Add Supervised Problem (#451)

* Add SuperviedProblem class in problem zoo

pina/problem/zoo/__init__.py

@@ -1,5 +1,8 @@
 __all__ = [
-    'Poisson2DSquareProblem'
+    'Poisson2DSquareProblem',
+    'SupervisedProblem'
+    
 ]
 
 from .poisson_2d_square import Poisson2DSquareProblem
+from .supervised_problem import SupervisedProblem

pina/problem/zoo/supervised_problem.py

@@ -0,0 +1,37 @@
+from pina.problem import AbstractProblem
+from pina import Condition
+from pina import Graph
+
+class SupervisedProblem(AbstractProblem):
+    """
+    A problem definition for supervised learning in PINA.
+
+    This class allows an easy and straightforward definition of a Supervised problem,
+    based on a single condition of type `InputOutputPointsCondition`
+
+    :Example:
+        >>> import torch
+        >>> input_data = torch.rand((100, 10))
+        >>> output_data = torch.rand((100, 10))
+        >>> problem = SupervisedProblem(input_data, output_data)
+    """
+    conditions = dict()
+    output_variables = None
+
+    def __init__(self, input_, output_):
+        """
+        Initialize the SupervisedProblem class
+
+        :param input_: Input data of the problem
+        :type input_: torch.Tensor | Graph
+        :param output_: Output data of the problem
+        :type output_: torch.Tensor
+        """
+        if isinstance(input_, Graph):
+            input_ = input_.data
+        self.conditions['data'] = Condition(
+            input_points=input_,
+            output_points = output_
+        )
+        super().__init__()
+        

tests/test_problem_zoo/test_supervised_problem.py

@@ -0,0 +1,31 @@
+import torch
+from pina.problem import AbstractProblem
+from pina.condition import InputOutputPointsCondition
+from pina.problem.zoo.supervised_problem import SupervisedProblem
+from pina import RadiusGraph
+
+def test_constructor():
+    input_ = torch.rand((100,10))
+    output_ = torch.rand((100,10))
+    problem = SupervisedProblem(input_=input_, output_=output_)
+    assert isinstance(problem, AbstractProblem)
+    assert hasattr(problem, "conditions")
+    assert isinstance(problem.conditions, dict)
+    assert list(problem.conditions.keys()) == ['data']
+    assert isinstance(problem.conditions['data'], InputOutputPointsCondition)
+
+def test_constructor_graph():
+    x = torch.rand((20,100,10))
+    pos = torch.rand((20,100,2))
+    input_ = RadiusGraph(
+        x=x, pos=pos, r=.2, build_edge_attr=True
+    )
+    output_ = torch.rand((100,10))
+    problem = SupervisedProblem(input_=input_, output_=output_)
+    assert isinstance(problem, AbstractProblem)
+    assert hasattr(problem, "conditions")
+    assert isinstance(problem.conditions, dict)
+    assert list(problem.conditions.keys()) == ['data']
+    assert isinstance(problem.conditions['data'], InputOutputPointsCondition)
+    assert isinstance(problem.conditions['data'].input_points, list) 
+    assert isinstance(problem.conditions['data'].output_points, torch.Tensor)