Updated the documentation for multiclass nll loss

ampligraph/latent_features/loss_functions.py

@@ -438,14 +438,27 @@ def _apply(self, scores_pos, scores_neg):
 
 @register_loss("multiclass_nll", [], {'require_same_size_pos_neg':False})   
 class NLLMulticlass(Loss):
-    """ Multiclass NLL Loss
+    """ Multiclass NLL Loss.
     
-        Introduced in :cite: `chen2015` where both the subject and objects are corrupted (to use it in this way pass corrupt_sides = ['s', 'o'] to embedding_model_params) . 
-        This loss was re-engineered in :cite: `kadlecBK17` where only the object was corrupted to get improved performance. (to use it in this way pass corrupt_sides = 'o' to embedding_model_params)
+        Introduced in :cite:`chen2015` where both the subject and objects are corrupted (to use it in this way pass corrupt_sides = ['s', 'o'] to embedding_model_params) . 
         
-        ..math::
+        This loss was re-engineered in :cite:`kadlecBK17` where only the object was corrupted to get improved performance (to use it in this way pass corrupt_sides = 'o' to embedding_model_params).
+
+        .. math::
         
             \mathcal{L(X)} = -\sum_{x_{e_1,e_2,r_k} \in X} log\,p(e_2|e_1,r_k) -\sum_{x_{e_1,e_2,r_k} \in X} log\,p(e_1|r_k, e_2)
+       
+       
+        
+        Examples
+        -------- 
+        >>> import numpy as np
+        >>> from ampligraph.latent_features import TransE
+        >>> model = TransE(batches_count=1, seed=555, epochs=20, k=10, 
+        >>>                embedding_model_params={'corrupt_side':['s', 'o']},
+        >>>                loss='multiclass_nll', loss_params={})
+        
+         
     """
     def __init__(self, eta, loss_params={}, verbose=False):
         """Initialize Loss

ampligraph/latent_features/models.py

@@ -1210,11 +1210,9 @@ def __init__(self,
             - **'normalize_ent_emb'** (bool): flag to indicate whether to normalize entity embeddings after each batch update (default: False).
             - **negative_corruption_entities** : entities to be used for generation of corruptions while training. It can take the following values : ``all`` (default: all entities), ``batch`` (entities present in each batch), list of entities or an int (which indicates how many entities that should be used for corruption generation).
             - **corrupt_sides** : Specifies how to generate corruptions for training. Takes values `s`, `o`, `s+o` or any combination passed as a list
+            
             Example: ``embedding_model_params={'norm': 1, 'normalize_ent_emb': False}``
 
-
-            
-            
         optimizer : string
             The optimizer used to minimize the loss function. Choose between 'sgd',
             'adagrad', 'adam', 'momentum'.

docs/ampligraph.latent_features.rst

@@ -100,6 +100,7 @@ and they can be thus used :ref:`during model selection <eval>`.
     NLLLoss
     AbsoluteMarginLoss
     SelfAdversarialLoss
+    NLLMulticlass
 
 .. _ref-reg:
 

docs/generated/ampligraph.latent_features.NLLMulticlass.rst

@@ -0,0 +1,20 @@
+NLLMulticlass
+==================================
+
+.. currentmodule:: ampligraph.latent_features
+
+.. autoclass:: NLLMulticlass
+
+
+
+
+
+   .. rubric:: Methods
+
+   .. autosummary::
+
+      ~NLLMulticlass.__init__
+
+   .. automethod:: NLLMulticlass.__init__
+
+