├── README.md ├── compare_models ├── .DS_Store ├── logistic_model_dynamic.py ├── randomforest_model_dynamic.py └── svm_model_dynamic.py ├── data ├── .DS_Store ├── bad_platforms.txt ├── dynamic_labels.csv ├── question_rate_table.csv └── scores │ ├── .DS_Store │ ├── deeplearning │ ├── .DS_Store │ ├── ppd_rankings_0.csv │ ├── ppd_rankings_1.csv │ ├── ppd_rankings_2.csv │ ├── ppd_rankings_3.csv │ ├── ppd_rankings_4.csv │ ├── ppd_rankings_5.csv │ └── scores.csv │ ├── logistic │ ├── ppd_rankings_0.csv │ ├── ppd_rankings_1.csv │ ├── ppd_rankings_2.csv │ ├── ppd_rankings_3.csv │ ├── ppd_rankings_4.csv │ ├── ppd_rankings_5.csv │ └── scores.csv │ ├── old_scores │ ├── ppd_deve_score.csv │ ├── ppd_rankings.csv │ ├── ppd_rankings_0.csv │ ├── ppd_rankings_1.csv │ ├── ppd_rankings_2.csv │ ├── ppd_rankings_3.csv │ ├── ppd_rankings_4.csv │ └── scores.csv │ ├── randomforest │ ├── ppd_rankings_0.csv │ ├── ppd_rankings_1.csv │ ├── ppd_rankings_2.csv │ ├── ppd_rankings_3.csv │ ├── ppd_rankings_4.csv │ ├── ppd_rankings_5.csv │ └── scores.csv │ └── svm │ ├── .DS_Store │ ├── ppd_rankings_0.csv │ ├── ppd_rankings_1.csv │ ├── ppd_rankings_2.csv │ ├── ppd_rankings_3.csv │ ├── ppd_rankings_4.csv │ ├── ppd_rankings_5.csv │ └── scores.csv ├── dl_model_dynamic.py ├── dynamic_scores.py ├── features.py ├── omnirank_kfold.py ├── omnirank_model.py ├── omnirank_model_dynamic.py └── omnirank_result └── omnirank_result.R /README.md: -------------------------------------------------------------------------------- 1 | # OMNIRank 2 | ##说明 3 | OMNIRank是一个用于对互联网金融行业各大P2P平台进行风险评估的深度学习模型，是上海交通大学[OMNILab](http://omnilab.sjtu.edu.cn/)的“若愚”团队在[魔镜杯数据产品开发大赛](http://mojing.ppdai.com/)中开发的作品。比赛之后我们会继续这一问题的研究。 4 | ##数据源 5 | 为了对P2P平台风险评估，我们选择了多个数据源，包括新闻门户、民意评论、网贷社区以及平台官网。我们将这些数据爬取并整理后，存放于我们的[开放数据平台](http://data.sjtu.edu.cn/dataset/ppd-stay-foolish)，供大家下载使用。 6 | ##模型介绍 7 | 工作包括特征提取、模型建立、对比试验以及结果呈现。我们将模型的代码以及数据开源，供大家参考、验证、改进，欢迎交流切磋。 8 | -------------------------------------------------------------------------------- /compare_models/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wang-haiyang/ppd_model/43c0237f3fbb4faf35e9026bf1a5e171e19eb5b4/compare_models/.DS_Store -------------------------------------------------------------------------------- /compare_models/logistic_model_dynamic.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | import MySQLdb 3 | import pandas as pd 4 | import numpy as np 5 | from sklearn import preprocessing 6 | from sklearn.linear_model import LogisticRegression 7 | from sklearn.metrics import roc_auc_score 8 | from sklearn.metrics import accuracy_score 9 | from sklearn.cross_validation import KFold 10 | 11 | '''确定当前时间，距离2016年3月份的时间''' 12 | interval = 6 13 | dynamic_labes = pd.read_csv('dynamic_labels.csv') 14 | fw = open('scores.csv', 'w') 15 | for rounds in xrange(0, interval): 16 | print 'rounds: ' + str(rounds) 17 | #读取p2p平台各个特征的数据 18 | conn = MySQLdb.connect(host="localhost",user="root",passwd="123",db="ppd",charset="utf8") 19 | sql = "select * from platform_features" 20 | platform_features = pd.read_sql(sql,conn,index_col="index") 21 | names = platform_features['platName'].values 22 | base = platform_features[['type', 'tzzj_cooperation', 'listed', 'vc', 'argue', 'third_party', 'join_society', 'score', 'averageProfit', 'registMoney', 'autobid', 'stockTransfer', 'fundsToken', 'ifGuarantee', 'ifGuaranteeOrg', 'lauchTime', 'category', 'lng', 'lat']].values 23 | 24 | #对特征进行预处理 25 | for i in xrange(0, len(base)): 26 | for j in xrange(0, len(base[i])): 27 | if isinstance(base[i][j], unicode): 28 | if base[i][j]=='': 29 | base[i][j] = 0 30 | else: 31 | base[i][j] = float(base[i][j]) 32 | #更改距离公司成立的时间 33 | for i in xrange(0, len(base[15])):#base[15]表示lauchTime 34 | base[15][i]-=rounds 35 | 36 | #base = preprocessing.scale(base) 37 | min_max_scaler = preprocessing.MinMaxScaler() 38 | base = min_max_scaler.fit_transform(base) 39 | for i in xrange(0, len(base)): 40 | base[i,7]*=60 41 | 42 | series_old = [platform_features['comment_pos'].values, 43 | platform_features['comment_neu'].values, 44 | platform_features['comment_neg'].values] 45 | series = [] 46 | for i in xrange(0, len(series_old)): 47 | #处理每个series 48 | series.append([]) 49 | for j in xrange(0, len(series_old[i])): 50 | tmp = series_old[i][j].split(',') 51 | tmp = [int(tmp[k]) for k in xrange(0, len(tmp))] 52 | tmp = tmp[0:len(tmp)-rounds] 53 | series[i].append(tmp) 54 | series[i] = np.array(series[i]) 55 | #获得序列的统计信息 56 | series_stat = [] 57 | for i in xrange(0, len(series[0])): 58 | series_stat.append([]) 59 | for i in xrange(0, len(series)):#feature数、sample数、序列点的个数 60 | for j in xrange(0, len(series[i])): 61 | series_stat[j].append(np.mean(series[i][j][0:(27-rounds)])) 62 | series_stat[j].append(np.var(series[i][j][0:(27-rounds)])) 63 | series_stat[j].append(np.percentile(series[i][j][0:(27-rounds)], 25)) 64 | series_stat[j].append(np.percentile(series[i][j][0:(27-rounds)], 50)) 65 | series_stat[j].append(np.percentile(series[i][j][0:(27-rounds)], 75)) 66 | 67 | series_stat = min_max_scaler.fit_transform(series_stat) 68 | #换成动态label查看效果 69 | labels = dynamic_labes[dynamic_labes.columns[rounds]].values 70 | labels = labels.reshape(len(labels),1) 71 | 72 | #样本个数 73 | N_Samples = 3050 74 | 75 | accuracy_list, auc_list = [],[] 76 | 77 | name_list = [] 78 | score_list = [] 79 | label_list = [] 80 | 81 | kf = KFold(N_Samples, n_folds=5) 82 | for train, test in kf: 83 | #基本型变量 84 | x_train_base = base[train] 85 | x_test_base = base[test] 86 | 87 | #序列型变量 88 | x_train_series = series_stat[train] 89 | x_test_series = series_stat[test] 90 | 91 | x_train = np.hstack((x_train_base, x_train_series)) 92 | x_test = np.hstack((x_test_base, x_test_series)) 93 | 94 | #标签 95 | y_train = labels[train] 96 | y_test = labels[test] 97 | 98 | #名称 99 | names_train = names[train] 100 | name_test = names[test] 101 | 102 | #建立Logistic分类模型 103 | clf = LogisticRegression() 104 | clf.fit(x_train, y_train) 105 | #计算auc 106 | y_scores = clf.predict_proba(x_test)[:, 1] 107 | y_true = y_test 108 | auc = roc_auc_score(y_true, y_scores) 109 | auc_list.append(auc) 110 | #计算accuracy 111 | accuracy = clf.score(x_test, y_true) 112 | accuracy_list.append(accuracy) 113 | 114 | name_list+=list(names[test]) 115 | score_list+=list(y_scores) 116 | label_list+=list(y_true[:,0]) 117 | print 'average results' 118 | print accuracy_list 119 | print auc_list 120 | print np.mean(accuracy_list), np.mean(auc_list) 121 | fw.write('month' + str(rounds) + '\n') 122 | for i in xrange(0, len(accuracy_list)): 123 | fw.write(str(accuracy_list[i]) + ',') 124 | fw.write('\n') 125 | for i in xrange(0, len(auc_list)): 126 | fw.write(str(auc_list[i]) + ',') 127 | fw.write('\n') 128 | fw.write(str(np.mean(accuracy_list)) + ',' + str(np.mean(auc_list)) + '\n') 129 | score_list = np.array([1-score_list[i] for i in xrange(0, len(score_list))]) 130 | rankings = pd.DataFrame({ 131 | 'name':[name_list[i].encode('utf-8') for i in xrange(0, len(name_list))], 132 | 'score':list(score_list), 133 | 'label':label_list 134 | }) 135 | rankings.sort(columns='score', ascending=False).to_csv('ppd_rankings_' + str(rounds) + '.csv',index=False) 136 | fw.close() -------------------------------------------------------------------------------- /compare_models/randomforest_model_dynamic.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | import MySQLdb 3 | import pandas as pd 4 | import numpy as np 5 | from sklearn import preprocessing 6 | from sklearn.ensemble import RandomForestClassifier 7 | from sklearn.metrics import roc_auc_score 8 | from sklearn.metrics import accuracy_score 9 | from sklearn.cross_validation import KFold 10 | 11 | '''确定当前时间，距离2016年3月份的时间''' 12 | interval = 6 13 | dynamic_labes = pd.read_csv('dynamic_labels.csv') 14 | fw = open('scores.csv', 'w') 15 | for rounds in xrange(0, interval): 16 | print 'rounds: ' + str(rounds) 17 | #读取p2p平台各个特征的数据 18 | conn = MySQLdb.connect(host="localhost",user="root",passwd="123",db="ppd",charset="utf8") 19 | sql = "select * from platform_features" 20 | platform_features = pd.read_sql(sql,conn,index_col="index") 21 | names = platform_features['platName'].values 22 | base = platform_features[['type', 'tzzj_cooperation', 'listed', 'vc', 'argue', 'third_party', 'join_society', 'score', 'averageProfit', 'registMoney', 'autobid', 'stockTransfer', 'fundsToken', 'ifGuarantee', 'ifGuaranteeOrg', 'lauchTime', 'category', 'lng', 'lat']].values 23 | 24 | #对特征进行预处理 25 | for i in xrange(0, len(base)): 26 | for j in xrange(0, len(base[i])): 27 | if isinstance(base[i][j], unicode): 28 | if base[i][j]=='': 29 | base[i][j] = 0 30 | else: 31 | base[i][j] = float(base[i][j]) 32 | #更改距离公司成立的时间 33 | for i in xrange(0, len(base[15])):#base[15]表示lauchTime 34 | base[15][i]-=rounds 35 | 36 | #base = preprocessing.scale(base) 37 | min_max_scaler = preprocessing.MinMaxScaler() 38 | base = min_max_scaler.fit_transform(base) 39 | for i in xrange(0, len(base)): 40 | base[i,7]*=60 41 | 42 | series_old = [platform_features['comment_pos'].values, 43 | platform_features['comment_neu'].values, 44 | platform_features['comment_neg'].values] 45 | series = [] 46 | for i in xrange(0, len(series_old)): 47 | #处理每个series 48 | series.append([]) 49 | for j in xrange(0, len(series_old[i])): 50 | tmp = series_old[i][j].split(',') 51 | tmp = [int(tmp[k]) for k in xrange(0, len(tmp))] 52 | tmp = tmp[0:len(tmp)-rounds] 53 | series[i].append(tmp) 54 | series[i] = np.array(series[i]) 55 | #获得序列的统计信息 56 | series_stat = [] 57 | for i in xrange(0, len(series[0])): 58 | series_stat.append([]) 59 | for i in xrange(0, len(series)):#feature数、sample数、序列点的个数 60 | for j in xrange(0, len(series[i])): 61 | series_stat[j].append(np.mean(series[i][j][0:(27-rounds)])) 62 | series_stat[j].append(np.var(series[i][j][0:(27-rounds)])) 63 | series_stat[j].append(np.percentile(series[i][j][0:(27-rounds)], 25)) 64 | series_stat[j].append(np.percentile(series[i][j][0:(27-rounds)], 50)) 65 | series_stat[j].append(np.percentile(series[i][j][0:(27-rounds)], 75)) 66 | 67 | series_stat = min_max_scaler.fit_transform(series_stat) 68 | #换成动态label查看效果 69 | labels = dynamic_labes[dynamic_labes.columns[rounds]].values 70 | labels = labels.reshape(len(labels),1) 71 | 72 | #样本个数 73 | N_Samples = 3050 74 | 75 | accuracy_list, auc_list = [],[] 76 | 77 | name_list = [] 78 | score_list = [] 79 | label_list = [] 80 | 81 | kf = KFold(N_Samples, n_folds=5) 82 | for train, test in kf: 83 | #基本型变量 84 | x_train_base = base[train] 85 | x_test_base = base[test] 86 | 87 | #序列型变量 88 | x_train_series = series_stat[train] 89 | x_test_series = series_stat[test] 90 | 91 | x_train = np.hstack((x_train_base, x_train_series)) 92 | x_test = np.hstack((x_test_base, x_test_series)) 93 | 94 | #标签 95 | y_train = labels[train] 96 | y_test = labels[test] 97 | 98 | #名称 99 | names_train = names[train] 100 | name_test = names[test] 101 | 102 | #建立SVM分类模型 103 | clf = RandomForestClassifier(n_estimators=100) 104 | clf.fit(x_train, y_train) 105 | #计算auc 106 | y_scores = clf.predict_proba(x_test)[:, 1] 107 | y_true = y_test 108 | auc = roc_auc_score(y_true, y_scores) 109 | auc_list.append(auc) 110 | #计算accuracy 111 | accuracy = clf.score(x_test, y_true) 112 | accuracy_list.append(accuracy) 113 | 114 | name_list+=list(names[test]) 115 | score_list+=list(y_scores) 116 | label_list+=list(y_true[:,0]) 117 | print 'average results' 118 | print accuracy_list 119 | print auc_list 120 | print np.mean(accuracy_list), np.mean(auc_list) 121 | fw.write('month' + str(rounds) + '\n') 122 | for i in xrange(0, len(accuracy_list)): 123 | fw.write(str(accuracy_list[i]) + ',') 124 | fw.write('\n') 125 | for i in xrange(0, len(auc_list)): 126 | fw.write(str(auc_list[i]) + ',') 127 | fw.write('\n') 128 | fw.write(str(np.mean(accuracy_list)) + ',' + str(np.mean(auc_list)) + '\n') 129 | score_list = np.array([1-score_list[i] for i in xrange(0, len(score_list))]) 130 | rankings = pd.DataFrame({ 131 | 'name':[name_list[i].encode('utf-8') for i in xrange(0, len(name_list))], 132 | 'score':list(score_list), 133 | 'label':label_list 134 | }) 135 | rankings.sort(columns='score', ascending=False).to_csv('ppd_rankings_' + str(rounds) + '.csv',index=False) 136 | fw.close() -------------------------------------------------------------------------------- /compare_models/svm_model_dynamic.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | import MySQLdb 3 | import pandas as pd 4 | import numpy as np 5 | from sklearn import preprocessing 6 | from sklearn import svm 7 | from sklearn.metrics import roc_auc_score 8 | from sklearn.metrics import accuracy_score 9 | from sklearn.cross_validation import KFold 10 | 11 | '''确定当前时间，距离2016年3月份的时间''' 12 | interval = 6 13 | dynamic_labes = pd.read_csv('dynamic_labels.csv') 14 | fw = open('scores.csv', 'w') 15 | for rounds in xrange(0, interval): 16 | print 'rounds: ' + str(rounds) 17 | #读取p2p平台各个特征的数据 18 | conn = MySQLdb.connect(host="localhost",user="root",passwd="123",db="ppd",charset="utf8") 19 | sql = "select * from platform_features" 20 | platform_features = pd.read_sql(sql,conn,index_col="index") 21 | names = platform_features['platName'].values 22 | base = platform_features[['type', 'tzzj_cooperation', 'listed', 'vc', 'argue', 'third_party', 'join_society', 'score', 'averageProfit', 'registMoney', 'autobid', 'stockTransfer', 'fundsToken', 'ifGuarantee', 'ifGuaranteeOrg', 'lauchTime', 'category', 'lng', 'lat']].values 23 | 24 | #对特征进行预处理 25 | for i in xrange(0, len(base)): 26 | for j in xrange(0, len(base[i])): 27 | if isinstance(base[i][j], unicode): 28 | if base[i][j]=='': 29 | base[i][j] = 0 30 | else: 31 | base[i][j] = float(base[i][j]) 32 | #更改距离公司成立的时间 33 | for i in xrange(0, len(base[15])):#base[15]表示lauchTime 34 | base[15][i]-=rounds 35 | 36 | #base = preprocessing.scale(base) 37 | min_max_scaler = preprocessing.MinMaxScaler() 38 | base = min_max_scaler.fit_transform(base) 39 | for i in xrange(0, len(base)): 40 | base[i,7]*=60 41 | 42 | series_old = [platform_features['comment_pos'].values, 43 | platform_features['comment_neu'].values, 44 | platform_features['comment_neg'].values] 45 | series = [] 46 | for i in xrange(0, len(series_old)): 47 | #处理每个series 48 | series.append([]) 49 | for j in xrange(0, len(series_old[i])): 50 | tmp = series_old[i][j].split(',') 51 | tmp = [int(tmp[k]) for k in xrange(0, len(tmp))] 52 | tmp = tmp[0:len(tmp)-rounds] 53 | series[i].append(tmp) 54 | series[i] = np.array(series[i]) 55 | #获得序列的统计信息 56 | series_stat = [] 57 | for i in xrange(0, len(series[0])): 58 | series_stat.append([]) 59 | for i in xrange(0, len(series)):#feature数、sample数、序列点的个数 60 | for j in xrange(0, len(series[i])): 61 | series_stat[j].append(np.mean(series[i][j][0:(27-rounds)])) 62 | series_stat[j].append(np.var(series[i][j][0:(27-rounds)])) 63 | series_stat[j].append(np.percentile(series[i][j][0:(27-rounds)], 25)) 64 | series_stat[j].append(np.percentile(series[i][j][0:(27-rounds)], 50)) 65 | series_stat[j].append(np.percentile(series[i][j][0:(27-rounds)], 75)) 66 | 67 | series_stat = min_max_scaler.fit_transform(series_stat) 68 | #换成动态label查看效果 69 | labels = dynamic_labes[dynamic_labes.columns[rounds]].values 70 | labels = labels.reshape(len(labels),1) 71 | 72 | #样本个数 73 | N_Samples = 3050 74 | 75 | accuracy_list, auc_list = [],[] 76 | 77 | name_list = [] 78 | score_list = [] 79 | label_list = [] 80 | 81 | kf = KFold(N_Samples, n_folds=5) 82 | for train, test in kf: 83 | #基本型变量 84 | x_train_base = base[train] 85 | x_test_base = base[test] 86 | 87 | #序列型变量 88 | x_train_series = series_stat[train] 89 | x_test_series = series_stat[test] 90 | 91 | x_train = np.hstack((x_train_base, x_train_series)) 92 | x_test = np.hstack((x_test_base, x_test_series)) 93 | 94 | #标签 95 | y_train = labels[train] 96 | y_test = labels[test] 97 | 98 | #名称 99 | names_train = names[train] 100 | name_test = names[test] 101 | 102 | #建立SVM分类模型 103 | clf = svm.SVC(probability=True) 104 | clf.fit(x_train, y_train) 105 | #计算auc 106 | y_scores = clf.predict_proba(x_test)[:, 1] 107 | y_true = y_test 108 | auc = roc_auc_score(y_true, y_scores) 109 | auc_list.append(auc) 110 | #计算accuracy 111 | accuracy = clf.score(x_test, y_true) 112 | accuracy_list.append(accuracy) 113 | 114 | name_list+=list(names[test]) 115 | score_list+=list(y_scores) 116 | label_list+=list(y_true[:,0]) 117 | print 'average results' 118 | print accuracy_list 119 | print auc_list 120 | print np.mean(accuracy_list), np.mean(auc_list) 121 | fw.write('month' + str(rounds) + '\n') 122 | for i in xrange(0, len(accuracy_list)): 123 | fw.write(str(accuracy_list[i]) + ',') 124 | fw.write('\n') 125 | for i in xrange(0, len(auc_list)): 126 | fw.write(str(auc_list[i]) + ',') 127 | fw.write('\n') 128 | fw.write(str(np.mean(accuracy_list)) + ',' + str(np.mean(auc_list)) + '\n') 129 | score_list = np.array([1-score_list[i] for i in xrange(0, len(score_list))]) 130 | rankings = pd.DataFrame({ 131 | 'name':[name_list[i].encode('utf-8') for i in xrange(0, len(name_list))], 132 | 'score':list(score_list), 133 | 'label':label_list 134 | }) 135 | rankings.sort(columns='score', ascending=False).to_csv('ppd_rankings_' + str(rounds) + '.csv',index=False) 136 | fw.close() -------------------------------------------------------------------------------- /data/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wang-haiyang/ppd_model/43c0237f3fbb4faf35e9026bf1a5e171e19eb5b4/data/.DS_Store -------------------------------------------------------------------------------- /data/dynamic_labels.csv: 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0,0,0,0,0,0 1467 | 1,1,1,1,1,1 1468 | 1,1,1,1,1,1 1469 | 0,0,0,0,0,0 1470 | 0,0,0,0,0,0 1471 | 1,1,1,1,1,1 1472 | 0,0,0,0,0,0 1473 | 0,0,0,0,0,0 1474 | 0,0,0,0,0,0 1475 | 0,0,0,0,0,0 1476 | 1,1,1,0,0,0 1477 | 0,0,0,0,0,0 1478 | 0,0,0,0,0,0 1479 | 0,0,0,0,0,0 1480 | 1,0,0,0,0,0 1481 | 0,0,0,0,0,0 1482 | 1,1,1,1,0,0 1483 | 0,0,0,0,0,0 1484 | 0,0,0,0,0,0 1485 | 1,1,1,1,1,1 1486 | 0,0,0,0,0,0 1487 | 1,1,1,1,1,1 1488 | 0,0,0,0,0,0 1489 | 0,0,0,0,0,0 1490 | 1,1,1,1,1,1 1491 | 1,1,1,0,0,0 1492 | 1,1,0,0,0,0 1493 | 0,0,0,0,0,0 1494 | 0,0,0,0,0,0 1495 | 1,0,0,0,0,0 1496 | 0,0,0,0,0,0 1497 | 0,0,0,0,0,0 1498 | 0,0,0,0,0,0 1499 | 0,0,0,0,0,0 1500 | 0,0,0,0,0,0 1501 | 0,0,0,0,0,0 1502 | 0,0,0,0,0,0 1503 | 0,0,0,0,0,0 1504 | 0,0,0,0,0,0 1505 | 0,0,0,0,0,0 1506 | 0,0,0,0,0,0 1507 | 0,0,0,0,0,0 1508 | 0,0,0,0,0,0 1509 | 1,1,1,1,0,0 1510 | 0,0,0,0,0,0 1511 | 0,0,0,0,0,0 1512 | 0,0,0,0,0,0 1513 | 1,1,1,1,1,1 1514 | 0,0,0,0,0,0 1515 | 0,0,0,0,0,0 1516 | 0,0,0,0,0,0 1517 | 1,1,1,0,0,0 1518 | 1,1,1,1,1,1 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1,0,0,0,0,0 1572 | 0,0,0,0,0,0 1573 | 0,0,0,0,0,0 1574 | 0,0,0,0,0,0 1575 | 1,1,0,0,0,0 1576 | 1,1,1,1,1,1 1577 | 0,0,0,0,0,0 1578 | 0,0,0,0,0,0 1579 | 0,0,0,0,0,0 1580 | 1,1,1,0,0,0 1581 | 1,1,1,1,0,0 1582 | 0,0,0,0,0,0 1583 | 0,0,0,0,0,0 1584 | 1,0,0,0,0,0 1585 | 0,0,0,0,0,0 1586 | 0,0,0,0,0,0 1587 | 0,0,0,0,0,0 1588 | 0,0,0,0,0,0 1589 | 0,0,0,0,0,0 1590 | 1,0,0,0,0,0 1591 | 0,0,0,0,0,0 1592 | 0,0,0,0,0,0 1593 | 0,0,0,0,0,0 1594 | 1,1,1,0,0,0 1595 | 0,0,0,0,0,0 1596 | 1,1,1,0,0,0 1597 | 0,0,0,0,0,0 1598 | 0,0,0,0,0,0 1599 | 0,0,0,0,0,0 1600 | 1,0,0,0,0,0 1601 | 0,0,0,0,0,0 1602 | 1,1,1,1,1,1 1603 | 0,0,0,0,0,0 1604 | 0,0,0,0,0,0 1605 | 0,0,0,0,0,0 1606 | 0,0,0,0,0,0 1607 | 0,0,0,0,0,0 1608 | 0,0,0,0,0,0 1609 | 0,0,0,0,0,0 1610 | 0,0,0,0,0,0 1611 | 0,0,0,0,0,0 1612 | 1,1,1,1,0,0 1613 | 0,0,0,0,0,0 1614 | 1,1,1,1,0,0 1615 | 0,0,0,0,0,0 1616 | 1,1,1,1,1,1 1617 | 0,0,0,0,0,0 1618 | 0,0,0,0,0,0 1619 | 1,0,0,0,0,0 1620 | 0,0,0,0,0,0 1621 | 0,0,0,0,0,0 1622 | 0,0,0,0,0,0 1623 | 0,0,0,0,0,0 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0,0,0,0,0,0 1677 | 0,0,0,0,0,0 1678 | 0,0,0,0,0,0 1679 | 0,0,0,0,0,0 1680 | 1,1,1,0,0,0 1681 | 1,1,0,0,0,0 1682 | 0,0,0,0,0,0 1683 | 0,0,0,0,0,0 1684 | 0,0,0,0,0,0 1685 | 1,1,1,0,0,0 1686 | 1,1,1,1,1,1 1687 | 0,0,0,0,0,0 1688 | 0,0,0,0,0,0 1689 | 0,0,0,0,0,0 1690 | 0,0,0,0,0,0 1691 | 0,0,0,0,0,0 1692 | 1,1,1,1,1,1 1693 | 0,0,0,0,0,0 1694 | 0,0,0,0,0,0 1695 | 1,0,0,0,0,0 1696 | 0,0,0,0,0,0 1697 | 0,0,0,0,0,0 1698 | 0,0,0,0,0,0 1699 | 0,0,0,0,0,0 1700 | 0,0,0,0,0,0 1701 | 0,0,0,0,0,0 1702 | 0,0,0,0,0,0 1703 | 1,1,1,0,0,0 1704 | 0,0,0,0,0,0 1705 | 0,0,0,0,0,0 1706 | 0,0,0,0,0,0 1707 | 0,0,0,0,0,0 1708 | 0,0,0,0,0,0 1709 | 1,1,0,0,0,0 1710 | 0,0,0,0,0,0 1711 | 0,0,0,0,0,0 1712 | 0,0,0,0,0,0 1713 | 1,1,1,1,1,1 1714 | 1,1,0,0,0,0 1715 | 0,0,0,0,0,0 1716 | 1,1,1,0,0,0 1717 | 0,0,0,0,0,0 1718 | 0,0,0,0,0,0 1719 | 0,0,0,0,0,0 1720 | 1,1,1,1,1,1 1721 | 0,0,0,0,0,0 1722 | 1,1,1,1,1,1 1723 | 1,1,1,1,1,1 1724 | 1,1,1,1,1,1 1725 | 1,1,1,1,1,1 1726 | 0,0,0,0,0,0 1727 | 1,1,1,1,1,1 1728 | 1,1,1,1,1,1 1729 | 1,1,1,1,1,1 1730 | 1,1,1,1,1,1 1731 | 1,1,1,1,1,1 1732 | 1,1,1,1,1,1 1733 | 1,1,1,1,1,1 1734 | 1,1,1,1,1,1 1735 | 1,1,1,1,1,1 1736 | 0,0,0,0,0,0 1737 | 1,1,1,1,1,1 1738 | 1,1,1,1,1,1 1739 | 0,0,0,0,0,0 1740 | 1,1,1,1,1,1 1741 | 1,1,1,1,1,1 1742 | 1,1,1,1,1,1 1743 | 1,1,1,1,1,1 1744 | 1,1,1,1,1,1 1745 | 1,1,1,1,1,1 1746 | 1,1,1,1,1,1 1747 | 1,1,1,1,1,1 1748 | 1,1,1,1,1,1 1749 | 1,1,1,1,1,1 1750 | 1,1,1,1,1,1 1751 | 1,1,1,1,1,1 1752 | 1,1,1,1,1,1 1753 | 0,0,0,0,0,0 1754 | 1,1,1,0,0,0 1755 | 1,1,1,1,0,0 1756 | 1,1,1,1,1,1 1757 | 1,1,1,1,1,1 1758 | 1,1,1,1,1,1 1759 | 1,1,1,1,1,1 1760 | 1,1,1,1,1,1 1761 | 1,1,1,1,1,1 1762 | 1,1,1,1,1,1 1763 | 1,1,1,1,1,1 1764 | 0,0,0,0,0,0 1765 | 1,1,1,1,1,1 1766 | 1,1,1,1,1,1 1767 | 1,1,1,1,1,1 1768 | 1,1,1,1,1,1 1769 | 1,1,1,1,1,1 1770 | 1,1,1,1,1,1 1771 | 1,1,1,1,1,1 1772 | 1,1,1,1,1,1 1773 | 1,1,1,1,1,1 1774 | 0,0,0,0,0,0 1775 | 1,1,1,1,1,1 1776 | 1,1,1,1,1,1 1777 | 1,1,1,1,1,1 1778 | 1,1,1,1,1,1 1779 | 1,1,1,1,1,1 1780 | 1,1,1,1,1,1 1781 | 1,1,1,1,1,1 1782 | 1,1,1,1,1,1 1783 | 1,1,1,1,1,1 1784 | 1,1,1,1,1,1 1785 | 1,1,1,1,1,1 1786 | 1,1,1,1,1,1 1787 | 1,1,1,1,1,1 1788 | 1,1,1,1,1,1 1789 | 1,1,1,1,1,1 1790 | 1,1,1,1,1,1 1791 | 1,1,1,1,1,1 1792 | 0,0,0,0,0,0 1793 | 1,1,1,1,1,1 1794 | 1,1,1,1,1,1 1795 | 0,0,0,0,0,0 1796 | 0,0,0,0,0,0 1797 | 1,1,1,1,1,1 1798 | 1,1,1,1,1,1 1799 | 1,1,1,1,1,1 1800 | 1,1,1,1,1,1 1801 | 1,1,1,1,1,1 1802 | 1,1,1,1,1,1 1803 | 1,1,1,1,1,1 1804 | 1,1,1,1,1,1 1805 | 1,1,1,1,1,1 1806 | 1,1,1,1,1,1 1807 | 1,1,1,1,1,1 1808 | 1,1,1,1,1,1 1809 | 1,0,0,0,0,0 1810 | 1,1,1,1,1,1 1811 | 1,1,1,1,1,1 1812 | 1,1,1,1,1,1 1813 | 1,1,1,1,1,1 1814 | 1,1,1,1,1,1 1815 | 1,1,1,1,1,1 1816 | 1,1,1,1,1,1 1817 | 0,0,0,0,0,0 1818 | 1,1,1,1,1,1 1819 | 1,1,1,1,1,1 1820 | 1,1,1,1,1,1 1821 | 1,1,1,1,1,1 1822 | 0,0,0,0,0,0 1823 | 0,0,0,0,0,0 1824 | 0,0,0,0,0,0 1825 | 1,1,1,1,1,1 1826 | 1,1,1,1,1,1 1827 | 1,1,1,1,1,1 1828 | 1,1,1,1,1,1 1829 | 1,1,1,1,1,1 1830 | 1,1,1,1,1,1 1831 | 1,1,1,1,1,1 1832 | 0,0,0,0,0,0 1833 | 1,1,1,1,1,1 1834 | 1,1,1,1,1,1 1835 | 1,1,1,1,1,1 1836 | 0,0,0,0,0,0 1837 | 1,1,1,1,1,1 1838 | 1,1,1,1,1,1 1839 | 1,1,1,1,1,1 1840 | 1,1,1,1,1,1 1841 | 0,0,0,0,0,0 1842 | 1,1,1,1,1,1 1843 | 1,1,1,1,1,1 1844 | 1,1,1,1,1,1 1845 | 1,1,1,1,1,1 1846 | 1,1,1,1,1,1 1847 | 1,1,1,1,1,1 1848 | 1,1,1,1,1,1 1849 | 1,1,1,1,1,1 1850 | 0,0,0,0,0,0 1851 | 1,1,1,1,1,1 1852 | 1,1,1,1,1,1 1853 | 0,0,0,0,0,0 1854 | 1,1,1,1,1,1 1855 | 1,1,1,0,0,0 1856 | 1,1,1,1,1,1 1857 | 0,0,0,0,0,0 1858 | 0,0,0,0,0,0 1859 | 0,0,0,0,0,0 1860 | 1,1,1,1,1,1 1861 | 0,0,0,0,0,0 1862 | 0,0,0,0,0,0 1863 | 0,0,0,0,0,0 1864 | 0,0,0,0,0,0 1865 | 0,0,0,0,0,0 1866 | 1,1,1,1,0,0 1867 | 0,0,0,0,0,0 1868 | 1,1,1,1,1,1 1869 | 1,1,1,1,1,1 1870 | 0,0,0,0,0,0 1871 | 0,0,0,0,0,0 1872 | 0,0,0,0,0,0 1873 | 0,0,0,0,0,0 1874 | 0,0,0,0,0,0 1875 | 0,0,0,0,0,0 1876 | 1,1,1,1,1,1 1877 | 1,1,1,1,1,1 1878 | 1,1,1,1,1,1 1879 | 1,1,1,1,1,1 1880 | 1,1,1,1,1,1 1881 | 1,1,1,1,1,1 1882 | 1,1,1,1,1,1 1883 | 1,1,1,1,1,1 1884 | 1,1,1,1,1,1 1885 | 1,1,1,1,1,1 1886 | 0,0,0,0,0,0 1887 | 1,0,0,0,0,0 1888 | 0,0,0,0,0,0 1889 | 1,1,1,1,1,1 1890 | 1,1,1,1,0,0 1891 | 1,1,1,1,1,1 1892 | 0,0,0,0,0,0 1893 | 0,0,0,0,0,0 1894 | 1,1,1,1,1,1 1895 | 1,1,1,1,1,1 1896 | 1,1,1,1,1,1 1897 | 1,1,1,1,1,1 1898 | 0,0,0,0,0,0 1899 | 1,1,1,1,1,1 1900 | 0,0,0,0,0,0 1901 | 0,0,0,0,0,0 1902 | 0,0,0,0,0,0 1903 | 1,1,1,1,1,1 1904 | 1,1,1,1,1,1 1905 | 1,1,1,1,1,1 1906 | 1,1,1,1,1,1 1907 | 1,1,1,1,1,1 1908 | 0,0,0,0,0,0 1909 | 1,1,1,1,1,1 1910 | 0,0,0,0,0,0 1911 | 1,1,1,1,1,1 1912 | 0,0,0,0,0,0 1913 | 1,1,1,1,1,1 1914 | 1,1,1,1,1,1 1915 | 1,1,1,1,1,1 1916 | 0,0,0,0,0,0 1917 | 1,1,1,1,1,1 1918 | 1,1,1,1,1,1 1919 | 1,1,1,1,1,1 1920 | 1,1,0,0,0,0 1921 | 1,1,1,1,1,1 1922 | 0,0,0,0,0,0 1923 | 1,1,1,1,1,1 1924 | 1,1,1,1,1,1 1925 | 1,1,1,1,1,1 1926 | 1,1,1,1,1,1 1927 | 1,1,1,1,1,1 1928 | 1,1,1,1,1,0 1929 | 1,1,1,1,1,1 1930 | 1,1,1,1,1,1 1931 | 0,0,0,0,0,0 1932 | 1,1,1,1,1,1 1933 | 1,1,1,1,1,1 1934 | 1,1,1,1,1,1 1935 | 1,1,1,1,1,1 1936 | 1,1,1,1,1,1 1937 | 1,1,1,1,1,1 1938 | 1,1,1,1,1,1 1939 | 1,1,1,1,1,1 1940 | 0,0,0,0,0,0 1941 | 1,1,1,0,0,0 1942 | 1,1,1,1,1,1 1943 | 1,1,1,1,1,1 1944 | 0,0,0,0,0,0 1945 | 0,0,0,0,0,0 1946 | 0,0,0,0,0,0 1947 | 0,0,0,0,0,0 1948 | 1,1,1,1,1,1 1949 | 1,1,1,1,1,1 1950 | 1,1,1,1,1,1 1951 | 0,0,0,0,0,0 1952 | 0,0,0,0,0,0 1953 | 1,1,1,1,1,1 1954 | 0,0,0,0,0,0 1955 | 1,1,1,1,1,1 1956 | 1,1,1,1,1,1 1957 | 0,0,0,0,0,0 1958 | 1,1,1,1,1,1 1959 | 0,0,0,0,0,0 1960 | 1,1,1,1,1,1 1961 | 1,1,1,1,1,1 1962 | 1,1,1,1,1,1 1963 | 0,0,0,0,0,0 1964 | 1,1,1,1,1,1 1965 | 0,0,0,0,0,0 1966 | 0,0,0,0,0,0 1967 | 1,1,1,1,1,1 1968 | 0,0,0,0,0,0 1969 | 1,1,1,1,1,1 1970 | 0,0,0,0,0,0 1971 | 1,1,1,1,1,1 1972 | 1,1,1,1,1,1 1973 | 0,0,0,0,0,0 1974 | 0,0,0,0,0,0 1975 | 1,1,1,1,1,1 1976 | 1,1,1,1,1,1 1977 | 1,1,1,1,1,1 1978 | 0,0,0,0,0,0 1979 | 1,1,1,1,1,1 1980 | 1,1,1,1,1,1 1981 | 1,1,1,1,1,1 1982 | 1,1,1,1,1,1 1983 | 1,1,1,1,1,1 1984 | 1,1,1,1,1,1 1985 | 0,0,0,0,0,0 1986 | 1,1,1,1,1,1 1987 | 1,1,0,0,0,0 1988 | 1,1,1,1,1,1 1989 | 1,1,1,1,1,1 1990 | 1,1,1,1,1,1 1991 | 1,1,1,1,1,1 1992 | 1,1,1,1,1,1 1993 | 1,1,1,1,1,1 1994 | 0,0,0,0,0,0 1995 | 1,1,1,1,1,1 1996 | 0,0,0,0,0,0 1997 | 0,0,0,0,0,0 1998 | 1,1,1,1,1,1 1999 | 0,0,0,0,0,0 2000 | 1,1,0,0,0,0 2001 | 1,1,1,1,1,1 2002 | 1,1,1,1,1,1 2003 | 0,0,0,0,0,0 2004 | 1,1,1,1,1,1 2005 | 1,0,0,0,0,0 2006 | 1,1,1,1,1,1 2007 | 0,0,0,0,0,0 2008 | 1,1,1,1,1,1 2009 | 0,0,0,0,0,0 2010 | 1,1,1,1,1,1 2011 | 1,1,1,1,1,1 2012 | 0,0,0,0,0,0 2013 | 0,0,0,0,0,0 2014 | 1,1,1,1,1,1 2015 | 1,1,1,1,1,1 2016 | 1,1,1,1,1,1 2017 | 1,1,0,0,0,0 2018 | 1,1,1,1,1,1 2019 | 1,1,1,1,1,1 2020 | 1,1,1,1,1,1 2021 | 1,1,1,1,1,1 2022 | 1,1,1,1,1,1 2023 | 1,1,1,1,1,1 2024 | 1,0,0,0,0,0 2025 | 0,0,0,0,0,0 2026 | 1,1,1,1,1,1 2027 | 1,1,1,1,1,1 2028 | 1,1,1,1,1,1 2029 | 0,0,0,0,0,0 2030 | 1,1,1,1,1,1 2031 | 1,1,1,1,1,1 2032 | 0,0,0,0,0,0 2033 | 1,1,1,1,1,1 2034 | 0,0,0,0,0,0 2035 | 0,0,0,0,0,0 2036 | 1,1,1,1,1,1 2037 | 0,0,0,0,0,0 2038 | 0,0,0,0,0,0 2039 | 0,0,0,0,0,0 2040 | 0,0,0,0,0,0 2041 | 0,0,0,0,0,0 2042 | 1,1,1,1,1,1 2043 | 1,1,1,1,1,1 2044 | 1,1,1,1,1,1 2045 | 1,1,1,1,1,1 2046 | 1,1,1,1,1,1 2047 | 1,1,1,1,1,1 2048 | 0,0,0,0,0,0 2049 | 1,1,0,0,0,0 2050 | 0,0,0,0,0,0 2051 | 0,0,0,0,0,0 2052 | 1,1,1,1,1,1 2053 | 1,1,1,1,1,1 2054 | 1,1,1,1,1,1 2055 | 1,1,1,1,1,1 2056 | 0,0,0,0,0,0 2057 | 1,1,1,1,1,1 2058 | 1,1,1,1,1,1 2059 | 1,1,1,1,1,1 2060 | 1,1,1,1,1,1 2061 | 1,1,1,1,1,1 2062 | 0,0,0,0,0,0 2063 | 0,0,0,0,0,0 2064 | 1,1,1,1,1,1 2065 | 0,0,0,0,0,0 2066 | 1,1,1,1,1,1 2067 | 1,1,1,1,1,1 2068 | 1,1,1,1,1,1 2069 | 1,1,1,1,1,1 2070 | 0,0,0,0,0,0 2071 | 1,1,1,1,1,1 2072 | 1,1,1,1,1,1 2073 | 1,1,1,1,1,1 2074 | 1,1,1,1,1,1 2075 | 0,0,0,0,0,0 2076 | 1,1,1,1,1,1 2077 | 1,1,1,1,1,1 2078 | 0,0,0,0,0,0 2079 | 1,1,1,1,1,1 2080 | 0,0,0,0,0,0 2081 | 1,1,0,0,0,0 2082 | 1,1,1,1,1,1 2083 | 1,1,1,1,1,1 2084 | 1,1,1,1,1,1 2085 | 1,1,1,1,1,1 2086 | 1,1,1,1,1,1 2087 | 1,1,1,1,1,1 2088 | 1,1,1,1,1,1 2089 | 1,1,1,1,1,1 2090 | 0,0,0,0,0,0 2091 | 1,1,1,1,1,1 2092 | 0,0,0,0,0,0 2093 | 0,0,0,0,0,0 2094 | 0,0,0,0,0,0 2095 | 1,1,1,1,1,1 2096 | 0,0,0,0,0,0 2097 | 0,0,0,0,0,0 2098 | 1,1,1,1,1,1 2099 | 1,1,1,1,1,1 2100 | 1,1,1,1,1,1 2101 | 0,0,0,0,0,0 2102 | 1,1,1,1,1,1 2103 | 1,1,1,1,1,1 2104 | 0,0,0,0,0,0 2105 | 1,1,1,1,1,1 2106 | 0,0,0,0,0,0 2107 | 0,0,0,0,0,0 2108 | 1,1,1,1,1,1 2109 | 1,1,1,1,1,1 2110 | 1,1,1,1,1,1 2111 | 1,1,1,1,1,1 2112 | 1,1,1,1,1,1 2113 | 1,1,1,1,1,1 2114 | 0,0,0,0,0,0 2115 | 1,1,1,1,1,1 2116 | 1,1,1,1,1,1 2117 | 1,1,1,1,1,1 2118 | 0,0,0,0,0,0 2119 | 1,1,1,1,1,1 2120 | 1,0,0,0,0,0 2121 | 1,1,0,0,0,0 2122 | 0,0,0,0,0,0 2123 | 1,1,1,1,1,1 2124 | 1,1,1,1,1,1 2125 | 1,1,1,1,1,1 2126 | 1,1,1,1,1,1 2127 | 0,0,0,0,0,0 2128 | 1,1,0,0,0,0 2129 | 1,1,1,1,1,1 2130 | 1,1,1,1,1,1 2131 | 1,1,1,1,1,1 2132 | 1,1,0,0,0,0 2133 | 1,1,1,1,1,1 2134 | 0,0,0,0,0,0 2135 | 0,0,0,0,0,0 2136 | 0,0,0,0,0,0 2137 | 1,1,1,1,1,1 2138 | 1,1,1,1,1,1 2139 | 1,1,1,1,1,1 2140 | 0,0,0,0,0,0 2141 | 1,1,1,1,1,1 2142 | 1,1,1,1,1,1 2143 | 1,1,1,1,1,1 2144 | 0,0,0,0,0,0 2145 | 0,0,0,0,0,0 2146 | 1,1,1,1,1,1 2147 | 1,1,1,1,1,1 2148 | 1,1,1,1,1,1 2149 | 1,1,1,1,1,1 2150 | 1,1,1,1,1,1 2151 | 1,1,1,1,1,1 2152 | 1,1,1,1,1,1 2153 | 0,0,0,0,0,0 2154 | 1,1,1,1,1,1 2155 | 1,1,1,1,1,1 2156 | 0,0,0,0,0,0 2157 | 1,1,1,1,1,1 2158 | 0,0,0,0,0,0 2159 | 0,0,0,0,0,0 2160 | 1,1,1,1,1,1 2161 | 1,1,1,1,1,1 2162 | 1,1,1,1,1,1 2163 | 1,1,1,1,1,1 2164 | 1,1,1,1,1,1 2165 | 0,0,0,0,0,0 2166 | 1,1,1,1,1,1 2167 | 0,0,0,0,0,0 2168 | 0,0,0,0,0,0 2169 | 0,0,0,0,0,0 2170 | 1,1,1,1,1,1 2171 | 1,1,1,1,1,1 2172 | 0,0,0,0,0,0 2173 | 0,0,0,0,0,0 2174 | 1,1,1,1,1,1 2175 | 1,1,1,1,1,1 2176 | 0,0,0,0,0,0 2177 | 1,1,0,0,0,0 2178 | 1,1,1,1,1,1 2179 | 1,1,1,1,1,1 2180 | 1,1,1,1,1,1 2181 | 1,1,1,1,0,0 2182 | 1,1,1,1,1,1 2183 | 0,0,0,0,0,0 2184 | 1,1,1,1,1,1 2185 | 0,0,0,0,0,0 2186 | 0,0,0,0,0,0 2187 | 0,0,0,0,0,0 2188 | 0,0,0,0,0,0 2189 | 1,1,1,1,1,1 2190 | 1,1,1,1,1,1 2191 | 1,1,1,1,1,1 2192 | 1,1,1,1,1,1 2193 | 1,1,1,1,1,1 2194 | 1,1,1,1,1,1 2195 | 0,0,0,0,0,0 2196 | 0,0,0,0,0,0 2197 | 1,1,1,1,1,1 2198 | 0,0,0,0,0,0 2199 | 1,1,1,1,1,1 2200 | 1,0,0,0,0,0 2201 | 1,1,1,1,1,1 2202 | 1,1,1,1,1,1 2203 | 1,1,1,1,1,1 2204 | 1,1,1,1,1,1 2205 | 1,1,1,1,1,1 2206 | 1,1,1,1,1,1 2207 | 1,1,1,1,1,1 2208 | 1,1,1,1,1,1 2209 | 0,0,0,0,0,0 2210 | 0,0,0,0,0,0 2211 | 0,0,0,0,0,0 2212 | 0,0,0,0,0,0 2213 | 0,0,0,0,0,0 2214 | 1,1,1,1,1,1 2215 | 1,1,1,1,1,1 2216 | 1,1,1,1,1,1 2217 | 1,1,1,1,1,1 2218 | 1,1,1,1,1,1 2219 | 1,1,1,1,1,1 2220 | 1,1,1,1,1,1 2221 | 1,1,1,1,1,1 2222 | 0,0,0,0,0,0 2223 | 1,1,1,1,1,1 2224 | 1,1,1,1,1,1 2225 | 0,0,0,0,0,0 2226 | 1,1,1,1,1,1 2227 | 0,0,0,0,0,0 2228 | 1,1,1,1,1,1 2229 | 1,1,1,1,1,1 2230 | 1,1,1,1,1,1 2231 | 1,1,1,1,1,1 2232 | 1,1,1,1,1,1 2233 | 1,1,1,1,1,1 2234 | 1,1,1,1,1,1 2235 | 1,1,1,1,1,1 2236 | 1,1,1,1,1,1 2237 | 1,1,1,1,1,1 2238 | 1,1,1,1,1,1 2239 | 0,0,0,0,0,0 2240 | 1,1,1,1,0,0 2241 | 1,1,1,1,1,1 2242 | 1,1,1,1,1,1 2243 | 0,0,0,0,0,0 2244 | 1,1,1,1,1,1 2245 | 1,1,1,1,1,1 2246 | 1,1,1,1,1,1 2247 | 1,1,1,1,1,1 2248 | 1,1,1,1,1,1 2249 | 1,1,1,1,1,1 2250 | 1,1,1,1,1,1 2251 | 1,1,1,1,1,1 2252 | 0,0,0,0,0,0 2253 | 0,0,0,0,0,0 2254 | 1,1,1,1,1,1 2255 | 0,0,0,0,0,0 2256 | 1,1,1,1,1,1 2257 | 1,1,1,1,1,1 2258 | 1,1,1,1,1,1 2259 | 1,1,1,1,1,1 2260 | 1,1,1,1,1,1 2261 | 1,1,1,1,1,1 2262 | 0,0,0,0,0,0 2263 | 1,1,1,1,1,1 2264 | 1,1,1,1,1,1 2265 | 0,0,0,0,0,0 2266 | 1,1,1,1,1,1 2267 | 1,1,1,1,1,1 2268 | 1,1,1,1,1,1 2269 | 1,1,1,1,1,1 2270 | 1,1,1,1,1,1 2271 | 1,1,1,1,1,1 2272 | 1,1,1,1,1,1 2273 | 1,1,1,1,1,1 2274 | 0,0,0,0,0,0 2275 | 1,1,1,1,1,0 2276 | 1,1,1,1,1,1 2277 | 1,1,1,1,1,1 2278 | 1,1,1,1,1,1 2279 | 0,0,0,0,0,0 2280 | 1,1,1,1,1,1 2281 | 1,1,1,1,1,1 2282 | 1,1,1,1,1,1 2283 | 1,1,1,1,1,1 2284 | 1,1,1,1,1,1 2285 | 1,1,1,1,1,1 2286 | 1,1,1,1,1,1 2287 | 1,1,1,1,1,1 2288 | 0,0,0,0,0,0 2289 | 1,1,1,1,1,1 2290 | 0,0,0,0,0,0 2291 | 1,1,1,1,1,1 2292 | 0,0,0,0,0,0 2293 | 0,0,0,0,0,0 2294 | 1,1,1,1,1,1 2295 | 0,0,0,0,0,0 2296 | 1,1,1,1,1,1 2297 | 1,1,1,1,1,1 2298 | 0,0,0,0,0,0 2299 | 1,1,1,1,1,1 2300 | 1,1,1,1,1,1 2301 | 1,1,1,1,1,1 2302 | 1,1,1,1,1,1 2303 | 0,0,0,0,0,0 2304 | 1,1,1,1,1,1 2305 | 1,1,1,1,1,1 2306 | 1,1,1,1,1,1 2307 | 1,1,1,1,1,1 2308 | 0,0,0,0,0,0 2309 | 1,1,1,1,0,0 2310 | 0,0,0,0,0,0 2311 | 1,1,1,1,1,1 2312 | 1,1,1,1,1,1 2313 | 1,1,1,1,1,1 2314 | 1,1,1,1,1,1 2315 | 1,1,1,1,1,1 2316 | 1,1,1,1,1,1 2317 | 1,1,1,1,1,1 2318 | 1,1,1,1,1,1 2319 | 1,1,1,1,1,1 2320 | 1,1,1,1,1,1 2321 | 1,1,1,1,1,1 2322 | 0,0,0,0,0,0 2323 | 0,0,0,0,0,0 2324 | 1,0,0,0,0,0 2325 | 0,0,0,0,0,0 2326 | 1,1,1,1,1,1 2327 | 1,1,1,1,1,1 2328 | 1,1,1,1,1,1 2329 | 1,1,1,1,1,1 2330 | 1,1,1,1,1,1 2331 | 1,1,1,1,1,1 2332 | 0,0,0,0,0,0 2333 | 1,1,1,1,1,1 2334 | 1,1,1,1,1,1 2335 | 1,1,1,1,1,1 2336 | 1,1,1,1,1,1 2337 | 1,1,1,1,1,1 2338 | 1,1,1,1,1,1 2339 | 0,0,0,0,0,0 2340 | 1,1,1,1,1,1 2341 | 0,0,0,0,0,0 2342 | 0,0,0,0,0,0 2343 | 1,1,1,1,1,0 2344 | 1,1,1,1,1,1 2345 | 1,1,1,1,1,1 2346 | 1,1,1,1,1,1 2347 | 0,0,0,0,0,0 2348 | 1,1,1,1,1,1 2349 | 1,1,1,1,1,1 2350 | 1,1,1,1,1,1 2351 | 1,1,1,1,1,1 2352 | 1,1,1,1,1,1 2353 | 1,1,1,1,1,1 2354 | 1,1,1,1,1,1 2355 | 1,1,1,1,1,1 2356 | 1,1,1,1,1,1 2357 | 0,0,0,0,0,0 2358 | 1,1,1,1,1,1 2359 | 1,1,1,1,1,1 2360 | 1,1,1,1,1,1 2361 | 1,1,1,1,1,1 2362 | 1,1,1,1,1,1 2363 | 1,1,1,1,1,1 2364 | 1,1,1,1,1,1 2365 | 1,1,1,1,1,1 2366 | 1,1,1,1,1,1 2367 | 1,1,1,1,1,1 2368 | 1,1,1,1,1,1 2369 | 1,1,1,1,1,1 2370 | 1,1,1,1,1,1 2371 | 0,0,0,0,0,0 2372 | 1,1,1,1,1,1 2373 | 1,1,1,1,1,1 2374 | 1,1,1,1,1,1 2375 | 0,0,0,0,0,0 2376 | 1,1,1,1,1,1 2377 | 1,1,1,1,1,1 2378 | 1,1,1,1,1,1 2379 | 1,1,1,1,1,1 2380 | 1,1,1,1,1,1 2381 | 1,1,1,1,1,1 2382 | 0,0,0,0,0,0 2383 | 0,0,0,0,0,0 2384 | 1,1,1,1,1,1 2385 | 1,1,1,1,1,1 2386 | 1,1,1,1,1,1 2387 | 1,1,1,1,1,1 2388 | 1,1,1,1,1,1 2389 | 0,0,0,0,0,0 2390 | 1,1,1,1,1,1 2391 | 1,1,1,1,1,1 2392 | 1,1,1,1,1,1 2393 | 1,1,1,1,1,1 2394 | 1,1,1,1,1,1 2395 | 0,0,0,0,0,0 2396 | 1,1,1,1,1,1 2397 | 1,1,1,1,1,1 2398 | 1,1,1,1,1,1 2399 | 1,1,1,1,1,1 2400 | 0,0,0,0,0,0 2401 | 1,1,1,1,1,1 2402 | 1,1,1,1,1,1 2403 | 0,0,0,0,0,0 2404 | 1,1,1,1,1,1 2405 | 1,1,1,1,1,1 2406 | 1,1,1,1,1,1 2407 | 0,0,0,0,0,0 2408 | 0,0,0,0,0,0 2409 | 1,1,1,1,1,1 2410 | 1,1,1,1,1,1 2411 | 1,1,1,1,1,1 2412 | 1,1,1,1,1,1 2413 | 1,1,1,1,1,1 2414 | 0,0,0,0,0,0 2415 | 1,1,1,1,1,1 2416 | 1,0,0,0,0,0 2417 | 1,1,1,1,1,1 2418 | 1,1,1,1,0,0 2419 | 1,1,1,1,1,1 2420 | 1,1,1,1,1,1 2421 | 1,1,1,1,1,1 2422 | 1,1,1,1,1,1 2423 | 1,1,1,1,1,1 2424 | 0,0,0,0,0,0 2425 | 0,0,0,0,0,0 2426 | 0,0,0,0,0,0 2427 | 1,1,1,1,1,1 2428 | 1,1,1,1,1,1 2429 | 0,0,0,0,0,0 2430 | 1,1,1,1,1,1 2431 | 0,0,0,0,0,0 2432 | 1,1,1,1,1,1 2433 | 1,1,1,1,1,1 2434 | 1,1,1,1,1,1 2435 | 0,0,0,0,0,0 2436 | 1,1,1,1,1,1 2437 | 1,1,1,1,1,1 2438 | 1,1,1,1,1,1 2439 | 0,0,0,0,0,0 2440 | 1,1,1,1,1,1 2441 | 1,1,1,1,1,1 2442 | 1,1,1,1,1,1 2443 | 1,1,1,1,1,1 2444 | 1,1,1,1,1,1 2445 | 0,0,0,0,0,0 2446 | 1,1,1,1,1,1 2447 | 1,1,1,1,1,1 2448 | 1,1,1,1,1,1 2449 | 1,1,1,1,1,1 2450 | 1,1,1,1,1,1 2451 | 1,1,1,1,1,1 2452 | 1,1,1,1,1,1 2453 | 0,0,0,0,0,0 2454 | 1,1,1,1,1,1 2455 | 1,1,1,1,1,1 2456 | 1,1,1,1,1,1 2457 | 1,1,1,1,0,0 2458 | 0,0,0,0,0,0 2459 | 0,0,0,0,0,0 2460 | 1,1,1,1,1,1 2461 | 1,1,1,1,1,1 2462 | 1,1,1,1,1,1 2463 | 1,1,1,1,1,1 2464 | 1,1,1,1,1,1 2465 | 1,1,0,0,0,0 2466 | 0,0,0,0,0,0 2467 | 1,1,1,1,1,1 2468 | 1,1,1,1,1,1 2469 | 1,1,1,1,1,1 2470 | 1,1,1,1,1,1 2471 | 1,1,1,1,1,1 2472 | 0,0,0,0,0,0 2473 | 0,0,0,0,0,0 2474 | 0,0,0,0,0,0 2475 | 1,1,1,1,1,1 2476 | 1,1,1,1,1,1 2477 | 1,1,1,1,1,1 2478 | 1,1,1,1,0,0 2479 | 1,1,1,1,1,1 2480 | 1,1,1,1,1,1 2481 | 1,1,1,1,1,1 2482 | 1,1,1,1,1,1 2483 | 1,1,1,1,1,1 2484 | 1,1,1,1,1,1 2485 | 1,1,1,1,0,0 2486 | 1,1,1,1,1,1 2487 | 1,1,1,1,1,1 2488 | 1,1,1,1,1,1 2489 | 0,0,0,0,0,0 2490 | 1,1,1,1,1,1 2491 | 0,0,0,0,0,0 2492 | 1,1,1,1,1,1 2493 | 1,1,1,1,1,1 2494 | 1,1,1,1,1,1 2495 | 1,1,1,1,1,1 2496 | 1,1,1,1,1,1 2497 | 1,1,1,1,1,1 2498 | 0,0,0,0,0,0 2499 | 0,0,0,0,0,0 2500 | 0,0,0,0,0,0 2501 | 0,0,0,0,0,0 2502 | 0,0,0,0,0,0 2503 | 0,0,0,0,0,0 2504 | 1,1,1,1,1,1 2505 | 1,1,1,1,1,1 2506 | 0,0,0,0,0,0 2507 | 1,1,1,1,1,1 2508 | 0,0,0,0,0,0 2509 | 1,1,1,1,1,1 2510 | 1,1,1,1,1,1 2511 | 1,1,1,1,1,1 2512 | 1,1,1,1,1,1 2513 | 1,1,1,1,1,1 2514 | 1,1,1,1,1,1 2515 | 1,1,1,1,1,1 2516 | 1,1,1,1,1,1 2517 | 1,1,1,1,1,1 2518 | 0,0,0,0,0,0 2519 | 1,1,1,1,1,1 2520 | 1,1,1,1,1,1 2521 | 1,1,1,1,1,1 2522 | 1,1,1,1,1,1 2523 | 0,0,0,0,0,0 2524 | 0,0,0,0,0,0 2525 | 1,1,1,1,1,1 2526 | 1,1,1,1,1,1 2527 | 1,1,1,1,1,1 2528 | 1,1,1,1,1,1 2529 | 1,1,1,1,1,1 2530 | 1,1,1,1,1,1 2531 | 1,1,1,1,1,1 2532 | 0,0,0,0,0,0 2533 | 1,1,1,1,1,1 2534 | 1,1,1,1,1,1 2535 | 1,1,1,1,1,1 2536 | 0,0,0,0,0,0 2537 | 0,0,0,0,0,0 2538 | 0,0,0,0,0,0 2539 | 1,1,1,1,1,1 2540 | 1,1,1,1,1,1 2541 | 1,1,1,1,1,1 2542 | 1,1,1,1,1,1 2543 | 1,1,1,1,1,1 2544 | 1,1,1,1,1,1 2545 | 0,0,0,0,0,0 2546 | 0,0,0,0,0,0 2547 | 1,1,1,1,1,1 2548 | 1,1,1,1,1,1 2549 | 1,1,1,1,1,1 2550 | 1,1,1,1,1,1 2551 | 1,1,1,1,1,1 2552 | 0,0,0,0,0,0 2553 | 1,1,1,1,1,1 2554 | 1,1,1,1,1,1 2555 | 1,1,1,1,1,1 2556 | 1,1,1,1,1,1 2557 | 1,1,1,1,1,1 2558 | 1,1,1,1,1,1 2559 | 1,1,1,1,1,1 2560 | 1,1,1,1,1,1 2561 | 1,1,1,1,1,1 2562 | 0,0,0,0,0,0 2563 | 1,1,1,1,1,1 2564 | 1,1,1,1,1,1 2565 | 1,1,1,1,1,1 2566 | 1,1,1,1,1,1 2567 | 1,1,1,1,1,1 2568 | 0,0,0,0,0,0 2569 | 1,1,1,1,1,1 2570 | 1,1,1,1,1,1 2571 | 0,0,0,0,0,0 2572 | 0,0,0,0,0,0 2573 | 0,0,0,0,0,0 2574 | 1,1,1,1,1,1 2575 | 0,0,0,0,0,0 2576 | 1,1,1,1,1,1 2577 | 0,0,0,0,0,0 2578 | 1,1,1,1,1,1 2579 | 1,1,1,1,1,1 2580 | 1,1,1,1,1,1 2581 | 1,1,1,1,1,1 2582 | 0,0,0,0,0,0 2583 | 0,0,0,0,0,0 2584 | 1,1,1,1,1,1 2585 | 1,1,1,1,1,1 2586 | 1,1,1,1,1,1 2587 | 1,1,1,1,1,1 2588 | 1,1,1,1,1,1 2589 | 1,1,1,1,1,1 2590 | 1,1,1,1,1,1 2591 | 1,1,1,1,1,1 2592 | 1,1,1,1,1,1 2593 | 1,1,1,1,1,1 2594 | 1,1,1,1,1,1 2595 | 1,1,1,1,1,1 2596 | 1,1,1,1,1,1 2597 | 1,1,1,1,1,1 2598 | 1,1,1,1,1,1 2599 | 1,1,1,1,1,1 2600 | 1,1,1,1,1,1 2601 | 1,1,1,1,1,1 2602 | 1,1,1,1,1,1 2603 | 1,1,1,1,1,1 2604 | 1,1,1,1,1,1 2605 | 0,0,0,0,0,0 2606 | 0,0,0,0,0,0 2607 | 0,0,0,0,0,0 2608 | 0,0,0,0,0,0 2609 | 0,0,0,0,0,0 2610 | 1,1,1,1,1,1 2611 | 1,1,1,1,1,1 2612 | 0,0,0,0,0,0 2613 | 1,1,1,1,1,1 2614 | 0,0,0,0,0,0 2615 | 0,0,0,0,0,0 2616 | 1,1,1,1,1,1 2617 | 1,1,1,1,1,1 2618 | 1,1,1,1,1,1 2619 | 1,1,1,1,1,1 2620 | 1,1,1,1,1,1 2621 | 1,1,1,1,1,1 2622 | 1,1,1,1,1,1 2623 | 0,0,0,0,0,0 2624 | 1,1,1,1,1,1 2625 | 0,0,0,0,0,0 2626 | 1,1,1,1,1,1 2627 | 1,1,1,1,1,1 2628 | 1,1,1,1,1,1 2629 | 0,0,0,0,0,0 2630 | 0,0,0,0,0,0 2631 | 0,0,0,0,0,0 2632 | 0,0,0,0,0,0 2633 | 0,0,0,0,0,0 2634 | 0,0,0,0,0,0 2635 | 0,0,0,0,0,0 2636 | 1,1,1,1,1,1 2637 | 1,1,1,1,1,1 2638 | 1,1,1,1,1,1 2639 | 1,1,1,1,1,1 2640 | 1,1,1,1,1,1 2641 | 1,1,1,1,1,1 2642 | 0,0,0,0,0,0 2643 | 1,1,1,1,1,1 2644 | 1,1,1,1,1,1 2645 | 0,0,0,0,0,0 2646 | 1,1,1,1,1,1 2647 | 1,1,1,1,1,1 2648 | 1,1,1,1,1,1 2649 | 1,1,1,1,1,0 2650 | 1,1,1,1,1,1 2651 | 1,1,1,1,1,1 2652 | 0,0,0,0,0,0 2653 | 0,0,0,0,0,0 2654 | 1,1,1,1,1,1 2655 | 1,1,1,1,1,1 2656 | 1,1,1,1,1,1 2657 | 1,1,1,1,1,1 2658 | 0,0,0,0,0,0 2659 | 1,1,0,0,0,0 2660 | 1,1,1,1,1,1 2661 | 1,1,1,1,1,1 2662 | 1,1,1,1,1,1 2663 | 1,1,1,1,1,1 2664 | 1,1,1,1,1,1 2665 | 0,0,0,0,0,0 2666 | 0,0,0,0,0,0 2667 | 1,1,1,1,1,1 2668 | 0,0,0,0,0,0 2669 | 0,0,0,0,0,0 2670 | 0,0,0,0,0,0 2671 | 1,1,1,1,1,1 2672 | 1,1,1,1,1,1 2673 | 1,1,1,1,1,1 2674 | 1,1,1,1,1,1 2675 | 1,1,1,1,1,1 2676 | 1,1,1,1,1,1 2677 | 0,0,0,0,0,0 2678 | 1,1,1,1,1,1 2679 | 1,1,1,1,1,1 2680 | 0,0,0,0,0,0 2681 | 1,1,1,1,1,1 2682 | 1,1,1,1,1,1 2683 | 1,1,1,1,1,1 2684 | 1,1,1,1,1,1 2685 | 1,1,1,1,1,1 2686 | 0,0,0,0,0,0 2687 | 1,1,1,1,1,1 2688 | 1,1,1,1,1,1 2689 | 1,1,1,0,0,0 2690 | 1,1,1,1,1,1 2691 | 1,1,1,1,1,1 2692 | 0,0,0,0,0,0 2693 | 0,0,0,0,0,0 2694 | 1,1,1,1,1,1 2695 | 0,0,0,0,0,0 2696 | 1,1,1,1,1,1 2697 | 1,1,1,1,1,1 2698 | 0,0,0,0,0,0 2699 | 0,0,0,0,0,0 2700 | 0,0,0,0,0,0 2701 | 0,0,0,0,0,0 2702 | 0,0,0,0,0,0 2703 | 0,0,0,0,0,0 2704 | 0,0,0,0,0,0 2705 | 0,0,0,0,0,0 2706 | 0,0,0,0,0,0 2707 | 1,1,1,1,1,0 2708 | 1,1,1,1,1,0 2709 | 0,0,0,0,0,0 2710 | 1,1,1,1,1,0 2711 | 1,1,1,1,1,1 2712 | 1,1,1,1,1,1 2713 | 1,1,1,1,1,1 2714 | 1,1,1,1,1,0 2715 | 1,1,1,1,1,1 2716 | 1,1,1,1,1,1 2717 | 1,1,1,1,1,1 2718 | 0,0,0,0,0,0 2719 | 1,1,1,1,1,1 2720 | 1,1,1,1,1,1 2721 | 1,1,1,1,1,1 2722 | 1,1,1,1,1,1 2723 | 1,1,1,1,1,1 2724 | 1,1,1,1,1,1 2725 | 0,0,0,0,0,0 2726 | 1,1,1,1,1,1 2727 | 1,1,1,1,1,1 2728 | 1,1,1,1,1,1 2729 | 1,1,1,1,1,1 2730 | 1,1,1,1,1,1 2731 | 1,1,1,1,1,1 2732 | 1,1,1,1,1,1 2733 | 1,1,1,1,1,1 2734 | 0,0,0,0,0,0 2735 | 1,1,1,1,1,1 2736 | 1,1,1,1,1,1 2737 | 1,1,1,1,1,1 2738 | 1,1,1,1,1,1 2739 | 0,0,0,0,0,0 2740 | 1,1,1,1,1,1 2741 | 1,1,1,1,1,1 2742 | 1,1,1,1,1,1 2743 | 1,1,1,1,1,1 2744 | 1,1,1,1,1,1 2745 | 1,1,1,1,1,1 2746 | 1,1,1,1,1,1 2747 | 0,0,0,0,0,0 2748 | 0,0,0,0,0,0 2749 | 1,1,1,1,1,1 2750 | 0,0,0,0,0,0 2751 | 0,0,0,0,0,0 2752 | 1,1,1,1,1,0 2753 | 0,0,0,0,0,0 2754 | 0,0,0,0,0,0 2755 | 1,1,1,1,1,1 2756 | 1,1,1,1,1,0 2757 | 1,1,1,1,1,0 2758 | 1,1,1,1,1,1 2759 | 1,1,1,1,1,1 2760 | 0,0,0,0,0,0 2761 | 1,1,1,1,1,1 2762 | 1,1,1,1,1,0 2763 | 1,1,1,1,1,0 2764 | 0,0,0,0,0,0 2765 | 1,1,1,1,1,0 2766 | 1,1,1,1,1,0 2767 | 1,0,0,0,0,0 2768 | 0,0,0,0,0,0 2769 | 1,1,1,1,1,1 2770 | 0,0,0,0,0,0 2771 | 0,0,0,0,0,0 2772 | 0,0,0,0,0,0 2773 | 1,1,1,1,1,0 2774 | 1,1,1,1,1,0 2775 | 0,0,0,0,0,0 2776 | 0,0,0,0,0,0 2777 | 0,0,0,0,0,0 2778 | 1,1,1,1,1,0 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-------------------------------------------------------------------------------- /data/scores/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wang-haiyang/ppd_model/43c0237f3fbb4faf35e9026bf1a5e171e19eb5b4/data/scores/.DS_Store -------------------------------------------------------------------------------- /data/scores/deeplearning/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wang-haiyang/ppd_model/43c0237f3fbb4faf35e9026bf1a5e171e19eb5b4/data/scores/deeplearning/.DS_Store -------------------------------------------------------------------------------- /data/scores/deeplearning/scores.csv: -------------------------------------------------------------------------------- 1 | 2016-04 2 | 0.783606557377,0.788524590164,0.795081967213,0.954098360656,0.924590163934, 3 | 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0,菠萝理财,0.97 117 | 0,银魅财富,0.97 118 | 0,穗金所,0.97 119 | 0,乐驰金服,0.97 120 | 1,基石在线·网贷,0.97 121 | 0,微车融,0.97 122 | 0,小小理财,0.97 123 | 0,旺财谷,0.97 124 | 0,招财网,0.97 125 | 0,利巨人,0.97 126 | 0,隆隆网,0.97 127 | 0,千店贷,0.97 128 | 0,仓储贷,0.97 129 | 0,中融宝,0.97 130 | 0,苏融贷,0.97 131 | 0,景金贷,0.97 132 | 0,爱钱帮,0.97 133 | 0,鉴丰金融,0.97 134 | 0,百融网,0.97 135 | 0,黄河金融,0.97 136 | 0,创益100,0.97 137 | 0,海英财富,0.97 138 | 0,我投点,0.97 139 | 0,玖融网,0.97 140 | 0,好利网,0.97 141 | 0,启腾财富,0.97 142 | 0,华策在线,0.97 143 | 0,浙金网,0.97 144 | 0,长江创投,0.97 145 | 0,正大财富,0.97 146 | 0,有鱼金服,0.97 147 | 0,中潮财富,0.97 148 | 0,钱道网,0.97 149 | 0,格林易贷,0.97 150 | 0,钱增增,0.97 151 | 0,爱银承,0.96 152 | 0,信投在线,0.96 153 | 0,小算盘,0.96 154 | 0,中融通贷,0.96 155 | 0,甬e贷,0.96 156 | 0,智富360,0.96 157 | 0,国富通,0.96 158 | 0,天池资本,0.96 159 | 0,荷银商务,0.96 160 | 0,光合种子,0.96 161 | 0,囧羊理财,0.96 162 | 0,电网贷,0.96 163 | 0,小油菜,0.96 164 | 1,申融财富,0.96 165 | 0,沃投资,0.96 166 | 0,库天下,0.96 167 | 0,酷融贷,0.96 168 | 0,千金e诺,0.96 169 | 1,浙楚金融,0.96 170 | 0,隆筹金融,0.96 171 | 0,广佛e家,0.96 172 | 0,信德过,0.96 173 | 0,放心贷,0.96 174 | 0,兴中宝,0.96 175 | 0,金融一号店,0.96 176 | 0,融裕贷,0.96 177 | 0,金控网贷,0.96 178 | 0,融头金融,0.96 179 | 0,安心贷,0.96 180 | 0,91旺财,0.96 181 | 0,艺融网,0.96 182 | 0,礼德财富,0.96 183 | 0,精融汇,0.96 184 | 0,汇票通,0.96 185 | 0,潮人贷,0.96 186 | 0,金梧桐,0.96 187 | 0,可溯贷,0.96 188 | 0,融之家,0.96 189 | 0,淘金家,0.96 190 | 0,领投羊,0.96 191 | 0,房金所,0.96 192 | 0,百达金融,0.96 193 | 0,创利投,0.96 194 | 0,壹理财,0.96 195 | 0,助赢普惠,0.96 196 | 0,中鑫投融,0.96 197 | 0,亿钱贷,0.96 198 | 0,顺顺贷,0.96 199 | 0,壹禾贷,0.96 200 | 0,希望金融,0.96 201 | 0,九坤贷,0.96 202 | 0,前海红筹,0.96 203 | 0,冀贷通,0.96 204 | 0,贷贷兴隆,0.96 205 | 0,丰投网,0.96 206 | 0,车富88,0.96 207 | 0,投资吧,0.96 208 | 0,麦钱网,0.96 209 | 0,理财团,0.96 210 | 0,钱趣网,0.96 211 | 0,懒猪理财,0.96 212 | 0,小宝金融,0.96 213 | 0,仁我行,0.96 214 | 0,e路同心,0.96 215 | 0,民民贷,0.96 216 | 0,聚火投资,0.96 217 | 0,快快贷,0.96 218 | 0,融租E投,0.96 219 | 0,红海贷,0.96 220 | 0,信和贷,0.96 221 | 0,财富观,0.95 222 | 0,58财福,0.95 223 | 0,方橙式,0.95 224 | 0,互融宝,0.95 225 | 0,银巴克,0.95 226 | 0,51信托,0.95 227 | 0,微投天下,0.95 228 | 0,欣欣贷,0.95 229 | 0,小鹅e贷,0.95 230 | 0,金佰汇金融,0.95 231 | 0,柳e贷,0.95 232 | 0,融通资产,0.95 233 | 0,新沃金融,0.95 234 | 0,万富宝,0.95 235 | 0,楚财友道,0.95 236 | 0,人文贷,0.95 237 | 0,新融网,0.95 238 | 1,汇贷中国,0.95 239 | 0,家合资本,0.95 240 | 0,典金所,0.95 241 | 0,富瑞财富,0.95 242 | 0,通宝金服,0.95 243 | 0,百泉贷,0.95 244 | 0,奇子贷,0.95 245 | 0,为为贷,0.95 246 | 0,福赢万宝,0.95 247 | 0,付融宝,0.95 248 | 0,酷宝盒,0.95 249 | 0,点车成金,0.95 250 | 0,利盈贷,0.95 251 | 0,富通财富,0.95 252 | 0,知商金融,0.95 253 | 0,致盈贷,0.95 254 | 0,乐投天下,0.95 255 | 0,增值e,0.95 256 | 1,深金在线,0.95 257 | 0,能成财富,0.95 258 | 0,融资易,0.95 259 | 0,网利宝,0.95 260 | 0,金融工场,0.95 261 | 1,徽金所,0.95 262 | 0,易简贷,0.95 263 | 0,汇投网,0.95 264 | 0,领投鸟理财,0.95 265 | 0,皖都金融,0.95 266 | 0,理财乐,0.95 267 | 0,东泽财富,0.95 268 | 0,鑫悦贷,0.95 269 | 0,房融所,0.95 270 | 0,向上金服,0.95 271 | 0,简易贷,0.95 272 | 0,优微贷,0.95 273 | 0,彩麒麟,0.95 274 | 0,元鑫贷,0.95 275 | 0,银象网,0.95 276 | 1,爱财网,0.95 277 | 0,搜贷天下,0.95 278 | 0,蜗牛客,0.95 279 | 0,有融网,0.95 280 | 0,真鑫贷,0.95 281 | 0,众信金融(京),0.95 282 | 0,支点金融,0.95 283 | 0,聚融在线,0.95 284 | 0,牛娃互联网金融,0.94 285 | 0,爱财在线,0.94 286 | 0,借贷之家,0.94 287 | 0,汇投资,0.94 288 | 0,一一贷,0.94 289 | 0,点金融,0.94 290 | 0,安信聚贷,0.94 291 | 0,币丰所,0.94 292 | 0,中瑞财富,0.94 293 | 0,搜搜金融网,0.94 294 | 0,银港在线,0.94 295 | 0,金砖联合,0.94 296 | 0,聚财宝,0.94 297 | 0,众信所,0.94 298 | 0,优车融,0.94 299 | 0,众易贷,0.94 300 | 0,左右逢园,0.94 301 | 0,融贝网,0.94 302 | 0,即利网,0.94 303 | 0,易合贷,0.94 304 | 1,金柿子,0.94 305 | 0,君融贷,0.94 306 | 0,金桥梁,0.94 307 | 0,信投财富,0.94 308 | 0,54贷客,0.94 309 | 0,宝象金融,0.94 310 | 0,钱保姆,0.94 311 | 0,普天贷,0.94 312 | 0,金宝保,0.94 313 | 0,冀鑫财富,0.94 314 | 0,e人e贷,0.94 315 | 0,钱庄网,0.94 316 | 0,理融宝,0.94 317 | 0,融投贷,0.94 318 | 0,中鼎在线,0.94 319 | 0,飞特金融,0.94 320 | 0,瀚翔e家,0.94 321 | 0,乾贷网,0.94 322 | 0,投宝金融,0.94 323 | 0,布袋资产,0.94 324 | 0,宜保通贷,0.94 325 | 0,惠众金融,0.94 326 | 0,金融圈,0.94 327 | 0,红顶金融,0.94 328 | 0,投德宝,0.94 329 | 0,宝点网,0.94 330 | 0,金福财富,0.94 331 | 0,如意财富,0.94 332 | 0,祺天优贷,0.94 333 | 0,薪金融,0.94 334 | 0,协众金融,0.94 335 | 0,贷先生,0.94 336 | 0,锦绣钱程,0.94 337 | 0,京都贷,0.94 338 | 0,蜜蜂财富,0.94 339 | 0,安平贷,0.94 340 | 0,百利市,0.94 341 | 0,丰收贷理财,0.94 342 | 0,天天理财,0.94 343 | 0,mM贷,0.94 344 | 0,楚金所,0.94 345 | 0,互助金服,0.94 346 | 0,向日葵贷,0.94 347 | 0,睿信财富,0.94 348 | 0,中赢投,0.94 349 | 0,满满贷,0.94 350 | 0,酷贷网,0.94 351 | 0,爱贷网,0.94 352 | 0,金牌理财,0.94 353 | 0,新富金融,0.94 354 | 0,玖信贷,0.94 355 | 1,建辉贷,0.94 356 | 0,合力贷,0.94 357 | 0,聚众金融,0.94 358 | 0,蜂投网,0.94 359 | 0,产融贷,0.94 360 | 0,民贷网,0.94 361 | 0,杉易贷,0.94 362 | 0,易天贷,0.94 363 | 0,小鹅网,0.94 364 | 0,e趣钱袋,0.94 365 | 0,九斗鱼,0.94 366 | 0,钱吧,0.94 367 | 0,渝都贷,0.94 368 | 1,校园贷,0.9299999999999999 369 | 0,车融资,0.9299999999999999 370 | 0,泰和网,0.9299999999999999 371 | 0,老账房,0.9299999999999999 372 | 0,今日捷财,0.9299999999999999 373 | 0,实在贷,0.9299999999999999 374 | 0,汇泉贷,0.9299999999999999 375 | 0,简单理财网,0.9299999999999999 376 | 0,前海P2P,0.9299999999999999 377 | 0,海金所,0.9299999999999999 378 | 0,汇钱庄,0.9299999999999999 379 | 0,星时代金融,0.9299999999999999 380 | 0,合众e贷,0.9299999999999999 381 | 0,微金易贷,0.9299999999999999 382 | 0,金银猫,0.9299999999999999 383 | 0,达人贷,0.9299999999999999 384 | 0,粒子财富,0.9299999999999999 385 | 0,深佰信,0.9299999999999999 386 | 0,三好贷,0.9299999999999999 387 | 0,华夏银帮,0.9299999999999999 388 | 0,银亿通网贷,0.9299999999999999 389 | 0,小平贷,0.9299999999999999 390 | 0,立马贷,0.9299999999999999 391 | 0,蚂蚁打的,0.9299999999999999 392 | 0,苏银财富,0.9299999999999999 393 | 0,保浩财富,0.9299999999999999 394 | 0,安宜投,0.9299999999999999 395 | 0,聚融易贷,0.9299999999999999 396 | 0,古德金融超市,0.9299999999999999 397 | 0,第一房贷,0.9299999999999999 398 | 0,中恒宝,0.9299999999999999 399 | 0,民生转赚,0.9299999999999999 400 | 0,广信贷,0.9299999999999999 401 | 0,景云金融,0.9299999999999999 402 | 0,钻库网,0.9299999999999999 403 | 1,九州宏瑞,0.9299999999999999 404 | 0,固金所,0.9299999999999999 405 | 0,壹金所,0.9299999999999999 406 | 0,印子坊,0.9299999999999999 407 | 0,宁创贷,0.9299999999999999 408 | 0,网筹金融,0.9299999999999999 409 | 0,一贷添交,0.9299999999999999 410 | 0,微金所,0.9299999999999999 411 | 0,橙旗贷,0.9299999999999999 412 | 0,互贷网,0.9299999999999999 413 | 0,麻袋理财,0.9299999999999999 414 | 0,好又贷,0.9299999999999999 415 | 0,利得行,0.9299999999999999 416 | 0,金钥匙网贷,0.9299999999999999 417 | 0,钱爸爸,0.9299999999999999 418 | 0,欧亚金融,0.9299999999999999 419 | 0,聚力金融,0.9299999999999999 420 | 0,惠投无忧,0.9299999999999999 421 | 0,金信网,0.9299999999999999 422 | 0,襄金所,0.9299999999999999 423 | 0,微邦金融,0.9299999999999999 424 | 0,投资去,0.9299999999999999 425 | 0,分钱网,0.9299999999999999 426 | 0,楚天财富网,0.9299999999999999 427 | 0,鄂金所,0.9299999999999999 428 | 0,民合贷,0.9299999999999999 429 | 0,易保利,0.9299999999999999 430 | 0,甜菜金融,0.9299999999999999 431 | 0,银票网,0.9299999999999999 432 | 0,阿凡达e贷,0.9299999999999999 433 | 1,中银号,0.9299999999999999 434 | 0,运盈e贷,0.9299999999999999 435 | 0,诺诺镑客,0.9299999999999999 436 | 0,梦想盈行,0.9299999999999999 437 | 0,任我贷,0.9299999999999999 438 | 0,猴子理财,0.9299999999999999 439 | 1,芝麻金融,0.9299999999999999 440 | 0,袋袋金,0.9299999999999999 441 | 0,丁丁贷,0.9299999999999999 442 | 0,可投可贷,0.92 443 | 0,招商贷,0.92 444 | 0,创世介贷,0.92 445 | 0,聚金资本,0.92 446 | 1,众信在线,0.92 447 | 0,开鑫贷,0.92 448 | 0,金屋魔方,0.92 449 | 1,武汉贷,0.92 450 | 0,十六铺金融,0.92 451 | 0,拓道金服,0.92 452 | 0,小狗钱钱,0.92 453 | 0,两只老虎,0.92 454 | 0,甬红财富,0.92 455 | 0,众利网,0.92 456 | 0,爱财狼,0.92 457 | 0,果树财富,0.92 458 | 0,金谷网盈,0.92 459 | 0,冰川财富,0.92 460 | 0,悦车贷,0.92 461 | 0,邦邦贷,0.92 462 | 0,速贷融创,0.92 463 | 0,达飞贷,0.92 464 | 1,COLA贷,0.92 465 | 0,万利泓通,0.92 466 | 0,众财金桥,0.92 467 | 0,东方车贷,0.92 468 | 0,首鸿财富,0.92 469 | 1,广惠人,0.92 470 | 0,股易贷金融网,0.92 471 | 0,合时代,0.92 472 | 0,优选宝,0.92 473 | 0,国惠金融,0.92 474 | 0,八戒宝,0.92 475 | 1,成融理财,0.92 476 | 0,量子金融,0.92 477 | 0,金柜贷,0.92 478 | 0,联投金融,0.92 479 | 0,多宝贷,0.92 480 | 1,小麦创投,0.92 481 | 0,众信易贷,0.92 482 | 0,浦小宝,0.92 483 | 0,最易贷,0.92 484 | 0,银豆网,0.92 485 | 0,恒丰钱包,0.92 486 | 0,企易贷,0.92 487 | 0,三信贷,0.92 488 | 0,融圆财富,0.92 489 | 0,东方汇,0.92 490 | 0,要借钱网,0.92 491 | 0,汇金益,0.92 492 | 0,米缸金融,0.92 493 | 0,新联在线,0.92 494 | 0,新纪元财富,0.92 495 | 0,钱在线,0.92 496 | 0,阿朋贷,0.92 497 | 0,五岳贷,0.92 498 | 0,亿富贷,0.92 499 | 0,可信贷,0.92 500 | 0,宝元贷,0.92 501 | 0,玖盈贷,0.92 502 | 0,倍多金,0.92 503 | 0,优聚投,0.92 504 | 0,利融网,0.92 505 | 0,e微贷,0.92 506 | 0,生菜金融,0.92 507 | 0,京金所,0.92 508 | 0,葫芦金融,0.92 509 | 0,卓安e贷,0.92 510 | 0,万盈金融,0.92 511 | 0,多多智富,0.92 512 | 0,优先贷,0.92 513 | 0,开心贷,0.91 514 | 0,粤商贷,0.91 515 | 0,鑫金所,0.91 516 | 0,宏骏时贷,0.91 517 | 1,涛财网,0.91 518 | 0,乐钱 ,0.91 519 | 1,亿峰借贷,0.91 520 | 0,金海贷,0.91 521 | 0,拉拉财富,0.91 522 | 0,珠宝盒子,0.91 523 | 0,日益网,0.91 524 | 0,融远财富,0.91 525 | 0,口碑贷,0.91 526 | 0,药业贷,0.91 527 | 0,多利宝,0.91 528 | 0,够力金融,0.91 529 | 0,众投未来,0.91 530 | 0,和合速融,0.91 531 | 0,e周行,0.91 532 | 0,贷贷网,0.91 533 | 0,道口贷,0.91 534 | 0,财来网,0.91 535 | 0,掌中财富,0.91 536 | 0,壹佰金融,0.91 537 | 0,联鑫贷,0.91 538 | 0,海投汇,0.91 539 | 1,深速贷,0.91 540 | 0,新博贷,0.91 541 | 0,抱财网,0.91 542 | 0,一诚一贷,0.91 543 | 0,聚有财,0.91 544 | 0,桔子理财,0.91 545 | 0,投资啦,0.91 546 | 0,钱诚理财,0.91 547 | 0,人人聚财,0.91 548 | 0,金花顺金融网,0.91 549 | 1,空中贷,0.91 550 | 1,天使计划,0.91 551 | 0,联众在线,0.91 552 | 1,贝尔创投,0.91 553 | 1,哈哈贷,0.91 554 | 0,聚诚贷,0.91 555 | 1,给力贷,0.91 556 | 0,金票通,0.91 557 | 1,蚂蚁贷,0.91 558 | 1,学生借贷网,0.91 559 | 1,利他人,0.91 560 | 0,金麦子,0.91 561 | 0,善行创投,0.91 562 | 0,缘天金服,0.91 563 | 0,久融金融,0.91 564 | 0,久金所,0.91 565 | 1,信他贷,0.91 566 | 0,宁商e贷,0.91 567 | 0,派财网,0.91 568 | 0,牛牛bank,0.91 569 | 1,鼎力贷,0.91 570 | 0,神州通宝,0.91 571 | 0,乐投壹佰,0.91 572 | 0,恒大财富,0.91 573 | 1,银畅金融,0.91 574 | 0,金易融,0.91 575 | 1,贷贷通,0.91 576 | 1,财金所,0.91 577 | 0,钱海湾金融,0.91 578 | 0,信贷通,0.91 579 | 0,e兴金融,0.91 580 | 0,盼贷网,0.9 581 | 0,168理财网,0.9 582 | 0,玉器贷,0.9 583 | 0,创具财富,0.9 584 | 0,盈盈理财,0.9 585 | 0,明志在线,0.9 586 | 0,易众网,0.9 587 | 0,力帆善融,0.9 588 | 0,轩辕财富,0.9 589 | 1,渤海创投,0.9 590 | 0,爱投投,0.9 591 | 0,人人融,0.9 592 | 0,安盈创富,0.9 593 | 1,金融聚投,0.9 594 | 1,天翼贷,0.9 595 | 1,金油所,0.9 596 | 0,融和贷,0.9 597 | 1,快时贷,0.9 598 | 0,第一理财,0.9 599 | 0,大唐e贷,0.9 600 | 0,钱趣多,0.9 601 | 0,掮客贷,0.9 602 | 0,趣钱,0.9 603 | 0,欧拉宝,0.9 604 | 0,金瑞龙,0.9 605 | 1,互利贷,0.9 606 | 0,月光宝盒,0.9 607 | 0,云钱袋,0.9 608 | 0,本利网,0.9 609 | 0,长征金融,0.9 610 | 1,非诚勿贷,0.9 611 | 0,合安易贷,0.9 612 | 0,中金贷,0.9 613 | 0,钱胖胖,0.9 614 | 0,利人金融（沪）,0.9 615 | 1,富宏金融,0.9 616 | 0,福银票号,0.9 617 | 0,创联汇通,0.9 618 | 0,信东创赢,0.9 619 | 0,易九金融,0.9 620 | 0,车e融,0.9 621 | 0,中投融,0.9 622 | 0,纳泓金服,0.9 623 | 0,利金行,0.9 624 | 0,ok贷,0.9 625 | 0,有车贷,0.9 626 | 1,同城人人贷,0.9 627 | 0,金开贷,0.9 628 | 0,九信金融,0.9 629 | 0,共富网,0.9 630 | 0,汇富宝,0.9 631 | 0,民宝贷,0.9 632 | 0,星月创投,0.9 633 | 0,融信网,0.9 634 | 1,易街金融,0.9 635 | 0,帮友贷,0.9 636 | 0,诺米宝,0.9 637 | 0,轻创贷,0.9 638 | 0,金e贷,0.9 639 | 0,润锦众成,0.9 640 | 0,小马金融,0.9 641 | 0,妥妥当,0.9 642 | 0,88财富网,0.9 643 | 0,爱本地,0.9 644 | 0,乐投贷,0.9 645 | 0,360金融,0.9 646 | 0,浙江车贷网,0.9 647 | 0,e路财富,0.9 648 | 0,地标金融,0.9 649 | 0,迷你贷,0.89 650 | 1,聚投融,0.89 651 | 0,火柴头,0.89 652 | 0,超银网贷,0.89 653 | 1,机器人金融,0.89 654 | 1,诚鑫易通,0.89 655 | 0,多贷贷,0.89 656 | 0,昂道招财猫,0.89 657 | 0,众贷汇,0.89 658 | 0,钱多多,0.89 659 | 0,天立鸿,0.89 660 | 0,担宝投,0.89 661 | 1,小石头金融,0.89 662 | 0,58money,0.89 663 | 1,联众贷,0.89 664 | 0,旺宝财富,0.89 665 | 0,淘珠网,0.89 666 | 0,融金汇,0.89 667 | 0,小微易贷,0.89 668 | 1,长安易贷,0.89 669 | 0,励国理财,0.89 670 | 0,后河财富,0.89 671 | 0,存利网,0.89 672 | 0,广西东盟贷,0.89 673 | 0,元宝365,0.89 674 | 0,京金联,0.89 675 | 0,丰汇金融,0.89 676 | 0,搜易贷,0.89 677 | 1,迅贷网,0.89 678 | 0,豫商贷,0.89 679 | 0,第1车贷,0.89 680 | 0,秦沪贷,0.89 681 | 0,钱景家财猫,0.89 682 | 0,吉盟财富,0.89 683 | 0,放心e贷,0.89 684 | 0,中融钱邦,0.89 685 | 0,丰收贷,0.89 686 | 0,资易贷,0.89 687 | 1,安穗金融,0.89 688 | 0,钱贷网,0.89 689 | 0,九一贷,0.89 690 | 0,东方金钰,0.89 691 | 0,圈圈贷,0.89 692 | 0,可乐贷,0.89 693 | 0,点点投,0.89 694 | 0,龙商e贷,0.89 695 | 0,微众筹,0.89 696 | 0,财富之门,0.89 697 | 1,积财99,0.89 698 | 0,汇通易贷,0.89 699 | 0,鲁金所,0.89 700 | 0,晋商贷,0.89 701 | 0,信和大金融,0.89 702 | 0,汉沃财富,0.89 703 | 0,融易投,0.89 704 | 0,信诚贷,0.89 705 | 0,城城理财,0.89 706 | 0,网投网,0.89 707 | 0,网银贷,0.89 708 | 0,第三方金融,0.89 709 | 0,天添钱,0.89 710 | 0,广富宝,0.89 711 | 0,锐盈财富,0.88 712 | 1,奥科创投,0.88 713 | 0,冀金宝,0.88 714 | 0,首E家,0.88 715 | 0,大麦理财,0.88 716 | 0,通金贷,0.88 717 | 0,种子拼图,0.88 718 | 0,易贷宝,0.88 719 | 0,福汇财富,0.88 720 | 0,生活贷,0.88 721 | 0,喜投网,0.88 722 | 0,财佰通,0.88 723 | 0,新财富365,0.88 724 | 0,泛湾天域,0.88 725 | 0,财富保姆,0.88 726 | 0,微金在线,0.88 727 | 0,中贝金融,0.88 728 | 0,e元贷,0.88 729 | 0,泰山宝,0.88 730 | 0,禾泰财富,0.88 731 | 0,连资贷,0.88 732 | 0,鲁班贷,0.88 733 | 0,车来贷,0.88 734 | 0,码头益,0.88 735 | 0,青蚨在线,0.88 736 | 0,信投宝,0.88 737 | 0,大同行,0.88 738 | 0,好车贷,0.88 739 | 0,自由财富,0.88 740 | 0,弘兴金融,0.88 741 | 0,就爱金融网,0.88 742 | 0,农泰金融,0.88 743 | 1,银通贷,0.88 744 | 0,向右转,0.88 745 | 0,中兴财富,0.88 746 | 0,恒瑞财富网,0.88 747 | 0,沃联财富,0.88 748 | 0,易贷在线,0.88 749 | 1,财玖有财,0.88 750 | 0,万家贷,0.88 751 | 1,星理财,0.88 752 | 0,财富派 (京),0.88 753 | 0,钱盆网,0.88 754 | 1,安心金融,0.88 755 | 0,赢多多,0.88 756 | 0,新新贷,0.88 757 | 0,迅融贷,0.88 758 | 0,天天添财,0.88 759 | 0,城商行,0.88 760 | 0,我要投,0.88 761 | 1,华夏银泰,0.88 762 | 0,融贷通赢,0.88 763 | 0,车邦贷,0.88 764 | 1,虹银财富,0.88 765 | 0,骑士贷,0.88 766 | 0,民贷天下,0.88 767 | 0,鹏金所,0.87 768 | 0,互利网龙宝宝,0.87 769 | 0,恒有钱,0.87 770 | 0,梧桐理财,0.87 771 | 0,小资钱包,0.87 772 | 0,好管家金融,0.87 773 | 0,华汇财富,0.87 774 | 0,中储贷,0.87 775 | 0,口贷网,0.87 776 | 0,温州贷,0.87 777 | 0,投促金融,0.87 778 | 0,宜聚网,0.87 779 | 0,信通袋,0.87 780 | 1,点点理财,0.87 781 | 0,吉贷网,0.87 782 | 0,超爱财,0.87 783 | 0,爱当宝,0.87 784 | 1,浩亚达e金融,0.87 785 | 1,鑫华士,0.87 786 | 0,亚丰金融,0.87 787 | 0,掌道财富,0.87 788 | 1,轩鸿在线,0.87 789 | 0,悦享金服,0.87 790 | 0,迅泊达,0.87 791 | 0,金牛在线,0.87 792 | 0,金蟾在线,0.87 793 | 0,富金利,0.87 794 | 0,财富中国,0.87 795 | 0,惠金贷,0.87 796 | 0,信客网,0.87 797 | 0,和贵网,0.87 798 | 0,好好理财,0.87 799 | 0,51帮你,0.87 800 | 0,178网贷,0.87 801 | 0,天天财富,0.87 802 | 0,爱钱进,0.87 803 | 1,聚融汇,0.87 804 | 0,点滴身边,0.87 805 | 0,华舵金服,0.87 806 | 0,龙江易贷,0.87 807 | 0,倍赢金融,0.87 808 | 0,大数云融,0.87 809 | 0,小微金融,0.87 810 | 0,国金宝,0.87 811 | 0,756金融网,0.87 812 | 1,汇德行,0.87 813 | 1,豫麟创投,0.87 814 | 0,温商贷,0.87 815 | 0,聚福鑫,0.87 816 | 0,蜜蜂融,0.87 817 | 0,万绿创投,0.86 818 | 0,利众e贷,0.86 819 | 0,雪莲借贷,0.86 820 | 0,钱江通,0.86 821 | 0,合拍贷,0.86 822 | 0,皮城金融,0.86 823 | 1,汇纳财富,0.86 824 | 0,恒利网,0.86 825 | 0,众合投资,0.86 826 | 0,鱼猫金服,0.86 827 | 0,金猪儿,0.86 828 | 0,e速贷,0.86 829 | 0,365易贷,0.86 830 | 0,益大家金融,0.86 831 | 0,湛卢宝,0.86 832 | 0,鼎信贷,0.86 833 | 0,金晟创投,0.86 834 | 0,财神驾到,0.86 835 | 0,文创汇,0.86 836 | 0,金府通,0.86 837 | 0,妙资金融,0.86 838 | 0,融信速贷,0.86 839 | 0,壹心贷,0.86 840 | 0,信互贷,0.86 841 | 0,联豪创投,0.86 842 | 0,黄金钱包,0.86 843 | 0,隆金宝,0.86 844 | 1,八陆融通,0.86 845 | 0,圆梦易贷,0.86 846 | 0,光华财缘网,0.86 847 | 0,财大狮,0.86 848 | 1,淘财网,0.86 849 | 0,多多理财,0.86 850 | 0,中唐财富,0.86 851 | 0,胖毛在线,0.86 852 | 0,一点投,0.86 853 | 1,众贷邦,0.86 854 | 0,鼠贷金融,0.86 855 | 1,金投资,0.86 856 | 0,监保贷,0.86 857 | 0,特易贷,0.86 858 | 0,钱内助,0.86 859 | 0,恒富在线,0.86 860 | 0,进弘创利,0.86 861 | 0,淘淘金,0.86 862 | 0,人人富,0.86 863 | 0,石投金融,0.86 864 | 0,非常有财,0.86 865 | 0,沪商财富,0.86 866 | 0,稳贷网,0.86 867 | 1,恋贷网,0.86 868 | 0,百川创投,0.85 869 | 0,币胜贷,0.85 870 | 0,利民网,0.85 871 | 1,通灵创投,0.85 872 | 0,资本在线,0.85 873 | 0,财猫网,0.85 874 | 0,人企贷,0.85 875 | 0,先锋e融,0.85 876 | 0,金玉普惠,0.85 877 | 0,国诚金融,0.85 878 | 1,信易安,0.85 879 | 0,利魔方,0.85 880 | 0,喔喔贷,0.85 881 | 0,融诺微贷,0.85 882 | 0,速e贷,0.85 883 | 0,小微时贷,0.85 884 | 0,陇e贷,0.85 885 | 0,飞鱼贷,0.85 886 | 0,永利宝,0.85 887 | 0,99财富,0.85 888 | 0,德众金融,0.85 889 | 0,MoneyHome,0.85 890 | 0,车易贷,0.85 891 | 0,爱财有道,0.85 892 | 1,广筹网,0.85 893 | 0,华人金融,0.85 894 | 0,赢贷通,0.85 895 | 0,中通国源,0.85 896 | 0,友信宝,0.85 897 | 0,1119贷,0.85 898 | 0,益民宝,0.85 899 | 0,大时贷,0.85 900 | 0,华涌贷,0.85 901 | 0,挖财猫,0.85 902 | 0,比亮贷,0.85 903 | 0,小金瓜理财,0.85 904 | 0,升隆财富,0.85 905 | 1,东起投资,0.85 906 | 0,多了口贷,0.85 907 | 1,渣丰投资,0.84 908 | 0,月月贷,0.84 909 | 0,蚂蚁白领,0.84 910 | 0,富春贷,0.84 911 | 0,中宝财富,0.84 912 | 0,广州e贷,0.84 913 | 0,旺财小强,0.84 914 | 0,安付贷,0.84 915 | 0,众可贷,0.84 916 | 0,医融网,0.84 917 | 0,西域财富,0.84 918 | 0,民信贷,0.84 919 | 0,安星财富网,0.84 920 | 0,懒财主,0.84 921 | 0,实实贷,0.84 922 | 0,信广立诚贷,0.84 923 | 0,易多财富,0.84 924 | 0,巨石财富,0.84 925 | 0,沃时贷,0.84 926 | 0,丰益贷,0.84 927 | 0,万同普惠,0.84 928 | 1,沪商在线,0.84 929 | 0,蚂蚁理财,0.84 930 | 0,钱途贷,0.84 931 | 0,进化互联,0.84 932 | 0,盛世开元,0.84 933 | 0,贷金所,0.84 934 | 0,房易贷,0.84 935 | 0,外星人理财,0.84 936 | 0,汇盈金服,0.84 937 | 0,前海航交所,0.84 938 | 0,E票宝,0.84 939 | 0,渥金,0.84 940 | 0,投哪网,0.84 941 | 0,安信网贷,0.84 942 | 1,红牛贷,0.84 943 | 0,链金所,0.84 944 | 0,玖富理财,0.84 945 | 0,小熊在线,0.84 946 | 0,大众贷,0.84 947 | 0,大印财富通,0.84 948 | 0,钱富通,0.84 949 | 0,鑫元汇,0.84 950 | 0,金硕果,0.84 951 | 1,宜佳财富,0.84 952 | 0,收获宝,0.84 953 | 0,牛牛理财,0.84 954 | 0,汇理财,0.83 955 | 0,金发所,0.83 956 | 0,彩云金融,0.83 957 | 0,博金贷,0.83 958 | 0,周转贷,0.83 959 | 1,91快车,0.83 960 | 0,袋鼠妈妈,0.83 961 | 0,锦联财富市场,0.83 962 | 0,贷贷平安,0.83 963 | 0,信任时贷,0.83 964 | 0,东领在线,0.83 965 | 1,云图资本,0.83 966 | 0,财路通,0.83 967 | 0,合银创投,0.83 968 | 1,汇商贷,0.83 969 | 0,易通贷,0.83 970 | 0,金易贷,0.83 971 | 0,黑豆金服,0.83 972 | 0,联合贷,0.83 973 | 0,雪山贷,0.83 974 | 1,宗亲贷,0.83 975 | 1,乾融易贷,0.83 976 | 1,快融在线,0.83 977 | 0,易融贷,0.83 978 | 0,石榴壳,0.83 979 | 0,地方金融,0.83 980 | 0,平安理财网,0.83 981 | 0,徽融通,0.83 982 | 1,优财树,0.83 983 | 0,金二代,0.83 984 | 0,懒财网,0.83 985 | 0,鼎天贷,0.83 986 | 0,融金宝,0.83 987 | 0,春言金融,0.83 988 | 0,阳光贷,0.83 989 | 0,拓天速贷,0.83 990 | 0,携银网,0.83 991 | 0,鸿森财富,0.83 992 | 0,智投贷,0.83 993 | 1,15贷,0.83 994 | 0,筷来财,0.83 995 | 0,贵州网贷,0.83 996 | 0,湖湘贷,0.83 997 | 0,房融界,0.83 998 | 0,银湖网,0.83 999 | 0,贷发发,0.83 1000 | 0,皖江贷,0.83 1001 | 0,速贷100,0.83 1002 | 0,粤易贷,0.83 1003 | 1,A资本,0.8200000000000001 1004 | 0,360贷贷网,0.8200000000000001 1005 | 1,贷投帮帮,0.8200000000000001 1006 | 0,鑫天下财富,0.8200000000000001 1007 | 0,世宇财富,0.8200000000000001 1008 | 1,众贷,0.8200000000000001 1009 | 1,储钱罐,0.8200000000000001 1010 | 0,芝麻e贷网,0.8200000000000001 1011 | 1,好收易,0.8200000000000001 1012 | 1,至尊宝,0.8200000000000001 1013 | 0,商汇通,0.8200000000000001 1014 | 0,当贷网,0.8200000000000001 1015 | 1,盈天下,0.8200000000000001 1016 | 0,日融财富,0.8200000000000001 1017 | 0,海星宝理财,0.8200000000000001 1018 | 0,建安金融,0.8200000000000001 1019 | 0,宏祺金融,0.8200000000000001 1020 | 1,方凯财富,0.8200000000000001 1021 | 0,万家兄弟,0.8200000000000001 1022 | 0,商富贷,0.8200000000000001 1023 | 0,智融财富,0.8200000000000001 1024 | 1,发现地,0.8200000000000001 1025 | 0,腾邦创投,0.8200000000000001 1026 | 1,鑫茂创投,0.8200000000000001 1027 | 0,诚投在线,0.8200000000000001 1028 | 0,财富智汇,0.8200000000000001 1029 | 1,银达金融,0.8200000000000001 1030 | 0,四达投资,0.8200000000000001 1031 | 0,星投资,0.8200000000000001 1032 | 0,天玑汇富,0.8200000000000001 1033 | 0,京城贷,0.8200000000000001 1034 | 0,分分贷,0.8200000000000001 1035 | 0,中青金服,0.8200000000000001 1036 | 1,e租宝,0.8200000000000001 1037 | 0,厦信贷,0.8200000000000001 1038 | 0,予财网,0.8200000000000001 1039 | 1,友友贷,0.8200000000000001 1040 | 0,阳光易贷,0.8200000000000001 1041 | 1,诚信万通,0.8200000000000001 1042 | 0,金钱窝,0.8200000000000001 1043 | 0,网信理财,0.8200000000000001 1044 | 0,中e财富,0.8200000000000001 1045 | 0,乐居财富,0.8200000000000001 1046 | 0,芒果金融,0.8200000000000001 1047 | 0,溢诚金融,0.81 1048 | 0,合信,0.81 1049 | 0,银海颐乐,0.81 1050 | 0,爱投资,0.81 1051 | 0,贷动中国,0.81 1052 | 0,德信贷,0.81 1053 | 0,民投金融,0.81 1054 | 0,良信财富,0.81 1055 | 0,广裕贷,0.81 1056 | 1,财神在线,0.81 1057 | 0,创富部落,0.81 1058 | 1,三农资本,0.81 1059 | 0,中航生意贷,0.81 1060 | 0,采壳365,0.81 1061 | 1,融讯网,0.81 1062 | 0,票据贷,0.81 1063 | 0,康恒财富,0.81 1064 | 0,易利贷,0.81 1065 | 0,永利财富,0.81 1066 | 0,红云创投,0.81 1067 | 0,汇付四海,0.81 1068 | 0,芝麻贷,0.81 1069 | 0,安心投,0.81 1070 | 0,中广核富盈,0.81 1071 | 0,三分贷,0.81 1072 | 0,启道金融,0.81 1073 | 0,益投网贷,0.81 1074 | 1,黄鹤金服,0.81 1075 | 0,龙城易贷,0.81 1076 | 0,中网国投,0.81 1077 | 0,小优贷,0.81 1078 | 0,飞鸟金融,0.81 1079 | 0,小微蚌壳,0.81 1080 | 1,美贷网,0.81 1081 | 0,易安贷,0.81 1082 | 0,乐乐贷,0.81 1083 | 0,我家贷,0.81 1084 | 0,金合社,0.81 1085 | 0,轻易贷,0.81 1086 | 0,好借贷,0.81 1087 | 0,他项车贷,0.81 1088 | 1,昊华财富,0.81 1089 | 0,我借我贷,0.81 1090 | 0,网信贷,0.81 1091 | 1,安鑫金融,0.81 1092 | 0,创客金融,0.81 1093 | 0,鄂汇金融,0.81 1094 | 1,淘金贷,0.8 1095 | 0,庆协投资,0.8 1096 | 0,阳光金服,0.8 1097 | 1,众融投,0.8 1098 | 0,兴玉融通,0.8 1099 | 1,大众金融,0.8 1100 | 0,融星行,0.8 1101 | 1,金豪利,0.8 1102 | 1,黑火金融,0.8 1103 | 0,惠农宝,0.8 1104 | 0,千和投,0.8 1105 | 0,物金所,0.8 1106 | 0,天天赢,0.8 1107 | 0,顺e贷,0.8 1108 | 0,合富吧,0.8 1109 | 0,玖玖金融,0.8 1110 | 0,今时贷,0.8 1111 | 0,海吉星金融网,0.8 1112 | 1,前海e贷,0.8 1113 | 0,绿谷贷融资,0.8 1114 | 1,存钱罐,0.8 1115 | 0,堆金网,0.8 1116 | 0,海金贷,0.8 1117 | 1,金顺创投,0.8 1118 | 1,乐贷网,0.8 1119 | 0,知财网,0.8 1120 | 0,紫马财行,0.8 1121 | 0,乐融巴巴,0.8 1122 | 0,微积金,0.8 1123 | 0,致富贷,0.8 1124 | 0,贷贷好,0.8 1125 | 0,富民投资网,0.8 1126 | 0,旺利网,0.8 1127 | 0,慧源融投,0.8 1128 | 1,坤一金融,0.8 1129 | 0,厚铺街,0.79 1130 | 0,逍遥贷,0.79 1131 | 0,遇财网,0.79 1132 | 0,前金所,0.79 1133 | 1,天天投,0.79 1134 | 0,亚太投资,0.79 1135 | 0,高安创投,0.79 1136 | 0,坤润财富,0.79 1137 | 0,金源贷,0.79 1138 | 1,如德易拍贷,0.79 1139 | 0,拾财贷,0.79 1140 | 1,Hi投吧,0.79 1141 | 0,富利网,0.79 1142 | 0,盛金所,0.79 1143 | 0,中国钱庄,0.79 1144 | 0,道口金融网,0.79 1145 | 0,U车贷,0.79 1146 | 0,惠恩资本,0.79 1147 | 0,融交所,0.79 1148 | 0,创一贷,0.79 1149 | 0,马尚贷,0.79 1150 | 0,贝贝投,0.79 1151 | 0,冀银理财,0.79 1152 | 1,融资贷,0.79 1153 | 0,蜀丰财富,0.79 1154 | 1,金口袋,0.79 1155 | 0,钱潮网,0.79 1156 | 0,慈E贷,0.79 1157 | 0,钱盒子,0.79 1158 | 0,钱升钱,0.79 1159 | 0,微信贷,0.79 1160 | 0,京贷网,0.79 1161 | 0,钱程在线,0.79 1162 | 1,润源财富,0.79 1163 | 1,中金e贷,0.79 1164 | 0,好融贷,0.79 1165 | 0,闲钱宝,0.79 1166 | 0,六六投资,0.79 1167 | 0,广州贷,0.79 1168 | 0,新安左右贷 ,0.79 1169 | 0,滴滴投资网,0.79 1170 | 0,善金网,0.79 1171 | 0,企额贷,0.79 1172 | 0,飞融网,0.79 1173 | 1,中纳资本,0.79 1174 | 1,商易贷,0.78 1175 | 0,富通贷,0.78 1176 | 1,存钱网,0.78 1177 | 0,巨涟金融,0.78 1178 | 0,亿人惠富,0.78 1179 | 0,奔宝贷,0.78 1180 | 0,A+理财,0.78 1181 | 1,财道金融,0.78 1182 | 0,易钱票贷,0.78 1183 | 0,握握贷,0.78 1184 | 0,360迪迪贷,0.78 1185 | 0,乾包,0.78 1186 | 0,生金所,0.78 1187 | 0,浙昌贷,0.78 1188 | 0,潜隆贷,0.78 1189 | 0,九能金融,0.78 1190 | 0,车能贷,0.78 1191 | 0,惠农时贷,0.78 1192 | 1,超钻金融,0.78 1193 | 0,住永金融,0.78 1194 | 0,安盈贷,0.78 1195 | 0,望洲易贷,0.78 1196 | 0,前海领投,0.78 1197 | 0,懒虫网,0.78 1198 | 0,双喆投资,0.78 1199 | 0,聚宝匯,0.78 1200 | 0,全民通金融,0.78 1201 | 0,添惠贷,0.78 1202 | 0,V5金融,0.78 1203 | 0,点融网,0.78 1204 | 0,金贝艺融,0.78 1205 | 0,车贷汇,0.78 1206 | 0,你投我融,0.78 1207 | 0,担保贷,0.78 1208 | 0,全民财富,0.78 1209 | 0,微镑客,0.78 1210 | 0,蚂蚁兄弟,0.78 1211 | 0,国民丰泰,0.78 1212 | 0,华融众信,0.78 1213 | 0,易友贷,0.78 1214 | 0,多维度,0.77 1215 | 0,微易贷,0.77 1216 | 0,一点钱,0.77 1217 | 0,汇金丰,0.77 1218 | 1,中乾在线,0.77 1219 | 1,慧财网,0.77 1220 | 0,亿诺信投,0.77 1221 | 0,远尚金融,0.77 1222 | 0,合盈网贷,0.77 1223 | 1,金裤兜,0.77 1224 | 0,百顺贷,0.77 1225 | 1,徽商财富,0.77 1226 | 1,华邦财富,0.77 1227 | 0,徽商贷,0.77 1228 | 1,速可贷,0.77 1229 | 0,益合贷,0.77 1230 | 0,快联财富,0.77 1231 | 0,丰利网,0.77 1232 | 1,金麦金融,0.77 1233 | 0,借贷云盘,0.77 1234 | 0,玖鑫汇,0.77 1235 | 0,网汇贷,0.77 1236 | 0,北方贷,0.77 1237 | 0,奇乐融,0.77 1238 | 0,臻鑫创投,0.77 1239 | 0,闪电金融,0.77 1240 | 0,网信财富,0.77 1241 | 0,芝麻开门,0.77 1242 | 0,点牛金融,0.77 1243 | 0,种钱网,0.77 1244 | 0,爱互融,0.77 1245 | 0,宜投通,0.77 1246 | 0,阳光e贷,0.77 1247 | 1,财富宝,0.77 1248 | 0,集优贷,0.77 1249 | 0,华银金融,0.77 1250 | 0,牛气贷,0.77 1251 | 0,新金所,0.77 1252 | 1,徽商金融,0.77 1253 | 0,本地贷,0.77 1254 | 0,51如易贷,0.7683333333333333 1255 | 0,招宝万金,0.76 1256 | 0,快手理财,0.76 1257 | 0,金果财富,0.76 1258 | 0,柘家贷,0.76 1259 | 0,资生管家,0.76 1260 | 0,惠人贷,0.76 1261 | 0,点滴聚,0.76 1262 | 0,人众金服,0.76 1263 | 0,浙联储,0.76 1264 | 1,科煌财富,0.76 1265 | 0,京东金融,0.76 1266 | 0,和润网,0.76 1267 | 1,融佳易贷,0.76 1268 | 0,金安国有金融,0.76 1269 | 0,三农金服,0.76 1270 | 0,电商贷,0.76 1271 | 1,老友贷,0.76 1272 | 0,点点财富,0.76 1273 | 0,排队金融,0.76 1274 | 0,诚汇通,0.76 1275 | 1,天天聚财,0.76 1276 | 1,中融易联,0.76 1277 | 0,优投在线,0.76 1278 | 0,钱贷子金融,0.76 1279 | 0,爱达财富,0.76 1280 | 0,网交所,0.76 1281 | 0,财加,0.76 1282 | 0,基因创投,0.76 1283 | 0,富田在线,0.76 1284 | 0,广电财富,0.76 1285 | 0,银点财富,0.76 1286 | 0,金粮宝,0.76 1287 | 0,潞商e贷,0.76 1288 | 0,优信金融,0.76 1289 | 0,意投财富,0.76 1290 | 0,学好贷,0.76 1291 | 1,联信财富,0.76 1292 | 0,钱涌金服,0.76 1293 | 0,招财猫理财,0.76 1294 | 0,明银贷,0.76 1295 | 0,世纪贷,0.76 1296 | 1,博贷网,0.76 1297 | 0,昊良创投,0.76 1298 | 0,好收益,0.76 1299 | 0,易融恒信,0.76 1300 | 0,微金客,0.75 1301 | 0,首贷通,0.75 1302 | 1,纳米时贷,0.75 1303 | 0,紫金所,0.75 1304 | 0,德得宝,0.75 1305 | 1,盈盈创投,0.75 1306 | 0,点滴金融,0.75 1307 | 0,海融易,0.75 1308 | 0,建工E贷,0.75 1309 | 1,惠宜投资,0.75 1310 | 0,抱团贷,0.75 1311 | 1,指南针,0.75 1312 | 1,盛融在线,0.75 1313 | 0,百年贷,0.75 1314 | 0,生意贷,0.75 1315 | 0,一点通,0.75 1316 | 0,利多财富,0.75 1317 | 0,西海诚鑫财富,0.75 1318 | 1,房司令,0.75 1319 | 0,世富资本,0.75 1320 | 0,易港金融,0.75 1321 | 0,易人贷,0.75 1322 | 0,钱途无忧,0.75 1323 | 0,利聚网,0.75 1324 | 0,德成贷,0.75 1325 | 0,上雨创投,0.75 1326 | 1,环鑫金融,0.75 1327 | 1,潮深E融,0.75 1328 | 0,微贷网,0.75 1329 | 0,方元在线,0.75 1330 | 0,财富加油站,0.75 1331 | 1,正和创投,0.75 1332 | 0,富桥金融,0.74 1333 | 0,融金交易,0.74 1334 | 0,金戈戈,0.74 1335 | 0,87汇财,0.74 1336 | 0,1688易贷,0.74 1337 | 0,金池e贷,0.74 1338 | 1,成峰创投,0.74 1339 | 0,团贷网,0.74 1340 | 1,金联投资,0.74 1341 | 1,盛凯禄财富,0.74 1342 | 1,贷乐网,0.74 1343 | 0,骏合金信,0.74 1344 | 0,明志财富,0.74 1345 | 0,宝筹贷,0.74 1346 | 0,利安贷,0.74 1347 | 0,东虹桥金融在线,0.74 1348 | 1,不差钱,0.74 1349 | 0,车行贷,0.74 1350 | 0,理财屋,0.74 1351 | 0,小米贷,0.74 1352 | 0,中富国帮,0.74 1353 | 1,投融无忧,0.74 1354 | 0,浙财理财 ,0.74 1355 | 1,钱贷贷,0.74 1356 | 1,69贷,0.74 1357 | 1,信财贷,0.74 1358 | 0,隆和财富,0.74 1359 | 0,金融街,0.74 1360 | 1,万利财富,0.74 1361 | 0,智联财富,0.74 1362 | 0,理想宝,0.74 1363 | 0,车财网,0.74 1364 | 0,恒昇贷,0.74 1365 | 0,云回通宝,0.74 1366 | 0,小富金融,0.74 1367 | 0,金投手,0.74 1368 | 0,亦融在线,0.74 1369 | 0,快捷财富,0.74 1370 | 0,联恒金融,0.74 1371 | 0,和信贷,0.74 1372 | 0,港信创富,0.74 1373 | 0,盛信金融,0.74 1374 | 0,人人贷,0.73 1375 | 0,乐饷吧,0.73 1376 | 0,钱一百,0.73 1377 | 0,投储在线,0.73 1378 | 0,爱投点,0.73 1379 | 0,聚千金融,0.73 1380 | 0,小牛在线,0.73 1381 | 1,晨诺贷,0.73 1382 | 1,紫枫信贷,0.73 1383 | 1,优信贷,0.73 1384 | 0,四海全球,0.73 1385 | 0,大地金融,0.73 1386 | 0,恒信易贷,0.73 1387 | 0,五A贷,0.73 1388 | 1,财源网,0.73 1389 | 1,永顺贷,0.73 1390 | 0,贷未来,0.73 1391 | 0,投融贷,0.73 1392 | 0,多多贷,0.73 1393 | 1,龙腾财富,0.73 1394 | 0,宏高财富,0.73 1395 | 0,富轩投资,0.73 1396 | 0,合和年在线,0.73 1397 | 0,图腾贷,0.73 1398 | 0,全通贷,0.73 1399 | 0,80资本,0.73 1400 | 0,真理财,0.73 1401 | 1,拾贝理财,0.73 1402 | 0,前海双赢,0.73 1403 | 0,艺点理财,0.73 1404 | 0,申金贷,0.73 1405 | 0,蜜蜂有钱,0.73 1406 | 0,融易理财,0.72 1407 | 0,投复利,0.72 1408 | 1,惠龙贷,0.72 1409 | 0,汉泰华泽,0.72 1410 | 0,余易贷,0.72 1411 | 0,保澜贷,0.72 1412 | 0,信而富,0.72 1413 | 1,808信贷,0.72 1414 | 0,智信创富,0.72 1415 | 0,小梦想,0.72 1416 | 0,存贷汇,0.72 1417 | 0,海汇众筹,0.72 1418 | 0,冰融贷,0.72 1419 | 0,宝玉金融超市,0.72 1420 | 0,满益网,0.72 1421 | 0,金陵贷,0.72 1422 | 1,全民贷,0.72 1423 | 1,银瑞贷,0.72 1424 | 1,鼎融资本,0.72 1425 | 1,乐投资,0.72 1426 | 1,国湘资本,0.72 1427 | 0,自金所,0.72 1428 | 0,煜达投资城,0.72 1429 | 0,大白金融,0.72 1430 | 0,天顺祥信贷,0.72 1431 | 0,仁和融兴,0.72 1432 | 0,中通融贷,0.72 1433 | 0,小猪理财,0.72 1434 | 0,投牛网,0.72 1435 | 0,博拓创投,0.72 1436 | 1,在线贷,0.72 1437 | 1,大国贷,0.72 1438 | 0,财富500,0.71 1439 | 0,兴泰财富,0.71 1440 | 0,奕盈贷,0.71 1441 | 0,徽盐金融,0.71 1442 | 0,行行贷,0.71 1443 | 0,金道贵网贷,0.71 1444 | 1,起点贷,0.71 1445 | 0,草根新贷,0.71 1446 | 1,合互贷,0.71 1447 | 0,飞翼聚财,0.71 1448 | 1,克克贷,0.71 1449 | 0,浙商e贷,0.71 1450 | 0,喵喵客,0.71 1451 | 0,鼓腰包,0.71 1452 | 1,蓝筹汇,0.71 1453 | 1,东方创投,0.71 1454 | 1,众人贷,0.71 1455 | 0,稳盈贷,0.71 1456 | 0,稳通金融,0.71 1457 | 0,中天易贷,0.71 1458 | 0,好借好贷,0.71 1459 | 0,联保投资网,0.71 1460 | 0,安全投,0.71 1461 | 0,鑫贷天下,0.71 1462 | 0,木头人,0.71 1463 | 1,中金服,0.71 1464 | 1,融信贷,0.71 1465 | 0,介投行,0.71 1466 | 0,夏日贷,0.71 1467 | 1,美好时贷,0.71 1468 | 0,银客网,0.71 1469 | 1,辽商贷,0.7 1470 | 0,共赢社,0.7 1471 | 0,商汇财富,0.7 1472 | 0,金陵e贷,0.7 1473 | 0,亨多财富,0.7 1474 | 0,金都e贷,0.7 1475 | 0,新华久久贷,0.7 1476 | 0,创融在线,0.7 1477 | 0,中仁财富,0.7 1478 | 0,家家聚财,0.7 1479 | 0,大圣理财,0.7 1480 | 1,红兆资本,0.7 1481 | 0,众力金融,0.7 1482 | 0,北方金融网,0.7 1483 | 1,昌泰网,0.7 1484 | 1,中信e贷,0.7 1485 | 0,诚天财富,0.7 1486 | 1,富裕财富,0.7 1487 | 0,小白菜理财,0.7 1488 | 1,开开贷,0.7 1489 | 1,金华财富,0.7 1490 | 0,e生财富,0.7 1491 | 1,大鑫富帮,0.7 1492 | 1,好借好还,0.7 1493 | 0,寻钱网,0.7 1494 | 0,中亿资本,0.7 1495 | 1,速银E贷,0.7 1496 | 1,佰家创投,0.7 1497 | 0,大志成,0.7 1498 | 0,新一贷,0.7 1499 | 1,通融易贷,0.7 1500 | 0,惠信贷,0.7 1501 | 0,东邦易贷,0.69 1502 | 0,财蜂发财树,0.69 1503 | 0,金帝财富,0.69 1504 | 0,魔方贷,0.69 1505 | 0,金米袋,0.69 1506 | 0,中信实力,0.69 1507 | 1,博源e贷,0.69 1508 | 0,世联集金,0.69 1509 | 1,投息宝,0.69 1510 | 0,德鸿金融,0.69 1511 | 0,小袋理财,0.69 1512 | 1,爱投网,0.69 1513 | 0,互联贷,0.69 1514 | 0,58车贷,0.69 1515 | 1,本利宝,0.69 1516 | 1,家园贷,0.69 1517 | 0,汇南创投 ,0.69 1518 | 0,银信贷,0.69 1519 | 1,财爷爷,0.69 1520 | 1,天瑞小小贷,0.69 1521 | 0,E借通,0.69 1522 | 1,工商贷,0.69 1523 | 1,迅拓创投,0.69 1524 | 0,众金在线,0.69 1525 | 0,互助贷,0.69 1526 | 1,贷贷巴,0.69 1527 | 0,贷信通,0.69 1528 | 1,众和贷,0.69 1529 | 0,甬商贷,0.69 1530 | 0,心意贷,0.69 1531 | 0,全球贷（皖）,0.69 1532 | 0,普惠利德,0.6799999999999999 1533 | 0,融100,0.6799999999999999 1534 | 0,民生电商,0.6799999999999999 1535 | 1,汇丰贷,0.6799999999999999 1536 | 1,信益贷,0.6799999999999999 1537 | 0,钱市网 ,0.6799999999999999 1538 | 0,银佰汇,0.6799999999999999 1539 | 0,百金贷,0.6799999999999999 1540 | 1,兴旺财富,0.6799999999999999 1541 | 0,信用本,0.6799999999999999 1542 | 0,汇贷天下,0.6799999999999999 1543 | 1,66财富,0.6799999999999999 1544 | 0,微金互助,0.6799999999999999 1545 | 1,e快钱,0.6799999999999999 1546 | 0,皓添金融,0.6799999999999999 1547 | 0,叮当口袋,0.6799999999999999 1548 | 0,安贷宝,0.6799999999999999 1549 | 0,唯信贷,0.6799999999999999 1550 | 0,人安资本,0.6799999999999999 1551 | 0,中益信金融,0.6799999999999999 1552 | 1,汇丰创投,0.6799999999999999 1553 | 1,现佳创富,0.6799999999999999 1554 | 1,名启财富,0.6799999999999999 1555 | 0,投贷宝,0.6799999999999999 1556 | 1,苏丰创投,0.6699999999999999 1557 | 0,红岭创投,0.6699999999999999 1558 | 0,安捷财富,0.6699999999999999 1559 | 1,快速贷,0.6699999999999999 1560 | 0,鸿学金信,0.6699999999999999 1561 | 1,优易贷,0.6699999999999999 1562 | 0,宏东资本,0.6699999999999999 1563 | 1,鲁源金融,0.6699999999999999 1564 | 0,提钱网,0.6699999999999999 1565 | 0,乐投创富,0.6699999999999999 1566 | 0,信贷宝,0.6699999999999999 1567 | 0,沃信财富,0.6699999999999999 1568 | 0,塞上贷,0.6699999999999999 1569 | 0,小龟在线,0.6699999999999999 1570 | 1,金诺鼎,0.6699999999999999 1571 | 0,智汇贷,0.6699999999999999 1572 | 0,安家贷,0.6699999999999999 1573 | 0,汇金宝,0.6699999999999999 1574 | 0,猪宝宝,0.6699999999999999 1575 | 1,沄银宝,0.6699999999999999 1576 | 0,来财街,0.6599999999999999 1577 | 0,惠车贷,0.6599999999999999 1578 | 0,融途e贷,0.6599999999999999 1579 | 0,投米网,0.6599999999999999 1580 | 0,爱银网,0.6599999999999999 1581 | 1,四联信投,0.6599999999999999 1582 | 0,通通贷,0.6599999999999999 1583 | 0,中金国泰,0.6599999999999999 1584 | 0,爱上贷,0.6599999999999999 1585 | 1,帝天财富,0.6599999999999999 1586 | 0,悦投融,0.6599999999999999 1587 | 0,知麻开门,0.6599999999999999 1588 | 0,德铢财富,0.6599999999999999 1589 | 0,平联Bank,0.6599999999999999 1590 | 1,好优贷,0.6599999999999999 1591 | 0,熊猫贷,0.6599999999999999 1592 | 1,建泰投资,0.6599999999999999 1593 | 0,聚宝网,0.6559999999999999 1594 | 1,喜来贷,0.65 1595 | 0,乐信,0.65 1596 | 1,中源资本,0.65 1597 | 1,徽鑫所,0.65 1598 | 0,隆发金融,0.65 1599 | 0,大丰收金融,0.65 1600 | 1,宜倡财富,0.65 1601 | 1,昌晟易贷,0.65 1602 | 0,票据客,0.65 1603 | 0,吉成贷,0.65 1604 | 0,普邦金控,0.65 1605 | 1,懒人财富,0.65 1606 | 1,飞速贷,0.65 1607 | 1,天勤在线,0.65 1608 | 1,一家人财富,0.65 1609 | 1,立贷网,0.65 1610 | 0,创绩宝,0.65 1611 | 0,融益盈,0.65 1612 | 1,荔园银河系,0.64 1613 | 0,财智魔方,0.64 1614 | 0,小树时代,0.64 1615 | 0,中海资本,0.64 1616 | 0,有利网,0.64 1617 | 1,众润金融,0.64 1618 | 0,皖平财富,0.64 1619 | 0,万泽财富,0.64 1620 | 1,爱口袋,0.64 1621 | 1,莱商贷,0.64 1622 | 0,翼龙贷,0.64 1623 | 0,利往行,0.64 1624 | 0,盛开网,0.64 1625 | 0,博宇金融,0.64 1626 | 0,我企贷,0.64 1627 | 1,365金融,0.64 1628 | 0,启迪金,0.63 1629 | 1,汇富创投,0.63 1630 | 1,乾客天下,0.63 1631 | 0,合拍在线,0.63 1632 | 0,安企金融,0.63 1633 | 1,永融贷,0.63 1634 | 1,财富天下,0.63 1635 | 0,医界贷,0.63 1636 | 0,鼎诚创投,0.63 1637 | 0,金蝶金链,0.63 1638 | 1,沃坤投资,0.63 1639 | 0,永银贷,0.63 1640 | 0,共信赢,0.63 1641 | 0,贷帮,0.63 1642 | 0,和缘融贷,0.63 1643 | 0,三惠富邦,0.63 1644 | 0,52金融,0.63 1645 | 0,易e贷,0.63 1646 | 1,鲁信宜贷,0.63 1647 | 0,金牛座,0.63 1648 | 1,钱好贷,0.63 1649 | 0,宜人贷,0.63 1650 | 1,泽业财富,0.63 1651 | 0,百合贷,0.63 1652 | 0,多美贷,0.63 1653 | 0,盛世通贷,0.63 1654 | 0,瑞银创投,0.62 1655 | 0,积木盒子,0.62 1656 | 1,华生贷,0.62 1657 | 0,惠富天下,0.62 1658 | 1,中宝投资,0.62 1659 | 0,乐升易贷,0.62 1660 | 1,瑞安贷,0.62 1661 | 0,丰盛金,0.62 1662 | 0,融金桥,0.62 1663 | 1,巽邦资产,0.62 1664 | 0,携行天下,0.62 1665 | 1,宏量财富,0.62 1666 | 0,盈金所,0.62 1667 | 1,斗金贷,0.62 1668 | 0,欧克普惠,0.62 1669 | 1,鄂商贷,0.62 1670 | 1,官银储,0.62 1671 | 1,联合易贷,0.62 1672 | 1,用心贷,0.62 1673 | 1,机智投,0.61 1674 | 1,宜企贷,0.61 1675 | 0,好时代(京),0.61 1676 | 1,铭胜投资,0.61 1677 | 0,皖乾商贷,0.61 1678 | 0,有友金融,0.61 1679 | 1,讯贷网,0.61 1680 | 0,五星财富,0.61 1681 | 0,融邦网,0.61 1682 | 1,力川金融,0.61 1683 | 0,大互联,0.61 1684 | 0,凤凰金融(京),0.61 1685 | 0,信融财富,0.61 1686 | 1,德利贷,0.61 1687 | 1,永贷宝,0.61 1688 | 0,福鑫e融,0.61 1689 | 1,融易贷,0.61 1690 | 1,蜜蜂兵团,0.61 1691 | 0,PPmoney万惠,0.61 1692 | 0,诚壹贷,0.61 1693 | 1,友贷网,0.61 1694 | 0,鑫合汇,0.61 1695 | 1,兆鑫财富,0.61 1696 | 0,稳展财富,0.6 1697 | 0,金融街在线,0.6 1698 | 1,21世纪财富,0.6 1699 | 1,银米贷,0.6 1700 | 0,睿银财富,0.6 1701 | 0,宏信创投,0.6 1702 | 0,享福宝,0.6 1703 | 0,薛国创投,0.6 1704 | 0,天天融,0.6 1705 | 1,国控小微,0.6 1706 | 0,善贷,0.6 1707 | 0,巴菲贷,0.6 1708 | 0,亿信天合,0.6 1709 | 0,鑫融贷,0.6 1710 | 1,强强财富,0.6 1711 | 0,银鑫源,0.6 1712 | 0,阜阳金融网,0.6 1713 | 1,尊荣财富,0.6 1714 | 0,中正易贷,0.6 1715 | 1,嘉润财富,0.6 1716 | 1,广安宏润,0.6 1717 | 0,智融会,0.6 1718 | 1,淘金城,0.5900000000000001 1719 | 0,助商贷,0.5900000000000001 1720 | 1,乐投网,0.5900000000000001 1721 | 1,万通财富,0.5900000000000001 1722 | 1,光世界金融,0.5900000000000001 1723 | 0,长投在线,0.5900000000000001 1724 | 1,君孚财富,0.5900000000000001 1725 | 0,小赢理财,0.5900000000000001 1726 | 0,恒丰财富,0.5900000000000001 1727 | 1,鲁商贷,0.5900000000000001 1728 | 1,坤玺金融,0.5900000000000001 1729 | 0,有道理财,0.5900000000000001 1730 | 1,红顶财富,0.5900000000000001 1731 | 1,天行信,0.5900000000000001 1732 | 1,壹方壹贷,0.5900000000000001 1733 | 1,银钱树,0.5900000000000001 1734 | 0,天昊财富,0.5900000000000001 1735 | 0,融金所,0.5800000000000001 1736 | 1,金马财富,0.5800000000000001 1737 | 1,家家贷,0.5800000000000001 1738 | 0,汇通贷,0.5800000000000001 1739 | 0,福润融通,0.5800000000000001 1740 | 1,不倒翁贷,0.5800000000000001 1741 | 1,盈汇融,0.5800000000000001 1742 | 1,财富在线,0.5800000000000001 1743 | 1,如通金融,0.5800000000000001 1744 | 1,呱呱贷,0.5800000000000001 1745 | 0,医疗贷,0.5800000000000001 1746 | 1,沃资本,0.5800000000000001 1747 | 0,奕诚金融,0.5800000000000001 1748 | 1,民商理财,0.5800000000000001 1749 | 1,彩讯贷,0.5800000000000001 1750 | 0,欢乐贷,0.5800000000000001 1751 | 1,网赢天下,0.5800000000000001 1752 | 1,好想贷,0.5800000000000001 1753 | 1,添亿创投,0.5800000000000001 1754 | 1,腾滕聚,0.5800000000000001 1755 | 0,正能贷,0.5700000000000001 1756 | 0,响當當,0.5700000000000001 1757 | 0,爱贷金服,0.5700000000000001 1758 | 1,首机贷,0.5700000000000001 1759 | 1,my标客,0.5700000000000001 1760 | 0,岁意讯,0.5700000000000001 1761 | 1,美冠信投,0.5700000000000001 1762 | 1,中联贷,0.5700000000000001 1763 | 0,Aoao贷,0.5700000000000001 1764 | 1,中州易贷,0.5700000000000001 1765 | 1,创赢易贷,0.5700000000000001 1766 | 1,去网贷,0.5700000000000001 1767 | 0,陆金所,0.5700000000000001 1768 | 1,成功贷,0.5700000000000001 1769 | 1,智慧树金融,0.5700000000000001 1770 | 1,朋友贷,0.5700000000000001 1771 | 1,放心投资,0.5700000000000001 1772 | 1,友禾创投,0.5700000000000001 1773 | 0,银通泰,0.5700000000000001 1774 | 0,拍拍贷,0.5700000000000001 1775 | 1,热贷网,0.56 1776 | 0,实名贷,0.56 1777 | 0,19贷,0.56 1778 | 1,品胜财富,0.56 1779 | 0,添添投,0.56 1780 | 0,好帮贷,0.56 1781 | 1,财大户,0.56 1782 | 0,银嘉在线,0.56 1783 | 1,金葫篓,0.56 1784 | 1,中祥金融,0.56 1785 | 0,长盈金融,0.56 1786 | 0,元贷通,0.56 1787 | 0,津融贷,0.56 1788 | 0,炜会贷,0.56 1789 | 1,诺辰财富,0.56 1790 | 0,久信e贷,0.55 1791 | 0,蓝融汇,0.55 1792 | 0,中金普惠,0.55 1793 | 1,汇信财富,0.55 1794 | 0,苏聚创投,0.55 1795 | 0,量子贷,0.55 1796 | 0,千企贷,0.55 1797 | 1,盈信贷,0.55 1798 | 0,吉强贷,0.55 1799 | 0,互融城,0.55 1800 | 1,唐人贷,0.55 1801 | 0,聚恒贷,0.55 1802 | 0,宁安贷,0.55 1803 | 1,快乐e贷,0.55 1804 | 1,信融汇金,0.55 1805 | 0,你我贷,0.55 1806 | 0,凤凰金融,0.55 1807 | 0,瑞涛财富,0.55 1808 | 1,中财在线,0.55 1809 | 1,安泰卓越,0.55 1810 | 1,星光财富,0.55 1811 | 0,异享金融,0.54 1812 | 0,宜联易贷,0.54 1813 | 0,龍亿贷,0.54 1814 | 1,钱掌柜,0.54 1815 | 0,吉信佳,0.54 1816 | 0,广发金融,0.54 1817 | 0,微贷在线,0.54 1818 | 1,湘商盟,0.54 1819 | 0,e融在线,0.54 1820 | 0,云贷网,0.54 1821 | 0,优企贷,0.54 1822 | 1,591车贷,0.54 1823 | 1,拓宏理财,0.54 1824 | 0,钱鼓鼓,0.54 1825 | 0,合盘贷,0.54 1826 | 0,众联财富,0.53 1827 | 0,节节贷,0.53 1828 | 0,苏富贷,0.53 1829 | 1,泰麟资本,0.53 1830 | 1,融融网,0.53 1831 | 0,日日昌,0.53 1832 | 1,方便贷,0.53 1833 | 1,畅贷网,0.53 1834 | 1,融合天下,0.53 1835 | 1,中银汇通,0.53 1836 | 0,中国融信贷,0.53 1837 | 1,秦皇资本,0.53 1838 | 1,速车贷,0.53 1839 | 1,自由金服,0.53 1840 | 1,昊天财富,0.53 1841 | 0,广亿贷,0.52 1842 | 0,飞阳易贷,0.52 1843 | 0,惠黎金融,0.52 1844 | 0,国阳财富,0.52 1845 | 0,百财车贷,0.52 1846 | 1,帮客网,0.52 1847 | 1,爱定投,0.51 1848 | 1,汇鑫在线,0.51 1849 | 1,钱窝窝,0.51 1850 | 0,平方贷,0.51 1851 | 1,春鹏易贷,0.51 1852 | 0,丰储宝,0.51 1853 | 0,隆泰贷,0.51 1854 | 1,邦诚金融,0.51 1855 | 1,学富贷,0.51 1856 | 0,梧桐E贷,0.5083333333333333 1857 | 1,中联乐银,0.5 1858 | 1,得利宝,0.5 1859 | 0,润恒贷,0.5 1860 | 0,1号钱庄,0.5 1861 | 1,上咸BANK,0.5 1862 | 0,合贷网,0.5 1863 | 0,票号网,0.5 1864 | 1,世纪创想,0.5 1865 | 0,粒粒贷,0.5 1866 | 1,投乐网,0.5 1867 | 1,苹果金融,0.5 1868 | 1,年享财富,0.5 1869 | 0,徽嘉金融,0.5 1870 | 1,融亿通,0.5 1871 | 1,理财邦,0.5 1872 | 1,钱柜理财,0.5 1873 | 1,苗木之家,0.5 1874 | 1,华南城贷,0.49 1875 | 1,华强财富,0.49 1876 | 0,快乐投,0.49 1877 | 0,长久贷,0.49 1878 | 1,富民网贷,0.49 1879 | 1,万城汇丰,0.49 1880 | 1,文妥财富,0.49 1881 | 0,同城投,0.49 1882 | 0,徽金猫,0.49 1883 | 1,大佳创投,0.49 1884 | 0,爱投金融,0.49 1885 | 1,南瓜P2P,0.48 1886 | 1,邦融理财,0.48 1887 | 0,微E贷,0.48 1888 | 1,汇利通,0.48 1889 | 1,财富钥匙,0.48 1890 | 0,科银信贷,0.48 1891 | 1,车当铺,0.48 1892 | 1,中盛金服,0.48 1893 | 1,中大财富,0.48 1894 | 1,广融贷,0.48 1895 | 0,银富汇,0.48 1896 | 1,新港e贷,0.48 1897 | 0,易捷贷,0.48 1898 | 0,广融在线,0.48 1899 | 0,民泰财富,0.48 1900 | 1,粮食贷,0.47 1901 | 0,资交所,0.47 1902 | 1,融通e贷,0.47 1903 | 0,易久贷,0.47 1904 | 1,幸福贷,0.47 1905 | 0,神州贷,0.47 1906 | 0,开元理财,0.47 1907 | 1,新易贷,0.47 1908 | 0,第一账房,0.47 1909 | 0,融生在线,0.47 1910 | 1,中金在线,0.47 1911 | 0,发发金融,0.47 1912 | 0,糖糖贷,0.47 1913 | 0,惠众e金,0.47 1914 | 0,好利农,0.47 1915 | 0,小企业E家,0.47 1916 | 1,广发财富,0.45999999999999996 1917 | 1,融资城,0.45999999999999996 1918 | 1,合泽财富,0.45999999999999996 1919 | 0,鑫隆财富,0.45999999999999996 1920 | 1,宿州易贷,0.45999999999999996 1921 | 1,企联融业,0.45999999999999996 1922 | 1,易网贷,0.45999999999999996 1923 | 1,济赢财富,0.45999999999999996 1924 | 1,玄谷金融,0.45999999999999996 1925 | 1,一站贷,0.45999999999999996 1926 | 0,宏伟贷,0.45999999999999996 1927 | 1,巨福易贷,0.45999999999999996 1928 | 1,盛世创投,0.45999999999999996 1929 | 1,积储在线,0.45999999999999996 1930 | 1,生财有道,0.45999999999999996 1931 | 0,白银巷子,0.45999999999999996 1932 | 1,亿贷网,0.45999999999999996 1933 | 0,家宝贷,0.45999999999999996 1934 | 0,钱立方,0.44999999999999996 1935 | 0,润通贷,0.44999999999999996 1936 | 1,人人爱投,0.44999999999999996 1937 | 1,齐鲁恒信贷,0.44999999999999996 1938 | 1,小商贷,0.44999999999999996 1939 | 1,圆梦巴巴,0.44999999999999996 1940 | 0,学贷网,0.44999999999999996 1941 | 0,盛鑫在线,0.44999999999999996 1942 | 0,金车贷,0.44999999999999996 1943 | 1,诚意贷,0.44999999999999996 1944 | 0,融邦财富,0.44999999999999996 1945 | 0,桔子金融,0.44999999999999996 1946 | 0,金联所,0.44999999999999996 1947 | 0,鑫胜贷,0.44999999999999996 1948 | 0,柚子理财,0.44999999999999996 1949 | 0,钜宝盆,0.44999999999999996 1950 | 0,小鹰速贷,0.44999999999999996 1951 | 1,东郊财富,0.44999999999999996 1952 | 0,哈哈金服,0.43999999999999995 1953 | 1,一元投资,0.43999999999999995 1954 | 1,可投可贷（鲁）,0.43999999999999995 1955 | 0,北城贷,0.43999999999999995 1956 | 1,爱增宝,0.43000000000000005 1957 | 0,中浩信投,0.43000000000000005 1958 | 0,中盈贷,0.43000000000000005 1959 | 0,金策通,0.43000000000000005 1960 | 1,和诚德在线,0.43000000000000005 1961 | 1,涌金贷,0.43000000000000005 1962 | 1,华东贷,0.43000000000000005 1963 | 1,民强财富,0.43000000000000005 1964 | 0,通融财富,0.43000000000000005 1965 | 1,富达亚金融,0.43000000000000005 1966 | 1,筑梦天使,0.43000000000000005 1967 | 1,小丫P2P,0.42500000000000004 1968 | 1,微小宝,0.42000000000000004 1969 | 0,余额E贷,0.42000000000000004 1970 | 1,如丰电子财富,0.42000000000000004 1971 | 1,华鑫恒生,0.42000000000000004 1972 | 1,宝利金融,0.42000000000000004 1973 | 0,巧巧财富,0.42000000000000004 1974 | 0,钱来网,0.42000000000000004 1975 | 0,淘收益,0.42000000000000004 1976 | 1,神州易贷,0.42000000000000004 1977 | 1,净净贷,0.42000000000000004 1978 | 0,融易贷（豫）,0.42000000000000004 1979 | 0,多好贷（赣）,0.42000000000000004 1980 | 1,益元贷,0.42000000000000004 1981 | 0,智富圈,0.42000000000000004 1982 | 0,财火火,0.42000000000000004 1983 | 0,汇诚金服,0.41000000000000003 1984 | 1,创新众投,0.41000000000000003 1985 | 0,658金融网,0.41000000000000003 1986 | 0,誉金所,0.41000000000000003 1987 | 1,良渚财富,0.41000000000000003 1988 | 1,中汇在线,0.41000000000000003 1989 | 0,富二贷,0.41000000000000003 1990 | 1,融e365,0.41000000000000003 1991 | 1,广通贷,0.41000000000000003 1992 | 1,中若财富,0.41000000000000003 1993 | 1,人人商贷,0.4 1994 | 1,贤贤贷,0.4 1995 | 1,瑞贷通,0.4 1996 | 1,九问贷,0.4 1997 | 1,淘贷宝,0.4 1998 | 1,铂利亚,0.4 1999 | 1,力合创投,0.4 2000 | 1,鼎玉财富,0.4 2001 | 1,盈赢贷,0.4 2002 | 1,惠民财富,0.4 2003 | 1,汇瑞财富,0.4 2004 | 1,汇众贷,0.4 2005 | 0,点银网,0.4 2006 | 1,兴飞财富,0.39 2007 | 0,鼎鑫财富,0.39 2008 | 1,宝盖头,0.39 2009 | 1,满地易贷,0.39 2010 | 1,拉手贷,0.39 2011 | 1,明贷网,0.39 2012 | 1,银禾财富,0.39 2013 | 0,十鼎贷,0.39 2014 | 1,鑫阳创投,0.38 2015 | 0,工程惠贷,0.38 2016 | 1,众筹投资,0.38 2017 | 1,利人金融,0.38 2018 | 0,金融社,0.38 2019 | 0,万度财富,0.38 2020 | 1,鸿诚贷,0.38 2021 | 0,中续投资,0.37 2022 | 1,泰堃财富,0.37 2023 | 0,温易贷,0.37 2024 | 1,百凯祺投资,0.37 2025 | 1,有钱金融,0.37 2026 | 0,恒安财富钱多多,0.37 2027 | 1,麦粒金融,0.37 2028 | 1,安客创投,0.37 2029 | 1,现贷网,0.37 2030 | 1,锦融运通,0.37 2031 | 1,旗融网,0.365 2032 | 1,万汇通,0.3603333333333333 2033 | 1,赢金贷,0.36 2034 | 0,迈信金融,0.36 2035 | 0,蜂缘财富,0.36 2036 | 1,国临创投,0.35 2037 | 1,点滴聚财,0.35 2038 | 0,帮侬贷,0.35 2039 | 1,胜源财富,0.35 2040 | 0,钱冠人人贷,0.35 2041 | 1,盈联网,0.35 2042 | 1,双富贷,0.35 2043 | 0,柚美理财,0.35 2044 | 0,聚金汇财,0.35 2045 | 1,钱江理财,0.3483333333333334 2046 | 1,力安创投,0.33999999999999997 2047 | 0,嘉嘉易贷,0.33999999999999997 2048 | 1,海西贷,0.33999999999999997 2049 | 1,交融所,0.33999999999999997 2050 | 0,e云贷,0.33999999999999997 2051 | 1,巨牛财富,0.33999999999999997 2052 | 0,淦源贷,0.33999999999999997 2053 | 0,宝典创投,0.33999999999999997 2054 | 1,天府投资网,0.33999999999999997 2055 | 0,冠腾投资,0.33999999999999997 2056 | 1,红酒金融,0.33999999999999997 2057 | 1,闽昌贷,0.33999999999999997 2058 | 1,利丰网,0.33999999999999997 2059 | 1,中贸易融,0.33999999999999997 2060 | 1,活宝汇,0.33999999999999997 2061 | 0,普惠贷,0.33999999999999997 2062 | 0,天脉投资,0.33999999999999997 2063 | 0,山水聚宝,0.32999999999999996 2064 | 0,佰易贷,0.32999999999999996 2065 | 0,点点贷,0.32999999999999996 2066 | 1,钰泰财富,0.32999999999999996 2067 | 0,河南贷,0.32999999999999996 2068 | 1,易诚金融,0.32999999999999996 2069 | 1,艺融易,0.32999999999999996 2070 | 1,融促汇,0.32999999999999996 2071 | 0,好安贷,0.32999999999999996 2072 | 1,中贷信创,0.325952380952381 2073 | 1,天之源贷,0.31999999999999995 2074 | 0,创亿财富,0.31999999999999995 2075 | 1,诚宜创投,0.31999999999999995 2076 | 0,惠融通,0.31999999999999995 2077 | 0,中纳联投,0.31999999999999995 2078 | 1,幸福e贷,0.31999999999999995 2079 | 1,沪联在线,0.31999999999999995 2080 | 0,孔方兄理财,0.31999999999999995 2081 | 1,正大金融,0.31000000000000005 2082 | 1,川信贷,0.31000000000000005 2083 | 1,一辰创投,0.31000000000000005 2084 | 0,笑着赚,0.31000000000000005 2085 | 1,有存网,0.31000000000000005 2086 | 1,金榜财富,0.31000000000000005 2087 | 1,湘信网,0.31000000000000005 2088 | 1,聚众贷,0.31000000000000005 2089 | 1,随e贷,0.31000000000000005 2090 | 1,齐鲁人贷,0.31000000000000005 2091 | 1,财富宝投融,0.30000000000000004 2092 | 1,3a借贷,0.30000000000000004 2093 | 1,微投网,0.30000000000000004 2094 | 1,华辰易贷,0.29000000000000004 2095 | 0,金蛋理财,0.29000000000000004 2096 | 1,中信创投,0.29000000000000004 2097 | 1,中E邦达,0.29000000000000004 2098 | 1,及时贷,0.29000000000000004 2099 | 0,益邦贷,0.29000000000000004 2100 | 0,儒商贷,0.28500000000000003 2101 | 1,城乡贷,0.28 2102 | 1,农银贷,0.28 2103 | 1,旭日贷,0.28 2104 | 0,投资无忧,0.28 2105 | 1,阳光快贷,0.28 2106 | 1,丰达财富,0.28 2107 | 1,益民贷,0.27 2108 | 1,好乐易贷,0.27 2109 | 0,安易融,0.2678571428571428 2110 | 0,融盛创投,0.2678571428571428 2111 | 1,开杰圆融网,0.2609999999999999 2112 | 0,英邦投资,0.26 2113 | 0,聚钱袋,0.26 2114 | 0,中润e在线,0.26 2115 | 0,平平贷,0.26 2116 | 1,有钱贷,0.26 2117 | 1,友融金融,0.26 2118 | 1,伯乐易贷,0.26 2119 | 1,有益贷,0.26 2120 | 1,洛卡贷,0.25 2121 | 0,联正金融,0.25 2122 | 1,湘商贷,0.25 2123 | 0,和盛在线,0.25 2124 | 1,百利财富,0.25 2125 | 1,优悦贷,0.25 2126 | 1,微通贷,0.25 2127 | 1,富壹代,0.25 2128 | 1,亿人易贷,0.25 2129 | 1,警安财富,0.25 2130 | 1,众贷网,0.24 2131 | 0,房贷通,0.24 2132 | 1,牡丹财富,0.24 2133 | 1,阿拉贷,0.24 2134 | 1,巢商金融,0.24 2135 | 0,和融天下,0.24 2136 | 1,融易贷（粤）,0.24 2137 | 0,恒硕金融,0.24 2138 | 1,互益贷,0.24 2139 | 0,褔易贷,0.24 2140 | 0,金元开泰,0.24 2141 | 0,煜生创投,0.24 2142 | 1,重庆贷,0.22999999999999998 2143 | 1,聚米立方,0.22999999999999998 2144 | 1,渝商创投,0.22999999999999998 2145 | 1,陆鑫所,0.22999999999999998 2146 | 1,当当贷,0.22999999999999998 2147 | 1,三人贷,0.22999999999999998 2148 | 1,万民投资,0.22999999999999998 2149 | 1,华夏商贷,0.21999999999999997 2150 | 1,融易融,0.21999999999999997 2151 | 1,互帮贷,0.21999999999999997 2152 | 1,发展投资,0.21999999999999997 2153 | 1,丰银凯利,0.21999999999999997 2154 | 1,携盛金融,0.21999999999999997 2155 | 1,联富金融,0.21999999999999997 2156 | 1,钱根源,0.21999999999999997 2157 | 1,汇易宝,0.21999999999999997 2158 | 0,鼎力投资网,0.21999999999999997 2159 | 1,萨贝尔贷,0.21999999999999997 2160 | 1,渝易贷,0.20999999999999996 2161 | 1,湘中银联,0.20999999999999996 2162 | 1,优区贷,0.20999999999999996 2163 | 1,农商贷,0.20999999999999996 2164 | 1,民生创投,0.20999999999999996 2165 | 1,安贷创投,0.20956440781440788 2166 | 1,天成投资,0.20123107448107447 2167 | 1,丰融贷,0.20123107448107447 2168 | 1,乐城投资,0.20123107448107447 2169 | 1,世昌金融,0.19999999999999996 2170 | 1,中融财富,0.19999999999999996 2171 | 0,瑞信富盈,0.19999999999999996 2172 | 1,融资谷,0.19999999999999996 2173 | 1,富润金融,0.18999999999999995 2174 | 1,百信财富,0.18999999999999995 2175 | 1,中国二手车,0.18000000000000005 2176 | 1,及时雨网贷,0.18000000000000005 2177 | 1,好时代,0.18000000000000005 2178 | 1,信诺宝,0.18000000000000005 2179 | 1,兴业易贷,0.18000000000000005 2180 | 0,融至鼎,0.18000000000000005 2181 | 1,高新盛,0.18000000000000005 2182 | 1,易贷乐投,0.18000000000000005 2183 | 1,艺商贷,0.18000000000000005 2184 | 0,众储财富,0.18000000000000005 2185 | 1,每天美贷,0.17000000000000004 2186 | 1,聚散贷,0.17000000000000004 2187 | 1,蜀易贷,0.17000000000000004 2188 | 1,亿元宝,0.17000000000000004 2189 | 1,徽州贷,0.17000000000000004 2190 | 1,金蜗牛财富,0.17000000000000004 2191 | 1,一起富,0.16000000000000003 2192 | 1,汉荣鼎盛,0.16000000000000003 2193 | 1,黔程金融,0.16000000000000003 2194 | 1,轻纺城金融,0.16000000000000003 2195 | 1,道和创鑫 ,0.16000000000000003 2196 | 1,鸿利贷,0.15000000000000002 2197 | 1,卓忠贷,0.1406666666666666 2198 | 1,元一创投,0.1406666666666666 2199 | 1,景煜贷,0.1406666666666666 2200 | 1,紫金贷,0.14 2201 | 1,乐贷通,0.14 2202 | 1,浙江商贷,0.14 2203 | 1,信安财富,0.14 2204 | 1,金汇丰,0.13 2205 | 1,融金通,0.13 2206 | 1,华源投资 ,0.13 2207 | 1,鑫潮创投,0.12 2208 | 1,融易信,0.12 2209 | 1,信业财富,0.12 2210 | 1,宝都e贷,0.12 2211 | 1,卡趣网,0.12 2212 | 1,金喜财富,0.10999999999999999 2213 | 1,钱客金融,0.10999999999999999 2214 | 1,金承易通,0.10999999999999999 2215 | 1,昆石投资,0.09999999999999998 2216 | 1,金点贷,0.09999999999999998 2217 | 1,汇聚贷,0.09999999999999998 2218 | 1,汇银财富,0.09999999999999998 2219 | 1,大融小贷,0.09999999999999998 2220 | 1,中富润德,0.08999999999999997 2221 | 1,富华金融,0.08999999999999997 2222 | 1,源鑫贷,0.08999999999999997 2223 | 1,迪丰资本,0.08999999999999997 2224 | 1,民间资本,0.08999999999999997 2225 | 1,顺昌财富,0.07999999999999996 2226 | 1,招金贷,0.07999999999999996 2227 | 1,仁信贷,0.07999999999999996 2228 | 1,永泰达投资,0.07999999999999996 2229 | 1,安宜贷,0.07999999999999996 2230 | 1,小微所,0.06999999999999995 2231 | 1,股民贷,0.06999999999999995 2232 | 1,力元投资,0.06999999999999995 2233 | 1,蓝岸创投,0.06999999999999995 2234 | 1,凌轩财富,0.06999999999999995 2235 | 1,民联贷,0.06999999999999995 2236 | 1,财富广域,0.06999999999999995 2237 | 1,敬友财富,0.06999999999999995 2238 | 1,金蝶理财,0.06000000000000005 2239 | 1,本息宝,0.06000000000000005 2240 | 1,乾丰鼎盛投资,0.06000000000000005 2241 | 1,平海金融,0.06000000000000005 2242 | 1,佳通创投,0.06000000000000005 2243 | 1,禾嘉创投,0.06000000000000005 2244 | 1,酬勤贷,0.06000000000000005 2245 | 1,益远赢,0.06000000000000005 2246 | 1,万家创投,0.06000000000000005 2247 | 0,天使贷,0.06000000000000005 2248 | 1,五洲财富,0.06000000000000005 2249 | 1,新华贷,0.06000000000000005 2250 | 1,嘉融贷,0.057595238095238144 2251 | 1,风瑞投资,0.057595238095238144 2252 | 1,银泰发,0.057595238095238144 2253 | 1,伟利投资,0.050000000000000044 2254 | 1,融信中盛 ,0.050000000000000044 2255 | 1,眉州商贷,0.050000000000000044 2256 | 1,酷跑金融,0.050000000000000044 2257 | 1,兴利贷,0.050000000000000044 2258 | 1,财利通 ,0.050000000000000044 2259 | 1,盈通投资,0.050000000000000044 2260 | 1,华盈金融,0.04233333333333322 2261 | 1,徽瑞贷,0.040000000000000036 2262 | 1,金线财富 ,0.040000000000000036 2263 | 1,财富联成,0.040000000000000036 2264 | 1,全诚在线,0.040000000000000036 2265 | 0,万佳金融,0.040000000000000036 2266 | 1,金银丰,0.040000000000000036 2267 | 1,中汇通投资,0.040000000000000036 2268 | 1,世成贷,0.040000000000000036 2269 | 1,青青贷,0.040000000000000036 2270 | 1,天源财富,0.040000000000000036 2271 | 1,鼎和资本,0.040000000000000036 2272 | 1,易信云投,0.03949999999999998 2273 | 1,富城贷,0.030000000000000027 2274 | 0,创贷网,0.030000000000000027 2275 | 1,莱诚贷,0.030000000000000027 2276 | 1,鑫汇融合,0.030000000000000027 2277 | 1,第四市场,0.030000000000000027 2278 | 1,深港易贷,0.030000000000000027 2279 | 1,缑城贷,0.030000000000000027 2280 | 1,聚商一百,0.030000000000000027 2281 | 1,中庚财富,0.030000000000000027 2282 | 1,华深拓业投资,0.030000000000000027 2283 | 1,乐网贷,0.030000000000000027 2284 | 1,汇宝信贷,0.030000000000000027 2285 | 1,富莱而金融,0.030000000000000027 2286 | 1,好运财富,0.030000000000000027 2287 | 1,中诚财富,0.030000000000000027 2288 | 1,浙商贷,0.030000000000000027 2289 | 1,首信贷,0.030000000000000027 2290 | 1,汇金众盈,0.030000000000000027 2291 | 1,南岭财富,0.030000000000000027 2292 | 1,壹号金融,0.030000000000000027 2293 | 1,马上有钱,0.025000000000000022 2294 | 1,聚融贷,0.020000000000000018 2295 | 1,保本儿,0.020000000000000018 2296 | 1,海巢贷,0.020000000000000018 2297 | 1,瓯江贷,0.020000000000000018 2298 | 1,潜力股 ,0.020000000000000018 2299 | 1,锦慧通,0.020000000000000018 2300 | 1,信誉财富,0.020000000000000018 2301 | 1,汇凯鑫创投,0.020000000000000018 2302 | 1,贷宝宝,0.020000000000000018 2303 | 1,人山贷,0.020000000000000018 2304 | 1,国银贷,0.020000000000000018 2305 | 1,温州金融港,0.020000000000000018 2306 | 1,盛世财富（鄂）,0.020000000000000018 2307 | 1,骏宝威,0.020000000000000018 2308 | 1,泯华创投,0.020000000000000018 2309 | 1,金陵财富,0.020000000000000018 2310 | 1,稳收宝,0.020000000000000018 2311 | 1,助民贷,0.020000000000000018 2312 | 1,名龙资本,0.020000000000000018 2313 | 1,众富贷,0.020000000000000018 2314 | 1,安琪贷,0.020000000000000018 2315 | 1,多利宝理财（鄂）,0.020000000000000018 2316 | 1,保诚财富,0.020000000000000018 2317 | 1,融邦贷,0.020000000000000018 2318 | 1,零一贷,0.020000000000000018 2319 | 1,华幕投资,0.020000000000000018 2320 | 1,浙亚财富,0.020000000000000018 2321 | 1,怀民贷,0.020000000000000018 2322 | 1,天达创投,0.020000000000000018 2323 | 1,天标贷,0.020000000000000018 2324 | 1,天弘创投,0.020000000000000018 2325 | 1,贷易网,0.020000000000000018 2326 | 1,合众贷,0.020000000000000018 2327 | 1,尚融财富,0.020000000000000018 2328 | 1,恒金贷,0.020000000000000018 2329 | 1,诚贷网,0.020000000000000018 2330 | 1,胜达投资,0.010000000000000009 2331 | 1,文茂创投 ,0.010000000000000009 2332 | 1,泓润恒业,0.010000000000000009 2333 | 1,壹投资,0.010000000000000009 2334 | 1,大地贷,0.010000000000000009 2335 | 1,发利网,0.010000000000000009 2336 | 1,弘富贷,0.010000000000000009 2337 | 1,青源贷,0.010000000000000009 2338 | 1,融大在线,0.010000000000000009 2339 | 1,嘉富财富,0.010000000000000009 2340 | 1,福润汇鑫,0.010000000000000009 2341 | 1,宝筹网,0.010000000000000009 2342 | 1,楚盈贷,0.010000000000000009 2343 | 1,粤利通,0.010000000000000009 2344 | 1,万元财富,0.010000000000000009 2345 | 1,隆晋贷,0.010000000000000009 2346 | 1,盛泰信投,0.010000000000000009 2347 | 1,孔礼贷 ,0.010000000000000009 2348 | 1,万博基业,0.010000000000000009 2349 | 1,亚投财富,0.010000000000000009 2350 | 1,乐投无忧,0.010000000000000009 2351 | 1,大家网,0.010000000000000009 2352 | 1,旺旺贷,0.010000000000000009 2353 | 1,周道财富,0.010000000000000009 2354 | 1,一诺创投,0.010000000000000009 2355 | 1,鑫汇在线,0.010000000000000009 2356 | 1,惠信财富,0.010000000000000009 2357 | 1,龙源金融,0.010000000000000009 2358 | 1,永合贷,0.010000000000000009 2359 | 1,日金贷,0.010000000000000009 2360 | 1,翰爽投资,0.010000000000000009 2361 | 1,恒友投资,0.010000000000000009 2362 | 1,润通创投,0.010000000000000009 2363 | 1,融益财富,0.010000000000000009 2364 | 1,御帮贷,0.010000000000000009 2365 | 1,重友财富,0.010000000000000009 2366 | 1,幸福财富,0.010000000000000009 2367 | 1,中投瑞银投资,0.010000000000000009 2368 | 1,多益贷,0.010000000000000009 2369 | 1,检顺财富,0.010000000000000009 2370 | 1,惠兴通,0.010000000000000009 2371 | 1,信邦财富,0.010000000000000009 2372 | 1,伊凡诺投资,0.010000000000000009 2373 | 1,慧众投,0.010000000000000009 2374 | 1,日升财富,0.010000000000000009 2375 | 1,宏德创投,0.010000000000000009 2376 | 1,瑞敏投资,0.010000000000000009 2377 | 1,建明财富,0.010000000000000009 2378 | 1,亿通投,0.010000000000000009 2379 | 1,安旺P2P,0.010000000000000009 2380 | 1,金满盆,0.010000000000000009 2381 | 1,农科城网贷,0.010000000000000009 2382 | 1,雨滴财富,0.010000000000000009 2383 | 1,盈通易贷,0.010000000000000009 2384 | 1,君茂财富,0.010000000000000009 2385 | 1,余下钱,0.010000000000000009 2386 | 1,丰谦创投,0.010000000000000009 2387 | 1,运兴嘉筑,0.010000000000000009 2388 | 1,海阳财富,0.010000000000000009 2389 | 1,起跑线借贷,0.010000000000000009 2390 | 1,满仓赢,0.010000000000000009 2391 | 1,辉煌财富,0.010000000000000009 2392 | 1,恒艳财富,0.010000000000000009 2393 | 1,贷贷通（蜀）,0.010000000000000009 2394 | 1,诚铭投资 ,0.010000000000000009 2395 | 1,常盈财富,0.010000000000000009 2396 | 1,宜信宜投,0.010000000000000009 2397 | 1,满堂金,0.010000000000000009 2398 | 1,琳鹏创投,0.010000000000000009 2399 | 1,三姐投融,0.010000000000000009 2400 | 1,华夏创银,0.010000000000000009 2401 | 1,聚宝盆,0.010000000000000009 2402 | 1,元亨泰富,0.010000000000000009 2403 | 1,一本贷,0.010000000000000009 2404 | 1,永盈财富,0.010000000000000009 2405 | 1,鼎元贷,0.010000000000000009 2406 | 1,恒通金融,0.010000000000000009 2407 | 1,晶玉贷,0.010000000000000009 2408 | 1,泰山贷,0.010000000000000009 2409 | 1,利德丰财富,0.010000000000000009 2410 | 1,金冠贷,0.010000000000000009 2411 | 1,得易贷,0.010000000000000009 2412 | 1,信邦创投,0.010000000000000009 2413 | 1,广发贷,0.010000000000000009 2414 | 1,融业网,0.010000000000000009 2415 | 1,幸汇财富,0.010000000000000009 2416 | 1,亿谷财富,0.010000000000000009 2417 | 1,中桂联投资,0.010000000000000009 2418 | 1,天力贷,0.010000000000000009 2419 | 1,高效贷,0.010000000000000009 2420 | 1,元宝街,0.010000000000000009 2421 | 1,成融贷,0.010000000000000009 2422 | 1,旺欣贷,0.010000000000000009 2423 | 1,易融投资,0.010000000000000009 2424 | 1,天利诚投资 ,0.010000000000000009 2425 | 1,亿德汇富,0.010000000000000009 2426 | 1,中银投资担保,0.010000000000000009 2427 | 1,群润贷,0.010000000000000009 2428 | 1,启隆信投,0.010000000000000009 2429 | 1,宝多多,0.010000000000000009 2430 | 1,众力投融,0.010000000000000009 2431 | 1,徽通金融,0.010000000000000009 2432 | 1,鹏城贷,0.010000000000000009 2433 | 1,沪易贷,0.010000000000000009 2434 | 1,保险贷,0.010000000000000009 2435 | 1,御声资本,0.010000000000000009 2436 | 1,诺信创投,0.010000000000000009 2437 | 1,元邦创投 ,0.010000000000000009 2438 | 1,华人贷,0.010000000000000009 2439 | 1,吉超贷,0.010000000000000009 2440 | 1,聚鑫贷,0.006666666666666599 2441 | 1,达通创投,0.006666666666666599 2442 | 1,顺融易贷,0.0050000000000000044 2443 | 1,祥富春投资(粤）,0.0050000000000000044 2444 | 1,零花钱,0.0045000000000000595 2445 | 1,益宝贷,0.0040000000000000036 2446 | 1,如信网,0.0040000000000000036 2447 | 1,浙江贷,0.0 2448 | 1,永融e贷,0.0 2449 | 1,中信速贷,0.0 2450 | 1,台商金融,0.0 2451 | 1,中汇金融,0.0 2452 | 1,汇富帮,0.0 2453 | 1,日宝网,0.0 2454 | 1,金栋投资,0.0 2455 | 1,鑫利源,0.0 2456 | 1,鹏天投资,0.0 2457 | 1,帝业投资,0.0 2458 | 1,龙腾理财,0.0 2459 | 1,微投所,0.0 2460 | 1,享投网,0.0 2461 | 1,荣桓世远,0.0 2462 | 1,华融天成,0.0 2463 | 1,999理财,0.0 2464 | 1,睿逸财富,0.0 2465 | 1,德州贷,0.0 2466 | 1,壹诺理财,0.0 2467 | 1,商贸财富,0.0 2468 | 1,草原理财,0.0 2469 | 0,OK贷,0.0 2470 | 0,瑞通易贷,0.0 2471 | 1,汇元贷,0.0 2472 | 1,舒心贷 ,0.0 2473 | 1,欣鼎贷,0.0 2474 | 0,叶子金服,0.0 2475 | 1,6000贷,0.0 2476 | 1,合众融通,0.0 2477 | 1,亚厚财富,0.0 2478 | 1,国鼎投融 ,0.0 2479 | 1,福祥创投,0.0 2480 | 1,福翔创投,0.0 2481 | 1,贷记宝 ,0.0 2482 | 1,宜商贷,0.0 2483 | 1,银实贷,0.0 2484 | 1,传通财富,0.0 2485 | 1,宝丰易贷,0.0 2486 | 1,祥晨财富,0.0 2487 | 1,鸿屹资本,0.0 2488 | 1,荣钻贷,0.0 2489 | 1,连云港e贷,0.0 2490 | 1,万利创投,0.0 2491 | 1,鑫满盈 ,0.0 2492 | 1,九鼎投资（冀）,0.0 2493 | 1,黄山资本,0.0 2494 | 1,财富园,0.0 2495 | 1,斯格瑞特投资 ,0.0 2496 | 1,云鼎汇融,0.0 2497 | 1,同鑫创投,0.0 2498 | 1,乘10理财,0.0 2499 | 1,益盛财富,0.0 2500 | 1,汇投汇贷 ,0.0 2501 | 1,龙华贷,0.0 2502 | 1,德鑫财富,0.0 2503 | 1,江淮财富,0.0 2504 | 1,上致财富,0.0 2505 | 1,金贷子,0.0 2506 | 1,优旺诺祥,0.0 2507 | 1,妙慈投资,0.0 2508 | 1,尧瑞投资,0.0 2509 | 1,永利天成,0.0 2510 | 1,时时贷,0.0 2511 | 1,易起创投,0.0 2512 | 1,天基投资,0.0 2513 | 1,旭贷网,0.0 2514 | 1,慧眼通贷,0.0 2515 | 1,纯嘉投资 ,0.0 2516 | 1,聚投网,0.0 2517 | 1,华纳汇盈,0.0 2518 | 1,德利汇金,0.0 2519 | 1,盛德多投资,0.0 2520 | 1,处州贷,0.0 2521 | 1,西北贷 ,0.0 2522 | 1,慧鸣贷 ,0.0 2523 | 1,宝金所（苏）,0.0 2524 | 1,欣融贷,0.0 2525 | 1,押押贷,0.0 2526 | 1,同发理财,0.0 2527 | 1,云峰理财,0.0 2528 | 1,蔬泽创投,0.0 2529 | 1,滨州财富,0.0 2530 | 1,乾恒来投资,0.0 2531 | 1,微方财富,0.0 2532 | 1,善安合财富,0.0 2533 | 1,奇旺投资,0.0 2534 | 1,云联贷,0.0 2535 | 1,油财宝 ,0.0 2536 | 1,国瑞财富,0.0 2537 | 1,纵横投资,0.0 2538 | 1,久鼎创投,0.0 2539 | 1,创金财富,0.0 2540 | 1,加诚金融,0.0 2541 | 1,慧众财富,0.0 2542 | 1,盛达投资,0.0 2543 | 1,鑫融城,0.0 2544 | 1,龙泰财富,0.0 2545 | 1,腾远投资,0.0 2546 | 1,威泰创投,0.0 2547 | 1,创鑫贷,0.0 2548 | 1,金财通,0.0 2549 | 1,网金宝,0.0 2550 | 1,盛宇财富,0.0 2551 | 1,咱去投 ,0.0 2552 | 1,拜腾财富,0.0 2553 | 1,三五八创投,0.0 2554 | 1,康意和,0.0 2555 | 1,洪升财富,0.0 2556 | 0,星之灵,0.0 2557 | 1,润宏贷,0.0 2558 | 1,光大富尊,0.0 2559 | 1,铜穗子,0.0 2560 | 1,百事得财富,0.0 2561 | 1,群众贷,0.0 2562 | 1,誉信贷,0.0 2563 | 1,亚盛财富,0.0 2564 | 1,阳天财富,0.0 2565 | 1,锋逸信投,0.0 2566 | 1,松特贷,0.0 2567 | 1,鸿商贷,0.0 2568 | 1,召鑫财富,0.0 2569 | 1,云腾信,0.0 2570 | 1,宜众贷,0.0 2571 | 0,易贷中国,0.0 2572 | 1,艾瑞贷,0.0 2573 | 1,江城贷,0.0 2574 | 1,锦煜贷,0.0 2575 | 0,德丰投资,0.0 2576 | 1,国银财富,0.0 2577 | 1,信博财富,0.0 2578 | 1,融海财富,0.0 2579 | 1,立洲金融,0.0 2580 | 1,龙跃贷,0.0 2581 | 1,大大宝,0.0 2582 | 1,汉本在线,0.0 2583 | 1,鼎方投资,0.0 2584 | 1,有人投,0.0 2585 | 1,鑫龙财富,0.0 2586 | 1,莲花财富,0.0 2587 | 1,赢瑞创投,0.0 2588 | 1,今鑫财富,0.0 2589 | 1,金坤投资,0.0 2590 | 1,米兰金融,0.0 2591 | 1,民大投资,0.0 2592 | 1,德塞财富,0.0 2593 | 1,茶金所,0.0 2594 | 1,惠卡贷,0.0 2595 | 1,国汽金融,0.0 2596 | 1,天天利,0.0 2597 | 1,星期八金融,0.0 2598 | 1,敏迎投资,0.0 2599 | 1,帮你贷,0.0 2600 | 1,沪发贷,0.0 2601 | 1,佳联财富,0.0 2602 | 1,腾略创投,0.0 2603 | 1,信贷365,0.0 2604 | 1,贵福财富,0.0 2605 | 1,携成贷,0.0 2606 | 1,海陵贷,0.0 2607 | 1,银都创投,0.0 2608 | 1,中银资本,0.0 2609 | 1,富豪创投,0.0 2610 | 1,维沃财富,0.0 2611 | 1,江腾金融,0.0 2612 | 1,窑湾贷,0.0 2613 | 1,创值贷,0.0 2614 | 1,东信财富,0.0 2615 | 1,速速贷,0.0 2616 | 1,迅益贷,0.0 2617 | 1,富创贷,0.0 2618 | 1,明启华投资,0.0 2619 | 1,云融通,0.0 2620 | 1,港兴投资,0.0 2621 | 1,凯盈财富,0.0 2622 | 1,瑞金资本,0.0 2623 | 1,诚德担保,0.0 2624 | 1,金瑞盈,0.0 2625 | 1,海发财富,0.0 2626 | 1,钱海创投,0.0 2627 | 1,桂融贷,0.0 2628 | 1,广佳贷,0.0 2629 | 1,华悦财富,0.0 2630 | 1,高益创投,0.0 2631 | 1,金蛋e贷,0.0 2632 | 1,你投我贷,0.0 2633 | 1,万遇金融,0.0 2634 | 1,楚商创投,0.0 2635 | 0,明利财富,0.0 2636 | 1,宏安财富,0.0 2637 | 1,铜都贷,0.0 2638 | 1,乐投微贷,0.0 2639 | 1,步华投资,0.0 2640 | 1,乾坤贷,0.0 2641 | 1,宝丰创投,0.0 2642 | 1,通达财富,0.0 2643 | 1,鑫巢财富,0.0 2644 | 0,律邦融安,0.0 2645 | 1,太阳金服,0.0 2646 | 1,浙信贷,0.0 2647 | 1,昌盛易贷,0.0 2648 | 1,慈鑫贷,0.0 2649 | 1,华飞资本,0.0 2650 | 1,信美财富,0.0 2651 | 1,牛车在线,0.0 2652 | 1,燕赵借贷网 ,0.0 2653 | 1,乐天在线,0.0 2654 | 1,金翌豪创投,0.0 2655 | 1,宏高资本,0.0 2656 | 1,银盛亿达,0.0 2657 | 1,壹唯世创投资 ,0.0 2658 | 1,通联贷,0.0 2659 | 1,好信财富,0.0 2660 | 1,君发贷,0.0 2661 | 1,融宝贷,0.0 2662 | 1,善德财富,0.0 2663 | 1,微美金融,0.0 2664 | 1,联合信贷,0.0 2665 | 1,宝丰财富,0.0 2666 | 1,天诚贷,0.0 2667 | 1,中银信投资（鄂）,0.0 2668 | 1,亚港投资,0.0 2669 | 1,中信贷,0.0 2670 | 1,瑞诚贷,0.0 2671 | 1,光大信投,0.0 2672 | 1,财意创投,0.0 2673 | 1,明铄财富,0.0 2674 | 1,汴京财富,0.0 2675 | 1,金瑞投资,0.0 2676 | 1,北部湾财富,0.0 2677 | 1,闽商贷,0.0 2678 | 1,民融通,0.0 2679 | 1,宝仕金融,0.0 2680 | 0,玖富,0.0 2681 | 1,有道财富,0.0 2682 | 1,快可贷,0.0 2683 | 1,玖信理财,0.0 2684 | 0,e理财,0.0 2685 | 1,迈龙财富,0.0 2686 | 1,鸿磊财富,0.0 2687 | 1,徽煌财富,0.0 2688 | 1,汉腾创投,0.0 2689 | 1,网梦创投,0.0 2690 | 1,微尘聚金,0.0 2691 | 1,滴水微贷,0.0 2692 | 1,珊湖财富,0.0 2693 | 1,鼎日投资,0.0 2694 | 1,浙配资网,0.0 2695 | 1,贺翔财富 ,0.0 2696 | 1,益得创投,0.0 2697 | 1,亚联贷,0.0 2698 | 1,尔达信投,0.0 2699 | 1,惠仁财富,0.0 2700 | 1,汇银投资,0.0 2701 | 1,都梁创投,0.0 2702 | 1,银鑫贷,0.0 2703 | 1,惠利宝,0.0 2704 | 1,融华财富,0.0 2705 | 1,福易贷,0.0 2706 | 1,中恒盛业,0.0 2707 | 1,百泓财富,0.0 2708 | 1,翼凤贷,0.0 2709 | 1,弘桥投资,0.0 2710 | 1,中青创投,0.0 2711 | 1,车妈妈,0.0 2712 | 1,帮你贷（皖）,0.0 2713 | 1,万利贷,0.0 2714 | 1,亿信金融 ,0.0 2715 | 1,联帮贷,0.0 2716 | 1,鸿利财富,0.0 2717 | 1,茂荣投资,0.0 2718 | 1,合筑资本,0.0 2719 | 1,金畅想,0.0 2720 | 1,鑫联创投,0.0 2721 | 1,益达易贷,0.0 2722 | 1,新锐联,0.0 2723 | 1,亿鑫创投,0.0 2724 | 1,鼎隆投资,0.0 2725 | 1,温商金融,0.0 2726 | 1,优利网,0.0 2727 | 1,人人金融,0.0 2728 | 1,锐忻投资,0.0 2729 | 1,佰信投,0.0 2730 | 1,环球财富,0.0 2731 | 1,凤城贷,0.0 2732 | 1,盛泰投资,0.0 2733 | 1,鑫华创投 ,0.0 2734 | 1,展翔投资,0.0 2735 | 1,金兴贷,0.0 2736 | 1,冠丰投资,0.0 2737 | 1,感投网,0.0 2738 | 1,中安永恒财富,0.0 2739 | 1,安农e商,0.0 2740 | 1,易通投资,0.0 2741 | 1,家融合,0.0 2742 | 1,永利财富（安徽）,0.0 2743 | 1,宏飞创投,0.0 2744 | 1,安凰贷,0.0 2745 | 1,广聚财富,0.0 2746 | 1,璀璨明珠 ,0.0 2747 | 1,银际财富,0.0 2748 | 1,多好贷,0.0 2749 | 1,赢城创投,0.0 2750 | 1,华容创投,0.0 2751 | 1,中山投资,0.0 2752 | 1,壹视财富,0.0 2753 | 1,保全财富,0.0 2754 | 1,云商贷,0.0 2755 | 1,众人贷（鲁）,0.0 2756 | 1,旭鑫投资,0.0 2757 | 1,名宏创投,0.0 2758 | 1,聚来融,0.0 2759 | 1,甬发贷,0.0 2760 | 1,泉旺创投,0.0 2761 | 1,汾湖投资,0.0 2762 | 1,汇众商贷,0.0 2763 | 1,春强财富,0.0 2764 | 1,鼎泰益,0.0 2765 | 1,元太在线,0.0 2766 | 1,水晶贷,0.0 2767 | 1,合利在线,0.0 2768 | 1,里外贷,0.0 2769 | 1,元一投资,0.0 2770 | 1,金鑫创投,0.0 2771 | 1,玺融资本,0.0 2772 | 1,九华金融,0.0 2773 | 1,新聪理财,0.0 2774 | 1,安融网,0.0 2775 | 1,众融网,0.0 2776 | 1,沃利资本,0.0 2777 | 1,富源财富,0.0 2778 | 1,德利创投,0.0 2779 | 1,小小易贷,0.0 2780 | 1,广融钱多多,0.0 2781 | 1,矩顺财富,0.0 2782 | 1,新乡贷,0.0 2783 | 1,太湖金融,0.0 2784 | 1,坤隆投资,0.0 2785 | 1,零薪投资,0.0 2786 | 1,速帮经济 ,0.0 2787 | 1,万鼎投资,0.0 2788 | 1,合汇富,0.0 2789 | 1,华信贷,0.0 2790 | 1,共赢在线,0.0 2791 | 1,祥富春投资（沪）,0.0 2792 | 1,诺贷投资,0.0 2793 | 1,足鞋贷,0.0 2794 | 1,惠民理财,0.0 2795 | 1,富弘易贷,0.0 2796 | 1,顺顺贷（浙）,0.0 2797 | 1,聚铢投资,0.0 2798 | 1,资本密码,0.0 2799 | 1,裕滨财富,0.0 2800 | 1,宏图创投,0.0 2801 | 1,新双贷,0.0 2802 | 1,信诚创投,0.0 2803 | 1,金誉丰投资,0.0 2804 | 1,万钧财富,0.0 2805 | 1,彩虹财富,0.0 2806 | 1,亿鑫贷,0.0 2807 | 1,微利众贷,0.0 2808 | 1,安泰创投,0.0 2809 | 1,惠金创投,0.0 2810 | 1,鑫益贷,0.0 2811 | 1,盈聚贷,0.0 2812 | 1,峰诺贷,0.0 2813 | 1,时诗运,0.0 2814 | 1,丰彩投资 ,0.0 2815 | 1,宏升投资,0.0 2816 | 1,轩正创投,0.0 2817 | 1,盛赢资本,0.0 2818 | 1,宝鼎投资,0.0 2819 | 1,易建财富,0.0 2820 | 1,金中信,0.0 2821 | 1,顺祥投资,0.0 2822 | 1,光华永贷,0.0 2823 | 1,浩万投资,0.0 2824 | 1,荣盛贷,0.0 2825 | 1,弘宇信投,0.0 2826 | 1,酬信投融,0.0 2827 | 1,卓彤网 ,0.0 2828 | 1,诺一创投,0.0 2829 | 1,汇丰恒创投,0.0 2830 | 1,晨升投资,0.0 2831 | 1,亿豪商贷,0.0 2832 | 1,创盛财富,0.0 2833 | 1,锦盛财富,0.0 2834 | 1,星光资本,0.0 2835 | 1,创信泰冠,0.0 2836 | 1,弘利创投 ,0.0 2837 | 1,鑫广顺,0.0 2838 | 1,亿润贷,0.0 2839 | 1,惠通贷,0.0 2840 | 1,泰信财富,0.0 2841 | 1,点道投资,0.0 2842 | 1,财安贷 ,0.0 2843 | 1,卓信财富,0.0 2844 | 1,鑫十贷,0.0 2845 | 1,荣京贷,0.0 2846 | 1,乾韵财富,0.0 2847 | 1,渤海金融,0.0 2848 | 1,北京软银赛富,0.0 2849 | 1,盈金创富,0.0 2850 | 1,善富资本,0.0 2851 | 1,欧亿投资,0.0 2852 | 1,闽台贷,0.0 2853 | 1,金政创投,0.0 2854 | 1,鲁滨易投,0.0 2855 | 1,兴民财富,0.0 2856 | 1,无棣盛世,0.0 2857 | 1,天之浩投资,0.0 2858 | 1,招商加盟贷,0.0 2859 | 1,益航贷 ,0.0 2860 | 1,鲁融贷,0.0 2861 | 1,融信达投资,0.0 2862 | 1,源泰易贷,0.0 2863 | 1,鸿湘堂,0.0 2864 | 1,民间财富,0.0 2865 | 1,鹏飞财富,0.0 2866 | 1,凌云创投,0.0 2867 | 1,钱布贷,0.0 2868 | 1,众利创投,0.0 2869 | 1,汪达财富,0.0 2870 | 1,福州财富,0.0 2871 | 1,凯祥财富,0.0 2872 | 1,和泰投资,0.0 2873 | 1,鑫发创投,0.0 2874 | 1,至鼎财富,0.0 2875 | 1,德亨在线,0.0 2876 | 1,万晶聚金融,0.0 2877 | 1,多彩多乐,0.0 2878 | 1,华商财富,0.0 2879 | 1,美E贷,0.0 2880 | 1,南汇财富,0.0 2881 | 1,汇财宝,0.0 2882 | 1,鼎瑞投资,0.0 2883 | 1,银银贷,0.0 2884 | 1,恒融财富,0.0 2885 | 1,金麒麟,0.0 2886 | 1,盛合金融,0.0 2887 | 1,凯大投资,0.0 2888 | 1,全家福,0.0 2889 | 1,云通金融,0.0 2890 | 1,58钱管家,0.0 2891 | 1,润海创投,0.0 2892 | 1,邦慧投资,0.0 2893 | 1,瑞言贷,0.0 2894 | 1,耀升信投,0.0 2895 | 1,久盈投资,0.0 2896 | 1,安农商贷,0.0 2897 | 1,泓然控股,0.0 2898 | 1,度尔投资,0.0 2899 | 1,好利贷,0.0 2900 | 1,融创财富,0.0 2901 | 1,爱网贷,0.0 2902 | 1,大华财富,0.0 2903 | 1,一诺财富,0.0 2904 | 1,昊泽贷,0.0 2905 | 1,鼎诚财富,0.0 2906 | 1,联创财富,0.0 2907 | 1,房车贷,0.0 2908 | 1,信达财富,0.0 2909 | 1,中融资本,0.0 2910 | 1,荣锦创投,0.0 2911 | 1,乐贷网（鲁）,0.0 2912 | 1,贵禄财富,0.0 2913 | 1,致雅财富,0.0 2914 | 1,聚融投,0.0 2915 | 1,时时宝,0.0 2916 | 1,万晶聚投资,0.0 2917 | 1,百e贷,0.0 2918 | 1,胜达易贷,0.0 2919 | 1,钱茂财富,0.0 2920 | 1,助行投资,0.0 2921 | 1,银坊金融,0.0 2922 | 1,华东财富,0.0 2923 | 1,百益贷,0.0 2924 | 1,汉国钱庄,0.0 2925 | 1,金升贷,0.0 2926 | 1,北斗金融,0.0 2927 | 1,金泰达投资,0.0 2928 | 1,国安贷,0.0 2929 | 1,温心创投,0.0 2930 | 1,亿豪通,0.0 2931 | 1,美美贷,0.0 2932 | 1,瑞资所,0.0 2933 | 1,小虎金融,0.0 2934 | 1,鸢都贷,0.0 2935 | 1,宝翔投资,0.0 2936 | 1,亚龙信投,0.0 2937 | 1,江南创投,0.0 2938 | 1,富赢财富,0.0 2939 | 1,润达贷,0.0 2940 | 1,易尚在线,0.0 2941 | 1,京银财富,0.0 2942 | 1,追潮投资,0.0 2943 | 1,西部聚财,0.0 2944 | 1,信优贷,0.0 2945 | 1,赢富达,0.0 2946 | 1,信丰财富,0.0 2947 | 1,朝助创投,0.0 2948 | 1,小蜜蜂理财 ,0.0 2949 | 1,德鸿贷,0.0 2950 | 1,京商网,0.0 2951 | 1,一禾网,0.0 2952 | 1,立鼎资本,0.0 2953 | 1,天晨财富,0.0 2954 | 1,D1金融,0.0 2955 | 1,鸿海资本,0.0 2956 | 1,布贷宝宝,0.0 2957 | 1,鼎丰投资,0.0 2958 | 1,祥仁投资,0.0 2959 | 1,高利投资,0.0 2960 | 1,益盈投资,0.0 2961 | 1,致盛贷,0.0 2962 | 1,巨莱财富,0.0 2963 | 1,鲁华财富,0.0 2964 | 1,京浙贷,0.0 2965 | 1,弘文贷,0.0 2966 | 1,三农创投,0.0 2967 | 1,聚信通,0.0 2968 | 1,方圆财富,0.0 2969 | 1,裕德财富,0.0 2970 | 1,锐畅易贷,0.0 2971 | 1,众鑫贷,0.0 2972 | 1,诚信宝,0.0 2973 | 1,圣达创投,0.0 2974 | 1,皇顺贷,0.0 2975 | 1,永信财富,0.0 2976 | 1,华夏信,0.0 2977 | 1,金地信投,0.0 2978 | 1,欣旺达财富,0.0 2979 | 1,金鼎投资,0.0 2980 | 1,宏中创投,0.0 2981 | 1,心诚创投,0.0 2982 | 1,稳益贷,0.0 2983 | 1,甬都贷,0.0 2984 | 1,四季贷,0.0 2985 | 1,格华财富,0.0 2986 | 1,融和创鑫,0.0 2987 | 1,乔赢财富,0.0 2988 | 1,聚宝通,0.0 2989 | 1,惠利银通,0.0 2990 | 1,聚贷网,0.0 2991 | 1,福人创投,0.0 2992 | 1,盛世汇盈,0.0 2993 | 1,三块钱,0.0 2994 | 1,玉丰投资,0.0 2995 | 1,国创投资,0.0 2996 | 1,众信财富,0.0 2997 | 1,墨竹轩电子,0.0 2998 | 1,金信贷,0.0 2999 | 1,中海融通,0.0 3000 | 1,金淮贷,0.0 3001 | 1,豫诚财富,0.0 3002 | 1,人贷贷,0.0 3003 | 1,鼎和贷,0.0 3004 | 1,鲁润创投,0.0 3005 | 1,18亮点贷,0.0 3006 | 1,联商贷,0.0 3007 | 1,英大创投,0.0 3008 | 1,汉泽天下,0.0 3009 | 1,金德易贷,0.0 3010 | 1,雅戈创投,0.0 3011 | 1,华富信投,0.0 3012 | 1,聚金投资,0.0 3013 | 1,德信财富,0.0 3014 | 1,皓峰财富,0.0 3015 | 1,姑苏财富,0.0 3016 | 1,标晨投资,0.0 3017 | 1,硕宝投资,0.0 3018 | 1,华迪易投,0.0 3019 | 1,泽鑫宝,0.0 3020 | 1,财益创投,0.0 3021 | 1,融丰创投,0.0 3022 | 1,钱塘人家,0.0 3023 | 1,汇亿财富,0.0 3024 | 1,煜尊财富,0.0 3025 | 1,宇鑫人人贷,0.0 3026 | 1,卓越财富,0.0 3027 | 1,龙腾投资,0.0 3028 | 1,佳伦资本,0.0 3029 | 1,甲富乾财富,0.0 3030 | 1,浙融创投,0.0 3031 | 1,恒丰信投,0.0 3032 | 1,宜客贷,0.0 3033 | 1,稳稳盈,0.0 3034 | 1,鸿康创投,0.0 3035 | 1,拓达贷,0.0 3036 | 1,基鼎贷,0.0 3037 | 1,众富随心贷,0.0 3038 | 1,冠宇投资,0.0 3039 | 1,金泰财富,0.0 3040 | 1,友信网投,0.0 3041 | 1,众达贷,0.0 3042 | 1,中银汇通（粤）,0.0 3043 | 1,中融速贷,0.0 3044 | 1,美嘉创投,0.0 3045 | 1,民生投资,0.0 3046 | 1,兴盛贷,0.0 3047 | 1,中广财富,0.0 3048 | 1,智邦创投,0.0 3049 | 1,瑞城财富,0.0 3050 | 1,新中金财富,0.0 3051 | 1,大禹资本,0.0 3052 | -------------------------------------------------------------------------------- /data/scores/randomforest/scores.csv: -------------------------------------------------------------------------------- 1 | month0 2 | 0.745901639344,0.798360655738,0.773770491803,0.947540983607,0.918032786885, 3 | 0.825769986894,0.806005141936,0.827677108047,0.986482758621,0.964748807504, 4 | 0.836721311475,0.8821367606 5 | month1 6 | 0.749180327869,0.804918032787,0.796721311475,0.937704918033,0.914754098361, 7 | 0.811535753917,0.797170483026,0.857891432435,0.981327667659,0.945898681162, 8 | 0.840655737705,0.87876480364 9 | month2 10 | 0.745901639344,0.819672131148,0.811475409836,0.937704918033,0.886885245902, 11 | 0.795886095196,0.800482173073,0.833579077584,0.978126246013,0.921843850221, 12 | 0.840327868852,0.865983488418 13 | month3 14 | 0.773770491803,0.837704918033,0.855737704918,0.918032786885,0.868852459016, 15 | 0.811462560524,0.799327950491,0.862056520259,0.972231859635,0.907666010901, 16 | 0.850819672131,0.870548980362 17 | month4 18 | 0.76393442623,0.852459016393,0.867213114754,0.875409836066,0.808196721311, 19 | 0.814942393758,0.8039,0.875869565217,0.962219512195,0.879148814937, 20 | 0.833442622951,0.867216057222 21 | month5 22 | 0.750819672131,0.86393442623,0.867213114754,0.737704918033,0.765573770492, 23 | 0.787438052144,0.827441176471,0.852747106755,0.933769864804,0.862587234043, 24 | 0.797049180328,0.852796686843 25 | -------------------------------------------------------------------------------- /data/scores/svm/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wang-haiyang/ppd_model/43c0237f3fbb4faf35e9026bf1a5e171e19eb5b4/data/scores/svm/.DS_Store -------------------------------------------------------------------------------- /data/scores/svm/scores.csv: -------------------------------------------------------------------------------- 1 | month0 2 | 0.659016393443,0.681967213115,0.760655737705,0.945901639344,0.922950819672, 3 | 0.71409848343,0.660592255125,0.763286685234,0.975448275862,0.96984323949, 4 | 0.794098360656,0.816653787828 5 | month1 6 | 0.660655737705,0.683606557377,0.796721311475,0.962295081967,0.92131147541, 7 | 0.696500375855,0.664260082023,0.785376219453,0.979678032248,0.940563179469, 8 | 0.804918032787,0.81327557781 9 | month2 10 | 0.662295081967,0.713114754098,0.829508196721,0.965573770492,0.867213114754, 11 | 0.664452797139,0.657432503257,0.799260268153,0.984088417065,0.882660136951, 12 | 0.807540983607,0.797578824513 13 | month3 14 | 0.704918032787,0.811475409836,0.845901639344,0.962295081967,0.824590163934, 15 | 0.583519803795,0.641170687682,0.818113721485,0.980645321174,0.858171810666, 16 | 0.829836065574,0.77632426896 17 | month4 18 | 0.72131147541,0.832786885246,0.822950819672,0.952459016393,0.734426229508, 19 | 0.496327031223,0.617690909091,0.835492753623,0.96968902439,0.825116972201, 20 | 0.812786885246,0.748863338106 21 | month5 22 | 0.73606557377,0.837704918033,0.795081967213,0.554098360656,0.652459016393, 23 | 0.452690691661,0.59237254902,0.833225082664,0.965420413696,0.802575886525, 24 | 0.715081967213,0.729256924713 25 | -------------------------------------------------------------------------------- /dl_model_dynamic.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | import MySQLdb 3 | import pandas as pd 4 | from keras.models import Model 5 | from keras.models import Sequential 6 | from keras.layers import Input, Embedding, LSTM, Dense, merge 7 | from keras.layers import Dense, Dropout, Activation 8 | from keras.layers.embeddings import Embedding 9 | from keras.layers.recurrent import LSTM, SimpleRNN, GRU 10 | import numpy as np 11 | from keras.utils.visualize_util import plot 12 | from sklearn import preprocessing 13 | from sklearn.metrics import roc_auc_score 14 | from sklearn.cross_validation import KFold 15 | import pickle 16 | 17 | #确定当前时间，距离2016年3月份的时间 18 | interval = 6 19 | dynamic_labes = pd.read_csv('dynamic_labels.csv') 20 | fw = open('scores.csv', 'a') 21 | for rounds in xrange(0, interval): 22 | print 'rounds: ' + str(rounds) 23 | #读取p2p平台各个特征的数据 24 | conn = MySQLdb.connect(host="localhost",user="root",passwd="123",db="ppd",charset="utf8") 25 | sql = "select * from platform_features" 26 | platform_features = pd.read_sql(sql,conn,index_col="index") 27 | names = platform_features['platName'].values 28 | base = platform_features[['type', 'tzzj_cooperation', 'listed', 'vc', 'argue', 'third_party', 'join_society', 'score', 'averageProfit', 'registMoney', 'autobid', 'stockTransfer', 'fundsToken', 'ifGuarantee', 'ifGuaranteeOrg', 'lauchTime', 'category', 'lng', 'lat']].values 29 | 30 | #对特征进行预处理 31 | for i in xrange(0, len(base)): 32 | for j in xrange(0, len(base[i])): 33 | if isinstance(base[i][j], unicode): 34 | if base[i][j]=='': 35 | base[i][j] = 0 36 | else: 37 | base[i][j] = float(base[i][j]) 38 | #更改距离公司成立的时间 39 | for i in xrange(0, len(base[15])):#base[15]表示lauchTime 40 | base[15][i]-=rounds 41 | 42 | #base = preprocessing.scale(base) 43 | min_max_scaler = preprocessing.MinMaxScaler() 44 | base = min_max_scaler.fit_transform(base) 45 | for i in xrange(0, len(base)): 46 | base[i,7]*=60 47 | 48 | series_old = [platform_features['comment_pos'].values, 49 | platform_features['comment_neu'].values, 50 | platform_features['comment_neg'].values] 51 | series = [] 52 | for i in xrange(0, len(series_old)): 53 | #处理每个series 54 | series.append([]) 55 | for j in xrange(0, len(series_old[i])): 56 | tmp = series_old[i][j].split(',') 57 | tmp = [int(tmp[k]) for k in xrange(0, len(tmp))] 58 | tmp = tmp[0:len(tmp)-rounds] 59 | series[i].append(tmp) 60 | series[i] = np.array(series[i]) 61 | #labels = platform_features['label'].values 62 | #换成动态label查看效果 63 | labels = dynamic_labes[dynamic_labes.columns[rounds]].values 64 | labels = labels.reshape(len(labels),1) 65 | 66 | #将中间变量存入pickle文件 67 | pkl_file = open('data.pkl', 'wb') 68 | pickle.dump([names, base, series, labels], pkl_file) 69 | pkl_file.close() 70 | #从pickle文件中读入中间变量 71 | # pkl_file = open('data.pkl', 'rb') 72 | # [names, base, series, labels] = pickle.load(pkl_file) 73 | # pkl_file.close() 74 | 75 | #样本个数 76 | N_Samples = 3050 77 | base_dim = 19 78 | series_dims = [27-rounds, 27-rounds, 27-rounds] 79 | 80 | accuracy_list, auc_list = [],[] 81 | 82 | name_list = [] 83 | score_list = [] 84 | label_list = [] 85 | 86 | kf = KFold(N_Samples, n_folds=5) 87 | for train, test in kf: 88 | #基本型变量 89 | x_train_base = base[train] 90 | x_test_base = base[test] 91 | 92 | #序列型变量 93 | x_train_series = [series[i][train] for i in xrange(0, len(series_dims))] 94 | x_test_series = [series[i][test] for i in xrange(0, len(series_dims))] 95 | 96 | #标签 97 | y_train = labels[train] 98 | y_test = labels[test] 99 | 100 | #名称 101 | names_train = names[train] 102 | name_test = names[test] 103 | 104 | #建立深度学习模型 105 | #基本特征 106 | base_input = Input(shape=(base_dim,), name='base_input') 107 | x = Dense(64, input_dim=base_dim, init='uniform', activation='relu')(base_input) 108 | base_model = Dropout(0.5)(x) 109 | #序列特征 110 | series_inputs, series_models = [], [] 111 | for i in xrange(0, len(series_dims)): 112 | series_input = Input(shape=(series_dims[i],), dtype='int32', name='series_input' + str(i)) 113 | series_inputs.append(series_input) 114 | x = Embedding(output_dim=128, input_dim=10000, input_length=series_dims[i])(series_input) 115 | lstm_out = LSTM(32)(x) 116 | series_models.append(lstm_out) 117 | x = merge([base_model] + series_models, mode='concat') 118 | x = Dense(64, activation='relu')(x) 119 | x = Dense(64, activation='relu')(x) 120 | x = Dense(64, activation='relu')(x) 121 | main_loss = Dense(1, activation='sigmoid', name='main_output')(x) 122 | 123 | model = Model(input=[base_input] + series_inputs, output=main_loss) 124 | plot(model, to_file='model.png') 125 | 126 | model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=["accuracy"]) 127 | 128 | model.fit([x_train_base] + x_train_series, y_train, 129 | nb_epoch=20, batch_size=32) 130 | 131 | score,accuracy = model.evaluate([x_test_base] + x_test_series, y_test, batch_size=32) 132 | a = model.predict([x_test_base] + x_test_series, batch_size=32) 133 | y_scores, y_true = [], [] 134 | for i in xrange(0, len(a)): 135 | y_scores.append(float(a[i][0])) 136 | y_true.append(int(y_test[i][0])) 137 | y_scores = np.array(y_scores) 138 | y_true = np.array(y_true) 139 | auc = roc_auc_score(y_true, y_scores) 140 | accuracy_list.append(accuracy) 141 | auc_list.append(auc) 142 | print accuracy, auc 143 | 144 | name_list+=list(names[test]) 145 | score_list+=list(y_scores) 146 | label_list+=list(y_true) 147 | print 'average results' 148 | print accuracy_list 149 | print auc_list 150 | print np.mean(accuracy_list), np.mean(auc_list) 151 | fw.write('month' + str(rounds) + '\n') 152 | for i in xrange(0, len(accuracy_list)): 153 | fw.write(str(accuracy_list[i]) + ',') 154 | fw.write('\n') 155 | for i in xrange(0, len(auc_list)): 156 | fw.write(str(auc_list[i]) + ',') 157 | fw.write('\n') 158 | fw.write(str(np.mean(accuracy_list)) + ',' + str(np.mean(auc_list)) + '\n') 159 | score_list = np.array([1-score_list[i] for i in xrange(0, len(score_list))]) 160 | rankings = pd.DataFrame({ 161 | 'name':[name_list[i].encode('utf-8') for i in xrange(0, len(name_list))], 162 | 'score':list(score_list), 163 | 'label':label_list 164 | }) 165 | rankings.sort(columns='score', ascending=False).to_csv('ppd_rankings_' + str(rounds) + '.csv',index=False) 166 | fw.close() -------------------------------------------------------------------------------- /dynamic_scores.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | import pandas as pd 3 | import MySQLdb 4 | 5 | conn = MySQLdb.connect(host="localhost",user="root",passwd="123",db="ppd",charset="utf8") 6 | sql = "select * from platform_features" 7 | platform_features = pd.read_sql(sql,conn,index_col="index") 8 | 9 | #得到平台的名称与标记 10 | platform = list(platform_features['platName'].values) 11 | labels = list(platform_features['label'].values) 12 | 13 | #跑路平台数据 14 | bad_platforms = pd.read_csv('bad_platforms.txt', sep='\t') 15 | bad_platforms_time = bad_platforms[['name', 'run_time']] 16 | 17 | #回到三月底，首先都更新一遍 18 | all_bad = list(bad_platforms_time[bad_platforms_time.run_time!=2016.04]['name'].values) 19 | all_bad = [all_bad[i].decode('utf-8') for i in xrange(0, len(all_bad))] 20 | for i in xrange(0, len(platform)): 21 | if platform[i] in all_bad and labels[i]==0: 22 | labels[i] = 1 23 | 24 | #我们现在在3月底，2月底，1月底，…… 25 | rslt_labels = [] 26 | months = [2016.04, 2016.03, 2016.02, 2016.01, 2015.12, 2015.11] 27 | for i in xrange(0, len(months)): 28 | print months[i] 29 | up_list = list(bad_platforms_time[bad_platforms_time.run_time==months[i]]['name'].values) 30 | up_list = [up_list[j].decode('utf-8') for j in xrange(0, len(up_list))] 31 | for j in xrange(0, len(platform)): 32 | if platform[j] in up_list and labels[j]==1: 33 | labels[j] = 0 34 | tmp = labels[:] 35 | rslt_labels.append(tmp) 36 | 37 | df_rslt = pd.DataFrame({'01':rslt_labels[0], 38 | '02':rslt_labels[1],'03':rslt_labels[2], 39 | '04':rslt_labels[3],'05':rslt_labels[4], 40 | '06':rslt_labels[5], 41 | }) 42 | df_rslt.to_csv('dynamic_labels.csv', index=False) 43 | 44 | 45 | -------------------------------------------------------------------------------- /features.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | import pandas as pd 3 | import time, datetime 4 | import MySQLdb 5 | from keras.utils import np_utils 6 | import numpy as np 7 | from sklearn import preprocessing 8 | 9 | def date_difference(d1, d2):#d1早于d2 10 | d1 = datetime.datetime.strptime(d1 + ' 00:00:00', '%Y-%m-%d %H:%M:%S') 11 | d2 = datetime.datetime.strptime(d2 + ' 00:00:00', '%Y-%m-%d %H:%M:%S') 12 | delta = d2 - d1 13 | return delta.days 14 | 15 | def extr_tags(x): 16 | #type：国资系/上市公司系/银行系/民营系（类别型标签） 17 | #tzzj_cooperation 投之家合作平台 18 | #listed 股权上市 19 | #vc 接受过风投 20 | #argue 争议 21 | #third_party 加入第三方征信 22 | #join_society 加入协会 23 | #label：停业/提现困难/跑路/经侦介入（含有这些关键字就为1，否则就为0） 24 | types = ['国资系', '上市公司系', '银行系', '民营系'] 25 | binas = ['投之家合作平台', '股权上市', '接受过风投', '争议', '加入第三方征信', '加入协会'] 26 | labels = ['停业', '提现困难', '跑路','经侦介入'] 27 | rslt = [ [0 for i in xrange(0, len(x))] for j in xrange(0, 8)] 28 | for i in xrange(0, len(x)): 29 | tag_list = x[i].split(',') 30 | for j in xrange(0, len(tag_list)): 31 | tag = tag_list[j].encode('utf-8') 32 | if tag in types: 33 | rslt[0][i]=types.index(tag)+1 34 | continue 35 | if tag in binas: 36 | rslt[binas.index(tag)+1][i] = 1 37 | continue 38 | if tag in labels: 39 | rslt[7][i] = 1 40 | return rslt 41 | 42 | def one_feature(x, fill, cut): 43 | x_new = [] 44 | for i in xrange(0, len(x)): 45 | if x[i]=='': 46 | x_new.append(0) 47 | else: 48 | x_new.append(x[i].strip(cut)) 49 | return x_new 50 | 51 | def autobid_feature(x): 52 | rslt = [] 53 | for i in xrange(0, len(x)): 54 | if x[i]=='': 55 | rslt.append(0) 56 | elif x[i]==u'支持': 57 | rslt.append(1) 58 | else: 59 | rslt.append(-1) 60 | return rslt 61 | 62 | def stockTransfer_feature(x): 63 | rslt = [] 64 | for i in xrange(0, len(x)): 65 | if x[i]=='': 66 | rslt.append(-1) 67 | elif x[i]==u'随时': 68 | rslt.append(0) 69 | elif x[i]==u'1年': 70 | rslt.append(12) 71 | elif x[i]==u'不可转让': 72 | rslt.append(300) 73 | else: 74 | rslt.append(x[i].strip(u'个月')) 75 | print x, 76 | return rslt 77 | 78 | def fundsToken_feature(x): 79 | rslt = [] 80 | for i in xrange(0, len(x)): 81 | if x[i]=='' or x[i]==u'无托管': 82 | rslt.append(0) 83 | else: 84 | rslt.append(1) 85 | return rslt 86 | 87 | def ifGuarantee_feature(x): 88 | rslt = [] 89 | for i in xrange(0, len(x)): 90 | if x[i]=='': 91 | rslt.append(0) 92 | else: 93 | rslt.append(1) 94 | return rslt 95 | 96 | def ifGuaranteeOrg_feature(x): 97 | rslt = [] 98 | for i in xrange(0, len(x)): 99 | if x[i]=='': 100 | rslt.append(0) 101 | else: 102 | rslt.append(1) 103 | return rslt 104 | 105 | def lauchTime_feature(x): 106 | rslt = [] 107 | for i in xrange(0, len(x)): 108 | if x[i]!='': 109 | rslt.append(date_difference(x[i], '2016-04-17')) 110 | else: 111 | rslt.append(0) 112 | avg = np.mean(rslt) 113 | for i in xrange(0, len(rslt)): 114 | if rslt[i]==0: 115 | rslt[i] = avg 116 | rslt[i] = rslt[i]/30 117 | return rslt 118 | 119 | def category_feature(x): 120 | dic = {'':-1, u'股份合作企业':0, u'私营企业':1, u'港、澳、台投资企业':2, 121 | u'股份制企业':3, u'集体所有制企业':4, u'外商投资企业':5, u'国有企业':6, u'联营企业':7} 122 | rslt = [] 123 | for i in xrange(0, len(x)): 124 | rslt.append(dic[x[i]]) 125 | return rslt 126 | 127 | conn = MySQLdb.connect(host="localhost",user="root",passwd="123",db="ppd",charset="utf8") 128 | 129 | 130 | #平台基本信息 131 | sql = "select id,platId,platName,platPin,tags,score,averageProfit,registMoney,\ 132 | autobid,stockTransfer,fundsToken,bidGuarantee,guaranteeMode,guaranteeOrg,\ 133 | lauchTime,category,lng,lat from platform" 134 | platform = pd.read_sql(sql,conn,index_col="id") 135 | #定义提取特征的DataFrame 136 | platform_features = platform[['platId', 'platName', 'platPin']] 137 | 138 | #开始提取特征 139 | #tags特征 140 | tags = list(platform['tags']) 141 | tag_features = ['type', 'tzzj_cooperation', 'listed', 'vc', 'argue' ,'third_party', 'join_society', 'label'] 142 | rslt = extr_tags(tags) 143 | for i in xrange(0, len(tag_features)): 144 | platform_features[tag_features[i]] = pd.Series(rslt[i], index = platform_features.index) 145 | #score 146 | score = list(platform['score']) 147 | platform_features['score'] = pd.Series(one_feature(score,0,''), index = platform_features.index) 148 | #averageProfit 149 | averageProfit = list(platform['averageProfit']) 150 | platform_features['averageProfit'] = pd.Series(one_feature(averageProfit,0,'%'), index = platform_features.index) 151 | #registMoney 152 | registMoney = list(platform['registMoney']) 153 | platform_features['registMoney'] = pd.Series(one_feature(registMoney,0,u' 万元'), index = platform_features.index) 154 | #autobid 155 | autobid = list(platform['autobid']) 156 | platform_features['autobid'] = pd.Series(autobid_feature(autobid), index = platform_features.index) 157 | #stockTransfer 158 | stockTransfer = list(platform['stockTransfer']) 159 | platform_features['stockTransfer'] = pd.Series(stockTransfer_feature(stockTransfer), index = platform_features.index) 160 | #fundsToken 161 | fundsToken = list(platform['fundsToken']) 162 | platform_features['fundsToken'] = pd.Series(fundsToken_feature(fundsToken), index = platform_features.index) 163 | #ifGuarantee 164 | guaranteeMode = list(platform['guaranteeMode']) 165 | platform_features['ifGuarantee'] = pd.Series(ifGuarantee_feature(guaranteeMode), index = platform_features.index) 166 | #ifGuaranteeOrg 167 | guaranteeOrg = list(platform['guaranteeOrg']) 168 | platform_features['ifGuaranteeOrg'] = pd.Series(ifGuaranteeOrg_feature(guaranteeOrg), index = platform_features.index) 169 | #lauchTime 170 | lauchTime = list(platform['lauchTime']) 171 | platform_features['lauchTime'] = pd.Series(lauchTime_feature(lauchTime), index = platform_features.index) 172 | #category 173 | category = list(platform['category']) 174 | platform_features['category'] = pd.Series(category_feature(category), index = platform_features.index) 175 | #lng 176 | lng = list(platform['lng']) 177 | platform_features['lng'] = pd.Series(one_feature(lng,0,''), index = platform_features.index) 178 | #lat 179 | lat = list(platform['lat']) 180 | platform_features['lat'] = pd.Series(one_feature(lat,0,''), index = platform_features.index) 181 | 182 | #用户评论信息comment 183 | sql = "select timestamp,platName,attitude from comment" 184 | comment = pd.read_sql(sql,conn) 185 | 186 | platName = list(set(list(comment['platName']))) 187 | comment_features = pd.DataFrame({'platName': pd.Series(platName)}) 188 | pos,neu,neg = [],[],[] 189 | for i in xrange(0, len(platName)): 190 | pos.append([0 for k in xrange(0, 12*2+3)]) 191 | neu.append([0 for k in xrange(0, 12*2+3)]) 192 | neg.append([0 for k in xrange(0, 12*2+3)]) 193 | 194 | tmp = comment[comment.platName==platName[i]] 195 | tsp = list(tmp['timestamp']) 196 | atti = list(tmp['attitude']) 197 | for j in xrange(0, len(tsp)): 198 | year = int(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(int(tsp[j]))).split(' ')[0].split('-')[0]) 199 | month = int(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(int(tsp[j]))).split(' ')[0].split('-')[1]) 200 | if year in [2014, 2015] or (year==2016 and month in [1, 2, 3]): 201 | idx = (year-2014)*12+month-1 202 | if atti[j]==u'推荐': 203 | pos[i][idx]+=1 204 | elif atti[j]==u'一般': 205 | neu[i][idx]+=1 206 | else: 207 | neg[i][idx]+=1 208 | pos[i] = ','.join([str(pos[i][j]) for j in xrange(0, len(pos[i]))]) 209 | neu[i] = ','.join([str(neu[i][j]) for j in xrange(0, len(neu[i]))]) 210 | neg[i] = ','.join([str(neg[i][j]) for j in xrange(0, len(neg[i]))]) 211 | comment_features['comment_pos'] = pd.Series(pos, index = comment_features.index) 212 | comment_features['comment_neu'] = pd.Series(neu, index = comment_features.index) 213 | comment_features['comment_neg'] = pd.Series(neg, index = comment_features.index) 214 | platform_features = platform_features.merge(comment_features, how='left', on='platName') 215 | 216 | platform_features.fillna(','.join(['0' for i in xrange(0, 12*2+3)]), inplace=True) 217 | 218 | #把特征写入数据库 219 | platform_features.to_sql('platform_features1', conn, flavor='mysql', if_exists='replace', index=True) -------------------------------------------------------------------------------- /omnirank_kfold.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | import MySQLdb 3 | import pandas as pd 4 | from keras.models import Model 5 | from keras.models import Sequential 6 | from keras.layers import Input, Embedding, LSTM, Dense, merge 7 | from keras.layers import Dense, Dropout, Activation 8 | from keras.layers.embeddings import Embedding 9 | from keras.layers.recurrent import LSTM, SimpleRNN, GRU 10 | import numpy as np 11 | from keras.utils.visualize_util import plot 12 | from sklearn import preprocessing 13 | from sklearn.metrics import roc_auc_score 14 | from sklearn.cross_validation import KFold 15 | import pickle 16 | 17 | #确定当前时间，距离2016年3月份的时间 18 | interval = 5 19 | fw = open('scores.csv', 'a') 20 | for rounds in xrange(0, interval): 21 | print 'rounds: ' + str(rounds) 22 | #读取p2p平台各个特征的数据 23 | conn = MySQLdb.connect(host="localhost",user="root",passwd="123",db="ppd",charset="utf8") 24 | sql = "select * from platform_features" 25 | platform_features = pd.read_sql(sql,conn,index_col="index") 26 | names = platform_features['platName'].values 27 | base = platform_features[['type', 'tzzj_cooperation', 'listed', 'vc', 'argue', 'third_party', 'join_society', 'score', 'averageProfit', 'registMoney', 'autobid', 'stockTransfer', 'fundsToken', 'ifGuarantee', 'ifGuaranteeOrg', 'lauchTime', 'category', 'lng', 'lat']].values 28 | 29 | #对特征进行预处理 30 | for i in xrange(0, len(base)): 31 | for j in xrange(0, len(base[i])): 32 | if isinstance(base[i][j], unicode): 33 | if base[i][j]=='': 34 | base[i][j] = 0 35 | else: 36 | base[i][j] = float(base[i][j]) 37 | #更改距离公司成立的时间 38 | for i in xrange(0, len(base[15])):#base[15]表示lauchTime 39 | base[15][i]-=rounds 40 | 41 | #base = preprocessing.scale(base) 42 | min_max_scaler = preprocessing.MinMaxScaler() 43 | base = min_max_scaler.fit_transform(base) 44 | for i in xrange(0, len(base)): 45 | base[i,7]*=60 46 | 47 | series_old = [platform_features['comment_pos'].values, 48 | platform_features['comment_neu'].values, 49 | platform_features['comment_neg'].values] 50 | series = [] 51 | for i in xrange(0, len(series_old)): 52 | #处理每个series 53 | series.append([]) 54 | for j in xrange(0, len(series_old[i])): 55 | tmp = series_old[i][j].split(',') 56 | tmp = [int(tmp[k]) for k in xrange(0, len(tmp))] 57 | tmp = tmp[0:len(tmp)-rounds] 58 | series[i].append(tmp) 59 | series[i] = np.array(series[i]) 60 | labels = platform_features['label'].values 61 | #在这里改变label的跑路情况 62 | 63 | labels = labels.reshape(len(labels),1) 64 | 65 | #将中间变量存入pickle文件 66 | pkl_file = open('data.pkl', 'wb') 67 | pickle.dump([names, base, series, labels], pkl_file) 68 | pkl_file.close() 69 | #从pickle文件中读入中间变量 70 | # pkl_file = open('data.pkl', 'rb') 71 | # [names, base, series, labels] = pickle.load(pkl_file) 72 | # pkl_file.close() 73 | 74 | 75 | 76 | #样本个数 77 | N_Samples = 3050 78 | base_dim = 19 79 | series_dims = [27-rounds, 27-rounds, 27-rounds] 80 | 81 | accuracy_list, auc_list = [],[] 82 | 83 | name_list = [] 84 | score_list = [] 85 | label_list = [] 86 | 87 | kf = KFold(N_Samples, n_folds=5) 88 | for train, test in kf: 89 | #基本型变量 90 | x_train_base = base[train] 91 | x_test_base = base[test] 92 | 93 | #序列型变量 94 | x_train_series = [series[i][train] for i in xrange(0, len(series_dims))] 95 | x_test_series = [series[i][test] for i in xrange(0, len(series_dims))] 96 | 97 | #标签 98 | y_train = labels[train] 99 | y_test = labels[test] 100 | 101 | #名称 102 | names_train = names[train] 103 | name_test = names[test] 104 | 105 | #建立深度学习模型 106 | #基本特征 107 | base_input = Input(shape=(base_dim,), name='base_input') 108 | x = Dense(64, input_dim=base_dim, init='uniform', activation='relu')(base_input) 109 | base_model = Dropout(0.5)(x) 110 | #序列特征 111 | series_inputs, series_models = [], [] 112 | for i in xrange(0, len(series_dims)): 113 | series_input = Input(shape=(series_dims[i],), dtype='int32', name='series_input' + str(i)) 114 | series_inputs.append(series_input) 115 | x = Embedding(output_dim=128, input_dim=10000, input_length=series_dims[i])(series_input) 116 | lstm_out = LSTM(32)(x) 117 | series_models.append(lstm_out) 118 | x = merge([base_model] + series_models, mode='concat') 119 | x = Dense(64, activation='relu')(x) 120 | x = Dense(64, activation='relu')(x) 121 | x = Dense(64, activation='relu')(x) 122 | main_loss = Dense(1, activation='sigmoid', name='main_output')(x) 123 | 124 | model = Model(input=[base_input] + series_inputs, output=main_loss) 125 | plot(model, to_file='model.png') 126 | 127 | model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=["accuracy"]) 128 | 129 | model.fit([x_train_base] + x_train_series, y_train, 130 | nb_epoch=20, batch_size=32) 131 | 132 | score,accuracy = model.evaluate([x_test_base] + x_test_series, y_test, batch_size=32) 133 | a = model.predict([x_test_base] + x_test_series, batch_size=32) 134 | y_scores, y_true = [], [] 135 | for i in xrange(0, len(a)): 136 | y_scores.append(float(a[i][0])) 137 | y_true.append(int(y_test[i][0])) 138 | y_scores = np.array(y_scores) 139 | y_true = np.array(y_true) 140 | auc = roc_auc_score(y_true, y_scores) 141 | accuracy_list.append(accuracy) 142 | auc_list.append(auc) 143 | print accuracy, auc 144 | 145 | name_list+=list(names[test]) 146 | score_list+=list(y_scores) 147 | label_list+=list(y_true) 148 | print 'average results' 149 | print accuracy_list 150 | print auc_list 151 | print np.mean(accuracy_list), np.mean(auc_list) 152 | fw.write('month' + str(i) + '\n') 153 | for i in xrange(0, len(accuracy_list)): 154 | fw.write(str(accuracy_list[i]) + ',') 155 | fw.write('\n') 156 | for i in xrange(0, len(auc_list)): 157 | fw.write(str(auc_list[i]) + ',') 158 | fw.write('\n') 159 | fw.write(str(np.mean(accuracy_list)) + ',' + str(np.mean(auc_list)) + '\n') 160 | score_list = np.array([1-score_list[i] for i in xrange(0, len(score_list))]) 161 | rankings = pd.DataFrame({ 162 | 'name':[name_list[i].encode('utf-8') for i in xrange(0, len(name_list))], 163 | 'score':list(score_list), 164 | 'label':label_list 165 | }) 166 | rankings.sort(columns='score', ascending=False).to_csv('ppd_rankings_' + str(rounds) + '.csv',index=False) 167 | fw.close() -------------------------------------------------------------------------------- /omnirank_model.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | import MySQLdb 3 | import pandas as pd 4 | from keras.models import Model 5 | from keras.models import Sequential 6 | from keras.layers import Input, Embedding, LSTM, Dense, merge 7 | from keras.layers import Dense, Dropout, Activation 8 | from keras.layers.embeddings import Embedding 9 | from keras.layers.recurrent import LSTM, SimpleRNN, GRU 10 | import numpy as np 11 | from keras.utils.visualize_util import plot 12 | from sklearn.metrics import roc_auc_score 13 | 14 | #读取p2p平台各个特征的数据 15 | conn = MySQLdb.connect(host="localhost",user="root",passwd="123",db="ppd",charset="utf8") 16 | sql = "select * from platform_features" 17 | platform_features = pd.read_sql(sql,conn,index_col="index") 18 | names = platform_features['platName'].values 19 | base = platform_features[['type', 'tzzj_cooperation', 'listed', 'vc', 'argue', 'third_party', 'join_society', 'score', 'averageProfit', 'registMoney', 'autobid', 'stockTransfer', 'fundsToken', 'ifGuarantee', 'ifGuaranteeOrg', 'lauchTime', 'category', 'lng', 'lat']].values 20 | for i in xrange(0, len(base)): 21 | for j in xrange(0, len(base[i])): 22 | if isinstance(base[i][j], unicode): 23 | if base[i][j]=='': 24 | base[i][j] = 0 25 | else: 26 | base[i][j] = float(base[i][j]) 27 | 28 | series_old = [platform_features['comment_pos'].values, 29 | platform_features['comment_neu'].values, 30 | platform_features['comment_neg'].values] 31 | series = [] 32 | for i in xrange(0, len(series_old)): 33 | #处理每个series 34 | series.append([]) 35 | for j in xrange(0, len(series_old[i])): 36 | tmp = series_old[i][j].split(',') 37 | tmp = [int(tmp[k]) for k in xrange(0, len(tmp))] 38 | series[i].append(tmp) 39 | series[i] = np.array(series[i]) 40 | labels = platform_features['label'].values 41 | labels = labels.reshape(len(labels),1) 42 | 43 | 44 | #样本个数 45 | N_train = 2000 46 | N_test = 1000 47 | 48 | #基本型变量 49 | base_dim = 19 50 | # x_train_base = np.random.random((N_train, base_dim)) 51 | # x_test_base = np.random.random((N_test, base_dim)) 52 | x_train_base = base[0:N_train] 53 | x_test_base = base[N_train:(N_train+N_test)] 54 | 55 | #序列型变量 56 | series_dims = [27, 27, 27] 57 | # x_train_series = [np.random.random((N_train, series_dims[i])) for i in xrange(0, len(series_dims))] 58 | # x_test_series = [np.random.random((N_test, series_dims[i])) for i in xrange(0, len(series_dims))] 59 | x_train_series = [series[i][0:N_train] for i in xrange(0, len(series_dims))] 60 | x_test_series = [series[i][N_train:(N_train+N_test)] for i in xrange(0, len(series_dims))] 61 | #标签 62 | # y_train = np.random.randint(2, size=(N_train, 1)) 63 | # y_test = np.random.randint(2, size=(N_test, 1)) 64 | y_train = labels[0:N_train] 65 | y_test = labels[N_train:(N_train+N_test)] 66 | 67 | #名称 68 | names_train = names[0:N_train] 69 | name_test = names[N_train:(N_train+N_test)] 70 | 71 | #建立深度学习模型 72 | #基本特征 73 | base_input = Input(shape=(base_dim,), name='base_input') 74 | x = Dense(64, input_dim=base_dim, init='uniform', activation='relu')(base_input) 75 | base_model = Dropout(0.5)(x) 76 | #序列特征 77 | series_inputs, series_models = [], [] 78 | for i in xrange(0, len(series_dims)): 79 | series_input = Input(shape=(series_dims[i],), dtype='int32', name='series_input' + str(i)) 80 | series_inputs.append(series_input) 81 | x = Embedding(output_dim=128, input_dim=10000, input_length=series_dims[i])(series_input) 82 | lstm_out = LSTM(32)(x) 83 | series_models.append(lstm_out) 84 | x = merge([base_model] + series_models, mode='concat') 85 | x = Dense(64, activation='relu')(x) 86 | main_loss = Dense(1, activation='sigmoid', name='main_output')(x) 87 | 88 | model = Model(input=[base_input] + series_inputs, output=main_loss) 89 | plot(model, to_file='model.png') 90 | 91 | model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=["accuracy"]) 92 | 93 | model.fit([x_train_base] + x_train_series, y_train, 94 | nb_epoch=16, batch_size=32) 95 | 96 | a = model.predict([x_test_base] + x_test_series, batch_size=32) 97 | b = model.evaluate([x_test_base] + x_test_series, y_test, batch_size=32) 98 | print b 99 | y_scores, y_true = [], [] 100 | for i in xrange(0, len(a)): 101 | y_scores.append(float(a[i][0])) 102 | y_true.append(int(y_test[i][0])) 103 | y_scores = np.array(y_scores) 104 | y_true = np.array(y_true) 105 | print roc_auc_score(y_true, y_scores) -------------------------------------------------------------------------------- /omnirank_model_dynamic.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | import MySQLdb 3 | import pandas as pd 4 | from keras.models import Model 5 | from keras.models import Sequential 6 | from keras.layers import Input, Embedding, LSTM, Dense, merge 7 | from keras.layers import Dense, Dropout, Activation 8 | from keras.layers.embeddings import Embedding 9 | from keras.layers.recurrent import LSTM, SimpleRNN, GRU 10 | import numpy as np 11 | from keras.utils.visualize_util import plot 12 | from sklearn import preprocessing 13 | from sklearn.metrics import roc_auc_score 14 | from sklearn.cross_validation import KFold 15 | import pickle 16 | 17 | #确定当前时间，距离2016年3月份的时间 18 | interval = 6 19 | dynamic_labes = pd.read_csv('dynamic_labels.csv') 20 | fw = open('scores.csv', 'a') 21 | for rounds in xrange(0, interval): 22 | print 'rounds: ' + str(rounds) 23 | #读取p2p平台各个特征的数据 24 | conn = MySQLdb.connect(host="localhost",user="root",passwd="123",db="ppd",charset="utf8") 25 | sql = "select * from platform_features" 26 | platform_features = pd.read_sql(sql,conn,index_col="index") 27 | names = platform_features['platName'].values 28 | base = platform_features[['type', 'tzzj_cooperation', 'listed', 'vc', 'argue', 'third_party', 'join_society', 'score', 'averageProfit', 'registMoney', 'autobid', 'stockTransfer', 'fundsToken', 'ifGuarantee', 'ifGuaranteeOrg', 'lauchTime', 'category', 'lng', 'lat']].values 29 | 30 | #对特征进行预处理 31 | for i in xrange(0, len(base)): 32 | for j in xrange(0, len(base[i])): 33 | if isinstance(base[i][j], unicode): 34 | if base[i][j]=='': 35 | base[i][j] = 0 36 | else: 37 | base[i][j] = float(base[i][j]) 38 | #更改距离公司成立的时间 39 | for i in xrange(0, len(base[15])):#base[15]表示lauchTime 40 | base[15][i]-=rounds 41 | 42 | #base = preprocessing.scale(base) 43 | min_max_scaler = preprocessing.MinMaxScaler() 44 | base = min_max_scaler.fit_transform(base) 45 | for i in xrange(0, len(base)): 46 | base[i,7]*=60 47 | 48 | series_old = [platform_features['comment_pos'].values, 49 | platform_features['comment_neu'].values, 50 | platform_features['comment_neg'].values] 51 | series = [] 52 | for i in xrange(0, len(series_old)): 53 | #处理每个series 54 | series.append([]) 55 | for j in xrange(0, len(series_old[i])): 56 | tmp = series_old[i][j].split(',') 57 | tmp = [int(tmp[k]) for k in xrange(0, len(tmp))] 58 | tmp = tmp[0:len(tmp)-rounds] 59 | series[i].append(tmp) 60 | series[i] = np.array(series[i]) 61 | #labels = platform_features['label'].values 62 | #换成动态label查看效果 63 | labels = dynamic_labes[dynamic_labes.columns[rounds]].values 64 | labels = labels.reshape(len(labels),1) 65 | 66 | #将中间变量存入pickle文件 67 | pkl_file = open('data.pkl', 'wb') 68 | pickle.dump([names, base, series, labels], pkl_file) 69 | pkl_file.close() 70 | #从pickle文件中读入中间变量 71 | # pkl_file = open('data.pkl', 'rb') 72 | # [names, base, series, labels] = pickle.load(pkl_file) 73 | # pkl_file.close() 74 | 75 | #样本个数 76 | N_Samples = 3050 77 | base_dim = 19 78 | series_dims = [27-rounds, 27-rounds, 27-rounds] 79 | 80 | accuracy_list, auc_list = [],[] 81 | 82 | name_list = [] 83 | score_list = [] 84 | label_list = [] 85 | 86 | kf = KFold(N_Samples, n_folds=5) 87 | for train, test in kf: 88 | #基本型变量 89 | x_train_base = base[train] 90 | x_test_base = base[test] 91 | 92 | #序列型变量 93 | x_train_series = [series[i][train] for i in xrange(0, len(series_dims))] 94 | x_test_series = [series[i][test] for i in xrange(0, len(series_dims))] 95 | 96 | #标签 97 | y_train = labels[train] 98 | y_test = labels[test] 99 | 100 | #名称 101 | names_train = names[train] 102 | name_test = names[test] 103 | 104 | #建立深度学习模型 105 | #基本特征 106 | base_input = Input(shape=(base_dim,), name='base_input') 107 | x = Dense(64, input_dim=base_dim, init='uniform', activation='relu')(base_input) 108 | base_model = Dropout(0.5)(x) 109 | #序列特征 110 | series_inputs, series_models = [], [] 111 | for i in xrange(0, len(series_dims)): 112 | series_input = Input(shape=(series_dims[i],), dtype='int32', name='series_input' + str(i)) 113 | series_inputs.append(series_input) 114 | x = Embedding(output_dim=128, input_dim=10000, input_length=series_dims[i])(series_input) 115 | lstm_out = LSTM(32)(x) 116 | series_models.append(lstm_out) 117 | x = merge([base_model] + series_models, mode='concat') 118 | x = Dense(64, activation='relu')(x) 119 | x = Dense(64, activation='relu')(x) 120 | x = Dense(64, activation='relu')(x) 121 | main_loss = Dense(1, activation='sigmoid', name='main_output')(x) 122 | 123 | model = Model(input=[base_input] + series_inputs, output=main_loss) 124 | plot(model, to_file='model.png') 125 | 126 | model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=["accuracy"]) 127 | 128 | model.fit([x_train_base] + x_train_series, y_train, 129 | nb_epoch=20, batch_size=32) 130 | 131 | score,accuracy = model.evaluate([x_test_base] + x_test_series, y_test, batch_size=32) 132 | a = model.predict([x_test_base] + x_test_series, batch_size=32) 133 | y_scores, y_true = [], [] 134 | for i in xrange(0, len(a)): 135 | y_scores.append(float(a[i][0])) 136 | y_true.append(int(y_test[i][0])) 137 | y_scores = np.array(y_scores) 138 | y_true = np.array(y_true) 139 | auc = roc_auc_score(y_true, y_scores) 140 | accuracy_list.append(accuracy) 141 | auc_list.append(auc) 142 | print accuracy, auc 143 | 144 | name_list+=list(names[test]) 145 | score_list+=list(y_scores) 146 | label_list+=list(y_true) 147 | print 'average results' 148 | print accuracy_list 149 | print auc_list 150 | print np.mean(accuracy_list), np.mean(auc_list) 151 | fw.write('month' + str(rounds) + '\n') 152 | for i in xrange(0, len(accuracy_list)): 153 | fw.write(str(accuracy_list[i]) + ',') 154 | fw.write('\n') 155 | for i in xrange(0, len(auc_list)): 156 | fw.write(str(auc_list[i]) + ',') 157 | fw.write('\n') 158 | fw.write(str(np.mean(accuracy_list)) + ',' + str(np.mean(auc_list)) + '\n') 159 | score_list = np.array([1-score_list[i] for i in xrange(0, len(score_list))]) 160 | rankings = pd.DataFrame({ 161 | 'name':[name_list[i].encode('utf-8') for i in xrange(0, len(name_list))], 162 | 'score':list(score_list), 163 | 'label':label_list 164 | }) 165 | rankings.sort(columns='score', ascending=False).to_csv('ppd_rankings_' + str(rounds) + '.csv',index=False) 166 | fw.close() -------------------------------------------------------------------------------- /omnirank_result/omnirank_result.R: -------------------------------------------------------------------------------- 1 | library(RODBC) 2 | library('RMySQL') 3 | library(minerva) 4 | library(ggplot2) 5 | library(reshape2) 6 | library(grid) 7 | library(gridExtra) 8 | setwd('/Users/wanghaiyang/workspace/R/ppd_omnirank') 9 | 10 | #各个月的accuracy曲线 11 | month <- 1:6 12 | SVM <- c(0.715081967213,0.812786885246,0.829836065574,0.807540983607,0.804918032787,0.794098360656) 13 | Logistic <- c(0.8,0.833770491803,0.840983606557,0.82262295082,0.82,0.804590163934) 14 | RF <- c(0.797049180328,0.833442622951,0.850819672131,0.840327868852,0.840655737705,0.836721311475) 15 | OMNIRank <- c(0.840327868852,0.844262295082,0.850819672131,0.840327868852,0.86,0.849180327869) 16 | timeValue <- data.frame(month,SVM,Logistic,RF,OMNIRank) 17 | timeValue_long <- melt(timeValue, id="month") # convert to long format 18 | #cairo_ps('paper_data/paper_accuracy.eps') 19 | ggplot(data=timeValue_long)+ 20 | geom_histogram(aes(x=month, y=value, fill=as.factor(variable)), position = "dodge", stat="identity", alpha = 1, width = 0.8)+ 21 | theme_bw()+ 22 | theme(text=element_text(family = "Microsoft YaHei",face="bold",size=25),legend.title=element_blank(), axis.text.x=element_text(angle=0, color = "black", size=20), axis.text.y=element_text(color = "black", size=20), plot.title=element_blank(), plot.margin = unit(c(1.5,0.7,0.7,0.5), "cm"), axis.title.x=element_text(vjust=-1), axis.title.y=element_text(vjust=1.5))+ 23 | labs(x='month',y='accuracy',title='OMNIRank评分准确率与AUC')+ 24 | scale_x_continuous(breaks=c(1,2,3,4,5,6), labels=c("2015-11", "2015-12", "2016-01", "2016-02", "2016-03", "2016-04"))+ 25 | scale_color_brewer(palette="Set3")+ 26 | scale_fill_brewer(palette="Set3")+ 27 | coord_cartesian(ylim=c(0.7, 0.87)) 28 | #dev.off() 29 | 30 | #各个月的auc曲线 31 | month <- 1:6 32 | SVM <- c(0.729256924713,0.748863338106,0.77632426896,0.797578824513,0.81327557781,0.816653787828) 33 | Logistic <- c(0.805231948091,0.814425451392,0.82320828334,0.820215115951,0.827436627889,0.825864402262) 34 | RF <- c(0.852796686843,0.867216057222,0.870548980362,0.865983488418,0.87876480364,0.8821367606) 35 | OMNIRank = c(0.852089625538,0.858188191289,0.869886839283,0.882064123965,0.899087775476,0.905489157118) 36 | timeValue <- data.frame(month,SVM,Logistic,RF,OMNIRank) 37 | timeValue_long <- melt(timeValue, id="month") # convert to long format 38 | ggplot(data=timeValue_long)+ 39 | geom_histogram(aes(x=month, y=value, fill=as.factor(variable)), position = "dodge", stat="identity", alpha = 1, width = 0.8)+ 40 | theme_bw()+ 41 | theme(text=element_text(family = "Microsoft YaHei",face="bold",size=25),legend.title=element_blank(), axis.text.x=element_text(angle=0, color = "black", size=20), axis.text.y=element_text(color = "black", size=20), plot.title=element_blank(), plot.margin = unit(c(1.5,0.7,0.7,0.5), "cm"), axis.title.x=element_text(vjust=-1), axis.title.y=element_text(vjust=1.5))+ 42 | labs(x='month',y='auc',title='OMNIRank评分准确率与AUC')+ 43 | scale_x_continuous(breaks=c(1,2,3,4,5,6), labels=c("2015-11", "2015-12", "2016-01", "2016-02", "2016-03", "2016-04"))+ 44 | scale_color_brewer(palette="Set3")+ 45 | scale_fill_brewer(palette="Set3")+ 46 | coord_cartesian(ylim=c(0.7, 0.92)) 47 | 48 | 49 | #各模型评分分布图 50 | data_source <- c('paper_data/dl_201504.csv', 'paper_data/logistic_201504.csv', 'paper_data/rf_201504.csv', 'paper_data/svm_201504.csv') 51 | model_type <- c('OMNIRank', 'Logistic', 'RandomForest', 'SVM') 52 | 53 | ppdFile <- data_source[4] 54 | options(digits=15) 55 | ppdData <- read.table(file=ppdFile, 56 | head=T, 57 | sep=',', 58 | col.names = c('label','name','score'), 59 | nrows = -1) 60 | 61 | ppdData$label[ppdData$label==0] <- 'normal' 62 | ppdData$label[ppdData$label==1] <- 'question' 63 | #频率直方图 64 | p4 <- ggplot(ppdData)+geom_histogram(aes(x=score, fill=as.factor(label)), position = "dodge", 65 | alpha = 0.8, binwidth=0.03)+ 66 | labs(x='score',y='amount',title=model_type[4])+ 67 | theme_bw()+ 68 | #theme(text=element_text(family = "Microsoft YaHei",face="bold",size=25),legend.title=element_blank(), axis.title.x=element_text(vjust=-1), axis.title.y=element_text(vjust=1.5), plot.title=element_text(vjust=2.5, size=28), axis.text.x=element_text(angle=0, color = "black", size=25),legend.position="right", axis.text.y=element_text(color = "black", size=25), plot.margin = unit(c(1.5,0.7,0.7,0.5), "cm"))+ 69 | theme(legend.position="none", text=element_text(family = "Microsoft YaHei",face="bold",size=25),legend.title=element_blank(), axis.title.x=element_text(vjust=-1), axis.title.y=element_text(vjust=1.5), plot.title=element_text(vjust=2.5, size=25), axis.text.x=element_text(angle=0, color = "black", size=20),legend.position="right", axis.text.y=element_text(color = "black", size=20), plot.margin = unit(c(1.5,0.7,0.7,0.5), "cm"))+ 70 | scale_color_brewer(palette="Dark2")+ 71 | scale_fill_brewer(palette="Dark2")+ 72 | scale_x_continuous(breaks=c(0,0.2,0.4,0.6,0.8,1.0), labels=c(0,0.2,0.4,0.6,0.8,1.0)) 73 | 74 | gp1<- ggplot_gtable(ggplot_build(p1)) 75 | gp2<- ggplot_gtable(ggplot_build(p2)) 76 | gp3<- ggplot_gtable(ggplot_build(p3)) 77 | gp4<- ggplot_gtable(ggplot_build(p4)) 78 | maxWidth = unit.pmax(gp1widths[2:3],gp2widths[2:3],gp2widths[2:3], gp3widths[2:3],gp4widths[2:3],gp4widths[2:3]) 79 | gp1$widths[2:3] <- maxWidth 80 | gp2$widths[2:3] <- maxWidth 81 | gp3$widths[2:3] <- maxWidth 82 | gp4$widths[2:3] <- maxWidth 83 | grid.arrange(gp1, gp2, gp3, gp4)#, nrow=4设置排列方式 84 | 85 | 86 | #平台分类统计饼图 87 | dt <- data.frame(A = c(1672, 240, 383, 737, 18), B = c('Normal','Closed Down','Withdraw Failure','Runaway Bosses','Investigation Involvements')) 88 | dt = dt[order(dt$A, decreasing = TRUE),] 89 | myLabel = as.vector(dt$B) 90 | #myLabel = paste(myLabel, "(", round(dt$A / sum(dt$A) * 100, 2), "%)", sep = "") 91 | myLabel = paste( round(dt$A / sum(dt$A) * 100, 2), "%" ) 92 | p <- ggplot(dt, aes(x = "", y = A, fill = B))+ 93 | theme_bw()+ 94 | geom_bar(stat = "identity", width = 1)+ 95 | coord_polar(theta = "y")+ 96 | labs(x = "", y = "", title = "")+ 97 | theme(axis.ticks = element_blank())+ 98 | theme(legend.title = element_blank(), legend.position = "right")+ 99 | #scale_fill_discrete(breaks = dt$B, labels = myLabel)+ 100 | theme(axis.text.x = element_blank())+ 101 | #geom_text(aes(y = A/2 + c(0, cumsum(A)[-length(A)]), x = sum(A)/2500, label = myLabel), size = 5)+## 在图中加上百分比：x 调节标签到圆心的距离, y 调节标签的左右位置 102 | scale_color_brewer(palette="Set3")+ 103 | scale_fill_brewer(palette="Set3") 104 | p --------------------------------------------------------------------------------