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Adjust domain-based weighting #696

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Merged
NicolaCourtier merged 2 commits into from
Mar 28, 2025
Merged

Adjust domain-based weighting #696

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2b7601f
Adjust domain-based weighting
NicolaCourtier Mar 27, 2025
f6bb9d0
Fix typo
NicolaCourtier Mar 28, 2025
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18 changes: 7 additions & 11 deletions pybop/costs/fitting_costs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,19 +32,15 @@ def __init__(self, problem, weighting: Union[str, np.ndarray] = None):
# Normalise the residuals by the domain spacing (for a uniform domain,
# this is the same as a uniform weighting)
domain_data = self.problem.domain_data
n_domain_data = len(domain_data)
domain_spacing = domain_data[1:] - domain_data[:-1]
self.weighting = (
np.concatenate(
(
[domain_spacing[0]],
(domain_spacing[1:] + domain_spacing[:-1]) / 2,
[domain_spacing[-1]],
)
mean_spacing = np.mean(domain_spacing)
self.weighting = np.concatenate(
(
[(mean_spacing + domain_spacing[0]) / 2],
(domain_spacing[1:] + domain_spacing[:-1]) / 2,
[(domain_spacing[-1] + mean_spacing) / 2],
)
* (n_domain_data - 1)
/ (domain_data[-1] - domain_data[0])
)
) * ((len(domain_data) - 1) / (domain_data[-1] - domain_data[0]))
else:
self.weighting = np.asarray(weighting)

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5 changes: 4 additions & 1 deletion tests/unit/test_cost.py
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Expand Up @@ -238,7 +238,7 @@ def test_error_weighting(
np.testing.assert_allclose(e, eE)
np.testing.assert_allclose(de, deE)

# Test that domain-basd weighting also matches for evenly spaced data
# Test that domain-based weighting also matches for evenly spaced data
costD = cost_class(problem, weighting="domain")
eD, deD = costD(x, calculate_grad=True)
np.testing.assert_allclose(e, eD)
Expand All @@ -251,6 +251,9 @@ def test_error_weighting(
np.testing.assert_allclose(e, eR, rtol=1e-2, atol=1e-9)
np.testing.assert_allclose(de, deR, rtol=1e-2, atol=1e-9)

# Check that the sum (and therefore mean) are the same as an even weighting
np.testing.assert_allclose(np.sum(costR.weighting), len(costR.weighting))

# Check gradient calculation using finite difference
delta = 1e-6 * x[0]
cost_right = costR(x[0] + delta / 2)
Expand Down