diff --git a/dashboard.html b/dashboard.html index 472441768..fd976b3bb 100644 --- a/dashboard.html +++ b/dashboard.html @@ -231,7 +231,7 @@

PyAutoMindDashboard

Intent. Priority. Flow.

Every task the Mind is holding. Tap a task's πŸ“‹ and its /start_dev command is on your clipboard β€” paste it into a Claude Code chat to route Claude straight to that task. Recent is the same work by date β€” what has been happening rather than what to do next.

- +

Last updated 2026-09-24. This page is generated from active/, draft/ and the registry files, so it is only as current as they are. dashboard_refresh.yml re-renders it on every push to main β€” that heals a stale page, but not a stale prompt: a task that shipped without its prompt advancing to complete/ keeps rendering here as pickable backlog. Reconciling those is the refresh below.

latent-nan-guard-honest-run β€” planned 2026-07-22

Backlog

-

216 filed prompts, not started β€” sorted most-pickable first (priority, then size). 38 of them belong to an epic and are listed only under Epics below.

+

217 filed prompts, not started β€” sorted most-pickable first (priority, then size). 39 of them belong to an epic and are listed only under Epics below.

bug β€” 66

SED chain waveband fit can spin forever in Nautilus explorationβ€¦πŸ› bugeuclidsmallsupervisedhigh

@@ -1256,7 +1256,7 @@

Epics

Source & Cluster arc β€” magnification science, PointSolver trust, cluster extended sources✨ featureautolenstoo-largesupervisedhigh

-Expectation propagation (EP) campaign β€” 8 queued prompt(s), in order +Expectation propagation (EP) campaign β€” 9 queued prompt(s), in order

Expectation propagation (EP) campaign β€” ledger: draft/research/graphical_ep/ep_campaign.md

Graphical Model Scale-Up β€” ScopingπŸ”¬ researchgraphical_eptoo-largehuman-requiredhigh

The project @z_projects/ic50_workspace is our IC50 use case which we✨ featureautofitmediumsafenormal

@@ -1264,6 +1264,7 @@

Epics

NaN in EP evidence accumulation: invalid value encountered in add in ep_mean_field.pyβ€¦πŸ› bugautofitmediumsupervisedmedium

analytic_gaussian.py's graphical column is an unseeded run, so its PARITY countβ€¦πŸ› buggraphical_epsmallsafelow

EP: moment-matching projection for the hierarchical scatterβ€¦βœ¨ featureautofitmediumsupervisednormal

+

EP: cut the per-factor-search wrapper overhead…♻️ refactorautofitmediumsupervisedhigh

EP campaign β€” phase map for the 2026 Q3 graphical/EP pushπŸ”¬ researchgraphical_eptoo-largehuman-requiredhigh

Expectation Propagation Scale-Up β€” ScopingπŸ”¬ researchgraphical_eptoo-largehuman-requiredhigh

diff --git a/dashboard.md b/dashboard.md index 6747e07eb..33eba4e1e 100644 --- a/dashboard.md +++ b/dashboard.md @@ -44,7 +44,7 @@ anything you could not verify. | [Human review](#human-review) (`draft/human_review/`) | 2 | | [Parked](#parked) (`parked.md`) | 4 | | [Planned](#planned) (`planned.md`) | 6 | -| [Backlog](#backlog) (`draft/`) | 216 | +| [Backlog](#backlog) (`draft/`) | 217 | > **No batch in flight.** @@ -588,7 +588,7 @@ Scoped but not started; some are not yet prompt files. Full detail in [`planned. ## Backlog -**216** filed prompts, not started. Each section is sorted most-pickable first (priority, then size). **38** of them belong to an epic and are listed only under [Epics](#epics) below. +**217** filed prompts, not started. Each section is sorted most-pickable first (priority, then size). **39** of them belong to an epic and are listed only under [Epics](#epics) below.
bug β€” 66 @@ -2536,7 +2536,7 @@ Continue the 'Cluster strong lensing β€” Source & Cluster arc' epic. Its canonic
-Expectation propagation (EP) campaign β€” 8 queued prompt(s), in order +Expectation propagation (EP) campaign β€” 9 queued prompt(s), in order
πŸ“‹ Expectation propagation (EP) campaign β€” ledger: `draft/research/graphical_ep/ep_campaign.md` @@ -2594,6 +2594,14 @@ Continue the 'Expectation propagation (EP) campaign' epic. Its canonical state l
+
πŸ“‹ EP: cut the per-factor-search wrapper overhead… β€” autofit Β· medium Β· supervised Β· high + +``` +/start_dev draft/refactor/autofit/ep_factor_search_wrapper_overhead.md +``` + +
+
πŸ“‹ EP campaign β€” phase map for the 2026 Q3 graphical/EP push β€” graphical_ep Β· too-large Β· human-required Β· high ``` diff --git a/draft/feature/autofit/ep_hierarchical_scatter_moment_matching.md b/draft/feature/autofit/ep_hierarchical_scatter_moment_matching.md index 768f1a798..831fc736b 100644 --- a/draft/feature/autofit/ep_hierarchical_scatter_moment_matching.md +++ b/draft/feature/autofit/ep_hierarchical_scatter_moment_matching.md @@ -76,3 +76,38 @@ A moment-matching projection option for `_HierarchicalFactor` updates - Mechanism records: `complete/2026/09/ep-prior-id-zero.md`, `ep-message-support.md`, `ep-laplace-hessian.md` - PyAutoFit#1405 (umbrella), #1561 (Hessian), autofit_workspace_test#91 (referee) - README caveat: `PyAutoFit/autofit/graphical/README.md` Β§3.5 + +## 2026-09-24 β€” slope_hierarchy_scale 343299: hierarchical factor 0/50 SUCCESS at N=25 + +RAL job 343299 (PyAutoCortex `projects/slope_hierarchy_scale.md`) completed +2026-09-16 02:03 BST: wall 5h20m, `MAX_STEPS=2`, 25 lenses, JAX-on-CPU, no +pool. It logged 50 "jit compiling vectorized" lines β€” one compile per factor +search (2 steps x 25), confirming `draft/refactor/autofit/ep_analysis_level_compile_cache.md`. +Every dataset factor reached SUCCESS (2/2 each). **The hierarchical factor +never did:** `HierarchicalFactor0` has 50 rows = 27 `BAD_PROJECTION` + 23 +`FAILURE`, zero SUCCESS (`PriorFactor239` and `PriorFactor263` also stale), +and the library's own STALE FACTORS warning fired. The parent recovery printed +exactly the prior: mean 2.0000 Β± 0.7071, sigma 0.5000 Β± 0.3536 (priors +TG(2,1) / TG(0.5,0.5); truth sigma 0.1). + +Under the Laplace optimiser's semantics `BAD_PROJECTION` = the Hessian at the +mode is not finite or not negative-definite (the scale parameter is driven to +a limit) and `FAILURE` = the line search failed and the mean field was handed +back unchanged. This is the Laplace-on-scatter caveat +(`PyAutoFit/autofit/graphical/README.md` Β§3.5, `analytic_gaussian` leg B) at +100 % on the lensing model: per-lens slope widths of 0.002-0.05 are far +tighter than the scatter prior, so the tilted density in sigma sits at the +boundary. The EP arm of campaign phase 3 therefore cannot estimate the slope +scatter with the Laplace projection; this prompt is now the blocking decision +for it (human call, `Autonomy: supervised`). EP output (local): +`output/sample_n25_seed42/ep/expectation_propagation/a7ec60f07f4261afcb98b2adef484199/` +(`ep_history.csv`, `ep_diagnostics.results`). + +Cross-reference β€” `draft/bug/autofit/ep_factors_end_the_run_with_zero.md`: +that bug records a `HierarchicalFactor1` ending with only `BAD_PROJECTION` and +never a SUCCESS on 6/200 leg-B seeds of the analytic toy (seeds 29, 71, 106, +152, 178, 193). The same shape now reproduces at 100 % on a real lensing model +(every hierarchical update over 2 steps x 25 factors), so it is no longer a +rare-seed edge case: whether a zero-SUCCESS hierarchical factor is legitimate +(the bug's open question) and whether moment matching removes it should be +judged together. diff --git a/draft/refactor/autofit/ep_factor_search_wrapper_overhead.md b/draft/refactor/autofit/ep_factor_search_wrapper_overhead.md new file mode 100644 index 000000000..3c35e9967 --- /dev/null +++ b/draft/refactor/autofit/ep_factor_search_wrapper_overhead.md @@ -0,0 +1,88 @@ +# EP: cut the per-factor-search wrapper overhead (redundant second run_nested pass, per-search plots, instance_from_vector fast path) + +Type: refactor +Target: PyAutoFit +Repos: +- PyAutoFit +Themes: +- graphical-ep +Difficulty: medium +Autonomy: supervised +Priority: high +Status: formalised +Consequence: judge +Witness: on the ep_toy_gaussian N=5, max_steps=3 toy, `run_nested` is called once per Dynesty factor search (15 calls for 15 searches, not 30) and per-search wall drops from ~2.45 s to <= 1.5 s at identical parent moments (mean and sigma within 1e-6 of a control run with the same seeds), with the full `test_autofit` suite green run serially. +Review-minutes: 5 +Unattended: ready +Epic: graphical-ep +Filed: 2026-09-24 + +## Why + +Every EP factor update is a full autofit non-linear search. On the +`ep_toy_gaussian` #1405 collapse toy (PyAutoCortex +`projects/ep_toy_gaussian.md`) a 1-parameter Dynesty factor search spends +about as much time in the autofit wrapper as in the sampler. A 1000-search +N=50 repeat carries ~15-20 min of pure overhead; best case on the toy is ~2x. + +Profile (2026-09-24, laptop, PyAutoFit at the RAL mirror a73684012, N=5, +max_steps=3, 15 factor searches, cProfile total 109 s; files in +`ep_factor_search_wrapper_overhead_assets/`): + +- **Per search 2.45 s = ~1.47 s dynesty `run_nested` + ~0.98 s autofit wrapper** + (plots, a redundant second `run_nested` pass, samples writes). +- **Redundant second `run_nested` pass (~5.5 s / 13 %).** 30 `run_nested` + calls for 15 searches. `run_search_internal` + (`autofit/non_linear/search/nest/dynesty/search/abstract.py:413-455`) + returns `finished = total_iterations == iterations_after_run`, which is + never true on a first pass that converges, so every search does a full + `perform_update(during_analysis=True)` + checkpoint restore + a no-op second + `run_nested`. +- **Per-search visuals.** `corner_anesthetic` + `model_fit.png` 7.7 s / 18 %; + `data.png` redrawn every search 2.3 s / 5 % although the data never changes. +- **`instance_from_vector`** (`autofit/mapper/prior_model/abstract.py:990`) + is 57 % of `fitness.call`, i.e. ~25-30 % of `run_nested` for a 1-parameter + model: `autofit/mapper/model.py:30` `cache` 486k calls, + `autofit/mapper/prior_model/attribute_pair.py` wrapper 481k calls, + `prior_model.py:483 _instance_for_arguments` 2.7 s tottime. +- **`truncated_normal.log_partition` / `kl`** call `scipy.stats.norm.cdf` + 18.6k times / 3.45 s β€” use `scipy.special.ndtr` / `log_ndtr`. +- **`manage_quick_update`** 1.8 s while quick-update is disabled (61k calls). +- **`gc.collect` per search** 75 ms (1.78 s over 15). + +## What (phases) + +1. **Dynesty finished-detection fix** so a converged first pass is recognised + and the second `perform_update` + checkpoint restore + `run_nested` is + skipped; unit test that counts `run_nested` calls (mock the sampler) and + asserts one per converged search. +2. **EP factor searches skip per-search visuals** β€” a config switch, default + off under EP (corner/model_fit per search), and `data.png` drawn once per + analysis rather than per search. +3. **Mapper fast path for fixed-structure models** in `instance_from_vector` + (precompute the prior-to-attribute map once per model; bypass the + `cache` / `attribute_pair` wrappers on the hot path). +4. **`ndtr` swap** in `autofit/messages/truncated_normal.py` (and + `mapper/prior/_erf_helpers.py` if it pays) β€” `scipy.special.ndtr` / + `log_ndtr` in place of `scipy.stats.norm.cdf`. Also look at the disabled + `manage_quick_update` early-out and the per-search `gc.collect`. + +Each phase should be measured on the same toy (per-search wall, `run_nested` +call count, parent moments against a same-seed control). + +## Side observation (reproducibility) + +Three runs of `ep_repeat.py --repeat 0` gave parent means 51.86 / 51.33 / +51.61: the repeat seed does not make EP reproducible at a73684012. Known cause +β€” the Laplace refine draws from global `np.random` and depends on variable-id +ordering (PyAutoFit#1352). The witness's "same seeds" control therefore needs +the global RNG seeded (or the #1352 fix) before the 1e-6 moment comparison is +meaningful; if it cannot be made deterministic, compare moments across +matched repeats within their spread and say so. + +## Links + +- Campaign ledger: `draft/research/graphical_ep/ep_campaign.md` (row 1b; phase 6 epic 2, EP profiling) +- Sibling: `draft/refactor/autofit/ep_analysis_level_compile_cache.md` (the JAX recompile per factor search) +- EMFILE fix that preceded this profile: PyAutoFit#1632 (dynesty single-core no pool), #1634 (release factor search internals) +- Evidence: `ep_factor_search_wrapper_overhead_assets/ep_profile_report.txt`, `ep_pstats_cumulative.txt`, `ep_pstats_tottime.txt` (paths shortened: `SP/` = site-packages, `PyAutoFit/`, `STDLIB/`, `SCRATCH/` = the profiling scratch dir) diff --git a/draft/refactor/autofit/ep_factor_search_wrapper_overhead_assets/ep_profile_report.txt b/draft/refactor/autofit/ep_factor_search_wrapper_overhead_assets/ep_profile_report.txt new file mode 100644 index 000000000..6e43421bc --- /dev/null +++ b/draft/refactor/autofit/ep_factor_search_wrapper_overhead_assets/ep_profile_report.txt @@ -0,0 +1,126 @@ +total tottime 109.12s + a_dynesty 7.95s 7.3% + b_likelihood+mapping 21.38s 19.6% + c_search_wrapper+output 22.54s 20.7% + d_ep_maths 6.72s 6.2% + e_other 50.53s 46.3% +e_other top: + numpy 7.91 + scipy 6.60 + stdlib 3.93 + 2.61 + 1.87 + 1.78 + 1.70 + 1.46 + 1.09 + pandas 0.90 + 0.83 + 0.83 + 0.70 + 0.67 + 0.64 +=== TOP 30 cumtime +ncalls tottime percall cumtime percall filename:lineno(function) + 30 0.004 0.000 50.303 1.677 autofit/non_linear/search/nest/dynesty/search/abstract.py:413(run_search_internal) + 30 0.051 0.002 50.124 1.671 SP/dynesty/sampler.py:920(run_nested) + 6691 0.504 0.000 47.989 0.007 SP/dynesty/sampler.py:599(sample) + 6661 0.340 0.000 42.432 0.006 SP/dynesty/sampler.py:413(_new_point) + 55007 0.273 0.000 32.615 0.001 SP/dynesty/sampler.py:399(_get_point_value) + 55007 2.452 0.000 32.287 0.001 SP/dynesty/sampler.py:336(_fill_queue) + 119178 0.587 0.000 26.534 0.000 SP/dynesty/dynesty.py:905(__call__) + 53878 0.352 0.000 24.318 0.000 SP/dynesty/sampling.py:30(sample_unif) + 59589 0.234 0.000 21.877 0.000 SP/dynesty/utils.py:183(__call__) + 61089 0.172 0.000 21.699 0.000 SP/timeout_decorator/timeout_decorator.py:71(new_function) + 61089 0.070 0.000 21.499 0.000 autofit/non_linear/fitness.py:764(__call__) + 61119 0.326 0.000 21.444 0.000 autofit/non_linear/fitness.py:552(call_wrap) + 61119 2.420 0.000 19.277 0.000 autofit/non_linear/fitness.py:351(call) + 62649 0.570 0.000 10.969 0.000 autofit/mapper/prior_model/abstract.py:990(instance_from_vector) + 55007 0.059 0.000 9.304 0.000 SP/dynesty/sampler.py:294(update_bound_if_needed) + 82 0.003 0.000 9.244 0.113 SP/dynesty/nestedsamplers.py:668(update) + 64220 0.446 0.000 9.185 0.000 autofit/mapper/prior_model/abstract.py:1818(instance_for_arguments) + 82 0.006 0.000 9.159 0.112 SP/dynesty/bounding.py:536(update) + 5664/82 0.371 0.000 8.711 0.106 SP/dynesty/bounding.py:1368(_bounding_ellipsoids) + 64220 2.723 0.000 8.020 0.000 autofit/mapper/prior_model/prior_model.py:483(_instance_for_arguments) + 12115 0.796 0.000 6.975 0.001 SP/scipy/special/_logsumexp.py:15(logsumexp) + 10826/11 0.084 0.000 6.492 0.590 SP/matplotlib/text.py:65(_get_text_metrics_with_cache) + 413/6 0.010 0.000 6.491 1.082 SP/matplotlib/text.py:73(_get_text_metrics_with_cache_impl) + 1771/22 0.022 0.000 6.182 0.281 :1349(_find_and_load) + 1729/22 0.017 0.000 6.181 0.281 :1304(_find_and_load_unlocked) + 1677/23 0.014 0.000 6.170 0.268 :911(_load_unlocked) + 1452/23 0.008 0.000 6.168 0.268 :993(exec_module) + 4033/41 0.011 0.000 6.157 0.150 :480(_call_with_frames_removed) + 1407 0.118 0.000 6.018 0.004 SP/matplotlib/axis.py:1276(_update_ticks) +107861/105294 1.460 0.000 5.999 0.000 {built-in method builtins.sum} + + + +=== TOP 30 tottime +ncalls tottime percall cumtime percall filename:lineno(function) + 64220 2.723 0.000 8.020 0.000 autofit/mapper/prior_model/prior_model.py:483(_instance_for_arguments) + 359323 2.614 0.000 2.618 0.000 {method 'reduce' of 'numpy.ufunc' objects} + 55007 2.452 0.000 32.287 0.001 SP/dynesty/sampler.py:336(_fill_queue) + 61119 2.420 0.000 19.277 0.000 autofit/non_linear/fitness.py:351(call) + 506/6 2.100 0.004 4.513 0.752 SP/matplotlib/backends/backend_agg.py:206(get_text_width_height_descent) + 61154 2.087 0.000 2.087 0.000 autofit/example/model.py:65(model_data_from) + 80 1.866 0.023 1.866 0.023 {method 'encode' of 'ImagingEncoder' objects} +485820/482427 1.780 0.000 3.150 0.000 autofit/mapper/model.py:30(cache) + 15 1.780 0.119 1.781 0.119 {built-in method gc.collect} +4117601/4116698 1.704 0.000 2.648 0.000 {built-in method builtins.isinstance} + 242/0 1.633 0.007 0.000 SP/matplotlib/backends/backend_agg.py:185(draw_text) +107861/105294 1.460 0.000 5.999 0.000 {built-in method builtins.sum} +481330/481315 1.398 0.000 5.760 0.000 autofit/mapper/prior_model/attribute_pair.py:6(wrapper) + 12115 1.249 0.000 4.370 0.000 SP/scipy/special/_logsumexp.py:201(_logsumexp) + 284917 1.092 0.000 1.180 0.000 {built-in method numpy.array} + 18598 1.036 0.000 3.452 0.000 SP/scipy/stats/_distn_infrastructure.py:2132(cdf) + 61119 1.025 0.000 1.818 0.000 autofit/non_linear/fitness.py:601(manage_quick_update) + 61119 0.847 0.000 3.571 0.000 autofit/example/analysis.py:152(model_data_1d_from) +1275694/1274269 0.831 0.000 1.329 0.000 {built-in method builtins.getattr} + 122178 0.829 0.000 1.090 0.000 autofit/mapper/prior/_erf_helpers.py:17(_norm_cdf) +2175963/2174688 0.828 0.000 1.618 0.000 {built-in method builtins.hasattr} + 1425 0.799 0.001 2.662 0.002 autofit/non_linear/samples/samples.py:257(parameter_lists) + 12115 0.796 0.000 6.975 0.001 SP/scipy/special/_logsumexp.py:15(logsumexp) + 4466 0.773 0.000 1.092 0.000 SP/matplotlib/lines.py:758(draw) +531330/531060 0.643 0.000 0.665 0.000 {built-in method numpy.asarray} + 33440 0.641 0.000 1.707 0.000 SP/scipy/cluster/vq.py:150(vq) + 42011 0.638 0.000 1.437 0.000 SP/scipy/_lib/array_api_extra/_lib/_at.py:224(_op) + 615536 0.637 0.000 0.810 0.000 autofit/non_linear/samples/sample.py:104(parameter_lists_for_paths) +266403/3600 0.632 0.000 1.608 0.000 STDLIB/copy.py:118(deepcopy) + 1950730 0.620 0.000 0.620 0.000 {method 'append' of 'list' objects} + + + +=== bucket c top cumtime +ncalls tottime percall cumtime percall filename:lineno(function) + 30 0.004 0.000 50.303 1.677 autofit/non_linear/search/nest/dynesty/search/abstract.py:413(run_search_internal) + 59589 0.298 0.000 4.485 0.000 autofit/non_linear/search/nest/dynesty/search/abstract.py:58(prior_transform) + 30 0.001 0.000 4.236 0.141 autofit/non_linear/search/updater.py:195(_save_samples) + 1470/510 0.009 0.000 3.303 0.006 autofit/non_linear/samples/interface.py:38(wrapper) + 60 0.007 0.000 2.707 0.045 autofit/non_linear/search/abstract_search.py:1572(samples_from) + 60 0.032 0.001 2.700 0.045 autofit/non_linear/search/nest/dynesty/search/abstract.py:329(samples_via_internal_from) + 60 0.003 0.000 2.690 0.045 autofit/non_linear/samples/pdf.py:56(summary) + 1290 0.036 0.000 2.673 0.002 autofit/non_linear/samples/samples.py:286(parameters_extract) + 1425 0.799 0.001 2.662 0.002 autofit/non_linear/samples/samples.py:257(parameter_lists) + 450 0.027 0.000 2.233 0.005 autofit/non_linear/samples/pdf.py:152(values_at_sigma) + 120 0.001 0.000 1.896 0.016 autofit/non_linear/samples/pdf.py:235(errors_at_sigma) + 30 0.000 0.000 1.146 0.038 autofit/non_linear/paths/directory.py:303(save_samples) + + + +=== bucket d top cumtime +ncalls tottime percall cumtime percall filename:lineno(function) + 36 0.002 0.000 4.149 0.115 autofit/graphical/laplace/optimiser.py:365(optimise) + 36 0.001 0.000 4.147 0.115 autofit/graphical/laplace/optimiser.py:272(optimise_approx) + 36 0.000 0.000 3.297 0.092 autofit/graphical/laplace/optimiser.py:158(optimise_state) + 36 0.004 0.000 3.297 0.092 autofit/graphical/laplace/newton.py:309(optimise_quasi_newton) + 182 0.003 0.000 2.966 0.016 autofit/graphical/laplace/newton.py:219(take_quasi_newton_step) + 182 0.002 0.000 2.749 0.015 autofit/graphical/laplace/line_search.py:380(line_search) + 143 0.001 0.000 2.565 0.018 autofit/graphical/mean_field.py:435(kl) + 1144 0.009 0.000 2.561 0.002 autofit/graphical/mean_field.py:447() + 1001 0.044 0.000 2.520 0.003 autofit/messages/truncated_normal.py:312(kl) + 1325 0.002 0.000 2.517 0.002 autofit/graphical/laplace/line_search.py:148(gradient) + 412 0.008 0.000 2.512 0.006 autofit/graphical/laplace/line_search.py:154(value_gradient) + 412 0.017 0.000 2.504 0.006 autofit/graphical/mean_field.py:735(func_gradient) + + + diff --git a/draft/refactor/autofit/ep_factor_search_wrapper_overhead_assets/ep_pstats_cumulative.txt b/draft/refactor/autofit/ep_factor_search_wrapper_overhead_assets/ep_pstats_cumulative.txt new file mode 100644 index 000000000..a194203ce --- /dev/null +++ b/draft/refactor/autofit/ep_factor_search_wrapper_overhead_assets/ep_pstats_cumulative.txt @@ -0,0 +1,410 @@ +Thu Sep 24 14:49:27 2026 ep.prof + + 51419624 function calls (50728443 primitive calls) in 109.119 seconds + + Ordered by: cumulative time + List reduced from 10814 to 400 due to restriction <400> + + ncalls tottime percall cumtime percall filename:lineno(function) + 30 0.004 0.000 50.303 1.677 PyAutoFit/autofit/non_linear/search/nest/dynesty/search/abstract.py:413(run_search_internal) + 30 0.051 0.002 50.124 1.671 SP/dynesty/sampler.py:920(run_nested) + 6691 0.504 0.000 47.989 0.007 SP/dynesty/sampler.py:599(sample) + 6661 0.340 0.000 42.432 0.006 SP/dynesty/sampler.py:413(_new_point) + 55007 0.273 0.000 32.615 0.001 SP/dynesty/sampler.py:399(_get_point_value) + 55007 2.452 0.000 32.287 0.001 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b/draft/research/graphical_ep/ep_campaign.md index fbd6dbc2e..0281bda32 100644 --- a/draft/research/graphical_ep/ep_campaign.md +++ b/draft/research/graphical_ep/ep_campaign.md @@ -40,6 +40,7 @@ its own prompt file. Update the table as phases ship. | 1b | Confidence test on the #1405 collapse toy at N=50 (NUTS vs EP x3) | β†’ PyAutoCortex `projects/ep_toy_gaussian.md` | **born 2026-09-10** as Cortex science project `ep_toy_gaussian` (Cortex PR #29, human merge pending β€” `projects.yaml` is code). The campaign's last confidence test before scaling up: the *original* collapse toy from `complete/2026/07/ep_scale_collapse_assets/ep_toy_diagnostic.py`, model and EP settings verbatim (`DynestyStatic(nlive=100, rwalk)` per factor, `LaplaceOptimiser`, `EPHistory(kl_tol=0.05)`, `max_steps=20`), but at **N=50**, with a joint **BlackJAX NUTS** reference and **three EP repeats on identical data** β€” none of which this toy has ever had. A pass on the no-collapse / parity / self-agreement / honest-width criteria is the evidence that the D1–D6 + review-wave fixes cured PyAutoFit#1405 on its own toy; a failure isolates it to the Dynesty-per-factor EP path, since the analytic ensemble used Laplace factors. Task `State: ready`, witness pre-registered (7 criteria), **awaiting submission** | | 2 | Scatter-collapse cure or caveat | `complete/2026/09/ep-scale-collapse-basin-cure-or-caveat.md` | **SHIPPED 2026-09-02** β€” cure of the mechanism (#1558/#1560/#1562) + caveat (README Β§3.5); record `complete/2026/09/ep-scale-collapse-basin-cure-or-caveat.md`; cure follow-on filed `draft/feature/autofit/ep_hierarchical_scatter_moment_matching.md` (human decision). Close-out 2026-09-02: `complete/2026/09/ep-collapse-unpark.md` (autofit_workspace_test#94 / PR #95) curated `analytic_gaussian_collapse.py` into the smoke gate (RECOVER 5/5). **Codex review of the fixes shipped 2026-09-07** as bundle `ep-phase2-review`: PyAutoFit#1572 coupled-transform covariance in `from_mode` (`transformed-from-mode-coupled-covariance.md`), #1573 P1 deterministic variables kept cavity covariance on the fd-Hessian path (`ep-laplace-deterministic-hessian.md`), #1574 fully reverted projection counted as updated (`ep-full-revert-not-updated.md`), #1576 + autofit_workspace_test#98 per-(factor, variable) stale tracking with `reverted_variables` in `ep_history.csv` (`ep-stale-tracking-per-variable.md`) | | 3 | Graphical (non-EP) JAX scaling | β†’ PyAutoCortex `projects/slope_hierarchy_scale.md` (the ledger of project `slope_hierarchy_scale`; the measurement addendum is in `archive/tasks/slope_hierarchy_scale/n25_scale_up.md`) and `graphical_scoping.md` sub-tasks | **gated 2026-09-08** β€” a fresh Cortex science project `slope_hierarchy_scale` was born for it (Cortex PR #25, issue Cortex#24): the wrapped-up N=5 tree was vaulted to `Science/z_projects_complete/slope_hierarchy` and its row retired, the code borrowed into `Science/slope_hierarchy_scale`, and the task moved `planned β†’ gated` on `Gates: PyAutoFit#1405, #1558, #1560, #1562`. PR #25 **merged 2026-09-08** (merge `866d8f0`; issue Cortex#24 closed, record `complete/2026/09/slope-hierarchy-scale-birth.md`). Next: `/cortex` β†’ `move tasks/slope_hierarchy_scale/n25_scale_up.md ready` β†’ submits the N=25 runs | +| 1b/3 | Check-in (EP arms of 1b and 3) | β†’ PyAutoCortex `projects/ep_toy_gaussian.md`, `projects/slope_hierarchy_scale.md` | 2026-09-24: 343299 shows the Laplace hierarchical projection fails on every update at N=25 lensing; the moment-matching cure is now the blocking decision for phase 3's EP arm; ep_toy_gaussian wave 1 failed on infra (EMFILE, fixed by #1632/#1634), wave 2 awaits a human go. | | 4 | IC50 EP end-to-end + scale ladder | β†’ PyAutoCortex `projects/ic50_workspace.md` (the ledger of project `ic50_workspace`; dev companion stays in the Mind as `feature/autofit/ep_lbfgs_jax.md`) | **RUNNING ON RAL 2026-09-09 β€” parity at N=5 achieved.** The 2026-09-08 blockers (dirty checkout, no `results/` witness path, row `dormant`) are all cleared; Cortex row `active` and task `submitted` via PyAutoCortex PR #26 (human merge pending). Runs `342408` (EP, sim, nlive 150, max_steps 12, 56.7 s) and `342409` (graphical joint Dynesty, sim, nlive 150, 21.4 s), partition `ral`, against a mirror **verified to contain #1580** β€” so unlike phase 3's EP arm these carry the full D1–D6 wave. **Result: 33/33 within 3Οƒ for both methods, 0/33 cross-method disagreements** (`results/graphical_ep_comparison_sim.txt`). Caveats: N=5 only, the scale ladder is not started; EP's median Οƒ is still 2.3Γ— the graphical fit's on `hill_coef` (0.980 vs 0.423); and the gain over the archived `_v1` run confounds the D1–D6 fixes, nlive 50β†’150 and laptopβ†’RAL. Setup shipped as 5 commits on `feature/ep-phase4-ral-setup` (unpushed, public repo β€” see Phase 4 notes below). The companion dev prompt `draft/feature/autofit/ep_lbfgs_jax.md` is still open and is now the scale lever, not a blocker | | 5 | Diagnostics sufficiency checkpoint | no prompt yet β€” deliberate | judge after phases 2 & 4 produce real runs; the 2026-07 diagnostics wave (#1330/#1335) shipped and caught #1383; graphical_scoping sub-tasks 5–6 (summary JSON, dashboard) are the likely follow-ups; concrete item from phase 1–2: STALE = zero SUCCESS updates is now warned by the library (#1562); 2026-09-07: #1574/#1576 extended STALE to full reverts and to per-(factor, variable) tracking, and `ep_history.csv` gained `reverted_variables`; the BIASED-TIGHT guard calibration stays a human call | | 6 | Profiling: autofit_profiling repo + two epics | `research/autofit/autofit_profiling_bootstrap.md` | filed; repo creation human-gated | @@ -141,7 +142,7 @@ profiling) last, once end-to-end runs exist to profile. end-to-end models are the goal; adopt only if a genuinely simple change dramatically simplifies/speeds things. (`feature/autofit/ep_lbfgs_jax.md` covers the *factor-fit sampler*, which is allowed; this gate is about the - EP update maths itself.) β€” 2026-09-02: the moment-matching cure prompt is the first candidate for this gate. 2026-09-07: still the human's call (`Autonomy: supervised`); the phase-2 review bundle did not touch the update maths. + EP update maths itself.) β€” 2026-09-02: the moment-matching cure prompt is the first candidate for this gate. 2026-09-07: still the human's call (`Autonomy: supervised`); the phase-2 review bundle did not touch the update maths. 2026-09-24: 343299 shows the Laplace hierarchical projection fails on every update at N=25 lensing; the moment-matching cure is now the blocking decision for phase 3's EP arm; ep_toy_gaussian wave 1 failed on infra (EMFILE, fixed by #1632/#1634), wave 2 awaits a human go. - **Sampler-result reuse across EP passes** β€” complicated for efficiency-only gains; revisit via profiling epic 2 evidence (ep_scoping sub-task 6). - **Parallel factor updates on HPC** β€” revisit when scaled runs approach