[refactor] Move probabilistic to Prototorch

prototorch/models/probabilistic.py

@@ -2,44 +2,13 @@
 
 import torch
 from prototorch.functions.competitions import stratified_sum
+from prototorch.functions.losses import (log_likelihood_ratio_loss,
+                                         robust_soft_loss)
 from prototorch.functions.transform import gaussian
 
 from .glvq import GLVQ
 
 
-def likelihood_loss(probabilities, target, prototype_labels):
-    uniques = prototype_labels.unique(sorted=True).tolist()
-    labels = target.tolist()
-
-    key_val = {key: val for key, val in zip(uniques, range(len(uniques)))}
-    target_indices = torch.LongTensor(list(map(key_val.get, labels)))
-
-    whole_probability = probabilities.sum(dim=1)
-    correct_probability = probabilities[torch.arange(len(probabilities)),
-                                        target_indices]
-    wrong_probability = whole_probability - correct_probability
-
-    likelihood = correct_probability / wrong_probability
-    log_likelihood = torch.log(likelihood)
-    return log_likelihood
-
-
-def robust_soft_loss(probabilities, target, prototype_labels):
-    uniques = prototype_labels.unique(sorted=True).tolist()
-    labels = target.tolist()
-
-    key_val = {key: val for key, val in zip(uniques, range(len(uniques)))}
-    target_indices = torch.LongTensor(list(map(key_val.get, labels)))
-
-    whole_probability = probabilities.sum(dim=1)
-    correct_probability = probabilities[torch.arange(len(probabilities)),
-                                        target_indices]
-
-    likelihood = correct_probability / whole_probability
-    log_likelihood = torch.log(likelihood)
-    return log_likelihood
-
-
 class ProbabilisticLVQ(GLVQ):
     def __init__(self, hparams, rejection_confidence=1.0, **kwargs):
         super().__init__(hparams, **kwargs)
@@ -78,17 +47,17 @@ class ProbabilisticLVQ(GLVQ):
 class LikelihoodRatioLVQ(ProbabilisticLVQ):
     """Learning Vector Quantization based on Likelihood Ratios
     """
-    @property
-    def loss_fn(self):
-        return likelihood_loss
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.loss_fn = log_likelihood_ratio_loss
 
 
 class RSLVQ(ProbabilisticLVQ):
     """Learning Vector Quantization based on Likelihood Ratios
     """
-    @property
-    def loss_fn(self):
-        return robust_soft_loss
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.loss_fn = robust_soft_loss
 
 
 __all__ = ["LikelihoodRatioLVQ", "RSLVQ"]