git地址：https://github.com/hxjcarrie/pyspark_study

本文以LogisticRegression为例。
下图是数据的一个截图，
从中可以知道，第一列是id，第二列是label，其他的列是特征。

lrDemo.py (мллиб на основе RDD)

import sys
reload(sys)
sys.setdefaultencoding('utf8')
from pyspark.sql import SparkSession,Row
from pyspark.sql.types import *
from time import *
import numpy
import os
 
from pyspark.mllib.linalg import Vectors
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.classification import LogisticRegressionWithLBFGS, LogisticRegressionModel
#os.environ['PYSPARK_PYTHON'] = './python_env/py27/bin/python2' 

# 该函数的作用是把x转化为浮点型
def parseFloat(x):
    try:
        rx = float(x)
    except:
        rx = 0.0
    return rx
 
def parse(line, ifUid=False):
    # 针对每一行数据，按照制表符分割
    l = line.split('\t')
    
    # 第一列是uid
    uid = l[0] 
    
    # 第二列label，需通过parseFloat()函数转化为浮点型
    label = parseFloat(l[1])
    
    # 剩下的列都是特征列了，同样需要parseFloat()函数转化为浮点型
    features = map(lambda x: parseFloat(x), l[2:])
    
    if ifUid:
        return (uid, LabeledPoint(label, features))
    else:
        return LabeledPoint(label, features)

# 主函数
def main():
    #spark = SparkSession.builder.master("yarn").appName("spark_demo").getOrCreate()
    spark = SparkSession.builder.getOrCreate()
    print( "Session created!" ) 
    
    sc = spark.sparkContext
    # 打印追踪任务url
    print("The url to track the job: http://namenode-01:8088/proxy/" + sc.applicationId )
 
    sampleHDFS_train = sys.argv[1] # 第一个参数是训练数据
    sampleHDFS_test = sys.argv[2]  # 第二个参数是测试数据
    outputHDFS = sys.argv[3]       # 第三个参数是输出目录
   
    # 此处开始构建rdd弹性分布式数据集
    sampleRDD = sc.textFile(sampleHDFS_train).map( parse )
    predictRDD = sc.textFile(sampleHDFS_test).map(lambda x: parse(x, True))
 
    # 训练
    model = LogisticRegressionWithLBFGS.train(sampleRDD)
    model.clearThreshold() # 删除默认阈值（否则后面直接输出0、1）
 
    # 预测，保存结果
    labelsAndPreds = predictRDD.map(lambda p: (p[0], p[1].label, model.predict(p[1].features)))
    labelsAndPreds.map(lambda p: '\t'.join(map(str, p))).saveAsTextFile(outputHDFS + "/target/output")
 
    # 评估不同阈值下的准确率、召回率
    labelsAndPreds_label_1 = labelsAndPreds.filter(lambda lp: int(lp[1]) == 1)
    labelsAndPreds_label_0 = labelsAndPreds.filter(lambda lp: int(lp[1]) == 0)
    t_cnt = labelsAndPreds_label_1.count()
    f_cnt = labelsAndPreds_label_0.count()
    print "thre\ttp\ttn\tfp\tfn\taccuracy\trecall"
    for thre in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95]:
        tp = labelsAndPreds_label_1.filter(lambda lp: lp[2] > thre).count()
        tn = t_cnt - tp
        fp = labelsAndPreds_label_0.filter(lambda lp: lp[2] > thre).count()
        fn = f_cnt - fp
        print("%.1f\t%d\t%d\t%d\t%d\t%.4f\t%.4f"%(thre, tp, tn, fp, fn, float(tp)/(tp+fp), float(tp)/(t_cnt)))
 
    # 保存模型、加载模型
    model.save(sc, outputHDFS + "/target/tmp/pythonLogisticRegressionWithLBFGSModel")
    sameModel = LogisticRegressionModel.load(sc, outputHDFS + "/target/tmp/pythonLogisticRegressionWithLBFGSModel")
 
    print "output:", outputHDFS
 
if __name__ == '__main__':
    main()

lrDemo_df.py (мл на основе DataFrame)

import sys
reload(sys)
sys.setdefaultencoding('utf8')
from pyspark.sql import SparkSession,Row
from pyspark.sql.types import *
from time import *
import numpy
import os
 
from pyspark.ml.linalg import Vectors
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.feature import StringIndexer, VectorAssembler
from pyspark.ml import Pipeline
from pyspark.sql.functions import udf, col
 
def getFeatureName():
    featureLst = ['feature1', 'feature2', 'feature3', 'feature4', 'feature5', 'feature6', 'feature7', 'feature8', 'feature9']
    colLst = ['uid', 'label'] + featureLst
    return featureLst, colLst
 
def parseFloat(x):
    try:
        rx = float(x)
    except:
        rx = 0.0
    return rx
 
 
def getDict(dictDataLst, colLst):
    dictData = {}
    for i in range(len(colLst)):
        dictData[colLst[i]] = parseFloat(dictDataLst[i])
        if colLst[i] == "label":
            dictData["weight"] = 1.0 if dictDataLst[i] == '1' else 1.0
    return dictData
 
def to_array(col):
    def to_array_(v):
        return v.toArray().tolist()
    return udf(to_array_, ArrayType(DoubleType())).asNondeterministic()(col)
 
 
def main():
    #spark = SparkSession.builder.master("yarn").appName("spark_demo").getOrCreate()
    spark = SparkSession.builder.getOrCreate()
    print "Session created!"
    sc = spark.sparkContext
    print "The url to track the job: http://bx-namenode-02:8088/proxy/" + sc.applicationId
 
    sampleHDFS_train = sys.argv[1]
    sampleHDFS_test = sys.argv[2]
    outputHDFS = sys.argv[3]
 
    featureLst, colLst = getFeatureName()
 
    #读取hdfs上数据，将RDD转为DataFrame
    #训练数据
    rdd_train = sc.textFile(sampleHDFS_train)
    rowRDD_train = rdd_train.map(lambda x: getDict(x.split('\t'), colLst))
    trainDF = spark.createDataFrame(rowRDD_train)
    #测试数据
    rdd_test = sc.textFile(sampleHDFS_test)
    rowRDD_test = rdd_test.map(lambda x: getDict(x.split('\t'), colLst))
    testDF = spark.createDataFrame(rowRDD_test)
 
    #用于训练的特征featureLst
    vectorAssembler = VectorAssembler().setInputCols(featureLst).setOutputCol("features")
 
    #### 训练 ####
    print "step 1"
    lr = LogisticRegression(regParam=0.01, maxIter=100, weightCol="weight")  # regParam 正则项参数
 
    pipeline = Pipeline(stages=[vectorAssembler, lr])
    model = pipeline.fit(trainDF)
    #打印参数
    print "\n-------------------------------------------------------------------------"
    print "LogisticRegression parameters:\n" + lr.explainParams() + "\n"
    print "-------------------------------------------------------------------------\n"
 
    #### 预测, 保存结果 ####
    print "step 2"
    labelsAndPreds = model.transform(testDF).withColumn("probability_xj", to_array(col("probability"))[1])\
                                            .select("uid", "label", "prediction", "probability_xj")
    labelsAndPreds.show()
    labelsAndPreds.write.mode("overwrite").options(header="true").csv(outputHDFS + "/target/output")
 
 
    #### 评估不同阈值下的准确率、召回率
    print "step 3"
    labelsAndPreds_label_1 = labelsAndPreds.where(labelsAndPreds.label == 1)
    labelsAndPreds_label_0 = labelsAndPreds.where(labelsAndPreds.label == 0)
    labelsAndPreds_label_1.show(3)
    labelsAndPreds_label_0.show(3)
    t_cnt = labelsAndPreds_label_1.count()
    f_cnt = labelsAndPreds_label_0.count()
    print "thre\ttp\ttn\tfp\tfn\taccuracy\trecall"
    for thre in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95]:
        tp = labelsAndPreds_label_1.where(labelsAndPreds_label_1.probability_xj > thre).count()
        tn = t_cnt - tp
        fp = labelsAndPreds_label_0.where(labelsAndPreds_label_0.probability_xj > thre).count()
        fn = f_cnt - fp
        print("%.1f\t%d\t%d\t%d\t%d\t%.4f\t%.4f"%(thre, tp, tn, fp, fn, float(tp)/(tp+fp), float(tp)/(t_cnt)))
 
    # 保存模型
    model.write().overwrite().save(outputHDFS + "/target/model/lrModel")
    #加载模型
    #model.load(outputHDFS + "/target/model/lrModel")
 
    print "output:", outputHDFS
 
 
if __name__ == '__main__':
    main()

日志打印模型效果如下所示：

以上是通过python语言开发的lr逻辑回归的代码，那么如何提交到spark集群来执行呢？
登陆spark集群的客户端，编辑spark-submit-lr.sh文件：

ModelType=lrDemo
ModelType=lrDemo_df
#ModelType=xgbDemo

CUR_PATH=$(cd "$(dirname "$0")";pwd)
echo $CUR_PATH

SPARK_PATH=/user/spark/spark
YARN_QUEUE=

DEPLOY_MODE=cluster
DEPLOY_MODE=client

input_path_train=hdfs://
input_path_test=hdfs://
output_path=hdfs://user/huangxiaojuan/program/sparkDemo/${ModelType}

hadoop fs -rmr $output_path

${SPARK_PATH}/bin/spark-submit \
--master yarn \
--name "spark_demo_lr" \
--queue ${YARN_QUEUE} \
--deploy-mode ${DEPLOY_MODE} \
--driver-memory 6g \
--driver-cores 4 \
--executor-memory 12g \
--executor-cores 15 \
--num-executors 10 \
--archives ./source/py27.zip#python_env \
--conf spark.default.parallelism=150 \
--conf spark.executor.memoryOverhead=4g \
--conf spark.driver.memoryOverhead=2g \
--conf spark.yarn.maxAppAttempts=3 \
--conf spark.yarn.submit.waitAppCompletion=true \
--conf spark.pyspark.driver.python=./source/py27/bin/python2 \
--conf spark.yarn.appMasterEnv.PYSPARK_PYTHON=./python_env/py27/bin/python2 \
--conf spark.pyspark.python=./python_env/py27/bin/python2 \
./${ModelType}.py $input_path_train $input_path_test $output_path

执行： nohup bash spakr-submit_lr.sh > spark-submit-ls.log 2>&1 &
kill任务： yarn application -kill appplication_xxxxxxxxx-xxxxx



需要保证driver和executor上的python版本一致
若executor上的python不满足要求，可通过如下参数上传打包好的python到executor上

#上传python包到executor
--archives ./source/py27.zip  

#指定executor上python路径
--conf spark.yarn.appMasterEnv.PYSPARK_PYTHON=./python_env/py27/bin/python2 \
--conf spark.pyspark.python=./python_env/py27/bin/python2 \

#指定driver上python路径
--conf spark.pyspark.driver.python=./source/py27/bin/python2 \
 
#或者先上传至hdfs
--conf spark.yarn.dist.archives=hdfs://user/huangxiaojuan/py27.zip#python_env\