[原创] 机器学习线上预测流程和方案

我们知道模型通过离线方式训练出来后，怎么进行预测呢？如何在线上实时打分。  我们可以将模型结果转为一种叫pmml的格式文件，然后交由openscoring的Web 服务组件来进行线上打分预测。openscoring是一套解决方案（官网是openscoring.io），github上有一位叫vruusmann的大神基于opencoring的组件，实现了一套REST Web服务，用于R，Scikit-Learn和Apache Spark模型的真实实时评分，名字还是叫openscoring，据说耗时小于1ms。 openscoring REST WEB服务代码 Fork 路劲：https://github.com/dearbaba/openscoring 原路径：https://github.com/openscoring/openscoring #### 一、部署OpenScoreing服务 ```shell java -Dconfig.file=application.conf -Djava.util.logging.config.file=logging.properties -jar openscoring-server-executable-${version}.jar ``` 推送模型： ```shell #lrmodel即为模型的ID curl -X PUT --data-binary @lrmodel.pmml -H "Content-type: text/xml" https://localhost:8080/openscoring/model/lrmodel ``` 预测打分： ```shell curl -X POST --data-binary @lrmodel.json -H "Content-type: application/json" https://localhost:8080/openscoring/model/lrmodel ``` 具体其它接口请参看GIT文档。 #### 二、模型结果 例如，我们的模型是使用spark ML将模型结果保存为一个pmml的格式文件，大致代码如下： ```scala import org.apache.spark.ml.feature.VectorIndexer import org.apache.spark.SparkConf import org.apache.spark.sql.SparkSession import org.apache.spark.ml.regression.DecisionTreeRegressor import org.apache.spark.ml.Pipeline import org.apache.spark.ml.evaluation.RegressionEvaluator import org.apache.spark.ml.regression.DecisionTreeRegressionModel import org.apache.spark.ml.classification.DecisionTreeClassifier import org.apache.spark.ml.classification.DecisionTreeClassificationModel import org.apache.spark.storage.StorageLevel import org.apache.spark.ml.linalg.{ Vector, Vectors } import org.apache.commons.lang3.StringUtils import scala.util.parsing.json.JSONObject import java.io.FileReader import scala.io.Source import scala.util.parsing.json.JSON import spray.json.JsObject import org.json4s.jackson.Json import org.json4s._ import org.json4s.JsonDSL._ import org.json4s.jackson.JsonMethods._ import org.apache.spark.util.LongAccumulator import cn.pa18.spark.util.DBConnectionDao import org.apache.spark.sql.Row import org.apache.spark.broadcast.Broadcast import scala.util.control.Breaks._ import scala.collection.mutable.ArrayBuffer import org.apache.spark.ml.feature.CountVectorizer import org.apache.spark.ml.classification.LogisticRegression import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator import org.apache.spark.ml.feature.RegexTokenizer import org.apache.commons.lang3.math.NumberUtils import com.sun.tools.example.debug.expr.ExpressionParser import scala.tools.reflect.Eval import scala.reflect.runtime.currentMirror import scala.tools.reflect.ToolBox import org.apache.spark.ml.feature.CountVectorizerModel import org.apache.spark.ml.classification.LogisticRegressionModel import spray.json.JsValue import spray.json.JsNumber import java.text.SimpleDateFormat import java.util.Calendar import org.apache.spark.sql.SaveMode import java.util.Date import org.apache.spark.sql.Dataset import org.apache.spark.ml.PipelineModel import org.jpmml.sparkml.PMMLBuilder import java.io.File import org.apache.hadoop.fs.Path def lr_training(hiveContext: SparkSession,appid: String): Unit = { import hiveContext.implicits._ val data = hiveContext.sql(s""" select userid,label,b.tagmap from table_name """.stripMargin).rdd.map( row => { val features = ArrayBuffer[String]() val tdid = row.getAs[String]("userid") val label = row.getAs[Int]("label") //正负样本标志 val tagmap = row.getAs[scala.collection.immutable.Map[String, String]]("tagmap") if (tagmap.size > 0) { for (key lrModel.coefficients(i)) } kvWeightData += ("intercept_b" -> lrModel.intercept) kvWeightData += ("auc" -> auc) //将特征权重保存至hdfs文件 val dateFormat = new SimpleDateFormat("yyyyMMdd") val todayDate = dateFormat.format(new Date()) val feature_importance_save_path = hdfs_uri+s"/model/result/${todayDate}/" //保存至HDFS文件 hiveContext.sparkContext.parallelize(kvWeightData.toArray[(String,Double)]).map(row => row._1 + "\t" + row._2).repartition(1).saveAsTextFile(feature_importance_save_path) //将模型结果保存至PMML文件路径 val hdfs_path = hdfs_uri+s"/data/spark/rym/models/" //将模型结果保存至PMML文件的名称 val hdfs_file_name = "lrmodel.pmml" //将模型结果保存至HDFS save_to_PMML(trainingDF, pipelineModel, hdfs_path, hdfs_file_name) } /** * 将模型保存至PMML文件 */ def save_to_PMML(trainingDF: Dataset[Row], pipelineModel: PipelineModel, hdfs_path: String, hdfs_file_name: String): Unit = { println("start to save model to pmml file ... ...") val pmmlBuilder = new PMMLBuilder(trainingDF.schema, pipelineModel) pmmlBuilder.buildFile(new File(hdfs_file_name)) val hdfs = org.apache.hadoop.fs.FileSystem.get(new java.net.URI(hdfs_uri), new org.apache.hadoop.conf.Configuration()) if (!HDFSHelper.exists(hdfs, hdfs_path)) { HDFSHelper.createFolder(hdfs, hdfs_path) } val path = new Path(hdfs_file_name) val dst_path = new Path(hdfs_path) hdfs.copyFromLocalFile(path, dst_path) } ```
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