feat: planner top-K set enumeration with protein/cuisine diversity penalty

backend/app/services/planner/select.py

@@ -0,0 +1,53 @@
+"""Top-K set enumeration with diversity penalty.
+
+Takes the top K (=20) scored recipes, enumerates all C(K, set_size)
+combinations, applies a pairwise diversity penalty for shared
+protein and cuisine, and returns the highest-scoring combination.
+"""
+from __future__ import annotations
+
+from itertools import combinations
+from typing import Iterable, List, Tuple
+
+from app.services.planner.config import PlannerConfig
+from app.services.planner.types import ScoredRecipe
+
+
+def set_diversity_penalty(
+    chosen: List[ScoredRecipe],
+    config: PlannerConfig,
+) -> float:
+    penalty = 0.0
+    for a, b in combinations(chosen, 2):
+        if a.protein and b.protein and a.protein == b.protein:
+            penalty += config.p_protein
+        if a.cuisine and b.cuisine and a.cuisine == b.cuisine:
+            penalty += config.p_cuisine
+    return penalty
+
+
+def _set_score(chosen: List[ScoredRecipe], config: PlannerConfig) -> float:
+    return sum(s.score for s in chosen) - set_diversity_penalty(chosen, config)
+
+
+def select_set(
+    scored: Iterable[ScoredRecipe],
+    config: PlannerConfig,
+) -> Tuple[List[ScoredRecipe], float]:
+    pool = list(scored)
+    if not pool:
+        return [], 0.0
+    if len(pool) <= config.set_size:
+        return pool, _set_score(pool, config)
+
+    pool.sort(key=lambda s: s.score, reverse=True)
+    candidate_pool = pool[: config.top_k]
+
+    best: List[ScoredRecipe] = []
+    best_score = float("-inf")
+    for combo in combinations(candidate_pool, config.set_size):
+        s = _set_score(list(combo), config)
+        if s > best_score:
+            best_score = s
+            best = list(combo)
+    return best, best_score