Loading and saving of StandardScaler

toolkit.py

@@ -10,13 +10,17 @@ from root_numpy import tree2array, rec2array
 import numpy as np
 import pandas as pd
 import h5py
+from sklearn.preprocessing import StandardScaler
+from sklearn.externals import joblib
 
 import ROOT
 
 class KerasROOTClassification:
 
+
     dataset_names = ["x_train", "x_test", "y_train", "y_test", "w_train", "w_test"]
 
+
     def __init__(self, name,
                  signal_trees, bkg_trees, branches, weight_expr, identifiers,
                  layers=3, nodes=64, out_dir="./outputs"):
@@ -50,6 +54,9 @@ class KerasROOTClassification:
         self.s_eventlist_train = None
         self.b_eventlist_train = None
 
+        self._scaler = None
+
+
     def load_data(self):
 
         try:
@@ -125,6 +132,29 @@ class KerasROOTClassification:
         logger.info("Data loaded")
 
 
+    @property
+    def scaler(self):
+        # create the scaler (and fit to training data) if not existent
+        if self._scaler is None:
+            filename = os.path.join(self.project_dir, "scaler.pkl")
+            try:
+                self._scaler = joblib.load(filename)
+                logger.info("Loaded existing StandardScaler from {}".format(filename))
+            except IOError:
+                logger.info("Creating new StandardScaler")
+                self._scaler = StandardScaler()
+                logger.info("Fitting StandardScaler to training data")
+                self._scaler.fit(self.x_train)
+                joblib.dump(self._scaler, filename)
+        return self._scaler
+
+
+    def _transform_data(self):
+        pass
+
+    def _create_model(self):
+        pass
+
     def train(self):
         pass
 
@@ -161,3 +191,5 @@ if __name__ == "__main__":
     c.load_data()
     print(c.x_train)
     print(len(c.x_train))
+
+    print(c.scaler)