From 12b8426733f632a7c32e7dbe98516875308bb7a1 Mon Sep 17 00:00:00 2001
From: Nikolai Hartmann <Nikolai.Hartmann@physik.uni-muenchen.de>
Date: Fri, 27 Apr 2018 16:54:07 +0200
Subject: [PATCH] scores managed as properties (saved and loaded from h5)
---
toolkit.py | 90 ++++++++++++++++++++++++++++++++++++++----------------
1 file changed, 64 insertions(+), 26 deletions(-)
diff --git a/toolkit.py b/toolkit.py
index 4fce688..3db07b0 100755
--- a/toolkit.py
+++ b/toolkit.py
@@ -36,7 +36,7 @@ K.set_session(session)
import ROOT
-class KerasROOTClassification:
+class KerasROOTClassification(object):
dataset_names = ["x_train", "x_test", "y_train", "y_test", "w_train", "w_test"]
@@ -51,7 +51,9 @@ class KerasROOTClassification:
validation_split=0.33,
activation_function='relu',
out_dir="./outputs",
- scaler_type="RobustScaler"):
+ scaler_type="RobustScaler",
+ step_signal=2,
+ step_bkg=2):
self.name = name
self.signal_trees = signal_trees
self.bkg_trees = bkg_trees
@@ -66,6 +68,8 @@ class KerasROOTClassification:
self.activation_function = activation_function
self.out_dir = out_dir
self.scaler_type = scaler_type
+ self.step_signal = step_signal
+ self.step_bkg = step_bkg
self.project_dir = os.path.join(self.out_dir, name)
@@ -94,8 +98,8 @@ class KerasROOTClassification:
self._model = None
self._history = None
- self.score_train = None
- self.score_test = None
+ self._scores_train = None
+ self._scores_test = None
# track the number of epochs this model has been trained
self.total_epochs = 0
@@ -124,19 +128,19 @@ class KerasROOTClassification:
self.s_train = tree2array(signal_chain,
branches=self.branches+[self.weight_expr]+self.identifiers,
selection=self.selection,
- start=0, step=2)
+ start=0, step=self.step_signal)
self.b_train = tree2array(bkg_chain,
branches=self.branches+[self.weight_expr]+self.identifiers,
selection=self.selection,
- start=0, step=200)
+ start=0, step=self.step_bkg)
self.s_test = tree2array(signal_chain,
branches=self.branches+[self.weight_expr],
selection=self.selection,
- start=1, step=2)
+ start=1, step=self.step_signal)
self.b_test = tree2array(bkg_chain,
branches=self.branches+[self.weight_expr],
selection=self.selection,
- start=1, step=200)
+ start=1, step=self.step_bkg)
self._dump_training_list()
self.s_eventlist_train = self.s_train[self.identifiers]
@@ -178,14 +182,20 @@ class KerasROOTClassification:
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:
+ def _dump_to_hdf5(self, dataset_names=None):
+ if dataset_names is None:
+ dataset_names = self.dataset_names
+ for dataset_name in dataset_names:
+ filename = os.path.join(self.project_dir, dataset_name+".h5")
+ logger.info("Writing {} to {}".format(dataset_name, filename))
+ with h5py.File(filename, "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:
+ def _load_from_hdf5(self, dataset_names=None):
+ if dataset_names is None:
+ dataset_names = self.dataset_names
+ for dataset_name in 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:
@@ -193,6 +203,33 @@ class KerasROOTClassification:
logger.info("Data loaded")
+ @property
+ def scores_train(self):
+ if self._scores_train is None:
+ self._load_from_hdf5(["_scores_train"])
+ return self._scores_train
+
+
+ @scores_train.setter
+ def scores_train(self, value):
+ self._scores_train = value
+ self._dump_to_hdf5(["_scores_train"])
+
+
+ @property
+ def scores_test(self):
+ if self._scores_test is None:
+ self._load_from_hdf5(["_scores_test"])
+ return self._scores_test
+
+
+ @scores_test.setter
+ def scores_test(self, value):
+ self._scores_test = value
+ logger.info("dump")
+ self._dump_to_hdf5(["_scores_test"])
+
+
@property
def scaler(self):
# create the scaler (and fit to training data) if not existent
@@ -309,13 +346,13 @@ class KerasROOTClassification:
logger.info("Train model")
self._history = self.model.fit(self.x_train,
- # the reshape might be unnescessary here
- self.y_train.reshape(-1, 1),
- epochs=epochs,
- validation_split = self.validation_split,
- class_weight=self.class_weight,
- shuffle=True,
- batch_size=self.batch_size)
+ # the reshape might be unnescessary here
+ self.y_train.reshape(-1, 1),
+ epochs=epochs,
+ validation_split = self.validation_split,
+ class_weight=self.class_weight,
+ shuffle=True,
+ batch_size=self.batch_size)
logger.info("Save weights")
self.model.save_weights(os.path.join(self.project_dir, "weights.h5"))
@@ -323,7 +360,7 @@ class KerasROOTClassification:
self.total_epochs += epochs
self._write_info("epochs", self.total_epochs)
- logger.info("Create scores for ROC curve")
+ 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)
@@ -454,12 +491,12 @@ class KerasROOTClassification:
if __name__ == "__main__":
logging.basicConfig()
- #logging.getLogger("KerasROOTClassification").setLevel(logging.INFO)
- logging.getLogger("KerasROOTClassification").setLevel(logging.DEBUG)
+ logging.getLogger("KerasROOTClassification").setLevel(logging.INFO)
+ #logging.getLogger("KerasROOTClassification").setLevel(logging.DEBUG)
filename = "/project/etp4/nhartmann/trees/allTrees_m1.8_NoSys.root"
- c = KerasROOTClassification("test2",
+ c = KerasROOTClassification("test3",
signal_trees = [(filename, "GG_oneStep_1705_1105_505_NoSys")],
bkg_trees = [(filename, "ttbar_NoSys"),
(filename, "wjets_Sherpa221_NoSys")
@@ -467,9 +504,10 @@ if __name__ == "__main__":
selection="lep1Pt<5000", # cut out a few very weird outliers
branches = ["met", "mt"],
weight_expr = "eventWeight*genWeight",
- identifiers = ["DatasetNumber", "EventNumber"])
+ identifiers = ["DatasetNumber", "EventNumber"],
+ step_bkg = 100)
- c.train(epochs=20)
+# c.train(epochs=10)
c.plot_ROC()
c.plot_loss()
c.plot_accuracy()
--
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