tests/test_numeric.py

"""Tests for the numeric block."""

from __future__ import annotations

import pandas as pd
import pytest

from src.numeric.obesity import (
    OBESITY_LEVELS,
    apply_favc_override,
    derive_high_caloric_meal,
)
from src.numeric.profile import (
    bmi_to_band,
    bmr_mifflin_st_jeor,
    daily_target_kcal,
    macro_targets,
    tdee,
)
from src.numeric.model import predict


def _profile(**overrides):
    """Build a complete profile dict with sensible defaults for tests."""
    base = {
        "age": 30,
        "weight_kg": 75,
        "height_cm": 175,
        "Gender": "Male",
        "family_history_with_overweight": "no",
        "FAVC": "no",
        "FCVC": 2.0,
        "NCP": 3.0,
        "CAEC": "Sometimes",
        "SMOKE": "no",
        "CH2O": 2.0,
        "SCC": "no",
        "FAF": 1.0,
        "TUE": 1.0,
        "CALC": "no",
        "MTRANS": "Public_Transportation",
        "activity_level": "moderate",
        "goal": "maintain",
    }
    base.update(overrides)
    return base


def test_bmr_known_values():
    # Mifflin-St Jeor for a 30-year-old, 75kg, 178cm male:
    # 10*75 + 6.25*178 - 5*30 + 5 = 1717.5 kcal
    assert bmr_mifflin_st_jeor(30, 75, 178, "male") == pytest.approx(1717.5, abs=0.5)


def test_tdee_scales_with_activity():
    bmr = 1700
    assert tdee(bmr, "sedentary") < tdee(bmr, "moderate") < tdee(bmr, "very active")


def test_target_respects_goal():
    args = (30, 75, 178, "male", "moderate")
    lose = daily_target_kcal(*args, "lose")
    maintain = daily_target_kcal(*args, "maintain")
    gain = daily_target_kcal(*args, "gain")
    assert lose < maintain < gain
    assert maintain - lose == 500
    assert gain - maintain == 300


def test_macro_targets_sum_to_target_kcal_within_rounding():
    target = 2500.0
    m = macro_targets(target, "maintain")
    total = m["protein_g"] * 4 + m["fat_g"] * 9 + m["carbohydrate_g"] * 4
    assert total == pytest.approx(target, rel=0.01)


def test_high_caloric_meal_threshold():
    high = {"calories_kcal": 850, "fat_g": 40}
    low = {"calories_kcal": 400, "fat_g": 15}
    borderline = {"calories_kcal": 750, "fat_g": 20}
    assert derive_high_caloric_meal(high) is True
    assert derive_high_caloric_meal(low) is False
    assert derive_high_caloric_meal(borderline) is False


def test_favc_override_flips_columns():
    row = pd.DataFrame([{"FAVC_yes": 0, "FAVC_no": 1, "Age": 30}])
    out = apply_favc_override(row, {"calories_kcal": 900, "fat_g": 35})
    assert int(out["FAVC_yes"].iloc[0]) == 1
    assert int(out["FAVC_no"].iloc[0]) == 0


def test_favc_override_noop_for_low_calorie_meal():
    row = pd.DataFrame([{"FAVC_yes": 0, "FAVC_no": 1}])
    out = apply_favc_override(row, {"calories_kcal": 400, "fat_g": 10})
    assert int(out["FAVC_yes"].iloc[0]) == 0


def test_predict_returns_expected_shape_without_models():
    """When models are absent, predict() must return a coherent fallback."""
    out = predict(_profile(weight_kg=75, height_cm=178), nutrition=None)
    assert out["obesity_class"] in OBESITY_LEVELS
    assert out["daily_target_kcal"] > 0
    assert out["predicted_bmi"] > 0
    assert "models" in out


# ───────────────────────────────────────────────────────────────────
# BMI-band rule (transparent, training-data-independent)
# ───────────────────────────────────────────────────────────────────


@pytest.mark.parametrize(
    "bmi, expected",
    [
        (12.0, "Insufficient_Weight"),
        (17.0, "Insufficient_Weight"),
        (18.4, "Insufficient_Weight"),
        (18.5, "Normal_Weight"),
        (22.0, "Normal_Weight"),
        (24.9, "Normal_Weight"),
        (25.0, "Overweight_Level_I"),
        (27.4, "Overweight_Level_I"),
        (27.5, "Overweight_Level_II"),
        (29.9, "Overweight_Level_II"),
        (30.0, "Obesity_Type_I"),
        (34.9, "Obesity_Type_I"),
        (35.0, "Obesity_Type_II"),
        (39.9, "Obesity_Type_II"),
        (40.0, "Obesity_Type_III"),
        (60.0, "Obesity_Type_III"),
        (100.0, "Obesity_Type_III"),
    ],
)
def test_bmi_to_band_covers_full_severity_range(bmi, expected):
    assert bmi_to_band(bmi) == expected


# ───────────────────────────────────────────────────────────────────
# Predict shape: every call must populate the anomaly fields
# ───────────────────────────────────────────────────────────────────


def test_predict_always_returns_bmi_band_and_anomaly_fields():
    out = predict(_profile(), nutrition=None)
    assert out["bmi_band"] in OBESITY_LEVELS
    assert "anomaly_flags" in out
    assert isinstance(out["anomaly_flags"], list)
    assert "ood_blended" in out
    assert "bmi_raw" in out


def test_predict_probabilities_sum_to_one():
    out = predict(_profile(), nutrition=None)
    total = sum(out["obesity_probabilities"].values())
    assert total == pytest.approx(1.0, abs=1e-3)


# ───────────────────────────────────────────────────────────────────
# Realistic profiles — model should land on (or near) the BMI band
# ───────────────────────────────────────────────────────────────────


@pytest.mark.parametrize(
    "label, profile_kwargs, expected_band",
    [
        ("lean male",         dict(weight_kg=65, height_cm=178),                      "Normal_Weight"),
        ("normal male",       dict(weight_kg=75, height_cm=178),                      "Normal_Weight"),
        ("overweight ii",     dict(weight_kg=90, height_cm=178),                      "Overweight_Level_II"),
        ("obese i",           dict(weight_kg=100, height_cm=178),                     "Obesity_Type_I"),
        ("obese ii",          dict(weight_kg=115, height_cm=178),                     "Obesity_Type_II"),
        ("obese iii female",  dict(weight_kg=110, height_cm=165, Gender="Female"),    "Obesity_Type_III"),
        ("insufficient fem.", dict(weight_kg=45,  height_cm=165, Gender="Female"),    "Insufficient_Weight"),
    ],
)
def test_predict_matches_bmi_band_on_realistic_profiles(label, profile_kwargs, expected_band):
    out = predict(_profile(**profile_kwargs), nutrition=None)
    assert out["bmi_band"] == expected_band, f"{label}: bmi_band wrong"
    # Final class within 1 step of the BMI band — the classifier is allowed
    # to land on an adjacent severity bucket for borderline BMIs.
    gap = abs(OBESITY_LEVELS.index(out["obesity_class"]) - OBESITY_LEVELS.index(expected_band))
    assert gap <= 1, (
        f"{label}: predicted {out['obesity_class']} is {gap} classes "
        f"from expected band {expected_band}"
    )
    # Most probability mass should sit on the band or an adjacent bucket.
    top_two = sorted(out["obesity_probabilities"].items(), key=lambda kv: -kv[1])[:2]
    assert sum(p for _, p in top_two) > 0.7, f"{label}: top-2 mass under 70%"


# ───────────────────────────────────────────────────────────────────
# Anomaly handling — out-of-distribution / rare-conditional inputs
# ───────────────────────────────────────────────────────────────────


def test_anomaly_extreme_overweight_male_routes_to_type_iii():
    """Male BMI 44.2 — Type_III training data is 99.7% female, so the
    raw classifier predicts Type_II. The BMI-band override must correct
    this and the final class must be Type_III."""
    out = predict(_profile(weight_kg=140, height_cm=178, Gender="Male"), nutrition=None)
    assert out["bmi_band"] == "Obesity_Type_III"
    assert out["obesity_class"] == "Obesity_Type_III"
    assert out["ood_blended"] is True
    # A flag mentioning the female-bias must be visible to the user.
    assert any("female" in f.lower() for f in out["anomaly_flags"])
    assert out["obesity_probabilities"]["Obesity_Type_III"] > 0.5


def test_anomaly_weight_above_training_range_is_flagged_and_blended():
    out = predict(_profile(weight_kg=220, height_cm=178), nutrition=None)
    assert out["ood_blended"] is True
    assert out["obesity_class"] == "Obesity_Type_III"
    assert any("trained range" in f.lower() for f in out["anomaly_flags"])
    # Despite the model wanting Type_II, the blended final class must
    # be at the top of the severity scale.
    assert out["obesity_probabilities"]["Obesity_Type_III"] > 0.5


def test_anomaly_weight_below_training_range_is_flagged():
    out = predict(_profile(weight_kg=35, height_cm=178), nutrition=None)
    assert out["ood_blended"] is True
    assert out["obesity_class"] == "Insufficient_Weight"
    assert any("below the trained range" in f.lower() for f in out["anomaly_flags"])


def test_anomaly_bmi_below_training_range_is_flagged():
    # Tall and very light → BMI ~10.1, below the trained 13.0 floor.
    out = predict(_profile(weight_kg=38, height_cm=195), nutrition=None)
    assert out["bmi_band"] == "Insufficient_Weight"
    assert any("bmi" in f.lower() and "outside" in f.lower() for f in out["anomaly_flags"])


def test_anomaly_age_outside_range_is_flagged():
    out = predict(_profile(age=80), nutrition=None)
    assert any("age" in f.lower() and "outside" in f.lower() for f in out["anomaly_flags"])


# ───────────────────────────────────────────────────────────────────
# In-distribution behavior — model should NOT be overridden when it
# is operating inside the trained envelope.
# ───────────────────────────────────────────────────────────────────


def test_in_distribution_borderline_is_not_blended():
    """BMI 26.8 is right on the Overweight I/II boundary. Adjacent-class
    disagreement is acceptable here — no OOD blend, no flags about
    training range."""
    out = predict(_profile(weight_kg=85, height_cm=178), nutrition=None)
    assert out["ood_blended"] is False
    # No range-violation flags for in-distribution input.
    assert not any("range" in f.lower() for f in out["anomaly_flags"])


def test_in_distribution_female_type_iii_is_not_blended():
    """Female Type_III is well-represented (323 examples) — the model
    should handle this without needing the BMI-band override."""
    out = predict(_profile(weight_kg=110, height_cm=165, Gender="Female"), nutrition=None)
    assert out["bmi_band"] == "Obesity_Type_III"
    assert out["obesity_class"] == "Obesity_Type_III"
    assert out["ood_blended"] is False