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Implement custom sampling logic

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This commit is contained in:
FilippoOlivo
2025-02-07 15:56:04 +01:00
committed by Nicola Demo
parent f578b2ed12
commit 195224794f
4 changed files with 114 additions and 46 deletions
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35
tests/test_collector.py
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@@ -11,22 +11,23 @@ from pina.operators import laplacian
from pina.collector import Collector
# def test_supervised_tensor_collector():
# class SupervisedProblem(AbstractProblem):
# output_variables = None
# conditions = {
# 'data1' : Condition(input_points=torch.rand((10,2)),
# output_points=torch.rand((10,2))),
# 'data2' : Condition(input_points=torch.rand((20,2)),
# output_points=torch.rand((20,2))),
# 'data3' : Condition(input_points=torch.rand((30,2)),
# output_points=torch.rand((30,2))),
# }
# problem = SupervisedProblem()
# collector = Collector(problem)
# for v in collector.conditions_name.values():
# assert v in problem.conditions.keys()
# assert all(collector._is_conditions_ready.values())
def test_supervised_tensor_collector():
class SupervisedProblem(AbstractProblem):
output_variables = None
conditions = {
'data1': Condition(input_points=torch.rand((10, 2)),
output_points=torch.rand((10, 2))),
'data2': Condition(input_points=torch.rand((20, 2)),
output_points=torch.rand((20, 2))),
'data3': Condition(input_points=torch.rand((30, 2)),
output_points=torch.rand((30, 2))),
}
problem = SupervisedProblem()
collector = Collector(problem)
for v in collector.conditions_name.values():
assert v in problem.conditions.keys()
def test_pinn_collector():
def laplace_equation(input_, output_):
@@ -81,7 +82,7 @@ def test_pinn_collector():
def poisson_sol(self, pts):
return -(torch.sin(pts.extract(['x']) * torch.pi) *
torch.sin(pts.extract(['y']) * torch.pi)) / (
2 * torch.pi ** 2)
2 * torch.pi ** 2)
truth_solution = poisson_sol

50
tests/test_problem.py
View File

@@ -2,6 +2,8 @@ import torch
import pytest
from pina.problem.zoo import Poisson2DSquareProblem as Poisson
from pina import LabelTensor
from pina.domain import Union
from pina.domain import CartesianDomain
def test_discretise_domain():
@@ -29,18 +31,6 @@ def test_discretise_domain():
poisson_problem.discretise_domain(n)
'''
def test_sampling_few_variables():
n = 10
poisson_problem = Poisson()
poisson_problem.discretise_domain(n,
'grid',
domains=['D'],
variables=['x'])
assert poisson_problem.discretised_domains['D'].shape[1] == 1
'''
def test_variables_correct_order_sampling():
n = 10
poisson_problem = Poisson()
@@ -66,3 +56,39 @@ def test_add_points():
new_pts.extract('x'))
assert torch.isclose(poisson_problem.discretised_domains['D'].extract('y'),
new_pts.extract('y'))
@pytest.mark.parametrize(
"mode",
[
'random',
'grid'
]
)
def test_custom_sampling_logic(mode):
poisson_problem = Poisson()
sampling_rules = {
'x': {'n': 100, 'mode': mode},
'y': {'n': 50, 'mode': mode}
}
poisson_problem.discretise_domain(sample_rules=sampling_rules)
for domain in ['g1', 'g2', 'g3', 'g4']:
assert poisson_problem.discretised_domains[domain].shape[0] == 100 * 50
assert poisson_problem.discretised_domains[domain].labels == ['x', 'y']
@pytest.mark.parametrize(
"mode",
[
'random',
'grid'
]
)
def test_wrong_custom_sampling_logic(mode):
d2 = CartesianDomain({'x': [1,2], 'y': [0,1] })
poisson_problem = Poisson()
poisson_problem.domains['D'] = Union([poisson_problem.domains['D'], d2])
sampling_rules = {
'x': {'n': 100, 'mode': mode},
'y': {'n': 50, 'mode': mode}
}
with pytest.raises(RuntimeError):
poisson_problem.discretise_domain(sample_rules=sampling_rules)