tests/test_risk_models/test_mrat_model.py

"""Tests for the MRAT Melanoma Risk Model.

Ground truth values collected from: https://mrisktool.cancer.gov/calculator.html.
All scenarios assume the patient is Non-Hispanic white, matching the published MRAT scope.
"""

import pytest

from sentinel.risk_models import MRATRiskModel
from sentinel.user_input import (
    Anthropometrics,
    ComplexionLevel,
    Demographics,
    DermatologicProfile,
    FemaleSmallMolesCategory,
    FemaleTanResponse,
    FrecklingIntensity,
    Lifestyle,
    MaleSmallMolesCategory,
    PersonalMedicalHistory,
    Sex,
    SmokingHistory,
    SmokingStatus,
    UserInput,
    USGeographicRegion,
)

GROUND_TRUTH_CASES = [
    {
        "name": "male_light_complexion_high_damage",
        "input": UserInput(
            demographics=Demographics(
                age_years=30,
                sex=Sex.MALE,
                anthropometrics=Anthropometrics(height_cm=175.0, weight_kg=70.0),
            ),
            lifestyle=Lifestyle(
                smoking=SmokingHistory(status=SmokingStatus.NEVER),
            ),
            personal_medical_history=PersonalMedicalHistory(),
            dermatologic=DermatologicProfile(
                region=USGeographicRegion.NORTHERN,
                complexion=ComplexionLevel.LIGHT,
                freckling=FrecklingIntensity.MILD,
                male_sunburn=True,  # True=YES, had sunburn
                male_has_two_or_more_big_moles=False,  # False=<2 moles
                male_small_moles=MaleSmallMolesCategory.LESS_THAN_SEVEN,
                solar_damage=False,  # False=NO damage
            ),
        ),
        "expected": 0.02,
    },
    {
        "name": "male_medium_complexion_average_moles",
        "input": UserInput(
            demographics=Demographics(
                age_years=45,
                sex=Sex.MALE,
                anthropometrics=Anthropometrics(height_cm=175.0, weight_kg=70.0),
            ),
            lifestyle=Lifestyle(
                smoking=SmokingHistory(status=SmokingStatus.NEVER),
            ),
            personal_medical_history=PersonalMedicalHistory(),
            dermatologic=DermatologicProfile(
                region=USGeographicRegion.CENTRAL,
                complexion=ComplexionLevel.MEDIUM,
                freckling=FrecklingIntensity.MODERATE,
                male_sunburn=False,  # False=NO sunburn
                male_has_two_or_more_big_moles=True,  # True=≥2 moles
                male_small_moles=MaleSmallMolesCategory.SEVEN_TO_SIXTEEN,
                solar_damage=True,  # True=YES damage
            ),
        ),
        "expected": 0.54,
    },
    {
        "name": "female_central_region_moderate_features",
        "input": UserInput(
            demographics=Demographics(
                age_years=50,
                sex=Sex.FEMALE,
                anthropometrics=Anthropometrics(height_cm=165.0, weight_kg=65.0),
            ),
            lifestyle=Lifestyle(
                smoking=SmokingHistory(status=SmokingStatus.NEVER),
            ),
            personal_medical_history=PersonalMedicalHistory(),
            dermatologic=DermatologicProfile(
                region=USGeographicRegion.SOUTHERN,
                complexion=ComplexionLevel.MEDIUM,
                freckling=FrecklingIntensity.MODERATE,
                female_tan=FemaleTanResponse.MODERATE,
                female_small_moles=FemaleSmallMolesCategory.FIVE_TO_ELEVEN,
            ),
        ),
        "expected": 0.16,
    },
    {
        "name": "female_northern_region_severe_freckling",
        "input": UserInput(
            demographics=Demographics(
                age_years=65,
                sex=Sex.FEMALE,
                anthropometrics=Anthropometrics(height_cm=165.0, weight_kg=65.0),
            ),
            lifestyle=Lifestyle(
                smoking=SmokingHistory(status=SmokingStatus.NEVER),
            ),
            personal_medical_history=PersonalMedicalHistory(),
            dermatologic=DermatologicProfile(
                region=USGeographicRegion.NORTHERN,
                complexion=ComplexionLevel.LIGHT,
                freckling=FrecklingIntensity.SEVERE,
                female_tan=FemaleTanResponse.NONE,
                female_small_moles=FemaleSmallMolesCategory.TWELVE_OR_MORE,
            ),
        ),
        "expected": 1.19,
    },
    {
        "name": "male_dark_complexion_extensive_moles",
        "input": UserInput(
            demographics=Demographics(
                age_years=55,
                sex=Sex.MALE,
                anthropometrics=Anthropometrics(height_cm=175.0, weight_kg=70.0),
            ),
            lifestyle=Lifestyle(
                smoking=SmokingHistory(status=SmokingStatus.NEVER),
            ),
            personal_medical_history=PersonalMedicalHistory(),
            dermatologic=DermatologicProfile(
                region=USGeographicRegion.SOUTHERN,
                complexion=ComplexionLevel.DARK,
                freckling=FrecklingIntensity.ABSENT,
                male_sunburn=False,  # False=NO sunburn
                male_has_two_or_more_big_moles=False,  # False=<2 moles
                male_small_moles=MaleSmallMolesCategory.SEVENTEEN_OR_MORE,
                solar_damage=False,  # False=NO damage
            ),
        ),
        "expected": 0.52,
    },
]


class TestMRATModel:
    """Test suite for MRATRiskModel."""

    def setup_method(self) -> None:
        """Initialise the MRAT model instance for each test."""
        self.model = MRATRiskModel()

    @pytest.mark.parametrize("case", GROUND_TRUTH_CASES, ids=lambda x: x["name"])
    def test_ground_truth_placeholders(self, case):
        """Check that absolute risk calculation returns a float for each scenario.

        Args:
            case (dict[str, MRATInput | float | str]): Test scenario definition.
        """
        result = self.model.absolute_risk(case["input"])
        assert isinstance(result, float)

    def test_compute_score_male_user(self):
        """Ensure male user profiles yield a percentage string from compute_score."""
        user = UserInput(
            demographics=Demographics(
                age_years=42,
                sex=Sex.MALE,
                anthropometrics=Anthropometrics(height_cm=175.0, weight_kg=70.0),
            ),
            lifestyle=Lifestyle(
                smoking=SmokingHistory(status=SmokingStatus.NEVER),
            ),
            personal_medical_history=PersonalMedicalHistory(),
            dermatologic=DermatologicProfile(
                region=USGeographicRegion.NORTHERN,
                complexion=ComplexionLevel.LIGHT,
                freckling=FrecklingIntensity.MILD,
                male_sunburn=True,
                male_has_two_or_more_big_moles=True,
                male_small_moles=MaleSmallMolesCategory.SEVEN_TO_SIXTEEN,
                solar_damage=False,
            ),
        )

        score = self.model.compute_score(user)
        assert score.endswith("%")

    def test_compute_score_female_user(self):
        """Ensure female user profiles yield a percentage string from compute_score."""
        user = UserInput(
            demographics=Demographics(
                age_years=37,
                sex=Sex.FEMALE,
                anthropometrics=Anthropometrics(height_cm=165.0, weight_kg=65.0),
            ),
            lifestyle=Lifestyle(
                smoking=SmokingHistory(status=SmokingStatus.NEVER),
            ),
            personal_medical_history=PersonalMedicalHistory(),
            dermatologic=DermatologicProfile(
                region=USGeographicRegion.SOUTHERN,
                complexion=ComplexionLevel.MEDIUM,
                freckling=FrecklingIntensity.MODERATE,
                female_tan=FemaleTanResponse.MODERATE,
                female_small_moles=FemaleSmallMolesCategory.FIVE_TO_ELEVEN,
            ),
        )

        score = self.model.compute_score(user)
        assert score.endswith("%")

    def test_missing_dermatologic(self):
        """Verify missing dermatologic information raises ValueError."""
        user = UserInput(
            demographics=Demographics(
                age_years=30,
                sex=Sex.MALE,
                anthropometrics=Anthropometrics(height_cm=175.0, weight_kg=70.0),
            ),
            lifestyle=Lifestyle(
                smoking=SmokingHistory(status=SmokingStatus.NEVER),
            ),
            personal_medical_history=PersonalMedicalHistory(),
            dermatologic=None,
        )

        with pytest.raises(ValueError, match=r"Invalid inputs for MRAT:"):
            self.model.compute_score(user)

    def test_validation_errors(self):
        """Test that model raises ValueError for invalid inputs."""
        user_input = UserInput(
            demographics=Demographics(
                age_years=15,  # Below minimum
                sex=Sex.MALE,
                anthropometrics=Anthropometrics(height_cm=175.0, weight_kg=70.0),
            ),
            lifestyle=Lifestyle(
                smoking=SmokingHistory(status=SmokingStatus.NEVER),
            ),
            personal_medical_history=PersonalMedicalHistory(),
            dermatologic=DermatologicProfile(
                region=USGeographicRegion.NORTHERN,
                complexion=ComplexionLevel.LIGHT,
                freckling=FrecklingIntensity.MILD,
            ),
        )

        with pytest.raises(ValueError, match=r"Invalid inputs for MRAT:"):
            self.model.compute_score(user_input)