From a35af907ce85caf367905f5be4f47c47a0542ae8 Mon Sep 17 00:00:00 2001
From: Nikolai Hartmann <Nikolai.Hartmann@physik.uni-muenchen.de>
Date: Tue, 8 May 2018 19:32:23 +0200
Subject: [PATCH] Plot score for training and test data, signal and background
---
toolkit.py | 48 ++++++++++++++++++++++++++++++++++++++++++------
1 file changed, 42 insertions(+), 6 deletions(-)
diff --git a/toolkit.py b/toolkit.py
index 205b3ca..a29b3af 100755
--- a/toolkit.py
+++ b/toolkit.py
@@ -472,8 +472,13 @@ class KerasROOTClassification(object):
return self._class_weight
- def load(self):
+ def load(self, reload=False):
"Load all data needed for plotting and training"
+
+ if reload:
+ self.data_loaded = False
+ self.data_transformed = False
+
if not self.data_loaded:
self._load_data()
@@ -489,6 +494,7 @@ class KerasROOTClassification(object):
np.random.set_state(rn_state)
np.random.shuffle(self.w_train)
if self._scores_train is not None:
+ logger.info("Shuffling scores, since they are also there")
np.random.set_state(rn_state)
np.random.shuffle(self._scores_train)
@@ -528,12 +534,18 @@ class KerasROOTClassification(object):
self.total_epochs += epochs
self._write_info("epochs", self.total_epochs)
+ logger.info("Reloading (and re-transforming) unshuffled training data")
+ self.load(reload=True)
+
logger.info("Create/Update scores for ROC curve")
self.scores_test = self.model.predict(self.x_test)
self.scores_train = self.model.predict(self.x_train)
self._dump_to_hdf5("scores_train", "scores_test")
+ logger.info("Creating all validation plots")
+ self.plot_all()
+
def evaluate(self, x_eval):
@@ -588,7 +600,8 @@ class KerasROOTClassification(object):
def get_bin_centered_hist(x, scale_factor=None, **np_kwargs):
hist, bins = np.histogram(x, **np_kwargs)
centers = (bins[:-1] + bins[1:]) / 2
- hist *= scale_factor
+ if scale_factor is not None:
+ hist *= scale_factor
return centers, hist
@@ -667,8 +680,24 @@ class KerasROOTClassification(object):
plt.savefig(os.path.join(self.project_dir, "ROC.pdf"))
plt.clf()
+
def plot_score(self):
- pass
+ plot_opts = dict(bins=50, range=(0, 1))
+ centers_sig_train, hist_sig_train = self.get_bin_centered_hist(self.scores_train[self.y_train==1].reshape(-1), density=True, weights=self.w_train[self.y_train==1], **plot_opts)
+ centers_bkg_train, hist_bkg_train = self.get_bin_centered_hist(self.scores_train[self.y_train==0].reshape(-1), density=True, weights=self.w_train[self.y_train==0], **plot_opts)
+ centers_sig_test, hist_sig_test = self.get_bin_centered_hist(self.scores_test[self.y_test==1].reshape(-1), density=True, weights=self.w_test[self.y_test==1], **plot_opts)
+ centers_bkg_test, hist_bkg_test = self.get_bin_centered_hist(self.scores_test[self.y_test==0].reshape(-1), density=True, weights=self.w_test[self.y_test==0], **plot_opts)
+ fig, ax = plt.subplots()
+ width = centers_sig_train[1]-centers_sig_train[0]
+ ax.bar(centers_bkg_train, hist_bkg_train, color="b", alpha=0.5, width=width, label="background train")
+ ax.bar(centers_sig_train, hist_sig_train, color="r", alpha=0.5, width=width, label="signal train")
+ ax.scatter(centers_bkg_test, hist_bkg_test, color="b", label="background test")
+ ax.scatter(centers_sig_test, hist_sig_test, color="r", label="signal test")
+ ax.set_yscale("log")
+ ax.set_xlabel("NN output")
+ plt.legend(loc='upper right', framealpha=1.0)
+ fig.savefig(os.path.join(self.project_dir, "scores.pdf"))
+
def plot_loss(self):
@@ -695,6 +724,15 @@ class KerasROOTClassification(object):
plt.savefig(os.path.join(self.project_dir, "accuracy.pdf"))
plt.clf()
+
+ def plot_all(self):
+ self.plot_ROC()
+ self.plot_accuracy()
+ self.plot_loss()
+ self.plot_score()
+ self.plot_weights()
+
+
def create_getter(dataset_name):
def getx(self):
if getattr(self, "_"+dataset_name) is None:
@@ -739,9 +777,7 @@ if __name__ == "__main__":
np.random.seed(42)
c.train(epochs=20)
- c.plot_ROC()
- c.plot_loss()
- c.plot_accuracy()
+ #c.plot_all()
# c.write_friend_tree("test4_score",
# source_filename=filename, source_treename="GG_oneStep_1705_1105_505_NoSys",
--
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