parser.add_argument('-o','--outpath', action='store',default='./',
dest='outpath',
help='Directory for storing output plots')
results = parser.parse_args()
listOfFiles = results.listOfFiles
listOfHists = results.listOfHists
listOfDetectorSysts = results.listOfSysts # just detector systematics
outpath = results.outpath
files = open(listOfFiles,"r").readlines()
histograms = open(listOfHists,"r").readlines()
detectorSystematics = open(listOfDetectorSysts,"r").readlines()
# setup for examples choices
x_labels = hpl.text_dicts()['variables']
rebins = {"leptonicT_m":40,"ST":200}
# For DataMC plots, one histogram with each sample goes on a single plot
# so I have this structured to loop over the histograms, then the ROOT files
# with lots of histograms, it is probably better to open root files only once,
# load all the histograms, then do the plotting
for histogram in histograms:
histogram = histogram.rstrip('\n')
histogramName = histogram.replace("_nominal","").replace("h_","")
print " :: Plotting "+histogram+"\n"
## setup histogram
hist = HepPlotterDataMC()