推荐算法 pd

from numpy import *
from numpy import linalg as la


def loadExData1():
    return [[2,0,0,4,4,0,0,0,0,0,0],
            [0,0,0,0,0,0,0,0,0,0,5],
            [0,0,0,0,0,0,0,1,0,4,0],
            [3,3,4,0,3,0,0,2,2,0,0],
            [5,5,5,0,0,0,0,0,0,0,0],
            [0,0,0,0,0,0,5,0,0,5,0],
            [4,0,4,0,0,0,0,0,0,0,5],
            [0,0,0,0,0,4,0,0,0,0,4],
            [0,0,0,0,0,0,5,0,0,5,0],
            [0,0,0,3,0,0,0,0,4,5,0],
            [1,1,2,1,1,2,1,0,4,5,0]]


#相似度计算
def ecludSim(inA,inB):
    return 1.0/(1.0 + la.norm(inA - inB))

def pearsSim(inA,inB):
    if len(inA) < 3 : return 1.0
    return 0.5+0.5*corrcoef(inA, inB, rowvar=0)[0][1]

def cosSim(inA, inB):
    """
    :param inA: [a]
    :param inB: [b]
    :return: 1 or 0
    """
    num = float(inA.T*inB)
    demon = la.norm(inA)*la.norm(inB)
    return 0.5+0.5*(num/demon)  # 0.5+0.5*(a*b/abs(a*b))

#  基于物品相似度的推荐引擎
def standEst(dataMat, user, simMeas, item):
    """
    :param dataMat: ex loadExdata1()
    :param user: ex user=1
    :param simMeas: cosSim()
    :param item: ex  # user=1对应的数据[0,0,0,0,0,0,0,0,0,0,5]的列=0的下标为 0 1 2 3 4 5 6 7 8 9
    :return: 相似度
    """
    n = shape(dataMat)[1]
    simTotal = 0.0; ratSimTotal = 0.0
    for j in range(n):  # shape(dataMat):[x, n] x:数据集长度  n:维度
        userRating = dataMat[user, j]  # 取出user这条数据 a = np.array[[1,2,3],[0,2,1]]
        if userRating == 0:                  #   for i in [0,1,2]: print(a[1, i])  # 0 2 1
            continue
        # logical_and----  numpy逻辑与的判断
        # logical_or----  numpy逻辑或的判断
        # logical_not----  numpy逻辑非的判断
        overLap = nonzero(logical_and(dataMat[:, item].A > 0, 
                                     dataMat[:, j].A > 0))[0]
        if len(overLap) == 0:
            similarity = 0
        else:
            similarity = simMeas(dataMat[overLap,item],
                                 dataMat[overLap,j])
        print('the %d and %d similarity is: %f'%(item, j, similarity))
        simTotal += similarity
        ratSimTotal += similarity * userRating
    if simTotal == 0:
        return 0
    return ratSimTotal/simTotal

# 将一个11纬的矩阵转换成一个5维的矩阵,基于SVD的评3分估计
def svdEst(dataMat, user, simMeas, item):
    n = shape(dataMat)[1]      #获取物品的数量
    simTotal = 0.0; ratSimTotal = 0.0
    U,Sigma, VT = la.svd(dataMat)
    Sig4 = mat(eye(4)*Sigma[:4])
    xformedItems = dataMat.T*U[:,:4]*Sig4.I
    for j in range(n):
        userRating = dataMat[user,j]
        if userRating == 0 or j==item: continue
        similarity = simMeas(xformedItems[item,:].T,
                            xformedItems[j,:].T)
        print('the %d and %d similarity is:%f'%(item, j, similarity))
        simTotal += similarity
        ratSimTotal += similarity * userRating
    if simTotal == 0:return 0
    else: return ratSimTotal/simTotal


def recommend(dataMat, user, N=3, simMeas=cosSim, estMethod=standEst):
    """
    :param dataMat: 测试数据集， ex loadExData1()
    :param user: 用户ID所对应的行号index， ex user=1
    :param N: default N=3 N个推荐结果，默认设为3
    :param simMeas: 默认相关性函数cosSim
    :param estMethod: 默认基于物品相似度的推荐函数standEst
    :return: N个推荐结果
    """
    unratedItems = nonzero(dataMat[user,:].A==0)[1]   #返回user=1行[0,0,0,0,0,0,0,0,0,0,5],中元素为0的列下标
    if len(unratedItems) == 0:
        return 'you rated everything'
    itemScores = []
    for item in unratedItems:  # [0 1 2 3 4 5 6 7 8 9]
        estimatedScore = estMethod(dataMat, user, simMeas, item)
        itemScores.append((item, estimatedScore))
    return sorted(itemScores, key=lambda jj: jj[1], reverse=True)[:N]


if __name__ == '__main__':
    data = mat(loadExData1())
    re = recommend(data, 1)
    print(re)

from django.db import connection

  select_sql = 'select * from model'
    datas = pd.read_sql(select_sql, connection)  # <pandas.core.frame.DataFrame'>
    temp = datas.iloc[:, 2:]  # 取出所有数据的 除了前两个字段
    tp = temp.sum(axis=0)  # 所有字段纵向相加
    top_sorts = tp.sort_values(ascending=False)  # 降序排序
    top3 = top_sorts.index[:4]
    top_recommends = top3.values.tolist()