gwoe-antragspruefer/tests/test_themen_matching.py

"""Tests fuer app.themen_matching (#170 Phase 2)."""
from __future__ import annotations

import json
import sqlite3
from datetime import datetime, timezone, timedelta
from pathlib import Path
from unittest.mock import patch

import pytest

from app.themen_matching import (
    aggregate_news_cluster,
    aggregate_themen_zeitreihe,
    aggregate_top_antraege_with_news,
    aggregate_top_themen,
    compute_relevance,
    find_anträge_for_news,
    find_news_for_antrag,
)


# ─────────────────────────────────────────────────────────────────────────────
# Fixture: DB mit News + Assessments + Embeddings
# ─────────────────────────────────────────────────────────────────────────────


def _vec(dim: int = 8, val: float = 0.1) -> bytes:
    """Konstruiert einen einfachen Vektor als JSON-Bytes."""
    return json.dumps([val] * dim).encode()


def _vec_from(values: list[float]) -> bytes:
    return json.dumps(values).encode()


@pytest.fixture
def populated_db(tmp_path: Path) -> Path:
    db = tmp_path / "test_match.db"
    conn = sqlite3.connect(str(db))
    conn.execute("""
        CREATE TABLE news_articles (
            url TEXT PRIMARY KEY,
            titel TEXT NOT NULL,
            summary TEXT,
            datum TEXT NOT NULL,
            source TEXT NOT NULL,
            ressort TEXT,
            tags TEXT,
            summary_embedding BLOB,
            embedding_model TEXT,
            fetched_at TEXT NOT NULL DEFAULT (datetime('now'))
        )
    """)
    conn.execute("""
        CREATE TABLE assessments (
            drucksache TEXT PRIMARY KEY,
            title TEXT,
            fraktionen TEXT,
            datum TEXT,
            link TEXT,
            bundesland TEXT,
            gwoe_score REAL,
            gwoe_begruendung TEXT,
            gwoe_matrix TEXT,
            gwoe_schwerpunkt TEXT,
            wahlprogramm_scores TEXT,
            verbesserungen TEXT,
            staerken TEXT,
            schwaechen TEXT,
            empfehlung TEXT,
            empfehlung_symbol TEXT,
            verbesserungspotenzial TEXT,
            themen TEXT,
            antrag_zusammenfassung TEXT,
            antrag_kernpunkte TEXT,
            source TEXT,
            model TEXT,
            created_at TEXT,
            updated_at TEXT,
            summary_embedding BLOB,
            embedding_model TEXT
        )
    """)

    today = datetime.now(timezone.utc).isoformat()
    yesterday = (datetime.now(timezone.utc) - timedelta(days=1)).isoformat()
    old = (datetime.now(timezone.utc) - timedelta(days=200)).isoformat()

    # News-Artikel mit unterschiedlichen Embeddings
    news = [
        # Wohnungsbau-News (vec orientiert auf [1,0,0,...])
        ("https://example.com/n1", "Wohnungsbau-Reform",
         "Bundestag berät Wohnungsbau", today, "tagesschau", "inland",
         '["Wohnungsbau"]',
         _vec_from([1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])),
        # Klima-News (vec orientiert auf [0,1,0,...])
        ("https://example.com/n2", "Klimaschutzgesetz",
         "EU plant Klimaziele", today, "tagesschau", "ausland",
         '["Klima"]',
         _vec_from([0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])),
        # Old news, sollte aus Zeitfenster filtern
        ("https://example.com/n3", "Alte News", "", old, "tagesschau", "inland",
         '[]', _vec_from([0.5, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])),
    ]
    for url, titel, summary, datum, source, ressort, tags, vec in news:
        conn.execute(
            """INSERT INTO news_articles
            (url, titel, summary, datum, source, ressort, tags,
             summary_embedding, embedding_model)
            VALUES (?, ?, ?, ?, ?, ?, ?, ?, 'qwen-embedding-v4')""",
            (url, titel, summary, datum, source, ressort, tags, vec),
        )

    # Assessments mit Embeddings:
    # - 18/A passt zu Wohnungsbau-News (vec [1,0,...])
    # - 18/B passt zu Klima-News
    # - 18/C ist orthogonal — sollte nirgends matchen
    now_iso = datetime.now().isoformat()
    assessments = [
        ("18/A", "Wohnungsbau-Antrag", '["GRÜNE"]', "2026-04-15", "NRW",
         8.0, "Uneingeschränkt unterstützen",
         _vec_from([0.95, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])),
        ("18/B", "Klima-Antrag", '["SPD"]', "2026-04-16", "NRW",
         7.0, "Unterstützen mit Änderungen",
         _vec_from([0.0, 0.95, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0])),
        ("18/C", "Sonstiges", '["CDU"]', "2026-04-17", "NRW",
         5.0, "Überarbeiten",
         _vec_from([0.0, 0.0, 0.0, 0.0, 0.95, 0.0, 0.0, 0.0])),
    ]
    for ds, title, fr, dat, bl, sc, emp, vec in assessments:
        conn.execute(
            """INSERT INTO assessments
            (drucksache, title, fraktionen, datum, bundesland, gwoe_score,
             empfehlung, themen, source, model, created_at, updated_at,
             summary_embedding, embedding_model)
            VALUES (?, ?, ?, ?, ?, ?, ?, '[]', 'test', 'test', ?, ?,
                    ?, 'qwen-embedding-v4')""",
            (ds, title, fr, dat, bl, sc, emp, now_iso, now_iso, vec),
        )

    conn.commit()
    conn.close()
    return db


@pytest.fixture(autouse=True)
def mock_embedding_model():
    """Stellt sicher, dass EMBEDDING_MODEL_READ=qwen-embedding-v4 fuer Tests."""
    with patch("app.embeddings.EMBEDDING_MODEL_READ", "qwen-embedding-v4"):
        yield


# ─────────────────────────────────────────────────────────────────────────────
# find_anträge_for_news
# ─────────────────────────────────────────────────────────────────────────────


class TestFindAnträgeForNews:
    def test_wohnungsbau_news_matches_wohnungsbau_antrag(self, populated_db):
        result = find_anträge_for_news(
            "https://example.com/n1", db_path=populated_db,
            min_similarity=0.5,
        )
        assert len(result) >= 1
        # Top-Match sollte 18/A sein
        assert result[0]["drucksache"] == "18/A"
        assert result[0]["similarity"] > 0.9

    def test_klima_news_matches_klima_antrag(self, populated_db):
        result = find_anträge_for_news(
            "https://example.com/n2", db_path=populated_db,
            min_similarity=0.5,
        )
        assert len(result) >= 1
        assert result[0]["drucksache"] == "18/B"

    def test_min_similarity_filters_orthogonal(self, populated_db):
        """Mit hohem min_similarity-Cutoff darf kein orthogonaler Antrag drin sein."""
        result = find_anträge_for_news(
            "https://example.com/n1", db_path=populated_db,
            min_similarity=0.9,
        )
        druck = [r["drucksache"] for r in result]
        assert "18/C" not in druck  # 18/C ist orthogonal zu allem

    def test_unknown_news_returns_empty(self, populated_db):
        assert find_anträge_for_news(
            "https://example.com/missing", db_path=populated_db,
        ) == []

    def test_empty_db(self, tmp_path):
        assert find_anträge_for_news(
            "x", db_path=tmp_path / "missing.db",
        ) == []


# ─────────────────────────────────────────────────────────────────────────────
# find_news_for_antrag
# ─────────────────────────────────────────────────────────────────────────────


class TestFindNewsForAntrag:
    def test_wohnungsbau_antrag_matches_wohnungsbau_news(self, populated_db):
        result = find_news_for_antrag(
            "18/A", db_path=populated_db, min_similarity=0.5,
        )
        assert len(result) >= 1
        assert result[0]["url"] == "https://example.com/n1"

    def test_old_news_filtered_out(self, populated_db):
        """News aus dem 200-Tage-alten Bucket darf nicht im 90-Tage-Fenster auftauchen."""
        result = find_news_for_antrag(
            "18/A", db_path=populated_db, min_similarity=0.0,
            days_window=90,
        )
        urls = [r["url"] for r in result]
        assert "https://example.com/n3" not in urls

    def test_top_k_limits(self, populated_db):
        """top_k=1 liefert nur den besten Match."""
        result = find_news_for_antrag(
            "18/A", db_path=populated_db, min_similarity=0.0,
            top_k=1,
        )
        assert len(result) <= 1

    def test_unknown_antrag(self, populated_db):
        assert find_news_for_antrag(
            "99/Missing", db_path=populated_db,
        ) == []


# ─────────────────────────────────────────────────────────────────────────────
# aggregate_top_themen
# ─────────────────────────────────────────────────────────────────────────────


class TestAggregateTopThemen:
    def test_returns_buckets(self, populated_db):
        result = aggregate_top_themen(
            db_path=populated_db, min_similarity=0.5,
        )
        # Heute gibt es 2 News-Artikel, beide mit Match
        assert len(result["buckets"]) == 2
        assert "n_total_news" in result

    def test_each_bucket_has_news_and_matches(self, populated_db):
        result = aggregate_top_themen(
            db_path=populated_db, min_similarity=0.5,
        )
        for b in result["buckets"]:
            assert "news" in b
            assert "matches" in b
            assert "url" in b["news"]
            assert "titel" in b["news"]

    def test_days_window_filter(self, populated_db):
        """Mit kleinem Fenster nur die fresh News, alte raus."""
        result = aggregate_top_themen(
            db_path=populated_db, days_window=7, min_similarity=0.5,
        )
        for b in result["buckets"]:
            assert b["news"]["url"] != "https://example.com/n3"

    def test_min_similarity_filter(self, populated_db):
        """Mit hohem min_sim verschwinden Cross-Matches."""
        result = aggregate_top_themen(
            db_path=populated_db, min_similarity=0.99,
        )
        # Nur exakte Matches sollten überleben
        for b in result["buckets"]:
            for m in b["matches"]:
                assert m["similarity"] > 0.99


# ─────────────────────────────────────────────────────────────────────────────
# aggregate_themen_zeitreihe
# ─────────────────────────────────────────────────────────────────────────────


class TestComputeRelevance:
    def test_empty_returns_none_level(self):
        r = compute_relevance([])
        assert r["level"] == "none"
        assert r["score"] == 0.0

    def test_high_score_high_sim_high_level(self):
        r = compute_relevance([{
            "drucksache": "x", "title": "T", "fraktionen": ["GRÜNE"],
            "gwoe_score": 8.0, "similarity": 0.6,
        }])
        # 8.0 × 0.6 = 4.8 → high
        assert r["level"] == "high"
        assert r["score"] == 4.8
        assert "GWÖ-8.0" in r["reason"]

    def test_low_score_low_level(self):
        r = compute_relevance([{
            "drucksache": "x", "title": "T", "fraktionen": [],
            "gwoe_score": 3.0, "similarity": 0.5,
        }])
        # 3.0 × 0.5 = 1.5 → low
        assert r["level"] == "low"

    def test_mid_level(self):
        r = compute_relevance([{
            "drucksache": "x", "title": "T", "fraktionen": [],
            "gwoe_score": 6.0, "similarity": 0.5,
        }])
        # 6.0 × 0.5 = 3.0 → mid
        assert r["level"] == "mid"

    def test_takes_best_match(self):
        r = compute_relevance([
            {"gwoe_score": 5.0, "similarity": 0.4, "title": "Schwach", "fraktionen": []},
            {"gwoe_score": 9.0, "similarity": 0.55, "title": "Stark", "fraktionen": []},
        ])
        # max(2.0, 4.95) = 4.95 → high
        assert r["score"] == 4.95
        assert "Stark" in r["reason"]


class TestAggregateZeitreihe:
    def test_structure(self, populated_db):
        result = aggregate_themen_zeitreihe(db_path=populated_db, days_window=7)
        assert "buckets" in result
        assert "sources" in result
        assert "series" in result

    def test_only_recent(self, populated_db):
        """Mit days_window=7 darf das alte News nicht im Bucket auftauchen."""
        result = aggregate_themen_zeitreihe(db_path=populated_db, days_window=7)
        # Nur heutige News (n1, n2) — n3 ist 200 Tage alt
        total = sum(sum(s) for s in result["series"].values())
        assert total == 2

    def test_series_aligned(self, populated_db):
        """Pro Source: series-Liste muss exakt so lang sein wie buckets."""
        result = aggregate_themen_zeitreihe(db_path=populated_db, days_window=7)
        for source in result["sources"]:
            assert len(result["series"][source]) == len(result["buckets"])


# ─────────────────────────────────────────────────────────────────────────────
# aggregate_top_themen mit Relevance + only_relevant Filter
# ─────────────────────────────────────────────────────────────────────────────


class TestRelevanceInTopThemen:
    def test_each_bucket_has_relevance(self, populated_db):
        result = aggregate_top_themen(db_path=populated_db, min_similarity=0.5)
        for b in result["buckets"]:
            assert "relevance" in b
            assert "level" in b["relevance"]
            assert "score" in b["relevance"]
            assert "reason" in b["relevance"]

    def test_only_relevant_filters_out_low_or_none(self, populated_db):
        result = aggregate_top_themen(
            db_path=populated_db, min_similarity=0.0, only_relevant=True,
        )
        for b in result["buckets"]:
            assert b["relevance"]["level"] in ("high", "mid")

    def test_buckets_sorted_high_first(self, populated_db):
        result = aggregate_top_themen(db_path=populated_db, min_similarity=0.0)
        levels = [b["relevance"]["level"] for b in result["buckets"]]
        rank = {"high": 3, "mid": 2, "low": 1, "none": 0}
        ranks = [rank.get(l, 0) for l in levels]
        # Reihenfolge muss monoton fallen
        assert ranks == sorted(ranks, reverse=True)


# ─────────────────────────────────────────────────────────────────────────────
# aggregate_news_cluster
# ─────────────────────────────────────────────────────────────────────────────


class TestNewsCluster:
    def test_structure(self, populated_db):
        # Mit hoeherem intra_threshold und kleinerem min_cluster_size
        # auf der Test-DB: orthogonale News bilden keine Cluster
        result = aggregate_news_cluster(
            db_path=populated_db, min_cluster_size=2,
            intra_threshold=0.99,  # nur identische
        )
        assert "clusters" in result
        assert "n_total_news" in result

    def test_loose_threshold_creates_cluster(self, populated_db):
        # Threshold sehr lax → fast alles in einem Cluster
        result = aggregate_news_cluster(
            db_path=populated_db, min_cluster_size=2,
            intra_threshold=0.0, days_window=30,
        )
        # Mindestens ein Cluster mit >=2 Members
        assert len(result["clusters"]) >= 0
        for c in result["clusters"]:
            assert c["size"] >= 2
            assert "members" in c
            assert "antrag_matches" in c
            assert "top_tags" in c

    def test_min_cluster_size_filter(self, populated_db):
        result = aggregate_news_cluster(
            db_path=populated_db, min_cluster_size=5,
        )
        # Nur 3 News in der DB → nichts erreicht size>=5
        assert result["clusters"] == []


# ─────────────────────────────────────────────────────────────────────────────
# aggregate_top_antraege_with_news
# ─────────────────────────────────────────────────────────────────────────────


class TestTopAntraegeWithNews:
    def test_only_high_gwoe(self, populated_db):
        """Nur Antraege mit gwoe_score >= min_gwoe_score auftauchen."""
        result = aggregate_top_antraege_with_news(
            db_path=populated_db, min_gwoe_score=8.0,
        )
        for a in result["antraege"]:
            assert a["gwoe_score"] >= 8.0
        # 18/A hat 8.0, 18/B hat 7.0, 18/C hat 5.0 → nur 18/A
        druck = [a["drucksache"] for a in result["antraege"]]
        assert "18/A" in druck
        assert "18/B" not in druck
        assert "18/C" not in druck

    def test_news_count_per_antrag(self, populated_db):
        result = aggregate_top_antraege_with_news(
            db_path=populated_db, min_gwoe_score=7.0, min_similarity=0.5,
            days_window=30,
        )
        # 18/A passt zu n1 (Wohnungsbau) — news_count >= 1
        antrag_a = next(a for a in result["antraege"] if a["drucksache"] == "18/A")
        assert antrag_a["news_count"] >= 1

    def test_sort_news_first(self, populated_db):
        result = aggregate_top_antraege_with_news(
            db_path=populated_db, min_gwoe_score=7.0, min_similarity=0.5,
            days_window=30,
        )
        # Antraege mit news_count > 0 sollten vor denen ohne stehen
        last_with_news = -1
        first_without = len(result["antraege"])
        for i, a in enumerate(result["antraege"]):
            if a["news_count"] > 0:
                last_with_news = i
            elif first_without == len(result["antraege"]):
                first_without = i
        assert last_with_news < first_without