diff --git a/README.md b/README.md index e584a6a02..921e2d8c0 100644 --- a/README.md +++ b/README.md @@ -334,7 +334,11 @@ calibrates to the SOI `state` surface by default, the 4,459-target contract of Build O and Build P; `--soi-mode totals` and `--soi-mode full` are explicit opt-ins. See [the ACS local-area SOI target surface](docs/us-acs-local-soi-target-surface.md) -for what each mode contains and where the build records it. +for what each mode contains and where the build records it. Its +`--l2-basis chi_square` and `--mass-parametrization softmax` options (defaults: +the historical `record` and `projection`) penalize distance from the design +weights; see [penalized calibration toward the design weights](docs/calibration-l2-basis.md) +for the algebra, the evidence and the measured frontier. National and ACS local-area builds now use the same typed schema-8 calibration diagnostics writer. The local builder adds its Census population marginals to a diff --git a/changelog.d/calibrate-chi-square-l2-basis.added.md b/changelog.d/calibrate-chi-square-l2-basis.added.md new file mode 100644 index 000000000..3fc01b2ff --- /dev/null +++ b/changelog.d/calibrate-chi-square-l2-basis.added.md @@ -0,0 +1 @@ +Add two opt-in options to Adam calibration and the ACS local-area tool. `l2_basis="chi_square"` (`--l2-basis chi_square`) makes the `l2_lambda` penalty GREG's design-weighted chi-square distance `sum(d * (w / d - 1) ** 2) / sum(d)`, which pulls toward the design weights themselves; the historical record-weighted `mean((w / d) ** 2)`, whose mass-conserved optimum is `w ∝ d ** 2`, stays the default. `mass_parametrization="softmax"` (`--mass-parametrization softmax`) conserves mass through `w = total * softmax(log_w)`; the historical per-step uniform shift, still the default, can stall away from the constrained optimum when every record's gradient shares a sign (on the ACS local release it does not). Defaults reproduce the previous solver's bytes. Results record both settings and `CalibrationResult.chi_square_distance`; the tool records them in its calibration summary and build manifest, names all three penalty flags in the manifest's refresh recipe, and refuses to resume a checkpoint solved under other settings. See docs/calibration-l2-basis.md. diff --git a/changelog.d/calibrate-softmax-exact-cap.changed.md b/changelog.d/calibrate-softmax-exact-cap.changed.md new file mode 100644 index 000000000..1908d4c0d --- /dev/null +++ b/changelog.d/calibrate-softmax-exact-cap.changed.md @@ -0,0 +1 @@ +`mass_parametrization="softmax"` now projects the log-weights exactly onto the capped simplex after every Adam step: one common shift, then a clamp to each cap, with the shift found in float64 by active-set rounds that a sort finishes if they run long. It replaces a renormalize/clamp loop of at most 32 rounds, which ran out on most epochs of the ACS local release because of float32 rounding (the overshoot it left was float32-sized, measured in `experiments/us-acs-local-l2-basis-20260928/results/exact_cap.md`). Softmax solves record `options["iterate_selection_receipt"]["softmax_in_loop_max_cap_ratio"]`, the largest in-loop weight over its cap, in place of the exhaustion count, and the ACS local-area tool carries it in its calibration summary and build manifest. The default `"projection"` parametrization is unchanged. diff --git a/docs/calibration-l2-basis.md b/docs/calibration-l2-basis.md new file mode 100644 index 000000000..f8c3ea94b --- /dev/null +++ b/docs/calibration-l2-basis.md @@ -0,0 +1,281 @@ +# Penalized calibration toward the design weights + +`microcosm.calibrate.calibrate` has two opt-in options for spreading calibrated +weights: `l2_basis`, the functional form of the `l2_lambda` penalty, and +`mass_parametrization`, how the Adam solve holds the total under +`mass="conserve"`. Their defaults (`"record"`, `"projection"`) are the historical +solve, byte for byte. This page gives the algebra, the evidence for each claim, +and what the options can and cannot do for the ACS local-area release. The +full-scale sweep and the recommendation are in +[`experiments/us-acs-local-l2-basis-20260928/README.md`](../experiments/us-acs-local-l2-basis-20260928/README.md). + +## The two bases + +Write `d` for the anchor (the initial, design weights unless `l2_anchor` says +otherwise), `w` for the calibrated weights, `r = w / d`, and `D = sum(d)`. + +| | `l2_basis="record"` (default) | `l2_basis="chi_square"` | +|---|---|---| +| penalty | `mean(r ** 2)` | `sum(d * (r - 1) ** 2) / D` = `sum((w - d) ** 2 / d) / D` | +| value at `w = d` | 1 | 0 | +| target-free optimum, `mass="conserve"` | `w ∝ d ** 2` | `w = d` (an explicit anchor whose total differs is rescaled to the input total) | +| target-free optimum, `mass="free"` | `w → 0` | `w = d` | +| cost of a record collapsing to 0 | none (0 is its cheapest ratio) | its anchor share `d_i / D` | +| `options["l2_penalty"]` (initial anchor) | `mean_initial_pre_gate_weight_ratio_squared` | `chi_square_initial_pre_gate_weight_distance` | + +The chi-square form is GREG's distance function. Minimizing target loss plus +`l2_lambda` times it is penalized ("ridge") calibration, the soft form of GREG: +it works where exact GREG cannot, with thousands of partly inconsistent soft +targets, nonnegativity, a ratio cap and 1.6 million records. + +Identities (tested in +`packages/microcosm-calibrate/tests/engine_free/shared/test_l2_basis.py`): + +- When `sum(w) = D`, the distance equals `sum(w ** 2 / d) / D - 1`: the + weighting effect of `w` relative to `d`, minus one. +- With uniform `d` it equals `n / ESS(w) - 1`, so it is a direct Kish ESS + control. +- The float32 penalty the solver differentiates agrees with the float64 + reference `microcosm.calibrate.chi_square_distance`. +- Under a mass constraint the reduced gradient of the chi-square penalty + vanishes at `w = d`, and that of the record penalty at `w ∝ d ** 2`. + +`CalibrationResult.chi_square_distance` reports the realized distance from the +input weights for every solve, whatever its penalty. + +Kish ESS is not in general monotone in `l2_lambda`. What is monotone, for exact +minimizers, is the penalty itself: adding the optimality inequalities of two +solves at `l2_lambda` values `a < b` gives +`(b - a) * (P(w_b) - P(w_a)) <= 0`. With unequal design weights, calibration can +raise Kish ESS above the design's, and pulling back toward `d` then lowers it. +In the ACS local release, calibration lowers ESS from the design's 36,288 to +13,631 in the published weights (13,646 in the sweep's reproduction), and as `l2_lambda → ∞` the solve returns to the design's figure; the +sweep measures the path in between. + +The path tests check each solve against the exact optimum of the same convex +program, computed by CLARABEL on the tests' own problems (fixture +`tests/fixtures/l2_basis_path_reference.json`, generator +`experiments/us-acs-local-l2-basis-20260928/path_reference.py`). The +largest excess measured there is 0.0026. The excess bound `eps` = 0.006 is +empirical (twice that maximum, rounded up to 1e-3); the monotonicity slack +`2·eps/Δλ` is derived from it, since two `eps`-optimal solves of the same +feasible set satisfy `(λb − λa)(Pb − Pa) ≤ 2·eps`. The exact optima also show the textbook structure: between +`l2_lambda = 0.01` and `0.3` the optimal distance is flat, because the solve +meets every feasible target exactly and picks the closest such point to `d`, +which is GREG's solution. + +## The mass parametrization + +Under `mass="conserve"` the historical solve takes an Adam step on the +log-weights, then shifts every log-weight by the same amount so the total +returns to the input total (`mass_parametrization="projection"`). Adam divides +each coordinate's gradient by its own running RMS plus `eps` (1e-8) before +stepping. When a gradient is well above `eps`, the coordinate moves about `lr` +in the direction of the gradient's sign, whatever its size. If every record's +gradient then has the same sign, all records step by about `lr` and the shift +takes them straight back. Every target missing on the same side produces +this; a net demand for mass with targets on both sides does not. Such a point +is a fixed point of the projected iteration whether or not it is the +constrained optimum. At the constrained optimum the log-weight gradient is +proportional to `w` (the constraint's normal), not zero, so the iteration has +no systematic pull toward it; it gets there only through records whose +gradients change sign. Records whose gradient is zero or near `eps` take +smaller steps, which the shift does not cancel, and that weakens the +mechanism. At the ACS release's scale it is weak in exactly this way +(`experiments/us-acs-local-l2-basis-20260928/gradient_scale.py`). At the +design weights the median record's log-weight gradient is 3.8e-8 and 23% are +at or below `eps`; at the release's weights the median is 1.2e-8 and 47% are +at or below `eps`. The signs are mixed, 33% positive at the design and 52% at +the release. So the sharp same-sign stall does not occur there, and whether +the projection solve still falls short is an empirical question the +full-scale sweep answers by solving the release's own surface both ways. + +`mass_parametrization="softmax"` optimizes `w = D * softmax(log_w)` instead. +The total holds by construction, and autograd hands Adam the gradient with its +component along the constraint already removed. For a smooth objective that +reduced gradient vanishes at the constrained optimum; the capped-MAPE loss has +kinks, so Adam still oscillates there on the scale of `lr`, as it does under +`mass="free"`. After every step the log-weights are projected exactly onto +the capped simplex `{w : sum(w) = D, w <= cap * d}`, and the closing float64 +projection that both parametrizations share makes the returned vector exact. +It requires `mass="conserve"` and no L0 gates. + +**The per-step cap projection.** It is the Kullback-Leibler projection: one +common shift `s` for every log-weight, then a clamp to each log cap, +`log_w <- min(log_w + s, log(cap * d))`. Uncapped records keep their +relative weights, the direction the softmax cannot see, and only records +whose share would pass their caps are held at them. `s` solves +`sum(min(exp(log_w + s), cap * d)) = D`, whose left side is continuous and +nondecreasing in `s`. Active-set rounds find it: with `C` the records already +at their caps, the next shift solves +`sum(cap * d[C]) + exp(s) * sum(exp(log_w)[not C]) = D`. That shift is a lower +bound on the answer for every `C`, so `C` only grows, and a round that adds +no record is exact. If eight rounds pass without that, a sort over the +remaining records finishes it, which bounds the worst case at `O(n log n)`. +The shift is computed in float64. The solve records +`options["iterate_selection_receipt"]["softmax_in_loop_max_cap_ratio"]`, the +largest ratio of a realized in-loop weight to its float64 cap over every +forward pass and the closing iterate, before the closing projection. +`test_softmax_cap_projection.py` pins the projection for every drawn input, +including zero design weights and a cap of exactly 1. It conserves the total, +respects every cap, is idempotent and invariant to a constant added to the +log-weights, and moves an input already within its caps by the shift alone. +It also meets the Kullback-Leibler projection's KKT conditions, equals a +float64 water-fill written from the definition, and gives the same answer by +its active-set and sorted paths, including on an input built to need one +round per record. + +**What the projection replaced.** Before, each step renormalized and clamped +in a loop of at most 32 rounds. On the ACS local release the rounds ran out on +most epochs, and how far that left the in-loop weights past their caps was +not recorded. It has since been measured +(`experiments/us-acs-local-l2-basis-20260928/results/exact_cap.md` and +`results/exact_cap_float32.json`): + +- **Why the loop ran out.** Each round's shift was `log(D)` in float64 minus a + float32 `logsumexp`. On a vector already within its caps that difference + does not reach zero. It bottoms out at the float32 rounding residual of + `log(D)` itself, 7.8e-8 here. That shift lifts the one to three capped + records with log caps below 2 in magnitude one float32 step over their caps, + the clamp puts them back, and the next round repeats it. On full-scale vectors + the projection had already made exact, the loop still ran out in 10 of 12 + trials. +- **How far past the cap it was.** Not materially. Two full-scale + configurations whose earlier runs ran out were rerun under both steps: share + 0.5 at `l2_lambda = 0.03`, and share 0.9 unpenalized. The largest in-loop + weight over its cap was 1 + 1.2e-5 and 1 + 8.7e-6 under the loop, and + 1 + 9.0e-6 under the projection at both. The loop ran out on 713 and 705 of + 800 steps. The projection settled in one to three rounds on every step and + took 12.7-22.5 ms a step, against the loop's 48.7-53.8 ms. +- **What sets that residual.** The float32 softmax. The projected + log-weights sit within their float32 log caps, but the solve realizes + `D * softmax(log_w)` with a float32 normalizer over 1.59M records, whose + total was off by up to 8.2e-6. The cap ratio exceeded that total error by + at most 6.1e-7. With a float64 normalizer on the same log-weights the largest + ratio was 1 + 4.9e-7, the float32 rounding of the log-weights themselves. +- **The solves.** National ESS agreed within 0.1% between the two steps + (21,834 against 21,835, and 93,609 against 93,598). At share 0.5 and + `l2_lambda = 0.03` the projection's final loss was 0.01899. Three loop runs + of that configuration gave 0.01910-0.01917, so the projection's loss is + 0.6-0.9% lower, from one run. At share 0.9 the loss was 0.2% lower (0.03129 + against 0.03137). + +Evidence: + +- `test_projection_parametrization_stalls_under_uniform_mass_pressure` pins + the stall in its sharpest form. When every target sits above its design + total, the projection + solve returns the design weights unchanged after 200 epochs, while the + softmax solve halves the loss. The same mechanism means the record penalty + alone never moves a projection solve: its log-space gradient is positive for + every record. +- `experiments/us-acs-local-l2-basis-20260928/optimizer_reference.py` runs the + kernel under each setting and compares it with CLARABEL's exact optimum of + the same convex program: + +| Setting | Problems | Mean excess objective at λ = 0 / 0.001 / 0.01 / 0.1 / 1 | Max excess | Problems with non-monotone distance | +|---|---|---|---:|---:| +| `mass="conserve"`, `"projection"` (default) | 16 small (120 records, 6 targets; half press on the total) | 0.0285 / 0.0268 / 0.0304 / 0.1608 / 0.0697 | 0.3384 | 94% | +| `mass="conserve"`, `"softmax"` | 16 small (120 records, 6 targets; half press on the total) | 0.0013 / 0.0016 / 0.0013 / 0.0017 / 0.0008 | 0.0034 | 0% | +| `mass="free"` | 16 small (120 records, 6 targets; half press on the total) | 0.0004 / 0.0015 / 0.0020 / 0.0019 / 0.0014 | 0.0042 | 0% | +| `mass="conserve"`, `"projection"` (default) | 3 large (3,000 records, 150 targets in both directions) | 0.0023 / 0.0037 / 0.0062 / 0.0081 / 0.0061 | 0.0139 | 0% | +| `mass="conserve"`, `"softmax"` | 3 large (3,000 records, 150 targets in both directions) | 0.0012 / 0.0016 / 0.0018 / 0.0012 / 0.0010 | 0.0023 | 0% | +| `mass="free"` | 3 large (3,000 records, 150 targets in both directions) | 0.0009 / 0.0016 / 0.0018 / 0.0013 / 0.0010 | 0.0023 | 0% | + + Excess objective is the kernel solve's loss plus `l2_lambda` times its + distance, minus CLARABEL's optimum of the same program; the loss cap never + binds on these surfaces. Receipts: `results/optimizer_reference.csv` and + `results/optimizer_reference_summary.json` in that experiment directory. + The small problems where every target presses on the total show the stall. + On the larger problems with targets in both directions, projection is never + non-monotone but still lands 1.9 to 6.7 times further from the optimum than + softmax. + +## At the ACS release's scale + +On the published ACS local release (1,588,854 households, 4,459 targets), +recalibrated from its own checkpoint (`experiments/us-acs-local-l2-basis-20260928/`): + +- **Parametrization.** The per-record gradients there are near Adam's `eps` + with mixed signs, so the projection stall does not occur. The two + parametrizations trace the same training frontier for `l2_lambda ≤ 0.03` + (at `0` softmax reaches 18% more ESS, 16,132 against 13,646, at a 3% higher + loss, about the size of the unpenalized solve's 2.7% run-to-run variation). At `0.1` and above, projection falls inside softmax's + frontier: at `1` it reaches ESS 30,582 at loss 0.0785, where softmax's path, + interpolated between its `0.3` and `1` solves, gives about 0.055. Equal + `l2_lambda` is not an equal-ESS comparison. At `0.03` the held-out + comparison is a toss-up: softmax has slightly lower capped error, projection + more held-out targets within 10% on both folds. Projection's training loss is + 6% lower there, against 0.3% run-to-run variation, so projection is the + recommended parametrization. Rerun with the exact cap projection, softmax's + training loss at `0.03` is 0.01899 (one run), still 5% above projection's + 0.01806. +- **Record basis.** The record basis at `0.1` lowers national ESS from 13,646 + to 8,689, as its `w ∝ d ** 2` optimum predicts. +- **Chi-square basis.** The chi-square basis raises ESS smoothly with + `l2_lambda`. At `0.03` under projection it costs 0.3 points of training fit + and no measurable held-out fit: it is ahead of the release on both measures + (capped error and share within 10%) on both rotated folds. Held-out + run-to-run variation was not measured and those folds also chose + `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. + +## Using it + +```python +from microcosm.calibrate import calibrate + +result = calibrate( + frame, + targets, + mass="conserve", + max_weight_ratio=5.0, + l2_lambda=..., + l2_basis="chi_square", + # mass_parametrization="softmax" for problems where every target presses + # the same way (above). +) +result.options["l2_basis"], result.options["mass_parametrization"] +result.chi_square_distance, result.effective_sample_size +``` + +The ACS local-area tool takes the same settings as +`--l2-lambda`, `--l2-basis {record,chi_square}` and +`--mass-parametrization {projection,softmax}`. It records them, with the +realized chi-square distance and, under softmax, the last epoch batch's +largest in-loop weight over its cap, in `calibration_summary.json` and in the +build manifest's `calibration` block. Both join the tool's solver-settings stamp, +so `--resume` and the already-complete shortcut refuse weights solved under +any other solver setting (cap, loss cap, `l2_lambda`, `l2_basis`, +`mass_parametrization`, seed, epoch batch); a stamp written before these two +options existed reads as the historical solve. The manifest's refresh recipe +carries the three flags whenever they differ from the historical solve. +`calibrate_l0_refit` takes `l2_basis` / `refit_l2_basis` +and `refit_mass_parametrization`; `refit_l0_selection` and `static_aging` take +`l2_basis`. + +## What the penalty cannot fix in the ACS local release + +The chi-square penalty pulls toward the design weights, so the most it can +recover is the design weights' own concentration. In the ACS local staging, +that concentration is already high by construction. The 57,240 donor-spine +records (the Build O sparse release) carry half the design mass with a within- +spine ESS of 9,174, while the 1,531,614 ACS records carry the other half with a +within-spine ESS of 812,434. National design ESS is therefore 36,288 (2.3% of +records). Massachusetts shows the same split +(`massachusetts_by_spine_at_release_design` in that experiment's +`results/seeding_options.json`). Its 35,056 ACS records have a +design ESS of 20,238, while its 1,522 donor records carry 49.6% of the state's +design mass with an ESS of 255, the national release's own Massachusetts figure, +since the donor spine is that release's records at half weight. Together the +state's design ESS is 1,020, and each of its nine districts is 108-134 (the ACS +records alone give 2,020-2,444 per district). As `l2_lambda → ∞` the solve +returns to those combined figures. The penalty bounds the distance from them, +not ESS itself, but no tested setting lifted national ESS above them. Reaching the ACS-only figures needs a larger ACS +share of the mass in the staging (`--acs-share`), which is a construction +decision outside calibration; `experiments/us-acs-local-l2-basis-20260928/ +seeding_options.py` measures the starting ESS under other shares and under +donor location clones. +(Measured on the calibration checkpoint of the 2026-09-23 release, +`populace-us-2024-buildo-acs-local-767312d60-20260923T074941Z`.) diff --git a/docs/evidence/spec-engine/us-f0-coverage.json b/docs/evidence/spec-engine/us-f0-coverage.json index 03b169c7d..d181a6b69 100644 --- a/docs/evidence/spec-engine/us-f0-coverage.json +++ b/docs/evidence/spec-engine/us-f0-coverage.json @@ -1656,13 +1656,13 @@ "compiler_ir.node_slices" ], "expected": { - "map_sha256": "3cf10a524310ea0ada18ddc65cfa585d1d0652d23e7d7a180c050ac686224e28", - "protocol_sha256": "91989ed5e366e19598f86335b2e66723dcb2b6481a78d0dbeb83183a1aa09bd4" + "map_sha256": "292ad19c691876eecd8d4b36458b2b7e6f0ec14a51a2e3ab3fdf0d2e6986a98f", + "protocol_sha256": "5795dc072b958dcafb6ba5c37e6124181ce37479f96cd4ca2dec3755d4e476f8" }, "failures": [], "observed": { - "map_sha256": "3cf10a524310ea0ada18ddc65cfa585d1d0652d23e7d7a180c050ac686224e28", - "protocol_sha256": "91989ed5e366e19598f86335b2e66723dcb2b6481a78d0dbeb83183a1aa09bd4" + "map_sha256": "292ad19c691876eecd8d4b36458b2b7e6f0ec14a51a2e3ab3fdf0d2e6986a98f", + "protocol_sha256": "5795dc072b958dcafb6ba5c37e6124181ce37479f96cd4ca2dec3755d4e476f8" }, "status": "covered" }, @@ -1677,7 +1677,7 @@ "compiler_ir.seed_stream_map" ], "expected": { - "implementation_sha256": "91989ed5e366e19598f86335b2e66723dcb2b6481a78d0dbeb83183a1aa09bd4", + "implementation_sha256": "5795dc072b958dcafb6ba5c37e6124181ce37479f96cd4ca2dec3755d4e476f8", "protocol": "legacy-v1", "streams": [ "build_model", @@ -1698,7 +1698,7 @@ }, "failures": [], "observed": { - "implementation_sha256": "91989ed5e366e19598f86335b2e66723dcb2b6481a78d0dbeb83183a1aa09bd4", + "implementation_sha256": "5795dc072b958dcafb6ba5c37e6124181ce37479f96cd4ca2dec3755d4e476f8", "protocol": "legacy-v1", "streams": [ "build_model", @@ -2599,7 +2599,7 @@ "country": "us", "schema_id": "country_spec", "schema_version": 1, - "spec_sha256": "ffbb93ed5ae22a95536ae475d6912a42628568609b442360dc1cd3bcf26772cd" + "spec_sha256": "edcd647524b51dd2fbcb417576a7421d2429ed7b51beb6756579ef3911e86d52" } }, "report_schema_version": 3, @@ -2609,7 +2609,7 @@ "country": "us", "schema_id": "country_spec", "schema_version": 1, - "spec_sha256": "ffbb93ed5ae22a95536ae475d6912a42628568609b442360dc1cd3bcf26772cd" + "spec_sha256": "edcd647524b51dd2fbcb417576a7421d2429ed7b51beb6756579ef3911e86d52" }, "status": "pass" } diff --git a/experiments/us-acs-local-l2-basis-20260928/README.md b/experiments/us-acs-local-l2-basis-20260928/README.md new file mode 100644 index 000000000..a9b578a73 --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/README.md @@ -0,0 +1,250 @@ +# ACS local release: effective sample size versus fit under a design-weight penalty + +Full-scale evidence for the chi-square L2 basis (`l2_basis="chi_square"`) and +the softmax mass parametrization added in this branch; the algebra and the +small-problem evidence are in [docs/calibration-l2-basis.md](../../docs/calibration-l2-basis.md). +Everything here recalibrates the published ACS local release, +`populace-us-2024-buildo-acs-local-767312d60-20260923T074941Z` (1,588,854 +households, 4,459 targets), from its own calibration checkpoint, on Modal. It is +research evidence: nothing was published, and no default changed. + +## The question + +On 2026-09-28 David Trimmer measured the release's weights in #microcosm: +national Kish ESS of about 14k of 1.59M, Massachusetts 477 with half its weight +on 152 records (over 36,491 Massachusetts records in the files he read), and +district ESS of 41-64. On this checkpoint's 36,578 Massachusetts households the +published weights give 446 with 149 records holding half, and the reproduction +below gives 448; the published national figure is 13,631 and the reproduction's +13,646 (`results/published_weights.json`; the record counts were not +reconciled with his). Threshold statistics (a 1.9 pp drop +in Massachusetts child poverty) rested on a few dozen households. He asked +whether to anchor calibration to the design weights. This experiment measures +what that anchor buys, what it costs, and what limits it. + +## Findings + +1. **Most of the concentration is in the starting weights, not the solve.** + - The staging's `--acs-share 0.5` gives the 57,240 donor (ASEC-by-PUF) rows half the mass. + - Those rows are the national release's records at half weight, with a within-spine ESS of 9,174. + - So the design weights start at a national ESS of 36,288, a Massachusetts ESS of 1,020 and a median district ESS of 80, before any calibration. + - The 1,531,614 ACS rows alone have an ESS of 812,434. + - Calibration then moves the donor share of mass from 50% to 68% and cuts ESS to 13,646. + - Receipts: `results/seeding_options.{json,md}`, and the national and per-spine blocks of every run. +2. **The anchor works, and at the current seeding it costs no measurable out-of-sample fit.** + - The chi-square penalty pulls toward the design weights themselves. The old record-weighted L2 penalty pulls toward their square: at λ = 0.1 it *lowers* ESS to 8,689. + - With the chi-square basis, ESS rises smoothly with λ. + - On held-out targets, projection at λ = 0.03 is ahead of the release on both measures and both folds (see the table): 3.6-3.8% lower capped error and 0.9-1.2 points more targets within 10%. Each fold has its own solves for both configurations, and all four comparisons agree in sign. But held-out run-to-run variation was not measured, and λ was picked on those two folds, so treat the gain as optimistic. +3. **The seeding sets the limit.** As λ grows the solve returns to the starting weights. On every tested path the national ESS stays below the prior's (at share 0.5, λ = 3 reaches 35,386 against the prior's 36,288), though the penalty is a distance, not an ESS bound, and single districts can exceed their prior's ESS (at λ = 1 some do). + - A larger ACS share raises that limit several-fold. At share 0.9 without a penalty: ESS 93,524, Massachusetts 2,704, no district below 50, and 95.8% of targets within 10%. + - It also worsens held-out fit, mainly on SOI targets (held-out SOI within 10% falls from 64% to 54% at share 0.9), while held-out district populations improve. + - That is consistent with how the staging builds the rows: the ACS rows' tax variables are transferred from the donor rows by QRF (the tool's `donor_sparse_selection_training_set` limitation), so the donor rows carry tax detail the ACS rows only approximate. +4. **The softmax parametrization is not needed at this scale for λ ≤ 0.03.** + - The per-record gradients here are near Adam's `eps`, with mixed signs (`results/gradient_scale.json`). The same-sign stall that the small-problem evidence shows does not occur. + - The two parametrizations trace the same training frontier for λ ≤ 0.03. At λ = 0 softmax reaches 18% more ESS (16,132 against 13,646) at a 3% higher loss (0.0160 against 0.0155), a loss gap about the size of the unpenalized solve's run-to-run variation (2.7%; see "How it was run"). At λ ≥ 0.1 projection falls inside softmax's training frontier. At λ = 0.1 projection reaches ESS 22,739 at loss 0.0228, where softmax's path (interpolated between its λ = 0.03 and 0.1 solves) gives about 0.021. At λ = 1 projection reaches 30,582 at 0.0785, against about 0.055 on softmax's path (between λ = 0.3 and 1). Equal λ is not an equal-ESS comparison. + - Softmax's per-step cap loop runs out of its 32 rounds on most epochs at this scale: in the runs that record it (heads 9ef71ed6, bc763cb4 and d82ff85b), 165-400 of the last batch's 400 epochs, including 400 at share 0.5, λ = 0.03 on the full surface (the `dup_` rerun) and on fold 0, and every share-0.9 run. Those epochs optimize total·softmax(log_w) past the 5x cap, by an amount these runs did not record; only the closing projection makes the returned weights exact. The loop has since been replaced by an exact projection, and the overshoot measured: it was float32-sized, 1 + 1.2e-5 at most, against 1 + 9.0e-6 for the projection, so the grid's softmax results stand (see "The softmax cap step, re-measured" below). Projection is the recommended parametrization. + +## The softmax cap step, re-measured + +Softmax's per-step cap loop (finding 4) was replaced by an exact projection +onto the capped simplex (docs/calibration-l2-basis.md). `exact_cap_modal.py` +reran two configurations whose grid runs ran out of rounds, under the new +step and under the old loop, at kernel head 4bddde64. It recorded the largest +in-loop weight over its cap at every forward pass, the cap step's rounds and +its time (`results/exact_cap.md`, `results/exact_cap/`): + +| Configuration | Cap step | Largest in-loop weight over its cap | Steps out of rounds | Cap step | Final loss | National ESS | +|---|---|---:|---:|---:|---:|---:| +| Share 0.5, softmax, chi-square λ 0.03 | exact projection | 1 + 9.0e-6 | 0 of 800 | 12.7 ms | 0.018991 | 21,834 | +| Share 0.5, softmax, chi-square λ 0.03 | 32-round loop | 1 + 1.2e-5 | 713 of 800 | 53.8 ms | 0.019169 | 21,835 | +| Share 0.9, softmax, no penalty | exact projection | 1 + 9.0e-6 | 0 of 800 | 22.5 ms | 0.031293 | 93,609 | +| Share 0.9, softmax, no penalty | 32-round loop | 1 + 8.7e-6 | 705 of 800 | 48.7 ms | 0.031365 | 93,598 | + +- The loop's overshoot was float32-sized, about the same as the projection's. + Both are set by the solve's float32 softmax, whose normalizer missed the + total by up to 8.2e-6. With a float64 normalizer the projected vectors sit + within 1 + 4.9e-7 of their caps (`exact_cap_float32.py`, + `results/exact_cap_float32.json`). +- The loop ran out because of float32 too. Its shift was `log(total)` in + float64 minus a float32 `logsumexp`, which bottoms out at the float32 + rounding residual of `log(total)` (7.8e-8) and lifts a few capped records + over their caps every round. On vectors the projection had already made + exact it still ran out in 10 of 12 trials. +- The projection settled in one to three active-set rounds on every step. +- The loop run at share 0.5 reproduced the grid's `soft_chi_s050_0.03` + (0.019169, ESS 21,835); the `dup_` rerun gave 0.019104. The projection's + 0.018991 is 0.6-0.9% below those three, from one run. ESS agreed within + 0.1% at both configurations. + +## Key configurations + +"Held-out error" is the mean capped scaled error, and "held-out within 10%" +the share of targets within 10%, on a rotated 20% of targets +(`microcosm.build.holdout.rotated_folds(4459, n_folds=5, seed=20260529)`) +never seen by that solve. MA is Massachusetts. Each configuration's training fit +and concentration come from its full-surface run; "–" marks a fold not run. + + +| Configuration | National ESS | Top-1% share | State ESS min | CD ESS median / min | CDs < 50 | MA ESS | Train within 10% | Held-out error, fold 0 / 1 | Held-out within 10%, fold 0 / 1 | +|---|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| Release (reproduced): share 0.5, projection, λ 0 | 13,646 | 69.3% | 195 | 33 / 12 | 352 | 448 | 97.6% | 0.1296 / 0.1236 | 63.3% / 65.8% | +| Share 0.5, projection, chi-square λ 0.03 | 20,690 | 63.7% | 204 | 46 / 22 | 278 | 589 | 97.3% | 0.1247 / 0.1191 | 64.2% / 67.0% | +| Share 0.5, softmax, chi-square λ 0.03 | 21,835 | 61.6% | 213 | 49 / 23 | 239 | 620 | 97.1% | 0.1242 / 0.1166 | 63.1% / 66.3% | +| Share 0.5, projection, chi-square λ 0.1 | 22,739 | 62.0% | 207 | 51 / 27 | 208 | 616 | 95.9% | 0.1285 / – | 61.7% / – | +| Share 0.5, softmax, chi-square λ 0.1 | 24,895 | 59.2% | 222 | 55 / 29 | 132 | 664 | 95.5% | 0.1263 / 0.1210 | 61.5% / 63.8% | +| Share 0.5, softmax, chi-square λ 1 | 33,418 | 53.3% | 269 | 73 / 47 | 4 | 907 | 75.7% | 0.1505 / – | 50.1% / – | +| Share 0.7, softmax, chi-square λ 0.01 | 34,280 | 46.9% | 319 | 81 / 24 | 30 | 992 | 97.1% | 0.1466 / 0.1416 | 60.4% / 60.5% | +| Share 0.9, softmax, no penalty | 93,524 | 27.8% | 599 | 227 / 64 | 0 | 2,704 | 95.8% | 0.1765 / 0.1767 | 56.1% / 56.7% | +| Share 0.9, softmax, chi-square λ 0.01 | 130,608 | 24.2% | 612 | 317 / 85 | 0 | 3,285 | 95.1% | 0.1785 / – | 54.9% / – | +| Share 0.964, softmax, no penalty | 244,979 | 17.9% | 594 | 578 / 265 | 0 | 6,591 | 94.9% | 0.1952 / 0.1984 | 51.6% / 52.4% | + + +The full grid, the per-family fit, the holdout detail and candidate floors are +in `results/frontier.md`, `results/floors.md` and `results/frontier.csv`: + +![ESS vs training fit by prior and λ](results/frontier.png) + +## Recommendation + +Two decisions, kept separate because they trade different things. + +**1. Calibration: adopt the chi-square penalty at the current seeding.** +On today's loss, use `--l2-basis chi_square --l2-lambda 0.03` with the +default `--mass-parametrization projection`. +- Against the release it improves every concentration measure: + - national ESS from 13,646 to 20,690; + - Massachusetts from 448 to 589; + - districts below ESS 50 from 352 to 278; + - the smallest district from 12 to 22. +- Out of sample it is ahead on both measures and both folds: held-out error 0.1247 and 0.1191 against 0.1296 and 0.1236, and held-out within 10% 64.2% and 67.0% against 63.3% and 65.8%. Held-out run-to-run variation was not measured and λ was picked on these folds, so treat that as "no worse, probably slightly better". +- 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 (0.0190 and 5% higher with the exact cap projection, from one run). 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. + +**2. Construction: the ACS share of the mass is the bigger lever, and it costs out-of-sample fit.** + +| ACS share | National ESS | MA ESS | Districts below 50 | Held-out error, fold 0 / 1 | +|---|---:|---:|---:|---:| +| 0.5, no penalty (release reproduced, projection) | 13,646 | 448 | 352 | 0.1296 / 0.1236 | +| 0.7, λ 0.01 (softmax) | 34,280 | 992 | 30 | 0.1466 / 0.1416 | +| 0.9, no penalty (softmax) | 93,524 | 2,704 | 0 | 0.1765 / 0.1767 | + +The share-0.7 and share-0.9 rows are softmax solves (at share 0.5, softmax +alone gave 18% more ESS than projection at λ = 0); a projection solve at those +shares was not run. + +That is a methodology call: more stable local estimates against worse +aggregate SOI fit. It also overlaps the support-mix bake-off (#1067, decision +d701), which recommends ACS rows plus CPS, seeded in proportion to their +counts, for the local file. + +**ESS floor as a release gate.** At the current seeding, no tested +configuration gets every district to ESS 50: even λ = 1 (76% within 10%) +leaves 4 districts below it, and λ = 3 leaves 1. So the gate should catch +calibration-induced collapse now and rise with the seeding. Across each +configuration's three solves (full surface, fold 0, fold 1): + +| Configuration | Districts below 25% of their starting ESS | Smallest district ESS | Smallest state ESS | +|---|---|---|---| +| Release reproduced (projection, λ 0) | 15 / 22 / 24 | 11.7 / 10.7 / 7.8 | 195 / 203 / 177 | +| Softmax, λ 0 | 8 / 5 / – | 13.1 / 14.8 / – | 201 / 210 / – | +| Projection, λ 0.03 | 0 / 0 / 0 | 22.4 / 22.9 / 24.2 | 204 / 205 / 193 | +| Softmax, λ 0.03 | 0 / 0 / 0 | 23.0 / 23.0 / 24.1 | 213 / 216 / 203 | + +- **The gate to adopt now:** no district below a quarter of its starting ESS. + It separates cleanly at share 0.5: every penalized solve there has 0, and + the release has 15-24. It measures collapse relative to the seeding, so it + binds harder as the seeding improves: at share 0.9 the unpenalized solves + leave 261-328 districts below a quarter of their (much higher) starting ESS, + λ = 0.01 leaves 72-121, λ = 0.03 leaves 33, and λ = 0.1 leaves 0. A + share-0.9 release would pass it only with a penalty around 0.1. +- **An absolute district floor of 15:** it also separates (release at most 11.7, λ = 0.03 at least 22.4), with some margin. +- **No state floor:** a state floor near 200 sits inside the solves' spread (177-216), so it would flap. +- **With an ACS share of 0.9 or more:** raise the floors to every district at least 50 and every state at least 500. Across every solve at shares 0.9 and 0.964 (full surface and folds, every λ), the smallest district is at least 63.7 and the smallest state at least 549, so the state floor's margin is about 10%. At share 0.964 the relative gate leaves 13-30 districts unpenalized and 0 from λ = 0.01. + +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. + +## How it was run + +- **Inputs.** The release's calibration checkpoint was a 28.7 GB dense + float32 frame. The sparse copy used here (`target_matrix.npz`, 4,459 × + 1,588,854, 24,773,532 nonzeros) was checked value for value against 3,000 + sampled dense rows (`results/csr_verification.json`). Design weights and + target values equal the release's. +- **Harness.** `sweep.py` calls `microcosm.calibrate.calibrate` with the + tool's settings: Adam, lr 0.02, `mass="conserve"`, `max_weight_ratio=5`, + `target_loss_cap=1`, seed 0, 800 epochs as two warm-started batches of 400. + Each run adds its own `l2_lambda`, `l2_basis`, `mass_parametrization` and + prior (`acs_share`). + - The prior rescales each spine to its share of the unchanged total, keeping + proportions within a spine. That matches `--acs-share` in the staging. + - The prior is the frame's weights, so it is also the chi-square anchor and + the base of the 5x cap. +- **Compute.** `modal_sweep.py` ran each configuration in its own 8-CPU + container from the committed kernel, gated on a reproduction of the release. + - The reproduction matched the release: loss 0.015485 against 0.015491, + within-10% 97.58% against 97.58%, ESS 13,646 against 13,631, and + chi-square distance 0.7269 against 0.7270. A second run of the same + configuration gave loss 0.015069 and ESS 13,635, so the loss match is + closer than the solve's run-to-run variation. + - Every run's metrics are in `results/runs/`. Weights stay in + `_build_artifacts/acs-local-l2-basis-20260928/runs/` and the Modal volume + `microcosm-acs-l2-basis-sweep`. +- **Analysis.** `analyze.py` builds the frontier, the floors, the key table + and the chart from those metrics. `seeding_options.py` measures starting + ESS under other shares and under donor location clones. + `gradient_scale.py` measures the gradient scale, and `published_weights.py` + the published weights' own concentration. `optimizer_reference.py` + and `path_reference.py` compare the kernel with CLARABEL's exact + optimum on small problems. +- **Kernel heads.** The grid ran on three heads: 35ad6657 (the full-surface + grid, `release_repro`, the first holdout runs), 9ef71ed6 (the second-pass + holdout runs) and bc763cb4 (projection at λ = 0.03 and every fold-1 run). + Each run records its head in `provenance` (and `kernel_head` in + `results/frontier.csv`). Between 35ad6657 and bc763cb4 (and this branch's + head), `git diff -- packages/microcosm-calibrate/src` changes `solve.py` + only in docstrings and comments, the softmax cap-exhaustion counter (a + receipt field), an `assert` on an unreachable branch, a guard that only + raises, and an `L0RefitResult` property; the other changed calibrate + modules came from main and are not on `calibrate`'s path. +- **Run-to-run variation.** Two first-head runs (`release_repro` and softmax + λ = 0.03) were repeated at d82ff85b as `dup_*` + (`results/rerun_variation.json`). Neither pair is byte-identical. The trajectories already differ at epoch 0 for + softmax (the loss at the starting weights, 1.2e-6 relative) and at epoch 1 + for the release (7e-8 relative), which no code change between the heads + touches. Over 800 epochs that grows to: + - final loss: −2.7% for the release, −0.3% at λ = 0.03; + - per-record weights: median 0.3-0.6% and 99th percentile 4-9% apart; + - every concentration measure in the key table within 1%, training within 10% within 0.05 points; + - districts below a quarter of their starting ESS: 15 and 16 for the release, 0 and 0 at λ = 0.03. + + No same-head pair was run, so a head change and the container are + confounded. But the receipts' kernel-module hashes show `solve.py` changed + once (35ad6657 to 9ef71ed6, then identical at bc763cb4 and d82ff85b), and + `matrix`, `target` and the frame modules are identical at every head. The + 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. + +## Limits + +- **Holdout is interpolation.** The holdout draws random targets, most of + them SOI state cells whose neighbours stay in training. It measures + aggregate generalization, not the variance of threshold statistics. ESS is + the variance side of that trade, measured here for every state and district. +- **Folds.** Folds 0 and 1 cover the configurations the recommendation + compares; λ = 1 and share 0.9 with λ = 0.01 have fold 0 only. λ was chosen + on the same folds it is reported on, so the held-out gains are optimistic by + that selection. Fold-to-fold noise is visible in the table. +- **Clones.** Donor location clones were measured only for their starting + 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 + 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. +- **Not measured.** Poverty and program estimates were not recomputed, since + the engine was not run. ESS is the proxy for their precision. diff --git a/experiments/us-acs-local-l2-basis-20260928/analyze.py b/experiments/us-acs-local-l2-basis-20260928/analyze.py new file mode 100644 index 000000000..c0c34ef88 --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/analyze.py @@ -0,0 +1,539 @@ +"""Build the ESS-versus-fit frontier from the sweep's metrics. + +Reads ``results/runs/*.json`` (one ``sweep.py`` payload per run) and writes +``results/frontier.csv``, ``results/frontier.md``, ``results/floors.md``, +``results/key_configs.md`` (also spliced into the README) and +``results/frontier.png``. ``dup_*`` runs repeat a first-head configuration at +a later head and are kept out of the frontier; ``results/rerun_variation.json`` +compares each with its original: whether weights and trajectory are +byte-identical, the first epoch where the trajectories differ, and every +frontier metric's relative change (``weights.npz`` beside the checkpoint). + +Run: ``uv run --with matplotlib python experiments/us-acs-local-l2-basis-20260928/analyze.py`` +""" + +from __future__ import annotations + +import hashlib +import json +from pathlib import Path + +import numpy as np +import pandas as pd + +HERE = Path(__file__).resolve().parent +RUNS = HERE / "results" / "runs" +LOCAL_RUNS = Path( + "/Users/maxghenis/PolicyEngine/_build_artifacts/acs-local-l2-basis-20260928/runs" +) +#: Reruns of a first-head run at a later kernel head, and the run they repeat. +#: The frontier metrics the rerun comparison reports. +RERUN_METRICS = ( + "final_loss", + "within_10pct", + "kish_ess", + "top_1pct_share", + "state_ess_min", + "cd_ess_median", + "cd_ess_min", + "cds_below_50", + "cds_below_0.25_of_prior", + "ma_ess", + "chi_square_distance_from_prior", + "donor_mass_share", + "within_10pct_soi", +) +HEAD_PAIRS = { + "dup_release_repro": "release_repro", + "dup_soft_chi_s050_0.03": "soft_chi_s050_0.03", +} +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). +STATE_FLOORS = (300, 1000) +CD_FLOORS = (50, 100, 200) +RELATIVE_FLOORS = (0.25, 0.5) +PRIOR_LABELS = {0.5: "0.5 (release)", 0.7: "0.7", 0.9: "0.9", 0.964: "0.964"} +#: Categorical slots 1-4 of the dataviz reference palette, in fixed order. +PRIOR_COLORS = {0.5: "#2a78d6", 0.7: "#eb6834", 0.9: "#1baf7a", 0.964: "#eda100"} + + +def _prior(payload: dict) -> float: + share = payload["spec"].get("acs_share") + return 0.5 if share is None else round(float(share), 3) + + +def _head(payload: dict) -> str | None: + head = payload["provenance"]["git"].get("head_from_env") + return head[:8] if head else None + + +def row(payload: dict) -> dict: + spec = payload["spec"] + metrics = payload["metrics"] + concentration = metrics["concentration"] + national = concentration["national"] + state = concentration["state_summary"] + district = concentration["cd_summary"] + ma = concentration["massachusetts"] + ma_districts = [entry["kish_ess"] for entry in ma["districts"].values()] + fit = metrics["fit_train"] + prior_fit = metrics["fit_train_design"]["overall"] + state_ess = np.array([v["kish_ess"] for v in concentration["per_state"].values()]) + state_ratio = np.array( + [v["kish_ess_ratio"] for v in concentration["per_state"].values()] + ) + cd_ess = np.array([v["kish_ess"] for v in concentration["per_cd"].values()]) + cd_ratio = np.array([v["kish_ess_ratio"] for v in concentration["per_cd"].values()]) + out = { + "run_id": spec["run_id"], + "kernel_head": _head(payload), + "prior_acs_share": _prior(payload), + "mass_parametrization": spec["mass_parametrization"], + "l2_basis": spec["l2_basis"], + "l2_lambda": float(spec["l2_lambda"]), + "holdout_fold": spec.get("holdout_fold"), + "epochs": spec["epochs"], + "kish_ess": national["kish_ess"], + "ess_fraction": national["ess_fraction"], + "top_1pct_share": national["top_1pct_weight_share"], + "chi_square_distance_from_prior": national["chi_square_distance"], + "chi_square_distance_from_release_design": metrics["prior"][ + "chi_square_distance_from_release_design" + ], + "prior_kish_ess": national["kish_ess_design"], + "donor_mass_share": concentration["per_spine"]["asec_puf"]["mass_share"], + "state_ess_median": state["kish_ess"]["median"], + "state_ess_min": state["kish_ess"]["min"], + "cd_ess_median": district["kish_ess"]["median"], + "cd_ess_p10": district["kish_ess"]["p10"], + "cd_ess_min": district["kish_ess"]["min"], + "prior_cd_ess_median": district["kish_ess_design"]["median"], + "ma_ess": ma["state"]["kish_ess"], + "ma_prior_ess": ma["state"]["kish_ess_design"], + "ma_cd_ess_min": float(min(ma_districts)), + "ma_cd_ess_median": float(np.median(ma_districts)), + "final_loss": payload["result"]["final_loss"], + "within_10pct": fit["overall"]["fraction_within_10pct"], + "mean_capped_error": fit["overall"]["mean_capped_scaled_error"], + "prior_within_10pct": prior_fit["fraction_within_10pct"], + "prior_mean_capped_error": prior_fit["mean_capped_scaled_error"], + "n_states": int(state_ess.size), + "n_cds": int(cd_ess.size), + } + for floor in STATE_FLOORS: + out[f"states_below_{floor}"] = int((state_ess < floor).sum()) + for floor in CD_FLOORS: + out[f"cds_below_{floor}"] = int((cd_ess < floor).sum()) + for floor in RELATIVE_FLOORS: + out[f"states_below_{floor:g}_of_prior"] = int((state_ratio < floor).sum()) + out[f"cds_below_{floor:g}_of_prior"] = int((cd_ratio < floor).sum()) + for family in FAMILIES: + block = fit["per_family"].get(family, {}) + out[f"within_10pct_{family}"] = block.get("fraction_within_10pct") + out[f"mean_abs_rel_error_{family}"] = block.get("mean_abs_rel_error") + if "fit_holdout" in metrics: + holdout = metrics["fit_holdout"] + prior_holdout = metrics["fit_holdout_design"] + out["holdout_within_10pct"] = holdout["overall"]["fraction_within_10pct"] + out["holdout_mean_capped_error"] = holdout["overall"][ + "mean_capped_scaled_error" + ] + out["prior_holdout_within_10pct"] = prior_holdout["overall"][ + "fraction_within_10pct" + ] + out["prior_holdout_mean_capped_error"] = prior_holdout["overall"][ + "mean_capped_scaled_error" + ] + for family in FAMILIES: + block = holdout["per_family"].get(family, {}) + out[f"holdout_within_10pct_{family}"] = block.get("fraction_within_10pct") + return out + + +#: Per-geography fields the committed receipts keep; the full payloads stay in +#: the artifacts directory and on the Modal volume. +GEOGRAPHY_FIELDS = ( + "n", + "kish_ess", + "kish_ess_design", + "kish_ess_ratio", + "n_records_holding_half_weight", +) + + +def compact(payload: dict) -> dict: + """The receipt with per-state and per-district rows cut to GEOGRAPHY_FIELDS.""" + + concentration = payload["metrics"]["concentration"] + for key in ("per_state", "per_cd"): + concentration[key] = { + code: {field: row[field] for field in GEOGRAPHY_FIELDS} + for code, row in concentration[key].items() + } + return payload + + +def load() -> pd.DataFrame: + for path in sorted(RUNS.glob("*.json")): + text = path.read_text() + if text.startswith("{\n"): # a full, indented payload from collect + path.write_text( + json.dumps(compact(json.loads(text)), separators=(",", ":")) + "\n" + ) + rows = [ + row(json.loads(path.read_text())) + for path in sorted(RUNS.glob("*.json")) + if path.stem not in HEAD_PAIRS + ] + frame = pd.DataFrame(rows) + frame["arm"] = [ + "full surface" if pd.isna(fold) else "holdout" for fold in frame.holdout_fold + ] + return frame.sort_values( + ["arm", "mass_parametrization", "l2_basis", "prior_acs_share", "l2_lambda"] + ) + + +def _fmt(value, spec: str) -> str: + if value is None or (isinstance(value, float) and np.isnan(value)): + return "" + return format(value, spec) + + +#: The configurations the write-up compares, in order, with a short label. +KEY_CONFIGS = ( + ("release_repro", "Release (reproduced): share 0.5, projection, λ 0"), + ("proj_chi_s050_0.03", "Share 0.5, projection, chi-square λ 0.03"), + ("soft_chi_s050_0.03", "Share 0.5, softmax, chi-square λ 0.03"), + ("proj_chi_s050_0.1", "Share 0.5, projection, chi-square λ 0.1"), + ("soft_chi_s050_0.1", "Share 0.5, softmax, chi-square λ 0.1"), + ("soft_chi_s050_1", "Share 0.5, softmax, chi-square λ 1"), + ("soft_chi_s700_0.01", "Share 0.7, softmax, chi-square λ 0.01"), + ("soft_chi_s900_0", "Share 0.9, softmax, no penalty"), + ("soft_chi_s900_0.01", "Share 0.9, softmax, chi-square λ 0.01"), + ("soft_chi_s964_0", "Share 0.964, softmax, no penalty"), +) + + +def _holdout_id(run_id: str, fold: int) -> str: + if run_id == "release_repro": + run_id = "proj_s050_0" + return ("hold_" if fold == 0 else f"hold{fold}_") + run_id + + +def key_config_table(frame: pd.DataFrame) -> str: + """The write-up's comparison: concentration, training fit and holdout.""" + + by_id = frame.set_index("run_id") + columns = ( + "Configuration", + "National ESS", + "Top-1% share", + "State ESS min", + "CD ESS median / min", + "CDs < 50", + "MA ESS", + "Train within 10%", + "Held-out error, fold 0 / 1", + "Held-out within 10%, fold 0 / 1", + ) + lines = [ + "| " + " | ".join(columns) + " |", + "|---|" + "---:|" * (len(columns) - 1), + ] + for run_id, label in KEY_CONFIGS: + if run_id not in by_id.index: + continue + r = by_id.loc[run_id] + held, held_within = [], [] + for fold in (0, 1): + hid = _holdout_id(run_id, fold) + found = hid in by_id.index + held.append( + f"{by_id.loc[hid, 'holdout_mean_capped_error']:.4f}" if found else "–" + ) + held_within.append( + f"{by_id.loc[hid, 'holdout_within_10pct']:.1%}" if found else "–" + ) + lines.append( + f"| {label} | {r.kish_ess:,.0f} | {r.top_1pct_share:.1%} | " + f"{r.state_ess_min:,.0f} | {r.cd_ess_median:,.0f} / {r.cd_ess_min:,.0f} | " + f"{int(r.cds_below_50)} | {r.ma_ess:,.0f} | {r.within_10pct:.1%} | " + f"{' / '.join(held)} | {' / '.join(held_within)} |" + ) + return "\n".join(lines) + "\n" + + +def floor_table(frame: pd.DataFrame) -> str: + """States and districts below each candidate ESS floor, per full-surface run.""" + + full = frame[frame.arm == "full surface"] + columns = [ + ("Run", "run_id", "s"), + ("Within 10%", "within_10pct", ".1%"), + ("State ESS min", "state_ess_min", ",.0f"), + *[(f"States < {f:,}", f"states_below_{f}", "d") for f in STATE_FLOORS], + ("CD ESS min", "cd_ess_min", ",.0f"), + *[(f"CDs < {f}", f"cds_below_{f}", "d") for f in CD_FLOORS], + *[ + (f"CDs < {f:.0%} of prior", f"cds_below_{f:g}_of_prior", "d") + for f in RELATIVE_FLOORS + ], + ] + lines = [ + "| " + " | ".join(name for name, _, _ in columns) + " |", + "|" + "---:|" * len(columns), + ] + for _, r in full.iterrows(): + lines.append( + "| " + " | ".join(_fmt(r[key], spec) for _, key, spec in columns) + " |" + ) + return "\n".join(lines) + "\n" + + +def markdown(frame: pd.DataFrame) -> str: + sections = [] + full = frame[frame.arm == "full surface"] + columns = ( + ("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"), + ("MA CD ESS min", "ma_cd_ess_min", ",.0f"), + ("Loss", "final_loss", ".4f"), + ("Within 10%", "within_10pct", ".1%"), + ("SOI within 10%", "within_10pct_soi", ".1%"), + ("SNAP", "within_10pct_snap", ".0%"), + ("Medicaid", "within_10pct_medicaid", ".0%"), + ("CD pop", "within_10pct_pop_cd", ".0%"), + ) + for (parametrization, basis), group in full.groupby( + ["mass_parametrization", "l2_basis"], sort=False + ): + 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", "prior_acs_share", "mass_parametrization", "l2_lambda"] + ) + if len(held): + hold_columns = ( + ("Fold", "holdout_fold", ".0f"), + ("Parametrization", "mass_parametrization", "s"), + ("Prior ACS share", "prior_acs_share", ".3g"), + ("λ", "l2_lambda", "g"), + ("National ESS", "kish_ess", ",.0f"), + ("Train within 10%", "within_10pct", ".1%"), + ("Held-out within 10%", "holdout_within_10pct", ".1%"), + ("Held-out capped error", "holdout_mean_capped_error", ".4f"), + ("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 within 10%", "prior_holdout_within_10pct", ".1%"), + ) + 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 chart(frame: pd.DataFrame, path: Path) -> None: + import matplotlib + + matplotlib.use("Agg") + import matplotlib.pyplot as plt + + surface, ink, muted, grid = "#fcfcfb", "#0b0b0b", "#52514e", "#e4e3df" + full = frame[frame.arm == "full surface"] + softmax = full[ + (full.mass_parametrization == "softmax") & (full.l2_basis == "chi_square") + ] + release = full[ + (full.mass_parametrization == "projection") + & (full.l2_lambda == 0.0) + & (full.prior_acs_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 prior, group in softmax.groupby("prior_acs_share"): + group = group.sort_values("l2_lambda") + color = PRIOR_COLORS.get(prior, muted) + ax.plot( + group.within_10pct, + group[key], + color=color, + linewidth=2, + marker="o", + markersize=8, + markeredgecolor=surface, + markeredgewidth=2, + label=f"ACS share {PRIOR_LABELS.get(prior, prior)}", + ) + # Label only each line's ends; lambda rises right to left. + for r in (group.iloc[0], group.iloc[-1]): + ax.annotate( + f"λ={r.l2_lambda:g}", + (r.within_10pct, r[key]), + textcoords="offset points", + xytext=(6, -12) if r.l2_lambda == 0 else (-8, 8), + ha="left" if r.l2_lambda == 0 else "right", + fontsize=8, + color=muted, + ) + if len(release): + r = release.iloc[0] + ax.scatter( + [r.within_10pct], + [r[key]], + s=90, + marker="D", + color=ink, + edgecolor=surface, + linewidth=2, + zorder=5, + label="Published release (projection, λ=0)", + ) + ax.set_yscale("log") + ax.set_xlabel("Training targets within 10%", 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: ESS vs fit under a chi-square penalty toward each " + "prior (softmax mass, 800 epochs; λ = 0, 0.01, 0.03, 0.1, 0.3, 1, 3 " + "from right to left)", + color=ink, + fontsize=11, + ) + fig.tight_layout() + fig.savefig(path, dpi=150, facecolor=surface) + + +def rerun_variation() -> dict: + """Each rerun against its original: bytes, divergence, metric changes.""" + + pairs = {} + 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)): + pairs[dup] = {"original": original, "status": "not collected"} + continue + payloads = [json.loads(path.read_text()) for path in receipts] + heads = [_head(payload) for payload in payloads] + rows = [row(payload) for payload in payloads] + arrays = [np.load(path) for path in paths] + weights = [a["weights"] for a in arrays] + trajectories = [ + np.concatenate( + [a[k] for k in sorted(a.files) if k.startswith("trajectory_")] + ) + for a in arrays + ] + pairs[dup] = { + "original": original, + "heads": {"rerun": heads[0], "original": heads[1]}, + "weights_sha256": [ + hashlib.sha256(np.ascontiguousarray(w).tobytes()).hexdigest() + for w in weights + ], + "weights_byte_identical": bool( + weights[0].dtype == weights[1].dtype + and weights[0].tobytes() == weights[1].tobytes() + ), + "trajectory_byte_identical": bool( + trajectories[0].tobytes() == trajectories[1].tobytes() + ), + "first_trajectory_difference_epoch": ( + int(np.argmax(trajectories[0] != trajectories[1])) + if (trajectories[0] != trajectories[1]).any() + else None + ), + "trajectory_relative_difference_at_epochs_0_1": [ + float(abs(a - b) / abs(b)) + for a, b in zip(trajectories[0][:2], trajectories[1][:2], strict=True) + ], + "weight_relative_difference_median_p99": [ + float(q) + for q in np.quantile( + np.abs(weights[0] - weights[1]) / weights[1], (0.5, 0.99) + ) + ], + "metrics": { + name: { + "rerun": float(rows[0][name]), + "original": float(rows[1][name]), + "relative_change": ( + float(rows[0][name] / rows[1][name] - 1.0) + if rows[1][name] + else None + ), + } + for name in RERUN_METRICS + }, + } + return pairs + + +def main() -> None: + frame = load() + out = HERE / "results" + frame.to_csv(out / "frontier.csv", index=False) + (out / "frontier.md").write_text(markdown(frame)) + (out / "floors.md").write_text(floor_table(frame)) + table = key_config_table(frame) + (out / "key_configs.md").write_text(table) + (out / "rerun_variation.json").write_text( + json.dumps(rerun_variation(), indent=1) + "\n" + ) + readme = HERE / "README.md" + start, end = ( + "", + "", + ) + text = readme.read_text() + head, _, rest = text.partition(start) + _, _, tail = rest.partition(end) + readme.write_text(f"{head}{start}\n{table}{end}{tail}") + chart(frame, out / "frontier.png") + print(markdown(frame)) + + +if __name__ == "__main__": + main() diff --git a/experiments/us-acs-local-l2-basis-20260928/exact_cap_float32.py b/experiments/us-acs-local-l2-basis-20260928/exact_cap_float32.py new file mode 100644 index 000000000..9d6cbde50 --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/exact_cap_float32.py @@ -0,0 +1,179 @@ +"""What float32 does to the softmax cap step at full scale. + +Applies the kernel's exact projection to full-scale log-weights (a softmax +run's final weights, moved by a step-sized random perturbation), so every +log-weight is at or below its float32 log cap and the vector meets the total. +Two measurements on each projected vector: + +- **The realized weights.** The solve realizes ``total * softmax(log_w)`` in + float32, whose normalizer is a float32 sum over 1.59M records. Realize the + same float32 log-weights with a float64 normalizer too; the difference is + what the float32 softmax alone adds to the cap ratio. +- **microcosm#1078's 32-round loop on that vector.** It needs no cap work, so + an exact stopping test would pass in the first round. Record the shift the + loop computes each round, the records it lifts over their float32 log caps, + and whether the rounds run out. + +Run on Modal, off the shared host, from the sweep's volume (it writes +``results/exact_cap_float32.json``):: + + modal run experiments/us-acs-local-l2-basis-20260928/exact_cap_modal.py::float32 + +or locally against the build artifacts with +``uv run python experiments/us-acs-local-l2-basis-20260928/exact_cap_float32.py``. +""" + +from __future__ import annotations + +import json +import math +from pathlib import Path + +import numpy as np +import pandas as pd +import torch + +from microcosm.calibrate import solve + +HERE = Path(__file__).resolve().parent +ARTIFACTS = Path("/Users/maxghenis/PolicyEngine/_build_artifacts") +CHECKPOINT = ARTIFACTS / "acs-local-l2-basis-20260928/checkpoint" +RUN_WEIGHTS = ( + ARTIFACTS / "acs-local-l2-basis-20260928/runs/dup_soft_chi_s050_0.03/weights.npz" +) +MAX_WEIGHT_RATIO = 5.0 +#: Adam's learning rate in the release's solve: the size of one log-weight step. +STEP = 0.02 +TRIALS = 12 +SEED = 3 + + +def rounds_32_on(log_w: torch.Tensor, total: float, log_upper: torch.Tensor) -> dict: + """microcosm#1078's cap step (b115745d1) on a copy, with what each round did.""" + log_w = log_w.clone() + shifts, lifted, lifted_log_cap = [], [], 0.0 + for _ in range(32): + shift = math.log(total) - float(torch.logsumexp(log_w, dim=0).item()) + shifts.append(shift) + log_w.add_(shift) + over = log_w > log_upper + lifted.append(int(over.sum())) + if not lifted[-1]: + break + lifted_log_cap = max(lifted_log_cap, float(log_upper[over].abs().max())) + log_w.clamp_(max=log_upper) + return { + "rounds": len(shifts), + "ran_out": bool(lifted[-1]), + "shift_min": min(shifts), + "shift_max": max(shifts), + "records_lifted_over_their_caps_per_round_max": max(lifted), + "largest_abs_log_cap_among_lifted_records": lifted_log_cap, + } + + +def measure(checkpoint: Path = CHECKPOINT, run_weights: Path = RUN_WEIGHTS) -> dict: + design = pd.read_parquet(checkpoint / "households.parquet")[ + "design_weight" + ].to_numpy(np.float64) + weights = np.load(run_weights)["weights"].astype(np.float64) + total = float(design.sum()) + cap = torch.tensor(MAX_WEIGHT_RATIO * design, dtype=torch.float64) + log_upper = torch.log(torch.tensor(MAX_WEIGHT_RATIO * design, dtype=torch.float32)) + rng = np.random.default_rng(SEED) + trials = [] + for _ in range(TRIALS): + log_w = torch.tensor( + np.log(weights) + rng.normal(0.0, STEP, design.size), dtype=torch.float32 + ) + rounds = solve._project_softmax_log_weights_( + log_w, total, log_upper.to(torch.float64) + ) + if not bool((log_w <= log_upper).all()): + raise SystemExit("a projected log-weight passed its float32 log cap") + loop = rounds_32_on(log_w, total, log_upper) + float32 = total * torch.softmax(log_w, dim=0) + exact = log_w.to(torch.float64) + float64 = total * torch.exp(exact - torch.logsumexp(exact, dim=0)) + trials.append( + { + "active_set_rounds": int(rounds), + "records_at_cap": int((log_w >= log_upper).sum()), + "float64_shift_still_needed": math.log(total) + - float(torch.logsumexp(log_w.to(torch.float64), dim=0)), + "rounds_32_loop": loop, + "float32_softmax": { + "max_cap_ratio_minus_one": solve._max_cap_ratio(float32, cap) - 1.0, + "total_error": float(float32.to(torch.float64).sum()) / total - 1.0, + }, + "float64_normalizer": { + "max_cap_ratio_minus_one": solve._max_cap_ratio(float64, cap) - 1.0, + "total_error": float(float64.sum()) / total - 1.0, + }, + } + ) + + def spread(realization: str, key: str) -> dict: + values = [trial[realization][key] for trial in trials] + return {"min": min(values), "max": max(values)} + + out = { + "records": int(design.size), + "max_weight_ratio": MAX_WEIGHT_RATIO, + "log_weight_step_sd": STEP, + "trials": len(trials), + "seed": SEED, + "torch": str(torch.__version__), + "torch_num_threads": torch.get_num_threads(), + "float32_softmax": { + key: spread("float32_softmax", key) + for key in ("max_cap_ratio_minus_one", "total_error") + }, + "float64_normalizer": { + key: spread("float64_normalizer", key) + for key in ("max_cap_ratio_minus_one", "total_error") + }, + # How far the float32 softmax's cap ratio sits above its own total + # error: what is left once the normalizer's error is taken out. + "float32_cap_ratio_minus_total_error": { + "min": min( + t["float32_softmax"]["max_cap_ratio_minus_one"] + - t["float32_softmax"]["total_error"] + for t in trials + ), + "max": max( + t["float32_softmax"]["max_cap_ratio_minus_one"] + - t["float32_softmax"]["total_error"] + for t in trials + ), + }, + "rounds_32_loop_on_the_projected_vectors": { + "trials_ran_out": sum(t["rounds_32_loop"]["ran_out"] for t in trials), + "shift": { + "min": min(t["rounds_32_loop"]["shift_min"] for t in trials), + "max": max(t["rounds_32_loop"]["shift_max"] for t in trials), + }, + "float32_log_total_residual": math.log(total) + - float(torch.tensor(math.log(total), dtype=torch.float32)), + "records_lifted_over_their_caps_per_round_max": max( + t["rounds_32_loop"]["records_lifted_over_their_caps_per_round_max"] + for t in trials + ), + "largest_abs_log_cap_among_lifted_records": max( + t["rounds_32_loop"]["largest_abs_log_cap_among_lifted_records"] + for t in trials + ), + }, + "by_trial": trials, + } + return out + + +def write(out: dict) -> None: + path = HERE / "results" / "exact_cap_float32.json" + path.write_text(json.dumps(out, indent=1) + "\n") + print(json.dumps({k: v for k, v in out.items() if k != "by_trial"}, indent=1)) + + +if __name__ == "__main__": + write(measure()) diff --git a/experiments/us-acs-local-l2-basis-20260928/exact_cap_modal.py b/experiments/us-acs-local-l2-basis-20260928/exact_cap_modal.py new file mode 100644 index 000000000..d7ae8fa8f --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/exact_cap_modal.py @@ -0,0 +1,333 @@ +"""Measure the softmax cap step at full scale, before and after the exact projection. + +Each run is one ``sweep.py`` softmax calibration of the 2026-09-23 release's +surface (1,588,854 households, 4,459 targets, a 5x cap; 800 epochs as two +warm-started batches) on Modal, in one of two variants of the same committed +kernel: + +- ``water_fill``: the kernel as committed, whose per-step cap is the exact + log-space projection ``_project_softmax_log_weights_``. +- ``rounds_32``: the same kernel with that one function replaced by + microcosm#1078's renormalize/clamp loop (32 rounds at most), copied here. + +Both record what the kernel's receipt records, the largest in-loop weight +over its cap, plus this script's own instrumentation: the ratio at every +forward pass, the cap step's rounds and its wall time. + + modal run --detach experiments/us-acs-local-l2-basis-20260928/exact_cap_modal.py::launch + modal run experiments/us-acs-local-l2-basis-20260928/exact_cap_modal.py::collect + modal run experiments/us-acs-local-l2-basis-20260928/exact_cap_modal.py::float32 + +``launch`` reads the checkpoint the sweep already uploaded to the volume +(``sweep.py`` checks every file against ``MANIFEST.json``) and uploads +nothing. ``collect`` copies each finished run's metrics into +``results/exact_cap/`` and writes ``results/exact_cap.md``. Weights stay on +the volume under ``/exact_cap``. ``float32`` runs ``exact_cap_float32.py`` +against the volume's checkpoint and the sweep's ``dup_soft_chi_s050_0.03`` +weights and writes ``results/exact_cap_float32.json``. +""" + +from __future__ import annotations + +import json +import subprocess +from pathlib import Path + +import modal + +HERE = Path(__file__).resolve().parent +REPO = ( + HERE.parents[1] + if len(HERE.parents) > 1 and (HERE.parents[1] / ".git").exists() + else None +) +KERNEL = ( + "microcosm-frame", + "microcosm-graph", + "microcosm-diagnostics", + "microcosm-calibrate", +) +VOLUME_ROOT = "/sweep" +OUT_ROOT = f"{VOLUME_ROOT}/exact_cap" +CPUS = 8 +VARIANTS = ("water_fill", "rounds_32") +#: The two configurations whose earlier runs ran out of cap rounds on every +#: epoch of their last batch: the recommended penalty at the release's +#: seeding, and the unpenalized solve at an ACS share of 0.9. +CONFIGURATIONS = { + "soft_chi_s050_0.03": { + "l2_lambda": 0.03, + "l2_basis": "chi_square", + "mass_parametrization": "softmax", + }, + "soft_chi_s900_0": { + "l2_lambda": 0.0, + "l2_basis": "chi_square", + "mass_parametrization": "softmax", + "acs_share": 0.9, + }, +} + + +def _git(*args: str) -> str: + return subprocess.run( + ["git", "-C", str(REPO), *args], capture_output=True, text=True, check=True + ).stdout.strip() + + +GIT_SHA = _git("rev-parse", "HEAD") if REPO is not None else "container" + +image = ( + modal.Image.debian_slim(python_version="3.13") + .uv_pip_install("torch==2.12.0", index_url="https://download.pytorch.org/whl/cpu") + .uv_pip_install( + "numpy==2.4.6", + "scipy==1.17.1", + "pandas==3.0.3", + "pyarrow==25.0.0", + "pydantic>=2.10,<3", + ) + .env( + { + "OMP_NUM_THREADS": str(CPUS), + "MKL_NUM_THREADS": str(CPUS), + "OPENBLAS_NUM_THREADS": str(CPUS), + "MICROCOSM_GIT_SHA": GIT_SHA, + "MICROCOSM_REPO": "/opt/none", + } + ) +) +if REPO is not None: + for name in KERNEL: + 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") + .add_local_file( + str(HERE / "exact_cap_float32.py"), "/opt/sweep/exact_cap_float32.py" + ) + ) + +app = modal.App("microcosm-acs-exact-cap", image=image) +volume = modal.Volume.from_name("microcosm-acs-l2-basis-sweep") + + +def run_ids() -> list[tuple[str, str, str]]: + return [ + (f"{variant}_{name}", variant, name) + for name in CONFIGURATIONS + for variant in VARIANTS + ] + + +def _instrument(variant: str) -> dict: + """Swap in the variant's cap step and record what each step and pass did.""" + + import math + import time + + import torch + + from microcosm.calibrate import solve + + record = {"variant": variant, "seconds": 0.0, "rounds": [], "ratios": []} + water_fill = solve._project_softmax_log_weights_ + float32_caps: dict[int, torch.Tensor] = {} + + def rounds_32(log_w, total, log_upper): + """microcosm#1078's cap step (b115745d1), plus the round count. + + Shift so ``exp(log_w)`` sums to ``total``, stop if no record is over + its float32 log cap, else clamp and go again. Returns the rounds + taken, negated when all 32 ran out with a record still over. + """ + if log_upper is not None and id(log_upper) not in float32_caps: + float32_caps[id(log_upper)] = log_upper.to(torch.float32) + upper = None if log_upper is None else float32_caps[id(log_upper)] + for taken in range(1, 33): + log_w.add_(math.log(total) - float(torch.logsumexp(log_w, dim=0).item())) + if upper is None or not bool((log_w > upper).any().item()): + return taken + log_w.clamp_(max=upper) + return -32 + + step = water_fill if variant == "water_fill" else rounds_32 + + def timed(log_w, total, log_upper): + started = time.perf_counter() + taken = step(log_w, total, log_upper) + record["seconds"] += time.perf_counter() - started + record["rounds"].append(int(taken)) + return taken + + max_cap_ratio = solve._max_cap_ratio + + def recorded(weights, cap): + ratio = max_cap_ratio(weights, cap) + record["ratios"].append(ratio) + return ratio + + solve._project_softmax_log_weights_ = timed + solve._max_cap_ratio = recorded + return record + + +def _cap_step_summary(record: dict) -> dict: + import numpy as np + + rounds = np.asarray(record["rounds"]) + ratios = np.asarray(record["ratios"], dtype=np.float64) + values, counts = np.unique(rounds, return_counts=True) + return { + "variant": record["variant"], + "cap_steps": int(rounds.size), + "cap_step_seconds_total": record["seconds"], + "cap_step_seconds_mean": record["seconds"] / max(1, rounds.size), + "rounds_histogram": { + str(int(v)): int(c) for v, c in zip(values, counts, strict=True) + }, + "rounds_exhausted_steps": int((rounds < 0).sum()), + "forward_passes": int(ratios.size), + "in_loop_cap_ratio_minus_one": { + "max": float(ratios.max() - 1.0), + "p99": float(np.quantile(ratios, 0.99) - 1.0), + "median": float(np.median(ratios) - 1.0), + "min": float(ratios.min() - 1.0), + "passes_above_cap": int((ratios > 1.0).sum()), + }, + "in_loop_cap_ratio_by_pass": [float(r) for r in ratios], + } + + +@app.function( + cpu=CPUS, memory=24576, timeout=3 * 3600, volumes={VOLUME_ROOT: volume}, retries=0 +) +def run_variant(run_id: str, variant: str, name: str) -> dict: + import sys + + import torch + + torch.set_num_threads(CPUS) + sys.path.insert(0, "/opt/sweep") + import sweep + + volume.reload() + out_root = Path(OUT_ROOT) / "runs" + out_root.mkdir(parents=True, exist_ok=True) + record = _instrument(variant) + payload = sweep.calibrate_mode( + sweep.RunSpec.from_mapping({"run_id": run_id, **CONFIGURATIONS[name]}), + Path(VOLUME_ROOT) / "checkpoint", + out_root, + resume=False, + verify=True, + ) + cap_step = _cap_step_summary(record) + (out_root / run_id / "cap_step.json").write_text( + json.dumps(cap_step, indent=1) + "\n" + ) + volume.commit() + return { + "run_id": run_id, + "final_loss": payload["result"]["final_loss"], + "kish_ess": payload["metrics"]["concentration"]["national"]["kish_ess"], + "wall_seconds": payload["timing"]["total_wall_seconds"], + **{k: v for k, v in cap_step.items() if k != "in_loop_cap_ratio_by_pass"}, + } + + +@app.local_entrypoint() +def launch() -> None: + kernel = [f"packages/{name}/src" for name in KERNEL] + if _git("status", "--porcelain", "--", *kernel): + raise SystemExit("kernel sources differ from HEAD; commit before launching") + print(f"kernel HEAD {GIT_SHA}") + calls = [] + for run_id, variant, name in run_ids(): + calls.append(run_variant.spawn(run_id, variant, name)) + print(run_id, calls[-1].object_id, flush=True) + # The runs outlive this process under --detach; waiting only prints them. + for call in calls: + print(json.dumps(call.get()), flush=True) + + +@app.function(cpu=CPUS, memory=8192, timeout=1800, volumes={VOLUME_ROOT: volume}) +def measure_float32() -> str: + import sys + + import torch + + torch.set_num_threads(CPUS) + sys.path.insert(0, "/opt/sweep") + import exact_cap_float32 + + out = exact_cap_float32.measure( + Path(VOLUME_ROOT) / "checkpoint", + Path(VOLUME_ROOT) / "runs" / "dup_soft_chi_s050_0.03" / "weights.npz", + ) + # JSON text: the local client has no torch to unpickle torch-typed values. + return json.dumps(out) + + +@app.local_entrypoint() +def float32() -> None: + kernel = [f"packages/{name}/src" for name in KERNEL] + if _git("status", "--porcelain", "--", *kernel): + raise SystemExit("kernel sources differ from HEAD; commit before launching") + out = {"kernel_head": GIT_SHA, **json.loads(measure_float32.remote())} + path = HERE / "results" / "exact_cap_float32.json" + path.write_text(json.dumps(out, indent=1) + "\n") + print(json.dumps({k: v for k, v in out.items() if k != "by_trial"}, indent=1)) + + +def _read(path: str) -> bytes | None: + try: + return b"".join(volume.read_file(path)) + except FileNotFoundError: + return None + + +@app.local_entrypoint() +def collect() -> None: + results = HERE / "results" / "exact_cap" + results.mkdir(parents=True, exist_ok=True) + rows = [] + for run_id, variant, name in run_ids(): + metrics = _read(f"/exact_cap/runs/{run_id}/metrics.json") + cap_step = _read(f"/exact_cap/runs/{run_id}/cap_step.json") + if metrics is None or cap_step is None: + print(f"{run_id}: not finished") + continue + (results / f"{run_id}.json").write_bytes(metrics) + (results / f"{run_id}.cap_step.json").write_bytes(cap_step) + rows.append((name, variant, json.loads(metrics), json.loads(cap_step))) + lines = [ + "| Configuration | Cap step | Largest in-loop weight over its cap, minus 1 " + "(max / median over passes) | Steps out of rounds | Rounds per step " + "| Cap step ms | s/epoch | Receipt (batch 1 / 2) | Final loss | National ESS |", + "|---|---|---:|---:|---|---:|---:|---|---:|---:|", + ] + for name, variant, metrics, cap_step in rows: + ratio = cap_step["in_loop_cap_ratio_minus_one"] + batches = metrics["batches"] + receipts = " / ".join( + f"{(b.get('iterate_selection_receipt') or {}).get('softmax_in_loop_max_cap_ratio', float('nan')) - 1.0:.2e}" + for b in batches + ) + per_epoch = " / ".join(f"{b['seconds_per_epoch']:.2f}" for b in batches) + lines.append( + f"| `{name}` | `{variant}` | {ratio['max']:.2e} / {ratio['median']:.2e} " + f"| {cap_step['rounds_exhausted_steps']} of {cap_step['cap_steps']} " + f"| {json.dumps(cap_step['rounds_histogram'])} " + f"| {1000 * cap_step['cap_step_seconds_mean']:.1f} | {per_epoch} " + f"| {receipts} | {metrics['result']['final_loss']:.6f} " + f"| {metrics['metrics']['concentration']['national']['kish_ess']:,.0f} |" + ) + (HERE / "results" / "exact_cap.md").write_text("\n".join(lines) + "\n") + print("\n".join(lines)) + print(f"collected {len(rows)} of {len(run_ids())} runs") diff --git a/experiments/us-acs-local-l2-basis-20260928/gradient_scale.py b/experiments/us-acs-local-l2-basis-20260928/gradient_scale.py new file mode 100644 index 000000000..9067ef055 --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/gradient_scale.py @@ -0,0 +1,74 @@ +"""Per-record gradient scale of the release's calibration loss at full size. + +Adam's step is ``lr * m / (sqrt(v) + eps)`` with ``eps = 1e-8``: a record whose +gradient is well above ``eps`` moves about ``lr`` whatever its size, one near +or below ``eps`` moves in proportion to it. This measures the exact log-weight +gradient of the release's capped-MAPE loss (cap 1.0, uniform target weights) +at the design weights and at the published release's weights, and the split +of its signs, which decides whether the projection parametrization's +same-sign stall can occur at this scale. + +Run: ``uv run python experiments/us-acs-local-l2-basis-20260928/gradient_scale.py`` +Writes ``results/gradient_scale.json``. +""" + +from __future__ import annotations + +import json +from pathlib import Path + +import numpy as np +import pandas as pd +from scipy import sparse + +HERE = Path(__file__).resolve().parent +CHECKPOINT = Path( + "/Users/maxghenis/PolicyEngine/_build_artifacts/acs-local-l2-basis-20260928/checkpoint" +) +RELEASE_WEIGHTS = Path( + "/Users/maxghenis/PolicyEngine/_recovered/scratch-backup/893/overnight-20260923/" + "run/release/checkpoints/weights_latest.npz" +) +EPS = 1e-8 +CAP = 1.0 + + +def main() -> None: + matrix = sparse.load_npz(CHECKPOINT / "target_matrix.npz").tocsr() + matrix = matrix.astype(np.float64) + goals = pd.read_parquet(CHECKPOINT / "targets_meta.parquet")["value"].to_numpy() + design = pd.read_parquet(CHECKPOINT / "households.parquet")[ + "design_weight" + ].to_numpy(np.float64) + scale = np.maximum(np.abs(goals), 1.0) + out = {"eps": EPS, "loss_cap": CAP, "n_targets": int(len(goals))} + for label, weights in ( + ("design", design), + ("release_0923", np.load(RELEASE_WEIGHTS)["weights"].astype(np.float64)), + ): + miss = (matrix @ weights - goals) / scale + # Rows past the cap contribute no gradient to capped MAPE. + live = np.abs(miss) < CAP + grad_w = matrix.T @ (np.sign(miss) * live / scale) / len(goals) + grad = grad_w * weights + magnitude = np.abs(grad) + signs = np.sign(grad[grad != 0]) + out[label] = { + "log_weight_grad_abs_quantiles": { + q: float(np.quantile(magnitude, float(q))) + for q in ("0.1", "0.25", "0.5", "0.75", "0.9", "0.99") + }, + "share_at_or_below_eps": float((magnitude <= EPS).mean()), + "share_at_least_10_eps": float((magnitude >= 10 * EPS).mean()), + "share_positive_of_nonzero": float((signs > 0).mean()), + "share_negative_of_nonzero": float((signs < 0).mean()), + "live_targets": int(live.sum()), + } + (HERE / "results" / "gradient_scale.json").write_text( + json.dumps(out, indent=1) + "\n" + ) + print(json.dumps(out, indent=1)) + + +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 new file mode 100644 index 000000000..d2650f63b --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/modal_sweep.py @@ -0,0 +1,361 @@ +"""Run the ACS local L2-basis sweep grid on Modal. + +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. + + 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 + +``launch`` uploads the verified checkpoint once (sha256-checked in the +container against ``MANIFEST.json``), runs ``release_repro`` first and stops +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. +""" + +from __future__ import annotations + +import json +import subprocess +from pathlib import Path + +import modal + +HERE = Path(__file__).resolve().parent +#: The worktree root when this file runs from the repository; ``None`` inside +#: a container, where Modal mounts the launcher alone at /root. +REPO = ( + HERE.parents[1] + if len(HERE.parents) > 1 and (HERE.parents[1] / ".git").exists() + else None +) +CHECKPOINT = Path( + "/Users/maxghenis/PolicyEngine/_build_artifacts/acs-local-l2-basis-20260928/checkpoint" +) +LOCAL_RUNS = CHECKPOINT.parent / "runs" +UPLOAD = ( + "MANIFEST.json", + "target_matrix.npz", + "households.parquet", + "targets_meta.parquet", + "holdout_folds.npz", +) +KERNEL = ( + "microcosm-frame", + "microcosm-graph", + "microcosm-diagnostics", + "microcosm-calibrate", +) +VOLUME_ROOT = "/sweep" +CPUS = 8 + + +def _head() -> str: + return subprocess.run( + ["git", "-C", str(REPO), "rev-parse", "HEAD"], + capture_output=True, + text=True, + check=True, + ).stdout.strip() + + +def _kernel_clean() -> bool: + status = subprocess.run( + ["git", "-C", str(REPO), "status", "--porcelain", "--"] + + [f"packages/{name}/src" for name in KERNEL], + capture_output=True, + text=True, + check=True, + ).stdout.strip() + return not status + + +GIT_SHA = _head() if REPO is not None else "container" + +image = ( + modal.Image.debian_slim(python_version="3.13") + .uv_pip_install( + "torch==2.12.0", + index_url="https://download.pytorch.org/whl/cpu", + ) + .uv_pip_install( + "numpy==2.4.6", + "scipy==1.17.1", + "pandas==3.0.3", + "pyarrow==25.0.0", + "pydantic>=2.10,<3", + ) + .env( + { + "OMP_NUM_THREADS": str(CPUS), + "MKL_NUM_THREADS": str(CPUS), + "OPENBLAS_NUM_THREADS": str(CPUS), + "MICROCOSM_GIT_SHA": GIT_SHA, + "MICROCOSM_REPO": "/opt/none", + } + ) +) +if REPO is not None: + for name in KERNEL: + 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") + +app = modal.App("microcosm-acs-l2-basis-sweep", image=image) +volume = modal.Volume.from_name("microcosm-acs-l2-basis-sweep", create_if_missing=True) + + +def grid() -> list[dict]: + """The sweep's run specs; ``release_repro`` must come first.""" + + specs = [ + {"run_id": "release_repro", "mass_parametrization": "projection"}, + ] + lambdas = (0.0, 0.01, 0.03, 0.1, 0.3, 1.0, 3.0) + for share in (None, 0.7, 0.9, 0.964): + tag = "s050" if share is None else f"s{round(share * 1000):03d}" + for lam in lambdas: + specs.append( + { + "run_id": f"soft_chi_{tag}_{lam:g}", + "l2_lambda": lam, + "l2_basis": "chi_square", + "mass_parametrization": "softmax", + "acs_share": share, + } + ) + for lam in (0.1, 1.0): + specs.append( + { + "run_id": f"proj_chi_s050_{lam:g}", + "l2_lambda": lam, + "l2_basis": "chi_square", + "mass_parametrization": "projection", + } + ) + specs.append( + { + "run_id": "proj_rec_s050_0.1", + "l2_lambda": 0.1, + "l2_basis": "record", + "mass_parametrization": "projection", + } + ) + for share in (None, 0.964): + tag = "s050" if share is None else "s964" + for lam in (0.0, 0.1, 1.0): + specs.append( + { + "run_id": f"hold_soft_chi_{tag}_{lam:g}", + "l2_lambda": lam, + "l2_basis": "chi_square", + "mass_parametrization": "softmax", + "acs_share": share, + "holdout_fold": 0, + } + ) + specs.append( + { + "run_id": "hold_proj_s050_0", + "mass_parametrization": "projection", + "holdout_fold": 0, + } + ) + # Second pass (after the first frontier): holdout at the knee lambdas and + # the intermediate priors, and projection at the recommended lambda. + for share, lams in ( + (None, (0.01, 0.03)), + (0.7, (0.0, 0.01, 0.1)), + (0.9, (0.0, 0.01, 0.1)), + ): + tag = "s050" if share is None else f"s{round(share * 1000):03d}" + for lam in lams: + specs.append( + { + "run_id": f"hold_soft_chi_{tag}_{lam:g}", + "l2_lambda": lam, + "l2_basis": "chi_square", + "mass_parametrization": "softmax", + "acs_share": share, + "holdout_fold": 0, + } + ) + # Third pass: projection at the held-out optimum, and a second holdout + # fold for the configurations the recommendation compares. + specs.append( + { + "run_id": "proj_chi_s050_0.03", + "l2_lambda": 0.03, + "l2_basis": "chi_square", + "mass_parametrization": "projection", + } + ) + for fold in (0, 1): + specs.append( + { + "run_id": f"hold{fold}_proj_chi_s050_0.03".replace("hold0_", "hold_"), + "l2_lambda": 0.03, + "l2_basis": "chi_square", + "mass_parametrization": "projection", + "holdout_fold": fold, + } + ) + specs.append( + { + "run_id": "hold1_proj_s050_0", + "mass_parametrization": "projection", + "holdout_fold": 1, + } + ) + for share, lam in ( + (None, 0.03), + (None, 0.1), + (0.7, 0.01), + (0.9, 0.0), + (0.964, 0.0), + ): + tag = "s050" if share is None else f"s{round(share * 1000):03d}" + specs.append( + { + "run_id": f"hold1_soft_chi_{tag}_{lam:g}", + "l2_lambda": lam, + "l2_basis": "chi_square", + "mass_parametrization": "softmax", + "acs_share": share, + "holdout_fold": 1, + } + ) + # Provenance: the grid ran on three kernel heads. These rerun two + # first-head configurations at a later head; analyze.py's + # rerun_variation compares them with the originals. + specs.append( + { + "run_id": "dup_release_repro", + "mass_parametrization": "projection", + } + ) + specs.append( + { + "run_id": "dup_soft_chi_s050_0.03", + "l2_lambda": 0.03, + "l2_basis": "chi_square", + "mass_parametrization": "softmax", + } + ) + specs.append( + { + "run_id": "hold_proj_chi_s050_0.1", + "l2_lambda": 0.1, + "l2_basis": "chi_square", + "mass_parametrization": "projection", + "holdout_fold": 0, + } + ) + return specs + + +@app.function( + cpu=CPUS, + memory=24576, + timeout=4 * 3600, + volumes={VOLUME_ROOT: volume}, + retries=1, +) +def run_point(spec: dict) -> dict: + import sys + + import torch + + torch.set_num_threads(CPUS) + sys.path.insert(0, "/opt/sweep") + import sweep + + volume.reload() + out_root = Path(VOLUME_ROOT) / "runs" + out_root.mkdir(parents=True, exist_ok=True) + finished = out_root / spec["run_id"] / "metrics.json" + if finished.exists(): + payload = json.loads(finished.read_text()) + if payload.get("spec", {}).get("run_id") == spec["run_id"]: + return {"run_id": spec["run_id"], "cached": True} + payload = sweep.calibrate_mode( + sweep.RunSpec.from_mapping(spec), + Path(VOLUME_ROOT) / "checkpoint", + out_root, + resume=True, + verify=True, + ) + volume.commit() + national = payload["metrics"]["concentration"]["national"] + return { + "run_id": spec["run_id"], + "final_loss": payload["result"]["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"], + } + + +def _upload_checkpoint() -> None: + with volume.batch_upload(force=True) as batch: + for name in UPLOAD: + batch.put_file(str(CHECKPOINT / name), f"/checkpoint/{name}") + + +@app.local_entrypoint() +def launch(skip_repro: bool = False) -> None: + 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: + repro = run_point.remote(specs[0]) + print("release_repro:", json.dumps(repro)) + if not repro.get("cached"): + reference = {"kish_ess": 13631.3, "final_loss": 0.015491} + for key, value in reference.items(): + if abs(repro[key] / value - 1.0) > 0.05: + raise SystemExit( + f"release_repro {key}={repro[key]} is not within 5% of the " + f"release's {value}; not launching the grid" + ) + if abs(repro["fraction_within_10pct"] - 0.9758) > 0.01: + raise SystemExit("release_repro within-10% is not within 1 pp") + for result in run_point.map( + specs[1:], return_exceptions=True, wrap_returned_exceptions=False + ): + print(json.dumps(result, default=str), flush=True) + + +@app.local_entrypoint() +def collect() -> None: + results = HERE / "results" / "runs" + results.mkdir(parents=True, exist_ok=True) + collected = 0 + for spec in grid(): + run_id = spec["run_id"] + local = LOCAL_RUNS / run_id + local.mkdir(parents=True, exist_ok=True) + try: + metrics = b"".join(volume.read_file(f"/runs/{run_id}/metrics.json")) + except FileNotFoundError: + print(f"{run_id}: not finished") + continue + (results / f"{run_id}.json").write_bytes(metrics) + (local / "metrics.json").write_bytes(metrics) + with open(local / "weights.npz", "wb") as handle: + 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") diff --git a/experiments/us-acs-local-l2-basis-20260928/optimizer_reference.py b/experiments/us-acs-local-l2-basis-20260928/optimizer_reference.py new file mode 100644 index 000000000..b586a3e0e --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/optimizer_reference.py @@ -0,0 +1,219 @@ +"""Measure the Adam calibrator's distance from the exact penalized optimum. + +For synthetic surfaces small enough for an exact convex solver, this runs +``microcosm.calibrate.calibrate`` with the chi-square L2 basis under three +mass settings, ``mass="conserve"`` with the default ``"projection"`` +parametrization, ``mass="conserve"`` with ``"softmax"``, and ``mass="free"``, +and compares each solve's objective with CLARABEL's optimum of the same +convex program: + + minimize mean_j |A_j w - b_j| / max(|b_j|, 1) + + lambda * sum((w - d) ** 2 / d) / sum(d) + subject to 0 <= w <= 5 d (and sum(w) = sum(d) under conservation). + +The capped-MAPE cap (1.0 in the ACS tool) never binds on these surfaces, so +the capped and uncapped losses coincide at every solution reported here; the +script checks this. + +Run (cvxpy is not a workspace dependency): + + uv run --with cvxpy python experiments/us-acs-local-l2-basis-20260928/optimizer_reference.py + +Writes ``results/optimizer_reference.csv`` and +``results/optimizer_reference_summary.json`` next to this file. +""" + +from __future__ import annotations + +import json +import time +from pathlib import Path + +import cvxpy as cp +import numpy as np +import pandas as pd + +from microcosm.calibrate import Target, TargetSet, calibrate +from microcosm.frame import EntitySchema, Frame, WeightKind, Weights + +HERE = Path(__file__).resolve().parent +LAMBDAS = (0.0, 1e-3, 1e-2, 1e-1, 1.0) +CAP = 5.0 +LOSS_CAP = 1.0 +VARIANTS = ( + ("conserve", "projection"), + ("conserve", "softmax"), + ("free", "projection"), +) + + +def small_problem(seed: int, factors: tuple[float, float]): + """120 records and six targets; the (1.02, 1.3) factors press on the total.""" + rng = np.random.default_rng(seed) + n, k = 120, 6 + values = rng.lognormal(0.0, 1.0, (n, k)) * (rng.random((n, k)) < 0.6) + design = rng.lognormal(2.0, 0.8, n) + targets = (values * design[:, None]).sum(0) * rng.uniform(*factors, k) + return values, design, targets + + +def large_problem(seed: int): + """3,000 records, 150 sparse targets in both directions, two design strata. + + Four percent of records carry heavier, more dispersed design weights, like + the ACS local staging's donor spine. + """ + rng = np.random.default_rng(seed) + n, k = 3000, 150 + values = rng.lognormal(0.0, 1.0, (n, k)) * (rng.random((n, k)) < 0.05) + values[:, :10] = rng.lognormal(0.0, 0.5, (n, 10)) * (rng.random((n, 10)) < 0.5) + heavy = rng.random(n) < 0.04 + design = np.where(heavy, rng.lognormal(4.0, 1.5, n), rng.lognormal(2.0, 0.6, n)) + targets = (values * design[:, None]).sum(0) * rng.normal(1.0, 0.12, k) + return values, design, targets + + +def kernel_solve(values, design, targets, lam, mass, parametrization, epochs): + n, k = values.shape + frame = Frame( + { + "person": pd.DataFrame( + {"person_id": range(n), "person_household_id": range(n)} + ), + "household": pd.DataFrame( + {"household_id": range(n), **{f"x{j}": values[:, j] for j in range(k)}} + ), + }, + EntitySchema(group_entities=("household",)), + {"household": Weights(values=design, kind=WeightKind.DESIGN)}, + ) + target_set = TargetSet( + tuple( + Target( + name=f"x{j}", + entity="household", + value=float(targets[j]), + measure=f"x{j}", + ) + for j in range(k) + ) + ) + result = calibrate( + frame, + target_set, + epochs=epochs, + learning_rate=0.02, + mass=mass, + max_weight_ratio=CAP, + target_loss_cap=LOSS_CAP, + l2_lambda=lam, + l2_basis="chi_square", + mass_parametrization=parametrization, + seed=0, + ) + return result.weights + + +def objective_parts(values, design, targets, weights): + scale = np.maximum(np.abs(targets), 1.0) + miss = np.abs(values.T @ weights - targets) / scale + distance = float(np.sum((weights - design) ** 2 / design) / design.sum()) + return float(miss.mean()), float(np.minimum(miss, LOSS_CAP).mean()), distance + + +def reference_solve(values, design, targets, lam, conserve): + w = cp.Variable(len(design)) + scale = np.maximum(np.abs(targets), 1.0) + objective = cp.sum(cp.abs(values.T @ w - targets) / scale) / len(targets) + objective += lam * cp.sum( + cp.multiply(1.0 / (design * design.sum()), cp.square(w - design)) + ) + constraints = [w >= 0, w <= CAP * design] + if conserve: + constraints.append(cp.sum(w) == design.sum()) + cp.Problem(cp.Minimize(objective), constraints).solve(solver=cp.CLARABEL) + return np.clip(np.asarray(w.value, dtype=np.float64), 0.0, None) + + +def main() -> None: + problems = [ + (f"small_s{seed}_{low}", small_problem(seed, (low, high)), 1500) + for seed in range(8) + for low, high in ((0.7, 1.4), (1.02, 1.3)) + ] + [(f"large_s{seed}", large_problem(seed), 800) for seed in range(3)] + rows = [] + started = time.time() + for name, (values, design, targets), epochs in problems: + for lam in LAMBDAS: + references = {} + for conserve in (True, False): + ref_w = reference_solve(values, design, targets, lam, conserve) + references[conserve] = objective_parts(values, design, targets, ref_w) + for mass, parametrization in VARIANTS: + weights = kernel_solve( + values, design, targets, lam, mass, parametrization, epochs + ) + loss, capped, distance = objective_parts( + values, design, targets, weights + ) + ref_loss, _, ref_distance = references[mass == "conserve"] + ess = float(weights.sum() ** 2 / np.square(weights).sum()) + rows.append( + { + "problem": name, + "records": len(design), + "targets": len(targets), + "epochs": epochs, + "lambda": lam, + "mass": mass, + "parametrization": parametrization, + "loss": loss, + "loss_cap_binds": capped != loss, + "chi_square_distance": distance, + "objective": loss + lam * distance, + "reference_loss": ref_loss, + "reference_chi_square_distance": ref_distance, + "reference_objective": ref_loss + lam * ref_distance, + "kish_ess": ess, + "mass_ratio": float(weights.sum() / design.sum()), + } + ) + print(f"{name} done ({time.time() - started:.0f}s)", flush=True) + frame = pd.DataFrame(rows) + frame["excess_objective"] = frame.objective - frame.reference_objective + out = HERE / "results" + out.mkdir(exist_ok=True) + frame.to_csv(out / "optimizer_reference.csv", index=False) + + def nonmonotone(group): + path = group.sort_values("lambda").chi_square_distance.to_numpy() + return bool((np.diff(path) > 1e-3).any()) + + summary = {} + for scale_name, subset in ( + ("small", frame[frame.problem.str.startswith("small")]), + ("large", frame[frame.problem.str.startswith("large")]), + ): + by_variant = {} + for (mass, parametrization), group in subset.groupby( + ["mass", "parametrization"] + ): + paths = group.groupby("problem").apply(nonmonotone, include_groups=False) + by_variant[f"{mass}/{parametrization}"] = { + "excess_objective_mean_by_lambda": { + str(lam): float(g.excess_objective.mean()) + for lam, g in group.groupby("lambda") + }, + "excess_objective_max": float(group.excess_objective.max()), + "share_of_problems_with_nonmonotone_distance": float(paths.mean()), + "loss_cap_ever_binds": bool(group.loss_cap_binds.any()), + } + summary[scale_name] = by_variant + (out / "optimizer_reference_summary.json").write_text( + json.dumps(summary, indent=2) + "\n" + ) + print(json.dumps(summary, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/experiments/us-acs-local-l2-basis-20260928/path_reference.py b/experiments/us-acs-local-l2-basis-20260928/path_reference.py new file mode 100644 index 000000000..ef0254f3d --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/path_reference.py @@ -0,0 +1,127 @@ +"""Exact optima for the regularization-path tests' own problems. + +``test_l2_basis.py`` checks the Adam solve's objective against the exact +optimum of the same convex program, on exactly the problems it generates. This +script computes those optima with CLARABEL and writes them to the test fixture +``packages/microcosm-calibrate/tests/fixtures/l2_basis_path_reference.json``. + +The program (``target_loss_cap`` never binds on these surfaces; checked): + + minimize mean_j |A_j w - b_j| / max(|b_j|, 1) + + lambda * sum((w - d) ** 2 / d) / sum(d) + subject to 0 <= w <= 5 d (and sum(w) = sum(d) under mass="conserve") + +Run (cvxpy is not a workspace dependency): + + uv run --with cvxpy python experiments/us-acs-local-l2-basis-20260928/path_reference.py +""" + +from __future__ import annotations + +import importlib.util +import json +import sys +from pathlib import Path + +import cvxpy as cp +import numpy as np + +HERE = Path(__file__).resolve().parent +REPO = HERE.parents[1] +TESTS = REPO / "packages" / "microcosm-calibrate" / "tests" +FIXTURE = TESTS / "fixtures" / "l2_basis_path_reference.json" + + +def _test_module(): + # The test module imports the repository's test_support package. + if str(REPO) not in sys.path: + sys.path.insert(0, str(REPO)) + path = TESTS / "engine_free" / "shared" / "test_l2_basis.py" + spec = importlib.util.spec_from_file_location("test_l2_basis_for_reference", path) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module + + +def _arrays(frame, targets): + household = frame.table("household") + names = [target.measure for target in targets] + values = np.column_stack([household[name].to_numpy(np.float64) for name in names]) + goals = np.array([target.value for target in targets], dtype=np.float64) + design = frame.resolve_weights("household").values.astype(np.float64) + return values, goals, design + + +def solve(values, goals, design, lam, conserve): + w = cp.Variable(len(design)) + scale = np.maximum(np.abs(goals), 1.0) + loss = cp.sum(cp.abs(values.T @ w - goals) / scale) / len(goals) + distance = cp.sum(cp.multiply(1.0 / (design * design.sum()), cp.square(w - design))) + constraints = [w >= 0, w <= 5.0 * design] + if conserve: + constraints.append(cp.sum(w) == design.sum()) + cp.Problem(cp.Minimize(loss + lam * distance), constraints).solve( + solver=cp.CLARABEL + ) + weights = np.clip(np.asarray(w.value, dtype=np.float64), 0.0, None) + miss = np.abs(values.T @ weights - goals) / scale + if miss.max() >= 10.0: + raise SystemExit("the test loss cap would bind at the optimum") + loss_value = float(miss.mean()) + distance_value = float(np.sum((weights - design) ** 2 / design) / design.sum()) + return { + "objective": loss_value + lam * distance_value, + "loss": loss_value, + "chi_square_distance": distance_value, + "kish_ess": float(weights.sum() ** 2 / np.square(weights).sum()), + } + + +def main() -> None: + module = _test_module() + entries = {} + measured = [] + for case in module.PATH_CASES: + case_id = module.path_case_id(case) + frame, targets, results = module._solved_path(case_id) + values, goals, design = _arrays(frame, targets) + conserve = case["mass"] == "conserve" + optimum = { + str(lam): solve(values, goals, design, lam, conserve) + for lam in module._LAMBDAS + } + excess = {} + for lam, result in zip(module._LAMBDAS, results, strict=True): + loss, distance = module._objective_parts(frame, targets, result.weights) + excess[str(lam)] = loss + lam * distance - optimum[str(lam)]["objective"] + measured.extend(excess.values()) + entries[case_id] = { + "problem_fingerprint": module._problem_fingerprint(frame, targets), + "optimum": optimum, + "solver_excess_when_generated": excess, + } + print(case_id, {k: round(v, 5) for k, v in excess.items()}, flush=True) + worst = max(measured) + # Twice the worst measured excess, rounded up to a 1e-3 step, absorbs + # platform float differences in the Adam path. + bound = float(np.ceil(2.0 * worst * 1000.0) / 1000.0) + payload = { + "schema": "microcosm.l2_basis_path_reference.v1", + "solver": "cvxpy CLARABEL", + "program": ( + "mean |A w - b| / max(|b|, 1) + lambda * sum((w - d)^2 / d) / sum(d); " + "0 <= w <= 5 d; sum(w) = sum(d) under conserve" + ), + "generator": "test_l2_basis._problem at the test module's PATH_CASES", + "solver_settings": "calibrate(epochs=1500, learning_rate=0.02, " + "max_weight_ratio=5.0, l2_basis='chi_square', seed=0) per case", + "measured_max_excess_objective": worst, + "excess_objective_bound": bound, + "cases": entries, + } + FIXTURE.write_text(json.dumps(payload, indent=1, sort_keys=True) + "\n") + print(f"wrote {FIXTURE}") + + +if __name__ == "__main__": + main() diff --git a/experiments/us-acs-local-l2-basis-20260928/prepare.py b/experiments/us-acs-local-l2-basis-20260928/prepare.py new file mode 100644 index 000000000..189bb30f5 --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/prepare.py @@ -0,0 +1,609 @@ +"""Build the L2-basis sweep inputs next to the copied sparse checkpoint. + +Inputs (read only): + +* ``checkpoint/target_matrix.npz``, ``targets.json``, ``target_frame_lean.h5`` + and ``identity.json``: the sparse copy of the 09-23 ACS local release's dense + calibration checkpoint (verified by ``verify_csr.py``, which must have passed). +* The 09-23 staging H5, for each household's spine (``acs_2024_1yr`` or the + ``asec_puf`` donor spine). +* The 09-23 ``calibration_diagnostics.json`` and ``weights_latest.npz``, for + the released per-target estimates. + +Outputs (under the checkpoint directory): + +* ``households.parquet``: one row per CSR column, in CSR column order — + ``household_id``, ``state_fips`` (2-digit string), ``congressional_district_geoid`` + (4-digit string), ``spine`` and ``design_weight``. +* ``targets_meta.parquet``: one row per CSR row — ``row``, ``name``, ``value``, + the target taxonomy (``family``, ``subfamily`` and the SOI fields parsed from + the target name), geography (``geography_level``, ``geography_code``, + ``state_fips``, ``state_postal``, ``congressional_district_geoid``), ``nnz``, + ``fold`` (rotated holdout fold), and the design and released 09-23 estimates. +* ``holdout_folds.npz``: ``microcosm.build.holdout.rotated_folds(4459, + n_folds=5, seed=20260529)`` as ``fold_0`` .. ``fold_4`` plus ``fold_of_target``. +* ``MANIFEST.json``: sha256 of every file above plus provenance and the + structural checks below. + +Checks (the script exits non-zero if any fails): the staging household ids +equal the checkpoint's; the staging spine equals the structure H5's +``household_spine``; every target parses into the taxonomy; every SOI name's +state postal code matches its ``state_fips``; every target's nonzero households +lie inside the target's geography (state or congressional district). +""" + +from __future__ import annotations + +import datetime as dt +import hashlib +import json +import resource +import subprocess +import sys +import time +from collections import Counter +from pathlib import Path + +import numpy as np +import pandas as pd +from scipy import sparse + +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" +) +DENSE_DIR = RUN_0923 / "release" / "checkpoints" +STAGING_H5 = RUN_0923 / "staging" / "acs_multispine_staging.h5" +COPY_SOURCE = Path( + "/Users/maxghenis/PolicyEngine/_build_artifacts/acs-local-state-cd-20260927/state" +) +RELEASE_TAG = "populace-us-2024-buildo-acs-local-767312d60-20260923T074941Z" +HOLDOUT_N_FOLDS = 5 +HOLDOUT_SEED = 20260529 +SPINES = ("acs_2024_1yr", "asec_puf") + +# SOI Historic Table 2 variables on the state_broad table, grouped by concept. +SOI_CONCEPTS = { + "income": ( + "adjusted_gross_income", + "wages_salaries", + "taxable_interest", + "tax_exempt_interest", + "ordinary_dividends", + "qualified_dividends", + "net_capital_gains", + "schedule_c_income", + "partnership_scorp_income", + "rental_royalty_income", + "taxable_ira_distributions", + "taxable_pension_income", + "taxable_social_security", + "unemployment_compensation", + ), + "deduction": ( + "itemized_deductions", + "limited_state_local_taxes", + "real_estate_taxes", + "medical_dental_expense", + ), + "tax": ( + "taxable_income", + "income_tax_before_credits", + "income_tax_liability", + ), + "credit": ("ctc", "actc", "premium_tax_credit"), + "eitc": ( + "eitc", + "eitc_no_children", + "eitc_one_child", + "eitc_two_children", + "eitc_three_or_more_children", + ), +} +CONCEPT_OF = { + base: concept for concept, bases in SOI_CONCEPTS.items() for base in bases +} +AGI_BANDS = ( + "all", + "under_1", + "1_to_10k", + "10k_to_25k", + "25k_to_50k", + "50k_to_75k", + "75k_to_100k", + "100k_to_200k", + "200k_to_500k", + "500k_plus", +) + + +def log(message: str) -> None: + print(f"[{time.strftime('%H:%M:%S')}] {message}", flush=True) + + +def peak_rss_gb() -> float: + peak = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss + return peak / 2**30 if sys.platform == "darwin" else peak / 2**20 + + +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_state() -> dict: + def run(*args: str) -> str: + return subprocess.run( + ["git", "-C", str(REPO), *args], capture_output=True, text=True, check=True + ).stdout.strip() + + return { + "head": run("rev-parse", "HEAD"), + "branch": run("rev-parse", "--abbrev-ref", "HEAD"), + "kernel_dirty_paths": run( + "status", + "--porcelain", + "--", + "packages/microcosm-calibrate", + "packages/microcosm-frame", + "packages/microcosm-build", + ).splitlines(), + } + + +def soi_fields(name: str, source_measure_id: str) -> dict: + """Parse ``irs_soi.ty2022.historic_table_2....``.""" + + parts = name.split(".") + if len(parts) != 7 or parts[:3] != ["irs_soi", "ty2022", "historic_table_2"]: + raise ValueError(f"unexpected SOI target name {name!r}") + table, postal, qualifier, variable = parts[3], parts[4], parts[5], parts[6] + if variable != source_measure_id: + raise ValueError(f"{name}: variable {variable!r} != {source_measure_id!r}") + if table == "state_eitc": + if qualifier != postal: + raise ValueError( + f"{name}: state_eitc qualifier {qualifier!r} != {postal!r}" + ) + band = "all" + elif table in ("state_broad", "state_agi"): + band = qualifier + else: + raise ValueError(f"{name}: unknown SOI table {table!r}") + if band not in AGI_BANDS or (table == "state_agi") == (band == "all"): + raise ValueError(f"{name}: unexpected AGI band {band!r} on {table}") + if variable == "return_count": + base, kind = "return_count", "count" + elif variable == "adjusted_gross_income": + base, kind = "adjusted_gross_income", "amount" + elif variable.endswith("_amount"): + base, kind = variable.removesuffix("_amount"), "amount" + elif variable.endswith("_returns"): + base, kind = variable.removesuffix("_returns"), "count" + elif variable.endswith("_claims"): + base, kind = variable.removesuffix("_claims"), "count" + else: + raise ValueError(f"{name}: cannot classify SOI variable {variable!r}") + if base == "return_count": + concept = "filing" + else: + concept = CONCEPT_OF.get(base) + if concept is None: + raise ValueError(f"{name}: SOI base {base!r} has no concept") + if table == "state_agi": + subfamily = f"soi_agi_band_{kind}" + elif table == "state_eitc": + if concept != "eitc": + raise ValueError(f"{name}: state_eitc row outside the EITC concept") + subfamily = f"soi_eitc_{kind}" + elif concept == "filing": + subfamily = "soi_return_count" + else: + if concept == "eitc": + raise ValueError(f"{name}: EITC variable on the state_broad table") + subfamily = f"soi_{concept}_{kind}" + return { + "subfamily": subfamily, + "soi_table": table, + "soi_variable": variable, + "soi_base": base, + "soi_kind": kind, + "soi_concept": concept, + "agi_band": band, + "name_postal": postal.upper(), + } + + +def classify(record: dict) -> dict: + source_family = record["family"] + level = record["geography_level"] + measure_id = record.get("source_measure_id") + fields: dict = { + "source_family": source_family, + "source_measure_id": measure_id, + "soi_table": None, + "soi_variable": None, + "soi_base": None, + "soi_kind": None, + "soi_concept": None, + "agi_band": None, + } + if source_family == "usda_snap": + subfamily = { + "average_monthly_households": "snap_households", + "total_benefits": "snap_benefits", + }[measure_id] + fields.update(family="snap", subfamily=subfamily) + elif source_family == "cms_medicaid": + if measure_id != "total_medicaid_enrollment": + raise ValueError(f"{record['name']}: unexpected Medicaid measure") + fields.update(family="medicaid", subfamily="medicaid_enrollment") + elif source_family == "census_population_ladder": + family = {"state": "pop_state", "congressional_district": "pop_cd"}[level] + expected = ( + f"pop_state_{record['state_fips']}" + if family == "pop_state" + else f"pop_cd_{record['congressional_district_geoid']}" + ) + if record["name"] != expected: + raise ValueError(f"{record['name']}: expected {expected}") + fields.update(family=family, subfamily=family) + elif source_family == "irs_soi": + parsed = soi_fields(record["name"], measure_id) + name_postal = parsed.pop("name_postal") + fields.update(family="soi", **parsed) + fields["_name_postal"] = name_postal + else: + raise ValueError(f"{record['name']}: unknown family {source_family!r}") + return fields + + +def main() -> int: + started = time.time() + failures: list[str] = [] + verification = json.loads((HERE / "results" / "csr_verification.json").read_text()) + if not verification.get("ok"): + raise SystemExit("csr_verification.json is not ok; run verify_csr.py first") + + from microcosm.build.holdout import rotated_folds + from microcosm.calibrate.geography_constants import US_STATE_FIPS_TO_POSTAL + + matrix = sparse.load_npz(CHECKPOINT / "target_matrix.npz").tocsr() + records = json.loads((CHECKPOINT / "targets.json").read_text()) + n_targets, n_households = matrix.shape + if len(records) != n_targets: + raise SystemExit("targets.json length differs from the CSR row count") + + # ---- households ------------------------------------------------------ + struct = pd.read_hdf(CHECKPOINT / "target_frame_lean.h5", "household") + if len(struct) != n_households: + raise SystemExit("structure H5 household count differs from the CSR") + household_id = struct["household_id"].to_numpy(np.int64) + if not np.all(np.diff(household_id) > 0): + raise SystemExit("household ids are not strictly increasing") + + log("reading staging spine") + with pd.HDFStore(STAGING_H5, mode="r") as store: + storer = store.get_storer("household") + group = storer.group + + def items(block: str) -> list[str]: + return [ + item.decode() if isinstance(item, bytes) else str(item) + for item in getattr(group, f"{block}_items")[:] + ] + + spine_block = next( + f"block{i}" + for i in range(storer.nblocks) + if items(f"block{i}") == ["household_spine"] + ) + id_block = next( + f"block{i}" + for i in range(storer.nblocks) + if "household_id" in items(f"block{i}") + ) + staging_spine = np.asarray( + storer.read_array(f"{spine_block}_values"), dtype=object + ) + staging_ids = storer.read_array(f"{id_block}_values")[ + items(id_block).index("household_id") + ] + staging_spine = staging_spine.astype(str) + ok = np.array_equal(np.asarray(staging_ids, dtype=np.int64), household_id) + if not ok: + failures.append("staging_household_ids_equal_checkpoint") + struct_spine = struct["household_spine"].astype(str).to_numpy() + if not np.array_equal(staging_spine, struct_spine): + failures.append("staging_spine_equals_structure_spine") + spine_counts = Counter(staging_spine.tolist()) + if set(spine_counts) != set(SPINES): + failures.append("spine_labels") + + state_fips = np.char.zfill(struct["state_fips"].to_numpy(np.int64).astype(str), 2) + cd_geoid = np.char.zfill( + struct["congressional_district_geoid"].to_numpy(np.int64).astype(str), 4 + ) + if not np.all(np.char.startswith(cd_geoid, state_fips)): + failures.append("cd_geoid_prefix_equals_state_fips") + design_weight = struct["household_weight"].to_numpy(np.float64) + households = pd.DataFrame( + { + "household_id": household_id, + "state_fips": pd.Categorical(state_fips), + "congressional_district_geoid": pd.Categorical(cd_geoid), + "spine": pd.Categorical(staging_spine, categories=list(SPINES)), + "design_weight": design_weight, + } + ) + + # ---- targets --------------------------------------------------------- + folds = rotated_folds(n_targets, n_folds=HOLDOUT_N_FOLDS, seed=HOLDOUT_SEED) + fold_of_target = np.full(n_targets, -1, dtype=np.int8) + for index, fold in enumerate(folds): + fold_of_target[fold] = index + if (fold_of_target < 0).any(): + failures.append("folds_cover_targets") + + diagnostics = json.loads((DENSE_DIR / "calibration_diagnostics.json").read_text()) + diag = diagnostics["targets"] + if [row["name"] for row in diag] != [record["name"] for record in records]: + raise SystemExit("09-23 diagnostics are not row-aligned with targets.json") + w_0923 = np.load(DENSE_DIR / "weights_latest.npz")["weights"].astype(np.float64) + matrix64 = matrix.astype(np.float64) + design_estimate = matrix64 @ design_weight + release_estimate = matrix64 @ w_0923 + nnz = np.diff(matrix.indptr) + + rows = [] + for index, record in enumerate(records): + fields = classify(record) + name_postal = fields.pop("_name_postal", None) + level = record["geography_level"] + state = record["state_fips"] + postal = US_STATE_FIPS_TO_POSTAL.get(state) + if postal is None: + failures.append(f"unknown_state_fips:{record['name']}") + if name_postal is not None and name_postal != postal: + failures.append(f"soi_name_postal_mismatch:{record['name']}") + cd = record["congressional_district_geoid"] + if level == "state": + code = state + if cd is not None: + failures.append(f"state_target_with_cd:{record['name']}") + elif level == "congressional_district": + code = cd + if cd is None or not cd.startswith(state): + failures.append(f"cd_target_geoid:{record['name']}") + else: + failures.append(f"unknown_level:{record['name']}") + code = None + value = float(record["value"]) + rows.append( + { + "row": index, + "name": record["name"], + "value": value, + "family": fields.pop("family"), + "subfamily": fields.pop("subfamily"), + **fields, + "geography_level": level, + "geography_code": code, + "state_fips": state, + "state_postal": postal, + "congressional_district_geoid": cd, + "nnz": int(nnz[index]), + "fold": int(fold_of_target[index]), + "design_estimate": float(design_estimate[index]), + "release_0923_estimate": float(release_estimate[index]), + "release_0923_final_estimate_diagnostics": float( + diag[index]["final_estimate"] + ), + } + ) + meta = pd.DataFrame(rows) + if not np.array_equal( + meta["release_0923_estimate"].to_numpy(), + meta["release_0923_final_estimate_diagnostics"].to_numpy(), + ): + failures.append("release_estimates_reproduce_diagnostics") + meta = meta.drop(columns=["release_0923_final_estimate_diagnostics"]) + + # ---- geography containment: every nonzero lies inside its geography ---- + state_codes = {code: i for i, code in enumerate(sorted(set(state_fips.tolist())))} + cd_codes = {code: i for i, code in enumerate(sorted(set(cd_geoid.tolist())))} + hh_state = np.asarray([state_codes[s] for s in state_fips], dtype=np.int32) + hh_cd_series = pd.Series(cd_geoid).map(cd_codes) + hh_cd = hh_cd_series.to_numpy(np.int32) + target_state = np.asarray( + [state_codes.get(s, -1) for s in meta["state_fips"]], dtype=np.int32 + ) + target_cd = np.asarray( + [ + cd_codes.get(c, -1) if c is not None else -1 + for c in meta["congressional_district_geoid"] + ], + dtype=np.int32, + ) + is_cd_target = (meta["geography_level"] == "congressional_district").to_numpy() + row_of_nnz = np.repeat(np.arange(n_targets), nnz) + outside = np.where( + is_cd_target[row_of_nnz], + hh_cd[matrix.indices] != target_cd[row_of_nnz], + hh_state[matrix.indices] != target_state[row_of_nnz], + ) + outside_per_row = np.bincount(row_of_nnz, weights=outside, minlength=n_targets) + containment = { + "rows_with_nonzeros_outside_geography": int((outside_per_row > 0).sum()), + "nonzeros_outside_geography": int(outside.sum()), + "empty_rows": int((nnz == 0).sum()), + "cd_targets_with_unknown_cd": int(((target_cd < 0) & is_cd_target).sum()), + "state_targets_with_unknown_state": int((target_state < 0).sum()), + } + if containment["rows_with_nonzeros_outside_geography"] or containment["empty_rows"]: + failures.append("geography_containment") + if ( + containment["cd_targets_with_unknown_cd"] + or containment["state_targets_with_unknown_state"] + ): + failures.append("target_geography_codes_known") + pop_cd_codes = set( + meta.loc[meta["family"] == "pop_cd", "congressional_district_geoid"] + ) + containment["households_cds"] = len(cd_codes) + containment["pop_cd_targets"] = len(pop_cd_codes) + containment["household_cds_without_pop_cd_target"] = sorted( + set(cd_codes) - pop_cd_codes + ) + containment["households_states"] = len(state_codes) + + # ---- write ---------------------------------------------------------- + CHECKPOINT.mkdir(parents=True, exist_ok=True) + households.to_parquet(CHECKPOINT / "households.parquet", index=False) + meta.to_parquet(CHECKPOINT / "targets_meta.parquet", index=False) + np.savez( + CHECKPOINT / "holdout_folds.npz", + **{ + f"fold_{i}": np.asarray(fold, dtype=np.int64) + for i, fold in enumerate(folds) + }, + fold_of_target=fold_of_target, + n_targets=np.int64(n_targets), + n_folds=np.int64(HOLDOUT_N_FOLDS), + seed=np.int64(HOLDOUT_SEED), + ) + + taxonomy = { + "family": meta["family"].value_counts().sort_index().to_dict(), + "subfamily": meta["subfamily"].value_counts().sort_index().to_dict(), + "family_by_fold": { + str(k): v + for k, v in meta.groupby("fold")["family"] + .value_counts() + .unstack(fill_value=0) + .to_dict("index") + .items() + }, + "soi_concept_of_base": CONCEPT_OF, + "rules": { + "snap": "usda_snap; subfamily by source_measure_id (households / benefits)", + "medicaid": "cms_medicaid total_medicaid_enrollment", + "pop_state": "census_population_ladder at state level", + "pop_cd": "census_population_ladder at congressional_district level", + "soi": ( + "irs_soi Historic Table 2 (TY2022). state_agi -> soi_agi_band_{count,amount} " + "(taxable interest by AGI band); state_eitc -> soi_eitc_{count,amount}; " + "state_broad -> soi_{income,deduction,tax,credit}_{count,amount} by " + "variable concept, plus soi_return_count. count = *_returns, *_claims, " + "return_count; amount = *_amount, adjusted_gross_income." + ), + }, + } + spine_summary = { + spine: { + "n": int(spine_counts[spine]), + "design_mass_share": float( + design_weight[staging_spine == spine].sum() / design_weight.sum() + ), + } + for spine in SPINES + } + + output_files = [ + "households.parquet", + "targets_meta.parquet", + "holdout_folds.npz", + ] + copied = [ + "target_matrix.npz", + "targets.json", + "target_frame_lean.h5", + "identity.json", + ] + log("hashing outputs and sources") + manifest = { + "created_utc": dt.datetime.now(dt.UTC).isoformat(timespec="seconds"), + "purpose": ( + "Inputs for the chi-square L2 basis / softmax mass sweep on the " + f"published ACS local release {RELEASE_TAG}." + ), + "release_tag": RELEASE_TAG, + "files": {name: sha256(CHECKPOINT / name) for name in copied + output_files}, + "copied_from": { + "directory": str(COPY_SOURCE), + "files": copied, + "note": "cp -p of the other session's sparse conversion; read-only source", + }, + "sources": { + "dense_checkpoint": str(DENSE_DIR), + "dense_calibration_diagnostics_sha256": sha256( + DENSE_DIR / "calibration_diagnostics.json" + ), + "dense_weights_latest_sha256": sha256(DENSE_DIR / "weights_latest.npz"), + "dense_targets_json_sha256": sha256(DENSE_DIR / "targets.json"), + "staging_h5": str(STAGING_H5), + "staging_h5_sha256": sha256(STAGING_H5), + "staging_h5_sha256_pinned_by_run_identity": json.loads( + (DENSE_DIR / "run_identity.json").read_text() + )["staging_sha256"], + }, + "csr_verification": { + "path": str(HERE / "results" / "csr_verification.json"), + "sha256": sha256(HERE / "results" / "csr_verification.json"), + "ok": verification["ok"], + }, + "shape": { + "targets": n_targets, + "households": n_households, + "nnz": int(matrix.nnz), + }, + "holdout": { + "function": "microcosm.build.holdout.rotated_folds", + "n_targets": n_targets, + "n_folds": HOLDOUT_N_FOLDS, + "seed": HOLDOUT_SEED, + "fold_sizes": [int(len(fold)) for fold in folds], + }, + "spines": spine_summary, + "taxonomy": taxonomy, + "geography_containment": containment, + "design": { + "total": float(design_weight.sum()), + "kish_ess": float( + design_weight.sum() ** 2 / np.square(design_weight).sum() + ), + }, + "git": git_state(), + "failures": failures, + "wall_seconds": round(time.time() - started, 1), + "peak_rss_gb": round(peak_rss_gb(), 3), + } + if ( + manifest["sources"]["staging_h5_sha256"] + != manifest["sources"]["staging_h5_sha256_pinned_by_run_identity"] + ): + failures.append("staging_sha256_matches_run_identity") + manifest["ok"] = not failures + (CHECKPOINT / "MANIFEST.json").write_text(json.dumps(manifest, indent=2) + "\n") + log( + f"wrote {CHECKPOINT / 'MANIFEST.json'} ok={manifest['ok']} " + f"failures={failures[:10]} peak_rss_gb={manifest['peak_rss_gb']}" + ) + print( + json.dumps( + {k: manifest[k] for k in ("taxonomy", "geography_containment", "spines")}, + indent=1, + )[:4000] + ) + return 0 if not failures else 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/experiments/us-acs-local-l2-basis-20260928/published_weights.py b/experiments/us-acs-local-l2-basis-20260928/published_weights.py new file mode 100644 index 000000000..bcbdb5603 --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/published_weights.py @@ -0,0 +1,58 @@ +"""Concentration of the published release's own weights, for the README. + +David Trimmer measured the published ACS local release's weights directly; the +sweep's ``release_repro`` re-solves them. This records the published weights' +national and Massachusetts Kish ESS and how many Massachusetts households hold +half the state's weight, on this checkpoint's households, so the README can set +the three figures side by side. + +Run: ``uv run python experiments/us-acs-local-l2-basis-20260928/published_weights.py`` +Writes ``results/published_weights.json``. +""" + +from __future__ import annotations + +import json +import sys +from pathlib import Path + +import numpy as np +import pandas as pd + +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, + MASSACHUSETTS, + kish, + n_records_holding_half, +) + + +def main() -> None: + households = pd.read_parquet(DEFAULT_CHECKPOINT / "households.parquet") + weights = np.load(RELEASE_WEIGHTS)["weights"].astype(np.float64) + if weights.shape != (len(households),): + raise SystemExit(f"weights {weights.shape} vs {len(households)} households") + ma = households["state_fips"].astype(str).to_numpy() == MASSACHUSETTS + out = { + "weights": str(RELEASE_WEIGHTS), + "checkpoint": str(DEFAULT_CHECKPOINT), + "households": int(len(households)), + "national_kish_ess": kish(weights), + "massachusetts": { + "households": int(ma.sum()), + "kish_ess": kish(weights[ma]), + "n_records_holding_half_weight": n_records_holding_half(weights[ma]), + }, + } + (HERE / "results" / "published_weights.json").write_text( + json.dumps(out, indent=1) + "\n" + ) + print(json.dumps(out, indent=1)) + + +if __name__ == "__main__": + main() diff --git a/experiments/us-acs-local-l2-basis-20260928/results/csr_verification.json b/experiments/us-acs-local-l2-basis-20260928/results/csr_verification.json new file mode 100644 index 000000000..ef0281aa4 --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/results/csr_verification.json @@ -0,0 +1,108 @@ +{ + "ok": true, + "failures": [], + "dense_checkpoint": "/Users/maxghenis/PolicyEngine/_recovered/scratch-backup/893/overnight-20260923/run/release/checkpoints", + "sparse_checkpoint": "/Users/maxghenis/PolicyEngine/_build_artifacts/acs-local-l2-basis-20260928/checkpoint", + "sample": { + "size": 3000, + "seed": 20260928, + "read_chunk": 500 + }, + "checks": { + "copy_digests_match_identity": { + "ok": true, + "digests": { + "target_matrix.npz": "19bc7dfaab57a380fe3152c9acbbcf99c0ab9369b17f1d7299a74e5607daf504", + "targets.json": "c5216b9495f9a2b932649fcd344a92d1497168109421aca11fec4933c85f79c3", + "target_frame_lean.h5": "0e21d35af0402a5421e9c66f1e6c2d434e7c5b2b1acfb4b8aa12c6a942d89be7", + "identity.json": "c317c05cb7246b80eb4d023107f3d48f39238f287cbb26dd009df1f35aed836a" + } + }, + "csr_shape_nnz": { + "ok": true, + "shape": [ + 4459, + 1588854 + ], + "nnz": 24773532, + "dtype": "float32", + "has_canonical_format": true + }, + "csr_finite_no_explicit_zeros": { + "ok": true, + "explicit_zeros": 0, + "nonfinite": 0 + }, + "dense_block2_shape_dtype": { + "ok": true, + "shape": [ + 1588854, + 4459 + ], + "dtype": "float32" + }, + "block2_items_equal_csr_target_order": { + "ok": true, + "n_items": 4459, + "first_mismatch": null + }, + "sampled_rows_exact_float32_equal": { + "ok": true, + "sample_size": 3000, + "seed": 20260928, + "cells_compared": 13377000, + "nonzero_cells_dense": 46047, + "nonzero_cells_csr": 46047, + "mismatched_cells": 0, + "nan_cells_dense": 0, + "sample_index_min": 644, + "sample_index_max": 1588156 + }, + "dense_and_copied_targets_json_agree": { + "ok": true, + "n": 4459 + }, + "design_weights_three_way_exact": { + "ok": true, + "dense_block": "block3_items", + "n": 1588854, + "total": 122537091.8856833, + "min": 2.7730940199080788e-05, + "max": 25332.453126797645, + "max_abs_diff_struct": 0.0, + "max_abs_diff_npz": 0.0 + }, + "household_structure_equal": { + "ok": true, + "household_id_sorted_unique": true + }, + "weights_latest_epochs": { + "ok": true, + "epochs_done": 800 + }, + "diagnostics_target_order": { + "ok": true, + "n": 4459 + }, + "full_matrix_reproduces_0923_estimates": { + "ok": true, + "max_rel_diff_initial_estimate": 0.0, + "max_rel_diff_final_estimate": 0.0 + }, + "full_matrix_reproduces_0923_headline": { + "ok": true, + "fraction_within_10pct": 0.9757793227180982, + "fraction_within_10pct_0923": 0.9758, + "final_loss": 0.015490699209467104, + "final_loss_0923": 0.015491, + "kish_ess": 13631.348394968125, + "kish_ess_0923": 13631.3, + "kish_ess_design": 36287.945780887014, + "chi_square_distance_0923_vs_design": 0.7270154670928841, + "mass_ratio": 0.9999999999999998, + "realized_max_weight_ratio": 5.000000000000001 + } + }, + "wall_seconds": 9.5, + "peak_rss_gb": 1.362 +} diff --git a/experiments/us-acs-local-l2-basis-20260928/results/exact_cap.md b/experiments/us-acs-local-l2-basis-20260928/results/exact_cap.md new file mode 100644 index 000000000..792a13181 --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/results/exact_cap.md @@ -0,0 +1,6 @@ +| Configuration | Cap step | Largest in-loop weight over its cap, minus 1 (max / median over passes) | Steps out of rounds | Rounds per step | Cap step ms | s/epoch | Receipt (batch 1 / 2) | Final loss | National ESS | +|---|---|---:|---:|---|---:|---:|---|---:|---:| +| `soft_chi_s050_0.03` | `water_fill` | 8.97e-06 / 3.19e-06 | 0 of 800 | {"1": 598, "2": 202} | 12.7 | 1.00 / 0.95 | 8.97e-06 / 7.80e-06 | 0.018991 | 21,834 | +| `soft_chi_s050_0.03` | `rounds_32` | 1.22e-05 / 2.96e-06 | 713 of 800 | {"-32": 713, "1": 68, "2": 2, "3": 15, "22": 1, "32": 1} | 53.8 | 1.33 / 1.37 | 1.14e-05 / 1.22e-05 | 0.019169 | 21,835 | +| `soft_chi_s900_0` | `water_fill` | 8.99e-06 / 1.09e-06 | 0 of 800 | {"1": 70, "2": 103, "3": 627} | 22.5 | 1.34 / 1.38 | 8.99e-06 / 8.27e-06 | 0.031293 | 93,609 | +| `soft_chi_s900_0` | `rounds_32` | 8.66e-06 / 1.18e-06 | 705 of 800 | {"-32": 705, "1": 65, "2": 1, "3": 6, "4": 18, "5": 5} | 48.7 | 1.33 / 1.28 | 7.74e-06 / 8.66e-06 | 0.031365 | 93,598 | diff --git a/experiments/us-acs-local-l2-basis-20260928/results/exact_cap/rounds_32_soft_chi_s050_0.03.cap_step.json b/experiments/us-acs-local-l2-basis-20260928/results/exact_cap/rounds_32_soft_chi_s050_0.03.cap_step.json new file mode 100644 index 000000000..8418f67b3 --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/results/exact_cap/rounds_32_soft_chi_s050_0.03.cap_step.json @@ -0,0 +1,827 @@ +{ + "variant": "rounds_32", + "cap_steps": 800, + "cap_step_seconds_total": 43.05271996800134, + "cap_step_seconds_mean": 0.053815899960001676, + "rounds_histogram": { + "-32": 713, + "1": 68, + "2": 2, + "3": 15, + "22": 1, + "32": 1 + }, + "rounds_exhausted_steps": 713, + "forward_passes": 802, + "in_loop_cap_ratio_minus_one": { + "max": 1.2231641854487663e-05, + "p99": 1.080651350005013e-05, + "median": 2.963704962555269e-06, + "min": -0.799999572981976, + "passes_above_cap": 606 + }, + "in_loop_cap_ratio_by_pass": [ + 0.20000042701802404, + 0.20542158747216505, + 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b/experiments/us-acs-local-l2-basis-20260928/results/floors.md @@ -0,0 +1,35 @@ +| Run | Within 10% | State ESS min | States < 300 | States < 1,000 | CD ESS min | CDs < 50 | CDs < 100 | CDs < 200 | CDs < 25% of prior | CDs < 50% of prior | +|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| proj_chi_s050_0.03 | 97.3% | 204 | 12 | 48 | 22 | 278 | 401 | 427 | 0 | 88 | +| proj_chi_s050_0.1 | 95.9% | 207 | 11 | 47 | 27 | 208 | 397 | 427 | 0 | 41 | +| proj_chi_s050_1 | 75.9% | 243 | 2 | 46 | 44 | 17 | 370 | 420 | 0 | 0 | +| release_repro | 97.6% | 195 | 25 | 50 | 12 | 352 | 413 | 429 | 15 | 375 | +| proj_rec_s050_0.1 | 97.6% | 120 | 46 | 51 | 8 | 401 | 423 | 435 | 203 | 436 | +| soft_chi_s050_0 | 97.5% | 201 | 24 | 49 | 13 | 319 | 410 | 428 | 8 | 276 | +| soft_chi_s050_0.01 | 97.4% | 206 | 16 | 48 | 19 | 282 | 405 | 427 | 0 | 139 | +| soft_chi_s050_0.03 | 97.1% | 213 | 10 | 47 | 23 | 239 | 399 | 426 | 0 | 49 | +| soft_chi_s050_0.1 | 95.5% | 222 | 4 | 47 | 29 | 132 | 390 | 424 | 0 | 8 | +| soft_chi_s050_0.3 | 89.5% | 230 | 3 | 46 | 38 | 33 | 379 | 421 | 0 | 0 | +| soft_chi_s050_1 | 75.7% | 269 | 1 | 44 | 47 | 4 | 348 | 418 | 0 | 0 | +| soft_chi_s050_3 | 59.2% | 325 | 0 | 43 | 50 | 1 | 332 | 415 | 0 | 0 | +| soft_chi_s700_0 | 97.3% | 309 | 0 | 46 | 19 | 128 | 358 | 420 | 157 | 431 | +| soft_chi_s700_0.01 | 97.1% | 319 | 0 | 45 | 24 | 30 | 310 | 414 | 15 | 414 | +| soft_chi_s700_0.03 | 96.2% | 333 | 0 | 38 | 36 | 4 | 249 | 406 | 4 | 345 | +| soft_chi_s700_0.1 | 92.6% | 371 | 0 | 32 | 54 | 0 | 98 | 392 | 0 | 171 | +| soft_chi_s700_0.3 | 83.6% | 411 | 0 | 23 | 79 | 0 | 10 | 346 | 0 | 33 | +| soft_chi_s700_1 | 66.4% | 573 | 0 | 11 | 105 | 0 | 0 | 245 | 0 | 0 | +| soft_chi_s700_3 | 49.6% | 782 | 0 | 8 | 114 | 0 | 0 | 201 | 0 | 0 | +| soft_chi_s900_0 | 95.8% | 599 | 0 | 10 | 64 | 0 | 4 | 164 | 328 | 436 | +| soft_chi_s900_0.01 | 95.1% | 612 | 0 | 8 | 85 | 0 | 1 | 37 | 121 | 435 | +| soft_chi_s900_0.03 | 93.7% | 637 | 0 | 6 | 147 | 0 | 0 | 11 | 33 | 382 | +| soft_chi_s900_0.1 | 87.3% | 748 | 0 | 5 | 263 | 0 | 0 | 0 | 0 | 179 | +| soft_chi_s900_0.3 | 75.5% | 945 | 0 | 3 | 412 | 0 | 0 | 0 | 0 | 4 | +| soft_chi_s900_1 | 56.1% | 1,172 | 0 | 0 | 571 | 0 | 0 | 0 | 0 | 0 | +| soft_chi_s900_3 | 40.3% | 1,269 | 0 | 0 | 603 | 0 | 0 | 0 | 0 | 0 | +| soft_chi_s964_0 | 94.9% | 594 | 0 | 7 | 265 | 0 | 0 | 0 | 30 | 436 | +| soft_chi_s964_0.01 | 94.3% | 602 | 0 | 6 | 353 | 0 | 0 | 0 | 0 | 380 | +| soft_chi_s964_0.03 | 92.3% | 616 | 0 | 5 | 462 | 0 | 0 | 0 | 0 | 213 | +| soft_chi_s964_0.1 | 85.4% | 669 | 0 | 4 | 620 | 0 | 0 | 0 | 0 | 11 | +| soft_chi_s964_0.3 | 72.3% | 925 | 0 | 3 | 725 | 0 | 0 | 0 | 0 | 0 | +| soft_chi_s964_1 | 52.8% | 1,117 | 0 | 0 | 812 | 0 | 0 | 0 | 0 | 0 | +| soft_chi_s964_3 | 37.4% | 1,208 | 0 | 0 | 833 | 0 | 0 | 0 | 0 | 0 | diff --git a/experiments/us-acs-local-l2-basis-20260928/results/frontier.csv b/experiments/us-acs-local-l2-basis-20260928/results/frontier.csv new file mode 100644 index 000000000..8ad51c3f7 --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/results/frontier.csv @@ -0,0 +1,58 @@ +run_id,kernel_head,prior_acs_share,mass_parametrization,l2_basis,l2_lambda,holdout_fold,epochs,kish_ess,ess_fraction,top_1pct_share,chi_square_distance_from_prior,chi_square_distance_from_release_design,prior_kish_ess,donor_mass_share,state_ess_median,state_ess_min,cd_ess_median,cd_ess_p10,cd_ess_min,prior_cd_ess_median,ma_ess,ma_prior_ess,ma_cd_ess_min,ma_cd_ess_median,final_loss,within_10pct,mean_capped_error,prior_within_10pct,prior_mean_capped_error,n_states,n_cds,states_below_300,states_below_1000,cds_below_50,cds_below_100,cds_below_200,states_below_0.25_of_prior,cds_below_0.25_of_prior,states_below_0.5_of_prior,cds_below_0.5_of_prior,within_10pct_soi,mean_abs_rel_error_soi,within_10pct_snap,mean_abs_rel_error_snap,within_10pct_medicaid,mean_abs_rel_error_medicaid,within_10pct_pop_state,mean_abs_rel_error_pop_state,within_10pct_pop_cd,mean_abs_rel_error_pop_cd,holdout_within_10pct,holdout_mean_capped_error,prior_holdout_within_10pct,prior_holdout_mean_capped_error,holdout_within_10pct_soi,holdout_within_10pct_snap,holdout_within_10pct_medicaid,holdout_within_10pct_pop_state,holdout_within_10pct_pop_cd,arm 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file mode 100644 index 000000000..c99503454 --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/results/frontier.md @@ -0,0 +1,76 @@ +### `projection` parametrization, `chi_square` basis + +| Prior ACS share | λ | National ESS | Top-1% share | Donor mass | χ² from prior | State ESS median | CD ESS median / min | MA ESS | MA CD ESS min | Loss | Within 10% | SOI within 10% | SNAP | Medicaid | CD pop | +|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| 0.5 | 0.03 | 20,690 | 63.7% | 62.8% | 0.328 | 378 | 46 / 22 | 589 | 55 | 0.0181 | 97.3% | 96.8% | 100% | 100% | 100% | +| 0.5 | 0.1 | 22,739 | 62.0% | 61.1% | 0.261 | 413 | 51 / 27 | 616 | 58 | 0.0228 | 95.9% | 95.4% | 98% | 94% | 100% | +| 0.5 | 1 | 30,582 | 55.5% | 54.7% | 0.057 | 551 | 67 / 44 | 843 | 85 | 0.0785 | 75.9% | 75.6% | 74% | 78% | 76% | + +### `projection` parametrization, `record` basis + +| Prior ACS share | λ | National ESS | Top-1% share | Donor mass | χ² from prior | State ESS median | CD ESS median / min | MA ESS | MA CD ESS min | Loss | Within 10% | SOI within 10% | SNAP | Medicaid | CD pop | +|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| 0.5 | 0 | 13,646 | 69.3% | 68.0% | 0.727 | 303 | 33 / 12 | 448 | 32 | 0.0155 | 97.6% | 97.2% | 100% | 100% | 100% | +| 0.5 | 0.1 | 8,689 | 88.8% | 88.2% | 1.075 | 199 | 20 / 8 | 279 | 26 | 0.0150 | 97.6% | 97.2% | 100% | 100% | 100% | + +### `softmax` parametrization, `chi_square` basis + +| Prior ACS share | λ | National ESS | Top-1% share | Donor mass | χ² from prior | State ESS median | CD ESS median / min | MA ESS | MA CD ESS min | Loss | Within 10% | SOI within 10% | SNAP | Medicaid | CD pop | +|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| 0.5 | 0 | 16,132 | 64.0% | 62.8% | 0.639 | 309 | 39 / 13 | 502 | 39 | 0.0160 | 97.5% | 97.1% | 100% | 100% | 100% | +| 0.5 | 0.01 | 19,844 | 62.8% | 61.8% | 0.389 | 354 | 45 / 19 | 591 | 56 | 0.0171 | 97.4% | 97.0% | 100% | 100% | 100% | +| 0.5 | 0.03 | 21,835 | 61.6% | 60.7% | 0.291 | 389 | 49 / 23 | 620 | 59 | 0.0192 | 97.1% | 96.6% | 100% | 100% | 100% | +| 0.5 | 0.1 | 24,895 | 59.2% | 58.4% | 0.196 | 441 | 55 / 29 | 664 | 64 | 0.0246 | 95.5% | 94.9% | 97% | 94% | 100% | +| 0.5 | 0.3 | 29,165 | 56.2% | 55.5% | 0.105 | 495 | 65 / 38 | 769 | 77 | 0.0417 | 89.5% | 89.1% | 84% | 90% | 93% | +| 0.5 | 1 | 33,418 | 53.3% | 52.6% | 0.036 | 597 | 73 / 47 | 907 | 92 | 0.0801 | 75.7% | 75.7% | 66% | 75% | 75% | +| 0.5 | 3 | 35,386 | 51.8% | 51.1% | 0.011 | 667 | 77 / 50 | 976 | 100 | 0.1232 | 59.2% | 58.3% | 46% | 71% | 64% | +| 0.7 | 0 | 25,531 | 49.5% | 47.3% | 0.989 | 503 | 65 / 19 | 777 | 53 | 0.0178 | 97.3% | 96.9% | 100% | 100% | 100% | +| 0.7 | 0.01 | 34,280 | 46.9% | 45.2% | 0.634 | 588 | 81 / 24 | 992 | 81 | 0.0193 | 97.1% | 96.6% | 100% | 100% | 100% | +| 0.7 | 0.03 | 40,799 | 44.5% | 43.0% | 0.470 | 652 | 95 / 36 | 1,085 | 90 | 0.0231 | 96.2% | 95.6% | 100% | 94% | 100% | +| 0.7 | 0.1 | 52,651 | 40.7% | 39.4% | 0.294 | 808 | 123 / 54 | 1,280 | 115 | 0.0342 | 92.6% | 91.7% | 95% | 82% | 100% | +| 0.7 | 0.3 | 69,250 | 36.7% | 35.5% | 0.144 | 1,065 | 159 / 79 | 1,736 | 150 | 0.0622 | 83.6% | 82.6% | 80% | 73% | 92% | +| 0.7 | 1 | 85,358 | 33.5% | 32.4% | 0.046 | 1,442 | 190 / 105 | 2,274 | 206 | 0.1165 | 66.4% | 65.0% | 61% | 65% | 77% | +| 0.7 | 3 | 92,668 | 32.0% | 31.0% | 0.013 | 1,666 | 206 / 114 | 2,535 | 239 | 0.1737 | 49.6% | 47.0% | 42% | 61% | 68% | +| 0.9 | 0 | 93,524 | 27.8% | 22.4% | 1.522 | 1,620 | 227 / 64 | 2,704 | 234 | 0.0313 | 95.8% | 95.1% | 100% | 100% | 100% | +| 0.9 | 0.01 | 130,608 | 24.2% | 20.1% | 1.030 | 1,897 | 317 / 85 | 3,285 | 271 | 0.0335 | 95.1% | 94.4% | 100% | 94% | 100% | +| 0.9 | 0.03 | 170,766 | 21.4% | 17.9% | 0.737 | 2,437 | 413 / 147 | 4,003 | 316 | 0.0384 | 93.7% | 92.8% | 100% | 84% | 100% | +| 0.9 | 0.1 | 254,544 | 17.9% | 15.1% | 0.399 | 3,345 | 609 / 263 | 6,021 | 485 | 0.0585 | 87.3% | 85.8% | 92% | 73% | 100% | +| 0.9 | 0.3 | 350,622 | 15.1% | 12.7% | 0.175 | 4,577 | 832 / 412 | 8,609 | 743 | 0.0994 | 75.5% | 74.1% | 74% | 63% | 87% | +| 0.9 | 1 | 433,108 | 13.2% | 11.1% | 0.051 | 6,073 | 1,007 / 571 | 11,296 | 1,027 | 0.1684 | 56.1% | 54.9% | 52% | 55% | 64% | +| 0.9 | 3 | 465,831 | 12.4% | 10.4% | 0.013 | 6,738 | 1,086 / 603 | 12,250 | 1,185 | 0.2332 | 40.3% | 38.3% | 37% | 55% | 53% | +| 0.964 | 0 | 244,979 | 17.9% | 9.1% | 1.767 | 3,393 | 578 / 265 | 6,591 | 671 | 0.0350 | 94.9% | 94.1% | 99% | 98% | 100% | +| 0.964 | 0.01 | 314,416 | 15.1% | 8.1% | 1.236 | 3,988 | 743 / 353 | 7,747 | 778 | 0.0376 | 94.3% | 93.5% | 99% | 90% | 100% | +| 0.964 | 0.03 | 392,036 | 12.9% | 7.1% | 0.855 | 4,732 | 910 / 462 | 9,626 | 951 | 0.0445 | 92.3% | 91.4% | 98% | 78% | 100% | +| 0.964 | 0.1 | 525,451 | 10.3% | 5.7% | 0.435 | 6,375 | 1,242 / 620 | 13,112 | 1,331 | 0.0688 | 85.4% | 83.6% | 91% | 71% | 99% | +| 0.964 | 0.3 | 649,730 | 8.5% | 4.7% | 0.183 | 8,164 | 1,528 / 725 | 16,481 | 1,676 | 0.1144 | 72.3% | 70.7% | 72% | 61% | 85% | +| 0.964 | 1 | 738,984 | 7.4% | 4.0% | 0.052 | 10,120 | 1,724 / 812 | 18,673 | 1,890 | 0.1873 | 52.8% | 51.8% | 50% | 55% | 59% | +| 0.964 | 3 | 769,037 | 7.0% | 3.8% | 0.013 | 10,981 | 1,797 / 833 | 19,362 | 1,959 | 0.2540 | 37.4% | 35.8% | 37% | 51% | 45% | + +### Rotated holdout (each fold is 20% of targets, never seen by the solve) + +| Fold | Parametrization | Prior ACS share | λ | National ESS | Train within 10% | Held-out within 10% | Held-out capped error | Held-out SOI within 10% | Held-out CD pop within 10% | Prior: held-out within 10% | +|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| 0 | projection | 0.5 | 0 | 14,068 | 97.7% | 63.3% | 0.1296 | 64.2% | 51.1% | 30.2% | +| 0 | projection | 0.5 | 0.03 | 21,572 | 97.3% | 64.2% | 0.1247 | 65.6% | 50.0% | 30.2% | +| 0 | projection | 0.5 | 0.1 | 23,569 | 96.3% | 61.7% | 0.1285 | 62.4% | 51.1% | 30.2% | +| 0 | softmax | 0.5 | 0 | 16,727 | 97.7% | 60.8% | 0.1355 | 61.9% | 45.6% | 30.2% | +| 0 | softmax | 0.5 | 0.01 | 20,791 | 97.5% | 63.5% | 0.1264 | 64.7% | 48.9% | 30.2% | +| 0 | softmax | 0.5 | 0.03 | 22,727 | 97.3% | 63.1% | 0.1242 | 64.0% | 53.3% | 30.2% | +| 0 | softmax | 0.5 | 0.1 | 25,682 | 95.9% | 61.5% | 0.1263 | 62.4% | 53.3% | 30.2% | +| 0 | softmax | 0.5 | 1 | 33,419 | 78.2% | 50.1% | 0.1505 | 49.9% | 51.1% | 30.2% | +| 0 | softmax | 0.7 | 0 | 27,764 | 97.3% | 58.3% | 0.1523 | 58.7% | 48.9% | 24.1% | +| 0 | softmax | 0.7 | 0.01 | 37,350 | 97.1% | 60.4% | 0.1466 | 60.1% | 57.8% | 24.1% | +| 0 | softmax | 0.7 | 0.1 | 54,870 | 93.4% | 52.8% | 0.1554 | 52.7% | 52.2% | 24.1% | +| 0 | softmax | 0.9 | 0 | 105,443 | 95.9% | 56.1% | 0.1765 | 54.3% | 67.8% | 17.0% | +| 0 | softmax | 0.9 | 0.01 | 144,403 | 95.4% | 54.9% | 0.1785 | 53.0% | 66.7% | 17.0% | +| 0 | softmax | 0.9 | 0.1 | 263,924 | 88.7% | 44.1% | 0.2008 | 43.5% | 43.3% | 17.0% | +| 0 | softmax | 0.964 | 0 | 265,093 | 95.0% | 51.6% | 0.1952 | 49.1% | 67.8% | 14.8% | +| 0 | softmax | 0.964 | 0.1 | 537,544 | 86.5% | 41.5% | 0.2234 | 40.7% | 42.2% | 14.8% | +| 0 | softmax | 0.964 | 1 | 737,537 | 55.8% | 29.4% | 0.2709 | 28.4% | 30.0% | 14.8% | +| 1 | projection | 0.5 | 0 | 14,118 | 97.6% | 65.8% | 0.1236 | 65.0% | 60.5% | 31.3% | +| 1 | projection | 0.5 | 0.03 | 21,220 | 97.3% | 67.0% | 0.1191 | 66.7% | 60.5% | 31.3% | +| 1 | softmax | 0.5 | 0.03 | 22,415 | 97.2% | 66.3% | 0.1166 | 65.4% | 61.6% | 31.3% | +| 1 | softmax | 0.5 | 0.1 | 25,434 | 95.7% | 63.8% | 0.1210 | 62.9% | 61.6% | 31.3% | +| 1 | softmax | 0.7 | 0.01 | 35,984 | 97.0% | 60.5% | 0.1416 | 59.4% | 60.5% | 27.4% | +| 1 | softmax | 0.9 | 0 | 100,207 | 95.7% | 56.7% | 0.1767 | 54.8% | 65.1% | 19.4% | +| 1 | softmax | 0.964 | 0 | 257,383 | 95.0% | 52.4% | 0.1984 | 50.2% | 61.6% | 17.3% | diff --git a/experiments/us-acs-local-l2-basis-20260928/results/frontier.png b/experiments/us-acs-local-l2-basis-20260928/results/frontier.png new file mode 100644 index 000000000..137584ef6 Binary files /dev/null and b/experiments/us-acs-local-l2-basis-20260928/results/frontier.png differ diff --git a/experiments/us-acs-local-l2-basis-20260928/results/gradient_scale.json b/experiments/us-acs-local-l2-basis-20260928/results/gradient_scale.json new file mode 100644 index 000000000..06484d38b --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/results/gradient_scale.json @@ -0,0 +1,35 @@ +{ + "eps": 1e-08, + "loss_cap": 1.0, + "n_targets": 4459, + "design": { + "log_weight_grad_abs_quantiles": { + "0.1": 2.9357636748371047e-09, + "0.25": 1.1367635048845279e-08, + "0.5": 3.8030910683097095e-08, + "0.75": 1.1260116339101827e-07, + "0.9": 3.1066832488468777e-07, + "0.99": 3.4493282236612487e-06 + }, + "share_at_or_below_eps": 0.22946664702987185, + "share_at_least_10_eps": 0.2740887457249061, + "share_positive_of_nonzero": 0.3279407673706961, + "share_negative_of_nonzero": 0.6720592326293039, + "live_targets": 4356 + }, + "release_0923": { + "log_weight_grad_abs_quantiles": { + "0.1": 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projection, λ 0 | 13,646 | 69.3% | 195 | 33 / 12 | 352 | 448 | 97.6% | 0.1296 / 0.1236 | 63.3% / 65.8% | +| Share 0.5, projection, chi-square λ 0.03 | 20,690 | 63.7% | 204 | 46 / 22 | 278 | 589 | 97.3% | 0.1247 / 0.1191 | 64.2% / 67.0% | +| Share 0.5, softmax, chi-square λ 0.03 | 21,835 | 61.6% | 213 | 49 / 23 | 239 | 620 | 97.1% | 0.1242 / 0.1166 | 63.1% / 66.3% | +| Share 0.5, projection, chi-square λ 0.1 | 22,739 | 62.0% | 207 | 51 / 27 | 208 | 616 | 95.9% | 0.1285 / – | 61.7% / – | +| Share 0.5, softmax, chi-square λ 0.1 | 24,895 | 59.2% | 222 | 55 / 29 | 132 | 664 | 95.5% | 0.1263 / 0.1210 | 61.5% / 63.8% | +| Share 0.5, softmax, chi-square λ 1 | 33,418 | 53.3% | 269 | 73 / 47 | 4 | 907 | 75.7% | 0.1505 / – | 50.1% / – | +| Share 0.7, softmax, chi-square λ 0.01 | 34,280 | 46.9% | 319 | 81 / 24 | 30 | 992 | 97.1% | 0.1466 / 0.1416 | 60.4% / 60.5% | +| Share 0.9, softmax, no penalty | 93,524 | 27.8% | 599 | 227 / 64 | 0 | 2,704 | 95.8% | 0.1765 / 0.1767 | 56.1% / 56.7% | +| Share 0.9, softmax, chi-square λ 0.01 | 130,608 | 24.2% | 612 | 317 / 85 | 0 | 3,285 | 95.1% | 0.1785 / – | 54.9% / – | +| Share 0.964, softmax, no penalty | 244,979 | 17.9% | 594 | 578 / 265 | 0 | 6,591 | 94.9% | 0.1952 / 0.1984 | 51.6% / 52.4% | diff --git a/experiments/us-acs-local-l2-basis-20260928/results/optimizer_reference.csv b/experiments/us-acs-local-l2-basis-20260928/results/optimizer_reference.csv new file mode 100644 index 000000000..19dc86482 --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/results/optimizer_reference.csv @@ -0,0 +1,286 @@ +problem,records,targets,epochs,lambda,mass,parametrization,loss,loss_cap_binds,chi_square_distance,objective,reference_loss,reference_chi_square_distance,reference_objective,kish_ess,mass_ratio,excess_objective 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2555.0926824507637, + "2508": 2110.6567887111105, + "2509": 2215.2333266884193 + }, + "district_kish_ess_distinct": { + "2501": 2331.912369964771, + "2502": 2527.9603006722164, + "2503": 2133.01588039559, + "2504": 2473.736644099596, + "2505": 2445.3341452119566, + "2506": 2359.9877221362894, + "2507": 2534.8267781045975, + "2508": 2104.660749482162, + "2509": 2196.042650412419 + } + } + }, + { + "acs_share": 0.9639740341151547, + "acs_share_label": "proportional", + "k": 16, + "rows": 2447454, + "donor_mass_share": 0.03602596588484531, + "national": { + "kish_ess_rows": 867587.7198291977, + "kish_ess_distinct": 778061.4203347411, + "top_1pct_share_rows": 0.0854183142407802 + }, + "state": { + "kish_ess_rows": { + "min": 1364.4882853547485, + "p10": 2143.0189246782043, + "median": 11809.409123570802, + "max": 102019.45457624477 + }, + "kish_ess_distinct": { + "min": 1351.1195057058414, + "p10": 2117.797018211337, + "median": 11181.387847635217, + "max": 89082.07529533777 + } + }, + "district": { + "kish_ess_rows": { + "min": 894.6054308784837, + "p10": 1596.071856756358, + "median": 2033.4942845540422, + "max": 3662.744554424218 + }, + "kish_ess_distinct": { + "min": 890.7926819834995, + "p10": 1587.1584465801407, + "median": 2015.381851054054, + "max": 3611.3196389202444 + } + }, + "massachusetts": { + "kish_ess_rows": 21610.463609280934, + "kish_ess_distinct": 19638.1912624535, + "district_kish_ess_rows": { + "2501": 2409.00677206135, + "2502": 2604.156095235854, + "2503": 2190.5075747265464, + "2504": 2536.556391218649, + "2505": 2537.4819672037124, + "2506": 2415.199224186811, + "2507": 2600.5605846409994, + "2508": 2159.8127248495925, + "2509": 2240.6229162178 + }, + "district_kish_ess_distinct": { + "2501": 2373.700458603401, + "2502": 2567.556154913803, + "2503": 2170.688081367939, + "2504": 2508.601131111788, + "2505": 2508.841255106897, + "2506": 2393.7260869844663, + "2507": 2571.3222671728868, + "2508": 2137.991239650569, + "2509": 2218.673469801649 + } + } + } + ], + "acs_rows_only_at_release_design": { + "rows": 1531614, + "donor_mass_share": 0.0, + "national": { + "kish_ess_rows": 812434.4564852872, + "kish_ess_distinct": 812434.4564852872, + "top_1pct_share_rows": 0.06183499269792848 + }, + "state": { + "kish_ess_rows": { + "min": 1279.1751497509695, + "p10": 2008.578992350391, + "median": 11018.404539687901, + "max": 95281.52756328302 + }, + "kish_ess_distinct": { + "min": 1279.1751497509695, + "p10": 2008.578992350391, + "median": 11018.404539687901, + "max": 95281.52756328302 + } + }, + "district": { + "kish_ess_rows": { + "min": 834.2004051362453, + "p10": 1492.5982359501002, + "median": 1905.1475659852658, + "max": 3480.480993641855 + }, + "kish_ess_distinct": { + "min": 834.2004051362453, + "p10": 1492.5982359501002, + "median": 1905.1475659852658, + "max": 3480.480993641855 + } + }, + "massachusetts": { + "kish_ess_rows": 20237.75081280998, + "kish_ess_distinct": 20237.75081280998, + "district_kish_ess_rows": { + "2501": 2245.440000658762, + "2502": 2429.64247712815, + "2503": 2056.491766048132, + "2504": 2376.31453254353, + "2505": 2375.451457700593, + "2506": 2274.714348026484, + "2507": 2443.778808256828, + "2508": 2020.412898933117, + "2509": 2094.734066413284 + }, + "district_kish_ess_distinct": { + "2501": 2245.440000658762, + "2502": 2429.64247712815, + "2503": 2056.491766048132, + "2504": 2376.31453254353, + "2505": 2375.451457700593, + "2506": 2274.714348026484, + "2507": 2443.778808256828, + "2508": 2020.412898933117, + "2509": 2094.734066413284 + } + } + } +} diff --git a/experiments/us-acs-local-l2-basis-20260928/results/seeding_options.md b/experiments/us-acs-local-l2-basis-20260928/results/seeding_options.md new file mode 100644 index 000000000..8f3299bf5 --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/results/seeding_options.md @@ -0,0 +1,15 @@ +| ACS share | Donor clones k | National ESS (rows / distinct) | Top-1% share | State ESS median (distinct) | District ESS median, min (distinct) | Massachusetts ESS (distinct) | MA district ESS range (distinct) | +|---|---:|---|---:|---:|---|---:|---| +| 0.5 | 1 | 36,288 / 36,288 | 50.7% | 736 | 80, 50 | 1,020 | 108-134 | +| 0.5 | 4 | 140,447 / 36,288 | 29.4% | 736 | 257, 144 | 1,020 | 302-371 | +| 0.5 | 16 | 497,310 / 36,288 | 13.1% | 736 | 572, 334 | 1,020 | 621-740 | +| 0.7 | 1 | 96,034 / 96,034 | 31.1% | 1,821 | 215, 114 | 2,697 | 267-386 | +| 0.7 | 4 | 327,269 / 96,034 | 17.6% | 1,821 | 622, 405 | 2,697 | 690-898 | +| 0.7 | 16 | 822,204 / 96,034 | 8.3% | 1,821 | 1,196, 724 | 2,697 | 1,244-1,444 | +| 0.9 | 1 | 479,159 / 479,159 | 12.1% | 7,065 | 1,111, 608 | 12,754 | 1,295-1,711 | +| 0.9 | 4 | 787,712 / 479,159 | 7.1% | 7,065 | 1,742, 881 | 12,754 | 1,903-2,266 | +| 0.9 | 16 | 938,855 / 479,159 | 8.0% | 7,065 | 2,027, 973 | 12,754 | 2,187-2,573 | +| proportional | 1 | 778,061 / 778,061 | 6.9% | 11,181 | 1,822, 837 | 19,638 | 1,983-2,399 | +| proportional | 4 | 848,071 / 778,061 | 6.6% | 11,181 | 1,967, 881 | 19,638 | 2,105-2,535 | +| proportional | 16 | 867,588 / 778,061 | 8.5% | 11,181 | 2,015, 891 | 19,638 | 2,138-2,571 | +| ACS rows only | - | 812,434 | 6.2% | 11,018 | 1,905, 834 | 20,238 | 2,020-2,444 | diff --git a/experiments/us-acs-local-l2-basis-20260928/seeding_options.py b/experiments/us-acs-local-l2-basis-20260928/seeding_options.py new file mode 100644 index 000000000..db0c2942c --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/seeding_options.py @@ -0,0 +1,271 @@ +"""Starting-weight concentration of the ACS local file under other seedings. + +Before any calibration, how concentrated are the starting weights if the +staging seeds them differently? Two levers, alone and together: + +``acs_share`` + The ACS rows' share of the household mass (``--acs-share`` in + ``tools/_legacy/build_us_acs_multispine_base.py``, applied through + ``base_pool._pooled_household_weights``; the release used 0.5). Each spine + keeps its internal proportions (``sweep.reseed_prior``). 0.964 is the + share proportional to record counts (1,531,614 of 1,588,854). +``k`` + Location clones of each donor (ASEC-by-PUF) record: ``k`` copies at + weight/``k``, as location v1's draw supports (PolicyEngine/microcosm#1047; + every line refuses k > 1 today). v1 draws a clone's block within the + record's finest source geography, its CPS county when one is coded, else + its state. This checkpoint carries no donor county, so here each clone's + congressional district is drawn within the record's state with probability + proportional to the ACS rows' starting mass in that district (a household + count proxy). That is the state-only case, the widest spread v1 allows, so + the per-district figures are an upper bound for donors with a coded county. + +Clones of one record are not independent observations. Every figure is +therefore given twice: Kish ESS over rows, and over distinct source households +within the group (a record's clones summed first), which is at most the row +figure. Nationally and by state, cloning leaves the distinct figure unchanged, +since all of a record's clones stay in its state. + +Run: ``uv run python experiments/us-acs-local-l2-basis-20260928/seeding_options.py`` +Writes ``results/seeding_options.json`` and ``results/seeding_options.md``. +""" + +from __future__ import annotations + +import json +import sys +from pathlib import Path + +import numpy as np +import pandas as pd + +HERE = Path(__file__).resolve().parent +sys.path.insert(0, str(HERE)) + +from sweep import ( # noqa: E402 + DEFAULT_CHECKPOINT, + MASSACHUSETTS, + SPINES, + kish, + reseed_prior, + top_share, +) + +ACS_SHARES = (0.5, 0.7, 0.9, None) # None: proportional to record counts +CLONES = (1, 4, 16) +SEED = 20260930 + + +def grouped_kish(weights: np.ndarray, groups: np.ndarray) -> pd.Series: + frame = pd.DataFrame({"g": groups, "w": weights, "w2": weights**2}) + sums = frame.groupby("g", observed=True)[["w", "w2"]].sum() + return sums.w**2 / sums.w2 + + +def distinct_kish( + weights: np.ndarray, groups: np.ndarray, source: np.ndarray +) -> pd.Series: + """Kish ESS per group after summing each source household's rows.""" + + frame = pd.DataFrame({"g": groups, "s": source, "w": weights}) + per_source = frame.groupby(["g", "s"], observed=True).w.sum().reset_index() + per_source["w2"] = per_source.w**2 + sums = per_source.groupby("g", observed=True)[["w", "w2"]].sum() + return sums.w**2 / sums.w2 + + +def summary(values: pd.Series) -> dict: + return { + "min": float(values.min()), + "p10": float(values.quantile(0.10)), + "median": float(values.median()), + "max": float(values.max()), + } + + +def cloned_rows( + households: pd.DataFrame, prior: np.ndarray, k: int, rng: np.random.Generator +) -> pd.DataFrame: + """ACS rows as they are; each donor row split into k clones at weight/k.""" + + spine = households["spine"].astype(str).to_numpy() + state = households["state_fips"].astype(str).to_numpy() + district = households["congressional_district_geoid"].astype(str).to_numpy() + acs = spine == SPINES[0] + rows = pd.DataFrame( + { + "source": households["household_id"].to_numpy(), + "state": state, + "district": district, + "spine": spine, + "weight": prior, + } + ) + if k == 1: + return rows + # Placement probabilities: ACS starting mass by district within each state. + acs_mass = ( + pd.DataFrame({"state": state[acs], "district": district[acs], "w": prior[acs]}) + .groupby(["state", "district"], observed=True) + .w.sum() + ) + donors = rows[~acs] + parts = [rows[acs]] + for state_code, group in donors.groupby("state", observed=True): + options = acs_mass.loc[state_code] + probabilities = (options / options.sum()).to_numpy() + draws = rng.choice(len(options), size=(len(group), k), p=probabilities) + parts.append( + pd.DataFrame( + { + "source": np.repeat(group["source"].to_numpy(), k), + "state": state_code, + "district": options.index.to_numpy()[draws.ravel()], + "spine": SPINES[1], + "weight": np.repeat(group["weight"].to_numpy() / k, k), + } + ) + ) + return pd.concat(parts, ignore_index=True) + + +def measure(rows: pd.DataFrame) -> dict: + weights = rows["weight"].to_numpy() + source = rows["source"].to_numpy() + donor = rows["spine"].to_numpy() == SPINES[1] + national_distinct = kish( + rows.groupby("source", observed=True).weight.sum().to_numpy() + ) + by_state_rows = grouped_kish(weights, rows["state"].to_numpy()) + by_state_distinct = distinct_kish(weights, rows["state"].to_numpy(), source) + by_cd_rows = grouped_kish(weights, rows["district"].to_numpy()) + by_cd_distinct = distinct_kish(weights, rows["district"].to_numpy(), source) + ma_cds = sorted(c for c in by_cd_rows.index if str(c).startswith(MASSACHUSETTS)) + return { + "rows": int(len(rows)), + "donor_mass_share": float(weights[donor].sum() / weights.sum()), + "national": { + "kish_ess_rows": kish(weights), + "kish_ess_distinct": national_distinct, + "top_1pct_share_rows": top_share(weights), + }, + "state": { + "kish_ess_rows": summary(by_state_rows), + "kish_ess_distinct": summary(by_state_distinct), + }, + "district": { + "kish_ess_rows": summary(by_cd_rows), + "kish_ess_distinct": summary(by_cd_distinct), + }, + "massachusetts": { + "kish_ess_rows": float(by_state_rows[MASSACHUSETTS]), + "kish_ess_distinct": float(by_state_distinct[MASSACHUSETTS]), + "district_kish_ess_rows": {c: float(by_cd_rows[c]) for c in ma_cds}, + "district_kish_ess_distinct": {c: float(by_cd_distinct[c]) for c in ma_cds}, + }, + } + + +def main() -> None: + households = pd.read_parquet(DEFAULT_CHECKPOINT / "households.parquet") + design = households["design_weight"].to_numpy(np.float64) + spine = households["spine"].astype(str).to_numpy() + proportional = float((spine == SPINES[0]).mean()) + results = [] + for share in ACS_SHARES: + acs_share = proportional if share is None else share + prior = reseed_prior(design, spine, acs_share) + for k in CLONES: + rng = np.random.default_rng([SEED, k, int(round(acs_share * 1e6))]) + rows = cloned_rows(households, prior, k, rng) + entry = { + "acs_share": acs_share, + "acs_share_label": "proportional" if share is None else str(share), + "k": k, + **measure(rows), + } + results.append(entry) + print( + f"acs_share={acs_share:.3f} k={k}: national ESS " + f"{entry['national']['kish_ess_rows']:,.0f} rows / " + f"{entry['national']['kish_ess_distinct']:,.0f} distinct; " + f"MA {entry['massachusetts']['kish_ess_distinct']:,.0f}; " + f"CD median {entry['district']['kish_ess_distinct']['median']:,.0f}", + flush=True, + ) + acs_only = measure( + cloned_rows( + households[spine == SPINES[0]].reset_index(drop=True), + design[spine == SPINES[0]], + 1, + np.random.default_rng(SEED), + ) + ) + # The release design's Massachusetts split by spine, which the doc quotes. + ma = households["state_fips"].astype(str).to_numpy() == MASSACHUSETTS + districts = households["congressional_district_geoid"].astype(str).to_numpy() + ma_by_spine = {} + for label in SPINES: + rows = ma & (spine == label) + ma_by_spine[label] = { + "n": int(rows.sum()), + "design_mass_share_of_state": float(design[rows].sum() / design[ma].sum()), + "kish_ess": kish(design[rows]), + "district_kish_ess": { + code: kish(design[rows & (districts == code)]) + for code in sorted(set(districts[ma])) + }, + } + out = HERE / "results" + out.mkdir(exist_ok=True) + payload = { + "massachusetts_by_spine_at_release_design": ma_by_spine, + "checkpoint": str(DEFAULT_CHECKPOINT), + "seed": SEED, + "placement": ( + "donor clones: district drawn within the record's state with " + "probability proportional to ACS starting mass in the district " + "(state-only case of location v1; an upper bound on spread)" + ), + "proportional_acs_share": proportional, + "options": results, + "acs_rows_only_at_release_design": acs_only, + } + (out / "seeding_options.json").write_text(json.dumps(payload, indent=1) + "\n") + + lines = [ + "| ACS share | Donor clones k | National ESS (rows / distinct) " + "| Top-1% share | State ESS median (distinct) | District ESS median, min " + "(distinct) | Massachusetts ESS (distinct) | MA district ESS range " + "(distinct) |", + "|---|---:|---|---:|---:|---|---:|---|", + ] + for entry in results: + ma = entry["massachusetts"]["district_kish_ess_distinct"].values() + lines.append( + f"| {entry['acs_share_label']} | {entry['k']} | " + f"{entry['national']['kish_ess_rows']:,.0f} / " + f"{entry['national']['kish_ess_distinct']:,.0f} | " + f"{entry['national']['top_1pct_share_rows']:.1%} | " + f"{entry['state']['kish_ess_distinct']['median']:,.0f} | " + f"{entry['district']['kish_ess_distinct']['median']:,.0f}, " + f"{entry['district']['kish_ess_distinct']['min']:,.0f} | " + f"{entry['massachusetts']['kish_ess_distinct']:,.0f} | " + f"{min(ma):,.0f}-{max(ma):,.0f} |" + ) + ma_acs = acs_only["massachusetts"]["district_kish_ess_distinct"].values() + lines.append( + f"| ACS rows only | - | {acs_only['national']['kish_ess_rows']:,.0f} | " + f"{acs_only['national']['top_1pct_share_rows']:.1%} | " + f"{acs_only['state']['kish_ess_distinct']['median']:,.0f} | " + f"{acs_only['district']['kish_ess_distinct']['median']:,.0f}, " + f"{acs_only['district']['kish_ess_distinct']['min']:,.0f} | " + f"{acs_only['massachusetts']['kish_ess_distinct']:,.0f} | " + f"{min(ma_acs):,.0f}-{max(ma_acs):,.0f} |" + ) + (out / "seeding_options.md").write_text("\n".join(lines) + "\n") + print("\n".join(lines)) + + +if __name__ == "__main__": + main() diff --git a/experiments/us-acs-local-l2-basis-20260928/sweep.py b/experiments/us-acs-local-l2-basis-20260928/sweep.py new file mode 100644 index 000000000..27c0155d3 --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/sweep.py @@ -0,0 +1,1242 @@ +"""Per-run harness for the chi-square L2 basis / softmax mass sweep. + +One run recalibrates the published ACS local release's 4,459-target surface +(``populace-us-2024-buildo-acs-local-767312d60-20260923T074941Z``) from its +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. +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} + +``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, +each spine keeping its internal proportions, as the staging's ``--acs-share`` +does through ``base_pool._pooled_household_weights``. ``null`` keeps the +checkpoint's weights (the release's ``--acs-share 0.5``). The prior becomes the +frame's weights, so it is also the chi-square anchor, the base of the 5x ratio +cap, and the "design" every metric is measured against; the original design +weights stay recorded alongside. + +Modes: + +``calibrate`` (default) + Calibrate, then write ``//metrics.json``, + ``weights.npz`` (float64 final weights, per-batch loss trajectories) and + ``progress.log``. A per-batch ``resume.npz`` lets a restarted run continue + from its last finished batch (``--resume``). +``evaluate`` + Score a given weight vector without calibrating: ``--weights design`` + (``w = d``, the design reference) or ``--weights [:key]``. With + ``--weights design`` every holdout fold's design block is also written. + +Metrics (``metrics.json``) are computed from the final weights ``w`` and the +design weights ``d``: national, per-spine, per-state, per-district and +Massachusetts concentration; fit on the training targets (overall, per family +and per subfamily); and, with a holdout fold, the same fit block on the +held-out targets for ``w`` and for ``d``. + +All heavy inputs are verified against the checkpoint's ``MANIFEST.json`` +sha256s before use. +""" + +from __future__ import annotations + +import argparse +import dataclasses +import gc +import hashlib +import json +import math +import os +import platform +import resource +import socket +import subprocess +import sys +import time +from collections.abc import Callable, Sequence +from pathlib import Path + +import numpy as np +import pandas as pd +from scipy import sparse + +HERE = Path(__file__).resolve().parent +REPO = Path( + os.environ.get("MICROCOSM_REPO", HERE.parents[1] if len(HERE.parents) > 1 else HERE) +) +DEFAULT_CHECKPOINT = Path( + "/Users/maxghenis/PolicyEngine/_build_artifacts/acs-local-l2-basis-20260928/checkpoint" +) +DEFAULT_OUT_ROOT = Path( + "/Users/maxghenis/PolicyEngine/_build_artifacts/acs-local-l2-basis-20260928/runs" +) +RELEASE_TAG = "populace-us-2024-buildo-acs-local-767312d60-20260923T074941Z" +#: The published release's calibration settings (calibration_diagnostics.json). +RELEASE_SETTINGS = { + "method": "adam", + "learning_rate": 0.02, + "mass": "conserve", + "max_weight_ratio": 5.0, + "target_loss_cap": 1.0, + "seed": 0, + "weight_entity": "household", +} +#: What the published release recorded at l2_lambda=0 (800 epochs, 2 x 400). +RELEASE_0923_RESULT = { + "final_loss": 0.015491, + "fraction_within_10pct": 0.9758, + "effective_sample_size": 13631.3, + "initial_loss_second_batch_start": 0.017768, +} +SPINES = ("acs_2024_1yr", "asec_puf") +MASSACHUSETTS = "25" +WITHIN = 0.10 +CAP = 1.0 +INPUT_FILES = ( + "target_matrix.npz", + "households.parquet", + "targets_meta.parquet", + "holdout_folds.npz", +) +KERNEL_MODULES = ( + "microcosm.calibrate.solve", + "microcosm.calibrate.matrix", + "microcosm.calibrate.target", + "microcosm.frame.bundle", + "microcosm.frame.weights", +) + + +# --------------------------------------------------------------------------- +# Small utilities +# --------------------------------------------------------------------------- + + +def peak_rss_gb() -> float: + """Process peak RSS (ru_maxrss is bytes on macOS, KiB on Linux).""" + + peak = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss + return peak / 2**30 if sys.platform == "darwin" else peak / 2**20 + + +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 jsonable(value): + """Recursively convert numpy/pandas scalars and containers for json.""" + + if isinstance(value, dict): + return {str(k): jsonable(v) for k, v in value.items()} + if isinstance(value, (list, tuple)): + return [jsonable(v) for v in value] + if isinstance(value, np.ndarray): + return [jsonable(v) for v in value.tolist()] + if isinstance(value, (np.integer,)): + return int(value) + if isinstance(value, (np.floating,)): + value = float(value) + if isinstance(value, float) and not math.isfinite(value): + return None if math.isnan(value) else ("inf" if value > 0 else "-inf") + if isinstance(value, (np.bool_,)): + return bool(value) + if isinstance(value, (str, int, float, bool)) or value is None: + return value + if hasattr(value, "items"): + return {str(k): jsonable(v) for k, v in value.items()} + return str(value) + + +class Logger: + def __init__(self, path: Path | None): + self.path = path + self.handle = open(path, "a") if path is not None else None + + def __call__(self, message: str) -> None: + line = f"[{time.strftime('%Y-%m-%d %H:%M:%S')}] {message}" + print(line, flush=True) + if self.handle is not None: + self.handle.write(line + "\n") + self.handle.flush() + + def close(self) -> None: + if self.handle is not None: + self.handle.close() + + +# --------------------------------------------------------------------------- +# Run spec +# --------------------------------------------------------------------------- + + +@dataclasses.dataclass(frozen=True) +class RunSpec: + run_id: str + l2_lambda: float = 0.0 + l2_basis: str = "record" + mass_parametrization: str = "projection" + epochs: int = 800 + epoch_batch: int = 400 + holdout_fold: int | None = None + acs_share: float | None = None + + def __post_init__(self) -> None: + if not self.run_id or "/" in self.run_id: + raise ValueError( + f"run_id must be a non-empty path segment: {self.run_id!r}" + ) + if not (math.isfinite(self.l2_lambda) and self.l2_lambda >= 0.0): + raise ValueError(f"l2_lambda must be finite and >= 0: {self.l2_lambda!r}") + if self.l2_basis not in ("record", "chi_square"): + raise ValueError(f"unknown l2_basis {self.l2_basis!r}") + if self.mass_parametrization not in ("projection", "softmax"): + raise ValueError( + f"unknown mass_parametrization {self.mass_parametrization!r}" + ) + if self.epochs <= 0 or self.epoch_batch <= 0: + raise ValueError("epochs and epoch_batch must be positive") + if self.holdout_fold is not None and not (0 <= self.holdout_fold < 5): + raise ValueError( + f"holdout_fold must be None or 0..4: {self.holdout_fold!r}" + ) + 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}") + + @classmethod + def from_mapping(cls, mapping: dict) -> RunSpec: + known = {field.name for field in dataclasses.fields(cls)} + unknown = sorted(set(mapping) - known) + if unknown: + raise ValueError(f"unknown run spec keys {unknown}") + values = dict(mapping) + for key in ("l2_lambda",): + if key in values: + values[key] = float(values[key]) + if values.get("acs_share") is not None: + values["acs_share"] = float(values["acs_share"]) + for key in ("epochs", "epoch_batch"): + if key in values: + values[key] = int(values[key]) + if values.get("holdout_fold") is not None: + values["holdout_fold"] = int(values["holdout_fold"]) + return cls(**values) + + +# --------------------------------------------------------------------------- +# Inputs +# --------------------------------------------------------------------------- + + +@dataclasses.dataclass +class Inputs: + checkpoint: Path + matrix: sparse.csr_array # (targets, households) float32 + households: pd.DataFrame + meta: pd.DataFrame + folds: dict[int, np.ndarray] + fold_of_target: np.ndarray + manifest: dict + input_sha256: dict[str, str] + _matrix64: sparse.csr_array | None = None + + def matrix64(self) -> sparse.csr_array: + """The CSR widened to float64 (exact), built once on first use.""" + + if self._matrix64 is None: + self._matrix64 = self.matrix.astype(np.float64) + return self._matrix64 + + @property + def design(self) -> np.ndarray: + return self.households["design_weight"].to_numpy(np.float64) + + @property + def values(self) -> np.ndarray: + return self.meta["value"].to_numpy(np.float64) + + def prior(self, acs_share: float | None) -> np.ndarray: + """The run's starting weights: the design, reseeded to ``acs_share``.""" + + return reseed_prior( + self.design, self.households["spine"].astype(str).to_numpy(), acs_share + ) + + +def reseed_prior( + design: np.ndarray, spine: np.ndarray, acs_share: float | None +) -> np.ndarray: + """Rescale each spine to its share of the unchanged total. + + ACS rows together get ``acs_share`` of the mass and donor rows the rest; + proportions within a spine are kept. ``None`` returns ``design`` itself. + """ + + design = np.asarray(design, dtype=np.float64) + if acs_share is None: + return design + acs = spine == SPINES[0] + donor = spine == SPINES[1] + if not (acs | donor).all() or not acs.any() or not donor.any(): + raise SystemExit("every household must be on exactly one of the two spines") + total = float(design.sum()) + prior = design.copy() + prior[acs] *= acs_share * total / float(design[acs].sum()) + prior[donor] *= (1.0 - acs_share) * total / float(design[donor].sum()) + return prior + + +def load_inputs( + checkpoint: Path, *, verify: bool = True, log: Callable = print +) -> Inputs: + started = time.time() + manifest = json.loads((checkpoint / "MANIFEST.json").read_text()) + if not manifest.get("ok"): + raise SystemExit(f"{checkpoint}/MANIFEST.json is not ok") + digests = {} + if verify: + for name in INPUT_FILES: + actual = sha256(checkpoint / name) + expected = manifest["files"][name] + if actual != expected: + raise SystemExit(f"{name}: sha256 {actual} != manifest {expected}") + digests[name] = actual + matrix = sparse.load_npz(checkpoint / "target_matrix.npz").tocsr() + matrix.sort_indices() + households = pd.read_parquet(checkpoint / "households.parquet") + meta = pd.read_parquet(checkpoint / "targets_meta.parquet") + folds_npz = np.load(checkpoint / "holdout_folds.npz") + n_folds = int(folds_npz["n_folds"]) + folds = { + i: np.asarray(folds_npz[f"fold_{i}"], dtype=np.int64) for i in range(n_folds) + } + fold_of_target = np.asarray(folds_npz["fold_of_target"], dtype=np.int64) + n_targets, n_households = matrix.shape + if len(households) != n_households or len(meta) != n_targets: + raise SystemExit("households/targets metadata do not align with the CSR") + if not np.array_equal(meta["row"].to_numpy(), np.arange(n_targets)): + raise SystemExit("targets_meta rows are not in CSR row order") + if not np.all(np.diff(households["household_id"].to_numpy()) > 0): + 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") + log( + f"inputs: {n_targets} targets x {n_households:,} households, nnz " + f"{matrix.nnz:,}, verified={verify}, {time.time() - started:.1f}s" + ) + return Inputs( + checkpoint=checkpoint, + matrix=matrix, + households=households, + meta=meta, + folds=folds, + fold_of_target=fold_of_target, + manifest=manifest, + input_sha256=digests, + ) + + +def build_frame(households: pd.DataFrame, weights: np.ndarray): + """A minimal frame: households with the run's prior weights, one person each.""" + + from microcosm.frame import EntitySchema, Frame, WeightKind, Weights + + household_id = households["household_id"].to_numpy(np.int64) + tables = { + "household": pd.DataFrame({"household_id": household_id}), + "person": pd.DataFrame( + { + "person_id": np.arange(len(household_id), dtype=np.int64), + "person_household_id": household_id, + } + ), + } + return Frame( + tables, + EntitySchema(group_entities=("household",)), + {"household": Weights(np.asarray(weights, np.float64), WeightKind.DESIGN)}, + ) + + +def csr_row_measure(matrix: sparse.csr_array, row: int, n: int): + """A callable measure returning CSR row ``row`` as a dense float64 vector. + + float32 values widen exactly to float64, as the dense checkpoint's float32 + columns did when the release compiled them. + """ + + start, stop = int(matrix.indptr[row]), int(matrix.indptr[row + 1]) + indices = matrix.indices[start:stop] + data = matrix.data[start:stop] + + def measure(frame) -> np.ndarray: + values = np.zeros(n, dtype=np.float64) + values[indices] = data + return values + + measure.__qualname__ = f"target_matrix_row[{row}]" + return measure + + +def build_target_set(inputs: Inputs, rows: Sequence[int]): + from microcosm.calibrate import Target, TargetSet + + n_households = inputs.matrix.shape[1] + names = inputs.meta["name"].to_numpy() + values = inputs.values + return TargetSet( + Target( + name=str(names[row]), + entity="household", + measure=csr_row_measure(inputs.matrix, int(row), n_households), + value=float(values[row]), + period=2024, + source="acs_local_0923_checkpoint", + ) + for row in rows + ) + + +def training_rows(inputs: Inputs, holdout_fold: int | None) -> np.ndarray: + n_targets = inputs.matrix.shape[0] + if holdout_fold is None: + return np.arange(n_targets, dtype=np.int64) + return np.flatnonzero(inputs.fold_of_target != holdout_fold).astype(np.int64) + + +# --------------------------------------------------------------------------- +# Metrics +# --------------------------------------------------------------------------- + + +def n_records_holding_half(weights: np.ndarray) -> int: + ordered = np.sort(weights)[::-1] + cumulative = np.cumsum(ordered) + return int(np.searchsorted(cumulative, 0.5 * cumulative[-1], side="left") + 1) + + +def top_share(weights: np.ndarray, fraction: float = 0.01) -> float: + total = float(weights.sum()) + if total <= 0: + return 0.0 + k = max(1, math.ceil(fraction * weights.size)) + return float( + np.partition(weights, weights.size - k)[weights.size - k :].sum() / total + ) + + +def kish(weights: np.ndarray) -> float: + denominator = float(np.square(weights).sum()) + return float(weights.sum() ** 2 / denominator) if denominator > 0 else 0.0 + + +def grouped_concentration( + weights: np.ndarray, codes: np.ndarray, n_groups: int +) -> dict[str, np.ndarray]: + """Per-group n, mass, Kish ESS, top-1% share and half-mass record count.""" + + n = np.bincount(codes, minlength=n_groups).astype(np.int64) + mass = np.bincount(codes, weights=weights, minlength=n_groups) + square = np.bincount(codes, weights=np.square(weights), minlength=n_groups) + ess = np.divide(mass**2, square, out=np.zeros(n_groups), where=square > 0) + order = np.lexsort((-weights, codes)) + sorted_weights = weights[order] + sorted_codes = codes[order] + starts = np.concatenate([[0], np.cumsum(n)[:-1]]) + rank = np.arange(weights.size) - starts[sorted_codes] + cumulative = np.cumsum(sorted_weights) + before = np.concatenate([[0.0], cumulative])[starts] + within = cumulative - before[sorted_codes] + below_half = within < 0.5 * mass[sorted_codes] + half = np.bincount(sorted_codes, weights=below_half, minlength=n_groups).astype( + np.int64 + ) + (n > 0) + k = np.maximum(1, np.ceil(0.01 * n)).astype(np.int64) + top = np.bincount( + sorted_codes, + weights=sorted_weights * (rank < k[sorted_codes]), + minlength=n_groups, + ) + top_share_values = np.divide(top, mass, out=np.zeros(n_groups), where=mass > 0) + return { + "n": n, + "mass": mass, + "kish_ess": ess, + "top_1pct_share": top_share_values, + "n_records_holding_half_weight": half, + } + + +def summary_stats(values: np.ndarray, labels: np.ndarray) -> dict: + values = np.asarray(values, dtype=np.float64) + return { + "min": float(values.min()), + "p10": float(np.percentile(values, 10)), + "median": float(np.median(values)), + "max": float(values.max()), + "argmin": str(labels[int(values.argmin())]), + "argmax": str(labels[int(values.argmax())]), + } + + +def concentration_metrics( + w: np.ndarray, d: np.ndarray, households: pd.DataFrame +) -> dict: + from microcosm.calibrate import chi_square_distance + + n = int(w.size) + total = float(w.sum()) + ratio = w / d + ess = kish(w) + national = { + "households": n, + "total_weight": total, + "mass_conserved_ratio": total / float(d.sum()), + "kish_ess": ess, + "kish_ess_design": kish(d), + "ess_fraction": ess / n, + "top_1pct_weight_share": top_share(w), + "chi_square_distance": chi_square_distance(w, d), + "realized_max_weight_ratio": float(ratio.max()), + "realized_min_weight_ratio": float(ratio.min()), + "share_of_design_mass_with_ratio_below_0_1": float( + d[ratio < 0.1].sum() / d.sum() + ), + "share_of_design_mass_with_ratio_below_0_01": float( + d[ratio < 0.01].sum() / d.sum() + ), + "share_of_design_mass_at_cap": float( + d[ratio >= 5.0 * (1 - 1e-9)].sum() / d.sum() + ), + "n_records_at_cap": int((ratio >= 5.0 * (1 - 1e-9)).sum()), + "n_records_holding_half_weight": n_records_holding_half(w), + "weight_ratio_quantiles": { + q: float(np.quantile(ratio, float(q))) + for q in ("0.01", "0.1", "0.25", "0.5", "0.75", "0.9", "0.99") + }, + "design_weighted_mean_log_ratio": float( + np.sum(d * np.log(np.maximum(ratio, 1e-300))) / d.sum() + ), + } + + spine = households["spine"].astype(str).to_numpy() + per_spine = {} + for label in SPINES: + mask = spine == label + per_spine[label] = { + "n": int(mask.sum()), + "mass_share": float(w[mask].sum() / total), + "design_mass_share": float(d[mask].sum() / d.sum()), + "kish_ess": kish(w[mask]), + "kish_ess_design": kish(d[mask]), + "n_records_holding_half_weight": n_records_holding_half(w[mask]), + } + + def geography(column: str) -> tuple[dict, dict, dict]: + codes_cat = households[column].astype("category") + labels = np.asarray(codes_cat.cat.categories.astype(str)) + codes = codes_cat.cat.codes.to_numpy(np.int64) + on_w = grouped_concentration(w, codes, len(labels)) + on_d = grouped_concentration(d, codes, len(labels)) + present = on_w["n"] > 0 + rows = {} + for i, label in enumerate(labels): + if not present[i]: + continue + rows[str(label)] = { + "n": int(on_w["n"][i]), + "kish_ess": float(on_w["kish_ess"][i]), + "kish_ess_design": float(on_d["kish_ess"][i]), + "kish_ess_ratio": float(on_w["kish_ess"][i] / on_d["kish_ess"][i]), + "mass_ratio_vs_design": float(on_w["mass"][i] / on_d["mass"][i]), + "top_1pct_share": float(on_w["top_1pct_share"][i]), + "n_records_holding_half_weight": int( + on_w["n_records_holding_half_weight"][i] + ), + "n_records_holding_half_weight_design": int( + on_d["n_records_holding_half_weight"][i] + ), + } + ess = on_w["kish_ess"][present] + ratio_ess = ess / on_d["kish_ess"][present] + summary = { + "n_groups": int(present.sum()), + "kish_ess": summary_stats(ess, labels[present]), + "kish_ess_design": summary_stats( + on_d["kish_ess"][present], labels[present] + ), + "kish_ess_ratio": summary_stats(ratio_ess, labels[present]), + "n_records_holding_half_weight": summary_stats( + on_w["n_records_holding_half_weight"][present], labels[present] + ), + "mass_ratio_vs_design": summary_stats( + on_w["mass"][present] / on_d["mass"][present], labels[present] + ), + } + return rows, summary, {"labels": labels} + + per_state, state_summary, _ = geography("state_fips") + per_cd, cd_summary, _ = geography("congressional_district_geoid") + massachusetts = { + "state": per_state[MASSACHUSETTS], + "districts": { + code: row for code, row in per_cd.items() if code.startswith(MASSACHUSETTS) + }, + } + return { + "national": national, + "per_spine": per_spine, + "state_summary": state_summary, + "cd_summary": cd_summary, + "massachusetts": massachusetts, + "per_state": per_state, + "per_cd": per_cd, + } + + +def fit_rows(estimates: np.ndarray, targets: np.ndarray) -> dict[str, np.ndarray]: + difference = estimates - targets + relative = np.where( + targets != 0, difference / np.where(targets != 0, targets, 1.0), difference + ) + capped = np.minimum(np.abs(difference) / np.maximum(np.abs(targets), 1.0), CAP) + return {"relative": relative, "abs_relative": np.abs(relative), "capped": capped} + + +def fit_summary(rows: dict[str, np.ndarray], mask: np.ndarray | None = None) -> dict: + 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 { + "n_targets": int(abs_rel.size), + "fraction_within_10pct": float(np.mean(abs_rel <= WITHIN)), + "mean_abs_rel_error": float(abs_rel.mean()), + "median_abs_rel_error": float(np.median(abs_rel)), + "p90_abs_rel_error": float(np.percentile(abs_rel, 90)), + "max_abs_rel_error": float(abs_rel.max()), + "mean_capped_scaled_error": float(capped.mean()), + } + + +def fit_block( + estimates: np.ndarray, meta: pd.DataFrame, rows: np.ndarray, worst: int = 10 +) -> dict: + """Fit of ``estimates`` (all targets) on the target subset ``rows``.""" + + 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": {}} + 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) + order = np.argsort(-errors["capped"])[:worst] + block["worst_targets"] = [ + { + "row": int(subset["row"].iloc[i]), + "name": str(subset["name"].iloc[i]), + "value": float(values[i]), + "estimate": float(est[i]), + "relative_error": float(errors["relative"][i]), + } + for i in order + ] + return block + + +def estimates_for(inputs: Inputs, weights: np.ndarray) -> np.ndarray: + return np.asarray(inputs.matrix64() @ np.asarray(weights, np.float64)) + + +# --------------------------------------------------------------------------- +# Provenance +# --------------------------------------------------------------------------- + + +def provenance() -> dict: + import importlib + + import torch + + info: dict = { + "hostname": socket.gethostname(), + "platform": platform.platform(), + "python": sys.version.split()[0], + "torch": torch.__version__, + "numpy": np.__version__, + "scipy": __import__("scipy").__version__, + "pandas": pd.__version__, + "torch_num_threads": torch.get_num_threads(), + "torch_num_interop_threads": torch.get_num_interop_threads(), + "env": { + key: os.environ.get(key) + for key in ("OMP_NUM_THREADS", "MKL_NUM_THREADS", "OPENBLAS_NUM_THREADS") + }, + "nice": os.nice(0) if hasattr(os, "nice") else None, + "kernel_module_sha256": {}, + } + for name in KERNEL_MODULES: + module = importlib.import_module(name) + info["kernel_module_sha256"][name] = sha256(Path(module.__file__)) + if name == "microcosm.calibrate.solve": + info["kernel_solve_path"] = module.__file__ + + def git(*args: str) -> str: + return subprocess.run( + ["git", "-C", str(REPO), *args], capture_output=True, text=True, check=True + ).stdout.strip() + + try: + info["git"] = { + "repo": str(REPO), + "head": git("rev-parse", "HEAD"), + "branch": git("rev-parse", "--abbrev-ref", "HEAD"), + } + for package in ("microcosm-calibrate", "microcosm-frame"): + path = f"packages/{package}" + status = git("status", "--porcelain", "--", path).splitlines() + src_status = git("status", "--porcelain", "--", f"{path}/src").splitlines() + info["git"][package] = { + "clean_vs_head": not status, + "src_clean_vs_head": not src_status, + "dirty_paths": status, + } + except (OSError, subprocess.CalledProcessError) as exc: + info["git"] = { + "unavailable": str(exc)[:200], + "head_from_env": os.environ.get("MICROCOSM_GIT_SHA"), + } + return info + + +# --------------------------------------------------------------------------- +# Calibration +# --------------------------------------------------------------------------- + + +def _result_block(result) -> dict: + return { + "final_loss": float(result.final_loss), + "fraction_within_10pct": float(result.fraction_within_10pct), + "effective_sample_size": float(result.effective_sample_size), + "chi_square_distance": float(result.chi_square_distance), + "realized_max_weight_ratio": float(result.realized_max_weight_ratio), + "top_1pct_weight_share": float(result.top_1pct_weight_share), + "n_targets": int(result.problem.n_targets), + "matrix_nnz": int(result.problem.matrix.nnz), + "skipped": len(result.problem.skipped), + "initial_loss_last_batch": float(result.initial_loss), + } + + +def run_calibration( + spec: RunSpec, + inputs: Inputs, + out_dir: Path, + log: Logger, + *, + resume: bool, + stop_after_batches: int | None = None, +) -> dict: + import inspect + + import torch + + from microcosm.calibrate import calibrate + + parameters = inspect.signature(calibrate).parameters + if "l2_basis" not in parameters or "mass_parametrization" not in parameters: + raise SystemExit( + "the imported microcosm.calibrate.calibrate lacks l2_basis / " + "mass_parametrization; run from the l2-design-basis kernel" + ) + + frame = build_frame(inputs.households, inputs.prior(spec.acs_share)) + 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]] + log( + f"run {spec.run_id}: {len(train)} training targets" + + ( + f" (fold {spec.holdout_fold} held out)" + if spec.holdout_fold is not None + else "" + ) + + 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"torch threads={torch.get_num_threads()}" + ) + + resume_path = out_dir / "resume.npz" + warm, done = None, 0 + batches: list[dict] = [] + trajectories: list[np.ndarray] = [] + options: dict | None = None + result_block: dict | None = None + spec_json = json.dumps(dataclasses.asdict(spec), sort_keys=True) + 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") + warm = np.asarray(saved["weights"], dtype=np.float64) + done = int(saved["epochs_done"]) + batches = json.loads(str(saved["batches_json"])) + trajectories = [ + np.asarray(saved[f"trajectory_{i}"]) for i in range(len(batches)) + ] + options = json.loads(str(saved["options_json"])) + result_block = json.loads(str(saved["result_json"])) + log(f"RESUME from {done} epochs ({len(batches)} batches)") + + result = None + batches_this_process = 0 + log_every = max(1, min(50, spec.epoch_batch // 8)) + while done < spec.epochs: + if ( + stop_after_batches is not None + and batches_this_process >= stop_after_batches + ): + log( + f"STOP after {batches_this_process} batch(es) at {done} epochs (resumable)" + ) + return {"stopped": True, "epochs_done": done} + this_batch = min(spec.epoch_batch, spec.epochs - done) + stamps: dict[str, float] = {} + + def on_progress(event: dict, _stamps=stamps, _done=done) -> None: + if event.get("kind") != "calibration_epoch": + return + now = time.time() + epoch = int(event["epoch"]) + if epoch == 1: + _stamps["first_epoch"] = now + _stamps["last_epoch"] = now + _stamps["last_epoch_index"] = epoch + if epoch == 1 or epoch % log_every == 0 or epoch == event.get("epochs"): + log( + f" epoch {_done + epoch}/{spec.epochs} " + f"loss={float(event['loss']):.6f} rss_peak={peak_rss_gb():.2f}GB" + ) + + if result is not None: + del result + gc.collect() + batch_started = time.time() + result = calibrate( + frame, + targets, + weight_entity=RELEASE_SETTINGS["weight_entity"], + method=RELEASE_SETTINGS["method"], + epochs=this_batch, + learning_rate=RELEASE_SETTINGS["learning_rate"], + mass=RELEASE_SETTINGS["mass"], + max_weight_ratio=RELEASE_SETTINGS["max_weight_ratio"], + target_loss_cap=RELEASE_SETTINGS["target_loss_cap"], + l2_lambda=spec.l2_lambda, + l2_basis=spec.l2_basis, + mass_parametrization=spec.mass_parametrization, + seed=RELEASE_SETTINGS["seed"], + warm_start_weights=warm, + progress_callback=on_progress, + ) + finished = time.time() + if result.problem.skipped: + skipped = [s.target.name for s in result.problem.skipped] + raise SystemExit( + f"calibration skipped {len(skipped)} targets: {skipped[:5]}" + ) + if list(result.problem.names) != expected_names: + raise SystemExit( + "compiled target rows differ from the requested training rows" + ) + done += this_batch + warm = np.asarray(result.weights, dtype=np.float64).copy() + first = stamps.get("first_epoch", finished) + last = stamps.get("last_epoch", finished) + n_epoch_events = int(stamps.get("last_epoch_index", 0)) + batch = { + "batch": len(batches), + "epochs": this_batch, + "epochs_done": done, + "wall_seconds": finished - batch_started, + "compile_seconds": first - batch_started, + "optimize_seconds": last - first, + "seconds_per_epoch": ( + (last - first) / (n_epoch_events - 1) if n_epoch_events > 1 else None + ), + "post_seconds": finished - last, + "start_loss": float(result.loss_trajectory[0]), + "last_trajectory_loss": float(result.loss_trajectory[-1]), + "final_loss": float(result.final_loss), + "fraction_within_10pct": float(result.fraction_within_10pct), + "effective_sample_size": float(result.effective_sample_size), + "chi_square_distance": float(result.chi_square_distance), + "realized_max_weight_ratio": float(result.realized_max_weight_ratio), + "iterate_selection_receipt": jsonable( + dict(result.options.get("iterate_selection_receipt") or {}) + ), + "peak_rss_gb": peak_rss_gb(), + } + batches.append(batch) + batches_this_process += 1 + trajectories.append(np.asarray(result.loss_trajectory, dtype=np.float64)) + options = jsonable(dict(result.options)) + result_block = _result_block(result) + np.savez( + resume_path, + weights=warm, + epochs_done=np.int64(done), + spec_json=np.str_(spec_json), + batches_json=np.str_(json.dumps(jsonable(batches))), + options_json=np.str_(json.dumps(options)), + result_json=np.str_(json.dumps(jsonable(result_block))), + **{f"trajectory_{i}": t for i, t in enumerate(trajectories)}, + ) + log( + f"batch -> {done}/{spec.epochs} ep, {batch['wall_seconds']:.1f}s " + f"(compile {batch['compile_seconds']:.1f}s, " + f"{(batch['seconds_per_epoch'] or float('nan')):.3f}s/epoch), " + f"loss={batch['final_loss']:.6f}, " + f"within10%={batch['fraction_within_10pct']:.2%}, " + f"ESS={batch['effective_sample_size']:,.0f}, " + f"chi2={batch['chi_square_distance']:.4f}, " + f"peak RSS {batch['peak_rss_gb']:.2f}GB" + ) + + if result is None: + # Resumed an already finished run: the saved weights and last-batch + # result/options are the run's; nothing was recalibrated. + if warm is None or result_block is None: + raise SystemExit("no batches ran and no finished resume state exists") + weights = warm + result_block = {**result_block, "restored_from_resume": True} + else: + weights = np.asarray(result.weights, dtype=np.float64) + del result + gc.collect() + + np.savez( + out_dir / "weights.npz", + weights=weights, + epochs_done=np.int64(done), + spec_json=np.str_(spec_json), + **{f"trajectory_{i}": t for i, t in enumerate(trajectories)}, + ) + return { + "stopped": False, + "resumed": bool(resume and batches_this_process < len(batches)), + "weights": weights, + "train_rows": train, + "batches": batches, + "options": options, + "result": result_block, + "trajectory_first_loss": float(trajectories[0][0]) if trajectories else None, + } + + +def score( + inputs: Inputs, + weights: np.ndarray, + train: np.ndarray, + holdout: np.ndarray | None, + acs_share: float | 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. + """ + + from microcosm.calibrate import chi_square_distance + + design = inputs.prior(acs_share) + estimates = estimates_for(inputs, weights) + design_estimates = estimates_for(inputs, design) + original = inputs.design + metrics = { + "concentration": concentration_metrics(weights, design, inputs.households), + "fit_train": fit_block(estimates, inputs.meta, train), + "prior": { + "acs_share": acs_share, + "realized_acs_share": float( + design[ + inputs.households["spine"].astype(str).to_numpy() == SPINES[0] + ].sum() + / design.sum() + ), + "kish_ess_prior": kish(design), + "kish_ess_release_design": kish(original), + "chi_square_distance_prior_from_release_design": chi_square_distance( + design, original + ), + "chi_square_distance_from_release_design": chi_square_distance( + weights, original + ), + }, + } + if holdout is not None: + metrics["fit_holdout"] = fit_block(estimates, inputs.meta, holdout) + metrics["fit_holdout_design"] = fit_block( + design_estimates, inputs.meta, holdout + ) + metrics["fit_train_design"] = { + "overall": fit_block(design_estimates, inputs.meta, train)["overall"] + } + return metrics + + +def calibrate_mode( + spec: RunSpec, + checkpoint: Path, + out_root: Path, + *, + resume: bool, + verify: bool, + stop_after_batches: int | None = None, +) -> dict: + out_dir = out_root / spec.run_id + out_dir.mkdir(parents=True, exist_ok=True) + log = Logger(out_dir / "progress.log") + started = time.time() + try: + (out_dir / "spec.json").write_text( + json.dumps(dataclasses.asdict(spec), indent=2) + "\n" + ) + inputs = load_inputs(checkpoint, verify=verify, log=log) + prov = provenance() + calibration = run_calibration( + spec, + inputs, + out_dir, + log, + resume=resume, + stop_after_batches=stop_after_batches, + ) + if calibration["stopped"]: + return calibration + weights = calibration["weights"] + train = calibration["train_rows"] + holdout = ( + 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) + 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"], + ) + consistency = { + "recomputed_final_loss": recomputed_loss, + "recomputed_minus_result_final_loss": recomputed_loss + - calibration["result"]["final_loss"], + "fit_train_fraction_within_minus_result": ( + metrics["fit_train"]["overall"]["fraction_within_10pct"] + - calibration["result"].get( + "fraction_within_10pct", + metrics["fit_train"]["overall"]["fraction_within_10pct"], + ) + ), + "mass_conserved_ratio": float(weights.sum() / inputs.design.sum()), + "max_ratio_to_prior": float((weights / inputs.prior(spec.acs_share)).max()), + } + payload = { + "kind": "calibrate", + "resumed": calibration["resumed"], + "release_tag": RELEASE_TAG, + "spec": dataclasses.asdict(spec), + "settings": RELEASE_SETTINGS, + "release_0923_reference": RELEASE_0923_RESULT, + "result": calibration["result"], + "design_loss_first_batch_start": calibration["trajectory_first_loss"], + "options": calibration["options"], + "batches": calibration["batches"], + "consistency": consistency, + "metrics": metrics, + "inputs": { + "checkpoint": str(checkpoint), + "manifest_sha256": sha256(checkpoint / "MANIFEST.json"), + "input_sha256": inputs.input_sha256, + "n_train_targets": int(len(train)), + "n_holdout_targets": 0 if holdout is None else int(len(holdout)), + }, + "provenance": prov, + "timing": { + "total_wall_seconds": time.time() - started, + "scoring_seconds": time.time() - scoring_started, + }, + "peak_rss_gb": peak_rss_gb(), + } + (out_dir / "metrics.json").write_text( + json.dumps(jsonable(payload), indent=1) + "\n" + ) + national = metrics["concentration"]["national"] + log( + f"DONE {spec.run_id}: loss={calibration['result']['final_loss']:.6f} " + f"within10%={metrics['fit_train']['overall']['fraction_within_10pct']:.4f} " + f"ESS={national['kish_ess']:,.1f} chi2={national['chi_square_distance']:.4f} " + f"peak RSS {payload['peak_rss_gb']:.2f}GB, {payload['timing']['total_wall_seconds']:.0f}s" + ) + return payload + finally: + log.close() + + +def evaluate_mode( + label: str, + weights_arg: str, + checkpoint: Path, + out_root: Path, + *, + verify: bool, + acs_share: float | None = None, +) -> dict: + 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) + if weights_arg == "design": + weights = inputs.prior(acs_share) + source = {"kind": "design", "acs_share": acs_share} + else: + path, _, key = weights_arg.partition(":") + key = key or "weights" + weights = np.asarray(np.load(path)[key], dtype=np.float64) + source = { + "kind": "npz", + "path": path, + "key": key, + "sha256": sha256(Path(path)), + } + if weights.shape != (inputs.matrix.shape[1],): + raise SystemExit( + 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) + estimates = estimates_for(inputs, weights) + metrics["fit_holdout_by_fold"] = { + str(fold): fit_block(estimates, inputs.meta, rows) + for fold, rows in inputs.folds.items() + } + payload = { + "kind": "evaluate", + "label": label, + "release_tag": RELEASE_TAG, + "weights_source": source, + "metrics": metrics, + "inputs": { + "checkpoint": str(checkpoint), + "manifest_sha256": sha256(checkpoint / "MANIFEST.json"), + "input_sha256": inputs.input_sha256, + }, + "provenance": provenance(), + "timing": {"total_wall_seconds": time.time() - started}, + "peak_rss_gb": peak_rss_gb(), + } + (out_dir / "metrics.json").write_text( + json.dumps(jsonable(payload), indent=1) + "\n" + ) + national = metrics["concentration"]["national"] + log( + f"DONE evaluate {label}: loss={metrics['fit_train']['overall']['mean_capped_scaled_error']:.6f} " + f"within10%={metrics['fit_train']['overall']['fraction_within_10pct']:.4f} " + f"ESS={national['kish_ess']:,.1f} chi2={national['chi_square_distance']:.4f}" + ) + return payload + finally: + log.close() + + +def main(argv: Sequence[str] | None = None) -> int: + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument( + "--mode", choices=("calibrate", "evaluate"), default="calibrate" + ) + parser.add_argument("--spec", help="run spec as a JSON object or a path to one") + parser.add_argument("--run-id") + parser.add_argument("--l2-lambda", type=float, default=0.0) + parser.add_argument("--l2-basis", default="record") + parser.add_argument("--mass-parametrization", default="projection") + parser.add_argument("--epochs", type=int, default=800) + 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( + "--weights", default="design", help="evaluate: 'design' or [:key]" + ) + parser.add_argument( + "--label", default="design", help="evaluate: output directory name" + ) + parser.add_argument("--checkpoint", type=Path, default=DEFAULT_CHECKPOINT) + parser.add_argument("--out-root", type=Path, default=DEFAULT_OUT_ROOT) + parser.add_argument("--resume", action="store_true") + parser.add_argument( + "--stop-after-batches", + type=int, + default=None, + help="calibrate: stop (resumably) after this many batches in this process", + ) + parser.add_argument( + "--no-verify", action="store_true", help="skip input sha256 checks" + ) + args = parser.parse_args(argv) + + if args.mode == "evaluate": + evaluate_mode( + args.label, + args.weights, + args.checkpoint, + args.out_root, + verify=not args.no_verify, + acs_share=args.acs_share, + ) + return 0 + if args.spec: + text = Path(args.spec).read_text() if Path(args.spec).exists() else args.spec + spec = RunSpec.from_mapping(json.loads(text)) + else: + if not args.run_id: + parser.error("--run-id or --spec is required in calibrate mode") + spec = RunSpec( + run_id=args.run_id, + l2_lambda=args.l2_lambda, + l2_basis=args.l2_basis, + mass_parametrization=args.mass_parametrization, + epochs=args.epochs, + epoch_batch=args.epoch_batch, + holdout_fold=args.holdout_fold, + acs_share=args.acs_share, + ) + calibrate_mode( + spec, + args.checkpoint, + args.out_root, + resume=args.resume, + verify=not args.no_verify, + stop_after_batches=args.stop_after_batches, + ) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/experiments/us-acs-local-l2-basis-20260928/verify_csr.py b/experiments/us-acs-local-l2-basis-20260928/verify_csr.py new file mode 100644 index 000000000..ed812e3e0 --- /dev/null +++ b/experiments/us-acs-local-l2-basis-20260928/verify_csr.py @@ -0,0 +1,326 @@ +"""Verify the copied sparse checkpoint against the dense 09-23 release checkpoint. + +The L2-basis sweep calibrates from a CSR copy (4,459 targets x 1,588,854 +households) that another session converted from the dense lean checkpoint of +the published ACS local release +(``populace-us-2024-buildo-acs-local-767312d60-20260923T074941Z``). Before any +sweep result can speak for that release, this script checks the copy against +the dense source: + +1. A seeded random sample of 3,000 households: their dense + ``household/block2_values`` rows (float32) must equal the corresponding CSR + columns exactly, zeros included. +2. The dense ``block2_items`` order must equal the CSR row order recorded in + the copied ``targets.json`` (``measure`` and ``name``), and the 09-23 + ``targets.json`` names/values must equal the copy's. +3. The design weights must agree three ways: the dense H5's + ``household_weight``, the structure H5's ``household_weight``, and the 09-23 + ``weights_latest.npz`` ``initial_weights``. Household ids, state FIPS and + district geoids must agree between the dense and structure H5 files. +4. Whole-matrix check (every CSR entry, not a sample): ``A @ d`` and + ``A @ w_0923`` must reproduce the 09-23 ``calibration_diagnostics.json`` + per-target ``initial_estimate`` and ``final_estimate``, and the 09-23 + headline metrics (ESS, fraction within 10%, final loss) must be recovered + from the CSR and the saved weights. + +Reads only; writes ``results/csr_verification.json``. Exits non-zero on any +mismatch. RAM stays well under 2 GB: the dense matrix is read 500 rows at a +time. +""" + +from __future__ import annotations + +import hashlib +import json +import math +import resource +import sys +import time +from pathlib import Path + +import h5py +import numpy as np +from scipy import sparse + +HERE = Path(__file__).resolve().parent +DENSE_DIR = Path( + "/Users/maxghenis/PolicyEngine/_recovered/scratch-backup/893/overnight-20260923/" + "run/release/checkpoints" +) +CHECKPOINT = Path( + "/Users/maxghenis/PolicyEngine/_build_artifacts/acs-local-l2-basis-20260928/checkpoint" +) +SAMPLE_SIZE = 3_000 +SEED = 20260928 +READ_CHUNK = 500 + + +def peak_rss_gb() -> float: + peak = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss + return peak / 2**30 if sys.platform == "darwin" else peak / 2**20 + + +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 decode(items) -> list[str]: + return [item.decode() if isinstance(item, bytes) else str(item) for item in items] + + +def block_column(group: h5py.Group, name: str) -> np.ndarray: + """One column of a pandas fixed-format frame group, found by item name.""" + + for key in group.keys(): + if key.endswith("_items") and name in decode(group[key][:]): + items = decode(group[key][:]) + values = group[key.replace("_items", "_values")] + if values.ndim != 2: + raise ValueError(f"{name} lives in an object block; read it via pandas") + return values[:, items.index(name)] + raise KeyError(name) + + +def kish_ess(weights: np.ndarray) -> float: + return float(weights.sum() ** 2 / np.square(weights).sum()) + + +def main() -> int: + started = time.time() + checks: dict[str, object] = {} + failures: list[str] = [] + + def record(name: str, ok: bool, **detail) -> None: + checks[name] = {"ok": bool(ok), **detail} + if not ok: + failures.append(name) + print( + f"[{'ok' if ok else 'FAIL'}] {name} {json.dumps(detail)[:300]}", flush=True + ) + + manifest_digests = { + name: sha256(CHECKPOINT / name) + for name in ( + "target_matrix.npz", + "targets.json", + "target_frame_lean.h5", + "identity.json", + ) + } + identity = json.loads((CHECKPOINT / "identity.json").read_text()) + record( + "copy_digests_match_identity", + manifest_digests["target_matrix.npz"] == identity["target_matrix_sha256"] + and manifest_digests["targets.json"] == identity["targets_sha256"], + digests=manifest_digests, + ) + + matrix = sparse.load_npz(CHECKPOINT / "target_matrix.npz").tocsr() + matrix.sort_indices() + n_targets, n_households = matrix.shape + record( + "csr_shape_nnz", + matrix.shape == (4459, 1_588_854) + and matrix.nnz == identity["published_matrix_nnz"] + and matrix.dtype == np.float32, + shape=list(matrix.shape), + nnz=int(matrix.nnz), + dtype=str(matrix.dtype), + has_canonical_format=bool(matrix.has_canonical_format), + ) + record( + "csr_finite_no_explicit_zeros", + bool(np.isfinite(matrix.data).all()) and int((matrix.data == 0).sum()) == 0, + explicit_zeros=int((matrix.data == 0).sum()), + nonfinite=int((~np.isfinite(matrix.data)).sum()), + ) + + targets = json.loads((CHECKPOINT / "targets.json").read_text()) + dense_targets = json.loads((DENSE_DIR / "targets.json").read_text()) + names = [target["name"] for target in targets] + measures = [target["measure"] for target in targets] + + rng = np.random.default_rng(SEED) + sample = np.sort(rng.choice(n_households, size=SAMPLE_SIZE, replace=False)) + # household-major view of just the sampled columns + csr_sample = matrix[:, sample].toarray().T # (SAMPLE_SIZE, n_targets) float32 + + dense_path = DENSE_DIR / "target_frame_lean.h5" + with h5py.File(dense_path, "r") as handle: + household = handle["household"] + block2_items = decode(household["block2_items"][:]) + values = household["block2_values"] + record( + "dense_block2_shape_dtype", + values.shape == (n_households, n_targets) and values.dtype == np.float32, + shape=list(values.shape), + dtype=str(values.dtype), + ) + record( + "block2_items_equal_csr_target_order", + block2_items == measures and block2_items == names, + n_items=len(block2_items), + first_mismatch=next( + ( + i + for i, (a, b) in enumerate( + zip(block2_items, measures, strict=False) + ) + if a != b + ), + None, + ), + ) + dense_rows = np.empty((SAMPLE_SIZE, n_targets), dtype=np.float32) + for low in range(0, SAMPLE_SIZE, READ_CHUNK): + chunk = sample[low : low + READ_CHUNK] + dense_rows[low : low + len(chunk)] = values[chunk.tolist(), :] + dense_ids = block_column(household, "household_id") + dense_state = block_column(household, "state_fips") + dense_cd = block_column(household, "congressional_district_geoid") + dense_weight = block_column(household, "household_weight").astype(np.float64) + weight_block = next( + key + for key in household.keys() + if key.endswith("_items") + and "household_weight" in decode(household[key][:]) + ) + + nan_dense = int(np.isnan(dense_rows).sum()) + mismatch = dense_rows != csr_sample + record( + "sampled_rows_exact_float32_equal", + nan_dense == 0 and not bool(mismatch.any()), + sample_size=SAMPLE_SIZE, + seed=SEED, + cells_compared=int(dense_rows.size), + nonzero_cells_dense=int(np.count_nonzero(dense_rows)), + nonzero_cells_csr=int(np.count_nonzero(csr_sample)), + mismatched_cells=int(mismatch.sum()), + nan_cells_dense=nan_dense, + sample_index_min=int(sample.min()), + sample_index_max=int(sample.max()), + ) + del dense_rows, csr_sample, mismatch + + record( + "dense_and_copied_targets_json_agree", + [t["name"] for t in dense_targets] == names + and [float(t["value"]) for t in dense_targets] + == [float(t["value"]) for t in targets] + and [t["measure"] for t in dense_targets] == measures, + n=len(dense_targets), + ) + + import pandas as pd + + struct = pd.read_hdf(CHECKPOINT / "target_frame_lean.h5", "household") + struct_weight = struct["household_weight"].to_numpy(np.float64) + saved = np.load(DENSE_DIR / "weights_latest.npz") + initial_weights = np.asarray(saved["initial_weights"], dtype=np.float64) + w_0923 = np.asarray(saved["weights"], dtype=np.float64) + record( + "design_weights_three_way_exact", + np.array_equal(dense_weight, struct_weight) + and np.array_equal(dense_weight, initial_weights), + dense_block=weight_block, + n=int(dense_weight.size), + total=float(dense_weight.sum()), + min=float(dense_weight.min()), + max=float(dense_weight.max()), + max_abs_diff_struct=float(np.abs(dense_weight - struct_weight).max()), + max_abs_diff_npz=float(np.abs(dense_weight - initial_weights).max()), + ) + record( + "household_structure_equal", + np.array_equal(dense_ids, struct["household_id"].to_numpy()) + and np.array_equal(dense_state, struct["state_fips"].to_numpy()) + and np.array_equal(dense_cd, struct["congressional_district_geoid"].to_numpy()), + household_id_sorted_unique=bool(np.all(np.diff(dense_ids) > 0)), + ) + record( + "weights_latest_epochs", + int(saved["epochs_done"]) == 800 and w_0923.shape == (n_households,), + epochs_done=int(saved["epochs_done"]), + ) + + # Whole-matrix check against the 09-23 per-target diagnostics. + diagnostics = json.loads((DENSE_DIR / "calibration_diagnostics.json").read_text()) + diag_targets = diagnostics["targets"] + record( + "diagnostics_target_order", + [t["name"] for t in diag_targets] == names, + n=len(diag_targets), + ) + matrix64 = matrix.astype(np.float64) + est_design = matrix64 @ dense_weight + est_0923 = matrix64 @ w_0923 + diag_initial = np.asarray([t["initial_estimate"] for t in diag_targets]) + diag_final = np.asarray([t["final_estimate"] for t in diag_targets]) + target_values = np.asarray([float(t["value"]) for t in targets]) + scale = np.maximum(np.abs(diag_initial), 1.0) + rel_initial = np.abs(est_design - diag_initial) / scale + rel_final = np.abs(est_0923 - diag_final) / np.maximum(np.abs(diag_final), 1.0) + record( + "full_matrix_reproduces_0923_estimates", + float(rel_initial.max()) < 1e-9 and float(rel_final.max()) < 1e-9, + max_rel_diff_initial_estimate=float(rel_initial.max()), + max_rel_diff_final_estimate=float(rel_final.max()), + ) + rel_error = np.where( + target_values != 0, + (est_0923 - target_values) / np.where(target_values != 0, target_values, 1.0), + est_0923 - target_values, + ) + within = float(np.mean(np.abs(rel_error) <= 0.10)) + capped = np.minimum( + np.abs(est_0923 - target_values) / np.maximum(np.abs(target_values), 1.0), 1.0 + ) + final_loss = float(capped.mean()) + ess_0923 = kish_ess(w_0923) + ess_design = kish_ess(dense_weight) + from microcosm.calibrate import chi_square_distance + + chi2 = chi_square_distance(w_0923, dense_weight) + record( + "full_matrix_reproduces_0923_headline", + abs(within - diagnostics["fraction_within_10pct"]) < 5e-5 + and abs(final_loss - diagnostics["final_loss"]) < 5e-7 + and abs(ess_0923 - diagnostics["effective_sample_size"]) < 0.05, + fraction_within_10pct=within, + fraction_within_10pct_0923=diagnostics["fraction_within_10pct"], + final_loss=final_loss, + final_loss_0923=diagnostics["final_loss"], + kish_ess=ess_0923, + kish_ess_0923=diagnostics["effective_sample_size"], + kish_ess_design=ess_design, + chi_square_distance_0923_vs_design=chi2, + mass_ratio=float(w_0923.sum() / dense_weight.sum()), + realized_max_weight_ratio=float((w_0923 / dense_weight).max()), + ) + + result = { + "ok": not failures, + "failures": failures, + "dense_checkpoint": str(DENSE_DIR), + "sparse_checkpoint": str(CHECKPOINT), + "sample": {"size": SAMPLE_SIZE, "seed": SEED, "read_chunk": READ_CHUNK}, + "checks": checks, + "wall_seconds": round(time.time() - started, 1), + "peak_rss_gb": round(peak_rss_gb(), 3), + } + out = HERE / "results" / "csr_verification.json" + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(result, indent=2, default=float) + "\n") + print(f"wrote {out} ok={result['ok']} peak_rss_gb={result['peak_rss_gb']}") + if not math.isfinite(result["peak_rss_gb"]): + return 1 + return 0 if not failures else 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/packages/microcosm-build/src/microcosm/build/spec_engine/inventory_coverage.py b/packages/microcosm-build/src/microcosm/build/spec_engine/inventory_coverage.py index 0ac879d20..779cd8f21 100644 --- a/packages/microcosm-build/src/microcosm/build/spec_engine/inventory_coverage.py +++ b/packages/microcosm-build/src/microcosm/build/spec_engine/inventory_coverage.py @@ -359,8 +359,8 @@ "late_schedule": "e59c019d3d454eac99ac0ac209b6c5b6faaf9bdfcaeee18c36a25be19bf7da2f", "ownership": "5f64f0aac49e2313177564f71876bffc8c81b3ded4df701e70930e60e9c98356", "primary_tuples": "fdf23da429f8c501e198a2f5b719763d523a41565c2153fdf17b0344f21bcd7c", - "seed_map": "3cf10a524310ea0ada18ddc65cfa585d1d0652d23e7d7a180c050ac686224e28", - "seed_protocol": "91989ed5e366e19598f86335b2e66723dcb2b6481a78d0dbeb83183a1aa09bd4", + "seed_map": "292ad19c691876eecd8d4b36458b2b7e6f0ec14a51a2e3ab3fdf0d2e6986a98f", + "seed_protocol": "5795dc072b958dcafb6ba5c37e6124181ce37479f96cd4ca2dec3755d4e476f8", "source_manifest": "f4f8bfeb79268a4043a843b47cec1d1c0e58e9f5dd6bc23417ada95446dd7b8e", "take_up": "9522ce40f7dea569312dd7a7beb474e5a5afadd2f1e10cb5876534c3ef623d35", "tail": "ac92829c88a1a4fb6460d61190918d5d99c6c377fc8dd8f62f02b332d09bf59c", diff --git a/packages/microcosm-build/tests/engine_free/shared/test_spec_engine_loader.py b/packages/microcosm-build/tests/engine_free/shared/test_spec_engine_loader.py index 1c170efe5..b94680f74 100644 --- a/packages/microcosm-build/tests/engine_free/shared/test_spec_engine_loader.py +++ b/packages/microcosm-build/tests/engine_free/shared/test_spec_engine_loader.py @@ -12,7 +12,7 @@ def test_semantic_hash_has_golden_vector_and_surface_separation(tmp_path) -> Non # Pin the domain separator, normalization rules, schema-set receipt, and # exact normative projection as one reviewable golden vector. assert first.spec_sha256 == ( - "6af478ff88b3f65b5f06bfa4345f601fe887bfb8b5a6809fc3fc3127a4733922" + "5135dd79dd7e21d7855d39909cb907b9ac20eb7c9e5b5922702a958bbed99472" ) second_root = _rich_minimal(tmp_path / "xy", note="second", store="local:b") diff --git a/packages/microcosm-build/tests/engine_free/us/test_us_acs_local_release_tool.py b/packages/microcosm-build/tests/engine_free/us/test_us_acs_local_release_tool.py index a8c4eea70..57c97509a 100644 --- a/packages/microcosm-build/tests/engine_free/us/test_us_acs_local_release_tool.py +++ b/packages/microcosm-build/tests/engine_free/us/test_us_acs_local_release_tool.py @@ -1636,3 +1636,398 @@ def changed_while_writing_sums(path): assert root_copy.exists(), "the calibrated H5 itself is never removed" else: assert not root_copy.exists(), "a refused copy is not left at the root" + + +def _calibrate_stage_argv(tmp_path: Path, *extra: str) -> list[str]: + return [ + "--stage", + "calibrate", + "--staging-h5", + str(tmp_path / "staging.h5"), + "--checkpoint-dir", + str(tmp_path / "ckpt"), + "--out-h5", + str(tmp_path / "out.h5"), + *extra, + ] + + +def test_penalty_options_default_to_the_historical_solve(tmp_path: Path) -> None: + """``--l2-basis`` and ``--mass-parametrization`` take the kernel's names. + + Their defaults are the kernel defaults, so a refresh that passes neither + solves exactly as every earlier ACS local release did. + """ + from microcosm.calibrate import ( + L2_BASES, + L2_BASIS_RECORD, + MASS_PARAMETRIZATION_PROJECTION, + MASS_PARAMETRIZATIONS, + calibrate, + ) + + module = _load_tool_module() + # The tool spells the kernel's values so parsing never imports torch. + assert module.L2_BASIS_CHOICES == tuple(sorted(L2_BASES)) + assert module.MASS_PARAMETRIZATION_CHOICES == tuple(sorted(MASS_PARAMETRIZATIONS)) + defaults = inspect.signature(calibrate).parameters + for key, value in module.HISTORICAL_PENALTY.items(): + assert defaults[key].default == value, key + args = module._parse_args(_calibrate_stage_argv(tmp_path)) + assert args.l2_basis == L2_BASIS_RECORD + assert args.mass_parametrization == MASS_PARAMETRIZATION_PROJECTION + assert args.l2_lambda == 0.0 + for basis in L2_BASES: + parsed = module._parse_args( + _calibrate_stage_argv(tmp_path, "--l2-basis", basis) + ) + assert parsed.l2_basis == basis + for parametrization in MASS_PARAMETRIZATIONS: + parsed = module._parse_args( + _calibrate_stage_argv(tmp_path, "--mass-parametrization", parametrization) + ) + assert parsed.mass_parametrization == parametrization + for flag in ("--l2-basis", "--mass-parametrization"): + with pytest.raises(SystemExit): + module._parse_args(_calibrate_stage_argv(tmp_path, flag, "bogus")) + + +def _tiny_calibration_surface(n: int = 30): + """A real frame, design weights and target set small enough to solve.""" + + from microcosm.calibrate import Target, TargetSet + from microcosm.frame import EntitySchema, Frame, WeightKind, Weights + + rng = np.random.default_rng(0) + income = rng.lognormal(10.0, 1.0, n) + design = rng.lognormal(2.0, 0.8, n) + frame = Frame( + { + "person": pd.DataFrame( + {"person_id": range(n), "person_household_id": range(n)} + ), + "household": pd.DataFrame({"household_id": range(n), "income": income}), + }, + EntitySchema(group_entities=("household",)), + {"household": Weights(values=design, kind=WeightKind.DESIGN)}, + ) + targets = TargetSet( + ( + Target( + name="income", + entity="household", + value=float((income * design).sum() * 1.2), + measure="income", + ), + ) + ) + return frame, design, targets + + +def _recording_calibrate(monkeypatch) -> list[dict]: + """Wrap the kernel's calibrate to record each call's keyword arguments.""" + + import microcosm.calibrate as calibrate_package + + calls: list[dict] = [] + real = calibrate_package.calibrate + + def recording(*args, **kwargs): + calls.append(kwargs) + return real(*args, **kwargs) + + monkeypatch.setattr(calibrate_package, "calibrate", recording) + return calls + + +def test_calibrate_surface_threads_the_penalty_settings(monkeypatch) -> None: + module = _load_tool_module() + calls = _recording_calibrate(monkeypatch) + frame, _design, targets = _tiny_calibration_surface() + result, done = module.calibrate_surface( + frame, + targets, + epochs=4, + epoch_batch=2, + max_weight_ratio=5.0, + target_loss_cap=1.0, + l2_lambda=0.01, + seed=0, + l2_basis="chi_square", + mass_parametrization="softmax", + ) + assert done == 4 and len(calls) == 2 + for kwargs in calls: + assert kwargs["l2_basis"] == "chi_square" + assert kwargs["mass_parametrization"] == "softmax" + assert kwargs["l2_lambda"] == 0.01 + assert result.options["mass_parametrization"] == "softmax" + # Callers that pass neither get the historical solve. + calls.clear() + module.calibrate_surface( + frame, + targets, + epochs=2, + epoch_batch=2, + max_weight_ratio=5.0, + target_loss_cap=1.0, + l2_lambda=0.0, + seed=0, + ) + assert calls[0]["l2_basis"] == "record" + assert calls[0]["mass_parametrization"] == "projection" + + +def test_do_calibrate_stamps_and_records_the_penalty_settings( + tmp_path: Path, monkeypatch +) -> None: + """The penalty settings reach the solve, its stamp, the checkpoint and summary.""" + + from scipy import sparse + + import microcosm.calibrate as calibrate_package + + module = _load_tool_module() + calls = _recording_calibrate(monkeypatch) + frame, design, targets = _tiny_calibration_surface() + identity = {"staging_sha256": "s", "target_roles_sha256": "r"} + monkeypatch.setattr(module, "_verify_run_identity", lambda args: identity) + monkeypatch.setattr( + module, + "load_checkpoint_surface", + lambda *a, **k: ( + frame, + design, + SimpleNamespace(specs=()), + [{"name": "income"}], + sparse.csr_array((1, len(design)), dtype=np.float32), + ), + ) + monkeypatch.setattr( + module.cd_surface, "calibration_target_set", lambda *a, **k: targets + ) + monkeypatch.setattr( + module, + "calibration_evidence", + lambda **kwargs: {"cd_holdout": {"n_targets": 0}, "n_holdout_targets": 0}, + ) + monkeypatch.setattr(module, "_write_calibrated_artifact", lambda *a, **k: None) + monkeypatch.setattr( + calibrate_package, + "write_calibration_diagnostics", + lambda *a, **k: SimpleNamespace( + status="available", schema_version=8, sha256="diagnostics" + ), + ) + args = module._parse_args( + _calibrate_stage_argv( + tmp_path, + "--epochs", + "4", + "--epoch-batch", + "2", + "--l2-lambda", + "0.01", + "--l2-basis", + "chi_square", + "--mass-parametrization", + "softmax", + ) + ) + args.checkpoint_dir.mkdir() + module.do_calibrate(args) + + assert len(calls) == 2 + for kwargs in calls: + assert kwargs["l2_basis"] == "chi_square" + assert kwargs["mass_parametrization"] == "softmax" + summary = json.loads((args.checkpoint_dir / "calibration_summary.json").read_text()) + assert summary["l2_basis"] == "chi_square" + assert summary["mass_parametrization"] == "softmax" + assert summary["chi_square_distance"] >= 0.0 + in_loop = summary["softmax_in_loop_max_cap_ratio"] + assert 0.0 < in_loop <= 1.0 + 1e-5 + assert summary["solver_settings"]["l2_basis"] == "chi_square" + assert summary["solver_settings"]["mass_parametrization"] == "softmax" + saved = np.load(args.checkpoint_dir / "weights_latest.npz") + stamp = json.loads(str(saved["solver_settings"])) + assert stamp["l2_basis"] == "chi_square" + assert stamp["mass_parametrization"] == "softmax" + + # Extending the run under another basis is refused before any solve. + calls.clear() + args.epochs = 6 + args.resume = True + args.l2_basis = "record" + with pytest.raises(SystemExit, match="different solver settings"): + module.do_calibrate(args) + assert calls == [] + + +def test_a_stamp_from_before_the_penalty_settings_reads_as_the_historical_solve( + tmp_path: Path, +) -> None: + module = _load_tool_module() + defaults = module._parse_args(_calibrate_stage_argv(tmp_path)) + softmax = module._parse_args( + _calibrate_stage_argv(tmp_path, "--mass-parametrization", "softmax") + ) + legacy = { + key: value + for key, value in module._solver_settings(defaults).items() + if key not in ("l2_basis", "mass_parametrization") + } + assert module._stamped_settings(legacy) == module._solver_settings(defaults) + assert module._stamped_settings(legacy) != module._solver_settings(softmax) + assert module._stamped_settings(None) is None + # A recorded value always wins over the fill. + recorded = {**legacy, "mass_parametrization": "softmax"} + assert module._stamped_settings(recorded) == module._solver_settings(softmax) + + +def test_package_manifest_records_the_penalty_settings( + tmp_path: Path, monkeypatch +) -> None: + module = _load_tool_module() + args = _package_evidence_args( + module, + tmp_path, + monkeypatch, + hours_report={"passed": True, "failures": [], "detail": {}}, + ) + summary_path = args.checkpoint_dir / "calibration_summary.json" + summary = json.loads(summary_path.read_text()) + summary.update( + l2_lambda=0.003, + l2_basis="chi_square", + mass_parametrization="softmax", + chi_square_distance=0.21, + ) + summary_path.write_text(json.dumps(summary)) + + result = module.do_package(args) + + manifest = json.loads( + (Path(result["release_dir"]) / "build_manifest.json").read_text() + ) + assert manifest["calibration"]["l2_lambda"] == 0.003 + assert manifest["calibration"]["l2_basis"] == "chi_square" + assert manifest["calibration"]["mass_parametrization"] == "softmax" + assert manifest["calibration"]["chi_square_distance"] == 0.21 + recipe = shlex.split(manifest["refresh_recipe"]["release"]) + assert recipe[recipe.index("--l2-lambda") + 1] == "0.003" + assert recipe[recipe.index("--l2-basis") + 1] == "chi_square" + assert recipe[recipe.index("--mass-parametrization") + 1] == "softmax" + + +def test_refresh_recipe_rebuilds_the_recorded_solve(tmp_path: Path) -> None: + """The recipe names every penalty setting, so a default change cannot move it.""" + + module = _load_tool_module() + plain = shlex.split(module.release_refresh_recipe("state")) + assert plain[plain.index("--l2-lambda") + 1] == "0.0" + assert plain[plain.index("--l2-basis") + 1] == "record" + assert plain[plain.index("--mass-parametrization") + 1] == "projection" + # A legacy summary records none of them: the historical solve. + legacy = {"l2_lambda": None, "l2_basis": None, "mass_parametrization": None} + assert shlex.split(module.release_refresh_recipe("state", penalty=legacy)) == plain + assert ( + shlex.split( + module.release_refresh_recipe("state", penalty=module.HISTORICAL_PENALTY) + ) + == plain + ) + # Every flag is spelled out (asserted above), so the parse below does not + # lean on the parser's defaults: the historical recipe is the historical + # solve whatever those defaults become. + historical = module._parse_args(plain[3:]) + assert historical.l2_lambda == 0.0 + assert historical.l2_basis == "record" + assert historical.mass_parametrization == "projection" + penalized = shlex.split( + module.release_refresh_recipe( + "state", + penalty={ + "l2_lambda": 0.03, + "l2_basis": "chi_square", + "mass_parametrization": "projection", + }, + ) + ) + assert penalized[penalized.index("--l2-lambda") + 1] == "0.03" + assert penalized[penalized.index("--l2-basis") + 1] == "chi_square" + assert penalized[penalized.index("--mass-parametrization") + 1] == "projection" + # The recipe's arguments are ones the tool itself accepts. + args = module._parse_args(penalized[3:]) + assert args.l2_lambda == 0.03 + assert args.l2_basis == "chi_square" + assert args.mass_parametrization == "projection" + + +def test_package_manifest_reads_a_legacy_summary_as_the_historical_solve( + tmp_path: Path, monkeypatch +) -> None: + """A summary from before the settings existed was solved the only way there was.""" + + module = _load_tool_module() + args = _package_evidence_args( + module, + tmp_path, + monkeypatch, + hours_report={"passed": True, "failures": [], "detail": {}}, + ) + result = module.do_package(args) + manifest = json.loads( + (Path(result["release_dir"]) / "build_manifest.json").read_text() + ) + assert manifest["calibration"]["l2_basis"] == "record" + assert manifest["calibration"]["mass_parametrization"] == "projection" + assert manifest["calibration"]["chi_square_distance"] is None + assert manifest["calibration"]["softmax_in_loop_max_cap_ratio"] is None + + +def test_concentration_limitation_is_true_to_the_penalty_solved() -> None: + module = _load_tool_module() + base = { + "effective_sample_size": 13631.3, + "ess_fraction": 0.0086, + "households": 1_588_854, + "max_weight_ratio": 5.0, + } + historical = module._ess_concentration_limitation({**base, "l2_lambda": 0.0}) + assert historical["id"] == "low_effective_sample_size_lambda_zero" + assert historical["status"] == "reviewed_concentration" + assert module._ess_concentration_limitation(base) == historical + + assert ( + module._ess_concentration_limitation( + {**base, "l2_lambda": 0.0, "mass_parametrization": "projection"} + ) + == historical + ) + # An unpenalized softmax solve is a new optimizer, not the certified default. + unpenalized_softmax = module._ess_concentration_limitation( + {**base, "l2_lambda": 0.0, "mass_parametrization": "softmax"} + ) + assert unpenalized_softmax["id"] == "effective_sample_size_under_nondefault_solve" + assert unpenalized_softmax["status"] == "recorded_concentration" + assert "certified default" not in unpenalized_softmax["reason"] + + penalized = module._ess_concentration_limitation( + { + **base, + "l2_lambda": 0.003, + "l2_basis": "chi_square", + "mass_parametrization": "softmax", + "chi_square_distance": 0.21, + } + ) + assert penalized["id"] == "effective_sample_size_under_nondefault_solve" + # A summary from before l2_basis was recorded used the record basis. + legacy_penalized = module._ess_concentration_limitation({**base, "l2_lambda": 0.1}) + assert "l2_basis='record'" in legacy_penalized["reason"] + assert penalized["status"] == "recorded_concentration" + assert "l2_lambda=0.003" in penalized["reason"] + assert "'chi_square'" in penalized["reason"] + assert "'softmax'" in penalized["reason"] + assert "l2_lambda=0 " not in penalized["reason"] diff --git a/packages/microcosm-build/tests/engine_free/us/test_us_acs_local_sparse_cd.py b/packages/microcosm-build/tests/engine_free/us/test_us_acs_local_sparse_cd.py index e8a647496..a5880051a 100644 --- a/packages/microcosm-build/tests/engine_free/us/test_us_acs_local_sparse_cd.py +++ b/packages/microcosm-build/tests/engine_free/us/test_us_acs_local_sparse_cd.py @@ -1204,6 +1204,8 @@ def test_resume_refuses_weights_without_the_run_identity_stamp( max_weight_ratio=5.0, target_loss_cap=1.0, l2_lambda=0.0, + l2_basis="record", + mass_parametrization="projection", seed=0, ) settings = module._solver_settings(args) diff --git a/packages/microcosm-build/tests/engine_workflow/us/test_us_multispine_pool_tool.py b/packages/microcosm-build/tests/engine_workflow/us/test_us_multispine_pool_tool.py index b30b5e0d8..04b0c21f9 100644 --- a/packages/microcosm-build/tests/engine_workflow/us/test_us_multispine_pool_tool.py +++ b/packages/microcosm-build/tests/engine_workflow/us/test_us_multispine_pool_tool.py @@ -338,7 +338,7 @@ def capture_equality(expected: object, actual: object) -> None: "country": "us", "schema_id": "country_spec", "schema_version": 1, - "spec_sha256": "ffbb93ed5ae22a95536ae475d6912a42628568609b442360dc1cd3bcf26772cd", + "spec_sha256": "edcd647524b51dd2fbcb417576a7421d2429ed7b51beb6756579ef3911e86d52", }, } diff --git a/packages/microcosm-calibrate/src/microcosm/calibrate/__init__.py b/packages/microcosm-calibrate/src/microcosm/calibrate/__init__.py index f3fd70266..bdab67f1c 100644 --- a/packages/microcosm-calibrate/src/microcosm/calibrate/__init__.py +++ b/packages/microcosm-calibrate/src/microcosm/calibrate/__init__.py @@ -20,7 +20,10 @@ proximal selection penalty; and experimental ``l2_lambda`` as a soft concentration penalty. Under L0 gates, ``l2_lambda`` penalizes latent pre-gate weights so a nearly closed gate cannot hide an exploding underlying weight. -``l0_lambda`` alone prunes at a fixed penalty. +``l2_basis`` selects that penalty's form: the historical record-weighted +``mean(r ** 2)`` (default) or GREG's design-weighted chi-square distance, which +pulls toward the design weights themselves. ``l0_lambda`` alone prunes at a +fixed penalty. Importing this shard asserts compatibility with the installed :mod:`microcosm.frame` kernel — the constellation mechanism from DESIGN.md: a @@ -129,11 +132,18 @@ def _assert_frame_compatible(version: str, required: tuple[int, int]) -> None: from microcosm.calibrate.solve import ( # noqa: E402 - after the compat gate CONSERVE_MASS, FREE_MASS, + L2_BASES, + L2_BASIS_CHI_SQUARE, + L2_BASIS_RECORD, + MASS_PARAMETRIZATION_PROJECTION, + MASS_PARAMETRIZATION_SOFTMAX, + MASS_PARAMETRIZATIONS, CalibrationResult, L0RefitResult, TargetDiagnostic, calibrate, calibrate_l0_refit, + chi_square_distance, default_target_loss_scales, effective_sample_size, rebuild_calibration_result, @@ -183,6 +193,12 @@ def _assert_frame_compatible(version: str, required: tuple[int, int]) -> None: "CALIBRATION_VARIABLE_LABELS_BY_COUNTRY", "CONSERVE_MASS", "FREE_MASS", + "L2_BASES", + "L2_BASIS_CHI_SQUARE", + "L2_BASIS_RECORD", + "MASS_PARAMETRIZATION_PROJECTION", + "MASS_PARAMETRIZATION_SOFTMAX", + "MASS_PARAMETRIZATIONS", "TARGET_LOSS_ATTRIBUTION_ABS_TOLERANCE", "TARGET_LOSS_ATTRIBUTION_REL_TOLERANCE", "TARGET_LOSS_ATTRIBUTION_WARNING_CODES", @@ -212,6 +228,7 @@ def _assert_frame_compatible(version: str, required: tuple[int, int]) -> None: "calibration_variable_label", "assert_exact_k_support", "exact_k_design_feasibility", + "chi_square_distance", "default_target_loss_scales", "effective_sample_size", "rebuild_calibration_result", diff --git a/packages/microcosm-calibrate/src/microcosm/calibrate/solve.py b/packages/microcosm-calibrate/src/microcosm/calibrate/solve.py index 4bda636c1..396bdaa61 100644 --- a/packages/microcosm-calibrate/src/microcosm/calibrate/solve.py +++ b/packages/microcosm-calibrate/src/microcosm/calibrate/solve.py @@ -19,7 +19,7 @@ are :class:`~microcosm.frame.WeightKind.CALIBRATED`, per-target diagnostics, and the loss trajectory. -Six declared options, each a real feature (and each its own test): +Declared options, each a real feature (and each its own test): - ``mass="free"`` (default) lets the total weight move to fit the targets; ``mass="conserve"`` projects every step's weights back to the input total, so @@ -51,6 +51,23 @@ This penalty is only implemented by ``method="adam"``. In the two-stage :func:`calibrate_l0_refit` path it applies to both stages unless ``refit_l2_lambda`` overrides the refit stage — the stage whose weights ship. +- ``l2_basis`` selects the penalty's functional form. ``"record"`` (default) + is the record-weighted ``mean(r ** 2)`` above, ``r = w / anchor``. Under + ``mass="conserve"`` its target-free minimizer is ``w ∝ anchor ** 2``, and + ``r = 0`` is each record's cheapest value, so it spreads weight without + anchoring to the design. ``"chi_square"`` is GREG's anchor-weighted + chi-square distance ``sum(anchor * (r - 1) ** 2) / sum(anchor)`` + (``= sum((w - anchor) ** 2 / anchor) / sum(anchor)``): zero exactly at + ``w = anchor`` and nonnegative everywhere. With no targets and no binding + cap its minimizer is ``w = anchor`` under ``mass="free"`` and the anchor + rescaled to the input total under ``mass="conserve"`` (the anchor itself + when it is the initial weights), so ``l2_lambda`` trades target fit against + distance from the design weights: penalized ("ridge") calibration, the soft + form of GREG. +- ``mass_parametrization`` selects how ``mass="conserve"`` holds the total + under Adam: the historical per-step uniform log shift (``"projection"``, + default) or ``w = total * softmax(log_w)`` (``"softmax"``), whose gradient + already has its mass-constraint component removed. An explicit exact-k frozen refit requires the selected records' aligned marginal inclusion probabilities ``q_i`` and benchmarks them with Horvitz--Thompson @@ -99,6 +116,7 @@ "rebuild_calibration_result", "refit_l0_selection", "default_target_loss_scales", + "chi_square_distance", "effective_sample_size", "relative_error_loss", "CalibrationResult", @@ -106,6 +124,12 @@ "TargetDiagnostic", "FREE_MASS", "CONSERVE_MASS", + "L2_BASIS_RECORD", + "L2_BASIS_CHI_SQUARE", + "L2_BASES", + "MASS_PARAMETRIZATION_PROJECTION", + "MASS_PARAMETRIZATION_SOFTMAX", + "MASS_PARAMETRIZATIONS", ] #: ``mass="free"`` — the total weight may move to fit the targets (the default). @@ -115,6 +139,55 @@ #: the calibrated population conserves the starting mass exactly. CONSERVE_MASS = "conserve" +#: ``l2_basis="record"`` — the historical record-weighted penalty +#: ``mean((w / anchor) ** 2)`` (the default). Its mass-conserved, target-free +#: minimizer is ``w ∝ anchor ** 2``; ``w -> 0`` is each record's cheapest value. +L2_BASIS_RECORD = "record" + +#: ``l2_basis="chi_square"`` — the anchor-weighted chi-square distance +#: ``sum(anchor * (w / anchor - 1) ** 2) / sum(anchor)``, GREG's distance +#: function. Zero exactly at ``w = anchor``; its target-free minimizer is +#: ``w ∝ anchor``, and a record's collapse toward zero costs ``anchor_i``. +L2_BASIS_CHI_SQUARE = "chi_square" + +L2_BASES = frozenset({L2_BASIS_RECORD, L2_BASIS_CHI_SQUARE}) + +#: ``mass_parametrization="projection"`` (the default) — the historical +#: ``mass="conserve"`` scheme: Adam steps the log-weights, then every step +#: clamps them to the ratio cap and shifts them uniformly back to the input +#: total. Adam divides each coordinate's gradient by its running RMS (plus +#: ``eps``) before that shift. When every record's gradient has the same sign +#: and is well above ``eps``, the steps are all about ``lr`` and the shift +#: cancels them, so the iterate can stall away from the constrained optimum; +#: every target missing on the same side produces that. Measured on small +#: problems in docs/calibration-l2-basis.md; how much it matters at a given +#: scale is an empirical question that document answers for the ACS release. +MASS_PARAMETRIZATION_PROJECTION = "projection" + +#: ``mass_parametrization="softmax"`` — ``w = total * softmax(log_w)``, so +#: the input total holds by construction and autograd hands Adam the +#: gradient with its component along the mass constraint already removed. +#: For a smooth objective that reduced gradient vanishes at the constrained +#: optimum, which the projection scheme's full gradient does not; with the +#: capped-MAPE kink Adam still oscillates there on the scale of ``lr``, as it +#: does under ``mass="free"``. After every step the log-weights are projected +#: exactly onto the capped simplex (:func:`_project_softmax_log_weights_`), +#: and the closing float64 projection is shared with ``"projection"``. +MASS_PARAMETRIZATION_SOFTMAX = "softmax" + +MASS_PARAMETRIZATIONS = frozenset( + {MASS_PARAMETRIZATION_PROJECTION, MASS_PARAMETRIZATION_SOFTMAX} +) + +#: Active-set rounds the softmax cap projection takes before it finishes with +#: a sort. Each round is one pass over the records and only ever adds records +#: to the capped set, so the rounds alone are exact and finite, but an +#: adversarial input can need one per record. The sort bounds that worst case +#: at ``O(n log n)``. On the ACS local release (1.59M records, a 5x cap) every +#: step of two full-scale solves settled in one to three rounds +#: (experiments/us-acs-local-l2-basis-20260928/results/exact_cap.md). +_SOFTMAX_CAP_ACTIVE_SET_ROUNDS = 8 + #: Threshold below which a weight counts as pruned (a "zero") when reporting the #: non-zero record count for the L0 path. Relative to the *initial* mean weight. _PRUNE_REL_ATOL = 1e-6 @@ -317,6 +390,20 @@ def top_1pct_weight_share(self) -> float: k = max(1, math.ceil(0.01 * weights.size)) return float(np.sort(weights)[-k:].sum() / total) + @property + def chi_square_distance(self) -> float: + """Chi-square distance of the calibrated weights from the input weights. + + ``sum(d * (w / d - 1) ** 2) / sum(d)`` with ``d`` the input weights — + the quantity ``l2_basis="chi_square"`` penalizes under the default + ``"initial"`` anchor, reported for every solve whatever its penalty. + It is zero only when calibration returned the input weights. Under + ``mass="conserve"`` it equals ``sum(w ** 2 / d) / sum(d) - 1``: the + calibration's weighting effect relative to the input weights, minus + one. See :func:`chi_square_distance` for zero input weights. + """ + return chi_square_distance(self.weights, self.initial_weights) + @dataclass(frozen=True) class L0RefitResult: @@ -437,6 +524,11 @@ def top_1pct_weight_share(self) -> float: """Weight share of the post-L0 refit's heaviest 1% of records.""" return self.refit.top_1pct_weight_share + @property + def chi_square_distance(self) -> float: + """Chi-square distance of the post-L0 refit from its own input weights.""" + return self.refit.chi_square_distance + @property def options(self) -> Mapping[str, object]: """Combined production options and selection provenance.""" @@ -564,6 +656,53 @@ def effective_sample_size(weights: np.ndarray) -> float: return float(weights.sum() ** 2 / denominator) +def chi_square_distance(weights: np.ndarray, anchor: np.ndarray) -> float: + """Anchor-weighted chi-square distance ``sum(d * (w / d - 1) ** 2) / sum(d)``. + + The float64 reference form of the ``l2_basis="chi_square"`` penalty, with + ``d = anchor``; equivalently ``sum((w - d) ** 2 / d) / sum(d)``. It is + zero exactly when ``w == d`` and positive otherwise. When + ``sum(w) == sum(d)`` it equals ``sum(w ** 2 / d) / sum(d) - 1``, the + weighting effect of ``w`` relative to ``d`` minus one; with a uniform + ``d`` that is ``n / effective_sample_size(w) - 1``. + + A record with ``d == 0`` contributes nothing when its weight is also zero + and makes the distance infinite otherwise (no finite rescaling of a zero + anchor reaches a positive weight). + + Args: + weights: Non-negative, finite weight values. + anchor: Non-negative, finite anchor values aligned to ``weights``, + with a positive total. + + Returns: + The distance, ``>= 0`` (``inf`` as above). + + Raises: + ValueError: If the vectors are misaligned, any value is negative or + non-finite, or the anchor total is not positive. + """ + weights = np.asarray(weights, dtype=np.float64) + anchor = np.asarray(anchor, dtype=np.float64) + if weights.shape != anchor.shape: + raise ValueError( + f"weights shape {weights.shape} must match anchor shape {anchor.shape}." + ) + for label, values in (("weights", weights), ("anchor", anchor)): + if not np.isfinite(values).all() or (values < 0.0).any(): + raise ValueError( + f"chi_square_distance requires finite, non-negative {label}." + ) + total = float(anchor.sum()) + if total <= 0.0: + raise ValueError("chi_square_distance requires a positive anchor total.") + supported = anchor > 0.0 + if (weights[~supported] > 0.0).any(): + return math.inf + residual = weights[supported] - anchor[supported] + return float(np.sum(residual * residual / anchor[supported]) / total) + + def _relative_error_loss( estimate: torch.Tensor, targets: torch.Tensor, @@ -764,6 +903,186 @@ def _prepare_warm_start_weights( return prepared +def _l2_penalty( + pre_gate_weights: torch.Tensor, + anchor: torch.Tensor, + anchor_share: torch.Tensor | None, + basis: str, +) -> torch.Tensor: + """The L2 concentration penalty on the pre-gate weights. + + ``"record"``: ``mean((pre_gate_weights / anchor) ** 2)`` — the historical + form, kept as the same torch expression so default runs are bit-identical. + ``"chi_square"``: ``sum(anchor_share * (pre_gate_weights / anchor - 1) ** 2)`` + with ``anchor_share = anchor / sum(anchor)`` precomputed in float64, i.e. + the anchor-weighted chi-square distance of :func:`chi_square_distance`. + + Under the projection parametrization the caller passes a fresh + ``torch.exp(log_w)``: its own autograd node, exactly as the historical + inline expression was. Reusing the loss path's ``exp`` would change the + order of the gradient accumulation into ``log_w`` and so the bits. + """ + if basis == L2_BASIS_RECORD: + return ((pre_gate_weights / anchor) ** 2).mean() + if basis == L2_BASIS_CHI_SQUARE: + if anchor_share is None: # pragma: no cover - set up with the anchor + raise RuntimeError("the chi-square L2 basis needs the anchor shares.") + return (anchor_share * (pre_gate_weights / anchor - 1.0) ** 2).sum() + raise ValueError(f"l2_basis must be one of {sorted(L2_BASES)}, got {basis!r}.") + + +def _cap_every_record_shift(cap_shift: torch.Tensor) -> float: + """The smallest shift that puts every record able to reach its cap at it.""" + reachable = cap_shift[torch.isfinite(cap_shift)] + return float(reachable.max()) if reachable.numel() else 0.0 + + +def _sorted_capped_log_shift( + cap_shift: torch.Tensor, + caps: torch.Tensor, + mass: torch.Tensor, + total: float, + capped: torch.Tensor, + lower: float, +) -> float: + """Finish :func:`_capped_log_shift` exactly with one sort. + + ``capped`` marks records known to be at their caps at the answer and + ``lower`` is a lower bound on it. Sort the remaining records by the shift + at which each meets its cap. With the first ``k`` of them capped too, the + shift that meets ``total`` is ``log(room_k / free_k)``, ``room_k`` being + the total less every capped record's cap and ``free_k`` the mass of the + records after the ``k``-th. The answer is that shift at the smallest + ``k`` where it does not pass the ``k``-th record's own cap shift: the + ``k`` records before it reach their caps at a smaller shift, so they are + capped, and every record from the ``k``-th on is not. + """ + rest = ~capped & (mass > 0.0) + order = torch.argsort(cap_shift[rest]) + rest_shift = cap_shift[rest][order] + rest_caps = caps[rest][order] + rest_mass = mass[rest][order] + capped_before = torch.cat( + (rest_caps.new_zeros(1), torch.cumsum(rest_caps, dim=0)[:-1]) + ) + room = (total - float(caps[capped].sum())) - capped_before + free = torch.flip(torch.cumsum(torch.flip(rest_mass, (0,)), dim=0), (0,)) + # Once the capped records' caps reach the total, no shift meets it short + # of capping every record (a cap of exactly 1, up to rounding). + candidate = torch.where( + room > 0.0, + torch.log(room.clamp_min(torch.finfo(torch.float64).tiny)) - torch.log(free), + math.inf, + ) + meets = candidate <= rest_shift + if not bool(meets.any()): + return max(lower, _cap_every_record_shift(cap_shift)) + return max(lower, float(candidate[int(meets.to(torch.uint8).argmax())])) + + +def _capped_log_shift( + log_weights: torch.Tensor, + log_upper: torch.Tensor, + total: float, + *, + active_set_rounds: int = _SOFTMAX_CAP_ACTIVE_SET_ROUNDS, +) -> tuple[float, int]: + """The shift ``s`` with ``sum(min(exp(log_weights + s), exp(log_upper))) == total``. + + Float64 tensors; ``log_weights`` sums to ``total`` (``exp`` of it does), + so ``s >= 0``. The left side is continuous and nondecreasing in ``s``, so + the shift exists and the projection it defines is unique whenever the caps + sum to at least ``total``. + + Active-set rounds: with ``C`` the records whose cap shift + ``log_upper - log_weights`` is at most the current shift, the next shift + solves ``sum(caps[C]) + exp(s) * sum(mass[~C]) == total``. That left side + bounds the true one from above for every ``C`` (a record outside ``C`` + contributes ``exp(...)`` where the true sum has the smaller ``min``), so + every round's shift is a lower bound on the answer and ``C`` only grows. + When a round adds no record, the bound is attained and the shift is exact. + If ``active_set_rounds`` pass without that, :func:`_sorted_capped_log_shift` + finishes from the current capped set and bound. Records with a zero cap + are capped at every shift; records with zero mass never reach a positive + cap and contribute nothing. + + Returns the shift and the rounds taken (``active_set_rounds + 1`` when + the sort finished it). + """ + cap_shift = torch.where( + torch.isneginf(log_upper), + torch.full_like(log_upper, -math.inf), + log_upper - log_weights, + ) + caps = torch.exp(log_upper) + mass = torch.exp(log_weights) + zero = torch.zeros((), dtype=torch.float64) + shift = 0.0 + capped = cap_shift <= shift + for rounds in range(1, active_set_rounds + 1): + room = total - float(torch.where(capped, caps, zero).sum()) + free = float(torch.where(capped, zero, mass).sum()) + if room <= 0.0 or free <= 0.0: + # The capped records alone hold the total (a cap of exactly 1, up + # to rounding): every record that can reach its cap sits at it. + return max(shift, _cap_every_record_shift(cap_shift)), rounds + shift = max(shift, math.log(room) - math.log(free)) + grown = capped | (cap_shift <= shift) + if int(grown.sum()) == int(capped.sum()): + return shift, rounds + capped = grown + return ( + _sorted_capped_log_shift(cap_shift, caps, mass, total, capped, shift), + active_set_rounds + 1, + ) + + +def _project_softmax_log_weights_( + log_w: torch.Tensor, total: float, log_upper: torch.Tensor | None +) -> int: + """Project ``log_w`` exactly onto the capped simplex, in place. + + The softmax parametrization's per-step cap. The target is + ``{w : sum(w) == total, w <= exp(log_upper)}``, reached in log space: + every log-weight moves by one common shift ``s``, then each is clamped to + its log cap, ``log_w <- min(log_w + s, log_upper)``, with ``s`` from + :func:`_capped_log_shift` (computed in float64; exact in a finite number + of steps). This is the projection in Kullback-Leibler divergence: the + uncapped records keep their relative weights, which is the direction + ``total * softmax(log_w)`` cannot see, and only records whose share + would pass their caps are held at them. A vector already within its caps + after the shift moves by the shift alone. + + Afterwards ``exp(log_w)`` sums to ``total`` up to float32 rounding of + ``log_w`` and never passes ``exp(log_upper)``, so the realized weights + ``total * softmax(log_w)`` sit within the caps up to the float32 error of + the softmax's own normalizer. ``log_upper`` is the float64 copy of the + float32 log caps, so the clamp is exact in float32. Without a cap the + projection is the shift alone. Returns the active-set rounds taken (0 + without a cap). + """ + log_weights = log_w.detach().to(torch.float64) + log_weights = log_weights + ( + math.log(total) - float(torch.logsumexp(log_weights, dim=0)) + ) + if log_upper is None: + log_w.copy_(log_weights) + return 0 + shift, rounds = _capped_log_shift(log_weights, log_upper, total) + log_w.copy_(torch.minimum(log_weights + shift, log_upper)) + return rounds + + +def _max_cap_ratio(weights: torch.Tensor, cap: torch.Tensor) -> float: + """The largest ``weights / cap``, in float64 against the float64 cap. + + A record with a zero cap reads ``0`` while its weight is zero (``0 / 0``) + and ``inf`` once it is not. + """ + ratio = weights.detach().to(torch.float64) / cap + return float(torch.nan_to_num(ratio, nan=0.0, posinf=math.inf).max()) + + def _optimize( matrix: torch.Tensor, targets: torch.Tensor, @@ -782,6 +1101,8 @@ def _optimize( l2_lambda: float, l2_anchor: str = "initial", l2_anchor_weights: np.ndarray | None = None, + l2_basis: str = L2_BASIS_RECORD, + mass_parametrization: str = MASS_PARAMETRIZATION_PROJECTION, target_records: int | None, init_mean: float, temperature: float, @@ -822,6 +1143,14 @@ def _optimize( # exactly what the reported non-zero count means. prune_atol = _PRUNE_REL_ATOL * float(np.mean(w0)) log_w = torch.tensor(np.log(start), dtype=torch.float32, requires_grad=True) + softmax_mass = mass_parametrization == MASS_PARAMETRIZATION_SOFTMAX + if softmax_mass and ( + not conserve_mass or l0_lambda > 0.0 or target_records is not None + ): + # calibrate() refuses these combinations first; this guards the seam. + raise ValueError( + "mass_parametrization='softmax' requires mass='conserve' without L0 gates." + ) gates: HardConcrete | None = None params: list[torch.Tensor] = [log_w] @@ -847,16 +1176,29 @@ def _optimize( if max_weight_ratio is not None else None ) + # Under softmax: the float32 log caps the per-step projection clamps to + # (float64 copies, so its arithmetic stays float64), and the float64 cap + # the in-loop receipt measures every realized weight against. + softmax_log_upper: torch.Tensor | None = None + softmax_cap: torch.Tensor | None = None + softmax_max_cap_ratio = 0.0 + if softmax_mass and upper is not None: + softmax_log_upper = torch.log(upper).to(torch.float64) + softmax_cap = torch.tensor(max_weight_ratio * w0, dtype=torch.float64) if l2_lambda > 0.0: # The L2 penalty's reference vector. "initial" divides by each record's # own starting weight; "uniform" divides by the shared mean weight; an # explicit vector (e.g. the pre-selection design weights during a - # refit) overrides both. Under mass conservation the penalty's + # refit) overrides both. Under mass conservation the record basis's # constrained optimum is w_i ∝ anchor_i², so anchoring on # heterogeneous starting weights (e.g. a refit whose start is a # concentrated selection vector) pulls toward MORE concentration; the # uniform anchor makes the penalty a direct 1/ESS control regardless # of the starting distribution. + # + # The chi-square basis instead weights each record's (r - 1)² by its + # anchor share; its constrained optimum is w_i ∝ anchor_i, so the + # "initial" anchor pulls toward the design weights themselves. if l2_anchor_weights is not None: anchor = np.asarray(l2_anchor_weights, dtype=np.float64) elif l2_anchor == "initial": @@ -864,8 +1206,14 @@ def _optimize( else: anchor = np.full_like(w0, w0.mean()) w0_t = torch.tensor(anchor, dtype=torch.float32) + anchor_share_t = ( + torch.tensor(anchor / anchor.sum(), dtype=torch.float32) + if l2_basis == L2_BASIS_CHI_SQUARE + else None + ) else: w0_t = None + anchor_share_t = None retain_best = gates is None and not conserve_mass and l2_lambda == 0.0 best_loss = float("inf") @@ -874,7 +1222,14 @@ def _optimize( trajectory = np.empty(epochs, dtype=np.float64) for epoch in range(epochs): optimizer.zero_grad() - weights = torch.exp(log_w) + if softmax_mass: + weights = total0 * torch.softmax(log_w, dim=0) + if softmax_cap is not None: + softmax_max_cap_ratio = max( + softmax_max_cap_ratio, _max_cap_ratio(weights, softmax_cap) + ) + else: + weights = torch.exp(log_w) if gates is not None: weights = weights * gates() estimate = _apply_constraint(matrix, weights) @@ -892,11 +1247,13 @@ def _optimize( ) # Penalize latent pre-gate weights. Under L0, a nearly closed gate # should not be able to hide a very large exp(log_w). - l2_penalty = ( - ((torch.exp(log_w) / w0_t) ** 2).mean() - if w0_t is not None - else torch.zeros((), dtype=torch.float32) - ) + if w0_t is None: + l2_penalty = torch.zeros((), dtype=torch.float32) + elif softmax_mass: + # No gates under softmax: the realized weights are the pre-gate ones. + l2_penalty = _l2_penalty(weights, w0_t, anchor_share_t, l2_basis) + else: + l2_penalty = _l2_penalty(torch.exp(log_w), w0_t, anchor_share_t, l2_basis) total_loss = loss + penalty + l2_lambda * l2_penalty trajectory[epoch] = float(loss.item()) if retain_best and trajectory[epoch] < best_loss: @@ -916,6 +1273,14 @@ def _optimize( total_loss.backward() optimizer.step() + if softmax_mass: + # The softmax weights sum to the input total whatever log_w is, so + # there is no mass shift for Adam's normalized step to fight; only + # the ratio cap needs a per-step projection, exact in one call. + with torch.no_grad(): + _project_softmax_log_weights_(log_w, total0, softmax_log_upper) + continue + # Hard projections, applied to the realized weights every step so the # guarantees hold on the returned vector, not just in expectation. with torch.no_grad(): @@ -940,7 +1305,20 @@ def _optimize( gate_open_probabilities: np.ndarray | None = None with torch.no_grad(): - weights = torch.exp(log_w) + if softmax_mass: + weights = total0 * torch.softmax(log_w, dim=0) + if softmax_cap is not None: + softmax_max_cap_ratio = max( + softmax_max_cap_ratio, _max_cap_ratio(weights, softmax_cap) + ) + if selection_receipt is not None: + # Every realized vector the solve evaluated, and the closing + # one, over its float64 cap, before the closing projection. + selection_receipt["softmax_in_loop_max_cap_ratio"] = ( + softmax_max_cap_ratio if softmax_cap is not None else None + ) + else: + weights = torch.exp(log_w) if best_log_w is not None: closing_loss = _relative_error_loss( _apply_constraint(matrix, weights), @@ -950,6 +1328,8 @@ def _optimize( target_loss_cap, ) if float(closing_loss.item()) > best_loss: + # retain_best requires free mass, so never under softmax. + assert not softmax_mass, "best-iterate retention under softmax" weights = torch.exp(best_log_w) selected_epoch = best_epoch selected_loss = best_loss @@ -1135,6 +1515,7 @@ def _search_l0_lambda_for_budget( l2_lambda: float, l2_anchor: str = "initial", l2_anchor_weights: np.ndarray | None = None, + l2_basis: str = L2_BASIS_RECORD, init_mean: float, temperature: float, seed: int, @@ -1205,6 +1586,8 @@ def _search_l0_lambda_for_budget( temperature: Passed through to :func:`_optimize`. l2_lambda: Fixed soft concentration penalty passed through to :func:`_optimize`; the budget search varies only ``l0_lambda``. + ``l2_anchor``, ``l2_anchor_weights`` and ``l2_basis`` pass + through with it. seed: Reseeded before every evaluation for a deterministic response. prune_atol: Threshold counting a weight as non-zero (a survivor). initial_lambda: A user-supplied ``l0_lambda`` to evaluate first as a warm @@ -1260,6 +1643,7 @@ def evaluate( "l2_lambda": l2_lambda, "l2_anchor": l2_anchor, "l2_anchor_weights": l2_anchor_weights, + "l2_basis": l2_basis, "target_records": target_records, "init_mean": init_mean, "temperature": temperature, @@ -1585,6 +1969,17 @@ def _project_to_total( return weights +def _l2_penalty_label(basis: str, anchor: str) -> str: + """The ``options["l2_penalty"]`` provenance label for a basis and anchor. + + ``anchor`` is ``"initial"``, ``"uniform"`` or ``"explicit_anchor"``. The + record labels are the historical strings, unchanged. + """ + if basis == L2_BASIS_CHI_SQUARE: + return f"chi_square_{anchor}_pre_gate_weight_distance" + return f"mean_{anchor}_pre_gate_weight_ratio_squared" + + def calibrate( frame: Frame, targets: TargetSet, @@ -1596,6 +1991,7 @@ def calibrate( learning_rate: float = 0.02, mass: str = FREE_MASS, mass_reason: str | None = None, + mass_parametrization: str = MASS_PARAMETRIZATION_PROJECTION, max_weight_ratio: float | None = None, target_records: int | None = None, l0_lambda: float = 0.0, @@ -1603,6 +1999,7 @@ def calibrate( l2_lambda: float = 0.0, l2_anchor: str = "initial", l2_anchor_weights: np.ndarray | None = None, + l2_basis: str = L2_BASIS_RECORD, init_mean: float = 0.999, temperature: float = 0.25, budget_iters: int = _DEFAULT_BUDGET_ITERS, @@ -1650,6 +2047,30 @@ def calibrate( (which target families moved the mass) instead of the generic default. Must be ``None`` under :data:`CONSERVE_MASS`, which appends no record. + mass_parametrization: How ``method="adam"`` holds the total under + :data:`CONSERVE_MASS`. :data:`MASS_PARAMETRIZATION_PROJECTION` + (``"projection"``, default) is the historical scheme, unchanged + bit for bit: step the log-weights, then shift them uniformly back + to the input total. Adam normalizes each coordinate's gradient + before that shift, so when every record's gradient shares a sign + and sits well above Adam's ``eps`` (every target missing on the + same side does that) the shift cancels the step and the solve can + stall away from the constrained optimum. + :data:`MASS_PARAMETRIZATION_SOFTMAX` (``"softmax"``) optimizes + ``w = total * softmax(log_w)`` instead: the total holds by + construction and Adam sees the gradient with its mass-constraint + component removed. It requires ``mass="conserve"`` and no L0 + gates. Recorded in ``options["mass_parametrization"]`` whatever + the mass mode, like ``l2_basis`` whatever ``l2_lambda``. Every + softmax step ends on an exact projection onto the capped simplex, + and a softmax solve records in + ``options["iterate_selection_receipt"] + ["softmax_in_loop_max_cap_ratio"]`` the largest ratio of a + realized weight it evaluated to its ``max_weight_ratio`` cap, + before the closing projection (``None`` without a cap). It is 1 + up to the float32 error of the softmax's normalizer. + docs/calibration-l2-basis.md compares the two parametrizations + at the ACS local release's scale. max_weight_ratio: If given, a hard per-record cap: no calibrated weight exceeds ``max_weight_ratio * initial_weight``. The landmine guard. target_records: If given, enable L0 pruning with **budget control**: the @@ -1675,7 +2096,8 @@ def calibrate( zero. ``l1_lambda`` must be zero unless ``method="prox"``. l2_lambda: Experimental soft concentration penalty strength. ``0.0`` (default) preserves the unpenalized path. Positive values add - ``l2_lambda * mean((pre_gate_weight / initial_weight) ** 2)`` to the + ``l2_lambda`` times the ``l2_basis`` penalty, by default + ``mean((pre_gate_weight / initial_weight) ** 2)``, to the optimization loss while leaving ``max_weight_ratio`` as the hard per-record cap. When L0 gates are active, this is a latent pre-gate penalty on ``exp(log_w)``, not the realized gated returned weight; @@ -1698,6 +2120,18 @@ def calibrate( cannot express, e.g. the pre-selection *design* weights during a post-L0 refit. When supplied, ``l2_anchor`` becomes a free-form provenance label recorded in options (e.g. ``"design"``). + l2_basis: The L2 penalty's functional form, with ``r = w / anchor``. + :data:`L2_BASIS_RECORD` (``"record"``, default) is the historical + record-weighted ``mean(r ** 2)`` above, unchanged bit for bit. + :data:`L2_BASIS_CHI_SQUARE` (``"chi_square"``) is GREG's + anchor-weighted chi-square distance + ``sum(anchor * (r - 1) ** 2) / sum(anchor)``: zero exactly at + ``w = anchor``, it pulls toward the anchor itself (target-free + optimum ``w ∝ anchor`` under ``mass="conserve"``, ``w = anchor`` + under ``mass="free"``) and makes a record's collapse toward zero + cost its anchor share, where the record basis pulls toward + ``w ∝ anchor ** 2`` and makes ``r = 0`` free of penalty. Recorded + in ``options["l2_basis"]``; only consulted when ``l2_lambda > 0``. gate_initialization: Optional informed L0 prior and protected-record mask, aligned to the weight entity. Requires an Adam L0 solve; protected gates remain open throughout training and selection. @@ -1743,7 +2177,10 @@ def calibrate( a positive integer, if ``l1_lambda`` or ``l2_lambda`` is negative or non-finite, if ``l1_lambda`` is used outside ``method="prox"``, if ``method="prox"`` is combined with L0/budget pruning or - ``l2_lambda``, or if no targets compile (from the matrix build). + ``l2_lambda``, if ``l2_basis`` is unknown, if + ``mass_parametrization`` is unknown or is ``"softmax"`` outside + an Adam ``mass="conserve"`` solve without L0 gates, or if no + targets compile (from the matrix build). """ if method == "apg": warnings.warn( @@ -1801,6 +2238,26 @@ def calibrate( raise ValueError( f"l2_lambda must be finite and non-negative, got {l2_lambda!r}." ) + if l2_basis not in L2_BASES: + raise ValueError( + f"l2_basis must be one of {sorted(L2_BASES)}, got {l2_basis!r}." + ) + if mass_parametrization not in MASS_PARAMETRIZATIONS: + raise ValueError( + f"mass_parametrization must be one of {sorted(MASS_PARAMETRIZATIONS)}, " + f"got {mass_parametrization!r}." + ) + if mass_parametrization == MASS_PARAMETRIZATION_SOFTMAX and ( + method != "adam" + or mass != CONSERVE_MASS + or l0_lambda > 0.0 + or target_records is not None + ): + raise ValueError( + "mass_parametrization='softmax' reparametrizes the Adam solve's " + "conserved total: it requires method='adam', mass='conserve', and " + "no L0 gates (l0_lambda=0, target_records=None)." + ) if l2_anchor_weights is None: if l2_anchor not in ("initial", "uniform"): raise ValueError( @@ -1998,6 +2455,7 @@ def calibrate( l2_lambda=l2_lambda, l2_anchor=l2_anchor, l2_anchor_weights=l2_anchor_weights, + l2_basis=l2_basis, init_mean=init_mean, temperature=temperature, seed=seed, @@ -2033,6 +2491,8 @@ def calibrate( l2_lambda=l2_lambda, l2_anchor=l2_anchor, l2_anchor_weights=l2_anchor_weights, + l2_basis=l2_basis, + mass_parametrization=mass_parametrization, target_records=target_records, init_mean=init_mean, temperature=temperature, @@ -2123,6 +2583,7 @@ def calibrate( "iterate_selection_receipt": iterate_selection_receipt, "mass": mass, "mass_reason": mass_reason, + "mass_parametrization": mass_parametrization, "max_weight_ratio": max_weight_ratio, "target_records": target_records, "l1_lambda": l1_lambda, @@ -2130,12 +2591,10 @@ def calibrate( "l2_lambda": l2_lambda, "l2_anchor": l2_anchor, "l2_anchor_weights_supplied": l2_anchor_weights is not None, - "l2_penalty": ( - "mean_explicit_anchor_pre_gate_weight_ratio_squared" - if l2_anchor_weights is not None - else "mean_initial_pre_gate_weight_ratio_squared" - if l2_anchor == "initial" - else "mean_uniform_pre_gate_weight_ratio_squared" + "l2_basis": l2_basis, + "l2_penalty": _l2_penalty_label( + l2_basis, + "explicit_anchor" if l2_anchor_weights is not None else l2_anchor, ), "seed": seed, "target_loss_weights": _target_loss_weight_options(target_loss_weights_np), @@ -2392,9 +2851,11 @@ def refit_l0_selection( learning_rate: float = 0.02, mass: str = FREE_MASS, mass_reason: str | None = None, + mass_parametrization: str = MASS_PARAMETRIZATION_PROJECTION, max_weight_ratio: float | None = None, l2_lambda: float = 0.0, l2_anchor: str = "initial", + l2_basis: str = L2_BASIS_RECORD, init_mean: float = 0.999, temperature: float = 0.25, budget_iters: int = _DEFAULT_BUDGET_ITERS, @@ -2441,6 +2902,11 @@ def refit_l0_selection( real design weights rather than a uniform reset. On the explicit exact-k path, the starting weights and design anchor instead use the original-frame design weights after the same normalized Horvitz--Thompson projection. + ``l2_basis`` selects the refit penalty's form (see :func:`calibrate`); + ``"chi_square"`` with the ``"design"`` anchor pulls the shipped weights + toward the survivors' design weights themselves rather than their square. + ``mass_parametrization`` applies to the refit, which has no gates (see + :func:`calibrate`). """ if l2_anchor not in ("initial", "uniform", "design"): raise ValueError( @@ -2567,12 +3033,14 @@ def refit_l0_selection( learning_rate=learning_rate, mass=mass, mass_reason=mass_reason, + mass_parametrization=mass_parametrization, max_weight_ratio=max_weight_ratio, target_records=None, l0_lambda=0.0, l2_lambda=l2_lambda, l2_anchor=l2_anchor, l2_anchor_weights=refit_l2_anchor_weights, + l2_basis=l2_basis, init_mean=init_mean, temperature=temperature, budget_iters=budget_iters, @@ -2607,6 +3075,9 @@ def calibrate_l0_refit( refit_l2_lambda: float | None = None, l2_anchor: str = "initial", refit_l2_anchor: str | None = None, + l2_basis: str = L2_BASIS_RECORD, + refit_l2_basis: str | None = None, + refit_mass_parametrization: str = MASS_PARAMETRIZATION_PROJECTION, init_mean: float = 0.999, temperature: float = 0.25, budget_iters: int = _DEFAULT_BUDGET_ITERS, @@ -2634,7 +3105,11 @@ def calibrate_l0_refit( ``refit_l2_anchor`` follow the same inherit-or-override pattern; note the refit starts from the selection stage's concentrated weights, so a penalized refit whose goal is spreading the shipped weights should anchor - ``"uniform"`` (see :func:`refit_l0_selection`). + ``"uniform"`` (see :func:`refit_l0_selection`). ``l2_basis`` / + ``refit_l2_basis`` follow the same pattern for the penalty's form. + ``refit_mass_parametrization`` applies to the refit stage only: the + selection stage has L0 gates, which the softmax parametrization does not + support. """ if target_records is None and not (math.isfinite(l0_lambda) and l0_lambda > 0.0): raise ValueError( @@ -2666,6 +3141,26 @@ def calibrate_l0_refit( "refit_l2_anchor must be 'initial', 'uniform', 'design', or None, " f"got {refit_l2_anchor!r}." ) + if l2_basis not in L2_BASES: + raise ValueError( + f"l2_basis must be one of {sorted(L2_BASES)}, got {l2_basis!r}." + ) + if refit_l2_basis is not None and refit_l2_basis not in L2_BASES: + # Same early check: an invalid refit basis must not cost a selection run. + raise ValueError( + f"refit_l2_basis must be one of {sorted(L2_BASES)} or None, got " + f"{refit_l2_basis!r}." + ) + if refit_mass_parametrization not in MASS_PARAMETRIZATIONS or ( + refit_mass_parametrization == MASS_PARAMETRIZATION_SOFTMAX + and mass != CONSERVE_MASS + ): + raise ValueError( + "refit_mass_parametrization must be one of " + f"{sorted(MASS_PARAMETRIZATIONS)}, and 'softmax' requires " + f"mass='conserve'; got {refit_mass_parametrization!r} with " + f"mass={mass!r}." + ) selection = calibrate( frame, targets, @@ -2681,6 +3176,7 @@ def calibrate_l0_refit( # At a fresh selection the frame's initial weights ARE the design # prior, so the "design" anchor is exactly the "initial" anchor. l2_anchor="initial" if l2_anchor == "design" else l2_anchor, + l2_basis=l2_basis, init_mean=init_mean, temperature=temperature, budget_iters=budget_iters, @@ -2701,9 +3197,11 @@ def calibrate_l0_refit( learning_rate if refit_learning_rate is None else refit_learning_rate ), mass=mass, + mass_parametrization=refit_mass_parametrization, max_weight_ratio=max_weight_ratio, l2_lambda=l2_lambda if refit_l2_lambda is None else refit_l2_lambda, l2_anchor=l2_anchor if refit_l2_anchor is None else refit_l2_anchor, + l2_basis=l2_basis if refit_l2_basis is None else refit_l2_basis, init_mean=init_mean, temperature=temperature, budget_iters=budget_iters, diff --git a/packages/microcosm-calibrate/src/microcosm/calibrate/static_aging.py b/packages/microcosm-calibrate/src/microcosm/calibrate/static_aging.py index 38d263bea..57ca4c9ec 100644 --- a/packages/microcosm-calibrate/src/microcosm/calibrate/static_aging.py +++ b/packages/microcosm-calibrate/src/microcosm/calibrate/static_aging.py @@ -40,7 +40,12 @@ import numpy as np import pandas as pd -from microcosm.calibrate.solve import FREE_MASS, CalibrationResult, calibrate +from microcosm.calibrate.solve import ( + FREE_MASS, + L2_BASIS_RECORD, + CalibrationResult, + calibrate, +) from microcosm.calibrate.target import Target, TargetSet from microcosm.frame import ( Frame, @@ -297,6 +302,7 @@ def static_aging( learning_rate: float = 0.02, max_weight_ratio: float | None = 5.0, l2_lambda: float = 0.0, + l2_basis: str = L2_BASIS_RECORD, seed: int = 0, ) -> StaticAgingResult: """Project ``frame`` from ``base_year`` to each of ``years``. @@ -319,9 +325,12 @@ def static_aging( cell, so the base-year calibration is kept and only projected change is applied. ``"projection"`` targets the projection's absolute counts. - epochs, learning_rate, max_weight_ratio, l2_lambda, seed: Passed to - :func:`~microcosm.calibrate.calibrate`. ``max_weight_ratio`` bounds - how far any record's weight may move from its base-year value. + epochs, learning_rate, max_weight_ratio, l2_lambda, l2_basis, seed: + Passed to :func:`~microcosm.calibrate.calibrate`. + ``max_weight_ratio`` bounds how far any record's weight may move + from its base-year value. Aging runs at free mass, where the + ``"record"`` basis also pulls the total down; ``"chi_square"`` + instead penalizes distance from the base-year weights. Returns: A :class:`StaticAgingResult` with one :class:`YearProjection` per year. @@ -413,6 +422,7 @@ def static_aging( mass_reason=f"static aging: {year} population by {', '.join(demographics.cells)}", max_weight_ratio=max_weight_ratio, l2_lambda=l2_lambda, + l2_basis=l2_basis, seed=seed, ) year_person_weights = result.frame.resolve_weights("person").values diff --git a/packages/microcosm-calibrate/tests/engine_free/shared/test_l2_basis.py b/packages/microcosm-calibrate/tests/engine_free/shared/test_l2_basis.py new file mode 100644 index 000000000..c56d417d8 --- /dev/null +++ b/packages/microcosm-calibrate/tests/engine_free/shared/test_l2_basis.py @@ -0,0 +1,857 @@ +"""Contracts for the L2 basis and the softmax mass parametrization. + +Invariants pinned here (each holds for every input the strategies draw): + +1. The chi-square penalty is zero exactly at ``w = anchor`` and nonnegative + everywhere; the solver's float32 torch form agrees with the float64 + reference :func:`chi_square_distance`. +2. When ``sum(w) == sum(d)`` the distance equals ``sum(w**2 / d) / sum(d) - 1`` + (the weighting effect relative to ``d`` minus one), and with uniform ``d`` + it equals ``n / ESS(w) - 1``. +3. Under a mass constraint the chi-square penalty is stationary at + ``w = d`` and the record penalty at ``w ∝ d**2`` (the reduced gradient + vanishes there). +4. With no targets and ``mass="conserve"``, the chi-square solve returns the + design weights from any warm start, under both mass parametrizations. +5. The softmax parametrization conserves the input total, respects the ratio + cap, and keeps every weight positive. Every vector it evaluates in the + loop, before the closing projection, also sits within the cap up to + float32 rounding (the receipt's ``softmax_in_loop_max_cap_ratio``), even + when the cap binds on nearly every record. The per-step projection's own + contracts are in ``test_softmax_cap_projection.py``. +6. Each solve under softmax-conserve and free mass lands within a bound of + the exact optimum of the same convex program (differential against + CLARABEL on these tests' own problems, fixture + ``tests/fixtures/l2_basis_path_reference.json``), and the realized + chi-square distance is nonincreasing in ``l2_lambda`` to within the slack + that bound implies (the regularization-path identity), so with uniform + design weights Kish ESS is nondecreasing too. +7. The defaults (``l2_basis="record"``, ``mass_parametrization="projection"``) + reproduce the pre-change optimizer's bytes. + +Intended violation, pinned as such: under the default projection +parametrization invariant 6 does not hold in general. When every record's +gradient shares a sign the per-step mass shift cancels Adam's normalized step, +so the solve stalls (``test_projection_parametrization_stalls_under_uniform +_mass_pressure``); the softmax option exists for that reason. +""" + +from __future__ import annotations + +import functools +import hashlib +import importlib.util +import inspect +import json +from itertools import pairwise + +import numpy as np +import pandas as pd +import pytest +import torch +from hypothesis import given, settings +from hypothesis import strategies as st + +from microcosm.calibrate import ( + L2_BASIS_CHI_SQUARE, + L2_BASIS_RECORD, + MASS_PARAMETRIZATION_PROJECTION, + MASS_PARAMETRIZATION_SOFTMAX, + Target, + TargetSet, + calibrate, + calibrate_l0_refit, + chi_square_distance, + effective_sample_size, + refit_l0_selection, +) +from microcosm.calibrate import solve as solve_module +from microcosm.calibrate.matrix import build_constraint_matrix +from microcosm.frame import EntitySchema, Frame, WeightKind, Weights +from test_support.paths import paths_for + +_TEST_PATHS = paths_for("microcosm-calibrate") + +# Solver-backed properties run a torch optimization per example, so they use +# few, derandomized examples; the pure penalty algebra runs many. +_SOLVER_SETTINGS = settings(max_examples=8, deadline=None, derandomize=True) +_ALGEBRA_SETTINGS = settings(max_examples=200, deadline=None) +#: Float32 slack for a softmax solve's in-loop weights over their caps: the +#: log-weights and the softmax's normalizer are float32, and on these sizes +#: their error stays below 1e-6 (measured 2e-7 to 6e-7). +_IN_LOOP_CAP_TOLERANCE = 1e-5 + + +def _frame(columns: dict[str, np.ndarray], design: np.ndarray) -> Frame: + n = len(design) + return Frame( + { + "person": pd.DataFrame( + {"person_id": range(n), "person_household_id": range(n)} + ), + "household": pd.DataFrame({"household_id": range(n), **columns}), + }, + EntitySchema(group_entities=("household",)), + { + "household": Weights( + values=np.asarray(design, float), kind=WeightKind.DESIGN + ) + }, + ) + + +def _problem( + seed: int, + *, + n: int = 80, + k: int = 5, + factors: tuple[float, float] = (0.7, 1.4), + uniform: bool = False, +) -> tuple[Frame, TargetSet, np.ndarray]: + """A sparse synthetic surface whose targets pull away from the design.""" + rng = np.random.default_rng(seed) + values = rng.lognormal(0.0, 1.0, (n, k)) * (rng.random((n, k)) < 0.6) + design = np.full(n, 10.0) if uniform else rng.lognormal(2.0, 0.8, n) + scale = rng.uniform(*factors, k) + columns = {f"x{j}": values[:, j] for j in range(k)} + targets = TargetSet( + tuple( + Target( + name=f"x{j}", + entity="household", + value=float((values[:, j] * design).sum() * scale[j]), + measure=f"x{j}", + ) + for j in range(k) + ) + ) + return _frame(columns, design), targets, design + + +def _null_problem(design: np.ndarray) -> tuple[Frame, TargetSet]: + """A target the weights cannot affect: zero measure, zero value. + + Its scaled miss is exactly zero for every weight vector, and so is its + gradient, which makes the solve's objective the L2 penalty alone. + """ + n = len(design) + frame = _frame({"null": np.zeros(n)}, design) + targets = TargetSet( + (Target(name="null", entity="household", value=0.0, measure="null"),) + ) + return frame, targets + + +def _positive_vectors(min_size: int = 1, max_size: int = 40): + return st.integers(min_size, max_size).flatmap( + lambda n: st.tuples( + st.lists( + st.floats(1e-3, 1e4, allow_nan=False, allow_infinity=False), + min_size=n, + max_size=n, + ), + st.lists( + st.floats(0.0, 1e4, allow_nan=False, allow_infinity=False), + min_size=n, + max_size=n, + ), + ) + ) + + +def _torch_penalty(weights: np.ndarray, anchor: np.ndarray, basis: str) -> float: + anchor_t = torch.tensor(anchor, dtype=torch.float32) + share_t = torch.tensor(anchor / anchor.sum(), dtype=torch.float32) + return float( + solve_module._l2_penalty( + torch.tensor(weights, dtype=torch.float32), anchor_t, share_t, basis + ) + ) + + +# --- 1-3: the penalty's algebra --------------------------------------------- + + +@_ALGEBRA_SETTINGS +@given(_positive_vectors()) +def test_chi_square_penalty_is_zero_at_the_anchor_and_nonnegative(vectors) -> None: + anchor, weights = (np.asarray(v, dtype=np.float64) for v in vectors) + assert _torch_penalty(anchor, anchor, L2_BASIS_CHI_SQUARE) == 0.0 + assert chi_square_distance(anchor, anchor) == 0.0 + assert _torch_penalty(weights, anchor, L2_BASIS_CHI_SQUARE) >= 0.0 + distance = chi_square_distance(weights, anchor) + assert distance >= 0.0 + if not np.array_equal(weights, anchor): + assert distance > 0.0 + + +@_ALGEBRA_SETTINGS +@given(_positive_vectors()) +def test_torch_chi_square_penalty_matches_the_float64_reference(vectors) -> None: + """Differential: the solver's float32 penalty vs the float64 definition.""" + anchor, weights = (np.asarray(v, dtype=np.float64) for v in vectors) + reference = chi_square_distance(weights, anchor) + assert _torch_penalty(weights, anchor, L2_BASIS_CHI_SQUARE) == pytest.approx( + reference, rel=1e-4, abs=1e-6 + ) + # The record basis is the historical mean((w / d) ** 2), 1 at w = d. + assert _torch_penalty(weights, anchor, L2_BASIS_RECORD) == pytest.approx( + float(np.mean((weights / anchor) ** 2)), rel=1e-4, abs=1e-6 + ) + assert _torch_penalty(anchor, anchor, L2_BASIS_RECORD) == pytest.approx(1.0) + + +@_ALGEBRA_SETTINGS +@given(_positive_vectors(min_size=2)) +def test_chi_square_distance_is_the_weighting_effect_at_equal_totals(vectors) -> None: + anchor, raw = (np.asarray(v, dtype=np.float64) for v in vectors) + if raw.sum() == 0.0: + raw = raw + 1.0 + weights = raw * (anchor.sum() / raw.sum()) + expected = float(np.sum(weights**2 / anchor) / anchor.sum() - 1.0) + assert chi_square_distance(weights, anchor) == pytest.approx( + expected, rel=1e-9, abs=1e-9 + ) + uniform = np.full_like(anchor, anchor.mean()) + rescaled = raw * (uniform.sum() / raw.sum()) + assert chi_square_distance(rescaled, uniform) == pytest.approx( + len(uniform) / effective_sample_size(rescaled) - 1.0, rel=1e-9, abs=1e-9 + ) + + +def test_chi_square_distance_edges_and_validation() -> None: + assert chi_square_distance(np.array([0.0, 2.0]), np.array([0.0, 2.0])) == 0.0 + assert chi_square_distance(np.array([1.0, 2.0]), np.array([0.0, 2.0])) == ( + float("inf") + ) + with pytest.raises(ValueError, match="shape"): + chi_square_distance(np.ones(2), np.ones(3)) + with pytest.raises(ValueError, match="non-negative anchor"): + chi_square_distance(np.ones(2), np.array([1.0, -1.0])) + with pytest.raises(ValueError, match="non-negative weights"): + chi_square_distance(np.array([1.0, np.nan]), np.ones(2)) + with pytest.raises(ValueError, match="positive anchor total"): + chi_square_distance(np.zeros(2), np.zeros(2)) + + +@_ALGEBRA_SETTINGS +@given( + st.lists(st.floats(1e-2, 1e3, allow_nan=False), min_size=2, max_size=30), +) +def test_mass_constrained_stationary_points_of_each_basis(anchor_values) -> None: + """Chi-square is stationary at ``w = d``; the record basis at ``w ∝ d**2``. + + Stationary under ``sum(w) = D`` means the log-weight gradient is + proportional to ``w`` (the constraint's normal in log space), i.e. the + reduced gradient ``grad_i / w_i - mean_w(grad / w)`` is zero. + """ + anchor = np.asarray(anchor_values, dtype=np.float64) + total = anchor.sum() + for basis, optimum in ( + (L2_BASIS_CHI_SQUARE, anchor.copy()), + (L2_BASIS_RECORD, anchor**2 * total / np.sum(anchor**2)), + ): + log_w = torch.tensor(np.log(optimum), dtype=torch.float64, requires_grad=True) + penalty = solve_module._l2_penalty( + torch.exp(log_w), + torch.tensor(anchor, dtype=torch.float64), + torch.tensor(anchor / total, dtype=torch.float64), + basis, + ) + penalty.backward() + per_unit = log_w.grad.numpy() / optimum + reduced = per_unit - np.average(per_unit, weights=optimum) + # 2 / total is the per-unit gradient of a unit ratio deviation; the + # chi-square gradient at its optimum is roundoff of exp(log d) - d. + scale = max(np.abs(per_unit).max(), 2.0 / total) + assert np.abs(reduced).max() <= 1e-9 * scale, basis + + +# --- 4-5: target-free and softmax solves ------------------------------------- + + +@_SOLVER_SETTINGS +@given(st.integers(0, 2**32 - 1)) +def test_target_free_chi_square_solve_returns_the_design_weights(seed: int) -> None: + rng = np.random.default_rng(seed) + n = int(rng.integers(2, 40)) + design = rng.lognormal(2.0, 1.2, n) + frame, targets = _null_problem(design) + warm = design * rng.uniform(0.25, 4.0, n) + for parametrization in ( + MASS_PARAMETRIZATION_PROJECTION, + MASS_PARAMETRIZATION_SOFTMAX, + ): + result = calibrate( + frame, + targets, + epochs=1500, + learning_rate=0.05, + mass="conserve", + l2_lambda=1.0, + l2_basis=L2_BASIS_CHI_SQUARE, + mass_parametrization=parametrization, + warm_start_weights=warm, + seed=0, + ) + np.testing.assert_allclose(result.weights, design, rtol=1e-2) + assert result.chi_square_distance < 1e-4 + assert result.weights.sum() == pytest.approx(design.sum(), rel=1e-12) + + +@_SOLVER_SETTINGS +@given( + st.integers(0, 2**32 - 1), + st.sampled_from([0.0, 1e-3, 1e-2, 1e-1, 1.0]), + st.sampled_from([None, 1.5, 5.0]), + st.sampled_from([L2_BASIS_RECORD, L2_BASIS_CHI_SQUARE]), +) +def test_softmax_parametrization_conserves_mass_and_respects_the_cap( + seed: int, l2_lambda: float, cap: float | None, basis: str +) -> None: + frame, targets, design = _problem(seed, n=40, k=3, factors=(0.5, 2.0)) + result = calibrate( + frame, + targets, + epochs=300, + learning_rate=0.05, + mass="conserve", + max_weight_ratio=cap, + l2_lambda=l2_lambda, + l2_basis=basis, + mass_parametrization=MASS_PARAMETRIZATION_SOFTMAX, + seed=0, + ) + assert result.weights.sum() == pytest.approx(design.sum(), rel=1e-12) + assert (result.weights > 0.0).all() + if cap is not None: + assert (result.weights <= cap * design * (1.0 + 1e-12)).all() + assert result.options["mass_parametrization"] == MASS_PARAMETRIZATION_SOFTMAX + assert result.options["iterate_selection"] == "closing_state" + receipt = result.options["iterate_selection_receipt"] + assert set(receipt) == {"softmax_in_loop_max_cap_ratio"} + in_loop = receipt["softmax_in_loop_max_cap_ratio"] + if cap is None: + assert in_loop is None + else: + assert 0.0 < in_loop <= 1.0 + _IN_LOOP_CAP_TOLERANCE + + +@_SOLVER_SETTINGS +@given(st.integers(0, 2**32 - 1), st.sampled_from([1.0, 1.001, 1.05])) +def test_softmax_iterates_stay_at_a_cap_that_binds_almost_everywhere( + seed: int, cap: float +) -> None: + """Targets far above the design push every record into a tight cap. + + The per-step projection holds every in-loop vector at the cap, where it + binds on nearly every record (and on all of them at a cap of 1, whose + only feasible vector is the design itself). + """ + frame, targets, design = _problem(seed, n=60, k=4, factors=(1.5, 3.0)) + result = calibrate( + frame, + targets, + epochs=200, + learning_rate=0.05, + mass="conserve", + max_weight_ratio=cap, + mass_parametrization=MASS_PARAMETRIZATION_SOFTMAX, + seed=0, + ) + in_loop = result.options["iterate_selection_receipt"][ + "softmax_in_loop_max_cap_ratio" + ] + assert 1.0 - _IN_LOOP_CAP_TOLERANCE <= in_loop <= 1.0 + _IN_LOOP_CAP_TOLERANCE + assert result.weights.sum() == pytest.approx(design.sum(), rel=1e-12) + assert (result.weights <= cap * design * (1.0 + 1e-12)).all() + if cap == 1.0: + np.testing.assert_allclose(result.weights, design, rtol=1e-12) + + +def test_projection_solves_record_no_softmax_receipt() -> None: + frame, targets, _ = _problem(0, n=40, k=3) + result = calibrate( + frame, targets, epochs=20, mass="conserve", max_weight_ratio=5.0, seed=0 + ) + assert result.options["iterate_selection_receipt"] == {} + + +# --- 6: the regularization path ---------------------------------------------- + +#: Dense at large lambda, where the derived slack 2 * eps / (lam_b - lam_a) is +#: small enough for the path identity to say something. +_LAMBDAS = (0.0, 1e-2, 0.1, 0.3, 1.0, 3.0, 10.0) +#: The path tests' problems: three seeds, heterogeneous and uniform design +#: weights, under the two settings whose Adam solves reach the optimum. +PATH_CASES = tuple( + {"seed": seed, "uniform": uniform, "mass": mass, "parametrization": param} + for seed in range(3) + for uniform in (False, True) + for mass, param in ( + ("conserve", MASS_PARAMETRIZATION_SOFTMAX), + ("free", MASS_PARAMETRIZATION_PROJECTION), + ) +) + + +def path_case_id(case: dict) -> str: + design = "uniform" if case["uniform"] else "lognormal" + return f"s{case['seed']}_{design}_{case['mass']}_{case['parametrization']}" + + +def _path_reference() -> dict: + path = _TEST_PATHS.tests / "fixtures" / "l2_basis_path_reference.json" + return json.loads(path.read_text()) + + +def _problem_fingerprint(frame: Frame, targets: TargetSet) -> list[float]: + """Moments that pin the generated problem, up to float rounding. + + Not a byte digest. A SHA-256 of the generated arrays, written on macOS + arm64, failed on Linux CI (run 36893146111) for + ``s0_lognormal_conserve_softmax`` while ``s0_lognormal_free_projection``, + which generates the same problem, passed in the same run; the cause was + not isolated. For each target, the measure's sum, sum of squares and + position-weighted sum, then the target value; then the same three moments + of the design weights. Any change to the generator moves these far more + than rounding does. + """ + household = frame.table("household") + design = frame.resolve_weights("household").values.astype(np.float64) + position = np.arange(1, len(design) + 1, dtype=np.float64) + + def moments(values: np.ndarray) -> list[float]: + return [ + float(values.sum()), + float((values**2).sum()), + float((position * values).sum()), + ] + + fingerprint: list[float] = [] + for target in targets: + fingerprint += moments(household[target.measure].to_numpy(np.float64)) + fingerprint.append(float(target.value)) + return fingerprint + moments(design) + + +def _objective_parts(frame, targets, weights: np.ndarray) -> tuple[float, float]: + household = frame.table("household") + design = frame.resolve_weights("household").values + misses = [ + abs(float(household[t.measure].to_numpy() @ weights) - t.value) + / max(abs(t.value), 1.0) + for t in targets + ] + return float(np.mean(misses)), chi_square_distance(weights, design) + + +@functools.cache +def _solved_path(case_id: str) -> tuple: + case = next(c for c in PATH_CASES if path_case_id(c) == case_id) + frame, targets, _ = _problem(case["seed"], uniform=case["uniform"]) + results = [ + calibrate( + frame, + targets, + epochs=1500, + learning_rate=0.02, + mass=case["mass"], + max_weight_ratio=5.0, + l2_lambda=lam, + l2_basis=L2_BASIS_CHI_SQUARE, + mass_parametrization=case["parametrization"], + seed=0, + ) + for lam in _LAMBDAS + ] + return frame, targets, results + + +@pytest.mark.parametrize("case", PATH_CASES, ids=path_case_id) +def test_path_solves_reach_the_exact_optimum(case: dict) -> None: + """Differential: each solve's objective against CLARABEL's exact optimum. + + The fixture holds the exact optimum of the same convex program for these + very problems (``experiments/us-acs-local-l2-basis-20260928/ + path_reference.py``); its problem fingerprints guard against generator + drift. The loss cap never binds on them, so the solver's capped loss and + the program's loss coincide. + """ + reference = _path_reference() + entry = reference["cases"][path_case_id(case)] + frame, targets, results = _solved_path(path_case_id(case)) + assert _problem_fingerprint(frame, targets) == pytest.approx( + entry["problem_fingerprint"], rel=1e-9, abs=1e-12 + ) + bound = reference["excess_objective_bound"] + for lam, result in zip(_LAMBDAS, results, strict=True): + loss, distance = _objective_parts(frame, targets, result.weights) + assert loss == pytest.approx(result.final_loss, rel=1e-4, abs=1e-6) + excess = loss + lam * distance - entry["optimum"][str(lam)]["objective"] + assert excess <= bound, (lam, excess) + + +@pytest.mark.parametrize("case", PATH_CASES, ids=path_case_id) +def test_chi_square_distance_is_nonincreasing_in_lambda(case: dict) -> None: + """The regularization-path identity, with its slack derived from ``eps``. + + ``eps`` itself is empirical: the fixture's bound, twice the largest + measured excess rounded up to 1e-3. For exact minimizers at ``lam_a < lam_b``, adding the two optimality + inequalities gives ``(lam_b - lam_a) * (P_b - P_a) <= 0``. Solves within + ``eps`` of optimal (the differential test above) weaken it to + ``P_b <= P_a + 2 * eps / (lam_b - lam_a)``. Only pairs where that slack is + below the exact optimum's own ``P_a`` say anything; the others are skipped + by name, and every case keeps at least two informative pairs. With uniform + design weights under mass conservation, ``ESS = n / (1 + P)`` exactly, so + Kish ESS is nondecreasing on the same pairs. + """ + reference = _path_reference() + optimum = reference["cases"][path_case_id(case)]["optimum"] + eps = reference["excess_objective_bound"] + frame, targets, results = _solved_path(path_case_id(case)) + exact = [optimum[str(lam)]["chi_square_distance"] for lam in _LAMBDAS] + assert all(b <= a + 1e-9 for a, b in pairwise(exact)), exact + informative = 0 + for (lam_a, a, exact_a), (lam_b, b, _) in pairwise( + zip(_LAMBDAS, results, exact, strict=True) + ): + slack = 2.0 * eps / (lam_b - lam_a) + if slack >= exact_a: + continue + informative += 1 + assert b.chi_square_distance <= a.chi_square_distance + slack, (lam_a, lam_b) + if case["uniform"] and case["mass"] == "conserve": + n = len(b.weights) + for result in (a, b): + assert result.effective_sample_size == pytest.approx( + n / (1.0 + result.chi_square_distance), rel=1e-9 + ) + assert informative >= 2 + + +def test_projection_parametrization_stalls_under_uniform_mass_pressure() -> None: + """Pins the mechanism the softmax option exists for (an intended violation). + + Every target sits above its design total, so every record's gradient + points up. Adam normalizes each coordinate to roughly the same step and + the per-step mass shift then cancels it: the projection solve leaves the + design weights untouched for hundreds of epochs, while the softmax solve, + whose gradient has the mass component removed, moves at once. + """ + frame, targets, design = _problem(4, factors=(1.02, 1.4)) + common = dict( + epochs=200, + learning_rate=0.02, + mass="conserve", + max_weight_ratio=5.0, + l2_lambda=0.01, + l2_basis=L2_BASIS_CHI_SQUARE, + seed=0, + ) + stalled = calibrate( + frame, targets, mass_parametrization=MASS_PARAMETRIZATION_PROJECTION, **common + ) + moved = calibrate( + frame, targets, mass_parametrization=MASS_PARAMETRIZATION_SOFTMAX, **common + ) + assert stalled.chi_square_distance < 1e-6 + assert stalled.final_loss == pytest.approx(stalled.initial_loss, rel=1e-4) + assert moved.final_loss < 0.5 * moved.initial_loss + # And the record basis alone, target-free: its log-space gradient is + # positive for every record, so the projection solve cannot reach w ∝ d². + free_frame, free_targets = _null_problem(design) + record = calibrate( + free_frame, + free_targets, + epochs=200, + learning_rate=0.05, + mass="conserve", + l2_lambda=1.0, + l2_basis=L2_BASIS_RECORD, + seed=0, + ) + np.testing.assert_allclose(record.weights, design, rtol=1e-4) + record_optimum = design**2 * design.sum() / np.sum(design**2) + assert np.abs(record.weights / record_optimum - 1.0).max() > 0.5 + + +# --- options, validation and threading --------------------------------------- + + +@pytest.mark.parametrize( + ("basis", "anchor", "explicit", "label"), + [ + ( + L2_BASIS_RECORD, + "initial", + False, + "mean_initial_pre_gate_weight_ratio_squared", + ), + ( + L2_BASIS_RECORD, + "uniform", + False, + "mean_uniform_pre_gate_weight_ratio_squared", + ), + ( + L2_BASIS_RECORD, + "design", + True, + "mean_explicit_anchor_pre_gate_weight_ratio_squared", + ), + ( + L2_BASIS_CHI_SQUARE, + "initial", + False, + "chi_square_initial_pre_gate_weight_distance", + ), + ( + L2_BASIS_CHI_SQUARE, + "uniform", + False, + "chi_square_uniform_pre_gate_weight_distance", + ), + ( + L2_BASIS_CHI_SQUARE, + "design", + True, + "chi_square_explicit_anchor_pre_gate_weight_distance", + ), + ], +) +def test_options_record_the_basis_and_penalty_label( + basis: str, anchor: str, explicit: bool, label: str +) -> None: + frame, targets, design = _problem(0, n=30, k=2) + result = calibrate( + frame, + targets, + epochs=5, + mass="conserve", + l2_lambda=0.01, + l2_basis=basis, + l2_anchor=anchor, + l2_anchor_weights=design.copy() if explicit else None, + ) + assert result.options["l2_basis"] == basis + assert result.options["l2_penalty"] == label + assert result.options["mass_parametrization"] == MASS_PARAMETRIZATION_PROJECTION + + +def test_explicit_design_anchor_is_the_initial_anchor_under_chi_square() -> None: + frame, targets, design = _problem(1, n=30, k=2) + common = dict( + epochs=60, mass="conserve", l2_lambda=0.05, l2_basis=L2_BASIS_CHI_SQUARE + ) + preset = calibrate(frame, targets, **common) + explicit = calibrate( + frame, targets, l2_anchor="design", l2_anchor_weights=design.copy(), **common + ) + np.testing.assert_array_equal(explicit.weights, preset.weights) + + +def test_invalid_basis_and_parametrization_are_refused() -> None: + frame, targets, _ = _problem(0, n=20, k=2) + with pytest.raises(ValueError, match="l2_basis"): + calibrate(frame, targets, epochs=2, l2_basis="bogus") + with pytest.raises(ValueError, match="mass_parametrization must be one of"): + calibrate(frame, targets, epochs=2, mass_parametrization="bogus") + for bad in ( + dict(mass="free"), + dict(mass="conserve", l0_lambda=0.01), + dict(mass="conserve", target_records=5), + dict(mass="conserve", method="prox"), + ): + with pytest.raises(ValueError, match="softmax"): + calibrate( + frame, + targets, + epochs=2, + mass_parametrization=MASS_PARAMETRIZATION_SOFTMAX, + **bad, + ) + with pytest.raises(ValueError, match="refit_l2_basis"): + calibrate_l0_refit( + frame, targets, epochs=2, l0_lambda=0.003, refit_l2_basis="bogus" + ) + with pytest.raises(ValueError, match="refit_mass_parametrization"): + calibrate_l0_refit( + frame, + targets, + epochs=2, + l0_lambda=0.003, + refit_mass_parametrization=MASS_PARAMETRIZATION_SOFTMAX, + ) + + +def test_l0_refit_threads_the_basis_and_the_refit_parametrization() -> None: + frame, targets, _ = _problem(2, n=60, k=3) + common = dict(epochs=40, seed=0, mass="conserve", l0_lambda=0.003) + inherited = calibrate_l0_refit( + frame, targets, l2_lambda=0.01, l2_basis=L2_BASIS_CHI_SQUARE, **common + ) + assert inherited.options["selection_options"]["l2_basis"] == L2_BASIS_CHI_SQUARE + assert inherited.options["l2_basis"] == L2_BASIS_CHI_SQUARE + overridden = calibrate_l0_refit( + frame, + targets, + l2_lambda=0.01, + l2_basis=L2_BASIS_CHI_SQUARE, + refit_l2_basis=L2_BASIS_RECORD, + refit_mass_parametrization=MASS_PARAMETRIZATION_SOFTMAX, + **common, + ) + assert overridden.options["selection_options"]["l2_basis"] == L2_BASIS_CHI_SQUARE + assert ( + overridden.options["selection_options"]["mass_parametrization"] + == MASS_PARAMETRIZATION_PROJECTION + ) + assert overridden.options["l2_basis"] == L2_BASIS_RECORD + assert overridden.options["mass_parametrization"] == MASS_PARAMETRIZATION_SOFTMAX + direct = refit_l0_selection( + frame, + targets, + overridden.selection, + epochs=10, + mass="conserve", + l2_lambda=0.01, + l2_anchor="design", + l2_basis=L2_BASIS_CHI_SQUARE, + mass_parametrization=MASS_PARAMETRIZATION_SOFTMAX, + ) + assert direct.refit.options["l2_penalty"] == ( + "chi_square_explicit_anchor_pre_gate_weight_distance" + ) + + +def test_result_reports_the_realized_chi_square_distance() -> None: + frame, targets, design = _problem(3, n=40, k=3) + result = calibrate(frame, targets, epochs=50, mass="conserve", seed=0) + assert result.chi_square_distance == pytest.approx( + chi_square_distance(result.weights, design), rel=1e-12 + ) + assert result.chi_square_distance > 0.0 + + +# --- 7: the defaults are the pre-change optimizer, byte for byte -------------- + +_ORACLE_MODULE = "optimizer_bda72cb02.py" +_ORACLE_MODULE_SHA256 = ( + "88ce8f77dcbfb48096dd16f4dc4de9de4c39367dc479a36db79b35f6c05ec55b" +) +_ORACLE_FUNCTION_SHA256 = ( + "c5c8923788d33ef50861599e5599028611b62fb4f5a7238f7c09773daaee5155" +) +#: The live helpers the oracle imports. These digests equal the helpers' +#: sources at bda72cb02 (checked with ``git show bda72cb02:.../solve.py`` when +#: the pin was written), so the oracle runs the pre-change closure exactly. +_HELPER_SOURCE_SHA256 = { + "_apply_constraint": ( + "86281bdf18bdc17acafbbe79e15fb6b4c4102840a1315c3b6bbf52b170d8749c" + ), + "_prepare_warm_start_weights": ( + "2e50a07a704ad2ab9d6529a965b7554f7aaba3972ec046a14e5a519e4f0904ed" + ), + "_project_to_total": ( + "f220ae141fc9144a4a1eec2c894f49065e88bbc1fd18be837ddec2d96cdd52f0" + ), + "_relative_error_loss": ( + "e7bba6d694211827780e2d5a0c60436decbaa1dc9310a7caa62945ac4d50843d" + ), +} + + +def _pre_l2_basis_oracle(): + path = _TEST_PATHS.tests / "fixtures/pre_l2_basis" / _ORACLE_MODULE + assert hashlib.sha256(path.read_bytes()).hexdigest() == _ORACLE_MODULE_SHA256 + for name, expected in _HELPER_SOURCE_SHA256.items(): + source = inspect.getsource(getattr(solve_module, name)).rstrip("\n") + assert hashlib.sha256(source.encode()).hexdigest() == expected, name + spec = importlib.util.spec_from_file_location("pre_l2_basis_oracle", path) + oracle = importlib.util.module_from_spec(spec) + spec.loader.exec_module(oracle) + source = inspect.getsource(oracle._optimize).rstrip("\n") + assert hashlib.sha256(source.encode()).hexdigest() == _ORACLE_FUNCTION_SHA256 + return oracle + + +_DEFAULT_CASES = { + "free_best_iterate": dict(mass="free"), + "conserve_capped": dict(mass="conserve", max_weight_ratio=5.0), + "conserve_l2_initial": dict(mass="conserve", max_weight_ratio=5.0, l2_lambda=0.05), + "free_l2_uniform": dict(mass="free", l2_lambda=0.05, l2_anchor="uniform"), + "conserve_l2_explicit": dict(mass="conserve", l2_lambda=0.05, l2_anchor="design"), + "conserve_l0_gated": dict(mass="conserve", l0_lambda=0.002, l2_lambda=0.01), + "conserve_warm_start": dict( + mass="conserve", max_weight_ratio=5.0, l2_lambda=0.02, warm=True + ), +} + + +@pytest.mark.parametrize("case_id", sorted(_DEFAULT_CASES)) +def test_defaults_reproduce_the_pre_change_optimizer_bytes(case_id: str) -> None: + """Regression pin: the default path is the bda72cb02 optimizer exactly. + + The oracle is the verbatim pre-change ``_optimize``; both sides get the + same compiled inputs and seed, and every returned array must match byte + for byte, with the defaults implicit and with them spelled out. + """ + oracle = _pre_l2_basis_oracle() + options = dict(_DEFAULT_CASES[case_id]) + warm = options.pop("warm", False) + frame, targets, design = _problem(7, n=90, k=4) + rng = np.random.default_rng(11) + warm_start = design * rng.uniform(0.5, 2.0, len(design)) if warm else None + anchor_weights = design * 1.5 if options.get("l2_anchor") == "design" else None + epochs, learning_rate, seed = 120, 0.03, 3 + + problem = build_constraint_matrix(frame, targets, "household") + torch.manual_seed(seed) + old = oracle._optimize( + solve_module._torch_constraint_matrix(problem.matrix), + torch.tensor(problem.target_vector, dtype=torch.float32), + None, + torch.tensor( + solve_module.default_target_loss_scales(problem.target_vector), + dtype=torch.float32, + ), + solve_module._DEFAULT_TARGET_LOSS_CAP, + problem.initial_weights.values, + warm_start_weights=warm_start, + epochs=epochs, + learning_rate=learning_rate, + conserve_mass=options["mass"] == "conserve", + max_weight_ratio=options.get("max_weight_ratio"), + l0_lambda=options.get("l0_lambda", 0.0), + l2_lambda=options.get("l2_lambda", 0.0), + l2_anchor=options.get("l2_anchor", "initial"), + l2_anchor_weights=anchor_weights, + target_records=None, + init_mean=0.999, + temperature=0.25, + return_gate_open_probabilities=True, + ) + old_weights, old_trajectory, old_gates = old + common = dict( + epochs=epochs, + learning_rate=learning_rate, + seed=seed, + warm_start_weights=warm_start, + l2_anchor_weights=anchor_weights, + **options, + ) + for spelled in ( + {}, + { + "l2_basis": L2_BASIS_RECORD, + "mass_parametrization": MASS_PARAMETRIZATION_PROJECTION, + }, + ): + result = calibrate(frame, targets, **common, **spelled) + assert result.weights.tobytes() == old_weights.tobytes() + assert result.loss_trajectory.tobytes() == old_trajectory.tobytes() + if old_gates is None: + assert result.gate_open_probabilities is None + else: + assert result.gate_open_probabilities.tobytes() == old_gates.tobytes() diff --git a/packages/microcosm-calibrate/tests/engine_free/shared/test_softmax_cap_projection.py b/packages/microcosm-calibrate/tests/engine_free/shared/test_softmax_cap_projection.py new file mode 100644 index 000000000..46a6d7a09 --- /dev/null +++ b/packages/microcosm-calibrate/tests/engine_free/shared/test_softmax_cap_projection.py @@ -0,0 +1,343 @@ +"""Contracts for the softmax parametrization's per-step cap projection. + +``_project_softmax_log_weights_`` maps log-weights onto the capped simplex +``{w : sum(w) == total, w <= cap}`` in log space: one common shift ``s``, then +``min(log_w + s, log_cap)``. Invariants pinned here, each for every input the +strategies draw (with records whose design weight, and so cap, is zero, and +with a cap of exactly 1, where the caps sum to the total): + +1. It conserves the total and never passes a cap; a record with zero weight + or a zero cap stays at zero. +2. It is idempotent, and invariant to a constant added to the input (the + direction ``total * softmax(log_w)`` cannot see). +3. An input already within its caps after the shift moves by the shift alone. +4. It is the Kullback-Leibler projection: every record below its cap moved by + one common shift, and every record at its cap would pass it under that + shift (the KKT conditions of ``min KL(w || exp(log_w))`` on the set). +5. Differential: it equals a float64 water-fill written from the definition, + and the active-set rounds and the sort that bounds them agree (an input + built to need one round per record exercises the sort). +6. In float32, as the solver runs it, the clamp is exact in log space and the + realized ``total * softmax(log_w)`` sits within the float64 caps up to the + float32 error of the softmax. +""" + +from __future__ import annotations + +import math + +import numpy as np +import pytest +import torch +from hypothesis import given, settings +from hypothesis import strategies as st + +from microcosm.calibrate import solve as solve_module + +_SETTINGS = settings(max_examples=300, deadline=None) + +#: Float64 agreement in log space, for differences that involve no sum: the +#: shift added to every uncapped record, the clamp. +_EXACT = 1e-10 +#: Float64 agreement in weight units, as a share of the total. Each path +#: computes the shift from a sum of up to 40 weights, whose rounding is a few +#: ulps of the total. When the caps nearly meet the total, the uncapped +#: records hold only the small remainder, so that absolute error is large in +#: log space for them; in weight units it stays a few ulps of the total. +_EXACT_MASS = 1e-12 +#: Float32 tolerance for the realized weights over their float64 caps. Two +#: float32 errors reach them: rounding ``log_w`` (half an ulp of a log-weight +#: below 16 is 4.8e-7 relative) and the softmax's float32 normalizer. On these +#: sizes they stay below 1e-6; 1e-5 leaves an order of magnitude. +_FLOAT32 = 1e-5 + + +@st.composite +def capped_problems(draw, *, max_size: int = 40): + """Log-weights, log caps and a total the caps can hold. + + The caps are ``ratio * design`` and the total a ``fill`` share of their + sum, so ``fill = 1 / ratio`` is calibrate's own total ``sum(design)`` + and ``fill = 1`` leaves no slack. Zero design weights have zero caps and + zero weight. + """ + n = draw(st.integers(1, max_size)) + design = np.array( + draw( + st.lists( + st.one_of(st.just(0.0), st.floats(1e-3, 1e4)), + min_size=n, + max_size=n, + ) + ) + ) + if design.sum() == 0.0: + design[0] = 1.0 + moves = np.array(draw(st.lists(st.floats(-8.0, 8.0), min_size=n, max_size=n))) + ratio = draw(st.one_of(st.just(1.0), st.floats(1.0, 10.0))) + fill = draw(st.one_of(st.just(1.0), st.just(1.0 / ratio), st.floats(0.05, 1.0))) + with np.errstate(divide="ignore"): + log_weights = np.log(design) + moves + log_caps = np.log(ratio * design) + total = fill * float(np.exp(log_caps).sum()) + return log_weights, log_caps, total + + +def _project(log_weights: np.ndarray, log_caps: np.ndarray, total: float): + """The solver's projection on a float64 copy.""" + log_w = torch.tensor(log_weights, dtype=torch.float64) + solve_module._project_softmax_log_weights_( + log_w, total, torch.tensor(log_caps, dtype=torch.float64) + ) + return log_w.numpy() + + +def _reference_water_fill( + log_weights: np.ndarray, log_caps: np.ndarray, total: float +) -> np.ndarray: + """Float64 water-fill from the definition, independent of the solver. + + In weight space: scale the masses ``v`` by ``c``, holding at its cap + ``u`` each record ``c * v`` would pass. Records reach their caps in + order of ``u / v``; try each count ``k`` of capped records in that order + and keep the one whose ``c`` caps exactly those ``k``. + """ + mass = np.exp(log_weights - np.max(log_weights)) + caps = np.exp(log_caps) + with np.errstate(divide="ignore", invalid="ignore"): + breakpoints = np.where(mass > 0.0, caps / mass, np.inf) + order = np.argsort(breakpoints, kind="stable") + for k in range(len(order) + 1): + held, scaled = order[:k], order[k:] + free = mass[scaled].sum() + room = total - caps[held].sum() + if free == 0.0: + # Nothing left to scale: only a total the held caps already meet. + assert room == pytest.approx(0.0, abs=1e-9 * total) + weights = np.where(np.isin(np.arange(len(mass)), held), caps, 0.0) + break + c = room / free + below = k == 0 or c >= breakpoints[order[k - 1]] * (1.0 - 1e-12) + above = k == len(order) or c <= breakpoints[order[k]] * (1.0 + 1e-12) + if below and above: + weights = np.minimum(c * mass, caps) + break + else: # pragma: no cover - a feasible total always has a count + raise AssertionError("no consistent capped count") + with np.errstate(divide="ignore"): + return np.log(weights) + + +def _assert_same_weights( + actual: np.ndarray, expected: np.ndarray, total: float +) -> None: + """Log-weight vectors with the same support and weights equal to rounding.""" + assert np.array_equal(np.isneginf(actual), np.isneginf(expected)) + np.testing.assert_allclose( + np.exp(actual), np.exp(expected), rtol=1e-9, atol=_EXACT_MASS * total + ) + + +# --- 1: total, caps, support -------------------------------------------------- + + +@_SETTINGS +@given(capped_problems()) +def test_projection_conserves_the_total_and_respects_the_caps(problem) -> None: + log_weights, log_caps, total = problem + projected = _project(log_weights, log_caps, total) + assert (projected <= log_caps).all() + assert float(np.exp(projected).sum()) == pytest.approx(total, rel=1e-12) + zero = np.isneginf(log_weights) | np.isneginf(log_caps) + assert np.isneginf(projected[zero]).all() + assert np.isfinite(projected[~zero]).all() + + +# --- 2: idempotence and shift invariance ------------------------------------- + + +@_SETTINGS +@given(capped_problems(), st.floats(-30.0, 30.0)) +def test_projection_is_idempotent_and_shift_invariant(problem, offset) -> None: + log_weights, log_caps, total = problem + projected = _project(log_weights, log_caps, total) + _assert_same_weights(_project(projected, log_caps, total), projected, total) + _assert_same_weights( + _project(log_weights + offset, log_caps, total), projected, total + ) + + +# --- 3: a feasible input moves by the shift alone ---------------------------- + + +@_SETTINGS +@given( + st.lists(st.floats(1e-3, 1e4), min_size=1, max_size=40), + st.data(), + st.floats(0.05, 1.0), + st.floats(-30.0, 30.0), +) +def test_a_feasible_input_moves_by_the_shift_alone(caps, data, fill, offset) -> None: + """Below its caps once scaled to the total: the projection is the shift. + + Each record sits ``slack`` below its cap, and the total is at most the + records' own sum, so scaling to it only lowers them. + """ + log_caps = np.log(np.asarray(caps, dtype=np.float64)) + slack = np.array( + data.draw(st.lists(st.floats(0.0, 8.0), min_size=len(caps), max_size=len(caps))) + ) + log_weights = log_caps - slack + total = fill * float(np.exp(log_weights).sum()) + shifted = log_weights + offset + expected_shift = math.log(total) - float(np.logaddexp.reduce(shifted)) + _assert_same_weights( + _project(shifted, log_caps, total), shifted + expected_shift, total + ) + + +# --- 4: the KKT conditions of the KL projection ------------------------------ + + +@_SETTINGS +@given(capped_problems()) +def test_projection_is_the_kullback_leibler_projection(problem) -> None: + log_weights, log_caps, total = problem + projected = _project(log_weights, log_caps, total) + live = np.isfinite(projected) + at_cap = live & (projected >= log_caps - _EXACT) + below = live & ~at_cap + if below.any(): + moved = projected[below] - log_weights[below] + shift = float(np.median(moved)) + np.testing.assert_allclose(moved, shift, rtol=0.0, atol=_EXACT) + # Every capped record would pass its cap under the common shift. + assert (log_weights[at_cap] + shift >= log_caps[at_cap] - _EXACT).all() + else: + # Every live record is held: the caps alone meet the total. + assert float(np.exp(log_caps[live]).sum()) == pytest.approx(total, rel=1e-12) + + +def test_a_cap_of_one_holds_every_record_at_its_design_weight() -> None: + design = np.array([3.0, 0.0, 1.5, 40.0, 2.25]) + with np.errstate(divide="ignore"): + log_caps = np.log(design) + log_weights = log_caps + np.array([2.0, 0.0, -3.0, 0.5, -0.25]) + projected = _project(log_weights, log_caps, float(design.sum())) + assert np.array_equal(projected, log_caps) + + +# --- 5: differential against the float64 water-fill -------------------------- + + +@_SETTINGS +@given(capped_problems()) +def test_projection_matches_the_reference_water_fill(problem) -> None: + log_weights, log_caps, total = problem + _assert_same_weights( + _project(log_weights, log_caps, total), + _reference_water_fill(log_weights, log_caps, total), + total, + ) + + +@_SETTINGS +@given(capped_problems(), st.sampled_from([0, 1, 2])) +def test_active_set_rounds_and_the_sort_agree(problem, rounds) -> None: + log_weights, log_caps, total = problem + log_w = torch.tensor(log_weights, dtype=torch.float64) + log_w = log_w + (math.log(total) - float(torch.logsumexp(log_w, dim=0))) + log_upper = torch.tensor(log_caps, dtype=torch.float64) + shift, taken = solve_module._capped_log_shift(log_w, log_upper, total) + assert 1 <= taken <= solve_module._SOFTMAX_CAP_ACTIVE_SET_ROUNDS + 1 + bounded, bounded_taken = solve_module._capped_log_shift( + log_w, log_upper, total, active_set_rounds=rounds + ) + assert 1 <= bounded_taken <= rounds + 1 + _assert_same_weights( + torch.minimum(log_w + bounded, log_upper).numpy(), + torch.minimum(log_w + shift, log_upper).numpy(), + total, + ) + + +def _active_set_chain(n: int, length: int): + """Unit masses with caps that the active-set rounds take one at a time. + + Round ``r`` caps the records whose cap shift is at most the previous + round's shift and solves for the next. Each record of the chain gets a + cap just inside the next round's root, so every round adds exactly one; + the margins shrink by more than the roots move, which keeps them in + order. The records after the chain never bind. + """ + caps = np.full(n, 10.0 * n) + caps[0] = 0.5 + previous, root = 1.0, (n - 0.5) / (n - 1) + margin = 0.5 * (root - previous) + for r in range(1, length): + caps[r] = root - margin + previous, root = root, root + margin / (n - r - 1) + margin = 0.5 * margin / (n - r - 1) + return np.zeros(n), np.log(caps), float(n) + + +def test_a_long_active_set_chain_is_finished_by_the_sort() -> None: + log_weights, log_caps, total = _active_set_chain(14, 12) + log_w = torch.tensor(log_weights, dtype=torch.float64) + log_upper = torch.tensor(log_caps, dtype=torch.float64) + unbounded, unbounded_taken = solve_module._capped_log_shift( + log_w, log_upper, total, active_set_rounds=10_000 + ) + assert unbounded_taken == 12 + shift, taken = solve_module._capped_log_shift(log_w, log_upper, total) + assert taken == solve_module._SOFTMAX_CAP_ACTIVE_SET_ROUNDS + 1 + assert shift == pytest.approx(unbounded, abs=_EXACT) + _assert_same_weights( + _project(log_weights, log_caps, total), + _reference_water_fill(log_weights, log_caps, total), + total, + ) + assert int(np.sum(_project(log_weights, log_caps, total) >= log_caps)) == 12 + + +# --- 6: float32, as the solver runs it ---------------------------------------- + + +@_SETTINGS +@given(capped_problems()) +def test_float32_projection_holds_the_realized_weights_at_their_caps(problem) -> None: + log_weights, log_caps, total = problem + caps = np.exp(log_caps) + upper = torch.tensor(caps, dtype=torch.float32) + log_upper32 = torch.log(upper) + log_w = torch.tensor(log_weights, dtype=torch.float32) + solve_module._project_softmax_log_weights_( + log_w, total, log_upper32.to(torch.float64) + ) + assert (log_w <= log_upper32).all() + realized = total * torch.softmax(log_w, dim=0) + ratio = solve_module._max_cap_ratio(realized, torch.tensor(caps)) + assert ratio <= 1.0 + _FLOAT32 + assert float(realized.double().sum()) == pytest.approx(total, rel=_FLOAT32) + + +def test_max_cap_ratio_reads_zero_caps() -> None: + caps = torch.tensor([2.0, 0.0, 4.0], dtype=torch.float64) + assert solve_module._max_cap_ratio( + torch.tensor([1.0, 0.0, 4.0]), caps + ) == pytest.approx(1.0) + assert solve_module._max_cap_ratio(torch.tensor([1.0, 1e-30, 1.0]), caps) == ( + math.inf + ) + + +def test_without_a_cap_the_projection_is_the_shift() -> None: + log_weights = np.array([0.0, -2.0, 3.5, 1.0]) + log_w = torch.tensor(log_weights, dtype=torch.float64) + assert solve_module._project_softmax_log_weights_(log_w, 50.0, None) == 0 + np.testing.assert_allclose( + log_w.numpy() - log_weights, + math.log(50.0) - float(np.logaddexp.reduce(log_weights)), + rtol=0.0, + atol=_EXACT, + ) diff --git a/packages/microcosm-calibrate/tests/engine_free/shared/test_static_aging.py b/packages/microcosm-calibrate/tests/engine_free/shared/test_static_aging.py index 16f3699ab..dbbc4ad35 100644 --- a/packages/microcosm-calibrate/tests/engine_free/shared/test_static_aging.py +++ b/packages/microcosm-calibrate/tests/engine_free/shared/test_static_aging.py @@ -14,6 +14,7 @@ import pytest from microcosm.calibrate import ( + L2_BASIS_CHI_SQUARE, DemographicProjection, SeriesProjection, score_predictions, @@ -637,3 +638,25 @@ def test_projected_frame_preserves_link_tables(): pd.testing.assert_frame_equal( result.frame_for(frame, 2025).link("jobs"), frame.link("jobs") ) + + +def test_l2_basis_reaches_each_years_calibration() -> None: + """``l2_basis`` is passed through to every projected year's solve. + + Aging runs at free mass, where the chi-square basis penalizes distance + from the base-year weights rather than the total weight scale. + """ + frame = _frame(n_households=120) + result = static_aging( + frame, + base_year=BASE_YEAR, + years=(2025,), + demographics=_projection(frame), + epochs=20, + max_weight_ratio=3.0, + l2_lambda=0.01, + l2_basis=L2_BASIS_CHI_SQUARE, + ) + options = result.years[0].calibration.options + assert options["l2_basis"] == 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b/packages/microcosm-calibrate/tests/fixtures/pre_l2_basis/optimizer_bda72cb02.py new file mode 100644 index 000000000..4df0cde11 --- /dev/null +++ b/packages/microcosm-calibrate/tests/fixtures/pre_l2_basis/optimizer_bda72cb02.py @@ -0,0 +1,265 @@ +"""Immutable pre-``l2_basis`` optimizer, extracted from commit bda72cb02. + +The ``_optimize`` body below is copied verbatim from +``packages/microcosm-calibrate/src/microcosm/calibrate/solve.py`` at +bda72cb024995f432df3107a6db05a22373e6b2d (origin/main when the chi-square L2 +basis and the softmax mass parametrization were added). Tests verify its SHA +and the unchanged installed helper closure before use. This module is only +an oracle for the default path's bytes; it never replaces a solver function. +""" + +from __future__ import annotations + +from collections.abc import Callable, Mapping + +import numpy as np +import torch + +from microcosm.calibrate.gates import HardConcrete +from microcosm.calibrate.initialization import GateInitialization +from microcosm.calibrate.solve import ( + _PRUNE_REL_ATOL, + _apply_constraint, + _prepare_warm_start_weights, + _project_to_total, + _relative_error_loss, +) + + +def _optimize( + matrix: torch.Tensor, + targets: torch.Tensor, + target_loss_weights: torch.Tensor | None, + target_loss_scales: torch.Tensor, + target_loss_cap: float, + initial_weights: np.ndarray, + *, + gate_initialization: GateInitialization | None = None, + warm_start_weights: np.ndarray | None = None, + epochs: int, + learning_rate: float, + conserve_mass: bool, + max_weight_ratio: float | None, + l0_lambda: float, + l2_lambda: float, + l2_anchor: str = "initial", + l2_anchor_weights: np.ndarray | None = None, + target_records: int | None, + init_mean: float, + temperature: float, + progress_callback: Callable[[dict[str, object]], None] | None = None, + progress_context: Mapping[str, object] | None = None, + return_gate_open_probabilities: bool = False, + selection_receipt: dict[str, object] | None = None, +) -> tuple[np.ndarray, np.ndarray] | tuple[np.ndarray, np.ndarray, np.ndarray | None]: + """Run the torch optimization and return weights plus its trajectory. + + Optimizes the log-weights with Adam against capped weighted MAPE. + Positivity is by construction (``w = exp(log_w)`` times optional gates). The + hard constraints — mass conservation and ``max_weight_ratio`` — are applied + by projecting the realized weights after each step, so they hold on the + returned vector exactly, not merely in expectation. + + For deterministic, unregularized free-mass runs, return the best feasible + iterate seen within the requested budget, including the warm start and the + final update. Constant-step Adam can otherwise discard an earlier better + fit when it oscillates across the absolute-error kink. Gated, regularized, + and conserved-mass runs keep their closing-state selection: their stochastic + penalties or closing mass projection need a different selection criterion. + + The private opt-in ``return_gate_open_probabilities`` adds a third return + value without changing the two-item tuple used by existing workspace + callers. The third value is aligned per-record ``pi_i`` for an L0 run and + ``None`` when no gates were active. + """ + w0 = np.asarray(initial_weights, dtype=np.float64) + start = _prepare_warm_start_weights( + w0, + warm_start_weights, + conserve_mass=conserve_mass, + max_weight_ratio=max_weight_ratio, + ) + total0 = float(w0.sum()) + # Same prune threshold the result's n_nonzero uses, so "pruned" here means + # exactly what the reported non-zero count means. + prune_atol = _PRUNE_REL_ATOL * float(np.mean(w0)) + log_w = torch.tensor(np.log(start), dtype=torch.float32, requires_grad=True) + + gates: HardConcrete | None = None + params: list[torch.Tensor] = [log_w] + if l0_lambda > 0.0 or target_records is not None: + gates = HardConcrete( + len(w0), + init_mean=init_mean, + temperature=temperature, + **( + {} + if gate_initialization is None + else { + "initial_probabilities": gate_initialization.probabilities, + "protected_mask": gate_initialization.protected, + } + ), + ) + params = [log_w, *gates.parameters()] + + optimizer = torch.optim.Adam(params, lr=learning_rate) + upper = ( + torch.tensor(max_weight_ratio * w0, dtype=torch.float32) + if max_weight_ratio is not None + else None + ) + if l2_lambda > 0.0: + # The L2 penalty's reference vector. "initial" divides by each record's + # own starting weight; "uniform" divides by the shared mean weight; an + # explicit vector (e.g. the pre-selection design weights during a + # refit) overrides both. Under mass conservation the penalty's + # constrained optimum is w_i ∝ anchor_i², so anchoring on + # heterogeneous starting weights (e.g. a refit whose start is a + # concentrated selection vector) pulls toward MORE concentration; the + # uniform anchor makes the penalty a direct 1/ESS control regardless + # of the starting distribution. + if l2_anchor_weights is not None: + anchor = np.asarray(l2_anchor_weights, dtype=np.float64) + elif l2_anchor == "initial": + anchor = w0 + else: + anchor = np.full_like(w0, w0.mean()) + w0_t = torch.tensor(anchor, dtype=torch.float32) + else: + w0_t = None + + retain_best = gates is None and not conserve_mass and l2_lambda == 0.0 + best_loss = float("inf") + best_epoch = 0 + best_log_w: torch.Tensor | None = None + trajectory = np.empty(epochs, dtype=np.float64) + for epoch in range(epochs): + optimizer.zero_grad() + weights = torch.exp(log_w) + if gates is not None: + weights = weights * gates() + estimate = _apply_constraint(matrix, weights) + loss = _relative_error_loss( + estimate, + targets, + target_loss_weights, + target_loss_scales, + target_loss_cap, + ) + penalty = ( + l0_lambda * gates.get_penalty() + if (gates is not None and l0_lambda > 0.0) + else torch.zeros((), dtype=torch.float32) + ) + # Penalize latent pre-gate weights. Under L0, a nearly closed gate + # should not be able to hide a very large exp(log_w). + l2_penalty = ( + ((torch.exp(log_w) / w0_t) ** 2).mean() + if w0_t is not None + else torch.zeros((), dtype=torch.float32) + ) + total_loss = loss + penalty + l2_lambda * l2_penalty + trajectory[epoch] = float(loss.item()) + if retain_best and trajectory[epoch] < best_loss: + best_loss = trajectory[epoch] + best_epoch = epoch + best_log_w = log_w.detach().clone() + if progress_callback is not None: + progress_callback( + { + **dict(progress_context or {}), + "kind": "calibration_epoch", + "epoch": epoch + 1, + "epochs": epochs, + "loss": trajectory[epoch], + } + ) + total_loss.backward() + optimizer.step() + + # Hard projections, applied to the realized weights every step so the + # guarantees hold on the returned vector, not just in expectation. + with torch.no_grad(): + if upper is not None: + # Clamp log-weights so exp(log_w) <= max_weight_ratio*w0. This is + # the landmine guard: a rare high-value near-zero-weight record + # can never be inflated past its bound. + log_w.clamp_(max=torch.log(upper)) + if conserve_mass: + realized = torch.exp(log_w) + if gates is not None: + realized = realized * gates() + current_total = float(realized.sum().item()) + if current_total > 0: + log_w.add_(float(np.log(total0 / current_total))) + if upper is not None: + # Re-clamp: the rescale may have pushed a record over its + # bound. The mass invariant is held within rtol by the + # final-vector rescale below; per-step we keep the bound + # hard so it can never be violated mid-run. + log_w.clamp_(max=torch.log(upper)) + + gate_open_probabilities: np.ndarray | None = None + with torch.no_grad(): + weights = torch.exp(log_w) + if best_log_w is not None: + closing_loss = _relative_error_loss( + _apply_constraint(matrix, weights), + targets, + target_loss_weights, + target_loss_scales, + target_loss_cap, + ) + if float(closing_loss.item()) > best_loss: + weights = torch.exp(best_log_w) + selected_epoch = best_epoch + selected_loss = best_loss + else: + selected_epoch = epochs + selected_loss = float(closing_loss.item()) + if selection_receipt is not None: + selection_receipt.update( + { + "rule": "best_feasible_loss", + "selected_epoch": selected_epoch, + "epochs_executed": epochs, + "epoch_convention": "completed_optimizer_updates; zero is start", + "selected_loss_float32": selected_loss, + "closing_iterate_loss_float32": float(closing_loss.item()), + } + ) + if gates is not None: + gates.eval() + weights = weights * gates() + if return_gate_open_probabilities: + gate_open_probabilities = ( + gates.get_active_prob() + .detach() + .cpu() + .numpy() + .astype(np.float64, copy=True) + ) + final = weights.detach().numpy().astype(np.float64) + + # Make the hard ratio bound exact on the returned vector. The per-step + # clamp is in float32, so exp() can overshoot the bound by a float epsilon + # (~1e-7 relative); a closing float64 cap guarantees no returned weight + # exceeds max_weight_ratio * w0, which downstream code may assert. + if max_weight_ratio is not None: + final = np.minimum(final, max_weight_ratio * np.asarray(w0, dtype=np.float64)) + + # Exact mass conservation on the returned vector: a single closing rescale + # to the input total. When a max_weight_ratio is also set, the rescale is + # capped at the bound and the residual is redistributed below the bound, so + # both invariants hold together. When L0 pruning is active, the deficit is + # redistributed only over surviving (gate-open) records so the cap fill never + # resurrects a pruned one. + if conserve_mass: + pruned = ( + final <= prune_atol if (gates is not None and l0_lambda > 0.0) else None + ) + final = _project_to_total(final, total0, max_weight_ratio, w0, pruned=pruned) + if return_gate_open_probabilities: + return final, trajectory, gate_open_probabilities + return final, trajectory diff --git a/packages/microcosm-graph/tests/fixtures/parity/kernels/calibrate/pins.json b/packages/microcosm-graph/tests/fixtures/parity/kernels/calibrate/pins.json index 04c2811a6..1572f3985 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/kernels/calibrate/pins.json +++ b/packages/microcosm-graph/tests/fixtures/parity/kernels/calibrate/pins.json @@ -1 +1 @@ -{"dependencies":{"numpy":"2.4.6","pandas":"3.0.3","scipy":"1.17.1","torch":"2.12.0"},"implementation_hash":"994c30fd23b95c659bb0c8d882b857f44b4747afd7d6a6eefd03630ad4d49471","kernel":"calibrate.adam@1","node":"calibrate","node_key":"d3bc86bba1d962fed07e9db03a76b0ac554c898a863390a83c5c5ab2bc6ac47d","numeric":"bitwise","platform":"arm64/darwin/py3.14","platforms":{"arm64/darwin/py3.14":{"direct":"direct.csv","node_key":"d3bc86bba1d962fed07e9db03a76b0ac554c898a863390a83c5c5ab2bc6ac47d"}},"seed":0} +{"dependencies":{"numpy":"2.4.6","pandas":"3.0.3","scipy":"1.17.1","torch":"2.12.0"},"implementation_hash":"ebaea824c978926a03642bb5060bc49a800ae82ffa013ec39e0e70cfe253d308","kernel":"calibrate.adam@1","node":"calibrate","node_key":"87f944c66137bcc26702f4b219d51cabe2d489a2d961061d0e3f0fb97984d230","numeric":"bitwise","platform":"arm64/darwin/py3.14","platforms":{"arm64/darwin/py3.14":{"direct":"direct.csv","node_key":"87f944c66137bcc26702f4b219d51cabe2d489a2d961061d0e3f0fb97984d230"}},"seed":0} diff --git a/tools/build_us_acs_local_release.py b/tools/build_us_acs_local_release.py index 7c3cf446c..e0b459a3f 100644 --- a/tools/build_us_acs_local_release.py +++ b/tools/build_us_acs_local_release.py @@ -28,8 +28,9 @@ rung. Heavy stage; a crash in calibrate never re-runs the microsim. calibrate : epoch-batched warm-start calibrate on the checkpoint's - training targets (adam, mass conserved, hard weight-ratio cap; - resumable with --resume), score the held-out district targets + training targets (adam, mass conserved, hard weight-ratio cap, + optional L2 penalty --l2-lambda of form --l2-basis under + --mass-parametrization; resumable with --resume), score the held-out district targets against the pro-rata baseline, record ESS over rows and distinct households, and write the calibrated weights onto a copy of the staging H5. @@ -134,6 +135,19 @@ #: ``state`` mode excludes them, which is what Build O's switch did. CONGRESSIONAL_DISTRICT_RECORD_SET_SPEC_PREFIX = "irs_soi.congressional_district_" +#: The kernel's ``l2_basis`` and ``mass_parametrization`` values +#: (``microcosm.calibrate.L2_BASES`` / ``MASS_PARAMETRIZATIONS``), spelled here +#: so parsing arguments never imports torch; a test pins them equal. +L2_BASIS_CHOICES = ("chi_square", "record") +MASS_PARAMETRIZATION_CHOICES = ("projection", "softmax") +#: The penalty settings every solve had before they were options: a solver +#: stamp or summary that lacks them was solved with these. +HISTORICAL_PENALTY = { + "l2_lambda": 0.0, + "l2_basis": "record", + "mass_parametrization": "projection", +} + def _require_soi_mode(soi_mode: str) -> str: if soi_mode not in SOI_MODES: @@ -145,7 +159,9 @@ def _require_soi_mode(soi_mode: str) -> str: def release_refresh_recipe( - soi_mode: str, cd_holdout_fraction: float | None = None + soi_mode: str, + cd_holdout_fraction: float | None = None, + penalty: dict | None = None, ) -> str: """The one-command release refresh, pinned to the SOI surface it built. @@ -153,7 +169,10 @@ def release_refresh_recipe( the recorded surface even if the parser default changes again, and, for every ``state_cd`` build, ``--cd-holdout-fraction`` at the recorded value (0 included, full precision), since ``state_cd`` otherwise defaults to a - 10% holdout. + 10% holdout. It names ``--l2-lambda``, ``--l2-basis`` and + ``--mass-parametrization`` at the recorded ``penalty`` (a legacy summary + that records none reads as the historical solve) for the same reason: the + recipe rebuilds the recorded solve even after a default changes. """ _require_soi_mode(soi_mode) @@ -162,12 +181,30 @@ def release_refresh_recipe( if soi_mode == SOI_MODE_STATE_CD and cd_holdout_fraction is not None else "" ) + flags = { + "l2_lambda": "--l2-lambda", + "l2_basis": "--l2-basis", + "mass_parametrization": "--mass-parametrization", + } + recorded = { + key: HISTORICAL_PENALTY[key] + if (penalty or {}).get(key) is None + else (penalty or {})[key] + for key in flags + } + solve = "".join( + f"{flag} {float(recorded[key])!r} " + if key == "l2_lambda" + else f"{flag} {recorded[key]} " + for key, flag in flags.items() + ) return ( "uv run tools/build_us_acs_local_release.py --stage all " "--staging-h5 /acs_multispine_staging.h5 " "--feed --feed-sha256 " f"--soi-mode {soi_mode} " f"{holdout}" + f"{solve}" "--ladder build/us/us_puma_ladder_2020.npz " "--checkpoint-dir /checkpoints " "--out-h5 /populace_us_2024_acs_local.h5 " @@ -1491,11 +1528,33 @@ def _solver_settings(args) -> dict: "max_weight_ratio": args.max_weight_ratio, "target_loss_cap": args.target_loss_cap, "l2_lambda": args.l2_lambda, + "l2_basis": args.l2_basis, + "mass_parametrization": args.mass_parametrization, "seed": args.seed, "epoch_batch": args.epoch_batch, } +def _stamped_settings(settings: dict | None) -> dict | None: + """A recorded solver stamp, with the penalty keys it predates filled in. + + A stamp written before ``l2_basis`` and ``mass_parametrization`` existed + was solved with their historical values, so it compares equal to a request + for those values and to nothing else. + """ + + if settings is None: + return None + return { + **{ + key: value + for key, value in HISTORICAL_PENALTY.items() + if key != "l2_lambda" + }, + **settings, + } + + def _weights_digest(weights) -> str: """Digest of a calibrated household weight vector (float64 bytes).""" @@ -1671,6 +1730,8 @@ def calibrate_surface( warm: np.ndarray | None = None, done: int = 0, on_batch=None, + l2_basis: str = HISTORICAL_PENALTY["l2_basis"], + mass_parametrization: str = HISTORICAL_PENALTY["mass_parametrization"], ): """Epoch-batched warm-start calibration of ``target_set``. @@ -1698,6 +1759,8 @@ def calibrate_surface( max_weight_ratio=max_weight_ratio, target_loss_cap=target_loss_cap, l2_lambda=l2_lambda, + l2_basis=l2_basis, + mass_parametrization=mass_parametrization, seed=seed, warm_start_weights=warm, ) @@ -1803,7 +1866,7 @@ def do_calibrate(args) -> None: if "solver_settings" in saved else None ) - if saved_stamp != stamp or saved_settings != settings: + if saved_stamp != stamp or _stamped_settings(saved_settings) != settings: raise SystemExit( "weights_latest.npz was calibrated for a different " "materialization (staging, surface, holdout or sample) or " @@ -1822,7 +1885,7 @@ def do_calibrate(args) -> None: previous = _load_json(summary_path) if ( previous.get("run_identity_sha256") == stamp - and previous.get("solver_settings") == settings + and _stamped_settings(previous.get("solver_settings")) == settings and previous.get("weights_sha256") == _weights_digest(warm) ): log( @@ -1869,6 +1932,8 @@ def save(weights: np.ndarray, epochs_done: int) -> None: max_weight_ratio=args.max_weight_ratio, target_loss_cap=args.target_loss_cap, l2_lambda=args.l2_lambda, + l2_basis=args.l2_basis, + mass_parametrization=args.mass_parametrization, seed=args.seed, warm=warm, done=done, @@ -1899,12 +1964,20 @@ def save(weights: np.ndarray, epochs_done: int) -> None: "max_weight_ratio": args.max_weight_ratio, "target_loss_cap": args.target_loss_cap, "l2_lambda": args.l2_lambda, + "l2_basis": args.l2_basis, + "mass_parametrization": args.mass_parametrization, "seed": args.seed, "initial_loss": round(result.initial_loss, 6), "final_loss": round(result.final_loss, 6), "fraction_within_10pct": round(result.fraction_within_10pct, 4), "effective_sample_size": round(result.effective_sample_size, 1), "ess_fraction": round(result.effective_sample_size / n_households, 4), + "chi_square_distance": round(result.chi_square_distance, 6), + # Softmax only: the largest in-loop weight over its cap, before the + # closing projection (the last epoch batch). + "softmax_in_loop_max_cap_ratio": ( + result.options.get("iterate_selection_receipt") or {} + ).get("softmax_in_loop_max_cap_ratio"), "realized_max_weight_ratio": round(result.realized_max_weight_ratio, 4), "mass_conserved_ratio": round( float(result.weights.sum()) / float(design_weights.sum()), 6 @@ -2239,6 +2312,54 @@ def _git(*parts: str) -> str: return {"sha": sha, "dirty": dirty, "branch": _git("branch", "--show-current")} +def _ess_concentration_limitation(diagnostics: dict) -> dict: + """The lineage's weight-concentration entry, true to the solve it describes. + + Only the historical solve (no penalty, projection mass parametrization) + keeps the reviewed entry. Any other solve, including an unpenalized softmax + one, records its own settings instead: its concentration is measured, but + whether it is acceptable is a separate review this register cannot claim. + """ + + ess = diagnostics.get("effective_sample_size") + ess_fraction = diagnostics.get("ess_fraction", 0.0) + households = diagnostics.get("households") or 0 + l2_lambda = float(diagnostics.get("l2_lambda") or 0.0) + parametrization = diagnostics.get( + "mass_parametrization", HISTORICAL_PENALTY["mass_parametrization"] + ) + if ( + l2_lambda == 0.0 + and parametrization == HISTORICAL_PENALTY["mass_parametrization"] + ): + return { + "id": "low_effective_sample_size_lambda_zero", + "status": "reviewed_concentration", + "reason": ( + f"Kish ESS is {ess} = {ess_fraction:.2%} of {households} " + "households under the hard 5x weight cap with l2_lambda=0 " + "(kept for consistency with the certified default and the " + "Build L doctrine; no new calibration knobs per " + "microcosm#492)." + ), + "calibration_blocker": False, + } + return { + "id": "effective_sample_size_under_nondefault_solve", + "status": "recorded_concentration", + "reason": ( + f"Kish ESS is {ess} = {ess_fraction:.2%} of {households} " + f"households with max_weight_ratio=" + f"{diagnostics.get('max_weight_ratio')}, l2_lambda={l2_lambda:g}, " + f"l2_basis=" + f"{diagnostics.get('l2_basis', HISTORICAL_PENALTY['l2_basis'])!r} and " + f"mass_parametrization={parametrization!r}; chi-square distance " + f"from the design weights {diagnostics.get('chi_square_distance')}." + ), + "calibration_blocker": False, + } + + def finalize_reviewed_limitations( staging_summary: dict, diagnostics: dict, @@ -2252,9 +2373,6 @@ def finalize_reviewed_limitations( duplicates. """ - ess = diagnostics.get("effective_sample_size") - ess_fraction = diagnostics.get("ess_fraction", 0.0) - households = diagnostics.get("households") or 0 donor_release = (staging_summary.get("base") or {}).get("donor_release") or {} limitations = list(staging_summary.get("reviewed_limitations", [])) aged_ssi = (spine_qa or {}).get("per_spine", {}) @@ -2319,18 +2437,7 @@ def finalize_reviewed_limitations( ), "calibration_blocker": False, }, - { - "id": "low_effective_sample_size_lambda_zero", - "status": "reviewed_concentration", - "reason": ( - f"Kish ESS is {ess} = {ess_fraction:.2%} of {households} " - "households under the hard 5x weight cap with l2_lambda=0 " - "(kept for consistency with the certified default and the " - "Build L doctrine; no new calibration knobs per " - "microcosm#492)." - ), - "calibration_blocker": False, - }, + _ess_concentration_limitation(diagnostics), { "id": "donor_sparse_selection_training_set", "status": "reviewed_construction", @@ -3032,7 +3139,9 @@ def _version(package: str) -> str: ), "staging": LEGACY_STAGING_REFRESH_RECIPE, "release": release_refresh_recipe( - soi_mode, (identity.get("cd_holdout") or {}).get("fraction") + soi_mode, + (identity.get("cd_holdout") or {}).get("fraction"), + penalty={key: diagnostics.get(key) for key in HISTORICAL_PENALTY}, ), "publish": ( "tools/publish_release.sh --no-latest " @@ -3062,8 +3171,10 @@ def _version(package: str) -> str: or ["acs_2024_1yr", "asec_puf"], "donor_release": donor_release, }, + # A summary from before the penalty settings were recorded was solved + # with the historical ones, the only solve that existed then. "calibration": { - key: diagnostics.get(key) + key: diagnostics.get(key, HISTORICAL_PENALTY.get(key)) for key in ( "families", "geographies", @@ -3071,11 +3182,15 @@ def _version(package: str) -> str: "epochs", "max_weight_ratio", "l2_lambda", + "l2_basis", + "mass_parametrization", "seed", "final_loss", "fraction_within_10pct", "effective_sample_size", "ess_fraction", + "chi_square_distance", + "softmax_in_loop_max_cap_ratio", "realized_max_weight_ratio", "mass_conserved_ratio", ) @@ -3337,6 +3452,31 @@ def _parse_args(argv: list[str] | None = None) -> argparse.Namespace: parser.add_argument("--max-weight-ratio", type=float, default=5.0) parser.add_argument("--target-loss-cap", type=float, default=1.0) parser.add_argument("--l2-lambda", type=float, default=0.0) + parser.add_argument( + "--l2-basis", + choices=L2_BASIS_CHOICES, + default=HISTORICAL_PENALTY["l2_basis"], + help=( + "Form of the --l2-lambda penalty (microcosm.calibrate l2_basis). " + "'record' (default) is the historical mean((w / d) ** 2); " + "'chi_square' is GREG's design-weighted chi-square distance " + "sum(d * (w / d - 1) ** 2) / sum(d), which pulls toward the " + "design weights d themselves." + ), + ) + parser.add_argument( + "--mass-parametrization", + choices=MASS_PARAMETRIZATION_CHOICES, + default=HISTORICAL_PENALTY["mass_parametrization"], + help=( + "How the mass-conserving Adam solve holds the total " + "(microcosm.calibrate mass_parametrization). 'projection' " + "(default) is the historical per-step uniform shift; 'softmax' " + "optimizes total * softmax(log_w), projecting exactly onto the " + "capped simplex after every step; the summary records the largest " + "in-loop weight over its cap (docs/calibration-l2-basis.md)." + ), + ) parser.add_argument("--seed", type=int, default=0) parser.add_argument("--resume", action="store_true") parser.add_argument("--batch", type=int, default=5_000)