概述
https://github.com/hxjcarrie/pyspark_study
以LogisticRegression为例
- 输入数据样例（第一列为label，后面为feature）
- lrDemo.py（基于RDD的mllib）
#!coding=utf8
'''
author: huangxiaojuan
'''
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'
def parseFloat(x):
try:
rx = float(x)
except:
rx = 0.0
return rx
def parse(line, ifUid=False):
l = line.split('t')
uid = l[0]
label = parseFloat(l[1])
features = map(lambda x: parseFloat(x), l[2:])
if ifUid:
return (uid, LabeledPoint(label, features))
else:
return LabeledPoint(label, features)
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]
sampleHDFS_test = sys.argv[2]
outputHDFS = sys.argv[3]
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 "threttpttntfptfntaccuracytrecall"
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("%.1ft%dt%dt%dt%dt%.4ft%.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（基于DataFrame的ml）
#!coding=utf8
'''
author: huangxiaojuan
'''
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 "threttpttntfptfntaccuracytrecall"
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("%.1ft%dt%dt%dt%dt%.4ft%.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：
spark-submit_lr.sh 提交任务到集群
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- nohup sh -x spark-submit_lr.sh > spark-submit_lr.log 2>&1 &
- kill任务: yarn application -kill application_xxxxxxxxx_xxxxx
上传python包
- 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
参考资料
初学者可直接参考：
Submitting Applications
https://spark.apache.org/docs/latest/quick-start.html
spark.mllib参考：
MLlib: Main Guidespark.apache.orgspark.ml参考：
https://spark.apache.org/docs/latest/api/python/pyspark.ml.htmlspark.apache.org两者区别：
- spark.sql参考：
- 使用dataFrame进行表的关联，例子：
https://github.com/hxjcarrie/pyspark_study/blob/master/df.py
- 使用spark.sql进行表的关联，例子：
https://github.com/hxjcarrie/pyspark_study/blob/master/df_sql.py
- 若要写入hive表，可参考: https://blog.csdn.net/lulynn/article/details/51543567
- xgboost 参考：
- scala参考：
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若需要RDD嵌套RDD，或要使用的算法只有python自己的sklearn里有，可以考虑对样本分组做分布式的（但模型训练是单机的，所以这种方法的前提是：分完组的数据量在单机训练占用的内存不多）
Say you find yourself in the peculiar situation where you need to train a whole bunch ofscikit-learnmodels over different groups from a large amount of data. And say you want to leverage Spark to distribute the process to do it all in a scalable fashion
最后
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