def plot_discriminators(self,
log=False,
printROC=False,
privateWork=False,
signal_class=None,
nbins=None,
bin_range=None,
sigScale=-1):
''' plot all events classified as one category '''
if not bin_range:
bin_range = [round(1. / self.data.n_output_neurons, 2), 1.]
if not nbins:
nbins = int(25 * (1. - bin_range[0]))
plotDiscrs = plottingScripts.plotDiscriminators(
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)
bkg_hist, sig_hist = plotDiscrs.plot(ratio=False,
printROC=printROC,
privateWork=privateWork)
def plot_discriminators(self, log = False): ''' plot all events classified as one category ''' nbins = 15 bin_range = [0.2, 0.7] plotDiscrs = plottingScripts.plotDiscriminators( data = self.data, prediction_vector = self.mainnet_predicted_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) plotDiscrs.set_printROCScore(True) plotDiscrs.plot(ratio = False)
def plot_discriminators(self,
log=False,
plot_nonTrainData=False,
signal_class="ttHbb"):
''' plot all events classified as one category '''
nbins = 18
bin_range = [0.1, 1.]
plotDiscrs = plottingScripts.plotDiscriminators(
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.categoryLabel,
plotdir=self.plot_path,
logscale=log,
plot_nonTrainData=plot_nonTrainData)
plotDiscrs.set_printROCScore(True)
plotDiscrs.plot(ratio=False)