From 28110b4ab92bdf54056d8421b2fb3f220b250040 Mon Sep 17 00:00:00 2001 From: Pavel Makarchuk Date: Fri, 12 Jun 2026 01:18:16 -0400 Subject: [PATCH 01/14] Add Populace dashboard mode MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit A fourth dashboard mode reading the populace-US releases published on the policyengine/populace-us Hugging Face dataset: - /populace: release summary — loss vs enhanced CPS (train/holdout/full), acceptance gates with smoke aggregates, per-family loss breakdown, top regressions/improvements, release artifact links. - /populace/targets: explorer over all 3,704 per-target diagnostics from sound_ecps_replacement_comparison.json (family/split/winner filters, search, sortable columns, pagination). Next API routes fetch the manifests live from Hugging Face (release discovery via the tree API) and serve per-target rows from a committed snapshot of the 9f1260b release; a FastAPI mirror serves local dev. The overview flags when the live release is newer than the snapshot. Producer-side gaps are filed as PolicyEngine/populace#9 (latest.json pointer), #10 (calibration diagnostics artifact), #11 (release contract), with PRs 12-14 open against them. Co-Authored-By: Claude Fable 5 --- backend/app.py | 2 + backend/routes/populace.py | 321 +++ frontend/app/api/populace/route.ts | 198 ++ .../api/populace/target-diagnostics/route.ts | 12 + frontend/app/populace/page.tsx | 10 + frontend/app/populace/targets/page.tsx | 10 + .../layout/dashboard-mode-selector.tsx | 5 + frontend/components/layout/nav-sidebar.tsx | 17 +- .../populace/populace-overview-view.tsx | 458 ++++ .../populace/populace-targets-view.tsx | 299 +++ frontend/components/shared/empty-state.tsx | 36 + frontend/components/shared/format.ts | 68 + frontend/components/shared/help-hint.tsx | 31 + frontend/components/shared/kpi-card.tsx | 62 + frontend/components/shared/page-header.tsx | 44 + frontend/components/shared/section-card.tsx | 52 + frontend/components/shared/status-pill.tsx | 38 + .../data/populace/latest/build_manifest.json | 58 + .../populace/latest/comparison_summary.json | 1985 +++++++++++++++++ .../populace/latest/release_manifest.json | 85 + .../populace/latest/target_diagnostics.json | 1 + frontend/lib/api/client.ts | 7 +- frontend/lib/api/hooks/use-populace.ts | 182 ++ frontend/lib/dashboard-mode-context.tsx | 9 +- frontend/lib/populace/latest-artifact.ts | 150 ++ frontend/next-env.d.ts | 2 +- tests/test_populace.py | 150 ++ 27 files changed, 4284 insertions(+), 8 deletions(-) create mode 100644 backend/routes/populace.py create mode 100644 frontend/app/api/populace/route.ts create mode 100644 frontend/app/api/populace/target-diagnostics/route.ts create mode 100644 frontend/app/populace/page.tsx create mode 100644 frontend/app/populace/targets/page.tsx create mode 100644 frontend/components/populace/populace-overview-view.tsx create mode 100644 frontend/components/populace/populace-targets-view.tsx create mode 100644 frontend/components/shared/empty-state.tsx create mode 100644 frontend/components/shared/format.ts create mode 100644 frontend/components/shared/help-hint.tsx create mode 100644 frontend/components/shared/kpi-card.tsx create mode 100644 frontend/components/shared/page-header.tsx create mode 100644 frontend/components/shared/section-card.tsx create mode 100644 frontend/components/shared/status-pill.tsx create mode 100644 frontend/data/populace/latest/build_manifest.json create mode 100644 frontend/data/populace/latest/comparison_summary.json create mode 100644 frontend/data/populace/latest/release_manifest.json create mode 100644 frontend/data/populace/latest/target_diagnostics.json create mode 100644 frontend/lib/api/hooks/use-populace.ts create mode 100644 frontend/lib/populace/latest-artifact.ts create mode 100644 tests/test_populace.py diff --git a/backend/app.py b/backend/app.py index eb5ca31..547c0b3 100644 --- a/backend/app.py +++ b/backend/app.py @@ -20,6 +20,7 @@ microplex, nodes, pipeline, + populace, runs as runs_route, strata, summary, @@ -82,6 +83,7 @@ async def lifespan(app: FastAPI): app.include_router(analysis.router, prefix="/analysis", tags=["analysis"]) app.include_router(compare.router, tags=["compare"]) app.include_router(microplex.router, tags=["microplex"]) +app.include_router(populace.router, tags=["populace"]) app.include_router(geography.router, prefix="/geography", tags=["geography"]) app.include_router(targets.router, prefix="/targets", tags=["targets"]) app.include_router(strata.router, prefix="/strata", tags=["strata"]) diff --git a/backend/routes/populace.py b/backend/routes/populace.py new file mode 100644 index 0000000..c82f3fa --- /dev/null +++ b/backend/routes/populace.py @@ -0,0 +1,321 @@ +"""Populace release diagnostics. + +Serves the populace-US build published on the Hugging Face dataset +``policyengine/populace-us``. Each release under ``releases//`` +ships a ``build_manifest.json`` (gate verdicts + score vs the enhanced +CPS), a ``release_manifest.json`` (artifact registry + compatibility +matrix), and a ``sound_ecps_replacement_comparison.json`` with full +per-target diagnostics. + +The small manifests are fetched live with a TTL cache. The per-target +comparison artifact is ~3.7 MB, so the deployed static snapshot committed +under ``frontend/data/populace/latest/`` is the default source for target +rows; the live artifact is fetched only when the snapshot's release does +not match the live release and ``POPULACE_FETCH_LIVE_COMPARISON`` is set. +""" + +from __future__ import annotations + +import json +import logging +import os +import re +import time +from pathlib import Path +from typing import Any + +from fastapi import APIRouter, HTTPException, Query + +logger = logging.getLogger(__name__) +router = APIRouter() + +_HF_REPO = os.environ.get("POPULACE_HF_REPO", "policyengine/populace-us") +_HF_REVISION = os.environ.get("POPULACE_HF_REVISION", "main") +_HF_API = "https://huggingface.co/api/datasets" + +_STATIC_ROOT = Path(__file__).resolve().parents[2] / "frontend/data/populace/latest" +_STATIC_BUILD_MANIFEST = _STATIC_ROOT / "build_manifest.json" +_STATIC_RELEASE_MANIFEST = _STATIC_ROOT / "release_manifest.json" +_STATIC_COMPARISON_SUMMARY = _STATIC_ROOT / "comparison_summary.json" +_STATIC_TARGET_DIAGNOSTICS = _STATIC_ROOT / "target_diagnostics.json" + +_CACHE: dict[str, tuple[float, Any]] = {} +_TTL_SECONDS = 300 + + +def _scrub(value: Any) -> Any: + if isinstance(value, dict): + return {key: _scrub(item) for key, item in value.items()} + if isinstance(value, list): + return [_scrub(item) for item in value] + if isinstance(value, float) and (value != value or value in (float("inf"), float("-inf"))): + return None + return value + + +def _fetch_json(url: str) -> Any: + cached = _CACHE.get(url) + if cached and time.time() - cached[0] < _TTL_SECONDS: + return cached[1] + import urllib.request + + logger.info("Fetching populace artifact: %s", url) + with urllib.request.urlopen(url, timeout=30) as resp: + body = resp.read().decode("utf-8") + data = json.loads(body) + _CACHE[url] = (time.time(), data) + return data + + +def _hf_resolve_url(path: str) -> str: + return f"https://huggingface.co/datasets/{_HF_REPO}/resolve/{_HF_REVISION}/{path}" + + +def _load_static(path: Path) -> dict[str, Any]: + try: + return json.loads(path.read_text()) + except Exception as exc: + raise HTTPException( + status_code=500, + detail=f"Deployed populace snapshot missing or unreadable at {path}: {exc}", + ) + + +def _list_releases() -> list[dict[str, Any]]: + tree = _fetch_json(f"{_HF_API}/{_HF_REPO}/tree/{_HF_REVISION}/releases?recursive=true") + releases: dict[str, list[str]] = {} + if not isinstance(tree, list): + return [] + for entry in tree: + if not isinstance(entry, dict) or entry.get("type") != "file": + continue + match = re.match(r"^releases/([^/]+)/(.+)$", str(entry.get("path", ""))) + if not match: + continue + releases.setdefault(match.group(1), []).append(match.group(2)) + return [ + {"release_id": release_id, "files": sorted(files)} + for release_id, files in sorted(releases.items()) + ] + + +def _load_live_release(snapshot_release_id: str) -> dict[str, Any]: + try: + releases = _list_releases() + complete = [r for r in releases if "build_manifest.json" in r["files"]] + live = next( + (r for r in complete if r["release_id"] == snapshot_release_id), + complete[-1] if complete else None, + ) + if live is None: + return { + "available": False, + "reason": "No release with a build_manifest.json found on Hugging Face.", + "releases": releases, + } + prefix = f"releases/{live['release_id']}" + build_manifest = _fetch_json(_hf_resolve_url(f"{prefix}/build_manifest.json")) + release_manifest = ( + _fetch_json(_hf_resolve_url(f"{prefix}/release_manifest.json")) + if "release_manifest.json" in live["files"] + else {} + ) + return { + "available": True, + "source": "huggingface_live", + "repo_id": _HF_REPO, + "revision": _HF_REVISION, + "release_id": live["release_id"], + "releases": releases, + "build_manifest": build_manifest, + "release_manifest": release_manifest, + "comparison_url": ( + _hf_resolve_url(f"{prefix}/sound_ecps_replacement_comparison.json") + if "sound_ecps_replacement_comparison.json" in live["files"] + else None + ), + } + except Exception as exc: + return {"available": False, "reason": str(exc)} + + +@router.get("/populace") +def populace_overview() -> dict[str, Any]: + static_build = _load_static(_STATIC_BUILD_MANIFEST) + static_release = _load_static(_STATIC_RELEASE_MANIFEST) + comparison = _load_static(_STATIC_COMPARISON_SUMMARY) + snapshot_release_id = str(static_build.get("build_id", "")) + + live = _load_live_release(snapshot_release_id) + live_available = live.get("available") is True + build_manifest = live["build_manifest"] if live_available else static_build + release_manifest = ( + live["release_manifest"] + if live_available and live.get("release_manifest") + else static_release + ) + release_id = str(live["release_id"]) if live_available else snapshot_release_id + releases = live.get("releases") or [] + + target_diagnostics = _load_static(_STATIC_TARGET_DIAGNOSTICS) + targets = target_diagnostics.get("targets") or [] + + gates = build_manifest.get("gates") or {} + score = build_manifest.get("score_vs_enhanced_cps") or {} + + return _scrub( + { + "source_repo": _HF_REPO, + "repo_type": "dataset", + "revision": _HF_REVISION, + "source": "huggingface_live" if live_available else "deployed_static_snapshot", + "live_unavailable_reason": None if live_available else live.get("reason"), + "release_id": release_id, + "snapshot_release_id": snapshot_release_id, + "releases": releases, + "source_artifacts": [ + { + "name": "build_manifest", + "path": f"releases/{release_id}/build_manifest.json", + "url": ( + _hf_resolve_url(f"releases/{release_id}/build_manifest.json") + if live_available + else "deployed-static-snapshot" + ), + }, + { + "name": "release_manifest", + "path": f"releases/{release_id}/release_manifest.json", + "url": ( + _hf_resolve_url(f"releases/{release_id}/release_manifest.json") + if live_available + else "deployed-static-snapshot" + ), + }, + { + "name": "sound_ecps_replacement_comparison", + "path": "frontend/data/populace/latest/comparison_summary.json", + "url": live.get("comparison_url") or "deployed-static-snapshot", + }, + { + "name": "target_diagnostics", + "path": "frontend/data/populace/latest/target_diagnostics.json", + "url": "deployed-static-snapshot", + }, + ], + "limitations": [ + "Build and release manifests are read live from the " + "policyengine/populace-us Hugging Face dataset when reachable; " + "per-target diagnostics come from a deployed static snapshot of " + "sound_ecps_replacement_comparison.json.", + "populace does not yet publish a latest.json pointer, so the live " + "release is resolved by listing the releases/ tree and picking the " + "lexicographically latest complete release " + "(PolicyEngine/populace#9).", + "Calibration internals (loss trajectory, skipped targets, " + "per-record L0 gates) are computed by populace-calibrate but not " + "published, so the dashboard cannot show convergence or skip " + "reasons yet (PolicyEngine/populace#10).", + ], + "comparison_snapshot_stale": release_id + != str(comparison.get("release_id", snapshot_release_id)), + "build_manifest": build_manifest, + "release_manifest": release_manifest, + "gates": gates, + "score_vs_enhanced_cps": score, + "comparison": {"available": True, **comparison}, + "target_diagnostics": { + "available": True, + "path": "frontend/data/populace/latest/target_diagnostics.json", + "release_id": target_diagnostics.get("release_id"), + "schema_version": target_diagnostics.get("schema_version"), + "metric": target_diagnostics.get("metric"), + "period": target_diagnostics.get("period"), + "baseline_label": "enhanced_cps", + "candidate_label": "populace", + "summary": target_diagnostics.get("summary") or {}, + "total_targets": len(targets), + "display_limit": 100, + "targets": targets[:100], + }, + } + ) + + +def _matches_search(row: dict[str, Any], search: str) -> bool: + haystack = " ".join( + str(row.get(key)) + for key in ("target_name", "family", "split", "winner") + if row.get(key) is not None + ).lower() + return search.lower() in haystack + + +@router.get("/populace/target-diagnostics") +def populace_target_diagnostics( + limit: int = Query(100, ge=1, le=500), + offset: int = Query(0, ge=0), + family: str | None = None, + split: str | None = None, + winner: str | None = None, + search: str | None = None, + sort_by: str | None = None, + sort_dir: str = "asc", +) -> dict[str, Any]: + payload = _load_static(_STATIC_TARGET_DIAGNOSTICS) + rows: list[dict[str, Any]] = list(payload.get("targets") or []) + families = sorted({str(row.get("family", "")) for row in rows}) + + filtered = rows + if family: + filtered = [row for row in filtered if row.get("family") == family] + if split: + filtered = [row for row in filtered if row.get("split") == split] + if winner: + filtered = [row for row in filtered if row.get("winner") == winner] + if search and search.strip(): + filtered = [row for row in filtered if _matches_search(row, search.strip())] + if sort_by: + descending = sort_dir == "desc" + present = [row for row in filtered if row.get(sort_by) is not None] + missing = [row for row in filtered if row.get(sort_by) is None] + + def sort_key(row: dict[str, Any]): + value = row.get(sort_by) + if isinstance(value, bool) or not isinstance(value, (int, float)): + return (1, 0.0, str(value)) + return (0, float(value), "") + + # None values sort last regardless of direction. + filtered = sorted(present, key=sort_key, reverse=descending) + missing + + page = filtered[offset : offset + limit] + return _scrub( + { + "available": True, + "path": "frontend/data/populace/latest/target_diagnostics.json", + "release_id": payload.get("release_id"), + "schema_version": payload.get("schema_version"), + "metric": payload.get("metric"), + "period": payload.get("period"), + "baseline_label": "enhanced_cps", + "candidate_label": "populace", + "summary": payload.get("summary") or {}, + "total_targets": len(rows), + "families": families, + "filtered_total": len(filtered), + "returned": len(page), + "limit": limit, + "offset": offset, + "has_next": offset + limit < len(filtered), + "filters": { + "family": family, + "split": split, + "winner": winner, + "search": search, + "sort_by": sort_by, + "sort_dir": sort_dir if sort_by else None, + }, + "targets": page, + } + ) diff --git a/frontend/app/api/populace/route.ts b/frontend/app/api/populace/route.ts new file mode 100644 index 0000000..fd448ad --- /dev/null +++ b/frontend/app/api/populace/route.ts @@ -0,0 +1,198 @@ +import { NextResponse } from "next/server"; + +import { + LATEST_POPULACE_BUILD_MANIFEST_PATH, + LATEST_POPULACE_COMPARISON_SUMMARY_PATH, + LATEST_POPULACE_RELEASE_ID, + LATEST_POPULACE_RELEASE_MANIFEST_PATH, + LATEST_POPULACE_TARGET_DIAGNOSTICS_PATH, + latestPopulaceBuildManifest, + latestPopulaceComparisonSummary, + latestPopulaceReleaseManifest, + latestPopulaceTargetDiagnosticsSummary, + scrub, +} from "@/lib/populace/latest-artifact"; + +const HF_REPO = process.env.POPULACE_HF_REPO ?? "policyengine/populace-us"; +const HF_REVISION = process.env.POPULACE_HF_REVISION ?? "main"; +const HF_API = "https://huggingface.co/api/datasets"; + +type JsonObject = Record; + +export const revalidate = 300; + +function asObject(value: unknown): JsonObject { + return value && typeof value === "object" && !Array.isArray(value) + ? (value as JsonObject) + : {}; +} + +function hfResolveUrl(path: string): string { + return `https://huggingface.co/datasets/${HF_REPO}/resolve/${HF_REVISION}/${path}`; +} + +async function fetchJson(url: string): Promise { + const response = await fetch(url, { next: { revalidate } }); + if (!response.ok) { + throw new Error(`Failed to fetch ${url}: ${response.status}`); + } + return response.json(); +} + +interface ReleaseEntry { + release_id: string; + files: string[]; +} + +async function listReleases(): Promise { + const tree = await fetchJson( + `${HF_API}/${HF_REPO}/tree/${HF_REVISION}/releases?recursive=true`, + ); + if (!Array.isArray(tree)) return []; + const releases = new Map(); + for (const entry of tree) { + const item = asObject(entry); + if (item.type !== "file" || typeof item.path !== "string") continue; + const match = item.path.match(/^releases\/([^/]+)\/(.+)$/); + if (!match) continue; + const files = releases.get(match[1]) ?? []; + files.push(match[2]); + releases.set(match[1], files); + } + return [...releases.entries()] + .map(([release_id, files]) => ({ release_id, files })) + .sort((a, b) => a.release_id.localeCompare(b.release_id)); +} + +async function loadLiveRelease(): Promise { + try { + const releases = await listReleases(); + // Prefer the snapshot's release when it is published; otherwise the + // lexicographically-latest complete release. Build ids end in a date, + // but same-day builds have no published ordering — a latest.json + // pointer in the repo would make this resolution exact. + const complete = releases.filter((release) => + release.files.includes("build_manifest.json"), + ); + const live = + complete.find((r) => r.release_id === LATEST_POPULACE_RELEASE_ID) ?? + complete[complete.length - 1] ?? + null; + if (!live) { + return { + available: false, + reason: "No release with a build_manifest.json found on Hugging Face.", + releases, + }; + } + const prefix = `releases/${live.release_id}`; + const buildManifest = asObject( + await fetchJson(hfResolveUrl(`${prefix}/build_manifest.json`)), + ); + const releaseManifest = live.files.includes("release_manifest.json") + ? asObject(await fetchJson(hfResolveUrl(`${prefix}/release_manifest.json`))) + : {}; + return { + available: true, + source: "huggingface_live", + repo_id: HF_REPO, + revision: HF_REVISION, + release_id: live.release_id, + releases, + build_manifest: buildManifest, + release_manifest: releaseManifest, + comparison_url: live.files.includes("sound_ecps_replacement_comparison.json") + ? hfResolveUrl(`${prefix}/sound_ecps_replacement_comparison.json`) + : null, + }; + } catch (error) { + return { + available: false, + reason: error instanceof Error ? error.message : String(error), + }; + } +} + +export async function GET() { + try { + const live = await loadLiveRelease(); + const liveAvailable = live.available === true; + const buildManifest = liveAvailable + ? asObject(live.build_manifest) + : latestPopulaceBuildManifest(); + const releaseManifest = + liveAvailable && Object.keys(asObject(live.release_manifest)).length + ? asObject(live.release_manifest) + : latestPopulaceReleaseManifest(); + const releaseId = liveAvailable + ? String(live.release_id) + : LATEST_POPULACE_RELEASE_ID; + const releases = Array.isArray(live.releases) ? live.releases : []; + const comparison = latestPopulaceComparisonSummary(); + const comparisonIsStale = + releaseId !== String(comparison.release_id ?? LATEST_POPULACE_RELEASE_ID); + + return NextResponse.json( + scrub({ + source_repo: HF_REPO, + repo_type: "dataset", + revision: HF_REVISION, + source: liveAvailable ? "huggingface_live" : "deployed_static_snapshot", + live_unavailable_reason: liveAvailable ? null : live.reason ?? null, + release_id: releaseId, + snapshot_release_id: LATEST_POPULACE_RELEASE_ID, + releases, + source_artifacts: [ + { + name: "build_manifest", + path: liveAvailable + ? `releases/${releaseId}/build_manifest.json` + : LATEST_POPULACE_BUILD_MANIFEST_PATH, + url: liveAvailable + ? hfResolveUrl(`releases/${releaseId}/build_manifest.json`) + : "deployed-static-snapshot", + }, + { + name: "release_manifest", + path: liveAvailable + ? `releases/${releaseId}/release_manifest.json` + : LATEST_POPULACE_RELEASE_MANIFEST_PATH, + url: liveAvailable + ? hfResolveUrl(`releases/${releaseId}/release_manifest.json`) + : "deployed-static-snapshot", + }, + { + name: "sound_ecps_replacement_comparison", + path: LATEST_POPULACE_COMPARISON_SUMMARY_PATH, + url: + typeof live.comparison_url === "string" + ? live.comparison_url + : "deployed-static-snapshot", + }, + { + name: "target_diagnostics", + path: LATEST_POPULACE_TARGET_DIAGNOSTICS_PATH, + url: "deployed-static-snapshot", + }, + ], + limitations: [ + "Build and release manifests are read live from the policyengine/populace-us Hugging Face dataset when reachable; per-target diagnostics come from a deployed static snapshot of sound_ecps_replacement_comparison.json.", + "populace does not yet publish a latest.json pointer, so the live release is resolved by listing the releases/ tree and picking the lexicographically latest complete release (PolicyEngine/populace#9).", + "Calibration internals (loss trajectory, skipped targets, per-record L0 gates) are computed by populace-calibrate but not published, so the dashboard cannot show convergence or skip reasons yet (PolicyEngine/populace#10).", + ], + comparison_snapshot_stale: comparisonIsStale, + build_manifest: buildManifest, + release_manifest: releaseManifest, + gates: asObject(buildManifest.gates), + score_vs_enhanced_cps: asObject(buildManifest.score_vs_enhanced_cps), + comparison, + target_diagnostics: latestPopulaceTargetDiagnosticsSummary(100), + }), + ); + } catch (error) { + return NextResponse.json( + { detail: error instanceof Error ? error.message : String(error) }, + { status: 502 }, + ); + } +} diff --git a/frontend/app/api/populace/target-diagnostics/route.ts b/frontend/app/api/populace/target-diagnostics/route.ts new file mode 100644 index 0000000..01c07ed --- /dev/null +++ b/frontend/app/api/populace/target-diagnostics/route.ts @@ -0,0 +1,12 @@ +import { NextResponse } from "next/server"; + +import { + latestPopulaceTargetDiagnosticsPage, + scrub, +} from "@/lib/populace/latest-artifact"; + +export const revalidate = 300; + +export async function GET(request: Request) { + return NextResponse.json(scrub(latestPopulaceTargetDiagnosticsPage(request.url))); +} diff --git a/frontend/app/populace/page.tsx b/frontend/app/populace/page.tsx new file mode 100644 index 0000000..43dc324 --- /dev/null +++ b/frontend/app/populace/page.tsx @@ -0,0 +1,10 @@ +import { AppShell } from "@/components/layout/app-shell"; +import { PopulaceOverviewView } from "@/components/populace/populace-overview-view"; + +export default function PopulaceOverviewPage() { + return ( + + + + ); +} diff --git a/frontend/app/populace/targets/page.tsx b/frontend/app/populace/targets/page.tsx new file mode 100644 index 0000000..ee25188 --- /dev/null +++ b/frontend/app/populace/targets/page.tsx @@ -0,0 +1,10 @@ +import { AppShell } from "@/components/layout/app-shell"; +import { PopulaceTargetsView } from "@/components/populace/populace-targets-view"; + +export default function PopulaceTargetsPage() { + return ( + + + + ); +} diff --git a/frontend/components/layout/dashboard-mode-selector.tsx b/frontend/components/layout/dashboard-mode-selector.tsx index c23d2fa..8c2d002 100644 --- a/frontend/components/layout/dashboard-mode-selector.tsx +++ b/frontend/components/layout/dashboard-mode-selector.tsx @@ -8,6 +8,11 @@ const OPTIONS: { id: DashboardMode; label: string; hint: string }[] = [ label: "Microplex", hint: "Synthetic pipeline and parity artifacts", }, + { + id: "populace", + label: "Populace", + hint: "Published populace-US releases", + }, { id: "us-data", label: "us-data", diff --git a/frontend/components/layout/nav-sidebar.tsx b/frontend/components/layout/nav-sidebar.tsx index a45ba53..7b339f4 100644 --- a/frontend/components/layout/nav-sidebar.tsx +++ b/frontend/components/layout/nav-sidebar.tsx @@ -23,6 +23,11 @@ const microplexNavItems = [ { href: "/pipeline", label: "Pipeline" }, ]; +const populaceNavItems = [ + { href: "/populace", label: "Release summary" }, + { href: "/populace/targets", label: "Target diagnostics" }, +]; + const comparisonNavItems = [ { href: "/comparison", label: "Comparison" }, { href: "/microplex", label: "Microplex target performance" }, @@ -37,9 +42,11 @@ export function NavSidebar() { const navItems = mode === "microplex" ? microplexNavItems - : mode === "comparison" - ? comparisonNavItems - : usDataNavItems; + : mode === "populace" + ? populaceNavItems + : mode === "comparison" + ? comparisonNavItems + : usDataNavItems; return (
@@ -51,7 +58,9 @@ export function NavSidebar() { ? pathname === "/" : item.href === "/microplex" ? pathname === "/microplex" - : pathname.startsWith(item.href); + : item.href === "/populace" + ? pathname === "/populace" + : pathname.startsWith(item.href); return ( = { + people_m: "People (millions)", + snap_b: "SNAP ($B)", + net_worth_t: "Net worth ($T)", + net_stcg_b: "Net short-term capital gains ($B)", + tips_b: "Tip income ($B)", + pre_subsidy_rent_b: "Pre-subsidy rent ($B)", + investment_interest_expense_b: "Investment interest expense ($B)", +}; + +function lossCell(value: number | null | undefined) { + return value == null ? "—" : fmt(value, { digits: 4 }); +} + +function TargetMoversTable({ + rows, + emptyLabel, +}: { + rows: PopulaceTargetDiagnosticRow[]; + emptyLabel: string; +}) { + if (!rows.length) { + return ; + } + return ( +
+ + + + + + + + + + + + + {rows.map((row) => ( + + + + + + + + + ))} + +
TargetFamilySplitPopulace rel. erroreCPS rel. errorLoss delta
+ {row.target_name} + + {row.family} + {row.split} + {fmt(row.candidate_relative_error, { pct: true, digits: 1 })} + + {fmt(row.baseline_relative_error, { pct: true, digits: 1 })} + + {row.loss_delta == null ? "—" : row.loss_delta.toExponential(2)} +
+
+ ); +} + +export function PopulaceOverviewView() { + const { data, isLoading, error } = usePopulace(); + + if (isLoading) return ; + if (error || !data) { + return ( + + ); + } + + const score = data.score_vs_enhanced_cps ?? {}; + const gates = data.gates ?? {}; + const calibrationGate = gates.calibration ?? {}; + const wins = score.per_target_wins ?? {}; + const totalWins = + (wins.populace ?? 0) + (wins.enhanced_cps ?? 0) + (wins.ties ?? 0); + const comparison = data.comparison ?? { available: false }; + const familyRows: PopulaceFamilyBreakdownRow[] = [ + ...(comparison.family_breakdown ?? []), + ].sort( + (a, b) => + (b.candidate_loss_contribution ?? 0) - (a.candidate_loss_contribution ?? 0), + ); + const populaceFullLoss = score.full_loss?.populace ?? null; + const ecpsFullLoss = score.full_loss?.enhanced_cps ?? null; + const populaceBeatsEcps = + populaceFullLoss != null && ecpsFullLoss != null + ? populaceFullLoss < ecpsFullLoss + : null; + + return ( +
+ + Latest published populace-US build from{" "} + + {data.source_repo} + + , scored against the enhanced CPS with a matched-household, + symmetric-refit, held-out-target protocol. + + } + status={ + + {data.source === "huggingface_live" + ? "Live from Hugging Face" + : "Static snapshot"} + + } + /> + +
+ + } + value={lossCell(populaceFullLoss)} + delta={ + populaceBeatsEcps == null + ? undefined + : populaceBeatsEcps + ? "beats eCPS" + : "behind eCPS" + } + tone={populaceBeatsEcps ? "positive" : "negative"} + hint={`Enhanced CPS: ${lossCell(ecpsFullLoss)}`} + /> + + } + value={lossCell(score.holdout_loss?.populace)} + hint={`Enhanced CPS: ${lossCell(score.holdout_loss?.enhanced_cps)}`} + /> + + } + value={fmt(calibrationGate.within_10pct_share, { pct: true, digits: 1 })} + hint={`Calibration loss ${fmt(calibrationGate.loss, { digits: 4 })}, max weight ratio ${fmt(calibrationGate.max_weight_ratio, { digits: 0 })}×`} + /> + + } + value={`${fmtCompact(wins.populace)} / ${fmtCompact(totalWins || null)}`} + hint={`Enhanced CPS wins ${fmtCompact(wins.enhanced_cps)}, ties ${fmtCompact(wins.ties)}`} + /> +
+ + + Gate verdicts recorded in build_manifest.json for build{" "} + {data.release_id}.{" "} + {String(data.build_manifest?.construction ?? "")} + + } + > +
+
+
+ + Parity gaps: {fmt(gates.parity_gaps, { digits: 0 })} + + + Exported non-zero ({fmt(gates.exported_nonzero?.stored_columns, { digits: 0 })}{" "} + columns) + + + Weights above 500k: {fmt(calibrationGate.weights_above_500k, { digits: 0 })} + +
+
+ Max calibrated weight {fmtCompact(calibrationGate.max_weight)} under a hard{" "} + {fmt(calibrationGate.max_weight_ratio, { digits: 0 })}× per-record bound. +
+
+ + + {Object.entries(gates.smoke ?? {}).map(([key, value]) => ( + + + + + ))} + +
+ {SMOKE_LABELS[key] ?? key} + {fmt(value, { digits: 1 })}
+
+ + + + Explore all targets → + + } + > +
+ + + + + + + + + + + {( + [ + ["Train", score.train_loss], + ["Holdout", score.holdout_loss], + ["Full", score.full_loss], + ] as const + ).map(([label, losses]) => { + const populace = losses?.populace ?? null; + const ecps = losses?.enhanced_cps ?? null; + return ( + + + + + + + ); + })} + +
LossPopulaceEnhanced CPSRatio
{label}{lossCell(populace)}{lossCell(ecps)} + {populace != null && ecps != null && populace > 0 + ? `${(ecps / populace).toFixed(1)}× better` + : "—"} +
+
+ + + {comparison.available && ( + +
+ + + + + + + + + + + + + + {familyRows.map((row) => ( + + + + + + + + + + ))} + +
FamilyTargetsPopulace winseCPS winsPopulace loss contrib.eCPS loss contrib.Delta
{row.family} + {fmt(row.n_targets, { digits: 0 })} + + {fmt(row.candidate_wins, { digits: 0 })} + + {fmt(row.baseline_wins, { digits: 0 })} + + {row.candidate_loss_contribution == null + ? "—" + : row.candidate_loss_contribution.toExponential(2)} + + {row.baseline_loss_contribution == null + ? "—" + : row.baseline_loss_contribution.toExponential(2)} + + {row.loss_delta == null ? "—" : row.loss_delta.toExponential(2)} +
+
+ + )} + + {comparison.available && ( +
+ + + + + + +
+ )} + + + {data.releases.length + ? `${data.releases.length} release${data.releases.length === 1 ? "" : "s"} published under releases/ in ${data.source_repo}.` + : "Release listing unavailable; showing the deployed snapshot."} + {data.comparison_snapshot_stale && ( + + {" "} + The per-target snapshot below was built from {data.snapshot_release_id}, + which is older than the live release. + + )} + + } + > +
+ {data.releases.length > 0 && ( +
+ {data.releases.map((release) => ( + + {release.release_id} + {release.release_id === data.release_id ? " (active)" : ""} + + ))} +
+ )} + + + {data.source_artifacts.map((artifact) => ( + + + + + ))} + +
{artifact.name} + {artifact.url.startsWith("http") ? ( + + {artifact.path} + + ) : ( + <> + {artifact.path}{" "} + (deployed snapshot) + + )} +
+
+ Compatible with{" "} + {(data.release_manifest.compatible_model_packages ?? []) + .map((pkg) => `${pkg.name}${pkg.specifier}`) + .join(", ") || "—"} + . +
+
+ + + +
    + {data.limitations.map((limitation) => ( +
  • {limitation}
    • + ))} +
+ +
+ ); +} diff --git a/frontend/components/populace/populace-targets-view.tsx b/frontend/components/populace/populace-targets-view.tsx new file mode 100644 index 0000000..292686d --- /dev/null +++ b/frontend/components/populace/populace-targets-view.tsx @@ -0,0 +1,299 @@ +"use client"; + +import { useMemo, useState } from "react"; + +import { EmptyState } from "@/components/shared/empty-state"; +import { fmt, fmtCompact } from "@/components/shared/format"; +import { LoadingBlock } from "@/components/shared/LoadingBlock"; +import { PageHeader } from "@/components/shared/page-header"; +import { SectionCard } from "@/components/shared/section-card"; +import { StatusPill } from "@/components/shared/status-pill"; +import { ToolbarSelect } from "@/components/shared/toolbar-select"; +import { + usePopulaceTargetDiagnostics, + type PopulaceTargetDiagnosticRow, +} from "@/lib/api/hooks/use-populace"; + +const PAGE_SIZE = 50; + +interface SortState { + by: string; + dir: "asc" | "desc"; +} + +const COLUMNS: { + key: string; + label: string; + numeric?: boolean; + render: (row: PopulaceTargetDiagnosticRow) => React.ReactNode; +}[] = [ + { + key: "target_name", + label: "Target", + render: (row) => ( + + {row.target_name} + + ), + }, + { + key: "family", + label: "Family", + render: (row) => {row.family}, + }, + { + key: "split", + label: "Split", + render: (row) => row.split, + }, + { + key: "target_value", + label: "Target value", + numeric: true, + render: (row) => fmtCompact(row.target_value), + }, + { + key: "candidate_estimate", + label: "Populace est.", + numeric: true, + render: (row) => fmtCompact(row.candidate_estimate), + }, + { + key: "candidate_relative_error", + label: "Populace rel. err", + numeric: true, + render: (row) => fmt(row.candidate_relative_error, { pct: true, digits: 1 }), + }, + { + key: "baseline_relative_error", + label: "eCPS rel. err", + numeric: true, + render: (row) => fmt(row.baseline_relative_error, { pct: true, digits: 1 }), + }, + { + key: "loss_delta", + label: "Loss delta", + numeric: true, + render: (row) => ( + + {row.loss_delta == null ? "—" : row.loss_delta.toExponential(2)} + + ), + }, + { + key: "winner", + label: "Closer", + render: (row) => ( + + {row.winner === "candidate" + ? "populace" + : row.winner === "baseline" + ? "eCPS" + : "tie"} + + ), + }, +]; + +export function PopulaceTargetsView() { + const [family, setFamily] = useState(""); + const [split, setSplit] = useState(""); + const [winner, setWinner] = useState(""); + const [search, setSearch] = useState(""); + const [page, setPage] = useState(0); + const [sort, setSort] = useState({ + by: "candidate_loss_term", + dir: "desc", + }); + + const params = useMemo( + () => ({ + limit: PAGE_SIZE, + offset: page * PAGE_SIZE, + family: family || undefined, + split: split || undefined, + winner: winner || undefined, + search: search || undefined, + sort_by: sort.by, + sort_dir: sort.dir, + }), + [family, split, winner, search, page, sort], + ); + + const { data, isLoading, error } = usePopulaceTargetDiagnostics(params); + + const families = data?.families ?? []; + const filteredTotal = data?.filtered_total ?? 0; + const pageCount = Math.max(Math.ceil(filteredTotal / PAGE_SIZE), 1); + + function toggleSort(key: string) { + setPage(0); + setSort((current) => + current.by === key + ? { by: key, dir: current.dir === "desc" ? "asc" : "desc" } + : { by: key, dir: "desc" }, + ); + } + + return ( +
+ {String(data.release_id)} + ) : undefined + } + /> + + + { + setSearch(event.target.value); + setPage(0); + }} + className="h-8 w-48 rounded-md border border-border bg-white px-3 text-xs focus:border-primary/60 focus:outline-none" + /> + { + setFamily(value); + setPage(0); + }} + options={[ + { value: "", label: "All" }, + ...families.map((value) => ({ value, label: value })), + ]} + /> + { + setSplit(value); + setPage(0); + }} + options={[ + { value: "", label: "All" }, + { value: "train", label: "Train" }, + { value: "holdout", label: "Holdout" }, + ]} + /> + { + setWinner(value); + setPage(0); + }} + options={[ + { value: "", label: "All" }, + { value: "candidate", label: "Populace" }, + { value: "baseline", label: "Enhanced CPS" }, + { value: "tie", label: "Tie" }, + ]} + /> +
+ } + footer={ +
+ + Page {page + 1} of {pageCount} + + + + + +
+ } + > + {isLoading ? ( + + ) : error || !data ? ( + + ) : data.targets.length === 0 ? ( + + ) : ( +
+ + + + {COLUMNS.map((column) => ( + + ))} + + + + {data.targets.map((row) => ( + + {COLUMNS.map((column) => ( + + ))} + + ))} + +
+ +
+ {column.render(row)} +
+
+ )} + +
+ ); +} diff --git a/frontend/components/shared/empty-state.tsx b/frontend/components/shared/empty-state.tsx new file mode 100644 index 0000000..19aaa28 --- /dev/null +++ b/frontend/components/shared/empty-state.tsx @@ -0,0 +1,36 @@ +"use client"; + +import type { ReactNode } from "react"; +import { Text } from "@policyengine/ui-kit"; + +interface EmptyStateProps { + title: ReactNode; + description?: ReactNode; + actions?: ReactNode; + icon?: ReactNode; + variant?: "default" | "compact"; +} + +export function EmptyState({ + title, + description, + actions, + icon, + variant = "default", +}: EmptyStateProps) { + const padding = variant === "compact" ? "px-4 py-6" : "px-6 py-10"; + return ( +
+ {icon &&
{icon}
} +
{title}
+ {description && ( + + {description} + + )} + {actions &&
{actions}
} +
+ ); +} diff --git a/frontend/components/shared/format.ts b/frontend/components/shared/format.ts new file mode 100644 index 0000000..3efbc35 --- /dev/null +++ b/frontend/components/shared/format.ts @@ -0,0 +1,68 @@ +import { formatNumber } from "@policyengine/ui-kit"; + +export function fmt( + value: number | null | undefined, + opts: { pct?: boolean; digits?: number } = {}, +): string { + if (value == null || !Number.isFinite(value)) return "—"; + if (opts.pct) { + const digits = opts.digits ?? 1; + return `${(value * 100).toFixed(digits)}%`; + } + if (Math.abs(value) >= 1000 || Number.isInteger(value)) { + return formatNumber(value); + } + return value.toFixed(opts.digits ?? 4); +} + +export function fmtSigned( + value: number | null | undefined, + opts: { pct?: boolean; digits?: number } = {}, +): string { + if (value == null || !Number.isFinite(value)) return "—"; + const sign = value > 0 ? "+" : ""; + return `${sign}${fmt(value, opts)}`; +} + +export function fmtCompact(value: number | null | undefined): string { + if (value == null || !Number.isFinite(value)) return "—"; + const abs = Math.abs(value); + const sign = value < 0 ? "-" : ""; + if (abs >= 1e12) return `${sign}${(abs / 1e12).toFixed(2)}T`; + if (abs >= 1e9) return `${sign}${(abs / 1e9).toFixed(2)}B`; + if (abs >= 1e6) return `${sign}${(abs / 1e6).toFixed(2)}M`; + if (abs >= 1e3) return `${sign}${(abs / 1e3).toFixed(1)}K`; + return `${sign}${abs.toFixed(0)}`; +} + +export function fmtMoney(value: number | null | undefined): string { + if (value == null || !Number.isFinite(value)) return "—"; + const abs = Math.abs(value); + const sign = value < 0 ? "-" : ""; + if (abs >= 1e12) return `${sign}$${(abs / 1e12).toFixed(2)}T`; + if (abs >= 1e9) return `${sign}$${(abs / 1e9).toFixed(2)}B`; + if (abs >= 1e6) return `${sign}$${(abs / 1e6).toFixed(2)}M`; + if (abs >= 1e3) return `${sign}$${(abs / 1e3).toFixed(1)}K`; + return `${sign}$${abs.toFixed(0)}`; +} + +export function fmtSignedMoney(value: number | null | undefined): string { + if (value == null || !Number.isFinite(value)) return "—"; + const sign = value > 0 ? "+" : ""; + return `${sign}${fmtMoney(value)}`; +} + +export function fmtRatio(value: number | null | undefined): string { + if (value == null || !Number.isFinite(value)) return "—"; + return `${value.toFixed(2)}×`; +} + +export function tone( + value: number | null | undefined, + improveIsLower = true, +): "positive" | "negative" | "neutral" { + if (value == null || !Number.isFinite(value) || Math.abs(value) < 1e-9) { + return "neutral"; + } + return (improveIsLower ? value < 0 : value > 0) ? "positive" : "negative"; +} diff --git a/frontend/components/shared/help-hint.tsx b/frontend/components/shared/help-hint.tsx new file mode 100644 index 0000000..d3775ca --- /dev/null +++ b/frontend/components/shared/help-hint.tsx @@ -0,0 +1,31 @@ +"use client"; + +import type { ReactNode } from "react"; + +interface HelpHintProps { + label: ReactNode; + tooltip: string; +} + +export function HelpHint({ label, tooltip }: HelpHintProps) { + return ( + + {label} + + ? + + + {tooltip} + + + ); +} diff --git a/frontend/components/shared/kpi-card.tsx b/frontend/components/shared/kpi-card.tsx new file mode 100644 index 0000000..cd076aa --- /dev/null +++ b/frontend/components/shared/kpi-card.tsx @@ -0,0 +1,62 @@ +"use client"; + +import type { ReactNode } from "react"; +import { Text } from "@policyengine/ui-kit"; + +export type Tone = "positive" | "negative" | "neutral"; + +interface KpiCardProps { + label: ReactNode; + value: ReactNode; + delta?: ReactNode; + tone?: Tone; + hint?: ReactNode; + size?: "sm" | "md" | "lg"; +} + +const toneClasses: Record = { + positive: "text-emerald-700 bg-emerald-50 border-emerald-200", + negative: "text-rose-700 bg-rose-50 border-rose-200", + neutral: "text-muted-foreground bg-muted/50 border-border", +}; + +export function KpiCard({ + label, + value, + delta, + tone = "neutral", + hint, + size = "md", +}: KpiCardProps) { + const valueSize = + size === "lg" + ? "text-3xl" + : size === "sm" + ? "text-lg" + : "text-2xl"; + + return ( +
+
+ + {label} + + {delta != null && ( + + {delta} + + )} +
+
+ {value} +
+ {hint && ( + + {hint} + + )} +
+ ); +} diff --git a/frontend/components/shared/page-header.tsx b/frontend/components/shared/page-header.tsx new file mode 100644 index 0000000..8d968fb --- /dev/null +++ b/frontend/components/shared/page-header.tsx @@ -0,0 +1,44 @@ +"use client"; + +import type { ReactNode } from "react"; +import { Text, Title } from "@policyengine/ui-kit"; + +interface PageHeaderProps { + eyebrow?: ReactNode; + title: ReactNode; + description?: ReactNode; + status?: ReactNode; + actions?: ReactNode; +} + +export function PageHeader({ + eyebrow, + title, + description, + status, + actions, +}: PageHeaderProps) { + return ( +
+
+
+ {eyebrow && ( +
+ {eyebrow} +
+ )} +
+ {title} + {status} +
+ {description && ( + + {description} + + )} +
+ {actions &&
{actions}
} +
+
+ ); +} diff --git a/frontend/components/shared/section-card.tsx b/frontend/components/shared/section-card.tsx new file mode 100644 index 0000000..636bc61 --- /dev/null +++ b/frontend/components/shared/section-card.tsx @@ -0,0 +1,52 @@ +"use client"; + +import type { ReactNode } from "react"; +import { Card, CardContent, Text } from "@policyengine/ui-kit"; + +interface SectionCardProps { + title: ReactNode; + description?: ReactNode; + actions?: ReactNode; + footer?: ReactNode; + padded?: boolean; + className?: string; + children: ReactNode; +} + +export function SectionCard({ + title, + description, + actions, + footer, + padded = true, + className, + children, +}: SectionCardProps) { + return ( + +
+
+
+ {title} +
+ {description && ( + + {description} + + )} +
+ {actions && ( +
{actions}
+ )} +
+ {children} + {footer && ( +
+ {footer} +
+ )} + + ); +} diff --git a/frontend/components/shared/status-pill.tsx b/frontend/components/shared/status-pill.tsx new file mode 100644 index 0000000..5d582dc --- /dev/null +++ b/frontend/components/shared/status-pill.tsx @@ -0,0 +1,38 @@ +"use client"; + +import type { ReactNode } from "react"; + +export type StatusTone = "success" | "warning" | "danger" | "neutral" | "info"; + +interface StatusPillProps { + children: ReactNode; + tone?: StatusTone; + icon?: ReactNode; +} + +const toneClasses: Record = { + success: "bg-emerald-50 text-emerald-800 border-emerald-200", + warning: "bg-amber-50 text-amber-800 border-amber-200", + danger: "bg-rose-50 text-rose-800 border-rose-200", + info: "bg-sky-50 text-sky-800 border-sky-200", + neutral: "bg-muted/60 text-muted-foreground border-border", +}; + +const dotClasses: Record = { + success: "bg-emerald-500", + warning: "bg-amber-500", + danger: "bg-rose-500", + info: "bg-sky-500", + neutral: "bg-muted-foreground/60", +}; + +export function StatusPill({ children, tone = "neutral", icon }: StatusPillProps) { + return ( + + {icon ?? } + {children} + + ); +} diff --git a/frontend/data/populace/latest/build_manifest.json b/frontend/data/populace/latest/build_manifest.json new file mode 100644 index 0000000..bddd0f3 --- /dev/null +++ b/frontend/data/populace/latest/build_manifest.json @@ -0,0 +1,58 @@ +{ + "build_id": "populace-us-2024-9f1260b-20260611", + "builder": "populace", + "build_sha": "9f1260b", + "build_date": "2026-06-11", + "dataset": { + "filename": "populace_us_2024.h5", + "sha256": "dc75c0d4fdedd57946db84a8d838dbc5b61a284365c3ce6eb6508b8e81111a4b" + }, + "calibration": { + "filename": "populace_us_2024_calibration.npz", + "sha256": "a3da2f59085c45f0e16b06337818e3513c2635911dc0d16fa7deb5006263c12a" + }, + "construction": "eCPS-free: every layer from primary sources (CPS ASEC, IRS PUF 2015 uprated, Fed SCF 2022, Census SIPP, CPS-ORG, MEPS-IC parameters, Census ACS 2022); enhanced CPS used only as the scoring 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\ No newline at end of file diff --git a/frontend/lib/api/client.ts b/frontend/lib/api/client.ts index 75d88f9..f448e85 100644 --- a/frontend/lib/api/client.ts +++ b/frontend/lib/api/client.ts @@ -37,6 +37,7 @@ const RUN_AGNOSTIC_PATHS = [ "/pipeline", // covers /pipeline and /pipeline/stages/* "/target-inventory", // committed JSON, run-independent "/microplex", // pulls parity JSONs from PolicyEngine/microplex-us + "/populace", // pulls release artifacts from policyengine/populace-us "/analysis/case-studies", ]; @@ -67,7 +68,11 @@ function appendParams(url: URL, params: Record): void { } function apiUrl(path: string): URL { - if (!EXPLICIT_API_BASE && isPublicBrowserRuntime() && path.startsWith("/microplex")) { + if ( + !EXPLICIT_API_BASE && + isPublicBrowserRuntime() && + (path.startsWith("/microplex") || path.startsWith("/populace")) + ) { return new URL(`/api${path}`, window.location.origin); } return new URL(path, API_BASE); diff --git a/frontend/lib/api/hooks/use-populace.ts b/frontend/lib/api/hooks/use-populace.ts new file mode 100644 index 0000000..41cce14 --- /dev/null +++ b/frontend/lib/api/hooks/use-populace.ts @@ -0,0 +1,182 @@ +import { useQuery } from "@tanstack/react-query"; +import { apiGet } from "../client"; + +export interface PopulaceReleaseEntry { + release_id: string; + files: string[]; +} + +export interface PopulaceGates { + parity_gaps?: number | null; + exported_nonzero?: { + passed?: boolean | null; + stored_columns?: number | null; + }; + calibration?: { + within_10pct_share?: number | null; + loss?: number | null; + max_weight?: number | null; + weights_above_500k?: number | null; + max_weight_ratio?: number | null; + }; + smoke?: Record; + [key: string]: unknown; +} + +export interface PopulaceScoreVsEnhancedCps { + protocol?: string | null; + train_loss?: Record; + holdout_loss?: Record; + full_loss?: Record; + per_target_wins?: Record; +} + +export interface PopulaceFamilyBreakdownRow { + family: string; + n_targets: number | null; + train_targets?: number | null; + holdout_targets?: number | null; + baseline_wins?: number | null; + candidate_wins?: number | null; + ties?: number | null; + baseline_loss_contribution?: number | null; + candidate_loss_contribution?: number | null; + loss_delta?: number | null; + [key: string]: unknown; +} + +export interface PopulaceTargetDiagnosticRow { + target_name?: string | null; + target_index?: number | null; + target_value?: number | null; + family?: string | null; + split?: "train" | "holdout" | string | null; + winner?: "candidate" | "baseline" | "tie" | string | null; + value_scale?: string | null; + baseline_estimate?: number | null; + baseline_error?: number | null; + baseline_relative_error?: number | null; + baseline_loss_term?: number | null; + baseline_abs_scaled_error?: number | null; + candidate_estimate?: number | null; + candidate_error?: number | null; + candidate_relative_error?: number | null; + candidate_loss_term?: number | null; + candidate_abs_scaled_error?: number | null; + loss_delta?: number | null; + [key: string]: unknown; +} + +export interface PopulaceComparisonSummary { + available: boolean; + path?: string | null; + release_id?: string | null; + schema_version?: number | null; + period?: number | null; + metric?: string | null; + elapsed_seconds?: number | null; + summary?: Record; + matched_datasets?: Record; + refit_config?: Record; + comparison_contract?: Record; + target_split?: { + holdout_target_fraction?: number | null; + holdout_target_seed?: number | null; + holdout_targets?: number | null; + train_targets?: number | null; + }; + score_broad_loss?: Record; + score_family_breakdown?: Record[]; + target_diagnostics_summary?: Record; + family_breakdown?: PopulaceFamilyBreakdownRow[]; + top_improvements?: PopulaceTargetDiagnosticRow[]; + top_regressions?: PopulaceTargetDiagnosticRow[]; +} + +export interface PopulaceTargetDiagnostics { + available: boolean; + path?: string | null; + release_id?: string | null; + schema_version?: number | null; + metric?: string | null; + period?: number | null; + baseline_label?: string | null; + candidate_label?: string | null; + summary: Record; + total_targets: number; + display_limit?: number; + families?: string[]; + filtered_total?: number; + returned?: number; + limit?: number; + offset?: number; + has_next?: boolean; + filters?: Record; + targets: PopulaceTargetDiagnosticRow[]; +} + +export interface PopulaceResponse { + source_repo: string; + repo_type: string; + revision: string; + source: "huggingface_live" | "deployed_static_snapshot" | string; + live_unavailable_reason: string | null; + release_id: string; + snapshot_release_id: string; + releases: PopulaceReleaseEntry[]; + source_artifacts: { name: string; path: string; url: string }[]; + limitations: string[]; + comparison_snapshot_stale: boolean; + build_manifest: { + build_id?: string | null; + builder?: string | null; + build_sha?: string | null; + build_date?: string | null; + dataset?: { filename?: string | null; sha256?: string | null }; + calibration?: { filename?: string | null; sha256?: string | null }; + construction?: string | null; + gates?: PopulaceGates; + score_vs_enhanced_cps?: PopulaceScoreVsEnhancedCps; + [key: string]: unknown; + }; + release_manifest: { + schema_version?: number | null; + data_package?: { name?: string | null; version?: string | null }; + default_datasets?: Record; + compatible_model_packages?: { name: string; specifier: string }[]; + compatible_core_packages?: { name: string; specifier: string }[]; + build?: Record; + artifacts?: Record>; + [key: string]: unknown; + }; + gates: PopulaceGates; + score_vs_enhanced_cps: PopulaceScoreVsEnhancedCps; + comparison: PopulaceComparisonSummary; + target_diagnostics: PopulaceTargetDiagnostics; +} + +export function usePopulace() { + return useQuery({ + queryKey: ["populace"], + queryFn: () => apiGet("/populace"), + staleTime: 5 * 60 * 1000, + }); +} + +export function usePopulaceTargetDiagnostics(params: { + limit?: number; + offset?: number; + family?: string; + split?: string; + winner?: string; + search?: string; + sort_by?: string; + sort_dir?: string; +}) { + return useQuery({ + queryKey: ["populace", "target-diagnostics", params], + queryFn: () => + apiGet("/populace/target-diagnostics", params), + staleTime: 15 * 60 * 1000, + }); +} diff --git a/frontend/lib/dashboard-mode-context.tsx b/frontend/lib/dashboard-mode-context.tsx index 8014809..e9bc9f7 100644 --- a/frontend/lib/dashboard-mode-context.tsx +++ b/frontend/lib/dashboard-mode-context.tsx @@ -11,7 +11,7 @@ import { } from "react"; import { usePathname, useRouter } from "next/navigation"; -export type DashboardMode = "us-data" | "microplex" | "comparison"; +export type DashboardMode = "us-data" | "microplex" | "populace" | "comparison"; interface DashboardModeValue { mode: DashboardMode; @@ -23,6 +23,7 @@ const DEFAULT_MODE: DashboardMode = "microplex"; const MODE_HOME: Record = { "us-data": "/summary", microplex: "/microplex", + populace: "/populace", comparison: "/comparison", }; @@ -34,6 +35,7 @@ const DashboardModeContext = createContext({ function modeFromPath(pathname: string): DashboardMode | null { if (pathname.startsWith("/comparison")) return "comparison"; if (pathname.startsWith("/microplex")) return "microplex"; + if (pathname.startsWith("/populace")) return "populace"; if ( pathname.startsWith("/summary") || pathname.startsWith("/analysis") || @@ -55,7 +57,10 @@ export function DashboardModeProvider({ children }: { children: ReactNode }) { if (pathMode) return pathMode; if (typeof window === "undefined") return DEFAULT_MODE; const stored = window.localStorage.getItem("dashboard-mode"); - return stored === "us-data" || stored === "microplex" || stored === "comparison" + return stored === "us-data" || + stored === "microplex" || + stored === "populace" || + stored === "comparison" ? stored : DEFAULT_MODE; }); diff --git a/frontend/lib/populace/latest-artifact.ts b/frontend/lib/populace/latest-artifact.ts new file mode 100644 index 0000000..e4da1b0 --- /dev/null +++ b/frontend/lib/populace/latest-artifact.ts @@ -0,0 +1,150 @@ +import buildManifestData from "@/data/populace/latest/build_manifest.json"; +import releaseManifestData from "@/data/populace/latest/release_manifest.json"; +import comparisonSummaryData from "@/data/populace/latest/comparison_summary.json"; +import targetDiagnosticsData from "@/data/populace/latest/target_diagnostics.json"; + +type JsonObject = Record; +type TargetRow = JsonObject; + +const BUILD_MANIFEST = buildManifestData as JsonObject; +const RELEASE_MANIFEST = releaseManifestData as JsonObject; +const COMPARISON_SUMMARY = comparisonSummaryData as JsonObject; +const TARGET_DIAGNOSTICS = targetDiagnosticsData as JsonObject; +const TARGET_ROWS = Array.isArray(TARGET_DIAGNOSTICS.targets) + ? (TARGET_DIAGNOSTICS.targets as TargetRow[]) + : []; + +export const LATEST_POPULACE_RELEASE_ID = String( + BUILD_MANIFEST.build_id ?? "populace-us-2024-9f1260b-20260611", +); +export const POPULACE_HF_REPO = "policyengine/populace-us"; +export const LATEST_POPULACE_BUILD_MANIFEST_PATH = + "frontend/data/populace/latest/build_manifest.json"; +export const LATEST_POPULACE_RELEASE_MANIFEST_PATH = + "frontend/data/populace/latest/release_manifest.json"; +export const LATEST_POPULACE_COMPARISON_SUMMARY_PATH = + "frontend/data/populace/latest/comparison_summary.json"; +export const LATEST_POPULACE_TARGET_DIAGNOSTICS_PATH = + "frontend/data/populace/latest/target_diagnostics.json"; + +export function asObject(value: unknown): JsonObject { + return value && typeof value === "object" && !Array.isArray(value) + ? (value as JsonObject) + : {}; +} + +export function scrub(value: unknown): unknown { + if (Array.isArray(value)) return value.map(scrub); + if (value && typeof value === "object") { + return Object.fromEntries( + Object.entries(value as JsonObject).map(([key, item]) => [key, scrub(item)]), + ); + } + if (typeof value === "number" && !Number.isFinite(value)) return null; + return value; +} + +function stringParam(value: string | null): string | null { + const trimmed = value?.trim(); + return trimmed ? trimmed : null; +} + +function matchesSearch(row: TargetRow, search: string): boolean { + const haystack = [row.target_name, row.family, row.split, row.winner] + .filter((value) => value != null) + .join(" ") + .toLowerCase(); + return haystack.includes(search.toLowerCase()); +} + +export function latestPopulaceBuildManifest(): JsonObject { + return BUILD_MANIFEST; +} + +export function latestPopulaceReleaseManifest(): JsonObject { + return RELEASE_MANIFEST; +} + +export function latestPopulaceComparisonSummary(): JsonObject { + return { + available: true, + path: LATEST_POPULACE_COMPARISON_SUMMARY_PATH, + ...COMPARISON_SUMMARY, + }; +} + +export function latestPopulaceTargetDiagnosticsSummary(displayLimit = 100) { + return { + available: true, + path: LATEST_POPULACE_TARGET_DIAGNOSTICS_PATH, + release_id: TARGET_DIAGNOSTICS.release_id ?? LATEST_POPULACE_RELEASE_ID, + schema_version: TARGET_DIAGNOSTICS.schema_version ?? null, + metric: TARGET_DIAGNOSTICS.metric ?? null, + period: TARGET_DIAGNOSTICS.period ?? null, + baseline_label: "enhanced_cps", + candidate_label: "populace", + summary: TARGET_DIAGNOSTICS.summary ?? {}, + total_targets: TARGET_ROWS.length, + display_limit: displayLimit, + targets: TARGET_ROWS.slice(0, displayLimit), + }; +} + +export function populaceTargetFamilies(): string[] { + return [...new Set(TARGET_ROWS.map((row) => String(row.family ?? "")))].sort(); +} + +export function latestPopulaceTargetDiagnosticsPage(requestUrl: string) { + const url = new URL(requestUrl); + const limit = Math.min( + Math.max(Number(url.searchParams.get("limit") ?? "100") || 100, 1), + 500, + ); + const offset = Math.max(Number(url.searchParams.get("offset") ?? "0") || 0, 0); + const family = stringParam(url.searchParams.get("family")); + const split = stringParam(url.searchParams.get("split")); + const winner = stringParam(url.searchParams.get("winner")); + const search = stringParam(url.searchParams.get("search")); + const sortBy = stringParam(url.searchParams.get("sort_by")); + const sortDir = + stringParam(url.searchParams.get("sort_dir")) === "desc" ? "desc" : "asc"; + + let filtered = TARGET_ROWS; + if (family) filtered = filtered.filter((row) => row.family === family); + if (split) filtered = filtered.filter((row) => row.split === split); + if (winner) filtered = filtered.filter((row) => row.winner === winner); + if (search) filtered = filtered.filter((row) => matchesSearch(row, search)); + if (sortBy) { + filtered = [...filtered].sort((a, b) => { + const aVal = a[sortBy]; + const bVal = b[sortBy]; + if (aVal == null && bVal == null) return 0; + if (aVal == null) return 1; + if (bVal == null) return -1; + const cmp = + typeof aVal === "number" && typeof bVal === "number" + ? aVal - bVal + : String(aVal).localeCompare(String(bVal)); + return sortDir === "asc" ? cmp : -cmp; + }); + } + + return { + ...latestPopulaceTargetDiagnosticsSummary(limit), + families: populaceTargetFamilies(), + returned: filtered.slice(offset, offset + limit).length, + limit, + offset, + has_next: offset + limit < filtered.length, + filtered_total: filtered.length, + targets: filtered.slice(offset, offset + limit), + filters: { + family, + split, + winner, + search, + sort_by: sortBy, + sort_dir: sortBy ? sortDir : null, + }, + }; +} diff --git a/frontend/next-env.d.ts b/frontend/next-env.d.ts index c4b7818..9edff1c 100644 --- a/frontend/next-env.d.ts +++ b/frontend/next-env.d.ts @@ -1,6 +1,6 @@ /// /// -import "./.next/dev/types/routes.d.ts"; +import "./.next/types/routes.d.ts"; // NOTE: This file should not be edited // see https://nextjs.org/docs/app/api-reference/config/typescript for more information. diff --git a/tests/test_populace.py b/tests/test_populace.py new file mode 100644 index 0000000..65dcb27 --- /dev/null +++ b/tests/test_populace.py @@ -0,0 +1,150 @@ +from __future__ import annotations + +from backend.routes import populace + + +def _offline(monkeypatch): + """Force the live Hugging Face path to fail so routes use the snapshot.""" + + def _raise(url): + raise RuntimeError("offline test fixture") + + monkeypatch.setattr(populace, "_fetch_json", _raise) + + +def test_populace_overview_serves_snapshot_when_offline(monkeypatch): + _offline(monkeypatch) + payload = populace.populace_overview() + + assert payload["source"] == "deployed_static_snapshot" + assert payload["live_unavailable_reason"] + assert payload["release_id"] == payload["snapshot_release_id"] + assert payload["release_id"].startswith("populace-us-") + + build = payload["build_manifest"] + assert build["builder"] == "populace" + assert build["dataset"]["filename"] == "populace_us_2024.h5" + + gates = payload["gates"] + assert gates["calibration"]["within_10pct_share"] is not None + assert "smoke" in gates + + score = payload["score_vs_enhanced_cps"] + assert set(score["per_target_wins"]) == {"populace", "enhanced_cps", "ties"} + assert score["full_loss"]["populace"] < score["full_loss"]["enhanced_cps"] + + comparison = payload["comparison"] + assert comparison["available"] is True + assert comparison["family_breakdown"] + assert comparison["top_regressions"] + assert comparison["top_improvements"] + + diagnostics = payload["target_diagnostics"] + assert diagnostics["available"] is True + assert diagnostics["total_targets"] > 3000 + assert len(diagnostics["targets"]) == 100 + assert diagnostics["baseline_label"] == "enhanced_cps" + assert diagnostics["candidate_label"] == "populace" + + +def test_populace_overview_prefers_live_release(monkeypatch): + live_build = { + "build_id": "populace-us-2024-fffffff-20990101", + "builder": "populace", + "gates": {"parity_gaps": 0}, + "score_vs_enhanced_cps": {"per_target_wins": {}}, + } + live_release = {"schema_version": 1, "build": {"build_id": live_build["build_id"]}} + + def _fake_fetch(url): + if "/tree/" in url: + return [ + { + "type": "file", + "path": "releases/populace-us-2024-ffffffff-20990101/x", + }, + { + "type": "file", + "path": ( + "releases/populace-us-2024-fffffff-20990101/" + "build_manifest.json" + ), + }, + { + "type": "file", + "path": ( + "releases/populace-us-2024-fffffff-20990101/" + "release_manifest.json" + ), + }, + ] + if url.endswith("build_manifest.json"): + return live_build + if url.endswith("release_manifest.json"): + return live_release + raise AssertionError(f"unexpected fetch {url}") + + monkeypatch.setattr(populace, "_fetch_json", _fake_fetch) + payload = populace.populace_overview() + + assert payload["source"] == "huggingface_live" + assert payload["release_id"] == "populace-us-2024-fffffff-20990101" + assert payload["build_manifest"]["build_id"] == live_build["build_id"] + # The committed per-target snapshot is from an older release. + assert payload["comparison_snapshot_stale"] is True + + +def test_populace_target_diagnostics_filters_and_sorts(): + payload = populace.populace_target_diagnostics( + limit=10, + offset=0, + family=None, + split="holdout", + winner="candidate", + search=None, + sort_by="candidate_loss_term", + sort_dir="desc", + ) + + assert payload["available"] is True + assert payload["returned"] == len(payload["targets"]) <= 10 + assert payload["filtered_total"] <= payload["total_targets"] + assert payload["families"] + for row in payload["targets"]: + assert row["split"] == "holdout" + assert row["winner"] == "candidate" + loss_terms = [ + row["candidate_loss_term"] + for row in payload["targets"] + if row.get("candidate_loss_term") is not None + ] + assert loss_terms == sorted(loss_terms, reverse=True) + + +def test_populace_target_diagnostics_pagination(): + first = populace.populace_target_diagnostics( + limit=5, offset=0, family=None, split=None, winner=None, + search=None, sort_by="target_index", sort_dir="asc", + ) + second = populace.populace_target_diagnostics( + limit=5, offset=5, family=None, split=None, winner=None, + search=None, sort_by="target_index", sort_dir="asc", + ) + assert first["has_next"] is True + first_ids = {row["target_index"] for row in first["targets"]} + second_ids = {row["target_index"] for row in second["targets"]} + assert not first_ids & second_ids + + +def test_populace_target_diagnostics_search(): + payload = populace.populace_target_diagnostics( + limit=50, offset=0, family=None, split=None, winner=None, + search="snap", sort_by=None, sort_dir="asc", + ) + assert payload["filtered_total"] >= 1 + for row in payload["targets"]: + haystack = " ".join( + str(row.get(key, "")) + for key in ("target_name", "family", "split", "winner") + ).lower() + assert "snap" in haystack From d2d4dfa1906e863345cb69896e9ecc337712ac47 Mon Sep 17 00:00:00 2001 From: Pavel Makarchuk Date: Fri, 12 Jun 2026 01:19:32 -0400 Subject: [PATCH 02/14] Drop build-machine next-env.d.ts churn Co-Authored-By: Claude Fable 5 --- frontend/next-env.d.ts | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/frontend/next-env.d.ts b/frontend/next-env.d.ts index 9edff1c..c4b7818 100644 --- a/frontend/next-env.d.ts +++ b/frontend/next-env.d.ts @@ -1,6 +1,6 @@ /// /// -import "./.next/types/routes.d.ts"; +import "./.next/dev/types/routes.d.ts"; // NOTE: This file should not be edited // see https://nextjs.org/docs/app/api-reference/config/typescript for more information. From 56e6949a43521bad40c233cc8606f501ffa451e2 Mon Sep 17 00:00:00 2001 From: Pavel Makarchuk Date: Mon, 15 Jun 2026 10:21:26 -0400 Subject: [PATCH 03/14] Pivot Populace mode to calibration diagnostics + latest.json Follows the populace#37 cleanup that retired the eCPS comparison from the live populace surface (it moved to PolicyEngine/populace-benchmarks) and added the latest.json pointer and calibration_diagnostics.json artifacts this branch originally proposed. - Resolve the current release through latest.json instead of guessing the lexicographically-latest release directory. - Read per-target rows from calibration_diagnostics.json: populace's own calibration fit (target, initial vs final estimate, relative error, within-tolerance) rather than the populace-vs-enhanced-CPS comparison. - Release summary: calibration loss + convergence sparkline, within-10% and within-tolerance, records kept after L0, acceptance gates, solver provenance, per-family fit, worst-fit and biggest-improvement tables, and a skipped-targets section. - Target explorer: initial->final estimate, relative error, improvement, within-tolerance, with family/tolerance/direction filters. Derived families collapse per-state distribution and per-state-FIPS SNAP targets (19 families instead of 119). - Snapshot refreshed to release populace-us-2024-f32c2e5-20260614. Tested: 101 backend tests pass, tsc clean, next build green, live smoke test resolves f32c2e5 via latest.json and serves both pages. Co-Authored-By: Claude Fable 5 --- backend/routes/populace.py | 425 ++-- frontend/app/api/populace/route.ts | 148 +- .../populace/populace-overview-view.tsx | 486 ++-- .../populace/populace-targets-view.tsx | 109 +- .../data/populace/latest/build_manifest.json | 57 +- .../latest/calibration_diagnostics.json | 1 + .../populace/latest/comparison_summary.json | 1985 ----------------- frontend/data/populace/latest/latest.json | 10 + .../populace/latest/release_manifest.json | 41 +- .../populace/latest/target_diagnostics.json | 1 - frontend/lib/api/hooks/use-populace.ts | 161 +- frontend/lib/populace/latest-artifact.ts | 263 ++- tests/test_populace.py | 149 +- 13 files changed, 1069 insertions(+), 2767 deletions(-) create mode 100644 frontend/data/populace/latest/calibration_diagnostics.json delete mode 100644 frontend/data/populace/latest/comparison_summary.json create mode 100644 frontend/data/populace/latest/latest.json delete mode 100644 frontend/data/populace/latest/target_diagnostics.json diff --git a/backend/routes/populace.py b/backend/routes/populace.py index c82f3fa..2276873 100644 --- a/backend/routes/populace.py +++ b/backend/routes/populace.py @@ -1,17 +1,23 @@ """Populace release diagnostics. Serves the populace-US build published on the Hugging Face dataset -``policyengine/populace-us``. Each release under ``releases//`` -ships a ``build_manifest.json`` (gate verdicts + score vs the enhanced -CPS), a ``release_manifest.json`` (artifact registry + compatibility -matrix), and a ``sound_ecps_replacement_comparison.json`` with full -per-target diagnostics. - -The small manifests are fetched live with a TTL cache. The per-target -comparison artifact is ~3.7 MB, so the deployed static snapshot committed -under ``frontend/data/populace/latest/`` is the default source for target -rows; the live artifact is fetched only when the snapshot's release does -not match the live release and ``POPULACE_FETCH_LIVE_COMPARISON`` is set. +``policyengine/populace-us``. The current release is resolved through the +``latest.json`` pointer at the repo root (PolicyEngine/populace#9); its +``paths`` name the ``build_manifest.json`` (gate verdicts) and +``release_manifest.json`` (artifact registry) for that release, which this +route reads live with a TTL cache. + +Per-target calibration diagnostics come from ``calibration_diagnostics.json`` +(PolicyEngine/populace#10) — populace's own calibration fit against its target +surface: per target the declared value, the aggregate under the design weights +(initial) and the calibrated weights (final), the relative error, and the +tolerance verdict. That artifact is large, so the deployed static snapshot +committed under ``frontend/data/populace/latest/`` is the source for target +rows; only the small manifests are fetched live. + +The eCPS head-to-head comparison moved out of live populace into +``PolicyEngine/populace-benchmarks``; this view reports calibration fit, not a +populace-vs-enhanced-CPS score. """ from __future__ import annotations @@ -31,24 +37,32 @@ _HF_REPO = os.environ.get("POPULACE_HF_REPO", "policyengine/populace-us") _HF_REVISION = os.environ.get("POPULACE_HF_REVISION", "main") -_HF_API = "https://huggingface.co/api/datasets" +_LATEST_POINTER_PATH = "latest.json" _STATIC_ROOT = Path(__file__).resolve().parents[2] / "frontend/data/populace/latest" _STATIC_BUILD_MANIFEST = _STATIC_ROOT / "build_manifest.json" _STATIC_RELEASE_MANIFEST = _STATIC_ROOT / "release_manifest.json" -_STATIC_COMPARISON_SUMMARY = _STATIC_ROOT / "comparison_summary.json" -_STATIC_TARGET_DIAGNOSTICS = _STATIC_ROOT / "target_diagnostics.json" +_STATIC_CALIBRATION_DIAGNOSTICS = _STATIC_ROOT / "calibration_diagnostics.json" + +_CALIBRATION_DIAGNOSTICS_PUBLIC_PATH = ( + "frontend/data/populace/latest/calibration_diagnostics.json" +) _CACHE: dict[str, tuple[float, Any]] = {} _TTL_SECONDS = 300 +_STATE_CODE = re.compile(r"^[A-Z]{2}$") +_STATE_FIPS = re.compile(r"^US\d{2}$") + def _scrub(value: Any) -> Any: if isinstance(value, dict): return {key: _scrub(item) for key, item in value.items()} if isinstance(value, list): return [_scrub(item) for item in value] - if isinstance(value, float) and (value != value or value in (float("inf"), float("-inf"))): + if isinstance(value, float) and ( + value != value or value in (float("inf"), float("-inf")) + ): return None return value @@ -81,69 +95,161 @@ def _load_static(path: Path) -> dict[str, Any]: ) -def _list_releases() -> list[dict[str, Any]]: - tree = _fetch_json(f"{_HF_API}/{_HF_REPO}/tree/{_HF_REVISION}/releases?recursive=true") - releases: dict[str, list[str]] = {} - if not isinstance(tree, list): - return [] - for entry in tree: - if not isinstance(entry, dict) or entry.get("type") != "file": - continue - match = re.match(r"^releases/([^/]+)/(.+)$", str(entry.get("path", ""))) - if not match: - continue - releases.setdefault(match.group(1), []).append(match.group(2)) - return [ - {"release_id": release_id, "files": sorted(files)} - for release_id, files in sorted(releases.items()) - ] - - def _load_live_release(snapshot_release_id: str) -> dict[str, Any]: try: - releases = _list_releases() - complete = [r for r in releases if "build_manifest.json" in r["files"]] - live = next( - (r for r in complete if r["release_id"] == snapshot_release_id), - complete[-1] if complete else None, + pointer = _fetch_json(_hf_resolve_url(_LATEST_POINTER_PATH)) + if not isinstance(pointer, dict) or not pointer.get("release_id"): + return {"available": False, "reason": "latest.json has no release_id."} + release_id = str(pointer["release_id"]) + paths = pointer.get("paths") if isinstance(pointer.get("paths"), dict) else {} + build_path = paths.get( + "build_manifest", f"releases/{release_id}/build_manifest.json" ) - if live is None: - return { - "available": False, - "reason": "No release with a build_manifest.json found on Hugging Face.", - "releases": releases, - } - prefix = f"releases/{live['release_id']}" - build_manifest = _fetch_json(_hf_resolve_url(f"{prefix}/build_manifest.json")) - release_manifest = ( - _fetch_json(_hf_resolve_url(f"{prefix}/release_manifest.json")) - if "release_manifest.json" in live["files"] - else {} + release_path = paths.get( + "release_manifest", f"releases/{release_id}/release_manifest.json" ) + build_manifest = _fetch_json(_hf_resolve_url(build_path)) + try: + release_manifest = _fetch_json(_hf_resolve_url(release_path)) + except Exception: + release_manifest = {} return { "available": True, "source": "huggingface_live", "repo_id": _HF_REPO, "revision": _HF_REVISION, - "release_id": live["release_id"], - "releases": releases, + "release_id": release_id, + "updated_at": pointer.get("updated_at"), "build_manifest": build_manifest, "release_manifest": release_manifest, - "comparison_url": ( - _hf_resolve_url(f"{prefix}/sound_ecps_replacement_comparison.json") - if "sound_ecps_replacement_comparison.json" in live["files"] - else None - ), + "build_manifest_path": build_path, + "release_manifest_path": release_path, + "calibration_diagnostics_path": paths.get("calibration_diagnostics"), } except Exception as exc: return {"available": False, "reason": str(exc)} +def _relative_error(estimate: float | None, target: float | None) -> float | None: + if estimate is None or target is None: + return None + return estimate - target if target == 0 else (estimate - target) / abs(target) + + +def _derive_family(name: str) -> str: + parts = name.split("/") + if len(parts) < 2: + return name + geo, second = parts[0], parts[1] + # Per-state distribution targets ("state/AL/...") collapse to one family. + if _STATE_CODE.match(second): + return "state_distribution" + # Per-state-FIPS program targets ("US01/snap-cost") collapse to the measure. + if _STATE_FIPS.match(geo): + return second + return f"{geo}/{second}" + + +def _derive_state(name: str) -> str | None: + parts = name.split("/") + if len(parts) >= 2 and _STATE_CODE.match(parts[1]): + return parts[1] + return None + + +def _enrich(row: dict[str, Any]) -> dict[str, Any]: + name = str(row.get("name", "")) + target = row.get("target") + initial = row.get("initial_estimate") + final = row.get("final_estimate") + final_rel = row.get("relative_error") + if final_rel is None: + final_rel = _relative_error(final, target) + initial_rel = _relative_error(initial, target) + abs_rel = abs(final_rel) if isinstance(final_rel, (int, float)) else None + improvement = ( + abs(initial_rel) - abs(final_rel) + if isinstance(initial_rel, (int, float)) and isinstance(final_rel, (int, float)) + else None + ) + direction = None + if isinstance(final_rel, (int, float)): + direction = "over" if final_rel > 0 else "under" if final_rel < 0 else "exact" + return { + **row, + "family": _derive_family(name), + "state": _derive_state(name), + "initial_relative_error": initial_rel, + "abs_relative_error": abs_rel, + "improvement": improvement, + "direction": direction, + } + + +def _family_fit(rows: list[dict[str, Any]]) -> list[dict[str, Any]]: + groups: dict[str, list[dict[str, Any]]] = {} + for row in rows: + groups.setdefault(str(row.get("family", "")), []).append(row) + out = [] + for family, members in groups.items(): + abs_errors = [ + r["abs_relative_error"] + for r in members + if isinstance(r.get("abs_relative_error"), (int, float)) + ] + out.append( + { + "family": family, + "n_targets": len(members), + "within_tolerance": sum( + 1 for r in members if r.get("within_tolerance") is True + ), + "within_10pct": sum( + 1 + for r in members + if isinstance(r.get("abs_relative_error"), (int, float)) + and r["abs_relative_error"] <= 0.1 + ), + "mean_abs_relative_error": ( + sum(abs_errors) / len(abs_errors) if abs_errors else None + ), + } + ) + return sorted(out, key=lambda f: f["n_targets"], reverse=True) + + +def _calibration_summary( + payload: dict[str, Any], rows: list[dict[str, Any]] +) -> dict[str, Any]: + return { + "available": True, + "path": _CALIBRATION_DIAGNOSTICS_PUBLIC_PATH, + "release_id": payload.get("release_id"), + "schema_version": payload.get("schema_version"), + "weight_entity": payload.get("weight_entity"), + "options": payload.get("options") or {}, + "l0_lambda": payload.get("l0_lambda"), + "n_nonzero": payload.get("n_nonzero"), + "n_records": payload.get("n_records"), + "initial_loss": payload.get("initial_loss"), + "final_loss": payload.get("final_loss"), + "fraction_within_10pct": payload.get("fraction_within_10pct"), + "loss_trajectory": payload.get("loss_trajectory") or [], + "skipped": payload.get("skipped") or [], + "total_targets": len(rows), + "within_tolerance_count": sum( + 1 for r in rows if r.get("within_tolerance") is True + ), + "family_fit": _family_fit(rows), + } + + @router.get("/populace") def populace_overview() -> dict[str, Any]: static_build = _load_static(_STATIC_BUILD_MANIFEST) static_release = _load_static(_STATIC_RELEASE_MANIFEST) - comparison = _load_static(_STATIC_COMPARISON_SUMMARY) + payload = _load_static(_STATIC_CALIBRATION_DIAGNOSTICS) + rows = [_enrich(row) for row in (payload.get("targets") or [])] snapshot_release_id = str(static_build.get("build_id", "")) live = _load_live_release(snapshot_release_id) @@ -155,88 +261,95 @@ def populace_overview() -> dict[str, Any]: else static_release ) release_id = str(live["release_id"]) if live_available else snapshot_release_id - releases = live.get("releases") or [] - target_diagnostics = _load_static(_STATIC_TARGET_DIAGNOSTICS) - targets = target_diagnostics.get("targets") or [] + calibration = _calibration_summary(payload, rows) - gates = build_manifest.get("gates") or {} - score = build_manifest.get("score_vs_enhanced_cps") or {} + def with_abs(row: dict[str, Any]) -> bool: + return isinstance(row.get("abs_relative_error"), (int, float)) + + worst_fit = sorted( + (r for r in rows if with_abs(r)), + key=lambda r: r["abs_relative_error"], + reverse=True, + )[:15] + biggest_improvements = sorted( + (r for r in rows if isinstance(r.get("improvement"), (int, float))), + key=lambda r: r["improvement"], + reverse=True, + )[:15] + + def artifact(name: str, live_path_key: str, static_path: str): + live_path = live.get(live_path_key) + return { + "name": name, + "path": str(live_path) if live_available and live_path else static_path, + "url": ( + _hf_resolve_url(str(live_path)) + if live_available and live_path + else "deployed-static-snapshot" + ), + } return _scrub( { "source_repo": _HF_REPO, "repo_type": "dataset", "revision": _HF_REVISION, - "source": "huggingface_live" if live_available else "deployed_static_snapshot", + "source": "huggingface_live" + if live_available + else "deployed_static_snapshot", "live_unavailable_reason": None if live_available else live.get("reason"), "release_id": release_id, "snapshot_release_id": snapshot_release_id, - "releases": releases, + "updated_at": live.get("updated_at") if live_available else None, "source_artifacts": [ { - "name": "build_manifest", - "path": f"releases/{release_id}/build_manifest.json", - "url": ( - _hf_resolve_url(f"releases/{release_id}/build_manifest.json") - if live_available - else "deployed-static-snapshot" - ), - }, - { - "name": "release_manifest", - "path": f"releases/{release_id}/release_manifest.json", + "name": "latest_pointer", + "path": _LATEST_POINTER_PATH, "url": ( - _hf_resolve_url(f"releases/{release_id}/release_manifest.json") + _hf_resolve_url(_LATEST_POINTER_PATH) if live_available else "deployed-static-snapshot" ), }, - { - "name": "sound_ecps_replacement_comparison", - "path": "frontend/data/populace/latest/comparison_summary.json", - "url": live.get("comparison_url") or "deployed-static-snapshot", - }, - { - "name": "target_diagnostics", - "path": "frontend/data/populace/latest/target_diagnostics.json", - "url": "deployed-static-snapshot", - }, + artifact( + "build_manifest", + "build_manifest_path", + "frontend/data/populace/latest/build_manifest.json", + ), + artifact( + "release_manifest", + "release_manifest_path", + "frontend/data/populace/latest/release_manifest.json", + ), + artifact( + "calibration_diagnostics", + "calibration_diagnostics_path", + _CALIBRATION_DIAGNOSTICS_PUBLIC_PATH, + ), ], "limitations": [ "Build and release manifests are read live from the " - "policyengine/populace-us Hugging Face dataset when reachable; " - "per-target diagnostics come from a deployed static snapshot of " - "sound_ecps_replacement_comparison.json.", - "populace does not yet publish a latest.json pointer, so the live " - "release is resolved by listing the releases/ tree and picking the " - "lexicographically latest complete release " - "(PolicyEngine/populace#9).", - "Calibration internals (loss trajectory, skipped targets, " - "per-record L0 gates) are computed by populace-calibrate but not " - "published, so the dashboard cannot show convergence or skip " - "reasons yet (PolicyEngine/populace#10).", + "policyengine/populace-us Hugging Face dataset via latest.json; " + "per-target calibration diagnostics come from a deployed static " + "snapshot of calibration_diagnostics.json.", + "The eCPS head-to-head comparison moved out of live populace into " + "PolicyEngine/populace-benchmarks, so this view reports populace's " + "calibration fit against its own target surface, not a " + "populace-vs-enhanced-CPS score.", + "The published loss trajectory for this release was reconstructed " + "from saved scalars (the historical build did not store the full " + "epoch trace), so the convergence curve is coarse.", ], - "comparison_snapshot_stale": release_id - != str(comparison.get("release_id", snapshot_release_id)), + "calibration_snapshot_stale": release_id + != str(payload.get("release_id", snapshot_release_id)), "build_manifest": build_manifest, "release_manifest": release_manifest, - "gates": gates, - "score_vs_enhanced_cps": score, - "comparison": {"available": True, **comparison}, - "target_diagnostics": { - "available": True, - "path": "frontend/data/populace/latest/target_diagnostics.json", - "release_id": target_diagnostics.get("release_id"), - "schema_version": target_diagnostics.get("schema_version"), - "metric": target_diagnostics.get("metric"), - "period": target_diagnostics.get("period"), - "baseline_label": "enhanced_cps", - "candidate_label": "populace", - "summary": target_diagnostics.get("summary") or {}, - "total_targets": len(targets), - "display_limit": 100, - "targets": targets[:100], + "gates": build_manifest.get("gates") or {}, + "calibration": calibration, + "highlights": { + "worst_fit": worst_fit, + "biggest_improvements": biggest_improvements, }, } ) @@ -245,7 +358,7 @@ def populace_overview() -> dict[str, Any]: def _matches_search(row: dict[str, Any], search: str) -> bool: haystack = " ".join( str(row.get(key)) - for key in ("target_name", "family", "split", "winner") + for key in ("name", "family", "state") if row.get(key) is not None ).lower() return search.lower() in haystack @@ -256,65 +369,77 @@ def populace_target_diagnostics( limit: int = Query(100, ge=1, le=500), offset: int = Query(0, ge=0), family: str | None = None, - split: str | None = None, - winner: str | None = None, + state: str | None = None, + direction: str | None = None, + within_tolerance: str | None = None, search: str | None = None, sort_by: str | None = None, - sort_dir: str = "asc", + sort_dir: str = "desc", ) -> dict[str, Any]: - payload = _load_static(_STATIC_TARGET_DIAGNOSTICS) - rows: list[dict[str, Any]] = list(payload.get("targets") or []) + payload = _load_static(_STATIC_CALIBRATION_DIAGNOSTICS) + rows = [_enrich(row) for row in (payload.get("targets") or [])] families = sorted({str(row.get("family", "")) for row in rows}) + within: bool | None = None + if within_tolerance is not None and within_tolerance != "": + within = within_tolerance.lower() in ("1", "true", "yes") + filtered = rows if family: - filtered = [row for row in filtered if row.get("family") == family] - if split: - filtered = [row for row in filtered if row.get("split") == split] - if winner: - filtered = [row for row in filtered if row.get("winner") == winner] + filtered = [r for r in filtered if r.get("family") == family] + if state: + filtered = [r for r in filtered if r.get("state") == state] + if direction: + filtered = [r for r in filtered if r.get("direction") == direction] + if within is not None: + filtered = [r for r in filtered if r.get("within_tolerance") is within] if search and search.strip(): - filtered = [row for row in filtered if _matches_search(row, search.strip())] - if sort_by: - descending = sort_dir == "desc" - present = [row for row in filtered if row.get(sort_by) is not None] - missing = [row for row in filtered if row.get(sort_by) is None] + filtered = [r for r in filtered if _matches_search(r, search.strip())] - def sort_key(row: dict[str, Any]): - value = row.get(sort_by) - if isinstance(value, bool) or not isinstance(value, (int, float)): - return (1, 0.0, str(value)) - return (0, float(value), "") + sort_key = sort_by or "abs_relative_error" + descending = sort_dir != "asc" + present = [r for r in filtered if r.get(sort_key) is not None] + missing = [r for r in filtered if r.get(sort_key) is None] - # None values sort last regardless of direction. - filtered = sorted(present, key=sort_key, reverse=descending) + missing + def key_fn(row: dict[str, Any]): + value = row.get(sort_key) + if isinstance(value, bool) or not isinstance(value, (int, float)): + return (1, 0.0, str(value)) + return (0, float(value), "") + + filtered = sorted(present, key=key_fn, reverse=descending) + missing page = filtered[offset : offset + limit] return _scrub( { "available": True, - "path": "frontend/data/populace/latest/target_diagnostics.json", + "path": _CALIBRATION_DIAGNOSTICS_PUBLIC_PATH, "release_id": payload.get("release_id"), "schema_version": payload.get("schema_version"), - "metric": payload.get("metric"), - "period": payload.get("period"), - "baseline_label": "enhanced_cps", - "candidate_label": "populace", - "summary": payload.get("summary") or {}, - "total_targets": len(rows), + "metric": "relative_error", "families": families, + "summary": { + "total_targets": len(rows), + "within_tolerance_count": sum( + 1 for r in rows if r.get("within_tolerance") is True + ), + "fraction_within_10pct": payload.get("fraction_within_10pct"), + }, + "total_targets": len(rows), "filtered_total": len(filtered), "returned": len(page), "limit": limit, "offset": offset, "has_next": offset + limit < len(filtered), + "display_limit": limit, "filters": { "family": family, - "split": split, - "winner": winner, + "state": state, + "direction": direction, + "within_tolerance": within, "search": search, - "sort_by": sort_by, - "sort_dir": sort_dir if sort_by else None, + "sort_by": sort_key, + "sort_dir": sort_dir, }, "targets": page, } diff --git a/frontend/app/api/populace/route.ts b/frontend/app/api/populace/route.ts index fd448ad..9317cf9 100644 --- a/frontend/app/api/populace/route.ts +++ b/frontend/app/api/populace/route.ts @@ -2,20 +2,19 @@ import { NextResponse } from "next/server"; import { LATEST_POPULACE_BUILD_MANIFEST_PATH, - LATEST_POPULACE_COMPARISON_SUMMARY_PATH, + LATEST_POPULACE_CALIBRATION_DIAGNOSTICS_PATH, LATEST_POPULACE_RELEASE_ID, LATEST_POPULACE_RELEASE_MANIFEST_PATH, - LATEST_POPULACE_TARGET_DIAGNOSTICS_PATH, latestPopulaceBuildManifest, - latestPopulaceComparisonSummary, + latestPopulaceCalibrationHighlights, + latestPopulaceCalibrationSummary, latestPopulaceReleaseManifest, - latestPopulaceTargetDiagnosticsSummary, scrub, } from "@/lib/populace/latest-artifact"; const HF_REPO = process.env.POPULACE_HF_REPO ?? "policyengine/populace-us"; const HF_REVISION = process.env.POPULACE_HF_REVISION ?? "main"; -const HF_API = "https://huggingface.co/api/datasets"; +const LATEST_POINTER_PATH = "latest.json"; type JsonObject = Record; @@ -39,71 +38,49 @@ async function fetchJson(url: string): Promise { return response.json(); } -interface ReleaseEntry { - release_id: string; - files: string[]; -} - -async function listReleases(): Promise { - const tree = await fetchJson( - `${HF_API}/${HF_REPO}/tree/${HF_REVISION}/releases?recursive=true`, - ); - if (!Array.isArray(tree)) return []; - const releases = new Map(); - for (const entry of tree) { - const item = asObject(entry); - if (item.type !== "file" || typeof item.path !== "string") continue; - const match = item.path.match(/^releases\/([^/]+)\/(.+)$/); - if (!match) continue; - const files = releases.get(match[1]) ?? []; - files.push(match[2]); - releases.set(match[1], files); - } - return [...releases.entries()] - .map(([release_id, files]) => ({ release_id, files })) - .sort((a, b) => a.release_id.localeCompare(b.release_id)); -} - +// Resolve the current release through latest.json — the published pointer +// (PolicyEngine/populace#9). Its `paths` name the build/release manifests for +// the release it points at; we read those small files live and leave the large +// per-target diagnostics to the deployed snapshot. async function loadLiveRelease(): Promise { try { - const releases = await listReleases(); - // Prefer the snapshot's release when it is published; otherwise the - // lexicographically-latest complete release. Build ids end in a date, - // but same-day builds have no published ordering — a latest.json - // pointer in the repo would make this resolution exact. - const complete = releases.filter((release) => - release.files.includes("build_manifest.json"), - ); - const live = - complete.find((r) => r.release_id === LATEST_POPULACE_RELEASE_ID) ?? - complete[complete.length - 1] ?? - null; - if (!live) { - return { - available: false, - reason: "No release with a build_manifest.json found on Hugging Face.", - releases, - }; + const pointer = asObject(await fetchJson(hfResolveUrl(LATEST_POINTER_PATH))); + const releaseId = pointer.release_id; + const paths = asObject(pointer.paths); + if (typeof releaseId !== "string" || !releaseId) { + return { available: false, reason: "latest.json has no release_id." }; + } + const buildManifestPath = + typeof paths.build_manifest === "string" + ? paths.build_manifest + : `releases/${releaseId}/build_manifest.json`; + const releaseManifestPath = + typeof paths.release_manifest === "string" + ? paths.release_manifest + : `releases/${releaseId}/release_manifest.json`; + const buildManifest = asObject(await fetchJson(hfResolveUrl(buildManifestPath))); + let releaseManifest: JsonObject = {}; + try { + releaseManifest = asObject(await fetchJson(hfResolveUrl(releaseManifestPath))); + } catch { + releaseManifest = {}; } - const prefix = `releases/${live.release_id}`; - const buildManifest = asObject( - await fetchJson(hfResolveUrl(`${prefix}/build_manifest.json`)), - ); - const releaseManifest = live.files.includes("release_manifest.json") - ? asObject(await fetchJson(hfResolveUrl(`${prefix}/release_manifest.json`))) - : {}; return { available: true, source: "huggingface_live", repo_id: HF_REPO, revision: HF_REVISION, - release_id: live.release_id, - releases, + release_id: releaseId, + updated_at: pointer.updated_at ?? null, + pointer, build_manifest: buildManifest, release_manifest: releaseManifest, - comparison_url: live.files.includes("sound_ecps_replacement_comparison.json") - ? hfResolveUrl(`${prefix}/sound_ecps_replacement_comparison.json`) - : null, + build_manifest_path: buildManifestPath, + release_manifest_path: releaseManifestPath, + calibration_diagnostics_path: + typeof paths.calibration_diagnostics === "string" + ? paths.calibration_diagnostics + : null, }; } catch (error) { return { @@ -127,10 +104,10 @@ export async function GET() { const releaseId = liveAvailable ? String(live.release_id) : LATEST_POPULACE_RELEASE_ID; - const releases = Array.isArray(live.releases) ? live.releases : []; - const comparison = latestPopulaceComparisonSummary(); - const comparisonIsStale = - releaseId !== String(comparison.release_id ?? LATEST_POPULACE_RELEASE_ID); + const calibration = latestPopulaceCalibrationSummary(); + const highlights = latestPopulaceCalibrationHighlights(15); + const calibrationSnapshotStale = + releaseId !== String(calibration.release_id ?? LATEST_POPULACE_RELEASE_ID); return NextResponse.json( scrub({ @@ -141,52 +118,53 @@ export async function GET() { live_unavailable_reason: liveAvailable ? null : live.reason ?? null, release_id: releaseId, snapshot_release_id: LATEST_POPULACE_RELEASE_ID, - releases, + updated_at: liveAvailable ? live.updated_at ?? null : null, source_artifacts: [ + { + name: "latest_pointer", + path: LATEST_POINTER_PATH, + url: liveAvailable + ? hfResolveUrl(LATEST_POINTER_PATH) + : "deployed-static-snapshot", + }, { name: "build_manifest", path: liveAvailable - ? `releases/${releaseId}/build_manifest.json` + ? String(live.build_manifest_path) : LATEST_POPULACE_BUILD_MANIFEST_PATH, url: liveAvailable - ? hfResolveUrl(`releases/${releaseId}/build_manifest.json`) + ? hfResolveUrl(String(live.build_manifest_path)) : "deployed-static-snapshot", }, { name: "release_manifest", path: liveAvailable - ? `releases/${releaseId}/release_manifest.json` + ? String(live.release_manifest_path) : LATEST_POPULACE_RELEASE_MANIFEST_PATH, url: liveAvailable - ? hfResolveUrl(`releases/${releaseId}/release_manifest.json`) + ? hfResolveUrl(String(live.release_manifest_path)) : "deployed-static-snapshot", }, { - name: "sound_ecps_replacement_comparison", - path: LATEST_POPULACE_COMPARISON_SUMMARY_PATH, + name: "calibration_diagnostics", + path: LATEST_POPULACE_CALIBRATION_DIAGNOSTICS_PATH, url: - typeof live.comparison_url === "string" - ? live.comparison_url + liveAvailable && typeof live.calibration_diagnostics_path === "string" + ? hfResolveUrl(live.calibration_diagnostics_path) : "deployed-static-snapshot", }, - { - name: "target_diagnostics", - path: LATEST_POPULACE_TARGET_DIAGNOSTICS_PATH, - url: "deployed-static-snapshot", - }, ], limitations: [ - "Build and release manifests are read live from the policyengine/populace-us Hugging Face dataset when reachable; per-target diagnostics come from a deployed static snapshot of sound_ecps_replacement_comparison.json.", - "populace does not yet publish a latest.json pointer, so the live release is resolved by listing the releases/ tree and picking the lexicographically latest complete release (PolicyEngine/populace#9).", - "Calibration internals (loss trajectory, skipped targets, per-record L0 gates) are computed by populace-calibrate but not published, so the dashboard cannot show convergence or skip reasons yet (PolicyEngine/populace#10).", + "Build and release manifests are read live from the policyengine/populace-us Hugging Face dataset via latest.json; per-target calibration diagnostics come from a deployed static snapshot of calibration_diagnostics.json.", + "The eCPS head-to-head comparison moved out of live populace into PolicyEngine/populace-benchmarks, so this view reports populace's calibration fit against its own target surface, not a populace-vs-enhanced-CPS score.", + "The published loss trajectory for this release was reconstructed from saved scalars (the historical build did not store the full epoch trace), so the convergence curve is coarse — see the solver provenance note.", ], - comparison_snapshot_stale: comparisonIsStale, + calibration_snapshot_stale: calibrationSnapshotStale, build_manifest: buildManifest, release_manifest: releaseManifest, gates: asObject(buildManifest.gates), - score_vs_enhanced_cps: asObject(buildManifest.score_vs_enhanced_cps), - comparison, - target_diagnostics: latestPopulaceTargetDiagnosticsSummary(100), + calibration, + highlights, }), ); } catch (error) { diff --git a/frontend/components/populace/populace-overview-view.tsx b/frontend/components/populace/populace-overview-view.tsx index 81ab490..571ec84 100644 --- a/frontend/components/populace/populace-overview-view.tsx +++ b/frontend/components/populace/populace-overview-view.tsx @@ -12,8 +12,8 @@ import { SectionCard } from "@/components/shared/section-card"; import { StatusPill } from "@/components/shared/status-pill"; import { usePopulace, - type PopulaceFamilyBreakdownRow, - type PopulaceTargetDiagnosticRow, + type PopulaceFamilyFitRow, + type PopulaceTargetRow, } from "@/lib/api/hooks/use-populace"; const SMOKE_LABELS: Record = { @@ -26,20 +26,55 @@ const SMOKE_LABELS: Record = { investment_interest_expense_b: "Investment interest expense ($B)", }; -function lossCell(value: number | null | undefined) { - return value == null ? "—" : fmt(value, { digits: 4 }); +const OPTION_LABELS: { key: string; label: string }[] = [ + { key: "epochs", label: "Epochs" }, + { key: "learning_rate", label: "Learning rate" }, + { key: "mass", label: "Mass" }, + { key: "max_weight_ratio", label: "Max weight ratio" }, + { key: "seed", label: "Seed" }, +]; + +function relErr(value: number | null | undefined) { + return value == null ? "—" : fmt(value, { pct: true, digits: 1 }); } -function TargetMoversTable({ +function LossSparkline({ trajectory }: { trajectory: number[] }) { + if (trajectory.length < 2) { + return trace unavailable; + } + const width = 220; + const height = 44; + const max = Math.max(...trajectory); + const min = Math.min(...trajectory); + const span = max - min || 1; + const points = trajectory + .map((value, index) => { + const x = (index / (trajectory.length - 1)) * width; + const y = height - ((value - min) / span) * height; + return `${x.toFixed(1)},${y.toFixed(1)}`; + }) + .join(" "); + return ( + + + + ); +} + +function TargetFitTable({ rows, emptyLabel, }: { - rows: PopulaceTargetDiagnosticRow[]; + rows: PopulaceTargetRow[]; emptyLabel: string; }) { - if (!rows.length) { - return ; - } + if (!rows.length) return ; return (
@@ -47,37 +82,32 @@ function TargetMoversTable({ - - - - + + + {rows.map((row) => ( - - + - - - ))} @@ -100,25 +130,25 @@ export function PopulaceOverviewView() { ); } - const score = data.score_vs_enhanced_cps ?? {}; const gates = data.gates ?? {}; const calibrationGate = gates.calibration ?? {}; - const wins = score.per_target_wins ?? {}; - const totalWins = - (wins.populace ?? 0) + (wins.enhanced_cps ?? 0) + (wins.ties ?? 0); - const comparison = data.comparison ?? { available: false }; - const familyRows: PopulaceFamilyBreakdownRow[] = [ - ...(comparison.family_breakdown ?? []), - ].sort( - (a, b) => - (b.candidate_loss_contribution ?? 0) - (a.candidate_loss_contribution ?? 0), - ); - const populaceFullLoss = score.full_loss?.populace ?? null; - const ecpsFullLoss = score.full_loss?.enhanced_cps ?? null; - const populaceBeatsEcps = - populaceFullLoss != null && ecpsFullLoss != null - ? populaceFullLoss < ecpsFullLoss + const cal = data.calibration ?? { available: false }; + const options = cal.options ?? {}; + const lossDelta = + cal.initial_loss != null && cal.final_loss != null + ? cal.final_loss - cal.initial_loss + : null; + const recordsKept = + cal.n_nonzero != null && cal.n_records != null && cal.n_records > 0 + ? cal.n_nonzero / cal.n_records + : null; + const withinTolShare = + cal.within_tolerance_count != null && cal.total_targets + ? cal.within_tolerance_count / cal.total_targets : null; + const familyFit: PopulaceFamilyFitRow[] = cal.family_fit ?? []; + const diagnosticsNote = + typeof options.diagnostics_source === "string" ? options.diagnostics_source : null; return (
@@ -127,7 +157,7 @@ export function PopulaceOverviewView() { title="Release summary" description={ <> - Latest published populace-US build from{" "} + Calibration diagnostics for the latest published populace-US build from{" "} {data.source_repo} - , scored against the enhanced CPS with a matched-household, - symmetric-refit, held-out-target protocol. + — how well the calibrated weights reproduce populace's own target + surface. } status={ {data.source === "huggingface_live" - ? "Live from Hugging Face" + ? "Live from latest.json" : "Static snapshot"} } @@ -153,50 +183,52 @@ export function PopulaceOverviewView() { } - value={lossCell(populaceFullLoss)} - delta={ - populaceBeatsEcps == null - ? undefined - : populaceBeatsEcps - ? "beats eCPS" - : "behind eCPS" - } - tone={populaceBeatsEcps ? "positive" : "negative"} - hint={`Enhanced CPS: ${lossCell(ecpsFullLoss)}`} + value={cal.final_loss == null ? "—" : fmt(cal.final_loss, { digits: 4 })} + delta={lossDelta == null ? undefined : fmt(lossDelta, { digits: 3 })} + tone={lossDelta != null && lossDelta < 0 ? "positive" : "neutral"} + hint={`From initial ${fmt(cal.initial_loss, { digits: 3 })}`} /> } - value={lossCell(score.holdout_loss?.populace)} - hint={`Enhanced CPS: ${lossCell(score.holdout_loss?.enhanced_cps)}`} + value={fmt(cal.fraction_within_10pct, { pct: true, digits: 1 })} + hint={ + withinTolShare == null + ? undefined + : `${fmt(withinTolShare, { pct: true, digits: 1 })} within declared tolerance` + } /> } - value={fmt(calibrationGate.within_10pct_share, { pct: true, digits: 1 })} - hint={`Calibration loss ${fmt(calibrationGate.loss, { digits: 4 })}, max weight ratio ${fmt(calibrationGate.max_weight_ratio, { digits: 0 })}×`} + value={cal.n_nonzero == null ? "—" : fmtCompact(cal.n_nonzero)} + hint={ + recordsKept == null + ? `of ${fmtCompact(cal.n_records)}` + : `${fmt(recordsKept, { pct: true, digits: 1 })} of ${fmtCompact(cal.n_records)}` + } /> } - value={`${fmtCompact(wins.populace)} / ${fmtCompact(totalWins || null)}`} - hint={`Enhanced CPS wins ${fmtCompact(wins.enhanced_cps)}, ties ${fmtCompact(wins.ties)}`} + value={fmtCompact(cal.total_targets ?? null)} + hint={`${(cal.skipped ?? []).length} skipped`} />
@@ -216,9 +248,7 @@ export function PopulaceOverviewView() { Parity gaps: {fmt(gates.parity_gaps, { digits: 0 })} - + Exported non-zero ({fmt(gates.exported_nonzero?.stored_columns, { digits: 0 })}{" "} columns) @@ -248,9 +278,62 @@ export function PopulaceOverviewView() { +
+ +
+
+
+
+ Initial → final loss +
+
+ {fmt(cal.initial_loss, { digits: 3 })} →{" "} + {fmt(cal.final_loss, { digits: 4 })} +
+
+ +
+ {diagnosticsNote && ( +

{diagnosticsNote}

+ )} +
+ + + +
Target FamilySplitPopulace rel. erroreCPS rel. errorLoss deltaInitial errFinal errIn tol.
- {row.target_name} +
+ {row.name} {row.family} {row.split} - {fmt(row.candidate_relative_error, { pct: true, digits: 1 })} + + {relErr(row.initial_relative_error)} - {fmt(row.baseline_relative_error, { pct: true, digits: 1 })} + {relErr(row.relative_error)} - {row.loss_delta == null ? "—" : row.loss_delta.toExponential(2)} + + {row.within_tolerance == null + ? "—" + : row.within_tolerance + ? "✓" + : "✗"}
+ + {OPTION_LABELS.map(({ key, label }) => + options[key] == null ? null : ( + + + + + ), + )} + + + + + + + + + +
{label}{String(options[key])}
L0 penalty + {cal.l0_lambda == null ? "—" : cal.l0_lambda.toExponential(2)} +
Weight entity{cal.weight_entity ?? "—"}
+ +
+ - Loss - Populace - Enhanced CPS - Ratio + Family + Targets + Within 10% + Within tolerance + Mean abs rel. error - {( - [ - ["Train", score.train_loss], - ["Holdout", score.holdout_loss], - ["Full", score.full_loss], - ] as const - ).map(([label, losses]) => { - const populace = losses?.populace ?? null; - const ecps = losses?.enhanced_cps ?? null; - return ( - - {label} - {lossCell(populace)} - {lossCell(ecps)} - - {populace != null && ecps != null && populace > 0 - ? `${(ecps / populace).toFixed(1)}× better` - : "—"} - - - ); - })} + {familyFit.map((row) => ( + + {row.family} + + {fmt(row.n_targets, { digits: 0 })} + + + {fmt(row.within_10pct, { digits: 0 })} ( + {fmt(row.within_10pct / row.n_targets, { pct: true, digits: 0 })}) + + + {fmt(row.within_tolerance, { digits: 0 })} + + + {relErr(row.mean_abs_relative_error)} + + + ))} - {comparison.available && ( +
+ + + + + + +
+ + {(cal.skipped ?? []).length > 0 && (
- - - - - - - + + - {familyRows.map((row) => ( - - - - - - - - + {(cal.skipped ?? []).map((skip) => ( + + + ))} @@ -354,95 +428,53 @@ export function PopulaceOverviewView() { )} - {comparison.available && ( -
- - - - - - -
- )} - - {data.releases.length - ? `${data.releases.length} release${data.releases.length === 1 ? "" : "s"} published under releases/ in ${data.source_repo}.` - : "Release listing unavailable; showing the deployed snapshot."} - {data.comparison_snapshot_stale && ( - - {" "} - The per-target snapshot below was built from {data.snapshot_release_id}, - which is older than the live release. - - )} - + data.calibration_snapshot_stale ? ( + + The per-target snapshot was built from {data.snapshot_release_id}, which is + older than the live release {data.release_id}. + + ) : ( + <> + Resolved through latest.json + {data.updated_at ? ` (published ${data.updated_at})` : ""}. + + ) } > -
- {data.releases.length > 0 && ( -
- {data.releases.map((release) => ( - - {release.release_id} - {release.release_id === data.release_id ? " (active)" : ""} - - ))} -
- )} -
FamilyTargetsPopulace winseCPS winsPopulace loss contrib.eCPS loss contrib.DeltaTargetReason
{row.family} - {fmt(row.n_targets, { digits: 0 })} - - {fmt(row.candidate_wins, { digits: 0 })} - - {fmt(row.baseline_wins, { digits: 0 })} - - {row.candidate_loss_contribution == null - ? "—" - : row.candidate_loss_contribution.toExponential(2)} - - {row.baseline_loss_contribution == null - ? "—" - : row.baseline_loss_contribution.toExponential(2)} - - {row.loss_delta == null ? "—" : row.loss_delta.toExponential(2)} -
{skip.name}{skip.reason}
- - {data.source_artifacts.map((artifact) => ( - - - - - ))} - -
{artifact.name} - {artifact.url.startsWith("http") ? ( - - {artifact.path} - - ) : ( - <> - {artifact.path}{" "} - (deployed snapshot) - - )} -
-
- Compatible with{" "} - {(data.release_manifest.compatible_model_packages ?? []) - .map((pkg) => `${pkg.name}${pkg.specifier}`) - .join(", ") || "—"} - . -
+ + + {data.source_artifacts.map((artifact) => ( + + + + + ))} + +
{artifact.name} + {artifact.url.startsWith("http") ? ( + + {artifact.path} + + ) : ( + <> + {artifact.path} (deployed snapshot) + + )} +
+
+ Compatible with{" "} + {(data.release_manifest.compatible_model_packages ?? []) + .map((pkg) => `${pkg.name}${pkg.specifier}`) + .join(", ") || "—"} + .
diff --git a/frontend/components/populace/populace-targets-view.tsx b/frontend/components/populace/populace-targets-view.tsx index 292686d..22bcf3c 100644 --- a/frontend/components/populace/populace-targets-view.tsx +++ b/frontend/components/populace/populace-targets-view.tsx @@ -11,7 +11,7 @@ import { StatusPill } from "@/components/shared/status-pill"; import { ToolbarSelect } from "@/components/shared/toolbar-select"; import { usePopulaceTargetDiagnostics, - type PopulaceTargetDiagnosticRow, + type PopulaceTargetRow, } from "@/lib/api/hooks/use-populace"; const PAGE_SIZE = 50; @@ -25,14 +25,14 @@ const COLUMNS: { key: string; label: string; numeric?: boolean; - render: (row: PopulaceTargetDiagnosticRow) => React.ReactNode; + render: (row: PopulaceTargetRow) => React.ReactNode; }[] = [ { - key: "target_name", + key: "name", label: "Target", render: (row) => ( - - {row.target_name} + + {row.name} ), }, @@ -42,62 +42,53 @@ const COLUMNS: { render: (row) => {row.family}, }, { - key: "split", - label: "Split", - render: (row) => row.split, - }, - { - key: "target_value", - label: "Target value", + key: "target", + label: "Target", numeric: true, - render: (row) => fmtCompact(row.target_value), + render: (row) => fmtCompact(row.target), }, { - key: "candidate_estimate", - label: "Populace est.", + key: "initial_estimate", + label: "Initial est.", numeric: true, - render: (row) => fmtCompact(row.candidate_estimate), + render: (row) => fmtCompact(row.initial_estimate), }, { - key: "candidate_relative_error", - label: "Populace rel. err", + key: "final_estimate", + label: "Final est.", numeric: true, - render: (row) => fmt(row.candidate_relative_error, { pct: true, digits: 1 }), + render: (row) => fmtCompact(row.final_estimate), }, { - key: "baseline_relative_error", - label: "eCPS rel. err", + key: "relative_error", + label: "Rel. error", numeric: true, - render: (row) => fmt(row.baseline_relative_error, { pct: true, digits: 1 }), + render: (row) => fmt(row.relative_error, { pct: true, digits: 1 }), }, { - key: "loss_delta", - label: "Loss delta", + key: "improvement", + label: "Improvement", numeric: true, render: (row) => ( - - {row.loss_delta == null ? "—" : row.loss_delta.toExponential(2)} + 0 ? "text-emerald-700" : "text-rose-700"}> + {row.improvement == null ? "—" : fmt(row.improvement, { pct: true, digits: 1 })} ), }, { - key: "winner", - label: "Closer", + key: "within_tolerance", + label: "In tol.", render: (row) => ( - {row.winner === "candidate" - ? "populace" - : row.winner === "baseline" - ? "eCPS" - : "tie"} + {row.within_tolerance == null ? "—" : row.within_tolerance ? "yes" : "no"} ), }, @@ -105,27 +96,24 @@ const COLUMNS: { export function PopulaceTargetsView() { const [family, setFamily] = useState(""); - const [split, setSplit] = useState(""); - const [winner, setWinner] = useState(""); + const [within, setWithin] = useState(""); + const [direction, setDirection] = useState(""); const [search, setSearch] = useState(""); const [page, setPage] = useState(0); - const [sort, setSort] = useState({ - by: "candidate_loss_term", - dir: "desc", - }); + const [sort, setSort] = useState({ by: "abs_relative_error", dir: "desc" }); const params = useMemo( () => ({ limit: PAGE_SIZE, offset: page * PAGE_SIZE, family: family || undefined, - split: split || undefined, - winner: winner || undefined, + within_tolerance: within || undefined, + direction: direction || undefined, search: search || undefined, sort_by: sort.by, sort_dir: sort.dir, }), - [family, split, winner, search, page, sort], + [family, within, direction, search, page, sort], ); const { data, isLoading, error } = usePopulaceTargetDiagnostics(params); @@ -148,7 +136,7 @@ export function PopulaceTargetsView() { {String(data.release_id)} @@ -161,7 +149,7 @@ export function PopulaceTargetsView() { data?.total_targets ?? null, { digits: 0 }, )})`} - description="Click a column header to sort. Loss terms use the calibrator's relative-error loss; negative loss delta means populace fits the target better." + description="Click a column header to sort. Relative error is the calibrated estimate's miss against the target; improvement is how much calibration reduced the absolute relative error from the design weights." padded={false} actions={
@@ -188,30 +176,29 @@ export function PopulaceTargetsView() { ]} /> { - setSplit(value); + setWithin(value); setPage(0); }} options={[ { value: "", label: "All" }, - { value: "train", label: "Train" }, - { value: "holdout", label: "Holdout" }, + { value: "true", label: "Within" }, + { value: "false", label: "Outside" }, ]} /> { - setWinner(value); + setDirection(value); setPage(0); }} options={[ { value: "", label: "All" }, - { value: "candidate", label: "Populace" }, - { value: "baseline", label: "Enhanced CPS" }, - { value: "tie", label: "Tie" }, + { value: "over", label: "Over target" }, + { value: "under", label: "Under target" }, ]} />
@@ -276,7 +263,7 @@ export function PopulaceTargetsView() { {data.targets.map((row) => ( {COLUMNS.map((column) => ( diff --git a/frontend/data/populace/latest/build_manifest.json b/frontend/data/populace/latest/build_manifest.json index bddd0f3..07b9f99 100644 --- a/frontend/data/populace/latest/build_manifest.json +++ b/frontend/data/populace/latest/build_manifest.json @@ -1,17 +1,19 @@ { - "build_id": "populace-us-2024-9f1260b-20260611", + "build_id": "populace-us-2024-f32c2e5-20260614", "builder": "populace", - "build_sha": "9f1260b", - "build_date": "2026-06-11", + "build_sha": "f32c2e5", + "build_date": "2026-06-14", "dataset": { "filename": "populace_us_2024.h5", - "sha256": "dc75c0d4fdedd57946db84a8d838dbc5b61a284365c3ce6eb6508b8e81111a4b" + "sha256": "f32c2e5e9098bc6540724fdd5debf963af495da4c29b3a7a63fb53c2a4bb5a34", + "size_bytes": 355658426 }, "calibration": { "filename": "populace_us_2024_calibration.npz", - "sha256": "a3da2f59085c45f0e16b06337818e3513c2635911dc0d16fa7deb5006263c12a" + "sha256": "9c5fd7397267b01212658d82f4e96d4d41e38efbd666671274a0f060565e60fa", + "size_bytes": 326592 }, - "construction": "eCPS-free: every layer from primary sources (CPS ASEC, IRS PUF 2015 uprated, Fed SCF 2022, Census SIPP, CPS-ORG, MEPS-IC parameters, Census ACS 2022); enhanced CPS used only as the scoring benchmark", + "construction": "Primary-source Populace build: CPS ASEC household/person spine; IRS PUF tax detail; Fed SCF wealth; Census SIPP vehicles and tipped income; CPS-ORG hourly/union signals; MEPS-IC employer-sponsored insurance premiums; Census ACS rent. No incumbent microdata is used as a build input.", "gates": { "parity_gaps": 0, "exported_nonzero": { @@ -19,40 +21,21 @@ "stored_columns": 309 }, "calibration": { - "within_10pct_share": 0.9509, - "loss": 0.022, - "max_weight": 297651, + "targets": 3704, + "within_10pct_share": 0.9465442764578834, + "initial_loss": 1.8307578877880506, + "loss": 0.021951236237680125, + "max_weight": 379622.78125, "weights_above_500k": 0, - "max_weight_ratio": 50 + "max_weight_ratio": 50.0 }, "smoke": { - "people_m": 332.3, - "snap_b": 97.8, - "net_worth_t": 176.5, - "net_stcg_b": -77.5, - "tips_b": 52.9, - "pre_subsidy_rent_b": 757.5, - "investment_interest_expense_b": 5.1 - } - }, - "score_vs_enhanced_cps": { - "protocol": "matched 41,314 households, symmetric refit, 739-target holdout (seed 20260529)", - "train_loss": { - "populace": 0.18957, - "enhanced_cps": 1.08879 - }, - "holdout_loss": { - "populace": 0.03837, - "enhanced_cps": 0.3167 - }, - "full_loss": { - "populace": 0.22794, - "enhanced_cps": 1.40549 - }, - "per_target_wins": { - "populace": 1040, - "enhanced_cps": 2613, - "ties": 51 + "people_m": 332.8, + "snap_b": 98.0, + "net_worth_t": 175.5, + "net_stcg_b": -77.4, + "tips_b": 53.1, + "pre_subsidy_rent_b": 759.7 } } } \ No newline at end of file diff --git a/frontend/data/populace/latest/calibration_diagnostics.json b/frontend/data/populace/latest/calibration_diagnostics.json new file mode 100644 index 0000000..193f8eb --- /dev/null +++ b/frontend/data/populace/latest/calibration_diagnostics.json @@ -0,0 +1 @@ +{"release_id":"populace-us-2024-f32c2e5-20260614","schema_version":1,"weight_entity":"household","options":{"epochs":3000,"learning_rate":0.15,"seed":0,"max_weight_ratio":50.0,"mass":"free","diagnostics_source":"Reconstructed on 2026-06-14 from target_surface_raw.npz, published calibration weights, and initial weights; the historical build saved final scalars but not the full epoch loss trace."},"l0_lambda":null,"n_nonzero":75112,"n_records":75112,"initial_loss":1.83076,"final_loss":0.0219512,"fraction_within_10pct":0.946544,"loss_trajectory":[1.83076,0.0219512],"skipped":[],"targets":[{"name":"nation/irs/adjusted gross income/total/AGI in -inf-0/all returns/All","target":-150574000000.0,"initial_estimate":-35824200000.0,"final_estimate":-140299000000.0,"relative_error":-0.0682404,"within_tolerance":true},{"name":"nation/irs/adjusted gross income/total/AGI in -inf-inf/taxable/All","target":15039500000000.0,"initial_estimate":21766400000000.0,"final_estimate":14554700000000.0,"relative_error":-0.0322404,"within_tolerance":true},{"name":"nation/irs/adjusted gross income/total/AGI in 10k-15k/taxable/All","target":9488630000.0,"initial_estimate":3478520000.0,"final_estimate":9544110000.0,"relative_error":0.00584792,"within_tolerance":true},{"name":"nation/irs/adjusted gross income/total/AGI in 15k-20k/taxable/All","target":73967300000.0,"initial_estimate":57028100000.0,"final_estimate":74012800000.0,"relative_error":0.000616252,"within_tolerance":true},{"name":"nation/irs/adjusted gross income/total/AGI in 20k-25k/taxable/All","target":98091700000.0,"initial_estimate":93597700000.0,"final_estimate":97470800000.0,"relative_error":-0.00632928,"within_tolerance":true},{"name":"nation/irs/adjusted gross income/total/AGI in 25k-30k/taxable/All","target":127091000000.0,"initial_estimate":138590000000.0,"final_estimate":127266000000.0,"relative_error":0.0013816,"within_tolerance":true},{"name":"nation/irs/adjusted gross income/total/AGI in 30k-40k/taxable/All","target":373374000000.0,"initial_estimate":377979000000.0,"final_estimate":390497000000.0,"relative_error":0.0458611,"within_tolerance":true},{"name":"nation/irs/adjusted gross income/total/AGI in 40k-50k/taxable/All","target":496744000000.0,"initial_estimate":422362000000.0,"final_estimate":521734000000.0,"relative_error":0.0503071,"within_tolerance":true},{"name":"nation/irs/adjusted gross income/total/AGI in 50k-75k/taxable/All","target":1386820000000.0,"initial_estimate":1387080000000.0,"final_estimate":1440350000000.0,"relative_error":0.0386012,"within_tolerance":true},{"name":"nation/irs/adjusted gross income/total/AGI in 75k-100k/taxable/All","target":1363990000000.0,"initial_estimate":1353800000000.0,"final_estimate":1338340000000.0,"relative_error":-0.0188034,"within_tolerance":true},{"name":"nation/irs/adjusted gross income/total/AGI in 100k-200k/taxable/All","target":3936240000000.0,"initial_estimate":4630160000000.0,"final_estimate":3707510000000.0,"relative_error":-0.0581069,"within_tolerance":true},{"name":"nation/irs/adjusted gross income/total/AGI in 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\ No newline at end of file diff --git a/frontend/lib/api/hooks/use-populace.ts b/frontend/lib/api/hooks/use-populace.ts index 41cce14..7785df8 100644 --- a/frontend/lib/api/hooks/use-populace.ts +++ b/frontend/lib/api/hooks/use-populace.ts @@ -1,11 +1,6 @@ import { useQuery } from "@tanstack/react-query"; import { apiGet } from "../client"; -export interface PopulaceReleaseEntry { - release_id: string; - files: string[]; -} - export interface PopulaceGates { parity_gaps?: number | null; exported_nonzero?: { @@ -23,96 +18,54 @@ export interface PopulaceGates { [key: string]: unknown; } -export interface PopulaceScoreVsEnhancedCps { - protocol?: string | null; - train_loss?: Record; - holdout_loss?: Record; - full_loss?: Record; - per_target_wins?: Record; -} - -export interface PopulaceFamilyBreakdownRow { - family: string; - n_targets: number | null; - train_targets?: number | null; - holdout_targets?: number | null; - baseline_wins?: number | null; - candidate_wins?: number | null; - ties?: number | null; - baseline_loss_contribution?: number | null; - candidate_loss_contribution?: number | null; - loss_delta?: number | null; - [key: string]: unknown; +export interface PopulaceSkippedTarget { + name: string; + reason: string; } -export interface PopulaceTargetDiagnosticRow { - target_name?: string | null; - target_index?: number | null; - target_value?: number | null; +export interface PopulaceTargetRow { + name?: string | null; + target?: number | null; + initial_estimate?: number | null; + final_estimate?: number | null; + relative_error?: number | null; + within_tolerance?: boolean | null; + // Derived at read time. family?: string | null; - split?: "train" | "holdout" | string | null; - winner?: "candidate" | "baseline" | "tie" | string | null; - value_scale?: string | null; - baseline_estimate?: number | null; - baseline_error?: number | null; - baseline_relative_error?: number | null; - baseline_loss_term?: number | null; - baseline_abs_scaled_error?: number | null; - candidate_estimate?: number | null; - candidate_error?: number | null; - candidate_relative_error?: number | null; - candidate_loss_term?: number | null; - candidate_abs_scaled_error?: number | null; - loss_delta?: number | null; + state?: string | null; + initial_relative_error?: number | null; + abs_relative_error?: number | null; + improvement?: number | null; + direction?: "over" | "under" | "exact" | null; [key: string]: unknown; } -export interface PopulaceComparisonSummary { - available: boolean; - path?: string | null; - release_id?: string | null; - schema_version?: number | null; - period?: number | null; - metric?: string | null; - elapsed_seconds?: number | null; - summary?: Record; - matched_datasets?: Record; - refit_config?: Record; - comparison_contract?: Record; - target_split?: { - holdout_target_fraction?: number | null; - holdout_target_seed?: number | null; - holdout_targets?: number | null; - train_targets?: number | null; - }; - score_broad_loss?: Record; - score_family_breakdown?: Record[]; - target_diagnostics_summary?: Record; - family_breakdown?: PopulaceFamilyBreakdownRow[]; - top_improvements?: PopulaceTargetDiagnosticRow[]; - top_regressions?: PopulaceTargetDiagnosticRow[]; +export interface PopulaceFamilyFitRow { + family: string; + n_targets: number; + within_tolerance: number; + within_10pct: number; + mean_abs_relative_error: number | null; } -export interface PopulaceTargetDiagnostics { +export interface PopulaceCalibration { available: boolean; path?: string | null; release_id?: string | null; schema_version?: number | null; - metric?: string | null; - period?: number | null; - baseline_label?: string | null; - candidate_label?: string | null; - summary: Record; - total_targets: number; - display_limit?: number; - families?: string[]; - filtered_total?: number; - returned?: number; - limit?: number; - offset?: number; - has_next?: boolean; - filters?: Record; - targets: PopulaceTargetDiagnosticRow[]; + weight_entity?: string | null; + options?: Record; + l0_lambda?: number | null; + n_nonzero?: number | null; + n_records?: number | null; + initial_loss?: number | null; + final_loss?: number | null; + fraction_within_10pct?: number | null; + loss_trajectory?: number[]; + skipped?: PopulaceSkippedTarget[]; + total_targets?: number; + within_tolerance_count?: number; + family_fit?: PopulaceFamilyFitRow[]; } export interface PopulaceResponse { @@ -123,10 +76,10 @@ export interface PopulaceResponse { live_unavailable_reason: string | null; release_id: string; snapshot_release_id: string; - releases: PopulaceReleaseEntry[]; + updated_at: string | null; source_artifacts: { name: string; path: string; url: string }[]; limitations: string[]; - comparison_snapshot_stale: boolean; + calibration_snapshot_stale: boolean; build_manifest: { build_id?: string | null; builder?: string | null; @@ -136,7 +89,6 @@ export interface PopulaceResponse { calibration?: { filename?: string | null; sha256?: string | null }; construction?: string | null; gates?: PopulaceGates; - score_vs_enhanced_cps?: PopulaceScoreVsEnhancedCps; [key: string]: unknown; }; release_manifest: { @@ -150,9 +102,35 @@ export interface PopulaceResponse { [key: string]: unknown; }; gates: PopulaceGates; - score_vs_enhanced_cps: PopulaceScoreVsEnhancedCps; - comparison: PopulaceComparisonSummary; - target_diagnostics: PopulaceTargetDiagnostics; + calibration: PopulaceCalibration; + highlights: { + worst_fit: PopulaceTargetRow[]; + biggest_improvements: PopulaceTargetRow[]; + }; +} + +export interface PopulaceTargetDiagnostics { + available: boolean; + path?: string | null; + release_id?: string | null; + schema_version?: number | null; + metric?: string | null; + families?: string[]; + summary: { + total_targets?: number | null; + within_tolerance_count?: number | null; + fraction_within_10pct?: number | null; + [key: string]: unknown; + }; + total_targets: number; + filtered_total?: number; + returned?: number; + limit?: number; + offset?: number; + has_next?: boolean; + display_limit?: number; + filters?: Record; + targets: PopulaceTargetRow[]; } export function usePopulace() { @@ -167,8 +145,9 @@ export function usePopulaceTargetDiagnostics(params: { limit?: number; offset?: number; family?: string; - split?: string; - winner?: string; + state?: string; + direction?: string; + within_tolerance?: string; search?: string; sort_by?: string; sort_dir?: string; diff --git a/frontend/lib/populace/latest-artifact.ts b/frontend/lib/populace/latest-artifact.ts index e4da1b0..90d0851 100644 --- a/frontend/lib/populace/latest-artifact.ts +++ b/frontend/lib/populace/latest-artifact.ts @@ -1,31 +1,29 @@ import buildManifestData from "@/data/populace/latest/build_manifest.json"; import releaseManifestData from "@/data/populace/latest/release_manifest.json"; -import comparisonSummaryData from "@/data/populace/latest/comparison_summary.json"; -import targetDiagnosticsData from "@/data/populace/latest/target_diagnostics.json"; +import latestPointerData from "@/data/populace/latest/latest.json"; +import calibrationDiagnosticsData from "@/data/populace/latest/calibration_diagnostics.json"; type JsonObject = Record; type TargetRow = JsonObject; const BUILD_MANIFEST = buildManifestData as JsonObject; const RELEASE_MANIFEST = releaseManifestData as JsonObject; -const COMPARISON_SUMMARY = comparisonSummaryData as JsonObject; -const TARGET_DIAGNOSTICS = targetDiagnosticsData as JsonObject; -const TARGET_ROWS = Array.isArray(TARGET_DIAGNOSTICS.targets) - ? (TARGET_DIAGNOSTICS.targets as TargetRow[]) +const LATEST_POINTER = latestPointerData as JsonObject; +const CALIBRATION_DIAGNOSTICS = calibrationDiagnosticsData as JsonObject; +const TARGET_ROWS = Array.isArray(CALIBRATION_DIAGNOSTICS.targets) + ? (CALIBRATION_DIAGNOSTICS.targets as TargetRow[]).map(enrichTargetRow) : []; export const LATEST_POPULACE_RELEASE_ID = String( - BUILD_MANIFEST.build_id ?? "populace-us-2024-9f1260b-20260611", + BUILD_MANIFEST.build_id ?? LATEST_POINTER.release_id ?? "populace-us", ); export const POPULACE_HF_REPO = "policyengine/populace-us"; export const LATEST_POPULACE_BUILD_MANIFEST_PATH = "frontend/data/populace/latest/build_manifest.json"; export const LATEST_POPULACE_RELEASE_MANIFEST_PATH = "frontend/data/populace/latest/release_manifest.json"; -export const LATEST_POPULACE_COMPARISON_SUMMARY_PATH = - "frontend/data/populace/latest/comparison_summary.json"; -export const LATEST_POPULACE_TARGET_DIAGNOSTICS_PATH = - "frontend/data/populace/latest/target_diagnostics.json"; +export const LATEST_POPULACE_CALIBRATION_DIAGNOSTICS_PATH = + "frontend/data/populace/latest/calibration_diagnostics.json"; export function asObject(value: unknown): JsonObject { return value && typeof value === "object" && !Array.isArray(value) @@ -44,19 +42,92 @@ export function scrub(value: unknown): unknown { return value; } +function numberOrNull(value: unknown): number | null { + return typeof value === "number" && Number.isFinite(value) ? value : null; +} + +function relativeError(estimate: number | null, target: number | null): number | null { + if (estimate == null || target == null) return null; + // The artifact's own convention: relative miss against the target, falling + // back to the absolute miss when the target is zero (relative is undefined). + return target === 0 ? estimate - target : (estimate - target) / Math.abs(target); +} + +// Targets are named hierarchically (e.g. "nation/irs/...", "state/census/...", +// "state/AL/adjusted_gross_income/..."). Derive a coarse source family for +// filtering: national/state sources keep their source segment; the per-state +// distribution targets (2-letter state code in slot 2) collapse to one family. +function deriveFamily(name: string): string { + const parts = name.split("/"); + if (parts.length < 2) return name; + const [geo, second] = parts; + // Per-state distribution targets ("state/AL/...") collapse to one family. + if (/^[A-Z]{2}$/.test(second)) return "state_distribution"; + // Per-state-FIPS program targets ("US01/snap-cost") collapse to the measure + // ("snap-cost") so all 51 states form one family, not 51. + if (/^US\d{2}$/.test(geo)) return second; + return `${geo}/${second}`; +} + +function deriveState(name: string): string | null { + const parts = name.split("/"); + return parts.length >= 2 && /^[A-Z]{2}$/.test(parts[1]) ? parts[1] : null; +} + +function enrichTargetRow(row: TargetRow): TargetRow { + const name = String(row.name ?? ""); + const target = numberOrNull(row.target); + const initial = numberOrNull(row.initial_estimate); + const final = numberOrNull(row.final_estimate); + const finalRel = numberOrNull(row.relative_error) ?? relativeError(final, target); + const initialRel = relativeError(initial, target); + const absFinalRel = finalRel == null ? null : Math.abs(finalRel); + // Did calibration help? Positive when the calibrated estimate is closer to + // the target (in relative terms) than the design-weight estimate was. + const improvement = + initialRel == null || finalRel == null + ? null + : Math.abs(initialRel) - Math.abs(finalRel); + return { + ...row, + family: deriveFamily(name), + state: deriveState(name), + initial_relative_error: initialRel, + abs_relative_error: absFinalRel, + improvement, + direction: + finalRel == null ? null : finalRel > 0 ? "over" : finalRel < 0 ? "under" : "exact", + }; +} + function stringParam(value: string | null): string | null { const trimmed = value?.trim(); return trimmed ? trimmed : null; } +function booleanParam(value: string | null): boolean | null { + if (value == null || value === "") return null; + if (["1", "true", "yes"].includes(value.toLowerCase())) return true; + if (["0", "false", "no"].includes(value.toLowerCase())) return false; + return null; +} + function matchesSearch(row: TargetRow, search: string): boolean { - const haystack = [row.target_name, row.family, row.split, row.winner] + const haystack = [row.name, row.family, row.state] .filter((value) => value != null) .join(" ") .toLowerCase(); return haystack.includes(search.toLowerCase()); } +export function populaceTargetFamilies(): string[] { + return [...new Set(TARGET_ROWS.map((row) => String(row.family ?? "")))].sort(); +} + +function withinToleranceCount(rows: TargetRow[]): number { + return rows.filter((row) => row.within_tolerance === true).length; +} + export function latestPopulaceBuildManifest(): JsonObject { return BUILD_MANIFEST; } @@ -65,35 +136,104 @@ export function latestPopulaceReleaseManifest(): JsonObject { return RELEASE_MANIFEST; } -export function latestPopulaceComparisonSummary(): JsonObject { +export function latestPopulacePointer(): JsonObject { + return LATEST_POINTER; +} + +// Per-family calibration fit: how well each source family is reproduced, +// computed from the per-target rows (replaces the eCPS family breakdown the +// retired comparison artifact used to carry). +function familyFitSummary() { + const groups = new Map(); + for (const row of TARGET_ROWS) { + const family = String(row.family ?? ""); + const bucket = groups.get(family) ?? []; + bucket.push(row); + groups.set(family, bucket); + } + return [...groups.entries()] + .map(([family, rows]) => { + const absErrors = rows + .map((row) => numberOrNull(row.abs_relative_error)) + .filter((value): value is number => value != null); + const meanAbsError = absErrors.length + ? absErrors.reduce((sum, value) => sum + value, 0) / absErrors.length + : null; + return { + family, + n_targets: rows.length, + within_tolerance: withinToleranceCount(rows), + within_10pct: rows.filter( + (row) => (numberOrNull(row.abs_relative_error) ?? Infinity) <= 0.1, + ).length, + mean_abs_relative_error: meanAbsError, + }; + }) + .sort((a, b) => b.n_targets - a.n_targets); +} + +export function latestPopulaceCalibrationSummary() { return { available: true, - path: LATEST_POPULACE_COMPARISON_SUMMARY_PATH, - ...COMPARISON_SUMMARY, + path: LATEST_POPULACE_CALIBRATION_DIAGNOSTICS_PATH, + release_id: CALIBRATION_DIAGNOSTICS.release_id ?? LATEST_POPULACE_RELEASE_ID, + schema_version: CALIBRATION_DIAGNOSTICS.schema_version ?? null, + weight_entity: CALIBRATION_DIAGNOSTICS.weight_entity ?? null, + options: asObject(CALIBRATION_DIAGNOSTICS.options), + l0_lambda: numberOrNull(CALIBRATION_DIAGNOSTICS.l0_lambda), + n_nonzero: numberOrNull(CALIBRATION_DIAGNOSTICS.n_nonzero), + n_records: numberOrNull(CALIBRATION_DIAGNOSTICS.n_records), + initial_loss: numberOrNull(CALIBRATION_DIAGNOSTICS.initial_loss), + final_loss: numberOrNull(CALIBRATION_DIAGNOSTICS.final_loss), + fraction_within_10pct: numberOrNull(CALIBRATION_DIAGNOSTICS.fraction_within_10pct), + loss_trajectory: Array.isArray(CALIBRATION_DIAGNOSTICS.loss_trajectory) + ? (CALIBRATION_DIAGNOSTICS.loss_trajectory as number[]) + : [], + skipped: Array.isArray(CALIBRATION_DIAGNOSTICS.skipped) + ? (CALIBRATION_DIAGNOSTICS.skipped as JsonObject[]) + : [], + total_targets: TARGET_ROWS.length, + within_tolerance_count: withinToleranceCount(TARGET_ROWS), + family_fit: familyFitSummary(), + }; +} + +function worstFit(limit: number): TargetRow[] { + return [...TARGET_ROWS] + .filter((row) => numberOrNull(row.abs_relative_error) != null) + .sort( + (a, b) => + (numberOrNull(b.abs_relative_error) ?? 0) - + (numberOrNull(a.abs_relative_error) ?? 0), + ) + .slice(0, limit); +} + +function biggestImprovements(limit: number): TargetRow[] { + return [...TARGET_ROWS] + .filter((row) => numberOrNull(row.improvement) != null) + .sort( + (a, b) => (numberOrNull(b.improvement) ?? 0) - (numberOrNull(a.improvement) ?? 0), + ) + .slice(0, limit); +} + +export function latestPopulaceCalibrationHighlights(limit = 15) { + return { + worst_fit: worstFit(limit), + biggest_improvements: biggestImprovements(limit), }; } export function latestPopulaceTargetDiagnosticsSummary(displayLimit = 100) { return { - available: true, - path: LATEST_POPULACE_TARGET_DIAGNOSTICS_PATH, - release_id: TARGET_DIAGNOSTICS.release_id ?? LATEST_POPULACE_RELEASE_ID, - schema_version: TARGET_DIAGNOSTICS.schema_version ?? null, - metric: TARGET_DIAGNOSTICS.metric ?? null, - period: TARGET_DIAGNOSTICS.period ?? null, - baseline_label: "enhanced_cps", - candidate_label: "populace", - summary: TARGET_DIAGNOSTICS.summary ?? {}, - total_targets: TARGET_ROWS.length, + ...latestPopulaceCalibrationSummary(), + families: populaceTargetFamilies(), display_limit: displayLimit, targets: TARGET_ROWS.slice(0, displayLimit), }; } -export function populaceTargetFamilies(): string[] { - return [...new Set(TARGET_ROWS.map((row) => String(row.family ?? "")))].sort(); -} - export function latestPopulaceTargetDiagnosticsPage(requestUrl: string) { const url = new URL(requestUrl); const limit = Math.min( @@ -102,49 +242,64 @@ export function latestPopulaceTargetDiagnosticsPage(requestUrl: string) { ); const offset = Math.max(Number(url.searchParams.get("offset") ?? "0") || 0, 0); const family = stringParam(url.searchParams.get("family")); - const split = stringParam(url.searchParams.get("split")); - const winner = stringParam(url.searchParams.get("winner")); + const state = stringParam(url.searchParams.get("state")); + const direction = stringParam(url.searchParams.get("direction")); + const within = booleanParam(url.searchParams.get("within_tolerance")); const search = stringParam(url.searchParams.get("search")); - const sortBy = stringParam(url.searchParams.get("sort_by")); + const sortBy = stringParam(url.searchParams.get("sort_by")) ?? "abs_relative_error"; const sortDir = - stringParam(url.searchParams.get("sort_dir")) === "desc" ? "desc" : "asc"; + stringParam(url.searchParams.get("sort_dir")) === "asc" ? "asc" : "desc"; let filtered = TARGET_ROWS; if (family) filtered = filtered.filter((row) => row.family === family); - if (split) filtered = filtered.filter((row) => row.split === split); - if (winner) filtered = filtered.filter((row) => row.winner === winner); + if (state) filtered = filtered.filter((row) => row.state === state); + if (direction) filtered = filtered.filter((row) => row.direction === direction); + if (within !== null) + filtered = filtered.filter((row) => row.within_tolerance === within); if (search) filtered = filtered.filter((row) => matchesSearch(row, search)); - if (sortBy) { - filtered = [...filtered].sort((a, b) => { - const aVal = a[sortBy]; - const bVal = b[sortBy]; - if (aVal == null && bVal == null) return 0; - if (aVal == null) return 1; - if (bVal == null) return -1; - const cmp = - typeof aVal === "number" && typeof bVal === "number" - ? aVal - bVal - : String(aVal).localeCompare(String(bVal)); - return sortDir === "asc" ? cmp : -cmp; - }); - } + filtered = [...filtered].sort((a, b) => { + const aVal = a[sortBy]; + const bVal = b[sortBy]; + if (aVal == null && bVal == null) return 0; + if (aVal == null) return 1; + if (bVal == null) return -1; + const cmp = + typeof aVal === "number" && typeof bVal === "number" + ? aVal - bVal + : String(aVal).localeCompare(String(bVal)); + return sortDir === "asc" ? cmp : -cmp; + }); return { - ...latestPopulaceTargetDiagnosticsSummary(limit), + available: true, + path: LATEST_POPULACE_CALIBRATION_DIAGNOSTICS_PATH, + release_id: CALIBRATION_DIAGNOSTICS.release_id ?? LATEST_POPULACE_RELEASE_ID, + schema_version: CALIBRATION_DIAGNOSTICS.schema_version ?? null, + metric: "relative_error", families: populaceTargetFamilies(), + summary: { + total_targets: TARGET_ROWS.length, + within_tolerance_count: withinToleranceCount(TARGET_ROWS), + fraction_within_10pct: numberOrNull( + CALIBRATION_DIAGNOSTICS.fraction_within_10pct, + ), + }, + total_targets: TARGET_ROWS.length, + filtered_total: filtered.length, returned: filtered.slice(offset, offset + limit).length, limit, offset, has_next: offset + limit < filtered.length, - filtered_total: filtered.length, + display_limit: limit, targets: filtered.slice(offset, offset + limit), filters: { family, - split, - winner, + state, + direction, + within_tolerance: within, search, sort_by: sortBy, - sort_dir: sortBy ? sortDir : null, + sort_dir: sortDir, }, }; } diff --git a/tests/test_populace.py b/tests/test_populace.py index 65dcb27..3542a8e 100644 --- a/tests/test_populace.py +++ b/tests/test_populace.py @@ -29,55 +29,54 @@ def test_populace_overview_serves_snapshot_when_offline(monkeypatch): assert gates["calibration"]["within_10pct_share"] is not None assert "smoke" in gates - score = payload["score_vs_enhanced_cps"] - assert set(score["per_target_wins"]) == {"populace", "enhanced_cps", "ties"} - assert score["full_loss"]["populace"] < score["full_loss"]["enhanced_cps"] - - comparison = payload["comparison"] - assert comparison["available"] is True - assert comparison["family_breakdown"] - assert comparison["top_regressions"] - assert comparison["top_improvements"] - - diagnostics = payload["target_diagnostics"] - assert diagnostics["available"] is True - assert diagnostics["total_targets"] > 3000 - assert len(diagnostics["targets"]) == 100 - assert diagnostics["baseline_label"] == "enhanced_cps" - assert diagnostics["candidate_label"] == "populace" + cal = payload["calibration"] + assert cal["available"] is True + assert cal["total_targets"] > 3000 + assert cal["final_loss"] < cal["initial_loss"] + assert cal["n_nonzero"] is not None and cal["n_records"] is not None + assert cal["family_fit"] + # Family fit rows are consistent: within-10% never exceeds the count. + for row in cal["family_fit"]: + assert 0 <= row["within_10pct"] <= row["n_targets"] + assert 0 <= row["within_tolerance"] <= row["n_targets"] + + highlights = payload["highlights"] + assert len(highlights["worst_fit"]) == 15 + assert len(highlights["biggest_improvements"]) == 15 + # Worst-fit is sorted by descending absolute relative error. + worst = [r["abs_relative_error"] for r in highlights["worst_fit"]] + assert worst == sorted(worst, reverse=True) def test_populace_overview_prefers_live_release(monkeypatch): live_build = { "build_id": "populace-us-2024-fffffff-20990101", "builder": "populace", - "gates": {"parity_gaps": 0}, - "score_vs_enhanced_cps": {"per_target_wins": {}}, + "dataset": {"filename": "populace_us_2024.h5"}, + "gates": {"parity_gaps": 0, "smoke": {}}, } live_release = {"schema_version": 1, "build": {"build_id": live_build["build_id"]}} def _fake_fetch(url): - if "/tree/" in url: - return [ - { - "type": "file", - "path": "releases/populace-us-2024-ffffffff-20990101/x", - }, - { - "type": "file", - "path": ( - "releases/populace-us-2024-fffffff-20990101/" - "build_manifest.json" + if url.endswith("latest.json"): + return { + "schema_version": 1, + "release_id": "populace-us-2024-fffffff-20990101", + "updated_at": "2099-01-01T00:00:00+00:00", + "paths": { + "build_manifest": ( + "releases/populace-us-2024-fffffff-20990101/build_manifest.json" ), - }, - { - "type": "file", - "path": ( + "release_manifest": ( "releases/populace-us-2024-fffffff-20990101/" "release_manifest.json" ), + "calibration_diagnostics": ( + "releases/populace-us-2024-fffffff-20990101/" + "calibration_diagnostics.json" + ), }, - ] + } if url.endswith("build_manifest.json"): return live_build if url.endswith("release_manifest.json"): @@ -89,9 +88,10 @@ def _fake_fetch(url): assert payload["source"] == "huggingface_live" assert payload["release_id"] == "populace-us-2024-fffffff-20990101" + assert payload["updated_at"] == "2099-01-01T00:00:00+00:00" assert payload["build_manifest"]["build_id"] == live_build["build_id"] # The committed per-target snapshot is from an older release. - assert payload["comparison_snapshot_stale"] is True + assert payload["calibration_snapshot_stale"] is True def test_populace_target_diagnostics_filters_and_sorts(): @@ -99,10 +99,11 @@ def test_populace_target_diagnostics_filters_and_sorts(): limit=10, offset=0, family=None, - split="holdout", - winner="candidate", + state=None, + direction="over", + within_tolerance="false", search=None, - sort_by="candidate_loss_term", + sort_by="abs_relative_error", sort_dir="desc", ) @@ -111,40 +112,76 @@ def test_populace_target_diagnostics_filters_and_sorts(): assert payload["filtered_total"] <= payload["total_targets"] assert payload["families"] for row in payload["targets"]: - assert row["split"] == "holdout" - assert row["winner"] == "candidate" - loss_terms = [ - row["candidate_loss_term"] - for row in payload["targets"] - if row.get("candidate_loss_term") is not None + assert row["direction"] == "over" + assert row["within_tolerance"] is False + errors = [ + r["abs_relative_error"] + for r in payload["targets"] + if r.get("abs_relative_error") is not None ] - assert loss_terms == sorted(loss_terms, reverse=True) + assert errors == sorted(errors, reverse=True) + + +def test_populace_target_diagnostics_default_sort_is_worst_fit(): + payload = populace.populace_target_diagnostics( + limit=20, offset=0, family=None, state=None, direction=None, + within_tolerance=None, search=None, sort_by=None, sort_dir="desc", + ) + errors = [r["abs_relative_error"] for r in payload["targets"]] + assert errors == sorted(errors, reverse=True) def test_populace_target_diagnostics_pagination(): first = populace.populace_target_diagnostics( - limit=5, offset=0, family=None, split=None, winner=None, - search=None, sort_by="target_index", sort_dir="asc", + limit=5, offset=0, family=None, state=None, direction=None, + within_tolerance=None, search=None, sort_by="name", sort_dir="asc", ) second = populace.populace_target_diagnostics( - limit=5, offset=5, family=None, split=None, winner=None, - search=None, sort_by="target_index", sort_dir="asc", + limit=5, offset=5, family=None, state=None, direction=None, + within_tolerance=None, search=None, sort_by="name", sort_dir="asc", ) assert first["has_next"] is True - first_ids = {row["target_index"] for row in first["targets"]} - second_ids = {row["target_index"] for row in second["targets"]} - assert not first_ids & second_ids + first_names = {r["name"] for r in first["targets"]} + second_names = {r["name"] for r in second["targets"]} + assert not first_names & second_names + + +def test_populace_target_diagnostics_family_filter(): + payload = populace.populace_target_diagnostics( + limit=50, offset=0, family="nation/irs", state=None, direction=None, + within_tolerance=None, search=None, sort_by=None, sort_dir="desc", + ) + assert payload["filtered_total"] >= 1 + for row in payload["targets"]: + assert row["family"] == "nation/irs" def test_populace_target_diagnostics_search(): payload = populace.populace_target_diagnostics( - limit=50, offset=0, family=None, split=None, winner=None, - search="snap", sort_by=None, sort_dir="asc", + limit=50, offset=0, family=None, state=None, direction=None, + within_tolerance=None, search="snap", sort_by=None, sort_dir="desc", ) assert payload["filtered_total"] >= 1 for row in payload["targets"]: haystack = " ".join( - str(row.get(key, "")) - for key in ("target_name", "family", "split", "winner") + str(row.get(key, "")) for key in ("name", "family", "state") ).lower() assert "snap" in haystack + + +def test_enrich_derives_state_distribution_family(): + row = populace._enrich( + { + "name": "state/AL/adjusted_gross_income/count/1_1", + "target": 100.0, + "initial_estimate": 80.0, + "final_estimate": 95.0, + "relative_error": -0.05, + "within_tolerance": True, + } + ) + assert row["family"] == "state_distribution" + assert row["state"] == "AL" + # Calibration moved the estimate closer to the target → positive improvement. + assert row["improvement"] > 0 + assert row["direction"] == "under" From c48f0a0351e67dae15c7971c4918e20f093c0e24 Mon Sep 17 00:00:00 2001 From: Pavel Makarchuk Date: Mon, 15 Jun 2026 10:35:38 -0400 Subject: [PATCH 04/14] Add Populace incumbent comparison view (eCPS head-to-head) Restores the populace-vs-enhanced-CPS comparison the pivot dropped, now as a dedicated "Incumbent comparison" page sourced from the benchmarks repo's scorecard rather than the live populace surface. - /populace/comparison: full/holdout/train loss and unweighted MSRE head to head, per-target win/loss/tie, per-family loss breakdown, top regressions and improvements. Candidate is populace; baseline is the enhanced CPS. - Route reads a live scorecard from POPULACE_BENCHMARKS_SCORECARD_URL when set, else serves a committed archived snapshot of the 9f1260b scorecard, flagged "archived". The normalizer flattens both the archived sound_ecps_replacement_comparison shape and the proposed flat benchmarks scorecard, so the live artifact drops in unchanged. - The live scorecard is not published yet; filed as PolicyEngine/populace-benchmarks#3 (machine-readable scorecard + latest pointer), referenced in the route notes. Tested: 105 backend tests pass (4 new: archived snapshot, live-prefer, live-fetch fallback, FIPS family collapse), tsc clean, next build green. Smoke-tested both the archived path and, with the env var set at build, the live benchmarks path. Co-Authored-By: Claude Fable 5 --- backend/routes/populace.py | 140 ++ frontend/app/api/populace/comparison/route.ts | 69 + frontend/app/populace/comparison/page.tsx | 10 + frontend/components/layout/nav-sidebar.tsx | 1 + .../populace/populace-comparison-view.tsx | 303 +++ .../populace/latest/comparison_scorecard.json | 1704 +++++++++++++++++ frontend/lib/api/hooks/use-populace.ts | 71 + frontend/lib/populace/comparison-artifact.ts | 87 + tests/test_populace.py | 86 + 9 files changed, 2471 insertions(+) create mode 100644 frontend/app/api/populace/comparison/route.ts create mode 100644 frontend/app/populace/comparison/page.tsx create mode 100644 frontend/components/populace/populace-comparison-view.tsx create mode 100644 frontend/data/populace/latest/comparison_scorecard.json create mode 100644 frontend/lib/populace/comparison-artifact.ts diff --git a/backend/routes/populace.py b/backend/routes/populace.py index 2276873..c1293dd 100644 --- a/backend/routes/populace.py +++ b/backend/routes/populace.py @@ -43,10 +43,19 @@ _STATIC_BUILD_MANIFEST = _STATIC_ROOT / "build_manifest.json" _STATIC_RELEASE_MANIFEST = _STATIC_ROOT / "release_manifest.json" _STATIC_CALIBRATION_DIAGNOSTICS = _STATIC_ROOT / "calibration_diagnostics.json" +_STATIC_COMPARISON_SCORECARD = _STATIC_ROOT / "comparison_scorecard.json" _CALIBRATION_DIAGNOSTICS_PUBLIC_PATH = ( "frontend/data/populace/latest/calibration_diagnostics.json" ) +_COMPARISON_SCORECARD_PUBLIC_PATH = ( + "frontend/data/populace/latest/comparison_scorecard.json" +) + +# The live incumbent-comparison scorecard is not published yet +# (PolicyEngine/populace-benchmarks#3). Set this to the artifact URL to serve it +# live; otherwise the route serves the archived 9f1260b snapshot. +_BENCHMARKS_SCORECARD_URL = os.environ.get("POPULACE_BENCHMARKS_SCORECARD_URL") _CACHE: dict[str, tuple[float, Any]] = {} _TTL_SECONDS = 300 @@ -444,3 +453,134 @@ def key_fn(row: dict[str, Any]): "targets": page, } ) + + +def _num(value: Any) -> float | None: + return value if isinstance(value, (int, float)) and not isinstance(value, bool) else None + + +def _normalize_comparison_scorecard(raw: dict[str, Any]) -> dict[str, Any]: + """Flatten an incumbent-comparison scorecard to the shared shape. + + The archived ``sound_ecps_replacement_comparison.json`` splits the clean + win/loss block (``target_diagnostics_summary``) from the refit/loss scalars + (``summary``); the proposed benchmarks scorecard + (PolicyEngine/populace-benchmarks#3) is a single flat ``summary``. Read both. + """ + summary = raw.get("summary") if isinstance(raw.get("summary"), dict) else {} + td = ( + raw.get("target_diagnostics_summary") + if isinstance(raw.get("target_diagnostics_summary"), dict) + else {} + ) + + def pick(*keys: str) -> float | None: + # First key wins, across both sources: the kept-target ``n_targets`` + # (target_diagnostics_summary) takes precedence over the pre-drop + # ``n_targets_total`` (summary), so it matches the win/loss/tie counts. + for key in keys: + for source in (td, summary): + if key in source: + value = _num(source[key]) + if value is not None: + return value + return None + + flat = { + "candidate_loss": pick("candidate_loss", "candidate_enhanced_cps_native_loss"), + "baseline_loss": pick("baseline_loss", "baseline_enhanced_cps_native_loss"), + "loss_delta": pick("loss_delta", "enhanced_cps_native_loss_delta"), + "candidate_holdout_loss": pick("candidate_holdout_loss"), + "baseline_holdout_loss": pick("baseline_holdout_loss"), + "candidate_train_loss": pick("candidate_train_loss"), + "baseline_train_loss": pick("baseline_train_loss"), + "candidate_unweighted_msre": pick("candidate_unweighted_msre"), + "baseline_unweighted_msre": pick("baseline_unweighted_msre"), + "candidate_wins": pick("candidate_wins"), + "baseline_wins": pick("baseline_wins"), + "ties": pick("ties"), + "n_targets": pick("n_targets", "n_targets_total"), + "holdout_targets": pick("holdout_targets"), + "train_targets": pick("train_targets"), + "candidate_beats_baseline": ( + summary.get("candidate_beats_baseline") + if isinstance(summary.get("candidate_beats_baseline"), bool) + else None + ), + # The archived summary's ``matched_household_count`` is a stray bool; + # the real matched count is the per-dataset household count. ``pick`` + # skips non-numeric values, so the count keys are the real source. + "matched_household_count": pick( + "matched_household_count", + "candidate_household_count", + "baseline_household_count", + ), + } + protocol = raw.get("protocol") + if not isinstance(protocol, str) and flat["matched_household_count"] is not None: + protocol = ( + f"Matched {int(flat['matched_household_count']):,} households, " + f"symmetric refit, {flat['holdout_targets']}-target holdout." + ) + return { + "release_id": raw.get("candidate_release_id") or raw.get("release_id"), + "incumbent_manifest": raw.get("incumbent_manifest", "pinned-production-ecps-2024"), + "period": _num(raw.get("period")), + "baseline_label": raw.get("baseline_label", "enhanced_cps"), + "candidate_label": raw.get("candidate_label", "populace"), + "protocol": protocol if isinstance(protocol, str) else None, + "summary": flat, + "family_breakdown": raw.get("family_breakdown") or [], + "top_improvements": raw.get("top_improvements") or [], + "top_regressions": raw.get("top_regressions") or [], + "gates": raw.get("gates") or {}, + } + + +@router.get("/populace/comparison") +def populace_comparison() -> dict[str, Any]: + snapshot = _normalize_comparison_scorecard(_load_static(_STATIC_COMPARISON_SCORECARD)) + source = "deployed_static_snapshot" + path = _COMPARISON_SCORECARD_PUBLIC_PATH + live_error = None + + if _BENCHMARKS_SCORECARD_URL: + try: + live = _normalize_comparison_scorecard(_fetch_json(_BENCHMARKS_SCORECARD_URL)) + snapshot = live + source = "populace_benchmarks_live" + path = _BENCHMARKS_SCORECARD_URL + except Exception as exc: + live_error = str(exc) + + archived = source != "populace_benchmarks_live" + return _scrub( + { + "available": True, + "source": source, + "path": path, + "archived": archived, + "live_scorecard_configured": bool(_BENCHMARKS_SCORECARD_URL), + "live_scorecard_error": live_error, + **snapshot, + "notes": [ + "Populace (candidate) is scored against the enhanced CPS " + "(incumbent) with a matched-household, symmetric-refit, " + "held-out-target protocol.", + ( + "This is the archived scorecard for release " + "populace-us-2024-9f1260b-20260611. The live incumbent " + "comparison is not published as a machine-readable artifact " + "yet (PolicyEngine/populace-benchmarks#3); set " + "POPULACE_BENCHMARKS_SCORECARD_URL to serve it live." + if archived + else "Served live from the configured populace-benchmarks " + "scorecard." + ), + "The eCPS comparison is benchmark-harness material and " + "intentionally lives outside live populace " + "(PolicyEngine/populace#37); the live calibration-fit view is " + "the populace release summary.", + ], + } + ) diff --git a/frontend/app/api/populace/comparison/route.ts b/frontend/app/api/populace/comparison/route.ts new file mode 100644 index 0000000..fa6ce85 --- /dev/null +++ b/frontend/app/api/populace/comparison/route.ts @@ -0,0 +1,69 @@ +import { NextResponse } from "next/server"; + +import { + normalizeComparisonScorecard, + snapshotComparisonScorecard, +} from "@/lib/populace/comparison-artifact"; +import { scrub } from "@/lib/populace/latest-artifact"; + +// The live incumbent-comparison scorecard is not published yet +// (PolicyEngine/populace-benchmarks#3). When it is, point this at the artifact +// (a raw GitHub URL or HF resolve URL) and the route serves it live, falling +// back to the committed archived 9f1260b snapshot on any failure. +const BENCHMARKS_SCORECARD_URL = process.env.POPULACE_BENCHMARKS_SCORECARD_URL?.trim(); + +type JsonObject = Record; + +export const revalidate = 300; + +function asObject(value: unknown): JsonObject { + return value && typeof value === "object" && !Array.isArray(value) + ? (value as JsonObject) + : {}; +} + +async function loadLiveScorecard(url: string): Promise { + const response = await fetch(url, { next: { revalidate } }); + if (!response.ok) { + throw new Error(`Failed to fetch scorecard ${url}: ${response.status}`); + } + return { + available: true, + source: "populace_benchmarks_live", + path: url, + ...normalizeComparisonScorecard(asObject(await response.json())), + }; +} + +export async function GET() { + const snapshot = snapshotComparisonScorecard(); + let payload = snapshot; + let liveError: string | null = null; + + if (BENCHMARKS_SCORECARD_URL) { + try { + payload = (await loadLiveScorecard(BENCHMARKS_SCORECARD_URL)) as typeof snapshot; + } catch (error) { + liveError = error instanceof Error ? error.message : String(error); + payload = snapshot; + } + } + + const archived = payload.source !== "populace_benchmarks_live"; + + return NextResponse.json( + scrub({ + ...payload, + archived, + live_scorecard_configured: Boolean(BENCHMARKS_SCORECARD_URL), + live_scorecard_error: liveError, + notes: [ + "Populace (candidate) is scored against the enhanced CPS (incumbent) with a matched-household, symmetric-refit, held-out-target protocol.", + archived + ? "This is the archived scorecard for release populace-us-2024-9f1260b-20260611. The live incumbent comparison is not published as a machine-readable artifact yet (PolicyEngine/populace-benchmarks#3); set POPULACE_BENCHMARKS_SCORECARD_URL to serve it live once it is." + : "Served live from the configured populace-benchmarks scorecard.", + "The eCPS comparison is benchmark-harness material and intentionally lives outside live populace (PolicyEngine/populace#37); the live calibration-fit view is the populace release summary.", + ], + }), + ); +} diff --git a/frontend/app/populace/comparison/page.tsx b/frontend/app/populace/comparison/page.tsx new file mode 100644 index 0000000..17deb17 --- /dev/null +++ b/frontend/app/populace/comparison/page.tsx @@ -0,0 +1,10 @@ +import { AppShell } from "@/components/layout/app-shell"; +import { PopulaceComparisonView } from "@/components/populace/populace-comparison-view"; + +export default function PopulaceComparisonPage() { + return ( + + + + ); +} diff --git a/frontend/components/layout/nav-sidebar.tsx b/frontend/components/layout/nav-sidebar.tsx index 7b339f4..1435d9a 100644 --- a/frontend/components/layout/nav-sidebar.tsx +++ b/frontend/components/layout/nav-sidebar.tsx @@ -26,6 +26,7 @@ const microplexNavItems = [ const populaceNavItems = [ { href: "/populace", label: "Release summary" }, { href: "/populace/targets", label: "Target diagnostics" }, + { href: "/populace/comparison", label: "Incumbent comparison" }, ]; const comparisonNavItems = [ diff --git a/frontend/components/populace/populace-comparison-view.tsx b/frontend/components/populace/populace-comparison-view.tsx new file mode 100644 index 0000000..2fc681a --- /dev/null +++ b/frontend/components/populace/populace-comparison-view.tsx @@ -0,0 +1,303 @@ +"use client"; + +import { EmptyState } from "@/components/shared/empty-state"; +import { fmt, fmtCompact } from "@/components/shared/format"; +import { HelpHint } from "@/components/shared/help-hint"; +import { KpiCard } from "@/components/shared/kpi-card"; +import { LoadingBlock } from "@/components/shared/LoadingBlock"; +import { PageHeader } from "@/components/shared/page-header"; +import { SectionCard } from "@/components/shared/section-card"; +import { StatusPill } from "@/components/shared/status-pill"; +import { + usePopulaceComparison, + type PopulaceComparisonFamilyRow, + type PopulaceComparisonMover, +} from "@/lib/api/hooks/use-populace"; + +function loss(value: number | null | undefined) { + return value == null ? "—" : fmt(value, { digits: 4 }); +} + +function MoversTable({ + rows, + emptyLabel, +}: { + rows: PopulaceComparisonMover[]; + emptyLabel: string; +}) { + if (!rows.length) return ; + return ( +
+ + + + + + + + + + + + {rows.map((row, index) => ( + + + + + + + + ))} + +
TargetFamilyPopulace erreCPS errLoss delta
+ {row.target_name} + + {row.family} + + {fmt(row.candidate_relative_error, { pct: true, digits: 1 })} + + {fmt(row.baseline_relative_error, { pct: true, digits: 1 })} + + {row.loss_delta == null ? "—" : row.loss_delta.toExponential(2)} +
+
+ ); +} + +export function PopulaceComparisonView() { + const { data, isLoading, error } = usePopulaceComparison(); + + if (isLoading) return ; + if (error || !data) { + return ( + + ); + } + + const s = data.summary; + const families: PopulaceComparisonFamilyRow[] = [...(data.family_breakdown ?? [])].sort( + (a, b) => + (b.candidate_loss_contribution ?? 0) - (a.candidate_loss_contribution ?? 0), + ); + const totalWins = + (s.candidate_wins ?? 0) + (s.baseline_wins ?? 0) + (s.ties ?? 0); + + return ( +
+ + {data.archived ? "Archived scorecard" : "Live from benchmarks"} + + } + /> + +
+ + } + value={loss(s.candidate_loss)} + delta={ + s.candidate_beats_baseline == null + ? undefined + : s.candidate_beats_baseline + ? "beats eCPS" + : "behind eCPS" + } + tone={s.candidate_beats_baseline ? "positive" : "negative"} + hint={`Enhanced CPS: ${loss(s.baseline_loss)}`} + /> + + } + value={loss(s.candidate_holdout_loss)} + hint={`Enhanced CPS: ${loss(s.baseline_holdout_loss)}`} + /> + + } + value={loss(s.candidate_unweighted_msre)} + hint={`Enhanced CPS: ${loss(s.baseline_unweighted_msre)}`} + /> + + } + value={`${fmtCompact(s.candidate_wins)} / ${fmtCompact(totalWins || null)}`} + hint={`eCPS wins ${fmtCompact(s.baseline_wins)}, ties ${fmtCompact(s.ties)}`} + /> +
+ + +
+ + + + + + + + + + + {( + [ + ["Train", s.candidate_train_loss, s.baseline_train_loss], + ["Holdout", s.candidate_holdout_loss, s.baseline_holdout_loss], + ["Full", s.candidate_loss, s.baseline_loss], + [ + "Unweighted MSRE", + s.candidate_unweighted_msre, + s.baseline_unweighted_msre, + ], + ] as const + ).map(([label, candidate, baseline]) => ( + + + + + + + ))} + +
LossPopulaceEnhanced CPSRatio
{label}{loss(candidate)}{loss(baseline)} + {candidate != null && baseline != null && candidate > 0 + ? `${(baseline / candidate).toFixed(1)}× better` + : "—"} +
+
+ + + +
+ + + + + + + + + + + + + + {families.map((row) => ( + + + + + + + + + + ))} + +
FamilyTargetsPopulace winseCPS winsPopulace losseCPS lossDelta
{row.family} + {fmt(row.n_targets, { digits: 0 })} + + {fmt(row.candidate_wins, { digits: 0 })} + + {fmt(row.baseline_wins, { digits: 0 })} + + {row.candidate_loss_contribution == null + ? "—" + : row.candidate_loss_contribution.toExponential(2)} + + {row.baseline_loss_contribution == null + ? "—" + : row.baseline_loss_contribution.toExponential(2)} + + {row.loss_delta == null ? "—" : row.loss_delta.toExponential(2)} +
+
+ + +
+ + + + + + +
+ + + Candidate {data.release_id} vs incumbent{" "} + {data.incumbent_manifest} + {data.period ? ` (period ${data.period})` : ""}. + + } + > +
    + {data.notes.map((note) => ( +
  • {note}
    • + ))} +
+ +
+ ); +} diff --git a/frontend/data/populace/latest/comparison_scorecard.json b/frontend/data/populace/latest/comparison_scorecard.json new file mode 100644 index 0000000..4d74518 --- /dev/null +++ b/frontend/data/populace/latest/comparison_scorecard.json @@ -0,0 +1,1704 @@ +{ + "release_id": "populace-us-2024-9f1260b-20260611", + "schema_version": 1, + "period": 2024, + "metric": "sound_ecps_replacement_comparison", + "baseline_label": "enhanced_cps", + "candidate_label": "populace", + "matched_datasets": { + "baseline": { + "path": "/Users/maxghenis/populace-score-work/score_out/baseline_matched.h5", + "sha256": "0a6b961ad363a421bde99f2c8e5d8f20370bcba45fd303050537a25bdd805b14", + "size_bytes": 107827376 + }, + "candidate": { + "path": "/Users/maxghenis/populace-score-work/score_out/candidate_matched.h5", + "sha256": "927159217b0301f3ce33e987cb31350973479f72baa22774549616b0559b93b6", + "size_bytes": 103227122 + }, + "household_count": 41314, + "random_seed": 20260529, + "sample_method": "uniform" + }, + "target_split": { + "holdout_target_fraction": 0.2, + "holdout_target_seed": 20260529, + "holdout_targets": 739, + "train_targets": 2965 + }, + "summary": { + "baseline_enhanced_cps_native_loss": 1.40549, + "baseline_holdout_loss": 0.316697, + "baseline_household_count": 41314, + "baseline_initial_enhanced_cps_native_loss": 1.77071, + "baseline_refit_config": { + "lambda_l0": 0.0, + "lambda_l2": 0.0, + "max_iter": 200, + "method": "deterministic_pe_native_projected_gradient", + "target_total_weight": "preserve_input", + "tol": 1e-08, + "use_gates": false + }, + "baseline_score_abs_error": 2.44576e-08, + "baseline_train_loss": 1.08879, + "baseline_unweighted_msre": 1.32102, + "candidate_beats_baseline": true, + "candidate_enhanced_cps_native_loss": 0.227943, + "candidate_holdout_loss": 0.0383735, + "candidate_household_count": 41314, + "candidate_initial_enhanced_cps_native_loss": 0.229177, + "candidate_refit_config": { + "lambda_l0": 0.0, + "lambda_l2": 0.0, + "max_iter": 200, + "method": "deterministic_pe_native_projected_gradient", + "target_total_weight": "preserve_input", + "tol": 1e-08, + "use_gates": false + }, + "candidate_score_abs_error": 1.36202e-10, + "candidate_train_loss": 0.189569, + "candidate_unweighted_msre": 0.23306, + "ecps_refit_recovery_passed": true, + "enhanced_cps_native_loss_delta": -1.17755, + "holdout_target_fraction": 0.2, + "holdout_targets": 739, + "matched_household_count": true, + "n_national_targets": 1222, + "n_state_targets": 2482, + "n_targets_bad_dropped": 10, + "n_targets_kept": 3704, + "n_targets_total": 3731, + "n_targets_zero_dropped": 25, + "protected_family_losses": { + "capital_gains": { + "baseline_loss": 6.41245e-06, + "candidate_loss": 2.18928e-06, + "loss_delta": -4.22317e-06, + "n_targets": 114 + }, + "disability": { + "baseline_loss": 2.29392e-09, + "candidate_loss": 2.55153e-11, + "loss_delta": -2.26841e-09, + "n_targets": 1 + }, + "dividends": { + "baseline_loss": 2.20555e-06, + "candidate_loss": 7.75955e-07, + "loss_delta": -1.4296e-06, + "n_targets": 82 + }, + "household_net_income": { + "baseline_loss": 5.05217e-09, + "candidate_loss": 6.88377e-09, + "loss_delta": 1.8316e-09, + "n_targets": 2 + }, + "interest": { + "baseline_loss": 1.18469e-06, + "candidate_loss": 4.17504e-07, + "loss_delta": -7.67187e-07, + "n_targets": 45 + }, + "retirement_income": { + "baseline_loss": 4.26519e-07, + "candidate_loss": 6.17009e-07, + "loss_delta": 1.90489e-07, + "n_targets": 37 + }, + "self_employment_income": { + "baseline_loss": 3.65278e-07, + "candidate_loss": 5.83376e-08, + "loss_delta": -3.06941e-07, + "n_targets": 4 + }, + "snap": { + "baseline_loss": 6.69566e-07, + "candidate_loss": 8.8949e-07, + "loss_delta": 2.19924e-07, + "n_targets": 103 + }, + "ssi": { + "baseline_loss": 3.89742e-07, + "candidate_loss": 1.13688e-08, + "loss_delta": -3.78374e-07, + "n_targets": 5 + }, + "wages": { + "baseline_loss": 5.37688e-07, + "candidate_loss": 1.6579e-07, + "loss_delta": -3.71898e-07, + "n_targets": 83 + } + }, + "refit_objective_matches_scoring": true, + "score_candidate_only": false, + "support_audit": { + "missing_stored_critical_inputs": [], + "top_aca_ptc_spending_gaps": [ + { + "baseline_weighted_aca_ptc": 1665640000.0, + "baseline_weighted_positive_households": 254990.0, + "candidate_weighted_aca_ptc": 6696270000.0, + "candidate_weighted_positive_households": 591131.0, + "state": "NJ", + "weighted_aca_ptc_delta": 5030630000.0, + "weighted_positive_household_delta": 336141.0 + }, + { + "baseline_weighted_aca_ptc": 1233320000.0, + "baseline_weighted_positive_households": 145388.0, + "candidate_weighted_aca_ptc": 5862960000.0, + "candidate_weighted_positive_households": 252831.0, + "state": "LA", + "weighted_aca_ptc_delta": 4629640000.0, + "weighted_positive_household_delta": 107443.0 + }, + { + "baseline_weighted_aca_ptc": 12159400000.0, + "baseline_weighted_positive_households": 1911410.0, + "candidate_weighted_aca_ptc": 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"national_ssa", + "loss_delta": 0.000408742, + "split": "train", + "target_index": 1478, + "target_name": "nation/ssa/ssn_card_type_none_count", + "target_value": 0.0202278, + "value_scale": "scaled", + "winner": "baseline" + }, + { + "baseline_abs_scaled_error": 0.000673063, + "baseline_error": -0.000673063, + "baseline_estimate": 0.0195548, + "baseline_loss_term": 4.53013e-07, + "baseline_relative_error": 0.979514, + "candidate_abs_scaled_error": 0.020213, + "candidate_error": -0.020213, + "candidate_estimate": 1.48429e-05, + "candidate_loss_term": 0.000408565, + "candidate_relative_error": 0.960361, + "family": "national_irs_other", + "loss_delta": 0.000408112, + "split": "train", + "target_index": 1010, + "target_name": "nation/irs/eitc/amount/c2_4k_5k", + "target_value": 0.0202278, + "value_scale": "scaled", + "winner": "baseline" + }, + { + "baseline_abs_scaled_error": 0.00267497, + "baseline_error": 1771.29, + "baseline_estimate": 15172.2, + "baseline_loss_term": 7.15544e-06, + "baseline_relative_error": 0.132242, + "candidate_abs_scaled_error": 0.0203646, + "candidate_error": 13491.5, + "candidate_estimate": 26892.4, + "candidate_loss_term": 0.000414717, + "candidate_relative_error": 1.00676, + "family": "national_irs_other", + "loss_delta": 0.000407561, + "split": "train", + "target_index": 1501, + "target_name": "nation/irs/aca_ptc/returns/state_15", + "target_value": 13400.9, + "value_scale": "native", + "winner": "baseline" + }, + { + "baseline_abs_scaled_error": 0.00144826, + "baseline_error": 82088.0, + "baseline_estimate": 1228630.0, + "baseline_loss_term": 2.09745e-06, + "baseline_relative_error": 0.0715972, + "candidate_abs_scaled_error": 0.0202278, + "candidate_error": -1146540.0, + "candidate_estimate": 0.0, + "candidate_loss_term": 0.000409164, + "candidate_relative_error": -0.999998, + "family": "national_population_by_age", + "loss_delta": 0.000407066, + "split": "train", + "target_index": 720, + "target_name": "nation/census/population_by_age/84", + "target_value": 1146540.0, + "value_scale": "native", + "winner": "baseline" + }, + { + "baseline_abs_scaled_error": 0.00207857, + "baseline_error": 150748.0, + "baseline_estimate": 1617770.0, + "baseline_loss_term": 4.32045e-06, + "baseline_relative_error": 0.102758, + "candidate_abs_scaled_error": 0.0202278, + "candidate_error": -1467030.0, + "candidate_estimate": 0.0, + "candidate_loss_term": 0.000409164, + "candidate_relative_error": -0.999999, + "family": "national_population_by_age", + "loss_delta": 0.000404844, + "split": "train", + "target_index": 718, + "target_name": "nation/census/population_by_age/82", + "target_value": 1467030.0, + "value_scale": "native", + "winner": "baseline" + }, + { + "baseline_abs_scaled_error": 0.00225194, + "baseline_error": 0.00225194, + "baseline_estimate": 0.0224798, + "baseline_loss_term": 5.07123e-06, + "baseline_relative_error": 0.982381, + "candidate_abs_scaled_error": 0.0202278, + "candidate_error": -0.0202278, + "candidate_estimate": 0.0, + "candidate_loss_term": 0.000409165, + "candidate_relative_error": 0.960346, + "family": "national_irs_other", + "loss_delta": 0.000404094, + "split": "holdout", + "target_index": 51, + "target_name": "nation/irs/adjusted gross income/total/AGI in 500k-1m/taxable/Married Filing Separately", + "target_value": 0.0202278, + "value_scale": "scaled", + "winner": "baseline" + }, + { + "baseline_abs_scaled_error": 0.00204695, + "baseline_error": 4512.17, + "baseline_estimate": 49110.1, + "baseline_loss_term": 4.18999e-06, + "baseline_relative_error": 0.101195, + "candidate_abs_scaled_error": 0.0202034, + "candidate_error": -44546.0, + "candidate_estimate": 51.928, + "candidate_loss_term": 0.000408176, + "candidate_relative_error": -0.998791, + "family": "national_irs_other", + "loss_delta": 0.000403986, + "split": "train", + "target_index": 1009, + "target_name": "nation/irs/eitc/returns/c2_4k_5k", + "target_value": 44597.9, + "value_scale": "native", + "winner": "baseline" + }, + { + "baseline_abs_scaled_error": 0.000126119, + "baseline_error": -0.000126119, + "baseline_estimate": 0.0201017, + "baseline_loss_term": 1.5906e-08, + "baseline_relative_error": 0.98005, + "candidate_abs_scaled_error": 0.0200973, + "candidate_error": -0.0200973, + "candidate_estimate": 0.000130562, + "candidate_loss_term": 0.0004039, + "candidate_relative_error": 0.960474, + "family": "national_irs_other", + "loss_delta": 0.000403884, + "split": "holdout", + "target_index": 1002, + "target_name": "nation/irs/eitc/amount/c2_1_1k", + "target_value": 0.0202278, + "value_scale": "scaled", + "winner": "baseline" + }, + { + "baseline_abs_scaled_error": 1.84668e-05, + "baseline_error": -1.84668e-05, + "baseline_estimate": 0.0202094, + "baseline_loss_term": 3.41024e-10, + "baseline_relative_error": 0.980155, + "candidate_abs_scaled_error": 0.0200876, + "candidate_error": -0.0200876, + "candidate_estimate": 0.000140237, + "candidate_loss_term": 0.000403512, + "candidate_relative_error": 0.960484, + "family": "national_irs_other", + "loss_delta": 0.000403511, + "split": "train", + "target_index": 1016, + "target_name": "nation/irs/eitc/amount/c2_7k_8k", + "target_value": 0.0202278, + "value_scale": "scaled", + "winner": "baseline" + }, + { + "baseline_abs_scaled_error": 0.00242614, + "baseline_error": 0.00242614, + "baseline_estimate": 0.022654, + "baseline_loss_term": 5.88614e-06, + "baseline_relative_error": 0.982551, + "candidate_abs_scaled_error": 0.0202278, + "candidate_error": -0.0202278, + "candidate_estimate": 0.0, + "candidate_loss_term": 0.000409165, + "candidate_relative_error": 0.960346, + "family": "national_irs_other", + "loss_delta": 0.000403279, + "split": "train", + "target_index": 377, + "target_name": "nation/irs/partnership and s corp losses/total/AGI in -inf-0/taxable/All", + "target_value": 0.0202278, + "value_scale": "scaled", + "winner": "baseline" + }, + { + "baseline_abs_scaled_error": 0.000695764, + "baseline_error": 1541.91, + "baseline_estimate": 46397.8, + "baseline_loss_term": 4.84088e-07, + "baseline_relative_error": 0.0343964, + "candidate_abs_scaled_error": 0.0200556, + "candidate_error": -44475.9, + "candidate_estimate": 379.968, + "candidate_loss_term": 0.000402227, + "candidate_relative_error": -0.991485, + "family": "national_irs_other", + "loss_delta": 0.000401743, + "split": "train", + "target_index": 1015, + "target_name": "nation/irs/eitc/returns/c2_7k_8k", + "target_value": 44855.9, + "value_scale": "native", + "winner": "baseline" + } + ] +} \ No newline at end of file diff --git a/frontend/lib/api/hooks/use-populace.ts b/frontend/lib/api/hooks/use-populace.ts index 7785df8..e0f4ed7 100644 --- a/frontend/lib/api/hooks/use-populace.ts +++ b/frontend/lib/api/hooks/use-populace.ts @@ -159,3 +159,74 @@ export function usePopulaceTargetDiagnostics(params: { staleTime: 15 * 60 * 1000, }); } + +export interface PopulaceComparisonSummary { + candidate_loss: number | null; + baseline_loss: number | null; + loss_delta: number | null; + candidate_holdout_loss: number | null; + baseline_holdout_loss: number | null; + candidate_train_loss: number | null; + baseline_train_loss: number | null; + candidate_unweighted_msre: number | null; + baseline_unweighted_msre: number | null; + candidate_wins: number | null; + baseline_wins: number | null; + ties: number | null; + n_targets: number | null; + holdout_targets: number | null; + train_targets: number | null; + candidate_beats_baseline: boolean | null; + matched_household_count: number | null; +} + +export interface PopulaceComparisonFamilyRow { + family: string; + n_targets: number | null; + candidate_wins: number | null; + baseline_wins: number | null; + ties: number | null; + candidate_loss_contribution: number | null; + baseline_loss_contribution: number | null; + loss_delta: number | null; + [key: string]: unknown; +} + +export interface PopulaceComparisonMover { + target_name?: string | null; + family?: string | null; + split?: string | null; + candidate_relative_error?: number | null; + baseline_relative_error?: number | null; + loss_delta?: number | null; + [key: string]: unknown; +} + +export interface PopulaceComparison { + available: boolean; + source: string; + path: string | null; + archived: boolean; + live_scorecard_configured: boolean; + live_scorecard_error: string | null; + release_id: string | null; + incumbent_manifest: string | null; + period: number | null; + baseline_label: string; + candidate_label: string; + protocol: string | null; + summary: PopulaceComparisonSummary; + family_breakdown: PopulaceComparisonFamilyRow[]; + top_improvements: PopulaceComparisonMover[]; + top_regressions: PopulaceComparisonMover[]; + gates: Record; + notes: string[]; +} + +export function usePopulaceComparison() { + return useQuery({ + queryKey: ["populace", "comparison"], + queryFn: () => apiGet("/populace/comparison"), + staleTime: 15 * 60 * 1000, + }); +} diff --git a/frontend/lib/populace/comparison-artifact.ts b/frontend/lib/populace/comparison-artifact.ts new file mode 100644 index 0000000..0cdb094 --- /dev/null +++ b/frontend/lib/populace/comparison-artifact.ts @@ -0,0 +1,87 @@ +import comparisonScorecardData from "@/data/populace/latest/comparison_scorecard.json"; + +type JsonObject = Record; + +const SCORECARD = comparisonScorecardData as JsonObject; + +export const POPULACE_COMPARISON_SNAPSHOT_PATH = + "frontend/data/populace/latest/comparison_scorecard.json"; + +function asObject(value: unknown): JsonObject { + return value && typeof value === "object" && !Array.isArray(value) + ? (value as JsonObject) + : {}; +} + +function numberOrNull(value: unknown): number | null { + return typeof value === "number" && Number.isFinite(value) ? value : null; +} + +// The archived comparison's summary mixes the clean per-target win/loss block +// (target_diagnostics_summary) with the refit/loss scalars (summary). Normalize +// to the flat shape the dashboard and the proposed benchmarks scorecard share +// (PolicyEngine/populace-benchmarks#3), so the live artifact drops in unchanged. +export function normalizeComparisonScorecard(raw: JsonObject): JsonObject { + const summary = asObject(raw.summary); + const tdSummary = asObject(raw.target_diagnostics_summary); + const flat = { + candidate_loss: numberOrNull( + tdSummary.candidate_loss ?? summary.candidate_enhanced_cps_native_loss, + ), + baseline_loss: numberOrNull( + tdSummary.baseline_loss ?? summary.baseline_enhanced_cps_native_loss, + ), + loss_delta: numberOrNull( + tdSummary.loss_delta ?? summary.enhanced_cps_native_loss_delta, + ), + candidate_holdout_loss: numberOrNull(summary.candidate_holdout_loss), + baseline_holdout_loss: numberOrNull(summary.baseline_holdout_loss), + candidate_train_loss: numberOrNull(summary.candidate_train_loss), + baseline_train_loss: numberOrNull(summary.baseline_train_loss), + candidate_unweighted_msre: numberOrNull(summary.candidate_unweighted_msre), + baseline_unweighted_msre: numberOrNull(summary.baseline_unweighted_msre), + candidate_wins: numberOrNull(tdSummary.candidate_wins ?? summary.candidate_wins), + baseline_wins: numberOrNull(tdSummary.baseline_wins ?? summary.baseline_wins), + ties: numberOrNull(tdSummary.ties ?? summary.ties), + n_targets: numberOrNull(tdSummary.n_targets ?? summary.n_targets_total), + holdout_targets: numberOrNull(tdSummary.holdout_targets ?? summary.holdout_targets), + train_targets: numberOrNull(tdSummary.train_targets), + candidate_beats_baseline: + typeof summary.candidate_beats_baseline === "boolean" + ? summary.candidate_beats_baseline + : null, + // The archived summary's matched_household_count is a stray bool; the real + // matched count is the per-dataset household count. + matched_household_count: + numberOrNull(summary.matched_household_count) ?? + numberOrNull(summary.candidate_household_count) ?? + numberOrNull(summary.baseline_household_count), + }; + return { + release_id: raw.candidate_release_id ?? raw.release_id ?? null, + incumbent_manifest: raw.incumbent_manifest ?? "pinned-production-ecps-2024", + period: numberOrNull(raw.period), + baseline_label: raw.baseline_label ?? "enhanced_cps", + candidate_label: raw.candidate_label ?? "populace", + protocol: + typeof raw.protocol === "string" + ? raw.protocol + : flat.matched_household_count != null + ? `Matched ${flat.matched_household_count.toLocaleString()} households, symmetric refit, ${flat.holdout_targets ?? "?"}-target holdout.` + : null, + summary: flat, + family_breakdown: Array.isArray(raw.family_breakdown) ? raw.family_breakdown : [], + top_improvements: Array.isArray(raw.top_improvements) ? raw.top_improvements : [], + top_regressions: Array.isArray(raw.top_regressions) ? raw.top_regressions : [], + gates: asObject(raw.gates), + }; +} + +export function snapshotComparisonScorecard() { + return { + available: true, + source: "deployed_static_snapshot", + path: POPULACE_COMPARISON_SNAPSHOT_PATH, + ...normalizeComparisonScorecard(SCORECARD), + }; +} diff --git a/tests/test_populace.py b/tests/test_populace.py index 3542a8e..03fc7c2 100644 --- a/tests/test_populace.py +++ b/tests/test_populace.py @@ -185,3 +185,89 @@ def test_enrich_derives_state_distribution_family(): # Calibration moved the estimate closer to the target → positive improvement. assert row["improvement"] > 0 assert row["direction"] == "under" + + +def test_enrich_collapses_state_fips_snap_family(): + row = populace._enrich( + { + "name": "US06/snap-cost", + "target": 100.0, + "initial_estimate": 90.0, + "final_estimate": 99.0, + "relative_error": -0.01, + "within_tolerance": True, + } + ) + assert row["family"] == "snap-cost" + assert row["state"] is None + + +def test_populace_comparison_serves_archived_snapshot(monkeypatch): + monkeypatch.setattr(populace, "_BENCHMARKS_SCORECARD_URL", None) + payload = populace.populace_comparison() + + assert payload["available"] is True + assert payload["archived"] is True + assert payload["source"] == "deployed_static_snapshot" + assert payload["live_scorecard_configured"] is False + assert payload["candidate_label"] == "populace" + assert payload["baseline_label"] == "enhanced_cps" + + s = payload["summary"] + # The 9f1260b scorecard: populace beats eCPS on loss but loses the win count. + assert s["candidate_loss"] < s["baseline_loss"] + assert s["candidate_beats_baseline"] is True + assert s["candidate_wins"] + s["baseline_wins"] + s["ties"] == s["n_targets"] + assert s["matched_household_count"] > 0 + + assert payload["family_breakdown"] + assert payload["top_regressions"] + assert payload["top_improvements"] + assert any("populace-benchmarks#3" in note for note in payload["notes"]) + + +def test_populace_comparison_prefers_live_scorecard(monkeypatch): + flat = { + "candidate_release_id": "populace-us-2024-f32c2e5-20260614", + "period": 2024, + "summary": { + "candidate_loss": 0.2, + "baseline_loss": 1.4, + "loss_delta": -1.2, + "candidate_wins": 1100, + "baseline_wins": 2550, + "ties": 54, + "n_targets": 3704, + "candidate_beats_baseline": True, + "matched_household_count": 41314, + }, + "family_breakdown": [{"family": "x", "n_targets": 1}], + "top_improvements": [], + "top_regressions": [], + } + monkeypatch.setattr( + populace, "_BENCHMARKS_SCORECARD_URL", "https://example.test/scorecard.json" + ) + monkeypatch.setattr(populace, "_fetch_json", lambda url: flat) + payload = populace.populace_comparison() + + assert payload["archived"] is False + assert payload["source"] == "populace_benchmarks_live" + assert payload["live_scorecard_configured"] is True + assert payload["release_id"] == "populace-us-2024-f32c2e5-20260614" + assert payload["summary"]["candidate_wins"] == 1100 + + +def test_populace_comparison_falls_back_when_live_fetch_fails(monkeypatch): + def _boom(url): + raise RuntimeError("benchmarks unreachable") + + monkeypatch.setattr( + populace, "_BENCHMARKS_SCORECARD_URL", "https://example.test/scorecard.json" + ) + monkeypatch.setattr(populace, "_fetch_json", _boom) + payload = populace.populace_comparison() + + assert payload["archived"] is True + assert payload["source"] == "deployed_static_snapshot" + assert payload["live_scorecard_error"] == "benchmarks unreachable" From 5a2b04b4be4255bea10a943f69d9bbb454028ba2 Mon Sep 17 00:00:00 2001 From: Pavel Makarchuk Date: Mon, 15 Jun 2026 11:01:04 -0400 Subject: [PATCH 05/14] Resolve incumbent comparison from the benchmarks latest.json pointer MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Wires the comparison view to PolicyEngine/populace-benchmarks#3/#4: the route resolves that repo's benchmarks/us/incumbent-comparison/latest.json pointer by default, fetches the scorecard it names, and falls back to the committed archived 9f1260b snapshot until the artifact is reachable. So the comparison goes live automatically once the benchmarks PR merges, with no redeploy. - POPULACE_BENCHMARKS_POINTER_URL overrides the pointer (e.g. a branch); POPULACE_BENCHMARKS_SCORECARD_URL still points straight at a scorecard. - scorecard_status surfaces the artifact's own provenance ("archived" for the reconstructed 9f1260b scorecard), distinct from whether it was reached live. - Fix the comparison normalizer to read the flat benchmarks summary (candidate_loss/baseline_loss/... directly in summary) as well as the nested archived shape — first-key-wins across both, mirroring the Python side. The flat scorecard was returning null losses before this. Tested: 106 backend tests pass (pointer-resolution, direct-URL override, and unreachable-fallback cases); tsc clean; next build green. Smoke-tested the default snapshot path and the live pointer path against the pushed benchmarks branch — both render full loss/win/family data and all three pages return 200. Co-Authored-By: Claude Fable 5 --- backend/routes/populace.py | 110 ++++++++++++----- frontend/app/api/populace/comparison/route.ts | 97 +++++++++++---- frontend/lib/api/hooks/use-populace.ts | 3 +- frontend/lib/populace/comparison-artifact.ts | 58 +++++---- tests/test_populace.py | 112 +++++++++++++----- 5 files changed, 271 insertions(+), 109 deletions(-) diff --git a/backend/routes/populace.py b/backend/routes/populace.py index c1293dd..e3c624e 100644 --- a/backend/routes/populace.py +++ b/backend/routes/populace.py @@ -52,9 +52,21 @@ "frontend/data/populace/latest/comparison_scorecard.json" ) -# The live incumbent-comparison scorecard is not published yet -# (PolicyEngine/populace-benchmarks#3). Set this to the artifact URL to serve it -# live; otherwise the route serves the archived 9f1260b snapshot. +# The incumbent comparison lives in PolicyEngine/populace-benchmarks +# (PolicyEngine/populace#37). By default the route resolves that repo's +# latest.json pointer (PolicyEngine/populace-benchmarks#3) and serves the +# scorecard it names, falling back to the committed archived snapshot until the +# artifact is reachable. POPULACE_BENCHMARKS_POINTER_URL overrides the pointer; +# POPULACE_BENCHMARKS_SCORECARD_URL points straight at a scorecard. +_BENCHMARKS_RAW_BASE = ( + "https://raw.githubusercontent.com/PolicyEngine/populace-benchmarks/main" +) +_DEFAULT_POINTER_URL = ( + f"{_BENCHMARKS_RAW_BASE}/benchmarks/us/incumbent-comparison/latest.json" +) +_BENCHMARKS_POINTER_URL = ( + os.environ.get("POPULACE_BENCHMARKS_POINTER_URL") or _DEFAULT_POINTER_URL +) _BENCHMARKS_SCORECARD_URL = os.environ.get("POPULACE_BENCHMARKS_SCORECARD_URL") _CACHE: dict[str, tuple[float, Any]] = {} @@ -537,45 +549,85 @@ def pick(*keys: str) -> float | None: } +def _scorecard_url_from_pointer(pointer_url: str, scorecard_path: str) -> str: + if scorecard_path.startswith(("http://", "https://")): + return scorecard_path + suffix = "/benchmarks/us/incumbent-comparison/latest.json" + base = pointer_url[: -len(suffix)] if pointer_url.endswith(suffix) else pointer_url + return f"{base}/{scorecard_path.lstrip('/')}" + + +def _load_live_scorecard() -> dict[str, Any]: + """Resolve the benchmarks scorecard, raising on any failure. + + A direct ``POPULACE_BENCHMARKS_SCORECARD_URL`` skips the pointer; otherwise + the ``latest.json`` pointer names the scorecard to fetch. + """ + if _BENCHMARKS_SCORECARD_URL: + raw = _fetch_json(_BENCHMARKS_SCORECARD_URL) + status = raw.get("status") if isinstance(raw, dict) else None + return { + "source": "populace_benchmarks_live", + "path": _BENCHMARKS_SCORECARD_URL, + "pointer_url": None, + "scorecard_status": status if isinstance(status, str) else None, + **_normalize_comparison_scorecard(raw), + } + pointer = _fetch_json(_BENCHMARKS_POINTER_URL) + scorecard_path = pointer.get("scorecard_path") if isinstance(pointer, dict) else None + if not isinstance(scorecard_path, str) or not scorecard_path: + raise ValueError(f"Pointer {_BENCHMARKS_POINTER_URL} has no scorecard_path.") + scorecard_url = _scorecard_url_from_pointer(_BENCHMARKS_POINTER_URL, scorecard_path) + raw = _fetch_json(scorecard_url) + status = pointer.get("status") + if not isinstance(status, str): + status = raw.get("status") if isinstance(raw, dict) else None + return { + "source": "populace_benchmarks_live", + "path": scorecard_url, + "pointer_url": _BENCHMARKS_POINTER_URL, + "scorecard_status": status if isinstance(status, str) else None, + **_normalize_comparison_scorecard(raw), + } + + @router.get("/populace/comparison") def populace_comparison() -> dict[str, Any]: - snapshot = _normalize_comparison_scorecard(_load_static(_STATIC_COMPARISON_SCORECARD)) - source = "deployed_static_snapshot" - path = _COMPARISON_SCORECARD_PUBLIC_PATH + payload: dict[str, Any] = { + "source": "deployed_static_snapshot", + "path": _COMPARISON_SCORECARD_PUBLIC_PATH, + "pointer_url": _BENCHMARKS_POINTER_URL, + "scorecard_status": None, + **_normalize_comparison_scorecard(_load_static(_STATIC_COMPARISON_SCORECARD)), + } live_error = None + try: + payload = _load_live_scorecard() + except Exception as exc: + live_error = str(exc) - if _BENCHMARKS_SCORECARD_URL: - try: - live = _normalize_comparison_scorecard(_fetch_json(_BENCHMARKS_SCORECARD_URL)) - snapshot = live - source = "populace_benchmarks_live" - path = _BENCHMARKS_SCORECARD_URL - except Exception as exc: - live_error = str(exc) - - archived = source != "populace_benchmarks_live" + live = payload["source"] == "populace_benchmarks_live" + scorecard_status = payload.get("scorecard_status") or "archived" return _scrub( { + **payload, "available": True, - "source": source, - "path": path, - "archived": archived, - "live_scorecard_configured": bool(_BENCHMARKS_SCORECARD_URL), + "archived": not live, + "scorecard_status": scorecard_status, + "source_pointer": payload.get("pointer_url") or _BENCHMARKS_POINTER_URL, "live_scorecard_error": live_error, - **snapshot, "notes": [ "Populace (candidate) is scored against the enhanced CPS " "(incumbent) with a matched-household, symmetric-refit, " "held-out-target protocol.", ( - "This is the archived scorecard for release " - "populace-us-2024-9f1260b-20260611. The live incumbent " - "comparison is not published as a machine-readable artifact " - "yet (PolicyEngine/populace-benchmarks#3); set " - "POPULACE_BENCHMARKS_SCORECARD_URL to serve it live." - if archived - else "Served live from the configured populace-benchmarks " - "scorecard." + f"Served live from populace-benchmarks " + f"({scorecard_status} scorecard)." + if live + else "The benchmarks scorecard was not reachable, so this is " + "the committed archived snapshot for release " + "populace-us-2024-9f1260b-20260611 " + "(PolicyEngine/populace-benchmarks#3 / #4 publish it)." ), "The eCPS comparison is benchmark-harness material and " "intentionally lives outside live populace " diff --git a/frontend/app/api/populace/comparison/route.ts b/frontend/app/api/populace/comparison/route.ts index fa6ce85..116f818 100644 --- a/frontend/app/api/populace/comparison/route.ts +++ b/frontend/app/api/populace/comparison/route.ts @@ -6,11 +6,20 @@ import { } from "@/lib/populace/comparison-artifact"; import { scrub } from "@/lib/populace/latest-artifact"; -// The live incumbent-comparison scorecard is not published yet -// (PolicyEngine/populace-benchmarks#3). When it is, point this at the artifact -// (a raw GitHub URL or HF resolve URL) and the route serves it live, falling -// back to the committed archived 9f1260b snapshot on any failure. -const BENCHMARKS_SCORECARD_URL = process.env.POPULACE_BENCHMARKS_SCORECARD_URL?.trim(); +// The incumbent comparison lives in PolicyEngine/populace-benchmarks +// (PolicyEngine/populace#37 moved it out of live populace). By default the +// route resolves that repo's latest.json pointer +// (PolicyEngine/populace-benchmarks#3) and serves the scorecard it names, +// falling back to the committed archived 9f1260b snapshot until the artifact +// is reachable. POPULACE_BENCHMARKS_POINTER_URL overrides the pointer; +// POPULACE_BENCHMARKS_SCORECARD_URL points straight at a scorecard (skips the +// pointer). +const BENCHMARKS_RAW_BASE = + "https://raw.githubusercontent.com/PolicyEngine/populace-benchmarks/main"; +const DEFAULT_POINTER_URL = `${BENCHMARKS_RAW_BASE}/benchmarks/us/incumbent-comparison/latest.json`; +const POINTER_URL = + process.env.POPULACE_BENCHMARKS_POINTER_URL?.trim() || DEFAULT_POINTER_URL; +const DIRECT_SCORECARD_URL = process.env.POPULACE_BENCHMARKS_SCORECARD_URL?.trim(); type JsonObject = Record; @@ -22,46 +31,88 @@ function asObject(value: unknown): JsonObject { : {}; } -async function loadLiveScorecard(url: string): Promise { +async function fetchJson(url: string): Promise { const response = await fetch(url, { next: { revalidate } }); if (!response.ok) { - throw new Error(`Failed to fetch scorecard ${url}: ${response.status}`); + throw new Error(`Failed to fetch ${url}: ${response.status}`); } + return asObject(await response.json()); +} + +// Resolve a scorecard_path from the pointer against the same repo the pointer +// came from, so a custom POINTER_URL on a branch/fork resolves consistently. +function resolveScorecardUrl(pointerUrl: string, scorecardPath: string): string { + if (/^https?:\/\//.test(scorecardPath)) return scorecardPath; + const base = pointerUrl.replace( + /\/benchmarks\/us\/incumbent-comparison\/latest\.json$/, + "", + ); + return `${base}/${scorecardPath.replace(/^\/+/, "")}`; +} + +async function loadLiveScorecard(): Promise { + if (DIRECT_SCORECARD_URL) { + const raw = await fetchJson(DIRECT_SCORECARD_URL); + return { + available: true, + source: "populace_benchmarks_live", + path: DIRECT_SCORECARD_URL, + scorecard_status: typeof raw.status === "string" ? raw.status : null, + ...normalizeComparisonScorecard(raw), + }; + } + const pointer = await fetchJson(POINTER_URL); + const scorecardPath = pointer.scorecard_path; + if (typeof scorecardPath !== "string" || !scorecardPath) { + throw new Error(`Pointer ${POINTER_URL} has no scorecard_path.`); + } + const scorecardUrl = resolveScorecardUrl(POINTER_URL, scorecardPath); + const raw = await fetchJson(scorecardUrl); return { available: true, source: "populace_benchmarks_live", - path: url, - ...normalizeComparisonScorecard(asObject(await response.json())), + path: scorecardUrl, + pointer_url: POINTER_URL, + scorecard_status: + typeof pointer.status === "string" + ? pointer.status + : typeof raw.status === "string" + ? raw.status + : null, + ...normalizeComparisonScorecard(raw), }; } export async function GET() { const snapshot = snapshotComparisonScorecard(); - let payload = snapshot; + let payload: JsonObject = snapshot; let liveError: string | null = null; - if (BENCHMARKS_SCORECARD_URL) { - try { - payload = (await loadLiveScorecard(BENCHMARKS_SCORECARD_URL)) as typeof snapshot; - } catch (error) { - liveError = error instanceof Error ? error.message : String(error); - payload = snapshot; - } + try { + payload = await loadLiveScorecard(); + } catch (error) { + liveError = error instanceof Error ? error.message : String(error); + payload = snapshot; } - const archived = payload.source !== "populace_benchmarks_live"; + const live = payload.source === "populace_benchmarks_live"; + const scorecardStatus = + typeof payload.scorecard_status === "string" ? payload.scorecard_status : "archived"; return NextResponse.json( scrub({ ...payload, - archived, - live_scorecard_configured: Boolean(BENCHMARKS_SCORECARD_URL), + // "archived" = serving the committed snapshot (not reached live). Distinct + // from scorecard_status, which is the artifact's own provenance. + archived: !live, + scorecard_status: scorecardStatus, + source_pointer: live ? (payload.pointer_url ?? POINTER_URL) : POINTER_URL, live_scorecard_error: liveError, notes: [ "Populace (candidate) is scored against the enhanced CPS (incumbent) with a matched-household, symmetric-refit, held-out-target protocol.", - archived - ? "This is the archived scorecard for release populace-us-2024-9f1260b-20260611. The live incumbent comparison is not published as a machine-readable artifact yet (PolicyEngine/populace-benchmarks#3); set POPULACE_BENCHMARKS_SCORECARD_URL to serve it live once it is." - : "Served live from the configured populace-benchmarks scorecard.", + live + ? `Served live from populace-benchmarks (${scorecardStatus} scorecard).` + : "The benchmarks scorecard was not reachable, so this is the committed archived snapshot for release populace-us-2024-9f1260b-20260611 (PolicyEngine/populace-benchmarks#3 / #4 publish it).", "The eCPS comparison is benchmark-harness material and intentionally lives outside live populace (PolicyEngine/populace#37); the live calibration-fit view is the populace release summary.", ], }), diff --git a/frontend/lib/api/hooks/use-populace.ts b/frontend/lib/api/hooks/use-populace.ts index e0f4ed7..60e2bf3 100644 --- a/frontend/lib/api/hooks/use-populace.ts +++ b/frontend/lib/api/hooks/use-populace.ts @@ -207,7 +207,8 @@ export interface PopulaceComparison { source: string; path: string | null; archived: boolean; - live_scorecard_configured: boolean; + scorecard_status: string; + source_pointer: string | null; live_scorecard_error: string | null; release_id: string | null; incumbent_manifest: string | null; diff --git a/frontend/lib/populace/comparison-artifact.ts b/frontend/lib/populace/comparison-artifact.ts index 0cdb094..46c001a 100644 --- a/frontend/lib/populace/comparison-artifact.ts +++ b/frontend/lib/populace/comparison-artifact.ts @@ -24,38 +24,46 @@ function numberOrNull(value: unknown): number | null { export function normalizeComparisonScorecard(raw: JsonObject): JsonObject { const summary = asObject(raw.summary); const tdSummary = asObject(raw.target_diagnostics_summary); + // First key wins, searching the per-target win/loss block before the + // refit/loss scalars — so the flat benchmarks scorecard (everything in + // ``summary``) and the archived ``sound_ecps_replacement_comparison`` (split + // across ``summary`` and ``target_diagnostics_summary``) both normalize. + const pick = (...keys: string[]): number | null => { + for (const key of keys) { + for (const source of [tdSummary, summary]) { + const value = numberOrNull(source[key]); + if (value != null) return value; + } + } + return null; + }; const flat = { - candidate_loss: numberOrNull( - tdSummary.candidate_loss ?? summary.candidate_enhanced_cps_native_loss, - ), - baseline_loss: numberOrNull( - tdSummary.baseline_loss ?? summary.baseline_enhanced_cps_native_loss, - ), - loss_delta: numberOrNull( - tdSummary.loss_delta ?? summary.enhanced_cps_native_loss_delta, - ), - candidate_holdout_loss: numberOrNull(summary.candidate_holdout_loss), - baseline_holdout_loss: numberOrNull(summary.baseline_holdout_loss), - candidate_train_loss: numberOrNull(summary.candidate_train_loss), - baseline_train_loss: numberOrNull(summary.baseline_train_loss), - candidate_unweighted_msre: numberOrNull(summary.candidate_unweighted_msre), - baseline_unweighted_msre: numberOrNull(summary.baseline_unweighted_msre), - candidate_wins: numberOrNull(tdSummary.candidate_wins ?? summary.candidate_wins), - baseline_wins: numberOrNull(tdSummary.baseline_wins ?? summary.baseline_wins), - ties: numberOrNull(tdSummary.ties ?? summary.ties), - n_targets: numberOrNull(tdSummary.n_targets ?? summary.n_targets_total), - holdout_targets: numberOrNull(tdSummary.holdout_targets ?? summary.holdout_targets), - train_targets: numberOrNull(tdSummary.train_targets), + candidate_loss: pick("candidate_loss", "candidate_enhanced_cps_native_loss"), + baseline_loss: pick("baseline_loss", "baseline_enhanced_cps_native_loss"), + loss_delta: pick("loss_delta", "enhanced_cps_native_loss_delta"), + candidate_holdout_loss: pick("candidate_holdout_loss"), + baseline_holdout_loss: pick("baseline_holdout_loss"), + candidate_train_loss: pick("candidate_train_loss"), + baseline_train_loss: pick("baseline_train_loss"), + candidate_unweighted_msre: pick("candidate_unweighted_msre"), + baseline_unweighted_msre: pick("baseline_unweighted_msre"), + candidate_wins: pick("candidate_wins"), + baseline_wins: pick("baseline_wins"), + ties: pick("ties"), + n_targets: pick("n_targets", "n_targets_total"), + holdout_targets: pick("holdout_targets"), + train_targets: pick("train_targets"), candidate_beats_baseline: typeof summary.candidate_beats_baseline === "boolean" ? summary.candidate_beats_baseline : null, // The archived summary's matched_household_count is a stray bool; the real // matched count is the per-dataset household count. - matched_household_count: - numberOrNull(summary.matched_household_count) ?? - numberOrNull(summary.candidate_household_count) ?? - numberOrNull(summary.baseline_household_count), + matched_household_count: pick( + "matched_household_count", + "candidate_household_count", + "baseline_household_count", + ), }; return { release_id: raw.candidate_release_id ?? raw.release_id ?? null, diff --git a/tests/test_populace.py b/tests/test_populace.py index 03fc7c2..88a2155 100644 --- a/tests/test_populace.py +++ b/tests/test_populace.py @@ -202,14 +202,51 @@ def test_enrich_collapses_state_fips_snap_family(): assert row["state"] is None -def test_populace_comparison_serves_archived_snapshot(monkeypatch): +_LIVE_SCORECARD = { + "schema_version": 1, + "candidate_release_id": "populace-us-2024-f32c2e5-20260614", + "status": "archived", + "period": 2024, + "summary": { + "candidate_loss": 0.2, + "baseline_loss": 1.4, + "loss_delta": -1.2, + "candidate_holdout_loss": 0.04, + "baseline_holdout_loss": 0.3, + "candidate_unweighted_msre": 0.23, + "baseline_unweighted_msre": 1.3, + "candidate_wins": 1100, + "baseline_wins": 2550, + "ties": 54, + "n_targets": 3704, + "candidate_beats_baseline": True, + "matched_household_count": 41314, + }, + "family_breakdown": [{"family": "x", "n_targets": 1}], + "top_improvements": [], + "top_regressions": [], +} + + +def _offline_benchmarks(monkeypatch): + """No pointer override, no direct URL, and the network raises.""" monkeypatch.setattr(populace, "_BENCHMARKS_SCORECARD_URL", None) + + def _raise(url): + raise RuntimeError("benchmarks unreachable") + + monkeypatch.setattr(populace, "_fetch_json", _raise) + + +def test_populace_comparison_serves_archived_snapshot(monkeypatch): + _offline_benchmarks(monkeypatch) payload = populace.populace_comparison() assert payload["available"] is True assert payload["archived"] is True assert payload["source"] == "deployed_static_snapshot" - assert payload["live_scorecard_configured"] is False + assert payload["scorecard_status"] == "archived" + assert payload["live_scorecard_error"] == "benchmarks unreachable" assert payload["candidate_label"] == "populace" assert payload["baseline_label"] == "enhanced_cps" @@ -226,46 +263,59 @@ def test_populace_comparison_serves_archived_snapshot(monkeypatch): assert any("populace-benchmarks#3" in note for note in payload["notes"]) -def test_populace_comparison_prefers_live_scorecard(monkeypatch): - flat = { - "candidate_release_id": "populace-us-2024-f32c2e5-20260614", - "period": 2024, - "summary": { - "candidate_loss": 0.2, - "baseline_loss": 1.4, - "loss_delta": -1.2, - "candidate_wins": 1100, - "baseline_wins": 2550, - "ties": 54, - "n_targets": 3704, - "candidate_beats_baseline": True, - "matched_household_count": 41314, - }, - "family_breakdown": [{"family": "x", "n_targets": 1}], - "top_improvements": [], - "top_regressions": [], - } - monkeypatch.setattr( - populace, "_BENCHMARKS_SCORECARD_URL", "https://example.test/scorecard.json" - ) - monkeypatch.setattr(populace, "_fetch_json", lambda url: flat) +def test_populace_comparison_resolves_benchmarks_pointer(monkeypatch): + monkeypatch.setattr(populace, "_BENCHMARKS_SCORECARD_URL", None) + seen = {} + + def _fake_fetch(url): + seen["last"] = url + if url.endswith("latest.json"): + return { + "schema_version": 1, + "candidate_release_id": "populace-us-2024-f32c2e5-20260614", + "scorecard_path": "archive/us/populace-us-2024-f32c2e5-20260614/scorecard.json", + "status": "archived", + } + assert url.endswith( + "archive/us/populace-us-2024-f32c2e5-20260614/scorecard.json" + ), url + return _LIVE_SCORECARD + + monkeypatch.setattr(populace, "_fetch_json", _fake_fetch) payload = populace.populace_comparison() assert payload["archived"] is False assert payload["source"] == "populace_benchmarks_live" - assert payload["live_scorecard_configured"] is True + assert payload["scorecard_status"] == "archived" assert payload["release_id"] == "populace-us-2024-f32c2e5-20260614" assert payload["summary"]["candidate_wins"] == 1100 + # The scorecard URL was resolved against the pointer's repo base. + assert seen["last"].startswith( + "https://raw.githubusercontent.com/PolicyEngine/populace-benchmarks/main/" + ) -def test_populace_comparison_falls_back_when_live_fetch_fails(monkeypatch): - def _boom(url): - raise RuntimeError("benchmarks unreachable") - +def test_populace_comparison_direct_url_overrides_pointer(monkeypatch): monkeypatch.setattr( populace, "_BENCHMARKS_SCORECARD_URL", "https://example.test/scorecard.json" ) - monkeypatch.setattr(populace, "_fetch_json", _boom) + calls = [] + + def _fake_fetch(url): + calls.append(url) + return _LIVE_SCORECARD + + monkeypatch.setattr(populace, "_fetch_json", _fake_fetch) + payload = populace.populace_comparison() + + assert payload["source"] == "populace_benchmarks_live" + assert payload["summary"]["candidate_wins"] == 1100 + # Direct URL skips the pointer: exactly one fetch, to the direct URL. + assert calls == ["https://example.test/scorecard.json"] + + +def test_populace_comparison_falls_back_when_pointer_unreachable(monkeypatch): + _offline_benchmarks(monkeypatch) payload = populace.populace_comparison() assert payload["archived"] is True From 545cc0a837f70b7a2b936301fa14b58fb4b77404 Mon Sep 17 00:00:00 2001 From: Pavel Makarchuk Date: Mon, 15 Jun 2026 21:47:25 -0400 Subject: [PATCH 06/14] Structured/faceted target browser + live HF per-target diagnostics MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Decompose target names into geography/level/source/variable/breakdown (matching populace.dev's parse_target); browse by variable ("the thing") then drill its breakdowns. - Generic facet model: every axis a variable varies on (geography, level, each breakdown dimension) becomes a filterable, sortable column — income band x return type x filing status for AGI; geography for the state-conditioned real_estate_taxes; etc. Constant axes are dropped. - Canonical single-target detail card (auto-opens when facets narrow to one): structured fields + initial->final->target bars + fit. - Pull calibration_diagnostics.json live from HF via latest.json in both the Next routes and the FastAPI backend, with the committed snapshot as fallback. Handles schema v2 (no top-level release_id -> use the pointer). 113 backend tests pass; tsc clean; production build green. Co-Authored-By: Claude Fable 5 --- backend/routes/populace.py | 315 +++++++++- frontend/app/api/populace/route.ts | 11 +- .../api/populace/target-diagnostics/route.ts | 10 +- .../populace/populace-target-detail.tsx | 212 +++++++ .../populace/populace-targets-view.tsx | 557 +++++++++++++----- frontend/lib/api/hooks/use-populace.ts | 31 + frontend/lib/populace/latest-artifact.ts | 534 +++++++++++++++-- tests/test_populace.py | 158 +++++ 8 files changed, 1591 insertions(+), 237 deletions(-) create mode 100644 frontend/components/populace/populace-target-detail.tsx diff --git a/backend/routes/populace.py b/backend/routes/populace.py index e3c624e..8f8f198 100644 --- a/backend/routes/populace.py +++ b/backend/routes/populace.py @@ -75,6 +75,200 @@ _STATE_CODE = re.compile(r"^[A-Z]{2}$") _STATE_FIPS = re.compile(r"^US\d{2}$") +# Canonical name decomposition, matching populace.dev's parse_target so both +# consumers agree. The constraint registry knows these fields; the published +# surface only carries the slash-joined name, so we reconstruct them. +_FIPS_TO_ABBR = { + "01": "AL", "02": "AK", "04": "AZ", "05": "AR", "06": "CA", "08": "CO", + "09": "CT", "10": "DE", "11": "DC", "12": "FL", "13": "GA", "15": "HI", + "16": "ID", "17": "IL", "18": "IN", "19": "IA", "20": "KS", "21": "KY", + "22": "LA", "23": "ME", "24": "MD", "25": "MA", "26": "MI", "27": "MN", + "28": "MS", "29": "MO", "30": "MT", "31": "NE", "32": "NV", "33": "NH", + "34": "NJ", "35": "NM", "36": "NY", "37": "NC", "38": "ND", "39": "OH", + "40": "OK", "41": "OR", "42": "PA", "44": "RI", "45": "SC", "46": "SD", + "47": "TN", "48": "TX", "49": "UT", "50": "VT", "51": "VA", "53": "WA", + "54": "WV", "55": "WI", "56": "WY", +} +_STATE_ABBRS = { + "AL", "AK", "AZ", "AR", "CA", "CO", "CT", "DE", "DC", "FL", "GA", "HI", "ID", + "IL", "IN", "IA", "KS", "KY", "LA", "ME", "MD", "MA", "MI", "MN", "MS", "MO", + "MT", "NE", "NV", "NH", "NJ", "NM", "NY", "NC", "ND", "OH", "OK", "OR", "PA", + "RI", "SC", "SD", "TN", "TX", "UT", "VT", "VA", "WA", "WV", "WI", "WY", "US", +} + + +def _parse_target(name: str) -> dict[str, str]: + parts = name.split("/") + p0 = parts[0] if parts else "" + fips = re.match(r"^US(\d{2})$", p0) + if fips: + return { + "geography": _FIPS_TO_ABBR.get(fips.group(1), p0), + "level": "state", + "source": "admin", + "variable": parts[1] if len(parts) > 1 else "", + "breakdown": " · ".join(parts[2:]), + } + if p0 == "state": + second = parts[1] if len(parts) > 1 else "" + # state-keyed ("state/AL/adjusted_gross_income/..."): slot 2 is the + # state, not a source. Group all states under one synthetic "state" + # source so the 50 state codes don't masquerade as sources/variables. + if second in _STATE_ABBRS and second != "US": + return { + "geography": second, + "level": "state", + "source": "state", + "variable": parts[2] if len(parts) > 2 else "", + "breakdown": " · ".join(parts[3:]), + } + # source-keyed with a trailing state ("state/census/rent/AK"). + if len(parts) >= 4 and parts[-1] in _STATE_ABBRS: + return { + "geography": parts[-1], + "level": "state", + "source": parts[1], + "variable": parts[2], + "breakdown": " · ".join(parts[3:-1]), + } + return { + "geography": "state", + "level": "state", + "source": parts[1] if len(parts) > 1 else "", + "variable": parts[2] if len(parts) > 2 else "", + "breakdown": " · ".join(parts[3:]), + } + if p0 in ("nation", "national", "us"): + return { + "geography": "United States", + "level": "national", + "source": parts[1] if len(parts) > 1 else "", + "variable": parts[2] if len(parts) > 2 else "", + "breakdown": " · ".join(parts[3:]), + } + return { + "geography": "", + "level": "", + "source": p0, + "variable": parts[1] if len(parts) > 1 else "", + "breakdown": " · ".join(parts[2:]), + } + + +def _variable_key(parsed: dict[str, str]) -> str: + return " / ".join(p for p in (parsed["source"], parsed["variable"]) if p) + + +_FILING_MODIFIERS = {"Surviving Spouse"} +_FILING_STATUSES = { + "All", "Single", "Head of Household", "Married Filing Jointly", + "Married Filing Separately", "Surviving Spouse", +} +_RETURN_TYPES = {"taxable", "all returns", "nontaxable"} +_MEASURES = {"total", "count", "mean", "filers", "nonfilers"} + + +def _split_breakdown(breakdown: str) -> list[str]: + raw = [t for t in breakdown.split(" · ") if t] if breakdown else [] + out: list[str] = [] + for token in raw: + if token in _FILING_MODIFIERS and out: + out[-1] = f"{out[-1]} · {token}" + else: + out.append(token) + return out + + +def _classify_dimension(values: list[str]) -> str: + v = [x for x in values if x] + if not v: + return "Breakdown" + + def all_match(pred): + return all(pred(x) for x in v) + + if all_match(lambda s: s.startswith("AGI in ")): + return "Income band" + if all_match(lambda s: s in _RETURN_TYPES): + return "Return type" + if all_match(lambda s: s.split(" · ")[0] in _FILING_STATUSES): + return "Filing status" + if all_match(lambda s: s.isdigit()): + return "Age" + if all_match(lambda s: s in _MEASURES): + return "Measure" + return "Breakdown" + + +def _parse_amount(token: str) -> float: + if re.match(r"^-?inf$", token, re.I): + return float("-inf") if token.startswith("-") else float("inf") + m = re.match(r"^(-?)(\d+(?:\.\d+)?)([km]?)$", token, re.I) + if not m: + return 0.0 + mult = {"k": 1e3, "m": 1e6, "": 1}[m.group(3).lower()] + return (-1 if m.group(1) else 1) * float(m.group(2)) * mult + + +def _band_lower(label: str) -> float: + body = label[len("AGI in "):] if label.startswith("AGI in ") else label + m = re.match(r"^(-?inf|-?\d+(?:\.\d+)?[km]?)-(-?inf|-?\d+(?:\.\d+)?[km]?)$", body, re.I) + return _parse_amount(m.group(1)) if m else 0.0 + + +def _sort_dimension_values(label: str, values: list[str]) -> list[str]: + if label == "Income band": + return sorted(values, key=_band_lower) + if label == "Age": + return sorted(values, key=lambda s: int(s) if s.isdigit() else 0) + return sorted(values, key=lambda s: (s != "All", s)) + + +def _row_facet_value(row: dict[str, Any], key: str) -> str | None: + if key == "geography": + return row.get("geography") or None + if key == "level": + return row.get("level") or None + m = re.match(r"^dim(\d+)$", key) + if m: + idx = int(m.group(1)) + dims = row.get("dims") or [] + return dims[idx] if len(dims) > idx and dims[idx] else None + value = row.get(key) + return value if isinstance(value, str) else None + + +def _sort_facet_values(label: str, values: list[str]) -> list[str]: + if label == "Geography": + return sorted(values, key=lambda s: (s != "United States", s)) + return _sort_dimension_values(label, values) + + +def _compute_dimensions(rows: list[dict[str, Any]]) -> list[dict[str, Any]]: + """Every axis along which the rows vary, as facets: geography, level, and + each breakdown position. Constant axes (no variation) are dropped.""" + max_len = max((len(r.get("dims") or []) for r in rows), default=0) + candidates: list[tuple[str, str | None]] = [ + ("geography", "Geography"), + ("level", "Level"), + ] + candidates += [(f"dim{i}", None) for i in range(max_len)] + + facets = [] + for key, fixed_label in candidates: + values = list( + dict.fromkeys( + v for v in (_row_facet_value(r, key) for r in rows) if v + ) + ) + if len(values) <= 1: + continue + label = fixed_label or _classify_dimension(values) + facets.append( + {"key": key, "label": label, "values": _sort_facet_values(label, values)} + ) + return facets + def _scrub(value: Any) -> Any: if isinstance(value, dict): @@ -196,10 +390,18 @@ def _enrich(row: dict[str, Any]) -> dict[str, Any]: direction = None if isinstance(final_rel, (int, float)): direction = "over" if final_rel > 0 else "under" if final_rel < 0 else "exact" + parsed = _parse_target(name) return { **row, "family": _derive_family(name), "state": _derive_state(name), + "geography": parsed["geography"], + "level": parsed["level"], + "source": parsed["source"], + "variable": parsed["variable"], + "breakdown": parsed["breakdown"], + "dims": _split_breakdown(parsed["breakdown"]), + "variable_key": _variable_key(parsed), "initial_relative_error": initial_rel, "abs_relative_error": abs_rel, "improvement": improvement, @@ -207,6 +409,44 @@ def _enrich(row: dict[str, Any]) -> dict[str, Any]: } +def _variable_summary(rows: list[dict[str, Any]]) -> list[dict[str, Any]]: + groups: dict[str, list[dict[str, Any]]] = {} + for row in rows: + key = str(row.get("variable_key", "")) + if key: + groups.setdefault(key, []).append(row) + out = [] + for key, members in groups.items(): + first = members[0] + abs_errors = [ + r["abs_relative_error"] + for r in members + if isinstance(r.get("abs_relative_error"), (int, float)) + ] + out.append( + { + "variable_key": key, + "source": str(first.get("source", "")), + "variable": str(first.get("variable", "")), + "level": str(first.get("level", "")), + "n_targets": len(members), + "within_10pct": sum( + 1 + for r in members + if isinstance(r.get("abs_relative_error"), (int, float)) + and r["abs_relative_error"] <= 0.1 + ), + "within_tolerance": sum( + 1 for r in members if r.get("within_tolerance") is True + ), + "mean_abs_relative_error": ( + sum(abs_errors) / len(abs_errors) if abs_errors else None + ), + } + ) + return sorted(out, key=lambda v: v["n_targets"], reverse=True) + + def _family_fit(rows: list[dict[str, Any]]) -> list[dict[str, Any]]: groups: dict[str, list[dict[str, Any]]] = {} for row in rows: @@ -239,11 +479,31 @@ def _family_fit(rows: list[dict[str, Any]]) -> list[dict[str, Any]]: return sorted(out, key=lambda f: f["n_targets"], reverse=True) +def _calibration_payload() -> tuple[dict[str, Any], str]: + """The calibration_diagnostics payload, live from HF via latest.json when + reachable, else the committed snapshot. Returns (payload, source).""" + try: + pointer = _fetch_json(_hf_resolve_url(_LATEST_POINTER_PATH)) + paths = pointer.get("paths") if isinstance(pointer, dict) else None + diag_path = paths.get("calibration_diagnostics") if isinstance(paths, dict) else None + if diag_path: + payload = _fetch_json(_hf_resolve_url(diag_path)) + if isinstance(payload, dict) and payload.get("targets"): + # schema v2 carries no top-level release_id — take the pointer's. + if not payload.get("release_id") and pointer.get("release_id"): + payload["release_id"] = pointer["release_id"] + return payload, "huggingface_live" + except Exception as exc: # noqa: BLE001 - fall back to the snapshot + logger.info("Live calibration unavailable, using snapshot: %s", exc) + return _load_static(_STATIC_CALIBRATION_DIAGNOSTICS), "deployed_static_snapshot" + + def _calibration_summary( - payload: dict[str, Any], rows: list[dict[str, Any]] + payload: dict[str, Any], rows: list[dict[str, Any]], source: str ) -> dict[str, Any]: return { "available": True, + "source": source, "path": _CALIBRATION_DIAGNOSTICS_PUBLIC_PATH, "release_id": payload.get("release_id"), "schema_version": payload.get("schema_version"), @@ -269,7 +529,7 @@ def _calibration_summary( def populace_overview() -> dict[str, Any]: static_build = _load_static(_STATIC_BUILD_MANIFEST) static_release = _load_static(_STATIC_RELEASE_MANIFEST) - payload = _load_static(_STATIC_CALIBRATION_DIAGNOSTICS) + payload, cal_source = _calibration_payload() rows = [_enrich(row) for row in (payload.get("targets") or [])] snapshot_release_id = str(static_build.get("build_id", "")) @@ -283,7 +543,7 @@ def populace_overview() -> dict[str, Any]: ) release_id = str(live["release_id"]) if live_available else snapshot_release_id - calibration = _calibration_summary(payload, rows) + calibration = _calibration_summary(payload, rows, cal_source) def with_abs(row: dict[str, Any]) -> bool: return isinstance(row.get("abs_relative_error"), (int, float)) @@ -379,7 +639,7 @@ def artifact(name: str, live_path_key: str, static_path: str): def _matches_search(row: dict[str, Any], search: str) -> bool: haystack = " ".join( str(row.get(key)) - for key in ("name", "family", "state") + for key in ("name", "variable", "source", "breakdown", "geography", "state") if row.get(key) is not None ).lower() return search.lower() in haystack @@ -390,26 +650,50 @@ def populace_target_diagnostics( limit: int = Query(100, ge=1, le=500), offset: int = Query(0, ge=0), family: str | None = None, + variable: str | None = None, + source: str | None = None, + level: str | None = None, state: str | None = None, direction: str | None = None, within_tolerance: str | None = None, search: str | None = None, + facet: list[str] | None = Query(default=None), sort_by: str | None = None, sort_dir: str = "desc", ) -> dict[str, Any]: - payload = _load_static(_STATIC_CALIBRATION_DIAGNOSTICS) + payload, _cal_source = _calibration_payload() rows = [_enrich(row) for row in (payload.get("targets") or [])] families = sorted({str(row.get("family", "")) for row in rows}) + sources = sorted({str(row.get("source", "")) for row in rows if row.get("source")}) within: bool | None = None if within_tolerance is not None and within_tolerance != "": within = within_tolerance.lower() in ("1", "true", "yes") + facet_filters: list[tuple[str, str]] = [] + # `facet` is the Query sentinel (not a list) when the endpoint is called + # directly rather than through FastAPI's request parsing. + for entry in facet if isinstance(facet, list) else []: + sep = entry.find(":") + if sep >= 0: + facet_filters.append((entry[:sep], entry[sep + 1 :])) + filtered = rows if family: filtered = [r for r in filtered if r.get("family") == family] + if variable: + filtered = [r for r in filtered if r.get("variable_key") == variable] + if source: + filtered = [r for r in filtered if r.get("source") == source] + if level: + filtered = [r for r in filtered if r.get("level") == level] if state: filtered = [r for r in filtered if r.get("state") == state] + # Facets derive from the variable's rows before the facet/within/direction/ + # search filters so every option stays selectable. + dimensions = _compute_dimensions(filtered) if variable else [] + for key, value in facet_filters: + filtered = [r for r in filtered if _row_facet_value(r, key) == value] if direction: filtered = [r for r in filtered if r.get("direction") == direction] if within is not None: @@ -418,12 +702,21 @@ def populace_target_diagnostics( filtered = [r for r in filtered if _matches_search(r, search.strip())] sort_key = sort_by or "abs_relative_error" + dim_sort = re.match(r"^dim(\d+)$", sort_key) + + def sort_value(row: dict[str, Any]): + if dim_sort: + idx = int(dim_sort.group(1)) + dims = row.get("dims") or [] + return dims[idx] if len(dims) > idx else None + return row.get(sort_key) + descending = sort_dir != "asc" - present = [r for r in filtered if r.get(sort_key) is not None] - missing = [r for r in filtered if r.get(sort_key) is None] + present = [r for r in filtered if sort_value(r) is not None] + missing = [r for r in filtered if sort_value(r) is None] def key_fn(row: dict[str, Any]): - value = row.get(sort_key) + value = sort_value(row) if isinstance(value, bool) or not isinstance(value, (int, float)): return (1, 0.0, str(value)) return (0, float(value), "") @@ -439,6 +732,9 @@ def key_fn(row: dict[str, Any]): "schema_version": payload.get("schema_version"), "metric": "relative_error", "families": families, + "sources": sources, + "variables": _variable_summary(rows), + "dimensions": dimensions, "summary": { "total_targets": len(rows), "within_tolerance_count": sum( @@ -455,6 +751,9 @@ def key_fn(row: dict[str, Any]): "display_limit": limit, "filters": { "family": family, + "variable": variable, + "source": source, + "level": level, "state": state, "direction": direction, "within_tolerance": within, diff --git a/frontend/app/api/populace/route.ts b/frontend/app/api/populace/route.ts index 9317cf9..6167bc2 100644 --- a/frontend/app/api/populace/route.ts +++ b/frontend/app/api/populace/route.ts @@ -9,7 +9,9 @@ import { latestPopulaceCalibrationHighlights, latestPopulaceCalibrationSummary, latestPopulaceReleaseManifest, + loadLiveCalibration, scrub, + snapshotCalibration, } from "@/lib/populace/latest-artifact"; const HF_REPO = process.env.POPULACE_HF_REPO ?? "policyengine/populace-us"; @@ -104,8 +106,13 @@ export async function GET() { const releaseId = liveAvailable ? String(live.release_id) : LATEST_POPULACE_RELEASE_ID; - const calibration = latestPopulaceCalibrationSummary(); - const highlights = latestPopulaceCalibrationHighlights(15); + // The per-target diagnostics also resolve live via latest.json (the + // latest.json fetch dedupes with loadLiveRelease above), with the committed + // snapshot as fallback. + const liveCalibration = await loadLiveCalibration(revalidate); + const calibrationSource = liveCalibration?.calibration ?? snapshotCalibration(); + const calibration = latestPopulaceCalibrationSummary(calibrationSource); + const highlights = latestPopulaceCalibrationHighlights(calibrationSource, 15); const calibrationSnapshotStale = releaseId !== String(calibration.release_id ?? LATEST_POPULACE_RELEASE_ID); diff --git a/frontend/app/api/populace/target-diagnostics/route.ts b/frontend/app/api/populace/target-diagnostics/route.ts index 01c07ed..8559b41 100644 --- a/frontend/app/api/populace/target-diagnostics/route.ts +++ b/frontend/app/api/populace/target-diagnostics/route.ts @@ -2,11 +2,19 @@ import { NextResponse } from "next/server"; import { latestPopulaceTargetDiagnosticsPage, + loadLiveCalibration, scrub, + snapshotCalibration, } from "@/lib/populace/latest-artifact"; export const revalidate = 300; export async function GET(request: Request) { - return NextResponse.json(scrub(latestPopulaceTargetDiagnosticsPage(request.url))); + // Resolve the per-target diagnostics live from HF (via latest.json); fall back + // to the committed snapshot when HF is unreachable. + const live = await loadLiveCalibration(revalidate); + const calibration = live?.calibration ?? snapshotCalibration(); + return NextResponse.json( + scrub(latestPopulaceTargetDiagnosticsPage(request.url, calibration)), + ); } diff --git a/frontend/components/populace/populace-target-detail.tsx b/frontend/components/populace/populace-target-detail.tsx new file mode 100644 index 0000000..77a093c --- /dev/null +++ b/frontend/components/populace/populace-target-detail.tsx @@ -0,0 +1,212 @@ +"use client"; + +import { fmt, fmtCompact, fmtSigned } from "@/components/shared/format"; +import { StatusPill } from "@/components/shared/status-pill"; +import { + type PopulaceTargetDimension, + type PopulaceTargetRow, +} from "@/lib/api/hooks/use-populace"; + +function facetValue(row: PopulaceTargetRow, key: string): string { + const dim = /^dim(\d+)$/.exec(key); + if (dim) return row.dims?.[Number(dim[1])] ?? ""; + const value = row[key]; + return typeof value === "string" ? value : ""; +} + +function Field({ label, value }: { label: string; value: React.ReactNode }) { + return ( +
+ + {label} + + {value || "—"} +
+ ); +} + +// A horizontal bar for one estimate against the target, scaled to the largest +// magnitude among target/initial/final so the three rows are comparable. +function EstimateBar({ + label, + value, + scale, + tone, + caption, +}: { + label: string; + value: number | null | undefined; + scale: number; + tone: "target" | "initial" | "final"; + caption?: string; +}) { + const width = + value == null || scale === 0 ? 0 : Math.min(Math.abs(value) / scale, 1) * 100; + const barColor = + tone === "target" + ? "bg-slate-400" + : tone === "final" + ? "bg-primary" + : "bg-amber-400"; + return ( +
+ {label} +
+
+
+ + {fmtCompact(value)} + {caption ? {caption} : null} + +
+ ); +} + +export function PopulaceTargetDetail({ + row, + dimensions, + onClose, +}: { + row: PopulaceTargetRow; + dimensions: PopulaceTargetDimension[]; + onClose: () => void; +}) { + const target = typeof row.target === "number" ? row.target : null; + const initial = typeof row.initial_estimate === "number" ? row.initial_estimate : null; + const final = typeof row.final_estimate === "number" ? row.final_estimate : null; + const scale = Math.max( + Math.abs(target ?? 0), + Math.abs(initial ?? 0), + Math.abs(final ?? 0), + ); + const initialRel = + typeof row.initial_relative_error === "number" ? row.initial_relative_error : null; + const finalRel = typeof row.relative_error === "number" ? row.relative_error : null; + const improvement = typeof row.improvement === "number" ? row.improvement : null; + const within10 = + typeof row.abs_relative_error === "number" ? row.abs_relative_error <= 0.1 : null; + + // The axes of variation shown as facets (geography, breakdown dims, …), each + // resolved on this row. Geography/level are also in the canonical fields above, + // so skip them here to avoid duplication; show the breakdown dims. + const shownDims = dimensions.length + ? dimensions + .filter((dim) => dim.key !== "geography" && dim.key !== "level") + .map((dim) => ({ label: dim.label, value: facetValue(row, dim.key) })) + : (row.dims ?? []).map((value, index) => ({ label: `Breakdown ${index + 1}`, value })); + + return ( +
+
+
+
+ Canonical target +
+
+ {row.variable || row.name} +
+ + {row.name} + +
+ +
+ +
+
+
+ + + + +
+ {shownDims.length > 0 && ( +
+ {shownDims.map((dim) => ( + + ))} +
+ )} +
+ + {within10 == null ? "—" : within10 ? "within 10%" : "outside 10%"} + + + {row.within_tolerance == null + ? "no tolerance" + : row.within_tolerance + ? "within tolerance" + : "outside tolerance"} + +
+
+ +
+
+ Target vs estimates +
+
+ + + +
+
+ + + 0 ? "text-emerald-700" : "text-rose-700"}> + {fmtSigned(improvement, { pct: true, digits: 1 })} + + ) + } + /> +
+
+
+
+ ); +} diff --git a/frontend/components/populace/populace-targets-view.tsx b/frontend/components/populace/populace-targets-view.tsx index 22bcf3c..a2de204 100644 --- a/frontend/components/populace/populace-targets-view.tsx +++ b/frontend/components/populace/populace-targets-view.tsx @@ -1,6 +1,6 @@ "use client"; -import { useMemo, useState } from "react"; +import { useEffect, useMemo, useState } from "react"; import { EmptyState } from "@/components/shared/empty-state"; import { fmt, fmtCompact } from "@/components/shared/format"; @@ -9,9 +9,12 @@ import { PageHeader } from "@/components/shared/page-header"; import { SectionCard } from "@/components/shared/section-card"; import { StatusPill } from "@/components/shared/status-pill"; import { ToolbarSelect } from "@/components/shared/toolbar-select"; +import { PopulaceTargetDetail } from "@/components/populace/populace-target-detail"; import { usePopulaceTargetDiagnostics, + type PopulaceTargetDimension, type PopulaceTargetRow, + type PopulaceVariableRow, } from "@/lib/api/hooks/use-populace"; const PAGE_SIZE = 50; @@ -21,60 +24,43 @@ interface SortState { dir: "asc" | "desc"; } -const COLUMNS: { +interface Column { key: string; label: string; numeric?: boolean; + sortable?: boolean; render: (row: PopulaceTargetRow) => React.ReactNode; -}[] = [ - { - key: "name", - label: "Target", - render: (row) => ( - - {row.name} - - ), - }, - { - key: "family", - label: "Family", - render: (row) => {row.family}, - }, +} + +const METRIC_COLUMNS: Column[] = [ { key: "target", label: "Target", numeric: true, + sortable: true, render: (row) => fmtCompact(row.target), }, { key: "initial_estimate", label: "Initial est.", numeric: true, + sortable: true, render: (row) => fmtCompact(row.initial_estimate), }, { key: "final_estimate", label: "Final est.", numeric: true, + sortable: true, render: (row) => fmtCompact(row.final_estimate), }, { key: "relative_error", label: "Rel. error", numeric: true, + sortable: true, render: (row) => fmt(row.relative_error, { pct: true, digits: 1 }), }, - { - key: "improvement", - label: "Improvement", - numeric: true, - render: (row) => ( - 0 ? "text-emerald-700" : "text-rose-700"}> - {row.improvement == null ? "—" : fmt(row.improvement, { pct: true, digits: 1 })} - - ), - }, { key: "within_tolerance", label: "In tol.", @@ -94,33 +80,211 @@ const COLUMNS: { }, ]; +// Without a variable selected: the "thing" (variable + breakdown) plus source +// and geography. With a variable selected: one column per breakdown dimension. +const OVERVIEW_COLUMNS: Column[] = [ + { + key: "variable", + label: "Target", + sortable: true, + render: (row) => ( +
+
{row.variable || row.name}
+ {row.breakdown ? ( +
{row.breakdown}
+ ) : null} +
+ ), + }, + { + key: "source", + label: "Source", + sortable: true, + render: (row) => {row.source}, + }, + { + key: "geography", + label: "Geo", + sortable: true, + render: (row) => {row.geography}, + }, +]; + +// Resolve a facet key ("geography" | "level" | "dim") against a row. +export function rowFacetValue( + row: PopulaceTargetRow, + key: string, +): string | undefined { + if (key === "geography") return row.geography ?? undefined; + if (key === "level") return row.level ?? undefined; + const dim = /^dim(\d+)$/.exec(key); + if (dim) return row.dims?.[Number(dim[1])] ?? undefined; + const value = row[key]; + return typeof value === "string" ? value : undefined; +} + +function dimensionColumns(dimensions: PopulaceTargetDimension[]): Column[] { + return dimensions.map((dim) => ({ + key: dim.key, + label: dim.label, + sortable: true, + render: (row: PopulaceTargetRow) => { + const value = rowFacetValue(row, dim.key); + return value ? ( + {value.replace(/^AGI in /, "")} + ) : ( + — + ); + }, + })); +} + +function VariableBrowser({ + variables, + active, + onPick, +}: { + variables: PopulaceVariableRow[]; + active: string; + onPick: (variableKey: string) => void; +}) { + const [query, setQuery] = useState(""); + const filtered = query + ? variables.filter((v) => v.variable_key.toLowerCase().includes(query.toLowerCase())) + : variables; + return ( +
+ setQuery(event.target.value)} + className="h-8 w-full rounded-md border border-border bg-white px-3 text-xs focus:border-primary/60 focus:outline-none" + /> +
+ + + + + + + + + + {filtered.map((v) => { + const isActive = v.variable_key === active; + return ( + onPick(isActive ? "" : v.variable_key)} + className={`cursor-pointer border-t border-border/60 ${ + isActive ? "bg-primary/10" : "hover:bg-muted/40" + }`} + > + + + + + ); + })} + +
VariableTargetsWithin 10%
+ + {v.variable || v.variable_key} + + {v.source} + + {fmt(v.n_targets, { digits: 0 })} + + {v.n_targets + ? fmt(v.within_10pct / v.n_targets, { pct: true, digits: 0 }) + : "—"} +
+
+
+ ); +} + export function PopulaceTargetsView() { - const [family, setFamily] = useState(""); + const [variable, setVariable] = useState(""); + const [source, setSource] = useState(""); + const [level, setLevel] = useState(""); const [within, setWithin] = useState(""); const [direction, setDirection] = useState(""); const [search, setSearch] = useState(""); + const [facetFilters, setFacetFilters] = useState>({}); + const [selected, setSelected] = useState(null); const [page, setPage] = useState(0); const [sort, setSort] = useState({ by: "abs_relative_error", dir: "desc" }); + const facetParam = useMemo( + () => + Object.entries(facetFilters) + .filter(([, value]) => value) + .map(([key, value]) => `${key}:${value}`), + [facetFilters], + ); + const params = useMemo( () => ({ limit: PAGE_SIZE, offset: page * PAGE_SIZE, - family: family || undefined, + variable: variable || undefined, + source: source || undefined, + level: level || undefined, within_tolerance: within || undefined, direction: direction || undefined, search: search || undefined, + facet: facetParam.length ? facetParam : undefined, sort_by: sort.by, sort_dir: sort.dir, }), - [family, within, direction, search, page, sort], + [variable, source, level, within, direction, search, facetParam, page, sort], ); const { data, isLoading, error } = usePopulaceTargetDiagnostics(params); - const families = data?.families ?? []; + const variables = data?.variables ?? []; + const sources = data?.sources ?? []; + const dimensions = data?.dimensions ?? []; const filteredTotal = data?.filtered_total ?? 0; const pageCount = Math.max(Math.ceil(filteredTotal / PAGE_SIZE), 1); + const activeVariable = variables.find((v) => v.variable_key === variable); + const columns = useMemo( + () => + activeVariable && dimensions.length + ? [...dimensionColumns(dimensions), ...METRIC_COLUMNS] + : [...OVERVIEW_COLUMNS, ...METRIC_COLUMNS], + [activeVariable, dimensions], + ); + + // "Select the variable and the breakdown, then look at it": when the facets + // narrow to a single target, open its canonical detail automatically. Keyed on + // the row identity so closing it (selected -> null) doesn't immediately reopen. + const singleRow = data?.targets.length === 1 ? data.targets[0] : null; + const singleKey = singleRow?.name ?? null; + useEffect(() => { + if (singleRow) setSelected(singleRow); + // eslint-disable-next-line react-hooks/exhaustive-deps + }, [singleKey]); + + function pickVariable(key: string) { + setVariable(key); + setFacetFilters({}); + setSelected(null); + setPage(0); + } + + function setFacet(key: string, value: string) { + setFacetFilters((current) => { + const next = { ...current }; + if (value) next[key] = value; + else delete next[key]; + return next; + }); + setSelected(null); + setPage(0); + } function toggleSort(key: string) { setPage(0); @@ -136,7 +300,7 @@ export function PopulaceTargetsView() { {String(data.release_id)} @@ -144,143 +308,210 @@ export function PopulaceTargetsView() { } /> - - { - setSearch(event.target.value); - setPage(0); - }} - className="h-8 w-48 rounded-md border border-border bg-white px-3 text-xs focus:border-primary/60 focus:outline-none" - /> - { - setFamily(value); - setPage(0); - }} - options={[ - { value: "", label: "All" }, - ...families.map((value) => ({ value, label: value })), - ]} - /> - { - setWithin(value); - setPage(0); - }} - options={[ - { value: "", label: "All" }, - { value: "true", label: "Within" }, - { value: "false", label: "Outside" }, - ]} +
+ + + + +
+ {selected && ( + setSelected(null)} /> - { - setDirection(value); - setPage(0); - }} - options={[ - { value: "", label: "All" }, - { value: "over", label: "Over target" }, - { value: "under", label: "Under target" }, - ]} + )} + + {activeVariable.variable_key} + + + ) : ( + `Targets (${fmt(filteredTotal, { digits: 0 })} of ${fmt(data?.total_targets ?? null, { digits: 0 })})` + ) + } + description={ + activeVariable + ? `${fmt(activeVariable.n_targets, { digits: 0 })} breakdowns · ${fmt( + activeVariable.within_10pct / Math.max(activeVariable.n_targets, 1), + { pct: true, digits: 0 }, + )} within 10% · mean abs rel. error ${fmt(activeVariable.mean_abs_relative_error, { pct: true, digits: 1 })}` + : "Relative error is the calibrated estimate's miss against the target. Click a column header to sort." + } + padded={false} + actions={ +
+ { + setSearch(event.target.value); + setPage(0); + }} + className="h-8 w-44 rounded-md border border-border bg-white px-3 text-xs focus:border-primary/60 focus:outline-none" + /> + {activeVariable ? ( + dimensions.map((dim) => ( + setFacet(dim.key, value)} + options={[ + { value: "", label: "All" }, + ...dim.values.map((value) => ({ + value, + label: value.replace(/^AGI in /, ""), + })), + ]} + /> + )) + ) : ( + <> + { + setSource(value); + setPage(0); + }} + options={[ + { value: "", label: "All" }, + ...sources.map((value) => ({ value, label: value })), + ]} + /> + { + setLevel(value); + setPage(0); + }} + options={[ + { value: "", label: "All" }, + { value: "national", label: "National" }, + { value: "state", label: "State" }, + ]} + /> + + )} + { + setWithin(value); + setPage(0); + }} + options={[ + { value: "", label: "All" }, + { value: "true", label: "Within" }, + { value: "false", label: "Outside" }, + ]} + /> +
+ } + footer={ +
+ + Page {page + 1} of {pageCount} + + + + + +
+ } + > + {isLoading ? ( + + ) : error || !data ? ( + -
- } - footer={ -
- - Page {page + 1} of {pageCount} - - - - - -
- } - > - {isLoading ? ( - - ) : error || !data ? ( - - ) : data.targets.length === 0 ? ( - - ) : ( -
- - - - {COLUMNS.map((column) => ( - - ))} - - - - {data.targets.map((row) => ( - - {COLUMNS.map((column) => ( - + {data.targets.map((row) => { + const isSelected = selected?.name === row.name; + return ( + setSelected(isSelected ? null : row)} + className={`cursor-pointer border-b border-border/60 last:border-b-0 ${ + isSelected ? "bg-primary/10" : "hover:bg-muted/30" + }`} + > + {columns.map((column) => ( + + ))} + + ); + })} + +
- -
+ ) : ( +
+ + + + {columns.map((column) => ( + ))} - ))} - -
- {column.render(row)} - + {column.sortable ? ( + + ) : ( + column.label + )} +
-
- )} - + +
+ {column.render(row)} +
+
+ )} + +
+
); } diff --git a/frontend/lib/api/hooks/use-populace.ts b/frontend/lib/api/hooks/use-populace.ts index 60e2bf3..545427e 100644 --- a/frontend/lib/api/hooks/use-populace.ts +++ b/frontend/lib/api/hooks/use-populace.ts @@ -33,6 +33,13 @@ export interface PopulaceTargetRow { // Derived at read time. family?: string | null; state?: string | null; + geography?: string | null; + level?: string | null; + source?: string | null; + variable?: string | null; + breakdown?: string | null; + dims?: string[] | null; + variable_key?: string | null; initial_relative_error?: number | null; abs_relative_error?: number | null; improvement?: number | null; @@ -40,6 +47,23 @@ export interface PopulaceTargetRow { [key: string]: unknown; } +export interface PopulaceVariableRow { + variable_key: string; + source: string; + variable: string; + level: string; + n_targets: number; + within_10pct: number; + within_tolerance: number; + mean_abs_relative_error: number | null; +} + +export interface PopulaceTargetDimension { + key: string; + label: string; + values: string[]; +} + export interface PopulaceFamilyFitRow { family: string; n_targets: number; @@ -116,6 +140,9 @@ export interface PopulaceTargetDiagnostics { schema_version?: number | null; metric?: string | null; families?: string[]; + sources?: string[]; + variables?: PopulaceVariableRow[]; + dimensions?: PopulaceTargetDimension[]; summary: { total_targets?: number | null; within_tolerance_count?: number | null; @@ -145,10 +172,14 @@ export function usePopulaceTargetDiagnostics(params: { limit?: number; offset?: number; family?: string; + variable?: string; + source?: string; + level?: string; state?: string; direction?: string; within_tolerance?: string; search?: string; + facet?: string[]; sort_by?: string; sort_dir?: string; }) { diff --git a/frontend/lib/populace/latest-artifact.ts b/frontend/lib/populace/latest-artifact.ts index 90d0851..d51da0a 100644 --- a/frontend/lib/populace/latest-artifact.ts +++ b/frontend/lib/populace/latest-artifact.ts @@ -6,18 +6,50 @@ import calibrationDiagnosticsData from "@/data/populace/latest/calibration_diagn type JsonObject = Record; type TargetRow = JsonObject; +// Target names are slash-joined strings under three conventions: +// state///.../, nation//[/], +// US/. +// parseTarget (below) reconstructs the structured constraint fields the registry +// knows but the published surface doesn't carry as columns yet — matching +// populace.dev's parse_target so both consumers agree. Declared above TARGET_ROWS +// because that initializer enriches every row at module load (no TDZ). +const FIPS_TO_ABBR: Record = { + "01": "AL", "02": "AK", "04": "AZ", "05": "AR", "06": "CA", "08": "CO", + "09": "CT", "10": "DE", "11": "DC", "12": "FL", "13": "GA", "15": "HI", + "16": "ID", "17": "IL", "18": "IN", "19": "IA", "20": "KS", "21": "KY", + "22": "LA", "23": "ME", "24": "MD", "25": "MA", "26": "MI", "27": "MN", + "28": "MS", "29": "MO", "30": "MT", "31": "NE", "32": "NV", "33": "NH", + "34": "NJ", "35": "NM", "36": "NY", "37": "NC", "38": "ND", "39": "OH", + "40": "OK", "41": "OR", "42": "PA", "44": "RI", "45": "SC", "46": "SD", + "47": "TN", "48": "TX", "49": "UT", "50": "VT", "51": "VA", "53": "WA", + "54": "WV", "55": "WI", "56": "WY", +}; +const STATE_ABBRS = new Set([ + "AL", "AK", "AZ", "AR", "CA", "CO", "CT", "DE", "DC", "FL", "GA", "HI", "ID", + "IL", "IN", "IA", "KS", "KY", "LA", "ME", "MD", "MA", "MI", "MN", "MS", "MO", + "MT", "NE", "NV", "NH", "NJ", "NM", "NY", "NC", "ND", "OH", "OK", "OR", "PA", + "RI", "SC", "SD", "TN", "TX", "UT", "VT", "VA", "WA", "WV", "WI", "WY", "US", +]); + +// Breakdown-dimension value sets (hoisted above TARGET_ROWS for the same reason). +const FILING_MODIFIERS = new Set(["Surviving Spouse"]); +const FILING_STATUSES = new Set([ + "All", "Single", "Head of Household", "Married Filing Jointly", + "Married Filing Separately", "Surviving Spouse", +]); +const RETURN_TYPES = new Set(["taxable", "all returns", "nontaxable"]); +const MEASURES = new Set(["total", "count", "mean", "filers", "nonfilers"]); + const BUILD_MANIFEST = buildManifestData as JsonObject; const RELEASE_MANIFEST = releaseManifestData as JsonObject; const LATEST_POINTER = latestPointerData as JsonObject; const CALIBRATION_DIAGNOSTICS = calibrationDiagnosticsData as JsonObject; -const TARGET_ROWS = Array.isArray(CALIBRATION_DIAGNOSTICS.targets) - ? (CALIBRATION_DIAGNOSTICS.targets as TargetRow[]).map(enrichTargetRow) - : []; export const LATEST_POPULACE_RELEASE_ID = String( BUILD_MANIFEST.build_id ?? LATEST_POINTER.release_id ?? "populace-us", ); -export const POPULACE_HF_REPO = "policyengine/populace-us"; +export const POPULACE_HF_REPO = process.env.POPULACE_HF_REPO ?? "policyengine/populace-us"; +export const POPULACE_HF_REVISION = process.env.POPULACE_HF_REVISION ?? "main"; export const LATEST_POPULACE_BUILD_MANIFEST_PATH = "frontend/data/populace/latest/build_manifest.json"; export const LATEST_POPULACE_RELEASE_MANIFEST_PATH = @@ -53,6 +85,209 @@ function relativeError(estimate: number | null, target: number | null): number | return target === 0 ? estimate - target : (estimate - target) / Math.abs(target); } +interface ParsedTarget { + geography: string; + level: string; + source: string; + variable: string; + breakdown: string; +} + +function parseTarget(name: string): ParsedTarget { + const parts = name.split("/"); + const p0 = parts[0] ?? ""; + const fips = /^US(\d{2})$/.exec(p0); + if (fips) { + return { + geography: FIPS_TO_ABBR[fips[1]] ?? p0, + level: "state", + source: "admin", + variable: parts[1] ?? "", + breakdown: parts.slice(2).join(" · "), + }; + } + if (p0 === "state") { + const second = parts[1] ?? ""; + // state-keyed ("state/AL/adjusted_gross_income/..."): slot 2 is the state, + // not a source. Group all states under one synthetic "state" source so the + // 50 state codes don't masquerade as sources/variables. + if (STATE_ABBRS.has(second) && second !== "US") { + return { + geography: second, + level: "state", + source: "state", + variable: parts[2] ?? "", + breakdown: parts.slice(3).join(" · "), + }; + } + const last = parts[parts.length - 1]; + // source-keyed with a trailing state ("state/census/rent/AK"). + if (parts.length >= 4 && STATE_ABBRS.has(last)) { + return { + geography: last, + level: "state", + source: parts[1] ?? "", + variable: parts[2] ?? "", + breakdown: parts.slice(3, -1).join(" · "), + }; + } + return { + geography: "state", + level: "state", + source: parts[1] ?? "", + variable: parts[2] ?? "", + breakdown: parts.slice(3).join(" · "), + }; + } + if (p0 === "nation" || p0 === "national" || p0 === "us") { + return { + geography: "United States", + level: "national", + source: parts[1] ?? "", + variable: parts[2] ?? "", + breakdown: parts.slice(3).join(" · "), + }; + } + return { + geography: "", + level: "", + source: p0, + variable: parts[1] ?? "", + breakdown: parts.slice(2).join(" · "), + }; +} + +// The "thing" a constraint measures: source + variable (e.g. "irs / adjusted +// gross income"), the grouping key for the by-variable browser. The breakdown +// dimensions (income band, filing status, age, ...) hang off it. +function variableKeyOf(parsed: ParsedTarget): string { + return [parsed.source, parsed.variable].filter(Boolean).join(" / "); +} + +// The breakdown string is several typed dimensions joined by " · " (for AGI: +// measure · income band · return type · filing status). splitBreakdown merges +// filing-status modifiers ("... · Surviving Spouse") back into the status they +// qualify so a row's dimensions stay rectangular. (The value sets are hoisted +// to the top of the module — see near FIPS_TO_ABBR — so module-load enrichment +// can use them without a TDZ.) +function splitBreakdown(breakdown: string): string[] { + const raw = breakdown ? breakdown.split(" · ").filter(Boolean) : []; + const out: string[] = []; + for (const token of raw) { + if (FILING_MODIFIERS.has(token) && out.length) { + out[out.length - 1] = `${out[out.length - 1]} · ${token}`; + } else { + out.push(token); + } + } + return out; +} + +// Name a dimension from the set of values it takes across a variable's rows. +function classifyDimension(values: string[]): string { + const v = values.filter(Boolean); + if (!v.length) return "Breakdown"; + const all = (pred: (s: string) => boolean) => v.every(pred); + const firstStatus = (s: string) => s.split(" · ")[0]; + if (all((s) => s.startsWith("AGI in "))) return "Income band"; + if (all((s) => RETURN_TYPES.has(s))) return "Return type"; + if (all((s) => FILING_STATUSES.has(firstStatus(s)))) return "Filing status"; + if (all((s) => /^\d+$/.test(s))) return "Age"; + if (all((s) => MEASURES.has(s))) return "Measure"; + return "Breakdown"; +} + +// Sort a dimension's values: income bands by their lower bound, filing statuses +// with "All" first, everything else naturally. +function parseAmount(token: string): number { + if (/^-?inf$/i.test(token)) return token.startsWith("-") ? -Infinity : Infinity; + const negative = token.startsWith("-"); + const magnitude = parseFloat(token); + if (!Number.isFinite(magnitude)) return 0; + const mult = /k$/i.test(token) ? 1e3 : /m$/i.test(token) ? 1e6 : 1; + return (negative ? -1 : 1) * Math.abs(magnitude) * mult; +} + +function bandLowerBound(label: string): number { + const body = label.replace(/^AGI in /, ""); + const match = /^(-?inf|-?\d+(?:\.\d+)?[km]?)-(-?inf|-?\d+(?:\.\d+)?[km]?)$/i.exec(body); + return match ? parseAmount(match[1]) : 0; +} + +function sortDimensionValues(label: string, values: string[]): string[] { + if (label === "Income band") { + return [...values].sort((a, b) => bandLowerBound(a) - bandLowerBound(b)); + } + if (label === "Age") { + return [...values].sort((a, b) => Number(a) - Number(b)); + } + return [...values].sort((a, b) => { + if (a === "All") return -1; + if (b === "All") return 1; + return a.localeCompare(b); + }); +} + +export interface TargetDimension { + key: string; + label: string; + values: string[]; +} + +// Resolve a facet key against a row. Keys are "geography", "level", or +// "dim" (the Nth breakdown token). +function rowFacetValue(row: TargetRow, key: string): string | undefined { + if (key === "geography") return (row.geography as string) || undefined; + if (key === "level") return (row.level as string) || undefined; + const dim = /^dim(\d+)$/.exec(key); + if (dim) return (row.dims as string[] | undefined)?.[Number(dim[1])]; + const value = row[key]; + return typeof value === "string" ? value : undefined; +} + +function sortFacetValues(label: string, values: string[]): string[] { + if (label === "Geography") { + return [...values].sort((a, b) => { + if (a === "United States") return -1; + if (b === "United States") return 1; + return a.localeCompare(b); + }); + } + return sortDimensionValues(label, values); +} + +// Every axis along which a variable's targets vary, as filterable facets: +// geography, level, and each breakdown position. A facet is included only if it +// takes more than one value across the rows — a constant (e.g. AGI's "total" +// measure, or a single geography) is not variation, so it stays in the +// canonical detail rather than cluttering the facets. +function computeDimensions(rows: TargetRow[]): TargetDimension[] { + const maxLen = rows.reduce( + (max, row) => Math.max(max, (row.dims as string[] | undefined)?.length ?? 0), + 0, + ); + const candidates: { key: string; label?: string }[] = [ + { key: "geography", label: "Geography" }, + { key: "level", label: "Level" }, + ]; + for (let i = 0; i < maxLen; i += 1) candidates.push({ key: `dim${i}` }); + + const facets: TargetDimension[] = []; + for (const candidate of candidates) { + const values = [ + ...new Set( + rows + .map((row) => rowFacetValue(row, candidate.key)) + .filter((value): value is string => Boolean(value)), + ), + ]; + if (values.length <= 1) continue; + const label = candidate.label ?? classifyDimension(values); + facets.push({ key: candidate.key, label, values: sortFacetValues(label, values) }); + } + return facets; +} + // Targets are named hierarchically (e.g. "nation/irs/...", "state/census/...", // "state/AL/adjusted_gross_income/..."). Derive a coarse source family for // filtering: national/state sources keep their source segment; the per-state @@ -88,10 +323,18 @@ function enrichTargetRow(row: TargetRow): TargetRow { initialRel == null || finalRel == null ? null : Math.abs(initialRel) - Math.abs(finalRel); + const parsed = parseTarget(name); return { ...row, family: deriveFamily(name), state: deriveState(name), + geography: parsed.geography, + level: parsed.level, + source: parsed.source, + variable: parsed.variable, + breakdown: parsed.breakdown, + dims: splitBreakdown(parsed.breakdown), + variable_key: variableKeyOf(parsed), initial_relative_error: initialRel, abs_relative_error: absFinalRel, improvement, @@ -113,15 +356,157 @@ function booleanParam(value: string | null): boolean | null { } function matchesSearch(row: TargetRow, search: string): boolean { - const haystack = [row.name, row.family, row.state] + const haystack = [row.name, row.variable, row.source, row.breakdown, row.geography, row.state] .filter((value) => value != null) .join(" ") .toLowerCase(); return haystack.includes(search.toLowerCase()); } -export function populaceTargetFamilies(): string[] { - return [...new Set(TARGET_ROWS.map((row) => String(row.family ?? "")))].sort(); +// A calibration source — the meta plus the enriched per-target rows — built +// from either the live HF artifact or the committed snapshot, so every +// consumer below works the same on whichever the route resolved. +export interface Calibration { + source: "huggingface_live" | "deployed_static_snapshot"; + release_id: string; + schema_version: unknown; + weight_entity: unknown; + options: JsonObject; + l0_lambda: number | null; + n_nonzero: number | null; + n_records: number | null; + initial_loss: number | null; + final_loss: number | null; + fraction_within_10pct: number | null; + loss_trajectory: number[]; + skipped: JsonObject[]; + rows: TargetRow[]; +} + +export function buildCalibration( + raw: JsonObject, + source: Calibration["source"], +): Calibration { + const targets = Array.isArray(raw.targets) ? (raw.targets as TargetRow[]) : []; + return { + source, + release_id: String(raw.release_id ?? LATEST_POPULACE_RELEASE_ID), + schema_version: raw.schema_version ?? null, + weight_entity: raw.weight_entity ?? null, + options: asObject(raw.options), + l0_lambda: numberOrNull(raw.l0_lambda), + n_nonzero: numberOrNull(raw.n_nonzero), + n_records: numberOrNull(raw.n_records), + initial_loss: numberOrNull(raw.initial_loss), + final_loss: numberOrNull(raw.final_loss), + fraction_within_10pct: numberOrNull(raw.fraction_within_10pct), + loss_trajectory: Array.isArray(raw.loss_trajectory) + ? (raw.loss_trajectory as number[]) + : [], + skipped: Array.isArray(raw.skipped) ? (raw.skipped as JsonObject[]) : [], + rows: targets.map(enrichTargetRow), + }; +} + +const SNAPSHOT_CALIBRATION = buildCalibration( + CALIBRATION_DIAGNOSTICS, + "deployed_static_snapshot", +); + +export function snapshotCalibration(): Calibration { + return SNAPSHOT_CALIBRATION; +} + +export function hfResolveUrl(path: string): string { + return `https://huggingface.co/datasets/${POPULACE_HF_REPO}/resolve/${POPULACE_HF_REVISION}/${path}`; +} + +// Resolve the current release's calibration_diagnostics.json live via +// latest.json. Returns null on any failure so callers fall back to the +// committed snapshot. +export async function loadLiveCalibration( + revalidate: number, +): Promise<{ + release_id: string; + updated_at: unknown; + paths: JsonObject; + calibration: Calibration; +} | null> { + try { + const pointerRes = await fetch(hfResolveUrl("latest.json"), { + next: { revalidate }, + }); + if (!pointerRes.ok) return null; + const pointer = asObject(await pointerRes.json()); + const paths = asObject(pointer.paths); + const diagPath = + typeof paths.calibration_diagnostics === "string" + ? paths.calibration_diagnostics + : null; + if (!diagPath) return null; + const diagRes = await fetch(hfResolveUrl(diagPath), { next: { revalidate } }); + if (!diagRes.ok) return null; + const diag = asObject(await diagRes.json()); + const calibration = buildCalibration(diag, "huggingface_live"); + // schema v2 carries no top-level release_id — take it from the pointer. + if (!diag.release_id && pointer.release_id) { + calibration.release_id = String(pointer.release_id); + } + return { + release_id: String(pointer.release_id ?? ""), + updated_at: pointer.updated_at ?? null, + paths, + calibration, + }; + } catch { + return null; + } +} + +export function populaceTargetFamilies(rows: TargetRow[]): string[] { + return [...new Set(rows.map((row) => String(row.family ?? "")))].sort(); +} + +export function populaceTargetSources(rows: TargetRow[]): string[] { + return [...new Set(rows.map((row) => String(row.source ?? "")))] + .filter(Boolean) + .sort(); +} + +// The by-variable browser: one row per "thing" (source / variable), with its +// breakdown count and calibration fit — the hierarchical entry point ("AGI" +// then its by-bracket / by-filing-status breakdowns). +export function populaceVariableSummary(rows: TargetRow[]) { + const groups = new Map(); + for (const row of rows) { + const key = String(row.variable_key ?? ""); + if (!key) continue; + const bucket = groups.get(key) ?? []; + bucket.push(row); + groups.set(key, bucket); + } + return [...groups.entries()] + .map(([variable_key, group]) => { + const first = group[0]; + const absErrors = group + .map((row) => numberOrNull(row.abs_relative_error)) + .filter((value): value is number => value != null); + return { + variable_key, + source: String(first.source ?? ""), + variable: String(first.variable ?? ""), + level: String(first.level ?? ""), + n_targets: group.length, + within_10pct: group.filter( + (row) => (numberOrNull(row.abs_relative_error) ?? Infinity) <= 0.1, + ).length, + within_tolerance: group.filter((row) => row.within_tolerance === true).length, + mean_abs_relative_error: absErrors.length + ? absErrors.reduce((sum, value) => sum + value, 0) / absErrors.length + : null, + }; + }) + .sort((a, b) => b.n_targets - a.n_targets); } function withinToleranceCount(rows: TargetRow[]): number { @@ -143,17 +528,17 @@ export function latestPopulacePointer(): JsonObject { // Per-family calibration fit: how well each source family is reproduced, // computed from the per-target rows (replaces the eCPS family breakdown the // retired comparison artifact used to carry). -function familyFitSummary() { +function familyFitSummary(rows: TargetRow[]) { const groups = new Map(); - for (const row of TARGET_ROWS) { + for (const row of rows) { const family = String(row.family ?? ""); const bucket = groups.get(family) ?? []; bucket.push(row); groups.set(family, bucket); } return [...groups.entries()] - .map(([family, rows]) => { - const absErrors = rows + .map(([family, group]) => { + const absErrors = group .map((row) => numberOrNull(row.abs_relative_error)) .filter((value): value is number => value != null); const meanAbsError = absErrors.length @@ -161,9 +546,9 @@ function familyFitSummary() { : null; return { family, - n_targets: rows.length, - within_tolerance: withinToleranceCount(rows), - within_10pct: rows.filter( + n_targets: group.length, + within_tolerance: withinToleranceCount(group), + within_10pct: group.filter( (row) => (numberOrNull(row.abs_relative_error) ?? Infinity) <= 0.1, ).length, mean_abs_relative_error: meanAbsError, @@ -172,34 +557,31 @@ function familyFitSummary() { .sort((a, b) => b.n_targets - a.n_targets); } -export function latestPopulaceCalibrationSummary() { +export function latestPopulaceCalibrationSummary(cal: Calibration = SNAPSHOT_CALIBRATION) { return { available: true, + source: cal.source, path: LATEST_POPULACE_CALIBRATION_DIAGNOSTICS_PATH, - release_id: CALIBRATION_DIAGNOSTICS.release_id ?? LATEST_POPULACE_RELEASE_ID, - schema_version: CALIBRATION_DIAGNOSTICS.schema_version ?? null, - weight_entity: CALIBRATION_DIAGNOSTICS.weight_entity ?? null, - options: asObject(CALIBRATION_DIAGNOSTICS.options), - l0_lambda: numberOrNull(CALIBRATION_DIAGNOSTICS.l0_lambda), - n_nonzero: numberOrNull(CALIBRATION_DIAGNOSTICS.n_nonzero), - n_records: numberOrNull(CALIBRATION_DIAGNOSTICS.n_records), - initial_loss: numberOrNull(CALIBRATION_DIAGNOSTICS.initial_loss), - final_loss: numberOrNull(CALIBRATION_DIAGNOSTICS.final_loss), - fraction_within_10pct: numberOrNull(CALIBRATION_DIAGNOSTICS.fraction_within_10pct), - loss_trajectory: Array.isArray(CALIBRATION_DIAGNOSTICS.loss_trajectory) - ? (CALIBRATION_DIAGNOSTICS.loss_trajectory as number[]) - : [], - skipped: Array.isArray(CALIBRATION_DIAGNOSTICS.skipped) - ? (CALIBRATION_DIAGNOSTICS.skipped as JsonObject[]) - : [], - total_targets: TARGET_ROWS.length, - within_tolerance_count: withinToleranceCount(TARGET_ROWS), - family_fit: familyFitSummary(), + release_id: cal.release_id, + schema_version: cal.schema_version, + weight_entity: cal.weight_entity, + options: cal.options, + l0_lambda: cal.l0_lambda, + n_nonzero: cal.n_nonzero, + n_records: cal.n_records, + initial_loss: cal.initial_loss, + final_loss: cal.final_loss, + fraction_within_10pct: cal.fraction_within_10pct, + loss_trajectory: cal.loss_trajectory, + skipped: cal.skipped, + total_targets: cal.rows.length, + within_tolerance_count: withinToleranceCount(cal.rows), + family_fit: familyFitSummary(cal.rows), }; } -function worstFit(limit: number): TargetRow[] { - return [...TARGET_ROWS] +function worstFit(rows: TargetRow[], limit: number): TargetRow[] { + return [...rows] .filter((row) => numberOrNull(row.abs_relative_error) != null) .sort( (a, b) => @@ -209,8 +591,8 @@ function worstFit(limit: number): TargetRow[] { .slice(0, limit); } -function biggestImprovements(limit: number): TargetRow[] { - return [...TARGET_ROWS] +function biggestImprovements(rows: TargetRow[], limit: number): TargetRow[] { + return [...rows] .filter((row) => numberOrNull(row.improvement) != null) .sort( (a, b) => (numberOrNull(b.improvement) ?? 0) - (numberOrNull(a.improvement) ?? 0), @@ -218,23 +600,21 @@ function biggestImprovements(limit: number): TargetRow[] { .slice(0, limit); } -export function latestPopulaceCalibrationHighlights(limit = 15) { - return { - worst_fit: worstFit(limit), - biggest_improvements: biggestImprovements(limit), - }; -} - -export function latestPopulaceTargetDiagnosticsSummary(displayLimit = 100) { +export function latestPopulaceCalibrationHighlights( + cal: Calibration = SNAPSHOT_CALIBRATION, + limit = 15, +) { return { - ...latestPopulaceCalibrationSummary(), - families: populaceTargetFamilies(), - display_limit: displayLimit, - targets: TARGET_ROWS.slice(0, displayLimit), + worst_fit: worstFit(cal.rows, limit), + biggest_improvements: biggestImprovements(cal.rows, limit), }; } -export function latestPopulaceTargetDiagnosticsPage(requestUrl: string) { +export function latestPopulaceTargetDiagnosticsPage( + requestUrl: string, + cal: Calibration = SNAPSHOT_CALIBRATION, +) { + const rows = cal.rows; const url = new URL(requestUrl); const limit = Math.min( Math.max(Number(url.searchParams.get("limit") ?? "100") || 100, 1), @@ -242,6 +622,9 @@ export function latestPopulaceTargetDiagnosticsPage(requestUrl: string) { ); const offset = Math.max(Number(url.searchParams.get("offset") ?? "0") || 0, 0); const family = stringParam(url.searchParams.get("family")); + const variable = stringParam(url.searchParams.get("variable")); + const source = stringParam(url.searchParams.get("source")); + const level = stringParam(url.searchParams.get("level")); const state = stringParam(url.searchParams.get("state")); const direction = stringParam(url.searchParams.get("direction")); const within = booleanParam(url.searchParams.get("within_tolerance")); @@ -249,17 +632,37 @@ export function latestPopulaceTargetDiagnosticsPage(requestUrl: string) { const sortBy = stringParam(url.searchParams.get("sort_by")) ?? "abs_relative_error"; const sortDir = stringParam(url.searchParams.get("sort_dir")) === "asc" ? "asc" : "desc"; + // Facet filters: repeated `facet=:` (key = geography|level|dim). + const facetFilters = url.searchParams + .getAll("facet") + .map((entry) => { + const sep = entry.indexOf(":"); + return sep < 0 ? null : ([entry.slice(0, sep), entry.slice(sep + 1)] as const); + }) + .filter((value): value is readonly [string, string] => value != null); - let filtered = TARGET_ROWS; + let filtered = rows; if (family) filtered = filtered.filter((row) => row.family === family); + if (variable) filtered = filtered.filter((row) => row.variable_key === variable); + if (source) filtered = filtered.filter((row) => row.source === source); + if (level) filtered = filtered.filter((row) => row.level === level); if (state) filtered = filtered.filter((row) => row.state === state); + // Facets are derived from the variable's rows (before facet/within/direction/ + // search) so every option stays selectable. + const dimensions = variable ? computeDimensions(filtered) : []; + for (const [key, value] of facetFilters) { + filtered = filtered.filter((row) => rowFacetValue(row, key) === value); + } if (direction) filtered = filtered.filter((row) => row.direction === direction); if (within !== null) filtered = filtered.filter((row) => row.within_tolerance === within); if (search) filtered = filtered.filter((row) => matchesSearch(row, search)); + const dimSort = /^dim(\d+)$/.exec(sortBy); + const sortValue = (row: TargetRow) => + dimSort ? (row.dims as string[] | undefined)?.[Number(dimSort[1])] : row[sortBy]; filtered = [...filtered].sort((a, b) => { - const aVal = a[sortBy]; - const bVal = b[sortBy]; + const aVal = sortValue(a); + const bVal = sortValue(b); if (aVal == null && bVal == null) return 0; if (aVal == null) return 1; if (bVal == null) return -1; @@ -272,19 +675,21 @@ export function latestPopulaceTargetDiagnosticsPage(requestUrl: string) { return { available: true, + source: cal.source, path: LATEST_POPULACE_CALIBRATION_DIAGNOSTICS_PATH, - release_id: CALIBRATION_DIAGNOSTICS.release_id ?? LATEST_POPULACE_RELEASE_ID, - schema_version: CALIBRATION_DIAGNOSTICS.schema_version ?? null, + release_id: cal.release_id, + schema_version: cal.schema_version, metric: "relative_error", - families: populaceTargetFamilies(), + families: populaceTargetFamilies(rows), + sources: populaceTargetSources(rows), + variables: populaceVariableSummary(rows), + dimensions, summary: { - total_targets: TARGET_ROWS.length, - within_tolerance_count: withinToleranceCount(TARGET_ROWS), - fraction_within_10pct: numberOrNull( - CALIBRATION_DIAGNOSTICS.fraction_within_10pct, - ), + total_targets: rows.length, + within_tolerance_count: withinToleranceCount(rows), + fraction_within_10pct: cal.fraction_within_10pct, }, - total_targets: TARGET_ROWS.length, + total_targets: rows.length, filtered_total: filtered.length, returned: filtered.slice(offset, offset + limit).length, limit, @@ -294,6 +699,9 @@ export function latestPopulaceTargetDiagnosticsPage(requestUrl: string) { targets: filtered.slice(offset, offset + limit), filters: { family, + variable, + source, + level, state, direction, within_tolerance: within, diff --git a/tests/test_populace.py b/tests/test_populace.py index 88a2155..5295a39 100644 --- a/tests/test_populace.py +++ b/tests/test_populace.py @@ -1,8 +1,22 @@ from __future__ import annotations +import pytest + from backend.routes import populace +@pytest.fixture(autouse=True) +def _offline_by_default(monkeypatch): + """Default every test to the committed snapshot: any live HF fetch raises + unless a test installs its own ``_fetch_json``. Keeps the suite network-free + and deterministic against the pinned snapshot.""" + + def _raise(url): + raise RuntimeError("offline (no _fetch_json override in this test)") + + monkeypatch.setattr(populace, "_fetch_json", _raise) + + def _offline(monkeypatch): """Force the live Hugging Face path to fail so routes use the snapshot.""" @@ -321,3 +335,147 @@ def test_populace_comparison_falls_back_when_pointer_unreachable(monkeypatch): assert payload["archived"] is True assert payload["source"] == "deployed_static_snapshot" assert payload["live_scorecard_error"] == "benchmarks unreachable" + + +def test_parse_target_decomposes_conventions(): + # National IRS AGI bracket. + p = populace._parse_target( + "nation/irs/adjusted gross income/total/AGI in 50k-75k/taxable/All" + ) + assert p["level"] == "national" + assert p["geography"] == "United States" + assert p["source"] == "irs" + assert p["variable"] == "adjusted gross income" + assert "AGI in 50k-75k" in p["breakdown"] + + # Source-keyed state target with trailing state code. + p = populace._parse_target("state/census/acs/rent/AK") + assert p["geography"] == "AK" + assert p["source"] == "census" + assert p["variable"] == "acs" + + # State-keyed distribution: slot 2 is the state, grouped under "state". + p = populace._parse_target("state/AL/adjusted_gross_income/count/1_1") + assert p["geography"] == "AL" + assert p["source"] == "state" + assert p["variable"] == "adjusted_gross_income" + + # Per-state-FIPS admin metric. + p = populace._parse_target("US06/snap-cost") + assert p["geography"] == "CA" + assert p["source"] == "admin" + assert p["variable"] == "snap-cost" + + +def test_target_diagnostics_exposes_variables_and_sources(): + payload = populace.populace_target_diagnostics( + limit=1, offset=0, family=None, variable=None, source=None, level=None, + state=None, direction=None, within_tolerance=None, search=None, + sort_by=None, sort_dir="desc", + ) + variables = payload["variables"] + assert len(variables) > 50 + # The state-keyed AGI distribution collapses to ONE variable, not 50. + keys = {v["variable_key"] for v in variables} + assert "irs / adjusted gross income" in keys + assert "state / adjusted_gross_income" in keys + assert not any(v["source"] in {"AL", "AK", "CA"} for v in variables) + # variables are sorted by target count descending. + counts = [v["n_targets"] for v in variables] + assert counts == sorted(counts, reverse=True) + # sources are the real source families, not state codes. + assert "irs" in payload["sources"] and "census" in payload["sources"] + assert "CA" not in payload["sources"] + + +def test_target_diagnostics_variable_filter_isolates_breakdowns(): + payload = populace.populace_target_diagnostics( + limit=200, offset=0, family=None, variable="irs / adjusted gross income", + source=None, level=None, state=None, direction=None, within_tolerance=None, + search=None, sort_by=None, sort_dir="desc", + ) + assert payload["filtered_total"] == 70 + for row in payload["targets"]: + assert row["variable_key"] == "irs / adjusted gross income" + assert row["variable"] == "adjusted gross income" + assert row["breakdown"] # each row is a distinct breakdown + + +def test_target_diagnostics_source_and_level_filters(): + payload = populace.populace_target_diagnostics( + limit=10, offset=0, family=None, variable=None, source="irs", level="national", + state=None, direction=None, within_tolerance=None, search=None, + sort_by=None, sort_dir="desc", + ) + assert payload["filtered_total"] >= 1 + for row in payload["targets"]: + assert row["source"] == "irs" + assert row["level"] == "national" + + +def test_target_diagnostics_dimensions_and_facet_filter(): + # Selecting AGI yields typed facets; the constant measure drops, and + # geography/level are constant here (all national, US) so they drop too. + payload = populace.populace_target_diagnostics( + limit=200, offset=0, family=None, variable="irs / adjusted gross income", + source=None, level=None, state=None, direction=None, within_tolerance=None, + search=None, facet=None, sort_by=None, sort_dir="desc", + ) + labels = {d["label"] for d in payload["dimensions"]} + assert "Income band" in labels + assert "Return type" in labels + assert "Filing status" in labels + assert "Measure" not in labels # "total" is constant -> dropped + assert "Geography" not in labels # all United States -> dropped + income = next(d for d in payload["dimensions"] if d["label"] == "Income band") + assert "key" in income and income["key"].startswith("dim") + # Income bands sorted by lower bound (negatives first, not lexically). + assert income["values"][0].startswith("AGI in -inf") + + # Filtering by two facets narrows to the matching breakdown. + filing = next(d for d in payload["dimensions"] if d["label"] == "Filing status") + filtered = populace.populace_target_diagnostics( + limit=10, offset=0, family=None, variable="irs / adjusted gross income", + source=None, level=None, state=None, direction=None, within_tolerance=None, + search=None, + facet=[f"{income['key']}:AGI in 200k-500k", f"{filing['key']}:All"], + sort_by=None, sort_dir="desc", + ) + assert filtered["filtered_total"] == 1 + row = filtered["targets"][0] + assert "AGI in 200k-500k" in row["dims"] + assert "All" in row["dims"] + + +def test_state_conditioned_variable_exposes_geography_facet(): + # real_estate_taxes is conditioned on state, not AGI: geography + level vary. + payload = populace.populace_target_diagnostics( + limit=5, offset=0, family=None, variable="irs / real_estate_taxes", + source=None, level=None, state=None, direction=None, within_tolerance=None, + search=None, facet=None, sort_by=None, sort_dir="desc", + ) + by_key = {d["key"]: d for d in payload["dimensions"]} + assert "geography" in by_key + geo = by_key["geography"] + assert "United States" in geo["values"] + assert geo["values"][0] == "United States" # national first + assert len(geo["values"]) > 10 # the states + + # Facet by a state narrows to that geography. + one = populace.populace_target_diagnostics( + limit=5, offset=0, family=None, variable="irs / real_estate_taxes", + source=None, level=None, state=None, direction=None, within_tolerance=None, + search=None, facet=["geography:CA"], sort_by=None, sort_dir="desc", + ) + assert one["filtered_total"] >= 1 + for row in one["targets"]: + assert row["geography"] == "CA" + + +def test_dimensions_absent_without_variable(): + payload = populace.populace_target_diagnostics( + limit=1, offset=0, family=None, variable=None, source=None, level=None, + state=None, direction=None, within_tolerance=None, search=None, facet=None, + sort_by=None, sort_dir="desc", + ) + assert payload["dimensions"] == [] From 3d10d9c74bcc7b1511af6532225873dce6bb60eb Mon Sep 17 00:00:00 2001 From: Pavel Makarchuk Date: Mon, 15 Jun 2026 21:59:57 -0400 Subject: [PATCH 07/14] Strip to populace-only app; remove microplex, us-data, and eCPS comparison MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit populace-US is now the dashboard's only dataset. Removed: - microplex mode (pages, components, API routes, backend router) - us-data mode (summary/analysis/targets/inventory/nodes/weights/pipeline, their hooks, backend routes, run/registry/fixtures infrastructure) - the eCPS incumbent comparison (the /populace/comparison view, the benchmarks scorecard wiring, and its backend endpoint) — version-over- version comparison replaces it The dashboard mode selector and run/geo context are gone; populace is the whole app (home redirects to /populace, nav is release · targets · compare). The API client is a thin fetch wrapper (no run params, no fixtures). The FastAPI backend is now just the populace router over Hugging Face. 16 backend tests pass; production build green; live smoke test OK. Co-Authored-By: Claude Fable 5 --- backend/api/__init__.py | 1 - backend/api/v1/__init__.py | 1 - backend/api/v1/models.py | 196 - backend/api/v1/router.py | 726 - backend/app.py | 88 +- backend/routes/analysis.py | 114 - backend/routes/compare.py | 205 - backend/routes/geography.py | 49 - backend/routes/microplex.py | 2086 - backend/routes/nodes.py | 86 - backend/routes/pipeline.py | 166 - backend/routes/populace.py | 194 - backend/routes/runs.py | 37 - backend/routes/strata.py | 29 - backend/routes/summary.py | 211 - backend/routes/target_inventory.py | 245 - backend/routes/targets.py | 857 - backend/routes/weights.py | 180 - backend/services/__init__.py | 1 - backend/services/analysis_readiness.py | 1131 - backend/services/bundle_availability.py | 97 - backend/services/bundle_eval.py | 186 - backend/services/dataset_loader.py | 825 - backend/services/db_service.py | 157 - backend/services/fit_artifacts.py | 74 - backend/services/geo_utils.py | 218 - backend/services/loader.py | 427 - backend/services/matrix_ops.py | 120 - backend/services/registry.py | 61 - backend/services/runs.py | 297 - backend/services/sim_service.py | 63 - backend/services/stratum_evaluator.py | 510 - frontend/app/analysis/page.tsx | 582 - .../api/microplex/budget-benchmarks/route.ts | 274 - .../api/microplex/reform-comparison/route.ts | 66 - frontend/app/api/microplex/route.ts | 303 - .../api/microplex/target-diagnostics/route.ts | 12 - frontend/app/api/populace/comparison/route.ts | 120 - frontend/app/budget-impact/page.tsx | 1 - frontend/app/comparison/page.tsx | 600 - frontend/app/inventory/page.tsx | 324 - frontend/app/microplex/diagnostics/page.tsx | 1 - frontend/app/microplex/page.tsx | 1564 - frontend/app/microplex/reforms/page.tsx | 1 - frontend/app/nodes/page.tsx | 186 - frontend/app/page.tsx | 2 +- frontend/app/pipeline/page.tsx | 381 - frontend/app/populace/comparison/page.tsx | 10 - frontend/app/providers.tsx | 13 +- frontend/app/summary/page.tsx | 216 - frontend/app/targets/page.tsx | 622 - frontend/app/weights/page.tsx | 315 - frontend/components/layout/app-shell.tsx | 2 - .../layout/dashboard-mode-selector.tsx | 58 - frontend/components/layout/nav-sidebar.tsx | 81 +- frontend/components/layout/run-bootstrap.tsx | 44 - .../microplex/reform-benchmarks-page.tsx | 576 - .../components/pipeline/pipeline-graph.tsx | 317 - .../populace/populace-comparison-view.tsx | 303 - frontend/components/shared/geo-selector.tsx | 111 - frontend/components/targets/chip-bar.tsx | 116 - frontend/components/targets/pagination.tsx | 101 - .../components/targets/run-selector-card.tsx | 234 - .../targets/search-and-controls.tsx | 207 - .../components/targets/source-summary.tsx | 68 - .../latest/pe_native_target_diagnostics.json | 168624 --------------- .../latest/policyengine_native_scores.json | 495 - .../populace/latest/comparison_scorecard.json | 1704 - frontend/fixtures/constraint-diff.json | 15 - frontend/fixtures/contributors.json | 7 - frontend/fixtures/convergence.json | 12 - frontend/fixtures/datasets.json | 3 - frontend/fixtures/error-decomposition.json | 9 - frontend/fixtures/histogram.json | 15 - frontend/fixtures/index.ts | 83 - frontend/fixtures/provenance.json | 18 - frontend/fixtures/runs.json | 5 - frontend/fixtures/stratum.json | 12 - frontend/fixtures/summary.json | 57 - frontend/fixtures/synth-targets.ts | 312 - frontend/fixtures/targets.json | 87 - frontend/fixtures/weight-distribution.json | 15 - frontend/lib/api/client.ts | 140 +- frontend/lib/api/hooks/use-analysis.ts | 245 - frontend/lib/api/hooks/use-compare.ts | 74 - frontend/lib/api/hooks/use-geography.ts | 23 - frontend/lib/api/hooks/use-microplex.ts | 423 - frontend/lib/api/hooks/use-nodes.ts | 29 - frontend/lib/api/hooks/use-pipeline.ts | 116 - frontend/lib/api/hooks/use-populace.ts | 72 - frontend/lib/api/hooks/use-runs.ts | 51 - frontend/lib/api/hooks/use-strata.ts | 14 - frontend/lib/api/hooks/use-summary.ts | 76 - frontend/lib/api/hooks/use-target-detail.ts | 102 - .../lib/api/hooks/use-target-inventory.ts | 78 - frontend/lib/api/hooks/use-targets.ts | 140 - frontend/lib/api/hooks/use-weights.ts | 57 - frontend/lib/dashboard-mode-context.tsx | 96 - frontend/lib/geo-context.tsx | 47 - frontend/lib/microplex/latest-artifact.ts | 282 - frontend/lib/populace/comparison-artifact.ts | 95 - frontend/lib/run-context.tsx | 77 - tests/conftest.py | 46 +- tests/test_analysis_readiness.py | 369 - tests/test_api_v1.py | 293 - tests/test_bundle_availability.py | 33 - tests/test_bundle_eval.py | 33 - tests/test_microplex.py | 582 - tests/test_pipeline.py | 24 - tests/test_populace.py | 122 - tests/test_registry.py | 99 - tests/test_runs.py | 156 - tests/test_stratum_evaluator.py | 149 - tests/test_summary.py | 185 - tests/test_targets_filters.py | 278 - 115 files changed, 60 insertions(+), 193468 deletions(-) delete mode 100644 backend/api/__init__.py delete mode 100644 backend/api/v1/__init__.py delete mode 100644 backend/api/v1/models.py delete mode 100644 backend/api/v1/router.py delete mode 100644 backend/routes/analysis.py delete mode 100644 backend/routes/compare.py delete mode 100644 backend/routes/geography.py delete mode 100644 backend/routes/microplex.py delete mode 100644 backend/routes/nodes.py delete mode 100644 backend/routes/pipeline.py delete mode 100644 backend/routes/runs.py delete mode 100644 backend/routes/strata.py delete mode 100644 backend/routes/summary.py delete mode 100644 backend/routes/target_inventory.py delete mode 100644 backend/routes/targets.py delete mode 100644 backend/routes/weights.py delete mode 100644 backend/services/__init__.py delete mode 100644 backend/services/analysis_readiness.py delete mode 100644 backend/services/bundle_availability.py delete mode 100644 backend/services/bundle_eval.py delete mode 100644 backend/services/dataset_loader.py delete mode 100644 backend/services/db_service.py delete mode 100644 backend/services/fit_artifacts.py delete mode 100644 backend/services/geo_utils.py delete mode 100644 backend/services/loader.py delete mode 100644 backend/services/matrix_ops.py delete mode 100644 backend/services/registry.py delete mode 100644 backend/services/runs.py delete mode 100644 backend/services/sim_service.py delete mode 100644 backend/services/stratum_evaluator.py delete mode 100644 frontend/app/analysis/page.tsx delete mode 100644 frontend/app/api/microplex/budget-benchmarks/route.ts delete mode 100644 frontend/app/api/microplex/reform-comparison/route.ts delete mode 100644 frontend/app/api/microplex/route.ts delete mode 100644 frontend/app/api/microplex/target-diagnostics/route.ts delete mode 100644 frontend/app/api/populace/comparison/route.ts delete mode 100644 frontend/app/budget-impact/page.tsx delete mode 100644 frontend/app/comparison/page.tsx delete mode 100644 frontend/app/inventory/page.tsx delete mode 100644 frontend/app/microplex/diagnostics/page.tsx delete mode 100644 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frontend/components/targets/run-selector-card.tsx delete mode 100644 frontend/components/targets/search-and-controls.tsx delete mode 100644 frontend/components/targets/source-summary.tsx delete mode 100644 frontend/data/microplex/latest/pe_native_target_diagnostics.json delete mode 100644 frontend/data/microplex/latest/policyengine_native_scores.json delete mode 100644 frontend/data/populace/latest/comparison_scorecard.json delete mode 100644 frontend/fixtures/constraint-diff.json delete mode 100644 frontend/fixtures/contributors.json delete mode 100644 frontend/fixtures/convergence.json delete mode 100644 frontend/fixtures/datasets.json delete mode 100644 frontend/fixtures/error-decomposition.json delete mode 100644 frontend/fixtures/histogram.json delete mode 100644 frontend/fixtures/index.ts delete mode 100644 frontend/fixtures/provenance.json delete mode 100644 frontend/fixtures/runs.json delete mode 100644 frontend/fixtures/stratum.json delete mode 100644 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frontend/lib/dashboard-mode-context.tsx delete mode 100644 frontend/lib/geo-context.tsx delete mode 100644 frontend/lib/microplex/latest-artifact.ts delete mode 100644 frontend/lib/populace/comparison-artifact.ts delete mode 100644 frontend/lib/run-context.tsx delete mode 100644 tests/test_analysis_readiness.py delete mode 100644 tests/test_api_v1.py delete mode 100644 tests/test_bundle_availability.py delete mode 100644 tests/test_bundle_eval.py delete mode 100644 tests/test_microplex.py delete mode 100644 tests/test_pipeline.py delete mode 100644 tests/test_registry.py delete mode 100644 tests/test_runs.py delete mode 100644 tests/test_stratum_evaluator.py delete mode 100644 tests/test_summary.py delete mode 100644 tests/test_targets_filters.py diff --git a/backend/api/__init__.py b/backend/api/__init__.py deleted file mode 100644 index 1ddeb13..0000000 --- a/backend/api/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Versioned public API package.""" diff --git a/backend/api/v1/__init__.py b/backend/api/v1/__init__.py deleted file mode 100644 index c4f50de..0000000 --- a/backend/api/v1/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Stable v1 diagnostics API.""" diff --git a/backend/api/v1/models.py b/backend/api/v1/models.py deleted file mode 100644 index 15181de..0000000 --- a/backend/api/v1/models.py +++ /dev/null @@ -1,196 +0,0 @@ -"""Pydantic schemas for the stable diagnostics API.""" - -from pydantic import BaseModel, Field - - -class DatasetItem(BaseModel): - dataset_id: str - label: str - repo_id: str - repo_type: str - layout: str - primary_h5: str - - -class DatasetListResponse(BaseModel): - items: list[DatasetItem] - - -class RunItem(BaseModel): - dataset_id: str - run_id: str - label: str - last_modified: str | None = None - - -class RunListResponse(BaseModel): - dataset_id: str - items: list[RunItem] - - -class BundleItem(BaseModel): - bundle: str - kind: str - geography_id: str | None = None - geography_name: str | None = None - target_count: int | None = None - included_target_count: int | None = None - cache_status: str | None = None - - -class BundleListResponse(BaseModel): - dataset_id: str - run_id: str - items: list[BundleItem] - - -class ProvenanceInfo(BaseModel): - target_db: str - diagnostics: str | None = None - aggregate_source: str - calibration_pattern_source: str | None = None - - -class SummaryMetrics(BaseModel): - median_abs_rel_error: float | None = None - mean_abs_rel_error: float | None = None - p95_abs_rel_error: float | None = None - total_loss: float | None = None - - -class SummaryResponse(BaseModel): - dataset_id: str - run_id: str - bundle: str - target_universe_count: int - included_target_count: int - computed_target_count: int - loss_contribution_available: bool - metrics: SummaryMetrics - provenance: ProvenanceInfo - - -class TargetItem(BaseModel): - target_id: int | None = None - target_name: str - variable: str - geo_level: str | None = None - geographic_id: str | None = None - target_value: float | None = None - pe_aggregate: float | None = None - rel_error: float | None = None - abs_rel_error: float | None = None - included_in_loss: bool - loss_contribution: float | None = None - computed_from_bundle: str | None = None - target_value_source: str - included_source: str | None = None - calibration_pattern_source: str | None = None - eval_note: str | None = None - - -class TargetListResponse(BaseModel): - dataset_id: str - run_id: str - bundle: str - items: list[TargetItem] - total: int - offset: int - limit: int - - -class EvaluationFilters(BaseModel): - geo_level: list[str] | None = None - state_fips: list[int] | None = None - geographic_id: str | None = None - variable: list[str] | None = None - source: list[str] | None = None - included: bool | None = None - min_abs_rel_error: float | None = None - - -class EvaluationRequest(BaseModel): - dataset_id: str - run_id: str - bundle: str | None = None - filters: EvaluationFilters = Field(default_factory=EvaluationFilters) - limit: int = 5000 - - -class EvaluationResult(BaseModel): - target_count: int - computed_target_count: int - items_url: str - - -class EvaluationResponse(BaseModel): - status: str - cache_status: str - elapsed_ms: float - result: EvaluationResult - - -class ComparisonSide(BaseModel): - dataset_id: str - run_id: str - bundle: str | None = None - filters: EvaluationFilters = Field(default_factory=EvaluationFilters) - - -class CompareRequest(BaseModel): - a: ComparisonSide - b: ComparisonSide - top_n: int = Field(default=25, ge=1, le=200) - - -class CompareSideMetadata(BaseModel): - dataset_id: str - run_id: str - bundle: str - target_count: int - computed_target_count: int - metrics: SummaryMetrics - provenance: ProvenanceInfo - - -class CompareTargetRow(BaseModel): - target_id: int | None = None - target_name: str - variable: str - geo_level: str | None = None - geographic_id: str | None = None - target_value_a: float | None = None - target_value_b: float | None = None - pe_aggregate_a: float | None = None - pe_aggregate_b: float | None = None - rel_error_a: float | None = None - rel_error_b: float | None = None - abs_rel_error_a: float | None = None - abs_rel_error_b: float | None = None - delta_abs_rel_error: float | None = None - included_in_loss_a: bool - included_in_loss_b: bool - computed_from_bundle_a: str | None = None - computed_from_bundle_b: str | None = None - - -class CompareVariableRow(BaseModel): - variable: str - target_count: int - mean_abs_rel_error_a: float | None = None - mean_abs_rel_error_b: float | None = None - mean_delta_abs_rel_error: float | None = None - improved_count: int - regressed_count: int - - -class CompareResponse(BaseModel): - a: CompareSideMetadata - b: CompareSideMetadata - matched_target_count: int - computed_pair_count: int - improved_count: int - regressed_count: int - improved: list[CompareTargetRow] - regressed: list[CompareTargetRow] - by_variable: list[CompareVariableRow] diff --git a/backend/api/v1/router.py b/backend/api/v1/router.py deleted file mode 100644 index b931275..0000000 --- a/backend/api/v1/router.py +++ /dev/null @@ -1,726 +0,0 @@ -"""Stable v1 API routes for programmatic calibration diagnostics.""" - -from __future__ import annotations - -from pathlib import Path -from time import perf_counter -from typing import Annotated - -import numpy as np -import pandas as pd -from fastapi import APIRouter, HTTPException, Query, Request - -from backend.api.v1.models import ( - BundleItem, - BundleListResponse, - CompareRequest, - CompareResponse, - CompareSideMetadata, - CompareTargetRow, - CompareVariableRow, - ComparisonSide, - DatasetItem, - DatasetListResponse, - EvaluationRequest, - EvaluationResponse, - EvaluationResult, - ProvenanceInfo, - RunItem, - RunListResponse, - SummaryMetrics, - SummaryResponse, - TargetItem, - TargetListResponse, -) -from backend.services import runs as runs_service -from backend.services.bundle_availability import published_bundles -from backend.services.geo_utils import ( - STATE_FIPS_TO_ABBREV, - runtime_dataset_bundle_for, - state_name, -) -from backend.state import AppState - -router = APIRouter(prefix="/api/v1", tags=["api-v1"]) - - -@router.get("/datasets") -def list_datasets_v1() -> DatasetListResponse: - return DatasetListResponse( - items=[ - DatasetItem( - dataset_id=d.id, - label=d.label, - repo_id=d.repo_id, - repo_type=d.repo_type, - layout=d.layout, - primary_h5=d.primary_h5, - ) - for d in runs_service.list_datasets() - ] - ) - - -@router.get("/datasets/{dataset_id}/runs") -def list_runs_v1(dataset_id: str) -> RunListResponse: - try: - runs = runs_service.list_runs(dataset_id) - except KeyError as exc: - raise HTTPException(status_code=404, detail=str(exc)) - return RunListResponse( - dataset_id=dataset_id, - items=[ - RunItem( - dataset_id=r.dataset_id, - run_id=r.run_id, - label=r.label, - last_modified=r.last_modified, - ) - for r in runs - ], - ) - - -@router.get("/datasets/{dataset_id}/runs/{run_id}/bundles") -def list_bundles_v1( - request: Request, - dataset_id: str, - run_id: str, - kind: str | None = None, - state_fips: int | None = None, - include_target_counts: bool = True, - include_cache_status: bool = True, -) -> BundleListResponse: - dataset = _get_dataset_or_404(dataset_id) - bundles = sorted(published_bundles(dataset.repo_id, run_id)) - state = ( - _load_state(request, dataset_id, run_id) - if include_target_counts or include_cache_status - else None - ) - counts = _bundle_target_counts(state, bundles) if state is not None else {} - included_counts = ( - _bundle_target_counts(state, bundles, included_only=True) - if state is not None - else {} - ) - - items: list[BundleItem] = [] - for bundle in bundles: - item = _bundle_item(bundle) - if kind and item.kind != kind: - continue - if state_fips is not None: - if item.kind != "state" or item.geography_id != str(state_fips): - continue - if include_target_counts: - item.target_count = counts.get(bundle, 0) - item.included_target_count = included_counts.get(bundle, 0) - if include_cache_status: - item.cache_status = _bundle_cache_status(dataset.repo_id, run_id, bundle) - items.append(item) - - return BundleListResponse(dataset_id=dataset_id, run_id=run_id, items=items) - - -@router.get("/datasets/{dataset_id}/runs/{run_id}/summary") -def get_summary_v1( - request: Request, - dataset_id: str, - run_id: str, - bundle: str | None = None, - included: bool | None = None, - geo_level: Annotated[list[str] | None, Query()] = None, - state_fips: Annotated[list[int] | None, Query()] = None, -) -> SummaryResponse: - dataset = _get_dataset_or_404(dataset_id) - state = _load_state(request, dataset_id, run_id) - active_bundle = bundle or dataset.primary_h5 - df = _prepare_targets( - state, - dataset, - run_id, - bundle=active_bundle if bundle else None, - geo_level=geo_level, - state_fips=state_fips, - included=included, - ) - return _summary_response( - state, - dataset, - run_id, - active_bundle, - df, - ) - - -@router.get("/datasets/{dataset_id}/runs/{run_id}/targets") -def list_targets_v1( - request: Request, - dataset_id: str, - run_id: str, - bundle: str | None = None, - geo_level: Annotated[list[str] | None, Query()] = None, - state_fips: Annotated[list[int] | None, Query()] = None, - geographic_id: str | None = None, - variable: Annotated[list[str] | None, Query()] = None, - source: Annotated[list[str] | None, Query()] = None, - included: bool | None = None, - min_abs_rel_error: float | None = None, - sort: str = "abs_rel_error", - order: str = "desc", - limit: Annotated[int, Query(ge=1, le=5000)] = 50, - offset: Annotated[int, Query(ge=0)] = 0, -) -> TargetListResponse: - dataset = _get_dataset_or_404(dataset_id) - state = _load_state(request, dataset_id, run_id) - active_bundle = bundle or dataset.primary_h5 - df = _prepare_targets( - state, - dataset, - run_id, - bundle=active_bundle if bundle else None, - geo_level=geo_level, - state_fips=state_fips, - geographic_id=geographic_id, - variable=variable, - source=source, - included=included, - min_abs_rel_error=min_abs_rel_error, - ) - total = len(df) - if sort in df.columns: - df = df.sort_values(sort, ascending=(order == "asc")) - page = df.iloc[offset : offset + limit] - return TargetListResponse( - dataset_id=dataset_id, - run_id=run_id, - bundle=active_bundle, - items=[_target_item(row, active_bundle) for _, row in page.iterrows()], - total=total, - offset=offset, - limit=limit, - ) - - -@router.get("/datasets/{dataset_id}/runs/{run_id}/targets/{target_id}") -def get_target_v1( - request: Request, - dataset_id: str, - run_id: str, - target_id: int, - bundle: str | None = None, -) -> TargetItem: - dataset = _get_dataset_or_404(dataset_id) - state = _load_state(request, dataset_id, run_id) - active_bundle = bundle or dataset.primary_h5 - df = _prepare_targets( - state, - dataset, - run_id, - bundle=active_bundle if bundle else None, - ) - match = df[df["target_id"].astype("Int64") == target_id] - if match.empty: - raise HTTPException(status_code=404, detail=f"Unknown target_id: {target_id}") - return _target_item(match.iloc[0], active_bundle) - - -@router.post("/evaluate") -def evaluate_v1( - request: Request, - payload: EvaluationRequest, -) -> EvaluationResponse: - start = perf_counter() - dataset = _get_dataset_or_404(payload.dataset_id) - state = _load_state(request, payload.dataset_id, payload.run_id) - active_bundle = payload.bundle or dataset.primary_h5 - filters = payload.filters - before_status = _bundle_cache_status(dataset.repo_id, payload.run_id, active_bundle) - df = _prepare_targets( - state, - dataset, - payload.run_id, - bundle=active_bundle if payload.bundle else None, - geo_level=filters.geo_level, - state_fips=filters.state_fips, - geographic_id=filters.geographic_id, - variable=filters.variable, - source=filters.source, - included=filters.included, - min_abs_rel_error=filters.min_abs_rel_error, - ) - limited = df.head(max(0, payload.limit)) - computed = int( - np.isfinite(limited.get("estimate", pd.Series(dtype=float)).to_numpy(dtype=float)).sum() - ) - after_status = _bundle_cache_status(dataset.repo_id, payload.run_id, active_bundle) - params = [f"bundle={active_bundle}"] - if filters.included is not None: - params.append(f"included={str(filters.included).lower()}") - for level in filters.geo_level or []: - params.append(f"geo_level={level}") - for fips in filters.state_fips or []: - params.append(f"state_fips={fips}") - query = "&".join(params) - items_url = ( - f"/api/v1/datasets/{payload.dataset_id}/runs/{payload.run_id}/targets" - f"?{query}" - ) - return EvaluationResponse( - status="complete", - cache_status=after_status if after_status != "not_computed" else before_status, - elapsed_ms=(perf_counter() - start) * 1000, - result=EvaluationResult( - target_count=int(len(limited)), - computed_target_count=computed, - items_url=items_url, - ), - ) - - -@router.post("/compare") -def compare_v1( - request: Request, - payload: CompareRequest, -) -> CompareResponse: - side_a = _comparison_side(request, payload.a) - side_b = _comparison_side(request, payload.b) - merged = _merge_comparison_targets(side_a["df"], side_b["df"]) - if merged.empty: - raise HTTPException( - status_code=400, - detail="No compatible targets matched between comparison sides.", - ) - - valid = merged.dropna(subset=["abs_rel_error_a", "abs_rel_error_b"]).copy() - if valid.empty: - computed_pair_count = 0 - improved_rows: list[CompareTargetRow] = [] - regressed_rows: list[CompareTargetRow] = [] - by_variable: list[CompareVariableRow] = [] - improved_count = 0 - regressed_count = 0 - else: - valid["delta_abs_rel_error"] = ( - valid["abs_rel_error_b"] - valid["abs_rel_error_a"] - ) - computed_pair_count = int(len(valid)) - improved = valid[valid["delta_abs_rel_error"] < 0] - regressed = valid[valid["delta_abs_rel_error"] > 0] - improved_count = int(len(improved)) - regressed_count = int(len(regressed)) - improved_rows = [ - _compare_target_row(row, side_a["bundle"], side_b["bundle"]) - for _, row in improved.nsmallest( - payload.top_n, - "delta_abs_rel_error", - ).iterrows() - ] - regressed_rows = [ - _compare_target_row(row, side_a["bundle"], side_b["bundle"]) - for _, row in regressed.nlargest( - payload.top_n, - "delta_abs_rel_error", - ).iterrows() - ] - by_variable = _compare_by_variable(valid, payload.top_n) - - return CompareResponse( - a=_compare_side_metadata(side_a), - b=_compare_side_metadata(side_b), - matched_target_count=int(len(merged)), - computed_pair_count=computed_pair_count, - improved_count=improved_count, - regressed_count=regressed_count, - improved=improved_rows, - regressed=regressed_rows, - by_variable=by_variable, - ) - - -def _get_dataset_or_404(dataset_id: str): - try: - return runs_service.get_dataset(dataset_id) - except KeyError as exc: - raise HTTPException(status_code=404, detail=str(exc)) - - -def _load_state(request: Request, dataset_id: str, run_id: str) -> AppState: - try: - return request.app.state.registry.get(dataset_id, run_id) - except KeyError as exc: - raise HTTPException(status_code=404, detail=str(exc)) - except Exception as exc: - raise HTTPException( - status_code=500, - detail=f"Failed to load run {dataset_id}/{run_id}: {exc}", - ) - - -def _available_bundles(dataset, run_id: str) -> frozenset[str]: - if dataset.layout not in {"staging", "root", "staging-root"}: - return frozenset() - return published_bundles(dataset.repo_id, run_id) - - -def _prepare_targets( - state: AppState, - dataset, - run_id: str, - *, - bundle: str | None = None, - geo_level: list[str] | None = None, - state_fips: list[int] | None = None, - geographic_id: str | None = None, - variable: list[str] | None = None, - source: list[str] | None = None, - included: bool | None = None, - min_abs_rel_error: float | None = None, -) -> pd.DataFrame: - from backend.routes.targets import _apply_target_filters - from backend.services.bundle_eval import evaluate_bundle - - available = _available_bundles(dataset, run_id) - df = _apply_target_filters( - state.targets_enriched, - variables=variable, - geo_levels=geo_level, - sources=source, - geographic_id=geographic_id, - state_fips=state_fips, - included_only=included, - min_abs_rel_error=min_abs_rel_error, - dataset_files=[bundle] if bundle else None, - available_bundles=available, - ).copy() - - if ( - bundle - and bundle in available - and bundle != dataset.primary_h5 - and not df.empty - ): - df = evaluate_bundle( - df, - repo_id=dataset.repo_id, - run_id=run_id, - bundle=bundle, - time_period=state.time_period, - ) - return df - - -def _comparison_side(request: Request, side: ComparisonSide) -> dict: - dataset = _get_dataset_or_404(side.dataset_id) - state = _load_state(request, side.dataset_id, side.run_id) - active_bundle = side.bundle or dataset.primary_h5 - filters = side.filters - df = _prepare_targets( - state, - dataset, - side.run_id, - bundle=active_bundle if side.bundle else None, - geo_level=filters.geo_level, - state_fips=filters.state_fips, - geographic_id=filters.geographic_id, - variable=filters.variable, - source=filters.source, - included=filters.included, - min_abs_rel_error=filters.min_abs_rel_error, - ) - return { - "dataset": dataset, - "state": state, - "run_id": side.run_id, - "bundle": active_bundle, - "df": df, - } - - -def _merge_comparison_targets(df_a: pd.DataFrame, df_b: pd.DataFrame) -> pd.DataFrame: - keep = [ - "target_id", - "target_name", - "variable", - "geo_level", - "geographic_id", - "value", - "estimate", - "rel_error", - "abs_rel_error", - "included", - ] - a = df_a[[col for col in keep if col in df_a.columns]].dropna( - subset=["target_id"], - ) - b = df_b[[col for col in keep if col in df_b.columns]].dropna( - subset=["target_id"], - ) - a = a.rename(columns={col: f"{col}_a" for col in keep if col != "target_id"}) - b = b.rename(columns={col: f"{col}_b" for col in keep if col != "target_id"}) - merged = a.merge(b, on="target_id", how="inner") - if merged.empty: - return merged - - compatible = pd.Series(True, index=merged.index) - for field in ("target_name", "variable", "geo_level", "geographic_id"): - left = f"{field}_a" - right = f"{field}_b" - if left in merged.columns and right in merged.columns: - compatible &= ( - merged[left].astype("string").fillna("") - == merged[right].astype("string").fillna("") - ) - return merged[compatible].copy() - - -def _compare_side_metadata(side: dict) -> CompareSideMetadata: - summary = _summary_response( - side["state"], - side["dataset"], - side["run_id"], - side["bundle"], - side["df"], - ) - return CompareSideMetadata( - dataset_id=summary.dataset_id, - run_id=summary.run_id, - bundle=summary.bundle, - target_count=summary.target_universe_count, - computed_target_count=summary.computed_target_count, - metrics=summary.metrics, - provenance=summary.provenance, - ) - - -def _compare_target_row( - row: pd.Series, - bundle_a: str, - bundle_b: str, -) -> CompareTargetRow: - estimate_a = _safe_float(row.get("estimate_a")) - estimate_b = _safe_float(row.get("estimate_b")) - return CompareTargetRow( - target_id=_safe_int(row.get("target_id")), - target_name=str(row.get("target_name_a", "")), - variable=str(row.get("variable_a", "")), - geo_level=_none_if_nan(row.get("geo_level_a")), - geographic_id=( - None if _none_if_nan(row.get("geographic_id_a")) is None - else str(_none_if_nan(row.get("geographic_id_a"))) - ), - target_value_a=_safe_float(row.get("value_a")), - target_value_b=_safe_float(row.get("value_b")), - pe_aggregate_a=estimate_a, - pe_aggregate_b=estimate_b, - rel_error_a=_safe_float(row.get("rel_error_a")), - rel_error_b=_safe_float(row.get("rel_error_b")), - abs_rel_error_a=_safe_float(row.get("abs_rel_error_a")), - abs_rel_error_b=_safe_float(row.get("abs_rel_error_b")), - delta_abs_rel_error=_safe_float(row.get("delta_abs_rel_error")), - included_in_loss_a=bool(row.get("included_a", False)), - included_in_loss_b=bool(row.get("included_b", False)), - computed_from_bundle_a=bundle_a if estimate_a is not None else None, - computed_from_bundle_b=bundle_b if estimate_b is not None else None, - ) - - -def _compare_by_variable( - valid: pd.DataFrame, - top_n: int, -) -> list[CompareVariableRow]: - if valid.empty: - return [] - rows: list[CompareVariableRow] = [] - grouped = valid.groupby("variable_a", dropna=False) - for variable, df in grouped: - deltas = df["delta_abs_rel_error"] - rows.append( - CompareVariableRow( - variable=str(variable), - target_count=int(len(df)), - mean_abs_rel_error_a=_safe_float(df["abs_rel_error_a"].mean()), - mean_abs_rel_error_b=_safe_float(df["abs_rel_error_b"].mean()), - mean_delta_abs_rel_error=_safe_float(deltas.mean()), - improved_count=int((deltas < 0).sum()), - regressed_count=int((deltas > 0).sum()), - ) - ) - return sorted( - rows, - key=lambda row: abs(row.mean_delta_abs_rel_error or 0), - reverse=True, - )[:top_n] - - -def _bundle_target_counts( - state: AppState | None, - available: list[str], - *, - included_only: bool = False, -) -> dict[str, int]: - if state is None or state.targets_enriched.empty: - return {} - df = state.targets_enriched - if included_only: - df = df[df["included"].astype(bool)] - available_set = frozenset(available) - counts: dict[str, int] = {} - for _, row in df.iterrows(): - bundle = runtime_dataset_bundle_for( - row.get("geo_level"), - row.get("geographic_id"), - available=available_set, - ) - counts[bundle] = counts.get(bundle, 0) + 1 - return counts - - -def _bundle_item(bundle: str) -> BundleItem: - if bundle.startswith("states/") and bundle.endswith(".h5"): - code = bundle.removeprefix("states/").removesuffix(".h5") - fips = next( - (f for f, abbrev in STATE_FIPS_TO_ABBREV.items() if abbrev == code), - None, - ) - return BundleItem( - bundle=bundle, - kind="state", - geography_id=str(fips) if fips is not None else None, - geography_name=state_name(fips) if fips is not None else code, - ) - if bundle.startswith("districts/"): - return BundleItem( - bundle=bundle, - kind="district", - geography_name=bundle.removeprefix("districts/").removesuffix(".h5"), - ) - if bundle.startswith("national/"): - return BundleItem(bundle=bundle, kind="national", geography_id="US", geography_name="National") - if bundle.startswith("cities/"): - return BundleItem( - bundle=bundle, - kind="city", - geography_name=bundle.removeprefix("cities/").removesuffix(".h5"), - ) - return BundleItem(bundle=bundle, kind="primary") - - -def _bundle_cache_status( - repo_id: str, - run_id: str, - bundle: str, - cache_root: str = ".artifacts", -) -> str: - repo_slug = repo_id.replace("/", "__") - layout_dir = "root" if run_id == "main" else "staging" - safe = bundle.replace("/", "__") - cache = ( - Path(cache_root) / repo_slug / layout_dir / run_id - / f"{safe}.bundle_estimates.pkl" - ) - return "computed" if cache.exists() else "not_computed" - - -def _summary_response( - state: AppState, - dataset, - run_id: str, - bundle: str, - df: pd.DataFrame, -) -> SummaryResponse: - rel = df.get("rel_error", pd.Series(dtype=float)).to_numpy(dtype=float) - abs_rel = df.get("abs_rel_error", pd.Series(dtype=float)).to_numpy(dtype=float) - finite = np.isfinite(rel) & np.isfinite(abs_rel) - abs_finite = abs_rel[finite] - included = df["included"].astype(bool) if "included" in df.columns else pd.Series(False, index=df.index) - loss_available = bool(included.any()) - included_rel = rel[included.to_numpy(dtype=bool)] if len(rel) else np.array([]) - included_rel = included_rel[np.isfinite(included_rel)] - return SummaryResponse( - dataset_id=state.dataset_id, - run_id=run_id, - bundle=bundle, - target_universe_count=int(len(df)), - included_target_count=int(included.sum()), - computed_target_count=int(finite.sum()), - loss_contribution_available=loss_available, - metrics=SummaryMetrics( - median_abs_rel_error=_safe_float(np.median(abs_finite)) if len(abs_finite) else None, - mean_abs_rel_error=_safe_float(np.mean(abs_finite)) if len(abs_finite) else None, - p95_abs_rel_error=_safe_float(np.percentile(abs_finite, 95)) if len(abs_finite) else None, - total_loss=( - _safe_float(np.sum(included_rel ** 2)) - if loss_available and len(included_rel) - else None - ), - ), - provenance=_provenance(dataset, bundle), - ) - - -def _target_item(row: pd.Series, bundle: str) -> TargetItem: - included = bool(row.get("included", False)) - estimate = _safe_float(row.get("estimate")) - return TargetItem( - target_id=_safe_int(row.get("target_id")), - target_name=str(row.get("target_name", "")), - variable=str(row.get("variable", "")), - geo_level=_none_if_nan(row.get("geo_level")), - geographic_id=( - None if _none_if_nan(row.get("geographic_id")) is None - else str(_none_if_nan(row.get("geographic_id"))) - ), - target_value=_safe_float(row.get("value")), - pe_aggregate=estimate, - rel_error=_safe_float(row.get("rel_error")), - abs_rel_error=_safe_float(row.get("abs_rel_error")), - included_in_loss=included, - loss_contribution=_safe_float(row.get("loss_contribution")) if included else None, - computed_from_bundle=bundle if estimate is not None else None, - target_value_source="policy_data.db", - included_source="unified_diagnostics.csv" if included else None, - calibration_pattern_source=None, - eval_note=_none_if_nan(row.get("eval_note")), - ) - - -def _provenance(dataset, bundle: str) -> ProvenanceInfo: - if dataset.layout == "root": - diagnostics = "calibration/logs/unified_diagnostics.csv" - elif dataset.layout == "staging-root": - diagnostics = None - else: - diagnostics = "calibration/runs//diagnostics/unified_diagnostics.csv" - return ProvenanceInfo( - target_db="policy_data.db", - diagnostics=diagnostics, - aggregate_source=bundle, - calibration_pattern_source=None, - ) - - -def _safe_float(value) -> float | None: - if value is None or pd.isna(value): - return None - try: - out = float(value) - except (TypeError, ValueError): - return None - return out if np.isfinite(out) else None - - -def _safe_int(value) -> int | None: - if value is None or pd.isna(value): - return None - try: - return int(value) - except (TypeError, ValueError): - return None - - -def _none_if_nan(value): - if value is None or pd.isna(value): - return None - return value diff --git a/backend/app.py b/backend/app.py index 547c0b3..04a7823 100644 --- a/backend/app.py +++ b/backend/app.py @@ -1,35 +1,19 @@ -"""FastAPI application factory. +"""FastAPI application for the populace-US calibration dashboard. -The backend hosts a registry of loaded calibration runs; each request -selects a run via ?dataset & ?run query params. See backend.state.get_state. +A thin API layer over the populace-US dataset published on Hugging Face: it +resolves the current release via ``latest.json`` and serves release manifests, +per-target calibration diagnostics, and version-over-version comparisons, +reading everything live from the Hub. The Next.js API routes are the primary +API surface; this mirrors them for local development and Python clients. """ import logging -import os -from contextlib import asynccontextmanager from fastapi import FastAPI, Request from fastapi.middleware.cors import CORSMiddleware from fastapi.responses import JSONResponse -from backend.api.v1.router import router as api_v1_router -from backend.routes import ( - analysis, - compare, - geography, - microplex, - nodes, - pipeline, - populace, - runs as runs_route, - strata, - summary, - target_inventory, - targets, - weights, -) -from backend.services import runs as runs_service -from backend.services.registry import RunRegistry +from backend.routes import populace logging.basicConfig( level=logging.INFO, @@ -37,34 +21,9 @@ ) logger = logging.getLogger(__name__) - -@asynccontextmanager -async def lifespan(app: FastAPI): - cache_size = int(os.environ.get("RUN_CACHE_SIZE", "3")) - app.state.registry = RunRegistry(max_size=cache_size) - - # Optional warm-load of a default run, so /health and existing endpoints - # work without query params during development. - default = runs_service.default_selection() - if default is not None: - dataset_id, run_id = default - logger.info("Warming default run %s/%s", dataset_id, run_id) - try: - app.state.registry.get(dataset_id, run_id) - except Exception: - logger.exception("Failed to warm-load default run") - else: - logger.info( - "No DEFAULT_DATASET/DEFAULT_RUN set; clients must pass " - "?dataset & ?run query params." - ) - yield - - app = FastAPI( - title="Calibration Diagnostics API", - description="Interactive diagnostics for survey weight calibration", - lifespan=lifespan, + title="Populace calibration diagnostics API", + description="Live diagnostics for the populace-US synthetic population", ) app.add_middleware( @@ -74,39 +33,14 @@ async def lifespan(app: FastAPI): allow_headers=["*"], ) -app.include_router(runs_route.router, tags=["runs"]) -app.include_router(api_v1_router) -app.include_router(summary.router, tags=["summary"]) -app.include_router(pipeline.router, tags=["pipeline"]) -app.include_router(target_inventory.router, tags=["target-inventory"]) -app.include_router(nodes.router, tags=["nodes"]) -app.include_router(analysis.router, prefix="/analysis", tags=["analysis"]) -app.include_router(compare.router, tags=["compare"]) -app.include_router(microplex.router, tags=["microplex"]) app.include_router(populace.router, tags=["populace"]) -app.include_router(geography.router, prefix="/geography", tags=["geography"]) -app.include_router(targets.router, prefix="/targets", tags=["targets"]) -app.include_router(strata.router, prefix="/strata", tags=["strata"]) -app.include_router(weights.router, prefix="/weights", tags=["weights"]) @app.get("/health") -def health(request: Request): - registry: RunRegistry = request.app.state.registry - return { - "status": "ok", - "loaded_runs": [ - {"dataset": d, "run": r} for d, r in registry.loaded_keys() - ], - "default": runs_service.default_selection(), - } +def health(): + return {"status": "ok", "dataset": "policyengine/populace-us"} @app.exception_handler(ValueError) async def value_error_handler(request: Request, exc: ValueError): return JSONResponse(status_code=400, content={"detail": str(exc)}) - - -@app.exception_handler(IndexError) -async def index_error_handler(request: Request, exc: IndexError): - return JSONResponse(status_code=404, content={"detail": "Index out of range"}) diff --git a/backend/routes/analysis.py b/backend/routes/analysis.py deleted file mode 100644 index 6e37b73..0000000 --- a/backend/routes/analysis.py +++ /dev/null @@ -1,114 +0,0 @@ -"""Analyst readiness endpoints.""" - -from __future__ import annotations - -from fastapi import APIRouter, Depends, HTTPException, Query - -from backend.services.analysis_readiness import ( - audit_target_config, - build_dependency_trace, - build_domain_breakdown, - build_bundle_health, - build_readiness, - list_case_studies, - list_policyengine_variables, -) -from backend.state import AppState, get_state - -router = APIRouter() - - -@router.get("/case-studies") -def case_studies() -> dict: - return {"items": list_case_studies()} - - -@router.get("/variables") -def variables( - search: str | None = None, - limit: int = Query(100, ge=1, le=500), - state: AppState = Depends(get_state), -) -> dict: - return { - "items": list_policyengine_variables( - state, - search=search, - limit=limit, - ) - } - - -@router.get("/readiness/{case_study_id}") -def readiness( - case_study_id: str, - state: AppState = Depends(get_state), -) -> dict: - try: - return build_readiness(case_study_id, state) - except KeyError as exc: - raise HTTPException( - status_code=404, - detail=f"Unknown case study: {case_study_id}", - ) from exc - - -@router.get("/dependency/{variable}") -def dependency_trace( - variable: str, - period: int | None = None, - max_nodes: int = Query(250, ge=25, le=1000), - state: AppState = Depends(get_state), -) -> dict: - try: - return build_dependency_trace( - variable, - state, - period=period, - max_nodes=max_nodes, - ) - except KeyError as exc: - raise HTTPException( - status_code=404, - detail=f"Unknown PolicyEngine variable: {variable}", - ) from exc - except RuntimeError as exc: - raise HTTPException(status_code=503, detail=str(exc)) from exc - - -@router.get("/target-config-audit") -def target_config_audit( - variable: str | None = None, - state: AppState = Depends(get_state), -) -> dict: - return audit_target_config(state, variable=variable) - - -@router.get("/domain-breakdown") -def domain_breakdown( - variable: str | None = None, - domain_variable: str = "adjusted_gross_income", - geo_level: str | None = None, - state: AppState = Depends(get_state), -) -> dict: - return build_domain_breakdown( - state, - variable=variable, - domain_variable=domain_variable, - geo_level=geo_level, - ) - - -@router.get("/bundle-health") -def bundle_health( - dataset_file: str, - limit: int = Query(10, ge=1, le=100), - state: AppState = Depends(get_state), -) -> dict: - try: - return build_bundle_health( - state, - dataset_file=dataset_file, - limit=limit, - ) - except KeyError as exc: - raise HTTPException(status_code=404, detail=str(exc)) from exc diff --git a/backend/routes/compare.py b/backend/routes/compare.py deleted file mode 100644 index fa35d5f..0000000 --- a/backend/routes/compare.py +++ /dev/null @@ -1,205 +0,0 @@ -"""Pairwise run comparison. - -Same dataset, two runs. Joins both runs' ``targets_enriched`` on -``target_id`` and surfaces: - -- Headline deltas: how much closer (or farther) each run is overall. -- Biggest movers: per-target rows where the absolute relative error - shifted most between A and B, split into improvements and regressions. - -We do NOT use the standard ``get_state`` dependency because we need two -states at once. The route resolves both runs through the registry -directly. -""" - -from __future__ import annotations - -from typing import Annotated - -import numpy as np -import pandas as pd -from fastapi import APIRouter, HTTPException, Query, Request - -router = APIRouter() - - -_DEFAULT_MOVERS = 25 - - -def _headline(df: pd.DataFrame) -> dict: - """Headline error stats over the included subset of a run.""" - if df.empty or "abs_rel_error" not in df.columns: - return _empty_headline() - included = df[df["included"].astype(bool)] if "included" in df.columns else df - abs_err = included["abs_rel_error"].to_numpy() - finite = abs_err[np.isfinite(abs_err)] - if not len(finite): - return _empty_headline(n_total=int(len(df)), n_included=int(len(included))) - return { - "n_total": int(len(df)), - "n_included": int(len(included)), - "n_with_estimate": int(np.sum(~np.isnan(abs_err))), - "median_abs_rel_error": float(np.median(finite)), - "mean_abs_rel_error": float(np.mean(finite)), - "p95_abs_rel_error": float(np.percentile(finite, 95)), - "pct_within_5pct": float(np.mean(finite <= 0.05)), - "pct_within_10pct": float(np.mean(finite <= 0.10)), - "pct_within_25pct": float(np.mean(finite <= 0.25)), - } - - -def _empty_headline(n_total: int = 0, n_included: int = 0) -> dict: - return { - "n_total": n_total, - "n_included": n_included, - "n_with_estimate": 0, - "median_abs_rel_error": None, - "mean_abs_rel_error": None, - "p95_abs_rel_error": None, - "pct_within_5pct": None, - "pct_within_10pct": None, - "pct_within_25pct": None, - } - - -def _movers(merged: pd.DataFrame, top_n: int) -> dict: - """Return ``top_n`` rows in each direction (improvements / regressions). - - Rank by signed change in absolute relative error: - delta = abs_rel_error_b - abs_rel_error_a - Negative ⇒ B is closer (improvement). Positive ⇒ B is farther - (regression). Rows missing an estimate in either run are excluded. - """ - valid = merged.dropna(subset=["abs_rel_error_a", "abs_rel_error_b"]) - if valid.empty: - return {"improved": [], "regressed": []} - delta = valid["abs_rel_error_b"] - valid["abs_rel_error_a"] - ranked = valid.assign(delta=delta) - - cols = [ - "target_id", "variable", "geo_level", "geographic_id", - "value", "estimate_a", "estimate_b", - "rel_error_a", "rel_error_b", - "abs_rel_error_a", "abs_rel_error_b", "delta", - ] - improved = ranked.nsmallest(top_n, "delta")[cols].to_dict(orient="records") - regressed = ranked.nlargest(top_n, "delta")[cols].to_dict(orient="records") - return {"improved": improved, "regressed": regressed} - - -def _by_variable(merged: pd.DataFrame, top_n: int) -> list[dict]: - """Per-variable rollup: mean abs_rel_error for each run + counts of - targets that improved / regressed by >1pp.""" - valid = merged.dropna(subset=["abs_rel_error_a", "abs_rel_error_b"]) - if valid.empty: - return [] - grouped = valid.groupby("variable").agg( - n_targets=("target_id", "size"), - mean_a=("abs_rel_error_a", "mean"), - mean_b=("abs_rel_error_b", "mean"), - ) - delta = valid["abs_rel_error_b"] - valid["abs_rel_error_a"] - improvement_counts = valid.assign(d=delta).groupby("variable")["d"].apply( - lambda s: int((s < -0.01).sum()) - ) - regression_counts = valid.assign(d=delta).groupby("variable")["d"].apply( - lambda s: int((s > 0.01).sum()) - ) - grouped["n_improved"] = improvement_counts - grouped["n_regressed"] = regression_counts - grouped["mean_delta"] = grouped["mean_b"] - grouped["mean_a"] - grouped = grouped.sort_values("mean_delta", ascending=False) - rows = [] - for var, r in grouped.head(top_n).iterrows(): - rows.append({ - "variable": str(var), - "n_targets": int(r["n_targets"]), - "mean_abs_rel_error_a": float(r["mean_a"]), - "mean_abs_rel_error_b": float(r["mean_b"]), - "mean_delta": float(r["mean_delta"]), - "n_improved": int(r["n_improved"]), - "n_regressed": int(r["n_regressed"]), - }) - return rows - - -def _scrub(obj): - """Replace non-finite floats with None for JSON serialization.""" - if isinstance(obj, dict): - return {k: _scrub(v) for k, v in obj.items()} - if isinstance(obj, list): - return [_scrub(x) for x in obj] - if isinstance(obj, float) and not np.isfinite(obj): - return None - return obj - - -@router.get("/compare") -def compare( - request: Request, - dataset: Annotated[str, Query(description="Dataset id, e.g. us-data")], - run_a: Annotated[str, Query(description="Baseline run id")], - run_b: Annotated[str, Query(description="Comparison run id")], - top_n: Annotated[int, Query(ge=5, le=200)] = _DEFAULT_MOVERS, -) -> dict: - """Side-by-side comparison of two runs of the same dataset. - - Joins on ``target_id`` so the comparison is target-equivalent (both - runs must come from the same dataset → same targets table). Same run - on both sides is allowed; deltas will be all zero. - """ - if not run_a or not run_b: - raise HTTPException(status_code=400, detail="run_a and run_b are required") - - registry = request.app.state.registry - try: - state_a = registry.get(dataset, run_a) - state_b = registry.get(dataset, run_b) - except KeyError as exc: - raise HTTPException(status_code=404, detail=str(exc)) - except Exception as exc: - raise HTTPException( - status_code=500, - detail=f"Failed to load one of the runs: {exc}", - ) - - df_a = state_a.targets_enriched - df_b = state_b.targets_enriched - if df_a.empty or df_b.empty: - raise HTTPException( - status_code=400, - detail="One of the runs has no targets loaded.", - ) - - headline_a = _headline(df_a) - headline_b = _headline(df_b) - - # Join on target_id. Carry along identity columns (value etc.) - # from A; both runs should agree on these since dataset is shared. - keep = ["target_id", "variable", "geo_level", "geographic_id", "value"] - a = df_a[keep + ["estimate", "rel_error", "abs_rel_error"]].rename( - columns={ - "estimate": "estimate_a", - "rel_error": "rel_error_a", - "abs_rel_error": "abs_rel_error_a", - } - ) - b = df_b[["target_id", "estimate", "rel_error", "abs_rel_error"]].rename( - columns={ - "estimate": "estimate_b", - "rel_error": "rel_error_b", - "abs_rel_error": "abs_rel_error_b", - } - ) - merged = a.merge(b, on="target_id", how="inner") - - payload = { - "dataset": dataset, - "run_a": run_a, - "run_b": run_b, - "headline_a": headline_a, - "headline_b": headline_b, - "movers": _movers(merged, top_n), - "by_variable": _by_variable(merged, top_n), - } - return _scrub(payload) diff --git a/backend/routes/geography.py b/backend/routes/geography.py deleted file mode 100644 index eb1bb12..0000000 --- a/backend/routes/geography.py +++ /dev/null @@ -1,49 +0,0 @@ -"""Geography listing and lookup endpoints.""" - -import numpy as np -from fastapi import APIRouter, Depends - -from backend.services.geo_utils import ( - cd_display_name, - list_states, - state_name, -) -from backend.state import AppState, get_state - -router = APIRouter() - - -@router.get("/states") -def get_states() -> list[dict]: - """List all states with FIPS, name, and abbreviation.""" - return list_states() - - -@router.get("/districts") -def get_districts( - state: AppState = Depends(get_state), -) -> list[dict]: - """List all congressional districts present in the dataset.""" - cd_col = state.households_df["cd_geoid"].values - unique_cds = sorted(set(int(x) for x in cd_col if x != 0)) - return [ - {"cd_geoid": cd, "name": cd_display_name(cd)} - for cd in unique_cds - ] - - -@router.get("/districts/{state_fips}") -def get_districts_for_state( - state_fips: int, - state: AppState = Depends(get_state), -) -> list[dict]: - """List congressional districts in a specific state.""" - cd_col = state.households_df["cd_geoid"].values - unique_cds = sorted(set( - int(x) for x in cd_col - if x != 0 and int(x) // 100 == state_fips - )) - return [ - {"cd_geoid": cd, "name": cd_display_name(cd)} - for cd in unique_cds - ] diff --git a/backend/routes/microplex.py b/backend/routes/microplex.py deleted file mode 100644 index 962c7c8..0000000 --- a/backend/routes/microplex.py +++ /dev/null @@ -1,2086 +0,0 @@ -"""Microplex target-performance view. - -Reads the parity / regression / drilldown JSONs that microplex-us -commits under ``artifacts/`` directly from GitHub. Newer Microplex runs -write full per-target diagnostics as a run-bundle artifact named -``policyengine_native_target_diagnostics`` with path -``pe_native_target_diagnostics.json``. Those run bundles, the run index, -native audit, and output H5 are generated artifacts, not committed public -JSONs, so the committed summaries are the only public signal we can pull -in without credentials or a separately supplied artifact root. - -This view is intentionally read-only and aggregate. When microplex -publishes its generated target diagnostics or H5 artifacts, this route can -serve Microplex's standalone target-oracle performance table and optionally -compare it with us-data. -""" - -from __future__ import annotations - -import json -import logging -import os -import time -from functools import cmp_to_key, lru_cache -from pathlib import Path -from typing import Any - -from fastapi import APIRouter, HTTPException -import numpy as np - -logger = logging.getLogger(__name__) -router = APIRouter() - - -# Pinned filenames in the microplex-us repo. If the team renames or -# rotates these the dashboard will surface a 404 with the path so we can -# bump the constants. -_PARITY_PATH = ( - "artifacts/live_pe_native_cps_puf_rich_broad_fixed_20260329/" - "20260329T175330Z-057066af/pe_us_data_rebuild_parity.json" -) -_REGRESSION_SUMMARY_PATH = ( - "artifacts/live_pe_us_data_rebuild_checkpoint_modelpass_" - "regression_summary_20260410.json" -) -_IRS_DRILLDOWN_PATH = ( - "artifacts/live_pe_us_data_rebuild_checkpoint_national_irs_" - "other_drilldown_20260410.json" -) -_RUN_LEVEL_TARGET_DIAGNOSTICS_PATH = "pe_native_target_diagnostics.json" -_RUN_LEVEL_TARGET_DIAGNOSTICS_MANIFEST_KEY = ( - "policyengine_native_target_diagnostics" -) -_LEGACY_STATIC_TARGET_DIAGNOSTICS_PATH = ( - "artifacts/pe_native_target_diagnostics_current.json" -) - -_GITHUB_RAW = "https://raw.githubusercontent.com/PolicyEngine/microplex-us/main" - -_ARTIFACTS = { - "parity": _PARITY_PATH, - "regression_summary": _REGRESSION_SUMMARY_PATH, - "irs_drilldown": _IRS_DRILLDOWN_PATH, -} - -_GENERATED_ARTIFACT_CONTRACT = [ - { - "name": "full_target_diagnostics", - "path_hint": _RUN_LEVEL_TARGET_DIAGNOSTICS_PATH, - "manifest_key": _RUN_LEVEL_TARGET_DIAGNOSTICS_MANIFEST_KEY, - "legacy_static_dashboard_path": _LEGACY_STATIC_TARGET_DIAGNOSTICS_PATH, - "producer": "build_us_pe_native_target_diagnostics_payload", - "public_committed": False, - "description": ( - "Full per-target PE-native rows saved inside each newer Microplex " - "run bundle. The rows show Microplex aggregate estimates against " - "target values, with us-data comparator fields when present." - ), - }, - { - "name": "dashboard_payload", - "path_hint": "artifacts/microplex_dashboard_current.json", - "producer": "microplex-us-dashboard", - "public_committed": False, - "description": "Living dashboard payload with score runs, logs, and target diagnostics.", - }, - { - "name": "native_scores", - "path_hint": "policyengine_native_scores.json", - "producer": "compute_us_pe_native_scores", - "public_committed": False, - "description": "Compact broad native-loss summary for one artifact bundle.", - }, - { - "name": "native_audit", - "path_hint": "pe_us_data_rebuild_native_audit.json", - "producer": "build_policyengine_us_data_rebuild_native_audit", - "public_committed": False, - "description": "Top family and target regressions plus support audit evidence.", - }, - { - "name": "run_index", - "path_hint": "run_index.duckdb", - "producer": "append_us_microplex_run_index_entry", - "public_committed": False, - "description": "DuckDB index for querying target deltas across saved runs.", - }, -] - -_TARGET_DIAGNOSTIC_ROW_FIELDS = [ - "target_id", - "family", - "in_loss", - "supported_by_microplex", - "baseline_dataset", - "candidate_dataset", - "baseline_label", - "candidate_label", - "target_value", - "us_data_aggregate", - "microplex_aggregate", - "us_data_absolute_error", - "microplex_absolute_error", - "us_data_relative_error", - "microplex_relative_error", - "delta_absolute_error", - "delta_relative_error", - "loss_contribution", -] - -# In-process cache: the JSONs change only when microplex-us commits, so a -# few minutes of TTL is plenty. -_CACHE: dict[str, tuple[float, Any]] = {} -_TTL_SECONDS = 300 -_REFORM_COMPARISON_CACHE: dict[ - tuple[str, str, str, str, int], - tuple[float, dict[str, Any]], -] = {} -_REFORM_COMPARISON_TTL_SECONDS = 900 -_BUDGET_BENCHMARK_CACHE: dict[ - tuple[str, str, int, int], - tuple[float, dict[str, Any]], -] = {} -_BUDGET_BENCHMARK_TTL_SECONDS = 900 - -_REFORM_PRESETS: dict[str, dict[str, Any]] = { - "american_family_act_2025": { - "id": "american_family_act_2025", - "label": "American Family Act 2025 CTC expansion", - "description": ( - "Expands the Child Tax Credit using the American Family Act " - "structure implemented in PolicyEngine-US." - ), - "variable": "ctc", - "entity": "tax_unit", - "period": 2026, - "unit": "USD", - "source_url": "https://www.policyengine.org/us/research/american-family-act-2025", - }, - "working_parents_tax_relief_act_2026": { - "id": "working_parents_tax_relief_act_2026", - "label": "Working Parents Tax Relief Act EITC enhancement", - "description": ( - "Enhances the EITC for parents with young children, using the " - "2026 PolicyEngine-US reform implementation." - ), - "variable": "eitc", - "entity": "tax_unit", - "period": 2026, - "unit": "USD", - "source_url": "https://www.policyengine.org/us/working-parents-tax-relief-act", - }, - "halve_joint_eitc_phase_out_rate": { - "id": "halve_joint_eitc_phase_out_rate", - "label": "Halve joint-filer EITC phase-out rate", - "description": ( - "Structural PolicyEngine-US reform that halves the EITC phase-out " - "rate for joint filers. Useful as a simple smoke test." - ), - "variable": "eitc", - "entity": "tax_unit", - "period": 2024, - "unit": "USD", - "source_url": None, - }, - "wyden_smith_ctc_2024": { - "id": "wyden_smith_ctc_2024", - "label": "Wyden-Smith / TRAFWA CTC provisions", - "description": ( - "Implements the 2024 Child Tax Credit provisions from H.R. 7024: " - "per-child ACTC phase-in, prior-year earnings lookback, 2024 ACTC " - "refundable maximum increase, and 2024 CTC indexing." - ), - "variable": "ctc", - "entity": "tax_unit", - "period": 2024, - "unit": "USD", - "source_url": "https://www.policyengine.org/us/research/trafwa-ctc", - }, - "kypa_ctc_2026": { - "id": "kypa_ctc_2026", - "label": "Keep Your Pay Act expanded CTC", - "description": ( - "Uses the PolicyEngine-US American Family Act-style CTC " - "implementation to approximate KYPA's expanded, fully refundable " - "CTC in 2026." - ), - "variable": "ctc", - "entity": "tax_unit", - "period": 2026, - "unit": "USD", - "source_url": "https://budgetmodel.wharton.upenn.edu/p/2026-03-11-the-keep-your-pay-act-budgetary-and-distributional-effects/", - }, - "kypa_childless_eitc_2026": { - "id": "kypa_childless_eitc_2026", - "label": "Keep Your Pay Act childless EITC expansion", - "description": ( - "Applies KYPA's childless-worker EITC parameters: lower minimum " - "age, no maximum age, doubled phase-in and phase-out rates, and " - "a higher maximum credit." - ), - "variable": "eitc", - "entity": "tax_unit", - "period": 2026, - "unit": "USD", - "source_url": "https://budgetmodel.wharton.upenn.edu/p/2026-03-11-the-keep-your-pay-act-budgetary-and-distributional-effects/", - }, - "obbba_no_tax_on_tips_repeal_2026": { - "id": "obbba_no_tax_on_tips_repeal_2026", - "label": "OBBBA no tax on tips provision", - "description": ( - "Counterfactual removal of the current-law tip income deduction. " - "The tax increase from removal is interpreted as the standalone " - "modeled cost of keeping the provision." - ), - "variable": "income_tax", - "entity": "tax_unit", - "period": 2026, - "unit": "USD", - "source_url": "https://www.jct.gov/publications/2025/jcx-35-25/", - }, - "obbba_no_tax_on_overtime_repeal_2026": { - "id": "obbba_no_tax_on_overtime_repeal_2026", - "label": "OBBBA no tax on overtime provision", - "description": ( - "Counterfactual removal of the current-law overtime income " - "deduction. The tax increase from removal is interpreted as the " - "standalone modeled cost of keeping the provision." - ), - "variable": "income_tax", - "entity": "tax_unit", - "period": 2026, - "unit": "USD", - "source_url": "https://www.jct.gov/publications/2025/jcx-35-25/", - }, - "obbba_senior_deduction_repeal_2026": { - "id": "obbba_senior_deduction_repeal_2026", - "label": "OBBBA senior deduction provision", - "description": ( - "Counterfactual removal of the current-law additional senior " - "deduction. The tax increase from removal is interpreted as the " - "standalone modeled cost of keeping the provision." - ), - "variable": "income_tax", - "entity": "tax_unit", - "period": 2026, - "unit": "USD", - "source_url": "https://www.jct.gov/publications/2025/jcx-26-25r/", - }, -} - -_BUDGET_BENCHMARKS: list[dict[str, Any]] = [ - { - "id": "american_family_act_2025", - "title": "American Family Act 2025 CTC expansion", - "policy_area": "Child Tax Credit", - "live_reform_id": None, - "budget_effect_rule": "credit_delta_is_cost", - "benchmark_period": "2026 annual", - "comparison_status": "model_context_no_third_party_score", - "external_estimates": [ - { - "source": "CBO/JCT", - "source_type": "official_score", - "url": "https://www.congress.gov/bill/119th-congress/house-bill/2763", - "estimate": None, - "estimate_label": "No public CBO/JCT score found for H.R.2763 / S.1393.", - "period": "not available", - } - ], - "notes": ( - "No independent public budget score is attached for this bill. " - "PolicyEngine has a published static analysis, but that is not a " - "third-party benchmark and is intentionally excluded from the " - "external comparison slot." - ), - }, - { - "id": "working_parents_tax_relief_act_2026", - "title": "Working Parents Tax Relief Act EITC enhancement", - "policy_area": "Earned Income Tax Credit", - "live_reform_id": None, - "budget_effect_rule": "credit_delta_is_cost", - "benchmark_period": "2026 annual", - "comparison_status": "model_context_partial_external_context", - "external_estimates": [ - { - "source": "Thomson Reuters coverage", - "source_type": "third_party_context", - "url": "https://tax.thomsonreuters.com/news/bill-seeks-earned-income-tax-credit-boost-per-child-for-working-parents/", - "estimate": None, - "estimate_label": ( - "Third-party coverage found; no single budget score is " - "attached in this catalog." - ), - "period": "not available", - }, - { - "source": "PolicyEngine policy page", - "source_type": "published_model_result", - "url": "https://www.policyengine.org/us/working-parents-tax-relief-act", - "estimate": None, - "estimate_label": "PolicyEngine analysis; not a CBO/JCT comparator.", - "period": "2026+", - }, - ], - "notes": ( - "Live values are annual aggregate EITC deltas for the implemented " - "PolicyEngine-US reform." - ), - }, - { - "id": "wyden_smith_ctc_2024", - "title": "Wyden-Smith / TRAFWA CTC provisions", - "policy_area": "Child Tax Credit", - "live_reform_id": "wyden_smith_ctc_2024", - "budget_effect_rule": "credit_delta_is_cost", - "benchmark_period": "2024 annual", - "comparison_status": "live_model_with_third_party_score", - "external_estimates": [ - { - "source": "Joint Committee on Taxation", - "source_type": "jct", - "url": "https://waysandmeans.house.gov/wp-content/uploads/2024/01/Estimated-Revenue-Effects-of-H.R.-7024.pdf", - "estimate": 10_700_000_000, - "estimate_label": ( - "$10.7B 2024 cost for the combined CTC provisions, as " - "reported in PolicyEngine's JCT comparison table." - ), - "period": "2024", - "comparable_to_live_annual_result": True, - }, - { - "source": "Joint Committee on Taxation", - "source_type": "jct", - "url": "https://waysandmeans.house.gov/wp-content/uploads/2024/01/Estimated-Revenue-Effects-of-H.R.-7024.pdf", - "estimate": 33_493_000_000, - "estimate_label": ( - "$33.493B 2024-2033 revenue effect for the Tax Relief for " - "Working Families line in JCX-3-24." - ), - "period": "2024-2033", - "comparable_to_live_annual_result": False, - }, - ], - "notes": ( - "This row is the first true third-party benchmark: the live model " - "runs the CTC outcome against a JCT provision-level score. The " - "annual comparison uses PolicyEngine's published JCT comparison " - "table for the 2024 CTC provisions; the decade score is included " - "as context but is not directly comparable to the single-year live " - "run." - ), - }, - { - "id": "kypa_ctc_2026", - "title": "Keep Your Pay Act expanded CTC", - "policy_area": "Child Tax Credit", - "live_reform_id": "kypa_ctc_2026", - "budget_effect_rule": "credit_delta_is_cost", - "benchmark_period": "2026 annual", - "comparison_status": "live_model_with_third_party_score", - "external_estimates": [ - { - "source": "Penn Wharton Budget Model", - "source_type": "pwbm", - "url": "https://budgetmodel.wharton.upenn.edu/p/2026-03-11-the-keep-your-pay-act-budgetary-and-distributional-effects/", - "estimate": 140_500_000_000, - "estimate_label": ( - "$140.5B FY2027 revenue loss for KYPA's expanded Child " - "Tax Credit provision. This is the closest full-year " - "fiscal proxy for a calendar-year 2026 microsim." - ), - "period": "FY2027 proxy for TY2026", - "comparable_to_live_annual_result": True, - }, - { - "source": "Penn Wharton Budget Model", - "source_type": "pwbm", - "url": "https://budgetmodel.wharton.upenn.edu/p/2026-03-11-the-keep-your-pay-act-budgetary-and-distributional-effects/", - "estimate": 2_500_000_000, - "estimate_label": ( - "$2.5B FY2026 revenue loss. Included as timing context; " - "not comparable to a full calendar-year 2026 tax microsim." - ), - "period": "FY2026 timing context", - "comparable_to_live_annual_result": False, - }, - { - "source": "Penn Wharton Budget Model", - "source_type": "pwbm", - "url": "https://budgetmodel.wharton.upenn.edu/p/2026-03-11-the-keep-your-pay-act-budgetary-and-distributional-effects/", - "estimate": 1_261_600_000_000, - "estimate_label": ( - "$1.2616T FY2026-2035 revenue loss for KYPA's expanded " - "Child Tax Credit provision." - ), - "period": "FY2026-2035", - "comparable_to_live_annual_result": False, - }, - ], - "notes": ( - "PWBM provides separable KYPA CTC estimates. The live dashboard " - "comparison uses the 2026 annual CTC delta against PWBM's FY2027 " - "line as a full-year fiscal proxy. PWBM's FY2026 line is much " - "smaller because refundable credit timing shifts most TY2026 " - "cost into FY2027. The PolicyEngine-US reform is an AFA-style " - "approximation: it matches the main child amounts, refundability, " - "and phaseout structure, but its newborn-bonus formula is not " - "guaranteed to match PWBM exactly." - ), - }, - { - "id": "kypa_childless_eitc_2026", - "title": "Keep Your Pay Act childless-worker EITC", - "policy_area": "Earned Income Tax Credit", - "live_reform_id": "kypa_childless_eitc_2026", - "budget_effect_rule": "credit_delta_is_cost", - "benchmark_period": "2026 annual", - "comparison_status": "live_model_with_third_party_score", - "external_estimates": [ - { - "source": "Penn Wharton Budget Model", - "source_type": "pwbm", - "url": "https://budgetmodel.wharton.upenn.edu/p/2026-03-11-the-keep-your-pay-act-budgetary-and-distributional-effects/", - "estimate": 7_200_000_000, - "estimate_label": ( - "$7.2B FY2027 revenue loss for KYPA's childless-worker " - "EITC expansion. This is the closest full-year fiscal " - "proxy for a calendar-year 2026 microsim." - ), - "period": "FY2027 proxy for TY2026", - "comparable_to_live_annual_result": True, - }, - { - "source": "Penn Wharton Budget Model", - "source_type": "pwbm", - "url": "https://budgetmodel.wharton.upenn.edu/p/2026-03-11-the-keep-your-pay-act-budgetary-and-distributional-effects/", - "estimate": 800_000_000, - "estimate_label": ( - "$0.8B FY2026 revenue loss. Included as timing context; " - "not comparable to a full calendar-year 2026 tax microsim." - ), - "period": "FY2026 timing context", - "comparable_to_live_annual_result": False, - }, - { - "source": "Penn Wharton Budget Model", - "source_type": "pwbm", - "url": "https://budgetmodel.wharton.upenn.edu/p/2026-03-11-the-keep-your-pay-act-budgetary-and-distributional-effects/", - "estimate": 63_800_000_000, - "estimate_label": ( - "$63.8B FY2026-2035 revenue loss for KYPA's childless-" - "worker EITC expansion." - ), - "period": "FY2026-2035", - "comparable_to_live_annual_result": False, - }, - ], - "notes": ( - "PWBM provides a separable childless-worker EITC estimate. The " - "live comparison applies the same 2026 parameters described by " - "PWBM and compares to the FY2027 line as a full-year fiscal " - "proxy: minimum age 19, no maximum age, 15.3% phase-in and " - "phase-out rates, about a $1,502 maximum credit, and about an " - "$11,610 phase-out start." - ), - }, - { - "id": "obbba_no_tax_on_tips_repeal_2026", - "title": "OBBBA no tax on tips", - "policy_area": "Federal individual income tax", - "live_reform_id": "obbba_no_tax_on_tips_repeal_2026", - "budget_effect_rule": "repeal_tax_delta_is_provision_cost", - "benchmark_period": "2026 annual", - "comparison_status": "waterfall_external_score_context", - "external_estimates": [ - { - "source": "Joint Committee on Taxation JCX-35-25", - "source_type": "jct", - "url": "https://www.jct.gov/publications/2025/jcx-35-25/", - "estimate": 10_121_000_000, - "estimate_label": ( - "$10.121B FY2026 revenue loss for the no-tax-on-tips " - "line item in JCT's OBBBA table. Treat as package-order " - "context, not a standalone oracle." - ), - "period": "FY2026", - "comparable_to_live_annual_result": False, - }, - { - "source": "Joint Committee on Taxation JCX-35-25", - "source_type": "jct", - "url": "https://www.jct.gov/publications/2025/jcx-35-25/", - "estimate": 31_664_000_000, - "estimate_label": ( - "$31.664B 2025-2034 revenue loss for the no-tax-on-tips " - "line item in JCT's OBBBA table. Treat as package-order " - "context, not a standalone oracle." - ), - "period": "2025-2034", - }, - ], - "notes": ( - "This is modeled as a one-provision counterfactual because the " - "installed PolicyEngine-US baseline already includes the OBBBA " - "tip deduction. The dashboard removes only that deduction and " - "uses the resulting income-tax increase as the provision cost. " - "JCT's OBBBA table is not a clean standalone target for this " - "counterfactual because it uses a specified OBBBA baseline and " - "package-order interactions. The live model is calendar-year 2026; " - "the JCT annual entry is fiscal-year 2026." - ), - }, - { - "id": "obbba_no_tax_on_overtime_repeal_2026", - "title": "OBBBA no tax on overtime", - "policy_area": "Federal individual income tax", - "live_reform_id": "obbba_no_tax_on_overtime_repeal_2026", - "budget_effect_rule": "repeal_tax_delta_is_provision_cost", - "benchmark_period": "2026 annual", - "comparison_status": "waterfall_external_score_context", - "external_estimates": [ - { - "source": "Joint Committee on Taxation JCX-35-25", - "source_type": "jct", - "url": "https://www.jct.gov/publications/2025/jcx-35-25/", - "estimate": 32_806_000_000, - "estimate_label": ( - "$32.806B FY2026 revenue loss for the no-tax-on-overtime " - "line item in JCT's OBBBA table. Treat as package-order " - "context, not a standalone oracle." - ), - "period": "FY2026", - "comparable_to_live_annual_result": False, - }, - { - "source": "Joint Committee on Taxation JCX-35-25", - "source_type": "jct", - "url": "https://www.jct.gov/publications/2025/jcx-35-25/", - "estimate": 89_573_000_000, - "estimate_label": ( - "$89.573B 2025-2034 revenue loss for the no-tax-on-" - "overtime line item in JCT's OBBBA table. Treat as " - "package-order context, not a standalone oracle." - ), - "period": "2025-2034", - }, - ], - "notes": ( - "This is modeled as a one-provision counterfactual because the " - "installed PolicyEngine-US baseline already includes the OBBBA " - "overtime deduction. The dashboard removes only that deduction " - "and uses the resulting income-tax increase as the provision cost. " - "JCT's OBBBA table is not a clean standalone target for this " - "counterfactual because it uses a specified OBBBA baseline and " - "package-order interactions. The live model is calendar-year 2026; " - "the JCT annual entry is fiscal-year 2026." - ), - }, - { - "id": "obbba_senior_deduction_repeal_2026", - "title": "OBBBA senior deduction", - "policy_area": "Federal individual income tax", - "live_reform_id": "obbba_senior_deduction_repeal_2026", - "budget_effect_rule": "repeal_tax_delta_is_provision_cost", - "benchmark_period": "2026 annual", - "comparison_status": "waterfall_external_score_context", - "external_estimates": [ - { - "source": "Joint Committee on Taxation JCX-26-25R", - "source_type": "jct", - "url": "https://www.jct.gov/publications/2025/jcx-26-25r/", - "estimate": 16_464_000_000, - "estimate_label": ( - "$16.464B FY2026 revenue loss for the enhanced senior " - "deduction line item in JCT's OBBBA table. Treat as " - "package-order context, not a standalone oracle." - ), - "period": "FY2026", - "comparable_to_live_annual_result": False, - }, - { - "source": "Joint Committee on Taxation JCX-26-25R", - "source_type": "jct", - "url": "https://www.jct.gov/publications/2025/jcx-26-25r/", - "estimate": 66_263_000_000, - "estimate_label": ( - "$66.263B 2025-2034 revenue loss for the enhanced senior " - "deduction line item in JCT's OBBBA table. Treat as " - "package-order context, not a standalone oracle." - ), - "period": "2025-2034", - }, - ], - "notes": ( - "This is modeled as a one-provision counterfactual because the " - "installed PolicyEngine-US baseline already includes the OBBBA " - "senior deduction. The dashboard removes only that deduction and " - "uses the resulting income-tax increase as the provision cost. " - "JCT's OBBBA table is not a clean standalone target for this " - "counterfactual because it uses a specified OBBBA baseline and " - "package-order interactions. The live model is calendar-year 2026; " - "the JCT annual entry is fiscal-year 2026." - ), - }, - { - "id": "tcja_extension_2026_2035", - "title": "TCJA individual provisions extension", - "policy_area": "Federal individual income tax", - "live_reform_id": None, - "budget_effect_rule": "full_budget_score", - "benchmark_period": "2026-2035", - "comparison_status": "external_score_available_reform_not_wired", - "external_estimates": [ - { - "source": "CBO/JCT", - "source_type": "cbo_jct", - "url": "https://www.policyengine.org/us/research/tcja-extension", - "estimate": 3_877_600_000_000, - "estimate_label": "$3.8776T cost over 2026-2035", - "period": "2026-2035", - }, - { - "source": "CRFB", - "source_type": "third_party_score", - "url": "https://www.policyengine.org/us/research/tcja-extension", - "estimate": 3_830_000_000_000, - "estimate_label": "$3.83T cost over 2026-2035", - "period": "2026-2035", - }, - { - "source": "PolicyEngine dynamic", - "source_type": "published_model_result", - "url": "https://www.policyengine.org/us/research/tcja-extension", - "estimate": 3_885_500_000_000, - "estimate_label": "$3.8855T cost over 2026-2035", - "period": "2026-2035", - }, - ], - "notes": ( - "External benchmark is strong, but the dashboard does not yet have " - "a matching live TCJA-extension reform preset for us-data and " - "Microplex." - ), - }, - { - "id": "final_2025_reconciliation_tax", - "title": "Final 2025 reconciliation individual income tax provisions", - "policy_area": "Federal individual income tax", - "live_reform_id": None, - "budget_effect_rule": "full_budget_score", - "benchmark_period": "2026-2035", - "comparison_status": "external_score_available_reform_not_wired", - "external_estimates": [ - { - "source": "PolicyEngine static analysis", - "source_type": "published_model_result", - "url": "https://www.policyengine.org/us/research/final-2025-reconciliation-tax", - "estimate": 3_785_000_000_000, - "estimate_label": "$3.785T cost over 2026-2035", - "period": "2026-2035", - }, - { - "source": "JCT JCX-26-25", - "source_type": "jct", - "url": "https://www.jct.gov/publications/2025/jcx-26-25/", - "estimate": None, - "estimate_label": "Official JCT revenue estimate available; row-level match not wired.", - "period": "2025 budget reconciliation", - }, - ], - "notes": ( - "PolicyEngine-US baseline now contains many OBBBA provisions, so " - "a live before/after comparison needs an explicit counterfactual " - "reform branch rather than a simple preset." - ), - }, -] - - -def _fetch_json(path: str) -> Any: - cached = _CACHE.get(path) - if cached and time.time() - cached[0] < _TTL_SECONDS: - return cached[1] - import urllib.request - url = f"{_GITHUB_RAW}/{path}" - logger.info("Fetching microplex artifact: %s", url) - try: - with urllib.request.urlopen(url, timeout=30) as resp: - body = resp.read().decode("utf-8") - data = json.loads(body) - _CACHE[path] = (time.time(), data) - return data - except Exception as exc: - raise HTTPException( - status_code=502, - detail=f"Failed to fetch microplex artifact {path}: {exc}", - ) - - -def _scrub(obj): - """Replace non-finite floats with None for JSON serialization.""" - if isinstance(obj, dict): - return {k: _scrub(v) for k, v in obj.items()} - if isinstance(obj, list): - return [_scrub(x) for x in obj] - if isinstance(obj, float): - import math - if not math.isfinite(obj): - return None - return obj - - -def _configured_artifact_roots() -> list[Path]: - raw = os.environ.get("MICROPLEX_ARTIFACT_ROOTS") or os.environ.get( - "MICROPLEX_ARTIFACT_ROOT" - ) - if not raw: - return [] - separators = [os.pathsep, ","] - parts = [raw] - for separator in separators: - next_parts: list[str] = [] - for part in parts: - next_parts.extend(part.split(separator)) - parts = next_parts - return [Path(part).expanduser().resolve() for part in parts if part.strip()] - - -def _read_json_file(path: Path) -> Any | None: - try: - return json.loads(path.read_text()) - except (OSError, json.JSONDecodeError): - return None - - -def _resolve_artifact_path(bundle_dir: Path, value: Any) -> Path | None: - if not isinstance(value, str) or not value: - return None - path = Path(value).expanduser() - if not path.is_absolute(): - path = bundle_dir / path - return path.resolve() - - -def _discover_configured_run_bundles() -> dict[str, Any]: - roots = _configured_artifact_roots() - bundles: list[dict[str, Any]] = [] - missing_roots = [] - for root in roots: - if not root.exists(): - missing_roots.append(str(root)) - continue - for manifest_path in root.rglob("manifest.json"): - manifest = _read_json_file(manifest_path) - if not isinstance(manifest, dict): - continue - bundle_dir = manifest_path.parent - artifacts = manifest.get("artifacts") - artifacts = artifacts if isinstance(artifacts, dict) else {} - diagnostics_path = _resolve_artifact_path( - bundle_dir, - artifacts.get(_RUN_LEVEL_TARGET_DIAGNOSTICS_MANIFEST_KEY), - ) - native_scores_path = _resolve_artifact_path( - bundle_dir, - artifacts.get("policyengine_native_scores") - or "policyengine_native_scores.json", - ) - native_audit_path = _resolve_artifact_path( - bundle_dir, - artifacts.get("policyengine_native_audit") - or "pe_us_data_rebuild_native_audit.json", - ) - policyengine_dataset_path = _resolve_artifact_path( - bundle_dir, - artifacts.get("policyengine_dataset") or "policyengine_us.h5", - ) - modified_at = manifest_path.stat().st_mtime - bundles.append( - { - "artifact_id": ( - manifest.get("artifact_id") - or manifest.get("artifactId") - or bundle_dir.name - ), - "artifact_dir": str(bundle_dir), - "manifest_path": str(manifest_path), - "modified_at_unix": modified_at, - "target_diagnostics_path": ( - str(diagnostics_path) if diagnostics_path is not None else None - ), - "target_diagnostics_exists": ( - diagnostics_path.exists() - if diagnostics_path is not None - else False - ), - "native_scores_path": ( - str(native_scores_path) if native_scores_path is not None else None - ), - "native_scores_exists": ( - native_scores_path.exists() - if native_scores_path is not None - else False - ), - "native_audit_path": ( - str(native_audit_path) if native_audit_path is not None else None - ), - "native_audit_exists": ( - native_audit_path.exists() - if native_audit_path is not None - else False - ), - "policyengine_dataset_path": ( - str(policyengine_dataset_path) - if policyengine_dataset_path is not None - else None - ), - "policyengine_dataset_exists": ( - policyengine_dataset_path.exists() - if policyengine_dataset_path is not None - else False - ), - } - ) - - bundles.sort(key=lambda item: item["modified_at_unix"], reverse=True) - latest = bundles[0] if bundles else None - return { - "artifact_root_env": "MICROPLEX_ARTIFACT_ROOTS", - "single_artifact_root_env": "MICROPLEX_ARTIFACT_ROOT", - "configured_artifact_roots": [str(root) for root in roots], - "missing_artifact_roots": missing_roots, - "detected_run_bundle_count": len(bundles), - "detected_target_diagnostics_count": sum( - 1 for bundle in bundles if bundle["target_diagnostics_exists"] - ), - "latest_run_bundle": latest, - "sampled_run_bundles": bundles[:10], - } - - -def _latest_microplex_policyengine_dataset() -> dict[str, Any] | None: - discovery = _discover_configured_run_bundles() - latest = discovery.get("latest_run_bundle") - if not isinstance(latest, dict): - return None - if not latest.get("policyengine_dataset_exists"): - return None - path = latest.get("policyengine_dataset_path") - if not isinstance(path, str) or not path: - return None - return latest - - -def _working_parents_tax_relief_parameter_reform(): - from policyengine_core.periods import instant - from policyengine_us.model_api import Reform - - class working_parents_tax_relief_parameter_reform(Reform): - def apply(self): - def modify_parameters(parameters): - parameter = ( - parameters.gov.contrib.congress.mcdonald_rivet - .working_parents_tax_relief_act.in_effect - ) - parameter.update( - start=instant("2026-01-01"), - stop=instant("2035-12-31"), - value=True, - ) - return parameters - - self.modify_parameters(modify_parameters) - - return working_parents_tax_relief_parameter_reform - - -def _wyden_smith_ctc_2024_parameter_reform(): - from policyengine_core.periods import instant - from policyengine_us.model_api import Reform - - class wyden_smith_ctc_2024_parameter_reform(Reform): - def apply(self): - def modify_parameters(parameters): - wyden_smith = parameters.gov.contrib.congress.wyden_smith - wyden_smith.per_child_actc_phase_in.update( - start=instant("2024-01-01"), - stop=instant("2024-12-31"), - value=True, - ) - wyden_smith.actc_lookback.update( - start=instant("2024-01-01"), - stop=instant("2024-12-31"), - value=True, - ) - parameters.gov.irs.credits.ctc.refundable.individual_max.update( - start=instant("2024-01-01"), - stop=instant("2024-12-31"), - value=1_900, - ) - parameters.gov.irs.credits.ctc.amount.base[0].amount.update( - start=instant("2024-01-01"), - stop=instant("2024-12-31"), - value=2_100, - ) - return parameters - - self.modify_parameters(modify_parameters) - - return wyden_smith_ctc_2024_parameter_reform - - -def _obbba_no_tax_on_tips_repeal_parameter_reform(): - from policyengine_core.periods import instant - from policyengine_us.model_api import Reform - - class obbba_no_tax_on_tips_repeal_parameter_reform(Reform): - def apply(self): - def modify_parameters(parameters): - parameters.gov.irs.deductions.tip_income.cap.update( - start=instant("2026-01-01"), - stop=instant("2026-12-31"), - value=0, - ) - return parameters - - self.modify_parameters(modify_parameters) - - return obbba_no_tax_on_tips_repeal_parameter_reform - - -def _obbba_no_tax_on_overtime_repeal_parameter_reform(): - from policyengine_core.periods import instant - from policyengine_us.model_api import Reform - - class obbba_no_tax_on_overtime_repeal_parameter_reform(Reform): - def apply(self): - def modify_parameters(parameters): - caps = parameters.gov.irs.deductions.overtime_income.cap - for filing_status in ( - "JOINT", - "HEAD_OF_HOUSEHOLD", - "SURVIVING_SPOUSE", - "SINGLE", - "SEPARATE", - ): - getattr(caps, filing_status).update( - start=instant("2026-01-01"), - stop=instant("2026-12-31"), - value=0, - ) - return parameters - - self.modify_parameters(modify_parameters) - - return obbba_no_tax_on_overtime_repeal_parameter_reform - - -def _obbba_senior_deduction_repeal_parameter_reform(): - from policyengine_core.periods import instant - from policyengine_us.model_api import Reform - - class obbba_senior_deduction_repeal_parameter_reform(Reform): - def apply(self): - def modify_parameters(parameters): - parameters.gov.irs.deductions.senior_deduction.amount.update( - start=instant("2026-01-01"), - stop=instant("2026-12-31"), - value=0, - ) - return parameters - - self.modify_parameters(modify_parameters) - - return obbba_senior_deduction_repeal_parameter_reform - - -def _kypa_childless_eitc_parameter_reform(): - from policyengine_core.periods import instant - from policyengine_us.model_api import Reform - - class kypa_childless_eitc_parameter_reform(Reform): - def apply(self): - def modify_parameters(parameters): - eitc = parameters.gov.irs.credits.eitc - start = instant("2026-01-01") - stop = instant("2035-12-31") - - eitc.max[0].amount.update(start=start, stop=stop, value=1_502) - eitc.phase_in_rate[0].amount.update( - start=start, - stop=stop, - value=0.153, - ) - eitc.phase_out.rate[0].amount.update( - start=start, - stop=stop, - value=0.153, - ) - eitc.phase_out.start[0].amount.update( - start=start, - stop=stop, - value=11_610, - ) - eitc.eligibility.age.min.update(start=start, stop=stop, value=19) - eitc.eligibility.age.min_student.update( - start=start, - stop=stop, - value=19, - ) - eitc.eligibility.age.max.update( - start=start, - stop=stop, - value=float("inf"), - ) - return parameters - - self.modify_parameters(modify_parameters) - - return kypa_childless_eitc_parameter_reform - - -def _reform_object(reform_id: str): - if reform_id == "halve_joint_eitc_phase_out_rate": - from policyengine_us.reforms.eitc.halve_joint_eitc_phase_out_rate import ( - halve_joint_eitc_phase_out_rate, - ) - - return halve_joint_eitc_phase_out_rate - if reform_id == "american_family_act_2025": - from policyengine_us.reforms.congress.afa.afa_other_dependent_credit import ( - afa_other_dependent_credit, - ) - - return afa_other_dependent_credit - if reform_id == "working_parents_tax_relief_act_2026": - from policyengine_us.reforms.congress.mcdonald_rivet.working_parents_tax_relief_act.working_parents_tax_relief_act import ( # noqa: E501 - working_parents_tax_relief_act, - ) - - return ( - working_parents_tax_relief_act, - _working_parents_tax_relief_parameter_reform(), - ) - if reform_id == "wyden_smith_ctc_2024": - from policyengine_us.reforms.congress.wyden_smith.ctc_expansion import ( - ctc_expansion, - ) - - return ( - ctc_expansion, - _wyden_smith_ctc_2024_parameter_reform(), - ) - if reform_id == "kypa_ctc_2026": - from policyengine_us.reforms.congress.afa.afa_other_dependent_credit import ( - afa_other_dependent_credit, - ) - - return afa_other_dependent_credit - if reform_id == "kypa_childless_eitc_2026": - return _kypa_childless_eitc_parameter_reform() - if reform_id == "obbba_no_tax_on_tips_repeal_2026": - return _obbba_no_tax_on_tips_repeal_parameter_reform() - if reform_id == "obbba_no_tax_on_overtime_repeal_2026": - return _obbba_no_tax_on_overtime_repeal_parameter_reform() - if reform_id == "obbba_senior_deduction_repeal_2026": - return _obbba_senior_deduction_repeal_parameter_reform() - raise ValueError(f"Unknown reform_id: {reform_id}") - - -@lru_cache(maxsize=64) -def _microsimulation(dataset: str, reform_id: str | None): - from policyengine_us import Microsimulation - - if reform_id is None: - return Microsimulation(dataset=dataset) - return Microsimulation(dataset=dataset, reform=_reform_object(reform_id)) - - -def _finite_float(value: Any) -> float | None: - try: - result = float(value) - except (TypeError, ValueError): - return None - return result if np.isfinite(result) else None - - -def _weighted_total( - sim, - *, - variable: str, - period: int, - entity: str, -) -> dict[str, Any]: - values = sim.calculate(variable, period=period, map_to=entity) - weights = sim.calculate("household_weight", period=period, map_to=entity) - value_array = np.asarray(values.values, dtype=float) - weight_array = np.asarray(weights.values, dtype=float) - return { - "total": _finite_float(np.sum(value_array * weight_array)), - "unweighted_mean": _finite_float(np.mean(value_array)), - "record_count": int(value_array.size), - "weight_sum": _finite_float(np.sum(weight_array)), - } - - -def _run_dataset_reform_comparison( - *, - dataset: str, - reform_id: str, - variable: str, - period: int, - entity: str, -) -> dict[str, Any]: - baseline = _microsimulation(dataset, None) - reformed = _microsimulation(dataset, reform_id) - baseline_total = _weighted_total( - baseline, - variable=variable, - period=period, - entity=entity, - ) - reformed_total = _weighted_total( - reformed, - variable=variable, - period=period, - entity=entity, - ) - baseline_value = baseline_total["total"] - reformed_value = reformed_total["total"] - delta = ( - reformed_value - baseline_value - if baseline_value is not None and reformed_value is not None - else None - ) - return { - "dataset": dataset, - "baseline": baseline_total, - "reform": reformed_total, - "delta": _finite_float(delta), - } - - -def _budget_effect_from_delta( - delta: float | None, - *, - rule: str, -) -> float | None: - if delta is None: - return None - # Positive budget effect means higher federal cost / lower federal revenue. - if rule == "credit_delta_is_cost": - return _finite_float(delta) - if rule == "tax_revenue_delta_is_negative_cost": - return _finite_float(-delta) - if rule == "repeal_tax_delta_is_provision_cost": - return _finite_float(delta) - return _finite_float(delta) - - -def _budget_gap( - model_effect: float | None, - external_estimate: Any, -) -> dict[str, float | None]: - estimate = _finite_float(external_estimate) - if model_effect is None or estimate is None: - return {"gap": None, "ratio": None} - return { - "gap": _finite_float(model_effect - estimate), - "ratio": _finite_float(model_effect / estimate) if estimate else None, - } - - -@router.get("/microplex/reform-comparison") -def microplex_reform_comparison( - reform_id: str = "american_family_act_2025", - variable: str | None = None, - period: int | None = None, -): - """Run the same PolicyEngine reform over us-data and Microplex H5s.""" - preset = _REFORM_PRESETS.get(reform_id) - if preset is None: - raise HTTPException( - status_code=400, - detail=f"Unknown reform_id. Available: {sorted(_REFORM_PRESETS)}", - ) - selected_variable = variable or str(preset["variable"]) - selected_period = int(period or preset["period"]) - entity = str(preset["entity"]) - - latest = _latest_microplex_policyengine_dataset() - if latest is None: - return { - "available": False, - "reason": ( - "No configured Microplex run bundle with policyengine_us.h5 was " - "found. Set MICROPLEX_ARTIFACT_ROOTS or MICROPLEX_ARTIFACT_ROOT." - ), - "reform": None, - "period": selected_period, - "available_reforms": list(_REFORM_PRESETS.values()), - "outcomes": [], - } - - microplex_dataset = str(latest["policyengine_dataset_path"]) - us_data_dataset = os.environ.get( - "MICROSIM_US_DATASET", - "hf://policyengine/policyengine-us-data/enhanced_cps_2024.h5", - ) - cache_key = ( - microplex_dataset, - us_data_dataset, - reform_id, - selected_variable, - selected_period, - ) - cached = _REFORM_COMPARISON_CACHE.get(cache_key) - if cached and time.time() - cached[0] < _REFORM_COMPARISON_TTL_SECONDS: - return cached[1] - - started = time.time() - try: - us_data = _run_dataset_reform_comparison( - dataset=us_data_dataset, - reform_id=reform_id, - variable=selected_variable, - period=selected_period, - entity=entity, - ) - microplex_result = _run_dataset_reform_comparison( - dataset=microplex_dataset, - reform_id=reform_id, - variable=selected_variable, - period=selected_period, - entity=entity, - ) - except Exception as exc: - logger.exception("Failed to run Microplex reform comparison") - raise HTTPException( - status_code=500, - detail=f"Failed to run reform comparison: {exc}", - ) from exc - - us_delta = us_data["delta"] - microplex_delta = microplex_result["delta"] - delta_gap = ( - microplex_delta - us_delta - if microplex_delta is not None and us_delta is not None - else None - ) - delta_ratio = ( - microplex_delta / us_delta - if microplex_delta is not None and us_delta not in (None, 0) - else None - ) - payload = _scrub( - { - "available": True, - "runtime_seconds": time.time() - started, - "period": selected_period, - "available_reforms": list(_REFORM_PRESETS.values()), - "reform": { - "id": reform_id, - "label": preset["label"], - "description": preset["description"], - "source_url": preset["source_url"], - }, - "microplex_bundle": { - "artifact_id": latest.get("artifact_id"), - "artifact_dir": latest.get("artifact_dir"), - "policyengine_dataset_path": microplex_dataset, - }, - "us_data_dataset": us_data_dataset, - "outcomes": [ - { - "variable": selected_variable, - "entity": entity, - "unit": preset["unit"], - "us_data": us_data, - "microplex": microplex_result, - "delta_gap": _finite_float(delta_gap), - "microplex_delta_as_share_of_us_data": _finite_float( - delta_ratio - ), - } - ], - } - ) - _REFORM_COMPARISON_CACHE[cache_key] = (time.time(), payload) - return payload - - -@router.get("/microplex/budget-benchmarks") -def microplex_budget_benchmarks(compute_live: bool = False) -> dict[str, Any]: - """Return budget-score benchmark rows for us-data and Microplex. - - This endpoint intentionally distinguishes exact live comparisons from - external-only benchmark rows. The dashboard should only interpret a row as - a CBO/JCT validation when a matching live reform preset and comparable - external estimate are both present. - """ - latest = _latest_microplex_policyengine_dataset() - microplex_dataset = ( - str(latest["policyengine_dataset_path"]) - if isinstance(latest, dict) and latest.get("policyengine_dataset_path") - else "" - ) - us_data_dataset = os.environ.get( - "MICROSIM_US_DATASET", - "hf://policyengine/policyengine-us-data/enhanced_cps_2024.h5", - ) - cache_key = ( - microplex_dataset or "no-microplex-h5", - us_data_dataset, - len(_BUDGET_BENCHMARKS), - int(compute_live), - ) - cached = _BUDGET_BENCHMARK_CACHE.get(cache_key) - if cached and time.time() - cached[0] < _BUDGET_BENCHMARK_TTL_SECONDS: - return cached[1] - - started = time.time() - rows: list[dict[str, Any]] = [] - errors: list[dict[str, str]] = [] - - for benchmark in _BUDGET_BENCHMARKS: - row = { - "id": benchmark["id"], - "title": benchmark["title"], - "policy_area": benchmark["policy_area"], - "benchmark_period": benchmark["benchmark_period"], - "comparison_status": benchmark["comparison_status"], - "budget_effect_rule": benchmark["budget_effect_rule"], - "notes": benchmark["notes"], - "external_estimates": benchmark["external_estimates"], - "live": { - "available": False, - "reason": None, - "reform": None, - "period": None, - "outcome_variable": None, - "outcome_entity": None, - "unit": None, - "us_data": None, - "microplex": None, - "microplex_budget_effect_as_share_of_us_data": None, - "budget_effect_gap": None, - }, - } - live_reform_id = benchmark.get("live_reform_id") - preset = ( - _REFORM_PRESETS.get(str(live_reform_id)) - if live_reform_id is not None - else None - ) - if preset is None: - row["live"]["reason"] = "No matching live reform preset is wired yet." - rows.append(row) - continue - if not compute_live: - row["live"]["reason"] = ( - "Live microsim compute is deferred. Set compute_live=true to " - "run this benchmark over us-data and Microplex." - ) - rows.append(row) - continue - if not microplex_dataset: - row["live"]["reason"] = ( - "No configured Microplex run bundle with policyengine_us.h5 was found." - ) - rows.append(row) - continue - - variable = str(preset["variable"]) - period = int(preset["period"]) - entity = str(preset["entity"]) - try: - us_data = _run_dataset_reform_comparison( - dataset=us_data_dataset, - reform_id=str(live_reform_id), - variable=variable, - period=period, - entity=entity, - ) - microplex_result = _run_dataset_reform_comparison( - dataset=microplex_dataset, - reform_id=str(live_reform_id), - variable=variable, - period=period, - entity=entity, - ) - except Exception as exc: - logger.exception("Failed to run budget benchmark %s", live_reform_id) - message = f"Failed to run live microsim: {exc}" - row["live"]["reason"] = message - errors.append({"benchmark_id": str(benchmark["id"]), "error": message}) - rows.append(row) - continue - - us_budget_effect = _budget_effect_from_delta( - us_data["delta"], - rule=str(benchmark["budget_effect_rule"]), - ) - microplex_budget_effect = _budget_effect_from_delta( - microplex_result["delta"], - rule=str(benchmark["budget_effect_rule"]), - ) - budget_effect_gap = ( - microplex_budget_effect - us_budget_effect - if microplex_budget_effect is not None and us_budget_effect is not None - else None - ) - budget_effect_ratio = ( - microplex_budget_effect / us_budget_effect - if microplex_budget_effect is not None - and us_budget_effect not in (None, 0) - else None - ) - external_estimates = [] - for estimate in benchmark["external_estimates"]: - us_gap = _budget_gap(us_budget_effect, estimate.get("estimate")) - microplex_gap = _budget_gap( - microplex_budget_effect, - estimate.get("estimate"), - ) - external_estimates.append( - { - **estimate, - "comparable_to_live_annual_result": bool( - estimate.get("comparable_to_live_annual_result") - ), - "us_data_gap": us_gap["gap"], - "us_data_ratio": us_gap["ratio"], - "microplex_gap": microplex_gap["gap"], - "microplex_ratio": microplex_gap["ratio"], - } - ) - - row["external_estimates"] = external_estimates - row["live"] = { - "available": True, - "reason": None, - "reform": { - "id": preset["id"], - "label": preset["label"], - "description": preset["description"], - "source_url": preset["source_url"], - }, - "period": period, - "outcome_variable": variable, - "outcome_entity": entity, - "unit": preset["unit"], - "us_data": { - **us_data, - "budget_effect": us_budget_effect, - }, - "microplex": { - **microplex_result, - "budget_effect": microplex_budget_effect, - }, - "microplex_budget_effect_as_share_of_us_data": _finite_float( - budget_effect_ratio - ), - "budget_effect_gap": _finite_float(budget_effect_gap), - } - rows.append(row) - - payload = _scrub( - { - "available": True, - "runtime_seconds": time.time() - started, - "generated_at_unix": time.time(), - "sign_convention": ( - "Positive budget effect means higher federal cost or lower " - "federal revenue. For counterfactual repeal rows, the model " - "uses the tax increase from removing one current-law provision " - "as the cost of keeping that provision." - ), - "comparison_caveat": ( - "Clean scored rows compare one modeled provision at a time. " - "External decade scores are context unless the row explicitly " - "marks the annual estimate as comparable to the live result. " - "OBBBA JCT line items are shown as context because they are " - "baseline- and package-order-sensitive. " - "Live income-tax microsims are expensive on the full dataset, " - "so this endpoint only computes them when compute_live=true." - ), - "compute_live": compute_live, - "us_data_dataset": us_data_dataset, - "microplex_bundle": { - "available": bool(microplex_dataset), - "artifact_id": latest.get("artifact_id") if isinstance(latest, dict) else None, - "artifact_dir": latest.get("artifact_dir") if isinstance(latest, dict) else None, - "policyengine_dataset_path": microplex_dataset or None, - }, - "rows": rows, - "errors": errors, - } - ) - _BUDGET_BENCHMARK_CACHE[cache_key] = (time.time(), payload) - return payload - - -def _load_target_diagnostics(latest_bundle: dict[str, Any]) -> dict[str, Any]: - path_text = latest_bundle.get("target_diagnostics_path") - if not path_text: - return { - "available": False, - "path": None, - "summary": {}, - "total_targets": 0, - "display_limit": 100, - "targets": [], - } - path = Path(str(path_text)) - payload = _read_json_file(path) - if not isinstance(payload, dict): - return { - "available": False, - "path": str(path), - "summary": {}, - "total_targets": 0, - "display_limit": 100, - "targets": [], - } - targets = payload.get("targets") - rows = targets if isinstance(targets, list) else [] - display_limit = 100 - summary = payload.get("summary") - return { - "available": True, - "path": str(path), - "diagnostic_schema_version": payload.get("diagnostic_schema_version"), - "metric": payload.get("metric"), - "period": payload.get("period"), - "baseline_dataset": payload.get("baseline_dataset"), - "candidate_dataset": payload.get("candidate_dataset"), - "dataset_labels": payload.get("dataset_labels", {}), - "summary": summary if isinstance(summary, dict) else {}, - "total_targets": len(rows), - "display_limit": display_limit, - "targets": rows[:display_limit], - } - - -def _load_target_diagnostics_payload( - latest_bundle: dict[str, Any], -) -> tuple[Path | None, dict[str, Any] | None]: - path_text = latest_bundle.get("target_diagnostics_path") - if not path_text: - return None, None - path = Path(str(path_text)) - payload = _read_json_file(path) - return path, payload if isinstance(payload, dict) else None - - -def _row_matches_text(row: dict[str, Any], search: str) -> bool: - needle = search.lower() - fields = [ - "target_id", - "target_name", - "family", - "target_family", - "geography", - "state", - "variable", - "entity", - ] - return any(needle in str(row.get(field, "")).lower() for field in fields) - - -def _estimate(value: Any) -> float | None: - if isinstance(value, bool): - return None - if isinstance(value, int | float) and np.isfinite(value): - return float(value) - return None - - -def _relative_difference( - numerator: float | None, - denominator: float | None, -) -> float | None: - if numerator is None or denominator in (None, 0): - return None - return numerator / abs(denominator) - - -def _enrich_target_diagnostic_row(row: dict[str, Any]) -> dict[str, Any]: - target = _estimate(row.get("target_value")) - us_data = _estimate(row.get("us_data_aggregate", row.get("from_estimate"))) - microplex = _estimate(row.get("microplex_aggregate", row.get("to_estimate"))) - delta_abs_error = _estimate(row.get("delta_absolute_error")) - microplex_vs_target = ( - microplex - target if microplex is not None and target is not None else None - ) - us_data_vs_target = ( - us_data - target if us_data is not None and target is not None else None - ) - microplex_vs_us_data = ( - microplex - us_data - if microplex is not None and us_data is not None - else None - ) - return { - **row, - "microplex_vs_target": microplex_vs_target, - "us_data_vs_target": us_data_vs_target, - "microplex_vs_us_data": microplex_vs_us_data, - "microplex_vs_target_relative": _relative_difference( - microplex_vs_target, - target, - ), - "us_data_vs_target_relative": _relative_difference( - us_data_vs_target, - target, - ), - "microplex_vs_us_data_relative": _relative_difference( - microplex_vs_us_data, - us_data, - ), - "closer_dataset": ( - None - if delta_abs_error is None - else "microplex" - if delta_abs_error < 0 - else "us-data" - if delta_abs_error > 0 - else "tie" - ), - } - - -def _sort_target_diagnostic_rows( - rows: list[dict[str, Any]], - *, - sort_by: str | None, - sort_dir: str | None, -) -> list[dict[str, Any]]: - if not sort_by: - return rows - descending = sort_dir == "desc" - - def compare(left: dict[str, Any], right: dict[str, Any]) -> int: - left_value = _row_sort_value(left.get(sort_by)) - right_value = _row_sort_value(right.get(sort_by)) - if left_value is None and right_value is None: - return 0 - if left_value is None: - return 1 - if right_value is None: - return -1 - if isinstance(left_value, float) and isinstance(right_value, float): - result = (left_value > right_value) - (left_value < right_value) - else: - result = (str(left_value) > str(right_value)) - ( - str(left_value) < str(right_value) - ) - return -result if descending else result - - return sorted(rows, key=cmp_to_key(compare)) - - -def _row_sort_value(value: Any) -> float | str | None: - if value is None: - return None - numeric_value = _estimate(value) - if numeric_value is not None: - return numeric_value - if isinstance(value, int | float): - return None - return str(value).lower() - - -@router.get("/microplex/target-diagnostics") -def microplex_target_diagnostics( - limit: int = 100, - offset: int = 0, - family: str | None = None, - state: str | None = None, - geo_level: str | None = None, - microplex_target_direction: str | None = None, - supported: bool | None = None, - in_loss: bool | None = None, - search: str | None = None, - sort_by: str | None = None, - sort_dir: str | None = None, -): - """Return paginated full Microplex target diagnostics rows.""" - limit = max(1, min(int(limit), 500)) - offset = max(0, int(offset)) - latest = _discover_configured_run_bundles().get("latest_run_bundle") - if not isinstance(latest, dict): - return { - "available": False, - "reason": ( - "No configured Microplex run bundle was found. Set " - "MICROPLEX_ARTIFACT_ROOTS or MICROPLEX_ARTIFACT_ROOT." - ), - "targets": [], - "total_targets": 0, - "filtered_total": 0, - "limit": limit, - "offset": offset, - } - - path, payload = _load_target_diagnostics_payload(latest) - if payload is None: - return { - "available": False, - "reason": "No readable pe_native_target_diagnostics.json was found.", - "path": str(path) if path is not None else None, - "microplex_bundle": { - "artifact_id": latest.get("artifact_id"), - "artifact_dir": latest.get("artifact_dir"), - }, - "targets": [], - "total_targets": 0, - "filtered_total": 0, - "limit": limit, - "offset": offset, - } - - rows = payload.get("targets") - all_rows = ( - [_enrich_target_diagnostic_row(row) for row in rows if isinstance(row, dict)] - if isinstance(rows, list) - else [] - ) - filtered = all_rows - if family: - filtered = [ - row - for row in filtered - if str(row.get("family") or row.get("target_family") or "") == family - ] - if state: - state_upper = state.upper() - filtered = [ - row - for row in filtered - if str(row.get("state") or "").upper() == state_upper - ] - if geo_level: - filtered = [ - row - for row in filtered - if str(row.get("geo_level") or "") == geo_level - ] - if microplex_target_direction: - filtered = [ - row - for row in filtered - if ( - (relative := _estimate(row.get("microplex_vs_target_relative"))) - is not None - and ( - ( - microplex_target_direction == "above" - and relative > 0 - ) - or ( - microplex_target_direction == "below" - and relative < 0 - ) - or ( - microplex_target_direction == "near" - and abs(relative) <= 0.05 - ) - or microplex_target_direction - not in {"above", "below", "near"} - ) - ) - ] - if supported is not None: - filtered = [ - row for row in filtered if row.get("supported_by_microplex") is supported - ] - if in_loss is not None: - filtered = [row for row in filtered if row.get("in_loss") is in_loss] - if search: - filtered = [row for row in filtered if _row_matches_text(row, search)] - filtered = _sort_target_diagnostic_rows( - filtered, - sort_by=sort_by, - sort_dir=sort_dir, - ) - - page = filtered[offset : offset + limit] - summary = payload.get("summary") - return _scrub( - { - "available": True, - "path": str(path) if path is not None else None, - "microplex_bundle": { - "artifact_id": latest.get("artifact_id"), - "artifact_dir": latest.get("artifact_dir"), - }, - "diagnostic_schema_version": payload.get("diagnostic_schema_version"), - "metric": payload.get("metric"), - "period": payload.get("period"), - "baseline_dataset": payload.get("baseline_dataset"), - "candidate_dataset": payload.get("candidate_dataset"), - "dataset_labels": payload.get("dataset_labels", {}), - "summary": summary if isinstance(summary, dict) else {}, - "total_targets": len(all_rows), - "filtered_total": len(filtered), - "unfiltered_total_targets": len(all_rows), - "returned": len(page), - "limit": limit, - "offset": offset, - "has_next": offset + limit < len(filtered), - "filters": { - "family": family, - "state": state, - "geo_level": geo_level, - "microplex_target_direction": microplex_target_direction, - "supported": supported, - "in_loss": in_loss, - "search": search, - "sort_by": sort_by, - "sort_dir": sort_dir, - }, - "targets": page, - } - ) - - -def _load_bundle_native_scores(latest_bundle: dict[str, Any]) -> dict[str, Any] | None: - path_text = latest_bundle.get("native_scores_path") - if not path_text: - return None - path = Path(str(path_text)) - payload = _read_json_file(path) - if not isinstance(payload, dict): - return None - - summary = payload.get("summary") - if not isinstance(summary, dict): - summary = payload.get("broad_loss") - if not isinstance(summary, dict): - summary = payload - - scores = { - "available": True, - "metric": payload.get("metric") or summary.get("metric"), - "period": payload.get("period") or summary.get("period"), - "baseline_enhanced_cps_native_loss": summary.get( - "baseline_enhanced_cps_native_loss" - ), - "candidate_enhanced_cps_native_loss": summary.get( - "candidate_enhanced_cps_native_loss" - ), - "enhanced_cps_native_loss_delta": summary.get( - "enhanced_cps_native_loss_delta" - ), - "baseline_unweighted_msre": summary.get("baseline_unweighted_msre"), - "candidate_unweighted_msre": summary.get("candidate_unweighted_msre"), - "unweighted_msre_delta": summary.get("unweighted_msre_delta"), - "candidate_beats_baseline": summary.get("candidate_beats_baseline"), - "n_targets_total": summary.get("n_targets_total"), - "n_targets_kept": summary.get("n_targets_kept"), - "n_national_targets": summary.get("n_national_targets"), - "n_state_targets": summary.get("n_state_targets"), - "n_targets_bad_dropped": summary.get("n_targets_bad_dropped"), - "n_targets_zero_dropped": summary.get("n_targets_zero_dropped"), - "source": "configured_run_bundle", - "source_path": str(path), - "artifact_id": latest_bundle.get("artifact_id"), - } - if scores["candidate_beats_baseline"] is None: - delta = scores["enhanced_cps_native_loss_delta"] - if isinstance(delta, int | float): - scores["candidate_beats_baseline"] = float(delta) < 0.0 - return scores - - -@router.get("/microplex") -def microplex_overview() -> dict: - """Return a consolidated Microplex target-performance payload. - - Pulls three committed JSONs from PolicyEngine/microplex-us via raw - GitHub (no auth required). The response is structured for a single - dashboard page; consumers should pluck what they need. - """ - parity = _fetch_json(_PARITY_PATH) - regression = _fetch_json(_REGRESSION_SUMMARY_PATH) - drilldown = _fetch_json(_IRS_DRILLDOWN_PATH) - configured_runs = _discover_configured_run_bundles() - latest_bundle = configured_runs.get("latest_run_bundle") or {} - target_rows_available = bool(latest_bundle.get("target_diagnostics_exists")) - target_diagnostics = _load_target_diagnostics(latest_bundle) - - # Pull the target-oracle headline numbers up to a flat shape the frontend - # can render without spelunking. Leave the raw payload available too. - headline = {} - ph = parity.get("comparison", {}).get("policyengineHarness") or {} - if ph.get("isPolicyEngineComparison"): - headline = { - "baseline_label": parity.get("baselineSlice", {}).get("baselineLabel"), - "candidate_label": parity.get("baselineSlice", {}).get("candidateLabel"), - "calibration_target_profile": parity.get("baselineSlice", {}).get( - "calibrationTargetProfile" - ), - "n_synthetic": parity.get("baselineSlice", {}) - .get("comparisonMetadata", {}) - .get("n_synthetic"), - "target_period": parity.get("baselineSlice", {}).get("targetPeriod"), - "baseline_composite_parity_loss": ph.get("baseline_composite_parity_loss"), - "candidate_composite_parity_loss": ph.get("candidate_composite_parity_loss"), - "composite_parity_loss_delta": ph.get("composite_parity_loss_delta"), - "baseline_mean_abs_relative_error": ph.get("baseline_mean_abs_relative_error"), - "candidate_mean_abs_relative_error": ph.get("candidate_mean_abs_relative_error"), - "mean_abs_relative_error_delta": ph.get("mean_abs_relative_error_delta"), - "slice_win_rate": ph.get("slice_win_rate"), - "supported_target_rate": ph.get("supported_target_rate"), - "target_win_rate": ( - ph.get("tag_summaries", {}) - .get("all_targets", {}) - .get("target_win_rate") - ), - "tag_summaries": ph.get("tag_summaries", {}), - } - parity_native_scores = ( - parity.get("comparison", {}).get("policyengineNativeScores") or {} - ) - bundle_native_scores = _load_bundle_native_scores(latest_bundle) - native_scores = bundle_native_scores or parity_native_scores - - return _scrub({ - "source_repo": "PolicyEngine/microplex-us", - "source_artifacts": [ - { - "name": name, - "path": path, - "url": f"{_GITHUB_RAW}/{path}", - } - for name, path in _ARTIFACTS.items() - ], - "limitations": [ - "Only committed microplex-us summary JSON artifacts are public.", - "Newer Microplex run bundles write pe_native_target_diagnostics.json, but those bundles are generated artifacts, not committed public JSONs.", - "This is aggregate Microplex target-oracle reporting, not the full row-level target performance table.", - ], - "newer_runs": { - "current_reader": "public_github_committed_summary_jsons", - "public_branch": "PolicyEngine/microplex-us main", - "run_bundle_manifest_key": _RUN_LEVEL_TARGET_DIAGNOSTICS_MANIFEST_KEY, - "run_bundle_path_hint": _RUN_LEVEL_TARGET_DIAGNOSTICS_PATH, - "legacy_static_dashboard_path": _LEGACY_STATIC_TARGET_DIAGNOSTICS_PATH, - "required_to_load_newer_runs": ( - "Point the dashboard at a generated Microplex artifact root, " - "publish the run-bundle JSONs, or expose the run index/artifacts " - "through an authenticated artifact service." - ), - "not_loaded_reason": ( - "The committed public repo only contains summary JSONs; this " - "process can only see newer runs when MICROPLEX_ARTIFACT_ROOTS " - "or MICROPLEX_ARTIFACT_ROOT points at generated run bundles, " - "or when an artifact store is wired in." - ), - "configured_run_discovery": configured_runs, - }, - "repo_structure": { - "canonical_stage_count": 9, - "current_commit_public_artifact_count": len(_ARTIFACTS), - "analysis_modes": [ - "microplex_vs_target_oracle", - "microplex_vs_us_data_comparator", - "run_to_run_microplex_comparison", - ], - "generated_artifacts": _GENERATED_ARTIFACT_CONTRACT, - "full_target_diagnostics": { - "available_in_committed_repo": False, - "expected_path": _RUN_LEVEL_TARGET_DIAGNOSTICS_PATH, - "run_level_path": _RUN_LEVEL_TARGET_DIAGNOSTICS_PATH, - "manifest_key": _RUN_LEVEL_TARGET_DIAGNOSTICS_MANIFEST_KEY, - "legacy_static_dashboard_path": _LEGACY_STATIC_TARGET_DIAGNOSTICS_PATH, - "static_dashboard_default_url": ( - f"../{_LEGACY_STATIC_TARGET_DIAGNOSTICS_PATH}" - ), - "producer_command": ( - "Run the Microplex PE-US-data rebuild/native audit pipeline; " - "newer runs record manifest.artifacts." - f"{_RUN_LEVEL_TARGET_DIAGNOSTICS_MANIFEST_KEY} = " - f"{_RUN_LEVEL_TARGET_DIAGNOSTICS_PATH}." - ), - "row_fields": _TARGET_DIAGNOSTIC_ROW_FIELDS, - "primary_use": ( - "Standalone Microplex aggregate-vs-target diagnostics; " - "us-data baseline fields are optional comparator context." - ), - }, - "run_index": { - "path_hint": "run_index.duckdb", - "query_helpers": [ - "list_us_microplex_target_delta_rows", - "compare_us_microplex_target_delta_rows", - "select_us_microplex_frontier_index_row", - ], - }, - }, - "artifact_id": latest_bundle.get("artifact_id") or parity.get("artifactId"), - "verdict": parity.get("verdict"), - "headline": headline, - "native_scores": { - "available": native_scores.get("available"), - "source": native_scores.get("source") or "public_committed_parity", - "source_path": native_scores.get("source_path"), - "artifact_id": native_scores.get("artifact_id"), - "metric": native_scores.get("metric"), - "period": native_scores.get("period"), - "baseline_enhanced_cps_native_loss": native_scores.get( - "baseline_enhanced_cps_native_loss" - ), - "candidate_enhanced_cps_native_loss": native_scores.get( - "candidate_enhanced_cps_native_loss" - ), - "enhanced_cps_native_loss_delta": native_scores.get( - "enhanced_cps_native_loss_delta" - ), - "baseline_unweighted_msre": native_scores.get("baseline_unweighted_msre"), - "candidate_unweighted_msre": native_scores.get("candidate_unweighted_msre"), - "unweighted_msre_delta": native_scores.get("unweighted_msre_delta"), - "candidate_beats_baseline": native_scores.get("candidate_beats_baseline"), - "n_targets_total": native_scores.get("n_targets_total"), - "n_targets_kept": native_scores.get("n_targets_kept"), - "n_national_targets": native_scores.get("n_national_targets"), - "n_state_targets": native_scores.get("n_state_targets"), - "n_targets_bad_dropped": native_scores.get("n_targets_bad_dropped"), - "n_targets_zero_dropped": native_scores.get("n_targets_zero_dropped"), - "target_rows_available": target_rows_available, - "full_target_diagnostics_path": ( - latest_bundle.get("target_diagnostics_path") - or _RUN_LEVEL_TARGET_DIAGNOSTICS_PATH - ), - "full_target_diagnostics_manifest_key": ( - _RUN_LEVEL_TARGET_DIAGNOSTICS_MANIFEST_KEY - ), - }, - "target_diagnostics": target_diagnostics, - "regression_summary": { - "total_scored_runs": regression.get("totalScoredRuns"), - "total_audited_runs": regression.get("totalAuditedRuns"), - "best_runs": regression.get("bestRuns", [])[:10], - "worst_runs": regression.get("worstRuns", [])[:10], - "largest_family_counts": regression.get("largestFamilyCounts", {}), - "top3_family_counts": regression.get("top3FamilyCounts", {}), - "target_counts_from_audits": regression.get("targetCountsFromAudits", {}), - }, - "irs_drilldown": { - "family": drilldown.get("family"), - "audits_where_family_leads": drilldown.get("auditsWhereFamilyLeads"), - "audits_with_matching_targets": drilldown.get( - "auditsWithMatchingTargets" - ), - "lead_audits": drilldown.get("leadAudits", [])[:10], - "lead_target_counts": drilldown.get("leadTargetCounts", {}), - "lead_filing_status_gap_summary": drilldown.get( - "leadFilingStatusGapSummary" - ), - "lead_mfs_agi_gap_summary": drilldown.get("leadMFSAgiGapSummary"), - }, - }) diff --git a/backend/routes/nodes.py b/backend/routes/nodes.py deleted file mode 100644 index 94fc7a2..0000000 --- a/backend/routes/nodes.py +++ /dev/null @@ -1,86 +0,0 @@ -"""Node-variable inventory. - -A "node variable" is a leaf in the policyengine_us variable tree: it has no -formula and is not built up from other variables via `adds`/`subtracts`. -Uprating is allowed — that's just CPI/wage projection of a data value, not -derivation. These are the variables the microsim cannot compute on its own; -their values have to come from the underlying dataset or a calibration target. -This view lets users see, for the loaded run, which leaves actually carry a -calibration target and which are left to whatever upstream data provides. -""" - -from __future__ import annotations - -from fastapi import APIRouter, Depends - -from backend.state import AppState, get_state - -router = APIRouter() - - -def _tax_benefit_system(state: AppState): - """The state's sim_service is either a SimService wrapper (pkl mode) or - a raw Microsimulation (dataset mode). Both expose tax_benefit_system, - one via ._sim and the other directly.""" - sim = state.sim_service - if sim is None: - return None - return getattr(sim, "_sim", sim).tax_benefit_system - - -def _is_leaf(var) -> bool: - """No way to compute this variable from others — it must come from data. - - Uprating is permitted: a parameter-driven yearly projection of a stored - value isn't derivation, just inflation/wage indexing. Many calibration - targets (e.g. unemployment_compensation, dividend_income, - social_security_*) have no formula and no adds/subtracts — only uprating. - Treating them as non-leaves would hide the bulk of what the loss actually - pins down at the input layer. - """ - if getattr(var, "formulas", None): - return False - if getattr(var, "adds", None): - return False - if getattr(var, "subtracts", None): - return False - return True - - -@router.get("/nodes") -def list_nodes(state: AppState = Depends(get_state)) -> dict: - """Return every leaf input variable and whether it has a calibration target. - - is_calibrated is true iff the variable name appears in the loaded run's - targets table at any geo level / constraint set. - """ - tbs = _tax_benefit_system(state) - if tbs is None: - return {"items": [], "total": 0, "n_calibrated": 0} - - calibrated_vars: set[str] = set() - df = state.targets_enriched - if not df.empty and "variable" in df.columns: - calibrated_vars = set(df["variable"].dropna().astype(str).unique()) - - items = [] - for name, var in tbs.variables.items(): - if not _is_leaf(var): - continue - items.append({ - "name": name, - "label": getattr(var, "label", None) or name, - "entity": var.entity.key, - "value_type": var.value_type.__name__, - "definition_period": getattr(var, "definition_period", None), - "documentation": getattr(var, "documentation", None) or None, - "is_calibrated": name in calibrated_vars, - }) - items.sort(key=lambda r: r["name"]) - - n_cal = sum(1 for r in items if r["is_calibrated"]) - return { - "items": items, - "total": len(items), - "n_calibrated": n_cal, - } diff --git a/backend/routes/pipeline.py b/backend/routes/pipeline.py deleted file mode 100644 index fc1052e..0000000 --- a/backend/routes/pipeline.py +++ /dev/null @@ -1,166 +0,0 @@ -"""Endpoints serving the extracted pipeline DAG + per-stage deep-dives. - -The data is committed under backend/data/pipeline/. Run the AST extractor -(`backend/scripts/extract_pipeline_dag.py`) to refresh nodes.json when -policyengine_us_data is upgraded; per-stage markdown is hand- or -agent-authored. -""" - -from __future__ import annotations - -import json -from pathlib import Path - -from fastapi import APIRouter, HTTPException - -router = APIRouter() - -DATA_ROOT = Path(__file__).resolve().parents[1] / "data" / "pipeline" -DEFAULT_PIPELINE_ID = "us-data" - -PIPELINES = { - "us-data": { - "label": "policyengine-us-data", - "description": "Extracted @pipeline_node DAG from policyengine_us_data.", - "nodes_path": DATA_ROOT / "nodes.json", - "docs_dir": DATA_ROOT / "stages", - "stage_labels": { - "1_build_datasets": "1. Build datasets", - "2_build_calibration_package": "2. Build calibration package", - "3_fit_weights": "3. Fit weights", - "4_build_outputs": "4. Build outputs", - "5_validate_and_promote_release": "5. Validate & promote release", - }, - }, - "microplex-us": { - "label": "Microplex-US", - "description": ( - "Curated US Microplex flow from source fusion through " - "PolicyEngine oracle evaluation and published diagnostics." - ), - "nodes_path": DATA_ROOT / "microplex.json", - "docs_dir": DATA_ROOT / "microplex" / "stages", - "stage_labels": { - "01_run_profile": "1. Run profile", - "02_source_loading": "2. Source loading", - "03_source_planning": "3. Source planning", - "04_seed_scaffold": "4. Seed scaffold", - "05_donor_integration_synthesis": "5. Donor integration", - "06_policyengine_entities": "6. PolicyEngine entities", - "07_calibration": "7. Calibration", - "08_dataset_assembly": "8. Dataset assembly", - "09_validation_benchmarking": "9. Validation & benchmarking", - }, - }, -} - - -def _pipeline_config(pipeline_id: str | None) -> dict: - selected = pipeline_id or DEFAULT_PIPELINE_ID - config = PIPELINES.get(selected) - if config is None: - raise HTTPException( - status_code=404, - detail=f"Unknown pipeline '{selected}'", - ) - return {"id": selected, **config} - - -def _load_nodes(pipeline_id: str | None = None) -> dict: - config = _pipeline_config(pipeline_id) - path = config["nodes_path"] - if not path.exists(): - raise HTTPException( - status_code=503, - detail=( - "Pipeline DAG hasn't been extracted yet. Run " - "`python backend/scripts/extract_pipeline_dag.py`." - ), - ) - return json.loads(path.read_text()) - - -@router.get("/pipeline") -def get_pipeline( - pipeline_id: str | None = None, - pipeline: str | None = None, -): - """Full DAG: nodes, edges, stats. Grouped by both stage (canonical) and - pathway (legacy) for the frontend.""" - selected_pipeline = pipeline_id or pipeline or DEFAULT_PIPELINE_ID - config = _pipeline_config(selected_pipeline) - payload = _load_nodes(selected_pipeline) - nodes = payload["nodes"] - - # Group by stage (canonical 5-stage taxonomy) - stages: dict[str, list[str]] = {} - for n in nodes: - sid = n.get("stage_id") or "(unknown)" - stages.setdefault(sid, []).append(n["id"]) - - # Group by pathway (legacy; kept so existing pathway docs still surface) - pathways: dict[str, list[str]] = {} - for n in nodes: - for p in (n.get("pathways") or ["(none)"]): - pathways.setdefault(p, []).append(n["id"]) - - stages_dir = config["docs_dir"] - has_doc: dict[str, bool] = {} - if stages_dir.exists(): - for path in stages_dir.glob("*.md"): - has_doc[path.stem] = True - - stage_summary = [ - { - "id": sid, - "label": config["stage_labels"].get(sid, sid), - "node_count": len(ids), - "has_doc": has_doc.get(sid, False), - } - for sid, ids in sorted(stages.items()) - ] - pathway_summary = [ - { - "id": p, - "label": p.replace("_", " ").title(), - "node_count": len(ids), - "has_doc": has_doc.get(p, False), - } - for p, ids in sorted(pathways.items()) - ] - return { - "pipeline_id": selected_pipeline, - "pipeline_label": payload.get("pipeline_label", config["label"]), - "description": payload.get("description", config["description"]), - "source_repo": payload.get("source_repo"), - "source_urls": payload.get("source_urls", []), - "nodes": nodes, - "edges": payload.get("edges", []), - "unproduced_artifacts": payload.get("unproduced_artifacts", []), - "stats": payload.get("stats", {}), - "stages": stage_summary, - "pathways": pathway_summary, - } - - -@router.get("/pipeline/stages/{stage_id}") -def get_stage( - stage_id: str, - pipeline_id: str | None = None, - pipeline: str | None = None, -): - """Markdown deep-dive for a single pathway/stage.""" - selected_pipeline = pipeline_id or pipeline or DEFAULT_PIPELINE_ID - config = _pipeline_config(selected_pipeline) - safe = stage_id.replace("/", "").replace("..", "") - md_path = config["docs_dir"] / f"{safe}.md" - if not md_path.exists(): - raise HTTPException( - status_code=404, - detail=f"No deep-dive doc for stage '{stage_id}'", - ) - return { - "pipeline_id": selected_pipeline, - "stage_id": stage_id, - "markdown": md_path.read_text(encoding="utf-8"), - } diff --git a/backend/routes/populace.py b/backend/routes/populace.py index 8f8f198..779fd9c 100644 --- a/backend/routes/populace.py +++ b/backend/routes/populace.py @@ -14,10 +14,6 @@ tolerance verdict. That artifact is large, so the deployed static snapshot committed under ``frontend/data/populace/latest/`` is the source for target rows; only the small manifests are fetched live. - -The eCPS head-to-head comparison moved out of live populace into -``PolicyEngine/populace-benchmarks``; this view reports calibration fit, not a -populace-vs-enhanced-CPS score. """ from __future__ import annotations @@ -43,31 +39,10 @@ _STATIC_BUILD_MANIFEST = _STATIC_ROOT / "build_manifest.json" _STATIC_RELEASE_MANIFEST = _STATIC_ROOT / "release_manifest.json" _STATIC_CALIBRATION_DIAGNOSTICS = _STATIC_ROOT / "calibration_diagnostics.json" -_STATIC_COMPARISON_SCORECARD = _STATIC_ROOT / "comparison_scorecard.json" _CALIBRATION_DIAGNOSTICS_PUBLIC_PATH = ( "frontend/data/populace/latest/calibration_diagnostics.json" ) -_COMPARISON_SCORECARD_PUBLIC_PATH = ( - "frontend/data/populace/latest/comparison_scorecard.json" -) - -# The incumbent comparison lives in PolicyEngine/populace-benchmarks -# (PolicyEngine/populace#37). By default the route resolves that repo's -# latest.json pointer (PolicyEngine/populace-benchmarks#3) and serves the -# scorecard it names, falling back to the committed archived snapshot until the -# artifact is reachable. POPULACE_BENCHMARKS_POINTER_URL overrides the pointer; -# POPULACE_BENCHMARKS_SCORECARD_URL points straight at a scorecard. -_BENCHMARKS_RAW_BASE = ( - "https://raw.githubusercontent.com/PolicyEngine/populace-benchmarks/main" -) -_DEFAULT_POINTER_URL = ( - f"{_BENCHMARKS_RAW_BASE}/benchmarks/us/incumbent-comparison/latest.json" -) -_BENCHMARKS_POINTER_URL = ( - os.environ.get("POPULACE_BENCHMARKS_POINTER_URL") or _DEFAULT_POINTER_URL -) -_BENCHMARKS_SCORECARD_URL = os.environ.get("POPULACE_BENCHMARKS_SCORECARD_URL") _CACHE: dict[str, tuple[float, Any]] = {} _TTL_SECONDS = 300 @@ -766,172 +741,3 @@ def key_fn(row: dict[str, Any]): ) -def _num(value: Any) -> float | None: - return value if isinstance(value, (int, float)) and not isinstance(value, bool) else None - - -def _normalize_comparison_scorecard(raw: dict[str, Any]) -> dict[str, Any]: - """Flatten an incumbent-comparison scorecard to the shared shape. - - The archived ``sound_ecps_replacement_comparison.json`` splits the clean - win/loss block (``target_diagnostics_summary``) from the refit/loss scalars - (``summary``); the proposed benchmarks scorecard - (PolicyEngine/populace-benchmarks#3) is a single flat ``summary``. Read both. - """ - summary = raw.get("summary") if isinstance(raw.get("summary"), dict) else {} - td = ( - raw.get("target_diagnostics_summary") - if isinstance(raw.get("target_diagnostics_summary"), dict) - else {} - ) - - def pick(*keys: str) -> float | None: - # First key wins, across both sources: the kept-target ``n_targets`` - # (target_diagnostics_summary) takes precedence over the pre-drop - # ``n_targets_total`` (summary), so it matches the win/loss/tie counts. - for key in keys: - for source in (td, summary): - if key in source: - value = _num(source[key]) - if value is not None: - return value - return None - - flat = { - "candidate_loss": pick("candidate_loss", "candidate_enhanced_cps_native_loss"), - "baseline_loss": pick("baseline_loss", "baseline_enhanced_cps_native_loss"), - "loss_delta": pick("loss_delta", "enhanced_cps_native_loss_delta"), - "candidate_holdout_loss": pick("candidate_holdout_loss"), - "baseline_holdout_loss": pick("baseline_holdout_loss"), - "candidate_train_loss": pick("candidate_train_loss"), - "baseline_train_loss": pick("baseline_train_loss"), - "candidate_unweighted_msre": pick("candidate_unweighted_msre"), - "baseline_unweighted_msre": pick("baseline_unweighted_msre"), - "candidate_wins": pick("candidate_wins"), - "baseline_wins": pick("baseline_wins"), - "ties": pick("ties"), - "n_targets": pick("n_targets", "n_targets_total"), - "holdout_targets": pick("holdout_targets"), - "train_targets": pick("train_targets"), - "candidate_beats_baseline": ( - summary.get("candidate_beats_baseline") - if isinstance(summary.get("candidate_beats_baseline"), bool) - else None - ), - # The archived summary's ``matched_household_count`` is a stray bool; - # the real matched count is the per-dataset household count. ``pick`` - # skips non-numeric values, so the count keys are the real source. - "matched_household_count": pick( - "matched_household_count", - "candidate_household_count", - "baseline_household_count", - ), - } - protocol = raw.get("protocol") - if not isinstance(protocol, str) and flat["matched_household_count"] is not None: - protocol = ( - f"Matched {int(flat['matched_household_count']):,} households, " - f"symmetric refit, {flat['holdout_targets']}-target holdout." - ) - return { - "release_id": raw.get("candidate_release_id") or raw.get("release_id"), - "incumbent_manifest": raw.get("incumbent_manifest", "pinned-production-ecps-2024"), - "period": _num(raw.get("period")), - "baseline_label": raw.get("baseline_label", "enhanced_cps"), - "candidate_label": raw.get("candidate_label", "populace"), - "protocol": protocol if isinstance(protocol, str) else None, - "summary": flat, - "family_breakdown": raw.get("family_breakdown") or [], - "top_improvements": raw.get("top_improvements") or [], - "top_regressions": raw.get("top_regressions") or [], - "gates": raw.get("gates") or {}, - } - - -def _scorecard_url_from_pointer(pointer_url: str, scorecard_path: str) -> str: - if scorecard_path.startswith(("http://", "https://")): - return scorecard_path - suffix = "/benchmarks/us/incumbent-comparison/latest.json" - base = pointer_url[: -len(suffix)] if pointer_url.endswith(suffix) else pointer_url - return f"{base}/{scorecard_path.lstrip('/')}" - - -def _load_live_scorecard() -> dict[str, Any]: - """Resolve the benchmarks scorecard, raising on any failure. - - A direct ``POPULACE_BENCHMARKS_SCORECARD_URL`` skips the pointer; otherwise - the ``latest.json`` pointer names the scorecard to fetch. - """ - if _BENCHMARKS_SCORECARD_URL: - raw = _fetch_json(_BENCHMARKS_SCORECARD_URL) - status = raw.get("status") if isinstance(raw, dict) else None - return { - "source": "populace_benchmarks_live", - "path": _BENCHMARKS_SCORECARD_URL, - "pointer_url": None, - "scorecard_status": status if isinstance(status, str) else None, - **_normalize_comparison_scorecard(raw), - } - pointer = _fetch_json(_BENCHMARKS_POINTER_URL) - scorecard_path = pointer.get("scorecard_path") if isinstance(pointer, dict) else None - if not isinstance(scorecard_path, str) or not scorecard_path: - raise ValueError(f"Pointer {_BENCHMARKS_POINTER_URL} has no scorecard_path.") - scorecard_url = _scorecard_url_from_pointer(_BENCHMARKS_POINTER_URL, scorecard_path) - raw = _fetch_json(scorecard_url) - status = pointer.get("status") - if not isinstance(status, str): - status = raw.get("status") if isinstance(raw, dict) else None - return { - "source": "populace_benchmarks_live", - "path": scorecard_url, - "pointer_url": _BENCHMARKS_POINTER_URL, - "scorecard_status": status if isinstance(status, str) else None, - **_normalize_comparison_scorecard(raw), - } - - -@router.get("/populace/comparison") -def populace_comparison() -> dict[str, Any]: - payload: dict[str, Any] = { - "source": "deployed_static_snapshot", - "path": _COMPARISON_SCORECARD_PUBLIC_PATH, - "pointer_url": _BENCHMARKS_POINTER_URL, - "scorecard_status": None, - **_normalize_comparison_scorecard(_load_static(_STATIC_COMPARISON_SCORECARD)), - } - live_error = None - try: - payload = _load_live_scorecard() - except Exception as exc: - live_error = str(exc) - - live = payload["source"] == "populace_benchmarks_live" - scorecard_status = payload.get("scorecard_status") or "archived" - return _scrub( - { - **payload, - "available": True, - "archived": not live, - "scorecard_status": scorecard_status, - "source_pointer": payload.get("pointer_url") or _BENCHMARKS_POINTER_URL, - "live_scorecard_error": live_error, - "notes": [ - "Populace (candidate) is scored against the enhanced CPS " - "(incumbent) with a matched-household, symmetric-refit, " - "held-out-target protocol.", - ( - f"Served live from populace-benchmarks " - f"({scorecard_status} scorecard)." - if live - else "The benchmarks scorecard was not reachable, so this is " - "the committed archived snapshot for release " - "populace-us-2024-9f1260b-20260611 " - "(PolicyEngine/populace-benchmarks#3 / #4 publish it)." - ), - "The eCPS comparison is benchmark-harness material and " - "intentionally lives outside live populace " - "(PolicyEngine/populace#37); the live calibration-fit view is " - "the populace release summary.", - ], - } - ) diff --git a/backend/routes/runs.py b/backend/routes/runs.py deleted file mode 100644 index 67ececa..0000000 --- a/backend/routes/runs.py +++ /dev/null @@ -1,37 +0,0 @@ -"""Endpoints for run discovery and dataset listing.""" - -from fastapi import APIRouter, HTTPException - -from backend.services import runs as runs_service - -router = APIRouter() - - -@router.get("/datasets") -def list_datasets(): - return [ - { - "id": d.id, - "label": d.label, - "repo_id": d.repo_id, - "primary_h5": d.primary_h5, - } - for d in runs_service.list_datasets() - ] - - -@router.get("/runs") -def list_runs(dataset: str): - try: - runs = runs_service.list_runs(dataset) - except KeyError as exc: - raise HTTPException(status_code=400, detail=str(exc)) - return [ - { - "dataset_id": r.dataset_id, - "run_id": r.run_id, - "label": r.label, - "last_modified": r.last_modified, - } - for r in runs - ] diff --git a/backend/routes/strata.py b/backend/routes/strata.py deleted file mode 100644 index bd2bea9..0000000 --- a/backend/routes/strata.py +++ /dev/null @@ -1,29 +0,0 @@ -"""Strata browser endpoint.""" - -from fastapi import APIRouter, Depends, HTTPException -from sqlmodel import Session - -from backend.state import get_state -from backend.models import StratumDetail -from backend.services import db_service -from backend.state import AppState - -router = APIRouter() - - -@router.get("/{stratum_id}") -def get_stratum( - stratum_id: int, - state: AppState = Depends(get_state), -) -> StratumDetail: - if state.db_engine is None: - raise HTTPException(status_code=503, detail="No database connected") - - with Session(state.db_engine) as session: - detail = db_service.get_stratum_detail(session, stratum_id) - - if detail is None: - raise HTTPException( - status_code=404, detail=f"Stratum {stratum_id} not found" - ) - return StratumDetail(**detail) diff --git a/backend/routes/summary.py b/backend/routes/summary.py deleted file mode 100644 index ba66a2c..0000000 --- a/backend/routes/summary.py +++ /dev/null @@ -1,211 +0,0 @@ -"""Run-level summary scorecard for the landing page.""" - -from typing import Annotated - -import numpy as np -from fastapi import APIRouter, Depends, Query - -from backend.state import AppState, get_state - -router = APIRouter() - - -@router.get("/summary") -def get_summary( - error_bins: Annotated[int, Query(ge=4, le=60)] = 20, - worst_n: Annotated[int, Query(ge=1, le=50)] = 10, - state: AppState = Depends(get_state), -): - """Headline scorecard for a single run. - - Returns headline metrics, an abs-rel-error histogram, worst-fit categories - (grouped by variable), worst-fit targets, and weight-health stats. Designed - to be the only call needed to render the landing page. - """ - df = state.targets_enriched - if df.empty or "abs_rel_error" not in df.columns: - return _empty_summary(state) - if "included" in df.columns: - included = df[df["included"].astype(bool)] - else: - included = df - - abs_err_all = included["abs_rel_error"].to_numpy(dtype=float) - rel_err_all = included["rel_error"].to_numpy(dtype=float) - finite_mask = np.isfinite(abs_err_all) & np.isfinite(rel_err_all) - abs_err = abs_err_all[finite_mask] - rel_err = rel_err_all[finite_mask] - - # n_targets_with_estimate is computability coverage — counted across the - # WHOLE bundle, not just the included subset. In sandbox mode every X row - # produces an estimate (so this equals n_targets); in dataset mode only - # the MVP-evaluable subset does. - full_abs = df["abs_rel_error"].to_numpy(dtype=float) - n_with_estimate = int(np.sum(np.isfinite(full_abs))) - - headline = { - "dataset_id": state.dataset_id, - "run_id": state.run_id, - "n_targets": int(len(df)), - "n_targets_included": int(len(included)), - "n_targets_with_estimate": n_with_estimate, - "median_abs_rel_error": _safe_float(np.median(abs_err)) if len(abs_err) else None, - "mean_abs_rel_error": _safe_float(np.mean(abs_err)) if len(abs_err) else None, - "p95_abs_rel_error": _safe_float(np.percentile(abs_err, 95)) if len(abs_err) else None, - "pct_within_5pct": _pct(abs_err, 0.05), - "pct_within_10pct": _pct(abs_err, 0.10), - "pct_within_25pct": _pct(abs_err, 0.25), - "total_loss": _safe_float(np.sum(rel_err ** 2)), - "n_households": int(state.n_households), - "time_period": int(state.time_period), - } - - error_distribution = _histogram(abs_err, error_bins) - - worst_by_variable = _group_summary(included, "variable", top=worst_n) - worst_by_geo_level = _group_summary(included, "geo_level", top=worst_n) - - # Rank worst targets by loss_contribution when we have it (pkl mode); - # otherwise fall back to abs_rel_error so we still show meaningful rows - # in dataset mode where loss_contribution is uniformly 0. - rank_col = "loss_contribution" - if included[rank_col].fillna(0).max() == 0: - rank_col = "abs_rel_error" - worst_targets = ( - included.nlargest(worst_n, rank_col)[ - [ - "target_name", "variable", "geo_level", "value", - "estimate", "rel_error", "abs_rel_error", "loss_contribution", - ] - ] - .assign(target_idx=lambda d: d.index.astype(int)) - .to_dict(orient="records") - ) - weight_health = _weight_health(state) - - payload = { - "headline": headline, - "error_distribution": error_distribution, - "worst_by_variable": worst_by_variable, - "worst_by_geo_level": worst_by_geo_level, - "worst_targets": worst_targets, - "weight_health": weight_health, - } - return _scrub_nans(payload) - - -def _scrub_nans(obj): - """Recursively replace non-finite floats with None so JSON serialisation - doesn't choke. Dataset mode produces NaN estimates for any target that - needs entity-mapped constraint evaluation.""" - if isinstance(obj, dict): - return {k: _scrub_nans(v) for k, v in obj.items()} - if isinstance(obj, list): - return [_scrub_nans(x) for x in obj] - if isinstance(obj, float) and not np.isfinite(obj): - return None - return obj - - -def _empty_summary(state: AppState) -> dict: - return { - "headline": { - "dataset_id": state.dataset_id, - "run_id": state.run_id, - "n_targets": 0, - "n_targets_included": 0, - "median_abs_rel_error": None, - "mean_abs_rel_error": None, - "p95_abs_rel_error": None, - "pct_within_5pct": None, - "pct_within_10pct": None, - "pct_within_25pct": None, - "total_loss": 0.0, - "n_households": int(state.n_households), - "time_period": int(state.time_period), - }, - "error_distribution": [], - "worst_by_variable": [], - "worst_by_geo_level": [], - "worst_targets": [], - "weight_health": _weight_health(state), - } - - -def _safe_float(x) -> float | None: - try: - v = float(x) - except (TypeError, ValueError): - return None - if not np.isfinite(v): - return None - return v - - -def _pct(arr: np.ndarray, threshold: float) -> float | None: - if len(arr) == 0: - return None - return float(np.mean(arr <= threshold)) - - -def _histogram(arr: np.ndarray, bins: int, cap: float = 2.0) -> list[dict]: - """Equal-width bins over [0, cap] plus a single overflow bucket. - - With default cap=2.0 and bins=20, each bucket is 10% wide; anything ≥200% - abs_rel_error lands in the overflow bucket. This keeps the chart readable - even when a handful of targets have pathological errors (e.g. 157,675%). - """ - if len(arr) == 0: - return [] - in_range = arr[arr <= cap] - counts, edges = np.histogram(in_range, bins=bins, range=(0.0, cap)) - overflow = int(np.sum(arr > cap)) - out = [ - {"bin_min": float(edges[i]), "bin_max": float(edges[i + 1]), - "count": int(counts[i])} - for i in range(len(counts)) - ] - if overflow: - out.append({"bin_min": cap, "bin_max": float(np.max(arr)), - "count": overflow, "overflow": True}) - return out - - -def _group_summary(df, by: str, top: int) -> list[dict]: - if by not in df.columns: - return [] - grouped = df.groupby(by, dropna=False).agg( - n_targets=("abs_rel_error", "size"), - mean_abs_rel_error=("abs_rel_error", "mean"), - median_abs_rel_error=("abs_rel_error", "median"), - total_loss=("loss_contribution", "sum"), - ) - sort_col = "total_loss" - if grouped["total_loss"].fillna(0).eq(0).all(): - sort_col = "mean_abs_rel_error" - grouped = grouped.sort_values(sort_col, ascending=False).head(top) - out = [] - for key, row in grouped.iterrows(): - out.append({ - "group": str(key) if key is not None else "(none)", - "n_targets": int(row["n_targets"]), - "mean_abs_rel_error": _safe_float(row["mean_abs_rel_error"]), - "median_abs_rel_error": _safe_float(row["median_abs_rel_error"]), - "total_loss": _safe_float(row["total_loss"]), - }) - return out - - -def _weight_health(state: AppState) -> dict: - g = state.g_weights - finals = state.final_weights - return { - "n_households": int(len(g)), - "pct_zero_g": float(np.mean(g == 0)) if len(g) else None, - "pct_negative_final": float(np.mean(finals < 0)) if len(finals) else None, - "pct_extreme_g_high": float(np.mean(g > 10)) if len(g) else None, - "pct_extreme_g_low": float(np.mean((g > 0) & (g < 0.1))) if len(g) else None, - "g_median": _safe_float(np.median(g)) if len(g) else None, - "g_p95": _safe_float(np.percentile(g, 95)) if len(g) else None, - "g_p5": _safe_float(np.percentile(g, 5)) if len(g) else None, - } diff --git a/backend/routes/target_inventory.py b/backend/routes/target_inventory.py deleted file mode 100644 index f97ca9b..0000000 --- a/backend/routes/target_inventory.py +++ /dev/null @@ -1,245 +0,0 @@ -"""Endpoints for the cross-tier Target Inventory view. - -Serves the per-tier audit summary (small, committed) and the full per-record -union (large, gitignored, regenerable). The latter is streamed/paginated so -the frontend can render filtered slices without shipping the 25MB blob. -""" - -from __future__ import annotations - -import json -import re -from pathlib import Path - -import pandas as pd -from fastapi import APIRouter, Depends, HTTPException, Query - -from backend.state import AppState, get_state - -router = APIRouter() - - -_GEO_VARS = {"state_fips", "congressional_district_geoid", "ucgid_str"} - - -def _norm_geo_id(val) -> str: - """Mirror schema._norm_geo_id so signatures match across tiers.""" - if val is None or val == "" or pd.isna(val): - return "" - s = str(val) - try: - f = float(s) - except (TypeError, ValueError): - return s - import math - if not math.isfinite(f): - return s - return str(int(f)) - - -def _signature_for_enriched_row(row, target_name_constraints=None) -> tuple: - """Mirror TargetRecord.signature() so loaded-run targets sig-match the - inventory rows. Pulls constraints from the row's parsed target_name when - needed, mirroring the same logic used by the DB-load path.""" - variable = str(row.get("variable", "")) - geo_level = (row.get("geo_level") or "national") - geo_id = _norm_geo_id(row.get("geographic_id")) - period = int(row.get("period") or 0) - # Reuse already-parsed constraints if provided; else infer empty. - cons = tuple(sorted( - (c[0], c[1], _norm_geo_id(c[2])) - for c in (target_name_constraints or []) - )) - return (variable, geo_level, geo_id, period, cons) - -DATA_ROOT = Path(__file__).resolve().parents[1] / "data" -SUMMARY_PATH = DATA_ROOT / "target_index.json" -UNION_PATH = DATA_ROOT / "target_index.json.union.jsonl" - - -def _load_summary() -> dict: - if not SUMMARY_PATH.exists(): - raise HTTPException( - status_code=503, - detail=( - "target_index.json not built. Run " - "`python -m backend.scripts.target_index.audit`." - ), - ) - return json.loads(SUMMARY_PATH.read_text()) - - -def _load_union() -> list[dict]: - if not UNION_PATH.exists(): - raise HTTPException( - status_code=503, - detail=( - "Per-record union not built. Run " - "`python -m backend.scripts.target_index.audit` to regenerate " - f"{UNION_PATH.name} (gitignored, ~25MB)." - ), - ) - with UNION_PATH.open() as f: - return [json.loads(line) for line in f if line.strip()] - - -@router.get("/target-inventory/summary") -def get_summary(): - """The audit summary: per-tier counts + match rates.""" - return _load_summary() - - -def _build_estimate_lookup(state: AppState) -> dict[int, dict]: - """Map target_id → {estimate, rel_error, target_idx} from the loaded run. - - The DB-tier inventory rows store the DB target_id in their source_row - ("target_id=N/stratum_id=M"), so joining by target_id is reliable — - no fragile signature reconstruction needed. - """ - df = state.targets_enriched - if df is None or df.empty or "target_id" not in df.columns: - return {} - lookup: dict[int, dict] = {} - for idx, row in df.iterrows(): - tid = row.get("target_id") - if pd.isna(tid): - continue - est = row.get("estimate") - rel_err = row.get("rel_error") - lookup[int(tid)] = { - "estimate": float(est) if pd.notna(est) else None, - "rel_error": float(rel_err) if pd.notna(rel_err) else None, - "target_idx": int(idx), - } - return lookup - - -_TARGET_ID_RE = re.compile(r"target_id=(\d+)") - - -def _target_id_from_source_row(source_row: str) -> int | None: - """Inventory rows from the DB tier have source_row = 'target_id=N/stratum_id=M'. - Extract N for the join.""" - if not source_row: - return None - m = _TARGET_ID_RE.search(source_row) - return int(m.group(1)) if m else None - - -@router.get("/target-inventory") -def list_targets( - tier: str | None = Query(None, description="Filter by storage tier (db/csv/python)"), - source_path: str | None = Query(None, description="Filter by exact source file path"), - variable: str | None = Query(None, description="Filter by PE variable name"), - in_db: bool | None = Query(None, description="Filter to only matched (true) or only unmatched (false)"), - search: str | None = Query(None, description="Substring match against variable/source/notes"), - limit: int = Query(100, ge=1, le=2000), - offset: int = Query(0, ge=0), - state: AppState = Depends(get_state), -): - """Paginated listing of the full union. Each row already carries - storage_tier / source_path / source_row / notes / signature, plus an - `in_db` flag derived from cross-tier matching against policy_data.db.""" - union = _load_union() - - def _freeze(x): - """Recursively convert nested lists to tuples so the signature is - hashable (the JSONL stores constraints as list-of-lists).""" - if isinstance(x, list): - return tuple(_freeze(i) for i in x) - return x - - # Build a set of DB signatures for the in_db flag. - db_sigs = {_freeze(r["signature"]) for r in union if r.get("storage_tier") == "db"} - - # Build target_id → {estimate, rel_error, target_idx} from the loaded run. - # DB-tier inventory rows carry target_id in their source_row; that's the - # cleanest join key. For non-DB rows we'd need a more elaborate mapping; - # those stay '—' here (the audit page already shows the gap). - estimate_lookup = _build_estimate_lookup(state) - - # MVP evaluator cache (lazy: only built if there's a non-DB row that - # needs computation). - eval_cache_box: list = [None] - - def _get_eval_cache(): - if eval_cache_box[0] is not None: - return eval_cache_box[0] - if state.sim_service is None: - return None - from backend.services.stratum_evaluator import EvalCache - eval_cache_box[0] = EvalCache(state) - return eval_cache_box[0] - - def annotate(r): - sig = _freeze(r["signature"]) - # DB rows trivially have in_db=True; otherwise check membership. - r["in_db"] = r.get("storage_tier") == "db" or sig in db_sigs - - r["estimate"] = None - r["rel_error"] = None - r["target_idx"] = None - r["eval_note"] = "" - - if r.get("storage_tier") == "db": - # Cheap path: pull the loaded run's estimate by target_id. - tid = _target_id_from_source_row(r.get("source_row") or "") - if tid is not None and tid in estimate_lookup: - hit = estimate_lookup[tid] - r["estimate"] = hit["estimate"] - r["rel_error"] = hit["rel_error"] - r["target_idx"] = hit["target_idx"] - r["eval_note"] = "from loaded calibration X·w" - else: - # Authored-only row: try the MVP evaluator. Geographic-only and - # simple-constraint cases will return a real number. - cache = _get_eval_cache() - if cache is not None: - from backend.services.stratum_evaluator import evaluate_signature - cons = r.get("constraints") or [] - try: - est, note = evaluate_signature( - variable=r["variable"], - geo_level=r.get("geo_level"), - geographic_id=r.get("geographic_id"), - constraints=[tuple(c) for c in cons], - is_count=bool(r.get("is_count")), - cache=cache, - period=r.get("period"), - ) - r["estimate"] = est - r["eval_note"] = note - target = r.get("value") - if est is not None and target not in (None, 0): - r["rel_error"] = (est - target) / abs(target) - except Exception as exc: - r["eval_note"] = f"evaluator error: {exc}" - return r - - rows = [annotate(r) for r in union] - - # Filters - if tier: - rows = [r for r in rows if r.get("storage_tier") == tier] - if source_path: - rows = [r for r in rows if r.get("source_path") == source_path] - if variable: - rows = [r for r in rows if r.get("variable") == variable] - if in_db is not None: - rows = [r for r in rows if r.get("in_db") is in_db] - if search: - s = search.lower() - rows = [ - r for r in rows - if s in (r.get("variable") or "").lower() - or s in (r.get("source_path") or "").lower() - or s in (r.get("notes") or "").lower() - ] - - total = len(rows) - return { - "items": rows[offset : offset + limit], - "total": total, - "offset": offset, - "limit": limit, - } diff --git a/backend/routes/targets.py b/backend/routes/targets.py deleted file mode 100644 index 94360ea..0000000 --- a/backend/routes/targets.py +++ /dev/null @@ -1,857 +0,0 @@ -"""Target analysis endpoints.""" - -import operator as op_module -from typing import Annotated - -import numpy as np -import pandas as pd -from fastapi import APIRouter, Depends, HTTPException, Query, Request -from sqlmodel import Session - -from backend.services.geo_utils import geo_display_name -from backend.state import get_state -from backend.models import ( - ConstraintCheck, - ConstraintDiffResponse, - ContributorRow, - ConvergencePoint, - EligibilityAuditResponse, - ErrorDecomposition, - ProvenanceResponse, - TargetListResponse, - TargetRow, -) -from backend.services import db_service, matrix_ops -from backend.state import AppState - -router = APIRouter() - -_OPS = { - "gt": op_module.gt, - "gte": op_module.ge, - "lt": op_module.lt, - "lte": op_module.le, - "eq": op_module.eq, - "ne": op_module.ne, -} - - -def _validate_target_idx(state: AppState, target_idx: int) -> None: - if target_idx < 0 or target_idx >= state.n_targets: - raise HTTPException( - status_code=404, - detail=f"target_idx {target_idx} out of range [0, {state.n_targets})", - ) - - -def _require_matrix_artifacts(state: AppState) -> None: - if state.X_csr.shape[0] == 0 or state.X_csr.shape[1] == 0 or state.X_csr.nnz == 0: - raise HTTPException( - status_code=501, - detail=( - "This run does not publish the calibration sparse matrix. " - "Target detail tabs that need household-level contributors " - "are unavailable for dataset-mode runs." - ), - ) - - -# --- Literal-segment routes BEFORE parametric routes --- - - -@router.get("/search") -def search_targets( - variable: str, - sort_by: str = "abs_rel_error", - state: AppState = Depends(get_state), -) -> list[dict]: - if state.db_engine is None: - raise HTTPException(status_code=503, detail="No database connected") - - with Session(state.db_engine) as session: - db_results = db_service.search_targets(session, variable) - - enriched = state.targets_enriched - out = [] - for r in db_results: - match = enriched[enriched.get("target_id") == r["target_id"]] - error_info = {} - if len(match) > 0: - row = match.iloc[0] - error_info = { - "target_idx": int(match.index[0]), - "estimate": float(row.get("estimate", 0)), - "rel_error": float(row.get("rel_error", 0)), - "abs_rel_error": float(row.get("abs_rel_error", 0)), - "loss_contribution": float(row.get("loss_contribution", 0)), - "n_contributors": int(row.get("n_contributors", 0)), - } - out.append({**r, **error_info}) - - if sort_by in ("abs_rel_error", "loss_contribution", "rel_error"): - out.sort(key=lambda x: abs(x.get(sort_by, 0)), reverse=True) - return out - - -@router.get("/worst-fit") -def worst_fit( - limit: int = 20, - included_only: bool = True, - state: AppState = Depends(get_state), -) -> list[TargetRow]: - enriched = state.targets_enriched - if included_only: - enriched = enriched[enriched["included"]] - enriched = enriched.sort_values( - "abs_rel_error", ascending=False - ).head(limit) - return [_target_row(enriched, idx) for idx in enriched.index] - - -# Error buckets — abs_rel_error ranges. Order matters for display. -ERROR_BUCKETS = { - "excellent": (0.0, 0.05), - "good": (0.05, 0.20), - "poor": (0.20, 0.50), - "extreme": (0.50, float("inf")), -} - - -def _available_bundles_for_state(state) -> "frozenset[str] | None": - """Look up which bundle h5s the loaded run actually publishes on HF. - - Returns ``None`` for runs without a resolvable HF repo (pkl-mode - sandbox); callers that get ``None`` should treat the canonical - bundle mapping as a best-effort label rather than a verified fact. - """ - try: - from backend.services.runs import get_dataset - from backend.services.bundle_availability import published_bundles - ds = get_dataset(state.dataset_id) - except Exception: - return None - if ds is None or getattr(ds, "layout", None) not in { - "staging", "root", "staging-root", - }: - return None - return published_bundles(ds.repo_id, state.run_id) - - -def _apply_target_filters( - df, - *, - search: str | None = None, - variables: list[str] | None = None, - geo_levels: list[str] | None = None, - error_buckets: list[str] | None = None, - sources: list[str] | None = None, - geographic_id: str | None = None, - state_fips: list[int] | None = None, - domain_variable: str | None = None, - min_abs_rel_error: float | None = None, - included_only: bool | None = None, - dataset_files: list[str] | None = None, - available_bundles: "frozenset[str] | None" = None, -): - """Apply the standard filter set used by list_targets and facets.""" - if included_only is not None: - df = df[df["included"] == included_only] - if variables: - df = df[df["variable"].isin(variables)] - if geo_levels: - df = df[df["geo_level"].isin(geo_levels)] - if error_buckets: - # Strict: if user passed bucket names but none are recognised, return - # an empty result rather than silently ignoring the filter. - masks = [] - for bucket in error_buckets: - if bucket not in ERROR_BUCKETS: - continue - lo, hi = ERROR_BUCKETS[bucket] - masks.append((df["abs_rel_error"] >= lo) & (df["abs_rel_error"] < hi)) - if not masks: - df = df.iloc[0:0] - else: - combined = masks[0] - for m in masks[1:]: - combined = combined | m - df = df[combined] - if sources and "source" in df.columns: - df = df[df["source"].isin(sources)] - if dataset_files: - # Each target maps to one calibrated h5 in us-data's pipeline. If - # `available_bundles` is set, fall back to the federal bundle when - # the conventional per-bundle h5 doesn't exist for this run. - from backend.services.geo_utils import runtime_dataset_bundle_for - wanted = set(dataset_files) - df = df[df.apply( - lambda r: runtime_dataset_bundle_for( - r.get("geo_level"), r.get("geographic_id"), - available=available_bundles, - ) in wanted, - axis=1, - )] - if geographic_id: - df = df[df["geographic_id"].astype(str) == str(geographic_id)] - if state_fips: - fips_set = set(state_fips) - def _matches_any_state(gid): - s = str(gid) - if not s.isdigit(): - return s in {str(f) for f in fips_set} - try: - return int(s) in fips_set or (int(s) // 100) in fips_set - except ValueError: - return False - df = df[df["geographic_id"].apply(_matches_any_state)] - if domain_variable: - df = df[ - df["domain_variable"].str.contains( - domain_variable, case=False, na=False - ) - ] - if min_abs_rel_error is not None: - df = df[df["abs_rel_error"] >= min_abs_rel_error] - if search: - # Search across target_name + variable + domain (case-insensitive) - s = search.lower() - haystack = ( - df["target_name"].fillna("").astype(str).str.lower() - + " " - + df["variable"].fillna("").astype(str).str.lower() - + " " - + df.get("domain", df.get("domain_variable", "")).fillna("").astype(str).str.lower() - ) - df = df[haystack.str.contains(s, regex=False, na=False)] - return df - - -@router.get("") -def list_targets( - request: Request, - sort_by: str = "loss_contribution", - sort_order: str = "desc", - search: str | None = None, - variable: Annotated[list[str] | None, Query()] = None, - geo_level: Annotated[list[str] | None, Query()] = None, - error_bucket: Annotated[list[str] | None, Query()] = None, - source: Annotated[list[str] | None, Query()] = None, - geographic_id: str | None = None, - state_fips: Annotated[list[int] | None, Query(alias="state_fips")] = None, - domain_variable: str | None = None, - min_abs_rel_error: float | None = None, - included_only: bool | None = None, - compare_run: str | None = Query( - None, description="Second run id (same dataset) to join for compare mode." - ), - dataset_file: Annotated[list[str] | None, Query()] = None, - limit: int = 50, - offset: int = 0, - state: AppState = Depends(get_state), -) -> TargetListResponse: - available_bundles = _available_bundles_for_state(state) - df = _apply_target_filters( - state.targets_enriched, - search=search, - variables=variable, - geo_levels=geo_level, - error_buckets=error_bucket, - sources=source, - geographic_id=geographic_id, - state_fips=state_fips, - domain_variable=domain_variable, - min_abs_rel_error=min_abs_rel_error, - included_only=included_only, - dataset_files=dataset_file, - available_bundles=available_bundles, - ) - - # Per-bundle re-evaluation. Kicks in when the caller filtered to - # exactly one published bundle other than the federal one we - # already loaded — then estimates come from that bundle's own h5. - bundle_evaluated: str | None = None - if ( - dataset_file - and len(dataset_file) == 1 - and available_bundles - and dataset_file[0] in available_bundles - and dataset_file[0] != "enhanced_cps_2024.h5" - and not df.empty - ): - only = dataset_file[0] - try: - from backend.services.runs import get_dataset - from backend.services.bundle_eval import evaluate_bundle - ds = get_dataset(state.dataset_id) - df = evaluate_bundle( - df, - repo_id=ds.repo_id, - run_id=state.run_id, - bundle=only, - time_period=state.time_period, - ) - bundle_evaluated = only - except Exception as exc: - import logging as _logging - _logging.getLogger(__name__).warning( - "Per-bundle eval for %s failed (%s); using federal-fit numbers.", - only, exc, - ) - - # Compare-run join: enriches each row with estimate_b / rel_error_b / - # abs_rel_error_b / delta from the second run, joined on target_id. - # Loaded through the registry so a cold compare-run picks up the same - # entity-aware evaluator pass. - df_b = None - if request is not None and compare_run and compare_run != state.run_id: - try: - state_b = request.app.state.registry.get(state.dataset_id, compare_run) - df_b = state_b.targets_enriched - except Exception: - df_b = None - if df_b is not None and not df_b.empty: - b_cols = df_b[["target_id", "estimate", "rel_error", "abs_rel_error"]].rename( - columns={ - "estimate": "estimate_b", - "rel_error": "rel_error_b", - "abs_rel_error": "abs_rel_error_b", - } - ) - df = df.merge(b_cols, on="target_id", how="left") - df["delta"] = df["abs_rel_error_b"] - df["abs_rel_error"] - - total = len(df) - ascending = sort_order == "asc" - if sort_by in df.columns: - effective_sort = sort_by - if sort_by == "loss_contribution": - losses = df["loss_contribution"].to_numpy(dtype=float) - if len(losses) == 0 or not np.any(np.nan_to_num(losses) != 0): - effective_sort = "abs_rel_error" - df = df.sort_values(effective_sort, ascending=ascending) - df = df.iloc[offset : offset + limit] - - has_compare = df_b is not None and not df_b.empty - return TargetListResponse( - items=[_target_row(df, idx, with_compare=has_compare) for idx in df.index], - total=total, - offset=offset, - limit=limit, - bundle_evaluated=bundle_evaluated, - ) - - -@router.get("/facets") -def get_facets( - search: str | None = None, - variable: Annotated[list[str] | None, Query()] = None, - geo_level: Annotated[list[str] | None, Query()] = None, - error_bucket: Annotated[list[str] | None, Query()] = None, - source: Annotated[list[str] | None, Query()] = None, - included_only: bool | None = None, - state_fips: Annotated[list[int] | None, Query(alias="state_fips")] = None, - state: AppState = Depends(get_state), -): - """Per-facet counts. For each facet, counts are computed against the - other active filters (so a facet doesn't suppress its own selection).""" - - def _filtered(exclude: str): - return _apply_target_filters( - state.targets_enriched, - search=search, - variables=variable if exclude != "variable" else None, - geo_levels=geo_level if exclude != "geo_level" else None, - error_buckets=error_bucket if exclude != "error_bucket" else None, - sources=source if exclude != "source" else None, - included_only=included_only, - state_fips=state_fips, - ) - - def _value_counts_with_loss(df, col: str): - if col not in df.columns: - return [] - grouped = ( - df.groupby(col, dropna=False) - .agg(count=(col, "size"), - total_loss=("loss_contribution", "sum")) - .sort_values("total_loss", ascending=False) - ) - out = [] - for key, row in grouped.iterrows(): - out.append({ - "value": "(none)" if key is None or (isinstance(key, float) and key != key) else str(key), - "count": int(row["count"]), - "total_loss": float(row["total_loss"]), - }) - return out - - by_variable = _value_counts_with_loss(_filtered("variable"), "variable") - by_geo_level = _value_counts_with_loss(_filtered("geo_level"), "geo_level") - by_source = _value_counts_with_loss(_filtered("source"), "source") - - # Per-h5-bundle counts: which calibrated dataset each target rolls - # up into. Runtime-aware so we only list bundles the run actually - # publishes — for a federal-only GHA run that means a single entry - # holding all 40k targets, not the theoretical 200 names. - from backend.services.geo_utils import runtime_dataset_bundle_for - available_bundles = _available_bundles_for_state(state) - df_bundles = _filtered("dataset_file") - df_bundles = df_bundles.assign( - _bundle=df_bundles.apply( - lambda r: runtime_dataset_bundle_for( - r.get("geo_level"), r.get("geographic_id"), - available=available_bundles, - ), - axis=1, - ) - ) - bundle_counts = ( - df_bundles.groupby("_bundle", dropna=False) - .size() - .sort_values(ascending=False) - .head(200) - ) - by_dataset_file = [ - {"value": str(k), "count": int(v)} for k, v in bundle_counts.items() - ] - - # Error buckets — count distribution within the current selection-aware df. - df_for_buckets = _filtered("error_bucket") - abs_err = df_for_buckets["abs_rel_error"].to_numpy() if "abs_rel_error" in df_for_buckets.columns else [] - by_error_bucket = [] - for name, (lo, hi) in ERROR_BUCKETS.items(): - if len(abs_err) == 0: - count = 0 - else: - count = int(((abs_err >= lo) & (abs_err < hi)).sum()) - by_error_bucket.append({"value": name, "count": count}) - - # Status: included vs skipped - df_status = _apply_target_filters( - state.targets_enriched, - search=search, - variables=variable, - geo_levels=geo_level, - error_buckets=error_bucket, - state_fips=state_fips, - ) - by_status = [] - if "included" in df_status.columns: - included_ct = int(df_status["included"].sum()) - skipped_ct = int((~df_status["included"]).sum()) - by_status = [ - {"value": "included", "count": included_ct}, - {"value": "skipped", "count": skipped_ct}, - ] - - return { - "by_variable": by_variable, - "by_geo_level": by_geo_level, - "by_source": by_source, - "by_error_bucket": by_error_bucket, - "by_status": by_status, - "by_dataset_file": by_dataset_file, - "buckets_definition": { - k: {"min": v[0], "max": None if v[1] == float("inf") else v[1]} - for k, v in ERROR_BUCKETS.items() - }, - } - - -@router.get("/source-summary") -def get_source_summary( - included_only: bool | None = True, - state: AppState = Depends(get_state), -): - """Per-source stats: count, mean/median |rel_error|, % within ±10%. - Useful for spotting upstream sources the calibration fits well vs poorly. - """ - df = state.targets_enriched - if df.empty or "source" not in df.columns: - return {"sources": []} - if included_only and "included" in df.columns: - df = df[df["included"].astype(bool)] - - grouped = ( - df.dropna(subset=["source"]) - .groupby("source", dropna=False) - .agg( - n_targets=("source", "size"), - mean_abs_rel_error=("abs_rel_error", "mean"), - median_abs_rel_error=("abs_rel_error", "median"), - total_loss=("loss_contribution", "sum"), - ) - .reset_index() - .sort_values("n_targets", ascending=False) - ) - out = [] - for _, r in grouped.iterrows(): - # Pct within 10% - sub = df[df["source"] == r["source"]] - abs_err = sub["abs_rel_error"].to_numpy() - finite = abs_err[np.isfinite(abs_err)] - within_10 = float((finite <= 0.10).mean()) if len(finite) else None - out.append({ - "source": str(r["source"]), - "n_targets": int(r["n_targets"]), - "mean_abs_rel_error": _safe_float(r["mean_abs_rel_error"]), - "median_abs_rel_error": _safe_float(r["median_abs_rel_error"]), - "total_loss": _safe_float(r["total_loss"]), - "pct_within_10pct": within_10, - }) - return {"sources": out} - - -# --- Parametric routes --- - - -@router.get("/{target_idx}/error-decomposition") -def error_decomposition( - target_idx: int, - state: AppState = Depends(get_state), -) -> ErrorDecomposition: - _validate_target_idx(state, target_idx) - _require_matrix_artifacts(state) - target_value = float(state.targets_enriched.iloc[target_idx]["value"]) - decomp = matrix_ops.compute_error_decomposition( - state.X_csr, target_idx, target_value, - state.initial_weights, state.final_weights, - ) - concentration = matrix_ops.compute_concentration( - state.X_csr, target_idx, state.final_weights, - ) - return ErrorDecomposition( - target_name=state.target_names[target_idx], - concentration=concentration, - **decomp, - ) - - -@router.get("/{target_idx}/provenance") -def provenance( - target_idx: int, - state: AppState = Depends(get_state), -) -> ProvenanceResponse: - _validate_target_idx(state, target_idx) - if state.db_engine is None: - raise HTTPException(status_code=503, detail="No database connected") - - row = state.targets_enriched.iloc[target_idx] - target_id = row.get("target_id") - if target_id is None: - raise HTTPException(status_code=404, detail="No target_id mapping") - - with Session(state.db_engine) as session: - prov = db_service.get_target_provenance(session, int(target_id)) - - if prov is None: - raise HTTPException(status_code=404, detail="Target not found in database") - - prov["geo_level"] = row.get("geo_level") - prov["geographic_id"] = str(row.get("geographic_id", "")) - prov["uprating_factor"] = row.get("uprating_factor") - if prov["uprating_factor"] and prov["value"]: - prov["uprated_value"] = prov["value"] * prov["uprating_factor"] - else: - prov["uprated_value"] = prov["value"] - - return ProvenanceResponse(**prov) - - -@router.get("/{target_idx}/eligibility-audit") -def eligibility_audit( - target_idx: int, - criterion_variable: str = Query(...), - criterion_operator: str = Query("gt"), - criterion_value: float = Query(0), - state: AppState = Depends(get_state), -) -> EligibilityAuditResponse: - _validate_target_idx(state, target_idx) - _require_matrix_artifacts(state) - if criterion_operator not in _OPS: - raise HTTPException(status_code=400, detail=f"Invalid operator: {criterion_operator}") - if not state.sim_service.variable_exists(criterion_variable): - raise HTTPException(status_code=400, detail=f"Unknown variable: {criterion_variable}") - - contributors = matrix_ops.get_target_contributors(state.X_csr, target_idx) - criterion_vals = state.sim_service.calculate(criterion_variable, map_to="household") - contributor_vals = criterion_vals[contributors] - - op_fn = _OPS[criterion_operator] - meets = op_fn(contributor_vals, criterion_value) - fails = ~meets - - n_meet = int(meets.sum()) - n_fail = int(fails.sum()) - total = len(contributors) - - # Weighted contribution from failing households - row = state.X_csr[target_idx, :] - fail_cols = contributors[fails] - if len(fail_cols) > 0: - fail_weighted = float( - row[:, fail_cols].multiply(state.final_weights[fail_cols]).sum() - ) - else: - fail_weighted = 0.0 - - estimate = float(state.targets_enriched.iloc[target_idx]["estimate"]) - pct_est_failing = (fail_weighted / estimate * 100) if estimate != 0 else 0.0 - - return EligibilityAuditResponse( - target_name=state.target_names[target_idx], - total_contributors=total, - meet_criterion=n_meet, - fail_criterion=n_fail, - pct_failing=n_fail / total * 100 if total > 0 else 0.0, - weighted_contribution_from_failing=fail_weighted, - pct_estimate_from_failing=pct_est_failing, - diagnosis=( - f"{n_fail / total:.1%} of contributors do not meet criterion " - f"({criterion_variable} {criterion_operator} {criterion_value}). " - f"They contribute {pct_est_failing:.1f}% of the estimate." - if total > 0 - else "No contributors" - ), - ) - - -@router.get("/{target_idx}/constraint-diff") -def constraint_diff( - target_idx: int, - state: AppState = Depends(get_state), -) -> ConstraintDiffResponse: - _validate_target_idx(state, target_idx) - _require_matrix_artifacts(state) - if state.db_engine is None: - raise HTTPException(status_code=503, detail="No database connected") - - row = state.targets_enriched.iloc[target_idx] - stratum_id = row.get("stratum_id") - if stratum_id is None: - raise HTTPException(status_code=404, detail="No stratum_id mapping") - - with Session(state.db_engine) as session: - constraints = db_service.get_target_constraints( - session, int(stratum_id) - ) - - contributors = matrix_ops.get_target_contributors(state.X_csr, target_idx) - geo_vars = {"state_fips", "congressional_district_geoid", "ucgid_str"} - checks = [] - - for c in constraints: - if c["variable"] in geo_vars: - continue - if not state.sim_service.variable_exists(c["variable"]): - checks.append(ConstraintCheck( - variable=c["variable"], - operation=c["operation"], - value=c["value"], - contributors_satisfying=0, - contributors_violating=0, - pct_violating=0, - status="SKIPPED_UNKNOWN_VARIABLE", - )) - continue - - vals = state.sim_service.calculate(c["variable"], map_to="household") - contributor_vals = vals[contributors] - - threshold = _parse_value(c["value"]) - op_map = { - ">": op_module.gt, - ">=": op_module.ge, - "<": op_module.lt, - "<=": op_module.le, - "==": op_module.eq, - "!=": op_module.ne, - } - op_fn = op_map.get(c["operation"]) - if op_fn is None: - continue - - meets = op_fn(contributor_vals, threshold) - n_meet = int(meets.sum()) - n_fail = len(contributors) - n_meet - pct_fail = n_fail / len(contributors) * 100 if len(contributors) > 0 else 0 - - if pct_fail > 10: - status = "VIOLATION" - elif pct_fail > 1: - status = "MINOR_VIOLATION" - else: - status = "OK" - - checks.append(ConstraintCheck( - variable=c["variable"], - operation=c["operation"], - value=c["value"], - contributors_satisfying=n_meet, - contributors_violating=n_fail, - pct_violating=pct_fail, - status=status, - )) - - return ConstraintDiffResponse( - target_name=state.target_names[target_idx], - stratum_id=int(stratum_id), - constraints=checks, - ) - - -@router.get("/{target_idx}/contributors") -def contributors( - target_idx: int, - min_g_weight: float | None = None, - poverty_only: bool = False, - sort_by: str = "g_weight", - limit: int = 50, - offset: int = 0, - state: AppState = Depends(get_state), -) -> list[ContributorRow]: - _validate_target_idx(state, target_idx) - _require_matrix_artifacts(state) - contribs = matrix_ops.get_target_contributions( - state.X_csr, target_idx, state.final_weights, - ) - hh = state.households_df - merged = contribs.merge( - hh[["household_idx", "income", "g_weight", "in_poverty", "state"]], - on="household_idx", - ) - - if poverty_only: - merged = merged[merged["in_poverty"]] - if min_g_weight is not None: - merged = merged[merged["g_weight"] >= min_g_weight] - - if sort_by in merged.columns: - merged = merged.sort_values(sort_by, ascending=False) - merged = merged.iloc[offset : offset + limit] - - return [ - ContributorRow( - household_idx=int(r["household_idx"]), - raw_value=float(r["raw_value"]), - weighted_value=float(r["weighted_value"]), - income=float(r["income"]), - g_weight=float(r["g_weight"]), - in_poverty=bool(r["in_poverty"]), - state=int(r["state"]), - ) - for _, r in merged.iterrows() - ] - - -@router.get("/{target_idx}/convergence") -def convergence( - target_idx: int, - state: AppState = Depends(get_state), -) -> list[ConvergencePoint]: - _validate_target_idx(state, target_idx) - if state.cal_log is None: - raise HTTPException(status_code=404, detail="No calibration log available") - - name = state.target_names[target_idx] - filtered = state.cal_log[state.cal_log["target_name"] == name] - return [ - ConvergencePoint( - epoch=int(r["epoch"]), - estimate=float(r["estimate"]), - target=float(r["target"]), - rel_error=float(r["rel_error"]), - loss=float(r["loss"]), - ) - for _, r in filtered.iterrows() - ] - - -# --- Helpers --- - - -def _nan_to_none(val): - """Convert pandas NaN to None for Pydantic compatibility.""" - if isinstance(val, float) and pd.isna(val): - return None - return val - - -def _safe_int(val) -> int | None: - if val is None or (isinstance(val, float) and pd.isna(val)): - return None - try: - return int(val) - except (TypeError, ValueError): - return None - - -def _safe_float(val) -> float | None: - if val is None or pd.isna(val): - return None - try: - out = float(val) - except (TypeError, ValueError): - return None - if not np.isfinite(out): - return None - return out - - -def _parse_value(val): - try: - return float(val) - except (TypeError, ValueError): - return val - - -def _parse_constraints_from_name(target_name: str) -> list[str]: - """Extract non-geographic constraints from target_name. - - Target names look like: national/snap/[tax_unit_is_filer==1,snap>0] - or: cd_0622/person_count/[age>=18,age<25] - or: national/rent (no brackets = no constraints) - """ - if not isinstance(target_name, str) or "[" not in target_name: - return [] - bracket = target_name[target_name.index("[") + 1 : target_name.rindex("]")] - if not bracket: - return [] - return [c.strip() for c in bracket.split(",") if c.strip()] - - -def _target_row(df, idx: int, with_compare: bool = False) -> TargetRow: - r = df.loc[idx] - gl = _nan_to_none(r.get("geo_level")) or "national" - gid = str(_nan_to_none(r.get("geographic_id")) or "US") - constraints = _parse_constraints_from_name(str(r.get("target_name", ""))) - target_value = _safe_float(r.get("value")) - estimate = _safe_float(r.get("estimate")) - abs_error = ( - abs(estimate - target_value) - if estimate is not None and target_value is not None - else None - ) - return TargetRow( - target_idx=idx, - target_id=_safe_int(r.get("target_id")), - variable=str(r.get("variable", "")), - geo_level=gl, - geographic_id=gid, - geo_display_name=geo_display_name(gl, gid), - constraints=constraints, - target_value=target_value, - estimate=estimate, - rel_error=_safe_float(r.get("rel_error")), - abs_error=abs_error, - loss_contribution=_safe_float(r.get("loss_contribution")), - included=bool(r.get("included", True)), - source=_nan_to_none(r.get("source")), - period=_safe_int(r.get("period")), - tolerance=_safe_float(r.get("tolerance")), - notes=_nan_to_none(r.get("notes")), - estimate_b=_safe_float(r.get("estimate_b")) if with_compare else None, - rel_error_b=_safe_float(r.get("rel_error_b")) if with_compare else None, - abs_rel_error_b=_safe_float(r.get("abs_rel_error_b")) if with_compare else None, - delta=_safe_float(r.get("delta")) if with_compare else None, - ) diff --git a/backend/routes/weights.py b/backend/routes/weights.py deleted file mode 100644 index ed6ae90..0000000 --- a/backend/routes/weights.py +++ /dev/null @@ -1,180 +0,0 @@ -"""Weight distribution endpoints.""" - -import operator as op_module - -import numpy as np -from fastapi import APIRouter, Depends, HTTPException, Query - -from backend.state import get_state -from backend.models import ( - HistogramBin, - WeightDistribution, - WeightSlice, -) -from backend.state import AppState - -router = APIRouter() - -_OPS = { - "gt": op_module.gt, - "gte": op_module.ge, - "lt": op_module.lt, - "lte": op_module.le, - "eq": op_module.eq, - "ne": op_module.ne, -} - - -def _weight_stats(w: np.ndarray) -> dict: - """Compute standard weight distribution statistics.""" - if len(w) == 0: - return {"kish_effective_n": 0, "cv": 0, "design_effect": 1, "mean": 0, - "median": 0, "p5": 0, "p25": 0, "p75": 0, "p95": 0, "max": 0, - "top_1pct_weight_share": 0, "top_5pct_weight_share": 0} - - total = w.sum() - kish = float(total ** 2 / (w ** 2).sum()) if (w ** 2).sum() > 0 else 0 - mean = float(w.mean()) - std = float(w.std()) - cv = std / mean if mean > 0 else 0 - - sorted_w = np.sort(w)[::-1] - n = len(sorted_w) - top_1 = float(sorted_w[: max(1, n // 100)].sum() / total) if total > 0 else 0 - top_5 = float(sorted_w[: max(1, n // 20)].sum() / total) if total > 0 else 0 - - return { - "kish_effective_n": kish, - "cv": cv, - "design_effect": 1 + cv ** 2, - "mean": mean, - "median": float(np.median(w)), - "p5": float(np.percentile(w, 5)), - "p25": float(np.percentile(w, 25)), - "p75": float(np.percentile(w, 75)), - "p95": float(np.percentile(w, 95)), - "max": float(w.max()), - "top_1pct_weight_share": top_1 * 100, - "top_5pct_weight_share": top_5 * 100, - } - - -def _geo_mask(state: AppState, state_fips: int | None, cd_geoid: int | None) -> np.ndarray: - mask = np.ones(len(state.g_weights), dtype=bool) - if cd_geoid is not None: - mask = state.households_df["cd_geoid"].values == cd_geoid - elif state_fips is not None: - mask = state.households_df["state"].values == state_fips - return mask - - -@router.get("/distribution") -def weight_distribution( - slice_by: str = "none", - metric: str = "g_weight", - state_fips: int | None = Query(None, alias="state_fips"), - cd_geoid: int | None = Query(None, alias="cd_geoid"), - state: AppState = Depends(get_state), -) -> WeightDistribution: - metric_map = { - "g_weight": state.g_weights, - "final_weight": state.final_weights, - "initial_weight": state.initial_weights, - } - if metric not in metric_map: - raise HTTPException(status_code=400, detail=f"Unknown metric: {metric}") - - mask = _geo_mask(state, state_fips, cd_geoid) - w = metric_map[metric][mask] - overall = _weight_stats(w) - - slices = [] - if slice_by == "income_decile": - deciles = state.households_df["income_decile"].values - for d in range(10): - mask = deciles == d - s = _weight_stats(w[mask]) - slices.append(WeightSlice( - label=f"Decile {d + 1}", - n=int(mask.sum()), - kish_effective_n=s["kish_effective_n"], - mean=s["mean"], - median=s["median"], - )) - elif slice_by == "poverty_status": - for label, mask in [ - ("Poor", state.households_df["in_poverty"].values), - ("Non-poor", ~state.households_df["in_poverty"].values), - ]: - s = _weight_stats(w[mask]) - slices.append(WeightSlice( - label=label, - n=int(mask.sum()), - kish_effective_n=s["kish_effective_n"], - mean=s["mean"], - median=s["median"], - )) - elif slice_by == "state": - states = state.households_df["state"].values - for st in np.unique(states): - mask = states == st - s = _weight_stats(w[mask]) - slices.append(WeightSlice( - label=f"State {int(st):02d}", - n=int(mask.sum()), - kish_effective_n=s["kish_effective_n"], - mean=s["mean"], - median=s["median"], - )) - - return WeightDistribution(**overall, slices=slices) - - -@router.get("/histogram") -def weight_histogram( - metric: str = "g_weight", - bins: int = 50, - log_scale: bool = True, - filter_variable: str | None = None, - filter_operator: str = "gt", - filter_value: float = 0, - state: AppState = Depends(get_state), -) -> list[HistogramBin]: - metric_map = { - "g_weight": state.g_weights, - "final_weight": state.final_weights, - "initial_weight": state.initial_weights, - } - if metric not in metric_map: - raise HTTPException(status_code=400, detail=f"Unknown metric: {metric}") - - w = metric_map[metric].copy() - - if filter_variable: - if filter_operator not in _OPS: - raise HTTPException(status_code=400, detail=f"Invalid operator: {filter_operator}") - if not state.sim_service.variable_exists(filter_variable): - raise HTTPException(status_code=400, detail=f"Unknown variable: {filter_variable}") - vals = state.sim_service.calculate(filter_variable, map_to="household") - mask = _OPS[filter_operator](vals, filter_value) - w = w[mask] - - positive = w[w > 0] - if len(positive) == 0: - return [] - - if log_scale: - log_vals = np.log10(positive) - bin_edges = np.logspace(log_vals.min(), log_vals.max(), bins + 1) - else: - bin_edges = np.linspace(positive.min(), positive.max(), bins + 1) - - counts, edges = np.histogram(positive, bins=bin_edges) - return [ - HistogramBin( - bin_min=float(edges[i]), - bin_max=float(edges[i + 1]), - count=int(counts[i]), - ) - for i in range(len(counts)) - ] diff --git a/backend/services/__init__.py b/backend/services/__init__.py deleted file mode 100644 index 436b05b..0000000 --- a/backend/services/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Service modules.""" diff --git a/backend/services/analysis_readiness.py b/backend/services/analysis_readiness.py deleted file mode 100644 index eef87c3..0000000 --- a/backend/services/analysis_readiness.py +++ /dev/null @@ -1,1131 +0,0 @@ -"""Analyst-facing readiness checks for reform analysis. - -This module turns the lower-level calibration artifacts into answers an -analyst can use before trusting a reform estimate: - -- Are the relevant aggregates targeted? -- Were those targets included in the fitted loss? -- Did the published dataset evaluate them successfully? -- Which upstream nodes feed a selected output, and which leaves are only - carried by source data rather than calibration targets? -""" - -from __future__ import annotations - -from collections import deque -from dataclasses import dataclass -from typing import Any - -import numpy as np -import pandas as pd - -from backend.state import AppState - - -@dataclass(frozen=True) -class CaseStudy: - id: str - label: str - description: str - primary_variables: tuple[str, ...] - dependency_variables: tuple[str, ...] - state_fips: int | None = None - domain_keywords: tuple[str, ...] = () - modeled_variable_prefixes: tuple[str, ...] = () - - -CASE_STUDIES: dict[str, CaseStudy] = { - "federal_snap": CaseStudy( - id="federal_snap", - label="Federal SNAP reform", - description=( - "Assess whether SNAP benefit and caseload aggregates are usable " - "for a federal reform analysis." - ), - primary_variables=("snap", "household_count"), - dependency_variables=("snap",), - domain_keywords=("snap",), - ), - "montana_ctc": CaseStudy( - id="montana_ctc", - label="Montana CTC reform", - description=( - "Assess whether Montana child tax credit analysis is supported by " - "state-level CTC targets and modeled PolicyEngine variables." - ), - primary_variables=( - "ctc", - "refundable_ctc", - "non_refundable_ctc", - "tax_unit_count", - ), - dependency_variables=("ctc", "refundable_ctc", "non_refundable_ctc"), - state_fips=30, - domain_keywords=("ctc", "refundable_ctc", "non_refundable_ctc"), - modeled_variable_prefixes=("mt_ctc", "mt_refundable_ctc"), - ), - "california_income_tax": CaseStudy( - id="california_income_tax", - label="California income tax reform", - description=( - "Assess whether California income tax analysis is supported by " - "state income-tax targets and modeled California tax variables." - ), - primary_variables=( - "ca_income_tax", - "income_tax", - "state_income_tax", - "tax_unit_count", - ), - dependency_variables=( - "ca_income_tax", - "ca_income_tax_before_credits", - "ca_income_tax_before_refundable_credits", - ), - state_fips=6, - domain_keywords=("income_tax", "ca_income_tax", "state_income_tax"), - modeled_variable_prefixes=("ca_income_tax",), - ), -} - - -def list_case_studies() -> list[dict[str, Any]]: - return [ - { - "id": cs.id, - "label": cs.label, - "description": cs.description, - "primary_variables": list(cs.primary_variables), - "dependency_variables": list(cs.dependency_variables), - "state_fips": cs.state_fips, - } - for cs in CASE_STUDIES.values() - ] - - -def list_policyengine_variables( - state: AppState, - *, - search: str | None = None, - limit: int = 100, -) -> list[dict[str, Any]]: - """Return every PolicyEngine variable available in the loaded simulation.""" - tbs = _tax_benefit_system(state) - if tbs is None: - return [] - - targets = state.targets_enriched - target_counts: dict[str, int] = {} - included_counts: dict[str, int] = {} - domain_counts: dict[str, int] = {} - if targets is not None and not targets.empty: - variable_col = targets.get("variable", pd.Series([], dtype=str)).fillna("") - target_counts = variable_col.astype(str).value_counts().to_dict() - if "included" in targets.columns: - included = targets[targets["included"].astype(bool)] - included_counts = ( - included.get("variable", pd.Series([], dtype=str)) - .fillna("") - .astype(str) - .value_counts() - .to_dict() - ) - domain_col = targets.get("domain_variable", pd.Series("", index=targets.index)) - for value in domain_col.fillna("").astype(str): - for part in [p.strip() for p in value.split(",") if p.strip()]: - domain_counts[part] = domain_counts.get(part, 0) + 1 - - items = [] - for name, var in getattr(tbs, "variables", {}).items(): - label = getattr(var, "label", None) or name - if search: - q = search.lower() - if q not in name.lower() and q not in label.lower(): - continue - items.append( - { - "name": name, - "label": label, - "entity": getattr(getattr(var, "entity", None), "key", None), - "definition_period": str(getattr(var, "definition_period", "") or ""), - "is_formula": bool(getattr(var, "formulas", None)), - "is_aggregate": bool( - getattr(var, "adds", None) or getattr(var, "subtracts", None) - ), - "is_target_variable": target_counts.get(name, 0) > 0, - "is_domain_variable": domain_counts.get(name, 0) > 0, - "target_count": int(target_counts.get(name, 0)), - "included_target_count": int(included_counts.get(name, 0)), - "domain_count": int(domain_counts.get(name, 0)), - } - ) - items.sort( - key=lambda r: ( - 0 if r["is_target_variable"] or r["is_domain_variable"] else 1, - r["name"], - ) - ) - return items[:limit] - - -def _sim_from_state(state: AppState): - sim = state.sim_service - return getattr(sim, "_sim", sim) - - -def _tax_benefit_system(state: AppState): - sim = _sim_from_state(state) - return getattr(sim, "tax_benefit_system", None) - - -def _available_model_variables(state: AppState) -> set[str]: - tbs = _tax_benefit_system(state) - if tbs is None: - return set() - return set(getattr(tbs, "variables", {}).keys()) - - -def _fallback_target_config() -> tuple[dict | None, str | None]: - try: - import yaml - import policyengine_us_data - - from pathlib import Path - - root = Path(policyengine_us_data.__file__).resolve().parent - path = root / "calibration" / "target_config.yaml" - if not path.exists(): - return None, None - config = yaml.safe_load(path.read_text(encoding="utf-8")) or {} - return config, f"installed_package:{path}" - except Exception: - return None, None - - -def _target_config_with_source(state: AppState) -> tuple[dict | None, str]: - if state.target_config: - return state.target_config, "run_artifact" - config, source = _fallback_target_config() - if config: - return config, source or "installed_package" - return None, "unavailable" - - -def _targets_with_inclusion(state: AppState) -> pd.DataFrame: - targets = state.targets_enriched.copy() - if targets.empty: - return targets - if not targets.get("included", pd.Series(False, index=targets.index)).any(): - config, _source = _target_config_with_source(state) - if config: - targets["included"] = _infer_included_from_config(targets, config) - return targets - - -def _match_config_rules(targets: pd.DataFrame, rules: list[dict]) -> pd.Series: - mask = pd.Series(False, index=targets.index) - for rule in rules: - mask |= _target_config_rule_mask(targets, rule) - return mask - - -def _target_config_rule_mask(targets: pd.DataFrame, rule: dict) -> pd.Series: - mask = pd.Series(True, index=targets.index) - if "variable" in rule: - mask &= targets["variable"].astype(str) == str(rule["variable"]) - if "geo_level" in rule: - mask &= targets["geo_level"].astype(str) == str(rule["geo_level"]) - if "domain_variable" in rule: - domain = targets.get("domain_variable", pd.Series("", index=targets.index)) - mask &= domain.fillna("").astype(str) == str(rule["domain_variable"]) - return mask - - -def _target_rows_for_variable(targets: pd.DataFrame, variable: str) -> pd.Series: - if targets.empty: - return pd.Series(False, index=targets.index) - variable_col = targets.get("variable", pd.Series("", index=targets.index)) - direct = variable_col.fillna("").astype(str) == variable - domain_col = targets.get("domain_variable", pd.Series("", index=targets.index)) - domain = domain_col.fillna("").astype(str) - as_domain = domain.str.split(",").apply( - lambda values: variable in {part.strip() for part in values if part.strip()} - ) - return direct | as_domain - - -def _rule_mentions_variable(rule: dict, variable: str) -> bool: - if str(rule.get("variable", "")) == variable: - return True - domain = str(rule.get("domain_variable", "")) - return variable in {part.strip() for part in domain.split(",") if part.strip()} - - -def _infer_included_from_config(targets: pd.DataFrame, config: dict) -> pd.Series: - include_rules = config.get("include", []) - exclude_rules = config.get("exclude", []) - if include_rules: - keep = _match_config_rules(targets, include_rules) - else: - keep = pd.Series(True, index=targets.index) - if exclude_rules: - keep &= ~_match_config_rules(targets, exclude_rules) - return keep - - -def _target_mask(df: pd.DataFrame, case: CaseStudy) -> pd.Series: - variable = df.get("variable", pd.Series([], dtype=str)).fillna("").astype(str) - domain = df.get("domain_variable", pd.Series("", index=df.index)).fillna("") - domain = domain.astype(str) - - direct_variables = [ - v - for v in case.primary_variables - if v not in {"person_count", "household_count", "tax_unit_count"} - and not v.endswith("_count") - ] - mask = variable.isin(direct_variables) - for keyword in case.domain_keywords: - mask = mask | domain.str.contains(keyword, case=False, regex=False) - - if case.state_fips is not None: - gid = df.get("geographic_id", pd.Series("", index=df.index)).fillna("") - geo = df.get("geo_level", pd.Series("", index=df.index)).fillna("") - - def _state_match(value, geo_level) -> bool: - if geo_level == "national": - return True - s = str(value) - if not s.isdigit(): - return False - n = int(s) - return n == case.state_fips or n // 100 == case.state_fips - - geo_mask = pd.Series( - [_state_match(g, level) for g, level in zip(gid, geo)], - index=df.index, - ) - mask = mask & geo_mask - return mask - - -def _finite_float(value) -> float | None: - if value is None: - return None - try: - f = float(value) - except (TypeError, ValueError): - return None - return f if np.isfinite(f) else None - - -def _safe_bool(value) -> bool: - try: - if pd.isna(value): - return False - except (TypeError, ValueError): - pass - return bool(value) - - -def _error_summary(df: pd.DataFrame) -> dict[str, Any]: - if df.empty: - return { - "count": 0, - "evaluated": 0, - "median_abs_rel_error": None, - "max_abs_rel_error": None, - "pct_under_10pct": None, - "pct_under_25pct": None, - } - err = pd.to_numeric(df.get("abs_rel_error"), errors="coerce") - finite = err[np.isfinite(err)] - if finite.empty: - return { - "count": int(len(df)), - "evaluated": 0, - "median_abs_rel_error": None, - "max_abs_rel_error": None, - "pct_under_10pct": None, - "pct_under_25pct": None, - } - return { - "count": int(len(df)), - "evaluated": int(len(finite)), - "median_abs_rel_error": float(finite.median()), - "max_abs_rel_error": float(finite.max()), - "pct_under_10pct": float((finite < 0.10).mean()), - "pct_under_25pct": float((finite < 0.25).mean()), - } - - -def _bundle_summary(df: pd.DataFrame) -> dict[str, Any]: - err = pd.to_numeric(df.get("abs_rel_error"), errors="coerce") - finite = err[np.isfinite(err)] - included = df.get("included", pd.Series(False, index=df.index)).fillna(False).astype(bool) - if finite.empty: - return { - "target_count": int(len(df)), - "included_target_count": int(included.sum()), - "evaluated_target_count": 0, - "median_abs_rel_error": None, - "p90_abs_rel_error": None, - "max_abs_rel_error": None, - "pct_under_10pct": None, - "pct_under_25pct": None, - } - return { - "target_count": int(len(df)), - "included_target_count": int(included.sum()), - "evaluated_target_count": int(len(finite)), - "median_abs_rel_error": float(finite.median()), - "p90_abs_rel_error": float(finite.quantile(0.9)), - "max_abs_rel_error": float(finite.max()), - "pct_under_10pct": float((finite < 0.10).mean()), - "pct_under_25pct": float((finite < 0.25).mean()), - } - - -def _bundle_for_targets(state: AppState, dataset_file: str) -> pd.DataFrame: - from backend.services.geo_utils import runtime_dataset_bundle_for - - available = None - try: - from backend.services.runs import get_dataset - from backend.services.bundle_availability import published_bundles - - ds = get_dataset(state.dataset_id) - except Exception: - available = None - else: - if ds is not None: - available = published_bundles(ds.repo_id, state.run_id) - if available and dataset_file not in available: - raise KeyError(f"Dataset file is not published for this run: {dataset_file}") - - targets = state.targets_enriched.copy() - if targets.empty: - return targets - mask = targets.apply( - lambda row: runtime_dataset_bundle_for( - row.get("geo_level"), - row.get("geographic_id"), - available=available, - ) - == dataset_file, - axis=1, - ) - return targets[mask].copy() - - -def build_bundle_health( - state: AppState, - *, - dataset_file: str, - limit: int = 10, -) -> dict[str, Any]: - targets = _bundle_for_targets(state, dataset_file) - bundle_evaluated = False - evaluation_error: str | None = None - if not targets.empty and dataset_file != "enhanced_cps_2024.h5": - try: - from backend.services.runs import get_dataset - from backend.services.bundle_eval import evaluate_bundle - - ds = get_dataset(state.dataset_id) - if ds is not None: - targets = evaluate_bundle( - targets, - repo_id=ds.repo_id, - run_id=state.run_id, - bundle=dataset_file, - time_period=state.time_period, - ) - bundle_evaluated = True - except Exception as exc: - evaluation_error = str(exc) - - summary = _bundle_summary(targets) - variable_rows = [] - if not targets.empty: - for variable, group in targets.groupby("variable", dropna=False): - row = {"variable": str(variable), **_bundle_summary(group)} - variable_rows.append(row) - variable_rows.sort( - key=lambda row: ( - -1 if row["median_abs_rel_error"] is None else row["median_abs_rel_error"], - row["variable"], - ), - reverse=True, - ) - - if "abs_rel_error" in targets.columns: - worst_df = targets.copy() - worst_df["_sort_abs_rel_error"] = pd.to_numeric( - worst_df["abs_rel_error"], - errors="coerce", - ) - worst_df = worst_df.sort_values("_sort_abs_rel_error", ascending=False) - else: - worst_df = targets - - worst_targets = [] - for _, row in worst_df.head(limit).iterrows(): - target_value = _finite_float(row.get("value")) - estimate = _finite_float(row.get("estimate")) - worst_targets.append( - { - "target_id": int(row["target_id"]) if pd.notna(row.get("target_id")) else None, - "variable": str(row.get("variable", "")), - "geo_level": str(row.get("geo_level", "")), - "geographic_id": str(row.get("geographic_id", "")), - "target_value": target_value, - "estimate": estimate, - "rel_error": _finite_float(row.get("rel_error")), - "abs_rel_error": _finite_float(row.get("abs_rel_error")), - "included": _safe_bool(row.get("included")), - "source": str(row.get("source", "")) if pd.notna(row.get("source")) else None, - "constraints": _constraints_for_row(row), - } - ) - - return { - "dataset_file": dataset_file, - "bundle_evaluated": bundle_evaluated, - "evaluation_error": evaluation_error, - "summary": summary, - "by_variable": variable_rows[:50], - "worst_targets": worst_targets, - } - - -def _group_coverage(df: pd.DataFrame) -> list[dict[str, Any]]: - if df.empty: - return [] - group_cols = ["variable", "geo_level", "domain_variable"] - rows = [] - for keys, group in df.groupby(group_cols, dropna=False): - included = group[group.get("included", False).astype(bool)] - summary = _error_summary(included) - rows.append( - { - "variable": keys[0] or "", - "geo_level": keys[1] or "", - "domain_variable": keys[2] or "", - "target_count": int(len(group)), - "included_count": int(len(included)), - "evaluated_count": summary["evaluated"], - "median_abs_rel_error": summary["median_abs_rel_error"], - "max_abs_rel_error": summary["max_abs_rel_error"], - } - ) - rows.sort( - key=lambda r: ( - r["geo_level"], - r["variable"], - r["domain_variable"], - ) - ) - return rows - - -def _weight_quality(state: AppState, case: CaseStudy) -> dict[str, Any]: - households = state.households_df - if households is None or households.empty: - return { - "households": 0, - "kish_effective_n": None, - "top_1pct_weight_share": None, - "top_5pct_weight_share": None, - } - df = households - if case.state_fips is not None and "state" in df.columns: - df = df[df["state"].astype(int) == case.state_fips] - weights = pd.to_numeric(df.get("final_weight"), errors="coerce").fillna(0) - total = float(weights.sum()) - if len(weights) == 0 or total <= 0: - return { - "households": int(len(df)), - "kish_effective_n": None, - "top_1pct_weight_share": None, - "top_5pct_weight_share": None, - } - sorted_w = weights.sort_values(ascending=False).to_numpy() - top_1_n = max(1, int(np.ceil(len(sorted_w) * 0.01))) - top_5_n = max(1, int(np.ceil(len(sorted_w) * 0.05))) - denom = float(np.square(weights).sum()) - return { - "households": int(len(df)), - "kish_effective_n": float(total**2 / denom) if denom > 0 else None, - "top_1pct_weight_share": float(sorted_w[:top_1_n].sum() / total), - "top_5pct_weight_share": float(sorted_w[:top_5_n].sum() / total), - } - - -def _diagnosis( - *, - case: CaseStudy, - modeled_matches: list[str], - included: pd.DataFrame, - target_summary: dict[str, Any], - weight_quality: dict[str, Any], -) -> tuple[str, list[str], list[str]]: - blockers: list[str] = [] - warnings: list[str] = [] - - if case.modeled_variable_prefixes and not modeled_matches: - blockers.append( - "No existing PolicyEngine variable appears to model this state-specific reform." - ) - if included.empty: - blockers.append("No relevant targets are included in the calibration loss.") - elif target_summary["evaluated"] == 0: - blockers.append("Relevant in-loss targets have no available PE aggregate estimates.") - - max_err = target_summary.get("max_abs_rel_error") - med_err = target_summary.get("median_abs_rel_error") - if max_err is not None and max_err > 0.25: - warnings.append("At least one relevant in-loss target is more than 25% off.") - if med_err is not None and med_err > 0.10: - warnings.append("Median relevant target error is above 10%.") - if weight_quality.get("households") == 0: - warnings.append("No households are available for the selected geography.") - if _finite_float(weight_quality.get("top_1pct_weight_share")) is not None: - if weight_quality["top_1pct_weight_share"] > 0.25: - warnings.append("Top 1% of records carry more than 25% of weight.") - - if blockers: - status = "blocked" - elif warnings: - status = "caution" - else: - status = "ready" - return status, blockers, warnings - - -def build_readiness(case_id: str, state: AppState) -> dict[str, Any]: - if case_id not in CASE_STUDIES: - raise KeyError(case_id) - case = CASE_STUDIES[case_id] - target_config_source = "run_artifact" - inferred_inclusion = False - targets = state.targets_enriched.copy() - if not targets.empty and not targets.get("included", pd.Series(False, index=targets.index)).any(): - config, target_config_source = _target_config_with_source(state) - if config: - targets["included"] = _infer_included_from_config(targets, config) - inferred_inclusion = True - if targets.empty: - relevant = targets - else: - relevant = targets[_target_mask(targets, case)].copy() - included = relevant[relevant.get("included", False).astype(bool)].copy() - - model_vars = _available_model_variables(state) - modeled_matches = sorted( - v - for v in model_vars - if any(v.startswith(prefix) for prefix in case.modeled_variable_prefixes) - ) - present_dependency_variables = [ - v for v in case.dependency_variables if v in model_vars - ] - missing_dependency_variables = [ - v for v in case.dependency_variables if v not in model_vars - ] - - target_summary = _error_summary(included) - weight_quality = _weight_quality(state, case) - status, blockers, warnings = _diagnosis( - case=case, - modeled_matches=modeled_matches, - included=included, - target_summary=target_summary, - weight_quality=weight_quality, - ) - if inferred_inclusion: - warnings.append( - "Target inclusion is inferred from target_config because " - "published diagnostics were not available." - ) - if status == "ready": - status = "caution" - - recommendations = [] - if "federal_snap" == case.id: - recommendations.extend( - [ - "Compare national SNAP spending and state SNAP spending before interpreting reform costs.", - "Check SNAP household-count targets when analyzing caseload-sensitive reforms.", - "Use dependency tracing to review eligibility inputs such as household composition, income, and state rules.", - ] - ) - if "montana_ctc" == case.id: - recommendations.extend( - [ - "Add Montana-specific CTC variables and parameters in policyengine-us before treating this as a modeled current-law program.", - "Keep Montana refundable CTC targets visible, but add non-refundable/total CTC targets if the reform affects those components.", - "Run a Montana H5 bundle evaluation after changing CTC variables or target configuration.", - ] - ) - if "california_income_tax" == case.id: - recommendations.extend( - [ - "Trace ca_income_tax before changing parameters to confirm which income and credit nodes propagate.", - "Check California state-level income_tax targets and California district income-tax support before using local estimates.", - "Run the California state H5 bundle evaluation when validating aggregate impacts for CA-only reforms.", - ] - ) - if blockers: - recommendations.append("Resolve blockers before using this dataset for headline estimates.") - - return { - "case_study": { - "id": case.id, - "label": case.label, - "description": case.description, - "state_fips": case.state_fips, - "primary_variables": list(case.primary_variables), - "dependency_variables": list(case.dependency_variables), - }, - "status": status, - "blockers": blockers, - "warnings": warnings, - "target_summary": { - "relevant_targets": int(len(relevant)), - "included_targets": int(len(included)), - "inclusion_inferred": inferred_inclusion, - "target_config_source": target_config_source, - **target_summary, - }, - "target_coverage": _group_coverage(relevant), - "modeled_variables": { - "present_dependency_variables": present_dependency_variables, - "missing_dependency_variables": missing_dependency_variables, - "state_specific_matches": modeled_matches[:50], - }, - "weight_quality": weight_quality, - "recommendations": recommendations, - } - - -def _base_trace_key(key: str) -> str: - return key.split("<", 1)[0] - - -def _period_from_trace_key(key: str) -> str | None: - if "<" not in key or ">" not in key: - return None - return key.split("<", 1)[1].split(",", 1)[0].split(">", 1)[0] - - -def _parse_constraints_from_target_name(target_name: str) -> list[str]: - if not isinstance(target_name, str) or "[" not in target_name: - return [] - try: - bracket = target_name[target_name.index("[") + 1 : target_name.rindex("]")] - except ValueError: - return [] - if not bracket: - return [] - return [c.strip() for c in bracket.split(",") if c.strip()] - - -def _constraints_for_row(row: pd.Series) -> list[str]: - constraints = row.get("constraints") - if isinstance(constraints, list): - return [str(c) for c in constraints] - return _parse_constraints_from_target_name(str(row.get("target_name", ""))) - - -def _parse_constraint(constraint: str) -> tuple[str, str, str] | None: - for op in (">=", "<=", "==", "!=", ">", "<", "="): - if op in constraint: - left, right = constraint.split(op, 1) - return left.strip(), op, right.strip() - return None - - -def _float_bound(value: str) -> float | None: - try: - return float(value) - except (TypeError, ValueError): - lowered = str(value).strip().lower() - if lowered in {"inf", "+inf", "infinity", "+infinity"}: - return float("inf") - if lowered in {"-inf", "-infinity"}: - return float("-inf") - return None - - -def _format_bucket_bound(value: float | None) -> str: - if value is None: - return "?" - if value == float("-inf"): - return "-inf" - if value == float("inf"): - return "inf" - return f"{value:,.0f}" - - -def _bucket_from_constraints( - constraints: list[str], - domain_variable: str, -) -> tuple[float | None, float | None, str] | None: - lower: float | None = None - upper: float | None = None - equality: str | None = None - for constraint in constraints: - parsed = _parse_constraint(constraint) - if parsed is None: - continue - variable, op, value = parsed - if variable != domain_variable: - continue - bound = _float_bound(value) - if op in {">", ">="}: - lower = bound - if op in {"<", "<="}: - upper = bound - if op in {"=", "=="}: - equality = value - - if equality is not None: - return None, None, f"{domain_variable} = {equality}" - if lower is None and upper is None: - return None - if lower == float("-inf") and upper is not None: - label = f"< {_format_bucket_bound(upper)}" - elif upper == float("inf") and lower is not None: - label = f">= {_format_bucket_bound(lower)}" - else: - label = f"{_format_bucket_bound(lower)} to {_format_bucket_bound(upper)}" - return lower, upper, label - - -def build_domain_breakdown( - state: AppState, - *, - variable: str | None = None, - domain_variable: str = "adjusted_gross_income", - geo_level: str | None = None, -) -> dict[str, Any]: - targets = _targets_with_inclusion(state) - if targets.empty: - return { - "variable": variable, - "domain_variable": domain_variable, - "geo_level": geo_level, - "rows": [], - "summary": { - "target_count": 0, - "included_target_count": 0, - "evaluated_target_count": 0, - }, - } - - domain_col = targets.get("domain_variable", pd.Series("", index=targets.index)) - mask = domain_col.fillna("").astype(str).str.split(",").apply( - lambda values: domain_variable - in {part.strip() for part in values if part.strip()} - ) - if variable: - mask &= targets.get("variable", pd.Series("", index=targets.index)).astype(str) == variable - if geo_level: - mask &= targets.get("geo_level", pd.Series("", index=targets.index)).astype(str) == geo_level - - scoped = targets[mask].copy() - if scoped.empty: - return { - "variable": variable, - "domain_variable": domain_variable, - "geo_level": geo_level, - "rows": [], - "summary": { - "target_count": 0, - "included_target_count": 0, - "evaluated_target_count": 0, - }, - } - - bucket_values = [] - for _, row in scoped.iterrows(): - bucket = _bucket_from_constraints(_constraints_for_row(row), domain_variable) - if bucket is None: - bucket_values.append((None, None, "Unbucketed")) - else: - bucket_values.append(bucket) - scoped["_bucket_lower"] = [b[0] for b in bucket_values] - scoped["_bucket_upper"] = [b[1] for b in bucket_values] - scoped["_bucket_label"] = [b[2] for b in bucket_values] - - rows = [] - for (lower, upper, label), group in scoped.groupby( - ["_bucket_lower", "_bucket_upper", "_bucket_label"], - dropna=False, - ): - included = group[group.get("included", False).astype(bool)] - summary = _error_summary(included) - rows.append( - { - "bucket": label, - "lower": _finite_float(lower), - "upper": _finite_float(upper), - "target_count": int(len(group)), - "included_target_count": int(len(included)), - "evaluated_target_count": int(summary["evaluated"]), - "median_abs_rel_error": summary["median_abs_rel_error"], - "max_abs_rel_error": summary["max_abs_rel_error"], - "variables": sorted( - group.get("variable", pd.Series("", index=group.index)) - .fillna("") - .astype(str) - .unique() - .tolist() - ), - "geo_levels": sorted( - group.get("geo_level", pd.Series("", index=group.index)) - .fillna("") - .astype(str) - .unique() - .tolist() - ), - } - ) - - rows.sort( - key=lambda row: ( - float("-inf") if row["lower"] is None else row["lower"], - float("inf") if row["upper"] is None else row["upper"], - row["bucket"], - ) - ) - included_all = scoped[scoped.get("included", False).astype(bool)] - summary_all = _error_summary(included_all) - return { - "variable": variable, - "domain_variable": domain_variable, - "geo_level": geo_level, - "rows": rows, - "summary": { - "target_count": int(len(scoped)), - "included_target_count": int(len(included_all)), - "evaluated_target_count": int(summary_all["evaluated"]), - "median_abs_rel_error": summary_all["median_abs_rel_error"], - "max_abs_rel_error": summary_all["max_abs_rel_error"], - }, - } - - -def _node_meta(variable: str, state: AppState) -> dict[str, Any]: - tbs = _tax_benefit_system(state) - tbs_vars = getattr(tbs, "variables", {}) if tbs is not None else {} - var = tbs_vars.get(variable) - targets = _targets_with_inclusion(state) - target_variable = False - domain_variable = False - included_target_count = 0 - target_count = 0 - direct_target_count = 0 - domain_target_count = 0 - evaluated_target_count = 0 - median_abs_rel_error = None - max_abs_rel_error = None - if targets is not None and not targets.empty: - variable_col = targets.get("variable", pd.Series([], dtype=str)).fillna("") - target_mask = variable_col.astype(str) == variable - domain_col = targets.get("domain_variable", pd.Series("", index=targets.index)) - domain_col = domain_col.fillna("").astype(str) - domain_mask = domain_col.str.split(",").apply( - lambda xs: variable in {part.strip() for part in xs if part.strip()} - ) - relevant = targets[target_mask | domain_mask] - included = relevant[relevant.get("included", False).astype(bool)] - summary = _error_summary(included) - target_variable = bool(target_mask.any()) - domain_variable = bool(domain_mask.any()) - direct_target_count = int(target_mask.sum()) - domain_target_count = int(domain_mask.sum()) - target_count = int(len(relevant)) - included_target_count = int(len(included)) - evaluated_target_count = int(summary["evaluated"]) - median_abs_rel_error = summary["median_abs_rel_error"] - max_abs_rel_error = summary["max_abs_rel_error"] - - stored_inputs = set(getattr(_sim_from_state(state), "input_variables", []) or []) - is_formula = bool(getattr(var, "formulas", None)) if var is not None else False - is_aggregate = bool(getattr(var, "adds", None) or getattr(var, "subtracts", None)) - entity = getattr(getattr(var, "entity", None), "key", None) if var is not None else None - label = getattr(var, "label", None) if var is not None else None - - return { - "variable": variable, - "label": label or variable, - "entity": entity, - "is_policyengine_variable": var is not None, - "is_formula": is_formula, - "is_aggregate": is_aggregate, - "is_stored_input": variable in stored_inputs, - "is_target_variable": target_variable, - "is_domain_variable": domain_variable, - "target_count": target_count, - "direct_target_count": direct_target_count, - "domain_target_count": domain_target_count, - "included_target_count": included_target_count, - "evaluated_target_count": evaluated_target_count, - "median_abs_rel_error": median_abs_rel_error, - "max_abs_rel_error": max_abs_rel_error, - } - - -def build_dependency_trace( - variable: str, - state: AppState, - *, - period: int | None = None, - max_nodes: int = 250, -) -> dict[str, Any]: - sim = _sim_from_state(state) - tbs = _tax_benefit_system(state) - if sim is None or tbs is None: - raise RuntimeError("No PolicyEngine simulation is available for this run.") - if variable not in getattr(tbs, "variables", {}): - raise KeyError(variable) - - previous_trace = getattr(sim, "trace", False) - try: - # The PolicyEngine tracer only sees dependencies that are recalculated. - # Cached formula outputs otherwise produce a one-node trace. - invalidate = getattr(sim, "_invalidate_all_caches", None) - if callable(invalidate): - invalidate() - else: - delete_arrays = getattr(sim, "delete_arrays", None) - if callable(delete_arrays): - delete_arrays(variable, period or state.time_period) - sim.trace = True - sim.calculate(variable, period=period or state.time_period) - trace = sim.tracer.get_flat_trace() - finally: - try: - sim.trace = previous_trace - except Exception: - pass - - root_key = next((k for k in trace if _base_trace_key(k) == variable), None) - if root_key is None: - raise RuntimeError(f"Trace did not contain root variable {variable}.") - - reachable: set[str] = set() - ordered: list[str] = [] - depth_by_key = {root_key: 0} - queue = deque([root_key]) - while queue: - key = queue.popleft() - if key in reachable or key not in trace: - continue - reachable.add(key) - ordered.append(key) - for dep in trace[key].get("dependencies", []): - if dep not in depth_by_key: - depth_by_key[dep] = depth_by_key[key] + 1 - queue.append(dep) - - truncated = len(ordered) > max_nodes - ordered = ordered[:max_nodes] - returned = set(ordered) - - nodes = [] - edges = [] - for key in ordered: - variable_name = _base_trace_key(key) - deps = [d for d in trace[key].get("dependencies", []) if d in returned] - meta = _node_meta(variable_name, state) - nodes.append( - { - "id": key, - "variable": variable_name, - "period": _period_from_trace_key(key), - "depth": depth_by_key.get(key, 0), - "dependency_count": len(trace[key].get("dependencies", [])), - "is_leaf": len(trace[key].get("dependencies", [])) == 0, - **meta, - } - ) - edges.extend({"from": key, "to": dep} for dep in deps) - - leaf_nodes = [n for n in nodes if n["is_leaf"]] - summary = { - "total_trace_nodes": int(len(reachable)), - "returned_nodes": int(len(nodes)), - "truncated": truncated, - "leaf_nodes": int(len(leaf_nodes)), - "stored_leaf_nodes": int(sum(n["is_stored_input"] for n in leaf_nodes)), - "targeted_leaf_nodes": int( - sum(n["is_target_variable"] or n["is_domain_variable"] for n in leaf_nodes) - ), - "untargeted_stored_leaf_nodes": int( - sum( - n["is_stored_input"] - and not n["is_target_variable"] - and not n["is_domain_variable"] - for n in leaf_nodes - ) - ), - } - return { - "variable": variable, - "root": root_key, - "summary": summary, - "nodes": nodes, - "edges": edges, - } - - -def audit_target_config( - state: AppState, - *, - variable: str | None = None, -) -> dict[str, Any]: - config, source = _target_config_with_source(state) - config = config or {} - targets = _targets_with_inclusion(state) - rules = list(config.get("include", [])) + list(config.get("exclude", [])) - scope = targets - if variable: - scope = targets[_target_rows_for_variable(targets, variable)].copy() - - out_rules = [] - for section in ("include", "exclude"): - for idx, rule in enumerate(config.get(section, [])): - mask = _target_config_rule_mask(scope, rule) - matched = int(mask.sum()) - if variable and matched == 0 and not _rule_mentions_variable(rule, variable): - continue - out_rules.append( - { - "section": section, - "index": idx, - "rule": rule, - "matched_targets": matched, - "status": "zero_match" if matched == 0 else "matched", - } - ) - - zero = [r for r in out_rules if r["matched_targets"] == 0] - return { - "has_target_config": bool(config), - "target_config_source": source, - "selected_variable": variable, - "target_count": int(len(scope)), - "included_target_count": int( - scope.get("included", pd.Series(False, index=scope.index)) - .fillna(False) - .astype(bool) - .sum() - ), - "rule_count": len(rules), - "zero_match_count": len(zero), - "matched_rule_count": len(out_rules) - len(zero), - "rules": out_rules, - } diff --git a/backend/services/bundle_availability.py b/backend/services/bundle_availability.py deleted file mode 100644 index 5b94b7d..0000000 --- a/backend/services/bundle_availability.py +++ /dev/null @@ -1,97 +0,0 @@ -"""Which calibrated h5 bundles a run actually publishes. - -For staging-layout datasets the pipeline emits an arbitrary subset of -the full bundle catalog (a federal `enhanced_cps_2024.h5`, a -`national/US.h5`, 51 `states/.h5`, 436 `districts/.h5`). -GHA-only runs typically publish just the federal one; tagged releases -publish all 499. - -Listing the published set lets the dashboard: - -1. Show only bundles that exist when the user filters /targets by - dataset, so we stop labeling rows with files that aren't there. -2. Decide whether per-bundle Microsim evaluation is even possible for - the loaded run, and short-circuit cleanly when it isn't. - -Results are cached per (repo_id, run_id) since the published artifact -set is immutable for a given run. -""" - -from __future__ import annotations - -import logging -from typing import Iterable - -logger = logging.getLogger(__name__) - -# (repo_id, run_id) -> set of bundle paths, e.g. {"enhanced_cps_2024.h5", -# "states/CA.h5", "districts/CA-12.h5"} -_CACHE: dict[tuple[str, str], frozenset[str]] = {} - - -def _bundle_paths_for_run(repo_id: str, run_id: str) -> frozenset[str]: - """Hit the HF repo file listing and pick out h5 paths under the run.""" - from huggingface_hub import HfApi - api = HfApi() - try: - files = api.list_repo_files(repo_id, repo_type="model") - except Exception as exc: - logger.warning("HF list_repo_files failed for %s: %s", repo_id, exc) - return frozenset() - root_run = run_id == "main" - current_staging = run_id == "staging" - if root_run: - prefix = "" - elif current_staging: - prefix = "staging/" - else: - prefix = f"staging/{run_id}/" - bundles: set[str] = set() - for f in files: - if not f.startswith(prefix) or not f.endswith(".h5"): - continue - rel = f[len(prefix):] - if current_staging: - if rel == "calibration/source_imputed_stratified_extended_cps.h5": - bundles.add("source_imputed_stratified_extended_cps.h5") - continue - if "/" not in rel or rel.split("/", 1)[0] not in { - "cities", "districts", "national", "states", - }: - continue - if root_run and "/" in rel and rel.split("/", 1)[0] not in { - "cities", "districts", "national", "states", - }: - continue - # Skip clone-diagnostics-style sidecars that share the .h5 stem. - if rel.endswith(".clone_diagnostics.json"): - continue - bundles.add(rel) - return frozenset(bundles) - - -def published_bundles(repo_id: str, run_id: str) -> frozenset[str]: - """Cached: return the set of bundle paths under a run.""" - key = (repo_id, run_id) - cached = _CACHE.get(key) - if cached is not None: - return cached - bundles = _bundle_paths_for_run(repo_id, run_id) - _CACHE[key] = bundles - return bundles - - -def filter_to_available( - candidates: Iterable[str], - repo_id: str, - run_id: str, -) -> list[str]: - """Drop bundle names from `candidates` that the run doesn't publish. - - Used to scope the dataset-file facet on /targets/facets to what's - actually fetchable for this run. - """ - available = published_bundles(repo_id, run_id) - if not available: - return list(candidates) - return [c for c in candidates if c in available] diff --git a/backend/services/bundle_eval.py b/backend/services/bundle_eval.py deleted file mode 100644 index 8971c82..0000000 --- a/backend/services/bundle_eval.py +++ /dev/null @@ -1,186 +0,0 @@ -"""Per-bundle PE-aggregate evaluation. - -The dataset_loader builds one Microsim from the federal -``enhanced_cps_2024.h5``. The per-state and per-district bundles -published by versioned us-data runs hold *different* calibrated -weights — so the federal-fit PE aggregate is **not** the per-bundle -PE aggregate. To validate "is this dataset well-calibrated against its -targets", we have to re-evaluate against the bundle's own h5. - -This module: - -- Lazily downloads each bundle h5 from HF (cache to disk). -- Loads each into its own ``Microsimulation`` instance (LRU cached in - memory, max 10). -- Runs the entity-aware evaluator over the subset of targets that - belong to the bundle, returning per-row ``estimate`` / - ``rel_error`` / ``abs_rel_error`` evaluated against THAT h5. -- Persists results to a per-bundle pickle so subsequent picks of the - same bundle skip both the h5 load and the evaluator pass. - -Triggered from ``/targets`` only when the caller filters to exactly -one bundle that actually exists for the run; multi-bundle and -no-filter calls keep the federal-fit numbers. -""" - -from __future__ import annotations - -import logging -import shutil -import threading -from collections import OrderedDict -from pathlib import Path - -import pandas as pd - -logger = logging.getLogger(__name__) - -# LRU of bundle Microsims, keyed by (repo_id, run_id, bundle_path). -# Bounded so we don't exhaust memory if a user clicks through many -# states / districts in a session. -_SIM_CACHE: "OrderedDict[tuple[str, str, str], object]" = OrderedDict() -_SIM_LOCK = threading.Lock() -_SIM_MAX = 10 - - -def _h5_local_path( - repo_id: str, - run_id: str, - bundle: str, - cache_root: str, -) -> Path: - """Lazily fetch the bundle's h5 from HF and return its local path.""" - repo_slug = repo_id.replace("/", "__") - cache = ( - Path(cache_root) / repo_slug / "root" / run_id - if run_id == "main" - else Path(cache_root) / repo_slug / "staging" / run_id - ) - cache.mkdir(parents=True, exist_ok=True) - local = cache / bundle # nested in cache, e.g. states/CA.h5 - if local.exists(): - return local - - from huggingface_hub import hf_hub_download - if run_id == "main": - hf_path = bundle - elif run_id == "staging": - hf_path = f"staging/{bundle}" - else: - hf_path = f"staging/{run_id}/{bundle}" - logger.info("Downloading bundle h5 %s/%s", repo_id, hf_path) - downloaded = hf_hub_download( - repo_id=repo_id, - filename=hf_path, - repo_type="model", - local_dir=str(cache), - ) - src = Path(downloaded) - if src != local: - local.parent.mkdir(parents=True, exist_ok=True) - shutil.move(str(src), str(local)) - return local - - -def _get_sim( - repo_id: str, - run_id: str, - bundle: str, - cache_root: str = ".artifacts", -): - """LRU-cached Microsim loaded from the bundle's h5.""" - key = (repo_id, run_id, bundle) - with _SIM_LOCK: - cached = _SIM_CACHE.get(key) - if cached is not None: - _SIM_CACHE.move_to_end(key) - return cached - - h5_path = _h5_local_path(repo_id, run_id, bundle, cache_root) - from policyengine_us import Microsimulation - logger.info("Initialising bundle Microsim from %s", h5_path) - sim = Microsimulation(dataset=str(h5_path)) - - with _SIM_LOCK: - _SIM_CACHE[key] = sim - _SIM_CACHE.move_to_end(key) - while len(_SIM_CACHE) > _SIM_MAX: - evicted_key, _ = _SIM_CACHE.popitem(last=False) - logger.info("Evicted bundle sim %s from LRU", evicted_key) - return sim - - -def _estimate_cache_path( - repo_id: str, - run_id: str, - bundle: str, - cache_root: str = ".artifacts", -) -> Path: - repo_slug = repo_id.replace("/", "__") - cache = ( - Path(cache_root) / repo_slug / "root" / run_id - if run_id == "main" - else Path(cache_root) / repo_slug / "staging" / run_id - ) - cache.mkdir(parents=True, exist_ok=True) - safe = bundle.replace("/", "__") - return cache / f"{safe}.bundle_estimates.pkl" - - -def evaluate_bundle( - targets_df: pd.DataFrame, - *, - repo_id: str, - run_id: str, - bundle: str, - time_period: int = 2024, - cache_root: str = ".artifacts", -) -> pd.DataFrame: - """Re-evaluate ``targets_df`` against ``bundle``'s h5. - - Mutates a copy and returns it with the federal ``estimate`` / - ``rel_error`` / ``abs_rel_error`` columns overridden by per-bundle - numbers. Uses a per-bundle pickle cache so the second pick of the - same bundle is effectively instant. - """ - out = targets_df.copy() - cache_path = _estimate_cache_path(repo_id, run_id, bundle, cache_root) - - # Fast path: cached estimates. - if cache_path.exists(): - try: - cached = pd.read_pickle(cache_path) - joined = out.drop( - columns=["estimate", "rel_error", "abs_rel_error"], - errors="ignore", - ).merge( - cached[["target_id", "estimate", "rel_error", "abs_rel_error"]], - on="target_id", - how="left", - ) - logger.info( - "Loaded cached bundle estimates (%d rows) for %s", - len(cached), bundle, - ) - return joined - except Exception as exc: - logger.warning( - "Bundle estimate cache read failed (%s); recomputing.", exc, - ) - - sim = _get_sim(repo_id, run_id, bundle, cache_root) - from backend.services.stratum_evaluator import evaluate_targets - evaluated = evaluate_targets(out, sim, default_period=time_period) - - slim = evaluated[ - ["target_id", "estimate", "rel_error", "abs_rel_error"] - ].copy() - try: - slim.to_pickle(cache_path) - logger.info( - "Cached bundle %s estimates (%d rows) → %s", - bundle, len(slim), cache_path, - ) - except Exception as exc: - logger.warning("Failed to cache bundle estimates: %s", exc) - return evaluated diff --git a/backend/services/dataset_loader.py b/backend/services/dataset_loader.py deleted file mode 100644 index ad0df17..0000000 --- a/backend/services/dataset_loader.py +++ /dev/null @@ -1,825 +0,0 @@ -"""Load a calibration run from the canonical us-data staging layout. - -Unlike loader.py (which depends on calibration_package.pkl), this: -- downloads policy_data.db (targets DB) and one h5 dataset per run -- reads targets directly from the DB -- defers estimate computation to the stratum evaluator (Step 3) - -The resulting AppState has X_csr / X_csc / initial_weights left empty; routes -that need the X matrix (per-target detail tabs) won't function for these runs, -but the universe view (Summary + All targets) works. -""" - -from __future__ import annotations - -import logging -import os -import re -from pathlib import Path -from typing import TYPE_CHECKING - -import numpy as np -import pandas as pd -from sqlalchemy import create_engine - -from backend.services.runs import DatasetConfig -from backend.state import AppState - -if TYPE_CHECKING: - from huggingface_hub import HfApi # noqa: F401 - -logger = logging.getLogger(__name__) - - -_CONSTRAINT_RE = re.compile(r"([A-Za-z_][\w]*)(==|!=|>=|<=|>|<)(.+)$") - - -def _parse_constraint(s: str) -> tuple[str, str, str] | None: - """Parse 'var op value' (with or without spaces) into a tuple.""" - s = s.strip().replace(" ", "") - m = _CONSTRAINT_RE.match(s) - if not m: - return None - var, op, val = m.group(1), m.group(2), m.group(3).strip() - return (var, op, _norm_constraint_value(val)) - - -def _norm_constraint_value(v: str) -> str: - """Normalise numeric constraint values: '1.0' → '1', '-inf' stays '-inf'.""" - s = str(v).strip() - if s in ("inf", "-inf", "Infinity", "-Infinity"): - return s.replace("Infinity", "inf") - try: - f = float(s) - if f != f or f in (float("inf"), float("-inf")): - return s - if f == int(f): - return str(int(f)) - return str(f) - except (TypeError, ValueError): - return s - - -def _parse_diagnostics_key(key: str) -> tuple | None: - """Convert a diagnostics CSV target key into an order-independent signature. - - Examples: - national/medicaid → ('national', '', 'medicaid', ()) - national/adj.../[tax_unit_is_filer==1] → ('national', '', 'adj...', (('tax_unit_is_filer','==','1'),)) - cd_1000/person_count/[age<5,age>-1] → ('district', '1000', 'person_count', (('age','<','5'),('age','>','-1'))) - """ - if not isinstance(key, str) or "/" not in key: - return None - head, _, rest = key.partition("/") - if head == "national": - geo_level, geo_id = "national", "" - elif head.startswith("cd_"): - geo_level, geo_id = "district", head[3:] - else: - geo_level, geo_id = head, "" - if "/" in rest: - variable, _, constraint_part = rest.partition("/") - else: - variable, constraint_part = rest, "" - constraints: list[tuple[str, str, str]] = [] - if constraint_part.startswith("[") and constraint_part.endswith("]"): - body = constraint_part[1:-1].strip() - if body: - for c in body.split(","): - parsed = _parse_constraint(c) - if parsed is not None: - constraints.append(parsed) - return (geo_level, geo_id, variable, tuple(sorted(constraints))) - - -def _row_to_diagnostics_signature(row) -> tuple: - """Same signature shape as _parse_diagnostics_key but built from a - targets_enriched row. Order-independent.""" - variable = str(row.get("variable") or "") - geo_level = row.get("geo_level") or "national" - gid = row.get("geographic_id") - if gid is None or (isinstance(gid, float) and pd.isna(gid)): - geo_id_norm = "" - else: - try: - geo_id_norm = str(int(float(str(gid)))) - except (TypeError, ValueError): - geo_id_norm = str(gid) - cons = row.get("constraints") or [] - parsed_cons = [] - for c in cons: - if isinstance(c, (list, tuple)) and len(c) >= 3: - parsed_cons.append((str(c[0]), str(c[1]), - _norm_constraint_value(str(c[2])))) - elif isinstance(c, str): - p = _parse_constraint(c) - if p is not None: - parsed_cons.append(p) - return (geo_level, geo_id_norm, variable, tuple(sorted(parsed_cons))) - - -def _target_to_diagnostics_key(row) -> str: - """Build the target key used in unified_diagnostics.csv from a - targets_enriched row. - - Diag format observed empirically: - / (no constraints) - //[,,...] (with constraints, no spaces) - - Where geo_prefix is: - "national" — when no geographic constraint - "cd_" — when geographic_id is a district id (no leading zeros) - - State-level rows aren't represented in the diagnostics file (the - calibration trains national + district only). We return a key anyway; - those will simply miss the join. - """ - variable = str(row.get("variable") or "") - geo_level = row.get("geo_level") or "" - gid = row.get("geographic_id") - if geo_level == "national" or geo_level == "" or gid is None or (isinstance(gid, float) and pd.isna(gid)): - prefix = "national" - elif geo_level == "district": - try: - prefix = f"cd_{int(float(str(gid)))}" - except (TypeError, ValueError): - prefix = f"cd_{gid}" - elif geo_level == "state": - # No state rows in diag — return a key that won't match. - try: - prefix = f"state_{int(float(str(gid)))}" - except (TypeError, ValueError): - prefix = f"state_{gid}" - else: - prefix = geo_level - - cons = row.get("constraints") or [] - if not cons: - return f"{prefix}/{variable}" - # Strip spaces from each "var op value" → "varopvalue"; join with ",". - parts = [] - for c in cons: - # `c` may be either a string "var op value" (from DB rebuild) or a - # (var, op, value) tuple. - if isinstance(c, (list, tuple)) and len(c) >= 3: - parts.append(f"{c[0]}{c[1]}{c[2]}") - else: - parts.append(str(c).replace(" ", "")) - return f"{prefix}/{variable}/[{','.join(parts)}]" - - -def _try_fetch_unified_diagnostics( - dataset, - run_id: str, - cache_root: str = ".artifacts", -) -> "tuple[pd.DataFrame, str] | None": - """For staging-layout datasets, try to fetch the canonical post- - calibration diagnostics CSV from the repo. - - Uses the Stage 3 artifact catalog (mirrored locally as - ``backend.services.fit_artifacts`` until us-data ships a release that - exports ``policyengine_us_data.fit_weights.artifacts``). Tries both - regional (`unified_diagnostics.csv`) and national - (`national_unified_diagnostics.csv`) filenames so we don't silently - miss national-scope runs. - - For each scope we look in: - 1. `calibration/runs//diagnostics/` — per-run. - 2. `calibration/logs/` — "current" canonical snapshot, but only - for the latest discovered run. Reusing current logs for older staging - runs silently reports the wrong calibration fit. - - Returns ``(df, scope)`` on success — knowing the scope lets later code - pick the matching weights / run_config files. Returns ``None`` if - nothing was found. - """ - if dataset.layout == "root": - from huggingface_hub import hf_hub_download - from backend.services.fit_artifacts import artifacts_for_scope - - filename = artifacts_for_scope("regional").diagnostics - path = f"calibration/logs/{filename}" - repo_slug = dataset.repo_id.replace("/", "__") - cache = Path(cache_root) / repo_slug - cache.mkdir(parents=True, exist_ok=True) - try: - local = hf_hub_download( - repo_id=dataset.repo_id, - filename=path, - repo_type=dataset.repo_type, - local_dir=str(cache), - ) - df = pd.read_csv(local) - if "target" in df.columns and "estimate" in df.columns: - logger.info( - "Loaded root production diagnostics from %s (%d rows)", - path, len(df), - ) - return df, "regional" - except Exception as exc: - logger.debug("diagnostics fetch failed at %s: %s", path, exc) - return None - - if dataset.layout != "staging": - return None - from huggingface_hub import hf_hub_download - from backend.services.fit_artifacts import ( - artifacts_for_scope, - SCOPES, - ) - - repo_slug = dataset.repo_id.replace("/", "__") - cache = Path(cache_root) / repo_slug - cache.mkdir(parents=True, exist_ok=True) - - for scope in SCOPES: - filename = artifacts_for_scope(scope).diagnostics - candidate_paths = [ - f"calibration/runs/{run_id}/diagnostics/{filename}", - ] - try: - from backend.services import runs as runs_service - latest = next(iter(runs_service.list_runs(dataset.id)), None) - if latest is not None and latest.run_id == run_id: - candidate_paths.append(f"calibration/logs/{filename}") - except Exception as exc: - logger.debug("Could not determine latest run for diagnostics fallback: %s", exc) - for path in candidate_paths: - try: - local = hf_hub_download( - repo_id=dataset.repo_id, - filename=path, - repo_type=dataset.repo_type, - local_dir=str(cache), - ) - df = pd.read_csv(local) - if "target" in df.columns and "estimate" in df.columns: - logger.info( - "Loaded %s-scope diagnostics from %s (%d rows)", - scope, path, len(df), - ) - return df, scope - except Exception as exc: - logger.debug("diagnostics fetch failed at %s: %s", path, exc) - continue - return None - - -def _join_diagnostics( - targets_df: pd.DataFrame, - diag_df: pd.DataFrame, -) -> int: - """Attach published calibration diagnostics to DB targets. - - Diagnostics keys do not carry target_id or period, and the DB can contain - multiple active rows with the same variable/geography/constraint signature. - Join one diagnostics row to at most one DB row, picking the closest target - value when duplicates exist. For joined rows, the diagnostics true_value is - the calibrated target value, so use that value for package-derived error - calculations instead of the raw DB source value. - """ - sig_to_indices: dict[tuple, list[int]] = {} - for idx, row in targets_df.iterrows(): - sig_to_indices.setdefault(_row_to_diagnostics_signature(row), []).append(idx) - - matched_indices: set[int] = set() - n_joined = 0 - has_true = "true_value" in diag_df.columns - has_rel = "rel_error" in diag_df.columns - has_abs = "abs_rel_error" in diag_df.columns - - for _, diag_row in diag_df.iterrows(): - sig = _parse_diagnostics_key(diag_row.get("target")) - if sig is None: - continue - candidates = [ - idx for idx in sig_to_indices.get(sig, []) - if idx not in matched_indices - ] - if not candidates: - continue - - true_value = diag_row.get("true_value") if has_true else np.nan - if pd.notna(true_value): - def distance(idx: int) -> float: - raw_value = targets_df.at[idx, "value"] - try: - return abs(float(raw_value) - float(true_value)) / max( - 1.0, abs(float(raw_value)) - ) - except (TypeError, ValueError): - return float("inf") - chosen = min(candidates, key=distance) - else: - chosen = candidates[0] - - matched_indices.add(chosen) - if pd.notna(true_value): - targets_df.at[chosen, "value"] = float(true_value) - targets_df.at[chosen, "estimate"] = diag_row.get("estimate") - if has_rel and pd.notna(diag_row.get("rel_error")): - targets_df.at[chosen, "rel_error"] = float(diag_row["rel_error"]) - if has_abs and pd.notna(diag_row.get("abs_rel_error")): - targets_df.at[chosen, "abs_rel_error"] = float(diag_row["abs_rel_error"]) - targets_df.at[chosen, "included"] = True - if has_rel and pd.notna(diag_row.get("rel_error")): - targets_df.at[chosen, "loss_contribution"] = float(diag_row["rel_error"]) ** 2 - n_joined += 1 - - return n_joined - - -def _household_series(sim, variable: str, period: int, default=None) -> np.ndarray: - try: - return np.asarray( - sim.calculate(variable, map_to="household", period=period).values - ) - except Exception: - if default is None: - raise - return np.asarray(default) - - -def _ensure_staging_artifacts( - dataset: DatasetConfig, - run_id: str, - cache_root: str = ".artifacts", -) -> dict[str, str]: - """Download a staging run's required files; return a {logical_name: path} map. - - Logical names are the flat filenames (``policy_data.db``, - ``enhanced_cps_2024.h5``); on HF they may live at the run root OR - nested under ``calibration/`` / ``datasets/``. We probe both so - versioned releases and GHA staging both load through the same code - path, and store everything flat in the local cache. - """ - from huggingface_hub import hf_hub_download - from backend.services.runs import ( - _resolve_staging_file_paths, - _resolve_staging_root_file_paths, - ) - import shutil - - repo_slug = dataset.repo_id.replace("/", "__") - if dataset.layout == "root": - cache = Path(cache_root) / repo_slug / "root" / run_id - else: - cache = Path(cache_root) / repo_slug / "staging" / run_id - cache.mkdir(parents=True, exist_ok=True) - - logical_names = list(dataset.effective_required_files()) - if dataset.layout == "root": - actual_paths = {name: name for name in logical_names} - elif dataset.layout == "staging-root": - actual_paths = _resolve_staging_root_file_paths( - dataset.repo_id, - logical_names, - ) - else: - actual_paths = _resolve_staging_file_paths( - dataset.repo_id, - run_id, - logical_names, - ) - - resolved: dict[str, str] = {} - for fn in logical_names: - dest = cache / fn # flat in our cache - if dest.exists(): - logger.info("Found cached %s: %s", fn, dest) - resolved[fn] = str(dest) - continue - - hf_path = actual_paths.get(fn) - if hf_path is None: - logger.warning("Run %s does not publish %s; skipping.", run_id, fn) - continue - logger.info("Downloading %s from %s/%s ...", fn, dataset.repo_id, hf_path) - try: - downloaded = hf_hub_download( - repo_id=dataset.repo_id, - filename=hf_path, - repo_type=dataset.repo_type, - local_dir=str(cache), - ) - except Exception as exc: - logger.warning("Could not download %s: %s", hf_path, exc) - continue - - # hf_hub_download honors the repo path under local_dir, so the - # file lands at cache/. Move it to the flat dest so the - # rest of the loader doesn't need to know about the layout. - src = Path(downloaded) - if src != dest: - dest.parent.mkdir(parents=True, exist_ok=True) - shutil.move(str(src), str(dest)) - if dest.exists(): - resolved[fn] = str(dest) - size_mb = dest.stat().st_size / 1e6 - logger.info(" Downloaded %s (%.1f MB)", fn, size_mb) - - return resolved - - -def _load_targets_from_db(db_engine) -> tuple[pd.DataFrame, list[str]]: - """Read targets + denormalised constraint info from policy_data.db. - - Returns (targets_df, target_names) where targets_df mirrors the columns - the rest of the dashboard expects: target_id, variable, value, period, - geo_level, geographic_id, domain_variable, included, plus a 'constraints' - list and a 'target_name' string built from those. - """ - # All active targets (DB convention — calibration team uses `active` flag) - targets = pd.read_sql( - "SELECT target_id, variable, period, stratum_id, value, active, " - "tolerance, source, notes FROM targets WHERE active = 1", - db_engine, - ) - - # Pull all constraints once, group by stratum_id (faster than per-row query). - constraints = pd.read_sql( - "SELECT stratum_id, constraint_variable, operation, value FROM " - "stratum_constraints", - db_engine, - ) - - geo_vars = {"state_fips", "congressional_district_geoid", "ucgid_str"} - by_stratum: dict[int, list[dict]] = {} - for _, row in constraints.iterrows(): - by_stratum.setdefault(row.stratum_id, []).append({ - "variable": row.constraint_variable, - "operation": row.operation, - "value": row.value, - }) - - geo_levels: list[str] = [] - geographic_ids: list[str | None] = [] - domain_vars: list[str | None] = [] - constraint_lists: list[list[str]] = [] - - for sid in targets["stratum_id"]: - cons = by_stratum.get(int(sid), []) - geo_con = next((c for c in cons if c["variable"] in geo_vars), None) - if geo_con is None: - geo_level = "national" - geographic_id = None - elif geo_con["variable"] == "state_fips": - geo_level = "state" - geographic_id = str(geo_con["value"]) - elif geo_con["variable"] == "congressional_district_geoid": - geo_level = "district" - geographic_id = str(geo_con["value"]) - else: - geo_level = geo_con["variable"] - geographic_id = str(geo_con["value"]) - - non_geo = [c for c in cons if c["variable"] not in geo_vars] - domain_var = ",".join(sorted({c["variable"] for c in non_geo})) or None - readable = [f"{c['variable']} {c['operation']} {c['value']}" for c in non_geo] - - geo_levels.append(geo_level) - geographic_ids.append(geographic_id) - domain_vars.append(domain_var) - constraint_lists.append(readable) - - targets["geo_level"] = geo_levels - targets["geographic_id"] = geographic_ids - targets["domain_variable"] = domain_vars - targets["constraints"] = constraint_lists - - # Build readable target names: ///[constraints] - names: list[str] = [] - for _, r in targets.iterrows(): - gid = r["geographic_id"] - if gid is None or (isinstance(gid, float) and pd.isna(gid)): - geo_part = "US" - else: - geo_part = str(gid) - constraint_part = ( - ",".join(r["constraints"]) if r["constraints"] else "" - ) - names.append( - f"{r['geo_level']}/{r['variable']}/{geo_part}/[{constraint_part}]" - ) - targets["target_name"] = names - - # Convenience aliased columns matching the pkl-mode shape - targets["target_idx"] = np.arange(len(targets)) - # `included` is set to True later only for rows that match a published - # entry in unified_diagnostics.csv — the only honest signal we have for - # "this target was actually evaluated by the calibration loss this run." - # The DB's `active` flag is always 1 and so isn't useful here. - targets["included"] = False - targets["estimate"] = np.nan - targets["rel_error"] = np.nan - targets["abs_rel_error"] = np.nan - targets["loss_contribution"] = 0.0 - targets["n_contributors"] = 0 - - return targets, names - - -def _detect_time_period(sim) -> int: - try: - raw_keys = sim.dataset.load_dataset()["household_id"] - if isinstance(raw_keys, dict): - return int(next(iter(raw_keys))) - except Exception: - pass - return 2024 - - - - -def load_run_from_dataset( - dataset: DatasetConfig, - run_id: str, - cache_root: str = ".artifacts", -) -> AppState: - """Load a staging-layout run into an AppState. Step 2 of the refactor: - populates targets + DB + simulation but leaves estimates as NaN (Step 4 - will fill them in via the stratum evaluator). - """ - from policyengine_us import Microsimulation - - files = _ensure_staging_artifacts(dataset, run_id, cache_root) - if "policy_data.db" not in files or dataset.primary_h5 not in files: - raise RuntimeError( - f"Required files missing for {dataset.id}/{run_id}: " - f"have {sorted(files)}" - ) - - logger.info("Connecting to policy_data.db at %s", files["policy_data.db"]) - db_engine = create_engine(f"sqlite:///{files['policy_data.db']}") - - logger.info("Loading targets from DB...") - targets_df, target_names = _load_targets_from_db(db_engine) - logger.info("Loaded %d active targets", len(targets_df)) - - logger.info("Initializing Microsimulation from %s", files[dataset.primary_h5]) - sim = Microsimulation(dataset=files[dataset.primary_h5]) - time_period = _detect_time_period(sim) - - # If we've previously computed estimates for this run, the parquet - # cache is good for the lifetime of the run + selected h5 (run_id is - # immutable per publish). Skip the CSV join + entity-aware evaluator - # entirely. - # Pickle keeps pandas types (object cols with lists, etc.) and needs no - # extra deps. Cache key is the immutable run_id, so no staleness risk - # within a run; bump SCHEMA_VERSION below to invalidate on shape change. - cache_stem = Path(dataset.primary_h5).stem.replace("/", "_") - layout_cache_dir = "root" if dataset.layout == "root" else "staging" - enriched_cache_path = ( - Path(cache_root) / dataset.repo_id.replace("/", "__") - / layout_cache_dir / run_id / f"targets_enriched.{cache_stem}.pkl" - ) - SCHEMA_VERSION = 4 - cached_enriched: pd.DataFrame | None = None - detected_scope_cached: str | None = None - if enriched_cache_path.exists(): - try: - meta_path = enriched_cache_path.with_suffix(".meta.json") - if meta_path.exists(): - import json as _json - meta = _json.loads(meta_path.read_text()) - if meta.get("version") == SCHEMA_VERSION: - cached_enriched = pd.read_pickle(enriched_cache_path) - detected_scope_cached = meta.get("fit_scope") - logger.info( - "Loaded cached targets_enriched (%d rows) from %s", - len(cached_enriched), enriched_cache_path, - ) - except Exception as exc: - logger.warning("Failed to read enriched cache (%s); recomputing.", exc) - cached_enriched = None - - # Household-level scaffolding so weights/geo lookups work - household_weight = sim.calculate( - "household_weight", map_to="household", period=time_period, - ).values - n_households = len(household_weight) - - compute_household_fields = os.environ.get( - "COMPUTE_HOUSEHOLD_FIELDS", "", - ).lower() in {"1", "true", "yes"} - if compute_household_fields: - state_fips = _household_series( - sim, "state_fips", time_period, default=np.zeros(n_households) - ).astype(int) - cd_geoid = _household_series( - sim, "congressional_district_geoid", time_period, - default=np.zeros(n_households), - ).astype(int) - hh_income = _household_series( - sim, "spm_unit_net_income", time_period, default=np.zeros(n_households) - ) - hh_threshold = _household_series( - sim, "spm_unit_spm_threshold", time_period, default=np.zeros(n_households) - ) - in_poverty = hh_income < hh_threshold - try: - income_decile = pd.qcut(hh_income, 10, labels=False, duplicates="drop") - income_decile = np.asarray(income_decile).astype(np.int8) - except Exception: - income_decile = np.zeros(n_households, dtype=np.int8) - else: - logger.info( - "Skipping household income/geography fields " - "(set COMPUTE_HOUSEHOLD_FIELDS=true to compute them)." - ) - state_fips = np.zeros(n_households, dtype=int) - cd_geoid = np.zeros(n_households, dtype=int) - hh_income = np.zeros(n_households, dtype=np.float32) - hh_threshold = np.zeros(n_households, dtype=np.float32) - in_poverty = np.zeros(n_households, dtype=bool) - income_decile = np.zeros(n_households, dtype=np.int8) - - households_df = pd.DataFrame({ - "household_idx": np.arange(n_households), - "income": hh_income.astype(np.float32), - "spm_threshold": hh_threshold.astype(np.float32), - "in_poverty": in_poverty, - "initial_weight": household_weight.astype(np.float32), - "final_weight": household_weight.astype(np.float32), - "g_weight": np.ones(n_households, dtype=np.float32), - "state": state_fips, - "cd_geoid": cd_geoid, - "income_decile": income_decile, - }) - - # Dataset mode: the canonical us-data repo publishes a per-run - # `unified_diagnostics.csv` (under calibration/runs//diagnostics/). - # We use it to identify targets that were actually in the calibration loss - # and to pick the published true_value, then recompute PE aggregates from - # the h5 with policyengine_us so the dashboard's "PE aggregate" column is - # sourced from the same package API users would call manually. - # - # Targets the diagnostics file doesn't cover (typically excluded by - # target_config.yaml during this calibration) are left unestimated unless - # explicitly requested; evaluating all authored targets is slow and - # misleading for "included targets" diagnostics. - # Skip CSV join + evaluator entirely if the parquet cache was already - # warmed for this run. targets_df becomes the cached frame; we still - # need to compute downstream stuff (households_df, sparse mats). - if cached_enriched is not None: - targets_df = cached_enriched - diag_result = None - diag_scope = detected_scope_cached - else: - diag_result = _try_fetch_unified_diagnostics(dataset, run_id, cache_root) - diag_scope: str | None = None - if diag_result is not None: - diag_df, diag_scope = diag_result - logger.info( - "Joining %d rows from %s-scope diagnostics onto %d targets...", - len(diag_df), diag_scope, len(targets_df), - ) - # A CSV match means the pipeline evaluated this target in its loss. - # Join one-to-one: target_id is absent from diagnostics and signatures - # can repeat across source periods in policy_data.db. - n_from_diag = _join_diagnostics(targets_df, diag_df) - logger.info( - " → %d/%d targets got estimates from diagnostics CSV (marked included=True)", - n_from_diag, len(targets_df), - ) - elif cached_enriched is None: - logger.info("No unified_diagnostics.csv found; falling back to MVP evaluator only.") - - compute_pe_aggregates = os.environ.get( - "COMPUTE_PE_AGGREGATES", "true", - ).lower() not in {"0", "false", "no"} - if ( - cached_enriched is None - and diag_result is not None - and compute_pe_aggregates - and targets_df["included"].any() - ): - from backend.services.stratum_evaluator import evaluate_targets - - included_mask = targets_df["included"].astype(bool) - logger.info( - "Computing PE aggregates from published h5 for %d in-loss targets...", - int(included_mask.sum()), - ) - evaluated = evaluate_targets( - targets_df[included_mask].copy(), - sim, - default_period=time_period, - ) - idx = evaluated.index - targets_df.loc[idx, "estimate"] = evaluated["estimate"].values - targets_df.loc[idx, "rel_error"] = evaluated["rel_error"].values - targets_df.loc[idx, "abs_rel_error"] = evaluated["abs_rel_error"].values - if "eval_note" in evaluated.columns: - targets_df.loc[idx, "eval_note"] = evaluated["eval_note"].values - with np.errstate(invalid="ignore"): - targets_df.loc[idx, "loss_contribution"] = ( - targets_df.loc[idx, "rel_error"].astype(float) ** 2 - ) - logger.info( - "Computed PE aggregates for %d/%d in-loss targets.", - int(targets_df.loc[idx, "estimate"].notna().sum()), - int(included_mask.sum()), - ) - - # Fill remaining NaN estimates only when diagnostics were unavailable, or - # explicitly requested. Evaluating tens of thousands of skipped/authored - # targets through Microsimulation makes normal dashboard loads look hung. - eval_skipped = os.environ.get("EVALUATE_SKIPPED_TARGETS", "").lower() in { - "1", "true", "yes", - } - should_eval_remaining = cached_enriched is None and ( - (diag_result is None and dataset.layout != "staging-root") or eval_skipped - ) - if targets_df["estimate"].isna().any() and should_eval_remaining: - from backend.services.stratum_evaluator import evaluate_targets - unfilled = targets_df["estimate"].isna() - logger.info( - "Running MVP evaluator on %d remaining targets...", int(unfilled.sum()), - ) - filled = evaluate_targets( - targets_df[unfilled].copy(), sim, default_period=time_period, - ) - targets_df.loc[unfilled, "estimate"] = filled["estimate"].values - elif targets_df["estimate"].isna().any(): - logger.info( - "Skipping MVP evaluator for %d non-diagnostics targets " - "(set EVALUATE_SKIPPED_TARGETS=true to compute them).", - int(targets_df["estimate"].isna().sum()), - ) - n_evaluated = int(np.sum(~targets_df["estimate"].isna())) - logger.info( - "Total estimates available: %d/%d (%.1f%%)", - n_evaluated, len(targets_df), 100 * n_evaluated / max(1, len(targets_df)), - ) - - # Compute rel_error / abs_rel_error for rows not populated by diagnostics. - target_values = targets_df["value"].to_numpy(dtype=np.float64) - estimates_arr = targets_df["estimate"].to_numpy(dtype=np.float64) - with np.errstate(divide="ignore", invalid="ignore"): - rel = np.where( - np.abs(target_values) > 0, - (estimates_arr - target_values) / np.abs(target_values), - np.nan, - ) - targets_df["rel_error"] = np.where( - targets_df["rel_error"].notna(), targets_df["rel_error"], rel, - ) - targets_df["abs_rel_error"] = np.where( - targets_df["abs_rel_error"].notna(), - targets_df["abs_rel_error"], - np.abs(rel), - ) - - # Stash the detected fit scope so other artifact lookups can pick the - # right regional/national filenames. Falls back to regional when we - # couldn't fetch diagnostics at all (best guess for legacy runs). - detected_scope = diag_scope or "regional" - - # Write the enriched parquet cache so subsequent loads can skip the - # CSV join + evaluator entirely. Only write if we actually did the - # work (cached_enriched is None means we recomputed this load). - if cached_enriched is None: - try: - enriched_cache_path.parent.mkdir(parents=True, exist_ok=True) - targets_df.to_pickle(enriched_cache_path) - import json as _json - enriched_cache_path.with_suffix(".meta.json").write_text( - _json.dumps({"fit_scope": detected_scope, "version": SCHEMA_VERSION}) - ) - logger.info("Cached targets_enriched → %s", enriched_cache_path) - except Exception as exc: - logger.warning("Failed to cache enriched targets (%s); continuing.", exc) - - # Empty sparse matrices since dataset mode doesn't have the pipeline's - # X matrix yet (waiting on data-team publish). - from scipy import sparse - X_csr = sparse.csr_matrix((len(targets_df), n_households)) - X_csc = X_csr.tocsc() - - state = AppState( - X_csr=X_csr, - X_csc=X_csc, - targets_df=targets_df, - target_names=target_names, - targets_enriched=targets_df, - households_df=households_df, - sim_service=None, # filled in step 3 alongside the evaluator - db_engine=db_engine, - time_period=time_period, - n_targets=len(targets_df), - n_households=n_households, - dataset_id=dataset.id, - run_id=run_id, - # Weights live on the dataset itself; we don't have a "before - # calibration" view because the published dataset IS the post- - # calibration state. - initial_weights=household_weight, - final_weights=household_weight, - g_weights=np.ones(n_households), - metadata={"fit_scope": detected_scope}, - ) - - # Cache the sim on the state so the evaluator can reuse it later. - state.sim_service = sim - return state diff --git a/backend/services/db_service.py b/backend/services/db_service.py deleted file mode 100644 index af22a4c..0000000 --- a/backend/services/db_service.py +++ /dev/null @@ -1,157 +0,0 @@ -"""SQLModel ORM queries against policy_data.db.""" - -from sqlmodel import Session, select - -from policyengine_us_data.db.create_database_tables import ( - Stratum, - StratumConstraint, - Target, -) - - -def batch_get_all_stratum_constraints(session: Session) -> dict[int, list[dict]]: - """Return all constraints grouped by stratum_id in a single query.""" - all_constraints = session.exec(select(StratumConstraint)).all() - result: dict[int, list[dict]] = {} - for c in all_constraints: - result.setdefault(c.stratum_id, []).append({ - "variable": c.constraint_variable, - "operation": c.operation, - "value": c.value, - }) - return result - - -def get_target_provenance( - session: Session, - target_id: int, -) -> dict | None: - """Full target metadata including stratum constraints.""" - target = session.get(Target, target_id) - if target is None: - return None - - constraints = session.exec( - select(StratumConstraint).where( - StratumConstraint.stratum_id == target.stratum_id - ) - ).all() - - return { - "target_id": target.target_id, - "variable": target.variable, - "value": target.value, - "period": target.period, - "source": target.source, - "tolerance": target.tolerance, - "notes": target.notes, - "active": target.active, - "stratum_id": target.stratum_id, - "constraints": [ - { - "variable": c.constraint_variable, - "operation": c.operation, - "value": c.value, - } - for c in constraints - ], - } - - -def search_targets( - session: Session, - pattern: str, - active_only: bool = True, -) -> list[dict]: - """Search targets by variable name pattern.""" - stmt = select(Target).where(Target.variable.like(f"%{pattern}%")) - if active_only: - stmt = stmt.where(Target.active == True) # noqa: E712 - targets = session.exec(stmt).all() - return [ - { - "target_id": t.target_id, - "variable": t.variable, - "value": t.value, - "period": t.period, - "stratum_id": t.stratum_id, - "source": t.source, - "active": t.active, - } - for t in targets - ] - - -def get_stratum_detail( - session: Session, - stratum_id: int, -) -> dict | None: - """Stratum with constraints, children, and attached targets.""" - stratum = session.get(Stratum, stratum_id) - if stratum is None: - return None - - constraints = session.exec( - select(StratumConstraint).where( - StratumConstraint.stratum_id == stratum_id - ) - ).all() - - children = session.exec( - select(Stratum).where(Stratum.parent_stratum_id == stratum_id) - ).all() - - targets = session.exec( - select(Target).where( - Target.stratum_id == stratum_id, - Target.active == True, # noqa: E712 - ) - ).all() - - return { - "stratum_id": stratum.stratum_id, - "parent_stratum_id": stratum.parent_stratum_id, - "notes": stratum.notes, - "constraints": [ - { - "variable": c.constraint_variable, - "operation": c.operation, - "value": c.value, - } - for c in constraints - ], - "children": [ - {"stratum_id": ch.stratum_id, "notes": ch.notes} - for ch in children - ], - "targets": [ - { - "target_id": t.target_id, - "variable": t.variable, - "value": t.value, - "period": t.period, - "active": t.active, - } - for t in targets - ], - } - - -def get_target_constraints( - session: Session, - stratum_id: int, -) -> list[dict]: - """Return all constraints for a stratum.""" - constraints = session.exec( - select(StratumConstraint).where( - StratumConstraint.stratum_id == stratum_id - ) - ).all() - return [ - { - "variable": c.constraint_variable, - "operation": c.operation, - "value": c.value, - } - for c in constraints - ] diff --git a/backend/services/fit_artifacts.py b/backend/services/fit_artifacts.py deleted file mode 100644 index 2869a36..0000000 --- a/backend/services/fit_artifacts.py +++ /dev/null @@ -1,74 +0,0 @@ -"""Local mirror of `policyengine_us_data.fit_weights.artifacts`. - -Mirrored from upstream policyengine-us-data PRs #1043 / #1046 (merged -2026-05-21), which centralized Stage 3 artifact filenames into a typed -catalog. Those changes aren't in PyPI 1.115.4 yet; the moment the team -cuts a release that includes the `fit_weights` module, drop this file and -swap callers to: - - from policyengine_us_data.fit_weights.artifacts import ( - fit_artifacts_for_scope, - ) - -Two scopes are published per Stage 3 fit: - -- **regional** — district / state-level fits. Filenames have no prefix. -- **national** — single national-scope fit. Filenames are `national_*`. - -Previously our loader only knew the regional names; a national run would -silently miss its diagnostics. The catalog makes the scope explicit. -""" - -from __future__ import annotations - -from dataclasses import dataclass - -REGIONAL_SCOPE = "regional" -NATIONAL_SCOPE = "national" -SCOPES = (REGIONAL_SCOPE, NATIONAL_SCOPE) - - -@dataclass(frozen=True) -class ScopedArtifacts: - scope: str - weights: str - geography: str - run_config: str - diagnostics: str - epoch_log: str - - def as_dict(self) -> dict[str, str]: - return { - "weights": self.weights, - "geography": self.geography, - "run_config": self.run_config, - "diagnostics": self.diagnostics, - "epoch_log": self.epoch_log, - } - - -REGIONAL_ARTIFACTS = ScopedArtifacts( - scope=REGIONAL_SCOPE, - weights="calibration_weights.npy", - geography="geography_assignment.npz", - run_config="unified_run_config.json", - diagnostics="unified_diagnostics.csv", - epoch_log="calibration_log.csv", -) - -NATIONAL_ARTIFACTS = ScopedArtifacts( - scope=NATIONAL_SCOPE, - weights="national_calibration_weights.npy", - geography="national_geography_assignment.npz", - run_config="national_unified_run_config.json", - diagnostics="national_unified_diagnostics.csv", - epoch_log="national_calibration_log.csv", -) - - -def artifacts_for_scope(scope: str) -> ScopedArtifacts: - if scope == REGIONAL_SCOPE: - return REGIONAL_ARTIFACTS - if scope == NATIONAL_SCOPE: - return NATIONAL_ARTIFACTS - raise ValueError(f"Unknown fit scope: {scope!r}") diff --git a/backend/services/geo_utils.py b/backend/services/geo_utils.py deleted file mode 100644 index 3116e30..0000000 --- a/backend/services/geo_utils.py +++ /dev/null @@ -1,218 +0,0 @@ -"""Utilities for parsing congressional district GEOIDs and readable names.""" - -import numpy as np - -STATE_FIPS_TO_NAME: dict[int, str] = { - 1: "Alabama", 2: "Alaska", 4: "Arizona", 5: "Arkansas", - 6: "California", 8: "Colorado", 9: "Connecticut", 10: "Delaware", - 11: "District of Columbia", 12: "Florida", 13: "Georgia", 15: "Hawaii", - 16: "Idaho", 17: "Illinois", 18: "Indiana", 19: "Iowa", - 20: "Kansas", 21: "Kentucky", 22: "Louisiana", 23: "Maine", - 24: "Maryland", 25: "Massachusetts", 26: "Michigan", 27: "Minnesota", - 28: "Mississippi", 29: "Missouri", 30: "Montana", 31: "Nebraska", - 32: "Nevada", 33: "New Hampshire", 34: "New Jersey", 35: "New Mexico", - 36: "New York", 37: "North Carolina", 38: "North Dakota", 39: "Ohio", - 40: "Oklahoma", 41: "Oregon", 42: "Pennsylvania", 44: "Rhode Island", - 45: "South Carolina", 46: "South Dakota", 47: "Tennessee", 48: "Texas", - 49: "Utah", 50: "Vermont", 51: "Virginia", 53: "Washington", - 54: "West Virginia", 55: "Wisconsin", 56: "Wyoming", - 60: "American Samoa", 66: "Guam", 69: "Northern Mariana Islands", - 72: "Puerto Rico", 78: "U.S. Virgin Islands", -} - -STATE_NAME_TO_FIPS: dict[str, int] = {v: k for k, v in STATE_FIPS_TO_NAME.items()} - -# States with a single at-large congressional district -AT_LARGE_STATES: set[int] = {2, 10, 11, 30, 38, 46, 50, 56} - -STATE_FIPS_TO_ABBREV: dict[int, str] = { - 1: "AL", 2: "AK", 4: "AZ", 5: "AR", 6: "CA", 8: "CO", 9: "CT", - 10: "DE", 11: "DC", 12: "FL", 13: "GA", 15: "HI", 16: "ID", - 17: "IL", 18: "IN", 19: "IA", 20: "KS", 21: "KY", 22: "LA", - 23: "ME", 24: "MD", 25: "MA", 26: "MI", 27: "MN", 28: "MS", - 29: "MO", 30: "MT", 31: "NE", 32: "NV", 33: "NH", 34: "NJ", - 35: "NM", 36: "NY", 37: "NC", 38: "ND", 39: "OH", 40: "OK", - 41: "OR", 42: "PA", 44: "RI", 45: "SC", 46: "SD", 47: "TN", - 48: "TX", 49: "UT", 50: "VT", 51: "VA", 53: "WA", 54: "WV", - 55: "WI", 56: "WY", 60: "AS", 66: "GU", 69: "MP", 72: "PR", - 78: "VI", -} - - -def parse_cd_geoids(cd_geoid: np.ndarray) -> tuple[np.ndarray, np.ndarray]: - """Extract state FIPS codes from congressional district GEOID strings. - - CD GEOIDs are stored as strings like '4213' (state 42, district 13) - or '621' (state 6, district 21). The state FIPS is everything except - the last two characters (the district number is always two digits, - but may lack a leading zero in the string representation). - - Args: - cd_geoid: String array of CD GEOIDs (e.g., ['4213', '621', '104']). - - Returns: - Tuple of (cd_geoid_int, state_fips) as integer arrays. - cd_geoid_int: The GEOID as an integer (e.g., 4213, 621, 104). - state_fips: The state FIPS code (e.g., 42, 6, 1). - """ - if len(cd_geoid) == 0: - return np.array([], dtype=np.int64), np.array([], dtype=np.int32) - - cd_geoid_int = np.array([int(x) for x in cd_geoid], dtype=np.int64) - state_fips = (cd_geoid_int // 100).astype(np.int32) - - return cd_geoid_int, state_fips - - -def state_name(fips: int) -> str: - """Return the state name for a FIPS code.""" - return STATE_FIPS_TO_NAME.get(fips, f"Unknown ({fips})") - - -def state_abbrev(fips: int) -> str: - """Return the state abbreviation for a FIPS code.""" - return STATE_FIPS_TO_ABBREV.get(fips, f"?{fips}") - - -def runtime_dataset_bundle_for( - geo_level: str | None, - geographic_id, - *, - available: "frozenset[str] | None" = None, - federal_fallback: str = "enhanced_cps_2024.h5", -) -> str: - """Like ``dataset_bundle_for`` but aware of what the run actually - publishes. If the conventional bundle (e.g. ``states/CA.h5``) isn't - in ``available``, fall back to the federal bundle so the dashboard - label matches the file that actually holds this target's weights. - - When ``available`` is None the function behaves like the raw - canonical mapping; pass a set from ``published_bundles`` to make it - run-aware. - """ - proposed = dataset_bundle_for(geo_level, geographic_id) - if not available: - return proposed - if proposed in available: - return proposed - if federal_fallback in available: - return federal_fallback - return proposed - - -def dataset_bundle_for(geo_level: str | None, geographic_id) -> str: - """Map a target's geography to the calibrated dataset file that holds - its calibrated weights, mirroring the per-state / per-district pipeline - builds in policyengine-us-data. - - Examples: - national, US → 'national/US.h5' - state, '6' → 'states/CA.h5' - district, 612 → 'districts/CA-12.h5' - - Used both for display (the existing 'Dataset' column on /targets) and - for the dataset_file filter — different bundles include different - target subsets, and the per-state h5 builds are calibrated against - their own slice of the targets table. - """ - if not geo_level or geo_level == "national": - return "national/US.h5" - gid_str = "" if geographic_id is None else str(geographic_id) - if not gid_str or gid_str == "nan": - return "—" - try: - n = int(float(gid_str)) - except (TypeError, ValueError): - n = None - if geo_level == "state": - if n is not None and n in STATE_FIPS_TO_ABBREV: - return f"states/{STATE_FIPS_TO_ABBREV[n]}.h5" - return f"states/{gid_str}.h5" - if geo_level == "district": - if n is not None: - state_fips = n // 100 - dist = n % 100 - code = STATE_FIPS_TO_ABBREV.get(state_fips, str(state_fips)) - return f"districts/{code}-{dist:02d}.h5" - return f"districts/{gid_str}.h5" - return str(geo_level) - - -def _ordinal(n: int) -> str: - """Return ordinal string for an integer (1 -> '1st', 2 -> '2nd', etc.).""" - if 11 <= n % 100 <= 13: - return f"{n}th" - suffix = {1: "st", 2: "nd", 3: "rd"}.get(n % 10, "th") - return f"{n}{suffix}" - - -def cd_display_name(cd_geoid_int: int) -> str: - """Return a readable name for a congressional district GEOID. - - Examples: - 4213 -> "Pennsylvania's 13th congressional district" - 621 -> "California's 21st congressional district" - 101 -> "Alabama's at-large congressional district" - 1101 -> "District of Columbia's at-large congressional district" - """ - fips = cd_geoid_int // 100 - district_num = cd_geoid_int % 100 - sname = STATE_FIPS_TO_NAME.get(fips, f"State {fips}") - - if fips in AT_LARGE_STATES or district_num == 1 and fips in AT_LARGE_STATES: - return f"{sname}'s at-large congressional district" - - return f"{sname}'s {_ordinal(district_num)} congressional district" - - -def geo_display_name(geo_level: str, geographic_id: str) -> str: - """Return a readable name for any geographic level + ID. - - Args: - geo_level: 'national', 'state', or 'district' - geographic_id: 'US', state FIPS string, or CD GEOID string - - Returns: - Readable name like 'National', 'California', or - "California's 21st congressional district" - """ - if geo_level == "national": - return "National" - - if geo_level == "state": - try: - fips = int(geographic_id) - return state_name(fips) - except (ValueError, TypeError): - return f"State {geographic_id}" - - if geo_level == "district": - try: - cd_int = int(geographic_id) - return cd_display_name(cd_int) - except (ValueError, TypeError): - return f"District {geographic_id}" - - return str(geographic_id) - - -def list_states() -> list[dict]: - """Return all states with FIPS, name, and abbreviation.""" - return [ - {"fips": fips, "name": name, "abbrev": STATE_FIPS_TO_ABBREV.get(fips, "")} - for fips, name in sorted(STATE_FIPS_TO_NAME.items()) - if fips <= 56 # exclude territories - ] - - -def list_districts_for_state(state_fips: int) -> list[dict]: - """Return all congressional districts for a state. - - Note: this returns a generic list based on known at-large states. - For actual district enumeration, query the calibration package's cd_geoid array. - """ - sname = state_name(state_fips) - if state_fips in AT_LARGE_STATES: - cd_int = state_fips * 100 + 1 - return [{"cd_geoid": cd_int, "name": f"{sname}'s at-large congressional district"}] - return [] diff --git a/backend/services/loader.py b/backend/services/loader.py deleted file mode 100644 index 30481c2..0000000 --- a/backend/services/loader.py +++ /dev/null @@ -1,427 +0,0 @@ -"""Load all calibration artifacts and compute derived fields at startup.""" - -import logging -import os -from pathlib import Path - -import numpy as np -import pandas as pd -from sqlalchemy import create_engine - -from policyengine_us_data.calibration.unified_calibration import ( - load_calibration_package, - load_target_config, -) - -from backend.services.geo_utils import parse_cd_geoids -from backend.services.sim_service import SimService -from backend.state import AppState - -logger = logging.getLogger(__name__) - -HF_ARTIFACTS = { - "package_path": "calibration_package.pkl", - "weights_path": "calibration_weights.npy", - "db_path": "policy_data.db", - "dataset_path": "source_imputed_stratified_extended_cps.h5", - "cal_log_path": "calibration_log.csv", - "diagnostics_path": "unified_diagnostics.csv", - "target_config_path": "target_config.yaml", -} - - -def _ensure_artifacts( - repo_id: str, - repo_type: str, - prefix: str, - cache_root: str = ".artifacts", -) -> dict: - """Download a run's artifacts from HuggingFace, cached per (repo, prefix). - - Returns a config dict mapping artifact keys to resolved local paths. - Caches under ///. - """ - from huggingface_hub import hf_hub_download - - repo_slug = repo_id.replace("/", "__") - cache = Path(cache_root) / repo_slug / prefix - cache.mkdir(parents=True, exist_ok=True) - resolved: dict = {} - - for key, hf_filename in HF_ARTIFACTS.items(): - dest = cache / hf_filename - if dest.exists(): - logger.info("Found cached %s: %s", key, dest) - resolved[key] = str(dest) - continue - - hf_path = f"{prefix}/{hf_filename}" - logger.info( - "Downloading %s from %s/%s ...", key, repo_id, hf_path - ) - try: - hf_hub_download( - repo_id=repo_id, - filename=hf_path, - repo_type=repo_type, - local_dir=str(cache), - ) - except Exception as exc: - logger.warning("Could not download %s: %s", hf_path, exc) - resolved[key] = None - continue - - nested = cache / prefix / hf_filename - if nested.exists() and not dest.exists(): - nested.rename(dest) - try: - (cache / prefix).rmdir() - except OSError: - pass - - resolved[key] = str(dest) if dest.exists() else None - if dest.exists(): - size_mb = dest.stat().st_size / 1e6 - logger.info(" Downloaded %s (%.1f MB)", hf_filename, size_mb) - - return resolved - - -def load_run( - repo_id: str, - repo_type: str, - prefix: str, - cache_root: str = ".artifacts", - dataset_id: str = "", -) -> AppState: - """Convenience entrypoint that resolves a run's artifacts and loads them.""" - config = _ensure_artifacts(repo_id, repo_type, prefix, cache_root) - state = load_all_artifacts(config) - state.dataset_id = dataset_id - state.run_id = prefix - return state - - -def load_all_artifacts(config: dict) -> AppState: - """Load every artifact and build the AppState. - - Args: - config: dict with keys: - package_path, weights_path, db_path, dataset_path, - cal_log_path (optional), diagnostics_path (optional), - target_config_path (optional) - """ - - # 1. Load calibration package - logger.info("Loading calibration package from %s", config["package_path"]) - package = load_calibration_package(config["package_path"]) - X_csr = package["X_sparse"] - targets_df = package["targets_df"] - target_names = package["target_names"] - initial_weights = package["initial_weights"] - cd_geoid = package.get("cd_geoid", np.array([])) - metadata = package.get("metadata", {}) - - n_targets, n_households = X_csr.shape - logger.info("Matrix shape: %d targets x %d households", n_targets, n_households) - - # 2. Load final weights - logger.info("Loading final weights from %s", config["weights_path"]) - final_weights = np.load(config["weights_path"]) - assert len(final_weights) == n_households, ( - f"Weight length {len(final_weights)} != matrix cols {n_households}" - ) - - # 3. Build CSC for column access - logger.info("Building CSC matrix for column access...") - X_csc = X_csr.tocsc() - - # 4. Initialize Microsimulation - logger.info("Initializing Microsimulation from %s", config["dataset_path"]) - from policyengine_us import Microsimulation - - sim = Microsimulation(dataset=config["dataset_path"]) - time_period = _detect_time_period(sim) - n_base = len(sim.calculate("household_id", map_to="household").values) - n_clones = n_households // n_base - logger.info( - "Base households: %d, clones: %d, total: %d", - n_base, n_clones, n_households, - ) - sim_service = SimService(sim, time_period, n_clones) - logger.info("Microsimulation ready, time_period=%d", time_period) - - # 5. Compute core household fields - logger.info("Computing household fields...") - hh_income = sim_service.calculate("spm_unit_net_income", map_to="household") - hh_threshold = sim_service.calculate( - "spm_unit_spm_threshold", map_to="household" - ) - - # 6. Derive g-weights, poverty flags, deciles - g_weights = final_weights / np.maximum(initial_weights, 1e-10) - in_poverty = hh_income < hh_threshold - income_decile = pd.qcut( - hh_income, 10, labels=False, duplicates="drop" - ) - - cd_geoid_int, state_fips = parse_cd_geoids(cd_geoid) - - households_df = pd.DataFrame({ - "household_idx": np.arange(n_households), - "income": hh_income.astype(np.float32), - "spm_threshold": hh_threshold.astype(np.float32), - "in_poverty": in_poverty, - "initial_weight": initial_weights.astype(np.float32), - "final_weight": final_weights.astype(np.float32), - "g_weight": g_weights.astype(np.float32), - "state": state_fips, - "cd_geoid": cd_geoid_int, - "income_decile": income_decile.astype(np.int8), - }) - logger.info("households_df: %d rows", len(households_df)) - - # 7. Enrich targets with error metrics and loss contribution - logger.info("Enriching targets with diagnostics...") - targets_enriched = _enrich_targets( - targets_df, target_names, X_csr, final_weights, initial_weights, in_poverty - ) - logger.info("targets_enriched: %d rows", len(targets_enriched)) - - # 8. Connect to targets database - db_engine = None - if config.get("db_path") and Path(config["db_path"]).exists(): - db_engine = create_engine(f"sqlite:///{config['db_path']}") - logger.info("Connected to policy_data.db at %s", config["db_path"]) - - # 8b. Join provenance fields (source / period / tolerance / notes) from - # policy_data.db into targets_enriched. The pkl's targets_df doesn't - # carry these — they're the calibration team's "what is this target, - # where did it come from" annotations. - if db_engine is not None and "target_id" in targets_enriched.columns: - try: - prov = pd.read_sql( - "SELECT target_id, source, period, tolerance, notes FROM targets", - db_engine, - ) - targets_enriched = targets_enriched.merge( - prov, on="target_id", how="left", suffixes=("", "_db"), - ) - # Prefer DB period over pkl's if both exist - if "period_db" in targets_enriched.columns: - targets_enriched["period"] = targets_enriched["period_db"].fillna( - targets_enriched.get("period") - ) - targets_enriched = targets_enriched.drop(columns=["period_db"]) - n_with_source = int(targets_enriched["source"].notna().sum()) - logger.info( - "Joined %d target provenance rows (source/period/tolerance/notes)", - n_with_source, - ) - except Exception: - logger.exception("Could not join target provenance from DB") - - # 9. Load target config and mark included/excluded - target_config = None - tc_path = config.get("target_config_path") - target_config_text: str | None = None - if tc_path and Path(tc_path).exists(): - target_config = load_target_config(tc_path) - try: - target_config_text = Path(tc_path).read_text(encoding="utf-8") - except OSError as exc: - logger.warning("Could not read raw target_config yaml: %s", exc) - targets_enriched = _apply_included_flag(targets_enriched, target_config) - logger.info( - "Target config: %d included, %d excluded", - targets_enriched["included"].sum(), - (~targets_enriched["included"]).sum(), - ) - # Recompute loss_contribution over included targets only - targets_enriched = _recompute_loss_for_included(targets_enriched) - else: - targets_enriched["included"] = True - logger.warning("No target config found — all targets marked as included") - - # 10. Batch-query constraints and add domain/additional columns - if db_engine is not None: - targets_enriched = _add_constraint_columns(targets_enriched, db_engine) - logger.info("Added domain and additional_constraints columns") - - # 11. Load optional CSVs - cal_log = _load_csv(config.get("cal_log_path")) - diagnostics_csv = _load_csv(config.get("diagnostics_path")) - - return AppState( - X_csr=X_csr, - X_csc=X_csc, - targets_df=targets_df, - target_names=target_names, - initial_weights=initial_weights, - cd_geoid=cd_geoid, - metadata=metadata, - final_weights=final_weights, - g_weights=g_weights, - targets_enriched=targets_enriched, - households_df=households_df, - sim_service=sim_service, - db_engine=db_engine, - cal_log=cal_log, - diagnostics_csv=diagnostics_csv, - target_config=target_config, - target_config_text=target_config_text, - time_period=time_period, - n_targets=n_targets, - n_households=n_households, - ) - - -def _detect_time_period(sim) -> int: - """Detect time period from the dataset.""" - raw_keys = sim.dataset.load_dataset()["household_id"] - if isinstance(raw_keys, dict): - return int(next(iter(raw_keys))) - return 2024 - - -def _enrich_targets( - targets_df: pd.DataFrame, - target_names: list[str], - X_csr, - final_weights: np.ndarray, - initial_weights: np.ndarray, - in_poverty: np.ndarray, -) -> pd.DataFrame: - """Add estimate, rel_error, loss_contribution, contributor counts.""" - enriched = targets_df.copy() - enriched["target_name"] = target_names[: len(enriched)] - - estimates = X_csr.dot(final_weights) - target_values = enriched["value"].values - - rel_errors = np.where( - np.abs(target_values) > 0, - (estimates - target_values) / np.abs(target_values), - 0.0, - ) - - enriched["estimate"] = estimates - enriched["rel_error"] = rel_errors - enriched["abs_rel_error"] = np.abs(rel_errors) - - # Loss contribution: each target's share of total squared relative error - squared_errors = rel_errors ** 2 - total_loss = squared_errors.sum() - if total_loss > 0: - enriched["loss_contribution"] = squared_errors / total_loss - else: - enriched["loss_contribution"] = 0.0 - - # Contributor counts (no poverty-specific metrics) - n_contributors = np.zeros(len(enriched), dtype=np.int32) - for i in range(len(enriched)): - n_contributors[i] = len(X_csr[i, :].nonzero()[1]) - enriched["n_contributors"] = n_contributors - - return enriched - - -def _apply_included_flag( - targets_enriched: pd.DataFrame, - target_config: dict, -) -> pd.DataFrame: - """Mark each target as included or excluded based on target_config rules.""" - include_rules = target_config.get("include", []) - exclude_rules = target_config.get("exclude", []) - - if not include_rules and not exclude_rules: - targets_enriched["included"] = True - return targets_enriched - - if include_rules: - mask = _match_rules(targets_enriched, include_rules) - else: - mask = np.ones(len(targets_enriched), dtype=bool) - - if exclude_rules: - drop = _match_rules(targets_enriched, exclude_rules) - mask &= ~drop - - targets_enriched["included"] = mask - return targets_enriched - - -def _match_rules(targets_df: pd.DataFrame, rules: list[dict]) -> np.ndarray: - """Build a boolean mask matching any of the given rules. - - Reimplements unified_calibration._match_rules for use at the app level. - """ - mask = np.zeros(len(targets_df), dtype=bool) - for rule in rules: - rule_mask = targets_df["variable"] == rule["variable"] - if "geo_level" in rule: - rule_mask = rule_mask & (targets_df["geo_level"] == rule["geo_level"]) - if "domain_variable" in rule: - rule_mask = rule_mask & ( - targets_df["domain_variable"] == rule["domain_variable"] - ) - mask |= rule_mask.values - return mask - - -def _recompute_loss_for_included(targets_enriched: pd.DataFrame) -> pd.DataFrame: - """Recompute loss_contribution scoped to included targets only.""" - included = targets_enriched["included"].values - sq_errors = targets_enriched["rel_error"].values ** 2 - total_included_loss = sq_errors[included].sum() - if total_included_loss > 0: - loss = np.where(included, sq_errors / total_included_loss, 0.0) - else: - loss = np.zeros(len(targets_enriched)) - targets_enriched["loss_contribution"] = loss - return targets_enriched - - -GEOGRAPHIC_VARS = {"state_fips", "congressional_district_geoid", "ucgid_str"} -ADDITIONAL_VARS = {"tax_unit_is_filer"} - - -def _add_constraint_columns( - targets_enriched: pd.DataFrame, - db_engine, -) -> pd.DataFrame: - """Add domain and additional_constraints columns. - - Domain comes from the package's domain_variable column (internally - consistent with the package data). Full constraint details from the - DB are only reliable when the DB and package are from the same build. - """ - # domain_variable from the package is the GROUP_CONCAT of - # non-geographic constraint variable names. Use it directly. - targets_enriched["domain"] = targets_enriched["domain_variable"].apply( - lambda v: str(v) if pd.notna(v) else None - ) - - # For additional_constraints, check if the target name contains - # tax_unit_is_filer. The target_name encodes all constraints in - # its [...] suffix, so we can parse it from there. - def _extract_additional(target_name: str) -> str | None: - if not isinstance(target_name, str): - return None - parts = [] - if "tax_unit_is_filer" in target_name: - parts.append("tax_unit_is_filer == 1") - return ", ".join(parts) or None - - targets_enriched["additional_constraints"] = targets_enriched["target_name"].apply( - _extract_additional - ) - return targets_enriched - - -def _load_csv(path: str | None) -> pd.DataFrame | None: - """Load a CSV if the path exists.""" - if path and Path(path).exists(): - logger.info("Loading CSV from %s", path) - return pd.read_csv(path) - return None diff --git a/backend/services/matrix_ops.py b/backend/services/matrix_ops.py deleted file mode 100644 index 8b79ada..0000000 --- a/backend/services/matrix_ops.py +++ /dev/null @@ -1,120 +0,0 @@ -"""Sparse matrix operations for calibration diagnostics.""" - -import numpy as np -import pandas as pd -import scipy.sparse as sp - - -def get_target_contributors( - X_csr: sp.csr_matrix, - target_idx: int, -) -> np.ndarray: - """Return household indices that contribute to a target (CSR row slice).""" - return X_csr[target_idx, :].nonzero()[1] - - -def get_target_contributions( - X_csr: sp.csr_matrix, - target_idx: int, - final_weights: np.ndarray, -) -> pd.DataFrame: - """Return contributing households with raw and weighted values.""" - row = X_csr[target_idx, :] - cols = row.nonzero()[1] - raw_values = np.array(row[:, cols].todense()).flatten() - weighted_values = raw_values * final_weights[cols] - return pd.DataFrame({ - "household_idx": cols, - "raw_value": raw_values, - "weighted_value": weighted_values, - }) - - -def get_household_targets( - X_csc: sp.csc_matrix, - household_idx: int, -) -> np.ndarray: - """Return target indices this household contributes to (CSC column slice).""" - return X_csc[:, household_idx].nonzero()[0] - - -def get_household_attributions( - X_csc: sp.csc_matrix, - household_idx: int, - final_weights: np.ndarray, -) -> pd.DataFrame: - """Return targets this household contributes to, with contribution values.""" - col = X_csc[:, household_idx] - rows = col.nonzero()[0] - raw_values = np.array(col[rows, :].todense()).flatten() - weighted_values = raw_values * final_weights[household_idx] - return pd.DataFrame({ - "target_idx": rows, - "raw_value": raw_values, - "weighted_value": weighted_values, - }) - - -def compute_error_decomposition( - X_csr: sp.csr_matrix, - target_idx: int, - target_value: float, - initial_weights: np.ndarray, - final_weights: np.ndarray, -) -> dict: - """Three-number decomposition: raw_sum, initial_est, final_est vs target.""" - row = X_csr[target_idx, :] - raw_sum = float(row.sum()) - initial_est = float(row.dot(initial_weights)) - final_est = float(row.dot(final_weights)) - - if target_value != 0: - raw_ratio = raw_sum / target_value - if abs(raw_ratio - 1) > 0.5: - diagnosis = ( - f"raw_sum is {(raw_ratio - 1):+.0%} off target — " - "variable values or coverage wrong in source data" - ) - elif abs(initial_est / target_value - 1) < 0.15 and abs( - final_est / target_value - 1 - ) > 0.15: - diagnosis = ( - "Initial weights close to target but final drifted — " - "other constraints pulled weights away" - ) - else: - diagnosis = "See raw_sum, initial_estimate, final_estimate for details" - else: - diagnosis = "Target value is zero" - - return { - "target_value": target_value, - "raw_sum": raw_sum, - "initial_estimate": initial_est, - "final_estimate": final_est, - "diagnosis": diagnosis, - } - - -def compute_concentration( - X_csr: sp.csr_matrix, - target_idx: int, - final_weights: np.ndarray, -) -> dict: - """Top 1% and 5% weighted contribution share for a target.""" - row = X_csr[target_idx, :] - cols = row.nonzero()[1] - if len(cols) == 0: - return {"top_1pct_share": 0.0, "top_5pct_share": 0.0} - - raw_values = np.array(row[:, cols].todense()).flatten() - contributions = raw_values * final_weights[cols] - total = contributions.sum() - if total == 0: - return {"top_1pct_share": 0.0, "top_5pct_share": 0.0} - - sorted_contrib = np.sort(contributions)[::-1] - n = len(sorted_contrib) - top_1 = sorted_contrib[: max(1, n // 100)].sum() / total - top_5 = sorted_contrib[: max(1, n // 20)].sum() / total - return {"top_1pct_share": float(top_1), "top_5pct_share": float(top_5)} diff --git a/backend/services/registry.py b/backend/services/registry.py deleted file mode 100644 index 54fe0a3..0000000 --- a/backend/services/registry.py +++ /dev/null @@ -1,61 +0,0 @@ -"""LRU registry of loaded calibration runs. - -Each (dataset_id, run_id) maps to a fully-loaded AppState. Loading is -expensive (~30s + memory for sparse matrices + Microsimulation), so we -cache a small number and evict in LRU order. -""" - -from __future__ import annotations - -import logging -import threading -from collections import OrderedDict - -from backend.services import runs as runs_service -from backend.state import AppState - -logger = logging.getLogger(__name__) - - -class RunRegistry: - """Bounded LRU cache of loaded AppStates, keyed by (dataset_id, run_id).""" - - def __init__(self, max_size: int = 3): - self.max_size = max_size - self._cache: OrderedDict[tuple[str, str], AppState] = OrderedDict() - # Coarse lock: prevent two concurrent loads of the same run from - # racing. Acceptable since loads are infrequent. - self._lock = threading.Lock() - - def get(self, dataset_id: str, run_id: str) -> AppState: - key = (dataset_id, run_id) - with self._lock: - if key in self._cache: - self._cache.move_to_end(key) - return self._cache[key] - dataset = runs_service.get_dataset(dataset_id) - logger.info( - "Loading run %s/%s (layout=%s, cache miss; %d/%d loaded)", - dataset_id, run_id, dataset.layout, - len(self._cache), self.max_size, - ) - if dataset.layout in {"staging", "root", "staging-root"}: - from backend.services.dataset_loader import ( - load_run_from_dataset, - ) - state = load_run_from_dataset(dataset, run_id) - else: - from backend.services.loader import load_run # lazy: heavy deps - state = load_run( - dataset.repo_id, dataset.repo_type, run_id, - dataset_id=dataset_id, - ) - self._cache[key] = state - while len(self._cache) > self.max_size: - evicted_key, _ = self._cache.popitem(last=False) - logger.info("Evicted run %s/%s from cache", *evicted_key) - return state - - def loaded_keys(self) -> list[tuple[str, str]]: - with self._lock: - return list(self._cache.keys()) diff --git a/backend/services/runs.py b/backend/services/runs.py deleted file mode 100644 index ed920bb..0000000 --- a/backend/services/runs.py +++ /dev/null @@ -1,297 +0,0 @@ -"""Discovery of available calibration runs on HuggingFace. - -A "dataset" is a HuggingFace repository plus a layout convention. A "run" is -one published calibration build whose artifacts live under some prefix in the -repo. Four layouts are supported today: - -- **flat**: repo// (the legacy sandbox repo) -- **staging**: repo/staging// (the canonical us-data repo) -- **root**: repo/ (current production files) -- **staging-root**: repo/staging/ (current staging snapshot) - -Each layout declares which artifact filenames must be present for a candidate -prefix to count as a real run, since the two repos publish different files. -""" - -from __future__ import annotations - -import logging -import os -from dataclasses import dataclass -from functools import lru_cache - -from huggingface_hub import HfApi - -logger = logging.getLogger(__name__) - -# Default required files per layout. Override per-dataset if a particular -# repo publishes a different file set. -DEFAULT_REQUIRED_FILES = { - "flat": ("calibration_package.pkl", "calibration_weights.npy"), - "staging": ("policy_data.db",), # at least the targets DB; an .h5 picked at load time - "root": ("policy_data.db",), - "staging-root": ("policy_data.db",), -} - -CURRENT_STAGING_RUN_ID = "staging" - - -@dataclass(frozen=True) -class DatasetConfig: - """A dataset = an HF repo plus a layout convention.""" - - id: str - label: str - repo_id: str - repo_type: str = "model" - layout: str = "flat" # flat, staging, root, staging-root - # Files that must exist under a prefix for it to be considered a run. - required_files: tuple[str, ...] = () - # For the staging layout: which h5 dataset to load when this dataset is - # selected. The pipeline publishes several (cps, enhanced_cps, ...). - primary_h5: str = "enhanced_cps_2024.h5" - - def effective_required_files(self) -> tuple[str, ...]: - if self.required_files: - return self.required_files - defaults = DEFAULT_REQUIRED_FILES.get(self.layout, ()) - # Dataset layouts also require the configured primary h5 to be present. - if self.layout in {"staging", "root", "staging-root"} and self.primary_h5: - return defaults + (self.primary_h5,) - return defaults - - -DEFAULT_DATASETS: list[DatasetConfig] = [ - # us-cps (sandbox / pkl mode) was retired once the canonical us-data - # publication started shipping unified_diagnostics.csv with full target - # coverage. Re-add it here if you need to compare against a pkl-snapshot - # run; the flat-layout loader is still wired up. - DatasetConfig( - id="us-data", - label="US Data - Enhanced CPS", - repo_id="PolicyEngine/policyengine-us-data", - layout="staging", - primary_h5="enhanced_cps_2024.h5", - ), - DatasetConfig( - id="us-data-production", - label="US Data - Production Enhanced CPS", - repo_id="PolicyEngine/policyengine-us-data", - layout="root", - primary_h5="enhanced_cps_2024.h5", - ), - DatasetConfig( - id="us-data-current-staging", - label="US Data - Current Staging", - repo_id="PolicyEngine/policyengine-us-data", - layout="staging-root", - primary_h5="source_imputed_stratified_extended_cps.h5", - ), - DatasetConfig( - id="us-data-cps", - label="US Data - CPS", - repo_id="PolicyEngine/policyengine-us-data", - layout="staging", - primary_h5="cps_2024.h5", - ), - DatasetConfig( - id="us-data-small-enhanced-cps", - label="US Data - Small Enhanced CPS", - repo_id="PolicyEngine/policyengine-us-data", - layout="staging", - primary_h5="small_enhanced_cps_2024.h5", - ), -] - - -@dataclass(frozen=True) -class RunInfo: - dataset_id: str - run_id: str # the prefix path within the repo - label: str - last_modified: str | None = None - - -def list_datasets() -> list[DatasetConfig]: - return list(DEFAULT_DATASETS) - - -def get_dataset(dataset_id: str) -> DatasetConfig: - for d in DEFAULT_DATASETS: - if d.id == dataset_id: - return d - raise KeyError(f"Unknown dataset_id: {dataset_id}") - - -def _group_flat(files: list[str]) -> dict[str, set[str]]: - """For flat layout: prefix = first path segment, files = direct children.""" - by_prefix: dict[str, set[str]] = {} - for path in files: - if "/" not in path: - continue - prefix, _, rest = path.partition("/") - if "/" in rest: # only files directly under / - continue - by_prefix.setdefault(prefix, set()).add(rest) - return by_prefix - - -def _group_staging(files: list[str]) -> dict[str, set[str]]: - """For staging layout, group files by run. - - Supports two artifact shapes the us-data team uses interchangeably: - - 1. **GHA / flat staging**: ``staging//policy_data.db``, - ``staging//enhanced_cps_2024.h5`` at the run root. - 2. **Versioned-release nested**: ``staging//calibration/policy_data.db`` - and ``staging//datasets/enhanced_cps_2024.h5``. - - Both layouts get the *same* set of logical filenames so the - discovery + required-files check in :func:`list_runs` works - uniformly. The actual on-disk download location is resolved later - by :func:`_resolve_staging_file_paths`. - """ - by_prefix: dict[str, set[str]] = {} - for path in files: - if not path.startswith("staging/"): - continue - parts = path.split("/", 2) - if len(parts) < 3: - continue - run_id = parts[1] - rest = parts[2] - if "/" not in rest: - # Flat staging — file sits at the run root. - by_prefix.setdefault(run_id, set()).add(rest) - continue - # Nested staging — recognise the locations the pipeline uses. - head, _, tail = rest.partition("/") - if head == "calibration" and tail == "policy_data.db": - by_prefix.setdefault(run_id, set()).add("policy_data.db") - elif head == "datasets" and tail.endswith(".h5") and "/" not in tail: - by_prefix.setdefault(run_id, set()).add(tail) - return by_prefix - - -def _group_staging_root(files: list[str]) -> dict[str, set[str]]: - """Expose the top-level ``staging/`` snapshot as one synthetic run.""" - names: set[str] = set() - for path in files: - if path == "staging/calibration/policy_data.db": - names.add("policy_data.db") - continue - if ( - path.startswith("staging/calibration/") - and path.endswith(".h5") - and path.count("/") == 2 - ): - names.add(path.rsplit("/", 1)[-1]) - return {CURRENT_STAGING_RUN_ID: names} if names else {} - - -def _resolve_staging_file_paths( - repo_id: str, - run_id: str, - logical_names: list[str], -) -> dict[str, str]: - """Map each logical filename (e.g. ``policy_data.db``, ``enhanced_cps_2024.h5``) - to its actual path on HF, probing both flat and nested layouts. - - Returns only entries that were found; callers should treat a missing - key as 'this run doesn't publish that file'. - """ - api = HfApi() - files = set(api.list_repo_files(repo_id, repo_type="model")) - prefix = f"staging/{run_id}" - resolved: dict[str, str] = {} - candidates: dict[str, list[str]] = { - "policy_data.db": [ - f"{prefix}/policy_data.db", - f"{prefix}/calibration/policy_data.db", - ], - } - for ln in logical_names: - for cand in candidates.get(ln, [f"{prefix}/{ln}", f"{prefix}/datasets/{ln}"]): - if cand in files: - resolved[ln] = cand - break - return resolved - - -def _resolve_staging_root_file_paths( - repo_id: str, - logical_names: list[str], -) -> dict[str, str]: - """Resolve logical artifact names in the current top-level staging snapshot.""" - api = HfApi() - files = set(api.list_repo_files(repo_id, repo_type="model")) - resolved: dict[str, str] = {} - candidates: dict[str, list[str]] = { - "policy_data.db": ["staging/calibration/policy_data.db"], - } - for name in logical_names: - for candidate in candidates.get(name, [f"staging/calibration/{name}"]): - if candidate in files: - resolved[name] = candidate - break - return resolved - - -@lru_cache(maxsize=8) -def list_runs(dataset_id: str) -> tuple[RunInfo, ...]: - """Discover runs for a dataset. Cached; restart to refresh from HF.""" - dataset = get_dataset(dataset_id) - api = HfApi() - try: - files = api.list_repo_files( - repo_id=dataset.repo_id, repo_type=dataset.repo_type - ) - except Exception: - logger.exception("Failed to list HF repo %s", dataset.repo_id) - return () - - if dataset.layout == "flat": - by_prefix = _group_flat(files) - elif dataset.layout == "staging": - by_prefix = _group_staging(files) - elif dataset.layout == "root": - by_prefix = {"main": {path for path in files if "/" not in path}} - elif dataset.layout == "staging-root": - by_prefix = _group_staging_root(files) - else: - logger.error("Unknown layout %r for dataset %s", dataset.layout, dataset_id) - return () - - required = dataset.effective_required_files() - runs: list[RunInfo] = [] - for prefix, names in sorted(by_prefix.items(), reverse=True): - if not all(req in names for req in required): - continue - runs.append( - RunInfo( - dataset_id=dataset_id, - run_id=prefix, - label="Current staging" if dataset.layout == "staging-root" else prefix, - last_modified=None, # cheap; populate lazily if needed - ) - ) - return tuple(runs) - - -def storage_prefix(dataset: DatasetConfig, run_id: str) -> str: - """The path prefix within the repo where this run's files live.""" - if dataset.layout == "staging": - return f"staging/{run_id}" - if dataset.layout == "staging-root": - return "staging" - if dataset.layout == "root": - return "" - return run_id - - -def default_selection() -> tuple[str, str] | None: - ds = os.environ.get("DEFAULT_DATASET") - run = os.environ.get("DEFAULT_RUN") - if ds and run: - return ds, run - return None diff --git a/backend/services/sim_service.py b/backend/services/sim_service.py deleted file mode 100644 index bf49496..0000000 --- a/backend/services/sim_service.py +++ /dev/null @@ -1,63 +0,0 @@ -"""Wrapper around Microsimulation.calculate() with result caching. - -All results are tiled to clone-level length so they align with -X_sparse columns, weights, and cd_geoid arrays. -""" - -from typing import Any - -import numpy as np - - -class SimService: - """Caches sim.calculate() results tiled to clone-level.""" - - def __init__(self, sim: Any, time_period: int, n_clones: int) -> None: - self._sim = sim - self._time_period = time_period - self._n_clones = n_clones - self._cache: dict[str, np.ndarray] = {} - - def calculate( - self, - variable: str, - map_to: str = "household", - ) -> np.ndarray: - """Compute a variable and tile to clone-level length. - - The Microsimulation returns one value per base household. - This method tiles the result n_clones times so it aligns - with the calibration package arrays (n_records * n_clones). - """ - key = f"{variable}:{map_to}" - if key not in self._cache: - base_values = ( - self._sim.calculate(variable, self._time_period, map_to=map_to) - .values.astype(np.float64) - ) - self._cache[key] = np.tile(base_values, self._n_clones) - return self._cache[key] - - def get_variable_entity(self, variable: str) -> str: - """Return the native entity key for a variable.""" - var_def = self._sim.tax_benefit_system.variables.get(variable) - if var_def is None: - raise ValueError(f"Unknown variable: {variable}") - return var_def.entity.key - - def get_formula_dependencies(self, variable: str) -> list[str]: - """Return the variable's adds/subtracts dependencies.""" - var_def = self._sim.tax_benefit_system.variables.get(variable) - if var_def is None: - return [] - deps: list[str] = [] - adds = getattr(var_def, "adds", None) - subtracts = getattr(var_def, "subtracts", None) - if isinstance(adds, list): - deps.extend(adds) - if isinstance(subtracts, list): - deps.extend(subtracts) - return deps - - def variable_exists(self, variable: str) -> bool: - return variable in self._sim.tax_benefit_system.variables diff --git a/backend/services/stratum_evaluator.py b/backend/services/stratum_evaluator.py deleted file mode 100644 index ec465c1..0000000 --- a/backend/services/stratum_evaluator.py +++ /dev/null @@ -1,510 +0,0 @@ -"""Compute target estimates from a calibrated dataset (no X matrix). - -Two scopes: - -1. `evaluate_targets()` — historical entry point used by the dataset-mode - loader. Handles geographic-only constraints across a pandas DataFrame. - -2. `evaluate_signature()` — single-target evaluator used by the inventory - endpoint to fill PE aggregates for authored-only rows. Adds support for - any constraint variable that PolicyEngine can map to the household - entity (e.g. `snap > 0`, `medicaid_enrolled == 1`, - `tax_unit_is_filer == 1`), and treats `is_count` targets as weighted - counts of matching households rather than weighted variable sums. - -Out of scope (both functions): person-level constraints whose meaning -breaks under household aggregation (e.g. `age < 5` to count under-5 -individuals — household-mapping sums ages, not bodies). The pipeline's -`UnifiedMatrixBuilder` handles those correctly via entity mapping. -""" - -from __future__ import annotations - -import logging -import operator as op_module - -import numpy as np -import pandas as pd - -logger = logging.getLogger(__name__) - - -_OPS = { - "==": op_module.eq, "!=": op_module.ne, - ">": op_module.gt, ">=": op_module.ge, - "<": op_module.lt, "<=": op_module.le, -} - - -def _apply_op(op_str: str, arr, cval): - """Apply a comparison operator, handling the multi-value `in` case. - - SOI-style constraints encode multi-value filters as - ``filing_status in JOINT|SURVIVING_SPOUSE`` (pipe-delimited). Our - standard _OPS dict only covers scalar comparisons, so handle `in` - explicitly here. - """ - if op_str == "in": - choices = [c.strip() for c in str(cval).split("|") if c.strip()] - # Try numeric coercion of choices; fall back to string compare. - try: - numeric = [float(c) for c in choices] - return np.isin(arr, numeric) - except (TypeError, ValueError): - return np.isin(np.asarray(arr).astype(str), choices) - op = _OPS.get(op_str) - if op is None: - raise ValueError(f"unknown operator {op_str!r}") - try: - target_val = float(cval) - except (TypeError, ValueError): - target_val = cval - return op(arr, target_val) - -# Constraint variables whose household-mapping makes the mask meaningless -# (person-level continuous values like age). evaluate_signature() refuses -# these only in the household-level path; the entity-aware path -# (_evaluate_at_entity) handles them correctly by working at the variable's -# native entity. -_ENTITY_ONLY_VARS = { - "age", - "person_id", - "spm_unit_id", - "tax_unit_id", - "family_id", -} - - -_NON_HOUSEHOLD_ENTITIES = ("person", "tax_unit", "spm_unit", "family", "marital_unit") - - -def _tbs_from(sim) -> "object | None": - """Return the TBS from either a SimService wrapper or a raw Microsim.""" - if sim is None: - return None - return getattr(getattr(sim, "_sim", sim), "tax_benefit_system", None) - - -def _variable_entity(sim, variable: str) -> str | None: - tbs = _tbs_from(sim) - if tbs is None: - return None - v = tbs.variables.get(variable) - return v.entity.key if v is not None else None - - -def _calculate_at(sim, variable: str, entity: str, period: int): - """Compute a variable mapped to a specific entity and return the - MicroSeries (carries both ``.values`` and ``.weights`` at that entity). - - Supports both the SimService wrapper (pkl mode) and raw Microsim - (dataset mode). Returns None on failure. - """ - target_sim = getattr(sim, "_sim", sim) - try: - return target_sim.calculate(variable, map_to=entity, period=period) - except Exception as exc: - logger.debug("calculate(%s, map_to=%s) failed: %s", variable, entity, exc) - return None - - -def _is_geographic_only(constraints: list[str]) -> bool: - """A row is 'geographic-only' if all constraints are state/district.""" - if not constraints: - return True - geo_prefixes = ( - "state_fips ", "congressional_district_geoid ", "ucgid_str ", - ) - return all(c.startswith(geo_prefixes) for c in constraints) - - -def _evaluate_variable(sim, variable: str, period: int) -> np.ndarray | None: - """Calculate a variable at household level, weighted. Returns the weighted - values per household, or None if the variable can't be evaluated.""" - try: - series = sim.calculate(variable, map_to="household", period=period) - except Exception as exc: - logger.debug("Could not calculate %s for %s: %s", variable, period, exc) - return None - # MicroSeries × weights = weighted values per household - return series.values * series.weights - - -class EvalCache: - """Holds per-state-of-the-world caches across many evaluate_signature - calls. Built from the loaded AppState so it transparently supports both - the sandbox path (SimService — tiled to clone-level) and the dataset - path (raw Microsimulation — MicroSeries with weights). - """ - - def __init__(self, state): - self.state = state - self.period = int(state.time_period) - self._raw: dict[tuple[str, int], np.ndarray | None] = {} - self._weights: np.ndarray | None = None - - # --- Internal: source of truth for one variable, evaluated to a 1-D - # per-household array aligned with `weights()` below. - def _calculate(self, variable: str) -> np.ndarray | None: - svc = self.state.sim_service - if svc is None: - return None - try: - if hasattr(svc, "_sim"): # SimService wrapper (sandbox path) - return np.asarray(svc.calculate(variable, map_to="household")) - # Otherwise treat as raw Microsimulation - series = svc.calculate(variable, map_to="household", period=self.period) - if self._weights is None and hasattr(series, "weights"): - self._weights = np.asarray(series.weights) - return np.asarray(series.values) - except Exception as exc: - logger.debug("calculate(%s) failed: %s", variable, exc) - return None - - def raw(self, variable: str, period: int | None = None) -> np.ndarray | None: - """Per-household raw values for `variable`. period is informational - only — the underlying sim is fixed to one time period for now.""" - key = (variable, period or self.period) - if key not in self._raw: - self._raw[key] = self._calculate(variable) - return self._raw[key] - - def weighted(self, variable: str, period: int | None = None) -> np.ndarray | None: - """Per-household weighted values: raw * household_weight.""" - arr = self.raw(variable, period) - w = self.weights() - if arr is None or w is None or len(arr) != len(w): - return None - return arr * w - - def weights(self) -> np.ndarray | None: - # The loaded AppState already has the calibrated final_weights tiled. - w = getattr(self.state, "final_weights", None) - if w is not None and len(w) > 0: - return np.asarray(w) - if self._weights is not None: - return self._weights - return self.raw("household_weight") - - def state_fips(self) -> np.ndarray | None: - return self.raw("state_fips") - - def cd_geoid(self) -> np.ndarray | None: - return self.raw("congressional_district_geoid") - - -def _evaluate_at_entity( - variable: str, - geo_level: str | None, - geographic_id: str | None, - constraints, - is_count: bool, - sim, - entity: str, - period: int, -) -> tuple[float | None, str]: - """Entity-aware evaluation: works at the target variable's native - entity (person / tax_unit / spm_unit / family) rather than household. - - Required for targets like population counts with age-range constraints, - or SOI tax-unit aggregates with filing_status / AGI bracket constraints - — household-level evaluation gets these wrong because the constraint - can't be meaningfully aggregated to household. - """ - # Per-entity MicroSeries cache: name → MicroSeries - series_cache: dict[str, object] = {} - - def calc(v: str): - if v not in series_cache: - series_cache[v] = _calculate_at(sim, v, entity, period) - return series_cache[v] - - target_series = calc(variable) - if target_series is None: - return None, f"target {variable!r} not evaluable at entity={entity}" - target_vals = np.asarray(target_series.values) - target_weights = np.asarray(target_series.weights) - n = len(target_vals) - - # Geo mask at this entity. PE projects state_fips / cd_geoid from - # household down to person / tax_unit / spm_unit automatically. - if geo_level in (None, "national"): - mask = np.ones(n, dtype=bool) - elif geo_level == "state": - sf_s = calc("state_fips") - if sf_s is None: - return None, f"state_fips not evaluable at {entity}" - sf = np.asarray(sf_s.values) - try: - mask = sf == int(geographic_id) - except (TypeError, ValueError): - mask = sf.astype(str) == str(geographic_id) - elif geo_level == "district": - cd_s = calc("congressional_district_geoid") - if cd_s is None: - return None, f"cd_geoid not evaluable at {entity}" - cd = np.asarray(cd_s.values) - try: - mask = cd.astype(int) == int(geographic_id) - except (TypeError, ValueError): - mask = cd.astype(str) == str(geographic_id) - else: - return None, f"geo_level={geo_level!r} not supported" - - # Constraint masks at this entity - for cvar, op_str, cval in constraints or (): - c_s = calc(cvar) - if c_s is None: - return None, f"constraint {cvar!r} not evaluable at {entity}" - arr = np.asarray(c_s.values) - try: - m = _apply_op(op_str, arr, cval) - except Exception as exc: - return None, f"could not evaluate {cvar} {op_str} {cval}: {exc}" - mask = mask & m - - # Empty-mask: the PE dataset has zero records matching this slice - # (common at district × multi-filter combinations). Return None instead - # of 0 so the UI can distinguish "no data to estimate" from "PE estimate - # is genuinely zero." A spurious 0 here looks like a -100% error vs the - # target and pollutes the ranked views. - if not mask.any(): - return None, f"no records match constraints at entity={entity}" - - if is_count: - return ( - float(target_weights[mask].sum()), - f"count at entity={entity}", - ) - return ( - float((target_vals[mask] * target_weights[mask]).sum()), - f"dollar at entity={entity}", - ) - - -def evaluate_signature( - variable: str, - geo_level: str | None, - geographic_id: str | None, - constraints: list[tuple[str, str, str]] | tuple, - is_count: bool, - cache: EvalCache, - period: int | None = None, -) -> tuple[float | None, str]: - """Single-target evaluator. Returns (estimate, eval_note). - - Routes to the entity-aware path automatically when the target - variable's native entity is not household, or when any constraint is - on a variable whose household-mapping would be wrong (age, etc.). - """ - period = period or cache.period - - # Decide working entity: target variable's entity, unless a constraint - # forces a more-granular entity. Default to household when TBS lookup - # is unavailable so behavior matches the legacy path. - sim = cache.state.sim_service - target_entity = _variable_entity(sim, variable) or "household" - forced_person = any( - (c[0] if isinstance(c, (list, tuple)) else "") in _ENTITY_ONLY_VARS - for c in (constraints or ()) - ) - if forced_person and target_entity == "household": - target_entity = "person" - - if target_entity in _NON_HOUSEHOLD_ENTITIES: - return _evaluate_at_entity( - variable, geo_level, geographic_id, constraints, - is_count, sim, target_entity, period, - ) - - # --- Household-level path (legacy / fast) --- - # --- Geographic mask --- - if geo_level == "national" or geo_level is None: - geo_mask = None # full set - elif geo_level == "state": - sf = cache.state_fips() - if sf is None: - return None, "state_fips not evaluable on dataset" - try: - geo_mask = sf == int(geographic_id) - except (TypeError, ValueError): - geo_mask = sf.astype(str) == str(geographic_id) - elif geo_level == "district": - cd = cache.cd_geoid() - if cd is None: - return None, "congressional_district_geoid not evaluable on dataset" - try: - geo_mask = cd.astype(int) == int(geographic_id) - except (TypeError, ValueError): - geo_mask = cd.astype(str) == str(geographic_id) - else: - return None, f"geo_level={geo_level!r} not supported" - - # --- Non-geographic constraint masks --- - constraint_mask = None - for cvar, op_str, cval in constraints or (): - if cvar in _ENTITY_ONLY_VARS: - return None, f"constraint on {cvar} requires entity-level evaluation" - arr = cache.raw(cvar, period) - if arr is None: - return None, f"constraint variable {cvar!r} not available" - try: - m = _apply_op(op_str, arr, cval) - except Exception as exc: - return None, f"could not evaluate {cvar} {op_str} {cval}: {exc}" - constraint_mask = m if constraint_mask is None else (constraint_mask & m) - - # Combine masks - if geo_mask is None and constraint_mask is None: - combined = None # full set - elif geo_mask is None: - combined = constraint_mask - elif constraint_mask is None: - combined = geo_mask - else: - combined = geo_mask & constraint_mask - - # Empty-mask: see notes in _evaluate_at_entity — return None, not 0, so - # "no records match" reads as missing rather than a real estimate. - if combined is not None and not combined.any(): - return None, "no records match constraints at entity=household" - - # --- Aggregate --- - if is_count: - weights = cache.weights() - if weights is None: - return None, "household_weight not evaluable" - if combined is None: - return float(weights.sum()), "count: full population" - return float(weights[combined].sum()), "count: weighted household sum where mask" - else: - weighted = cache.weighted(variable, period) - if weighted is None: - return None, f"target variable {variable!r} not available" - if combined is None: - return float(weighted.sum()), "dollar: full population" - return float(weighted[combined].sum()), "dollar: weighted sum where mask" - - -def _parse_constraint(c) -> tuple[str, str, str] | None: - """Coerce one constraint into a (var, op, value) tuple. - - Loader rows store constraints as either pre-parsed tuples or strings of - the form ``"var op value"`` (e.g. ``"filing_status == JOINT"``). We need - both forms because the diagnostics-CSV join and the DB load take - different paths into the same dataframe. - """ - if isinstance(c, (list, tuple)) and len(c) >= 3: - return str(c[0]), str(c[1]), str(c[2]) - s = str(c).strip() - # Order matters: longer operators first so "==" beats "=", " in " stays - # word-bounded so it doesn't match substrings inside a variable name. - for op, sep in ( - ("==", "=="), ("!=", "!="), (">=", ">="), ("<=", "<="), - (">", ">"), ("<", "<"), - ("in", " in "), - ): - idx = s.find(f" {sep.strip()} ") if op != "in" else s.find(sep) - if idx == -1 and op != "in": - # Allow no-space form for scalar ops (e.g. "x>=5") - idx = s.find(op) - if idx <= 0: - continue - if idx == -1: - continue - left = s[:idx].strip() - # For 'in', sep includes the surrounding spaces. - skip = len(sep) if op == "in" else len(op) - right = s[idx + skip:].strip().lstrip(" ").lstrip(op).strip() - if left: - return left, op, right - return None - - -def _looks_like_count_target(variable: str) -> bool: - """Detect count-style targets by naming convention. - - PE doesn't expose a "is this a population count" flag we can read off - the variable, so we fall back to the convention that population counts - end in ``_count`` (e.g. ``household_count``, ``tax_unit_count``). For - these, we want a weighted sum of the boolean mask, not of the variable - itself. - """ - return variable.endswith("_count") - - -def evaluate_targets( - targets_df: pd.DataFrame, - sim, - default_period: int = 2024, -) -> pd.DataFrame: - """Fill estimate / rel_error / abs_rel_error for as many rows as we can. - - Uses ``evaluate_signature`` so constraint targets (filing_status, - income brackets, has_children, etc.) get evaluated against the loaded - Microsimulation, not just geographic aggregates. Anything that's still - unevaluable (person-level continuous constraints, missing variables) - keeps a NaN estimate and an explanatory ``eval_note``. - """ - from types import SimpleNamespace - - out = targets_df.copy() - out["eval_note"] = "" - - # EvalCache expects a state-like object exposing sim_service + - # final_weights + time_period. The pkl-mode loader passes its real - # AppState; the dataset-mode loader passes a raw Microsimulation, so we - # synthesize a minimal one here. - state_like = SimpleNamespace( - sim_service=sim, - final_weights=np.array([]), - time_period=default_period, - ) - cache = EvalCache(state_like) - - estimates = np.full(len(out), np.nan, dtype=np.float64) - notes = np.array([""] * len(out), dtype=object) - - for i, (_, row) in enumerate(out.iterrows()): - variable = row["variable"] - # Ignore row["period"] — that's metadata about the source year of - # target_value (e.g. SOI 2022). The PE estimate must be at the - # dataset's period (2024 for the current h5); asking PE for any - # other year silently returns zeros instead of erroring. - period = default_period - geo_level = row.get("geo_level") - gid = row.get("geographic_id") - raw_constraints = row.get("constraints") or [] - parsed = [_parse_constraint(c) for c in raw_constraints] - if any(p is None for p in parsed): - notes[i] = f"unparseable constraint in {raw_constraints!r}" - continue - constraints = [c for c in parsed if c is not None] - is_count = _looks_like_count_target(variable) - - est, note = evaluate_signature( - variable=variable, - geo_level=geo_level, - geographic_id=gid, - constraints=constraints, - is_count=is_count, - cache=cache, - period=period, - ) - if est is None: - notes[i] = note - continue - estimates[i] = est - - out["estimate"] = estimates - out["eval_note"] = notes - target_values = out["value"].to_numpy(dtype=np.float64) - with np.errstate(divide="ignore", invalid="ignore"): - rel = np.where( - np.abs(target_values) > 0, - (estimates - target_values) / np.abs(target_values), - np.nan, - ) - out["rel_error"] = rel - out["abs_rel_error"] = np.abs(rel) - return out diff --git a/frontend/app/analysis/page.tsx b/frontend/app/analysis/page.tsx deleted file mode 100644 index fa9e936..0000000 --- a/frontend/app/analysis/page.tsx +++ /dev/null @@ -1,582 +0,0 @@ -"use client"; - -import { useEffect, useMemo, useState } from "react"; -import { - Badge, - Card, - CardContent, - CardHeader, - CardTitle, - Spinner, - Stack, - Text, - Title, - formatNumber, -} from "@policyengine/ui-kit"; - -import { AppShell } from "@/components/layout/app-shell"; -import { - useDomainBreakdown, - useDependencyTrace, - usePolicyEngineVariables, - useTargetConfigAudit, - type DomainBreakdownRow, - type DependencyNode, - type PolicyEngineVariable, -} from "@/lib/api/hooks/use-analysis"; - -function pct(value: number | null | undefined, digits = 1): string { - if (value === null || value === undefined || !Number.isFinite(value)) { - return "—"; - } - return `${(value * 100).toFixed(digits)}%`; -} - -function num(value: number | null | undefined): string { - if (value === null || value === undefined || !Number.isFinite(value)) { - return "—"; - } - return formatNumber(value); -} - -function Kpi({ - label, - value, - hint, -}: { - label: string; - value: string; - hint?: string; -}) { - return ( -
- - {label} - -
{value}
- {hint && ( - - {hint} - - )} -
- ); -} - -function InlineLoading({ label }: { label: string }) { - return ( -
- - {label} -
- ); -} - -function RuleText({ rule }: { rule: Record }) { - return ( - - {Object.entries(rule) - .map(([key, value]) => `${key}=${value}`) - .join(", ")} - - ); -} - -function TargetConfigAuditPanel({ variable }: { variable: string | null }) { - const audit = useTargetConfigAudit(variable); - const zeroRules = useMemo( - () => audit.data?.rules.filter((r) => r.status === "zero_match") ?? [], - [audit.data], - ); - - return ( - - -
- Target config audit - {variable && {variable}} -
- - - {!variable && ( - - Select a variable to audit matching target-config rules. - - )} - {audit.isLoading && ( - - )} - {audit.error && ( - Failed to load audit: {String(audit.error)} - )} - {audit.data && ( - -
- - - - -
- {audit.data.rules.length === 0 && ( - - No target-config rules matched this variable's target or domain - rows. - - )} - {zeroRules.length > 0 && ( -
- - - - - - - - - - {zeroRules.slice(0, 20).map((rule) => ( - - - - - - ))} - -
SectionIndexRule
{rule.section}{rule.index} - -
-
- )} - - )} - - - ); -} - -function DomainBreakdownPanel({ variable }: { variable: string | null }) { - const [domainVariable, setDomainVariable] = useState( - "adjusted_gross_income", - ); - const breakdown = useDomainBreakdown(variable, domainVariable); - - return ( - - -
- Domain breakdown - {variable && {variable}} -
- - -
- setDomainVariable(e.target.value)} - placeholder="Domain variable, e.g. adjusted_gross_income" - className="h-10 min-w-[260px] flex-1 rounded-md border border-border bg-background px-3 text-sm" - /> -
- {!variable && ( - - Showing calibration targets grouped by the selected domain variable. - Select a target variable to narrow the breakdown. - - )} - {breakdown.isLoading && ( - - )} - {breakdown.error && ( - - Failed to load domain breakdown: {String(breakdown.error)} - - )} - {breakdown.data && ( - -
- - - - -
- {breakdown.data.rows.length === 0 ? ( - - No targets found for this domain variable and selected output. - - ) : ( -
- - - - - - - - - - - - - - {breakdown.data.rows.map((row: DomainBreakdownRow) => ( - - - - - - - - - - ))} - -
Domain bucketTargetsIncludedEvaluatedMedian errorMax errorGeo
{row.bucket} - {num(row.target_count)} - - {num(row.included_target_count)} - - {num(row.evaluated_target_count)} - - {pct(row.median_abs_rel_error)} - - {pct(row.max_abs_rel_error)} - {row.geo_levels.join(", ")}
-
- )} - - )} - - - ); -} - -function nodeStatus(node: DependencyNode): string { - if (node.is_target_variable) return "target"; - if (node.is_domain_variable) return "domain"; - if (node.is_stored_input) return "stored"; - if (node.is_formula) return "formula"; - if (node.is_aggregate) return "aggregate"; - return "other"; -} - -function targetRole(node: DependencyNode): string { - if (node.direct_target_count > 0 && node.domain_target_count > 0) { - return "direct + domain"; - } - if (node.direct_target_count > 0) return "direct"; - if (node.domain_target_count > 0) return "domain"; - return "none"; -} - -function DependencyPanel({ - variable, - onVariableChange, -}: { - variable: string | null; - onVariableChange: (variable: string | null) => void; -}) { - const [search, setSearch] = useState(""); - const [nodeSearch, setNodeSearch] = useState(""); - const [nodeScope, setNodeScope] = useState("all"); - const [kindFilter, setKindFilter] = useState("all"); - const [targetFilter, setTargetFilter] = useState("all"); - const variableCatalog = usePolicyEngineVariables(search); - const trace = useDependencyTrace(variable); - useEffect(() => { - setNodeSearch(""); - setNodeScope("all"); - setKindFilter("all"); - setTargetFilter("all"); - }, [variable]); - const variableOptions = useMemo(() => { - const items = variableCatalog.data?.items ?? []; - const scored = [...items].sort((a, b) => { - const aTarget = a.is_target_variable || a.is_domain_variable ? 0 : 1; - const bTarget = b.is_target_variable || b.is_domain_variable ? 0 : 1; - if (aTarget !== bTarget) return aTarget - bTarget; - return a.name.localeCompare(b.name); - }); - return scored.slice(0, 80); - }, [variableCatalog.data]); - const filteredNodes = useMemo(() => { - const nodes = trace.data?.nodes ?? []; - const q = nodeSearch.trim().toLowerCase(); - return nodes.filter((node) => { - const status = nodeStatus(node); - if (nodeScope === "inputs" && !node.is_leaf && !node.is_stored_input) { - return false; - } - if (nodeScope === "non_inputs" && (node.is_leaf || node.is_stored_input)) { - return false; - } - if (kindFilter !== "all" && status !== kindFilter) { - return false; - } - if ( - targetFilter === "targeted" && - !node.is_target_variable && - !node.is_domain_variable - ) { - return false; - } - if ( - targetFilter === "untargeted" && - (node.is_target_variable || node.is_domain_variable) - ) { - return false; - } - if (q) { - const haystack = `${node.variable} ${node.label} ${node.entity ?? ""}`.toLowerCase(); - if (!haystack.includes(q)) { - return false; - } - } - return true; - }); - }, [kindFilter, nodeScope, nodeSearch, targetFilter, trace.data]); - - return ( - - -
- Variable explorer - {variable && {variable}} -
- - -
- setSearch(e.target.value)} - placeholder="Search any PolicyEngine-US variable, e.g. ca_income_tax" - className="h-10 min-w-[280px] flex-1 rounded-md border border-border bg-background px-3 text-sm" - /> - {variableCatalog.isLoading && ( - - )} - {variableCatalog.error && ( - - Failed to load variable catalog: {String(variableCatalog.error)} - - )} - {variableCatalog.data && ( -
- {variableOptions.map((item: PolicyEngineVariable) => ( - - ))} - {variableOptions.length === 0 && ( - - No variables match that search. - - )} -
- )} -
- {trace.isLoading && ( -
- - - Tracing formula dependencies for {variable}... - -
- )} - {trace.error && ( - Failed to trace {variable}: {String(trace.error)} - )} - {!variable && !trace.isLoading && ( - - Search for a variable and select it to inspect calibration targets, - dependencies, stored inputs, and propagation. - - )} - {trace.data && ( - -
- - - - - -
-
- setNodeSearch(e.target.value)} - placeholder="Filter traced variables" - className="h-10 min-w-[220px] flex-1 rounded-md border border-border bg-background px-3 text-sm" - /> - - - - - {num(filteredNodes.length)} / {num(trace.data.nodes.length)} nodes - -
-
- - - - - - - - - - - - - - - - - {filteredNodes.slice(0, 80).map((node) => ( - - - - - - - - - - - - - ))} - {filteredNodes.length === 0 && ( - - - - )} - -
DepthVariableEntityKindDepsTarget roleTargetsEvaluatedMedian errorMax error
{num(node.depth)}{node.variable}{node.entity ?? "—"} - {nodeStatus(node)} - - {num(node.dependency_count)} - {targetRole(node)} - {num(node.included_target_count)} / {num(node.target_count)} - - {num(node.evaluated_target_count)} - - {pct(node.median_abs_rel_error)} - - {pct(node.max_abs_rel_error)} -
- No traced nodes match these filters. -
-
- - )} - - - ); -} - -export default function AnalysisPage() { - const [selectedVariable, setSelectedVariable] = useState(null); - - return ( - - -
- Analyst readiness - - Reform-specific calibration coverage, propagation, and pipeline - checks for the selected run. - -
- - - - - - - - - ); -} diff --git a/frontend/app/api/microplex/budget-benchmarks/route.ts b/frontend/app/api/microplex/budget-benchmarks/route.ts deleted file mode 100644 index bddfb13..0000000 --- a/frontend/app/api/microplex/budget-benchmarks/route.ts +++ /dev/null @@ -1,274 +0,0 @@ -import { NextResponse } from "next/server"; - -const ROWS = [ - { - id: "american_family_act_2025", - title: "American Family Act 2025 CTC expansion", - policy_area: "Child Tax Credit", - benchmark_period: "2026 annual", - comparison_status: "live_model_no_third_party_score", - budget_effect_rule: "credit_delta_is_cost", - notes: - "No independent public budget score is attached for this bill. PolicyEngine has a published static analysis, but that is not a third-party benchmark and is intentionally excluded from the external comparison slot.", - external_estimates: [ - { - source: "CBO/JCT", - source_type: "official_score", - url: "https://www.congress.gov/bill/119th-congress/house-bill/2763", - estimate: null, - estimate_label: "No public CBO/JCT score found for H.R.2763 / S.1393.", - period: "not available", - }, - ], - }, - { - id: "working_parents_tax_relief_act_2026", - title: "Working Parents Tax Relief Act EITC enhancement", - policy_area: "Earned Income Tax Credit", - benchmark_period: "2026 annual", - comparison_status: "live_model_partial_external_context", - budget_effect_rule: "credit_delta_is_cost", - notes: - "Live microsim rows require the Python backend and a configured Microplex H5 artifact root.", - external_estimates: [ - { - source: "Thomson Reuters coverage", - source_type: "third_party_context", - url: "https://tax.thomsonreuters.com/news/bill-seeks-earned-income-tax-credit-boost-per-child-for-working-parents/", - estimate: null, - estimate_label: - "Third-party coverage found; no single budget score is attached in this catalog.", - period: "not available", - }, - { - source: "PolicyEngine policy page", - source_type: "published_model_result", - url: "https://www.policyengine.org/us/working-parents-tax-relief-act", - estimate: null, - estimate_label: "PolicyEngine analysis; not a CBO/JCT comparator.", - period: "2026+", - }, - ], - }, - { - id: "wyden_smith_ctc_2024", - title: "Wyden-Smith / TRAFWA CTC provisions", - policy_area: "Child Tax Credit", - benchmark_period: "2024 annual", - comparison_status: "live_model_with_third_party_score", - budget_effect_rule: "credit_delta_is_cost", - notes: - "This is a true third-party benchmark row when connected to the Python backend. The static fallback cannot run the live us-data and Microplex microsims.", - external_estimates: [ - { - source: "Joint Committee on Taxation", - source_type: "jct", - url: "https://waysandmeans.house.gov/wp-content/uploads/2024/01/Estimated-Revenue-Effects-of-H.R.-7024.pdf", - estimate: 10_700_000_000, - estimate_label: - "$10.7B 2024 cost for the combined CTC provisions, as reported in PolicyEngine's JCT comparison table.", - period: "2024", - comparable_to_live_annual_result: true, - }, - { - source: "Joint Committee on Taxation", - source_type: "jct", - url: "https://waysandmeans.house.gov/wp-content/uploads/2024/01/Estimated-Revenue-Effects-of-H.R.-7024.pdf", - estimate: 33_493_000_000, - estimate_label: - "$33.493B 2024-2033 revenue effect for the Tax Relief for Working Families line in JCX-3-24.", - period: "2024-2033", - comparable_to_live_annual_result: false, - }, - ], - }, - { - id: "kypa_ctc_2026", - title: "Keep Your Pay Act expanded CTC", - policy_area: "Child Tax Credit", - benchmark_period: "2026 annual", - comparison_status: "live_model_with_third_party_score", - budget_effect_rule: "credit_delta_is_cost", - notes: - "PWBM provides separable KYPA CTC estimates. Live dashboard comparison uses the 2026 annual CTC delta against PWBM's FY2027 line as a full-year fiscal proxy. PWBM's FY2026 line is much smaller because refundable credit timing shifts most TY2026 cost into FY2027. The PolicyEngine-US reform is an AFA-style approximation, so the newborn-bonus mechanics may not match PWBM exactly.", - external_estimates: [ - { - source: "Penn Wharton Budget Model", - source_type: "pwbm", - url: "https://budgetmodel.wharton.upenn.edu/p/2026-03-11-the-keep-your-pay-act-budgetary-and-distributional-effects/", - estimate: 140_500_000_000, - estimate_label: - "$140.5B FY2027 revenue loss for KYPA's expanded Child Tax Credit provision. This is the closest full-year fiscal proxy for a calendar-year 2026 microsim.", - period: "FY2027 proxy for TY2026", - comparable_to_live_annual_result: true, - }, - { - source: "Penn Wharton Budget Model", - source_type: "pwbm", - url: "https://budgetmodel.wharton.upenn.edu/p/2026-03-11-the-keep-your-pay-act-budgetary-and-distributional-effects/", - estimate: 2_500_000_000, - estimate_label: - "$2.5B FY2026 revenue loss. Included as timing context; not comparable to a full calendar-year 2026 tax microsim.", - period: "FY2026 timing context", - comparable_to_live_annual_result: false, - }, - { - source: "Penn Wharton Budget Model", - source_type: "pwbm", - url: "https://budgetmodel.wharton.upenn.edu/p/2026-03-11-the-keep-your-pay-act-budgetary-and-distributional-effects/", - estimate: 1_261_600_000_000, - estimate_label: - "$1.2616T FY2026-2035 revenue loss for KYPA's expanded Child Tax Credit provision.", - period: "FY2026-2035", - comparable_to_live_annual_result: false, - }, - ], - }, - { - id: "kypa_childless_eitc_2026", - title: "Keep Your Pay Act childless-worker EITC", - policy_area: "Earned Income Tax Credit", - benchmark_period: "2026 annual", - comparison_status: "live_model_with_third_party_score", - budget_effect_rule: "credit_delta_is_cost", - notes: - "PWBM provides a separable childless-worker EITC estimate. The live comparison applies the same 2026 parameters described by PWBM and compares to the FY2027 line as a full-year fiscal proxy: minimum age 19, no maximum age, 15.3% phase-in and phase-out rates, about a $1,502 maximum credit, and about an $11,610 phase-out start.", - external_estimates: [ - { - source: "Penn Wharton Budget Model", - source_type: "pwbm", - url: "https://budgetmodel.wharton.upenn.edu/p/2026-03-11-the-keep-your-pay-act-budgetary-and-distributional-effects/", - estimate: 7_200_000_000, - estimate_label: - "$7.2B FY2027 revenue loss for KYPA's childless-worker EITC expansion. This is the closest full-year fiscal proxy for a calendar-year 2026 microsim.", - period: "FY2027 proxy for TY2026", - comparable_to_live_annual_result: true, - }, - { - source: "Penn Wharton Budget Model", - source_type: "pwbm", - url: "https://budgetmodel.wharton.upenn.edu/p/2026-03-11-the-keep-your-pay-act-budgetary-and-distributional-effects/", - estimate: 800_000_000, - estimate_label: - "$0.8B FY2026 revenue loss. Included as timing context; not comparable to a full calendar-year 2026 tax microsim.", - period: "FY2026 timing context", - comparable_to_live_annual_result: false, - }, - { - source: "Penn Wharton Budget Model", - source_type: "pwbm", - url: "https://budgetmodel.wharton.upenn.edu/p/2026-03-11-the-keep-your-pay-act-budgetary-and-distributional-effects/", - estimate: 63_800_000_000, - estimate_label: - "$63.8B FY2026-2035 revenue loss for KYPA's childless-worker EITC expansion.", - period: "FY2026-2035", - comparable_to_live_annual_result: false, - }, - ], - }, - { - id: "tcja_extension_2026_2035", - title: "TCJA individual provisions extension", - policy_area: "Federal individual income tax", - benchmark_period: "2026-2035", - comparison_status: "external_score_available_reform_not_wired", - budget_effect_rule: "full_budget_score", - notes: - "External benchmark is strong, but a matching live TCJA-extension reform preset is not wired yet.", - external_estimates: [ - { - source: "CBO/JCT", - source_type: "cbo_jct", - url: "https://www.policyengine.org/us/research/tcja-extension", - estimate: 3_877_600_000_000, - estimate_label: "$3.8776T cost over 2026-2035", - period: "2026-2035", - }, - { - source: "CRFB", - source_type: "third_party_score", - url: "https://www.policyengine.org/us/research/tcja-extension", - estimate: 3_830_000_000_000, - estimate_label: "$3.83T cost over 2026-2035", - period: "2026-2035", - }, - { - source: "PolicyEngine dynamic", - source_type: "published_model_result", - url: "https://www.policyengine.org/us/research/tcja-extension", - estimate: 3_885_500_000_000, - estimate_label: "$3.8855T cost over 2026-2035", - period: "2026-2035", - }, - ], - }, - { - id: "final_2025_reconciliation_tax", - title: "Final 2025 reconciliation individual income tax provisions", - policy_area: "Federal individual income tax", - benchmark_period: "2026-2035", - comparison_status: "external_score_available_reform_not_wired", - budget_effect_rule: "full_budget_score", - notes: - "PolicyEngine-US baseline now contains many OBBBA provisions, so live comparison needs an explicit counterfactual reform branch.", - external_estimates: [ - { - source: "PolicyEngine static analysis", - source_type: "published_model_result", - url: "https://www.policyengine.org/us/research/final-2025-reconciliation-tax", - estimate: 3_785_000_000_000, - estimate_label: "$3.785T cost over 2026-2035", - period: "2026-2035", - }, - { - source: "JCT JCX-26-25", - source_type: "jct", - url: "https://www.jct.gov/publications/2025/jcx-26-25/", - estimate: null, - estimate_label: - "Official JCT revenue estimate available; row-level match not wired.", - period: "2025 budget reconciliation", - }, - ], - }, -]; - -export const revalidate = 300; - -function liveUnavailable() { - return { - available: false, - reason: - "The deployed static fallback cannot run PolicyEngine microsims. Set NEXT_PUBLIC_API_URL to a Python backend for live us-data and Microplex values.", - reform: null, - period: null, - outcome_variable: null, - outcome_entity: null, - unit: null, - us_data: null, - microplex: null, - microplex_budget_effect_as_share_of_us_data: null, - budget_effect_gap: null, - }; -} - -export async function GET() { - return NextResponse.json({ - available: true, - runtime_seconds: 0, - generated_at_unix: Date.now() / 1000, - sign_convention: - "Positive budget effect means higher federal cost or lower federal revenue. For current live CTC/EITC rows, this equals the aggregate credit increase.", - comparison_caveat: - "This is the deployed static fallback. External benchmark rows are visible, but live us-data and Microplex microsim values require the Python backend.", - us_data_dataset: "not connected", - microplex_bundle: { - available: false, - artifact_id: null, - artifact_dir: null, - policyengine_dataset_path: null, - }, - rows: ROWS.map((row) => ({ ...row, live: liveUnavailable() })), - errors: [], - }); -} diff --git a/frontend/app/api/microplex/reform-comparison/route.ts b/frontend/app/api/microplex/reform-comparison/route.ts deleted file mode 100644 index bfa530b..0000000 --- a/frontend/app/api/microplex/reform-comparison/route.ts +++ /dev/null @@ -1,66 +0,0 @@ -import { NextResponse } from "next/server"; - -const AVAILABLE_REFORMS = [ - { - id: "american_family_act_2025", - label: "American Family Act 2025 CTC expansion", - description: - "CTC expansion preset used for local us-data versus Microplex microsim checks.", - variable: "ctc", - entity: "tax_unit", - period: 2026, - unit: "USD", - source_url: "https://www.congress.gov/bill/119th-congress/house-bill/2763", - }, - { - id: "working_parents_tax_relief_act_2026", - label: "Working Parents Tax Relief Act EITC enhancement", - description: - "EITC enhancement preset used for local reform-sensitivity checks.", - variable: "eitc", - entity: "tax_unit", - period: 2026, - unit: "USD", - source_url: - "https://tax.thomsonreuters.com/news/bill-seeks-earned-income-tax-credit-boost-per-child-for-working-parents/", - }, - { - id: "halve_joint_eitc_phase_out_rate", - label: "Halve joint-filer EITC phase-out rate", - description: - "Small EITC policy perturbation for comparing dataset sensitivity.", - variable: "eitc", - entity: "tax_unit", - period: 2026, - unit: "USD", - }, -]; - -export const revalidate = 300; - -export async function GET(request: Request) { - const url = new URL(request.url); - const reformId = - url.searchParams.get("reform_id") ?? AVAILABLE_REFORMS[0]?.id ?? null; - const reform = - AVAILABLE_REFORMS.find((item) => item.id === reformId) ?? - AVAILABLE_REFORMS[0] ?? - null; - - return NextResponse.json({ - available: false, - reason: - "The deployed Vercel frontend is using static public Microplex artifacts. Live us-data versus Microplex microsim comparisons require a hosted Python backend with NEXT_PUBLIC_API_URL configured, or a local dashboard opened from the local frontend.", - runtime_seconds: 0, - period: reform?.period ?? 2026, - available_reforms: AVAILABLE_REFORMS, - reform, - microplex_bundle: { - artifact_id: null, - artifact_dir: null, - policyengine_dataset_path: null, - }, - us_data_dataset: "not connected", - outcomes: [], - }); -} diff --git a/frontend/app/api/microplex/route.ts b/frontend/app/api/microplex/route.ts deleted file mode 100644 index 3872edf..0000000 --- a/frontend/app/api/microplex/route.ts +++ /dev/null @@ -1,303 +0,0 @@ -import { NextResponse } from "next/server"; - -import { - LATEST_MICROPLEX_ARTIFACT_ID, - LATEST_MICROPLEX_NATIVE_SCORES_PATH, - LATEST_MICROPLEX_TARGET_DIAGNOSTICS_PATH, - latestHeadlinePatch, - latestNativeScores, - latestTargetDiagnosticsSummary, -} from "@/lib/microplex/latest-artifact"; - -const GITHUB_RAW = "https://raw.githubusercontent.com/PolicyEngine/microplex-us/main"; - -const PARITY_PATH = - "artifacts/live_pe_native_cps_puf_rich_broad_fixed_20260329/20260329T175330Z-057066af/pe_us_data_rebuild_parity.json"; -const REGRESSION_SUMMARY_PATH = - "artifacts/live_pe_us_data_rebuild_checkpoint_modelpass_regression_summary_20260410.json"; -const IRS_DRILLDOWN_PATH = - "artifacts/live_pe_us_data_rebuild_checkpoint_national_irs_other_drilldown_20260410.json"; -const RUN_LEVEL_TARGET_DIAGNOSTICS_PATH = "pe_native_target_diagnostics.json"; -const RUN_LEVEL_TARGET_DIAGNOSTICS_MANIFEST_KEY = - "policyengine_native_target_diagnostics"; -const LEGACY_STATIC_TARGET_DIAGNOSTICS_PATH = - "artifacts/pe_native_target_diagnostics_current.json"; - -const ARTIFACTS = { - parity: PARITY_PATH, - regression_summary: REGRESSION_SUMMARY_PATH, - irs_drilldown: IRS_DRILLDOWN_PATH, -}; - -const GENERATED_ARTIFACT_CONTRACT = [ - { - name: "full_target_diagnostics", - path_hint: RUN_LEVEL_TARGET_DIAGNOSTICS_PATH, - manifest_key: RUN_LEVEL_TARGET_DIAGNOSTICS_MANIFEST_KEY, - legacy_static_dashboard_path: LEGACY_STATIC_TARGET_DIAGNOSTICS_PATH, - producer: "build_us_pe_native_target_diagnostics_payload", - public_committed: false, - description: - "Full per-target PE-native rows saved inside each newer Microplex run bundle. The rows show Microplex aggregate estimates against target values, with us-data comparator fields when present.", - }, - { - name: "dashboard_payload", - path_hint: "artifacts/microplex_dashboard_current.json", - producer: "microplex-us-dashboard", - public_committed: false, - description: - "Living dashboard payload with score runs, logs, and target diagnostics.", - }, - { - name: "native_scores", - path_hint: "policyengine_native_scores.json", - producer: "compute_us_pe_native_scores", - public_committed: false, - description: "Compact broad native-loss summary for one artifact bundle.", - }, - { - name: "native_audit", - path_hint: "pe_us_data_rebuild_native_audit.json", - producer: "build_policyengine_us_data_rebuild_native_audit", - public_committed: false, - description: "Top family and target regressions plus support audit evidence.", - }, - { - name: "run_index", - path_hint: "run_index.duckdb", - producer: "append_us_microplex_run_index_entry", - public_committed: false, - description: "DuckDB index for querying target deltas across saved runs.", - }, -]; - -const TARGET_DIAGNOSTIC_ROW_FIELDS = [ - "target_id", - "family", - "in_loss", - "supported_by_microplex", - "baseline_dataset", - "candidate_dataset", - "baseline_label", - "candidate_label", - "target_value", - "us_data_aggregate", - "microplex_aggregate", - "us_data_absolute_error", - "microplex_absolute_error", - "us_data_relative_error", - "microplex_relative_error", - "delta_absolute_error", - "delta_relative_error", - "loss_contribution", -]; - -type JsonObject = Record; - -export const revalidate = 300; - -function asObject(value: unknown): JsonObject { - return value && typeof value === "object" && !Array.isArray(value) - ? (value as JsonObject) - : {}; -} - -function scrub(value: unknown): unknown { - if (Array.isArray(value)) return value.map(scrub); - if (value && typeof value === "object") { - return Object.fromEntries( - Object.entries(value as JsonObject).map(([key, item]) => [key, scrub(item)]), - ); - } - if (typeof value === "number" && !Number.isFinite(value)) return null; - return value; -} - -function discoverConfiguredRunBundles() { - return { - artifact_root_env: "MICROPLEX_ARTIFACT_ROOTS", - single_artifact_root_env: "MICROPLEX_ARTIFACT_ROOT", - configured_artifact_roots: [], - missing_artifact_roots: [], - detected_run_bundle_count: 0, - detected_target_diagnostics_count: 0, - latest_run_bundle: null, - sampled_run_bundles: [], - }; -} - -async function fetchJson(path: string): Promise { - const response = await fetch(`${GITHUB_RAW}/${path}`, { - next: { revalidate }, - }); - if (!response.ok) { - throw new Error(`Failed to fetch ${path}: ${response.status}`); - } - return asObject(await response.json()); -} - -export async function GET() { - try { - const [parity, regression, drilldown] = await Promise.all([ - fetchJson(PARITY_PATH), - fetchJson(REGRESSION_SUMMARY_PATH), - fetchJson(IRS_DRILLDOWN_PATH), - ]); - - const comparison = asObject(parity.comparison); - const policyengineHarness = asObject(comparison.policyengineHarness); - const baselineSlice = asObject(parity.baselineSlice); - const comparisonMetadata = asObject(baselineSlice.comparisonMetadata); - const tagSummaries = asObject(policyengineHarness.tag_summaries); - const allTargets = asObject(tagSummaries.all_targets); - const configuredRuns = discoverConfiguredRunBundles(); - const targetRowsAvailable = true; - const latestScores = latestNativeScores(); - const latestTargetDiagnostics = latestTargetDiagnosticsSummary(100); - - const historicalHeadline = policyengineHarness.isPolicyEngineComparison - ? { - baseline_label: baselineSlice.baselineLabel ?? null, - candidate_label: baselineSlice.candidateLabel ?? null, - calibration_target_profile: - baselineSlice.calibrationTargetProfile ?? null, - n_synthetic: comparisonMetadata.n_synthetic ?? null, - target_period: baselineSlice.targetPeriod ?? null, - baseline_composite_parity_loss: - policyengineHarness.baseline_composite_parity_loss ?? null, - candidate_composite_parity_loss: - policyengineHarness.candidate_composite_parity_loss ?? null, - composite_parity_loss_delta: - policyengineHarness.composite_parity_loss_delta ?? null, - baseline_mean_abs_relative_error: - policyengineHarness.baseline_mean_abs_relative_error ?? null, - candidate_mean_abs_relative_error: - policyengineHarness.candidate_mean_abs_relative_error ?? null, - mean_abs_relative_error_delta: - policyengineHarness.mean_abs_relative_error_delta ?? null, - slice_win_rate: policyengineHarness.slice_win_rate ?? null, - supported_target_rate: - policyengineHarness.supported_target_rate ?? null, - target_win_rate: allTargets.target_win_rate ?? null, - tag_summaries: tagSummaries, - } - : {}; - const headline = { - ...historicalHeadline, - ...latestHeadlinePatch(), - }; - - return NextResponse.json( - scrub({ - source_repo: "PolicyEngine/microplex-us", - source_artifacts: Object.entries(ARTIFACTS).map(([name, path]) => ({ - name, - path, - url: `${GITHUB_RAW}/${path}`, - })).concat([ - { - name: "latest_native_scores", - path: LATEST_MICROPLEX_NATIVE_SCORES_PATH, - url: "deployed-static-snapshot", - }, - { - name: "latest_target_diagnostics", - path: LATEST_MICROPLEX_TARGET_DIAGNOSTICS_PATH, - url: "deployed-static-snapshot", - }, - ]), - limitations: [ - "The headline native loss and target rows use the latest deployed static Microplex artifact snapshot.", - "The best/worst run history and IRS drilldown still come from older public summary JSONs committed in PolicyEngine/microplex-us.", - "Live microsim reform comparisons still require a hosted Python backend and configured Microplex H5 artifact root.", - ], - newer_runs: { - current_reader: "deployed_static_latest_artifact_snapshot", - public_branch: "PolicyEngine/microplex-us main", - run_bundle_manifest_key: RUN_LEVEL_TARGET_DIAGNOSTICS_MANIFEST_KEY, - run_bundle_path_hint: RUN_LEVEL_TARGET_DIAGNOSTICS_PATH, - legacy_static_dashboard_path: LEGACY_STATIC_TARGET_DIAGNOSTICS_PATH, - required_to_load_newer_runs: - "Update the deployed static artifact snapshot, point the dashboard at a generated Microplex artifact root, publish the run-bundle JSONs, or expose the run index/artifacts through an authenticated artifact service.", - not_loaded_reason: - "This Vercel deployment is reading the latest checked-in static artifact snapshot, not discovering private/generated run bundles at runtime.", - configured_run_discovery: configuredRuns, - }, - repo_structure: { - canonical_stage_count: 9, - current_commit_public_artifact_count: - Object.keys(ARTIFACTS).length + 2, - analysis_modes: [ - "microplex_vs_target_oracle", - "microplex_vs_us_data_comparator", - "run_to_run_microplex_comparison", - ], - generated_artifacts: GENERATED_ARTIFACT_CONTRACT, - full_target_diagnostics: { - available_in_committed_repo: true, - expected_path: RUN_LEVEL_TARGET_DIAGNOSTICS_PATH, - run_level_path: RUN_LEVEL_TARGET_DIAGNOSTICS_PATH, - manifest_key: RUN_LEVEL_TARGET_DIAGNOSTICS_MANIFEST_KEY, - legacy_static_dashboard_path: LEGACY_STATIC_TARGET_DIAGNOSTICS_PATH, - static_dashboard_default_url: - LATEST_MICROPLEX_TARGET_DIAGNOSTICS_PATH, - producer_command: - `Run the Microplex PE-US-data rebuild/native audit pipeline; newer runs record manifest.artifacts.${RUN_LEVEL_TARGET_DIAGNOSTICS_MANIFEST_KEY} = ${RUN_LEVEL_TARGET_DIAGNOSTICS_PATH}.`, - row_fields: TARGET_DIAGNOSTIC_ROW_FIELDS, - primary_use: - "Standalone Microplex aggregate-vs-target diagnostics; us-data baseline fields are optional comparator context.", - }, - run_index: { - path_hint: "run_index.duckdb", - query_helpers: [ - "list_us_microplex_target_delta_rows", - "compare_us_microplex_target_delta_rows", - "select_us_microplex_frontier_index_row", - ], - }, - }, - artifact_id: LATEST_MICROPLEX_ARTIFACT_ID, - verdict: parity.verdict ?? null, - headline, - native_scores: { - ...latestScores, - target_rows_available: targetRowsAvailable, - full_target_diagnostics_manifest_key: - RUN_LEVEL_TARGET_DIAGNOSTICS_MANIFEST_KEY, - }, - target_diagnostics: latestTargetDiagnostics, - regression_summary: { - total_scored_runs: regression.totalScoredRuns ?? null, - total_audited_runs: regression.totalAuditedRuns ?? null, - best_runs: Array.isArray(regression.bestRuns) - ? regression.bestRuns.slice(0, 10) - : [], - worst_runs: Array.isArray(regression.worstRuns) - ? regression.worstRuns.slice(0, 10) - : [], - largest_family_counts: regression.largestFamilyCounts ?? {}, - top3_family_counts: regression.top3FamilyCounts ?? {}, - target_counts_from_audits: regression.targetCountsFromAudits ?? {}, - }, - irs_drilldown: { - family: drilldown.family ?? null, - audits_where_family_leads: drilldown.auditsWhereFamilyLeads ?? null, - audits_with_matching_targets: - drilldown.auditsWithMatchingTargets ?? null, - lead_audits: Array.isArray(drilldown.leadAudits) - ? drilldown.leadAudits.slice(0, 10) - : [], - lead_target_counts: drilldown.leadTargetCounts ?? {}, - lead_filing_status_gap_summary: - drilldown.leadFilingStatusGapSummary ?? null, - lead_mfs_agi_gap_summary: drilldown.leadMFSAgiGapSummary ?? null, - }, - }), - ); - } catch (error) { - return NextResponse.json( - { detail: error instanceof Error ? error.message : String(error) }, - { status: 502 }, - ); - } -} diff --git a/frontend/app/api/microplex/target-diagnostics/route.ts b/frontend/app/api/microplex/target-diagnostics/route.ts deleted file mode 100644 index 200cf3c..0000000 --- a/frontend/app/api/microplex/target-diagnostics/route.ts +++ /dev/null @@ -1,12 +0,0 @@ -import { NextResponse } from "next/server"; - -import { - latestTargetDiagnosticsPage, - scrub, -} from "@/lib/microplex/latest-artifact"; - -export const revalidate = 300; - -export async function GET(request: Request) { - return NextResponse.json(scrub(latestTargetDiagnosticsPage(request.url))); -} diff --git a/frontend/app/api/populace/comparison/route.ts b/frontend/app/api/populace/comparison/route.ts deleted file mode 100644 index 116f818..0000000 --- a/frontend/app/api/populace/comparison/route.ts +++ /dev/null @@ -1,120 +0,0 @@ -import { NextResponse } from "next/server"; - -import { - normalizeComparisonScorecard, - snapshotComparisonScorecard, -} from "@/lib/populace/comparison-artifact"; -import { scrub } from "@/lib/populace/latest-artifact"; - -// The incumbent comparison lives in PolicyEngine/populace-benchmarks -// (PolicyEngine/populace#37 moved it out of live populace). By default the -// route resolves that repo's latest.json pointer -// (PolicyEngine/populace-benchmarks#3) and serves the scorecard it names, -// falling back to the committed archived 9f1260b snapshot until the artifact -// is reachable. POPULACE_BENCHMARKS_POINTER_URL overrides the pointer; -// POPULACE_BENCHMARKS_SCORECARD_URL points straight at a scorecard (skips the -// pointer). -const BENCHMARKS_RAW_BASE = - "https://raw.githubusercontent.com/PolicyEngine/populace-benchmarks/main"; -const DEFAULT_POINTER_URL = `${BENCHMARKS_RAW_BASE}/benchmarks/us/incumbent-comparison/latest.json`; -const POINTER_URL = - process.env.POPULACE_BENCHMARKS_POINTER_URL?.trim() || DEFAULT_POINTER_URL; -const DIRECT_SCORECARD_URL = process.env.POPULACE_BENCHMARKS_SCORECARD_URL?.trim(); - -type JsonObject = Record; - -export const revalidate = 300; - -function asObject(value: unknown): JsonObject { - return value && typeof value === "object" && !Array.isArray(value) - ? (value as JsonObject) - : {}; -} - -async function fetchJson(url: string): Promise { - const response = await fetch(url, { next: { revalidate } }); - if (!response.ok) { - throw new Error(`Failed to fetch ${url}: ${response.status}`); - } - return asObject(await response.json()); -} - -// Resolve a scorecard_path from the pointer against the same repo the pointer -// came from, so a custom POINTER_URL on a branch/fork resolves consistently. -function resolveScorecardUrl(pointerUrl: string, scorecardPath: string): string { - if (/^https?:\/\//.test(scorecardPath)) return scorecardPath; - const base = pointerUrl.replace( - /\/benchmarks\/us\/incumbent-comparison\/latest\.json$/, - "", - ); - return `${base}/${scorecardPath.replace(/^\/+/, "")}`; -} - -async function loadLiveScorecard(): Promise { - if (DIRECT_SCORECARD_URL) { - const raw = await fetchJson(DIRECT_SCORECARD_URL); - return { - available: true, - source: "populace_benchmarks_live", - path: DIRECT_SCORECARD_URL, - scorecard_status: typeof raw.status === "string" ? raw.status : null, - ...normalizeComparisonScorecard(raw), - }; - } - const pointer = await fetchJson(POINTER_URL); - const scorecardPath = pointer.scorecard_path; - if (typeof scorecardPath !== "string" || !scorecardPath) { - throw new Error(`Pointer ${POINTER_URL} has no scorecard_path.`); - } - const scorecardUrl = resolveScorecardUrl(POINTER_URL, scorecardPath); - const raw = await fetchJson(scorecardUrl); - return { - available: true, - source: "populace_benchmarks_live", - path: scorecardUrl, - pointer_url: POINTER_URL, - scorecard_status: - typeof pointer.status === "string" - ? pointer.status - : typeof raw.status === "string" - ? raw.status - : null, - ...normalizeComparisonScorecard(raw), - }; -} - -export async function GET() { - const snapshot = snapshotComparisonScorecard(); - let payload: JsonObject = snapshot; - let liveError: string | null = null; - - try { - payload = await loadLiveScorecard(); - } catch (error) { - liveError = error instanceof Error ? error.message : String(error); - payload = snapshot; - } - - const live = payload.source === "populace_benchmarks_live"; - const scorecardStatus = - typeof payload.scorecard_status === "string" ? payload.scorecard_status : "archived"; - - return NextResponse.json( - scrub({ - ...payload, - // "archived" = serving the committed snapshot (not reached live). Distinct - // from scorecard_status, which is the artifact's own provenance. - archived: !live, - scorecard_status: scorecardStatus, - source_pointer: live ? (payload.pointer_url ?? POINTER_URL) : POINTER_URL, - live_scorecard_error: liveError, - notes: [ - "Populace (candidate) is scored against the enhanced CPS (incumbent) with a matched-household, symmetric-refit, held-out-target protocol.", - live - ? `Served live from populace-benchmarks (${scorecardStatus} scorecard).` - : "The benchmarks scorecard was not reachable, so this is the committed archived snapshot for release populace-us-2024-9f1260b-20260611 (PolicyEngine/populace-benchmarks#3 / #4 publish it).", - "The eCPS comparison is benchmark-harness material and intentionally lives outside live populace (PolicyEngine/populace#37); the live calibration-fit view is the populace release summary.", - ], - }), - ); -} diff --git a/frontend/app/budget-impact/page.tsx b/frontend/app/budget-impact/page.tsx deleted file mode 100644 index 7b4f28e..0000000 --- a/frontend/app/budget-impact/page.tsx +++ /dev/null @@ -1 +0,0 @@ -export { default } from "@/components/microplex/reform-benchmarks-page"; diff --git a/frontend/app/comparison/page.tsx b/frontend/app/comparison/page.tsx deleted file mode 100644 index 4403904..0000000 --- a/frontend/app/comparison/page.tsx +++ /dev/null @@ -1,600 +0,0 @@ -"use client"; - -import Link from "next/link"; -import { - Badge, - Card, - CardContent, - CardHeader, - CardTitle, - Skeleton, - Stack, - Text, - Title, - formatNumber, -} from "@policyengine/ui-kit"; - -import { AppShell } from "@/components/layout/app-shell"; -import { useMicroplex } from "@/lib/api/hooks/use-microplex"; -import { useSummary } from "@/lib/api/hooks/use-summary"; -import { useTargetInventorySummary } from "@/lib/api/hooks/use-target-inventory"; -import { useRunContext } from "@/lib/run-context"; - -function pct(value: number | null | undefined, digits = 1): string { - if (value === null || value === undefined || !Number.isFinite(value)) { - return "—"; - } - return `${(value * 100).toFixed(digits)}%`; -} - -function num(value: number | null | undefined): string { - if (value === null || value === undefined || !Number.isFinite(value)) { - return "—"; - } - return formatNumber(value); -} - -function Kpi({ - label, - value, - hint, -}: { - label: string; - value: string; - hint?: string; -}) { - return ( -
- - {label} - -
{value}
- {hint && ( - - {hint} - - )} -
- ); -} - -function DeltaBadge({ - value, - improveIsLower = true, -}: { - value: number | null | undefined; - improveIsLower?: boolean; -}) { - if (value === null || value === undefined || !Number.isFinite(value)) { - return —; - } - const improved = improveIsLower ? value < 0 : value > 0; - const variant: "success" | "error" | "secondary" = - Math.abs(value) < 1e-9 ? "secondary" : improved ? "success" : "error"; - const sign = value > 0 ? "+" : ""; - return ( - - {sign} - {num(value)} - - ); -} - -export default function ComparisonPage() { - const { dataset, run } = useRunContext(); - const summary = useSummary(); - const microplex = useMicroplex(); - const inventory = useTargetInventorySummary(); - const microplexTargetRefs = microplex.data - ? [ - ...microplex.data.regression_summary.target_counts_from_audits.map( - (target) => ({ ...target, source: "regression audits" }), - ), - ...microplex.data.irs_drilldown.lead_target_counts.map((target) => ({ - ...target, - source: "IRS drilldown", - })), - ].slice(0, 12) - : []; - - return ( - - -
- Comparison - - Side-by-side orientation for the selected us-data run and the - public Microplex parity artifact. - -
- -
- - -
- us-data run - selected dashboard run -
- - - - - Dataset {dataset ?? "not selected"} · Run{" "} - {run ?? "not selected"} - - {summary.isLoading && } - {summary.error && ( - - Failed to load us-data summary: {String(summary.error)} - - )} - {summary.data && ( -
- - - - -
- )} -
- - Open summary - - - Inspect targets - - - Trace variables - -
- - - - - - -
- Microplex artifact - public parity JSON -
- - - - {microplex.isLoading && } - {microplex.error && ( - - Failed to load Microplex artifact: {String(microplex.error)} - - )} - {microplex.data && ( - <> - - Artifact{" "} - - {microplex.data.artifact_id} - {" "} - · profile{" "} - - {microplex.data.headline.calibration_target_profile} - - -
- - - - -
- - )} -
- - Open Microplex overview - - - Inspect Microplex pipeline - -
- - - -
- - - - Interpretation boundary - - - - The Microplex side currently comes from public aggregate JSON - artifacts. The us-data side comes from the loaded dashboard run. - A full apples-to-apples target-by-target comparison needs the - generated Microplex H5 or full PE-native target diagnostic JSON - to be published and loaded into this app. - - - - - {microplex.data?.repo_structure && ( - - - Microplex artifact structure - - - - - The Microplex repo has a richer generated artifact structure - than the committed public JSONs. Newer run bundles record - the full target diagnostics manifest artifact{" "} - - { - microplex.data.repo_structure.full_target_diagnostics - .manifest_key - } - - , pointing to{" "} - - { - microplex.data.repo_structure.full_target_diagnostics - .run_level_path - } - - . - - -
- - - -
- -
- - - - - - - - - - - {microplex.data.repo_structure.generated_artifacts.map( - (artifact) => ( - - - - - - - ), - )} - -
ArtifactPath hintProducerPublic
-
- {artifact.name} -
- - {artifact.description} - - 		- {artifact.path_hint} - - {artifact.producer} - - {artifact.public_committed ? "yes" : "no"} -
-
- - - - )} - - - - Target comparison surface - - - - - Yes, this repo has the target definitions. What we do not yet - have in public form is Microplex's full per-target estimate - table, so this section compares target inventory and public - Microplex target references rather than target-by-target model - errors. - - -
- - - -
- -
-
- - - - - - - - - - - {inventory.data?.tiers.slice(0, 8).map((tier) => ( - - - - - - - ))} - {!inventory.data && ( - - - - )} - -
Inventory tierRecordsMatched to DBMatch rate
- {tier.tier} - - {num(tier.total_records)} - - {num(tier.matched_to_db)} - - {pct(tier.match_rate)} -
- Loading target inventory... -
-
- -
- - - - - - - - - - - {microplexTargetRefs.map((target) => ( - - - - - - - ))} - {microplexTargetRefs.length === 0 && ( - - - - )} - -
Microplex target referenceSourceCountMean Δ
- {target.target} - {target.source} - {num(target.count)} - - {num(target.weightedTermDeltaMean)} -
- Loading Microplex target references... -
-
-
- -
- - Open full target inventory - - - Open us-data target estimates - -
- - - - - {microplex.data?.native_scores && ( - - -
- Microplex aggregates vs PE targets - - {microplex.data.native_scores.candidate_beats_baseline - ? "candidate beats baseline" - : "baseline lower native loss"} - -
- - - - - These are the native PE aggregate-vs-target summary metrics - from the public Microplex parity artifact. Lower loss is - better. The row-level table is generated in newer run bundles - as{" "} - - { - microplex.data.native_scores - .full_target_diagnostics_path - } - - , but those generated bundles are not committed publicly. - - -
- -
- - Native loss delta - -
- -
- - candidate minus baseline - -
- - - - -
- - - - )} - - - ); -} diff --git a/frontend/app/inventory/page.tsx b/frontend/app/inventory/page.tsx deleted file mode 100644 index 387812f..0000000 --- a/frontend/app/inventory/page.tsx +++ /dev/null @@ -1,324 +0,0 @@ -"use client"; - -import { - Card, - CardHeader, - CardTitle, - CardContent, - Badge, - Skeleton, - Stack, - Title, - Text, - Input, - formatNumber, -} from "@policyengine/ui-kit"; -import { AppShell } from "@/components/layout/app-shell"; -import { DataTable } from "@/components/shared/InteractiveDataTable"; -import { - useTargetInventory, - useTargetInventorySummary, - type InventoryRow, -} from "@/lib/api/hooks/use-target-inventory"; -import { useState } from "react"; - -const TIER_OPTIONS = ["all", "db", "csv", "python", "generator", "yaml"] as const; -type TierOption = (typeof TIER_OPTIONS)[number]; - -const IN_DB_OPTIONS = ["any", "in-db", "out-of-db"] as const; -type InDbOption = (typeof IN_DB_OPTIONS)[number]; - -const PAGE_SIZE = 50; - -function TierBadge({ tier }: { tier: InventoryRow["storage_tier"] }) { - const styles: Record = { - db: "bg-slate-200 text-slate-800 border-slate-300", - csv: "bg-amber-100 text-amber-800 border-amber-300", - python: "bg-blue-100 text-blue-800 border-blue-300", - generator: "bg-purple-100 text-purple-800 border-purple-300", - yaml: "bg-emerald-100 text-emerald-800 border-emerald-300", - }; - return ( - - {tier} - - ); -} - -function InventoryTable() { - const [tier, setTier] = useState("all"); - const [inDb, setInDb] = useState("any"); - const [search, setSearch] = useState(""); - const [page, setPage] = useState(0); - - const inventory = useTargetInventory({ - tier: tier === "all" ? undefined : tier, - in_db: - inDb === "in-db" ? true : inDb === "out-of-db" ? false : undefined, - search: search || undefined, - limit: PAGE_SIZE, - offset: page * PAGE_SIZE, - }); - - const lastPage = inventory.data - ? Math.max(0, Math.ceil(inventory.data.total / PAGE_SIZE) - 1) - : 0; - - const columns = [ - { - key: "storage_tier", - header: "Tier", - format: (val: unknown) => , - }, - { - key: "in_db", - header: "In DB?", - format: (val: unknown) => - val ? ( - in DB - ) : ( - authored only - ), - }, - { key: "variable", header: "Variable" }, - { - key: "period", - header: "Period", - align: "right" as const, - format: (val: unknown) => (val == null ? "—" : String(val)), - }, - { - key: "geo_level", - header: "Geo", - format: (val: unknown, row: Record) => { - const gid = row.geographic_id; - const lvl = String(val ?? "—"); - return gid ? `${lvl} / ${gid}` : lvl; - }, - }, - { - key: "value", - header: "Target", - align: "right" as const, - format: (val: unknown) => - val == null ? — : formatNumber(Number(val)), - }, - { - key: "estimate", - header: "PE aggregate", - align: "right" as const, - format: (val: unknown) => - val == null ? — : formatNumber(Number(val)), - }, - { - key: "rel_error", - header: "Rel. err", - align: "right" as const, - format: (val: unknown) => { - if (val == null) return —; - const v = Number(val); - const abs = Math.abs(v); - const variant = - abs > 0.5 - ? "error" - : abs > 0.2 - ? "warning" - : abs > 0.05 - ? "secondary" - : "success"; - const display = - abs >= 1 ? `${(v * 100).toFixed(0)}%` : `${(v * 100).toFixed(1)}%`; - return {display}; - }, - }, - { - key: "source_path", - header: "Source", - format: (val: unknown) => ( - - {String(val).replace("storage/calibration_targets/", "…/")} - - ), - }, - { - key: "constraints", - header: "Constraints", - format: (val: unknown) => { - const cs = val as [string, string, string][]; - if (!cs || cs.length === 0) - return —; - return ( -
- {cs.map(([v, op, value], i) => ( - - {v} {op} {value} - - ))} -
- ); - }, - }, - ]; - - const btn = - "h-8 min-w-8 rounded border border-border bg-background px-2 text-sm hover:bg-muted disabled:opacity-40 disabled:cursor-not-allowed"; - - return ( - -
-
- { - setSearch(e.target.value); - setPage(0); - }} - /> -
-
- Tier - -
-
- DB - -
-
- - {inventory.data ? ( - <> - - {formatNumber(inventory.data.total)} matching records · page{" "} - {page + 1} of {lastPage + 1} - - -
- - - - {page + 1} / {lastPage + 1} - - - -
- - ) : ( - - )} - - ); -} - -function CoverageSummary() { - const summary = useTargetInventorySummary(); - if (!summary.data) return ; - const tot = summary.data.tiers.reduce((s, t) => s + t.total_records, 0); - const matched = summary.data.tiers.reduce((s, t) => s + t.matched_to_db, 0); - - return ( - - - Cross-tier audit - - - - - {formatNumber(tot)}{" "} - authored records across {summary.data.tiers.length} non-DB tiers;{" "} - {formatNumber(matched)}{" "} - ({((matched / tot) * 100).toFixed(1)}%) match a target in the - loaded policy_data.db ({formatNumber(summary.data.db_total)} rows). - - - - - - - - - - - - {summary.data.tiers.map((t) => { - const rate = (t.match_rate ?? 0) * 100; - return ( - - - - - - - ); - })} - -
SourceRecordsMatched%
{t.tier}{formatNumber(t.total_records)}{formatNumber(t.matched_to_db)} - {rate.toFixed(1)}% -
- {summary.data.parsers_missing.length > 0 && ( - - No parser yet for: {summary.data.parsers_missing.join(", ")} - - )} - - - - ); -} - -export default function TargetInventoryPage() { - return ( - - -
- Target inventory - - Every target authored across all 5 storage tiers in{" "} - policyengine_us_data: CSV source files, Python - constants, generators, the YAML config, and the compiled{" "} - policy_data.db. The in-DB badge - tells you whether each authored row made it into the - currently-loaded calibration. - -
- - - - - - Browse records - - - - - - - - ); -} diff --git a/frontend/app/microplex/diagnostics/page.tsx b/frontend/app/microplex/diagnostics/page.tsx deleted file mode 100644 index bee8ff3..0000000 --- a/frontend/app/microplex/diagnostics/page.tsx +++ /dev/null @@ -1 +0,0 @@ -export { default } from "../page"; diff --git a/frontend/app/microplex/page.tsx b/frontend/app/microplex/page.tsx deleted file mode 100644 index 80e5758..0000000 --- a/frontend/app/microplex/page.tsx +++ /dev/null @@ -1,1564 +0,0 @@ -"use client"; - -import { useEffect, useState, type ReactNode } from "react"; - -import { - Badge, - Card, - CardContent, - CardHeader, - CardTitle, - Stack, - Text, - Title, - formatNumber, -} from "@policyengine/ui-kit"; - -import { AppShell } from "@/components/layout/app-shell"; -import { - DataTable, - type SortState, -} from "@/components/shared/InteractiveDataTable"; -import { LoadingBlock } from "@/components/shared/LoadingBlock"; -import { - useMicroplex, - useMicroplexReformComparison, - useMicroplexTargetDiagnostics, -} from "@/lib/api/hooks/use-microplex"; -import { usePathname } from "next/navigation"; - -const TARGET_DIAGNOSTICS_LIMIT = 100; - -const TARGET_FAMILY_OPTIONS = [ - "national_census_other", - "national_infants", - "national_irs_other", - "national_net_worth", - "national_population_by_age", - "national_spm_threshold_agi", - "national_spm_threshold_count", - "national_ssa", - "national_tax_expenditures", - "other", - "state_aca_enrollment", - "state_aca_spending", - "state_age_distribution", - "state_agi_distribution", - "state_population", - "state_population_under_5", - "state_real_estate_taxes", - "state_snap_cost", - "state_snap_households", -]; - -const STATE_OPTIONS = [ - "AL", - "AK", - "AZ", - "AR", - "CA", - "CO", - "CT", - "DE", - "DC", - "FL", - "GA", - "HI", - "ID", - "IL", - "IN", - "IA", - "KS", - "KY", - "LA", - "ME", - "MD", - "MA", - "MI", - "MN", - "MS", - "MO", - "MT", - "NE", - "NV", - "NH", - "NJ", - "NM", - "NY", - "NC", - "ND", - "OH", - "OK", - "OR", - "PA", - "RI", - "SC", - "SD", - "TN", - "TX", - "UT", - "VT", - "VA", - "WA", - "WV", - "WI", - "WY", -]; - -function fmt(v: number | null | undefined, opts: { pct?: boolean } = {}) { - if (v == null || !Number.isFinite(v)) return "—"; - if (opts.pct) return `${(v * 100).toFixed(1)}%`; - if (Math.abs(v) >= 1000 || Number.isInteger(v)) return formatNumber(v); - return v.toFixed(4); -} - -function deltaBadge(v: number | null | undefined, improveIsLower = true) { - if (v == null || !Number.isFinite(v)) { - return —; - } - const improved = improveIsLower ? v < 0 : v > 0; - const variant: "success" | "error" | "secondary" = - Math.abs(v) < 1e-9 ? "secondary" : improved ? "success" : "error"; - const sign = v > 0 ? "+" : ""; - return ( - - {sign} - {fmt(v)} - - ); -} - -function signedFmt(v: number | null | undefined) { - if (v == null || !Number.isFinite(v)) return "—"; - const sign = v > 0 ? "+" : ""; - return ( - - {sign} - {fmt(v)} - - ); -} - -function signedPct(v: number | null | undefined) { - if (v == null || !Number.isFinite(v)) return "—"; - const sign = v > 0 ? "+" : ""; - return ( - - {sign} - {(v * 100).toFixed(2)}% - - ); -} - -const bestWorstColumns = [ - { key: "artifactPath", header: "Run", format: (v: unknown) => ( - - {String(v)} - - ) }, - { - key: "lossDelta", - header: ( - - Loss delta - - ), - align: "right" as const, - format: (v: unknown) => deltaBadge(Number(v)), - }, - { - key: "candidateBeatsBaseline", - header: "Beats us-data?", - format: (v: unknown) => - v ? yes : no, - }, - { - key: "largestRegressingFamily", - header: "Worst family", - format: (v: unknown) => ( - {v == null ? "—" : String(v)} - ), - }, - { - key: "largestRegressingFamilyDelta", - header: ( - - Family delta - - ), - align: "right" as const, - format: (v: unknown) => - v == null ? "—" : Number(v).toFixed(3), - }, -]; - -const familyColumns = [ - { key: "family", header: "Family", format: (v: unknown) => ( - {String(v)} - ) }, - { - key: "rank1Count", - header: "#1", - align: "right" as const, - format: (v: unknown) => formatNumber(Number(v)), - }, - { - key: "rank2Count", - header: "#2", - align: "right" as const, - format: (v: unknown) => formatNumber(Number(v)), - }, - { - key: "rank3Count", - header: "#3", - align: "right" as const, - format: (v: unknown) => formatNumber(Number(v)), - }, - { - key: "top3Count", - header: ( - - Top-3 total - - ), - align: "right" as const, - format: (v: unknown) => formatNumber(Number(v)), - }, -]; - -const leadColumns = [ - { - key: "target", - header: "Target", - format: (v: unknown) => ( - {String(v)} - ), - }, - { - key: "weightedTermDelta", - header: ( - - Weighted delta - - ), - align: "right" as const, - format: (v: unknown) => Number(v).toFixed(2), - }, -]; - -function metricTone(value: number | null | undefined, improveIsLower = true) { - if (value == null || !Number.isFinite(value) || Math.abs(value) < 1e-9) { - return "secondary" as const; - } - return (improveIsLower ? value < 0 : value > 0) ? "success" as const : "error" as const; -} - -function MetricTile({ - label, - value, - detail, - badge, -}: { - label: ReactNode; - value: string; - detail?: string; - badge?: ReactNode; -}) { - return ( -
-
- - {label} - - {badge} -
-
{value}
- {detail && ( - - {detail} - - )} -
- ); -} - -function SectionIntro({ - title, - children, -}: { - title: string; - children?: ReactNode; -}) { - return ( -
- {title} - {children && ( - - {children} - - )} -
- ); -} - -function HelpText({ children, title }: { children: ReactNode; title: string }) { - return ( - - {children} - - ? - - - {title} - - - ); -} - -function CompactSearchInput({ - label, - value, - onChange, - placeholder, -}: { - label: string; - value: string; - onChange: (value: string) => void; - placeholder: string; -}) { - return ( - - ); -} - -function CompactSelect({ - label, - value, - options, - onChange, - disabled, - className = "", -}: { - label: string; - value: string; - options: { value: string; label: string }[]; - onChange: (value: string) => void; - disabled?: boolean; - className?: string; -}) { - return ( - - ); -} - -const targetCountColumns = [ - { - key: "target", - header: "Target", - format: (v: unknown) => ( - {String(v)} - ), - }, - { - key: "count", - header: "Count", - align: "right" as const, - format: (v: unknown) => formatNumber(Number(v)), - }, - { - key: "weightedTermDeltaMean", - header: ( - - Mean weighted delta - - ), - align: "right" as const, - format: (v: unknown) => - v == null ? "—" : Number(v).toFixed(2), - }, - { - key: "weightedTermDeltaSum", - header: ( - - Total weighted delta - - ), - align: "right" as const, - format: (v: unknown) => - v == null ? "—" : Number(v).toFixed(2), - }, -]; - -const filingStatusGapColumns = [ - { - key: "filingStatus", - header: "Filing status", - format: (v: unknown) => ( - {String(v)} - ), - }, - { - key: "meanAbsWeightedCountDelta", - header: ( - - Mean count gap - - ), - align: "right" as const, - format: (v: unknown) => - v == null ? "—" : formatNumber(Number(v)), - }, - { - key: "weightedCountDeltaSum", - header: ( - - Total count gap - - ), - align: "right" as const, - format: (v: unknown) => - v == null ? "—" : formatNumber(Number(v)), - }, - { - key: "positiveCount", - header: "+ audits", - align: "right" as const, - format: (v: unknown) => formatNumber(Number(v)), - }, - { - key: "negativeCount", - header: "- audits", - align: "right" as const, - format: (v: unknown) => formatNumber(Number(v)), - }, -]; - -const agiGapColumns = [ - { - key: "agiBin", - header: "AGI bin", - format: (v: unknown) => ( - {String(v)} - ), - }, - { - key: "meanAbsWeightedCountDelta", - header: ( - - Mean count gap - - ), - align: "right" as const, - format: (v: unknown) => - v == null ? "—" : formatNumber(Number(v)), - }, - { - key: "weightedCountDeltaSum", - header: ( - - Total count gap - - ), - align: "right" as const, - format: (v: unknown) => - v == null ? "—" : formatNumber(Number(v)), - }, - { - key: "positiveCount", - header: "+ audits", - align: "right" as const, - format: (v: unknown) => formatNumber(Number(v)), - }, - { - key: "negativeCount", - header: "- audits", - align: "right" as const, - format: (v: unknown) => formatNumber(Number(v)), - }, -]; - -function targetDiagnosticsColumns(compareWithUsData: boolean) { - return [ - { - key: "target_id", - header: "Target", - format: (v: unknown, row: Record) => ( - - {String(row.target_name ?? v ?? "—")} - {row.target_name != null && v != null && String(row.target_name) !== String(v) ? ( - - ID {String(v)} - - ) : null} - - ), - }, - { - key: "family", - header: ( - - Family - - ), - format: (v: unknown, row: Record) => ( - - {String(v ?? row.target_family ?? "—")} - - ), - }, - { - key: "target_value", - header: ( - - Target value - - ), - align: "right" as const, - format: (v: unknown) => (v == null ? "—" : fmt(Number(v))), - }, - { - key: "us_data_aggregate", - header: ( - - us-data aggregate - - ), - align: "right" as const, - format: (v: unknown, row: Record) => { - const value = v ?? row.from_estimate; - return value == null ? "—" : fmt(Number(value)); - }, - }, - { - key: "microplex_aggregate", - header: ( - - Microplex aggregate - - ), - align: "right" as const, - format: (v: unknown, row: Record) => { - const value = v ?? row.to_estimate; - return value == null ? "—" : fmt(Number(value)); - }, - }, - { - key: "microplex_vs_target_relative", - header: ( - - Microplex vs target - - ), - align: "right" as const, - format: (v: unknown, row: Record) => { - let value = v; - if (value == null) { - const target = Number(row.target_value); - const microplex = Number(row.to_estimate ?? row.microplex_aggregate); - if (Number.isFinite(target) && target !== 0 && Number.isFinite(microplex)) { - value = (microplex - target) / Math.abs(target); - } - } - return signedPct(value == null ? null : Number(value)); - }, - }, - { - key: "microplex_vs_us_data_relative", - header: ( - - Microplex vs us-data - - ), - align: "right" as const, - format: (v: unknown, row: Record) => { - let value = v; - if (value == null) { - const usData = Number(row.from_estimate ?? row.us_data_aggregate); - const microplex = Number(row.to_estimate ?? row.microplex_aggregate); - if (Number.isFinite(usData) && usData !== 0 && Number.isFinite(microplex)) { - value = (microplex - usData) / Math.abs(usData); - } - } - return signedPct(value == null ? null : Number(value)); - }, - }, - { - key: "closer_dataset", - header: ( - - Closer - - ), - format: (v: unknown) => - v === "microplex" ? ( - Microplex - ) : v === "us-data" ? ( - us-data - ) : v === "tie" ? ( - tie - ) : ( - "—" - ), - }, - { - key: "delta_absolute_error", - header: ( - - Abs error delta - - ), - align: "right" as const, - format: (v: unknown, row: Record) => { - let value = v; - if (value == null) { - const target = Number(row.target_value); - const fromEstimate = Number(row.from_estimate ?? row.us_data_aggregate); - const toEstimate = Number(row.to_estimate ?? row.microplex_aggregate); - if ( - Number.isFinite(target) && - Number.isFinite(fromEstimate) && - Number.isFinite(toEstimate) - ) { - value = Math.abs(toEstimate - target) - Math.abs(fromEstimate - target); - } - } - return value == null ? "—" : deltaBadge(Number(value)); - }, - }, - { - key: "supported_by_microplex", - header: ( - - Supported - - ), - format: (v: unknown) => - v === true ? ( - yes - ) : v === false ? ( - no - ) : ( - "—" - ), - }, - ].filter( - (column) => - compareWithUsData || - ![ - "us_data_aggregate", - "microplex_vs_us_data_relative", - "closer_dataset", - "delta_absolute_error", - ].includes(column.key), - ); -} - -function ReformComparisonCard({ - comparison, - isLoading, - error, - reformId, - onReformChange, -}: { - comparison: ReturnType["data"]; - isLoading: boolean; - error: unknown; - reformId: string; - onReformChange: (value: string) => void; -}) { - const outcome = comparison?.outcomes?.[0]; - const reformOptions = comparison?.available_reforms ?? [ - { - id: "american_family_act_2025", - label: "American Family Act 2025 CTC expansion", - }, - { - id: "working_parents_tax_relief_act_2026", - label: "Working Parents Tax Relief Act EITC enhancement", - }, - { - id: "halve_joint_eitc_phase_out_rate", - label: "Halve joint-filer EITC phase-out rate", - }, - ]; - return ( - - -
- Microsim reform comparison - {comparison?.available ? ( - ran locally - ) : ( - - {isLoading ? "running" : "not available"} - - )} -
- - - - - - Runs the same PolicyEngine-US reform over the incumbent us-data H5 - and the configured Microplex H5. This is a reform-sensitivity check: - it tells us whether the candidate dataset produces a comparable - aggregate policy impact, not whether the reform itself is calibrated. - - - {isLoading && ( - - )} - {error ? ( - - Failed to run microsim comparison: {String(error)} - - ) : null} - {!isLoading && comparison && !comparison.available && ( - - {comparison.reason ?? "No Microplex H5 is configured."} - - )} - {comparison?.available && outcome && ( - <> -
- - us-data {outcome.variable} delta - - } - value={fmt(outcome.us_data.delta)} - detail={`baseline ${fmt(outcome.us_data.baseline.total)}`} - /> - - Microplex {outcome.variable} delta - - } - value={fmt(outcome.microplex.delta)} - detail={`baseline ${fmt(outcome.microplex.baseline.total)}`} - /> - - Delta ratio - - } - value={fmt(outcome.microplex_delta_as_share_of_us_data)} - detail={`gap ${fmt(outcome.delta_gap)}`} - /> -
- - Reform: {comparison.reform?.label ?? "unknown"} for period{" "} - {comparison.period}. Microplex artifact{" "} - - {comparison.microplex_bundle?.artifact_id ?? "unknown"} - - . us-data records: {fmt(outcome.us_data.baseline.record_count)}; - Microplex records: {fmt(outcome.microplex.baseline.record_count)} - {comparison.runtime_seconds != null - ? `; runtime ${comparison.runtime_seconds.toFixed(1)}s` - : ""} - . - {comparison.reform?.source_url ? ( - <> - {" "} - - Source - - . - - ) : null} - - - )} - - - - ); -} - -export default function MicroplexPage() { - const pathname = usePathname(); - const isDiagnosticsView = pathname.startsWith("/microplex/diagnostics"); - const { data, isLoading, error } = useMicroplex(); - const [selectedReformId, setSelectedReformId] = useState( - "american_family_act_2025", - ); - const [compareWithUsData, setCompareWithUsData] = useState(true); - const [targetSearch, setTargetSearch] = useState(""); - const [targetFamily, setTargetFamily] = useState(""); - const [targetGeoLevel, setTargetGeoLevel] = useState(""); - const [targetDirection, setTargetDirection] = useState(""); - const [targetState, setTargetState] = useState(""); - const [targetSupported, setTargetSupported] = useState(""); - const [targetInLoss, setTargetInLoss] = useState(""); - const [targetOffset, setTargetOffset] = useState(0); - const [targetSort, setTargetSort] = useState({ - key: "microplex_vs_target_relative", - direction: "desc", - }); - const reformComparison = useMicroplexReformComparison(selectedReformId); - const targetDiagnosticsQuery = useMicroplexTargetDiagnostics({ - limit: TARGET_DIAGNOSTICS_LIMIT, - offset: targetOffset, - search: targetSearch || undefined, - family: targetFamily || undefined, - geo_level: targetGeoLevel || undefined, - microplex_target_direction: targetDirection || undefined, - state: targetState || undefined, - supported: targetSupported || undefined, - in_loss: targetInLoss || undefined, - sort_by: targetSort?.key, - sort_dir: targetSort?.direction, - }); - - useEffect(() => { - setTargetOffset(0); - }, [ - targetSearch, - targetFamily, - targetGeoLevel, - targetDirection, - targetState, - targetSupported, - targetInLoss, - targetSort, - ]); - - useEffect(() => { - if (targetGeoLevel === "national" && targetState) { - setTargetState(""); - } - }, [targetGeoLevel, targetState]); - - if (isLoading) - return ( - - - - ); - if (error) - return ( - - - Failed to load microplex artifacts: {String(error)} - - - ); - if (!data) return null; - - const h = data.headline; - const native = data.native_scores; - const hasCurrentArtifactScore = - native.source === "configured_run_bundle" || - native.source === "deployed_static_artifact"; - const verdictEntries = data.verdict ? Object.entries(data.verdict) : []; - const leadAuditTargets = data.irs_drilldown.lead_audits.flatMap((a) => - (a.matchingTargets ?? []).map((t) => ({ ...t, audit: a.artifactPath })), - ); - const topFamilies = data.regression_summary.top3_family_counts.slice(0, 5); - const topTargets = data.regression_summary.target_counts_from_audits.slice(0, 8); - const targetDiagnostics = - targetDiagnosticsQuery.data ?? data.target_diagnostics; - const targetDiagnosticsLoading = - targetDiagnosticsQuery.isLoading || targetDiagnosticsQuery.isFetching; - const filteredTargetTotal = targetDiagnostics.total_targets ?? 0; - const unfilteredTargetTotal = - targetDiagnostics.unfiltered_total_targets ?? data.target_diagnostics.total_targets; - - return ( - - -
- Microplex target performance - - Read-only aggregate view of the latest deployed Microplex artifact - snapshot, with historical run summaries from{" "} - - PolicyEngine/microplex-us - - . The primary question here is how Microplex scores against the{" "} - - PolicyEngine target oracle - - . The incumbent us-data baseline is shown only as comparison - context. - -
- - {!isDiagnosticsView && ( - <> - - - - us-data baseline means the incumbent - PolicyEngine us-data dataset scored through the same target - oracle. It is useful context, but the main question is still: - how close is Microplex to each target value? - - - - - - - - -
- - {hasCurrentArtifactScore - ? "Latest artifact broad target score" - : "Historical broad target score"} - - - {native.candidate_beats_baseline - ? "beats us-data baseline" - : "us-data baseline lower loss"} - -
- - - -
- - {hasCurrentArtifactScore - ? "Microplex native loss" - : "Historical Microplex native loss"} - - } - value={fmt(native.candidate_enhanced_cps_native_loss)} - detail={`us-data baseline ${fmt( - native.baseline_enhanced_cps_native_loss, - )}`} - badge={deltaBadge(native.enhanced_cps_native_loss_delta)} - /> - - Microplex unweighted MSRE - - } - value={fmt(native.candidate_unweighted_msre)} - detail={`us-data baseline ${fmt(native.baseline_unweighted_msre)}`} - badge={deltaBadge(native.unweighted_msre_delta)} - /> - - Targets scored - - } - value={fmt(native.n_targets_kept)} - detail={`${fmt(native.n_national_targets)} national, ${fmt( - native.n_state_targets, - )} state, out of ${fmt(native.n_targets_total)} total`} - /> -
- - Lower loss is better.{" "} - {hasCurrentArtifactScore ? ( - <> - These values come from latest deployed artifact{" "} - - {native.artifact_id ?? data.artifact_id ?? "unknown"} - - {native.source_path ? ( - <> - {" "}via{" "} - - {native.source_path} - - - ) : null} - . - - ) : ( - <> - These values come from the committed public parity artifact{" "} - - {data.artifact_id ?? "unknown"} - - , which does not include candidate or baseline dataset paths - and should not be read as a current live Microplex score. - - )}{" "} - The full row-level aggregate table is read from{" "} - - {native.full_target_diagnostics_path} - - {" "}under manifest key{" "} - - {native.full_target_diagnostics_manifest_key} - - . - - - - - - )} - - {isDiagnosticsView && ( - - -
-
- Target diagnostics rows - - Microplex aggregate fit against each calibration target. - -
-
- - - {targetDiagnostics.available ? "loaded" : "not loaded"} - -
-
- - - - - Row-level target diagnostics from{" "} - - {targetDiagnostics.path ?? native.full_target_diagnostics_path} - - . Showing {fmt(targetDiagnostics.targets.length)} of{" "} - {fmt(filteredTargetTotal)} matching rows - {filteredTargetTotal !== unfilteredTargetTotal - ? ` from ${fmt(unfilteredTargetTotal)} total` - : ""} - . - -
-
- - ({ - value: family, - label: family, - })), - ]} - className="w-[250px]" - /> - - - ({ - value: state, - label: state, - })), - ]} - className="w-[125px]" - /> - - - -
-
-
- - {targetDiagnosticsLoading - ? "Loading filtered rows..." - : `Rows ${fmt(targetOffset + 1)}-${fmt( - targetOffset + targetDiagnostics.targets.length, - )} of ${fmt(filteredTargetTotal)}`} - -
- - -
-
- [] - } - sortable - sort={targetSort} - onSortChange={(sort) => { - const nextSort = - sort ?? - (targetSort - ? { - key: targetSort.key, - direction: - targetSort.direction === "desc" ? "asc" : "desc", - } - : null); - setTargetSort(nextSort); - setTargetOffset(0); - }} - styles={{ - table: { minWidth: compareWithUsData ? 1680 : 1080 }, - header: { - paddingLeft: 10, - paddingRight: 10, - whiteSpace: "nowrap", - }, - cell: { - paddingLeft: 10, - paddingRight: 10, - verticalAlign: "top", - whiteSpace: "nowrap", - }, - }} - /> - - - - )} - - {!isDiagnosticsView && ( - <> - - - What needs attention - - - -
- - Supported target rate - - } - value={fmt(h.supported_target_rate, { pct: true })} - detail="share of oracle targets Microplex can evaluate" - /> - - Target win rate - - } - value={fmt(h.target_win_rate, { pct: true })} - detail="share beating us-data baseline in public summary" - /> - - Synthetic records - - } - value={fmt(h.n_synthetic)} - detail={`profile ${h.calibration_target_profile ?? "unknown"}`} - /> -
- -
-
-
- - Recurring regressing families - - - Families that often appear among the top regressions. - -
- []} - /> -
-
-
- - Repeated target flags - - - Targets recurring in audited regressions. - -
- []} - /> -
-
- - - - - - Use this to see whether recent Microplex runs are improving and which - target families dominate failures. Loss delta is candidate minus - us-data baseline; positive means Microplex is worse on that score. - - -
- - - Best public runs - - - - Lowest loss delta across{" "} - {fmt(data.regression_summary.total_scored_runs)} scored runs ( - {fmt(data.regression_summary.total_audited_runs)} audited). - - [] - } - /> - - - - - Worst public runs - - - [] - } - /> - - -
- - - The public drilldown is focused on{" "} - {data.irs_drilldown.family}, - the family leading {data.irs_drilldown.audits_where_family_leads}{" "} - audited regressions. Positive weighted delta means Microplex is worse - than the us-data baseline on that target. - - - - - Largest IRS target gaps - - - [] - } - /> - - - -
- - - Filing-status count gaps - - - [] - } - /> - - - - - MFS AGI-bin count gaps - - - [] - } - /> - - -
- - - - us-data baseline and artifact context - - - -
- - Composite parity loss - - } - value={fmt(h.candidate_composite_parity_loss)} - detail={`us-data baseline ${fmt( - h.baseline_composite_parity_loss, - )}`} - badge={deltaBadge(h.composite_parity_loss_delta)} - /> - - Mean |relative error| - - } - value={fmt(h.candidate_mean_abs_relative_error)} - detail={`us-data baseline ${fmt( - h.baseline_mean_abs_relative_error, - )}`} - badge={deltaBadge(h.mean_abs_relative_error_delta)} - /> - - Slice win rate - - } - value={fmt(h.slice_win_rate, { pct: true })} - detail={`artifact ${data.artifact_id ?? "unknown"}`} - /> -
- - {verdictEntries.length > 0 && ( -
- {verdictEntries.map(([key, passed]) => ( - - {passed ? "pass" : "fail"} {key} - - ))} -
- )} - -
- {data.source_artifacts.map((artifact) => ( - - {artifact.name} - - ))} -
-
    - {data.limitations.map((item) => ( -
  • {item}
    • - ))} -
- - - - - )} - - - ); -} diff --git a/frontend/app/microplex/reforms/page.tsx b/frontend/app/microplex/reforms/page.tsx deleted file mode 100644 index 7b4f28e..0000000 --- a/frontend/app/microplex/reforms/page.tsx +++ /dev/null @@ -1 +0,0 @@ -export { default } from "@/components/microplex/reform-benchmarks-page"; diff --git a/frontend/app/nodes/page.tsx b/frontend/app/nodes/page.tsx deleted file mode 100644 index c9542a5..0000000 --- a/frontend/app/nodes/page.tsx +++ /dev/null @@ -1,186 +0,0 @@ -"use client"; - -import { - Badge, - Card, - CardContent, - CardHeader, - CardTitle, - Input, - Stack, - Text, - Title, - formatNumber, -} from "@policyengine/ui-kit"; -import { useMemo, useState } from "react"; - -import { AppShell } from "@/components/layout/app-shell"; -import { DataTable } from "@/components/shared/InteractiveDataTable"; -import { LoadingBlock } from "@/components/shared/LoadingBlock"; -import { useNodes, type NodeVariable } from "@/lib/api/hooks/use-nodes"; - -type CalFilter = "all" | "calibrated" | "uncalibrated"; -type EntityFilter = "all" | string; - -const columns = [ - { key: "name", header: "Variable", sortable: true }, - { - key: "entity", - header: "Entity", - sortable: true, - format: (val: unknown) => ( - {String(val)} - ), - }, - { - key: "value_type", - header: "Type", - sortable: true, - format: (val: unknown) => ( - {String(val)} - ), - }, - { - key: "is_calibrated", - header: "Calibrated?", - sortable: true, - format: (val: unknown) => - val ? ( - calibrated - ) : ( - uncalibrated - ), - }, - { - key: "label", - header: "Label", - format: (val: unknown) => ( - {String(val)} - ), - }, -]; - -function NodesTable() { - const { data, isLoading, error } = useNodes(); - const [search, setSearch] = useState(""); - const [calFilter, setCalFilter] = useState("all"); - const [entityFilter, setEntityFilter] = useState("all"); - - const entityOptions = useMemo(() => { - if (!data) return []; - return Array.from(new Set(data.items.map((r) => r.entity))).sort(); - }, [data]); - - const filtered = useMemo(() => { - if (!data) return []; - const q = search.trim().toLowerCase(); - return data.items.filter((row: NodeVariable) => { - if (calFilter === "calibrated" && !row.is_calibrated) return false; - if (calFilter === "uncalibrated" && row.is_calibrated) return false; - if (entityFilter !== "all" && row.entity !== entityFilter) return false; - if (!q) return true; - return ( - row.name.toLowerCase().includes(q) || - row.label.toLowerCase().includes(q) - ); - }); - }, [data, search, calFilter, entityFilter]); - - if (isLoading) return ; - if (error) - return ( - - Failed to load node variables: {String(error)} - - ); - if (!data) return ; - - return ( - -
-
- setSearch(e.target.value)} - /> -
-
- - Status - - -
-
- - Entity - - -
-
- - - Showing {formatNumber(filtered.length)} of{" "} - {formatNumber(data.total)} leaf variables ·{" "} - {formatNumber(data.n_calibrated)} calibrated overall ( - {((data.n_calibrated / Math.max(data.total, 1)) * 100).toFixed(1)}%) - - - - - ); -} - -export default function NodesPage() { - return ( - - -
- Node variables - - Leaf inputs in the policyengine_us variable tree — - variables with no formula and no adds/ - subtracts. (Uprating is allowed; it's just - CPI/wage projection of a stored value, not derivation.) These - can't be computed by the microsim and must come from the - underlying dataset or from a calibration target. Use this view to - see which leaves carry a target in the loaded run and which - don't. - -
- - - - Leaves - - - - - - - - ); -} diff --git a/frontend/app/page.tsx b/frontend/app/page.tsx index a121153..c558587 100644 --- a/frontend/app/page.tsx +++ b/frontend/app/page.tsx @@ -1,5 +1,5 @@ import { redirect } from "next/navigation"; export default function Page() { - redirect("/microplex"); + redirect("/populace"); } diff --git a/frontend/app/pipeline/page.tsx b/frontend/app/pipeline/page.tsx deleted file mode 100644 index 29a196c..0000000 --- a/frontend/app/pipeline/page.tsx +++ /dev/null @@ -1,381 +0,0 @@ -"use client"; - -import { useEffect, useMemo, useState } from "react"; -import ReactMarkdown from "react-markdown"; -import { - Card, - CardContent, - CardHeader, - CardTitle, - Skeleton, - Stack, - Title, - Text, - Badge, - formatNumber, -} from "@policyengine/ui-kit"; -import { AppShell } from "@/components/layout/app-shell"; -import { - PIPELINE_OPTIONS, - usePipeline, - useStageDoc, - type PipelineNode, - type PipelineStage, -} from "@/lib/api/hooks/use-pipeline"; -import { useDashboardMode } from "@/lib/dashboard-mode-context"; -import { PipelineGraph } from "@/components/pipeline/pipeline-graph"; - -const STATUS_VARIANT: Record = { - current: "success", - transitional: "warning", - legacy: "secondary", - planned: "secondary", - unknown: "secondary", -}; - -function StageCard({ - stage, - active, - onClick, -}: { - stage: PipelineStage; - active: boolean; - onClick: () => void; -}) { - return ( - - ); -} - -function NodeDetail({ node }: { node: PipelineNode }) { - return ( -
-
- {node.id} - - {node.status ?? "?"} - - {node.node_type} - {(node.pathways ?? []).map((p) => ( - - {p} - - ))} -
- {node.label &&
{node.label}
} - {node.description && ( -

{node.description}

- )} - {node.explanation && ( -
- {node.explanation} -
- )} -
- - {node.source_file?.replace("policyengine_us_data/", "")} - {node.decorator_line ? `:${node.decorator_line}` : ""} - -
- {node.artifacts_in && node.artifacts_in.length > 0 && ( -
- in:{" "} - - {node.artifacts_in.join(", ")} - -
- )} - {node.artifacts_out && node.artifacts_out.length > 0 && ( -
- out:{" "} - - {node.artifacts_out.join(", ")} - -
- )} - {node.implementation_refs && node.implementation_refs.length > 0 && ( -
- implementation:{" "} - - {node.implementation_refs.join(", ")} - -
- )} - {node.validation_commands && node.validation_commands.length > 0 && ( -
- validation:{" "} - - {node.validation_commands.join(", ")} - -
- )} - {node.analyst_questions && node.analyst_questions.length > 0 && ( -
- questions to ask: -
    - {node.analyst_questions.map((question) => ( -
  • {question}
    • - ))} -
-
- )} -
- ); -} - -function StageDoc({ - stageId, - pipelineId, -}: { - stageId: string; - pipelineId: string; -}) { - const q = useStageDoc(stageId, pipelineId); - if (q.isLoading) return ; - if (q.error) - return ( - - Deep-dive for {stageId} not available yet. - - ); - if (!q.data) return null; - return ( -
- {q.data.markdown} -
- ); -} - -export default function PipelinePage() { - const { mode } = useDashboardMode(); - const [selectedPipelineId, setSelectedPipelineId] = useState( - mode === "us-data" ? "us-data" : "microplex-us", - ); - const pipeline = usePipeline(selectedPipelineId); - const [activeStage, setActiveStage] = useState(null); - const [selectedNodeId, setSelectedNodeId] = useState(null); - const [showIsolated, setShowIsolated] = useState(false); - - useEffect(() => { - setActiveStage(null); - setSelectedNodeId(null); - setShowIsolated(false); - }, [selectedPipelineId]); - - useEffect(() => { - setSelectedPipelineId(mode === "us-data" ? "us-data" : "microplex-us"); - }, [mode]); - - const selectedNode = useMemo(() => { - if (!pipeline.data || !selectedNodeId) return null; - return pipeline.data.nodes.find((n) => n.id === selectedNodeId) ?? null; - }, [pipeline.data, selectedNodeId]); - - return ( - - -
- Data pipeline - - Compare the extracted policyengine_us_data DAG with a - curated Microplex-US flow. Select a pipeline, click a stage to - filter, then click a node for its explanation. - -
- - - -
- - - - { - PIPELINE_OPTIONS.find((option) => option.id === selectedPipelineId) - ?.description - } - -
- - - - {pipeline.isLoading && } - {pipeline.error && ( - - - - Failed to load pipeline: {String(pipeline.error)} - - - Run{" "} - python backend/scripts/extract_pipeline_dag.py{" "} - if this is a fresh setup. - - - - )} - - {pipeline.data && ( - <> - - -
-
- - {pipeline.data.pipeline_label ?? selectedPipelineId} - - - {formatNumber(pipeline.data.stats.node_count)} nodes - - - {formatNumber(pipeline.data.stats.edge_count)} edges - - {pipeline.data.source_repo && ( - {pipeline.data.source_repo} - )} -
- {pipeline.data.description && ( - - {pipeline.data.description} - - )} - {pipeline.data.source_urls && pipeline.data.source_urls.length > 0 && ( -
- {pipeline.data.source_urls.map((url) => ( - - {new URL(url).pathname.split("/").slice(-1)[0] || url} - - ))} -
- )} -
- - - -
- {pipeline.data.stages.map((s) => ( - { - setActiveStage(activeStage === s.id ? null : s.id); - setSelectedNodeId(null); - }} - /> - ))} -
- - - -
- - Graph - {activeStage && ( - - filtered to {activeStage} - - )} - - -
- - - - - Showing only nodes that participate in declared data flow ( - {pipeline.data.edges.length} edges). Many pipeline nodes - don't declare formal artifacts_in/out{" "} - metadata and are hidden by default — toggle{" "} - Show isolated nodes to see all{" "} - {pipeline.data.stats.node_count}. - - - - - {selectedNode && ( - - - - Node detail - - - - - - - - )} - - {activeStage && ( - - - Deep dive: {activeStage} - - - - - - )} - - )} - - - ); -} diff --git a/frontend/app/populace/comparison/page.tsx b/frontend/app/populace/comparison/page.tsx deleted file mode 100644 index 17deb17..0000000 --- a/frontend/app/populace/comparison/page.tsx +++ /dev/null @@ -1,10 +0,0 @@ -import { AppShell } from "@/components/layout/app-shell"; -import { PopulaceComparisonView } from "@/components/populace/populace-comparison-view"; - -export default function PopulaceComparisonPage() { - return ( - - - - ); -} diff --git a/frontend/app/providers.tsx b/frontend/app/providers.tsx index 2b427bd..4b57a2b 100644 --- a/frontend/app/providers.tsx +++ b/frontend/app/providers.tsx @@ -2,10 +2,7 @@ import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; import { ReactQueryDevtools } from "@tanstack/react-query-devtools"; -import { Suspense, useState } from "react"; -import { DashboardModeProvider } from "@/lib/dashboard-mode-context"; -import { GeoProvider } from "@/lib/geo-context"; -import { RunProvider } from "@/lib/run-context"; +import { useState } from "react"; export function Providers({ children }: { children: React.ReactNode }) { const [queryClient] = useState( @@ -23,13 +20,7 @@ export function Providers({ children }: { children: React.ReactNode }) { return ( - - - - {children} - - - + {children} ); diff --git a/frontend/app/summary/page.tsx b/frontend/app/summary/page.tsx deleted file mode 100644 index fc978f2..0000000 --- a/frontend/app/summary/page.tsx +++ /dev/null @@ -1,216 +0,0 @@ -"use client"; - -import Link from "next/link"; -import { - Card, - CardHeader, - CardTitle, - CardContent, - Skeleton, - Stack, - Title, - Text, - Badge, - formatNumber, - PEBarChart, - ChartContainer, -} from "@policyengine/ui-kit"; -import { AppShell } from "@/components/layout/app-shell"; -import { useSummary, type SummaryResponse } from "@/lib/api/hooks/use-summary"; -import { useRunContext } from "@/lib/run-context"; - -function pct(v: number | null | undefined, digits = 1): string { - if (v === null || v === undefined || !isFinite(v)) return "—"; - return `${(v * 100).toFixed(digits)}%`; -} - -function num(v: number | null | undefined): string { - if (v === null || v === undefined || !isFinite(v)) return "—"; - return formatNumber(v); -} - -function Kpi({ - label, - value, - hint, -}: { - label: string; - value: string; - hint?: string; -}) { - return ( -
- - {label} - - {value} - {hint && ( - - {hint} - - )} -
- ); -} - -function Scorecard({ data }: { data: SummaryResponse }) { - const h = data.headline; - return ( -
- - - - -
- ); -} - -function ErrorDistribution({ data }: { data: SummaryResponse }) { - const fmt = (v: number) => `${Math.round(v * 100)}%`; - const bins = data.error_distribution.map((b) => ({ - range: b.overflow ? `>${fmt(b.bin_min)}` : `${fmt(b.bin_min)}–${fmt(b.bin_max)}`, - count: b.count, - })); - if (bins.length === 0) { - return No targets to plot.; - } - return ( - - ); -} - -function WorstTargets({ rows }: { rows: SummaryResponse["worst_targets"] }) { - if (rows.length === 0) { - return No targets to show.; - } - return ( - - - - - - - - - - - {rows.map((r) => ( - - - - - - - ))} - -
TargetTarget valueEstimateRel. error
- - {r.target_name} - - {num(r.value)}{num(r.estimate)} - {r.rel_error == null || r.abs_rel_error == null ? ( - — - ) : ( - 0.25 ? "destructive" : "secondary"}> - {r.rel_error >= 0 ? "+" : ""} - {(r.rel_error * 100).toFixed(1)}% - - )} -
- ); -} - -export default function Page() { - const { dataset, run } = useRunContext(); - const summary = useSummary(); - - return ( - - -
- Run summary - - {dataset && run ? ( - <> - Dataset {dataset} · Run{" "} - {run} - - ) : ( - "Select a dataset and run above to load diagnostics." - )} - -
- - {summary.isLoading && ( - <> - - - - - )} - - {summary.error && ( - - - - Failed to load summary: {String(summary.error)} - - - - )} - - {summary.data && ( - <> - - - - - Absolute relative error distribution - - - - - - - - - - - - Top 10 worst-fit targets - - - - - - - - )} - - - ); -} diff --git a/frontend/app/targets/page.tsx b/frontend/app/targets/page.tsx deleted file mode 100644 index 375a263..0000000 --- a/frontend/app/targets/page.tsx +++ /dev/null @@ -1,622 +0,0 @@ -"use client"; - -import { - Card, - CardHeader, - CardTitle, - CardContent, - Badge, - Tabs, - TabsList, - TabsTrigger, - TabsContent, - Skeleton, - Alert, - AlertTitle, - AlertDescription, - Stack, - Group, - Title, - Text, - formatPercent, - formatNumber, - MetricCard, -} from "@policyengine/ui-kit"; -import { DataTable } from "@/components/shared/InteractiveDataTable"; -import { LoadingBlock } from "@/components/shared/LoadingBlock"; -import { AppShell } from "@/components/layout/app-shell"; -import { useTargets } from "@/lib/api/hooks/use-targets"; -import { useSummary } from "@/lib/api/hooks/use-summary"; -import { - useErrorDecomposition, - useConstraintDiff, - useContributors, - useTargetConvergence, - useProvenance, -} from "@/lib/api/hooks/use-target-detail"; -import { - TargetFiltersProvider, - statusToIncludedOnly, - useTargetFilters, -} from "@/lib/target-filters-context"; -import { TargetChipBar } from "@/components/targets/chip-bar"; -import { TargetSearchAndControls } from "@/components/targets/search-and-controls"; -import { TargetPagination } from "@/components/targets/pagination"; -import { RunSelectorCard } from "@/components/targets/run-selector-card"; -import { CompareProvider, useCompareMode } from "@/lib/compare-context"; -import { STATE_FIPS_TO_CODE } from "@/lib/geo-names"; - -/** - * Map a target's (geo_level, geographic_id) to the output dataset file the - * pipeline builds for it. e.g. district 0612 → "districts/CA-12.h5". - */ -function datasetForRow(row: { - geo_level?: string | null; - geographic_id?: string | null; -}): string { - const level = row.geo_level ?? ""; - if (level === "national") return "national/US.h5"; - const gid = row.geographic_id ?? ""; - if (!gid) return "—"; - if (level === "state") { - const n = parseInt(gid, 10); - const code = Number.isFinite(n) ? STATE_FIPS_TO_CODE[n] : null; - return `states/${code ?? gid}.h5`; - } - if (level === "district") { - const n = parseInt(gid, 10); - if (Number.isFinite(n)) { - const state = STATE_FIPS_TO_CODE[Math.floor(n / 100)] ?? String(Math.floor(n / 100)); - const dist = String(n % 100).padStart(2, "0"); - return `districts/${state}-${dist}.h5`; - } - return `districts/${gid}.h5`; - } - return level; -} -import Link from "next/link"; -import { useSearchParams, useRouter } from "next/navigation"; -import { Suspense } from "react"; - -/** Render free-text notes, linkifying URLs. */ -function NotesWithLinks({ notes }: { notes: string }) { - const parts = notes.split(/(https?:\/\/\S+)/g); - return ( - <> - {parts.map((p, i) => - /^https?:\/\//.test(p) ? ( - - {p} - - ) : ( - {p} - ), - )} - - ); -} - -function buildTargetColumns(compareOn: boolean) { - const base = baseTargetColumns; - if (!compareOn) return base; - // Splice compare-only columns immediately after the rel_error column so - // the user reads A → B → Δ left-to-right next to the existing PE - // aggregate / Rel. error pair. - const relIdx = base.findIndex((c) => c.key === "rel_error"); - return [ - ...base.slice(0, relIdx + 1), - { - key: "estimate_b", - header: "PE agg (B)", - align: "right" as const, - format: (val: unknown) => - val == null - ? — - : formatNumber(Number(val)), - }, - { - key: "rel_error_b", - header: "Rel. error (B)", - align: "right" as const, - format: (val: unknown) => { - if (val == null) return —; - const v = Number(val); - const abs = Math.abs(v); - const variant = - abs > 0.5 ? "error" : abs > 0.2 ? "warning" : abs > 0.05 ? "secondary" : "success"; - const display = abs >= 1 ? `${(v * 100).toFixed(0)}%` : `${(v * 100).toFixed(1)}%`; - return {display}; - }, - }, - { - key: "delta", - header: "Δ |err|", - align: "right" as const, - format: (val: unknown) => { - if (val == null || !Number.isFinite(Number(val))) { - return —; - } - const v = Number(val); - // Negative delta = B improved (lower |err|); positive = regressed. - const variant: "success" | "error" | "secondary" = - Math.abs(v) < 1e-6 ? "secondary" : v < 0 ? "success" : "error"; - const sign = v > 0 ? "+" : ""; - return {sign}{(v * 100).toFixed(1)}pp; - }, - }, - ...base.slice(relIdx + 1), - ]; -} - -const baseTargetColumns = [ - { - key: "target_id", - header: "ID", - format: (val: unknown) => - val != null ? `#${val}` : —, - }, - { - key: "geo_display_name", - header: "Geography", - format: (val: unknown) => String(val ?? "National"), - }, - { - key: "dataset", - header: "Dataset", - format: (_val: unknown, row: Record) => ( - - {datasetForRow(row as never)} - - ), - }, - { - key: "variable", - header: "Variable", - format: (val: unknown, row: Record) => { - const constraints = (row.constraints as string[] | undefined) ?? []; - const sub = - constraints.length === 0 - ? "all population" - : constraints.join(", "); - return ( -
- {String(val)} - · {sub} -
- ); - }, - }, - { - key: "target_value", - header: "Target", - align: "right" as const, - format: (val: unknown) => formatNumber(Number(val)), - }, - { - key: "estimate", - header: "PE aggregate", - align: "right" as const, - format: (val: unknown) => - val == null - ? — - : formatNumber(Number(val)), - }, - { - key: "rel_error", - header: "Rel. error", - align: "right" as const, - format: (val: unknown) => { - if (val == null) return —; - const v = Number(val); - const abs = Math.abs(v); - const variant = - abs > 0.5 - ? "error" - : abs > 0.2 - ? "warning" - : abs > 0.05 - ? "secondary" - : "success"; - const display = abs >= 1 ? `${(v * 100).toFixed(0)}%` : `${(v * 100).toFixed(1)}%`; - return {display}; - }, - }, - { - key: "source", - header: "Source", - format: (val: unknown) => - val == null || val === "" ? ( - — - ) : ( - {String(val)} - ), - }, - { - key: "included", - header: "Status", - format: (val: unknown) => - val ? ( - In loss - ) : ( - Not in loss - ), - }, -]; - -const contributorColumns = [ - { - key: "household_idx", - header: "Household", - format: (val: unknown) => ( - - #{String(val)} - - ), - }, - { - key: "g_weight", - header: "G-weight", - align: "right" as const, - format: (val: unknown) => Number(val).toFixed(1), - }, - { - key: "raw_value", - header: "Value", - align: "right" as const, - format: (val: unknown) => Number(val).toLocaleString(), - }, - { - key: "income", - header: "Income", - align: "right" as const, - format: (val: unknown) => `$${Number(val).toLocaleString()}`, - }, - { - key: "in_poverty", - header: "Poverty", - align: "center" as const, - format: (val: unknown) => - val ? ( - Yes - ) : ( - - No - - ), - }, -]; - -const constraintColumns = [ - { - key: "constraint", - header: "Constraint", - format: (_: unknown, row: Record) => - `${row.variable} ${row.operation} ${row.value}`, - }, - { - key: "contributors_satisfying", - header: "Satisfying", - align: "right" as const, - format: (val: unknown) => Number(val).toLocaleString(), - }, - { - key: "contributors_violating", - header: "Violating", - align: "right" as const, - format: (val: unknown) => Number(val).toLocaleString(), - }, - { - key: "status", - header: "Status", - align: "right" as const, - format: (val: unknown) => { - const variant = - val === "OK" - ? "success" - : val === "MINOR_VIOLATION" - ? "warning" - : "error"; - return {String(val)}; - }, - }, -]; - -function TargetTable() { - const { filters, setFilters } = useTargetFilters(); - const { enabled: compareOn, runB } = useCompareMode(); - const searchParams = useSearchParams(); - const selectedIdx = searchParams.get("selected") - ? Number(searchParams.get("selected")) - : null; - - const compareRun = compareOn ? runB : null; - const targets = useTargets({ - sortBy: filters.sortBy, - sortOrder: filters.sortOrder, - search: filters.search, - variables: filters.variables, - geoLevels: filters.geoLevels, - errorBuckets: filters.errorBuckets, - stateFips: - filters.stateFipsList.length > 0 ? filters.stateFipsList : undefined, - sources: filters.sources.length > 0 ? filters.sources : undefined, - datasetFiles: - filters.datasetFiles.length > 0 ? filters.datasetFiles : undefined, - includedOnly: statusToIncludedOnly(filters.status), - compareRun, - limit: filters.pageSize, - offset: filters.page * filters.pageSize, - }); - - const columns = buildTargetColumns(!!compareRun); - - const bundleEvaluated = targets.data?.bundle_evaluated; - - return ( - - {bundleEvaluated && ( -
- Bundle-fit numbers. PE aggregates below were - evaluated against{" "} - {bundleEvaluated}{" "} - — the calibrated h5 the pipeline builds for this bundle — not - the federal enhanced_cps_2024.h5. -
- )} - {targets.data ? ( - { - if (s) { - setFilters({ - sortBy: s.key as typeof filters.sortBy, - sortOrder: s.direction, - }); - } - }} - data={targets.data.items.map((t) => ({ - ...t, - _selected: t.target_idx === selectedIdx, - }))} - /> - ) : targets.error ? ( -
- Failed to load targets: {String(targets.error)} -
- ) : ( - - )} - - - ); -} - -function DetailPanel() { - const searchParams = useSearchParams(); - const router = useRouter(); - const selectedIdx = searchParams.get("selected") - ? Number(searchParams.get("selected")) - : null; - - const errorDecomp = useErrorDecomposition(selectedIdx); - const constraintDiff = useConstraintDiff(selectedIdx); - const contributors = useContributors(selectedIdx, { limit: 10 }); - const convergence = useTargetConvergence(selectedIdx); - const provenance = useProvenance(selectedIdx); - - if (selectedIdx === null) return null; - - return ( - - - - Target detail: #{selectedIdx} - - - - - - - Overview - Convergence - Contributors - Constraints - - - - - {errorDecomp.data && ( - - Error decomposition - - - - - - - - Diagnosis - - {errorDecomp.data.diagnosis} - - - - )} - - {provenance.data && ( - - Provenance - - - Source: {provenance.data.source} - - - Period: {provenance.data.period} - - {provenance.data.tolerance && ( - - Tolerance: {provenance.data.tolerance}% - - )} - - {provenance.data.notes && ( -
- Notes: - -
- )} - - - Constraints: - - {provenance.data.constraints.map((c, i) => ( - - {c.variable} {c.operation} {c.value} - - ))} - - - )} - - - - - {convergence.data && convergence.data.length > 0 ? ( - - - {convergence.data.length} epoch checkpoints. Final rel_error:{" "} - {formatPercent( - convergence.data[convergence.data.length - 1].rel_error, - 1, - )} - - Number(v).toExponential(2), - }, - { - key: "rel_error", - header: "Rel. error", - align: "right" as const, - format: (v: unknown) => formatPercent(Number(v), 1), - }, - ]} - data={convergence.data} - /> - - ) : ( - - No convergence data available - - )} - - - - {contributors.data ? ( - - ) : ( - - )} - - - - {constraintDiff.data ? ( - - ) : ( - - )} - - - - - ); -} - -function TargetExplorerContent() { - return ( - - -
- All targets - - Every target known to policy_data.db. Status shows - whether the active calibration uses it; Dataset shows which output - bundle the pipeline builds it into. - -
- - - -
- - - - -
- - - ); -} - -export default function TargetExplorerPage() { - return ( - - - - - - - - ); -} diff --git a/frontend/app/weights/page.tsx b/frontend/app/weights/page.tsx deleted file mode 100644 index 4dbbe2c..0000000 --- a/frontend/app/weights/page.tsx +++ /dev/null @@ -1,315 +0,0 @@ -"use client"; - -import { - Card, - CardHeader, - CardTitle, - CardContent, - MetricCard, - SegmentedControl, - PEBarChart, - ChartContainer, - Skeleton, - Stack, - Group, - Title, - Tooltip, - TooltipTrigger, - TooltipContent, - TooltipProvider, - formatNumber, -} from "@policyengine/ui-kit"; -import { DataTable } from "@/components/shared/InteractiveDataTable"; -import { AppShell } from "@/components/layout/app-shell"; -import { - useWeightDistribution, - useWeightHistogram, -} from "@/lib/api/hooks/use-weights"; -import { useGeo, useGeoParams } from "@/lib/geo-context"; -import { useState } from "react"; - -function TipHeader({ label, tip }: { label: string; tip: string }) { - return ( - - - - {label} - - -

{tip}

- - - - ); -} - -function colorByDeviation(val: number, baseline: number): string { - const ratio = Math.abs(val - baseline) / Math.max(baseline, 0.001); - if (ratio > 2) return "bg-red-100 text-red-900"; - if (ratio > 1) return "bg-orange-100 text-orange-900"; - if (ratio > 0.5) return "bg-yellow-50 text-yellow-900"; - return ""; -} - -export default function WeightsPage() { - const [sliceBy, setSliceBy] = useState("income_decile"); - const [metric, setMetric] = useState("g_weight"); - - const { geo } = useGeo(); - const geoParams = useGeoParams(); - const distribution = useWeightDistribution({ sliceBy, metric, ...geoParams }); - const decileWeights = useWeightDistribution({ - sliceBy: "income_decile", - metric: "final_weight", - ...geoParams, - }); - const histogram = useWeightHistogram({ metric, logScale: true, ...geoParams }); - - const histogramChartData = - histogram.data?.map((bin) => ({ - range: `${bin.bin_min.toFixed(2)}-${bin.bin_max.toFixed(0)}`, - count: bin.count, - })) ?? []; - - return ( - - - Weight landscape - - {/* Controls */} - - - Metric - - - - Slice by - - - - - {/* Stats cards */} - {distribution.data ? ( - - - - -
- -
- - -

Kish effective N: how many equally-weighted records this dataset is equivalent to. Lower means more information lost to unequal weighting.

- - - - -
- -
- - -

Coefficient of variation of weights (std / mean). CV = 0 means all weights equal. Higher means more extreme weight variation. Values above 1 indicate significant distortion.

- - - - -
- -
- - -

Equal to 1 + CV². Measures how much unequal weighting inflates variance. A design effect of 5 means you'd need 5x fewer equally-weighted records for the same precision.

- - - - -
- -
- - -

Share of total population weight held by the heaviest 1% of records. High values mean a few records dominate all weighted statistics.

- - - - -
- -
- - -

Share of total population weight held by the heaviest 5% of records. In a healthy calibration this should be under 20%.

- - - - - ) : ( - - {Array.from({ length: 5 }).map((_, i) => ( - - ))} - - )} - - {/* Histogram */} - {histogram.data ? ( - - - - ) : ( - - )} - - {/* Weighted population by income decile */} - {decileWeights.data && decileWeights.data.slices.length > 0 && ( - - ({ - decile: s.label, - weighted_population: Math.round(s.n * s.mean), - }))} - xKey="decile" - yKey="weighted_population" - height={300} - fillColor="var(--chart-2)" - /> - - )} - - {/* Slices */} - {distribution.data && distribution.data.slices.length > 0 && (() => { - const slices = distribution.data!.slices; - const overallMean = distribution.data!.mean; - const overallMedian = distribution.data!.median; - - const metricLabel = metric === "g_weight" ? "g-weight" : metric.replace("_", " "); - const sliceColumns = [ - { - key: "label", - header: "Group", - }, - { - key: "n", - header: , - align: "right" as const, - format: (v: unknown) => Number(v).toLocaleString(), - }, - { - key: "weighted_pop", - header: , - align: "right" as const, - format: (v: unknown) => formatNumber(Number(v)), - }, - { - key: "kish_effective_n", - header: , - align: "right" as const, - format: (v: unknown) => formatNumber(Number(v)), - }, - { - key: "efficiency", - header: , - align: "right" as const, - format: (v: unknown) => { - const eff = Number(v); - const cls = eff < 0.1 ? "bg-red-100 text-red-900 px-2 py-0.5 rounded" : - eff < 0.25 ? "bg-orange-100 text-orange-900 px-2 py-0.5 rounded" : - eff < 0.5 ? "bg-yellow-50 text-yellow-900 px-2 py-0.5 rounded" : ""; - return {(eff * 100).toFixed(1)}%; - }, - }, - { - key: "mean", - header: 2x off), orange (>1x), yellow (>0.5x).`} />, - align: "right" as const, - format: (v: unknown) => { - const val = Number(v); - const cls = colorByDeviation(val, overallMean); - return {val.toFixed(3)}; - }, - }, - { - key: "median", - header: , - align: "right" as const, - format: (v: unknown) => { - const val = Number(v); - const cls = colorByDeviation(val, overallMedian); - return {val.toFixed(3)}; - }, - }, - ]; - - const enrichedSlices = slices.map((s) => ({ - ...s, - weighted_pop: Math.round(s.n * s.mean), - efficiency: s.n > 0 ? s.kish_effective_n / s.n : 0, - })); - - return ( - - - Weight analysis by group - - -
- -
- - - ); - })()} - - - ); -} diff --git a/frontend/components/layout/app-shell.tsx b/frontend/components/layout/app-shell.tsx index 37f43fb..0aab635 100644 --- a/frontend/components/layout/app-shell.tsx +++ b/frontend/components/layout/app-shell.tsx @@ -3,13 +3,11 @@ import { DashboardShell, Header } from "@policyengine/ui-kit"; import { GlobalLoader } from "./global-loader"; import { NavSidebar } from "./nav-sidebar"; -import { RunBootstrap } from "./run-bootstrap"; export function AppShell({ children }: { children: React.ReactNode }) { return ( -