diff --git a/.github/workflows/notebooks.yml b/.github/workflows/notebooks.yml new file mode 100644 index 0000000..d291f06 --- /dev/null +++ b/.github/workflows/notebooks.yml @@ -0,0 +1,27 @@ +name: notebooks + +# A LIGHT, NON-BLOCKING drift check (issue #151): it only runs the showcase notebooks +# end-to-end to catch breakage in the frozen `views_frames` surface. It is deliberately +# separate from the core `CI` gate and `continue-on-error`, so a slow or flaky notebook +# never blocks a merge — the notebooks are un-gated dev artifacts (like research/). +on: + push: + branches: [main, development] + pull_request: + branches: [main, development] + +jobs: + nbmake: + runs-on: ubuntu-latest + continue-on-error: true + steps: + - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v4 + - run: uv python install 3.12 + - run: uv sync --python 3.12 --extra docs + # cwd = notebooks/ (working-directory) so each notebook's `import _synthetic` + # resolves to the in-repo helper next to it; the path arg is therefore `.` (the + # notebooks live directly in this dir — `notebooks/` here would mean notebooks/notebooks/ + # and collect nothing). The notebooks ship with cleared outputs and re-execute here. + - run: uv run --python 3.12 pytest --nbmake . + working-directory: notebooks diff --git a/CHANGELOG.md b/CHANGELOG.md index 534aed7..0b89eb1 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -4,6 +4,62 @@ All notable changes to `views-frames` are documented here. The format is based o [Keep a Changelog](https://keepachangelog.com/), and this project adheres to [Semantic Versioning](https://semver.org/) as governed in `GOVERNANCE.md`. +## [1.8.0] — 2026-06-28 + +**Native point-country broadcast in `views_frames_reconcile` (ADR-023 amendment, #143 / Epic #142), +the three showcase notebooks (Epic #166), and a governance/test hardening pass (Epic #179).** All +additive — the frozen leaf and summarize public surface are unchanged, and the hardening work makes +**no `src/` behaviour change**; `CONFORMANCE_FLOOR` stays `1.0.0`. + +### Added +- **`ReconciliationModule.reconcile` accepts a point country** (`cm.sample_count == 1`) against a draws + grid (`pgm.sample_count == S`): the point is broadcast across the `S` draws inside the orchestrator + (`np.tile`), so callers no longer tile it themselves (the DRY home of pipeline-core's WET + `align_country_to_grid`, #143). The **aligned-draws** path (`cm.sample_count == S`) is byte-for-byte + unchanged; any other count still fails loud. +- **`ReconciliationModule.reconcile_result(cm, pgm) -> ReconciliationResult`** (#144) — reconciles and + **reports the mode** (`POINT_BROADCAST` | `ALIGNED_DRAWS`) + method (`proportional`) on a returned + `ReconciliationResult`. The mode is *returned*, never stamped on the leaf's generic `FrameMetadata` + (ADR-020 / register C-47 — the numpy leaf carries no reconciliation vocabulary). `reconcile` is + unchanged (it returns `reconcile_result(...).frame`). New public names: `ReconciliationResult`, + `POINT_BROADCAST`, `ALIGNED_DRAWS`, `METHOD_PROPORTIONAL`. + +### Notes +- The broadcast lives entirely in `views_frames_reconcile/module.py`; the leaf `proportional` and the + parity-frozen `grouping` hot loop are untouched, so the torch-oracle parity is exact (0.000e+00). +- The aligned-draws mode remains the documented per-draw approximation. **ADR-024** (#145) records the + design direction + deferral for the principled joint upgrade (and corrects `proportional.py`'s + ambiguous "C-37" reference; register C-62). Design-only — no code. + +### Documentation +- **Three showcase notebooks** (`notebooks/01_frames`, `02_summaries`, `03_reconciliation`; Epic #166): + public-frozen-API-only, synthetic-data teaching notebooks for the frames contract, the posterior + summaries (with a calibration/coverage + PIT panel), and reconciliation — including a + bit-identity-≠-method-quality panel and a toy-lattice spatial view (register C-59/C-60/C-61). +- **`docs/CICs/Reconcile.md`** (Epic #179) — the package-level Class Intent Contract for + `views_frames_reconcile` (§1–§11): the sum-to-country / zero-preservation / non-negativity / + de-mutation guarantees, the point/aligned **mode** contract, the five fail-loud validation guards + + the per-draw-approximation caveat, and the green/beige/red test alignment. The reconcile package + was the last non-trivial surface without a CIC (ADR-006); **register C-64 resolved**. +- **ADR-025 — value-buffer immutability is by convention; only the index is enforced** (Epic #179). + Corrects the "immutable value objects" contract (the three frame CICs §9/§3 + README design + principle 2) to match the code: the index (`time`/`unit`) is `setflags(write=False)`-enforced; the + value buffer is immutable *by convention* (left writeable to preserve zero-copy / `mmap` — mutating + `.values` in place is unsupported). The `setflags`-enforce on `.values` would be a MAJOR + ("tightening an invariant" on a frozen-surface member, GOVERNANCE/ADR-018), so it is recorded as a + **deferred MAJOR-rider**, not done now; **register C-63 resolved** (contract corrected). + +### Tests +- **Adversarial (red) test hardening** (Epic #179), no `src/` change, 100% line+branch coverage held: + - the non-finite (NaN / ±inf) fail-loud guard in `exceedance`/`expected_shortfall` is now pinned on + the **blocked (multi-block) path** — the bad draw placed in a non-first block via the `block_rows` + kwarg with block 0 all-finite (**register C-65 resolved**); + - **conformance-suite negatives** — `assert_reconcile_contract` and `assert_summarizer_contract` are + shown to reject a deliberately non-conforming implementation (the leaf's C-51 envelope-negative + pattern, extended to the sibling packages); + - **reconcile mode-corners** — `reconcile_result.mode` for both-points and pre-tiled-cm inputs (both + `ALIGNED_DRAWS`); and **`ReconciliationResult` frozen-ness** (`FrozenInstanceError`). + ## [1.7.0] — 2026-06-26 **Forecast reconciliation is a third sibling package (ADR-023, Epic 11).** A new importable diff --git a/README.md b/README.md index 9bbf3a6..1a05f95 100644 --- a/README.md +++ b/README.md @@ -223,7 +223,13 @@ makes it safe to depend on from everywhere (SDP). 3. **Immutable value objects.** A frame is validated at construction and then treated as read-only. Operations (`collapse`, `select`, `with_metadata`) **return new frames**; they never mutate in place. (Directly forbids the - C-184 cross-repo-mutation anti-pattern.) **Copy-vs-view:** structural and + C-184 cross-repo-mutation anti-pattern.) **Enforced for the index, by + convention for the value buffer:** the identifier arrays (`time`/`unit`) are + write-protected (`setflags(write=False)`); the `values` buffer is left + **writeable on purpose** — so structural ops can share it zero-copy — and is + immutable *by convention*: mutating `.values` in place is unsupported and may + silently corrupt buffer-sharing frames (ADR-025 / register C-63; enforcing it + is a deferred MAJOR-rider). **Copy-vs-view:** structural and metadata-only operations (`with_metadata`, contiguous `select`) return frames that **share** the underlying `values` buffer (numpy view / zero-copy), and a `mmap`-backed frame stays `mmap`-backed — a new frame must never copy a diff --git a/docs/ADRs/023_reconciliation_is_a_sibling_package.md b/docs/ADRs/023_reconciliation_is_a_sibling_package.md index 0af7f1e..f4b58a7 100644 --- a/docs/ADRs/023_reconciliation_is_a_sibling_package.md +++ b/docs/ADRs/023_reconciliation_is_a_sibling_package.md @@ -3,6 +3,7 @@ **Status:** Accepted **Date:** 2026-06-26 +**Amended:** 2026-06-27 — sample-count contract (point-broadcast vs aligned-draws), v1.8.0, #143 **Deciders:** VIEWS platform maintainers **Consulted:** views-postprocessing (the current, mis-homed host), views-reporting (the frozen torch oracle) **Informed:** views-pipeline-core, views-models, views-evaluation @@ -68,6 +69,19 @@ any `views_*` except `views_frames` (and `views_frames_summarize` only if a real > map_vals)` arrays. `views_frames_reconcile` owns the *operation*; it never embeds or fetches geography. This > is asserted in the conformance suite, not just documented. +### Sample-count contract: point-broadcast vs aligned-draws (amended 2026-06-27, #143) + +`ReconciliationModule.reconcile` accepts a country (`cm`) frame whose `sample_count` is either **`1`** +(a *point* forecast) or **`S`** (the grid's draw count); any other count fails loud in validation. A point +`cm` is **broadcast** to `S` draws by tiling its single column (`np.tile`) inside the orchestrator +(`module.py`), *before* the unchanged aligned-draws reconcile path runs — so the equal-count path stays +**bit-for-bit identical** (the broadcast triggers only for `sample_count == 1`; the leaf `proportional` and the +parity-frozen `grouping` hot loop are untouched). This is the DRY home of pipeline-core's WET +`align_country_to_grid` (the consumer explicitly earmarked it for here, #143). The **aligned-draws** case +remains the documented *per-draw approximation*: pairing grid-draw `s` with country-draw `s` across +independently-trained models has no shared draw identity — the principled joint upgrade is a separate design +(#145; the pragmatic-vs-principled boundary the `proportional` docstring names). + ### Import-DAG `views_frames_reconcile → {views_frames}` (+ numpy / stdlib). Added to `ALLOWED_INTERNAL` in @@ -78,6 +92,11 @@ unchanged. A new sibling package is **additive ⇒ MINOR**: **1.6.0 → 1.7.0**. `CONFORMANCE_FLOOR` stays `1.0.0`. +The 2026-06-27 amendment (the point-broadcast sample-count contract, #143) is an **additive input-contract +relaxation** — `reconcile` accepts a new input shape (`cm.sample_count == 1`) it previously rejected, and no +existing call changes ⇒ **MINOR: 1.7.0 → 1.8.0**. `CONFORMANCE_FLOOR` stays `1.0.0` (the frozen leaf surface +is untouched; the broadcast lives entirely in `views_frames_reconcile`). + --- ## Rationale @@ -177,6 +196,10 @@ module*, never a modification of the proportional method. story, only after parity is locked. - The principled probabilistic-reconciliation method (views-postprocessing C-37) — a future *sibling module*, never a modification of `proportional`. +- **Reconciliation-mode provenance (#144):** the mode (`point-broadcast` vs `aligned-draws`) is **returned** + on a `ReconciliationResult` from `reconcile_result`, **not** stamped on the leaf's generic `FrameMetadata` + — that header is governed *generic-only* (ADR-020 / register C-47), so reconciliation vocabulary stays in + the sibling package, off the numpy leaf (decision recorded as register D-12). --- diff --git a/docs/ADRs/024_principled_joint_reconciliation_design.md b/docs/ADRs/024_principled_joint_reconciliation_design.md new file mode 100644 index 0000000..13419d9 --- /dev/null +++ b/docs/ADRs/024_principled_joint_reconciliation_design.md @@ -0,0 +1,172 @@ + +# ADR-024: Principled joint probabilistic reconciliation — design direction & deferral + +**Status:** Accepted (design direction; implementation deferred) +**Date:** 2026-06-27 +**Deciders:** VIEWS platform maintainers +**Consulted:** views-models (the country/grid model owner), views-postprocessing (the C-37 lineage) +**Informed:** views-pipeline-core, views-evaluation + +--- + +## Context + +`views_frames_reconcile.reconcile_proportional` makes grid (`pgm`) forecasts sum to their +country (`cm`) total by **top-down proportional disaggregation, applied per posterior draw**: +within draw `s`, each grid cell keeps its relative share and the cells are rescaled so they +sum to draw `s` of the country total. It is a *faithful, parity-proven port* and the right +**pragmatic baseline** — but its own docstring names it "a pragmatic per-draw approximation, +not principled joint probabilistic reconciliation." + +Two things make it an approximation, and they are *not* fixed by more samples: + +1. **No shared draw identity.** Pairing grid-draw `s` with country-draw `s` is only meaningful + if the two are *the same scenario*. When the grid and country models are trained + **independently** (the current platform reality — `rusty_bucket` and a separate country + model), draw index `s` carries **no shared meaning** across them; the pairing is arbitrary, + even though the arithmetic runs and the output looks reasonable. +2. **Dependence is discarded.** Proportional rescaling ignores the cross-cell error structure + a principled reconciler exploits (MinT) and does not reconcile the *joint* distribution + (probabilistic reconciliation) — so the reconciled joint tails (the thing FAO-style + decisions key on) are not guaranteed calibrated. + +This matters **now** because the reconciliation path is live (pipeline-core #233 wired the +frames-native ensemble) and a UN/FAO deliverable consumes reconciled grid→country draws. We +need the upgrade path *written down* — what "principled" requires and when it's worth +building — so the known approximation is **planned debt, not silent debt**. This ADR is the +design; it deliberately ships **no code**. + +--- + +## Decision + +1. **The draw-identity contract is the gate.** Principled joint reconciliation requires a + defined notion of *"grid-draw `s` and country-draw `s` are the same scenario."* That holds + only when the grid and country posteriors come from a **shared generative process** — shared + ensemble members / shared seeds, or an explicit **copula coupling**. Absent a declared + shared draw-identity, per-draw pairing is not principled and must not be presented as such. + +2. **When built, it is a new sibling module, not a change to `proportional`.** Consistent with + ADR-023's open question, the principled method (probabilistic reconciliation — + Panagiotelis et al. 2023; or MinT — Wickramasuriya et al. 2019) lands as a **new module in + `views_frames_reconcile`** behind the existing injected-mapping interface. `proportional` + stays the documented baseline; nothing about its parity-frozen behaviour changes. + +3. **Implementation is deferred** until *both* preconditions hold: (a) a consumer needs + calibrated **joint** country tails (proportional's marginal-rescale is demonstrably + insufficient for its decision), **and** (b) the country model can **supply what the method + needs** — either draws carrying a declared shared draw-identity, or a covariance/coupling the + reconciler can consume. Until then, per-draw proportional remains the shipped method, + labelled as the approximation it is (the `reconcile_result` mode `aligned-draws`, D-12). + +**In scope:** the design direction, the draw-identity contract, and the deferral preconditions. +**Out of scope:** any implementation; choosing the *specific* method (that is the future epic's +call, driven by what the country model can supply); the country model's own changes +(views-models); changing `proportional`. + +--- + +## Rationale + +Correctness-of-method is a *country-model-shaped* problem, not a reconciler-shaped one: the +reconciler cannot manufacture a joint distribution that the upstream models never shared. So the +honest, lowest-regret move is to **name the precondition** (draw-identity / coupling) and defer +until it can be met, rather than ship a more elaborate method that still pairs independent draws +and merely *looks* principled. WET-before-DRY's sibling logic applies again: keep the baseline, +add the principled method beside it only when its inputs exist — never by mutating the +parity-frozen `proportional`. + +--- + +## Considered Alternatives + +### Alternative A: keep proportional as the only method (status quo) +- **Pros:** simplest; parity-proven; no new inputs required. +- **Cons:** information-losing; per-draw pairing of independent draws is not principled; joint + tails not guaranteed calibrated. +- **Revisit when:** a consumer's decision provably needs calibrated joint country tails. + +### Alternative B: MinT (trace-minimization; Wickramasuriya 2019) +- **Pros:** minimum-variance unbiased *linear* reconciliation; well-established. +- **Cons:** needs an estimate of the base-forecast **error covariance** the platform does not + currently produce; still a point/linear view, not a full joint posterior. +- **Revisit when:** reconciliation residuals / a covariance estimate become available. + +### Alternative C: copula / shared-ensemble coupling +- **Pros:** models grid↔country dependence explicitly; can deliver a calibrated joint. +- **Cons:** heaviest; needs a fitted copula or a genuinely shared ensemble — a country-model + contract change. +- **Revisit when:** the country and grid models share members, or a coupling is fitted upstream. + +### Alternative D: marginal (non-per-draw) reconciliation +- **Pros:** honest when draws are *not* shared — reconcile the country **marginal**, don't fake + per-draw identity. +- **Cons:** loses the per-draw coherence downstream summaries (joint `expected_shortfall`, etc.) + rely on. +- **Revisit when:** a consumer wants marginal-only coherence. + +--- + +## Consequences + +### Positive +- The known approximation becomes **planned debt with a named precondition**, not a silent one. +- The upgrade path is fixed (new sibling module, injected interface unchanged) so a future epic + is scoped, not exploratory. +- Keeps `proportional` parity-frozen and the leaf untouched. + +### Negative +- No improvement now; per-draw proportional persists as the shipped method (documented). +- The hardest dependency (a shared draw-identity / coupling from the country model) is *upstream* + and outside this repo's control — the deferral may be long. + +--- + +## Implementation Notes + +**No code in this ADR.** When the preconditions are met, a future implementation epic: +- adds a sibling module under `src/views_frames_reconcile/` (e.g. `probabilistic.py`) behind the + same injected-mapping interface; does **not** modify `proportional`/`grouping`; +- requires the country model (views-models) to declare the draw-identity / supply the coupling — + a cross-repo contract, gated there; +- ships its own parity/validation suite (below) and is additive/MINOR; `CONFORMANCE_FLOOR` stays + `1.0.0`. + +This ADR's only repo change is documentation: `proportional.py`'s docstring is corrected to point +at **this ADR** (the prior "tracked as C-37" was ambiguous — views-frames' own C-37 is an +*unrelated, resolved* protocol-conformance item; the principled-reconciliation lineage is +*views-postprocessing* C-37). Tracked as register **C-62**. + +--- + +## Validation & Monitoring + +A principled implementation must demonstrate what proportional cannot: **calibrated joint +coverage** — e.g. the reconciled country total's predictive intervals cover at their nominal +rate against held-out actuals, and the joint grid→country dependence is preserved (not just the +per-cell marginals). That coverage check (on synthetic data with a known joint truth) is the +acceptance gate for the future epic, and the signal that would justify lifting the deferral. + +--- + +## Open Questions + +- **Which method** (probabilistic reconciliation vs MinT vs copula) — undecided on purpose; it + depends on what the country model can supply (draws + identity, or a covariance, or a coupling). +- **Where draw-identity is declared** — a country-model (views-models) contract; what does the + reconciler require as its injected interface? +- **Is marginal reconciliation (D) a useful interim** for consumers that only need marginal + coherence, shipped before the full joint method? + +--- + +## References + +- Wickramasuriya, Athanasopoulos & Hyndman (2019), *Optimal forecast reconciliation* (MinT). +- Panagiotelis, Gamakumara, Athanasopoulos & Hyndman (2023), *Probabilistic forecast + reconciliation*. +- Hyndman et al. (2011), *Optimal combination forecasts for hierarchical time series*. +- `src/views_frames_reconcile/proportional.py` (the per-draw approximation / C-37 anchor); + ADR-023 (reconciliation sibling; Open-Questions deferring this as a future sibling module). +- GH #145 (this story), #142 (epic); views-pipeline-core #233 / C-198 / C-200b; views-postprocessing + C-37 (the cross-repo principled-reconciliation lineage); register C-62, D-12. diff --git a/docs/ADRs/025_value_buffer_immutability_by_convention.md b/docs/ADRs/025_value_buffer_immutability_by_convention.md new file mode 100644 index 0000000..a716072 --- /dev/null +++ b/docs/ADRs/025_value_buffer_immutability_by_convention.md @@ -0,0 +1,151 @@ + +# ADR-025: Value-buffer immutability is by convention; only the index is enforced + +**Status:** Accepted +**Date:** 2026-06-28 +**Deciders:** VIEWS platform maintainers +**Consulted:** repo-assimilation + test-review (2026-06-27, the audit that surfaced C-63) +**Informed:** views-pipeline-core, views-datafactory (consumers that hold frames) + +--- + +## Context + +The leaf advertises **immutable value objects** (README design principle 3; ADR-013; +PredictionFrame CIC §9 lists `pf.values[:] = 0` as forbidden). The audit chain +(repo-assimilation + test-review, 2026-06-27, register **C-63**) established empirically that +the guarantee is enforced **only for the index**, not the value buffer: + +- `SpatioTemporalIndex` stores its `time`/`unit` arrays `setflags(write=False)` + (`index.py:55-56`) — they are genuinely write-protected. +- The frame **value buffer is not.** `_validation.coerce_values` returns a float32 input + **without copy** and without `setflags` (`_validation.py:64`); each frame constructor does + `self._values = values` with no write-protection; `frame.values.flags.writeable` is `True`. +- `with_metadata` returns a new frame **sharing** that buffer (zero-copy, C-07). So an + in-place mutation of `frame.values` (`f.values[:] = 0`, `f.values *= k`) **silently + corrupts every frame sharing the buffer**, with no error signal — the contract claims an + invariant it does not enforce. + +The obvious fix — add `self._values.setflags(write=False)` in each constructor — is trivial +code, and the audit confirmed **zero** in-place `.values`/`_values` mutations anywhere in +`src/` or `tests/`, so it would be safe. **But it is a breaking change under our own +governance:** `GOVERNANCE.md` lists **"tightening an invariant"** as a **MAJOR** bump, and +`values` is an explicit member of the frozen public surface (**ADR-018**). A MAJOR triggers +the full **cross-repo coordinated-bump process** (propose ADR → consumer shims → bump +`CONFORMANCE_FLOOR` → merge-train across every consumer). That is disproportionate to a hole +**no code in the ecosystem actually hits**. + +We must stop the contract from lying, without paying a platform-wide MAJOR for a latent, +unexercised gap. That is the decision this ADR records. + +--- + +## Decision + +**Value-buffer immutability is a guarantee held *by convention*, not by enforcement. Only the +index is write-protected.** + +1. Mutating a frame's `.values` buffer in place is **unsupported**: it may silently corrupt + other frames that share the buffer (the zero-copy / `mmap` path). Consumers must build a + new frame instead. This is the documented contract; it is **not** mechanically enforced. +2. The leaf deliberately leaves the value buffer **writeable** so that structural/metadata + operations stay **zero-copy** and `mmap`-backed frames stay `mmap`-backed (design + principle 2 / C-07). The enforced invariant is on the **index** (`time`/`unit`), which is + small, never shared mutably, and already `setflags(write=False)`. +3. **The `setflags`-enforce on `.values` is a deferred MAJOR-rider.** When a MAJOR bump + happens for any reason, enforcement is added **for free** as part of that coordinated bump: + `self._values.setflags(write=False)` after assignment in each frame constructor, plus a red + test that `frame.values.flags.writeable is False`. It is **out of scope** to do this on its + own (it does not justify a standalone MAJOR). + +In scope: correcting the docs (the three frame CICs, README principle 2) to state the +by-convention reality. Out of scope: any code change; any change to `CONFORMANCE_FLOOR` (stays +`1.0.0`); enforcing read-only `.values` now. + +--- + +## Rationale + +- **Correctness > convenience, but proportionate.** The contract must be honest, but + enforcement here buys near-zero real-world safety (nothing mutates `.values`) at the cost of + a full cross-repo merge-train. Correcting the docs removes the dishonesty at zero cost and + keeps the enforcement option open for when a MAJOR is already being paid. +- **Zero-copy is the load-bearing guarantee.** Write-protecting the *shared* value buffer + interacts with the C-07 copy-vs-view semantics (a frozen buffer shared across frames changes + caller-visible flags); the index, which is never shared mutably, is the clean place to + enforce. Keeping the value buffer writeable preserves the §7 OOM-avoidance the whole frame + contract exists for. +- **Asymmetry made explicit, not hidden.** The index-enforced / value-by-convention split is + now stated, so a reader is not misled into relying on a guard that is not there. + +--- + +## Considered Alternatives + +### Alternative A: Enforce read-only `.values` now (a MAJOR) +- **Pros:** the strongest guarantee; the contract becomes self-enforcing. +- **Cons:** a **MAJOR** (`GOVERNANCE.md` "tightening an invariant"; `values` is frozen, + ADR-018) → the full cross-repo coordinated-bump process for a hole nothing exercises. +- **Reason for rejection:** disproportionate. Deferred to ride a future MAJOR for free. + +### Alternative B: Leave the docs as written +- **Pros:** no work. +- **Cons:** the contract keeps claiming an enforced invariant it does not enforce — the docs + lie, and a consumer could rely on the lie. +- **Reason for rejection:** a contract that lies is worse than an honestly-scoped one. + +### Alternative C: Correct the contract + defer the enforce (**chosen**) +- Make the docs match the code (by-convention, index-enforced); record the `setflags`-enforce + as a deferred MAJOR-rider. No MAJOR, no merge-train, honest contract. + +--- + +## Consequences + +### Positive +- The immutability contract is **honest**: docs match code (C-63 resolved). +- **No MAJOR, no cross-repo coordination**; `CONFORMANCE_FLOOR` stays `1.0.0`; the release + stays the additive `1.8.0`. +- Zero-copy / `mmap` is untouched. + +### Negative +- The value buffer stays **writeable**, so a careless consumer *can* still corrupt + buffer-sharing frames. Mitigated: documented as unsupported in all three frame CICs + this + ADR; no code in the ecosystem does it; the deferred-enforce removes the gap on the next + MAJOR. (The risk is recorded as the resolution-with-residual of C-63.) + +--- + +## Implementation Notes + +- **Docs (this release, S2 / #181):** reword PredictionFrame / TargetFrame / FeatureFrame CIC + §9 (the `pf.values[:] = 0` example) and §3 immutability wording, and README design principle + 2, to state: the **index** is enforced read-only; the **value buffer is immutable by + convention** (mutating it is unsupported and may corrupt shares — build a new frame), left + writeable to preserve zero-copy. Mark register **C-63 Resolved** (contract corrected) with + the deferred-enforce recorded. +- **The deferred MAJOR-rider (future):** in `prediction_frame.py` (~line 44), `target_frame.py` + (~line 43), `feature_frame.py` (~line 52), add `self._values.setflags(write=False)` after + the `self._values = ...` assignment; add a red test mirroring `tests/test_properties.py:38` + asserting `frame.values.flags.writeable is False` and that an in-place write raises. Do this + **only** as part of a MAJOR that is already happening. + +--- + +## Validation & Monitoring + +- This release: no code change → the import-DAG, parity, coverage, and `mypy` gates are + unaffected; `validate_docs` stays green; no doc asserts an enforced read-only `.values`. +- Reconsider if a consumer is found mutating `.values` in place (promote the deferred-enforce, + or schedule the next MAJOR sooner), or whenever a MAJOR is next opened (do the enforce then). + +--- + +## Open Questions + +- **A zero-copy-preserving enforcement for the future MAJOR.** Returning a read-only *view* + from the `.values` property (`arr.view()` + `setflags(write=False)` on the view) would make + `frame.values[:] = 0` raise while leaving the underlying shared buffer writeable internally — + cleaner for zero-copy than freezing the shared buffer. It still changes observable behavior + (mutation now raises) and so is still a MAJOR, but it is the preferable mechanism when that + MAJOR is paid. Recorded for the principled redesign, not decided here. diff --git a/docs/ADRs/README.md b/docs/ADRs/README.md index cd776ca..baf4184 100644 --- a/docs/ADRs/README.md +++ b/docs/ADRs/README.md @@ -67,7 +67,9 @@ These ratify the six resolved decisions from the design bible (README §13a). Al - **ADR-020** — [The `MetricFrame` contract lives in views-evaluation, on the views-frames substrate](020_metricframe_contract_home.md). Ratifies (under the GH#109 driver) that evaluation-output types stay out of the leaf; the leaf provides only the `FrameMetadata` substrate and the conformance/IO patterns. Records the provenance split (C-47) and the envelope-drift mitigation (C-46); keeps `CONFORMANCE_FLOOR` at 1.0.0. Extends C-01. - **ADR-021** — [Threshold exceedance-probability estimator (`exceedance`)](021_exceedance_probability_estimator.md). Adds the `views_frames_summarize` per-row survival-fraction estimators (`exceedance`/`exceedance_reducer`, `P(Y > c)`): caller-supplied required thresholds (no default, no config), strict `>`, fail-loud NaN, geography-blind (country = `aggregate_distributions` → `exceedance`). Settles D-07/D-08; records C-49/C-50; additive/MINOR, `CONFORMANCE_FLOOR` stays 1.0.0. - **ADR-022** — [Worst-case scenario summary — the `expected_shortfall` tail mean](022_worst_case_expected_shortfall.md). Adds the `views_frames_summarize` worst-case estimator `expected_shortfall` (mean of the worst `⌈t·S⌉` draws — a coherent tail-risk measure, the companion to `exceedance`): caller-supplied required tail levels (no default, no config), upper-tail only, **never `max`**, fail-loud NaN, geography-blind. Best-case ships no code (low quantile + `exceedance(0)`). Settles D-10; records C-55/C-56; additive/MINOR (v1.6.0), `CONFORMANCE_FLOOR` stays 1.0.0. -- **ADR-023** — [Forecast reconciliation is a sibling package (`views_frames_reconcile`)](023_reconciliation_is_a_sibling_package.md). Adds a third sibling package (mirrors ADR-017): the numpy-only, `views_frames`-only forecast reconciler (`reconcile_proportional`/`ReconciliationModule` — make pgm grid forecasts sum to cm country totals per draw), relocated **WET** from views-postprocessing and proven bit-identical. Charter: frame-reconciliation algorithms only; **never fetch the mapping** (injected as arrays, like `cross_level_align`); no IO/scoring/plotting/foreign `views_*`. Import-DAG `views_frames_reconcile → {views_frames}`; additive/MINOR (v1.7.0), `CONFORMANCE_FLOOR` stays 1.0.0. +- **ADR-023** — [Forecast reconciliation is a sibling package (`views_frames_reconcile`)](023_reconciliation_is_a_sibling_package.md). Adds a third sibling package (mirrors ADR-017): the numpy-only, `views_frames`-only forecast reconciler (`reconcile_proportional`/`ReconciliationModule` — make pgm grid forecasts sum to cm country totals per draw), relocated **WET** from views-postprocessing and proven bit-identical. Charter: frame-reconciliation algorithms only; **never fetch the mapping** (injected as arrays, like `cross_level_align`); no IO/scoring/plotting/foreign `views_*`. Import-DAG `views_frames_reconcile → {views_frames}`; additive/MINOR (v1.7.0), `CONFORMANCE_FLOOR` stays 1.0.0. **Amended 2026-06-27 (#143, v1.8.0):** the sample-count contract — a point cm (`sample_count == 1`) is broadcast across the grid's draws; the equal-count path stays bit-exact. +- **ADR-024** — [Principled joint probabilistic reconciliation — design direction & deferral](024_principled_joint_reconciliation_design.md). Design-only (no code): per-draw `proportional` is an information-losing approximation that pairs grid-draw `s` with country-draw `s` across **independently-trained** models with no shared draw identity. Names the **draw-identity contract** principled reconciliation requires (shared ensemble / copula coupling), fixes the upgrade as a **future sibling module** (probabilistic reconciliation / MinT — never a change to `proportional`), and **defers** implementation until a consumer needs calibrated joint tails *and* the country model can supply the identity/coupling. Records C-62; corrects the ambiguous `proportional.py` "C-37" reference (views-frames C-37 is unrelated/resolved; the lineage is views-postprocessing C-37). +- **ADR-025** — [Value-buffer immutability is by convention; only the index is enforced](025_value_buffer_immutability_by_convention.md). Corrects the "immutable value objects" contract to match the code: only the index (`time`/`unit`) is `setflags(write=False)`-enforced; the frame **value buffer is immutable by convention** (mutating `.values` in place is unsupported and may corrupt buffer-sharing frames), left writeable to preserve zero-copy / `mmap` (C-07). The `setflags`-enforce on `.values` would be "tightening an invariant" on a frozen-surface member (a MAJOR, GOVERNANCE/ADR-018) for a hole nothing exercises, so it is recorded as a **deferred MAJOR-rider** (added free on the next MAJOR), not done now. Resolves C-63; docs-only, `CONFORMANCE_FLOOR` stays 1.0.0. --- diff --git a/docs/CICs/FeatureFrame.md b/docs/CICs/FeatureFrame.md index 248f3be..ce1cdbe 100644 --- a/docs/CICs/FeatureFrame.md +++ b/docs/CICs/FeatureFrame.md @@ -38,7 +38,9 @@ array `y_features (N, F, S)` float32 aligned to a `SpatioTemporalIndex`, carryin - Carries `feature_names: list[str]` (length `F`) and a typed `metadata` header (ADR-013); both serialize **with** the frame (register C-09). `from_2d` lifts a legacy `(N, F)` array to `(N, F, 1)`. -- Immutable with copy-vs-view semantics identical to `PredictionFrame` (C-07). +- Immutable with copy-vs-view semantics identical to `PredictionFrame` (C-07): the + **index is enforced** read-only; the **value buffer is immutable by convention** + (writeable for zero-copy — in-place `.values` mutation is unsupported; ADR-025 / C-63). - Row ops return new frames preserving `feature_names`: `select(positions | mask)` and `reindex(other)` — the latter raises unless this index is a superset of `other`; selection **copies** the selected `values`. @@ -98,6 +100,10 @@ FeatureFrame(y_features=x2d, index=idx, feature_names=names) # raises # WRONG: calling a grid constructor on the frame — that adapter lives in datafactory FeatureFrame.from_grid(cube) # no such method here + +# WRONG: mutating the value buffer in place — immutable *by convention*, not +# write-protected, so it does NOT raise; build a new frame (ADR-025 / register C-63). +ff.values[:] = 0 # unsupported: silent shared-buffer corruption, no error ``` --- diff --git a/docs/CICs/PredictionFrame.md b/docs/CICs/PredictionFrame.md index cad4ab6..1c8cedd 100644 --- a/docs/CICs/PredictionFrame.md +++ b/docs/CICs/PredictionFrame.md @@ -36,7 +36,10 @@ ensemble samples `y_pred (N, S)` float32 aligned to a `SpatioTemporalIndex`. identifiers integer, length-`N`, complete; the sample axis is the **trailing** axis and always explicit (`S >= 1`; ADR-012). - Immutable: `with_metadata` returns a **new** frame sharing the `values` buffer - (zero-copy); `mmap` propagates (register C-07). + (zero-copy); `mmap` propagates (register C-07). **Immutability is enforced for the + index** (`time`/`unit` are `setflags(write=False)`) and held **by convention for the + value buffer** (left writeable to preserve zero-copy — mutating `.values` in place is + unsupported and may corrupt buffer-sharing frames; ADR-025 / register C-63). - Row ops return new frames: `select(positions | mask)` and `reindex(other)` — the latter raises unless this frame's index is a superset of `other`. Selection **copies** the selected `values` (only structural/metadata ops share the buffer). @@ -95,8 +98,11 @@ point = collapse(pf, np.mean) # (N, 1) fra # WRONG: list-in-cell / object dtype (the measured non-scaler) — raises PredictionFrame(y_pred=np.array(list_of_lists, dtype=object), index=idx) -# WRONG: mutating in place -pf.values[:] = 0 # frames are immutable; build a new frame instead +# WRONG: mutating the value buffer in place. Immutable *by convention* — NOT +# write-protected (the buffer stays writeable for zero-copy), so this does NOT raise; +# it silently corrupts every frame sharing the buffer (e.g. via with_metadata). +# Build a new frame instead. (ADR-025 / register C-63; the index IS write-protected.) +pf.values[:] = 0 # unsupported: silent shared-buffer corruption, no error ``` --- diff --git a/docs/CICs/README.md b/docs/CICs/README.md index 19eff0c..30ff9c4 100644 --- a/docs/CICs/README.md +++ b/docs/CICs/README.md @@ -70,6 +70,9 @@ These CICs govern the classes implemented in v0.1.0 (`src/views_frames/`). - `TargetFrame.md` — observed actuals `(N, 1)`; the array-native evaluation boundary. - `Summarize.md` — the `views_frames_summarize` sibling package (collapse / MAP / HDI / quantiles / aggregation over frames; ADR-017). +- `Reconcile.md` — the `views_frames_reconcile` sibling package (`ReconciliationModule` / + `reconcile_proportional` / `ReconciliationResult`; pgm→cm top-down proportional + reconciliation, injected mapping, self-describing mode; ADR-023/ADR-024). The `_validation` helper and the tiny `SpatialLevel` value object are governed primarily by tests (and ADR-015 for `SpatialLevel`) rather than a CIC. diff --git a/docs/CICs/Reconcile.md b/docs/CICs/Reconcile.md new file mode 100644 index 0000000..5341e44 --- /dev/null +++ b/docs/CICs/Reconcile.md @@ -0,0 +1,280 @@ + +# Class Intent Contract: views_frames_reconcile + +**Status:** Active +**Owner:** VIEWS platform maintainers +**Last reviewed:** 2026-06-28 +**Related ADRs:** ADR-002, ADR-003, ADR-006, ADR-008, ADR-014, ADR-020, ADR-023, ADR-024 + +> The `views_frames_reconcile` package — forecast reconciliation as a `views_frames` +> sibling (numpy + `views_frames` only). Implemented in **v1.7.0** (Epic 11, ADR-023): a +> faithful numpy port of views-reporting's top-down proportional reconciler. Extended +> **additively in v1.8.0** (epic #142) with a native **point-country broadcast** and a +> self-describing **reconciliation mode** (`reconcile_result` → `ReconciliationResult`; +> ADR-024 designs the deferred principled upgrade). `CONFORMANCE_FLOOR` stays `1.0.0`. +> +> This is a **package-level** contract (the package is functions + two small classes, not +> one class). It governs the public surface re-exported from +> `src/views_frames_reconcile/__init__.py`: `ReconciliationModule`, `ReconciliationResult`, +> `reconcile_proportional`, and the `POINT_BROADCAST` / `ALIGNED_DRAWS` / +> `METHOD_PROPORTIONAL` mode/method constants — plus the package's own array→frame builder +> `prediction_frame_from_arrays` (`frames.py`) and the `assert_reconcile_contract` +> conformance check (`conformance.py`). + +--- + +## 1. Purpose + +Reconcile **PRIO-GRID-month (`pgm`) forecasts to their country-month (`cm`) totals**: make +each country's grid cells sum, **per posterior draw**, to that country's forecast, by +**top-down proportional disaggregation** (FPP3 forecast-proportions). It is the +frames-native home of the algorithm views-reporting used to own — moved here because +reconciliation belongs in post-processing, not reporting (views-reporting #72). One target +per call; geography is **injected**, never embedded (ADR-014). + +--- + +## 2. Non-Goals (Explicit Exclusions) + +- **No geography embedding** — the `(time, priogrid_gid) → country_id` mapping is injected + as arrays (`map_keys`/`map_vals`); the package never fetches, embeds, or infers it + (ADR-014). It holds no shapefiles, no PRIO-GRID↔country table. +- **No IO of domain data, no plotting, no scoring** — it transforms frames to frames. + `prediction_frame_from_arrays` is the package's *own* array→frame adapter, not a + general loader; comparing forecasts to actuals is views-evaluation. +- **No principled joint probabilistic reconciliation (C-62 / ADR-024)** — the shipped + method is a **pragmatic per-draw approximation**: it pairs grid-draw `s` with + country-draw `s`. When grid and country models are trained independently (today's + platform reality), draw index `s` has **no shared identity**, so the joint country + distribution is **not guaranteed calibrated**. This is a known, documented method-quality + limitation with a designed-but-deferred upgrade (ADR-024); it is **not** silently treated + as principled. +- **No reconciliation *mode* stamped on the leaf frame (ADR-020 / register C-47, D-12)** — + the mode is *reported* on `ReconciliationResult`, never written into the leaf's generic + `FrameMetadata` (which must carry no reconciliation/operation vocabulary). +- **No `views_*` import except `views_frames`; no torch, no pandas, no viewser, no wandb** + (enforced by the import-DAG test). The original's `ProcessPoolExecutor` + WandB alerting + are deliberately dropped. + +--- + +## 3. Responsibilities and Guarantees + +`ReconciliationModule(map_keys, map_vals)` holds the injected mapping and applies it: + +- `reconcile(cm_frame, pgm_frame)` → a **new** `pgm` `PredictionFrame`: validates the + inputs, then scales each country's grid cells to the country total. Returns only the + frame. +- `reconcile_result(cm_frame, pgm_frame)` → a `ReconciliationResult(frame, mode, method)`: + the same reconciled frame **plus** the mode it was produced by (`POINT_BROADCAST` | + `ALIGNED_DRAWS`) and the method (`METHOD_PROPORTIONAL`). `reconcile` is exactly + `reconcile_result(...).frame`. + +Guarantees on the reconciled frame (per the conformance laws, +`conformance.assert_reconcile_contract`): + +- **Sum-to-country per active draw** — for each `(time, country)` group, the cells' per-draw + sum equals that country's forecast for that draw, to `rtol=1e-4, atol=1e-3`. +- **Zero-preservation** — an input zero cell stays exactly zero; an **all-zero country draw + stays zero** (the documented edge — it is not rescaled to the country total, since there + is no mass to distribute). +- **Non-negativity** — every reconciled value is `≥ 0` (the leaf clamps). +- **Structure preserved** — same `(N, S)` shape, same `PredictionFrame` type, same + `SpatialLevel.PGM` level, same `time`/`unit` index as the input `pgm_frame`. +- **De-mutation** — the input `pgm_frame` (and its `values` buffer) is **never mutated**; a + new frame is returned (register C-184). +- **Injected mapping is honoured, not fetched** — the sum-to-country law holds only because + each cell is grouped under its *injected* country; permuting `map_vals` changes the result. + +**Mode contract (`reconcile_result`).** The mode describes **what this call did**, not +upstream provenance: + +- `POINT_BROADCAST` — `cm.sample_count == 1` (a point country) against a draws grid + (`sample_count == S`): the single country column is tiled across the `S` draws (every draw + rescaled to the same total), bit-identical to the WET broadcast a consumer would do. +- `ALIGNED_DRAWS` — `cm.sample_count == S`: scaled draw-for-draw (the per-draw + approximation). A caller who **pre-tiles** a point country to `S` before calling also reads + as `ALIGNED_DRAWS` (nothing was broadcast inside this call); both-points (`cm == 1` and + `pgm == 1`) likewise reads as `ALIGNED_DRAWS`. + +The `POINT_BROADCAST` / `ALIGNED_DRAWS` / `METHOD_PROPORTIONAL` string values match +pipeline-core's `reconcile_frames` constants verbatim, so the mode vocabulary is shared +across the repos that produce and consume it. + +`reconcile_proportional(grid, country)` is the **leaf kernel** (public): rescales a +`(S, n_cells)` (or point `(n_cells,)`) grid so each draw sums to its country total; preserves +zeros, clamps to non-negative. The orchestration (grouping, validation, broadcast) is built +**on top of** it; new methods are **new sibling modules**, never edits to this kernel. + +--- + +## 4. Inputs and Assumptions + +- `cm_frame` at `SpatialLevel.CM`, `pgm_frame` at `SpatialLevel.PGM` (both + `PredictionFrame`, values `(N, S)`, `S ≥ 1`). +- `cm_frame.sample_count ∈ {1, pgm_frame.sample_count}` — a point country (`1`, broadcast) + or aligned draws (`S`). Any other count fails loud. +- **Identical time coverage** — `cm` and `pgm` cover the same set of `time` values. +- **Country coverage** — every `(time, country)` the grid maps to has a forecast row in + `cm_frame`. +- `map_keys` is an `(M, 2)` int array of `(time, priogrid_gid)` pairs covering every `pgm` + row; `map_vals` is the length-`M` int `country_id` for each key. The mapping is the + consumer's (ADR-014); the package only applies it. + +--- + +## 5. Outputs and Side Effects + +- `reconcile` → a new `pgm` `PredictionFrame`; `reconcile_result` → a frozen + `ReconciliationResult(frame, mode, method)`. No IO, no global state, no input mutation. +- The result is **approximate** in the joint sense (§2 / C-62): conservation (sum / zeros / + non-negativity) holds **exactly**; joint-distribution calibration does **not**. + +--- + +## 6. Failure Modes and Loudness + +All input inconsistencies **raise `ValueError`** before any scaling work +(`validation.validate_reconciliation_inputs`), per ADR-003 (fail loud): + +- `cm_frame` not at `SpatialLevel.CM` → raises (message names `SpatialLevel.CM`). +- `pgm_frame` not at `SpatialLevel.PGM` → raises (message names `SpatialLevel.PGM`). +- `cm.sample_count ∉ {1, pgm.sample_count}` → raises (`"sample-count mismatch"` — names the + allowed `1` / `S`). +- `cm` and `pgm` cover **different time steps** → raises (`"different time steps"`, listing + the asymmetric difference). +- A grid group has **no country forecast** in `cm_frame` → raises (`"no country forecast"` / + `"has no country forecast in cm_frame"`). +- `ReconciliationModule.__init__` with `map_keys` not `(M, 2)` or `map_vals` not length `M` + → raises (`"map_keys must be"` / `"map_vals must be"`). +- A grid row whose `(time, priogrid_gid)` is absent from the injected mapping → raises (via + `cross_level_align`). + +**Never silent:** a level/coverage/shape inconsistency must surface as a `ValueError`, not a +mid-compute crash or a wrong-but-plausible frame. **The all-zero-country draw is an edge, not +a failure:** it stays `0` (no mass to distribute), not `NaN` and not the country total. + +**The per-draw approximation is loud-by-documentation, not by exception (C-62):** an +`aligned-draws` result of independently-trained models is a documented approximation +(`proportional.py` docstring, ADR-024, this §2, and the runtime `aligned-draws` mode). It does +not raise — conservation is exact — but consumers keying on **joint** country tails (e.g. an +FAO-style worst-case) must treat the joint distribution as uncalibrated. + +--- + +## 7. Boundaries and Interactions + +- Depends **only** on `views_frames` + numpy. `views_frames` never imports this package + (import-DAG test). It is a sibling of `views_frames_summarize`, peer to it, both depending + *toward* the leaf (ADR-023). +- Internal SRP split: `proportional` (the scaling math / leaf kernel), `grouping` (group rows + by `(time, country)`, call the kernel per group), `validation` (the fail-loud guards), + `frames` (array→frame IO for the package), `result` (the mode/result value object), + `module` (orchestration), `conformance` (the contract checks). The cm↔pgm labelling uses + `views_frames`' `cross_level_align_arrays` — the sanctioned primitive — never an embedded + table. +- Consumers (pipeline-core's frames-native PFE) call `ReconciliationModule.reconcile` / + `reconcile_result` instead of re-deriving the broadcast + scaling. + +--- + +## 8. Examples of Correct Usage + +```python +from views_frames import SpatialLevel +from views_frames_reconcile import ReconciliationModule, ALIGNED_DRAWS, POINT_BROADCAST +from views_frames_reconcile.frames import prediction_frame_from_arrays + +# inject the (time, priogrid_gid) -> country_id mapping +module = ReconciliationModule(map_keys, map_vals) # map_keys (M,2), map_vals (M,) + +# aligned draws: cm carries the grid's S draws -> scaled draw-for-draw +out = module.reconcile(cm_frame, pgm_frame) # new pgm PredictionFrame, (N, S) + +# also report the mode (audit-friendly) +res = module.reconcile_result(cm_frame, pgm_frame) +res.frame # the reconciled frame +res.mode # ALIGNED_DRAWS or POINT_BROADCAST +res.method # "proportional" + +# point country (sample_count == 1) vs a draws grid -> broadcast inside reconcile +point_cm = prediction_frame_from_arrays(t, u, country_point, level=SpatialLevel.CM) # (N, 1) +res2 = module.reconcile_result(point_cm, pgm_frame) +assert res2.mode == POINT_BROADCAST +``` + +--- + +## 9. Examples of Incorrect Usage + +```python +# WRONG: passing the grid frame where the country frame is expected -> ValueError +module.reconcile(pgm_frame, pgm_frame) # cm must be at SpatialLevel.CM + +# WRONG: a country with neither 1 nor S draws -> ValueError ("sample-count mismatch") +module.reconcile(cm_half_samples, pgm_frame) # cm.sample_count must be 1 or S + +# WRONG: embedding / fetching the geography. The mapping is injected, always: +ReconciliationModule().reconcile(cm, pgm) # TypeError — map_keys/map_vals required + +# WRONG: treating an aligned-draws result as a calibrated JOINT country distribution. +# Conservation holds exactly, but per-draw pairing of independently-trained models is an +# approximation (C-62 / ADR-024) — read res.mode and treat joint tails as uncalibrated. +res = module.reconcile_result(cm, pgm) # res.mode == ALIGNED_DRAWS is a caveat + +# WRONG: mutating the returned frame's value buffer in place (the leaf value buffer is +# immutable-by-convention; see ADR-025 / PredictionFrame CIC §9) -> may corrupt shares +out.values[:] = 0 # unsupported + +# WRONG: stamping the mode onto the leaf frame's metadata. It is reported on the result. +out.with_metadata(mode="point-broadcast") # the leaf carries no reconcile vocabulary +``` + +--- + +## 10. Test Alignment + +The package is **heavily tested** (100% line+branch coverage, a frozen torch-oracle parity +gate); these are the contract-bearing suites: + +- **Green (it reproduces the truth):** `tests/test_reconciliation_e2e_parity.py` — + `ReconciliationModule.reconcile` reproduces the frozen views-reporting oracle on the S0 + fixture at `0.000e+00` drift (`TestEndToEndParity`); the sum-to-country law on active draws + (`test_sum_constraint_on_active_draws`); de-mutation (`test_de_mutated`); native + point-broadcast ≡ manual tile (`test_point_country_broadcast_equals_manual_tile`). +- **Beige (edges):** `tests/test_reconcile_conformance.py` — `assert_reconcile_contract` + across the oracle fixture, synthetic probabilistic frames (`S ∈ {1, 16, 100}`), the + all-zero-country edge, and the point-country (`cm_samples=1`) case; the injected-mapping + proof (a permuted mapping changes the result). Mode reporting at the corners (point grid, + both-points, pre-tiled cm) — `TestReconciliationResult`. +- **Red (it fails loud):** `tests/test_reconciliation_validation.py` — + `validate_reconciliation_inputs` raises on wrong cm level, wrong grid level, sample-count + mismatch, time mismatch, and a missing country forecast; the `(M, 2)` map-keys guard + (`test_bad_mapping_shape_raises`). The conformance-suite **negative** (a deliberately + non-conforming impl/input makes `assert_reconcile_contract` raise) and the + `ReconciliationResult` frozen-ness assertion are the red gaps being closed alongside this + contract (register C-65 batch, epic #179 / S3). + +Each §3 guarantee maps to a green/beige test; each §6 failure mode maps to a red test in +`test_reconciliation_validation.py`. + +--- + +## 11. Evolution Notes + +- **Additive only** (ADR-023 charter; ADR-018 freeze posture): new reconciliation methods are + **new sibling modules**, never edits to `proportional`/`grouping`. The v1.8.0 point-broadcast + + mode surface was added without touching the parity-frozen equal-count path (byte-exact). +- **The principled joint upgrade is designed and deferred (ADR-024 / C-62):** pairing + grid-draw `s` with country-draw `s` needs a shared draw-identity / coupling contract and a + consumer that needs calibrated joint tails. Until those preconditions hold, the per-draw + proportional method stays the shipped method, and `aligned-draws` results carry the caveat. + The upgrade, when built, gets its **own** module + its own §3/§6 additions to this contract. + +--- + +## End of Contract + +This document defines the **intended meaning** of `views_frames_reconcile`. +Changes to behavior that violate this intent are bugs. +Changes to intent must update this contract. diff --git a/docs/CICs/TargetFrame.md b/docs/CICs/TargetFrame.md index 026582e..6764704 100644 --- a/docs/CICs/TargetFrame.md +++ b/docs/CICs/TargetFrame.md @@ -34,7 +34,9 @@ boundary array-native, replacing the pandas actuals the eval adapter takes today - Validates at construction: `values` contiguous `float32` of shape `(N, 1)` — the trailing sample axis is explicit with `S == 1` (ADR-012); no object dtype; identifiers integer, length-`N`, complete. -- Immutable with the same copy-vs-view semantics as the other frames (register C-07). +- Immutable with the same copy-vs-view semantics as the other frames (register C-07): + the **index is enforced** read-only; the **value buffer is immutable by convention** + (writeable for zero-copy — in-place `.values` mutation is unsupported; ADR-025 / C-63). - Carries a typed `metadata` header (ADR-013) and the same row/metadata surface as the sibling frames: `with_metadata`, `select(positions | mask)`, `reindex(other)` (raises unless this index is a superset of `other`); `sample_count == 1`, `is_sample == False`. @@ -90,6 +92,10 @@ assert tf.is_sample is False # S == 1 TargetFrame(y_true=actuals_1d, index=idx) # raises # WRONG: treating it as a sampled frame and asking for many quantiles + +# WRONG: mutating the value buffer in place — immutable *by convention*, not +# write-protected, so it does NOT raise; build a new frame (ADR-025 / register C-63). +tf.values[:] = 0 # unsupported: silent shared-buffer corruption, no error ``` --- diff --git a/docs/guides/reconciliation_migration_and_cutover_runbook.md b/docs/guides/reconciliation_migration_and_cutover_runbook.md new file mode 100644 index 0000000..94f4983 --- /dev/null +++ b/docs/guides/reconciliation_migration_and_cutover_runbook.md @@ -0,0 +1,144 @@ +# Reconciliation migration & cutover runbook + +How to relocate a reconciliation implementation into `views_frames_reconcile` (or move the +platform onto a new reconciler) **without a silent change to served numbers**. This is the +procedural companion to ADR-023 and the `views_frames_reconcile` package; it captures the +order, the verification gates, and the rollback so the next cutover is boring. + +> **Status — the Epic 11 instance is COMPLETE.** This runbook was written from, and is the +> record of, the first cutover (the relocation out of views-postprocessing). Every step +> below is marked ✅ with its actual PR/commit. Re-use it as the template for any future +> reconciler change. + +## What is special about reconciliation (read once) + +- It is a **frame→frame numpy operation** (`views_frames_reconcile`, ADR-023) — make grid + (`pgm`) predictions sum, per posterior draw, to country (`cm`) totals. It must stay + numpy-only and `views_frames`-only (import-DAG enforced). +- It is a **re-baseline of served numbers.** Repointing a consumer from one reconciler to + another changes a production code path. Even at *intended* bit-identity, that is a + consumer-visible change and must be verified, versioned, and reversible — never silent. +- The mapping is **injected, never fetched** (ADR-014). A cutover never moves geography into + the leaf; it only swaps which `ReconciliationModule` the composition root injects. +- There can be **several copies in flight** (during Epic 11: views-reporting torch = the + oracle source; views-postprocessing numpy = the stranded copy; views_frames_reconcile = + the new canonical). Know which one production actually serves before you touch anything. + +## The order (never reorder these) + +**Release → repoint → verify green → delete → (optional) collapse the injection.** The two +hard rules: + +1. **Never delete the old copy before the consumer repoint is merged, green, and observed.** + The old copy is the rollback. +2. **Sequence each consumer/injection change as its own deploy** — never batch the repoint + and the injection-collapse, so a regression is attributable. + +## Prerequisites + +- [ ] The new reconciler is **released** on PyPI and pinned by the consumers (`views-frames` + ≥ the version that carries it). _Epic 11: ✅ v1.7.0._ +- [ ] **In-repo parity is green** in views-frames: the frozen views-reporting **torch oracle** + fixtures (`tests/fixtures/reconciliation_*.npz`) pass, and the **new-vs-old bit-identity + head-to-head** (`tests/test_reconcile_head_to_head.py`, 136 cases, `np.array_equal`) + passes. _Epic 11: ✅._ +- [ ] You can run the verifier: `uv run python scripts/verify_reconcile_parity.py --oracle` + reports **PASS**. _Epic 11: ✅ (0.000e+00 abs/rel error vs the torch oracle)._ + +## Phase 1 — Verify before you move anything + +```bash +# 1. the canonical copy still reproduces the torch oracle the cutover is validated against +uv run python scripts/verify_reconcile_parity.py --oracle # expect: VERDICT: PASS + +# 2. the new == old bit-identity gate (needs the old sibling repo checked out adjacently) +uv run pytest tests/test_reconcile_head_to_head.py -q # expect: all green +``` + +> The head-to-head is **transitional** — it `importorskip`s the old reconciler, so it only +> runs while both copies coexist (during the cutover window). Once the old copy is retired +> (Phase 4) it skips permanently; that is expected. The durable, always-runnable gate is the +> `--oracle` check above, which needs no sibling repo. + +The safety argument is a **chain**: `new (views_frames_reconcile) == old vpp copy` +(head-to-head, bit-identical) and `old vpp == views-reporting torch oracle` (the frozen +`.npz`), therefore `new == torch oracle` — the numbers production was built on. Do not +proceed if either link is red. + +## Phase 2 — Production-slice check (belt-and-suspenders) + +The oracle is synthetic-but-real (captured from the torch path). For extra assurance, compare +the **old served output** against the **new reconciler** on an actual production slice: + +```bash +# old_served.parquet = what the current path produced; new_served.parquet = a run through +# views_frames_reconcile on the same inputs. Align on the served identity columns. +uv run --with pandas python scripts/verify_reconcile_parity.py \ + --compare old_served.parquet new_served.parquet --keys month_id priogrid_id +# expect: VERDICT: PASS (drift within rtol=1e-5 / atol=1e-6) +``` + +If this diverges, **stop** — the in-repo oracle and the live path have drifted; investigate +before repointing. + +## Phase 3 — Repoint the consumer (one deploy) + +Repoint the **single concrete-import seam** to `views_frames_reconcile`. By ADR-014 there is +exactly one such file per consumer (the composition root / factory), so this is a one-line +import change plus its tests. + +- views-models: `reconciliation/reconciler_factory.py` → + `from views_frames_reconcile import ReconciliationModule`. _Epic 11: ✅ views-models #191, + PR #202 (commit `97a66ed`)._ +- **Bump the consumer's methodology / re-baseline version** (e.g. faoapi's methodology-version + field) so the change in numbers is auditable from the served output, not reconstructed. +- Merge, run the consumer's own reconciliation tests, and **observe it green in the consumer's + CI/production** before Phase 4. + +## Phase 4 — Retire the old copy (a later, separate deploy) + +Only after Phase 3 is merged + green + observed: delete the stranded copy. + +- views-postprocessing: remove `views_postprocessing/reconciliation/`, drop its CIC entry, + and confirm **no live `import` of the old module remains** — grep for `import.*` + (or run the consumers' suites). Note that *transitional* `pytest.importorskip("")` + calls and docstring/ADR/test-name mentions are **not** live imports and may linger; sweep that + prose in a follow-up, but it does not block the delete. _Epic 11: ✅ views-postprocessing #62, + PR #63 (commit `6af2020`); residual stale prose references remain in views-models, tracked as a + doc-hygiene follow-up._ + +## Phase 5 — (Optional) collapse the cycle-dodging injection + +If the old home sat *above* a consumer in the DAG, a DIP port + adapter may have been built to +inject the reconciler and dodge a cycle. Once the new reconciler is *below* the consumer, that +port can collapse to a direct import — but it is its **own** deploy, sequenced last. + +- _Epic 11: the pipeline-core injection (DIP port + adapter, PRs #195/#217) already breaks the + cycle cleanly — the reconciler is injected at the composition root and the concrete is never + imported — so #221's collapse is **optional, not required**, and was deferred as a pure + simplification._ + +## Rollback + +- **Before Phase 4:** revert the Phase 3 repoint PR; the old copy still exists and the consumer + falls back to it. Single-commit revert, no data migration. +- **After Phase 4:** restore the deleted copy from git history, then revert the repoint. This is + why Phase 4 must never precede a green, observed Phase 3. + +## Verification gates (the checklist) + +| Gate | Tool | Pass condition | +|---|---|---| +| Oracle parity | `verify_reconcile_parity.py --oracle` | `PASS` (abs/rel err ≈ 0 vs torch oracle) | +| New == old bit-identity | `pytest tests/test_reconcile_head_to_head.py` | all `np.array_equal` | +| Production slice | `verify_reconcile_parity.py --compare` | within `rtol=1e-5/atol=1e-6` | +| Consumer repoint | the consumer's reconciliation tests | green in the consumer's CI | +| No stranded imports | `grep -rn "import.*"` | no live `import` after Phase 4 (transitional `importorskip`/prose may linger) | +| Re-baseline audit | methodology-version field | bumped + visible in served output | + +## Provenance + +ADR-023 (reconciliation is a sibling package); `reports/postmortems/2026-06_views_frames_reconcile.md` +(building the package) and `2026-06_reconciliation_cutover_epic11.md` (this cutover); +Epic 11 tracker `#138`; cross-repo issues views-models #191, views-postprocessing #62, +pipeline-core #221; register C-58 (the production-slice verification residual). diff --git a/notebooks/01_frames.ipynb b/notebooks/01_frames.ipynb new file mode 100644 index 0000000..b9f56fa --- /dev/null +++ b/notebooks/01_frames.ipynb @@ -0,0 +1,581 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "927a42aa", + "metadata": {}, + "source": [ + "# 01 · The frames — a typed storage contract for `(time, unit)` arrays\n", + "\n", + "**What this teaches:** what a `views_frames` frame *is* and what it can do as a\n", + "storage device — the immutable, numpy-only **array + identifier** value objects at\n", + "the root of the VIEWS dependency graph, and why they exist (to replace the\n", + "list-in-cell `object`-dtype DataFrame that caused the ~33× memory blow-up /\n", + "report-stage OOM, README §7).\n", + "\n", + "**Audience:** a consumer-repo author about to migrate a DataFrame-based path onto frames." + ] + }, + { + "cell_type": "markdown", + "id": "a9adac23", + "metadata": {}, + "source": [ + "> **How to read this notebook.** Everything below uses only the **public, frozen**\n", + "> `views_frames` API, on **synthetic data** generated in-repo with fixed seeds\n", + "> (`notebooks/_synthetic.py`). It is an un-gated dev artifact — the package never\n", + "> imports it. A full *Run All* finishes in well under a minute." + ] + }, + { + "cell_type": "markdown", + "id": "8bd26352", + "metadata": {}, + "source": [ + "### Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "620adbdb", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "from pathlib import Path\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# `_synthetic.py` sits next to this notebook; make it importable whether the kernel\n", + "# starts in notebooks/ or at the repo root.\n", + "for _p in (Path.cwd(), Path.cwd() / \"notebooks\"):\n", + " if (_p / \"_synthetic.py\").exists():\n", + " sys.path.insert(0, str(_p))\n", + " break\n", + "import _synthetic as syn\n", + "\n", + "from views_frames import (\n", + " FeatureFrame,\n", + " FrameMetadata,\n", + " PredictionFrame,\n", + " SpatialLevel,\n", + " SpatioTemporalIndex,\n", + " TargetFrame,\n", + ")\n", + "\n", + "RNG_SEED = 0\n", + "np.set_printoptions(precision=3, suppress=True)" + ] + }, + { + "cell_type": "markdown", + "id": "f8c645f7", + "metadata": {}, + "source": [ + "## 1 · Motivation — the problem the frame solves\n", + "\n", + "A forecast posterior is **`S` samples per `(month, cell)` row**. The pre-frames\n", + "encoding stored that as a *list-in-cell* `DataFrame` — one column whose every cell\n", + "holds a Python `list` of `S` floats. That is an `object`-dtype column of pointers to\n", + "millions of boxed Python `float`s: it cannot be vectorised, it cannot memory-map, and\n", + "it blows up in RAM (the report-stage OOM, README §7).\n", + "\n", + "The frame replaces it with a single **dense `float32 (N, S)`** array plus **integer\n", + "identifiers**. Below: the same posterior both ways, and the memory ratio." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ea6c8d6", + "metadata": {}, + "outputs": [], + "source": [ + "N, S = 2_000, 256\n", + "\n", + "# dense, frame-style: one contiguous float32 (N, S) block\n", + "dense = np.random.default_rng(RNG_SEED).standard_normal((N, S)).astype(np.float32)\n", + "\n", + "# list-in-cell, DataFrame-style: an object array of Python lists of Python floats\n", + "list_in_cell = np.empty(N, dtype=object)\n", + "for i in range(N):\n", + " list_in_cell[i] = [float(x) for x in dense[i]]\n", + "\n", + "def list_in_cell_bytes(col, sample_rows=200):\n", + " \"\"\"Rough memory of the object column: array of pointers + lists + boxed floats.\"\"\"\n", + " rows = col[:sample_rows]\n", + " per_row = np.mean([\n", + " sys.getsizeof(lst) + sum(sys.getsizeof(x) for x in lst) for lst in rows\n", + " ])\n", + " return col.nbytes + per_row * len(col)\n", + "\n", + "dense_mb = dense.nbytes / 1e6\n", + "listcell_mb = list_in_cell_bytes(list_in_cell) / 1e6\n", + "print(f\"dense float32 (N, S): {dense_mb:7.2f} MB\")\n", + "print(f\"list-in-cell (object): {listcell_mb:7.2f} MB\")\n", + "print(f\"blow-up factor: {listcell_mb / dense_mb:7.1f}x\")\n", + "print(\"\\n(README §7 reports ~33x on the production payload — bigger lists, \"\n", + " \"wider rows. The contract exists to make that encoding unrepresentable.)\")" + ] + }, + { + "cell_type": "markdown", + "id": "ec73c800", + "metadata": {}, + "source": [ + "## 2 · `SpatioTemporalIndex` — the shared identifier object\n", + "\n", + "Every frame carries a `SpatioTemporalIndex`: integer `time` + `unit` arrays and a\n", + "`SpatialLevel` (`cm` country-month / `pgm` PRIO-GRID-month — a label-only vocabulary,\n", + "never geography). The index is the **one genuinely-reused primitive**: it provides\n", + "pure-numpy, same-level alignment (`searchsorted` / `intersect` / `reindex`), the\n", + "numpy unwrap of `pd.Index.get_indexer`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cf1bf3fe", + "metadata": {}, + "outputs": [], + "source": [ + "time = np.full(5, 500, dtype=np.int64) # month_id 500\n", + "unit = np.array([1001, 1002, 1003, 1004, 1005], np.int64) # priogrid_id\n", + "index = SpatioTemporalIndex(time=time, unit=unit, level=SpatialLevel.PGM)\n", + "\n", + "print(\"level :\", index.level, \"->\", index.level.index_names)\n", + "print(\"n_rows :\", index.n_rows)\n", + "print(\"identifiers :\", {k: v.tolist() for k, v in index.identifiers.items()})" + ] + }, + { + "cell_type": "markdown", + "id": "ab16b2ac", + "metadata": {}, + "source": [ + "Same-level alignment is the operation consumers actually reuse — *where* does each\n", + "row of one index land in another, with no pandas:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49f49178", + "metadata": {}, + "outputs": [], + "source": [ + "subset = SpatioTemporalIndex(\n", + " time=np.array([500, 500], np.int64),\n", + " unit=np.array([1003, 1001], np.int64),\n", + " level=SpatialLevel.PGM,\n", + ")\n", + "print(\"positions of `subset` rows within `index`:\", index.searchsorted(subset))\n", + "print(\"is `index` a superset of `subset`? :\", index.is_superset_of(subset))" + ] + }, + { + "cell_type": "markdown", + "id": "1e821dac", + "metadata": {}, + "source": [ + "## 3 · The three frames as *siblings* (no base class — ADR-011)\n", + "\n", + "There is **no `_BaseFrame`**. `PredictionFrame`, `TargetFrame` and `FeatureFrame` are\n", + "independent sibling classes that *share an index*, not an inheritance tree — so a\n", + "change to one cannot silently reshape the others (ADR-011, Option C). The three differ\n", + "only in the shape of their value block:\n", + "\n", + "| frame | values | meaning |\n", + "|---|---|---|\n", + "| `PredictionFrame` | `(N, S)` | a posterior of `S` samples per row |\n", + "| `TargetFrame` | `(N, 1)` | an observed actual (an explicit 1-sample frame, ADR-012) |\n", + "| `FeatureFrame` | `(N, F, S)` | `F` model-input channels per row |\n", + "\n", + "`cm`/`pgm` is a **`SpatialLevel` value on the index**, never a class axis. Below we\n", + "build all three on one shared index from the synthetic zoo (one posterior per\n", + "distribution shape)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca8544fd", + "metadata": {}, + "outputs": [], + "source": [ + "zoo = syn.zoo_frames(n_samples=256, seed=RNG_SEED)\n", + "pred, tgt, feat = zoo.prediction, zoo.target, zoo.features\n", + "\n", + "for name, frame in [(\"PredictionFrame\", pred), (\"TargetFrame\", tgt), (\"FeatureFrame\", feat)]:\n", + " print(f\"{name:16s} values{str(frame.values.shape):12s} \"\n", + " f\"sample_count={frame.sample_count:<4d} is_sample={frame.is_sample}\")\n", + "\n", + "print(\"\\nall three share ONE index:\",\n", + " pred.index == tgt.index == feat.index)\n", + "print(\"shapes on the index :\", zoo.names)" + ] + }, + { + "cell_type": "markdown", + "id": "53eb83ad", + "metadata": {}, + "source": [ + "`is_sample` (from the `Sampled` protocol) is the honest distinction — a\n", + "`PredictionFrame` of `S>1` draws is a sample; a `TargetFrame` is a single realised\n", + "value (`is_sample == False`), not \"a posterior with one draw\". A quick look at what a\n", + "\"posterior per row\" means:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "125f7da0", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 3, figsize=(10, 2.6))\n", + "for ax, i in zip(axes, [0, 1, 5]): # zero-inflated, skewed, bimodal\n", + " ax.hist(pred.values[i], bins=40, color=\"steelblue\")\n", + " ax.axvline(tgt.values[i, 0], color=\"crimson\", lw=2, label=\"actual\")\n", + " ax.set_title(zoo.names[i], fontsize=9)\n", + " ax.set_yticks([])\n", + "axes[0].legend(fontsize=8)\n", + "fig.suptitle(\"one PredictionFrame row = one posterior; the TargetFrame row = its actual\", fontsize=10)\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "61f6d797", + "metadata": {}, + "source": [ + "## 4 · Immutability & structural sharing (register C-07)\n", + "\n", + "Frames are **value objects**: every operation returns a *new* frame and never mutates\n", + "the input. Structural operations (`with_metadata`, slicing into a shared buffer) are\n", + "**zero-copy** — they share the underlying values array; only operations that must\n", + "allocate (a row `select`, a reduction) copy. This is the contract that lets the leaf\n", + "be passed across repos without defensive copying." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2591a50c", + "metadata": {}, + "outputs": [], + "source": [ + "original_values = pred.values.copy() # snapshot to prove non-mutation\n", + "\n", + "# with_metadata: a NEW frame that SHARES the values buffer (zero-copy)\n", + "tagged = pred.with_metadata(FrameMetadata(run_id=\"demo-run\", data_version=\"v2026.06\"))\n", + "print(\"with_metadata shares the values buffer :\", np.shares_memory(pred.values, tagged.values))\n", + "print(\"new metadata on the new frame :\", tagged.metadata.run_id, tagged.metadata.data_version)\n", + "\n", + "# select: a NEW frame that COPIES (row gather cannot be a view)\n", + "first_three = pred.select(np.array([0, 1, 2]))\n", + "print(\"select copies (no shared buffer) :\", not np.shares_memory(pred.values, first_three.values))\n", + "print(\"select result shape :\", first_three.values.shape)\n", + "\n", + "# a reduction allocates a new (N, 1) block — it does NOT share the source buffer\n", + "row_means = pred.values.mean(axis=1, keepdims=True)\n", + "print(\"a reduction allocates a fresh block :\", row_means.shape,\n", + " \"| shares buffer:\", np.shares_memory(pred.values, row_means))\n", + "print(\"source frame never mutated :\", np.array_equal(pred.values, original_values))" + ] + }, + { + "cell_type": "markdown", + "id": "9e08f736", + "metadata": {}, + "source": [ + "**Why it matters:** because `with_metadata` shares the buffer, tagging provenance\n", + "onto a multi-gigabyte posterior is free; because `select` copies, a consumer can hand\n", + "out a slice without fear the parent changes under it. The frame chooses copy-vs-share\n", + "by what is *safe*, not by guesswork — see §8 for the provenance header `with_metadata`\n", + "attaches." + ] + }, + { + "cell_type": "markdown", + "id": "5ee9c4e3", + "metadata": {}, + "source": [ + "## 5 · Cross-level alignment (ADR-014)\n", + "\n", + "Grid (`pgm`) rows often need rolling up to their country (`cm`) — but a cell's country\n", + "**changes by month**, so the join is keyed by `(time, unit)`, not `unit` alone. The\n", + "leaf owns the *operation* (`cross_level_align`); the **mapping is injected by the\n", + "caller** and is never embedded or fetched here. This is the rule that keeps geography\n", + "out of the leaf (ADR-014): the frame knows *how* to remap, never *what the map is*." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f4f954e", + "metadata": {}, + "outputs": [], + "source": [ + "sc = syn.cm_pgm_scenario(n_samples=8, seed=RNG_SEED, lattice=(2, 2), n_months=1)\n", + "pgm_index = sc.pgm_index\n", + "print(\"pgm rows (time, unit):\",\n", + " list(zip(pgm_index.time.tolist(), pgm_index.unit.tolist())))\n", + "print(\"injected (time, unit) -> country mapping:\", sc.mapping)\n", + "\n", + "# remap every pgm row's unit to its country, via the INJECTED mapping; time is kept\n", + "cm_index = pgm_index.cross_level_align(sc.mapping, SpatialLevel.CM)\n", + "print(\"\\nafter cross_level_align -> level:\", cm_index.level)\n", + "print(\"remapped units (country_id) :\", cm_index.unit.tolist())\n", + "print(\"time preserved row-for-row :\", np.array_equal(cm_index.time, pgm_index.time))" + ] + }, + { + "cell_type": "markdown", + "id": "06de8452", + "metadata": {}, + "source": [ + "The leaf never guesses a mapping — an uncovered `(time, unit)` **fails loud**, and a\n", + "grid-scale mapping can be injected as columnar arrays (`cross_level_align_arrays`) to\n", + "skip building a 10M-key dict:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9b984a3", + "metadata": {}, + "outputs": [], + "source": [ + "# columnar form (what a producer holding the mapping as arrays would use)\n", + "cm_arrays = pgm_index.cross_level_align_arrays(sc.map_keys, sc.map_vals, SpatialLevel.CM)\n", + "print(\"array form agrees with dict form:\", np.array_equal(cm_arrays.unit, cm_index.unit))\n", + "\n", + "# a mapping that omits a row is rejected — the leaf does not invent geography\n", + "try:\n", + " pgm_index.cross_level_align({(500, 1000): 70}, SpatialLevel.CM) # missing 3 rows\n", + "except ValueError as e:\n", + " print(\"fails loud on an incomplete mapping:\", str(e)[:60], \"...\")" + ] + }, + { + "cell_type": "markdown", + "id": "285563e7", + "metadata": {}, + "source": [ + "## 6 · Serialization round-trips\n", + "\n", + "Two codecs, both proving `frame in == frame out`:\n", + "\n", + "* **npz** — the native format. `frame.save(dir)` / `Frame.load(dir)`, mmap-capable for\n", + " large posteriors. This is the method on the frame.\n", + "* **arrow/parquet** — the scalable flat-columnar interchange format (one scalar cell per\n", + " `(time, unit, sample)`), under `views_frames.io.arrow`. It is the **only** place\n", + " `pyarrow` may be imported — an *internal codec boundary*, not part of the frozen public\n", + " surface (which is exactly why there is no `frame.to_parquet`; see the D-11 note below)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9440f9f", + "metadata": {}, + "outputs": [], + "source": [ + "import tempfile\n", + "\n", + "# native npz round-trip on the frame itself\n", + "with tempfile.TemporaryDirectory() as d:\n", + " pred.save(d)\n", + " reloaded = PredictionFrame.load(d)\n", + "print(\"npz round-trip preserves values :\", np.array_equal(reloaded.values, pred.values))\n", + "print(\"npz round-trip preserves index :\", reloaded.index == pred.index)" + ] + }, + { + "cell_type": "markdown", + "id": "ce3ae78f", + "metadata": {}, + "source": [ + "**The `to_parquet` ergonomic gap (register D-11).** There is deliberately *no*\n", + "`frame.to_parquet()` method. To use the parquet codec you reach into `io.arrow` and\n", + "**destructure the frame's state** yourself — `arrow.save` takes the raw arrays, not a\n", + "frame. That friction is a recorded *demand signal* (D-11), not a reason to bolt a\n", + "serialization method onto the frozen value object:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "73e9e642", + "metadata": {}, + "outputs": [], + "source": [ + "from views_frames.io import arrow # the one module allowed to import pyarrow\n", + "\n", + "with tempfile.TemporaryDirectory() as d:\n", + " parquet_path = Path(d) / \"pred.parquet\"\n", + " # no frame.to_parquet — destructure the frame's state into the codec (D-11)\n", + " arrow.save(\n", + " parquet_path,\n", + " values=pred.values,\n", + " time=pred.index.time,\n", + " unit=pred.index.unit,\n", + " level=pred.index.level.value,\n", + " metadata=pred.metadata.to_dict(),\n", + " )\n", + " state = arrow.load(parquet_path) # -> a state dict, not a frame\n", + " rebuilt = PredictionFrame(\n", + " state[\"values\"],\n", + " SpatioTemporalIndex(state[\"time\"], state[\"unit\"], SpatialLevel(state[\"level\"])),\n", + " FrameMetadata.from_dict(state[\"metadata\"]),\n", + " )\n", + "print(\"parquet round-trip preserves values:\", np.array_equal(rebuilt.values, pred.values))\n", + "print(\"parquet round-trip preserves index :\", rebuilt.index == pred.index)" + ] + }, + { + "cell_type": "markdown", + "id": "b4bdeba6", + "metadata": {}, + "source": [ + "## 7 · Fail-loud construction (ADR-008/009)\n", + "\n", + "The contract is enforced **at construction** — a malformed frame cannot exist. The\n", + "guarantee is *structural* (dtype / shape / completeness), not temporal: `time` is an\n", + "opaque integer, so the leaf never judges its range or monotonicity (register C-11). A\n", + "downstream repo can therefore trust the shape of any frame it is handed without\n", + "re-validating. Each attempt below raises at build time:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "df6bc7af", + "metadata": {}, + "outputs": [], + "source": [ + "good_index = SpatioTemporalIndex(\n", + " time=np.array([500, 500], np.int64),\n", + " unit=np.array([1, 2], np.int64),\n", + " level=SpatialLevel.PGM,\n", + ")\n", + "\n", + "def expect_raise(label, thunk):\n", + " try:\n", + " thunk()\n", + " except (ValueError, TypeError) as e:\n", + " print(f\"{label:34s} -> {type(e).__name__}: {str(e)[:54]}\")\n", + " else:\n", + " print(f\"{label:34s} -> NO ERROR (contract hole!)\")\n", + "\n", + "# object dtype (the banned list-in-cell encoding)\n", + "expect_raise(\"object-dtype values\",\n", + " lambda: PredictionFrame(np.array([[1.0], [2.0]], dtype=object), good_index))\n", + "# missing the explicit trailing sample axis (1-D)\n", + "expect_raise(\"1-D values (no sample axis)\",\n", + " lambda: PredictionFrame(np.array([1.0, 2.0], np.float32), good_index))\n", + "# rows disagree with the index length\n", + "expect_raise(\"rows != index length\",\n", + " lambda: PredictionFrame(np.zeros((3, 4), np.float32), good_index))\n", + "# float identifiers (integers cannot be NaN -> identifiers must be integer)\n", + "expect_raise(\"non-integer identifiers\",\n", + " lambda: SpatioTemporalIndex(np.array([0.5, 1.5]), np.array([1, 2], np.int64),\n", + " SpatialLevel.PGM))" + ] + }, + { + "cell_type": "markdown", + "id": "c8b5e784", + "metadata": {}, + "source": [ + "## 8 · Metadata & provenance\n", + "\n", + "Every frame carries a typed `FrameMetadata` header — a frozen dataclass of all-optional,\n", + "validated fields (not a free-form dict, so consumers cannot diverge on key names). It is\n", + "the home for generic run identity (`run_id`, `data_version`); it travels through\n", + "`with_metadata` (zero-copy, §4) and survives serialization." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92e70c97", + "metadata": {}, + "outputs": [], + "source": [ + "meta = FrameMetadata(model=\"ensemble-A\", run_id=\"run-2026-06-27\", data_version=\"v2026.06.1\")\n", + "print(\"to_dict (unset fields omitted):\", meta.to_dict())\n", + "print(\"round-trips through a dict :\", FrameMetadata.from_dict(meta.to_dict()) == meta)\n", + "\n", + "tagged = pred.with_metadata(meta)\n", + "with tempfile.TemporaryDirectory() as d:\n", + " tagged.save(d)\n", + " print(\"provenance survives save/load :\", PredictionFrame.load(d).metadata.run_id)" + ] + }, + { + "cell_type": "markdown", + "id": "3895ea96", + "metadata": {}, + "source": [ + "**Conformance coda.** The leaf *publishes* the contract checks consumers re-run in\n", + "their own CI (ADR-016), so every repo tests the same guarantee. `assert_frame_envelope`\n", + "is the shape/dtype/round-trip envelope any frame-like object must satisfy;\n", + "`assert_frame_contract` adds the spatiotemporal identifier rule. All three frames pass:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "980bc51a", + "metadata": {}, + "outputs": [], + "source": [ + "from views_frames.conformance import assert_frame_contract, assert_frame_envelope\n", + "\n", + "for name, frame in [(\"PredictionFrame\", pred), (\"TargetFrame\", tgt), (\"FeatureFrame\", feat)]:\n", + " assert_frame_envelope(frame) # float32 + trailing axis + round-trip\n", + " assert_frame_contract(frame) # + integer (time, unit) of length n_rows\n", + " print(f\"{name:16s} satisfies the published frame contract ✓\")" + ] + }, + { + "cell_type": "markdown", + "id": "e2d50bf7", + "metadata": {}, + "source": [ + "## The contract in one screen\n", + "\n", + "| You saw | The guarantee |\n", + "|---|---|\n", + "| dense `float32 (N, S)` + integer ids (§1–2) | no list-in-cell `object` dtype; vectorisable, mmap-able |\n", + "| three sibling frames on one index (§3) | no base-class god-object; `cm`/`pgm` is a value, not a class |\n", + "| return-new, share-the-buffer (§4) | immutable value objects; zero-copy where safe, copy where needed |\n", + "| injected `(time, unit) → unit` remap (§5) | the leaf owns the *operation*; geography is never embedded (ADR-014) |\n", + "| npz + parquet round-trips (§6) | `frame in == frame out`; the `to_parquet` gap is a demand signal (D-11) |\n", + "| raises at construction (§7) | a malformed frame cannot exist; the guarantee is *structural* |\n", + "| typed `FrameMetadata` + published checks (§8) | provenance travels; every consumer re-runs the *same* contract |\n", + "\n", + "Everything here is the **frozen** `views_frames` surface on synthetic data. Next:\n", + "[`02_summaries.ipynb`](02_summaries.ipynb) reads point estimates, intervals, exceedance\n", + "and tail risk *off the sample axis* of a `PredictionFrame` — and asks whether those\n", + "intervals are actually *honest*." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/02_summaries.ipynb b/notebooks/02_summaries.ipynb new file mode 100644 index 0000000..e31d265 --- /dev/null +++ b/notebooks/02_summaries.ipynb @@ -0,0 +1,765 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c90040b9", + "metadata": {}, + "source": [ + "# 02 · Posterior summaries over the sample axis\n", + "\n", + "**What this teaches:** how to read a single number, an interval, an exceedance\n", + "probability or a worst-case off the **sample axis** of a `PredictionFrame` — using\n", + "`views_frames_summarize` — and, just as important, **when each summary lies**. Every\n", + "summary is swept over a *zoo* of distribution shapes typical of the VIEWS estimand, and\n", + "checked for *honesty* (calibration / coverage) against a **known latent truth**.\n", + "\n", + "**Audience:** anyone about to report a posterior to a downstream consumer (a dashboard,\n", + "an FAO food-security alert) and wanting to trust the number they print." + ] + }, + { + "cell_type": "markdown", + "id": "b8e96349", + "metadata": {}, + "source": [ + "> **How to read this notebook.** Public, **frozen** `views_frames` /\n", + "> `views_frames_summarize` API only, on **synthetic** data with a *known* data-generating\n", + "> process (`notebooks/_synthetic.py`) — so we can check whether the intervals actually\n", + "> cover. Fixed seeds; a full *Run All* is well under a minute. All plots are **static**\n", + "> small-multiples (a deliberate choice — reproducible and cheap; an interactive widget\n", + "> tour is out of scope for an un-gated artifact).\n", + ">\n", + "> Scoring rules live in *views-evaluation*, not here — this notebook **shows** calibration\n", + "> to build trust in the summaries; it does not add a scoring API." + ] + }, + { + "cell_type": "markdown", + "id": "ca717887", + "metadata": {}, + "source": [ + "### Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef418f87", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "from pathlib import Path\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "for _p in (Path.cwd(), Path.cwd() / \"notebooks\"):\n", + " if (_p / \"_synthetic.py\").exists():\n", + " sys.path.insert(0, str(_p))\n", + " break\n", + "import _synthetic as syn\n", + "\n", + "from views_frames import SpatialLevel\n", + "from views_frames_summarize import (\n", + " aggregate_distributions,\n", + " bimodality,\n", + " collapse,\n", + " exceedance,\n", + " expected_shortfall,\n", + " hdi,\n", + " hdi_tower,\n", + " map_estimate,\n", + " quantiles,\n", + " tower_point,\n", + ")\n", + "from views_frames_summarize.conformance import assert_summarizer_contract\n", + "\n", + "np.set_printoptions(precision=3, suppress=True)" + ] + }, + { + "cell_type": "markdown", + "id": "2030827e", + "metadata": {}, + "source": [ + "## 1 · The estimand, and the philosophy\n", + "\n", + "A VIEWS forecast for a `(month, cell)` is not a number — it is a **posterior of `S`\n", + "predictive samples** (MC-dropout × a distributional head × an ensemble, all pooled onto\n", + "one sample axis). That posterior is, by the nature of conflict, **low-sample,\n", + "zero-inflated, right-skewed, heavy-tailed and occasionally multimodal**.\n", + "\n", + "A *summary* projects that distribution onto something a consumer can act on — a point, an\n", + "interval, a probability. **The summary you choose encodes what you care about**, and on\n", + "these shapes the choices genuinely disagree: the mean is not the mode, the\n", + "highest-density interval is not the equal-tailed one, the worst-case is not the max. The\n", + "rest of this notebook makes those disagreements concrete — and asks which summaries are\n", + "*honest* on which shapes." + ] + }, + { + "cell_type": "markdown", + "id": "829f5018", + "metadata": {}, + "source": [ + "## 2 · The distribution zoo\n", + "\n", + "Six shapes spanning the estimand, each generated with a **known** mode and mean (the red\n", + "/ green marks). We show the pooled regime (`S=1024`) and the low-sample regime (`S=32`)\n", + "side by side — most of the summaries below behave very differently between the two." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12144bd6", + "metadata": {}, + "outputs": [], + "source": [ + "SHOW = [\"zero_inflated\", \"right_skewed_gamma\", \"near_symmetric\", \"heavy_tailed\", \"bimodal\"]\n", + "\n", + "def plot_zoo(n_samples, seed=0):\n", + " zoo = syn.distribution_zoo(n_samples=n_samples, seed=seed)\n", + " fig, axes = plt.subplots(1, len(SHOW), figsize=(13, 2.4))\n", + " for ax, name in zip(axes, SHOW):\n", + " samples, truth = zoo[name]\n", + " ax.hist(samples, bins=30, color=\"steelblue\", density=True)\n", + " ax.axvline(truth.mean, color=\"crimson\", lw=1.5, label=\"true mean\")\n", + " ax.axvline(truth.mode, color=\"seagreen\", lw=1.5, ls=\"--\", label=\"true mode\")\n", + " ax.set_title(name, fontsize=8.5)\n", + " ax.set_yticks([])\n", + " axes[0].legend(fontsize=7)\n", + " fig.suptitle(f\"the zoo at S={n_samples}\", fontsize=10)\n", + " plt.tight_layout(); plt.show()\n", + "\n", + "plot_zoo(1024)\n", + "plot_zoo(32)" + ] + }, + { + "cell_type": "markdown", + "id": "605dc65f", + "metadata": {}, + "source": [ + "## 3 · Point estimates — and why the *mode* is contested\n", + "\n", + "Four ways to collapse a posterior to one number, all on the **frozen** surface:\n", + "\n", + "* `collapse(frame, np.mean)` / `collapse(frame, np.median)` — generic folds (the package\n", + " owns the *mechanism*, you inject the statistic);\n", + "* `map_estimate(frame)` — the histogram MAP (mode) as faoapi/reporting compute it;\n", + "* `tower_point(frame)` — the tower-tip \"shorth\" (median of the 50%-mass HDI floor).\n", + "\n", + "On a symmetric posterior these agree. On the skewed, zero-inflated shapes they **don't**,\n", + "and the mode is the most contested of all — `map_estimate` carries a documented\n", + "low-sample, lowest-bin tie-break **bias toward zero** on right-skewed posteriors\n", + "(register C-32); `tower_point` is the unbinned, duplicate-robust alternative." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1acfea4", + "metadata": {}, + "outputs": [], + "source": [ + "zoo = syn.zoo_frames(n_samples=1024, seed=0)\n", + "frame = zoo.prediction\n", + "\n", + "est = {\n", + " \"true_mode\": np.array([t.mode for t in zoo.truths]),\n", + " \"true_mean\": np.array([t.mean for t in zoo.truths]),\n", + " \"map_estimate\": map_estimate(frame).values[:, 0],\n", + " \"tower_point\": tower_point(frame).values[:, 0],\n", + " \"mean\": collapse(frame, np.mean).values[:, 0],\n", + " \"median\": collapse(frame, np.median).values[:, 0],\n", + "}\n", + "print(f\"{'shape':24s}\" + \"\".join(f\"{k:>13s}\" for k in est))\n", + "for i, name in enumerate(zoo.names):\n", + " print(f\"{name:24s}\" + \"\".join(f\"{est[k][i]:13.2f}\" for k in est))" + ] + }, + { + "cell_type": "markdown", + "id": "4f5cc083", + "metadata": {}, + "source": [ + "**The C-32 bias, made visible.** The histogram MAP's lowest-bin tie-break biases the\n", + "mode **toward zero at low `S`** — but it only *bites* when the true mode sits **away from**\n", + "zero, where there is room to fall. On `right_skewed_gamma` (true mode 1.8) the `S=32` MAP\n", + "drops well below it while the tower tip does not. On shapes whose mode is *already* near zero\n", + "(`right_skewed_lognormal`, `heavy_tailed`) a low MAP is indistinguishable from a correct one —\n", + "so the bias is a property of the **(shape, `S`)** pair, not of \"right-skewed\" alone." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc898483", + "metadata": {}, + "outputs": [], + "source": [ + "low = syn.zoo_frames(n_samples=32, seed=0)\n", + "m_low = map_estimate(low.prediction).values[:, 0]\n", + "t_low = tower_point(low.prediction).values[:, 0]\n", + "print(f\"{'shape':24s}{'true_mode':>12s}{'MAP@S=32':>12s}{'tower@S=32':>12s}\")\n", + "for i, name in enumerate(low.names):\n", + " if \"skewed\" in name or name == \"heavy_tailed\":\n", + " print(f\"{name:24s}{low.truths[i].mode:12.2f}{m_low[i]:12.2f}{t_low[i]:12.2f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "4b5fd413", + "metadata": {}, + "source": [ + "## 4 · The HDI tower\n", + "\n", + "`hdi_tower(frame, masses=(...))` returns nested **highest-density** intervals — the\n", + "*shortest* interval holding each mass — built so wider always contains narrower (nested by\n", + "construction). On a right-skewed posterior the HDI hugs the dense left and is **not** the\n", + "equal-tailed interval (we put those head-to-head in the honesty panel below)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ceb6d230", + "metadata": {}, + "outputs": [], + "source": [ + "masses = (0.5, 0.9, 0.95, 0.99)\n", + "tower = hdi_tower(frame, masses=masses) # (N, 4, 2)\n", + "\n", + "fig, axes = plt.subplots(1, len(SHOW), figsize=(13, 2.6), sharey=False)\n", + "for ax, name in zip(axes, SHOW):\n", + " i = zoo.names.index(name)\n", + " for j, m in enumerate(masses):\n", + " lo, hi = tower[i, j]\n", + " ax.plot([lo, hi], [m, m], lw=3, solid_capstyle=\"butt\")\n", + " ax.axvline(zoo.truths[i].mode, color=\"seagreen\", ls=\"--\", lw=1)\n", + " ax.set_title(name, fontsize=8.5); ax.set_xlabel(\"value\", fontsize=8)\n", + "axes[0].set_ylabel(\"HDI mass\"); axes[0].set_yticks(masses)\n", + "fig.suptitle(\"nested HDI tower (50 / 90 / 95 / 99%); green = true mode\", fontsize=10)\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "75eaa826", + "metadata": {}, + "source": [ + "## 5 · Quantiles & exceedance — `P(Y > c)` and the onset flag\n", + "\n", + "`quantiles(frame, qs)` and `exceedance(frame, thresholds)` are index-aligned arrays. The\n", + "flagship exceedance is **`P(Y > 0)`** — the probability of *any* activity (onset). It is a\n", + "distribution-agnostic counting reducer (no histogram, no unimodality assumption), so it is\n", + "robust exactly where the mode/HDI are weakest." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "59f57d92", + "metadata": {}, + "outputs": [], + "source": [ + "qs = quantiles(frame, [0.05, 0.5, 0.95]) # (N, 3)\n", + "thresholds = [0.0, 1.0, 5.0, 10.0]\n", + "exc = exceedance(frame, thresholds) # (N, 4)\n", + "\n", + "print(f\"{'shape':24s}{'q05':>7s}{'q50':>7s}{'q95':>7s} | \" +\n", + " \"\".join(f\"P(>{c:g})\".rjust(9) for c in thresholds))\n", + "for i, name in enumerate(zoo.names):\n", + " print(f\"{name:24s}\" + \"\".join(f\"{qs[i, j]:7.2f}\" for j in range(3)) + \" | \" +\n", + " \"\".join(f\"{exc[i, j]:9.2f}\" for j in range(len(thresholds))))\n", + "print(\"\\nonset P(Y>0): high for active shapes, ~\", round(float(exc[0, 0]), 2),\n", + " \"for the zero-inflated cell; exceedance is non-increasing in the threshold.\")" + ] + }, + { + "cell_type": "markdown", + "id": "7c5fb3f2", + "metadata": {}, + "source": [ + "## 6 · Worst-case — `expected_shortfall` (tail mean / CVaR)\n", + "\n", + "`expected_shortfall(frame, tails)` is the mean of the worst `⌈t·S⌉` draws — a *coherent*\n", + "worst-case, deliberately **not** the `max` (the high-variance summary it replaces). It is\n", + "non-decreasing as the tail deepens (`t → 0`).\n", + "\n", + "**The caveat that matters (small-tail → max).** When `⌈t·S⌉ = 1` the \"tail mean\"\n", + "collapses to the single worst draw — i.e. the `max` you were trying to avoid. That happens\n", + "whenever `t` is smaller than `1/S`. At `S=32`, a `t=0.01` tail is *one draw*. Always read\n", + "ES with `S` in mind." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d943f8a9", + "metadata": {}, + "outputs": [], + "source": [ + "tails = [1.0, 0.5, 0.1, 0.01]\n", + "es = expected_shortfall(frame, tails) # (N, 4), S=1024\n", + "print(f\"S=1024 {'shape':22s}\" + \"\".join(f\"ES(t={t:g})\".rjust(12) for t in tails))\n", + "for i, name in enumerate(zoo.names):\n", + " print(f\" {name:22s}\" + \"\".join(f\"{es[i, j]:12.2f}\" for j in range(len(tails))))\n", + "\n", + "# small-tail -> max degeneracy at low S\n", + "es_small = expected_shortfall(low.prediction, [0.01])[:, 0] # S=32 -> ceil(0.01*32)=1\n", + "mx = low.prediction.values.max(axis=1)\n", + "print(\"\\nS=32, t=0.01 => ceil(t*S)=1 => ES == max(draws):\",\n", + " bool(np.allclose(es_small, mx)))" + ] + }, + { + "cell_type": "markdown", + "id": "1531459d", + "metadata": {}, + "source": [ + "## 7 · The bimodality flag\n", + "\n", + "`bimodality(frame)` is a deliberately **conservative** 0/1 flag — tuned for *zero false\n", + "positives* on the normal (unimodal, skewed, zero-inflated) regime, at the cost of recall\n", + "on ambiguous mixtures. Its job is to warn a consumer when a single point / interval would\n", + "**hide a second mode**, so you reach for it instead of silently reporting a misleading tip." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "022d9c40", + "metadata": {}, + "outputs": [], + "source": [ + "flag = bimodality(frame)[:, 0]\n", + "for i, name in enumerate(zoo.names):\n", + " print(f\"{name:24s} bimodal={int(flag[i])}\")\n", + "print(\"\\nonly the genuinely two-peaked shape is flagged; the skewed/zero-inflated \"\n", + " \"shapes read as unimodal (no crying wolf).\")" + ] + }, + { + "cell_type": "markdown", + "id": "3d6608cd", + "metadata": {}, + "source": [ + "## 8 · Cross-level aggregation — `HDI(sum) ≠ sum(HDI)`\n", + "\n", + "`aggregate_distributions` sums the per-cell sample arrays **preserving the sample index**\n", + "(joint sampling), then you summarize the *aggregate distribution*. You must **aggregate\n", + "then summarize**, never summarize then add — the HDI of the country total is not the sum of\n", + "the cells' HDIs (joint uncertainty does not add linearly)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "487805f3", + "metadata": {}, + "outputs": [], + "source": [ + "sc = syn.cm_pgm_scenario(n_samples=512, seed=0, lattice=(2, 2), n_months=1)\n", + "pgm = sc.pgm_prediction\n", + "country = aggregate_distributions(pgm, sc.mapping, SpatialLevel.CM) # joint-sampled sum\n", + "\n", + "# country 70's constituent grid cells (this month)\n", + "cm_units = pgm.index.cross_level_align_arrays(sc.map_keys, sc.map_vals, SpatialLevel.CM).unit\n", + "rows70 = np.where(cm_units == 70)[0]\n", + "crow = int(np.where(country.index.unit == 70)[0][0])\n", + "\n", + "agg_hdi = hdi(country, mass=0.9)[crow] # HDI of the joint sum\n", + "sum_of_hdis = hdi(pgm, mass=0.9)[rows70].sum(axis=0) # naive sum of per-cell HDIs\n", + "print(\"country 70, 90% interval:\")\n", + "print(f\" HDI(sum) (correct, joint) : [{agg_hdi[0]:8.1f}, {agg_hdi[1]:8.1f}] \"\n", + " f\"width {agg_hdi[1]-agg_hdi[0]:7.1f}\")\n", + "print(f\" sum(HDI) (wrong, naive) : [{sum_of_hdis[0]:8.1f}, {sum_of_hdis[1]:8.1f}] \"\n", + " f\"width {sum_of_hdis[1]-sum_of_hdis[0]:7.1f}\")\n", + "print(\" -> summing per-cell intervals OVER-states the joint interval width.\")" + ] + }, + { + "cell_type": "markdown", + "id": "ee453faf", + "metadata": {}, + "source": [ + "## 9 · The laws — `assert_summarizer_contract`\n", + "\n", + "The summarize package *publishes* its contract: point estimates return same-type `(N,…,1)`\n", + "frames; intervals align to the frame's rows; exceedance is a `[0,1]` survival function;\n", + "expected-shortfall lies in `[min, max]`, deepens monotonically and dominates its quantile;\n", + "the tower nests. A consumer re-runs this in its own CI." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d5350ce", + "metadata": {}, + "outputs": [], + "source": [ + "assert_summarizer_contract(frame)\n", + "print(\"all summarizer laws hold on the zoo frame ✓\")" + ] + }, + { + "cell_type": "markdown", + "id": "413752e4", + "metadata": {}, + "source": [ + "---\n", + "## 🔬 Are the intervals *honest*? (panel additions)\n", + "\n", + "The sections above show *how* to compute each summary. A sharp reviewer (Gelman /\n", + "Gneiting / Hyndman / Wilke / the FAO analyst) would next ask *whether they can be\n", + "trusted*. Because our data has a **known** DGP, we can check." + ] + }, + { + "cell_type": "markdown", + "id": "e918402a", + "metadata": {}, + "source": [ + "### A · Calibration & coverage + PIT, and recovery (register C-59)\n", + "\n", + "**Coverage.** If we draw many independent posteriors *and* a held-out actual from the same\n", + "truth, the 90% HDI should contain the actual ~90% of the time. We check the 50/90/95/99\n", + "levels. **Calibration is judged *subject to sharpness*** — a uselessly wide interval is\n", + "\"calibrated\" trivially, so coverage is only half the story." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "087986e4", + "metadata": {}, + "outputs": [], + "source": [ + "def coverage(truth, levels, R=600, S=256, seed=0, shrink=1.0):\n", + " \"\"\"Empirical coverage of each HDI level over R replica posteriors + actuals.\n", + "\n", + " shrink<1 narrows each posterior toward its median -> a deliberately MIScalibrated\n", + " (over-confident) forecaster, to contrast against the calibrated shrink=1.0 case.\n", + " \"\"\"\n", + " rng = np.random.default_rng(seed)\n", + " post = np.stack([truth.draw(S, rng) for _ in range(R)]).astype(np.float32)\n", + " if shrink != 1.0:\n", + " med = np.median(post, axis=1, keepdims=True)\n", + " post = (med + shrink * (post - med)).astype(np.float32)\n", + " actuals = np.array([truth.draw(1, rng)[0] for _ in range(R)], np.float32)\n", + " fr = syn.frame_from_samples(post)\n", + " return {lv: float(((actuals >= hdi(fr, mass=lv)[:, 0]) &\n", + " (actuals <= hdi(fr, mass=lv)[:, 1])).mean()) for lv in levels}\n", + "\n", + "levels = [0.5, 0.9, 0.95, 0.99]\n", + "truth = syn.distribution_zoo(seed=0)[\"right_skewed_gamma\"][1]\n", + "cal = coverage(truth, levels, shrink=1.0)\n", + "mis = coverage(truth, levels, shrink=0.6)\n", + "print(f\"{'nominal':>10s}{'calibrated':>14s}{'over-confident':>16s}\")\n", + "for lv in levels:\n", + " print(f\"{lv:10.2f}{cal[lv]:14.2f}{mis[lv]:16.2f}\")\n", + "print(\"\\ncalibrated coverage tracks the nominal level; the over-confident \"\n", + " \"forecaster under-covers (its intervals are too narrow).\")" + ] + }, + { + "cell_type": "markdown", + "id": "020c790b", + "metadata": {}, + "source": [ + "**PIT histogram.** The probability-integral transform `PIT = F_post(actual)` is\n", + "`Uniform(0,1)` iff the forecaster is calibrated. A `∩` shape ⇒ over-dispersed, a `∪` shape\n", + "⇒ over-confident. The same calibrated-vs-over-confident pair:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16e5aff9", + "metadata": {}, + "outputs": [], + "source": [ + "def pit(truth, R=3000, S=256, seed=1, shrink=1.0):\n", + " rng = np.random.default_rng(seed)\n", + " out = np.empty(R)\n", + " for r in range(R):\n", + " post = truth.draw(S, rng)\n", + " if shrink != 1.0:\n", + " m = np.median(post); post = m + shrink * (post - m)\n", + " out[r] = (post < truth.draw(1, rng)[0]).mean()\n", + " return out\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(8, 2.6), sharey=True)\n", + "for ax, (lab, sh) in zip(axes, [(\"calibrated\", 1.0), (\"over-confident\", 0.6)]):\n", + " ax.hist(pit(truth, shrink=sh), bins=12, range=(0, 1), color=\"slateblue\", density=True)\n", + " ax.axhline(1.0, color=\"k\", ls=\":\", lw=1)\n", + " ax.set_title(f\"PIT — {lab}\", fontsize=9); ax.set_xlabel(\"PIT\", fontsize=8)\n", + "fig.suptitle(\"PIT ~ Uniform(0,1) iff calibrated (flat = good; U = over-confident)\", fontsize=10)\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "7b13f66b", + "metadata": {}, + "source": [ + "**Recovery.** As `S` grows, the point estimate should converge to the known truth.\n", + "The tower tip's error vs the true mode shrinks with sample size (posterior-predictive\n", + "sanity — a model that can't recover the truth on synthetic data won't on real data):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2b227b3", + "metadata": {}, + "outputs": [], + "source": [ + "truth_g = syn.distribution_zoo(seed=0)[\"right_skewed_gamma\"][1]\n", + "Ss = [8, 16, 32, 64, 128, 256, 512, 1024]\n", + "errs = []\n", + "for S in Ss:\n", + " rng = np.random.default_rng(7)\n", + " post = np.stack([truth_g.draw(S, rng) for _ in range(300)]).astype(np.float32)\n", + " tip = tower_point(syn.frame_from_samples(post)).values[:, 0]\n", + " errs.append(np.abs(tip - truth_g.mode).mean())\n", + "\n", + "fig, ax = plt.subplots(figsize=(5, 2.6))\n", + "ax.plot(Ss, errs, \"o-\"); ax.set_xscale(\"log\", base=2)\n", + "ax.set_xlabel(\"sample count S\"); ax.set_ylabel(\"mean |tip − true mode|\")\n", + "ax.set_title(\"recovery of the mode vs S\", fontsize=10)\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "374d93d8", + "metadata": {}, + "source": [ + "### B · Equal-tailed vs HDI, and the point-estimate family\n", + "\n", + "On a right-skewed posterior the **equal-tailed** interval (`quantiles` at `5/95`) and the\n", + "**HDI** (`hdi` at 0.9) cover the same mass but are *different intervals* — the HDI is\n", + "shorter and shifted toward the dense left. Shown with the mean / median / MAP / tower tip\n", + "all marked, so you can see how far apart the \"central value\" candidates sit." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "445072db", + "metadata": {}, + "outputs": [], + "source": [ + "i = zoo.names.index(\"right_skewed_gamma\")\n", + "et = quantiles(frame, [0.05, 0.95])[i] # equal-tailed 90%\n", + "hd = hdi(frame, mass=0.9)[i] # HDI 90%\n", + "pts = {\"mean\": est[\"mean\"][i], \"median\": est[\"median\"][i],\n", + " \"MAP\": est[\"map_estimate\"][i], \"tower tip\": est[\"tower_point\"][i]}\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 2.8))\n", + "ax.hist(frame.values[i], bins=50, density=True, color=\"lightgray\")\n", + "ax.plot(et, [0.02, 0.02], lw=4, color=\"darkorange\", label=f\"equal-tailed (w={et[1]-et[0]:.1f})\")\n", + "ax.plot(hd, [0.05, 0.05], lw=4, color=\"steelblue\", label=f\"HDI 90% (w={hd[1]-hd[0]:.1f})\")\n", + "for k, v in pts.items():\n", + " ax.axvline(v, lw=1.2, ls=\"--\"); ax.text(v, 0.30, k, rotation=90, fontsize=7, va=\"top\")\n", + "ax.legend(fontsize=8); ax.set_yticks([])\n", + "ax.set_title(\"right-skewed gamma: equal-tailed vs HDI; the point-estimate family\", fontsize=10)\n", + "plt.tight_layout(); plt.show()\n", + "print(f\"HDI is {(et[1]-et[0]) - (hd[1]-hd[0]):.1f} narrower than the equal-tailed interval \"\n", + " \"at the same 90% mass.\")" + ] + }, + { + "cell_type": "markdown", + "id": "239965c8", + "metadata": {}, + "source": [ + "### C · Sample-size / Monte-Carlo error\n", + "\n", + "Every summary is itself a random variable in `S`. At `S=32` the point estimate scatters;\n", + "by `S=1024` it has settled. The spread of `tower_point` across replicas, by `S`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a8819d16", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(6, 2.8))\n", + "for S in [8, 32, 128, 1024]:\n", + " rng = np.random.default_rng(3)\n", + " post = np.stack([truth_g.draw(S, rng) for _ in range(400)]).astype(np.float32)\n", + " tips = tower_point(syn.frame_from_samples(post)).values[:, 0]\n", + " ax.hist(tips, bins=30, density=True, alpha=0.5, label=f\"S={S}\")\n", + "ax.axvline(truth_g.mode, color=\"k\", ls=\"--\", lw=1, label=\"true mode\")\n", + "ax.legend(fontsize=8); ax.set_yticks([])\n", + "ax.set_title(\"Monte-Carlo error of the tower tip shrinks with S\", fontsize=10)\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3462ff86", + "metadata": {}, + "source": [ + "### D · When each summary misleads\n", + "\n", + "A compact failure gallery — each summary on the shape where it is **most misleading**:\n", + "the MAP on a bimodal posterior (lands in one peak, ignores the other); the **mean** on a\n", + "heavy tail (dragged right of the bulk, representing no likely outcome); expected-shortfall\n", + "at a tiny tail (collapses to the volatile `max`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38fb1bd2", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 3, figsize=(12, 2.8))\n", + "# MAP on bimodal\n", + "ib = zoo.names.index(\"bimodal\")\n", + "axes[0].hist(frame.values[ib], bins=50, density=True, color=\"lightgray\")\n", + "axes[0].axvline(est[\"map_estimate\"][ib], color=\"crimson\", lw=2, label=\"MAP\")\n", + "axes[0].set_title(\"MAP on a bimodal posterior\\n(one peak, hides the other)\", fontsize=8.5)\n", + "axes[0].legend(fontsize=8)\n", + "# mean on heavy tail\n", + "ih = zoo.names.index(\"heavy_tailed\")\n", + "axes[1].hist(frame.values[ih], bins=60, density=True, color=\"lightgray\")\n", + "axes[1].axvline(est[\"mean\"][ih], color=\"crimson\", lw=2, label=\"mean\")\n", + "axes[1].axvline(est[\"median\"][ih], color=\"seagreen\", lw=2, label=\"median\")\n", + "axes[1].set_xlim(0, np.quantile(frame.values[ih], 0.99))\n", + "axes[1].set_title(\"mean on a heavy tail\\n(right of the bulk)\", fontsize=8.5)\n", + "axes[1].legend(fontsize=8)\n", + "# ES tiny tail -> max\n", + "es_curve = expected_shortfall(low.prediction, [0.5, 0.2, 0.1, 1/32])[ih]\n", + "axes[2].plot([0.5, 0.2, 0.1, 1/32], es_curve, \"o-\")\n", + "axes[2].axhline(low.prediction.values[ih].max(), color=\"crimson\", ls=\"--\", label=\"max draw\")\n", + "axes[2].set_xlabel(\"tail fraction t (S=32)\", fontsize=8)\n", + "axes[2].set_title(\"ES at a tiny tail -> max\\n(t<1/S = one draw)\", fontsize=8.5)\n", + "axes[2].legend(fontsize=8)\n", + "for a in axes[:2]: a.set_yticks([])\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f4c1f25d", + "metadata": {}, + "source": [ + "### F · A spatial / map view (register C-61)\n", + "\n", + "Per-cell summaries have spatial structure an analyst needs to *see*. On a **toy lattice**\n", + "(synthetic coordinates — no real geography, ADR-001), the point estimate and the interval\n", + "width as small-multiple maps. Width is itself a map: where is the forecast *uncertain*?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "278f25a0", + "metadata": {}, + "outputs": [], + "source": [ + "msc = syn.cm_pgm_scenario(n_samples=256, seed=0, lattice=(6, 6), n_months=1)\n", + "mpgm = msc.pgm_prediction\n", + "nr, nc = msc.lattice_shape\n", + "tip = tower_point(mpgm).values[:, 0]\n", + "band = hdi(mpgm, mass=0.9)\n", + "width = band[:, 1] - band[:, 0]\n", + "\n", + "def to_grid(vals):\n", + " g = np.full((nr, nc), np.nan)\n", + " for row, pg in enumerate(mpgm.index.unit.tolist()):\n", + " r, c = msc.coords[int(pg)]; g[r, c] = vals[row]\n", + " return g\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(8, 3.4))\n", + "for ax, (lab, vals) in zip(axes, [(\"tower tip (point)\", tip), (\"90% HDI width (uncertainty)\", width)]):\n", + " im = ax.imshow(to_grid(vals), cmap=\"magma\"); ax.set_title(lab, fontsize=9)\n", + " ax.set_xticks([]); ax.set_yticks([]); fig.colorbar(im, ax=ax, shrink=0.8)\n", + "fig.suptitle(\"toy lattice — synthetic coordinates, illustrative only\", fontsize=10)\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "7e148777", + "metadata": {}, + "source": [ + "### G · Decision relevance\n", + "\n", + "The operationally-relevant readout for an FAO-style consumer is not the point — it is a\n", + "**probability against a threshold**. Onset `P(Y>0)` and a severe-scenario exceedance\n", + "`P(Y>c)`, mapped, with a simple alert rule (`P > 0.5`). This is the summary a\n", + "food-security decision actually keys on." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7fd7cb98", + "metadata": {}, + "outputs": [], + "source": [ + "onset = exceedance(mpgm, [0.0])[:, 0]\n", + "severe_c = float(np.quantile(mpgm.values, 0.85)) # a synthetic \"severe\" level\n", + "severe = exceedance(mpgm, [severe_c])[:, 0]\n", + "\n", + "fig, axes = plt.subplots(1, 3, figsize=(12, 3.4))\n", + "for ax, (lab, vals) in zip(\n", + " axes,\n", + " [(\"onset P(Y>0)\", onset),\n", + " (f\"severe P(Y>{severe_c:.1f})\", severe),\n", + " (\"alert P(severe)>0.5\", (severe > 0.5).astype(float))],\n", + "):\n", + " im = ax.imshow(to_grid(vals), cmap=\"viridis\", vmin=0, vmax=1)\n", + " ax.set_title(lab, fontsize=9); ax.set_xticks([]); ax.set_yticks([])\n", + " fig.colorbar(im, ax=ax, shrink=0.8)\n", + "fig.suptitle(\"decision-relevant exceedance maps (synthetic thresholds, illustrative)\", fontsize=10)\n", + "plt.tight_layout(); plt.show()\n", + "print(f\"cells on alert (severe exceedance > 0.5): {int((severe > 0.5).sum())} / {mpgm.n_rows}\")" + ] + }, + { + "cell_type": "markdown", + "id": "8916d1f8", + "metadata": {}, + "source": [ + "## The summaries, and their honesty — in one screen\n", + "\n", + "| Summary | Reach for it when… | It misleads when… |\n", + "|---|---|---|\n", + "| `collapse(np.mean)` | you need an additive, conservable quantity | heavy tails drag it off the bulk (§D) |\n", + "| `map_estimate` / `tower_point` | you want the \"most likely\" value | the posterior is multimodal; MAP is low-`S` biased (§3, C-32) |\n", + "| `hdi_tower` | you want the *shortest* credible interval | you actually wanted equal-tailed (§B) |\n", + "| `quantiles` / `exceedance` | you need `P(Y>c)`, onset, decisions | — (distribution-agnostic; the robust workhorse) |\n", + "| `expected_shortfall` | you need a coherent worst-case | `t < 1/S` collapses it to `max` (§6) |\n", + "| `bimodality` | to *guard* a point/interval | (conservative; misses subtle bumps by design) |\n", + "\n", + "And the meta-summary: **an interval is only as good as its coverage** (§A). Next:\n", + "[`03_reconciliation.ipynb`](03_reconciliation.ipynb) takes these grid posteriors and makes\n", + "them sum, per draw, to country totals — and asks whether doing so *helps*." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/03_reconciliation.ipynb b/notebooks/03_reconciliation.ipynb new file mode 100644 index 0000000..45c6ee2 --- /dev/null +++ b/notebooks/03_reconciliation.ipynb @@ -0,0 +1,503 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "332b08b2", + "metadata": {}, + "source": [ + "# 03 · Reconciling grid forecasts to country totals\n", + "\n", + "**What this teaches:** how `views_frames_reconcile` makes PRIO-GRID (`pgm`) forecasts\n", + "sum, **per posterior draw**, to their country (`cm`) totals — `reconcile_proportional`\n", + "and the `ReconciliationModule` orchestrator — with conservation, zero-preservation,\n", + "joint sampling and the bit-identity provenance. And, honestly: what reconciliation\n", + "*guarantees* (coherence) versus what it does **not** (it is a pragmatic approximation,\n", + "and bit-identity to the old path proves *faithful relocation*, **not** method quality).\n", + "\n", + "**Audience:** anyone wiring reconciliation into a served pipeline, or deciding whether\n", + "proportional top-down is good enough." + ] + }, + { + "cell_type": "markdown", + "id": "3183e87a", + "metadata": {}, + "source": [ + "> **How to read this notebook.** Public, **frozen** `views_frames` /\n", + "> `views_frames_reconcile` API only, on **synthetic** data with a *known* true country\n", + "> split (`notebooks/_synthetic.py`), so we can ask whether reconciliation moves estimates\n", + "> toward the truth. Fixed seeds; *Run All* < 1 min; static plots; toy lattice only (no\n", + "> real geography — ADR-001)." + ] + }, + { + "cell_type": "markdown", + "id": "b0eba7e8", + "metadata": {}, + "source": [ + "### Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4c9ec98b", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "from pathlib import Path\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "for _p in (Path.cwd(), Path.cwd() / \"notebooks\"):\n", + " if (_p / \"_synthetic.py\").exists():\n", + " sys.path.insert(0, str(_p))\n", + " break\n", + "import _synthetic as syn\n", + "\n", + "from views_frames import PredictionFrame, SpatialLevel\n", + "from views_frames_reconcile import ReconciliationModule, reconcile_proportional\n", + "from views_frames_reconcile.conformance import assert_reconcile_contract\n", + "from views_frames_summarize import aggregate_distributions, expected_shortfall, hdi\n", + "\n", + "np.set_printoptions(precision=3, suppress=True)\n", + "\n", + "# one scenario reused throughout: a 4x4 grid lattice, 3 months, 2 countries\n", + "sc = syn.cm_pgm_scenario(n_samples=512, seed=0, lattice=(4, 4), n_months=3, n_countries=2)" + ] + }, + { + "cell_type": "markdown", + "id": "97fb3e36", + "metadata": {}, + "source": [ + "## 1 · The problem\n", + "\n", + "A grid model and a country model are trained separately, so the grid cells of a country\n", + "do **not** sum to the country forecast. Downstream consumers need them *coherent* — a\n", + "food-security map that doesn't add up to the national number is not usable.\n", + "\n", + "Reconciliation enforces that. Crucially it works **per posterior draw**, in distribution,\n", + "not on point estimates: within each draw, the grid cells of a country are rescaled to sum\n", + "to that draw's country total. The result is a new grid posterior that is **conservation-\n", + "correct sample-by-sample**, so every downstream summary (HDI, exceedance, tail risk) stays\n", + "internally consistent." + ] + }, + { + "cell_type": "markdown", + "id": "bb47159a", + "metadata": {}, + "source": [ + "## 2 · `reconcile_proportional` — the leaf operation\n", + "\n", + "The numpy core: `reconcile_proportional(grid, country)` rescales `grid` `(S, cells)` so\n", + "each draw sums to `country` `(S,)`, **top-down by forecast proportion**. Three guarantees,\n", + "shown on a tiny explicit example: per-draw conservation, **zeros stay zero**, and the\n", + "**all-zero-draw edge** (a draw where every cell is 0 has no proportions to scale — it stays\n", + "all-zero rather than dividing by zero)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d0ea6fb5", + "metadata": {}, + "outputs": [], + "source": [ + "rng = np.random.default_rng(0)\n", + "S, C = 4000, 4\n", + "grid = rng.gamma(2.0, 1.5, size=(S, C)).astype(np.float32)\n", + "grid[:, 3] = 0.0 # cell 3 is a structural zero (quiet cell)\n", + "grid[0, :] = 0.0 # draw 0 is all-zero (the edge case)\n", + "country = rng.gamma(20.0, 1.5, size=S).astype(np.float32) # country forecast per draw\n", + "country[0] = 0.0\n", + "\n", + "recon = reconcile_proportional(grid, country)\n", + "\n", + "draw_sums = recon.sum(axis=1)\n", + "print(\"per-draw conservation (active draws): max |sum - country| =\",\n", + " f\"{np.abs(draw_sums[1:] - country[1:]).max():.2e}\")\n", + "print(\"zero cell stays zero :\", bool((recon[:, 3] == 0).all()))\n", + "print(\"all-zero draw stays all-zero :\", bool((recon[0] == 0).all()))\n", + "print(\"non-negative :\", bool((recon >= 0).all()))" + ] + }, + { + "cell_type": "markdown", + "id": "bf492838", + "metadata": {}, + "source": [ + "## 3 · Make it visible\n", + "\n", + "On the scenario, reconcile the whole grid (via the module, §4) and look at one country in\n", + "one month: the per-cell means barely move (proportions are preserved) but the **per-draw\n", + "country sum** collapses from the grid model's own spread onto the country forecast — that\n", + "collapse *is* reconciliation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4d90ad20", + "metadata": {}, + "outputs": [], + "source": [ + "module = ReconciliationModule(sc.map_keys, sc.map_vals)\n", + "recon_pgm = module.reconcile(sc.cm_prediction, sc.pgm_prediction)\n", + "\n", + "# pick country 70, month 500\n", + "cm_units = sc.pgm_index.cross_level_align_arrays(sc.map_keys, sc.map_vals, SpatialLevel.CM).unit\n", + "rows = np.where((sc.pgm_index.time == 500) & (cm_units == 70))[0]\n", + "ci = int(np.where((sc.cm_index.time == 500) & (sc.cm_index.unit == 70))[0][0])\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(9, 3))\n", + "x = np.arange(len(rows))\n", + "axes[0].bar(x - 0.2, sc.pgm_prediction.values[rows].mean(1), 0.4, label=\"raw\")\n", + "axes[0].bar(x + 0.2, recon_pgm.values[rows].mean(1), 0.4, label=\"reconciled\")\n", + "axes[0].set_title(\"per-cell mean (proportions kept)\", fontsize=9)\n", + "axes[0].set_xlabel(\"grid cell\"); axes[0].legend(fontsize=8)\n", + "axes[1].hist(sc.pgm_prediction.values[rows].sum(0), bins=40, alpha=0.6, label=\"raw grid sum\")\n", + "axes[1].hist(recon_pgm.values[rows].sum(0), bins=40, alpha=0.6, label=\"reconciled sum\")\n", + "axes[1].hist(sc.cm_prediction.values[ci], bins=40, alpha=0.6, label=\"country forecast\")\n", + "axes[1].set_title(\"per-draw country sum\", fontsize=9); axes[1].legend(fontsize=8); axes[1].set_yticks([])\n", + "fig.suptitle(\"country 70, month 500 — reconciliation snaps the grid sum onto the country\", fontsize=10)\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "81ee0c6c", + "metadata": {}, + "source": [ + "## 4 · `ReconciliationModule` — the orchestrator\n", + "\n", + "The module holds the **injected** `(time, priogrid) → country` mapping (geography is\n", + "injected, never embedded or fetched — ADR-014) and applies it: `reconcile(cm, pgm)`\n", + "validates the inputs and returns a **new** pgm frame (de-mutated — the caller's frame is\n", + "never touched). It **fails loud** when the inputs are inconsistent." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2dfb7a98", + "metadata": {}, + "outputs": [], + "source": [ + "before = sc.pgm_prediction.values.copy()\n", + "out = module.reconcile(sc.cm_prediction, sc.pgm_prediction)\n", + "print(\"returns a new frame, input de-mutated :\", np.array_equal(sc.pgm_prediction.values, before))\n", + "print(\"output stays at PGM level :\", out.index.level is SpatialLevel.PGM)\n", + "\n", + "# fail-loud: a mapping that doesn't cover every grid (time, unit)\n", + "bad = ReconciliationModule(sc.map_keys[:2], sc.map_vals[:2])\n", + "try:\n", + " bad.reconcile(sc.cm_prediction, sc.pgm_prediction)\n", + "except ValueError as e:\n", + " print(\"fails loud on an incomplete mapping :\", str(e)[:58], \"...\")\n", + "\n", + "# fail-loud: levels swapped (cm where pgm expected)\n", + "try:\n", + " module.reconcile(sc.pgm_prediction, sc.pgm_prediction)\n", + "except ValueError as e:\n", + " print(\"fails loud on a wrong-level input :\", str(e)[:58], \"...\")" + ] + }, + { + "cell_type": "markdown", + "id": "3eea4c0d", + "metadata": {}, + "source": [ + "## 5 · Contract properties\n", + "\n", + "The package *publishes* its contract — a consumer re-runs `assert_reconcile_contract` in\n", + "its own CI. And the mapping is genuinely load-bearing: change the injected\n", + "`(time, unit) → country` map and the reconciliation changes, because each cell is grouped\n", + "under (and scaled to) a *different* country total." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3ab147b", + "metadata": {}, + "outputs": [], + "source": [ + "assert_reconcile_contract(sc.cm_prediction, sc.pgm_prediction, sc.map_keys, sc.map_vals)\n", + "print(\"all reconcile-contract laws hold ✓ (sum-to-country, zero-preservation, \"\n", + " \"non-negativity, level/shape)\")\n", + "\n", + "# mapping sensitivity: flip every grid cell to the OTHER country\n", + "flipped_vals = np.where(sc.map_vals == sc.countries[0], sc.countries[1], sc.countries[0])\n", + "recon_flipped = ReconciliationModule(sc.map_keys, flipped_vals).reconcile(\n", + " sc.cm_prediction, sc.pgm_prediction\n", + ")\n", + "moved = np.abs(recon_pgm.values - recon_flipped.values).mean()\n", + "print(f\"\\nre-injecting a different mapping changes the result (mean |Δ| = {moved:.3f}) — \"\n", + " \"the mapping is injected and load-bearing, not decorative.\")" + ] + }, + { + "cell_type": "markdown", + "id": "deb5d655", + "metadata": {}, + "source": [ + "## 6 · Joint sampling & subadditivity\n", + "\n", + "Because reconciliation is per-draw, the country distribution is a **joint** sum — so, as\n", + "in notebook 02 §8, you must aggregate the *distribution* then summarize. A consequence for\n", + "risk: expected-shortfall is **subadditive** — the worst-case of the joint country total is\n", + "*less than* the sum of the cells' worst-cases (the cells' bad draws don't all line up). So\n", + "`ES(aggregate) ≠ Σ ES(cell)`; summing per-cell tail risk **overstates** country risk." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d0bf5222", + "metadata": {}, + "outputs": [], + "source": [ + "# raw grid, country 70, month 500\n", + "country_dist = aggregate_distributions(sc.pgm_prediction, sc.mapping, SpatialLevel.CM)\n", + "crow = int(np.where((country_dist.index.time == 500) & (country_dist.index.unit == 70))[0][0])\n", + "\n", + "es_joint = expected_shortfall(country_dist, [0.1])[crow, 0] # ES of the joint sum\n", + "es_cells = expected_shortfall(sc.pgm_prediction, [0.1])[rows, 0].sum() # sum of per-cell ES\n", + "print(f\"worst-10% expected shortfall, country 70:\")\n", + "print(f\" ES(aggregate) (joint, correct) : {es_joint:8.1f}\")\n", + "print(f\" Σ ES(cell) (naive sum) : {es_cells:8.1f}\")\n", + "print(f\" diversification (Σ - joint) : {es_cells - es_joint:8.1f} (subadditivity)\")" + ] + }, + { + "cell_type": "markdown", + "id": "2e46a205", + "metadata": {}, + "source": [ + "## 7 · Provenance — bit-identity is *faithful relocation*, not method validation\n", + "\n", + "`reconcile_proportional` is a **byte-faithful numpy port** of views-reporting's torch\n", + "reconciler, proven bit-identical to a frozen torch oracle\n", + "(`scripts/verify_reconcile_parity.py --oracle` → `0.000e+00` error; the 136-case\n", + "head-to-head). The reconciler is also deterministic — same inputs, same bytes out:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a97fab8", + "metadata": {}, + "outputs": [], + "source": [ + "a = module.reconcile(sc.cm_prediction, sc.pgm_prediction).values\n", + "b = module.reconcile(sc.cm_prediction, sc.pgm_prediction).values\n", + "print(\"deterministic (bit-identical reruns):\", np.array_equal(a, b))" + ] + }, + { + "cell_type": "markdown", + "id": "39c32939", + "metadata": {}, + "source": [ + "**Read this carefully.** Bit-identity proves the relocation is *faithful* — the new\n", + "numpy reconciler reproduces the old served numbers exactly. It says **nothing** about\n", + "whether per-draw proportional top-down is the *right* method. That question — and the\n", + "literature — is the next section." + ] + }, + { + "cell_type": "markdown", + "id": "1b823ad2", + "metadata": {}, + "source": [ + "---\n", + "## 🔬 Is it a *good* method? (panel additions)" + ] + }, + { + "cell_type": "markdown", + "id": "2906a303", + "metadata": {}, + "source": [ + "### A · Where proportional sits in the literature (register C-60)\n", + "\n", + "Hierarchical-forecast reconciliation has a well-developed theory. **Top-down proportional**\n", + "(what ships here, FPP3 terminology) is the **pragmatic baseline**: simple, conservative,\n", + "and — applied per draw — distribution-aware. But it is known to be *suboptimal*:\n", + "\n", + "* **MinT** (Wickramasuriya et al. 2019) reconciles by minimising the trace of the\n", + " reconciled error covariance — provably the minimum-variance unbiased linear reconciliation.\n", + "* **Probabilistic reconciliation** (Panagiotelis et al. 2023) reconciles the *joint\n", + " distribution* coherently, rather than rescaling per draw.\n", + "\n", + "Adopting a principled reconciler is **deliberately deferred** (views-postprocessing C-37):\n", + "this package ships the faithful port first, behind a stable interface, so a better method\n", + "can be added as a sibling without breaking consumers. **Bit-identity to the torch oracle is\n", + "not evidence the method is good — only that the port is faithful.**" + ] + }, + { + "cell_type": "markdown", + "id": "7f7263cd", + "metadata": {}, + "source": [ + "### B · Does reconciliation *help*? (against the known truth)\n", + "\n", + "Because the synthetic DGP has a known true cell mean and country total, we can ask whether\n", + "reconciling moves estimates *toward* the truth — or just enforces the constraint. The honest\n", + "answer is the latter: reconciliation buys **coherence**, and accuracy at the country level\n", + "becomes the **country model's** accuracy; per cell it can move *away* from truth when the\n", + "country forecast disagrees with the grid." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de01b14c", + "metadata": {}, + "outputs": [], + "source": [ + "# country-level: raw grid sum vs reconciled (== country forecast) vs the TRUE total\n", + "true_tot = np.array([sc.country_total_truth[(int(t), int(u))]\n", + " for t, u in zip(sc.cm_index.time, sc.cm_index.unit)])\n", + "raw_country = np.array([ # raw grid summed to each (time, country)\n", + " sc.pgm_prediction.values[(sc.pgm_index.time == t) & (cm_units == u)].sum(0).mean()\n", + " for t, u in zip(sc.cm_index.time, sc.cm_index.unit)])\n", + "recon_country = np.array([\n", + " recon_pgm.values[(sc.pgm_index.time == t) & (cm_units == u)].sum(0).mean()\n", + " for t, u in zip(sc.cm_index.time, sc.cm_index.unit)])\n", + "print(\"country-total mean abs error vs truth:\")\n", + "print(f\" raw grid sum : {np.abs(raw_country - true_tot).mean():7.3f}\")\n", + "print(f\" reconciled (=cm fc) : {np.abs(recon_country - true_tot).mean():7.3f}\")\n", + "\n", + "# per-cell: did reconciliation move cells toward or away from their true means?\n", + "true_cell = np.array([sc.cell_total_truth[(int(t), int(u))]\n", + " for t, u in zip(sc.pgm_index.time, sc.pgm_index.unit)])\n", + "raw_cell = sc.pgm_prediction.values.mean(1)\n", + "rec_cell = recon_pgm.values.mean(1)\n", + "print(\"\\nper-cell mean abs error vs truth:\")\n", + "print(f\" raw grid : {np.abs(raw_cell - true_cell).mean():7.3f}\")\n", + "print(f\" reconciled : {np.abs(rec_cell - true_cell).mean():7.3f}\")\n", + "print(\"\\nVerdict: reconciliation guarantees coherence (the grid sums to the country, \"\n", + " \"exactly),\\nbut coherence is not accuracy. Here it *improved* the country total \"\n", + " \"(it adopted the\\ntighter country model) yet *worsened* the per-cell estimates \"\n", + " \"(top-down scaling injects\\nthe country forecast's per-draw noise into every cell \"\n", + " \"and overrides the grid's own\\nper-cell signal). Which way it nets depends on the \"\n", + " \"level you care about and on whether\\nthe country model beats the grid aggregate — \"\n", + " \"exactly the trade a principled reconciler\\n(MinT / probabilistic, §A) is designed \"\n", + " \"to optimise. Coherence is the deliverable here;\\naccuracy stays the model's job.\")" + ] + }, + { + "cell_type": "markdown", + "id": "c96b42f4", + "metadata": {}, + "source": [ + "### C · A spatial / map view (register C-61)\n", + "\n", + "Reconciliation *redistributes* mass across the grid. On the toy lattice (synthetic\n", + "coordinates — no real geography), raw vs reconciled point estimates and the change map show\n", + "*where* it moves forecasts." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce1c76fe", + "metadata": {}, + "outputs": [], + "source": [ + "nr, nc = sc.lattice_shape\n", + "m0 = (sc.pgm_index.time == 500)\n", + "def to_grid(vals_full):\n", + " g = np.full((nr, nc), np.nan)\n", + " for row in np.where(m0)[0]:\n", + " r, c = sc.coords[int(sc.pgm_index.unit[row])]; g[r, c] = vals_full[row]\n", + " return g\n", + "\n", + "raw_pt = sc.pgm_prediction.values.mean(1)\n", + "rec_pt = recon_pgm.values.mean(1)\n", + "fig, axes = plt.subplots(1, 3, figsize=(12, 3.4))\n", + "g_raw, g_rec = to_grid(raw_pt), to_grid(rec_pt)\n", + "vmax = np.nanmax([g_raw, g_rec])\n", + "for ax, (lab, g, kw) in zip(axes, [\n", + " (\"raw grid (mean)\", g_raw, dict(cmap=\"magma\", vmin=0, vmax=vmax)),\n", + " (\"reconciled (mean)\", g_rec, dict(cmap=\"magma\", vmin=0, vmax=vmax)),\n", + " (\"change (rec − raw)\", g_rec - g_raw, dict(cmap=\"coolwarm\")),\n", + "]):\n", + " im = ax.imshow(g, **kw); ax.set_title(lab, fontsize=9)\n", + " ax.set_xticks([]); ax.set_yticks([]); fig.colorbar(im, ax=ax, shrink=0.8)\n", + "fig.suptitle(\"toy lattice, month 500 — reconciliation redistributes mass (illustrative)\", fontsize=10)\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0d71cb91", + "metadata": {}, + "source": [ + "### D · Decision relevance\n", + "\n", + "The reconciled country posterior is what a national food-security decision keys on. A\n", + "threshold exceedance on the **coherent** country total — `P(total > c)` — is the readout,\n", + "now guaranteed consistent with the grid map above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c4a6113", + "metadata": {}, + "outputs": [], + "source": [ + "recon_country_dist = aggregate_distributions(recon_pgm, sc.mapping, SpatialLevel.CM)\n", + "c70 = int(np.where((recon_country_dist.index.time == 500) & (recon_country_dist.index.unit == 70))[0][0])\n", + "draws = recon_country_dist.values[c70]\n", + "severe = float(np.quantile(draws, 0.75))\n", + "band = hdi(recon_country_dist, mass=0.9)[c70]\n", + "print(f\"country 70, month 500 — reconciled country total:\")\n", + "print(f\" 90% HDI : [{band[0]:.1f}, {band[1]:.1f}]\")\n", + "print(f\" P(total > {severe:.0f}) (severe) : {float((draws > severe).mean()):.2f}\")\n", + "print(\" this exceedance is coherent with the per-cell grid map by construction.\")" + ] + }, + { + "cell_type": "markdown", + "id": "af2f08c3", + "metadata": {}, + "source": [ + "## Reconciliation in one screen\n", + "\n", + "| You saw | The takeaway |\n", + "|---|---|\n", + "| `reconcile_proportional` (§2) | per-draw top-down; conserves, preserves zeros, handles the all-zero edge |\n", + "| `ReconciliationModule` (§4) | injected mapping (ADR-014), de-mutated, fail-loud |\n", + "| `assert_reconcile_contract` (§5) | published contract; the mapping is load-bearing |\n", + "| `ES(aggregate) ≠ Σ ES(cell)` (§6) | joint sampling; tail risk is subadditive |\n", + "| bit-identity to the oracle (§7) | proves *faithful relocation*, **not** method quality |\n", + "| proportional vs MinT/probabilistic (§A) | a pragmatic baseline; the principled upgrade is deferred (C-37) |\n", + "| does-it-help (§B) | reconciliation buys **coherence**, not accuracy — that stays the model's job |\n", + "\n", + "That closes the tour: [`01`](01_frames.ipynb) the storage contract, [`02`](02_summaries.ipynb)\n", + "reading honest summaries off a posterior, and `03` making those posteriors coherent across\n", + "spatial levels — all on the frozen `views_frames` surface, all on synthetic data." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/README.md b/notebooks/README.md new file mode 100644 index 0000000..0496ba2 --- /dev/null +++ b/notebooks/README.md @@ -0,0 +1,72 @@ +# `views-frames` notebooks + +Runnable, synthetic-data showcases of the three packages in this repo — what the frames +**are** and what they can do, the **summaries** you can read off a posterior, and how +**reconciliation** works. They double as living, public-API-only demos for consumer-repo +authors migrating from pandas DataFrames onto frames. + +> **Status: all three built.** `01_frames`, `02_summaries` and `03_reconciliation` are +> built and runnable — public frozen API only, synthetic data, *Run All* < 1 min each. +> Built via the notebook epics [#148](../../../issues/148) / [#156](../../../issues/156) / +> [#166](../../../issues/166). The optional non-blocking `nbmake` CI check is the one +> remaining item ([#151](../../../issues/151)). + +## The three notebooks + +| Notebook | Package | Shows | +|---|---|---| +| [`01_frames.ipynb`](01_frames.ipynb) ✅ | `views_frames` | The frames as a typed storage contract — `SpatioTemporalIndex`, the three sibling frames, immutability/zero-copy, cross-level alignment, `save`/`load` (npz + arrow), fail-loud construction. | +| [`02_summaries.ipynb`](02_summaries.ipynb) ✅ | `views_frames_summarize` | Posterior summaries over the sample axis — MAP, the HDI tower, quantiles, exceedance, expected-shortfall, the bimodality flag — swept over a **zoo of distribution shapes**, with **calibration/coverage checks**, ET-vs-HDI, **failure modes**, a **toy-lattice map view**, and a decision-relevance framing (panel additions, register C-59/C-61). | +| [`03_reconciliation.ipynb`](03_reconciliation.ipynb) ✅ | `views_frames_reconcile` | Reconciling grid forecasts to country totals — `reconcile_proportional` + `ReconciliationModule`, with conservation / zero-preservation / joint-sampling, plots/animation, the **reconciliation-literature context** (proportional vs MinT/probabilistic; bit-identity ≠ method-quality) and a **does-it-help** check (panel additions, register C-60). | + +## Conventions + +- **Public, frozen API only** — so each notebook is also a contract demo. A cell that *wants* a + convenience that doesn't exist (e.g. `frame.to_parquet`) is a **demand signal** to record (see + register D-11), never a reason to reach into the leaf. +- **Synthetic data only** — fixed seeds, generated in-notebook; no `viewser`, no domain fetches, + no `views_*` consumer imports (zero domain knowledge — ADR-001). +- **Un-gated dev artifact** — `notebooks/` sits beside `research/` and `scripts/`, outside `src/`; + the package never imports it, so the import-DAG and the frozen core are untouched. +- **Light & reproducible** — small sample sizes that render fast; *Run All* should finish in well + under a minute. + +## Running them + +The notebooks need plotting/jupyter deps the package itself does not. These live in the +**optional `[docs]` extra**, never the runtime dependencies (the core stays numpy-only): + +```bash +uv sync --extra docs # matplotlib, scipy, jupyterlab, + the [arrow] extra for parquet +uv run jupyter lab notebooks/ +``` + +The `[docs]` extra self-references `[arrow]`, so a single `--extra docs` also enables the +parquet round-trip demo in `01_frames.ipynb`. The `notebooks/` tree is excluded from the +lint/coverage gate (`[tool.ruff] extend-exclude`), so it never touches the frozen core or the +import-DAG. + +## Setup decisions (resolved during the build) + +- [x] Add the optional `[docs]` dependency group to `pyproject.toml` (matplotlib, scipy, jupyter, …). — #149 +- [x] Exclude `notebooks/` from the lint/coverage gate (as `research/` is) — they are not gated src. — #149 +- [x] **`nbmake` CI check** — adopted and **wired** as a **non-blocking** job + (`.github/workflows/notebooks.yml`: `pytest --nbmake notebooks/`, `continue-on-error: true`) so it + catches frozen-API drift without ever blocking the core gate. `nbmake` rides in the `[docs]` extra. — #151 +- [x] Confirm the per-notebook roadmaps — all three built and runnable. + +## Source material + +**Self-contained by rule:** each notebook generates its own synthetic data and renders its own +plots/animations from code in the notebook — it depends on **no file outside this repo**. Where +prior exploration exists (earlier HDI/tower or reconciliation prototypes), we **port the useful +logic into the notebook** during the build; we never reference notebooks or figures scattered +elsewhere on disk. + +In-repo material we may adapt: + +- `research/map_hdi/` — the synthetic distribution battery + tower/density plotting (notebook 02). +- `tests/fixtures/reconciliation_*.npz` — a realistic cm/pgm example if we want one (notebook 03). +- `scripts/verify_reconcile_parity.py`, `tests/test_reconcile_conformance.py` — drift/conservation + + scenario-builder logic to borrow (notebook 03). +- `views-faoapi/notebooks/quickstart_*.ipynb` — in-platform tone/structure reference. diff --git a/notebooks/_synthetic.py b/notebooks/_synthetic.py new file mode 100644 index 0000000..8cb27db --- /dev/null +++ b/notebooks/_synthetic.py @@ -0,0 +1,398 @@ +"""Synthetic, ground-truth-carrying data generators for the showcase notebooks. + +An **un-gated dev helper** — it lives in ``notebooks/`` (outside ``src/``, excluded +from the lint/coverage gate) and is imported by ``01_frames`` / ``02_summaries`` / +``03_reconciliation``. It is *not* part of the package and is never imported by +``src/``. + +Design rules it obeys (the same conventions as the notebooks): + +* **numpy only**, and frames are built through the **public** ``views_frames`` API; + no ``views_*`` consumer, ``viewser``, or domain import (ADR-001, the import-DAG). +* **Self-contained** — the distribution-family parametrisations are *copied in* from + ``research/map_hdi/benchmark/battery.py``, never imported, so the notebooks depend + on nothing outside this repo. +* **Ground-truth-carrying** — every generator returns the data **and the known latent + truth**: the true mean / mode / quantiles of each shape, and for the cm/pgm scenario + the true country total. That is what lets ``02_summaries`` check interval *coverage* + and point-estimate *recovery*, and ``03_reconciliation`` ask whether reconciliation + moves the grid estimates *toward* the truth. +* **Reproducible & light** — fixed seeds, small sample sizes; everything here renders + fast inside the notebooks' < 1 min *Run All* budget. +""" + +from __future__ import annotations + +from dataclasses import dataclass + +import numpy as np +from numpy.typing import NDArray + +from views_frames import ( + FeatureFrame, + FrameMetadata, + PredictionFrame, + SpatialLevel, + SpatioTemporalIndex, + TargetFrame, +) + +__all__ = [ + "Truth", + "ZooFrames", + "CmPgmScenario", + "ZOO_SHAPES", + "distribution_zoo", + "zoo_frames", + "cm_pgm_scenario", + "frame_from_samples", +] + +_REF_SIZE = 40_000 # oracle-sample size behind each Truth (tight MC error on the truth) +_BASE_MONTH = 500 +_BASE_PRIOGRID = 1000 +_BASE_COUNTRY = 70 + +# The six canonical shapes of the VIEWS estimand. Params are FIXED (not random) so +# the zoo is fully reproducible and each shape is a labelled teaching example. +ZOO_SHAPES: dict[str, tuple[str, dict[str, float]]] = { + "zero_inflated": ("zi_gamma", {"p0": 0.55, "shape": 2.0, "scale": 1.5}), + "right_skewed_gamma": ("gamma", {"shape": 2.5, "scale": 1.2}), + "right_skewed_lognormal": ("lognormal", {"mu": 0.6, "sigma": 0.7}), + "near_symmetric": ("gamma", {"shape": 30.0, "scale": 0.2}), + "heavy_tailed": ("lognormal", {"mu": 0.5, "sigma": 1.5}), + # two well-separated, tight (clipped-normal) bumps — a *genuinely* two-peaked + # posterior the conservative `bimodality` detector should flag (vs a merely + # skewed/heavy tail, which it should not). + "bimodal": ("bimodal", {"w": 0.5, "mu1": 2.0, "s1": 0.7, "mu2": 10.0, "s2": 1.0}), +} + + +# ---- distribution families (copied from battery.py; the data-generating process) ---- + + +def _sample( + family: str, params: dict[str, float], n: int, rng: np.random.Generator +) -> NDArray[np.float64]: + """Draw ``n`` samples from the named family (the DGP behind a shape).""" + if family == "gamma": + x = rng.gamma(params["shape"], params["scale"], n) + elif family == "lognormal": + x = rng.lognormal(params["mu"], params["sigma"], n) + elif family == "zi_gamma": + x = rng.gamma(params["shape"], params["scale"], n) + x = np.where(rng.random(n) < params["p0"], 0.0, x) + elif family == "bimodal": + a = rng.normal(params["mu1"], params["s1"], n) + b = rng.normal(params["mu2"], params["s2"], n) + x = np.clip(np.where(rng.random(n) < params["w"], a, b), 0.0, None) + else: # pragma: no cover - guards a typo in ZOO_SHAPES + raise ValueError(f"unknown family: {family}") + return np.asarray(x, dtype=np.float64) + + +def _analytic_mode(family: str, params: dict[str, float]) -> float: + """The **true** mode of the DGP, in closed form. + + The notebooks rely on this being the genuine density peak. A histogram mode over the + reference sample is a binning artifact for heavy tails (it can land ~10x off) and + never returns exactly 0 for a zero-inflated atom — both would mislabel the "known + truth" the teaching tables compare estimators against. + """ + if family == "gamma": + k, theta = params["shape"], params["scale"] + return (k - 1.0) * theta if k >= 1.0 else 0.0 # gamma mode (k-1)θ, else at 0 + if family == "lognormal": + return float(np.exp(params["mu"] - params["sigma"] ** 2)) # lognormal mode + if family == "zi_gamma": + return 0.0 # the zero atom is the mode of a zero-inflated distribution + if family == "bimodal": + # the taller component peak (density ∝ weight / sigma) is the mode + d1 = params["w"] / params["s1"] + d2 = (1.0 - params["w"]) / params["s2"] + return params["mu1"] if d1 >= d2 else params["mu2"] + raise ValueError(f"unknown family: {family}") # pragma: no cover + + +@dataclass(frozen=True) +class Truth: + """The known latent truth behind a synthetic shape. + + ``reference`` is a large sorted oracle sample from the DGP; ``mean``/``mode`` and + :meth:`quantile` are read off it (tight MC error). :meth:`draw` produces *fresh* + actuals from the same DGP — used by ``02_summaries`` to test interval coverage. + """ + + name: str + family: str + params: dict[str, float] + reference: NDArray[np.float64] + mean: float + mode: float + + def quantile(self, p: float | NDArray[np.float64]) -> NDArray[np.float64]: + """True quantile(s) of the DGP at probability level(s) ``p``.""" + return np.quantile(self.reference, p) + + def draw(self, n: int, rng: np.random.Generator) -> NDArray[np.float64]: + """``n`` fresh draws from the same DGP (held-out 'actuals').""" + return _sample(self.family, self.params, n, rng) + + +def _make_truth( + name: str, family: str, params: dict[str, float], rng: np.random.Generator +) -> Truth: + ref = np.sort(_sample(family, params, _REF_SIZE, rng)) + mode = _analytic_mode(family, params) + return Truth(name, family, dict(params), ref, float(ref.mean()), mode) + + +# ---- the distribution zoo ---------------------------------------------------- + + +def distribution_zoo( + *, n_samples: int = 1024, seed: int = 0 +) -> dict[str, tuple[NDArray[np.float32], Truth]]: + """``{shape_name: (samples (n_samples,), Truth)}`` for the six canonical shapes. + + Call with ``n_samples=32`` for the low-sample regime and ``n_samples=1024`` for + the pooled regime (``02_summaries`` contrasts the two). + """ + rng = np.random.default_rng(seed) + out: dict[str, tuple[NDArray[np.float32], Truth]] = {} + for name, (family, params) in ZOO_SHAPES.items(): + cell_rng = np.random.default_rng(rng.integers(1 << 62)) + truth = _make_truth(name, family, params, cell_rng) + samples = _sample(family, params, n_samples, cell_rng).astype(np.float32) + out[name] = (samples, truth) + return out + + +def frame_from_samples( + samples: NDArray[np.float64], *, level: SpatialLevel = SpatialLevel.PGM +) -> PredictionFrame: + """Wrap a raw ``(R, S)`` array of posteriors as a ``PredictionFrame``. + + A convenience for the notebooks' coverage / recovery / Monte-Carlo replica loops, + which build many one-shot frames from arrays of draws; it owns the synthetic index + (one synthetic ``unit`` per row at ``_BASE_MONTH``) so those cells don't each + re-hardcode it. + """ + arr = np.asarray(samples, dtype=np.float32) + r = arr.shape[0] + index = SpatioTemporalIndex( + time=np.full(r, _BASE_MONTH, dtype=np.int64), + unit=np.arange(r, dtype=np.int64), + level=level, + ) + return PredictionFrame(arr, index) + + +@dataclass(frozen=True) +class ZooFrames: + """The zoo as the three sibling frames (all on one shared index).""" + + names: list[str] + truths: list[Truth] + index: SpatioTemporalIndex + prediction: PredictionFrame # (N, S) — one posterior per shape + target: TargetFrame # (N, 1) — a held-out actual per shape + features: FeatureFrame # (N, F, S) — illustrative model inputs + + +def zoo_frames( + *, n_samples: int = 1024, seed: int = 0, n_features: int = 2 +) -> ZooFrames: + """Build the distribution zoo as a ``PredictionFrame`` / ``TargetFrame`` / + ``FeatureFrame`` trio sharing one ``SpatioTemporalIndex`` (one row per shape).""" + zoo = distribution_zoo(n_samples=n_samples, seed=seed) + names = list(zoo) + truths = [zoo[k][1] for k in names] + samples = np.stack([zoo[k][0] for k in names]).astype(np.float32) # (N, S) + n = len(names) + + time = np.full(n, _BASE_MONTH, dtype=np.int64) + unit = np.arange(_BASE_PRIOGRID, _BASE_PRIOGRID + n, dtype=np.int64) + index = SpatioTemporalIndex(time=time, unit=unit, level=SpatialLevel.PGM) + meta = FrameMetadata(model="synthetic-zoo", run_type="notebook", seed=seed) + + prediction = PredictionFrame(samples, index, meta) + + arng = np.random.default_rng(seed + 1) + actuals = np.array([[float(t.draw(1, arng)[0])] for t in truths], dtype=np.float32) + target = TargetFrame(actuals, index, meta) + + frng = np.random.default_rng(seed + 2) + base = samples.mean(axis=1).reshape(n, 1, 1) + feats = (base + frng.normal(0.0, 1.0, size=(n, n_features, n_samples))).astype( + np.float32 + ) + feat_names = [f"feature_{i}" for i in range(n_features)] + features = FeatureFrame(feats, index, feat_names, meta) + + return ZooFrames(names, truths, index, prediction, target, features) + + +# ---- the cm/pgm reconciliation scenario -------------------------------------- + + +@dataclass(frozen=True) +class CmPgmScenario: + """A grid (pgm) ↔ country (cm) scenario with an injected mapping + known totals. + + Everything ``03_reconciliation`` needs: a pgm ``PredictionFrame`` of grid + forecasts, a cm ``PredictionFrame`` of country totals, the injected ``(time, + priogrid) -> country`` mapping in both ``dict`` and array form, the lattice + coordinates for the toy map view, and the **true** per-``(time, country)`` total + (the sum of the cells' true means) to check does-reconciliation-help against. + """ + + months: list[int] + countries: list[int] + lattice_shape: tuple[int, int] + coords: dict[int, tuple[int, int]] # priogrid_id -> (row, col) + pgm_to_country: dict[int, int] # priogrid_id -> country_id + pgm_index: SpatioTemporalIndex + cm_index: SpatioTemporalIndex + pgm_prediction: PredictionFrame # (n_months * G, S) + cm_prediction: PredictionFrame # (n_months * C, S) + map_keys: NDArray[np.int64] # (M, 2) (time, priogrid) + map_vals: NDArray[np.int64] # (M,) country + mapping: dict[tuple[int, int], int] # {(time, priogrid): country} + country_total_truth: dict[tuple[int, int], float] # (time, country) -> true sum + cell_total_truth: dict[tuple[int, int], float] # (time, priogrid) -> true cell mean + + +def cm_pgm_scenario( + *, + n_samples: int = 256, + seed: int = 0, + lattice: tuple[int, int] = (4, 4), + n_months: int = 3, + n_countries: int = 2, +) -> CmPgmScenario: + """Build a small reproducible cm/pgm reconciliation scenario. + + The grid cells are laid on a ``lattice`` (so the notebook can draw a toy map), the + countries split the columns, and each cell draws from a quiet-leaning family. The + cm country forecast is the true total times per-draw lognormal noise — so the grid + draws do **not** already sum to the country forecast (that is the job of + reconciliation), and the *true* total is known for the does-it-help check. + """ + nrows, ncols = lattice + rng = np.random.default_rng(seed) + + priogrids: list[int] = [] + coords: dict[int, tuple[int, int]] = {} + pgm_to_country: dict[int, int] = {} + for r in range(nrows): + for c in range(ncols): + pg = _BASE_PRIOGRID + r * ncols + c + priogrids.append(pg) + coords[pg] = (r, c) + pgm_to_country[pg] = _BASE_COUNTRY + (c * n_countries) // ncols + countries = sorted(set(pgm_to_country.values())) + months = [_BASE_MONTH + i for i in range(n_months)] + month_factor = {m: 1.0 + 0.25 * i for i, m in enumerate(months)} + + # per-cell DGP (round-robin families, quiet-leaning) + its true (base) mean + fam_cycle = [ + "zero_inflated", + "right_skewed_gamma", + "heavy_tailed", + "zero_inflated", + "near_symmetric", + "right_skewed_lognormal", + ] + cell_family: dict[int, str] = {} + cell_params: dict[int, dict[str, float]] = {} + cell_base_mean: dict[int, float] = {} + for k, pg in enumerate(priogrids): + family, params = ZOO_SHAPES[fam_cycle[k % len(fam_cycle)]] + cell_family[pg] = family + cell_params[pg] = params + cr = np.random.default_rng(rng.integers(1 << 62)) + cell_base_mean[pg] = float(_sample(family, params, _REF_SIZE, cr).mean()) + + # true per-(time, cell) means and per-(time, country) totals + cell_total_truth: dict[tuple[int, int], float] = { + (m, pg): month_factor[m] * cell_base_mean[pg] + for m in months + for pg in priogrids + } + country_total_truth: dict[tuple[int, int], float] = {} + for m in months: + for country in countries: + country_total_truth[(m, country)] = sum( + cell_total_truth[(m, pg)] + for pg in priogrids + if pgm_to_country[pg] == country + ) + + # pgm grid forecasts (time-major rows) + pg_time, pg_unit = [], [] + pg_vals: list[NDArray[np.float64]] = [] + for m in months: + for pg in priogrids: + cr = np.random.default_rng(rng.integers(1 << 62)) + pg_time.append(m) + pg_unit.append(pg) + draws = _sample(cell_family[pg], cell_params[pg], n_samples, cr) + pg_vals.append(month_factor[m] * draws) + pgm_index = SpatioTemporalIndex( + time=np.array(pg_time, dtype=np.int64), + unit=np.array(pg_unit, dtype=np.int64), + level=SpatialLevel.PGM, + ) + pgm_prediction = PredictionFrame( + np.array(pg_vals, dtype=np.float32), + pgm_index, + FrameMetadata(model="synthetic-pgm", run_type="notebook", seed=seed), + ) + + # cm country forecasts: true total × per-draw lognormal noise + cm_time, cm_unit = [], [] + cm_vals: list[NDArray[np.float64]] = [] + crng = np.random.default_rng(seed + 5) + for m in months: + for country in countries: + tt = country_total_truth[(m, country)] + cm_time.append(m) + cm_unit.append(country) + cm_vals.append(tt * np.exp(crng.normal(0.0, 0.12, n_samples))) + cm_index = SpatioTemporalIndex( + time=np.array(cm_time, dtype=np.int64), + unit=np.array(cm_unit, dtype=np.int64), + level=SpatialLevel.CM, + ) + cm_prediction = PredictionFrame( + np.array(cm_vals, dtype=np.float32), + cm_index, + FrameMetadata(model="synthetic-cm", run_type="notebook", seed=seed), + ) + + # injected mapping, both forms (dict for cross_level_align; arrays for the module) + map_keys = np.array([[m, pg] for m in months for pg in priogrids], dtype=np.int64) + map_vals = np.array( + [pgm_to_country[pg] for _ in months for pg in priogrids], dtype=np.int64 + ) + mapping = { + (int(m), int(pg)): int(pgm_to_country[pg]) for m in months for pg in priogrids + } + + return CmPgmScenario( + months=months, + countries=countries, + lattice_shape=lattice, + coords=coords, + pgm_to_country=pgm_to_country, + pgm_index=pgm_index, + cm_index=cm_index, + pgm_prediction=pgm_prediction, + cm_prediction=cm_prediction, + map_keys=map_keys, + map_vals=map_vals, + mapping=mapping, + country_total_truth=country_total_truth, + cell_total_truth=cell_total_truth, + ) diff --git a/pyproject.toml b/pyproject.toml index 7f4fc96..dc5a04f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "hatchling.build" [project] name = "views-frames" -version = "1.7.0" +version = "1.8.0" description = "The VIEWS platform data-contract layer: immutable array+identifier frames (numpy only, root of the dependency DAG)." authors = [ { name = "Simon Polichinel von der Maase", email = "simmaa@prio.org" }, @@ -39,6 +39,22 @@ Changelog = "https://github.com/views-platform/views-frames/blob/main/CHANGELOG. # Optional serialization extra — pyarrow is allowed ONLY behind io/ (README §3.1). [project.optional-dependencies] arrow = ["pyarrow>=14,<20"] +# Optional docs/notebooks extra — plotting + jupyter for the showcase notebooks +# (notebooks/, an un-gated dev artifact outside src/). NEVER runtime dependencies; +# self-references [arrow] so `uv sync --extra docs` also enables the parquet round-trip +# demo in notebooks/01_frames.ipynb. matplotlib/scipy are notebook-only (the core is +# numpy-only); these are imported by notebooks, never by the package. +docs = [ + "views-frames[arrow]", + "matplotlib>=3.8,<4", + "scipy>=1.11,<2", + "jupyterlab>=4,<5", + # nbmake drives the non-blocking notebook-execution CI check (issue #151): it just + # runs the notebooks end-to-end to catch frozen-API drift. A test tool, but it rides + # in [docs] so the single `uv sync --extra docs` provisions everything the notebooks + # job needs (kernel + plotting + the checker). + "nbmake>=1.5,<2", +] [dependency-groups] dev = [ @@ -76,9 +92,10 @@ show_missing = true [tool.ruff] line-length = 88 target-version = "py310" -# research/ is the volatile estimator lab (outside the frozen, gated src/); it is -# not held to the leaf's lint/format bar. -extend-exclude = ["research"] +# research/ is the volatile estimator lab and notebooks/ holds the un-gated showcase +# notebooks + their helper (notebooks/_synthetic.py) — both sit outside the frozen, +# gated src/ and are not held to the leaf's lint/format bar. +extend-exclude = ["research", "notebooks"] [tool.ruff.lint] select = ["E", "F", "I", "N", "UP", "B", "A", "C4", "SIM"] @@ -87,11 +104,13 @@ select = ["E", "F", "I", "N", "UP", "B", "A", "C4", "SIM"] # The falsification stubs assert against the README via long, literal regexes # whose lines cannot wrap without changing their semantics. "tests/test_falsification_*.py" = ["E501"] -# The reconciliation parity tests + their oracle-regeneration scripts are -# faithfully relocated from views-postprocessing (Epic 11 / ADR-023); their dense -# numpy/dict-comprehension assertions are kept verbatim rather than re-wrapped. +# The reconciliation parity tests are faithfully relocated from views-postprocessing +# (Epic 11 / ADR-023); their dense numpy/dict-comprehension assertions are kept verbatim +# rather than re-wrapped. "tests/test_reconciliation_*.py" = ["E501"] -"scripts/gen_reconciliation_*.py" = ["E501"] +# The dev/ops scripts (oracle regeneration; the reconciliation parity verifier) are not +# CI-run and carry dense numpy one-liners + report f-strings; not held to the wrap bar. +"scripts/*.py" = ["E501"] [tool.mypy] python_version = "3.10" diff --git a/reports/postmortems/2026-06_reconciliation_cutover_epic11.md b/reports/postmortems/2026-06_reconciliation_cutover_epic11.md new file mode 100644 index 0000000..f620470 --- /dev/null +++ b/reports/postmortems/2026-06_reconciliation_cutover_epic11.md @@ -0,0 +1,135 @@ +# Postmortem — the Epic 11 reconciliation cutover (cross-repo migration onto `views_frames_reconcile`) + +| Field | Value | +|---|---| +| Subject | The cross-repo half of Epic 11: the platform migrating *onto* the relocated reconciler and retiring the old copies — what it took to flip a live, served code path safely, and the stale-tracking gap that hid that it was already done | +| Window | 2026-06-26 — package released (v1.7.0), consumer repointed (views-models #191), old copy retired (views-postprocessing #62), all within the same window | +| Repos | `views-frames` (the canonical copy + the verification evidence + this record); `views-models` (the consumer repoint, C1); `views-postprocessing` (the retired copy, C2); `views-reporting` (the torch oracle source, now stranded); `views-pipeline-core` (the DIP-injection that made the cycle moot) | +| Governing docs | ADR-023; the Epic 11 tracker `#138`; cross-repo issues views-models #191, views-postprocessing #62, pipeline-core #221; the companion build postmortem `2026-06_views_frames_reconcile.md` | +| Outcome | The live reconciliation path is now served by `views_frames_reconcile` (numpy), injected at the views-models composition root; the stranded views-postprocessing copy is deleted; the views-reporting torch path is retired. The cutover was **safe by an established bit-identity chain** and landed as **phased** PRs (repoint, then delete), exactly as a re-baseline of served numbers should. The one real miss was process, not engineering: the in-repo `#138` tracker was never ticked when the sibling work landed, so a "go execute the cutover" request found it already complete. | + +--- + +## 1. What we did, and why + +The companion postmortem (`2026-06_views_frames_reconcile.md`) covers *building* the sibling +package — the faithful WET relocation of the reconciler into `views_frames_reconcile`, proven +bit-identical, released as v1.7.0. This one covers the **cross-repo cutover that followed**: +moving the platform's consumers onto the new canonical copy and retiring the old ones, so the +relocation actually *takes effect* rather than shipping a second unused copy. + +The work matters because reconciliation is a **served, production code path**. At the start of +Epic 11 there were three reconciler copies in flight: views-reporting (torch — the oracle +source), views-postprocessing (numpy — parity-proven but stranded, never repointed-to), and +views_frames_reconcile (the new numpy canonical). A cutover that simply "ships and hopes" would +re-baseline served numbers silently. The goal was the opposite: flip the path with an explicit +verification chain, in a reversible order, with the change auditable from the output. + +## 2. How it unfolded — the arc + +The cutover order was the one the runbook now codifies — **release → repoint → delete** — each +step its own PR: + +- **Release (✅ v1.7.0).** `views_frames_reconcile` published, pinned by the consumers. The + in-repo gates were green: the frozen views-reporting torch-oracle fixtures + (`tests/fixtures/reconciliation_*.npz`) and the 136-case new-vs-old bit-identity head-to-head + (`tests/test_reconcile_head_to_head.py`). +- **C1 — repoint the consumer (✅ views-models #191, PR #202, commit `97a66ed`).** The single + concrete-import seam — `views-models/reconciliation/reconciler_factory.py` — was repointed to + `from views_frames_reconcile import ReconciliationModule`. By ADR-014 that factory is the only + file that names the concrete, so C1 was a one-line import change plus its tests; the reconciler + is injected at the composition root (the ensemble `main`), not imported at module scope. +- **C2 — retire the old copy (✅ views-postprocessing #62, PR #63, commit `6af2020`).** Only after + C1 was merged and green: `views_postprocessing/reconciliation/` was deleted and its CIC entry + dropped, with a grep confirming no live imports of the old module remained anywhere. +- **The cycle was already moot.** pipeline-core's DIP port + adapter (PRs #195/#217) already break + the views-reporting/views-postprocessing cycle: the adapter imports only `views_frames`, and the + reconciler is injected, so pipeline-core never imports the concrete. Issue #221 ("collapse the + injection to a direct import") is therefore an *optional* simplification, not a required step, + and was deferred. + +The whole cutover was verified, after the fact, by re-running the parity verifier +(`scripts/verify_reconcile_parity.py --oracle`): the canonical copy reproduces the torch oracle +at **0.000e+00** absolute and relative error, with conservation within float32 summation noise. + +## 3. What went well + +- **The safety chain held end-to-end.** `new (views_frames_reconcile) == old vpp copy` + (bit-identical, 136 cases) and `old vpp == views-reporting torch oracle` (the frozen `.npz`), + therefore `new == the numbers production was built on`. The cutover never relied on "trust me" — + it relied on a transitive, re-runnable equality. +- **The order was right and the steps were phased.** Repoint (PR #202) and delete (PR #63) were + *separate* PRs, never batched. The old copy stayed as the rollback until the repoint was green. + This is exactly the discipline a served-numbers re-baseline needs, and it was followed without + drama. +- **ADR-014's single-seam design paid off.** Because the concrete is named in exactly one file per + consumer (the factory / composition root), C1 was genuinely a one-line change — not a + repo-wide find-and-replace. The injected-mapping boundary meant no geography moved anywhere. +- **The injection was already the right shape.** pipeline-core's DIP port (built earlier, #195/#217) + meant the cutover did *not* require a risky pipeline-core change — the consumer just swapped which + concrete the composition root injects. The earlier investment in decoupling de-risked this. + +## 4. What went wrong, and what we missed + +- **The in-repo tracker went stale — the cutover was *done* and `#138` didn't know.** C1 and C2 + merged and their sibling issues (views-models #191, views-postprocessing #62) closed, but the + views-frames `#138` checklist was never ticked and the issue stayed open. A later "investigate + and execute the cutover" request had to *discover by grep* that there was nothing left to execute. + Cross-repo tracking that lives in one repo but is satisfied in others drifts unless someone closes + the loop explicitly. (This postmortem + the `#138` closeout are that loop, late.) +- **Verification was against the oracle, not a live production slice.** The cutover was validated by + the frozen torch-oracle fixtures + the synthetic head-to-head — strong evidence, since the oracle + *is* the torch path's output — but no comparison was run against an actual served production + parquet (old path vs new path) at the repoint. The risk is low (the oracle chain is sound), but it + is a belt the runbook now provides a buckle for: `verify_reconcile_parity.py --compare`. Registered + as **C-58**. +- **Stale references were left in the consumer's docs.** views-models still carries + `views_postprocessing.reconciliation` mentions in a package docstring, an ADR, and test names + (the live import is correct; only the prose lags). Cosmetic, cross-repo, but the kind of residue a + cutover should sweep. + +## 5. What surprised us + +- **The hardest part of a "flip the path" cutover was *knowing it had already happened*.** The + engineering was a one-line import (C1) and a directory deletion (C2); the actual difficulty was + reconstructing the true state across four repos because the central tracker had drifted. The work + was trivial; the *bookkeeping* was where the time went. +- **The cycle never needed collapsing.** The instinct was that the cutover would unlock a satisfying + "delete the injection" simplification (#221). But the DIP port was already clean — it imports only + `views_frames` and injects the concrete — so there was nothing to fix, only an optional cosmetic + tidy. The earlier decoupling had already done the load-bearing work. +- **"Bit-identical" was, again, guaranteed by construction.** As in the build effort, the relocation + was byte-identical code modulo imports, so the verifier's `0.000e+00` was never in doubt — its + value is the *record*, not the discovery. + +## 6. What would be easier next time + +- **Close the cross-repo loop the moment the sibling work merges.** A tracker in repo A satisfied by + PRs in repos B and C must be ticked/closed deliberately — wire it into the consumer PR's checklist + ("update views-frames #138") so it never drifts into "is this done?" Add it to the runbook's + Phase 3/4 exit criteria. +- **Run the production-slice check at the repoint, not retrospectively.** `verify_reconcile_parity.py + --compare old_served.parquet new_served.parquet` is one command; make it a Phase-2 gate on every + future cutover so the evidence is live, not reconstructed. +- **Sweep the consumer's prose in the same PR as the code.** A repoint PR that updates the import + should also update the docstring/ADR/test-name references to the old module — otherwise the + cutover looks half-done in the consumer for months. +- **Keep the phased order and the rollback discipline.** Release → repoint → (green, observed) → + delete, each its own deploy, old copy retained until the repoint is proven. It cost nothing here + and it is the whole reason a served-numbers re-baseline can be undone with a single revert. + +## 7. Final state + +The platform's reconciliation is served by `views_frames_reconcile` (numpy, injected via the +views-models composition root, consumed by pipeline-core's EnsembleManager without importing the +concrete). The stranded views-postprocessing copy is deleted (#62); the views-reporting torch path is +retired but preserved as the parity oracle. views-models #191 and views-postprocessing #62 are closed; +the views-frames tracker `#138` is now closed too, with this record. The cutover's safety evidence is +re-runnable via `scripts/verify_reconcile_parity.py --oracle` (PASS, exact). The optional +injection-collapse (pipeline-core #221) remains a deferred cosmetic simplification, not a blocker. +`CONFORMANCE_FLOOR` stayed `1.0.0`; the frozen v1 surface was untouched throughout. + +The one-sentence lesson: **a cross-repo cutover is finished in the sibling repos but not *closed* +until the tracker that lives somewhere else is deliberately shut — the riskiest part of flipping a +served path turned out to be not the flip, which a one-line seam and a bit-identity chain made safe, +but knowing, across four repos, that it was already done.** diff --git a/reports/postmortems/2026-06_views_frames_leaf.md b/reports/postmortems/2026-06_views_frames_leaf.md new file mode 100644 index 0000000..3965ca4 --- /dev/null +++ b/reports/postmortems/2026-06_views_frames_leaf.md @@ -0,0 +1,164 @@ +# Postmortem — `views_frames`, the data-contract leaf (v0.1.0 → v1.4.0) + +| Field | Value | +|---|---| +| Subject | The leaf data contract: a numpy-only, immutable array+identifier value-object library at the root of the VIEWS dependency DAG — its constitution-first design, its freeze, and the boundary fights it provoked | +| Window | 2026-06-21 (`v0.1.0`, Epic 2) → 2026-06-24 (`v1.4.0`, provenance + envelope checker). The frozen v1.0 surface (ADR-018) has been unchanged since 2026-06-21. | +| Repos | `views-frames` (all code lives here); consumers `views-faoapi`, `views-pipeline-core`, `views-hydranet`, `views-baseline`, `views-reporting`, `views-evaluation` (each pins the leaf) | +| Governing docs | Constitutional ADRs 000–010; project ADRs 011–016, 018, 020; the conformance suite (ADR-005/016); README design bible; register C-01..C-31 (the design + first-code cluster) | +| Outcome | A small, stable, **provably pure** (import-DAG-enforced) leaf that froze at v1.0.0 and has carried two sibling packages and four consumers without a single breaking change. The leaf itself shipped almost no correctness bugs; every real defect lived at its *boundary* — what a consumer or a sibling leaked into it. The recurring tax was tooling (numpy-version mypy/determinism skew), not design. | + +--- + +## 1. What we did, and why + +The VIEWS platform's scaling failure was a **list-in-cell `object`-dtype DataFrame** — a pandas +cell holding a Python list of `S` posterior samples — measured at ~33× memory blow-up over dense +`float32` (register C-40/C-66) and the root cause of the report-stage OOM (#181). Worse, the +"prediction frame" concept was re-implemented, subtly differently, in every repo that touched +forecasts. There was no single, stable, dependency-light definition of *"an array aligned to +`(time, unit)`"* that the whole platform could agree on. + +`views_frames` is the answer: a **numpy-only** library of immutable array+identifier value objects +— `PredictionFrame (N, S)`, `TargetFrame (N, 1)`, `FeatureFrame (N, F, S)`, sharing a +`SpatioTemporalIndex {time, unit, level}` — that **depends on nothing internal** and sits at the +root of the dependency DAG, so every other repo can depend *toward* it without a cycle. The design +was settled before any code: a design-bible README, eleven constitutional/project ADRs, and a set +of falsification stubs, all ratified up front (Epic 1), then realised in Epic 2. + +The bet was deliberately conservative: make the contract **small, abstract, immutable, and frozen**, +push everything volatile (statistics, IO formats, domain geography) *out* of it, and enforce the +purity mechanically so it can never rot into a god-library. The whole platform's stability would +rest on this one package not changing underneath it. + +## 2. How it unfolded — the arc + +- **Design first (Epic 1).** The README design bible + ADRs 000–010 (the constitution) and the six + contract decisions (ADRs 011–016) were ratified *before code*: twin-unification as **Option C** + (frames are separate sibling classes sharing only `SpatioTemporalIndex` + `_validation` + + protocols + `io`; **no shared base** — ADR-011, register C-16/C-03); the **sample axis** as an + always-explicit trailing `S ≥ 1` (ADR-012); a **typed, optional-extensible metadata header** with + fixed `{time, unit}` identifiers (ADR-013); cross-level cm↔pgm alignment as a **consumer-injected + mapping**, never embedded or fetched (ADR-014); `SpatialLevel` as identifier vocabulary only + (ADR-015); and a **governed conformance floor** every consumer re-runs in CI (ADR-016). +- **v0.1.0 (Epic 2, 2026-06-21).** `SpatioTemporalIndex` + `_validation` + `cross_level_align`; the + three frames + `FrameMetadata`; `io/npz` (mmap) + `io/arrow` (flat-columnar parquet — the *only* + place `pyarrow` may be imported); and the **published conformance suite** + property tests + + falsification stubs. The import-DAG test (`tests/test_import_enforcement.py`) was wired from the + start as the executable form of ADR-002. +- **v0.2.0 / v0.3.0.** The summarize sibling was carved off (ADR-017 — its own postmortem); the leaf + got a **time-aware, vectorized `cross_level_align`** (`(time, unit)`-keyed, register C-20), the + columnar `cross_level_align_arrays` for grid scale (C-26), `select`/`reindex` frame ops, and a + string of numpy-floor hardening fixes (C-19/C-23/C-24/C-25). +- **v1.0.0 — the freeze (ADR-018, 2026-06-21).** After a second round of consumer review, the public + surface was frozen and `CONFORMANCE_FLOOR` set to `1.0.0`. PyPI Trusted Publishing was stood up. + From here, **everything is additive** (MINOR) or a fix (PATCH); the frozen surface never changes. +- **v1.0.1.** Test hardening (Epic 6) — protocol runtime-conformance assertions (C-37), IO + red-team paths (C-29), branch-coverage gate (C-36). No API change. +- **v1.4.0 (2026-06-24).** The one substantive *leaf* addition after the freeze: generic provenance + on `FrameMetadata` (`run_id` / `data_version`) and a **published `assert_frame_envelope`** checker + (ADR-020, register C-46/C-47), plus the C-51 direct reject-path tests. Additive; floor stayed + `1.0.0`. (ADR-020 also ratified that the `MetricFrame` evaluation-output contract stays *out* of + the leaf and lives in views-evaluation on the leaf's substrate — a boundary defence, not a feature.) + +## 3. What went well + +- **Constitution-first paid off.** Settling eleven ADRs before code felt heavy, but it meant every + later question (`where does reconciliation live?`, `can a magnitude rule sit in the leaf?`, `is + `MetricFrame` ours?`) had a ratified principle to answer it. The two sibling packages (summarize, + reconcile) and the ADR-020 boundary defence are all *applications* of ADR-001/014/017, not new + arguments. +- **The import-DAG test is the leaf's spine.** `FORBIDDEN = {pandas, polars, geopandas, wandb, + viewser, torch}` + a per-package allow-list, asserted by AST scan in CI, makes "the leaf is pure" + an executable, non-negotiable fact rather than a guideline. It has never had to fail loudly — + which is the point; it shapes what people even attempt. +- **The freeze held — completely.** `CONFORMANCE_FLOOR` has been `1.0.0` across **seven** subsequent + releases (v1.1–v1.7). Two sibling packages and provenance all landed as additive surface. No + consumer has ever eaten a breaking change. For an N-repo contract, this is the whole value + proposition, delivered. +- **Conformance-as-a-published-artifact works.** ADR-016's "one contract, N consumers, each runs the + suite in its own CI" is real: `views-faoapi` re-runs `assert_frame_contract` + + `assert_summarizer_contract` + the cross-level alignment law on its *own* real frames and injected + GAUL mapping. The contract is checked end-to-end at the boundary, not just in the leaf's own tests. +- **Honesty over symmetry.** ADR-011's "no shared base" (Option C) resisted the tempting `_BaseFrame` + god-class. The frames are ≥6-axis divergent (C-16); forcing a base would have created exactly the + dumping-ground the package exists to avoid. Three small sibling files beat one clever hierarchy. +- **Fail-loud, structural, at construction.** Invariants raise `ValueError`/`TypeError` at build + time (ADR-008/009); the guarantee is *structural*, not temporal (`time` stays opaque). This is + what lets a downstream repo trust a frame it received without re-validating it. + +## 4. What went wrong, and what we missed + +- **The numpy-version tooling tax was underestimated, and it never fully went away.** A numpy-only + leaf is checked under at least two numpy worlds — the **floor** (1.26.4, stricter generic stubs) + and the **ceiling** (2.x). They disagree, in *both* directions: the floor flags `type-arg` + (bare `np.floating`/`np.integer` need a parameter); the ceiling flags `no-any-return`. This bit at + v0.1 (`argsort` typing, C-19; the floor mypy job had to be added), bit again on determinism + (~1-ulp histogram binning differences forced a deterministic tie-break, C-24), and bit *yet again* + on the reconcile port two weeks later — the same class of bug, freshly rediscovered. **We never + turned "run mypy at both the floor and the ceiling" into a reflex that precedes every push.** +- **Coverage that was line-only hid untested branches (C-36).** The 100% gate initially measured + lines, not branches; a conditional could be "covered" with one outcome untested. Real on a leaf + whose whole job is fail-loud guards. Fixed by switching to branch coverage — but it was a latent + hole in the very thing (testing) the leaf treats as critical infrastructure (ADR-005). +- **Reject paths were tested only transitively (C-51).** `assert_frame_envelope`'s structural + rejections (non-float32, object dtype, wrong ndim) were exercised *through* the higher-level + contract, not by direct adversarial tests. 100% coverage is not the same as "every documented + failure mode has a dedicated red-team test." We learned this twice (C-29 on IO, C-51 on the + envelope). +- **The docs drifted from the code almost immediately (C-39/C-23/C-35).** Stale CIC signatures, + fossil examples, a README "Status" header still saying v1.0.0 after v1.1.0 shipped. The design + bible is load-bearing ("if code and README disagree, that is a bug") — but keeping it true needs a + validator run every release, which we only systematised after it had already drifted. +- **The first-publish PyPI token was account-wide (C-28).** An over-privileged credential on the + very first release. Migrated to Trusted Publishing (OIDC, no token) — but it shipped first and was + fixed after. + +## 5. What surprised us + +- **The leaf itself was almost bug-free; the danger was always inbound.** We braced for subtle + correctness bugs in the contract. There were essentially none. Every real defect in the *family* + (the tower's C-44/C-45, the summarizer's NaN/`inf` guards) was a **domain or shape assumption + leaking across the boundary** — usually into a sibling, occasionally pushed back at the leaf + (the `MetricFrame` question, ADR-020). The leaf's constitution was right; the violations happened + where the constitution was *not applied*. +- **The hardest design call was about asymmetry, not symmetry.** The instinct to unify the "twin" + frames under a base class was strong and wrong. Naming their ≥6 divergence axes (C-16) and + choosing three honest siblings over one false abstraction was the decision that aged best. +- **"Declare, don't infer" (ADR-003/014) turned out to be the load-bearing principle.** The leaf + never embeds or fetches geography; the consumer injects the time-varying mapping. Every later + effort that respected this stayed clean; the one place a sibling *violated* it (a hard-coded + magnitude threshold) became a production bug. The principle wasn't decoration — it was the fault + line. + +## 6. What would be easier next time + +- **Run the full mypy matrix (floor *and* ceiling) as a pre-push reflex, from commit one.** The + single most repeated failure in this family is "local mypy green, CI floor/ceiling red." It is a + two-command check; make it muscle memory, not a thing rediscovered each effort. +- **Branch coverage and direct reject-path tests from day one.** For a fail-loud leaf, every + documented failure mode deserves its *own* adversarial test, and the gate must be branch-level. Do + not let "100% line coverage" stand in for "every guard is directly proven to fire." +- **Make the docs validator a release gate, not an afterthought.** `validate_docs.sh` + a + README-status check should block release; doc drift on a design-bible repo is a correctness bug by + the repo's own rule. +- **Keep settling boundary questions at ADR time.** The leaf's calmest moments came from having a + ratified answer ready (ADR-020 for `MetricFrame`, ADR-017 for summarize, ADR-023 for reconcile). + The expensive moments came from a principle that existed but went unapplied. Ask "does this concept + belong here, by our own constitution?" *before* the code, every time. +- **Treat Trusted Publishing and least-privilege credentials as table stakes for release one**, not a + follow-up. + +## 7. Final state + +`views-frames` is a stable, pure, frozen leaf. The v1.0 public surface (ADR-018) is unchanged; +`CONFORMANCE_FLOOR` is `1.0.0` and has been since the freeze. The import-DAG is enforced; the +conformance suite is published and re-run by consumers; provenance (`run_id`/`data_version`) and the +`assert_frame_envelope` checker (v1.4.0) are the only post-freeze leaf additions, both additive. The +design-decision cluster (C-01..C-31) is resolved; the leaf carries two sibling packages +(`views_frames_summarize`, `views_frames_reconcile`) without a cycle or a breaking change. + +The one-sentence lesson: **a maximally-stable leaf earns its stability not by being clever but by +being frozen and provably pure — and the only forces that can still hurt it come from outside, when +a consumer or a sibling smuggles a domain assumption across a boundary the leaf's own constitution +already forbade.** diff --git a/reports/postmortems/2026-06_views_frames_reconcile.md b/reports/postmortems/2026-06_views_frames_reconcile.md new file mode 100644 index 0000000..b7f1a38 --- /dev/null +++ b/reports/postmortems/2026-06_views_frames_reconcile.md @@ -0,0 +1,157 @@ +# Postmortem — `views_frames_reconcile`, the reconciliation sibling (Epic 11, v1.7.0) + +| Field | Value | +|---|---| +| Subject | The third sibling package: a faithful **WET relocation** of the forecast reconciler from a repo where it was mis-homed, into the frames foundation — proven bit-identical, released as v1.7.0 | +| Window | 2026-06-26 (one focused session: ADR-023 → S1–S6 → published to PyPI), including a mid-effort process crash and a clean recovery | +| Repos | `views-frames` (where the code now lives); `views-postprocessing` (the **read-only** source it was relocated from); downstream `views-pipeline-core`, `views-models` (the gated cutover, not part of this effort) | +| Governing docs | ADR-023 (reconciliation is a sibling); the reconcile conformance suite; views-postprocessing risk C-42 (the stranded migration this completes); the Epic 11 tracking issue (#131–#138) | +| Outcome | A numpy-only, `views_frames`-only reconciler in the foundation, **100% coverage, mypy-strict, import-enforced**, proven bit-identical to its source by both the frozen torch oracle and a 136-case new-vs-old head-to-head. The work was almost entirely *discipline*: no algorithm was written, only relocated and proven. The only friction was tooling (the recurring numpy-floor mypy skew) and the leftover divergence of a multi-release branch graph. | + +--- + +## 1. What we did, and why + +Forecast **reconciliation** — making grid (`pgm`) predictions sum, per posterior draw, to their +country (`cm`) totals — is a numpy-only frame→frame operation, structurally the *same kind of thing* +as `views_frames_summarize` (ADR-017). But it lived in **views-postprocessing**, a partner-delivery +repo that does not even use it (the UN-FAO path sums by construction). Housing it there *stretched* +that repo's scope and forced an **injection dance**: views-postprocessing sits *above* pipeline-core +in the DAG, so pipeline-core could not import the reconciler directly and had to have it injected +(pipeline-core #195 / PR #217). The migration had been parity-proven there (vpp PR #30) but +**stranded** — pipeline-core was never repointed to it (vpp risk C-42). + +ADR-023 ratified the fix, mirroring ADR-017: reconciliation is a **third sibling package**, +`views_frames_reconcile`, depending on `views_frames` + numpy only. Landing it **low** in the DAG +(SDP) is the structural payoff — pipeline-core already depends on `views_frames`, so the +cycle-dodging injection can later collapse to a direct import; the port's only reason to exist +vanishes. The charter, like summarize's, is bounded: frame-reconciliation algorithms only; **never +fetch the `(time, unit) → country` mapping** (it is injected as arrays, exactly like +`cross_level_align` — ADR-014); no IO, scoring, plotting, or foreign `views_*`. + +The non-negotiable constraint was **WET before DRY**: a *faithful move + parity first*, not a +rewrite. The ported algorithm files had to differ from their source by **import lines only**. + +## 2. How it unfolded — the arc + +Six stories, executed in dependency order on one epic branch, each gated and committed separately. + +- **S1 — ADR-023.** The sibling charter, mirroring ADR-017; import-DAG `→ {views_frames}`; additive + MINOR (1.6.0 → 1.7.0); `CONFORMANCE_FLOOR` stays `1.0.0`. +- **S2 — scaffold.** A flat package mirroring summarize (one concept per file); `pyproject` wheel + packages; the import-enforcement allow-list entry. The empty scaffold had to keep the 100% gate + green — solved by making the stubs docstring-only (zero countable statements) rather than carrying + an uncovered `from __future__ import annotations`. +- **S3 — the faithful port.** The 6 reconciler files copied verbatim, then a single substitution + (`views_postprocessing.reconciliation → views_frames_reconcile`) applied only where it occurs (the + import lines). `diff` against the source proved it: `proportional`/`frames`/`validation` + byte-identical; `grouping`/`module`/`__init__` differ on import lines only. `grouping.py` was + *deliberately not* folded into the leaf's overlapping `cross_level_align` — that DRY pass is a + later story. +- **S4 — the parity + bit-identity gate.** Relocated the 6 parity tests + 2 frozen oracle fixtures + + 2 regeneration scripts (pointed at the new package), and added a **transitional 136-case + head-to-head**: the new package vs the old `views_postprocessing.reconciliation`, on fresh + synthetic seeds (probabilistic + point + edge cases, both the leaf function and the full module), + asserting `np.array_equal` (float32). All bit-identical. Closed the lint/type/coverage gaps the + port surfaced (import-sort, two fail-loud branches vpp's tests missed) → 100% line+branch. +- **S5 — conformance.** `assert_reconcile_contract` mirroring the summarizer contract: sum-to-country + per draw (with the all-zero-draw edge), zero-preservation, non-negativity, level correctness, and + an injected-mapping sensitivity check (a permuted mapping changes the result — proof the mapping is + used, not fetched). +- **S6 — release v1.7.0.** Version bump, README §6a charter + layout, CHANGELOG. **The crash hit + here** (see below). On recovery: full gate, PR → development → main (merge commit, trial-merged + first), tag `v1.7.0`, GitHub Release → PyPI; clean-env install verified. + +## 3. What went well + +- **WET-before-DRY done right, with two independent proofs.** The *import-lines-only diff* proves the + relocation introduced no algorithmic change by construction; the *136-case bit-identity head-to-head* + proves it behaviourally. Belt and suspenders — and exactly the right rigor for "this is now the + canonical copy." +- **Per-story commits made the crash a non-event.** A full process crash struck mid-S4, with the + head-to-head test unwritten. Because S1–S3 were committed and S4's work was on disk, the damage + assessment was a five-minute `git status` + gate re-run; nothing was lost, and the only remaining + work was the one unwritten test. Incremental, gated commits are cheap insurance that paid out. +- **Verifying against the repo, not the prose.** The story said "8 parity tests"; the repo had **6**. + The quest had explicitly warned that planning prose drifts from reality — and it had. Porting what + *actually existed* (verified by `find`), not what the card claimed, avoided a phantom-file hunt. +- **The conformance suite earned its keep on the mapping charter.** The sensitivity check (permute + the mapping → output must change) turns "the mapping is injected, never fetched" from a docstring + claim into an executed assertion. +- **The release machinery was boring.** The dev→main trial-merge-first / merge-commit discipline and + the Trusted-Publishing workflow made the irreversible steps uneventful. PyPI went 1.6.0 → 1.7.0 + with a passing version guard and a clean-env import check. + +## 4. What went wrong, and what we missed + +- **The numpy-floor mypy skew bit again — the same class of bug as the leaf's first week.** The + vpp-ported type hints used bare generics (`NDArray[np.floating]`, `NDArray[np.integer]`). Local + `mypy src/` (numpy 2.x) passed; the CI **floor** job (`mypy --strict` under py3.10 + numpy 1.26.4) + failed with `type-arg` errors. I had run the floor *pytest* locally but **not the floor *mypy***, + so the first PR CI run was red. Fixed by parametrising to `np.floating[Any]` / `np.integer[Any]` + (the leaf's house idiom) — annotations only, re-confirmed by the bit-identity gate. This is the + third time this family has eaten this exact lesson; it is now written into the pre-push checklist. +- **The "import-lines-only" ideal collided with the destination's stricter bar.** The source passed + *its* CI; the leaf's CI is stricter (ruff line-length, B905 `zip(strict=)`, mypy-strict floor, 100% + *branch* coverage). So the pristine S3 diff could not also be "ruff + mypy clean" — those gaps had + to close in S4. The honest resolution was to keep S3 a pristine relocation and isolate the + mechanical conformance in S4, but it is a genuine tension: a faithful port into a stricter home is + *not* import-lines-only end-to-end, and the story card's acceptance criteria overstated that. +- **The source's tests had real coverage gaps the port inherited.** Two fail-loud branches (the + pgm-level guard; the `map_vals`-shape guard) were never exercised by vpp's suite. 100% coverage in + the new home required writing the adversarial tests the source lacked. A relocation is only as good + as the tests that come with it — and these needed topping up. +- **Branch-graph divergence is a standing tax at release.** `main` carries squash release-commits + that `development` never receives, so dev→main is always a *divergent merge*, never a fast-forward. + The plan assumed "main is strictly behind"; it wasn't. A non-destructive trial-merge caught it and + it merged cleanly, but the assumption was wrong and would have been a surprise without the check. + +## 5. What surprised us + +- **The head-to-head was guaranteed to pass — and that was the point.** Because the port is + byte-identical to the source except for import lines, the algorithm *is* literally the same code; + `np.array_equal` could not fail. The value of the 136-case gate is not that it might find a + difference, but that it *proves the absence* of one on the record, on fresh seeds, for the release + note and the downstream consumers who will trust this as canonical. +- **The hardest part of a "no new code" effort was the tooling, not the code.** Zero algorithm was + authored. The entire cost was in the destination's stricter gate (floor mypy, branch coverage, + lint) and in release mechanics. A faithful relocation is a *conformance* exercise, not an + engineering one — and conformance to a stricter home is where the friction lives. +- **An empty package can fail a 100% coverage gate.** The scaffold's only countable line was a + `from __future__` import; with no test importing it, coverage dropped below 100%. Making the stubs + truly statement-free (docstring only) was the fix. A small, instructive reminder that `source = + ["src"]` + a 100% gate sees *every* file, including the empty ones. + +## 6. What would be easier next time + +- **Run the full mypy matrix — floor *and* ceiling — before pushing, every time.** This is now the + single most repeated lesson across all three packages. Running the floor *pytest* is not running + the floor *mypy*; they catch different errors. Make both a pre-push reflex, not a CI discovery. +- **State the WET acceptance criterion honestly: "import-lines-only diff *plus* mechanical + conformance to the destination gate, no algorithmic change — proven by bit-identity."** The pure + "import-lines-only" framing is the right *intent* but cannot survive a stricter home end-to-end; + the bit-identity gate, not the diff shape, is the real guarantee of "no behaviour change." +- **Budget for the source's test debt up front.** Assume a relocated component's tests do not meet + the new home's bar (branch coverage, direct reject-path tests) and plan the gap-closing as part of + the port, not a surprise. +- **Trial-merge dev→main before every release** (a non-destructive `--no-commit --no-ff` then abort) + and promote with a **merge commit**. The divergence is permanent and expected; verify it is clean + rather than assuming a fast-forward. +- **Verify the work-list against the repo, never the planning prose.** "8 tests" was 6. A 30-second + `find` beats trusting a stale card — the quest warned about this and it was right. + +## 7. Final state + +`views-frames==1.7.0` is on PyPI, shipping `views_frames_reconcile` (`ReconciliationModule`, +`reconcile_proportional`, `assert_reconcile_contract`) alongside the leaf and the summarize sibling — +numpy + `views_frames` only, import-enforced, 100% line+branch, mypy-strict at the floor and ceiling, +proven bit-identical to its views-postprocessing source. ADR-023 is merged; the Epic 11 stories +(#132–#137) and the epic (#131) are closed; `CONFORMANCE_FLOOR` stayed `1.0.0`. The **cross-repo +cutover** — repointing views-models and deleting views-postprocessing's now-redundant copy — is +explicitly *gated on this release* and out of scope here; the injection that existed only to dodge +the old cycle can now collapse to a direct import, which was the whole structural point. + +The one-sentence lesson: **relocating a proven component is an exercise in *discipline, not +invention* — its risks are bit-identity, the destination's stricter gate, and release mechanics, not +the algorithm — so prove "nothing changed" two ways, hold the algorithm byte-stable, and let the +tooling, not the code, be where you spend the effort.** diff --git a/reports/postmortems/2026-06_views_frames_summarize.md b/reports/postmortems/2026-06_views_frames_summarize.md new file mode 100644 index 0000000..6db8b48 --- /dev/null +++ b/reports/postmortems/2026-06_views_frames_summarize.md @@ -0,0 +1,176 @@ +# Postmortem — `views_frames_summarize`, the posterior-summary sibling (v0.2.0 → v1.6.0) + +| Field | Value | +|---|---| +| Subject | The sample-axis summarization package: why it was carved out of the leaf, the estimator family it grew (point, interval, tower, exceedance, expected shortfall), the bugs its consumers found, and the falsification discipline that became its release gate | +| Window | 2026-06-21 (ADR-017, `collapse` removed from the leaf) → 2026-06-25 (`v1.6.0`, `expected_shortfall` published to PyPI) | +| Repos | `views-frames` (all code); `views-faoapi` + `views-reporting` (the consumers of record whose adoption found every real bug) | +| Governing docs | ADR-017 (sibling charter), ADR-019 (tower), ADR-021 (exceedance), ADR-022 (expected shortfall); the Summarize CIC; register C-32/C-33/C-34/C-44/C-45/C-49/C-50/C-55/C-56/C-57; research lab `research/map_hdi/` | +| Outcome | A single, tested, **distribution-agnostic** home for the volatile posterior statistics that were duplicated ~500–600 LOC across two repos. Five estimators shipped additively under the v1.0 freeze. Every correctness bug was found by a consumer running real data; the deepest lessons were epistemic — *a green number, a passing test, or a low benchmark score is the easiest place to hide a wrong answer.* | +| Companion | The tower's two production bugs (C-44/C-45) and the four-release scramble are documented in full in [`2026-06_tower_estimator_c44_c45.md`](2026-06_tower_estimator_c44_c45.md); this postmortem covers the **package as a whole** and refers there for that chapter rather than repeating it. | + +--- + +## 1. What we did, and why + +The leaf carries posteriors; it must not *interpret* them. But two consumers — `views-faoapi` +(serving VIEWS conflict forecasts to the UN FAO) and `views-reporting` — each independently +re-derived the same statistics from a `(month, priogrid)` posterior: a MAP-like mode, highest-density +intervals, quantiles, and a conservation-correct cross-level aggregation where `HDI(sum) ≠ sum(HDI)`. +The code was ~500–600 LOC, subtle (zero-mass MAP rules, nesting, joint-sampling), and **volatile** — +"the way we collapse uncertainty will change and expand." + +ADR-017 ratified the answer: this lives in a **second package in the same repo**, +`views_frames_summarize`, depending on `views_frames` + numpy only, never the reverse (import-DAG +enforced). Volatile, expanding statistical logic does **not** belong in the maximally-pinned leaf +(SAP); but it *is* worth centralising once, because correctness is hard and it was being +re-implemented in 2–3 places. `collapse` and the string-keyed statistics registry were **removed +from the leaf** (a breaking change made while pre-1.0); the leaf kept only the *structural* sample +facts (`sample_count`, `is_sample`). + +The charter is bounded — that boundedness is the whole anti-bloat mechanism. The package **may** +contain sample-axis point/interval estimators and conservation-correct aggregation; it **must not** +contain IO, domain/geographic data, actuals or scoring (that is views-evaluation), or any `views_*` +import except `views_frames`. + +## 2. How it unfolded — the arc + +- **The base estimators (v0.2.0).** `collapse(frame, reducer)`, `map_estimate` (histogram MAP), + `hdi`, `quantiles`, and `aggregate_distributions` (element-wise joint-sample sum to a coarser + level). Index-aligned arrays out; the caller holds the index (ADR-017 convention). +- **The tower (v1.1.0 → v1.3.0, ADR-019).** A coherent, reproducible, eventually + **distribution-agnostic** point+interval summary — the constrained-nested HDI tower, the + mass-aware tower-tip MAP, and a conservative bimodality flag. This is the eventful chapter: an + unauthorized publish, two silent-correctness bugs (C-44 minority-duplicate collapse; C-45 a + count-domain magnitude rule smuggled into a domain-agnostic leaf), both found by the faoapi + integration spike, four releases in ~36 hours. **See the companion postmortem.** Its design was + derived in the `research/map_hdi/` lab against a 108-cell synthetic battery with known analytic + modes. +- **`exceedance` (v1.5.0, ADR-021, Epic 9).** Per-row survival fraction `P(Y > c)` + a + `collapse`-compatible `exceedance_reducer`. Required, no-default thresholds (the policy is the + consumer's, never a config); strict `>` (D-08); geography-blind (country exceedance = + `aggregate_distributions` → `exceedance`). Registered C-49 (aggregate-tail joint-sampling) and + C-50 (silent NaN onset deflation), both resolved by fail-loud guards. +- **`expected_shortfall` (v1.6.0, ADR-022, Epic 10).** The worst-case tail mean / CVaR — the mean of + the worst `⌈t·S⌉` draws — as the principled, coherent companion to `exceedance`. **`max` is never + offered** (D-10: it is the highest-variance, non-reproducible summary). **Best-case ships no code** + (a low quantile + `exceedance(0)` express it — CRP, don't add a symbol no one reuses). Registered + C-55/C-56. +- **The pre-publish falsification audit (2026-06-25).** Before promoting v1.6.0 to PyPI, a combined + `/falsify` audit of `exceedance` + `expected_shortfall` found one real gap: the fail-loud guard was + `np.isnan(...)`, which catches NaN but **not ±inf**. An infinite draw — always an upstream bug — + silently produced an `inf` "worst case" or a bug-masking probability. Fixed by widening the guard + to `not np.isfinite(...).all()` in both cores (C-50/C-56), with a new C-57 recording that the + frozen `map_estimate` still *crashes* (an `IndexError`) on `inf` draws — a pre-existing, + freeze-locked inconsistency, not a v1.6.0 blocker. Then published. + +## 3. What went well + +- **The consumer feedback loop was the best test suite we had.** Every real bug (C-44, C-45) was + found by *adopting the leaf in faoapi against the real forecast cache*, not by our own tests. The + cadence — adopt → audit → report → fix → re-adopt — was fast and decisive. The package's + correctness is, in practice, underwritten by faoapi's CI re-running the published conformance + contracts on real frames. +- **WET before DRY, applied deliberately.** Each estimator is its own module with its own private + core (`exceedance._exceed`, `expected_shortfall._expected_shortfall`); they reuse only the stable + primitives (`block_apply`, `ROW_BLOCK`, `AnyFrame`). We explicitly *refused* to extract a shared + "tail reducer" abstraction. Two shallow, independent duplications beat one premature coupling — the + concerns (a survival count vs a tail mean) change for different reasons. +- **Falsification as a release gate caught what green tests could not.** The `inf`-draw finding is + the canonical case: every test passed, coverage was 100%, and the guard was *still* an incomplete + expression of its own stated intent ("a draw must be a usable finite number"). `/falsify` — + attacking the claim rather than confirming it — found it before PyPI did. +- **Laws as tests.** The conformance suite asserts the estimators' *mathematical* laws (exceedance in + `[0,1]`, non-increasing, `P(>−inf)=1`; expected shortfall in `[min,max]`, non-decreasing as the + tail deepens, `ES(t) ≥ the (1−t) quantile`). These caught regressions a value-based test would + miss and document the contract executably. +- **Saying no shipped less code.** ADR-022's "best-case ships no code" and "`max` is never offered" + are the package at its best: the right answer was often a *deletion* or a *non-addition* (C-45 was + fixed by removing a harmful special case; the best-case need was met by composing existing + functions). The bounded charter did its job. + +## 4. What went wrong, and what we missed + +- **A domain assumption was smuggled into a domain-agnostic package, and our own tests masked it + (C-45).** ADR-019 introduced a `max(draws) ≤ 1.0 → 0` "quiet row" rule — a hard-coded + *magnitude* threshold — in a package whose constitution (ADR-003/014) forbids inferring meaning + from values. It zeroed every cell of a `[0,1]`-valued target and erased low-intensity counts. Our + synthetic tests used values above 1, so it never tripped. **We had the principle and did not apply + it; we had the tests and they tested the wrong shapes.** +- **A premature "fixed" claim on unrepresentative data (C-44).** A partial fix was declared done + against a synthetic case (30 identical body values) that did not resemble real cells (a distinct + continuous body + a few exact zeros). Only the consumer's "case L" exposed that the fix never + engaged. Confidence outran evidence. +- **A process failure: an unauthorized publish.** During the v1.1.0 release a pre-confirmation + ("we're ready") was treated as standing authorization for the specific irreversible steps + (merge-to-main, create-Release → PyPI). It was not. Nothing was lost (PyPI versions are + immutable), but trust was; a strict *per-step* authorization protocol followed and made every + subsequent release boring. +- **An incomplete guard that read as complete.** The NaN guard (`np.isnan`) looked like "reject + malformed draws" but only covered half of "non-finite." `inf` slipped through for a full release + cycle until the falsify audit. The guard expressed a *narrower* policy than it intended. +- **The `research/map_hdi/` benchmark oracle was circular, and we nearly trusted it.** The lab that + designed the tower scored point estimates against a stored `true_mode` that was *itself* a + histogram argmax — so a histogram-MAP "won" by sharing the oracle's binning. A naive + optimize-the-metric loop would have *kept the circular artifact and rejected the real C-32 fix*. + What saved us was the side-diagnostics that re-scored against an independent analytic mode — i.e., + distrusting the measure, not optimizing it. + +## 5. What surprised us + +- **The robust estimate *disagrees* with the incumbent, and the incumbent is the suspect.** On real + faoapi cells the tower-tip shorth lands on the dense low cluster while the old histogram MAP lands + on a sparse high cluster. The downward disagreement is largely the **C-32 bias being corrected** — + but it is a large, visible semantic shift a consumer must consciously accept, not a silent + improvement. +- **The best fixes were subtractions.** C-45 was resolved by *deleting* the magnitude rule (the + mass-aware tip already handled zero-inflation by density); the `inf` fix *completed* an existing + guard rather than adding a new policy; the best-case summary was met without a new function. The + package improved most when it grew *less*. +- **A metric can be deterministic, un-gamed, and still wrong.** The map_hdi oracle and the `inf` + guard are the same lesson in two domains: the immutable-harness / passing-test discipline protects + against *gaming* and *regression*, but not against a measure (or a guard) that is honestly built + and *incomplete*. Only adversarial falsification closes that gap. +- **`hdi_tower` was affected by both tower bugs**, contrary to the first guess that "only the point + is at risk." The shared zero-mask and the nested-through-the-narrowest-floor construction + propagated both defects into the published intervals. + +## 6. What would be easier next time + +- **Test against real (or realistic) distribution shapes from commit one.** A `[0,1]`/`beta` field, a + tight sub-1 mode, a distinct continuous body with a few exact zeros, a replay of the real cache + shape — a handful of cases would have caught *both* tower bugs before release. This matrix is now + baked into the suite; keep it, and keep a read-only replay against the consumer's data shape. +- **Audit every default for a hidden domain assumption, at ADR time.** Any magic constant (`1.0`, a + bin count, a magnitude threshold) in a domain-agnostic package is guilty until proven agnostic. Ask + before the code, not at the third release. +- **Make `/falsify` a standing pre-publish gate for every estimator.** It found the `inf` gap that + 100% coverage missed. The cost is small; the class of bug it catches (incomplete guards, + silent-wrong outputs) is exactly the one this package exists to avoid. +- **Validate the benchmark before trusting it.** For any optimize-a-metric work, check the oracle for + *circularity* and *proxy divergence* before believing a single score. The map_hdi loop would have + shipped the wrong estimator on a trusted-but-circular metric; the audit, not the loop, produced the + result. +- **Treat "fixed" as a claim that requires the consumer's exact data.** When a report ships a + paste-ready truth table, run *that table* (and the real cells) before asserting resolution. +- **Keep the per-step release protocol and the WET-before-DRY default.** Both cost almost nothing + once internalised and each removed a whole class of failure (an unauthorized publish; a premature + shared abstraction). + +## 7. Final state + +`views_frames_summarize` ships five estimator families — `collapse`/`map_estimate`, `hdi`/`quantiles`, +the coherent tower (`hdi_tower`/`tower_point`/`bimodality`/`summarize_tower`), `exceedance`, and +`expected_shortfall` — plus conservation-correct `aggregate_distributions`, all additive under the +v1.0 freeze (`CONFORMANCE_FLOOR` stayed `1.0.0` throughout). The summary is coherent (nested, +reproducible), robust (immune to minority-duplicate collapse), distribution-agnostic (no magnitude +assumption; an off-by-default opt-in for count consumers), and fail-loud on non-finite draws. +Resolved: C-33/C-44/C-45/C-49/C-50/C-55/C-56. Open and accepted: C-32 (a fully principled convergent +mode is still #89), C-34 (bimodality recall is a documented conservative trade), C-57 (the frozen +`map_estimate`'s ugly failure on `inf` draws, a future hardening). The consumer (faoapi) re-runs the +published conformance suite on its own real frames in CI. + +The one-sentence lesson: **a posterior summarizer is judged not by whether its tests pass but by +whether it tells the truth on the ugly real distributions it will actually meet — and the fastest way +to a wrong number is to trust a green test, a clean metric, or a tidy synthetic case instead of +attacking it on the data and shapes the consumer actually carries.** diff --git a/reports/technical_risk_register.md b/reports/technical_risk_register.md index b467dac..9cbb600 100644 --- a/reports/technical_risk_register.md +++ b/reports/technical_risk_register.md @@ -4,11 +4,11 @@ |-------------------|--------------------------------------| | Project | views-frames | | Owner | VIEWS platform maintainers | -| Last Updated | 2026-06-25 | -| Total Concerns | 54 | -| Open Concerns | 9 | -| Resolved Concerns | 45 | -| Disagreements | 10 | +| Last Updated | 2026-06-27 | +| Total Concerns | 62 | +| Open Concerns | 11 | +| Resolved Concerns | 51 | +| Disagreements | 12 | --- @@ -35,14 +35,23 @@ > and formalised by ADRs 011–016, all of which merged and shipped/froze in **v1.0.0** > (ADR-018) — they are now in **Resolved Concerns**. C-01/C-08/C-12 are resolved-by-decision > and persist only as **frozen-invariant guards** (their triggers protect the frozen scope). -> The **3 currently open** items fall into two live clusters (detailed under *Causal -> clusters* in Register Conventions): **(1) summarize-estimator coherence (#89)** — **C-32** -> (`map_estimate` mode bias) and **C-34** (`bimodality` recall); their tower-nesting sibling -> **C-33** was **resolved** by ADR-019 (2026-06-23). **(2) cross-repo coordination** — the -> inherent **concentration risk** **C-13** (accepted / monitored), with disagreements -> D-04/D-05/D-06. The **post-1.1.0 polish** cluster {C-35, C-36, C-37, C-38} was **closed by -> Epic 7** (2026-06-24, now in Resolved Concerns), as were the 2026-06-22 test-review gaps -> {C-29, C-31} by **Epic 6**. +> The **12 currently open** concerns fall into four clusters plus a cross-cutting theme +> (detailed under *Causal clusters* in Register Conventions): **(1) summarize-estimator +> coherence (#89)** — C-32, C-34, C-43, C-57 (the under-determined frozen MAP/bimodality +> estimators); **(2) reconcile method + governance** — C-58, C-62 (+ D-12): a pragmatic +> per-draw port with a deferred principled upgrade (ADR-024); the package's missing CIC (C-64) +> was closed by `Reconcile.md` (epic #179 / S1); **(3) +> construction-convenience accretion (#113)** — C-52, C-53, C-54 (+ D-09), the `from_arrays` +> "camel's nose"; **(4) cross-repo coordination** — C-13, C-46 (+ D-04/D-05/D-06). The +> former **immutability-enforcement** cluster (C-63) is resolved: the contract was corrected +> to the by-convention reality and the `setflags`-enforce deferred to a future MAJOR (ADR-025, +> epic #179 / S2). The cross-cutting +> **verification-completeness** theme now narrows to C-58 (the reconciler's production-slice +> check); its sibling C-65 (non-finite fail-loud on the blocked path) was pinned with a red +> test (epic #179 / S3). Earlier clusters are closed: +> **post-1.1.0 polish** {C-35, C-36, C-37, C-38} by **Epic 7** (2026-06-24) and the 2026-06-22 +> test-review gaps {C-29, C-31} by **Epic 6** — both now in Resolved Concerns. (`map_estimate`'s +> tower-nesting sibling **C-33** was resolved by ADR-019, 2026-06-23.) ### C-13: concentration risk — single point of coordination failure (accepted / monitored) @@ -130,9 +139,9 @@ Under Option B (the ratified boundary; C-01), `MetricFrame` lives in `views-eval | ID | C-52 | | Tier | 3 | | Source | expert-code-review (2026-06-24, GH #113 `build_prediction_frame` / `PredictionFrame.from_arrays`); pipeline-core owner calibration | -| Trigger | When a future PR extends the planned `PredictionFrame.from_arrays` with a `value_fn` / `from_grid` / dtype-guessing parameter, or adds a new `src/views_frames/factory.py` that accretes loosely-related construction helpers — pulling consumer-edge responsibility (ADR-001 non-entities: adapters, "convenience abstractions") into the data-contract leaf. | -| Location | (planned) `src/views_frames/prediction_frame.py` (`from_arrays`); guard against a future `src/views_frames/factory.py`. | -| Cross-refs | ADR-001 (ontology / explicit non-entities / accretion = the leaf's #1 failure mode), ADR-011, D-09, C-53, C-54, GH #113. | +| Trigger | When a future PR extends the planned `PredictionFrame.from_arrays` with a `value_fn` / `from_grid` / dtype-guessing parameter, or adds a new `src/views_frames/factory.py` that accretes loosely-related construction helpers — **or adds frame-level serialization/adapter conveniences (`to_parquet`/`from_parquet`, and especially `to_pddf`/`from_pddf`) to the frame classes** — pulling consumer-edge responsibility (ADR-001 non-entities: adapters, "convenience abstractions") into the data-contract leaf. | +| Location | (planned) `src/views_frames/prediction_frame.py` (`from_arrays`); guard against a future `src/views_frames/factory.py` and against `to_parquet`/`to_pddf`/`from_pddf` accreting onto `src/views_frames/*_frame.py`. | +| Cross-refs | ADR-001 (ontology / explicit non-entities / accretion = the leaf's #1 failure mode), ADR-011, D-09, **D-11** (serialization-convenience placement — settled off-leaf), C-53, C-54, GH #113. | ADR-001 (line 17) names accretion as the leaf's existential failure mode — "someone adds an adapter, then grid knowledge … becomes pipeline-core-lite." A construction convenience is the most innocuous possible first step onto that slope. **Tier 3** (recalibrated down from an initial Tier 2 with the pipeline-core owner): under the ratified mitigations the residual risk is boundary-erosion a code-review gate catches, not inherent structural fragility. **Mitigations:** ship the **classmethod** form (a class resists becoming a dumping ground far better than an open `factory.py`); keep it **zero-own-logic**; add a CIC "Construction" **non-goal** that explicitly fences out grid / `value_fn` / inference / adapters. Resolved when `from_arrays` lands with those guards, or closed if #113 is declined. @@ -183,6 +192,36 @@ The frozen surface is **inconsistent** on non-finite draws: `collapse(np.mean)` --- +### C-58: a reconciler cutover verified against the oracle but not a live production slice + +| Field | Value | +|-------|-------| +| ID | C-58 | +| Tier | 3 | +| Source | expert-code-review (2026-06-26, #138 cutover review) + the Epic 11 cutover closeout | +| Trigger | When a future reconciliation cutover repoints a consumer to a new reconciler relying only on the in-repo evidence — the frozen torch-oracle fixtures + the synthetic new-vs-old head-to-head — and **skips a comparison against an actual served production slice** at the repoint. If the in-repo oracle and the live served path have drifted (e.g. an upstream input or mapping change not reflected in the fixtures), the re-baseline of served numbers goes unverified. | +| Location | The cutover gates (runbook `docs/guides/reconciliation_migration_and_cutover_runbook.md`, Phase 2); mitigation tool `scripts/verify_reconcile_parity.py` (`--compare`). | +| Cross-refs | ADR-023, Epic 11 `#138`, the cutover postmortem (`reports/postmortems/2026-06_reconciliation_cutover_epic11.md`), views-models #191, views-postprocessing #62. | + +The Epic 11 cutover was validated by a sound transitive chain — `new == old vpp copy` (136-case bit-identity) and `old vpp == views-reporting torch oracle` (frozen `.npz`), so `new == torch oracle` — but **no comparison was run against an actual served production parquet** (old path vs new path) at the repoint. The residual risk is low (the oracle *is* the torch path's captured output), so this is **not** silent corruption — it is a missing belt on a sound buckle. **Mitigation shipped:** `verify_reconcile_parity.py --compare OLD.parquet NEW.parquet` makes the production-slice check a one-command gate, and the cutover runbook (Phase 2) now requires it for future cutovers. **Tier 3** — a verification-completeness gap, not a known defect. **Open** — watch-item for the next reconciler change. (The expert review's companion "batched-deploy blast radius" concern did **not** materialize: the Epic 11 cutover landed as phased PRs — repoint #202, then delete #63 — so no separate entry is warranted.) + +--- + +### C-62: `reconcile_proportional` is an information-losing per-draw approximation (no joint-calibration guarantee) + +| Field | Value | +|-------|-------| +| ID | C-62 | +| Tier | 3 | +| Source | expert-method-review lineage + S3 design (#145), 2026-06-27 | +| Trigger | When a consumer's decision provably needs **calibrated joint** country tails that proportional's per-draw marginal rescale cannot provide, **or** when the country model (views-models) gains a shared draw-identity / coupling that makes principled joint reconciliation buildable — i.e. when ADR-024's two deferral preconditions are met, build the principled sibling module (never by modifying `proportional`). | +| Location | `src/views_frames_reconcile/proportional.py` (the per-draw method); designed in `docs/ADRs/024_principled_joint_reconciliation_design.md`. | +| Cross-refs | ADR-024 (the design + deferral), ADR-023 (sibling charter; future-sibling-module open question), views-postprocessing C-37 (the cross-repo principled-reconciliation lineage), views-pipeline-core C-198 / C-200b (consumer-side), C-60 (the notebook presentation of this — resolved), D-12 (mode reporting). GH #145 / #142. | + +`reconcile_proportional` rescales grid cells to sum, **per draw**, to the country total — pairing grid-draw `s` with country-draw `s`. When the grid and country models are trained **independently** (the current platform reality), draw index `s` has **no shared identity** across them, so the pairing is arbitrary and the reconciled **joint** distribution (the joint country tails an FAO-style worst-case keys on) is not guaranteed calibrated — even though conservation (sum-to-country per draw, zeros preserved, non-negative) holds **exactly**. This is **not silent** (hence **Tier 3**, not Tier 1): it is documented at every layer — the `proportional.py` docstring, ADR-024, the `03_reconciliation.ipynb` bit-identity-≠-method-quality panel (C-60), and surfaced at runtime as the `reconcile_result` mode `aligned-draws` (D-12). It is a **known method-quality limitation with a designed upgrade path** (ADR-024), deliberately deferred until its preconditions hold. **Open** — the limitation persists until the principled sibling module is built. + +--- + ## Disagreements ### D-01: `SpatioTemporalIndex` domain-purity fork (where does cross-level alignment live?) @@ -225,7 +264,7 @@ The frozen surface is **inconsistent** on non-finite draws: `collapse(np.mean)` | ID | D-04 | | Source | expert-review (2026-06-20) | | Perspectives | Critique_01 §5 ("uniform structure/idioms suggest one author wrote all three — they are the proposer's hypotheses, not stakeholder buy-in"), Author ("they pressure-test the design from multiple angles") | -| Resolution | Unresolved — **and now load-bearing.** The simulated perspectives have been *acted upon*: five consumer-adoption issue sets were filed in real repos (datafactory #219–221, pipeline-core #186–190, reporting #137–140, postprocessing #27–29, faoapi #87–91). Those issues are **proposals derived from the simulated perspectives, not consumer buy-in** — treat each repo's adoption as unconfirmed until that team responds. Only `from_views-datafactory` is ratified. Add a "Ratified by: " header to each perspective; do not count unratified perspectives (or the issues filed from them) as agreement. See D-05. | +| Resolution | **Resolved-by-events (2026-06-27).** The simulated perspectives were acted on and then *ratified by real adoption*: the Epic 11 reconciliation cutover shipped through pipeline-core (#233) and views-models (#191/#202), with adoption work progressing in datafactory/reporting/faoapi — demonstrated buy-in, not assumed. The original caveat (five filed issue-sets — datafactory #219–221, pipeline-core #186–190, reporting #137–140, postprocessing #27–29, faoapi #87–91 — "unconfirmed until the team responds"; only `from_views-datafactory` ratified at filing) is now overtaken. See D-05, C-13. | --- @@ -236,7 +275,7 @@ The frozen surface is **inconsistent** on non-finite draws: `collapse(np.mean)` | ID | D-05 | | Source | expert-review (2026-06-20) | | Perspectives | Critique_01 §5b ("views-evaluation owns `EvaluationFrame`/would produce `MetricFrame`; a model repo produces `PredictionFrame` — both absent, and they would stress the riskiest claims"), Scope ("write them before promoting `MetricFrame` from exploratory") | -| Resolution | Unresolved — write the views-evaluation + a model-repo perspective before any `MetricFrame` work. See C-01. | +| Resolution | **Resolved-by-events (2026-06-27).** The substance — the views-evaluation boundary — was settled by **ADR-020** (`MetricFrame` lives in views-evaluation on the views-frames substrate; only generic provenance on the leaf header; C-47 resolved), decided with views-evaluation Informed/Consulted. The formal "write the perspectives before `MetricFrame`" step was overtaken by that ADR decision. See C-01, C-47, ADR-020. | --- @@ -247,7 +286,7 @@ The frozen surface is **inconsistent** on non-finite draws: `collapse(np.mean)` | ID | D-06 | | Source | expert-review (2026-06-20) | | Perspectives | Critique_01 §6 ("viewser→datafactory migration, views-appwrite extraction, and views-frames relocation compete for the same coordination budget and destroy change attribution if run concurrently"), Leverage ("views-frames is highest-leverage but also the largest coordination load") | -| Resolution | Unresolved — WIP limit: do not run views-frames relocation and views-appwrite extraction in the same repo concurrently; queue consumer adoption behind the data-migration baseline. See C-13. | +| Resolution | **Resolved-by-events (2026-06-27).** The sequencing played out as advised: views-frames relocation ran as the highest-leverage track through Epics 2–11 without being run concurrently with the views-appwrite extraction in the same repo, and consumer adoption queued behind the data-migration baseline. The concurrent-WIP / change-attribution concern is now historical. See C-13. | --- @@ -295,8 +334,125 @@ The frozen surface is **inconsistent** on non-finite draws: `collapse(np.mean)` --- +### D-11: frame serialization-convenience placement — `to_parquet`/`to_pddf` on the leaf vs off it + +| Field | Value | +|-------|-------| +| ID | D-11 | +| Source | expert-code-review (2026-06-26) — the DataFrame→views-frames transition question (consumer sites that load/distribute parquet) | +| Perspectives | **Add frame-level conveniences to the leaf** (`frame.to_parquet`/`from_parquet` via pyarrow, and/or `frame.to_pddf`/`from_pddf` via pandas) — migration ergonomics at the many parquet load/distribute sites; conceived as **transitional**, possibly removed once the migration completes. **Keep them off the frozen leaf** (all eight lenses + the constitution): the leaf is **frozen** (ADR-018 — additions are permanent, removal = platform-wide MAJOR), so "transitional/removable" + "frozen leaf" is **self-contradictory** (Beck/reversibility); **pandas is ratified out** (ADR-001 line 120 non-entities, README §11, import-DAG `FORBIDDEN`) and a `to_pddf` emitting the object-dtype **list-in-cell** encoding would re-arm the ~33× #181 OOM the frame exists to kill (README §7); serialization formats change for **different reasons** than the data contract (CCP) and concrete format/pandas sugar on the most-stable-most-abstract leaf breaks **SAP/SDP**; it is the C-52 camel's nose (`to_parquet` → `to_pddf` → `from_grid` → store knowledge). | +| Resolution | **Settled — keep them off the frozen leaf.** (1) **pandas (`to_pddf`/`from_pddf`): never on the leaf** — place in a **consumer adapter** (pipeline-core, beside `PredictionFrameConverter`), or, only if ≥2 repos need the identical adapter (REP/CRP), an explicitly-transitional **unfrozen sibling** (e.g. `views_frames_compat`, pandas an optional extra) so it stays removable post-transition (the ADR-017/ADR-023 sibling precedent). (2) **parquet:** the codec already exists (`io.arrow`) — **reject new frozen frame symbols** (`to_parquet`/`from_parquet`); if frame-level ergonomics are wanted, do **Option B'** — make the existing `Frame.save/load` **format-selectable** (`save(path, format="parquet"\|"npz")`, pyarrow imported lazily; additive, floor stays 1.0.0), and only **if/when a consumer actually reaches for it** (no demand yet → **defer**). (3) **Governing rule:** anything that might be removed post-transition must **not** touch the frozen leaf surface. **No work required in views-frames now**; B' is the only candidate leaf change and it is deferred. | + +Cross-refs: ADR-018 (freeze — additive-only, removal = MAJOR), ADR-001 (adapters/pandas = consumer edges; accretion = the leaf's #1 failure mode), ADR-017 + ADR-023 (unfrozen sibling-package precedent), README §7 (the #181 list-in-cell foot-gun) / §11 (scope — pandas/parquet-store → consumer repos), `tests/test_import_enforcement.py` (`FORBIDDEN` pandas), C-52 (the accretion this extends). + +--- + +### D-12: reconciliation-mode provenance placement — stamp the leaf frame vs report it from the sibling + +| Field | Value | +|-------|-------| +| ID | D-12 | +| Source | implementation + review-diff (2026-06-27) — S2 of the reconciliation right-home epic (#144) | +| Perspectives | **Stamp the mode on the frame** (#144 as originally written): write the reconciliation mode (`point-broadcast` / `aligned-draws`) into the reconciled frame's `FrameMetadata`, so an auditor reads it off the frame in isolation (a "self-describing" frame). **Keep reconciliation vocabulary off the leaf**: the leaf's `FrameMetadata` is governed **generic-only** (ADR-020 / register C-47) — it carries provenance meaningful for *any* frame (`run_id`, `data_version`) and deliberately excludes domain/operation-specific fields (the precedent: eval's `scoring_code_version` lives in views-evaluation's `MetricFrame`, never the leaf). Stamping `reconciliation_mode` would push sibling-only vocabulary into the numpy leaf that must not know reconciliation exists (ADR-001 accretion guard), and `FrameMetadata` lives in `views_frames` while reconciliation lives in the sibling — a layering inversion. | +| Resolution | **Settled — report the mode from the sibling; do not stamp the leaf.** `ReconciliationModule.reconcile_result(cm, pgm)` returns a `ReconciliationResult` (`frame`, `mode`, `method`) carrying the mode; `reconcile()` still returns just the frame (bit-unchanged). The mode lives in `views_frames_reconcile` (`result.py`), off the leaf's generic header, so the C-47 / ADR-020 generic-only guard holds and the leaf stays free of reconciliation vocabulary. The mode literals (`point-broadcast`/`aligned-draws`) match pipeline-core's `reconcile_frames` constants verbatim for cross-repo consistency. A caller needing in-isolation auditability records the returned mode in its own (consumer-side) metadata. | + +Cross-refs: C-47 (eval provenance kept out of the generic header — the precedent), ADR-020 (provenance split by concern), ADR-013 (`FrameMetadata` optional-extensible but generic), ADR-001 (leaf accretion guard), ADR-023 (the reconciliation sibling; its Open-Questions records this), D-11 (the analogous "keep removable/edge conveniences off the frozen leaf" decision); GH #144; pipeline-core C-200b (the silent-mode risk this addresses consumer-side). + +--- + ## Resolved Concerns +### C-65: non-finite fail-loud proven only on the single-shot path, not the blocked (multi-block) path — RESOLVED + +| Field | Value | +|-------|-------| +| ID | C-65 | +| Tier | 3 | +| Source | test-review (2026-06-27) | +| Trigger | When the per-block `np.isfinite(...)` guard in `exceedance`/`expected_shortfall` is hoisted, vectorized, or otherwise moved out of the per-block reducer — a refactor the existing single-row tests would not catch. | +| Location | `src/views_frames_summarize/exceedance.py` + `expected_shortfall.py` (the `np.isfinite` guard, called per block by `_common.block_apply`); pinned by `tests/test_summarize_exceedance.py::test_nonfinite_in_a_non_first_block_raises` + the matching test in `tests/test_summarize_expected_shortfall.py`. | +| Cross-refs | C-50 (exceedance fail-loud on NaN — RESOLVED), C-56 (expected_shortfall fail-loud on NaN/±inf — RESOLVED; the guard *exists*, this was the **verification-completeness gap** on its blocked path), C-25 (the block_apply memory-bounding that introduces the path). | + +The non-finite guard (reject NaN/±inf draws) lives **inside** `_exceed`/`_expected_shortfall`, which `block_apply` calls **per row-block** — but every committed adversarial test placed the non-finite draw in a **1–2 row** frame (the single-shot path, `n ≤ ROW_BLOCK`), so the blocked path had **no red test** and a future hoist of the guard above the per-block loop would have silently regressed. **Tier 3** — verification completeness, not a current defect. **Resolved** (2026-06-28, epic #179 / S3 #182): a parametrized red test (NaN, +inf, −inf) now forces `>1` block via the `block_rows` kwarg with the non-finite draw in a **non-first** block and **block 0 all-finite** — so a guard that inspected only the first block would pass silently, and the test fails loud as required. 100% line+branch coverage held; no `src/` change. + +--- + +### C-63: the frame `values` buffer is not write-protected, so the immutability guarantee is unenforced — RESOLVED (contract corrected; enforce deferred) + +| Field | Value | +|-------|-------| +| ID | C-63 | +| Tier | 2 | +| Source | repo-assimilation (2026-06-27), empirically verified; confirmed + extended by test-review (2026-06-27) | +| Trigger | When a consumer applies an in-place numpy mutation to `frame.values` (a natural idiom — e.g. `frame.values[mask] = 0`, `frame.values *= scale`, a clamp/normalise in place) on a frame obtained from `with_metadata`/`select`'s buffer-sharing path. | +| Location | `src/views_frames/_validation.py:64` (`coerce_values` returns a float32 input **without copy** and without `setflags(write=False)`); contrast `src/views_frames/index.py:55-56` (the index arrays **are** write-protected); `with_metadata` (`prediction_frame.py`/`target_frame.py`/`feature_frame.py`) shares the buffer; `tests/test_properties.py:38` pins only the *sharing*, not read-only-ness. **Contract side:** the three frame CICs §9 + README design principle 3 (now corrected). | +| Cross-refs | ADR-025 (the resolving decision + the deferred-enforce MAJOR-rider), C-07 (copy-vs-view / structural-sharing semantics — RESOLVED), ADR-013 + README design principle 3 (immutable value objects), GOVERNANCE.md (SemVer: "tightening an invariant" = MAJOR), ADR-018 (`values` is frozen-surface). | + +The leaf advertised **immutable value objects**, but the guarantee was enforced only for the *index* (`time`/`unit` are stored `setflags(write=False)`), not for the *value block*: `frame.values.flags.writeable` is **True**, `with_metadata` shares the buffer, so an in-place mutation of `frame.values` would **silently corrupt every frame sharing it**, with no error signal. **Tier 2** (silent-corruption potential + structural fragility with a realistic trigger), not Tier 1 — the system itself never mutates `.values` (the audit found **zero** in-place `.values`/`_values` mutations in `src/` or `tests/`). **Resolved** (2026-06-28, epic #179 / S2 #181, **ADR-025**): the enforce — `setflags(write=False)` on `.values` — is a **MAJOR** under `GOVERNANCE.md` ("tightening an invariant" on the frozen-surface `values`, ADR-018), triggering the cross-repo coordinated-bump process; disproportionate for an unexercised hole. So the contract was **corrected to match the code** instead: the value buffer is immutable **by convention** (writeable on purpose, to preserve zero-copy / `mmap`; mutating it in place is unsupported and may corrupt shares), the **index** is the enforced one. Corrected in the three frame CICs §9/§3 + README design principle 3; ADR-025 records the decision **and** the `setflags`-enforce as a **deferred MAJOR-rider** (added free on the next MAJOR — the exact one-line-per-constructor change + a red test). **Resolution carries a residual:** the value buffer is still writeable, so a careless in-place mutation is still *possible* until the deferred enforce lands — mitigated by the documented unsupported-status and the absence of any such mutation in the ecosystem. + +--- + +### C-64: `views_frames_reconcile` ships without a CIC — the package is contract-less — RESOLVED + +| Field | Value | +|-------|-------| +| ID | C-64 | +| Tier | 3 | +| Source | test-review (2026-06-27) | +| Trigger | When a behavior change to the reconcile package needs a written contract to test against and amend — e.g. adding a third reconciliation mode, the ADR-024 principled-reconciliation **sibling module**, or a new public symbol on `ReconciliationModule`/`ReconciliationResult` — and there is no §3-guarantee / §6-failure-mode CIC to extend. | +| Location | `docs/CICs/Reconcile.md` (now authored); governs `src/views_frames_reconcile/{module,result,proportional,grouping,validation,conformance,frames}.py` incl. `reconcile_result`/`ReconciliationResult`/`POINT_BROADCAST`/`ALIGNED_DRAWS`/`METHOD_PROPORTIONAL`. | +| Cross-refs | ADR-006 (intent contracts for non-trivial classes — the governing requirement), ADR-023 (the reconcile sibling), ADR-024 (the principled-reconciliation design the CIC §11 points to), C-62 (the per-draw-approximation limitation, documented in §2/§6), D-12 (the mode-reporting decision, §2/§3). | + +The leaf and summarize surfaces are CIC-governed, but the **entire `views_frames_reconcile` package** (shipped Epic 11, v1.7.0; extended v1.8.0) had **none**, and `Summarize.md §2` explicitly fences reconciliation out — so the package's red/beige/green completeness had nothing authoritative to be measured against. **Tier 3** — governance/maintainability (contract↔test alignment), not silent corruption. **Resolved** (2026-06-28, epic #179 / S1 #180): `docs/CICs/Reconcile.md` authored as a package-level CIC (§1–§11) modeled on `Summarize.md` — documenting the sum-to-country / zero-preservation / non-negativity / de-mutation guarantees (§3), the point/aligned **mode** contract (§3), the five fail-loud validation guards + the per-draw-approximation caveat (§6), and the green/beige/red test alignment (§10); listed under Active Contracts in `docs/CICs/README.md`; `validate_docs` green. + +--- + +### C-59: the summaries notebook teaches named intervals with no calibration / coverage demonstration — RESOLVED + +| Field | Value | +|-------|-------| +| ID | C-59 | +| Tier | 3 | +| Source | expert-method-review (2026-06-26 — Gneiting/Gelman seats) | +| Trigger | When `notebooks/02_summaries.ipynb` is built and shipped **without** a calibration/coverage section — displaying named X% HDI/quantile intervals (and the MAP) whose nominal coverage is never demonstrated on synthetic data with a known latent truth. | +| Location | `notebooks/02_summaries.ipynb` (planned — §4 HDI tower / §5 quantiles). | +| Cross-refs | C-60, C-61 (the notebook-completeness cluster); the views-evaluation boundary (scoring lives there — but coverage-on-synthetic-truth is a property demo, not scoring). | + +A notebook whose purpose is to build trust in these summaries that shows a "90% HDI" but never that it covers ~90% asserts the very claim it should prove. On synthetic draws the latent truth is known, so empirical coverage of each band + a PIT histogram are free to compute and are the highest-value addition (Gneiting2014; Kuleshov2018). **Highest-priority of the three notebook gaps.** **Resolved** (2026-06-27, PR #174 / commit `ea39cc6`): `02_summaries.ipynb` ships a calibration/coverage panel — empirical coverage of the 50/90/95/99 HDIs against the known synthetic truth, a PIT histogram (calibrated vs over-confident), and a recovery-vs-`S` view (Gneiting2014/Kuleshov2018), all on the ground-truth-carrying `notebooks/_synthetic.py`. No scoring API was added (scoring stays in views-evaluation). + +--- + +### C-60: the reconciliation notebook risks presenting bit-identity as method validation — RESOLVED + +| Field | Value | +|-------|-------| +| ID | C-60 | +| Tier | 3 | +| Source | expert-method-review (2026-06-26 — Hyndman seat) | +| Trigger | When `notebooks/03_reconciliation.ipynb` headlines the oracle / bit-identity parity story **without** (a) situating per-draw *proportional* reconciliation in its literature (MinT / bottom-up / probabilistic reconciliation; proportional = the pragmatic, information-losing baseline; the principled upgrade is deferred as views-postprocessing C-37) and (b) separating *implementation fidelity* (bit-identical to the torch oracle) from *method quality*. | +| Location | `notebooks/03_reconciliation.ipynb` (planned — §7 provenance). | +| Cross-refs | C-59, C-61; views-postprocessing C-37 (probabilistic-reconciliation upgrade); C-58 (the analogous "don't over-read bit-identity" caution at the cutover). | +| | Lit: Wickramasuriya2019 (MinT), Hyndman2011, Panagiotelis2023 (probabilistic reconciliation). | + +Bit-identity proves the *port* is faithful; it says nothing about whether proportional reconciliation is *good*. A reader infers methodological endorsement of a method the forecasting literature considers superseded. **Resolved** (2026-06-27, PR #174 / commit `ea39cc6`): `03_reconciliation.ipynb` panel §A situates proportional top-down against MinT (Wickramasuriya2019) / probabilistic reconciliation (Panagiotelis2023), states explicitly that **bit-identity proves faithful relocation, not method quality**, and frames the principled upgrade as deferred (views-postprocessing C-37); panel §B adds a does-it-help check against the known truth (it improves the country total but *worsens* per-cell — coherence, not accuracy). + +--- + +### C-61: the notebooks have no spatial / map view despite showcasing spatial forecasts — RESOLVED + +| Field | Value | +|-------|-------| +| ID | C-61 | +| Tier | 3 | +| Source | expert-method-review (2026-06-26 — Wilke/Kay + VIEWS/FAO domain seats) | +| Trigger | When the notebooks are shipped with only per-cell / histogram displays and **no map/lattice view** — the most operationally-expected visualization for PRIO-GRID forecasts is absent, so adopters can't connect the summaries to the spatial product they consume. | +| Location | `notebooks/02_summaries.ipynb` / `notebooks/03_reconciliation.ipynb` (planned). | +| Cross-refs | C-59, C-60. Constraint: views-frames is geography-blind (ADR-014) — the map view must use a **toy synthetic lattice**, embedding no domain geography. | + +A spatial-forecasting showcase with no spatial display under-serves the audience. Achievable on a synthetic square lattice with zero domain knowledge. **Resolved** (2026-06-27, PR #174 / commit `ea39cc6`): both `02_summaries.ipynb` and `03_reconciliation.ipynb` render toy-lattice map views on a synthetic square lattice (no domain geography — ADR-014): 02 maps the point estimate, the 90% HDI width, and decision-relevant exceedance; 03 maps raw-vs-reconciled point estimates and the change map. + +--- + ### C-55: aggregate `expected_shortfall` worst-case is silently wrong when summed samples are not a true joint posterior — RESOLVED | Field | Value | @@ -800,9 +956,13 @@ The frozen surface is **inconsistent** on non-finite draws: `collapse(np.mean)` - **ID format:** `C-xx` for concerns, `D-xx` for disagreements. IDs are permanent — gaps in numbering indicate merged or resolved entries. - **Skipped ids:** **C-04** was merged into C-18 (the "SpatialLevel slippery slope"). **C-30** is intentionally skipped — it is *pipeline-core's* external id for the cross-repo contract-test gap (referenced in ADR-005 / ADR-016), not a views-frames concern. **C-48** is intentionally skipped — it is *views-reporting's* external id for the run-identity concern (referenced in D-02 / ADR-020), not a views-frames concern. -- **Causal clusters** (assigned by `review-rr`, last reviewed 2026-06-24): - - **summarize-estimator coherence (#89)** = {C-32, C-34, + resolved C-33} — point/interval estimation over zero-inflated, heavy-tailed, potentially-multimodal conflict posteriors is mathematically under-determined; a single number can mislead. The register's live work; tracked in #89. - - **cross-repo coordination** = {C-13, D-04, D-05, D-06} — an N-consumer leaf whose consumer buy-in is *assumed, not elicited*; the concentration/fan-out risk plus the unratified-perspective disagreements. Resolvable only across repos, not within the leaf. +- **Causal clusters** (assigned by `review-rr`, last reviewed 2026-06-27): + - **summarize-estimator coherence (#89)** = {C-32, C-34, C-43, C-57, + resolved C-33} — point/interval/mode estimation over zero-inflated, heavy-tailed, potentially-multimodal conflict posteriors is mathematically under-determined; a single number can mislead, and the frozen `map_estimate` additionally carries an obscure inf-error (C-57) and a per-row binning duplication with `bimodality` (C-43). The register's live estimator work; tracked in #89. + - **reconcile method + governance** = {C-58, C-62, + D-12; resolved C-64, C-37-lineage} — the per-draw `proportional` reconciler is a pragmatic, information-losing port (C-62) whose principled joint upgrade is deferred (ADR-024); its cutover-verification residual (C-58) is the remaining governance debt, with the mode-reporting decision recorded as D-12. The package's **missing CIC** (C-64) was the other half of the governance debt — closed by `docs/CICs/Reconcile.md` (epic #179 / S1). + - **construction-convenience accretion (#113)** = {C-52, C-53, C-54, + D-09} — the planned `PredictionFrame.from_arrays` factory is the "camel's nose" for leaf bloat: accretion (C-52), two frozen construction paths diverging (C-53), and a DoD that overstates scope (C-54). Gated on the #113 decision (D-09). + - **cross-repo coordination** = {C-13, C-46, D-04, D-05, D-06} — an N-consumer leaf whose buy-in is *assumed, not elicited*: the concentration/fan-out risk (C-13), the envelope re-assertion in views-evaluation (C-46), plus the unratified-perspective disagreements. Resolvable only across repos, not within the leaf. + - **immutability enforcement** = {resolved C-63, C-07} — **resolved by ADR-025 (2026-06-28, epic #179 / S2).** Immutability is enforced for the *index* (`setflags(write=False)`) and held *by convention* for the *value buffer* (writeable on purpose, to preserve zero-copy / `mmap`; mutating `.values` in place is unsupported). The contract was corrected to this reality across the three frame CICs + README design principle 3; the `setflags`-enforce on `.values` would be a MAJOR ("tightening an invariant", GOVERNANCE/ADR-018) and is recorded as a deferred MAJOR-rider. + - cross-cutting **verification-completeness** = {C-58, resolved C-65} — a guard or path is structurally correct but not adversarially pinned: the reconciler's production-slice check (C-58, still open). Its sibling — the non-finite fail-loud on the blocked/multi-block path (C-65) — was **resolved by a red test (2026-06-28, epic #179 / S3)** placing a non-finite draw in a non-first block via `block_rows`. - **post-1.1.0 polish** = {C-35, C-36, C-37, C-38} — **resolved by Epic 7 (2026-06-24)**. Low-severity doc/test-completeness items from the 2026-06-24 repo-assimilation + test-review; closed before the v1.1.0 `main` merge, no `src/` behaviour change. - **test-coverage debt** = {C-29, C-31} — **resolved by Epic 6 (2026-06-23)**. Fail-loud / parity paths that existed in code but lacked tests (root cause: the v1.0.0 suite optimized happy-path coverage over failure/parity branches); now closed with a CI 100%-coverage gate. - **Sources:** `repo-assimilation`, `expert-review`, `test-review`, `falsification-audit`, `persona-critique`, `clean-architecture-review`, `pr-review`, `tech-debt-audit`, `incident`, `manual`. diff --git a/scripts/verify_reconcile_parity.py b/scripts/verify_reconcile_parity.py new file mode 100644 index 0000000..bc96a08 --- /dev/null +++ b/scripts/verify_reconcile_parity.py @@ -0,0 +1,202 @@ +"""Reconciliation production-parity verifier — a standalone drift report (Epic 11). + +Two modes, both human-readable: + + * ``--oracle`` (no extra deps): re-run ``views_frames_reconcile`` against the frozen + views-reporting **torch oracle** fixtures (``tests/fixtures/reconciliation_*.npz``) + and print a drift report — max absolute / relative error, the per-row sum-to-country + conservation residual, and zero-preservation — with a PASS/FAIL verdict at the same + ``rtol=1e-5, atol=1e-6`` the test suite uses. This re-confirms that the canonical + numpy reconciler still reproduces the torch numbers the cutover was validated against. + + * ``--compare OLD.parquet NEW.parquet`` (needs pandas): align two reconciled forecast + outputs on their identity columns and report the same drift statistics across the + shared numeric columns — the *retrospective production-slice check* (compare what the + old served path produced against what the new ``views_frames_reconcile`` path produces + on a real slice). + +This is a **dev / ops tool**, not run in CI and not part of the package (like +``scripts/gen_reconciliation_*.py``). The Epic 11 cutover already shipped and was verified +by the in-repo gates (the 136-case new-vs-old bit-identity head-to-head + these oracle +parity fixtures); this script makes that evidence re-runnable and gives any *future* +reconciler change — or a belt-and-suspenders production check — a one-command verifier. + +Usage: + uv run python scripts/verify_reconcile_parity.py --oracle + uv run --with pandas python scripts/verify_reconcile_parity.py \ + --compare old_served.parquet new_served.parquet --keys month_id priogrid_id +""" + +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +import numpy as np + +from views_frames import PredictionFrame, SpatialLevel, SpatioTemporalIndex +from views_frames_reconcile import ReconciliationModule, reconcile_proportional + +_RTOL = 1e-5 +_ATOL = 1e-6 +_FIX = Path(__file__).resolve().parent.parent / "tests" / "fixtures" + + +def _stats(got: np.ndarray, expected: np.ndarray) -> tuple[float, float, bool]: + """Return (max abs error, max rel error, allclose) at the suite's rtol/atol.""" + got = np.asarray(got, dtype=np.float64) + expected = np.asarray(expected, dtype=np.float64) + abs_err = np.abs(got - expected) + rel_err = abs_err / np.maximum(np.abs(expected), _ATOL) + ok = bool(np.allclose(got, expected, rtol=_RTOL, atol=_ATOL)) + return float(abs_err.max(initial=0.0)), float(rel_err.max(initial=0.0)), ok + + +def _oracle_leaf() -> bool: + """Re-run reconcile_proportional against the frozen leaf-parity oracle.""" + data = np.load(_FIX / "reconciliation_parity.npz") + n = int(data["n_cases"]) + if n == 0: + print(" leaf parity fixture is EMPTY — regenerate it; nothing verified.") + return False + worst_abs = worst_rel = cons = 0.0 + all_ok = zero_ok = True + for i in range(n): + grid = data[f"grid_{i}"] + country = data[f"country_{i}"] + got = reconcile_proportional(grid, country) + a, r, ok = _stats(got, data[f"expected_{i}"]) + worst_abs, worst_rel, all_ok = max(worst_abs, a), max(worst_rel, r), all_ok and ok + # conservation: each active (non-all-zero) draw sums to its country total + g2, adj = np.atleast_2d(grid), np.atleast_2d(got) + active = g2.sum(axis=1) > 0 + if active.any(): + tot = np.atleast_1d(country).astype(np.float64).reshape(-1)[active] + cons = max(cons, float(np.abs(adj.sum(axis=1)[active] - tot).max(initial=0.0))) + zero_ok = zero_ok and bool(np.all(adj[g2 == 0] == 0)) + print(f" leaf reconcile_proportional ({n} oracle cases):") + print(f" max abs err = {worst_abs:.3e} max rel err = {worst_rel:.3e}") + print(f" conservation = {cons:.3e} (worst |sum(adjusted) - country| on active draws)") + print(f" zeros preserved = {zero_ok}") + return all_ok and zero_ok + + +def _oracle_e2e() -> bool: + """Re-run ReconciliationModule.reconcile against the frozen end-to-end oracle.""" + data = np.load(_FIX / "reconciliation_e2e_parity.npz") + targets = [str(t) for t in data["targets"]] + if not targets: + print(" e2e parity fixture has NO targets — regenerate it; nothing verified.") + return False + module = ReconciliationModule( + np.stack([data["pg_time"], data["pg_unit"]], axis=1), data["pg_country"] + ) + worst_abs = worst_rel = 0.0 + all_ok = True + for tgt in targets: + cm = PredictionFrame( + np.asarray(data[f"cm__{tgt}"], dtype=np.float32), + SpatioTemporalIndex(data["cm_time"], data["cm_unit"], SpatialLevel.CM), + ) + pgm = PredictionFrame( + np.asarray(data[f"pg__{tgt}"], dtype=np.float32), + SpatioTemporalIndex(data["pg_time"], data["pg_unit"], SpatialLevel.PGM), + ) + got = module.reconcile(cm, pgm).values + a, r, ok = _stats(got, data[f"recon__{tgt}"]) + worst_abs, worst_rel, all_ok = max(worst_abs, a), max(worst_rel, r), all_ok and ok + print(f" end-to-end ReconciliationModule ({len(targets)} targets {targets}):") + print(f" max abs err = {worst_abs:.3e} max rel err = {worst_rel:.3e}") + return all_ok + + +def run_oracle() -> int: + """Re-confirm the canonical reconciler still matches the frozen torch oracle.""" + print("Reconciliation oracle parity — views_frames_reconcile vs the frozen") + print(f"views-reporting torch oracle (rtol={_RTOL:g}, atol={_ATOL:g}):\n") + ok = _oracle_leaf() and _oracle_e2e() + print( + f"\n VERDICT: {'PASS' if ok else 'FAIL'} — the canonical copy " + f"{'reproduces' if ok else 'DIVERGES from'} the torch oracle." + ) + return 0 if ok else 1 + + +def run_compare(old_path: str, new_path: str, keys: list[str]) -> int: + """Align two reconciled outputs on `keys` and report drift across shared columns.""" + try: + import pandas as pd + except ImportError: + print( + "--compare needs pandas: `uv run --with pandas python " + "scripts/verify_reconcile_parity.py --compare ...`", + file=sys.stderr, + ) + return 2 + old = pd.read_parquet(old_path) + new = pd.read_parquet(new_path) + missing = [k for k in keys if k not in old.columns or k not in new.columns] + if missing: + print(f"key column(s) absent from one side: {missing}", file=sys.stderr) + return 2 + merged = old.merge(new, on=keys, suffixes=("_old", "_new"), how="inner") + print( + f"compare: {len(old)} old rows, {len(new)} new rows, " + f"{len(merged)} matched on {keys}\n" + ) + cols = sorted( + c[:-4] + for c in merged.columns + if c.endswith("_old") + and f"{c[:-4]}_new" in merged.columns + and np.issubdtype(merged[c].dtype, np.number) + ) + # An empty overlap is a verification *failure*, not a pass: the two outputs share no + # comparable rows/columns, so nothing was checked. Reporting PASS here would defeat the + # tool's purpose (catching drift the consumer would otherwise serve silently). + if len(merged) == 0 or not cols: + why = "no rows matched on the keys" if len(merged) == 0 else "no shared numeric columns" + print(f"\n VERDICT: FAIL — nothing to compare ({why}); check --keys and the inputs.") + return 1 + all_ok = True + for col in cols: + a, r, ok = _stats(merged[f"{col}_new"].to_numpy(), merged[f"{col}_old"].to_numpy()) + all_ok = all_ok and ok + flag = "" if ok else " <-- beyond tolerance" + print(f" {col:<28} max abs = {a:.3e} max rel = {r:.3e}{flag}") + print( + f"\n VERDICT: {'PASS' if all_ok else 'FAIL'} — old vs new reconciled outputs " + f"{'agree' if all_ok else 'DIVERGE'} within rtol={_RTOL:g}/atol={_ATOL:g}." + ) + return 0 if all_ok else 1 + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser(description="Reconciliation parity verifier (Epic 11).") + group = parser.add_mutually_exclusive_group(required=True) + group.add_argument( + "--oracle", + action="store_true", + help="re-run the reconciler against the frozen torch-oracle fixtures", + ) + group.add_argument( + "--compare", + nargs=2, + metavar=("OLD", "NEW"), + help="compare two reconciled-output parquet files (needs pandas)", + ) + parser.add_argument( + "--keys", + nargs="+", + default=["month_id", "priogrid_id"], + help="identity columns to align --compare on (default: month_id priogrid_id)", + ) + args = parser.parse_args(argv) + if args.oracle: + return run_oracle() + return run_compare(args.compare[0], args.compare[1], args.keys) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/views_frames_reconcile/__init__.py b/src/views_frames_reconcile/__init__.py index a7aa456..e9d470f 100644 --- a/src/views_frames_reconcile/__init__.py +++ b/src/views_frames_reconcile/__init__.py @@ -7,5 +7,18 @@ from views_frames_reconcile.module import ReconciliationModule from views_frames_reconcile.proportional import reconcile_proportional +from views_frames_reconcile.result import ( + ALIGNED_DRAWS, + METHOD_PROPORTIONAL, + POINT_BROADCAST, + ReconciliationResult, +) -__all__ = ["ReconciliationModule", "reconcile_proportional"] +__all__ = [ + "ALIGNED_DRAWS", + "METHOD_PROPORTIONAL", + "POINT_BROADCAST", + "ReconciliationModule", + "ReconciliationResult", + "reconcile_proportional", +] diff --git a/src/views_frames_reconcile/conformance.py b/src/views_frames_reconcile/conformance.py index 441fb8c..437eb40 100644 --- a/src/views_frames_reconcile/conformance.py +++ b/src/views_frames_reconcile/conformance.py @@ -66,7 +66,10 @@ def assert_reconcile_contract( rows = (pg_time == t) & (cm_units == c) in_sum = pgm_frame.values[rows].sum(axis=0) # (S,) out_sum = out.values[rows].sum(axis=0) # (S,) - total = cm_frame.values[cm_pos[(int(t), int(c))]] # (S,) + # cm may be a point (sample_count == 1, broadcast inside reconcile) or aligned + # draws — broadcast its per-(time, country) total to the draw axis either way. + cm_total = cm_frame.values[cm_pos[(int(t), int(c))]] + total = np.broadcast_to(cm_total, out_sum.shape) active = in_sum != 0 np.testing.assert_allclose( out_sum[active], total[active], rtol=1e-4, atol=1e-3 diff --git a/src/views_frames_reconcile/module.py b/src/views_frames_reconcile/module.py index dff71a9..82785d4 100644 --- a/src/views_frames_reconcile/module.py +++ b/src/views_frames_reconcile/module.py @@ -23,9 +23,30 @@ from views_frames import PredictionFrame from views_frames_reconcile.grouping import reconcile_pgm_to_cm +from views_frames_reconcile.result import ( + ALIGNED_DRAWS, + METHOD_PROPORTIONAL, + POINT_BROADCAST, + ReconciliationResult, +) from views_frames_reconcile.validation import validate_reconciliation_inputs +def _broadcast_point_country( + cm_frame: PredictionFrame, n_samples: int +) -> PredictionFrame: + """Tile a point country forecast (``sample_count == 1``) across ``n_samples`` draws. + + Bit-identical to the WET broadcast pipeline-core's ``align_country_to_grid`` does + (views-frames#143): the single column is repeated, so every draw is rescaled to the + same total. It lives in the orchestrator so the leaf ``proportional`` and the + parity-frozen ``grouping`` hot loop stay **untouched** — the equal-count path is + byte-for-byte identical and only ``sample_count == 1`` takes this new path. + """ + tiled = np.tile(np.asarray(cm_frame.values, dtype=np.float32), (1, n_samples)) + return PredictionFrame(tiled, cm_frame.index, cm_frame.metadata) + + class ReconciliationModule: """Reconcile pgm forecasts to cm country totals (one target per call).""" @@ -53,16 +74,47 @@ def __init__( self._map_keys = keys self._map_vals = vals - def reconcile( + def reconcile_result( self, cm_frame: PredictionFrame, pgm_frame: PredictionFrame - ) -> PredictionFrame: - """Validate the inputs, then return a new pgm frame reconciled to cm totals. + ) -> ReconciliationResult: + """Reconcile, also reporting the mode — a ``ReconciliationResult``. + + A **point** country (``cm.sample_count == 1``) is broadcast across the grid's + draws before reconciling (mode ``point-broadcast``); an **aligned** country + (``cm.sample_count == S``) scales draw-for-draw (mode ``aligned-draws``, the + documented per-draw approximation). Any other count fails loud. + + The mode is **returned**, never stamped on the leaf's generic ``FrameMetadata`` + (ADR-020 / register C-47 — the leaf carries no reconciliation vocabulary; see + ``result.py``). Raises: - ValueError: the inputs fail validation (level / sample-count / time - coverage / missing country forecast). + ValueError: the inputs fail validation (level / sample-count — cm must be 1 + or pgm's ``S`` / time coverage / missing country forecast). """ validate_reconciliation_inputs( cm_frame, pgm_frame, self._map_keys, self._map_vals ) - return reconcile_pgm_to_cm(pgm_frame, cm_frame, self._map_keys, self._map_vals) + if cm_frame.sample_count != pgm_frame.sample_count: + cm = _broadcast_point_country(cm_frame, pgm_frame.sample_count) + mode = POINT_BROADCAST + else: + cm = cm_frame + mode = ALIGNED_DRAWS + frame = reconcile_pgm_to_cm(pgm_frame, cm, self._map_keys, self._map_vals) + return ReconciliationResult(frame=frame, mode=mode, method=METHOD_PROPORTIONAL) + + def reconcile( + self, cm_frame: PredictionFrame, pgm_frame: PredictionFrame + ) -> PredictionFrame: + """Validate the inputs, then return a new pgm frame reconciled to cm totals. + + Point/aligned handling and fail-loud are as :meth:`reconcile_result`; this + returns only the reconciled frame. Call :meth:`reconcile_result` when you also + need the reconciliation *mode* (point-broadcast vs aligned-draws). + + Raises: + ValueError: the inputs fail validation (level / sample-count — cm must be 1 + or pgm's ``S`` / time coverage / missing country forecast). + """ + return self.reconcile_result(cm_frame, pgm_frame).frame diff --git a/src/views_frames_reconcile/proportional.py b/src/views_frames_reconcile/proportional.py index 74f537c..e3174cd 100644 --- a/src/views_frames_reconcile/proportional.py +++ b/src/views_frames_reconcile/proportional.py @@ -10,9 +10,12 @@ ``ForecastReconciler.reconcile_forecast`` (torch), migrated here because the algorithm belongs in post-processing, not reporting (views-reporting issue #72). It is intentionally the **same** method — a pragmatic per-draw approximation, not -principled joint probabilistic reconciliation. The upgrade to the latter is -tracked as **C-37** and is deliberately deferred until this port's parity with -the original is proven and the move is wired. +principled joint probabilistic reconciliation. The upgrade to the latter is designed +in **ADR-024** (register **C-62**; the cross-repo lineage is views-postprocessing +C-37) and is deliberately **deferred**: per-draw pairing of independently-trained +grid and country draws has no shared draw identity, so the principled method waits on +a defined draw-identity/coupling contract and a consumer that needs calibrated joint +tails. No torch, no pandas — numpy only. """ diff --git a/src/views_frames_reconcile/result.py b/src/views_frames_reconcile/result.py new file mode 100644 index 0000000..4675f2d --- /dev/null +++ b/src/views_frames_reconcile/result.py @@ -0,0 +1,55 @@ +"""The reconciliation result — the reconciled frame plus *how* it was produced (#144). + +The reconciliation **mode** (`point-broadcast` vs `aligned-draws`) and **method** are +reported here, on a reconcile-package result object — deliberately **not** stamped onto +the leaf's generic ``FrameMetadata``. That header is governed *generic-only* (ADR-020 / +register C-47): the numpy leaf carries provenance meaningful for *any* frame (`run_id`, +`data_version`) and must never carry domain/operation vocabulary it cannot know — as +evaluation provenance lives in views-evaluation's ``MetricFrame``, not the leaf. +Reconciliation is a sibling operation, so its provenance is reported by the sibling. + +A caller that needs the mode reads it off :class:`ReconciliationResult` +(``ReconciliationModule.reconcile_result``); a caller that only needs the frame uses +``ReconciliationModule.reconcile``, which returns the frame directly. + +The mode reflects **what this reconcile call did** (broadcast a point, or scaled aligned +draws) — not upstream provenance: a caller that pre-tiles a point country to ``S`` draws +before calling reconciles in ``aligned-draws`` mode. The string values match +pipeline-core's ``reconcile_frames`` constants verbatim, so the *vocabulary* is shared +across the repos that produce and consume the mode. +""" + +from __future__ import annotations + +from dataclasses import dataclass + +from views_frames import PredictionFrame + +#: Mode — the call **broadcast a point**: the country arrived as a point +#: (``cm.sample_count == 1``) against a multi-draw grid and was tiled across the grid's +#: ``S`` draws (every draw rescaled to the same total). +POINT_BROADCAST = "point-broadcast" +#: Mode — the call **scaled aligned draws**: the country already carried the grid's draw +#: count (``cm.sample_count == S``) and was scaled draw-for-draw — the per-draw +#: approximation (the principled joint upgrade is a separate design, #145). The mode +#: describes *what this call did*: a point against a single-draw grid, or a point the +#: caller pre-tiled to ``S``, also reads as aligned-draws (nothing was broadcast). +ALIGNED_DRAWS = "aligned-draws" +#: The reconciliation algorithm — top-down proportional, per draw. +METHOD_PROPORTIONAL = "proportional" + + +@dataclass(frozen=True) +class ReconciliationResult: + """A reconciled grid frame plus how it was produced. + + Attributes: + frame: the reconciled pgm ``PredictionFrame`` (identical to what ``reconcile`` + returns). + mode: :data:`POINT_BROADCAST` or :data:`ALIGNED_DRAWS`. + method: the reconciliation method, e.g. :data:`METHOD_PROPORTIONAL`. + """ + + frame: PredictionFrame + mode: str + method: str diff --git a/src/views_frames_reconcile/validation.py b/src/views_frames_reconcile/validation.py index 1f8cd11..ab594b6 100644 --- a/src/views_frames_reconcile/validation.py +++ b/src/views_frames_reconcile/validation.py @@ -6,7 +6,8 @@ - dataset type checks -> spatial-level guard (cm@CM, pgm@PGM) - same time steps + exact overlap -> identical set of time values - - (per-draw scaling needs it) -> equal `sample_count` + - (per-draw scaling needs it) -> equal `sample_count`, or a point cm + (`sample_count == 1`) broadcast across the draws - valid countries -> every (time, country) the grid maps to has a country forecast in the cm frame @@ -44,10 +45,11 @@ def validate_reconciliation_inputs( raise ValueError( f"grid frame must be at SpatialLevel.PGM, got {pgm_frame.index.level}" ) - if cm_frame.sample_count != pgm_frame.sample_count: + if cm_frame.sample_count not in (1, pgm_frame.sample_count): raise ValueError( - f"sample-count mismatch: cm has {cm_frame.sample_count}, " - f"pgm has {pgm_frame.sample_count}" + f"sample-count mismatch: cm has {cm_frame.sample_count}, pgm has " + f"{pgm_frame.sample_count}; cm must be 1 (point, broadcast across draws) " + f"or {pgm_frame.sample_count} (aligned draws)" ) cm_times = {int(t) for t in np.unique(cm_frame.index.time)} diff --git a/tests/test_reconcile_conformance.py b/tests/test_reconcile_conformance.py index 9675e37..b390a9c 100644 --- a/tests/test_reconcile_conformance.py +++ b/tests/test_reconcile_conformance.py @@ -11,25 +11,31 @@ import numpy as np import pytest -from views_frames import SpatialLevel +from views_frames import PredictionFrame, SpatialLevel from views_frames_reconcile import ReconciliationModule +from views_frames_reconcile import conformance as _conformance from views_frames_reconcile.conformance import assert_reconcile_contract from views_frames_reconcile.frames import prediction_frame_from_arrays _FIX = Path(__file__).resolve().parent / "fixtures" / "reconciliation_e2e_parity.npz" -def _synthetic(seed, *, samples, all_zero_country=False): - """Build a (cm, pgm, map_keys, map_vals) scenario: 2 countries, 1 month.""" +def _synthetic(seed, *, samples, all_zero_country=False, cm_samples=None): + """Build a (cm, pgm, map_keys, map_vals) scenario: 2 countries, 1 month. + + ``cm_samples`` (default = ``samples``) lets the country frame carry a different + sample count than the grid — set ``cm_samples=1`` for the point-broadcast case. + """ rng = np.random.default_rng(seed) month = 500 + n_cm = samples if cm_samples is None else cm_samples # country 1 owns gids 1000-1002, country 2 owns gids 2000-2001 layout = {1: [1000, 1001, 1002], 2: [2000, 2001]} cm_t, cm_u, cm_v, pg_t, pg_u, pg_v, mk, mv = [], [], [], [], [], [], [], [] for c, gids in layout.items(): cm_t.append(month) cm_u.append(c) - cm_v.append(rng.gamma(2.0, 50.0, size=samples).astype(np.float32)) + cm_v.append(rng.gamma(2.0, 50.0, size=n_cm).astype(np.float32)) for g in gids: pg_t.append(month) pg_u.append(g) @@ -72,6 +78,13 @@ def test_contract_with_all_zero_country(): assert_reconcile_contract(*_synthetic(7, samples=32, all_zero_country=True)) +def test_contract_on_point_country(): + # S1 (#143): a point country (sample_count == 1) vs a draws grid is broadcast inside + # reconcile; the full contract must still hold (sum-to-country, zeros, non-negative, + # draws kept). + assert_reconcile_contract(*_synthetic(11, samples=64, cm_samples=1)) + + def test_injected_mapping_is_honored(): # Reconciling with a mapping that reassigns cells to the other country must change # the result — proof the mapping is the injected one, used not fetched/ignored. @@ -80,3 +93,24 @@ def test_injected_mapping_is_honored(): swapped_vals = np.where(mv == 1, 2, 1) # flip every cell's country swapped = ReconciliationModule(mk, swapped_vals).reconcile(cm, pgm) assert not np.array_equal(correct.values, swapped.values) + + +def test_conformance_rejects_a_non_conforming_reconciler(monkeypatch): + # The conformance suite must have TEETH: every positive test above runs the REAL + # (conforming) ReconciliationModule, so none exercises an assertion's raise-path + # (100% branch coverage does not — cf. the leaf's register C-51 envelope negatives). + # Substitute a deliberately broken reconciler (returns all -1.0 → violates the + # non-negativity law) and prove assert_reconcile_contract fails loud on it. + cm, pgm, mk, mv = _synthetic(5, samples=16) + + class _BadModule: + def __init__(self, _mk, _mv): + pass + + def reconcile(self, _cm, pgm_frame): + bad = np.full_like(pgm_frame.values, -1.0) # non-negativity-violating + return PredictionFrame(bad, pgm_frame.index, pgm_frame.metadata) + + monkeypatch.setattr(_conformance, "ReconciliationModule", _BadModule) + with pytest.raises(AssertionError, match="non-negative"): + assert_reconcile_contract(cm, pgm, mk, mv) diff --git a/tests/test_reconciliation_e2e_parity.py b/tests/test_reconciliation_e2e_parity.py index 2eaa250..6086f6f 100644 --- a/tests/test_reconciliation_e2e_parity.py +++ b/tests/test_reconciliation_e2e_parity.py @@ -5,13 +5,19 @@ fixture. Offline — numpy + views-frames + the committed fixture only. """ +from dataclasses import FrozenInstanceError from pathlib import Path import numpy as np import pytest from views_frames import SpatialLevel -from views_frames_reconcile import ReconciliationModule +from views_frames_reconcile import ( + ALIGNED_DRAWS, + POINT_BROADCAST, + ReconciliationModule, + ReconciliationResult, +) from views_frames_reconcile.frames import prediction_frame_from_arrays _FIX = Path(__file__).resolve().parent / "fixtures" / "reconciliation_e2e_parity.npz" @@ -81,6 +87,23 @@ def test_sum_constraint_on_active_draws(self, fix, module): ) assert (grid_sum[allzero] == 0).all() # all-zero draws stay zero + def test_point_country_broadcast_equals_manual_tile(self, fix, module): + # S1 (#143): native point-broadcast must be bit-identical to manually tiling the + # point country to S draws and running the (oracle-proven) aligned path. + _, pgm = _frames(fix, "pred_ged_sb") + s = pgm.sample_count + point_vals = fix["cm__pred_ged_sb"][:, :1] # a point country (sample_count == 1) + point_cm = prediction_frame_from_arrays( + fix["cm_time"], fix["cm_unit"], point_vals, level=SpatialLevel.CM + ) + tiled_cm = prediction_frame_from_arrays( + fix["cm_time"], fix["cm_unit"], np.tile(point_vals, (1, s)), + level=SpatialLevel.CM, + ) + out_point = module.reconcile(point_cm, pgm) + out_tiled = module.reconcile(tiled_cm, pgm) + np.testing.assert_array_equal(out_point.values, out_tiled.values) + def test_bad_mapping_shape_raises(self, fix): with pytest.raises(ValueError, match="map_keys must be"): ReconciliationModule(fix["pg_time"], fix["pg_country"]) # 1-D keys @@ -90,3 +113,69 @@ def test_validates_before_reconciling(self, fix, module): cm_wrong, pgm = _frames(fix, "pred_ged_sb") with pytest.raises(ValueError, match="SpatialLevel.CM"): module.reconcile(pgm, pgm) # pass pgm where cm expected + + +class TestReconciliationResult: + """S2 (#144): the mode is *returned* on a result, not stamped on the leaf frame.""" + + def test_aligned_draws_mode(self, fix, module): + cm, pgm = _frames(fix, "pred_ged_sb") # cm carries S draws + result = module.reconcile_result(cm, pgm) + assert isinstance(result, ReconciliationResult) + assert result.mode == ALIGNED_DRAWS + assert result.method == "proportional" + # the result frame is exactly what reconcile() returns + np.testing.assert_array_equal( + result.frame.values, module.reconcile(cm, pgm).values + ) + + def test_point_broadcast_mode(self, fix, module): + _, pgm = _frames(fix, "pred_ged_sb") + point_cm = prediction_frame_from_arrays( + fix["cm_time"], fix["cm_unit"], fix["cm__pred_ged_sb"][:, :1], + level=SpatialLevel.CM, + ) + result = module.reconcile_result(point_cm, pgm) + assert result.mode == POINT_BROADCAST + assert result.frame.sample_count == pgm.sample_count + + def test_both_points_is_aligned_draws(self, fix, module): + # mode-corner (S3): cm and pgm both points (sample_count == 1). Nothing is + # broadcast (counts already match), so the mode is ALIGNED_DRAWS — not + # POINT_BROADCAST — and the result stays a single-draw frame. + point_cm = prediction_frame_from_arrays( + fix["cm_time"], fix["cm_unit"], fix["cm__pred_ged_sb"][:, :1], + level=SpatialLevel.CM, + ) + point_pgm = prediction_frame_from_arrays( + fix["pg_time"], fix["pg_unit"], fix["pg__pred_ged_sb"][:, :1], + level=SpatialLevel.PGM, + ) + result = module.reconcile_result(point_cm, point_pgm) + assert result.mode == ALIGNED_DRAWS + assert result.frame.sample_count == 1 + + def test_pre_tiled_point_cm_is_aligned_draws(self, fix, module): + # mode-corner (S3): a caller who pre-tiles a point country to S draws BEFORE + # calling reads as ALIGNED_DRAWS (nothing was broadcast inside this call) — the + # mode describes what THIS call did, per result.py. Contrast test_point_broadcast_mode + # (a genuine sample_count==1 cm), which reads POINT_BROADCAST. + _, pgm = _frames(fix, "pred_ged_sb") + s = pgm.sample_count + tiled_cm = prediction_frame_from_arrays( + fix["cm_time"], fix["cm_unit"], + np.tile(fix["cm__pred_ged_sb"][:, :1], (1, s)), + level=SpatialLevel.CM, + ) + result = module.reconcile_result(tiled_cm, pgm) + assert result.mode == ALIGNED_DRAWS + assert result.frame.sample_count == s + + def test_result_is_frozen(self, fix, module): + # ReconciliationResult is a @dataclass(frozen=True) — its mode/method/frame must + # be immutable so a reported mode can't be retro-edited (mirrors + # test_frames.py::test_metadata_is_frozen). + cm, pgm = _frames(fix, "pred_ged_sb") + result = module.reconcile_result(cm, pgm) + with pytest.raises(FrozenInstanceError): + result.mode = POINT_BROADCAST # type: ignore[misc] diff --git a/tests/test_reconciliation_validation.py b/tests/test_reconciliation_validation.py index afde808..46ea2a5 100644 --- a/tests/test_reconciliation_validation.py +++ b/tests/test_reconciliation_validation.py @@ -44,6 +44,12 @@ def test_no_raise_on_consistent_inputs(self, fix): mk, mv = _mk(fix) validate_reconciliation_inputs(_cm(fix), _pgm(fix), mk, mv) # must not raise + def test_point_country_passes(self, fix): + # S1 (#143): a point cm (sample_count == 1) is the broadcast case — must not raise. + mk, mv = _mk(fix) + cm = _cm(fix, vals=fix["cm__pred_ged_sb"][:, :1]) + validate_reconciliation_inputs(cm, _pgm(fix), mk, mv) # must not raise + class TestGuards: def test_wrong_level_raises(self, fix): diff --git a/tests/test_summarize_conformance.py b/tests/test_summarize_conformance.py new file mode 100644 index 0000000..5c25a09 --- /dev/null +++ b/tests/test_summarize_conformance.py @@ -0,0 +1,43 @@ +"""Negative tests for the summarize conformance suite (register C-51 analogue). + +`assert_summarizer_contract` is exercised positively elsewhere (`test_proxy_adapter.py`, +`test_summarize_aggregate.py`) — but only against the REAL, conforming summarizers, so +no test exercises an assertion's raise-path (100% branch coverage does not). These +substitute a deliberately non-conforming summarizer and prove the conformance suite +fails loud — i.e. it has teeth and would reject a bad consumer implementation. + +numpy + views-frames only. +""" + +from __future__ import annotations + +import numpy as np +import pytest + +from views_frames import PredictionFrame, SpatialLevel, SpatioTemporalIndex +from views_frames_summarize import conformance as _conformance +from views_frames_summarize.conformance import assert_summarizer_contract + + +def _pf(rows): + arr = np.array(rows, dtype=np.float32) + index = SpatioTemporalIndex( + time=np.arange(arr.shape[0], dtype=np.int64), + unit=np.arange(100, 100 + arr.shape[0], dtype=np.int32), + level=SpatialLevel.PGM, + ) + return PredictionFrame(arr, index) + + +def test_conformance_accepts_a_conforming_frame(): + # baseline: the real summarizers pass (so the negative below isolates the bad impl). + assert_summarizer_contract(_pf([[0.0, 1.0, 2.0, 3.0], [1.0, 1.0, 1.0, 9.0]])) + + +def test_conformance_rejects_a_non_collapsing_point_estimator(monkeypatch): + # A point estimator that fails to reduce the sample axis to 1 (here: returns the + # frame unchanged, trailing axis S=4) must trip the `map_estimate → (N,…,1)` law. + pf = _pf([[0.0, 1.0, 2.0, 3.0], [1.0, 1.0, 1.0, 9.0]]) + monkeypatch.setattr(_conformance, "map_estimate", lambda frame: frame) + with pytest.raises(AssertionError, match="map_estimate"): + assert_summarizer_contract(pf) diff --git a/tests/test_summarize_exceedance.py b/tests/test_summarize_exceedance.py index 4716ade..0bfc485 100644 --- a/tests/test_summarize_exceedance.py +++ b/tests/test_summarize_exceedance.py @@ -197,3 +197,25 @@ def test_reducer_path_also_fails_loud_on_nonfinite(): pf = _pf([[0.0, np.nan, 2.0, 3.0]]) with pytest.raises(ValueError, match="non-finite"): collapse(pf, exceedance_reducer(1.0)) + + +@pytest.mark.parametrize("bad", [np.nan, np.inf, -np.inf]) +def test_nonfinite_in_a_non_first_block_raises(bad): + # register C-65: the np.isfinite guard lives INSIDE the per-block reducer, which + # block_apply calls once per row-block — but every other non-finite test uses a + # 1-row frame (the single-shot path, n <= ROW_BLOCK), so the multi-block path was + # never exercised. Force >1 block with a tiny block_rows and put the bad draw ONLY + # in a LATER block (block 0 is all-finite) — so a guard that checked only the first + # block would silently pass, and this fails loud as it must. + rows = [ + [0.0, 1.0, 2.0, 3.0], # block 0, row 0 — finite + [1.0, 2.0, 3.0, 4.0], # block 0, row 1 — finite + [0.0, 1.0, 2.0, 3.0], # block 1, row 2 — finite + [0.0, bad, 2.0, 3.0], # block 1, row 3 — the non-finite draw (later block) + ] + pf = _pf(rows) + # sanity: block 0 alone is clean, so the regression target (guard sees only block 0) + # would NOT raise — only the per-block guard on block 1 catches this. + assert np.isfinite(np.array(rows[:2], dtype=np.float32)).all() + with pytest.raises(ValueError, match="non-finite"): + exceedance(pf, [1.0], block_rows=2) diff --git a/tests/test_summarize_expected_shortfall.py b/tests/test_summarize_expected_shortfall.py index a9e9807..9033229 100644 --- a/tests/test_summarize_expected_shortfall.py +++ b/tests/test_summarize_expected_shortfall.py @@ -177,3 +177,24 @@ def test_aggregate_composition_is_joint_worst_case(): per_cell = expected_shortfall(grid, [0.5]) assert np.allclose(per_cell[:, 0], [51.5, 51.5]) assert not np.isclose(float(per_cell[:, 0].sum()), 101.0) # 103 ≠ 101 (subadditive) + + +@pytest.mark.parametrize("bad", [np.nan, np.inf, -np.inf]) +def test_nonfinite_in_a_non_first_block_raises(bad): + # register C-65: the np.isfinite guard lives INSIDE the per-block reducer, called + # once per row-block by block_apply — but every other non-finite test uses a 1-row + # frame (single-shot path, n <= ROW_BLOCK), leaving the multi-block path untested. + # numpy sorts NaN/+inf LAST, so without the per-block guard a top-k mean would + # silently select the garbage. Force >1 block (block_rows=2) with the bad draw ONLY + # in a LATER block (block 0 all-finite): a guard that saw only the first block would + # pass silently — this must fail loud. + rows = [ + [0.0, 1.0, 2.0, 3.0], # block 0, row 0 — finite + [1.0, 2.0, 3.0, 4.0], # block 0, row 1 — finite + [0.0, 1.0, 2.0, 3.0], # block 1, row 2 — finite + [0.0, bad, 2.0, 3.0], # block 1, row 3 — the non-finite draw (later block) + ] + pf = _pf(rows) + assert np.isfinite(np.array(rows[:2], dtype=np.float32)).all() # block 0 is clean + with pytest.raises(ValueError, match="non-finite"): + expected_shortfall(pf, [0.5], block_rows=2) diff --git a/uv.lock b/uv.lock index d234956..106d0ed 100644 --- a/uv.lock +++ b/uv.lock @@ -3,10 +3,96 @@ revision = 3 requires-python = ">=3.10" resolution-markers = [ "python_full_version >= '3.15'", - "python_full_version >= '3.11' and python_full_version < '3.15'", + "python_full_version == '3.14.*'", + "python_full_version >= '3.12' and python_full_version < '3.14'", 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