如何在pyspark中生成具有数量的关联规则？

Question

现在我试图解决在PySpark环境中使用关联规则生成与数量相关的项的问题。在

原始数据如下：

----------------------------
trans   item_code   item_qty
----------------------------
001         A          2
001         B          3
002         A          4
002         B          6
002         C          10
003         D          1
----------------------------  

我的目的是预测给定项目的可能附加项目。例如，给定{A:2，B:3}可能的新集合{A:2，B:3，C:5}。因此，我的第一步是生成具有数量系数的关联规则。我的方法是使用FP-growth算法生成频繁项集，然后找到一对一的关联规则。通过置信阈值对规则进行过滤后，可以计算出数量系数。我想要的关联规则数据帧如下（item1->；item2）：

^{pr2}$

我的spark版本是1.5.1。我的代码在这里：

#!/usr/bin/python
from pyspark import SparkContext,HiveContext
from pyspark.mllib.fpm import FPGrowth
import time

#read raw data from database    
def read_data():
    sql="""select t.orderno_nosplit, 
          t.prod_code, 
          t.item_code, 
          sum(t.item_qty) 
          as item_qty 
          from ioc_fdm.fdm_dwr_ioc_fcs_pk_spu_item_f_chain t
          group by t.prod_code, t.orderno_nosplit,t.item_code """
    data=sql_context.sql(sql)                  
    return data.cache()

#calculate quantity coefficient of two items 
def qty_coef(item1,item2):    
    sql =""" select t1.item, t1.qty from table t1
           where t1.trans in 
           (select t2.trans from spu_table t2 where t2.item ='%s'
           and
           (select t3.trans from spu_table t3 where t3.item = '%s' """ %  (item1,item2)
    df=sql_context.sql(sql)
    qty_item1=df.filter(df.item_code==item1).agg({"item_qty":"sum"}).first()[0]
    qty_item2=df.filter(df.item_code==item2).agg({"item_qty":"sum"}).first()[0]
    coef=float(qty_item2)/qty_item1
    return coef

def train(prod):

    spu=total_spu.filter(total_spu.prod_code == prod)
    print 'data length',spu.count(),time.strftime("%H:%M:%S")
    supp=0.1  
    conf=0.7
    sql_context.registerDataFrameAsTable(spu,'spu_table')
    sql_context.cacheTable('spu_table')
    print 'table register over', time.strftime("%H:%M:%S")

    trans_sets=spu.rdd.repartition(32).map(lambda x:(x[0],x[2])).groupByKey().mapvalues(list).values().cache()
    print 'trans group over',time.strftime("%H:%M:%S")

    model=FPGrowth.train(trans_sets,supp,10)
    print 'model train over',time.strftime("%H:%M:%S")

    model_f1=model.freqItemsets().filter(lambda x: len(x[0]==1))
    model_f2=model.freqItemsets().filter(lambda x: len(x[0]==2))

    #register model_f1 as dictionary
    model_f1_tuple=model_f1.map(lambda (U,V):(tuple(U)[0],V))
    model_f1Map=model_f1_tuple.collectAsMap()

    #convert model_f1Map to broadcast
    bc_model=sc.broadcast(model_f1Map)

    #generate association rules
    model_f2_conf=model_f2.map(lambda x:(x[0][0],x[0][1],float(x[1])/bc_model.value[x[0][0]],float(x[1]/bc_model.value[x[0][1]])))
    print 'conf calculation over',time.strftime("%H:%M:%S")

    model_f2_conf_flt=model_f2_conf.flatMap(lambda x: (x[0],x[1]))

    #filter the association rules by confidence threshold
    model_f2_conf_flt_ftr=model_f2_conf_flt.filter(lambda x: x[2]>=conf)

    #calculate the quantity coefficient for the filtered association rules
    #since we cannot use nested sql operations in rdd, I have to collect the rules to list first
    asso_list=model_f2_conf_flt_ftr.map(lambda x: list(x)).collect()
    print 'coef calculation over',time.strftime("%H:%M:%S")
    for row in asso_list:
        row.append(qty_coef(row[0],row[1]))

    #rewrite the list to dataframe
    asso_df=sql_context.createDataFrame(asso_list,['item1','item2','conf','coef'])
    sql_context.clearCache()
    path = "hdfs:/user/hive/wilber/%s"%(prod)
    asso_df.write.mode('overwrite').parquet(path)

if __name__ == '__main__':
    sc = SparkContext()
    sql_context=HiveContext(sc)
    prod_list=sc.textFile('hdfs:/user/hive/wilber/prod_list').collect()  
    total_spu=read_data()
    print 'spu read over',time.strftime("%H:%M:%S")
    for prod in list(prod_list):
        print 'prod',prod
        train(prod)

代码可以工作，但它工作得非常慢。我知道这可能主要是因为计算末端数量系数的步骤。但是我不知道是否有更有效地使用Spark（1.5.1）方法来达到这个结果。非常感谢。在

Answer 1

如果你粘贴这些东西呢？在

       
trans   item
       
001       A2   
001       B3   
002       A4   
002       B6    
002       C10  
003       D1    
       -

然后是交易

^{pr2}$

然后运行关联