plot_AUC_2D Namespace Reference

Variables
tuple	log = logging.getLogger(__name__)
tuple	parser
string	help = "output file pattern"
string	default = "mt"
list	choices = ["tt", "mt", "et", "em", "mm", "ee"]
tuple	args = parser.parse_args()
tuple	outputfolder = os.path.dirname(args.output_dir)
	outputname = args.score_name
string	ntuple_string = args.channel+"_jecUncNom/ntuple"
tuple	Output = open(os.path.join(outputfolder, outputname.replace("$N$", "_N_").replace("$X$", "_X_") + ".txt"),"w")
tuple	fig2 = plt.figure()
tuple	ax2 = fig2.add_subplot(111, xlabel='number of trees', ylabel='integral over ROC curve', xlim=[1.1*min(args.NTrees)-0.1*max(args.NTrees),1.1*max(args.NTrees)-0.1*min(args.NTrees)])
list	colors = ["r", "b", "g", "y"]
list	keys = []
string	filename = args.channel+"_"
dictionary	TestLeafs = {}
dictionary	ROCintegral = {}
dictionary	ROCintegralTrain = {}
list	y1 = [1.0]
list	y2 = [1.0]
list	ymean = [1.0]
list	y1T = [1.0]
list	y2T = [1.0]
list	ymeanT = [1.0]
tuple	f = ROOT.TFile.Open(TestLeafs[key]+"T%i.root"%(i))
tuple	hist = f.Get("Method_BDT/BDT_"+TestLeafs[key]+"/MVA_BDT_"+TestLeafs[key]+"_rejBvsS")
tuple	hist2 = f.Get("Method_BDT/BDT_"+TestLeafs[key]+"/MVA_BDT_"+TestLeafs[key]+"_trainingRejBvsS")
float	ROCcrossing = 0.0
list	x_values = [0.0]
tuple	fig = plt.figure()
tuple	ax = fig.add_subplot(111, xlabel='signal efficiency', ylabel='background rejection')
list	y_values = []
list	yT_values = []

Variable Documentation

tuple plot_AUC_2D.args = parser.parse_args()

tuple plot_AUC_2D.ax = fig.add_subplot(111, xlabel='signal efficiency', ylabel='background rejection')

tuple plot_AUC_2D.ax2 = fig2.add_subplot(111, xlabel='number of trees', ylabel='integral over ROC curve', xlim=[1.1*min(args.NTrees)-0.1*max(args.NTrees),1.1*max(args.NTrees)-0.1*min(args.NTrees)])

list plot_AUC_2D.choices = ["tt", "mt", "et", "em", "mm", "ee"]

list plot_AUC_2D.colors = ["r", "b", "g", "y"]

string plot_AUC_2D.default = "mt"

tuple plot_AUC_2D.f = ROOT.TFile.Open(TestLeafs[key]+"T%i.root"%(i))

tuple plot_AUC_2D.fig = plt.figure()

tuple plot_AUC_2D.fig2 = plt.figure()

string plot_AUC_2D.filename = args.channel+"_"

string plot_AUC_2D.help = "output file pattern"

tuple plot_AUC_2D.hist = f.Get("Method_BDT/BDT_"+TestLeafs[key]+"/MVA_BDT_"+TestLeafs[key]+"_rejBvsS")

tuple plot_AUC_2D.hist2 = f.Get("Method_BDT/BDT_"+TestLeafs[key]+"/MVA_BDT_"+TestLeafs[key]+"_trainingRejBvsS")

list plot_AUC_2D.keys = []

tuple plot_AUC_2D.log = logging.getLogger(__name__)

string plot_AUC_2D.ntuple_string = args.channel+"_jecUncNom/ntuple"

tuple plot_AUC_2D.Output = open(os.path.join(outputfolder, outputname.replace("$N$", "_N_").replace("$X$", "_X_") + ".txt"),"w")

tuple plot_AUC_2D.outputfolder = os.path.dirname(args.output_dir)

plot_AUC_2D.outputname = args.score_name

tuple plot_AUC_2D.parser

Initial value:

= argparse.ArgumentParser(description="Get information on overtraining.",
                                                                         parents=[logger.loggingParser])

float plot_AUC_2D.ROCcrossing = 0.0

dictionary plot_AUC_2D.ROCintegral = {}

dictionary plot_AUC_2D.ROCintegralTrain = {}

dictionary plot_AUC_2D.TestLeafs = {}

list plot_AUC_2D.x_values = [0.0]

list plot_AUC_2D.y1 = [1.0]

list plot_AUC_2D.y1T = [1.0]

list plot_AUC_2D.y2 = [1.0]

list plot_AUC_2D.y2T = [1.0]

list plot_AUC_2D.y_values = []

list plot_AUC_2D.ymean = [1.0]

list plot_AUC_2D.ymeanT = [1.0]

list plot_AUC_2D.yT_values = []