Identifiers option now optional

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)