在生成ALS和LR模型以后,接下来就可以用在代码中了。
首先ALS,其实在数据已经存在数据库中了,只要从中取出来,去掉个逗号之类的就好
@Service
public class RecommendService {
@Autowired
private RecommendDOMapper recommendDOMapper;
//找回数据,根据userid召回shopidList
public List<Integer> recall(Integer userId){
RecommendDO recommendDO = recommendDOMapper.selectByPrimaryKey(userId);
if (recommendDO == null){
recommendDO = recommendDOMapper.selectByPrimaryKey(99999);
}
String[] shopIdArr = recommendDO.getRecommend().split(",");
List<Integer> shopIdList = new ArrayList<>();
for (int i = 0 ; i < shopIdArr.length ; i ++){
shopIdList.add(Integer.valueOf(shopIdArr[i]));
}
return shopIdList;
}
}
对于LR:
@Service
public class RecommendSortService {
private SparkSession spark;
private LogisticRegressionModel lrModel;
@PostConstruct
public void init(){
//初始化spark运行环境
spark = SparkSession.builder()
.master("local")
.appName("DianpingApp")
.getOrCreate();
lrModel = LogisticRegressionModel.load("file:///F:/mouseSpace/project/background/lr/lrmodel");
}
public List<Integer> sort(List<Integer> shopIdList , Integer userId){
//需要根据lrmodel所需要的11维的x生成特征,然后调用预测方法
List<ShopSortModel> list = new ArrayList<>();
for (Integer shopId : shopIdList){
//造的假数据
Vector v = Vectors.dense(1,0,0,0,0,1,0.6,0,0,1,0);
Vector result = lrModel.predictProbability(v);
double[] arr = result.toArray();
double score = arr[1];
// lrModel.predict(v); 如果用这个,就是返回1或者0
ShopSortModel shopSortModel = new ShopSortModel();
shopSortModel.setShopId(shopId);
shopSortModel.setScore(score);
list.add(shopSortModel);
}
list.sort(new Comparator<ShopSortModel>() {
@Override
public int compare(ShopSortModel o1, ShopSortModel o2) {
if (o1.getScore() < o2.getScore()){
return -1;
}else if (o1.getScore() > o2.getScore()){
return 1;
}else {
return 0;
}
}
});
return list.stream().map(shopSortModel -> shopSortModel.getShopId()).collect(Collectors.toList());
}
}
代码中自己造了一个数据,所以结果会有些偏差。
对于GBDT
跟lr算法非常像
public class GBDTRecommendSortService {
private SparkSession spark;
private GBTClassificationModel gbtClassificationModel;
@PostConstruct
public void init(){
//初始化spark运行环境
spark = SparkSession.builder()
.master("local")
.appName("DianpingApp")
.getOrCreate();
gbtClassificationModel = GBTClassificationModel.load("file:///F:/mouseSpace/project/background/lr/gbdtmodel");
}
public List<Integer> sort(List<Integer> shopIdList , Integer userId){
//需要根据lrmodel所需要的11维的x生成特征,然后调用预测方法
List<ShopSortModel> list = new ArrayList<>();
for (Integer shopId : shopIdList){
//造的假数据
Vector v = Vectors.dense(1,0,0,0,0,1,0.6,0,0,1,0);
Vector result = gbtClassificationModel.predictProbability(v);
double[] arr = result.toArray();
double score = arr[1];
// lrModel.predict(v); 如果用这个,就是返回1或者0
ShopSortModel shopSortModel = new ShopSortModel();
shopSortModel.setShopId(shopId);
shopSortModel.setScore(score);
list.add(shopSortModel);
}
list.sort(new Comparator<ShopSortModel>() {
@Override
public int compare(ShopSortModel o1, ShopSortModel o2) {
if (o1.getScore() < o2.getScore()){
return -1;
}else if (o1.getScore() > o2.getScore()){
return 1;
}else {
return 0;
}
}
});
return list.stream().map(shopSortModel -> shopSortModel.getShopId()).collect(Collectors.toList());
}
}
A/B Test
它可以帮助我们决策算法的好坏,提供更多的真实依据的手段。
在真实场景中,假如现有的是LR算法,那么我现在马上在线上换成GBDT,当然是有很大风险的,那么AB TEST就出现了,假如有10条数据,我可以分5条用lr算法,5条用GBDT算法,然后将两个依次穿插,形成一个结果集发给前端,然后通过记录点击率来验证哪种算法更好。