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tests/test_icd_mapper.py
────────────────────────────────────────────────────────────────
Unit tests for src/nlp/icd_mapper.py
Uses an in-memory ICD-10 DataFrame so no CSV file is needed.
Covers exact match, fuzzy match, the describe() helper, and
graceful handling of missing dependencies.
────────────────────────────────────────────────────────────────
"""
from __future__ import annotations
from unittest.mock import patch
import pandas as pd
import pytest
from src.nlp.icd_mapper import ICD10Mapper, ICD10Match
# ── Fixtures ──────────────────────────────────────────────────────
@pytest.fixture
def small_icd10_df():
"""Minimal ICD-10 reference table for testing."""
return pd.DataFrame({
"icd10_code": ["I10", "E11", "J18.9", "K21.0"],
"description": ["Essential hypertension",
"Type 2 diabetes mellitus",
"Unspecified pneumonia",
"Gastro-oesophageal reflux disease"],
})
@pytest.fixture
def mapper(small_icd10_df, tmp_path):
"""ICD10Mapper loaded from a temporary parquet file."""
pq = tmp_path / "icd10_codes.parquet"
small_icd10_df.to_parquet(pq)
return ICD10Mapper(icd10_path=pq, top_k=3)
# ── ICD10Match dataclass ──────────────────────────────────────────
class TestICD10Match:
def test_to_dict_rounds_confidence(self):
m = ICD10Match(icd10_code="I10", description="Hypertension",
confidence=0.94567, match_method="fuzzy", rank=1)
assert m.to_dict()["confidence"] == 0.946
def test_to_dict_all_fields(self):
m = ICD10Match(icd10_code="E11", description="Diabetes",
confidence=1.0, match_method="exact", rank=1)
d = m.to_dict()
assert d["icd10_code"] == "E11"
assert d["match_method"] == "exact"
assert d["rank"] == 1
# ── Exact match ───────────────────────────────────────────────────
class TestExactMatch:
def test_exact_match_found(self, mapper):
results = mapper.map("essential hypertension")
assert len(results) == 1
assert results[0].icd10_code == "I10"
assert results[0].match_method == "exact"
assert results[0].confidence == 1.0
def test_case_insensitive_lookup(self, mapper):
results = mapper.map("ESSENTIAL HYPERTENSION")
assert len(results) == 1
assert results[0].icd10_code == "I10"
def test_code_reverse_lookup(self, mapper):
# Looking up "i10" should return the description
results = mapper.map("i10")
assert results and results[0].icd10_code == "I10"
def test_no_match_returns_empty(self, mapper):
# "zymurgy" will not match anything
with (
patch.object(mapper, "_fuzzy_match", return_value=[]),
patch.object(mapper, "_embedding_match", return_value=[]),
):
results = mapper.map("zymurgy")
assert results == []
def test_empty_string_returns_empty(self, mapper):
assert mapper.map("") == []
assert mapper.map(" ") == []
# ── Fuzzy match ───────────────────────────────────────────────────
class TestFuzzyMatch:
def test_fuzzy_match_spelling_variant(self, mapper):
# "hypertensioon" — one extra char — should still fuzzy-match
results = mapper._fuzzy_match("hypertensioon")
# If rapidfuzz is installed and score >= 80, we get a result
# If not installed, we get []
if results:
assert results[0].match_method == "fuzzy"
assert results[0].confidence > 0.7
def test_fuzzy_returns_empty_when_rapidfuzz_missing(self, mapper):
with (
patch.dict("sys.modules", {"rapidfuzz": None}),
patch("builtins.__import__", side_effect=ImportError),
):
results = mapper._fuzzy_match("hypertension")
# Should return [] gracefully, not raise
assert isinstance(results, list)
# ── describe() ───────────────────────────────────────────────────
class TestDescribe:
def test_describe_known_code(self, mapper):
desc = mapper.describe("I10")
assert desc == "Essential hypertension"
def test_describe_lowercase_code(self, mapper):
desc = mapper.describe("i10")
assert desc == "Essential hypertension"
def test_describe_unknown_code_returns_none(self, mapper):
assert mapper.describe("Z99.99") is None
# ── map_entities() ────────────────────────────────────────────────
class TestMapEntities:
def test_only_maps_disease_and_symptom(self, mapper):
from src.nlp.ner import Entity
entities = [
Entity(text="essential hypertension", label="DISEASE",
start=0, end=22),
Entity(text="metformin", label="MEDICATION",
start=30, end=39), # should be skipped
]
results = mapper.map_entities(entities)
assert "essential hypertension" in results
assert "metformin" not in results
def test_returns_empty_for_no_matches(self, mapper):
from src.nlp.ner import Entity
entities = [
Entity(text="xyzxyz", label="DISEASE", start=0, end=6),
]
with (
patch.object(mapper, "_fuzzy_match", return_value=[]),
patch.object(mapper, "_embedding_match", return_value=[]),
):
results = mapper.map_entities(entities)
assert results == {}
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