新推荐接口first commit
This commit is contained in:
litianxiang
@@ -1,25 +1,34 @@
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import io
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import logging
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import os
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import sys
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import time
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from typing import List
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from collections import defaultdict
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import numpy as np
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import pymysql
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import torch
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from PIL import Image
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from apscheduler.schedulers.background import BackgroundScheduler
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from apscheduler.triggers.cron import CronTrigger
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from fastapi import HTTPException, APIRouter
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from fastapi.responses import JSONResponse
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from minio import Minio
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from torchvision import models, transforms
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from app.core.mysql_config import DB_CONFIG
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from app.core.new_config import settings
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import pymysql
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from app.core.config import DB_CONFIG, TABLE_CATEGORIES, RECOMMEND_PATH_PREFIX
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from minio import Minio
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import torch
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from torchvision import models, transforms
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from PIL import Image
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import os
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from fastapi.responses import JSONResponse
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sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
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logger = logging.getLogger()
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router = APIRouter()
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minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE)
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# MinIO 配置
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minio_client = Minio(
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"www.minio.aida.com.hk:12024",
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access_key="admin",
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secret_key="Aidlab123123!",
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secure=True
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)
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transform = transforms.Compose([
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transforms.Resize((224, 224)),
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@@ -58,8 +67,8 @@ def extract_feature_vector_from_resnet(sketch_path: str) -> np.ndarray:
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# 预加载
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BRAND_FEATURES = np.load(f'{settings.RECOMMEND_PATH_PREFIX}brand_feature.npy', allow_pickle=True).item()
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SYSTEM_FEATURES = np.load(f'{settings.RECOMMEND_PATH_PREFIX}sketch_feature_dict.npy', allow_pickle=True).item()
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BRAND_FEATURES = np.load(f'{RECOMMEND_PATH_PREFIX}brand_feature.npy', allow_pickle=True).item()
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SYSTEM_FEATURES = np.load(f'{RECOMMEND_PATH_PREFIX}sketch_feature_dict.npy', allow_pickle=True).item()
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def save_sketch_to_iid():
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@@ -67,11 +76,11 @@ def save_sketch_to_iid():
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sketch_path: iid
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for iid, sketch_path in enumerate(SYSTEM_FEATURES.keys(), start=1)
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}
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np.save(f"{settings.RECOMMEND_PATH_PREFIX}sketch_to_iid.npy", sketch_to_iid)
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np.save(f"{RECOMMEND_PATH_PREFIX}sketch_to_iid.npy", sketch_to_iid)
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def load_sketch_to_iid():
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path = f"{settings.RECOMMEND_PATH_PREFIX}sketch_to_iid.npy"
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path = f"{RECOMMEND_PATH_PREFIX}sketch_to_iid.npy"
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if os.path.exists(path):
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return np.load(path, allow_pickle=True).item()
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save_sketch_to_iid()
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@@ -81,7 +90,7 @@ def load_sketch_to_iid():
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sketch_to_iid = load_sketch_to_iid()
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def get_new_category(gender: str, sketch_category: str) -> str:
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def getNewCategory(gender: str, sketch_category: str) -> str:
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return f"{gender.lower()}_{sketch_category.lower()}"
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@@ -94,8 +103,8 @@ def get_category_from_path(path: str) -> str:
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def load_brand_matrix():
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"""单独加载 brand_matrix 和 brand_index_map"""
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mat_path = f"{settings.RECOMMEND_PATH_PREFIX}brand_matrix.npy"
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idx_path = f"{settings.RECOMMEND_PATH_PREFIX}brand_index_map.npy"
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mat_path = f"{RECOMMEND_PATH_PREFIX}brand_matrix.npy"
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idx_path = f"{RECOMMEND_PATH_PREFIX}brand_index_map.npy"
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try:
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matrix = np.load(mat_path)
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index_map = np.load(idx_path, allow_pickle=True).item()
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@@ -104,19 +113,11 @@ def load_brand_matrix():
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index_map = {}
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return matrix, index_map
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def cosine_similarity(vec1, vec2):
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"""计算余弦相似度（增加零值处理）"""
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norm = np.linalg.norm(vec1) * np.linalg.norm(vec2)
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return np.dot(vec1, vec2) / (norm + 1e-10) if norm != 0 else 0.0
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def getNewCategory(gender, sketch_category):
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print(gender)
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print(sketch_category)
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return "None"
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def calculate_brand_matrix(sketch_data, brand_id: int) -> np.ndarray:
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# 1. 收集品牌-分类-特征
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brand_feature = defaultdict(lambda: defaultdict(list))
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@@ -163,11 +164,11 @@ def calculate_brand_matrix(sketch_data, brand_id: int) -> np.ndarray:
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brand_matrix[row_idx, sketch_index[iid]] = cos_sim
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# 7. 持久化
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np.save(f"{settings.RECOMMEND_PATH_PREFIX}brand_feature_matrix.npy", brand_matrix)
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np.save(f"{settings.RECOMMEND_PATH_PREFIX}brand_index_map.npy", brand_index_map)
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np.save(f"{RECOMMEND_PATH_PREFIX}brand_feature_matrix.npy", brand_matrix)
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np.save(f"{RECOMMEND_PATH_PREFIX}brand_index_map.npy", brand_index_map)
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# 返回该品牌对应行
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return brand_matrix[row_idx:row_idx + 1]
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return brand_matrix[row_idx:row_idx+1]
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@router.get("/brand_dna_initialize/{brand_id}")
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@@ -177,12 +178,14 @@ async def brand_dna_initialize(brand_id: int):
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conn = pymysql.connect(**DB_CONFIG)
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cursor = conn.cursor()
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cursor.execute("""
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SELECT id, img_url, gender, category
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FROM product_image_attribute
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WHERE library_id IN (SELECT library_id
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FROM brand_rel_library
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WHERE brand_id = %s)
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""", (brand_id,))
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SELECT id, img_url, gender, category
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FROM product_image_attribute
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WHERE library_id IN (
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SELECT library_id
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FROM brand_rel_library
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WHERE brand_id = %s
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)
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""", (brand_id,))
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sketch_data = cursor.fetchall()
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# 触发计算并持久化，若内部出错会抛异常
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116
app/api/api_import_sys_sketch.py
Normal file
116
app/api/api_import_sys_sketch.py
Normal file
@@ -0,0 +1,116 @@
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import logging
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import sys
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from typing import Optional
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from fastapi import APIRouter, HTTPException, Query
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from concurrent.futures import ThreadPoolExecutor
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import threading
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from app.schemas.response_template import ResponseModel
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from app.service.recommendation_system.import_sys_sketch_to_milvus import main as import_main
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logger = logging.getLogger()
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router = APIRouter()
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# 使用线程池执行器来运行长时间任务
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executor = ThreadPoolExecutor(max_workers=1)
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# 用于跟踪任务状态
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task_status = {"running": False}
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def run_import_task(batch_size: int, retry_times: int, limit: Optional[int], offset: int, skip_create_collection: bool):
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"""在后台线程中运行导入任务"""
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original_argv = None
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try:
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task_status["running"] = True
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# 保存原始 sys.argv
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original_argv = sys.argv.copy()
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# 模拟命令行参数
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sys.argv = [
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"import_sys_sketch_to_milvus.py",
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"--batch-size", str(batch_size),
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"--retry-times", str(retry_times),
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]
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if limit is not None:
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sys.argv.extend(["--limit", str(limit)])
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if offset > 0:
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sys.argv.extend(["--offset", str(offset)])
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if skip_create_collection:
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sys.argv.append("--skip-create-collection")
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import_main()
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task_status["running"] = False
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logger.info("导入任务完成")
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except Exception as e:
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task_status["running"] = False
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logger.error(f"导入任务失败: {e}", exc_info=True)
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raise
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finally:
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# 恢复原始 sys.argv
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if original_argv is not None:
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sys.argv = original_argv
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@router.post("/import-sys-sketch", response_model=ResponseModel)
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async def import_sys_sketch(
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batch_size: int = Query(1000, description="批量处理大小（默认：1000）"),
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retry_times: int = Query(3, description="失败重试次数（默认：3）"),
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limit: Optional[int] = Query(None, description="限制处理数量（用于测试，默认：不限制）"),
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offset: int = Query(0, description="起始偏移量（默认：0）"),
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skip_create_collection: bool = Query(False, description="跳过创建集合（如果集合已存在）"),
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):
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"""
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从 t_sys_file 导入系统图向量到 Milvus
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该接口会异步执行导入任务，任务在后台运行。
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"""
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try:
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# 检查是否有任务正在运行
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if task_status["running"]:
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raise HTTPException(
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status_code=409,
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detail="已有导入任务正在运行，请等待完成后再试"
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)
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# 在后台线程中执行任务
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executor.submit(
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run_import_task,
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batch_size,
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retry_times,
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limit,
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offset,
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skip_create_collection
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)
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return ResponseModel(
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code=200,
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msg="导入任务已启动，正在后台执行",
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data={
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"status": "started",
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"batch_size": batch_size,
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"retry_times": retry_times,
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"limit": limit,
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"offset": offset,
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"skip_create_collection": skip_create_collection
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}
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)
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"启动导入任务失败: {e}", exc_info=True)
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raise HTTPException(status_code=500, detail=f"启动导入任务失败: {str(e)}")
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@router.get("/import-sys-sketch/status", response_model=ResponseModel)
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async def get_import_status():
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"""
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获取导入任务状态
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"""
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return ResponseModel(
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code=200,
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msg="OK",
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data={
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"running": task_status["running"]
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}
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)
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85
app/api/api_precompute.py
Normal file
85
app/api/api_precompute.py
Normal file
@@ -0,0 +1,85 @@
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import logging
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from fastapi import APIRouter, HTTPException
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from concurrent.futures import ThreadPoolExecutor
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from app.schemas.response_template import ResponseModel
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from app.service.recommendation_system.precompute import run_precompute
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logger = logging.getLogger()
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router = APIRouter()
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# 使用线程池执行器来运行长时间任务
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executor = ThreadPoolExecutor(max_workers=1)
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# 用于跟踪任务状态
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task_status = {"running": False}
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def run_precompute_task():
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"""在后台线程中运行预计算任务"""
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try:
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task_status["running"] = True
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logger.info("开始执行预计算任务...")
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run_precompute()
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task_status["running"] = False
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logger.info("预计算任务完成")
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except Exception as e:
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task_status["running"] = False
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logger.error(f"预计算任务失败: {e}", exc_info=True)
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raise
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@router.post("/precompute", response_model=ResponseModel)
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async def precompute():
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"""
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运行预计算任务
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该接口会异步执行预计算任务，包括：
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1. 优化数据库表结构
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2. 历史数据迁移
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3. 初始用户偏好向量生成
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任务在后台运行。
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"""
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try:
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# 检查是否有任务正在运行
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if task_status["running"]:
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raise HTTPException(
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status_code=409,
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detail="已有预计算任务正在运行，请等待完成后再试"
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)
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# 在后台线程中执行任务
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executor.submit(run_precompute_task)
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return ResponseModel(
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code=200,
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msg="预计算任务已启动，正在后台执行",
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data={
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"status": "started",
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"tasks": [
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"优化数据库表结构",
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"历史数据迁移",
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"初始用户偏好向量生成"
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]
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}
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)
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"启动预计算任务失败: {e}", exc_info=True)
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raise HTTPException(status_code=500, detail=f"启动预计算任务失败: {str(e)}")
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@router.get("/precompute/status", response_model=ResponseModel)
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async def get_precompute_status():
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"""
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获取预计算任务状态
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"""
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return ResponseModel(
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code=200,
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msg="OK",
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data={
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"running": task_status["running"]
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}
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)
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@@ -1,206 +1,175 @@
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import io
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import logging
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import math
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import sys
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import time
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from typing import List
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import numpy as np
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from typing import List, Optional
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from fastapi import HTTPException, APIRouter, Query
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from apscheduler.schedulers.background import BackgroundScheduler
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from apscheduler.triggers.cron import CronTrigger
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from fastapi import HTTPException, APIRouter
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from app.service.recommend.service import load_resources, matrix_data
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from app.service.recommendation_system.recommendation_api import get_recommendations as get_new_recommendations
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from app.service.recommendation_system.incremental_listener import start_background_listener
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from app.service.recommendation_system.milvus_client import create_collection
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sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
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logger = logging.getLogger()
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router = APIRouter()
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@router.on_event("startup")
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async def startup_event():
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# 初始加载
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load_resources()
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# 配置定时任务
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scheduler = BackgroundScheduler()
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scheduler.add_job(
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load_resources,
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trigger=CronTrigger(hour=0, minute=30),
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name="每日资源刷新"
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)
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scheduler.start()
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logger.info("定时任务已启动")
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def softmax(scores):
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max_score = max(scores)
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exp_scores = [math.exp(s - max_score) for s in scores]
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sum_exp = sum(exp_scores)
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return [s / sum_exp for s in exp_scores]
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# def get_random_recommendations(category: str, num: int) -> List[str]:
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# """根据预加载热度向量推荐（冷启动）"""
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# ========== 旧版推荐接口（基于 npy 矩阵，已废弃）==========
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# @router.get("/recommend/{user_id}/{category}/{num_recommendations}/{brand_id}/{brand_scale}", response_model=List[str])
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# async def get_recommendations(user_id: int, category: str, brand_id: int, brand_scale: float, num_recommendations: int = 10):
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# """
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# :param user_id: 4
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# :param category: female_skirt
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# :param num_recommendations: 1
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# :return:
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# [
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# "aida-sys-image/images/female/skirt/903000017.jpg"
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# ]
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# """
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# try:
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# heat_data = matrix_data.get("heat_data", {})
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# start_time = time.time()
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# cache_key = (user_id, category)
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# # === 新增：用户存在性检查 ===
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# user_exists_inter = user_id in matrix_data["user_index_interaction"]
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# user_exists_feat = user_id in matrix_data["user_index_feature"]
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#
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# if category not in heat_data:
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# raise ValueError(f"热度数据缺少类别 {category}，使用随机推荐")
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# # 任一矩阵不存在用户则返回随机推荐
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# if not (user_exists_inter and user_exists_feat):
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# logger.info(f"用户 {user_id} 数据不完整，触发随机推荐")
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# return get_random_recommendations(category, num_recommendations)
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#
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# heat_dict = heat_data[category] # {url: score}
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# urls = list(heat_dict.keys())
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# scores = list(heat_dict.values())
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# # 检查缓存
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# if cache_key in matrix_data["cached_scores"]:
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# processed_inter, processed_feat = matrix_data["cached_scores"][cache_key]
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||||
# 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)
|
||||
#
|
||||
# if not urls:
|
||||
# raise ValueError("该类别下无热度记录，使用随机推荐")
|
||||
# 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
|
||||
# ]
|
||||
#
|
||||
# probs = softmax(scores)
|
||||
# sample_size = min(num, len(urls))
|
||||
# sampled_urls = random.choices(urls, weights=probs, k=sample_size)
|
||||
# # 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
|
||||
#
|
||||
# return sampled_urls
|
||||
# # 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:
|
||||
# # 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]
|
||||
# logger.error(f"推荐失败: {str(e)}", exc_info=True)
|
||||
# raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
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"
|
||||
]
|
||||
|
||||
"""
|
||||
# @router.on_event("startup")
|
||||
async def startup_event():
|
||||
"""启动时初始化增量监听任务"""
|
||||
try:
|
||||
logger.info(f"user_id:{user_id}-----category:{category}-----brand_id:{brand_id}-----brand_scale:{brand_scale}-----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
|
||||
|
||||
# 确保 Milvus 集合已创建（若已存在则直接返回）
|
||||
try:
|
||||
create_collection()
|
||||
except Exception as exc:
|
||||
logger.error("Milvus 集合创建/检查失败，不影响服务继续启动: %s", exc, exc_info=True)
|
||||
|
||||
# 配置定时任务
|
||||
scheduler = BackgroundScheduler()
|
||||
start_background_listener(scheduler)
|
||||
scheduler.start()
|
||||
logger.info("增量监听定时任务已启动")
|
||||
except Exception as e:
|
||||
logger.error(f"推荐失败: {str(e)}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
logger.error(f"启动增量监听任务失败: {e}", exc_info=True)
|
||||
|
||||
|
||||
@router.get("/recommend/{user_id}/{category}", response_model=List[str])
|
||||
async def recommend(
|
||||
user_id: int,
|
||||
category: str,
|
||||
style: Optional[str] = Query(
|
||||
None,
|
||||
description="风格样式（可选）：若传入，则在利用分支对同 style 的候选进行加分",
|
||||
),
|
||||
):
|
||||
"""新版推荐接口（Milvus + Redis 偏好向量）。"""
|
||||
try:
|
||||
results = get_new_recommendations(user_id, category, style)
|
||||
path = results[0] if results else ""
|
||||
return [path]
|
||||
except Exception as e:
|
||||
logger.error("新版推荐接口失败 [user=%s, category=%s]: %s", user_id, category, e, exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
Reference in New Issue
Block a user