[WIP] WLS_sparse2 solver

[bdestombe]

Introduces a new solver that accepts prior knowledge of the parameter set. And should generally be faster than wls_spare.

Solves the normal equations of X . p_sol = y with weights. Supports a priori
information. The parameter estimates from a previous calibration instance
and their covariances can be passed to `p_sol_prior` and `p_cov_prior`.
Allows for sequential calibration in chunks.

Normally, the X matrix is very tall (nobs>>npar), more observations than
unknowns. By using the normal equations, the coefficient matrix reduces to a
square matrix of shape (npar, npar), and the observation matrix to (npar,).
This results in a much smaller system to solve.

Todo:

• Test / demo that sequential calibration works
• Similar tests as the other solvers
• Basic implementation in the ds.calibration_single_ended() and ds.calibration_double_ended() functions.
• Example notebook

[bdestombe]

#110

[codecov]

Codecov Report

Merging #121 into master will decrease coverage by 0.38%.
The diff coverage is 58.97%.

@@            Coverage Diff             @@
##           master     #121      +/-   ##
==========================================
- Coverage   75.25%   74.86%   -0.39%     
==========================================
  Files           8        8              
  Lines        3180     3258      +78     
  Branches      687      713      +26     
==========================================
+ Hits         2393     2439      +46     
- Misses        585      610      +25     
- Partials      202      209       +7     

Impacted Files	Coverage Δ
src/dtscalibration/datastore.py	75.72% <7.69%> (-0.59%)	⬇️
src/dtscalibration/calibrate_utils.py	84.46% <69.23%> (-1.95%)	⬇️

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[BSchilperoort]

Nice work!

One question though; is sparse2 an appropriate name? I think something more self-descriptive might be better for clarity. Or is the plan that this solver supersedes the old one?

[bdestombe]

I would think the latter. There would be no need for two sparse solvers so wls_sparse2 would supersede wls_sparse, at least I can't come up with a reason to keep both. We should keep the statsmodels version for testing purposes.

It is going to be a pain to fully implement this though in ds.calibration_single_ended() and ds.calibration_double_ended() and keep on support all those fixed parameter options..

[bdestombe]

But I'm open to using other names :) we could drop the wls_ since all implemented solvers support weights.

[BSchilperoort]

wls_sparse2 is fine as a temporary name as it's going to replace the old one. For continuity sake I think we have to keep the name 'wls_sparse' though, to avoid breaking people's code (which is something we have to worry about now we're at version 1.0).

Fixing parameters is a mess yes, but extremely useful! Would there be a cleaner way to handle it in the back-end? Also wrap it in one or multiple functions perhaps?

[bdestombe]

For now, I think the path forward would be to:

1. Invoke calibration_single_ended_solver() and calibration_double_ended_solver() directly with solver='external' for each calibration instance. It returns for each instance the matrices needed for calibration (X, y, w, p0_est).
2. Manually stretch X, p0_est to account for the parameters that are in the first calibration instance but not in the second.
3. Call wls_sparse2 directly to compute p_sol, p_cov for each calibration instance.
4. Invoke ds.calibration_single_ended() and ds.calibration_double_ended() with solver='external' and pass the p_sol, p_cov from the final calibration instance as arguments.

Thus quite some manual labor, but full control.. And If you'd like to fix certain parameters, you could use solver='external_split' so you retrieve an X, y, w, p0_est for every parameter separately.

Maybe there should be two example notebooks. 1. Without fixing any parameters and 2. With fixing gamma.