推荐系统——SVD/SVD++

1，SVD

源代码：svd.py

#Ver1.0#Zero @2012.5.2#import mathimport randomimport cPickle as pickle#calculate the overall averagedef Average(fileName):    fi = open(fileName, 'r')    result = 0.0    cnt = 0    for line in fi:        cnt += 1        arr = line.split()        result += int(arr[2].strip())    return result / cntdef InerProduct(v1, v2):    result = 0    for i in range(len(v1)):        result += v1[i] * v2[i]    return resultdef PredictScore(av, bu, bi, pu, qi):    pScore = av + bu + bi + InerProduct(pu, qi)    if pScore < 1:        pScore = 1    elif pScore > 5:        pScore = 5    return pScoredef SVD(configureFile, testDataFile, trainDataFile, modelSaveFile):    #get the configure    fi = open(configureFile, 'r')    line = fi.readline()    arr = line.split()    averageScore = float(arr[0].strip())    userNum = int(arr[1].strip())    itemNum = int(arr[2].strip())    factorNum = int(arr[3].strip())    learnRate = float(arr[4].strip())    regularization = float(arr[5].strip())    fi.close()    bi = [0.0 for i in range(itemNum)]    bu = [0.0 for i in range(userNum)]    temp = math.sqrt(factorNum)    qi = [[(0.1 * random.random() / temp) for j in range(factorNum)] for i in range(itemNum)]       pu = [[(0.1 * random.random() / temp)  for j in range(factorNum)] for i in range(userNum)]    print("initialization end\nstart training\n")    #train model    preRmse = 1000000.0    for step in range(100):        fi = open(trainDataFile, 'r')           for line in fi:            arr = line.split()            uid = int(arr[0].strip()) - 1            iid = int(arr[1].strip()) - 1            score = int(arr[2].strip())                     prediction = PredictScore(averageScore, bu[uid], bi[iid], pu[uid], qi[iid])            eui = score - prediction            #update parameters            bu[uid] += learnRate * (eui - regularization * bu[uid])            bi[iid] += learnRate * (eui - regularization * bi[iid])             for k in range(factorNum):                temp = pu[uid][k]   #attention here, must save the value of pu before updating                pu[uid][k] += learnRate * (eui * qi[iid][k] - regularization * pu[uid][k])                qi[iid][k] += learnRate * (eui * temp - regularization * qi[iid][k])        fi.close()        #learnRate *= 0.9        curRmse = Validate(testDataFile, averageScore, bu, bi, pu, qi)        print("test_RMSE in step %d: %f" %(step, curRmse))        if curRmse >= preRmse:            break        else:            preRmse = curRmse    #write the model to files    fo = file(modelSaveFile, 'wb')    pickle.dump(bu, fo, True)    pickle.dump(bi, fo, True)    pickle.dump(qi, fo, True)    pickle.dump(pu, fo, True)    fo.close()    print("model generation over")#validate the modeldef Validate(testDataFile, av, bu, bi, pu, qi):    cnt = 0    rmse = 0.0    fi = open(testDataFile, 'r')            for line in fi:        cnt += 1        arr = line.split()        uid = int(arr[0].strip()) - 1        iid = int(arr[1].strip()) - 1        pScore = PredictScore(av, bu[uid], bi[iid], pu[uid], qi[iid])        tScore = int(arr[2].strip())        rmse += (tScore - pScore) * (tScore - pScore)    fi.close()    return math.sqrt(rmse / cnt)#use the model to make predictdef Predict(configureFile, modelSaveFile, testDataFile, resultSaveFile):    #get parameter    fi = open(configureFile, 'r')    line = fi.readline()    arr = line.split()    averageScore = float(arr[0].strip())    fi.close()    #get model    fi = file(modelSaveFile, 'rb')    bu = pickle.load(fi)    bi = pickle.load(fi)    qi = pickle.load(fi)    pu = pickle.load(fi)    fi.close()    #predict    fi = open(testDataFile, 'r')    fo = open(resultSaveFile, 'w')    for line in fi:        arr = line.split()        uid = int(arr[0].strip()) - 1        iid = int(arr[1].strip()) - 1        pScore = PredictScore(averageScore, bu[uid], bi[iid], pu[uid], qi[iid])        fo.write("%f\n" %pScore)    fi.close()    fo.close()    print("predict over")if __name__ == '__main__':    configureFile = 'svd.conf'    trainDataFile = 'u1.base'    testDataFile = 'u1.test'    modelSaveFile = 'svd_model.pkl'    resultSaveFile = 'prediction'    #print("%f" %Average("u1.base"))    SVD(configureFile, testDataFile, trainDataFile, modelSaveFile)    #Predict(configureFile, modelSaveFile, testDataFile, resultSaveFile)

配置文件svd.conf：

3.528350 6040 3900 10 0.01 0.05averageScore userNum itemNum factorNum learnRate regularization 

实验结果：
数据集一：movielen（u1.base/u1.test）

迭代了39次，最终的RMSE=0.919047

数据集二：movielen（u2.base/u2.test）

迭代了38次，最终的RMSE=0.916170

注：跑程序之前，记得在SVD.conf中修改