feat:备份

src/main/java/examples/utils/SimilarityUtils.java

@@ -3,6 +3,9 @@ package examples.utils;
 import com.tzld.piaoquan.recommend.similarity.word2vec.Segment;
 import com.tzld.piaoquan.recommend.similarity.word2vec.Word2Vec;
 import lombok.extern.slf4j.Slf4j;
+import org.ansj.library.AmbiguityLibrary;
+import org.ansj.library.DicLibrary;
+import org.ansj.library.SynonymsLibrary;
 
 import java.io.IOException;
 import java.util.List;
@@ -35,9 +38,38 @@ public final class SimilarityUtils {
         }
     }
 
+    public static void newInit() {
+        initLibrary();
+        Segment.getWords("1");
+        try {
+            long start = System.currentTimeMillis();
+            String endpoint = "oss-cn-hangzhou-internal.aliyuncs.com";
+            String bucketName = "art-recommend";
+            String path = "similarity/word2vec/Google_word2vec_zhwiki210720_300d.bin";
+            String accessKeyId = "LTAIP6x1l3DXfSxm";
+            String accessKetSecret = "KbTaM9ars4OX3PMS6Xm7rtxGr1FLon";
+            Word2Vec temp = new Word2Vec();
+            temp.loadGoogleModelFromOss(endpoint, bucketName, path, accessKeyId, accessKetSecret);
+            vec = temp;
+            long end = System.currentTimeMillis();
+            log.info("Model loaded successfully cost {}. Scheduled tasks cancelled.", end - start);
+        } catch (IOException e) {
+            log.error("loadGoogleModelFromOss error", e);
+        }
+    }
+
     public static float word2VecSimilarity(String str1, String str2) {
         List<String> words1 = Segment.getWords(str1);
         List<String> words2 = Segment.getWords(str2);
         return vec.sentenceSimilarity(words1, words2);
     }
+
+    public static void initLibrary() {
+        AmbiguityLibrary.put(AmbiguityLibrary.DEFAULT, "jar://library/ambiguity.dic");
+        AmbiguityLibrary.reload(AmbiguityLibrary.DEFAULT);
+        DicLibrary.put(DicLibrary.DEFAULT, "jar://library/default.dic");
+        DicLibrary.reload(DicLibrary.DEFAULT);
+        SynonymsLibrary.put(SynonymsLibrary.DEFAULT, "jar://library/synonyms.dic");
+        SynonymsLibrary.reload(SynonymsLibrary.DEFAULT);
+    }
 }

src/main/resources/20250317_recsys_nor_name.txt

@@ -0,0 +1,450 @@
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+b0_12h@is_share
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src/main/resources/20250317_recsys_rov_name.txt

@@ -0,0 +1,13895 @@
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src/main/resources/20250729_recsys_nor_name.txt

@@ -0,0 +1,385 @@
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src/main/resources/20260115_recsys_rov_bucket.txt

@@ -0,0 +1,452 @@
+b1_24h@share_cnt	200	0.109861,0.179176,0.219722,0.248491,0.277259,0.299573,0.325810,0.346574,0.366356,0.382864,0.398898,0.414313,0.429046,0.443082,0.456435,0.469135,0.481218,0.493447,0.504986,0.515906,0.526269,0.535659,0.544674,0.553733,0.562040,0.571043,0.579606,0.587774,0.595064,0.602102,0.609357,0.616331,0.622654,0.628972,0.635089,0.640688,0.646147,0.651323,0.656526,0.661740,0.666823,0.671417,0.676157,0.680683,0.685013,0.689669,0.693828,0.697915,0.701840,0.705618,0.710003,0.713728,0.717472,0.721008,0.724637,0.728345,0.732251,0.736074,0.739634,0.743130,0.746794,0.750163,0.753316,0.756528,0.759890,0.762998,0.766341,0.769257,0.772445,0.775961,0.779152,0.782164,0.785205,0.788269,0.791352,0.794343,0.797212,0.800336,0.803625,0.806527,0.809590,0.812445,0.815162,0.818032,0.820740,0.823748,0.826924,0.829630,0.832506,0.835255,0.838229,0.841139,0.843685,0.846632,0.849270,0.851659,0.854403,0.856998,0.859933,0.862227,0.865207,0.867539,0.870201,0.872778,0.875306,0.878140,0.881076,0.883928,0.886616,0.889179,0.891985,0.894494,0.897030,0.899479,0.902365,0.905532,0.907943,0.910376,0.913119,0.915356,0.917926,0.920683,0.923376,0.925617,0.928350,0.931389,0.934461,0.937594,0.940434,0.943348,0.945923,0.948994,0.951731,0.954079,0.956962,0.959988,0.962490,0.964737,0.967878,0.970540,0.972896,0.975551,0.977940,0.981504,0.983991,0.986573,0.989737,0.992334,0.995218,0.997785,1.000717,1.004520,1.007141,1.010279,1.013927,1.015728,1.018547,1.021101,1.023333,1.025699,1.028192,1.030273,1.032876,1.035099,1.037611,1.039843,1.041772,1.043432,1.045418,1.047365,1.049299,1.050791,1.053646,1.056401,1.059386,1.062213,1.065242,1.068620,1.070549,1.073553,1.076247,1.078343,1.080385,1.083094,1.085915,1.088688,1.093055,1.097157,1.101043,1.104286,1.108669,1.114054,1.119631,1.123502,1.127897,1.132940,1.137724,1.150044,1.186147
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+b5_6h@rovn_#	200	0.002252,0.002941,0.003484,0.003953,0.004386,0.004785,0.005139,0.005482,0.005814,0.006134,0.006426,0.006711,0.007003,0.007299,0.007576,0.007843,0.008104,0.008368,0.008621,0.008856,0.009104,0.009346,0.009589,0.009821,0.010066,0.010305,0.010526,0.010753,0.010989,0.011204,0.011429,0.011653,0.011892,0.012107,0.012342,0.012544,0.012761,0.012987,0.013193,0.013422,0.013629,0.013834,0.014066,0.014286,0.014493,0.014706,0.014925,0.015152,0.015360,0.015576,0.015789,0.016012,0.016229,0.016442,0.016667,0.016900,0.017118,0.017341,0.017556,0.017784,0.018003,0.018212,0.018456,0.018671,0.018875,0.019114,0.019334,0.019563,0.019778,0.020000,0.020225,0.020451,0.020681,0.020913,0.021154,0.021368,0.021608,0.021832,0.022065,0.022293,0.022534,0.022754,0.023006,0.023256,0.023479,0.023733,0.023961,0.024208,0.024444,0.024691,0.024938,0.025182,0.025435,0.025682,0.025943,0.026189,0.026451,0.026712,0.026976,0.027223,0.027483,0.027752,0.028000,0.028266,0.028537,0.028795,0.029070,0.029332,0.029595,0.029877,0.030160,0.030435,0.030717,0.030988,0.031250,0.031561,0.031842,0.032137,0.032419,0.032713,0.033010,0.033321,0.033599,0.033898,0.034188,0.034483,0.034791,0.035088,0.035387,0.035714,0.036030,0.036351,0.036683,0.037025,0.037338,0.037686,0.038015,0.038359,0.038696,0.039038,0.039390,0.039750,0.040103,0.040466,0.040832,0.041206,0.041602,0.041979,0.042362,0.042740,0.043148,0.043547,0.043987,0.044410,0.044853,0.045288,0.045707,0.046130,0.046579,0.047025,0.047493,0.047959,0.048448,0.048952,0.049469,0.050000,0.050489,0.051041,0.051621,0.052180,0.052768,0.053403,0.054052,0.054694,0.055394,0.056103,0.056851,0.057673,0.058496,0.059357,0.060328,0.061290,0.062367,0.063424,0.064655,0.065924,0.067392,0.068867,0.070501,0.072400,0.074513,0.076862,0.079545,0.082645,0.086623,0.091418,0.097392,0.105868,0.120659
+b8_24h@str_plus	200	0.007568,0.007942,0.008232,0.008386,0.008513,0.008582,0.008641,0.008715,0.008766,0.008821,0.008841,0.008859,0.008879,0.008925,0.008953,0.008967,0.008992,0.009023,0.009059,0.009078,0.009096,0.009116,0.009129,0.009151,0.009196,0.009223,0.009243,0.009281,0.009301,0.009328,0.009381,0.009411,0.009447,0.009481,0.009504,0.009537,0.009573,0.009631,0.009694,0.009738,0.009781,0.009866,0.009981,0.010116,0.010217,0.010300,0.010397,0.010459,0.010505,0.010570,0.010659,0.010769,0.010848,0.010903,0.010975,0.011026,0.011177,0.011207,0.011298,0.011407,0.011449,0.011532,0.011542,0.011611,0.011658,0.011710,0.011716,0.011730,0.011751,0.011777,0.011849,0.011870,0.011895,0.011929,0.011977,0.012026,0.012085,0.012110,0.012152,0.012182,0.012230,0.012280,0.012299,0.012311,0.012323,0.012344,0.012380,0.012382,0.012420,0.012473,0.012496,0.012506,0.012511,0.012549,0.012620,0.012717,0.012760,0.012789,0.012848,0.012878,0.012918,0.012946,0.012947,0.012970,0.012976,0.012987,0.012997,0.013044,0.013083,0.013100,0.013130,0.013146,0.013152,0.013165,0.013196,0.013220,0.013246,0.013300,0.013339,0.013365,0.013387,0.013409,0.013438,0.013452,0.013456,0.013465,0.013488,0.013503,0.013510,0.013527,0.013540,0.013555,0.013567,0.013576,0.013589,0.013605,0.013612,0.013623,0.013625,0.013649,0.013660,0.013673,0.013682,0.013697,0.013712,0.013727,0.013733,0.013745,0.013765,0.013786,0.013811,0.013818,0.013828,0.013839,0.013859,0.013875,0.013910,0.013939,0.013964,0.013999,0.014021,0.014064,0.014092,0.014157,0.014204,0.014244,0.014348,0.014391,0.014472,0.014535,0.014649,0.014837,0.014928,0.015019,0.015133,0.015184,0.015252,0.015320,0.015416,0.015475,0.015586,0.015655,0.015703,0.015781,0.015809,0.015847,0.015870,0.015906,0.015971,0.016044,0.016088,0.016204,0.016344,0.016502,0.016570,0.016694,0.016894,0.017350,0.018594
+b5_12h@exp	200	0.069315,0.109861,0.138629,0.160944,0.179176,0.194591,0.207944,0.230259,0.248491,0.256495,0.277259,0.289037,0.299573,0.313549,0.321888,0.333220,0.343399,0.352636,0.361092,0.371357,0.378419,0.387120,0.395124,0.404305,0.411087,0.418965,0.426268,0.433073,0.440672,0.447734,0.454329,0.460517,0.467283,0.472739,0.479579,0.485203,0.491265,0.497673,0.503044,0.508760,0.514749,0.519850,0.525750,0.530827,0.536129,0.541610,0.546806,0.551745,0.556834,0.561677,0.566643,0.571373,0.576205,0.580814,0.585507,0.589990,0.594542,0.599146,0.603548,0.607993,0.612249,0.616542,0.620859,0.625190,0.629342,0.633328,0.637502,0.641510,0.645362,0.649375,0.653379,0.657368,0.661204,0.665028,0.668960,0.672743,0.676504,0.680239,0.684055,0.687730,0.691274,0.694794,0.698286,0.701751,0.705272,0.708674,0.712125,0.715618,0.718992,0.722257,0.725559,0.728893,0.732185,0.735372,0.738647,0.741818,0.744892,0.747986,0.751098,0.754168,0.757147,0.760190,0.763192,0.766200,0.769074,0.771957,0.774760,0.777444,0.780139,0.782963,0.785671,0.788307,0.790949,0.793595,0.796172,0.798820,0.801434,0.803980,0.806527,0.809071,0.811612,0.814090,0.816593,0.818980,0.821365,0.823801,0.826204,0.828602,0.830918,0.833159,0.835444,0.837770,0.840088,0.842266,0.844527,0.846800,0.849023,0.851238,0.853484,0.855622,0.857754,0.859859,0.862029,0.864100,0.866199,0.868305,0.870500,0.872648,0.874846,0.877059,0.879225,0.881344,0.883434,0.885595,0.887808,0.890000,0.892199,0.894442,0.896533,0.898657,0.900945,0.903097,0.905415,0.907669,0.909919,0.912402,0.914825,0.917264,0.919827,0.922326,0.924975,0.927603,0.930173,0.932866,0.935729,0.938748,0.941679,0.944857,0.947953,0.951141,0.954538,0.957976,0.961554,0.965394,0.969184,0.973436,0.977781,0.982325,0.987344,0.992740,0.998308,1.004438,1.011371,1.019002,1.028004,1.037891,1.049211,1.064645,1.087447
+b5_3h@exp	196	0.069315,0.109861,0.138629,0.160944,0.179176,0.194591,0.207944,0.219722,0.230259,0.248491,0.256495,0.263906,0.277259,0.283321,0.294444,0.299573,0.309104,0.317805,0.321888,0.329584,0.336730,0.343399,0.349651,0.358352,0.363759,0.368888,0.376120,0.382864,0.387120,0.393183,0.398898,0.404305,0.411087,0.415888,0.421951,0.426268,0.431749,0.436945,0.443082,0.447734,0.452179,0.457471,0.462497,0.467283,0.471850,0.477068,0.482028,0.486753,0.491265,0.496284,0.500395,0.504986,0.509375,0.514166,0.518739,0.523111,0.527300,0.531321,0.535659,0.539816,0.543808,0.548064,0.552146,0.556068,0.559842,0.563835,0.567675,0.571373,0.575257,0.578690,0.582600,0.586079,0.589715,0.593225,0.596615,0.600141,0.603548,0.606843,0.610256,0.613556,0.616752,0.619848,0.623048,0.626340,0.629157,0.632257,0.635263,0.638351,0.641182,0.644095,0.646925,0.649828,0.652796,0.655536,0.658341,0.661070,0.663726,0.666568,0.669208,0.671780,0.674406,0.676964,0.679571,0.682220,0.684801,0.687213,0.689669,0.692166,0.694601,0.697073,0.699485,0.701840,0.704316,0.706561,0.708924,0.711314,0.713648,0.716007,0.718311,0.720564,0.722984,0.725205,0.727448,0.729709,0.731788,0.734084,0.736265,0.738523,0.740671,0.742893,0.744950,0.747136,0.749276,0.751425,0.753530,0.755643,0.757763,0.759840,0.761874,0.763964,0.766059,0.768156,0.770256,0.772268,0.774327,0.776430,0.778489,0.780629,0.782604,0.784659,0.786711,0.788758,0.790839,0.792913,0.795050,0.797212,0.799362,0.801632,0.803916,0.806149,0.808456,0.810862,0.813124,0.815507,0.817892,0.820303,0.822657,0.825271,0.827843,0.830425,0.833327,0.836054,0.838754,0.841649,0.844634,0.847762,0.850775,0.854267,0.858036,0.861650,0.865696,0.869751,0.874592,0.879604,0.884736,0.890123,0.895841,0.902063,0.908534,0.915894,0.923834,0.933123,0.943548,0.956808,0.976365
+b5_3h@ros_#	196	0.004545,0.005000,0.005263,0.005405,0.005556,0.005714,0.005882,0.006061,0.006250,0.006452,0.008000,0.009091,0.009756,0.010256,0.010811,0.011111,0.011429,0.011765,0.012121,0.012245,0.012500,0.012903,0.013953,0.014634,0.015385,0.015789,0.016393,0.017021,0.017544,0.018182,0.018519,0.019048,0.019512,0.020408,0.021053,0.021622,0.022222,0.022857,0.023529,0.024242,0.025000,0.025532,0.026087,0.026923,0.027586,0.028205,0.028947,0.029508,0.030303,0.030986,0.031579,0.032353,0.033149,0.033846,0.034483,0.035294,0.036000,0.036735,0.037500,0.038095,0.038889,0.040000,0.040541,0.041176,0.042105,0.042857,0.043636,0.044444,0.045161,0.045946,0.046809,0.047619,0.048485,0.049123,0.050000,0.050909,0.051656,0.052586,0.053333,0.054194,0.055072,0.056000,0.056757,0.057692,0.058586,0.059459,0.060377,0.061290,0.062222,0.063158,0.064000,0.065000,0.065823,0.066667,0.067797,0.068657,0.069697,0.070588,0.071642,0.072581,0.073548,0.074510,0.075510,0.076471,0.077419,0.078431,0.079452,0.080569,0.081633,0.082667,0.083761,0.084783,0.085714,0.086957,0.088000,0.089119,0.090196,0.091304,0.092308,0.093506,0.094624,0.095726,0.096916,0.098065,0.099231,0.100000,0.101667,0.102825,0.104000,0.105263,0.106422,0.107692,0.108943,0.110145,0.111373,0.112676,0.113924,0.115217,0.116514,0.117895,0.119231,0.120557,0.121933,0.123232,0.124675,0.126154,0.127649,0.129167,0.130667,0.132143,0.133708,0.135354,0.137008,0.138605,0.140323,0.142029,0.143791,0.145665,0.147368,0.149231,0.151087,0.153030,0.155027,0.157009,0.158974,0.160991,0.163090,0.165278,0.167568,0.169767,0.172093,0.174648,0.177202,0.179856,0.182609,0.185596,0.188571,0.191525,0.194690,0.197973,0.201702,0.205208,0.209091,0.213527,0.218391,0.223529,0.228788,0.235135,0.241962,0.250239,0.259459,0.270990,0.284758,0.303196,0.333766
+b2_3h@ros_n_#	200	0.004255,0.005128,0.005882,0.006452,0.007692,0.008889,0.010000,0.010811,0.011538,0.012360,0.013223,0.014098,0.014925,0.015758,0.016548,0.017301,0.018130,0.018861,0.019544,0.020290,0.021053,0.021708,0.022430,0.023137,0.023784,0.024419,0.025083,0.025740,0.026419,0.027061,0.027708,0.028436,0.029117,0.029834,0.030508,0.031214,0.031837,0.032500,0.033118,0.033708,0.034286,0.034831,0.035443,0.036066,0.036697,0.037358,0.038024,0.038593,0.039250,0.039872,0.040536,0.041113,0.041729,0.042318,0.043038,0.043640,0.044211,0.044898,0.045588,0.046182,0.046764,0.047320,0.047853,0.048502,0.049090,0.049720,0.050322,0.050909,0.051487,0.052000,0.052486,0.052980,0.053471,0.054082,0.054545,0.055072,0.055637,0.056151,0.056667,0.057212,0.057820,0.058333,0.058896,0.059474,0.060000,0.060517,0.060996,0.061579,0.062112,0.062595,0.063158,0.063736,0.064286,0.064824,0.065347,0.065909,0.066461,0.067010,0.067543,0.068023,0.068551,0.069118,0.069643,0.070251,0.070819,0.071411,0.071981,0.072608,0.073115,0.073617,0.074133,0.074738,0.075296,0.075862,0.076412,0.076923,0.077442,0.078063,0.078571,0.079211,0.079837,0.080438,0.080930,0.081437,0.082067,0.082597,0.083186,0.083794,0.084304,0.084961,0.085637,0.086242,0.086934,0.087600,0.088240,0.088864,0.089524,0.090140,0.090828,0.091511,0.092292,0.092803,0.093530,0.094320,0.094941,0.095789,0.096556,0.097273,0.098129,0.098812,0.099580,0.100404,0.101306,0.101925,0.102697,0.103442,0.104382,0.105194,0.105929,0.106818,0.107741,0.108763,0.109670,0.110617,0.111435,0.112219,0.113063,0.113783,0.114947,0.116162,0.117535,0.118860,0.120044,0.121262,0.122792,0.124194,0.125513,0.126908,0.128571,0.130209,0.131745,0.133572,0.135346,0.137349,0.139509,0.141988,0.144897,0.147735,0.151009,0.154168,0.157768,0.162566,0.168271,0.173262,0.179665,0.189656,0.200545,0.219829,0.239590
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+b1_6h@ros_n_#	200	0.005333,0.006452,0.009302,0.010959,0.012500,0.014063,0.015385,0.016667,0.017778,0.019048,0.020000,0.021053,0.022222,0.023108,0.023885,0.024779,0.025616,0.026490,0.027375,0.028242,0.029154,0.030056,0.030886,0.031579,0.032432,0.033236,0.033898,0.034667,0.035437,0.036149,0.036842,0.037525,0.038340,0.039057,0.039861,0.040511,0.041270,0.042051,0.042718,0.043564,0.044237,0.045031,0.045816,0.046570,0.047314,0.048000,0.048707,0.049351,0.050072,0.050817,0.051485,0.052167,0.052853,0.053585,0.054250,0.054987,0.055702,0.056371,0.057162,0.057806,0.058503,0.059232,0.059919,0.060550,0.061261,0.061945,0.062680,0.063366,0.064067,0.064734,0.065402,0.066084,0.066667,0.067422,0.068085,0.068768,0.069442,0.070025,0.070628,0.071272,0.071889,0.072561,0.073265,0.073915,0.074613,0.075176,0.075748,0.076423,0.077143,0.077803,0.078417,0.078947,0.079648,0.080342,0.080979,0.081715,0.082280,0.083003,0.083648,0.084220,0.084913,0.085529,0.086176,0.086774,0.087360,0.088066,0.088801,0.089565,0.090163,0.090889,0.091563,0.092286,0.092932,0.093574,0.094259,0.095085,0.095837,0.096552,0.097308,0.097964,0.098693,0.099482,0.100202,0.100888,0.101643,0.102381,0.103159,0.103920,0.104694,0.105576,0.106338,0.107213,0.108035,0.108874,0.109666,0.110487,0.111147,0.111923,0.112678,0.113575,0.114413,0.115212,0.116000,0.116977,0.117911,0.118680,0.119537,0.120631,0.121368,0.122347,0.123429,0.124395,0.125331,0.126174,0.127038,0.127910,0.128824,0.129717,0.130774,0.131773,0.132792,0.134030,0.135119,0.136202,0.137343,0.138618,0.140011,0.141315,0.142610,0.144060,0.145542,0.146817,0.148249,0.150026,0.151515,0.153255,0.154947,0.156906,0.159150,0.160724,0.162869,0.164960,0.167133,0.169715,0.171720,0.174847,0.177753,0.181639,0.185912,0.189403,0.195142,0.201384,0.208139,0.216542,0.224087,0.232597,0.246801,0.263907,0.295900
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+b4_3h@ros_minus_#	194	0.006452,0.012500,0.012903,0.018182,0.018750,0.023529,0.024242,0.025000,0.029412,0.030303,0.033333,0.035294,0.037838,0.041176,0.043243,0.045714,0.048649,0.051429,0.054545,0.057895,0.060606,0.063158,0.066667,0.070000,0.072727,0.076471,0.080000,0.082927,0.086275,0.089362,0.092683,0.095652,0.100000,0.102439,0.106250,0.110000,0.113514,0.116667,0.120000,0.123404,0.127273,0.130435,0.134286,0.137500,0.140909,0.144444,0.147826,0.151111,0.154839,0.158333,0.161702,0.165079,0.168421,0.171429,0.175000,0.178462,0.181818,0.185455,0.189011,0.192308,0.195833,0.200000,0.202632,0.205263,0.208649,0.212121,0.215464,0.218421,0.221429,0.224615,0.227491,0.230588,0.233473,0.237143,0.240000,0.243636,0.246789,0.250000,0.252874,0.256000,0.259341,0.262385,0.265323,0.268056,0.270833,0.273684,0.277083,0.280303,0.282857,0.285849,0.289041,0.292308,0.295349,0.298148,0.301163,0.303846,0.307087,0.310000,0.312779,0.315385,0.318049,0.321111,0.323871,0.326797,0.329915,0.332936,0.336082,0.339286,0.342353,0.345631,0.348899,0.351938,0.354639,0.357692,0.360784,0.363704,0.366667,0.369748,0.372932,0.375904,0.379085,0.382090,0.385055,0.387912,0.390877,0.393407,0.396091,0.398851,0.402137,0.404878,0.407705,0.410687,0.413699,0.416185,0.419231,0.422430,0.425352,0.428135,0.431579,0.434615,0.437662,0.440647,0.443705,0.446386,0.449464,0.452850,0.456198,0.459364,0.463043,0.466667,0.470780,0.474510,0.477833,0.481496,0.484786,0.488120,0.491429,0.495098,0.499438,0.502820,0.506716,0.511198,0.514729,0.519796,0.524091,0.528612,0.533333,0.537055,0.542029,0.546160,0.550835,0.555942,0.561247,0.566144,0.570775,0.576493,0.582567,0.589831,0.596653,0.603295,0.610822,0.619130,0.627753,0.639088,0.650833,0.660960,0.674172,0.689768,0.706574,0.725891,0.747307,0.776000,0.813687
+b7_1h@str	200	0.001433,0.002080,0.002680,0.003217,0.003714,0.004215,0.004660,0.005084,0.005530,0.005914,0.006306,0.006630,0.006951,0.007268,0.007563,0.007844,0.008089,0.008365,0.008610,0.008865,0.009108,0.009346,0.009569,0.009805,0.010036,0.010290,0.010517,0.010766,0.010987,0.011222,0.011444,0.011692,0.011897,0.012135,0.012368,0.012602,0.012822,0.013038,0.013251,0.013469,0.013694,0.013876,0.014104,0.014305,0.014533,0.014752,0.014947,0.015139,0.015354,0.015543,0.015768,0.015944,0.016161,0.016334,0.016535,0.016719,0.016914,0.017105,0.017275,0.017449,0.017663,0.017857,0.018024,0.018211,0.018392,0.018570,0.018748,0.018925,0.019091,0.019279,0.019443,0.019631,0.019815,0.019993,0.020172,0.020353,0.020532,0.020696,0.020882,0.021053,0.021232,0.021395,0.021587,0.021763,0.021955,0.022132,0.022317,0.022481,0.022665,0.022840,0.023028,0.023208,0.023392,0.023564,0.023766,0.023967,0.024167,0.024355,0.024545,0.024733,0.024930,0.025135,0.025341,0.025545,0.025733,0.025932,0.026134,0.026335,0.026531,0.026722,0.026951,0.027175,0.027391,0.027611,0.027825,0.028059,0.028279,0.028506,0.028717,0.028929,0.029179,0.029399,0.029661,0.029918,0.030139,0.030381,0.030627,0.030876,0.031137,0.031403,0.031665,0.031933,0.032200,0.032446,0.032741,0.033018,0.033304,0.033589,0.033892,0.034203,0.034502,0.034793,0.035130,0.035441,0.035762,0.036107,0.036431,0.036793,0.037166,0.037544,0.037938,0.038301,0.038672,0.039062,0.039473,0.039878,0.040241,0.040637,0.041103,0.041590,0.042090,0.042586,0.043069,0.043547,0.044046,0.044555,0.045101,0.045630,0.046210,0.046840,0.047444,0.048032,0.048671,0.049400,0.050140,0.050846,0.051623,0.052447,0.053280,0.054203,0.055002,0.055964,0.057161,0.058140,0.059392,0.060614,0.061956,0.063585,0.065316,0.066871,0.068783,0.070831,0.073364,0.076331,0.079336,0.083621,0.088938,0.096720,0.112473
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+b4_6h@share_cnt	198	0.069315,0.109861,0.138629,0.160944,0.179176,0.194591,0.207944,0.219722,0.230259,0.248491,0.256495,0.270805,0.277259,0.289037,0.299573,0.309104,0.317805,0.325810,0.333220,0.343399,0.349651,0.358352,0.366356,0.373767,0.380666,0.387120,0.393183,0.400733,0.406044,0.412713,0.417439,0.423411,0.429046,0.435671,0.440672,0.445435,0.449981,0.455388,0.460517,0.465396,0.470048,0.474493,0.479579,0.483628,0.488280,0.492725,0.496981,0.501064,0.504986,0.509375,0.512990,0.517048,0.520949,0.524702,0.528320,0.531812,0.535186,0.538907,0.542495,0.545532,0.548894,0.552146,0.555683,0.558725,0.562040,0.565249,0.568698,0.571703,0.574939,0.578074,0.581114,0.584064,0.587212,0.590263,0.592959,0.595842,0.598645,0.601616,0.604501,0.607535,0.610479,0.613340,0.616121,0.619032,0.621661,0.624611,0.627664,0.630445,0.633150,0.635611,0.638351,0.641182,0.643775,0.646459,0.649072,0.651915,0.654535,0.656948,0.659578,0.662141,0.664509,0.666950,0.669332,0.671780,0.674406,0.676849,0.679234,0.681783,0.684268,0.686589,0.688959,0.691473,0.693731,0.695845,0.698286,0.700397,0.702643,0.705012,0.707581,0.710003,0.712287,0.714756,0.717166,0.719444,0.721744,0.723993,0.726263,0.728551,0.730854,0.733106,0.735436,0.737713,0.740001,0.742118,0.744366,0.746794,0.749332,0.751698,0.753849,0.756476,0.759136,0.761923,0.764348,0.766716,0.768937,0.771468,0.773936,0.776217,0.778821,0.781116,0.783360,0.785748,0.788231,0.790433,0.792913,0.795648,0.798105,0.800470,0.803204,0.805643,0.807589,0.809986,0.812622,0.814642,0.817273,0.820193,0.822791,0.825271,0.827843,0.830474,0.832773,0.835279,0.837977,0.840760,0.843793,0.846421,0.849433,0.852298,0.856064,0.859693,0.863052,0.866527,0.870118,0.873295,0.876748,0.881180,0.885595,0.889891,0.894416,0.899032,0.905427,0.912238,0.917502,0.923903,0.932242,0.945892,0.963770
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+b5_24h@share_cnt	188	0.069315,0.109861,0.138629,0.160944,0.179176,0.194591,0.207944,0.219722,0.230259,0.239790,0.248491,0.256495,0.263906,0.270805,0.277259,0.283321,0.289037,0.294444,0.299573,0.304452,0.309104,0.313549,0.317805,0.325810,0.329584,0.333220,0.340120,0.343399,0.346574,0.352636,0.355535,0.361092,0.366356,0.368888,0.373767,0.378419,0.382864,0.385015,0.389182,0.393183,0.397029,0.400733,0.406044,0.409434,0.412713,0.415888,0.420469,0.423411,0.427667,0.430407,0.434381,0.436945,0.440672,0.444265,0.447734,0.451086,0.454329,0.457471,0.460517,0.464439,0.467283,0.470048,0.473620,0.477068,0.479579,0.482831,0.485981,0.489035,0.491998,0.494876,0.497673,0.501064,0.503695,0.506890,0.509987,0.512396,0.515329,0.518178,0.521494,0.524175,0.526786,0.529832,0.532301,0.535186,0.537990,0.540717,0.543372,0.546383,0.548894,0.551745,0.554126,0.556834,0.559471,0.562040,0.564545,0.567332,0.569709,0.572359,0.574939,0.577455,0.579909,0.582305,0.584644,0.587212,0.589715,0.592158,0.594803,0.597126,0.599645,0.601859,0.604263,0.606843,0.609357,0.611589,0.613988,0.616331,0.618621,0.621060,0.623245,0.625767,0.628040,0.630445,0.632794,0.635089,0.637332,0.639693,0.641999,0.644254,0.646459,0.648768,0.651175,0.653524,0.655820,0.658203,0.660394,0.662539,0.664769,0.667077,0.669456,0.671780,0.674052,0.676273,0.678672,0.681014,0.683303,0.685541,0.687936,0.690274,0.692658,0.694986,0.697448,0.699851,0.702376,0.704925,0.707327,0.709920,0.712528,0.714992,0.717778,0.720341,0.722984,0.725841,0.728756,0.731455,0.734213,0.737275,0.740306,0.743248,0.746394,0.749665,0.753209,0.756683,0.760440,0.764300,0.768340,0.772754,0.776980,0.781843,0.786978,0.792769,0.798548,0.805452,0.812740,0.820740,0.830028,0.843229,0.866957
+b9_3h@ros_minus_#	200	0.111538,0.155556,0.181006,0.199145,0.209765,0.217582,0.230137,0.232526,0.236917,0.240454,0.245946,0.252632,0.256767,0.261053,0.264438,0.267516,0.268691,0.271389,0.274123,0.274182,0.278593,0.281491,0.283426,0.286268,0.291045,0.294760,0.298597,0.303630,0.305572,0.306893,0.307747,0.312069,0.316222,0.317003,0.318018,0.321835,0.323669,0.324236,0.326250,0.330291,0.332934,0.336298,0.341356,0.344092,0.345902,0.348681,0.351538,0.353967,0.356500,0.358466,0.360000,0.361909,0.363924,0.365162,0.366773,0.369176,0.370406,0.371725,0.373561,0.375126,0.376683,0.379109,0.381333,0.382838,0.384054,0.386617,0.388031,0.389486,0.391330,0.392581,0.394780,0.397158,0.398834,0.400111,0.400808,0.402540,0.403285,0.404522,0.405802,0.407324,0.408730,0.410085,0.411597,0.413345,0.414679,0.415791,0.416773,0.418117,0.419531,0.420559,0.422465,0.424411,0.425592,0.426912,0.428541,0.430044,0.431458,0.432502,0.433844,0.436854,0.438326,0.440033,0.441767,0.443219,0.445193,0.446906,0.448201,0.449394,0.450888,0.453307,0.455414,0.457671,0.459084,0.461955,0.464314,0.465603,0.468143,0.469588,0.471489,0.472762,0.475020,0.476556,0.478707,0.479943,0.480550,0.482887,0.485251,0.486998,0.487776,0.488959,0.490610,0.491610,0.493929,0.495124,0.497421,0.498217,0.500100,0.502337,0.504368,0.506467,0.511033,0.512065,0.514259,0.516319,0.520178,0.523223,0.525179,0.527758,0.529273,0.532355,0.533973,0.535653,0.537869,0.540309,0.542330,0.544074,0.546802,0.549254,0.552799,0.556627,0.558719,0.561581,0.564409,0.568015,0.570594,0.572258,0.574724,0.576942,0.578534,0.581098,0.585333,0.587831,0.591498,0.593801,0.597095,0.598852,0.605644,0.608583,0.611578,0.616579,0.620355,0.622358,0.625844,0.627393,0.630149,0.631862,0.636366,0.638629,0.642190,0.645500,0.650665,0.654962,0.661078,0.667162,0.676684,0.690509,0.701515,0.735244,0.775626
+b2_24h@ros_minus_#	200	0.006452,0.012903,0.018750,0.025000,0.032258,0.043750,0.054545,0.066667,0.076471,0.085714,0.095000,0.105000,0.115556,0.126154,0.137778,0.150000,0.160784,0.171930,0.182500,0.193750,0.203636,0.212903,0.221429,0.230233,0.237500,0.245614,0.252381,0.259459,0.265823,0.273171,0.280000,0.286293,0.292857,0.299454,0.305419,0.311111,0.316667,0.322248,0.327711,0.332743,0.338807,0.344216,0.349149,0.354382,0.359434,0.363754,0.367730,0.372055,0.376596,0.381065,0.385714,0.390187,0.394591,0.399662,0.403895,0.407941,0.411837,0.415873,0.419745,0.424096,0.428414,0.432265,0.435965,0.439669,0.443179,0.446328,0.449657,0.452952,0.456356,0.460073,0.463473,0.466726,0.470588,0.474393,0.478689,0.482585,0.486035,0.489431,0.493578,0.497378,0.501097,0.504972,0.509291,0.513812,0.517834,0.522028,0.526103,0.529825,0.534001,0.537984,0.542260,0.546853,0.551119,0.554757,0.558418,0.562596,0.565385,0.569572,0.573247,0.577391,0.580652,0.584746,0.588052,0.591860,0.595455,0.598890,0.602502,0.605896,0.609706,0.613483,0.616847,0.620370,0.623866,0.627384,0.630821,0.634747,0.639127,0.643403,0.647407,0.651781,0.656904,0.662454,0.667651,0.672566,0.678149,0.683237,0.689305,0.694550,0.699167,0.703784,0.707947,0.712162,0.715799,0.719497,0.724400,0.727700,0.731772,0.735592,0.739816,0.743824,0.747241,0.751171,0.754188,0.757329,0.761192,0.764703,0.768094,0.771406,0.775566,0.779807,0.784019,0.788276,0.792781,0.796719,0.800428,0.805934,0.811370,0.816744,0.822498,0.828666,0.834876,0.841076,0.848963,0.854720,0.862982,0.870822,0.878529,0.887182,0.895477,0.904420,0.914876,0.922167,0.932223,0.941750,0.951359,0.963120,0.972003,0.980392,0.988954,0.996744,1.005470,1.012447,1.023074,1.032029,1.040612,1.050761,1.060703,1.070835,1.081673,1.091274,1.102975,1.115496,1.129769,1.151880,1.167887,1.188978,1.205341,1.252224,1.463475
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+b13_24h@is_return_1	199	0.069315,0.109861,0.138629,0.160944,0.194591,0.207944,0.230259,0.239790,0.256495,0.270805,0.283321,0.294444,0.309104,0.321888,0.329584,0.343399,0.352636,0.361092,0.368888,0.378419,0.387120,0.397029,0.404305,0.412713,0.420469,0.427667,0.434381,0.440672,0.446591,0.453260,0.459512,0.465396,0.470953,0.476217,0.482028,0.487520,0.492725,0.497673,0.503044,0.507517,0.512396,0.517048,0.521494,0.526269,0.530330,0.534711,0.539363,0.542935,0.547227,0.550939,0.554908,0.558725,0.562762,0.566643,0.570711,0.574620,0.578383,0.582305,0.586079,0.589164,0.592959,0.596615,0.599645,0.602828,0.606146,0.609357,0.612249,0.615273,0.618415,0.621661,0.624804,0.627664,0.630992,0.634036,0.637161,0.640357,0.643455,0.646614,0.649375,0.652796,0.655536,0.658341,0.661607,0.664509,0.667077,0.669950,0.673221,0.676041,0.678784,0.681674,0.684482,0.687109,0.689972,0.692756,0.695559,0.698286,0.700760,0.703966,0.706647,0.709506,0.712206,0.714835,0.717855,0.720712,0.723921,0.726822,0.729506,0.732449,0.735052,0.737838,0.740489,0.742893,0.745356,0.747986,0.750934,0.754009,0.756838,0.758984,0.761184,0.763434,0.765539,0.767925,0.770120,0.772754,0.775319,0.777737,0.779934,0.782764,0.785010,0.787474,0.789841,0.792624,0.795648,0.798480,0.800936,0.803819,0.806306,0.808856,0.811522,0.813740,0.816337,0.818924,0.821311,0.823986,0.826822,0.829205,0.831728,0.834022,0.836497,0.838549,0.840492,0.842880,0.845659,0.848198,0.850694,0.853267,0.855757,0.858728,0.861123,0.863728,0.866302,0.868609,0.870715,0.873488,0.875605,0.877879,0.880011,0.883054,0.886163,0.889220,0.892132,0.894207,0.896674,0.898795,0.901833,0.903741,0.906855,0.909493,0.913389,0.916670,0.919391,0.922533,0.925273,0.927987,0.930846,0.933397,0.936048,0.940327,0.943428,0.946475,0.948037,0.951007,0.955258,0.960238,0.966377,0.975016,0.985734,1.013066
+b0_6h@ros_one	200	0.012722,0.018671,0.024590,0.030053,0.034738,0.038051,0.043443,0.046964,0.051342,0.055610,0.059604,0.062214,0.065915,0.069599,0.072482,0.075977,0.079565,0.082217,0.085080,0.088750,0.091559,0.094115,0.095606,0.097804,0.100879,0.103510,0.106389,0.108663,0.110747,0.113604,0.115494,0.117618,0.119675,0.121752,0.124409,0.126651,0.128756,0.131249,0.133089,0.135247,0.137236,0.139391,0.141644,0.143803,0.146147,0.148535,0.150432,0.152421,0.154584,0.156843,0.158742,0.160875,0.162971,0.165023,0.167303,0.169364,0.171351,0.173240,0.175605,0.177490,0.179330,0.181024,0.182952,0.184913,0.186593,0.188291,0.190369,0.192130,0.193931,0.195658,0.197319,0.199145,0.201073,0.202566,0.204288,0.206096,0.207335,0.208606,0.210405,0.212157,0.213587,0.215035,0.216491,0.218139,0.219657,0.221169,0.222615,0.224327,0.225837,0.227419,0.228828,0.230296,0.231887,0.233198,0.234640,0.236114,0.237713,0.239243,0.240590,0.241976,0.243374,0.244706,0.246174,0.247405,0.248753,0.250034,0.251387,0.252819,0.254158,0.255406,0.256764,0.258093,0.259352,0.260671,0.262160,0.263497,0.264805,0.266313,0.267490,0.268891,0.270307,0.271553,0.272893,0.274245,0.275749,0.277166,0.278373,0.279702,0.280992,0.282329,0.283717,0.285180,0.286424,0.287880,0.289327,0.290919,0.292014,0.293638,0.295084,0.296427,0.297964,0.299334,0.300768,0.302243,0.303702,0.305185,0.306685,0.308129,0.309611,0.311153,0.312479,0.313619,0.314981,0.316341,0.317527,0.318857,0.320152,0.321760,0.323266,0.324843,0.326396,0.327686,0.328946,0.330571,0.332004,0.333610,0.334946,0.336350,0.337616,0.339195,0.340671,0.342342,0.343653,0.345062,0.347020,0.348637,0.350629,0.352201,0.353737,0.355505,0.357738,0.359356,0.361151,0.363215,0.365444,0.367702,0.369841,0.371944,0.375030,0.377362,0.379295,0.382186,0.384699,0.387298,0.390698,0.394180,0.397975,0.402160,0.409306
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+b9_3h@rovn_#	200	0.002430,0.002980,0.003294,0.003562,0.003874,0.004167,0.004496,0.004818,0.005057,0.005465,0.005734,0.005910,0.006169,0.006439,0.006883,0.007333,0.007762,0.008289,0.008632,0.009064,0.009474,0.009795,0.010088,0.010403,0.010648,0.010877,0.011075,0.011315,0.011438,0.011579,0.011695,0.011835,0.012023,0.012129,0.012277,0.012385,0.012468,0.012566,0.012673,0.012764,0.012854,0.012982,0.013148,0.013260,0.013388,0.013459,0.013537,0.013571,0.013768,0.013990,0.014135,0.014295,0.014407,0.014546,0.014725,0.014775,0.014877,0.015002,0.015063,0.015163,0.015380,0.015440,0.015578,0.015742,0.015905,0.015994,0.016170,0.016385,0.016603,0.016747,0.016894,0.016995,0.017217,0.017366,0.017587,0.017714,0.017810,0.017903,0.018051,0.018211,0.018535,0.018778,0.018934,0.019064,0.019294,0.019470,0.019673,0.019776,0.019852,0.019969,0.020253,0.020353,0.020557,0.020751,0.020847,0.020993,0.021129,0.021290,0.021451,0.021565,0.021724,0.021888,0.021984,0.022086,0.022198,0.022359,0.022459,0.022593,0.022692,0.022814,0.022890,0.022993,0.023160,0.023342,0.023510,0.023570,0.023704,0.023888,0.023947,0.024054,0.024157,0.024249,0.024360,0.024471,0.024599,0.024736,0.024815,0.024906,0.024994,0.025034,0.025195,0.025339,0.025484,0.025594,0.025740,0.025872,0.026031,0.026155,0.026301,0.026388,0.026406,0.026525,0.026689,0.026934,0.027060,0.027195,0.027307,0.027564,0.027691,0.027789,0.027884,0.028018,0.028192,0.028394,0.028633,0.028740,0.028907,0.029026,0.029193,0.029294,0.029360,0.029473,0.029629,0.029756,0.029951,0.030148,0.030413,0.030570,0.030741,0.030994,0.031221,0.031416,0.031671,0.031881,0.032059,0.032283,0.032465,0.032645,0.032994,0.033242,0.033443,0.033670,0.033884,0.034034,0.034288,0.034433,0.034610,0.034722,0.034928,0.035370,0.035755,0.036172,0.036685,0.037625,0.039213,0.040142,0.041430,0.043558,0.052604
+b10_12h@str_plus	199	0.005661,0.006472,0.006898,0.007308,0.007524,0.007613,0.007664,0.007748,0.007870,0.007919,0.007995,0.008101,0.008203,0.008321,0.008403,0.008490,0.008535,0.008575,0.008666,0.008768,0.008808,0.008892,0.008999,0.009027,0.009077,0.009168,0.009221,0.009294,0.009343,0.009376,0.009407,0.009464,0.009504,0.009516,0.009548,0.009619,0.009646,0.009702,0.009731,0.009734,0.009746,0.009767,0.009813,0.009833,0.009892,0.009949,0.010008,0.010037,0.010078,0.010127,0.010163,0.010214,0.010222,0.010238,0.010269,0.010303,0.010345,0.010383,0.010419,0.010454,0.010517,0.010537,0.010543,0.010571,0.010633,0.010645,0.010709,0.010771,0.010795,0.010821,0.010865,0.010900,0.010925,0.010945,0.010979,0.011010,0.011043,0.011092,0.011110,0.011118,0.011139,0.011175,0.011214,0.011231,0.011261,0.011326,0.011364,0.011383,0.011407,0.011418,0.011441,0.011466,0.011512,0.011519,0.011541,0.011586,0.011600,0.011627,0.011655,0.011664,0.011685,0.011718,0.011764,0.011783,0.011785,0.011796,0.011809,0.011839,0.011860,0.011869,0.011872,0.011895,0.011899,0.011901,0.011943,0.011954,0.012001,0.012010,0.012013,0.012063,0.012067,0.012104,0.012115,0.012121,0.012145,0.012161,0.012176,0.012183,0.012223,0.012240,0.012244,0.012261,0.012281,0.012293,0.012317,0.012324,0.012360,0.012374,0.012376,0.012394,0.012416,0.012429,0.012456,0.012472,0.012478,0.012480,0.012483,0.012484,0.012499,0.012515,0.012521,0.012525,0.012530,0.012537,0.012580,0.012664,0.012731,0.012776,0.012795,0.012925,0.012962,0.013060,0.013135,0.013167,0.013188,0.013222,0.013239,0.013268,0.013288,0.013312,0.013413,0.013511,0.013608,0.013653,0.013733,0.013778,0.013901,0.013945,0.014072,0.014187,0.014268,0.014336,0.014391,0.014448,0.014548,0.014598,0.014684,0.014855,0.014965,0.015097,0.015239,0.015441,0.015555,0.015721,0.015991,0.016137,0.016571,0.017170
+b5_24h@is_return_1	152	0.069315,0.109861,0.138629,0.160944,0.179176,0.194591,0.207944,0.219722,0.230259,0.239790,0.248491,0.256495,0.263906,0.270805,0.277259,0.283321,0.289037,0.294444,0.299573,0.304452,0.309104,0.313549,0.317805,0.321888,0.325810,0.329584,0.333220,0.336730,0.340120,0.343399,0.346574,0.349651,0.352636,0.355535,0.358352,0.361092,0.363759,0.368888,0.371357,0.373767,0.376120,0.378419,0.382864,0.385015,0.387120,0.391202,0.393183,0.397029,0.398898,0.402535,0.404305,0.407754,0.411087,0.412713,0.415888,0.418965,0.421951,0.423411,0.426268,0.429046,0.431749,0.434381,0.436945,0.439445,0.441884,0.445435,0.447734,0.449981,0.452179,0.455388,0.457471,0.459512,0.462497,0.464439,0.467283,0.469135,0.471850,0.474493,0.477068,0.479579,0.481218,0.483628,0.486753,0.489035,0.491265,0.493447,0.495583,0.497673,0.500395,0.502388,0.504986,0.507517,0.509375,0.511799,0.514166,0.516479,0.518739,0.520949,0.523111,0.525227,0.527811,0.529832,0.532301,0.534711,0.537064,0.538907,0.541165,0.543808,0.545959,0.548480,0.550533,0.552943,0.555296,0.557973,0.560212,0.562762,0.565249,0.567675,0.570378,0.573010,0.575890,0.578690,0.581413,0.584354,0.587212,0.589990,0.592959,0.595842,0.598645,0.601859,0.604737,0.607993,0.611368,0.614847,0.618002,0.621661,0.625575,0.629527,0.633859,0.638182,0.642811,0.648158,0.653524,0.659304,0.665157,0.671659,0.678897,0.686589,0.695750,0.709423,0.732975
+b0_12h@str_plus	200	0.000447,0.000724,0.000929,0.001113,0.001299,0.001504,0.001710,0.001909,0.002088,0.002249,0.002407,0.002547,0.002686,0.002835,0.002962,0.003080,0.003173,0.003268,0.003345,0.003424,0.003502,0.003574,0.003652,0.003723,0.003799,0.003872,0.003947,0.004018,0.004086,0.004145,0.004208,0.004268,0.004326,0.004382,0.004440,0.004496,0.004545,0.004595,0.004657,0.004707,0.004761,0.004814,0.004866,0.004923,0.004979,0.005036,0.005094,0.005148,0.005201,0.005251,0.005305,0.005359,0.005416,0.005476,0.005535,0.005594,0.005655,0.005720,0.005783,0.005842,0.005903,0.005972,0.006038,0.006110,0.006172,0.006238,0.006294,0.006354,0.006425,0.006486,0.006549,0.006615,0.006678,0.006750,0.006816,0.006884,0.006949,0.007021,0.007079,0.007138,0.007208,0.007285,0.007361,0.007427,0.007491,0.007556,0.007631,0.007699,0.007763,0.007839,0.007911,0.007985,0.008045,0.008114,0.008179,0.008248,0.008319,0.008385,0.008444,0.008503,0.008573,0.008635,0.008696,0.008756,0.008813,0.008877,0.008937,0.009006,0.009072,0.009127,0.009191,0.009255,0.009317,0.009378,0.009433,0.009486,0.009546,0.009606,0.009676,0.009744,0.009798,0.009863,0.009928,0.009986,0.010034,0.010094,0.010155,0.010213,0.010276,0.010332,0.010389,0.010454,0.010519,0.010573,0.010628,0.010682,0.010736,0.010804,0.010860,0.010924,0.010983,0.011040,0.011094,0.011157,0.011213,0.011267,0.011325,0.011378,0.011449,0.011516,0.011601,0.011680,0.011774,0.011866,0.011945,0.012038,0.012126,0.012205,0.012300,0.012379,0.012464,0.012545,0.012640,0.012732,0.012829,0.012912,0.013009,0.013079,0.013157,0.013242,0.013324,0.013409,0.013494,0.013615,0.013717,0.013814,0.013946,0.014057,0.014172,0.014277,0.014390,0.014515,0.014680,0.014853,0.015007,0.015215,0.015441,0.015657,0.015932,0.016157,0.016419,0.016730,0.017024,0.017367,0.017713,0.018232,0.019093,0.020371,0.024441
+b0_6h@share_cnt	200	0.069315,0.109861,0.160944,0.179176,0.207944,0.230259,0.248491,0.270805,0.289037,0.304452,0.317805,0.333220,0.346574,0.358352,0.368888,0.380666,0.391202,0.402535,0.411087,0.420469,0.429046,0.438203,0.446591,0.454329,0.461512,0.469135,0.476217,0.482831,0.489784,0.496284,0.502388,0.508140,0.514166,0.519850,0.525750,0.530827,0.535659,0.540717,0.545532,0.550533,0.555296,0.559842,0.564545,0.569036,0.573657,0.578074,0.582600,0.586647,0.590808,0.594803,0.598896,0.602828,0.606843,0.610256,0.613988,0.617379,0.620859,0.624611,0.628040,0.631536,0.634739,0.638012,0.641346,0.644413,0.647543,0.650728,0.653959,0.657228,0.660123,0.663200,0.666696,0.669580,0.672623,0.675693,0.678559,0.681344,0.684162,0.687005,0.690073,0.693147,0.695940,0.698841,0.701571,0.704316,0.706817,0.709589,0.712528,0.715540,0.718235,0.720934,0.723993,0.726753,0.729641,0.732647,0.735116,0.737838,0.740550,0.743426,0.745819,0.748381,0.750879,0.753530,0.755956,0.758325,0.761135,0.763916,0.766153,0.768891,0.771468,0.773979,0.776472,0.779235,0.781601,0.783795,0.786057,0.788382,0.790728,0.793057,0.795648,0.797900,0.800001,0.802158,0.804783,0.807309,0.809651,0.812148,0.814787,0.817160,0.819395,0.821825,0.824276,0.826591,0.828828,0.831312,0.833735,0.836101,0.838640,0.841183,0.843772,0.846274,0.848673,0.850734,0.853503,0.856159,0.858933,0.861577,0.864242,0.866837,0.869651,0.871997,0.874465,0.877354,0.879710,0.882321,0.885524,0.888211,0.890463,0.893446,0.896137,0.898457,0.900761,0.902942,0.904982,0.907521,0.909952,0.912522,0.915842,0.918122,0.920834,0.924009,0.926540,0.928749,0.931064,0.933935,0.936777,0.939491,0.942594,0.945407,0.948273,0.951628,0.955230,0.958363,0.961006,0.964251,0.968016,0.971287,0.975022,0.979963,0.984321,0.987843,0.993042,0.997660,1.003060,1.009192,1.015432,1.022361,1.031407,1.046906,1.060413
+b1_168h@ros_#	200	0.011111,0.016216,0.020513,0.025974,0.032653,0.039564,0.045045,0.050000,0.054315,0.058228,0.062069,0.065600,0.070320,0.074336,0.078351,0.081967,0.085681,0.089291,0.092593,0.096418,0.100599,0.104375,0.107794,0.111506,0.114671,0.117984,0.121665,0.125009,0.128303,0.130880,0.133658,0.137079,0.140295,0.143772,0.146896,0.150127,0.152868,0.155530,0.158621,0.161446,0.164277,0.166667,0.168828,0.171041,0.173266,0.175369,0.177379,0.178992,0.180812,0.182575,0.184078,0.185508,0.187106,0.188809,0.190432,0.191974,0.193828,0.195663,0.197620,0.199350,0.200938,0.202466,0.203920,0.205632,0.207392,0.209168,0.210868,0.212402,0.213926,0.215289,0.216692,0.218455,0.220323,0.222056,0.223925,0.226004,0.227966,0.230250,0.231918,0.233846,0.235501,0.237064,0.238402,0.239778,0.241534,0.243412,0.244863,0.246135,0.247752,0.249529,0.250918,0.252399,0.254053,0.255863,0.257806,0.259512,0.261622,0.264155,0.266417,0.268619,0.270897,0.273407,0.275482,0.277334,0.279599,0.281467,0.283900,0.286245,0.288299,0.290373,0.292426,0.294875,0.297045,0.298962,0.301223,0.303693,0.306170,0.309230,0.311289,0.313269,0.315481,0.317767,0.319355,0.321139,0.323489,0.325896,0.328108,0.330741,0.333868,0.336546,0.338931,0.340824,0.342605,0.345146,0.346712,0.348952,0.350915,0.352881,0.355119,0.357139,0.358616,0.360207,0.363062,0.365793,0.368535,0.371047,0.374611,0.377343,0.379258,0.380981,0.383235,0.386753,0.390515,0.395301,0.397375,0.399599,0.403012,0.405951,0.409229,0.413981,0.416787,0.419945,0.425161,0.429066,0.434324,0.437447,0.440236,0.443560,0.447986,0.452083,0.455760,0.460781,0.464443,0.466129,0.469308,0.475002,0.481183,0.486831,0.493084,0.499454,0.505061,0.509672,0.514609,0.519868,0.524939,0.530987,0.535621,0.543262,0.551792,0.561114,0.568997,0.578191,0.596501,0.618374,0.629640,0.668617,0.737109,0.775756,0.793982
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+b5_72h@exp	200	0.109861,0.138629,0.179176,0.207944,0.230259,0.248491,0.277259,0.294444,0.313549,0.333220,0.349651,0.363759,0.378419,0.393183,0.406044,0.418965,0.431749,0.444265,0.456435,0.468213,0.479579,0.490527,0.501064,0.511799,0.521494,0.531321,0.541165,0.550126,0.559099,0.567675,0.576205,0.584064,0.591889,0.599645,0.607074,0.614419,0.621461,0.628413,0.635089,0.641673,0.648004,0.654247,0.660530,0.666568,0.672503,0.678333,0.684055,0.689669,0.695081,0.700579,0.705704,0.710906,0.715929,0.721082,0.726052,0.730988,0.735819,0.740610,0.745356,0.749942,0.754380,0.758883,0.763192,0.767647,0.771957,0.776217,0.780507,0.784581,0.788758,0.792805,0.796867,0.800770,0.804687,0.808641,0.812534,0.816423,0.820412,0.824249,0.828096,0.831923,0.835632,0.839254,0.843033,0.846758,0.850391,0.853974,0.857546,0.861123,0.864611,0.868067,0.871440,0.874846,0.878217,0.881567,0.884779,0.888128,0.891449,0.894664,0.897954,0.901408,0.904582,0.907772,0.911041,0.914270,0.917482,0.920593,0.923708,0.926691,0.929652,0.932617,0.935669,0.938614,0.941524,0.944383,0.947263,0.950159,0.953097,0.955952,0.958699,0.961460,0.964186,0.966871,0.969579,0.972316,0.974882,0.977600,0.980306,0.983038,0.985729,0.988502,0.991299,0.994069,0.996829,0.999438,1.002020,1.004577,1.007171,1.009671,1.012138,1.014663,1.017183,1.019716,1.022096,1.024509,1.026872,1.029198,1.031546,1.033884,1.036180,1.038523,1.040747,1.043055,1.045447,1.047777,1.050188,1.052509,1.054962,1.057262,1.059626,1.061854,1.064024,1.066219,1.068458,1.070972,1.073429,1.075858,1.078347,1.080633,1.083003,1.085725,1.088257,1.090792,1.093316,1.095931,1.098519,1.101344,1.104102,1.107063,1.110128,1.113278,1.116310,1.119389,1.122753,1.126222,1.129895,1.133413,1.137492,1.141887,1.146429,1.151339,1.156628,1.162480,1.168842,1.175000,1.181275,1.188072,1.195803,1.205617,1.219040
+b0_1h@ros1_#	200	0.003226,0.003922,0.004444,0.004878,0.005263,0.005556,0.005882,0.006250,0.006452,0.007317,0.008108,0.008696,0.009302,0.009877,0.010417,0.010929,0.011429,0.012000,0.012500,0.013043,0.013636,0.014286,0.015000,0.015584,0.016162,0.016667,0.017391,0.017978,0.018519,0.019067,0.019608,0.020264,0.020896,0.021505,0.022105,0.022642,0.023256,0.023792,0.024365,0.025000,0.025575,0.026087,0.026733,0.027358,0.027907,0.028571,0.029060,0.029630,0.030211,0.030769,0.031325,0.031856,0.032432,0.032955,0.033517,0.034146,0.034722,0.035249,0.035832,0.036364,0.036876,0.037500,0.038023,0.038554,0.038961,0.039448,0.040000,0.040559,0.041116,0.041718,0.042254,0.042735,0.043290,0.043885,0.044444,0.044956,0.045455,0.046009,0.046512,0.046983,0.047500,0.048026,0.048571,0.049123,0.049672,0.050153,0.050746,0.051299,0.051852,0.052342,0.052747,0.053333,0.053952,0.054490,0.055000,0.055556,0.056098,0.056667,0.057179,0.057754,0.058228,0.058737,0.059281,0.059740,0.060360,0.060963,0.061481,0.062046,0.062535,0.063141,0.063704,0.064220,0.064802,0.065428,0.066077,0.066667,0.067164,0.067826,0.068387,0.068975,0.069577,0.070164,0.070775,0.071429,0.072093,0.072802,0.073461,0.074043,0.074631,0.075183,0.075676,0.076261,0.076972,0.077744,0.078339,0.079012,0.079574,0.080323,0.080741,0.081316,0.081989,0.082680,0.083265,0.083953,0.084570,0.085240,0.086119,0.086724,0.087500,0.088462,0.089152,0.089855,0.090385,0.091111,0.092075,0.092857,0.093646,0.094382,0.095158,0.095980,0.096689,0.097665,0.098561,0.099611,0.100460,0.101206,0.101996,0.102945,0.103944,0.104839,0.105836,0.106998,0.108163,0.109446,0.110627,0.111765,0.112477,0.113973,0.115250,0.116609,0.118563,0.120034,0.121784,0.123440,0.125182,0.127331,0.129874,0.132432,0.134452,0.136425,0.138931,0.141525,0.144643,0.147741,0.152376,0.157388,0.163931,0.173199,0.192004
+b4_3h@is_share	191	0.069315,0.109861,0.138629,0.160944,0.179176,0.194591,0.207944,0.219722,0.230259,0.239790,0.248491,0.256495,0.263906,0.270805,0.277259,0.283321,0.289037,0.294444,0.299573,0.304452,0.313549,0.317805,0.321888,0.329584,0.333220,0.340120,0.346574,0.349651,0.355535,0.358352,0.363759,0.368888,0.373767,0.378419,0.382864,0.387120,0.391202,0.395124,0.398898,0.402535,0.406044,0.411087,0.414313,0.418965,0.421951,0.426268,0.429046,0.433073,0.435671,0.439445,0.443082,0.446591,0.449981,0.453260,0.456435,0.459512,0.462497,0.466344,0.469135,0.472739,0.475359,0.478749,0.482028,0.485203,0.488280,0.491265,0.494164,0.496981,0.499721,0.503044,0.505625,0.508760,0.511199,0.513580,0.516479,0.519296,0.522036,0.525227,0.527811,0.530330,0.533272,0.536129,0.538450,0.541165,0.543372,0.545959,0.548894,0.550939,0.553733,0.556068,0.558350,0.560947,0.563479,0.565948,0.568358,0.570711,0.573334,0.575574,0.578074,0.580513,0.582895,0.585220,0.587774,0.589990,0.592158,0.594542,0.596871,0.599146,0.601616,0.604025,0.606379,0.608450,0.610925,0.613123,0.615486,0.617794,0.620051,0.622456,0.624611,0.626910,0.629157,0.631355,0.633683,0.635784,0.638182,0.640192,0.642487,0.644889,0.647080,0.649224,0.651767,0.654103,0.656103,0.658064,0.660123,0.662007,0.663988,0.666185,0.667960,0.670073,0.672503,0.675110,0.677194,0.679234,0.681564,0.683733,0.685961,0.688346,0.690475,0.692560,0.694890,0.697073,0.699118,0.701302,0.703703,0.705962,0.708003,0.710250,0.712367,0.715070,0.717702,0.720266,0.722694,0.724779,0.727379,0.730047,0.732909,0.736391,0.739142,0.742476,0.745530,0.748381,0.751262,0.754221,0.757302,0.759990,0.764252,0.767369,0.770931,0.775148,0.779729,0.784776,0.789469,0.794839,0.800703,0.806746,0.814555,0.826873,0.842398,0.868203
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+b7_24h@ros_minus_#	199	0.006452,0.012500,0.018182,0.018750,0.024242,0.029412,0.034286,0.038889,0.044444,0.050000,0.056410,0.062857,0.070000,0.076471,0.083721,0.090909,0.097872,0.106383,0.113725,0.121569,0.129730,0.137313,0.145763,0.154098,0.162500,0.170588,0.177778,0.185366,0.192793,0.200000,0.207500,0.214634,0.221277,0.228571,0.235897,0.243137,0.249600,0.256585,0.263158,0.269231,0.275610,0.281333,0.287273,0.292857,0.298462,0.303797,0.308955,0.314286,0.319527,0.325000,0.330275,0.335248,0.340153,0.344427,0.348485,0.352727,0.357296,0.361474,0.366033,0.370370,0.374704,0.378621,0.382759,0.386813,0.390625,0.394901,0.398585,0.402740,0.407132,0.411229,0.415577,0.420000,0.424961,0.428994,0.433028,0.437709,0.441947,0.446377,0.450894,0.454985,0.459623,0.463964,0.467925,0.472368,0.477049,0.481538,0.485891,0.490256,0.493992,0.497787,0.501844,0.506311,0.510769,0.514839,0.519324,0.523408,0.527677,0.531868,0.535784,0.540643,0.544807,0.549005,0.553608,0.557647,0.562162,0.566667,0.571429,0.576059,0.580178,0.584202,0.588827,0.593850,0.598100,0.602389,0.606924,0.611414,0.616071,0.620684,0.625221,0.629692,0.634286,0.638764,0.643321,0.647831,0.652232,0.656835,0.661394,0.666323,0.670866,0.675075,0.679528,0.683873,0.688136,0.692272,0.696897,0.701815,0.707200,0.712348,0.716909,0.722527,0.728046,0.733607,0.739258,0.745455,0.752225,0.759007,0.765217,0.771210,0.777242,0.782099,0.787995,0.793114,0.798276,0.803774,0.808917,0.814286,0.819134,0.824419,0.829740,0.835274,0.840820,0.846606,0.852305,0.858462,0.865049,0.872409,0.878577,0.885164,0.892731,0.898898,0.907407,0.915815,0.923558,0.931733,0.941534,0.950631,0.960536,0.971831,0.983803,0.995329,1.008704,1.023801,1.037765,1.051751,1.066977,1.082331,1.097030,1.113258,1.130776,1.151525,1.170000,1.190192,1.208743,1.230754,1.260212,1.292413,1.337577,1.466445
+b4_3h@str_plus	200	0.000309,0.000441,0.000567,0.000682,0.000792,0.000906,0.001020,0.001128,0.001235,0.001339,0.001445,0.001543,0.001642,0.001742,0.001840,0.001931,0.002026,0.002119,0.002210,0.002297,0.002386,0.002467,0.002551,0.002635,0.002709,0.002789,0.002860,0.002934,0.003001,0.003066,0.003134,0.003202,0.003274,0.003337,0.003401,0.003458,0.003524,0.003592,0.003649,0.003710,0.003772,0.003836,0.003896,0.003955,0.004019,0.004079,0.004142,0.004202,0.004261,0.004319,0.004375,0.004431,0.004487,0.004544,0.004602,0.004660,0.004725,0.004783,0.004844,0.004903,0.004957,0.005020,0.005080,0.005144,0.005211,0.005276,0.005342,0.005410,0.005476,0.005538,0.005599,0.005663,0.005724,0.005789,0.005855,0.005915,0.005976,0.006043,0.006110,0.006168,0.006234,0.006295,0.006357,0.006417,0.006481,0.006543,0.006609,0.006677,0.006735,0.006797,0.006858,0.006928,0.006991,0.007057,0.007121,0.007181,0.007240,0.007310,0.007372,0.007429,0.007492,0.007555,0.007615,0.007681,0.007742,0.007804,0.007869,0.007929,0.007988,0.008049,0.008113,0.008176,0.008233,0.008298,0.008363,0.008435,0.008497,0.008568,0.008634,0.008702,0.008766,0.008824,0.008884,0.008945,0.009009,0.009062,0.009122,0.009181,0.009243,0.009303,0.009359,0.009416,0.009477,0.009543,0.009611,0.009682,0.009748,0.009813,0.009875,0.009950,0.010018,0.010087,0.010165,0.010242,0.010319,0.010400,0.010472,0.010549,0.010631,0.010709,0.010788,0.010864,0.010956,0.011036,0.011123,0.011195,0.011282,0.011373,0.011457,0.011564,0.011682,0.011787,0.011893,0.012007,0.012108,0.012221,0.012334,0.012447,0.012565,0.012689,0.012799,0.012932,0.013074,0.013198,0.013328,0.013471,0.013615,0.013740,0.013914,0.014063,0.014230,0.014396,0.014584,0.014783,0.014988,0.015233,0.015476,0.015718,0.015998,0.016326,0.016802,0.017328,0.017879,0.018495,0.019405,0.020721,0.022169,0.024105,0.026659
+b8_3h@return_n_uv	200	0.429046,0.490527,0.533754,0.556452,0.580814,0.600635,0.614847,0.624804,0.632615,0.642649,0.651323,0.659851,0.668085,0.676964,0.683841,0.690073,0.698101,0.704752,0.711964,0.720266,0.725771,0.733237,0.739142,0.745124,0.748043,0.751207,0.757507,0.761727,0.771110,0.777863,0.784620,0.791826,0.797247,0.801863,0.806054,0.810651,0.812089,0.815995,0.820686,0.827308,0.831041,0.835232,0.838526,0.843642,0.848982,0.853287,0.856922,0.860502,0.863391,0.867846,0.870268,0.871375,0.875227,0.877416,0.880627,0.883113,0.886418,0.889179,0.891610,0.893564,0.896354,0.897816,0.899194,0.901165,0.902810,0.904429,0.905754,0.908148,0.908998,0.909974,0.911085,0.912794,0.914974,0.916451,0.917139,0.917988,0.919085,0.920412,0.922444,0.924126,0.925531,0.926426,0.928108,0.928786,0.929523,0.931109,0.932715,0.933406,0.934303,0.936589,0.937602,0.939798,0.941157,0.942287,0.942666,0.943811,0.944225,0.945132,0.946939,0.948098,0.949424,0.949627,0.950837,0.951856,0.952988,0.954445,0.955329,0.956662,0.958211,0.959240,0.960824,0.961340,0.962536,0.964147,0.964789,0.965676,0.968980,0.970467,0.972298,0.973844,0.974748,0.976463,0.977395,0.978993,0.980361,0.982027,0.983740,0.985067,0.986407,0.988614,0.990838,0.993953,0.996034,0.997525,0.998907,1.000595,1.002234,1.003723,1.004902,1.006943,1.008297,1.010254,1.012166,1.013257,1.014855,1.016685,1.018829,1.019126,1.019999,1.020980,1.022027,1.024363,1.026298,1.026993,1.029695,1.030762,1.031917,1.032928,1.034990,1.036458,1.038360,1.041070,1.042023,1.044159,1.045438,1.047481,1.049449,1.050529,1.052645,1.053399,1.054271,1.056775,1.058590,1.059205,1.062743,1.063602,1.066284,1.066572,1.070918,1.071994,1.072084,1.073197,1.074820,1.075216,1.076240,1.078771,1.080023,1.080858,1.088755,1.093612,1.133125,1.137938,1.144054,1.145370,1.150270,1.154692,1.158266,1.169299,1.173170
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+b5_72h@is_return_1	174	0.069315,0.109861,0.138629,0.160944,0.179176,0.194591,0.207944,0.219722,0.230259,0.239790,0.248491,0.256495,0.263906,0.270805,0.277259,0.283321,0.289037,0.294444,0.299573,0.304452,0.309104,0.313549,0.317805,0.321888,0.325810,0.329584,0.333220,0.336730,0.340120,0.343399,0.346574,0.352636,0.355535,0.358352,0.363759,0.366356,0.368888,0.373767,0.376120,0.380666,0.385015,0.387120,0.391202,0.395124,0.398898,0.400733,0.404305,0.407754,0.411087,0.415888,0.418965,0.421951,0.424850,0.429046,0.431749,0.435671,0.438203,0.441884,0.445435,0.447734,0.451086,0.454329,0.457471,0.460517,0.463473,0.466344,0.470048,0.472739,0.475359,0.478749,0.482028,0.484419,0.487520,0.490527,0.493447,0.496981,0.499721,0.502388,0.505625,0.508760,0.511799,0.514749,0.517615,0.520401,0.523111,0.526269,0.528827,0.531812,0.534233,0.537064,0.539816,0.542495,0.545532,0.548064,0.550939,0.553733,0.556068,0.558725,0.561313,0.564191,0.566988,0.569709,0.572359,0.574939,0.577455,0.579909,0.582600,0.585220,0.587493,0.589990,0.592693,0.595324,0.597889,0.600389,0.602828,0.605209,0.607764,0.610256,0.612687,0.615060,0.617379,0.619848,0.622258,0.624804,0.627288,0.629711,0.632257,0.634564,0.636990,0.639526,0.641999,0.644731,0.647389,0.649828,0.652503,0.654965,0.657786,0.660394,0.663068,0.665544,0.668211,0.670930,0.673697,0.676388,0.679347,0.682111,0.685118,0.687936,0.690575,0.693245,0.696224,0.699210,0.702376,0.705790,0.709174,0.712769,0.716240,0.719818,0.723921,0.728070,0.732383,0.736265,0.740489,0.745066,0.750329,0.755433,0.760639,0.766622,0.773762,0.781319,0.791279,0.802682,0.823297
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+b2_3h@is_share	197	0.069315,0.109861,0.138629,0.160944,0.179176,0.194591,0.207944,0.219722,0.230259,0.239790,0.248491,0.263906,0.270805,0.277259,0.289037,0.299573,0.304452,0.313549,0.321888,0.329584,0.336730,0.343399,0.349651,0.358352,0.363759,0.368888,0.376120,0.382864,0.389182,0.393183,0.398898,0.404305,0.411087,0.415888,0.420469,0.424850,0.430407,0.434381,0.439445,0.444265,0.447734,0.452179,0.456435,0.461512,0.465396,0.469135,0.472739,0.477068,0.481218,0.484419,0.489035,0.492725,0.496284,0.500395,0.503695,0.507517,0.511199,0.514166,0.517615,0.520401,0.523644,0.526786,0.530330,0.533754,0.537064,0.540268,0.543372,0.546806,0.549717,0.552943,0.556068,0.559099,0.562040,0.564897,0.568017,0.571043,0.573979,0.576519,0.579301,0.582305,0.584932,0.587774,0.590263,0.593225,0.595842,0.598141,0.601372,0.603787,0.606611,0.609131,0.612030,0.614419,0.616961,0.619236,0.621860,0.624222,0.626910,0.629157,0.631536,0.634388,0.636819,0.639693,0.641999,0.644254,0.646770,0.649224,0.651323,0.653669,0.655962,0.658203,0.660259,0.662672,0.665157,0.667708,0.669950,0.672143,0.674406,0.676734,0.679010,0.681564,0.683626,0.686380,0.688959,0.691274,0.693537,0.695750,0.698286,0.700397,0.702108,0.704578,0.706987,0.709589,0.711802,0.713887,0.715774,0.718159,0.720415,0.722766,0.724566,0.726473,0.728688,0.730988,0.733106,0.735564,0.737337,0.739449,0.741818,0.743780,0.745819,0.747817,0.750274,0.752456,0.754221,0.756268,0.758376,0.760639,0.763240,0.765255,0.767694,0.769894,0.772268,0.774846,0.777486,0.779523,0.782164,0.784385,0.786787,0.788908,0.791279,0.793630,0.796520,0.799429,0.802813,0.805452,0.808364,0.810621,0.814090,0.816849,0.819919,0.823271,0.825635,0.829079,0.832409,0.835984,0.839503,0.844355,0.849064,0.853660,0.860172,0.864118,0.870018,0.876421,0.884578,0.891086,0.913130,0.941532
+b1_1h@str_plus	200	0.000222,0.000312,0.000396,0.000475,0.000552,0.000632,0.000712,0.000788,0.000862,0.000935,0.001008,0.001078,0.001146,0.001213,0.001282,0.001350,0.001412,0.001472,0.001533,0.001597,0.001661,0.001719,0.001775,0.001831,0.001882,0.001938,0.001992,0.002044,0.002093,0.002138,0.002186,0.002234,0.002278,0.002326,0.002371,0.002419,0.002463,0.002508,0.002548,0.002593,0.002633,0.002677,0.002722,0.002766,0.002807,0.002852,0.002898,0.002944,0.002990,0.003034,0.003071,0.003115,0.003159,0.003198,0.003237,0.003276,0.003321,0.003364,0.003404,0.003450,0.003495,0.003540,0.003591,0.003632,0.003682,0.003730,0.003766,0.003810,0.003856,0.003903,0.003945,0.003997,0.004042,0.004086,0.004130,0.004179,0.004225,0.004268,0.004312,0.004356,0.004399,0.004441,0.004496,0.004544,0.004586,0.004626,0.004673,0.004713,0.004752,0.004804,0.004850,0.004899,0.004945,0.004989,0.005033,0.005075,0.005129,0.005173,0.005219,0.005254,0.005307,0.005353,0.005399,0.005446,0.005489,0.005532,0.005579,0.005630,0.005679,0.005724,0.005768,0.005815,0.005860,0.005906,0.005954,0.005993,0.006037,0.006083,0.006124,0.006173,0.006220,0.006263,0.006312,0.006354,0.006401,0.006447,0.006498,0.006540,0.006588,0.006635,0.006688,0.006738,0.006785,0.006845,0.006884,0.006936,0.007001,0.007055,0.007104,0.007144,0.007196,0.007254,0.007313,0.007366,0.007426,0.007485,0.007541,0.007597,0.007651,0.007717,0.007772,0.007829,0.007893,0.007966,0.008028,0.008100,0.008175,0.008254,0.008340,0.008410,0.008491,0.008567,0.008643,0.008733,0.008834,0.008921,0.009017,0.009124,0.009213,0.009294,0.009388,0.009480,0.009566,0.009667,0.009783,0.009860,0.010004,0.010101,0.010201,0.010324,0.010456,0.010618,0.010770,0.010935,0.011109,0.011292,0.011563,0.011831,0.012123,0.012416,0.012745,0.013302,0.014013,0.014943,0.015682,0.016593,0.017782,0.019890,0.023425
+b9_1h@return_n_uv	200	0.194591,0.248491,0.289037,0.313549,0.336730,0.352636,0.366356,0.380666,0.391202,0.402535,0.412713,0.420469,0.429046,0.436945,0.444265,0.452179,0.458497,0.465396,0.470953,0.476217,0.483628,0.491265,0.496981,0.503695,0.508760,0.514166,0.519296,0.524702,0.529832,0.536129,0.540717,0.545532,0.550126,0.554908,0.559099,0.563479,0.568017,0.571373,0.575890,0.579606,0.584932,0.588888,0.593489,0.597889,0.603069,0.606843,0.610925,0.615060,0.619236,0.623832,0.626910,0.630810,0.634212,0.638012,0.642162,0.645520,0.648768,0.652062,0.655108,0.657786,0.661740,0.664118,0.666823,0.669332,0.671780,0.675344,0.676849,0.679682,0.681344,0.683411,0.685857,0.687833,0.689770,0.690875,0.692264,0.694409,0.696319,0.698657,0.700851,0.702465,0.703527,0.705186,0.706133,0.708339,0.709920,0.711151,0.712930,0.714756,0.717089,0.719068,0.720638,0.722257,0.724494,0.725771,0.727863,0.729097,0.730586,0.732383,0.733498,0.735692,0.737776,0.739326,0.740062,0.742237,0.743662,0.744950,0.746049,0.747364,0.749443,0.750439,0.751643,0.753476,0.754908,0.755799,0.757405,0.758934,0.760290,0.761825,0.762852,0.764348,0.765302,0.766575,0.767786,0.768662,0.769485,0.770976,0.772533,0.773587,0.774544,0.776260,0.777275,0.778281,0.779359,0.780873,0.781521,0.782684,0.783795,0.784854,0.785941,0.786672,0.788080,0.790212,0.791863,0.792516,0.793200,0.794733,0.796276,0.797281,0.798514,0.799497,0.800136,0.801136,0.801961,0.804045,0.805896,0.807091,0.808395,0.810440,0.811313,0.813535,0.815708,0.817103,0.819035,0.820467,0.821906,0.823616,0.825764,0.827155,0.829054,0.831336,0.831654,0.833567,0.835749,0.837471,0.840178,0.842332,0.845127,0.849617,0.851298,0.852278,0.854052,0.856236,0.860392,0.862981,0.868542,0.870434,0.872648,0.875951,0.877833,0.884289,0.886658,0.901140,0.908251,0.986698,0.995166,0.996152,1.000987,1.005117,1.017881
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+b5_6h@return_n_uv	143	0.069315,0.109861,0.138629,0.160944,0.179176,0.194591,0.207944,0.219722,0.230259,0.239790,0.248491,0.256495,0.263906,0.270805,0.277259,0.283321,0.289037,0.294444,0.299573,0.304452,0.309104,0.313549,0.317805,0.321888,0.325810,0.329584,0.333220,0.336730,0.340120,0.343399,0.346574,0.349651,0.352636,0.355535,0.358352,0.361092,0.363759,0.366356,0.368888,0.371357,0.373767,0.376120,0.378419,0.380666,0.382864,0.385015,0.389182,0.391202,0.393183,0.397029,0.398898,0.400733,0.404305,0.406044,0.409434,0.411087,0.414313,0.415888,0.418965,0.421951,0.423411,0.426268,0.429046,0.431749,0.434381,0.435671,0.438203,0.440672,0.444265,0.446591,0.448864,0.451086,0.454329,0.456435,0.458497,0.461512,0.464439,0.466344,0.469135,0.471850,0.474493,0.476217,0.478749,0.482028,0.484419,0.486753,0.489784,0.491998,0.494876,0.497673,0.500395,0.502388,0.504986,0.508140,0.510595,0.512990,0.515906,0.518178,0.520949,0.523644,0.526269,0.529330,0.531812,0.534711,0.537528,0.540717,0.543372,0.546383,0.549306,0.552146,0.555296,0.558350,0.561313,0.564545,0.567675,0.571043,0.574300,0.577765,0.581413,0.585220,0.588888,0.592693,0.596615,0.600635,0.604501,0.608904,0.613340,0.617587,0.622258,0.627099,0.632436,0.638351,0.644731,0.651619,0.658755,0.666696,0.675110,0.685013,0.695368,0.707412,0.724922,0.748381
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+b4_6h@str_plus	200	0.000369,0.000549,0.000709,0.000870,0.001033,0.001185,0.001330,0.001473,0.001612,0.001758,0.001896,0.002031,0.002159,0.002286,0.002412,0.002536,0.002651,0.002766,0.002878,0.002984,0.003087,0.003184,0.003277,0.003362,0.003448,0.003529,0.003612,0.003689,0.003772,0.003849,0.003932,0.003997,0.004064,0.004134,0.004206,0.004273,0.004343,0.004413,0.004478,0.004543,0.004609,0.004677,0.004741,0.004804,0.004871,0.004935,0.005000,0.005061,0.005123,0.005181,0.005242,0.005307,0.005366,0.005435,0.005501,0.005565,0.005638,0.005706,0.005772,0.005841,0.005908,0.005975,0.006047,0.006119,0.006189,0.006261,0.006333,0.006405,0.006477,0.006554,0.006626,0.006695,0.006773,0.006839,0.006904,0.006977,0.007050,0.007125,0.007198,0.007269,0.007345,0.007416,0.007489,0.007565,0.007635,0.007712,0.007785,0.007859,0.007927,0.008000,0.008069,0.008137,0.008201,0.008264,0.008348,0.008414,0.008478,0.008549,0.008618,0.008689,0.008753,0.008820,0.008887,0.008955,0.009029,0.009096,0.009166,0.009230,0.009293,0.009363,0.009430,0.009497,0.009565,0.009622,0.009687,0.009748,0.009806,0.009873,0.009936,0.010005,0.010065,0.010132,0.010198,0.010267,0.010332,0.010395,0.010459,0.010526,0.010601,0.010665,0.010732,0.010796,0.010869,0.010945,0.011016,0.011087,0.011151,0.011217,0.011289,0.011352,0.011436,0.011515,0.011590,0.011665,0.011738,0.011819,0.011890,0.011966,0.012046,0.012131,0.012212,0.012309,0.012403,0.012494,0.012586,0.012668,0.012757,0.012853,0.012940,0.013033,0.013138,0.013249,0.013355,0.013470,0.013579,0.013699,0.013828,0.013953,0.014092,0.014220,0.014335,0.014478,0.014615,0.014760,0.014894,0.015026,0.015212,0.015372,0.015553,0.015752,0.015926,0.016144,0.016322,0.016513,0.016705,0.016930,0.017164,0.017365,0.017637,0.017976,0.018341,0.018793,0.019452,0.020164,0.020781,0.021743,0.023088,0.025212,0.028257
+b8_24h@exp	200	1.175150,1.204385,1.219773,1.252493,1.272312,1.277144,1.282137,1.288021,1.297959,1.307366,1.316152,1.321236,1.327196,1.335891,1.344860,1.353261,1.360658,1.367991,1.383274,1.389816,1.400321,1.405283,1.408409,1.411870,1.414882,1.417119,1.418873,1.420394,1.422217,1.426166,1.429180,1.433667,1.436744,1.439557,1.442086,1.444353,1.446200,1.447524,1.449486,1.451331,1.453010,1.454438,1.456027,1.457429,1.458899,1.460100,1.461443,1.462757,1.464481,1.465492,1.467463,1.468648,1.471507,1.473937,1.479872,1.487314,1.489199,1.491563,1.493210,1.494845,1.495523,1.497121,1.498332,1.500165,1.502476,1.503934,1.505029,1.505745,1.506300,1.506603,1.507717,1.508417,1.508860,1.509928,1.510638,1.511645,1.512726,1.513232,1.513728,1.514390,1.515142,1.517149,1.518416,1.520027,1.521807,1.522354,1.522860,1.523623,1.524081,1.524447,1.524847,1.525382,1.525883,1.526380,1.527172,1.527879,1.528135,1.529888,1.530669,1.531708,1.533473,1.534926,1.535917,1.536906,1.538071,1.539127,1.540116,1.541084,1.541485,1.541882,1.542630,1.543072,1.543342,1.543538,1.544165,1.544684,1.544937,1.545477,1.546026,1.546832,1.547772,1.548636,1.549263,1.549723,1.550266,1.550845,1.551656,1.552217,1.552750,1.553462,1.553836,1.554286,1.555159,1.556248,1.557450,1.559927,1.561467,1.562417,1.567521,1.568551,1.569175,1.570165,1.570718,1.571683,1.576373,1.643890,1.644241,1.644490,1.644759,1.645034,1.645371,1.645829,1.646306,1.646465,1.646679,1.646939,1.647226,1.647329,1.647647,1.648102,1.648339,1.648592,1.648983,1.649357,1.649538,1.649727,1.649759,1.650042,1.650230,1.650288,1.650562,1.650610,1.650760,1.650917,1.651048,1.651411,1.651473,1.651667,1.651827,1.651971,1.652331,1.652906,1.653372,1.653568,1.653956,1.654394,1.654428,1.654526,1.654646,1.655544,1.657014,1.657668,1.658531,1.659714,1.660763,1.661829,1.662435,1.662613,1.662991
+b0_3h@exp	200	0.160944,0.248491,0.304452,0.349651,0.387120,0.420469,0.447734,0.470953,0.493447,0.508760,0.525227,0.542935,0.560212,0.576205,0.589990,0.603309,0.614419,0.624222,0.633328,0.642162,0.649979,0.657786,0.665673,0.673578,0.681124,0.688244,0.695177,0.702019,0.708674,0.714992,0.721377,0.727379,0.733041,0.738709,0.744191,0.749610,0.754961,0.760340,0.765539,0.770661,0.775534,0.780384,0.785283,0.790138,0.794909,0.799733,0.804302,0.808641,0.813182,0.817723,0.821960,0.826230,0.830375,0.834617,0.838800,0.842989,0.846863,0.850694,0.854442,0.858298,0.862011,0.865730,0.869266,0.873069,0.876639,0.880297,0.884015,0.887640,0.890869,0.894416,0.897879,0.901079,0.903860,0.907360,0.910930,0.914228,0.917658,0.920884,0.923980,0.927181,0.930501,0.933335,0.936538,0.939541,0.942392,0.945642,0.948903,0.951628,0.954931,0.958197,0.961073,0.963672,0.966186,0.968924,0.972041,0.974835,0.977821,0.980444,0.983441,0.986261,0.989233,0.992162,0.995085,0.997669,1.000650,1.003601,1.006237,1.008843,1.011533,1.014294,1.016958,1.019317,1.022238,1.025217,1.027701,1.030802,1.033702,1.036448,1.038869,1.041781,1.044418,1.046809,1.049238,1.051838,1.054363,1.056896,1.059641,1.062530,1.065164,1.067672,1.070192,1.073095,1.075253,1.077754,1.080679,1.082888,1.085374,1.087614,1.089968,1.092179,1.094585,1.096734,1.098920,1.100974,1.102881,1.105240,1.107330,1.110042,1.112281,1.114322,1.116681,1.118428,1.120535,1.122260,1.124230,1.126355,1.128963,1.130810,1.133160,1.135193,1.136895,1.139007,1.140790,1.143042,1.144957,1.147006,1.148865,1.151117,1.152658,1.155454,1.158156,1.160761,1.162640,1.165314,1.168177,1.170707,1.173831,1.176386,1.179328,1.181973,1.184800,1.188105,1.191405,1.194538,1.197265,1.200766,1.204255,1.208394,1.211612,1.216141,1.219831,1.223534,1.229000,1.233749,1.239649,1.246404,1.256198,1.267436,1.294533
+b5_72h@str_plus	200	0.000715,0.001008,0.001262,0.001498,0.001724,0.001936,0.002130,0.002326,0.002504,0.002680,0.002843,0.002998,0.003154,0.003296,0.003438,0.003574,0.003703,0.003828,0.003945,0.004062,0.004171,0.004280,0.004388,0.004495,0.004604,0.004709,0.004811,0.004917,0.005009,0.005099,0.005198,0.005288,0.005376,0.005467,0.005548,0.005633,0.005717,0.005804,0.005894,0.005979,0.006068,0.006153,0.006246,0.006332,0.006415,0.006506,0.006590,0.006679,0.006763,0.006842,0.006924,0.007008,0.007089,0.007166,0.007251,0.007334,0.007412,0.007493,0.007573,0.007657,0.007743,0.007827,0.007909,0.007992,0.008071,0.008154,0.008236,0.008321,0.008402,0.008479,0.008564,0.008649,0.008735,0.008813,0.008891,0.008972,0.009050,0.009135,0.009216,0.009298,0.009379,0.009465,0.009550,0.009636,0.009718,0.009803,0.009885,0.009973,0.010063,0.010150,0.010232,0.010319,0.010404,0.010487,0.010572,0.010659,0.010747,0.010835,0.010926,0.011013,0.011101,0.011181,0.011265,0.011346,0.011428,0.011513,0.011597,0.011686,0.011775,0.011865,0.011953,0.012041,0.012129,0.012216,0.012305,0.012395,0.012485,0.012576,0.012668,0.012755,0.012848,0.012942,0.013035,0.013126,0.013215,0.013308,0.013401,0.013490,0.013576,0.013667,0.013757,0.013853,0.013950,0.014047,0.014143,0.014234,0.014333,0.014436,0.014538,0.014642,0.014749,0.014863,0.014965,0.015074,0.015182,0.015295,0.015406,0.015520,0.015635,0.015755,0.015879,0.015998,0.016125,0.016241,0.016361,0.016492,0.016612,0.016745,0.016874,0.017006,0.017139,0.017266,0.017393,0.017527,0.017671,0.017823,0.017971,0.018131,0.018300,0.018478,0.018660,0.018850,0.019032,0.019229,0.019435,0.019644,0.019846,0.020072,0.020311,0.020568,0.020856,0.021160,0.021500,0.021827,0.022213,0.022585,0.023017,0.023508,0.024069,0.024706,0.025435,0.026209,0.027015,0.027759,0.028618,0.029458,0.030326,0.031869,0.034412
+b0_12h@ros_minus1_#	200	0.006452,0.012500,0.018182,0.019355,0.025000,0.030303,0.037500,0.045161,0.052632,0.060000,0.066667,0.075000,0.082051,0.089474,0.097561,0.105556,0.113725,0.121951,0.130612,0.138983,0.146939,0.155000,0.162791,0.171084,0.178947,0.186047,0.192857,0.200000,0.207317,0.214433,0.220462,0.225974,0.231746,0.237209,0.242857,0.248649,0.254098,0.258974,0.264516,0.269841,0.275862,0.280851,0.285294,0.290323,0.295455,0.300000,0.304274,0.307865,0.312613,0.316418,0.320588,0.324057,0.328283,0.332248,0.336111,0.340525,0.344509,0.348000,0.352113,0.355556,0.359494,0.363333,0.367172,0.371556,0.375552,0.378800,0.381870,0.386142,0.389610,0.393585,0.397600,0.400920,0.404678,0.408730,0.412422,0.415526,0.418502,0.421764,0.425171,0.427715,0.431071,0.434221,0.437467,0.440550,0.443928,0.447059,0.450704,0.454327,0.457627,0.460723,0.463978,0.467496,0.470213,0.473016,0.476361,0.479495,0.482817,0.485475,0.488584,0.491919,0.495094,0.498000,0.501350,0.503864,0.507006,0.509774,0.512930,0.516071,0.518580,0.521294,0.524138,0.526873,0.529621,0.532184,0.535758,0.538649,0.541562,0.544595,0.547303,0.549957,0.552979,0.555589,0.558639,0.561207,0.564148,0.567005,0.570192,0.573293,0.576011,0.579389,0.582474,0.584983,0.588173,0.591316,0.594888,0.598506,0.601613,0.604778,0.607804,0.611621,0.615232,0.618415,0.621730,0.625240,0.628700,0.632264,0.635249,0.639212,0.642956,0.647268,0.650676,0.654560,0.658184,0.661707,0.665608,0.669663,0.673842,0.677851,0.682143,0.687527,0.692358,0.697380,0.702078,0.707348,0.712195,0.717431,0.722486,0.727806,0.733086,0.738260,0.744121,0.750276,0.757020,0.763521,0.771343,0.778074,0.785650,0.793978,0.802291,0.809910,0.818074,0.826868,0.835058,0.843295,0.853575,0.864649,0.874843,0.884975,0.896233,0.906907,0.918712,0.931341,0.946659,0.964071,0.985366,1.008756,1.038304,1.073813,1.164235
+b1_168h@exp	200	0.363759,0.457471,0.501064,0.524702,0.555683,0.597889,0.631536,0.652942,0.670196,0.684268,0.697635,0.716627,0.737086,0.754539,0.768524,0.781278,0.793845,0.807184,0.822202,0.837101,0.850188,0.862335,0.875621,0.888889,0.901152,0.912674,0.925177,0.936803,0.948888,0.960945,0.970613,0.980206,0.990753,1.000618,1.009473,1.018746,1.026594,1.034644,1.042064,1.049005,1.055932,1.062697,1.068746,1.075214,1.081017,1.086431,1.091888,1.096968,1.101742,1.106573,1.111124,1.115695,1.120197,1.124429,1.128600,1.133060,1.136870,1.141356,1.145470,1.149360,1.153666,1.157598,1.162032,1.166301,1.170145,1.173912,1.177877,1.181824,1.185559,1.189262,1.193077,1.197260,1.201098,1.204909,1.208644,1.212185,1.215671,1.219033,1.222516,1.225771,1.229461,1.233352,1.236441,1.239937,1.243628,1.247071,1.250114,1.253385,1.256529,1.259969,1.262806,1.266377,1.270267,1.273397,1.277116,1.280908,1.284218,1.287528,1.291526,1.295625,1.299386,1.303154,1.306591,1.309910,1.313210,1.316336,1.319651,1.322730,1.325957,1.329466,1.332374,1.335535,1.338626,1.341116,1.343834,1.347197,1.350766,1.354127,1.357046,1.360077,1.363384,1.366347,1.369293,1.372438,1.376056,1.378279,1.382160,1.385388,1.387711,1.390449,1.393125,1.396034,1.399410,1.402820,1.405863,1.409475,1.413234,1.416452,1.419875,1.423273,1.426208,1.429221,1.431965,1.433878,1.436482,1.439074,1.440959,1.443848,1.446407,1.448203,1.450012,1.452031,1.454064,1.456052,1.458263,1.460468,1.462493,1.464473,1.466501,1.468384,1.469982,1.471953,1.473860,1.475953,1.477880,1.479682,1.481868,1.484336,1.486303,1.488535,1.490357,1.492121,1.494591,1.497048,1.498641,1.499962,1.501567,1.503589,1.505613,1.507322,1.509214,1.513088,1.516058,1.518611,1.520940,1.523489,1.525623,1.527682,1.530300,1.531956,1.533750,1.535989,1.538137,1.541686,1.546367,1.550433,1.555531,1.560783,1.569694
+b9_24h@str_plus	200	0.005230,0.005305,0.005454,0.005788,0.006128,0.006358,0.006737,0.006896,0.007013,0.007152,0.007270,0.007391,0.007445,0.007560,0.007720,0.007859,0.007976,0.008086,0.008181,0.008257,0.008323,0.008400,0.008448,0.008515,0.008572,0.008632,0.008657,0.008703,0.008732,0.008768,0.008793,0.008836,0.008887,0.008928,0.008974,0.009027,0.009066,0.009110,0.009171,0.009224,0.009271,0.009305,0.009342,0.009381,0.009435,0.009465,0.009525,0.009573,0.009625,0.009741,0.009825,0.009930,0.010027,0.010126,0.010215,0.010295,0.010378,0.010462,0.010527,0.010593,0.010630,0.010692,0.010765,0.010818,0.010871,0.010914,0.010950,0.010984,0.011053,0.011118,0.011149,0.011189,0.011230,0.011265,0.011322,0.011352,0.011438,0.011518,0.011547,0.011592,0.011671,0.011773,0.011855,0.011893,0.011955,0.012001,0.012075,0.012179,0.012235,0.012285,0.012330,0.012394,0.012427,0.012465,0.012555,0.012608,0.012632,0.012685,0.012740,0.012814,0.012840,0.012870,0.012884,0.012935,0.012969,0.012989,0.013023,0.013060,0.013088,0.013114,0.013129,0.013167,0.013203,0.013244,0.013258,0.013277,0.013303,0.013337,0.013369,0.013382,0.013407,0.013411,0.013433,0.013458,0.013470,0.013501,0.013541,0.013596,0.013618,0.013668,0.013691,0.013731,0.013761,0.013782,0.013829,0.013861,0.013931,0.013968,0.014024,0.014085,0.014163,0.014216,0.014269,0.014309,0.014354,0.014385,0.014433,0.014519,0.014624,0.014664,0.014718,0.014834,0.014908,0.015022,0.015102,0.015195,0.015281,0.015308,0.015397,0.015515,0.015568,0.015644,0.015685,0.015758,0.015802,0.015883,0.015912,0.015955,0.015988,0.016033,0.016061,0.016119,0.016170,0.016246,0.016353,0.016478,0.016550,0.016634,0.016725,0.016822,0.016922,0.017033,0.017047,0.017154,0.017280,0.017578,0.017887,0.018030,0.018059,0.018432,0.018664,0.019009,0.019130,0.019344,0.019442,0.019811,0.020235,0.020631,0.022862
+b1_72h@exp	200	0.333220,0.440672,0.493447,0.515329,0.543372,0.578074,0.615273,0.642649,0.660259,0.676388,0.688551,0.703439,0.723201,0.741878,0.757558,0.770526,0.782445,0.794414,0.806778,0.820713,0.833878,0.846674,0.857546,0.868963,0.879830,0.891045,0.901457,0.910875,0.921354,0.930164,0.938488,0.947532,0.956184,0.964705,0.971051,0.978267,0.984866,0.991695,0.998682,1.004408,1.010443,1.016462,1.021742,1.027049,1.032348,1.037124,1.042300,1.046897,1.051797,1.056282,1.061138,1.065717,1.070484,1.074882,1.079158,1.083327,1.088018,1.092233,1.096599,1.101042,1.105404,1.109539,1.113784,1.117949,1.121503,1.125246,1.129275,1.133174,1.137028,1.140904,1.144911,1.148206,1.151768,1.155589,1.159229,1.162718,1.166442,1.169491,1.172820,1.176282,1.179660,1.182801,1.185936,1.189348,1.192669,1.196106,1.199346,1.202602,1.205958,1.209260,1.212191,1.215698,1.218749,1.222158,1.225381,1.228741,1.232472,1.235687,1.239240,1.242808,1.246256,1.249345,1.252797,1.256020,1.259063,1.262194,1.265608,1.269277,1.272468,1.275525,1.278705,1.282052,1.285485,1.289355,1.292549,1.295641,1.298721,1.302168,1.305056,1.308742,1.311469,1.313937,1.316472,1.319446,1.321682,1.324659,1.326950,1.329917,1.332414,1.334612,1.337678,1.340121,1.342735,1.345666,1.348903,1.352718,1.355354,1.358800,1.362564,1.365498,1.368558,1.371360,1.374406,1.376900,1.379408,1.381963,1.384705,1.387215,1.389073,1.391279,1.393124,1.394926,1.397019,1.398977,1.401169,1.403420,1.405145,1.406729,1.409041,1.410967,1.412554,1.414206,1.415782,1.418028,1.420473,1.422817,1.424626,1.425497,1.427018,1.429310,1.431148,1.432635,1.434542,1.436307,1.438782,1.440943,1.444125,1.446887,1.449406,1.451837,1.453552,1.455617,1.457676,1.459753,1.463490,1.467093,1.472075,1.475273,1.479103,1.482282,1.486589,1.489912,1.494361,1.496776,1.501392,1.508905,1.514435,1.521614,1.533257
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+b4_1h@is_share	175	0.069315,0.109861,0.138629,0.160944,0.179176,0.194591,0.207944,0.219722,0.230259,0.239790,0.248491,0.256495,0.263906,0.270805,0.277259,0.283321,0.289037,0.294444,0.299573,0.304452,0.309104,0.313549,0.317805,0.321888,0.325810,0.329584,0.333220,0.336730,0.340120,0.346574,0.349651,0.352636,0.355535,0.358352,0.361092,0.363759,0.366356,0.371357,0.373767,0.376120,0.380666,0.382864,0.385015,0.389182,0.391202,0.393183,0.397029,0.398898,0.402535,0.404305,0.407754,0.409434,0.412713,0.415888,0.418965,0.420469,0.423411,0.426268,0.429046,0.431749,0.433073,0.436945,0.439445,0.441884,0.444265,0.446591,0.448864,0.451086,0.453260,0.455388,0.458497,0.460517,0.463473,0.465396,0.468213,0.470953,0.472739,0.475359,0.477912,0.479579,0.482028,0.484419,0.486753,0.488280,0.490527,0.493447,0.495583,0.497673,0.499721,0.502388,0.504343,0.506260,0.508760,0.510595,0.512990,0.515329,0.517048,0.519850,0.521494,0.523644,0.525750,0.527811,0.529832,0.531812,0.534233,0.536129,0.538450,0.540717,0.542495,0.544674,0.546383,0.548480,0.550939,0.552943,0.554908,0.556834,0.559099,0.560947,0.562762,0.564897,0.566988,0.569036,0.571043,0.573657,0.575574,0.577765,0.579606,0.581711,0.583773,0.585507,0.587774,0.589440,0.591620,0.593754,0.595842,0.597635,0.599645,0.602102,0.604263,0.606611,0.608904,0.610925,0.613556,0.615698,0.618621,0.621060,0.623832,0.626340,0.628413,0.630810,0.632972,0.635437,0.638012,0.641182,0.644254,0.647543,0.651175,0.653669,0.657088,0.660530,0.664639,0.668336,0.671901,0.676273,0.680351,0.685435,0.690274,0.696035,0.702820,0.710250,0.719594,0.730384,0.745819,0.791169
+b5_24h@ros_one	196	0.019890,0.025679,0.032879,0.036223,0.045586,0.054974,0.061490,0.063224,0.071478,0.076511,0.082217,0.088605,0.094529,0.094757,0.096769,0.101229,0.107086,0.110337,0.117212,0.117618,0.120582,0.125484,0.131506,0.136842,0.138118,0.141644,0.146010,0.150036,0.151662,0.153642,0.158217,0.160956,0.163462,0.167465,0.170449,0.173095,0.177094,0.179746,0.183689,0.186431,0.188111,0.190844,0.193257,0.195833,0.198828,0.202144,0.204102,0.206543,0.206692,0.207655,0.210147,0.212634,0.214630,0.216108,0.218756,0.221503,0.224200,0.226864,0.229337,0.230984,0.232438,0.234730,0.236690,0.239083,0.241899,0.244633,0.245997,0.248092,0.250454,0.252350,0.254614,0.256246,0.258195,0.260648,0.262992,0.264703,0.266647,0.268342,0.270417,0.272251,0.274346,0.276270,0.278299,0.280088,0.281622,0.283794,0.285317,0.287542,0.289053,0.291045,0.292591,0.294350,0.296238,0.298214,0.299915,0.300870,0.302483,0.304355,0.305851,0.307545,0.309344,0.310869,0.312670,0.314190,0.315817,0.317483,0.319249,0.320613,0.322406,0.324034,0.325551,0.326963,0.328622,0.330210,0.331782,0.333403,0.334951,0.336542,0.337999,0.339631,0.341135,0.342372,0.343342,0.344896,0.346420,0.347938,0.349352,0.350778,0.352354,0.353797,0.355132,0.356483,0.357922,0.359225,0.360717,0.362255,0.363745,0.365193,0.366549,0.368061,0.369567,0.371026,0.372523,0.374011,0.375477,0.376756,0.378282,0.379774,0.381309,0.382892,0.384383,0.385916,0.387372,0.388751,0.390386,0.391870,0.393415,0.394992,0.396465,0.397932,0.399603,0.401200,0.402692,0.404236,0.405871,0.407363,0.408941,0.410434,0.412174,0.413826,0.415490,0.417212,0.418974,0.420802,0.422604,0.424384,0.426269,0.428363,0.430447,0.432685,0.434843,0.437192,0.439255,0.441713,0.444430,0.447382,0.450342,0.453681,0.457361,0.461912,0.466941,0.473009,0.481316,0.491294,0.511055
+b0_3h@ros_n_#	200	0.002148,0.003030,0.003704,0.004310,0.004878,0.005405,0.005882,0.006452,0.007143,0.007752,0.008333,0.008955,0.009524,0.010022,0.010526,0.011101,0.011592,0.012121,0.012683,0.013185,0.013681,0.014201,0.014716,0.015249,0.015827,0.016327,0.016854,0.017319,0.017857,0.018413,0.018886,0.019409,0.019898,0.020455,0.021023,0.021538,0.022041,0.022526,0.023026,0.023492,0.024000,0.024444,0.024975,0.025441,0.025946,0.026498,0.027027,0.027586,0.028088,0.028571,0.029060,0.029545,0.030000,0.030534,0.031070,0.031510,0.032021,0.032432,0.032939,0.033395,0.033913,0.034440,0.034848,0.035294,0.035762,0.036267,0.036782,0.037313,0.037806,0.038298,0.038795,0.039303,0.039760,0.040197,0.040646,0.041060,0.041481,0.041942,0.042402,0.042902,0.043324,0.043752,0.044231,0.044724,0.045196,0.045652,0.046081,0.046552,0.046953,0.047364,0.047806,0.048215,0.048673,0.049119,0.049565,0.050068,0.050545,0.050960,0.051335,0.051807,0.052260,0.052734,0.053105,0.053566,0.054051,0.054555,0.054983,0.055446,0.055869,0.056355,0.056790,0.057326,0.057770,0.058166,0.058603,0.059091,0.059497,0.059946,0.060407,0.060865,0.061329,0.061741,0.062232,0.062701,0.063086,0.063538,0.063964,0.064435,0.064968,0.065526,0.065963,0.066495,0.066933,0.067548,0.068137,0.068533,0.069032,0.069592,0.070175,0.070733,0.071279,0.071783,0.072318,0.072903,0.073567,0.074174,0.074710,0.075314,0.075912,0.076469,0.076898,0.077424,0.078083,0.078689,0.079279,0.080000,0.080695,0.081510,0.082151,0.082741,0.083339,0.084065,0.084836,0.085593,0.086354,0.086971,0.087769,0.088554,0.089547,0.090451,0.091244,0.092308,0.093324,0.094308,0.095455,0.096361,0.097448,0.098617,0.099859,0.101112,0.102266,0.103786,0.105325,0.107211,0.108619,0.110974,0.113055,0.115298,0.117045,0.119944,0.122621,0.127018,0.131611,0.137948,0.143002,0.152479,0.165077,0.178594,0.198295
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+b1_6h@is_return_1	191	0.069315,0.109861,0.138629,0.160944,0.179176,0.194591,0.207944,0.219722,0.230259,0.239790,0.248491,0.256495,0.263906,0.277259,0.283321,0.289037,0.294444,0.299573,0.304452,0.313549,0.317805,0.325810,0.329584,0.336730,0.343399,0.346574,0.352636,0.358352,0.361092,0.366356,0.371357,0.376120,0.380666,0.387120,0.391202,0.395124,0.398898,0.404305,0.407754,0.412713,0.415888,0.420469,0.424850,0.429046,0.431749,0.435671,0.439445,0.443082,0.446591,0.451086,0.454329,0.457471,0.460517,0.464439,0.468213,0.470953,0.474493,0.477912,0.481218,0.484419,0.488280,0.491265,0.494876,0.498361,0.501064,0.504343,0.507517,0.510595,0.513580,0.516479,0.519296,0.522575,0.525227,0.528320,0.531321,0.534233,0.537064,0.540268,0.542935,0.546383,0.549306,0.552146,0.555296,0.557973,0.560580,0.563835,0.566643,0.569373,0.572359,0.574939,0.577765,0.580513,0.583481,0.586647,0.589164,0.592426,0.595064,0.597635,0.600141,0.603069,0.605678,0.607993,0.610702,0.613773,0.616542,0.618415,0.620859,0.623832,0.626340,0.629157,0.631716,0.634739,0.637332,0.640192,0.642811,0.645047,0.647697,0.650429,0.652796,0.655393,0.657925,0.660259,0.663200,0.665929,0.668711,0.671296,0.674052,0.676388,0.679347,0.682002,0.684482,0.687833,0.690575,0.693440,0.696508,0.699118,0.701840,0.704491,0.707496,0.710743,0.713409,0.716472,0.718917,0.722037,0.724993,0.727656,0.730586,0.733368,0.736581,0.738895,0.741276,0.744425,0.747477,0.750274,0.753316,0.756372,0.759085,0.762364,0.764874,0.767647,0.770165,0.772798,0.775491,0.778945,0.781883,0.784971,0.788080,0.790765,0.794662,0.798139,0.801632,0.805706,0.808764,0.812237,0.815708,0.819229,0.823350,0.826822,0.831801,0.836544,0.841804,0.846210,0.850086,0.855564,0.862173,0.867146,0.877940,0.884765,0.898632,0.916754
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+b5_1h@ros_minus_#	127	0.006452,0.012500,0.012903,0.018182,0.018750,0.019355,0.023529,0.024242,0.025000,0.025806,0.028571,0.029412,0.030303,0.031250,0.032258,0.034286,0.035294,0.036364,0.037500,0.037838,0.038889,0.040000,0.041176,0.042424,0.043243,0.043750,0.044444,0.045714,0.047059,0.047368,0.048485,0.050000,0.051429,0.052941,0.054054,0.054545,0.055556,0.056250,0.057143,0.057895,0.058824,0.060000,0.061111,0.061905,0.062857,0.064516,0.064865,0.066667,0.068421,0.069767,0.070270,0.071795,0.072727,0.073913,0.075000,0.076471,0.077778,0.078947,0.080000,0.081081,0.082353,0.083721,0.085000,0.086364,0.087805,0.088889,0.090476,0.091892,0.093617,0.095000,0.097143,0.097674,0.100000,0.102128,0.102703,0.104762,0.106383,0.108108,0.110000,0.111628,0.113514,0.115556,0.117391,0.119149,0.121053,0.123404,0.125490,0.127273,0.129630,0.131818,0.134375,0.136364,0.138776,0.141463,0.144000,0.146667,0.149153,0.152174,0.155000,0.157895,0.160976,0.164444,0.167647,0.171014,0.175000,0.178378,0.182222,0.186364,0.190698,0.195349,0.200000,0.204494,0.209524,0.215190,0.220833,0.227586,0.234286,0.241176,0.250000,0.259302,0.268750,0.280000,0.293333,0.310714,0.332653,0.367500
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+b4_6h@ros_#	200	0.005128,0.005714,0.006250,0.007143,0.009836,0.011111,0.012121,0.013793,0.015686,0.017021,0.018286,0.019697,0.021277,0.022727,0.024242,0.025532,0.027027,0.028571,0.030000,0.031496,0.032836,0.034286,0.035616,0.036885,0.038274,0.039560,0.040909,0.042308,0.043636,0.044944,0.046154,0.047500,0.048780,0.050000,0.051223,0.052381,0.053727,0.054878,0.055994,0.057143,0.058369,0.059649,0.060838,0.062222,0.063514,0.064706,0.065909,0.067136,0.068333,0.069512,0.070807,0.072139,0.073455,0.074576,0.075949,0.077293,0.078715,0.080000,0.081481,0.082828,0.084034,0.085294,0.086647,0.087957,0.089263,0.090536,0.091788,0.092929,0.094215,0.095385,0.096429,0.097638,0.098851,0.100000,0.101370,0.102488,0.103704,0.105017,0.106230,0.107500,0.108676,0.109877,0.111111,0.112381,0.113520,0.114815,0.115972,0.117279,0.118395,0.119654,0.120898,0.122034,0.123077,0.124429,0.125683,0.126878,0.128049,0.129341,0.130526,0.131908,0.133333,0.134529,0.135766,0.137024,0.138213,0.139562,0.140824,0.142187,0.143662,0.144939,0.146154,0.147530,0.148906,0.150139,0.151429,0.152833,0.154153,0.155369,0.156899,0.158378,0.159712,0.161111,0.162468,0.164066,0.165468,0.166990,0.168341,0.169816,0.171390,0.172866,0.174468,0.176073,0.177604,0.179212,0.180843,0.182143,0.183838,0.185439,0.187228,0.188936,0.190476,0.192000,0.193750,0.195410,0.197222,0.199048,0.200755,0.202512,0.204180,0.205714,0.207813,0.209603,0.211355,0.213241,0.215119,0.217245,0.219226,0.220928,0.223204,0.225397,0.227517,0.229687,0.232039,0.234321,0.236874,0.239153,0.241551,0.244000,0.246422,0.248941,0.251781,0.254526,0.256993,0.259682,0.262443,0.265339,0.268050,0.271000,0.274185,0.277301,0.281084,0.284589,0.288435,0.292421,0.296415,0.300659,0.304881,0.309939,0.314667,0.319801,0.325987,0.332391,0.341287,0.351298,0.360360,0.372283,0.386527,0.408026,0.432019
+b5_24h@str_plus	200	0.000585,0.000803,0.000987,0.001155,0.001312,0.001460,0.001607,0.001748,0.001880,0.002011,0.002137,0.002266,0.002383,0.002497,0.002604,0.002720,0.002824,0.002929,0.003032,0.003129,0.003224,0.003321,0.003413,0.003508,0.003598,0.003687,0.003767,0.003854,0.003937,0.004025,0.004113,0.004195,0.004274,0.004351,0.004428,0.004507,0.004582,0.004660,0.004735,0.004808,0.004885,0.004955,0.005033,0.005102,0.005177,0.005245,0.005321,0.005391,0.005465,0.005538,0.005610,0.005682,0.005754,0.005832,0.005908,0.005980,0.006055,0.006128,0.006206,0.006284,0.006356,0.006432,0.006508,0.006579,0.006656,0.006731,0.006805,0.006879,0.006957,0.007032,0.007101,0.007177,0.007255,0.007330,0.007398,0.007474,0.007550,0.007628,0.007702,0.007768,0.007846,0.007924,0.007999,0.008070,0.008145,0.008225,0.008302,0.008381,0.008458,0.008538,0.008617,0.008697,0.008771,0.008845,0.008918,0.008996,0.009075,0.009158,0.009240,0.009318,0.009391,0.009473,0.009550,0.009635,0.009718,0.009796,0.009875,0.009950,0.010029,0.010110,0.010193,0.010273,0.010355,0.010438,0.010516,0.010596,0.010676,0.010761,0.010843,0.010927,0.011019,0.011103,0.011181,0.011269,0.011354,0.011445,0.011532,0.011625,0.011711,0.011797,0.011878,0.011970,0.012061,0.012154,0.012250,0.012340,0.012432,0.012529,0.012627,0.012725,0.012824,0.012924,0.013027,0.013129,0.013235,0.013347,0.013456,0.013562,0.013678,0.013790,0.013908,0.014030,0.014151,0.014270,0.014393,0.014516,0.014644,0.014770,0.014896,0.015027,0.015166,0.015303,0.015435,0.015574,0.015711,0.015852,0.016008,0.016158,0.016311,0.016476,0.016647,0.016817,0.017007,0.017190,0.017385,0.017562,0.017753,0.017943,0.018154,0.018383,0.018627,0.018883,0.019173,0.019483,0.019821,0.020182,0.020579,0.021018,0.021512,0.022042,0.022560,0.023192,0.023893,0.024656,0.025673,0.026746,0.027974,0.029658,0.032373
+b7_1h@str_plus	200	0.000199,0.000273,0.000336,0.000392,0.000445,0.000499,0.000549,0.000601,0.000652,0.000701,0.000750,0.000800,0.000851,0.000901,0.000950,0.000998,0.001048,0.001095,0.001142,0.001188,0.001235,0.001280,0.001328,0.001374,0.001420,0.001464,0.001508,0.001551,0.001595,0.001639,0.001683,0.001728,0.001771,0.001812,0.001856,0.001900,0.001941,0.001983,0.002029,0.002072,0.002112,0.002153,0.002196,0.002236,0.002276,0.002319,0.002357,0.002398,0.002441,0.002485,0.002524,0.002566,0.002611,0.002653,0.002696,0.002736,0.002779,0.002820,0.002861,0.002904,0.002944,0.002986,0.003025,0.003068,0.003109,0.003157,0.003196,0.003237,0.003279,0.003321,0.003365,0.003412,0.003456,0.003498,0.003539,0.003583,0.003621,0.003660,0.003700,0.003741,0.003779,0.003823,0.003869,0.003914,0.003953,0.003995,0.004041,0.004086,0.004128,0.004167,0.004210,0.004255,0.004297,0.004342,0.004396,0.004439,0.004482,0.004527,0.004570,0.004617,0.004660,0.004701,0.004742,0.004790,0.004836,0.004886,0.004931,0.004979,0.005020,0.005069,0.005119,0.005169,0.005211,0.005260,0.005308,0.005359,0.005405,0.005455,0.005502,0.005547,0.005597,0.005651,0.005699,0.005747,0.005804,0.005861,0.005909,0.005959,0.006014,0.006075,0.006128,0.006187,0.006250,0.006318,0.006375,0.006439,0.006499,0.006555,0.006617,0.006679,0.006734,0.006808,0.006866,0.006946,0.007012,0.007087,0.007153,0.007224,0.007296,0.007379,0.007463,0.007543,0.007625,0.007713,0.007791,0.007877,0.007974,0.008065,0.008157,0.008257,0.008340,0.008445,0.008539,0.008635,0.008746,0.008852,0.008966,0.009075,0.009191,0.009310,0.009441,0.009575,0.009710,0.009856,0.010015,0.010188,0.010373,0.010549,0.010766,0.010936,0.011130,0.011382,0.011661,0.011897,0.012187,0.012468,0.012785,0.013096,0.013495,0.013872,0.014319,0.014852,0.015375,0.016196,0.017291,0.018525,0.020176,0.022868,0.026862
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+b2_1h@rovn_#	200	0.001104,0.001461,0.001745,0.002006,0.002244,0.002452,0.002660,0.002853,0.003037,0.003221,0.003400,0.003570,0.003737,0.003906,0.004065,0.004208,0.004363,0.004499,0.004632,0.004772,0.004902,0.005025,0.005162,0.005291,0.005415,0.005529,0.005638,0.005751,0.005877,0.005988,0.006098,0.006209,0.006322,0.006427,0.006532,0.006630,0.006732,0.006849,0.006943,0.007047,0.007143,0.007226,0.007332,0.007429,0.007522,0.007632,0.007728,0.007843,0.007942,0.008032,0.008128,0.008219,0.008312,0.008408,0.008503,0.008616,0.008721,0.008830,0.008921,0.009036,0.009132,0.009224,0.009330,0.009426,0.009545,0.009627,0.009720,0.009834,0.009932,0.010035,0.010142,0.010245,0.010330,0.010424,0.010526,0.010629,0.010736,0.010824,0.010929,0.011020,0.011125,0.011218,0.011303,0.011406,0.011512,0.011625,0.011725,0.011820,0.011916,0.012016,0.012096,0.012200,0.012305,0.012425,0.012544,0.012683,0.012788,0.012907,0.013004,0.013102,0.013193,0.013308,0.013408,0.013514,0.013638,0.013746,0.013835,0.013953,0.014071,0.014192,0.014298,0.014403,0.014498,0.014610,0.014731,0.014842,0.014981,0.015092,0.015206,0.015312,0.015423,0.015521,0.015625,0.015720,0.015850,0.015977,0.016093,0.016223,0.016343,0.016455,0.016566,0.016692,0.016814,0.016949,0.017079,0.017201,0.017305,0.017443,0.017572,0.017708,0.017825,0.017939,0.018080,0.018224,0.018356,0.018494,0.018640,0.018791,0.018927,0.019054,0.019191,0.019318,0.019460,0.019598,0.019746,0.019884,0.020023,0.020185,0.020348,0.020542,0.020719,0.020903,0.021072,0.021277,0.021463,0.021674,0.021831,0.022044,0.022288,0.022485,0.022724,0.022936,0.023155,0.023376,0.023625,0.023895,0.024149,0.024441,0.024785,0.025069,0.025453,0.025844,0.026222,0.026600,0.027227,0.027746,0.028164,0.028710,0.029281,0.029973,0.030871,0.031699,0.032703,0.034033,0.035744,0.037145,0.039146,0.041881,0.049645
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+b1_168h@str_plus	200	0.001015,0.001529,0.002192,0.002699,0.003140,0.003464,0.003830,0.004130,0.004274,0.004399,0.004594,0.004798,0.004953,0.005123,0.005266,0.005442,0.005619,0.005743,0.005867,0.005974,0.006079,0.006201,0.006310,0.006409,0.006513,0.006645,0.006772,0.006869,0.006970,0.007084,0.007197,0.007308,0.007396,0.007467,0.007550,0.007647,0.007733,0.007830,0.007925,0.008039,0.008116,0.008168,0.008233,0.008318,0.008392,0.008461,0.008534,0.008614,0.008693,0.008790,0.008885,0.008989,0.009082,0.009188,0.009293,0.009381,0.009475,0.009562,0.009653,0.009740,0.009824,0.009880,0.009955,0.010042,0.010115,0.010189,0.010272,0.010327,0.010405,0.010502,0.010610,0.010687,0.010768,0.010827,0.010892,0.010970,0.011054,0.011152,0.011243,0.011307,0.011379,0.011441,0.011517,0.011601,0.011672,0.011753,0.011824,0.011891,0.011952,0.012032,0.012103,0.012193,0.012280,0.012357,0.012439,0.012528,0.012628,0.012727,0.012822,0.012938,0.013034,0.013117,0.013213,0.013299,0.013381,0.013464,0.013560,0.013650,0.013746,0.013829,0.013923,0.014025,0.014134,0.014242,0.014323,0.014376,0.014442,0.014500,0.014557,0.014608,0.014669,0.014728,0.014785,0.014852,0.014940,0.015028,0.015107,0.015184,0.015260,0.015333,0.015396,0.015456,0.015531,0.015605,0.015695,0.015799,0.015884,0.015986,0.016059,0.016139,0.016216,0.016302,0.016394,0.016501,0.016586,0.016682,0.016778,0.016868,0.016937,0.017016,0.017113,0.017215,0.017313,0.017436,0.017566,0.017671,0.017767,0.017860,0.017979,0.018107,0.018212,0.018315,0.018399,0.018508,0.018634,0.018779,0.018925,0.019047,0.019201,0.019371,0.019486,0.019614,0.019748,0.019948,0.020087,0.020238,0.020356,0.020492,0.020673,0.020851,0.021009,0.021141,0.021490,0.021738,0.021972,0.022315,0.022875,0.023253,0.023888,0.024300,0.024598,0.024823,0.025657,0.026261,0.027211,0.028233,0.029016,0.030193,0.031329
+b1_6h@share_cnt	200	0.069315,0.109861,0.160944,0.179176,0.207944,0.230259,0.248491,0.270805,0.289037,0.304452,0.317805,0.333220,0.346574,0.358352,0.368888,0.380666,0.391202,0.402535,0.411087,0.420469,0.429046,0.438203,0.446591,0.454329,0.461512,0.469135,0.476217,0.482831,0.489784,0.496284,0.502388,0.508140,0.514166,0.519850,0.525750,0.530827,0.535659,0.540717,0.545532,0.550533,0.555296,0.559842,0.564545,0.569036,0.573657,0.578074,0.582600,0.586647,0.590808,0.594803,0.598896,0.602828,0.606843,0.610256,0.613988,0.617379,0.620859,0.624611,0.628040,0.631536,0.634739,0.638012,0.641346,0.644413,0.647543,0.650728,0.653959,0.657228,0.660123,0.663200,0.666696,0.669580,0.672623,0.675693,0.678559,0.681344,0.684162,0.687005,0.690073,0.693147,0.695940,0.698841,0.701571,0.704316,0.706817,0.709589,0.712528,0.715540,0.718235,0.720934,0.723993,0.726753,0.729641,0.732647,0.735116,0.737838,0.740550,0.743426,0.745819,0.748381,0.750879,0.753530,0.755956,0.758325,0.761135,0.763916,0.766153,0.768891,0.771468,0.773979,0.776472,0.779235,0.781682,0.783795,0.786057,0.788382,0.790728,0.793057,0.795648,0.797900,0.800001,0.802158,0.804783,0.807309,0.809651,0.812148,0.814787,0.817160,0.819395,0.821825,0.824276,0.826591,0.828828,0.831312,0.833711,0.836147,0.838640,0.841183,0.843772,0.846274,0.848673,0.850734,0.853503,0.856159,0.858951,0.861577,0.864242,0.866837,0.869651,0.871997,0.874465,0.877354,0.879710,0.882321,0.885524,0.888211,0.890463,0.893446,0.896137,0.898457,0.900761,0.902942,0.904982,0.907521,0.909952,0.912522,0.915842,0.918122,0.920834,0.924009,0.926540,0.928749,0.931064,0.933926,0.936777,0.939491,0.942594,0.945407,0.948273,0.951628,0.955230,0.958363,0.961006,0.964251,0.968016,0.971287,0.975022,0.979963,0.984321,0.987843,0.993042,0.997776,1.003060,1.009192,1.015366,1.023023,1.031407,1.046906,1.060413
+b10_12h@is_share	200	0.632436,0.692854,0.734601,0.780954,0.818952,0.847700,0.876764,0.900319,0.916597,0.929918,0.948896,0.973720,0.991046,1.011739,1.025990,1.034077,1.040344,1.043831,1.049058,1.051667,1.053734,1.056277,1.058752,1.061492,1.063732,1.066548,1.068981,1.070228,1.073084,1.075954,1.078432,1.080700,1.082371,1.084770,1.086847,1.088777,1.090265,1.092067,1.092989,1.093962,1.095284,1.096740,1.097968,1.099188,1.101633,1.102582,1.103792,1.105404,1.106310,1.107470,1.108503,1.110720,1.111745,1.113954,1.114960,1.116456,1.117842,1.120240,1.123387,1.124994,1.127225,1.130056,1.133880,1.137978,1.144226,1.151906,1.185224,1.188150,1.190573,1.196939,1.200233,1.201195,1.203668,1.204635,1.206250,1.207726,1.209431,1.209945,1.210309,1.211155,1.211778,1.212322,1.212748,1.213870,1.214423,1.215015,1.215327,1.215693,1.216722,1.217345,1.218398,1.218810,1.219778,1.220130,1.222300,1.223469,1.223882,1.224797,1.225988,1.226456,1.226819,1.227636,1.228881,1.229939,1.230788,1.231201,1.231902,1.232187,1.233506,1.235277,1.236110,1.236722,1.237416,1.238222,1.238525,1.238591,1.239888,1.240307,1.240377,1.241071,1.241582,1.242410,1.243621,1.244640,1.245328,1.245837,1.246442,1.247432,1.247522,1.247583,1.248279,1.249035,1.250627,1.250818,1.252219,1.253113,1.253840,1.255450,1.256555,1.256725,1.257022,1.258734,1.259847,1.260906,1.262621,1.263568,1.264793,1.265043,1.266567,1.268407,1.269907,1.270124,1.272196,1.272620,1.274982,1.275425,1.277363,1.279879,1.280062,1.281705,1.282502,1.284052,1.285395,1.285915,1.286534,1.288181,1.289938,1.290631,1.291489,1.291570,1.291954,1.293350,1.294186,1.295415,1.296142,1.296596,1.296825,1.297846,1.298327,1.298699,1.298983,1.300233,1.300614,1.301878,1.302302,1.302837,1.303181,1.304103,1.304619,1.306467,1.306657,1.307465,1.308814,1.309229,1.310462,1.311058,1.311475,1.312308,1.315035
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+b10_12h@exp	200	0.973714,1.041840,1.091973,1.138556,1.175542,1.211120,1.229504,1.247749,1.266795,1.282503,1.295347,1.309709,1.326567,1.341633,1.353008,1.359029,1.365105,1.371421,1.376043,1.382696,1.385526,1.388720,1.392629,1.394405,1.396851,1.399643,1.401529,1.404189,1.407988,1.410278,1.413108,1.415089,1.417579,1.420795,1.422969,1.425670,1.427436,1.429524,1.430558,1.433031,1.433911,1.436092,1.436561,1.437513,1.438931,1.440150,1.441305,1.442377,1.443607,1.444389,1.445381,1.447242,1.448481,1.449514,1.451371,1.452558,1.454235,1.456365,1.458324,1.459086,1.460439,1.462383,1.464527,1.467966,1.474669,1.485719,1.535271,1.536753,1.538695,1.540670,1.541693,1.542087,1.543217,1.545236,1.547345,1.547823,1.548110,1.548328,1.548731,1.549151,1.549919,1.551906,1.552542,1.553494,1.554503,1.554973,1.556323,1.558608,1.559015,1.559785,1.562008,1.562692,1.563798,1.564351,1.564875,1.565675,1.566478,1.567389,1.567565,1.568795,1.569211,1.569644,1.570550,1.570732,1.571135,1.571272,1.571968,1.572554,1.573329,1.573810,1.574411,1.574744,1.575078,1.575155,1.575588,1.576225,1.576366,1.577718,1.578523,1.579045,1.579936,1.580298,1.580373,1.581073,1.581439,1.581932,1.582355,1.582727,1.583174,1.584557,1.585280,1.586291,1.587676,1.589518,1.589760,1.590629,1.591785,1.591821,1.592642,1.593312,1.595015,1.596847,1.599265,1.600199,1.600290,1.601285,1.602549,1.603794,1.604417,1.605114,1.607997,1.610187,1.610443,1.611359,1.612593,1.613671,1.613936,1.617355,1.619415,1.620015,1.620767,1.622347,1.623071,1.623905,1.626961,1.628579,1.628895,1.630365,1.631040,1.631091,1.633986,1.635415,1.636738,1.637191,1.637447,1.638391,1.640759,1.641855,1.641985,1.642693,1.643518,1.644478,1.645900,1.646410,1.647065,1.648226,1.648595,1.649381,1.650751,1.651198,1.651935,1.653098,1.653480,1.654300,1.655674,1.656233,1.656728,1.657054,1.658636
+b13_3h@rovn_#	200	0.001560,0.002160,0.002646,0.003076,0.003467,0.003846,0.004202,0.004555,0.004914,0.005236,0.005547,0.005848,0.006151,0.006473,0.006753,0.007021,0.007307,0.007535,0.007799,0.008039,0.008264,0.008474,0.008687,0.008889,0.009110,0.009315,0.009504,0.009683,0.009840,0.010028,0.010184,0.010363,0.010524,0.010697,0.010870,0.011042,0.011193,0.011354,0.011516,0.011677,0.011828,0.011993,0.012157,0.012313,0.012470,0.012647,0.012801,0.012957,0.013121,0.013295,0.013455,0.013613,0.013783,0.013966,0.014137,0.014332,0.014519,0.014692,0.014878,0.015051,0.015213,0.015377,0.015535,0.015727,0.015891,0.016063,0.016241,0.016408,0.016562,0.016729,0.016891,0.017094,0.017272,0.017444,0.017614,0.017761,0.017917,0.018069,0.018236,0.018403,0.018603,0.018772,0.018942,0.019094,0.019282,0.019434,0.019574,0.019754,0.019930,0.020104,0.020270,0.020445,0.020609,0.020781,0.020953,0.021124,0.021290,0.021471,0.021662,0.021838,0.021992,0.022190,0.022351,0.022517,0.022706,0.022879,0.023045,0.023213,0.023342,0.023508,0.023716,0.023891,0.024051,0.024221,0.024418,0.024561,0.024720,0.024907,0.025049,0.025226,0.025404,0.025596,0.025785,0.025957,0.026108,0.026304,0.026477,0.026623,0.026822,0.027001,0.027196,0.027351,0.027526,0.027708,0.027841,0.028055,0.028202,0.028410,0.028598,0.028804,0.029011,0.029241,0.029448,0.029648,0.029853,0.030077,0.030304,0.030529,0.030718,0.030973,0.031212,0.031426,0.031654,0.031848,0.032103,0.032333,0.032533,0.032777,0.033027,0.033249,0.033452,0.033705,0.033953,0.034220,0.034458,0.034728,0.034984,0.035301,0.035615,0.035830,0.036106,0.036420,0.036771,0.037037,0.037381,0.037762,0.038095,0.038457,0.038816,0.039231,0.039659,0.040060,0.040658,0.041215,0.041753,0.042427,0.043058,0.043644,0.044347,0.045029,0.046181,0.047143,0.048485,0.050040,0.052152,0.054692,0.057646,0.062044,0.069812
+b7_24h@is_return_1	190	0.069315,0.109861,0.138629,0.160944,0.179176,0.194591,0.207944,0.219722,0.230259,0.239790,0.248491,0.256495,0.263906,0.270805,0.283321,0.289037,0.294444,0.299573,0.309104,0.313549,0.321888,0.325810,0.333220,0.336730,0.343399,0.349651,0.355535,0.361092,0.366356,0.371357,0.376120,0.380666,0.385015,0.391202,0.395124,0.398898,0.404305,0.409434,0.412713,0.417439,0.421951,0.426268,0.430407,0.434381,0.438203,0.443082,0.446591,0.451086,0.454329,0.458497,0.462497,0.466344,0.470048,0.473620,0.477068,0.481218,0.484419,0.488280,0.491998,0.495583,0.499721,0.503044,0.506260,0.509987,0.512990,0.516479,0.520401,0.523644,0.526786,0.529832,0.533272,0.536598,0.539363,0.542495,0.545532,0.548480,0.551745,0.554908,0.557973,0.560947,0.563835,0.566988,0.570044,0.573010,0.575890,0.579301,0.582305,0.585220,0.588053,0.591080,0.594017,0.597126,0.599645,0.602587,0.605678,0.608677,0.611589,0.614204,0.616961,0.619848,0.622851,0.625575,0.628600,0.631355,0.634036,0.636819,0.639693,0.642649,0.645362,0.648158,0.650728,0.653379,0.655678,0.658203,0.660935,0.663332,0.665673,0.668211,0.671174,0.673934,0.676734,0.679459,0.681892,0.684801,0.687316,0.690174,0.692854,0.695464,0.698472,0.701212,0.704141,0.706902,0.709340,0.711639,0.713966,0.716317,0.718539,0.721450,0.724137,0.727170,0.729709,0.732185,0.734407,0.737275,0.740184,0.742774,0.745240,0.748156,0.751098,0.753903,0.756786,0.759237,0.761678,0.764204,0.766809,0.769439,0.772046,0.774803,0.777444,0.779934,0.782365,0.784737,0.787131,0.789730,0.792769,0.795578,0.798173,0.800803,0.803431,0.806338,0.809010,0.811761,0.815018,0.818535,0.821986,0.826462,0.830202,0.834022,0.838252,0.842222,0.845638,0.849638,0.853346,0.858167,0.864330,0.872079,0.881537,0.894037,0.919197
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+b5_12h@str_plus	200	0.000443,0.000601,0.000736,0.000857,0.000976,0.001084,0.001194,0.001299,0.001400,0.001498,0.001592,0.001689,0.001785,0.001879,0.001965,0.002056,0.002143,0.002232,0.002316,0.002396,0.002477,0.002561,0.002640,0.002715,0.002791,0.002862,0.002940,0.003009,0.003086,0.003158,0.003223,0.003294,0.003368,0.003436,0.003507,0.003575,0.003643,0.003714,0.003781,0.003848,0.003918,0.003987,0.004055,0.004122,0.004197,0.004262,0.004325,0.004394,0.004457,0.004527,0.004590,0.004660,0.004720,0.004787,0.004857,0.004920,0.004987,0.005055,0.005114,0.005180,0.005240,0.005308,0.005372,0.005439,0.005506,0.005567,0.005635,0.005702,0.005764,0.005833,0.005899,0.005963,0.006031,0.006099,0.006163,0.006231,0.006300,0.006367,0.006434,0.006502,0.006568,0.006636,0.006709,0.006777,0.006841,0.006916,0.006987,0.007060,0.007124,0.007195,0.007262,0.007336,0.007403,0.007473,0.007546,0.007619,0.007693,0.007759,0.007831,0.007903,0.007974,0.008048,0.008116,0.008191,0.008261,0.008335,0.008405,0.008476,0.008547,0.008621,0.008694,0.008766,0.008841,0.008909,0.008986,0.009062,0.009137,0.009210,0.009292,0.009359,0.009438,0.009518,0.009594,0.009675,0.009761,0.009840,0.009911,0.009993,0.010077,0.010156,0.010237,0.010321,0.010406,0.010479,0.010564,0.010646,0.010731,0.010817,0.010904,0.010996,0.011085,0.011167,0.011257,0.011345,0.011441,0.011533,0.011632,0.011727,0.011820,0.011916,0.012012,0.012118,0.012223,0.012329,0.012433,0.012542,0.012656,0.012760,0.012874,0.012997,0.013115,0.013232,0.013358,0.013482,0.013602,0.013719,0.013852,0.013992,0.014131,0.014272,0.014418,0.014569,0.014733,0.014886,0.015052,0.015229,0.015407,0.015591,0.015804,0.016024,0.016232,0.016443,0.016695,0.016948,0.017219,0.017491,0.017797,0.018097,0.018477,0.018890,0.019357,0.019881,0.020447,0.021181,0.022103,0.023155,0.024692,0.026814,0.030385
+b4_1h@ros_minus_#	181	0.006452,0.012500,0.012903,0.018182,0.018750,0.019355,0.023529,0.024242,0.025000,0.028571,0.029412,0.030303,0.032258,0.034286,0.035294,0.036364,0.037838,0.038889,0.040000,0.042105,0.043243,0.044444,0.046154,0.047368,0.050000,0.051282,0.052632,0.054054,0.056250,0.057895,0.059459,0.061111,0.063158,0.064865,0.066667,0.068421,0.070270,0.071795,0.073913,0.075676,0.077778,0.080000,0.081633,0.083333,0.085106,0.087500,0.089474,0.091304,0.093333,0.095455,0.097674,0.100000,0.102128,0.104167,0.106122,0.108333,0.110526,0.112500,0.114894,0.117073,0.119231,0.121569,0.123636,0.125806,0.128000,0.130233,0.132258,0.134694,0.137037,0.139130,0.141463,0.143478,0.145763,0.148148,0.150000,0.152542,0.154930,0.157143,0.159375,0.162025,0.164286,0.166667,0.168831,0.171134,0.173626,0.176000,0.178313,0.180769,0.182979,0.185106,0.187234,0.189474,0.191781,0.193846,0.196154,0.198182,0.200000,0.203333,0.205333,0.207595,0.209901,0.212346,0.214516,0.216667,0.218519,0.220779,0.222785,0.225000,0.227174,0.229730,0.232000,0.234286,0.236620,0.238776,0.240964,0.243333,0.245570,0.247826,0.250000,0.252308,0.254645,0.256667,0.258824,0.261290,0.263600,0.265789,0.268132,0.270492,0.272581,0.275200,0.277551,0.280000,0.282222,0.284298,0.286842,0.288972,0.291429,0.293750,0.296183,0.298667,0.301333,0.303922,0.306806,0.309259,0.311321,0.314474,0.317037,0.320000,0.322353,0.325000,0.327711,0.330323,0.332955,0.335802,0.338182,0.341333,0.344776,0.348066,0.351887,0.355405,0.359341,0.363636,0.368254,0.372014,0.376471,0.381377,0.385455,0.390000,0.396330,0.402151,0.407493,0.416393,0.423881,0.432353,0.440559,0.450394,0.464000,0.478981,0.507781,0.545251
+b1_168h@is_share	200	0.109861,0.160944,0.194591,0.219722,0.248491,0.283321,0.304452,0.325810,0.343399,0.361092,0.378419,0.395124,0.412713,0.429046,0.445435,0.459512,0.473620,0.487520,0.501728,0.516479,0.530827,0.544242,0.557595,0.571043,0.584064,0.595842,0.607535,0.618826,0.629895,0.640523,0.650728,0.660123,0.669084,0.677765,0.686797,0.695273,0.703351,0.710497,0.717549,0.724494,0.731055,0.737337,0.742536,0.748549,0.753476,0.758172,0.762998,0.767833,0.772268,0.776217,0.780262,0.784581,0.788533,0.792624,0.796901,0.801136,0.805293,0.809377,0.813505,0.817301,0.821257,0.825088,0.828652,0.832215,0.836077,0.839570,0.842836,0.846274,0.849699,0.852794,0.856522,0.859360,0.862641,0.865469,0.869081,0.872567,0.876092,0.879543,0.882879,0.885965,0.889069,0.892212,0.895209,0.898469,0.901942,0.904841,0.907943,0.911151,0.914099,0.917596,0.920804,0.923903,0.927162,0.930109,0.932928,0.935876,0.938849,0.941930,0.944904,0.948820,0.952544,0.956015,0.959431,0.962879,0.966479,0.969492,0.972955,0.975996,0.979512,0.982233,0.985156,0.987781,0.990753,0.993972,0.996703,1.000609,1.003867,1.008034,1.011391,1.014867,1.018059,1.021009,1.023351,1.026088,1.028463,1.031268,1.033767,1.036458,1.039136,1.042130,1.044819,1.048167,1.050671,1.053196,1.055826,1.058403,1.060693,1.063021,1.065283,1.067195,1.069437,1.071504,1.073457,1.076264,1.079309,1.081816,1.084129,1.086357,1.088590,1.090621,1.092660,1.095209,1.097823,1.099833,1.102345,1.105052,1.107755,1.110541,1.113678,1.116788,1.119950,1.122755,1.125688,1.128570,1.131237,1.134348,1.137301,1.140190,1.142768,1.145054,1.147345,1.150038,1.153356,1.155975,1.159455,1.162121,1.164614,1.166821,1.169381,1.171443,1.173516,1.175609,1.177961,1.180001,1.181692,1.184414,1.187272,1.190002,1.191971,1.193728,1.195488,1.198153,1.201631,1.203893,1.207149,1.210610,1.215918,1.224648,1.242441
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+b5_12h@rovn_#	200	0.002650,0.003509,0.004184,0.004785,0.005319,0.005814,0.006260,0.006695,0.007098,0.007491,0.007874,0.008236,0.008582,0.008929,0.009259,0.009583,0.009901,0.010204,0.010499,0.010769,0.011050,0.011345,0.011613,0.011892,0.012158,0.012425,0.012684,0.012950,0.013204,0.013471,0.013724,0.013986,0.014239,0.014493,0.014728,0.014993,0.015244,0.015494,0.015745,0.015999,0.016243,0.016494,0.016748,0.017000,0.017241,0.017492,0.017734,0.017984,0.018225,0.018486,0.018732,0.018978,0.019231,0.019466,0.019704,0.019973,0.020225,0.020488,0.020747,0.020997,0.021277,0.021505,0.021769,0.022036,0.022287,0.022546,0.022804,0.023068,0.023313,0.023586,0.023852,0.024108,0.024390,0.024635,0.024909,0.025167,0.025455,0.025733,0.025984,0.026266,0.026540,0.026820,0.027100,0.027397,0.027674,0.027956,0.028253,0.028557,0.028835,0.029125,0.029412,0.029707,0.030008,0.030303,0.030610,0.030914,0.031230,0.031500,0.031792,0.032099,0.032390,0.032700,0.033003,0.033333,0.033634,0.033941,0.034247,0.034542,0.034871,0.035169,0.035483,0.035798,0.036137,0.036465,0.036797,0.037118,0.037433,0.037763,0.038095,0.038433,0.038760,0.039102,0.039445,0.039806,0.040140,0.040488,0.040836,0.041204,0.041569,0.041929,0.042319,0.042695,0.043062,0.043436,0.043808,0.044199,0.044586,0.044962,0.045351,0.045751,0.046154,0.046544,0.046955,0.047357,0.047758,0.048186,0.048602,0.049040,0.049483,0.049965,0.050404,0.050873,0.051343,0.051809,0.052270,0.052762,0.053268,0.053763,0.054284,0.054814,0.055380,0.055934,0.056481,0.057094,0.057704,0.058314,0.058939,0.059596,0.060228,0.060878,0.061589,0.062344,0.063144,0.063886,0.064709,0.065574,0.066426,0.067356,0.068246,0.069285,0.070280,0.071341,0.072519,0.073729,0.075000,0.076424,0.077964,0.079553,0.081301,0.083333,0.085637,0.088235,0.091038,0.094419,0.098642,0.104052,0.111768,0.122438,0.138220
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+b13_1h@ros_minus_#	190	0.006452,0.012500,0.012903,0.018182,0.018750,0.023529,0.024242,0.025000,0.028571,0.029412,0.031250,0.034286,0.035294,0.037500,0.038889,0.041176,0.043243,0.045714,0.047368,0.050000,0.051429,0.052941,0.055556,0.057895,0.060000,0.061905,0.064865,0.066667,0.068571,0.071429,0.073684,0.075676,0.078049,0.080952,0.082927,0.085714,0.087805,0.090196,0.092308,0.095000,0.097561,0.100000,0.102222,0.105000,0.107692,0.110204,0.112821,0.115385,0.117857,0.120000,0.122727,0.125000,0.127660,0.130435,0.133333,0.135593,0.138095,0.140541,0.143210,0.145455,0.148000,0.150877,0.153247,0.155932,0.158491,0.160784,0.163636,0.166197,0.168750,0.171429,0.174194,0.176296,0.178667,0.180952,0.183333,0.185600,0.187879,0.190000,0.192424,0.194667,0.196970,0.200000,0.201980,0.203846,0.206250,0.208621,0.211236,0.213913,0.215873,0.218182,0.220000,0.221739,0.223881,0.225926,0.228188,0.230088,0.232061,0.234286,0.236364,0.238298,0.240268,0.242373,0.244681,0.246617,0.248951,0.251136,0.253061,0.255000,0.256790,0.258824,0.260606,0.262687,0.264615,0.266667,0.268809,0.271053,0.273394,0.275377,0.277027,0.279070,0.281356,0.283770,0.285393,0.287234,0.288889,0.290698,0.292593,0.294366,0.296259,0.298333,0.300000,0.302041,0.304202,0.306667,0.308923,0.310811,0.312977,0.315771,0.317808,0.319745,0.322330,0.324464,0.326316,0.328736,0.330435,0.332484,0.334641,0.336538,0.338868,0.341085,0.342598,0.345035,0.347253,0.349541,0.351282,0.353846,0.356579,0.359375,0.362080,0.364103,0.366612,0.369231,0.372016,0.375333,0.377869,0.381081,0.383766,0.386592,0.389183,0.391566,0.394872,0.398587,0.402186,0.405628,0.411719,0.416129,0.421635,0.427411,0.434043,0.439344,0.445161,0.454927,0.462551,0.470627,0.477990,0.492712,0.509600,0.536723,0.568421
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+b10_12h@rovn_#	200	0.013007,0.016687,0.017780,0.018960,0.020183,0.020468,0.020651,0.021083,0.021296,0.021529,0.021701,0.022085,0.022181,0.022564,0.022809,0.023199,0.023328,0.023601,0.023703,0.024115,0.024268,0.024525,0.024792,0.024910,0.025220,0.025478,0.025912,0.025980,0.026231,0.026493,0.026778,0.027083,0.027193,0.027587,0.027673,0.027981,0.028119,0.028312,0.028490,0.028548,0.028687,0.028766,0.028875,0.028961,0.029017,0.029146,0.029193,0.029297,0.029412,0.029562,0.029599,0.029665,0.029768,0.029830,0.029832,0.029924,0.029973,0.030011,0.030108,0.030247,0.030348,0.030426,0.030483,0.030582,0.030606,0.030668,0.030729,0.030881,0.031041,0.031085,0.031159,0.031175,0.031402,0.031428,0.031509,0.031602,0.031874,0.031966,0.031988,0.032000,0.032050,0.032168,0.032330,0.032401,0.032561,0.032583,0.032650,0.032830,0.032989,0.033225,0.033450,0.033490,0.033564,0.033610,0.033819,0.033950,0.033977,0.034159,0.034294,0.034524,0.034661,0.034770,0.034858,0.035123,0.035161,0.035213,0.035448,0.035589,0.035888,0.036084,0.036104,0.036184,0.036239,0.036400,0.036524,0.036600,0.036666,0.036845,0.036935,0.037077,0.037113,0.037245,0.037295,0.037455,0.037542,0.037716,0.037755,0.037853,0.038061,0.038156,0.038168,0.038310,0.038389,0.038424,0.038533,0.038745,0.038886,0.038990,0.039123,0.039222,0.039274,0.039341,0.039625,0.039745,0.039870,0.039998,0.040069,0.040096,0.040193,0.040321,0.040353,0.040496,0.040768,0.041071,0.041081,0.041193,0.041423,0.041548,0.041692,0.041709,0.041742,0.041815,0.041922,0.041933,0.041958,0.041980,0.042048,0.042468,0.042735,0.042834,0.042963,0.042969,0.043127,0.043283,0.043296,0.043637,0.043662,0.043743,0.043784,0.043837,0.043869,0.043942,0.044094,0.044238,0.044252,0.044303,0.044580,0.044622,0.044633,0.044778,0.045205,0.045511,0.046623,0.046750,0.047113,0.047588,0.047785,0.048017,0.051252
+b2_1h@ros_n_#	200	0.003125,0.003774,0.004255,0.004651,0.005000,0.005405,0.005714,0.005926,0.006250,0.006557,0.007273,0.007843,0.008333,0.008805,0.009302,0.009730,0.010169,0.010526,0.011002,0.011429,0.011765,0.012245,0.012698,0.013115,0.013636,0.014035,0.014504,0.014953,0.015385,0.015789,0.016216,0.016667,0.017073,0.017460,0.017834,0.018182,0.018627,0.019048,0.019403,0.019792,0.020163,0.020513,0.020958,0.021333,0.021687,0.022018,0.022388,0.022772,0.023166,0.023529,0.023894,0.024242,0.024599,0.025000,0.025316,0.025714,0.026131,0.026531,0.026901,0.027273,0.027633,0.028000,0.028333,0.028685,0.029029,0.029412,0.029703,0.030075,0.030418,0.030792,0.031169,0.031525,0.031858,0.032168,0.032494,0.032865,0.033198,0.033488,0.033822,0.034177,0.034568,0.034914,0.035255,0.035588,0.035866,0.036223,0.036563,0.036879,0.037209,0.037538,0.037903,0.038200,0.038496,0.038824,0.039144,0.039469,0.039851,0.040196,0.040460,0.040876,0.041219,0.041558,0.041877,0.042202,0.042515,0.042857,0.043206,0.043542,0.043850,0.044204,0.044474,0.044758,0.045104,0.045461,0.045767,0.046113,0.046424,0.046753,0.047131,0.047531,0.047879,0.048254,0.048560,0.048860,0.049169,0.049510,0.049880,0.050312,0.050683,0.051020,0.051348,0.051741,0.052146,0.052522,0.052830,0.053219,0.053534,0.053935,0.054307,0.054726,0.055118,0.055507,0.055922,0.056321,0.056723,0.057143,0.057624,0.058095,0.058585,0.059091,0.059459,0.059927,0.060412,0.060841,0.061278,0.061740,0.062231,0.062684,0.063158,0.063645,0.064286,0.064826,0.065419,0.065996,0.066551,0.067093,0.067571,0.068158,0.068778,0.069538,0.070051,0.070786,0.071345,0.072162,0.072928,0.073876,0.074620,0.075362,0.076201,0.077419,0.078566,0.079755,0.081127,0.082427,0.083680,0.085300,0.086957,0.088415,0.089744,0.092101,0.093888,0.096187,0.099106,0.102564,0.108006,0.114512,0.123488,0.132484,0.149333
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+b2_1h@share_cnt	195	0.069315,0.109861,0.138629,0.160944,0.179176,0.194591,0.207944,0.219722,0.230259,0.239790,0.248491,0.263906,0.270805,0.277259,0.283321,0.294444,0.299573,0.309104,0.313549,0.321888,0.325810,0.333220,0.340120,0.343399,0.349651,0.355535,0.361092,0.366356,0.371357,0.376120,0.382864,0.387120,0.391202,0.395124,0.400733,0.404305,0.409434,0.412713,0.417439,0.421951,0.424850,0.429046,0.433073,0.436945,0.440672,0.444265,0.447734,0.452179,0.455388,0.458497,0.462497,0.465396,0.469135,0.471850,0.475359,0.478749,0.482028,0.485203,0.488280,0.491265,0.494164,0.496981,0.499721,0.503044,0.505625,0.508760,0.511199,0.514749,0.517615,0.520401,0.523111,0.525750,0.528827,0.531321,0.533754,0.536129,0.538907,0.541165,0.543808,0.546383,0.549306,0.551745,0.554126,0.556834,0.559099,0.561677,0.563835,0.566296,0.568698,0.571043,0.573334,0.575890,0.578383,0.580814,0.583188,0.585220,0.587493,0.589715,0.591889,0.594542,0.596871,0.599645,0.601859,0.604025,0.606379,0.608677,0.610925,0.613340,0.615273,0.617587,0.619848,0.622059,0.624417,0.626720,0.629157,0.630992,0.633150,0.635437,0.637843,0.640192,0.642325,0.644254,0.646303,0.648616,0.650728,0.653088,0.655393,0.657508,0.659715,0.661874,0.664509,0.666441,0.668461,0.670686,0.672863,0.674993,0.676964,0.679010,0.681235,0.683195,0.685435,0.687316,0.689669,0.691771,0.693925,0.695940,0.698286,0.700579,0.702731,0.705272,0.707496,0.710003,0.712287,0.714520,0.716240,0.718690,0.720489,0.723273,0.725418,0.727379,0.729438,0.731588,0.734019,0.736771,0.739080,0.741638,0.743721,0.746623,0.748997,0.751643,0.754009,0.757147,0.759890,0.762754,0.766950,0.770165,0.772842,0.775662,0.779070,0.782844,0.787093,0.792190,0.795788,0.800803,0.804591,0.808764,0.812859,0.819506,0.825998,0.834640,0.843055,0.858054,0.872713,0.919401
+b4_3h@exp	200	0.069315,0.138629,0.160944,0.194591,0.230259,0.263906,0.294444,0.317805,0.343399,0.366356,0.389182,0.407754,0.424850,0.440672,0.455388,0.469135,0.480402,0.491998,0.503044,0.514166,0.524175,0.533754,0.542495,0.550939,0.558725,0.566643,0.573657,0.581114,0.588332,0.595324,0.602345,0.608677,0.615273,0.621461,0.627664,0.633328,0.639192,0.644889,0.650578,0.655962,0.661338,0.666696,0.671780,0.676849,0.681783,0.686693,0.691672,0.696791,0.701661,0.706305,0.710743,0.715618,0.720266,0.724494,0.728961,0.733368,0.737963,0.742297,0.746566,0.750769,0.754961,0.759035,0.762900,0.766809,0.770931,0.774716,0.778364,0.782244,0.786019,0.789841,0.793451,0.797109,0.800803,0.804559,0.807993,0.811522,0.814671,0.818116,0.821528,0.824722,0.827995,0.831312,0.834546,0.838046,0.841139,0.844269,0.847554,0.850633,0.853817,0.856713,0.859804,0.862605,0.865626,0.868592,0.871260,0.874146,0.877292,0.880087,0.882761,0.885652,0.888655,0.891584,0.894572,0.897196,0.899974,0.902714,0.905626,0.908410,0.911063,0.913626,0.915999,0.918881,0.921363,0.924223,0.926918,0.929817,0.932608,0.935279,0.937949,0.940443,0.942795,0.945352,0.947685,0.950122,0.952646,0.955216,0.958032,0.960616,0.963135,0.965535,0.968159,0.970911,0.973122,0.975400,0.978087,0.980306,0.982655,0.984755,0.986926,0.989258,0.991279,0.993523,0.995660,0.997455,0.999383,1.001288,1.003298,1.005018,1.007078,1.008910,1.010936,1.012747,1.014937,1.016938,1.018784,1.020474,1.022430,1.024338,1.026088,1.027701,1.029762,1.031659,1.033890,1.035863,1.037720,1.039528,1.041835,1.043899,1.046076,1.048114,1.050268,1.052441,1.055305,1.057974,1.060296,1.062922,1.065669,1.068787,1.072553,1.075596,1.078572,1.081249,1.084658,1.088334,1.092426,1.095352,1.100053,1.103763,1.108140,1.112603,1.117594,1.122913,1.127554,1.133590,1.139585,1.145853,1.156492,1.171957,1.205517
+b0_12h@rovn_#	200	0.000703,0.001221,0.001742,0.002257,0.002771,0.003311,0.003794,0.004248,0.004720,0.005155,0.005573,0.006006,0.006421,0.006750,0.007055,0.007364,0.007645,0.007913,0.008153,0.008403,0.008643,0.008837,0.009031,0.009217,0.009419,0.009606,0.009777,0.009929,0.010101,0.010255,0.010403,0.010550,0.010697,0.010851,0.011005,0.011152,0.011316,0.011465,0.011618,0.011764,0.011925,0.012071,0.012227,0.012377,0.012534,0.012691,0.012850,0.013010,0.013174,0.013321,0.013462,0.013621,0.013780,0.013937,0.014113,0.014281,0.014413,0.014567,0.014731,0.014905,0.015064,0.015217,0.015366,0.015523,0.015681,0.015849,0.016007,0.016184,0.016336,0.016506,0.016692,0.016871,0.017060,0.017238,0.017422,0.017604,0.017766,0.017942,0.018128,0.018317,0.018497,0.018709,0.018914,0.019112,0.019301,0.019483,0.019671,0.019897,0.020098,0.020293,0.020504,0.020715,0.020915,0.021132,0.021328,0.021496,0.021680,0.021881,0.022050,0.022227,0.022442,0.022641,0.022830,0.023037,0.023245,0.023443,0.023654,0.023850,0.024039,0.024229,0.024423,0.024653,0.024884,0.025052,0.025264,0.025413,0.025592,0.025811,0.026023,0.026251,0.026467,0.026676,0.026941,0.027157,0.027383,0.027604,0.027823,0.028065,0.028281,0.028528,0.028744,0.029018,0.029236,0.029458,0.029710,0.029945,0.030195,0.030426,0.030663,0.030913,0.031124,0.031356,0.031588,0.031831,0.032028,0.032258,0.032465,0.032723,0.033005,0.033248,0.033504,0.033786,0.034036,0.034317,0.034577,0.034865,0.035146,0.035392,0.035703,0.035975,0.036211,0.036498,0.036841,0.037079,0.037383,0.037740,0.038067,0.038457,0.038761,0.039123,0.039506,0.039885,0.040205,0.040674,0.041159,0.041599,0.042098,0.042636,0.043163,0.043763,0.044381,0.044873,0.045575,0.046261,0.046868,0.047622,0.048444,0.049187,0.050029,0.050998,0.051887,0.052864,0.054012,0.055165,0.056575,0.058756,0.062014,0.068971,0.081313
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+b7_1h@ros_one	197	0.007651,0.009875,0.011867,0.013710,0.016232,0.017876,0.019890,0.021355,0.022417,0.025295,0.025679,0.028541,0.030053,0.031019,0.032983,0.035814,0.036223,0.037360,0.040032,0.041702,0.043493,0.045586,0.047331,0.049809,0.051367,0.053096,0.055384,0.056711,0.058755,0.061490,0.061910,0.063224,0.065478,0.066786,0.069507,0.070925,0.071582,0.073432,0.075712,0.076674,0.078784,0.080960,0.081806,0.082642,0.085069,0.086107,0.088605,0.089082,0.090857,0.092447,0.094529,0.095073,0.096638,0.097193,0.097995,0.099997,0.101819,0.102609,0.104634,0.106279,0.107789,0.108960,0.110303,0.111773,0.113546,0.114817,0.116906,0.117618,0.118062,0.119992,0.120657,0.122514,0.124367,0.125407,0.126794,0.128125,0.129412,0.131245,0.132402,0.133540,0.134901,0.136636,0.137241,0.138277,0.140003,0.141629,0.142555,0.143840,0.145475,0.146308,0.147917,0.149294,0.150036,0.151087,0.152321,0.153295,0.154884,0.156007,0.157517,0.158276,0.159744,0.160956,0.162269,0.163447,0.164409,0.165618,0.166893,0.168213,0.169579,0.170919,0.171981,0.173229,0.174416,0.175933,0.177094,0.178379,0.179568,0.180792,0.181941,0.183188,0.184697,0.185692,0.186839,0.187956,0.189030,0.190445,0.191416,0.192387,0.193527,0.194792,0.195844,0.197286,0.198263,0.199405,0.200531,0.201896,0.203050,0.204489,0.205848,0.206543,0.206591,0.207655,0.208447,0.209956,0.211162,0.212309,0.213662,0.214877,0.215874,0.217122,0.218153,0.219543,0.220920,0.222163,0.223641,0.225156,0.226748,0.228183,0.229733,0.230930,0.232323,0.233840,0.235602,0.236790,0.238536,0.239854,0.241509,0.243195,0.244744,0.246571,0.248376,0.250255,0.252023,0.254218,0.256027,0.257924,0.260018,0.262040,0.263974,0.265983,0.268266,0.270738,0.273433,0.276159,0.278624,0.281397,0.284982,0.289114,0.293386,0.297521,0.301992,0.307814,0.313949,0.324034,0.342372,0.359755
+b8_3h@is_return_1	200	0.397029,0.451086,0.487520,0.508140,0.525227,0.546806,0.562402,0.576205,0.584644,0.592959,0.598645,0.604973,0.611368,0.618415,0.624611,0.630992,0.636303,0.642487,0.648311,0.654535,0.660800,0.667077,0.674524,0.680904,0.688755,0.695845,0.700397,0.703878,0.709340,0.713966,0.718159,0.720934,0.723993,0.729980,0.734472,0.739080,0.745008,0.751915,0.755591,0.759689,0.763240,0.765776,0.768018,0.772842,0.775061,0.777695,0.781035,0.782405,0.784228,0.785671,0.786596,0.788420,0.789915,0.792407,0.794980,0.797625,0.799396,0.801301,0.804495,0.805643,0.806966,0.808579,0.809895,0.812178,0.814148,0.815909,0.817808,0.819340,0.820904,0.822711,0.825297,0.827461,0.828577,0.830326,0.831825,0.833878,0.834664,0.835679,0.836591,0.839503,0.840447,0.842552,0.843966,0.845127,0.846590,0.848011,0.848921,0.849923,0.851218,0.852714,0.854013,0.855526,0.856579,0.857904,0.859174,0.861050,0.861650,0.862623,0.863693,0.864962,0.867505,0.868101,0.868946,0.869651,0.871424,0.872843,0.873697,0.874337,0.875731,0.877802,0.879498,0.880162,0.881996,0.882879,0.884837,0.886954,0.888184,0.889918,0.891288,0.893314,0.893801,0.894481,0.895519,0.895931,0.897436,0.898919,0.900675,0.902003,0.902750,0.904334,0.905777,0.906693,0.907567,0.908103,0.908308,0.909268,0.909515,0.910030,0.911537,0.912413,0.913335,0.914292,0.914484,0.916691,0.917378,0.918891,0.920533,0.921861,0.923132,0.924397,0.926075,0.927500,0.928182,0.931470,0.932625,0.934093,0.935054,0.935729,0.937535,0.938932,0.939997,0.941995,0.944034,0.946452,0.948174,0.948380,0.949785,0.951422,0.954738,0.956149,0.959560,0.962013,0.966852,0.967074,0.970449,0.972239,0.976847,0.980162,0.981820,0.987360,0.990703,0.996913,1.005186,1.009926,1.018690,1.021739,1.026664,1.027205,1.027970,1.030652,1.065888,1.069863,1.074398,1.087784,1.089988,1.094339,1.096244,1.101487,1.105678
+b2_3h@exp	200	0.109861,0.194591,0.239790,0.283321,0.325810,0.361092,0.393183,0.418965,0.441884,0.464439,0.480402,0.495583,0.510595,0.527300,0.544242,0.558350,0.571373,0.583481,0.594017,0.603548,0.612249,0.620859,0.628786,0.635957,0.643294,0.650877,0.658479,0.665415,0.672143,0.678784,0.685118,0.691473,0.697448,0.703351,0.708841,0.714520,0.719968,0.725418,0.730988,0.736074,0.741156,0.746221,0.751152,0.755904,0.760639,0.765634,0.770210,0.774760,0.779400,0.783834,0.788420,0.792949,0.797488,0.801863,0.806306,0.810832,0.814873,0.819008,0.822978,0.827257,0.831287,0.835137,0.838913,0.842661,0.846569,0.850269,0.853798,0.857622,0.861178,0.865085,0.868862,0.872307,0.875920,0.879118,0.882394,0.885680,0.888945,0.892306,0.895661,0.898695,0.901918,0.905392,0.908364,0.911910,0.915208,0.918163,0.921074,0.924077,0.926804,0.930100,0.933141,0.936143,0.938941,0.941662,0.944106,0.946877,0.949807,0.952697,0.955542,0.958252,0.961146,0.964154,0.967042,0.969935,0.972484,0.975580,0.978070,0.980494,0.983006,0.985498,0.988160,0.990798,0.993799,0.996515,0.999232,1.001922,1.004160,1.006748,1.009464,1.012122,1.014502,1.017011,1.019653,1.022045,1.024512,1.026914,1.029337,1.031606,1.034236,1.036788,1.038970,1.041607,1.044255,1.046536,1.049299,1.051905,1.054083,1.056134,1.058360,1.060366,1.062042,1.064235,1.065989,1.067865,1.069732,1.071890,1.074099,1.076013,1.078002,1.079671,1.081613,1.083423,1.085401,1.087180,1.089078,1.090751,1.092524,1.094369,1.096176,1.097987,1.099717,1.101935,1.103954,1.105873,1.108156,1.110075,1.111907,1.113541,1.115860,1.117952,1.120189,1.122783,1.125246,1.127902,1.130784,1.133423,1.135955,1.138588,1.141467,1.144843,1.148572,1.152188,1.155908,1.158918,1.162507,1.166222,1.170975,1.174217,1.178002,1.182529,1.188061,1.192674,1.196772,1.202819,1.208410,1.217008,1.225562,1.243715,1.276133
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+b5_3h@ros_one	187	0.011119,0.014865,0.017876,0.019890,0.022417,0.024179,0.025679,0.029358,0.030053,0.031019,0.035814,0.036223,0.040093,0.043257,0.045586,0.046964,0.051367,0.052368,0.056681,0.058365,0.061490,0.061499,0.063224,0.065556,0.068643,0.070925,0.071478,0.074590,0.076674,0.080656,0.081806,0.082217,0.085069,0.088605,0.088940,0.091998,0.094529,0.095549,0.096769,0.097459,0.100952,0.102123,0.106071,0.107789,0.108960,0.110337,0.113024,0.115562,0.117301,0.117618,0.118062,0.120045,0.120657,0.123414,0.125446,0.126805,0.128756,0.131535,0.132798,0.135935,0.136842,0.138118,0.138249,0.141644,0.142663,0.145475,0.146945,0.149212,0.150036,0.151662,0.152538,0.153973,0.156038,0.158217,0.158529,0.161079,0.162803,0.163604,0.165433,0.167465,0.168345,0.170908,0.172050,0.173240,0.175746,0.177094,0.179330,0.180747,0.182075,0.184810,0.185701,0.187552,0.187956,0.188775,0.190998,0.192234,0.193622,0.195324,0.197330,0.198910,0.199991,0.202375,0.203275,0.205383,0.206543,0.207245,0.207655,0.208224,0.210247,0.212122,0.213057,0.214630,0.215351,0.216874,0.218737,0.220337,0.221870,0.224011,0.225421,0.227230,0.229242,0.230720,0.231416,0.232189,0.233840,0.235609,0.236849,0.238725,0.240386,0.242228,0.244380,0.245592,0.247041,0.248753,0.250454,0.251719,0.253687,0.255194,0.256535,0.258230,0.260200,0.262235,0.263930,0.265641,0.267180,0.269271,0.270791,0.272613,0.274919,0.276574,0.278587,0.280504,0.282240,0.284550,0.286332,0.288330,0.290730,0.292369,0.294697,0.296876,0.299295,0.300636,0.302321,0.304800,0.306863,0.309350,0.312008,0.314271,0.317139,0.319886,0.323080,0.326030,0.329346,0.332581,0.336204,0.339917,0.342372,0.344891,0.349494,0.354197,0.358929,0.364843,0.373255,0.383198,0.395117,0.420561
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+b9_24h@ros_#	200	0.081705,0.087833,0.096131,0.107598,0.118940,0.126630,0.132983,0.135993,0.140533,0.145191,0.147949,0.149738,0.152779,0.154559,0.155440,0.158437,0.159746,0.161597,0.163589,0.165061,0.167758,0.169148,0.169866,0.171034,0.171611,0.172447,0.173574,0.175497,0.177250,0.179164,0.180558,0.182169,0.184172,0.185877,0.186911,0.187162,0.189008,0.189954,0.190666,0.191373,0.191610,0.192588,0.193713,0.195003,0.196193,0.197206,0.198348,0.199316,0.199977,0.201243,0.202202,0.203514,0.205461,0.207269,0.208131,0.209195,0.209551,0.210312,0.211613,0.212699,0.213728,0.214351,0.214782,0.215899,0.216635,0.217696,0.218331,0.218843,0.219309,0.220127,0.220590,0.221387,0.222055,0.222652,0.223548,0.224274,0.224830,0.225470,0.225837,0.226405,0.227055,0.227887,0.228767,0.229253,0.229710,0.230673,0.231559,0.232429,0.233010,0.233714,0.234432,0.235023,0.235891,0.236672,0.237178,0.237993,0.239229,0.240019,0.240744,0.241791,0.241982,0.242781,0.243209,0.244172,0.244768,0.245089,0.245802,0.246908,0.247522,0.248096,0.249059,0.250227,0.251470,0.252589,0.253561,0.256411,0.258890,0.260591,0.263176,0.265220,0.266875,0.268148,0.269901,0.271860,0.273488,0.275571,0.276642,0.278764,0.280302,0.282368,0.284137,0.287215,0.290195,0.293480,0.296513,0.299138,0.302218,0.304607,0.306033,0.307762,0.309878,0.313973,0.318778,0.322401,0.324679,0.326165,0.329222,0.330802,0.332465,0.334104,0.335412,0.336523,0.337807,0.338980,0.341055,0.342476,0.343751,0.344543,0.345777,0.346918,0.348264,0.348940,0.350684,0.351681,0.352650,0.354036,0.355099,0.356582,0.357581,0.358632,0.359586,0.360734,0.361785,0.363427,0.364691,0.365883,0.367207,0.368499,0.371185,0.374398,0.375723,0.378380,0.382339,0.384597,0.389497,0.393452,0.396265,0.400331,0.402933,0.405839,0.412218,0.419492,0.425531,0.431352,0.437665,0.443450,0.450751,0.466921,0.614658
+b0_1h@is_return_1	149	0.069315,0.109861,0.138629,0.160944,0.179176,0.194591,0.207944,0.219722,0.230259,0.239790,0.248491,0.256495,0.263906,0.270805,0.277259,0.283321,0.289037,0.294444,0.299573,0.304452,0.309104,0.313549,0.317805,0.321888,0.325810,0.329584,0.333220,0.336730,0.340120,0.343399,0.346574,0.349651,0.352636,0.355535,0.358352,0.361092,0.363759,0.366356,0.368888,0.371357,0.373767,0.376120,0.378419,0.380666,0.385015,0.387120,0.389182,0.391202,0.395124,0.397029,0.400733,0.402535,0.404305,0.407754,0.409434,0.412713,0.414313,0.417439,0.418965,0.421951,0.424850,0.426268,0.429046,0.431749,0.434381,0.436945,0.439445,0.441884,0.443082,0.446591,0.448864,0.451086,0.453260,0.456435,0.458497,0.461512,0.463473,0.466344,0.469135,0.471850,0.474493,0.477068,0.479579,0.482028,0.484419,0.485981,0.489035,0.491998,0.494164,0.496981,0.499721,0.502388,0.504986,0.507517,0.509987,0.512396,0.514166,0.516479,0.519296,0.521494,0.524175,0.526269,0.528320,0.530827,0.533272,0.535186,0.537528,0.540268,0.542935,0.545104,0.547646,0.550126,0.552545,0.555296,0.557595,0.559842,0.562762,0.565599,0.568698,0.572031,0.574939,0.577455,0.580212,0.583188,0.586930,0.590536,0.592959,0.595324,0.598896,0.602828,0.606146,0.609131,0.612687,0.616752,0.620254,0.626530,0.629895,0.634212,0.638012,0.643455,0.647389,0.652209,0.658755,0.665028,0.674052,0.683518,0.701392,0.743426
+b3_24h@share_cnt	144	0.069315,0.109861,0.138629,0.160944,0.179176,0.194591,0.207944,0.219722,0.230259,0.239790,0.248491,0.256495,0.263906,0.270805,0.277259,0.283321,0.289037,0.294444,0.299573,0.304452,0.309104,0.313549,0.317805,0.321888,0.325810,0.329584,0.333220,0.336730,0.340120,0.343399,0.346574,0.349651,0.352636,0.355535,0.358352,0.361092,0.363759,0.366356,0.368888,0.371357,0.373767,0.376120,0.378419,0.380666,0.382864,0.385015,0.387120,0.389182,0.391202,0.395124,0.397029,0.398898,0.400733,0.402535,0.404305,0.407754,0.409434,0.411087,0.412713,0.414313,0.415888,0.418965,0.420469,0.421951,0.424850,0.426268,0.427667,0.430407,0.431749,0.434381,0.435671,0.436945,0.439445,0.440672,0.443082,0.444265,0.446591,0.447734,0.449981,0.451086,0.453260,0.455388,0.457471,0.458497,0.460517,0.462497,0.464439,0.466344,0.468213,0.470048,0.471850,0.473620,0.475359,0.477068,0.479579,0.481218,0.483628,0.485981,0.488280,0.490527,0.491998,0.494876,0.496981,0.499043,0.501728,0.503044,0.504986,0.506890,0.508760,0.511199,0.512990,0.515329,0.517048,0.519296,0.522036,0.523644,0.526269,0.527811,0.530330,0.532788,0.535659,0.537064,0.539816,0.542053,0.544242,0.546383,0.548480,0.550939,0.552943,0.556068,0.558725,0.562040,0.564897,0.568017,0.572031,0.575574,0.577144,0.581114,0.585793,0.591889,0.598645,0.609357,0.624028
+b5_24h@return_n_uv	179	0.069315,0.109861,0.138629,0.160944,0.179176,0.194591,0.207944,0.219722,0.230259,0.239790,0.248491,0.256495,0.263906,0.270805,0.277259,0.283321,0.289037,0.294444,0.299573,0.304452,0.309104,0.317805,0.321888,0.325810,0.329584,0.333220,0.336730,0.340120,0.346574,0.349651,0.352636,0.358352,0.361092,0.366356,0.368888,0.373767,0.378419,0.380666,0.385015,0.389182,0.393183,0.397029,0.400733,0.404305,0.407754,0.411087,0.414313,0.418965,0.421951,0.424850,0.429046,0.431749,0.435671,0.439445,0.443082,0.445435,0.448864,0.452179,0.456435,0.459512,0.462497,0.466344,0.469135,0.472739,0.475359,0.478749,0.482028,0.485203,0.488280,0.491998,0.494876,0.498361,0.501064,0.504343,0.507517,0.510595,0.513580,0.516479,0.519850,0.522575,0.525750,0.528827,0.531812,0.534711,0.537528,0.540717,0.543808,0.546806,0.549717,0.552545,0.555683,0.558350,0.561677,0.564545,0.567675,0.570378,0.573334,0.576205,0.578996,0.582008,0.584932,0.587774,0.590536,0.593225,0.596101,0.598645,0.601372,0.604025,0.606843,0.609357,0.612030,0.614847,0.617379,0.620051,0.622654,0.625190,0.627852,0.630445,0.633150,0.635784,0.638519,0.641182,0.643935,0.646614,0.649224,0.651915,0.654535,0.657368,0.660259,0.662936,0.665544,0.668336,0.670930,0.673578,0.676388,0.679122,0.681674,0.684482,0.687213,0.689871,0.692756,0.695273,0.697915,0.700760,0.703615,0.706476,0.709506,0.712448,0.715540,0.718614,0.721671,0.724566,0.727863,0.731122,0.734343,0.737526,0.740792,0.744366,0.748099,0.752023,0.755904,0.760140,0.764779,0.769348,0.773805,0.779235,0.784581,0.790433,0.796346,0.803106,0.810380,0.817245,0.825166,0.833327,0.843229,0.855603,0.871735,0.898457
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+b9_24h@is_share	200	0.693828,0.753369,0.782124,0.808086,0.831140,0.849577,0.864699,0.877230,0.888696,0.897500,0.904900,0.912956,0.920794,0.927557,0.932510,0.939665,0.947278,0.953481,0.958679,0.962305,0.966720,0.970125,0.972860,0.978808,0.984559,0.993334,0.999725,1.003250,1.006113,1.009299,1.012835,1.015596,1.017805,1.019888,1.024576,1.028285,1.032751,1.036335,1.039400,1.043185,1.046806,1.048651,1.051200,1.053492,1.056988,1.061125,1.064559,1.066097,1.067362,1.068215,1.069451,1.070670,1.072303,1.074257,1.075411,1.076539,1.077480,1.078614,1.079111,1.079606,1.080098,1.080756,1.081728,1.082864,1.084367,1.085314,1.086215,1.087290,1.089193,1.090383,1.091638,1.092732,1.093765,1.094891,1.096031,1.097423,1.099134,1.100899,1.102337,1.103928,1.105750,1.107679,1.109693,1.111412,1.112336,1.113368,1.115045,1.116576,1.118485,1.119924,1.122178,1.124176,1.125037,1.125770,1.126203,1.126687,1.127304,1.128224,1.128827,1.129260,1.130757,1.132107,1.133662,1.134925,1.136031,1.137571,1.139281,1.139925,1.140841,1.141849,1.142310,1.143223,1.143672,1.144385,1.144759,1.145733,1.146802,1.147679,1.149212,1.151120,1.152296,1.153402,1.154559,1.155380,1.156476,1.157447,1.158109,1.158872,1.159873,1.160601,1.161128,1.161761,1.162346,1.162765,1.163305,1.163531,1.164026,1.164419,1.164830,1.165414,1.166653,1.167442,1.168709,1.170105,1.172320,1.175102,1.176426,1.177759,1.178988,1.180219,1.181389,1.182411,1.183404,1.185040,1.185784,1.189128,1.190839,1.194039,1.200019,1.204884,1.205345,1.206868,1.208989,1.210336,1.211068,1.211422,1.211625,1.212003,1.212289,1.212631,1.213135,1.214441,1.215394,1.215441,1.216007,1.216243,1.217079,1.218354,1.218917,1.219081,1.220522,1.221240,1.221598,1.222351,1.222856,1.224038,1.226604,1.232791,1.235626,1.237185,1.238540,1.246390,1.246728,1.248586,1.249349,1.250287,1.251711,1.251958,1.252316
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+b5_1h@return_n_uv	65	0.069315,0.109861,0.138629,0.160944,0.179176,0.194591,0.207944,0.219722,0.230259,0.239790,0.248491,0.256495,0.263906,0.270805,0.277259,0.283321,0.289037,0.294444,0.299573,0.304452,0.309104,0.313549,0.317805,0.321888,0.325810,0.329584,0.333220,0.336730,0.340120,0.343399,0.346574,0.349651,0.352636,0.355535,0.358352,0.361092,0.363759,0.366356,0.368888,0.371357,0.376120,0.378419,0.382864,0.385015,0.389182,0.393183,0.397029,0.398898,0.404305,0.407754,0.412713,0.417439,0.421951,0.427667,0.433073,0.438203,0.445435,0.452179,0.460517,0.468213,0.477912,0.489784,0.506260,0.530827
+b5_3h@ros_minus_#	171	0.006452,0.012500,0.012903,0.018182,0.018750,0.019355,0.023529,0.024242,0.025000,0.025806,0.028571,0.029412,0.030303,0.031250,0.032258,0.034286,0.035294,0.036364,0.037500,0.037838,0.038889,0.040000,0.041176,0.042424,0.043750,0.045161,0.046154,0.047368,0.048649,0.050000,0.051429,0.052941,0.054054,0.055556,0.057143,0.058065,0.059459,0.061111,0.062857,0.064516,0.065000,0.066667,0.068571,0.070270,0.071795,0.073171,0.075000,0.076471,0.078049,0.080000,0.081818,0.083333,0.085000,0.086957,0.088889,0.090476,0.092308,0.094444,0.095833,0.097561,0.100000,0.102222,0.103226,0.105556,0.107692,0.109524,0.111628,0.113636,0.115789,0.117647,0.120000,0.121951,0.124324,0.126316,0.128571,0.130612,0.133333,0.135000,0.137500,0.139623,0.142105,0.144444,0.146667,0.149020,0.151515,0.154167,0.156667,0.159091,0.161644,0.164103,0.166667,0.169231,0.171429,0.174359,0.177049,0.179592,0.182353,0.185075,0.187952,0.190769,0.193548,0.196040,0.200000,0.202500,0.204651,0.207843,0.210811,0.213793,0.216667,0.219672,0.222642,0.225641,0.228571,0.231884,0.235088,0.238462,0.241791,0.245070,0.248421,0.252000,0.255319,0.258824,0.262295,0.265625,0.269231,0.272727,0.276471,0.280556,0.284375,0.288235,0.292308,0.296183,0.300000,0.304000,0.308235,0.312644,0.316832,0.321622,0.326316,0.331034,0.335897,0.340845,0.346087,0.351282,0.356800,0.362500,0.368000,0.373684,0.379545,0.385366,0.391367,0.398507,0.405505,0.412903,0.420408,0.428777,0.437500,0.446429,0.455484,0.464865,0.475304,0.487179,0.500000,0.513978,0.531148,0.549296,0.571429,0.596610,0.634783,0.689299
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src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/calibration_user_fission.scala

@@ -0,0 +1,125 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.aliyun.odps.spark.examples.myUtils.{FissionFeature, MyDateUtils, MyHdfsUtils, ParamUtils}
+import org.apache.commons.lang.math.NumberUtils
+import org.apache.commons.lang3.StringUtils
+import org.apache.hadoop.io.compress.GzipCodec
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+import scala.io.Source
+
+
+object calibration_user_fission {
+  def main(args: Array[String]): Unit = {
+    // 1. 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val readPath = param.getOrElse("readPath", "/dw/recommend/model/general_model/fission/predict")
+    val beginStr = param.getOrElse("beginStr", "20260201")
+    val endStr = param.getOrElse("endStr", "20260201")
+    val clibFile = param.getOrElse("clibFile", "")
+    val repartition = param.getOrElse("repartition", "100").toInt
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/general_model/fission/calibration")
+
+    // 2. content
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 3. 加载数据
+    val clibData = loadData(clibFile)
+    val clibData_br = sc.broadcast(clibData)
+
+    // 4. 处理数据
+    val dateRange = MyDateUtils.getDateRange(beginStr, endStr)
+    for (dt <- dateRange) {
+      val partition = "%s".format(dt)
+      println("开始执行:" + partition)
+      val data = sc.textFile(readPath + "/" + partition)
+        .map(row => {
+          val cells = row.split("\t")
+          val label = cells(0).toDouble
+          val score = parseScore(cells(2)).toDouble
+          val hh = cells(3).toDouble
+          (label, score, hh)
+        })
+        .mapPartitions(row => {
+          val clibDataData = clibData_br.value
+          val endArray = clibDataData._1
+          val valArray = clibDataData._2
+          val coefArray = clibDataData._3
+          val result = new ArrayBuffer[String]()
+          row.foreach {
+            case (label, score, hh) =>
+              var value = -1.0
+              val pos = FissionFeature.bucket(endArray, score)
+              if (pos >= 0 && pos < coefArray.length) {
+                value = score * coefArray(pos)
+              }
+              result.add(label + "\t" + score + "\t[0," + value + "]\t" + "1" + "\t" + hh)
+          }
+          result.iterator
+        })
+
+      // 4. 保存数据到hdfs
+      val hdfsPath = savePath + "/" + partition
+      if (hdfsPath.nonEmpty && hdfsPath.startsWith("/dw/recommend/model/")) {
+        println("删除路径并开始数据写入:" + hdfsPath)
+        MyHdfsUtils.delete_hdfs_path(hdfsPath)
+        data.repartition(repartition).saveAsTextFile(hdfsPath, classOf[GzipCodec])
+      } else {
+        println("路径不合法，无法写入:" + hdfsPath)
+      }
+    }
+  }
+
+  private def loadData(clibFile: String): (Array[Double], Array[Double], Array[Double]) = {
+    val buffer = Source.fromFile(clibFile)
+    val lines = buffer.getLines().toList
+    buffer.close()
+
+    var index = 0
+    val endArray = new Array[Double](lines.size - 1)
+    val avgArray = new Array[Double](lines.size - 1)
+    val coefArray = new Array[Double](lines.size - 1)
+    for (line <- lines) {
+      if (index > 0) {
+        val cells: Array[String] = StringUtils.split(line, ',')
+        if (cells.length > 6) {
+          val end = NumberUtils.toDouble(cells(2), 0.0)
+          val pred = NumberUtils.toDouble(cells(3), 0.0)
+          val real = NumberUtils.toDouble(cells(6), 0.0)
+          endArray(index - 1) = end
+          avgArray(index - 1) = real
+          if (pred > 0) {
+            coefArray(index - 1) = real / pred
+          } else {
+            coefArray(index - 1) = 0
+          }
+        }
+      }
+      index += 1
+    }
+    endArray(endArray.length - 1) = 2.0
+    println("endArray.size=" + endArray.length)
+    println("avgArray.size=" + avgArray.length)
+    println("coefArray.size=" + coefArray.length)
+    println(endArray.mkString(","))
+    println(avgArray.mkString(","))
+    println(coefArray.mkString(","))
+    (endArray, avgArray, coefArray)
+  }
+
+  private def parseScore(data: String): String = {
+    if (data.nonEmpty) {
+      val pair = data.replace("[", "").replace("]", "").split(",")
+      if (pair.length > 1) {
+        return pair(1).toDouble.formatted("%.6f")
+      }
+    }
+    "-1"
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/case_bucket_sample.scala

@@ -0,0 +1,148 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.aliyun.odps.spark.examples.myUtils.{DataUtils, MyDateUtils, MyHdfsUtils, ParamUtils}
+import org.apache.hadoop.io.compress.GzipCodec
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+import scala.io.Source
+import scala.util.Random
+
+object case_bucket_sample {
+  def main(args: Array[String]): Unit = {
+    // 1. 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val readPath = param.getOrElse("readPath", "/dw/recommend/model/83_origin_data/")
+    val beginStr = param.getOrElse("beginStr", "20250317")
+    val endStr = param.getOrElse("endStr", "20250317")
+    val whatApps = param.getOrElse("whatApps", "0,4,5,21,3,6").split(",").toSet
+    val whatLabel = param.getOrElse("whatLabel", "is_return_n_noself")
+    val fuSampleRate = param.getOrElse("fuSampleRate", "-1.0").toDouble
+    val notUseBucket = param.getOrElse("notUseBucket", "0").toInt
+    val featureNameFile = param.getOrElse("featureName", "20250317_recsys_rov_name.txt")
+    val featureBucketFile = param.getOrElse("featureBucket", "20241209_recsys_nor_bucket.txt")
+    val repartition = param.getOrElse("repartition", "100").toInt
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/85_recsys_rov_train_data/")
+
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 2. 加载特征
+    val featureNameSet = loadFeatureNames(featureNameFile)
+    val featureBucketMap = loadFeatureBuckets(notUseBucket, featureBucketFile)
+    val bucketsMap_br = sc.broadcast(featureBucketMap)
+
+    // 3. 处理数据
+    val dateRange = MyDateUtils.getDateRange(beginStr, endStr)
+    for (date <- dateRange) {
+      println("开始执行:" + date)
+      val data = sc.textFile(readPath + "/" + date + "*").map(r => {
+          val rList = r.split("\t")
+          val logKey = rList(0)
+          val labelKey = rList(1)
+          val scoresMap = rList(2)
+          val featData = rList(3)
+          (logKey, labelKey, scoresMap, featData)
+        })
+        .filter {
+          case (logKey, labelKey, scoresMap, featData) =>
+            validData(logKey, whatApps)
+        }.filter {
+          case (logKey, labelKey, scoresMap, featData) =>
+            val label = DataUtils.parseLabel(labelKey, whatLabel).toDouble
+            label > 0 || new Random().nextDouble() <= fuSampleRate
+        }
+        .map {
+          case (logKey, labelKey, scoresMap, featData) =>
+            val label = DataUtils.parseLabel(labelKey, whatLabel).toDouble
+            val features = DataUtils.parseFeature(featData)
+            (logKey, label, scoresMap, features)
+        }
+        .mapPartitions(row => {
+          val result = new ArrayBuffer[String]()
+          val bucketsMap = bucketsMap_br.value
+          row.foreach {
+            case (logKey, label, scoresMap, features) =>
+              val featuresBucket = DataUtils.bucketFeature(featureNameSet, bucketsMap, features)
+              result.add(logKey + "\t" + label + "\t" + featuresBucket.mkString("\t"))
+          }
+          result.iterator
+        })
+
+      // 4. 保存数据到hdfs
+      val hdfsPath = savePath + "/" + date
+      if (hdfsPath.nonEmpty && hdfsPath.startsWith("/dw/recommend/model/")) {
+        println("删除路径并开始数据写入:" + hdfsPath)
+        MyHdfsUtils.delete_hdfs_path(hdfsPath)
+        data.repartition(repartition).saveAsTextFile(hdfsPath, classOf[GzipCodec])
+      } else {
+        println("路径不合法，无法写入:" + hdfsPath)
+      }
+    }
+  }
+
+  private def recommendFlow(flowPool: String): Boolean = {
+    if (flowPool.isEmpty || flowPool.endsWith("#1")) {
+      return true
+    }
+    false
+  }
+
+  private def validData(logKey: String, whatApps: Set[String]): Boolean = {
+    // apptype, page, pagesource, recommendpagetype, flowpool, abcode, mid, vid, level, ts
+    val cells = logKey.split(",")
+    val apptype = cells(0)
+    val page = cells(1)
+    //val pagesource = cells(2)
+    val recommendpagetype = cells(3)
+    val flowpool = cells(4)
+    if (whatApps.contains(apptype)) {
+      if (recommendFlow(flowpool)) {
+        if (page.equals("详情后沉浸页")) {
+          return true
+        } else if (page.equals("回流后沉浸页&内页feed")) {
+          return true
+        } else if (page.equals("首页feed")) {
+          return true
+        }
+      }
+    }
+    false
+  }
+
+  def loadFeatureNames(nameFile: String): Set[String] = {
+    val buffer = Source.fromFile(nameFile)
+    val names = buffer.getLines().mkString("\n")
+    buffer.close()
+    val featSet = names.split("\n")
+      .map(r => r.replace(" ", "").replaceAll("\n", ""))
+      .filter(r => r.nonEmpty)
+      .toSet
+    println("featSet.size=" + featSet.size)
+    println(featSet)
+    featSet
+  }
+
+  def loadFeatureBuckets(notUseBucket: Int, bucketFile: String): Map[String, (Double, Array[Double])] = {
+    if (notUseBucket > 0) {
+      return Map[String, (Double, Array[Double])]()
+    }
+    val buffer = Source.fromFile(bucketFile)
+    val buckets = buffer.getLines().mkString("\n")
+    buffer.close()
+    val bucketMap = buckets.split("\n")
+      .map(r => r.replace(" ", "").replaceAll("\n", ""))
+      .filter(r => r.nonEmpty)
+      .map(r => {
+        val rList = r.split("\t")
+        (rList(0), (rList(1).toDouble, rList(2).split(",").map(_.toDouble)))
+      }).toMap
+    println("bucketMap.size=" + bucketMap.size)
+    println(bucketMap)
+    bucketMap
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/case_sample.scala

@@ -0,0 +1,130 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.aliyun.odps.spark.examples.myUtils.{DataUtils, MyDateUtils, MyHdfsUtils, ParamUtils}
+import org.apache.hadoop.io.compress.GzipCodec
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+import scala.io.Source
+import scala.util.Random
+
+object case_sample {
+  def main(args: Array[String]): Unit = {
+    // 1. 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val readPath = param.getOrElse("readPath", "/dw/recommend/model/83_origin_data/")
+    val beginStr = param.getOrElse("beginStr", "20250317")
+    val endStr = param.getOrElse("endStr", "20250317")
+    val whatApps = param.getOrElse("whatApps", "0,4,5,21,3,6").split(",").toSet
+    val whatLabel = param.getOrElse("whatLabel", "is_return_n_noself")
+    val fuSampleRate = param.getOrElse("fuSampleRate", "-1.0").toDouble
+    val notUseBucket = param.getOrElse("notUseBucket", "0").toInt
+    val featureNameFile = param.getOrElse("featureName", "20250317_recsys_rov_name.txt")
+    val featureBucketFile = param.getOrElse("featureBucket", "20241209_recsys_nor_bucket.txt")
+    val repartition = param.getOrElse("repartition", "100").toInt
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/83_recsys_rov_train_data/")
+
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 2. 加载特征
+    val loader = getClass.getClassLoader
+    val featureNameSet = loadFeatureNames(featureNameFile)
+    val featureBucketMap = DataUtils.loadUseFeatureBuckets(loader, notUseBucket, featureBucketFile)
+    val bucketsMap_br = sc.broadcast(featureBucketMap)
+
+    // 3. 处理数据
+    val dateRange = MyDateUtils.getDateRange(beginStr, endStr)
+    for (date <- dateRange) {
+      println("开始执行:" + date)
+      val data = sc.textFile(readPath + "/" + date + "*").map(r => {
+          val rList = r.split("\t")
+          val logKey = rList(0)
+          val labelKey = rList(1)
+          val scoresMap = rList(2)
+          val featData = rList(3)
+          (logKey, labelKey, scoresMap, featData)
+        })
+        .filter {
+          case (logKey, labelKey, scoresMap, featData) =>
+            validData(logKey, whatApps)
+        }.filter {
+          case (logKey, labelKey, scoresMap, featData) =>
+            val label = DataUtils.parseLabel(labelKey, whatLabel).toDouble
+            label > 0 || new Random().nextDouble() <= fuSampleRate
+        }
+        .map {
+          case (logKey, labelKey, scoresMap, featData) =>
+            val label = DataUtils.parseLabel(labelKey, whatLabel).toDouble
+            val features = DataUtils.parseFeature(featData)
+            (logKey, label, scoresMap, features)
+        }
+        .mapPartitions(row => {
+          val result = new ArrayBuffer[String]()
+          val bucketsMap = bucketsMap_br.value
+          row.foreach {
+            case (logKey, label, scoresMap, features) =>
+              val featuresBucket = DataUtils.bucketFeature(featureNameSet, bucketsMap, features)
+              result.add(logKey + "\t" + label + "\t" + featuresBucket.mkString("\t"))
+          }
+          result.iterator
+        })
+
+      // 4. 保存数据到hdfs
+      val hdfsPath = savePath + "/" + date
+      if (hdfsPath.nonEmpty && hdfsPath.startsWith("/dw/recommend/model/")) {
+        println("删除路径并开始数据写入:" + hdfsPath)
+        MyHdfsUtils.delete_hdfs_path(hdfsPath)
+        data.repartition(repartition).saveAsTextFile(hdfsPath, classOf[GzipCodec])
+      } else {
+        println("路径不合法，无法写入:" + hdfsPath)
+      }
+    }
+  }
+
+  private def recommendFlow(flowPool: String): Boolean = {
+    if (flowPool.isEmpty || flowPool.endsWith("#1")) {
+      return true
+    }
+    false
+  }
+
+  private def validData(logKey: String, whatApps: Set[String]): Boolean = {
+    // apptype, page, pagesource, recommendpagetype, flowpool, abcode, mid, vid, level, ts
+    val cells = logKey.split(",")
+    val apptype = cells(0)
+    val page = cells(1)
+    //val pagesource = cells(2)
+    val recommendpagetype = cells(3)
+    val flowpool = cells(4)
+    if (whatApps.contains(apptype)) {
+      if (recommendFlow(flowpool)) {
+        if (page.equals("详情后沉浸页")) {
+          return true
+        } else if (page.equals("回流后沉浸页&内页feed")) {
+          return true
+        } else if (page.equals("首页feed")) {
+          return true
+        }
+      }
+    }
+    false
+  }
+
+  def loadFeatureNames(nameFile: String): Set[String] = {
+    val buffer = Source.fromFile(nameFile)
+    val names = buffer.getLines().mkString("\n")
+    buffer.close()
+    val featSet = names.split("\n")
+      .map(r => r.replace(" ", "").replaceAll("\n", ""))
+      .filter(r => r.nonEmpty)
+      .toSet
+    println("featSet.size=" + featSet.size)
+    println(featSet)
+    featSet
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/case_value_range.scala

@@ -0,0 +1,114 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.aliyun.odps.spark.examples.myUtils.{MyDateUtils, MyHdfsUtils, ParamUtils}
+import org.apache.commons.lang.math.NumberUtils
+import org.apache.commons.lang3.StringUtils
+import org.apache.hadoop.io.compress.GzipCodec
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+
+object case_value_range {
+  def main(args: Array[String]): Unit = {
+    // 1. 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val readPath = param.getOrElse("readPath", "/dw/recommend/model/83_recsys_rov_train_data/")
+    val beginStr = param.getOrElse("beginStr", "20250317")
+    val endStr = param.getOrElse("endStr", "20250317")
+    val useMid = param.getOrElse("useMid", "0").toInt
+    val useVid = param.getOrElse("useVid", "0").toInt
+    val repartition = param.getOrElse("repartition", "10").toInt
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/83_recsys_analysis_data/")
+
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 2. 处理数据
+    val dateRange = MyDateUtils.getDateRange(beginStr, endStr)
+    val pathArray = getPathArray(readPath, dateRange)
+    val data = sc.textFile(pathArray.mkString(","))
+      .map(r => {
+        // logKey + "\t" + k:v + "\t" + k:v ...
+        parseFeature(r, useMid, useVid)
+      })
+      .flatMap(features => {
+        features.map {
+          case (key, value) =>
+            (key, value)
+        }
+      })
+
+    // 3. feat min
+    val minRdd = data.reduceByKey((a, b) => if (a < b) a else b)
+
+    // 4. feat max
+    val maxRdd = data.reduceByKey((a, b) => if (a > b) a else b)
+
+    // 4. feat sum
+    val sumRdd = data.reduceByKey((a, b) => a + b)
+
+    // 6. feat cnt
+    val cntRdd = data.map(r => (r._1, 1)).reduceByKey((a, b) => a + b)
+
+    // 7. join
+    val result = minRdd
+      .leftOuterJoin(maxRdd)
+      .leftOuterJoin(cntRdd)
+      .leftOuterJoin(sumRdd)
+      .map(raw => {
+        // (String, (((Double, Option[Double]), Option[Int]), Option[Double]))
+        val key = raw._1
+        val featMin = raw._2._1._1._1
+        val featMax = raw._2._1._1._2.getOrElse(0)
+        val featCnt = raw._2._1._2.getOrElse(0)
+        val featSum = raw._2._2.getOrElse(0)
+        key + "\t" + featMin.toString + "\t" + featMax.toString + "\t" + featCnt.toString + "\t" + featSum.toString
+      })
+
+    // 8. 保存数据到hdfs
+    val hdfsPath = savePath + "/" + dateRange.get(dateRange.size - 1)
+    if (hdfsPath.nonEmpty && hdfsPath.startsWith("/dw/recommend/model/")) {
+      println("删除路径并开始数据写入:" + hdfsPath)
+      MyHdfsUtils.delete_hdfs_path(hdfsPath)
+      result.repartition(repartition).saveAsTextFile(hdfsPath, classOf[GzipCodec])
+    } else {
+      println("路径不合法，无法写入:" + hdfsPath)
+    }
+  }
+
+  private def parseFeature(data: String, useMid: Int, useVid: Int): scala.collection.mutable.Map[String, Double] = {
+    val features = scala.collection.mutable.Map[String, Double]()
+    if (data.nonEmpty) {
+      val cells = data.split("\t")
+      val logKeys = cells(0).split(",")
+      val mid = logKeys(6)
+      val vid = logKeys(7)
+      for (i <- 2 until cells.length) {
+        val fv: Array[String] = StringUtils.split(cells(i), ':')
+        var prefix = ""
+        if (useMid > 0 && useVid > 0) {
+          prefix = mid + "#" + vid + "#"
+        } else if (useMid > 0) {
+          prefix = mid + "#"
+        } else if (useVid > 0) {
+          prefix = vid + "#"
+        }
+        features.put(prefix + fv(0), NumberUtils.toDouble(fv(1), 0.0))
+      }
+    }
+    features
+  }
+
+  def getPathArray(basePath: String, dateRange: ArrayBuffer[String]): ArrayBuffer[String] = {
+    val pathArray = new ArrayBuffer[String]
+    for (date <- dateRange) {
+      val path = basePath + "/" + date + "*"
+      pathArray += path
+    }
+    pathArray
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/check_case.scala

@@ -0,0 +1,191 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.aliyun.odps.data.Record
+import com.aliyun.odps.spark.examples.myUtils.DataUtils.getStringValue
+import com.aliyun.odps.spark.examples.myUtils._
+import examples.utils.SimilarityUtils
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+
+/*
+   20250317 提取特征
+ */
+
+object check_case {
+  private val labelNames = List(
+    "is_share", "share_cnt",
+    "is_return_1", "return_1_uv",
+    "is_return_n", "return_n_uv",
+    "is_return_noself", "return_1_uv_noself",
+    "is_return_n_noself", "return_n_uv_noself"
+  )
+
+  private def parseVideoRdd(videoRdd: RDD[Record]): RDD[(String, java.util.Map[String, String])] = {
+    videoRdd
+      .map(record => {
+        val vid = record.getString("vid")
+        val feature = ConvertUtils.getRecordCol(record, "feature")
+        feature.put("vid", vid)
+        (vid, feature)
+      })
+      .reduceByKey((a, b) => if (a.size() > b.size()) a else b)
+  }
+
+  private def joinVideoStat(logRdd: RDD[java.util.Map[String, String]], videoStat: RDD[(String, String)]): RDD[java.util.Map[String, String]] = {
+    logRdd
+      .map(raw => {
+        val vid = raw.getOrElse("vid", "")
+        (vid, raw)
+      })
+      .leftOuterJoin(videoStat)
+      .map(raw => {
+        val info = raw._2._1
+        if (raw._2._2.isDefined) {
+          info.put("b0_feature", raw._2._2.get)
+        }
+        info
+      })
+  }
+
+  private def getVidMidRdd(logRdd: RDD[java.util.Map[String, String]]): RDD[(String, String)] = {
+    logRdd
+      .map(raw => {
+        val mid = raw.getOrElse("mid", "")
+        val c9 = raw.getOrElse("c9_feature", "")
+        (mid, c9)
+      })
+      .filter(_._1.nonEmpty)
+      .reduceByKey((a, b) => a)
+      .flatMap(raw => {
+        val result = new ArrayBuffer[(String, String)]
+        for (hVid <- ConvertUtils.getVidList(raw._2)) {
+          result += ((hVid, raw._1)) // (vid, mid)
+        }
+        result
+      })
+  }
+
+  private def getMidSeqRdd(vidMidRdd: RDD[(String, String)], videoRdd: RDD[(String, java.util.Map[String, String])]): RDD[(String, List[java.util.Map[String, String]])] = {
+    vidMidRdd
+      .join(videoRdd)
+      .map(raw => {
+        (raw._2._1, raw._2._2)
+      })
+      .groupByKey()
+      .map(raw => {
+        (raw._1, raw._2.toList)
+      })
+  }
+
+  private def joinMidSeq(logRdd: RDD[java.util.Map[String, String]], midSeqRdd: RDD[(String, List[java.util.Map[String, String]])]): RDD[(java.util.Map[String, String], List[java.util.Map[String, String]])] = {
+    logRdd
+      .map(raw => {
+        val mid = raw.getOrElse("mid", "")
+        (mid, raw)
+      })
+      .leftOuterJoin(midSeqRdd)
+      .map(raw => {
+        if (raw._2._2.isDefined) {
+          (raw._2._1, raw._2._2.get)
+        } else {
+          (raw._2._1, List())
+        }
+      })
+  }
+
+  private def getFeature(rdd: RDD[(java.util.Map[String, String], List[java.util.Map[String, String]])]): RDD[String] = {
+    rdd.mapPartitions(partition => {
+      SimilarityUtils.init()
+      partition.map(raw => {
+        val record = raw._1
+        val videoSeq = raw._2
+        val logKey = DataUtils.getLogKey(record)
+        val labels = DataUtils.getLabels(labelNames, record).toString
+        val features = ConvertV2.getFeature(record, videoSeq, 6).toString
+        val scoresMap = DataUtils.getSubJson(record, "extend_alg", "scoresMap").toString
+        logKey + "\t" + labels + "\t" + scoresMap + "\t" + features
+      })
+    })
+  }
+
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 1. 解析参数
+    val param = ParamUtils.parseArgs(args)
+    val project = param.getOrElse("project", "loghubods")
+    val table = param.getOrElse("table", "dwd_recsys_alg_sample_all_20250212")
+    val tablePart = param.getOrElse("tablePart", "64").toInt
+    val beginStr = param.getOrElse("beginStr", "2025031700")
+    val endStr = param.getOrElse("endStr", "2025031700")
+    val whatLabel = param.getOrElse("whatLabel", "is_return_noself")
+    val fuSampleRate = param.getOrElse("fuSampleRate", "0.03").toDouble
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/83_origin_data/")
+    val repartition = param.getOrElse("repartition", "64").toInt
+
+    // 2. 数据处理
+    val timeRange = MyDateUtils.getDateHourRange(beginStr, endStr)
+    for (dt_hh <- timeRange) {
+      // 2.1 分区
+      val dt = dt_hh.substring(0, 8)
+      val hh = dt_hh.substring(8, 10)
+      val partition = "dt=%s,hh=%s".format(dt, hh)
+      println("开始执行partition:" + partition)
+
+      // 2.2 加载视频信息
+      val originVideo = DataUtils.getODPSData(sc, project, "alg_recsys_feature_user_behavior_video", partition, tablePart)
+      val uniqVideo = parseVideoRdd(originVideo)
+
+      // 2.3 加载视频统计信息
+      val videoStat = DataUtils.getODPSData(sc, project, "alg_recsys_feature_video_clean_stat", partition, tablePart)
+        .map(record => {
+          val vid = record.getString("vid")
+          val feature = record.getString("feature")
+          (vid, feature)
+        })
+
+      // 2.4 加载样本数据
+      val odpsData = DataUtils.getODPSData(sc, project, table, partition, tablePart)
+
+      // 2.5 样本重采样
+      val filterColumns = Set("allfeaturemap", "metafeaturemap")
+      val vidSet = Set("52841732", "51696080")
+      val resampleData = odpsData
+        .filter(record => {
+          val vid = getStringValue(record, "vid", "0")
+          val apptype = getStringValue(record, "apptype", "0")
+          vidSet.contains(vid) && apptype.equals("4")
+        })
+        .map(record => {
+          ConvertUtils.record2Map(record, filterColumns)
+        })
+
+      // 2.6 join video stat
+      val sampleData = joinVideoStat(resampleData, videoStat)
+
+      // 2.7 get vid mid rdd
+      val vidMidRdd = getVidMidRdd(sampleData)
+
+      // 2.8 get mid seq rdd
+      val midSeqRdd = getMidSeqRdd(vidMidRdd, uniqVideo)
+
+      // 2.9 历史行为关联video
+      val seqSampleData = joinMidSeq(sampleData, midSeqRdd)
+
+      // 2.10 特征转换
+      val featureData = getFeature(seqSampleData)
+
+      // 2.11 保存数据
+      val hdfsPath = "%s/%s%s".format(savePath, dt, hh)
+      DataUtils.saveData(featureData, hdfsPath, repartition)
+    }
+  }
+}
+

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/dnn_feature_stat.scala

@@ -0,0 +1,78 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.alibaba.fastjson.JSON
+import com.aliyun.odps.spark.examples.myUtils.{MyDateUtils, MyHdfsUtils, ParamUtils}
+import org.apache.hadoop.io.compress.GzipCodec
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+
+object dnn_feature_stat {
+  def main(args: Array[String]): Unit = {
+    // 1. 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val readPath = param.getOrElse("readPath", "/dw/recommend/model/70_origin_data/")
+    val beginStr = param.getOrElse("beginStr", "20250317")
+    val endStr = param.getOrElse("endStr", "20250317")
+    val repartition = param.getOrElse("repartition", "10").toInt
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/70_recsys_feature/")
+
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 2. 处理数据
+    val dateRange = MyDateUtils.getDateRange(beginStr, endStr)
+    val pathArray = getPathArray(readPath, dateRange)
+    val data = sc.textFile(pathArray.mkString(","))
+      .map(r => {
+        // logKey + "\t" + labelKey + "\t" + scoresMap + "\t" + featureKey
+        val rList = r.split("\t")
+        val featData = rList(3)
+        parseFeature(featData)
+      })
+      .flatMap(features => {
+        features.map {
+          case (key, value) =>
+            (key, 1)
+        }
+      })
+      .reduceByKey((a, b) => a + b)
+      .map(raw => {
+        raw._1 + "\t" + raw._2.toString
+      })
+
+    // 3. 保存数据到hdfs
+    val hdfsPath = savePath + "/" + dateRange.get(dateRange.size - 1)
+    if (hdfsPath.nonEmpty && hdfsPath.startsWith("/dw/recommend/model/")) {
+      println("删除路径并开始数据写入:" + hdfsPath)
+      MyHdfsUtils.delete_hdfs_path(hdfsPath)
+      data.repartition(repartition).saveAsTextFile(hdfsPath, classOf[GzipCodec])
+    } else {
+      println("路径不合法，无法写入:" + hdfsPath)
+    }
+  }
+
+  private def parseFeature(data: String): scala.collection.mutable.Map[String, Object] = {
+    val features = scala.collection.mutable.Map[String, Object]()
+    if (data.nonEmpty) {
+      val obj = JSON.parseObject(data)
+      obj.foreach(r => {
+	features.put(r._1, r._2)
+      })
+    }
+    features
+  }
+
+  def getPathArray(basePath: String, dateRange: ArrayBuffer[String]): ArrayBuffer[String] = {
+    val pathArray = new ArrayBuffer[String]
+    for (date <- dateRange) {
+      val path = basePath + "/" + date + "*"
+      pathArray += path
+    }
+    pathArray
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/feature_bucket.scala

@@ -0,0 +1,104 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.alibaba.fastjson.JSON
+import com.aliyun.odps.spark.examples.myUtils.{MyHdfsUtils, ParamUtils}
+import org.apache.commons.lang3.StringUtils
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.{DataFrame, SparkSession}
+
+import java.util
+import scala.collection.JavaConversions.mapAsScalaMap
+import scala.collection.mutable.ArrayBuffer
+import scala.io.Source
+
+
+object feature_bucket {
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 1.param
+    val param = ParamUtils.parseArgs(args)
+    val dataPath = param.getOrElse("dataPath", "/dw/recommend/model/61_recsys_nor_train_data/20241210")
+    val featureIndex = param.getOrElse("featureIndex", "2").toInt
+    val featureFile = param.getOrElse("featureFile", "20241209_recsys_nor_name.txt")
+    val bucketNum = param.getOrElse("bucketNum", "100").toInt
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/user_profile/data/")
+
+    // 2.feature name
+    val featureNameSet = loadFeatureNames(featureFile)
+
+    // 3.load data
+    val df = loadDataFrame(
+      spark,
+      featureIndex,
+      featureNameSet,
+      sc.textFile(dataPath)
+    )
+
+    // 4. quantiles
+    val probs = getProbs(bucketNum)
+    val quantileDF = df
+      .groupBy("name")
+      .agg(expr(s"approx_percentile(value, array(0.1, 0.2)) as quantiles"))
+    quantileDF.show(100)
+
+    // 5. save
+    val hdfsPath = savePath
+    if (hdfsPath.nonEmpty && hdfsPath.startsWith("/dw/recommend/model/")) {
+      println("删除路径并开始数据写入:" + hdfsPath)
+      MyHdfsUtils.delete_hdfs_path(hdfsPath)
+      quantileDF.write.format("csv").option("header", "true").save(hdfsPath)
+    } else {
+      println("路径不合法，无法写入:" + hdfsPath)
+    }
+  }
+
+  def loadFeatureNames(nameFile: String): Set[String] = {
+    val buffer = Source.fromFile(nameFile)
+    val names = buffer.getLines().mkString("\n")
+    buffer.close()
+    val featSet = names.split("\n")
+      .map(r => r.replace(" ", "").replaceAll("\n", ""))
+      .filter(r => r.nonEmpty)
+      .toSet
+    println("featSet.size=" + featSet.size)
+    println(featSet)
+    featSet
+  }
+
+  def getProbs(bucketNum: Int): Array[Double] = {
+    val arr = ArrayBuffer[Double]()
+    for (i <- 1 until bucketNum) {
+      val prob = 1.0 * i / bucketNum
+      arr += prob
+    }
+    arr.toArray
+  }
+
+  def loadDataFrame(spark: SparkSession, featureIndex: Int, featureNameSet: Set[String], data: RDD[String]): DataFrame = {
+    import spark.implicits._
+    data
+      .flatMap(line => {
+        val map: util.Map[String, Double] = new util.HashMap[String, Double]
+        val cells = StringUtils.split(line, '\t')
+        if (cells.length > featureIndex) {
+          val data = cells(featureIndex)
+          if (data.nonEmpty) {
+            val obj = JSON.parseObject(data)
+            obj.foreach(r => {
+              if (featureNameSet.contains(r._1)) {
+                map.put(r._1, obj.getDoubleValue(r._1))
+              }
+            })
+          }
+        }
+        map
+      })
+      .toDF("name", "value")
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/feature_value_range.scala

@@ -0,0 +1,102 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.aliyun.odps.spark.examples.myUtils.{MyDateUtils, MyHdfsUtils, ParamUtils}
+import org.apache.commons.lang.math.NumberUtils
+import org.apache.commons.lang3.StringUtils
+import org.apache.hadoop.io.compress.GzipCodec
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+
+object feature_value_range {
+  def main(args: Array[String]): Unit = {
+    // 1. 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val readPath = param.getOrElse("readPath", "/dw/recommend/model/83_recsys_rov_train_data/")
+    val beginStr = param.getOrElse("beginStr", "20250317")
+    val endStr = param.getOrElse("endStr", "20250317")
+    val repartition = param.getOrElse("repartition", "10").toInt
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/83_recsys_analysis_data/")
+
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 2. 处理数据
+    val dateRange = MyDateUtils.getDateRange(beginStr, endStr)
+    val pathArray = getPathArray(readPath, dateRange)
+    val data = sc.textFile(pathArray.mkString(","))
+      .map(r => {
+        // logKey + "\t" + k:v + "\t" + k:v ...
+        parseFeature(r)
+      })
+      .flatMap(features => {
+        features.map {
+          case (key, value) =>
+            (key, value)
+        }
+      })
+
+    // 3. feat min
+    val minRdd = data.reduceByKey((a, b) => if (a < b) a else b)
+
+    // 4. feat max
+    val maxRdd = data.reduceByKey((a, b) => if (a > b) a else b)
+
+    // 5. feat sum
+    val sumRdd = data.reduceByKey((a, b) => a + b)
+
+    // 6. feat cnt
+    val cntRdd = data.map(r => (r._1, 1)).reduceByKey((a, b) => a + b)
+
+    // 7. join
+    val result = minRdd
+      .leftOuterJoin(maxRdd)
+      .leftOuterJoin(cntRdd)
+      .leftOuterJoin(sumRdd)
+      .map(raw => {
+        val key = raw._1
+        val featMin = raw._2._1._1._1
+        val featMax = raw._2._1._1._2.getOrElse(0)
+        val featCnt = raw._2._1._2.getOrElse(0)
+        val featSum = raw._2._2.getOrElse(0)
+        key + "\t" + featMin.toString + "\t" + featMax.toString + "\t" + featCnt.toString + "\t" + featSum.toString
+      })
+
+    // 8. 保存数据到hdfs
+    val hdfsPath = savePath + "/" + dateRange.get(dateRange.size - 1)
+    if (hdfsPath.nonEmpty && hdfsPath.startsWith("/dw/recommend/model/")) {
+      println("删除路径并开始数据写入:" + hdfsPath)
+      MyHdfsUtils.delete_hdfs_path(hdfsPath)
+      result.repartition(repartition).saveAsTextFile(hdfsPath, classOf[GzipCodec])
+    } else {
+      println("路径不合法，无法写入:" + hdfsPath)
+    }
+  }
+
+  private def parseFeature(data: String): scala.collection.mutable.Map[String, Double] = {
+    val features = scala.collection.mutable.Map[String, Double]()
+    if (data.nonEmpty) {
+      val cells = data.split("\t")
+      for (i <- 1 until cells.length) {
+        val fv: Array[String] = StringUtils.split(cells(i), ':')
+        if (fv.length == 2) {
+          features.put(fv(0), NumberUtils.toDouble(fv(1), 0.0))
+        }
+      }
+    }
+    features
+  }
+
+  def getPathArray(basePath: String, dateRange: ArrayBuffer[String]): ArrayBuffer[String] = {
+    val pathArray = new ArrayBuffer[String]
+    for (date <- dateRange) {
+      val path = basePath + "/" + date + "*"
+      pathArray += path
+    }
+    pathArray
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/gender_bucket_feature.scala

@@ -0,0 +1,135 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.aliyun.odps.spark.examples.myUtils.{MyHdfsUtils, ParamUtils}
+import examples.extractor.ExtractorUtils
+import org.apache.commons.lang.math.NumberUtils
+import org.apache.commons.lang3.StringUtils
+import org.apache.hadoop.io.compress.GzipCodec
+import org.apache.spark.sql.SparkSession
+
+import java.util
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+import scala.io.Source
+
+
+object gender_bucket_feature {
+  def main(args: Array[String]): Unit = {
+    // 1. 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val readPath = param.getOrElse("readPath", "/dw/recommend/model/user_profile/gender/sample/train/")
+    val year = param.getOrElse("year", "2025")
+    val suffixSet = param.getOrElse("suffix", "y,8,4,0,e,a,c,k,o,w,g,s,u,q,i,m").split(",").toSet
+    val featureFile = param.getOrElse("featureFile", "20241209_recsys_nor_name.txt")
+    val featureBucketFile = param.getOrElse("featureBucket", "20241209_recsys_nor_bucket.txt")
+    val repartition = param.getOrElse("repartition", "100").toInt
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/user_profile/gender/analysis")
+
+    // 2. content
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 3. 处理数据
+    val featureSet = loadFeatureNames(featureFile)
+    val featureBucketMap = loadUseFeatureBuckets(featureBucketFile)
+    val bucketsMap_br = sc.broadcast(featureBucketMap)
+    for (suffix <- suffixSet) {
+      val partition = "%s_%s".format(year, suffix)
+      println("开始执行:" + partition)
+      val data = sc.textFile(readPath + "/" + partition + "*").map(row => {
+          val cells: Array[String] = StringUtils.split(row, '\t')
+          val mid = cells(0)
+          val label = NumberUtils.toInt(cells(1))
+          val features: util.Map[String, Double] = new util.HashMap[String, Double]
+          for (i <- 2 until cells.length) {
+            val fv: Array[String] = StringUtils.split(cells(i), ':')
+            features.put(fv(0), NumberUtils.toDouble(fv(1), 0.0))
+          }
+          (mid, label, features)
+        })
+        .mapPartitions(row => {
+          val result = new ArrayBuffer[String]()
+          row.foreach {
+            case (mid, label, features) =>
+              val bucketsMap = bucketsMap_br.value
+              val featuresBucket = bucketFeature(featureSet, bucketsMap, features)
+              result.add(mid + "\t" + label + "\t" + featuresBucket.mkString("\t"))
+          }
+          result.iterator
+        })
+
+      // 4. 保存数据到hdfs
+      val hdfsPath = savePath + "/" + partition
+      if (hdfsPath.nonEmpty && hdfsPath.startsWith("/dw/recommend/model/")) {
+        println("删除路径并开始数据写入:" + hdfsPath)
+        MyHdfsUtils.delete_hdfs_path(hdfsPath)
+        data.repartition(repartition).saveAsTextFile(hdfsPath, classOf[GzipCodec])
+      } else {
+        println("路径不合法，无法写入:" + hdfsPath)
+      }
+    }
+  }
+
+  def loadFeatureNames(nameFile: String): Set[String] = {
+    val buffer = Source.fromFile(nameFile)
+    val names = buffer.getLines().mkString("\n")
+    buffer.close()
+    val featSet = names.split("\n")
+      .map(r => r.replace(" ", "").replaceAll("\n", ""))
+      .filter(r => r.nonEmpty)
+      .toSet
+    println("featSet.size=" + featSet.size)
+    println(featSet)
+    featSet
+  }
+
+  private def loadUseFeatureBuckets(bucketFile: String): Map[String, (Double, Array[Double])] = {
+    val buffer = Source.fromFile(bucketFile)
+    val lines = buffer.getLines().mkString("\n")
+    buffer.close()
+    val bucketMap = lines.split("\n")
+      .map(r => r.replace(" ", "").replaceAll("\n", ""))
+      .filter(r => r.nonEmpty)
+      .map(r => {
+        val rList = r.split("\t")
+        (rList(0), (rList(1).toDouble, rList(2).split(",").map(_.toDouble)))
+      }).toMap
+    println("bucketMap.size=" + bucketMap.size)
+    println(bucketMap)
+    bucketMap
+  }
+
+  def bucketFeature(nameSet: Set[String], bucketMap: Map[String, (Double, Array[Double])], features: scala.collection.mutable.Map[String, Double]): Iterable[String] = {
+    features.map {
+      case (name, score) =>
+        if (!nameSet.contains(name)) {
+          ""
+        } else {
+          if (score > 1E-8) {
+            if (bucketMap.contains(name)) {
+              val (bucketsNum, buckets) = bucketMap(name)
+              if (score <= buckets(0)) {
+                "%s:%.6f:%.6f".format(name, score, buckets(0))
+              } else if (score > buckets.last) {
+                "%s:%.6f:%.6f+".format(name, score, buckets.last)
+              } else {
+                val index = ExtractorUtils.findInsertPosition(buckets, score)
+                if (index >= buckets.length) {
+                  "%s:%.6f:%.6f+".format(name, score, buckets.last)
+                } else {
+                  "%s:%.6f:%.6f".format(name, score, buckets(index))
+                }
+              }
+            } else {
+              ""
+            }
+          } else {
+            ""
+          }
+        }
+    }.filter(_.nonEmpty)
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/gender_bucket_stat.scala

@@ -0,0 +1,74 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.aliyun.odps.spark.examples.myUtils.{MyHdfsUtils, ParamUtils}
+import org.apache.commons.lang3.StringUtils
+import org.apache.hadoop.io.compress.GzipCodec
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.mutable.ArrayBuffer
+
+object gender_bucket_stat {
+  def main(args: Array[String]): Unit = {
+    // 1. 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val readPath = param.getOrElse("readPath", "/dw/recommend/model/user_profile/gender/analysis")
+    val year = param.getOrElse("year", "2025")
+    val suffixSet = param.getOrElse("suffix", "y,8,4,0,e,a,c,k,o,w,g,s,u,q").split(",").toSet
+    val repartition = param.getOrElse("repartition", "1").toInt
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/user_profile/gender/bucket")
+
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 2. 处理数据
+    val pathArray = getPathArray(readPath, year, suffixSet)
+    val data = sc.textFile(pathArray.mkString(","))
+      .map(r => {
+        parseFeature(r)
+      })
+      .flatMap(features => {
+        features.map(key =>
+          (key, 1))
+      })
+      .reduceByKey((a, b) => a + b)
+      .map(raw => {
+        raw._1 + "\t" + raw._2.toString
+      })
+
+    // 3. 保存数据到hdfs
+    val hdfsPath = savePath + "/" + year
+    if (hdfsPath.nonEmpty && hdfsPath.startsWith("/dw/recommend/model/")) {
+      println("删除路径并开始数据写入:" + hdfsPath)
+      MyHdfsUtils.delete_hdfs_path(hdfsPath)
+      data.repartition(repartition).saveAsTextFile(hdfsPath, classOf[GzipCodec])
+    } else {
+      println("路径不合法，无法写入:" + hdfsPath)
+    }
+  }
+
+  private def parseFeature(data: String): scala.collection.mutable.Set[String] = {
+    val features = scala.collection.mutable.Set[String]()
+    if (data.nonEmpty) {
+      val cells: Array[String] = StringUtils.split(data, '\t')
+      val label = cells(1)
+      for (i <- 2 until cells.length) {
+        val fv: Array[String] = StringUtils.split(cells(i), ':')
+        val key = "%s:%s:%s".format(fv(0), fv.last, label)
+        features.add(key)
+      }
+    }
+    features
+  }
+
+  def getPathArray(basePath: String, year: String, suffixSet: Set[String]): ArrayBuffer[String] = {
+    val pathArray = new ArrayBuffer[String]
+    for (suffix <- suffixSet) {
+      val path = "%s/%s_%s/*".format(basePath, year, suffix)
+      pathArray += path
+    }
+    pathArray
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/make_bucket_sparse_sample.scala

@@ -0,0 +1,145 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.aliyun.odps.spark.examples.myUtils.{DataUtils, MyHdfsUtils, ParamUtils}
+import examples.extractor.ExtractorUtils
+import org.apache.hadoop.io.compress.GzipCodec
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+import scala.io.Source
+
+
+object make_bucket_sparse_sample {
+  def main(args: Array[String]): Unit = {
+    // 1. 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val readPath = param.getOrElse("readPath", "/dw/recommend/model/user_profile/data/")
+    val year = param.getOrElse("year", "2025")
+    val suffixSet = param.getOrElse("suffix", "y,8,4,0,e,a,c,k,o,w,g,s,u,q,i,m").split(",").toSet
+    val whatLabel = param.getOrElse("whatLabel", "gender")
+    val classSet = param.getOrElse("class", "1,2").split(",").toSet
+    val notUseBucket = param.getOrElse("notUseBucket", "1").toInt
+    val featureFile = param.getOrElse("featureFile", "20241209_recsys_nor_name.txt")
+    val featureBucketFile = param.getOrElse("featureBucket", "20241209_recsys_nor_bucket.txt")
+    val minCnt = param.getOrElse("minCnt", "10").toDouble
+    val repartition = param.getOrElse("repartition", "100").toInt
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/user_profile/gender/sample/")
+
+    // 2. content
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 3. 处理数据
+    val featureSet = loadFeatureNames(featureFile)
+    val featureBucketMap = loadUseFeatureBuckets(notUseBucket, featureBucketFile)
+    val bucketsMap_br = sc.broadcast(featureBucketMap)
+    for (suffix <- suffixSet) {
+      val partition = "%s_%s".format(year, suffix)
+      println("开始执行:" + partition)
+      val data = sc.textFile(readPath + "/" + partition + "*").map(row => {
+          val cells = row.split("\t")
+          val mid = cells(0)
+          val labels = cells(1)
+          val featData = cells(2)
+          (mid, labels, featData)
+        })
+        .filter {
+          case (mid, labels, featData) =>
+            val label = DataUtils.parseLabel(labels, whatLabel)
+            classSet.contains(label)
+        }
+        .map {
+          case (mid, labels, featData) =>
+            var label = DataUtils.parseLabel(labels, whatLabel).toInt
+            val features = DataUtils.parseFeature(featData)
+            if (label > 0) {
+              label -= 1
+            }
+            (mid, label, features)
+        }
+        .filter {
+          case (mid, label, features) =>
+            val cnt = features.getOrElse("cnt", 0d)
+            cnt >= minCnt
+        }
+        .mapPartitions(row => {
+          val result = new ArrayBuffer[String]()
+          row.foreach {
+            case (mid, label, features) =>
+              val bucketsMap = bucketsMap_br.value
+              val featuresBucket = bucketFeature(featureSet, bucketsMap, features)
+              result.add(mid + "\t" + label + "\t" + featuresBucket.mkString("\t"))
+          }
+          result.iterator
+        })
+
+      // 4. 保存数据到hdfs
+      val hdfsPath = savePath + "/" + partition
+      if (hdfsPath.nonEmpty && hdfsPath.startsWith("/dw/recommend/model/")) {
+        println("删除路径并开始数据写入:" + hdfsPath)
+        MyHdfsUtils.delete_hdfs_path(hdfsPath)
+        data.repartition(repartition).saveAsTextFile(hdfsPath, classOf[GzipCodec])
+      } else {
+        println("路径不合法，无法写入:" + hdfsPath)
+      }
+    }
+  }
+
+  def loadFeatureNames(nameFile: String): Set[String] = {
+    val buffer = Source.fromFile(nameFile)
+    val names = buffer.getLines().mkString("\n")
+    buffer.close()
+    val featSet = names.split("\n")
+      .map(r => r.replace(" ", "").replaceAll("\n", ""))
+      .filter(r => r.nonEmpty)
+      .toSet
+    println("featSet.size=" + featSet.size)
+    println(featSet)
+    featSet
+  }
+
+  def loadUseFeatureBuckets(notUseBucket: Int, bucketFile: String): Map[String, (Double, Array[Double])] = {
+    if (notUseBucket > 0) {
+      return Map[String, (Double, Array[Double])]()
+    }
+
+    val buffer = Source.fromFile(bucketFile)
+    val lines = buffer.getLines().mkString("\n")
+    buffer.close()
+    val bucketMap = lines.split("\n")
+      .map(r => r.replace(" ", "").replaceAll("\n", ""))
+      .filter(r => r.nonEmpty)
+      .map(r => {
+        val rList = r.split("\t")
+        (rList(0), (rList(1).toDouble, rList(2).split(",").map(_.toDouble)))
+      }).toMap
+    println("bucketMap.size=" + bucketMap.size)
+    println(bucketMap)
+    bucketMap
+  }
+
+  def bucketFeature(nameSet: Set[String], bucketMap: Map[String, (Double, Array[Double])], features: scala.collection.mutable.Map[String, Double]): Iterable[String] = {
+    features.map {
+      case (name, score) =>
+        if (!nameSet.contains(name)) {
+          ""
+        } else {
+          if (score > 1E-8) {
+            if (bucketMap.contains(name)) {
+              val (bucketsNum, buckets) = bucketMap(name)
+              val position = ExtractorUtils.findInsertPosition(buckets, score) + 1
+              "%s@%d:1".format(name, position)
+            } else {
+              name + ":" + score.toString
+            }
+          } else {
+            ""
+          }
+        }
+    }.filter(_.nonEmpty)
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/make_title.scala

@@ -0,0 +1,297 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.aliyun.odps.data.Record
+import com.aliyun.odps.spark.examples.myUtils.DataUtils.getStringValue
+import com.aliyun.odps.spark.examples.myUtils._
+import examples.utils.SimilarityUtils
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+
+/*
+   20250317 提取特征
+ */
+
+object make_title {
+  private val labelNames = List(
+    "is_share", "share_cnt",
+    "is_return_1", "return_1_uv",
+    "is_return_n", "return_n_uv",
+    "is_return_noself", "return_1_uv_noself",
+    "is_return_n_noself", "return_n_uv_noself"
+  )
+  private val o2oMap = Map(
+    "creative" -> "creativeInfo"
+  )
+  private val detailPageO2OMap = Map(
+    "d1_feature" -> "scene_type_vid_cf_feature_20250212",
+    "d2_feature" -> "vid_click_cf_feature_20250212",
+    "d3_feature" -> "alg_recsys_feature_cf_i2i_v2",
+    "v2_feature" -> "head_video",
+    "creative" -> "creativeInfo"
+  )
+  private val returnPageO2OMap = Map(
+    "d1_feature" -> "null1",
+    "d2_feature" -> "null2",
+    "d3_feature" -> "null3",
+    "v2_feature" -> "null4",
+    "creative" -> "creativeInfo"
+  )
+
+  private def parseVideoRdd(videoRdd: RDD[Record]): RDD[(String, java.util.Map[String, String])] = {
+    videoRdd
+      .map(record => {
+        val vid = record.getString("vid")
+        val feature = ConvertUtils.getRecordCol(record, "feature")
+        feature.put("vid", vid)
+        (vid, feature)
+      })
+      .reduceByKey((a, b) => if (a.size() > b.size()) a else b)
+  }
+
+  private def joinVideoStat(logRdd: RDD[java.util.Map[String, String]], videoStat: RDD[(String, String)]): RDD[java.util.Map[String, String]] = {
+    logRdd
+      .map(raw => {
+        val vid = raw.getOrElse("vid", "")
+        (vid, raw)
+      })
+      .leftOuterJoin(videoStat)
+      .map(raw => {
+        val info = raw._2._1
+        if (raw._2._2.isDefined) {
+          info.put("b0_feature", raw._2._2.get)
+        }
+        info
+      })
+  }
+
+  private def joinChannelLayerStat(logRdd: RDD[java.util.Map[String, String]], videoStat: RDD[(String, String)]): RDD[java.util.Map[String, String]] = {
+    logRdd
+      .map(raw => {
+        val extendMap = ConvertUtils.getRecordCol(raw, "extend")
+        val channel = FeatureUtils.getUserChannel(raw.get("rootsourceid"), extendMap.get("group_name"))
+        val level = FeatureUtils.getUserLevel(extendMap.get("rootsessionid"), raw.get("subsessionid"))
+        val vid = raw.getOrElse("vid", "")
+        if (level.isEmpty) {
+          val key = Seq(channel, "非0层", vid).mkString(":")
+          (key, raw)
+        } else {
+          val key = Seq(channel, "0层", vid).mkString(":")
+          (key, raw)
+        }
+      })
+      .leftOuterJoin(videoStat)
+      .map(raw => {
+        val info = raw._2._1
+        if (raw._2._2.isDefined) {
+          info.put("b14_feature", raw._2._2.get)
+        }
+        info
+      })
+  }
+
+  private def joinChannelLayerHeadStat(logRdd: RDD[java.util.Map[String, String]], videoStat: RDD[(String, String)]): RDD[java.util.Map[String, String]] = {
+    logRdd
+      .map(raw => {
+        val extendMap = ConvertUtils.getRecordCol(raw, "extend")
+        val channel = FeatureUtils.getUserChannel(raw.get("rootsourceid"), extendMap.get("group_name"))
+        val level = FeatureUtils.getUserLevel(extendMap.get("rootsessionid"), raw.get("subsessionid"))
+        val headInfo = ConvertUtils.getRecordCol(raw, "v2_feature")
+        val unionid = headInfo.getOrElse("title_time_w_h_unionid", "")
+        val vid = raw.getOrElse("vid", "")
+        if (level.isEmpty) {
+          val key = Seq(channel, "非0层", unionid, vid).mkString(":")
+          (key, raw)
+        } else {
+          val key = Seq(channel, "0层", unionid, vid).mkString(":")
+          (key, raw)
+        }
+      })
+      .leftOuterJoin(videoStat)
+      .map(raw => {
+        val info = raw._2._1
+        if (raw._2._2.isDefined) {
+          info.put("b15_feature", raw._2._2.get)
+        }
+        info
+      })
+  }
+
+  private def getVidMidRdd(logRdd: RDD[java.util.Map[String, String]]): RDD[(String, String)] = {
+    logRdd
+      .map(raw => {
+        val mid = raw.getOrElse("mid", "")
+        val c9 = raw.getOrElse("c9_feature", "")
+        (mid, c9)
+      })
+      .filter(_._1.nonEmpty)
+      .reduceByKey((a, b) => a)
+      .flatMap(raw => {
+        val result = new ArrayBuffer[(String, String)]
+        for (hVid <- ConvertUtils.getVidList(raw._2)) {
+          result += ((hVid, raw._1)) // (vid, mid)
+        }
+        result
+      })
+  }
+
+  private def getMidSeqRdd(vidMidRdd: RDD[(String, String)], videoRdd: RDD[(String, java.util.Map[String, String])]): RDD[(String, List[java.util.Map[String, String]])] = {
+    vidMidRdd
+      .join(videoRdd)
+      .map(raw => {
+        (raw._2._1, raw._2._2)
+      })
+      .groupByKey()
+      .map(raw => {
+        (raw._1, raw._2.toList)
+      })
+  }
+
+  private def joinMidSeq(logRdd: RDD[java.util.Map[String, String]], midSeqRdd: RDD[(String, List[java.util.Map[String, String]])]): RDD[(java.util.Map[String, String], List[java.util.Map[String, String]])] = {
+    logRdd
+      .map(raw => {
+        val mid = raw.getOrElse("mid", "")
+        (mid, raw)
+      })
+      .leftOuterJoin(midSeqRdd)
+      .map(raw => {
+        if (raw._2._2.isDefined) {
+          (raw._2._1, raw._2._2.get)
+        } else {
+          (raw._2._1, List())
+        }
+      })
+  }
+
+  private def getFeature(rdd: RDD[(java.util.Map[String, String], List[java.util.Map[String, String]])]): RDD[String] = {
+    rdd.mapPartitions(partition => {
+        // SimilarityUtils.init()
+        SimilarityUtils.newInit()
+        partition.map(raw => {
+          val record = raw._1
+          val videoSeq = raw._2
+          val headvideoid = record.getOrElse("headvideoid", "")
+          val features = TitleConvert.getFeature(record, videoSeq, 6)
+          if (features.nonEmpty) {
+            (headvideoid, features)
+          } else {
+            ("", "")
+          }
+        })
+      })
+      .filter(_._1.nonEmpty)
+      .reduceByKey((a, b) => a)
+      .map(raw => {
+        raw._1 + "\t" + raw._2
+      })
+  }
+
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 1. 解析参数
+    val param = ParamUtils.parseArgs(args)
+    val project = param.getOrElse("project", "loghubods")
+    val table = param.getOrElse("table", "dwd_recsys_alg_sample_all_20250212")
+    val tablePart = param.getOrElse("tablePart", "64").toInt
+    val beginStr = param.getOrElse("beginStr", "2025031700")
+    val endStr = param.getOrElse("endStr", "2025031700")
+    val whatPages = param.getOrElse("whatPages", "详情后沉浸页,回流后沉浸页&内页feed,首页feed,详情页,回流页").split(",").toSet
+    val whatLabel = param.getOrElse("whatLabel", "is_return_noself")
+    val fuSampleRate = param.getOrElse("fuSampleRate", "0.03").toDouble
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/83_origin_data/")
+    val repartition = param.getOrElse("repartition", "64").toInt
+
+    // 2. 数据处理
+    val timeRange = MyDateUtils.getDateHourRange(beginStr, endStr)
+    for (dt_hh <- timeRange) {
+      // 2.1 分区
+      val dt = dt_hh.substring(0, 8)
+      val hh = dt_hh.substring(8, 10)
+      val partition = "dt=%s,hh=%s".format(dt, hh)
+      println("开始执行partition:" + partition)
+
+      // 2.2 加载视频信息
+      val originVideo = DataUtils.getODPSData(sc, project, "alg_recsys_feature_user_behavior_video", partition, tablePart)
+      val uniqVideo = parseVideoRdd(originVideo)
+
+      // 2.3 加载视频统计信息
+      val videoStat = DataUtils.getODPSData(sc, project, "alg_recsys_feature_video_clean_stat", partition, tablePart)
+        .map(record => {
+          val vid = record.getString("vid")
+          val feature = record.getString("feature")
+          (vid, feature)
+        })
+
+      // 2.4 加载渠道特征
+      val channelLayerStat = DataUtils.getODPSData(sc, project, "alg_recsys_feature_video_recommend_channel_layer", partition, 8)
+        .map(record => {
+          val channel = record.getString("channel")
+          val layer = record.getString("layer")
+          val vid = record.getString("vid")
+          val feature = record.getString("feature")
+          val key = Seq(channel, layer, vid).mkString(":")
+          (key, feature)
+        })
+
+      // 2.5 加载渠道头部特征
+      val channelLayerHeadStat = DataUtils.getODPSData(sc, project, "alg_recsys_feature_video_recommend_channel_layer_head", partition, 8)
+        .map(record => {
+          val channel = record.getString("channel")
+          val layer = record.getString("layer")
+          val unionid = record.getString("unionid")
+          val vid = record.getString("vid")
+          val feature = record.getString("feature")
+          val key = Seq(channel, layer, unionid, vid).mkString(":")
+          (key, feature)
+        })
+
+      // 2.6 加载样本数据
+      val odpsData = DataUtils.getODPSData(sc, project, table, partition, tablePart)
+        .filter(record => {
+          val page = getStringValue(record, "page")
+          whatPages.contains(page)
+        })
+
+      // 2.7 样本重采样
+      val resampleData = DataUtils.resampleWithoutInterception(whatLabel, fuSampleRate, odpsData)
+        .map(record => {
+          val page = getStringValue(record, "page")
+          if (page.equals("详情页")) {
+            OnlineLogUtils.log2Map(record, detailPageO2OMap)
+          } else if (page.equals("回流页")) {
+            OnlineLogUtils.log2Map(record, returnPageO2OMap)
+          } else {
+            OnlineLogUtils.log2Map(record, o2oMap)
+          }
+        })
+
+      // 2.8 join video stat
+      val sampleDataTmp1 = joinVideoStat(resampleData, videoStat)
+      val sampleDataTmp2 = joinChannelLayerStat(sampleDataTmp1, channelLayerStat)
+      val sampleData = joinChannelLayerHeadStat(sampleDataTmp2, channelLayerHeadStat)
+
+      // 2.9 get vid mid rdd
+      val vidMidRdd = getVidMidRdd(sampleData)
+
+      // 2.10 get mid seq rdd
+      val midSeqRdd = getMidSeqRdd(vidMidRdd, uniqVideo)
+
+      // 2.11 历史行为关联video
+      val seqSampleData = joinMidSeq(sampleData, midSeqRdd)
+
+      // 2.12 特征转换
+      val featureData = getFeature(seqSampleData)
+
+      // 2.13 保存数据
+      val hdfsPath = "%s/%s%s".format(savePath, dt, hh)
+      DataUtils.saveData(featureData, hdfsPath, repartition)
+    }
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/makedata_fission_20260106.scala

@@ -0,0 +1,68 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.aliyun.odps.spark.examples.myUtils._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.JavaConversions._
+
+object makedata_fission_20260106 {
+  private val o2oMap = Map(
+    "creative" -> "creativeInfo"
+  )
+
+  private def getFeature(dt: String, rdd: RDD[java.util.Map[String, String]]): RDD[String] = {
+    rdd.mapPartitions(partition => {
+        partition.map(raw => {
+          val mid = raw.getOrElse("mid", "")
+          val labels = raw.getOrElse("labels", "")
+          val features = FissionConvert.getFeature(dt, raw, 6).toString
+          if (mid.nonEmpty && labels.nonEmpty && features.nonEmpty) {
+            mid + "\t" + labels + "\t" + features
+          } else {
+            ""
+          }
+        })
+      })
+      .filter(_.nonEmpty)
+  }
+
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 1. 解析参数
+    val param = ParamUtils.parseArgs(args)
+    val project = param.getOrElse("project", "loghubods")
+    val table = param.getOrElse("table", "alg_ad_display_ad_sample_20251218")
+    val beginStr = param.getOrElse("beginStr", "20251216")
+    val endStr = param.getOrElse("endStr", "20251216")
+    val tablePart = param.getOrElse("tablePart", "64").toInt
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/ad_display/data")
+    val repartition = param.getOrElse("repartition", "64").toInt
+    val dateRange = MyDateUtils.getDateRange(beginStr, endStr)
+
+    // 2. 数据处理
+    for (dt <- dateRange) {
+      // 2.1 分区
+      val partition = "dt=%s".format(dt)
+      println("开始执行partition:" + partition)
+
+      // 2.2 加载样本数据
+      val odpsData = DataUtils.getODPSData(sc, project, table, partition, tablePart)
+        .map(record => {
+          OnlineLogUtils.log2Map(record, o2oMap)
+        })
+
+      // 2.3 特征转换
+      val featureData = getFeature(dt, odpsData)
+
+      // 2.4 保存数据
+      val hdfsPath = "%s/%s".format(savePath, dt)
+      DataUtils.saveData(featureData, hdfsPath, repartition)
+    }
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/makedata_fission_20260205.scala

@@ -0,0 +1,76 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.alibaba.fastjson.JSONObject
+import com.aliyun.odps.spark.examples.myUtils._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.JavaConversions._
+
+object makedata_fission_20260205 {
+  private val o2oMap = Map(
+    "creative" -> "creativeInfo"
+  )
+
+  private def getFeature(dt: String, hh: String, rdd: RDD[java.util.Map[String, String]]): RDD[String] = {
+    rdd.mapPartitions(partition => {
+        partition.map(raw => {
+          val subsessionid = raw.getOrElse("subsessionid", "")
+          val mid = raw.getOrElse("user_curr_mid", "")
+          val key = Seq(subsessionid, mid).mkString(":")
+          val fissions = raw.getOrElse("fissions", "0")
+          val labels = new JSONObject()
+          val features = FissionConvertV2.getFeature(dt, hh, raw, 6).toString
+          if (key.nonEmpty && fissions.nonEmpty && features.nonEmpty) {
+            labels.put("r1_uv", fissions)
+            key + "\t" + labels.toString() + "\t" + features
+          } else {
+            ""
+          }
+        })
+      })
+      .filter(_.nonEmpty)
+  }
+
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 1. 解析参数
+    val param = ParamUtils.parseArgs(args)
+    val project = param.getOrElse("project", "loghubods")
+    val table = param.getOrElse("table", "alg_fission_feature_subsessionid_base")
+    val beginStr = param.getOrElse("beginStr", "2025121600")
+    val endStr = param.getOrElse("endStr", "2025121623")
+    val tablePart = param.getOrElse("tablePart", "64").toInt
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/ad_display/data")
+    val repartition = param.getOrElse("repartition", "64").toInt
+    val dateRange = MyDateUtils.getDateRange(beginStr, endStr)
+
+    // 2. 数据处理
+    val timeRange = MyDateUtils.getDateHourRange(beginStr, endStr)
+    for (dt_hh <- timeRange) {
+      // 2.1 分区
+      val dt = dt_hh.substring(0, 8)
+      val hh = dt_hh.substring(8, 10)
+      val partition = "dt=%s,hh=%s".format(dt, hh)
+      println("开始执行partition:" + partition)
+
+      // 2.2 加载样本数据
+      val odpsData = DataUtils.getODPSData(sc, project, table, partition, tablePart)
+        .map(record => {
+          OnlineLogUtils.log2Map(record, o2oMap)
+        })
+
+      // 2.3 特征转换
+      val featureData = getFeature(dt, hh, odpsData)
+
+      // 2.4 保存数据
+      val hdfsPath = "%s/%s%s".format(savePath, dt, hh)
+      DataUtils.saveData(featureData, hdfsPath, repartition)
+    }
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/makedata_fission_sample_20260205.scala

@@ -0,0 +1,134 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.aliyun.odps.spark.examples.myUtils.{DataUtils, MyDateUtils, MyHdfsUtils, ParamUtils}
+import org.apache.hadoop.io.compress.GzipCodec
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+import scala.io.Source
+import scala.util.Random
+
+
+object makedata_fission_sample_20260205 {
+  def main(args: Array[String]): Unit = {
+    // 1. 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val readPath = param.getOrElse("readPath", "/dw/recommend/model/general_model/fission/data")
+    val beginStr = param.getOrElse("beginStr", "20251216")
+    val endStr = param.getOrElse("endStr", "20251216")
+    val originSample = param.getOrElse("originSample", "0").toInt
+    val withKey = param.getOrElse("withKey", "0").toInt
+    val whatLabel = param.getOrElse("whatLabel", "r1_uv")
+    val maxLabel = param.getOrElse("maxLabel", "500").toInt
+    val fuSampleRate = param.getOrElse("fuSampleRate", "0.1").toDouble
+    val notUseBucket = param.getOrElse("notUseBucket", "1").toInt
+    val featureFile = param.getOrElse("featureFile", "20241209_recsys_nor_name.txt")
+    val featureBucketFile = param.getOrElse("featureBucket", "20241209_recsys_nor_bucket.txt")
+    val repartition = param.getOrElse("repartition", "100").toInt
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/general_model/fission/sample")
+
+    // 2. content
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 3. 处理数据
+    val featureSet = loadFeatureNames(featureFile)
+    val featureBucketMap = loadUseFeatureBuckets(notUseBucket, featureBucketFile)
+    val bucketsMap_br = sc.broadcast(featureBucketMap)
+    val dateRange = MyDateUtils.getDateRange(beginStr, endStr)
+    for (dt <- dateRange) {
+      val partition = "%s".format(dt)
+      println("开始执行:" + partition)
+      val data = sc.textFile(readPath + "/" + partition + "*").map(row => {
+          val cells = row.split("\t")
+          val mid = cells(0)
+          val labels = cells(1)
+          val featData = cells(2)
+          (mid, labels, featData)
+        })
+        .filter {
+          case (mid, labels, featData) =>
+            val label = DataUtils.parseLabel(labels, whatLabel).toInt
+            label > 0 || new Random().nextDouble() <= fuSampleRate
+        }
+        .map {
+          case (mid, labels, featData) =>
+            val label = DataUtils.parseLabel(labels, whatLabel).toInt
+            val features = DataUtils.parseFeature(featData)
+            (mid, label, features)
+        }
+        .mapPartitions(row => {
+          val result = new ArrayBuffer[String]()
+          row.foreach {
+            case (mid, label, features) =>
+              val bucketsMap = bucketsMap_br.value
+              val featuresBucket = DataUtils.bucketFeature(featureSet, bucketsMap, features)
+              var logKey = ""
+              if (withKey > 0) {
+                logKey = mid + "\t"
+              }
+              if (originSample > 0) {
+                val newLabel = math.min(maxLabel, label)
+                result.add(logKey + newLabel + "\t" + featuresBucket.mkString("\t"))
+              } else {
+                result.add(logKey + "0" + "\t" + featuresBucket.mkString("\t"))
+                if (label > 0) {
+                  val repeat = math.min(maxLabel, label)
+                  for (_ <- 1 to repeat) {
+                    result.add(logKey + "1" + "\t" + featuresBucket.mkString("\t"))
+                  }
+                }
+              }
+          }
+          result.iterator
+        })
+
+      // 4. 保存数据到hdfs
+      val hdfsPath = savePath + "/" + partition
+      if (hdfsPath.nonEmpty && hdfsPath.startsWith("/dw/recommend/model/")) {
+        println("删除路径并开始数据写入:" + hdfsPath)
+        MyHdfsUtils.delete_hdfs_path(hdfsPath)
+        data.repartition(repartition).saveAsTextFile(hdfsPath, classOf[GzipCodec])
+      } else {
+        println("路径不合法，无法写入:" + hdfsPath)
+      }
+    }
+  }
+
+  def loadFeatureNames(nameFile: String): Set[String] = {
+    val buffer = Source.fromFile(nameFile)
+    val names = buffer.getLines().mkString("\n")
+    buffer.close()
+    val featSet = names.split("\n")
+      .map(r => r.replace(" ", "").replaceAll("\n", ""))
+      .filter(r => r.nonEmpty)
+      .toSet
+    println("featSet.size=" + featSet.size)
+    println(featSet)
+    featSet
+  }
+
+  def loadUseFeatureBuckets(notUseBucket: Int, bucketFile: String): Map[String, (Double, Array[Double])] = {
+    if (notUseBucket > 0) {
+      return Map[String, (Double, Array[Double])]()
+    }
+
+    val buffer = Source.fromFile(bucketFile)
+    val lines = buffer.getLines().mkString("\n")
+    buffer.close()
+    val bucketMap = lines.split("\n")
+      .map(r => r.replace(" ", "").replaceAll("\n", ""))
+      .filter(r => r.nonEmpty)
+      .map(r => {
+        val rList = r.split("\t")
+        (rList(0), (rList(1).toDouble, rList(2).split(",").map(_.toDouble)))
+      }).toMap
+    println("bucketMap.size=" + bucketMap.size)
+    println(bucketMap)
+    bucketMap
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/makedata_profile_20251209.scala

@@ -0,0 +1,76 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.aliyun.odps.spark.examples.myUtils._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.JavaConversions._
+
+object makedata_profile_20251209 {
+  private val o2oMap = Map(
+    "creative" -> "creativeInfo"
+  )
+
+  private def getFeature(rdd: RDD[java.util.Map[String, String]]): RDD[String] = {
+    rdd.mapPartitions(partition => {
+        partition.map(raw => {
+          val mid = raw.getOrElse("mid", "")
+          val labels = raw.getOrElse("labels", "")
+          val features = ConvertV1.getFeature(0, raw, 6).toString
+          if (mid.nonEmpty && labels.nonEmpty && features.nonEmpty) {
+            mid + "\t" + labels + "\t" + features
+          } else {
+            ""
+          }
+        })
+      })
+      .filter(_.nonEmpty)
+  }
+
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 1. 解析参数
+    val param = ParamUtils.parseArgs(args)
+    val project = param.getOrElse("project", "loghubods")
+    val table = param.getOrElse("table", "alg_recsys_feature_behavior_profile")
+    val year = param.getOrElse("year", "2025")
+    val suffixSet = param.getOrElse("suffix", "y,8,4,0,e,a,c,k,o,w,g,s,u,q,i,m").split(",").toSet
+    val weight = param.getOrElse("weight", "1")
+    val ts = param.getOrElse("ts", "0").toLong
+    val tablePart = param.getOrElse("tablePart", "64").toInt
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/user_profile/data/")
+    val repartition = param.getOrElse("repartition", "64").toInt
+
+    var currentMs = 0L
+    if (0 != ts) {
+      currentMs = ts * 1000
+    } else {
+      currentMs = System.currentTimeMillis()
+    }
+
+    // 2. 数据处理
+    for (suffix <- suffixSet) {
+      // 2.1 分区
+      val partition = "year=%s,suffix=%s,weight=%s".format(year, suffix, weight)
+      println("开始执行partition:" + partition)
+
+      // 2.2 加载样本数据
+      val odpsData = DataUtils.getODPSData(sc, project, table, partition, tablePart)
+        .map(record => {
+          OnlineLogUtils.log2Map(record, o2oMap)
+        })
+
+      // 2.3 特征转换
+      val featureData = getFeature(odpsData)
+
+      // 2.4 保存数据
+      val hdfsPath = "%s/%s_%s".format(savePath, year, suffix)
+      DataUtils.saveData(featureData, hdfsPath, repartition)
+    }
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/makedata_profile_20251211.scala

@@ -0,0 +1,76 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.aliyun.odps.spark.examples.myUtils._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.JavaConversions._
+
+object makedata_profile_20251211 {
+  private val o2oMap = Map(
+    "creative" -> "creativeInfo"
+  )
+
+  private def getFeature(rdd: RDD[java.util.Map[String, String]]): RDD[String] = {
+    rdd.mapPartitions(partition => {
+        partition.map(raw => {
+          val mid = raw.getOrElse("mid", "")
+          val labels = raw.getOrElse("labels", "")
+          val features = ConvertV5.getFeature(0, raw, 6).toString
+          if (mid.nonEmpty && labels.nonEmpty && features.nonEmpty) {
+            mid + "\t" + labels + "\t" + features
+          } else {
+            ""
+          }
+        })
+      })
+      .filter(_.nonEmpty)
+  }
+
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 1. 解析参数
+    val param = ParamUtils.parseArgs(args)
+    val project = param.getOrElse("project", "loghubods")
+    val table = param.getOrElse("table", "alg_recsys_feature_behavior_profile")
+    val year = param.getOrElse("year", "2025")
+    val suffixSet = param.getOrElse("suffix", "y,8,4,0,e,a,c,k,o,w,g,s,u,q,i,m").split(",").toSet
+    val weight = param.getOrElse("weight", "1")
+    val ts = param.getOrElse("ts", "0").toLong
+    val tablePart = param.getOrElse("tablePart", "64").toInt
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/user_profile/data/")
+    val repartition = param.getOrElse("repartition", "64").toInt
+
+    var currentMs = 0L
+    if (0 != ts) {
+      currentMs = ts * 1000
+    } else {
+      currentMs = System.currentTimeMillis()
+    }
+
+    // 2. 数据处理
+    for (suffix <- suffixSet) {
+      // 2.1 分区
+      val partition = "year=%s,suffix=%s,weight=%s".format(year, suffix, weight)
+      println("开始执行partition:" + partition)
+
+      // 2.2 加载样本数据
+      val odpsData = DataUtils.getODPSData(sc, project, table, partition, tablePart)
+        .map(record => {
+          OnlineLogUtils.log2Map(record, o2oMap)
+        })
+
+      // 2.3 特征转换
+      val featureData = getFeature(odpsData)
+
+      // 2.4 保存数据
+      val hdfsPath = "%s/%s_%s".format(savePath, year, suffix)
+      DataUtils.saveData(featureData, hdfsPath, repartition)
+    }
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/makedata_recsys_70_dnn_str_sample.scala

@@ -0,0 +1,234 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.alibaba.fastjson.{JSON, JSONObject}
+import com.aliyun.odps.TableSchema
+import com.aliyun.odps.data.Record
+import com.aliyun.odps.spark.examples.myUtils.{MyDateUtils, MyHdfsUtils, ParamUtils, env}
+import examples.extractor.ExtractorUtils
+import org.apache.hadoop.io.compress.GzipCodec
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+import scala.io.Source
+import scala.util.Random
+
+object makedata_recsys_70_dnn_str_sample {
+  def main(args: Array[String]): Unit = {
+    // 1. 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val inputPath = param.getOrElse("inputPath", "/dw/recommend/model/70_origin_data/")
+    val startDate = param.getOrElse("startDate", "20250705")
+    val endDate = param.getOrElse("endDate", "20250705")
+    val whatApps = param.getOrElse("whatApps", "0,4,2,5,21,3,6").split(",").toSet
+    val whatPages = param.getOrElse("whatPages", "详情后沉浸页,回流后沉浸页&内页feed,首页feed,详情页").split(",").toSet
+    val whatLabel = param.getOrElse("whatLabel", "is_return_n_noself")
+    val allLabel = param.getOrElse("allLabel", "is_return_noself,is_return_n_noself").split(",").toSet
+    val traceKeys = param.getOrElse("traceKeys", "apptype,mid,vid").split(",").toSet
+    val negSampleRate = param.getOrElse("negSampleRate", "0.03").toDouble
+    val featureType = param.getOrElse("featureType", "")
+    val featureBucket = param.getOrElse("featureBucket", "")
+    val saveType = param.getOrElse("saveType", "1").toInt // 1:hdfs, 2:table, 3:hdfs&table
+    val project = param.getOrElse("project", "loghubods")
+    val outputTable = param.getOrElse("outputTable", "alg_recsys_model_train_sample_dnn_v1")
+    val outputPath = param.getOrElse("outputPath", "/dw/recommend/model/70_recsys_str_train_data/")
+    val repartition = param.getOrElse("repartition", "64").toInt
+
+    // 1. sc
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 2. 读取odps+表信息
+    val odpsOps = env.getODPS(sc)
+    // val tableSchema = odpsOps.getTableSchema(project, outputTable, isPartition = false)
+
+    // 3. 加载特征
+    val featureTypeMap = loadFeatureNames(featureType)
+    val featureBucketMap = loadFeatureBuckets(featureBucket)
+    val typeMapB = sc.broadcast(featureTypeMap)
+    val bucketsMapB = sc.broadcast(featureBucketMap)
+
+    // 4. 处理数据
+    val dateRange = MyDateUtils.getDateRange(startDate, endDate)
+    for (dt <- dateRange) {
+      println("开始执行:" + dt)
+      val sampleRdd = sc.textFile(inputPath + "/" + dt + "*").map(r => {
+          val rList = r.split("\t")
+          val tracesObj = JSON.parseObject(rList(0))
+          val labelsObj = JSON.parseObject(rList(1))
+          val scoresStr = rList(2)
+          val featStr = rList(3)
+          (tracesObj, labelsObj, scoresStr, featStr)
+        })
+        .filter {
+          case (tracesObj, labelsObj, scoresStr, featStr) =>
+            validData(tracesObj, whatApps, whatPages)
+        }.filter {
+          case (tracesObj, labelsObj, scoresStr, featStr) =>
+            val label = labelsObj.getOrDefault(whatLabel, "0").toString.toDouble
+            label > 0 || new Random().nextDouble() < negSampleRate
+        }
+        .mapPartitions(rows => {
+          val samples = ArrayBuffer[scala.collection.mutable.Map[String, Any]]()
+          val typeMap = typeMapB.value
+          val bucketsMap = bucketsMapB.value
+          rows.foreach {
+            case (tracesObj, labelsObj, scoresStr, featStr) =>
+              val labels = getValue(allLabel, labelsObj)
+              val traces = getValue(traceKeys, tracesObj)
+              val features = parseFeature(typeMap, bucketsMap, featStr)
+              if (features.nonEmpty) {
+                val sample = labels ++ traces ++ features
+                samples += sample
+              }
+          }
+          samples.iterator
+        })
+
+      // 5. 保存数据
+      if ((saveType & 1) > 0) {
+        // hdfs
+        val hdfsPath = outputPath + "/" + dt
+        if (hdfsPath.nonEmpty && hdfsPath.startsWith("/dw/recommend/model/")) {
+          println("删除路径并开始数据写入:" + hdfsPath)
+          MyHdfsUtils.delete_hdfs_path(hdfsPath)
+          sampleRdd.map(a => a.mkString("\t")).repartition(repartition).saveAsTextFile(hdfsPath, classOf[GzipCodec])
+        } else {
+          println("路径不合法，无法写入:" + hdfsPath)
+        }
+      }
+      if ((saveType & 2) > 0) {
+        // table
+        val partition = s"dt=$dt"
+        odpsOps.saveToTable(project, outputTable, partition, sampleRdd, write, defaultCreate = true, overwrite = true)
+      }
+    }
+  }
+
+  private def recommendFlow(flowPool: String): Boolean = {
+    if (flowPool.isEmpty || flowPool.endsWith("#1")) {
+      return true
+    }
+    false
+  }
+
+  def loadFeatureNames(nameFile: String): Map[String, String] = {
+    if (nameFile.isEmpty) {
+      return Map[String, String]()
+    }
+    val buffer = Source.fromFile(nameFile)
+    val names = buffer.getLines().mkString("\n")
+    buffer.close()
+    val featMap = names.split("\n")
+      .map(r => r.replace(" ", "").replaceAll("\n", ""))
+      .filter(r => r.nonEmpty)
+      .map(r => {
+        val rList = r.split("\t")
+        (rList(0), rList(1))
+      })
+      .toMap
+    println("featMap.size=" + featMap.size)
+    println(featMap)
+    featMap
+  }
+
+  def loadFeatureBuckets(bucketFile: String): Map[String, (Double, Array[Double])] = {
+    if (bucketFile.length <= 0) {
+      return Map[String, (Double, Array[Double])]()
+    }
+    val buffer = Source.fromFile(bucketFile)
+    val buckets = buffer.getLines().mkString("\n")
+    buffer.close()
+    val bucketsMap = buckets.split("\n")
+      .map(r => r.replace(" ", "").replaceAll("\n", ""))
+      .filter(r => r.nonEmpty)
+      .map(r => {
+        val rList = r.split("\t")
+        (rList(0), (rList(1).toDouble, rList(2).split(",").map(_.toDouble)))
+      }).toMap
+    println("bucketsMap.size=" + bucketsMap.size)
+    println(bucketsMap)
+    bucketsMap
+  }
+
+  private def validData(obj: JSONObject, whatApps: Set[String], whatPages: Set[String]): Boolean = {
+    val apptype = obj.getOrDefault("apptype", "").toString
+    val page = obj.getOrDefault("page", "").toString
+    val flowpool = obj.getOrDefault("flowpool", "").toString
+    if (whatApps.contains(apptype)) {
+      if (whatPages.contains(page)) {
+        if (recommendFlow(flowpool)) {
+          return true
+        }
+      }
+    }
+    false
+  }
+
+  private def getValue(names: Set[String], obj: JSONObject, default: String = ""): scala.collection.mutable.Map[String, Any] = {
+    val map = scala.collection.mutable.Map[String, Any]()
+    for (name <- names) {
+      map.put(name, obj.getOrDefault(name, default))
+    }
+    map
+  }
+
+  private def parseFeature(featureMap: Map[String, String], bucketsMap: Map[String, (Double, Array[Double])], data: String): scala.collection.mutable.Map[String, Any] = {
+    val features = scala.collection.mutable.Map[String, Any]()
+    if (data.nonEmpty) {
+      val obj = JSON.parseObject(data)
+      obj.foreach(r => {
+        if (featureMap.contains(r._1)) {
+          if (!bucketsMap.contains(r._1)) {
+            features.put(r._1, r._2)
+          } else {
+            val (bucketsNum, buckets) = bucketsMap(r._1)
+            val value = obj.getDoubleValue(r._1)
+            if (value > 1E-8) {
+              val scoreNew = 1.0 / bucketsNum * (ExtractorUtils.findInsertPosition(buckets, value).toDouble + 1.0)
+              features.put(r._1, scoreNew)
+            }
+          }
+        }
+      })
+    }
+    features
+  }
+
+  private def write(map: scala.collection.mutable.Map[String, Any], record: Record, schema: TableSchema): Unit = {
+    for ((key, value) <- map) {
+      try {
+        // 查找列名在表结构中的索引
+        val columnIndex = schema.getColumnIndex(key.toLowerCase)
+        // 获取列的类型
+        val columnType = schema.getColumn(columnIndex).getTypeInfo
+        try {
+          columnType.getTypeName match {
+            case "STRING" =>
+              record.setString(columnIndex, value.toString)
+            case "BIGINT" =>
+              record.setBigint(columnIndex, value.toString.toLong)
+            case "DOUBLE" =>
+              record.setDouble(columnIndex, value.toString.toDouble)
+            case "BOOLEAN" =>
+              record.setBoolean(columnIndex, value.toString.toBoolean)
+            case other =>
+              throw new IllegalArgumentException(s"Unsupported column type: $other")
+          }
+        } catch {
+          case e: NumberFormatException =>
+            println(s"Error converting value $value to type ${columnType.getTypeName} for column $key: ${e.getMessage}")
+          case e: Exception =>
+            println(s"Unexpected error writing value $value to column $key: ${e.getMessage}")
+        }
+      } catch {
+        case e: IllegalArgumentException => {
+          println(e.getMessage)
+        }
+      }
+    }
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/makedata_recsys_70_origin_data_20250718.scala

@@ -0,0 +1,216 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.alibaba.fastjson.JSONObject
+import com.aliyun.odps.data.Record
+import com.aliyun.odps.spark.examples.myUtils.DataUtils.getStringValue
+import com.aliyun.odps.spark.examples.myUtils._
+import examples.utils.SimilarityUtils
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+
+/*
+   20250718 提取特征
+ */
+
+object makedata_recsys_70_origin_data_20250718 {
+  private val labelNames = List(
+    "is_share", "share_cnt",
+    "is_return_1", "return_1_uv",
+    "is_return_n", "return_n_uv",
+    "is_return_noself", "return_1_uv_noself",
+    "is_return_n_noself", "return_n_uv_noself"
+  )
+  private val o2oMap = Map(
+    "d1_feature" -> "scene_type_vid_cf_feature_20250212",
+    "d2_feature" -> "vid_click_cf_feature_20250212",
+    "d3_feature" -> "alg_recsys_feature_cf_i2i_v2",
+    "v2_feature" -> "head_video"
+  )
+
+  private def parseVideoRdd(videoRdd: RDD[Record]): RDD[(String, java.util.Map[String, String])] = {
+    videoRdd
+      .map(record => {
+        val vid = record.getString("vid")
+        val feature = ConvertUtils.getRecordCol(record, "feature")
+        feature.put("vid", vid)
+        (vid, feature)
+      })
+      .reduceByKey((a, b) => if (a.size() > b.size()) a else b)
+  }
+
+  private def joinVideoStat(logRdd: RDD[java.util.Map[String, String]], videoStat: RDD[(String, String)]): RDD[java.util.Map[String, String]] = {
+    logRdd
+      .map(raw => {
+        val vid = raw.getOrElse("vid", "")
+        (vid, raw)
+      })
+      .leftOuterJoin(videoStat)
+      .map(raw => {
+        val info = raw._2._1
+        if (raw._2._2.isDefined) {
+          info.put("b0_feature", raw._2._2.get)
+        }
+        info
+      })
+  }
+
+  private def getVidMidRdd(logRdd: RDD[java.util.Map[String, String]]): RDD[(String, String)] = {
+    logRdd
+      .map(raw => {
+        val mid = raw.getOrElse("mid", "")
+        val c9 = raw.getOrElse("c9_feature", "")
+        (mid, c9)
+      })
+      .filter(_._1.nonEmpty)
+      .reduceByKey((a, b) => a)
+      .flatMap(raw => {
+        val result = new ArrayBuffer[(String, String)]
+        for (hVid <- ConvertUtils.getVidList(raw._2)) {
+          result += ((hVid, raw._1)) // (vid, mid)
+        }
+        result
+      })
+  }
+
+  private def getMidSeqRdd(vidMidRdd: RDD[(String, String)], videoRdd: RDD[(String, java.util.Map[String, String])]): RDD[(String, List[java.util.Map[String, String]])] = {
+    vidMidRdd
+      .join(videoRdd)
+      .map(raw => {
+        (raw._2._1, raw._2._2)
+      })
+      .groupByKey()
+      .map(raw => {
+        (raw._1, raw._2.toList)
+      })
+  }
+
+  private def joinMidSeq(logRdd: RDD[java.util.Map[String, String]], midSeqRdd: RDD[(String, List[java.util.Map[String, String]])]): RDD[(java.util.Map[String, String], List[java.util.Map[String, String]])] = {
+    logRdd
+      .map(raw => {
+        val mid = raw.getOrElse("mid", "")
+        (mid, raw)
+      })
+      .leftOuterJoin(midSeqRdd)
+      .map(raw => {
+        if (raw._2._2.isDefined) {
+          (raw._2._1, raw._2._2.get)
+        } else {
+          (raw._2._1, List())
+        }
+      })
+  }
+
+  private def getFeature(rdd: RDD[(java.util.Map[String, String], List[java.util.Map[String, String]])]): RDD[String] = {
+    rdd.mapPartitions(partition => {
+      SimilarityUtils.init()
+      partition.map(raw => {
+        val record = raw._1
+        val videoSeq = raw._2
+        val traces = getTraces(record).toString()
+        val labels = DataUtils.getLabels(labelNames, record).toString
+        val features = ConvertV4.getFeature(record, videoSeq, 6).toString
+        val scoresMap = DataUtils.getSubJson(record, "extend_alg", "scoresMap").toString
+        traces + "\t" + labels + "\t" + scoresMap + "\t" + features
+      })
+    })
+  }
+
+  def getTraces(record: java.util.Map[String, String]): JSONObject = {
+    val traces = new JSONObject
+    traces.put("apptype", record.getOrElse("apptype", ""))
+    traces.put("page", record.getOrElse("page", ""))
+    traces.put("pagesource", record.getOrElse("pagesource", ""))
+    traces.put("recommendpagetype", record.getOrElse("recommendpagetype", ""))
+    traces.put("flowpool", record.getOrElse("flowpool", ""))
+    traces.put("abcode", record.getOrElse("abcode", ""))
+    traces.put("mid", record.getOrElse("mid", ""))
+    traces.put("vid", record.getOrElse("vid", ""))
+    traces.put("level", record.getOrElse("level", "0"))
+    traces.put("ts", record.getOrElse("ts", "0"))
+    traces
+  }
+
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 1. 解析参数
+    val param = ParamUtils.parseArgs(args)
+    val project = param.getOrElse("project", "loghubods")
+    val table = param.getOrElse("table", "dwd_recsys_alg_sample_all_20250212")
+    val tablePart = param.getOrElse("tablePart", "64").toInt
+    val beginStr = param.getOrElse("beginStr", "2025031700")
+    val endStr = param.getOrElse("endStr", "2025031700")
+    val whatPages = param.getOrElse("whatPages", "详情后沉浸页,回流后沉浸页&内页feed,首页feed,详情页").split(",").toSet
+    val whatLabel = param.getOrElse("whatLabel", "is_return_noself")
+    val negSampleRate = param.getOrElse("negSampleRate", "0.03").toDouble
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/70_origin_data/")
+    val repartition = param.getOrElse("repartition", "64").toInt
+
+    // 2. 数据处理
+    val timeRange = MyDateUtils.getDateHourRange(beginStr, endStr)
+    for (dt_hh <- timeRange) {
+      // 2.1 分区
+      val dt = dt_hh.substring(0, 8)
+      val hh = dt_hh.substring(8, 10)
+      val partition = "dt=%s,hh=%s".format(dt, hh)
+      println("开始执行partition:" + partition)
+
+      // 2.2 加载视频信息
+      val originVideo = DataUtils.getODPSData(sc, project, "alg_recsys_feature_user_behavior_video", partition, tablePart)
+      val uniqVideo = parseVideoRdd(originVideo)
+
+      // 2.3 加载视频统计信息
+      val videoStat = DataUtils.getODPSData(sc, project, "alg_recsys_feature_video_clean_stat", partition, tablePart)
+        .map(record => {
+          val vid = record.getString("vid")
+          val feature = record.getString("feature")
+          (vid, feature)
+        })
+
+      // 2.4 加载样本数据
+      val odpsData = DataUtils.getODPSData(sc, project, table, partition, tablePart)
+        .filter(record => {
+          val page = getStringValue(record, "page")
+          whatPages.contains(page)
+        })
+
+      // 2.5 样本重采样
+      val filterColumns = Set("allfeaturemap", "metafeaturemap")
+      val resampleData = DataUtils.resampleWithoutInterception(whatLabel, negSampleRate, odpsData)
+        .map(record => {
+          val page = getStringValue(record, "page")
+          if (page.equals("详情页")) {
+            OnlineLogUtils.log2Map(record, o2oMap)
+          } else {
+            ConvertUtils.record2Map(record, filterColumns)
+          }
+        })
+
+      // 2.6 join video stat
+      val sampleData = joinVideoStat(resampleData, videoStat)
+
+      // 2.7 get vid mid rdd
+      val vidMidRdd = getVidMidRdd(sampleData)
+
+      // 2.8 get mid seq rdd
+      val midSeqRdd = getMidSeqRdd(vidMidRdd, uniqVideo)
+
+      // 2.9 历史行为关联video
+      val seqSampleData = joinMidSeq(sampleData, midSeqRdd)
+
+      // 2.10 特征转换
+      val featureData = getFeature(seqSampleData)
+
+      // 2.11 保存数据
+      val hdfsPath = "%s/%s%s".format(savePath, dt, hh)
+      DataUtils.saveData(featureData, hdfsPath, repartition)
+    }
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/makedata_recsys_83_bucket_20250512.scala

@@ -0,0 +1,130 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.alibaba.fastjson.JSON
+import com.aliyun.odps.spark.examples.myUtils.{MyHdfsUtils, ParamUtils}
+import org.apache.hadoop.io.compress.GzipCodec
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+import scala.io.Source
+
+object makedata_recsys_83_bucket_20250512 {
+  def main(args: Array[String]): Unit = {
+    // 1. param
+    val param = ParamUtils.parseArgs(args)
+    val readPath = param.getOrElse("readPath", "/dw/recommend/model/83_origin_data/20241210*")
+    val featureNameFile = param.getOrElse("featureName", "20250423_recsys_rov_name.txt")
+    val sampleRate = param.getOrElse("sampleRate", "1.0").toDouble
+    val bucketNum = param.getOrElse("bucketNum", "200").toInt
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/83_recsys_bucket/")
+    val fileName = param.getOrElse("fileName", "20250512_200")
+
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 2.load feature
+    val contentList = loadFeatureNames(featureNameFile)
+    val nameSet = contentList.toSet
+
+    // 3. load data
+    val data = sc.textFile(readPath)
+    val data1 = data
+      .map(r => {
+        val rList = r.split("\t")
+        val logKey = rList(0)
+        val featData = rList(rList.length - 1)
+        (logKey, featData)
+      })
+      .filter {
+        case (logKey, featData) =>
+          validData(logKey)
+      }
+      .map {
+        case (logKey, featData) =>
+          parseFeature(featData, nameSet)
+      }
+      .sample(withReplacement = false, sampleRate).repartition(24).persist()
+
+    // 4. bucket
+    val result = new ArrayBuffer[String]()
+    for (i <- contentList.indices) {
+      printf("特征(%d/%d): %s, at %d\n", i + 1, contentList.size, contentList(i), System.currentTimeMillis() / 1000L)
+      val data2 = data1.map(r => r.getOrDefault(contentList(i), 0D)).filter(_ > 1E-8).collect().sorted
+      val len = data2.length
+      if (len == 0) {
+        result.add(contentList(i) + "\t" + bucketNum.toString + "\t" + "0")
+      } else {
+        val oneBucketNum = (len - 1) / (bucketNum - 1) + 1 // 确保每个桶至少有一个元素
+        val buffers = new ArrayBuffer[Double]()
+
+        var lastBucketValue = data2(0) // 记录上一个桶的切分点
+        for (j <- 0 until len by oneBucketNum) {
+          val d = data2(j)
+          if (j > 0 && d != lastBucketValue) {
+            // 如果当前切分点不同于上一个切分点，则保存当前切分点
+            buffers += d
+          }
+          lastBucketValue = d // 更新上一个桶的切分点
+        }
+
+        // 最后一个桶的结束点应该是数组的最后一个元素
+        if (!buffers.contains(data2.last)) {
+          buffers += data2.last
+        }
+        result.add(contentList(i) + "\t" + bucketNum.toString + "\t" + buffers.mkString(","))
+      }
+    }
+
+    // 5. save
+    val hdfsPath = savePath + "/" + fileName
+    if (hdfsPath.nonEmpty && hdfsPath.startsWith("/dw/recommend/model/")) {
+      println("删除路径并开始数据写入:" + hdfsPath)
+      MyHdfsUtils.delete_hdfs_path(hdfsPath)
+      val data3 = sc.parallelize(result)
+      data3.repartition(1).saveAsTextFile(hdfsPath, classOf[GzipCodec])
+    } else {
+      println("路径不合法，无法写入:" + hdfsPath)
+    }
+  }
+
+  def loadFeatureNames(nameFile: String): List[String] = {
+    val buffer = Source.fromFile(nameFile)
+    val names = buffer.getLines().mkString("\n")
+    buffer.close()
+    val featList = names.split("\n")
+      .map(r => r.replace(" ", "").replaceAll("\n", ""))
+      .filter(r => r.nonEmpty)
+      .toList
+    println("featList.size=" + featList.size)
+    println(featList)
+    featList
+  }
+
+  private def validData(logKey: String): Boolean = {
+    // apptype, page, pagesource, recommendpagetype, flowpool, abcode, mid, vid, level, ts
+    val cells = logKey.split(",")
+    val flowPool = cells(4)
+    if (flowPool.isEmpty || flowPool.endsWith("#1")) {
+      true
+    } else {
+      false
+    }
+  }
+
+  def parseFeature(data: String, nameSet: Set[String]): scala.collection.mutable.Map[String, Double] = {
+    val features = scala.collection.mutable.Map[String, Double]()
+    if (data.nonEmpty) {
+      val obj = JSON.parseObject(data)
+      obj.foreach(r => {
+        if (nameSet.contains(r._1)) {
+          features.put(r._1, obj.getDoubleValue(r._1))
+        }
+      })
+    }
+    features
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/makedata_recsys_83_bucket_originData.scala

@@ -0,0 +1,191 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.aliyun.odps.data.Record
+import com.aliyun.odps.spark.examples.myUtils.DataUtils.getStringValue
+import com.aliyun.odps.spark.examples.myUtils._
+import examples.utils.SimilarityUtils
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+import scala.util.Random
+
+/*
+   20250317 提取特征
+ */
+
+object makedata_recsys_83_bucket_originData {
+  private val labelNames = List(
+    "is_share", "share_cnt",
+    "is_return_1", "return_1_uv",
+    "is_return_n", "return_n_uv",
+    "is_return_noself", "return_1_uv_noself",
+    "is_return_n_noself", "return_n_uv_noself"
+  )
+
+  private def parseVideoRdd(videoRdd: RDD[Record]): RDD[(String, java.util.Map[String, String])] = {
+    videoRdd
+      .map(record => {
+        val vid = record.getString("vid")
+        val feature = ConvertUtils.getRecordCol(record, "feature")
+        feature.put("vid", vid)
+        (vid, feature)
+      })
+      .reduceByKey((a, b) => if (a.size() > b.size()) a else b)
+  }
+
+  private def joinVideoStat(logRdd: RDD[java.util.Map[String, String]], videoStat: RDD[(String, String)]): RDD[java.util.Map[String, String]] = {
+    logRdd
+      .map(raw => {
+        val vid = raw.getOrElse("vid", "")
+        (vid, raw)
+      })
+      .leftOuterJoin(videoStat)
+      .map(raw => {
+        val info = raw._2._1
+        if (raw._2._2.isDefined) {
+          info.put("b0_feature", raw._2._2.get)
+        }
+        info
+      })
+  }
+
+  private def getVidMidRdd(logRdd: RDD[java.util.Map[String, String]]): RDD[(String, String)] = {
+    logRdd
+      .map(raw => {
+        val mid = raw.getOrElse("mid", "")
+        val c9 = raw.getOrElse("c9_feature", "")
+        (mid, c9)
+      })
+      .filter(_._1.nonEmpty)
+      .reduceByKey((a, b) => a)
+      .flatMap(raw => {
+        val result = new ArrayBuffer[(String, String)]
+        for (hVid <- ConvertUtils.getVidList(raw._2)) {
+          result += ((hVid, raw._1)) // (vid, mid)
+        }
+        result
+      })
+  }
+
+  private def getMidSeqRdd(vidMidRdd: RDD[(String, String)], videoRdd: RDD[(String, java.util.Map[String, String])]): RDD[(String, List[java.util.Map[String, String]])] = {
+    vidMidRdd
+      .join(videoRdd)
+      .map(raw => {
+        (raw._2._1, raw._2._2)
+      })
+      .groupByKey()
+      .map(raw => {
+        (raw._1, raw._2.toList)
+      })
+  }
+
+  private def joinMidSeq(logRdd: RDD[java.util.Map[String, String]], midSeqRdd: RDD[(String, List[java.util.Map[String, String]])]): RDD[(java.util.Map[String, String], List[java.util.Map[String, String]])] = {
+    logRdd
+      .map(raw => {
+        val mid = raw.getOrElse("mid", "")
+        (mid, raw)
+      })
+      .leftOuterJoin(midSeqRdd)
+      .map(raw => {
+        if (raw._2._2.isDefined) {
+          (raw._2._1, raw._2._2.get)
+        } else {
+          (raw._2._1, List())
+        }
+      })
+  }
+
+  private def getFeature(rdd: RDD[(java.util.Map[String, String], List[java.util.Map[String, String]])]): RDD[String] = {
+    rdd.mapPartitions(partition => {
+      SimilarityUtils.init()
+      partition.map(raw => {
+        val record = raw._1
+        val videoSeq = raw._2
+        val logKey = DataUtils.getLogKey(record)
+        val labels = DataUtils.getLabels(labelNames, record).toString
+        val features = ConvertV2.getFeature(record, videoSeq, 6).toString
+        val scoresMap = DataUtils.getSubJson(record, "extend_alg", "scoresMap").toString
+        logKey + "\t" + labels + "\t" + scoresMap + "\t" + features
+      })
+    })
+  }
+
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 1. 解析参数
+    val param = ParamUtils.parseArgs(args)
+    val project = param.getOrElse("project", "loghubods")
+    val table = param.getOrElse("table", "dwd_recsys_alg_sample_all_20250212")
+    val tablePart = param.getOrElse("tablePart", "64").toInt
+    val beginStr = param.getOrElse("beginStr", "2025031700")
+    val endStr = param.getOrElse("endStr", "2025031700")
+    val whatLabel = param.getOrElse("whatLabel", "is_return_noself")
+    val fuSampleRate = param.getOrElse("fuSampleRate", "0.03").toDouble
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/83_origin_data/")
+    val repartition = param.getOrElse("repartition", "64").toInt
+
+    // 2. 数据处理
+    val timeRange = MyDateUtils.getDateHourRange(beginStr, endStr)
+    for (dt_hh <- timeRange) {
+      // 2.1 分区
+      val dt = dt_hh.substring(0, 8)
+      val hh = dt_hh.substring(8, 10)
+      val partition = "dt=%s,hh=%s".format(dt, hh)
+      println("开始执行partition:" + partition)
+
+      // 2.2 加载视频信息
+      val originVideo = DataUtils.getODPSData(sc, project, "alg_recsys_feature_user_behavior_video", partition, tablePart)
+      val uniqVideo = parseVideoRdd(originVideo)
+
+      // 2.3 加载视频统计信息
+      val videoStat = DataUtils.getODPSData(sc, project, "alg_recsys_feature_video_clean_stat", partition, tablePart)
+        .map(record => {
+          val vid = record.getString("vid")
+          val feature = record.getString("feature")
+          (vid, feature)
+        })
+
+      // 2.4 加载样本数据
+      val odpsData = DataUtils.getODPSData(sc, project, table, partition, tablePart)
+
+      // 2.5 样本重采样
+      val pageSet = Set("详情后沉浸页", "回流后沉浸页&内页feed", "首页feed")
+      val filterColumns = Set("allfeaturemap", "metafeaturemap")
+      val resampleData = odpsData
+        .filter(record => {
+          val page = getStringValue(record, "page")
+          pageSet.contains(page) && new Random().nextDouble() <= fuSampleRate
+        })
+        .map(record => {
+          ConvertUtils.record2Map(record, filterColumns)
+        })
+
+      // 2.6 join video stat
+      val sampleData = joinVideoStat(resampleData, videoStat)
+
+      // 2.7 get vid mid rdd
+      val vidMidRdd = getVidMidRdd(sampleData)
+
+      // 2.8 get mid seq rdd
+      val midSeqRdd = getMidSeqRdd(vidMidRdd, uniqVideo)
+
+      // 2.9 历史行为关联video
+      val seqSampleData = joinMidSeq(sampleData, midSeqRdd)
+
+      // 2.10 特征转换
+      val featureData = getFeature(seqSampleData)
+
+      // 2.11 保存数据
+      val hdfsPath = "%s/%s%s".format(savePath, dt, hh)
+      DataUtils.saveData(featureData, hdfsPath, repartition)
+    }
+  }
+}
+

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/makedata_recsys_84_fm_sample_20250423.scala

@@ -0,0 +1,112 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.aliyun.odps.spark.examples.myUtils.{DataUtils, MyDateUtils, MyHdfsUtils, ParamUtils}
+import org.apache.hadoop.io.compress.GzipCodec
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+import scala.util.Random
+
+object makedata_recsys_84_fm_sample_20250423 {
+  def main(args: Array[String]): Unit = {
+    // 1. 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val readPath = param.getOrElse("readPath", "/dw/recommend/model/83_origin_data/")
+    val beginStr = param.getOrElse("beginStr", "20250317")
+    val endStr = param.getOrElse("endStr", "20250317")
+    val whatApps = param.getOrElse("whatApps", "0,4,5,21,3,6").split(",").toSet
+    val whatPages = param.getOrElse("whatPages", "详情后沉浸页,回流后沉浸页&内页feed,首页feed,详情页").split(",").toSet
+    val whatLabel = param.getOrElse("whatLabel", "is_return_n_noself")
+    val fuSampleRate = param.getOrElse("fuSampleRate", "-1.0").toDouble
+    val notUseBucket = param.getOrElse("notUseBucket", "0").toInt
+    val featureNameFile = param.getOrElse("featureName", "20250317_recsys_rov_name.txt")
+    val featureBucketFile = param.getOrElse("featureBucket", "20241209_recsys_nor_bucket.txt")
+    val repartition = param.getOrElse("repartition", "100").toInt
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/85_recsys_rov_train_data/")
+
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 2. 加载特征
+    val featureNameSet = DataUtils.loadFeatureNames(featureNameFile)
+    val featureBucketMap = DataUtils.loadFeatureBuckets(notUseBucket, featureBucketFile)
+    val bucketsMap_br = sc.broadcast(featureBucketMap)
+
+    // 3. 处理数据
+    val dateRange = MyDateUtils.getDateRange(beginStr, endStr)
+    for (date <- dateRange) {
+      println("开始执行:" + date)
+      val data = sc.textFile(readPath + "/" + date + "*").map(r => {
+          val rList = r.split("\t")
+          val logKey = rList(0)
+          val labelKey = rList(1)
+          val scoresMap = rList(2)
+          val featData = rList(3)
+          (logKey, labelKey, scoresMap, featData)
+        })
+        .filter {
+          case (logKey, labelKey, scoresMap, featData) =>
+            validData(logKey, whatApps, whatPages)
+        }.filter {
+          case (logKey, labelKey, scoresMap, featData) =>
+            val label = DataUtils.parseLabel(labelKey, whatLabel).toDouble
+            label > 0 || new Random().nextDouble() <= fuSampleRate
+        }
+        .map {
+          case (logKey, labelKey, scoresMap, featData) =>
+            val label = DataUtils.parseLabel(labelKey, whatLabel).toDouble
+            val features = DataUtils.parseFeature(featData)
+            (logKey, label, scoresMap, features)
+        }
+        .mapPartitions(row => {
+          val result = new ArrayBuffer[String]()
+          val bucketsMap = bucketsMap_br.value
+          row.foreach {
+            case (logKey, label, scoresMap, features) =>
+              val featuresBucket = DataUtils.bucketFeature(featureNameSet, bucketsMap, features)
+              result.add(label + "\t" + featuresBucket.mkString("\t"))
+          }
+          result.iterator
+        })
+
+      // 4. 保存数据到hdfs
+      val hdfsPath = savePath + "/" + date
+      if (hdfsPath.nonEmpty && hdfsPath.startsWith("/dw/recommend/model/")) {
+        println("删除路径并开始数据写入:" + hdfsPath)
+        MyHdfsUtils.delete_hdfs_path(hdfsPath)
+        data.repartition(repartition).saveAsTextFile(hdfsPath, classOf[GzipCodec])
+      } else {
+        println("路径不合法，无法写入:" + hdfsPath)
+      }
+    }
+  }
+
+  private def recommendFlow(flowPool: String): Boolean = {
+    if (flowPool.isEmpty || flowPool.endsWith("#1")) {
+      return true
+    }
+    false
+  }
+
+  private def validData(logKey: String, whatApps: Set[String], whatPages: Set[String]): Boolean = {
+    // apptype, page, pagesource, recommendpagetype, flowpool, abcode, mid, vid, level, ts
+    val cells = logKey.split(",")
+    val apptype = cells(0)
+    val page = cells(1)
+    //val pagesource = cells(2)
+    val recommendpagetype = cells(3)
+    val flowpool = cells(4)
+    if (whatApps.contains(apptype)) {
+      if (recommendFlow(flowpool)) {
+        if (whatPages.contains(page)) {
+          return true
+        }
+      }
+    }
+    false
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/makedata_recsys_84_originData_20250812.scala

@@ -0,0 +1,295 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.aliyun.odps.data.Record
+import com.aliyun.odps.spark.examples.myUtils.DataUtils.getStringValue
+import com.aliyun.odps.spark.examples.myUtils._
+import examples.utils.SimilarityUtils
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+
+/*
+   20250317 提取特征
+ */
+
+object makedata_recsys_84_originData_20250812 {
+  private val labelNames = List(
+    "is_share", "share_cnt",
+    "is_return_1", "return_1_uv",
+    "is_return_n", "return_n_uv",
+    "is_return_noself", "return_1_uv_noself",
+    "is_return_n_noself", "return_n_uv_noself"
+  )
+  private val o2oMap = Map(
+    "creative" -> "creativeInfo"
+  )
+  private val detailPageO2OMap = Map(
+    "d1_feature" -> "scene_type_vid_cf_feature_20250212",
+    "d2_feature" -> "vid_click_cf_feature_20250212",
+    "d3_feature" -> "alg_recsys_feature_cf_i2i_v2",
+    "v2_feature" -> "head_video",
+    "creative" -> "creativeInfo"
+  )
+  private val returnPageO2OMap = Map(
+    "d1_feature" -> "null1",
+    "d2_feature" -> "null2",
+    "d3_feature" -> "null3",
+    "v2_feature" -> "null4",
+    "creative" -> "creativeInfo"
+  )
+
+  private def parseVideoRdd(videoRdd: RDD[Record]): RDD[(String, java.util.Map[String, String])] = {
+    videoRdd
+      .map(record => {
+        val vid = record.getString("vid")
+        val feature = ConvertUtils.getRecordCol(record, "feature")
+        feature.put("vid", vid)
+        (vid, feature)
+      })
+      .reduceByKey((a, b) => if (a.size() > b.size()) a else b)
+  }
+
+  private def joinVideoStat(logRdd: RDD[java.util.Map[String, String]], videoStat: RDD[(String, String)]): RDD[java.util.Map[String, String]] = {
+    logRdd
+      .map(raw => {
+        val vid = raw.getOrElse("vid", "")
+        (vid, raw)
+      })
+      .leftOuterJoin(videoStat)
+      .map(raw => {
+        val info = raw._2._1
+        if (raw._2._2.isDefined) {
+          info.put("b0_feature", raw._2._2.get)
+        }
+        info
+      })
+  }
+
+  private def joinChannelLayerStat(logRdd: RDD[java.util.Map[String, String]], videoStat: RDD[(String, String)]): RDD[java.util.Map[String, String]] = {
+    logRdd
+      .map(raw => {
+        val extendMap = ConvertUtils.getRecordCol(raw, "extend")
+        val channel = FeatureUtils.getUserChannel(raw.get("rootsourceid"), extendMap.get("group_name"))
+        val level = FeatureUtils.getUserLevel(extendMap.get("rootsessionid"), raw.get("subsessionid"))
+        val vid = raw.getOrElse("vid", "")
+        if (level.isEmpty) {
+          val key = Seq(channel, "非0层", vid).mkString(":")
+          (key, raw)
+        } else {
+          val key = Seq(channel, "0层", vid).mkString(":")
+          (key, raw)
+        }
+      })
+      .leftOuterJoin(videoStat)
+      .map(raw => {
+        val info = raw._2._1
+        if (raw._2._2.isDefined) {
+          info.put("b14_feature", raw._2._2.get)
+        }
+        info
+      })
+  }
+
+  private def joinChannelLayerHeadStat(logRdd: RDD[java.util.Map[String, String]], videoStat: RDD[(String, String)]): RDD[java.util.Map[String, String]] = {
+    logRdd
+      .map(raw => {
+        val extendMap = ConvertUtils.getRecordCol(raw, "extend")
+        val channel = FeatureUtils.getUserChannel(raw.get("rootsourceid"), extendMap.get("group_name"))
+        val level = FeatureUtils.getUserLevel(extendMap.get("rootsessionid"), raw.get("subsessionid"))
+        val headInfo = ConvertUtils.getRecordCol(raw, "v2_feature")
+        val unionid = headInfo.getOrElse("title_time_w_h_unionid", "")
+        val vid = raw.getOrElse("vid", "")
+        if (level.isEmpty) {
+          val key = Seq(channel, "非0层", unionid, vid).mkString(":")
+          (key, raw)
+        } else {
+          val key = Seq(channel, "0层", unionid, vid).mkString(":")
+          (key, raw)
+        }
+      })
+      .leftOuterJoin(videoStat)
+      .map(raw => {
+        val info = raw._2._1
+        if (raw._2._2.isDefined) {
+          info.put("b15_feature", raw._2._2.get)
+        }
+        info
+      })
+  }
+
+  private def getVidMidRdd(logRdd: RDD[java.util.Map[String, String]]): RDD[(String, String)] = {
+    logRdd
+      .map(raw => {
+        val mid = raw.getOrElse("mid", "")
+        val c9 = raw.getOrElse("c9_feature", "")
+        (mid, c9)
+      })
+      .filter(_._1.nonEmpty)
+      .reduceByKey((a, b) => a)
+      .flatMap(raw => {
+        val result = new ArrayBuffer[(String, String)]
+        for (hVid <- ConvertUtils.getVidList(raw._2)) {
+          result += ((hVid, raw._1)) // (vid, mid)
+        }
+        result
+      })
+  }
+
+  private def getMidSeqRdd(vidMidRdd: RDD[(String, String)], videoRdd: RDD[(String, java.util.Map[String, String])]): RDD[(String, List[java.util.Map[String, String]])] = {
+    vidMidRdd
+      .join(videoRdd)
+      .map(raw => {
+        (raw._2._1, raw._2._2)
+      })
+      .groupByKey()
+      .map(raw => {
+        (raw._1, raw._2.toList)
+      })
+  }
+
+  private def joinMidSeq(logRdd: RDD[java.util.Map[String, String]], midSeqRdd: RDD[(String, List[java.util.Map[String, String]])]): RDD[(java.util.Map[String, String], List[java.util.Map[String, String]])] = {
+    logRdd
+      .map(raw => {
+        val mid = raw.getOrElse("mid", "")
+        (mid, raw)
+      })
+      .leftOuterJoin(midSeqRdd)
+      .map(raw => {
+        if (raw._2._2.isDefined) {
+          (raw._2._1, raw._2._2.get)
+        } else {
+          (raw._2._1, List())
+        }
+      })
+  }
+
+  private def getFeature(rdd: RDD[(java.util.Map[String, String], List[java.util.Map[String, String]])]): RDD[String] = {
+    rdd.mapPartitions(partition => {
+      SimilarityUtils.newInit()
+      partition.map(raw => {
+        val record = raw._1
+        val videoSeq = raw._2
+        val logKey = DataUtils.getLogKey(record)
+        val labels = DataUtils.getLabels(labelNames, record).toString
+        val features = ConvertV2.getFeature(record, videoSeq, 6).toString
+        val scoresMap = DataUtils.getSubJson(record, "extend_alg", "scoresMap").toString
+        if (features.nonEmpty) {
+          logKey + "\t" + labels + "\t" + scoresMap + "\t" + features
+        } else {
+          ""
+        }
+      })
+    })
+      .filter(_.nonEmpty)
+  }
+
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 1. 解析参数
+    val param = ParamUtils.parseArgs(args)
+    val project = param.getOrElse("project", "loghubods")
+    val table = param.getOrElse("table", "dwd_recsys_alg_sample_all_20250212")
+    val tablePart = param.getOrElse("tablePart", "64").toInt
+    val beginStr = param.getOrElse("beginStr", "2025031700")
+    val endStr = param.getOrElse("endStr", "2025031700")
+    val whatPages = param.getOrElse("whatPages", "详情后沉浸页,回流后沉浸页&内页feed,首页feed,详情页,回流页").split(",").toSet
+    val whatLabel = param.getOrElse("whatLabel", "is_return_noself")
+    val fuSampleRate = param.getOrElse("fuSampleRate", "0.03").toDouble
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/83_origin_data/")
+    val repartition = param.getOrElse("repartition", "64").toInt
+
+    // 2. 数据处理
+    val timeRange = MyDateUtils.getDateHourRange(beginStr, endStr)
+    for (dt_hh <- timeRange) {
+      // 2.1 分区
+      val dt = dt_hh.substring(0, 8)
+      val hh = dt_hh.substring(8, 10)
+      val partition = "dt=%s,hh=%s".format(dt, hh)
+      println("开始执行partition:" + partition)
+
+      // 2.2 加载视频信息
+      val originVideo = DataUtils.getODPSData(sc, project, "alg_recsys_feature_user_behavior_video", partition, tablePart)
+      val uniqVideo = parseVideoRdd(originVideo)
+
+      // 2.3 加载视频统计信息
+      val videoStat = DataUtils.getODPSData(sc, project, "alg_recsys_feature_video_clean_stat", partition, tablePart)
+        .map(record => {
+          val vid = record.getString("vid")
+          val feature = record.getString("feature")
+          (vid, feature)
+        })
+
+      // 2.4 加载渠道特征
+      val channelLayerStat = DataUtils.getODPSData(sc, project, "alg_recsys_feature_video_recommend_channel_layer", partition, 8)
+        .map(record => {
+          val channel = record.getString("channel")
+          val layer = record.getString("layer")
+          val vid = record.getString("vid")
+          val feature = record.getString("feature")
+          val key = Seq(channel, layer, vid).mkString(":")
+          (key, feature)
+        })
+
+      // 2.5 加载渠道头部特征
+      val channelLayerHeadStat = DataUtils.getODPSData(sc, project, "alg_recsys_feature_video_recommend_channel_layer_head", partition, 8)
+        .map(record => {
+          val channel = record.getString("channel")
+          val layer = record.getString("layer")
+          val unionid = record.getString("unionid")
+          val vid = record.getString("vid")
+          val feature = record.getString("feature")
+          val key = Seq(channel, layer, unionid, vid).mkString(":")
+          (key, feature)
+        })
+
+      // 2.6 加载样本数据
+      val odpsData = DataUtils.getODPSData(sc, project, table, partition, tablePart)
+        .filter(record => {
+          val page = getStringValue(record, "page")
+          whatPages.contains(page)
+        })
+
+      // 2.7 样本重采样
+      val filterColumns = Set("allfeaturemap", "metafeaturemap")
+      val resampleData = DataUtils.resampleWithoutInterception(whatLabel, fuSampleRate, odpsData)
+        .map(record => {
+          val page = getStringValue(record, "page")
+          if (page.equals("详情页")) {
+            OnlineLogUtils.log2Map(record, detailPageO2OMap)
+          } else if (page.equals("回流页")) {
+            OnlineLogUtils.log2Map(record, returnPageO2OMap)
+          } else {
+            OnlineLogUtils.log2Map(record, o2oMap)
+          }
+        })
+
+      // 2.8 join video stat
+      val sampleDataTmp1 = joinVideoStat(resampleData, videoStat)
+      val sampleDataTmp2 = joinChannelLayerStat(sampleDataTmp1, channelLayerStat)
+      val sampleData = joinChannelLayerHeadStat(sampleDataTmp2, channelLayerHeadStat)
+
+      // 2.9 get vid mid rdd
+      val vidMidRdd = getVidMidRdd(sampleData)
+
+      // 2.10 get mid seq rdd
+      val midSeqRdd = getMidSeqRdd(vidMidRdd, uniqVideo)
+
+      // 2.11 历史行为关联video
+      val seqSampleData = joinMidSeq(sampleData, midSeqRdd)
+
+      // 2.12 特征转换
+      val featureData = getFeature(seqSampleData)
+
+      // 2.13 保存数据
+      val hdfsPath = "%s/%s%s".format(savePath, dt, hh)
+      DataUtils.saveData(featureData, hdfsPath, repartition)
+    }
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/makedata_recsys_85_fm_sample_20250512.scala

@@ -0,0 +1,157 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.aliyun.odps.spark.examples.myUtils.{DataUtils, MyDateUtils, MyHdfsUtils, ParamUtils}
+import examples.extractor.ExtractorUtils
+import org.apache.hadoop.io.compress.GzipCodec
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+import scala.io.Source
+import scala.util.Random
+
+object makedata_recsys_85_fm_sample_20250512 {
+  def main(args: Array[String]): Unit = {
+    // 1. 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val readPath = param.getOrElse("readPath", "/dw/recommend/model/83_origin_data/")
+    val beginStr = param.getOrElse("beginStr", "20250317")
+    val endStr = param.getOrElse("endStr", "20250317")
+    val whatApps = param.getOrElse("whatApps", "0,4,5,21,3,6").split(",").toSet
+    val whatPages = param.getOrElse("whatPages", "详情后沉浸页,回流后沉浸页&内页feed,首页feed,详情页").split(",").toSet
+    val whatLabel = param.getOrElse("whatLabel", "is_return_n_noself")
+    val fuSampleRate = param.getOrElse("fuSampleRate", "-1.0").toDouble
+    val notUseBucket = param.getOrElse("notUseBucket", "0").toInt
+    val featureSampleRate = param.getOrElse("featureSampleRate", "0.5").toDouble
+    val noSampleFeatureFile = param.getOrElse("noSampleFeatureFile", "20250317_recsys_rov_name.txt")
+    val featureNameFile = param.getOrElse("featureName", "20250317_recsys_rov_name.txt")
+    val featureBucketFile = param.getOrElse("featureBucket", "20241209_recsys_nor_bucket.txt")
+    val repartition = param.getOrElse("repartition", "100").toInt
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/83_recsys_rov_train_data/")
+
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 2. 加载特征
+    val loader = getClass.getClassLoader
+    val noSampleSet = loadFeatureNames(noSampleFeatureFile)
+    val featureNameSet = loadFeatureNames(featureNameFile)
+    val featureBucketMap = DataUtils.loadUseFeatureBuckets(loader, notUseBucket, featureBucketFile)
+    val bucketsMap_br = sc.broadcast(featureBucketMap)
+
+    // 3. 处理数据
+    val dateRange = MyDateUtils.getDateRange(beginStr, endStr)
+    for (date <- dateRange) {
+      println("开始执行:" + date)
+      val data = sc.textFile(readPath + "/" + date + "*").map(r => {
+          val rList = r.split("\t")
+          val logKey = rList(0)
+          val labelKey = rList(1)
+          val scoresMap = rList(2)
+          val featData = rList(3)
+          (logKey, labelKey, scoresMap, featData)
+        })
+        .filter {
+          case (logKey, labelKey, scoresMap, featData) =>
+            validData(logKey, whatApps, whatPages)
+        }.filter {
+          case (logKey, labelKey, scoresMap, featData) =>
+            val label = DataUtils.parseLabel(labelKey, whatLabel).toDouble
+            label > 0 || new Random().nextDouble() <= fuSampleRate
+        }
+        .map {
+          case (logKey, labelKey, scoresMap, featData) =>
+            val label = DataUtils.parseLabel(labelKey, whatLabel).toDouble
+            val features = DataUtils.parseFeature(featData)
+            (logKey, label, scoresMap, features)
+        }
+        .mapPartitions(row => {
+          val result = new ArrayBuffer[String]()
+          val bucketsMap = bucketsMap_br.value
+          row.foreach {
+            case (logKey, label, scoresMap, features) =>
+              val flag = new Random().nextDouble() <= featureSampleRate
+              val featuresBucket = bucketFeature(flag, noSampleSet, featureNameSet, bucketsMap, features)
+              result.add(label + "\t" + featuresBucket.mkString("\t"))
+          }
+          result.iterator
+        })
+
+      // 4. 保存数据到hdfs
+      val hdfsPath = savePath + "/" + date
+      if (hdfsPath.nonEmpty && hdfsPath.startsWith("/dw/recommend/model/")) {
+        println("删除路径并开始数据写入:" + hdfsPath)
+        MyHdfsUtils.delete_hdfs_path(hdfsPath)
+        data.repartition(repartition).saveAsTextFile(hdfsPath, classOf[GzipCodec])
+      } else {
+        println("路径不合法，无法写入:" + hdfsPath)
+      }
+    }
+  }
+
+  private def recommendFlow(flowPool: String): Boolean = {
+    if (flowPool.isEmpty || flowPool.endsWith("#1")) {
+      return true
+    }
+    false
+  }
+
+  private def validData(logKey: String, whatApps: Set[String], whatPages: Set[String]): Boolean = {
+    // apptype, page, pagesource, recommendpagetype, flowpool, abcode, mid, vid, level, ts
+    val cells = logKey.split(",")
+    val apptype = cells(0)
+    val page = cells(1)
+    //val pagesource = cells(2)
+    val recommendpagetype = cells(3)
+    val flowpool = cells(4)
+    if (whatApps.contains(apptype)) {
+      if (recommendFlow(flowpool)) {
+        if (whatPages.contains(page)) {
+          return true
+        }
+      }
+    }
+    false
+  }
+
+  def loadFeatureNames(nameFile: String): Set[String] = {
+    val buffer = Source.fromFile(nameFile)
+    val names = buffer.getLines().mkString("\n")
+    buffer.close()
+    val featSet = names.split("\n")
+      .map(r => r.replace(" ", "").replaceAll("\n", ""))
+      .filter(r => r.nonEmpty)
+      .toSet
+    println("featSet.size=" + featSet.size)
+    println(featSet)
+    featSet
+  }
+
+  def bucketFeature(flag: Boolean, noSampleSet: Set[String], nameSet: Set[String], bucketMap: Map[String, (Double, Array[Double])], features: scala.collection.mutable.Map[String, Double]): Iterable[String] = {
+    features.map {
+      case (name, score) =>
+        if (!nameSet.contains(name)) {
+          ""
+        } else {
+          if (score > 1E-8) {
+            if (bucketMap.contains(name)) {
+              val (bucketsNum, buckets) = bucketMap(name)
+              val scoreNew = 1.0 / bucketsNum * (ExtractorUtils.findInsertPosition(buckets, score).toDouble + 1.0)
+              name + ":" + scoreNew.toString
+            } else {
+              if (flag || noSampleSet.contains(name)) {
+                name + ":" + score.toString
+              } else {
+                ""
+              }
+            }
+          } else {
+            ""
+          }
+        }
+    }.filter(_.nonEmpty)
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/makedata_recsys_85_nor_sample_20250512.scala

@@ -0,0 +1,155 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.aliyun.odps.spark.examples.myUtils.{DataUtils, MyDateUtils, MyHdfsUtils, ParamUtils}
+import examples.extractor.ExtractorUtils
+import org.apache.hadoop.io.compress.GzipCodec
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+import scala.io.Source
+import scala.util.Random
+
+object makedata_recsys_85_nor_sample_20250512 {
+  def main(args: Array[String]): Unit = {
+    // 1. 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val readPath = param.getOrElse("readPath", "/dw/recommend/model/83_origin_data/")
+    val beginStr = param.getOrElse("beginStr", "20250221")
+    val endStr = param.getOrElse("endStr", "20250221")
+    val whatApps = param.getOrElse("whatApps", "0,4,5,21,3,6").split(",").toSet
+    val whatPages = param.getOrElse("whatPages", "详情后沉浸页,回流后沉浸页&内页feed,首页feed,详情页").split(",").toSet
+    val whatLabel = param.getOrElse("whatLabel", "return_n_uv_noself")
+    val fuSampleRate = param.getOrElse("fuSampleRate", "-1.0").toDouble
+    val notUseBucket = param.getOrElse("notUseBucket", "0").toInt
+    val featureSampleRate = param.getOrElse("featureSampleRate", "0.5").toDouble
+    val noSampleFeatureFile = param.getOrElse("noSampleFeatureFile", "20250317_recsys_rov_name.txt")
+    val featureNameFile = param.getOrElse("featureName", "20241209_recsys_nor_name.txt")
+    val featureBucketFile = param.getOrElse("featureBucket", "20241209_recsys_nor_bucket.txt")
+    val repartition = param.getOrElse("repartition", "100").toInt
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/83_recsys_nor_train_data/")
+
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    val loader = getClass.getClassLoader
+    val noSampleSet = loadFeatureNames(noSampleFeatureFile)
+    val featureNameSet = loadFeatureNames(featureNameFile)
+    val featureBucketMap = DataUtils.loadUseFeatureBuckets(loader, notUseBucket, featureBucketFile)
+    val bucketsMap_br = sc.broadcast(featureBucketMap)
+
+    val dateRange = MyDateUtils.getDateRange(beginStr, endStr)
+    for (date <- dateRange) {
+      println("开始执行:" + date)
+      val data = sc.textFile(readPath + "/" + date + "*").map(r => {
+          val rList = r.split("\t")
+          val logKey = rList(0)
+          val labelKey = rList(1)
+          val scoresMap = rList(2)
+          val featData = rList(3)
+          (logKey, labelKey, scoresMap, featData)
+        })
+        .filter {
+          case (logKey, labelKey, scoresMap, featData) =>
+            validData(logKey, whatApps, whatPages)
+        }.filter {
+          case (logKey, labelKey, scoresMap, featData) =>
+            val label = DataUtils.parseLabel(labelKey, whatLabel).toDouble
+            label > 0 || new Random().nextDouble() <= fuSampleRate
+        }
+        .map {
+          case (logKey, labelKey, scoresMap, featData) =>
+            val label = DataUtils.parseLabel(labelKey, whatLabel).toDouble
+            val features = DataUtils.parseFeature(featData)
+            (logKey, label, scoresMap, features)
+        }
+        .mapPartitions(row => {
+          val result = new ArrayBuffer[String]()
+          val bucketsMap = bucketsMap_br.value
+          row.foreach {
+            case (logKey, label, scoresMap, features) =>
+              val flag = new Random().nextDouble() <= featureSampleRate
+              val featuresBucket = bucketFeature(flag, noSampleSet, featureNameSet, bucketsMap, features)
+              result.add(logKey + "\t" + label + "\t" + scoresMap + "\t" + featuresBucket.mkString("\t"))
+          }
+          result.iterator
+        })
+
+      // 4. 保存数据到hdfs
+      val hdfsPath = savePath + "/" + date
+      if (hdfsPath.nonEmpty && hdfsPath.startsWith("/dw/recommend/model/")) {
+        println("删除路径并开始数据写入:" + hdfsPath)
+        MyHdfsUtils.delete_hdfs_path(hdfsPath)
+        data.repartition(repartition).saveAsTextFile(hdfsPath, classOf[GzipCodec])
+      } else {
+        println("路径不合法，无法写入:" + hdfsPath)
+      }
+    }
+  }
+
+  private def recommendFlow(flowPool: String): Boolean = {
+    if (flowPool.isEmpty || flowPool.endsWith("#1")) {
+      return true
+    }
+    false
+  }
+
+  private def validData(logKey: String, whatApps: Set[String], whatPages: Set[String]): Boolean = {
+    // apptype, page, pagesource, recommendpagetype, flowpool, abcode, mid, vid, level, ts
+    val cells = logKey.split(",")
+    val apptype = cells(0)
+    val page = cells(1)
+    //val pagesource = cells(2)
+    val recommendpagetype = cells(3)
+    val flowpool = cells(4)
+    if (whatApps.contains(apptype)) {
+      if (recommendFlow(flowpool)) {
+        if (whatPages.contains(page)) {
+          return true
+        }
+      }
+    }
+    false
+  }
+
+  def loadFeatureNames(nameFile: String): Set[String] = {
+    val buffer = Source.fromFile(nameFile)
+    val names = buffer.getLines().mkString("\n")
+    buffer.close()
+    val featSet = names.split("\n")
+      .map(r => r.replace(" ", "").replaceAll("\n", ""))
+      .filter(r => r.nonEmpty)
+      .toSet
+    println("featSet.size=" + featSet.size)
+    println(featSet)
+    featSet
+  }
+
+  def bucketFeature(flag: Boolean, noSampleSet: Set[String], nameSet: Set[String], bucketMap: Map[String, (Double, Array[Double])], features: scala.collection.mutable.Map[String, Double]): Iterable[String] = {
+    features.map {
+      case (name, score) =>
+        if (!nameSet.contains(name)) {
+          ""
+        } else {
+          if (score > 1E-8) {
+            if (bucketMap.contains(name)) {
+              val (bucketsNum, buckets) = bucketMap(name)
+              val scoreNew = 1.0 / bucketsNum * (ExtractorUtils.findInsertPosition(buckets, score).toDouble + 1.0)
+              name + ":" + scoreNew.toString
+            } else {
+              if (flag || noSampleSet.contains(name)) {
+                name + ":" + score.toString
+              } else {
+                ""
+              }
+            }
+          } else {
+            ""
+          }
+        }
+    }.filter(_.nonEmpty)
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/mid_case_sample.scala

@@ -0,0 +1,132 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.aliyun.odps.spark.examples.myUtils.{DataUtils, MyDateUtils, MyHdfsUtils, ParamUtils}
+import org.apache.hadoop.io.compress.GzipCodec
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+import scala.io.Source
+import scala.util.Random
+
+object mid_case_sample {
+  def main(args: Array[String]): Unit = {
+    // 1. 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val readPath = param.getOrElse("readPath", "/dw/recommend/model/83_origin_data/")
+    val beginStr = param.getOrElse("beginStr", "20250317")
+    val endStr = param.getOrElse("endStr", "20250317")
+    val whatMids = param.getOrElse("whatMids", "0,4,5,21,3,6").split(",").toSet
+    val whatApps = param.getOrElse("whatApps", "0,4,5,21,3,6").split(",").toSet
+    val whatLabel = param.getOrElse("whatLabel", "is_return_n_noself")
+    val fuSampleRate = param.getOrElse("fuSampleRate", "-1.0").toDouble
+    val notUseBucket = param.getOrElse("notUseBucket", "0").toInt
+    val featureNameFile = param.getOrElse("featureName", "20250317_recsys_rov_name.txt")
+    val featureBucketFile = param.getOrElse("featureBucket", "20241209_recsys_nor_bucket.txt")
+    val repartition = param.getOrElse("repartition", "100").toInt
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/83_recsys_rov_train_data/")
+
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 2. 加载特征
+    val loader = getClass.getClassLoader
+    val featureNameSet = loadFeatureNames(featureNameFile)
+    val featureBucketMap = DataUtils.loadUseFeatureBuckets(loader, notUseBucket, featureBucketFile)
+    val bucketsMap_br = sc.broadcast(featureBucketMap)
+
+    // 3. 处理数据
+    val dateRange = MyDateUtils.getDateRange(beginStr, endStr)
+    for (date <- dateRange) {
+      println("开始执行:" + date)
+      val data = sc.textFile(readPath + "/" + date + "*").map(r => {
+          val rList = r.split("\t")
+          val logKey = rList(0)
+          val labelKey = rList(1)
+          val scoresMap = rList(2)
+          val featData = rList(3)
+          (logKey, labelKey, scoresMap, featData)
+        })
+        .filter {
+          case (logKey, labelKey, scoresMap, featData) =>
+            validData(logKey, whatApps, whatMids)
+        }.filter {
+          case (logKey, labelKey, scoresMap, featData) =>
+            val label = DataUtils.parseLabel(labelKey, whatLabel).toDouble
+            label > 0 || new Random().nextDouble() <= fuSampleRate
+        }
+        .map {
+          case (logKey, labelKey, scoresMap, featData) =>
+            val label = DataUtils.parseLabel(labelKey, whatLabel).toDouble
+            val features = DataUtils.parseFeature(featData)
+            (logKey, label, scoresMap, features)
+        }
+        .mapPartitions(row => {
+          val result = new ArrayBuffer[String]()
+          val bucketsMap = bucketsMap_br.value
+          row.foreach {
+            case (logKey, label, scoresMap, features) =>
+              val featuresBucket = DataUtils.bucketFeature(featureNameSet, bucketsMap, features)
+              result.add(logKey + "\t" + label + "\t" + featuresBucket.mkString("\t"))
+          }
+          result.iterator
+        })
+
+      // 4. 保存数据到hdfs
+      val hdfsPath = savePath + "/" + date
+      if (hdfsPath.nonEmpty && hdfsPath.startsWith("/dw/recommend/model/")) {
+        println("删除路径并开始数据写入:" + hdfsPath)
+        MyHdfsUtils.delete_hdfs_path(hdfsPath)
+        data.repartition(repartition).saveAsTextFile(hdfsPath, classOf[GzipCodec])
+      } else {
+        println("路径不合法，无法写入:" + hdfsPath)
+      }
+    }
+  }
+
+  private def recommendFlow(flowPool: String): Boolean = {
+    if (flowPool.isEmpty || flowPool.endsWith("#1")) {
+      return true
+    }
+    false
+  }
+
+  private def validData(logKey: String, whatApps: Set[String], whatMids: Set[String]): Boolean = {
+    // apptype, page, pagesource, recommendpagetype, flowpool, abcode, mid, vid, level, ts
+    val cells = logKey.split(",")
+    val apptype = cells(0)
+    val page = cells(1)
+    //val pagesource = cells(2)
+    val recommendpagetype = cells(3)
+    val flowpool = cells(4)
+    val mid = cells(6)
+    if (whatApps.contains(apptype)) {
+      if (recommendFlow(flowpool)) {
+        if (page.equals("详情后沉浸页")) {
+          return false
+        } else if (page.equals("回流后沉浸页&内页feed")) {
+          return true
+        } else if (page.equals("首页feed")) {
+          return false
+        }
+      }
+    }
+    false
+  }
+
+  def loadFeatureNames(nameFile: String): Set[String] = {
+    val buffer = Source.fromFile(nameFile)
+    val names = buffer.getLines().mkString("\n")
+    buffer.close()
+    val featSet = names.split("\n")
+      .map(r => r.replace(" ", "").replaceAll("\n", ""))
+      .filter(r => r.nonEmpty)
+      .toSet
+    println("featSet.size=" + featSet.size)
+    println(featSet)
+    featSet
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/parse_fission_eval_to_hive.scala

@@ -0,0 +1,97 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.aliyun.odps.TableSchema
+import com.aliyun.odps.data.Record
+import com.aliyun.odps.spark.examples.myUtils.{ParamUtils, env}
+import org.apache.spark.sql.SparkSession
+
+object parse_fission_eval_to_hive {
+  def main(args: Array[String]): Unit = {
+    // 1. 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val inputPath = param.getOrElse("inputPath", "/dw/recommend/model/user_profile/gender/eval")
+    val project = param.getOrElse("project", "loghubods")
+    val outputTable = param.getOrElse("outputTable", "alg_recsys_user_behavior_gender_eval_tmp")
+    val version = param.getOrElse("version", "20251119")
+
+    // 2. sc
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 3. 读取odps+表信息
+    val odpsOps = env.getODPS(sc)
+
+    // 4. 处理数据
+    val predictRdd = sc.textFile(inputPath)
+      .map(line => {
+        val cells = line.split("\t")
+        val label = cells(0)
+        val score = cells(1)
+        val clibScore = parseScore(cells(2))
+        val mid = cells(3)
+        val hh = cells(4).toDouble.toInt
+        (label, score, clibScore, hh)
+      })
+      .map {
+        case (label, score, clibScore, hh) =>
+          val colsMap = scala.collection.mutable.Map[String, Any]()
+          colsMap.put("label", label)
+          colsMap.put("score", score)
+          colsMap.put("clibscore", clibScore)
+          colsMap.put("hh", hh)
+          colsMap
+      }
+
+    // 5. write to table
+    val partition = s"version=$version"
+    odpsOps.saveToTable(project, outputTable, partition, predictRdd, write, defaultCreate = true, overwrite = true)
+
+  }
+
+  private def parseScore(data: String): String = {
+    if (data.nonEmpty) {
+      val pair = data.replace("[", "").replace("]", "").split(",")
+      if (pair.length > 1) {
+        return pair(1).toDouble.formatted("%.4f")
+      }
+    }
+    "-1"
+  }
+
+  private def write(map: scala.collection.mutable.Map[String, Any], record: Record, schema: TableSchema): Unit = {
+    for ((key, value) <- map) {
+      try {
+        // 查找列名在表结构中的索引
+        val columnIndex = schema.getColumnIndex(key.toLowerCase)
+        // 获取列的类型
+        val columnType = schema.getColumn(columnIndex).getTypeInfo
+        try {
+          columnType.getTypeName match {
+            case "STRING" =>
+              record.setString(columnIndex, value.toString)
+            case "BIGINT" =>
+              record.setBigint(columnIndex, value.toString.toLong)
+            case "DOUBLE" =>
+              record.setDouble(columnIndex, value.toString.toDouble)
+            case "BOOLEAN" =>
+              record.setBoolean(columnIndex, value.toString.toBoolean)
+            case other =>
+              throw new IllegalArgumentException(s"Unsupported column type: $other")
+          }
+        } catch {
+          case e: NumberFormatException =>
+            println(s"Error converting value $value to type ${columnType.getTypeName} for column $key: ${e.getMessage}")
+          case e: Exception =>
+            println(s"Unexpected error writing value $value to column $key: ${e.getMessage}")
+        }
+      } catch {
+        case e: IllegalArgumentException => {
+          println(e.getMessage)
+        }
+      }
+    }
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/parse_gender_eval_to_hive.scala

@@ -0,0 +1,97 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.aliyun.odps.TableSchema
+import com.aliyun.odps.data.Record
+import com.aliyun.odps.spark.examples.myUtils.{ParamUtils, env}
+import org.apache.spark.sql.SparkSession
+
+object parse_gender_eval_to_hive {
+  def main(args: Array[String]): Unit = {
+    // 1. 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val inputPath = param.getOrElse("inputPath", "/dw/recommend/model/user_profile/gender/eval")
+    val project = param.getOrElse("project", "loghubods")
+    val outputTable = param.getOrElse("outputTable", "alg_recsys_user_behavior_gender_eval_tmp")
+    val version = param.getOrElse("version", "20251119")
+
+    // 2. sc
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 3. 读取odps+表信息
+    val odpsOps = env.getODPS(sc)
+
+    // 4. 处理数据
+    val predictRdd = sc.textFile(inputPath)
+      .map(line => {
+        val cells = line.split("\t")
+        val label = cells(0)
+        val logit = cells(1)
+        val score = parseScore(cells(2))
+        val mid = cells(3)
+        val total = cells(4)
+        (mid, total, label, score)
+      })
+      .map {
+        case (mid, total, label, score) =>
+          val colsMap = scala.collection.mutable.Map[String, Any]()
+          colsMap.put("mid", mid)
+          colsMap.put("total", total)
+          colsMap.put("label", label)
+          colsMap.put("score", score)
+          colsMap
+      }
+
+    // 5. write to table
+    val partition = s"version=$version"
+    odpsOps.saveToTable(project, outputTable, partition, predictRdd, write, defaultCreate = true, overwrite = true)
+
+  }
+
+  private def parseScore(data: String): String = {
+    if (data.nonEmpty) {
+      val pair = data.replace("[", "").replace("]", "").split(",")
+      if (pair.length > 1) {
+        return pair(1).toDouble.formatted("%.4f")
+      }
+    }
+    "-1"
+  }
+
+  private def write(map: scala.collection.mutable.Map[String, Any], record: Record, schema: TableSchema): Unit = {
+    for ((key, value) <- map) {
+      try {
+        // 查找列名在表结构中的索引
+        val columnIndex = schema.getColumnIndex(key.toLowerCase)
+        // 获取列的类型
+        val columnType = schema.getColumn(columnIndex).getTypeInfo
+        try {
+          columnType.getTypeName match {
+            case "STRING" =>
+              record.setString(columnIndex, value.toString)
+            case "BIGINT" =>
+              record.setBigint(columnIndex, value.toString.toLong)
+            case "DOUBLE" =>
+              record.setDouble(columnIndex, value.toString.toDouble)
+            case "BOOLEAN" =>
+              record.setBoolean(columnIndex, value.toString.toBoolean)
+            case other =>
+              throw new IllegalArgumentException(s"Unsupported column type: $other")
+          }
+        } catch {
+          case e: NumberFormatException =>
+            println(s"Error converting value $value to type ${columnType.getTypeName} for column $key: ${e.getMessage}")
+          case e: Exception =>
+            println(s"Unexpected error writing value $value to column $key: ${e.getMessage}")
+        }
+      } catch {
+        case e: IllegalArgumentException => {
+          println(e.getMessage)
+        }
+      }
+    }
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/parse_org_log.scala

@@ -0,0 +1,192 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.aliyun.odps.data.Record
+import com.aliyun.odps.spark.examples.myUtils.DataUtils.getStringValue
+import com.aliyun.odps.spark.examples.myUtils._
+import examples.utils.SimilarityUtils
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+
+/*
+   20250317 提取特征
+ */
+
+object parse_org_log {
+  private val labelNames = List(
+    "is_share", "share_cnt",
+    "is_return_1", "return_1_uv",
+    "is_return_n", "return_n_uv",
+    "is_return_noself", "return_1_uv_noself",
+    "is_return_n_noself", "return_n_uv_noself"
+  )
+
+  private val o2oMap = Map(
+    "b0_feature" -> "alg_recsys_feature_video_clean_stat",
+    "b1_feature" -> "alg_vid_global_feature_20250212",
+    "b2_feature" -> "alg_vid_recommend_exp_feature_20250212",
+    "b3_feature" -> "alg_vid_recommend_flowpool_exp_feature_20250212",
+    "b4_feature" -> "alg_vid_apptype_recommend_exp_feature_20250212",
+    "b5_feature" -> "alg_vid_province_recommend_exp_feature_20250212",
+    "b6_feature" -> "alg_vid_brand_recommend_exp_feature_20250212",
+    "b7_feature" -> "alg_vid_hotsencetype_recommend_exp_feature_20250212",
+    "b8_feature" -> "alg_merge_cate1_recommend_exp_feature_20250212",
+    "b9_feature" -> "alg_merge_cate2_recommend_exp_feature_20250212",
+    "b10_feature" -> "alg_channel_recommend_exp_feature_20250212",
+    "b11_feature" -> "alg_festive_recommend_exp_feature_20250212",
+    "b13_feature" -> "alg_video_unionid_recommend_exp_feature_20250212",
+    "d1_feature" -> "scene_type_vid_cf_feature_20250212",
+    "d2_feature" -> "vid_click_cf_feature_20250212",
+    "d3_feature" -> "alg_recsys_feature_cf_i2i_v2",
+    "c1_feature" -> "mid_global_feature_20250212",
+    "c5_feature" -> "alg_mid_feature_return_tags",
+    "c6_feature" -> "alg_mid_feature_share_tags",
+    "c7_feature" -> "alg_mid_feature_sharecf",
+    "c8_feature" -> "alg_mid_feature_returncf",
+    "c9_feature" -> "alg_recsys_feature_user_share_return_stat",
+    "v2_feature" -> "head_video"
+  )
+
+  private def parseVideoRdd(videoRdd: RDD[Record]): RDD[(String, java.util.Map[String, String])] = {
+    videoRdd
+      .map(record => {
+        val vid = record.getString("vid")
+        val feature = ConvertUtils.getRecordCol(record, "feature")
+        feature.put("vid", vid)
+        (vid, feature)
+      })
+      .reduceByKey((a, b) => if (a.size() > b.size()) a else b)
+  }
+
+  private def getVidMidRdd(logRdd: RDD[java.util.Map[String, String]]): RDD[(String, String)] = {
+    logRdd
+      .map(raw => {
+        val mid = raw.getOrElse("mid", "")
+        val c9 = raw.getOrElse("c9_feature", "")
+        (mid, c9)
+      })
+      .filter(_._1.nonEmpty)
+      .reduceByKey((a, b) => a)
+      .flatMap(raw => {
+        val result = new ArrayBuffer[(String, String)]
+        for (hVid <- ConvertUtils.getVidList(raw._2)) {
+          result += ((hVid, raw._1)) // (vid, mid)
+        }
+        result
+      })
+  }
+
+  private def getMidSeqRdd(vidMidRdd: RDD[(String, String)], videoRdd: RDD[(String, java.util.Map[String, String])]): RDD[(String, List[java.util.Map[String, String]])] = {
+    vidMidRdd
+      .join(videoRdd)
+      .map(raw => {
+        (raw._2._1, raw._2._2)
+      })
+      .groupByKey()
+      .map(raw => {
+        (raw._1, raw._2.toList)
+      })
+  }
+
+  private def joinMidSeq(logRdd: RDD[java.util.Map[String, String]], midSeqRdd: RDD[(String, List[java.util.Map[String, String]])]): RDD[(java.util.Map[String, String], List[java.util.Map[String, String]])] = {
+    logRdd
+      .map(raw => {
+        val mid = raw.getOrElse("mid", "")
+        (mid, raw)
+      })
+      .leftOuterJoin(midSeqRdd)
+      .map(raw => {
+        if (raw._2._2.isDefined) {
+          (raw._2._1, raw._2._2.get)
+        } else {
+          (raw._2._1, List())
+        }
+      })
+  }
+
+  private def getFeature(rdd: RDD[(java.util.Map[String, String], List[java.util.Map[String, String]])]): RDD[String] = {
+    rdd.mapPartitions(partition => {
+      SimilarityUtils.init()
+      partition.map(raw => {
+        val record = raw._1
+        val videoSeq = raw._2
+        val logKey = DataUtils.getLogKey(record)
+        val labels = DataUtils.getLabels(labelNames, record).toString
+        val features = ConvertV2.getFeature(record, videoSeq, 6).toString
+        val scoresMap = DataUtils.getSubJson(record, "extend_alg", "scoresMap").toString
+        logKey + "\t" + labels + "\t" + scoresMap + "\t" + features
+      })
+    })
+  }
+
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 1. 解析参数
+    val param = ParamUtils.parseArgs(args)
+    val project = param.getOrElse("project", "loghubods")
+    val table = param.getOrElse("table", "dwd_recsys_alg_sample_all_20250212")
+    val tablePart = param.getOrElse("tablePart", "64").toInt
+    val beginStr = param.getOrElse("beginStr", "2025031700")
+    val endStr = param.getOrElse("endStr", "2025031700")
+    val whatLabel = param.getOrElse("whatLabel", "is_return_noself")
+    val fuSampleRate = param.getOrElse("fuSampleRate", "0.03").toDouble
+    val vidSet = param.getOrElse("vids", "54450911,54514940").split(",").toSet
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/83_origin_data/")
+    val repartition = param.getOrElse("repartition", "64").toInt
+
+    println("vidSet.size=" + vidSet.size)
+    println(vidSet)
+
+    // 2. 数据处理
+    val timeRange = MyDateUtils.getDateHourRange(beginStr, endStr)
+    for (dt_hh <- timeRange) {
+      // 2.1 分区
+      val dt = dt_hh.substring(0, 8)
+      val hh = dt_hh.substring(8, 10)
+      val partition = "dt=%s,hh=%s".format(dt, hh)
+      println("开始执行partition:" + partition)
+
+      // 2.2 加载视频信息
+      val originVideo = DataUtils.getODPSData(sc, project, "alg_recsys_feature_user_behavior_video", partition, tablePart)
+      val uniqVideo = parseVideoRdd(originVideo)
+
+      // 2.4 加载样本数据
+      val odpsData = DataUtils.getODPSData(sc, project, table, partition, tablePart)
+
+      // 2.5 样本重采样
+      val resampleData = odpsData
+        .filter(record => {
+          val vid = getStringValue(record, "vid", "0")
+          val apptype = getStringValue(record, "apptype", "0")
+          vidSet.contains(vid) && apptype.equals("4")
+        })
+        .map(record => {
+          OnlineLogUtils.log2Map(record, o2oMap)
+        })
+
+      // 2.7 get vid mid rdd
+      val vidMidRdd = getVidMidRdd(resampleData)
+
+      // 2.8 get mid seq rdd
+      val midSeqRdd = getMidSeqRdd(vidMidRdd, uniqVideo)
+
+      // 2.9 历史行为关联video
+      val seqSampleData = joinMidSeq(resampleData, midSeqRdd)
+
+      // 2.10 特征转换
+      val featureData = getFeature(seqSampleData)
+
+      // 2.11 保存数据
+      val hdfsPath = "%s/%s%s".format(savePath, dt, hh)
+      DataUtils.saveData(featureData, hdfsPath, repartition)
+    }
+  }
+}
+

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/predict_fission_20260205.scala

@@ -0,0 +1,104 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.aliyun.odps.spark.examples.model.FMModel
+import com.aliyun.odps.spark.examples.myUtils.{MyDateUtils, MyHdfsUtils, ParamUtils}
+import org.apache.commons.lang.math.NumberUtils
+import org.apache.commons.lang3.StringUtils
+import org.apache.hadoop.io.compress.GzipCodec
+import org.apache.spark.sql.SparkSession
+
+import java.util
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+import scala.io.Source
+
+
+object predict_fission_20260205 {
+  def main(args: Array[String]): Unit = {
+    // 1. 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val readPath = param.getOrElse("readPath", "/dw/recommend/model/general_model/fission/sample")
+    val beginStr = param.getOrElse("beginStr", "20251216")
+    val endStr = param.getOrElse("endStr", "20251216")
+    val modelFile = param.getOrElse("modelFile", "")
+    val vectorDim = param.getOrElse("vectorDim", "8").toInt
+    val repartition = param.getOrElse("repartition", "100").toInt
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/general_model/fission/predict")
+
+    // 2. content
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 3. 处理数据
+    val modelData = loadData(modelFile)
+    val modelData_br = sc.broadcast(modelData)
+    val dateRange = MyDateUtils.getDateRange(beginStr, endStr)
+    for (dt <- dateRange) {
+      val partition = "%s".format(dt)
+      println("开始执行:" + partition)
+      val data = sc.textFile(readPath + "/" + partition)
+        .map(row => {
+          val cells = row.split("\t")
+          val label = cells(0)
+          val features = parseFeature(cells, 1)
+          (label, features)
+        })
+        .mapPartitions(row => {
+          val localModelData = modelData_br.value
+          val model = FMModel.builder(localModelData, vectorDim)
+          val result = new ArrayBuffer[String]()
+          row.foreach {
+            case (label, features) =>
+              val logKey = if (features.contains("ad@1")) "ad@1" else "ad@0"
+              val tmp_hour = features.getOrElse("tmp_hour", -1)
+              val score = model.predict(features)
+              result.add(label + "\t" + logKey + "\t[0," + score + "]\t" + tmp_hour)
+          }
+          result.iterator
+        })
+
+      // 4. 保存数据到hdfs
+      val hdfsPath = savePath + "/" + partition
+      if (hdfsPath.nonEmpty && hdfsPath.startsWith("/dw/recommend/model/")) {
+        println("删除路径并开始数据写入:" + hdfsPath)
+        MyHdfsUtils.delete_hdfs_path(hdfsPath)
+        data.repartition(repartition).saveAsTextFile(hdfsPath, classOf[GzipCodec])
+      } else {
+        println("路径不合法，无法写入:" + hdfsPath)
+      }
+    }
+  }
+
+  private def loadData(nameFile: String): List[String] = {
+    val buffer = Source.fromFile(nameFile)
+    val names = buffer.getLines().mkString("\n")
+    buffer.close()
+    val featList = names.split("\n")
+      .map(r => r.replace(" ", "").replaceAll("\n", ""))
+      .filter(r => r.nonEmpty)
+      .toList
+    println("featList.size=" + featList.size)
+    println(featList)
+    featList
+  }
+
+  def parseFeature(data: Array[String], startIndex: Int): util.Map[String, java.lang.Double] = {
+    val features = new util.HashMap[String, java.lang.Double]()
+    for (i <- startIndex until data.length) {
+      val fv: Array[String] = StringUtils.split(data(i), ':')
+      if (2 == fv.length) {
+        features.put(fv(0), NumberUtils.toDouble(fv(1), 0.0))
+        if (fv(0).startsWith("hh@")) {
+          val cells = StringUtils.split(fv(0), '@')
+          if (2 == cells.length) {
+            features.put("tmp_hour", NumberUtils.toDouble(cells(1), -1))
+          }
+        }
+      }
+    }
+    features
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/relevant_sample.scala

@@ -0,0 +1,130 @@
+package com.aliyun.odps.spark.examples.makedata_recsys_r_rate
+
+import com.aliyun.odps.spark.examples.myUtils.{DataUtils, MyDateUtils, MyHdfsUtils, ParamUtils}
+import org.apache.hadoop.io.compress.GzipCodec
+import org.apache.spark.sql.SparkSession
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+import scala.io.Source
+import scala.util.Random
+
+object relevant_sample {
+  def main(args: Array[String]): Unit = {
+    // 1. 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val readPath = param.getOrElse("readPath", "/dw/recommend/model/83_origin_data/")
+    val beginStr = param.getOrElse("beginStr", "20250317")
+    val endStr = param.getOrElse("endStr", "20250317")
+    val whatApps = param.getOrElse("whatApps", "0,4,5,21,3,6").split(",").toSet
+    val whatPages = param.getOrElse("whatPages", "详情后沉浸页,回流后沉浸页&内页feed,首页feed,详情页").split(",").toSet
+    val whatLabel = param.getOrElse("whatLabel", "is_return_n_noself")
+    val fuSampleRate = param.getOrElse("fuSampleRate", "-1.0").toDouble
+    val notUseBucket = param.getOrElse("notUseBucket", "0").toInt
+    val featureNameFile = param.getOrElse("featureName", "20250317_recsys_rov_name.txt")
+    val featureBucketFile = param.getOrElse("featureBucket", "20241209_recsys_nor_bucket.txt")
+    val minFeatureCnt = param.getOrElse("minFeatureCnt", "100").toInt
+    val repartition = param.getOrElse("repartition", "10").toInt
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/83_recsys_rov_train_data/")
+
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 2. 加载特征
+    val loader = getClass.getClassLoader
+    val featureNameSet = loadFeatureNames(featureNameFile)
+    val featureBucketMap = DataUtils.loadUseFeatureBuckets(loader, notUseBucket, featureBucketFile)
+    val bucketsMap_br = sc.broadcast(featureBucketMap)
+
+    // 3. 处理数据
+    val dateRange = MyDateUtils.getDateRange(beginStr, endStr)
+    for (date <- dateRange) {
+      println("开始执行:" + date)
+      val data = sc.textFile(readPath + "/" + date + "*").map(r => {
+          val rList = r.split("\t")
+          val logKey = rList(0)
+          val labelKey = rList(1)
+          val scoresMap = rList(2)
+          val featData = rList(3)
+          (logKey, labelKey, scoresMap, featData)
+        })
+        .filter {
+          case (logKey, labelKey, scoresMap, featData) =>
+            validData(logKey, whatApps, whatPages)
+        }.filter {
+          case (logKey, labelKey, scoresMap, featData) =>
+            val label = DataUtils.parseLabel(labelKey, whatLabel).toDouble
+            label > 0 || new Random().nextDouble() <= fuSampleRate
+        }
+        .map {
+          case (logKey, labelKey, scoresMap, featData) =>
+            val label = DataUtils.parseLabel(labelKey, whatLabel).toDouble
+            val features = DataUtils.parseFeature(featData)
+            (logKey, label, scoresMap, features)
+        }
+        .mapPartitions(row => {
+          val result = new ArrayBuffer[String]()
+          val bucketsMap = bucketsMap_br.value
+          row.foreach {
+            case (logKey, label, scoresMap, features) =>
+              val featuresBucket = DataUtils.bucketFeature(featureNameSet, bucketsMap, features)
+              if (featuresBucket.size >= minFeatureCnt) {
+                result.add(label + "\t" + featuresBucket.mkString("\t"))
+              }
+          }
+          result.iterator
+        })
+
+      // 4. 保存数据到hdfs
+      val hdfsPath = savePath + "/" + date
+      if (hdfsPath.nonEmpty && hdfsPath.startsWith("/dw/recommend/model/")) {
+        println("删除路径并开始数据写入:" + hdfsPath)
+        MyHdfsUtils.delete_hdfs_path(hdfsPath)
+        data.repartition(repartition).saveAsTextFile(hdfsPath, classOf[GzipCodec])
+      } else {
+        println("路径不合法，无法写入:" + hdfsPath)
+      }
+    }
+  }
+
+  private def recommendFlow(flowPool: String): Boolean = {
+    if (flowPool.isEmpty || flowPool.endsWith("#1")) {
+      return true
+    }
+    false
+  }
+
+  private def validData(logKey: String, whatApps: Set[String], whatPages: Set[String]): Boolean = {
+    // apptype, page, pagesource, recommendpagetype, flowpool, abcode, mid, vid, level, ts
+    val cells = logKey.split(",")
+    val apptype = cells(0)
+    val page = cells(1)
+    //val pagesource = cells(2)
+    val recommendpagetype = cells(3)
+    val flowpool = cells(4)
+    if (whatApps.contains(apptype)) {
+      if (recommendFlow(flowpool)) {
+        if (whatPages.contains(page)) {
+          return true
+        }
+      }
+    }
+    false
+  }
+
+  def loadFeatureNames(nameFile: String): Set[String] = {
+    val buffer = Source.fromFile(nameFile)
+    val names = buffer.getLines().mkString("\n")
+    buffer.close()
+    val featSet = names.split("\n")
+      .map(r => r.replace(" ", "").replaceAll("\n", ""))
+      .filter(r => r.nonEmpty)
+      .toSet
+    println("featSet.size=" + featSet.size)
+    println(featSet)
+    featSet
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/myUtils/ConvertV4.java

@@ -0,0 +1,116 @@
+package com.aliyun.odps.spark.examples.myUtils;
+
+import com.alibaba.fastjson.JSON;
+import com.alibaba.fastjson.JSONObject;
+import com.aliyun.odps.spark.examples.transform.TransBaseFeature;
+import com.aliyun.odps.spark.examples.transform.TransCrossFeature;
+import com.aliyun.odps.spark.examples.transform.TransItemFeature;
+import com.aliyun.odps.spark.examples.transform.TransUserFeature;
+
+import java.math.BigDecimal;
+import java.math.RoundingMode;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+
+public class ConvertV4 {
+    public static JSONObject getFeature(Map<String, String> record, List<Map<String, String>> videoSeq, int scale) {
+        Map<String, Object> featMap = new HashMap<>();
+
+        // origin info
+        String ts = record.get("ts");
+        long currentMs = Long.parseLong(ts) * 1000;
+        String uid = record.getOrDefault("uid", "");
+        String mid = record.getOrDefault("mid", "");
+        String vid = record.get("vid");
+        String appType = record.getOrDefault("apptype", "");
+        String scene = record.getOrDefault("hotsencetype", "");
+        Map<String, String> extendMap = ConvertUtils.getRecordCol(record, "extend");
+        record.put("user_channel", FeatureUtils.getUserChannel(record.get("rootsourceid"), extendMap.get("group_name")));
+        record.put("user_level", FeatureUtils.getUserLevel(extendMap.get("rootsessionid"), record.get("subsessionid")));
+        Map<String, String> headInfo = ConvertUtils.getRecordCol(record, "v2_feature");
+        Map<String, String> rankInfo = ConvertUtils.getRecordCol(record, "v1_feature");
+        Map<String, Map<String, String>> userOriginInfo = getUserOriginInfo(record);
+        Map<String, Map<String, Map<String, String>>> videoOriginInfo = getVideoOriginInfo(record);
+
+        // parse info
+        Map<String, String> c9Map = userOriginInfo.get("alg_recsys_feature_user_share_return_stat");
+        UserShareReturnProfile userProfile = JSON.parseObject(ConvertUtils.toJson(c9Map), UserShareReturnProfile.class);
+        Map<String, Map<String, String>> historyVideoMap = ConvertUtils.list2Map(videoSeq);
+
+        // context feature
+        TransBaseFeature.getBaseFeature(currentMs, appType, scene, featMap);
+
+        // head video feature
+        TransItemFeature.getVideoBaseFeature("h", currentMs, headInfo, featMap);
+
+        // user feature
+        TransUserFeature.getUserFeature(uid, mid, currentMs, userProfile, record, userOriginInfo, featMap);
+
+        // user & video feature
+        TransCrossFeature.getUserTagsVideoCrossFeature("c5", rankInfo, userOriginInfo.get("alg_mid_feature_return_tags"), featMap);
+        TransCrossFeature.getUserTagsVideoCrossFeature("c6", rankInfo, userOriginInfo.get("alg_mid_feature_share_tags"), featMap);
+
+        // rank video feature
+        featMap.put("item", vid);
+        TransItemFeature.getVideoBaseFeature("r", currentMs, rankInfo, featMap);
+        TransItemFeature.getVideoStatFeature(vid, videoOriginInfo, featMap);
+
+        // head&rank cross feature
+        TransCrossFeature.getHeadVideoCrossFeature("hr_sim", headInfo, rankInfo, featMap);
+
+        // user profile & rank cross
+        TransCrossFeature.getUserProfileVideoCrossFeature(currentMs, userProfile, rankInfo, historyVideoMap, featMap);
+
+        return filterAndTruncate(featMap, scale);
+    }
+
+    private static Map<String, Map<String, String>> getUserOriginInfo(Map<String, String> record) {
+        Map<String, Map<String, String>> map = new HashMap<>();
+        map.put("mid_global_feature_20250212", ConvertUtils.getRecordCol(record, "c1_feature"));
+        map.put("alg_mid_feature_return_tags", ConvertUtils.getRecordCol(record, "c5_feature"));
+        map.put("alg_mid_feature_share_tags", ConvertUtils.getRecordCol(record, "c6_feature"));
+        map.put("alg_recsys_feature_user_share_return_stat", ConvertUtils.getRecordCol(record, "c9_feature"));
+        return map;
+    }
+
+    private static Map<String, Map<String, Map<String, String>>> getVideoOriginInfo(Map<String, String> record) {
+        Map<String, Map<String, String>> map = new HashMap<>();
+        map.put("alg_recsys_feature_video_clean_stat", ConvertUtils.getRecordCol(record, "b0_feature"));
+        map.put("alg_vid_global_feature_20250212", ConvertUtils.getRecordCol(record, "b1_feature"));
+        map.put("alg_vid_recommend_exp_feature_20250212", ConvertUtils.getRecordCol(record, "b2_feature"));
+        map.put("alg_vid_recommend_flowpool_exp_feature_20250212", ConvertUtils.getRecordCol(record, "b3_feature"));
+        map.put("alg_vid_apptype_recommend_exp_feature_20250212", ConvertUtils.getRecordCol(record, "b4_feature"));
+        map.put("alg_vid_province_recommend_exp_feature_20250212", ConvertUtils.getRecordCol(record, "b5_feature"));
+        map.put("alg_vid_brand_recommend_exp_feature_20250212", ConvertUtils.getRecordCol(record, "b6_feature"));
+        map.put("alg_vid_hotsencetype_recommend_exp_feature_20250212", ConvertUtils.getRecordCol(record, "b7_feature"));
+        map.put("alg_merge_cate1_recommend_exp_feature_20250212", ConvertUtils.getRecordCol(record, "b8_feature"));
+        map.put("alg_merge_cate2_recommend_exp_feature_20250212", ConvertUtils.getRecordCol(record, "b9_feature"));
+        map.put("alg_channel_recommend_exp_feature_20250212", ConvertUtils.getRecordCol(record, "b10_feature"));
+        map.put("alg_video_unionid_recommend_exp_feature_20250212", ConvertUtils.getRecordCol(record, "b13_feature"));
+
+        Map<String, Map<String, Map<String, String>>> allMap = new HashMap<>();
+        String vid = record.get("vid");
+        allMap.put(vid, map);
+        return allMap;
+    }
+
+    private static JSONObject filterAndTruncate(Map<String, Object> featMap, int scale) {
+        JSONObject jsonObject = new JSONObject();
+        for (Map.Entry<String, Object> entry : featMap.entrySet()) {
+            Object obj = entry.getValue();
+            if (null != obj) {
+                if (obj instanceof Double) {
+                    double value = (double) obj;
+                    if (value > 1E-8) {
+                        double newValue = new BigDecimal(value).setScale(scale, RoundingMode.HALF_UP).doubleValue();
+                        jsonObject.put(entry.getKey(), newValue);
+                    }
+                } else {
+                    jsonObject.put(entry.getKey(), obj);
+                }
+            }
+        }
+        return jsonObject;
+    }
+}

src/main/scala/com/aliyun/odps/spark/examples/myUtils/ConvertV5.java

@@ -0,0 +1,123 @@
+package com.aliyun.odps.spark.examples.myUtils;
+
+import com.alibaba.fastjson.JSONObject;
+
+import java.util.*;
+
+public class ConvertV5 {
+    private static final Map<String, String> cate_stat_map = new HashMap<>();
+
+    static {
+        cate_stat_map.put("cate1_stat", "cate1");
+        cate_stat_map.put("cate2_stat", "cate2");
+        cate_stat_map.put("title_keywords_stat", "title");
+        cate_stat_map.put("video_keywords_stat", "video");
+    }
+
+    public static JSONObject getFeature(long currentMs, Map<String, String> record, int accurate) {
+        Map<String, Double> featMap = new HashMap<>();
+        try {
+            // base
+            Map<String, String> total_stat = ConvertUtils.getRecordCol(record, "total_stat");
+            double p_total = Double.parseDouble(total_stat.getOrDefault("p_t", "0"));
+            double s_total = Double.parseDouble(total_stat.getOrDefault("s_t", "0"));
+            double s2r_total = Double.parseDouble(total_stat.getOrDefault("s2r_t", "0"));
+            double r_total = Double.parseDouble(total_stat.getOrDefault("r_t", "0"));
+
+            double p_weight = Double.parseDouble(total_stat.getOrDefault("p_w", "0"));
+            double s_weight = Double.parseDouble(total_stat.getOrDefault("s_w", "0"));
+            double s2r_weight = Double.parseDouble(total_stat.getOrDefault("s2r_w", "0"));
+            double r_weight = Double.parseDouble(total_stat.getOrDefault("r_w", "0"));
+
+            // detail
+            if (p_total > 0) {
+                featMap.put("p_t", p_total);
+            }
+            if (s_total > 0) {
+                featMap.put("s_t", s_total);
+            }
+            if (s2r_total > 0) {
+                featMap.put("s2r_t", s2r_total);
+            }
+            if (r_total > 0) {
+                featMap.put("r_t", r_total);
+            }
+            if (p_weight > 0) {
+                featMap.put("p_w", p_weight);
+            }
+            if (s_weight > 0) {
+                featMap.put("s_w", s_weight);
+            }
+            if (s2r_weight > 0) {
+                featMap.put("s2r_w", s2r_weight);
+            }
+            if (r_weight > 0) {
+                featMap.put("r_w", r_weight);
+            }
+
+            // sum
+            double total = p_total + s_total + r_total;
+            double weight = p_weight + s_weight + r_weight;
+            long behaviorMs = Long.parseLong(total_stat.get("ts")) * 1000;
+
+            // context
+            FeatureTransformV1.getContextFeature(currentMs, behaviorMs, total, featMap);
+
+            // cate stat
+            for (Map.Entry<String, String> cate_entry : cate_stat_map.entrySet()) {
+                Map<String, Map<String, String>> map2map = new HashMap<>();
+                Map<String, String> one_cate_map = ConvertUtils.getRecordCol(record, cate_entry.getKey());
+                for (Map.Entry<String, String> behavior_entry : one_cate_map.entrySet()) {
+                    // behavior
+                    String behavior = behavior_entry.getKey();
+                    Map<String, String> behavior_map = ConvertUtils.getRecordCol(one_cate_map, behavior);
+
+                    // feat
+                    String prefix = String.format("%s@%s", behavior, cate_entry.getValue());
+                    FeatureTransformV1.getCateFeature(prefix, weight, behavior_map, featMap);
+
+                    // cache
+                    map2map.put(behavior, behavior_map);
+                }
+
+                // share + return
+                Set<String> s_r_set = new HashSet<>(Arrays.asList("2", "4"));
+                String s_r_prefix = String.format("%s@%s", "6", cate_entry.getValue());
+                Map<String, String> share_return_map = megreMap(map2map, s_r_set);
+                FeatureTransformV1.getCateFeature(s_r_prefix, weight, share_return_map, featMap);
+
+                // all
+                Set<String> all_set = new HashSet<>(Arrays.asList("1", "2", "4"));
+                String all_prefix = String.format("%s@%s", "7", cate_entry.getValue());
+                Map<String, String> all_map = megreMap(map2map, all_set);
+                FeatureTransformV1.getCateFeature(all_prefix, weight, all_map, featMap);
+            }
+        } catch (Exception e) {
+            e.printStackTrace();
+        }
+        return ConvertUtils.filterAndTruncate(featMap, accurate);
+    }
+
+    private static Map<String, String> megreMap(Map<String, Map<String, String>> map2map, Set<String> behaviorSet) {
+        Map<String, Double> sumMap = new HashMap<>();
+        for (Map.Entry<String, Map<String, String>> entry : map2map.entrySet()) {
+            if (behaviorSet.contains(entry.getKey())) {
+                for (Map.Entry<String, String> entry2st : entry.getValue().entrySet()) {
+                    String key = entry2st.getKey();
+                    double value = Double.parseDouble(entry2st.getValue());
+                    if (sumMap.containsKey(key)) {
+                        sumMap.put(key, sumMap.get(key) + value);
+                    } else {
+                        sumMap.put(key, value);
+                    }
+                }
+            }
+        }
+
+        Map<String, String> result = new HashMap<>();
+        for (Map.Entry<String, Double> entry : sumMap.entrySet()) {
+            result.put(entry.getKey(), String.valueOf(entry.getValue()));
+        }
+        return result;
+    }
+}

src/main/scala/com/aliyun/odps/spark/examples/myUtils/FissionConvert.java

@@ -0,0 +1,64 @@
+package com.aliyun.odps.spark.examples.myUtils;
+
+import com.alibaba.fastjson.JSONObject;
+
+import java.util.*;
+
+public class FissionConvert {
+    private static final double plusSmooth = 3.0;
+    private static final Set<String> realSet = new HashSet<>(Arrays.asList("hour", "group_n", "user_launchs", "user_shares", "user_return_1_uv", "user_return_n_uv"));
+    private static final Set<String> hotSceneSet = new HashSet<>(Arrays.asList("1008", "1007", "1074", "1058", "1067", "1010", "1095"));
+
+    public static JSONObject getFeature(String dt, Map<String, String> record, int accurate) {
+        Map<String, Double> featMap = new HashMap<>();
+        try {
+            String dtKey = String.format("dt@%s", dt);
+            featMap.put(dtKey, 1.0);
+            Map<String, String> feature = ConvertUtils.getRecordCol(record, "feature");
+            for (Map.Entry<String, String> entry : feature.entrySet()) {
+                String key = entry.getKey();
+                String value = entry.getValue();
+                if (realSet.contains(key)) {
+                    double featValue = Double.parseDouble(value);
+                    featMap.put(key, featValue);
+                } else {
+                    if ("scene".equals(key)) {
+                        String featKey;
+                        if (hotSceneSet.contains(value)) {
+                            featKey = String.format("%s@%s", key, value);
+                        } else {
+                            featKey = String.format("%s@%s", key, "other");
+                        }
+                        featMap.put(featKey, 1.0);
+                    } else {
+                        String featKey = String.format("%s@%s", key, value);
+                        featMap.put(featKey, 1.0);
+                    }
+
+                }
+            }
+            // rate
+            double launchs = Double.parseDouble(feature.getOrDefault("user_launchs", "0"));
+            double shares = Double.parseDouble(feature.getOrDefault("user_shares", "0"));
+            double return_1_uv = Double.parseDouble(feature.getOrDefault("user_return_1_uv", "0"));
+            double return_n_uv = Double.parseDouble(feature.getOrDefault("user_return_n_uv", "0"));
+            if (launchs > 0) {
+                double rov_1 = FeatureUtils.plusSmooth(return_1_uv, launchs, plusSmooth);
+                double rov_n = FeatureUtils.plusSmooth(return_n_uv, launchs, plusSmooth);
+                double str = FeatureUtils.plusSmooth(shares, launchs, plusSmooth);
+                featMap.put("rov_1", rov_1);
+                featMap.put("rov_n", rov_n);
+                featMap.put("str", str);
+            }
+            if (shares > 0) {
+                double ros_1 = FeatureUtils.plusSmooth(return_1_uv, shares, plusSmooth);
+                double ros_n = FeatureUtils.plusSmooth(return_n_uv, shares, plusSmooth);
+                featMap.put("ros_1", ros_1);
+                featMap.put("ros_n", ros_n);
+            }
+        } catch (Exception e) {
+            e.printStackTrace();
+        }
+        return ConvertUtils.filterAndTruncate(featMap, accurate);
+    }
+}

src/main/scala/com/aliyun/odps/spark/examples/myUtils/FissionConvertV2.java

@@ -0,0 +1,199 @@
+package com.aliyun.odps.spark.examples.myUtils;
+
+import com.alibaba.fastjson.JSONObject;
+import org.apache.commons.lang.math.NumberUtils;
+import org.apache.commons.lang3.StringUtils;
+
+import java.util.*;
+
+public class FissionConvertV2 {
+    public static final double CTR_SMOOTH_BETA_FACTOR = 25;
+    public static final double CVR_SMOOTH_BETA_FACTOR = 10;
+    public static final double CTCVR_SMOOTH_BETA_FACTOR = 100;
+    private static final double[] groupNBoundaries = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 11.0, 14.0};
+    private static final double[] userLaunch30dBoundaries = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 14.0, 16.0, 18.0, 21.0, 24.0, 28.0, 34.0, 42.0, 59.0};
+    private static final double[] userShares30dBoundaries = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 11.0, 13.0, 15.0, 18.0, 22.0, 26.0, 32.0, 40.0, 50.0, 66.0, 92.0, 141.0, 269.0};
+    private static final double[] userReturn1UV30dBoundaries = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 10.0, 11.0, 13.0, 15.0, 17.0, 20.0, 23.0, 26.0, 31.0, 36.0, 43.0, 51.0, 63.0, 80.0, 109.0, 169.0};
+    private static final double[] userRov1_30dBoundaries = {0.07, 0.11, 0.15, 0.2, 0.25, 0.29, 0.33, 0.4, 0.47, 0.5, 0.58, 0.67, 0.75, 0.83, 0.95, 1.0, 1.13, 1.25, 1.4, 1.57, 1.77, 2.0, 2.25, 2.59, 3.0, 3.59, 4.4, 5.71, 8.49};
+    private static final double[] userRos1_30dBoundaries = {0.1, 0.15, 0.2, 0.24, 0.27, 0.32, 0.33, 0.39, 0.43, 0.5, 0.5, 0.56, 0.63, 0.68, 0.75, 0.86, 1.0, 1.0, 1.0, 1.19, 1.35, 1.54, 1.8, 2.0, 2.4, 3.0, 3.6, 4.8, 7.11};
+    private static final double[] vidRosBoundaries = {0.21, 0.29, 0.35, 0.38, 0.42, 0.47, 0.5, 0.53, 0.55, 0.57, 0.6, 0.64, 0.67, 0.69, 0.72, 0.72, 0.75, 0.77, 0.8, 0.85, 0.88, 0.9, 0.96, 1.03, 1.07, 1.2, 1.21, 1.29, 1.61};
+    private static final double[] vidStrBoundaries = {0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.12, 0.13};
+    private static final double[] vidRovBoundaries = {0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.11};
+    private static final double[] ecpmBoundaries = {9.58, 14.41, 18.57, 22.64, 26.61, 30.54, 34.5, 38.58, 42.84, 47.35, 52.03, 56.94, 62.19, 67.88, 74.19, 80.26, 87.92, 95.98, 105.87, 117.54, 130.96, 147.58, 167.92, 192.05, 220.37, 259.65, 325.1, 458.61, 750.42};
+    private static final Set<String> hasChannelScenes = new HashSet<>(Arrays.asList(
+            "DaiTou",
+            "GzhTouLiu",
+            "daitou",
+            "dyyjs",
+            "dyyqw",
+            "fwhdyy",
+            "gzhhz",
+            "gzhhzzx",
+            "shequn",
+            "touliu",
+            "xcxdt"
+    ));
+
+    public static JSONObject getFeature(String dt, String hh, Map<String, String> record, int accurate) {
+        Map<String, Double> featMap = new HashMap<>();
+        try {
+            // dt
+            String dtKey = String.format("dt@%s", dt);
+            featMap.put(dtKey, 1.0);
+
+            // cid info
+            String has_ad = record.getOrDefault("has_ad", "0");
+            String ad_profession = record.getOrDefault("ad_profession", "");
+            String is_filted = record.getOrDefault("is_filted", "0");
+            String ad_level = record.getOrDefault("ad_level", "");
+            String ad_sku = record.getOrDefault("ad_sku", "");
+            double cid_ecpm = NumberUtils.toDouble(record.getOrDefault("cid_ecpm", "0"));
+            Map<String, String> cid_feature = ConvertUtils.getRecordCol(record, "cid_feature");
+            Map<String, String> cid_province_feature = ConvertUtils.getRecordCol(record, "cid_province_feature");
+            Map<String, String> extParams = ConvertUtils.getRecordCol(record, "rootsourceid");
+
+            // rootSourceId
+            String rootSourceScene = "";
+            String subSourceScene = "";
+            String rootSourceId = extParams.getOrDefault("rootsourceid", "");
+            if (StringUtils.isNotEmpty(rootSourceId)) {
+                String[] cells = rootSourceId.split("_");
+                rootSourceScene = cells[0];
+                if (hasChannelScenes.contains(rootSourceScene) && cells.length > 1) {
+                    subSourceScene = String.format("%s_%s", rootSourceScene, cells[1]);
+                }
+            }
+            FissionFeature.valCrossAdFeature("rootSource", rootSourceScene, has_ad, ad_profession, ad_sku, featMap);
+            FissionFeature.valCrossAdFeature("subSource", subSourceScene, has_ad, ad_profession, ad_sku, featMap);
+
+            // video info
+            String merge_leve1 = record.getOrDefault("merge_leve1", "");
+            String merge_leve2 = record.getOrDefault("merge_leve2", "");
+            double vid_ros = NumberUtils.toDouble(record.getOrDefault("vid_ros", "0"));
+            double vid_str = NumberUtils.toDouble(record.getOrDefault("vid_str", "0"));
+            double vid_rov = NumberUtils.toDouble(record.getOrDefault("vid_rov", "0"));
+
+            // label
+            String fissions = record.getOrDefault("fissions", "0");
+
+            // context
+            String week_day = record.getOrDefault("week_day", "");
+            String scene = record.getOrDefault("scene", "");
+            String channel = record.getOrDefault("channel", "");
+            String depth = record.getOrDefault("depth", "");
+            String channel_depth = record.getOrDefault("channel_depth", "");
+
+            // id
+            String gender_t = record.getOrDefault("gender_t", "");
+            String gender_70 = record.getOrDefault("gender_70", "");
+            String gender_80 = record.getOrDefault("gender_80", "");
+            String machine_brand = record.getOrDefault("machine_brand", "");
+            String benchmarklevel = record.getOrDefault("benchmarklevel", "");
+            String modellevel = record.getOrDefault("modellevel", "");
+            String province = record.getOrDefault("province", "");
+            String city = record.getOrDefault("city", "");
+            String device_os = record.getOrDefault("device_os", "");
+            String is_regist = record.getOrDefault("is_regist", "");
+
+            // real
+            double group_n = NumberUtils.toDouble(record.getOrDefault("group_n", "0"));
+            double user_launchs_30day = NumberUtils.toDouble(record.getOrDefault("user_launchs_30day", "0"));
+            double user_shares_30day = NumberUtils.toDouble(record.getOrDefault("user_shares_30day", "0"));
+            double user_return_1_uvs_30day = NumberUtils.toDouble(record.getOrDefault("user_return_1_uvs_30day", "0"));
+            double user_rov_1_30day = NumberUtils.toDouble(record.getOrDefault("user_rov_1_30day", "0"));
+            double user_ros_1_30day = NumberUtils.toDouble(record.getOrDefault("user_ros_1_30day", "0"));
+            String user_launchs_90day = record.getOrDefault("user_launchs_90day", "0");
+            String user_shares_90day = record.getOrDefault("user_shares_90day", "0");
+            String user_return_1_uvs_90day = record.getOrDefault("user_return_1_uvs_90day", "0");
+            String user_rov_1_90day = record.getOrDefault("user_rov_1_90day", "0");
+            String user_launchs_180day = record.getOrDefault("user_launchs_180day", "0");
+            String user_shares_180day = record.getOrDefault("user_shares_180day", "0");
+            String user_return_1_uvs_180day = record.getOrDefault("user_return_1_uvs_180day", "0");
+            String user_rov_1_180day = record.getOrDefault("user_rov_1_180day", "0");
+            String user_launchs_1year = record.getOrDefault("user_launchs_1year", "0");
+            String user_shares_1year = record.getOrDefault("user_shares_1year", "0");
+            String user_return_1_uvs_1year = record.getOrDefault("user_return_1_uvs_1year", "0");
+            String user_rov_1_1year = record.getOrDefault("user_rov_1_1year", "0");
+            String user_launchs_all = record.getOrDefault("user_launchs_all", "0");
+            String user_shares_all = record.getOrDefault("user_shares_all", "0");
+            String user_return_1_uvs_all = record.getOrDefault("user_return_1_uvs_all", "0");
+            String user_rov_1_all = record.getOrDefault("user_rov_1_all", "0");
+
+            // gender
+            FissionFeature.idCrossAdFeature("gender1", gender_t, has_ad, ad_profession, ad_sku, featMap);
+            FissionFeature.idCrossAdFeature("gender2", gender_70, has_ad, ad_profession, ad_sku, featMap);
+            FissionFeature.idCrossAdFeature("gender3", gender_80, has_ad, ad_profession, ad_sku, featMap);
+
+            // context
+            FissionFeature.idCrossAdFeature("week", week_day, has_ad, ad_profession, ad_sku, featMap);
+            FissionFeature.idCrossAdFeature("hh", hh, has_ad, ad_profession, ad_sku, featMap);
+            FissionFeature.idCrossAdFeature("province", province, has_ad, ad_profession, ad_sku, featMap);
+            FissionFeature.idCrossAdFeature("city", city, has_ad, ad_profession, ad_sku, featMap);
+            FissionFeature.idCrossAdFeature("os", device_os, has_ad, ad_profession, ad_sku, featMap);
+            FissionFeature.idCrossAdFeature("reg", is_regist, has_ad, ad_profession, ad_sku, featMap);
+            FissionFeature.idCrossAdFeature("brand", machine_brand, has_ad, ad_profession, ad_sku, featMap);
+            FissionFeature.idCrossAdFeature("scene", scene, has_ad, ad_profession, ad_sku, featMap);
+            FissionFeature.idCrossAdFeature("channel", channel, has_ad, ad_profession, ad_sku, featMap);
+            FissionFeature.idCrossAdFeature("depth", depth, has_ad, ad_profession, ad_sku, featMap);
+            FissionFeature.idCrossAdFeature("channel_depth", channel_depth, has_ad, ad_profession, ad_sku, featMap);
+
+            // user
+            FissionFeature.realCrossAdFeature("groupN", group_n, groupNBoundaries, has_ad, ad_profession, ad_sku, featMap);
+            FissionFeature.realCrossAdFeature("user_launchs_30day", user_launchs_30day, userLaunch30dBoundaries, has_ad, ad_profession, ad_sku, featMap);
+            FissionFeature.realCrossAdFeature("user_shares_30day", user_shares_30day, userShares30dBoundaries, has_ad, ad_profession, ad_sku, featMap);
+            FissionFeature.realCrossAdFeature("user_return_1_uvs_30day", user_return_1_uvs_30day, userReturn1UV30dBoundaries, has_ad, ad_profession, ad_sku, featMap);
+            FissionFeature.realCrossAdFeature("user_rov_1_30day", user_rov_1_30day, userRov1_30dBoundaries, has_ad, ad_profession, ad_sku, featMap);
+            FissionFeature.realCrossAdFeature("user_ros_1_30day", user_ros_1_30day, userRos1_30dBoundaries, has_ad, ad_profession, ad_sku, featMap);
+
+            // video
+            FissionFeature.idCrossAdFeature("cate1", merge_leve1, has_ad, ad_profession, ad_sku, featMap);
+            FissionFeature.idCrossAdFeature("cate2", merge_leve2, has_ad, ad_profession, ad_sku, featMap);
+            FissionFeature.realCrossAdFeature("vid_ros", vid_ros, vidRosBoundaries, has_ad, ad_profession, ad_sku, featMap);
+            FissionFeature.realCrossAdFeature("vid_str", vid_str, vidStrBoundaries, has_ad, ad_profession, ad_sku, featMap);
+            FissionFeature.realCrossAdFeature("vid_rov", vid_rov, vidRovBoundaries, has_ad, ad_profession, ad_sku, featMap);
+
+            // cid ad
+            featMap.put("benchmarklevel", FeatureUtils.log1(NumberUtils.toDouble(benchmarklevel), 10));
+            featMap.put(String.format("%s@%s", "benchmarklevel", benchmarklevel), 1.0);
+            featMap.put(String.format("%s@%s", "modellevel", modellevel), 1.0);
+            featMap.put(String.format("%s@%s", "ad_filtered", is_filted), 1.0);
+            featMap.put(String.format("%s@%s", "ad", has_ad), 1.0);
+            featMap.put(String.format("%s@%s", "ad_prof", ad_profession), 1.0);
+            featMap.put(String.format("%s@%s", "ad_sku", ad_sku), 1.0);
+            featMap.put(String.format("%s@%s", "ad_level", ad_level), 1.0);
+            FissionFeature.realCrossAdFeature("ad_epcm", cid_ecpm, ecpmBoundaries, has_ad, ad_profession, ad_sku, featMap);
+            oneTypeStatFeature("b3", cid_feature, featMap);
+            oneTypeStatFeature("b4", cid_province_feature, featMap);
+        } catch (Exception e) {
+            e.printStackTrace();
+        }
+        return ConvertUtils.filterAndTruncate(featMap, accurate);
+    }
+
+    private static void oneTypeStatFeature(String prefix, Map<String, String> feature, Map<String, Double> featMap) {
+        List<String> timeList = Arrays.asList("1h", "2h", "3h", "6h", "12h", "1d", "3d", "7d", "yesterday", "today");
+        for (String time : timeList) {
+            double view = Double.parseDouble(feature.getOrDefault("ad_view_" + time, "0"));
+            double click = Double.parseDouble(feature.getOrDefault("ad_click_" + time, "0"));
+            double conver = Double.parseDouble(feature.getOrDefault("ad_conversion_" + time, "0"));
+            double income = Double.parseDouble(feature.getOrDefault("ad_income_" + time, "0"));
+            double cpc = div(income, click);
+            double ctr = FeatureUtils.plusSmooth(click, view, CTR_SMOOTH_BETA_FACTOR);
+            double ctcvr = FeatureUtils.plusSmooth(conver, view, CTCVR_SMOOTH_BETA_FACTOR);
+            double ecpm = ctr * cpc * 1000;
+            featMap.put(prefix + "_" + time + "_ctr", ctr);
+            featMap.put(prefix + "_" + time + "_ctcvr", ctcvr);
+            featMap.put(prefix + "_" + time + "_cvr", FeatureUtils.plusSmooth(conver, click, CVR_SMOOTH_BETA_FACTOR));
+            featMap.put(prefix + "_" + time + "_conver", FeatureUtils.log1(conver, 10));
+            featMap.put(prefix + "_" + time + "_ecpm", FeatureUtils.log1(ecpm, 10));
+            featMap.put(prefix + "_" + time + "_click", FeatureUtils.log1(click, 10));
+        }
+    }
+
+    private static double div(double d1, double d2) {
+        if (d1 == 0 || d2 == 0) {
+            return 0d;
+        }
+        return d1 / d2;
+    }
+}

src/main/scala/com/aliyun/odps/spark/examples/myUtils/FissionFeature.java

@@ -0,0 +1,72 @@
+package com.aliyun.odps.spark.examples.myUtils;
+
+import java.util.Arrays;
+import java.util.Map;
+
+public class FissionFeature {
+    public static int bucket(double[] boundaries, double value) {
+        int pos = Arrays.binarySearch(boundaries, value);
+        return pos >= 0 ? pos : -pos - 1;
+    }
+
+    public static void valCrossAdFeature(String key, String value, String hasAd, String adProfession, String adSKU, Map<String, Double> featMap) {
+        String val = value.replaceAll("(\\s+|\\t|:)", "").trim();
+        if (val.isEmpty()) {
+            return;
+        }
+
+        String ad = hasAd.toLowerCase().replaceAll("(\\s+|\\t|:)", "").trim();
+        String profession = adProfession.toLowerCase().replaceAll("(\\s+|\\t|:)", "").trim();
+        String sku = adSKU.toLowerCase().replaceAll("(\\s+|\\t|:)", "").trim();
+
+        featMap.put(String.format("%s@%s", key, val), 1.0);
+        if (!ad.isEmpty()) {
+            featMap.put(String.format("%s@%s@%s", key, val, ad), 1.0);
+        }
+        if (!profession.isEmpty()) {
+            featMap.put(String.format("%s@%s@%s", key, val, profession), 1.0);
+        }
+        if (!sku.isEmpty()) {
+            featMap.put(String.format("%s@%s@%s", key, val, sku), 1.0);
+        }
+    }
+
+    public static void idCrossAdFeature(String key, String value, String hasAd, String adProfession, String adSKU, Map<String, Double> featMap) {
+        String val = value.toLowerCase().replaceAll("(\\s+|\\t|:)", "").trim();
+        String ad = hasAd.toLowerCase().replaceAll("(\\s+|\\t|:)", "").trim();
+        String profession = adProfession.toLowerCase().replaceAll("(\\s+|\\t|:)", "").trim();
+        String sku = adSKU.toLowerCase().replaceAll("(\\s+|\\t|:)", "").trim();
+
+        featMap.put(String.format("%s@%s", key, val), 1.0);
+        if (!ad.isEmpty()) {
+            featMap.put(String.format("%s@%s@%s", key, val, ad), 1.0);
+        }
+        if (!profession.isEmpty()) {
+            featMap.put(String.format("%s@%s@%s", key, val, profession), 1.0);
+        }
+        if (!sku.isEmpty()) {
+            featMap.put(String.format("%s@%s@%s", key, val, sku), 1.0);
+        }
+    }
+
+    public static void realCrossAdFeature(String key, double value, double[] boundaries, String hasAd, String adProfession, String adSKU, Map<String, Double> featMap) {
+        if (value > 0 && null != boundaries) {
+            int pos = bucket(boundaries, value);
+            String ad = hasAd.toLowerCase().replaceAll("(\\s+|\\t|:)", "").trim();
+            String profession = adProfession.toLowerCase().replaceAll("(\\s+|\\t|:)", "").trim();
+            String sku = adSKU.toLowerCase().replaceAll("(\\s+|\\t|:)", "").trim();
+
+            featMap.put(key, FeatureUtils.log1(value, 10));
+            featMap.put(String.format("%s@%d", key, pos), 1.0);
+            if (!ad.isEmpty()) {
+                featMap.put(String.format("%s@%d@%s", key, pos, ad), 1.0);
+            }
+            if (!profession.isEmpty()) {
+                featMap.put(String.format("%s@%d@%s", key, pos, profession), 1.0);
+            }
+            if (!sku.isEmpty()) {
+                featMap.put(String.format("%s@%d@%s", key, pos, sku), 1.0);
+            }
+        }
+    }
+}

src/main/scala/com/aliyun/odps/spark/examples/myUtils/TitleConvert.java

@@ -0,0 +1,63 @@
+package com.aliyun.odps.spark.examples.myUtils;
+
+import com.tzld.piaoquan.recommend.similarity.word2vec.Segment;
+
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+
+public class TitleConvert {
+    public static String getFeature(Map<String, String> record, List<Map<String, String>> videoSeq, int scale) {
+        String result = "";
+        try {
+
+            Map<String, String> headInfo = ConvertUtils.getRecordCol(record, "v2_feature");
+            String title = headInfo.getOrDefault("title", "");
+            if (!title.isEmpty()) {
+                List<String> words = Segment.getWords(title);
+                result = String.join(" ", words);
+            }
+        } catch (Exception e) {
+            e.printStackTrace();
+        }
+        return result;
+    }
+
+    private static Map<String, Map<String, String>> getUserOriginInfo(Map<String, String> record) {
+        Map<String, Map<String, String>> map = new HashMap<>();
+        map.put("mid_global_feature_20250212", ConvertUtils.getRecordCol(record, "c1_feature"));
+        map.put("alg_mid_feature_return_tags", ConvertUtils.getRecordCol(record, "c5_feature"));
+        map.put("alg_mid_feature_share_tags", ConvertUtils.getRecordCol(record, "c6_feature"));
+        map.put("alg_mid_feature_sharecf", new HashMap<>());
+        map.put("alg_mid_feature_returncf", new HashMap<>());
+        map.put("alg_recsys_feature_user_share_return_stat", ConvertUtils.getRecordCol(record, "c9_feature"));
+        return map;
+    }
+
+    private static Map<String, Map<String, Map<String, String>>> getVideoOriginInfo(Map<String, String> record) {
+        Map<String, Map<String, String>> map = new HashMap<>();
+        map.put("alg_recsys_feature_video_clean_stat", ConvertUtils.getRecordCol(record, "b0_feature"));
+        map.put("alg_vid_global_feature_20250212", ConvertUtils.getRecordCol(record, "b1_feature"));
+        map.put("alg_vid_recommend_exp_feature_20250212", ConvertUtils.getRecordCol(record, "b2_feature"));
+        map.put("alg_vid_recommend_flowpool_exp_feature_20250212", ConvertUtils.getRecordCol(record, "b3_feature"));
+        map.put("alg_vid_apptype_recommend_exp_feature_20250212", ConvertUtils.getRecordCol(record, "b4_feature"));
+        map.put("alg_vid_province_recommend_exp_feature_20250212", ConvertUtils.getRecordCol(record, "b5_feature"));
+        map.put("alg_vid_brand_recommend_exp_feature_20250212", ConvertUtils.getRecordCol(record, "b6_feature"));
+        map.put("alg_vid_hotsencetype_recommend_exp_feature_20250212", ConvertUtils.getRecordCol(record, "b7_feature"));
+        map.put("alg_merge_cate1_recommend_exp_feature_20250212", ConvertUtils.getRecordCol(record, "b8_feature"));
+        map.put("alg_merge_cate2_recommend_exp_feature_20250212", ConvertUtils.getRecordCol(record, "b9_feature"));
+        map.put("alg_channel_recommend_exp_feature_20250212", ConvertUtils.getRecordCol(record, "b10_feature"));
+        map.put("alg_festive_recommend_exp_feature_20250212", ConvertUtils.getRecordCol(record, "b11_feature"));
+        map.put("alg_video_unionid_recommend_exp_feature_20250212", ConvertUtils.getRecordCol(record, "b13_feature"));
+        map.put("scene_type_vid_cf_feature_20250212", ConvertUtils.getRecordCol(record, "d1_feature"));
+        map.put("vid_click_cf_feature_20250212", ConvertUtils.getRecordCol(record, "d2_feature"));
+        map.put("alg_recsys_feature_cf_i2i_v2", ConvertUtils.getRecordCol(record, "d3_feature"));
+        map.put("alg_recsys_feature_video_recommend_channel_layer", ConvertUtils.getRecordCol(record, "b14_feature"));
+        map.put("alg_recsys_feature_video_recommend_channel_layer_head", ConvertUtils.getRecordCol(record, "b15_feature"));
+
+        Map<String, Map<String, Map<String, String>>> allMap = new HashMap<>();
+        String vid = record.get("vid");
+        allMap.put(vid, map);
+        return allMap;
+    }
+}

src/main/scala/com/aliyun/odps/spark/examples/transform/CommonFeature.java

@@ -0,0 +1,119 @@
+package com.aliyun.odps.spark.examples.transform;
+
+
+import com.aliyun.odps.spark.examples.myUtils.FeatureUtils;
+
+import java.util.List;
+import java.util.Map;
+
+public class CommonFeature {
+    private static final double rovnSmoothPlus = 200.0;
+
+    public static double getOneInfo(String name, Map<String, String> map) {
+        if (null == map) {
+            return 0.0;
+        }
+        return map.isEmpty() ? 0 : Double.parseDouble(map.getOrDefault(name, "0.0"));
+    }
+
+    public static void oneTypeStatFeature(String prefix, String uvPrefix, List<String> periods, Map<String, String> infoMap, double log1Scale, double smoothPlus, Map<String, Object> featMap) {
+        if (null == infoMap || infoMap.isEmpty()) {
+            return;
+        }
+        for (String period : periods) {
+            double exp = getOneInfo("exp_" + period, infoMap);
+            if (!FeatureUtils.greaterThanZero(exp)) {
+                continue;
+            }
+            double is_share = getOneInfo("is_share_" + period, infoMap);
+            double share_cnt = getOneInfo("share_cnt_" + period, infoMap);
+            double is_return_1 = getOneInfo("is_return_1_" + period, infoMap);
+            double return_n_uv = getOneInfo(uvPrefix + "_" + period, infoMap);
+
+            double exp_s = FeatureUtils.log1(exp, log1Scale);
+            double is_share_s = FeatureUtils.log1(is_share, log1Scale);
+            double share_cnt_s = FeatureUtils.log1(share_cnt, log1Scale);
+            double is_return_1_s = FeatureUtils.log1(is_return_1, log1Scale);
+            double return_n_uv_s = FeatureUtils.log1(return_n_uv, log1Scale);
+
+            double str = FeatureUtils.wilsonScore(is_share, exp);
+            double str_plus = FeatureUtils.wilsonScore(is_return_1, exp);
+            double ros_one = FeatureUtils.wilsonScore(is_return_1, is_share);
+
+            double rovn = FeatureUtils.plusSmooth(return_n_uv, exp, rovnSmoothPlus, 1);
+            double ros = FeatureUtils.plusSmooth(return_n_uv / 10, is_share, smoothPlus, 1);
+            double ros_n = FeatureUtils.plusSmooth(return_n_uv / 10, share_cnt, smoothPlus, 1);
+            double ros_minus = FeatureUtils.plusSmooth(return_n_uv / 10, is_return_1, smoothPlus, 1);
+
+            featMap.put(prefix + "_" + period + "_exp", exp_s);
+            featMap.put(prefix + "_" + period + "_is_share", is_share_s);
+            featMap.put(prefix + "_" + period + "_share_cnt", share_cnt_s);
+            featMap.put(prefix + "_" + period + "_is_return_1", is_return_1_s);
+            featMap.put(prefix + "_" + period + "_return_n_uv", return_n_uv_s);
+            featMap.put(prefix + "_" + period + "_str", str);
+            featMap.put(prefix + "_" + period + "_str_plus", str_plus);
+            featMap.put(prefix + "_" + period + "_ros_one", ros_one);
+            featMap.put(prefix + "_" + period + "_rovn", rovn);
+            featMap.put(prefix + "_" + period + "_ros", ros);
+            featMap.put(prefix + "_" + period + "_ros_n", ros_n);
+            featMap.put(prefix + "_" + period + "_ros_minus", ros_minus);
+        }
+    }
+
+    public static void oneTypeStatFeature(String prefix, List<String> periods, Map<String, String> infoMap, double log1Scale, double smoothPlus, Map<String, Object> featMap) {
+        if (null == infoMap || infoMap.isEmpty()) {
+            return;
+        }
+        for (String period : periods) {
+            double exp = getOneInfo("exp_" + period, infoMap);
+            if (!FeatureUtils.greaterThanZero(exp)) {
+                continue;
+            }
+            double is_share = getOneInfo("is_share_" + period, infoMap);
+            double share_cnt = getOneInfo("share_cnt_" + period, infoMap);
+            double is_return_1 = getOneInfo("is_return_1_" + period, infoMap);
+            double return_1_uv = getOneInfo("return_1_uv_" + period, infoMap);
+            double return_n_uv = getOneInfo("return_n_uv_" + period, infoMap);
+
+            double exp_s = FeatureUtils.log1(exp, log1Scale);
+            double is_share_s = FeatureUtils.log1(is_share, log1Scale);
+            double share_cnt_s = FeatureUtils.log1(share_cnt, log1Scale);
+            double is_return_1_s = FeatureUtils.log1(is_return_1, log1Scale);
+            double return_1_uv_s = FeatureUtils.log1(return_1_uv, log1Scale);
+            double return_n_uv_s = FeatureUtils.log1(return_n_uv, log1Scale);
+
+            double str = FeatureUtils.wilsonScore(is_share, exp);
+            double str_plus = FeatureUtils.wilsonScore(is_return_1, exp);
+            double ros_one = FeatureUtils.wilsonScore(is_return_1, is_share);
+
+            double rovn1 = FeatureUtils.plusSmooth(return_1_uv, exp, rovnSmoothPlus, 1);
+            double ros1 = FeatureUtils.plusSmooth(return_1_uv / 10, is_share, smoothPlus, 1);
+            double ros_n1 = FeatureUtils.plusSmooth(return_1_uv / 10, share_cnt, smoothPlus, 1);
+            double ros_minus1 = FeatureUtils.plusSmooth(return_1_uv / 10, is_return_1, smoothPlus, 1);
+
+            double rovn = FeatureUtils.plusSmooth(return_n_uv, exp, rovnSmoothPlus, 1);
+            double ros = FeatureUtils.plusSmooth(return_n_uv / 10, is_share, smoothPlus, 1);
+            double ros_n = FeatureUtils.plusSmooth(return_n_uv / 10, share_cnt, smoothPlus, 1);
+            double ros_minus = FeatureUtils.plusSmooth(return_n_uv / 10, is_return_1, smoothPlus, 1);
+
+            featMap.put(prefix + "_" + period + "_exp", exp_s);
+            featMap.put(prefix + "_" + period + "_is_share", is_share_s);
+            featMap.put(prefix + "_" + period + "_share_cnt", share_cnt_s);
+            featMap.put(prefix + "_" + period + "_is_return_1", is_return_1_s);
+            featMap.put(prefix + "_" + period + "_return_1_uv", return_1_uv_s);
+            featMap.put(prefix + "_" + period + "_return_n_uv", return_n_uv_s);
+            featMap.put(prefix + "_" + period + "_str", str);
+            featMap.put(prefix + "_" + period + "_str_plus", str_plus);
+            featMap.put(prefix + "_" + period + "_ros_one", ros_one);
+
+            featMap.put(prefix + "_" + period + "_rovn1", rovn1);
+            featMap.put(prefix + "_" + period + "_ros1", ros1);
+            featMap.put(prefix + "_" + period + "_ros_n1", ros_n1);
+            featMap.put(prefix + "_" + period + "_ros_minus1", ros_minus1);
+            featMap.put(prefix + "_" + period + "_rovn", rovn);
+            featMap.put(prefix + "_" + period + "_ros", ros);
+            featMap.put(prefix + "_" + period + "_ros_n", ros_n);
+            featMap.put(prefix + "_" + period + "_ros_minus", ros_minus);
+        }
+    }
+}

src/main/scala/com/aliyun/odps/spark/examples/transform/TransBaseFeature.java

@@ -0,0 +1,28 @@
+package com.aliyun.odps.spark.examples.transform;
+
+import java.util.*;
+
+public class TransBaseFeature {
+    private static final Set<String> appSet = new HashSet<>(Arrays.asList("0", "2", "4"));
+    private static final Set<String> sceneSet = new HashSet<>(Arrays.asList("1008", "1007", "1058", "1074", "1010"));
+
+    public static void getBaseFeature(long currentMs, String appType, String hotSceneType, Map<String, Object> featMap) {
+        Calendar calendar = Calendar.getInstance();
+        calendar.setTimeInMillis(currentMs);
+
+        featMap.put("week", calendar.get(Calendar.DAY_OF_WEEK));
+        featMap.put("hour", calendar.get(Calendar.HOUR_OF_DAY));
+
+        String app = "other";
+        if (appSet.contains(appType)) {
+            app = appType;
+        }
+        featMap.put("app", app);
+
+        String scene = "other";
+        if (sceneSet.contains(hotSceneType)) {
+            scene = hotSceneType;
+        }
+        featMap.put("scene", scene);
+    }
+}

src/main/scala/com/aliyun/odps/spark/examples/transform/TransCrossFeature.java

@@ -0,0 +1,155 @@
+package com.aliyun.odps.spark.examples.transform;
+
+import com.aliyun.odps.spark.examples.myUtils.FeatureUtils;
+import com.aliyun.odps.spark.examples.myUtils.UserSRBO;
+import com.aliyun.odps.spark.examples.myUtils.UserShareReturnProfile;
+import com.aliyun.odps.spark.examples.myUtils.VideoAttrSRBO;
+import examples.utils.SimilarityUtils;
+
+import java.util.*;
+
+public class TransCrossFeature {
+    private static final int seqMaxN = 2;
+    private static final int seqLastN = 2;
+    private static final double log1Scale = 16.0;
+    private static final double smoothPlus = 30.0;
+    private static final Map<String, String> historyVideoMap = new HashMap<>();
+    private static final Map<String, String> headVideoSimMap = new HashMap<>();
+
+    static {
+        historyVideoMap.put("title", "title");
+
+        headVideoSimMap.put("title", "title");
+        headVideoSimMap.put("merge_first_level_cate", "cate1");
+        headVideoSimMap.put("merge_second_level_cate", "cate2");
+        headVideoSimMap.put("keywords", "kw");
+    }
+
+    public static void getHeadVideoCrossFeature(String prefix, Map<String, String> video1, Map<String, String> video2, Map<String, Object> featMap) {
+        getVideoCrossFeature(prefix, headVideoSimMap, video1, video2, featMap);
+    }
+
+    public static void getUserTagsVideoCrossFeature(String prefix, Map<String, String> videoInfo, Map<String, String> infoMap, Map<String, Object> featMap) {
+        if (null == videoInfo || videoInfo.isEmpty() || null == infoMap || infoMap.isEmpty()) {
+            return;
+        }
+        String title = videoInfo.getOrDefault("title", "");
+        if (title.isEmpty()) {
+            return;
+        }
+        for (String period : Arrays.asList("tags_1d", "tags_3d", "tags_7d")) {
+            String tags = infoMap.getOrDefault(period, "");
+            if (!tags.isEmpty()) {
+                Double[] doubles = FeatureUtils.funcC34567ForTagsNew(tags, title);
+                featMap.put(prefix + "_" + period + "_matchnum", doubles[0]);
+                featMap.put(prefix + "_" + period + "_maxscore", doubles[1]);
+                featMap.put(prefix + "_" + period + "_avgscore", doubles[2]);
+            }
+        }
+    }
+
+    public static void getUserProfileVideoCrossFeature(long currentMs, UserShareReturnProfile profile, Map<String, String> rankVideo, Map<String, Map<String, String>> hVideoMap, Map<String, Object> featMap) {
+        if (null == profile) {
+            return;
+        }
+        getUserSeqVideoCrossFeature("c9_mss", currentMs, seqMaxN, profile.getM_s_s(), rankVideo, hVideoMap, featMap);
+        getUserSeqVideoCrossFeature("c9_mrs", currentMs, seqMaxN, profile.getM_r_s(), rankVideo, hVideoMap, featMap);
+        getUserSeqVideoCrossFeature("c9_lss", currentMs, seqLastN, profile.getL_s_s(), rankVideo, hVideoMap, featMap);
+        getUserSeqVideoCrossFeature("c9_lrs", currentMs, seqLastN, profile.getL_r_s(), rankVideo, hVideoMap, featMap);
+
+        if (null == rankVideo || rankVideo.isEmpty()) {
+            return;
+        }
+        getUserStatVideoCrossFeature("c9_c1s", rankVideo.getOrDefault("merge_first_level_cate", ""), profile.getC1_s(), featMap);
+        getUserStatVideoCrossFeature("c9_c2s", rankVideo.getOrDefault("merge_second_level_cate", ""), profile.getC2_s(), featMap);
+        getUserStatVideoCrossFeature("c9_l1s", rankVideo.getOrDefault("festive_label1", ""), profile.getL1_s(), featMap);
+        getUserStatVideoCrossFeature("c9_l2s", rankVideo.getOrDefault("festive_label2", ""), profile.getL2_s(), featMap);
+    }
+
+    private static void getUserSeqVideoCrossFeature(String prefix, long currentMs, int maxN, List<UserSRBO> list, Map<String, String> rankVideo, Map<String, Map<String, String>> hVideoMap, Map<String, Object> featMap) {
+        if (null != list && !list.isEmpty()) {
+            for (int i = 0; i < list.size() && i < maxN; i++) {
+                UserSRBO u = list.get(i);
+                if (null != u) {
+                    long id = u.getId();
+                    long cnt = u.getCnt();
+                    long uv = u.getUv();
+                    long ts = u.getTs();
+                    if (id > 0) {
+                        String vid = id + "";
+                        String baseKey = String.format("%s_%d", prefix, i + 1);
+                        if (cnt > 0) {
+                            featMap.put(baseKey + "_cnt", FeatureUtils.log1(cnt, log1Scale));
+                        }
+                        if (uv > 0) {
+                            featMap.put(baseKey + "_uv", FeatureUtils.log1(uv, log1Scale));
+                        }
+                        if (ts > 0) {
+                            long historyMs = ts * 1000;
+                            featMap.put(baseKey + "_ts", 1 - FeatureUtils.getTimeDiff(currentMs, historyMs));
+
+                            // history week & hour
+                            Calendar calendar = Calendar.getInstance();
+                            calendar.setTimeInMillis(historyMs);
+                            featMap.put(baseKey + "_week", calendar.get(Calendar.DAY_OF_WEEK));
+                            featMap.put(baseKey + "_hour", calendar.get(Calendar.HOUR_OF_DAY));
+                        }
+                        if (null != hVideoMap && hVideoMap.containsKey(vid)) {
+                            Map<String, String> hVideo = hVideoMap.get(vid);
+                            getVideoCrossFeature(baseKey, historyVideoMap, hVideo, rankVideo, featMap);
+                        }
+                    }
+                }
+            }
+        }
+    }
+
+    private static void getUserStatVideoCrossFeature(String prefix, String attr, Map<String, VideoAttrSRBO> attrMap, Map<String, Object> featMap) {
+        if (null == attrMap || attrMap.isEmpty()) {
+            return;
+        }
+        attr = attr.trim();
+        if (attrMap.containsKey(attr)) {
+            VideoAttrSRBO bo = attrMap.get(attr);
+            if (null != bo) {
+                long sp = bo.getSp();    // share_pv
+                long rp = bo.getRp();    // return_n_pv_noself
+                long ru = bo.getRu();    // return_n_uv_noself
+                long mu = bo.getMu();    // max_return_uv
+                if (sp > 0) {
+                    double sp_s = FeatureUtils.log1(sp, log1Scale);
+                    double rp_s = FeatureUtils.log1(rp, log1Scale);
+                    double ru_s = FeatureUtils.log1(ru, log1Scale);
+                    double mu_s = FeatureUtils.log1(mu, log1Scale);
+
+                    double ros_one = FeatureUtils.wilsonScore(rp, sp);
+                    double ros = FeatureUtils.plusSmooth(ru / 10.0, sp, smoothPlus, 1);
+                    double ros_minus = FeatureUtils.plusSmooth(ru / 10.0, rp, smoothPlus, 1);
+
+                    featMap.put(prefix + "_sp", sp_s);
+                    featMap.put(prefix + "_rp", rp_s);
+                    featMap.put(prefix + "_ru", ru_s);
+                    featMap.put(prefix + "_mu", mu_s);
+                    featMap.put(prefix + "_ros_one", ros_one);
+                    featMap.put(prefix + "_ros", ros);
+                    featMap.put(prefix + "_ros_minus", ros_minus);
+                }
+            }
+        }
+    }
+
+    private static void getVideoCrossFeature(String prefix, Map<String, String> attrMap, Map<String, String> video1, Map<String, String> video2, Map<String, Object> featMap) {
+        if (null == video1 || video1.isEmpty() || null == video2 || video2.isEmpty()) {
+            return;
+        }
+        for (Map.Entry<String, String> entry : attrMap.entrySet()) {
+            String attr = entry.getKey();
+            String attr1 = video1.getOrDefault(attr, "");
+            String attr2 = video2.getOrDefault(attr, "");
+            if (!"".equals(attr1) && !"unknown".equals(attr1) && !"".equals(attr2) && !"unknown".equals(attr2)) {
+                double simScore = SimilarityUtils.word2VecSimilarity(attr1, attr2);
+                featMap.put(prefix + "_" + entry.getValue(), simScore);
+            }
+        }
+    }
+}

src/main/scala/com/aliyun/odps/spark/examples/transform/TransItemFeature.java

@@ -0,0 +1,107 @@
+package com.aliyun.odps.spark.examples.transform;
+
+import com.aliyun.odps.spark.examples.myUtils.FeatureUtils;
+
+import java.util.*;
+
+public class TransItemFeature {
+    private static final int kwNum = 6;
+    private static final double log1Scale = 16.0;
+    private static final double smoothPlus = 30.0;
+    private static final List<String> b0Periods = Arrays.asList("1h", "3h", "6h", "12h");
+    private static final List<String> b1Periods = Arrays.asList("1h", "3h", "6h", "12h", "24h", "72h");
+    private static final List<String> b2Periods = Arrays.asList("1h", "3h", "6h", "24h");
+    private static final List<String> b3Periods = Arrays.asList("1h", "3h", "6h", "12h");
+    private static final List<String> b4Periods = Arrays.asList("1h", "3h", "6h", "12h");
+    private static final List<String> b5Periods = Arrays.asList("1h", "3h", "6h", "12h", "24h", "72h");
+    private static final List<String> b6Periods = Arrays.asList("1h", "12h");
+    private static final List<String> b7Periods = Arrays.asList("1h", "3h", "6h", "12h", "24h", "72h");
+    private static final List<String> b8Periods = Arrays.asList("1h", "3h", "6h");
+    private static final List<String> b9Periods = Arrays.asList("1h", "3h", "6h");
+    private static final List<String> b10Periods = Arrays.asList("1h", "3h", "6h");
+    private static final List<String> b13Periods = Arrays.asList("1h", "3h", "6h", "12h");
+    private static final Map<String, String> videoAttrMAP = new HashMap<>();
+
+    static {
+        videoAttrMAP.put("merge_first_level_cate", "cate1");
+        videoAttrMAP.put("merge_second_level_cate", "cate2");
+        videoAttrMAP.put("channel", "channel");
+        videoAttrMAP.put("vid_source", "source");
+        videoAttrMAP.put("uid", "author");
+    }
+
+    public static void getVideoBaseFeature(String prefix, long currentMs, Map<String, String> videoInfo, Map<String, Object> featMap) {
+        if (null == videoInfo || videoInfo.isEmpty()) {
+            return;
+        }
+        featMap.put(prefix + "_duration", FeatureUtils.log1(Double.parseDouble(videoInfo.getOrDefault("total_time", "0")), log1Scale));
+        featMap.put(prefix + "_rate", FeatureUtils.log1(Double.parseDouble(videoInfo.getOrDefault("bit_rate", "0")), log1Scale));
+        String resolution = FeatureUtils.getResolution(videoInfo);
+        if (!resolution.isEmpty()) {
+            featMap.put(prefix + "_res", resolution);
+        }
+
+        // cate
+        getVideoCateFeature(prefix, videoInfo, featMap);
+
+        // public time
+        try {
+            if (videoInfo.containsKey("gmt_create_timestamp")) {
+                String createMsStr = videoInfo.get("gmt_create_timestamp");
+                long createMs = Long.parseLong(createMsStr);
+                double createTime = FeatureUtils.getTimeDiff(currentMs, createMs);
+                featMap.put(prefix + "_ts", 1 - createTime);
+            }
+        } catch (Exception ignored) {
+        }
+    }
+
+
+    public static void getVideoStatFeature(String vid, Map<String, Map<String, Map<String, String>>> videoOriginInfo, Map<String, Object> featMap) {
+        CommonFeature.oneTypeStatFeature("b0", b0Periods, videoOriginInfo.getOrDefault(vid, new HashMap<>()).get("alg_recsys_feature_video_clean_stat"), log1Scale, smoothPlus, featMap);
+        CommonFeature.oneTypeStatFeature("b1", "return_1_uv", b1Periods, videoOriginInfo.getOrDefault(vid, new HashMap<>()).get("alg_vid_global_feature_20250212"), log1Scale, smoothPlus, featMap);
+        CommonFeature.oneTypeStatFeature("b2", "return_n_uv", b2Periods, videoOriginInfo.getOrDefault(vid, new HashMap<>()).get("alg_vid_recommend_exp_feature_20250212"), log1Scale, smoothPlus, featMap);
+        CommonFeature.oneTypeStatFeature("b3", "return_n_uv", b3Periods, videoOriginInfo.getOrDefault(vid, new HashMap<>()).get("alg_vid_recommend_flowpool_exp_feature_20250212"), log1Scale, smoothPlus, featMap);
+        CommonFeature.oneTypeStatFeature("b4", "return_n_uv", b4Periods, videoOriginInfo.getOrDefault(vid, new HashMap<>()).get("alg_vid_apptype_recommend_exp_feature_20250212"), log1Scale, smoothPlus, featMap);
+        CommonFeature.oneTypeStatFeature("b5", "return_n_uv", b5Periods, videoOriginInfo.getOrDefault(vid, new HashMap<>()).get("alg_vid_province_recommend_exp_feature_20250212"), log1Scale, smoothPlus, featMap);
+        CommonFeature.oneTypeStatFeature("b6", "return_n_uv", b6Periods, videoOriginInfo.getOrDefault(vid, new HashMap<>()).get("alg_vid_brand_recommend_exp_feature_20250212"), log1Scale, smoothPlus, featMap);
+        CommonFeature.oneTypeStatFeature("b7", "return_n_uv", b7Periods, videoOriginInfo.getOrDefault(vid, new HashMap<>()).get("alg_vid_hotsencetype_recommend_exp_feature_20250212"), log1Scale, smoothPlus, featMap);
+        CommonFeature.oneTypeStatFeature("b8", "return_n_uv", b8Periods, videoOriginInfo.getOrDefault(vid, new HashMap<>()).get("alg_merge_cate1_recommend_exp_feature_20250212"), log1Scale, smoothPlus, featMap);
+        CommonFeature.oneTypeStatFeature("b9", "return_n_uv", b9Periods, videoOriginInfo.getOrDefault(vid, new HashMap<>()).get("alg_merge_cate2_recommend_exp_feature_20250212"), log1Scale, smoothPlus, featMap);
+        CommonFeature.oneTypeStatFeature("b10", "return_n_uv", b10Periods, videoOriginInfo.getOrDefault(vid, new HashMap<>()).get("alg_channel_recommend_exp_feature_20250212"), log1Scale, smoothPlus, featMap);
+        // CommonFeature.oneTypeStatFeature("b13", "return_n_uv", b13Periods, videoOriginInfo.getOrDefault(vid, new HashMap<>()).get("alg_video_unionid_recommend_exp_feature_20250212"), log1Scale, smoothPlus, featMap);
+    }
+
+    private static void getVideoCateFeature(String prefix, Map<String, String> videoInfo, Map<String, Object> featMap) {
+        if (null == videoInfo || videoInfo.isEmpty()) {
+            return;
+        }
+        for (Map.Entry<String, String> entry : videoAttrMAP.entrySet()) {
+            String attr = entry.getKey();
+            String attrVal = videoInfo.getOrDefault(attr, "");
+            attrVal = attrVal.trim();
+            if (!attrVal.isEmpty() && !attrVal.equals("unknown")) {
+                String key = String.format("%s_%s", prefix, entry.getValue());
+                featMap.put(key, attrVal);
+            }
+        }
+        if (videoInfo.containsKey("keywords")) {
+            String keywords = videoInfo.get("keywords");
+            if (null != keywords && !keywords.isEmpty()) {
+                Set<String> kws = new HashSet<>();
+                for (String kw : keywords.split("[,，、]")) {
+                    kw = kw.replaceAll("(\\s+|\\t|:)", "");
+                    if (!kw.isEmpty()) {
+                        kws.add(kw);
+                        if (kws.size() >= kwNum) {
+                            break;
+                        }
+                    }
+                }
+                if (!kws.isEmpty()) {
+                    featMap.put(prefix + "_kw", String.join("|", kws));
+                }
+            }
+        }
+    }
+}

src/main/scala/com/aliyun/odps/spark/examples/transform/TransUserFeature.java

@@ -0,0 +1,109 @@
+package com.aliyun.odps.spark.examples.transform;
+
+import com.aliyun.odps.spark.examples.myUtils.FeatureUtils;
+import com.aliyun.odps.spark.examples.myUtils.UserSRBO;
+import com.aliyun.odps.spark.examples.myUtils.UserShareReturnProfile;
+
+import java.util.Arrays;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+
+public class TransUserFeature {
+    private static final double log1Scale = 16.0;
+    private static final double smoothPlus = 30.0;
+    private static final long midValidTs = 14 * 24 * 3600 * 1000;
+    private static final List<String> c1Periods = Arrays.asList("72h", "168h");
+    private static final Map<String, String> userAttrMAP = new HashMap<>();
+
+    static {
+        userAttrMAP.put("province", "province");
+        userAttrMAP.put("city", "city");
+        userAttrMAP.put("model", "model");
+        userAttrMAP.put("brand", "brand");
+        userAttrMAP.put("system", "system");
+        userAttrMAP.put("user_channel", "channel");
+        userAttrMAP.put("user_level", "level");
+    }
+
+    public static void getUserFeature(String uid, String mid, long currentMs, UserShareReturnProfile profile,
+                                      Map<String, String> userBaseInfo,
+                                      Map<String, Map<String, String>> userOriginInfo,
+                                      Map<String, Object> featMap) {
+        String user = getUser(uid, mid, currentMs, profile);
+        if (null != user) {
+            featMap.put("user", user);
+        }
+        // user cate
+        getUserCateFeature(userBaseInfo, featMap);
+        // user c9 stat
+        getUserC9Feature(profile, log1Scale, smoothPlus, featMap);
+        // user c1 stat
+        CommonFeature.oneTypeStatFeature("c1", "return_1_uv", c1Periods, userOriginInfo.get("mid_global_feature_20250212"), log1Scale, smoothPlus, featMap);
+    }
+
+    private static String getUser(String uid, String mid, long currentMs, UserShareReturnProfile profile) {
+        if (null != profile) {
+            List<UserSRBO> l_r_s = profile.getL_r_s();
+            if (null != l_r_s && !l_r_s.isEmpty()) {
+                UserSRBO user = l_r_s.get(0);
+                if (null != user) {
+                    long ts = user.getTs() * 1000;
+                    if (ts > currentMs - midValidTs) {
+                        if (null != uid && !uid.isEmpty() && !uid.equals("null")) {
+                            return uid;
+                        } else if (null != mid && !mid.isEmpty() && !mid.equals("null")) {
+                            return mid;
+                        }
+                    }
+                }
+            }
+        }
+        return null;
+    }
+
+    private static void getUserCateFeature(Map<String, String> userInfo, Map<String, Object> featMap) {
+        if (null != userInfo && !userInfo.isEmpty()) {
+            for (Map.Entry<String, String> entry : userAttrMAP.entrySet()) {
+                String attr = entry.getKey();
+                if (userInfo.containsKey(attr)) {
+                    String value = userInfo.get(attr).trim().replaceAll("(\\s+|\\t|:)", "_");
+                    if (!value.isEmpty()) {
+                        featMap.put(entry.getValue(), value.toLowerCase());
+                    }
+                }
+            }
+        }
+    }
+
+    private static void getUserC9Feature(UserShareReturnProfile profile, double log1Scale, double smoothPlus, Map<String, Object> featMap) {
+        if (null != profile) {
+            long s_pv = profile.getS_pv();              // share_pv(分享pv)
+            long s_cnt = profile.getS_cnt();            // share_cnt(分享次数)
+            long r_pv = profile.getR_pv();              // return_pv(回流pv)
+            long r_uv = profile.getR_uv();              // return_uv(回流uv)
+            long m_s_cnt = profile.getM_s_cnt();        // max_share_cnt(最大分享次数)
+            long m_r_uv = profile.getM_r_uv();          // max_return_uv(最大回流uv)
+            if (s_pv > 0) {
+                double s_pv_s = FeatureUtils.log1(s_pv, log1Scale);
+                double s_cnt_s = FeatureUtils.log1(s_cnt, log1Scale);
+                double r_pv_s = FeatureUtils.log1(r_pv, log1Scale);
+                double r_uv_s = FeatureUtils.log1(r_uv, log1Scale);
+                double m_s_cnt_s = FeatureUtils.log1(m_s_cnt, log1Scale);
+                double m_r_uv_s = FeatureUtils.log1(m_r_uv, log1Scale);
+                double ros_one = FeatureUtils.wilsonScore(r_pv, s_pv);
+                double ros = FeatureUtils.plusSmooth(r_uv / 10.0, s_pv, smoothPlus, 1);
+                double ros_minus = FeatureUtils.plusSmooth(r_uv / 10.0, r_pv, smoothPlus, 1);
+                featMap.put("c9_s_pv", s_pv_s);
+                featMap.put("c9_s_cnt", s_cnt_s);
+                featMap.put("c9_r_pv", r_pv_s);
+                featMap.put("c9_r_uv", r_uv_s);
+                featMap.put("c9_m_s_cnt", m_s_cnt_s);
+                featMap.put("c9_m_r_uv", m_r_uv_s);
+                featMap.put("c9_ros_one", ros_one);
+                featMap.put("c9_ros", ros);
+                featMap.put("c9_ros_minus", ros_minus);
+            }
+        }
+    }
+}