Merge branch 'master' into feature_zhaohaipeng

src/main/resources/20241128_recsys_i2i_bucket_47.txt

@@ -0,0 +1,47 @@
+action:rov_day336	100	0.019854,0.024964,0.025876,0.027656,0.03079,0.033211,0.034639,0.036093,0.036864,0.037524,0.037918,0.038907,0.039264,0.039654,0.040559,0.041864,0.042842,0.044022,0.04473,0.045306,0.04625,0.046736,0.047414,0.047688,0.047849,0.048131,0.048751,0.0488,0.049114,0.049622,0.050601,0.050742,0.050954,0.051239,0.052302,0.052783,0.052804,0.053545,0.054264,0.054668,0.055453,0.056391,0.057024,0.057573,0.058317,0.058989,0.059534,0.061089,0.06275,0.063433,0.064061,0.065245,0.065346,0.067091,0.067592,0.068102,0.068852,0.069307,0.070297,0.071266,0.072067,0.072495,0.073921,0.07496,0.075024,0.078836,0.079371,0.080406,0.081501,0.090802,0.103937,0.206944,0.23445
+cate1:ros_day30	100	0.568913,0.775484,0.780939,0.802548,0.869337,0.893388,0.928003,0.928705,0.935544,0.986266,0.994482,1.022163,1.054331,1.085911,1.176906,1.193622,1.227002,1.294849,1.402791
+action:ros_day21	100	0.25,0.328602,0.365854,0.387535,0.404136,0.452126,0.465116,0.479065,0.496933,0.516129,0.52012,0.53876,0.543434,0.556543,0.589769,0.6,0.604136,0.609922,0.617376,0.631866,0.651121,0.673048,0.688716,0.707252,0.720015,0.728878,0.746781,0.757868,0.767714,0.778018,0.793709,0.814338,0.81546,0.820637,0.835823,0.848272,0.860068,0.875434,0.893281,0.899405,0.908121,0.912633,0.916364,0.922581,0.92456,0.937168,0.956125,0.965547,0.966331,0.994555,1.004505,1.010526,1.017194,1.018069,1.023735,1.0359,1.054629,1.076291,1.096155,1.114377,1.120894,1.131766,1.169232,1.184152,1.187872,1.20162,1.209869,1.210319,1.223057,1.278414,1.302882,1.31542,1.359835,1.383904,1.418945,1.451771,1.493695,1.542568,1.622861,1.66065,1.793735,22.0
+action:str_day336	100	0.027696,0.030167,0.031642,0.033286,0.0335,0.03405,0.034097,0.034306,0.034368,0.034781,0.03511,0.03518,0.035215,0.035287,0.035835,0.036406,0.037134,0.037399,0.03829,0.038314,0.038991,0.039151,0.039866,0.040019,0.040565,0.041311,0.041606,0.041796,0.042902,0.042939,0.043499,0.043906,0.044136,0.044519,0.045218,0.045581,0.04618,0.046208,0.046557,0.047247,0.047858,0.048742,0.04893,0.049489,0.049953,0.050708,0.051685,0.052182,0.052284,0.05309,0.053489,0.054153,0.055099,0.056472,0.056837,0.056984,0.058334,0.059659,0.060481,0.061499,0.06314,0.064178,0.066172,0.0669,0.069459,0.070702,0.072543,0.076603,0.08157,0.084894,0.088682,0.093633,0.102991,1.278912
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+cate2:rov_day1	100	0.026118,0.02717,0.0278,0.032997,0.033808,0.033995,0.037557,0.037572,0.040033,0.041213,0.04411,0.045117,0.047059,0.048886,0.049236,0.052352,0.053487,0.056874,0.059551,0.060377,0.063341,0.095622
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+cate2:str_day1	100	0.035124,0.038644,0.039721,0.040222,0.040396,0.040966,0.041068,0.043825,0.045943,0.046239,0.046699,0.050678,0.052013,0.052262,0.059016,0.068581,0.071479,0.071686,0.080863,0.08236
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+cate1:rov_day1	100	0.032978,0.041151,0.041269,0.04214,0.04282,0.043561,0.04411,0.046826,0.048145,0.048886,0.050366,0.051036,0.052371,0.054471,0.057223,0.0585,0.063412,0.068202,0.069998
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+cate2:str_day7	100	0.038319,0.039771,0.042056,0.042651,0.043288,0.043932,0.04456,0.044671,0.044821,0.045327,0.046423,0.046512,0.047975,0.054029,0.05444,0.055424,0.063758,0.068376,0.07106,0.081865,0.08189,0.094847
+cate1:rov_day3	100	0.033398,0.040212,0.042089,0.042606,0.042607,0.044162,0.045844,0.046173,0.047263,0.04779,0.0479,0.048133,0.04828,0.050624,0.051353,0.051611,0.054355,0.064011,0.064333,0.068423
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+action:ros_day336	100	0.348613,0.434862,0.453572,0.513781,0.534658,0.556676,0.576444,0.603072,0.654221,0.693712,0.715447,0.731941,0.752071,0.761062,0.772958,0.797642,0.807481,0.836035,0.853369,0.88675,0.904697,0.931785,0.947704,0.965427,0.981116,0.996465,1.011519,1.027801,1.051123,1.055017,1.059061,1.060222,1.065558,1.076977,1.080115,1.10106,1.101198,1.113232,1.133651,1.142515,1.15843,1.19254,1.219361,1.245118,1.269212,1.26929,1.281102,1.316645,1.369174,1.418259,1.424134,1.457729,1.481606,1.508852,1.532746,1.552832,1.55388,1.561422,1.636015,1.703974,1.751432,1.776608,1.803191,1.80948,1.820939,1.822301,1.823567,1.855319,1.862946,1.899702,1.981808,2.041562,2.313638,2.334647,3.364988,5.065342
+action:vovd1_day7	100	0.137666,0.205238,0.239319,0.257397,0.278364,0.297826,0.308853,0.323599,0.330846,0.339474,0.345131,0.355611,0.365971,0.378241,0.386534,0.394485,0.401745,0.4053,0.4096,0.418047,0.423613,0.43029,0.437099,0.440698,0.44838,0.452893,0.457572,0.460936,0.461213,0.469853,0.472214,0.478025,0.485588,0.491587,0.492244,0.497435,0.498857,0.50138,0.502244,0.510647,0.51313,0.51375,0.518796,0.522852,0.52421,0.526356,0.531474,0.534258,0.54725,0.553808,0.555702,0.561948,0.57056,0.573226,0.574231,0.580617,0.592342,0.606824,0.61233,0.616977,0.619873,0.624001,0.629072,0.630574,0.638138,0.641281,0.6527,0.653959,0.659965,0.661916,0.665125,0.665898,0.676628,0.691067,0.69406,0.697332,0.697739,0.708397,0.720571,0.739653,0.765283,0.788233,0.820842,0.974022,66.0
+cate1:str_day30	100	0.043294,0.043718,0.044014,0.045551,0.045938,0.046901,0.04757,0.048139,0.050299,0.051885,0.052461,0.052698,0.053114,0.054098,0.060293,0.062666,0.063203,0.072972,0.083519
+cate1:rov_day30	100	0.041354,0.041515,0.045137,0.046642,0.047085,0.047451,0.047478,0.048904,0.049013,0.04908,0.049602,0.050241,0.050293,0.053031,0.053143,0.053609,0.054005,0.059483,0.060733
+cate2:ros_day7	100	0.390503,0.39766,0.578565,0.716623,0.752324,0.754474,0.776025,0.838096,0.868822,0.871489,0.878833,0.885504,0.888563,0.925512,1.012344,1.081793,1.102885,1.11219,1.127939,1.163207,1.238196,1.394579
+cate2:str_day30	100	0.036034,0.040884,0.042769,0.044628,0.04554,0.045578,0.046651,0.046863,0.047346,0.049194,0.051813,0.052461,0.052698,0.056434,0.061652,0.061676,0.065602,0.072413,0.076976
+cate1:vovd1_day1	100	0.381544,0.441132,0.445324,0.467233,0.469086,0.469838,0.478436,0.48103,0.519413,0.522035,0.523529,0.52888,0.53239,0.537782,0.565363,0.574103,0.575852,0.589783,0.706623,0.743416,0.912354
+cate1:vovd1_day3	100	0.373584,0.417485,0.445449,0.452683,0.460663,0.47318,0.473297,0.473597,0.475075,0.488597,0.492541,0.509136,0.510421,0.519506,0.555438,0.593366,0.653946,0.685791,0.798356
+cate1:ros_day3	100	0.540985,0.651424,0.687296,0.70954,0.760934,0.821386,0.838121,0.872193,0.900231,0.905328,0.967716,1.021737,1.063502,1.086678,1.097226,1.167411,1.181074,1.342978,1.369609,1.400263
+cate1:str_day7	100	0.040618,0.040838,0.041237,0.041273,0.042872,0.046365,0.046746,0.046876,0.047723,0.047737,0.048656,0.051212,0.053431,0.05444,0.055187,0.055424,0.06004,0.061695,0.062462,0.067108,0.067844,0.068583,0.085334
+cate2:rov_day7	100	0.030134,0.031978,0.033098,0.036565,0.037636,0.038902,0.04181,0.044573,0.045799,0.046316,0.046451,0.047662,0.048373,0.049034,0.049267,0.05026,0.051588,0.051632,0.056584,0.06822
+action:ros_day7	100	0.235294,0.285472,0.3,0.336364,0.346555,0.365527,0.370192,0.38741,0.401695,0.406203,0.435685,0.451705,0.472144,0.483173,0.506849,0.533366,0.548633,0.565574,0.584775,0.601219,0.628705,0.655896,0.670468,0.688501,0.696132,0.706748,0.722412,0.740503,0.753801,0.781575,0.791011,0.813642,0.815923,0.817254,0.82943,0.836958,0.847162,0.853241,0.856176,0.864239,0.886174,0.889372,0.894357,0.904244,0.922517,0.9375,0.950874,0.969653,0.991328,1.011533,1.020426,1.022207,1.041193,1.063075,1.069047,1.071736,1.076515,1.084513,1.085469,1.102302,1.110046,1.146137,1.165161,1.186323,1.189572,1.210379,1.217369,1.218697,1.230732,1.251783,1.289474,1.302491,1.318563,1.337901,1.339483,1.399507,1.431193,1.446613,1.448433,1.533333,1.602524,1.68916,1.705158,2.134192,56.0
+action:str_day21	100	0.02529,0.025918,0.026903,0.027762,0.028975,0.029358,0.029759,0.031034,0.031404,0.031486,0.031523,0.03199,0.033401,0.033404,0.033816,0.034923,0.035043,0.035242,0.035405,0.036101,0.036718,0.036796,0.036905,0.036934,0.037151,0.037436,0.037648,0.037743,0.038547,0.039119,0.03998,0.040804,0.041602,0.041932,0.042623,0.042741,0.042858,0.043654,0.043674,0.043831,0.044052,0.044966,0.045036,0.045468,0.04597,0.046178,0.047072,0.047778,0.047981,0.048224,0.048268,0.048517,0.048907,0.049482,0.050441,0.051567,0.052579,0.053673,0.055797,0.056378,0.057306,0.057499,0.059821,0.061331,0.061444,0.06305,0.063712,0.064352,0.067355,0.071703,0.076664,0.078362,0.080203,0.082609,0.083713,0.089455,0.104439,12.2
+action:str_day1	100	0.017324,0.021725,0.02407,0.025399,0.026721,0.027972,0.028949,0.030162,0.031023,0.032131,0.032741,0.033061,0.034113,0.034893,0.035262,0.035872,0.035971,0.036071,0.036502,0.037155,0.037493,0.037617,0.037677,0.038439,0.039401,0.040038,0.040611,0.040757,0.041109,0.041609,0.042446,0.042696,0.043116,0.043287,0.043652,0.043956,0.044368,0.044531,0.045105,0.045271,0.045521,0.045849,0.045905,0.046234,0.046542,0.046948,0.046957,0.047394,0.047995,0.048265,0.049201,0.049995,0.050314,0.051602,0.052819,0.053446,0.054108,0.05495,0.055071,0.055349,0.056438,0.056748,0.057858,0.059144,0.060907,0.063513,0.063704,0.065497,0.065569,0.066413,0.067175,0.067752,0.069989,0.07229,0.074536,0.076372,0.078269,0.079786,0.081651,0.087351,0.092959,0.09716,0.098375,0.111731,0.114471,4.0
+cate2:vovd1_day7	100	0.356425,0.362591,0.373599,0.40264,0.420118,0.426541,0.447309,0.452845,0.459098,0.463879,0.487661,0.491812,0.493725,0.499914,0.507697,0.5156,0.523791,0.534578,0.547378,0.561814,0.574644
+cate2:vovd1_day30	100	0.370944,0.443582,0.463276,0.482661,0.483314,0.48982,0.491756,0.49963,0.513591,0.514496,0.517509,0.518859,0.529267,0.537233,0.542512,0.551456,0.564547,0.62894,0.685266
+action:vovd1_day336	100	0.248048,0.296243,0.309127,0.337845,0.343714,0.372605,0.390476,0.400762,0.410739,0.429253,0.443089,0.451999,0.456237,0.463742,0.46671,0.473092,0.481418,0.482771,0.491281,0.497179,0.503485,0.509267,0.511139,0.511264,0.51902,0.522388,0.52348,0.529949,0.534409,0.537132,0.541402,0.543096,0.544218,0.546881,0.556071,0.55956,0.566337,0.570893,0.575426,0.577667,0.582255,0.585322,0.586476,0.592316,0.596397,0.597903,0.60241,0.610119,0.615851,0.624853,0.636624,0.649087,0.659438,0.662265,0.67485,0.679742,0.688169,0.693111,0.694488,0.699781,0.702592,0.708096,0.727242,0.749829,0.755498,0.758281,0.785222,0.786199,0.790469,0.791362,0.794589,0.827586,0.924981,1.108323,1.803599,2.663537
+cate1:str_day1	100	0.035608,0.03656,0.041586,0.042091,0.043074,0.046,0.046257,0.046782,0.047716,0.047823,0.048981,0.04941,0.052013,0.052262,0.053882,0.060194,0.064039,0.066976,0.067135,0.081139
+action:vovd1_day21	100	0.156087,0.199765,0.226021,0.251534,0.267941,0.280154,0.285975,0.291124,0.296886,0.313966,0.32,0.322216,0.327358,0.330584,0.334689,0.340277,0.343639,0.347172,0.350845,0.354745,0.362177,0.367886,0.371314,0.371816,0.38003,0.383879,0.392,0.396224,0.401546,0.408392,0.413505,0.417704,0.419192,0.419686,0.430666,0.43259,0.438847,0.443279,0.445256,0.447127,0.452189,0.461607,0.472475,0.473825,0.479948,0.487464,0.494902,0.500678,0.50193,0.506677,0.507536,0.511972,0.521198,0.529263,0.532299,0.54172,0.543133,0.544893,0.548595,0.551223,0.56902,0.579565,0.586334,0.5894,0.603461,0.611138,0.611801,0.620005,0.636173,0.663272,0.698996,0.713037,0.750082,0.761945,0.821366,0.844093,0.851573,29.0
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+cate1:str_day3	100	0.038626,0.040474,0.043725,0.043868,0.044441,0.045714,0.046753,0.046972,0.047257,0.049522,0.050513,0.052909,0.053765,0.055499,0.058611,0.060402,0.065406,0.066702,0.067354,0.074332,0.084941
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+action:rov_day21	100	0.012194,0.015795,0.018156,0.019479,0.020871,0.022283,0.02243,0.022818,0.023734,0.023792,0.024912,0.02574,0.026363,0.026752,0.027949,0.027969,0.029016,0.029888,0.030381,0.030485,0.030778,0.030791,0.031497,0.031525,0.031665,0.032079,0.032328,0.033126,0.033836,0.034304,0.034613,0.034621,0.035005,0.035409,0.036235,0.036529,0.037285,0.037697,0.037845,0.03808,0.039235,0.039677,0.04048,0.040792,0.040898,0.040997,0.041597,0.042572,0.043476,0.043678,0.044648,0.045501,0.046636,0.046883,0.04704,0.047114,0.048054,0.048444,0.048891,0.049643,0.050005,0.050938,0.051163,0.052014,0.052591,0.053466,0.054724,0.055605,0.057282,0.058256,0.058721,0.060107,0.062902,0.063506,0.065871,0.068452,0.074924,0.075672,0.080895,0.083062,3.0
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+cate2:str_day3	100	0.03577,0.039348,0.039679,0.040957,0.04204,0.042171,0.043124,0.043777,0.044224,0.044981,0.045699,0.046297,0.047884,0.052909,0.053765,0.055292,0.066415,0.0721,0.082512,0.088704

src/main/resources/20241128_recsys_i2i_bucket_47_v2.txt

@@ -0,0 +1,47 @@
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+cate1:rov_day3	100	0.021761,0.026643,0.027054,0.033398,0.037379,0.038136,0.040212,0.042089,0.042231,0.042606,0.042607,0.044162,0.045844,0.046173,0.047263,0.048133,0.04828,0.050624,0.051353,0.051611,0.054355,0.064011,0.064333,0.068423
+cate1:ros_day3	100	0.344306,0.540985,0.594047,0.651424,0.687296,0.70954,0.760934,0.821386,0.838121,0.845957,0.900231,0.967716,0.985816,1.021737,1.063502,1.086678,1.097226,1.167411,1.181074,1.251183,1.342978,1.369609,1.400263
+cate1:str_day7	100	0.036657,0.040618,0.040838,0.041237,0.041273,0.042872,0.044776,0.046365,0.046746,0.046876,0.047723,0.047737,0.048173,0.048656,0.051212,0.055187,0.055325,0.055424,0.06004,0.061695,0.066902,0.067108,0.068583,0.085334
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+action:vovd1_day336	100	0.248048,0.269801,0.296243,0.316646,0.337845,0.343714,0.359085,0.386732,0.390476,0.405387,0.415979,0.43131,0.444287,0.452781,0.456237,0.464449,0.46796,0.477302,0.482771,0.488934,0.495389,0.50004,0.509267,0.511139,0.513168,0.521635,0.522388,0.52348,0.524909,0.528353,0.532809,0.534409,0.536096,0.541402,0.543096,0.544218,0.549014,0.557489,0.561005,0.56691,0.57146,0.575501,0.579341,0.582255,0.586476,0.592978,0.597525,0.597903,0.603812,0.610836,0.615851,0.622983,0.629389,0.639957,0.65397,0.659438,0.663116,0.67485,0.68259,0.688169,0.693111,0.694488,0.699781,0.702592,0.707336,0.710641,0.733625,0.750316,0.755498,0.758281,0.77156,0.786199,0.790469,0.791362,0.809808,0.827586,0.84378,0.924981,1.108323,1.803599,3.268374
+cate1:str_day1	100	0.035608,0.03656,0.037703,0.041586,0.042091,0.043074,0.046,0.046257,0.046782,0.047716,0.047823,0.048384,0.048981,0.04941,0.052262,0.053882,0.05913,0.060194,0.064039,0.066976,0.074171,0.081139
+action:rov_day7	100	0.01034,0.01391,0.015094,0.01703,0.019049,0.019896,0.020764,0.021611,0.023042,0.024871,0.025981,0.027062,0.028324,0.029674,0.030081,0.030522,0.031056,0.031481,0.032027,0.032543,0.033259,0.033448,0.034077,0.034491,0.035017,0.035842,0.036802,0.037338,0.03786,0.039083,0.039618,0.039713,0.040451,0.04097,0.041458,0.041793,0.04232,0.043376,0.044168,0.044634,0.045382,0.046262,0.046521,0.046571,0.047264,0.047882,0.048069,0.048778,0.049052,0.049129,0.049817,0.050058,0.050329,0.050754,0.050921,0.051414,0.051769,0.051897,0.052922,0.053216,0.053455,0.053699,0.055632,0.055906,0.056628,0.056761,0.057194,0.057487,0.058291,0.058677,0.058729,0.059125,0.059958,0.062278,0.062582,0.063155,0.064239,0.066725,0.067458,0.06892,0.070718,0.073396,0.075292,0.080075,0.082271,0.083973,10.538462
+cate1:rov_day7	100	0.024745,0.032212,0.036666,0.042028,0.042247,0.042418,0.042667,0.043535,0.044368,0.044822,0.045227,0.046451,0.047062,0.047219,0.04723,0.04767,0.050948,0.05276,0.054466,0.060435,0.06132,0.061887
+cate1:rov_day1	100	0.02241,0.027995,0.032978,0.039364,0.041151,0.041269,0.04214,0.04282,0.043408,0.043561,0.04411,0.046826,0.048145,0.049265,0.050366,0.051036,0.052371,0.054471,0.057223,0.0585,0.063412,0.068202,0.069998
+cate2:ros_day1	100	0.337544,0.361674,0.507606,0.525643,0.603445,0.69085,0.737146,0.746133,0.823211,0.841549,0.850929,0.871358,0.921015,0.939885,0.947934,1.03452,1.135761,1.18473,1.206837,1.217894,1.275245,1.287086,1.290502,1.358827,1.369865,1.436415,1.694856
+cate2:str_day3	100	0.034438,0.039306,0.039348,0.040144,0.040957,0.04204,0.042171,0.043322,0.043777,0.044224,0.044777,0.044981,0.045699,0.046082,0.047884,0.049615,0.052909,0.055292,0.065738,0.066415,0.0721,0.080923,0.082512,0.087943,0.088704
+cate2:vovd1_day7	100	0.34348,0.362372,0.362591,0.373599,0.40264,0.412673,0.426541,0.44663,0.452845,0.459098,0.463879,0.482624,0.490028,0.493596,0.493725,0.499914,0.502578,0.509512,0.513889,0.5156,0.523791,0.530674,0.540608,0.547378,0.561814,0.574644
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src/main/scala/com/aliyun/odps/spark/examples/makedata_dssm/makedata_i2i_01_originData_20241127.scala

@@ -0,0 +1,94 @@
+package com.aliyun.odps.spark.examples.makedata_dssm
+
+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 org.apache.hadoop.io.compress.GzipCodec
+import org.apache.spark.sql.SparkSession
+import scala.util.Random
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+object makedata_i2i_01_originData_20241127 {
+  def func(record: Record, schema: TableSchema): Record = {
+    record
+  }
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 1 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val tablePart = param.getOrElse("tablePart", "64").toInt
+    val beginStr = param.getOrElse("beginStr", "2024062008")
+    val endStr = param.getOrElse("endStr", "2024062023")
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/51_dssm_i2i_sample/")
+    val project = param.getOrElse("project", "loghubods")
+    val repartition = param.getOrElse("repartition", "100").toInt
+    val filterHours = param.getOrElse("filterHours", "25").split(",").toSet
+    val negCnt = param.getOrElse("negCnt", "20").toInt
+    // 2 读取odps+表信息
+    val odpsOps = env.getODPS(sc)
+    // 3 循环执行数据生产
+    val timeRange = MyDateUtils.getDateHourRange(beginStr, endStr)
+    for (dt_hh <- timeRange) {
+      val dt = dt_hh.substring(0, 8)
+      val hh = dt_hh.substring(8, 10)
+      val partition = s"dt=$dt,hh=$hh"
+      val vidsArr = odpsOps.readTable(project = project,
+          table = "t_vid_tag_feature",
+          partition = s"dt=$dt",
+          transfer = func,
+          numPartition = tablePart)
+        .map(r => {
+          r.getString("vid")
+        }).collect().toList
+      val vids_br = sc.broadcast(vidsArr)
+      if (filterHours.nonEmpty && filterHours.contains(hh)) {
+        println("不执行partiton:" + partition)
+      } else {
+        println("开始执行partiton:" + partition)
+        val odpsData = odpsOps.readTable(project = project,
+          table = "alg_dssm_sample",
+          partition = partition,
+          transfer = func,
+          numPartition = tablePart)
+          .map(record =>{
+            val apptype = record.getString("apptype")
+            val pagesource = record.getString("pagesource")
+            val mid = record.getString("mid")
+            val vid_right = record.getString("vid_right")
+            val vid_left = record.getString("vid_left")
+            val total_return_uv = record.getString("total_return_uv")
+            val view_24h = record.getString("view_24h")
+            val logKey = (apptype, pagesource, mid, vid_right, vid_left, total_return_uv, view_24h).productIterator.mkString(",")
+            (logKey, vid_left, vid_right)
+          }).mapPartitions(row =>{
+            val result = new ArrayBuffer[String]()
+            val vids = vids_br.value
+            row.foreach {
+              case (logKey, vid_left, vid_right) =>
+                val negs = Random.shuffle(vids).take(negCnt).filter(r => !r.equals(vid_left) && !r.equals(vid_right))
+                negs.foreach(negVid =>{
+                  result.add((logKey, "0", vid_left, negVid).productIterator.mkString("\t"))
+                })
+                result.add((logKey, "1", vid_left, vid_right).productIterator.mkString("\t"))
+            }
+            result.iterator
+          })
+        val savePartition = dt + hh
+        val hdfsPath = savePath + "/" + savePartition
+        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)
+          odpsData.coalesce(repartition).saveAsTextFile(hdfsPath, classOf[GzipCodec])
+        } else {
+          println("路径不合法，无法写入:" + hdfsPath)
+        }
+      }
+    }
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_dssm/makedata_i2i_02_joinFeatureData_20241128.scala

@@ -0,0 +1,158 @@
+package com.aliyun.odps.spark.examples.makedata_dssm
+
+import com.alibaba.fastjson.JSON
+import com.aliyun.odps.TableSchema
+import com.aliyun.odps.data.Record
+import com.aliyun.odps.spark.examples.myUtils.{MyDateUtils, MyHdfsUtils, ParamUtils, env}
+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_i2i_02_joinFeatureData_20241128 {
+  def func(record: Record, schema: TableSchema): Record = {
+    record
+  }
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 1 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val tablePart = param.getOrElse("tablePart", "64").toInt
+    val beginStr = param.getOrElse("beginStr", "2024062008")
+    val endStr = param.getOrElse("endStr", "2024062023")
+    val readPath = param.getOrElse("readPath", "/dw/recommend/model/51_dssm_i2i_sample/")
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/52_dssm_i2i_joinfeature/")
+    val project = param.getOrElse("project", "loghubods")
+    val repartition = param.getOrElse("repartition", "100").toInt
+    val filterHours = param.getOrElse("filterHours", "25").split(",").toSet
+    val ifDebug = param.getOrElse("ifDebug", "false").toBoolean
+    // 2 读取odps+表信息
+    val odpsOps = env.getODPS(sc)
+    // 3 循环执行数据生产
+    val timeRange = MyDateUtils.getDateHourRange(beginStr, endStr)
+    for (dt_hh <- timeRange) {
+      val dt = dt_hh.substring(0, 8)
+      val hh = dt_hh.substring(8, 10)
+      val partition = s"dt=$dt,hh=$hh"
+      // 1 类目特征用broadcast
+      val category1_br = sc.broadcast(
+        odpsOps.readTable(project = project,
+          table = "t_vid_l1_cat_stat_feature",
+          partition = s"dt=$dt",
+          transfer = func,
+          numPartition = tablePart)
+          .map(record =>{
+            val category = record.getString("category1")
+            val feature = record.getString("feature")
+            (category, feature)
+        }).collectAsMap()
+      )
+      val category2_br = sc.broadcast(
+        odpsOps.readTable(project = project,
+            table = "t_vid_l2_cat_stat_feature",
+            partition = s"dt=$dt",
+            transfer = func,
+            numPartition = tablePart)
+          .map(record => {
+            val category = record.getString("category2")
+            val feature = record.getString("feature")
+            (category, feature)
+          }).collectAsMap()
+      )
+      // 2 视频特征用join
+      val vidStaticFeature = odpsOps.readTable(project = project,
+        table = "t_vid_tag_feature",
+        partition = s"dt=$dt",
+        transfer = func,
+        numPartition = tablePart)
+      .map(record => {
+        val vid = record.getString("vid")
+        val feature = record.getString("feature")
+        (vid, feature)
+      })
+      val vidActionFeature = odpsOps.readTable(project = project,
+          table = "t_vid_stat_feature",
+          partition = s"dt=$dt",
+          transfer = func,
+          numPartition = tablePart)
+        .map(record => {
+          val vid = record.getString("vid")
+          val feature = record.getString("feature")
+          (vid, feature)
+        })
+
+      if (filterHours.nonEmpty && filterHours.contains(hh)) {
+        println("不执行partiton:" + partition)
+      } else {
+        println("开始执行partiton:" + partition)
+        val savePartition = dt + hh
+        val sampleData1 = sc.textFile( readPath + "/" + savePartition).map(r=>{
+          val rList = r.split("\t")
+          val logKey = rList(0)
+          val label = rList(1)
+          val vid_left = rList(2)
+          val vid_right = rList(3)
+          (vid_left, (logKey, label, vid_right))
+        }).leftOuterJoin(vidStaticFeature).map{
+          case (vid_left, ((logKey, label, vid_right), Some(feature))) =>
+            (vid_right, (logKey, label, vid_left, feature))
+          case (vid_left, ((logKey, label, vid_right), None)) =>
+            (vid_right, (logKey, label, vid_left, "{}"))
+        }.leftOuterJoin(vidStaticFeature).map{
+          case (vid_right, ((logKey, label, vid_left, feature_left), Some(feature_right))) =>
+            (vid_left, (logKey, label, vid_right, feature_left, feature_right))
+          case (vid_right, ((logKey, label, vid_left, feature_left), None)) =>
+            (vid_left, (logKey, label, vid_right, feature_left, "{}"))
+        }.leftOuterJoin(vidActionFeature).map{
+          case (vid_left, ((logKey, label, vid_right, feature_left, feature_right), Some(feature))) =>
+            (vid_right, (logKey, label, vid_left, feature_left, feature_right, feature))
+          case (vid_left, ((logKey, label, vid_right, feature_left, feature_right), None)) =>
+            (vid_right, (logKey, label, vid_left, feature_left, feature_right, "{}"))
+        }.leftOuterJoin(vidActionFeature).map{
+          case (vid_right, ((logKey, label, vid_left, feature_left, feature_right, feature_left_action), Some(feature))) =>
+            (logKey, label, vid_left, vid_right, feature_left, feature_right, feature_left_action, feature)
+          case (vid_right, ((logKey, label, vid_left, feature_left, feature_right, feature_left_action), None)) =>
+            (logKey, label, vid_left, vid_right, feature_left, feature_right, feature_left_action, "{}")
+        }.mapPartitions(row =>{
+          val result = new ArrayBuffer[String]()
+          val category1 = category1_br.value
+          val category2 = category2_br.value
+          row.foreach{
+            case (logKey, label, vid_left, vid_right, feature_left, feature_right, feature_left_action, feature_right_action) =>
+              val cate1_left = JSON.parseObject(feature_left).getOrDefault("category1", "无").toString
+              val cate2_left = JSON.parseObject(feature_left).getOrDefault("category2_1", "无").toString
+              val cate1_right = JSON.parseObject(feature_right).getOrDefault("category1", "无").toString
+              val cate2_right = JSON.parseObject(feature_right).getOrDefault("category2_1", "无").toString
+              val feature_left_cate1 = category1.getOrElse(cate1_left, "{}")
+              val feature_left_cate2 = category2.getOrElse(cate2_left, "{}")
+              val feature_right_cate1 = category1.getOrElse(cate1_right, "{}")
+              val feature_right_cate2 = category2.getOrElse(cate2_right, "{}")
+              result.add(
+                (logKey, label, vid_left, vid_right, feature_left, feature_right, feature_left_action, feature_right_action,
+                feature_left_cate1, feature_right_cate1, feature_left_cate2, feature_right_cate2).productIterator.mkString("\t")
+              )
+          }
+          result.iterator
+        })
+        if (ifDebug){
+          println("数据量:" + sampleData1.count())
+        }
+        val hdfsPath = savePath + "/" + savePartition
+        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)
+          sampleData1.coalesce(repartition).saveAsTextFile(hdfsPath, classOf[GzipCodec])
+        } else {
+          println("路径不合法，无法写入:" + hdfsPath)
+        }
+      }
+    }
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_dssm/makedata_i2i_03_onehotFile_20241128.scala

@@ -0,0 +1,119 @@
+package com.aliyun.odps.spark.examples.makedata_dssm
+
+import com.alibaba.fastjson.JSON
+import com.aliyun.odps.TableSchema
+import com.aliyun.odps.data.Record
+import com.aliyun.odps.spark.examples.myUtils.{MyDateUtils, MyHdfsUtils, ParamUtils, env}
+import org.apache.hadoop.io.compress.GzipCodec
+import org.apache.spark.sql.SparkSession
+import scala.collection.mutable
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+
+object makedata_i2i_03_onehotFile_20241128 {
+  def func(record: Record, schema: TableSchema): Record = {
+    record
+  }
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 1 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val tablePart = param.getOrElse("tablePart", "64").toInt
+    val dt = param.getOrElse("dt", "20240620")
+    val readPath = param.getOrElse("readPath", "/dw/recommend/model/53_dssm_i2i_onehot/20240101")
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/53_dssm_i2i_onehot/20250101")
+    val project = param.getOrElse("project", "loghubods")
+    val repartition = param.getOrElse("repartition", "100").toInt
+    val ifDebug = param.getOrElse("ifDebug", "false").toBoolean
+    // 2 读取odps+表信息
+    val odpsOps = env.getODPS(sc)
+    // 3 vid中的sparse特征
+    if (MyHdfsUtils.hdfs_exits(readPath)){
+      val onehot = sc.textFile(readPath).map(r => {
+        val rList = r.split("\t")
+        (rList(0), rList(1))
+      })
+      val onehotMapOldStatic = onehot.map(_._1.split(":")(0)).map(r => (r, 1)).reduceByKey(_ + _).collect().sortBy(-_._2)
+      print(s"读入路径:$readPath \n数据量:" + onehot.count())
+      print("打印各个特征多少枚举值：")
+      onehotMapOldStatic.foreach(r => println(r.productIterator.mkString("\t")))
+    }
+    val onehotMap = if (MyHdfsUtils.hdfs_exits(readPath)){
+      mutable.Map(sc.textFile(readPath).map(r => {
+        val rList = r.split("\t")
+        (rList(0), rList(1))
+      }).collectAsMap().toSeq: _*)
+    }else{
+      mutable.Map[String, String]()
+    }
+
+    val vidStaticFeature = odpsOps.readTable(project = project,
+        table = "t_vid_tag_feature",
+        partition = s"dt=$dt",
+        transfer = func,
+        numPartition = tablePart)
+      .map(record => {
+        val vid = record.getString("vid")
+        val feature = record.getString("feature")
+        (vid, feature)
+      }).flatMap{
+        case (vid, feature) =>
+          val result = new ArrayBuffer[String]()
+          result.add("vid:" + vid)
+          JSON.parseObject(feature).foreach(r =>{
+            val value = if (r._2 == null) "无" else r._2.toString
+            r._1 match {
+              case "category1" => result += "cate1:" + value
+              case "category2_1" => result += "cate2:" + value
+              case "category2_2" => result += "cate2:" + value
+              case "category2_3" => result += "cate2:" + value
+              case "valid_time" => result += "valid_time:" + value
+              case "timeliness" => result += "timeliness:" + value
+              case "sentiment_tendency" => result += "sentiment_tendency:" + value
+              case "has_end_credit_guide" => result += "has_end_credit_guide:" + value
+              case "background_music_type" => result += "background_music_type:" + value
+              case "cover_persons_num" => result += "cover_persons_num:" + value
+              case "captions" => result += "captions:" + value
+              case "captions_color" => result += "captions_color:" + value
+              case "audience_value_type" => result += "audience_value_type:" + value
+              case "audience_gender" => result += "audience_gender:" + value
+              case "audience_age_group" => result += "audience_age_group:" + value
+              case "font_size" => result += "font_size:" + value
+              case "video_type" => result += "video_type:" + value
+              case "video_style" => result += "video_style:" + value
+              case _ =>
+            }
+          })
+          result.distinct
+      }.distinct().collect()
+    val maxEnumMap = mutable.Map[String, Int]()
+    onehotMap.foreach { case (key, value) =>
+      val prefix = key.split(":")(0)
+      val currentMax = maxEnumMap.getOrElse(prefix, 0)
+      maxEnumMap(prefix) = Math.max(currentMax, value.toInt)
+    }
+    vidStaticFeature.foreach { feature =>
+      val prefix = feature.split(":")(0)
+      if (!onehotMap.contains(feature)) {
+        val newEnumValue = maxEnumMap.getOrElse(prefix, 0) + 1
+        maxEnumMap(prefix) = newEnumValue
+        onehotMap(feature) = newEnumValue.toString
+      }
+    }
+
+    val dataRdd = sc.parallelize(onehotMap.toSeq.map(_.productIterator.mkString("\t")))
+    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)
+      dataRdd.repartition(10).saveAsTextFile(hdfsPath, classOf[GzipCodec])
+    } else {
+      println("路径不合法，无法写入:" + hdfsPath)
+    }
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_dssm/makedata_i2i_04_bucketFile_20241128.scala

@@ -0,0 +1,208 @@
+package com.aliyun.odps.spark.examples.makedata_dssm
+
+import com.alibaba.fastjson.JSON
+import com.aliyun.odps.TableSchema
+import com.aliyun.odps.data.Record
+import com.aliyun.odps.spark.examples.myUtils.{MyDateUtils, MyHdfsUtils, ParamUtils, env}
+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_i2i_04_bucketFile_20241128 {
+  def func(record: Record, schema: TableSchema): Record = {
+    record
+  }
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 1 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val readPath = param.getOrElse("readPath", "/dw/recommend/model/52_dssm_i2i_joinfeature/20241128*")
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/54_dssm_i2i_bucketfile/")
+    val fileName = param.getOrElse("fileName", "XXXXX")
+    val bucketNum = param.getOrElse("bucketNum", "100").toInt
+    // 3 循环执行数据生产
+    val data = sc.textFile(readPath).flatMap(r=>{
+      val rList = r.split("\t")
+      val f1 = rList(6)
+      val f11 = rList(7)
+      val f2 = rList(8)
+      val f22 = rList(9)
+      val f3 = rList(10)
+      val f33 = rList(11)
+      val result = new ArrayBuffer[(String, Double)]()
+      Set(f1, f11).toSeq.foreach(f=> {
+        if (f != null && f.nonEmpty){
+          val jsonOpt = Option(JSON.parseObject(f))
+          jsonOpt.foreach { json =>
+            if (json.nonEmpty) { // 确保 JSON 对象非空
+              json.foreach {
+                case (k, v) =>
+                  val value = try {
+                    v.toString.toDouble
+                  } catch {
+                    case _: Exception => 0D
+                  }
+                  k match {
+                    case "str_day1" => result += (("action:str_day1", value))
+                    case "rov_day1" => result += (("action:rov_day1", value))
+                    case "ros_day1" => result += (("action:ros_day1", value))
+                    case "str_day7" => result += (("action:str_day7", value))
+                    case "rov_day7" => result += (("action:rov_day7", value))
+                    case "ros_day7" => result += (("action:ros_day7", value))
+                    case "str_day21" => result += (("action:str_day21", value))
+                    case "rov_day21" => result += (("action:rov_day21", value))
+                    case "ros_day21" => result += (("action:ros_day21", value))
+                    case "str_day336" => result += (("action:str_day336", value))
+                    case "rov_day336" => result += (("action:rov_day336", value))
+                    case "ros_day336" => result += (("action:ros_day336", value))
+                    case "vovd1_day7" => result += (("action:vovd1_day7", value))
+                    case "vovd1_day21" => result += (("action:vovd1_day21", value))
+                    case "vovd1_day336" => result += (("action:vovd1_day336", value))
+                    case _ =>
+                  }
+              }
+            }
+          }
+        }
+      })
+      Set(f2, f22).toSeq.foreach(f => {
+        if (f != null && f.nonEmpty) {
+          val jsonOpt = Option(JSON.parseObject(f))
+          jsonOpt.foreach { json =>
+            if (json.nonEmpty) { // 确保 JSON 对象非空
+              json.foreach {
+                case (k, v) =>
+                  val value = try {
+                    v.toString.toDouble
+                  } catch {
+                    case _: Exception => 0D
+                  }
+                  k match {
+                    case "str_day1" => result += (("cate1:str_day1", value))
+                    case "rov_day1" => result += (("cate1:rov_day1", value))
+                    case "ros_day1" => result += (("cate1:ros_day1", value))
+                    case "str_day3" => result += (("cate1:str_day3", value))
+                    case "rov_day3" => result += (("cate1:rov_day3", value))
+                    case "ros_day3" => result += (("cate1:ros_day3", value))
+                    case "str_day7" => result += (("cate1:str_day7", value))
+                    case "rov_day7" => result += (("cate1:rov_day7", value))
+                    case "ros_day7" => result += (("cate1:ros_day7", value))
+                    case "str_day30" => result += (("cate1:str_day30", value))
+                    case "rov_day30" => result += (("cate1:rov_day30", value))
+                    case "ros_day30" => result += (("cate1:ros_day30", value))
+                    case "vovd1_day1" => result += (("cate1:vovd1_day1", value))
+                    case "vovd1_day3" => result += (("cate1:vovd1_day3", value))
+                    case "vovd1_day7" => result += (("cate1:vovd1_day7", value))
+                    case "vovd1_day30" => result += (("cate1:vovd1_day30", value))
+                    case _ =>
+                  }
+              }
+            }
+          }
+        }
+      })
+      Set(f3, f33).toSeq.foreach(f => {
+        if (f != null && f.nonEmpty) {
+          val jsonOpt = Option(JSON.parseObject(f))
+          jsonOpt.foreach { json =>
+            if (json.nonEmpty) { // 确保 JSON 对象非空
+              json.foreach {
+                case (k, v) =>
+                  val value = try {
+                    v.toString.toDouble
+                  } catch {
+                    case _: Exception => 0D
+                  }
+                  k match {
+                    case "str_day1" => result += (("cate2:str_day1", value))
+                    case "rov_day1" => result += (("cate2:rov_day1", value))
+                    case "ros_day1" => result += (("cate2:ros_day1", value))
+                    case "str_day3" => result += (("cate2:str_day3", value))
+                    case "rov_day3" => result += (("cate2:rov_day3", value))
+                    case "ros_day3" => result += (("cate2:ros_day3", value))
+                    case "str_day7" => result += (("cate2:str_day7", value))
+                    case "rov_day7" => result += (("cate2:rov_day7", value))
+                    case "ros_day7" => result += (("cate2:ros_day7", value))
+                    case "str_day30" => result += (("cate2:str_day30", value))
+                    case "rov_day30" => result += (("cate2:rov_day30", value))
+                    case "ros_day30" => result += (("cate2:ros_day30", value))
+                    case "vovd1_day1" => result += (("cate2:vovd1_day1", value))
+                    case "vovd1_day3" => result += (("cate2:vovd1_day3", value))
+                    case "vovd1_day7" => result += (("cate2:vovd1_day7", value))
+                    case "vovd1_day30" => result += (("cate2:vovd1_day30", value))
+                    case _ =>
+                  }
+              }
+            }
+          }
+        }
+      })
+      result
+    })
+
+    val contentList = List[String](
+      "action:str_day1", "action:rov_day1", "action:ros_day1", "action:str_day7", "action:rov_day7","action:ros_day7",
+      "action:str_day21", "action:rov_day21", "action:ros_day21", "action:str_day336","action:rov_day336", "action:ros_day336",
+      "action:vovd1_day7", "action:vovd1_day21", "action:vovd1_day336",
+
+      "cate1:str_day1", "cate1:rov_day1", "cate1:ros_day1", "cate1:str_day3", "cate1:rov_day3", "cate1:ros_day3",
+      "cate1:str_day7", "cate1:rov_day7", "cate1:ros_day7", "cate1:str_day30", "cate1:rov_day30", "cate1:ros_day30",
+      "cate1:vovd1_day1", "cate1:vovd1_day3", "cate1:vovd1_day7", "cate1:vovd1_day30",
+
+      "cate2:str_day1", "cate2:rov_day1", "cate2:ros_day1", "cate2:str_day3", "cate2:rov_day3", "cate2:ros_day3",
+      "cate2:str_day7", "cate2:rov_day7", "cate2:ros_day7", "cate2:str_day30", "cate2:rov_day30", "cate2:ros_day30",
+      "cate2:vovd1_day1", "cate2:vovd1_day3", "cate2:vovd1_day7", "cate2:vovd1_day30"
+
+
+    )
+    val result = new ArrayBuffer[String]()
+
+    for (i <- contentList.indices) {
+      println("特征:" + contentList(i))
+      val data2 = data.filter(_._1.equals(contentList(i))).map(_._2).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(","))
+      }
+    }
+    val data3 = sc.parallelize(result)
+
+    // 4 保存数据到hdfs
+    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)
+      data3.repartition(1).saveAsTextFile(hdfsPath, classOf[GzipCodec])
+    } else {
+      println("路径不合法，无法写入:" + hdfsPath)
+    }
+
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_dssm/makedata_i2i_05_trainData_20241129.scala

@@ -0,0 +1,588 @@
+package com.aliyun.odps.spark.examples.makedata_dssm
+
+import com.alibaba.fastjson.JSON
+import com.alibaba.fastjson.JSONObject
+import com.aliyun.odps.TableSchema
+import com.aliyun.odps.data.Record
+import com.aliyun.odps.spark.examples.myUtils.{MyDateUtils, MyHdfsUtils, ParamUtils, env}
+import org.apache.hadoop.io.compress.GzipCodec
+import org.apache.spark.sql.SparkSession
+import examples.extractor.ExtractorUtils
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+import scala.io.Source
+
+object makedata_i2i_05_trainData_20241129 {
+  def func(record: Record, schema: TableSchema): Record = {
+    record
+  }
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 1 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val beginStr = param.getOrElse("beginStr", "2024062008")
+    val endStr = param.getOrElse("endStr", "2024062023")
+    val readPath = param.getOrElse("readPath", "/dw/recommend/model/52_dssm_i2i_joinfeature/")
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/55_dssm_i2i_traindata/")
+    val onehotPath = param.getOrElse("onehotPath", "/dw/recommend/model/53_dssm_i2i_onehot/20241128")
+    val bucketFile = param.getOrElse("bucketFile", "20241128_recsys_i2i_bucket_47_v2.txt")
+    val repartition = param.getOrElse("repartition", "100").toInt
+    val filterHours = param.getOrElse("filterHours", "25").split(",").toSet
+    val ifDebug = param.getOrElse("ifDebug", "false").toBoolean
+
+    // 2 读取onehot文件
+    val onehotMap_br = sc.broadcast(
+      sc.textFile(onehotPath).map(r => {
+        val rList = r.split("\t")
+        (rList(0), rList(1))
+      }).collectAsMap()
+    )
+
+    // 3 读取dense分桶文件
+    val resourceUrlBucket = this.getClass.getClassLoader.getResource(bucketFile)
+    val buckets =
+      if (resourceUrlBucket != null) {
+        val buckets = Source.fromURL(resourceUrlBucket).getLines().mkString("\n")
+        Source.fromURL(resourceUrlBucket).close()
+        buckets
+      } else {
+        ""
+      }
+    println(buckets)
+    val bucketsMap_br = sc.broadcast(
+      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
+    )
+
+    // 4 循环执行数据生产
+    val timeRange = MyDateUtils.getDateHourRange(beginStr, endStr)
+    for (dt_hh <- timeRange) {
+      val dt = dt_hh.substring(0, 8)
+      val hh = dt_hh.substring(8, 10)
+      val data = sc.textFile(readPath + "/" + dt_hh).map(r=>{
+        val rList = r.split("\t")
+        val logKey = rList(0)
+        val label = rList(1)
+        val vid_left = rList(2)
+        val vid_right = rList(3)
+        val feature_left = rList(4)
+        val feature_right = rList(5)
+        val feature_left_action = rList(6)
+        val feature_right_action = rList(7)
+        val feature_left_cate1 = rList(8)
+        val feature_right_cate1 = rList(9)
+        val feature_left_cate2 = rList(10)
+        val feature_right_cate2 = rList(11)
+        (logKey, label, vid_left, vid_right, feature_left, feature_right, feature_left_action, feature_right_action,
+          feature_left_cate1, feature_right_cate1, feature_left_cate2, feature_right_cate2)
+      }).mapPartitions(row =>{
+        val result = new ArrayBuffer[String]()
+        val onehotMap = onehotMap_br.value
+        val bucketsMap = bucketsMap_br.value
+        row.foreach{
+          case (logKey, label, vid_left, vid_right, feature_left, feature_right, feature_left_action, feature_right_action,
+          feature_left_cate1, feature_right_cate1, feature_left_cate2, feature_right_cate2) =>
+            val left = new ArrayBuffer[String]()
+            val right = new ArrayBuffer[String]()
+            val left_dense1 = new ArrayBuffer[String]()
+            val right_dense1 = new ArrayBuffer[String]()
+            val left_dense2 = new ArrayBuffer[String]()
+            val right_dense2 = new ArrayBuffer[String]()
+            // 1 sparse 特征 16个
+            // vid cate1 cate2 video_style valid_time captions_color audience_age_group
+            // audience_value_type font_size cover_persons_num audience_gender sentiment_tendency
+            // video_type background_music_type captions has_end_credit_guide
+            left += onehotMap.getOrElse("vid:" + vid_left, "0")
+            right += onehotMap.getOrElse("vid:" + vid_right, "0")
+            var jsonLeft = JSON.parseObject(feature_left)
+            left += getOnehotValue(jsonLeft, onehotMap, "category1", "cate1:")
+            left += getOnehotValue(jsonLeft, onehotMap, "category2_1", "cate2:")
+            left += getOnehotValue(jsonLeft, onehotMap, "video_style", "video_style:")
+            left += getOnehotValue(jsonLeft, onehotMap, "valid_time", "valid_time:")
+            left += getOnehotValue(jsonLeft, onehotMap, "captions_color", "captions_color:")
+            left += getOnehotValue(jsonLeft, onehotMap, "audience_age_group", "audience_age_group:")
+            left += getOnehotValue(jsonLeft, onehotMap, "audience_value_type", "audience_value_type:")
+            left += getOnehotValue(jsonLeft, onehotMap, "font_size", "font_size:")
+            left += getOnehotValue(jsonLeft, onehotMap, "cover_persons_num", "cover_persons_num:")
+            left += getOnehotValue(jsonLeft, onehotMap, "audience_gender", "audience_gender:")
+            left += getOnehotValue(jsonLeft, onehotMap, "sentiment_tendency", "sentiment_tendency:")
+            left += getOnehotValue(jsonLeft, onehotMap, "video_type", "video_type:")
+            left += getOnehotValue(jsonLeft, onehotMap, "background_music_type", "background_music_type:")
+            left += getOnehotValue(jsonLeft, onehotMap, "captions", "captions:")
+            left += getOnehotValue(jsonLeft, onehotMap, "has_end_credit_guide", "has_end_credit_guide:")
+            var jsonRight = JSON.parseObject(feature_right)
+            right += getOnehotValue(jsonRight, onehotMap, "category1", "cate1:")
+            right += getOnehotValue(jsonRight, onehotMap, "category2_1", "cate2:")
+            right += getOnehotValue(jsonRight, onehotMap, "video_style", "video_style:")
+            right += getOnehotValue(jsonRight, onehotMap, "valid_time", "valid_time:")
+            right += getOnehotValue(jsonRight, onehotMap, "captions_color", "captions_color:")
+            right += getOnehotValue(jsonRight, onehotMap, "audience_age_group", "audience_age_group:")
+            right += getOnehotValue(jsonRight, onehotMap, "audience_value_type", "audience_value_type:")
+            right += getOnehotValue(jsonRight, onehotMap, "font_size", "font_size:")
+            right += getOnehotValue(jsonRight, onehotMap, "cover_persons_num", "cover_persons_num:")
+            right += getOnehotValue(jsonRight, onehotMap, "audience_gender", "audience_gender:")
+            right += getOnehotValue(jsonRight, onehotMap, "sentiment_tendency", "sentiment_tendency:")
+            right += getOnehotValue(jsonRight, onehotMap, "video_type", "video_type:")
+            right += getOnehotValue(jsonRight, onehotMap, "background_music_type", "background_music_type:")
+            right += getOnehotValue(jsonRight, onehotMap, "captions", "captions:")
+            right += getOnehotValue(jsonRight, onehotMap, "has_end_credit_guide", "has_end_credit_guide:")
+            // 2 dense通过分桶转换成sparse特征 47个 * 3 * 2
+            jsonLeft = JSON.parseObject(feature_left_action)
+            var res = getDenseBucketValue(jsonLeft, bucketsMap, "str_day1", "action:str_day1")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "rov_day1", "action:rov_day1")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "ros_day1", "action:ros_day1")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "str_day7", "action:str_day7")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "rov_day7", "action:rov_day7")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "ros_day7", "action:ros_day7")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "str_day21", "action:str_day21")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "rov_day21", "action:rov_day21")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "ros_day21", "action:ros_day21")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "str_day336", "action:str_day336")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "rov_day336", "action:rov_day336")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "ros_day336", "action:ros_day336")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "vovd1_day7", "action:vovd1_day7")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "vovd1_day21", "action:vovd1_day21")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "vovd1_day336", "action:vovd1_day336")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+
+            jsonRight = JSON.parseObject(feature_right_action)
+            res = getDenseBucketValue(jsonRight, bucketsMap, "str_day1", "action:str_day1")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "rov_day1", "action:rov_day1")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "ros_day1", "action:ros_day1")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "str_day7", "action:str_day7")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "rov_day7", "action:rov_day7")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "ros_day7", "action:ros_day7")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "str_day21", "action:str_day21")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "rov_day21", "action:rov_day21")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "ros_day21", "action:ros_day21")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "str_day336", "action:str_day336")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "rov_day336", "action:rov_day336")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "ros_day336", "action:ros_day336")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "vovd1_day7", "action:vovd1_day7")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "vovd1_day21", "action:vovd1_day21")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "vovd1_day336", "action:vovd1_day336")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+
+            //----------------------cate1-----------------------------cate1---------------------------cate1----------------------
+            jsonLeft = JSON.parseObject(feature_left_cate1)
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "str_day1", "cate1:str_day1")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "rov_day1", "cate1:rov_day1")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "ros_day1", "cate1:ros_day1")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "str_day3", "cate1:str_day3")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "rov_day3", "cate1:rov_day3")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "ros_day3", "cate1:ros_day3")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "str_day7", "cate1:str_day7")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "rov_day7", "cate1:rov_day7")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "ros_day7", "cate1:ros_day7")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "str_day30", "cate1:str_day30")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "rov_day30", "cate1:rov_day30")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "ros_day30", "cate1:ros_day30")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "vovd1_day1", "cate1:vovd1_day1")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "vovd1_day3", "cate1:vovd1_day3")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "vovd1_day7", "cate1:vovd1_day7")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "vovd1_day30", "cate1:vovd1_day30")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+
+            jsonRight = JSON.parseObject(feature_right_cate1)
+            res = getDenseBucketValue(jsonRight, bucketsMap, "str_day1", "cate1:str_day1")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "rov_day1", "cate1:rov_day1")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "ros_day1", "cate1:ros_day1")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "str_day3", "cate1:str_day3")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "rov_day3", "cate1:rov_day7")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "ros_day3", "cate1:ros_day3")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "str_day7", "cate1:str_day7")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "rov_day7", "cate1:rov_day7")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "ros_day7", "cate1:ros_day7")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "str_day30", "cate1:str_day30")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "rov_day30", "cate1:rov_day30")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "ros_day30", "cate1:ros_day30")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "vovd1_day1", "cate1:vovd1_day1")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "vovd1_day3", "cate1:vovd1_day3")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "vovd1_day7", "cate1:vovd1_day7")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "vovd1_day30", "cate1:vovd1_day30")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+
+          //----------------------cate2-----------------------------cate2---------------------------cate2----------------------
+            jsonLeft = JSON.parseObject(feature_left_cate2)
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "str_day1", "cate2:str_day1")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "rov_day1", "cate2:rov_day1")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "ros_day1", "cate2:ros_day1")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "str_day3", "cate2:str_day3")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "rov_day3", "cate2:rov_day3")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "ros_day3", "cate2:ros_day3")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "str_day7", "cate2:str_day7")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "rov_day7", "cate2:rov_day7")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "ros_day7", "cate2:ros_day7")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "str_day30", "cate2:str_day30")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "rov_day30", "cate2:rov_day30")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "ros_day30", "cate2:ros_day30")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "vovd1_day1", "cate2:vovd1_day1")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "vovd1_day3", "cate2:vovd1_day3")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "vovd1_day7", "cate2:vovd1_day7")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonLeft, bucketsMap, "vovd1_day30", "cate2:vovd1_day30")
+            left += res._1.toString
+            left_dense1 += res._2.toString
+            left_dense2 += res._3.toString
+
+            jsonRight = JSON.parseObject(feature_right_cate2)
+            res = getDenseBucketValue(jsonRight, bucketsMap, "str_day1", "cate2:str_day1")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "rov_day1", "cate2:rov_day1")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "ros_day1", "cate2:ros_day1")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "str_day3", "cate2:str_day3")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "rov_day3", "cate2:rov_day7")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "ros_day3", "cate2:ros_day3")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "str_day7", "cate2:str_day7")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "rov_day7", "cate2:rov_day7")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "ros_day7", "cate2:ros_day7")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "str_day30", "cate2:str_day30")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "rov_day30", "cate2:rov_day30")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "ros_day30", "cate2:ros_day30")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "vovd1_day1", "cate2:vovd1_day1")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "vovd1_day3", "cate2:vovd1_day3")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "vovd1_day7", "cate2:vovd1_day7")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+            res = getDenseBucketValue(jsonRight, bucketsMap, "vovd1_day30", "cate2:vovd1_day30")
+            right += res._1.toString
+            right_dense1 += res._2.toString
+            right_dense2 += res._3.toString
+
+          // 3 left 和 right 分别 16+47*3=16+141 = 157
+            left ++= left_dense1
+            left ++= left_dense2
+            right ++= right_dense1
+            right ++= right_dense2
+
+            result.add(
+              (logKey, label, vid_left, vid_right, left.mkString(","), right.mkString(",")).productIterator.mkString("\t")
+            )
+        }
+        result.iterator
+      })
+
+      // 4 保存数据到hdfs
+      val hdfsPath = savePath + "/" + dt_hh
+      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.coalesce(repartition).saveAsTextFile(hdfsPath, classOf[GzipCodec])
+      } else {
+        println("路径不合法，无法写入:" + hdfsPath)
+      }
+    }
+  }
+
+  def getOnehotValue(obj: JSONObject, m: scala.collection.Map[String, String], key1: String, key2: String): String = {
+    if (obj.containsKey(key1)) {
+      val value1 = obj.get(key1)
+      val value2 = if (value1 == null) "无" else value1.toString
+      m.getOrElse(key2 + value2, "0")
+    } else {
+      "0"
+    }
+  }
+  def getDenseBucketValue(obj: JSONObject, bucketsMap: Map[String, (Double, Array[Double])], key1: String, name: String): (Int, Double, Double) = {
+    if (obj.containsKey(key1)) {
+      val value1 = obj.get(key1)
+      val score = try {
+        value1.toString.toDouble
+      } catch {
+        case _: Exception => 0D
+      }
+      if (score > 1E-8) {
+        if (bucketsMap.contains(name)) {
+          val (bucketsNum, buckets) = bucketsMap(name)
+          val index = ExtractorUtils.findInsertPosition(buckets, score).toDouble + 1.0
+          val scoreNew = 1.0 / bucketsNum * index
+          (index.toInt, scoreNew, score)
+        } else {
+          (0, 0D, score)
+        }
+      } else {
+        (0, 0D, 0D)
+      }
+    } else {
+      (0, 0D, 0D)
+    }
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_dssm/makedata_i2i_06_itemPred_20241206.scala

@@ -0,0 +1,374 @@
+package com.aliyun.odps.spark.examples.makedata_dssm
+
+import com.alibaba.fastjson.JSON
+import com.aliyun.odps.TableSchema
+import com.aliyun.odps.data.Record
+import com.aliyun.odps.spark.examples.myUtils.{MyDateUtils, MyHdfsUtils, ParamUtils, env}
+import org.apache.hadoop.io.compress.GzipCodec
+import org.apache.spark.sql.SparkSession
+import com.aliyun.odps.spark.examples.makedata_dssm.makedata_i2i_05_trainData_20241129.{getOnehotValue, getDenseBucketValue}
+import scala.collection.mutable
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+import scala.io.Source
+
+object makedata_i2i_06_itemPred_20241206 {
+  def func(record: Record, schema: TableSchema): Record = {
+    record
+  }
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 1 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val tablePart = param.getOrElse("tablePart", "64").toInt
+    val dt = param.getOrElse("dt", "20240620")
+    val onehotPath = param.getOrElse("onehotPath", "/dw/recommend/model/53_dssm_i2i_onehot/20241128")
+    val bucketFile = param.getOrElse("bucketFile", "20241128_recsys_i2i_bucket_47_v2.txt")
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/56_dssm_i2i_itempredData/")
+    val project = param.getOrElse("project", "loghubods")
+    val repartition = param.getOrElse("repartition", "100").toInt
+
+    // 2 读取onehot文件
+    val onehotMap_br = sc.broadcast(
+      sc.textFile(onehotPath).map(r => {
+        val rList = r.split("\t")
+        (rList(0), rList(1))
+      }).collectAsMap()
+    )
+    // 3 读取dense分桶文件
+    val resourceUrlBucket = this.getClass.getClassLoader.getResource(bucketFile)
+    val buckets =
+      if (resourceUrlBucket != null) {
+        val buckets = Source.fromURL(resourceUrlBucket).getLines().mkString("\n")
+        Source.fromURL(resourceUrlBucket).close()
+        buckets
+      } else {
+        ""
+      }
+    println(buckets)
+    val bucketsMap_br = sc.broadcast(
+      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
+    )
+
+    val odpsOps = env.getODPS(sc)
+    val category1_br = sc.broadcast(
+      odpsOps.readTable(project = project,
+          table = "t_vid_l1_cat_stat_feature",
+          partition = s"dt=$dt",
+          transfer = func,
+          numPartition = tablePart)
+        .map(record => {
+          val category = record.getString("category1")
+          val feature = record.getString("feature")
+          (category, feature)
+        }).collectAsMap()
+    )
+    val category2_br = sc.broadcast(
+      odpsOps.readTable(project = project,
+          table = "t_vid_l2_cat_stat_feature",
+          partition = s"dt=$dt",
+          transfer = func,
+          numPartition = tablePart)
+        .map(record => {
+          val category = record.getString("category2")
+          val feature = record.getString("feature")
+          (category, feature)
+        }).collectAsMap()
+    )
+    // 2 视频特征用join
+    val vidStaticFeature = odpsOps.readTable(project = project,
+        table = "t_vid_tag_feature",
+        partition = s"dt=$dt",
+        transfer = func,
+        numPartition = tablePart)
+      .map(record => {
+        val vid = record.getString("vid")
+        val feature = record.getString("feature")
+        (vid, feature)
+      })
+    val vidActionFeature = odpsOps.readTable(project = project,
+        table = "t_vid_stat_feature",
+        partition = s"dt=$dt",
+        transfer = func,
+        numPartition = tablePart)
+      .map(record => {
+        val vid = record.getString("vid")
+        val feature = record.getString("feature")
+        (vid, feature)
+      })
+
+    val data = vidStaticFeature.leftOuterJoin(vidActionFeature).map{
+      case (vid, (feature, Some(feature_action))) =>
+        (vid, (feature, feature_action))
+      case (vid, (feature, None)) =>
+        (vid, (feature, "{}"))
+    }.mapPartitions(row => {
+      val result = new ArrayBuffer[(String, (String, String, String, String))]()
+      val category1 = category1_br.value
+      val category2 = category2_br.value
+      row.foreach {
+        case (vid, (feature, feature_action)) =>
+          val cate1 = JSON.parseObject(feature).getOrDefault("category1", "无").toString
+          val cate2 = JSON.parseObject(feature).getOrDefault("category2_1", "无").toString
+          val feature_cate1 = category1.getOrElse(cate1, "{}")
+          val feature_cate2 = category2.getOrElse(cate2, "{}")
+          result.add((vid, (feature, feature_action, feature_cate1, feature_cate2)))
+      }
+      result.iterator
+    }).mapPartitions(row =>{
+      val result = new ArrayBuffer[String]()
+      val onehotMap = onehotMap_br.value
+      val bucketsMap = bucketsMap_br.value
+      row.foreach {
+        case (vid_left, (feature_left, feature_left_action, feature_left_cate1, feature_left_cate2)) =>
+          val left = new ArrayBuffer[String]()
+          val left_dense1 = new ArrayBuffer[String]()
+          val left_dense2 = new ArrayBuffer[String]()
+          // 1 sparse 特征 16个
+          // vid cate1 cate2 video_style valid_time captions_color audience_age_group
+          // audience_value_type font_size cover_persons_num audience_gender sentiment_tendency
+          // video_type background_music_type captions has_end_credit_guide
+          left += onehotMap.getOrElse("vid:" + vid_left, "0")
+          var jsonLeft = JSON.parseObject(feature_left)
+          left += getOnehotValue(jsonLeft, onehotMap, "category1", "cate1:")
+          left += getOnehotValue(jsonLeft, onehotMap, "category2_1", "cate2:")
+          left += getOnehotValue(jsonLeft, onehotMap, "video_style", "video_style:")
+          left += getOnehotValue(jsonLeft, onehotMap, "valid_time", "valid_time:")
+          left += getOnehotValue(jsonLeft, onehotMap, "captions_color", "captions_color:")
+          left += getOnehotValue(jsonLeft, onehotMap, "audience_age_group", "audience_age_group:")
+          left += getOnehotValue(jsonLeft, onehotMap, "audience_value_type", "audience_value_type:")
+          left += getOnehotValue(jsonLeft, onehotMap, "font_size", "font_size:")
+          left += getOnehotValue(jsonLeft, onehotMap, "cover_persons_num", "cover_persons_num:")
+          left += getOnehotValue(jsonLeft, onehotMap, "audience_gender", "audience_gender:")
+          left += getOnehotValue(jsonLeft, onehotMap, "sentiment_tendency", "sentiment_tendency:")
+          left += getOnehotValue(jsonLeft, onehotMap, "video_type", "video_type:")
+          left += getOnehotValue(jsonLeft, onehotMap, "background_music_type", "background_music_type:")
+          left += getOnehotValue(jsonLeft, onehotMap, "captions", "captions:")
+          left += getOnehotValue(jsonLeft, onehotMap, "has_end_credit_guide", "has_end_credit_guide:")
+          // 2 dense通过分桶转换成sparse特征 47个 * 3 * 2
+          jsonLeft = JSON.parseObject(feature_left_action)
+          var res = getDenseBucketValue(jsonLeft, bucketsMap, "str_day1", "action:str_day1")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "rov_day1", "action:rov_day1")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "ros_day1", "action:ros_day1")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "str_day7", "action:str_day7")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "rov_day7", "action:rov_day7")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "ros_day7", "action:ros_day7")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "str_day21", "action:str_day21")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "rov_day21", "action:rov_day21")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "ros_day21", "action:ros_day21")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "str_day336", "action:str_day336")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "rov_day336", "action:rov_day336")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "ros_day336", "action:ros_day336")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "vovd1_day7", "action:vovd1_day7")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "vovd1_day21", "action:vovd1_day21")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "vovd1_day336", "action:vovd1_day336")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          //----------------------cate1-----------------------------cate1---------------------------cate1----------------------
+          jsonLeft = JSON.parseObject(feature_left_cate1)
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "str_day1", "cate1:str_day1")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "rov_day1", "cate1:rov_day1")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "ros_day1", "cate1:ros_day1")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "str_day3", "cate1:str_day3")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "rov_day3", "cate1:rov_day3")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "ros_day3", "cate1:ros_day3")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "str_day7", "cate1:str_day7")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "rov_day7", "cate1:rov_day7")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "ros_day7", "cate1:ros_day7")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "str_day30", "cate1:str_day30")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "rov_day30", "cate1:rov_day30")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "ros_day30", "cate1:ros_day30")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "vovd1_day1", "cate1:vovd1_day1")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "vovd1_day3", "cate1:vovd1_day3")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "vovd1_day7", "cate1:vovd1_day7")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "vovd1_day30", "cate1:vovd1_day30")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          //----------------------cate2-----------------------------cate2---------------------------cate2----------------------
+          jsonLeft = JSON.parseObject(feature_left_cate2)
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "str_day1", "cate2:str_day1")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "rov_day1", "cate2:rov_day1")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "ros_day1", "cate2:ros_day1")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "str_day3", "cate2:str_day3")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "rov_day3", "cate2:rov_day3")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "ros_day3", "cate2:ros_day3")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "str_day7", "cate2:str_day7")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "rov_day7", "cate2:rov_day7")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "ros_day7", "cate2:ros_day7")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "str_day30", "cate2:str_day30")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "rov_day30", "cate2:rov_day30")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "ros_day30", "cate2:ros_day30")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "vovd1_day1", "cate2:vovd1_day1")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "vovd1_day3", "cate2:vovd1_day3")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "vovd1_day7", "cate2:vovd1_day7")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+          res = getDenseBucketValue(jsonLeft, bucketsMap, "vovd1_day30", "cate2:vovd1_day30")
+          left += res._1.toString
+          left_dense1 += res._2.toString
+          left_dense2 += res._3.toString
+
+          // 3 left 和 right 分别 16+47*3=16+141 = 157
+          left ++= left_dense1
+          left ++= left_dense2
+          result.add(
+            (vid_left, left.mkString(",")).productIterator.mkString("\t")
+          )
+      }
+      result.iterator
+    })
+
+
+    val hdfsPath = savePath + "/" + 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)
+      data.coalesce(repartition).saveAsTextFile(hdfsPath, classOf[GzipCodec])
+    } else {
+      println("路径不合法，无法写入:" + hdfsPath)
+    }
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/makedata_recsys_r_rate/makedata_recsys_61_originData_20241209.scala

@@ -0,0 +1,280 @@
+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.RankExtractorFeature_20240530
+import org.apache.hadoop.io.compress.GzipCodec
+import org.apache.spark.sql.SparkSession
+import org.xm.Similarity
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+/*
+
+ */
+
+object makedata_recsys_61_originData_20241209 {
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder()
+      .appName(this.getClass.getName)
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // 1 读取参数
+    val param = ParamUtils.parseArgs(args)
+    val tablePart = param.getOrElse("tablePart", "64").toInt
+    val beginStr = param.getOrElse("beginStr", "2023010100")
+    val endStr = param.getOrElse("endStr", "2023010123")
+    val savePath = param.getOrElse("savePath", "/dw/recommend/model/61_origin_data/")
+    val project = param.getOrElse("project", "loghubods")
+    val table = param.getOrElse("table", "XXXX")
+    val repartition = param.getOrElse("repartition", "32").toInt
+
+    // 2 读取odps+表信息
+    val odpsOps = env.getODPS(sc)
+
+    // 3 循环执行数据生产
+    val timeRange = MyDateUtils.getDateHourRange(beginStr, endStr)
+    for (dt_hh <- timeRange) {
+      val dt = dt_hh.substring(0, 8)
+      val hh = dt_hh.substring(8, 10)
+      val partition = s"dt=$dt,hh=$hh"
+      println("开始执行partiton:" + partition)
+      val odpsData = odpsOps.readTable(project = project,
+          table = table,
+          partition = partition,
+          transfer = func,
+          numPartition = tablePart)
+        .map(record => {
+
+          val featureMap = new JSONObject()
+
+          // a 视频特征
+          val b1: JSONObject = if (record.isNull("b1_feature")) new JSONObject() else
+            JSON.parseObject(record.getString("b1_feature"))
+          val b2: JSONObject = if (record.isNull("b2_feature")) new JSONObject() else
+            JSON.parseObject(record.getString("b2_feature"))
+          val b3: JSONObject = if (record.isNull("b3_feature")) new JSONObject() else
+            JSON.parseObject(record.getString("b3_feature"))
+          val b6: JSONObject = if (record.isNull("b6_feature")) new JSONObject() else
+            JSON.parseObject(record.getString("b6_feature"))
+          val b7: JSONObject = if (record.isNull("b7_feature")) new JSONObject() else
+            JSON.parseObject(record.getString("b7_feature"))
+
+          val b8: JSONObject = if (record.isNull("b8_feature")) new JSONObject() else
+            JSON.parseObject(record.getString("b8_feature"))
+          val b9: JSONObject = if (record.isNull("b9_feature")) new JSONObject() else
+            JSON.parseObject(record.getString("b9_feature"))
+          val b10: JSONObject = if (record.isNull("b10_feature")) new JSONObject() else
+            JSON.parseObject(record.getString("b10_feature"))
+          val b11: JSONObject = if (record.isNull("b11_feature")) new JSONObject() else
+            JSON.parseObject(record.getString("b11_feature"))
+          val b12: JSONObject = if (record.isNull("b12_feature")) new JSONObject() else
+            JSON.parseObject(record.getString("b12_feature"))
+          val b13: JSONObject = if (record.isNull("b13_feature")) new JSONObject() else
+            JSON.parseObject(record.getString("b13_feature"))
+          val b17: JSONObject = if (record.isNull("b17_feature")) new JSONObject() else
+            JSON.parseObject(record.getString("b17_feature"))
+          val b18: JSONObject = if (record.isNull("b18_feature")) new JSONObject() else
+            JSON.parseObject(record.getString("b18_feature"))
+          val b19: JSONObject = if (record.isNull("b19_feature")) new JSONObject() else
+            JSON.parseObject(record.getString("b19_feature"))
+
+
+          val origin_data = List(
+            (b1, b2, b3, "b123"), (b1, b6, b7, "b167"),
+            (b8, b9, b10, "b8910"), (b11, b12, b13, "b111213"),
+            (b17, b18, b19, "b171819")
+          )
+          for ((b_1, b_2, b_3, prefix1) <- origin_data) {
+            for (prefix2 <- List(
+              "1h", "2h", "3h", "4h", "12h", "1d", "3d", "7d"
+            )) {
+              val exp = if (b_1.isEmpty) 0D else b_1.getIntValue("exp_pv_" + prefix2).toDouble
+              val share = if (b_2.isEmpty) 0D else b_2.getIntValue("share_pv_" + prefix2).toDouble
+              val returns = if (b_3.isEmpty) 0D else b_3.getIntValue("return_uv_" + prefix2).toDouble
+              val f1 = RankExtractorFeature_20240530.calDiv(share, exp)
+              val f2 = RankExtractorFeature_20240530.calLog(share)
+              val f3 = RankExtractorFeature_20240530.calDiv(returns, exp)
+              val f4 = RankExtractorFeature_20240530.calLog(returns)
+              val f5 = f3 * f4
+              val f6 = RankExtractorFeature_20240530.calDiv(returns, share)
+              featureMap.put(prefix1 + "_" + prefix2 + "_" + "STR", f1)
+              featureMap.put(prefix1 + "_" + prefix2 + "_" + "log(share)", f2)
+              featureMap.put(prefix1 + "_" + prefix2 + "_" + "ROV", f3)
+              featureMap.put(prefix1 + "_" + prefix2 + "_" + "log(return)", f4)
+              featureMap.put(prefix1 + "_" + prefix2 + "_" + "ROV*log(return)", f5)
+              featureMap.put(prefix1 + "_" + prefix2 + "_" + "ROS", f6)
+            }
+          }
+
+          val video_info: JSONObject = if (record.isNull("t_v_info_feature")) new JSONObject() else
+            JSON.parseObject(record.getString("t_v_info_feature"))
+          featureMap.put("total_time", if (video_info.containsKey("total_time")) video_info.getIntValue("total_time").toDouble else 0D)
+          featureMap.put("bit_rate", if (video_info.containsKey("bit_rate")) video_info.getIntValue("bit_rate").toDouble else 0D)
+
+          val c1: JSONObject = if (record.isNull("c1_feature")) new JSONObject() else
+            JSON.parseObject(record.getString("c1_feature"))
+          if (c1.nonEmpty) {
+            featureMap.put("playcnt_6h", if (c1.containsKey("playcnt_6h")) c1.getIntValue("playcnt_6h").toDouble else 0D)
+            featureMap.put("playcnt_1d", if (c1.containsKey("playcnt_1d")) c1.getIntValue("playcnt_1d").toDouble else 0D)
+            featureMap.put("playcnt_3d", if (c1.containsKey("playcnt_3d")) c1.getIntValue("playcnt_3d").toDouble else 0D)
+            featureMap.put("playcnt_7d", if (c1.containsKey("playcnt_7d")) c1.getIntValue("playcnt_7d").toDouble else 0D)
+          }
+          val c2: JSONObject = if (record.isNull("c2_feature")) new JSONObject() else
+            JSON.parseObject(record.getString("c2_feature"))
+          if (c2.nonEmpty) {
+            featureMap.put("share_pv_12h", if (c2.containsKey("share_pv_12h")) c2.getIntValue("share_pv_12h").toDouble else 0D)
+            featureMap.put("share_pv_1d", if (c2.containsKey("share_pv_1d")) c2.getIntValue("share_pv_1d").toDouble else 0D)
+            featureMap.put("share_pv_3d", if (c2.containsKey("share_pv_3d")) c2.getIntValue("share_pv_3d").toDouble else 0D)
+            featureMap.put("share_pv_7d", if (c2.containsKey("share_pv_7d")) c2.getIntValue("share_pv_7d").toDouble else 0D)
+            featureMap.put("return_uv_12h", if (c2.containsKey("return_uv_12h")) c2.getIntValue("return_uv_12h").toDouble else 0D)
+            featureMap.put("return_uv_1d", if (c2.containsKey("return_uv_1d")) c2.getIntValue("return_uv_1d").toDouble else 0D)
+            featureMap.put("return_uv_3d", if (c2.containsKey("return_uv_3d")) c2.getIntValue("return_uv_3d").toDouble else 0D)
+            featureMap.put("return_uv_7d", if (c2.containsKey("return_uv_7d")) c2.getIntValue("return_uv_7d").toDouble else 0D)
+          }
+
+          val title = if (video_info.containsKey("title")) video_info.getString("title") else ""
+          if (!title.equals("")) {
+            for (key_feature <- List("c3_feature", "c4_feature", "c5_feature", "c6_feature", "c7_feature")) {
+              val c34567: JSONObject = if (record.isNull(key_feature)) new JSONObject() else
+                JSON.parseObject(record.getString(key_feature))
+              for (key_time <- List("tags_1d", "tags_3d", "tags_7d")) {
+                val tags = if (c34567.containsKey(key_time)) c34567.getString(key_time) else ""
+                if (!tags.equals("")) {
+                  val (f1, f2, f3, f4) = funcC34567ForTags(tags, title)
+                  featureMap.put(key_feature + "_" + key_time + "_matchnum", f1)
+                  featureMap.put(key_feature + "_" + key_time + "_maxscore", f3)
+                  featureMap.put(key_feature + "_" + key_time + "_avgscore", f4)
+                }
+              }
+            }
+          }
+
+          val vid = if (record.isNull("vid")) "" else record.getString("vid")
+          if (!vid.equals("")) {
+            for (key_feature <- List("c8_feature", "c9_feature")) {
+              val c89: JSONObject = if (record.isNull(key_feature)) new JSONObject() else
+                JSON.parseObject(record.getString(key_feature))
+              for (key_action <- List("share", "return")) {
+                val cfListStr = if (c89.containsKey(key_action)) c89.getString(key_action) else ""
+                if (!cfListStr.equals("")) {
+                  val cfMap = cfListStr.split(",").map(r => {
+                    val rList = r.split(":")
+                    (rList(0), (rList(1), rList(2), rList(3)))
+                  }).toMap
+                  if (cfMap.contains(vid)) {
+                    val (score, num, rank) = cfMap(vid)
+                    featureMap.put(key_feature + "_" + key_action + "_score", score.toDouble)
+                    featureMap.put(key_feature + "_" + key_action + "_num", num.toDouble)
+                    featureMap.put(key_feature + "_" + key_action + "_rank", 1.0 / rank.toDouble)
+                  }
+                }
+              }
+            }
+          }
+
+          val d1: JSONObject = if (record.isNull("d1_feature")) new JSONObject() else
+            JSON.parseObject(record.getString("d1_feature"))
+          if (d1.nonEmpty) {
+            featureMap.put("d1_exp", if (d1.containsKey("exp")) d1.getString("exp").toDouble else 0D)
+            featureMap.put("d1_return_n", if (d1.containsKey("return_n")) d1.getString("return_n").toDouble else 0D)
+            featureMap.put("d1_rovn", if (d1.containsKey("rovn")) d1.getString("rovn").toDouble else 0D)
+          }
+
+
+          /*
+
+
+          视频：
+          曝光使用pv 分享使用pv 回流使用uv --> 1h 2h 3h 4h 12h 1d 3d 7d
+          STR log(share) ROV log(return) ROV*log(return)
+          40个特征组合
+          整体、整体曝光对应、推荐非冷启root、推荐冷启root、分省份root
+          200个特征值
+
+          视频：
+          视频时长、比特率
+
+          人：
+          播放次数 --> 6h 1d 3d 7d --> 4个
+          带回来的分享pv 回流uv --> 12h 1d 3d 7d --> 8个
+          人+vid-title:
+          播放点/回流点/分享点/累积分享/累积回流 --> 1d 3d 7d --> 匹配数量 语义最高相似度分 语义平均相似度分 --> 45个
+          人+vid-cf
+          基于分享行为/基于回流行为 -->  “分享cf”+”回流点击cf“ 相似分 相似数量 相似rank的倒数 --> 12个
+
+          头部视频：
+          曝光 回流 ROVn 3个特征
+
+          场景：
+          小时 星期 apptype city province pagesource 机器型号
+           */
+
+
+          //4 处理label信息。
+          val labels = new JSONObject
+          for (labelKey <- List(
+            "is_play", "is_share", "is_return", "noself_is_return", "return_uv", "noself_return_uv", "total_return_uv",
+            "share_pv", "total_share_uv"
+          )) {
+            if (!record.isNull(labelKey)) {
+              labels.put(labelKey, record.getString(labelKey))
+            }
+          }
+          //5 处理log key表头。
+          val apptype = record.getString("apptype")
+          val pagesource = record.getString("pagesource")
+          val mid = record.getString("mid")
+          // vid 已经提取了
+          val ts = record.getString("ts")
+          val abcode = record.getString("abcode")
+          val level = if (record.isNull("level")) "0" else record.getString("level")
+          val logKey = (apptype, pagesource, mid, vid, ts, abcode, level).productIterator.mkString(",")
+          val labelKey = labels.toString()
+          val featureKey = featureMap.toString()
+          //6 拼接数据，保存。
+          logKey + "\t" + labelKey + "\t" + featureKey
+
+        })
+
+      // 4 保存数据到hdfs
+      val savePartition = dt + hh
+      val hdfsPath = savePath + "/" + savePartition
+      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)
+        odpsData.coalesce(repartition).saveAsTextFile(hdfsPath, classOf[GzipCodec])
+      } else {
+        println("路径不合法，无法写入:" + hdfsPath)
+      }
+    }
+  }
+
+  def func(record: Record, schema: TableSchema): Record = {
+    record
+  }
+
+  def funcC34567ForTags(tags: String, title: String): Tuple4[Double, String, Double, Double] = {
+    // 匹配数量 匹配词 语义最高相似度分 语义平均相似度分
+    val tagsList = tags.split(",")
+    var d1 = 0.0
+    val d2 = new ArrayBuffer[String]()
+    var d3 = 0.0
+    var d4 = 0.0
+    for (tag <- tagsList) {
+      if (title.contains(tag)) {
+        d1 = d1 + 1.0
+        d2.add(tag)
+      }
+      val score = Similarity.conceptSimilarity(tag, title)
+      d3 = if (score > d3) score else d3
+      d4 = d4 + score
+    }
+    d4 = if (tagsList.nonEmpty) d4 / tagsList.size else d4
+    (d1, d2.mkString(","), d3, d4)
+  }
+}

src/main/scala/com/aliyun/odps/spark/examples/临时记录的脚本-I2I

@@ -0,0 +1,73 @@
+nohup /opt/apps/SPARK2/spark-2.4.8-hadoop3.2-1.0.8/bin/spark-class2 org.apache.spark.deploy.SparkSubmit \
+--class com.aliyun.odps.spark.examples.makedata_dssm.makedata_i2i_01_originData_20241127 \
+--master yarn --driver-memory 2G --executor-memory 2G --executor-cores 1 --num-executors 16 \
+./target/spark-examples-1.0.0-SNAPSHOT-shaded.jar \
+beginStr:2024113000 endStr:2024113023 negCnt:20 \
+tablePart:64 savePath:/dw/recommend/model/51_dssm_i2i_sample/ > p51_2.log 2>&1 &
+
+nohup /opt/apps/SPARK2/spark-2.4.8-hadoop3.2-1.0.8/bin/spark-class2 org.apache.spark.deploy.SparkSubmit \
+--class com.aliyun.odps.spark.examples.makedata_dssm.makedata_i2i_02_joinFeatureData_20241128 \
+--master yarn --driver-memory 2G --executor-memory 2G --executor-cores 1 --num-executors 32 \
+./target/spark-examples-1.0.0-SNAPSHOT-shaded.jar \
+beginStr:2024113000 endStr:2024113023 \
+tablePart:64 \
+readPath:/dw/recommend/model/51_dssm_i2i_sample/ \
+savePath:/dw/recommend/model/52_dssm_i2i_joinfeature/ > p52_2.log 2>&1 &
+
+nohup /opt/apps/SPARK2/spark-2.4.8-hadoop3.2-1.0.8/bin/spark-class2 org.apache.spark.deploy.SparkSubmit \
+--class com.aliyun.odps.spark.examples.makedata_dssm.makedata_i2i_03_onehotFile_20241128 \
+--master yarn --driver-memory 16G --executor-memory 1G --executor-cores 1 --num-executors 32 \
+./target/spark-examples-1.0.0-SNAPSHOT-shaded.jar \
+dt:20241201 \
+tablePart:64 \
+readPath:/dw/recommend/model/53_dssm_i2i_onehot/after_20241201_file \
+savePath:/dw/recommend/model/53_dssm_i2i_onehot/after_20241201_file > p53.log 2>&1 &
+
+
+
+
+数据量:3544265打印各个特征多少枚举值：vid       3534920
+video_style     7382
+captions_color  782
+valid_time      754
+audience_age_group      80
+cate2   67
+audience_value_type     65
+font_size       49
+audience_gender 47
+cover_persons_num       45
+cate1   42
+sentiment_tendency      11
+video_type      8
+background_music_type   6
+captions        3
+has_end_credit_guide    2
+timeliness      2
+
+nohup /opt/apps/SPARK2/spark-2.4.8-hadoop3.2-1.0.8/bin/spark-class2 org.apache.spark.deploy.SparkSubmit \
+--class com.aliyun.odps.spark.examples.makedata_dssm.makedata_i2i_04_bucketFile_20241128 \
+--master yarn --driver-memory 16G --executor-memory 2G --executor-cores 1 --num-executors 32 \
+./target/spark-examples-1.0.0-SNAPSHOT-shaded.jar \
+readPath:/dw/recommend/model/52_dssm_i2i_joinfeature/20241126* \
+savePath:/dw/recommend/model/54_dssm_i2i_bucketfile/ \
+fileName:47_rate_v2  bucketNum:100 > p54.log 2>&1 &
+
+nohup /opt/apps/SPARK2/spark-2.4.8-hadoop3.2-1.0.8/bin/spark-class2 org.apache.spark.deploy.SparkSubmit \
+--class com.aliyun.odps.spark.examples.makedata_dssm.makedata_i2i_05_trainData_20241129 \
+--master yarn --driver-memory 2G --executor-memory 4G --executor-cores 1 --num-executors 32 \
+./target/spark-examples-1.0.0-SNAPSHOT-shaded.jar \
+beginStr:2024113000 endStr:2024113023 \
+readPath:/dw/recommend/model/52_dssm_i2i_joinfeature/ \
+savePath:/dw/recommend/model/55_dssm_i2i_traindata/ \
+onehotPath:/dw/recommend/model/53_dssm_i2i_onehot/after_20241201_file \
+bucketFile:20241128_recsys_i2i_bucket_47_v2.txt > p55_2.log 2>&1 &
+
+nohup /opt/apps/SPARK2/spark-2.4.8-hadoop3.2-1.0.8/bin/spark-class2 org.apache.spark.deploy.SparkSubmit \
+--class com.aliyun.odps.spark.examples.makedata_dssm.makedata_i2i_06_itemPred_20241206 \
+--master yarn --driver-memory 2G --executor-memory 2G --executor-cores 1 --num-executors 16 \
+./target/spark-examples-1.0.0-SNAPSHOT-shaded.jar \
+onehotPath:/dw/recommend/model/53_dssm_i2i_onehot/after_20241201_file \
+bucketFile:20241128_recsys_i2i_bucket_47_v2.txt repartition:100 \
+dt:20241206 \
+savePath:/dw/recommend/model/56_dssm_i2i_itempredData/ \
+> p56.log 2>&1 &