In [7]:

    

import os
execfile("../script/utils.py")
eventsPath = os.environ["YAHOO_DATA"]
splitedRdd = sc.textFile(eventsPath + "/splitedData")
splitedRdd = splitedRdd.map(parseContextData2)
#splitedRdd = sc.parallelize(splitedRdd.take(2000))
splitedRdd.take(1)
#(uid,[[train],[test]]


    

    
Out[7]:





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    train(itemId=29030, context=context(ts=1421012413, city=2995, lat=42.897648, lon=-87.853859, moving=2, location=3, time_of_day=4)),
    train(itemId=94874, context=context(ts=1421012523, city=2995, lat=42.897648, lon=-87.853859, moving=2, location=3, time_of_day=4)),
    train(itemId=29030, context=context(ts=1421012541, city=2995, lat=42.897697, lon=-87.853836, moving=2, location=3, time_of_day=4)),
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    train(itemId=94874, context=context(ts=1421012579, city=2995, lat=42.897697, lon=-87.853836, moving=2, location=3, time_of_day=4)),
    train(itemId=29345, context=context(ts=1421012598, city=2995, lat=42.897697, lon=-87.853836, moving=2, location=3, time_of_day=4)),
    train(itemId=94874, context=context(ts=1421012611, city=2995, lat=42.897697, lon=-87.853836, moving=2, location=3, time_of_day=4)),
    train(itemId=94874, context=context(ts=1421017863, city=2995, lat=42.897629, lon=-87.853935, moving=1, location=3, time_of_day=4)),
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    train(itemId=30593, context=context(ts=1421435081, city=713, lat=43.039051, lon=-87.910385, moving=2, location=3, time_of_day=3)),
    train(itemId=29345, context=context(ts=1421435092, city=713, lat=43.038147, lon=-87.90963, moving=4, location=3, time_of_day=3)),
    train(itemId=94874, context=context(ts=1421435100, city=713, lat=43.038147, lon=-87.90963, moving=2, location=3, time_of_day=3)),
    train(itemId=94874, context=context(ts=1421435342, city=713, lat=43.038147, lon=-87.90963, moving=2, location=3, time_of_day=3)),
    train(itemId=94874, context=context(ts=1421436007, city=713, lat=43.038116, lon=-87.909592, moving=1, location=3, time_of_day=3)),
    train(itemId=77111, context=context(ts=1421437510, city=713, lat=43.037964, lon=-87.909691, moving=1, location=3, time_of_day=3)),
    train(itemId=14861, context=context(ts=1421440487, city=713, lat=43.037994, lon=-87.909485, moving=1, location=3, time_of_day=4)),
    train(itemId=29345, context=context(ts=1421440495, city=713, lat=43.038601, lon=-87.909416, moving=4, location=3, time_of_day=4)),
    train(itemId=94874, context=context(ts=1421440501, city=713, lat=43.038601, lon=-87.909416, moving=2, location=3, time_of_day=4)),
    train(itemId=29345, context=context(ts=1421442127, city=713, lat=43.038185, lon=-87.909576, moving=1, location=3, time_of_day=4)),
    train(itemId=14861, context=context(ts=1421443785, city=713, lat=43.038147, lon=-87.909508, moving=1, location=3, time_of_day=4)),
    train(itemId=29345, context=context(ts=1421448211, city=713, lat=43.038143, lon=-87.909615, moving=1, location=3, time_of_day=4)),
    train(itemId=14861, context=context(ts=1421448806, city=713, lat=43.038696, lon=-87.909904, moving=1, location=3, time_of_day=4)),
    train(itemId=29345, context=context(ts=1421448995, city=713, lat=43.03854, lon=-87.909698, moving=2, location=3, time_of_day=4)),
    train(itemId=79614, context=context(ts=1421449280, city=713, lat=43.03537, lon=-87.906876, moving=3, location=3, time_of_day=4)),
    train(itemId=79614, context=context(ts=1421449282, city=713, lat=43.03537, lon=-87.906876, moving=2, location=3, time_of_day=4)),
    train(itemId=37219, context=context(ts=1421449283, city=713, lat=43.03537, lon=-87.906876, moving=2, location=3, time_of_day=4)),
    train(itemId=77111, context=context(ts=1421449285, city=713, lat=43.03537, lon=-87.906876, moving=2, location=3, time_of_day=4)),
    train(itemId=29345, context=context(ts=1421449382, city=713, lat=43.03537, lon=-87.906876, moving=2, location=3, time_of_day=4)),
    train(itemId=29345, context=context(ts=1421455547, city=2995, lat=42.902782, lon=-87.863365, moving=1, location=3, time_of_day=4)),
    train(itemId=14861, context=context(ts=1421455575, city=2995, lat=42.902782, lon=-87.863365, moving=2, location=3, time_of_day=4)),
    train(itemId=29345, context=context(ts=1421455823, city=2995, lat=42.902245, lon=-87.864265, moving=2, location=3, time_of_day=4)),
    train(itemId=94874, context=context(ts=1421457076, city=2995, lat=42.897663, lon=-87.853752, moving=1, location=3, time_of_day=4)),
    train(itemId=94874, context=context(ts=1421457663, city=2995, lat=42.897758, lon=-87.853867, moving=1, location=3, time_of_day=4)),
    train(itemId=94874, context=context(ts=1421458373, city=2995, lat=42.897995, lon=-87.854088, moving=1, location=3, time_of_day=4)),
    train(itemId=81629, context=context(ts=1421458481, city=2995, lat=42.897995, lon=-87.854088, moving=2, location=3, time_of_day=4)),
    train(itemId=94874, context=context(ts=1421458648, city=2995, lat=42.897995, lon=-87.854088, moving=2, location=3, time_of_day=4)),
    train(itemId=42869, context=context(ts=1421459202, city=2995, lat=42.897724, lon=-87.853813, moving=1, location=3, time_of_day=4)),
    train(itemId=17592, context=context(ts=1421459264, city=2995, lat=42.897724, lon=-87.853813, moving=2, location=3, time_of_day=4)),
    train(itemId=17592, context=context(ts=1421459998, city=2995, lat=42.897682, lon=-87.853783, moving=1, location=3, time_of_day=4)),
    train(itemId=81629, context=context(ts=1421460002, city=2995, lat=42.897552, lon=-87.853775, moving=4, location=3, time_of_day=4)),
    train(itemId=17592, context=context(ts=1421460090, city=2995, lat=42.897751, lon=-87.853806, moving=2, location=3, time_of_day=4)),
    train(itemId=88438, context=context(ts=1421460106, city=2995, lat=42.897751, lon=-87.853806, moving=2, location=3, time_of_day=4)),
    train(itemId=17592, context=context(ts=1421460279, city=2995, lat=42.897751, lon=-87.853806, moving=2, location=3, time_of_day=4)),
    train(itemId=14861, context=context(ts=1421460468, city=2995, lat=42.897678, lon=-87.853851, moving=2, location=3, time_of_day=4)),
    train(itemId=30593, context=context(ts=1421460499, city=2995, lat=42.897678, lon=-87.853851, moving=2, location=3, time_of_day=4)),
    train(itemId=17592, context=context(ts=1421460626, city=2995, lat=42.897678, lon=-87.853851, moving=2, location=3, time_of_day=4)),
    train(itemId=17592, context=context(ts=1421463991, city=2995, lat=42.897694, lon=-87.853798, moving=1, location=3, time_of_day=4)),
    train(itemId=81629, context=context(ts=1421464208, city=2995, lat=42.897621, lon=-87.853806, moving=2, location=3, time_of_day=4)),
    train(itemId=17592, context=context(ts=1421464341, city=2995, lat=42.897621, lon=-87.853806, moving=2, location=3, time_of_day=4)),
    train(itemId=29345, context=context(ts=1421464349, city=2995, lat=42.897671, lon=-87.853783, moving=2, location=3, time_of_day=4)),
    train(itemId=94874, context=context(ts=1421464352, city=2995, lat=42.897671, lon=-87.853783, moving=2, location=3, time_of_day=4)),
    train(itemId=17592, context=context(ts=1421464571, city=2995, lat=42.897633, lon=-87.853752, moving=2, location=3, time_of_day=4)),
    train(itemId=94874, context=context(ts=1421464605, city=2995, lat=42.897633, lon=-87.853752, moving=2, location=3, time_of_day=4)),
    train(itemId=94874, context=context(ts=1421464896, city=2995, lat=42.897724, lon=-87.85379, moving=2, location=3, time_of_day=4)),
    train(itemId=17592, context=context(ts=1421465401, city=2995, lat=42.897629, lon=-87.853844, moving=1, location=3, time_of_day=4)),
    train(itemId=94874, context=context(ts=1421465427, city=2995, lat=42.897629, lon=-87.853844, moving=2, location=3, time_of_day=4)),
    train(itemId=94874, context=context(ts=1421465702, city=2995, lat=42.897633, lon=-87.853813, moving=2, location=3, time_of_day=4)),
    train(itemId=94874, context=context(ts=1421465728, city=2995, lat=42.897633, lon=-87.853813, moving=2, location=3, time_of_day=4)),
    train(itemId=17592, context=context(ts=1421466726, city=2995, lat=42.897591, lon=-87.853859, moving=1, location=3, time_of_day=4)),
    train(itemId=81629, context=context(ts=1421467499, city=2995, lat=42.89761, lon=-87.853798, moving=1, location=3, time_of_day=1)),
    train(itemId=94874, context=context(ts=1421467576, city=2995, lat=42.89761, lon=-87.853798, moving=2, location=3, time_of_day=1)),
    train(itemId=94874, context=context(ts=1421467642, city=2995, lat=42.89761, lon=-87.853798, moving=2, location=3, time_of_day=1)),
    train(itemId=94874, context=context(ts=1421467708, city=2995, lat=42.89761, lon=-87.853798, moving=2, location=3, time_of_day=1)),
    train(itemId=17592, context=context(ts=1421468041, city=2995, lat=42.897602, lon=-87.853798, moving=1, location=3, time_of_day=1)),
    train(itemId=94874, context=context(ts=1421470037, city=2995, lat=42.897663, lon=-87.853821, moving=1, location=3, time_of_day=1)),
    train(itemId=94874, context=context(ts=1421471173, city=2995, lat=42.897739, lon=-87.853867, moving=1, location=1, time_of_day=1)),
    train(itemId=17592, context=context(ts=1421472955, city=2995, lat=42.897598, lon=-87.853821, moving=1, location=1, time_of_day=1)),
    train(itemId=94874, context=context(ts=1421473999, city=2995, lat=42.897591, lon=-87.85379, moving=1, location=1, time_of_day=1)),
    train(itemId=94874, context=context(ts=1421474396, city=2995, lat=42.897621, lon=-87.85379, moving=1, location=1, time_of_day=1)),
    train(itemId=42869, context=context(ts=1421503297, city=2995, lat=42.8979, lon=-87.853775, moving=1, location=2, time_of_day=2)),
    train(itemId=44053, context=context(ts=1421503310, city=2995, lat=42.8979, lon=-87.853775, moving=2, location=2, time_of_day=2)),
    train(itemId=79614, context=context(ts=1421503558, city=2995, lat=42.898178, lon=-87.85495, moving=2, location=2, time_of_day=2)),
    train(itemId=79614, context=context(ts=1421503559, city=2995, lat=42.902794, lon=-87.863396, moving=4, location=2, time_of_day=2)),
    train(itemId=37219, context=context(ts=1421503561, city=2995, lat=42.902794, lon=-87.863396, moving=2, location=2, time_of_day=2)),
    train(itemId=77111, context=context(ts=1421503562, city=2995, lat=42.902794, lon=-87.863396, moving=2, location=2, time_of_day=2)),
    train(itemId=79614, context=context(ts=1421503895, city=2995, lat=42.902794, lon=-87.863396, moving=1, location=2, time_of_day=2)),
    train(itemId=29345, context=context(ts=1421504770, city=2995, lat=42.897606, lon=-87.85379, moving=1, location=2, time_of_day=2)),
    train(itemId=14861, context=context(ts=1421506519, city=2995, lat=42.897556, lon=-87.853828, moving=1, location=2, time_of_day=2)),
    train(itemId=94874, context=context(ts=1421506530, city=2995, lat=42.897556, lon=-87.853828, moving=2, location=2, time_of_day=2)),
    train(itemId=81629, context=context(ts=1421506969, city=2995, lat=42.897629, lon=-87.853752, moving=1, location=2, time_of_day=2)),
    train(itemId=79463, context=context(ts=1421507337, city=2995, lat=42.897644, lon=-87.85379, moving=1, location=2, time_of_day=2)),
    train(itemId=81629, context=context(ts=1421507347, city=2995, lat=42.897743, lon=-87.853806, moving=3, location=2, time_of_day=2)),
    train(itemId=30593, context=context(ts=1421507551, city=2995, lat=42.897743, lon=-87.853806, moving=2, location=2, time_of_day=2)),
    train(itemId=94874, context=context(ts=1421509780, city=2995, lat=42.897583, lon=-87.85379, moving=1, location=2, time_of_day=2)),
    train(itemId=14861, context=context(ts=1421518650, city=9149, lat=42.897865, lon=-87.915138, moving=1, location=3, time_of_day=3)),
    train(itemId=79463, context=context(ts=1421518658, city=9149, lat=42.897865, lon=-87.915138, moving=2, location=3, time_of_day=3)),
    train(itemId=85325, context=context(ts=1421518769, city=9149, lat=42.897865, lon=-87.915138, moving=2, location=3, time_of_day=3)),
    train(itemId=14861, context=context(ts=1421520437, city=2995, lat=42.897877, lon=-87.853935, moving=1, location=3, time_of_day=3)),
    train(itemId=79614, context=context(ts=1421520442, city=2995, lat=42.897526, lon=-87.853737, moving=4, location=3, time_of_day=3)),
    train(itemId=79614, context=context(ts=1421520446, city=2995, lat=42.897526, lon=-87.853737, moving=2, location=3, time_of_day=3)),
    train(itemId=37219, context=context(ts=1421520450, city=2995, lat=42.897526, lon=-87.853737, moving=2, location=3, time_of_day=3)),
    train(itemId=77111, context=context(ts=1421520453, city=2995, lat=42.897526, lon=-87.853737, moving=2, location=3, time_of_day=3)),
    train(itemId=14861, context=context(ts=1421522196, city=2995, lat=42.897579, lon=-87.853836, moving=1, location=3, time_of_day=3)),
    train(itemId=94874, context=context(ts=1421522203, city=2995, lat=42.897579, lon=-87.853836, moving=2, location=3, time_of_day=3)),
    train(itemId=81109, context=context(ts=1421524758, city=2995, lat=42.898571, lon=-87.85199, moving=1, location=3, time_of_day=3)),
    train(itemId=81109, context=context(ts=1421524789, city=2995, lat=42.898571, lon=-87.85199, moving=2, location=3, time_of_day=3)),
    train(itemId=14861, context=context(ts=1421527375, city=9110, lat=42.523922, lon=-87.954636, moving=1, location=3, time_of_day=4)),
    train(itemId=30593, context=context(ts=1421529372, city=4853, lat=42.518532, lon=-87.948402, moving=1, location=3, time_of_day=4)),
    train(itemId=42869, context=context(ts=1421531376, city=4853, lat=42.517887, lon=-87.947517, moving=1, location=3, time_of_day=4)),
    train(itemId=94874, context=context(ts=1421531400, city=4853, lat=42.517887, lon=-87.947517, moving=2, location=3, time_of_day=4)),
    train(itemId=79463, context=context(ts=1421532231, city=4853, lat=42.516857, lon=-87.949348, moving=1, location=3, time_of_day=4)),
    train(itemId=94874, context=context(ts=1421532906, city=4853, lat=42.516888, lon=-87.94928, moving=1, location=3, time_of_day=4)),
    train(itemId=29345, context=context(ts=1421534352, city=4853, lat=42.516884, lon=-87.949341, moving=1, location=3, time_of_day=4)),
    train(itemId=29345, context=context(ts=1421536262, city=4853, lat=42.518337, lon=-87.948875, moving=1, location=3, time_of_day=4)),
    train(itemId=94874, context=context(ts=1421536290, city=4853, lat=42.518337, lon=-87.948875, moving=2, location=3, time_of_day=4)),
    train(itemId=94874, context=context(ts=1421541857, city=713, lat=42.952927, lon=-87.9496, moving=1, location=3, time_of_day=4)),
    train(itemId=94874, context=context(ts=1421542431, city=713, lat=42.953178, lon=-87.948677, moving=1, location=3, time_of_day=4)),
    train(itemId=77111, context=context(ts=1421545759, city=972, lat=42.926182, lon=-87.949654, moving=1, location=3, time_of_day=4)),
    train(itemId=29345, context=context(ts=1421548197, city=2995, lat=42.898632, lon=-87.85186, moving=1, location=3, time_of_day=4)),
    train(itemId=29345, context=context(ts=1421548320, city=2995, lat=42.898533, lon=-87.85173, moving=2, location=3, time_of_day=4)),
    train(itemId=68168, context=context(ts=1421551200, city=2995, lat=42.897579, lon=-87.853821, moving=1, location=3, time_of_day=4)),
    train(itemId=29345, context=context(ts=1421551465, city=2995, lat=42.897579, lon=-87.853821, moving=2, location=3, time_of_day=4)),
    train(itemId=42869, context=context(ts=1421552038, city=2995, lat=42.897572, lon=-87.85376, moving=1, location=3, time_of_day=4)),
    train(itemId=68168, context=context(ts=1421552043, city=2995, lat=42.897549, lon=-87.853737, moving=2, location=3, time_of_day=4)),
    train(itemId=81629, context=context(ts=1421552171, city=2995, lat=42.897549, lon=-87.853737, moving=2, location=3, time_of_day=4)),
    train(itemId=94874, context=context(ts=1421552222, city=2995, lat=42.897549, lon=-87.853737, moving=2, location=3, time_of_day=4)),
    train(itemId=88438, context=context(ts=1421553044, city=2995, lat=42.897713, lon=-87.853752, moving=1, location=3, time_of_day=4)),
    train(itemId=94874, context=context(ts=1421553237, city=2995, lat=42.897728, lon=-87.853813, moving=2, location=3, time_of_day=4)),
    train(itemId=94874, context=context(ts=1421556327, city=2995, lat=42.897614, lon=-87.85379, moving=1, location=3, time_of_day=1)),
    train(itemId=94874, context=context(ts=1421557811, city=2995, lat=42.897736, lon=-87.853806, moving=1, location=1, time_of_day=1)),
    train(itemId=94874, context=context(ts=1421562244, city=2995, lat=42.897774, lon=-87.853882, moving=1, location=1, time_of_day=1))]])]

In [ ]:

    

def remove_duplicates(values):
    output = []
    seen = set()
    for value in values:
        # If value has not been encountered yet,
        # ... add it to both list and set.
        if value not in seen:
            output.append(value)
            seen.add(value)
    return output

def evaluation_rr(testList, recList):
    scores = 0
    for t in testList:
        for i in range(len(recList)):
            if t == recList[i]:
                scores = scores + 1.0 / (i+1)
                break
    scores = scores / len(testList)
    return scores

def evaluation_ap(testList, recList):
    scores = 0
    num = 0 
    rr = []
    rr[:len(recList)] = [0 for i in range(len(recList))]
    for t in testList:
        for i in range(len(recList)):
            if t == recList[i]:
                rr[i] = 1
    
    for t in testList:    
        for i in range(len(recList)):
            if t == recList[i]:
                for j in range(i,len(recList)):    
                    if rr[j] == 1:
                        scores = scores + 1.0 / (j+1)
                        num = num + 1
                break
    if num == 0:
        scores = 0
    else:
        scores = scores / num
    return scores

def evaluation_recall(testList, recList):
    scores = 0
    num = 0
    recall = []
    recall[:len(recList)] = [0 for i in range(len(recList))]
    rr=[]
    rr[:len(recList)] = [0 for i in range(len(recList))]
    for t in testList:
        for i in range(len(recList)):
            if t == recList[i]:
                rr[i] = 1
    for t in testList:    
        for i in range(len(recList)):
            if t == recList[i]:
                for j in range(i,len(recList)):    
                    recall[j] = recall[j] + 1.0
                    num = num + 1  
                break    
    recall = [0 if num == 0 else x*sum(rr)/num for x in recall]
    return recall
def print_result(finalScore, algorithm_name):
    f = open('result/resultOfEvaluation1.txt','a')
    f.write(algorithm_name + ' by mrr:'+str(finalScore.map(lambda x:x[0]).mean()))
    f.write('\n')
    f.write(algorithm_name + ' by map:'+str(finalScore.map(lambda x:x[1]).mean()))
    f.write('\n')
    f.write(algorithm_name + ' by recall:')
    for i in range(numRecList):
        if i == 0:
            f.write(' '+str(finalScore.map(lambda x:x[2][i]).mean()))
        elif i == numRecList-1:
            f.write(','+str(finalScore.map(lambda x:x[2][i]).mean()) + '\n')
        else:
            f.write(','+str(finalScore.map(lambda x:x[2][i]).mean()))
    f.close()


    

In [ ]:

    

numRecList = 5
def lruFunction(line):
    listGroup = line[1]
    l = len(listGroup)  
    trainList = listGroup[0]      #0.8 train set
    testList = listGroup[1]       #0.2 test set
    numTrain = len(trainList)
    numTest = l - numTrain
    trainList = sorted(trainList,key=lambda x:int(x[1][0]), reverse=True);  #sort by timestamp with descending
    testList = [t[0] for t in testList]                      #take only id for test set
    RecommenderDuplicate =  [t[0] for t in trainList]        #take only id for train set
    Recommender = remove_duplicates(RecommenderDuplicate)    #remove duplicate
    
    if len(Recommender) > numRecList-1:
        finalRecommender = Recommender[:numRecList]
    else:    
        finalRecommender = [-1,-1,-1,-1,-1]
        numRec = len(Recommender)
        finalRecommender[:numRec] = Recommender
    
    scores_rr = evaluation_rr(testList, finalRecommender)
    scores_ap = evaluation_ap(testList, finalRecommender)
    scores_recall = evaluation_recall(testList, finalRecommender)
    return scores_rr, scores_ap, scores_recall#[0],scores_recall[1],scores_recall[2],scores_recall[3],scores_recall[4]

finalScore = splitedRdd.map(lruFunction)
#finalScore.map(lambda x:x[2][0]).mean()
#finalScore.take(10)#,finalScore.map(lambda x:x[2]).mean()
print_result(finalScore, 'lru')


    

In [ ]:

    

def mruFunction(line):
    listGroup = line[1]
    l = len(listGroup)  
    trainList = listGroup[0]      #0.8 train set
    testList = listGroup[1]       #0.2 test set
    numTrain = len(trainList)
    numTest = l - numTrain    
    #trainList = sorted(trainList,key=lambda x:int(x[1]), reverse=True);  #sort by timestamp with descending
    testList = [t[0] for t in testList]                      #take only id for test set
    RecommenderDuplicate =  [t[0] for t in trainList]        #take only id for train set
    Recommender = remove_duplicates(RecommenderDuplicate)    #remove duplicate
    
    if len(Recommender) > 4:
        finalRecommender = Recommender[:5]
    else:
        finalRecommender = [-1,-1,-1,-1,-1]
        numRec = len(Recommender)
        finalRecommender[:numRec] = Recommender
    scores_rr = evaluation_rr(testList, finalRecommender)
    scores_ap = evaluation_ap(testList, finalRecommender)
    scores_recall = evaluation_recall(testList, finalRecommender)
    return scores_rr, scores_ap, scores_recall
finalScore = splitedRdd.map(mruFunction)
print_result(finalScore,'mru')


    

In [ ]:

    

from collections import Counter
def mruFunction(line):
    listGroup = line[1]
    l = len(listGroup)  
    trainList = listGroup[0]      #0.8 train set
    testList = listGroup[1]       #0.2 test set
    numTrain = len(trainList)
    numTest = l - numTrain   
    
    testList = [t[0] for t in testList]                      #take only id for test set
    RecommenderDuplicate =  [t[0] for t in trainList]        #take only id for train set
    #Recommender = remove_duplicates(RecommenderDuplicate)    #remove duplicate
    Recommender = Counter(RecommenderDuplicate).most_common()
    Recommender =  [t[0] for t in Recommender] 

    if len(Recommender) > 4:
        finalRecommender = Recommender[:5]
    else:
        finalRecommender = [-1,-1,-1,-1,-1]
        numRec = len(Recommender)
        finalRecommender[:numRec] = Recommender
    scores_rr = evaluation_rr(testList, finalRecommender)
    scores_ap = evaluation_ap(testList, finalRecommender)
    scores_recall = evaluation_recall(testList, finalRecommender)
    return scores_rr, scores_ap, scores_recall
finalScore = splitedRdd.map(mruFunction)
print_result(finalScore,'mfu')


    

In [12]:

    

def evaluation_ap(testList, recList):
    scores = 0
    num = 0 
    rr = []
    rr[:len(recList)] = [0 for i in range(len(recList))]
    for t in testList:
        for i in range(len(recList)):
            if t == recList[i]:
                rr[i] = 1
    
    for t in testList:    
        for i in range(len(recList)):
            if t == recList[i]:
                for j in range(i,len(recList)):    
                    if rr[j] == 1:
                        scores = scores + 1.0 / (j+1)
                        num = num + 1
                break
    if num == 0:
        scores = 0
    else:
        scores = scores / num
    return scores
evaluation_ap('ASFDG','ABCDE')


    

    
Out[12]:





0.5

In [ ]: