25437f24e3
Plateforme de decisions collectives pour Duniter/G1. Backend FastAPI async + PostgreSQL (14 tables, 8 routers, 6 services, moteur de vote avec formule d'inertie WoT/Smith/TechComm). Frontend Nuxt 4 + Nuxt UI v3 + Pinia (9 pages, 5 stores). Infrastructure Docker + Woodpecker CI + Traefik. Documentation technique et utilisateur (15 fichiers). Seed : Licence G1, Engagement Forgeron v2.0.0, 4 protocoles de vote. 30 tests unitaires (formules, mode params, vote nuance) -- tous verts. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
96 lines
2.5 KiB
Python
96 lines
2.5 KiB
Python
"""Six-level nuanced vote evaluation.
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Levels:
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0 - CONTRE
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1 - PAS DU TOUT
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2 - PAS D'ACCORD
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3 - NEUTRE
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4 - D'ACCORD
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5 - TOUT A FAIT
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Adoption rule:
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The sum of votes at levels 3 + 4 + 5 must be >= threshold_pct% of total votes.
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A minimum number of participants is also required.
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"""
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from __future__ import annotations
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LEVEL_LABELS: dict[int, str] = {
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0: "CONTRE",
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1: "PAS DU TOUT",
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2: "PAS D'ACCORD",
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3: "NEUTRE",
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4: "D'ACCORD",
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5: "TOUT A FAIT",
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}
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NUM_LEVELS = 6
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def evaluate_nuanced(
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votes: list[int],
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threshold_pct: int = 80,
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min_participants: int = 59,
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) -> dict:
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"""Evaluate a nuanced vote from a list of individual vote levels.
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Parameters
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----------
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votes:
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List of vote levels (each 0-5). One entry per voter.
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threshold_pct:
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Minimum percentage of positive votes (levels 3-5) out of total
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for adoption.
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min_participants:
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Minimum number of participants required for validity.
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Returns
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-------
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dict
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{
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"total": int,
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"per_level_counts": {0: int, 1: int, ..., 5: int},
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"positive_count": int, # levels 3 + 4 + 5
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"positive_pct": float, # 0.0 - 100.0
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"threshold_met": bool,
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"min_participants_met": bool,
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"adopted": bool,
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}
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Raises
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------
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ValueError
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If any vote value is outside the 0-5 range.
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"""
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# Validate vote levels
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for v in votes:
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if v < 0 or v > 5:
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raise ValueError(
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f"Niveau de vote invalide : {v}. Les niveaux valides sont 0-5."
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)
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total = len(votes)
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per_level_counts: dict[int, int] = {level: 0 for level in range(NUM_LEVELS)}
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for v in votes:
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per_level_counts[v] += 1
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# Positive = levels 3 (NEUTRE), 4 (D'ACCORD), 5 (TOUT A FAIT)
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positive_count = per_level_counts[3] + per_level_counts[4] + per_level_counts[5]
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positive_pct = (positive_count / total * 100.0) if total > 0 else 0.0
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threshold_met = positive_pct >= threshold_pct
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min_participants_met = total >= min_participants
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adopted = threshold_met and min_participants_met
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return {
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"total": total,
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"per_level_counts": per_level_counts,
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"positive_count": positive_count,
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"positive_pct": round(positive_pct, 2),
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"threshold_met": threshold_met,
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"min_participants_met": min_participants_met,
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"adopted": adopted,
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}
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