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#!/usr/bin/env python
import os
from root_numpy import tree2array, rec2array
import numpy as np
import pandas as pd
import h5py
import ROOT
class KerasROOTClassification:
def __init__(self, name,
signal_trees, bkg_trees, branches, weight_expr, identifiers,
layers=3, nodes=64, out_dir="./outputs"):
self.name = name
self.signal_trees = signal_trees
self.bkg_trees = bkg_trees
self.branches = branches
self.weight_expr = weight_expr
self.identifiers = identifiers
self.layers = layers
self.nodes = nodes
self.out_dir = out_dir
self.project_dir = os.path.join(self.out_dir, name)
if not os.path.exists(self.out_dir):
os.mkdir(self.out_dir)
if not os.path.exists(self.project_dir):
os.mkdir(self.project_dir)
self.s_train = None
self.b_train = None
self.s_test = None
self.b_test = None
self.x_train = None
self.x_test = None
self.y_train = None
self.y_test = None
self.s_eventlist_train = None
self.b_eventlist_train = None
def load_data(self):
# Read signal and background trees into structured numpy arrays
signal_chain = ROOT.TChain()
bkg_chain = ROOT.TChain()
for filename, treename in self.signal_trees:
signal_chain.AddFile(filename, -1, treename)
for filename, treename in self.bkg_trees:
bkg_chain.AddFile(filename, -1, treename)
self.s_train = tree2array(signal_chain, branches=self.branches+[self.weight_expr]+self.identifiers, start=0, step=2)
self.b_train = tree2array(bkg_chain, branches=self.branches+[self.weight_expr]+self.identifiers, start=0, step=2)
self.s_test = tree2array(signal_chain, branches=self.branches+[self.weight_expr], start=1, step=2)
self.b_test = tree2array(bkg_chain, branches=self.branches+[self.weight_expr], start=1, step=2)
self._dump_training_list()
self.s_eventlist_train = self.s_train[self.identifiers]
self.b_eventlist_train = self.b_train[self.identifiers]
# now we don't need the identifiers anymore
self.s_train = self.s_train[self.branches+[self.weight_expr]]
self.b_train = self.b_train[self.branches+[self.weight_expr]]
# create x (input), y (target) and w (weights) arrays
# the first block will be signals, the second block backgrounds
self.x_train = rec2array(self.s_train[self.branches])
self.x_train = np.concatenate((self.x_train, rec2array(self.b_train[self.branches])))
self.x_test = rec2array(self.s_test[self.branches])
self.x_test = np.concatenate((self.x_test, rec2array(self.b_test[self.branches])))
self.w_train = self.s_train[self.weight_expr]
self.w_train = np.concatenate((self.w_train, self.b_train[self.weight_expr]))
self.w_test = self.s_test[self.weight_expr]
self.w_test = np.concatenate((self.w_test, self.b_test[self.weight_expr]))
self._dump_to_hdf5()
def _dump_training_list(self):
s_eventlist = pd.DataFrame(self.s_train[self.identifiers])
b_eventlist = pd.DataFrame(self.b_train[self.identifiers])
s_eventlist.to_csv(os.path.join(self.project_dir, "s_eventlist_train.csv"))
s_eventlist.to_csv(os.path.join(self.project_dir, "b_eventlist_train.csv"))
def _dump_to_hdf5(self):
for dataset_name in ["x_train", "x_test"]:
with h5py.File(os.path.join(self.project_dir, dataset_name+".h5"), "w") as hf:
hf.create_dataset(dataset_name, data=getattr(self, dataset_name))
def _load_from_hdf5(self):
dataset_names = ["x_train", "x_test"]
# example:
with h5py.File("x_test.h5") as hf:
self.x_test = hf["x_test"][:]
def train(self):
pass
def evaluate(self):
pass
def writeFriendTree(self):
pass
def plotROC(self):
pass
def plotScore(self):
pass
if __name__ == "__main__":
filename = "/project/etp4/nhartmann/trees/allTrees_m1.8_NoSys.root"
c = KerasROOTClassification("test",
signal_trees = [(filename, "GG_oneStep_1705_1105_505_NoSys")],
bkg_trees = [(filename, "ttbar_NoSys"),
(filename, "wjets_Sherpa221_NoSys")
],
branches = ["met", "mt"],
weight_expr = "eventWeight*genWeight",
identifiers = ["DatasetNumber", "EventNumber"])
c.load_data()
print(c.x_train)
print(len(c.x_train))