import pandas as pd
import seaborn as sns

from nnfwtbn import Variable, Process, Cut, hist, McStack, DataStack, Stack, TmvaBdt
from nnfwtbn import toydata

df = toydata.get()
df = df.compute()

Evaluation of TMVA BDT

The file tmva_bdt.xml contains a sample BDT trained on the toy dataset. The input variables are

• $\eta^{j_1}$,
• $\eta^{j_2}$,
• $\tau\;\mathrm{centrality}$ and
• $\ell\;\mathrm{centrality}$.

The BDT was trained with regular TMVA in ROOT. The weights stored in the XML file can be read by nnfwtbn.TmvaBdt directly.

bdt = TmvaBdt("tmva_bdt.xml")
df['bdt_prediction'] = bdt.predict(df)

Plotting

p_ztt = Process(r"$Z\rightarrow\tau\tau$", range=(0, 0))
p_sig = Process(r"Signal", range=(1, 1))

colors = ["windows blue", "amber", "greyish", "faded green", "dusty purple"]
colors = sns.xkcd_palette(colors)
s_ztt = McStack(p_ztt, color=colors[0], histtype='step')
s_sig = McStack(p_sig, color=colors[1], histtype='step')

v_bdt = Variable("BDT Score", "bdt_prediction")
hist(df, v_bdt, 22, [s_ztt, s_sig], range=(-1.1, 1.1), 
     weight="weight",  numerator=None)
None