"""Step 10 ensemble — weighted z-blend, top_n cut, 1-based rank, reranker dominance, empty guard."""

from __future__ import annotations

import polars as pl

from config import EnsembleSettings, Settings
from rank import ensemble


def _pool() -> pl.DataFrame:
    return pl.DataFrame({
        "candidate_id": ["c1", "c2", "c3", "c4"],
        "reranker_score": [2.0, 1.0, 0.5, 0.1],
        "granite_score": [0.8, 0.7, 0.6, 0.5],
        "composite_score": [1.0, 2.0, 0.5, 0.2],
    })


def test_final_score_rank_and_top_n() -> None:
    kept = ensemble(_pool(), Settings(ensemble=EnsembleSettings(top_n=2)))
    assert kept.height == 2
    assert {"final_score", "rank"} <= set(kept.columns)
    assert kept["rank"].to_list() == [1, 2]  # 1-based, in sorted order


def test_reranker_dominates_blend() -> None:
    full = ensemble(_pool(), Settings(ensemble=EnsembleSettings(top_n=4)))
    # c1 has the top reranker_score (weight 0.50) → should rank #1
    assert full.sort("rank")["candidate_id"].to_list()[0] == "c1"


def test_missing_signal_contributes_zero() -> None:
    pool = _pool().drop("granite_score", "composite_score")  # only reranker drives the blend
    kept = ensemble(pool, Settings(ensemble=EnsembleSettings(top_n=4)))
    assert kept["candidate_id"].to_list() == ["c1", "c2", "c3", "c4"]  # reranker desc order


def test_empty_pool_passthrough() -> None:
    out = ensemble(_pool().head(0), Settings())
    assert out.height == 0 and {"final_score", "rank"} <= set(out.columns)