From d23f0440cdbcbf80e16ea4de6e7459862a7804eb Mon Sep 17 00:00:00 2001
From: Nikolai <osterei33@gmx.de>
Date: Thu, 9 Aug 2018 14:45:41 +0200
Subject: [PATCH] weighted accuracy
---
toolkit.py | 19 ++++++++++---------
1 file changed, 10 insertions(+), 9 deletions(-)
diff --git a/toolkit.py b/toolkit.py
index 6f24378..3c2d3d9 100755
--- a/toolkit.py
+++ b/toolkit.py
@@ -605,7 +605,8 @@ class ClassificationProject(object):
np.random.seed(self.random_seed)
self._model.compile(optimizer=optimizer,
loss=self.loss,
- metrics=['accuracy'])
+ weighted_metrics=['accuracy']
+ )
np.random.set_state(rn_state)
if os.path.exists(os.path.join(self.project_dir, "weights.h5")):
if self.is_training:
@@ -1090,7 +1091,7 @@ class ClassificationProject(object):
plt.clf()
- def plot_accuracy(self, all_trainings=False, log=False):
+ def plot_accuracy(self, all_trainings=False, log=False, acc_suffix="weighted_acc"):
"""
Plot the value of the accuracy metric for each epoch
@@ -1102,14 +1103,14 @@ class ClassificationProject(object):
else:
hist_dict = self.history.history
- if (not 'acc' in hist_dict) or (not 'val_acc' in hist_dict):
+ if (not acc_suffix in hist_dict) or (not 'val_'+acc_suffix in hist_dict):
logger.warning("No previous history found for plotting, try global history")
hist_dict = self.csv_hist
logger.info("Plot accuracy")
- plt.plot(hist_dict['acc'])
- plt.plot(hist_dict['val_acc'])
+ plt.plot(hist_dict[acc_suffix])
+ plt.plot(hist_dict['val_'+acc_suffix])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
@@ -1122,11 +1123,11 @@ class ClassificationProject(object):
def plot_all(self):
self.plot_ROC()
- self.plot_accuracy()
+ # self.plot_accuracy()
self.plot_loss()
self.plot_score()
self.plot_weights()
- self.plot_significance()
+ # self.plot_significance()
def create_getter(dataset_name):
@@ -1165,8 +1166,8 @@ if __name__ == "__main__":
optimizer="Adam",
#optimizer="SGD",
#optimizer_opts=dict(lr=100., decay=1e-6, momentum=0.9),
- earlystopping_opts=dict(monitor='val_loss',
- min_delta=0, patience=2, verbose=0, mode='auto'),
+ earlystopping_opts=dict(monitor='val_loss',
+ min_delta=0, patience=2, verbose=0, mode='auto'),
selection="1",
branches = ["met", "mt"],
weight_expr = "eventWeight*genWeight",
--
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