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57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
"""
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app/core/ranking.py — Kombiniertes Scoring (WP-04)
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Zweck:
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Zusammenführen von semantischem Score (normalisiert), Edge-Bonus und
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Centrality-Bonus in einen Gesamtscore für die Ergebnisreihung.
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Kompatibilität:
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Python 3.12+
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Version:
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0.1.0 (Erstanlage)
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Stand:
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2025-10-07
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Bezug:
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WP-04 Ranking-Formel (w_sem, w_edge, w_cent)
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Nutzung:
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from app.core.ranking import combine_scores
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Änderungsverlauf:
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0.1.0 (2025-10-07) – Erstanlage.
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"""
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from __future__ import annotations
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from typing import List, Tuple, Dict
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def normalize_scores(values: List[float]) -> List[float]:
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"""Min-Max-Normalisierung über die Kandidatenmenge (Fallback 0.5 bei Konstanz)."""
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if not values:
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return values
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lo, hi = min(values), max(values)
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if hi - lo < 1e-9:
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return [0.5] * len(values)
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return [(v - lo) / (hi - lo) for v in values]
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def combine_scores(
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hits: List[Tuple[str, float, dict]], # (id, semantic_score, payload)
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edge_bonus_map: Dict[str, float],
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centrality_map: Dict[str, float],
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w_sem: float = 0.70,
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w_edge: float = 0.25,
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w_cent: float = 0.05,
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) -> List[Tuple[str, float, float, float, float]]:
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"""
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Liefert Liste von (point_id, total_score, edge_bonus, centrality_bonus, raw_semantic_score),
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absteigend nach total_score sortiert.
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"""
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sem = [h[1] for h in hits]
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sem_n = normalize_scores(sem)
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out = []
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for (pid, s, payload), s_norm in zip(hits, sem_n):
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e = edge_bonus_map.get(pid, 0.0)
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c = centrality_map.get(pid, 0.0)
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total = w_sem * s_norm + w_edge * e + w_cent * c
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out.append((pid, total, e, c, s))
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out.sort(key=lambda t: t[1], reverse=True)
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return out
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