CDH:5.8.0 , CDH-hadoop :2.6.0 ; CDH-spark :1.6.0
使用Spark 加载PMML文件到模型,并使用Spark平台进行预测(这里测试使用的是Spark on YARN的方式)。
具体小目标:
1. 参考https://github.com/jpmml/jpmml-spark 实现,能运行简单例子;
2. 直接读取HDFS上面的输入数据文件,使用PMML生成的模型进行预测;
(第1点和第2点的不一样的地方体现在输入数据的构造上,可以参看下面的代码)
1. 准备原始数据,原始数据包括PMML文件,以及测试数据;分别如下:
<?xml version="1.0" encoding="UTF-8" standalone="yes"?> <PMML version="4.2" xmlns="http://www.dmg.org/PMML-4_2"> <Header description="linear SVM"> <Application name="Apache Spark MLlib"/> <Timestamp>2016-11-16T22:17:47</Timestamp> </Header> <DataDictionary numberOfFields="4"> <DataField name="field_0" optype="continuous" dataType="double"/> <DataField name="field_1" optype="continuous" dataType="double"/> <DataField name="field_2" optype="continuous" dataType="double"/> <DataField name="target" optype="categorical" dataType="string"/> </DataDictionary> <RegressionModel modelName="linear SVM" functionName="classification" normalizationMethod="none"> <MiningSchema> <MiningField name="field_0" usageType="active"/> <MiningField name="field_1" usageType="active"/> <MiningField name="field_2" usageType="active"/> <MiningField name="target" usageType="target"/> </MiningSchema> <RegressionTable intercept="0.0" targetCategory="1"> <NumericPredictor name="field_0" coefficient="-0.36682158807862086"/> <NumericPredictor name="field_1" coefficient="3.8787681305811765"/> <NumericPredictor name="field_2" coefficient="-1.6134308474471166"/> </RegressionTable> <RegressionTable intercept="0.0" targetCategory="0"/> </RegressionModel> </PMML>以上pmml文件是由一个svm模型构建的,其输入有三个字段,有一个目标输出,代表类别;
输入测试数据,如下:
field_0,field_1,field_2 98,97,96 1,2,7这个数据由列名和数据组成,这里需要注意,列名需要和pmml里面的列名对应;
2. 把https://github.com/jpmml/jpmml-spark工程下载到本地,并添加如下代码:
package org.jpmml.spark; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; import org.apache.spark.SparkConf; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.ml.Transformer; import org.apache.spark.sql.*; import org.jpmml.evaluator.Evaluator; public class SVMEvaluationSparkExample { static public void main(String... args) throws Exception { if(args.length != 3){ System.err.println("Usage: java " + SVMEvaluationSparkExample.class.getName() + " <PMML file> <Input file> <Output directory>"); System.exit(-1); } /** * 根据pmml文件,构建模型 */ FileSystem fs = FileSystem.get(new Configuration()); Evaluator evaluator = EvaluatorUtil.createEvaluator(fs.open(new Path(args[0]))); TransformerBuilder modelBuilder = new TransformerBuilder(evaluator) .withTargetCols() .withOutputCols() .exploded(true); Transformer transformer = modelBuilder.build(); /** * 利用DataFrameReader从原始数据中构造 DataFrame对象 * 需要原始数据包含列名 */ SparkConf conf = new SparkConf(); try(JavaSparkContext sparkContext = new JavaSparkContext(conf)){ SQLContext sqlContext = new SQLContext(sparkContext); DataFrameReader reader = sqlContext.read() .format("com.databricks.spark.csv") .option("header", "true") .option("inferSchema", "true"); DataFrame dataFrame = reader.load(args[1]);// 输入数据需要包含列名 /** * 使用模型进行预测 */ dataFrame = transformer.transform(dataFrame); /** * 写入数据 */ DataFrameWriter writer = dataFrame.write() .format("com.databricks.spark.csv") .option("header", "true"); writer.save(args[2]); } } }这个代码主要实现的是小目标1,即参考jpmml-spark工程给的示例,编写代码;代码有四个部分,第一部分读取HDFS上面的PMML文件,然后构建模型;第二部分使用DataFrameReader根据输入数据构建DataFrame数据结构;第三部分,使用模型对构造的DataFrame数据进行预测;第四部分,把预测的结果写入HDFS。
注意里面在构造数据的时候.option("header","true")是一定要加的,原因如下:1)原始数据中确实有列名;2)如果这里不加,那么将读取不到列名的相关信息,将不能和模型中的列名对应;(当然,下面有其他方法处理这种情况)。
3. 上传测试数据以及pmml文件到HDFS,进行测试,代码如下:
spark-submit --master yarn --class org.jpmml.spark.SVMEvaluationSparkExample /opt/tmp/example-1.0-SNAPSHOT.jar hdfs://quickstart.cloudera:8020/tmp/svm/part-00000 sample_test_data.txt sample_out00其中,example-1.0-SNAPSHOT.jar 是编译后的jar包;/tmp/svm/part-00000时svm模型的pmml文件;sample_test_data.txt 是测试数据;sample_out00是输出目录;
查看结果:
根据输出的结果,也可以看出预测结果是对的。
4. 如何实现小目标2呢?
编写代码:
/* * Copyright (c) 2015 Villu Ruusmann * * This file is part of JPMML-Spark * * JPMML-Spark is free software: you can redistribute it and/or modify * it under the terms of the GNU Affero General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * JPMML-Spark is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU Affero General Public License for more details. * * You should have received a copy of the GNU Affero General Public License * along with JPMML-Spark. If not, see <http://www.gnu.org/licenses/>. */ package org.jpmml.spark; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; import org.apache.spark.SparkConf; import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.api.java.function.Function; import org.apache.spark.ml.Transformer; import org.apache.spark.sql.*; import org.apache.spark.sql.types.DataTypes; import org.apache.spark.sql.types.StructField; import org.apache.spark.sql.types.StructType; import org.dmg.pmml.FieldName; import org.jpmml.evaluator.Evaluator; import java.util.ArrayList; import java.util.List; //import org.jpmml.evaluator.FieldValue; public class EvaluationSparkExample { static public void main(String... args) throws Exception { if(args.length != 3){ System.err.println("Usage: java " + EvaluationSparkExample.class.getName() + " <PMML file> <Input file> <Output directory>"); System.exit(-1); } /** * 构造模型 */ FileSystem fs = FileSystem.get(new Configuration()); Evaluator evaluator = EvaluatorUtil.createEvaluator(fs.open(new Path(args[0]))); TransformerBuilder modelBuilder = new TransformerBuilder(evaluator) .withTargetCols() .withOutputCols() .exploded(true); Transformer transformer = modelBuilder.build(); /** * 构造列名,schema */ List<StructField> fields = new ArrayList<>(); for (FieldName fieldName: evaluator.getActiveFields()) { fields.add(DataTypes.createStructField(fieldName.getValue(), DataTypes.StringType, true)); } StructType schema = DataTypes.createStructType(fields); /** * 原始数据构造成DataFrame */ SparkConf conf = new SparkConf(); final String splitter = ","; try(JavaSparkContext sparkContext = new JavaSparkContext(conf)){ JavaRDD<Row> data = sparkContext.textFile(args[1]).map(new Function<String, Row>() { @Override public Row call(String line) throws Exception { String[] lineArr = line.split(splitter,-1); return RowFactory.create(lineArr); } }); SQLContext sqlContext = new SQLContext(sparkContext); DataFrame dataFrame = sqlContext.createDataFrame(data, schema); /** * 预测,并生成新的DataFrame */ dataFrame = transformer.transform(dataFrame); /** * 把评估后的数据写入HDFS,不要写入列名 */ DataFrameWriter writer = dataFrame.write() .format("com.databricks.spark.csv"); writer.save(args[2]); } } }这个代码和上一个代码的不同之处只是从原始测试数据中构造DataFrame不同,这里使用的PMML模型中的列名信息,代码参考:http://spark.apache.org/docs/1.6.0/sql-programming-guide.html#interoperating-with-rdds;同时,这时,原始测试数据就不需要再添加列名信息了。由于在代码中,在输出的时候也把列名信息给去掉了,所以只输出数据。运行后,其结果如下所示:
其调用代码如下所示:
spark-submit --master yarn --class org.jpmml.spark.EvaluationSparkExample /opt/tmp/example-1.0-SNAPSHOT.jar hdfs://quickstart.cloudera:8020/tmp/svm/part-00000 sample_test_data1.txt sample_out02其中,sample_test_data1.txt是没有列名的数据。
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