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from scipy.optimize import minimize_scalar
from chatbot.agents.states.state import SwapState
import numpy as np
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def calculate_top_options(state: SwapState):
logger.info("---NODE: SCIPY RANKING (MATH FILTER)---")
candidates = state.get("candidates", [])
food_old = state["food_old"]
if not candidates or not food_old:
logger.warning("⚠️ Candidates hoặc Food_old rỗng, bỏ qua tính toán.")
return {"top_candidates": []}
# 1. Xác định "KPI" từ món cũ
old_scale = float(food_old.get("portion_scale", 1.0))
target_vector = np.array([
float(food_old.get("kcal", 0)) * old_scale,
float(food_old.get("protein", 0)) * old_scale,
float(food_old.get("totalfat", 0)) * old_scale,
float(food_old.get("carbs", 0)) * old_scale
])
weights = np.array([3.0, 2.0, 1.0, 1.0])
# Bound của món cũ
bounds = food_old.get("solver_bounds", (0.5, 2.0))
# Hàm tính toán
def calculate_score(candidate):
try:
base_vector = np.array([
float(candidate.get("kcal", 0)),
float(candidate.get("protein", 0)),
float(candidate.get("totalfat", 0)),
float(candidate.get("carbs", 0))
])
if np.sum(base_vector) == 0: return float('inf'), 1.0
def objective(x):
current_vector = base_vector * x
diff = (current_vector - target_vector) / (target_vector + 1e-5)
loss = np.sum(weights * (diff ** 2))
return loss
res = minimize_scalar(objective, bounds=bounds, method='bounded')
if res.success:
return res.fun, res.x
else:
return float('inf'), 1.0
except Exception as inner_e:
logger.debug(f"Bỏ qua món {candidate.get('name')} do lỗi toán học: {inner_e}")
return float('inf'), 1.0
# 3. Chấm điểm hàng loạt
scored_candidates = []
for item in candidates:
try:
loss, scale = calculate_score(item)
item_score = item.copy()
item_score["optimization_loss"] = round(loss, 4)
item_score["portion_scale"] = round(scale, 2)
# Tính chỉ số hiển thị sau khi scale
item_score["final_kcal"] = int(item["kcal"] * scale)
item_score["final_protein"] = int(item["protein"] * scale)
item_score["final_totalfat"] = int(item["totalfat"] * scale)
item_score["final_carbs"] = int(item["carbs"] * scale)
scored_candidates.append(item_score)
except Exception as e:
logger.warning(f"Lỗi khi xử lý món {item.get('name', 'N/A')}: {e}")
continue
# 4. Lấy Top 10 tốt nhất
scored_candidates.sort(key=lambda x: x["optimization_loss"])
top_10 = scored_candidates[:10]
logger.info(f"📊 Scipy đã lọc ra {len(top_10)} ứng viên tiềm năng.")
for item in top_10:
logger.info(f" - {item['name']} (Scale x{item['portion_scale']} | Loss: {item['optimization_loss']})")
return {"top_candidates": top_10} |