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Commit 365ba76d authored by Nikolai.Hartmann's avatar Nikolai.Hartmann
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Selection argument, RobustScaler and workaround for numpy bug

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toolkit.py
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......@@ -11,7 +11,7 @@ from root_numpy import tree2array, rec2array
import numpy as np
import pandas as pd
import h5py
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler, RobustScaler
from sklearn.externals import joblib
from sklearn.metrics import roc_curve
......@@ -44,12 +44,20 @@ class KerasROOTClassification:
def __init__(self, name,
signal_trees, bkg_trees, branches, weight_expr, identifiers,
layers=3, nodes=64, batch_size=128, validation_split=0.33, activation_function='relu', out_dir="./outputs"):
selection=None,
layers=3,
nodes=64,
batch_size=128,
validation_split=0.33,
activation_function='relu',
out_dir="./outputs",
scaler_type="RobustScaler"):
self.name = name
self.signal_trees = signal_trees
self.bkg_trees = bkg_trees
self.branches = branches
self.weight_expr = weight_expr
self.selection = selection
self.identifiers = identifiers
self.layers = layers
self.nodes = nodes
......@@ -57,6 +65,7 @@ class KerasROOTClassification:
self.validation_split = validation_split
self.activation_function = activation_function
self.out_dir = out_dir
self.scaler_type = scaler_type
self.project_dir = os.path.join(self.out_dir, name)
......@@ -112,10 +121,22 @@ class KerasROOTClassification:
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.s_train = tree2array(signal_chain,
branches=self.branches+[self.weight_expr]+self.identifiers,
selection=self.selection,
start=0, step=2)
self.b_train = tree2array(bkg_chain,
branches=self.branches+[self.weight_expr]+self.identifiers,
selection=self.selection,
start=0, step=2)
self.s_test = tree2array(signal_chain,
branches=self.branches+[self.weight_expr],
selection=self.selection,
start=1, step=2)
self.b_test = tree2array(bkg_chain,
branches=self.branches+[self.weight_expr],
selection=self.selection,
start=1, step=2)
self._dump_training_list()
self.s_eventlist_train = self.s_train[self.identifiers]
......@@ -179,11 +200,16 @@ class KerasROOTClassification:
filename = os.path.join(self.project_dir, "scaler.pkl")
try:
self._scaler = joblib.load(filename)
logger.info("Loaded existing StandardScaler from {}".format(filename))
logger.info("Loaded existing scaler from {}".format(filename))
except IOError:
logger.info("Creating new StandardScaler")
self._scaler = StandardScaler()
logger.info("Fitting StandardScaler to training data")
logger.info("Creating new {}".format(self.scaler_type))
if self.scaler_type == "StandardScaler":
self._scaler = StandardScaler()
elif self.scaler_type == "RobustScaler":
self._scaler = RobustScaler()
else:
raise ValueError("Scaler type {} unknown".format(self.scaler_type))
logger.info("Fitting {} to training data".format(self.scaler_type))
self._scaler.fit(self.x_train)
# i think this would refit to test data (and overwrite the parameters)
# probably we either want to fit only training data or training and test data together
......@@ -239,7 +265,7 @@ class KerasROOTClassification:
self._model.add(Dense(self.nodes, activation=self.activation_function))
# last layer is one neuron (binary classification)
self._model.add(Dense(1, activation='sigmoid'))
logger.info("Compile model")
self._model.compile(optimizer='SGD',
loss='binary_crossentropy',
......@@ -321,6 +347,7 @@ class KerasROOTClassification:
self._bkg_weights = np.empty(sum(self.y_train == 0))
self._bkg_weights.fill(self.class_weight[0])
self._bkg_weights *= self.w_train[self.y_train == 0]
logger.debug("Background weights: {}".format(self._bkg_weights))
return self._bkg_weights
......@@ -335,6 +362,7 @@ class KerasROOTClassification:
self._sig_weights = np.empty(sum(self.y_train == 1))
self._sig_weights.fill(self.class_weight[1])
self._sig_weights *= self.w_train[self.y_train == 1]
logger.debug("Signal weights: {}".format(self._sig_weights))
return self._sig_weights
......@@ -344,10 +372,27 @@ class KerasROOTClassification:
fig, ax = plt.subplots()
bkg = self.x_train[:,var_index][self.y_train == 0]
sig = self.x_train[:,var_index][self.y_train == 1]
logger.debug("Plotting bkg (min={}, max={}) from {}".format(np.min(bkg), np.max(bkg), bkg))
logger.debug("Plotting sig (min={}, max={}) from {}".format(np.min(sig), np.max(sig), sig))
ax.hist(bkg, color="b", alpha=0.5, bins=50, weights=self.bkg_weights)
ax.hist(sig, color="r", alpha=0.5, bins=50, weights=self.sig_weights)
# calculate percentiles to get a heuristic for the range to be plotted
# (should in principle also be done with weights, but for now do it unweighted)
range_sig = np.percentile(sig, [1, 99])
range_bkg = np.percentile(sig, [1, 99])
plot_range = (min(range_sig[0], range_bkg[0]), max(range_sig[1], range_sig[1]))
logger.debug("Calculated range based on percentiles: {}".format(plot_range))
try:
ax.hist(bkg, color="b", alpha=0.5, bins=50, range=plot_range, weights=self.bkg_weights)
ax.hist(sig, color="r", alpha=0.5, bins=50, range=plot_range, weights=self.sig_weights)
except ValueError:
# weird, probably not always working workaround for a numpy bug
plot_range = (float("{:.2f}".format(plot_range[0])), float("{:.2f}".format(plot_range[1])))
logger.warn("Got a value error during plotting, maybe this is due to a numpy bug - changing range to {}".format(plot_range))
ax.hist(bkg, color="b", alpha=0.5, bins=50, range=plot_range, weights=self.bkg_weights)
ax.hist(sig, color="r", alpha=0.5, bins=50, range=plot_range, weights=self.sig_weights)
ax.set_xlabel(branch+" (transformed)")
plot_dir = os.path.join(self.project_dir, "plots")
if not os.path.exists(plot_dir):
......@@ -411,11 +456,12 @@ if __name__ == "__main__":
filename = "/project/etp4/nhartmann/trees/allTrees_m1.8_NoSys.root"
c = KerasROOTClassification("test",
c = KerasROOTClassification("test2",
signal_trees = [(filename, "GG_oneStep_1705_1105_505_NoSys")],
bkg_trees = [(filename, "ttbar_NoSys"),
(filename, "wjets_Sherpa221_NoSys")
],
selection="lep1Pt<5000", # cut out a few very weird outliers
branches = ["met", "mt"],
weight_expr = "eventWeight*genWeight",
identifiers = ["DatasetNumber", "EventNumber"])
......
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