def plot_outputNodes(self,
log=False,
cut_on_variable=None,
plot_nonTrainData=False):
''' plot distribution in outputNodes '''
nbins = 20
bin_range = [0., 1.]
plotNodes = plottingScripts.plotOutputNodes(
data=self.data,
prediction_vector=self.model_prediction_vector,
event_classes=self.event_classes,
nbins=nbins,
bin_range=bin_range,
signal_class="ttHbb",
event_category=self.categoryLabel,
plotdir=self.plot_path,
logscale=log,
plot_nonTrainData=plot_nonTrainData)
if cut_on_variable:
plotNodes.set_cutVariable(cutClass=cut_on_variable["class"],
cutValue=cut_on_variable["value"])
plotNodes.set_printROCScore(True)
plotNodes.plot(ratio=False)
def plot_outputNodes(self,
log=False,
printROC=False,
signal_class=None,
privateWork=False,
nbins=30,
bin_range=[0., 1.],
sigScale=-1):
''' plot distribution in outputNodes '''
plotNodes = plottingScripts.plotOutputNodes(
data=self.data,
prediction_vector=self.model_prediction_vector,
event_classes=self.event_classes,
nbins=nbins,
bin_range=bin_range,
signal_class=signal_class,
event_category=self.category_label,
plotdir=self.plot_path,
logscale=log,
sigScale=sigScale)
plotNodes.plot(ratio=False, printROC=printROC, privateWork=privateWork)