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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.
88 lines
2.6 KiB
Python
88 lines
2.6 KiB
Python
"""lead.express.stop_loss: beneficiary-grain Stop-Loss on polars and DuckDB,
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matching Box 5.3.3 and the scalar engine."""
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from __future__ import annotations
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import random
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import polars as pl
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from lead.express.stop_loss import (
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band_status,
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distance_to_attachment_point,
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predicted_expenditure,
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residual_expenditure,
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stop_loss_payout,
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)
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from lead.stop_loss import band_status as scalar_band_status
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from lead.stop_loss import stop_loss_payout as scalar_payout
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from tests.lead.paper import STOP_LOSS_PAYOUT
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SCHEMA = {"actual": pl.Float64, "predicted": pl.Float64, "ap": pl.Float64}
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def test_box_5_3_3_illustration(backend):
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data = [
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{
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"actual": STOP_LOSS_PAYOUT["actual"],
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"predicted": STOP_LOSS_PAYOUT["predicted"],
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"ap": STOP_LOSS_PAYOUT["attachment_point"],
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}
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]
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residual = residual_expenditure("actual", "predicted")
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(result,) = backend.select(
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data,
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SCHEMA,
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residual=residual,
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payout=stop_loss_payout(residual, "ap"),
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status=band_status(residual, "ap"),
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distance=distance_to_attachment_point("ap", residual),
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)
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assert result == {
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"residual": 400_000.0,
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"payout": 220_000.0,
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"status": "band_2",
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"distance": -250_000.0,
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}
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def test_predicted_expenditure(backend):
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data = {"regional": [1_000.0], "risk": [1.2], "months": [12.0]}
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(result,) = backend.select(
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data, predicted=predicted_expenditure("regional", "risk", "months")
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)
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assert result["predicted"] == 14_400.0
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def test_payout_and_band_match_the_scalar(backend):
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rng = random.Random(3)
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data = [
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{
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"actual": rng.uniform(0, 900_000),
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"predicted": rng.uniform(0, 200_000),
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"ap": rng.choice([150_000.0, 98_765.43]),
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}
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for _ in range(200)
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]
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data += [
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{"actual": None, "predicted": 1.0, "ap": 150_000.0},
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{"actual": 1.0, "predicted": 0.0, "ap": None},
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]
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residual = residual_expenditure("actual", "predicted")
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rows = backend.select(
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data,
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SCHEMA,
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residual=residual,
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payout=stop_loss_payout(residual, "ap"),
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status=band_status(residual, "ap"),
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)
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for inputs, result in zip(data, rows, strict=True):
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if inputs["actual"] is None or inputs["ap"] is None:
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assert (result["payout"], result["status"]) == (None, None)
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continue
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assert (
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result["payout"]
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== scalar_payout(result["residual"], inputs["ap"]).total_payout
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)
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assert result["status"] == scalar_band_status(result["residual"], inputs["ap"])
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