From c5c9734a852d1ff6868ec5291d2eeddafa1e5f0c Mon Sep 17 00:00:00 2001
From: Nikolai Hartmann <Nikolai.Hartmann@physik.uni-muenchen.de>
Date: Tue, 5 Jun 2018 14:26:36 +0200
Subject: [PATCH] Identifiers option now optional
---
toolkit.py | 20 +++++++++++++-------
1 file changed, 13 insertions(+), 7 deletions(-)
diff --git a/toolkit.py b/toolkit.py
index 96fb4c9..fa96e41 100755
--- a/toolkit.py
+++ b/toolkit.py
@@ -151,7 +151,8 @@ class ClassificationProject(object):
def _init_from_args(self, name,
- signal_trees, bkg_trees, branches, weight_expr, identifiers,
+ signal_trees, bkg_trees, branches, weight_expr,
+ identifiers=None,
selection=None,
layers=3,
nodes=64,
@@ -178,6 +179,8 @@ class ClassificationProject(object):
self.branches = branches
self.weight_expr = weight_expr
self.selection = selection
+ if identifiers is None:
+ identifiers = []
self.identifiers = identifiers
self.layers = layers
self.nodes = nodes
@@ -730,12 +733,15 @@ class ClassificationProject(object):
start=start, stop=start+batch_size)
x_eval = rec2array(x_from_tree[self.branches])
- # create list of booleans that indicate which events where used for training
- df_identifiers = pd.DataFrame(x_from_tree[self.identifiers])
- total_train_list = self.s_eventlist_train
- total_train_list = np.concatenate((total_train_list, self.b_eventlist_train))
- merged = df_identifiers.merge(pd.DataFrame(total_train_list), on=tuple(self.identifiers), indicator=True, how="left")
- is_train = np.array(merged["_merge"] == "both")
+ if len(self.identifiers) > 0:
+ # create list of booleans that indicate which events where used for training
+ df_identifiers = pd.DataFrame(x_from_tree[self.identifiers])
+ total_train_list = self.s_eventlist_train
+ total_train_list = np.concatenate((total_train_list, self.b_eventlist_train))
+ merged = df_identifiers.merge(pd.DataFrame(total_train_list), on=tuple(self.identifiers), indicator=True, how="left")
+ is_train = np.array(merged["_merge"] == "both")
+ else:
+ is_train = np.zeros(len(x_eval))
# join scores and is_train array
scores = self.evaluate(x_eval).reshape(-1)
--
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