remove state-dependent standardscaler

materialsvirtuallab · Apr 8, 2019 · 8128de2 · 8128de2
1 parent 83a197a
commit 8128de2
Showing 1 changed file with 4 additions and 6 deletions.
diff --git a/megnet/models.py b/megnet/models.py
@@ -11,7 +11,6 @@
 from megnet.data.graph import GraphBatchDistanceConvert, GraphBatchGenerator, GaussianDistance
 from megnet.data.crystal import graphs2inputs, CrystalGraph
 import numpy as np
-from sklearn.preprocessing import StandardScaler
 import os
 
 from monty.serialization import dumpfn, loadfn
@@ -41,7 +40,6 @@ def __init__(self,
         self.model = model
         self.graph_convertor = graph_convertor
         self.distance_convertor = distance_convertor
-        self.yscaler = StandardScaler()
 
     def __getattr__(self, p):
         return getattr(self.model, p)
@@ -111,10 +109,10 @@ def train_from_graphs(self,
             os.makedirs(dirname)
         if callbacks is None:
             callbacks = [ManualStop()]
-        train_targets = self.yscaler.fit_transform(np.array(train_targets).reshape((-1, 1))).ravel()
+        train_targets = np.array(train_targets).ravel()
         if validation_graphs is not None:
             filepath = pjoin(dirname, 'val_mae_{epoch:05d}_{val_mae:.6f}.hdf5')
-            validation_targets = self.yscaler.transform(np.array(validation_targets).reshape((-1, 1))).ravel()
+            validation_targets = np.array(validation_targets).ravel()
             val_inputs = graphs2inputs(validation_graphs, validation_targets)
 
             val_generator = self._create_generator(*val_inputs,
@@ -126,7 +124,7 @@ def train_from_graphs(self,
                                                  save_weights_only=False,
                                                  val_gen=val_generator,
                                                  steps_per_val=steps_per_val,
-                                                 y_scaler=self.yscaler)])
+                                                 y_scaler=None)])
         else:
             val_generator = None
             steps_per_val = None
@@ -154,7 +152,7 @@ def predict_structure(self, structure):
                expand_1st(np.array(gnode)),
                expand_1st(np.array(gbond)),
                ]
-        return self.yscaler.inverse_transform(self.predict(inp).reshape((-1, 1))).ravel()
+        return self.predict(inp).ravel()
 
     def _create_generator(self, *args, **kwargs):
         if self.distance_convertor is not None: