fix optim doc

pina/optim/__init__.py

@@ -1,4 +1,4 @@
-"""Module for Optimizer class."""
+"""Module for the Optimizers and Schedulers."""
 
 __all__ = [
     "Optimizer",

pina/optim/optimizer_interface.py

@@ -1,24 +1,23 @@
-"""Module for PINA Optimizer."""
+"""Module for the PINA Optimizer."""
 
 from abc import ABCMeta, abstractmethod
 
 
 class Optimizer(metaclass=ABCMeta):
     """
-    TODO
+    Abstract base class for defining an optimizer. All specific optimizers
-    :param metaclass: _description_, defaults to ABCMeta
+    should inherit form this class and implement the required methods.
-    :type metaclass: _type_, optional
     """
 
     @property
     @abstractmethod
     def instance(self):
         """
-        TODO
+        Abstract property to retrieve the optimizer instance.
         """
 
     @abstractmethod
     def hook(self):
         """
-        TODO
+        Abstract method to define the hook logic for the optimizer.
         """

pina/optim/scheduler_interface.py

@@ -1,25 +1,23 @@
-"""Module for PINA Scheduler."""
+"""Module for the PINA Scheduler."""
 
 from abc import ABCMeta, abstractmethod
 
 
 class Scheduler(metaclass=ABCMeta):
     """
-    TODO
+    Abstract base class for defining a scheduler. All specific schedulers should
-
+    inherit form this class and implement the required methods.
-    :param metaclass: _description_, defaults to ABCMeta
-    :type metaclass: _type_, optional
     """
 
     @property
     @abstractmethod
     def instance(self):
         """
-        TODO
+        Abstract property to retrieve the scheduler instance.
         """
 
     @abstractmethod
     def hook(self):
         """
-        TODO
+        Abstract method to define the hook logic for the scheduler.
         """

pina/optim/torch_optimizer.py

@@ -1,4 +1,4 @@
-"""Module for PINA Torch Optimizer"""
+"""Module for the PINA Torch Optimizer"""
 
 import torch
 
@@ -8,18 +8,17 @@ from .optimizer_interface import Optimizer
 
 class TorchOptimizer(Optimizer):
     """
-    TODO
+    A wrapper class for using PyTorch optimizers.
-
-    :param Optimizer: _description_
-    :type Optimizer: _type_
     """
 
     def __init__(self, optimizer_class, **kwargs):
         """
-        TODO
+        Initialization of the :class:`TorchOptimizer` class.
 
-        :param optimizer_class: _description_
+        :param torch.optim.Optimizer optimizer_class: The PyTorch optimizer
-        :type optimizer_class: _type_
+            class.
+        :param dict kwargs: Additional parameters passed to `optimizer_class`,
+            see more: <https://pytorch.org/docs/stable/optim.html#algorithms>_.
         """
         check_consistency(optimizer_class, torch.optim.Optimizer, subclass=True)
 
@@ -29,10 +28,9 @@ class TorchOptimizer(Optimizer):
 
     def hook(self, parameters):
         """
-        TODO
+        Initialize the optimizer instance with the given parameters.
 
-        :param parameters: _description_
+        :param dict parameters: The parameters of the model to be optimized.
-        :type parameters: _type_
         """
         self._optimizer_instance = self.optimizer_class(
             parameters, **self.kwargs
@@ -41,6 +39,9 @@ class TorchOptimizer(Optimizer):
     @property
     def instance(self):
         """
-        Optimizer instance.
+        Get the optimizer instance.
+
+        :return: The optimizer instance.
+        :rtype: torch.optim.Optimizer
         """
         return self._optimizer_instance

pina/optim/torch_scheduler.py

@@ -1,4 +1,4 @@
-"""Module for PINA Torch Optimizer"""
+"""Module for the PINA Torch Optimizer"""
 
 try:
     from torch.optim.lr_scheduler import LRScheduler  # torch >= 2.0
@@ -14,18 +14,17 @@ from .scheduler_interface import Scheduler
 
 class TorchScheduler(Scheduler):
     """
-    TODO
+    A wrapper class for using PyTorch schedulers.
-
-    :param Scheduler: _description_
-    :type Scheduler: _type_
     """
 
     def __init__(self, scheduler_class, **kwargs):
         """
-        TODO
+        Initialization of the :class:`TorchScheduler` class.
 
-        :param scheduler_class: _description_
+        :param torch.optim.LRScheduler scheduler_class: The PyTorch scheduler
-        :type scheduler_class: _type_
+            class.
+        :param dict kwargs: Additional parameters passed to `scheduler_class`,
+            see more: <https://pytorch.org/docs/stable/optim.html#algorithms>_.
         """
         check_consistency(scheduler_class, LRScheduler, subclass=True)
 
@@ -35,10 +34,9 @@ class TorchScheduler(Scheduler):
 
     def hook(self, optimizer):
         """
-        TODO
+        Initialize the scheduler instance with the given parameters.
 
-        :param optimizer: _description_
+        :param dict parameters: The parameters of the optimizer.
-        :type optimizer: _type_
         """
         check_consistency(optimizer, Optimizer)
         self._scheduler_instance = self.scheduler_class(
@@ -48,6 +46,9 @@ class TorchScheduler(Scheduler):
     @property
     def instance(self):
         """
-        Scheduler instance.
+        Get the scheduler instance.
+
+        :return: The scheduelr instance.
+        :rtype: torch.optim.LRScheduler
         """
         return self._scheduler_instance