Documentation for v0.1 version (#199)

* Adding Equations, solving typos
* improve _code.rst
* the team rst and restuctore index.rst
* fixing errors

---------

Co-authored-by: Dario Coscia <dariocoscia@dhcp-015.eduroam.sissa.it>

pina/solvers/solver.py

@@ -2,30 +2,38 @@
 
 from abc import ABCMeta, abstractmethod
 from ..model.network import Network
-import pytorch_lightning as pl
+import pytorch_lightning
 from ..utils import check_consistency
 from ..problem import AbstractProblem
 import torch
 
 
-class SolverInterface(pl.LightningModule, metaclass=ABCMeta):
-    """ Solver base class. """
+class SolverInterface(pytorch_lightning.LightningModule, metaclass=ABCMeta):
+    """
+    Solver base class. This class inherits is a wrapper of
+    LightningModule class, inheriting all the
+    LightningModule methods.
+    """
+
     def __init__(self,
                  models,
                  problem,
-                 optimizers, 
-                 optimizers_kwargs, 
+                 optimizers,
+                 optimizers_kwargs,
                  extra_features=None):
         """
         :param models: A torch neural network model instance.
         :type models: torch.nn.Module
         :param problem: A problem definition instance.
         :type problem: AbstractProblem
-        :param list(torch.nn.Module) extra_features: the additional input
-        features to use as augmented input. If ``None`` no extra features 
-        are passed. If it is a list of ``torch.nn.Module``, the extra feature
-        list is passed to all models. If it is a list of extra features' lists,
-        each single list of extra feature is passed to a model.
+        :param list(torch.optim.Optimizer) optimizer: A list of neural network optimizers to
+            use.
+        :param list(dict) optimizer_kwargs: A list of optimizer constructor keyword args.
+        :param list(torch.nn.Module) extra_features: The additional input
+            features to use as augmented input. If ``None`` no extra features 
+            are passed. If it is a list of :class:`torch.nn.Module`, the extra feature
+            list is passed to all models. If it is a list of extra features' lists,
+            each single list of extra feature is passed to a model.
         """
         super().__init__()
 
@@ -52,37 +60,40 @@ class SolverInterface(pl.LightningModule, metaclass=ABCMeta):
             raise ValueError('You must define one optimizer for each model.'
                              f'Got {len_model} models, and {len_optimizer}'
                              ' optimizers.')
-        
+
         # check length consistency optimizers kwargs
         if len_optimizer_kwargs != len_optimizer:
             raise ValueError('You must define one dictionary of keyword'
                              ' arguments for each optimizers.'
                              f'Got {len_optimizer} optimizers, and'
                              f' {len_optimizer_kwargs} dicitionaries')
-        
+
         # extra features handling
-        if extra_features is  None:
+        if extra_features is None:
             extra_features = [None] * len_model
         else:
             # if we only have a list of extra features
             if not isinstance(extra_features[0], (tuple, list)):
                 extra_features = [extra_features] * len_model
-            else: # if we have a list of list extra features
+            else:  # if we have a list of list extra features
                 if len(extra_features) != len_model:
-                    raise ValueError('You passed a list of extrafeatures list with len'
-                                     f'different of models len. Expected {len_model} '
-                                     f'got {len(extra_features)}. If you want to use'
-                                     'the same list of extra features for all models, '
-                                     'just pass a list of extrafeatures and not a list '
-                                     'of list of extra features.')
-            
+                    raise ValueError(
+                        'You passed a list of extrafeatures list with len'
+                        f'different of models len. Expected {len_model} '
+                        f'got {len(extra_features)}. If you want to use'
+                        'the same list of extra features for all models, '
+                        'just pass a list of extrafeatures and not a list '
+                        'of list of extra features.')
+
         # assigning model and optimizers
         self._pina_models = []
         self._pina_optimizers = []
 
         for idx in range(len_model):
-            model_ = Network(model=models[idx], extra_features=extra_features[idx])
-            optim_ = optimizers[idx](model_.parameters(), **optimizers_kwargs[idx])
+            model_ = Network(model=models[idx],
+                             extra_features=extra_features[idx])
+            optim_ = optimizers[idx](model_.parameters(),
+                                     **optimizers_kwargs[idx])
             self._pina_models.append(model_)
             self._pina_optimizers.append(optim_)
 
@@ -90,9 +101,9 @@ class SolverInterface(pl.LightningModule, metaclass=ABCMeta):
         self._pina_problem = problem
 
     @abstractmethod
-    def forward(self):
+    def forward(self, *args, **kwargs):
         pass
-    
+
     @abstractmethod
     def training_step(self):
         pass
@@ -131,4 +142,4 @@ class SolverInterface(pl.LightningModule, metaclass=ABCMeta):
     #     """
     #     Set the problem formulation."""
     #     check_consistency(problem, AbstractProblem, 'pina problem')
-    #     self._problem = problem
+    #     self._problem = problem