"""Tests for eval/ts_metrics.py (T5.2)."""
import numpy as np
import pytest

from tsmm.eval.ts_metrics import (
    affiliation_f1,
    auc_pr,
    point_f1,
    point_f1_with_pa,
    vus_pr,
)


def labels(segs, T=100):
    y = np.zeros(T, dtype=int)
    for a, b in segs:
        y[a:b + 1] = 1
    return y


class TestPointF1:
    def test_perfect(self):
        y = labels([(20, 40)], T=100)
        s = np.zeros(100); s[20:41] = 1.0
        f1, prec, rec = point_f1(s, y)
        assert f1 == pytest.approx(1.0, abs=1e-6)

    def test_no_overlap(self):
        y = labels([(20, 40)])
        s = np.zeros(100); s[60:70] = 1.0
        f1, _, _ = point_f1(s, y)
        assert f1 == pytest.approx(0.0, abs=1e-6)

    def test_partial(self):
        y = labels([(20, 40)])
        s = np.zeros(100); s[30:41] = 0.9
        f1, prec, rec = point_f1(s, y, threshold=0.5)
        # predicted 11 anomalous all in true region: precision 1, recall 11/21
        assert prec == pytest.approx(1.0, abs=1e-6)
        assert rec == pytest.approx(11 / 21, abs=1e-3)


class TestAUCPR:
    def test_perfect_ranking(self):
        y = labels([(10, 20)])
        s = np.zeros(100); s[10:21] = 1.0
        assert auc_pr(s, y) == pytest.approx(1.0, abs=1e-6)

    def test_inverse_ranking_low(self):
        y = labels([(10, 20)])
        s = np.ones(100); s[10:21] = 0.0  # anomalies score LOW
        assert auc_pr(s, y) < 0.5


class TestPAF1:
    def test_pa_inflates(self):
        # PA: if ANY predicted point is in the true anomaly, the whole segment
        # counts as caught → much higher F1 than point-wise
        y = labels([(20, 40)])
        s = np.zeros(100); s[40] = 1.0  # single overlap point
        f1_pa, _, _ = point_f1_with_pa(s, y)
        f1_pt, _, _ = point_f1(s, y)
        assert f1_pa > f1_pt


class TestAffiliationF1:
    def test_perfect(self):
        y = labels([(20, 40)])
        s = np.zeros(100); s[20:41] = 1.0
        f1, _, _ = affiliation_f1(s, y)
        assert f1 == pytest.approx(1.0, abs=1e-6)

    def test_far_predictions_lower(self):
        y = labels([(20, 40)])
        s_near = np.zeros(100); s_near[41:45] = 1.0
        s_far = np.zeros(100); s_far[90:95] = 1.0
        f1_near, _, _ = affiliation_f1(s_near, y)
        f1_far, _, _ = affiliation_f1(s_far, y)
        assert f1_near > f1_far


class TestVUSPR:
    def test_perfect_high(self):
        y = labels([(20, 40)])
        s = np.zeros(100); s[20:41] = 1.0
        v = vus_pr(s, y)
        assert v == pytest.approx(1.0, abs=1e-3)

    def test_random_scores_moderate(self):
        rng = np.random.default_rng(0)
        y = labels([(20, 40)])
        s = rng.random(100)
        v = vus_pr(s, y)
        # random ranking → VUS-PR around the anomaly prevalence (~0.21)
        assert 0.0 < v < 0.6

    def test_all_report_not_inflated(self):
        # spec REQUIREMENT: "全报异常" trivial baseline must NOT have inflated VUS-PR
        y = labels([(20, 40)], T=100)
        s = np.ones(100)  # report everything as anomalous
        v = vus_pr(s, y)
        # prevalence = 21/100; all-ones gives AUC-PR == prevalence everywhere
        assert v == pytest.approx(21 / 100, abs=1e-3)

    def test_returns_scalar(self):
        y = labels([(5, 10)])
        s = np.random.default_rng(1).random(100)
        v = vus_pr(s, y)
        assert np.isscalar(v) or v.shape == ()