553实验排序同步567

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/rank/strategy/RankStrategy4RegionMergeModelV553.java

@@ -354,108 +354,31 @@ public class RankStrategy4RegionMergeModelV553 extends RankStrategy4RegionMergeM
             }
             item.featureMap = featureMap;
         }
-
-        // 3 排序
+        // 4 排序模型计算
         Map<String, String> sceneFeatureMap = new HashMap<>(0);
-
-        List<RankItem> items = ScorerUtils.getScorerPipeline("feeds_score_config_20240807.conf")
-                .scoring(sceneFeatureMap, userFeatureMap, rankItems);
-
-
-        // 获取VoV预测模型参数
-        // 融合权重
-        double alpha_vov = mergeWeight.getOrDefault("alpha_vov", 1.0);
-
-        double vov_thresh = mergeWeight.getOrDefault("vov_thresh", 0.1);
-
-        double view_thresh = mergeWeight.getOrDefault("view_thresh", 1535.0);
-
-        double level50_vov = mergeWeight.getOrDefault("level50_vov", 0.123);
-
-        double level_95_vov = mergeWeight.getOrDefault("level_95_vov", 0.178);
-
-        double beta_vov = mergeWeight.getOrDefault("beta_vov", 100.0);
-
-        List<Double> weightList = new ArrayList<>(7);
-        weightList.add(mergeWeight.getOrDefault("d2_ago_vov_w", 0.0));
-        weightList.add(mergeWeight.getOrDefault("d1_ago_vov_w", 0.0));
-        weightList.add(mergeWeight.getOrDefault("h48_ago_vov_w", 0.0));
-        weightList.add(mergeWeight.getOrDefault("h24_ago_vov_w", 0.0));
-        weightList.add(mergeWeight.getOrDefault("h3_ago_vov_w", 0.0));
-        weightList.add(mergeWeight.getOrDefault("h2_ago_vov_w", 0.0));
-        weightList.add(mergeWeight.getOrDefault("h1_ago_vov_w", 0.0));
-
-
+        List<RankItem> items = ScorerUtils.getScorerPipeline("feeds_score_config_20240807.conf").scoring(sceneFeatureMap, userFeatureMap, rankItems);
+        // 5 排序公式特征
         Map<String, Map<String, String>> vid2MapFeature = this.getVideoRedisFeature(vids, "redis:vid_hasreturn_rov:");
-        Map<String, Map<String, String>> vid2VovFeatureMap = this.getVideoRedisFeature(vids, "redis:vid_vovhour4rank:");
+        Map<String, Map<String, String>> vid2VovFeatureMap = this.getVideoRedisFeature(vids, "redis:vid_vovh24pred:");
+        double alpha_vov = mergeWeight.getOrDefault("alpha_vov", 0.05);
+        double func = mergeWeight.getOrDefault("func", 1.0);
         List<Video> result = new ArrayList<>();
-//        String hasReturnRovKey = mergeWeight.getOrDefault("hasReturnRovKey", 1.0) < 0.5 ? "rate_1" : "rate_n";
-//        Double chooseFunction = mergeWeight.getOrDefault("chooseFunction", 0.0);
-
         for (RankItem item : items) {
+            item.getScoresMap().put("alpha_vov", alpha_vov);
             double score = 0.0;
-            // 获取其他模型输出score
             double fmRovOrigin = item.getScoreRov();
             item.getScoresMap().put("fmRovOrigin", fmRovOrigin);
             double fmRov = restoreScore(fmRovOrigin);
             item.getScoresMap().put("fmRov", fmRov);
-
-
-            // 获取VoV输入特征
-            double h1_ago_vov = Double.parseDouble(vid2VovFeatureMap.getOrDefault(item.getVideoId() + "", new HashMap<>())
-                    .getOrDefault("h1_ago_vov", "-2")); // 如果没有时，默认为多少？？ 需要考虑
-            double h2_ago_vov = Double.parseDouble(vid2VovFeatureMap.getOrDefault(item.getVideoId() + "", new HashMap<>())
-                    .getOrDefault("h2_ago_vov", "-2")); // 如果没有时，默认为多少？？ 需要考虑
-            double h3_ago_vov = Double.parseDouble(vid2VovFeatureMap.getOrDefault(item.getVideoId() + "", new HashMap<>())
-                    .getOrDefault("h3_ago_vov", "-2")); // 如果没有时，默认为多少？？ 需要考虑
-            double h24_ago_vov = Double.parseDouble(vid2VovFeatureMap.getOrDefault(item.getVideoId() + "", new HashMap<>())
-                    .getOrDefault("h24_ago_vov", "-2")); // 如果没有时，默认为多少？？ 需要考虑
-            double h48_ago_vov = Double.parseDouble(vid2VovFeatureMap.getOrDefault(item.getVideoId() + "", new HashMap<>())
-                    .getOrDefault("h48_ago_vov", "-2")); // 如果没有时，默认为多少？？ 需要考虑
-            double d1_ago_vov = Double.parseDouble(vid2VovFeatureMap.getOrDefault(item.getVideoId() + "", new HashMap<>())
-                    .getOrDefault("d1_ago_vov", "-2")); // 如果没有时，默认为多少？？ 需要考虑
-            double d2_ago_vov = Double.parseDouble(vid2VovFeatureMap.getOrDefault(item.getVideoId() + "", new HashMap<>())
-                    .getOrDefault("d2_ago_vov", "-2")); // 如果没有时，默认为多少？？ 需要考虑
-
-            double h1_ago_view = Double.parseDouble(vid2VovFeatureMap.getOrDefault(item.getVideoId() + "", new HashMap<>())
-                    .getOrDefault("h1_ago_view", "-2")); // 如果没有时，默认为多少？？ 需要考虑
-
-            item.getScoresMap().put("h1_ago_vov", h1_ago_vov);
-            item.getScoresMap().put("h2_ago_vov", h2_ago_vov);
-            item.getScoresMap().put("h3_ago_vov", h3_ago_vov);
-            item.getScoresMap().put("h24_ago_vov", h24_ago_vov);
-            item.getScoresMap().put("h48_ago_vov", h48_ago_vov);
-            item.getScoresMap().put("d1_ago_vov", d1_ago_vov);
-            item.getScoresMap().put("d2_ago_vov", d2_ago_vov);
-
-            item.getScoresMap().put("h1_ago_view", h1_ago_view);
-            item.getScoresMap().put("alpha_vov", alpha_vov);
-            item.getScoresMap().put("view_thresh", view_thresh);
-            item.getScoresMap().put("vov_thresh", vov_thresh);
-
-
-            List<Double> featureList = new ArrayList<>(7);
-            featureList.add(d2_ago_vov);
-            featureList.add(d1_ago_vov);
-            featureList.add(h48_ago_vov);
-            featureList.add(h24_ago_vov);
-            featureList.add(h3_ago_vov);
-            featureList.add(h2_ago_vov);
-            featureList.add(h1_ago_vov);
-
-            // todo 线性加权 预测VoV
-
-
-            double vov_p = calculateScore(featureList, weightList, item, vov_thresh, view_thresh, h1_ago_view, level50_vov, level_95_vov, beta_vov);
-
-
-            double hasReturnRovScore = Double.parseDouble(vid2MapFeature.getOrDefault(item.getVideoId() + "", new HashMap<>())
-                    .getOrDefault("rate_n", "0"));
+            double hasReturnRovScore = Double.parseDouble(vid2MapFeature.getOrDefault(item.getVideoId() + "", new HashMap<>()).getOrDefault("rate_n", "0"));
             item.getScoresMap().put("hasReturnRovScore", hasReturnRovScore);
-            score = fmRov * (1 + hasReturnRovScore) * (1.0 + alpha_vov * vov_p);
-
-
-            item.getScoresMap().put("vov_p", vov_p);
+            double vovScore = this.calVovScore(item, vid2VovFeatureMap);
+            item.getScoresMap().put("vovScore", vovScore);
+            if (func == 1){
+                score = fmRov * (1 + hasReturnRovScore) + alpha_vov * vovScore;
+            }else{
+                score = fmRov * (1 + hasReturnRovScore) * (1.0 + alpha_vov * vovScore);
+            }
 
             Video video = item.getVideo();
             video.setScore(score);
@@ -474,58 +397,29 @@ public class RankStrategy4RegionMergeModelV553 extends RankStrategy4RegionMergeM
             result.add(video);
         }
         result.sort(Comparator.comparingDouble(o -> -o.getSortScore()));
-
         return result;
     }
 
-
-    private double calculateScore(List<Double> featureList, List<Double> weightList, RankItem rankItem,
-                                  double vov_thresh, double view_thresh, double h1_ago_view, double level50_vov, double level_95_vov, double beta_vov) {
-        // 检查 h1_ago_view 条件
-        if (h1_ago_view == -2 || h1_ago_view == -1 || h1_ago_view < view_thresh) {
-            rankItem.getScoresMap().put("origin_vov_p", 0d);
-            return 0;
-        }
-
-        // // 检查 featureList 是否全为 -1
-        // if (featureList.stream().allMatch(f -> f == -1)) {
-        //     rankItem.getScoresMap().put("origin_vov_p", 0d);
-        //     return 0;
-        // }
-
-        // 计算有效特征的总权重和得分
-        double score = 0;
-        List<Integer> validIndices = new ArrayList<>();
-
-        for (int i = 0; i < featureList.size(); i++) {
-            if (featureList.get(i) != -1) {
-                validIndices.add(i);
-            }
-        }
-
-        // 如果没有有效特征，返回 0
-        if (validIndices.isEmpty()) {
-            rankItem.getScoresMap().put("origin_vov_p", 0d);
-            return 0;
-        }
-
-        // 计算得分，动态调整权重
-        for (int index : validIndices) {
-            double weight = weightList.get(index);
-            score += featureList.get(index) * weight;
+    public double calVovScore(RankItem item, Map<String, Map<String, String>> vid2VovFeatureMap){
+        String id = item.getVideoId() + "";
+        Map<String, String> featureMap = vid2VovFeatureMap.getOrDefault(id, new HashMap<>());
+        double numerator = 0D;
+        final Set<String> ups = new HashSet<>(Arrays.asList(
+            "1_vovh0分子", "2_vovh1分子", "3_vovh2分子", "4_vovh3分子", "7_vovh6分子", "13_vovh12分子", "25_vovh24分子", "2_vovd1分子"
+        ));
+        for (String key: ups){
+            numerator += Double.parseDouble(featureMap.getOrDefault(key, "0"));
         }
-        rankItem.getScoresMap().put("origin_vov_p", score);
-        // 调整vov
-        if (score < vov_thresh) {
-            score = 0;
-        } else {
-            double term1 = 1 / (1 + Math.exp(-1 * beta_vov * (score - level50_vov)));
-            double term2 = 1 + Math.exp(-1 * beta_vov * (level_95_vov - level50_vov));
-            score = term1 * term2;
+        double denominator = 0D;
+        final Set<String> downs = new HashSet<>(Arrays.asList(
+                "1_vovh分母", "2_vovh分母", "3_vovh分母", "4_vovh分母", "7_vovh分母", "13_vovh分母", "25_vovh分母", "2_vovd分母"
+        ));
+        for (String key: downs){
+            denominator += Double.parseDouble(featureMap.getOrDefault(key, "0"));
         }
-        return score;
+        item.getScoresMap().put("numerator", numerator);
+        item.getScoresMap().put("denominator", denominator);
+        return denominator != 0.0? numerator / denominator: 0.0;
     }
 
-
-
 }