code/tests/test_decode.py

"""Tests for src.decode. Run with `python -m pytest tests` or `python tests/test_decode.py`."""
import itertools
import math
import sys
from pathlib import Path

import numpy as np

sys.path.insert(0, str(Path(__file__).resolve().parents[1]))

from src.decode import (best_subset, decode_all, decode_arrays,  # noqa: E402
                        expected_f05_topk, threshold_decode)
from src.metric import f05_entity  # noqa: E402


# ---------------------------------------------------------------------------
# helpers: independent reference implementations
# ---------------------------------------------------------------------------
def _pb_pmf(p):
    pmf = np.array([1.0])
    for x in p:
        pmf = np.convolve(pmf, [1.0 - x, x])
    return pmf


def _expected_naive(p):
    """O(m^3) prefix x suffix double sum, straight from the definition."""
    p = np.sort(np.asarray(p, float))[::-1]
    m = len(p)
    out = np.zeros(m + 1)
    out[0] = np.prod(1 - p)
    for k in range(1, m + 1):
        pre, suf = _pb_pmf(p[:k]), _pb_pmf(p[k:])
        for a in range(1, k + 1):
            for b in range(len(suf)):
                out[k] += pre[a] * suf[b] * 1.25 * (a / k) * (a / (a + b)) / (0.25 * a / k + a / (a + b))
    return out


def _expected_enumerate(p):
    """Exact expectation by enumerating all 2^m label vectors, scored with src.metric."""
    p = np.sort(np.asarray(p, float))[::-1]
    m = len(p)
    out = np.zeros(m + 1)
    for y in itertools.product([0, 1], repeat=m):
        w = np.prod([pi if yi else 1 - pi for pi, yi in zip(p, y)])
        true = {i for i in range(m) if y[i]}
        for k in range(m + 1):
            out[k] += w * f05_entity(set(range(k)), true)
    return out


def _crossover(p1=0.95, lo=0.0, hi=None, iters=60):
    """Smallest p2 at which predicting both beats predicting only the first."""
    hi = p1 if hi is None else hi
    assert best_subset([p1, lo])[0] == 1 and best_subset([p1, hi])[0] == 2
    for _ in range(iters):
        mid = 0.5 * (lo + hi)
        if best_subset([p1, mid])[0] == 2:
            hi = mid
        else:
            lo = mid
    return 0.5 * (lo + hi)


# ---------------------------------------------------------------------------
# (a) single candidate: include iff p > 0.5
# ---------------------------------------------------------------------------
def test_single_candidate():
    assert best_subset(np.array([0.49]))[0] == 0
    assert best_subset(np.array([0.51]))[0] == 1
    k, v = best_subset(np.array([0.49]))
    assert math.isclose(v, 0.51)
    k, v = best_subset(np.array([0.51]))
    assert math.isclose(v, 0.51)
    assert best_subset(np.array([0.5]))[0] == 0      # tie -> prefer the smaller set
    assert best_subset(np.array([]))[0] == 0
    assert best_subset(np.array([]))[1] == 1.0


# ---------------------------------------------------------------------------
# (b) second-candidate crossover with p1 = 0.95
# ---------------------------------------------------------------------------
def test_two_candidate_crossover():
    x = _crossover(0.95)
    # closed form: E1 = p1 (1 - p2/6), E2 = 5/9 (p1 + p2 - 2 p1 p2) + p1 p2
    p1 = 0.95
    analytic = (p1 - 5 / 9 * p1) / (5 / 9 * (1 - 2 * p1) + p1 + p1 / 6)
    print(f"  p2 crossover at p1=0.95: {x:.6f} (closed form {analytic:.6f})")
    assert 0.65 < x < 0.72
    assert abs(x - analytic) < 1e-9


# ---------------------------------------------------------------------------
# (c) Monte Carlo agreement, and exact agreement with brute force
# ---------------------------------------------------------------------------
def test_monte_carlo_matches_dp():
    rng = np.random.default_rng(12345)
    n_draws = 200_000
    worst = 0.0
    for trial in range(16):
        m = int(rng.integers(1, 9))
        if trial % 3 == 0:
            p = rng.random(m)
        elif trial % 3 == 1:
            p = rng.beta(0.5, 0.5, m)
        else:
            p = rng.beta(0.4, 3.0, m)
        p = np.sort(p)[::-1]
        dp = expected_f05_topk(p)
        y = rng.random((n_draws, m)) < p                # independent labels
        n = y.sum(axis=1)
        tp = np.cumsum(y, axis=1)
        mc = np.empty(m + 1)
        mc[0] = (n == 0).mean()
        for k in range(1, m + 1):
            t = tp[:, k - 1]
            mc[k] = np.where(t > 0, 5.0 * t / (4.0 * k + n), 0.0).mean()
        err = np.abs(mc - dp).max()
        worst = max(worst, err)
        assert err < 0.005, (p, mc, dp)
    print(f"  max |MC - DP| over 16 vectors: {worst:.5f}")


def test_exact_vs_enumeration_and_naive():
    rng = np.random.default_rng(7)
    for m in range(1, 9):
        p = rng.random(m)
        dp = expected_f05_topk(p)
        assert np.allclose(dp, _expected_enumerate(p), atol=1e-12, rtol=0)
        assert np.allclose(dp, _expected_naive(p), atol=1e-12, rtol=0)
    # large m against the O(m^3) reference
    for m in (30, 60):
        p = np.concatenate([rng.beta(5, 1, 3), rng.beta(0.3, 6, m - 3)])
        dp = expected_f05_topk(p)
        assert np.allclose(dp, _expected_naive(p), atol=1e-12, rtol=0)
        assert (dp >= -1e-15).all() and (dp <= 1 + 1e-12).all()


def test_input_order_irrelevant_and_edge_probs():
    p = np.array([0.1, 0.9, 0.0, 1.0, 0.3])
    assert np.allclose(expected_f05_topk(p), expected_f05_topk(p[::-1]))
    k, v = best_subset(np.array([1.0, 1.0, 0.0]))
    assert k == 2 and math.isclose(v, 1.0)
    assert best_subset(np.array([np.nan, 0.2]))[0] == 0


def test_min_prob_and_max_k():
    p = np.array([0.99, 0.97, 0.96, 0.95])
    assert best_subset(p)[0] == 4
    assert best_subset(p, max_k=2)[0] == 2
    assert best_subset(p, min_prob=0.965)[0] == 2
    E = expected_f05_topk(p)
    assert math.isclose(best_subset(p, max_k=2)[1], E[:3].max())
    assert best_subset(np.array([0.6]), min_prob=0.7)[0] == 0


def test_threshold_decode():
    p = np.array([0.2, 0.5, 0.51, 0.9])
    assert threshold_decode(p, 0.5).tolist() == [False, False, True, True]


# ---------------------------------------------------------------------------
# (d) decode_all agrees with best_subset per entity
# ---------------------------------------------------------------------------
def _synthetic(n_ent=3000, max_m=60, seed=3):
    rng = np.random.default_rng(seed)
    m = rng.integers(1, max_m + 1, size=n_ent)
    ids = np.array([f"S1-{i:06d}" for i in range(n_ent)], dtype=object)
    s1 = np.repeat(ids, m)
    N = int(m.sum())
    p = np.where(rng.random(N) < 0.08, rng.beta(4, 1, N), rng.beta(0.4, 4, N))
    perm = rng.permutation(N)                          # rows not grouped by entity
    return s1[perm], p[perm]


def _check_against_best_subset(s1, p, out, **kw):
    by_ent = {}
    for i, sid in enumerate(s1.tolist()):
        by_ent.setdefault(sid, []).append(i)
    assert set(out) == set(by_ent)
    for sid, rows in by_ent.items():
        rows = np.array(rows)
        k, _ = best_subset(p[rows], **kw)
        top = rows[np.argsort(-p[rows], kind="stable")[:k]]
        assert sorted(out[sid]) == sorted(top.tolist()), sid


def test_decode_all_matches_best_subset():
    s1, p = _synthetic()
    assert len(p) > 50_000                             # large enough to use the pool
    out_mp = decode_all(s1, p, n_jobs=2)
    out_1 = decode_all(s1, p, n_jobs=1)
    assert out_mp == out_1
    _check_against_best_subset(s1, p, out_mp)
    # expected values agree too
    r = decode_arrays(s1, p, n_jobs=1)
    for e in range(0, len(r.entity_ids), 97):
        rows = np.flatnonzero(s1 == r.entity_ids[e])
        k, v = best_subset(p[rows])
        assert k == r.k[e] and math.isclose(v, r.expected_f05[e], rel_tol=1e-12)


def test_decode_all_caps_and_threshold():
    s1, p = _synthetic(n_ent=800, seed=11)
    out = decode_all(s1, p, n_jobs=1, max_k=2, min_prob=0.3)
    _check_against_best_subset(s1, p, out, max_k=2, min_prob=0.3)
    out_t = decode_all(s1, p, n_jobs=1, method="threshold", threshold=0.4)
    mask = threshold_decode(p, 0.4)
    assert sorted(i for v in out_t.values() for i in v) == np.flatnonzero(mask).tolist()


def test_decode_frame_roundtrip():
    import pandas as pd
    from src.decode import decode_frame, pred_dict_from_frame
    from src.metric import macro_f05
    df = pd.DataFrame({
        "source1_entity_id": ["a", "a", "b", "c", "c", "c"],
        "candidate_entity_id": ["x1", "x2", "y1", "z1", "z2", "z3"],
        "prob": [0.9, 0.2, 0.3, 0.97, 0.96, 0.1],
    })
    fr = decode_frame(df, n_jobs=1)
    pred = pred_dict_from_frame(fr)
    assert pred == {"a": {"x1"}, "b": set(), "c": {"z1", "z2"}}
    assert fr.set_index("source1_entity_id").loc["c", "matched_entity_ids"] == ["z1", "z2"]
    truth = {"a": {"x1"}, "b": set(), "c": {"z1", "z2"}, "d": set()}
    assert macro_f05(pred, truth) == 1.0


def _run_all():
    mod = sys.modules[__name__]
    names = [n for n in dir(mod) if n.startswith("test_") and callable(getattr(mod, n))]
    failed = 0
    for n in names:
        try:
            getattr(mod, n)()
            print(f"PASS {n}")
        except Exception as e:  # noqa: BLE001
            failed += 1
            import traceback
            traceback.print_exc()
            print(f"FAIL {n}: {type(e).__name__}: {e}")
    print(f"{len(names) - failed}/{len(names)} passed")
    return failed


if __name__ == "__main__":
    raise SystemExit(1 if _run_all() else 0)