autoarray.inversion.regularization.AdaptPower#

class AdaptPower[source]#

Bases: Adapt

Regularization which uses the neighbors of the mesh (e.g. shared Delaunay vertexes) and values adapted to the data being fitted to smooth an inversion’s solution, with the coefficient convention of Constant.

This is the corrected sibling of Adapt. It reconstructs the same adaptive weighting – high smoothing where there is no signal, less where there is (Nightingale, Dye and Massey 2018) – but fixes the two ways in which Adapt diverges from Constant:

The coefficient enters at ``lambda^2``, not ``lambda^4``. Adapt squares its coefficients twice (once when interpolating them into per-pixel weights, once in the matrix builder), so its matrix scales as the fourth power of the coefficient. This class raises the interpolated coefficient to power before the builder squares it, so the effective exponent is 2 * power and the default power=1.0 matches Constant. Under the shared LogUniform(1e-6, 1e6) prior that means the prior now spans lambda^2, and the regularization matrix stays positive-definite to c ~ 1e6 rather than collapsing from c ~ 1e4 – the fragility that produced the likelihood-overflow floods seen in free-coefficient adaptive fits.
Every mesh edge is scattered once. Adapt adds each ordered neighbor pair to both the (i, j) and (j, i) entries, and the neighbor list already holds each unordered edge twice, so its matrix is exactly 2 x Constant’s. This class uses weighted_regularization_matrix_single_scatter_from, which scatters each ordered pair once with the symmetric edge weight 0.5 * (w_i ** 2 + w_j ** 2) – a weighted graph Laplacian, so still symmetric and positive semi-definite.

Together these make AdaptPower(inner_coefficient=c, outer_coefficient=c) exactly equal to Constant(coefficient=c) for any c, which is what Adapt’s docstring always claimed and never delivered.

A full description of regularization and this matrix can be found in the parent AbstractRegularization class; the B matrix construction is described on Adapt.

Migration from ``Adapt``. The coefficient scale is squared: c_new = c_old ** 2. To reproduce the legacy class exactly, pass power=2.0 – but note the factor-2 scatter is fixed regardless, so AdaptPower(power=2.0) is 0.5 x Adapt with the same coefficients.

JAX & gradient support: as for Adapt – JAX-differentiable and FD-certified on the rectangular mesh family, but raising TracerArrayConversionError on the Delaunay mesh family, whose neighbors come from a direct scipy call on the traced mesh grid (use AdaptSplitPower there).

Parameters:

inner_coefficient (float) – The inner regularization coefficient which controls the degree of smoothing of the inversion reconstruction in the inner (high signal) regions of a mesh’s reconstruction.
outer_coefficient (float) – The outer regularization coefficient which controls the degree of smoothing of the inversion reconstruction in the outer (low signal) regions of a mesh’s reconstruction.
signal_scale (float) – A factor which controls how rapidly the smoothness of regularization varies from high signal regions to low signal regions.
power (float) – The exponent the interpolated coefficient is raised to before the matrix builder squares it, so the coefficient enters the regularization matrix at the power 2 * power. The default 1.0 is the Constant convention; 2.0 is the legacy Adapt convention. This is a convention switch, not a model parameter – the shipped prior config fixes it as a Constant prior so a search never samples it.

Methods

`log_det_regularization_matrix_term_from`	Returns `log det H` of this scheme's regularization matrix computed from a factorization the scheme itself knows about, or `None` when no such shortcut exists (the default).
`regularization_matrix_from`	Returns the regularization matrix with shape [pixels, pixels].
`regularization_term_from`	Returns this scheme's contribution to the regularization term `s^T H s` computed from a factorization the scheme itself knows about, or `None` when no such shortcut exists (the default).
`regularization_weights_from`	Returns the regularization weights of this regularization scheme.

Attributes

is_split_regularization

Whether this scheme is a "split" regularization variant, which regularizes using a split-cross calculation of the mesh's mappings rather than the mappings themselves.

regularization_weights_from(linear_obj, xp=<module 'numpy' from '/home/docs/checkouts/readthedocs.org/user_builds/pyautolens/envs/latest/lib/python3.12/site-packages/numpy/__init__.py'>)[source]#

Returns the regularization weights of this regularization scheme.

These are the interpolated inner / outer coefficients raised to self.power (default 1.0), as opposed to Adapt, which squares them.

Parameters:: linear_obj (LinearObj) – The linear object (e.g. a Mapper) which uses these weights when performing regularization.
Return type:: The regularization weights.

regularization_matrix_from(linear_obj, xp=<module 'numpy' from '/home/docs/checkouts/readthedocs.org/user_builds/pyautolens/envs/latest/lib/python3.12/site-packages/numpy/__init__.py'>)[source]#

Returns the regularization matrix with shape [pixels, pixels].

Every mesh edge is scattered once (unlike Adapt), so uniform weights reproduce Constant exactly.

Parameters:: linear_obj (LinearObj) – The linear object (e.g. a Mapper) which uses this matrix to perform regularization.
Return type:: The regularization matrix.