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)