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
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.
45 lines
1.3 KiB
Python
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()
|