read and save training list

toolkit.py

@@ -112,8 +112,8 @@ class KerasROOTClassification(object):
         self._scores_train = None
         self._scores_test = None
 
-        self.s_eventlist_train = None
-        self.b_eventlist_train = None
+        self._s_eventlist_train = None
+        self._b_eventlist_train = None
 
         self._scaler = None
         self._class_weight = None
@@ -164,9 +164,9 @@ class KerasROOTClassification(object):
                                      selection=self.selection,
                                      start=1, step=self.step_bkg)
 
-            self._dump_training_list()
             self.s_eventlist_train = self.s_train[self.identifiers]
             self.b_eventlist_train = self.b_train[self.identifiers]
+            self._dump_training_list()
 
             # now we don't need the identifiers anymore
             self.s_train = self.s_train[self.branches+[self.weight_expr]]
@@ -196,11 +196,37 @@ class KerasROOTClassification(object):
 
 
     def _dump_training_list(self):
-        s_eventlist = pd.DataFrame(self.s_train[self.identifiers])
-        b_eventlist = pd.DataFrame(self.b_train[self.identifiers])
+        s_eventlist_df = pd.DataFrame(self.s_eventlist_train)
+        b_eventlist_df = pd.DataFrame(self.b_eventlist_train)
+
+        s_eventlist_df.to_csv(os.path.join(self.project_dir, "s_eventlist_train.csv"))
+        b_eventlist_df.to_csv(os.path.join(self.project_dir, "b_eventlist_train.csv"))
+
+
+    @property
+    def s_eventlist_train(self):
+        if self._s_eventlist_train is None:
+            df = pd.read_csv(os.path.join(self.project_dir, "s_eventlist_train.csv"))
+            self._s_eventlist_train = df.to_records()[self.identifiers]
+        return self._s_eventlist_train
+
+
+    @s_eventlist_train.setter
+    def s_eventlist_train(self, value):
+        self._s_eventlist_train = value
+
+
+    @property
+    def b_eventlist_train(self):
+        if self._b_eventlist_train is None:
+            df = pd.read_csv(os.path.join(self.project_dir, "b_eventlist_train.csv"))
+            self._b_eventlist_train = df.to_records()[self.identifiers]
+        return self._b_eventlist_train
+
 
-        s_eventlist.to_csv(os.path.join(self.project_dir, "s_eventlist_train.csv"))
-        s_eventlist.to_csv(os.path.join(self.project_dir, "b_eventlist_train.csv"))
+    @b_eventlist_train.setter
+    def b_eventlist_train(self, value):
+        self._b_eventlist_train = value
 
 
     def _dump_to_hdf5(self, *dataset_names):
@@ -435,19 +461,20 @@ class KerasROOTClassification(object):
         if os.path.exists(target_filename):
             raise IOError("{} already exists, if you want to recreate it, delete it first".format(target_filename))
         for start in range(0, entries, batch_size):
+            logger.info("Evaluating score for entry {}/{}".format(start, entries))
             logger.debug("Loading next batch")
             x_eval = rec2array(tree2array(tree,
                                           branches=self.branches,
                                           start=start, stop=start+batch_size))
+            logger.debug("Done")
             scores = np.array(self.evaluate(x_eval), dtype=[(score_name, np.float64)])
-            print(len(scores))
-            print(scores)
             if start == 0:
                 mode = "recreate"
             else:
                 mode = "update"
             logger.debug("Write to root file")
             array2root(scores, target_filename, treename=target_treename, mode=mode)
+            logger.debug("Done")
 
     def write_all_friend_trees(self):
         pass
@@ -589,7 +616,7 @@ if __name__ == "__main__":
 
     logging.basicConfig()
     logging.getLogger("KerasROOTClassification").setLevel(logging.INFO)
-    logging.getLogger("KerasROOTClassification").setLevel(logging.DEBUG)
+    #logging.getLogger("KerasROOTClassification").setLevel(logging.DEBUG)
 
     filename = "/project/etp4/nhartmann/trees/allTrees_m1.8_NoSys.root"