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Simplify Graph class (#459)

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* Simplifying Graph class and adjust tests

---------

Co-authored-by: Dario Coscia <dariocos99@gmail.com>
This commit is contained in:
Filippo Olivo
2025-03-03 09:30:44 +01:00
committed by Nicola Demo
parent 4c3e305b09
commit ab6ca78d85
7 changed files with 909 additions and 719 deletions
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31
tests/test_problem_zoo/test_supervised_problem.py
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@@ -4,28 +4,31 @@ from pina.condition import InputOutputPointsCondition
from pina.problem.zoo.supervised_problem import SupervisedProblem
from pina.graph import RadiusGraph
def test_constructor():
input_ = torch.rand((100,10))
output_ = torch.rand((100,10))
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)
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))
x = torch.rand((20, 100, 10))
pos = torch.rand((20, 100, 2))
input_ = [
RadiusGraph(x=x_, pos=pos_, radius=0.2, edge_attr=True)
for x_, pos_ in zip(x, pos)
]
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)
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)