From 3bc3c4f1ead06a3f52c8131afd83f7fadcf1f369 Mon Sep 17 00:00:00 2001
From: Nikolai Hartmann <Nikolai.Hartmann@physik.uni-muenchen.de>
Date: Tue, 5 Jun 2018 14:13:17 +0200
Subject: [PATCH] Adding appropriate class weights for balance_dataset mode
---
toolkit.py | 31 +++++++++++++++++++++++++++++--
1 file changed, 29 insertions(+), 2 deletions(-)
diff --git a/toolkit.py b/toolkit.py
index 3c65869..96fb4c9 100755
--- a/toolkit.py
+++ b/toolkit.py
@@ -230,6 +230,7 @@ class ClassificationProject(object):
self._scaler = None
self._class_weight = None
+ self._balanced_class_weight = None
self._model = None
self._history = None
self._callbacks_list = []
@@ -534,10 +535,29 @@ class ClassificationProject(object):
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)]
- logger.debug("Calculated class_weight: {}".format(self._class_weight))
+ logger.debug("Calculated class_weight: {}".format(self._class_weight))
return self._class_weight
+ @property
+ def balanced_class_weight(self):
+ """
+ Class weight for the balance_dataset method
+ Since we have equal number of signal and background events in
+ each batch, we need to balance the ratio of sum of weights per
+ event with class weights
+ """
+ if self._balanced_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])
+ # use sumw *per event* in this case
+ sumw_bkg /= len(self.w_train[self.y_train == 0])
+ sumw_sig /= len(self.w_train[self.y_train == 1])
+ self._balanced_class_weight = [(sumw_sig+sumw_bkg)/(2*sumw_bkg), (sumw_sig+sumw_bkg)/(2*sumw_sig)]
+ logger.debug("Calculated balanced_class_weight: {}".format(self._balanced_class_weight))
+ return self._balanced_class_weight
+
+
def load(self, reload=False):
"Load all data needed for plotting and training"
@@ -601,13 +621,19 @@ class ClassificationProject(object):
for start in range(0, len(x_train[y_train==class_label]), int(self.batch_size/2)):
yield (x_train[y_train==class_label][start:start+int(self.batch_size/2)],
y_train[y_train==class_label][start:start+int(self.batch_size/2)],
- w_train[y_train==class_label][start:start+int(self.batch_size/2)])
+ w_train[y_train==class_label][start:start+int(self.batch_size/2)]*self.balanced_class_weight[class_label])
# restart
def yield_balanced_batch(self):
"generate batches with equal amounts of both classes"
+ logcounter = 0
for batch_0, batch_1 in izip(self.yield_batch(0), self.yield_batch(1)):
+ if logcounter == 10:
+ logger.debug("\rSumw sig*balanced_class_weight[1]: {}".format(np.sum(batch_1[2])))
+ logger.debug("\rSumw bkg*balanced_class_weight[0]: {}".format(np.sum(batch_0[2])))
+ logcounter = 0
+ logcounter += 1
yield (np.concatenate((batch_0[0], batch_1[0])),
np.concatenate((batch_0[1], batch_1[1])),
np.concatenate((batch_0[2], batch_1[2])))
@@ -645,6 +671,7 @@ class ClassificationProject(object):
self.is_training = True
labels, label_counts = np.unique(self.y_train, return_counts=True)
logger.info("Training on balanced batches")
+ # note: the batches have balanced_class_weight already applied
self.model.fit_generator(self.yield_balanced_batch(),
steps_per_epoch=int(min(label_counts)/self.batch_size),
epochs=epochs,
--
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