fix connection issue

tests/test_problem_zoo/test_inverse_poisson_2d_square.py

@@ -1,12 +1,25 @@
 from pina.problem.zoo import InversePoisson2DSquareProblem
 from pina.problem import InverseProblem, SpatialProblem
+import pytest
 
 
-def test_constructor():
-    problem = InversePoisson2DSquareProblem()
+@pytest.mark.parametrize("load", [True, False])
+@pytest.mark.parametrize("data_size", [0.01, 0.05])
+def test_constructor(load, data_size):
+
+    # Define the problem with or without loading data
+    problem = InversePoisson2DSquareProblem(load=load, data_size=data_size)
+
+    # Discretise the domain
     problem.discretise_domain(n=10, mode="random", domains="all")
+
+    # Check if the problem is correctly set up
     assert problem.are_all_domains_discretised
     assert isinstance(problem, InverseProblem)
     assert isinstance(problem, SpatialProblem)
     assert hasattr(problem, "conditions")
     assert isinstance(problem.conditions, dict)
+
+    # Should fail if data_size is not in the range [0.0, 1.0]
+    with pytest.raises(ValueError):
+        problem = InversePoisson2DSquareProblem(load=load, data_size=3.0)

tests/test_solver/test_competitive_pinn.py

@@ -20,15 +20,9 @@ from torch._dynamo.eval_frame import OptimizedModule
 # define problems
 problem = Poisson()
 problem.discretise_domain(10)
-inverse_problem = InversePoisson()
+inverse_problem = InversePoisson(load=True, data_size=0.01)
 inverse_problem.discretise_domain(10)
 
-# reduce the number of data points to speed up testing
-if hasattr(inverse_problem.conditions, "data"):
-    data_condition = inverse_problem.conditions["data"]
-    data_condition.input = data_condition.input[:10]
-    data_condition.target = data_condition.target[:10]
-
 # add input-output condition to test supervised learning
 input_pts = torch.rand(10, len(problem.input_variables))
 input_pts = LabelTensor(input_pts, problem.input_variables)

tests/test_solver/test_gradient_pinn.py

@@ -31,15 +31,9 @@ class DummyTimeProblem(TimeDependentProblem):
 # define problems
 problem = Poisson()
 problem.discretise_domain(10)
-inverse_problem = InversePoisson()
+inverse_problem = InversePoisson(load=True, data_size=0.01)
 inverse_problem.discretise_domain(10)
 
-# reduce the number of data points to speed up testing
-if hasattr(inverse_problem.conditions, "data"):
-    data_condition = inverse_problem.conditions["data"]
-    data_condition.input = data_condition.input[:10]
-    data_condition.target = data_condition.target[:10]
-
 # add input-output condition to test supervised learning
 input_pts = torch.rand(10, len(problem.input_variables))
 input_pts = LabelTensor(input_pts, problem.input_variables)

tests/test_solver/test_pinn.py

@@ -20,15 +20,9 @@ from torch._dynamo.eval_frame import OptimizedModule
 # define problems
 problem = Poisson()
 problem.discretise_domain(10)
-inverse_problem = InversePoisson()
+inverse_problem = InversePoisson(load=True, data_size=0.01)
 inverse_problem.discretise_domain(10)
 
-# reduce the number of data points to speed up testing
-if hasattr(inverse_problem.conditions, "data"):
-    data_condition = inverse_problem.conditions["data"]
-    data_condition.input = data_condition.input[:10]
-    data_condition.target = data_condition.target[:10]
-
 # add input-output condition to test supervised learning
 input_pts = torch.rand(10, len(problem.input_variables))
 input_pts = LabelTensor(input_pts, problem.input_variables)

tests/test_solver/test_rba_pinn.py

@@ -19,15 +19,9 @@ from torch._dynamo.eval_frame import OptimizedModule
 # define problems
 problem = Poisson()
 problem.discretise_domain(10)
-inverse_problem = InversePoisson()
+inverse_problem = InversePoisson(load=True, data_size=0.01)
 inverse_problem.discretise_domain(10)
 
-# reduce the number of data points to speed up testing
-if hasattr(inverse_problem.conditions, "data"):
-    data_condition = inverse_problem.conditions["data"]
-    data_condition.input = data_condition.input[:10]
-    data_condition.target = data_condition.target[:10]
-
 # add input-output condition to test supervised learning
 input_pts = torch.rand(10, len(problem.input_variables))
 input_pts = LabelTensor(input_pts, problem.input_variables)

tests/test_solver/test_self_adaptive_pinn.py

@@ -20,15 +20,9 @@ from torch._dynamo.eval_frame import OptimizedModule
 # define problems
 problem = Poisson()
 problem.discretise_domain(10)
-inverse_problem = InversePoisson()
+inverse_problem = InversePoisson(load=True, data_size=0.01)
 inverse_problem.discretise_domain(10)
 
-# reduce the number of data points to speed up testing
-if hasattr(inverse_problem.conditions, "data"):
-    data_condition = inverse_problem.conditions["data"]
-    data_condition.input = data_condition.input[:10]
-    data_condition.target = data_condition.target[:10]
-
 # add input-output condition to test supervised learning
 input_pts = torch.rand(10, len(problem.input_variables))
 input_pts = LabelTensor(input_pts, problem.input_variables)