handle zero scales for WeightedRobustScaler

utils.py

@@ -6,6 +6,7 @@ import numpy as np
 
 import keras.backend as K
 from sklearn.preprocessing import RobustScaler
+from sklearn.preprocessing.data import _handle_zeros_in_scale
 
 from meme import cache
 
@@ -140,5 +141,6 @@ class WeightedRobustScaler(RobustScaler):
             wqs = np.array([weighted_quantile(X[:,i], [0.25, 0.5, 0.75], sample_weight=weights) for i in range(X.shape[1])])
             self.center_ = wqs[:,1]
             self.scale_ = wqs[:,2]-wqs[:,0]
+            self.scale_ = _handle_zeros_in_scale(self.scale_, copy=False)
             return self