diff --git a/docs/calibration-l2-basis.md b/docs/calibration-l2-basis.md index 9956fa770..8ec55c00c 100644 --- a/docs/calibration-l2-basis.md +++ b/docs/calibration-l2-basis.md @@ -177,6 +177,15 @@ recalibrated from its own checkpoint (`experiments/us-acs-local-l2-basis-2026092 `l2_lambda`, so the gain is optimistic. - **What limits ESS is the starting weights.** The experiment's README has the frontier, the holdout, candidate ESS floors and the recommendation. +- **On the weighted loss.** With the national release's target weighting + (microcosm#1104), `l2_lambda` is in units of a different loss, so the + README's "On the weighted loss" section re-picks it. Held-out weighted error + is lowest at projection `l2_lambda = 0.1` on the default weights. But the + weighting gives each state's district populations one shared budget, and at + 0.1 it leaves 135 of 436 trained district populations more than 10% off. + `l2_lambda = 0.03` with `--target-family-loss-multiplier + census_population=8` misfits 3 and has slightly lower held-out error (by + 0.6%). The multiplier was chosen after the first pass of runs. ## Using it diff --git a/experiments/us-acs-local-l2-basis-20260928/README.md b/experiments/us-acs-local-l2-basis-20260928/README.md index 0fa601ef1..a626c9d00 100644 --- a/experiments/us-acs-local-l2-basis-20260928/README.md +++ b/experiments/us-acs-local-l2-basis-20260928/README.md @@ -8,6 +8,12 @@ Everything here recalibrates the published ACS local release, households, 4,459 targets), from its own calibration checkpoint, on Modal. It is research evidence: nothing was published, and no default changed. +Two losses are covered. The sections through "Recommendation" use the loss +the release calibrated on, every target weighted equally. "On the weighted +loss" reruns the frontier with the national release's target weighting +(microcosm#1104) and re-picks λ there; that re-pick supersedes the λ in +"Recommendation". + ## The question On 2026-09-28 David Trimmer measured the release's weights in #microcosm: @@ -88,7 +94,7 @@ default `--mass-parametrization projection`. - In sample it costs a little: training loss rises from 0.0155 to 0.0181, and training targets within 10% fall from 97.6% to 97.3%. - Softmax at λ = 0.03 gives a little more ESS (21,835). Against projection, its held-out fit is a toss-up (lower capped error, fewer targets within 10%, both by small margins). What separates them is training fit (loss 0.0192 against 0.0181, 6% higher, against 0.3% run-to-run variation at this λ) and softmax's cap loop (finding 4). Projection is the default and the better-supported choice. - λ = 0.1 (softmax) buys more ESS (24,895; 132 districts below 50) and lower held-out error than the release, but lower held-out within 10% on both folds (61.5% and 63.8%), and 2 pp of training fit. -- **λ is in units of the loss.** The ACS local build weights every target equally today (`calibrate` gets no `target_loss_weights`), and IRS SOI cells are 3,819 of the 4,459 targets. A change to weight them as the national release does is planned. Re-pick λ on that loss by rerunning this harness with its target weights before shipping a default. +- **λ is in units of the loss.** The release weighted every target equally (`calibrate` got no `target_loss_weights`), and IRS SOI cells are 3,819 of the 4,459 targets. microcosm#1104 weights them as the national release does; "On the weighted loss" below re-picks λ on that loss and supersedes this recommendation's λ. **2. Construction: the ACS share of the mass is the bigger lever, and it costs out-of-sample fit.** @@ -134,6 +140,218 @@ configuration's three solves (full surface, fold 0, fold 1): Main's tool already records ESS by state and district in the calibration summary, so the gate is a finalize-stage check on data it already has. +## On the weighted loss + +The equal-weight frontier above calibrates with every target counting once, +so the 3,819 IRS SOI cells carry 85.6% of the loss weight. microcosm#1104 weights +the ACS local build's targets like the national release, through one shared +implementation (`microcosm.build.us_runtime.target_loss_weights. +us_acs_local_target_loss_weights`, row mapping `us_acs_local.v1`, formula +`sqrt_value_concept_budget_weighted_mape_50_50_amount_count_target_scale_cap_100pct`, +no family multipliers by default). This section reruns the frontier on that +loss and re-picks λ (decision d797; d792 is superseded by it). + +**The weights.** `registry.py` rebuilds this checkpoint's 4,459 target specs +from the 09-23 release's own feed and settings (every name and value matches +the checkpoint exactly), and `sweep.py` hands the training specs to the +shared function. On the full surface the weights run from 0.026 to 20.1, the +effective number of targets is 1,405, and the loss vector's digest is +`ba36f887407181dc…` (the figure #1104 records for this surface). Shares of +the loss weight (`results/weighting_shift.md`, per target in +`results/target_loss_weights.csv`): + +| Family | Targets | Equal weights | Shared weights | +|---|---:|---:|---:| +| IRS SOI (state) | 3,819 | 85.6% | 90.2% | +| State population | 51 | 1.1% | 4.4% | +| District population | 436 | 9.8% | 1.7% | +| Medicaid enrollment | 51 | 1.1% | 2.0% | +| SNAP | 102 | 2.3% | 1.7% | + +District population falls because each state's district rows form one +concept group, scaled to sum to its largest member's weight: California's 52 +districts carry 1.51 between them, an at-large district 1.30-1.70. + +**Findings.** + +1. **The release settings fit the weighted loss as they fit the old one.** + At share 0.5, projection, λ 0, the weighted solve reaches national ESS + 13,707 (13,646 on the equal loss) and weighted loss 0.0097; the + equal-weight release scores 0.0107 on the weighted loss. +2. **The penalty still buys ESS and held-out fit, now with an interior + optimum.** Held-out weighted error (the objective's out-of-sample form) + at projection λ 0, 0.03, 0.1, 0.2 and 0.3 is 0.0784, 0.0721, 0.0708, + 0.0742 and 0.0787: lowest at 0.1, 9.3% and 10.1% below the release + settings on folds 0 and 1, while ESS rises from 13,707 to 21,751. + Projection is ahead of softmax at every λ held out (0.0721 against 0.0736 + at 0.03, 0.0708 against 0.0740 at 0.1, 0.0784 against 0.0828 at 0). +3. **The weighted loss costs district population fit, and the penalty + multiplies it.** On the equal loss, at share 0.5 and λ 0 and 0.03, every + trained district population is within 0.75% on the full surface (2% on + the holdout folds' trained districts). On the weighted loss, 11 of + 436 districts miss by more than 10% at λ 0 (worst TX-14 −33%, CA-29 + −32%), 73 at λ 0.03 (worst 46%) and 135 at λ 0.1 (worst 50%). The misses + sit in states with many districts (at λ 0.1: California 27 of 52, Texas + 18 of 38, Florida 14 of 28, Pennsylvania 10 of 17, New York 10 of 26); + state populations stay within 3.8%. Held-out district populations are + predicted poorly in every configuration: at share 0.5 their fold-mean + held-out capped error is 0.095-0.126 in every configuration with both + folds, weighted or equal-weight. At share 0.5 they carry 1.1-1.9% of the + held-out weighted loss, so the held-out measure does not see this cost. +4. **Multiplying the population family by 8 buys it back and is the best + held-out configuration.** `--target-family-loss-multiplier + census_population=8` (in the training weights; scoring keeps the default + weights) returns district population to 9.5% of the loss weight, about its + equal-weight share, and state population to 24.7%. At λ 0 it leaves no + district beyond 10% and already cuts held-out weighted error by 8% + (0.0721). At λ 0.03 it gives 0.0704, the lowest of every configuration + held out, weighted or equal-weight, on each fold separately too. Its margin + over the next best, default weights at λ 0.1, is small: 0.9% on fold 0 and + 0.3% on fold 1, the latter inside the rerun noise. It has ESS 19,874, + Massachusetts 574, the smallest district 22, no district + below a quarter of its starting ESS on any of its three solves, and 3 + districts beyond 10% (worst 15%): better district fit than the release + settings on the weighted loss (11, worst 33%). Most of the held-out gain is + on SOI, which carries 92-96% of the held-out weighted loss: its held-out + capped error falls from 0.132 to 0.119 here and at default λ 0.1 alike. Held-out district error falls + most with the multiplier: 0.110 to 0.095, against 0.106 at default λ 0.03 + and 0.111 at λ 0.1. At λ 0.1 the multiplier + leaves 24 districts beyond 10% and held-out error rises to 0.0724. + ×4 sits between (λ 0.03: 8 districts beyond 10%, worst 32%). +5. **Whether training on the weighted loss helps out of sample depends on + λ.** On the weighted held-out yardstick, against the same settings trained + on the equal loss (`results/cross_scores.json`): + - At λ 0 the weighted solve is worse on both folds (0.0786 / 0.0783 + against 0.0779 / 0.0766). + - At λ 0.03 the folds split (0.0723 / 0.0719 against 0.0734 / 0.0715; + the mean is 0.5% lower). + - At its own optimum, λ 0.1, the weighted solve (0.0713 / 0.0704) is below + every equal-weight configuration held out on both folds (the best are + 0.0734 on fold 0 and 0.0715 on fold 1), by 1.6-2.9%. + + λ is in units of each loss, so equal λ is not an equal setting across the + two losses. The equal-weight solves keep trained district populations + within 2% at λ 0 and 0.03 (0.75% on the full surface), which the + weighted ones do not. +6. **Share 0.9 behaves as on the equal loss.** ESS 81,714 (93,524 there), + held-out weighted error 36-41% above the release settings, and the + relative gate fails on every solve (318-371 districts). That remains the + construction call in d793. + + +### Key configurations on the weighted loss + + +| Configuration | National ESS | CD ESS median / min | CDs < 50 | CDs < ¼ of prior | MA ESS | Weighted loss (default weights) | Train within 10% | Trained CD populations > 10% off / worst | Held-out weighted error, fold 0 / 1 | Held-out within 10%, fold 0 / 1 | +|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| Release settings: share 0.5, projection, λ 0 | 13,707 | 34 / 11 | 351 | 28 | 478 | 0.0097 | 97.2% | 11 / 33% | 0.0786 / 0.0783 | 62.3% / 63.9% | +| Share 0.5, projection, chi-square λ 0.01 | 17,758 | 40 / 16 | 320 | 0 | 546 | 0.0113 | 96.5% | 38 / 45% | – / – | – / – | +| Share 0.5, projection, chi-square λ 0.03 | 19,618 | 44 / 19 | 302 | 0 | 580 | 0.0134 | 95.5% | 73 / 46% | 0.0723 / 0.0719 | 64.0% / 65.9% | +| Share 0.5, projection, chi-square λ 0.1 | 21,751 | 49 / 28 | 240 | 0 | 609 | 0.0192 | 92.3% | 135 / 50% | 0.0713 / 0.0704 | 64.3% / 66.3% | +| Share 0.5, projection, chi-square λ 0.2 | 23,363 | 52 / 31 | 178 | 0 | 645 | 0.0268 | 89.2% | 171 / 51% | 0.0750 / 0.0735 | 63.1% / 64.6% | +| Share 0.5, projection, chi-square λ 0.3 | 24,816 | 55 / 34 | 125 | 0 | 684 | 0.0350 | 85.7% | 182 / 52% | 0.0803 / 0.0771 | 59.6% / 63.1% | +| Population ×4: share 0.5, projection, λ 0 | 14,201 | 34 / 11 | 348 | 21 | 483 | 0.0111 | 97.2% | 0 / 0% | – / – | – / – | +| Population ×4: projection, chi-square λ 0.03 | 19,711 | 44 / 20 | 301 | 0 | 576 | 0.0141 | 96.7% | 8 / 32% | – / – | – / – | +| Population ×4: projection, chi-square λ 0.1 | 21,904 | 49 / 27 | 245 | 0 | 608 | 0.0205 | 93.6% | 55 / 41% | – / – | – / – | +| Population ×8: share 0.5, projection, λ 0 | 14,114 | 34 / 12 | 348 | 18 | 472 | 0.0121 | 96.8% | 0 / 0% | 0.0719 / 0.0722 | 64.8% / 67.2% | +| Population ×8: projection, chi-square λ 0.03 | 19,874 | 44 / 22 | 301 | 0 | 574 | 0.0153 | 96.4% | 3 / 15% | 0.0706 / 0.0702 | 64.9% / 68.2% | +| Population ×8: projection, chi-square λ 0.1 | 22,146 | 49 / 27 | 233 | 0 | 609 | 0.0222 | 93.7% | 24 / 36% | 0.0734 / 0.0714 | 62.7% / 67.7% | +| Share 0.5, softmax, no penalty | 14,424 | 36 / 11 | 343 | 17 | 497 | 0.0093 | 97.2% | 13 / 35% | 0.0833 / 0.0823 | 60.1% / 63.2% | +| Share 0.5, softmax, chi-square λ 0.01 | 18,167 | 41 / 16 | 307 | 0 | 560 | 0.0113 | 96.6% | 36 / 46% | – / – | – / – | +| Share 0.5, softmax, chi-square λ 0.03 | 20,387 | 46 / 20 | 278 | 0 | 605 | 0.0139 | 95.3% | 73 / 45% | 0.0739 / 0.0732 | 62.9% / 65.7% | +| Share 0.5, softmax, chi-square λ 0.1 | 23,507 | 53 / 29 | 175 | 0 | 651 | 0.0208 | 91.7% | 136 / 46% | 0.0744 / 0.0737 | 62.8% / 65.4% | +| Share 0.5, softmax, chi-square λ 0.3 | 28,506 | 64 / 37 | 36 | 0 | 772 | 0.0408 | 83.3% | 173 / 47% | – / – | – / – | +| Share 0.5, softmax, chi-square λ 1 | 33,058 | 73 / 44 | 7 | 0 | 901 | 0.0742 | 65.5% | 182 / 45% | – / – | – / – | +| Share 0.9, softmax, no penalty | 81,714 | 198 / 64 | 0 | 371 | 2,503 | 0.0170 | 95.8% | 2 / 12% | 0.1108 / 0.1068 | 57.2% / 57.6% | +| Share 0.9, softmax, chi-square λ 0.01 | 112,432 | 270 / 75 | 0 | 220 | 3,054 | 0.0199 | 95.2% | 7 / 16% | – / – | – / – | + + +### Re-picking λ + +The rule, fixed before any weighted held-out result was read (commit +"Fix the weighted λ re-pick rule before the results"): + +1. **Measure.** Held-out weighted capped error: each held-out target's + capped scaled miss, weighted by its full-surface weight. It is the + objective's own out-of-sample form, averaged over folds 0 and 1. +2. **Noise.** The two `w_dup_hold_` reruns give the run-to-run change in that + measure on one fold. Two configurations whose fold means differ by less + than the larger rerun change are not distinguished. +3. **Pick.** Take the configuration with the lowest mean. Then take the + largest λ of the same parametrization whose mean is within the noise of + it: more ESS at no measurable held-out cost. +4. **Constraints.** The pick must pass the d797 gate (no district below a + quarter of its starting ESS) on all three of its solves (full surface and + both folds), and its held-out unweighted share within 10% must not trail + the release settings' by more than the noise on both folds. + + +| Configuration | Loss trained on | National ESS | Held-out weighted error, fold 0 / 1 (mean) | vs release settings, fold 0 / 1 | Held-out capped error, fold 0 / 1 (mean) | Held-out within 10%, fold 0 / 1 (mean) | Trained CD populations > 10% off | CDs < ¼ of prior: full / fold 0 / 1 | +|---|---|---:|---:|---:|---:|---:|---:|---:| +| Release settings: share 0.5, projection, λ 0 | weighted | 13,707 | 0.0786 / 0.0783 (0.0784) | +0.0% / +0.0% | 0.1314 / 0.1225 (0.1270) | 62.3% / 63.9% (63.1%) | 11 | 28 / 21 / 17 | +| Share 0.5, projection, chi-square λ 0.03 | weighted | 19,618 | 0.0723 / 0.0719 (0.0721) | -8.1% / -8.2% | 0.1201 / 0.1150 (0.1175) | 64.0% / 65.9% (65.0%) | 73 | 0 / 0 / 0 | +| Share 0.5, projection, chi-square λ 0.1 | weighted | 21,751 | 0.0713 / 0.0704 (0.0708) | -9.3% / -10.1% | 0.1182 / 0.1134 (0.1158) | 64.3% / 66.3% (65.3%) | 135 | 0 / 0 / 0 | +| Share 0.5, projection, chi-square λ 0.2 | weighted | 23,363 | 0.0750 / 0.0735 (0.0742) | -4.5% / -6.1% | 0.1221 / 0.1176 (0.1198) | 63.1% / 64.6% (63.8%) | 171 | 0 / 0 / 0 | +| Share 0.5, projection, chi-square λ 0.3 | weighted | 24,816 | 0.0803 / 0.0771 (0.0787) | +2.1% / -1.5% | 0.1273 / 0.1224 (0.1248) | 59.6% / 63.1% (61.4%) | 182 | 0 / 0 / 0 | +| Population ×8: share 0.5, projection, λ 0 | weighted | 14,114 | 0.0719 / 0.0722 (0.0721) | -8.5% / -7.7% | 0.1218 / 0.1147 (0.1183) | 64.8% / 67.2% (66.0%) | 0 | 18 / 8 / 12 | +| Population ×8: projection, chi-square λ 0.03 | weighted | 19,874 | 0.0706 / 0.0702 (0.0704) | -10.2% / -10.4% | 0.1168 / 0.1119 (0.1144) | 64.9% / 68.2% (66.5%) | 3 | 0 / 0 / 0 | +| Population ×8: projection, chi-square λ 0.1 | weighted | 22,146 | 0.0734 / 0.0714 (0.0724) | -6.6% / -8.8% | 0.1207 / 0.1137 (0.1172) | 62.7% / 67.7% (65.2%) | 24 | 0 / 0 / 0 | +| Share 0.5, softmax, no penalty | weighted | 14,424 | 0.0833 / 0.0823 (0.0828) | +6.0% / +5.1% | 0.1387 / 0.1274 (0.1331) | 60.1% / 63.2% (61.7%) | 13 | 17 / 13 / 20 | +| Share 0.5, softmax, chi-square λ 0.03 | weighted | 20,387 | 0.0739 / 0.0732 (0.0736) | -6.0% / -6.4% | 0.1217 / 0.1154 (0.1186) | 62.9% / 65.7% (64.3%) | 73 | 0 / 0 / 0 | +| Share 0.5, softmax, chi-square λ 0.1 | weighted | 23,507 | 0.0744 / 0.0737 (0.0740) | -5.4% / -5.9% | 0.1216 / 0.1164 (0.1190) | 62.8% / 65.4% (64.1%) | 136 | 0 / 0 / 0 | +| Share 0.9, softmax, no penalty | weighted | 81,714 | 0.1108 / 0.1068 (0.1088) | +41.0% / +36.4% | 0.1697 / 0.1733 (0.1715) | 57.2% / 57.6% (57.4%) | 2 | 371 / 318 / 331 | +| Equal-weight release (projection, λ 0) | equal | 13,646 | 0.0779 / 0.0766 (0.0772) | -0.9% / -2.2% | 0.1296 / 0.1236 (0.1266) | 63.3% / 65.8% (64.6%) | 0 | 15 / 22 / 24 | +| Equal-weight pick (projection, λ 0.03) | equal | 20,690 | 0.0734 / 0.0715 (0.0724) | -6.7% / -8.6% | 0.1247 / 0.1191 (0.1219) | 64.2% / 67.0% (65.6%) | 0 | 0 / 0 / 0 | +| Equal-weight softmax λ 0.03 | equal | 21,835 | 0.0750 / 0.0727 (0.0738) | -4.6% / -7.1% | 0.1242 / 0.1166 (0.1204) | 63.1% / 66.3% (64.7%) | 0 | 0 / 0 / 0 | + + +The `w_dup_hold_` reruns moved held-out weighted error by +0.37% (release +settings) and −0.12% (softmax λ 0.03), so the noise threshold is 0.37%. + +**The rule's pick: projection λ 0.1.** It has the lowest mean held-out +weighted error among the configurations on the default weights (0.0708). +λ 0.2 is 4.8% higher, beyond the noise, so step 3 stays at 0.1. It passes +both constraints: no district below a quarter of its starting ESS on any of +its three solves, and held-out within 10% of 64.3% and 66.3% against the +release settings' 62.3% and 63.9%. + +**But the rule did not anticipate finding 3.** At λ 0.1 the default +weighting leaves 135 of 436 trained district populations more than 10% off, +up to 50%. For a local-area file that is disqualifying, and nothing in the +rule weighs it. + +**Recommendation.** Calibrate the ACS local release with +`--l2-basis chi_square --l2-lambda 0.03` (projection, the λ d797 names) and +`--target-family-loss-multiplier census_population=8`. + +- It has the lowest held-out weighted error of every configuration with + held-out folds, 10.2% and 10.4% below the release settings on the two folds. +- Against the release settings: ESS 19,874 against 13,707, Massachusetts 574 + against 478, the smallest district 22 against 11. +- Against the rule's pick (λ 0.1, no multiplier) it gives up about 9% of + national ESS: 19,874 against 21,751; Massachusetts 574 against 609; 301 + districts under ESS 50 against 240; the smallest district 22 against 28. In + exchange it has 132 fewer district misses (3 against 135) and slightly + lower held-out error (0.0704 against 0.0708, inside the noise on fold 1). +- Against the equal-weight pick (λ 0.03 on the equal loss; ESS 20,690, no + district misses) it gives up 4% of ESS for held-out error of 0.0704 + against 0.0724. +- It passes d797's gate on every solve, and it misfits 3 district + populations (worst 15%) against the release settings' 11 (worst 33%). +- Its λ is d797's; the multiplier is the new call (d952). + +The multiplier was tried after the first pass showed the district cost, so it +was not chosen under the pre-registered rule. Several things were not tested: +- only ×4 and ×8 were run, and only ×8 has held-out folds; +- no value between them, and no λ strictly between 0 and 0.03 (such as 0.01) + or between 0.03 and 0.1 with ×8; +- no softmax or share-0.9 run with a multiplier; +- no district-only lever (the family multiplier also raises state population + to 24.7% of the loss weight). + +If the build keeps #1104's default (no multiplier), the rule's λ is 0.1, with +the district cost above. + ## How it was run - **Inputs.** The release's calibration checkpoint was a 28.7 GB dense @@ -195,6 +413,48 @@ summary, so the gate is a finalize-stage check on data it already has. cause (thread scheduling, CPU type or library code path) was not isolated. The penalized solve varies much less than the unpenalized one. Only training metrics were rerun; held-out variation was not measured. +- **The weighted grid** (`modal_sweep.py --grid weighted`, then + `--grid weighted2`; run ids `w_*`) ran the same harness with + `target_weighting="shared"`. The first pass is the asked grid plus + projection at λ 0.01, 0.03 and 0.1 (d797's parametrization) and two + same-head held-out reruns; the second pass, launched after reading the + first, extends projection to λ 0.2 and 0.3 and tries the population + multiplier. + - Kernel: main at 45d1ae398 (#1078 merged) plus this harness, heads + c028b6ad4 and 5b51c776b, which differ only in experiment files. Their + `solve.py` differs from the equal grid's last head (d82ff85b) by a + docstring paragraph and an `assert` on an unreachable softmax path; the + frame modules the solve uses are unchanged. + - Weights: microcosm#1104's `target_loss_weights.py` at 4d8785498 (sha256 + `849fbede…`), shipped to the containers as that one file and loaded on + its own, since importing it through `microcosm.build.us_runtime` needs + the whole build stack. Loaded either way, it gives the same weights. The + containers rebuild the weights from `target_registry.json`, which they + check against `registry.py`'s receipt. #1104's later review fixes + changed the file's bytes but not these weights: `check_weights.py` + rebuilds every `w_` run's training and yardstick weights with the + current module and compares both digests with the run's receipt + (`results/weights_check.json`: 46 runs, 7 distinct weightings, no + mismatch against main's module after #1104 merged as e34712cc8). + - Gate: before the first pass fanned out, its release-settings run had to + show its epoch-0 loss equal to the weighted loss of the starting weights + recomputed outside the solver (observed 5e-6 relative; the gate's + tolerance is 1e-4), and its final loss equal to the recomputed weighted + loss of its returned weights (exact). The second pass reused that cached + run, so its launch did not gate. Every `w_` run records both checks, and + `check_weights.py` asserts them on all 46 receipts: epoch-0 within 1e-5 + (at most 9.3e-6) and final differences exactly 0. + - Held-out scoring weights each held-out target by its full-surface + weight under the default weighting, the same yardstick for every run. + Training weights are recomputed on each fold's training rows, as a + build calibrating on them would, and carry the run's family multipliers. + - `cross_score.py` scores every equal-weight solve on the weighted loss + (`results/cross_scores.json`). +- **Same-head reruns.** The two `w_dup_hold_` pairs ran at one kernel head + and still diverge from epoch 1 and epoch 0, so the run-to-run variation is + the container's, not a head change. Their held-out weighted error moved + +0.37% and −0.12%, held-out within 10% by 0.1 points, and ESS by 0.3% and + 0.05% (`results/weighted_rerun_variation.json`). ## Limits @@ -210,9 +470,28 @@ summary, so the gate is a finalize-stage check on data it already has. ESS. A solve on cloned rows needs a rebuilt target matrix. The support-mix bake-off (#1067) compares clones and ACS rows in the solve. - **Epochs.** 800 epochs, like the release; longer solves were not run. -- **One run per configuration.** Apart from the two reruns, each configuration +- **One run per configuration.** Apart from the four reruns (two `dup_` on + the equal loss, two `w_dup_hold_` on the weighted one), each configuration was solved once, so differences smaller than the run-to-run variation above - (notably the held-out margins and the softmax-versus-projection comparison - at λ = 0.03) are not resolved. + are not resolved. That includes the held-out margins, the + softmax-versus-projection comparison at λ = 0.03, and the fold-1 margin + between the weighted recommendation and λ 0.1. - **Not measured.** Poverty and program estimates were not recomputed, since the engine was not run. ESS is the proxy for their precision. +- **Weighted grid.** The softmax runs predate microcosm#1096, which replaces + the softmax cap loop with an exact projection; projection runs are not + affected. The population multiplier was chosen after the first pass, only + ×4 and ×8 were run, and only ×8 has held-out folds. The weights came from + #1104 before it merged; they hold for main only while + `check_weights.py` passes against main's module. +- **One target surface.** Everything here is the 09-23 release's + `--soi-mode state` surface: 4,459 targets, with no district SOI rows. Under + `--soi-mode state_cd`, #1104 gives district SOI 16.7% of the loss weight and the + population families other shares, so neither the λ nor the multiplier + carries over without a rerun on that surface. +- **Registry metadata.** `registry.py` compiles the specs with this branch's + code, as a rebuilt release would, not with the release's head 767312d60. + Names and values are anchored to the release: they match its checkpoint bit + for bit. The metadata the weighting reads (measure mode, units, ledger + geography, roles) was not compared with a compile at 767312d60. The + receipt records the compiling head. diff --git a/experiments/us-acs-local-l2-basis-20260928/analyze.py b/experiments/us-acs-local-l2-basis-20260928/analyze.py index c0c34ef88..497ebb93e 100644 --- a/experiments/us-acs-local-l2-basis-20260928/analyze.py +++ b/experiments/us-acs-local-l2-basis-20260928/analyze.py @@ -9,6 +9,17 @@ byte-identical, the first epoch where the trajectories differ, and every frontier metric's relative change (``weights.npz`` beside the checkpoint). +Weighted-loss runs (``target_weighting="shared"``, ids ``w_*``) are kept out +of every equal-weight output above, which stay as they were. They get their +own: ``results/weighted_frontier.{csv,md}``, ``results/weighted_floors.md``, +``results/weighted_key_configs.md`` (spliced into the README's weighted +section), ``results/weighted_lambda.md`` (the λ re-pick's evidence), +``results/weighting_shift.md`` (how the weights divide the loss), +``results/target_loss_weights.csv`` (the full-surface weight of every +target), ``results/weighted_rerun_variation.json`` and +``results/weighted_frontier.png``. ``results/cross_scores.json`` +(``cross_score.py``) scores the equal-weight solves on the weighted loss. + Run: ``uv run --with matplotlib python experiments/us-acs-local-l2-basis-20260928/analyze.py`` """ @@ -47,6 +58,19 @@ "dup_release_repro": "release_repro", "dup_soft_chi_s050_0.03": "soft_chi_s050_0.03", } +#: Same-head reruns of weighted holdout runs, and the run they repeat. +WEIGHTED_PAIRS = { + "w_dup_hold_proj_s050_0": "w_hold_proj_s050_0", + "w_dup_hold_soft_chi_s050_0.03": "w_hold_soft_chi_s050_0.03", +} +WEIGHTED_RERUN_METRICS = ( + *RERUN_METRICS, + "weighted_within_10pct", + "holdout_weighted_capped_error", + "holdout_weighted_within_10pct", + "holdout_mean_capped_error", + "holdout_within_10pct", +) FAMILIES = ("soi", "snap", "medicaid", "pop_state", "pop_cd") #: Candidate release-gate floors on Kish ESS, and the relative floor (share of #: the prior's own ESS in the same geography). @@ -68,6 +92,10 @@ def _head(payload: dict) -> str | None: return head[:8] if head else None +def _weighting(payload: dict) -> str: + return payload["spec"].get("target_weighting", "equal") + + def row(payload: dict) -> dict: spec = payload["spec"] metrics = payload["metrics"] @@ -148,9 +176,82 @@ def row(payload: dict) -> dict: for family in FAMILIES: block = holdout["per_family"].get(family, {}) out[f"holdout_within_10pct_{family}"] = block.get("fraction_within_10pct") + if _weighting(payload) != "equal": + out.update(weighted_columns(payload)) + return out + + +def weighted_columns(payload: dict) -> dict: + """A weighted run's extra columns: both losses, weighted fit, loss shares.""" + + metrics = payload["metrics"] + fit = metrics["fit_train"] + prior_fit = metrics["fit_train_design"]["overall"] + multipliers = dict(payload["spec"].get("family_loss_multipliers") or ()) + yardstick = metrics.get("fit_train_yardstick", {}).get("overall", {}) + district = fit["per_family"].get("pop_cd", {}) + out = { + "target_weighting": _weighting(payload), + "pop_multiplier": float(multipliers.get("census_population", 1.0)), + # Training fit on the default full-surface weights. Second-pass runs + # record it; a first-pass full-surface run without multipliers trained + # on exactly those weights (sweep.loss_weights_for returns + # LossWeights(full, full)), so its final loss is it. A first-pass + # holdout run trained on its fold's own weights and has none. + "yardstick_loss": yardstick.get( + "weighted_mean_capped_scaled_error", + payload["result"]["final_loss"] + if payload["spec"].get("holdout_fold") is None and not multipliers + else None, + ), + "train_cds_off_10pct": int( + round( + district.get("n_targets", 0) + * (1.0 - district.get("fraction_within_10pct", 1.0)) + ) + ), + "train_cd_pop_worst_miss": district.get("max_abs_rel_error"), + "unweighted_loss": payload["consistency"]["unweighted_final_loss"], + "weighted_within_10pct": fit["overall"]["weighted_fraction_within_10pct"], + "prior_weighted_loss": prior_fit["weighted_mean_capped_scaled_error"], + "loss_vector_sha256": payload["target_loss_weights"]["train"][ + "loss_vector_sha256" + ], + } + for family in FAMILIES: + block = fit["per_family"].get(family, {}) + out[f"loss_share_{family}"] = block.get("share_of_weighted_loss") + if "fit_holdout" in metrics: + holdout = metrics["fit_holdout"]["overall"] + prior_holdout = metrics["fit_holdout_design"]["overall"] + out["holdout_weighted_capped_error"] = holdout[ + "weighted_mean_capped_scaled_error" + ] + out["holdout_weighted_within_10pct"] = holdout["weighted_fraction_within_10pct"] + out["prior_holdout_weighted_capped_error"] = prior_holdout[ + "weighted_mean_capped_scaled_error" + ] return out +#: Columns only weighted runs carry; equal-weight outputs drop them. +WEIGHTED_ONLY_COLUMNS = ( + "target_weighting", + "pop_multiplier", + "yardstick_loss", + "train_cds_off_10pct", + "train_cd_pop_worst_miss", + "unweighted_loss", + "weighted_within_10pct", + "prior_weighted_loss", + "loss_vector_sha256", + *(f"loss_share_{family}" for family in FAMILIES), + "holdout_weighted_capped_error", + "holdout_weighted_within_10pct", + "prior_holdout_weighted_capped_error", +) + + #: Per-geography fields the committed receipts keep; the full payloads stay in #: the artifacts directory and on the Modal volume. GEOGRAPHY_FIELDS = ( @@ -184,7 +285,7 @@ def load() -> pd.DataFrame: rows = [ row(json.loads(path.read_text())) for path in sorted(RUNS.glob("*.json")) - if path.stem not in HEAD_PAIRS + if path.stem not in HEAD_PAIRS and path.stem not in WEIGHTED_PAIRS ] frame = pd.DataFrame(rows) frame["arm"] = [ @@ -195,6 +296,18 @@ def load() -> pd.DataFrame: ) +def split(frame: pd.DataFrame) -> tuple[pd.DataFrame, pd.DataFrame]: + """The equal-weight frame (as before this grid) and the weighted frame.""" + + if "target_weighting" not in frame.columns: + return frame, frame.iloc[0:0] + weighted = frame.target_weighting.fillna("equal") != "equal" + equal = frame[~weighted].drop( + columns=[c for c in WEIGHTED_ONLY_COLUMNS if c in frame.columns] + ) + return equal, frame[weighted] + + def _fmt(value, spec: str) -> str: if value is None or (isinstance(value, float) and np.isnan(value)): return "" @@ -445,11 +558,14 @@ def chart(frame: pd.DataFrame, path: Path) -> None: fig.savefig(path, dpi=150, facecolor=surface) -def rerun_variation() -> dict: +def rerun_variation( + head_pairs: dict[str, str] = HEAD_PAIRS, + metrics: tuple[str, ...] = RERUN_METRICS, +) -> dict: """Each rerun against its original: bytes, divergence, metric changes.""" pairs = {} - for dup, original in HEAD_PAIRS.items(): + for dup, original in head_pairs.items(): paths = [LOCAL_RUNS / run / "weights.npz" for run in (dup, original)] receipts = [RUNS / f"{run}.json" for run in (dup, original)] if not all(path.exists() for path in (*paths, *receipts)): @@ -505,15 +621,528 @@ def rerun_variation() -> dict: else None ), } - for name in RERUN_METRICS + for name in metrics + if name in rows[0] and name in rows[1] }, } return pairs +# --------------------------------------------------------------------------- +# Weighted loss +# --------------------------------------------------------------------------- + +#: The weighted write-up's configurations, in order, with a short label. +WEIGHTED_KEY_CONFIGS = ( + ("w_release_repro", "Release settings: share 0.5, projection, λ 0"), + ("w_proj_chi_s050_0.01", "Share 0.5, projection, chi-square λ 0.01"), + ("w_proj_chi_s050_0.03", "Share 0.5, projection, chi-square λ 0.03"), + ("w_proj_chi_s050_0.1", "Share 0.5, projection, chi-square λ 0.1"), + ("w_proj_chi_s050_0.2", "Share 0.5, projection, chi-square λ 0.2"), + ("w_proj_chi_s050_0.3", "Share 0.5, projection, chi-square λ 0.3"), + ("w_pop4_proj_s050_0", "Population ×4: share 0.5, projection, λ 0"), + ("w_pop4_proj_chi_s050_0.03", "Population ×4: projection, chi-square λ 0.03"), + ("w_pop4_proj_chi_s050_0.1", "Population ×4: projection, chi-square λ 0.1"), + ("w_pop8_proj_s050_0", "Population ×8: share 0.5, projection, λ 0"), + ("w_pop8_proj_chi_s050_0.03", "Population ×8: projection, chi-square λ 0.03"), + ("w_pop8_proj_chi_s050_0.1", "Population ×8: projection, chi-square λ 0.1"), + ("w_soft_chi_s050_0", "Share 0.5, softmax, no penalty"), + ("w_soft_chi_s050_0.01", "Share 0.5, softmax, chi-square λ 0.01"), + ("w_soft_chi_s050_0.03", "Share 0.5, softmax, chi-square λ 0.03"), + ("w_soft_chi_s050_0.1", "Share 0.5, softmax, chi-square λ 0.1"), + ("w_soft_chi_s050_0.3", "Share 0.5, softmax, chi-square λ 0.3"), + ("w_soft_chi_s050_1", "Share 0.5, softmax, chi-square λ 1"), + ("w_soft_chi_s900_0", "Share 0.9, softmax, no penalty"), + ("w_soft_chi_s900_0.01", "Share 0.9, softmax, chi-square λ 0.01"), +) +#: The equal-weight configurations the λ re-pick sets beside the weighted ones. +EQUAL_COMPARED = ( + ("release_repro", "Equal-weight release (projection, λ 0)"), + ("proj_chi_s050_0.03", "Equal-weight pick (projection, λ 0.03)"), + ("soft_chi_s050_0.03", "Equal-weight softmax λ 0.03"), +) + + +def _weighted_holdout_id(run_id: str, fold: int) -> str: + base = run_id.removeprefix("w_") + if base == "release_repro": + base = "proj_s050_0" + return "w_" + ("hold_" if fold == 0 else f"hold{fold}_") + base + + +def _fold_cells(by_id: pd.DataFrame, ids: list[str], key: str, spec: str) -> str: + cells = [format(by_id.loc[i, key], spec) if i in by_id.index else "–" for i in ids] + return " / ".join(cells) + + +def weighted_key_config_table(frame: pd.DataFrame) -> str: + """The weighted write-up's comparison: concentration, both fits, holdout.""" + + by_id = frame.set_index("run_id") + columns = ( + "Configuration", + "National ESS", + "CD ESS median / min", + "CDs < 50", + "CDs < ¼ of prior", + "MA ESS", + "Weighted loss (default weights)", + "Train within 10%", + "Trained CD populations > 10% off / worst", + "Held-out weighted error, fold 0 / 1", + "Held-out within 10%, fold 0 / 1", + ) + lines = [ + "| " + " | ".join(columns) + " |", + "|---|" + "---:|" * (len(columns) - 1), + ] + for run_id, label in WEIGHTED_KEY_CONFIGS: + if run_id not in by_id.index: + continue + r = by_id.loc[run_id] + held = [_weighted_holdout_id(run_id, fold) for fold in (0, 1)] + lines.append( + f"| {label} | {r.kish_ess:,.0f} | {r.cd_ess_median:,.0f} / " + f"{r.cd_ess_min:,.0f} | {int(r.cds_below_50)} | " + f"{int(r['cds_below_0.25_of_prior'])} | {r.ma_ess:,.0f} | " + f"{r.yardstick_loss:.4f} | {r.within_10pct:.1%} | " + f"{int(r.train_cds_off_10pct)} / {r.train_cd_pop_worst_miss:.0%} | " + f"{_fold_cells(by_id, held, 'holdout_weighted_capped_error', '.4f')} | " + f"{_fold_cells(by_id, held, 'holdout_within_10pct', '.1%')} |" + ) + return "\n".join(lines) + "\n" + + +def weighted_markdown(frame: pd.DataFrame) -> str: + sections = [] + full = frame[frame.arm == "full surface"] + columns = ( + ("Population ×", "pop_multiplier", "g"), + ("Prior ACS share", "prior_acs_share", ".3g"), + ("λ", "l2_lambda", "g"), + ("National ESS", "kish_ess", ",.0f"), + ("Top-1% share", "top_1pct_share", ".1%"), + ("Donor mass", "donor_mass_share", ".1%"), + ("χ² from prior", "chi_square_distance_from_prior", ".3f"), + ("State ESS median", "state_ess_median", ",.0f"), + ("CD ESS median / min", None, None), + ("MA ESS", "ma_ess", ",.0f"), + ("Weighted loss (default weights)", "yardstick_loss", ".4f"), + ("Training objective (with multipliers)", "final_loss", ".4f"), + ("Unweighted loss", "unweighted_loss", ".4f"), + ("Within 10%", "within_10pct", ".1%"), + ("Weighted within 10% (training weights)", "weighted_within_10pct", ".1%"), + ("SOI within 10%", "within_10pct_soi", ".1%"), + ("SNAP", "within_10pct_snap", ".0%"), + ("Medicaid", "within_10pct_medicaid", ".0%"), + ("State pop", "within_10pct_pop_state", ".0%"), + ("CD pop", "within_10pct_pop_cd", ".0%"), + ) + for (parametrization, basis), group in full.groupby( + ["mass_parametrization", "l2_basis"], sort=False + ): + group = group.sort_values(["pop_multiplier", "prior_acs_share", "l2_lambda"]) + lines = [ + f"### `{parametrization}` parametrization, `{basis}` basis", + "", + "| " + " | ".join(name for name, _, _ in columns) + " |", + "|" + "---:|" * len(columns), + ] + for _, r in group.iterrows(): + cells = [] + for _name, key, spec in columns: + if key is None: + cells.append(f"{r.cd_ess_median:,.0f} / {r.cd_ess_min:,.0f}") + else: + cells.append(_fmt(r[key], spec)) + lines.append("| " + " | ".join(cells) + " |") + sections.append("\n".join(lines)) + held = frame[frame.arm == "holdout"].sort_values( + [ + "holdout_fold", + "pop_multiplier", + "prior_acs_share", + "mass_parametrization", + "l2_lambda", + ] + ) + if len(held): + hold_columns = ( + ("Fold", "holdout_fold", ".0f"), + ("Population ×", "pop_multiplier", "g"), + ("Parametrization", "mass_parametrization", "s"), + ("Prior ACS share", "prior_acs_share", ".3g"), + ("λ", "l2_lambda", "g"), + ("National ESS", "kish_ess", ",.0f"), + ("Training objective (run's own weights)", "final_loss", ".4f"), + ("Held-out weighted error", "holdout_weighted_capped_error", ".4f"), + ("Held-out capped error", "holdout_mean_capped_error", ".4f"), + ("Held-out within 10%", "holdout_within_10pct", ".1%"), + ("Held-out SOI within 10%", "holdout_within_10pct_soi", ".1%"), + ("Held-out CD pop within 10%", "holdout_within_10pct_pop_cd", ".1%"), + ( + "Prior: held-out weighted error", + "prior_holdout_weighted_capped_error", + ".4f", + ), + ) + lines = [ + "### Rotated holdout (each fold is 20% of targets, never seen by the solve)", + "", + "| " + " | ".join(name for name, _, _ in hold_columns) + " |", + "|" + "---:|" * len(hold_columns), + ] + for _, r in held.iterrows(): + lines.append( + "| " + + " | ".join(_fmt(r[key], spec) for _, key, spec in hold_columns) + + " |" + ) + sections.append("\n".join(lines)) + return "\n\n".join(sections) + "\n" + + +def load_cross_scores() -> dict: + path = HERE / "results" / "cross_scores.json" + return json.loads(path.read_text()) if path.exists() else {} + + +def lambda_table(weighted: pd.DataFrame, equal: pd.DataFrame, cross: dict) -> str: + """Held-out fit by configuration on both losses, folds 0 and 1 and mean. + + Weighted solves come from their own receipts; equal-weight solves are + scored on the weighted loss by ``cross_score.py``. Differences are against + the weighted release-settings solve on the same fold. + """ + + by_id = weighted.set_index("run_id") + cross_runs = cross.get("runs", {}) + rows = [] + for run_id, label in WEIGHTED_KEY_CONFIGS: + ids = [_weighted_holdout_id(run_id, fold) for fold in (0, 1)] + if not all(i in by_id.index for i in ids): + continue + rows.append( + { + "label": label, + "trained_on": "weighted", + "weighted": [ + by_id.loc[i, "holdout_weighted_capped_error"] for i in ids + ], + "capped": [by_id.loc[i, "holdout_mean_capped_error"] for i in ids], + "within": [by_id.loc[i, "holdout_within_10pct"] for i in ids], + "ess": by_id.loc[run_id, "kish_ess"] if run_id in by_id.index else None, + "cds_off": ( + int(by_id.loc[run_id, "train_cds_off_10pct"]) + if run_id in by_id.index + else None + ), + "gate": [ + int(by_id.loc[i, "cds_below_0.25_of_prior"]) + for i in (run_id, *ids) + if i in by_id.index + ], + } + ) + equal_by_id = equal.set_index("run_id") + for run_id, label in EQUAL_COMPARED: + base = "proj_s050_0" if run_id == "release_repro" else run_id + ids = [("hold_" if fold == 0 else f"hold{fold}_") + base for fold in (0, 1)] + if not all(i in cross_runs and "holdout" in cross_runs[i] for i in ids): + continue + rows.append( + { + "label": label, + "trained_on": "equal", + "weighted": [ + cross_runs[i]["holdout"]["weighted_mean_capped_scaled_error"] + for i in ids + ], + "capped": [ + cross_runs[i]["holdout"]["mean_capped_scaled_error"] for i in ids + ], + "within": [ + cross_runs[i]["holdout"]["fraction_within_10pct"] for i in ids + ], + "ess": ( + equal_by_id.loc[run_id, "kish_ess"] + if run_id in equal_by_id.index + else None + ), + "cds_off": ( + int( + round( + 436 * (1 - equal_by_id.loc[run_id, "within_10pct_pop_cd"]) + ) + ) + if run_id in equal_by_id.index + else None + ), + "gate": [ + int(equal_by_id.loc[i, "cds_below_0.25_of_prior"]) + for i in (run_id, *ids) + if i in equal_by_id.index + ], + } + ) + if not rows: + return "" + baseline = next( + (r for r in rows if r["label"].startswith("Release settings")), rows[0] + ) + columns = ( + "Configuration", + "Loss trained on", + "National ESS", + "Held-out weighted error, fold 0 / 1 (mean)", + "vs release settings, fold 0 / 1", + "Held-out capped error, fold 0 / 1 (mean)", + "Held-out within 10%, fold 0 / 1 (mean)", + "Trained CD populations > 10% off", + "CDs < ¼ of prior: full / fold 0 / 1", + ) + lines = [ + "| " + " | ".join(columns) + " |", + "|---|---|" + "---:|" * (len(columns) - 2), + ] + for r in rows: + delta = [ + f"{a / b - 1:+.1%}" + for a, b in zip(r["weighted"], baseline["weighted"], strict=True) + ] + lines.append( + f"| {r['label']} | {r['trained_on']} | " + f"{_fmt(r['ess'], ',.0f')} | " + f"{r['weighted'][0]:.4f} / {r['weighted'][1]:.4f} " + f"({np.mean(r['weighted']):.4f}) | {' / '.join(delta)} | " + f"{r['capped'][0]:.4f} / {r['capped'][1]:.4f} " + f"({np.mean(r['capped']):.4f}) | " + f"{r['within'][0]:.1%} / {r['within'][1]:.1%} " + f"({np.mean(r['within']):.1%}) | " + f"{_fmt(r['cds_off'], 'd')} | " + f"{' / '.join(str(g) for g in r['gate'])} |" + ) + return "\n".join(lines) + "\n" + + +def weighting_shift(weighted: pd.DataFrame) -> tuple[str, pd.DataFrame | None]: + """How the shared weights divide the loss, against equal weighting. + + Reads the full-surface weight vector from the release-settings run's + ``weights.npz`` and checks its content hash against that run's receipt. + Returns the markdown and a per-target frame for the CSV. + """ + + import sys + + sys.path.insert(0, str(HERE)) + from sweep import DEFAULT_CHECKPOINT, loss_vector_sha256 + + receipt = RUNS / "w_release_repro.json" + weights_path = LOCAL_RUNS / "w_release_repro" / "weights.npz" + if not (receipt.exists() and weights_path.exists()): + return "", None + payload = json.loads(receipt.read_text()) + weights = np.load(weights_path)["target_loss_weights_full"] + meta = pd.read_parquet(DEFAULT_CHECKPOINT / "targets_meta.parquet") + expected = payload["target_loss_weights"]["full_surface"]["loss_vector_sha256"] + actual = loss_vector_sha256([f"{name}@2024" for name in meta["name"]], weights) + if actual != expected: + raise SystemExit(f"w_release_repro target weights hash {actual} != {expected}") + per_target = meta[ + ["row", "name", "family", "subfamily", "geography_level", "state_fips", "value"] + ].copy() + per_target["weight"] = weights + per_target["weight_share"] = weights / weights.sum() + sections = [ + f"Full-surface weights of {len(weights):,} targets " + f"(`{payload['target_loss_weights']['function']}`, loss vector " + f"`{expected[:12]}`). Under the equal-weight loss every target's share " + "is its count share. Effective number of targets (Σw)²/Σw²: " + f"{weights.sum() ** 2 / np.square(weights).sum():,.0f} of {len(weights):,}." + ] + for column, title in ( + ("family", "Family"), + ("subfamily", "Subfamily"), + ("geography_level", "Geography"), + ): + grouped = per_target.groupby(column).agg( + n=("weight", "size"), + weight_share=("weight_share", "sum"), + mean_weight=("weight", "mean"), + ) + grouped["count_share"] = grouped.n / grouped.n.sum() + lines = [ + f"| {title} | Targets | Equal-weight share | Weighted share | Mean weight |", + "|---|---:|---:|---:|---:|", + ] + for label, r in grouped.sort_values("weight_share", ascending=False).iterrows(): + lines.append( + f"| {label} | {int(r.n):,} | {r.count_share:.2%} | " + f"{r.weight_share:.2%} | {r.mean_weight:.3f} |" + ) + sections.append("\n".join(lines)) + return "\n\n".join(sections) + "\n", per_target + + +def weighted_chart(weighted: pd.DataFrame, equal: pd.DataFrame, path: Path) -> None: + import matplotlib + + matplotlib.use("Agg") + import matplotlib.pyplot as plt + + surface, ink, muted, grid = "#fcfcfb", "#0b0b0b", "#52514e", "#e4e3df" + full_all = weighted[weighted.arm == "full surface"] + full = full_all[full_all.pop_multiplier == 1.0] + equal_full = equal[equal.arm == "full surface"] + paths = ( + ( + full[ + (full.mass_parametrization == "softmax") & (full.prior_acs_share == 0.5) + ], + PRIOR_COLORS[0.5], + "-", + "Weighted, softmax, share 0.5", + ), + ( + full[ + (full.mass_parametrization == "projection") + & (full.prior_acs_share == 0.5) + ], + "#6b4fbb", + "-", + "Weighted, projection, share 0.5", + ), + ( + full_all[ + (full_all.mass_parametrization == "projection") + & (full_all.pop_multiplier == 8.0) + ], + "#c43b6b", + ":", + "Weighted, projection, population ×8", + ), + ( + full[ + (full.mass_parametrization == "softmax") & (full.prior_acs_share == 0.9) + ], + PRIOR_COLORS[0.9], + "-", + "Weighted, softmax, share 0.9", + ), + ( + equal_full[ + (equal_full.mass_parametrization == "softmax") + & (equal_full.l2_basis == "chi_square") + & (equal_full.prior_acs_share == 0.5) + & (equal_full.l2_lambda <= 1.0) + ], + muted, + "--", + "Equal-weight, softmax, share 0.5", + ), + ) + fig, axes = plt.subplots(1, 2, figsize=(12, 5), facecolor=surface) + panels = ( + ("kish_ess", "National Kish ESS (log scale)"), + ("cd_ess_median", "Median district Kish ESS (log scale)"), + ) + for ax, (key, label) in zip(axes, panels, strict=True): + ax.set_facecolor(surface) + for group, color, style, name in paths: + if group.empty: + continue + group = group.sort_values("l2_lambda") + ax.plot( + group.within_10pct, + group[key], + color=color, + linestyle=style, + linewidth=2, + marker="o", + markersize=7, + markeredgecolor=surface, + markeredgewidth=2, + label=name, + ) + # Label each path's largest λ; every path starts at λ = 0 on the + # right, which the title states. + r = group.iloc[-1] + ax.annotate( + f"λ={r.l2_lambda:g}", + (r.within_10pct, r[key]), + textcoords="offset points", + xytext=(-8, 8), + ha="right", + fontsize=8, + color=color, + ) + ax.set_yscale("log") + ax.set_xlabel("Training targets within 10% (unweighted count)", color=muted) + ax.set_ylabel(label, color=muted) + ax.xaxis.set_major_formatter(matplotlib.ticker.PercentFormatter(1.0)) + ax.grid(True, color=grid, linewidth=0.8) + ax.tick_params(colors=muted) + for spine in ax.spines.values(): + spine.set_visible(False) + axes[0].legend(frameon=False, fontsize=9, labelcolor=ink) + fig.suptitle( + "ACS local release on the weighted loss: ESS vs fit under a chi-square " + "penalty (800 epochs; each path starts at λ = 0 on the right)", + color=ink, + fontsize=11, + ) + fig.tight_layout() + fig.savefig(path, dpi=150, facecolor=surface) + + +def _splice(text: str, start: str, end: str, body: str) -> str: + head, found, rest = text.partition(start) + if not found: + return text + _, _, tail = rest.partition(end) + return f"{head}{start}\n{body}{end}{tail}" + + +def write_weighted(weighted: pd.DataFrame, equal: pd.DataFrame, out: Path) -> None: + weighted.to_csv(out / "weighted_frontier.csv", index=False) + (out / "weighted_frontier.md").write_text(weighted_markdown(weighted)) + (out / "weighted_floors.md").write_text(floor_table(weighted)) + table = weighted_key_config_table(weighted) + (out / "weighted_key_configs.md").write_text(table) + lambdas = lambda_table(weighted, equal, load_cross_scores()) + (out / "weighted_lambda.md").write_text(lambdas) + shift, per_target = weighting_shift(weighted) + if per_target is not None: + (out / "weighting_shift.md").write_text(shift) + per_target.to_csv(out / "target_loss_weights.csv", index=False) + (out / "weighted_rerun_variation.json").write_text( + json.dumps(rerun_variation(WEIGHTED_PAIRS, WEIGHTED_RERUN_METRICS), indent=1) + + "\n" + ) + readme = HERE / "README.md" + text = readme.read_text() + text = _splice( + text, + "", + "", + table, + ) + text = _splice( + text, + "", + "", + lambdas, + ) + readme.write_text(text) + weighted_chart(weighted, equal, out / "weighted_frontier.png") + + def main() -> None: - frame = load() + frame, weighted = split(load()) out = HERE / "results" + if len(weighted): + write_weighted(weighted, frame, out) frame.to_csv(out / "frontier.csv", index=False) (out / "frontier.md").write_text(markdown(frame)) (out / "floors.md").write_text(floor_table(frame)) diff --git a/experiments/us-acs-local-l2-basis-20260928/check_weights.py b/experiments/us-acs-local-l2-basis-20260928/check_weights.py new file mode 100644 index 000000000..5fb03b227 --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/check_weights.py @@ -0,0 +1,147 @@ +"""Check that the current shared weighting reproduces every weighted run's weights. + +The ``w_`` runs used microcosm#1104's ``target_loss_weights.py`` before it +merged. They stay valid for main as long as main's module gives each run +the same weights. The module's bytes can change without that: review fixes +that touch only other surfaces leave these weights alone. So this checks the +weights, not the file. + +For every ``results/runs/w_*.json``, it rebuilds the run's training weights +the way ``sweep.py`` did (the run's training rows and family multipliers) and +its full-surface yardstick, with whichever module ``sweep.py`` loads now: +the package import, or the file named by ``MICROCOSM_TARGET_LOSS_WEIGHTS_FILE``. +It compares both loss-vector digests with the run's receipt. It also checks +each receipt's own consistency block: the solver's epoch-0 loss within 1e-5 +of the weighted loss of the starting weights recomputed outside it, and its +final loss equal to the recomputed weighted loss of its returned weights. +The launch gate checked those only on the first pass's first run. It writes +``results/weights_check.json`` and exits non-zero on any failure. + +Run: ``uv run python experiments/us-acs-local-l2-basis-20260928/check_weights.py`` +""" + +from __future__ import annotations + +import json +import math +import os +import sys +from pathlib import Path + +HERE = Path(__file__).resolve().parent +sys.path.insert(0, str(HERE)) + +from sweep import ( # noqa: E402 + DEFAULT_CHECKPOINT, + SHARED_WEIGHTS_FILE_ENV, + RunSpec, + load_inputs, + loss_vector_sha256, + loss_weights_for, + row_names, + sha256, + shared_weight_module, + training_rows, +) + +RUNS = HERE / "results" / "runs" +OUT = HERE / "results" / "weights_check.json" +#: float32 solver against float64 recomputation; observed at most 9.3e-6. +EPOCH0_RTOL = 1e-5 + + +def _finite(value) -> bool: + # bool is an int subclass, and a JSON false must not pass as 0.0. + return ( + isinstance(value, (int, float)) + and not isinstance(value, bool) + and math.isfinite(value) + ) + + +def main() -> int: + inputs = load_inputs(DEFAULT_CHECKPOINT, verify=True, need_registry=True) + module = shared_weight_module() + all_rows = list(range(inputs.matrix.shape[0])) + full_names = row_names(inputs, all_rows) + cache: dict[tuple, tuple[str, str]] = {} + checked, mismatches = [], [] + for path in sorted(RUNS.glob("w_*.json")): + payload = json.loads(path.read_text()) + spec = RunSpec.from_mapping(payload["spec"]) + recorded = payload["target_loss_weights"] + key = (spec.holdout_fold, spec.family_loss_multipliers) + if key not in cache: + train = training_rows(inputs, spec.holdout_fold) + weights = loss_weights_for(spec, inputs, train) + cache[key] = ( + loss_vector_sha256(row_names(inputs, train), weights.train), + loss_vector_sha256(full_names, weights.full), + ) + train_digest, full_digest = cache[key] + consistency = payload.get("consistency") or {} + epoch0 = consistency.get("recomputed_design_loss_relative_to_epoch0") + final = consistency.get("recomputed_minus_result_final_loss") + row = { + "epoch0_relative": epoch0, + "final_difference": final, + "consistent": _finite(epoch0) + and abs(epoch0) < EPOCH0_RTOL + and _finite(final) + and final == 0.0, + "run_id": spec.run_id, + "train_matches": train_digest == recorded["train"]["loss_vector_sha256"], + "full_matches": full_digest + == recorded["full_surface"]["loss_vector_sha256"], + "recorded_module_sha256": recorded["module_sha256"], + } + checked.append(row) + if not (row["train_matches"] and row["full_matches"] and row["consistent"]): + mismatches.append( + { + **row, + "train_digest_now": train_digest, + "train_digest_recorded": recorded["train"]["loss_vector_sha256"], + } + ) + result = { + "ok": not mismatches and bool(checked), + "module_file": module.__file__, + "module_sha256": sha256(Path(module.__file__)), + "module_loaded_from_file": bool(os.environ.get(SHARED_WEIGHTS_FILE_ENV)) + and Path(module.__file__).resolve() + == Path(os.environ[SHARED_WEIGHTS_FILE_ENV]).resolve(), + "recorded_module_sha256": sorted( + {row["recorded_module_sha256"] for row in checked} + ), + "registry_sha256": inputs.registry_receipt["verified_registry_sha256"], + "n_runs": len(checked), + "n_distinct_weightings": len(cache), + # Over the receipts that record finite values; the others are in + # mismatches already. + "max_abs_epoch0_relative": max( + ( + abs(r["epoch0_relative"]) + for r in checked + if _finite(r["epoch0_relative"]) + ), + default=None, + ), + "max_abs_final_difference": max( + ( + abs(r["final_difference"]) + for r in checked + if _finite(r["final_difference"]) + ), + default=None, + ), + "n_mismatches": len(mismatches), + "mismatches": mismatches, + } + OUT.write_text(json.dumps(result, indent=1) + "\n") + print(json.dumps({k: v for k, v in result.items() if k != "mismatches"}, indent=1)) + return 0 if result["ok"] else 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/experiments/us-acs-local-l2-basis-20260928/cross_score.py b/experiments/us-acs-local-l2-basis-20260928/cross_score.py new file mode 100644 index 000000000..9855c1018 --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/cross_score.py @@ -0,0 +1,153 @@ +"""Score the equal-weight solves on the weighted loss. + +The weighted grid's solves record both losses; the equal-weight grid's record +only the unweighted one. This scores every equal-weight run's final weights +(``weights.npz`` beside the checkpoint), the published release's weights and +the design weights on the shared module's target-loss weights, the same way +``sweep.py`` scores a weighted run: + +* training fit over the run's training rows, weighted as a build calibrating + on those rows would weight them (the full surface, or one fold's + complement); +* held-out fit over the run's held-out fold, with each target's full-surface + weight. + +It writes ``results/cross_scores.json``: per run, the weighted capped error +and weighted share within 10% on training and held-out targets, the run's +receipt sha256 and the weights' sha256, plus the full-surface weight vector's +content hash, so analyze.py can set the two grids side by side on one loss. + +Run: ``uv run python experiments/us-acs-local-l2-basis-20260928/cross_score.py`` +""" + +from __future__ import annotations + +import hashlib +import json +import sys +from pathlib import Path + +import numpy as np + +HERE = Path(__file__).resolve().parent +sys.path.insert(0, str(HERE)) + +from gradient_scale import RELEASE_WEIGHTS # noqa: E402 +from sweep import ( # noqa: E402 + DEFAULT_CHECKPOINT, + DEFAULT_OUT_ROOT, + SHARED_TARGET_LOSS_WEIGHTS, + RunSpec, + estimates_for, + fit_block, + load_inputs, + loss_vector_sha256, + loss_weights_for, + row_names, + sha256, + training_rows, +) + +RUNS = HERE / "results" / "runs" +OUT = HERE / "results" / "cross_scores.json" + + +def _measures(block: dict) -> dict: + overall = block["overall"] + return { + "weighted_mean_capped_scaled_error": overall[ + "weighted_mean_capped_scaled_error" + ], + "weighted_fraction_within_10pct": overall["weighted_fraction_within_10pct"], + "mean_capped_scaled_error": overall["mean_capped_scaled_error"], + "fraction_within_10pct": overall["fraction_within_10pct"], + } + + +def main() -> None: + inputs = load_inputs(DEFAULT_CHECKPOINT, verify=True, need_registry=True) + all_rows = np.arange(inputs.matrix.shape[0], dtype=np.int64) + full = loss_weights_for( + RunSpec(run_id="full", target_weighting="shared"), inputs, all_rows + ) + fold_weights = { + fold: loss_weights_for( + RunSpec(run_id=f"fold{fold}", target_weighting="shared"), + inputs, + training_rows(inputs, fold), + ) + for fold in inputs.folds + } + + def score(weights: np.ndarray, fold: int | None) -> dict: + estimates = estimates_for(inputs, weights) + if fold is None: + return { + "train": _measures( + fit_block(estimates, inputs.meta, all_rows, loss_weights=full.train) + ) + } + train = training_rows(inputs, fold) + held = inputs.folds[fold] + return { + "train": _measures( + fit_block( + estimates, + inputs.meta, + train, + loss_weights=fold_weights[fold].train, + ) + ), + "holdout": _measures( + fit_block(estimates, inputs.meta, held, loss_weights=full.full[held]) + ), + } + + runs: dict[str, dict] = {} + for receipt in sorted(RUNS.glob("*.json")): + payload = json.loads(receipt.read_text()) + spec = payload.get("spec") or {} + if payload.get("kind") != "calibrate": + continue + if spec.get("target_weighting", "equal") != "equal": + continue + path = DEFAULT_OUT_ROOT / spec["run_id"] / "weights.npz" + if not path.exists(): + runs[spec["run_id"]] = {"status": "weights not found", "path": str(path)} + continue + weights = np.asarray(np.load(path)["weights"], dtype=np.float64) + fold = spec.get("holdout_fold") + runs[spec["run_id"]] = { + "holdout_fold": fold, + "receipt_sha256": sha256(receipt), + "weights_sha256": sha256(path), + **score(weights, fold), + } + published = np.asarray(np.load(RELEASE_WEIGHTS)["weights"], dtype=np.float64) + references = { + "published_release": (published, str(RELEASE_WEIGHTS), sha256(RELEASE_WEIGHTS)), + "design": (inputs.design, "checkpoint households.parquet design_weight", None), + } + for label, (weights, source, digest) in references.items(): + runs[label] = { + "source": source, + "weights_sha256": digest, + "full_surface": score(weights, None), + **{f"fold_{fold}": score(weights, fold) for fold in (0, 1)}, + } + payload = { + "function": SHARED_TARGET_LOSS_WEIGHTS, + "registry_sha256": inputs.registry_receipt["verified_registry_sha256"], + "full_surface_loss_vector_sha256": loss_vector_sha256( + row_names(inputs, all_rows), full.full + ), + "runs": runs, + } + OUT.write_text(json.dumps(payload, indent=1) + "\n") + print(f"wrote {OUT}: {len(runs)} entries") + digest = hashlib.sha256(OUT.read_bytes()).hexdigest() + print(f"sha256 {digest}") + + +if __name__ == "__main__": + main() diff --git a/experiments/us-acs-local-l2-basis-20260928/modal_sweep.py b/experiments/us-acs-local-l2-basis-20260928/modal_sweep.py index d2650f63b..951ba513d 100644 --- a/experiments/us-acs-local-l2-basis-20260928/modal_sweep.py +++ b/experiments/us-acs-local-l2-basis-20260928/modal_sweep.py @@ -3,8 +3,8 @@ Each grid point is one ``sweep.py`` calibration of the 2026-09-23 release's 4,459-target surface (1,588,854 households; ~8 GB peak) in its own container. The kernel packages are installed from this worktree's committed tree; the -launch refuses a tree whose kernel sources differ from HEAD, and every run -records that HEAD. +launch refuses a tree whose kernel sources (or ``sweep.py``) differ from HEAD, +and every run records that HEAD. modal run --detach experiments/us-acs-local-l2-basis-20260928/modal_sweep.py::launch modal run experiments/us-acs-local-l2-basis-20260928/modal_sweep.py::collect @@ -14,6 +14,14 @@ unless it reproduces the published release, then fans out the rest. ``collect`` copies every finished run's ``metrics.json`` into ``results/runs/`` and its ``weights.npz`` into the local artifacts directory. + +``--grid weighted`` runs the weighted-loss grid instead (``weighted_grid``; +run ids ``w_*``): every point passes the shared module's target weights. It +also uploads ``registry.py``'s ``target_registry.json`` and receipt. Its first +point, ``w_release_repro``, has no published result to match, so it gates on +the harness instead: the solve's epoch-0 loss must equal the weighted loss of +the starting weights recomputed outside the solver, and its final loss the +weighted loss of its returned weights. """ from __future__ import annotations @@ -43,12 +51,22 @@ "targets_meta.parquet", "holdout_folds.npz", ) +#: The weighted grid also needs the rebuilt registry (``registry.py``). +UPLOAD_WEIGHTED = ("target_registry.json", "target_registry.receipt.json") KERNEL = ( "microcosm-frame", "microcosm-graph", "microcosm-diagnostics", "microcosm-calibrate", ) +#: The shared target-loss weighting's source. It imports only numpy and the +#: standard library, so the image carries this one file and ``sweep.py`` loads +#: it on its own (``SHARED_WEIGHTS_FILE_ENV``) rather than installing the +#: build package whose ``us_runtime`` ``__init__`` needs the whole build stack. +SHARED_WEIGHTS_FILE = ( + "packages/microcosm-build/src/microcosm/build/us_runtime/target_loss_weights.py" +) +SHARED_WEIGHTS_CONTAINER_PATH = "/opt/shared/target_loss_weights.py" VOLUME_ROOT = "/sweep" CPUS = 8 @@ -65,7 +83,8 @@ def _head() -> str: def _kernel_clean() -> bool: status = subprocess.run( ["git", "-C", str(REPO), "status", "--porcelain", "--"] - + [f"packages/{name}/src" for name in KERNEL], + + [f"packages/{name}/src" for name in KERNEL] + + [SHARED_WEIGHTS_FILE, str(HERE / "sweep.py")], capture_output=True, text=True, check=True, @@ -95,6 +114,7 @@ def _kernel_clean() -> bool: "OPENBLAS_NUM_THREADS": str(CPUS), "MICROCOSM_GIT_SHA": GIT_SHA, "MICROCOSM_REPO": "/opt/none", + "MICROCOSM_TARGET_LOSS_WEIGHTS_FILE": SHARED_WEIGHTS_CONTAINER_PATH, } ) ) @@ -103,10 +123,14 @@ def _kernel_clean() -> bool: image = image.add_local_dir( str(REPO / "packages" / name), f"/opt/kernel/{name}", copy=True ) - image = image.run_commands( - "python -m pip install --no-deps " - + " ".join(f"/opt/kernel/{name}" for name in KERNEL) - ).add_local_file(str(HERE / "sweep.py"), "/opt/sweep/sweep.py") + image = ( + image.run_commands( + "python -m pip install --no-deps " + + " ".join(f"/opt/kernel/{name}" for name in KERNEL) + ) + .add_local_file(str(HERE / "sweep.py"), "/opt/sweep/sweep.py") + .add_local_file(str(REPO / SHARED_WEIGHTS_FILE), SHARED_WEIGHTS_CONTAINER_PATH) + ) app = modal.App("microcosm-acs-l2-basis-sweep", image=image) volume = modal.Volume.from_name("microcosm-acs-l2-basis-sweep", create_if_missing=True) @@ -261,6 +285,124 @@ def grid() -> list[dict]: return specs +def weighted_grid() -> list[dict]: + """The weighted-loss grid; ``w_release_repro`` must come first. + + Every point passes the shared module's target weights. Full surface: the + release's settings, softmax chi-square at share 0.5 over the λ path and at + share 0.9, and projection chi-square around the equal-weight pick (d797 + proposes projection). Rotated holdout folds 0 and 1 for the configurations + the λ re-pick compares. Two ``w_dup_`` reruns of fold-0 points measure the + held-out run-to-run variation the equal-weight grid left unmeasured. + """ + + def point(run_id: str, **fields) -> dict: + return {"run_id": f"w_{run_id}", "target_weighting": "shared", **fields} + + def softmax(lam: float, share: float | None = None, **fields) -> dict: + tag = "s050" if share is None else f"s{round(share * 1000):03d}" + return point( + f"soft_chi_{tag}_{lam:g}", + l2_lambda=lam, + l2_basis="chi_square", + mass_parametrization="softmax", + acs_share=share, + **fields, + ) + + def projection(lam: float, **fields) -> dict: + if lam == 0: + return point("proj_s050_0", mass_parametrization="projection", **fields) + return point( + f"proj_chi_s050_{lam:g}", + l2_lambda=lam, + l2_basis="chi_square", + mass_parametrization="projection", + **fields, + ) + + def held(spec: dict, fold: int, prefix: str = "") -> dict: + tag = "hold" if fold == 0 else f"hold{fold}" + return { + **spec, + "run_id": "w_" + prefix + tag + "_" + spec["run_id"].removeprefix("w_"), + "holdout_fold": fold, + } + + specs = [point("release_repro", mass_parametrization="projection")] + specs += [softmax(lam) for lam in (0.0, 0.01, 0.03, 0.1, 0.3, 1.0)] + specs += [softmax(lam, 0.9) for lam in (0.0, 0.01)] + specs += [projection(lam) for lam in (0.01, 0.03, 0.1)] + compared = [ + projection(0.0), + softmax(0.0), + softmax(0.03), + softmax(0.1), + softmax(0.0, 0.9), + projection(0.03), + projection(0.1), + ] + for fold in (0, 1): + specs += [held(spec, fold) for spec in compared] + specs += [held(projection(0.0), 0, "dup_"), held(softmax(0.03), 0, "dup_")] + return specs + + +def weighted_grid_2() -> list[dict]: + """The weighted grid's second pass, after the first one's results. + + 1. Projection at λ 0.2 and 0.3 (full surface and folds 0 and 1): the + first pass's held-out weighted error was still falling at λ 0.1, the + edge of its projection holdout. + 2. The family lever: ``census_population`` multiplied by 4 or 8 in the + training weights (the tool's ``--target-family-loss-multiplier``) at + λ 0, 0.03 and 0.1, folds 0 and 1 at 8. The shared weighting gives the + 436 district population rows 1.7% of the loss (9.8% under equal + weights; a multiplier of about 8.4 restores that share), and the first + pass's weighted solves miss 11-135 trained districts by more than 10%. + ``w_release_repro`` leads so the launch's gate step finds it cached. + """ + + first = weighted_grid() + specs = [first[0]] + + def projection(lam: float, multiplier: float | None = None, **fields) -> dict: + tag = "" if multiplier is None else f"pop{multiplier:g}_" + base = "proj_s050_0" if lam == 0 else f"proj_chi_s050_{lam:g}" + spec = { + "run_id": f"w_{tag}{base}", + "target_weighting": "shared", + "mass_parametrization": "projection", + **fields, + } + if lam: + spec.update(l2_lambda=lam, l2_basis="chi_square") + if multiplier is not None: + spec["family_loss_multipliers"] = [["census_population", multiplier]] + return spec + + def held(spec: dict, fold: int) -> dict: + tag = "hold" if fold == 0 else f"hold{fold}" + return { + **spec, + "run_id": "w_" + tag + "_" + spec["run_id"].removeprefix("w_"), + "holdout_fold": fold, + } + + for lam in (0.2, 0.3): + specs.append(projection(lam)) + specs += [held(projection(lam), fold) for fold in (0, 1)] + for multiplier in (4.0, 8.0): + for lam in (0.0, 0.03, 0.1): + specs.append(projection(lam, multiplier)) + for lam in (0.0, 0.03, 0.1): + specs += [held(projection(lam, 8.0), fold) for fold in (0, 1)] + return specs + + +GRIDS = {"equal": grid, "weighted": weighted_grid, "weighted2": weighted_grid_2} + + @app.function( cpu=CPUS, memory=24576, @@ -294,32 +436,91 @@ def run_point(spec: dict) -> dict: ) volume.commit() national = payload["metrics"]["concentration"]["national"] + consistency = payload["consistency"] + final_loss = payload["result"]["final_loss"] return { "run_id": spec["run_id"], - "final_loss": payload["result"]["final_loss"], + "final_loss": final_loss, "fraction_within_10pct": payload["metrics"]["fit_train"]["overall"][ "fraction_within_10pct" ], "kish_ess": national["kish_ess"], "chi_square_distance": national["chi_square_distance"], "wall_seconds": payload["timing"]["total_wall_seconds"], + "weighted": payload["target_loss_weights"]["target_weighting"] != "equal", + "epoch0_vs_recomputed": consistency.get( + "recomputed_design_loss_relative_to_epoch0" + ), + "final_vs_recomputed": ( + consistency["recomputed_minus_result_final_loss"] / final_loss + if final_loss + else None + ), } -def _upload_checkpoint() -> None: +def _upload_checkpoint(weighted: bool = False) -> None: + """Upload the checkpoint files the volume lacks or holds at another size. + + Other sessions run from this volume, so files already there are not + rewritten; every container still checks each input's sha256 against + ``MANIFEST.json`` (and the registry against its receipt) before use. + """ + + names = UPLOAD + (UPLOAD_WEIGHTED if weighted else ()) + present = { + Path(entry.path).name: entry.size for entry in volume.listdir("/checkpoint") + } + missing = [ + name + for name in names + if present.get(name) != (CHECKPOINT / name).stat().st_size + ] + print(f"checkpoint upload: {missing or 'nothing missing'}") + if not missing: + return with volume.batch_upload(force=True) as batch: - for name in UPLOAD: + for name in missing: batch.put_file(str(CHECKPOINT / name), f"/checkpoint/{name}") +#: The weighted harness gate: the solve's epoch-0 loss against the weighted +#: loss of the starting weights recomputed in float64 outside the solver +#: (the solver's float32 path differs at ~1e-6), and its final loss against +#: the weighted loss of its returned weights. +WEIGHTED_GATE_RTOL = 1e-4 + + +def _weighted_gate(repro: dict) -> None: + for key in ("epoch0_vs_recomputed", "final_vs_recomputed"): + value = repro.get(key) + if value is None or abs(value) > WEIGHTED_GATE_RTOL: + raise SystemExit( + f"w_release_repro {key}={value} exceeds {WEIGHTED_GATE_RTOL}; " + "the solve did not optimize the weighted loss the harness " + "recomputes; not launching the grid" + ) + if not repro.get("weighted"): + raise SystemExit("w_release_repro did not record target-loss weights") + + @app.local_entrypoint() -def launch(skip_repro: bool = False) -> None: +def launch(skip_repro: bool = False, grid: str = "equal") -> None: + if grid not in GRIDS: + raise SystemExit(f"unknown grid {grid!r}; choose one of {sorted(GRIDS)}") if not _kernel_clean(): - raise SystemExit("kernel sources differ from HEAD; commit before launching") - print(f"kernel HEAD {GIT_SHA}") - _upload_checkpoint() - specs = grid() - if not skip_repro: + raise SystemExit( + "kernel sources or sweep.py differ from HEAD; commit before launching" + ) + print(f"kernel HEAD {GIT_SHA}, grid {grid}") + _upload_checkpoint(weighted=grid != "equal") + specs = GRIDS[grid]() + if not skip_repro and grid != "equal": + repro = run_point.remote(specs[0]) + print(f"{specs[0]['run_id']}:", json.dumps(repro)) + if not repro.get("cached"): + _weighted_gate(repro) + elif not skip_repro: repro = run_point.remote(specs[0]) print("release_repro:", json.dumps(repro)) if not repro.get("cached"): @@ -339,11 +540,14 @@ def launch(skip_repro: bool = False) -> None: @app.local_entrypoint() -def collect() -> None: +def collect(grid: str = "equal") -> None: + if grid not in GRIDS: + raise SystemExit(f"unknown grid {grid!r}; choose one of {sorted(GRIDS)}") results = HERE / "results" / "runs" results.mkdir(parents=True, exist_ok=True) collected = 0 - for spec in grid(): + specs = GRIDS[grid]() + for spec in specs: run_id = spec["run_id"] local = LOCAL_RUNS / run_id local.mkdir(parents=True, exist_ok=True) @@ -358,4 +562,4 @@ def collect() -> None: for chunk in volume.read_file(f"/runs/{run_id}/weights.npz"): handle.write(chunk) collected += 1 - print(f"collected {collected} of {len(grid())} runs") + print(f"collected {collected} of {len(specs)} runs") diff --git a/experiments/us-acs-local-l2-basis-20260928/registry.py b/experiments/us-acs-local-l2-basis-20260928/registry.py new file mode 100644 index 000000000..a63f4af50 --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/registry.py @@ -0,0 +1,241 @@ +"""Rebuild the 09-23 release's target specs, row-aligned with the checkpoint. + +The checkpoint's ``targets.json`` and ``targets_meta.parquet`` carry each +target's name, value and a parsed taxonomy, but not the ``TargetSpec`` +metadata that target-loss weighting reads (``measure_mode``, +``source_measure_id``, the ledger geography, ``target_role``, the filter, the +hierarchy). The 09-23 release wrote no registry, so this recompiles one the +way its materialize stage did (``run/materialize-sup/COMMAND.json``): + +* the admin families through the tool's production compile path, + ``state_admin_surface(feed, ["snap", "medicaid", "soi"], soi_mode="state")``, + on the release's consumer-facts feed (sha256-pinned below); +* the Census ladder populations through ``population_target_specs`` on the + checkpoint's own ``pop_state_*`` / ``pop_cd_*`` names and values. + +Then it checks that the compiled surface is the checkpoint's: the same 4,459 +names (no target missing or extra, none duplicated), equal values, and the +same family and ``source_measure_id`` as ``targets.json``. It writes, beside +the checkpoint (``MANIFEST.json`` is left untouched, so earlier receipts stay +valid): + +* ``target_registry.json``: the ``TargetRegistry``, spec ``i`` = CSR row ``i``; +* ``target_registry.receipt.json``: its sha256, the feed's, the compile head, + and every check above. + +Run (engine-free; reads the 164 MB feed):: + + uv run python experiments/us-acs-local-l2-basis-20260928/registry.py +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import subprocess +import sys +import time +from collections import Counter +from pathlib import Path + +import numpy as np +import pandas as pd + +HERE = Path(__file__).resolve().parent +REPO = HERE.parents[1] +CHECKPOINT = Path( + "/Users/maxghenis/PolicyEngine/_build_artifacts/acs-local-l2-basis-20260928/checkpoint" +) +RUN_0923 = Path( + "/Users/maxghenis/PolicyEngine/_recovered/scratch-backup/893/overnight-20260923/run" +) +#: The feed, families and SOI mode the 09-23 materialize stage compiled from. +FEED = Path( + "/Users/maxghenis/PolicyEngine/_buildh-runtime/inputs/chronicle_us_b571381/" + "artifact/consumer_facts.jsonl" +) +FEED_SHA256 = "4d1dba8c1b6274877bf184fa6de5d99b13fc61f34709ccab1487db2b5c64a79f" +FAMILIES = ["snap", "medicaid", "soi"] +SOI_MODE = "state" +RELEASE_HEAD = "767312d60430893617af57034f62edfb4920dca3" +REGISTRY_FILENAME = "target_registry.json" +RECEIPT_FILENAME = "target_registry.receipt.json" +#: The checkpoint's values are the release's (targets_meta.parquet carries the +#: 09-23 checkpoint's targets.json values); a recompile of the same feed must +#: reproduce them bit for bit, and anything else means a different compile. +VALUE_RTOL = 0.0 + + +def sha256(path: Path) -> str: + digest = hashlib.sha256() + with open(path, "rb") as handle: + for block in iter(lambda: handle.read(1 << 24), b""): + digest.update(block) + return digest.hexdigest() + + +def git(*args: str) -> str: + return subprocess.run( + ["git", "-C", str(REPO), *args], capture_output=True, text=True, check=True + ).stdout.strip() + + +def compile_specs(feed: Path, population_names, population_values): + """The admin surface and the ladder population specs, as the tool builds them.""" + + sys.path.insert(0, str(REPO / "tools")) + from build_us_acs_local_release import ( + population_target_specs, + state_admin_surface, + ) + + surface = state_admin_surface(feed, FAMILIES, soi_mode=SOI_MODE) + population = population_target_specs(population_names, population_values) + return list(surface.registry.specs), list(population), surface + + +def main(argv=None) -> int: + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("--checkpoint", type=Path, default=CHECKPOINT) + parser.add_argument("--feed", type=Path, default=FEED) + args = parser.parse_args(argv) + + from microcosm.calibrate import TargetRegistry + + started = time.time() + checkpoint = args.checkpoint + feed_sha = sha256(args.feed) + if feed_sha != FEED_SHA256: + raise SystemExit(f"feed sha256 {feed_sha} != the release's {FEED_SHA256}") + meta = pd.read_parquet(checkpoint / "targets_meta.parquet") + records = json.loads((checkpoint / "targets.json").read_text()) + if [r["name"] for r in records] != meta["name"].tolist(): + raise SystemExit("targets.json and targets_meta.parquet disagree on names") + names = meta["name"].tolist() + values = meta["value"].to_numpy(np.float64) + is_population = meta["name"].str.startswith(("pop_state_", "pop_cd_")).to_numpy() + + admin, population, surface = compile_specs( + args.feed, + [names[i] for i in np.flatnonzero(is_population)], + [float(values[i]) for i in np.flatnonzero(is_population)], + ) + compiled = admin + population + counts = Counter(spec.name for spec in compiled) + duplicated = sorted(name for name, n in counts.items() if n > 1) + by_name = {spec.name: spec for spec in compiled} + missing = [name for name in names if name not in by_name] + extra = sorted(set(by_name) - set(names)) + + ordered = [by_name[name] for name in names if name in by_name] + compiled_values = np.asarray([float(spec.value) for spec in ordered]) + value_check: dict = {"n_compared": len(ordered)} + if not missing: + relative = np.abs(compiled_values - values) / np.maximum(np.abs(values), 1.0) + value_check.update( + { + "n_unequal": int((compiled_values != values).sum()), + "max_relative_difference": float(relative.max()), + "unequal_examples": [ + { + "name": names[i], + "checkpoint": float(values[i]), + "compiled": float(compiled_values[i]), + } + for i in np.flatnonzero(relative > VALUE_RTOL)[:10] + ], + } + ) + field_mismatches = [] + for record, spec in zip(records, ordered, strict=False): + expected_family = record.get("family") + if expected_family is not None and spec.family != expected_family: + if not ( + record["name"].startswith("pop_") + and expected_family == "census_population_ladder" + ): + field_mismatches.append( + {"name": record["name"], "field": "family", "spec": spec.family} + ) + expected_measure = record.get("source_measure_id") + actual_measure = spec.metadata.get("source_measure_id") + if expected_measure is not None and actual_measure != expected_measure: + field_mismatches.append( + { + "name": record["name"], + "field": "source_measure_id", + "spec": actual_measure, + "checkpoint": expected_measure, + } + ) + checks = { + "admin_specs_compiled": len(admin), + "admin_specs_in_checkpoint": int((~is_population).sum()), + "population_specs": len(population), + "duplicated_names": duplicated, + "missing_from_compile": missing[:20], + "n_missing_from_compile": len(missing), + "extra_in_compile": extra[:20], + "n_extra_in_compile": len(extra), + "values": value_check, + "field_mismatches": field_mismatches[:20], + "n_field_mismatches": len(field_mismatches), + } + ok = ( + not duplicated + and not missing + and not extra + and len(admin) == int((~is_population).sum()) + and value_check.get("n_unequal", 1) == 0 + and not field_mismatches + ) + receipt = { + "ok": ok, + "created_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), + "purpose": ( + "TargetSpecs (with the metadata target-loss weighting reads) for " + "the checkpoint's 4,459 targets, recompiled from the 09-23 " + "release's feed; spec i is CSR row i." + ), + "feed": {"path": str(args.feed), "sha256": feed_sha}, + "families": FAMILIES, + "soi_mode": SOI_MODE, + "release_materialize_head": RELEASE_HEAD, + "compile_head": git("rev-parse", "HEAD"), + "compile_tree_clean": not git( + "status", "--porcelain", "--", "tools", "packages" + ), + "ri_substitutions": [ + getattr(item, "__dict__", str(item)) for item in surface.ri_substitutions + ] + if isinstance(surface.ri_substitutions, (list, tuple)) + else str(surface.ri_substitutions), + "soi_receipt": surface.soi_receipt, + "checkpoint_manifest_sha256": sha256(checkpoint / "MANIFEST.json"), + "targets_meta_sha256": sha256(checkpoint / "targets_meta.parquet"), + "checks": checks, + "wall_seconds": None, + } + if ok: + registry = TargetRegistry(ordered, country="us") + path = registry.to_json(checkpoint / REGISTRY_FILENAME) + reloaded = TargetRegistry.from_json(path) + if [spec.name for spec in reloaded.specs] != names: + raise SystemExit("the written registry does not reload in row order") + receipt["registry"] = { + "path": str(path), + "sha256": sha256(path), + "n_specs": len(reloaded.specs), + "family_counts": dict(Counter(spec.family for spec in reloaded.specs)), + } + receipt["wall_seconds"] = round(time.time() - started, 1) + (checkpoint / RECEIPT_FILENAME).write_text( + json.dumps(receipt, indent=1, default=str) + "\n" + ) + print(json.dumps({"ok": ok, **checks}, indent=1, default=str)[:4000]) + return 0 if ok else 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/experiments/us-acs-local-l2-basis-20260928/results/cross_scores.json b/experiments/us-acs-local-l2-basis-20260928/results/cross_scores.json new file mode 100644 index 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+1370,irs_soi.ty2022.historic_table_2.state_broad.ar.all.premium_tax_credit_amount,soi,soi_credit_amount,state,05,329323458.59867686,0.2442081125996294,5.476746189720328e-05 +1371,irs_soi.ty2022.historic_table_2.state_broad.tn.all.taxable_income_amount,soi,soi_tax_amount,state,47,229205475977.14722,6.4426011804857675,0.0014448533708198627 +1372,irs_soi.ty2022.historic_table_2.state_broad.md.all.limited_state_local_taxes_returns,soi,soi_deduction_count,state,24,614240.0,1.3380263033754762,0.00030007317859956855 +1373,irs_soi.ty2022.historic_table_2.state_agi.mn.100k_to_200k.taxable_interest_amount,soi,soi_agi_band_amount,state,27,852213159.1589298,0.3928464845954007,8.810192522884072e-05 +1374,irs_soi.ty2022.historic_table_2.state_agi.ia.500k_plus.taxable_interest_amount,soi,soi_agi_band_amount,state,19,926941735.9282186,0.4097084976932232,9.188349353963292e-05 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+1390,irs_soi.ty2022.historic_table_2.state_broad.wa.all.wages_salaries_returns,soi,soi_income_count,state,53,3028550.0,2.9710706398889375,0.0006663087328748458 +1391,irs_soi.ty2022.historic_table_2.state_broad.ak.all.qualified_dividends_returns,soi,soi_income_count,state,02,66810.0,0.44128212872155087,9.896437064847519e-05 +1392,irs_soi.ty2022.historic_table_2.state_agi.nv.10k_to_25k.taxable_interest_returns,soi,soi_agi_band_count,state,32,33117.50261064642,0.3106879383626379,6.967659528204484e-05 +1393,irs_soi.ty2022.historic_table_2.state_broad.vt.all.partnership_scorp_income_returns,soi,soi_income_count,state,50,21210.0,0.24863710353585847,5.576073189860024e-05 +1394,irs_soi.ty2022.historic_table_2.state_broad.il.all.limited_state_local_taxes_amount,soi,soi_deduction_amount,state,17,5107560949.33093,0.9617352452067018,0.0002156840648590944 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+1421,irs_soi.ty2022.historic_table_2.state_broad.nc.all.income_tax_before_credits_amount,soi,soi_tax_amount,state,37,64377292394.9498,3.4144055740546624,0.0007657334770250421 +1422,irs_soi.ty2022.historic_table_2.state_broad.wa.all.tax_exempt_interest_returns,soi,soi_income_count,state,53,206920.0,0.7765990206030648,0.00017416439125433165 +1423,irs_soi.ty2022.historic_table_2.state_broad.nc.all.ctc_amount,soi,soi_credit_amount,state,37,2885052214.914308,0.7228123961220613,0.00016210190538732032 +1424,irs_soi.ty2022.historic_table_2.state_broad.nm.all.taxable_income_amount,soi,soi_tax_amount,state,35,53414488564.127754,3.110128195466231,0.0006974945493308435 +1425,irs_soi.ty2022.historic_table_2.state_broad.hi.all.taxable_pension_income_amount,soi,soi_income_amount,state,15,5866344389.75535,1.030700604551176,0.00023115061775088046 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+1446,irs_soi.ty2022.historic_table_2.state_agi.id.200k_to_500k.taxable_interest_amount,soi,soi_agi_band_amount,state,16,322891281.13056856,0.24181147841732484,5.422997946116278e-05 +1447,irs_soi.ty2022.historic_table_2.state_broad.nv.all.actc_claims,soi,soi_credit_count,state,32,189210.0,0.7426217187860389,0.00016654445364118389 +1448,irs_soi.ty2022.historic_table_2.state_agi.mi.200k_to_500k.taxable_interest_returns,soi,soi_agi_band_count,state,26,181500.12260788464,0.7273342937666991,0.00016311601116095518 +1449,irs_soi.ty2022.historic_table_2.state_broad.sc.all.taxable_social_security_amount,soi,soi_income_amount,state,45,9320746345.946075,1.2991942741683673,0.00029136449297339476 +1450,irs_soi.ty2022.historic_table_2.state_broad.ak.all.actc_amount,soi,soi_credit_amount,state,02,67705868.29471144,0.11072913550645697,2.483272830375801e-05 +1451,irs_soi.ty2022.historic_table_2.state_broad.mi.all.ctc_claims,soi,soi_credit_count,state,26,1050440.0,1.7497710592821518,0.00039241333466744825 +1452,irs_soi.ty2022.historic_table_2.state_broad.nm.all.taxable_interest_amount,soi,soi_income_amount,state,35,1133111491.1714199,0.45298630796816497,0.00010158921461497308 +1453,irs_soi.ty2022.historic_table_2.state_agi.mi.75k_to_100k.taxable_interest_returns,soi,soi_agi_band_count,state,26,201902.8697519436,0.767126259895712,0.00017203997755005877 +1454,irs_soi.ty2022.historic_table_2.state_broad.nd.all.taxable_income_returns,soi,soi_tax_count,state,38,311740.0,0.9532171976948207,0.00021377376041597235 +1455,irs_soi.ty2022.historic_table_2.state_agi.mi.under_1.taxable_interest_amount,soi,soi_agi_band_amount,state,26,147846072.7308076,0.16362650329934772,3.669578454795867e-05 +1456,irs_soi.ty2022.historic_table_2.state_broad.ar.all.adjusted_gross_income,soi,soi_income_amount,state,05,107337920055.26299,4.408850179330097,0.000988753123868602 +1457,irs_soi.ty2022.historic_table_2.state_broad.nm.all.return_count,soi,soi_return_count,state,35,987790.0,1.696789297668895,0.00038053135179836174 +1458,irs_soi.ty2022.historic_table_2.state_broad.ny.all.qualified_dividends_amount,soi,soi_income_amount,state,36,27804404696.90928,2.2439106188468982,0.0005032318050789186 +1459,irs_soi.ty2022.historic_table_2.state_eitc.ia.ia.eitc_claims,soi,soi_eitc_count,state,19,177190.0,0.7186463228085216,0.00016116759874602413 +1460,irs_soi.ty2022.historic_table_2.state_agi.fl.10k_to_25k.taxable_interest_amount,soi,soi_agi_band_amount,state,12,549956498.723433,0.31558250737861426,7.077427839843335e-05 +1461,irs_soi.ty2022.historic_table_2.state_broad.oh.all.taxable_income_amount,soi,soi_tax_amount,state,39,373148272969.06903,8.220334141935975,0.0018435375963076867 +1462,irs_soi.ty2022.historic_table_2.state_eitc.ma.ma.eitc_three_or_more_children_claims,soi,soi_eitc_count,state,25,32370.0,0.30716162276174325,6.888576424349478e-05 +1463,irs_soi.ty2022.historic_table_2.state_eitc.ma.ma.eitc_no_children_claims,soi,soi_eitc_count,state,25,112270.0,0.5720414054041142,0.0001282891691868388 +1464,irs_soi.ty2022.historic_table_2.state_broad.la.all.net_capital_gains_amount,soi,soi_income_amount,state,22,7515789161.602456,1.1666378097964292,0.0002616366471846668 +1465,irs_soi.ty2022.historic_table_2.state_agi.va.75k_to_100k.taxable_interest_returns,soi,soi_agi_band_count,state,51,166124.13925294165,0.6958441420657601,0.00015605385558774615 +1466,irs_soi.ty2022.historic_table_2.state_broad.nd.all.wages_salaries_returns,soi,soi_income_count,state,38,306610.0,0.9453415818344741,0.00021200753124791974 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+1472,irs_soi.ty2022.historic_table_2.state_agi.tn.10k_to_25k.taxable_interest_returns,soi,soi_agi_band_count,state,47,92967.75121090525,0.5205491799242614,0.00011674123792874219 +1473,irs_soi.ty2022.historic_table_2.state_agi.ks.1_to_10k.taxable_interest_returns,soi,soi_agi_band_count,state,20,36359.162919095936,0.32553863766353114,7.300709523739204e-05 +1474,irs_soi.ty2022.historic_table_2.state_eitc.ms.ms.eitc_one_child_amount,soi,soi_eitc_amount,state,28,411535566.5750179,0.272993584224875,6.122305095870711e-05 +1475,irs_soi.ty2022.historic_table_2.state_eitc.pa.pa.eitc_no_children_amount,soi,soi_eitc_amount,state,42,93387559.07490885,0.13004488211167964,2.9164584461018083e-05 +1476,irs_soi.ty2022.historic_table_2.state_eitc.co.co.eitc_no_children_claims,soi,soi_eitc_count,state,08,102360.0,0.54621137757426,0.00012249638429564027 +1477,irs_soi.ty2022.historic_table_2.state_eitc.md.md.eitc_one_child_claims,soi,soi_eitc_count,state,24,137370.0,0.6327636940212324,0.00014190708544992877 +1478,irs_soi.ty2022.historic_table_2.state_broad.or.all.partnership_scorp_income_amount,soi,soi_income_amount,state,41,12954383491.876257,1.5316415100741043,0.00034349439562101464 +1479,irs_soi.ty2022.historic_table_2.state_agi.al.10k_to_25k.taxable_interest_amount,soi,soi_agi_band_amount,state,01,81374924.41912043,0.12139311404550145,2.722429110686285e-05 +1480,irs_soi.ty2022.historic_table_2.state_eitc.me.me.eitc_three_or_more_children_claims,soi,soi_eitc_count,state,23,8550.0,0.15786238540714956,3.540309159164601e-05 +1481,irs_soi.ty2022.historic_table_2.state_broad.ct.all.taxable_ira_distributions_amount,soi,soi_income_amount,state,09,7273067600.89602,1.1476449905662731,0.0002573772125064528 +1482,irs_soi.ty2022.historic_table_2.state_broad.mo.all.limited_state_local_taxes_returns,soi,soi_deduction_count,state,29,177580.0,0.7194367681321256,0.00016134486838576489 +1483,irs_soi.ty2022.historic_table_2.state_broad.ma.all.unemployment_compensation_amount,soi,soi_income_amount,state,25,2047153302.7878768,0.6088691030551586,0.0001365483523335184 +1484,irs_soi.ty2022.historic_table_2.state_agi.mi.200k_to_500k.taxable_interest_amount,soi,soi_agi_band_amount,state,26,1149713295.30482,0.45629270972264896,0.00010233072655811818 +1485,irs_soi.ty2022.historic_table_2.state_broad.ct.all.tax_exempt_interest_amount,soi,soi_income_amount,state,09,1146220365.0816243,0.4555990537605994,0.00010217516343588234 +1486,irs_soi.ty2022.historic_table_2.state_eitc.mo.mo.eitc_two_children_amount,soi,soi_eitc_amount,state,29,439010931.26086015,0.28195929780897605,6.323375147095225e-05 +1487,irs_soi.ty2022.historic_table_2.state_agi.ia.10k_to_25k.taxable_interest_amount,soi,soi_agi_band_amount,state,19,84221167.17951393,0.12349784550498913,2.769630982394912e-05 +1488,irs_soi.ty2022.historic_table_2.state_agi.la.100k_to_200k.taxable_interest_amount,soi,soi_agi_band_amount,state,22,400351023.4596958,0.269258377674945,6.03853728806784e-05 +1489,irs_soi.ty2022.historic_table_2.state_eitc.ks.ks.eitc_claims,soi,soi_eitc_count,state,20,176390.0,0.7170221694857338,0.00016080335713965774 +1490,irs_soi.ty2022.historic_table_2.state_broad.nm.all.ctc_amount,soi,soi_credit_amount,state,35,512735298.13216364,0.3047160721090744,6.833731152928335e-05 +1491,irs_soi.ty2022.historic_table_2.state_eitc.mi.mi.eitc_two_children_amount,soi,soi_eitc_amount,state,26,709114021.9779135,0.3583494892538181,8.036543827176902e-05 +1492,irs_soi.ty2022.historic_table_2.state_agi.ms.under_1.taxable_interest_returns,soi,soi_agi_band_count,state,28,5103.424674788767,0.1219625393666785,2.735199357853297e-05 +1493,irs_soi.ty2022.historic_table_2.state_broad.nd.all.unemployment_compensation_amount,soi,soi_income_amount,state,38,60503164.022762336,0.10467375320490362,2.3474714780198164e-05 +1494,irs_soi.ty2022.historic_table_2.state_broad.dc.all.taxable_ira_distributions_amount,soi,soi_income_amount,state,11,943326829.3924487,0.41331374331355947,9.269202586085657e-05 +1495,irs_soi.ty2022.historic_table_2.state_broad.mn.all.limited_state_local_taxes_amount,soi,soi_deduction_amount,state,27,2408150062.439948,0.6603748519756217,0.00014809931643319617 +1496,irs_soi.ty2022.historic_table_2.state_broad.ms.all.income_tax_before_credits_amount,soi,soi_tax_amount,state,28,10590073687.775963,1.384835679123635,0.0003105709080788596 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+1508,irs_soi.ty2022.historic_table_2.state_broad.ny.all.net_capital_gains_returns,soi,soi_income_count,state,36,1974434.8073859748,2.3989268369108143,0.0005379965994417614 +1509,irs_soi.ty2022.historic_table_2.state_agi.vt.10k_to_25k.taxable_interest_amount,soi,soi_agi_band_amount,state,50,18187312.22993572,0.05738957578615185,1.2870503652422482e-05 +1510,irs_soi.ty2022.historic_table_2.state_broad.nh.all.taxable_ira_distributions_amount,soi,soi_income_amount,state,33,3129613658.0980134,0.7528251467045872,0.00016883273081511264 +1511,irs_soi.ty2022.historic_table_2.state_broad.az.all.unemployment_compensation_returns,soi,soi_income_count,state,04,55070.0,0.4006389249882816,8.984950100656685e-05 +1512,irs_soi.ty2022.historic_table_2.state_agi.la.25k_to_50k.taxable_interest_returns,soi,soi_agi_band_count,state,22,85619.25774141327,0.4995527256249315,0.000112032456969036 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+1524,irs_soi.ty2022.historic_table_2.state_agi.de.100k_to_200k.taxable_interest_returns,soi,soi_agi_band_count,state,10,48986.3059449145,0.377861765071067,8.47413691570009e-05 +1525,irs_soi.ty2022.historic_table_2.state_broad.ne.all.ctc_amount,soi,soi_credit_amount,state,31,693897482.9482646,0.3544838136239316,7.949850047632465e-05 +1526,irs_soi.ty2022.historic_table_2.state_broad.or.all.partnership_scorp_income_returns,soi,soi_income_count,state,41,126260.0,0.6066364424743875,0.00013604764352419545 +1527,irs_soi.ty2022.historic_table_2.state_agi.nm.50k_to_75k.taxable_interest_amount,soi,soi_agi_band_amount,state,35,83725056.58156574,0.12313357197688528,2.761461582796261e-05 +1528,irs_soi.ty2022.historic_table_2.state_broad.ok.all.taxable_income_amount,soi,soi_tax_amount,state,40,103330132167.59068,4.3257582593903505,0.0009701184703723594 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+1830,irs_soi.ty2022.historic_table_2.state_broad.vt.all.medical_dental_expense_amount,soi,soi_deduction_amount,state,50,183666158.069765,0.1823741681488359,4.0900239548965216e-05 +1831,irs_soi.ty2022.historic_table_2.state_broad.nv.all.tax_exempt_interest_returns,soi,soi_income_count,state,32,47810.0,0.3732974089311665,8.371774140640648e-05 +1832,irs_soi.ty2022.historic_table_2.state_broad.mt.all.return_count,soi,soi_return_count,state,30,550380.0,1.2665633753811085,0.0002840465071498337 +1833,irs_soi.ty2022.historic_table_2.state_agi.or.25k_to_50k.taxable_interest_amount,soi,soi_agi_band_amount,state,41,167473128.60324246,0.17414913433829088,3.9055647978984276e-05 +1834,irs_soi.ty2022.historic_table_2.state_agi.dc.10k_to_25k.taxable_interest_amount,soi,soi_agi_band_amount,state,11,5879166.3127983995,0.03262921504583095,7.3176082183967155e-06 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+1840,irs_soi.ty2022.historic_table_2.state_agi.wv.200k_to_500k.taxable_interest_amount,soi,soi_agi_band_amount,state,54,112709167.49468319,0.14286583183583124,3.203988155098256e-05 +1841,irs_soi.ty2022.historic_table_2.state_broad.mi.all.unemployment_compensation_returns,soi,soi_income_count,state,26,173540.0,0.7112059809168392,0.00015949898652541807 +1842,irs_soi.ty2022.historic_table_2.state_broad.ut.all.unemployment_compensation_returns,soi,soi_income_count,state,49,23950.0,0.26420945943686697,5.925307455413029e-05 +1843,irs_soi.ty2022.historic_table_2.state_broad.ga.all.itemized_deductions_amount,soi,soi_deduction_amount,state,13,23724929231.347847,2.0727704856048903,0.00046485097232673025 +1844,irs_soi.ty2022.historic_table_2.state_broad.mi.all.actc_claims,soi,soi_credit_count,state,26,489550.0,1.1945219404537186,0.0002678900965359315 +1845,irs_soi.ty2022.historic_table_2.state_eitc.ok.ok.eitc_claims,soi,soi_eitc_count,state,40,322440.0,0.969438046198203,0.00021741153760892643 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+1852,irs_soi.ty2022.historic_table_2.state_agi.mo.10k_to_25k.taxable_interest_returns,soi,soi_agi_band_count,state,29,92376.36723571514,0.5188908867765449,0.0001163693399364308 +1853,irs_soi.ty2022.historic_table_2.state_agi.tx.50k_to_75k.taxable_interest_amount,soi,soi_agi_band_amount,state,48,833816150.6968715,0.3885830965105925,8.71457942387514e-05 +1854,irs_soi.ty2022.historic_table_2.state_broad.ri.all.limited_state_local_taxes_returns,soi,soi_deduction_count,state,44,46070.0,0.36644154855000904,8.218020824176026e-05 +1855,irs_soi.ty2022.historic_table_2.state_broad.pa.all.income_tax_before_credits_returns,soi,soi_tax_count,state,42,5034800.0,3.830775625294661,0.0008591109274040504 +1856,irs_soi.ty2022.historic_table_2.state_eitc.nv.nv.eitc_two_children_claims,soi,soi_eitc_count,state,32,56460.0,0.40566360011736396,9.09763624394178e-05 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+1939,irs_soi.ty2022.historic_table_2.state_broad.al.all.actc_claims,soi,soi_credit_count,state,01,330180.0,0.9810044676600311,0.00022000548725275424 +1940,irs_soi.ty2022.historic_table_2.state_agi.hi.1_to_10k.taxable_interest_returns,soi,soi_agi_band_count,state,15,18869.529430603103,0.2345179766442886,5.2594298417647145e-05 +1941,irs_soi.ty2022.historic_table_2.state_broad.in.all.itemized_deductions_amount,soi,soi_deduction_amount,state,18,7879212865.022589,1.1945110455890804,0.0002678876531933349 +1942,irs_soi.ty2022.historic_table_2.state_agi.nh.10k_to_25k.taxable_interest_amount,soi,soi_agi_band_amount,state,33,28106966.917776085,0.07134371900311202,1.599993698208388e-05 +1943,irs_soi.ty2022.historic_table_2.state_agi.ca.25k_to_50k.taxable_interest_returns,soi,soi_agi_band_count,state,06,860945.5523258458,1.584103111580412,0.00035525972450782957 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+1949,irs_soi.ty2022.historic_table_2.state_agi.nm.200k_to_500k.taxable_interest_returns,soi,soi_agi_band_count,state,35,30040.115628638603,0.29590098008478044,6.63603902410362e-05 +1950,irs_soi.ty2022.historic_table_2.state_agi.pa.500k_plus.taxable_interest_returns,soi,soi_agi_band_count,state,42,81665.74635171639,0.48788287164327826,0.00010941531097629026 +1951,irs_soi.ty2022.historic_table_2.state_broad.ny.all.wages_salaries_amount,soi,soi_income_amount,state,36,731878945182.9071,11.512466513616898,0.0025818494087501455 +1952,irs_soi.ty2022.historic_table_2.state_eitc.ut.ut.eitc_one_child_amount,soi,soi_eitc_amount,state,49,131786297.01499026,0.154484133344724,3.4645466101081855e-05 +1953,irs_soi.ty2022.historic_table_2.state_agi.wv.500k_plus.taxable_interest_returns,soi,soi_agi_band_count,state,54,4336.815818060841,0.11242971060708962,2.521410868066598e-05 +1954,irs_soi.ty2022.historic_table_2.state_agi.co.1_to_10k.taxable_interest_amount,soi,soi_agi_band_amount,state,08,55907061.30073885,0.10061948393008478,2.256548193094523e-05 +1955,irs_soi.ty2022.historic_table_2.state_broad.sc.all.medical_dental_expense_returns,soi,soi_deduction_count,state,45,54430.0,0.39830409439776177,8.932587898581785e-05 +1956,irs_soi.ty2022.historic_table_2.state_broad.ne.all.wages_salaries_returns,soi,soi_income_count,state,31,780910.0,1.5086763964381729,0.0003383441122310323 +1957,irs_soi.ty2022.historic_table_2.state_eitc.me.me.eitc_one_child_claims,soi,soi_eitc_count,state,23,27790.0,0.2846032643284168,6.382670202476269e-05 +1958,irs_soi.ty2022.historic_table_2.state_eitc.sd.sd.eitc_claims,soi,soi_eitc_count,state,46,52530.0,0.39129050007966076,8.77529715361428e-05 +1959,irs_soi.ty2022.historic_table_2.state_broad.in.all.schedule_c_income_amount,soi,soi_income_amount,state,18,6971785281.700457,1.1236233477307116,0.000251989986035145 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+1965,irs_soi.ty2022.historic_table_2.state_broad.de.all.income_tax_before_credits_amount,soi,soi_tax_amount,state,10,6471752466.230504,1.0825792588254466,0.00024278521166751438 +1966,irs_soi.ty2022.historic_table_2.state_broad.ar.all.limited_state_local_taxes_amount,soi,soi_deduction_amount,state,05,590023485.5492861,0.3268762768373325,7.330708159617235e-05 +1967,irs_soi.ty2022.historic_table_2.state_eitc.wi.wi.eitc_no_children_amount,soi,soi_eitc_amount,state,55,37010629.277311936,0.0818675866006993,1.8360077730589662e-05 +1968,irs_soi.ty2022.historic_table_2.state_agi.tx.under_1.taxable_interest_returns,soi,soi_agi_band_count,state,48,56148.62297777255,0.40454343573842416,9.072514818085314e-05 +1969,irs_soi.ty2022.historic_table_2.state_broad.ri.all.income_tax_liability_amount,soi,soi_tax_amount,state,44,7124576945.396854,1.1358691225136366,0.00025473629121184943 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| +| w_proj_chi_s050_0.03 | 95.5% | 211 | 12 | 48 | 19 | 302 | 401 | 426 | 0 | 117 | +| w_pop4_proj_chi_s050_0.1 | 93.6% | 212 | 8 | 47 | 27 | 245 | 395 | 425 | 0 | 28 | +| w_pop8_proj_chi_s050_0.1 | 93.7% | 212 | 7 | 47 | 27 | 233 | 393 | 425 | 0 | 23 | +| w_proj_chi_s050_0.1 | 92.3% | 216 | 8 | 47 | 28 | 240 | 396 | 426 | 0 | 28 | +| w_proj_chi_s050_0.2 | 89.2% | 219 | 4 | 47 | 31 | 178 | 391 | 422 | 0 | 10 | +| w_proj_chi_s050_0.3 | 85.7% | 228 | 3 | 47 | 34 | 125 | 388 | 421 | 0 | 2 | +| w_pop4_proj_s050_0 | 97.2% | 208 | 20 | 50 | 11 | 348 | 410 | 427 | 21 | 367 | +| w_pop8_proj_s050_0 | 96.8% | 208 | 20 | 50 | 12 | 348 | 406 | 427 | 18 | 372 | +| w_release_repro | 97.2% | 208 | 22 | 50 | 11 | 351 | 413 | 428 | 28 | 368 | +| w_soft_chi_s050_0 | 97.2% | 207 | 22 | 50 | 11 | 343 | 411 | 428 | 17 | 325 | +| w_soft_chi_s050_0.01 | 96.6% | 210 | 15 | 49 | 16 | 307 | 405 | 427 | 0 | 202 | +| w_soft_chi_s050_0.03 | 95.3% | 215 | 8 | 48 | 20 | 278 | 399 | 426 | 0 | 79 | +| w_soft_chi_s050_0.1 | 91.7% | 224 | 4 | 47 | 29 | 175 | 389 | 423 | 0 | 10 | +| w_soft_chi_s050_0.3 | 83.3% | 248 | 2 | 46 | 37 | 36 | 374 | 420 | 0 | 0 | +| w_soft_chi_s050_1 | 65.5% | 314 | 0 | 45 | 44 | 7 | 348 | 417 | 0 | 0 | +| w_soft_chi_s900_0 | 95.8% | 609 | 0 | 11 | 64 | 0 | 16 | 222 | 371 | 436 | +| w_soft_chi_s900_0.01 | 95.2% | 630 | 0 | 9 | 75 | 0 | 1 | 84 | 220 | 436 | diff --git a/experiments/us-acs-local-l2-basis-20260928/results/weighted_frontier.csv b/experiments/us-acs-local-l2-basis-20260928/results/weighted_frontier.csv new file mode 100644 index 000000000..683c32e9e --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/results/weighted_frontier.csv @@ -0,0 +1,45 @@ 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diff --git a/experiments/us-acs-local-l2-basis-20260928/results/weighted_frontier.md b/experiments/us-acs-local-l2-basis-20260928/results/weighted_frontier.md new file mode 100644 index 000000000..79f6ad310 --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/results/weighted_frontier.md @@ -0,0 +1,63 @@ +### `projection` parametrization, `chi_square` basis + +| Population × | Prior ACS share | λ | National ESS | Top-1% share | Donor mass | χ² from prior | State ESS median | CD ESS median / min | MA ESS | Weighted loss (default weights) | Training objective (with multipliers) | Unweighted loss | Within 10% | Weighted within 10% (training weights) | SOI within 10% | SNAP | Medicaid | State pop | CD pop | +|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| 1 | 0.5 | 0.01 | 17,758 | 66.1% | 65.1% | 0.453 | 354 | 40 / 16 | 546 | 0.0113 | 0.0113 | 0.0192 | 96.5% | 98.3% | 97.0% | 100% | 100% | 100% | 91% | +| 1 | 0.5 | 0.03 | 19,618 | 65.2% | 64.3% | 0.332 | 382 | 44 / 19 | 580 | 0.0134 | 0.0134 | 0.0227 | 95.5% | 97.9% | 96.7% | 100% | 100% | 100% | 83% | +| 1 | 0.5 | 0.1 | 21,751 | 63.5% | 62.8% | 0.236 | 415 | 49 / 28 | 609 | 0.0192 | 0.0192 | 0.0314 | 92.3% | 96.0% | 94.6% | 99% | 100% | 100% | 69% | +| 1 | 0.5 | 0.2 | 23,363 | 62.1% | 61.4% | 0.183 | 438 | 52 / 31 | 645 | 0.0268 | 0.0268 | 0.0418 | 89.2% | 94.2% | 92.0% | 96% | 98% | 100% | 61% | +| 1 | 0.5 | 0.3 | 24,816 | 60.7% | 60.0% | 0.142 | 460 | 55 / 34 | 684 | 0.0350 | 0.0350 | 0.0530 | 85.7% | 91.5% | 88.3% | 91% | 98% | 100% | 58% | +| 4 | 0.5 | 0.03 | 19,711 | 65.2% | 64.3% | 0.329 | 386 | 44 / 20 | 576 | 0.0141 | 0.0121 | 0.0204 | 96.7% | 98.2% | 96.3% | 100% | 100% | 100% | 98% | +| 4 | 0.5 | 0.1 | 21,904 | 63.5% | 62.7% | 0.238 | 422 | 49 / 27 | 608 | 0.0205 | 0.0179 | 0.0297 | 93.6% | 96.4% | 94.0% | 99% | 98% | 100% | 87% | +| 8 | 0.5 | 0.03 | 19,874 | 65.1% | 64.3% | 0.313 | 400 | 44 / 22 | 574 | 0.0153 | 0.0112 | 0.0215 | 96.4% | 98.4% | 95.8% | 100% | 100% | 100% | 99% | +| 8 | 0.5 | 0.1 | 22,146 | 63.4% | 62.6% | 0.232 | 430 | 49 / 27 | 609 | 0.0222 | 0.0164 | 0.0302 | 93.7% | 96.6% | 93.3% | 98% | 96% | 100% | 94% | + +### `projection` parametrization, `record` basis + +| Population × | Prior ACS share | λ | National ESS | Top-1% share | Donor mass | χ² from prior | State ESS median | CD ESS median / min | MA ESS | Weighted loss (default weights) | Training objective (with multipliers) | Unweighted loss | Within 10% | Weighted within 10% (training weights) | SOI within 10% | SNAP | Medicaid | State pop | CD pop | +|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| 1 | 0.5 | 0 | 13,707 | 68.6% | 67.4% | 0.757 | 306 | 34 / 11 | 478 | 0.0097 | 0.0097 | 0.0169 | 97.2% | 98.3% | 97.0% | 100% | 100% | 100% | 97% | +| 4 | 0.5 | 0 | 14,201 | 69.8% | 68.6% | 0.639 | 324 | 34 / 11 | 483 | 0.0111 | 0.0095 | 0.0177 | 97.2% | 98.4% | 96.7% | 100% | 100% | 100% | 100% | +| 8 | 0.5 | 0 | 14,114 | 71.3% | 70.3% | 0.584 | 346 | 34 / 12 | 472 | 0.0121 | 0.0088 | 0.0188 | 96.8% | 98.6% | 96.3% | 100% | 100% | 100% | 100% | + +### `softmax` parametrization, `chi_square` basis + +| Population × | Prior ACS share | λ | National ESS | Top-1% share | Donor mass | χ² from prior | State ESS median | CD ESS median / min | MA ESS | Weighted loss (default weights) | Training objective (with multipliers) | Unweighted loss | Within 10% | Weighted within 10% (training weights) | SOI within 10% | SNAP | Medicaid | State pop | CD pop | +|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| 1 | 0.5 | 0 | 14,424 | 65.6% | 64.3% | 0.772 | 315 | 36 / 11 | 497 | 0.0093 | 0.0093 | 0.0161 | 97.2% | 98.3% | 97.0% | 100% | 100% | 100% | 97% | +| 1 | 0.5 | 0.01 | 18,167 | 64.6% | 63.7% | 0.449 | 356 | 41 / 16 | 560 | 0.0113 | 0.0113 | 0.0189 | 96.6% | 98.3% | 96.9% | 100% | 100% | 100% | 92% | +| 1 | 0.5 | 0.03 | 20,387 | 63.3% | 62.5% | 0.312 | 397 | 46 / 20 | 605 | 0.0139 | 0.0139 | 0.0231 | 95.3% | 97.8% | 96.4% | 100% | 100% | 100% | 83% | +| 1 | 0.5 | 0.1 | 23,507 | 60.6% | 59.9% | 0.194 | 447 | 53 / 29 | 651 | 0.0208 | 0.0208 | 0.0332 | 91.7% | 95.5% | 93.9% | 99% | 98% | 100% | 69% | +| 1 | 0.5 | 0.3 | 28,506 | 56.6% | 55.9% | 0.087 | 535 | 64 / 37 | 772 | 0.0408 | 0.0408 | 0.0593 | 83.3% | 88.9% | 85.6% | 81% | 94% | 100% | 60% | +| 1 | 0.5 | 1 | 33,058 | 53.2% | 52.5% | 0.024 | 629 | 73 / 44 | 901 | 0.0742 | 0.0742 | 0.1103 | 65.5% | 76.6% | 65.9% | 58% | 78% | 100% | 58% | +| 1 | 0.9 | 0 | 81,714 | 30.0% | 24.3% | 1.736 | 1,564 | 198 / 64 | 2,503 | 0.0170 | 0.0170 | 0.0303 | 95.8% | 97.1% | 95.1% | 100% | 100% | 100% | 100% | +| 1 | 0.9 | 0.01 | 112,432 | 26.0% | 21.8% | 1.188 | 1,825 | 270 / 75 | 3,054 | 0.0199 | 0.0199 | 0.0328 | 95.2% | 96.3% | 94.6% | 100% | 100% | 100% | 98% | + +### Rotated holdout (each fold is 20% of targets, never seen by the solve) + +| Fold | Population × | Parametrization | Prior ACS share | λ | National ESS | Training objective (run's own weights) | Held-out weighted error | Held-out capped error | Held-out within 10% | Held-out SOI within 10% | Held-out CD pop within 10% | Prior: held-out weighted error | +|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| 0 | 1 | projection | 0.5 | 0 | 14,471 | 0.0084 | 0.0786 | 0.1314 | 62.3% | 62.8% | 51.1% | 0.1580 | +| 0 | 1 | projection | 0.5 | 0.03 | 20,497 | 0.0120 | 0.0723 | 0.1201 | 64.0% | 65.1% | 50.0% | 0.1580 | +| 0 | 1 | projection | 0.5 | 0.1 | 22,443 | 0.0176 | 0.0713 | 0.1182 | 64.3% | 65.5% | 51.1% | 0.1580 | +| 0 | 1 | projection | 0.5 | 0.2 | 24,007 | 0.0251 | 0.0750 | 0.1221 | 63.1% | 64.2% | 51.1% | 0.1580 | +| 0 | 1 | projection | 0.5 | 0.3 | 25,245 | 0.0325 | 0.0803 | 0.1273 | 59.6% | 60.7% | 47.8% | 0.1580 | +| 0 | 1 | softmax | 0.5 | 0 | 15,171 | 0.0081 | 0.0833 | 0.1387 | 60.1% | 61.8% | 43.3% | 0.1580 | +| 0 | 1 | softmax | 0.5 | 0.03 | 21,351 | 0.0126 | 0.0739 | 0.1217 | 62.9% | 63.8% | 50.0% | 0.1580 | +| 0 | 1 | softmax | 0.5 | 0.1 | 24,182 | 0.0188 | 0.0744 | 0.1216 | 62.8% | 63.8% | 52.2% | 0.1580 | +| 0 | 1 | softmax | 0.9 | 0 | 91,910 | 0.0158 | 0.1108 | 0.1697 | 57.2% | 54.3% | 77.8% | 0.2649 | +| 0 | 8 | projection | 0.5 | 0 | 15,113 | 0.0078 | 0.0719 | 0.1218 | 64.8% | 65.5% | 55.6% | 0.1580 | +| 0 | 8 | projection | 0.5 | 0.03 | 20,780 | 0.0099 | 0.0706 | 0.1168 | 64.9% | 65.5% | 55.6% | 0.1580 | +| 0 | 8 | projection | 0.5 | 0.1 | 22,904 | 0.0147 | 0.0734 | 0.1207 | 62.7% | 63.6% | 52.2% | 0.1580 | +| 1 | 1 | projection | 0.5 | 0 | 14,730 | 0.0083 | 0.0783 | 0.1225 | 63.9% | 63.8% | 55.8% | 0.1572 | +| 1 | 1 | projection | 0.5 | 0.03 | 20,278 | 0.0116 | 0.0719 | 0.1150 | 65.9% | 65.4% | 61.6% | 0.1572 | +| 1 | 1 | projection | 0.5 | 0.1 | 22,105 | 0.0174 | 0.0704 | 0.1134 | 66.3% | 66.3% | 55.8% | 0.1572 | +| 1 | 1 | projection | 0.5 | 0.2 | 23,751 | 0.0248 | 0.0735 | 0.1176 | 64.6% | 64.4% | 57.0% | 0.1572 | +| 1 | 1 | projection | 0.5 | 0.3 | 25,030 | 0.0325 | 0.0771 | 0.1224 | 63.1% | 62.8% | 55.8% | 0.1572 | +| 1 | 1 | softmax | 0.5 | 0 | 15,096 | 0.0078 | 0.0823 | 0.1274 | 63.2% | 62.8% | 57.0% | 0.1572 | +| 1 | 1 | softmax | 0.5 | 0.03 | 20,936 | 0.0123 | 0.0732 | 0.1154 | 65.7% | 65.4% | 60.5% | 0.1572 | +| 1 | 1 | softmax | 0.5 | 0.1 | 23,866 | 0.0190 | 0.0737 | 0.1164 | 65.4% | 65.1% | 59.3% | 0.1572 | +| 1 | 1 | softmax | 0.9 | 0 | 88,414 | 0.0160 | 0.1068 | 0.1733 | 57.6% | 55.6% | 72.1% | 0.2612 | +| 1 | 8 | projection | 0.5 | 0 | 15,093 | 0.0079 | 0.0722 | 0.1147 | 67.2% | 67.0% | 61.6% | 0.1572 | +| 1 | 8 | projection | 0.5 | 0.03 | 20,427 | 0.0098 | 0.0702 | 0.1119 | 68.2% | 67.1% | 68.6% | 0.1572 | +| 1 | 8 | projection | 0.5 | 0.1 | 22,664 | 0.0150 | 0.0714 | 0.1137 | 67.7% | 66.8% | 66.3% | 0.1572 | diff --git a/experiments/us-acs-local-l2-basis-20260928/results/weighted_frontier.png b/experiments/us-acs-local-l2-basis-20260928/results/weighted_frontier.png new file mode 100644 index 000000000..89a36ccc3 Binary files /dev/null and b/experiments/us-acs-local-l2-basis-20260928/results/weighted_frontier.png differ diff --git a/experiments/us-acs-local-l2-basis-20260928/results/weighted_key_configs.md b/experiments/us-acs-local-l2-basis-20260928/results/weighted_key_configs.md new file mode 100644 index 000000000..bc1b4021c --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/results/weighted_key_configs.md @@ -0,0 +1,22 @@ +| Configuration | National ESS | CD ESS median / min | CDs < 50 | CDs < ¼ of prior | MA ESS | Weighted loss (default weights) | Train within 10% | Trained CD populations > 10% off / worst | Held-out weighted error, fold 0 / 1 | Held-out within 10%, fold 0 / 1 | +|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| Release settings: share 0.5, projection, λ 0 | 13,707 | 34 / 11 | 351 | 28 | 478 | 0.0097 | 97.2% | 11 / 33% | 0.0786 / 0.0783 | 62.3% / 63.9% | +| Share 0.5, projection, chi-square λ 0.01 | 17,758 | 40 / 16 | 320 | 0 | 546 | 0.0113 | 96.5% | 38 / 45% | – / – | – / – | +| Share 0.5, projection, chi-square λ 0.03 | 19,618 | 44 / 19 | 302 | 0 | 580 | 0.0134 | 95.5% | 73 / 46% | 0.0723 / 0.0719 | 64.0% / 65.9% | +| Share 0.5, projection, chi-square λ 0.1 | 21,751 | 49 / 28 | 240 | 0 | 609 | 0.0192 | 92.3% | 135 / 50% | 0.0713 / 0.0704 | 64.3% / 66.3% | +| Share 0.5, projection, chi-square λ 0.2 | 23,363 | 52 / 31 | 178 | 0 | 645 | 0.0268 | 89.2% | 171 / 51% | 0.0750 / 0.0735 | 63.1% / 64.6% | +| Share 0.5, projection, chi-square λ 0.3 | 24,816 | 55 / 34 | 125 | 0 | 684 | 0.0350 | 85.7% | 182 / 52% | 0.0803 / 0.0771 | 59.6% / 63.1% | +| Population ×4: share 0.5, projection, λ 0 | 14,201 | 34 / 11 | 348 | 21 | 483 | 0.0111 | 97.2% | 0 / 0% | – / – | – / – | +| Population ×4: projection, chi-square λ 0.03 | 19,711 | 44 / 20 | 301 | 0 | 576 | 0.0141 | 96.7% | 8 / 32% | – / – | – / – | +| Population ×4: projection, chi-square λ 0.1 | 21,904 | 49 / 27 | 245 | 0 | 608 | 0.0205 | 93.6% | 55 / 41% | – / – | – / – | +| Population ×8: share 0.5, projection, λ 0 | 14,114 | 34 / 12 | 348 | 18 | 472 | 0.0121 | 96.8% | 0 / 0% | 0.0719 / 0.0722 | 64.8% / 67.2% | +| Population ×8: projection, chi-square λ 0.03 | 19,874 | 44 / 22 | 301 | 0 | 574 | 0.0153 | 96.4% | 3 / 15% | 0.0706 / 0.0702 | 64.9% / 68.2% | +| Population ×8: projection, chi-square λ 0.1 | 22,146 | 49 / 27 | 233 | 0 | 609 | 0.0222 | 93.7% | 24 / 36% | 0.0734 / 0.0714 | 62.7% / 67.7% | +| Share 0.5, softmax, no penalty | 14,424 | 36 / 11 | 343 | 17 | 497 | 0.0093 | 97.2% | 13 / 35% | 0.0833 / 0.0823 | 60.1% / 63.2% | +| Share 0.5, softmax, chi-square λ 0.01 | 18,167 | 41 / 16 | 307 | 0 | 560 | 0.0113 | 96.6% | 36 / 46% | – / – | – / – | +| Share 0.5, softmax, chi-square λ 0.03 | 20,387 | 46 / 20 | 278 | 0 | 605 | 0.0139 | 95.3% | 73 / 45% | 0.0739 / 0.0732 | 62.9% / 65.7% | +| Share 0.5, softmax, chi-square λ 0.1 | 23,507 | 53 / 29 | 175 | 0 | 651 | 0.0208 | 91.7% | 136 / 46% | 0.0744 / 0.0737 | 62.8% / 65.4% | +| Share 0.5, softmax, chi-square λ 0.3 | 28,506 | 64 / 37 | 36 | 0 | 772 | 0.0408 | 83.3% | 173 / 47% | – / – | – / – | +| Share 0.5, softmax, chi-square λ 1 | 33,058 | 73 / 44 | 7 | 0 | 901 | 0.0742 | 65.5% | 182 / 45% | – / – | – / – | +| Share 0.9, softmax, no penalty | 81,714 | 198 / 64 | 0 | 371 | 2,503 | 0.0170 | 95.8% | 2 / 12% | 0.1108 / 0.1068 | 57.2% / 57.6% | +| Share 0.9, softmax, chi-square λ 0.01 | 112,432 | 270 / 75 | 0 | 220 | 3,054 | 0.0199 | 95.2% | 7 / 16% | – / – | – / – | diff --git a/experiments/us-acs-local-l2-basis-20260928/results/weighted_lambda.md b/experiments/us-acs-local-l2-basis-20260928/results/weighted_lambda.md new file mode 100644 index 000000000..09a065092 --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/results/weighted_lambda.md @@ -0,0 +1,17 @@ +| Configuration | Loss trained on | National ESS | Held-out weighted error, fold 0 / 1 (mean) | vs release settings, fold 0 / 1 | Held-out capped error, fold 0 / 1 (mean) | Held-out within 10%, fold 0 / 1 (mean) | Trained CD populations > 10% off | CDs < ¼ of prior: full / fold 0 / 1 | +|---|---|---:|---:|---:|---:|---:|---:|---:| +| Release settings: share 0.5, projection, λ 0 | weighted | 13,707 | 0.0786 / 0.0783 (0.0784) | +0.0% / +0.0% | 0.1314 / 0.1225 (0.1270) | 62.3% / 63.9% (63.1%) | 11 | 28 / 21 / 17 | +| Share 0.5, projection, chi-square λ 0.03 | weighted | 19,618 | 0.0723 / 0.0719 (0.0721) | -8.1% / -8.2% | 0.1201 / 0.1150 (0.1175) | 64.0% / 65.9% (65.0%) | 73 | 0 / 0 / 0 | +| Share 0.5, projection, chi-square λ 0.1 | weighted | 21,751 | 0.0713 / 0.0704 (0.0708) | -9.3% / -10.1% | 0.1182 / 0.1134 (0.1158) | 64.3% / 66.3% (65.3%) | 135 | 0 / 0 / 0 | +| Share 0.5, projection, chi-square λ 0.2 | weighted | 23,363 | 0.0750 / 0.0735 (0.0742) | -4.5% / -6.1% | 0.1221 / 0.1176 (0.1198) | 63.1% / 64.6% (63.8%) | 171 | 0 / 0 / 0 | +| Share 0.5, projection, chi-square λ 0.3 | weighted | 24,816 | 0.0803 / 0.0771 (0.0787) | +2.1% / -1.5% | 0.1273 / 0.1224 (0.1248) | 59.6% / 63.1% (61.4%) | 182 | 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"chi_square_distance_from_prior": { + "rerun": 0.2916879587747659, + "original": 0.29115880286531826, + "relative_change": 0.0018174133985995322 + }, + "donor_mass_share": { + "rerun": 0.6101527679964142, + "original": 0.6100505138562665, + "relative_change": 0.00016761585774482946 + }, + "within_10pct_soi": { + "rerun": 0.9671592775041051, + "original": 0.967487684729064, + "relative_change": -0.0003394433129666474 + }, + "weighted_within_10pct": { + "rerun": 0.9788088605406341, + "original": 0.9794522286968751, + "relative_change": -0.0006568652736611558 + }, + "holdout_weighted_capped_error": { + "rerun": 0.07381586587123028, + "original": 0.07390253003104826, + "relative_change": -0.0011726819065811522 + }, + "holdout_weighted_within_10pct": { + "rerun": 0.7894430119169832, + "original": 0.7853384147855269, + "relative_change": 0.005226532987791188 + }, + "holdout_mean_capped_error": { + "rerun": 0.12167867780121039, + "original": 0.12171812209774432, + "relative_change": -0.0003240626445276673 + }, + "holdout_within_10pct": { + "rerun": 0.6300448430493274, + "original": 0.6289237668161435, + "relative_change": 0.0017825311942960553 + } + } + } +} diff --git a/experiments/us-acs-local-l2-basis-20260928/results/weighting_shift.md b/experiments/us-acs-local-l2-basis-20260928/results/weighting_shift.md new file mode 100644 index 000000000..fc2453883 --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/results/weighting_shift.md @@ -0,0 +1,35 @@ +Full-surface weights of 4,459 targets (`microcosm.build.us_runtime.target_loss_weights:us_acs_local_target_loss_weights`, loss vector `ba36f8874071`). Under the equal-weight loss every target's share is its count share. Effective number of targets (Σw)²/Σw²: 1,405 of 4,459. + +| Family | Targets | Equal-weight share | Weighted share | Mean weight | +|---|---:|---:|---:|---:| +| soi | 3,819 | 85.65% | 90.20% | 1.053 | +| pop_state | 51 | 1.14% | 4.41% | 3.854 | +| medicaid | 51 | 1.14% | 2.02% | 1.766 | +| pop_cd | 436 | 9.78% | 1.69% | 0.173 | +| snap | 102 | 2.29% | 1.68% | 0.733 | + +| Subfamily | Targets | Equal-weight share | Weighted share | Mean weight | +|---|---:|---:|---:|---:| +| soi_income_amount | 714 | 16.01% | 28.27% | 1.766 | +| soi_income_count | 663 | 14.87% | 15.94% | 1.072 | +| soi_tax_amount | 153 | 3.43% | 12.45% | 3.629 | +| soi_tax_count | 153 | 3.43% | 8.06% | 2.349 | +| soi_agi_band_count | 456 | 10.23% | 4.98% | 0.487 | +| pop_state | 51 | 1.14% | 4.41% | 3.854 | +| soi_eitc_count | 255 | 5.72% | 3.44% | 0.601 | +| soi_deduction_amount | 204 | 4.58% | 3.35% | 0.733 | +| soi_credit_count | 153 | 3.43% | 3.11% | 0.907 | +| soi_return_count | 51 | 1.14% | 3.05% | 2.667 | +| soi_agi_band_amount | 456 | 10.23% | 2.59% | 0.253 | +| soi_deduction_count | 153 | 3.43% | 2.19% | 0.637 | +| medicaid_enrollment | 51 | 1.14% | 2.02% | 1.766 | +| pop_cd | 436 | 9.78% | 1.69% | 0.173 | +| soi_eitc_amount | 255 | 5.72% | 1.38% | 0.242 | +| soi_credit_amount | 153 | 3.43% | 1.38% | 0.402 | +| snap_households | 51 | 1.14% | 1.11% | 0.969 | +| snap_benefits | 51 | 1.14% | 0.57% | 0.498 | + +| Geography | Targets | Equal-weight share | Weighted share | Mean weight | +|---|---:|---:|---:|---:| +| state | 4,023 | 90.22% | 98.31% | 1.090 | +| congressional_district | 436 | 9.78% | 1.69% | 0.173 | diff --git a/experiments/us-acs-local-l2-basis-20260928/results/weights_check.json b/experiments/us-acs-local-l2-basis-20260928/results/weights_check.json new file mode 100644 index 000000000..82c558362 --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/results/weights_check.json @@ -0,0 +1,16 @@ +{ + "ok": true, + "module_file": "/Users/maxghenis/PolicyEngine/_worktrees/acs-local-weighted-frontier-20261002/packages/microcosm-build/src/microcosm/build/us_runtime/target_loss_weights.py", + "module_sha256": "5cb426635611ff43423e58181b32943cfeb19972893fb14aae9fa82f106c1640", + "module_loaded_from_file": false, + "recorded_module_sha256": [ + "849fbede68e8f5a4c68af26e713dad053213d767ba5f03c12ac1b8685cd33ac7" + ], + "registry_sha256": "fd25c6002cae0c991842c81399d43a3840f12cba0294021ca8ff6718e22a209f", + "n_runs": 46, + "n_distinct_weightings": 7, + "max_abs_epoch0_relative": 9.256193909346067e-06, + "max_abs_final_difference": 0.0, + "n_mismatches": 0, + "mismatches": [] +} diff --git a/experiments/us-acs-local-l2-basis-20260928/sweep.py b/experiments/us-acs-local-l2-basis-20260928/sweep.py index c1a5251ce..8fe833f5e 100644 --- a/experiments/us-acs-local-l2-basis-20260928/sweep.py +++ b/experiments/us-acs-local-l2-basis-20260928/sweep.py @@ -5,15 +5,27 @@ verified sparse checkpoint with the release's settings — Adam, 800 epochs as warm-started batches of 400, lr 0.02, ``mass="conserve"``, ``max_weight_ratio=5.0``, ``target_loss_cap=1.0``, seed 0, default target loss -weights and scales — plus the run's ``l2_lambda``, ``l2_basis`` and -``mass_parametrization``, optionally holding one rotated fold of targets out. +scales — plus the run's ``l2_lambda``, ``l2_basis``, ``mass_parametrization`` +and target-loss weighting, optionally holding one rotated fold of targets out. The batch loop mirrors ``tools/build_us_acs_local_release.py`` ``do_calibrate``. Run spec (JSON or flags):: {"run_id": "...", "l2_lambda": 0.0, "l2_basis": "record", "mass_parametrization": "projection", "epochs": 800, "epoch_batch": 400, - "holdout_fold": null, "acs_share": null} + "holdout_fold": null, "acs_share": null, "target_weighting": "equal"} + +``target_weighting`` is ``equal`` (the release's loss: no +``target_loss_weights``, every target counts once) or ``shared``: the shared +module's national-release weighting (``SHARED_TARGET_LOSS_WEIGHTS``) of the +specs ``registry.py`` rebuilt for this checkpoint (``target_registry.json``, +verified against its receipt), computed on the run's training specs and passed +to ``calibrate`` as ``target_loss_weights``. The weights, their summary by +family, subfamily and geography, and a content hash go in the metrics' +``target_loss_weights`` block and in ``weights.npz``; every fit block gains the +weighted measures (training fit with the training weights, so its weighted +capped error is the solve's loss; held-out fit with each held-out target's +full-surface weight). ``acs_share`` reseeds the prior: the ACS spine's rows are rescaled together to that share of the household mass and the donor (ASEC-by-PUF) rows to the rest, @@ -106,6 +118,27 @@ "targets_meta.parquet", "holdout_folds.npz", ) +#: The rebuilt target specs (``registry.py``) and their receipt. Only runs +#: with ``target_weighting="shared"`` read them. +REGISTRY_FILE = "target_registry.json" +REGISTRY_RECEIPT_FILE = "target_registry.receipt.json" +#: ``equal`` is the ACS local tool's loss as released: no +#: ``target_loss_weights``, every target counts once. ``shared`` weights the +#: targets like the national release, through the shared module both builds +#: call (``SHARED_TARGET_LOSS_WEIGHTS``). +TARGET_WEIGHTINGS = ("equal", "shared") +#: ``module:function`` of the shared target-loss weighting the ACS local +#: build calls: it takes the training ``TargetSpec``s and returns one positive +#: weight per spec, row-aligned (``us_acs_local.v1`` row mapping, no family +#: multipliers, as the tool's default). +SHARED_TARGET_LOSS_WEIGHTS = ( + "microcosm.build.us_runtime.target_loss_weights:us_acs_local_target_loss_weights" +) +#: Set by the Modal image: the shared module's source file, loaded on its own. +#: Importing it as a package module runs ``microcosm.build.us_runtime``'s +#: package ``__init__``, which needs the whole build stack; the module itself +#: imports only numpy and the standard library. +SHARED_WEIGHTS_FILE_ENV = "MICROCOSM_TARGET_LOSS_WEIGHTS_FILE" KERNEL_MODULES = ( "microcosm.calibrate.solve", "microcosm.calibrate.matrix", @@ -191,6 +224,13 @@ class RunSpec: epoch_batch: int = 400 holdout_fold: int | None = None acs_share: float | None = None + target_weighting: str = "equal" + #: ``(family, multiplier)`` pairs passed to the shared weighting as its + #: ``family_multipliers`` (the tool's ``--target-family-loss-multiplier``); + #: empty is the tool's default. Training weights only: held-out scoring + #: always uses the unmultiplied full-surface weights, one yardstick for + #: every run. + family_loss_multipliers: tuple[tuple[str, float], ...] = () def __post_init__(self) -> None: if not self.run_id or "/" in self.run_id: @@ -213,6 +253,18 @@ def __post_init__(self) -> None: ) if self.acs_share is not None and not (0.0 < self.acs_share < 1.0): raise ValueError(f"acs_share must be in (0, 1): {self.acs_share!r}") + if self.target_weighting not in TARGET_WEIGHTINGS: + raise ValueError(f"unknown target_weighting {self.target_weighting!r}") + if self.family_loss_multipliers and self.target_weighting == "equal": + raise ValueError("family_loss_multipliers need target_weighting='shared'") + families = [family for family, _ in self.family_loss_multipliers] + if len(set(families)) != len(families): + raise ValueError("family_loss_multipliers repeats a family") + for family, multiplier in self.family_loss_multipliers: + if not (isinstance(family, str) and family): + raise ValueError(f"bad family in family_loss_multipliers: {family!r}") + if not (math.isfinite(multiplier) and multiplier > 0): + raise ValueError(f"multiplier for {family!r} must be positive") @classmethod def from_mapping(cls, mapping: dict) -> RunSpec: @@ -231,6 +283,16 @@ def from_mapping(cls, mapping: dict) -> RunSpec: values[key] = int(values[key]) if values.get("holdout_fold") is not None: values["holdout_fold"] = int(values["holdout_fold"]) + multipliers = values.get("family_loss_multipliers") + if multipliers: + pairs = ( + multipliers.items() if isinstance(multipliers, dict) else multipliers + ) + values["family_loss_multipliers"] = tuple( + sorted((str(family), float(value)) for family, value in pairs) + ) + elif "family_loss_multipliers" in values: + values["family_loss_multipliers"] = () return cls(**values) @@ -249,6 +311,10 @@ class Inputs: fold_of_target: np.ndarray manifest: dict input_sha256: dict[str, str] + #: The rebuilt ``TargetRegistry`` (spec ``i`` = CSR row ``i``), loaded only + #: for a weighted run, with its sha256 and ``registry.py``'s receipt. + registry: object | None = None + registry_receipt: dict | None = None _matrix64: sparse.csr_array | None = None def matrix64(self) -> sparse.csr_array: @@ -297,8 +363,43 @@ def reseed_prior( return prior +def load_registry(checkpoint: Path, meta: pd.DataFrame, manifest_sha256: str): + """The rebuilt registry, checked against ``registry.py``'s receipt. + + Refuses a receipt that did not pass, one written for another checkpoint + (its ``targets_meta.parquet`` or ``MANIFEST.json`` digest differs), a + registry whose bytes are not the receipt's, and specs out of CSR row + order or with values other than the checkpoint's. + """ + + from microcosm.calibrate import TargetRegistry + + receipt = json.loads((checkpoint / REGISTRY_RECEIPT_FILE).read_text()) + if not receipt.get("ok"): + raise SystemExit(f"{REGISTRY_RECEIPT_FILE} is not ok; rerun registry.py") + if receipt["checkpoint_manifest_sha256"] != manifest_sha256: + raise SystemExit(f"{REGISTRY_RECEIPT_FILE} was written for another MANIFEST") + if receipt["targets_meta_sha256"] != sha256(checkpoint / "targets_meta.parquet"): + raise SystemExit(f"{REGISTRY_RECEIPT_FILE} was written for other targets") + path = checkpoint / REGISTRY_FILE + digest = sha256(path) + if digest != receipt["registry"]["sha256"]: + raise SystemExit(f"{REGISTRY_FILE}: sha256 {digest} != receipt") + registry = TargetRegistry.from_json(path) + if [spec.name for spec in registry.specs] != meta["name"].tolist(): + raise SystemExit(f"{REGISTRY_FILE} is not in CSR row order") + values = np.asarray([float(spec.value) for spec in registry.specs]) + if not np.array_equal(values, meta["value"].to_numpy(np.float64)): + raise SystemExit(f"{REGISTRY_FILE} values differ from targets_meta") + return registry, {**receipt, "verified_registry_sha256": digest} + + def load_inputs( - checkpoint: Path, *, verify: bool = True, log: Callable = print + checkpoint: Path, + *, + verify: bool = True, + log: Callable = print, + need_registry: bool = False, ) -> Inputs: started = time.time() manifest = json.loads((checkpoint / "MANIFEST.json").read_text()) @@ -331,9 +432,15 @@ def load_inputs( raise SystemExit("household ids must be strictly increasing (Frame contract)") if not np.array_equal(meta["fold"].to_numpy(), fold_of_target): raise SystemExit("targets_meta fold column differs from holdout_folds.npz") + registry, registry_receipt = None, None + if need_registry: + registry, registry_receipt = load_registry( + checkpoint, meta, sha256(checkpoint / "MANIFEST.json") + ) log( f"inputs: {n_targets} targets x {n_households:,} households, nnz " - f"{matrix.nnz:,}, verified={verify}, {time.time() - started:.1f}s" + f"{matrix.nnz:,}, verified={verify}, registry={need_registry}, " + f"{time.time() - started:.1f}s" ) return Inputs( checkpoint=checkpoint, @@ -344,6 +451,8 @@ def load_inputs( fold_of_target=fold_of_target, manifest=manifest, input_sha256=digests, + registry=registry, + registry_receipt=registry_receipt, ) @@ -415,6 +524,220 @@ def training_rows(inputs: Inputs, holdout_fold: int | None) -> np.ndarray: return np.flatnonzero(inputs.fold_of_target != holdout_fold).astype(np.int64) +# --------------------------------------------------------------------------- +# Target-loss weights +# --------------------------------------------------------------------------- + + +def shared_weight_module(): + """The shared target-loss weighting module. + + Imported by name, or, when ``SHARED_WEIGHTS_FILE_ENV`` names its source + file (the Modal image), executed from that file alone. Either way it is + the committed module at the launch's HEAD; the metrics record its path and + sha256. + """ + + import importlib + import importlib.util + + module_name, _, _ = SHARED_TARGET_LOSS_WEIGHTS.partition(":") + path = os.environ.get(SHARED_WEIGHTS_FILE_ENV) + if not path: + return importlib.import_module(module_name) + if module_name in sys.modules: + return sys.modules[module_name] + spec = importlib.util.spec_from_file_location(module_name, path) + if spec is None or spec.loader is None: + raise SystemExit(f"cannot load the shared weighting from {path}") + module = importlib.util.module_from_spec(spec) + sys.modules[module_name] = module + spec.loader.exec_module(module) + return module + + +def shared_weight_function(): + """The shared module and its weighting function.""" + + module = shared_weight_module() + _, _, function_name = SHARED_TARGET_LOSS_WEIGHTS.partition(":") + return module, getattr(module, function_name) + + +def target_loss_weights_for( + inputs: Inputs, + rows: np.ndarray, + family_multipliers: dict[str, float] | None = None, +) -> np.ndarray: + """The shared weighting of the specs at ``rows``, in that order. + + The function sees exactly the specs a build calibrating on ``rows`` would + weight, so a holdout run's training rows are weighted among themselves. + """ + + if inputs.registry is None: + raise SystemExit("a weighted run needs the rebuilt target registry") + _, function = shared_weight_function() + specs = inputs.registry.specs + weights = np.asarray( + function([specs[int(i)] for i in rows], family_multipliers or None), + dtype=np.float64, + ) + if weights.shape != (len(rows),): + raise SystemExit( + f"the shared weighting returned shape {weights.shape} for {len(rows)} specs" + ) + if not (np.isfinite(weights).all() and (weights > 0).all()): + raise SystemExit("the shared weighting returned a non-positive or NaN weight") + return weights + + +def loss_vector_sha256(names: Sequence[str], weights: np.ndarray) -> str: + """Content address of a weight vector, as the builds record it. + + The national ``loss_vector_sha256`` form: one ``{"row_name", + "weight_hex"}`` object per row, in row order, compact sorted-key JSON. + Row names are ``name@period`` (the shared module's ``target_row_name``). + """ + + vector = [ + {"row_name": str(name), "weight_hex": float(weight).hex()} + for name, weight in zip(names, weights, strict=True) + ] + return hashlib.sha256( + json.dumps( + vector, sort_keys=True, separators=(",", ":"), allow_nan=False + ).encode("utf-8") + ).hexdigest() + + +def row_names(inputs: Inputs, rows: np.ndarray) -> list[str]: + """``name@period`` of the targets at ``rows`` (every target is 2024's).""" + + names = inputs.meta["name"].to_numpy() + return [f"{names[int(i)]}@2024" for i in rows] + + +def weight_summary( + weights: np.ndarray, meta: pd.DataFrame, rows: np.ndarray, names: Sequence[str] +) -> dict: + """How a weight vector over ``rows`` divides the loss among the targets.""" + + subset = meta.iloc[rows] + weights = np.asarray(weights, dtype=np.float64) + total = float(weights.sum()) + + def shares(column: str) -> dict: + labels = subset[column].astype(str).to_numpy() + out = {} + for label in sorted(set(labels.tolist())): + mask = labels == label + out[label] = { + "n": int(mask.sum()), + "weight_share": float(weights[mask].sum() / total), + "count_share": float(mask.mean()), + "mean_weight": float(weights[mask].mean()), + } + return out + + order = np.argsort(-weights, kind="stable") + return { + "n": int(weights.size), + "sum": total, + "mean": float(weights.mean()), + "min": float(weights.min()), + "max": float(weights.max()), + "effective_n_targets": total**2 / float(np.square(weights).sum()), + "loss_vector_sha256": loss_vector_sha256(names, weights), + "per_family": shares("family"), + "per_subfamily": shares("subfamily"), + "per_geography_level": shares("geography_level"), + "largest": [ + {"row": int(rows[i]), "name": str(names[i]), "weight": float(weights[i])} + for i in order[:10] + ], + "smallest": [ + {"row": int(rows[i]), "name": str(names[i]), "weight": float(weights[i])} + for i in order[::-1][:10] + ], + } + + +@dataclasses.dataclass(frozen=True) +class LossWeights: + """A run's target-loss weights: on its training rows and on every row. + + ``train`` is what ``calibrate`` receives, with the run's family + multipliers. ``full`` weights the whole surface at once without + multipliers; held-out fit is weighted with its held-out rows, so every + fold and every run scores a target with the weight the default + full-surface objective gives it. + """ + + train: np.ndarray | None + full: np.ndarray | None + + @property + def weighted(self) -> bool: + return self.train is not None + + +def loss_weights_for(spec: RunSpec, inputs: Inputs, train: np.ndarray) -> LossWeights: + if spec.target_weighting == "equal": + return LossWeights(None, None) + full_rows = np.arange(inputs.matrix.shape[0], dtype=np.int64) + full = target_loss_weights_for(inputs, full_rows) + multipliers = dict(spec.family_loss_multipliers) + if np.array_equal(train, full_rows) and not multipliers: + return LossWeights(full, full) + return LossWeights(target_loss_weights_for(inputs, train, multipliers), full) + + +def loss_weights_block( + spec: RunSpec, inputs: Inputs, train: np.ndarray, weights: LossWeights +) -> dict: + """The metrics' record of the run's target-loss weights.""" + + if not weights.weighted: + return {"target_weighting": "equal", "target_loss_weights": None} + module, function = shared_weight_function() + full_rows = np.arange(inputs.matrix.shape[0], dtype=np.int64) + receipt = inputs.registry_receipt or {} + specs = inputs.registry.specs + train_names = row_names(inputs, train) + full_names = row_names(inputs, full_rows) + # The shared module's own digest and summary, beside the harness's. + module_digest = module.target_loss_weights_sha256( + [module.target_row_name(specs[int(i)]) for i in train], weights.train + ) + if module_digest != loss_vector_sha256(train_names, weights.train): + raise SystemExit("the harness's weight digest differs from the module's") + return { + "target_weighting": spec.target_weighting, + "function": SHARED_TARGET_LOSS_WEIGHTS, + "function_qualname": getattr(function, "__qualname__", None), + "row_mapping": module.US_ACS_LOCAL_TARGET_LOSS_ROW_MAPPING.mapping_id, + "formula": module.US_FISCAL_TARGET_LOSS_WEIGHTING, + "family_multipliers": dict(spec.family_loss_multipliers), + "module_file": getattr(module, "__file__", None), + "module_loaded_from_file": bool(os.environ.get(SHARED_WEIGHTS_FILE_ENV)) + and Path(module.__file__).resolve() + == Path(os.environ[SHARED_WEIGHTS_FILE_ENV]).resolve(), + "module_sha256": sha256(Path(module.__file__)), + "registry_sha256": receipt.get("verified_registry_sha256"), + "registry_compile_head": receipt.get("compile_head"), + "train": weight_summary(weights.train, inputs.meta, train, train_names), + "full_surface": weight_summary( + weights.full, inputs.meta, full_rows, full_names + ), + "train_distribution": module.target_loss_weight_distribution( + [specs[int(i)] for i in train], + weights.train, + row_mapping=module.US_ACS_LOCAL_TARGET_LOSS_ROW_MAPPING, + ), + } + + # --------------------------------------------------------------------------- # Metrics # --------------------------------------------------------------------------- @@ -614,12 +937,24 @@ def fit_rows(estimates: np.ndarray, targets: np.ndarray) -> dict[str, np.ndarray return {"relative": relative, "abs_relative": np.abs(relative), "capped": capped} -def fit_summary(rows: dict[str, np.ndarray], mask: np.ndarray | None = None) -> dict: +def fit_summary( + rows: dict[str, np.ndarray], + mask: np.ndarray | None = None, + loss_weights: np.ndarray | None = None, +) -> dict: + """Unweighted fit; with ``loss_weights`` (aligned with ``rows``) also weighted. + + The weighted capped error is the loss's own form, ``sum(w * capped) / + sum(w)`` over the rows in ``mask``; ``weighted_loss_numerator`` is its + numerator scaled by the whole block's weight, so each family's + ``share_of_weighted_loss`` sums to one across families. + """ + abs_rel = rows["abs_relative"] if mask is None else rows["abs_relative"][mask] capped = rows["capped"] if mask is None else rows["capped"][mask] if abs_rel.size == 0: return {"n_targets": 0} - return { + summary = { "n_targets": int(abs_rel.size), "fraction_within_10pct": float(np.mean(abs_rel <= WITHIN)), "mean_abs_rel_error": float(abs_rel.mean()), @@ -628,22 +963,60 @@ def fit_summary(rows: dict[str, np.ndarray], mask: np.ndarray | None = None) -> "max_abs_rel_error": float(abs_rel.max()), "mean_capped_scaled_error": float(capped.mean()), } + if loss_weights is not None: + all_weights = np.asarray(loss_weights, dtype=np.float64) + weights = all_weights if mask is None else all_weights[mask] + summary.update( + { + "weighted_mean_capped_scaled_error": float( + np.sum(weights * capped) / weights.sum() + ), + "weighted_fraction_within_10pct": float( + np.sum(weights * (abs_rel <= WITHIN)) / weights.sum() + ), + "weight_share": float(weights.sum() / all_weights.sum()), + "weighted_loss_numerator": float( + np.sum(weights * capped) / all_weights.sum() + ), + } + ) + return summary def fit_block( - estimates: np.ndarray, meta: pd.DataFrame, rows: np.ndarray, worst: int = 10 + estimates: np.ndarray, + meta: pd.DataFrame, + rows: np.ndarray, + worst: int = 10, + loss_weights: np.ndarray | None = None, ) -> dict: - """Fit of ``estimates`` (all targets) on the target subset ``rows``.""" + """Fit of ``estimates`` (all targets) on the target subset ``rows``. + + ``loss_weights``, aligned with ``rows``, adds the weighted measures. + """ subset = meta.iloc[rows] values = subset["value"].to_numpy(np.float64) est = estimates[rows] errors = fit_rows(est, values) - block = {"overall": fit_summary(errors), "per_family": {}, "per_subfamily": {}} + block = { + "overall": fit_summary(errors, loss_weights=loss_weights), + "per_family": {}, + "per_subfamily": {}, + } for column, key in (("family", "per_family"), ("subfamily", "per_subfamily")): labels = subset[column].to_numpy() for label in sorted(set(labels.tolist())): - block[key][label] = fit_summary(errors, labels == label) + block[key][label] = fit_summary( + errors, labels == label, loss_weights=loss_weights + ) + if loss_weights is not None: + total = block["overall"]["weighted_loss_numerator"] + for key in ("per_family", "per_subfamily"): + for entry in block[key].values(): + entry["share_of_weighted_loss"] = ( + entry["weighted_loss_numerator"] / total if total > 0 else None + ) order = np.argsort(-errors["capped"])[:worst] block["worst_targets"] = [ { @@ -655,6 +1028,19 @@ def fit_block( } for i in order ] + if loss_weights is not None: + weights = np.asarray(loss_weights, dtype=np.float64) + contribution = weights * errors["capped"] / weights.sum() + block["largest_weighted_loss_contributions"] = [ + { + "row": int(subset["row"].iloc[i]), + "name": str(subset["name"].iloc[i]), + "weight": float(weights[i]), + "relative_error": float(errors["relative"][i]), + "contribution": float(contribution[i]), + } + for i in np.argsort(-contribution, kind="stable")[:worst] + ] return block @@ -769,6 +1155,7 @@ def run_calibration( train = training_rows(inputs, spec.holdout_fold) targets = build_target_set(inputs, train) expected_names = [f"{name}@2024" for name in inputs.meta["name"].to_numpy()[train]] + loss_weights = loss_weights_for(spec, inputs, train) log( f"run {spec.run_id}: {len(train)} training targets" + ( @@ -778,7 +1165,8 @@ def run_calibration( ) + f"; l2_lambda={spec.l2_lambda} l2_basis={spec.l2_basis} " f"mass_parametrization={spec.mass_parametrization} epochs={spec.epochs} " - f"batch={spec.epoch_batch} acs_share={spec.acs_share}; " + f"batch={spec.epoch_batch} acs_share={spec.acs_share} " + f"target_weighting={spec.target_weighting}; " f"torch threads={torch.get_num_threads()}" ) @@ -789,10 +1177,23 @@ def run_calibration( options: dict | None = None result_block: dict | None = None spec_json = json.dumps(dataclasses.asdict(spec), sort_keys=True) + # The weights' content, not just the spec: a resumed weighted run must + # continue under the same weights (module, registry and multipliers). + loss_digest = ( + loss_vector_sha256(row_names(inputs, train), loss_weights.train) + if loss_weights.weighted + else "" + ) if resume and resume_path.exists(): saved = np.load(resume_path, allow_pickle=False) if str(saved["spec_json"]) != spec_json: raise SystemExit(f"{resume_path} belongs to a different run spec") + saved_digest = str(saved["loss_digest"]) if "loss_digest" in saved.files else "" + if saved_digest != loss_digest: + raise SystemExit( + f"{resume_path} was solved under other target-loss weights " + f"({saved_digest or 'none recorded'} != {loss_digest or 'none'})" + ) warm = np.asarray(saved["weights"], dtype=np.float64) done = int(saved["epochs_done"]) batches = json.loads(str(saved["batches_json"])) @@ -853,6 +1254,11 @@ def on_progress(event: dict, _stamps=stamps, _done=done) -> None: seed=RELEASE_SETTINGS["seed"], warm_start_weights=warm, progress_callback=on_progress, + **( + {"target_loss_weights": loss_weights.train} + if loss_weights.weighted + else {} + ), ) finished = time.time() if result.problem.skipped: @@ -899,6 +1305,7 @@ def on_progress(event: dict, _stamps=stamps, _done=done) -> None: weights=warm, epochs_done=np.int64(done), spec_json=np.str_(spec_json), + loss_digest=np.str_(loss_digest), batches_json=np.str_(json.dumps(jsonable(batches))), options_json=np.str_(json.dumps(options)), result_json=np.str_(json.dumps(jsonable(result_block))), @@ -933,11 +1340,21 @@ def on_progress(event: dict, _stamps=stamps, _done=done) -> None: epochs_done=np.int64(done), spec_json=np.str_(spec_json), **{f"trajectory_{i}": t for i, t in enumerate(trajectories)}, + **( + { + "target_loss_weights_train": loss_weights.train, + "target_loss_weights_full": loss_weights.full, + "train_rows": train, + } + if loss_weights.weighted + else {} + ), ) return { "stopped": False, "resumed": bool(resume and batches_this_process < len(batches)), "weights": weights, + "loss_weights": loss_weights, "train_rows": train, "batches": batches, "options": options, @@ -952,13 +1369,25 @@ def score( train: np.ndarray, holdout: np.ndarray | None, acs_share: float | None = None, + loss_weights: LossWeights | None = None, ) -> dict: """Metrics against the run's prior (the "design" blocks) and the release's. With ``acs_share`` unset the prior is the release's design weights and the - two coincide. + two coincide. With weighted ``loss_weights`` every fit block also carries + the weighted measures: training fit with the training weights (its + weighted capped error is the solve's loss), held-out fit with the + full-surface weights of the held-out rows. """ + loss_weights = loss_weights or LossWeights(None, None) + train_w = loss_weights.train + holdout_w = ( + loss_weights.full[holdout] + if loss_weights.weighted and holdout is not None + else None + ) + from microcosm.calibrate import chi_square_distance design = inputs.prior(acs_share) @@ -967,7 +1396,7 @@ def score( original = inputs.design metrics = { "concentration": concentration_metrics(weights, design, inputs.households), - "fit_train": fit_block(estimates, inputs.meta, train), + "fit_train": fit_block(estimates, inputs.meta, train, loss_weights=train_w), "prior": { "acs_share": acs_share, "realized_acs_share": float( @@ -987,13 +1416,29 @@ def score( }, } if holdout is not None: - metrics["fit_holdout"] = fit_block(estimates, inputs.meta, holdout) + metrics["fit_holdout"] = fit_block( + estimates, inputs.meta, holdout, loss_weights=holdout_w + ) metrics["fit_holdout_design"] = fit_block( - design_estimates, inputs.meta, holdout + design_estimates, inputs.meta, holdout, loss_weights=holdout_w ) metrics["fit_train_design"] = { - "overall": fit_block(design_estimates, inputs.meta, train)["overall"] + "overall": fit_block( + design_estimates, inputs.meta, train, loss_weights=train_w + )["overall"] } + if loss_weights.weighted: + # Training fit on the yardstick (unmultiplied full-surface weights of + # the training rows), comparable across runs with and without family + # multipliers. + metrics["fit_train_yardstick"] = { + "overall": fit_block( + estimates, + inputs.meta, + train, + loss_weights=loss_weights.full[train], + )["overall"] + } return metrics @@ -1014,7 +1459,12 @@ def calibrate_mode( (out_dir / "spec.json").write_text( json.dumps(dataclasses.asdict(spec), indent=2) + "\n" ) - inputs = load_inputs(checkpoint, verify=verify, log=log) + inputs = load_inputs( + checkpoint, + verify=verify, + log=log, + need_registry=spec.target_weighting != "equal", + ) prov = provenance() calibration = run_calibration( spec, @@ -1032,13 +1482,15 @@ def calibrate_mode( inputs.folds[spec.holdout_fold] if spec.holdout_fold is not None else None ) scoring_started = time.time() - metrics = score(inputs, weights, train, holdout, spec.acs_share) + loss_weights = calibration["loss_weights"] + metrics = score(inputs, weights, train, holdout, spec.acs_share, loss_weights) from microcosm.calibrate import relative_error_loss recomputed_loss = relative_error_loss( estimates_for(inputs, weights)[train], inputs.values[train], target_loss_cap=RELEASE_SETTINGS["target_loss_cap"], + target_loss_weights=loss_weights.train, ) consistency = { "recomputed_final_loss": recomputed_loss, @@ -1054,6 +1506,26 @@ def calibrate_mode( "mass_conserved_ratio": float(weights.sum() / inputs.design.sum()), "max_ratio_to_prior": float((weights / inputs.prior(spec.acs_share)).max()), } + if loss_weights.weighted: + # The loss at the starting weights, recomputed here, against the + # solve's own epoch-0 loss: the weights calibrate saw are these. + design_loss = relative_error_loss( + estimates_for(inputs, inputs.prior(spec.acs_share))[train], + inputs.values[train], + target_loss_cap=RELEASE_SETTINGS["target_loss_cap"], + target_loss_weights=loss_weights.train, + ) + consistency["recomputed_design_loss"] = design_loss + consistency["recomputed_design_loss_relative_to_epoch0"] = ( + calibration["trajectory_first_loss"] / design_loss - 1.0 + if calibration["trajectory_first_loss"] is not None + else None + ) + consistency["unweighted_final_loss"] = relative_error_loss( + estimates_for(inputs, weights)[train], + inputs.values[train], + target_loss_cap=RELEASE_SETTINGS["target_loss_cap"], + ) payload = { "kind": "calibrate", "resumed": calibration["resumed"], @@ -1066,6 +1538,9 @@ def calibrate_mode( "options": calibration["options"], "batches": calibration["batches"], "consistency": consistency, + "target_loss_weights": loss_weights_block( + spec, inputs, train, loss_weights + ), "metrics": metrics, "inputs": { "checkpoint": str(checkpoint), @@ -1104,13 +1579,27 @@ def evaluate_mode( *, verify: bool, acs_share: float | None = None, + target_weighting: str = "equal", ) -> dict: + """Score a weight vector; ``target_weighting="shared"`` adds weighted fit. + + The weighted blocks use the full-surface weights: training fit over every + target, and each fold's held-out rows with their full-surface weights. + """ + + if target_weighting not in TARGET_WEIGHTINGS: + raise SystemExit(f"unknown target_weighting {target_weighting!r}") out_dir = out_root / label out_dir.mkdir(parents=True, exist_ok=True) log = Logger(out_dir / "progress.log") started = time.time() try: - inputs = load_inputs(checkpoint, verify=verify, log=log) + inputs = load_inputs( + checkpoint, + verify=verify, + log=log, + need_registry=target_weighting != "equal", + ) if weights_arg == "design": weights = inputs.prior(acs_share) source = {"kind": "design", "acs_share": acs_share} @@ -1129,10 +1618,19 @@ def evaluate_mode( f"weights shape {weights.shape} does not match the checkpoint" ) all_rows = np.arange(inputs.matrix.shape[0], dtype=np.int64) - metrics = score(inputs, weights, all_rows, None, acs_share) + spec = RunSpec(run_id=label, target_weighting=target_weighting) + loss_weights = loss_weights_for(spec, inputs, all_rows) + metrics = score(inputs, weights, all_rows, None, acs_share, loss_weights) estimates = estimates_for(inputs, weights) metrics["fit_holdout_by_fold"] = { - str(fold): fit_block(estimates, inputs.meta, rows) + str(fold): fit_block( + estimates, + inputs.meta, + rows, + loss_weights=( + loss_weights.full[rows] if loss_weights.weighted else None + ), + ) for fold, rows in inputs.folds.items() } payload = { @@ -1140,6 +1638,9 @@ def evaluate_mode( "label": label, "release_tag": RELEASE_TAG, "weights_source": source, + "target_loss_weights": loss_weights_block( + spec, inputs, all_rows, loss_weights + ), "metrics": metrics, "inputs": { "checkpoint": str(checkpoint), @@ -1178,6 +1679,16 @@ def main(argv: Sequence[str] | None = None) -> int: parser.add_argument("--epoch-batch", type=int, default=400) parser.add_argument("--holdout-fold", type=int, default=None) parser.add_argument("--acs-share", type=float, default=None) + parser.add_argument( + "--target-weighting", choices=TARGET_WEIGHTINGS, default="equal" + ) + parser.add_argument( + "--family-loss-multiplier", + action="append", + default=[], + metavar="FAMILY=MULTIPLIER", + help="shared weighting only: scale one family's training weights", + ) parser.add_argument( "--weights", default="design", help="evaluate: 'design' or [:key]" ) @@ -1206,6 +1717,7 @@ def main(argv: Sequence[str] | None = None) -> int: args.out_root, verify=not args.no_verify, acs_share=args.acs_share, + target_weighting=args.target_weighting, ) return 0 if args.spec: @@ -1223,6 +1735,15 @@ def main(argv: Sequence[str] | None = None) -> int: epoch_batch=args.epoch_batch, holdout_fold=args.holdout_fold, acs_share=args.acs_share, + target_weighting=args.target_weighting, + family_loss_multipliers=tuple( + sorted( + (family, float(value)) + for family, _, value in ( + entry.partition("=") for entry in args.family_loss_multiplier + ) + ) + ), ) calibrate_mode( spec,