renamed overly specific loss function

sup3r/utilities/loss_metrics.py

@@ -173,14 +173,14 @@ def __call__(self, x1, x2):
  return self.MSE_LOSS(x1_coarse, x2_coarse)
 
 
-class TRHLoss(tf.keras.losses.Loss):
- """Loss class for Temperature and Relative Humidity Sup3rCC GAN that
- encourages accuracy of the min/max values in the timeseries"""
+class TemporalExtremesLoss(tf.keras.losses.Loss):
+ """Loss class that encourages accuracy of the min/max values in the
+ timeseries"""
 
  MAE_LOSS = MeanAbsoluteError()
 
  def __call__(self, x1, x2):
- """Custom Sup3rCC content loss function for Temp + RH
+ """Custom content loss that encourages temporal min/max accuracy
 
  Parameters
  ----------

tests/test_custom_loss.py

@@ -3,7 +3,8 @@
 import numpy as np
 import tensorflow as tf
 
-from sup3r.utilities.loss_metrics import MmdMseLoss, CoarseMseLoss, TRHLoss
+from sup3r.utilities.loss_metrics import (MmdMseLoss, CoarseMseLoss,
+ TemporalExtremesLoss)
 
 
 def test_mmd_loss():
@@ -54,24 +55,24 @@ def test_coarse_mse_loss():
  assert mse.numpy() > 10 * coarse_mse.numpy()
 
 
-def test_trh_loss():
- """Test custom Sup3rCC Temp + RH loss function that looks at min/max values
+def test_tex_loss():
+ """Test custom TemporalExtremesLoss function that looks at min/max values
  in the timeseries."""
- trh_loss = TRHLoss()
+ loss_obj = TemporalExtremesLoss()
 
  x = np.zeros((1, 1, 1, 72, 1))
  y = np.zeros((1, 1, 1, 72, 1))
 
  # loss should be dominated by special min/max values
  x[..., 24, 0] = 20
  y[..., 25, 0] = 25
- loss = trh_loss(x, y)
+ loss = loss_obj(x, y)
  assert loss.numpy() > 5
  assert loss.numpy() < 6
 
  # loss should be dominated by special min/max values
  x[..., 24, 0] = -10
  y[..., 25, 0] = -15
- loss = trh_loss(x, y)
+ loss = loss_obj(x, y)
  assert loss.numpy() > 5
  assert loss.numpy() < 6