fix utils and trainer doc

Author: GiovanniCanali

pina/trainer.py

@@ -1,4 +1,4 @@
-"""Trainer module."""
+"""Module for the Trainer."""
 
 import sys
 import torch
@@ -10,8 +10,11 @@
 
 class Trainer(lightning.pytorch.Trainer):
     """
-    PINA custom Trainer class which allows to customize standard Lightning
-    Trainer class for PINNs training.
+    PINA custom Trainer class to extend the standard Lightning functionality.
+
+    This class enables specific features or behaviors required by the PINA
+    framework. It modifies the standard :class:`lightning.pytorch.Trainer` class
+    to better support the training process in PINA.
     """
 
     def __init__(
@@ -29,42 +32,35 @@ def __init__(
         **kwargs,
     ):
         """
-        Initialize the Trainer class for by calling Lightning costructor and
-        adding many other functionalities.
-
-        :param solver: A pina:class:`SolverInterface` solver for the
-            differential problem.
-        :type solver: SolverInterface
-        :param batch_size: How many samples per batch to load.
-            If ``batch_size=None`` all
-            samples are loaded and data are not batched, defaults to None.
-        :type batch_size: int | None
-        :param train_size: Percentage of elements in the train dataset.
-        :type train_size: float
-        :param test_size: Percentage of elements in the test dataset.
-        :type test_size: float
-        :param val_size: Percentage of elements in the val dataset.
-        :type val_size: float
-        :param compile: if True model is compiled before training,
-            default False. For Windows users compilation is always disabled.
-        :type compile: bool
-        :param automatic_batching: if True automatic PyTorch batching is
-            performed. Please avoid using automatic batching when batch_size is
-            large, default False.
-        :type automatic_batching: bool
-        :param num_workers: Number of worker threads for data loading.
-            Default 0 (serial loading).
-        :type num_workers: int
-        :param pin_memory: Whether to use pinned memory for faster data
-            transfer to GPU. Default False.
-        :type pin_memory: bool
-        :param shuffle: Whether to shuffle the data for training. Default True.
-        :type pin_memory: bool
+        Initialization of the :class:`Trainer` class.
+
+        :param SolverInterface solver: A :class:`~pina.solver.SolverInterface`
+            solver used to solve a :class:`~pina.problem.AbstractProblem`.
+        :param int batch_size: The number of samples per batch to load.
+            If ``None``, all samples are loaded and data is not batched.
+            Default is ``None``.
+        :param float train_size: The percentage of elements to include in the
+            training dataset. Default is ``1.0``.
+        :param float test_size: The percentage of elements to include in the
+            test dataset. Default is ``0.0``.
+        :param float val_size: The percentage of elements to include in the
+            validation dataset. Default is ``0.0``.
+        :param bool compile: If ``True``, the model is compiled before training.
+            Default is ``False``. For Windows users, it is always disabled.
+        :param bool automatic_batching: If ``True``, automatic PyTorch batching
+            is performed. Avoid using automatic batching when ``batch_size`` is
+            large. Default is ``False``.
+        :param int num_workers: The number of worker threads for data loading.
+            Default is ``0`` (serial loading).
+        :param bool pin_memory: Whether to use pinned memory for faster data
+            transfer to GPU. Default is ``False``.
+        :param bool shuffle: Whether to shuffle the data during training.
+            Default is ``True``.
 
         :Keyword Arguments:
-            The additional keyword arguments specify the training setup
-            and can be choosen from the `pytorch-lightning
-            Trainer API <https://lightning.ai/docs/pytorch/stable/common/trainer.html#trainer-class-api>`_
+            Additional keyword arguments that specify the training setup.
+            These can be selected from the pytorch-lightning Trainer API
+            <https://lightning.ai/docs/pytorch/stable/common/trainer.html#trainer-class-api>_.
         """
         # check consistency for init types
         self._check_input_consistency(
@@ -134,6 +130,10 @@ def __init__(
         }
 
     def _move_to_device(self):
+        """
+        Moves the ``unknown_parameters`` of an instance of
+        :class:`~pina.problem.AbstractProblem` to the :class:`Trainer` device.
+        """
         device = self._accelerator_connector._parallel_devices[0]
         # move parameters to device
         pb = self.solver.problem
@@ -155,9 +155,25 @@ def _create_datamodule(
         shuffle,
     ):
         """
-        This method is used here because is resampling is needed
-        during training, there is no need to define to touch the
-        trainer dataloader, just call the method.
+        This method is designed to handle the creation of a data module when
+        resampling is needed during training. Instead of manually defining and
+        modifying the trainer's dataloaders, this method is called to
+        automatically configure the data module.
+
+        :param float train_size: The percentage of elements to include in the
+            training dataset.
+        :param float test_size: The percentage of elements to include in the
+            test dataset.
+        :param float val_size: The percentage of elements to include in the
+            validation dataset.
+        :param int batch_size: The number of samples per batch to load.
+        :param bool automatic_batching: Whether to perform automatic batching
+            with PyTorch.
+        :param bool pin_memory: Whether to use pinned memory for faster data
+            transfer to GPU.
+        :param int num_workers: The number of worker threads for data loading.
+        :param bool shuffle: Whether to shuffle the data during training.
+        :raises RuntimeError: If not all conditions are sampled.
         """
         if not self.solver.problem.are_all_domains_discretised:
             error_message = "\n".join(
@@ -188,33 +204,52 @@ def _create_datamodule(
 
     def train(self, **kwargs):
         """
-        Train the solver method.
+        Manage the training process of the solver.
         """
         return super().fit(self.solver, datamodule=self.data_module, **kwargs)
 
     def test(self, **kwargs):
         """
-        Test the solver method.
+        Manage the test process of the solver.
         """
         return super().test(self.solver, datamodule=self.data_module, **kwargs)
 
     @property
     def solver(self):
         """
-        Returning trainer solver.
+        Get the solver.
+
+        :return: The solver.
+        :rtype: SolverInterface
         """
         return self._solver
 
     @solver.setter
     def solver(self, solver):
+        """
+        Set the solver.
+
+        :param SolverInterface solver: The solver to set.
+        """
         self._solver = solver
 
     @staticmethod
     def _check_input_consistency(
         solver, train_size, test_size, val_size, automatic_batching, compile
     ):
         """
-        Check the consistency of the input parameters."
+        Verifies the consistency of the parameters for the solver configuration.
+
+        :param SolverInterface solver: The solver.
+        :param float train_size: The percentage of elements to include in the
+            training dataset.
+        :param float test_size: The percentage of elements to include in the
+            test dataset.
+        :param float val_size: The percentage of elements to include in the
+            validation dataset.
+        :param bool automatic_batching: Whether to perform automatic batching
+            with PyTorch.
+        :param bool compile: If ``True``, the model is compiled before training.
         """
 
         check_consistency(solver, SolverInterface)
@@ -231,8 +266,14 @@ def _check_consistency_and_set_defaults(
         pin_memory, num_workers, shuffle, batch_size
     ):
         """
-        Check the consistency of the input parameters and set the default
-        values.
+        Checks the consistency of input parameters and sets default values
+        for missing or invalid parameters.
+
+        :param bool pin_memory: Whether to use pinned memory for faster data
+            transfer to GPU.
+        :param int num_workers: The number of worker threads for data loading.
+        :param bool shuffle: Whether to shuffle the data during training.
+        :param int batch_size: The number of samples per batch to load.
         """
         if pin_memory is not None:
             check_consistency(pin_memory, bool)

pina/utils.py

@@ -1,4 +1,4 @@
-"""Utils module."""
+"""Module for utility functions."""
 
 import types
 from functools import reduce
@@ -12,35 +12,36 @@ def custom_warning_format(
     message, category, filename, lineno, file=None, line=None
 ):
     """
-    Depewarning custom format.
+    Custom warning formatting function.
 
     :param str message: The warning message.
-    :param class category: The warning category.
-    :param str filename: The filename where the warning was raised.
-    :param int lineno: The line number where the warning was raised.
-    :param str file: The file object where the warning was raised.
-    :param inr line: The line where the warning was raised.
+    :param Warning category: The warning category.
+    :param str filename: The filename where the warning is raised.
+    :param int lineno: The line number where the warning is raised.
+    :param str file: The file object where the warning is raised.
+        Default is None.
+    :param int line: The line where the warning is raised.
     :return: The formatted warning message.
     :rtype: str
     """
     return f"{filename}: {category.__name__}: {message}\n"
 
 
 def check_consistency(object_, object_instance, subclass=False):
-    """Helper function to check object inheritance consistency.
-       Given a specific ``'object'`` we check if the object is
-       instance of a specific ``'object_instance'``, or in case
-       ``'subclass=True'`` we check if the object is subclass
-       if the ``'object_instance'``.
-
-    :param (iterable or class object) object: The object to check the
-        inheritance
-    :param Object object_instance: The parent class from where the object
-        is expected to inherit
-    :param str object_name: The name of the object
-    :param bool subclass: Check if is a subclass and not instance
-    :raises ValueError: If the object does not inherit from the
-        specified class
+    """
+    Check if an object maintains inheritance consistency.
+
+    This function checks whether a given object is an instance of a specified
+    class or, if ``subclass=True``, whether it is a subclass of the specified
+    class.
+
+    :param object: The object to check.
+    :type object: Iterable | Object
+    :param Object object_instance: The expected parent class.
+    :param bool subclass: If True, checks whether ``object_`` is a subclass
+        of ``object_instance`` instead of an instance. Default is ``False``.
+    :raises ValueError: If ``object_`` does not inherit from ``object_instance``
+        as expected.
     """
     if not isinstance(object_, (list, set, tuple)):
         object_ = [object_]
@@ -59,18 +60,28 @@ def check_consistency(object_, object_instance, subclass=False):
 
 def labelize_forward(forward, input_variables, output_variables):
     """
-    Wrapper decorator to allow users to enable or disable the use of
-    LabelTensors during the forward pass.
-
-    :param forward: The torch.nn.Module forward function.
-    :type forward: Callable
-    :param input_variables: The problem input variables.
-    :type input_variables: list[str] | tuple[str]
-    :param output_variables: The problem output variables.
-    :type output_variables: list[str] | tuple[str]
+    Decorator to enable or disable the use of :class:`~pina.LabelTensor`
+    during the forward pass.
+
+    :param Callable forward: The forward function of a :class:`torch.nn.Module`.
+    :param list[str] input_variables: The names of the input variables of a
+        :class:`~pina.problem.AbstractProblem`.
+    :param list[str] output_variables: The names of the output variables of a
+        :class:`~pina.problem.AbstractProblem`.
+    :return: The decorated forward function.
+    :rtype: Callable
     """
 
     def wrapper(x):
+        """
+        Decorated forward function.
+
+        :param LabelTensor x: The labelized input of the forward pass of an
+            instance of :class:`torch.nn.Module`.
+        :return: The labelized output of the forward pass of an instance of
+            :class:`torch.nn.Module`.
+        :rtype: LabelTensor
+        """
         x = x.extract(input_variables)
         output = forward(x)
         # keep it like this, directly using LabelTensor(...) raises errors
@@ -82,15 +93,32 @@ def wrapper(x):
     return wrapper
 
 
-def merge_tensors(tensors):  # name to be changed
-    """TODO"""
+def merge_tensors(tensors):
+    """
+    Merge a list of :class:`~pina.LabelTensor` instances into a single
+    :class:`~pina.LabelTensor` tensor, by applying iteratively the cartesian
+    product.
+
+    :param list[LabelTensor] tensors: The list of tensors to merge.
+    :raises ValueError: If the list of tensors is empty.
+    :return: The merged tensor.
+    :rtype: LabelTensor
+    """
     if tensors:
         return reduce(merge_two_tensors, tensors[1:], tensors[0])
     raise ValueError("Expected at least one tensor")
 
 
 def merge_two_tensors(tensor1, tensor2):
-    """TODO"""
+    """
+    Merge two :class:`~pina.LabelTensor` instances into a single
+    :class:`~pina.LabelTensor` tensor, by applying the cartesian product.
+
+    :param LabelTensor tensor1: The first tensor to merge.
+    :param LabelTensor tensor2: The second tensor to merge.
+    :return: The merged tensor.
+    :rtype: LabelTensor
+    """
     n1 = tensor1.shape[0]
     n2 = tensor2.shape[0]
 
@@ -102,12 +130,14 @@ def merge_two_tensors(tensor1, tensor2):
 
 
 def torch_lhs(n, dim):
-    """Latin Hypercube Sampling torch routine.
-    Sampling in range $[0, 1)^d$.
+    """
+    The Latin Hypercube Sampling torch routine, sampling in :math:`[0, 1)`$.
 
-    :param int n: number of samples
-    :param int dim: dimensions of latin hypercube
-    :return: samples
+    :param int n: The number of points to sample.
+    :param int dim: The number of dimensions of the sampling space.
+    :raises TypeError: If `n` or `dim` are not integers.
+    :raises ValueError: If `dim` is less than 1.
+    :return: The sampled points.
     :rtype: torch.tensor
     """
 
@@ -137,22 +167,22 @@ def torch_lhs(n, dim):
 
 def is_function(f):
     """
-    Checks whether the given object `f` is a function or lambda.
+    Check if the given object is a function or a lambda.
 
-    :param object f: The object to be checked.
-    :return: `True` if `f` is a function, `False` otherwise.
+    :param Object f: The object to be checked.
+    :return: ``True`` if ``f`` is a function, ``False`` otherwise.
     :rtype: bool
     """
     return isinstance(f, (types.FunctionType, types.LambdaType))
 
 
 def chebyshev_roots(n):
     """
-    Return the roots of *n* Chebyshev polynomials (between [-1, 1]).
+    Compute the roots of the Chebyshev polynomial of degree ``n``.
 
-    :param int n: number of roots
-    :return: roots
-    :rtype: torch.tensor
+    :param int n: The number of roots to return.
+    :return: The roots of the Chebyshev polynomials.
+    :rtype: torch.Tensor
     """
     pi = torch.acos(torch.zeros(1)).item() * 2
     k = torch.arange(n)