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f266901 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 | """Guards for the premium correctness work (#38 + #36-B + #37b).
Pins the bugs the user surfaced 2026-05-18:
- canonical-match: doctype-suffixed ids reach their real sample
- sample sanity guard: a bad curated sample (SBI Arogya Supreme
brochure-extract, ~₹10k/L) can NEVER emit an absurd premium
- sample normalization (#38): floater-priced samples are NOT
double-counted by the floater multiplier
- attribute model (#36-B): quote-less policies are differentiated by
product TYPE — a top-up is not priced like a comprehensive plan
- provenance label (#37b): sample-anchored vs modelled is explicit
- REGRESSION: the sample-anchored policies that were already sane
must stay sane (no swing) when none of the above misfires.
"""
from __future__ import annotations
import sys
from pathlib import Path
_REPO = Path(__file__).resolve().parent.parent
if str(_REPO) not in sys.path:
sys.path.insert(0, str(_REPO))
from backend.premium_calculator import ( # noqa: E402
_attribute_base_factor,
estimate,
)
PROFILE = dict(
age=34, sum_insured_inr=1_200_000, city_tier="metro", smoker=False,
family_size=3, pre_existing_conditions="none", copayment_pct=0.0,
)
def _pt(pid, **over):
return estimate(policy_id=pid, **{**PROFILE, **over})
# --- attribute base factor: type-differentiated, comprehensive == 1.0 ----
def test_attribute_factor_by_type():
# Post-rebuild factors via the real-fact-aware _policy_product_type
# classifier (synthetic ids fall back to id keywords).
assert _attribute_base_factor("x-insurer__no-such-comprehensive-plan") == 1.0
assert _attribute_base_factor("acko__x-super-top-up__wordings") == 0.32
assert _attribute_base_factor("x__hospital-cash") == 0.30
assert _attribute_base_factor("aditya-birla__activ-secure-cancer-secure") == 0.50
assert _attribute_base_factor("acko__arogya-sanjeevani") == 0.70
assert _attribute_base_factor(None) == 1.0
# --- #38/#44: SBI bad data REPLACED by real harvested samples ----------
def test_sbi_now_real_anchored_and_sane():
# The bad brochure-extract was physically replaced by 2 real official
# SBI rate-chart figures (UIN SBIHLIP21043V012122) + unquarantined.
# It must now be sample-anchored AND sane (never the ₹146,800 absurd).
e = _pt("sbi-general__arogya-supreme__brochure")
assert e.base_sample_used is not None, "SBI should now use its real harvested sample"
assert 3_000 < e.point_estimate_inr < 60_000, (
f"SBI out of sane band: ₹{e.point_estimate_inr:,}"
)
# --- #38 regression: real-sample policies stay sample-anchored & sane ---
def test_sample_anchored_policies_not_regressed():
for pid, lo, hi in [
("icici-lombard__elevate__brochure", 8_000, 60_000),
("hdfc-ergo__optima-secure__wordings", 6_000, 50_000),
("aditya-birla__group-activ-health__wordings", 6_000, 50_000),
]:
e = _pt(pid)
assert e.base_sample_used is not None, f"{pid} lost its real sample"
assert lo < e.point_estimate_inr < hi, (
f"{pid} swung out of sane band: ₹{e.point_estimate_inr:,}"
)
def test_legit_topup_sample_preserved_cheap():
e = _pt("royal-sundaram__advanced-top-up__brochure", sum_insured_inr=4_500_000)
assert e.base_sample_used is not None
assert e.point_estimate_inr < 15_000, (
f"legit top-up sample broken: ₹{e.point_estimate_inr:,}"
)
# --- #36-B: quote-less policies of different TYPE must NOT collide ------
def test_quoteless_types_do_not_collide():
comprehensive = _pt("royal-sundaram__family-plus__cis").point_estimate_inr
topup = _pt("acko__acko-health-iii-platinum-super-top-up__wordings").point_estimate_inr
cancer = _pt("aditya-birla__activ-secure-cancer-secure__brochure").point_estimate_inr
assert comprehensive != topup, "top-up priced same as comprehensive (collision)"
assert comprehensive != cancer, "cancer plan priced same as comprehensive"
assert topup < comprehensive, "top-up must be materially cheaper"
# --- #37b: provenance label is explicit and correct --------------------
def test_provenance_label_distinguishes_sample_vs_model():
s = _pt("icici-lombard__elevate__brochure")
assert s.base_sample_used is not None
assert "public quote we collected" in s.methodology
# A policy with no curated sample AND no extraction → model path
# (royal-sundaram__family-plus now HAS a real harvested sample, so use
# a synthetic id that can never resolve to a sample).
m = _pt("nonexistent-insurer__no-such-plan-zzz__wordings")
assert m.base_sample_used is None
assert "Modelled" in m.methodology and "not a quote" in m.methodology
if __name__ == "__main__":
import pytest
raise SystemExit(pytest.main([__file__, "-v"]))
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