update comments

torchcp/classification/loss/conftr.py

@@ -17,11 +17,11 @@ class ConfTr(nn.Module):
     Paper: https://arxiv.org/abs/2110.09192.
     
     
-    :param weights: the weight of each loss function
+    :param weight: the weight of each loss function
     :param predictor: the CP predictors
     :param alpha: the significance level for each training batch
     :param fraction: the fraction of the calibration set in each training batch
-    :param loss_types: the selected (multi-selected) loss functions, which can be "valid", "classification",  "probs", "coverage".
+    :param loss_type: the selected (multi-selected) loss functions, which can be "valid", "classification",  "probs", "coverage".
     :param target_size: Optional: 0 | 1.
     :param loss_transform: a transform for loss
     :param base_loss_fn: a base loss function. For example, cross entropy in classification.

torchcp/classification/scores/thr.py

@@ -14,7 +14,7 @@ class THR(BaseScore):
     Threshold conformal predictors (Sadinle et al., 2016).
     paper : https://arxiv.org/abs/1609.00451.
     
-    param score_type: a transformation on logits. Default: "softmax". Optional: "softmax", "Identity", "log_softmax" or "log". 
+    :param score_type: a transformation on logits. Default: "softmax". Optional: "softmax", "Identity", "log_softmax" or "log".
     """
 
     def __init__(self, score_type="softmax") -> None:

torchcp/regression/loss/quantile.py

@@ -3,16 +3,18 @@
 
 __all__ = ["QuantileLoss"]
 
+
 class QuantileLoss(nn.Module):
-    """ Pinball loss function
+    """
+    Pinball loss function (Romano et al., 2019)
+    Paper: https://proceedings.neurips.cc/paper_files/paper/2019/file/5103c3584b063c431bd1268e9b5e76fb-Paper.pdf
+
+    :param quantiles: a list of quantiles, such as $[\frac{alpha}{2}, 1-\frac{alpha}{2}]$.
     """
 
     def __init__(self, quantiles):
         """
-        A loss to training a quantile-regression model (Romano et al., 2019).
-        Paper: https://proceedings.neurips.cc/paper_files/paper/2019/file/5103c3584b063c431bd1268e9b5e76fb-Paper.pdf.
-        
-        :param quantiles: a list of quantiles, such as $[\frac{alpha}{2}, 1-\frac{alpha}{2}]$.
+
         """
         super().__init__()
         self.quantiles = quantiles
@@ -30,7 +32,7 @@ def forward(self, preds, target):
         losses = preds.new_zeros(len(self.quantiles))
 
         for i, q in enumerate(self.quantiles):
-            errors = target - preds[:, i:i+1]
+            errors = target - preds[:, i:i + 1]
             losses[i] = torch.sum(torch.max((q - 1) * errors, q * errors).squeeze(1))
         loss = torch.mean(losses)
         return loss