Public Access
43 lines
1.6 KiB
Python
43 lines
1.6 KiB
Python
"""Planner constants. Tune here without touching algorithm code."""
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from __future__ import annotations
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from dataclasses import dataclass
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@dataclass(frozen=True)
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class PlannerConfig:
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# Hard constraints
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recency_weeks: int = 4 # constraint #3: no repeat within N weeks
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calorie_tolerance_pct: int = 20 # constraint #4: ±X% of family.calorie_target
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max_total_minutes: int = 45 # constraint #5: prep + cook
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max_meal_cost: float = 30.00 # constraint #6: dollars per meal
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# Scoring weights (must sum to 1.0)
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w_savings: float = 0.30
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w_coverage: float = 0.25
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w_pantry: float = 0.10
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w_time: float = 0.15
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w_recency: float = 0.20
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# Time bonus boundaries
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time_ideal_minutes: int = 25 # full bonus at <= this
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time_full_minutes: int = 45 # zero bonus at this; matches max_total_minutes
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# Recency bonus boundary
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recency_full_weeks: int = 12 # full bonus when last cooked >= this many weeks ago
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# Set selection
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top_k: int = 20 # how many feasible recipes to enumerate over
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set_size: int = 3 # 3 dinners/week
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p_protein: float = 0.15 # diversity penalty per shared-protein pair
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p_cuisine: float = 0.10 # diversity penalty per shared-cuisine pair
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def validate(self) -> None:
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total = self.w_savings + self.w_coverage + self.w_pantry + self.w_time + self.w_recency
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if abs(total - 1.0) > 1e-6:
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raise ValueError(f"weights must sum to 1.0, got {total}")
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DEFAULT = PlannerConfig()
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DEFAULT.validate()
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