Add combined results and plot generation

[avolkov-intel]

Description

New CLI options

• --combined-results — builds two extra sheets:
  • All cases — all algorithm results on one page (group name, sklearn time[ms], sklearnex time[ms], speedup, then parameters). KNN is split into
    brute/kd_tree; rows sorted by dtype then dataset; each group ends with per-dtype and total GEOMEAN formula rows with a per-group speedup color scale.
  • Summary (for plots) — one row per algorithm referencing the group GEOMEAN cells (total + fp32 + fp64), split into Training/Inference sections with
    section and total geomeans.
• --draw-plots / --plot-output <path> — renders a Training/Inference speedup bar chart (geomean per algorithm, combining fp32 & fp64) with
  Intel-branded titles and disclaimer footnote.

Other changes

• --performance-stability-metrics now additionally emits a median time[ms] column (alongside existing 1st run time[ms], time CV), included in the
  individual sheets and "All cases".

Use --compatibility-mode together with --combined-results so sklearn/sklearnex rows merge correctly.

Completeness and readability

• I have commented my code, particularly in hard-to-understand areas.
• I have updated the documentation to reflect the changes or created a separate PR with updates and provided its number in the description, if necessary.
• Git commit message contains an appropriate signed-off-by string (see CONTRIBUTING.md for details).
• I have resolved any merge conflicts that might occur with the base branch.

Testing

• I have run it locally and tested the changes extensively.
• All CI jobs are green or I have provided justification why they aren't.
• I have extended testing suite if new functionality was introduced in this PR.

[david-cortes-intel]

@avolkov-intel Please document these options in the .md files where the other options are described.

[avolkov-intel]

@avolkov-intel Please document these options in the .md files where the other options are described.

Done

[avolkov-intel]

CI failures are not related to this PR

[ethanglaser]

Would it make more sense to drop and results["n_jobs"].notna().any() and just drop n_jobs col if any are na? Or what is the reason for this change

[ethanglaser]

Is there a way to make this knn-specific logic more flexible? ie right now we have 2 different knn methods but what it we wanted to use 2 different LogReg solvers - would we need to keep adding in algo-specific logic or can we set it up to be more general?