feat:修改文件名

recommend-model-produce/src/main/scala/com/tzld/piaoquan/recommend/model/ana_01_xgb_ad_20240809.scala

@@ -1,75 +0,0 @@
-package com.tzld.piaoquan.recommend.model
-
-import ml.dmlc.xgboost4j.scala.spark.XGBoostClassifier
-import org.apache.commons.lang.math.NumberUtils
-import org.apache.commons.lang3.StringUtils
-import org.apache.hadoop.io.compress.GzipCodec
-import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
-import org.apache.spark.ml.feature.VectorAssembler
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.types.DataTypes
-import org.apache.spark.sql.{Dataset, Row, SparkSession}
-
-import java.util
-import scala.io.Source
-
-object ana_01_xgb_ad_20240809{
-  def main(args: Array[String]): Unit = {
-    val spark = SparkSession
-      .builder()
-      .appName(this.getClass.getName)
-      .getOrCreate()
-    val sc = spark.sparkContext
-
-    val param = ParamUtils.parseArgs(args)
-    val savePath = param.getOrElse("savePath", "/dw/recommend/model/34_ad_predict_data/")
-
-    val hdfsPath = savePath
-    // 统计分cid的分数
-    sc.textFile(hdfsPath).map(r=>{
-      val rList = r.split("\t")
-      val cid = rList(3)
-      val score = rList(2).replace("[", "").replace("]", "")
-        .split(",")(1).toDouble
-      val label = rList(0).toDouble
-      (cid, (1, label, score))
-    }).reduceByKey{
-      case (a, b) => (a._1 + b._1, a._2 + b._2, a._3 + b._3)
-    }.map{
-      case (cid, (all, zheng, scores)) =>
-        (cid, all, zheng, scores, zheng / all, scores / all)
-    }.collect().sortBy(_._1).map(_.productIterator.mkString("\t")).foreach(println)
-
-  }
-
-
-}
-
-
-
-//rabit_timeout -> -1
-//scale_pos_weight -> 1.0
-//seed -> 0
-//handle_invalid -> error
-//features_col -> features
-//label_col -> label
-//num_workers -> 1
-//subsample -> 0.8
-//max_depth -> 5
-//probability_col -> probability
-//raw_prediction_col -> rawPrediction
-//tree_limit -> 0
-//dmlc_worker_connect_retry -> 5
-//train_test_ratio -> 1.0
-//use_external_memory -> false
-//objective -> binary:logistic
-//eval_metric -> auc
-//num_round -> 1000
-//missing -> 0.0
-//rabit_ring_reduce_threshold -> 32768
-//tracker_conf -> TrackerConf(0,python,,)
-//eta -> 0.009999999776482582
-//colsample_bytree -> 0.8
-//allow_non_zero_for_missing -> false
-//nthread -> 8
-//prediction_col -> prediction

recommend-model-produce/src/main/scala/com/tzld/piaoquan/recommend/model/recsys_01_xgb_train.scala → recommend-model-produce/src/main/scala/com/tzld/piaoquan/recommend/model/recsys_01_str_xgb_train.scala

@@ -1,6 +1,7 @@
 package com.tzld.piaoquan.recommend.model
 
 import com.alibaba.fastjson.JSON
+import com.tzld.piaoquan.recommend.utils.{MyHdfsUtils, ParamUtils}
 import ml.dmlc.xgboost4j.scala.spark.XGBoostClassifier
 import org.apache.commons.lang.math.NumberUtils
 import org.apache.commons.lang3.StringUtils
@@ -14,7 +15,7 @@ import org.apache.spark.sql.{Dataset, Row, SparkSession}
 import java.util
 import scala.io.Source
 
-object recsys_01_xgb_train {
+object recsys_01_str_xgb_train {
   def main(args: Array[String]): Unit = {
     val spark = SparkSession
       .builder()

recommend-model-produce/src/main/scala/com/tzld/piaoquan/recommend/model/MyDateUtils.scala → recommend-model-produce/src/main/scala/com/tzld/piaoquan/recommend/utils/MyDateUtils.scala

@@ -1,4 +1,4 @@
-package com.tzld.piaoquan.recommend.model
+package com.tzld.piaoquan.recommend.utils
 
 import org.apache.commons.lang.time.DateUtils
 import org.apache.commons.lang3.time.DateUtils.addDays

recommend-model-produce/src/main/scala/com/tzld/piaoquan/recommend/model/MyHdfsUtils.scala → recommend-model-produce/src/main/scala/com/tzld/piaoquan/recommend/utils/MyHdfsUtils.scala

@@ -1,4 +1,4 @@
-package com.tzld.piaoquan.recommend.model
+package com.tzld.piaoquan.recommend.utils
 
 /**
  * Author: zhangbo58

recommend-model-produce/src/main/scala/com/tzld/piaoquan/recommend/model/ParamUtils.scala → recommend-model-produce/src/main/scala/com/tzld/piaoquan/recommend/utils/ParamUtils.scala

@@ -1,4 +1,4 @@
-package com.tzld.piaoquan.recommend.model
+package com.tzld.piaoquan.recommend.utils
 
 import scala.collection.mutable