Python推荐算法

# coding: utf-8import timeimport randomimport osimport reimport xlwtimport requestsimport numpy as npimport xlsxwriterimport pandas as pdfrom matplotlib import pyplot as pltfrom sklearn.cluster import KMeansimport sysimport mathreload(sys)sys.setdefaultencoding('utf8')os.chdir(u'**********')guangdabase = pd.read_csv('guangdabase.csv' ,header=None)guangdabase.head()def xybasenames(data1):    data1. colnames =['id', 'UpdateFlag', 'branch', 'ajbh', 'kehu', 'ajlx', 'shfzh', 'shfzh18', 'shebaoID', 'xm', 'pinyin', 'sex',                     'zhiwu', 'zjqkje', 'zjshje', 'zjzxqke', 'zjzxqkerq', 'zjyhlx', 'jdsj', 'dqsj', 'zu', 'ywy', 'states', 'period',                    'yjbl', 'fenpeisj', 'urgent', 'lasttime', 'closetime', 'czy', 'addtime', 'pici', 'inpici', 'shengfen', 'chengshi',                    'remark1', 'remark2', 'remark3', 'lastJzSj', 'kongguan', 'PromisedDate', 'PromisedJe', 'nextStep', 'hint',                     'dingyueTime', 'fabuTime', 'gaNum', 'Ajsx', 'ajInfo', 'kehuAjBh', 'ajStop', 'ajLock', 'yxAj', 'isShare', 'zxxddm',                    'picipizhu']    return data1guangdabase =xybasenames(guangdabase[:])guangdabase.columns = guangdabase.colnamesguangdabaseguangda1 = guangdabase[["ajbh", "shfzh18", "ywy", "zjqkje", "zjshje"]]guangda1.shapeguangda1["hkzhb"] = guangda1["zjshje"] / guangda1["zjqkje"]guangda1.describe()guangda1["bornyear"] = guangda1["shfzh18"].str.slice(6, 10)guangda1["sex"] = guangda1["shfzh18"].str.get(16)guangda1["address"] = guangda1["shfzh18"].str.slice(0, 6)guangda1["shfzhnum"] = guangda1["shfzh18"].str.len()guangda2 = guangda1[["ajbh", "shfzh18", "bornyear", "shfzhnum", "sex", "address", "zjqkje", "zjshje", "ywy"]]guangda2.shapeguangda2 = guangda2[guangda2["shfzhnum"] == 18]guangda2["yearlen"] = guangda2["bornyear"].str.len()guangda2 = guangda2[guangda2["yearlen"] == 4]list(set(guangda2["bornyear"]))guangda2 = guangda2[guangda2["bornyear"] != '\xe7\xac\xac2']guangda2["bornyear"] = guangda2["bornyear"].astype(int)guangda2["age"] = 2017 - guangda2["bornyear"]guangda2["sex"] = guangda2["sex"].astype(int)guangda2guangda2["sex"][guangda2["sex"] % 2 == 0] = 0guangda2["sex"][guangda2["sex"] != 0] = 1guangda2["address"] = guangda2["address"].astype(int)guangda2["hkzhb"] = guangda2["zjshje"] / guangda2["zjqkje"]guangda2["ywy"] = guangda2["ywy"].str.upper()guangda3 = guangda2.dropna()guangda3.shapeguangda = guangda3[["ajbh", "shfzh18", "age", "sex", "address", "zjqkje", "zjshje", "hkzhb", "ywy"]]guangda = guangda[guangda.zjqkje > 0]guangda.head()def maxminscale(normal):    max1 = np.max(normal)    min1 = np.min(normal)    normal = (normal - min1) / (max1 - min1)    return normaldef datascale(scaledata):    mean1 = np.mean(scaledata)    std1 = np.std(scaledata)    scaledata = (scaledata - mean1) / std1    return scaledataguangda4 = guangdaguangda4["age"] = maxminscale(guangda4["age"])guangda4["address"] = maxminscale(guangda4["address"])guangda4["zjqkje"] = maxminscale(guangda4["zjqkje"])guangda4["zjshje"] = maxminscale(guangda4["zjshje"])guangda4["hkzhb"] = maxminscale(guangda4["hkzhb"])guangda4.head()testclust = guangda4[["age", "sex", "address", "zjqkje", "zjshje", "hkzhb"]]testclust.describe()a = np.array(range(10))for j in range(2, 12):    kmeanss = KMeans(n_clusters=j, init='k-means++', n_init=10, max_iter=300, algorithm='auto').fit(testclust)    a[j - 2] = kmeanss.inertia_ax = np.array(range(1, 11))xy = ayplt.rc('font', family='SimHei', size=13)plt.xlabel("聚类个数")plt.ylabel("均方误差")plt.plot(x, y)plt.show()kmeans = KMeans(n_clusters=5, init='k-means++', n_init=10, max_iter=300, algorithm='auto').fit(testclust)testclust["label"] = kmeans.labels_kcenters = kmeans.cluster_centers_kcenterskmeans.inertia_def centerDistance(labeldata, centerdata):    ldata0 = labeldata[labeldata.label == 0]    ldata0 = ldata0.iloc[:, 0:6]    ldata00 = np.array(ldata0)    ldata1 = labeldata[labeldata.label == 1]    ldata1 = ldata1.iloc[:, 0:6]    ldata11 = np.array(ldata1)    ldata2 = labeldata[labeldata.label == 2]    ldata2 = ldata2.iloc[:, 0:6]    ldata22 = np.array(ldata2)    ldata3 = labeldata[labeldata.label == 3]    ldata3 = ldata3.iloc[:, 0:6]    ldata33 = np.array(ldata3)    ldata4 = labeldata[labeldata.label == 4]    ldata4 = ldata4.iloc[:, 0:6]    ldata44 = np.array(ldata4)    test0 = ldata00 - kcenters[0]    test0 = test0 * test0    test0 = test0.sum(axis=1)    test0 = np.sqrt(test0)    test1 = ldata11 - kcenters[1]    test1 = test1 * test1    test1 = test1.sum(axis=1)    test1 = np.sqrt(test1)    test2 = ldata22 - kcenters[2]    test2 = test2 * test2    test2 = test2.sum(axis=1)    test2 = np.sqrt(test2)    test3 = ldata33 - kcenters[3]    test3 = test3 * test3    test3 = test3.sum(axis=1)    test3 = np.sqrt(test3)    test4 = ldata44 - kcenters[4]    test4 = test4 * test4    test4 = test4.sum(axis=1)    test4 = np.sqrt(test4)    test = np.concatenate((test0, test1, test2, test3, test4))    return testttttt = centerDistance(testclust, kcenters)testclust["ddd"] = ttttttestclust.head()guangda["label"] = testclust["label"]guangda["ddd"] = testclust["ddd"]testclust["ywy"] = guangda["ywy"]guangda["nnn"] = 1guangda.head()data1 = guangda.groupby("label").sum().reset_index()data1 = data1[["label", "nnn"]]data1data2 = guangda.groupby(["label", "ywy"]).sum().reset_index()data2 = data2.sort(["label", "zjshje"], ascending=False)data2 = data2[["label", "ywy", "zjshje", "nnn"]]data2fpresult = pd.merge(data2, data1, on="label", how="left")fpresult["fpb"] = fpresult["nnn_x"] / fpresult["nnn_y"]fpresult = fpresult[["label", "ywy", "zjshje", "nnn_x", "fpb"]]fpresultguangdaresult = pd.merge(guangda, data3, on=["label", "ywy"], how="left")rankdata = guangdaresult.sort(["label", "ddd"])rankdata[rankdata.label == 4]label4 = fpresult[fpresult.label == 4].shape[0]fpdata4 = fpresult[fpresult.label == 4]rankdata4 = rankdata[rankdata.label == 4]sumnum = 0for i in range(label4):    rank4 = int(fpdata4["nnn_x"][i])    sumnum = sumnum + rank4    sumnum1 = sumnum - rank4    if sumnum < rank4 + 1:        rankdata4["ywy"][0:sumnum] = fpdata4["ywy"][i]    else:        rankdata4["ywy"][sumnum1:sumnum] = fpdata4["ywy"][i]label3 = fpresult[fpresult.label == 3].shape[0]fpdata3 = fpresult[fpresult.label == 3]rankdata3 = rankdata[rankdata.label == 3]sumnum = 0for i in range(label3):    rank3 = int(fpdata3["nnn_x"][i])    sumnum = sumnum + rank3    sumnum1 = sumnum - rank3    if sumnum < rank3 + 1:        rankdata3["ywy"][0:sumnum] = fpdata3["ywy"][i]    else:        rankdata3["ywy"][sumnum1:sumnum] = fpdata3["ywy"][i]label2 = fpresult[fpresult.label == 2].shape[0]fpdata2 = fpresult[fpresult.label == 2]rankdata2 = rankdata[rankdata.label == 2]sumnum = 0for i in range(label2):    rank2 = int(fpdata2["nnn_x"][i])    sumnum = sumnum + rank2    sumnum1 = sumnum - rank2    if sumnum < rank2 + 1:        rankdata2["ywy"][0:sumnum] = fpdata2["ywy"][i]    else:        rankdata2["ywy"][sumnum1:sumnum] = fpdata2["ywy"][i]label1 = fpresult[fpresult.label == 1].shape[0]fpdata1 = fpresult[fpresult.label == 1]rankdata1 = rankdata[rankdata.label == 1]sumnum = 0for i in range(label1):    rank1 = int(fpdata1["nnn_x"][i])    sumnum = sumnum + rank1    sumnum1 = sumnum - rank1    if sumnum < rank1 + 1:        rankdata1["ywy"][0:sumnum] = fpdata1["ywy"][i]    else:        rankdata1["ywy"][sumnum1:sumnum] = fpdata1["ywy"][i]label0 = fpresult[fpresult.label == 0].shape[0]fpdata0 = fpresult[fpresult.label == 0]rankdata0 = rankdata[rankdata.label == 0]sumnum = 0sumnum1 = 0for i in range(label0):    rank0 = int(fpdata0["nnn_x"][i])    sumnum = sumnum + rank0    sumnum1 = sumnum - rank0    if sumnum < rank0 + 1:        rankdata0["ywy"][0:sumnum] = fpdata0["ywy"][i]    else:        rankdata0["ywy"][sumnum1:sumnum] = fpdata0["ywy"][i]resultrankdata = pd.concat(rankdata0, rankdata1, rankdata2, rankdata3, rankdata4)resultrankdata