├── LSTM评分卡.py ├── 决策树规则挖掘.ipynb ├── 常用反欺诈特征.ipynb ├── 异常检测.ipynb ├── 深度学习与金融.ipynb └── 社交网络分析.ipynb /LSTM评分卡.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Tue Oct 15 00:33:10 2019 4 | RNN时序建模 5 | @author: kjc 6 | """ 7 | 8 | import torch 9 | import torch.nn as nn 10 | import random 11 | from sklearn.model_selection import train_test_split 12 | import torchvision.transforms as transforms 13 | import torchvision.datasets as dsets 14 | from torch.autograd import Variable 15 | 16 | random_st = random.choice(range(10000)) 17 | train_images, test_images = train_test_split(train_images,test_size=0.15, 18 | random_state=random_st) 19 | 20 | train_data = MyDataset(train_images) 21 | test_data = MyDataset(test_images) 22 | 23 | train_loader = torch.utils.data.DataLoader(train_data, batch_size=50, 24 | shuffle=True, num_workers=0) 25 | test_loader = torch.utils.data.DataLoader(test_data, batch_size=25, 26 | shuffle=False, num_workers=0) 27 | 28 | #搭建LSTM网络 29 | class Rnn(nn.Module): 30 | def __init__(self, in_dim, hidden_dim, n_layer, n_class): 31 | super(Rnn, self).__init__() 32 | self.n_layer = n_layer 33 | self.hidden_dim = hidden_dim 34 | self.LSTM = nn.LSTM(in_dim, hidden_dim, 35 | n_layer,batch_first=True) 36 | self.linear = nn.Linear(hidden_dim,n_class) 37 | self.sigmoid = nn.Sigmoid() 38 | 39 | def forward(self, x): 40 | x = x.sum(dim = 1) 41 | out, _ = self.LSTM(x) 42 | out = out[:, -1, :] 43 | out = self.linear(out) 44 | out = self.sigmoid(out) 45 | return out 46 | 47 | #指定网络参数。 48 | 49 | #28个特征，42个月切片，2个隐层，2分类 50 | model = Rnn(28,42,2,2) 51 | device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") 52 | model = model.to(device) 53 | #使用二分类对数损失函数 54 | criterion = nn.SoftMarginLoss(reduction='mean') 55 | opt = torch.optim.Adam(model.parameters()) 56 | total_step = len(train_loader) 57 | total_step_test = len(test_loader) 58 | num_epochs = 50 59 | 60 | #训练得到LSTM模型并计算单模型的KS值和AUC值。 61 | for epoch in range(num_epochs): 62 | train_label = [] 63 | train_pred = [] 64 | model.train() 65 | for i, (images, labels) in enumerate(train_loader): 66 | images = images.to(device) 67 | labels = labels.to(device) 68 | #网络训练 69 | out = model(images) 70 | loss = criterion(out, labels) 71 | opt.zero_grad() 72 | loss.backward() 73 | opt.step() 74 | #每一百轮打印一次 75 | if i%100 == 0: 76 | print('train epoch: {}/{}, round: {}/{}, 77 | loss: {}'.format(epoch + 1, num_epochs, 78 | i + 1, total_step, loss)) 79 | #真实标记和预测值 80 | train_label.extend(labels.cpu().numpy().flatten().tolist()) 81 | train_pred.extend(out.detach().cpu().numpy().flatten().tolist()) 82 | #计算真正率和假正率 83 | fpr_lm_train, tpr_lm_train, _ = roc_curve(np.array(train_label), 84 | np.array(train_pred)) 85 | #计算KS和AUC 86 | print('train epoch: {}/{}, KS: {}, ROC: {}'.format( 87 | epoch + 1, num_epochs,abs(fpr_lm_train - tpr_lm_train).max(), 88 | metrics.auc(fpr_lm_train, tpr_lm_train))) 89 | 90 | test_label = [] 91 | test_pred = [] 92 | 93 | model.eval() 94 | #计算测试集上的KS值和AUC值 95 | for i, (images, labels) in enumerate(test_loader): 96 | 97 | images = images.to(device) 98 | labels = labels.to(device) 99 | out = model(images) 100 | loss = criterion(out, labels) 101 | 102 | #计算KS和AUC 103 | if i%100 == 0: 104 | print('test epoch: {}/{}, round: {}/{}, 105 | loss: {}'.format(epoch + 1, num_epochs, 106 | i + 1, total_step_test, loss)) 107 | test_label.extend(labels.cpu().numpy().flatten().tolist()) 108 | test_pred.extend(out.detach().cpu().numpy().flatten().tolist()) 109 | 110 | fpr_lm_test, tpr_lm_test, _ = roc_curve(np.array(test_label), 111 | np.array(test_pred)) 112 | 113 | print('test epoch: {}/{}, KS: {}, ROC: {}'.format( 114 | epoch + 1, num_epochs, 115 | abs(fpr_lm_test - tpr_lm_test).max(), 116 | 117 | -------------------------------------------------------------------------------- /决策树规则挖掘.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "import numpy as np\n", 11 | "import os\n", 12 | "os.environ[\"PATH\"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": 2, 18 | "metadata": {}, 19 | "outputs": [ 20 | { 21 | "data": { 22 | "text/html": [ 23 | "
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uidoil_actv_dtcreate_dttotal_oil_cntpay_amount_totalclass_newbad_indoil_amountdiscount_amountsale_amountamountpay_amountcoupon_amountpayment_coupon_amountchannel_codeoil_codescenesource_appcall_source
0A82177102018-08-192018-08-17137.024147747.2B01653.78880040.0898000.0865540.04327700.00.0100.013203
1A82177102018-08-192018-08-16137.024147747.2B02336.841243522.01268900.01218922.06094610.00.0100.013203
2A82177102018-08-192018-08-15137.024147747.2B0936.03488922.0498900.0480922.02404610.00.0200.012203
3A82177102018-08-192018-08-14137.024147747.2B02418.391263220.01289000.01242220.06211100.00.0300.012203
4A82177102018-08-192018-08-13137.024147747.2B01292.69675220.0689000.0664220.03321100.00.0100.012203
\n", 175 | "
" 176 | ], 177 | "text/plain": [ 178 | " uid oil_actv_dt create_dt total_oil_cnt pay_amount_total class_new \\\n", 179 | "0 A8217710 2018-08-19 2018-08-17 137.0 24147747.2 B \n", 180 | "1 A8217710 2018-08-19 2018-08-16 137.0 24147747.2 B \n", 181 | "2 A8217710 2018-08-19 2018-08-15 137.0 24147747.2 B \n", 182 | "3 A8217710 2018-08-19 2018-08-14 137.0 24147747.2 B \n", 183 | "4 A8217710 2018-08-19 2018-08-13 137.0 24147747.2 B \n", 184 | "\n", 185 | " bad_ind oil_amount discount_amount sale_amount amount pay_amount \\\n", 186 | "0 0 1653.78 880040.0 898000.0 865540.0 4327700.0 \n", 187 | "1 0 2336.84 1243522.0 1268900.0 1218922.0 6094610.0 \n", 188 | "2 0 936.03 488922.0 498900.0 480922.0 2404610.0 \n", 189 | "3 0 2418.39 1263220.0 1289000.0 1242220.0 6211100.0 \n", 190 | "4 0 1292.69 675220.0 689000.0 664220.0 3321100.0 \n", 191 | "\n", 192 | " coupon_amount payment_coupon_amount channel_code oil_code scene \\\n", 193 | "0 0.0 100.0 1 3 2 \n", 194 | "1 0.0 100.0 1 3 2 \n", 195 | "2 0.0 200.0 1 2 2 \n", 196 | "3 0.0 300.0 1 2 2 \n", 197 | "4 0.0 100.0 1 2 2 \n", 198 | "\n", 199 | " source_app call_source \n", 200 | "0 0 3 \n", 201 | "1 0 3 \n", 202 | "2 0 3 \n", 203 | "3 0 3 \n", 204 | "4 0 3 " 205 | ] 206 | }, 207 | "execution_count": 2, 208 | "metadata": {}, 209 | "output_type": "execute_result" 210 | } 211 | ], 212 | "source": [ 213 | "data = pd.read_excel( 'oil_data_for_tree.xlsx')\n", 214 | "data.head()" 215 | ] 216 | }, 217 | { 218 | "cell_type": "code", 219 | "execution_count": 3, 220 | "metadata": {}, 221 | "outputs": [ 222 | { 223 | "data": { 224 | "text/plain": [ 225 | "{'A', 'B', 'C', 'D', 'E', 'F'}" 226 | ] 227 | }, 228 | "execution_count": 3, 229 | "metadata": {}, 230 | "output_type": "execute_result" 231 | } 232 | ], 233 | "source": [ 234 | "set(data.class_new)" 235 | ] 236 | }, 237 | { 238 | "cell_type": "markdown", 239 | "metadata": {}, 240 | "source": [ 241 | "org_lst 不需要做特殊变换，直接去重 \n", 242 | "agg_lst 数值型变量做聚合 \n", 243 | "dstc_lst 文本型变量做cnt " 244 | ] 245 | }, 246 | { 247 | "cell_type": "code", 248 | "execution_count": 14, 249 | "metadata": {}, 250 | "outputs": [], 251 | "source": [ 252 | "org_lst = ['uid','create_dt','oil_actv_dt','class_new','bad_ind']\n", 253 | "agg_lst = ['oil_amount','discount_amount','sale_amount','amount','pay_amount','coupon_amount','payment_coupon_amount']\n", 254 | "dstc_lst = ['channel_code','oil_code','scene','source_app','call_source']" 255 | ] 256 | }, 257 | { 258 | "cell_type": "markdown", 259 | "metadata": {}, 260 | "source": [ 261 | "数据重组" 262 | ] 263 | }, 264 | { 265 | "cell_type": "code", 266 | "execution_count": 15, 267 | "metadata": {}, 268 | "outputs": [], 269 | "source": [ 270 | "df = data[org_lst].copy()\n", 271 | "df[agg_lst] = data[agg_lst].copy()\n", 272 | "df[dstc_lst] = data[dstc_lst].copy()" 273 | ] 274 | }, 275 | { 276 | "cell_type": "markdown", 277 | "metadata": {}, 278 | "source": [ 279 | "看一下缺失情况" 280 | ] 281 | }, 282 | { 283 | "cell_type": "code", 284 | "execution_count": 16, 285 | "metadata": {}, 286 | "outputs": [ 287 | { 288 | "data": { 289 | "text/plain": [ 290 | "uid 0\n", 291 | "create_dt 4944\n", 292 | "oil_actv_dt 0\n", 293 | "class_new 0\n", 294 | "bad_ind 0\n", 295 | "oil_amount 4944\n", 296 | "discount_amount 4944\n", 297 | "sale_amount 4944\n", 298 | "amount 4944\n", 299 | "pay_amount 4944\n", 300 | "coupon_amount 4944\n", 301 | "payment_coupon_amount 4946\n", 302 | "channel_code 0\n", 303 | "oil_code 0\n", 304 | "scene 0\n", 305 | "source_app 0\n", 306 | "call_source 0\n", 307 | "dtype: int64" 308 | ] 309 | }, 310 | "execution_count": 16, 311 | "metadata": {}, 312 | "output_type": "execute_result" 313 | } 314 | ], 315 | "source": [ 316 | "df.isna().sum()" 317 | ] 318 | }, 319 | { 320 | "cell_type": "markdown", 321 | "metadata": {}, 322 | "source": [ 323 | "看一下基础变量的describe" 324 | ] 325 | }, 326 | { 327 | "cell_type": "code", 328 | "execution_count": 17, 329 | "metadata": {}, 330 | "outputs": [ 331 | { 332 | "data": { 333 | "text/html": [ 334 | "
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bad_indoil_amountdiscount_amountsale_amountamountpay_amountcoupon_amountpayment_coupon_amountchannel_codeoil_codescenesource_appcall_source
count50609.00000045665.0000004.566500e+044.566500e+044.566500e+044.566500e+0445665.045663.00000050609.00000050609.00000050609.00000050609.00000050609.000000
mean0.017764212.1880541.091035e+051.121195e+051.077312e+055.386562e+050.0417.0553841.4763781.6178941.9065190.3060722.900729
std0.132093200.2981221.010993e+051.031804e+059.953775e+044.976888e+050.0968.2502731.5114703.0741660.3672800.8936820.726231
min0.0000000.0000000.000000e+000.000000e+001.000000e+005.000000e+000.00.0000000.0000000.0000000.0000000.0000000.000000
25%0.00000087.2200004.854000e+045.000000e+044.820000e+042.410000e+050.00.0000001.0000000.0000002.0000000.0000003.000000
50%0.000000167.5800008.820000e+049.000000e+048.709600e+044.354800e+050.0100.0000001.0000000.0000002.0000000.0000003.000000
75%0.000000278.3000001.391600e+051.430000e+051.371150e+056.855750e+050.0500.0000001.0000000.0000002.0000000.0000003.000000
max1.0000003975.9100001.958040e+061.998000e+061.925540e+069.627700e+060.050000.0000006.0000009.0000002.0000003.0000004.000000
\n", 498 | "
" 499 | ], 500 | "text/plain": [ 501 | " bad_ind oil_amount discount_amount sale_amount \\\n", 502 | "count 50609.000000 45665.000000 4.566500e+04 4.566500e+04 \n", 503 | "mean 0.017764 212.188054 1.091035e+05 1.121195e+05 \n", 504 | "std 0.132093 200.298122 1.010993e+05 1.031804e+05 \n", 505 | "min 0.000000 0.000000 0.000000e+00 0.000000e+00 \n", 506 | "25% 0.000000 87.220000 4.854000e+04 5.000000e+04 \n", 507 | "50% 0.000000 167.580000 8.820000e+04 9.000000e+04 \n", 508 | "75% 0.000000 278.300000 1.391600e+05 1.430000e+05 \n", 509 | "max 1.000000 3975.910000 1.958040e+06 1.998000e+06 \n", 510 | "\n", 511 | " amount pay_amount coupon_amount payment_coupon_amount \\\n", 512 | "count 4.566500e+04 4.566500e+04 45665.0 45663.000000 \n", 513 | "mean 1.077312e+05 5.386562e+05 0.0 417.055384 \n", 514 | "std 9.953775e+04 4.976888e+05 0.0 968.250273 \n", 515 | "min 1.000000e+00 5.000000e+00 0.0 0.000000 \n", 516 | "25% 4.820000e+04 2.410000e+05 0.0 0.000000 \n", 517 | "50% 8.709600e+04 4.354800e+05 0.0 100.000000 \n", 518 | "75% 1.371150e+05 6.855750e+05 0.0 500.000000 \n", 519 | "max 1.925540e+06 9.627700e+06 0.0 50000.000000 \n", 520 | "\n", 521 | " channel_code oil_code scene source_app call_source \n", 522 | "count 50609.000000 50609.000000 50609.000000 50609.000000 50609.000000 \n", 523 | "mean 1.476378 1.617894 1.906519 0.306072 2.900729 \n", 524 | "std 1.511470 3.074166 0.367280 0.893682 0.726231 \n", 525 | "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", 526 | "25% 1.000000 0.000000 2.000000 0.000000 3.000000 \n", 527 | "50% 1.000000 0.000000 2.000000 0.000000 3.000000 \n", 528 | "75% 1.000000 0.000000 2.000000 0.000000 3.000000 \n", 529 | "max 6.000000 9.000000 2.000000 3.000000 4.000000 " 530 | ] 531 | }, 532 | "execution_count": 17, 533 | "metadata": {}, 534 | "output_type": "execute_result" 535 | } 536 | ], 537 | "source": [ 538 | "df.describe()" 539 | ] 540 | }, 541 | { 542 | "cell_type": "markdown", 543 | "metadata": {}, 544 | "source": [ 545 | "对creat_dt做补全，用oil_actv_dt来填补，并且截取6个月的数据。 \n", 546 | "构造变量的时候不能直接对历史所有数据做累加。 \n", 547 | "否则随着时间推移，变量分布会有很大的变化。" 548 | ] 549 | }, 550 | { 551 | "cell_type": "code", 552 | "execution_count": 18, 553 | "metadata": {}, 554 | "outputs": [ 555 | { 556 | "data": { 557 | "text/html": [ 558 | "
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uidcreate_dtoil_actv_dtclass_newbad_indoil_amountdiscount_amountsale_amountamountpay_amountcoupon_amountpayment_coupon_amountchannel_codeoil_codescenesource_appcall_sourcedtn
50608B964363919850357032018-10-082018-10-08B0NaNNaNNaNNaNNaNNaNNaN692340
50607B964363919846933972018-10-112018-10-11E0NaNNaNNaNNaNNaNNaNNaN692340
50606B964363919772174682018-10-172018-10-17B0NaNNaNNaNNaNNaNNaNNaN692340
50605B964363919764808922018-09-282018-09-28B0NaNNaNNaNNaNNaNNaNNaN692340
50604B964363919721060432018-10-192018-10-19A0NaNNaNNaNNaNNaNNaNNaN692340
\n", 704 | "
" 705 | ], 706 | "text/plain": [ 707 | " uid create_dt oil_actv_dt class_new bad_ind \\\n", 708 | "50608 B96436391985035703 2018-10-08 2018-10-08 B 0 \n", 709 | "50607 B96436391984693397 2018-10-11 2018-10-11 E 0 \n", 710 | "50606 B96436391977217468 2018-10-17 2018-10-17 B 0 \n", 711 | "50605 B96436391976480892 2018-09-28 2018-09-28 B 0 \n", 712 | "50604 B96436391972106043 2018-10-19 2018-10-19 A 0 \n", 713 | "\n", 714 | " oil_amount discount_amount sale_amount amount pay_amount \\\n", 715 | "50608 NaN NaN NaN NaN NaN \n", 716 | "50607 NaN NaN NaN NaN NaN \n", 717 | "50606 NaN NaN NaN NaN NaN \n", 718 | "50605 NaN NaN NaN NaN NaN \n", 719 | "50604 NaN NaN NaN NaN NaN \n", 720 | "\n", 721 | " coupon_amount payment_coupon_amount channel_code oil_code scene \\\n", 722 | "50608 NaN NaN 6 9 2 \n", 723 | "50607 NaN NaN 6 9 2 \n", 724 | "50606 NaN NaN 6 9 2 \n", 725 | "50605 NaN NaN 6 9 2 \n", 726 | "50604 NaN NaN 6 9 2 \n", 727 | "\n", 728 | " source_app call_source dtn \n", 729 | "50608 3 4 0 \n", 730 | "50607 3 4 0 \n", 731 | "50606 3 4 0 \n", 732 | "50605 3 4 0 \n", 733 | "50604 3 4 0 " 734 | ] 735 | }, 736 | "execution_count": 18, 737 | "metadata": {}, 738 | "output_type": "execute_result" 739 | } 740 | ], 741 | "source": [ 742 | "def time_isna(x,y):\n", 743 | " if str(x) == 'NaT':\n", 744 | " x = y\n", 745 | " else:\n", 746 | " x = x\n", 747 | " return x\n", 748 | "df2 = df.sort_values(['uid','create_dt'],ascending = False)\n", 749 | "df2['create_dt'] = df2.apply(lambda x: time_isna(x.create_dt,x.oil_actv_dt),axis = 1)\n", 750 | "df2['dtn'] = (df2.oil_actv_dt - df2.create_dt).apply(lambda x :x.days)\n", 751 | "df = df2[df2['dtn']<180]\n", 752 | "df.head()" 753 | ] 754 | }, 755 | { 756 | "cell_type": "markdown", 757 | "metadata": {}, 758 | "source": [ 759 | "对org_list变量求历史贷款天数的最大间隔，并且去重" 760 | ] 761 | }, 762 | { 763 | "cell_type": "code", 764 | "execution_count": 28, 765 | "metadata": {}, 766 | "outputs": [ 767 | { 768 | "name": "stderr", 769 | "output_type": "stream", 770 | "text": [ 771 | "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n", 772 | "A value is trying to be set on a copy of a slice from a DataFrame.\n", 773 | "Try using .loc[row_indexer,col_indexer] = value instead\n", 774 | "\n", 775 | "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", 776 | " \n" 777 | ] 778 | }, 779 | { 780 | "data": { 781 | "text/plain": [ 782 | "(11099, 6)" 783 | ] 784 | }, 785 | "execution_count": 28, 786 | "metadata": {}, 787 | "output_type": "execute_result" 788 | } 789 | ], 790 | "source": [ 791 | "base = df[org_lst]\n", 792 | "base['dtn'] = df['dtn']\n", 793 | "base = base.sort_values(['uid','create_dt'],ascending = False)\n", 794 | "base = base.drop_duplicates(['uid'],keep = 'first')\n", 795 | "base.shape" 796 | ] 797 | }, 798 | { 799 | "cell_type": "markdown", 800 | "metadata": {}, 801 | "source": [ 802 | "做变量衍生" 803 | ] 804 | }, 805 | { 806 | "cell_type": "code", 807 | "execution_count": 20, 808 | "metadata": {}, 809 | "outputs": [ 810 | { 811 | "name": "stderr", 812 | "output_type": "stream", 813 | "text": [ 814 | "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:21: RuntimeWarning: Mean of empty slice\n", 815 | "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:27: RuntimeWarning: All-NaN axis encountered\n", 816 | "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:33: RuntimeWarning: All-NaN axis encountered\n", 817 | "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:39: RuntimeWarning: Degrees of freedom <= 0 for slice.\n", 818 | "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:45: RuntimeWarning: All-NaN axis encountered\n", 819 | "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:51: RuntimeWarning: Mean of empty slice\n", 820 | "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:51: RuntimeWarning: Degrees of freedom <= 0 for slice.\n" 821 | ] 822 | } 823 | ], 824 | "source": [ 825 | "gn = pd.DataFrame()\n", 826 | "for i in agg_lst:\n", 827 | " tp = pd.DataFrame(df.groupby('uid').apply(lambda df:len(df[i])).reset_index())\n", 828 | " tp.columns = ['uid',i + '_cnt']\n", 829 | " if gn.empty == True:\n", 830 | " gn = tp\n", 831 | " else:\n", 832 | " gn = pd.merge(gn,tp,on = 'uid',how = 'left')\n", 833 | " tp = pd.DataFrame(df.groupby('uid').apply(lambda df:np.where(df[i]>0,1,0).sum()).reset_index())\n", 834 | " tp.columns = ['uid',i + '_num']\n", 835 | " if gn.empty == True:\n", 836 | " gn = tp\n", 837 | " else:\n", 838 | " gn = pd.merge(gn,tp,on = 'uid',how = 'left')\n", 839 | " tp = pd.DataFrame(df.groupby('uid').apply(lambda df:np.nansum(df[i])).reset_index())\n", 840 | " tp.columns = ['uid',i + '_tot']\n", 841 | " if gn.empty == True:\n", 842 | " gn = tp\n", 843 | " else:\n", 844 | " gn = pd.merge(gn,tp,on = 'uid',how = 'left')\n", 845 | " tp = pd.DataFrame(df.groupby('uid').apply(lambda df:np.nanmean(df[i])).reset_index())\n", 846 | " tp.columns = ['uid',i + '_avg']\n", 847 | " if gn.empty == True:\n", 848 | " gn = tp\n", 849 | " else:\n", 850 | " gn = pd.merge(gn,tp,on = 'uid',how = 'left')\n", 851 | " tp = pd.DataFrame(df.groupby('uid').apply(lambda df:np.nanmax(df[i])).reset_index())\n", 852 | " tp.columns = ['uid',i + '_max']\n", 853 | " if gn.empty == True:\n", 854 | " gn = tp\n", 855 | " else:\n", 856 | " gn = pd.merge(gn,tp,on = 'uid',how = 'left')\n", 857 | " tp = pd.DataFrame(df.groupby('uid').apply(lambda df:np.nanmin(df[i])).reset_index())\n", 858 | " tp.columns = ['uid',i + '_min']\n", 859 | " if gn.empty == True:\n", 860 | " gn = tp\n", 861 | " else:\n", 862 | " gn = pd.merge(gn,tp,on = 'uid',how = 'left')\n", 863 | " tp = pd.DataFrame(df.groupby('uid').apply(lambda df:np.nanvar(df[i])).reset_index())\n", 864 | " tp.columns = ['uid',i + '_var']\n", 865 | " if gn.empty == True:\n", 866 | " gn = tp\n", 867 | " else:\n", 868 | " gn = pd.merge(gn,tp,on = 'uid',how = 'left')\n", 869 | " tp = pd.DataFrame(df.groupby('uid').apply(lambda df:np.nanmax(df[i]) -np.nanmin(df[i]) ).reset_index())\n", 870 | " tp.columns = ['uid',i + '_var']\n", 871 | " if gn.empty == True:\n", 872 | " gn = tp\n", 873 | " else:\n", 874 | " gn = pd.merge(gn,tp,on = 'uid',how = 'left')\n", 875 | " tp = pd.DataFrame(df.groupby('uid').apply(lambda df:np.nanmean(df[i])/max(np.nanvar(df[i]),1)).reset_index())\n", 876 | " tp.columns = ['uid',i + '_var']\n", 877 | " if gn.empty == True:\n", 878 | " gn = tp\n", 879 | " else:\n", 880 | " gn = pd.merge(gn,tp,on = 'uid',how = 'left')" 881 | ] 882 | }, 883 | { 884 | "cell_type": "markdown", 885 | "metadata": {}, 886 | "source": [ 887 | "对dstc_lst变量求distinct个数" 888 | ] 889 | }, 890 | { 891 | "cell_type": "code", 892 | "execution_count": 22, 893 | "metadata": {}, 894 | "outputs": [], 895 | "source": [ 896 | "gc = pd.DataFrame()\n", 897 | "for i in dstc_lst:\n", 898 | " tp = pd.DataFrame(df.groupby('uid').apply(lambda df: len(set(df[i]))).reset_index())\n", 899 | " tp.columns = ['uid',i + '_dstc']\n", 900 | " if gc.empty == True:\n", 901 | " gc = tp\n", 902 | " else:\n", 903 | " gc = pd.merge(gc,tp,on = 'uid',how = 'left')" 904 | ] 905 | }, 906 | { 907 | "cell_type": "markdown", 908 | "metadata": {}, 909 | "source": [ 910 | "将变量组合在一起" 911 | ] 912 | }, 913 | { 914 | "cell_type": "code", 915 | "execution_count": 29, 916 | "metadata": {}, 917 | "outputs": [ 918 | { 919 | "data": { 920 | "text/plain": [ 921 | "(11099, 74)" 922 | ] 923 | }, 924 | "execution_count": 29, 925 | "metadata": {}, 926 | "output_type": "execute_result" 927 | } 928 | ], 929 | "source": [ 930 | "fn = pd.merge(base,gn,on= 'uid')\n", 931 | "fn = pd.merge(fn,gc,on= 'uid') \n", 932 | "fn.shape" 933 | ] 934 | }, 935 | { 936 | "cell_type": "code", 937 | "execution_count": 35, 938 | "metadata": {}, 939 | "outputs": [], 940 | "source": [ 941 | "fn = fn.fillna(0)" 942 | ] 943 | }, 944 | { 945 | "cell_type": "code", 946 | "execution_count": 36, 947 | "metadata": {}, 948 | "outputs": [ 949 | { 950 | "data": { 951 | "text/html": [ 952 | "
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uidcreate_dtoil_actv_dtclass_newbad_inddtnoil_amount_cntoil_amount_numoil_amount_totoil_amount_avg...payment_coupon_amount_maxpayment_coupon_amount_minpayment_coupon_amount_var_xpayment_coupon_amount_var_ypayment_coupon_amount_varchannel_code_dstcoil_code_dstcscene_dstcsource_app_dstccall_source_dstc
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5B964363919644893552018-09-292018-09-29C00100.000.00...0.00.00.00.00.011111
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12B964363918514652872018-10-192018-10-19D00100.000.00...0.00.00.00.00.011111
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27B964363915435114432018-10-242018-10-24C00100.000.00...0.00.00.00.00.011111
28B964363914772006312018-10-052018-10-05B00100.000.00...0.00.00.00.00.011111
29B964363914743107272018-10-192018-10-19F00100.000.00...0.00.00.00.00.011111
..................................................................
70B964363905076025212018-10-292018-10-29C00100.000.00...0.00.00.00.00.011111
71B964363904975970082018-09-242018-09-24E00100.000.00...0.00.00.00.00.011111
72B964363904952359322018-10-032018-10-03D00100.000.00...0.00.00.00.00.011111
73B964363904877778322018-09-222018-09-22D00100.000.00...0.00.00.00.00.011111
74B964363904807773862018-09-162018-09-16F00100.000.00...0.00.00.00.00.011111
75B964363904790741522018-10-152018-10-15C00100.000.00...0.00.00.00.00.011111
76B964363904753334492018-10-162018-10-16C00100.000.00...0.00.00.00.00.011111
77B964363904545805562018-09-262018-09-26D00100.000.00...0.00.00.00.00.011111
78B964363904278923162018-10-112018-10-11D00100.000.00...0.00.00.00.00.011111
79B964363904256848332018-09-272018-09-27D00100.000.00...0.00.00.00.00.011111
80B964363904158601382018-10-282018-10-28F00100.000.00...0.00.00.00.00.011111
81B964363904146972172018-10-112018-10-11D00100.000.00...0.00.00.00.00.011111
82B964363904137161572018-10-302018-10-30A00100.000.00...0.00.00.00.00.011111
83B963805880675882032018-07-202018-07-20E00100.000.00...0.00.00.00.00.011111
84B963805880548696252018-10-292018-10-29C00100.000.00...0.00.00.00.00.011111
85B963805880507984442018-10-212018-10-21F00100.000.00...0.00.00.00.00.011111
86B963805880296328822018-10-262018-10-26B00100.000.00...0.00.00.00.00.011111
87B963152737592942632018-10-312018-10-31B00100.000.00...0.00.00.00.00.011111
88B963152737481632472018-10-142018-10-14E00100.000.00...0.00.00.00.00.011111
89B963152737474686402018-09-132018-10-18D03511201.68201.68...300.0300.00.00.0300.011111
90B963152737371579512018-09-242018-09-24B00100.000.00...0.00.00.00.00.011111
91B963152737325817022018-10-162018-10-16B00100.000.00...0.00.00.00.00.011111
92B962681917493707312018-09-282018-09-28B00100.000.00...0.00.00.00.00.011111
93B962681917461042922018-09-272018-09-27C00100.000.00...0.00.00.00.00.011111
94B962681917350403742018-10-242018-10-24E00100.000.00...0.00.00.00.00.011111
95B961173703323551902018-10-192018-10-19B00100.000.00...0.00.00.00.00.011111
96B961173703301016582018-10-122018-10-12B00100.000.00...0.00.00.00.00.011111
97B961173703300663472018-10-012018-10-01D00100.000.00...0.00.00.00.00.011111
98B961173703287243502018-09-202018-09-20C00100.000.00...0.00.00.00.00.011111
99B961173703211590332018-10-082018-10-08D00100.000.00...0.00.00.00.00.011111
\n", 2460 | "

100 rows × 74 columns

\n", 2461 | "
" 2462 | ], 2463 | "text/plain": [ 2464 | " uid create_dt oil_actv_dt class_new bad_ind dtn \\\n", 2465 | "0 B96436391985035703 2018-10-08 2018-10-08 B 0 0 \n", 2466 | "1 B96436391984693397 2018-10-11 2018-10-11 E 0 0 \n", 2467 | "2 B96436391977217468 2018-10-17 2018-10-17 B 0 0 \n", 2468 | "3 B96436391976480892 2018-09-28 2018-09-28 B 0 0 \n", 2469 | "4 B96436391972106043 2018-10-19 2018-10-19 A 0 0 \n", 2470 | "5 B96436391964489355 2018-09-29 2018-09-29 C 0 0 \n", 2471 | "6 B96436391942199711 2018-10-18 2018-10-18 E 0 0 \n", 2472 | "7 B96436391941530690 2018-09-29 2018-09-29 B 0 0 \n", 2473 | "8 B96436391899881051 2018-10-10 2018-10-10 A 0 0 \n", 2474 | "9 B96436391889563211 2018-09-25 2018-09-25 E 0 0 \n", 2475 | "10 B96436391864423458 2018-10-27 2018-10-27 D 0 0 \n", 2476 | "11 B96436391861783745 2018-09-27 2018-09-27 E 0 0 \n", 2477 | "12 B96436391851465287 2018-10-19 2018-10-19 D 0 0 \n", 2478 | "13 B96436391812222712 2018-10-12 2018-10-12 B 0 0 \n", 2479 | "14 B96436391761778117 2018-10-11 2018-10-11 C 0 0 \n", 2480 | "15 B96436391758836159 2018-09-29 2018-09-29 E 0 0 \n", 2481 | "16 B96436391733014891 2018-06-26 2018-06-26 E 0 0 \n", 2482 | "17 B96436391679580342 2018-10-10 2018-10-10 D 0 0 \n", 2483 | "18 B96436391635123385 2018-09-19 2018-09-19 D 0 0 \n", 2484 | "19 B96436391607145327 2018-10-20 2018-10-20 B 0 0 \n", 2485 | "20 B96436391601943473 2018-10-25 2018-10-25 D 0 0 \n", 2486 | "21 B96436391594695697 2018-09-24 2018-09-24 F 0 0 \n", 2487 | "22 B96436391593055178 2018-09-28 2018-09-28 D 0 0 \n", 2488 | "23 B96436391584119601 2018-10-10 2018-10-10 D 0 0 \n", 2489 | "24 B96436391558820968 2018-10-16 2018-10-16 D 0 0 \n", 2490 | "25 B96436391546006453 2018-10-26 2018-10-26 B 0 0 \n", 2491 | "26 B96436391544818920 2018-10-06 2018-10-06 C 0 0 \n", 2492 | "27 B96436391543511443 2018-10-24 2018-10-24 C 0 0 \n", 2493 | "28 B96436391477200631 2018-10-05 2018-10-05 B 0 0 \n", 2494 | "29 B96436391474310727 2018-10-19 2018-10-19 F 0 0 \n", 2495 | ".. ... ... ... ... ... ... \n", 2496 | "70 B96436390507602521 2018-10-29 2018-10-29 C 0 0 \n", 2497 | "71 B96436390497597008 2018-09-24 2018-09-24 E 0 0 \n", 2498 | "72 B96436390495235932 2018-10-03 2018-10-03 D 0 0 \n", 2499 | "73 B96436390487777832 2018-09-22 2018-09-22 D 0 0 \n", 2500 | "74 B96436390480777386 2018-09-16 2018-09-16 F 0 0 \n", 2501 | "75 B96436390479074152 2018-10-15 2018-10-15 C 0 0 \n", 2502 | "76 B96436390475333449 2018-10-16 2018-10-16 C 0 0 \n", 2503 | "77 B96436390454580556 2018-09-26 2018-09-26 D 0 0 \n", 2504 | "78 B96436390427892316 2018-10-11 2018-10-11 D 0 0 \n", 2505 | "79 B96436390425684833 2018-09-27 2018-09-27 D 0 0 \n", 2506 | "80 B96436390415860138 2018-10-28 2018-10-28 F 0 0 \n", 2507 | "81 B96436390414697217 2018-10-11 2018-10-11 D 0 0 \n", 2508 | "82 B96436390413716157 2018-10-30 2018-10-30 A 0 0 \n", 2509 | "83 B96380588067588203 2018-07-20 2018-07-20 E 0 0 \n", 2510 | "84 B96380588054869625 2018-10-29 2018-10-29 C 0 0 \n", 2511 | "85 B96380588050798444 2018-10-21 2018-10-21 F 0 0 \n", 2512 | "86 B96380588029632882 2018-10-26 2018-10-26 B 0 0 \n", 2513 | "87 B96315273759294263 2018-10-31 2018-10-31 B 0 0 \n", 2514 | "88 B96315273748163247 2018-10-14 2018-10-14 E 0 0 \n", 2515 | "89 B96315273747468640 2018-09-13 2018-10-18 D 0 35 \n", 2516 | "90 B96315273737157951 2018-09-24 2018-09-24 B 0 0 \n", 2517 | "91 B96315273732581702 2018-10-16 2018-10-16 B 0 0 \n", 2518 | "92 B96268191749370731 2018-09-28 2018-09-28 B 0 0 \n", 2519 | "93 B96268191746104292 2018-09-27 2018-09-27 C 0 0 \n", 2520 | "94 B96268191735040374 2018-10-24 2018-10-24 E 0 0 \n", 2521 | "95 B96117370332355190 2018-10-19 2018-10-19 B 0 0 \n", 2522 | "96 B96117370330101658 2018-10-12 2018-10-12 B 0 0 \n", 2523 | "97 B96117370330066347 2018-10-01 2018-10-01 D 0 0 \n", 2524 | "98 B96117370328724350 2018-09-20 2018-09-20 C 0 0 \n", 2525 | "99 B96117370321159033 2018-10-08 2018-10-08 D 0 0 \n", 2526 | "\n", 2527 | " oil_amount_cnt oil_amount_num oil_amount_tot oil_amount_avg \\\n", 2528 | "0 1 0 0.00 0.00 \n", 2529 | "1 1 0 0.00 0.00 \n", 2530 | "2 1 0 0.00 0.00 \n", 2531 | "3 1 0 0.00 0.00 \n", 2532 | "4 1 0 0.00 0.00 \n", 2533 | "5 1 0 0.00 0.00 \n", 2534 | "6 1 0 0.00 0.00 \n", 2535 | "7 1 0 0.00 0.00 \n", 2536 | "8 1 0 0.00 0.00 \n", 2537 | "9 1 0 0.00 0.00 \n", 2538 | "10 1 0 0.00 0.00 \n", 2539 | "11 1 0 0.00 0.00 \n", 2540 | "12 1 0 0.00 0.00 \n", 2541 | "13 1 0 0.00 0.00 \n", 2542 | "14 1 0 0.00 0.00 \n", 2543 | "15 1 0 0.00 0.00 \n", 2544 | "16 1 0 0.00 0.00 \n", 2545 | "17 1 0 0.00 0.00 \n", 2546 | "18 1 0 0.00 0.00 \n", 2547 | "19 1 0 0.00 0.00 \n", 2548 | "20 1 0 0.00 0.00 \n", 2549 | "21 1 0 0.00 0.00 \n", 2550 | "22 1 0 0.00 0.00 \n", 2551 | "23 1 0 0.00 0.00 \n", 2552 | "24 1 0 0.00 0.00 \n", 2553 | "25 1 0 0.00 0.00 \n", 2554 | "26 1 0 0.00 0.00 \n", 2555 | "27 1 0 0.00 0.00 \n", 2556 | "28 1 0 0.00 0.00 \n", 2557 | "29 1 0 0.00 0.00 \n", 2558 | ".. ... ... ... ... \n", 2559 | "70 1 0 0.00 0.00 \n", 2560 | "71 1 0 0.00 0.00 \n", 2561 | "72 1 0 0.00 0.00 \n", 2562 | "73 1 0 0.00 0.00 \n", 2563 | "74 1 0 0.00 0.00 \n", 2564 | "75 1 0 0.00 0.00 \n", 2565 | "76 1 0 0.00 0.00 \n", 2566 | "77 1 0 0.00 0.00 \n", 2567 | "78 1 0 0.00 0.00 \n", 2568 | "79 1 0 0.00 0.00 \n", 2569 | "80 1 0 0.00 0.00 \n", 2570 | "81 1 0 0.00 0.00 \n", 2571 | "82 1 0 0.00 0.00 \n", 2572 | "83 1 0 0.00 0.00 \n", 2573 | "84 1 0 0.00 0.00 \n", 2574 | "85 1 0 0.00 0.00 \n", 2575 | "86 1 0 0.00 0.00 \n", 2576 | "87 1 0 0.00 0.00 \n", 2577 | "88 1 0 0.00 0.00 \n", 2578 | "89 1 1 201.68 201.68 \n", 2579 | "90 1 0 0.00 0.00 \n", 2580 | "91 1 0 0.00 0.00 \n", 2581 | "92 1 0 0.00 0.00 \n", 2582 | "93 1 0 0.00 0.00 \n", 2583 | "94 1 0 0.00 0.00 \n", 2584 | "95 1 0 0.00 0.00 \n", 2585 | "96 1 0 0.00 0.00 \n", 2586 | "97 1 0 0.00 0.00 \n", 2587 | "98 1 0 0.00 0.00 \n", 2588 | "99 1 0 0.00 0.00 \n", 2589 | "\n", 2590 | " ... payment_coupon_amount_max payment_coupon_amount_min \\\n", 2591 | "0 ... 0.0 0.0 \n", 2592 | "1 ... 0.0 0.0 \n", 2593 | "2 ... 0.0 0.0 \n", 2594 | "3 ... 0.0 0.0 \n", 2595 | "4 ... 0.0 0.0 \n", 2596 | "5 ... 0.0 0.0 \n", 2597 | "6 ... 0.0 0.0 \n", 2598 | "7 ... 0.0 0.0 \n", 2599 | "8 ... 0.0 0.0 \n", 2600 | "9 ... 0.0 0.0 \n", 2601 | "10 ... 0.0 0.0 \n", 2602 | "11 ... 0.0 0.0 \n", 2603 | "12 ... 0.0 0.0 \n", 2604 | "13 ... 0.0 0.0 \n", 2605 | "14 ... 0.0 0.0 \n", 2606 | "15 ... 0.0 0.0 \n", 2607 | "16 ... 0.0 0.0 \n", 2608 | "17 ... 0.0 0.0 \n", 2609 | "18 ... 0.0 0.0 \n", 2610 | "19 ... 0.0 0.0 \n", 2611 | "20 ... 0.0 0.0 \n", 2612 | "21 ... 0.0 0.0 \n", 2613 | "22 ... 0.0 0.0 \n", 2614 | "23 ... 0.0 0.0 \n", 2615 | "24 ... 0.0 0.0 \n", 2616 | "25 ... 0.0 0.0 \n", 2617 | "26 ... 0.0 0.0 \n", 2618 | "27 ... 0.0 0.0 \n", 2619 | "28 ... 0.0 0.0 \n", 2620 | "29 ... 0.0 0.0 \n", 2621 | ".. ... ... ... \n", 2622 | "70 ... 0.0 0.0 \n", 2623 | "71 ... 0.0 0.0 \n", 2624 | "72 ... 0.0 0.0 \n", 2625 | "73 ... 0.0 0.0 \n", 2626 | "74 ... 0.0 0.0 \n", 2627 | "75 ... 0.0 0.0 \n", 2628 | "76 ... 0.0 0.0 \n", 2629 | "77 ... 0.0 0.0 \n", 2630 | "78 ... 0.0 0.0 \n", 2631 | "79 ... 0.0 0.0 \n", 2632 | "80 ... 0.0 0.0 \n", 2633 | "81 ... 0.0 0.0 \n", 2634 | "82 ... 0.0 0.0 \n", 2635 | "83 ... 0.0 0.0 \n", 2636 | "84 ... 0.0 0.0 \n", 2637 | "85 ... 0.0 0.0 \n", 2638 | "86 ... 0.0 0.0 \n", 2639 | "87 ... 0.0 0.0 \n", 2640 | "88 ... 0.0 0.0 \n", 2641 | "89 ... 300.0 300.0 \n", 2642 | "90 ... 0.0 0.0 \n", 2643 | "91 ... 0.0 0.0 \n", 2644 | "92 ... 0.0 0.0 \n", 2645 | "93 ... 0.0 0.0 \n", 2646 | "94 ... 0.0 0.0 \n", 2647 | "95 ... 0.0 0.0 \n", 2648 | "96 ... 0.0 0.0 \n", 2649 | "97 ... 0.0 0.0 \n", 2650 | "98 ... 0.0 0.0 \n", 2651 | "99 ... 0.0 0.0 \n", 2652 | "\n", 2653 | " payment_coupon_amount_var_x payment_coupon_amount_var_y \\\n", 2654 | "0 0.0 0.0 \n", 2655 | "1 0.0 0.0 \n", 2656 | "2 0.0 0.0 \n", 2657 | "3 0.0 0.0 \n", 2658 | "4 0.0 0.0 \n", 2659 | "5 0.0 0.0 \n", 2660 | "6 0.0 0.0 \n", 2661 | "7 0.0 0.0 \n", 2662 | "8 0.0 0.0 \n", 2663 | "9 0.0 0.0 \n", 2664 | "10 0.0 0.0 \n", 2665 | "11 0.0 0.0 \n", 2666 | "12 0.0 0.0 \n", 2667 | "13 0.0 0.0 \n", 2668 | "14 0.0 0.0 \n", 2669 | "15 0.0 0.0 \n", 2670 | "16 0.0 0.0 \n", 2671 | "17 0.0 0.0 \n", 2672 | "18 0.0 0.0 \n", 2673 | "19 0.0 0.0 \n", 2674 | "20 0.0 0.0 \n", 2675 | "21 0.0 0.0 \n", 2676 | "22 0.0 0.0 \n", 2677 | "23 0.0 0.0 \n", 2678 | "24 0.0 0.0 \n", 2679 | "25 0.0 0.0 \n", 2680 | "26 0.0 0.0 \n", 2681 | "27 0.0 0.0 \n", 2682 | "28 0.0 0.0 \n", 2683 | "29 0.0 0.0 \n", 2684 | ".. ... ... \n", 2685 | "70 0.0 0.0 \n", 2686 | "71 0.0 0.0 \n", 2687 | "72 0.0 0.0 \n", 2688 | "73 0.0 0.0 \n", 2689 | "74 0.0 0.0 \n", 2690 | "75 0.0 0.0 \n", 2691 | "76 0.0 0.0 \n", 2692 | "77 0.0 0.0 \n", 2693 | "78 0.0 0.0 \n", 2694 | "79 0.0 0.0 \n", 2695 | "80 0.0 0.0 \n", 2696 | "81 0.0 0.0 \n", 2697 | "82 0.0 0.0 \n", 2698 | "83 0.0 0.0 \n", 2699 | "84 0.0 0.0 \n", 2700 | "85 0.0 0.0 \n", 2701 | "86 0.0 0.0 \n", 2702 | "87 0.0 0.0 \n", 2703 | "88 0.0 0.0 \n", 2704 | "89 0.0 0.0 \n", 2705 | "90 0.0 0.0 \n", 2706 | "91 0.0 0.0 \n", 2707 | "92 0.0 0.0 \n", 2708 | "93 0.0 0.0 \n", 2709 | "94 0.0 0.0 \n", 2710 | "95 0.0 0.0 \n", 2711 | "96 0.0 0.0 \n", 2712 | "97 0.0 0.0 \n", 2713 | "98 0.0 0.0 \n", 2714 | "99 0.0 0.0 \n", 2715 | "\n", 2716 | " payment_coupon_amount_var channel_code_dstc oil_code_dstc scene_dstc \\\n", 2717 | "0 0.0 1 1 1 \n", 2718 | "1 0.0 1 1 1 \n", 2719 | "2 0.0 1 1 1 \n", 2720 | "3 0.0 1 1 1 \n", 2721 | "4 0.0 1 1 1 \n", 2722 | "5 0.0 1 1 1 \n", 2723 | "6 0.0 1 1 1 \n", 2724 | "7 0.0 1 1 1 \n", 2725 | "8 0.0 1 1 1 \n", 2726 | "9 0.0 1 1 1 \n", 2727 | "10 0.0 1 1 1 \n", 2728 | "11 0.0 1 1 1 \n", 2729 | "12 0.0 1 1 1 \n", 2730 | "13 0.0 1 1 1 \n", 2731 | "14 0.0 1 1 1 \n", 2732 | "15 0.0 1 1 1 \n", 2733 | "16 0.0 1 1 1 \n", 2734 | "17 0.0 1 1 1 \n", 2735 | "18 0.0 1 1 1 \n", 2736 | "19 0.0 1 1 1 \n", 2737 | "20 0.0 1 1 1 \n", 2738 | "21 0.0 1 1 1 \n", 2739 | "22 0.0 1 1 1 \n", 2740 | "23 0.0 1 1 1 \n", 2741 | "24 0.0 1 1 1 \n", 2742 | "25 0.0 1 1 1 \n", 2743 | "26 0.0 1 1 1 \n", 2744 | "27 0.0 1 1 1 \n", 2745 | "28 0.0 1 1 1 \n", 2746 | "29 0.0 1 1 1 \n", 2747 | ".. ... ... ... ... \n", 2748 | "70 0.0 1 1 1 \n", 2749 | "71 0.0 1 1 1 \n", 2750 | "72 0.0 1 1 1 \n", 2751 | "73 0.0 1 1 1 \n", 2752 | "74 0.0 1 1 1 \n", 2753 | "75 0.0 1 1 1 \n", 2754 | "76 0.0 1 1 1 \n", 2755 | "77 0.0 1 1 1 \n", 2756 | "78 0.0 1 1 1 \n", 2757 | "79 0.0 1 1 1 \n", 2758 | "80 0.0 1 1 1 \n", 2759 | "81 0.0 1 1 1 \n", 2760 | "82 0.0 1 1 1 \n", 2761 | "83 0.0 1 1 1 \n", 2762 | "84 0.0 1 1 1 \n", 2763 | "85 0.0 1 1 1 \n", 2764 | "86 0.0 1 1 1 \n", 2765 | "87 0.0 1 1 1 \n", 2766 | "88 0.0 1 1 1 \n", 2767 | "89 300.0 1 1 1 \n", 2768 | "90 0.0 1 1 1 \n", 2769 | "91 0.0 1 1 1 \n", 2770 | "92 0.0 1 1 1 \n", 2771 | "93 0.0 1 1 1 \n", 2772 | "94 0.0 1 1 1 \n", 2773 | "95 0.0 1 1 1 \n", 2774 | "96 0.0 1 1 1 \n", 2775 | "97 0.0 1 1 1 \n", 2776 | "98 0.0 1 1 1 \n", 2777 | "99 0.0 1 1 1 \n", 2778 | "\n", 2779 | " source_app_dstc call_source_dstc \n", 2780 | "0 1 1 \n", 2781 | "1 1 1 \n", 2782 | "2 1 1 \n", 2783 | "3 1 1 \n", 2784 | "4 1 1 \n", 2785 | "5 1 1 \n", 2786 | "6 1 1 \n", 2787 | "7 1 1 \n", 2788 | "8 1 1 \n", 2789 | "9 1 1 \n", 2790 | "10 1 1 \n", 2791 | "11 1 1 \n", 2792 | "12 1 1 \n", 2793 | "13 1 1 \n", 2794 | "14 1 1 \n", 2795 | "15 1 1 \n", 2796 | "16 1 1 \n", 2797 | "17 1 1 \n", 2798 | "18 1 1 \n", 2799 | "19 1 1 \n", 2800 | "20 1 1 \n", 2801 | "21 1 1 \n", 2802 | "22 1 1 \n", 2803 | "23 1 1 \n", 2804 | "24 1 1 \n", 2805 | "25 1 1 \n", 2806 | "26 1 1 \n", 2807 | "27 1 1 \n", 2808 | "28 1 1 \n", 2809 | "29 1 1 \n", 2810 | ".. ... ... \n", 2811 | "70 1 1 \n", 2812 | "71 1 1 \n", 2813 | "72 1 1 \n", 2814 | "73 1 1 \n", 2815 | "74 1 1 \n", 2816 | "75 1 1 \n", 2817 | "76 1 1 \n", 2818 | "77 1 1 \n", 2819 | "78 1 1 \n", 2820 | "79 1 1 \n", 2821 | "80 1 1 \n", 2822 | "81 1 1 \n", 2823 | "82 1 1 \n", 2824 | "83 1 1 \n", 2825 | "84 1 1 \n", 2826 | "85 1 1 \n", 2827 | "86 1 1 \n", 2828 | "87 1 1 \n", 2829 | "88 1 1 \n", 2830 | "89 1 1 \n", 2831 | "90 1 1 \n", 2832 | "91 1 1 \n", 2833 | "92 1 1 \n", 2834 | "93 1 1 \n", 2835 | "94 1 1 \n", 2836 | "95 1 1 \n", 2837 | "96 1 1 \n", 2838 | "97 1 1 \n", 2839 | "98 1 1 \n", 2840 | "99 1 1 \n", 2841 | "\n", 2842 | "[100 rows x 74 columns]" 2843 | ] 2844 | }, 2845 | "execution_count": 36, 2846 | "metadata": {}, 2847 | "output_type": "execute_result" 2848 | } 2849 | ], 2850 | "source": [ 2851 | "fn.head(100)" 2852 | ] 2853 | }, 2854 | { 2855 | "cell_type": "markdown", 2856 | "metadata": {}, 2857 | "source": [ 2858 | "训练决策树模型" 2859 | ] 2860 | }, 2861 | { 2862 | "cell_type": "code", 2863 | "execution_count": 37, 2864 | "metadata": {}, 2865 | "outputs": [], 2866 | "source": [ 2867 | "x = fn.drop(['uid','oil_actv_dt','create_dt','bad_ind','class_new'],axis = 1)\n", 2868 | "y = fn.bad_ind.copy()\n", 2869 | "from sklearn import tree\n", 2870 | "\n", 2871 | "dtree = tree.DecisionTreeRegressor(max_depth = 2,min_samples_leaf = 500,min_samples_split = 5000)\n", 2872 | "dtree = dtree.fit(x,y)" 2873 | ] 2874 | }, 2875 | { 2876 | "cell_type": "markdown", 2877 | "metadata": {}, 2878 | "source": [ 2879 | "输出决策树图像，并作出决策" 2880 | ] 2881 | }, 2882 | { 2883 | "cell_type": "code", 2884 | "execution_count": 49, 2885 | "metadata": {}, 2886 | "outputs": [ 2887 | { 2888 | "data": { 2889 | "image/png": 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\n", 2890 | "text/plain": [ 2891 | "" 2892 | ] 2893 | }, 2894 | "execution_count": 49, 2895 | "metadata": {}, 2896 | "output_type": "execute_result" 2897 | } 2898 | ], 2899 | "source": [ 2900 | "import pydotplus \n", 2901 | "from IPython.display import Image\n", 2902 | "from sklearn.externals.six import StringIO\n", 2903 | "import os\n", 2904 | "os.environ[\"PATH\"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'\n", 2905 | "with open(path + \"dt.dot\", \"w\") as f:\n", 2906 | " tree.export_graphviz(dtree, out_file=f)\n", 2907 | "dot_data = StringIO()\n", 2908 | "tree.export_graphviz(dtree, out_file=dot_data,\n", 2909 | " feature_names=x.columns,\n", 2910 | " class_names=['bad_ind'],\n", 2911 | " filled=True, rounded=True,\n", 2912 | " special_characters=True)\n", 2913 | "graph = pydotplus.graph_from_dot_data(dot_data.getvalue()) \n", 2914 | "Image(graph.create_png())" 2915 | ] 2916 | }, 2917 | { 2918 | "cell_type": "markdown", 2919 | "metadata": {}, 2920 | "source": [ 2921 | "value = badrate" 2922 | ] 2923 | }, 2924 | { 2925 | "cell_type": "code", 2926 | "execution_count": 53, 2927 | "metadata": {}, 2928 | "outputs": [ 2929 | { 2930 | "data": { 2931 | "text/plain": [ 2932 | "0.04658077304261645" 2933 | ] 2934 | }, 2935 | "execution_count": 53, 2936 | "metadata": {}, 2937 | "output_type": "execute_result" 2938 | } 2939 | ], 2940 | "source": [ 2941 | "sum(fn.bad_ind)/len(fn.bad_ind)" 2942 | ] 2943 | } 2944 | ], 2945 | "metadata": { 2946 | "kernelspec": { 2947 | "display_name": "Python 3", 2948 | "language": "python", 2949 | "name": "python3" 2950 | }, 2951 | "language_info": { 2952 | "codemirror_mode": { 2953 | "name": "ipython", 2954 | "version": 3 2955 | }, 2956 | "file_extension": ".py", 2957 | "mimetype": "text/x-python", 2958 | "name": "python", 2959 | "nbconvert_exporter": "python", 2960 | "pygments_lexer": "ipython3", 2961 | "version": "3.7.3" 2962 | } 2963 | }, 2964 | "nbformat": 4, 2965 | "nbformat_minor": 2 2966 | } 2967 | -------------------------------------------------------------------------------- /常用反欺诈特征.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "### 金融反欺诈 常用特征处理方法 " 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "#### 用户基本属性\n", 15 | "\n", 16 | "phone_nember \n", 17 | "\n", 18 | "- 手机号前缀是否相同\n", 19 | "- 手机号归属地是否相同\n", 20 | "- 是否是虚拟运营商\n", 21 | "- 流量卡还是通话卡\n", 22 | "\n", 23 | "nickname\n", 24 | "\n", 25 | "- 昵称符合固定的规律（中文+数字）\n", 26 | "- 备注是否符合某种亲密的称呼\n", 27 | "\n", 28 | "birthday \n", 29 | "\n", 30 | "- 年纪\n", 31 | "- 星座\n", 32 | "- 生肖\n", 33 | "\n", 34 | "sex \n", 35 | "\n", 36 | "- 性别是否失衡\n", 37 | "\n", 38 | "password \n", 39 | "\n", 40 | "- 是否都相同\n", 41 | "\n", 42 | "\n", 43 | "身份证号码\n", 44 | "\n", 45 | "- 年龄 核对\n", 46 | "- 性比 核对\n", 47 | "- 城市\n", 48 | "\n", 49 | "邮箱\n", 50 | "- 是否是一次性邮箱\n", 51 | "- username 满足规律\n", 52 | "- 是否同一邮箱服务商\n", 53 | "- 邮箱里面的数据（账单）\n", 54 | "\n", 55 | "\n", 56 | "学历\n", 57 | "- 相似性\n", 58 | "\n", 59 | "\n", 60 | "住房\n", 61 | "- 租房情况是否雷同\n", 62 | "\n", 63 | "\n", 64 | "积分 \n", 65 | "- 是不是超过某个阈值\n", 66 | "\n", 67 | "\n", 68 | "签到 \n", 69 | "- 相似性\n", 70 | "\n", 71 | "\n", 72 | "ip \n", 73 | "\n", 74 | "- 是否是同一个号段\n", 75 | "- 每次登录ip地址是否相同\n", 76 | "- 是不是临时ip 和 gps\n", 77 | "- ip 和 gps 是否能对的上\n", 78 | "\n", 79 | "gps\n", 80 | "\n", 81 | "- 经纬度相似性分析\n", 82 | "- 国家 省份 城市 相似性\n", 83 | "- ip 和 gps 是否能对的上\n", 84 | "\n", 85 | "\n", 86 | "wifi\n", 87 | "\n", 88 | "- ssid\n", 89 | "- wifi list\n", 90 | "- 贷款前的几分钟有没有切换过wifi\n", 91 | "\n", 92 | "\n", 93 | "application time\n", 94 | "\n", 95 | "- 时间切片\n", 96 | "- 注册用了多长时间（太快太慢都有问题）\n", 97 | "- 一共申请了几次\n", 98 | "\n", 99 | "login time \n", 100 | "\n", 101 | "- 时间切片\n", 102 | "- 登陆了几次、频率\n", 103 | "- 最后一次登录时间距贷款时间的间隔\n", 104 | "- 同一时间登录做一个校验（同一时间多人登录）\n", 105 | "\n", 106 | "\n", 107 | "ua（user agent）\n", 108 | "\n", 109 | "- 每次打开是否是同一个ua\n", 110 | "\n", 111 | "\n", 112 | "渠道\n", 113 | "\n", 114 | "- app/H5/微信\n", 115 | "- 渠道ID属于违规渠道\n", 116 | "\n", 117 | "app version\n", 118 | "\n", 119 | "- 每次app的版本号是否相同\n", 120 | "- app版本会不会太老了（老版本的app有bug，可能会被黑中介用来攻击我们） \n", 121 | "\n", 122 | "推荐人/联系人 \n", 123 | "\n", 124 | "- 名字匹配\n", 125 | "- 手机号匹配\n" 126 | ] 127 | }, 128 | { 129 | "cell_type": "markdown", 130 | "metadata": {}, 131 | "source": [ 132 | "#### 设备指纹 \n", 133 | "\n", 134 | "imei \n", 135 | "\n", 136 | "- 受否都相同\n", 137 | "- 每次登录imei号是否都相同\n", 138 | " \n", 139 | " \n", 140 | "device id\n", 141 | "\n", 142 | "- 受否都相同\n", 143 | "- 每次登录device id号是否都相同\n", 144 | "\n", 145 | "\n", 146 | "分辨率 \n", 147 | "\n", 148 | "- 手机型号和屏幕分辨率是否一致\n", 149 | "\n", 150 | "mobile type\n", 151 | "\n", 152 | "- 手机品牌\n", 153 | "- 手机型号\n", 154 | "\n", 155 | "os（operating system）\n", 156 | "\n", 157 | "- 每次打开操作系统是否都相同\n", 158 | "- 来申请的人是否os都相同\n", 159 | "- os的版本是否太旧" 160 | ] 161 | }, 162 | { 163 | "cell_type": "markdown", 164 | "metadata": {}, 165 | "source": [ 166 | "#### 中文错别字可以考虑转换成拼音做相似度匹配 \n", 167 | "\n", 168 | "address \n", 169 | "\n", 170 | "- 地址要标准化\n", 171 | "- 模糊匹配\n", 172 | "- 相似度计算（cos距离，词向量）\n", 173 | "\n", 174 | "company \n", 175 | "\n", 176 | "- 正则\n", 177 | "- 字节拆分\n", 178 | "- 关键字提取\n", 179 | "- 相似度计算\n", 180 | "- 错别字/同音字识别\n", 181 | "\n" 182 | ] 183 | }, 184 | { 185 | "cell_type": "markdown", 186 | "metadata": {}, 187 | "source": [ 188 | "#### 第三方数据 \n", 189 | "\n", 190 | "人行征信 \n", 191 | "\n", 192 | "- 公司信息是否一致\n", 193 | "- 学历是否一致\n", 194 | "- 居住地址是否一致\n", 195 | "- 手机号码是否一致\n", 196 | "- 逾期数据\n", 197 | "\n", 198 | "运营商 \n", 199 | "\n", 200 | "- 是否有相同的联系人\n", 201 | "- 是否有黑名单客户在通讯录中\n", 202 | "- 通话最频繁的几个人（所在地是否和他相同）\n", 203 | "\n", 204 | "社保公积金 \n", 205 | "\n", 206 | "- 工资\n", 207 | "- 社保\n", 208 | "- 公积金\n" 209 | ] 210 | } 211 | ], 212 | "metadata": { 213 | "kernelspec": { 214 | "display_name": "Python 3", 215 | "language": "python", 216 | "name": "python3" 217 | }, 218 | "language_info": { 219 | "codemirror_mode": { 220 | "name": "ipython", 221 | "version": 3 222 | }, 223 | "file_extension": ".py", 224 | "mimetype": "text/x-python", 225 | "name": "python", 226 | "nbconvert_exporter": "python", 227 | "pygments_lexer": "ipython3", 228 | "version": "3.7.1" 229 | } 230 | }, 231 | "nbformat": 4, 232 | "nbformat_minor": 2 233 | } 234 | --------------------------------------------------------------------------------