Files
stack/tests/lead/conftest.py
kert cb9976e160
All checks were successful
CI / lint (push) Successful in 44s
CI / notebooks-smoke (push) Successful in 1m33s
Deploy / changes (push) Successful in 14s
CI / test (push) Successful in 3m9s
Infra CI / zotero (push) Successful in 14s
Infra CI / notebooks (push) Successful in 55s
Infra CI / api (push) Successful in 1m9s
Infra CI / docs (push) Successful in 1m23s
Infra CI / llm (push) Successful in 1m1s
Infra CI / mc (push) Successful in 16s
Infra CI / bcda (push) Successful in 1m43s
Deploy / notebooks (push) Has been skipped
Deploy / zotero (push) Has been skipped
Deploy / docs (push) Has been skipped
Package Supply Chain / pkg-supply-chain (push) Successful in 1m30s
Deploy / api (push) Successful in 1m17s
Deploy / llm (push) Successful in 1m18s
Deploy / mc (push) Has been skipped
Deploy / bcda (push) Successful in 1m42s
Deploy / report (push) Successful in 1m36s
feat(lead): LEAD PY2027 financial methodology — settlement and benchmark engines
Ports the public CMS "LEAD Model PY 2027 Alignment and Financial
Methodology Paper" v1.2 (bib E4PEU3HV) as generic engines: parameters
come from the paper, everything CMS computes per ACO or per year comes
from the caller. No participant data, no organization-specific rules.

- lead.params: Table 5.2.1 discounts, 3% Quality Withhold, 2% retention
  incentive, Box 5.3.3 Stop-Loss bands, Tables 5.4.1/5.4.3 corridors, 2%
  sequestration, base-year weights, Table 3.5.1 ACPT guardrails,
  risk-score growth caps, REA/PSA shares and caps, voluntary guardrail
  and 500 minimum, Table 2.6.1 base-year alignment minimums.
- lead.settlement: Final Financial Settlement, Table 5.6.1 line by line
  (settle(SettlementInputs) -> SettlementLines).
- lead.stop_loss: Box 5.3.3 charge, banded payout, neutrality factor,
  application to PY expenditure.
- lead.monies_owed: Table 5.5.1 Total Monies Owed, APO reconciliation.
- lead.timing: run-out end, settlement-basis rule, settlement-stage inputs.
- lead.benchmark: §3 formulas — risk standardization, two-/three-way
  trend, base-year weights and sufficiency, voluntary guardrail,
  regional FFS, spending designation, REA, PSA, risk-score cap, hybrid
  alignment dollars, category blend.
- lead.express: the same as narwhals Exprs; express.settlement.settle
  settles a frame of scenarios on any backend.

Rounding: every dollar line rounds to the cent (half away from zero) as
it is produced; PBPMs, rates and factors never do. It is the only rule
under which Tables 3.3.3, 3.5.3.2, 3.5.4, 5.5.1 and 5.6.1 all reproduce
(3.5.4 line 24 sums cent-rounded cells; 3.3.3 carries unrounded PBPMs).
The Expr cents snaps its ÷100 with round(2): polars 2.0 divides a column
by a literal as a reciprocal multiply (k × 0.01), one ulp off k/100 for
~15% of values.

Missing inputs stay missing (None/null or a refused input model); the
only zeros are the paper's own (Professional "N/A" discount, no
Stop-Loss election, sequestration on losses, ineligible REA/PSA).

Paper inconsistencies recorded in tests: Table 5.5.1 line 2 is a cent
below Table 5.6.1 line 28 (.43 vs .44); Box 3.3.2 prints 1.104 then says
1.103; §3.5.1 prose widens the ACPT lower bound a year later than
Table 3.5.1 (the table is followed).

Tests: 283 in tests/lead — every published line to the cent, Expr
builders on polars and DuckDB, bit-for-bit scalar/frame parity over 160
scenarios; 100% of src/lead.
2026-10-27 14:11:10 -04:00

45 lines
1.3 KiB
Python

"""Backends for the lead Expr tests: every frame test runs on polars and on
DuckDB through narwhals."""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
import duckdb
import narwhals as nw
import polars as pl
import pytest
class Backend:
"""Build a native frame on one backend and read results back as rows."""
def __init__(self, name: str) -> None:
self.name = name
self.con = duckdb.connect() if name == "duckdb" else None
def frame(self, data: Any, schema: dict[str, Any] | None = None) -> Any:
df = pl.DataFrame(data, schema=schema)
return df if self.con is None else self.con.from_arrow(df.to_arrow())
def rows(self, native: Any) -> list[dict[str, Any]]:
return nw.from_native(native).lazy().collect("polars").to_native().to_dicts()
def select(self, data: Any, schema: dict[str, Any] | None = None, **exprs: nw.Expr):
frame = nw.from_native(self.frame(data, schema))
return self.rows(frame.select(**exprs).to_native())
def close(self) -> None:
if self.con is not None:
self.con.close()
@pytest.fixture(params=["polars", "duckdb"])
def backend(request: pytest.FixtureRequest) -> Iterator[Backend]:
engine = Backend(request.param)
try:
yield engine
finally:
engine.close()