daniel156161
c704fbc742
Build and Push Docker Container / build-and-push (push) Successful in 8m3s
- Add bitboard-accelerated Prism and versioned Supreme snake implementations. - Add database-backed move benchmarks and focused strategy tests. - Normalize gameplay storage while preserving replay compatibility. - Add deterministic game quality scoring and replay retention tiers. - Add backup-first SQLite cleanup, verification, and replacement tooling. - Add safe compact-plus-delta database merging with conflict detection. - Extend SQLite and PostgreSQL schemas for replay and quality metadata. - Add PostgreSQL development service and pytest import configuration. - Update gameplay documentation and the quart_common submodule revision.
101 lines
3.0 KiB
Python
101 lines
3.0 KiB
Python
"""Deterministic gameplay quality scoring.
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Quality controls replay retention, never whether a game's result contributes to
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historical rates. Structural failures produce ``invalid``; otherwise strategic
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signals produce a 0-100 score and high/medium/low tier.
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"""
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from dataclasses import dataclass
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QUALITY_ORDER = {"invalid": 0, "low": 1, "medium": 2, "high": 3}
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@dataclass(frozen=True)
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class GameQualityInput:
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status:str
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final_turn:int
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turn_rows:int
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min_turn:int|None
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max_turn:int|None
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valid_moves:int
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thinking_rows:int
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distinct_moves:int
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snake_turn_rows:int
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winner_name:str|None
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@dataclass(frozen=True)
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class GameQuality:
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score:int
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tier:str
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reasons:tuple[str, ...]
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def rate_game_quality(data:GameQualityInput) -> GameQuality:
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reasons:list[str] = []
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expected_turns = max(1, data.final_turn)
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coverage = min(1.0, data.turn_rows / expected_turns)
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valid_ratio = data.valid_moves / data.turn_rows if data.turn_rows else 0.0
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thinking_ratio = data.thinking_rows / data.turn_rows if data.turn_rows else 0.0
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average_snakes = data.snake_turn_rows / data.turn_rows if data.turn_rows else 0.0
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if data.status != "finished":
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reasons.append("unfinished_game")
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if data.turn_rows == 0:
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reasons.append("missing_turns")
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observed_span = (
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data.max_turn - data.min_turn + 1
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if data.min_turn is not None and data.max_turn is not None
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else 0
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)
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if coverage < 0.8 or observed_span != data.turn_rows:
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reasons.append("incomplete_turn_sequence")
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if valid_ratio < 0.95:
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reasons.append("invalid_or_missing_moves")
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if reasons:
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return GameQuality(score=0, tier="invalid", reasons=tuple(reasons))
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score = 25.0 * coverage
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score += 10.0 * valid_ratio
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score += 20.0 * min(1.0, data.final_turn / 40.0)
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score += 15.0 * thinking_ratio
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score += 10.0 * min(1.0, data.distinct_moves / 3.0)
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if average_snakes >= 3.0:
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score += 15.0
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elif average_snakes >= 1.8:
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score += 10.0
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elif average_snakes >= 1.0:
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score += 3.0
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if data.winner_name:
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score += 5.0
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if coverage >= 0.98:
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reasons.append("complete_turn_sequence")
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if valid_ratio == 1.0:
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reasons.append("valid_moves")
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if thinking_ratio >= 0.9:
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reasons.append("complete_thinking_data")
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elif thinking_ratio < 0.25:
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reasons.append("sparse_thinking_data")
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if average_snakes >= 1.8:
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reasons.append("competitive_game")
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else:
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reasons.append("limited_opposition_data")
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if data.final_turn < 3:
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reasons.append("very_short_game")
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elif data.final_turn < 10:
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reasons.append("short_game")
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else:
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reasons.append("substantial_game_length")
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if data.distinct_moves <= 1:
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reasons.append("low_move_diversity")
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rounded_score = max(0, min(100, round(score)))
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if rounded_score >= 80 and data.final_turn >= 10:
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tier = "high"
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elif rounded_score >= 55:
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tier = "medium"
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else:
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tier = "low"
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return GameQuality(score=rounded_score, tier=tier, reasons=tuple(reasons))
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def quality_meets_minimum(tier:str, minimum_tier:str) -> bool:
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return QUALITY_ORDER.get(tier, 0) >= QUALITY_ORDER[minimum_tier]
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