#!/usr/bin/env python
import os
import json
import logging
logger = logging.getLogger("KerasROOTClassification")
logger.addHandler(logging.NullHandler())
from root_numpy import tree2array, rec2array
import numpy as np
import pandas as pd
import h5py
from sklearn.preprocessing import StandardScaler
from sklearn.externals import joblib
from keras.models import Sequential
from keras.layers import Dense
from keras.models import model_from_json
# configure number of cores
# this doesn't seem to work, but at least with these settings keras only uses 4 processes
import tensorflow as tf
from keras import backend as K
num_cores = 1
config = tf.ConfigProto(intra_op_parallelism_threads=num_cores,
inter_op_parallelism_threads=num_cores,
allow_soft_placement=True,
device_count = {'CPU': num_cores})
session = tf.Session(config=config)
K.set_session(session)
import ROOT
class KerasROOTClassification:
dataset_names = ["x_train", "x_test", "y_train", "y_test", "w_train", "w_test"]
def __init__(self, name,
signal_trees, bkg_trees, branches, weight_expr, identifiers,
layers=3, nodes=64, batch_size=128, activation_function='relu', 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.batch_size = batch_size
self.activation_function = activation_function
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
self._scaler = None
self._class_weight = None
self._model = None
# track the number of epochs this model has been trained
self.total_epochs = 0
self.data_loaded = False
def _load_data(self):
try:
self._load_from_hdf5()
except KeyError:
logger.info("Couldn't load all datasets - reading from ROOT trees")
# 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.y_train = np.empty(len(self.x_train))
self.y_train[:len(self.s_train)] = 1
self.y_train[len(self.s_train):] = 0
self.y_test = np.empty(len(self.x_test))
self.y_test[:len(self.s_test)] = 1
self.y_test[len(self.s_test):] = 0
logger.info("Writing to hdf5")
self._dump_to_hdf5()
self.data_loaded = True
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 self.dataset_names:
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):
for dataset_name in self.dataset_names:
filename = os.path.join(self.project_dir, dataset_name+".h5")
logger.info("Trying to load {} from {}".format(dataset_name, filename))
with h5py.File(filename) as hf:
setattr(self, dataset_name, hf[dataset_name][:])
logger.info("Data loaded")
@property
def scaler(self):
# create the scaler (and fit to training data) if not existent
if self._scaler is None:
filename = os.path.join(self.project_dir, "scaler.pkl")
try:
self._scaler = joblib.load(filename)
logger.info("Loaded existing StandardScaler from {}".format(filename))
except IOError:
logger.info("Creating new StandardScaler")
self._scaler = StandardScaler()
logger.info("Fitting StandardScaler to training data")
self._scaler.fit(self.x_train)
joblib.dump(self._scaler, filename)
return self._scaler
def _read_info(self, key, default):
filename = os.path.join(self.project_dir, "info.json")
if not os.path.exists(filename):
with open(filename, "w") as of:
json.dump({}, of)
with open(filename) as f:
info = json.load(f)
return info.get(key, default)
def _write_info(self, key, value):
filename = os.path.join(self.project_dir, "info.json")
with open(filename) as f:
info = json.load(f)
info[key] = value
with open(filename, "w") as of:
json.dump(info, of)
@property
def model(self):
"Simple MLP"
if self._model is None:
self._model = Sequential()
# first hidden layer
self._model.add(Dense(self.nodes, input_dim=len(self.branches), activation=self.activation_function))
# the other hidden layers
for layer_number in range(self.layers-1):
self._model.add(Dense(self.nodes, activation=self.activation_function))
# last layer is one neuron (binary classification)
self._model.add(Dense(1, activation='sigmoid'))
self._model.compile(optimizer='SGD',
loss='binary_crossentropy',
metrics=['accuracy'])
# dump to json for documentation
with open(os.path.join(self.project_dir, "model.json"), "w") as of:
of.write(self._model.to_json())
return self._model
@property
def class_weight(self):
if self._class_weight is None:
sumw_bkg = np.sum(self.w_train[self.y_train == 0])
sumw_sig = np.sum(self.w_train[self.y_train == 1])
self._class_weight = [(sumw_sig+sumw_bkg)/(2*sumw_bkg), (sumw_sig+sumw_bkg)/(2*sumw_sig)]
return self._class_weight
def train(self, epochs=10):
if not self.data_loaded:
self._load_data()
try:
self.model.load_weights(os.path.join(self.project_dir, "weights.h5"))
logger.info("Weights found and loaded")
logger.info("Continue training")
except IOError:
logger.info("No weights found, starting completely new training")
self.total_epochs = self._read_info("epochs", 0)
self.model.fit(self.x_train, self.y_train,
epochs=epochs,
class_weight=self.class_weight,
shuffle=True,
batch_size=self.batch_size)
self.model.save_weights(os.path.join(self.project_dir, "weights.h5"))
self.total_epochs += epochs
self._write_info("epochs", self.total_epochs)
def evaluate(self):
pass
def writeFriendTree(self):
pass
def plotROC(self):
pass
def plotScore(self):
pass
if __name__ == "__main__":
logging.basicConfig()
logging.getLogger("KerasROOTClassification").setLevel(logging.INFO)
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.train(epochs=1)