205 lines
8.2 KiB
Python
205 lines
8.2 KiB
Python
import io
|
||
import logging
|
||
import sys
|
||
import time
|
||
from typing import List
|
||
import os
|
||
import json
|
||
import math
|
||
import random
|
||
import numpy as np
|
||
from apscheduler.schedulers.background import BackgroundScheduler
|
||
from apscheduler.triggers.cron import CronTrigger
|
||
from fastapi import HTTPException, APIRouter
|
||
|
||
from app.service.recommend.service import load_resources, matrix_data
|
||
|
||
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
|
||
logger = logging.getLogger()
|
||
router = APIRouter()
|
||
|
||
|
||
@router.on_event("startup")
|
||
async def startup_event():
|
||
# 初始加载
|
||
load_resources()
|
||
|
||
# 配置定时任务
|
||
scheduler = BackgroundScheduler()
|
||
scheduler.add_job(
|
||
load_resources,
|
||
trigger=CronTrigger(hour=0, minute=30),
|
||
name="每日资源刷新"
|
||
)
|
||
scheduler.start()
|
||
logger.info("定时任务已启动")
|
||
|
||
def softmax(scores):
|
||
max_score = max(scores)
|
||
exp_scores = [math.exp(s - max_score) for s in scores]
|
||
sum_exp = sum(exp_scores)
|
||
return [s / sum_exp for s in exp_scores]
|
||
|
||
# def get_random_recommendations(category: str, num: int) -> List[str]:
|
||
# """根据预加载热度向量推荐（冷启动）"""
|
||
# try:
|
||
# heat_data = matrix_data.get("heat_data", {})
|
||
#
|
||
# if category not in heat_data:
|
||
# raise ValueError(f"热度数据缺少类别 {category}，使用随机推荐")
|
||
#
|
||
# heat_dict = heat_data[category] # {url: score}
|
||
# urls = list(heat_dict.keys())
|
||
# scores = list(heat_dict.values())
|
||
#
|
||
# if not urls:
|
||
# raise ValueError("该类别下无热度记录，使用随机推荐")
|
||
#
|
||
# probs = softmax(scores)
|
||
# sample_size = min(num, len(urls))
|
||
# sampled_urls = random.choices(urls, weights=probs, k=sample_size)
|
||
#
|
||
# return sampled_urls
|
||
#
|
||
# except Exception as e:
|
||
# # 回退：完全随机推荐
|
||
# all_iids = list(matrix_data["iid_to_sketch"].keys())
|
||
# category_iids = matrix_data["category_to_iids"].get(category, all_iids)
|
||
# sample_size = min(num, len(category_iids))
|
||
# sampled = np.random.choice(category_iids, size=sample_size, replace=False)
|
||
# return [matrix_data["iid_to_sketch"][iid] for iid in sampled]
|
||
|
||
def get_random_recommendations(category: str, num: int) -> List[str]:
|
||
"""全品类随机推荐"""
|
||
all_iids = list(matrix_data["iid_to_sketch"].keys())
|
||
# 优先从当前品类选择
|
||
category_iids = matrix_data["category_to_iids"].get(category, all_iids)
|
||
# 确保不超出实际数量
|
||
sample_size = min(num, len(category_iids))
|
||
sampled = np.random.choice(category_iids, size=sample_size, replace=False)
|
||
return [matrix_data["iid_to_sketch"][iid] for iid in sampled]
|
||
|
||
|
||
@router.get("/recommend/{user_id}/{category}/{num_recommendations}/{brand_id}/{brand_scale}", response_model=List[str])
|
||
async def get_recommendations(user_id: int, category: str, brand_id: int, brand_scale: float, num_recommendations: int = 10):
|
||
"""
|
||
:param user_id: 4
|
||
:param category: female_skirt
|
||
:param num_recommendations: 1
|
||
:return:
|
||
[
|
||
"aida-sys-image/images/female/skirt/903000017.jpg"
|
||
]
|
||
"""
|
||
try:
|
||
logger.info(f"user_id:{user_id}, category:{category}, num_recommendations:{num_recommendations}")
|
||
start_time = time.time()
|
||
cache_key = (user_id, category)
|
||
# === 新增：用户存在性检查 ===
|
||
user_exists_inter = user_id in matrix_data["user_index_interaction"]
|
||
user_exists_feat = user_id in matrix_data["user_index_feature"]
|
||
|
||
# 任一矩阵不存在用户则返回随机推荐
|
||
if not (user_exists_inter and user_exists_feat):
|
||
logger.info(f"用户 {user_id} 数据不完整，触发随机推荐")
|
||
return get_random_recommendations(category, num_recommendations)
|
||
|
||
# 检查缓存
|
||
if cache_key in matrix_data["cached_scores"]:
|
||
processed_inter, processed_feat = matrix_data["cached_scores"][cache_key]
|
||
valid_sketch_idxs_inter = matrix_data["cached_valid_idxs"][cache_key]
|
||
else:
|
||
# 实时计算逻辑（同原代码）
|
||
user_idx_inter = matrix_data["user_index_interaction"].get(user_id)
|
||
user_idx_feature = matrix_data["user_index_feature"].get(user_id)
|
||
|
||
category_iids = matrix_data["category_to_iids"].get(category, [])
|
||
valid_sketch_idxs_inter = [
|
||
idx for iid, idx in matrix_data["sketch_index_interaction"].items()
|
||
if iid in category_iids
|
||
]
|
||
|
||
# 处理交互分数
|
||
raw_inter_scores = []
|
||
if user_idx_inter is not None and valid_sketch_idxs_inter:
|
||
raw_inter_scores = matrix_data["interaction_matrix"][user_idx_inter, valid_sketch_idxs_inter]
|
||
processed_inter = raw_inter_scores * 0.7
|
||
|
||
# 处理特征分数
|
||
valid_sketch_idxs_feature = [
|
||
idx for iid, idx in matrix_data["sketch_index_feature"].items()
|
||
if iid in category_iids
|
||
]
|
||
raw_feat_scores = []
|
||
if user_idx_feature is not None and valid_sketch_idxs_feature:
|
||
raw_feat_scores = matrix_data["feature_matrix"][user_idx_feature, valid_sketch_idxs_feature]
|
||
raw_feat_scores = (raw_feat_scores - np.min(raw_feat_scores)) / (
|
||
np.max(raw_feat_scores) - np.min(raw_feat_scores) + 1e-8)
|
||
processed_feat = raw_feat_scores
|
||
else:
|
||
processed_feat = np.array([])
|
||
|
||
# 更新缓存
|
||
matrix_data["cached_scores"][cache_key] = (processed_inter, processed_feat)
|
||
matrix_data["cached_valid_idxs"][cache_key] = valid_sketch_idxs_inter
|
||
|
||
# 合并分数
|
||
if brand_id is not None:
|
||
brand_idx_feature = matrix_data["brand_index_map"].get(brand_id)
|
||
|
||
brand_feat_valid = (
|
||
matrix_data["brand_feature_matrix"].size > 0 and # 矩阵非空
|
||
brand_idx_feature is not None and
|
||
valid_sketch_idxs_feature # 有可用索引
|
||
)
|
||
|
||
if brand_feat_valid:
|
||
raw_brand_feat_scores = matrix_data["brand_feature_matrix"][
|
||
brand_idx_feature, valid_sketch_idxs_feature
|
||
]
|
||
raw_brand_feat_scores = (raw_brand_feat_scores - np.min(raw_brand_feat_scores)) / (
|
||
np.max(raw_brand_feat_scores) - np.min(raw_brand_feat_scores) + 1e-8
|
||
)
|
||
processed_brand_feat = raw_brand_feat_scores
|
||
|
||
# 如果 processed_feat 是空的，替换为全 0，避免 shape 不一致
|
||
if processed_feat.size == 0:
|
||
processed_feat = np.zeros_like(processed_brand_feat)
|
||
|
||
final_scores = processed_inter + 0.3 * (
|
||
(1 - brand_scale) * processed_feat + brand_scale * processed_brand_feat
|
||
)
|
||
else:
|
||
# brand 信息不可用
|
||
final_scores = processed_inter + 0.3 * processed_feat if processed_feat.size > 0 else processed_inter
|
||
else:
|
||
final_scores = processed_inter + 0.3 * processed_feat if processed_feat.size > 0 else processed_inter
|
||
|
||
valid_sketch_idxs = matrix_data["cached_valid_idxs"][cache_key]
|
||
|
||
# 概率采样
|
||
scores = np.array(final_scores)
|
||
|
||
# 调整后的概率转换（带温度控制的softmax）
|
||
def calibrated_softmax(scores, temperature=1.0):
|
||
scores = scores / temperature
|
||
scale = scores - max(scores)
|
||
exps = np.exp(scale)
|
||
return exps / np.sum(exps)
|
||
|
||
probs = calibrated_softmax(scores, 0.09)
|
||
|
||
chosen_indices = np.random.choice(
|
||
len(valid_sketch_idxs),
|
||
size=min(num_recommendations, len(valid_sketch_idxs)),
|
||
p=probs,
|
||
replace=False
|
||
)
|
||
recommendations = [matrix_data["iid_to_sketch"][valid_sketch_idxs[idx]] for idx in chosen_indices]
|
||
|
||
logger.info(f"推荐生成完成，耗时: {time.time() - start_time:.2f}秒")
|
||
return recommendations
|
||
|
||
except Exception as e:
|
||
logger.error(f"推荐失败: {str(e)}", exc_info=True)
|
||
raise HTTPException(status_code=500, detail=str(e)) |