Cross Validation or Optimization for EquivalentSources #543
Description
Description of the desired feature:
Is there a reason there is no cross-validation equivalent for harmonica.EquivalentSources like there is for verde.SplineCV? I find myself quite often manually running cross-validations for the equivalent source parameters as shown in the Estimating damping and depth parameters user guide:
dampings = [0.01, 0.1, 1, 10,]
depths = [5e3, 10e3, 20e3, 50e3]
equivalent_sources = hm.EquivalentSources()
scores = []
for params in parameter_sets:
equivalent_sources.set_params(**params)
score = np.mean(
vd.cross_val_score(
equivalent_sources,
coordinates,
data.gravity_disturbance_mgal,
)
)
scores.append(score)It would be great to be able to instead use
eqs = hm.EquivalentSourcesCV(
dampings=[0.01, 0.1, 1, 10,],
depths=[5e3, 10e3, 20e3, 50e3],
)
eqs.fit(coordinates, data.gravity_disturbance_mgal,)Also, related to this verde issue; I have an equivalent function for performing an optimization for the optimal equivalent source parameters, instead of a grid-search which would be done with the above cross-validation.
Are you willing to help implement and maintain this feature?
Yes if there is enough interest to justify doing this.