"""Week 4: formal recovery test against the pre-registered criteria (PLAN §6).

Pre-registered criteria (committed in docs/recovery_test_report.md before this run):
  - hydroxyurea in top 10% (top 30 of 300), AND
  - L-glutamine in top 25% (top 75) OR documented unscorable due to missing LINCS signature, AND
  - >=4 of 5 pre-specified negative controls in the bottom half.

The 5 negative controls are pre-specified here by a category rule (one per category, alphabetically
first available) so the choice does not peek at ranks. Primary ranking = raw connectivity.
"""

from __future__ import annotations

from pathlib import Path

import pandas as pd

RANKED = Path("data/results/ranked_candidates_v1.csv")

# One per unrelated category, alphabetical-first — chosen without looking at ranks.
NEG_CONTROL_CATEGORIES = {
    "antifungal": ["clotrimazole", "fluconazole", "itraconazole", "ketoconazole", "miconazole", "terbinafine"],
    "antihistamine": ["astemizole", "cetirizine", "diphenhydramine", "fexofenadine", "loratadine"],
    "antibiotic": ["azithromycin", "ciprofloxacin", "doxycycline", "tetracycline", "trimethoprim"],
    "hormone": ["ethinyl-estradiol", "levonorgestrel", "medroxyprogesterone-acetate", "norethindrone"],
    "misc": ["caffeine", "lidocaine", "loperamide", "omeprazole", "ranitidine"],
}


def main() -> None:
    df = pd.read_csv(RANKED).set_index("drug_name")
    n = len(df)
    top10_cut, top25_cut, half = int(n * 0.10), int(n * 0.25), n // 2

    def rk(name):
        return int(df.loc[name, "rank"]) if name in df.index else None

    hu, glut = rk("hydroxyurea"), rk("glutamine")

    # pick negative controls present in the ranking
    negs = {}
    for cat, options in NEG_CONTROL_CATEGORIES.items():
        pick = next((d for d in options if d in df.index), None)
        if pick:
            negs[pick] = (cat, rk(pick))

    print("=" * 60)
    print(f"N = {n}; top10 cut = {top10_cut}, top25 cut = {top25_cut}, bottom-half > {half}")
    print(f"\nhydroxyurea: rank {hu} (top {100*hu/n:.1f}%)  -> top-10%? {hu <= top10_cut}")
    glut_score = df.loc["glutamine", "connectivity_score"]
    print(f"L-glutamine: rank {glut} (top {100*glut/n:.1f}%), WTCS={glut_score:.3f}  "
          f"-> top-25%? {glut <= top25_cut}  (has signature, so NOT 'missing-signature unscorable')")
    print("\nnegative controls (pre-specified, 1 per category):")
    n_bottom = 0
    for d, (cat, r) in negs.items():
        in_bottom = r > half
        n_bottom += in_bottom
        print(f"  {d:18s} [{cat:13s}] rank {r:3d}  bottom-half? {in_bottom}")
    print(f"  -> {n_bottom}/5 in bottom half (need >=4)")

    crit_hu = hu <= top10_cut
    crit_glut = glut <= top25_cut
    crit_neg = n_bottom >= 4
    overall = crit_hu and crit_glut and crit_neg
    print(f"\nCRITERIA: hydroxyurea={crit_hu}, L-glutamine={crit_glut}, neg-controls={crit_neg}")
    print(f"OVERALL (raw ranking): {'PASS' if overall else 'FAIL'}")

    # secondary prior-weighted view (reported, not the primary criterion)
    hu_b = int(df.loc["hydroxyurea", "blended_rank"])
    print(f"\nsecondary (mechanistic-prior) ranking: hydroxyurea blended_rank {hu_b} "
          f"(top {100*hu_b/n:.1f}%)")

    print("\n--- TOP 10 (raw connectivity) ---")
    top10 = df.nsmallest(10, "connectivity_score")
    for name, r in top10.iterrows():
        print(f"  {int(r['rank']):2d}  {name:18s} {r['connectivity_score']:+.3f}  "
              f"[{r['inclusion_reason']}]  {str(r['known_targets'])[:45]}")


if __name__ == "__main__":
    main()